From ca69f5e4889033f6de7f187c4b81e751ed8cb7f9 Mon Sep 17 00:00:00 2001 From: Tony Liu Date: Fri, 13 Dec 2019 09:48:43 -0600 Subject: [PATCH 1/2] add metalearning capability --- metalearning/metalearning_files.zip | Bin 0 -> 3593164 bytes .../algorithm_runs.arff | 152 +++++++++++++ .../configurations.csv | 141 ++++++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 152 +++++++++++++ .../configurations.csv | 141 ++++++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 152 +++++++++++++ .../configurations.csv | 141 ++++++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 152 +++++++++++++ .../configurations.csv | 141 ++++++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 107 +++++++++ .../configurations.csv | 96 ++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 107 +++++++++ .../configurations.csv | 96 ++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 107 +++++++++ .../configurations.csv | 96 ++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 107 +++++++++ .../configurations.csv | 96 ++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 152 +++++++++++++ .../configurations.csv | 141 ++++++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 152 +++++++++++++ .../configurations.csv | 141 ++++++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 152 +++++++++++++ .../configurations.csv | 141 ++++++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 152 +++++++++++++ .../configurations.csv | 141 ++++++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 107 +++++++++ .../configurations.csv | 96 ++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../f1_binary.classification_dense/readme.txt | 0 .../algorithm_runs.arff | 107 +++++++++ .../configurations.csv | 96 ++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 152 +++++++++++++ .../configurations.csv | 141 ++++++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 152 +++++++++++++ .../configurations.csv | 141 ++++++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 152 +++++++++++++ .../configurations.csv | 141 ++++++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 152 +++++++++++++ .../configurations.csv | 141 ++++++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 152 +++++++++++++ .../configurations.csv | 141 ++++++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 152 +++++++++++++ .../configurations.csv | 141 ++++++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 152 +++++++++++++ .../configurations.csv | 141 ++++++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 152 +++++++++++++ .../configurations.csv | 141 ++++++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 107 +++++++++ .../configurations.csv | 96 ++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 107 +++++++++ .../configurations.csv | 96 ++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 152 +++++++++++++ .../configurations.csv | 141 ++++++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 152 +++++++++++++ .../configurations.csv | 141 ++++++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 152 +++++++++++++ .../configurations.csv | 141 ++++++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 152 +++++++++++++ .../configurations.csv | 141 ++++++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 152 +++++++++++++ .../configurations.csv | 141 ++++++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 152 +++++++++++++ .../configurations.csv | 141 ++++++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 152 +++++++++++++ .../configurations.csv | 141 ++++++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 152 +++++++++++++ .../configurations.csv | 141 ++++++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 152 +++++++++++++ .../configurations.csv | 141 ++++++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 152 +++++++++++++ .../configurations.csv | 141 ++++++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 152 +++++++++++++ .../configurations.csv | 141 ++++++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 152 +++++++++++++ .../configurations.csv | 141 ++++++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 107 +++++++++ .../configurations.csv | 96 ++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 107 +++++++++ .../configurations.csv | 96 ++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 152 +++++++++++++ .../configurations.csv | 141 ++++++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 152 +++++++++++++ .../configurations.csv | 141 ++++++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 152 +++++++++++++ .../configurations.csv | 141 ++++++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 152 +++++++++++++ .../configurations.csv | 141 ++++++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 152 +++++++++++++ .../configurations.csv | 141 ++++++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 152 +++++++++++++ .../configurations.csv | 141 ++++++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 152 +++++++++++++ .../configurations.csv | 141 ++++++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 152 +++++++++++++ .../configurations.csv | 141 ++++++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 107 +++++++++ .../configurations.csv | 96 ++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 107 +++++++++ .../configurations.csv | 96 ++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 152 +++++++++++++ .../configurations.csv | 141 ++++++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 152 +++++++++++++ .../configurations.csv | 141 ++++++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 152 +++++++++++++ .../configurations.csv | 141 ++++++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 152 +++++++++++++ .../configurations.csv | 141 ++++++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 107 +++++++++ .../configurations.csv | 96 ++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 107 +++++++++ .../configurations.csv | 96 ++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 152 +++++++++++++ .../configurations.csv | 141 ++++++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 152 +++++++++++++ .../configurations.csv | 141 ++++++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 152 +++++++++++++ .../configurations.csv | 141 ++++++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 152 +++++++++++++ .../configurations.csv | 141 ++++++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 152 +++++++++++++ .../configurations.csv | 141 ++++++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 152 +++++++++++++ .../configurations.csv | 141 ++++++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 152 +++++++++++++ .../configurations.csv | 141 ++++++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 152 +++++++++++++ .../configurations.csv | 141 ++++++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 107 +++++++++ .../configurations.csv | 96 ++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 107 +++++++++ .../configurations.csv | 96 ++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 152 +++++++++++++ .../configurations.csv | 141 ++++++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 152 +++++++++++++ .../configurations.csv | 141 ++++++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 152 +++++++++++++ .../configurations.csv | 141 ++++++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 152 +++++++++++++ .../configurations.csv | 141 ++++++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 107 +++++++++ .../configurations.csv | 96 ++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 107 +++++++++ .../configurations.csv | 96 ++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 107 +++++++++ .../configurations.csv | 96 ++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 .../algorithm_runs.arff | 107 +++++++++ .../configurations.csv | 96 ++++++++ .../description.txt | 68 ++++++ .../feature_costs.arff | 205 ++++++++++++++++++ .../feature_runstatus.arff | 205 ++++++++++++++++++ .../feature_values.arff | 199 +++++++++++++++++ .../readme.txt | 0 505 files changed, 68040 insertions(+) create mode 100644 metalearning/metalearning_files.zip create mode 100755 metalearning/metalearning_files/accuracy_binary.classification_dense/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/accuracy_binary.classification_dense/configurations.csv create mode 100755 metalearning/metalearning_files/accuracy_binary.classification_dense/description.txt create mode 100755 metalearning/metalearning_files/accuracy_binary.classification_dense/feature_costs.arff create mode 100755 metalearning/metalearning_files/accuracy_binary.classification_dense/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/accuracy_binary.classification_dense/feature_values.arff create mode 100755 metalearning/metalearning_files/accuracy_binary.classification_dense/readme.txt create mode 100755 metalearning/metalearning_files/accuracy_binary.classification_sparse/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/accuracy_binary.classification_sparse/configurations.csv create mode 100755 metalearning/metalearning_files/accuracy_binary.classification_sparse/description.txt create mode 100755 metalearning/metalearning_files/accuracy_binary.classification_sparse/feature_costs.arff create mode 100755 metalearning/metalearning_files/accuracy_binary.classification_sparse/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/accuracy_binary.classification_sparse/feature_values.arff create mode 100755 metalearning/metalearning_files/accuracy_binary.classification_sparse/readme.txt create mode 100755 metalearning/metalearning_files/accuracy_multiclass.classification_dense/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/accuracy_multiclass.classification_dense/configurations.csv create mode 100755 metalearning/metalearning_files/accuracy_multiclass.classification_dense/description.txt create mode 100755 metalearning/metalearning_files/accuracy_multiclass.classification_dense/feature_costs.arff create mode 100755 metalearning/metalearning_files/accuracy_multiclass.classification_dense/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/accuracy_multiclass.classification_dense/feature_values.arff create mode 100755 metalearning/metalearning_files/accuracy_multiclass.classification_dense/readme.txt create mode 100755 metalearning/metalearning_files/accuracy_multiclass.classification_sparse/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/accuracy_multiclass.classification_sparse/configurations.csv create mode 100755 metalearning/metalearning_files/accuracy_multiclass.classification_sparse/description.txt create mode 100755 metalearning/metalearning_files/accuracy_multiclass.classification_sparse/feature_costs.arff create mode 100755 metalearning/metalearning_files/accuracy_multiclass.classification_sparse/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/accuracy_multiclass.classification_sparse/feature_values.arff create mode 100755 metalearning/metalearning_files/accuracy_multiclass.classification_sparse/readme.txt create mode 100755 metalearning/metalearning_files/average_precision_binary.classification_dense/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/average_precision_binary.classification_dense/configurations.csv create mode 100755 metalearning/metalearning_files/average_precision_binary.classification_dense/description.txt create mode 100755 metalearning/metalearning_files/average_precision_binary.classification_dense/feature_costs.arff create mode 100755 metalearning/metalearning_files/average_precision_binary.classification_dense/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/average_precision_binary.classification_dense/feature_values.arff create mode 100755 metalearning/metalearning_files/average_precision_binary.classification_dense/readme.txt create mode 100755 metalearning/metalearning_files/average_precision_binary.classification_sparse/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/average_precision_binary.classification_sparse/configurations.csv create mode 100755 metalearning/metalearning_files/average_precision_binary.classification_sparse/description.txt create mode 100755 metalearning/metalearning_files/average_precision_binary.classification_sparse/feature_costs.arff create mode 100755 metalearning/metalearning_files/average_precision_binary.classification_sparse/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/average_precision_binary.classification_sparse/feature_values.arff create mode 100755 metalearning/metalearning_files/average_precision_binary.classification_sparse/readme.txt create mode 100755 metalearning/metalearning_files/average_precision_multiclass.classification_dense/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/average_precision_multiclass.classification_dense/configurations.csv create mode 100755 metalearning/metalearning_files/average_precision_multiclass.classification_dense/description.txt create mode 100755 metalearning/metalearning_files/average_precision_multiclass.classification_dense/feature_costs.arff create mode 100755 metalearning/metalearning_files/average_precision_multiclass.classification_dense/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/average_precision_multiclass.classification_dense/feature_values.arff create mode 100755 metalearning/metalearning_files/average_precision_multiclass.classification_dense/readme.txt create mode 100755 metalearning/metalearning_files/average_precision_multiclass.classification_sparse/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/average_precision_multiclass.classification_sparse/configurations.csv create mode 100755 metalearning/metalearning_files/average_precision_multiclass.classification_sparse/description.txt create mode 100755 metalearning/metalearning_files/average_precision_multiclass.classification_sparse/feature_costs.arff create mode 100755 metalearning/metalearning_files/average_precision_multiclass.classification_sparse/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/average_precision_multiclass.classification_sparse/feature_values.arff create mode 100755 metalearning/metalearning_files/average_precision_multiclass.classification_sparse/readme.txt create mode 100755 metalearning/metalearning_files/balanced_accuracy_binary.classification_dense/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/balanced_accuracy_binary.classification_dense/configurations.csv create mode 100755 metalearning/metalearning_files/balanced_accuracy_binary.classification_dense/description.txt create mode 100755 metalearning/metalearning_files/balanced_accuracy_binary.classification_dense/feature_costs.arff create mode 100755 metalearning/metalearning_files/balanced_accuracy_binary.classification_dense/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/balanced_accuracy_binary.classification_dense/feature_values.arff create mode 100755 metalearning/metalearning_files/balanced_accuracy_binary.classification_dense/readme.txt create mode 100755 metalearning/metalearning_files/balanced_accuracy_binary.classification_sparse/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/balanced_accuracy_binary.classification_sparse/configurations.csv create mode 100755 metalearning/metalearning_files/balanced_accuracy_binary.classification_sparse/description.txt create mode 100755 metalearning/metalearning_files/balanced_accuracy_binary.classification_sparse/feature_costs.arff create mode 100755 metalearning/metalearning_files/balanced_accuracy_binary.classification_sparse/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/balanced_accuracy_binary.classification_sparse/feature_values.arff create mode 100755 metalearning/metalearning_files/balanced_accuracy_binary.classification_sparse/readme.txt create mode 100755 metalearning/metalearning_files/balanced_accuracy_multiclass.classification_dense/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/balanced_accuracy_multiclass.classification_dense/configurations.csv create mode 100755 metalearning/metalearning_files/balanced_accuracy_multiclass.classification_dense/description.txt create mode 100755 metalearning/metalearning_files/balanced_accuracy_multiclass.classification_dense/feature_costs.arff create mode 100755 metalearning/metalearning_files/balanced_accuracy_multiclass.classification_dense/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/balanced_accuracy_multiclass.classification_dense/feature_values.arff create mode 100755 metalearning/metalearning_files/balanced_accuracy_multiclass.classification_dense/readme.txt create mode 100755 metalearning/metalearning_files/balanced_accuracy_multiclass.classification_sparse/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/balanced_accuracy_multiclass.classification_sparse/configurations.csv create mode 100755 metalearning/metalearning_files/balanced_accuracy_multiclass.classification_sparse/description.txt create mode 100755 metalearning/metalearning_files/balanced_accuracy_multiclass.classification_sparse/feature_costs.arff create mode 100755 metalearning/metalearning_files/balanced_accuracy_multiclass.classification_sparse/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/balanced_accuracy_multiclass.classification_sparse/feature_values.arff create mode 100755 metalearning/metalearning_files/balanced_accuracy_multiclass.classification_sparse/readme.txt create mode 100755 metalearning/metalearning_files/f1_binary.classification_dense/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/f1_binary.classification_dense/configurations.csv create mode 100755 metalearning/metalearning_files/f1_binary.classification_dense/description.txt create mode 100755 metalearning/metalearning_files/f1_binary.classification_dense/feature_costs.arff create mode 100755 metalearning/metalearning_files/f1_binary.classification_dense/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/f1_binary.classification_dense/feature_values.arff create mode 100755 metalearning/metalearning_files/f1_binary.classification_dense/readme.txt create mode 100755 metalearning/metalearning_files/f1_binary.classification_sparse/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/f1_binary.classification_sparse/configurations.csv create mode 100755 metalearning/metalearning_files/f1_binary.classification_sparse/description.txt create mode 100755 metalearning/metalearning_files/f1_binary.classification_sparse/feature_costs.arff create mode 100755 metalearning/metalearning_files/f1_binary.classification_sparse/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/f1_binary.classification_sparse/feature_values.arff create mode 100755 metalearning/metalearning_files/f1_binary.classification_sparse/readme.txt create mode 100755 metalearning/metalearning_files/f1_macro_binary.classification_dense/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/f1_macro_binary.classification_dense/configurations.csv create mode 100755 metalearning/metalearning_files/f1_macro_binary.classification_dense/description.txt create mode 100755 metalearning/metalearning_files/f1_macro_binary.classification_dense/feature_costs.arff create mode 100755 metalearning/metalearning_files/f1_macro_binary.classification_dense/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/f1_macro_binary.classification_dense/feature_values.arff create mode 100755 metalearning/metalearning_files/f1_macro_binary.classification_dense/readme.txt create mode 100755 metalearning/metalearning_files/f1_macro_binary.classification_sparse/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/f1_macro_binary.classification_sparse/configurations.csv create mode 100755 metalearning/metalearning_files/f1_macro_binary.classification_sparse/description.txt create mode 100755 metalearning/metalearning_files/f1_macro_binary.classification_sparse/feature_costs.arff create mode 100755 metalearning/metalearning_files/f1_macro_binary.classification_sparse/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/f1_macro_binary.classification_sparse/feature_values.arff create mode 100755 metalearning/metalearning_files/f1_macro_binary.classification_sparse/readme.txt create mode 100755 metalearning/metalearning_files/f1_macro_multiclass.classification_dense/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/f1_macro_multiclass.classification_dense/configurations.csv create mode 100755 metalearning/metalearning_files/f1_macro_multiclass.classification_dense/description.txt create mode 100755 metalearning/metalearning_files/f1_macro_multiclass.classification_dense/feature_costs.arff create mode 100755 metalearning/metalearning_files/f1_macro_multiclass.classification_dense/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/f1_macro_multiclass.classification_dense/feature_values.arff create mode 100755 metalearning/metalearning_files/f1_macro_multiclass.classification_dense/readme.txt create mode 100755 metalearning/metalearning_files/f1_macro_multiclass.classification_sparse/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/f1_macro_multiclass.classification_sparse/configurations.csv create mode 100755 metalearning/metalearning_files/f1_macro_multiclass.classification_sparse/description.txt create mode 100755 metalearning/metalearning_files/f1_macro_multiclass.classification_sparse/feature_costs.arff create mode 100755 metalearning/metalearning_files/f1_macro_multiclass.classification_sparse/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/f1_macro_multiclass.classification_sparse/feature_values.arff create mode 100755 metalearning/metalearning_files/f1_macro_multiclass.classification_sparse/readme.txt create mode 100755 metalearning/metalearning_files/f1_micro_binary.classification_dense/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/f1_micro_binary.classification_dense/configurations.csv create mode 100755 metalearning/metalearning_files/f1_micro_binary.classification_dense/description.txt create mode 100755 metalearning/metalearning_files/f1_micro_binary.classification_dense/feature_costs.arff create mode 100755 metalearning/metalearning_files/f1_micro_binary.classification_dense/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/f1_micro_binary.classification_dense/feature_values.arff create mode 100755 metalearning/metalearning_files/f1_micro_binary.classification_dense/readme.txt create mode 100755 metalearning/metalearning_files/f1_micro_binary.classification_sparse/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/f1_micro_binary.classification_sparse/configurations.csv create mode 100755 metalearning/metalearning_files/f1_micro_binary.classification_sparse/description.txt create mode 100755 metalearning/metalearning_files/f1_micro_binary.classification_sparse/feature_costs.arff create mode 100755 metalearning/metalearning_files/f1_micro_binary.classification_sparse/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/f1_micro_binary.classification_sparse/feature_values.arff create mode 100755 metalearning/metalearning_files/f1_micro_binary.classification_sparse/readme.txt create mode 100755 metalearning/metalearning_files/f1_micro_multiclass.classification_dense/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/f1_micro_multiclass.classification_dense/configurations.csv create mode 100755 metalearning/metalearning_files/f1_micro_multiclass.classification_dense/description.txt create mode 100755 metalearning/metalearning_files/f1_micro_multiclass.classification_dense/feature_costs.arff create mode 100755 metalearning/metalearning_files/f1_micro_multiclass.classification_dense/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/f1_micro_multiclass.classification_dense/feature_values.arff create mode 100755 metalearning/metalearning_files/f1_micro_multiclass.classification_dense/readme.txt create mode 100755 metalearning/metalearning_files/f1_micro_multiclass.classification_sparse/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/f1_micro_multiclass.classification_sparse/configurations.csv create mode 100755 metalearning/metalearning_files/f1_micro_multiclass.classification_sparse/description.txt create mode 100755 metalearning/metalearning_files/f1_micro_multiclass.classification_sparse/feature_costs.arff create mode 100755 metalearning/metalearning_files/f1_micro_multiclass.classification_sparse/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/f1_micro_multiclass.classification_sparse/feature_values.arff create mode 100755 metalearning/metalearning_files/f1_micro_multiclass.classification_sparse/readme.txt create mode 100755 metalearning/metalearning_files/f1_multiclass.classification_dense/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/f1_multiclass.classification_dense/configurations.csv create mode 100755 metalearning/metalearning_files/f1_multiclass.classification_dense/description.txt create mode 100755 metalearning/metalearning_files/f1_multiclass.classification_dense/feature_costs.arff create mode 100755 metalearning/metalearning_files/f1_multiclass.classification_dense/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/f1_multiclass.classification_dense/feature_values.arff create mode 100755 metalearning/metalearning_files/f1_multiclass.classification_dense/readme.txt create mode 100755 metalearning/metalearning_files/f1_multiclass.classification_sparse/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/f1_multiclass.classification_sparse/configurations.csv create mode 100755 metalearning/metalearning_files/f1_multiclass.classification_sparse/description.txt create mode 100755 metalearning/metalearning_files/f1_multiclass.classification_sparse/feature_costs.arff create mode 100755 metalearning/metalearning_files/f1_multiclass.classification_sparse/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/f1_multiclass.classification_sparse/feature_values.arff create mode 100755 metalearning/metalearning_files/f1_multiclass.classification_sparse/readme.txt create mode 100755 metalearning/metalearning_files/f1_weighted_binary.classification_dense/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/f1_weighted_binary.classification_dense/configurations.csv create mode 100755 metalearning/metalearning_files/f1_weighted_binary.classification_dense/description.txt create mode 100755 metalearning/metalearning_files/f1_weighted_binary.classification_dense/feature_costs.arff create mode 100755 metalearning/metalearning_files/f1_weighted_binary.classification_dense/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/f1_weighted_binary.classification_dense/feature_values.arff create mode 100755 metalearning/metalearning_files/f1_weighted_binary.classification_dense/readme.txt create mode 100755 metalearning/metalearning_files/f1_weighted_binary.classification_sparse/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/f1_weighted_binary.classification_sparse/configurations.csv create mode 100755 metalearning/metalearning_files/f1_weighted_binary.classification_sparse/description.txt create mode 100755 metalearning/metalearning_files/f1_weighted_binary.classification_sparse/feature_costs.arff create mode 100755 metalearning/metalearning_files/f1_weighted_binary.classification_sparse/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/f1_weighted_binary.classification_sparse/feature_values.arff create mode 100755 metalearning/metalearning_files/f1_weighted_binary.classification_sparse/readme.txt create mode 100755 metalearning/metalearning_files/f1_weighted_multiclass.classification_dense/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/f1_weighted_multiclass.classification_dense/configurations.csv create mode 100755 metalearning/metalearning_files/f1_weighted_multiclass.classification_dense/description.txt create mode 100755 metalearning/metalearning_files/f1_weighted_multiclass.classification_dense/feature_costs.arff create mode 100755 metalearning/metalearning_files/f1_weighted_multiclass.classification_dense/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/f1_weighted_multiclass.classification_dense/feature_values.arff create mode 100755 metalearning/metalearning_files/f1_weighted_multiclass.classification_dense/readme.txt create mode 100755 metalearning/metalearning_files/f1_weighted_multiclass.classification_sparse/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/f1_weighted_multiclass.classification_sparse/configurations.csv create mode 100755 metalearning/metalearning_files/f1_weighted_multiclass.classification_sparse/description.txt create mode 100755 metalearning/metalearning_files/f1_weighted_multiclass.classification_sparse/feature_costs.arff create mode 100755 metalearning/metalearning_files/f1_weighted_multiclass.classification_sparse/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/f1_weighted_multiclass.classification_sparse/feature_values.arff create mode 100755 metalearning/metalearning_files/f1_weighted_multiclass.classification_sparse/readme.txt create mode 100755 metalearning/metalearning_files/log_loss_binary.classification_dense/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/log_loss_binary.classification_dense/configurations.csv create mode 100755 metalearning/metalearning_files/log_loss_binary.classification_dense/description.txt create mode 100755 metalearning/metalearning_files/log_loss_binary.classification_dense/feature_costs.arff create mode 100755 metalearning/metalearning_files/log_loss_binary.classification_dense/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/log_loss_binary.classification_dense/feature_values.arff create mode 100755 metalearning/metalearning_files/log_loss_binary.classification_dense/readme.txt create mode 100755 metalearning/metalearning_files/log_loss_binary.classification_sparse/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/log_loss_binary.classification_sparse/configurations.csv create mode 100755 metalearning/metalearning_files/log_loss_binary.classification_sparse/description.txt create mode 100755 metalearning/metalearning_files/log_loss_binary.classification_sparse/feature_costs.arff create mode 100755 metalearning/metalearning_files/log_loss_binary.classification_sparse/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/log_loss_binary.classification_sparse/feature_values.arff create mode 100755 metalearning/metalearning_files/log_loss_binary.classification_sparse/readme.txt create mode 100755 metalearning/metalearning_files/log_loss_multiclass.classification_dense/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/log_loss_multiclass.classification_dense/configurations.csv create mode 100755 metalearning/metalearning_files/log_loss_multiclass.classification_dense/description.txt create mode 100755 metalearning/metalearning_files/log_loss_multiclass.classification_dense/feature_costs.arff create mode 100755 metalearning/metalearning_files/log_loss_multiclass.classification_dense/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/log_loss_multiclass.classification_dense/feature_values.arff create mode 100755 metalearning/metalearning_files/log_loss_multiclass.classification_dense/readme.txt create mode 100755 metalearning/metalearning_files/log_loss_multiclass.classification_sparse/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/log_loss_multiclass.classification_sparse/configurations.csv create mode 100755 metalearning/metalearning_files/log_loss_multiclass.classification_sparse/description.txt create mode 100755 metalearning/metalearning_files/log_loss_multiclass.classification_sparse/feature_costs.arff create mode 100755 metalearning/metalearning_files/log_loss_multiclass.classification_sparse/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/log_loss_multiclass.classification_sparse/feature_values.arff create mode 100755 metalearning/metalearning_files/log_loss_multiclass.classification_sparse/readme.txt create mode 100755 metalearning/metalearning_files/pac_score_binary.classification_dense/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/pac_score_binary.classification_dense/configurations.csv create mode 100755 metalearning/metalearning_files/pac_score_binary.classification_dense/description.txt create mode 100755 metalearning/metalearning_files/pac_score_binary.classification_dense/feature_costs.arff create mode 100755 metalearning/metalearning_files/pac_score_binary.classification_dense/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/pac_score_binary.classification_dense/feature_values.arff create mode 100755 metalearning/metalearning_files/pac_score_binary.classification_dense/readme.txt create mode 100755 metalearning/metalearning_files/pac_score_binary.classification_sparse/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/pac_score_binary.classification_sparse/configurations.csv create mode 100755 metalearning/metalearning_files/pac_score_binary.classification_sparse/description.txt create mode 100755 metalearning/metalearning_files/pac_score_binary.classification_sparse/feature_costs.arff create mode 100755 metalearning/metalearning_files/pac_score_binary.classification_sparse/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/pac_score_binary.classification_sparse/feature_values.arff create mode 100755 metalearning/metalearning_files/pac_score_binary.classification_sparse/readme.txt create mode 100755 metalearning/metalearning_files/pac_score_multiclass.classification_dense/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/pac_score_multiclass.classification_dense/configurations.csv create mode 100755 metalearning/metalearning_files/pac_score_multiclass.classification_dense/description.txt create mode 100755 metalearning/metalearning_files/pac_score_multiclass.classification_dense/feature_costs.arff create mode 100755 metalearning/metalearning_files/pac_score_multiclass.classification_dense/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/pac_score_multiclass.classification_dense/feature_values.arff create mode 100755 metalearning/metalearning_files/pac_score_multiclass.classification_dense/readme.txt create mode 100755 metalearning/metalearning_files/pac_score_multiclass.classification_sparse/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/pac_score_multiclass.classification_sparse/configurations.csv create mode 100755 metalearning/metalearning_files/pac_score_multiclass.classification_sparse/description.txt create mode 100755 metalearning/metalearning_files/pac_score_multiclass.classification_sparse/feature_costs.arff create mode 100755 metalearning/metalearning_files/pac_score_multiclass.classification_sparse/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/pac_score_multiclass.classification_sparse/feature_values.arff create mode 100755 metalearning/metalearning_files/pac_score_multiclass.classification_sparse/readme.txt create mode 100755 metalearning/metalearning_files/precision_binary.classification_dense/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/precision_binary.classification_dense/configurations.csv create mode 100755 metalearning/metalearning_files/precision_binary.classification_dense/description.txt create mode 100755 metalearning/metalearning_files/precision_binary.classification_dense/feature_costs.arff create mode 100755 metalearning/metalearning_files/precision_binary.classification_dense/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/precision_binary.classification_dense/feature_values.arff create mode 100755 metalearning/metalearning_files/precision_binary.classification_dense/readme.txt create mode 100755 metalearning/metalearning_files/precision_binary.classification_sparse/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/precision_binary.classification_sparse/configurations.csv create mode 100755 metalearning/metalearning_files/precision_binary.classification_sparse/description.txt create mode 100755 metalearning/metalearning_files/precision_binary.classification_sparse/feature_costs.arff create mode 100755 metalearning/metalearning_files/precision_binary.classification_sparse/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/precision_binary.classification_sparse/feature_values.arff create mode 100755 metalearning/metalearning_files/precision_binary.classification_sparse/readme.txt create mode 100755 metalearning/metalearning_files/precision_macro_binary.classification_dense/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/precision_macro_binary.classification_dense/configurations.csv create mode 100755 metalearning/metalearning_files/precision_macro_binary.classification_dense/description.txt create mode 100755 metalearning/metalearning_files/precision_macro_binary.classification_dense/feature_costs.arff create mode 100755 metalearning/metalearning_files/precision_macro_binary.classification_dense/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/precision_macro_binary.classification_dense/feature_values.arff create mode 100755 metalearning/metalearning_files/precision_macro_binary.classification_dense/readme.txt create mode 100755 metalearning/metalearning_files/precision_macro_binary.classification_sparse/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/precision_macro_binary.classification_sparse/configurations.csv create mode 100755 metalearning/metalearning_files/precision_macro_binary.classification_sparse/description.txt create mode 100755 metalearning/metalearning_files/precision_macro_binary.classification_sparse/feature_costs.arff create mode 100755 metalearning/metalearning_files/precision_macro_binary.classification_sparse/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/precision_macro_binary.classification_sparse/feature_values.arff create mode 100755 metalearning/metalearning_files/precision_macro_binary.classification_sparse/readme.txt create mode 100755 metalearning/metalearning_files/precision_macro_multiclass.classification_dense/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/precision_macro_multiclass.classification_dense/configurations.csv create mode 100755 metalearning/metalearning_files/precision_macro_multiclass.classification_dense/description.txt create mode 100755 metalearning/metalearning_files/precision_macro_multiclass.classification_dense/feature_costs.arff create mode 100755 metalearning/metalearning_files/precision_macro_multiclass.classification_dense/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/precision_macro_multiclass.classification_dense/feature_values.arff create mode 100755 metalearning/metalearning_files/precision_macro_multiclass.classification_dense/readme.txt create mode 100755 metalearning/metalearning_files/precision_macro_multiclass.classification_sparse/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/precision_macro_multiclass.classification_sparse/configurations.csv create mode 100755 metalearning/metalearning_files/precision_macro_multiclass.classification_sparse/description.txt create mode 100755 metalearning/metalearning_files/precision_macro_multiclass.classification_sparse/feature_costs.arff create mode 100755 metalearning/metalearning_files/precision_macro_multiclass.classification_sparse/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/precision_macro_multiclass.classification_sparse/feature_values.arff create mode 100755 metalearning/metalearning_files/precision_macro_multiclass.classification_sparse/readme.txt create mode 100755 metalearning/metalearning_files/precision_micro_binary.classification_dense/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/precision_micro_binary.classification_dense/configurations.csv create mode 100755 metalearning/metalearning_files/precision_micro_binary.classification_dense/description.txt create mode 100755 metalearning/metalearning_files/precision_micro_binary.classification_dense/feature_costs.arff create mode 100755 metalearning/metalearning_files/precision_micro_binary.classification_dense/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/precision_micro_binary.classification_dense/feature_values.arff create mode 100755 metalearning/metalearning_files/precision_micro_binary.classification_dense/readme.txt create mode 100755 metalearning/metalearning_files/precision_micro_binary.classification_sparse/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/precision_micro_binary.classification_sparse/configurations.csv create mode 100755 metalearning/metalearning_files/precision_micro_binary.classification_sparse/description.txt create mode 100755 metalearning/metalearning_files/precision_micro_binary.classification_sparse/feature_costs.arff create mode 100755 metalearning/metalearning_files/precision_micro_binary.classification_sparse/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/precision_micro_binary.classification_sparse/feature_values.arff create mode 100755 metalearning/metalearning_files/precision_micro_binary.classification_sparse/readme.txt create mode 100755 metalearning/metalearning_files/precision_micro_multiclass.classification_dense/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/precision_micro_multiclass.classification_dense/configurations.csv create mode 100755 metalearning/metalearning_files/precision_micro_multiclass.classification_dense/description.txt create mode 100755 metalearning/metalearning_files/precision_micro_multiclass.classification_dense/feature_costs.arff create mode 100755 metalearning/metalearning_files/precision_micro_multiclass.classification_dense/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/precision_micro_multiclass.classification_dense/feature_values.arff create mode 100755 metalearning/metalearning_files/precision_micro_multiclass.classification_dense/readme.txt create mode 100755 metalearning/metalearning_files/precision_micro_multiclass.classification_sparse/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/precision_micro_multiclass.classification_sparse/configurations.csv create mode 100755 metalearning/metalearning_files/precision_micro_multiclass.classification_sparse/description.txt create mode 100755 metalearning/metalearning_files/precision_micro_multiclass.classification_sparse/feature_costs.arff create mode 100755 metalearning/metalearning_files/precision_micro_multiclass.classification_sparse/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/precision_micro_multiclass.classification_sparse/feature_values.arff create mode 100755 metalearning/metalearning_files/precision_micro_multiclass.classification_sparse/readme.txt create mode 100755 metalearning/metalearning_files/precision_multiclass.classification_dense/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/precision_multiclass.classification_dense/configurations.csv create mode 100755 metalearning/metalearning_files/precision_multiclass.classification_dense/description.txt create mode 100755 metalearning/metalearning_files/precision_multiclass.classification_dense/feature_costs.arff create mode 100755 metalearning/metalearning_files/precision_multiclass.classification_dense/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/precision_multiclass.classification_dense/feature_values.arff create mode 100755 metalearning/metalearning_files/precision_multiclass.classification_dense/readme.txt create mode 100755 metalearning/metalearning_files/precision_multiclass.classification_sparse/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/precision_multiclass.classification_sparse/configurations.csv create mode 100755 metalearning/metalearning_files/precision_multiclass.classification_sparse/description.txt create mode 100755 metalearning/metalearning_files/precision_multiclass.classification_sparse/feature_costs.arff create mode 100755 metalearning/metalearning_files/precision_multiclass.classification_sparse/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/precision_multiclass.classification_sparse/feature_values.arff create mode 100755 metalearning/metalearning_files/precision_multiclass.classification_sparse/readme.txt create mode 100755 metalearning/metalearning_files/precision_weighted_binary.classification_dense/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/precision_weighted_binary.classification_dense/configurations.csv create mode 100755 metalearning/metalearning_files/precision_weighted_binary.classification_dense/description.txt create mode 100755 metalearning/metalearning_files/precision_weighted_binary.classification_dense/feature_costs.arff create mode 100755 metalearning/metalearning_files/precision_weighted_binary.classification_dense/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/precision_weighted_binary.classification_dense/feature_values.arff create mode 100755 metalearning/metalearning_files/precision_weighted_binary.classification_dense/readme.txt create mode 100755 metalearning/metalearning_files/precision_weighted_binary.classification_sparse/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/precision_weighted_binary.classification_sparse/configurations.csv create mode 100755 metalearning/metalearning_files/precision_weighted_binary.classification_sparse/description.txt create mode 100755 metalearning/metalearning_files/precision_weighted_binary.classification_sparse/feature_costs.arff create mode 100755 metalearning/metalearning_files/precision_weighted_binary.classification_sparse/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/precision_weighted_binary.classification_sparse/feature_values.arff create mode 100755 metalearning/metalearning_files/precision_weighted_binary.classification_sparse/readme.txt create mode 100755 metalearning/metalearning_files/precision_weighted_multiclass.classification_dense/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/precision_weighted_multiclass.classification_dense/configurations.csv create mode 100755 metalearning/metalearning_files/precision_weighted_multiclass.classification_dense/description.txt create mode 100755 metalearning/metalearning_files/precision_weighted_multiclass.classification_dense/feature_costs.arff create mode 100755 metalearning/metalearning_files/precision_weighted_multiclass.classification_dense/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/precision_weighted_multiclass.classification_dense/feature_values.arff create mode 100755 metalearning/metalearning_files/precision_weighted_multiclass.classification_dense/readme.txt create mode 100755 metalearning/metalearning_files/precision_weighted_multiclass.classification_sparse/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/precision_weighted_multiclass.classification_sparse/configurations.csv create mode 100755 metalearning/metalearning_files/precision_weighted_multiclass.classification_sparse/description.txt create mode 100755 metalearning/metalearning_files/precision_weighted_multiclass.classification_sparse/feature_costs.arff create mode 100755 metalearning/metalearning_files/precision_weighted_multiclass.classification_sparse/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/precision_weighted_multiclass.classification_sparse/feature_values.arff create mode 100755 metalearning/metalearning_files/precision_weighted_multiclass.classification_sparse/readme.txt create mode 100755 metalearning/metalearning_files/recall_binary.classification_dense/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/recall_binary.classification_dense/configurations.csv create mode 100755 metalearning/metalearning_files/recall_binary.classification_dense/description.txt create mode 100755 metalearning/metalearning_files/recall_binary.classification_dense/feature_costs.arff create mode 100755 metalearning/metalearning_files/recall_binary.classification_dense/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/recall_binary.classification_dense/feature_values.arff create mode 100755 metalearning/metalearning_files/recall_binary.classification_dense/readme.txt create mode 100755 metalearning/metalearning_files/recall_binary.classification_sparse/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/recall_binary.classification_sparse/configurations.csv create mode 100755 metalearning/metalearning_files/recall_binary.classification_sparse/description.txt create mode 100755 metalearning/metalearning_files/recall_binary.classification_sparse/feature_costs.arff create mode 100755 metalearning/metalearning_files/recall_binary.classification_sparse/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/recall_binary.classification_sparse/feature_values.arff create mode 100755 metalearning/metalearning_files/recall_binary.classification_sparse/readme.txt create mode 100755 metalearning/metalearning_files/recall_macro_binary.classification_dense/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/recall_macro_binary.classification_dense/configurations.csv create mode 100755 metalearning/metalearning_files/recall_macro_binary.classification_dense/description.txt create mode 100755 metalearning/metalearning_files/recall_macro_binary.classification_dense/feature_costs.arff create mode 100755 metalearning/metalearning_files/recall_macro_binary.classification_dense/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/recall_macro_binary.classification_dense/feature_values.arff create mode 100755 metalearning/metalearning_files/recall_macro_binary.classification_dense/readme.txt create mode 100755 metalearning/metalearning_files/recall_macro_binary.classification_sparse/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/recall_macro_binary.classification_sparse/configurations.csv create mode 100755 metalearning/metalearning_files/recall_macro_binary.classification_sparse/description.txt create mode 100755 metalearning/metalearning_files/recall_macro_binary.classification_sparse/feature_costs.arff create mode 100755 metalearning/metalearning_files/recall_macro_binary.classification_sparse/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/recall_macro_binary.classification_sparse/feature_values.arff create mode 100755 metalearning/metalearning_files/recall_macro_binary.classification_sparse/readme.txt create mode 100755 metalearning/metalearning_files/recall_macro_multiclass.classification_dense/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/recall_macro_multiclass.classification_dense/configurations.csv create mode 100755 metalearning/metalearning_files/recall_macro_multiclass.classification_dense/description.txt create mode 100755 metalearning/metalearning_files/recall_macro_multiclass.classification_dense/feature_costs.arff create mode 100755 metalearning/metalearning_files/recall_macro_multiclass.classification_dense/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/recall_macro_multiclass.classification_dense/feature_values.arff create mode 100755 metalearning/metalearning_files/recall_macro_multiclass.classification_dense/readme.txt create mode 100755 metalearning/metalearning_files/recall_macro_multiclass.classification_sparse/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/recall_macro_multiclass.classification_sparse/configurations.csv create mode 100755 metalearning/metalearning_files/recall_macro_multiclass.classification_sparse/description.txt create mode 100755 metalearning/metalearning_files/recall_macro_multiclass.classification_sparse/feature_costs.arff create mode 100755 metalearning/metalearning_files/recall_macro_multiclass.classification_sparse/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/recall_macro_multiclass.classification_sparse/feature_values.arff create mode 100755 metalearning/metalearning_files/recall_macro_multiclass.classification_sparse/readme.txt create mode 100755 metalearning/metalearning_files/recall_micro_binary.classification_dense/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/recall_micro_binary.classification_dense/configurations.csv create mode 100755 metalearning/metalearning_files/recall_micro_binary.classification_dense/description.txt create mode 100755 metalearning/metalearning_files/recall_micro_binary.classification_dense/feature_costs.arff create mode 100755 metalearning/metalearning_files/recall_micro_binary.classification_dense/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/recall_micro_binary.classification_dense/feature_values.arff create mode 100755 metalearning/metalearning_files/recall_micro_binary.classification_dense/readme.txt create mode 100755 metalearning/metalearning_files/recall_micro_binary.classification_sparse/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/recall_micro_binary.classification_sparse/configurations.csv create mode 100755 metalearning/metalearning_files/recall_micro_binary.classification_sparse/description.txt create mode 100755 metalearning/metalearning_files/recall_micro_binary.classification_sparse/feature_costs.arff create mode 100755 metalearning/metalearning_files/recall_micro_binary.classification_sparse/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/recall_micro_binary.classification_sparse/feature_values.arff create mode 100755 metalearning/metalearning_files/recall_micro_binary.classification_sparse/readme.txt create mode 100755 metalearning/metalearning_files/recall_micro_multiclass.classification_dense/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/recall_micro_multiclass.classification_dense/configurations.csv create mode 100755 metalearning/metalearning_files/recall_micro_multiclass.classification_dense/description.txt create mode 100755 metalearning/metalearning_files/recall_micro_multiclass.classification_dense/feature_costs.arff create mode 100755 metalearning/metalearning_files/recall_micro_multiclass.classification_dense/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/recall_micro_multiclass.classification_dense/feature_values.arff create mode 100755 metalearning/metalearning_files/recall_micro_multiclass.classification_dense/readme.txt create mode 100755 metalearning/metalearning_files/recall_micro_multiclass.classification_sparse/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/recall_micro_multiclass.classification_sparse/configurations.csv create mode 100755 metalearning/metalearning_files/recall_micro_multiclass.classification_sparse/description.txt create mode 100755 metalearning/metalearning_files/recall_micro_multiclass.classification_sparse/feature_costs.arff create mode 100755 metalearning/metalearning_files/recall_micro_multiclass.classification_sparse/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/recall_micro_multiclass.classification_sparse/feature_values.arff create mode 100755 metalearning/metalearning_files/recall_micro_multiclass.classification_sparse/readme.txt create mode 100755 metalearning/metalearning_files/recall_multiclass.classification_dense/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/recall_multiclass.classification_dense/configurations.csv create mode 100755 metalearning/metalearning_files/recall_multiclass.classification_dense/description.txt create mode 100755 metalearning/metalearning_files/recall_multiclass.classification_dense/feature_costs.arff create mode 100755 metalearning/metalearning_files/recall_multiclass.classification_dense/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/recall_multiclass.classification_dense/feature_values.arff create mode 100755 metalearning/metalearning_files/recall_multiclass.classification_dense/readme.txt create mode 100755 metalearning/metalearning_files/recall_multiclass.classification_sparse/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/recall_multiclass.classification_sparse/configurations.csv create mode 100755 metalearning/metalearning_files/recall_multiclass.classification_sparse/description.txt create mode 100755 metalearning/metalearning_files/recall_multiclass.classification_sparse/feature_costs.arff create mode 100755 metalearning/metalearning_files/recall_multiclass.classification_sparse/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/recall_multiclass.classification_sparse/feature_values.arff create mode 100755 metalearning/metalearning_files/recall_multiclass.classification_sparse/readme.txt create mode 100755 metalearning/metalearning_files/recall_weighted_binary.classification_dense/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/recall_weighted_binary.classification_dense/configurations.csv create mode 100755 metalearning/metalearning_files/recall_weighted_binary.classification_dense/description.txt create mode 100755 metalearning/metalearning_files/recall_weighted_binary.classification_dense/feature_costs.arff create mode 100755 metalearning/metalearning_files/recall_weighted_binary.classification_dense/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/recall_weighted_binary.classification_dense/feature_values.arff create mode 100755 metalearning/metalearning_files/recall_weighted_binary.classification_dense/readme.txt create mode 100755 metalearning/metalearning_files/recall_weighted_binary.classification_sparse/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/recall_weighted_binary.classification_sparse/configurations.csv create mode 100755 metalearning/metalearning_files/recall_weighted_binary.classification_sparse/description.txt create mode 100755 metalearning/metalearning_files/recall_weighted_binary.classification_sparse/feature_costs.arff create mode 100755 metalearning/metalearning_files/recall_weighted_binary.classification_sparse/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/recall_weighted_binary.classification_sparse/feature_values.arff create mode 100755 metalearning/metalearning_files/recall_weighted_binary.classification_sparse/readme.txt create mode 100755 metalearning/metalearning_files/recall_weighted_multiclass.classification_dense/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/recall_weighted_multiclass.classification_dense/configurations.csv create mode 100755 metalearning/metalearning_files/recall_weighted_multiclass.classification_dense/description.txt create mode 100755 metalearning/metalearning_files/recall_weighted_multiclass.classification_dense/feature_costs.arff create mode 100755 metalearning/metalearning_files/recall_weighted_multiclass.classification_dense/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/recall_weighted_multiclass.classification_dense/feature_values.arff create mode 100755 metalearning/metalearning_files/recall_weighted_multiclass.classification_dense/readme.txt create mode 100755 metalearning/metalearning_files/recall_weighted_multiclass.classification_sparse/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/recall_weighted_multiclass.classification_sparse/configurations.csv create mode 100755 metalearning/metalearning_files/recall_weighted_multiclass.classification_sparse/description.txt create mode 100755 metalearning/metalearning_files/recall_weighted_multiclass.classification_sparse/feature_costs.arff create mode 100755 metalearning/metalearning_files/recall_weighted_multiclass.classification_sparse/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/recall_weighted_multiclass.classification_sparse/feature_values.arff create mode 100755 metalearning/metalearning_files/recall_weighted_multiclass.classification_sparse/readme.txt create mode 100755 metalearning/metalearning_files/roc_auc_binary.classification_dense/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/roc_auc_binary.classification_dense/configurations.csv create mode 100755 metalearning/metalearning_files/roc_auc_binary.classification_dense/description.txt create mode 100755 metalearning/metalearning_files/roc_auc_binary.classification_dense/feature_costs.arff create mode 100755 metalearning/metalearning_files/roc_auc_binary.classification_dense/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/roc_auc_binary.classification_dense/feature_values.arff create mode 100755 metalearning/metalearning_files/roc_auc_binary.classification_dense/readme.txt create mode 100755 metalearning/metalearning_files/roc_auc_binary.classification_sparse/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/roc_auc_binary.classification_sparse/configurations.csv create mode 100755 metalearning/metalearning_files/roc_auc_binary.classification_sparse/description.txt create mode 100755 metalearning/metalearning_files/roc_auc_binary.classification_sparse/feature_costs.arff create mode 100755 metalearning/metalearning_files/roc_auc_binary.classification_sparse/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/roc_auc_binary.classification_sparse/feature_values.arff create mode 100755 metalearning/metalearning_files/roc_auc_binary.classification_sparse/readme.txt create mode 100755 metalearning/metalearning_files/roc_auc_multiclass.classification_dense/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/roc_auc_multiclass.classification_dense/configurations.csv create mode 100755 metalearning/metalearning_files/roc_auc_multiclass.classification_dense/description.txt create mode 100755 metalearning/metalearning_files/roc_auc_multiclass.classification_dense/feature_costs.arff create mode 100755 metalearning/metalearning_files/roc_auc_multiclass.classification_dense/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/roc_auc_multiclass.classification_dense/feature_values.arff create mode 100755 metalearning/metalearning_files/roc_auc_multiclass.classification_dense/readme.txt create mode 100755 metalearning/metalearning_files/roc_auc_multiclass.classification_sparse/algorithm_runs.arff create mode 100755 metalearning/metalearning_files/roc_auc_multiclass.classification_sparse/configurations.csv create mode 100755 metalearning/metalearning_files/roc_auc_multiclass.classification_sparse/description.txt create mode 100755 metalearning/metalearning_files/roc_auc_multiclass.classification_sparse/feature_costs.arff create mode 100755 metalearning/metalearning_files/roc_auc_multiclass.classification_sparse/feature_runstatus.arff create mode 100755 metalearning/metalearning_files/roc_auc_multiclass.classification_sparse/feature_values.arff create mode 100755 metalearning/metalearning_files/roc_auc_multiclass.classification_sparse/readme.txt diff --git a/metalearning/metalearning_files.zip b/metalearning/metalearning_files.zip new file mode 100644 index 0000000000000000000000000000000000000000..061e5c4c35904a5329c8f357a8739e07076b08f9 GIT binary patch literal 3593164 zcmbTcV{j%w*ESeUFtKghwsXg}ZQIVo$sOCa?POxxGqG)c^E}_y?$*}s{&>5pPn|z~ zuIuz^o^C}MaEPBE|0$%7f93zH{2u}p1P{c{%+<)&%*ff^%HG1j+{)I>g;7lv1_az$ z6Gt!0>_E@W6BYy%;tL!EztYOw z%EZXk%E8{i)Xd(+?0@6+cl`bT!bAH90}b*YJZh8`X;^d+5T-N`5UKw$o{59Kxs`>R z^M5(~95D%%)jh^JDtFf^lNQlUC!z1YWdZnJaO>PXcJOMo z=@~s-bWQ0^J^Z@S=`5AR6X2L{I;Y!4_mmc)kCnDv5KK~Q<@Xhv#Q?Ml9%MZzUOmYL zt=eiNk3RA!L}_#nK9S<{y{8faFBup;o!89P9c0*uHr+P1F(P}90;TRC z>GgdG0KI0Xf7>UYR!j2x+KQ<(G!E}b#yK}hac;pG>$l8K1-ck|6*xEDS`pg_DLZy+ z?}yyxueH9uoVYd>uaDOA>Ys^vzu|j)i0*e0V8ptLsgW)dYK3IS^)lL{bd=lBw$KTb zuM>0?I`js7RC(D%H!URLrl0xw67faSU2pP8Z?hb$oKWI5_^^t zZ)L(e?>k-VRQC7UI$HylUl4W(4XWC#qCRucP87GmzDJ7;oG`cUKHll)FX@9Sg=^lc zHK++1=s0ye^9lp(zZ)i_cNd>+Z^R&!Pd^HD+XN;IR=InW8oygVF0Y5m)JGpC!VZ}4 zz(h@8MDa0rqse?cHBWprn|T>FEI3CWi4_Jd^o_0Bu#IpqqQrB^D zx!NXnf+)Z#cNeEA@VTCOeH}O+DA+V%Tp;B&wzpRgPJPQFg&o8@#s?gX+45Zkq#;@# zHrwa`yYo9YHadEV_or>Ps}0M)mhzFsO}mbekx$t^5wX4*d=y$w8SI$r^t7v`kvf19 zW?R#(;)U}0^e7`2y;5*>@5)ZqS7Y0Qr@b#o5{NQSHyw=LuvwcLaDVT<{Fd9MZ;W;m zPWElPW>+u2pa}_;S3Yg$#!e10lSXGIcaBBB__8n3N&GN^a&Meo`8UaXM&0QiXM1(L z#T~#{3tDu7A7pUpNt7p@^&YM6-b`}*ChhrqsRK>|b;Y`&+>tYcQzT5Q=9>kIVK!Rv z$+xh*nYlI3???^mth0KE1xzcM=bhVvJl^~0xIfl*tZ~@|wJs-grb$lD;3Gjh+&aDV zwo$5iU59Cbbu&P_4#Khu*suwW00!t}1h&#BnVa5ivrWAY2>G-c!;VLjyJGFmD=9)jistt1j93UoL-{^`a*k?vm9-L!zRv(?CGU0vCp~Wtd`$? z=a3H6QhdWWuh}oJZ6?f)r6V`{B)fu!1>5JeRm6==44mt=-ir^H^o(=yCZ?-yH%uaE z*X;s8h0|UmRd1ct;d9hxul3YAP5VaiEE)`c*VdzX(K6a&mVe5EXjugMmyf@uzbTWT zr8%LWl#JnvL=6(DRnFVH|}Y&Ywk5TZQyi z*)y;)C+O|Ex$cD9Xu)1ff3z^p)jdPQb!?=x%E+23Qh_d!oVpS{Gr$2Q^hoR=wM z+o|?KYIWTn8f4>uN@*&N5L)48z+qXUeB3j$8;IiQ|fLE zL651B6Z(^DbEc!eshF5Bx4rUy_!#-#_H~)GT81@5OXB@)y>|Q){as+ugz5JdM$h#H z^8wq(`sQ`{Ct5BQBf8KwkgVr(*d#zmXL0-IE!?M81th1SjakFV2*{Af=7nfdabpzg zb@BZUE+t0Qg-)0oO|uHF1+i(h%NF%4Zl~kJelh%rF8M6`zOHvkifQI#MXVj6af7fA zQ+|_RoaN5mN`B@IG^2hD+KfzH$KwM1j{I|Rlis!lwCK;P^`pJMMRwOs!q77tmLqh?D>E#P>=JO}4#0{)}GgfVlVRVZUEEu5V3*1#Nui!w_cmWq4r{Pt>3 z*sY7*k5yf(*#q%sHn4Vr8gDiDXCrSmUuqwvoxLAyzhh06dDZ$X=fDbP2rRoM)yJXO zAca0!b*jDQ@b+b~ngdzQ@+C;8`&q-~+GB}bPVvH9Xxjv5%4ve=v^^Qg+QK)($8p~V zB+vCUyz@X&hhZI+9PMj3qm`PG&O^m#HV3Z409VSbNNZwxDODaHUCqCHq=Xb1QfSC}0x_bz=_gm%ik!_330_cae5#tp!A zFIK_Q>SQ;!x|=UK+}g87oQyh_2Bob!8gnxkqFH4N-r)~~p0S?cmmzqYTb#4u`nbaM zp>PKhAgB+#trs^V0;=_k4hm!IIJq^UsF{VDL6v`&!hfNv&|5AHXhG>KWQ>MS(fH_S z%-DMQPRrL|`89)8{~!)Q)us(Ghb^kU0mh8GkR~r?&PW*^yLleQ4Gl;MBZHY7LDW9a z8~kBm3OAI{EKnO2ovwM|k&ix7;%&@oevv}NnunFfsc8ZSSwfA9;EmzIaeq%y0!CAc zwJ~o)S0EW{2emKJl$dUpc99y;BcCvv^h39cS8ssG>5u4W!_=Y$vrr;Wam)#2lQ!L? zRr@zB@Y450Y%Yb8s<6)sAc=6EURsP{SSBfB-7IKg81~4uzMcGGALI6zf*z+s3J&L0 zEx^NK7=v4l>#|7NEWPFF4iDNy%?(+nTC6woGk_aVgYspBS(~quR7cWph@ZG+3JP28 zoLieEcinOKY6TM@Z}Fo&Dp3L(meWkItzdkzv$=o5k|Ki0v?ONLdl!I$tXwDpm65#W zHRp#L5}fFLtj@f~ZCQRHen4A5imvVc@d_qoIn$BYYZYSQ&t7(a25Dx}t?8~?dyif) z;X%XzR#|~?K^fQa$fd2KU0r*=qu6 z-igjf>VLB#ob!7C8|?1z2X!Tgn)LBa2M5;MN?WqsnPRK5FZor9^9*a~mO9A;#&e6u z$)BqwIaH)z*+OM`LK3hG2K!QE=-RA^Tv@($aJ{1qEQP2(sxB6kIm<@dc0EuMd)o-O zckT6s^h`I$FlaqZa02;`*a*Cjfq@M!cMXpjs~^*Q2H&so0Uwx$jK#;q(LbIFx{*L{ zN?2V$tRIlr1q4XRnk?ym!yj6arB@I;jPy6Bo8Xuv+nhf#@j_;xZ(!Snp&(Qa=KJZ> z5rYuwbNvMk4Zp3W#X}6<8&I)077=>euS;M++p{@@jrb*IQ=;y7Ieu@fOGV7Lhr&f^ zUc485<9Cp3+fKqQxM5w4>qV(Koc(g4C%+lwntznu2ZTA0fMk-1Q{ArH)85=xa9 ze2i;!!r;$P{W!MSveqU)X!cFnIQN?>U8w|Ih)<@eE3uhRr$Ha@VP4`L8hygW52c85 zi!O!|S0!dgj^-q0MoVMD{b2_8I)~({1=8OlWC~Oz4>#>QJSK3k86c&h^Kb#gH=y** zt2uAm60k@Ru)qbPxUlcV?d;Zrq+NtCr(Z*!3vbJQV)s7RO~^RQ#y|=&BbxJ8Ql;N*q)3p0UNe= z7o9#y%drzumI>^3AiJ^*jjmBHow&9lR&@G~vt_WJT%bb5Q=qs{y;gjg6#bTp1@=w* zt+fDlts&w(JF<3IyCn5*bmKfG8u(xQa&1y10;0q2)u~;qyZSQuA&L{$Ff5C*?nyF9 ztmhjZuK0C4-N&hFtdEh0p>&du`^hiRl48%fs?(rrB32edVu&RU^f7r#v1uxtp3IK= z=l$n@C7fVcVN7p+E>KbQ|8um}>f;E8igZ1)lE0Estcr2i3X9gq`_mI19O|k&rrM}H zuQ|YgUlSMaBb-7BX`iUt{HA4nSAK@HIH3pm=5`RLsAZw_%GvmhpnWX4Iarg(6l1cH zD8TIm0t!2Qlh^=b-N-2liAzqy$E1T(=bX58J3}f^3lL?pklr^CWg>_^{8LQ^FAy)2QPhp?1Y z%0i1XsatR}77mnMY6mRo3iU8W49FqU(?JHUN@_a?BKaWnQ?)X!AkPMzaSNoORbHm} z#Lh;wO_qv42OwJZbsxAW<1#S;r*bi_B|Yx)ijgRtD4!=(F~ZF_C?5DkVHJwGzoPWW ze)Y5sY;SvvU4?Eg!V>igIsIxDtO;k}lHpP%0^a}ZatBVmjcmEHF0Qi0j(X*=`JCE#sm|CWDs-{Rd5&Bg;(v$vcxlZu6Y^M zp(4udynrE$xg)D{jZ>gI^6PIOHaBu@wb~S0l6YZaFL=H>E0|G5VemO{iuy5Mi~2Ee zp?1TAOQL*`Ik)~L$MdY_E zK!ZMTtPtTEU~;UDHnPeW$?(F-G9g#G9}elu{1& z>~u!y6H(?`_ujg+<-H>&jsEatt0L5j_B;BGiLq0Oh##EwZ%Hw-X~J`+y0G#{*1Udf zQaFAYL&#kycaS*Av0F#7Wftof(iTV5KLSc&)oHs+sHH7>&PuGW)AN>$of-Bfyu%Gb z8P1>-58<8~?WrEBkDSZ8 zUD3(8c5Tm0GA}wN164va)YbM3;!YmiyMPD|L`^wx3201s#8YI|0Sw-hgS>oK6LkN8 z4wB!ILJ-!-G18ho9xXXVC9o32VlDF2i+|pGjiLrMu$`-{5O#dg&$5uXSO%aAWGq(i|2h6s8V4y~~lSFs|@j{qrs{a{4*W|$x& zF5>!BaJ&^A9!6=skx7dP2p9!|8VE{mHX;K#KvY&|Qqp6cKO&62m1cN-*90|mroXnV zFd%g&Ume8m7s?l}pvI(Wib}n=rXZX;IYN`j+K?31(=&l341*IC*>-EfhvKS zwV_?xpOF1A2to~nC7$n*%ZBy}S24@Edkn8%S=@r>nPI9Nc^8vpL#WEFX9!zijr7ca z8jty2U2|I%XlJ^{%m{2fqD7Bhug{+6Z6qtzGr5z>1=6_~TX@)nbKhy4{fHBXiM)Xs z9%<|IC+*#fQGfE~`Qc(0LHt#iv$sQ2MlCAjW7+C~T=Y zn>8>rA8atNHU|p_WD#-w9BWsf-LzA%lEfv{nd6h$FCAj|eH@G*ur|mvLE~IkQy>Q| zIsi`(-zD^#Uc*6I+wfbIU9<9eiggtJn#}2VM{_t*$pvZox44JJu%x}xo6Vn)5ABzm z6s>w6M~Eld)9^{CH1n8p$g@jd8~DqYV60Ty5w?5K74(3eN+bV`7>fzV;-%(2)d|ej zu7R;$6E+?*;oWY-9USK5jF^aza8}lYJAZ%f`sC(YjDhIKO!l_*K9?NCwCy_K4>ATLUg7z+lrkx+f{J_<=qoFIS>_?ii!S7j?|sYY8$$8_lX_p2^e!v{ObqFDaF?bzo$RF0+*C9nTI+%fmy1}uf~a2>fM7I@#G@&(wA?< z|0I9^uVk!H0j^ZEEeHrM1}F&Nf1HdpH#2f|b2c+@H?nmzb73%YHaGwO%f?>z=rob7 ztJS>D)cq;hB^mPFP7}m;6>2tu=Se3!k*p51?S!lNSw)j$|K%6&Fm;H0#Y-wZD2rn5 zH>TG?%fCfB=%;t&HpTpXaV+?Gp7(KA_cc2E^-#6@adTOA{r#ws*Y|dn74R~d_jQmK z@O+>5HMIMEQ1^XR*7uSm_}cWZMVjDu)%TZ2-^Xd5|L63xpzq_fpx;+S-S?|PKmlgJ zQ(xY!|MTmv!1v7~qCvp>bl%s+_196^zgB{Rd7t+NevhBef}ckb*S()@0RcW=4t?)W zgne(Lbpel$h~HnF2A_R>i)ML!-rsJ==e=J!4DTb1-+Rku0k_MD27ceK5qSY6Wj=0S zb-k~7d42C+_YPeb-!Ic1g1+yctGj((t)r)$vkzR~Z(4DS4!M8p`reN60zQ{LzBB!w znh1G6G4sAp&#&1d1mB)ZvzuzWj4L)zOu6MTn+&A`V z`y3p)jfw|+EhFZAA8uavewEevKbjD}w`Z%p?WcV(5W48z2l#&ueSdNq1bqLa{V};4 z@c1tZFIoO&$6mS*gl{LiuOgq{O_1lKxwj7AACv*F+Zx}Y5A&XW@8<#UPg%47LiTCk z|GvHR_4JQIaY*0ySyx@hIH!YP>#TwQ=k~MU$7Nn$#~J4L+w1r1(LXo5)qMZixO)}k zIymP18kKnXnA!DizA^?j@|@bCKlRAtc9_t3tVHrl1H=M6W7yZ*%WDbdP5 z)0b-ZuV))CF~7gg13sQK9Qu56{GON3AF>?cngqW_`d+5%2ra%3X213*`##3=4wUUr zd$(KPd#}9?I-awm5Ol0vlBfH)mv(tO27Ud+l^i zO^!CPo*R~Rf9p&1_(?zeoeUj~^<1~=E}8avSH0d`QtK(++PL&qy|PP9J<>0| zGCGlY?rhsy`0ZieR^3Tvn2`>gEz_^5-cqR@NUB_P5XAi(?h_pP**GIqHNadFFi)~?56`jXBE-^onQ zs!nIeVv{eC&%ye3bF`%;dveLwxtyB8t(d>8Aui9sp{;`3K1Oc!qB#3`z~X`1dNwn- zcHn)uzqkwI+$>h8K8-`6m+Kx4?pTgGF8XE|^U6Fk? zZ%uA-myHjAK;=_3WVh75X-nX-E492=H`;cK0qRvi!oIpM`&@3)Z4v*{yPUt2g0<*0 z{Yv%BonY5Z#G@{P)+MWg-LFOCxD;x+Pok2!+u%d)x!9hbv5FV(ZzU%PF6; z1bL5Yb9iFYtkjlnM5d6XwV4+ku>vej>^e^Hk?**0nzh=!JAqePdfBPKuZ%5$pLG@_ zZ1KUSNAw#%QNh)oRD!j;@{)6kiuKLiUmPQ#1YY@muJ@EZCKi#faa`ZVva_?_=(#*O z_dW&!LnGfSQvtvfITCA2dUe4e4i{;F$?XeZ_g59G0xe6?3)7J53i`d_qL# zWjiLG6SY^LFdmsI*WeP`s)LLhvrzV&4?t@RjCh@XGQVQG>%cfKqQR$rr3F=b1@W`n z@Q6K)**18b_Y-CA))mOS{PM}N%ZtSC-EG8hfpN;A8vw3!=P9kqMH*}ug+FON0C*i; zZECAIr7+LSB0h4vg|qxHW?KaGcr@Q~qRBbJOXUZvhet#kdmn&)yPeFjNnbW2S#sYD zS!CL)=Cmg?@Vi^hs`md#ua?hfkts_Da4z@s=AJ#iCBPU(R>7Aj0f$$n&fT_!Rw0gn zi(UBiw;Y)iHSrkALYr*>1^LTg7iP*;Xi)^p#sk4E(q#yKk_-;!USB)rUO6VYlX985 zUB;h(XUKgX<{RlN9nePF9Q0TVmBcXsE*|jK1bki9X}Kk(kAethMX|w53b?!~$}+sd zQTgi8AlODY9dyLAesKJe;YE~Ijy7klP2B9p#K;eZ+K}R$p6^RnZYJzTL`R#CRNIf; zRB!@sm#TdkR^PiK9H#?Oq02&^r@E_m^NnfxH4dE4nc6MN{rX2zM5ixWnnye2+h_7J zt@kjU%B}TqDDdOw+^NcPMj&G>$y2x|Oz_FQ<`epfK?h2)H8hVra}dPh&F(xTO1oyb zfICI)ACnfMx0f6ExpGU5bal3{ss^RkiHEB#-(0p>T04HeiR#Nsd@=}H7w&$|7$^Py zQC&b)J2?k}l?jv~p5%~ztEY2VqyKl@9Kz_XoA z*#?<2FY}haed$`4>wH-OKI&DjONMnV?2{-+o#NYe{#Jaa2rPtjX5^q#Cek0*;6*$> z10HsC*!rwKyVjcIQ?5-q0VdHcyIJ(D024!TnF;CN2NjQPZqypw;5e87HM{Ddu{fi( zgHAiRu)~%kHOTN>8W%xrP;vW+r3AJ6nQhBkhbO4wm;;)VFtlEbjf(tkv=Odj&XTtac#xdoz!zvx$rpR;8p6JzcZzX-hMF{d} zyQxTGJHCS*HU~WV%P8zGb@s{9gyu!?kwv%*jo6?1l!R2w4C1Rbuv()jF4`yb#eb__ z8Ej;H4kv!YGFDiZTxiqT_)nZ7a?-Gpn-ENL9-0(m)`Lkr;e~8Qx8j2oud>3d>PZclRZqa5oL&quCE45|gUUln(Z*dGQ)mieYSBt?k_L zPOsPtFImi%W^(E5E1z0=i?5oV|~yJs?Oy>5~UkrW6ZAs8JB)=@PKIQ3wsGyEt(PPV}m zybM<>(n{^m(+2PARsmRdgvnLG@SacvQ6x4P1(tN!19UXI)Hl30z*h0k%EwnvkjWDt z5=onzqo&kzn=Du8ARu8ytqBzXD4kY^Hp65I3;%XeBsJTW@Dldx&es?ZeTnwAVPR(k zKQ9x^8-)!2+3!Q>6gIyxs;^6sgcYfI!xHl!8gtSo=|`K}aSeYX7M&A1CGrA)LA6nM zsQ@PS$s%ofCnK*ip|I(_;r5hxoyhT^V)J6;`NfjtS8?VklEg`>jxSgIwM|iUQxxTY zIg?d%=U}Jin02&isM&mnq!G1X9n1}m*LhAI=o zC{tGS9s07uq3)$&aq-s|qbR^eFs&E3mNS4^8y8 zq4HV^B3sU|O*R&or}=;;gLoGCID4TcDh0>AJXCqTowwhLqr=|vK7x}+k8kzngDpeq zHxqN!Xa3SLXK76pKWdk0a|$-+ywegEmrl{G@Q3;2S)33D1vlVvnE7A;-DyT_au(iW z!rSj04JJSbx&tR}3RdG3j*@ipoF$AzuEF!27lz{aR58(At!QiEMl~2AYgl_J0KEFo zXhjj-5Q9NoAkR=7aP}ZE>6px}tzis*Ji(2U*I}aYfnb;_wS&5^!QZ!zgRhKDT>Sjb zoqu8rrBeE?36OZ^l*-y6Xm(?=7;~>!lcq!uEMU2;!mKY&v)vCC{NtHJCu8&7A#e-3 zNR6tqiH}X^ZeHIMFy_)Se85{^eU?my3$%(`pIhGq(3y*rzCaTq-0QJ3G92K^tX#SC?%Ae zJQGvMB1f(1yF=3R!*$*HYZJ}+uRUVP!c#9JbF$?rCYh=+-Lt(!CYK=_IO@?9f%g`q zfs2hbCHy%_M72F8aj(Xa*4Vl5!nv9X%1gj(mIE#CG}3L}N-;uOPSH)x*>!SFm?l0k zH{K;`iDfO}-{%d*{Il9eZ=RQ!8Mj3VBWo}sfb_{5KkhzpZ%xF(w-(rPexkg5fLY?u zz*`y*DR-$`J+cUpD8Pi#swToghgqY*4;+ zt_9t+K*nPNf4(%u(Yss2xzG?kiFvu)UL2elyFFz?l=?j0TvWs(Kl$+&4(V|c+XETg z7z%F>Rab!~xuz}GUz&R>v8mtyRP7~_XP@Yh+pv~;0(&z1!nOOx$!xMuS5N-R^v{ic zgf?yiIy0(*bZt0F)J;Ndt`wOYnlECv$fK%X+Iql6y-lPt+k~H~uMzCr0c2XTE9l3m z>&Z6qYh~iv(mcyuc1TzW%*y~<8=T*`f*v!ZaRaH8#hXJ{ewqr0lN}P=HM+MyjfmIS zMuEw_r-&7MT_qOAg^CT%u{lV@eLduW4Wq*%?iEEOLg!kkovtNiQbMEAlx`F92OOX{ zOUK5w8-96W?;YB!2pA_FB^&SmAET$8@WMTi(V|Q#J?Nnp4+u2 z$a{`SCY;M-`cbbUbxt*>lBb9w=&WG|5BmS#q!Rq}kxo z)@hXBZKVqsV@jx72weh+kV>*afvSy-Cl7v9=WcFrBmYy9j)bH>)~GojPe(zaO;POL z!N+Y5dYlj;dMAc5VuZnn@`~-uS*+B9|9d!jWLDH5FTU2Y&TBN0 zXj#xZh6Z67{8Ien#nzbKbj%9``D!dr7;YHX{7AN29qc)lJWgi-Cv_`|kHbD-C9pe1nz1c~a(Kd@ap5`9^CXh4E`5usjqM9-7R% zFq*ZITZ~%C7#o)ztVQph4f?&ft;v^5Z$m+Jw~`*WUn)Z@@K_qcO+eFTqtMyv)|k{$ z$w}K?w)t1DG#eRNPIs<+bsSYxF2FwcXyAp&Spj#(m(;p22issTr*JyZabs$e26lkbKA;jT=5W;${0q&e${t1En= zYfO0PHAfx{<)}1P@9AmFU7P`wCWsqQh}PUz&R%kb`Y2WwA^Las9WOBcfce}A5!59k zh}&l>U6svR-e=QuN&x#<{%#i>IvBxByb;drNXpe8yX+M;162|Nmr|loFW#^^NEV9@ zKfAY?K)9;_NqBgvRiazBfLv#;8lBlKD>tx4GWRbkMZ7=pwVBn)v$Uk;cwlMLv~kFH zUyeT$|3e_ULQM0)DF}I`Ffj@Zb?O{CnZz>=G2*61W{GHYVTAl_kNikm_lEX)toFE% zejonOqd7T-vEjWZei;d3yMS<^ybYulN<&B{NZp>{5qqIFEN?rZyZ3<#W11la;(~~- zbJo7RCXlPAIkOt(^7m2T<*>`pRZ=UZ+<8IHOqTU8?_zgF2xlc!WG#ds1Yj3|zIXX= zIl^U0?9mb6fyt~a^D^ZZ6J8zjgMaFUX1N((H1kwaE^iKQ{q-~d*xZc?Kn!s{ij5T)B%AH z)!QVCCrk{>!g$sD;(*QOeF7d!fVK-8q;^FWLZt?cy_>84clw0uc_6zjNp>6$O_}`| zONND~WQ=ZOZopdi#>=Q|!jU(^u@lKP^d)z*?${*SLh9Wnvwy!nvYZRj=OIPyUBy5; z`!*A;!>Ugcp6}ccS4RdpoPHG356{*f6swE6L$anUMEG^n*Cc0$9XJj%PZA!&sEo1AVsh4+ZWT%j)^2UT4O{lt& zJw1`O%QzLJNORyq`joUdlOWSzsw0hA*=3;D>-PfKo5CNY3%$qB!4Cb?M31@-gb3b0 z=ajD0W3_n#FVqbg&T!-Ui%sX(eIrATi_Rn2;Q7j*tgdDhPWR`y9h?Ie>b-M+x{ean z%WM4_cvm7)ES_`~rW8{9)eE+=c+$Pw7x5=GbzJDDbo}3bNb*$;0w*{Sy@hQ_CqRe3 zu%|!BGx>P3J&VSbLZ2>a>t@G%@|a^)*8yDlk2|%nIe#b&SZR)ZIP>wS!(}NfwsB_q ztQrA(+J)8Z8VEY(S6f=EQUK60YttYFgH*oP>OSq?ZG8y?C@oMRe05C9)X1)D5;@WV z!kJH;I}l@=4av)i%dUA%3rJ5RfiTS4Z~NpM$EttkO+=k)q)FmoVS9D8(-itM6!Dp_ z#@7e{VNZbKz$RSKg%v6lWOnIigwTVooy-?;50qqeG*J#!gmPq+GxUqiHlxSuEV5N4 z1~gHWF?$TBCr@K^Y$)zY>?ExiqMQNkm)>M|X}BXK@FQ)vfN7oN zGx>5v(N0KfDRaPYQp&AL>~2)0f`*}-7*uy)fSpKGLHtw5?gh}-Z^u+^xl~Z1mxH7;54h7a8k_pk4s}V92&Aj$01=j# z6=MVCxU*B-QD$RcVu!sr_oYJM+klQQT{~RqXEo~5$YM# zjC4Af?iW(+z=#CJE<$^lZR83Je_9wTR7Mm80GUVnIJlryh6T^b_LUu#JU#Hwc-EXq zf(k1PEg|=d7>9~W5#ujayo$g`3qo?xb*TiL8LXoz8dj* zj9w#Da8S_hWng5$qks2Ic6U_o%*wEyWtYggp|xd8gP-4Vtf6X;PH*191=`$(@xq_& zopph{>jpYy40MYg6ADZFs3=fYN3zCioj*1?LuAX>Mh+BsmA~DZ)5fZRiR|UYlT=8 z^PP0D&SloZ){Z=53f)?n&O9V|3T8y-_Bu%oQ7Md^qS&UVAK3m_B1T!I-WmC88pkJEaZD{!$J2)H)0_jqtzDsn zGVtYc69DxdG1tNpm{+4z1o+Mf716#B9Q^%*TQ_3B1u-bqAW%OAvYC6>)g=1C3b2Mi zoC64GhCW<9XU8=scMOhYOT*|UwfrJQCvCglp*q=k+-XHLjk{2ge0N-+?T4*!vhoPV z5?4XK<&zaDtK%c-t;272h8IF-4us&DcaI0XQsxHbD1%FYqkk=lzy6`89~5~a-j7}u z5>NawB}yFpWMO)WJebDnB)?Li*=BVMESX|`hR$=&}6q6P`P@cSdb z9ccq#v@KQ`=7v7lpG!bk>|LqcIscj|D0sRT%8bAGxiaR%k)bzqnE$F2#Yh`=@CO59 z*uB&E$GNZ&C7BuVkuo$N9kbv0>y?D z_f0GPuUQZU94WkzS&9-8l(pXgo)N~&g~7#C5Ync_!h{RY@_v8mXcW`mGHW#|3X-1h z*gZl*lkD&i>5)J9V%L3#PanY8(=JW{FVambC;zuT4GhobPA&|HgZ4SRkRMxsN@9Bvu#N#+mU1z*5CU_*hZIgZ==s=lMfl;#7gE){GAw9*Q%!g;y<{8MJTg4 zcKnN8n1qy4nPo2y&oR=ia=VNK{g9f#Yv0Sx3sk=_>hu;~6fN2s&WzC!k@##4C`Ac! zDN4H^bfy6+)sq(lGnR$=p~@2&%JyIukZ~IX;dJbrRd7>g9IG;8IuDXTtxKvV{)<$B zEU@kDt)1>v43(iG6#Hq=8QjfOoVfTrxSs*xL zRiF!oPc}=JR@UXep?rYPmrlz@i&sv&Xghe1v|`;YVn>5G^k#gZv@#fV^*rn*WD_he z1}qf31y`XjB#Nr;XI|c3H4*%TpKErk*a+o*XRjawPt|?*fiiRaNOGKINp5u+CARB_Y;k%_4+fE5JQt9z=zG^K^E$P<2)YaViWWLodIk?5yJE; z%c31~P#%SdbV(^{oml5EX(iPXWCnwIRHglyC!24qgJ)bZ_M4$K5JsL6YvGnn=&7RC zwO>~ha%yJ8{@(z(xinJJnXDLsfKE21GWh$Y6BioD#m8*R1csVvJHsSr4e7M02J9W{)OqJX(L zxJMRtjxSM=ND`0g#3>#%_%firN|OIq^PU9qN_wv8&*lT?gBS0g;0@n8nnWpNN+w3H zHK9orvxAb9=JLRvh6dMw)uO>3gfSS|Dz-q=GlDoN$|s5O_;D#rMv}U1kqx*ayb~-G zx_zrA`KM3z2v~-KqSVa^5G4apO{8f zHG&+`{!u>(8#qmkS>o@IKdx)5XcX5j#bezDJFswJX5ipB>H`s^ z^fFdCvhJ_3GE^#uUkl`X^1hBREdyhKv73HUhyb!51_nuK z$z6mt!^j z9S0E|`?tR>eQ-fW9C5~&3IG@#CZlU(t8Ys)sdPR`Hup!RXIVKy`~_c#77K6-9IGiN zO)|NlvLL)*&?ViIE&Nqw)?(9-zn)NarD|a|HKukpeL}PE2@%0bR}MsjTW09FQR#E-QG+1qIQ40Huv2(1Oary|6E7|it5Uw9x|Hbp>^NPnS0c<%^ zUPKGn3%X5*L<%O{aoIS2Xlr`2q`tCN(P-gaPc32Ah9SCDTxIyzZDWtFuosm(dO0w$ z(hMzb_pm2`%?bflnxc6*HGnzlQlabvz~$l=z$!e$?tmc+*$2Ob-s=!|FlOG1Q6CJ6 zj^lc4+uyZ5Bbl4>ljPtFi&hi{bRiJ(6uFD>MEa*;gec~J23p=_qzMhPYLXQ_FIpRG zDqmoml4D+wkz3^~$m?)r6%j&^e5{+!o!=v|>Z-U-!MB zO=C$>d5f)5njFg%^Uf2_!|8bGUB2O_XX{_gD@Y?o4ldFQD84D#ws7Vm;+@JX-(p=> zGdshm^M9YO*UTa~W1p=Tt06GO`3>%PZSBvL0Yw^h$`~+P^m)2-E3V<%9e+*vN`ieN z4+I*v3_|5L@^26Ud$#$1Kd7GLk)pjNLH<&C^OyQE-jgELCH$f-#BDN*^4w z^n$z>JwOyQh1Z4s0;?CNM|>|(U_4Q-jWmVIxBCq*2P)2hu6_wsR+*sdSi^r6QJ_i{ z{Rqh&hlXqd?~2doTQ1Pk8MTbST4%wYhyFr>dg5$*uJ@wISBaJO`qTNVO7nC4alA$f zLnXi`U`R(Ij&(#9-UjFB6isiPrYcx+0WZrB{-~+TK{Uu3EP)3u2j0tZ(d}P@CKD&R zd>nSwX@GoWj~a}_kWkCoNk326S^r}RV&o9$a8ollK>1!9(oQS7n`zYKK+W`Rng8;B zh%X26{5Dw-rtxuz#Fq2z?@M`6xd!R{yYu$XaZ!A>h~a&HQD)gj6_kiM?eQ(2@_Bxn z)3^I5CzUX%Y)9cFi)-mEqCSV4l^Kl>grnztb9&J11S7b(GNHrgabv8bU1>VB z)3RwhijEO@@tIh(X&8yQeD>AF$}dhd~Gw+c6PWn9%2tXbLtm` z+uB2WeX=gO{@nf}YA@TU;5cv7Ulie1pju77mn*o2@`706)xIZz~_}HN0a` zhAdOIfi^XBSADRpRyd7vKN5APD_lcDFj*T(ml>E2wQ@h`qAw}_N2)&|q7zB;5=FCIsNN3ba}T}c@=UWb65d5j5;)GV&~=alHyJ;2IAIA6 zIxy1l16J4?F%YQUKp-s4XE$}z)>o4kK(1{PmU00UgA3?&@9SZ9;{4lcvOqN#@cW3C zUJl3I0cW!|o5c#W>U7hX=kjk4u5dlxr3m6Ulc1H8v8=70A*!W{Gf@O)W=d0yXm7kR zTxy4#iu!A_#5zuxs9$fUs&W&gY2($nWNL5oGRj_0@)2dIj&6~p9Kq)a9T~jG&mba+ zq!K~*n_1P)SI9;_ot7Re(OtoXK-BVMG2`Xm@(DmqAtZ0vHP9F0y z$&X@1$%%8icD??5as;Yo43jpZ{E3h_uJI!O`*MPbw?erBXl2}>Io)7_9c`;C#{lS| zo6Z+A57A7O<=env$q!c(W*DH95pRc71w70X@+py3An<`=?PmPQ^O8wV7UFc;3%d)R zk{l{$tAUwhI}P(~9b7AAQUU1?ifR#KT`Fag5z6OwTXAO2 zQ)eFdhRCx++E<;pCTHD2mA#x`nIKL{cghOK&TP}pY>Rdtllrv{Iw51?cVIf*Uf|pL zJrcMZm6Bu-O0-dcexO3LvCT&;finI~s)0}yQ+&Nfcd_pqNwf~+xp#AEKnN(4N>UTp zfN}QPYDBdw<*(Wlr{p-5-HKPr&# zkZSFS^)RdGZiY1K5EdGel3?Noy2YlDY~7fri1R^60zGz#@|tz4ZChNSa1T0w;OD?Z z$aHrIsJZkRmOB8O4{!bCjs(=Nli<8vk3N`6+VvRDWI`5^Z93UX*m&)utpuFqL=0<% zTB&CpIq0P%4=CFLxv;(QJGD%9pO4aKnr$`PqbY3DjWj68BFhVio2MhBwZG5fmqSaV zo;&+48Xk3Yl^MVK1AL<$LyX70=G<59e8u*$5mUq#Ql+fQeeyu_4Xw!1C?%^4o>Am% z!ksH?E@(j-^g9}-zCG_l_V53>`_H8ICX@B3dc9#l_3n zg%Fm)a=I80ZL^!z7Q~G(L*Cp8;eClQ+21vpG%y1n6Q(HEj3lU5#eVpe6=wE{6Btwp zrz>KEry2+!G69mnW=?8|RA?ojOX7nx`6yC|2wY!G?e~Bj74`&f)s#L58$^JegMEmK zCZfbqT9Mp32Ocv^^$nb5r4S@2Z&Nl4X)vM2!(ak*cS8g|OC?Ts&G`A~8UdV`UkxEM z+cQ*jOsZV1j}gEF-?G!$q?I5UO4P+VRQVN>_`E4f<^v7v^;N$#f7S=fJoYhz&Cs zy`<27G8U@KTf7)?B{YZWV5&vU^##0xFuYSm5WzdzLpGp!cKhjJk-$C%q>5}7TG2P5#8(2hstrtCmU7q^GtI2m=zZ4dADu% zN8LEx6{u{7HjIA1O(B(gjK z*&-7WHh2eG$8EObnTYsSTU8sYp{nEdz210BZQO_XV%@i2Bh;P!0%d z$4j274}9IzHg?WX2HD!i&ns3OUuH>y4GLCbS6URrbC=WP*cCXa0m`DnIaJ4f5LVC` z2p(%qdZFSM?)l)Fr5#D3gw1<)z_w0@sE|^G>Iq`Qt{Z9>su1eLXHhW%d9Y&mJl4?1l5W_tE3&uYav_jZ zo>DnJ;IeDby;oxg2&;xfsmV!?93|{@ss_>?j0;dk4Ek)1n9~aC9oPw2;0D_%Es3ej zzg>?$cE8iNU&ymiHa#Ip8xX}i;||I2pw*o#O2GQbQUhpxyB?Xi6*9m{H7s^k zrhoZpg zxb6=$J^?jM%(pVQoqEG}OVvG0sYK_5KIW5Gf_@+skXrCcY^N(?O5qfA%>{`iZ60jT zAU7tbyNA{1!<|G+1IOu=sHverX9`4*4B|wO)gG7|vf;G&$o>|}?~_{{v`Cra%tJcq zN9>+}vRYkMkRHh^`|(6r=DYhQ!OPYIF>$*deJB=sWhFm~g+7?c>-F7TaXy)+$jOTm z2`sI8;*lHe#oq1Q4X+3S?$TBlP*P%1YFRI*;;%^3`h zC-F;I!s%~>dzDr{C){j$R<5*vX76SYGkXB9DCVLk_2T%4yI{~1(t>@9x&sPL{N@5f zJ~ZLjZyCXX5~BI|?Rs?Wd)5gZG4l0@T_x9WK3fb+QJw{KyxvxiVMnTn>!cjG64>A? zF65i|MFs&t%}#Qk!px$hC8He^LU?*YeWKkRkQ{Qc#^U#QIl~Wo3ZEa8Q1dF2vVvHb z;Qj$)tmYmhgU}ai@Nq>j*KNdNa5+Im&~N3zhe)2=b(#(a%RYw)i}2mP>TIR4@9vwE zRsn=0J|%{W@pc&8MvE&e(qZ~Y>D&PFcOotT*vg0|9|O=nQJ~;hmtZ6Bnqox}eNE=# z*rP)AbV#QlR>AJ%?*Q2hes&(UdiI9Kqfa<3<c#fQdUXLD)95xqV2NmywWqgL2kn$O1^Ut=REvb%0NHKz zC^4bhltX_pyY+d+V(TJsMYL+8l4SN%_tDt6@FP?sHn5N>?b{l#}6C&YT z#=0$ne$<`TfTR~!DAx!>ve0po-EhkA2@+Pa0Rpp1Rni7?aEq3B+FjIV3_Fh3qcg)3 zs*8rpTdAko>_#7p5$Gpq31-P_YSYBe^?$eXzVu~VsWdEg%0hmG2vzDiIZS1eoWUC@e>#^!D< zby-@OUJ2!BR|3$1PBWq8alartS!f$d?SXl8W92Y3(IN7yJ&E0h3)IFzP}`g>Dv|Sr zq74BRyn+RdF1Mn66ezI6k|{~8KrG2BaTc0FVz=O=?2S|pEbzQ zp<0Iyb(Mv}^yvQ8R>i0RXptz=mUNy}Hn2>0%&7CxN1{^r(;>(&r?!^Iq{?SLdrAjU zsY~=^`wl`XbNRFA;N6ug`)lA5CKTD?iB0A*`AqX6)AmJAD!47kXhoF0m;<*$e)N71 zKTRxsH+&5Aq z*YB_c2`q#ABw=(jsWA451}R*iIqW6O=pjm_3}U$ld)J`*GoDt7_ZAuGa1t9!+n{zb zrF-jt@T`Kp1E#a3l}|+}5s*mlvc4WsbUfv#V|BEIs)TF;Wv~mLyjkMI)EGM+jLXR5 zbycvZc&_{}Du@w^00|9Y=}1ZqGnWo5!T7yArU#+2g=s?S0utL?Nyj}r0W*ZXbNmL0 z2n-w`!0JLsgbHX|U3~+f&&o9E>chqc30>0bE~|fH+jQ4t^I37xs^)n#;3&qU22Y5a zlV~9VHTZIKmf`*&N7%MA*yeGD?uQT;@Z0EefS85AIqnOA@MI#wmoo-92m1lYrVUx3 znwh@FvCcuxLk>X8tGk2s2)?&uvZ;r~1m0=VDjwm?xk@WFjFV`fbMz?Sw~tL{)}YM` zE$Gg&?9%W#wDAIrzK6I%BGctU3o23Mp*;=Pzrjq?Up!Z{S*=A-(m_lQI%VgrIR%o> zj1nEImT#5eKm%ePBM8k=Ur6y5B+;N9W@!r{20tS#a8Z)KLCIKXgXG>(`A%z*3c;cq zOP!Bs#!Njanhgu45fHRPvM4f{S53jOJ@bY3|2R>$d&FzVcOu?KjJE@eL@3{w zQ7l=SAM~hTK;Y66rV^|J_X=r|*)y<^hR2$Yb8qv1<7!C}-HzT#s2rNLY}-=`PdX3E zuHBc=eO$5qxD+-UHq}apAqCI+W@H3X7~_wQ@R5FyBrN8e54v#t_|4TGdu-`a@f6c) zW7pg;7Yb!tQn<$Yu(a}jxLag&qPJ!R5{ZFMo3THF9Hte*ZvQfYM3d(LGyz)fu$RYaNkb0dYWJ*wk za|eJ>si`TUZfWZ-V~}_)_{@PtSwJ4T!T~Y^63%nfTix9$TUvBGQvbuHT0BokV2FI& zh=aCcH+M9tG<^o7cagyE7qYw8O5}{6hZDao(~4mN2%^lGb|!m2aT1sGNB~GypT^^H zLPJ7jGrpvkxC8+-p#&k&41Lmrk6N48GI#(W2K}=I=vI4<@<++DJy^3VxH8xjO<}vq z=%ckndYcm%<-8RQafmRwNu^Dv{@95ox2MUvx6I@GR>Lc%_2cF??^riCx`Ese0Et`T zfj$|8CT>wcm7KmAtMRvN9>3Lh?jZQ28KdPVvNcLN0+bKZWf-OI4W|tVBb9HAH@R2S z+KphH_v*4rZ*2KW_*C>Z)NV8uuLMNpdUVbEOz2KYN?;K_cH^OLHDzbB_vl^a3OKEU zhBdc#5k0(j12JD4saDb~IvC^jWfdU_f(t3i5ivLDUa*o`iO$yUEc?E&ZX6|76FLZT z#1RLec$@YCbv}w_ZDCGqH#;%;dWC%&jj^;f@?Oi(5?6~_?){Jzi#Z?}Mzg9$ooKKU z1CDWfm?R}tslYHN%_^IMYRx<}+>tgGR{RLdkvc%~D|HE5co|^q?7=X%x!G{XnELo$ zdo_quqb+aOqifOC?iR<>(d;Mp^rPyEY|^DVW0hSs(?mD-V~cjR#eM%O*20_mSPDak zLBI`c?h2~ua0ip~RZOf|wjjWwAALu`ZSVK*QEO#SzC$Tt5!>Lm#Z^lCBc^DQ44N*4 zM}5754ebzA?UTqG9T9^xZP2XBCELL0X(!;4kqA%#Pu5zYvo`Qvh*3ch)F2hF8@ZDj z#0HQ>Zy@mVB0euRgrj!@Fm{5SEqa4OOGd$(Q8Q%vK}O5KggNlj0E0y8Q3LNl@#*NQ zL^Xt@jNN1HIj;}gn^gXnRg#S`@o-nu4wafab<@b8zfqsiBN~W5D96Bls}cK*p0bh| zu;1+rH+g_9gB)w)J+5LraTWNbq#=S>YEZdRA<%#1^ztL)6ymnO_T%x z6*#K#-tK)css2|VyZ@E?*ulKHT6-XE>C_qBd}&fgk9K%*Ym+p=}iJP;~ZbXZ3;|#+JHWk3Mwvh6-rI zd>sqmA0gsL{&wJ|oi%Zsc_0B(^yjFqS=%CUOURHgRNK?booO3KCKcA}2s14KKcW9P z4DO$t-3H{?S2>l&oEGt0;1piZ_ydNH?+1oI67fCC_5k}HL1d{=SSmUi(aB0*DM45KHK%teK%?-A&8N!b6*FQM+Q>oA7!-1tBexw3v%xXs}3O!3%)! zG}2=?G2|_9b+bDb5Fh1!Qh_b~L!&avSQW6q3$RC>90fHnKxXvUnb#29G!Nf{D5g`0 z3s!Oj)HP!nMOCtQ`yR+{atrUKccvA)qVkeZmemkwc4@;gRwT=B&oBYwG;XVdFBSCA z0TCadQijSyTI)s)Yy$dre^7YsFloZV0qKxV%w{KLM2L|1TEcEEz)0P|Ny~mboI0wy zyZxbO=cWNLP%Fr-tKx~eQ@AjlL#HnbRjUO84)6lkqid>`b~D9s)hLl#J?aj}w*Y!R zPve@!$4`7ZC1F|@ZaYToj%;=%rFz>J zfi^gD7sZd&q(_5B046#7ITj($g-;MOibhmpp%=HR4w}u247K`lZc~&zHS1CKzFF~T& zFbxc3!#Rjnoav_7WVu04c?ZF=3QZumBSf(U%Llo2j8R~Ama){8Xw62v5gte_O=1%P zj@+G@=z-56VlnAq+LCb=b=g0B^7RO~l3DiJfS>@qV`V!|h?>XD!nkr)w97dkUgJ`4_7WfrnK?>^OloFQnw6HK7gE_l&XwsUaf9sOi4hP1u(C{UEvN7HVOxz2Z+D} zlLeh8WCZ)7=F_A$+v^MwHS92DM_g#qM#A~(;CQ5IWig&L7(U&srYzPCY^p+1$_5HG zuV5_5aRJymA7%I90%!tAZj#0x&9db&&-vlZ?(LEjJSHlzWK|xmeJru;@+7nN6z5wTz?rAgRizXSLRnPgpEzdmsB$D`*qDarNTxCWYb_Rdf z0(;DEYWuS8(wIrI&N;cCDipmbx)h?kyaZ%)-1Id-mOF?puVx%!x+b|k+mJGF8LZGe z>@qMCtY4tr&;NazMl?dzcQh~pA1Ze44k%k{bVT3Cz=iW>mn+B<~<=v zhCRjTyS94RZYDR_dq8G0I27XC2A>ue_tKmCEiz(092Q77$dm-C4?7gboiQJUS_JfI ziIXGqlg?LA&p|~Dy1Lt-tx!a`be0Z#yBoiDx70KJIXK>cm{%B)9V?_9nKsm9ThkZ( zFU(?cM(uVzx>_#qN9@zMt?gNUb8G?Ggr$5LB?<=vkT*El7UT|kTyNa5zp+Zw(N(U+ zAXR8wXaGif%G{t}f8n|E`bMaN5PFa5-!BPLihuSIl^V&_hP6E*NHFL~=}302bog;a zKQg9N^w5%#`c285J5=XzqSFk}shUz< zaeUFSgLX1Op;Fxao1zLrwR5^f?MQD!H~YC(m7xYN;i)iNeRCuEN6`#;*~;1q3H7+5 zt{P-`^W*~4VMlVP-MxRN+m+?$OW?3jhmo!=elLk0h<+M5ZZ4m&OXa z!!DJEN%O`{UKt!98|9h!h0;BM$8i{Ma3+980)=fmcqcvpYmEdN6}IGUO3;{rv4nbg z1Ptl8M{s^OLwYIv)edVpv@wCz0-#g$S>zPlM01aeGyk@_m7PHW1z(tZ2CyA06V0luzS7**zZSH zjGRZOxHCXd1Z2Pnxq{`0sDgIdY(a;BJzudUcguxbq`^7oCVwm5Hb8BF2p^D~?GzNz zu2j;uMfBX{c~rtk-QVFkn8B-NjxKs?>?2Yt0A8`r7Lzk7cw$31r2;9j+{G3}=Cp01 z&+3Yz(G(>?3Y?-OM~YeUKqlOVK06=fJJrRKiJLhF9slWY?VL&0?g2>9(6T!nq47W0 ztcS&;$(Sr#Ds?=H;C>|Aa&(-qP=jrVp=k>@#yVwEWiUPsnM@`}t`!vu)m>sK2(B(0 zb?XM`%xKPH{woA_Ayo7x1Lt`k%O{{-M9!G}+UUwO;kucIx?uf37*ONsQ*&#pbnt&j z$C|PUP9JYmBs7e00pVgL?9NG1<^U^eqZt53JNBuGUC3O(|1kq^3t3LyjK%3EtB6~p z)0^&~N4ekSco4oet9$U|RX0AzXbuTy$eu*>V?Szm&im>(gYP17ac}0z->Q4&CfeA; zm-%K>W|zl;E^?~{hz1ia7Kt2Xie4xZ!}N=OU<8g^ecQ1D1|Ey?PQk4XvKV5T+>ff4VUdk<YXlDV zT<&u+XK<;D)F?-1`2yMEWe#ZLH(g4fqZQ`!a0l8zQU~NTo5nS4Kvct$GscP>%41*j zOJB!z>PH;tY!K)S0<1eciiivZv}OQe|6$f?qz`7iiPg$Gk1cNp(s5vG_hh)gBNz4f z%cWr*+60TgA)89Au0}Nje2$G2~N)8cG93O6fd3e3kn_1@-6oaL1IV0S@sJ_Qpuj9BX-B-Wx#C zkENdKg`VsLo?xysw3%LMQDg3QOlOM4q`)X$Dr@(Bs{jHwJtJ^j_?3#P1Ozii< z_Orun?;x=Ux^HNa&E=2Q93&VhI)Ipk%y)J(ie!;R2)+~=LCR(+M6qE50g#YfLOuKq ziv2C!Cv5}mwCqZLyy$K{ z9kK^}45-TB)&N;g5~8}^QC_O!(#Kh-Sq#7gyw&v=>l)43 zD%c?_0{(jKY+2b@?RUFU8`sXtV-7()RvcwSK!5L-48#J(mY`3V*g;lnJ*vErO#Eku8B~9B571l%3kV8N7`6 zO9f_x6tG3#@g($f&FEJPr9bVtB4T(wJBdU>OOnUC3bg!e|GEQYArTk# zqcp~|3A8x8DMffw@5vTaQ9;7&#S)8nd$&Ju8v5f1{HE4|T$Whr@p7?`@>Kx~o)QTBDk)GZ^F!Rt| z>R^GQmbeq-dA-k@yyFd`b4uM=j?^0vi;pNfBV0{)U0gwGZ-=PUdz(K!5eQ%^CgF!@ z=~E0CS>W~H^MHLvhJ1v&x9b%or0I&D?YEi-eFxMF8*!SppKxm(YjF*a>9C^a5k1x3`xDdUls#wb+|b=Lj*ql~goM zQgrY1qPQM0phWx35@qXXBcBWu{JFo60o1lTTcqptN{z1T9%sOiuuSsq<_e_mcb99w z#&GU;6YSRm5^nYzs(A^(eX6ihP<2D~ryG1T@x7O~2*13VNP?LmkyZEI50W9MH{+MJ zafCNMcn=^!s0Wm2_Uuwgkbs44)2$*;SXH^$AQ;>Ux0;>L1uk_JG4d)$RpP(8d4rpV z2DE-RPsFye*jiyNI*;|dG8(Zo39^t~O3|y>z1j4pY`YjIXwVYg?Ypi=FlthU0l5+7 z0z|{MeZjLsBtN>B)O_Br>-GK&AzQJcJ0Ka4_CFPbrF$T4ucwsJAlXLKdL|750{~O- zs(uO%9UxK$6BVG2@Q9`Umfk-<+C;*OPY$p}>n!QX3bxtLeeBmk?^gQKM0D|a#VUd- zjsqIJC+!RS`+=6Bt!IJ>5|I53jK90WZ6u+jpb4degV#c&3K6*J026BC!YsRa6|13@ z_i47-sB;5vkyiamZwiPV%(X1{ZYk@=%Zbt^p=@Bu|>&=gG9`{XSh6M1=2# z4@+2LZl%55wfP8ySx;kN1b4r-Gf;Sb-M)19QD-h8uThVRsZCWw1Yn@*FUJ+P2{t4{ zfq`(9Z)o;=`9m}sAM*|wIGsVGDG`PQH4*OjO)8BH&K`Ufz{!V8soHUm$$dzpfU6K8 zfKe$T@RYQ6)5h9$Gd>$~EW;KNAFvFgd60Hx+Qo8#-Sw!r9T;#fxC?Z<=2@e%{TWw# z1f1(LLBBm;m)Rr_6vvzofbJPdUs|+q&mbVT?X`7G=#<4k0bvl0fI6V8A|im(t&E$A z9e3Cy3(@WInkCcoeVE!6!~>hMEXM5tL&7XE;lb#i=~RR%(jyyqxnzSl$CZ4bIa}0` zr(qZanr8#7Ow}d`&Ka1Mv4O6pKB$0TC4We#+OVk#060vlD4Q|m;WU%}I*S6w9=R=E zVF=r!%bn!dPFQOT^pYjA+)nDcnB5IZr(|FBc0IaQHFiM2HhNBUOX;Xioo-0tO&q_T zbfwsd2vP~0=u4EAL0~~0!2_uBc9-JU#Rw{cP=aGPC-mIluCglfY>KI5CgV(Hwq4L+ zhIy=a$I6JBwZ${{db-OBXgE4fb4Z80s0gJ|=^^|tDAl;N4TdvJ#)vjHgO{KxZHrdC z&=Z_??a)i=mWqvvU`d4z+$oV}PYJWpCRm0ZV%mAj?rWQufWJ08v@mb6`>QcvSdW7^#{>8GP>$?|@mQ8Ns? zZp_8JKWml=o1O81@jA6$G>Gi9KO-%&nWc3PI~95(_;*`mHD3e{*@wySDdWkInsYi) zy!c%x9-*CCTFurrSpD7LFaz>7>cepI)qk5+$9LPK5@VL7#6RyoXaflz; z5uDW1$gbD5e2+g7IDyZ-Q*F8jvIuDh|U}G`LUS#>F0u1hxLb1LoGEGbj{=5X|;R_YN8Zc6h8yiPk5VD>gf*eRn1$$$02b zvm2i7Oc}CP%GOsGyd6Mov5s_aA&MTfU*WnZ_aK7}CWYwVK#n!-b^;#v#uxyd=nbs2EUiTkkHwCsH3ME$k! zOTjH;(@@S|R>p*~bYPRCtWpLxCXKwJ)E&4MP0C3p-CTE-K=Pr1ZvlG4Iv~F535HXi zm{M-#k1)Y~F;^KS2mCyS5!Rhze*D)y72r)MjVU^$oo|9?OnPRKL}piIfDB^t2ZqZw z-}vL6-beN#<2=woFh?OkbrYE~9*iPdD|9$2tJ840Ua`4fC=J<=i;3pFhmS3(cPE!E zPeIdE>D2?!HZOHd)nj7rW!db+Z5w?x!t;f`ZlK-ML2?S6MyG+gr+=fSXj>fjV9%2? z!K?RH-rT%AwJ6mh7wyXfbptTm@3>u$3So(cebr6c)svY%+o_kq;8_5w{VhC{BvZkw8vGb>z0&%&G@(*ZB${J-J0&N&KTu{_bE&>}kIP&qz&6P1cty zgCuqglBM<=o@hF)CCV_|`ys7v%sIMb?m=7IEM$#%@aBh98j6WP(2|zwZmV;%rELRg zM2S&6P&YlGgV!WZaYiF~b2_^66u&M`f9Tr}#p&zy4(sFa@9DgEah+v0%OOo@a++@@ zg^w7l01EhuJGnKO&@(yFPxcmRH%^HocK?j#7poxW^%U1<_?Ldd4sI}~*g}A-+k<`J zq?N;NeqIsz^%uo*)E;S`-6|$v8Q@Wgs<~cLA4n6H$hslA29214U}HF?2@^rk2W$x0 z6aXI5@o=)X97Iq~;sR0Jf`^3E9ZU&<^Tk9bd$>inr9W_yoaujQ!3ud(1VtJZWxc(b zhN$Ei1l}pG8^MuUs4U(cMRubukb;xcc&I{1MRePFf1h*;e*|COT=TZUnMMs#GdDw| z52GS(y|P8I3|f|AqSexcd`Y7jn?-)hPp)mEHp5YX7On5tMFg*Uh#XMc5{Bdk5IGr^ zW|tj>$OUa~r^b758Zwb%dF}9C0`jZDZ%cD0GDXoR8NxtXJ@&G!F`)C%2Q@e8(a4i< zh3WHlJ)-5}giRe@J>YAG%FtCLtX=~)A0-QV@berm=s*CWDW8DdaRDD`Jw!aZ&Gfp| z)oti=s)R{QT$jvYbtr4XPG(J!Dd34C!2%F%dq2Vb!4k|RQ@Am4D(>gwr3VGR{zmRBli1+kV-?>jdKU=<1S5XUYmrfo+D4nL>;{-gOd( z*9e&Q7WF+7T6O&PE$Cq+^(n(+(R1`lj+1>)_=*L8 zJ-U*NWHUt%de5915+AeOYr1m9#B854JRSuIHz2u*j#rdh5Sx9SNs5XHct2^)-49_+ z_yJXw15E`GhJZ-W#L!o-LzMLv7WC{)OS_FqX<3weQ$|kGILIGtcY$k#R5o(E>3W5m zcS2A*P`H#OGQZ zCO^8c!hBAp7935U{sFQc;8*$hhEIhOc+5facejs6XDkrpWg8d;gvE1$A2X~mIL~jq z-qPbIJBUFWMoMVh@5Z3XvAV;4IlaxzXvs?k!vh~<2Ap7zqE1dr*MUuky{O-k(3@|k zwm#DBbu2>1979rR{Iu8Y5y>(Tvzl()QMBt7o732nrJ3@LbmB8aT^+=w6&n_aR3_cj zyYsaRTimj%=cp*L{FO+Xpp64BU@rp;IRK23QZmA+ISLv-*?j|JtuK}*D9@mMiM1ew zMq#^2jQL*nDiJ&M%Jqn^fTEk+9H3iR2Io9@z)hR#X~j=s6FmP%+44Bfn*;Lr3BQ=s z8<@tnK!&7EY7Or+KgQ|d-Xtqu_*}N<#7Q^6unChSIe-Se4Ui%tyPq<>5}l+W_>@sY zfETyM=LWl^2Y&GiLZihK`=xEs!=TOLe zA--Y!p)UYn?zCv0Htds%t@EZg7qC;qDSO~q!J#!Hc2{q4P$k|e!6HUuabTXkyzMPc z`040Mvu|MTLALqBfv9d)F}EWXrPXZ0pOv7$Tmg#{CEUrq95mN3zaB^#=rmnIEOTYv z=xap?l91*q@gN*^ml#HVhTBW0Zs^Ja-^F*q&>QuhJ9WIIPJ>d4a$WF@5ybB%N@-L& zeDT&#(2(g%%C_qH&j;ur60lf6lRgy*6xmEq4Q;#2xd*63p8tc&xgO_1LBMW>GZJlR zBkOI}%@x6cLzw1p!9#O!mcgNK19P0fm&6$yoJkabN9i)|>qa{m-6kXlF876%3!*z? zJ>S+A=SL@0*n-^6wFV3E4iL&cF%NDqZrThzVN;ar6+9EbNs8q5+YVmu__-0S@_f4y zqi&FbL8sFnbRP%lLg(;!2qt7g|NXoQ+>X7{%YyPB0^syQGg{;m zo$lFtWwoE)??mRzue-o@8+;r za6w?!k^PN&`X?wqwmNa|4h$&Ih2q}{vQZCV)?qpiLF85Ju;Kp}eMM=<@i&I_J4>lPT6D&P2B1OX#et5ETOSksmJ{Zv6!$%j5)nTHlKsNn2w7Q_8 zfv%1p-;0|@dWiM{#dJI8qf500ZJlAh))*h0%;$0q1X{cFyS{G=sz(Udv|2%!1|J&k z=9{|XpjaR>KO&G|aue2m4(lC0TZ%BXG$V>gnH~VEHw~{RqW5rcpeP$ONUsEIqjw9T z*g+p}5+a9(mV8O>(2YHK0VDl(m6Q90(DcQro2$j!3y%W9Ci|Z#S zCWim*6kBJ7K00|`+M=J%Y<6quT34Lqh#V~JgZkoqvnOQFwa{vbH9;#N%+S~CsM6vK z;>(?am~MP?SL;Yt8`>#cq$R3`G#ADb#-d4L7&(}2kSjtD(Dp+Jw1cT z!r}P&?EE>Gbh}>J0{2C6xaK?R;GzqJL^DO$)av5O;r)YV+8t{cBO)R4wJA2%hT8+k z8NoMjle@cokCj-Vs=K=QY4!I+%4=D}T?D43FLjJz4r*?O2E|n&+K}87ddZ*W##J;Y z-{T_9powF(N^^9=U_1&eP~oV4LEu% zG7P_CRn0R%=6M2K4yG|k?-4!Mc3R`o?V`KmM&Q`0d?q(5)J8Th_p1d)Kpka|=?wyI zMu+R5n=&(9jb3=wW+m0U1}-?9PhJgaPPNjW-gJd#i9H+I(P;?gNJJw8T2CazY07lHAg z2nSO#$vC?NHw~Oq#6ijRH^BGoWK5$IEp9&mmU};WxhotEt>+>r8E!uVJaY9fMdk)X z5~dJ$vL#B8qdI+?mv=_$;NzSH1~+8`vlA@P)VDpYBdT@Wk2ljTVvino;N1dclsdWK zDDly(kTC*A>RJ*CWhoA=EZleA5^Hfj)*@&*8guUU6BzzS8=*i}Y?nV#Ts%$ka;Dm{ zy`=di3FOY6i|(m{#4&6gGd}t%PWX*s6CmKd0(V+Ql-5oaSz@M^n+_|@Q>^oVhj!U$<`gaw1t`|iVbZq zn!(4dHxj%+VDhM*adeZ(v>|l;<2n8y*_@g_u(=7%@wppdDz>GFv0*x&T4H*Vy?$OWrMrPVe4IlYPG$5+S4WTkJ z)$!?a9pqrm2*&5j3qIc?OBVmqHcDpELd;ZJ-I*n7KzO#9JssxLsB%wkj5Pr96HzJ7 zSL3~LrUihTR%563p9U5cKtsu`1yiJWsPz3n1URB)MqgJ3!l9MZOXbzag9xGAW5XIC zC*qa;#cTbZT;NTy=mx_F-nh&pa(uLbULen~ZXQ)T^SqpnmnA|?vqc;$npVQzy+hwk z(p{frq}qUd0)|OyVdq=J2BN0vxztqluwtM`fv^Iv$Y2Wx5Va%#<+`o%>UBe737ot_ z-^SOmRuqM#F7MSJ?d9_S2oKmu1&WwEV8eJqZ*+-3I zaBekM^=Og7z0&u7qG-)euK#md{dk=icj!(UC#32|A>?agO~8CP@svh&Tin+wDYN~gfLnI#tp$g`f> z2itJ1r)L5xl2D))K3u6iLXG3|=N4ud@oB(?J2l%05nre*1Ac=?T-_&3igYVW)Lp zfsZdG6D+tt0-**igP?BnWM>3$J~{XRh}MK$I!2sMNS|a#mgE_^zZ?am7_H_4Wnt;d z>{OLxF6Okqp}wJ^h3nFw;&93>67(RgHuHj0HXB?d1QlS^0jnAv1t^Z(O*E@7&F|OK z6GcfNV7j`6dUz`e3aF9`!iZKJ>xop`c$QcNjp60eDxu#Bsq`)_-RR-ISe@NLoK&jL zY;Y}MHE3{(S?Wy7sj(T90f9XMs1YTV?j2d(GXj_pB;3=%gdOUIwBo;K1$8#x2p*iE zjZx7u_p=RmTkN2x55W00I)xnsn0&2AjCGxnpa2w}b#ix-}(=d<(2RYz&l`VqwHH zO@bCsJJ{nKE+l~i)TJ=xN36Y5uqaWmrnzypZQHhO+qP|cpKaT=ZQHhO+k4Kv(Q#+G zW1>6a&QwHYtg4rlPq`{8|3AO1;wOJ|*$+&MT0wGTSiJn($5~Cj=Bwic9e)dl{3Rsp1n)lOVQfCj<#IbTsk8uL5T;`=&n* z@y<&Fn#4SQIHkH!#d4q>^M{zilxxH8Td=kCKI79|2IiQwXo94#x8N^)vA zgtl#xjHcG$aYx2%3SZg&Zj6MCSZtuM(K|quR}XG z1IPWeyoLI*6BC!S!xD?pC@HUUT-ZK}6eIE?klZ@VTEY=8jpC4tUj$=sK2BYqU!!un zeAGPB)t8@~iX=vf^C@LP1z!)vs~_n=(y!NkjNt zWi%l3Y?q3UZ=g*Mi-yv*`_N5GqyrG2$_`#8y3_ib6d**;`0esKwz_4WKm&~rpG^}I zZ<;7@S1fVQexHkPx4*#uGYzd^Z&6L*4*e@b;}J z)@Mu80=Y39X;)$Hl|Bg(pwO^N3qhmO+71==<6PDgBrqLyTu(+xay0qsug$N9t|0|6 zJv1B1c5Ga7P&~&=jYp2sF;`Yed`v7af&&k>VvL3P6bOc7Ja)C49EYGp@HTL4t-W|K zj#oP8PQ_?!At}fVlmulk{6J2xSdzNFtUWVrXFJ?Ow?6Svy^nBni(w&=B~iYRGe?=0 zfKHyMPnuP z)mC~}HK{QxTaGSqoUgh{Ck6l-h_KwhSaFq1Budo&mPwKj+ufeWm?zw4mo?sJn=i~O zgbW7+#Q^ECECu7s>tZ)Ug&D?P=SSNi6WCO0?VghMDR zUFW3nH$UUbyJ~CQQ_N#j>pv%swVjv_0Ib+DPwSV zPN8htkBd0yF{Y@uKRW&!@x&ID0zxHo-(R9LY4%O$SP5%1SrbsDIv&T~(MRX9^EB%s z!PcRI_R)g;E{ptelCoN$4H|0o1x4e*!=4Z726xIrSKQHU?XE)t$2~aF4(dCGH8>G( z7XH_44BI1TN|t38GU!YmW?e)-3wVY&sdq4wOlVsHB8r z3dSNWqnDmV)4g|>-?DZTGDq}ge+;bw@E4S}27#^UYa9I5x2Y&D{sDpqvd_Zw<#DF1usWw<`n!|zT zy$^ffHi9j1TWB|k*#NOUi)37GJY4{~V6^_oeVTo)I|y&Io|dygxV_LjqBn*w5+E=@ zfjz=Ic=J%8KLml`0#W+ma{}V<9pF5{yn@$uVf2FNMB0Ro@Cd@RgmCe( zTXroJ88_rVvvYuo$0|B9F8Qc5@$!ZRF99|k*tjd#2 z6P`~-^-3hJ*iUxO7N7Y3qn<$m{KrLPzc2fri~kY$|J(J<(Zs;m#^k?BnWH+U&VSr4 z?oa@Le|~@g0RA)l&y_R*$AF@X8~{Lj3IIU-zrC~R|KLg***Q7?pRtnLIvR;bBM!d1 zdi`j<(VX%cubJeyup#Bef!gD-5drsS@*3pKbEAroR72P2pdDD> z1XPccjlQ4PXnKAhCzW=(eh+Vxp>Dk%zpJ@9xOzK3?~`=AK98ezy>E|ZyWX!Soocnd zpXZruyS~qzd_C_6iKnf%eB3?X`{%Q>e(y(}4+ocicCkI&-`D4ZkD;e-c6=V+qq~0h z*J`=VVtT(1kFRocyI-qnUl_MyWIo?Fqe7?S61m?`Z$IDfN=rP*RQj; zyL`Rh_j0j(-;O_>CsseRr*OaBpEZ2k-CuW4FN24%a;ZIyy=RfVHwA1HtvXLIp?uBU zyxrWt_OQS2wtT+sANM*O@AsunrGB64#&LWv_oZyNy1k$8@7ITNo23i)Y?EH1rM^FJ z`}g0AVBVu4)YsV$%bUG?-Ccg1cM@Q;!uP)?p?HvpT2vr%?}L+_w|KpNKj-g*e7?Wj z+S!$hxWBJ+7ILwnyf>X}xprG?w?FrfueZLKx}10$ZH642J@OnK4nE%QhYd$(bbPTf zKcjSXcst#GI}2Qn|6ZOB9$x0a*=e_7?{+-&@_x^5mR3t87HWk}?8As3-=UUxdmk>y z6Zg1(?jLXO{xBqcjJEo{zuzC%>itWjeXtfU_xIllivgt#;JUfOqg>mM%A8$*P4Ta2FC{W0uwr1C54P z6Af?6k$0?9=W+T;qR)(&SM)dJ1lsqhp4SwM(O1avO$N5=(C%bZd)96Ik7sVcl5TtC z6S`7A8@czri`B9d*7-+OI1MA}hS{DfxX`0k3Ku9<@Uqlg5HP)-G*r4uE8+d8rhnr{ z)Jxc<-GMMb#yF`bbfO5rWV|PLa4@@EeE|rmnc&=?&N10`u5{h(Jrs8Nwiog}57N3V z&A-Nqvj-tZ9XjJSu)2RK(_{j&Z{K5tH$R}F!;yXzGZknX9eM_!FbI2W87J!La>c;~ z1xniCxjShCwe4NPE(4F`iHzsLw-s%3aa15|L8`FIg4d{{=#4T_GHCcc@Z&cTH+vHO zGDhtj0+R6DwU+=LC%{c6z)8}S^}v|!KclCszqC|AI8e=-Des8%EeMcN5IKdD_OciM zs7X~}NmeOUdcdi4hE@#ko)UG#qRu~#5HEqnVMDU^DbKTn&(cZh7G9|LtBS;dM(~$w zt%t7J)3P!NlRD?Afng9ax#s3=-T1P6f2gw;Qgp3eG5yqXNT0c-lt{1=tIV&6X zCUwkP_2-?|UzapjRiLL(p-@rqBdBDUbu$j0Pe=}iT``LQ38daw6MK-Cf|({rqp5ti zRWUJ@r1q*Cp$3Hyw_uVJ0y0c>DtHaM@v9I;u7o1K7|ey~^oF3jn3R%HBN9w!* zM~hynzybTp$YT$!B1(78kh4t#i*=B$8Enrzea(m+Bh1)+&P`|6kPU@-8Rb8s-~mfU zQ)8u&5k)-RL`a4<_x{=(d`K0t<;H{Rz`=nPEs`G56|s+MSS0w@jdu>HKM0daZm2&s zv=g2@P{2W%q|6y~|`fGzr zJHcmp&@2h*>fz-X1t4S)`Hfd8>i&}1B8!Md))pgEjD@1`i>2sVD28nt+Od>()8(Mm zd<33)q&lndhW~3djg|g`hXg_V(KdXKIs-t?U2M2zb^=e3Z&B+<<2Sgz%nTV@{G_Q2 zm#~nx&z{q*mAk&sNj3DUoM$^qSx~I@tK$pq_`AM8gawnYmby<5fUH0Pef(!9&!_a=`&a8qZ|Ejvc}iB#Ll+8qqf8G z0wkp*8g{hIJ6jSoasy$BY)$6GzZEK*v0NcCDA$41G?(z2dQH{A7jVqQ?zmTK8Uu+a zH+$VxurFI{kW6fNohpR{mC70=-d7r?lCfZuw(;-frmEQ#DUJzIpj!ca-Ba!yOSExJ z>Hfha&5$j3e|&DENDyzKCi>|&*}qY+2vF6RN$%V+U&q~?x-7;V1WioZG`sOEbZMlV zgX(rMi~Rt=rSPJh!a?Iwc4#OSe4npDMguTu4f&b$K$YzWF4rb^wSDIv%r%2~Rupn7?dr{Hiv6rAX*{o%}0Bwa#^TXOlT4#>uq2Tt1FU9v0NFw|rSV!rHH(U~- zW}<&lu;|E>O{u?ZP-Up13iyX=YeW1o{muD(-Qk(`xe<+buIA>m1Nb0AG(yNsd+8X% zq3U2<(HLNkf76(e&a>1|B_>F2UD??nxF&j=DyNWvw%;*j@!r20{W+Q-h3Eu|))?d( zBP&cu%8<&hT8dXGaK`Ip{V}+|VgTpj0+y^!$xuJ99~GUntp2w`CWaEISMiUVgH9{7 z8kW(V1AldJYf^tzk1IPM1$!pxODp`=xLAW|NOM53m$g8drD~TrxdnNeCf^b$2OFWX zx>yySvh(xOk-5zB!(?t8GgKdsRp?%~9>l{Yk=)fEF zp)#pbPke0^dC1z&kBFk~x**vLBRRAu_UUhG+JWv%G>Cm?1(^=tjROGu!ZB()ZUdR9vt$kj>%9$}@m za%)jg2QXm^%|2!rjY*>7NS*IH%kW}oA_Zy@F5C`6eDg8tn-Y2L;wF0;p<7sr{d)%m zVM|?ZK4Pb7Gu<845D=Paaq>9`q1EDsJ7DGDdRV-?XAI~A94xjA$e<@|nv;pd`tcHE zbx{S-fu)<_%9p`kU5cGrqm`4BwC8Vb>Rv@qucnE(^>}{(cjRXvDzQdj3332W&HT4w z?EDrD3Sii*lBm9qZ)*L2Y|N{^hAgP&AIK=nMEU80b`Z)<7(@v|){%KjT5(535{G!m z?34y^OKb^Bi`6>TuM#(4X`n6uBvd7q{Bt`issRI@x)|_3&l?+X>pv4EQ%d8R!llk#gQpDllCa;41s8Xj5Jc%0fGR}S@#HORg<-uWI9_Wr$jU2(Z+#j?-~?D z7zP+9#Lw%DeZm(5+ zhlcKXlg72th!JtM_K`jJ-hwRbCth3KVc;F)}+rzPpJgy{tK5Ujm9W7ZX3GuP4VZns7{gr znS#KX1=(nddCavIQM*f1TUiHP$6%gwb6awfL(nS}u^RZ;=0mqu0#L+Bt>@3Qqw92G?A(ouWem1j&tdx&GBD; z$!({dWx`%+>Q(#+eQq^ejHy*k*KaH53Kz__lIk`n@ERQdO@qremdT5p{Gum8UcGWa zvw9t?9V@7o*<{n4(k@UF2H{QfKdFL|PHc`=*|hfp;XZh3l=q!~P7nq;_jhIYZ!{A=6k%YdCNhtKwkV-_1WsWdgK<-7r7KPV*Zm zF9+%SuNY+}k|3^!VOD$TFq*7LH>hDYX*-AL%G4=vr~V0Q&61`NnHww*dAM)UgnxQC z6gU{z#Jq}X4PkdAr{rt=!|RX-=+dRXX~*$+kCZAHvO_h^%&hj!Y~)*-`IvICFM)y8 zr*Z0trW%_pJx;5{WPAv3y44sH-1ka)Boql#9NcQYZ9+`W9m8)aukqqtV#U(}=GNPxf6M=~Ect?WQ6j~?gLF3_SfG;NJ?(3(hBeXk zQm^8gDId?T=5!X=SnjbCJ!JLLZIo7~RdsHbo zMxibk5xcgAtmR8h1~N3;i(<8UofX5e($Ok(RiJ_gnGq}+=qmWjP=&Yt12lJkP;W%poYkkjc)7yD zI*McwmJ<}4mRHp0$!gcsKYpHr`~`Xoh8n79vFq`Cd3Pp2g8*1qjR55@)Qtov;T{DZ zS`ThIKSME(;~+~Rp9ilhKum-ekP{r6%%mkyN>g=C4GV&ZNz-9(|6N$BpTb0^Pt?ku zJ-|eagKs~2-yL;$+QR z%kUr0C@wS*=yGg|iVZZM>vkK4zocz6{aSbAoh@{$<|0Z*sh_xHca-+~Pg`Bh@U7>S zPW{*Od-bOG8zLM{Qk+zIFO+ewW7+wVhkgC5;NXc8)NL~+v=9Xi>FDcgE26cmPWVZ5 z1RXxsSiAYTLMIvu0xWZZInwZaie{_vq_fw6v7<+={vjCa0~2v^?dmviO1kvrW!7Gf z5M_iV;OZJZ_KpFks3@fCFVh||!Jrkv^&W9)_1$-lZ-TRU&OXOi>aKp#FGmWRCk4(l zGsjhbQ1sjY%`0HpI*?a49s_GfW?c}jy8MP>loSd%5{=8!TOjGMbHQfr4REnFOlUGsaVBD4o@1)+*TB5zACg=YI_@3>i5(f* z(u=vNAR8Hs!8MuIy{4+tMQHFb26j*f+UA*7D)$-u9Ulvj@VsGJ3Y0O`fM#Kd|dGs;>OBq7Bq&ne|v!!X3Im&Y)CvU48)-* z9Z#Y9@WG+&o|Nxj@{mrs09{z!4H#UvH`KC2qH&Z@Vh|{B!hgd^453CIYakNHWGL@Q z2;2vlZZYT}?k*{YKmWY6J#RQdw!n86-Mxw5QyYws;39kOmv;d6jd0w?0@CeeA52(HzQ5b^`~IhP2dt=0z$s`=aN<+~LRxRgrfFC{50~a_1=X6SaTPI0wf$C>=0Gq| zza1bzERD>e)txYj;7GfUDUebge*+LjfX&UxzfzfelQi#~B}860=9=RF`5GaOnuOcy!s_ z%0ERE1FdnnR69#muuY4y@l5UOgsW<5H%E?U6=SQxispm{noPBW9C{SYA!tSqgg_Vj z=|-DJ_W9}yYgOm}xD3o#a73@r(1V;ZNF?8<5AMiV}TYMv; zp6~#(430>#lA&zl1wcwSibk$%h@-N>hM@P8s|y3_s`i?cYV|&7z_n zFzhUyW%XZuwnRTbi}bb2lNW*7OXH3FVaWP^#Rni#cu_T)!%L8Q?M zTRbt*LV=+1)y_mEtez=q@4%M5OBC2778kAJyM3g8t9kPJ-cz&M>_CRuFr{vUV3D!v zn_R%q)0MToMzPFK&%M)NV+VOZU2+L$i(5qV4L_qjN6vnr-V8hU5@_$ZrB^tY`cVD7 z>!u3fX;`QNr>`Pa4tD~nSZ>ZDhuOu+oaKKPK_zk53(dp~;bs!2;-wMdT-~@u7sm2_ z_=7xT8U&M)a-I{6@IP5H@J#X-L1_`vokagaX=|k^lL6rcyV?xf;-NV?L4c)bp-_{` zmDg$u?GfZ!Yiyhls2Q|?tGcY=I>N+&+3HnhPOy+*UzvcM=z8#V&OM!7=uhc1LK*e* zDDF0Q;|&PRAI}-wqxp-p)|gBZ#o$jw@j^qf+i!K`-PXICiNhyoVAaoI)sJxz3Rjk; z-m*Tl=~~*+GO^Pa+u6Y*vUG6Mz?K5JukiBK3OH%Zb{zQ2L!Y>c;H3o!={j8wOM;SF(X!G z@k7F9;W_?cHX4V?yQVU(ntGF!RDE81?*|y_EDQFU652LtdEy2T8K5D-?T#d-<5jo< zxd_R5VJ?;+y`?ff^(I2xJOUFKmR**tJfn2k6z#SZeh!(Nid)e|@U3uxv4)>+bxllk7Caq? zGmWSMHokU_F7(R&D)6}NGEEKPCJ}eyZ<2@+*P$Y=nL`qhkjT5$tRfMY`AU!R*D|U; z5eo|`*bTYKg~nhIqJ_mg@<=W^W9k}-FZO&Lao6?P4TvZDG1jKa6gJHTsLClxD;;bz zyS{&@OQo^?2#W&|MV3}$$IsL9sXLwY20=(NbCij68ES8w4zN%ZS2Ga{f@KS`m%?UJ zM)Q#4;$Sh-1B0Zv+$OSYt$t}Yw(uFpRFMdjrJ#8+wIcPB&hFx!q*6(#3%vY9tMI4T z16u-~TZ_g!ck9iuDS4`z$7jRL3n8eR=DfXWv2mRc#j@)Vu|pb-U{fI|u_KyEht-3* zjH%5wt(C~FLl(*=RCU$1UgSqf9Bb`n(n5J+oN{UQuu_TD>!%vo(B`cb&?@v2eV{<` z;;A~&oxW7xYB25Dh41!&wMe^B)znc0McT7B(NtEC%}rO?<<#5Z+;MIv02jvbQ?!XK zu6q0Zv6BQW?SZx7(z^&TD2-yz5~tJ=2Udk0WAgfpJ>Bk|LNwzqlV^*@Kab1g%EbGY zfK$zxdA3m(FTGbN4>|18*qE-f@*m(UV~5%1eVK1d!u)i~;2IuywHFU=EaESi^t=FAchPuH6oGgTTtYUtwXAgkW2@jNCkvo+Us8B2LGT4;zrZg617P__pB*SANR*f+B1f?`W;(bv4ob!*4+>9tb5s9O9t z&4$HTm;xTh8_3YaEKYd zjM4xv9t61L>GJ2MYje9<4Gd&LPI z`Q&&QW9TI@J%Y1$9JqEJjU>SltWSpD6H3fH+F45oTjz}5oiFR;s01XT4~%$xG}(Yv z!|xf>Wc8HUp$ zivFkJc-_x*LSfD!;%^Khv@&Y>MA@~qeJJC|ZS@e*DFg<%{x-j{0G__roj*QX%fhj- z?C7eFe-CKZHqDhN)vS@PBXDqxa;}t0z*z(lX<#)J)fV$wXK5|PfF|oWIE!JyBT=eB zud$YP?uP33T2}g*WvAq6P`&_6XVX8@88nJ(iguhmT@L}h-`X}+L5rYZG?>ZrFk%^X zMDR=;lHzXIh9G}htU8u4YQxWp#y^J*3z_We5-+_$N`3kh$Wg(6p~^!q!$1kXX#)l= zMisa69^6>=?;~~jq9v1?I}WAPAay=oVk$rs(9{Cq)bysKbu_3l8%Db0!c}GRJ6lOJ z9QCJ6Qj~y4iDZ>xfHmdLVd=bnqi_?l(|uo4Yz5EXP2<40#>6g~EG)yMVX0_yftX&R z3>RYvkTFasb=>0i>5RUXrOB40;oZ=j4q^e8TIHaDR)Q~|Tx8F)1zx-!^|hVXjT9Ut zY_NgWx*iZ+#oG;E>j``Wpm{ncxE}e;~AGmva?+C zKqUqLAKA+gUv^7zc5%num)aPD(3)IL`==#$mlsj_e7YLxN>kXmdw-><(N}U;x5O1> z3PtIoM!(iLA>mZu4X`v3Jrt?b3Jb{1yABSk@U13KbI2qy3>iVEQBMcoWxqwOO!Uwm zWpE?i&pp-nx<}7nyqJA{3ns%3J)(FdI??)wyHKQ^X4GV$ibb+JVyi$;|CM9K@pM(8 z%c)!MTSv37W5?!R{C4%Z%Y{h|O1g;DwgjRtF&eQrFDfdzfiShbnn%^EE{JDIv6cDo z%)XDsSU=jjupATxYsWPcdxi+zD(s;Fz2CNyUB#|wYLOM&v$bFGykB_<@HuE9n;e@! zf-baOIv~BjAY$4h0hF%T zP{icG9Jmh5HDO>M+?fxVFmHMAz1nJ4A*fq5_P!?BGEgbLv?bHGJM`NarN+P>DCR5+ z2re!UA%T8<-SNH#4c*m2cSntCE2Wb>llz^2OP{$MSL%FI&6<>K2vns65YyJJDZ{GT zq4pdA>(HjHnXaIms0nKC#MP6Hul;vdVKJ`Hz!88%3B(rI$V4hzCGl5x)P5RsGD<5j zg3Dq~2nx$KoevIsm#VCtO8sXd3qN+mGsaY6#F47SrQh1xJGPe)yPo&zyQ`(!$IWe zKWdLd6=0hKkxV5Pe@pc1H>>=q#=!E#{sN43vkm6iB9qIWQ}-qC&8?^syZND}9rWsA zBHF%{I%STafqE0UA)q?}F{?NoIbbFj#;|OHNn)Gr;bSip}trp0t+aO)yLLEACc_Ghs(PL6{9o61s)h>>F zd+Gvrn~zAd*+V*Kr4jgoY7qJw^oC%4GAWUecu-?y{52p7BswCLNJepF26T3q?b_FP zFdE6yoTFWLdZG>XhOEbL1UVtXF8obn`?j)>_|(da1Qbs`QXmrzw>oGULxz021P5bm zAAoQxsep(gz&97VA!e7;X$v*p*xQ&&tyxWbmJ0{R{1Gc30WOphJ*cFAf2KMveA*J7 z_`M8-BqtR!E&U+sBlfuWaL(3;#O=h#8(JD4%znnsxaQ<$yE{PP>LTPA~^az<{?6*-hl8!!$@Gh^4=CwMmaoZ4yrrNp@~a z+Wf%1Z|Pbo`^r=@)oLkN=B$!db~g7%Be|>UWnorI1~c?>oqt0VSZ$AJKc|@-( z&#BJc6fd@H$D54hSpQ>|m$B2v?_-vPN4A!-vlX1}XDXV-AZ{cty&zIH%-f8GCCsP$ z`R){~&z3$>NgLo7Pj5(XU!YOASbAl)XF6Q}q2toM$qG2u>A(MBt)ec>m-o>x1My7E zkQu_tPc=<6hu_Fijq2Bl=o6cC%X-2PBNQ8!Jl*>{GL3Iwq6k2_?>GXMVy>*(M;V~o zR2P4~t8{{?&@!CjW=h|OQ2?Kk@ZFLrdoTjKZ^Pv{1qmOJ{}%17Wo(endeTs>>!$Lg`QPd%~Lophb)n8n}5yiESt3Hwf3rQlaA=(robF_$FlMTKkqR3WTL)RE(&laLmOuPU}{u&X@050=bRSu`bL5zwL{D4e(rvBd9sh2&r^A_^Oh6dSeG^h`Rma(IfK z)&f}JE}7rX7ZTp(Fh%4bIPcvcm0A_#2*wFA6j?d;T~$n9Dx@~Nuh$_JuEU;VVYNP< zr)UMvY_w^3u{d_tqN&-u6_C_!qyOLz z_LV-g=ncl64WxTFYBf<{T{Wg0L^#FuaxV^+;|UPJ#q}`2E&a?yHz(8uEPILmI8cET z)ri@;P&5$%5*1Z+R7vX6=f#hy`md*9GCi|txc_9Qgyenp-2QOrg!0|tVljkMMYhr1 z)AsNtG0cgt4Y8pkTXni}YnZ9`y-D`;`@ibYNxCcU;{NH-FKht-r2jvwCmmgEotzDv zUH;Ds%H=Ety90LQzq{`!9_wtbC|pJPb|#Qfb4?Z?=T}C@igs2k;Z&o}t(w<S{<9^}RJ&(Geh6g4T0m&YnSlpB0% zLH*-JX#9|q_eDiYoGgJlWJFZc6fqN#EZuISa#<~O17Ew2k-t+g&Cw}KNcnx)0&8TgV; z6jvj`oFf31rxg~;KCdcCnfPFDj8}e?UU-sUiW$vS)oqD*Pk?-IXJY++!M>wE2wnLY zn_?DAoP8ZW5HbeG*|sbq82m02nyDKC5e;+gHOe%ScEh)f!*CXG!{v1a@vc-?W<&u= zsLzSa(a@tPs`gepXqPUX%-oCps^<%5SPq4fCb_dJIEpe~NQyEMarTvl7id+Lrx&8B zyoJ3=cUVudLsnH>2)zV5r=nHcCCxO9F3)9` zE#$T*8`!0tknffB6W79Dyv2`c?Tpdg+|Njtf*1Vt{o~Gx(xBWDAy={PYdz@~)h65p zv+M@|QA7Zu!R@$$fVAhVgS2B z>!AT3&;b&F*VqpMaL3vG0ZAyr&s{q&dOp>3S;Y?!z6E9$-qME1UlkW(DaFDW4xgAh z3ybtdIV2IsyH~SyJ*HD~e7}##&sRw`CJg`JnJ|3oloSfzDWE3a%?n*3xd9MUzwkdA zGWb9L{N53@(M1w$r3$ps36&XQ1lj9}{%*Au62}vHQb-U7>PjMp>`FoGrvj8NV3P

a=paeNHb4r4z&E&6|L=b)f zJA-phf}aPTBRV4|aD|gYQog_@g9Ru;qOU1aRi&k_4}Tn@6{*AlPOBst{>R%-x%cIB+xyPs&GX@X(zE z@5>Q#H^~r;H2>B;oDU;pgb4dQ5|ZDoz(=~T0cJ1Mh8~@Btp<6jCgvjry>AZxy$N0F z=Y@y49>NigdK7dnk+$)P3SBxc=W`dAc-I+p4u}&X)U@ar=Bf!hFFQ$qslp))MILGn zcnm+CjJkkidSpDg}YS0}x$*$X?=*(v+Iu8^tC0fw7>l5bGc&!8f8M z>HnDeA6o#NgW*?66x)BdANCaJ@0!qdvIL&`TV%{ZgcbO==6Af?w?$| z%)Yv@<<{`&YIpT?b$7RaTrXZ{F5le0*Li$3bZ>q)e}sju+R63$Z20ctx`uB5IJkVD ze`jV^?#lIUZ~Zpt`2oF4wz zx4!>;`@WhyzWa9S`gm+``{~ragw}4B{=&cFrM9M`NgG>vCa*Nf){?i=H^xJSJB?p{ zw?a`VHwm=~XeSLpYN4Wbfd*TJg5&>P&W=(_5gwPsR|kp@5PuX~!b^S+W5FfIH6B=P zM-^ZXuijuMErqRr!EzmE;oyo?ku0Bb0zSk^RFoY@vOturKuX{$YROC*gGn7W!(@vW zSxMSp7}Maks0jI7b2-T-=8xtjzOqe{DKZ|h3F=jr@(<(>J}nPZHN>8SGsGHY{!vsg zbCNyMtl*k;Cp5N%R-|DE`s=W)X_b@4ICGwq(MjYX)NrBfA<9G_eXO_@nJRpuNExj} zE-8|2Y%kcbsmb&a7cWZ~)QL!LY_&6{z8Kd;!ohUWh;#4;PiAK$%ecdWU zw7P(UGCwhMh9qM4I;=ENN|oxs@Zq2tVd30XnaEIHWywT@@dY*fF+$<1@pwd1f|Q}S zsbypEK>JHvG2z~^&qD?fQS|D>Tr~p=s;0AvPj=TvlMhPvbfy6 zR;J{hJ$!r)o4CMf!8VQAB1TlGdvPRMv&haEC^lKs))d!UBD`Iou?~}B!HNuUApz<+ zHW?`Zae&cER~C~pS@fay?7!)9L|Fv7>cGyyrs5o4=?rkA2{pQkGh5n$nT2806_H0y zWvvD8%S=LOabwWt_Gz6G_~{WjPsF-wX7l^{0T9oGC6G=o^5Y9Nx)~(GIi0@yl7D8) zF>nOxtaKo;B4)B}rpF-Bmr~!y5JM*%&9G2HogGz|pgT@IbYMH23b|BX5+2CH7g%1atl_lWU$!TUkT{m-=^2HnDAC z#mP3Ft@nz&5fmUYp8_4^c^`{1eqvh^j4qg~4tagBecER&TA+qCgHSg^u+c@M!zg1; zTAS6L^O;~Ts)u|kr^yTpw+=Bw83@F%CJ^vVaY=`<#?4JLR7(a|I$IVTszocZsq6mb zt7I5i?Be>X*u-FoUjWf5I5>WBz#1RmO0{y7h8j^ynoL?quBtC-XSbxd%o!>_aKzM9 zoh>J=719CS$UhyLP|${1fHH=fh>b94nK-TQK6E*6WzRjA%mAqwE7uZoZbgJ_6<{`z z#tD%^yI2*Tb$?ipnSCEU8NLZguITwQ3dXIKMKAvK-kpK*3|UJ_Q0jZ zXlbOwnt`mYQl&gb0yI-g7(+m-3Na1?8>ra77H`v&UvgIiQ=|^`Xm%RVw0VTKL=2~h zfqdGt0i@WbQzQj4-^Gw*n)}0h(u_`nlQ6=XWkX%zq#(oxF{U2D-DVI5zH4J(=`~`1qYX7duKJ0^)!{+XRsXZO&GS z=T=Yk{@3sSHcC|_`N;i0jZ$L&??;QhqluA)(?3tAo|}n}ph$iT1<~%FP9gXV~N6-iYzBcSVCU5>25Y zqx!UmCBiiK(m6#~q0OgTvS8N5_v;_ya^ZND(R21dBd10UhwgKORN-+!q-(q6)*?aW z!{LiHXVmWUne`Wy?u+PQ)}+oQerP=+7x6tFlp*CFZ=QSoD7Sw_CCU2H8RPWQ+AHEN z#klf$)i7SPlI>>Tgvr*%NAA%)vH0=|*(hgffd-CwqLKKW^$?I)!)8k*NuNzizQC>I zp#9Wi>Xmq=x53y8H}d=yw|eEV(fyoKGBuM9cL?sLk6P-$#u3*Ci;gYQSZk{X>%_HD z{PD4@m+XAh%y%z;WIB;AZ5Qh4nk{F0tMRu4hJc-<6ucjzoG3UV@KEB)P7^YJbxU@D5R9$-^+q< zFtthM)Z=QTC}f^a#^$l&2C3`YbMyHG`FhoS7-^z)Z};nrtp{rN;qD#r;skNPmOuHE z`h3C%My zzDAwdbgJyy{NH@vNz|0{?HJ9}NXyFytN2od`K_2H3Dtj4^!b>%qT!cMM)9!k*G88I zTFs4CSUoMaNxTEJK0TWzX~mKO{$B%WDBK z{X;$I&yNpU^4`hkv)5H|@qw)stGXD(AAJ~joq^OFk{8&8i<2D=B- zdy~ZYuhntWOxlV^1@G+9f*&2K#P<;p_sHxTtlif2$AbEih@VdX3yL1y-yCCw!+I9k z??Q`|9d}j@Gsb0Ff)6+q8-%`$YP3#{=pA3`gxm4r(cFVm77{dmJMvBdSt<$OHZBXz zBZncQO=rQHa9!}&c9)lQ+7pslU4A>GW%vtDtp=CRUg8NqYue~(IKB+COR2$!ib?`R z8uv)|ch=ruby@RJT2HNP;mqe2tl}72WzJutgJi`U6H6E99X5@I@fD_(#Y@RL?f0J{ zf!GPqTGoQ^L9@V|wgvAZ&6{?OGXr~NIi;kmefJ+;B zUfqngi-th6_Et??%Q#`WL*GfUesZaGqgBV%+QQ?&z+==-uWIAo-<>KusnG6jpK&FJ zlJ;v1j}bkFk+0p~&)nSy_D}H#^Bh_-YZf;{-vfa!o8d3E^3zK)LrVPzzI8Rm@?g9~ zg1okCu?|VH-{V}G63br!X@9>szlhm^UoTkw2I+%k<7VD@ke*i_{mjR=1M8Wd(_L5S z6T3b)?l?n7FvKq=-MYoTtU=wY*zYqKNi!4(M*U<`tzyWt8Djy`Np6z?XQnk1J6W?K ziJQneObM$xQM^vWw^A=T$UAIT!A;QG>vD%H>u*??n0cUNl@WgelfRtjdIF^O-=1q| z<_*TP*(?eVn`m+e`p|Dv4di%4k!Ed0<0Cm@0P=Ax=2PQy%H!j1M+nPG;F0NN7Jr-} z<5&U-DT+`uRgU3>dkMNAE6)uyuG1w9Zb)1RJi+Z!sUf9^vycTdyb#JLCPp-XOyH5l zkN@C6kn-d!n?CFVPHyhQeYj8Of8H^={M&|eZvXi-;SHAO zZ?CayMAGOjJc`hc$!3t9`A1v)73F#6$cfx0*%4C-2&%E~bTYkvWW--nVmTKtF)PZ` zE%+i)c5ad9`>puoaeMvsywdZ@^5Nj$O_`I}UjsNYZz&TzBx`Pi-hcwV2z<$vU8@($ zM2B8zxzcL7M1-ZfEFI&XB1~nOUp!de))+FzaoLP_RFNU6-*y@XpgFQ`v`l7vKrYC0 z4>Ljc{@PD1ErE<_S_MbK_vhzeSZrjJ<+L=+N{Fh);YQ5h9_9x^yzvy2L2DvA4d*!; zuC2L%-A8&cG{6e1P<&q)fxD@chF2wbKVEA=%QMkXP=NcAtn(Q_yePnS#id%?cssyS zT;5VmV2PiqR~EH#@3;M}>W5Sp4H^c>(VWnmzxY90h))Y%n*tDdBmOC=VlAP~x4ScK zt%p>gPq_Y>_Czm#G~xM!q1C@AFbLeYuu0jH9y@C?@tWSb(KOf2L=apFUoyUFdNwjZ zBjQd!aP(rfnHe*iG?Y4oV=)}!GSG~iSy8%`ZoTXBv^Rd&mcYWjoaaSoTaCo(ETkQLJl)^7|O3gcZO zre3h!yXSfPe|evOF1~X(icG667#m8xGR2dZmI8n~baI?f{c_yBBxp)n%o} zDry3HC7SG`UORn&qipZw&d%C9hI6yqR?|-iSr+YJ`YHV2bM3u?U^@wnCC5+Z=YwP;u7yxCQB9@6vot`)aBt1?0MW_mW1&`#`17@ui^A6(ObOW&Wla5TLRiFR3rV z^YT!#pr^lkTV2za0k>|!=*hD0z!aoQD?xEj3}hs0A>iA2ED8$z9O+z zAmMpEa-M-VpXJ9cxo8rxl5^G>_ZdH${1e&w-^r9?y#TwMGhebHkB`OSpj*gTVVJxL zl$PgGUfwiX5G1!yMXbQ)7CSscl>2;{cZDB11D5_n8Mzjj!TP6Mq(QkYK?>B6IuH}p zhSH6C+_Yanyud8+-7*6-;QY$Tr!|j<6Nc`J%}`R>n9`lW`5nBv{C0rixaY^I#?ZY0 z{)GsetspAx-Ea1R}jbD&_hC%Wz3aZdF#>zJ*q6wTGvzkLNefh558^nQ6tq0`* zT{%?cCxrgq>}6Zsad7R=aoyGno_iE*Wj@7%QiQ$(zr<{~sbX(e9gMs@<$j{W;tB72 zeYeJG-pf>!Y~gV2?2>l@r>oB9#l_!(uh`P@%p4iQnu@UQ>E(^Tk|1(+K4>TnpM`$a zbVZGrUnw`}{nXc($fK8H&4Za|kgTE&#JVf>FGa-J@2otW>1$)+T5MG_m_mtj`&-=j zZo7#dzOV>vz!ql@Ky}&dSN!s zfw;tw%8Lr7E6dVdltqB^d?g;)tR^jH(DW||PQQ%bAT=0jZ1SiSgyls$nXxeT*ec+R zB8d97`*QM>TMyicn7HA{nVq=8sUsUYJAKIAt!pSf>ixxbm+H2xdih%4Fl_(CeRON) zd1|Go0b8RNuOfcXM2Zv|NYIoKu>6taUFk4k4VaZlWRf7WSp&UFKiEr~vt}URvst!+ z*qnM1SSBk<*^P6{D_~XbA2S}DCTdl^4t(?f#~qE)!kW{3a1wbm?duJ* zzL`M~^mQZb^euCZ`Ib*t`6>NYnpcc5CUA6kg`ncqhw=&obRtlsj}WQCn|b5opsTWX z?6U=O)OjcEBbs6QPocdpynkcxtr?pZ8h zgj2;HNhipOezFHlo&ew4M;cy3{k*qf)8_c1GHb_^UQHyxX5`-OdHdGNN+7}eZj{sx zFS7w69rvR1C%4{T%IiD7@mIH|*+Tb3R#luPZO2ub4<6w2tN^~YwOANK)NIgNRSwvd ze-UJa_urY^CQlz4#1FQfU1@~R?*oG=ebCupcX?QB+C4kH8U_tm;jQ@a(2gtKnY2z7 zL3_CT;mn)}8GFGGi#(f81)k^xA_EaVv=@#khxaS=qJ-5^aeFqV>oK15 z4l_*Iba3XWH3_80@+I^rHru2z9vzqn&a3QAyI6*NL3$Wm$R%Mxz)SPKoA;e~Cq}hn zS^570gn37lnF~1dR4vMy(cYo6Z=fG~j=->b(`E!gb~QzvL9ky5GU961t1hH&P@!=V z*I%f+KD%QCZx%k?GCrE(>!yiVcV~V*`RwyLltMC)8;oYi<|XEdWhQ>o4!P6?_#FRn zZ3rhB`Y4XeZ9^BU$ju~~!RPh4aA2}_{)!eNlSO%1Fnjh0-!`d-6P3&dj;4~m4*c{H zcq)P(Spm8;J$*{ZH7EDy;*AsL?a*JkkMukfEb=1{MTP9~IV{8-@@Bgr_?i8DW|My1 z--}vFxUV#ho4a=V2Y=LM1I zlf07tM2zzaK50bR&&T=bQ=C;<$XA>V+RHBZh;u80t&vz(f*WO)Mlz2>SL@u5;hj+e zttIs`LHYvO25xpJyemt>6)jCCki-%Uj`d-?uOB~iQg1W!_5$y0M#nYf{6X^O?gT(a ze+le-LH~bTLpUE#`nN3z2p%RVi1vT4_w#?^IqpWbZf5@(=(z0CX(V6!S^Y9yyIZ_N zI^esNDn#Ha++>8vn?`;jRTXI4j!+I)Ntp>&8=k+Qx;CUkV^BcE#y373>*!eoB z{kkgcc}^61Y5a~z75b|D`t;~|Kh5?3n0gZOeV7vR`wXxBdQl9>#|n7t$(`|kdf5^D z`uBir5b!pY`+0Hwc~trxB_x#lacAK7@bM({aTI>t{m~i_;PdIw^Y%#8^Ey%+@bG~A z^~q)M(bKbFmfPd~<#v4D{h7`9HpKL`w`3M@vxIEm_w^E<8&F*8_Mx&;9kP6}#Y&^S8F=^(Z&sW69$y!~e07i0=a{_v`fh znj>83?J-yAivxb|&ZB4Iu;=~i?wF|Ks`hJ-O33}y!ocrCNa*XS_VeTJNyvR^=HbgB z;A7+JVk6+i?k-Q(e*|YCS?F!5_Uq=(<7@xxbz;Wg<6q|W_U7(gLyxx4!J*rTM8M|~ za_-mR#&!2+X|4Z*3DMi1te>y@sqc(LF1mLC{vQKhA6y0jU*BoJPwWIdd{^N))4%lC zOZT4W^U<>U#^LLoD&S>H<16HT&eQMhJmBpybLP8b9|rz!Tic(H-zgLY z_k5jo)V7UrIS94P82Eo|Jqf*E=JvFmVST;6e7zifKZf^D-|u_ez6fz29CLk+NZ!9s z?|9c^z0GugJgn^aK2#d`cMLyP8g%vC|Jh3&>Co5nMwrA~d*uF*Y!R65NwNDL*@jE3 zug~*<_eTwf9-nN#r=|1zOo!M;q0gb7=c!sEi?4&3&poQ1_p#gq75mfft(Ld$Yp;X0 zr>rO>9c!1QsUDui9bTJpzvHLbzwTYevVQh{2b;$^_I4{e_kjIwJDpRLqYdn*`X$|A zeaS9AnJ2%Kfuqr`i&mB6u2zPad7G8eskNt;IF&n2w^g4xp^fD?x$c#Ur>Uf;B%Ug} zWlpH;U46ITE-jsQn{ISI*CyA+Q(kXs*BgsJyNWi~FTK^S>{3#W^h>UcPGq0jTQ}#2 zJ?vYndWwDOT;r|vzczDfS9E>wHTt4YO zsW?A8=-{a;c3*Y~igE=A@;&^0Eoq^OS*k={*mR$<>++bor1v3kGLyHe)!DY#;7{Op zu)f(CX>QJ%SoC!+qhWL_5-6>Y&2?~SE$6Y1mS4Fj%6jUvxaYB+$q1_HdmHR6Du6wA z^JGp}lvn(V%KuA?X!VsdjL7F?;!Xb*(JHuJ@WiG` zxi!s*TrpE?BlmasGO#3}<2c?&q3yzH#%kyG1W|eMdAppTBBmH|##xA{*$0OK*>CJb z6;FFY8Q$*7OWr9e#y4kwVU&;xc;)-C)>ZnDP)N$od3_VZ!NKuQ&*jm%`ymh*68Tn< z0stnQJ%vsqW(xlR3=Y&CwsjuuV}4l1;o4hS%; z^TEY++;!=Woyw03j$EEa_jpVzE23!iW#=^tc=AG5td&CVR9zg2aZ%alt>`!|^lp8k zI8^E!gG(5zHgX=U0=aX30G%x`{AKFV{EGdy4fDK^mVoAk4pjLC#LsTsBjz-E%iv+o zPmHBgS1{x9(cJPB-dUsr#qoV z+}UhWv;R(drEFS@Tty~;YpJU{=j`D%9@Z$b60ukrIJi7{?zSbo0&xUf=pbOY;mn|{ zj>A+D-e?6VDqKcfn5k4^L=mbO_XRb}lp^^_F*=xgeQuk3Wt-$o$YHw^p@p~!y|N2^a1TjC`LEtdU;-_an9NvxY;4i+-Hgv@q|L_ z`IAh>gv>1K<;)0mr*#M>Kqd#@l6ZhLkkEO8ZmJCR7BJ)j1nu=@LD6uF4`vyMSm+_7;R*I4#$V# znaZt;FSO}x{Krp`xoFuaOb92q4o!-%>cAu)^Tv?@+#ptTo*-z=^kQE)t?f3%=trTh zjBh51L!%E?LbM zr*r7-D;}G>i>?}<;;uhJf_d)$dfT+?AvIur%ll{DsWh8V0WHOdhaM#@FKyV1l@v|z zY9Ezm27b#~7wb$}?u=seQCBhv>}cw#ikc7JTD&$*ljmy|stmB=k+*nkF8}(RlRR_C zI#HhiMbj$cPE&`{cF$nmeAyrqCe0T_K{7fNs->>)bLzrLV}#5{O|rofx(xeSsFl*2 zs}0`KsS2=e3ze^gnUGb}cT)R>hyNjuuuimm?}zTh0+E}0t`0o_XJr3x6|Cy%u0 zo`}54fW~3)M%Yv4bE3e9j>(Ny5D-sPSizm6Oq3w4Jic7^*EU7dO;%Efa3(MB%*IK{ zHfw7MhFIthjp?vUlL(%>qSIXFzx| zht+jZAvL}jq~JhZaj)u#I0tSDrZxv zP*u@YbfofCF_k+qa~lnrKU@asfavL@r03(T;RwC&WuMQP_h{(a~7vjWG`YSaQB~YKQk7^rHG5|X#KVpY4`~%Yz=QO4M0?f`>iCZ8*I?8 z3*;S$1Exgmyi%>1A!c+5~Ul7iiFRHVAM? zy=lp;oZ-t}6w?YJ?l~dw^MA&G{(9iqhNycTlerQ%+d!*!M&Y>78o&Aqr6fAoFFY9>yk#> z>yb^>cC+5Mp<7&aUe^gp4Kqyvl^tM-~ z2fV4AQJfOEXTl*@gAQD*7>*UrZ(d(WP0Rp1b5K@0x^P(N7&5B)9(Vdz;%{nBljaEF zF8+4%Tp3%%PEOLr&egqUV&!gjktSF;Ba>4?1)@;@`WfFNP6qgg&tw<1wf-|Lw zwNl4R&OE;~eNlnSU6#cU@xAlq4U+eE9jzR=0C*`T9tsk0Y7zqSj!WGA4J6~uM_Q)q zCG%Oy_$~Pmm+cy<^%2&1^5X=!29Y9ND7w5FX(nS%0!)^hraSzRbB0{erbo}x6|6qc z6a&hWMaMD&YmpI8x`dzAXh3~07e__yRT0Efa`{olMiF!SF7&k7@nu|p^m*(!hyqH5 zTxE4%;S3et6xUV$!TPN1Y+c}Gm@AKBp2TuQ)z3?NoCKBo3;nGkPAW;2M$d#~^2iZu zhR)!$yf9sNftmz!foqQ#^01W4$m}cy$_eHwEcYxg(TOFfdd@lwCE%R}S>QrLb+JHp zB5_TZN$iVpq%}?sqDYRWqRJvLi}gUuJC$sUuR@%Nj!SGqb7qY~6Sk3G+>LLMMsi6@ zWca+kNMJ_$=+*NQEB&T0erOd|6p%LY&yS}^!dnx$|Fs#uOn^8y4`7xs(D$0kOU6^; zR);DIBn~iPvZ@Yu&|%RiHZ5JkG!{97SV%DXIN4C|S9y)yYxGWB?zq3ZwQK%inlI}y zPB2%J?C9O8;ap&dn8>nJW-kFwg7YVNU5w^D&Rk5?BQNP80+;MKk^P<=VHAz8i@GCU zlS0#$`!DUCmH1>(0J`=f>66dz;G59qIzoGL`-0WGhKVfl4_8ltinNdQUZhqY19~&+ z{4{L@D)bE^ZSG{*f3%+@Zjnco-|CXU1-%WFQrmdAl$UT0o&a(!x#b_nDQihK3ah0O z+A_RL9d;<#@hnRKTN~VAJRy&1ve>>9s-lg7D?d%e!-+OYo@(72I3to(_7PxG_bGDu zUPrNoae-33b4)f0NlzCAVBP4jkY`y5g~+)^db?v$g^b8(B)QXs;vN?$!P>Td?M7*3 z$Yd4fq8X#wAe&fxcrDVDs1BRHZb5ZX`|WI{#n1wa;K~kR6n&@MhE7jd)YJ5FtXETZ zAf5V>OQiY%qxtQPS3>j2LN=%2#6@>1fULN4Q34xg#DLR zCer!?Qw;Q3903)vW)lR!T1`T8A?`m{h783)=IOAW;2{u&4;+OAtgc_58JP_doX(YC zCAyvv+N=w_>v} z%0A0D05W*rS8dJ0!S?MpIT&D)^<<$?y^wSyY@yamXfcS#7W;R?-toA0{qmk=mJQ?f zn0nA|1J1st2?$)aFuEy8_ccU+{~W-n%F_Y3YB_Wx<~+y`4OdL`ff3aX8Ux)I>;N$} zh2LdJn69Zs@GxQ*cJ5O*tW1Ew3qmlsJFs8j<36*y^r5-7+Gy$3r9w zpUZbFge97ywRCg-Nd|?%s6NL$O_A#@R_E=0v44(=+SkQ9Q%10OP0k&m4T)i-^*5Y6RnZ-H){{e* z&`zWaU}BvgViOO8oy5iK!zC(=ER37=gU9KbDpJLV)(fq*VpRxoa|VgJ{D_M(o&BAq z94#qw(4;6Ht^rAG2076?AS{_>H1IfH`rpw8uaTpcl1VOV*P?rcC+#}7woZd2UkiP} zD06)6e8?h5xOAco8gxxe97WK#8fR0z8^vyM8VZW~XoKcl96cqaHf51}8$a81aCzpu zEoYE0{+R*Z%W-_T*sVC)kP#*m+6#{Jx2|dz!SG8KY7>eXnj2*Mzq`H@_wI@nVTMXXLAZpvm9Kd+5eRNSVP8*V8&3>a0j z@ih5$Q~FDehiU-#^86rceUlgi5=SB$M~D^a%34l<=(6oCCwwmy4>zd5LXx2ZNPZ;U zH(krUiXM=@-m+B3RJ=viwiuHZsunOjvJBN)cMYN7W{uL{dur0?z=<$d96YJpkl$B2 zKDxsCbYew9(Swq2H|s<2oEKvWAQ>Bv>~TO}5Ha!G%=9@PSQdhg2t)2&@SClHN1R64 z7zd97tXc1l9p{%7#^YloXBApaqGyEwEgvxMfaTl>8Pp~H7mv?mni{*c zg3pHMq#(|*!tD+?Oc0WpL<54`k+iEnPU#DJI=U1D9+hN)UYudoFF9;_f~@W)LXnPq z6p_Kj7RgTCd)Ps(xlA(AeVf?B|+ZM$S4;3T@bD?)1@%f+PSWM9-}?xqu)a?@L*1X zX>54sNl;3P{6|ovK*0t|3#~pl1EhA(@Q9;88=mhEk-PVSDpRT);3~0&O8z`QdpgrP!n?>_3Bp+!9aRhI7ZR|8P~W?3Se|G}3TI>p zcwjPPRBF;tC^gQXGnn?9*`GE&5A?9(OFjcCYBZyAnqTUP+}f!LTSX3O_r9A(HXMz`$N_N zu;;r!fiMO_7v2Ov3fzluTrFCoCm7E$k$sBYif^tw^LZRcsCjmag~=R{_|d&hGI>MA zu`P^Oye|&eZQjNau?6Wma6oF7)gV->F*rK8>xR?DUC#qKY)P|Xd1*`SM_JP?Jf)&_ z8*&0xJJ+8_LKk&c~6uVF5Ensi4eFy>QkH(308^-<+rP(BVRYi`T?(m1x5=^R#k z8u5K+2e{kPDG>Cdm?1q|y3njHY7fa9Gm#P3OkWb69kvl%G-(iff7|F#RS@!0gWzc$ zdWYJkj`+AM{atOl*^w9Xk;(`EoN84r8f_k36HB?g{Yicbb*f-YwBCrW8`;$rX}g45 zPKGiIF04;QhdTi>1*SICkeO8qdcAfhh_fN`PB!0t{1oKSJ4O7U>p+C$4L7TNr5>Zr z8+f5^$asbq+goHhx8@red|Y@Q$&Sch_GooAt$4aW%j4i2Fkk1L1LrzIT&JKF(f6iI ztW-4NDncdvGol-8dEumUrzdC3D zRy71Y%Zn|YRS5uSnYm$*j7g@@ZFQIWy<1_Ng)g~_gg>s$FS?3_!7`38)y@GS=nD)?KFjV2f{z{e)2Z}Kse%| zIdO>Qb>W3egjig9nIQCFY9{hT-2gNC1tLr7RwU zX-QLmuP1|`^8te9a^4nNrFh*O<-y}#dMxL@oy)T z)#O>=7a7%N1x_cra(;d93?iLTBG*yV7x8sFXuP{jML|0}No%i*r3+YUwvl4)&Re8Y zLdr!-TkCm3m7M6p0E(+FaU}JwX%|0Tdh95q)9pfF%i%#@O0P7#umQ~PN&ZH7eRJ0b zigls&_uC5wYnU=cVjUiQtuaM;t}vH`Q$>{-p1@Z0^|(o~dB)_wYPMcd)D4YjG}q#? z>l5y~2PoHBm}73xK4RQM8!vxY9^Vl81&YzavQwC5K3D%zv2sGHF@<8Zp3nVgA_3Ly z>*F96Qt&D zxbT-r<`#t4lRrKq-0`;&4XH8sBu{VcWp>8EmU^{~D$eWO9XY9_EHrs#hnv3%&MzlX z9-G&uDXy5dHzxZ8zsMOz-Q8UucZMd`vKuUQbcBEGJSd!Kd(?xuF+o3pnvqQf(MO=v z^bJW;?jZdMwT)bc6-W(bgHDfv0HE^990%pM$g<))*}ia~Q=|p%j%Ch@#;dZy(h>1I zi*u^F6f#Aq;g<(SS`blyu1Uw^E_-e%IF?kJ9BS6Er zmx7T4kG}Vr9Pa4e85N;jOD>VK1FK7x25?`oY#~1%oL;?y^0j#k<3v9GwATjmtr_T) zGSV-2j4LkgqoYAv9myH5wnJ`k1LB%EIxh>i6C)4Q)JWr1K}c@cn9A=01Y%B})Ygv9a37B$K&@lMZM)i^%Uie+w|Jf1RipW^BRZte)jmx3>qnE+_^ zNVw-0!Mqxz!@;+QsEPN55fJb0-8zv2E=WMB`+@q&P)$68t|q_ltpKY?B-wy~CYZyO za}GRn3df)r_EgMHGRsf0A7ri9+terP58Exsrm+`_Qg4p)biMH9PF5a4*b=I!H~ey< zrM3K|-L(Ww&WOT4SOOt<=iK8!uT*$IIZF}Z5g1;Izb5dBYT3K-!X`Hwa<&FoA^pLF5_%vCub>-~Cf&7F5Uij@QY(?4tm~4v_ z2YFx)_Gjaf7P^-!w$DGO^Yb6?gfrqU;Fd>yIMek84)b1=qnPMI4|Xvz2i@C^A_QO=1a+_^vl(xgI{&pWM9hFF z;!5KO&rlYVqOA@Cc!!uS=ld5@K*$;w3gRz3%XO6FAI5*EpC_fEy^A@5BKbK&QpdID8#1J`nD+gZuBS0_ z(e|J*1PXlZayj$uhnwW+s;1uHsHJJG=lh9Estl!)azb)ELQnArKJ0qBqf(Qh>{knG|K6{5}H+6gRpVG&VD zXOuoWJVnd2$nP)__Cjd_uYE7uFVOu$Y0{ec(6s2Pxzb05MB}nFpp_&kq^ay6=}iMv zt0pc8r!5QgLsZ5wRqVkmpkmhv!{|AMirQA=2N#|*zrqF}2JzQKLF%-z)O+1Gz_vh)q$>c6-huB<=D5Lf@GO6$ z^hba1Q4PV|2;5SNY$t^qsP*`{UbGe`7j_;iW9+92+l4{@*xaqDT?M1(``yf=mmM#VtP-fq%mG{-gSRM+||*@s0A ze}XhsQm7PLuz0-EiU>u#A7ZoZ80bl_ysOOmsZ?wu(0g=7A*#C>K_MkLIqG1_=RRwt zrWj^q3?PZ$;WWx-@vWUQduEbr9K5T;{P- zb635TosI0 z++UGo%WXJiVW5BLM->K@zKqJykVBc7nDPiK5yj|4qt=-9E35=!sKL*;9`RwPBr6gf zrFyD?HmObR+d))dd$Zn)*m(9(GUmv_Yj=*RuYn6jE7HKjEjo04z*`oj7tanf0f{2e)0joSk+pD}@8WG`~i_H2@{6m{2Ta5a1r#l_DhS3D&{ zc2U5gUJYa-m3c>h9do4CPS8;7eW<^XF`s!1csJ1y$Aj;fL#C}TTp)(X#;*<1_Ct^* zsu5KxFY58vKbRU3z`E6W?S;gsEPP)swO8Z_22zM6$;!dV<%7)2$f&VVjq}Gmh=m%# zv=`~*#JrEfsTNFHc#Y{y^i-rN}Re`N)SPi@yC}wdaUb`&wyy z4`j#@ca%9FfZ1j;vO2o*x;ULe?~`bAcT{qgnJp}k|A}ldk1)@hHNThxJ@hQRX;^xvZT@@jd5g?Al|q**Jb*k)0s;a|7)J^F%fbe`W!fe9667_mEtT>SW&%e{;=q5YO_vtr=GXk|;`4{TL-%>8BL zRxtzeGFVYbjPy$(#!cte?*Y^+e*y)U+$SzGr7w4R)1fZrAyluY^G?X7p}4TD*;YAK zo^_IC`w{p4bgbky&v3)D_p}!aoFN$#g1)h-r_gK#FG)RsWl9b93j-GNFQU_w4H$oS>6G;&$%&js;9juN~Eh z=!FN23$QKNxM`?A!v5^2zKM+Yq7cE3LJh6Gb$u9~WnvJdVjN_uS)p;$7j_x4&HgMI zfnb`128g4_lRq|$T9`McCwUAQyJH2Krxv55;Tg4BszqZ5VUe7b+&gIL{qkP$08z>i zSrhRKtXh~F^1VQV^+dZi(iAS+>NULVD?bCe`o&jTWq__>5B^m`gDz3>BcgB|7_bSv zEjpiTzW9;Gq-6xwG6VKB@D~cy6L-sVts70DQoN+wpWa_hMu2nI@fsx*osfW#F%5+z z#t~Iy3!JlEEUjgVI)BmS`yg!4dv$F#vO#8lF(Pm&@J^nae*YRYi6p`0{jj4}1LQqx z#9$1Tlt#`@=4sr{`a4SyBZokTf7ShcRBtuGf9S+^GK?A>Xqdk&^Pb-h3FIN3UMKQH zHQo~(;DwcM^Aa?44_%@MhLN`!iP^|#@I(YGV~ayrBim4Z9|9> z(=ixRy5wR<@9RP5csV@b{)NtDx@<9{m_K6)w9QQaa3Hkul6c^o(>yEQ)Z7SD-$8HV zR+08HlE7|l^XWe4ix0a93&3dbB3poWQcbd6qovxT@duIJeS`^ao=YmRi_zd{b^f3% z3?60Kmnx%Q)t-2X+9Amy1;Erxm06~)9%F}Y!O{pw{c6DO)ZM_}XfjN9JWH}22k;r< zj3WJz6D*pw+t8S*EQxGKfqw)Lq$X*?xm%4X&}v0oeU_&0;Dc$BtXd&n9zk2_&;+M8 zA_nD*8|mocRMh7LY54|^;b0;qMQ2tEQ-cUi>NwC5U2$}pD236hw*2DcaMAFqt5)ZZ zaTY>zE2YyF6J^%I1Ek`lAvHS-ofI(9d`eXakrbc>>KHGCVOXvD|K=di z$o&gmEKfA$5y1cQN51@YxSsKnj?Ln)B1a#-M&mFR&e-kp!faT&kc8woTV*`X2dzk( z9(pUkB`^CncymGSI=q!@WJTMSkVE61uA6m>2j{{Oa^^OIMdD?izX4ZY#brS$+ZHCK zpS0j|TUgde#1gKtLrZZ6#R!H!al1Y@a^HL3ux=3M!20(Vk)n?@vIT0<$eB3=*L3E( zkam3QfL!A!uw4wZ$DNp$oLFB~okLPl9L1&#VQwy?8e{3u$)al=o*DIO(>TbhkXB~k zP*UNHUSzeu^vBfN;f{4&U-1^ztSr_(p$Nb47?uF1b7Y2?G_nXyBzecU zAHxzkow~B7_=z;30>b9|-|E@W%obEH>8aU=6YVO?I9mn6JC?CHV~Mdymv;BAPhV$< z3Uh=+&Vu*IcF8)!cXFVzl<-<-$(C&07c9_*LC;~_#7ZUtwrUpbW=Wb)?A0| zE79HxgW|T|xE4%7Db+&2w*vafi7?Bb?`$JzOV|N`V^J!HJ!soEjMzM8`>_3zi zTO!F_S%&Gv3ILCBxRGhwg=yR0?u3c2&4|1tCVL)+yj%La8!rNAfAErlrYJH^%7OlQ zqK)-aQuM!AKjkWZDg99O?UMAUzs3VJAsw`NxL66pP$$Z$psxlxJ+51eH}hUIfp(-d zspnPbZ*&EVT7_f$#{0`Nzu!%1%G!t7rEzBy9<*ijV?49>OFz%L#n9}6OmvZZ;hrDZX zzU3W1A>?G7ld3W?G=<|iQq;CFMm#!B!OI}$N>|nLr7e1dXydQrtuG@Um@7~0!`RPI zFmZuE!#(Ksp^|vpt+djJI}vFx0r$w;47W-<9o>9KWZuBt<6iMmR}Yx7wtJiuK9Edg zhmV6;h{f8(-A=tY!8Dom2kdE6QOpE8-Zhz3&&$GUh^#|#1e;Lpati{UlVWmY>J6~A zx~?0(W;yrVxOs~VJC2P)8ry{!cIr{m3HXT2QnI4vo|*krG1h^hnpyrpBf3&|^KS>L8{{an-ort?(UU_2@h`MJ0TW@_}uCHk}Qlr3mglA8u*4 zz_r-pKg^|>B4-+`SZl5R&^GPb4qY*Q*jPQfjnA8|-NVV~b}}!A6|#%>!3@_K*hop5 zF9Z^IKVy5s*Hu?XivcUUv#m}psiYVL0zeK@-p=y|L5c%=8uu+*lMt)bjHD@xG+Slpuk>p z1?vbkONu<%yFO)cOZ_+lnDoRkEoZ7O#cnCy{D?XyaQ^kH?b1y5s*ioU}k68Kb z?!Q}LF7uH6k_XFl(2Sq^4f2ZK&Qk>G>Z_})UO6%4P>P=(fyjp4RW$3*idiwC(~0dw z!5)5USRpa>0NEML+^H&o(#N+h#+m2k{&s4V&?^X46lJ-uP;BFDBgjjF)*R!Qwgwoayz_Ef zf)a2(bJ18t(TOSnBdp#LL>5#*$~9-pS^nkO5up8Q16@PwUuTkdwHy^5!QBR%*)9ze zXqd_{@$xZ196rT`YX#IiVKlswB*Lq0Oxgn68Nn*7<}TC*4MSx4WY71k?cRjh<(l-O7iFU%I5ce|n1ghL z#B=yrdcE<8E4uOCa6{ov=DKsKrQB%fhcNspPd$5eLAm@tLZMpEe=Ix^3co3Eq_{(; zy0W6hbQ#b|oFT!Go+2t!%+LX3vZX&&C(RaqKy$Ej7r;U9u3i%G;va3GA0UvB zD(%#HMpZz9m(*_lCQ=0;nrCXeaHwNGc#W@GQ~IfNTvJ2>ZA}BwV4zK8{!cpSo>c-#_U8eYTSrsvU5%<=0)9fBt5^u0+j-czZIoWKTcJ@t6P zF}k-Kam}NSH_E<=WpEa!4njX_Om8y^p{P*7w6XJtEj=f}^wQC>-)OM()!usv|HIlh z1!n?u+s3wS+qP}nwr$(V#I|itoJ?#xUu-9H=0A1oR-OBDUrtwbS64rEKlG~Jd-Yxm z_j+{qdKO<=LQ0uR^#dHj-kxgpn;W`h@acR%;mG z80MR@A}YXkuGv<<1;@zN9mKVISJ^($tvfAyY=pBBpORrM_rcUb)u~A(=r_Ayoo~q z3(YFH6b#Q4mynm>ql$R>i1-)p;3wg^G>sj;dU1~+`oa_trYSwrq&A0WW$ym~o7SEZ zkcG};+TJ;fGp*VGHgVz>7N2)K8H$w3-*V4JfS4ADgo?e}>A$cmYr4J3N!0)jBNHHx zh`sl~+Lnl=rCl`skOFK2J$RBZ0Ig3&Q4oU83n&-vYh@ad-e#MKBm2vr9MVQGH!l@4O>bBjspJqQ2fDr8jXgQlXPdlNdHf!H6eviJeW{ zI?}CzaGJ&0K^pY7QK{r;Hdfqri8Qi@&50r4L86JkR2l)|JVBRlqxi%p67SMaEQDn) zfo>A;3Zihf#yZM>_{dt@oIAR3q54aokS!cYD6NPzX^mGqVEE-eAhjL1nYFzQTPO^U znqG;gN(J;@^f(o&p+ly*xK-=LdziqsoHywo4Kko_ToAVM1EsDEQ$ISa#EY-fkO?GR z(tuB)>^UAzxl+b`V{S6fWZse#(3$JrGT#tq;i$)li4SN z2#xD|%lZCJI_;OaBPcXo z=qH)68*3HQOEd(vULp4t`OG=7Oc|U>7o_i6;U{apM)WHN_PG-ZuIyH!2TPgnW#C7pAVQdtk3leA{q(~k7)2h zL@Er%N>|?>v~X^U4RA3s1_7rLnOK0&bof;9B~tggn74h?h=$0KFECo8+>O*@{j9;;DY=Fo+yXcF3kSbKi2d| z_>U-p&~!~t=rdG*hgow~)qY2!yya@)EG+%`pXRH@ zJMPwdkhuQHP7#u&6A@S%<>MlL__cu{xg|qB%@*xCaiv9JIYb`wvpR0@l({$*{F)5M z^aCMK69Q7GS!F}1dknILLVOErSYil4QaUH)lmX#MEvwMuUFCNL+hmxbVqDq0M5{45 ze(G#^*m7i~R?%iEmS39kLrwgT;V*x=l!4$-^&EYF+fI0r>`KISfX0eZ?PV#`X=f8Y zRT>HHFQSyA)PlakSSN3=HYP=kXD7HiJ_a7yNF(ng>`GVSWw+UFslq7oAWqfqWX}8R zHGOxYao}>S$V3u@&wW^!fvI2}B_`Y@2PBcvo#r5RJAJ*)t`PXz0aWhMmTXMdwwW+t z%ym^d$37Zc9|Ajd&D@GREWlBsgB5a`1Vix3(T5%WvIL8zo&m7|>s33`I*KuWH|kTm z8V%kHzy*@1VeUkrB60!5QQ&*XC9FneCa<=l1$gv<&D`?3Kxc&PTFjA(&rzm9<<+mLb+HT6} z$yUYY4^irwJa~iI+_NTg99IyDIzYb^V+;aSJd@=?;VbBjlKCVBB&AU>cH}G~Dq_ys zUz*to2Fz9jMm!JazZjaRle5O+1w;h#-2#5D;hXRvIoZOm)k53JvW_Ni+%tJz(kAAU zgNk+1iw^G`#ndfbCSUQh9!=3vG1=WQbM&P#LR53ROG;JZrSpBu73iu}H^*b@XD6P7M7|UU3SUne*JBF-1H2&*}}`XMdvu9=Rqe50uMqWXM9V zoEKnsffx&WV+A`-Ugme7aDSZmrdIX`gXwt`%~+kA?H+t*uVTXV&JQEFr4t^JNsb(v4na;p@$Y=$sN|Gp6T zYb3Sn)CpXC8_u2h`g5J}>W zM0bxQ0*oACC7z&69wB-3KA|GA5<}U_tY#m=64QfW+;>Yrp<)-xptnNzfb(iG_OQ~p z9n)*>2E^?gwVzD&lz66lC(R{<^lV=DrHR4;<$`%qoG3x6@xg!^kSTD{>!aQ|KEd7l!^2;PTKrA*B_!Y~{4V1x`-+vFy5qAq~f{ zhN>ixZ&5{BMR;Mql6Q=FP{*5hN!ZVYJ+{AJTrJ+7Bg^*YfWo<@MG8duG%z4^uIl^U z8-J#%A5FrZzv&A|9zh8%?Ct~%lI1e0pd;7~b#ELQNdLQQ9!rh^@AlIi+%%5hV(&eFnaiU>VQ0cqlW zGL02Ru2%u{H~0!P`h)~1Dt@dp6ByY=_Gq_CZZFoB&ms6@8QuRPCAXId7^@LKeAsrdBG(0#*r`a8k=#IwNJzI22 ziLAvSQE*sDvgZ}GzlzLu$2u}i3g6Y3SOIW6~&>II~8zuAbz}cCEES}bWts!l*A?}4zm8(X!|YzRk9BM1;Tg-kdJ`kQTFJ# zq8|^ru@0sKuf$b?o%@#I=K%ZO5GZlXZ8P6J;_WYzN! zGtZ&YswfuR-BZ&M)U#XIMb5g*zhgR`rI_W#_;1^BN5b24Wa{YGw4zSBUL9QL3iRr4 z;4a|@(fI+g;a7CenPB(gpC;Wpd^u*GVK_S?z%VgluK?Z}tO%OaysM1D#T!iUaAqUi zQ5v~`@(r^tVMTAqp=KC)qE1q=Ch%#yb4j-<1@pZE*h=V+&rwRpf<&UG=(KN z>`qX1Xm1sal-MNRuLLA#Lg{uaY@^^YZYJPjrN9ehmbvcH;A&@E+yP$wSjpuMI{LO) z@Q-YKy7zCr)D=m&du@u&RAsm@X*`PHA9_Pq({bjgTef zg;pXf-Q6IwB9w2_3oDf&D1@p|*^w3Y-;GsfSB5SRkQ6t|@sP2Qi$1+vBi%t=Bnt*d zDvmEGL~y^jWg_HkEL5`7GuD!dM0A8G%#3BoG(Hjs++)tTz-eHy7I;AXm4&wH7^C->0 zK8sJ6N;$SJ;gH#{n9L>htQIAP-!JyPO0CVA^VzWkc52J%R^jccMO3CBVAvas=q8+j z*LeudUk^a6K%^W}#t3GcPnhT44`yzzQ&9ORXc~{%D&$=c7G^7(Mmk=)XEFu~GpL1y zHQxErejjY2D!TRqon_ouvmVD%U>B&*z4z14@qLk{yrmaQ%{A$nOW=4w1g?Sj&N$e7 zHs35}NjLxD?kb=`>!a>QEAH(DGjrR1Vi(Hbjbdo9z%%Zq=n~jUVsXN*o#(q^VM3`} zoR^0Z=qaIX6bS+H`U9d1JuU|_y3=6ukzmo+6~Im#7f4eEyerAv23Qhg!#odW8xxl z{jan_hv820YN@mCT06ki4tOK1%nzF!1`;%hlWPOx7{N!+al2`tNoHw5y?&HP-Ad02 zNWy1oyU-}`pzGm7qA~=@C-GvB5n3fC@C7*?XL2Q?jh|SG>0AmR4LsTu&ds7)=lrfWz&;P;uF3;SbS=`HJA3O2CbS-h)oW zryh|Jazai%?@MLkmm!Sf_iPjVfJDi!veGCPOam>4HA5)D*hO{i=QbJ!kLresWlwpU zIZ~NKz~5V45nvG01vikWV_L0BBYsE|#gmrW}-j2{;!OZlkc53>w;n z8wL}DzhR-lqV8)oNH8&7t(hJTSy)B)FI_LWh<||eXx5kD#yc;wDV!$6O8rdU9)V^i z3AzJ-_rUcn{ZnaUJmXG<3D@vP*0g+0jQcVz9L2KxkZJ(pEiG;I?FV1KmDGsKm8|Gw z6;c8-8-(f#If<$a@z}Hzjtt^AY$ErZ4%aIk;y$AYq}}BZvJZkHOv=4RLtCgEn9#SlMjwJ)B#h$&t` zBNv9rEhdwU(S8u3*p3k3B|PC>8BK}K;fBEf3vjy5m0rC8mYSb$=EX0%7hJ2294(p5 zqSK{u=Zo17Alq_r%WYH&wT~*YhUFaNF;_8X5`h0DM6PuADw`4rnh_pgXb~CgB zp`$7pTbvW{feHE5A8F-NAX?iJY#<5Vm#S#}3BAF;%%G02+6udIqX5d9DBY5!oSQ<) zuDug4q<)SX-@9XVPJwYN1Zc~Ne6c4}K^w0>pBG%AwuOMB|yq7*L^%JjDhWfhFkrZ#4%Ym)Uqc|hREFMZe;_9qmqXUWUis_+K4t>hPu zwA#VwegoAsYg1gJ+v*b2Po~oxHNqA|PR^{1CWo7yBSufdADID|##C@mgP*In%G}KJ zd>X)uW{ay42XkUUh3izqC6R^X!gznX$Ehtx`W|I?2Dlp7S6LUes$MIFSS6)t1Ot|V zV@sH$!0~mPzyy5~rJ=Wt$~h2%k#BQ+5pBq9$YvLF98;<(7Ab{uXF_0X!4O{bZd_R` zovLD&sU$7mK{gJ}5v-0l^<@f5&`%$oAUB0nAdlH{#@OwI)J9TR$71lNObwr84aeOt zB=OGekj~*iYI*r$qTs+9dgng`s3`B*g^M+Q_v2EfPfm;NaX{(Sb2@Jg!yMz}|L8IxMTv-LLI#T8meEX%(L z*LTnRK)z?YDSv8!?E+G#{;Cs8C2S)9w6VCT7_@8uuFIdO+2Y{4%J!|4yLugdX&JG< zC2E4~cC<>KJxN@}Bg3NN`ImH>@$_JgF-sPQ>PN#7p0c18Ycd7}PD;`f;s1h<&9L7M zRSkLRoym6H9?|}Bm77yz2Z~)NDqxW01)l<9_VfW$=s>lZFDf7Xg{dp7V#CkB5cJsD zpn5xw&(TE%YaX_qdbNX4cn5sr9w{;Y1d(*zCkZ@!=`1KmX%BkcdL=0T)YnY*kux8QkM>i8H%FvrbG4 zQqRz|z%XcwBX&S*?-4Q=xg>ATta4tpa2u3Z_;XbQY)RDc7+UP2mv(;yMvb>Ag~qf; zMkS0HnjMpiv!Rm&^v)hpxJQp8Tvf4B2CctQ+3*B^G<$R-){`PMH^5+LYO$CaQ`U{V zNKg#~nX5&pf94=rC8*YlB?`>qWuT0Kq|1T)yPrZPtiWk|q#V*j=V_tUn%x0J4+PGD z^+5wIeXci4lJ)?hJ=v@6I6ERN4G=5`bbT9gBtF)EfH8v~XsRqP?w;m?KM6~9w%;h&Z zPaV_i)M5D(_g^Ab!*R3iv*((yWrEjTBqm<`ywPGJvMJ*|Fs)y9f4f1=h>aA~re zn;e<8!KYvfW{uibWJFV;R4COVH5zsu9$=nJd3MA!K?4-#N=)gQU zBQy9vR87SP3T~HggZDgnBxU{ULuB8eH%4`-s_tUD&@m>L8lM$bvBBO0K6y$-Kpyoy z0SOS{%U~z3ByS6>7$RCuGASn*Ts8fICnZbJ^6J?oV8(^HbB>9i8zyB(lf5>H6T<7fe0w-I1TW5sjrPv%eo8M~u|9G%n zQl9L=r

O3F2PLe7cFWD0ytXb!fBEClJ%}j*d(SACFAqw)GsPGbrZ1bTu}46}Q`` z;!XnQLb1;o6!S|f(gHZNudaba;N^B+-O|fvnfUrk0?WaqM!AQZQ<&0Q&rI8aF{c|( z)T=d66o(hwYH#R4|LR5+C3n&13S2+d`@YN$sDa*WsgPg9$?C5=h$+OCoJW~$&S9Ja zUWi1(mH$i|UanN6lY+wSm+D9qwbZ-TiLkbzJF@fUz!}`pnIC8*)ei@{y+d;%AX)=B z6@Rxkuj?4o^CUj^vfwnaK{qGq%PiEa_ia3t+x1{kTFs;EUiZ{XGTEp^0CypufQXH> z`wz8PHB_W65N${-`B1=vfC8!BRR_Qz z;1j7pprRKh+(niBWb6tGZi)@ZDk3zBU(x|4VcIw5{IAx8Z#6y^#oX?_M`=Q65MTQ`QIfvl;|6+$?a7Kt3!%$GL)Sz~VURlkK&UoH!*o5~=ru*@uh#7w zm9C(U$vR9@LkSY0tw!@8RFDjThJ6cjx25N3YMhu#5bivkAmN445ZEIR0S=p#Z#V&y zk6*u|UB(PV9buyIXWHmq9jGtIW^QF-wK08;>wzge$rQ79&n8;|-vauWVqv!th_YHH z4rP5iZSPQF+KQ$|V4ef*tV&)1JN<62%9aeo4a$izRr2bhU`B-(jN|AX&>Uoqe+c~02PY$Ct2UhzC;g>Lf@+Y6gOsUB z?$TO4mc};SSp{%i=teM*2^--`ypuf4H%DE9cb+Rd@v)p3I$_;f&;u@jchc zU>C0Mv&okp{j)aFe}m>U(~VsNiw@H<9}Jxr0Mx>$7fErwy39hk#?$=@xpkM5D~z!~ zvn@eu>IeZk{WC4{5c@9#C@^}OJZe{Dy zP%6;5=dx)T!bZaQL!gxp9+`)0qfj)VN+G6Pluz6H_02R7b}?0E6i0IA?fUb^5#F_) z<5N-DZKJHOKHbwWR$M#^u2PY`l~85M0OBu3s+G<*_y|_Hu_z9X(9A*_`&fv`FWjF|WsBk6F2ZR%tTMd+3V3atnNlTGjG8{3@9e$P@do^Y6GwGu@@ zg0>mZIeGU%_dIO|Y*AOOf~54#zslSZdC`4H-#m5A*bTuDHXD_O^1!=r*eRpi zlPZ27!Vikwyb_5xYa87>z%)&z5y%qM_*Nq>&)`lGigihZSA??F5_v;|h?Xn8DF@+K zjO=4}k-5FHqIM?8LBJOT`qf{wT_h6fJv-^nN_;BF@g{hW?>c)JQR`>R z*g6&W#zqO^!PW|%9m^C1b5cT)Mah^+#QE1zvQW_`Y)&kcIjHX(uh zHNQGJe>ZG+8lrL#_bOAkg*x{N2tSL4ZlhiA^~afjFRK>|)DPr`@0X0&&=!rI zSsYZ6+h}!MrjegPVR@?nev*wc{c+K$ohbu_@(%%4yiL4~oZ#&QFytEF#}H#&N59ue z=xft(@WH6?$vo%RKWPWVfI0Uyd&X5fSCEUX>>GkhHb;t*n2z5 z-<5nJIpUiaW|AZpa!38eLJ&uyL+i#xQ?umMZP3(ZNCm%<6dQfo7nw+3JG}ZecP#@2xA-_;lCokxsv)zV5=%~HT3rj%6hG2j;o!)-|Exf z;%kS5I)}|QkOrr=Efe3(GnPedaarfi)t(j^_$F+ADBK|IMUFok9g`XQE=_~62&^al2S^3pW+E6kN zxRkON+c$aW13jr$|c*8B0LeR z76|4ohPR77Yo3*RLGcB{no}W~)Z=g5DP|4P*)!UjDlP9@zgzPLvTLsCfUvR9>ul(^fLcVK}VOdy!yp59~o&@Tyi6;@8sQp6Gx zhrVX>TlcX~lZ`VvZ5zAdI@(b#br;pC~+~EtH632qbRo42w+s z@Ck5xhl(*@Cmfb!Q}63L1CWQBAxBwPN*3`2O6*}F9sZZ*TIRyXc}R9nz`{hTQ#jz! zgTMIXFialbfPddMVrpKUtkwYZAMpn3bN7hHcrFA~L|qWH;|xq_%`e8+J^R$XykdXu zS{?RJcq((o*qzFDSiwR*nR-8(bO;2=-dAwwdkDSd*BAE_SFNpE?N~)Oz8P~FE)si3 zdV1jyyE_c~upAO7YHMUY#4olKc&SKh>RhSd>&U*;)kW7W%qe|B;RnlDifbjNIh45N zr?c#Gekj4(g3W+=tny^PNanB#g zYli_lE(g0Un4Lwn{u7jIL-RMko`ld`fX3*_Rt|eVRWPD}NH(>s=!HtX z5wVa8r-AnZ5Mzh><_p!MT3X@xKxV+&v=c}tYOFbdm~i)R-zWE3DiTut72jgO1NpmC z;>=jZ5q;)zaZm_bc5J&{Mt|ECu<{XrLva#yMJH&c=_2L$=Sj!JKzRvT&+Z@^%fA7u zr-RCaBSi#B&HtX)Y!FR*#=v;Gl9TO;m$BAP^Hr5lSmc@@wDSzM4OioE=>hnJ=Xi=J z@hWwukslvLs`2=%P*KUX;?Hb6K_!Xv42c}^aunm1hYs%PV_tP};6DjqcosRrLQ)8J zHxBzLlwsh>=l=jV6YOigzeOm9qj=2-KY0F&B`xWYj?Hngz{5seLkL-GnYrd1dv_O) zD0!oVaV2D|wr|G@C!4xQ0=WBTXRRh>Ax1z6O(AlP5{nfSWY>b_Bl{}%Qs#3UE7}DX zuifcH#_>c|tiM{M3YReM zk&b&ec%eb&eVTfXY6vRM*~Tl>)q*(V^BR=Txv(BpG(qM2E4lH&^(P1P;SG8qYZ*6d ztX(LgRNe;umQ8RBaIi~8Yxt(ue)e0MbFdLQm5e(Gq7Ocagrui{r4J>ql=v-8Su_Z< z<6VGPnee0p>1WBbkszPB*s-;OU1G}8Ik*(zV=WNJE@1Jqq7qEF7*(JgsHPpeqZl3y}}#d zT&Bg*=tj8xeI&TjL4&!2^q+K{-EcvA;eZ}+EGqf-WFH>e8q)xQ6!tlrz6hPG`R=)` zI8;i~Da}0*o_i*iL_oxrY=y)56v*DF=g>Uvb3U(oZ>d74N{oyCT^#c7TM8L#H6FkF zRmJ%vfb3NJ#rquOIT1>umQbnyIyBmboFcq^&vX}LrC9#o(5WthGhyLDhj3ODdn?jQ z`_)P(OSEr1jB1cfy+`-J>*m$|pXhD(ymYYinUaTDoGDwEk@O0Q>UQ_

Q=dgo1gOm>BoEHg!f zEZNM_4C@~<7?Eb4bjdUCLVwfNvj<&X)E>cuApZ`Q-eR+z(4z=lW?N*Xsnnd>2&` zK*FkN+QX`mg(sEPUT?pfh6nRX3mYBSDW_2hB_6HukP)C4w-TU1@45Fd|Pksn7ekalxG# z;4W`kw|Chck{Yco&8ag|s#8*2PjDxEQe;f#Wp&7e(&g3wy9B?yCM-KPt~G`9zAX|x zzt#&2CG@i`Y=ll(PeR;s3$try7wMo$vtN%gPL6IsvnWq+;-3>vUEcb}P{+i2PJ2Jg z=JoopX2P|@R`ANutm=MBjOv%OsRgdpE1jX+(0Z4U)70q|X&uBoPBL*1!av##8ZkTx zNxYX~G)-rE$U)uvHnC%pZ!F|-IVLk=l{TfAC7sk3kvPKZ-uN!0kCsG3-&u=lL9$f| zKxzwaNJ$M=3X4!eT!UZ2T#`D&j*`#hk(1{nZMU(=9m$Y&Cp&M-qVyibD3-Y?zp;LN zuQoiVG(|=0@^(GS+!Crv^0P^i>T#Acxj$v_W7CZDP_36yEs~)!?W-5Cq?c^tftpw`A?Xm1oSop9HCW>0fF^7-UP7v$x7}-W!n<1 zauGX$QET=Pt#YFWenfLHNLo}HPZvpm##-GnE{i0ngBsc$cjEaD@DfLp5wqJx*JiE~ z(2|JFu2I*flFK5g$-PJt;=fCmT6JpUYb(^)a@t#jmxg4PJt1i**@s(?e8VIvqFJvI z)t_2Yj+_*G3^MT&Qd%G0(LtI7*`EZ<`|WykjYvLuwNcAE+htpUUA*I>z`(%kRtN6XJic%Bg0yS*7P zD=nI>JNDng*O2k9>6FnP3hd#$IqqUQSwY6@v7|lETITwA?H8Hxqmaee2|`Jj%kX_UzeRdj55oGj z3#dIHpP)%)k;B3ImpUUkrgg9pLXseVV=rl~hi_XkSEXd9)fQqw>IBn~Vg2aaR>D&H zj^eX=lh(WR^YU%A6=b1Hcgh{nrj>AdyjOCfU=5Qo)u?Vo10fl>mD)oo>yKtbLU*8) z^@tiH=aV$_Bn-$BJH05op;#qYY&hX6^JzrPf`1cQ)FStLi-YlnwXhy;ByOVE^RmmF zB>i-FJ*t@f+Q1ICIbSSgD502I3?~!YA6ht-OYkcGA$5u7tB6`%lG9z=5T|UA3YPhY zH93DISxnXy=a$)&YfjjwMOsQa)w^vF7?3g5No;Ex#v4C(&LfV&mM5g!!|r`pPvy(G zMmeoq@d{xf*fhtIl)mW?sp7k(XJVpIv2u-_5Fx+4B}v+0<)b;)P!Jz-u1May)KViK zVaJucV+4u4;47!j6l|buFyVHh~WD>T%Hn^1m2o$aB7U`Buz^68XEw_j{PAvFt z-L8R^fX8We3WY$9J|vAm&fp;9Zm$O*nbjD!4eWxf8UnC-cI@Np5fQDaLD5>T*CYdz+F{u)iiNs^1kP5O6L(dPBdVhwMG01@DkVx?6+d5%zSx{r2 z5xvxQs(32eI9gk>Mmx4Ff5pjgKXQ^;S8wv2`WjrI-*9R>-ng z(T^-9CR!$>cDXz=?}MqE(rh+@Kpg5{8~NvuhePs262)N_FB16~ayO(=y&cBHl*P|O zU!RYzXs>z4ks#GSdW=201tZJ=2t6P5_~aEAXbKcC0K(ncBA1urHdtMx3KR7PIYP! z!ocZcvu`#G(WQgTT(Y6Tr^?9JGWY0dAtk>Mp>5E69r&P)Xh|W-%89>k6VpLqd_fQz z+J<^|ea$Y%=5{^0rW>{Eumu`9shFWO@27mD6#kb9$0gb`9&*b|^vT$lVwCV%!Idxm z%7CbZ6AcI?dZLRnvN=R4QDPYzH3GF=^E3$iq_?wtI9=LLL*J?`8^B1JS%BwC1a&@G zYuci6Y4Ktsa8Yquc1*O0zDhJCB%QpTPOOe7ik?Ysg66^aAaYL|&U_@3k@jfdNJLI(WZr5Knx^4%5FjPO08!&v@J|Evpu$q`5 zuFGaK{B(2;qI8Uggzq+=2`9%&7{2N0wepT%2Uq}gl0YkFY)x)Q)+y$VOb(02yz)De zkK{4yVGjXwnSnWb%$QU(YEFCGc+5T-=T7q90|>KPei^6v6PXi71REzt zqFs+*tbt4zEFGM_v>rjbp%6jD)V4;vT%jdf(5w|Pb(@}mtw)~W6DE&-A@5~r==VxT zXvu)4lg(m-|G%AnFg^5u6;F(`y28;dtSdC!}Vq*7GhxYcpaxA54b)-qE2fDSrV~>ngFljVCFTVS_?b4A9i}= zQ!kfbCJ)j=TymS^PFRvSy|kLF3GK$_fbGO{!|s!|Cr#_2oz9zThxud@TVrtOv zC{d7F^(~(=eJDc06ujg4znZKebEwHDp15E{NmTYipgUt=X~zR2**wh02mv}irc&qd z$$%w2w^2r86aa=1f}0K(p)O<;eY&3Qs1Ii^+ndmaT#kJ^LeMfzv@y02k{Y-vg&;Xv z9!L$Q+%dNO$j>90^yMvL=7-wSFdhFyhYx~L#7(Oez833-zFy%K5cujM4On-2}yWcibFZ{7@Z zSSby_i)&jasvbHh02^85dWCG4$9r~A;n=zH-%Ue%N1+4$&9_mc$hFAXLzeCGRV3*K z=}uxF+Orc}iM_pvY}Fd6BM{I7Kwc*+8p{C90#wDao1CWs@7eP{8QVepBhtR-_kwT0 z%^>w$ZyyeN3eZvRR*hOZfb3RaF6tYOz@%i+*?ftE=cDV1Gz~nc_G#|UPwC^(Id5`* z(y)4-3UtRHuufTs!TV7lP^8OrK_@^}h`s{W)9Ae4zlY$KmdQX0N{`{d1|`>59&ro+ zUDDl0aY?yeL3n-u_a(`cZZcQyPrqCvw%fVUl*n+ayW|Y01@cve(0&IN3raTQ3}(rS z<<&?SG|4e7o2@=>E7I4TtpqGF`D`m`rb&`$o7-QN79&o+Y4ewl@BP#kl1e_ILGI@x zFtRmVV~NGN05_l^p23W4oCZ5L8#P$(D@mA_A_QQYx}$7VAxX0)r!jZLn3T;Hx!O?TM&6S(2{+_=3DqvuKC*0C?GoyeZq)ir-zqtW zMjeT4+>)2NCC;EtAd7Jj*p<+d|05^dsu{c!QK7NG&kZYKlQ-tH{~+(W0g!e|s-n6J zA-BMDXmbLWoncR(-}{Mik(TV1xwq?)tV}}F!w_^_Ac-j8WW78qnU2T>aq`oEw&moY zmcU3fKwk)p%sjITUS@RlCE7pQ{POHAY6z%LAX_;cS;YO=}%J6Ie@nl+K(LdoApu;$!8IU5PeuJeZTLyQz-=(BXw;#lU7ZkH(Mj@MVaUw>T7+|z zMu04^HUL3ROhht6I2O~x>yM%=gq)8=*aFK?NCd=m?C+=ih)Wj$shTjBoR@u?R7i~W z0NP2QBnf@+I8M&h>m>xHHyqX>(bGWMmHqyRb{yUfU?>zjM(OfyO5e!>)xcU|s_U_K za5g7LXIMH*#ow8_KTR;`x^-kzo zcd-I>F9-JlWh2dkEQk=U!No=<-wwEiY>CzhJ(qWMFL}i%3WrvQ>9k$0x-QsTp<-nA}t ztAr#$*rFIF(Yh>+a;IuRYH&KpPrO;0a>LGwyCQin>!^oqLnMG$SC~3d43)Cm&7r+X zfELW?ux);MucjFbsWA_7xUWZ7Lr4~#qm8rn1sQ%ALSdVxdYPcuSLf(ijxe>%=gnYt z%{Rf4+}}WurvuffV@WCEAvn_10(r7XhXdZlC?MlN3=Q-2b-WLM>>&Xt!jfK<2DCZE zbYqM3X-U`)A)QHovopr@C2)d`G?x~acOsg7)4VN@jOgIr>{IBzONm$at%NgF@OX*(RPmk?ov4cHm3}b3@!PkkM9Lt zUV(||MZguOgO4Hn%tIl9_Ge`25yF=d~^SeZf+n`%j7mMU%lZG{Y? zK3f+?oi6b&yzsUyh|d5|8b}<2&nBfJ64=H`I>j&T(!$f003);0qLA!(sK9Gfs%Wh<|w+%wm@GZ)%J1s&z>rxiry^)}z2 z*JYz#vJvr(jqA;^Nr>PAyvmA zus7m-gvDFbnAPhg+L$gkE5K52RokOIwz77WjWL&(Yq4qx0;K3v=vy0u1S0BBMUFS= zbk2F~%ir)S$4A5+ZJnK2X?g^{$e?V8DZ_v`p5U`%k-IsAyH;=J-QZY?^NkY zA>bQ7qY}>=Z9UU@7au1`ny2`0_l!!mP2Vpbzy?^7?|kjL>=S*3DVVi85?L#)H<`!9 zLXVtR2T(>ONGELtEM$LEtjp{k&S;`ugi^{DQf>lg29DYuhAcHVs|?I#HrGqYTl7bP zw$xN(e$yy`G7Gphwz_sGE~eJp z3*#YbG6Xk`QwX8N=-Z~$*4a+~2YZtD?Qzv%)9!KK6y6;5D@44tMCQU8EoD3bD@zuy zusStkB+4@QUos{;q3(qEB2ZLz>Uny3t%p4y{n%|Y84jp@>iGb{_N>42fJ@Ll5KT9y zldmOuzrXpwo^=T|IoAJm=jP$`5nuv!9d(wZY|KEiC|x`^`B~J=)LgFkudVWXnU2TT zNmC5z`4V*t#@ms--j7^ZqT0zdDo)Q|&g)%cuyryKX+q{W;$>1WRLO+l3S{bQrP=O! znUb{ejS)oA_kUh0+1W0s)$b;6+_Uv6cd#CI@mB?mnSpF2jVMX zgj3*}f`q0So>l@;>wDR!WE5p1EdXdU4`3kxXcpX4PEFl)E3A0wOfuLbKm#&aC}Y*~ zDG6IpK4wJ&cbgfVd?0~BlR8w&=^QBo)k(0cM?HwN;zkkHn4V+`wB&N+_<9NhvHx3G zAeAIhV`2HNgp;Yy!bc}&PLjQ`v2=geqr$xo$O%lVG=OC49*#&S_D0E%$w(J3BCp*- z+6GRa^#~ayPv_H5EvdRyQIRlNNsPxk|zbi&SjI^ zB_=lmx2?sVNw!tPQ!ek7dkAE^NEA$RP^W`yM4aRt@O&-*_AIt|1jKp7vvc^UV>AKG zB&rM|gW;QymT1Y1`8r=Mr<58V-DI)nW(YP>su`qIU^kkqgl2jGIlWYlZtV@OhiQGd zghbXmwN>F)fp4e;Ny{UH#e@R-mjUDO&h-+Y9~6uvs#&B(?`3=J_vI3jW|D^0D*%0y zRQJB%2zY|z_y%Q3p;oCQpz<(1?q;^*=D;E}11ha$k;a4IblBGvChv2k$h=?P3k-#2 z0|OdX!G|L)OKF`{UyB}*M?VvI!II6D&fG79h5P%9PYPDL_;8z~gzt`ku0IU-TTTsEAuSlR7 z+J=qkbwC9eNlB1gH>xh~IRA+w*0~v|h5@)N$WFvi|6!;S4}~xh&9)XQ3s=sY6PFInQMDH=2gT7#kOQwPQ$5|S zV&H|j4aPaK>aNi4soQZCEKEs6?l-QbsRKKfFw=N^yLxx zEUbhPPSo{q!Vhr3-T!EN8vw;{8ptrANfxmt6<@E+Ttvt zz7si%)qs(zWfYFz<-LD2X$}iYCbZR-e=&;fPpAAzICFJ>GeOQZZx&1jzYWwCzHg4? z$Y#D4u!05^8g%Duz`NPbN1l}mBIN!UD?OL)e5716Wo=-J>*C6K*ky+|jrVHPtOOTDJzw%WY;NjONuyVU!vwEDZP4HKi}F4GCN7#f%n6^6LqQ~N>6zz`n^$OgsP`@Q{O>YEfD zmlWiiRA~}8ETg?(GtgD0#{@6T2T4Q|kR6CNENE|?G@jc^e`-2*ndLzuSBD52DI1E-*Z{H?E@4_}+Yr45qRfb&f_ zEaUG1lp&FG{N=l^R{;E+$6v__A}LI5O83fbYJGsL0AX){Z}6?c!6*B!gSab0-N2-uP$f>U?y zhVxLyt{s)%Z{qeyh{=>~usB+u9ez~}>F4P^e5>NcQ_ffX4(9iuW-S4nkYNmtr$FWR z$zmAh-%2bL>8H(YCb`88Mb3>B&0Hiy=KR`%>l>aqhkm1SBV03$$QD(O9a?2%xHSy7 zIw5x)W-0hj@viYeDDwy28muC~E+Gls-aB0Qp+v@WKs^XoIuB<9yeG^u?-G}85_!DO>ew#lp#iFx&c*ww@+7F<4&-wWz2)*uvoaFiMNASn&alvRkOMavKXN!> z2@X0i((waU*cveqsNO&zEX-#&b<@^YlNUg)Z4#Dp0ThD^=ymVwVRqvD+iJ2vH5c&v zh?ZUs$K3&Evo@Q>3bg8U)0pS-Zx60;J>I1V;y9C_m6Nfot)3yOrHV6A1ZHMRQ;leE zyfIvAhntG}YqP{UPMD})Z>Fkp6QpV5)wpD8Z}T$BUQhB7WvGsBk)#~K=LsDdyvNTV zB8j9DLHCQKm0~6^3?!$iAUGXdD`ipv=?{u(5o28{Ws(uf=XP6h zX3kS*9{7gHvqRcfowz1v-9eSToM4$CPDyvl3dhcD)6Q&*b{>=ZwGBEUW8!yUI^AC2 z+xa~bxEqy{WDrWUQGkA+LbI{WM=XId{!FTYP!&^ry+(Jj?;A<94&=Fab7?>bD3eN3 z6WD-p_S$MhwL0%F0lS#AR-me0+r5KAX@?Yx8{s+9Om?4-(r21&HQS>pY}1W2D99qq3y7PiBc!#z&*PUv zOQW7U`z{(Db##>(zxo4wqa8zx$GzsMetjc}zK=Tc)$kHe!s|%h{ zNuC*wFd!mYDY(VO z%h`nxmcnwn7!Yl(_>~oA_K6c1 zR0*dmVuPm|2p=*5lE7w8YKT;5C7?^9kn#8Fz2+&TvyGfVXioMojDBq(oFHVbJmp~k~t0(5sn1U^e8PIt}t`REz}oS0t? zAv4=ERCG+LT&<50zysg1)7hkzAQ?*3#X3~^CK-?WNNXBWPzHE5T1$aB9YJEM{fdW| zLyPY?)=>FvClK_ zGZ?+3(0(!&s>@rv7;z;uhv{IdMa}gEyn`^jQ$-NLJK94wpm}!t>0yz;J_e+UXyZ<$ zYj9o`xEpD=5yW}MNXb>2u{QCoD64n4l#UkJCIb=O>(PhGamXhdRq^voa`~7Q7V3Go zZTCmrINcSfY=<_Cle7SY0~9Kmms7l%wY!r@we=8Pb|hsQmSBlA??&I8f*Z803hn`} znch;-Ugh;vKNvtJrW35}!T)c{0KL?=BNpgdVL0tY&I-mzAJyYqpxsfEY zJOSAv6A?Ce2U^E%w&R(I_*Pq7I}G@k>;;SKe00^m!z|ZQLnW7I3Ho?U^ktsMV;^sP z`EbX=Z8f_gBT%+C=@oxZ^zs1YPxl%E4pLn!{eh`SQ$Z~QatQ!x__X&0ul0!f(MV7Z z2y4eno~jRg-P1O9&QJ!~+Q!c-RvceuNrDXuR$^CL6vT6v)8yC{IH&>2qQW^;$9@o2 z&>09GYfXBg;ur4u;F_f!Nuh+zdv?IKPKT(FQiSRWV#BT*Y8R>y>iA3zK}{+Bs)Eph zju+7`$y!$e1#V2F!|i%>r6As6En>>I1;r1o`|yu)I$P4~^$PkPWlf?GCWZ_JhT{qC zO>G=?(8rQ)*s?3Kx8ZUj zkW`*hIX>XBYtX${V+RPUhD52!Nsk;Q>~yLI(jJTpP(}>;Y>k-H3hEu$30U9;+bJ!H zsm#A!k3M$4)3;y+)PU2*oIV;(Zx;{vNmW?27Qxx4fCLmkgsj^z@m~pK@H6ydFmvxB z?7$G)vV(<~+k-VC$02ye_(BE>L;Qh8~F9zUdjHB8L6GPs?3!*@&7Jxr-Y=Y&4ylUIU%AQg~W@Jei_D`HCF6m-o6i6w0w zY|kJ!Ca1fH)#t;VL`wt5>6NIdp+RQ~M2`&OM32=Tm>aU;wD`#W7Rv9FTOG7Wnc~bt zI_XF3o`AAiT~?4D$t(NuL|Ep#`zFE5)&ntdyB>Wg7J6kRKZ=Dun91w)-Cc1$nWxCf zixLSet$N~-8|}s3?c5Eo2mA0P=StE&$ldh$bHc&^}S1;8>SnBk!7GMG<{X z=Hl3+LiKb=ryy3r?&R+P*$jSm9<_S*hQ^~$I4$MRQ*)d_z;>(%He*KUo9}-&(4kUR zS17J|6PYw92ecd5TA0zBC^xyHbG#CXq2_5w^ax2lsN_Z(jruggxmnx zZS^QIq1u!~e=)oDdBtMuB5*~tYNL{5_EY!K*tqZ`R3kR9kSXokBQ0WbU{Wkdlt#j3 z5*Cdha*NH6+v8iQ25`+9-4MWeYEq~+vSCl$a))4T3!91_IuI&`7f{0hH{}&gwI=%= zAVs-dkDv=NqY1G-cz`BxX&B?4^7@$9+Rx+aawz{Tj+#M#A4yLT$!Q#{zhC@yzewDF z+L+wj3!p&-6P2hOQz!uWq4UavR$3%i(%Yi33a-V% zP&*x3ua^Kxqpuw%QFq6MC)K4n?qy=oFlD-q3a%p;)|>c(K7^2DZE2Hp|GHxdE|Lsd0JW9^8kG+Md2jP#;$;TkBoWijv0W zZY_0LTA5x6kL3~6y*E4du;+S^{l1aOX z+uj}7g6a3%a)%t z$kCx%hYod>g~Igc{?%5+r~zn^DAJa6o>VrlOn1zv^U+75Quxy$$SAZ#*Nwoa9g1rN#v!#_!MJW-GNbj<~9#M2W<)~wIw1ldJYyxGl3!c1L;=|M!J06V7 z$m4ZYu%~#g{4Xkq5sCl_4Pog>N)0oY4lKd=y*#D|p|XW(Lh1q%+gwS=%0m!Be zS)iJkzQ(c6LC!-CK+CJUgY^i$w`8)Zhs6ZmY0@el;mo;8D>aOhXrOcSDB!n`O=s4i z%?mB)&a&*%@Hw>c0*t5IMNrZ~Ob+E3#Jhev_rBeGMQIR!LdE_h4%kAQ2-1RTyJ~CYshyZ-bReK1B*l` z-S(+d8s9-?g(h{Z;tONH7X_478u#kqwnvQdC^MB)NNfF(S-btt&nzd}(QwmQy z56Z6Hm(YD&vHiFdHXAn8N{1l@&-!L$1X38|kB;z>evl+A=9>??aQyhq)gF6n=~D3& z(`sYa+%OjkWm{6X#`>_d@_)EnWOSmpW(5+7fliyTKY|>l6~b=+GJ!;s=KwSTT&i&^ zZ$vTuuw5r}V8HP{41Xe27u|*v4>;*X5)Hd0Mrg%K$?V&SJ~~|iWOc|P`rMFuog`#R zP=s>_fKaKaDWPs@>n>xEcrEzMfkjzB9=gH-G6NFMbJSbi-6>mIbURZ2!=+k0Pe@>h zeB6kGwqrMUG^sRw2Bde9!0s2ayVpwOjGu=Szb(^>VFC!E%$Rm2dp~g!m-I*gNLHW5 z<8eYmLS-|)q?fn^0W_fmA!V4e5svPy4k`AYay^fuIPG#0M}MCE#P&HGH~PDx5&5k7X~p>8#0XS4U{UF8Zm zt%HU&w{{UdymtdJUmK}b(kwa{6i(2mekQIwLAQ?upsz#k? zuo44~aeJ5~B~_`wFel9_n}TZ1JT%;qHWpU=2+NT=K=Lbf30rs>VC?L{Ft@qcaL1VX z_+EQ8h*YC3Z`Y%1(beu2$J5d5C-?ND>WXaAr8;AkT{Y80H}_+UcD2QQ|0>qPoBCJ^ zLx@4Z4Q%cTs_AeClk-(ftXZ}oz@i_0N5O6H_wP|_Wlz3CDPa-Y;J3w9O8XtjZPw58Rtn{+CsfjWF?WSJMuanmcvV$e_PbpU@*3h(9RDz<#R{`;4Bl zk{Phy?F=`0fGvX@YvVnxVmxsb_@$&Ff>>%$xltj|f8_M?H2whws9T7aZ|?a=3;WUEH2?fKvw3&J)NN(ypwH9NCJ#-N z1OOE{s`1|LeK4v1S0B6omHODhyt!I?AZ_W?8QpwoQb*-Yespjq%}$$!@sP^oGC@s1 zq7+r%#CY|UfkT&HHXkK(T7``s$yD_P2%FAk)g73FW6W^Nfp2P&EWKK$8i?0zlo9Zo7;A084?d^60*7Mh`L6z@=U_GxGJf*r<|x?PVxboPb{ zXv2IR3*a9i;z#~=;HI55ah!P|0aNtnsIFPtB5_N|kT6u+)61P{8%HJ;*6Ro}Edf8F z|2PcppPbzW6 zZ($j`+bmFzGok{B3>Xkg=%=iip=8}n%gVw-n{83MV%wYWdO!ssEt0gDi(+W7NMXSX zfbcZZV>dD6EpThV;MzNvUmF)$Zm2A@1}RA6}zJHl2De_5NLL3!!cGQ%Wuyx0pm1otAj5U z^w0qjAD~i(%0pV~Mh$EN`gVU%choPk3O1O zuRNoNsrA7zI^s~)Ll_M3^s(az^>l>lo7i%pXXkL|l4PBVecWt0hYf8ums(w`v=hm5 z?BVU4KD=Go8`B+tEPUyj4b@xdFbm&}^vQI$dp6AV3OBcg1Q!;I?CE({GY(fNJJv5j zqS-JF3}nMOh*q5GrrBhM!XRoNG(lb z69JCgotfx?&mm$l>0#QEaTayiKYa4_2)L42_S%4;0KH>nq@7ccWl60cVRl2f5HTNbtv@Mu?KaIs?Gc`}O}wosky8^!IDAVRziV%dvf&^Z8 zBg@asbfle$qRYZabQ4uz^MuLEIIwRC4mv8Oa>;_X6-0?=!R^bV94}bao(@ds4CI1$ zH`h>SU$zn0b<|Q05`xTR!q^{+H#NhkIo@cR@8FL;xmcVv(DcuUZZKa6H zBm@k5ogv-06Yv@jq1o#`h$V=Weex*5Oye>0?EC)o&2=&=9|cYQ5nGwO%l`aKdBafK zOXqZYA0VAtSXkqoA8mMl169$b7w9zo#)|bQh5|cZefGVVewOcxEcq?1P-?b8*GvM( z9U@>A#An*x`m^z7Ayc~X4|hjC4O$O%Ct6{5H<+pG))Tu>I&UOHojIOSCq;+AW+ICt zcJ&6B6Uk$OCSmF$t{3kz<$TWheUZWl6S(zE+e!`bifO88qUOWcnd$V6w{g1xs-dY z>zk9fAWNy*(_C`Whx|k?uiB|&;Ue5zMV5-*VN^DbyjA2Zxo2_Jrqn_jYNF~p6E+Y6 zydL%3wr;{6TG5zhw0rbghbxMlmEbu*a#JCqW0th@&-9fZM2j>cdg4GW2#lBN6ck^a z2Et30lka>(h&zjLfy&l*b|H*Ni%X~YwUmP>=VymTS^)&(eg%_`p9|@`(Y>|VW6L{criKVuX@q(2U~Jww@LkGUU259TX^ zR|)|)4tf_l5ubW^dhjth`J4}xv2VICj_vy9i&`*1cM+&#VoMK zj8T}bA%?VY>BmudeQZ1;+~ePGVGI0$V@_BOjue3sAYpdAo|FPWHgRA$=&eU~GIN&D}>8B+V38V1b+Z_YaS|lV_yEbBM%ClFOtacu)N#O^{D?sL3ibid-YqKmi(NcToP32wZz5^KN| zAy&#~($)|(GfCjxId~Ub&te4fIO8dI3QU-WAF`&!YeMXo319@v_Cu-;h_|@7-lrFQ z?N(AFHb=6oomEH)%(Ne>BltM7JlK81RyZP%-=KlqV=7Fyc!2wqB7k;>L&z==iZC(f z8Vzl}R&Ak8{AugROKnhUdv~8ZbD+vRp26Gtid2OLNWWngBZZa5(^!TfS2%?cX=c^Dw`Vu|L?iub&mAP4Y1VQTq7@j z!M)&Wb;L-~L?)dMjT>L|oAhQ5c35|78YsfYnmii5)qo ztcuhrTFkxyNOtTVdm{C6RQcQ;sdEa9St3ANjOU3xnFv~Y{`tJ%3b`e5a&kDC+^zIE zq1ad(%6R{GH!5Q+n8CRUJQ4zx9wQ3RELxumW#}o!-w-Tb$8WPipeFtq{)E024oSK$ zRb=0fyM4k?oY&tN)Lu!GoX1=2-Ne0?b=|ANqcKau8e5aB3(6e=M}G0c+MqW+e=Sp9 z&PIhdsAV}1Fw|@dqw@_^)u=^riEg7qOh1uEb65|XA2Bh#JdzYBp1p%>=vuG6ybB2?&0sEZ&zVe(5!l` z6l|H8su6U)1RPVu90`uE(*P#uogfXpWmw9A5QKc2?Sp7dW=%G;knNCMNwGjFoHHE^ zV*`fpqI=`QV&Pa3vqU9n@eZ2>a=GC;l zj}-IyZK}iwe*-M0k2qVVrv55emyM3Q?d`JoO5qBgzj%nJK_VJCaK53dYoDSe2yzXN zUj}pB`foCdb0+adiwzA55^rd?BedAnqoeg=gV9+P^qa5jAHiAB%gk*Dr)JgqH@>$7 zR!5nvBQmX}j?XgCe&IaWSi;CP8ykb0VtTMQyc&?e1|^tA!0)#5_+TUB-j^wU#^W{Y zw_C^iUu0T`g^VFncT+XWY@h8K^F^wILPN@xI^5DW>PqY`%!@+AWQQ%Cebs1_+hkft z6#Ib1!{peV1pghJO=%u*W9B2(FdP|B37K;~e&4iDVeseEe+949GGK&zl$TMdc|030 zgB@I<`Gk_Zi!eR6+z;e?wwuzYI@k^%b?UDgu@u4v@=t5?i?V*(*6*6U@v2P@zN;*s za=EM5!I!2XyIZ0L$W8~#q?zM{6+AL5DxQC))3k>>YqV*SI8-khj_{;8wOE4@C~#t; zt_c4Zd`!CCPKavoOZRk^%hr(AkBi)_8aq(Td_g{gBro_R5VMCjm_i$>^;|*e$Zt#? zVHInB{`tVi_Bz$uF?@~=Dp<47wUn!E1i&5eja!7o*b_wJb&n+Q;H8tG9HkxTb@P>s zfA^U^KYk;mFI@TL-U^VGB1&QBjih^Bi5BHG~35u zYhu2T5?#`Xyg*P11ex=TQ18@UvO-X;8A}wH#ZzAy14)MidAOHCCN$r1Yp4{`So>+d z*^1pBL>B~3pY=flEp4_tQ<8QczcP6=QsIwO8caZ|UMh4OH+^ssQWAZT6`rO1dTQ=Q zH+FaIn=H6_C|bSv?mgM7%@{i(EaS@BwRf?&{1kqDL{vBWOtZA5?KL(l--!mqyq7K} z!g!SC?~J84IS*}<>y$zHW4GTTmV>b~tutquuqA@m9VEt{{Jc?OBC^S2T`bHd!S^x5%BVYU z`rE*G$WAfrI&lLPjYHZJB$<3f&cKjbQh*_e75pwt?6;fO;2BD*D8r$7%9FSif)z+g zE7(bs!qI_waE7Mwf2bM?_Z3_(-}>))a!E>h*9OSGL2nFeQdHf*UEbfiMYT!&0LDc7EH?9 z5oCIt)4MPXDok%jJ@V?A&F(&gFLaUF){0-kMT$wF;w9CU_t4W7wswdpU-ZcqpYRz}F!MAZjSXzw9 zhO-`4VIlVq8umgrp%)oPmPqv)?I@(9eDyVDQ#=Zfbe$DFlt%LRyu`Z z?nzT)lUH%QeJX4xU*qA^6C^{KFh?_UJ_XJC)Ue7+?>D^ z-+E-&^o=^+c%WXbf}+^J;8uD;_xn}Wt0=jNK9}M8vflS(v_bWCXGsPBCQed+-9}6% zF6TVVXmJYV6!1hO5-x{KwS2i!kxmQ@vsKgFI}6U>hR*yzBdLDS z*Xb3K9S+g#@v+PJ1|M9-7(+|7d1zy{rzs3$XDwbrx#RBGFWMQJ&QvUA;4E6HT7 z5)Ryfd<-Hs)ap0TBuXaOnX%-%Q+N6N14pdWP{>P!o$w-v9$NCxAF3-$ohMqCSoEfV z2LT0Ay{q(xLBJi#etrg}7t~k;L!%^q!^ie@4cd_v6a%13LPAzWieZr3&w)^_4+rVG zywIx((q64v*UMc%9g?(}qz2+8LYfWdK&T)Y0t|ZQXK#zo(9}3Fl_1=B+CjnqQ4rWe z5B~NWly5lx6OUiRQO=|Kq7E>T_|q+PulCfJqtmxCF#l~mcx$NOw4KRhuY zV+0-a`bJQjMN_#)YYM+?V4sr78@CiLIy1N7Wy(wqp8lOCHQ3#S`Z>H6TxHM86>{yR z4&?Fr@?1;SqM%fubIxQ_(}fL%@drT5AKWtzR!5*{LX?6{xG0~t_G%kx9&G=isVENR z%v$y4jKaODJ;o*@vsy-2U%fl0U@W-E*ip?$>j&7wOS)(T37t2wT)i=8Diw^Ax|r35++qrfzJq%X>YF#dyMCqST5M z@#F}e4A+BY4Vu)jcd@Tz!<>pyCJsw$s@Gj-w0H^rkf%566{;v6%9(6zv!9n0*-Sk6 z=PR?qb=gmkbPL*~LucpS2j26v=(9y$H4Bo`H~ubhL*zyGCVlhJF=aOZLs*ZPxT8Lt zNX&BKQSN@zqarBC)zY3>;rR8vy?1z>L#WZ(g%gaJU`h-qmPrYxw7CwK*e&pb%9QBQ z56%Vez+tD1YE3Nrfe70#boERi;;gQBbqCWlkwzekSL0g=zdVIIK`7KA5ndL`QcK_s z2_#x7_oD2FUpBOh-a+Q}%#7R~CkFwa7wA=g(Q=lEuk~oBJ1z1qBgY%(J-Tb}VnnT- z;d6>4Tu07XS{$!kZShSIMCJ;b?NPPsglvSef;>F1p$NbWw6zyspGVFjF{AKnbU+TB z!&Ewj2caUslVEFC+#MYuhy!c+82~+5DP*NW3qzWIk$gqK0d@DRmW^7W%%J7qE%)=F zAfNC&Q*6S`yOG0g)RwMBYx}|3z%+Z&dzlvp(bf z4SlBHt+oLPeV-DM}L=l+cb-}utQLx$}-!S8Wk79 zp*}K{_K9vV%#!wH!kOt%7&L($744uv0C>WhP^%3UgqRf0>!T~0!wO(&?{v=ZkdS2T zoARuD-S?~=EXEcuUGCOO68>I6+0%a|^tV4Bnn^B6V$g9?Rv@EUAnJ`Z247My6sYaX z3)?FivZgHlfW)jTGj_SBl%|SXl@zDUC&Th!^x=eI}j@+Uf@DZlgZ#4`Y**>lS z2jjFVrz$%@Z?dTL$3SB=w9(ZtrKnm3f`cNiyKo7=9YcOnnsQs$BTeX`AjkpOdx(dq z_Yyi>H7`@e{y2eYz>irIn;i0sFC2L#*VzASRRDcYo+^%-r#ROk*h0?RJW0%4(<~6q z_yIC}ievZfFmFflh2)TLPMArOSjY|aHw!^5i8ie(7fscoW2b&YhXED*dSXn}Nl!!q zeT4^Q2EC&2qT2Z~=o|;5o7}RA@r~K~)vz6|AN$~ASg$=`dr{aKCRd^?sIx4fsMJyi z@6~WNg>|6nRbywXo3DjxnFRBC3e*jhY3sIKF9F6VD%@{beq%Z1x4>pa#!ATW3(8ux zWVVZ~{@==zVe!=iLhXabDoFhko2K#asTl4A_to)9yK{zOY!fpAJqieXf^y?M4;?MM zG)BrdDQP*ZSx4kH##THrC4~bJ<*nd|&?S61=%B|DF-o6y@wO5{$E2L|0IPw2-XNGb z4!T)S&+qW^BP_5T>RQ}^B<*s#9ei>3B8J4^lqJt{5h-!kO|O7_SC{}WgI(Rn)`8y= z@G7jFqQ!_sB=$Xx;tg=x>Bfc23t_&}McCQm{!@yzc7+^6%7GX)w`<~Hb>5M9N}DL* zM-WKd*y-jO_+jJVcJ^hXK8`pnNhV&`clsa?RRa#PE|e_d^OV?wLfZT46vwdhM|b|hJjOF2paSarz-=dBLMwhT zzRsDa&ZT9$GndNHf5B53(?)JoHiHW0@=4TtQKSPPNOnGg1K$JaO~1dmpSY@RT&qXR zI`K`JOK_3c+tShi1MF@v?1OShps39ewGhAAlHsKytf+IOg03TaQdSmRHZdpl2!$Uk zVkoYam}XI8m!3|uO8KD#tLJY~Z8P=o(|M9fMJkDQ9TzSx&P9z-uuyf(Cv-=B&QM6? zkc(2Zo(Y;%YI%M9;>O@E8cASuMb{=9Xy|1BuiUN!YBmUg~$IE zi51v!Ly1_0%6!3i=10|b`+A{2Ga`GY9x|EtnMnZ!x6Rb&jJl^ItYrjo>JR16IPY5m zTTAQY|JRf%Jd?Q&qT<~<$PR@@d*kEYLhg6;0_itSxjHa+^ZQ8%%^7Hvo^1J``%?uY z5{P8umnFSWu@@p15?~5=*B>!vpl1%C9@*3k&j&IM)}j?pI$mYP3B-iEcl$oE$5Ixb z;-~l)4IaSXnH+1%Dvszqn}dTw*tBia={z!Qo6pKe01m}T)Dac0nW}@7?UySZ9S!9v zXf?BqXe9pzteyrc4~`TbC^a`cr&%YO`iz0`bR{R-6(?h*mFlA^p|HR;PH5{9WD};w z@sBv{9hU7OqQtAzo=Sdn7@@}Fr$R*~*Ni{C{sfgM&NCo#$jecPTN={8tA}~j!GZrI zfZ43GE&Sl|?<{FShje6yiv=D!;u1{A zTFuNg>(ITka7f7;DU2&2W4U!Z3Yci<9P;PxnVGSikcAimB{YG^F-#~_P>@{>l8@*q z-%Xy&b|`BVSh#kh6B)x3Rk8YNUcE-1U_olivfol}zC_PnTuUkW{SfDRz$&JPKU{*2 zixH_tvAxvw*aEAw)mU{}PSx<0CY23{17$R11cl`WicMBw8LZ%j!4X{d12w5(OsAxp z1^7j^5QRw?bxFs*>%Y(-^FB>JN7e-vW^dsEbbdjc@_F{lXJ1&2C>o>k{gqsQ;QEsd z`tSzbm$`(SIoc`|UMz17f6FE~dcMCyMr-h<+j=@I&DmcMokGSP2+;$dNJ7$;&(edE zTTJ|xsw^6a+4e5Lt4w%Yg!Hp$QcsY_T*EYp!_m^sEljS+( zt#PX>l$_CUr-5n%Z;Dw@Fg}yR_?02l#s_nG8<5!3C$!&PpOQo(p+arhFShL?xZTez z*HKTE#(jxclSWy|c(CP8*bJO<`)pYwR#x{DP;RHB{r?qZRL zZz*J~)OdXFR}|+G&t)fDFWzS%&xla!e+i`sphKgr%PGR!bxn0ZmW$>64Vmm9I29J| zw+~}Qv9lz-v|E`$9|#i7JaHTlb#Y7|C~yFE$?4A+#>UH$MhlcYXSunQ;KkaeAcJIh z4NHS9Aj4RH_uYcq(R`97YhAr#1+nG=#uQHhh4iv4 z7k%S;8~Nh^PA=(1+42=7H1up)RGq+ZRXS99Vyi;_DRzsKn)OvTGg9K*KfMu^Ebym` z8JYmp4y=d}+fD)Y#MV@n_l4xM2a@K6St;IJh3`n@^v)BC!c^q%cQ?#de9yvE@B<;p z-jfKc_7Q6C&AN|SJ0syS*G*~KhmKBV>TD@yRB-Q+^%wqy_nDw*w z#BD}v+JS0!`q^0ACeB*W+KZv0VOp#@@GR(!TGS^94J$QylsmfDJO+r~U&fUclO4^z zt%Jtfllotd!iFnJ-*Mg_lvIMPJWjU=a8$El2fNUX%7{iQMm&hZN9tGg`+qTxaLUzC zqVMi_6hj8ZY>v*vrROqZRP|O6Ik3@C>G+vQ#D~!QJsVwQ+g|nru;zA#Br)TbjS4G; zIKH`>I{}43wef_zG1(H{kuM-4uDqv3vMmsHxkkM!ScEy#r_S(T+Fegwy$-OIl2EJ7 z+QOL-gg7@R*`0@gUqmc|B#DO3s4M;mm-qrY(M&CDq|s8p34p3N;p-?1UmYC*RJd$X zbzn9{98?7_3v)A;xrfU{Im59`C^0}rN>_)+^9!LQwozK^)5A@b%IZ6)`Mti>>qGreX|qB- zs67iYmy}c#;eW5ubS%OIc)avC3iJQiy4##vUpRn0n92>#w2x(e!3MUH$c3T34rFAkay2#cq(Lrz57!BIbF;1$&R-LQyyVxV zIYTGl=MR|}+<$w&`r^)T?b2Tdc@=kRKjFRDD})UdV-;Upx;U8J{|+(Vz_-C7B_V!j zq2^c#-vk}U9^iCL>FV-LNc&Y#+12@G_53O}X`?ly2Uc3b=oV{)U~L}`F``bqk+_BC zJ&}Jhs?J8adq^?^h%#xBad90q3c4llP`BeFmUB4>cd+pv@GQ9xQ3ZW@#AjHTS1*jiIIY8 zD>9veQ=O)4PN>E+-M{S1qzE}=OyQ;YX;_$Egh-b;uLWj{P&~g%>8$33!yAGz`jZ|s1XmXrCwz|Tu<*n)XaDP!saz2%y}ck%x{sh&2Q40a%_#{Lw!I)I zpYt=L_z8pRA~T4@g*FO|88t!;-Im7=ij9-N3W+5*j?2vpD%OuAZh?@7ZHAz- zE}#5^bLmE7Pw_iB^wFQZn45~=VP;!=R)G&1g||2(RHD5e5D#sM)a?NP5`u`M@}KXR zng#{WD8IKM+06)f6Gzels?!muluWgg4-}onNIw@Mpd6Y#Rw*#mK3M@u!YDwut@+-~^w$&F#N>YP8K zxBZxdVIbKo-_P>oB}v3bI4IU-$(k9jE<`dCgg@iNyqlfpZ*R^qJ(PT;B%B3fNOC8l zE?7=N^o(g7&-j(>sOml3^w8UXn z1JYiJ$4VG^`TpD)|HyC#YV>ppR$!M@7US;%hm85W$h4aF_HS})Av^oeHc?FrO*AG>qnGss2co5b;G zvor>GdPH~S7xePYiZ=p%LBgf2jZSrjAPP&*JC@X5>Wu-CLG>VY zBzV^87;14NG;SrhcYx6I9gUAp6uQB97(#GUe#FbT&BN?gJQmZG*7jF4k=B4snGo-1 zf@++U-?D*~A8CBtQ91dpD(idNPh$|uejSE@GnK1AUW!%hVq~7cd9}zf=cqIq{E79w z$I&BWS(l76akOXGe6~y)=vjF6FSMLdto`ev{q32H<#DEfLs@(wL40ImBH!=JeV;1f zrn9w5L)~E;rFUV(8AkjA^qa18Um85_%x+!E&M*Ni8&E(UP8E#4j=8&g`TP{fE>qUu z_M03NYP>0L19H=`rpy9_CRqu9A)s5qFA{h7c#2_;b#K)l#>>75jcia(2kIXpsk==n z7n24w3#csJC^rlr@5|Vxmu40ms6x$OGQI?M-s)ppJzd^tt8i&~>WQwsaHOwJaA4yN zvD3f6Fe-C12uffiN2O~Q1Sx=^4*}Renp%K43UI#xMXrSE@<`+;NadSKr5|B;^x=UQ zW7V91m>Hct4J&QYuUW9vw7#BS*rt#u(cRndT*3LL}_T#ihhc7w0A%?gr3;V%iL8oPA@12cPBQ(YNSlTTCN_L(Qd$$uE>lf+(y z&tCS;f1HwBmj<=T_yh3E^kGXBAv)&|al~lWMm)C=>X`igaXgJIG4d#%L|uQ;&IfY0 zu1u*aYT}dFx681Y+d?HCnQ|(BY_40uGj$z`lcF#$?3MrHTA*z#e zeFL-5zi$VR2N(p33>79PFK0)j7vs>=Uln=r{8Xq%mBeBA^w@O7qF3iK$KU*1 zx<9|h6n6#b_~mLYKY3KhO;YC5%R>sk9!gd}l2)Fc6w;8ikID@$#$=6Fr3FzAURXBe zn+a2giFnKD!RI+IRUY3Unw=Jn^!})5? zHLy*|kcM-yNV3w-tI_X|crqLE->Fyf98@q*BP(#Tx}H)9k{i;>8Xa^i*_CQt+nuqh z*z|S@4I?*}RB08HuWaMKHc9H|J=3e!Z36u(;?(&umgm|r1?C;rf%)!)jfH6I5-4J- zMqV`pY}acE(8#$)tMCS7g;e45h%l@l7fi2Gi?PnvsVNmN{tDWDu`win2zn!nS(2YD zbOqv>=A5&GVwWg2q+DF|IXGE%$E(p0oe?JZs7S;^K9Z+b#9tbELIR9n-1E3v=5vC3U_V)3xisV8tjP)Lr>Bg!ao`R;8nKzW! z@Z!x1wJ)8CeObB|=+T6p_w=+}yUep$=sXRt3K5F`IHkeoy~c;n&0=P!{BFWtWMV>! zEjRD|w^S{eH(d!(^Qwq$?q5=xgj{vSkio)YeR{27O>|r3le({3@tUV_u1?v!5}N^P^*OBwWT#qOC+GUtNOJ!>Qz9j$ilC zK#X8D4%OPRjl1rko63O!N3SgzG8f}`3rH|E4&#`rX&kU<8_$wpZmwG0)|kq@edeS* zn%!g|>G*X5c- za3}~CQ2-=#4=yJ}XOE*D^d{J6Prwt1V9>aJd^Ff>$l0)?p6{1HHIN2W_P|N~L<#(6 z(DmSp;b%SQ8ZjP3#8AVYk{Dz>>{!U*5cFQCBepKWYOu{vNvLi+fi9sv5NGJvo|7jA zA8sCK!o&04`yGfUu`fjaP}|<-9sV1GZ&o~_U?3rhAruz~Y*1Xk@E*w>(wr?Q7_mrr zB552E>KhpU>hliTh1V`#9C)|9OpR5b$wQ<)9z%@HQ3U(dYGdH9wEQ zVCUz3ic!Gtam=Cb?a^Y__w}Spv(EqXJezaZ|G7)B_x&K{wCz@qulIZZe0DbA{iy5V z;4;7=p_lLb`h4gy;?%=I(Cd3_H{kwSGrvXB;P1oZt0Lp>*Q(|h_N^qP-}lX!=;?%X z{`b?{&-c4(udnyl-PF|0YA)~f>+J2WVBhz>VuIke^H0}_&Cl#9;@_UnT0y>^ue+z0 zp~D2ljNYccvzWe{Le9xHy(jnx!4^J&9=^Yh|2XLOg8rT#_j=s#_hl|+0iVB269r%H z%Q$cK`##^_uMZVB%NFiAr+g&J{D0o|@4puzeaFJ-u5%xjH~R#8x&wIbq#D0&u*4U0VuDA7lq zbY2MB&}G@V5KtxqIoRLoZ6x=f+JQ|Uu`iLA4hLdDSrZiAa4F(IQ%T-@p`l!g4Tazo z79w+CUE>NJyubBx_t3eN+Fz*lyeR6ow8KnQW)H%TI`t;(5cI=nGUbDEZ{Op^Ha}qF zqELTSvXvQ{oO%agu}OOEStc78^QE9fgiAY+__`Q^bsgOzFN2SiNKNOVwpHx&@ztU2 z!K-o0L)YkHnM|_Lv*?Aqh>|u@HhWV7vc?>of>H@Rb(eshC!x$Hp~y4U3?NzWKjUU< zzH~IexiKtTXzoZ2t%%XkQFz2s_i`7(G-a!CWvW%Hyb#s9A}U9APf2?aFy6^UlItBh3P}T%;oM5mGwBv6+Y@ zD5?n0rIJI83f^F)AZ|?$k$^BSW>l zYS14%c01^^-U&%?9xq=}<($x+qhA6+M~glhph5d;XyXrU;;MJ9Ftg2ri}f(>S)9+k z{Vgb+qilHnuFYrHFpWiktco8AsGud2>G3j{=wkjJQdDER`#@c8LDWix3e%xqprOH) ztukJ5l?jiUxa36FO?OThKgd(59vDBh6_f_lATB;3`tf06wu|5($k7s1O4vygB$_kc z_r3w$KHx?;bClrJ0ZuDY|A0N219hQgT~M?9nARkW4M<8X!qD<4LZ++K^Ouzt(pUvLPpn@+2P}hpY&Bx(pE~2xpVq;iq{u<8OAj+7rCkp)m%ONY zbQ@^mWL_u+|4@WkQp3wJ%2{$g=*DlN18f`oev|XT)R$7JqaOHz9WgTOqEch`DHvQm z!ydx${g%r8jAI`zwnTcDc)9j>bPo7Fz!Ws3BhJ=a9`>G?f@>ZO3 zc7c6-w6&Y!CGp|Pj4QyedumjazAmnAqu5Gkpf7B}8SZcQ|Eusv=T2_HZN)IK!R_?WzEPK{+E@AI{2m_TN2 z;XhMe7zzWR6}nXJ_V0W{`4({P(e^w405o?%R^;m*FX}q{j?zqKn{_M~5N$A-0l539 z>ugE<)O>+RWkdnOsU%<`^)vy5Bc;)rW`-Aqi_ZMHG=|GY)yC=?z+kjn8&Z$I-(26< zot_z9xUW;+o7k3DtzQr4Cg0x^n?jb7fP!v>|OxNH$7@ zw*-~=*b0|hYjjIdSy5$b3ob!$bCRh2lB_0FbA4VqvXozbn2Kv-l@2`FohuJ{u|JCI zYvZfs+O^1avd_~ha*Vm)n8WTL8+?O1RHIPsO{%M=3SayA5m(V)7omJ%p@Q?qJN=uH zxZh_Au+C6R;>20QWcr=_fxwlHgJj~r5NT_kh4GGd7zES2B<&oW#Ab2B6S!(< zJu*qjJ09Wz5dqH)e8?Li)5T1B{dftcrnnO1z}mxj<;y5cpL(axWaZ=}^ZA>Pu1^KR zr+G4QJt+{#6YUv5wJ{}6}d1C{SD;YC$;tXe-e528iXo_`);g|A`>2U3 z@GKDB<8s(iTgpDDeNZf6f@n0P9mrJ$*ZiOgEqIlWxDKdQG!Qbm3&G3)!=0VA;*U%$ zS^kyjGlZX~Cx#}tI~=U$Q^lDP=BN$wfShP)rNKD0D#KWKDIcuukQ#mFCG^01WPcD< zj5?WmcAHfM;+#n}8cV)2-Cx@BFkMo0^WZ{h8*u|VKKA{zHzjo0u4UjDFZDxRj1aAu=T;Fzv%n6KOz!642y0StZzX zyuCYpnObU~dK~eu87bmohD31PU8BNi;}GK(ifZ&LWmtFlMxpFFyIYy9pU0D`Xsd66 zQ_u%cDqBJVfFrFcS-m+VSBqKn4}IGlhQW$iJP`zW$Ccu`6x60@!QgnWMBFo5<_E{b zh}E<_syl^-C!~Upuz>j`1r{9wRXm-DT@EcoX}b|K$!f6e#8a2jpNPWr0VaHSW;WUp z8Q*EtLH|%DR}FZQ4vNvXCnFlVk`6cGn&lyZ!46*cIQKi-uGoEVJn7#+f+|}5M`qu_ zN0sYFNcoW(#1;h+%9$jc7}hjhs!mUsBs_a9zl6*58+0*Df*s5_%CEe(Ds5h8c43)n z#yf+iTQ8ic`&RDhRs(Uw*6&FLA(ge@Dgy;VDjG5>Wo=r8-CmALwIiS}iz2P?jA43G zolDB{H#|+?-8faURwDvDH7=-)`0=RjMG*Fy7=#I_fmu;oy4TF!3_wo z;UiwGNxr_|aa$eNFL2@3ja2EU$kx~bXP>$F-@-jC#_N0TMlbFZ{lT7@Lt z$)on$F5K+gIqH^1$tcXP8_uVq|H@43;Ot=NC~i?Lg(}APB~X^)LmLML_N+jIp$t_l zp|#n!N>bgVJ~-;1siY?9mNM$oSj0bKKag+G68!U3^=mhDO@QzcH;Dl_zrg|PTj^i3 zls{BA4Qk>$cuym;6$Uxs)4tYfWHVzQ-70~F+VT8qURR-=^&S`LLrx##M(I{ow6F3< zvBrrOa^VSg#Swq>ccs5W`I)p@oNFF^v_6BPZMDwc)yPgo1nu${@+S?_i?x2$t=*Xq zdwx~cD{{gr$zMxaSvl}i&q-Z1^ZwPd&bkGQUq>m)lLt3S^__QeV7&>Ph(U^Qd{i;o zPYH{&`*3$X<82owAl5EIC*aIw$jdms978hh7?D}^x@-q{ThGp6_cRz zgT`OjbtpJSr?krec+@4<5mE<|kLsnz=yZjnlGnB{b%GfwJQ*K-5steBGRrOjm1auy z;hksjIF);u@{~ihe3y1bkh0U;eWB&NO7g^@AjTGZu^cw9vy%8WdOAh!%Ctz}e?*E0 zy9>jZs|nYCK<4fb8cb+fa{6@_FIU((#!#&y^Fk6b0mc2^91hI`6X$toUl6yD7!fK~ zyI#+icW1)%$iPK4$gqK;J*dFap0Q974N&Iuf2im2ofN2*07x3bWTb>ad7%kutUAJF z^wsBd2;e9<^qr25-$iAH>8y-~q-|WegREruM2=%Oo*pG&T~m?f^W*~pWG;;isCR$J ztrx^#HCkMl4%`*+QJ)U{CB*zw@ycscyvcR4>Qbt4rci__&FUQJgM*6ORE1ueLYW&o zOlI1iJ@5GPXWnPmy`L5)I|SlAFQ*5}`5Bs8+XUDlN+i6~ckQL`sSi_e?$t|TXe3(m zd9XxkE{tQITA%4>;#eYPsbGrhVbY%t6hE?vsVbAQI#uKO48W#LqYI;^yPw89E9J_8 zZNxm<*2ibS6|Swz`Y^4XJ&HVwKaR~Sg=LBmoQTS)`m>}lgUHMOi?z3msVs=rL~)14 zwUNdh8h3YhcXxL<&@|RK9NgXA-Q62^ch`f<^u5WOH#3>c{J8U{lAWE}wQ5&VSy}57 zq-FGVdQZ{{T4Wklu}dS^59ULS(>OXOvhJ`IF}*xL;UL9y^&t($VsW<9x1S7H$(L`G z7T94wvRNU{iBMh=TgUj~J|r~%)|ce-iBcGI8%^*ICQMX6HxEJaMDgN4Bb9xpDPO|@ zT(;T&454gg=+nKS?r3IOwh~u8NO>cox}kO2ec0%1LTx#%aP7OC-Kn#i*H&p}XvQj%=7x+@rVmK}BZYP((V{9zS z)yFCCm|&Rl;5zTP)Vi)4muHa~VmH9yg_cJ^^y7iD&QZP_!}MV#6qcbUxK%j{XFKNd z+WqgE;c1wJB5(ZzY*Pm0HsMXO`sbin zB42I1UpsE=Ndj$Yl&MB+-O5t+&jy8)^ET&#l5>M_zSKtSM9y5O%@1cl&q$NElfJ2w zfMN$#BMQKrEP_EyG;K(TZGkGgP~A2za%jSM`no({8^sqt>_@~$3{UYc-}UXX&7+m- z+3~pfa-0EMLL8zgay&X@|ESOl31z%FRIG5dUiPwopRl8C6i+->|)_J?nVH>)!p`dR(%S4jXl$gbd6fS%uY4DETq3XXV9r zh%vzdN2iQJBZm*TGXvx`Tz+4a7=0+4JjKA0L@{H%%#=}SnCRB{P8cpmuGUWf_oEKt6}grwej#l|i6A_XuQ(l<4@`Uj~tM>iD0)enWQbdI4dv%U|#( zpqZkVm1xTaQ1kN!-@k5tjC&pgZd~PSO-r^eKXa~%UzJdkUMN{VI74TchnJ~qhM5{R zOQi$Yfz#G~=KjgPM#{)M6B9e%TxhM}gK=rqp0a|n&ge?b_Csp_)R+9dn#Px?L%AiGxg@pBXz8C+fij|H1-vX#YXkUNh>jER6SlBc z*fnTHWc*kfUe0jPzHvw?mx$qH3EPgLVHrJ0qwQLOVNc|@Q5#sGWGa<&ix+9)j|070 z*59;BBx~SU!knJ2zbe$I*C`|4YzGY(vvqf#PzlxW$ds~SF_qZ{?@pdDy>=Ln{h}RV zcv`&5*Fye21%6{O8vc3B5I1enyTIn%G1;lo30X2sFBM&Sv-A&(^^?)8Oty`^GT6RZ z&1|~nY1~62rHeaTr;??`cu8km8%Ms%SrIo1c^@Xd8(z4R>v*lzJL`1$k)yKXavkPL z+}3Tup?%=uclTFm*9f9n)dk9QuQXoYJULR%85GyM z-pKT$q%CZ`ULjoFw~6LU-1`a@c4a;w^iz8iP$#GwO;*34df7;ofyGnEu$Z86p$Q}| zv+i*pX>pia?Nf={NM!ZZKtk|+5|ju;PDWs^tPO|dv5bnk!*jH8Q!si0Y)HJq6fI6% z&k;7I+*E(0ZDc+l1%od#r1R_~F7a|=%@0c){*dw3!j-lYg5`|4i>UbdlaI!Dj{BAo z1ic()?#MXWku(p{RuXUe3x%p+sm>x*zL7<@dq-&`2U-r7wAlwACkz6KMDs*K+7?7p za|65RSFFq_JwI<7-vyBSs^ZV>eM!k|b%Y+`#FxDiMZqMfYjlUjO;gkJ9U-tfKJ`mQ zi5(DlbI&22DQcF;GkJ^l89w<$d^YLW0W#k3$u03L^kV<=tDP)BWnicKJ#`VOzQ6UG zj{WK+a)?Wc%1!BO9!7>}wZKx+6}>|sCTK#b$+3J;sCi$zbVSXQGay7ME)PJ8X>ho*5e zoQC@fkqdGRtb<`i#yC60_X~6ABLi>2j=v8lXGW9y4Tz?F{3_c`UBvyuvxk3;Z*hK! zw^W-?kj0RUM+qPyayf0Z=U&%&SxTV-wF&C}64Z_Il8RN7rd)H}wHnwsGO}_p7dbkj zqO*7M1SLU;_nFMLaB?f3WXvHp1@j4N1&mt*jnRrXb(dT^N-0E>B`zQNJ7c&r7Ug%E zucKSnqCg9eL=U!!4jVF8PJw|f_1?r0MkvXY1g&=74{Yd_nL-FCnZzzoY^Gz#xtDZi zm6Oj3G8&+jmp-t;j?!S?NzqO7<_A77@qPvhqOM4CCIRIO=(Eu5N46qq%4<5aV?Q$V z^#fSpA%#VmiW6G*b%`zqF;M8=$+#s0bio}D{!_KXH3+HfFva`fgw7f&IpgfFf+z9B ztm210zx022f@n$)G^GM~MP3l%aL_WY*#l=75wCdacEiV9WLPPnoo-Ga{|@9)y6e7X#NIUCO|jY*SL z{D3t}U$meuhSRpj`G!?eESt`K^maL%AM46N3GLCW`W)VDrL3(EsV!ul?FxwYVH(R0 zb>g3@QUohk6V__uW3&r1`xUAjzMoZ?Cic%Q;I?7sxc&JmXAf1sy_idkYzI<7?t<5Q z9EExf8Wt|%SaLo+2^I>5oSp`1?#F)4r!IfD0*R1a-Xt11z02+P`|OC2sd!yy$l7cv6dbBUt=bY5^Q$#wQYYz4wOdLz6wf|1`ceHNv3 z?L7m%#Vq2q$(4zd=a9NJnmElOo2v%C8E16$+oV4U*>$mPOQb!EV-?n??bl=ai9QV( z$4QnkVQmcjTHPFBR!{A9ff~j@ znWb{{&!$bP`_UU$w*aR-D-E`nXjRKu3BX^{e+aa9jgDlVDM{P4_K>wH!5>XZ%=arP z95W9n42F9`qOT^m5=R2n3#x zw5oqCe#o_ zYHQm3hPCbBa97Hm)8Z|zUjPYq`C)Zs4LNmdFnf5il1XT9)81JX{}R}wXOSaaqEoF@ z`@`8K%B?~cNH`BIUe947p(p9P%HC3fheXxBcM`)+OrcthTWu%j)`i&TyQum(!$r$q zuXYBW#%Xk*KVTZy80|81yc!B~yRm7ZjuSz{VmzJeZOT64g65MjD9cyB2~YhnUwJ5R z+DeicO>&CzCv>8>Q>x?&Bjo|=H+Q+vnFc?z{7+iAb$eJONxHbDm*9rde@W`%S#t&- zUmRA6amsAG^kkp}xP>*^vBgz;%Scc~7P4IXnTPtsN0zEiIQD0$jKmLqRf=VrevafD z=Y`X{wSsko4zFDu$tB`np4xk6)#i@TRDaUV>lX^w=g66*ONsCX!CC%@ri@u%Kb$bv zus1qT)W4WmF~QHF(5vs&GfE5QQH$^RG^0w@VL!DAcv8Ya{~4%fw5tQhRrm8m5>l}! z{zlY1Q>_7A3tZ)R1(<0KUS?FQ&Y)`&>gMXS^n)>n?P-yL8|BaX(CN$Q+oxb2Lqc>Y zf-h*9W9f0FD7Ma;5w1`edmhhh`u(;;m=+de#D8LR(%DGJMc)XGM4YzQf@P)#HJ2r* zttyP1cJpjsRx!ucnn-LXGBGBDs>M=_}etts4{0*)#jHZT2! zmxH}EvBZ|I;Va}gPC3Al3G2H?o6F{0Ti22%4LGQ8Q34&)2lq{mggBKBHOBdbL!W4N zwEbttAJj9}&*P|N#LA(0GMt*1I`7Oc~Mq;}MrdY{cX+XConr&CoJ7dm2YUAs$#4gNCQ2E`uGlUS;+)kZaDK;W_PGk8e? zZWu<1Edhk5U+p)7g6Ha7ok8=2KbUCx4Tkz8?z_#Jr4sv2SOaTm0bVI)m)(ZHq>8v^ zH;~gEaU+U`qZ8~7_zJ|^7)H$dE7@hbA~y03jh?udT#lEOJ6*dBKlF79+PCa)rLLDj zo$jofh;oIL4#n`j3DM|9xlvI`^`t3nRs0&hwL$y~DlKgLCr-WWW=7F=1!XW;1X~^% z->1oNZU5ZWZ6UtCgPuBUW}zpj6z$48=|B(WTT7|>^Ya-7iD#PVX)MZ=}{S+&&s_K6XMF6 z=diuaz>Q`_I`6lyzNQrsU5}cR79qD%ndrc@zWj(OZy*d)k%_qZo)u&*tOu}v7tT!( zlQeg6;HAoTMmeZUBlfmB$>z6ed`WYLe^=Ot8CJEi6GY5OCd9Y6Tr?o_>Z;3aH4d(a zv%!`o-9~Z;bq3#y(1sCP8IkPix~3f^)gX*|F*v@1XJfi;m2=H07{RrLz2-?YuXW~Y zAv_hS;6gb=IYY2XN4Z3;`;IhV6WyG_0TLxdn$Kstg65_d1_i>$(1VSui^@PmQNkR% zXZ+&9-rwn?v!I9w(0>3XT@1egJ3OAkSwa5Z6}6knmW0&;iRM0^9g4-i$pj$e>eNth zRIhtWU>Eux0m7S1h&T}5blU{#L4f+PIJXiq(fOsDf>wKJ(g!ISrJ%7*$$dEz_u($R zOy3NQ0@H*!dkA9KR*8%aY!&n3Tb%_suA5dB8ua;r$$7VRp`P$h$?d$jpV}CdNP3bR z;~m{oG+#?HqiZI;y~xLSxcan#*9l#sUBHz^y;q{`$B~B(5QO3+BY1EleC@CYdA|?K zS-WE35f)zVE)(UvQfhAoRU&~mUR20(~ZGB~hO?i9A1IunaOK*$3NR%w%g^^_lk{ zXo{kwoh0jKPOYV(@hKH&KrA0YN(ggp&srpTlb?BxKxeb>0I+ad*}#ZG@Mm|XK{of} zDQiuE*z1@I-5DJx_A_UfykT2_FfUfIA!6dc%2fLqpf$l&$k$j@Ws z@MLvR%28^pJ~5|mD<1tP_Zs$mMOgJ8@9@8mG5}cb2D_&6hqT!}TF{njlYt<*B80Z_ zap%z55&i%LEaavMm$~B9A8kr$a=DKjJ<5Y)`-Ee3immJ7)&MxaYo-?3-ct1p%^Di^ zzqW}>TkE?ck$jbP3dl>v1L=l2ZeO9w91aJJZ&R#yeiYEl2qe`e1MB$rT5!^D5Kcg@ znIw{k>y_2!ADup>fD&a)9t%(l-kwiqvjt(h6|TgY8E&%O7)7y@Z+nsTmB~?I-U0FV z8eys_xXOZrL_C;F2DoPO`r~1UFf5)YQMa4oVgzm|U4~(kt<*GnX#))!YvWJ1RgdtMn}?D;Etq@p z@==q4AI%vGd&4NZ_Pj2W2&jR1&(VImW)7+BgVIM+eJ{#MTo7Trr7f-Wj@CBE+?@)G z$rk#^KTCpjRJkIxlVXREfBXaj=ydOnyrlAx6gjhTIHZ>OJ)_oo2ULQ-ALHY1vC~h% z7q%;I(Tx1b>?NgnHts1xMMFLaG%1&yR$cyaaOrkMV z{$L)rIty+=T!piynyDemi8EBah(uMu{AiabkH6w*>p45Tp*D3A!acQgYB^Miku_Vc%9FMwe?uk_n!4Mx ze4YR@7Ct=Bo~MxyXHXmG2Kxc6U{}T|=JssX=cu5dg8Ix4F0pn}r9 z*H;K%<6lq1d}?~#WcR^Q6~pfY)ONS;iuKX%Zaqj?NwwD1-Fo*dJ;Xzz2fwDTPgsAC|%qgT;2YmDgW;<%Ee4&$2~60knI;N z?^R9@EZ)LAM{|Uzzm3+=rx&J&Dvq}7;dCQzEjpJ0IjKJg*~&Q>JS3l_;L0Jwn3}KK zmUn>(hZlA#^o(~>c9ox9-qc0RAdnviO?7g^`Js9@?V6xwP~TV~b#kPl<`BJ#yQz3r z^70vnPSe+<;I*LbK|$jT;)`7y*Jc36wi@T0-h0!EHQQ{@t1+&&x^!3FKq+wX z@pg7pB1Q4e1Gg&L>MAV0`DyOGrN+JZT4dZ^r=-X#okYf!=3+RQXBf=pxZGL|@T8uY zK?3{CauGo5ODyxQlHOEV)tW&30516LM6xd+*ni{`tuqgAUDA4ir?(vdFK>L9<-jhE zC*)3}ld>ipQUABCT8%-*ap;<5=$rMop|aZicn`V@OR~U3Y)}GQG|~u`hLi0U&bfOB z8{d4N#_8M%fpdY3dCrVFp^E%FhKhVdoKuC#8BS%z@tH)5KtYe{4Z(xLplu~DTF;NI zvgc?!s`@fMv2MGl#ppYFXCAB`cpv54^gJA_9-1>VzT*?&Pkq^$*-J6Llz@`GmQP-+ z8L*YH=jf}YGK(hm*V|R9%RjHp=tsMDJn*?dL=d@PxMhz>02N1)?P6m_Nxu}H6612q z-Q?wEA$)GT9kDx>M#5p6BBAdYYripZ_G_a4u(%U^di=4r917c%A7BMhHDQjiVRF5xQ%GuQVtJ{ zOlYzY>@yW&&>aDlRr2S*XFX!@K@gfy_83?vmFy{N40vZ)9TNB*E*KDUg=-&-XpHL@ zI0a4ksYl0I_nW2xhtwY0hw${=bLt@VlgeBytz`Jm{Rh^Lf@SP~v>9}Ex_0~EXRp3vlY`-|F_C=8`4k-y=0 z3;}lH>7IzHO_a;DtSr)M4Wk@sM$Xrhg5G~Hf=Ve!X?)j}i0aQBdNc7e7-ROiYbXy{ z)D#^BG#r}Or7TFfs}1WU+KL;UeW{6XtSJeQMcTDO{a8mT3GgMxUk&As#y$u-l}=rI z!$vBZRRr9`CERoboq`jFiZ;%>{PEC1nN^thfv?UjhDaS|1%8M+mGph*cp(OzbofU) z9k-Y(#h3b2l7Aw+7?C1LCS1Ne`fhzd4EpZEPeqb<^c~WO81wb-)e7c@T2964% z)he){u;==P;nn{Q=6^<)H~g%t?uP>dyTJehQ~0l4jmCDCjxN@2R`!PfaXB&?yI5HK zp9n(ssq&6lBgi37pIB>dB{28GE*2ijx@7EeT))U_pcmVsq1S;w3=1Ojzq-DKvDRFX z3fn=lY~$VC+}uP3_&vQIXRLGfba%CX`TM_~wateMuoOf`xe7zrDZN9bi zboqexr!Fr$I0JkG-d@jkZ`WTwZwF4VcAqM?pLf@D7{4B09zbsXT!MW323>%!iE~fK zPyd&er<^Y@AAhcZ_d6=XPu}g12HP#irX{G znuZF7M>(;I#~p7qhJu2AtzXN>^Pt7B<2Og*nwsosV#S%Sf?oj}8(W>UZH4FWkUiD& z-^!@Qia2a3P0A9_OB8F;kCZK?ZsXP>Ba-JNnr$qIR9qZb^U>pVSqRcQqV;sg+EJ7< zeuzj(H%VdBt-VQQHi(R;{x~Z+m~GOTC}c^bBP*0DG|3)+rIiG?_cx!bC}fe4t(wp> zT;yu)@l%)i&h}0>S7&E>Bgw?SjxrV~M=Ie0T;R2qzQR9^^M{hVK-Y{|+VDwMR*%hJ zqA@o#V5^>vNtPlC(IE(HT7po}&5^5A8eM>Gg8<856y8%89#?pp7iM?scMGhZ^8))1n9~jL4!jbfxFSnMXZe*{Ut)wi2 zX{S5Kq^Ci#YM%400G`(UDNJ(5$N!0;+FQxi%IP9Yf?3-lJ`zq1CUa?{g7Fd9%zIv1 z`Og3qC501@W|7BZ^h`89*Wa2Mg`-arW%INWH>*)yT7si#Q7!M9(x|pVF4uOEBcjQH zf)i`qOlEd|Zm-^98nCjK+R|)tR}LW6$ESGtlN#tC(5_5sfu^~@o17R(VCuO@nwM@N z67`_OUS!%OC5RpoR1P`y%W1yUDeAT9x-NY09BNHPm9<)0v~sE4G{0JhN~m^{XAFJQ z@E1{EE^Qx)J$WKfSi{;LTfzX1#krahsTPwIfpGOZ!8z?nT6*SqxLYSiM`@MlB`!xK zO=%#F=oSTgvAI^uh6gOra0u;MYf94M>YK_#JshiA}hpx5(crQY(t!ogNdQ4X#Ip=a6GM$JI=19j6>LRq{R?(4M`B>m!nD@#*_4 zBH{Sq4!=uxe#T)M^(Vn(}o?qFIH{5iymG(~|W4 z5qTPkCIqA{cQ!;9i(abBDw@OD+{nx|@^!0xa7P-OB@J2nj^$d`+52|v1NB_y3GEKz zz0Gjzjmlqt%pjH+6botOip0jz`>@0mSEb@7S0lQ@ho8_-yw=tSs-{@)06Qqo%$b^l z<^dsct+!l&=c7FL$`Sne4pooFkTe20#v|bkd@;iyv#!hT`q&LEZwVS0z27r{-;*1x zdvc8TGf>1tOlymxl&N}37?gR(1GJBDcnQDCVR%M1az;rntyjsC zvjv0r>OFMDwhLO!0+_A25^LtV)A~zrmZ2vNRfw_{awF{vK3N6EE*tGA z0e}9^`dc}ZaP>nZpo;w@Oh(^ao6SUnZtsV1)pDF-mz65nQ=q8g*33yYuMiSu@|9<9~*vj9}`P8)1KC`mCX(7?hO<&kb}uA zE4wy0Ts(U5k^LpqWT(p$;z7JsQe)%N%Fb*zi7^r6UgUdv8FSPG%EWC9)Wi`5Xl;Qe zCoYS!>#f;|?U~~#gGjc={Ot_iNxzTlu1m3vS~NExMSa4CxYX1YTa?x9_HWxa5r5{M!IoQTJHO-~p(3 z>efnilwlJ&1RJW6NY+o1;cm~s`5J7uCRN)&(V8ENl4+BYb>9zeW~tIk3ILBcl$5Sn z1FQY+8x*!8{zp!_jL{pPbO-I@4;bph#oiMki-8Cbdor}$-PS6wGH$Bl^p{YKdW8Vm zML*d=e(w0XDvS&SV_9c#VhXBSVaNMxLkx1a>lhCEHSySx=9xop-r(&6{sIWMtGBP@ zdC&9P%Y^#-SHS;I6D&^zDE^N$LH_@JCgtQ}ZffoNk34N??{4R2ZE9!i>dN?^qqT*# zsquf%stwJ|9bCha$L`QK5z^|f91ThaWtxAWb11&$b!WVV1|1D54v$SvKQ$9iS*G*J4YBE#3B)IsN%bj} z53ZT+$bA?*juA816gl7I8yWWx85!>4TP`ce@wd{4)RU^`3oraeUTiueaw7BBKK`&i zMQolAuV+v=r|mPu6O^TxV0__K0WYr>*E1xgn`Aj}tEnqE`dn3xh6E<&r~`(n>IT7p^_*9X;ZLlPFopNum5sR-8cKKlNgK!AM@UW!w!alsZl^P9uk$8|5wF zzD`5tk*_x&IpSGuSYv5*=RB{yKg;&P3(be>NnRI6{HLzSymy)pbD960ttxM)?K_m_ z+wqa@npuxHHzj6l@7L{_B_DJw--lncn{RY3p3*V*D(x^`@{k4Mb;7r|@@1B(fJj52 zQP@af`=@@qJ7dv|_E|S|`Bt*vC;pYrJ35dpJ=opLG+AiUIG*iTv6iCFqb* z(RG|}-=rN+dPJWm4k*@!%{PP%%rk2cz}ot)i?*jcq&pS&Xm+f4iV&e3x4KzeoE16KB$MhAN)zjUN3%WZarN4BPM)fQuvPKjl z`#^DmHN3w0C)M6^ih#CfGMa)uTVLSo@m~ghQx0R+z9!q?tFY>(8zqLw%Cz~D7t7>M zie-cC;%!h=W|*JR1s|JmvX2#k{kVs7aGkv*+LPLhB&@Z5ck${ONrUF7X#N=)!#Hy1 zdO(8JldG@2ox}5NIej(qL|S#A4Ae=z*L`QZgjeccw44(^cXazmo9`vlsI>YYZB7!_ z$8NNOu-EG>tI0#?9_=Sk6ItgBzlaEVGFSh^Fd1@u<6l3g@ywBbN$i-(=Kx(LK&HO_ zN_I?rfU8+geJzZU^u89ww`2v;7znZ*8WVP1^BECB4hB^w&D=iKJbCAq1l@d)2Xu;v zyghNM?Dr~Y*a7Qb=DfNTTqpN&*Dg@`qDY1~EqsqvUky)+wy;A6Kxo6A7jvA{oNFd> z$Pp+YA}6=f4rj)^Ey!dP_b|Lq-FDq>N>o9o;=ArbQB;2=s=+@z`T>OU+LF4a;T>@n z7y1KQb{ARfJ9x4iF$=?6u40O&r-c4zdZPjT6qsX2MshMagUD8XE}SHHaKpcbse&N$ zYr)t}GVKalTIKfr(vUg;62`h_f*GSzyg&`lbkPFMUKi_)0QU^IJ!j@@@oW&~KG;6N zd752z!s6L40l+ku5TR2JPO0b?quYdd4%;4O>_2zTW%nG6R_+Vaa&p<^KYdxnM;(~V zmB-oB{G3}_uoy(FfY#X`O=qYMX!BU73pUUvu%UaA$N zw=V9{jVsq+tOe=_r?NRu?!rfYvf6wDL5m*K6cD6;=8Cr1zKYUhEHUr*)Sk5Iv9mBg z^R1a~+7VyPaM>krX$vWSvv&2e*FRIS`3c`Zju4OEq`ZwpMKf}YAy}BsW`|2Fk}6KR z^oJxx;L#KwM1qLNOaO`GwUjm9H?kk1Dat{$B=6bs>aKqf8K~<*32duakUITsCv!^M zD3fMMRMv29f{QGdROXMWNQ(1HfJ)<4albL7U&x7&@OAB&iB7!k-iQWt_2wJkYici~ zoh|5FfHo8Ik^5GQC>-RBns7{;f&^Pnbcr!&ZnrruIrKd6DIK-B{&oxxVR&&>WxYB3 zVoEOZ3*2T{K!h>w^|;7=O$a2M~Yn7d`yd=FO+LufivqdQ;Oy2IvOTTl7vDo63=qPUjbq_!D3tkurx@U4f zF5t1_l*^mceiNAAlz*V?f{}c-J7EgI{&B<^2Dl~o`Z#su9yKAxPU3$kQ#MrOR+&kE zp^2Usrxw*B;tvZ`582yRI@B)25M35p#K6JD&yDID#nT9=>&Rf%h8fkT)f=2cWWy$r zyrn7NV%Mx9_TlXsa|Z7S<*IrNqIXycm@^d zQekQ@xX|3kS=ZK{c)ctXp zAxqvY?*J%o6ql0xa1y^XF@A%KQmBY+2W>f%q`&0?eJ1ASW66!MX-`H5Ol60Own{$t4QUo0-LHafnhsZg*vQdf;)>FYNCp_VE^zPVWYZD;eg->N&N{E zZZgPYJ;KTep^90=RX&>h(rCPSKHJq;-`6a!Wox~_06Xr=Y?oG7yIz*$Yx;AMmbu(p zjx-EPS~{wCrwCi&fs1^c);c&pao1Z)2SXN|X1t_QAl1RQpMtVo%W-1pj@|sTvlvY*AgPh^ZNVo@ z)qWuQej;LkwmiHH#{w#Z#tg*)^@q>r=%gL;_4i{h^O(NrjW#LlOySB!bpJGM#`T-)W@SYiFMUl@roP@vFL1DckdJb~Va+}`PDLvkqwO*^ux>Ri zD2U!531?)hd~#2aEgoA`*6#;-vL&E^b#aobh6=cQY^e_G7w(&+B1asYO^>JVBME0pjc#OPC|e3o>EvKu$IM9inUV+&t} zal?F*50X1xJfXVM$^P+M7dM5BN9POKy27VEA3VqZFrW=3pkls&4Qn0M*D{uw{y9o- zdOnaVA)2A~Hk7cR+h7>d*V~`<7Q8Q9E~UD1QAwx$QN3_FT`*>J&E*RZDhlay@rZb@ko z4b|cRj#$JP3ardvNaeO3{O09vf<~>>P1xa9?OWLY26HF&6Il#S47z_&&>`yfFI1ZS zVY!MT+?rlpFD*@?k-U!QZZt!Yxr(w3+dldpm~9klhE!kTQLD_QsBKj$UXkLcq>roD z0M{3BKdoDBr+FSb;WqN%zCI5BVD+ zz0~?zViH9@_+jMYWtJ9Ii;H%D#`FHHUm>%_Tm1+2oG+*jtw&2zl;mvhTEDvhzNqe} zB=W6&Dbn`QJT+Waw6z-Adwz&mI*9pe{gWt7Yirv#lw;H9n_>_|(HLa8wVu`@odIKi zy?6VB_H=)}_yF&NIMm@L(&$ElQX;5rI;uZH=@Yoba7?lB?e^Z;w}eq}Mlg(9on{2J z5|!;MLs|j$Nb{J$Qto9`30buOx(pX@^$H;>l5+dxMJy7Zs8KnxMi>J$lzH4ekKx%e z>nQ^a&uS$-Ql` z;evyEDtW0flu)uNWE1B|6BEcVI-_cgEi;i#N43|}e9%fByF^{9<-)p-d2FH0Ma*ef z=d?z{ugFRpIMcW3i9-v`W!lOtL>C1%uvk`tO>H%X>8@Lu3Kf$lql&P@S})DhbEfiI zm`X=Zs#7&vgxi#XChFpGB3t(2y_cx#PujFzd=)t> z)zqh{C&I46a=YCTu40byUlThYjkNoeHQQF(=1LP-xDp1Uhb%Pjz* zLAE0D?wZs6%Yl4sLW=)_A0FrLGZV7>#f zL|wh(2MrnPUl`YO3t5oAnd&&y0b1^)B;m0ES1~5B?l;q-k>|%m7|{C`aQT{=zO|t? z{7n!jwSj_qPAh|t_}_ge-=?+fRQE*cE;~y>t-qg_+^asx!pST0mQw!gEoP+%0BE2; z61z}TlkXIX_6m!OM5@{fGY1pyv62u|Zmz1%5ynwos1>_q2oS39 z7BA&oq-eLhKm_n;{&Fp02y?jyd4$olIo;H#PrAU+E5d6+2<7?SI0A%vX*ROzPI+NB@OyN;I6(InXfp?2Pq3}hh<&uOcKfuQJ6d*3DF8TPUkP4+*(Cuh}?XpQ9)=$Fw=r# zCE9)c{`N6VoAG6bmcr;5dGa-K=06?1w}2JdlvD`tyNl-b8-MG$KH)!cR2qlNido^6 zwPN=e>v%KrpX_2Frzav$*YP~b`sLQMFO|D=f9>&*$LlX-bCQv)W-V(Dz}$OD;rFIc zK()SLwqHHe0@eE*bA=h|65r6EMhMmZQOAUlU*`A=Y9mu&K!a#qV0g4fDby=`S-;hk z^+2Ut9VkkC3*l2xJNLxRr*W-dPD;q8p==3#$9>8RBhdiA9(7rWZ3}F>P7p0)RUD zlLi_11^z#`<%IGGq$2IWzzDFx!Ib{1Rh|D&_Q}K8&fWa~-k7`W(rx%x6KXU@=1C<#k*xe>*N#+%P(hpJ@Ck@@oIFIk;w6*mmqWJzjOw@1@o$jz z1N4vFCx3oj91DJ&=e*z5e2&a~K2&VK-&~ele}Ob}dS0(G0-h&wJ`Yj?p6+u#2e!Ws zYQC;YdY%&mUmE_+ND=(1`1Hd!m#JnFkIbWye z*X&_}Z;v^GU+jo`_ntiyhdu9C_s7H?S2bUIRDvF_mWF^2LBX%5n$M5>Cqa+J8PJzw zz{mR4#d^St{e7;i{|MfElHl7^&DZU{=hyz%>%@%V$4$od*2d0#eUFas!J+$zc);i4 zx16uT_3Q4>k{W-IDe+rtruyrC$~z;mtKNNp|Hr`B2d81c*T1meC$P^DFpa)(h}<9`N>|AkN( z)bn-LQPVcY=_uGdW9a{}`6T#$nbXsDhV%9M^7V4`ZyP@9e*eC4_aex3aLoBRBJuD( zz3o$n^ET7{0b1Vn163IMcMLyP7s~H@no4|1sG`ZSJ(+aHsRVHoY#I@_tjhUSCk}D%@DR^ijLAPfk8ED84d2k$q}!*_a#l zbZDvUDe|p#i?cEK+Q_b1*7GHhf6^JbOt_-A1T3wvoAF6ps*5(JG%vq;oxeCQ0fzJ$)^^42xFTbAp5@qCUp zx9cNKO_>u5elDdnjP8Z}C3P`5j*cy5+zwIl%NK>2PkojT+%_}mzpMM+273$h;m_T@ zn9~&H6?f720=0=(UO7UEeNQId3|_Hq^HR2K=m3fvS1W8_?Dho?mAqBig&nrO(nP8s zqCwlm4h`KzlT&$JefCesL_X`l*oi8E z&V(|e{gt=8b40XX_Wt}R5f$*t?_;&A1QcID%Eobh8_mwnexvXD=+X`P1q=>TJVHIO{fYZk&9%b3SQ0J7Cz$)T6}}+g%&>c>ygU%?lm4@(UQie$6xbG-}fjGz$>@ z*{LUxe);K}VV@ID*u7nk?F#RlMc)Tp>daAImJc`FEC_wndXVOIaRA5FBshITrZjvcM0Z1}BT6lkMS$JoeW>3hc>vfoX>`qhoKFroL zlsjS$wK(dt<|~V1OS^g^+Ys?}RHkGX6@vs(%nPD_GbM1tWN zML16TB7zr%yiWC&?`G>$45}PCUD9=$RC*1Lq)1MmwY846C^pX&WSj5d+f|xtk{t+EOHG6qSqNQ>4 z0iML5UhUI!#v*%tJNRn#MDm#irtVJ7gbMJv7#Y8r!=3$wdf?gCx?G*?nYTsLZcnQA z;Ao8T%0at5QpjP`kvdFh zHm$3m4!F2O*g~B8{q&~Qt>YtXVblTbNeE^)_F7qPr%Cqe9;Epp&Fp8gHOYiR%lVT` z`h?71mdR1uO_zU?NiM{(2TY?XDTfuhehuO0UR{yP=RV2?M)ObpqzmoTRy=o!6oturl$M@4K_WVm0^M&bb zdWZ7IrtZS4hNsx;kKiEghg~1rc72q3?0=RRnfEG9rc^*HQIerY39Cz6wjw1(Q-YdD zrI~?9Ih!KgDXZ;K%s%Q0CjMQ!8^;>#%c0Atpb$+76S5S&yA(P&sm8x$IKIr z8E|y%LatN|7#)vv=8cziG9l7D0aO&@L%|yAx<2PFyi`W$JhVhx9Kp*_^#bkW-W(ms zj!so+o3;@73V7ZV%0SBaI^$nOZ4T19S{)i|K5G!mglDB=%O`064PHQ@za^3Mkt4LU zip*A?vuM^V1O^L3Y9CKY61r*SYs4gw((QUw5LIl}hNU&{y7`QEUPidL#6fZneR*RS z@K2e3&;R8Jl{9_GDE{pLkk&}iV5MdSSI|o6nC9Evj zB#CRj%Zcuq2N^AT4B}BcaU~oFjh~E8Jw%gE*Ba3#lSLw{=k3;hT4WMjl~7KIawMs| zof(#$Wx2M41k=8Sjk|4_MT61qD_GVw8~6cw=S_M{c7>EIGev|f+04-l>mM>0k!GHB z$usXn30!YdB4X^sB{dP$ILk6)hL;2Gl?J9edPI;+gM|3CjV~Equ;zy6O*6Vp`Eom< zDdJ*-pjl^tZy%4&mi9z=Nug@_jb`h7gZ8K!m4T7!>W;mCzaM- zZ@-&{2lGk`8y(o>C?i6osy z?Xwo~1U7xe(AY~8RjVFMVi|lTf-st0X=*7jB2PD|&-=J>!JQf4E^k`5ciA118m%qO zsWVclQ&L<{a3_3HWK8B|b;yL$<<7YroUym|Qj&4A+C{J+WpA$}9-ulK+$HaP0dq2zO_4=@8!nMOz@XFAv z>V8U$>X);r1+LXAouS*%dY6#X)aeyz9mG6NGI0;WKiUl%F+2%Lyq94#O=o(@LEZZ{ zv15{NEaY)HCNpA{Hl>&)ozxbQIKu1R_%5W6mPA9}S&M2xvQ-H{Y71^iNexyCi%>#b zgI~g2k~+hVlF#IkljkIDx3S0_$&hs?J8#OO^d7`0mbodvv3`87Haw>^MMdlKc0J16 z5~@n_vq_Qaah5Z=KV|S^(~R>_t(Q?PlA$u~s~51Omu%yLKj3(sEK6(uxjyj=(p;e6mf%Q4w1hD$aO728u+Y+sE5j%lVYxWSWa-#=+ zL~}4mT2vZO7fFD|THP`(izKLn8rmIq;`t8n5=WB}v)e`2X08&@l8DW&QP-xD%Oa`C zy+{(`ze|@|b!y{lE7aI>+FOK|hGdpKA!#Vthg*+)!z3!AS+5b*pITCmoD_QuGVu~p zS|8rgL7D{Fp9IVM?Rs>LNKdw_K2iqT{@itsV5R)bcAlMnwwJV|G$5MZ7H+w1V&ujC zdTvoHN&8ajCFvb@n+gxD0mJOqV9BG>-0$p1%g;%8o)Fl(y%{kpEt;)6_TR$SknyhR zl+hjv?BTpQ?qX?<%*-o}(Xjg@c_q^<(i0m1*HIGoHVH{v*=yO{N|QMyPu7t%X(0_C zgPTYoS(nPD>UjTHd=Zk?y=bNNkigQZvPSf6ySFtONef3WIVB+jC?OpryX_j|+YVyf z_9`#$)n?E}kljs$bKb5;+Q%}GBn)!`wDx$X z*B8M7mR~05)NrU@YC*?%E0I;L5|Undb8t^u*g*~HBu&UJvq5HVy%U(w%GMfw6Ve!u z1fnKRpHWtPK$=oImUP%W#Ic;6om`UbhIBbkpheb*tX1u98Ie-->|WiZHYY|>vv`q} zB#FmrExd!6poUy_J+6^vJ@0poB!-sliJhEICd)9a7j~R^Ld(?#u{N+2P?Ga!BY@Mg zt))GklOV3WV~+V^iE0_14MCn*Pg2y9oHFEDdY2>JJE}wp;iSkKudSP8Q{M0GpV zDMr;HO zIwLuzb+8dak|2L$FKMlZZ(A`}rDUho7Ggo_1k;gW{pj0P!czK<;VuX++F|e-m2NBKLcXgYkv6upVwCZlc)pvdf$#{d9Oes+j%Szz(-L zUo2%Pp_p0>CllKrS~!(U@GAZxb&2Myh+18e(_PyTr)-c4midP@Ie#QsOx6|Wmf4hR zPS~eKT1qBaXqAC#2iM?tNHK<;%H7Ijvmr3SlAGG{=&Z zzUdFC;=841Vxmy7a*dr3A-}yPN!nrMqdC@45Fc``NZz~DQX?N>$CbQeCG;ASMKZbC z?{y|=U@vKgb6Aa2vP+YtT&5}hF(c7eaW^aJJr7Uc@uQk##OvrJ9uis9B46FBm7FjN zBu$nXsS5}0iBQ{)OBgO1thYXpY}X~QnW_mRis1F7;BY1Dxk#{|c9FoWdN!Z%&>S1NX z9qG}g7n5}89&6e@wiK0AY2xlf1Z-C4(#iAVdp`}rDHD01nY&$&G7y4|jlE-)I>DzE z)WQx3d7bK-`3JJmEv~OP)Bfp#wC;1_}k=+roMOiIGj-`IQ3G+F0y@7hr zFiVCwcg^)$xsuTOm_0r1wk~y0NWrp@-P3R8EVi4i}__<@iH$a-I&}HETdaIp0@gyxKK8MTwx*{yKG$9_wp_b zx{w5wK6T2ur#6Buw}?4TEckBSu7Q<+$7yy7g+Puz zB#l7M;2`5}uLmHR)fl!7?1HQs0L?<6>gS_KqgS>cBymrr&Y z(0Dam9&JIrNn>iEglmup7?d|UdpANr4?r566*+P5Cy7#fDtQvOs@OjT+|LduspdB( znKb+{sS=cl#ABq83bGDE&kvJ&e}~GP-CAFz0`K9cq-aBT3fP4 zJGLx;$S{zv(uC9ihe(ra5(lo0k0pZjRynk>btT}Xm=F+F$g)_`k1Qr8S|+4+xjZxP zgQ=U+Y&L>G9O_>i`R9;_L-Irt#bFjN68RZ&H>6R$9md3z#m_@upO3C+uX)FjAk{y5 zj4BS*1MNlV_-tY=WXO2{5$?#JK@7wjX68)1Rs#2pC5~Ic8kXFOJUVMxijUk`bsDD= z2m&XyV$LM*w3+#ycT;~j*3!f>v9v=i4mFb!HH2v**V&$bLe^~ZHVeQ8IXD45)0ALN z0HKL1@TR7AtSm{H+P2lOFQwflxzvrAg;amW$JPe7uI~iN9|X(?MZ;K@b|+hI)2=%`V60c0IbL z8@21O1sXZ2n4vW9r+lLn{+9{ICE7C{a?4Bf$=H`-l<-->l`sCvfT)BM4G1K9qKh-K zIYcQ@Vi_AX0<~T9Gzj~ox3hdWUD{7W->NMez(|={fagjCbv{^Y+M;r4@nR!zQE^&! zOtgr;N;D)SoxGk-td1y(o=I+k=E3+Na!(u1d?RY2ff)KUEp+YC;$5+oKnn!210QeT z4a~SVtQ~^!J}VJB3(aC&x+{zUk?;@{V5zSO9gBKr3c!O>RflDdvq#4vWUT@;j1`T9zny+C zJ@kMTPmHwW=>R3FK_L<xIRImPHP5P60w7t0I%X;<~5>P3p=wq09{dv;La*tzlFO+$M}p#%QSw^5|XwaD2+mhJLYBEJoTmZr+4DXb+d=#z(!S^Sf^WdhAoW~t9}ap7&{6JI zjaoW@>{ehd>Kl&0q-4?Ae2IhSqw9$@4Lqp!Y3|NX>EqBjZ*qUquzH>fbjKjDPFaY- z`%xfJq|0?M=)B**hv1f$$v_H9kKw=uCD&ITaSQ-m(%naKNx5D@czyu) zCCQX-GFR?Tzg#1>+quz{$Z)H>PY)kqjL$uTXPtv+rm z($}1=1S~Q6Y%6J|Ns?%r+h3IyBTl|)^OukB{nQqcN|iN(19H=rS& z!HjI220J$!HCXQ}Ntl-+1Yn!vxBWcH7_j#{krT|aDw8@gH2HLGSU}*zhy9>cJhDju zHIN5386VPI3qYnnF>}B#1YHQO%b-GbfJZZMV^V}r2XHe@V1138Wi1@Kqij?mNwX%W zF?Yn6l+6~o+EC&~-jg*6H{^N=)h^XOvTRxH66%s})cQ={DmjNn9f@q*l9#$A&Y(>o zi*XRxmC%y^BPZOd8N3rwp|QZv4J%=jH|DheAn&>XkakL{qPhzqx4?5~a{`y0VNajm z`-yRpmh6_fx9gFtOhVJc5OiE1i74P?y*w+Kj>rXZ^3#B}<>a82z(_PeUkHoLJhKa4 zW_0x>+CSR-^6V{Y2&hjWTR9wA$xlr;hI5hWPg2i0fVUIckIX~TE|uX_YwyMsj{`r&FoSf8xyD)805ML(^C~r&WWgC#fyIVeI$6T}!!daCRXc1tvz9dX3 z??g-5%XZ-85bvB8$#n;NLpa6T`M5Fm`wcujr4ojnmM_o#*x@HKHU(5Kg%r2z(G|2I z0bIesH6TMzMg2f+_({LOZ7{58)S;zaoe*2mN%CD`$jts)gmaZffGn^!06|VnL^4A- z7SqG)kD@GuoR37<0?SZH1jKag@2C8TOBVpCnlP4}mwlR4NR0LX+DV`!34QQ5PR`Zq zB?P879M&Px(?Hsl{r-q{9NrCJC=@$J>GEz$-^l{iz*=Fd>#=rlHYZ1CSUO9_wQLyK z`RIyw-0x(TbQWy!W8)oxBrV8xc69dN#mm{9vS?ddKo#HhPUu>9u>y532loMGBh7*= zh!C#9#YQII4!DJEiPi}{mxVJ2nTiBS5jslef}yrcG8}Pi`85+In1JBPo6tg#Q!@!G zA369$HdAw>FNjnesep-RA0+|VNRS%U1+t|udbBS>gLV{VNBmis6VB{>SC3sUdBrFU z2MBhj0wj{=?Rs=Ib`yG0dZGL&LrWp&V zF%NRMuSZuyNEV%=jkERz8GaZ-VVkCUnV{HL=jd6EFtyC*&0u!TH^GwJ-$0P31J$Tw zNh#tXIMUPtd9p}{1K!0bAmcy`4fFJMybpiuApt1Dl3tYtv^m6dV~g}@N!Sh{ok@SR z2<6x7H5+CnaDt6AmlnJuX#OJ=Be(C~DCpYt-nI{9j_glI`*6}LzzeByX?KDcMN<*+ zJWN8TuRJ2YuN|~<4L}t!fCM_xc8>w>QaJ%Orwot`E%~L7?*(06fr;ovz!j&1k0Jcb zLm`6pXJqLS!k05~PS&i~*K^0S%j`lpkdnv0A=oKriEBwO zY@|=bISAzA(2x$(zSgSV>;BH6q4x7Ynvd)X*2sG{nO(!>p!rdXFDr%Q;1(9dcDiB( z>p?&#BxNDyQV2XOF@5X#?nGE}#o;tqnL-kqYDr?2DsBL6g$$xTTNg&1F7YqC@U|_8 z&j3#vNF0ODCZ!@0*v3gZ#V_sB!qb)jBeT<@knDJX^qGbsQD$*Hx)y2gROv|};2S@q63-fKJ=1v? zA16qfr}%I8j7qjm-!C4(23V5seC@jI6Mcm#n6*0+Su3qKna9OKkDONrP(~$4Cv62R zWPekv%j_P`Xrf+(Qpy%mZUScpj@llEEHyW)49sOV*GtG-^hbfV)Kp`B(jkNPIl}RqIHbA@;~{D?1UHRS2%*I2 z+osgk*-rlldy@CHSM7*^`=E52+Wjp~ZOBS!NIyGY?$};$0GA28r z?u7UvP*is6d3t%Rhdm$t*ljZz4yb+V`2fN8tiSVsOVB+KO*f~LuO)iFzxlwPbqO^& z*8g?q=Hc`aU;=d=b(W-T%s{dzT|77WS=7wbT(0=9t@3-Bj>p$YQw-_(5_Jp4+mXHA zk6c)y+Q~I4PS0P?>s@28butlYLgqN)Wl}Iy$%NqwWa?|B+3tFolC=^*7kQ_fE68tB z#{`LUm7})G**0UgBUwK&0txk0D+-@XpkiAbVyVc+F1COPPA?eJ@iaj8sjhUuTtf!T z&c$x$qjo0?Lir6GE0a1}0CrgQj|onX+L&4&9>sP#@nF+orIHrFz9QBVaM>uAbg}MB z_0%*cn;D&a zAb~=YI#kN(94Q0UNwBL&J&3g8MiJJSo@5HNnDH#@|iQCchnbU*RoPI^>61j9r?`vLjL^!7Wu<8ABjy& z)`lCBqiSFKjZ*L@Ut5t@+xLz?=X~(>3Ie`>3p|Mwabz|vBE<8;dcobsH3YGrS`T`O ztxU6VO;|bz$LV2m6wpGJZ zF7K6l2xPlR6ijkZr-N%moa7wvd@cX>EVg(A#CgQCbNHxZGy%*csth87;hT__XvvNF zI$tfPlo}r0WU=RF2sTlw8KhKTH=3-3W_kcQy;P2F?G3JnX??haMAkdCRpD2GZ>R)G z%OiuugaZ1P0psw_^%9^T6pSRQS)@hpWqa)RorC->kGfC?#UwOZ8RS02;9*W_jZtP==ZP{Ew9NT3zpavMm$bUxf+qsv(0bGu8*T4n(3C(&D81Z+tqZgz>{Zot z8&F_0LP_Oat_71PX1l_$Fo{{;g2j&Xg?>L`J7$doz8}Rk^?b>1n`}S|fD}R7f!iXw z-L}o z)2=?4`AC(Mp#7uGwiYT2SI(Ogmk!NQwHGW0#nDTU1FtJnJ>9Kh;DxylBnP21Ry1#t za6kff!{d+{mN>|GSn~R|xtH)mE5xXfC zW~hx3FxuA>ek4~jY-|#uz@!oQ^oACYaI(d)+)rn3*S(3g1 z-7F9z0ztX$vdQIaWbo8h*4U}CXL_(HMrF4aL)@gRVBvMz;w+-R6FH03fRUqwAGe>F^cU^r~FAcb9H|+LC!UA7EA`e4b&CBZ;s^1X1*4%f(8{Dbmwfq zyV=f1o|OtB$Jb<@R zaG!x;b*j$*`NXQLAP#{}jdfe{ef0r(q00~&BzupWmcgIa+;6ar^@Ux(yz@V6vZbS_ zwQNzBO)|_e-u4>w>B*+@pkUj0w&54<24OEYI3w%_Jj_hDhB2cd-m30=q+By)ZD5M) z;>vp1WrsJ7_iEFu1Q$g;U-CO_Zt7A=qgSs|775%fpIp$^bcR~F;bc6ef_sVNKC#?R z%qxPL5WSIn4DbvvMwurqh|YJ4fF<}LQ-1N@W@d8Ss6V2qcAf988G`7zL~VAVaH21? z`n#RfqK(- z08pAi*+G4PtkccKeC!elcoOenSU{-Vz+mmcsp^t+eUPhZngQ7u#Nkj92c}ScA0=?Z z!)t)@r0dbO>=l?VSz!XU&jR12(!tn6}L*q11B&r=WuTt*@OAUz@_E4U*M>^G!G`U2SR2m=kYvJ>!7!B|8~bW zC_y7kxuVN5bf)nSZ?L>1+d2|K8^m-iFgt7MCnz%r*pecGQ+Mu$^H9dF9hKj2;`T_0 z$&_udI9i__epLqbyCLn#?q zycH&+gUB)CN2n~=TFd#0-yxnNNeHn=sA!ki=NN_E2`N2CfwCC9T-zMmv|yQV>XdBC zNjqQ}$PN`uKBUlX+gHu2rir=`3-(DJmy}ovc4^5@Dp`o`{->GW90FJ&*l7*z<_Bq8 zDT9}@4T7G^VOv}os2L?lrt)P=+j1wt+S^b60(^ ztyVaVaz7Gvrz>1TLNHkyNtYRz4z+SW=%Ozv{zs}mA)*sW^Ab!T=OY`}urBOC2r_BH z;hqoAOz?x$sgq87P8gsMg=wM*e>Ux z0jikJ#rvu9B&gmF9kG7{cJOcFTGu+Vjo12-8zayVfL4mvQ>@dH-a8Zi*4 z-asHM%x5=s)7Dp$7eKCU5|(lS6oU)sb?@t8cH;coYO+8z7x4RtmR=6W-2rE_Hk-u? zwCZ%znCJ3u53X=M-lYiQIFq22ld-I=o*}BGiZf9JW@buLjc9MYFTxMXT?^D@d_Px29EsE%%tq#VKL2^|@{$Il=liKG%i_nTSO&R57r zKAn~xE74uSg+SEuV=?39-||MTbsE|Uj(|cXlZQ&#BmF3&O zVaX3y6J{8olo4--R0TZD6Y?pMRUq(zV(n)9$n%m(PZr{I+6%i2o{}6YXRCpkWIGcw zpKqP+LcpqcisMxz@7n4u|-Fw^IJa| zB-LE3x-ZDo<4D`Mkf={^ivq4`>=Sa%&uM-TJSzhl9FKJ2fD?kkZj$Ur-<`GNCG`}iSn9tt8H6cp>Pj6fZ*rAM96e^2&lRA8J0T$ zn-6dO@du@O_m7E-0G%6;-c^9`-Y(kLaX3!YKrY{H!@Yc6O(8uU9F zr@lS!L-z0%)z74IDHo1vS;B?6TCclYLtY7vT`SxE@x9>X%#6Atd2iPv?EJ)ID=yf;UbjSL-0o*9lXAR<~RxW&cG*@Y06!g9J85N)%Y z)fU8!Fhkzl3E_Q-G1=cWnKUp19}}i1){G>mR>gk!l@(_8i4z!938yP!gQpq@A2I=w zz-CTrh*W4LpiAO|H2Ek}hzML?Ozro892NEiZq<}N2OC6yor8UdiYB7OQCgAQItLyz zOZ5$$Wu*`#C~s3X3u!Q+#=~F&baz7pK1(G|cg^_u=o$f>m|qPcGuty%bWEyTt&b7F z1K+aK*`$>q8A{Z}I#l^48ISu&YZ_8e26#4FOMy8ZL1L=?iiej&i|;trQ2A{q5cJ1s zec#>elixS%Uiq{ zaV0c|>0qiw&GiMmgD|{PMG(O|+Cw&=d3O8hVUfT-2BeB;<4&b(a9$R;8)>%@#CgU@ z$yJ)MHu0?}t9Q7RjuzP_0}Up8hc=9p zv;c$y6e^jQQ@okAyOT(@^$=ZlBxM?wV2L#EM&F!*8?>zo?g6fu-cr$C<@HoQ7(gbb z6RhmP|8L3wz0|iO7U)`GK>H@^PHX;9e-cr*x8ce*>6(r1C`VEd*zpa&>DQ|Y+5ktDJ_0ofuG5jJ=STE}g+ z@#NwD$$C^@#e>NKg(4YsX8Tst8X_ zPzKrB#?LEO9A9Qhf(;5*Vpm!e#B-O^$`@7pf5I_)HB!O)34Vg3yAF7tt=sT2}%EZcL=Z z?Rs>jAl_juV#>D##Sg9f@Q-pjThi^N*xWOn_z-ZfCbhwWz zqNW2oWK)h#Sl|ZRDJ_Yq%)ecaK6by;w_pX- zfYZjDJ{nGM7Z3PJRamta!P%#P1QbAotlKd0UkPOJGxTFHbMGSTz!2NAgN2yegEb<@ zA$Z34NZPaJH;l9NVB1dfO7FbYwy9yo6+jE=Ik7aYqNz>r9zU;m@G&`vPKzZOM>vJe zy_}3gTrCqJas>ho_lT>OWfMswOQ%&+%w^LR2Q@Uv$BZjbgCQJ?mkdRZcG8<6*ISeu z80*XiZ8kk2NgEKwJL3+?@SxS5D@wrn$x;JoeY+l+xD_(MNi{5XR;GXXevDsUe?5_x zNO8RCC!!%6l)7g7L99t1(l5E}Z?G`r`jD*IP*qUpuB0IcYAH!OC%EnpG(G_}Ow6}3 zxSe{#cT3ehOsPcYgg)kzSAu>Z6_8r+N^GYqVoKo@bj<~cC2byT&mcD@r@M#M=fj;u zO9RL0m8hwqL1zj?j|}2OkJTQS8?xcF_{jbi%I}j~9kfW9;><%j=|}9IfU;U$R*)Xa zEBo<8SmwL?Cc(?r12J*C9(^bldSxX)iiJLy$?NsqU2#5{r^v~R5(zA=dg75A?Zw{h z+zqb?0`AgQ7f@1SnXW0IcEdFuGrE)P`(i1ju@$_I?=7j-#NB?uXH5 zK90F0g1b)ng+#%E2I*)6a5|6jg!NyN^5Na@@F@^>OG0NBciCLaUPK2f0HSeIZU@0wyo5q(YO;@G1?^>j$5AXdTd zxbP!XBQ~&*Dec=MEn;$DQY=W6M#5zh7L6csi_MSQ<6Ef) zaLpRs5WsnAQm8hvVNcw0hhS|Bn~EMf5GsZjP{RN>vnn8abNly^TX&kJ-U;K5yNZfzgnB3e8pg{!_m8cw3 zC;<7P^U8x(S|nG}+pKsy;*K=boymQP0>52hpM=%^2vt!EuEoMoI~`iDmjFqluN@{) zcgKY%)ulP^Wn$1UWx9?Et|J%LoA`o0gpg$9+h}wsq#@x#4ih5bTgJLAf_~JU)_|lJ zS18vAL$c6ulHG91@Cg!DvH=3KN>$PZb8w56c-mdmXAC=z*P}DT6RL}b%Uh|Z+U!R? z_Os`=_lE&t4Ep=I1nO+IyXjcNz zflf1_@og`y1s6}*BJ<>Jx0 z+3_DIXjO3>h?uTcN;p2d$>ZJ#XPZ79PXVw_3^Shq{=g*x!TZw-d{%W^AuD>^&^BYd zPC0s>pC&bEeyHAKV#`Jcvst2SX>mM3d_+3eGjh7(n0E=1NxO;L-Ws*LS}tyzQDRoG znQFA6j0uOXt&nr@rX}plc!m`Pvl*prDMa$E3< z=-}OzEBkBU5+)Sc;)zY>GWksNA=CCnPb#=A$Y@2By_f^HLVomq4?j&TeK&j!Gf=Cr zrkcWUJR{GvE07r6!XrFgu?W0gWfYh~@~-$$v&R+&%SC6#6ojU zGpR84i3TZLpgHU%%;+IXr3_-Z2Yc6``!k+aiT4&6=x`DnOWUA!GNpU#fAFk=y#uDR zrIk-bDG`uJ@3OugQFJ`zsAF}sgsOyW0%foZp1fJ&!_*i%9*oP#<8@WAr+BXXFDi%; ziU0`>Vd+Rp4KtSxEW!A_Jf;VsvW00v>H-qmTuH}0JOML=y>t8qi3kiFAi(NENQ4S# zTU~tvpwG%Q>FUGA1_@o#>n^K*V%v1rWb;{Z(W>ToG~g)4qXtiio0DiE0yX$@bC%)$ zAV=7?GuY;FhVF+D7x3HYa)6kHz&Y*$79`Q29cF0@AqGDqEO1ehzd^}ZXoKY5QTa}5kqW`08%v#!XvR!EDVhxn zrV$XdL$W9`nO9B0u|4yJ_Ww9h01OjcZ+paR$af;%MvS)ui$o~jm{BZQnjiG2U_ju~ z5~dQY1NRDPk=Zk_kcP*aj&pDGf8%ON5#5g7NvIr}wQSo{3Qsx@%C6m)(0yF7{kRl1 z8#dKSham;e`etMVQW)cpj_{FwkR&YTn-98h{P@k)9(!!*Qt=eiYGc>jFc%7CTT-~j z`mnU}f4Ey@bfULr1rmvYPMfhmf*ht5!fyXEfkcz%05kzys&OlCL^1uaT_m;mBxFiZgmVXgP^qaYp>ApG zE@O~*E%?lVMOi=|y21f60}{@2)LY%%DO*}}J5v9{rCK~sNMMM3+=zp=V>fp+sWg2C zq<4|P?iaGV*GlA!pNA8_Ez^o&0tlkan06+6KXDS5^hf|mR-eY>aY92vWi!5{m$(E0 zG@%3`&^iCM0%SO80EYb z4RMGtx=E!?r~cT9Cby@_y0^^Z{8qy&ruF0IHt$$BH@bn`4*-c<;(?hU662qTqmj5oPg)7p(-o%iapN^flW zO88XtHq>r37Owv>(EIJtD_GJ|z34#kL$`LU)=w7grS&7cp?kxMhux=bBR}(r2a>Nk_pm>}10d+o# zW^G|kY&SbG`Fe$Y8jZ2EHS%7|&=OaRTJHUj6^l6_8Ah|JMxAJ|5(AELdzd68RjI%* zC(SCGf@;k?G~AIk7FPTS%aJ-j@+)--TX-2@?CilXx4GGH$C&!~UVAl&RHH3#*Q0CE z)$SI@)6wiF_w=Lcifq!QI%AbxHPb{l_hXB8wZ(n^D%QfA`dA7>h(W*&Z0-uG>2L>= z^Hof&S+*d+q91)n!ENvN?@?=IPrgGbVG-Nlx5ZUT`y-}kk_?(Ighze7f(`8uRPB?< z8yyjYG;PqV$|c*t=xHb5l932d08iFhp|dvdUWida5Y!+QuN%3O8pH;WMQQMvlK=JA5szfz}q>SBT?K!Ux z+?!PXmsOIDF!69#(+-uIJ9X2@pubU{&?6d%KPbn*eyb7tjGnTR8L;2&3^#dzErT3u z<2|loJaHBHrKBN(SZYwYQ6bQOj*O~0Y9PtI1KKeoZSZG*jG7~ z#+(-MTi_I4&-ep|j_(JCKN9gh%Ju;J9zkTOP*^HD8sp}gxAPTmVHvyIEKrX#q5_Bv z7!XV7r>vQwWZg~6%ECjNZBe^o+nexuKm{Q!lC+qMVrZ~PVZjT4@HEn6H!L9?okOQD3stKH0uJy3*Q0BymUc76an&f1T0QCx$F~4_K2PJC#m7&4IwfIR z7j8R7?2c@8+h6;?4gLY3c}{_}yRkJOhK~TUYXK=Mqtud3faj49llSFR=HHuVBq3PI zK0x$4fYZ!7TnG{qcL;M51{9tI{s9ze^P_7-zz(u9azYVHqNRG<7lAf7au>yq)ucy* zMgT3AZ6Uy%T>{A=u~-C4gvtllZZPcwhiu$U4NiwjQ;D39KAKvuJfnxH^}#VZ;!xH@ z7!2_AvEvB!bcE`g*m9v~=WyqeWSxqA+-y0A4Q)1;T3xHO6UlSz;q9D0yj|HF(;a{; zeCe7E)m!K=3*U|O$#l1SHq7-3H@Aia7Z!``>3LQ&4p%8V)-OS#*)R$7IoP_eDd`OxRP1++JK+{y<=rNPKcVv%)+>GRROj0{et5nZk*021U27N(^E5nL z@xZpb5e<%3JkKj4hKxm(iGYKlEAXK_tchf_t|o&HkfDz|4Pj{en3fRU$)oF{h!tZ~ z0cMZN>x~5cMm8)cP^d?t_|^|b^*`O~T^Qks>P*N~w?T0kabVM-)c`j5YLisv{5Dhn9VIs$!BLEHuy<%m?e7NX2x zvEEKstp1^z5I%sMo|LMLYF@2wW=u&ym<2Ge!Cm1F4>k%1pa+P+1Cs@vCu9WsqUO`2 zHrwkA5jE^EWk+0S(niAh>fm^!X=O2^PV&8V@%xAwZsmcO8uDM3M2`8^cG z;o1{VbG`uQmhNdYSM*8+RYZfg6o?$Ve^ zvd%fVpDGl+DY_J*yu1WtblmhcK$bg*F0W=BVY(){KHHEoa2c%7JnS+s60OqEO$O&P zU0@m=N~c-rj=rA`Wxq0^z@H3Eg$$gwdHrN4%_o`-`|BbqedawONrpYe=)1Oh*ls2_ z*n2=`GdL9D+yfbL3Qi^}}5tSOr)rPe_AxJRjNa;v+u5|cuML#m8RP@l2k@`)^ zojX+LaH7);(5aeIUE_(H8ck`4&n0-ItJ_G@l@pLt`7jv(4h>x?(A`{*;1r|BSBiFu z*tv8POld#qphw$Ihq=!qy3Wk(T?VyD4;7HXt^x{qQ_BcEjaCl!nsI#5v4eIpL7`II z{hOi+LbY?cMeRs$LpS@mR+XU!FX5>$TYYmQ`A5+Vc-hL@3JLYNqOKZbc=O}}(_u$) zsNKDPrrVX}=u6%C@u2E7;e4tbLvIT{ta6(BrShL(cm zB_+^%C}Mn;K^lDZYJu_)Cp}Wtutb3>2}~HwA4V^^DD8Z7#e@wLbQ2hDC+3&KsE{5z z@3Rvoj6yV8{AnGoO5{NWJ8Jli)^S$}Nt&$%44OM2!PeZi2T???)UCS3tT8-*dS^Yb zYOm(^V4NIMSmav5#L?CzW=}n(Q*39h16vTC9S&cCPfwZzvX{mRyTdM(hDr0rOoc?1mUxJPh)H$!?U z{M8O?IkX1ep4<4^4!T_aNjEfc(De-TRJHBT3>YzWqAA{=cINdEmuus!wq;|kyiry6)3mBGv!{MBg#(cvQQzSN~;+sz(xvw=tDuZCT&N6!QKqQ1Vjz) zGAbA;y|iAAVq?~;%f*7x))jrE*rV!=@&|UZT}C+^=%s3)%O4n%`;pwk2{9so@aYA+ z487Bb5y&s+Ig<*7R(%myXZMec{9~ZS7;O4TuK+uwq_BIw1=#OLR*alSr?@jfPy}Sa z2)Tmgh^T^g+H66GfIVNaC3nk(T%^G{=O%wE-ZnsOfCwLuob418(XLd|w?*{aoTPk%t zir{`E+j4ZAuuy|-h@oi!x z!`Q8kdBd~Py(vq~3R9h=8^R3?OLpngO202Ie?3E1##)IxuyrM`V7SEwO8W<(x=EAl z3e{ShkajYa>Zk!aKYVg#Wi&C=(?+y>zs2B?Oc zCn^F4q{R%t=&#|bWeL1lPvpuJcRZ~<_@bkM^_xjYKQa2t`*f)x?j}fdKVi01ZNqht z4hs!u`}z7E%nnjn$0VA|onPg^1A=+bG58Vd z*4FyBCA1*#IJH0ljS5gr1)1#?aY2SgeXrBJ^e5|>?{^Ll8YG%WMfAbb_tUkCEMFaJ z3&qMq0>g?`+MJTsstT;mj7tK9BuA}X{WU04J0zONWCsOHM@cce@qW8lTasL$MvTYG zp;%JDVp118ygsR4f}k&F{|v8`azMBTq}MTt1srQn{atK|_tNFsR(M)u0K_x3*-i3gtj(X+0aa^`e3TV@i z_2lavxPp7YTi0;0@n^7vn_h9ip({sz8FE|To0e-Szn*hDUfd>dADD`%{Z#->d8DGQ zT}8dBqs4K}3Qd>+kY1fe4#~J{K{J?W{9}uf!mGe;w=1mK4XSlQYM?s0#s#_oYb=oi zYFoFUx$q@fTSlex%7wdt_=4Z-YCv;>`lsL`C!LgsBM?fQO$ii+JrW8*^x&+h9IOqk z1b`3Lu!22WEWyh1l~PFEjf(nb*rVB_TaoT0fw_J<8)LJ@l+?MN3~}lM z+^VFp2)REJsZjoHx+#$DoU}p32#K_TmN*u&8!5S)J(xW)@6w=VAt()^dk-XU*5j=3 z(DbY8H(n*8veUQ?;gLP4vn`SqHaD2eJf~`43!XaYa1)X0nd!^#GVWT&H_1b?C$5=7 z7DF+!ZL{a<(53u0okT_+yxfr@Lefd&-B2yxHh;T-O$ZUuUy50V!5&ocovoiSBR?ZR z`Rg@W<%9WVP?%e<$#!nuJ0)8WE0utx>9Qhn&=sL{>g<$^*s!Uxni}mHwn3+*nxQ2D zh2OLH?Jiiif(wu&NUU5z90CH0wq`ilK@ZSG$|<{U2iifnNY2n~yRZY~je^_b#TmSX z&Os1blL5gA<$N!V?RHw$Vd=`M$-^MIDiS&6g5-$GDp`pW!%zXau!d)Fe<>P^4&+>} z-UlAIa*0a&)(1&`fNu?Ila*aXb|IsTuGGHDEusTG`@M1%3IW{ey8Yt7LYF~K-iY28 zn9+nZ9Hf#?&^W65_)m(LAZ1mvia`tua^~#gfj3S-{anCGcDrktY7z~61;&tjqBTG_ zB$7%MFU9lrI&u4Q=lMg47lypa-2egY4X)i0F0o;+)lL`$*Dm*J0uamJd+|y*GBtB* zLMCJ9SsP=n7FaD}9w!txH1pnr!!L2pGM1w-`4jVY`5B((buSG9i_$ufk3DnoeyTn{s1JBKg-%wf)(H?7O&Sz zk_iE!woA1{@*1k0>jW6vknLHybD(stsEm(P;;M)JU7o?&VPGwO?D8XRP3u~Qv|RBo zJxo}QERanJx>5_(>%AM#Wj5Uy@ZN7ue!X*4%>B~O5 z^;a*yFofETMcf3K@vr=-A*KK7L-pk;3j`ZtOI~EKK)?XX_f>vSaJU3YV92NiarY6W zziGSt{F@>}(eiK&qE|E*lTd9NbH3MWf_G}4iy|%$o}*MjRB^yqJ>)mgUtfV~g|+5E zkVpwXurdAJL$)M^B?XYB!NF@nB~XYR7XZjsM?*B-o~YG@sc)8T8x_vL_K8{y5`%GK z!7T>!KosC~{`$QOb9W`@C@Spe3Sh2W9YCQ4kzkm^kA8NW5SB(VFO9$8~^Y?j-VAyJwRv7e9Qu=prF^Verx#Ms}sWJFOoOA)4~W z1|aVJZOjTDemi|GZ;Ix0g!PK?QI)c)!XO3(m-OSP?T~CF!hwMfsrDAH-XFp&SolwH zm>ju+#gvF)AgqYDZ?aUeFn{oTPzNT%r7Jfb|Dix6iUX<<2m=%;iSAQc+?K{RU77i? zooI&95b+yeiaitDOgBfJ0(V|2I&d)@=sKWXx=%HWn|?6Wy+LxWm<9WJ_0u;<;8DhN z-~(Q9M#bJvvQ-ImT0c3OKcw6Uz-TfV0ejYh+f)ApT%x zq&5cDmGM;s7?Ao)ly5dvLIH5dR8eFzo*b|w|8hkGjpKFF>KQ_86;J0LughszLq8{x zHf_sYc4h3qN|l@FKmWT;s^u?43WP}xSS^~@vZxZTW4U#3OHMj1$k z!fjI~n#n^pFOX@3;TdguPTyK*SM<4;h;W5MMXD6bo==l?qOa_hdP!Z zO&*oi)@-=UYI5WKAx&%1EmD#{k}=-gVZA6Xww`?S%U5KE>9(60?cujhgUrr-2zcOX z)nkdcZs8}UZOSZlh37`~B7S$*HeuBVf!he5yr(>xOvrNPQtWxxrNAr9)zq3@Wz+cC z*+06;!B=bR#tMRuHz6!2kxC9Dx4sDz+bi^i$Pnw*3(5uU#9}3nY)dHr1q(eWa`A{K zV6SO#aRX5|mV_gTQ{h<+yE=n8g)7n~5?m3;Qir@FX9AT`{nY+C}8_$cWgP zAO!+l;OkR;)pQbzt8?$5IV<)mC&iiIKEChhrbn)u<#CL_-$2Y+UYe*|YxRleN8|{c z>s7Yx0&jw_1V1{oCiBM$u(1=}SU}7oG9~kEvPTS=M^`w51){*i5o76)-y0jniv?-@ z9RxmIEn=oX2}PKB6@P=n0(SGLk&av?Pp4+%uJCmyBc1d(mv6?-yOqIgQkz;;NAJNE zux*TUu_uheYtcXGB7Y+Yh9wI<-xG*qy>SD=dmF<5xkKwzaQY$~cT;8&I^9XPPCMu6 z?0BcY3nfWZ5}IKw$IJuAN8Cx~)gZL+Gpu;A-kA0JfjrmiQP~6s@KyirU>~X9aMwp> zBkWNma|w3r;S+oj4&Fw&*~hX)GT{|NReeSkQSLjDnTeMRDyU+8)#4P?Avd>A`tVmE z6LN>Ba)K7{?9(&sK>d(;-!hFew}n%p$THoW9upP7qC7T`^p0vY$ddG7z?$up8#0C- z6YeB~EAW6erc@a&3^p!W&_k6sgXY7~+U;7{B_c{cFyUJLe&}64T#6}KzS^r3$Io0v z+Shx-_p`ego=qxEq|9B6Lze*uJ zgTU}qy7)~rNcY7?rF10s7sxxju;6awZeRy(!-FDK`#A<1<2V{wC!(!M!NLWhz$I~? zU;m=+7Xf77SMMHIa$i9#vaqcWEZ!VO6ht!uQXhee5)wY7d_-z|_`(k?!MuUTOl|_MQqja{|r?bup9x5=$Md$fpT1vQI;O0HC|Hqqpvm=(&S>0 zTwJ3B#zq#~Q?!iRfhId8Nxq}wo+|KI7-$dZHOxiOcLf=yoR^_wcM{Jq=*y^%NeceW z6Nb2&YvlL6#)rBuOA$-SRg!BTWG>@nmMCJTZW;h%^avg{&9?V`l(#GXN_50CFUTNH zDBy~m$%Gd}q($w*K~=rv(52VdsZRmBkq{ku+8Z8ETj@@oPAf0Cq;jzWJkLh&Dzjp2 zbZfeCJz|UP%R2NF+Gkg=vn1#Ql`B>r*j4UdTxOw-^JXxY%sg2ArnbA?!_!K!LWF)Z z4eSccuzlB|8xLg|8RokpySb8_$+uORz8XC8inLxMp6zU-_qXbFM0D*CU+b``8eH$x zx_RPfI+`=yZEa%8_JXbi)7TVGmkbOSufk~GU0V|;m7e@vLQ)1}&H=F)Y zPSo|dZEL0q+-*5m#(Tm>sCQeZwQ?b5E=xw+I9*is}?kw<}@w;*_pM9xu+ z*wdM->k*}&PR{>)3x4$C>ILrV=>G1a$K5g4lx><&*1{>vo-8yTqXq!#J&L=NHDjKc zb3y(U&5~U%lGyEU%qe;`!PyJSn)!RW-w>P~L;1Q;K=oFz?|w<@u$`YC;>Qd6sM9i@ zW!?@InE~d$Vq{gPo)W$Ua@s@}VHa3zMv#FqM=2z-;5@hu#0Clg-r|EpQyY$mg4`jH z;&xDY$TF@BWZ=`N2)TEMXnQHYLt@T_zvbY++8~H17OH7)Z)RAI{+`ZL+hC>`tP(T3a0z@dkzH?-Xd{ePno-fp zVyKT~F=n=?-?V(4Z4ukBVgOq0-MT19y;lT|!WL54F&F|TW|~<#Zs-K4tzG$;w*wYa zqOs@Ay&lkG^`O19GdYv!0y*Z8fEMp-Qw?Lm(>ypU`^CaUvP0;_lN)c*$zg~rt{(5c zb=cIrDoKqV@IT`X+Ux!ahyGjuppY^@V8;;<-;!5^r)&1PYk9@?+_@^`U+`4=jG-%q z^^lyIY$D};B=H~+f~`0I;LjjxbLKbaGe?cJOU+n$7p@6oDK-LYdunRIAge1B>yQir zFmg+H9atty60AhHC1s98;7xdM^6H}V7W$MfzTl&IG}(;;!yHn~^7C0%87~BX&B7hB zO@=OR8dnmrP!++R!{X)Tg|HzK2C}x90$J6BQLZ`P(hOjiD=$!S~cT{C#o&k0T{6N?>;8?nabmmedXVyK>c~Ul44AlMd7{XaSQb>=F!d19@ zl_)4=T5xAJo*@!Mxdw%fxY>%Z%Yp~?bkVOn*>Iov(A*2{p~1=cyBdakkI1gvN1%l`Owo z)@~3dnGl+@?6wtKu28d=){{#!A7fn(nMHJQM@mt#(IQmHc9y%JTA{VKo2t($C>p<0 zC9(@*f$0tCfuT78Vv>}YhAKIsu=qE8fsJeF)5t023Vb752|~pTyCq{j^j@hDxu2(A zBI*N*vbS*xv^BuacsvGVvo9@2<&BVe{)%rra{S2#etd`Q&sfIJ7;6&VIkM)+Ma zZotc9EV6GYXBC?=cRMf0-H|5?6LF)1yPO3r5iAh0?Djd{M@da*a*W6Y(6W%;?nfd~ zAR8%wE=q+5nc6bD4cp%*;Q~T8Sl<$Bo7DPnc}^6HfC#Z+z1*=2=X5)_*g!s0nD8O&GyE>32Qav8vB+b) zPL8U5Dq}})P#&iA-q#!~WF>Y}w|7(^-K88P5;Jx*$m{L7p_O~TIG1X+H@FpS`xp+a zuv24fC;k^*XET(aTF|cp7>!K2J=u%Hvc}MlCxLm+qANt>Y`S}HEees8a7y(+fa98u zA?6phC0%ZJJ_WQl>OMG+{gTJ++EXGItQ6&>dmn>1az`d*slw&+uqr>Fa3MX_cKI;} zeolbYpdpaVhYE?ZAtMiK+dbV0ULlhAH+ZTO?@Ul|z%G;-$<~7S%64@YbudskiBgFPVer$6S4NuryH@@E+I&;d}aXez|3Z365pS0Mo2thTaaRWOj|kVQyUX$?pLC( zUT~^cMuj*tC7xrUvwIH+G83V{KRr;}alMPvL67)A`_DqmTF1z_w;SH39rXAo9Jghu zpW52Jd|KX#(6|IBN;afEC=D+05flq^Qcv-Sp;j+mlXvNDsfWrvY3JjyTUhIX>#qj# z2B|TsfOEjNDv@76RLqp5k#4A-3ur*Pf9Y3Ojd#`iw-1}{P8&2F1PxXbe`38p$tn2T zxE$}`U?}E75BDIO6yXh54Y}Y2k5#W54*sGYV^ydiMcvEkfbQKf@*2UrPMQ4k7MZ5xy zx^SNrOSgjA<{I{`V&Lb@oH@aQXmvk#_t`^Ni9@WlXbEP3;bYyNW_KMGXb>>*6D1fp zA+P$uUE%U+M=`Xr5JyR5;sI2%!`71*y*W4(P++r2)PqvP*&5M}3k=@g~G$mu#6BJW&bW zm3%qG&3-5s1qHRgjusW`6CeV(}3Aa5^_A!!CyL6%)`> zEEkIUCV-y4+Qr1wg9@?aBTPHY)zzv_Fn?VH{EAnH>Kql1mp6EF=-~b1`kOP|rCVN>_P!Gn{<9f9Uj?7r?Sna-jWYe$m2hG(YKXB zBaAk<%zU3za__N*$OQYnK<-*%DLaD&2yj9Uihq8hYa8V}B7NV7rMJRmjU9*!Db7YA zk~36JKasSTB7B_*0CK4InI%A!`=$BF@goa*Y|Q?C=}05f>xqXB6?V%ieeww7^)+>e zL?s0`9E0y>$Ws6$`zk1&5+D}P20^|TFpGM^PS|bE7Bx`;6l=3c;bT~sz)V>qkY5?_ zqY|_(>5HGx!3%}gIl}M4JO^9b-N#>J8NtB5O|Ig0w}fUUze79qU#L1^6P>*1)!46NA`>7`769HEs2;H0be@zPb;7&50vXm4z-Q4MV^7$|it zI?jy~839r)BUw#@G}V07rudVvh<*UhE^&@W-%6dLR*Y+dhOQ1fT)qN>dVf|oL9bp~NgaQ6xHn7%F@jV+*@rlaY4Vj(ix~ieK z<18AkT;nJhl%YZi{7R&94=v*q#8&W98+SMBXJD#}iqNZm_A_E5rKfJ~aM z14+=>bK4CGTZ4Fz?1DndFp4154fMUetC!~hR;luV_DoWAh>7OBP4F#;+H!Lssw9O1 zG(Me5UZL2dr!zD&j0eksP;S;ONJRY#8eqR*ah)ADY_Dbj0=iaEgO9$GTczYJ^U|YRQG{bUN{lIv7Qe>JAexO1y z+Ta4aClhlJ2R_bsfQZ!)9WJpPIf;A|iL_(P&VC%w5{%kYAXCGO>1&l#6S~uu{=&Y( z!qBV5ff5X1@_^JJona! z=`CVglK?X8AgU9%(6jf3f${QICLa| zz{{l!C*nD!&jA*4pIDalLKG-)_=R^*b6WhZ*C&g-=H2@B#X3tsl=*&nK$L^KQpI!- zEL0^Y$sF~4_@5WrAPm^4m)ZfWJ$?|rh5P}1*I~4z?ArxW*|$LpNFPl^K}#k2*FN2f zY+wC9pmrIjgh_wsX%j`>1kYa&%zhmcU6zNmNO=8m%k`j3 z<-xiZj<7_i)<-?I;p!QDCug}0Ezt7FpM_mCs22h_+g7KQ<<)VC>^h{F%xoYMj!oDV zzjFiJnTVLM*}>pqcY&&}rzV*;ot3$s-9C6bq#5F8)8TgKcR8cN+M*qlfK>QL*5t-; z3v#Rpfk*BS7QL2$cXaar`%j?aF!nBpJd4N_3WAlV=KBZdAT#fVP6p}tiVftZrmp5j zC6{7R)86E{aeS31#}q`Ncyw8`MWSAtC7_nR2`AotoqN8%$L06Rh9JLG9KB8TW(~F|L5W`oIu&&7^eVi7hMFJ0TBao5wNT-&SrcCTzn0$b z{sR9`D4MAOI)e%b0Kg_10D#PY3Pm$9bvAagba1h>x1)3MaQT07?lpGow%Abm-jt64 z`v8l6YrAPR1B@!MSp>Z;8JKb*5=G)mx;*6hrJATFOH9};8DQ39XPw`3{U7cnw8d0O zGjz&R54Uc#OhKjTdgdL`wu)FR*}1i_6sm;WlgQCyi7rs)!PV2Rqb0q)6r&> zyyq&S$4I4GNGY(t;K8XkJ#I+g6aJ=lRC|;$=I~_poY=O zpXu87upI^}DKeRd!7|-x^Pf5BYB=NRy1*K>$pntB0eaWn=egE`O1qWPK3dPlqtd?4 z6IS%;eTP{8LZWJL^5y;8gPuPjD(&g8_0`9L<{F%91Mr^48k~^0mr<-h|NekI@aY-}K7BH( z%7;4MA$Q#G6AMH@Fs2c}K>#`m@*v=o06+>7C1999t_4<`>U7}g;M)PPhhPuJ9?%_d z-b3)zE(V_sL+^*)6L~Q9koti01>Oy=O_`=z!n>-6#YQh#(L~ zAkrXmK|l(j7lzM^SNy;tia{8MNQ=M`1x<(^6Fwq>M!0Yw7GW&>OPkIha6|Nta245< zF4`f{!!3qetoG!RxuIV8gLFf?aEr8uNoKcf=q_W^NpVS411)Cym1{;0cY=RY{UZk- zoOGtDyZn-mT06I3MCeNEO8UXIMS|X<^o{0RKJw zPialUHKgpJ007XL0RWKyPd8v@YUtwXWU6m$@9gsb$4&0&Y9=3#Ir{DC52E+Saw%xO zWmDk6g;kaY=}g8)5wez%i`-I~r8m9Y>864!Xi~7uk1Ih@kK9~M`+eP@ z>-&G6R@>|OKfX_gyZ3wkUCYnI)8GC5n5O6ReHyp#e}6LH^Lab%R>-}8UCQO|D`*Z=$Y z^d?Wg_r0e6jd>?d?)!5)E_^mAng8?r{`>Qx+~?!YyOzs+^EP+4$KU_+ARo{F z2cw2b{KJT^(k<6Ir{o|95o%E)APs2{f^Vqw+3R}j=lhx4DzBAJF4m5iI)IfpxkoGW@i|&lAnEh?Iyl+c z`(;e|9B=pk_;@&}*Z;ST&f$9e{J+1?-Q)f?6fS&MPv!V|d+5T?rzaN|6Q1>ZKPMMg zR^NGge{z3qJ$>Gu29sy6)`UqvHtye2)BAlqzYDeIiq3a`zT4gHqp5uVo^+>QYu5i> zJe+iHM#Q#9Tljo@KmHEm^KEfR-zxd9R`2=!US!Mb2e!hWvhd1$>wA389^=FOPS?vS zGd*L>IGJR{oS1v^ZL!?7z1NTX8}bcb&e7*5EnBy72JJyXZqCv9W1`dYX`$n9JMm3) z>psmsOAeTm@QM8mJB9IkZsaq=X7UqudY6T(IkG<;*O_yl1o6rbT-NK1e#TH9WT)_X zaJ61>#=iKhiKJyh+ce)-gAjhwPU8ln4q1_&4+dt?mx0byZ6|v8(h6w)jCqZ?vOg37 z$eJYcf=Lzym`?QK2?^nlZ!7{OGZ&f%>7J15!orlKz zD-^|QTNTl2WC{q(pZG62w1G@?ABrn*1O+jeGJ+>dt#PP^LO+llYECZVUHXKt2IPY- zK#we6O|Y+6u~9m}#hj4cV-3E*<0Wr3;J^bF zl!?c7QRRCVsJWJ*r3NUsEcTbafmWohF&3NwmzMJzsHWnAtjb?8h`?pznTc|!s1n{@ zVq_!RhX5T;e&i~-N|Rv?;Ejnn#3(Te1k0t!TON$ZiqQvbSonI zMg#>WK}cC70h2Y#hEQ@l6j8D0`cf3iiEvZ_@iaY4rHCCPd)CTc`aJZy&!98UbQg8L z$bS~pMEO6cNH8Rbj?qWVIRHxjQqvuaGenX?oBANSfZ@$mcG$$y7j1QT z{LQ6qx{-JF0{d|~m#|+EbIWrzaolB!iU8ukCz}H!V>Hz~Thrw`BdQ5}hn(JSnI{Ur z=JF_CMRwd0+EPDwS@)yV6*saj%_fQ{i6@f&KTu$n#K=mtVwQ|Is?odfAj>AN@6(dny;fop8k;t5ssdN?j-|3rX*pw{%?sL8pA2t_S6o3DBrY^S3=khl=$k zspO`&nQ|yF>6~$r1LaX_Sxa^q+kk!^>bfn_(zq~1`c=TUeUl zN^hzeTy$;~$EI?jkA*rEbO6)#u-|Dd~=wND7)Q%c$j+tOVSPZ zS5<9Z2T2Cgt$LcbAV1#}~SnW^}&!y4$ZV;KMAjC}DG*RxM2AW6&J&hy4W4+ykbVWL8r8(hk#m)msr9y;!|ik6VMyXoCslQBWnLz zX+GtkKi;n!Pa%U9ilJU<}nAxhu&e1RLGS366+ z9$-b#nkFlaHTZq7jxL2KQ=*mN!S5m_ww|E9D^t`jZE;i(xkscqd~{I~wKe1yB6XX! z(%(~$0HK?ere1&&SubsR09FrgL?kMB#eqG-!{NAs4tv36IGajtoGe4tmQ(>9TDcpo zejA4BQSR0oub!S}y!`Oc^ecgRw@f8$BnAL@pu7N4i#G#HQUG{q6~33^6t-zn0>kB$ z#SDD@&=~wLe4B1saljV)NwNjNEyIwnHprZq`e;Yd`WDe3%sJEx#frDVWhz7pMY?zSW|iiN(3(Su5IQPhx(kj-HoDdS?Z{{ zYTAt^fkL&FvQODy1VjsDteM6E5Da+1wogQ-ma4-n+ub%jBdSHU36$lv!&u;J=y305 zfJjabNd5TROzdB%05CN{QUx*-XXi#+u96a<8cV1#D?wO7mjJ4>XIKQidndL1`*d0zW%+}52K)$2VS|rX;6SZR(qIP8(P|p?OWQt=roU<$M*v3J zc`d&o0kI`qI5g2G7W=}I@yRwhYB?i|>_(>M0WRk)$Y*v%hCu^I5l16rn?ns&)?vs< zv=(GD`P{AWC%hfAsCC9CuxD4g132>dC*58y0K{}yc*k2Rpg-z7g!_0#>%nDN} zP`9H9>-|hvEjHv^vjQIJg|=3H=6swhU|@||+y>&A<`yf@ zvl?+(U!vPyb*3bb{jxquC89J(_qre3Fw+aC$U7)@ zifEtL0BN#MEi5GH^FlSca%7SCwicgiaaH4npvaV1G!=2zJJpx^Im zjlGa{KKv`}L^{CyMth7Og@0d}{2+R$kP|*Ydz%m~(Ma*14>Z>zTIl;}*6_?#P8Qbk zx{GYB_Bn_jbNcBw%eJ$kd=x)R)J`=Ki%vN!k9nhhs{HIL&Lvf1UGiw7^yuVmYP9#S zM|Ug3saL)czo-yit@NtzY|p(}^Q*Jo5aZW~{#sB=%YdGFOzEhY4XmAa)i0WB949AD z9o{N5bluB<^u@Eo2g<|nP(*7z$1lwtz})vvv|pYASvd=wf-;sPu3-7F4NJM9=`PJD z1$Do?w`EDuhE3!RsQtl}PlU`5oOor`CSx0$)+#T+p)9oumpBxEQY||{r70Q{zp;U; z=TA@OO8@K+ci1bGT5pF+Us@l(xB_FQixw0(=m!8?_52@f*kR=2LGBV$f zVY7al6UVjI)h>2Zq(%V!BUCcfQxwWrgTL_$H2-keXiU|bGoZ6{waUsij%*o`7aX5a zP%_}fX5TV6d69?m4R!~P7OrHu=lODVe=bOi2v}T;2pu5Yiwr2~5d#t42w}GHhjIbe zQI1lf06|TVgcv_CFC;#dSzEB2w&sEc4ipKCw#&icr?}i8jhWtnxSb<+h?xYJz+wE> z!@U%wdpg2wfpn0M#JPzM`Th^7)uITrTB|d|p_?2o^7Emen228rPDNd^7pZnueR2)f zG?E~>X}vvdP+&>BvcPL|2xC*H@of8x$30K}?8n@O*Yo03CtsY$)y!Z8FI{t6J0B}p zshC&Vo}J_aA+Y9Y%EK~R#1yo4`RNC{Q{AU&+MO9)} zmvS7BKFG95R8izi&-1uPl}rVowTOHB#>6bB+>KRvKf0BZd$C8!=ZRUBpj0uOBS8hl zK$awWAZZ268qyYxJ5e&&S~y`IGtd!H##Ux zB@SiPCc5uUr!8YBSqJT)_B};+8~vJvsPb|87asXNmBYdFc26rp`$e_$;LXB*quIlz zC})c_7j?laRl?guZlToCz#tn0M6x7J#~)KV$fBl9jE(hGv3fRVf)skfE?*n$y~2Fq zQ%yxd*7=}383cYM^R+~>xf{Uv@#FS@FwBjisf2`14P1C-J%-8(8*eAb3ZgOy4b46W zr@%8bRI-iNS-S=#bk+PWQb@lLCeEaV8)OmOSb&J3l`Qq{WD*LWm4l?vw`<; z@6%L&M?_M18WMfFFgnhYsVZWO)#4l1SL+y?<<74<9gEAa^dfmOTF_G13qW>$9DV!8 zTRk0gjUD*pyUAPNd`*d?XhnoGM+I1x$@5B7trMb0rw#wyRTXK%d1HkWM*YNbl^yWh zJ*-*1S}0$hO_{DG=&>XxKpVp*A^{Ig2z(HbC7J>skzN)%?HvY-9~;>*h`Xzz7#mK& zx0p4&WvaFjDWZq^)tPpN;VRB4za-3uAKo5XCPLp092GQS5SMpbsR|@#ql`++ZTy1B zPI|m6u6zSc@(;&G2bQOZ80zGtPe_8rb|m(IsQ~Js zV+~@dXsj*)d8Y?=X-ch0y-4^Mv1$D}!4A~w1=@0s1D8t(PhHN_3T06wGA0e_v!XTe z35whVK%T$Mm42+pnlHb!y>IjU^V;KF3xO~aYCh}R^R#&x3Y2_m1zL5&h=%}ad&1p3 zjGePhRM41e>;R4(cAs*N&X3g6$Cv2oS$P)28u*_L{Ts;moP z{X!|iy5$4);Pf_O@!a3hDvpRJQNKt)p&^L=g_IaUk3H2vCXvfh-IEe}3^Ctf(nH=~ zQI39rymq{7IzhD|^p@PeOFYmRj*;S_cpX%Bjdn38?i2PH<~`blB@}%?4ae2@!54CX zEpC=n8M+>P7u0>x{SLMwPty;k;nY!j<8>>xhcJ|9R2r=Q3ldO-Hm`=1#_MPX+zi!n zNcsgYX%KV{UKg4=lZ2GfAF*v2RVcutJzqt$p>1A63Rdg9lcPNp3Nq*f2o%pCcWn0{ zN+CSfX!7~B6}{uoCbidae+DEm2bV}-@-IOk?#b^6jEVaq1I@EIqs7Zc za!nQisoW`>xpN_pD~6kbKTfYN4QXmR>)sz!ha1!GdH=K?g(->xm^gJn#jTGa@`UO) z7Z2cp>=usyc@?(?Pt+-aXoR0`yTN#>R$^7;NdkTAYE9A#u0xd8Ev{QLP^M*a7tk-m zZdq=95tZ6CU!z7<*9)oX(Is69`_hk&B(|@ zX}HMwK=%T=7H(=!H_@HC1k_ocXdD1XUcB09mLc21q}{i#FrE)8gF)2hg@qmt1Vf;H zE+%RHLRo(gzT#7+$S%3GWS!XSEAzLOw_xBSJ*UGSbd()S`c@bY1-r4u6&xc|MaO#_ z+v4oPCj%~inD5IqpJ=YMO{~!9E7oi5{1^J&sCz$&?w&_xm1}tbEx@N?rWk>il{#ql zI$HH;H;9_`_B?u&LxS8z;qMZd6y8R$xwsMhpX8ZD8N>uPcOJ3DiGp8&U{Bd5p_G)o zmn36?FV-x4)50Y%I>bz8u~29o?FWcI`w#N=VD|Ddjurb9Q0{kFTlC|fsauD@!1g9j{3rvsG&1ZC8_#DJq?#N7D7YMaClD6n zN!WrLYLl9ocR4Awm-UZ9fRXNs5bqh`9n-dF9stoHT2j2;XcBt9|Hax@1=kTo%Sp1> zB1;xCGc#EXSIo@J%xGE6j8|weGcz+YGn2)v8@;5GN>Zuh+q3dm7I+QLzjn6 z)75e%LJ)G^(mZwSUpS#?9O!Q)pa_zJWqO@~;k0R-_%K3d#fBe>XV@!5>DHHIq$fd> zarl#{sxYG~r&uCSTu(xGo34{|aPEM(qu-{9*l}&Dl3Lm15ebREHX2m{alj`g>|m>? z+C*Gjv>^AN4X*Tt{cug}7LfshtE10AaMwFkJ|sOWMGnp^%}R`;Fh6jwh)q=3V$6pP;S9hYBoIaIZWCuN8Pzk0cv0x^^O&Ou@gR$Di`+naljc|L%lOjR z%Z8A1)rm!xamOzv?@)d!q=k_YJr@6#s3M&JxyE8{~S-s6y+*W0+ zr~~UWRd;C$f}}5ZkK{3Fr+U*i z{a!a8lwfBKxErmb^0+_=rKiYJI{GqElk9r*#L~{kY|BcA=^R%89`-DJOMP?z4D0fSnmeA*32RD*{V^`c0zE6u! z#`12Q27Y!c#H+xV2U$s!A)dN=y|wZwviJMdLS2~28oM6$=zYgt>a)Q37L3xJp6_PFm-2d0}!>z9{qQ^sGF@Ckc8DYA;N@y zaIAB*+CufyF3=gBEt^!ZK9I!Pxakdj+s^K`ggd9%Q%pDi8`$NC#g*l^$s4`d!;_Uv zd|Ruwj>`C#fJPm&96+&Fl|l`%qf?YixpV^lJd{`+hmp9Bg!d|Yb1^moW!v6K3_BsY zaursUjf_j@_g?Qs<5a^qYO*2)@2x=C?sa#KE z_F<Px;pQWwvffP8#$=*5Pqv+;n50C8|L%OA&PS8dJ1f#q3lum`|d!bDO>EwArSn5x+u?8xh0j4T-8=8))A_v#n`0(n$oJ6=sFlC>C5t-stU zV4=eL>lkcm!Ld|*+z|wo%!-f+nx?DNplT9UIbMBDGzKn#Rm#(->iF8ZTFrf6Ord)k zq~HdBXT4~3rF3mme;$8>XqN|H&@e^U;YyWnnKi**AvbhCp5FBNV*@`WB+7vE#NeQ{ zk(i6R5fb@*%2or0iRz)LG*M+$Zuqo|XOqLO2`xM{uQMC8jq-3rb}t&vKOXIh@!x_k{+F2ixk0gi}Y*E!NuI_KQ7mOP=yL3M)^ zV4pFtZ*(Ncsc@(^#wQf=M7^WwH#>H(3S2*rqmmLWgW>@?G`-gVyYmOH(wsi18b_LM zz_(y~9ev>y1T64hr^IylV8c07A%JzFh%P@;l`1n#Dnl88fC%*UrI&A0i_vWl1oTqv zrCNKcDMVk6Uq(c78%opjTONLNCU7J+RO&jPO*z}YP*qMRE72~rL|r;}mkR3rq_*{n z+@L1Vm0zn2s!b9Sj)k7VixaUz(Tc5cA>4gxka6>$t8%plOcTR?{?M)0*Cle@ZBj20 z-*-UoUrYD*NHw|a(*G@4$Thoxm|>3uNos2)znXVVApe3= zGu!@&Ll3)&L9|VNDKt9nmKzXniWJK_?6wZ8*RGsP)xKb2o&(RTxmW45S7ibGA#g5> z3Xd4T7~s}g5Q-OArIt#B{N2({z3x7j|#ENu;VVphZ((|IU zmM;{BDl#Jq$oZ_;6EQBPsD2L9)0D8$Bv0%42I_5G5!QCAK4}(oDUpf}Nbmg{G3l8A z%~)t8X1Ztbtp>&|p>G$~Mc^k%?qdH-rS-I8V5eH_O;xhhALaPsCZJzu=!Xe8&@Z#p0K>QDMt6$#l5Yc!$(JcA%Sn-0T`eNZUbg$ES0mI?7cH;H;pYB zz4_Y@*ZJ%aboNa~Uwp0(H933L+P6e@LA;0u?1{vP1EEcq%>*5Yhdy-9t;9@Jen4a3 zY7ceB00o026oxUGH%HPw?4^hCo1Q^Hx)5hKZVcNhfuWwYd|rHu<1dcu##Om`UH*iW zyqnq(cetmNHeRexO*9Gw9f^&x_O3~4(2~^1no&;=;xRUsE=|C7Vy8%F!pfq~E5Y{T z@cqUEg!}{pcu)ge&5#>epBKwnn|#0#I(F_ZBgMQzS`Uyi30E*&Gq$~AXrQ*Q4|k(( zFLL^6@m6uT2h^w~I1YdC-s7IEs%Sfdl;?*jCNP(rR)}5@@ z!JX$o_t(SrHPU=~pV38m2<2}j6jK#eU628#v{*& z>Zf=Zda2^J^uVc!!r?ni|&m5idhOB*sc+rdWzbAd2Otqc) zwj??WdK-#JjVl4I{NZXNcKCMiPgVyc?IlO+l5%Rd;!&Bo*D&VGL#x6(!)G3)d|^E6 zZ5qq&(`WZ+9=4nt^#oAmAv6t-I|kQ|aQew%zHJ(DnaWRwX;J{lWIl3qC=QNo6OU2J zx2}s?{9%2r8JlT(N>qXB)zs`W)=5iS>$}5|d=<5Fh)YHN8TvUcpb$k4y90)|N!D8* za;Rl^qAH{Qb(}j5Sil>ELju=yGEv0!%4!pFhgXSjvEol2vxjEvJ+F=?GyFCSEb%iF ztQ4D3@*)TCwnED*qoabn146uNA<9YEiu}YRZ0JjR*e0^N;~|Jpbe<;>mw!b?@Z6Ay zCrV^9YBN`b^G%!arX$%lUtZ=VTnzEMI7Q)+&Ba`te@}LSO6D=BYsm|bs5Eu6wj*H) zv+4eRTYp!li|=V=42cRS*JL)&FsWUwyn(Gi$4f{?UcIZ#fJ5Ehn@^4ky8Ju^UxP1Z zX<~-#0B&BYS)v8XTDDqLuWm$-#JGFrJ+=gi#GusiPVmqqk)f#)7|pKJ5KM}Nidqj% zfL=pQ{OPvx5sqThV2ZmLQxEoEl$3;zCZOEj5YnzKuhRrPN z}c3_KpE9tY<~y99as`Yhx{S-_alggFh3DDwH1J^#Xkz}uxc`yAE9DS#e85FLwRge3 ze~gvZ1YYJLmDef|65ioBLFyr*G4S`p|3!vi%CSw8YnQOHy*qCU8*-zFWd%av_u zy*irx+dHXXkIa#6_0a6cN}I1!0mWk(k_=bj&KPg5k~J)w@w(C3{K!d@mR93NKvJu% z!L0|(d-}k<4-8ipgx#@H!6(cHvBon-kHfUSI*dytjRPG7qebGa?<|T#Oq3dSO!qM`~8$|+oXyajR8zH%C-lT+(PyZ83WXg((ot+)Hm=pSvamIL?| zlxv+`Ew|5rK^`IJw_We}l^P1mi5SZ0*nWc*FlWG!>2Y7kQ-Arqh3cZy5;5Ex2vL*6hY0ckwH`)tx z(tsq4heWn$gkf|w2kR}&bJun@zWH9Y)43B|$9yT%oM}~jCE0g0CE18LhjODc%!=~k zGx5}4`Q6I@aPQ>?tSfkbbQ5otK1bV7)|K*!cG*NNM&Hso@}PIac`4pxPk<~UW)Ri`WNptfAXSFgRP7{M_(;T5;TzmzkLU5dW{D!fZ;Wr^K@9r~zbu^rTQjwx!{AQ^AVo z@W@3Q;T>2e6@{P4L;p!5?WTS)u@8C2wmA6~{k!=&!q(D74*o#?rGYvzcTPfK018%& z{}6qIIYD+wLYe(GLwZ6*?1UU`FYirFyhJWYn4KRKmi^NRGe_7&@^`k71l*5(FyFwK z1ko$cadnQj8I;~6AxLHMzZ*Ws5MU?nu1P4G1i6gMioz|H(29{JWPIJJsC@^+C=>z| zhPRzbD1O`_|Hhev&}N@I2lEg`j8TyuhC=c>6$L1EHDMe?TCk$CFV*3X)g^qT5q2$5 zKGqS6{k;isRztX>F%AMx0cmS*7zo9)^1lD#692Uao`U0th&0SQg}G@V&B~1vTOvc+eUWh>@86wW0J@uVaW2+T z0w6G?E%o~@^*@FHpQ_`Lf~ACLu?Wa7=)QiTfAxEV{$Cm9RHTx=f1tp?R1v|z6#lcp z(a^@+-pSI%!dCx3!XtyBlbPB7t$@c>P1atU6Q$!#{bUY^df}AXen4-BTL66v-ZHi7 zpqux@NIQf+C*t!7sr4Wcl2RTzS>bKuaN>e)Tel13;pcjDcz1SW+U)R z-@YPH%jx#>^7p)1zp3VQ2VG=0z1cqrc--AsI=B0Njz-Ee+Ae>L-<@r5K0R1Tp@uLHZUIL;R@=O^!)AFP@|O`fmFryxH+SHF}r-${GRNoz0Z zbRBs5z=MaYw{-KVC1X^h7OT9DC_7TJMFJ97YYFpZAsw-trIr2!j{vf7sD?oN6 zc^ylt;u-N4nu^!RGP~h9Qw=3>sLZv25C{RSaz&{FRcw8I3nF#W?8Lg9}kp1Yr14A zuGdB5K@#$QfoaA3u8TZb_(BxQ1P8BoWn$iYyD@ge;)KNyQ~15LMrYtpd#Q(3tsE^uu^v zPf-K(E7gQp7!j0$B)_4Tv!-S5lawgtZXh!Vsn+9WGI z2TKZYvwP7S%d1*gU8o9o%ng2LQN*_+SttuS9{nZ+cViW=WK|pWr-8@RAP?P=N_nxY zh)M*^q?SLM?NRGGG5+dIgbK!B{4!X17_b)T}ecqD|H>u*NJ&Usol#EIVQ;A z5h9?!%}k-7K(~{tO+`{dtdp$7@`z2%iYZrzRvp}wEP8tqHv<=mb+*dz?;dm;9+s(s zve^|u*%DGqFP1_V94$ur!#H(P5xzZ#!*1cO9kk~3CL7o?5?7D~Hdxl5Q9h$S`b-06 zcp>Y=anz*YQ=GksK=Tqv`ay-yNJ(+Yoyp{yO)Nv$O)67K0nQfZA;yya;K-jdCIm5L zCDjn*B={=B8>W!K1&SQ4fXUJf#_yW{l6i-)C*>hA;6{slERf=DuQn$t`*4pn%p$4t za`905Z4BYSu*jud-9n3+2^O@oZ$?=kco4X~U9f2jn2_v)^@~{yf`2Sjipb@R_GuKm z-I)u!((_vrY<(ljzfzhp2&l9e8|t@Gj){tnwRP$E61`hA(eR96C37(vR)>hmtZZj& z6DwNy7jiXe)*q2s8L;BDaAUs5sxyxh@I0E>Zar_DvK zVLu)uA5eDgwERwb7?P*%LeoN)p>J&3sujf1Aw(5(7B~R8B`7V8y^ME_^7j|+^5xR+ zq`5hG67%biC@FM_y!<9M%$#!vgcwWw}IddZ8ZtsKxEOMF$BS;t~)?%KB5Rx3ZWxeJn5=`_nS&q*C#wd>xvD*qYj z)5T|yTfhHn+CSYG?4^tOiU)AZ%qwX&y|Y*N#(e0Ujlcc)vde=0{KU6zgNb>X0$g?K z^zJ9}+XFv(6ag(e5IS68dQ^HIFpy;-1htfXD$Z2QLv3pvM-T$PpsFpQ(O{hBUklD;wV0&pSwbftr|ATBVKRB;4 z>X&89l=lq(9<42~tNkzex?7)gQLYXSW%n=W%$D z)ZyLmW!dBGt!3i@Wd^?Ag#U~AQ|X|#+pcgE);MCy5Z<3P*+)4R-k!cc+nWWGu8IQf ze%kn{^&V|Ni&Xo%#T#=@As}Wb;W-(pT+Vd!mjNJG^N4<~ERS55$+_#2wKO zqqc79luK8}9hg{r7fmeQW!rR4{!bPjnr(_QpeM%vEq=NVGm7(5%UpfW=aF=q(}Z1U zky;dPt;mB+5=R7t*w;O+Z`N*;vlzGhYn`qM%t>1>*^*V85`YuH15h$=1}MF@$tEu? zBDPt3waIQ3=C@O8zqZPH$#O1NpUjPiriy8E?1$P4?Zw`08n%{AOT0g)g&&z`2@g@K zC(u7St-p4RXAdVLDjrOumQnh3eDH5iYc<kvxBQZPiL*@eb~g zR#7fk|G-(g1l_LQ9AB-ics$n+`gz-GRJ3VH{|&TDuFSz$h8>5yya}@@zgxBQJ{c}$ z4bo!xOsi4ixy@M1%zE1VwSC#_d~#|#safT#2Yyn23t1k$dU|xXCq4c8no>Sp_WsrU zbvs|YhTd6XELjNwc9B?e*nJdbh_yaR!y7xFHWs z{6d(g&+nnevM!m5AjI6W=p9{_Py1S2vgm8SF6=@;ZG2Pu&S8XH&XQXN;xiv&?3l)C zybse`Nwe!`(mJ7RzSUN5M!wZizl#|i7nRSwC*k-X3eDeD-VE;0C=7ws(x!Ft0G&t* za(DW_K?V&LN-QfRXBfG{QFogc5>5JMK+mmTSxWHjmuKd?t>h9)+?^W+VvN5JQ#9|E zR7t`lO8J_Bs>Uo*So#*zJ|9Vp804=q*!xIlNOztAZR2aMx=ync4=q%NfebpXn@27> z8@9$sFEly*FvrPl+zjCTI7g>GW;?;N>5?Ur_jzq*H1bhva|VVLCdOv^m3 z-*@fpHE)tovM0gKJn&OTPWSuS=PuWOFgZ7A2*0Mt+cOh8cbRm5{Y*r9#e4&24Vx>h zAD`U*k^wGUk@=JvP=O^bOq`;bn}U>oHofhBYxbFg=ue#5_w1g)Jhx%h{^qdD+o-rD zP2brhC0+Pn5)YzE=^n6*Q;5UhM2rYeQZu`?=fl(I&3h(Q+OQ{xlGS}cY@vec`#d&wvyKx1^ma^Rf}gzF0e5M1&w2v# z6)pvVoKjSjowZ{*nshI*^qO=~pbi(5m4=>s+1K8QTT4B#9l2%%oN1B{^5E9Hscgc* zU3n0tB|h#a0iJ(kcfo-+#vI?T=}0jp?1EC(G_P1Gel-MA>oZg?1KU(-Ez9CUSiKjN zKMyW?vpg~L8rOm*_!wQwLTVnpv>Mqhd1AG>(QFEQG#2^EOiG(PE3bK!)=@BOw4h7G zexOc09_I$h%ukv*81yI?@ba^l`z#-h(Kkk#l%r|J6ceJv7}F{2?~S(%Nx@Qz&rD$` zNvh&dWm?#!&!K$G{VmVHkSxcDksoAPrxM&30RL=a%WG@THh5ng$|kRScFtm}h*8k{ zzCP%c@hHQ6jJN4A01~5tzM_tTUFBoTr)5aEGoCe`63V+w0ugF=+vqGf0Vm>nAO)0R zkAxC7%6pBOC=I+B1Mhq3DdT#Zw9SMpF*b9xOJwWdVm!Nj=^04-KhbKdG2T(- zq&PJ{e=k6aAON8J646~1EBs=Q7q$}nEcacwoHer?EDifi09UyNlGcX7&Q@eHI0S7h zJIFQtOfP#yfQVS(D}b!v7;gS*z>g)7*q@}l=4JL?(A=&}ylX=58nB&a=VeT= zE!wntZn22b1pN+O&dzBY+0w;4xi(lUc>U9KS$?MfQTqC%9ajFLwddt2wS#EPQUTuw z@X(PGPzk}!(D{xy9KF-#37}9pB14E^0RKVJ55o-RGd~aE;^k4?hlMSPOWJqfS(`JV zpL*YUAMlnbX+=)7`W{g*IqbD7Swd<^_nEIf>^)EHE7Bq7c>_O5;;)CUVfgu&B##RF zxfk!?6CO_!B8xG@B07v&g=|Tr@hd-9nz7mzF6cap1nCmaP=4%@=G^*5|HQHcZ;9t% z%(9thHM-~e#7 zGpQ7Yy)|$G=F+!(jWXVd1W zqrtXgPwjt~pa^YyG6Lk07m<2ljYr*Cp*R?h*M%=-Ci;DB58rlFmMN<&F^S@SBcbBJ z8&v=4AUVUSE4u7q`TY|E7HNgo!7%K*@EUwdxcFX-)Nk-lE9zzUVQV-HDToy+KtS{w znb#3S47XfQEA&>A3(^vXJGlZ%O&lvz3S^eQNK48_KSa#d{)^4OF^94VE={iT#L)v^ zN-$79j5mx3?2ZkxWg`I=M2b=tx=p~3 zP9{>vI70ID^`(mN8V^kK^|$^jyq}`0RE)h;mBgv2JC&&NG)9t6V<-eza*oi5Tz(4K z0e#`NlY6szsj1vBJ3>;Y!M{2pdl}^(5-|UX{{&ZkRYc{~xJ$S@B~bdlsxl|m`JIg@ zO>r%egLteaeQ#UX8n;K}x-v>w1#rkj0OQy5vu7T<5C`FVlEn(B)<(b*vUMma~#kmUkff7z2-7(Yfysj*xKBB!dibEH*NBM*eg}& z-|2r=^0nQ>LMgz)(;|^*om=-pggQL?aunFtU}JCMr4#6up{V!d=2$GJTLn{sQ&N&V zq|lFWeFl983leW*rJy@Q4jm^Sxl?eUp!@0DF1>4U0I75i_&G zPDTAcxXPM>n-WtSQD|a=QbS&>8JLQy`VXpSH1w!o$2+xMV6sxrNJN@2Gp;o`|Vl*ugEh@Q_d$_%__F}qmZ5;23L@0s#QkS{c!<8S16u~7{OU@gO)_y?U?dCT#;VCUE<iIq0dkr5Q7m2l=F6%r2NG8JzuU=luUruV|A#9g}(~0v~&5hd)EOJ)O!D` zN(%IyGHN4%4^Q4(S&zfh{jM%++MN7G?Bl)o87v67$bDNt2eKSw%Fn|c zX)gR8SO;Mj-uyXXPU&yxu6~aZ6whe;8ra@v~c%R4au{uOK?+!RL`iE~2dt zm*l-ffFz{HcxKqF6#&SUgZMqiw$^JaNV`PC!I0LWnZHa+<3z8AXVs*KhUCgRwkADR z@MCeHnU(A-#iQl9Su$}&TD-MCW;I&hO9P{I$E!moos0{ z5esE)L&-^n9t`w2-K7hGN(e7wMrlU}cmt50H~UGfu@Zlt0yODsQ6zD%jS9)t@3}CW z;ci|*b2rd;>E2aIWDFg(F?a)Ieg}ef0vxVnSY@Z{zcF8`W8Em03EZ~c^wR+t&ST9cZ6`~Ak z*s`InR9*D)C{*c21#l~_f0D8h&T8!m)zZr=JE30XFZ_ahj!o1Mfx{Xr^e_pimwYz! zl!eR_>UlJbXr=y*bLNQ6PZt7J!^;)&P-uEa`YP>_}ZnREsL#xMXBao&H6>0_b@q0`?$`tLnF_SV{FMudNdbaI* zUh?)Q_+QMHASzLB>P)9ae5tC6%@gVf(&EsBcCCbTVh_TBgn>7;3Cs2w{N!Z{jT6Vx ztZ(R^jQXQT8pTz}V2kM*!Gb(~=aJQIBhR$w`GO|ZBe6GVdr1Qx- zwCO8DK;-273mDSZoj|ABawMd#s3>INOmZfLZ)&AG_y{7M%;2)kI3w)Zl2kPXpD5I^5|M*VN)NVkm2T;*(y9SfxEZJOu~Ma%BBLQ{j*$q z7R3?g{CGjzko$RN6p93`Y@z+5Ql%}l9jadRTu;N=#I0-W8++jzUNbx(Ii?Z6i1iyW zk2}vwG=dPLggl`Y6M5#N6Ud3sGV@Pi{}6Uow%CyuD4qW9}2b)+gwy_Qw2tSX3z*{?&s&X0NEUcG+ETu*X321!ZiVAzBK ztauFnT_Cc;8FYHKIZia=xtU0iE$$2Ajw05HDrEC(F!jP9F3V9B8ywf-Rk;g6*W(?3 zmqq!~e#y2)(!!@uL8<-qn^k@h360J7+00ivr7e1$waOjEX!ZxSG~OA1FKTNnpr58E zVgWCmU6u4s7!WqzeEdRgt~18 zKfBnwtb}KJS<*D%BK1=L&CPFA44>aP*VDWO%NJ?0U~#`G@TF{5Ow&+TUnWKR0tIoq zG!f<&_p=v|LfZCkp%~~M+`IWTQA$CCCkVH<^z4h6&EhDnY-4|n?z;FJk+$F;4aX9= zi5o?U!}*o+C;_y6vD*d>(X&*RhaxuEao}vsZc8Ud6Haomlp#uKT?I$iFTro_w7ZQ2 zr7|1r*0n>IJ)gS2#h)u2blZFm&F~#)=5dgNpNlAGHIZg`44yj=6XdUo3R=eH+fd%# zRuDjd>v`;^SzImY#3S*z4b{f}Q=kP2XlI~T*L0}T5CVW} zpP7i%lgzut-gJ5&>$vUmY*t>4yu5vp;C+QJxh1vN%sXqV$U2M*NPhU!khJ)iJ7}#% z(6K!VoSI}iW`K0O4*h;@UGo$_Lg$=lB9=WKSQwTC%XL!NK~Ysyi-0E3&fCSZ!AGQV z)njqfu=x1g53ksPU+<=@#T1cw_{lA}6dsMw&#{U0@1k(GFtZcCxmXn8U3O5LZ-iH7 zXXXlivcFxzwWkF$c=LQIl=@n_X=6y(X&64no9fC7*8g`4KPo4=9q+@2aHhMSk&wge zu|n+Do6v_1rSLj+<`3H?^hhb?{yHMF*N!Yug1Do5{ zpS4Y?giesTiN)IJg|q4*H2Kfa-um>m=V}FU=Q)eK*NN7x1%0V8B?rC3I$ImVK_zc4 zB|6;${u*1+2D%46n9&l!2h}c~Uny~Chfpz;wWl9HdwTbvJRlhz!cMc%Uug9ql(sAw zY#Y6stm~6zQ|sHe=R+uS>Vf4>6wFg@M|*(0XAskvSzd~jbYmofLtO5-*bawQM;5Tf zQRL!@^5xK2zx|k>>eGp-4T<_!qr|@_kKU~3L|!5h{a2bOOg&mTul}l*uIpZ_?mq}a zLjxf^EGkb=!N{<#(s^?5_eKLZOIwPkc8npS&kT;(VT<}+_2+SW${eAf7hFGSL&Dv7 zL={7EYWNr%BF4bHP&k!W-|-uEw(N7{R&u>T#+c_?f6I=alXVsAdfJmd^rQ;Qu?OoN zVAh?F;QrE;q!B3_vxovw$V^&iWm%M%4Tn_+TZv@6!XM);Reyg@!k00RDT31`&4#pZ z7%og&EnF9(5b9{VA~tA@3ei-z#LVQ3ef&CV?3nUKBpVtnX;JKW5pDoiQ2KRNN#X8Z z?5**cbT_r-gsid4hNJ2UuTs-X7_WsiXL7}a$)q5ijy`Xn&#&HQmpWDI0~y_T{N zVt9gl$&^B_eS6)o>E?0L<+6TV9fO&M0vip0MU2$PMl~WkK5;{uoM&GxB0df&` zNf?n|NX~tNB*F8ImSX2Bv=8pyWD!S4R65C8t)1Jw`iL$*C9_6=gB%fCq+7QcA{xtd3~OZY&_Z}OOdgw6<@kH`kB}SjqaJ(&OWd3&eMuBX zW9TUU(3|7Q)mP#;)_!@*#^8p+N{~i7-->1A&(T$w86yuwx=l#~(?tn4jO#kx+zZ6Q z3>uE8u@jr^jp^GXOf3Y#Ua`RV6Me_!6DDVjIt=dhdbNG^br_*W-tg7$tqK1yq9R!8 zc(0m+ZUhAXT^)KbxUk)%=YZ0;GJ=hIemS&8A*ssHxVmdP_twb(kljSHY^}WiV$e75 zSQ75mPAo`a_*&OvN)LUM^y-I4$M3fB%UTxgZRq8gb7)B;@9%IKsQ2OWZ>?%xIeZn1ATPMI3ZNhOdYa zz-n%(3g@w-W&RmvfxNL>fiGUujhOVALI`CPl8Z7$LQfs!gLlRrBr)(&V+;S@AsWa6 z!%4(!r<(IXZp)-eP?{?yENr*Og5ULNGohu5pod*0uKB%?C+;2jf%Qp(wzzonCT23H zW%ubD zYd>-L3ah~&jTm3ma{dr%${r>N=_!{(} zp40t$1@wO&&-pw^^?$m{`Rv~Y9aMv^io2f^1zzgEdZY?~%0Zv*-S4M4ejk%h0zMCu z0=}Q2)u0!7|2$0p$L^eIzo(b&U!Z>v$ol?olR2Lk*PlnlU%dnbaz5_#eIGua1U`;J zue&~){r$Z^?YrL|3Am&c6Ku^gQV<;Pdveyxr~DG zem%d%k6Cjqy`=?9R#|Ht~(#k&8C?Om?4-!S%kvcTJ9HR$Hf9kdU69iP_! z_y@e++Ss|P>(=r)iSKee{Dq zIQ9KOU$EcDxBVZ!R^b`wSA6WLb5Hnsvi&0b0jh^SAI`q92fb7Jzig_5g6?NMeBaLf z-yVU}UrY9(@AtO3_4)V(p&+mubk<(oI?8D;&@}zU&)Iwuc)!f)Zau>Uy}p26j=rYh zrRwwbjN2Chu7hLF&tdWV_o;2KTFkfUu8)W1ZJ&p7eZTgh$8!D7?)#R#)ZunLT`zx9|4O@_kx7zX#gK)C+tLc0W&66Pkk#ra$*6 zyWdB14wURpyEdENx~@GBTAwl_khHCwlP0^l7q)q<$9#{UW_H~=jii0;d=EB`v+Zn` zweJD@UAEe%#z*T|Pqm9WLwe$!zEV%VC;dkwofpkY$DPge(R0?z#gnT~O|eRM9Ih+g zvjXc&Z!%rWWlxhyPf6Srwo4q)*E@QykhdZ3N$>SY~oT-hhB;mgD;I&>ahSa9zSC8`-`^zVtCncwc2W?zs`L4_MU&5UJzjz;ZUyGV3qZiAO=QrG@Z9Cm3 zFX_DT9Zh8|tF^bx*ZJc4?5%FrhZ`F+#}|B@N~jrJ3;2s`V{+{6n@hRvqGXpZ3NoL1 z&F{IbrZfIj^}Y@C6#RudclBUOmzR~_{lOQYNx1UL5klyFGXAFbiea6Xx@AS{E5C8I z!Uo1}n{QXaTbW(ZZtV>qQ2r1J+%B@K-}v>=o>J1K6KS)-0Q2%o+^(WK^IUe^bsqoH ztAxLZf;Im%?Mmg$jbPhV*u6TG))`pJ?%Sw-Tm-Y&Enddlq5m%XRA5+2ej4wgmr_e$ zX{}Z8*`YFTgHxvD*|*lfH~ecjDi-AQ*ZNU3)q6BUhrX)K*>uimKCbwOgigD2%_5KZ z(@jyS9Di)(b$Ytw2XiZ6Ke?G@Prx@|tAKj`>T1wEy_uuYID#PMQs;5$E0I~V;$~B@ zP&PPP#VMDy0DXsMeRyKspxB&dNG1=|T+fLNT}mj5Z$FOnmTSFmoVMJ)Jwa4lc-|_- zFN-cjoOTi*Z1l#aNA?{(QO4C8SNv{!kwP!MARrEjm~o6z3GVa`s8TGb8-fq6c#u!gfH*}h%=alxL$z2FvyVQEPirMBd>O8%WJ-vx6y z-z!xITWm~N`gt=dmJ_W@k1+NJRkr>mtYs@1H|AfNb3OpAO+x6)q3ebBE5ajLT1Nplwb#e%E##hBKUF7F}<`Qb&&BvTV5i zW`4+{#yx=7!Nt0&f>RRnyg2kdyF&=*i!t5!OP5FEH7k;wBcw>KuVP?Oq^|2e!FRij zIXdyvdJrfJVgQn9Et}Dv&>(JaG^p5pfnF||(j-%o^5A zW#*Y>oINg^q0?^ku{%ZXeLq`AUuOSvu-RUh^{=8B2Ef@J(Taety&^Tcu;@Vm$uvLu z50gADudr}4~gQoDK6kvK?`c!T;%3*4L@6Up^mQF=DUi1(RKXcawCY#21|3x*C$?Wae+@7 zN%O+Zw*lj%$1kEiLB&?qo?vMVwVx*`ut)RsoT1QG*9NXiHHmDxp0TS#J+T~oE=I~{ z`fz7|p)TQUYh9*R`pna;ako27^Kz9B=>;dD5QtDy3woiTdxkqQ@ z^0}9yp20jMd8F+`II%6?!4{i69{ptm_NN;Acu`!#{I|h*gbVd(cs)u&sxQ{&a^-i; z;bdp66Z(SP@)rhcY45|aq3?{PR)rT@bk=@jr^uW%tmMW7t~iAUUZ{n$wMgV6ZI3RA^{{R&>Z zI^_a58y72EH@wp;_P>`brVCTqbarKrja>y-^-rYze zH1d`DS#Zgk+&7j2K4-@!c)romA)3%JtMptamGm^NP4Nd-yrexV>49tu=b)%H4e zVy7`c<^4#q#uT^=QO(y(>B-Uh*508EuxbsKEr;Vhp$MReuQmK#*lGvR)@WB-^IC&g z#y=|=T|W6i9)BNC+RzX&p_W~5u|x+6{awV0P#%EVW_f5mKo&RmwTq&t+AM_>vR}8o zM7isUw=@q3IU)Ia8e{&GPxqS!9l|EF`HoP1UV_CfNzNJ+nn9`0NS&k|t#8KE?uO1g z#kGm&{0@U@rtnk-jO~+!TX&6zUuD2x(|aN8De^j!x%*ej73T?~-3|5)G}XE6>*{dScMUBv4p8YdGN^xPQD zh^<$16ml%f+9vzr#G6OGXB3#LY5v=uFm@{CQPcyFXZ3RMp0t51t$qAzNwj(`jF$X%J#C|n?KK2 z8@E^*!{B|Cy8aDt@)z~89^(d*!*_6IMH)@DoIu71+lTxQQ--dk6*UuyJZI_j+kXL6 zK&!vFalxG#;4W`kw|Chck{Yco&8ag|s#8*2PjDxEQe;f#Wp&7e(&g3wy9B?yCM-KP zt~G`9zAX|xzt#&2CG@i`Y=ll(PeR;s3$try7wMo$vtN%gPL6IsvnWq+;-3>vUEcb} zP{+i2PJ2Jg=JoopX2P|@R`ANutm=MBjOv%OsRgdpE1jX+(0Z4U)70q|X&uBoPBL*1 z!av##8ZkTxNxYX~G)-rE$U)uvHnC%pZ!F|-IVLk=l{TfAC7sk3kvPKZ-uN!0kCsG3 z-&u=lL9$f|KxzwaNJ$M=3X4!eT!UZ2T#`D&j*`#hk(1{nZMU(=9m$Y&Cp&M-qVyib zD3-Y?zp;LNuQoiVG(|=0@^(GS+!Crv^0P^i>T#Acxj$v_W7CZDP_36yEs~)!?W-5C zq?c^tftpw`A?Xm1oSop9HCW>0fF^7-UP7v z$x7}-W!n<1auGX$QET=Pt#YFWenfLHNLo}HPZvpm##-GnE{i0ngBsc$cjEaD@DfLp z5wqJx*JiE~(2|JFu2I*flFK5g$-PJt;=fCmT6JpUYb(^)a@t#jmxg4PJt1i**@s(? ze8VIvqFJvI)t_2Yj+_*G3^MT&Qd%G0(LtI7*`EZ<`|WykjYvLuwNcAE+htpUUA*I>z`(%kRtN6XJi zc%Bg0yS*7PD=nI>JNDng*O2k9>6FnP3hd#$IqqUQSwY6@v7|lETITwA?H8Hxqmaee2|`Jj%kX_U zzeRdj55oGj3#dIHpP)%)k;B3ImpUUkrgg9pLXseVV=rl~hi_XkSEXd9)fQqw>IBn~ zVg2aaR>D&Hj^eX=lh(WR^YU%A6=b1Hcgh{nrj>AdyjOCfU=5Qo)u?Vo10fl>mD)oo z>yKtbLU*8)^@tiH=aV$_Bn-$BJH05op;#qYY&hX6^JzrPf`1cQ)FStLi-YlnwXhy; zByOVE^RmmFB>i-FJ*t@f+Q1ICIbSSgD502I3?~!YA6ht-OYkcGA$5u7tB6`%lG9z= z5T|UA3YPhYH93DISxnXy=a$)&YfjjwMOsQa)w^vF7?3g5No;Ex#v4C(&LfV&mM5g! z!|r`pPvy(GMmeoq@d{xf*fhtIl)mW?sp7k(XJVpIv2u-_5Fx+4B}v+0<)b;)P!Jz- zu1May)KViKVaJucV+4u4;47!j6l|buFyVHh~WD>T%Hn^1m2o$aB7U`Buz^68X zEw_j{PAvFt-L8R^fX8We3WY$9J|vAm&fp;9Zm$O*nbjD!4eWxf8UnC-cI@Np5fQDaLD5>T*CYdz+F{u)iiNs^1kP5O6L(dPBdVhwMG01@DkVx?6 z+d5%zSx{r25xvxQs(32eI9gk>Mmx4Ff5pjgKXQ^;S8wv2`Wj zrI-*9R>-ng(T^-9CR!$>cDXz=?}MqE(rh+@Kpg5{8~NvuhePs262)N_FB16~ayO(= zy&cBHl*P|OU!RYzXs>z4ks#GSdW=201tZJ=2t6P5_~aEAXbKcC0K(ncBA1urHdtM zx3KR7PIYP!!ocZcvu`#G(WQgTT(Y6Tr^?9JGWY0dAtk>Mp>5E69r&P)Xh|W-%89>k z6VpLqd_fQz+J<^|ea$Y%=5{^0rW>{Eumu`9shFWO@27mD6#kb9$0gb`9&*b|^vT$l zVwCV%!Idxm%7CbZ6AcI?dZLRnvN=R4QDPYzH3GF=^E3$iq_?wtI9=LLL*J?`8^B1J zS%BwC1a&@GYuci6Y4Ktsa8Yquc1*O0zDhJCB%QpTPOOe7ik?Ysg66^aAaYL|&U_@3k@jfdNJLI(WZr5Knx^4%5FjPO08!&v@ zJ|Evpu$q`5uFGaK{B(2;qI8Uggzq+=2`9%&7{2N0wepT%2Uq}gl0YkFY)x)Q)+y$V zOb(02yz)DekK{4yVGjXwnSnWb%$QU(YEFCGc+5T-=T7q90|>KPei^6v z6PXi71REztqFs+*tbt4zEFGM_v>rjbp%6jD)V4;vT%jdf(5w|Pb(@}mtw)~W6DE&- zA@5~r==VxTXvu)4lg(m-|G%AnFg^5u6;F(`y28;dtSdC!}Vq*7GhxYcpaxA54b)-qE2fDSrV~>ngFljVCFTV zS_?b4A9i}=Q!kfbCJ)j=TymS^PFRvSy|kLF3GK$_fbGO{!|s!|Cr#_2oz9zTh zxud@TVrtOvC{d7F^(~(=eJDc06ujg4znZKebEwHDp15E{NmTYipgUt=X~zR2**wh0 z2mv}irc&qd$$%w2w^2r86aa=1f}0K(p)O<;eY&3Qs1Ii^+ndmaT#kJ^LeMfzv@y02 zk{Y-vg&;Xv9!L$Q+%dNO$j>90^yMvL=7-wSFdhFyhYx~L#7(Oez833-zFy%K5cujM4On-2}y zWcibFZ{7@ZSSby_i)&jasvbHh02^85dWCG4$9r~A;n=zH-%Ue%N1+4$&9_mc$hFAX zLzeCGRV3*K=}uxF+Orc}iM_pvY}Fd6BM{I7Kwc*+8p{C90#wDao1CWs@7eP{8QVep zBhtR-_kwT0%^>w$ZyyeN3eZvRR*hOZfb3RaF6tYOz@%i+*?ftE=cDV1Gz~nc_G#|U zPwC^(Id5`*(y)4-3UtRHuufTs!TV7lP^8OrK_@^}h`s{W)9Ae4zlY$KmdQX0N{`{d z1|`>59&ro+UDDl0aY?yeL3n-u_a(`cZZcQyPrqCvw%fVUl*n+ayW|Y01@cve(0&IN z3raTQ3}(rS<<&?SG|4e7o2@=>E7I4TtpqGF`D`m`rb&`$o7-QN79&o+Y4ewl@BP#k zl1e_ILGI@xFtRmVV~NGN05_l^p23W4oCZ5L8#P$(D@mA_A_QQYx}$7VAxX0)r!jZLn3T;Hx!O?TM&6S(2{+_=3DqvuKC*0C?Goye zZq)ir-zqtWMjeT4+>)2NCC;EtAd7Jj*p<+d|05^dsu{c!QK7NG&kZYKlQ-tH{~+(W z0g!e|s-n6JA-BMDXmbLWoncR(-}{Mik(TV1xwq?)tV}}F!w_^_Ac-j8WW78qnU2T> zaq`oEw&moYmcU3fKwk)p%sjITUS@RlCE7pQ{POHAY6z%LAX_;cS;YO=}%J6 zIe@nl+K(LdoApu;$!8IU5PeuJeZTLyQz-=(BXw;#lU7ZkH(Mj@M zVaUw>T7+|zMu04^HUL3ROhht6I2O~x>yM%=gq)8=*aFK?NCd=m?C+=ih)Wj$shTjB zoR@u?R7i~W0NP2QBnf@+I8M&h>m>xHHyqX>(bGWMmHqyRb{yUfU?>zjM(OfyO5e!> z)xcU|s_U_Ka5g7LXIMH*#ow8_K zTR;`x^-kzocd-I>F9-JlWh2dkEQk=U!No=<-wwEiY>CzhJ(qWMFL}i%3WrvQ>9k$0x- zQsTp<-nA}ttAr#$*rFIF(Yh>+a;IuRYH&KpPrO;0a>LGwyCQin>!^oqLnMG$SC~3d z43)Cm&7r+XfELW?ux);MucjFbsWA_7xUWZ7Lr4~#qm8rn1sQ%ALSdVxdYPcuSLf(i zjxe>%=gnYt%{Rf4+}}WurvuffV@WCEAvn_10(r7XhXdZlC?MlN3=Q-2b-WLM>>&Xt z!jfK<2DCZEbYqM3X-U`)A)QHovopr@C2)d`G?x~acOsg7)4VN@jOgIr>{IBzONm$at%NgF@OX*(RPmk?ov4cHm3}b z3@!PkkM9LtUV(||MZguOgO4Hn%tIl9_Ge`25yF=d~^SeZf+n`%j7 zmMU%lZG{Y?K3f+?oi6b&yzsUyh|d5|8b}<2&nBfJ64=H`I>j&T(!$f003);0qLA!( zsK9Gfs%Wh<|w+%wm@GZ)%J1s&z> zrxiry^)}z2*JYz#vJvr(jqA; z^Nr>PAyvmAus7m-gvDFbnAPhg+L$gkE5K52RokOIwz77WjWL&(Yq4qx0;K3v=vy0u z1S0BBMUFS=bk2F~%ir)S$4A5+ZJnK2X?g^{$e?V8DZ_v`p5U`%k-IsAyH;= zJ-QZY?^NkYA>bQ7qY}>=Z9UU@7au1`ny2`0_l!!mP2Vpbzy?^7?|kjL>=S*3DVVi8 z5?L#)H<`!9LXVtR2T(>ONGELtEM$LEtjp{k&S;`ugi^{DQf>lg29DYuhAcHVs|?I# zHrGqYTl7bPw$xN(e$yy`G7Gphwz_sGE~eJp3*#YbG6Xk`QwX8N=-Z~$*4a+~2YZtD?Qzv%)9!KK6y6;5D@44tMCQU8 zEoD3bD@zuyusStkB+4@QUos{;q3(qEB2ZLz>Uny3t%p4y{n%|Y84jp@>iGb{_N>42 zfJ@Ll5KT9yldmOuzrXpwo^=T|IoAJm=jP$`5nuv!9d(wZY|KEiC|x`^`B~J=)LgFk zudVWXnU2TTNmC5z`4V*t#@ms--j7^ZqT0zdDo)Q|&g)%cuyryKX+q{W;$>1WRLO+l z3S{bQrP=O!nUb{ejS)oA_kUh0+1W0s)$b;6+_Uv6cd#CI@mB? zmnSpF2jVMXgj3*}f`q0So>l@;>wDR!WE5p1EdXdU4`3kxXcpX4PEFl)E3A0wOfuLb zKm#&aC}Y*~DG6IpK4wJ&cbgfVd?0~BlR8w&=^QBo)k(0cM?HwN;zkkHn4V+`wB&N+ z_<9NhvHx3GAeAIhV`2HNgp;Yy!bc}&PLjQ`v2=geqr$xo$O%lVG=OC49*#&S_D0E% z$w(J3BCp*-+6GRa^#~ayPv_H5EvdRyQIRlNNsPx zk|zbi&SjI^B_=lmx2?sVNw!tPQ!ek7dkAE^NEA$RP^W`yM4aRt@O&-*_AIt|1jKp7 zvvc^UV>AKGB&rM|gW;QymT1Y1`8r=Mr<58V-DI)nW(YP>su`qIU^kkqgl2jGIlWYl zZtV@OhiQGdghbXmwN>F)fp4e;Ny{UH#e@R-mjUDO&h-+Y9~6uvs#&B(?`3=J_vI3j zW|D^0D*%0yRQJB%2zY|z_y%Q3p;oCQpz<(1?q;^*=D;E}11ha$k;a4IblBGvChv2k z$h=?P3k-#20|OdX!G|L)OKF`{UyB}*M?VvI!II6D&fG79h5P%9PYPDL_;8z~gzt`ku0IU-T zTTsEAuSlR7+J=qkbwC9eNlB1gH>xh~IRA+w*0~v|h5@)N$WFvi|6!;S4}~xh&9)XQ3s=sY6PFInQMDH=2gT7# zkOQwPQ$5|SV&H|j4aPaK>aNi4soQZCEKEs6?l-QbsRKK zfFw=N^yLxxEUbhPPSo{q!Vhr3-T!EN8vw;{8ptrANfxm zt6<@E+Ttvtz7si%)qs(zWfYFz<-LD2X$}iYCbZR-e=&;fPpAAzICFJ>GeOQZZx&1j zzYWwCzHg4?$Y#D4u!05^8g%Duz`NPbN1l}mBIN!UD?OL)e5716Wo=-J>*C6K*ky+|jrVHPtOOTDJzw%WY;NjONuyVU!vwEDZP4HKi}F4GCN7#f%n6^6LqQ~N>6zz`n^$OgsP z`@Q{O>YEfDmlWiiRA~}8ETg?(GtgD0#{@6T2T4Q|kR6CNENE|?G@jc^e`-2*ndLzuSBD52DI1E-*Z{H?E@4_}+Y zr45qRfb&f_EaUG1lp&FG{N=l^R{;E+$6v__A}LI5O83fbYJGsL0AX){Z}6?c!6*B! zgSab0-N z2-uP$f>U?yhVxLyt{s)%Z{qeyh{=>~usB+u9ez~}>F4P^e5>NcQ_ffX4(9iuW-S4n zkYNmtr$FWR$zmAh-%2bL>8H(YCb`88Mb3>B&0Hiy=KR`%>l>aqhkm1SBV03$$QD(O z9a?2%xHSy7Iw5x)W-0hj@viYeDDwy28muC~E+Gls-aB0Qp+v@WKs^XoIuB<9yeG^u z?-G}85_!DO>ew#lp#iFx&c*ww@+7F<4&-wWz2)*uvoaFiMNASn&alvR zkOMavKXN!>2@X0i((waU*cveqsNO&zEX-#&b<@^YlNUg)Z4#Dp0ThD^=ymVwVRqvD z+iJ2vH5c&vh?ZUs$K3&Evo@Q>3bg8U)0pS-Zx60;J>I1V;y9C_m6Nfot)3yOrHV6A z1ZHMRQ;leEyfIvAhntG}YqP{UPMD})Z>Fkp6QpV5)wpD8Z}T$BUQhB7WvGsBk)#~K z=LsDdyvNTVB8j9DLHCQKm0~6^3?!$iAUGXdD`ipv=?{u(5o28{ zWs(uf=XP6hX3kS*9{7gHvqRcfowz1v-9eSToM4$CPDyvl3dhcD)6Q&*b{>=ZwGBEU zW8!yUI^AC2+xa~bxEqy{WDrWUQGkA+LbI{WM=XId{!FTYP!&^ry+(Jj?;A<94&=Fa zb7?>bD3eN36WD-p_S$MhwL0%F0lS#AR-me0+r5KAX@?Yx8{s+9Om?4-(r21&HQS>pY}1W2D99qq3y7Pi zBc!#z&*PUvOQW7U`z{(Db##>(zxo4wqa8zx$GzsMetjc}zK=Tc) z$kHe!s|%h{NuC*w zFd!mYDY(VO%h`nxmcnwn7!Yl( z_>~oA_K6c1R0*dmVuPm|2p=*5lE7w8YKT;5C7?^9kn#8Fz2+&TvyGfVXioMojDBq(oFHVbJmp~k~t0(5sn1U^e8PIt}t z`REz}oS0t?Av4=ERCG+LT&<50zysg1)7hkzAQ?*3#X3~^CK-?WNNXBWPzHE5T1$aB z9YJEM{fdW|LyPY?)=>FvClK_GZ?+3(0(!&s>@rv7;z;uhv{IdMa}gEyn`^jQ$-NLJK94wpm}!t>0yz; zJ_e+UXyZ<$Yj9o`xEpD=5yW}MNXb>2u{QCoD64n4l#UkJCIb=O>(PhGamXhdRq^vo za`~7Q7V3GoZTCmrINcSfY=<_Cle7SY0~9Kmms7l%wY!r@we=8Pb|hsQmSBlA??&I8 zf*Z803hn`}nch;-Ugh;vKNvtJrW35}!T)c{0KL?=BNpgdVL0tY&I-mzA zJyYqpxsfEYJOSAv6A?Ce2U^E%w&R(I_*Pq7I}G@k>;;SKe00^m!z|ZQLnW7I3Ho?U z^ktsMV;^sP`EbX=Z8f_gBT%+C=@oxZ^zs1YPxl%E4pLn!{eh`SQ$Z~QatQ!x__X&0 zul0!f(MV7Z2y4eno~jRg-P1O9&QJ!~+Q!c-RvceuNrDXuR$^CL6vT6v)8yC{IH&>2 zqQW^;$9@o2&>09GYfXBg;ur4u;F_f!Nuh+zdv?IKPKT(FQiSRWV#BT*Y8R>y>iA3z zK}{+Bs)Ephju+7`$y!$e1#V2F!|i%>r6As6En>>I1;r1o`|yu)I$P4~^$PkPWlf?G zCWZ_JhT{qCO>G=?(8rQ) z*s?3Kx8ZUjkW`*hIX>XBYtX${V+RPUhD52!Nsk;Q>~yLI(jJTpP(}>;Y>k-H3hEu$ z30U9;+bJ!Hsm#A!k3M$4)3;y+)PU2*oIV;(Zx;{vNmW?27Qxx4fCLmkgsj^z@m~pK z@H6ydFmvxB?7$G)vV(<~+k-VC$02ye_(BE>L;Qh8~F9zUdjHB8L6GPs?3!*@&7Jxr-Y=Y&4ylUIU%AQg~W@Jei_D`HCF z6m-o6i6w0wY|kJ!Ca1fH)#t;VL`wt5>6NIdp+RQ~M2`&OM32=Tm>aU;wD`#W7Rv9F zTOG7Wnc~btI_XF3o`AAiT~?4D$t(NuL|Ep#`zFE5)&ntdyB>Wg7J6kRKZ=Dun91w) z-Cc1$nWxCfixLSet$N~-8|}s3?c5Eo2mA0P=StE&$ldh$bHc&^}S1;8>Sn zBk!7GMG<{X=Hl3+LiKb=ryy3r?&R+P*$jSm9<_S*hQ^~$I4$MRQ*)d_z;>(%He*KU zo9}-&(4kURS17J|6PYw92ecd5TA0zBC^xyHbG#CXq2_5w^ax2lsN_Z z(jruggxmnxZS^QIq1u!~e=)oDdBtMuB5*~tYNL{5_EY!K*tqZ`R3kR9kSXokBQ0Wb zU{Wkdlt#j35*Cdha*NH6+v8iQ25`+9-4MWeYEq~+vSCl$a))4T3!91_IuI&`7f{0h zH{}&gwI=%=AVs-dkDv=NqY1G-cz`BxX&B?4^7@$9+Rx+aawz{Tj+#M#A4yLT$!Q#{ zzhC@yzewDF+L+wj3!p&-6P2hOQz!uWq4UavR$3%i(%Yi33a-V%P&*x3ua^Kxqpuw%QFq6MC)K4n?qy=oFlD-q3a%p;)|>c(K7^2DZE2Hp|GHxdE|Lsd0JW9^8kG+Md2jP#;$; zTkBoWijv0WZY_0LTA5x6kL3~6y*E4du z;+S^{l1aOX+uj} z7g6a3%a)%t$kCx%hYod>g~Igc{?%5+r~zn^DAJa6o>VrlOn1zv^U+75Quxy$$SAZ#*Nwoa9g1rN#v!#_!MJW-GNbj<~9#M2W<)~wIw1ldJYyxGl3!c1L z;=|M!J06V7$m4ZYu%~#g{4Xkq5sCl_4Pog>N)0oY4lKd=y*#D|p|XW(Lh1q%+gwS< zJv;$3guQe828jp^93a5zLP&%PXj@%<1E9~!H0kQY#s&#p((5j(e`4Eo*JSfqanY*g zc{JcC#-j#Lh?|pWAp$k{a&wm9{vb!#wlmn~afa@P5Et;<=yHIVg}^!P3xV)tBEpw5 z1~>=%0m!BeS)iJkzQ(c6LC!-CK+CJUgY^i$w`8)Zhs6ZmY0@el;mo;8D>aOhXrOcS zDB!n`O=s4i%?mB)&a&*%@Hw>c0*t5IMNrZ~ zOb+E3#Jhev_rBeGMQIR!LdE_h4%kAQ2-1RTyJ~CYshyZ z-bReK1B*l`-S(+d8s9-?g(h{Z;tONH7X_478u#kqwnvQdC^MB)NNfF(S-btt& znzd}(QwmQy56Z6Hm(YD&vHiFdHXAn8N{1l@&-!L$1X38|kB;z>evl+A=9>??aQyhq z)gF6n=~D3&(`sYa+%OjkWm{6X#`>_d@_)EnWOSmpW(5+7fliyTKY|>l6~b=+GJ!;s z=KwSTT&i&^Z$vTuuw5r}V8HP{41Xe27u|*v4>;*X5)Hd0Mrg%K$?V&SJ~~|iWOc|P z`rMFuog`#RP=s>_fKaKaDWPs@>n>xEcrEzMfkjzB9=gH-G6NFMbJSbi-6>mIbURZ2 z!=+k0Pe@>heB6kGwqrMUG^sRw2Bde9!0s2ayVpwOjGu=Szb(^>VFC!E%$Rm2dp~g! zm-I*gNLHW5<8eYmLS-|)q?fn^0W_fmA!V4e5svPy4k`AYay^fuIPG#0M}MCE#P&HGH~PDx5&5k7X~p>8#0 zXS4U{UF8Zmt%HU&w{{UdymtdJUmK}b(kwa{6i(2mekQIwL zAQ?upsz#k?uo44~aeJ5~B~_`wFel9_n}TZ1JT%;qHWpU=2+NT=K=Lbf30rs>VC?L{ zFt@qcaL1VX_+EQ8h*YC3Z`Y%1(beu2$J5d5C-?ND>WXaAr8;AkT{Y80H}_+UcD2QQ z|0>qPoBCJ^Lx@4Z4Q%cTs_AeClk-(ftXZ}oz@i_0N5O6H_wP|_Wlz3CDPa-Y;J3w9 zO8XtjZPw58Rtn{+CsfjWF?WSJMuanmcvV$e_PbpU@*3h(9RD zz<#R{`;4Blk{Phy?F=`0fGvX@YvVnxVmxsb_@$&Ff>>%$xltj|f8_M?H2whws9T7a zZ|?a=3;WUEH2?fKvw3&J)NN(y zpwH9NCJ#-N1OOE{s`1|LeK4v1S0B6omHODhyt!I?AZ_W?8QpwoQb*-Yespjq%}$$! z@sP^oGC@s1q7+r%#CY|UfkT&HHXkK(T7``s$yD_P2%FAk)g73FW6W^Nfp2P&EWKK$ z8i?0zlo9Zo7;A084?d^60*7Mh`L6z@=U_GxGJf*r<| zx?PVxboPb{Xv2IR3*a9i;z#~=;HI55ah!P|0aNtnsIFPtB5_N|kT6u+)61P{8%HJ; z*6Ro}Edf8F|2PcppPbzW6Z($j`+bmFzGok{B3>Xkg=%=iip=8}n%gVw-n{83MV%wYWdO!ssEt0gD zi(+W7NMXSXfbcZZV>dD6EpThV;MzNvUmF)$Zm2A@1}RA6}zJHl2De_5NLL3!!cGQ%Wuyx z0pm1otAj5U^w0qjAD~i(%0pV~Mh$EN`gVU%choPk3O1OuRNoNsrA7zI^s~)Ll_M3^s(az^>l>lo7i%pXXkL|l4PBVecWt0hYf8u zms(w`v=hm5?BVU4KD=Go8`B+tEPUyj4b@xdFbm&}^vQI$dp6AV3OBcg1Q!;I?CE({ zGY(fNJJv5jqS-JF3}nMOh*q5GrrBh zM!XRoNG(lb69JCgotfx?&mm$l>0#QEaTayiKYa4_2)L42_S%4;0KH>nJ5Gq2$IQaG za#pm*Sny3sb$h z7jlx(aFhfu^zYt8um_{2plCwXy1D|*LMQmk(bcGe5+73tnun)t{jjNI>|b*72PW>+ zjR%Vd(t5l&!@7XEBGF#sQg8MWAPku~N`_2oW>T7!me-J>LRvs3WMN7qR{D>>b&BUW z1u6>|(K-TsQ9;}W80Cmmx)!3$V6om#SFHY_nh-vKoSu}bjA~x3Ze~nLK$rzEufbj6 z4i7d82cQRtzyp&7ohM`j`=aL4q&C~@3=uW#Fl9$vXwpW)`Rd?!q-kX_o;4Ug-K?f8 z)(vc`LQ={G3N^1_EXZ*I*g79&_u&F)0!VI>#vaYG?#NpZ#PjkKi=a%kiGvtdV8J|_p`MxdBJp3e*_?4na z&#_!(MjCbof7b$g%x-G?vhLEDNwUs4xt}T&y(zjBqP)BWWOUs0H9(d-h%T>Y9AUa9 zxjx&FGH@BJ&^+uiFcPiO&`k#CGhJXB9ZIKJ>5jgi4rRYGp}?OEOoa@bwt4+zD9tCD z4g2dNDt+cXAxVZk#pt`Xdf09zH`seXW-~Yx;@k$G78m!@oBAy>Vm=%eNH@ro1gZ}^ z6vmw~AB9>3^l6EcBlDBaS5VJEMGU&S+n}vbM7VU84tu*Bzjn9OGyOR@-hh}_7?B+- zq#T(x)MQ)J7yK{GVsb|9c0IaUF7QX})3~keS$=bD0ojD5d>JJQ2Lq5dIN28D4tiW~ z+_As0O4HF*uEii#Xk2IjMtaKJpkRODx$^o(sDcoBkLuqq2~vuG_7RmD$<>CnJt0Ui z=t${EcCK{zaYa8erd0IMl9BpN$(=h?=WwFa4A7~XQeESTn;K1Nh|eW>q^sLV(v=gC zQ~59%01gdZDbU?qkKh!e$5)DWirBey5=?17>7Ym3PKUYABf8Ga>|F-6Ne>l}!L9-d zc~i>>JdIWk_L^~g(XoSeGC`qI-2I!P3PQDWx<&0sZ$mfxxmJ~-1~1{MFk5|dBl$*V`uuzAQt}T8qi5`f48aZw*r|Z3D zeFnW3_YQfJ9yuBnyA>cjRfd*=EuRJMXg-CX7NfTKs7pu1e%V1v_f^jn;8j2}zo*1q_-y zAi>t$wg*u}uhgx&#jG(rfO=;=uxhX7_h6hHQds0#!o<TogEHe zflp7G1hSXL3cJHDm4-?4#!X%s93UIznfQg$J%Gn?7;kVUfJOp^Z98};J^*Wt1R52# z9|L5em6sUDg4zAYdN$A-k#g|+77y0{z*49anSV)^yNGqz_AVH zdxNsbMGkt4=65dXM?kh6n-xOL>-^Rq)mvKmcheruplv0(PF295%JKH;dRo3^&!m{s zk5sko&kPtbb)qTWpLXW;5aa0vkn1Ark;4smUXfP<`xPj+z%%7uo+HXm>9SBEqDre7 zC%{Gue&|C%wI*#xfWh7j!URMO?lLMEDZR8_j$&ihtINfL(bg4xq}ZeCjq(R}vRy_w z9O$KLpvxZ^llzg}!wE4Wfbi)By9~Y4hY`px=Q)!Kg;sqLS7-N+jQnGu#Tab*NUs1p zq@=KWz6IFtM^=oSN2j2U3wN!IQG zNYK!-I~}3%Ki8~>#iPlXEL$pdJc{6cB-?UyoUl-XZHS?13pd6(Wm07@J`I^nCP%In z6$;f|Vkro&E*o|02I$Oa&SL&61a=`*^dNtb%B5`qV z=E~oyd*&wE*u$6kW>aRD$AT_$s|AP#6D<~r9A%1LC=$c;i+*4Pj$3`(u|BPi^$q4N zwhRzSuarXJeL1+vU`0EAVgqhaS0y@nMEPTOt~1$rsduuPMrs*XB)bT91{zJ%`f6Xk zj-S|=O-yT2JAt*;o}k0DYzkZb0IRZDChHYzTOlpUl`7+~3!jI{%+C^#>qcMmD9b2&kKYEWz3EW_&HV%Oi@0)uMY9wkT&C;81lBy=s zCqbOdf(mN{4)$E`b24Xesf*MoM`!s0+2UmmXyZ3sN}r<@=JRj|+CWkVplj#k?LYS+(4`Vapc@Kwf+y<>}V!#mG= zJhOvT)-j3Za_3h$@PJ?*bPRsPy0x|bZ3!*NJ5DW7K%)XwQ$c2XMO=`fQQzw{Fa60n z=KGz)g9eG_Q4xLc^!;?LBFk5Y+Cs7Nkif8Fl{TlOwW~A@YXe4Z2TE4;igv{aOldBUxwTk_@?Dr z%CG0#ju*EH+y|y&YJU|#Qy!_PYgbXP>S%FXvqBSQ0Hjx^kwY@>TF?w88vod$r0^=R z+wBT#c7tl2kQ%6tu5p2Gz#2>BfZEnAXfAw7)|OG}ymH|#Aim)Dx*E`&p#CYi$Vn&V z;Ru8hXHx=&VUL7D5Is06DhF#rD*@nxHLPHd7E7?Qe5DjpccY^I8TM%Q=vJgVNnoy@ z&c@hmF*&NV3vm&z3IIGu17G*dPP~#|r3FJ6kjX<&5e-3`4RNH8Oe!SbVSBg?+(_$r zp~aHb4oC+GMvwVX4JCE1Cqtb20Jkb>EJE&&L@Jbjn{EnZJ11>WF+w73pe2rl>_$rN zW)Egh%)2zGSqMsl=-va#oAo#=JT(34`i)nKsO&UuLwIBl>THXoh0P5nGta3S*n+1H zI^0C0dS?3ayNtV*@lEoO?1^ipki}5UY}@R)I&>-jO(&6&2QPP|h>&#BcsEqbx6R)! zU=u<_^p|3mVXy~Pd}r%t%*f9OQ2u(2R{3DQ85HK$YqFi2_fE;y!%8I}X}YY49CSq} zojN-uBQ|WRtfoeLhHcPksb*+NK;if7eY*?Rt>6MA2@)$;5Ql((qOBQDcF+Sfk#fqe z+ktivE|N1e+b-+?d86R=cyR`Ap>q&~)?`3%LOI_{W4oP}by&KxYVt5hu8KrXxga^B zvPxFs#4uDqF0A1h++T{uq60aXtM`Eiu3VzhzV$(pAK+Vq+GJ%{kzL3rqbs$qa*OCd z&wj65g+c(gx^BNXu+U|YlQ*LG1!goM4F{>D6Eu$MKK_&9B}iG-tYQ$uf}A<~c;Jl_ zP(K&2lHKlFrkX?pUx6{?o@fow4T+>u#Y^$Ly-wV|+`;)Nk^ayLLgdxL9tgiCDL zYqb*w!L`f1ngGP|_g=gbj!ey*nvluZdDh05s|8kzn8yjl4b8mw;P6YFvy9~^O#Z~Y zU4DkAdEHBcz@oGcK`Z<4iY4!CeqKZf z11hqEaEl9N7!1w*m;I*zU-2-XkgoPCRFaa5sJxr@EO1}&WoT^2thRgyYg}tbxaFnj zY#6H%72bb7*e)?w*1&UfBKZVi4|yKVL`sA#rp`K~$>=kXaantNI+&MRx?$^jw!#?% zV{fVoi>#8%-E&a~9%F&X*9?;Bl?8D=49a(Be>~uF8@G1J)r(YY-4&ksKthAer>2KODKmVr4P_#T;gXk5_#UxbQ#+>ijnGPze-b#{~eg)zJ`5 zwmuzjKygT!E*Sa6HMJP-vqoxgtX!rWcSIf@E9x&oLhR|imNK_nRF z@S~sICiy#--{jNxNTk!4p0GVs1nx{L&6^$N)!59PRJ10#*Kr*nnLCMm*6!J4%f%0$ zF1kp_T^PKyhLK%q?@sFnM2M!mu>pvCe;c!chu==0%bTJ(9bvs12f7Yum+n)| z;-()=b#IWID`vrdUj6h95_pvH9Qc4+Oi6vM7Kb?kg7%wMSDEvaQ2{_hgxCPCf*K;C zfTh})>_}YqbrR@>n};mYr6=EvP1F(K>?WGA8$jV98peac^ZW~y5XwbjY>!TpV2<%r zp8`(p<-~GB4B#wt;2N0~GKfEz8L5qdb!B`N0S2W066Ko>l~4d2GF24Wj3);y$-i9D zK;w9ww0edRTgB74$Ln$$*3i#Mq)pp$mt7e_>yZ#jkU8hlDQSWRg1Cdg6_0M|hijt{RKW^C#vJ6&+xvA*RF5|Q@Kj_+ zGNx_1^M+xbHSXh65m~LH%x_*@(@+*1TyoA5;XM@)rHU7X-}DqK9j&lo%ravUY;3{l z1yr`tYCZGFIc_)e(wAvbu~7z+p>W%jiDvSU%?o52VR%NHp3}G1*%f{6B_dp*P?0Ld z@;EYh&juSo()!IRn0uJl(xHweNRvlpwKW?qvzpv^e@N3>bc>Yak7SHDcUUjVi>)Ug z{qhx=VY=;RMtk_J(;%~R9|9h@TJ=~Wu3PwtX`3=jUE#S=y@=o4wM|&{LEtvRC+{hb zCKIxpxfFZebt&))b2YVQSJ^avcJ_~Ma`4sKy0L=b<4p()N~Ds*$gOX}#P$k(Au_~z z^@4IiJF!^FBij-O?PT`8Qi3C>!vQ*-^ zg98YbD?G^uVE+L-qjnKFJu)J8CP;xm7x?;AUp1Y?;_BQxXwHhg%1Lo1xR38Uy6KVY zW_cVV@HY^1mX{{#)>?hy`4Kq+=X#ZGyTF?uEWwWst;zgx0&MI=Hx>}Hh)l_Ro9q!o z=Ft_-V1X#`aKuYNR7q$@LKc_y2#%Mf?>%*&-Vo4SZ~~b@ZQES zK<>~w6`Z~Z$K8}!gid$Tt<%nVIy>I!??Ooum4s#(%Q5r7@ey~Dc{K*S0escJJJ?6+H{A7+*$8_S$y|aRd-w!jgoC$HZuYTkkxY2SP*tB%MU?wa zWM<;!f(oiwU$r;|b;!-_lRo?v$b{S>s+^z&Jp1$vJ5WDl-nUHS%x&S6D6&j9r^iGE zuqclWB)y{=4YDMC7_eqL<%W!*$Amk{;0ipTjVV=z3xkb|7W7c%&7k=(w064|c8Q46 z4@|gLzaM(n50_#}maq2e#PKs%k@of8@cryAhG&yX6X~=a73D~%776-dj6j!Fi}>pL z^FsHFhpng!$Ii?SDoL$0+OJYb&mb^7l`ei04bpwFQ7IkC{RQ$4FD$qlxf|F)+wh=B z)qak_#yE~f)`@6qQm}AAC~!&K=hwfe`$YiR_tm?{mE2bli!5yG1B*9@5e3nVfYe8z zqJ)GGDIbv4(C_bl(>R_$)fqfYE65@uIoL>)*`nq znn;H*7QYOjn@?Uq%8i4VS&8{L|2T#wjd`?3x_h4$GM>?{d7LFI~-2X>YF7nfOR z4ra9T8nS#Me4(ss`6PwQipHnU3a+cUzm7vb~@y!8A6-(<6Bc zkd>BQO{Wefaf3-JeZ|hR;Ho8^V%Yv7thJlr>@v9()ZU^oiS@WY4OhX6iWHbnO1reH zc5W`U1-2B4ROC@$=Pd|cBaw3yBldLW>Uu=!r<3zP-+~{#xO#!RI=a8R=y7+p*tvS$9C?l%Ny$56g5 z6i~et?7Lr*I&9~shxqY=KI*iLXPLJ{MP`7xuNYa?si%Z5ft)tcMc4%vn-OGS%ux!7 zEI1Et1F?YufVcSI(A0(_q9At&q_`av9NhQ4XIsQJtQde+d$%qMQtuUkqp*b( zb_|BViJ4}WjvG1wYHL?M=IwyRlxXaEbFT;VSUqSj?M%)jx@H7w3 z%6_phk?at9@#Mx^baEIXi>t@GZyh!@uS!y*2mH@?gZ8?A!l6GG04Su)57==8#JA)X z;pv)v?pj{4J$J4O`4>EuK4a)gVLc>gCYwmPA4xn2gkbB6)AiTBI6b9g%?@N_4}3f+>qpja@csmdo~#u)FwlZJ@SDDzm6WraUg_E?V^7f zWIeTu_ur>f!P$%rASJK9AyxRWHOR#M;NS8(46^0mSF+stPH6eoZ&T9TE+o-ZY| z2mqo@4GUU<5>I#xgo0_nJwN#9!QS}-)rjU6SRSAmkXFq&;)!ZYb^r$K{kxCJeWvob zWMBFBC{TaiuA~?fW>I*rxg0Dc{N^3&E~n8En|x*-JWvRBg3ib|^%QM{Y~NhTs3-^z ze#_Y%cthEDK-E-WSx|(q0EzjLdG&hXlovF#=W7}1?pP^H%@l8CF}X#K349y(Ke+9E12GIXUUIK-uu# zioK-yZ2R&yzQr3?8liCugL zY_td!vYqAbr&eg~?WXFp3W~<>REg|@SYUbsdSGZyfS4pDrlCqsC@lUBUtr@}`ZRKi zxdPwFR)SD5!*0o#54~3^MDFLQmx%g+qU>#)0&NYjGaiot+3ZWpQF$X|p1n#%%2~yx%-zlla(Cp(!bIHY;4WuDO9Tsq zEW3S<_fb;QnH(c>0kkZnxBHPu6v##jpo>!BL8i9MZo~HXNw|Q}4c50r>JFCcsFP&b zWv#GlDixeiux9}30&Yu~Ptm@TLU|RzR3?UUxf>B!(ZIGTP*U}u9Kr`pUT+L8ZfND+FV3Z!?G0`P+dhT^E9}%5+ll{0*Vzo^rxx_<07fH|Zcp~&u&gok<4Iti zv*-%ZIGgUCTZ=*@C7e<{5a76`V~F{MZAq8golgPnjk*ucW548ayY`gG1uI24>E6d6 zj@*$+S*mdPJgmykCtOHRwOxM9fu9o~HE0MV^PxhbY{(&-{SBV##5)rd z9Iy*zMzXaazOr4NMI8(j&Ny`#6n1t<8!WU3cFq|{AHl@Qkwgg)zhJt(6640$AtM2& zdkam4F5))k-CIRF6DL<$589q>1%rNoLzv=agtud`+l1jYR8F}<bZg44Tu z??kM9^yx+{wo3>SEMLNRArVn?D_%0zXCpz|`0&|=*;Rj*Bxzf_X9lw307Mr}1_t-E zs1SbVcpv>^4@xTTN#6P$DKPwEQCt&GcU?AIc50(U`XzFQm6G*cKRa6LG%&LnnZ);} zn-LNZ*cPN1AJbM2`qaimn){XLs~4Q=l~EziOo``M=Zi7LFQ1loA~Y@mijobf4@!eed<4b9oYYf1VyM-N z*W_J#Tk4^5Pulr->=xE~;QFh9yg_P=D&QROtxDt<5EU~eX`~yf=K>m#?qB-VRpVXt z{_Vr2yVC{@2SJ0?#GhENPjU+WHZI3II2ek#(8E2*CPjF|RYNX#!DH3yhJ(Lo$5<6A zNKyCqT=Ky~BG$*}qLT9&(aO53@NAeU$TYkRM54neejZKE((SMNd>He)!{X?%D~3f? z0&G7VEnNjgfpu~Cd(qiqUJGiOe)AX?qe-F^1ZRpJn9 zEn0#ZVE9w@_9NJQb`X+Dy-^RWuJhzxn~=~gupa)Fpv39O>Ma)9mP;8+_?Bqf&E zzU)$<_EDcCQoITA*d<#g22WIicO_pAakC%FMLEDiGHYvbb_q+gS>3Ek$zc)k^GG5| z^Jy;HQH%4l7P&_&1UVxy3`o%ch6>k5Cku-qL^hF{8#5!#Rf?*+$oYLflpDhX5UFzl zy~w?b(O2XYWMO}AP_)fM`M5mwHjDEA*tl6=XsPF%{7Y@V4*op!Z6-IFYn``>WEG?) zn3-R@s#rXPKAg@C%CL)Je8mK`6w8I8z6qeGuXZsp^`Js5`3Tbvb9J?<6U<*10l(ta zp*ly!^SAV+%JL-5n&cxU%ot?I`|1T+r+iTASNPw zY^7vd4ch{qz#L?ENbc_TiBHuitm^K1w|sdMnX=X#)&(gmrFV@ngtM{>0~=K(+)UU; z@tVv(9aCi?-#a3jEr>L3m2!3>?!dl?B8P7G=oABK1nyflD{``#^#_?UUF}>|Z54 z_O(#qg5~g{VS?#5R-I)QIQ0b5fh%K_xI^S!bJIFnyeaBF65C&|@*!iVUKV4cf2#%+ z6Tmf%zkmg@6AQm8$Yx^Jjn(s5F(X~+0i)x{k=KK@lxtng+jR*wi{!=LT(E>Vl@K8Y z){R&9AN8ux5~W2XGv@s zzHIu1Ll1n&hX7aR5nvZ`9Qt=qbK?BFy$x5M>u*=5OMQl~7sPe`JZyfECdc3Q$IR+1 z|D0bZmtQE#cxAiKW0IpAgdH|Vc|>sXg?-@d+C-)pnbwXUAkCZKvy&PJw)TvO0@IEk zn8)dbUi1`91gQ+cy@^yRp_t1zJKbIEa{nj?6pY;zxEYydez~2umbl7wJDz=s)+ip& zeSZhYG=-~kpW%1>{1H}%9IkWmS)?17s=X@Z!FOa*ISjx^|0Ro(mgW{i` z=-Ng(k4WG5Vd<@KSz`y{LW;9dh~x~F(@!Mrr3hbV0)QN}`Z-hzQK&dXMeIQ7Be5kS1@r)E8@Tz~s=PG})yBgA3M>4THH z;B`Q9;-h9wV!0-+l~2RjFA5SW+bi(1kH^)=$4yG@ z=hVRM6u2w}5mz2AoN$i~W+BGTrlI-vh^5i~F0x6PEeOr1XzgT@S_ycxW%A;lE>$?m z+r_W~JRmDl{!u^bbu0r7$W}8hf6WI&LlqF7tOZpr@u+zI5dh&RG8NNT2OBC(U6MXo z84rdi+%_J@A|O9Z{Ta3X$x!H!ax=@Bs>wv@$%?9tJBayXah2d2KK%5D#nss-G}`21jTm0XXTZ zeY|uPHipdU5!xGDYg9v93kFIZi;i>SL`HyA%Scw!AWb!2wJH82ETSKPvrC-g(YI2k zs1@VdprNb74p+Mq2We+(XMah4E~aVa!o#(*G0V z^MIvG!n7eCYwTdhs{Ue;IM}=R=2v7fD_{55S?k9m7sLHr4vW0xQjGA}+E{kLhw~vt z%vF1Pjf%3p&7T_S|+u!qy-jB)g!HGK?Y!bpw5G@9O0_fK{q| zpgofm9b%$6ZxejWp|;!{h$=~;0F6(ll2<79=;;j24CBFaAe5VR3ldSkf(FUF)3m23k zZd;b6ntt|fZ3$UalE9#=E7Z$_8Vaf+33@0(4d*ISWy>Q?12p#FWRa?H532H+j&|q# zpid)rM=(dF>W_^Bts$$0LzL#F%yeZVW(r^+FJJ{CY3ePm<_)w8=i{tcoI?@;n!=~Lm%tB2Rh1qYr zmC1r;FRuZ>Ps|%fZh?KcBG0`wVtR|%)+7KB(n6NY5)@c=M{B3CwP$+>fM^#hX_OCi z4UY{>N`{f)2(xSungaLw5e^+mAn2rXE+$WZ0y$}To9Dd>5)0`H6>-EVZ zuX(qAeX-6G5M{n!9uVc=u2eA{1PfKkNis)$AO7csHV6ZD>ZNu7YmXmTH$e%5q%y2RA;G^kz1DPhtddfG&h zH^KAQ1G8VpM3?0uEfQXT+;TnWQhBhhg(EBxs`XKiZMb>{-+wGmLkqM#@@HWe4eEse z&bHNQWqEa6BD)SLCNmp|gkuwS#qZnzcP1hxY<4iX*j=FN>#0emO=o4UXSWaD4rzw? z*>t$w`CZPau(oIiB_I|4ku|w7+=3iyLg116gGH|;;2qsO!2T1cIE=juBF`c+g@Rz^ zsrmlFImpbrp_4&6zG4Hpsi~{EQOTuP)U-EwZX91F$}t5|C>~uFZIP(gW(laJZ^DUp zU+12$?{WD(ei~kx+N&=vB~s($g|v#`qVLDjwa>)W=V!T81g+x={mXG_!!=2Mq{COH zE!h_Qlo0~%3R=(w_A8~Qcd!=6B_o;o1DKX&vLVPX6-RGVy;*}TN>Jh#f=&fpJG}}o zprPi+ua+svcP&)-Yu1Dp|F5O@yT8Ey6N?sItV!Af0ss(*4getczsI7Pm^vFfSvt5_ z+S}2&c(|ylKmq^{bROvbr@MMU0|0{j0s{d2_i&@JWp~7e;QOZj4lD<#Xzi1>OmcJ&33KuohKQ_*k?2d@l*L3MjOjc(%YF5(&FOQ`^-9I?wiDNZ^=botO7Q9qF!m}+nmU& zO0N6FdcHRDIEen@zxZENywQ{1xx9Y4{gAH&nzi>9y&8Nq0Iw6`fQ1h??kk9Z$3c$=9}Pq9hd5yBz^w&Y50{4Mbl~aW+X1kLjPE;o zpz&bm0mnbS?0?(?c@X-5I2|S;C+1`vi;$Ajb?VZC97M>;`8W}!AQ$IQ zoQzPquRFMkuMITMxKqhr)i%kMQf$Uca`*j`9fgl8*@;Iu{gvUhE=;Q0^}gz-QpA57 zEh?ZD$Cj%-vTZ?8t@NYJOY-_lr}ThS{;+4#iY!U^#WS|I;5m@N$M%`<3;!Ptg#!3b zBX&5D`|rvB86^Mr8tPll%VMjtien zO6LDOzyJPxDEIkzecw+{->&6y-@MJ;?eX{jJjloM|2X}2pIZOUox%U@{i@^V>HWTc zejPrFmrw6&?mv(2zb#^)YS(>+4d-v=;p^r3>+p|@ZpZKE@%f<3`SDQhT<-s+VUocA z`cTe(r`P}W@o{q`zg52Yz&`CQR_^!ve(>p9|J_Ayr+ z{d_-jTjjOV$;H|cQwOjTC--P&K0Zf_3M73VUk4{Ud%uh+pX2TRA0H1V_4@y|(K%d? zpa1vwxqIB-hQfvK>Zu$*Zx3Dg`Sj%CV#2e2@8{&=%IZ5$?@#Wpt*6i1(_r%K)tWHr z$Hx6zYI?tq=XasjT+#XN&v(1KeKeKt-;?h2Yt8!Ki-(iW&4}3cXbYc@@5kR^e7-FX z>02fL)#^RJ--~Q{{lHfEQx;yCZ+(x?*<*Z|-|2c;Wu|A087Gshm=kkPzAcu!w)gsR ze?z|E%Q^b|q-EOG-QiWEduPPGaA*rpVxf->>gE~t_1JzVqkX89B1`)VyJTp-a6;;C9ON?w}`w*bR$&XwmS7|b=0UQ!k z)h6W`TNVGLj!jB%(|qrU_KP^3;*R!PS4pl<3FPb@tQQw5VzUGagcv18p@5k*;|*$vHBSyo>F>BI@ekgUF<2i`-VHIwi*7|k--w{VBnT;sBw(^e*$_%@haxH# zU0;eqIT4O3AfBdYsT8qeWY1dJOP_~c_Zf8NneL*_7x~ZdnkfH=FbReP(J}gnIR`+= zUuwEzafV1zXj30V7cjiJ$_|@Y`l79ll(bZE$eq`#m%q8xO*itcUSK~?=MwfSVs3e^ zCXTyIQ4v5K_+)cnWQ?YoXKT8AXGArD?~v2mE%QX-*IXXutH_R9LR;zwFYA7ky5dIG zrP)LgCGkYk|A!^ak{DTuR?L#|Mm2gD9%R|%^_^M>qP&tw8FR`*a-cj)Eo;dx zV;j)VLtVEeS{fIoNWTjBwy)AXk!mbQsx>e6~3Dypk;g5ZQ zyup&lOUV;}P)^`4m_h_1)IjBrKUx;0ZfbB@wB*E_OJ%TPSYxEB1_(mEy(#gO`R?+u z;rK%L(u~eGUw8Y}1$>w#7A0)1vwVW-SaUe3WCAcRuw}wT?^SN38W$|Tq2gi~QWv{J zomb3AH|UhM^bk;s@e)gzMtllQX99YInG+!_ZA9%~E6t}I^vC;k<0)jYY6$o85{|q< z*~p+^5DkN@qVaE)Y#bF(zY>VMqi#ElI=1n=qd;v)d&*!dYdgaBqO-h4&ytKD&I ze>+bd$DVnvqg|e6u|xDF+dSsL_|QAdkqVh|Ut)a?McDf9uc(sVh7kEH69tSH&e`Ae zgoA#Q0;_bDM0TuobcW2NPdJV=EFAMshEsd!#p_xwR87a`7cW%b1;pSAt9r4EFF>ro z+PB6J*RUXSDH?4?jhwa)(CgJEe4=U_mG+Y0E?}ZI+5@ZzTGM2uu?D{n*3qT#WJH4($ou3BI~7155Vf- zjfg}AuQ;$rcsLwa&|xpQ3};ixjgw`l+L9`uLo0Wq)o;U4J<8pBg!V7zA z>LEkkhBye2m(5LhjwJMq$#bk7(#b2j zfb#%U_p1>LEeX58jvp!g_!<;Q;WYZa7nYG&fe}%0E&u zB>C4SFJQhN9%$;IZZOd5&z0wf=wsH1gEGQpRr(W@%5>vlWjxR}!z#2HSCE4r5d(n~ z(W)e>+3l9$@bktsC`|cIG=HfpLUo8$&4P*~twr@|cvufo-xYARlbxx8aDi8JG`IY) zJd6}r{Sy$56>Cb*K#9PG-nGr#;!vLxx4Ti)ElV9WS53RoBv7cfQuZkujDTo?j5X6Z z0D=K8*!GF&)KYbrWxLy^XGFEAHi5Fdb{GqM4IS>?3=ql50jVEW+ezq=n_D6_6&=njDn3?$!buq6`|c^n*_4!ZSSPEf1ggPqbz^$ z&VV0*DQxiZ3LL1FNgB++Ia*Dleren1(ezhM;|Rb=JFn$8Bp|kg3x_89#A07qGCtWR zM=fV$k=@ADJiz6=1^LXb$S`Q&DB@^@Y;&l=$~p`giPnN_CZD?%{)88$4Km=uGO|#Q zO8Lwn5BY^KxTwJrb&`#>KO0ifly+S6RQ!?m;uu zPILv%v|Tz;_OCwBtOa0+Y&;P2gDYynR0Z$_S2m_sNn5uGy1pKlX@x^x6-QX&7(w-> zIF**?Z+aNRy0R-}tzGX6@iL$-`HEhdl16)EZ3~Xd^oTblDb%v4wI%G9xYh znAD)IV5)wyzTa+$D^-J^qrJGWvnsOVzo8ZK~c?MO{? z3VDYk)dHW`e(Kdr0;(Em+rcq$68h}|qYjqIAfYL(L{vt&8wkQurA|Us_`n@{mw^0{ zqRg#ZZdv1mAy)I|vuh~Dbt#{=JqZ<%+Hu}pA?l~0StFP-;8DlJoLSRy`>}SZa>eQ> ztL=b>sKX7|GQ8?woxaQ~EO{2<)2{?HZ`8HkwT5n+OEt?Y?*TJK>FB2g_OjTRA7V|5 z-iO+vBFLx;&1orGIyYs<*?@GI=%Zx^r{GQdJ9_MR&9*bb86sL4?s9K5n zi)Ol$X(9)MS z^nnb%c(WZk8o+o*X21TNHv-`mHXAXPD4$g%6)CgL;|2O`3Gh=iIG)R_2zW;ip!XfS zjthUeCIPPV)B2lIAxJ0G3;S#0ys(M-dYF0ehFM`M1?qMbVZEOTtHp+Vixy#q~UotIm|NG#1LIveaBZr2cz!s19J{2FOcCw#8X!&fsfC3EeO{`D{Xm1muB^oLI^MU4CL<@aC%^IG$%E`i7UU!kL)jkLDV@^N)X4!UDl#k+P ziQ1_qV$msQc_2_P8IQ7am;ujUdtCe2$o$a|d zYkqas8)Ez#(O(N{X&KNnk0~7$vw^kquKGoDjpO8`sl!`^hOT=VkiK|!_&|9W9*StK z=lG?$1DN~XiT2A=AS-8qQ&7fo#1$+bwqYqZG~K27q@eDX_qHr4+OUbd0kuE4@`;f7 zffKK++GK2F(^};PIFzMU;Sz`9PpV}ns5C`m;x{%>_5A6{TRi+9J zVO{62SXKKOvgE^cJXf~G;LKavbKaQY zwOSN`R%>-;ICPW4MSec?6BF@E!KtWA_9E5Js!y)Lnnn^NH?6m)4GJu2R~C3}4qg0>_xSAQP;H7JBYv*GHD;4uf+q08=pgc;!dQdHmrV?w* z=fV)Gy)=q`ZhN7fjb#d-qkt-DfJ%Ell>f{kq^L^F>Qav5(Fd6}i7JYm>3JUasFJAw zv=(u1-2b0nys7|4=D49l1GCF`Ia)V`G)$x2U*mViLtT1Dpt?tOproP*yU@3y;qnoe5$D^$T}aCCxgJRWWJV2Hg^LU zKYrXE5Qe!iG?kFhseucxtjADUVdL!tSwU0=p`qF5;1qac=Fcm;ObgV%v6^+#;An)|xE={R5sTT?V zA~vmGC)j~ny+B*eao}2ceO9z4K0%S20Lb%~xzdmISo7tVw)btG zf6jiKYatLuLd|D=d!9BgLxGY{tw5_z81WDwZBMwHhp}_Ei3%E1jUB+T!|qeg(fN^D z`uGw(JuA;*SOfpFp??DzpFGQPB?VxM5*&b}LZR|;-DadlUGpMWS=hXE0Xt%gA-vY` z?XC)NvTVR~TPsOgkVUW*pCwctW9D5(C&js|fYbEdc9do44ztK@ftU6mu-hz}p2Dn> zE%bPW%|uSpJ%)K}c?Qi~SRx%PU(s&)sfSaLO4oSb@B@Kek!Rta(XOfUI`Xjb2 zqY4FhwCAg6Hnh!aNWp5IcXG6cLO}+d0D%iDwxCydEh9DEF+KSZzvx7bZ0)X_F!D?9#xy6^KXIhOa^0& zceDwA%m$WOJiBLllzM>6$7m#CEAChSy~|>xGpUsBWUUFYZBsG%Q~x&Qrk38znWt65 z)NZ({HKmCzTk9x~5d(V!memI-*u!zQ+2NUcvG&SV(|xlA_9klWvTWBid>z#Hx1x6( z+NAax?$3ZE=HL`@W<)( zr6EmCXWjdQ>TqM)J@22^qcBBL028MUsJQhpM4nLn=HdZ7kln)ZKd<7};E6gV5RLHD zZ8sQC)k>_2JV~H$U9Cx4!F7nzy2W*C2FkQd?gIK{*e%PgFQQUgZcou>Ck1t0H5e^; z7Jr)wuyJNyQj%cPpcrZzBEh-LV`3gL?X6to4BmXV#XiAGSElb4aa+^x>wYq~bKXxw z09R7qtr93q-By9x1+2bCgsu>x1C=C}mALw2{*TPNh=_a~Umw-CU z6O9Al$ctAS%`#+Ln6&%$6~^;HWiW`^ys*&2fnW&K&&4FIUnuME!B>3B6xk)0maG$d zeP#aE@)ityq~~?po&Q3=8+Gp|(cSaNta2?6pau9e%oHQgvQh`lUPr4Q?FLb^-kwK~a!8Q7DEwUl zlfv65HWxR7|C2nED1(^b=FTIwI8pE`5bP=2B$Sep_mX5x@Wq;iZ(6tnMu(W`EEWo_ zqn)8b4ul`#W;<$!kM8UY37)2nN<$`JS+66!Pnd6`xp_*cZrBE)=DLpO1RDo#r(c~t z#Y&2EZ3=p-=gHqa|9pOFFr(WHZ9K@UwAb2;KP0$tGH>{R9w6FYXF5$BM=%w`2LsLF zu-#Q~*XUs`fsmw$-8hfkIKfRMQeBaL$M)EvXJt>v%)wA{2#Wtn7%Shl0R>eO99Dx4;E^UB*3$Ce_u00ozW?*zhPJPBKHLv2zs^DZZ)_Okvl2r$xJ z5#l`~ykpw-%mW}gL`#a-8%;vbr+5u|8J73TQYuMyM{RQELyWX_3@$h-w<1-2PUX5K z)@vv75;i}Ru&Rf|zwgF-p>nbbB#{>`|1_4|Q%^2qnD@8%O*AF9j8QB5(=MzKHJO9- zeDE@yV$SRj0D>>^XGKs*d4|0}~&$NqyIl*)njQ5+O6B~)h6IXjX% zgB=%K(5&3>NAVJKogmZthJ^S$WHteJ7D*L+a{U5B_>JRD@M*_&mIl&YGT}79G#N9Y zOI1QEk2ESNnQyyQRWc#_jR7;%DyA_R8yh*q9i_#U)^HfIjnyLhSUxsu<_4KR{$c}Z z&+Ww>h&T2r-nPaJF2fb5#yLeh6MQSTad4zZwYl*an-d9Dj!tve|G!xK#^6k%u*+y- zXJXr!*fuAg*qPW)-e6+ewr$&**v1>%dUw99TGrO?k8l5UbywHz``o@=w@>$T&U3u1 z9=g-1uF%9}Ge_B|mJoKwnZXMsiL?{(;W#$odueSa6|@g{&h{4~J#i_E%WWgeR_hmc zVhbM$%~i;uISQH=QY%u=neESBDJxY~yI{+Ybqc?VJqct``E?k5ayMU`nv$og1pw>6 zz0m@@8Bg1r78=*cFs-@{&^zR@NjH=O6FZ{W^f*1)%h=lN(pt&gI}{OYLp4_H>LtEZ zrSVsr@*bQQYZ=Pc4~ z)G&9Fz?ApuO*B_D;Bwbja|QZ1o;v;A4j@8udY5eCimTpwz3-%iNxS20IQJ<+4@{%o zwIVEaB1Bf_#+|%8;ZC=IrIpMGX7g&%`lqy+T%LH{6mhOOvB);*5@hiX6`(<092?Vj zQGH`WyXY``_Mj?$7g)n zZ_X_F=H+%(^IL<_j{&i`I?%c|Ydnw5`}eBbnt~N=U}Kj09$isz^mI3D5mZPa5V*H_ z*AdXyPgvd&U1oRL6%MJSxtJNykw+9A)^)+TEZ@}+u^j+w?+O3j1V(Yo{8gOVz5kNn zDQ+I8MWIZbGLP7!(adEU*-|}-VwBlEV3qMKY}3uYBbojpfmzg$e$epUN9=jnC{C)J z8FO>ce{E}&je9+dIWV@=4rkS;T)%cKpG7Cto4&<=2V21Xcms{c1!^cGRkq6U zKg%}ho+nQnokHx6>~xquqP1U+KLG-z14jwA&e4%9v!&^KmTq!ZrFdh>Nd-QoMdN0E zMM1F7hzvCp7NSUscl2oq1MUrQV+nY6rG4QC^n?_wA0}zwXLlleKbiBPs!1{=Q`fGy zSHHye{ogD!MOmzI8)1(>mYlkOUg+@_l76rp;CRQ09RhMZO>qs9*zRGuJNDhW4o8wu zNY^IAZ^>k4?(J=4#BFlMZ%&u=a?}Hoa0W&^-@YWB(+EgP2ma2B*eG-lZF%-+M{*YnGc%ph4{n zJe|w%P;byAt|{7S7PuAyeY?43u8ti+%W5=}=V`(*>V)Q%I3&m4umwl+uuyfRVA4jC z6-{!A92PR!*Ckzgg^~II^@pcI_)J59Md1e>?1n82q7;4H@@r6I**}oFbk>r|&mV_b zYLq$`FEbS&32ttQ1~k9wXdMl#%tn&$ICE2<{LEI>4#)Z`la(YDP^Da<9pFs4aa=sD zUoYH%@ATNymRctEch}lCtueEYrVh(6YgjDWn5SToDI>xi0%r{qOC7hoemG&NDX6V3HBar;*t8YC)B*$9irTbf<#-7B<+xWK$20qweF5D6C>$f_!$v#CcI}R2kp(oJoL?wNQ4>NUob2*k1geiYHNz4 zr#-w|oOUf3;i386IiMZX$76~I@o<63XjiOHw@eH;=dth9Lza}T4t~c~c3YReA}c|j znwa7%*Kn2coTr@NNQ4dDV=d+L&TZ?-Q~I1Vx5xqZ8AAug$HH7ck2EIuMM9owceQ-y zCLYu?H_qc|WW_7K@n$--eAH$377kyfIepSJkG0-{Z^QOG0^olNSrER>Na*szhI6Vz zWHyK+xuB)0RA-o0hcai%L||?#ze3I|MRz``!r0GAFV2gKN^T%aZLb#4@U9CKSX60cKR9vd<1jUhwka%!#>C%t%Osc~ z$FUB(YryHZtK?Rn%p|-`xG6?vMLxzf$&q{Mc~M@^9|}VgnGu!A^`hJtF)5*}c@ERp zlCasLMDO_y>Tg~Z)p4smX%%)UlZ_5Y?=Og$_Dq0gE;g1h+qZzMgKn$I%ptq6{q zOlYx!v4SzkxU)jC&UIG?u!U~MXa|WLEW__LQ%QT%2aOEjW#Gon-A%18sw8QK)jM(V zVC(Df*;QCf1Q<93lPQ7Qgc+Gg<*KCk=#JV;V^79xg+y~*$O*yZ*kT3{a(8Jc+N;;U zCvpfAL_Fe7B}N>IY`JVD=t4XWU~+9IW}yqnGzYHr(Pj)$F-d>JGNJJ1OgezQ^e}nX zHw;J@;p)YYVP7LM(zjO1k8g7nF+;qxRJJMpW|T|WlM7ocRX0g*SQ=I?e!XiO#~_Mq;v)Dw$S(aP<+p{JVrF zYeUlZ(&G(Dx%J!e=s$SYu@)*rYr;Ik|31kAU_2XbnkydC=l1Cyx1F2yh0v8Cw2XjV z!|TU*gOo6kTgKdGO4DIlR5BFupSikJhd|pzAUfstbxE5atj{%bD_viidZuPAEyrK$ zq~-06z0pYis(MAF<&wb+gIpI-h%%?$A=CRb+no>Pw-tDj8sotYy!&6UGVc%$3EZ>E zBoWuEYb~T*US)t% z)YGt4g^5YH(3gy`EffvF5r|Ms-e)nFo01ZE9w?*}6^g$af3J!cTDIcN#&T@_Ij5I# zGsW-Wm4rvOmU43yoa|+){Ek6iPhNaNr)!wA9ScjCOZW5LE?Ao_eV~^&A}O9;m)|ko@jx(o}&@fuNToL zHR+!9fGb5NH7pC<4IY^$F)~vDquX;Dfl0AY)99lM&~K`XKiyG1##3$?PH{J9>BB8R zO-cA{$yD4QLEf|FbDDxj4ak3q_R%r5OXC=lIiBu+RZixH2<0nlZDX*vv;y*UDK4d$ z>mmIp4boQSj?_wy9Y!MkkpQ6AxjXidE3)^`E^T2Wy+`ChesLz*6rymr zfK!tVyC|W;Ra?W{80EkfqFzif4^8tN5&Z*J;qmf2zv>{3!wVD63VUiCJkZT)Cr9Ij z5<_mAiPz)QZt-*p1XK=f-g_S8kXH;!}x4r?4(6oyLmGpsomD_&I9HneQ3c4hC3TV|7O&BqQItV zOf8UXitp)G8YagJEP#*iZh&9@k&XGUSQn(?InMn+1z}VpUh90(M8vnKsG`G4D%U=5 zVLXk0ISsSvnGNH;2YXcvpOeS-y8~y;&kk41A;K!^_3oawyBC>ZUJ_lnbv?x@;PSO` zrorb1^#kaCXTwu=SKP$?W5b`>fq|+2|D#blx!O6q{9{!9zp0cqfNohPkQtzQ#7t##uA1|UwiB!@YW>9f8k?2lY zIpfrB22BZF3t1l)Hcim!Nxd$;+O%_T`El;3an0*~G_P8+&-K0;;b^JLbk`4-B`iJN z&W%Z?D&4u^R7YD}g~qo$&wsSmx|UpvPPl597F%SH$U4(rj0EwHfLQ@6EY$$d>PeX- zFfXhZestc%vL7lL%~jQHiNp`!LMSIv{eD5dqhDxU`M4WWmW#Z79RN55qoZs)4hdXg zS6c1Vb&-gMzwI?@jI#E_*Q~=RmMFvJbp`Qm^cTO$1Cp>F6WOB?M=>=VthcexT|3$N z7y30$=TGn*3uVo6XVnQ+6h1Ii6e8jrDvi&ut15wKlBt4)y{b3(4~j$9ReWf@q}$~$ z(Kgf#<^1A3Hc?B_cMOian7wdb%C{N$*qFVvXQuqX6Ok`Hx!Ji(asE`l(*4#iKFnFL z)$y0;tL1X@W{$VpHR{W-w-)r{JsV!Qyaa?t3Zd}dy`p~9oXOTpO_`+w(zrjFR$A|- zE-#DV@;YpYT`@HhkJ^<8z0cSZFFS#fRa+&^jI6GYW#=t4cE{@krJeAvl`IpN;va&= z_i62{(cS!y80VsA!u9>*E-LcS{4yaIu^y{E>1Wku`~|bz`(RPzV3I-YL<_&=aly!i zr;5P7(jbOh;ZfP7e*8PuB?cdQL=(;#2kWAiJ7tRj?+UF)1i!-pOMqPEJ^&*c=k^Du zqzylH>pbgu*VN~f-becsnVEk{8=`qunUAHD3jcBNz}8t>R@#jDq1w_}=)3|Kk_vhP;hAl5{gwq>Wju z%osP&!9WtU*;YsqPwqu4L-9vn7CmG~4sI_MtaP4$QoI@7fpuD0^o26?Mhi+{Bim{*iV+bp4#BTXszdsEQ|4o6X`gs6<}x|2|Sc|vX`e*|I7y>t)fBZ--y zBR`IW0CI?X7O0;ah^2ns#CU5VJkeN(fu}NQ>+e{I zrE^Mvo4CZA&cIV}!Vs~h1*b4KZR9z{Nm4v@9&rSkPz&%Q)ahh`UEpF2I@t(m20f3s zGbKoUI@vb~PMk=IBn!4c0e!E*F9v;Y@rM%02l_5qL=5^MGbvpwT2Q4}Qx=&n?}Bmy z_rZ!B9GU=Fbo~K$iDODrYJzVRpX?j%yz+dkqm+!$h>ooPed>R70Y1~fD+@~v(Pj}) zSk!y{%J}B{4*kEf%o~5y*9^ddf!$z$fvNoGmq#O;-}X+HE*7>1|FJ+a89AAo|K9{d zwrL9X*`r9o&tI79E~U`-B2MOR$~xp6aoqmowcnQ7qrYt=kQx+57J#}@LfLAs$V6-) zS$A;nZfAYb3N^H!x?rRUjLcP=5&!+9rHE9m3sYU{nVx7+LS zVEXd1lgrQB@BQs;?{?$$>vr(;YVWyn=VfmrmkIRr`ta!D%Pqt&px+GuO`f~kfBC+) zKIeiwynMO+KJKUuzW8=Nfe$yMqxYxJLhlc+ojcxNUzcaSO1U+)Gbjd%N;|VFng)sn z$GNdez|Qwt10f-wHqZ)i;c+Px_-;>JTbnaOtTYQM^!MA`-0q@lFFOB#?5$ZqDW@JU z=Cr0VE>AiyRjSQ6R{kx08@C=AkuopYVr5RG;$+8GfF7^Iil5OLt*bNMfvlWKDk?40 zER99K{w|%>C_0fwdRBTk*Q`BR#F|7;UL;*)oHOx8Ck1ZnYc^k5#40IQJ^9OEiMy@W zM_rbH{eynK-p1rcidkR-c|1U#OwuV~kM#E{qWG}5$Fg^oX_|D6!{+Xg8 zr*h%aF7djJor%Tg`C76dbM{LiZ=F`*WH+hHNOCqWspVf&8P-+G=UFdtMl{<|a$#E@Z09M!2T3d|oDgb19c$BYUX#sYE9m-_pXqtk zicPwuh0r4cD%;fYq1IJY*=l6Os+K!U3Tm{eh3lqx$I-V8{E7PW z==w=)DUuRIG%RhgB=yl)9c!2n>%No06Rr{9pVN(|XJk!;yL4f6mQ{;g;&4XNmIcs? zZBufTnEh(qbc2C>;a$tzlMQu$C@XMikfQ8Eg-J%+GZ!yGi<-Y64I`s4C|fw|j=9FAFt(=%qwYC&{PzQG>eA-_sXuGE9R_mX^P5FH7R8$Su=?3QP)Ud=BBEz=fy>CDvb?GkJX6l$*ey2m{KL9g zo=3PZoi=15b&IDByf@;X*r{~$A*4#y0KaqT^SN~Tr2`8y5 z+#db;8K+6qrx6mnO+iFQiFm~olSQA%X0qv&;9Fk>^y=B>zcb0a;Rw=U>X2?0^k%?c zPwM&548&gK9swfswz0QP2v}BNPYk>LL@)&5K|bguz_hjs=V6~kpK+VY1_9+vt3KGH z^;ffRG83KSkxtc6f*~ce+W`_S*izcbt$1?%2u|_M@a&XyDxhhVmE(zO435>{+R{*nc8IAmuI@4 zg(4kZn z0dMa8lGQS--b-I%z4(ho@OxXHBdlC52%(ef#W+2n!bb92<@l*I{kr_%NzIzLdqXFFcwr3k(`ed~fWdGyo2FI5|VJ7DnsC zJ1}%rB1~SUoXHc9Y!~e_3=H+*V*ihgB3{trT$ppefVr@c%9oB!d-hCcps$bC?7Jll#wfX<~0@Zu#5Q>A!~I%w*#1_TPi} zKS;eV^mG#U$6|bidV@(-JZF73xa;5Do{Zdg;NW%DWA-?OU6pbTdUJhfeZJw_RFv0b zrP<+4j+Cp(=CV>AE%wQ6qd=cgAAhFEOk$w8-`FW-{ux-jZcZ zXVltr&0V;9^8@N*xNhHU1-!Rsu}3{RPdzp^NHdn~^`=omUwC4kil4awdo;E1OZrcyMD`wYgz*bpUH)fp`B#GJ zSo`-AhaC71?;P8f$wrr3bF5l-`;#H1u0CVpX_Pt7yFs6B9f{Dx+btTDPIVuCr8n{j z1O3=*RyA*r2d+Z_QX!wg6C$k-cU!1vF%=36f;c)6jKoUzX$B@`J!6ODw&-qK7mi~M zxc9mi+lj6V;3W4t!SC^@{q&u&1759j0y~l|BBl@XgF22x4kUdxvwKv|zxsG%$2#jF z_x!H5KQqUfk!(L}t{{Cpyk&Pvq@RE`f#AdBH(b7Na7z8;J3}ZRy?Ch z3@zt`RkJ0&mp2`;Tz)eK1QKKpYwONzQ>X+IJeNM zCg38r)ZWzTgoZ}_*KYG?T{H%Iz114W{%nRc795w||S< zxT~_^Y&1)dB>zcexwz(SeOh=HPhov zWIq2hWFXZk)s+-4npQyy4$c+9 z;yGl!`?~oqW`SK#WK@)f0WZb-M_{YMWHS1IJ=NkkBZ8 zlnM5_`Z2!?$uM8d_G*dRPF=iL>lSNGZvJc}DEP{-yMebE@ZF zN-;Sd^K8C!fcjUs#JcF^bk;R48Qtg-nJX)Cq(91qmI?iM%AH`v#srtR=rPof!2|n?@XikQzw*ly8N|YF7dGY@wBs-=IXdnh$E%zY}!H+lV%};nNf}i zu92_&Vq4~y>vlZWiM*SOhJiDiu4~6lo11nf$a8eseK5fE4xSNzBg}i3j`{nMp>&I% z3v1C)i^%CH%f5i%GY80npJR#~=g7_lVpc6pJts!n2?!9DM; zQ^N_Iej4)SuWk!sfu+}mmV&WQ`We$zKamO(+7TNwg}a61Xl{QG!H^0v{p*3fSFN_3 z;QsVLcg4xGcH{h4+f(oUYCX4E@P@s#`-;PpO7H^|fU%6{KgXA|E5o z_tgog!-j2-WS<*tZF>j`GN?_nx5wJ*ay5^+mBc&VjrMyayg+sMbs_YaWfw`o5ChkTmounB(4qjwlRfL@;73E6@qj(cVT*ISW?S|s0nUIMq8^ME5 z+`2b?hyF;hK=d^irtAgKl-2!5%<%a6(^R5-Jvb~?+u~rm7~E*NM2NeKQ-(+@evBWBl}Ctz4hJzrU4;9O zb^b#ctFG4fc~O%QxfdFTTm_manUiL8NGX6rB!u&RH%rK$#gRTPU;QiU$dzBdbV9*b z$7M+hvOCRsQ`q_(E-E;Ypu^_R?uz)@u$7yU;+~Ro&+>_a4Rj4@9ANYsj_ksHi! zdS1SEiPT3?2`S&Ser>sGb(^NywAShC z0nca~D0_Gq^Oqo^qREOQJW84r*^qZkm(NKTNi4VXz_}9@c_6yHRswS&P-Qv>D-1F0 zDLm-LWUhl2v@5HWx>uT_Sr3Dnaj2Z&&u*J6;;OU)xlkpl17R-aWyj!~N(gl1l6{>b zxU59`>hLu>R-6hpiwNW?f+gJZ2$NmO)`n<0yV24pD-7HK0ne&q`UW$O7s z?%a}R6UVO9rRjr-3I}(zog2PcB9z-1bSht3b17sFe`tJY+h?Zrwt3QCcoIv=(cEiM z?cn7tTEYFL&sS<@rG9lw_kfY7Kmx^+HkWn896Dc}`Yb>@OVTsz`Wpu2AL_(TyoLxE zBwk?ix7-nW${lmvNEUyxZ(BSaJZ>ghujOzIuq)>Tsorqr(;1Q>Y{_>@s1=-fC5Et= z0E+_$QtI(hPU}6QBih)4F*wbmK{3C?c@z$K%o-^SS&dSOT9v||jX{01kjLWYNRJ=J zs?tCe+9Clke_7(9Fyz0>mY`$a3x03iY1DZkMqv!KGF7tj&#S;|amm2uHuMuv%R-{Q zOzjZGx`QIa{IyFG`fZuzX)3-r<&+vm)rz5>9-4n|t66}V>yhZz&7HWpgVeU>mk0!x z2c|gwG?>_?`;lUKf0rT>7^$?f;U^^DzvSC@E}Y2wmxki2ZZLT3iaeBXY0()|T$Yab zwHKnq<+{6Z+`dZnCec(k$SQAW1y1SNtB!^6S|)a??@~{Ho`uWb3MAl{ttPR^qz@SR z2Ri%TDC99b^!t)We_EKPctd5$-go{VAVmVFCx#l}`!0(EPL)VTJC> zMf%gui3))W;e&wF>x5%Y%(-q9Jx|TLLVo?h&u;$_Dr)sheve?Zo@Q4Y^J=8-NgZy@ zUR^ZUz+2r|*m|p}Hu{INVZ!1J4~m$>e&IwarBu_hc2h=7fGtNB8@K8RJ~etEpUj=& zAF_bj?}&NxC77+LXYOV~@%Qn$hy??Q$c zMGBt%^g2^d>tC@{+p}}Bjgs**n&z%d(XvG!LDYl{yCpYSl)vh80HCYtJ z{=E?Wmln-pf6*ge_s2xN^sY7zgF#fFcVf`nik~7Vlr~>Yc*-Y0@(tn&a4`#1S5Hpubb$6=-M#H1w={}3_O%TkE9UcP}Z zSIC|N$e@1rS?!{ef9j>ayMzjNyGt~z_XryrfRDl`Q|Z&s3YM(59xFW6-e0I40BPl*>8mRT&3 zx=^O9${gEtM>;7q5(2$;eMM@$b3iEFL`FkWRZE*cU?#o%tq&ypPf<8q7lwju!gZh3Vk!jS2Z1#_wy|%J0&bzfbq6qYGl%OU}r8S!{0>+VmIR zneT38$I6A-h>mm|90-#ysR^TU;w^w;ms2O?wW z1AF_DR>e-CB^-@Lper)|x=U(A(BJ<`Ek588I1UGV$&Zft%;Rpwr=0UNDRqdcCjDla zl_;P#ItdaZw19a#SysMe_8M-zX-{)&Ot;W>rNiNM zDRwkFmSDFG#4oT6rGXUrv#a^z)3f4mwq~!6U~94Ezewa@6nPq#ZR5%6_jWQKHNwy! z!ZIn%C)rMB=C^+Zf18uv2vD>~nkO&W7%ku9Q{S2U@dY(P=#5_tU&XVUek-(V$=G8k z;3iSXNhX3Z+zhBYHzv%-xl7O7y_F;nr*s#E+^C3%7*{~mdTym85Gqgy1`IM6J7_f7 zlKrL`b0+C|Ah@7+C-*>Eyrz(Jo#KBq+V zO!kcRg;u`(`1vO5nL(a-bRFn@RLTv=RZ zjl0T`mp4^)pWjZsKZ>ru;(T4-3&oLa5DU6!KD5qonu;~xm zRL{p9@;zdGy*# zuu=NL&oRpIO8gxM%%VsA0k-|F(+LfU+(K{5Dii*<6zxt@xEeAs^3+$d4nfI^Nat;L zrAIGKJ$TwCY*q>p&3O%!_rEF8f4CAyGKCbOGi|RNj|Dxqqd!xHr z#+!r5dAbF&iWB74KEt>OR`!MdIIs;ta=VFz|Fb4N5@N_rQS5Y)o|KbRmx7|Vhtn== zmZUoVH_rEt>Bc-OB>T@#Zh(=?KU<1`&ooK;;dEwx$c5>sP)$?=eBA12!Lz34_oTQ7 zjXSvD`3iq6z3{mcosYqnqYs$=_zvl{8O_fVKXN2U+k$Mp}QB^N0H52ogNoJ8?%?_ZqZlSb8w+zFod= zS3TDWObflbgkuP2S&6+h#vpu^6kLs*o$(KIY=>l`plWgwhVc=k`x_DvTEaB?GnEop zy=#KanDdQUI6YtnVps2e1KvqrQQ(~sYMVc$PpBp2>#j;0f4p|#yAJ+%Ct>LesaeMB zFBaRj4hZgN<=8&@4ymV= zYAJke4F4s>+{1yT_XtBKy<$qiA5b;c{)JLLyIz))R6F(KCiKOjqsZ>lw<2LfdlG@{ zDh=Mru_2r8VKYrqSKd1fDy>*`RC4S{ijJm2xj!Tlg>bO&^Sjlc;b#*vP3tj4JNl>} zJU^Iiz)Tc@u@v)_?RB)ZNr_a*Y(F)V(5cj?iB3mfx(48bi;jii2l<+3>%~iZ$VRpC z%e}>8qN7!;K5q6woqUD(U?MnScH-K!m-QfH6$~M9`6TJEn!mA6G1kNqS!H!}Q>t& z)Muq-?v1-W$Xy@!018v|CM1PoR2RZ zX#)m^j|C1U|DQ=v{I?2*Zbmk)X8&()$z``r%UtJ2<=M4{Kle=Sml zK$V~`_uh}wT;I>>XCa@*X(7N@Xf5bf$uA$<@2NL;*7x~!M-X)Lh+^RPKArn@as72% z`mdFcQ10iw0pRiTS?KdP^t$J>)z8oS%f9#hiMaP|wASzO5e4+cW$@YCyI_{v>jiQJ zp7(rZGrbQpgZ7uq{BD;}3;>|l&|JUbQg7F<+Md_k+}`)Ed;87{(95*Dkk9+)%1*Cm z%jhZB>;pIGO)F->K4-7C_w6{>?{mo=l;Qi-NX++%oeMfWzvc)PdVk6l0&yVh-@Ert z9`$})-2;g`uWCX2)Ix4=zYPGNLPDVD+ON<1XCb$x*+-DQ-{;2F#fIOj?R}n{?)!kMehz{@xeWY3|HA&5-0^$-Hw!PBzNJ7<-3Q{glbu)5Pf#QD`Do6q zJ?Mkl?{!NP6!b9X0eC<6dwPX_O_p4gWg_2ugCv};ic~L?;3ZnLfnTyuCGzahmV;ZuX^nF*`CkGl^vhQN(0}{ zk*7+7?%s#C{nXJ;eLXM4Dg3o3?oY`UftlVE+kbbq;SwA4b?*1^q-o#loeg+iI)BKt zk7*S88t#3Wt|k5rI-LF5r|$h2&plMJJMGzOdGERQJZyi?ia^$}a!#7=;Q+Gz#Kc<6`co}gC1L*Q1=!un z7(H*jQaZi%+!CvD&*{4AJtwrW{4U?KQt>>U^qj;~WxLD?eZ8yi8tL59WxMH0?|p4@ zT|Djiu6Dh#sNP+)xqj)Tc4eEAa;#r+WppC<+|jx@KjLoJTGdS|m_f zACqfu-&)RN7p1UrQIz%E|NDW*YBu9fP5=8)Ur_h8K8b4as(>db%F5Gt4yGTimmW8?Mm&;jcCVJ)V(&8-Z`_J1JJAqEP+|-m8|&D zW$>Z!Tx3*Ec^dDbpHfd`X{}xK)upyzgIA&AIk4WuKPosH6$^4IuznIx^&ZdAWvuCN zHk&tIh$}^t((QDvTjG^`zAY(N6o{?9PEVKq{LwCRklf0;F9ZnKE}~tyx*B#*Z{=(@ zi6Bb3)O}iBU-hV4b+aj2tQa1z;Zn?AguchHK02{(Qf^H%qEO1z+Q^LzT}~*8?*ztq zE4E)a&RXu=oggVMzHFBhRzw#g%{mDYH+$nUq5#HERPnVZl@V;OJQW-xqJ44>7RHFE z6Rvzd*Sbp|;|s~yIj?V{IXF0O^qilZdLI1~g2LY`Qe+a6709eC7&U|jIh|!?On_VY zsm~3`dP-Z{@*bZ6g*L3M+Y|+2x^m5gq8QU}21aXPQyIRj<9b`IE&CVQ8_N*OX)y^5 zg&5~e*7fp1cYm|4ymFfcezDa65;|Kre{)cy*K|TbV4n{yuH&ssckWbvUU1~{EV{*E zSy~cDX)HUfQ6f+jx?ryqdZp^(N=%5#y=+Cra$)r76UU;_C@r^UKiN zB!s?BKbc>#-?d|%7t#^ZzS4s$zk&g5*WIH}qqYnl=Kx|qx^x9IF2B4pZF9p3dv+SI zoZ%d^8Tu2JyK}~1Wk*tS zhLk7{R1FP_HS|0r0CqZlL??b(4`(WXm@+A}SIp^8=#X|co7C+71-()>qeY=2>&La! z-IH_n_!b9e6kdr`teh~kJaz86CA8T!%_a9U?l?_&HCCih>c?7;DlL{s{|G zQjhtBetf{8a&$G_Bkx}*Qi&!vUNYq!Gklr5pW5Fhe~aB-t`p`cEH*II+90SIlw8Lj ztu%wUZE&=<0Y32>ON;z+$XXX}fF`VyKHrGW1T|X)d!pqD^g-UFz&@?hbEaZjJsY?h z^(2bfM&_O_&BRLZ`50NB*`wWq#fF5l?G5>Qxie4m=DprDt;;q3Oh0dpN|!~$+GdVP zbkq)sEn8np{!?UDVg@rx@F^47k88+6UhjT)TLxTxHt!v)UzAfWjXHiNknmmwr{|aH+Rt1d37_A<5*dhiWH6N=(hvd*X3u%K(*o7{}so&3R zS=`z`!4yRu(wzi<@4;Fx&+9VIS=)y+JEEQYO0guFRBSzemd%)y{mVKvX1(R~ulk=8 zaqJ=Um`dtVrH)Ty__;@SfJptoo1n!px$7D%d(*or1 z0^)^cG`v1FG0hJqiIr*ut?GwhLy~tsBAV6-U7(tJ&gA4uf6AQ*%$zRpWE)^=D8Z z&%>UVb%!2u1J*yMr>uLGW)tcJ3o(-6CrOJ-Yxd%wN+$TVPd{e|Bjv4%b*3$L#=iH{ zR5A03Tpk~Ox_Wr%vAR!U#4He_d)s*F{8utFsq-w9O! zReZgXe{s8=jLxr4jdibeh!w)KvhkG@G|Kpgc=D#Eh$)SnMvG+zC};#RD`F)X^bX4- z>miD``G0RwRCSx>kYbMOj@Kx6eaW`gArU8JfTs!ejZ(VrEa(U}nH?}j^K}Upw=6wp zSZw}H^RMhl+VRF#O#NQyf>T_FWUhZ0Oe>YAs?5XzMYwg(WcXDE3@)P=;=VGUBP9V$ zbZ(TQfOw+f3f>%5q6Aqb@N(H#+Z0_l`KMx-6GeGfHf~C`S$j($)Iv{ibf;~aMBv;N z{jX&X{1&zBe-j=3W*7pK$e%hFr9$qLJ`t zxL_2_wA>NrQ;qFqr?)^pKnK564$JUkoVM{@=A;1qvJvYRiW4yWcU2BUy^=`Y7}tmL z4@-ugr4=m;nG#p|?0cJIe>Uzelf8DU`x;>aUNz%%Ih#s_s*0|X1GSHesr<2->sZkI z(K1*kRBsm*10QD%NAN=rBf3EEG6DkSLnEV2kfN57=%y1wqxEm>(>$3*gIHF@7(3xc z8YPGQTnt6M?YEJiqeEVb-a?bdk8gG61I>dPHxqxW&V2t109rt$zemtizbXL`s%-TOANW0G$yU~V&*YJl z=Ok^nvB(|CkaZ_JZ_1+d9>ge?xhcP~etfSsJf}28MeFi*J<8k?s!H;+Ns;PtmNU6O zW$fmr*T}p)&2O7qFz4Y~zAI;CP)ZOKbnRKJg3FDHB`U9T_5eDkEuf3#j=| zn4<*rHUu1@RgD3G^*P=Iu=>eL?nGtV60LF(JAqMa_7JUdqX&LOb1+C+R2okgNr1*$ z-7+qVB&dTL+8uY|`3~?BN0Sk=+eO!At`g9ah|R81*QS!oBB{x}ND|_|OP5-8YU67w z)Yx*`TZET}WR^W4X(-u;TaSFhBr2j=uMyRsT2hXj6nhLZ@e)#6AKuYHngrRO1k3yF zdUTCQPqwQ*QU=@p+;xv&rTolxo}GTSm$albAe!D5Zny9=^b{P z3Jh>@ z5t7!uXr=X#z|yI*M)YmFw>26`3r8HJ zbl5z^v7DWqT$1gEbU9C;Mb?O{RqbvWky7;RUfrZNCq`1Uc#)MPiN|U!yn~pahFo?% zu90Rv?{|$PhL-J#ot#c4%P_1LcAR-a%hd+4Hn0>>lJjRHfYY+Ar9GXKAg;Y*j`?DV zY8jplL7rGoQq+>1GUQo$mm}Rfsze_vH$&T4&)Fqd39~#zbvx84M%7tC#_O@9Jo zX|0EETQOIqWT({@VnONz(~)8Q=-XDpQu>bKvwD-(yY%z&ZM79-p-Xql9nq$haC*E~ za-v`jlQGq(Zbbtj8Mu|&Ln-TzW*0;`G++*eyGP&CCbtY+GFKLEzSdCM% zOOvJL??JSaRtcHfXD95h6{Xc#Ew%{^bTkBEjPp^s6D%I2zqI}5U9PC)NSFDaA5;AZ zSV>G=rYZh0BhgoJH!JBq4^Q9mqnc#I>*ypN5?RzDU)`&foG=O`O_mv{3kU9rP}`16 z7%m#Dw?2?;*Cnu-sa6RY_DH&Ayb;+$PGi~=!av1*jzCMkUcpY)7~v{PiE8~8Ljm|i z3=s{k+yF^3tsyDwM1Ikz4+b?OcyHQ~cRh+6lE?)~*xK*vVP(V}>CvValXU1FYuY}x z6qQtI;_gEPY*y#e$@AlTKMleu6M3MSyIqem5Q2@3yFVImy6Y+Tv*@-7UzkOY-L>rK1UhLdCx zw!SvFl>rD8t?L%)mQ28>Hi9j;h&fIy_-@^uzGgvR%iA z=a7d(@AAx&qH6IkFIF1dB>3;)jxWSDh|~H?M3MLY+@~B z$aw$}?#Q1(48$8|=1ja+0{4w2j$6SRmfVUwI%`>qkK9>x8mAKo0w=X%&Lr=&nfab~ zQ-3(t(!?^cv_mZpHIouGglQtz*`9ww)@<@N3%~|BH~~G=lweK(p@}Q-rlxkREJ>N# zw$-pNrQIjF)Qy;hRDZ_D)&{q*?*vYDY7oM}>0`5RHVo0FgUnpAp~0uh$k#IW=xHG( zzYw8q(0d*Dpp9rrA<4>#zi$)ML1BDB5E|NsdUk!yF309}J-VhFwd=428ab($p)~KO ze4`ZpmkGxu+A|(<%S-gh*q36I@L9o?FaFAasDu*@2qb!YbBvwS#R+D}8@sx2G9NSRrH=Sl>1K3HqoqH<~RVk2--aawjvw1~b+G$bUQyq-?1 zjwp(rNp6DX!T2C@PaDpBBWj|782U6VbnVgNU9puw3k0$QA8+6d%(yqK9fI*bD-k>7 zwDoS+UpBgK2Y)bBKam?SeA+%A-%GHXm?5sqW;6VBbPb|(jE02oHlGP6$4VH!>FKrd zj$a2@0Ckc;D`sp>Zb#NB=8a4ai^jb2JCcv&G3#Lu0dtvwIeW~QR5WT%d)s)-J{jju z^4|jpf(~UxvJ7?RNOXP~r}-0^6GsFaCq|-Ok6^5UOc*R3oW8UkLA#+4LB!OyM!a01 zC0o#}6)|<2o`9`Kp5YTFkA5NVWohX5N=InPfTokpVuSy`oqjMq^nevljI`wG041tH zArdMnme3U>M)w*a58GFor9+FNhfdGEPQ|yp+FI+4TFHA}za7K%W+fJ4VDoq#ry>uy zK0%^RYX(^ov4ffbui{|lHKJMzJGdWqdgN0tmtZCj(n4Hvo8wMck~qDznyd-!#^!+S z#B;;$leQ;K>!F>_n`(%9q;^mQa#0fzSq^~?lDi&WdZ@-tAJs=0$HkK5W6xoyW3r=1 z$~>OLn2RueDw&`G?#j`r(^0vjzW`!t(C{cxkXrREpE7+YLc$chq#1N)w~Wg!>lP1trFEt8?s%a&aK|V);H5w#Zw@>YdSjb9g+v%_#=yg0DNn96kPfHiC)oQ^swr*6nHnP+%Mj9jvwf7ra*_{U6; z=6Vk2aU$;s?K!ku$jcPcmh#&%e!yN4O>+?aIw$}eS><|#Y?sG-c2MEi zx$)mkLwiS|1OCmoQKZPV$k{`d?ebM5=?3XeVjtSG6I_YCy@_nq8mS`?&;vkTCo3Au z0L=na#j=~6rvdNT^FA5dLHr}qzUTLXZ@|qU^;~Zs4tfgEQSMfaS~`I2R$wmb8;-!F zWYO7tiG$~(>xncCJgD|*?#@r?0TqCyI zxzUu!aI3rI45$V2RfW)g2NnxTHscIt$%^IGNEkH9F)f>|K5i@0*PN{cEHU|PD`}=l zl4zUTUzHXkPQGdLmyhrL)E1ITKA}PG=OZw(HC$tf#kl}Cpdp^YjBK0+J2x9OSnn%I zn3p01V4LH&{XEGSu=hKW6U?$IlR7dq`E+eqK;XlN{h(DmvPl3nkOwvyAJSY4K&C%2 zbHFbIT?nqrph9+lM>B9^QiMK%@(=ZP~t}3 zlQju9XL5M`b^&{Ifq6aiEP}Gm%1g+piLl)aS+&*(31ZnC)}zT zyc1EOvB1v_D`As2=CuDH@45kyc1o(Ex(gwyfNX zLes+#bX*{bDBxthJS&-w$OUoo(}1?+@8{t zs81kUIUHHZPfa(5bCKy!QqMVnw-efr%tO*HmElxuc}FO+vmR{I`fIzks?MkW`IFb2 zoYaE5Fl|u~UndPHZ%gN88<4`gTRvyUT(l6vS(OxM5n!~wBupvqL`&MscHra?@0=FN zbq9MxIK|ufxH0zo4Lm)i5{8|YFVFti;U_XS1ynDE6u0Zq6|^A%T*1LLAVW_@{XlK_ zNx#5tFsx|Qp`~4&5L?kn@?Bxb%>G)0bCpJbEU-2JK~79WGDA2P)5GhJqAY}*k3`r4 z%TP!J#B}WMr~HUZ7XYc6FqWK`eVSBAjP?N9NuVSNeegI=&eiKB1g19})*;c;K-!i4 z{)l!Q-VIXj@xA72owv=vsHN0(CD3_W@-i&4Mh55U#<+Mke15xP@$q z)(JhAg);}4iUdg!I!fn)p|(ph9C2*~w;kQ&tmvZXM3v@b$~b`)ku{8^Y2&g^_wk6kZ$#V8C12zI9eB$DRsdUQ2* z6MCAeg}v2tiO|E?)o5P||Bf?+RrPWDdV--^Ibe@L{kXcXfIHb=Tc<88(|5{G)Z-Hp z2WjR^V%WL`+c$$usMy8iPdifob+nhs-49qBhRohvK6i5(ITL6_7#)_7o>f8|Jc2Eu z>lLyi06E3qTOX$Yk|dFLrtDJU!=>J}E_ADeBth7s7$(uWERAxfYC&pnI>=AFS(OfetEB^84IZ~4|2G#M^{5g7M-Jw zv-SlUei%Ywo2Gi1px9UE=vj_1wan+uV0O(n!IIqHK#->c)u>}hDdHhG($oTZvPg#m z-o+>&<3J1z^YnGR4}a_-0Vu+fUX=#4ImC2hi}Yzp*bX6`Nq@8m<=5*q8)hYNf{iqn z7Q7>9{v#D5x9{F4=-Tz(whv^E>`zDgaMCNl3#oBwcY+v2QxWkzOhTuxJR-iY9kg-{ zKov271Uk`nj{)vdIRQ4O43G>h`K6EV1zlc&iReYZ6{mxbA^glkA%ga2Wa$yYmosrr z)~wgpbH}sG>_Rw@lE=Rx*eQFoX*0c^;|c&(29oT3;7;9%Ye_F`q))^-2;}3?kPg$n z)~ep?{?4JH_VYlRkL(K8$a^-KUBl&|`B925D~06X78b;Ix?%5W>KtRhi4Fs5p1w4(}e8lPX zW&33-ucX{F*Sa$o+Cv2$m}NlE;lQ{Qf^h-qdm5= zc9o4WmzQg?Y6$|Q=v3%i8-oNQ>Q6MkPomZ3Qf3e^ac>>>kc&qF#hj z$`(>?0%rz}+8%~1H8-ma%w;y$OUPUFM}fA~RAYYAD1b5xxHY!Ab|@~YghD3cmk60T zv?wKA2yErl+Q(o$nmUu~1*v{H!tt9pq`C{^A!;%NH;q#Wp~UFhrqtHiPX7mclK1U# z)nU`_ao-f)9Q7+iytPE;!Wu1QJOL|97O${6HDe^oGWcIICOe_-g!m#*RCelldU>sf zJs}g&^-`MH>Z=YC3?TV`M{oa2{k#^|8?i);q(z;0(BjA zmZWUVK(Z)ZJU97S)XdaeuK2I5@_U(%$Ja?y4C(n2bqmJZk-grJTv(#o$u%lY&tJ~# zU1P9yG7)J)<~ZVIQZQ7>gy9Ng>T9Lh?s}P$wGu!Vd8eBz$Zt}|1c`H%qqfS~Hegy>Pa73O(7F^8l=?c@FkY7@Gsg$wD`A9F;F^MjrWu}A0#WOG*{5U_Wg{&BXfqFB zApmF=+*3|X-E}Lhc zu&YNsh_vEH5!RTVWD2z8a^(1W3InnKTUa2KBvE5w`K^SLsn5bkCuUBPy|J-$f7heJ zy$;9;Osh12Wa=J{NGJA2$&blM7cU~O-9p+7qa5fM5-j6svrhK?HHQl>p`(`D$^cPc z0ay_0Cx8L+nKPkx)E8yfvQjnmZ|5}~`ON=9{`~(I`NK3HiA_w_h8vQjYG3<}Qt&5V zTai}V_l`g3eDL)O0=|F?Jc$%>WHv1##Ph;>!QI9+1hJo54|<8MOtW!KSUL#D>0xr@ zqba+e!JZHp9xA(}!nR0lZ@`i#1;Wl{liMXGHv_k=#hyvFRl`#*@0EK9WV=WdOma}C zgKI>bq*P!xnyiFodH^}SRE}=#4X%f2eYk`~);qOT;a7ogs02yNBZI|+0{WK$xHWl5n{sUx8BFg@;Ow&Ui& zA~XXkt!0tMgWz=7*Ayo2bEL?;U)~E0g=GT+8dkxFBP~m5olJK5jY}v~<^IwoJWhOY z3AMq>NoZr3C%{TlW<4Bt&(e(w7TAc zCj#Qodej&jZU#zb4?x-TaF_ak(_z<0lI7hi6JAOCoF;iM?J=+A3JZIjYGz76C*+V( z#Kj<#>b6buBsZ>g^*TFZ9X^S`4}#v%lt4!)z1)MX3$!KdRn>GGP+&AdN#$Lx1(PRc zyTY(AiCN!*#g6oaem`P6W{m^BAH_BGe93Q{Y(NTt6hYg8+akK<0M#eQQzv;S%KEq# zSBR3Z*wR|llZu=id^c434~Q)y1l2~X#EI7Ao9&5;$;emh_MimQu0ELgNR^YI{iDsc z7Agx@&YKgL4$V=u7c2+G(Mym6uPakM-K}Eag}DzT2ca}pG;fk{KmvBdVG_fysY$f|l(nPZr|6UW0bk#=Wx9|0Q#c$u-^2uay>ozN~Wtfr(D%uaKPRFXnPv~#c&$PFri5n zxnsxJ$k+PR&Xz2DlWX(lrs|uWKs}-U4`%h3yBe4bIYVk6-}^_KZ~2b|_YJ}@ z@H*RWjf_Kv#L6^suZaab-#hv;!(~goA>6f_i=iE>$6n?UyD1iCsErXY+Se0)Bv&(R zY!aftq#2!Rv>Lx35vnzx{0{H+bjfd<@cr&Od5{9tiB3XUlD+}mED$3CLAmX+$>nTh z@YGh;*r~E-dax-*Ww#bX+@z~u;dR>LETX;>Ig8bRk*Z}Bj^E|Ie>7HK&NXiqOa{LV)D^yOj^xN@z80{81{E4~=WM{c+0I9vl?o!{{unDm z2?5f82I&#IFX&1NV248&^?b>1pY4fbDlt@(X)QYxw=QYIgSFchQ3EVB8T+r5ZhFZDdWIUyUdx_*evD{9~D}tI3y^(wj@C-0U znI|oX&UcD{CHNsze(~OBW^&x9KccC2o$sz0g6OzJZFZq>qA#@iyR8iqqvS5r3APv- zm=P6*xZhLzLCU}o9|_0?#o7D4{b1^w6djio=Frh67OMHK&aioVC})F>XLMQkgI8$0ofSD;ZPC>rciw!C2+&TYk=~k>(RCB6__ts zVFI?#0{6q=1_5{)-FVr)5>Bf`roMhGepE_5HsEmY5QP&S9Ud;25Fw3l5kcJpHo8NY zw#_J^)N=!;po09ZubmHHo5H0HlGT9oO*ky$?*Wt{k#qdzyRTOO{G7*M$q6DUOl?Z{ z%57?WfUE#vZ-8&`t-`@4`>unyD?{I!VY6>3F#l+v`1|D@O$7J)%b|r={N)fdo&SD( zC+w=&26D!_@!;p9kDNka^Pr=er`oFrLS`%H@jO!Npto`VcE>j;K_g7LqRTRLrtuGN zu)HMOIubz}#B?n%J8S7DC^HDyk|KgrckYJsP{ytumEUjT_DG1ylx?s$TAv+$RSoIq z={AAi0i2Lw434Kj<@d>A80OzfEEMUd&21*R#SKNyjTFsXBtz!> z+JfsFo;inpqjDo$GmXd=RgN86Wn{QD47WNVcN}IZ_)qb!@jxi^2i_X2BET*o3Ekd1 zT==0x#&bYD2v<4}X9K(^%rfs1mu?a~f+O?&hCbZuMo=_EDH&P36(*yD$T8zbs4Uo8 z%lV4mA)X>h2(d?~XqVXM7=_&lDLqGlvKYKv+Z@}pV3~00lx)gLJ75{e4i!v3q|j~K zSIw)YiMkLA_DLO=lvoOOX~|A1S%~ibr2WeX=gO{@nf}YA@TU;5c zv7Ulie1pju77mn*o2@`706)xIZz~_}HN0a`hAdOIfi^XBSADRpRyd7vKN5APD_lcD zFj*T(ml>E2wQ@h`qAw}_N2)&|q7zB;5=FCIsNN3b za}T}c@=UWb65d5j5;)GV&~=alHyJ;2IAIA6Ixy1l16J4?F%YQUKp-s4XE$}z)>o4k zK(1{PmU00UgA3?&@9SZ9;{4lcvOqN#@cW3CUJl3I0cW!|o5c#W>U7hX=kjk4u5dlx zr3m6Ulc1H8v8=70A*!W{Gf@O)W=d0yXm7kRTxy4#iu!A_#5zuxs9$fUs&W&gY2($n zWNL5oGRj_0@)2dIj&6~p9Kq)a9T~jG&mba+q!K~*n_1P)SI9;_ot7Re(OtoXK-BVM zG2`Xm@(DmqAtZ0vHP9F0y$&X@1$%%8icD??5as;Yo43jpZ{E3h_ zuJI!O`*MPbw?erBXl2}>Io)7_9c`;C#{lS|o6Z+A57A7O<=env$q!c(W*DH95pRc7 z1w70X@+py3An<`=?PmPQ^O8wV7UFc;3%d)Rk{l{$tAUwhI}P(~9b7AAQUU1?ifR#KT`Fag5z6OwTXAO2Q)eFdhRCx++E<;pCTHD2mA#x`nIKL{ zcghOK&TP}pY>Rdtllrv{Iw51?cVIf*Uf|pLJrcMZm6Bu-O0-dcexO3LvCT&;finI~ zs)0}yQ+&Nfcd_pqNwf~+xp#AEKnN(4N>UTpfN}QPYDBdw<*(Wlr{p-5-HKPr&#kZSFS^)RdGZiY1K5EdGel3?Noy2YlD zY~7fri1R^60zGz#@|tz4ZChNSa1T0w;OD?Z$aHrIsJZkRmOB8O4{!bCjs(=Nli<8v zk3N`6+VvRDWI`5^Z93UX*m&)utpuFqL=0<%TB&CpIq0P%4=CFLxv;(QJGD%9pO4aK znr$`PqbY3DjWj68BFhVio2MhBwZG5fmqSaVo;&+48Xk3Yl^MVK1AL<$LyX70=G<59 ze8u*$5mUq#Ql+fQeeyu_4Xw!1C?%^4o>Am%!ksH?E@(j-^g9}-zCG_l_V53>`_H8ICX@B3dc9#l_3ng%Fm)a=I80ZL^!z7Q~G(L*Cp8;eClQ z+21vpG%y1n6Q(HEj3lU5#eVpe6=wE{6Btwprz>KEry2+!G69mnW=?8|RA?ojOX7nx z`6yC|2wY!G?e~Bj74`&f)s#L58$^JegMEmKCZfbqT9Mp32Ocv^^$nb5r4S@2Z&Nl4 zX)vM2!(ak*cS8g|OC?Ts&G`A~8UdV`UkxEM+cQ*jOsZV1j}gEF-?G!$q?I5UO4P+V zRQVN>_`E4f<^v7v^;N$#f7S=fJoYhz&Csy`<27G8U@KTf7)?B{YZWV5&vU^##0x zFuYSm5WzdzLpGp!cKhjJk-$C%q>5} z7TG2P5#8(2hstrtCmU7q^GtI2m=zZ4dADu%N8LEx6{u{7HjIA1O(B(gjK*&-7WHh2eG$8EObnTYsSTU8sYp{nEdz210BZQO_XV%@i2Bh;P!0%d$4j274}9IzHg?WX2HD!i&ns3OUuH>y z4GLCbS6URrbC=WP*cCXa0m`DnIaJ4f5LVC`2p(%qdZFSM?)l)Fr5#D3gw1<)z_w0@ zsE|^G>Iq`Qt{Z9>su1eLXHhW%d9Y&mJl4?1l5W_tE3&uYav_jZo>DnJ;IeDby;oxg2&;xfsmV!?93|{@ zss_>?j0;dk4Ek)1n9~aC9oPw2;0D_%Es3ejzg>?$cE8iNU&ymiHa#Ip8xX}i z;||I2pw*o#O2GQbQUhpxyB?Xi6*9m{H7s^krhoZpgxb6=$J^?jM%(pVQoqEG}OVvG0sYK_5 zKIW5Gf_@+skXrCcY^N(?O5qfA%>{`iZ60jTAU7tbyNA{1!<|G+1IOu=sHverX9`4* z4B|wO)gG7|vf;G&$o>|}?~_{{v`Cra%tJcqN9>+}vRYkMkRHh^`|(6r=DYhQ!OPYI zF>$*deJB=sWhFm~g+7?c>-F7TaXy)+$jOTm2`sI8;*lHe#oq1Q4X+3S?$TBlP*P%< zt|_2)!!;f=x|8htVkxDu6}*n`EvePyuG1xeh7fs`0wtsFhtX#~j=3a)yH5FqM8Sdv z>1YFRI*;;%^3`hC-F;I!s%~>dzDr{C){j$R<5*vX76SY zGkXB9DCVLk_2T%4yI{~1(t>@9x&sPL{N@5fJ~ZLjZyCXX5~BI|?Rs?Wd)5gZG4l0@ zT_x9WK3fb+QJw{KyxvxiVMnTn>!cjG64>A?F65i|MFs&t%}#Qk!px$hC8He^LU?*Y zeWKkRkQ{Qc#^U#QIl~Wo3ZEa8Q1dF2vVvHb;Qj$)tmYmhgU}ai@Nq>j*KNdNa5+Im z&~N3zhe)2=b(#(a%RYw)i}2mP>TIR4@9vwERsn=0J|%{W@pc&8MvE&e(qZ~Y>D&PF zcOotT*vg0|9|O=nQJ~;hmtZ6Bnqox}eNE=#*rP)AbV#QlR>AJ%?*Q2hes&(UdiI9K zqfa<3<c#fQdUXLD z)95xqV2NmywWqgL2kn$O1^Ut=REvb%0NHKzC^4bhltX_pyY+d+V(TJsMYL+8l4SN% z_tDt6@FP?sHn5N>?b{l#}6C&YT#=0$ne$<`TfTR~!DAx!>ve0po-EhkA z2@+Pa0Rpp1Rni7?aEq3B+FjIV3_Fh3qcg)3s*8rpTdAko>_#7p5$Gpq31-P_YSYBe^?$eXzV zu~VsWdEg%0hmG2vzDiIZS1eoWUC@e>#^!DDv|Srq74BRyn+RdF1Mn66ez zI6k|{~8KrG2BaTc0FVz=O=?2S|pEbzQp<0Iyb(Mv}^yvQ8R>i0RXptz=mUNy} zHn2>0%&7CxN1{^r(;>(&r?!^Iq{?SLdrAjUsY~=^`wl`XbNRFA;N6ug`)lA5CKTD? ziB0A*`AqX6)AmJAD!47kXhoF0m;<*$e)N71KTRxsH+&5Aq*YB_c2`q#ABw=(jsWA451}R*iIqW6O z=pjm_3}U$ld)J`*GoDt7_ZAuGa1t9!+n{zbrF-jt@T`Kp1E#a3l}|+}5s*mlvc4Ws zbUfv#V|BEIs)TF;Wv~mLyjkMI)EGM+jLXR5bycvZc&_{}Du@w^00|9Y=}1ZqGnWo5 z!T7yArU#+2g=s?S0utL?Nyj}r0W*ZXbNmL02n-w`!0JLsgbHX|U3~+f&&o9E>chqc z30>0bE~|fH+jQ4t^I37xs^)n#;3&qU22Y5alV~9VHTZIKmf`*&N7%MA*yeGD?uQT; z@Z0EefS85AIqnOA@MI#wmoo-92m1lYrVUx3nwh@FvCcuxLk>X8tGk2s2)?&uvZ;r~ z1m0=VDjwm?xk@WFjFV`fbMz?Sw~tL{)}YM`E$Gg&?9%W#wDAIrzK6I%BGctU3o23M zp*;=Pzrjq?Up!Z{S*=A-(m_lQI%VgrIR%o>j1nEImT#5eKm%ePBM8k=Ur6y5B+;N9 zW@!r{20tS#a8Z)KLCIKXgXG>(`A%z*3c;cqOP!Bs#!Njanhgu45fHRPvM4f{S53jO zJ@bY3|2R>$d&FzVcOu?KjJE@eL@3{wQ7l=SAM~hTK;Y66rV^|J_X=r|*)y<^ zhR2$Yb8qv1<7!C}-HzT#s2rNLY}-=`PdX3EuHBc=eO$5qxD+-UHq}apAqCI+W@H3X z7~_wQ@R5FyBrN8e54v#t_|4TGdu-`a@f6c)W7pg;7Yb!tQn<$Yu(a}jxLag&qPJ!R z5{ZFMo3THF9Hte*ZvQfYM3d(LGyz)fu$RYaNkb0dYWJ*wka|eJ>si`TUZfWZ-V~}_)_{@PtSwJ4T z!T~Y^63%nfTix9$TUvBGQvbuHT0BokV2FI&h=aCcH+M9tG<^o7cagyE7qYw8O5}{6 zhZDao(~4mN2%^lGb|!m2aT1sGNB~GypT^^HLPJ7jGrpvkxC8+-p#&k&41Lmrk6N48 zGI#(W2K}=I=vI4<@<++DJy^3VxH8xjO<}vq=%ckndYcm%<-8RQafmRwNu^Dv{@95o zx2MUvx6I@GR>Lc%_2cF??^riCx`Ese0Et`Tfj$|8CT>wcm7KmAtMRvN9>3Lh?jZQ2 z8KdPVvNcLN0+bKZWf-OI4W|tVBb9HAH@R2S+KphH_v*4rZ*2KW_*C>Z)NV8uuLMNp zdUVbEOz2KYN?;K_cH^OLHDzbB_vl^a3OKEUhBdc#5k0(j12JD4saDb~IvC^jWfdU_ zf(t3i5ivLDUa*o`iO$yUEc?E&ZX6|76FLZT#1RLec$@YCbv}w_ZDCGqH#;%;dWC%& zjj^;f@?Oi(5?6~_?){Jzi#Z?}Mzg9$ooKKU1CDWfm?R}tslYHN%_^IMYRx<}+>tgG zR{RLdkvc%~D|HE5co|^q?7=X%x!G{XnELo$do_quqb+aOqifOC?iR<>(d;Mp^rPyE zY|^DVW0hSs(?mD-V~cjR#eM%O*20_mSPDakLBI`c?h2~ua0ip~RZOf|wjjWwAALu` zZSVK*QEO#SzC$Tt5!>Lm#Z^lCBc^DQ44N*4M}5754ebzA?UTqG9T9^xZP2XBCELL0 zX(!;4kqA%#Pu5zYvo`Qvh*3ch)F2hF8@ZDj#0HQ>Zy@mVB0euRgrj!@Fm{5SEqa4O zOGd$(Q8Q%vK}O5KggNlj0E0y8Q3LNl@#*NQL^Xt@jNN1HIj;}gn^gXnRg#S`@o-nu z4wafab<@b8zfqsiBN~W5D96Bls}cK*p0bh|u;1+rH+g_9gB)w)J+5LraTWNbq#=S> zYEZdRA<%#1^ztL)6ymnO_T%x6*#K#-tK)css2|VyZ@E?*ulKHT6-XE z>C_qBd}&fgk9K z%*Ym+p=}iJP;~ZbXZ3;|#+JHWk3Mwvh6-rId>sqmA0gsL{&wJ|oi%Zsc_0B(^yjFq zS=%CUOURHgRNK?booO3KCKcA}2s14KKcW9P4DO$t-3H{?S2>l&oEGt0;1piZ_ydNH z?+1oI67fCC_5k}HL1d{=SSmUi(aB0*DM45KHK%teK%?-A&8N z!b6*FQM+Q>oA7!-1tBexw3v%xXs}3O!3%)!G}2=?G2|_9b+bDb5Fh1!Qh_b~L!&av zSQW6q3$RC>90fHnKxXvUnb#29G!Nf{D5g`03s!Oj)HP!nMOCtQ`yR+{atrUKccvA) zqVkeZmemkwc4@;gRwT=B&oBYwG;XVdFBSCA0TCadQijSyTI)s)Yy$dre^7YsFloZV z0qKxV%w{KLM2L|1TEcEEz)0P|Ny~mboI0wyyZxbO=cWNLP%Fr-tKx~eQ@AjlL#Hnb zRjUO84)6lkqid>`b~D9s)hLl#J?aj}w*Y!RPve@!$4`7ZC1F|@ZaYToj%;=%rFz>Jfi^gD7sZd&q(_5B046#7ITj($g-;MOi zbhmpp%=HR4w}u247K`lZc~&zHS1CKzFF~T&Fbxc3!#Rjnoav_7WVu04c?ZF=3QZum zBSf(U%Llo2j8R~Ama){8Xw62v5gte_O=1%Pj@+G@=z-56VlnAq+LCb=b=g0B^7RO~ zl3DiJfS>@qV`V!|h?>XD!nkr)w97dkUgJ`4_7WfrnK?>^OloFQnw6H< zkfB0aKqh2iN+eeLkH2+_=Qsr_3mDNl0)0_I+y)rsh*Y{3qRe2i-cDDn{-K%>K7gE_ zl&XwsUaf9sOi4hP1u(C{UEvN7HVOxz2Z+D}lLeh8WCZ)7=F_A$+v^MwHS92DM_g#q zM#A~(;CQ5IWig&L7(U&srYzPCY^p+1$_5HGuV5_5aRJymA7%I90%!tAZj#0x&9db& z&-vlZ?(LEjJSHlzWK|xmeJru;@+7nN6z5wTz?rAgR zizXSLRnPgpEzdmsB$D`*qDarNTxCWYb_Rdf0(;DEYWuS8(wIrI&N;cCDipmbx)h?k zyaZ%)-1Id-mOF?puVx%!x+b|k+mJGF8LZGe>@qMCtY4tr&;NazMl?d zzcQh~pA1Ze44k%k{bVT3Cz=iW>mn+B<~<=vhCRjTyS94RZYDR_dq8G0I27XC2A>ue z_tKmCEiz(092Q77$dm-C4?7gboiQJUS_JfIiIXGqlg?LA&p|~Dy1Lt-tx!a`be0Z# zyBoiDx70KJIXK>cm{%B)9V?_9nKsm9ThkZ(FU(?cM(uVzx>_#qN9@zMt?gNUb8G?G zgr$5LB?<=vkT*El7UT|kTyNa5zp+Zw(N(U+AXR8wXaGif%G{t}f8n|E`bMaN5PFa5 z-!BPLihuSIl^V&_hP6E*NHFL~=}302bog;aKQg9N^w5%#`c285J5=XzqSFk}shUz< zaeUFSgLX1Op;Fxao1zLrwR5^f?MQD! zH~YC(m7xYN;i)iNeRCuEN6`#;*~;1q3H7+5t{P-`^W*~4VMlVP-MxRN+m+?$OW?3j zhmo!=elLk0h<+M5ZZ4m&OXa!!DJEN%O`{UKt!98|9h!h0;BM$8i{M za3+980)=fmcqcvpYmEdN6}IGUO3;{rv4nbg1Ptl8M{s^OLwYIv)edVpv@wCz0-#g$S>zPlM01a zeGyk@_m7PHW1z(tZ2CyA06V0luzS7**zZSHjGRZOxHCXd1Z2Pnxq{`0sDgIdY(a;B zJzudUcguxbq`^7oCVwm5Hb8BF2p^D~?GzNzu2j;uMfBX{c~rtk-QVFkn8B-NjxKs? z>?2Yt0A8`r7Lzk7cw$31r2;9j+{G3}=Cp01&+3Yz(G(>?3Y?-OM~YeUKqlOVK06=f zJJrRKiJLhF9slWY?VL&0?g2>9(6T!nq47W0tcS&;$(Sr#Ds?=H;C>|Aa&(-qP=jrV zp=k>@#yVwEWiUPsnM@`}t`!vu)m>sK2(B(0b?XM`%xKPH{woA_Ayo7x1Lt`k%O{{- zM9!G}+UUwO;kucIx?uf37*ONsQ*&#pbnt&j$C|PUP9JYmBs7e00pVgL?9NG1<^U^e zqZt53JNBuGUC3O(|1kq^3t3LyjK%3EtB6~p)0^&~N4ekSco4oet9$U|RX0AzXbuTy z$eu*>V?Szm&im>(gYP17ac}0z->Q4&CfeA;m-%K>W|zl;E^?~{hz1ia7Kt2Xie4xZ z!}N=OU<8g^ecQ1D1|Ey?PQk4XvKV5T+> zff4VUdk<YXlDVT<&u+XK<;D)F?-1`2yMEWe#ZLH(g4f zqZQ`!a0l8zQU~NTo5nS4Kvct$GscP>%41*jOJB!z>PH;tY!K)S0<1eciiivZv}OQe z|6$f?qz`7iiPg$Gk1cNp(s5vG_hh)gBNz4f%cWr*+60TgA)89AuLAR~9ZQJ%f+qP}nwr$(C zZQDBAwr%^K|K{Z-_jO+5R#KIf)mhaVsX520@x}S>U~Nipf*LX&DTQE30*gwXbMtto zduJSS-Gz=y~?GpUI$;AhHP#L8o)d4%oApg<5qA;Feo_xHJzs2 zT$v(`6GR|-QLqFiO(}&N41fXSBv zM8}@M;;(ze00%D}_@v2gfUldcBz?QjYV*>4S%f*Wc+%{N=H*aB$-!RRtQsG_9IcBhL`@8eb`j)u$r6;FZk zYt>1HY~!E}EJ8@24Y0s5mswBF+33dXj((E@H4R3o7umfhd9@m2frqAFS-bWu7Ll34 ztq+UnMxAMvFt@(OWa2(i1)KNKMu!`ZP|HYLdXsk3G`db2lsR_E5Huf*o@t#qQ-dzy zyY3(|bm!rU5Ehh59P5H={<8k<1U4o_M1L+~9s;{p!FRHH!i@L`2j#0%Z;=b)okn45 zxgy)Xe(R8EIjB$slA_BD&qh~((yp~tG-So5%4}?~W7q0KKj z`3AnxuSrsJ5#E7}G`v*(EHjS^@aXf*kuLynt?lxS1q)dMIesO2n`c53RJWH*JVs-$ z>g78wT7;BQ$t(gf$j_d&ivwOi2K99YE8gj<`BR;s@54We+#RJ3x-OnrqHrOWyW4@= zn={83LOeg{MeYg+Xs3VWhH!xmd!>5JAh3F|TkVfn_SS<}%$}i_Y2wUhRxvdeqgpCjt zN5Ms`E#s!6EnsOER=((!#m9pPp-)A&A7*~81cRZm_q_M$?;{rK9o*S=iAqv@9+`XH zmI>|yz66c!klC7NZ-r~+0JpRll?7uttjzPz2iqa$%?Y5p8BY$E!PH)Z zG#+^ZGAeCrO9S(CO*3d&%aT8ZVC+dzW|mQOzI`fe$79SF{+vcKxilxvgF*S~=!*kf zYUR=@zI>L9slCKA>yNLOez-Y-D!z3~v+5hQzi~spS_MY3eZj8ufb93FtXGtG5qd7e z_GY^8No#}X>&_Gp%pgoqdEJIjBrIn;OlxrnX5)2-ClDwHPd0zKQk01I53yOSA(B&9 z=~yGc*n(`!%$WtHb3tW%pb}F#=&)xZqd7_TEa zU$xe={#0t+g+XpUhqQCuRV&6|sTc~_fp`ogJk;tl&?H2{*O|8Dy;FDj`~yR%)lkSq zfEo9~hZaZcrEX|j(zD(24iT&&XQU6}*4N4;@9w+Z z>-?%_un#Hmqcs zvEI|aYEqTOmA{|WQ`TAPyj(WNR{TH)r!UvJWGwexxEE8lu3FtoRu{wEuYFJO0|0qIotJmPU<2xGA2@AA_Q)WGQm_1vT2@7 zJrvJy!(-~kDyzKLtyq{d1S&$gNDfCD?@50>P)e^!8FLr&N-D&m7-{0Lw5EF9c}9Z^ z?=NX;vreI++@ZA5#x~1&S&`MmgKwS!6HJ%w^hh_KRVrjw&b|LVXNxX#_*F9>F>PZ; zi3>azsweTAo0c()9thle*u)*>;Y56<6Q@G=n+^qDL5_yz%nGae_x9f5bvC|gYZq1^ ze4H_1ez9ayD7n>jsOWBi4@A0Xk8WTNXa^Pxc|>b`*$-IAexb8_906N(y|XKbnvnz? zNvtyWO6cV&%n4kf7LmX*f2MLASCBu!Qn?3tKkTx;P2>(DhkJVX_BbgJ=sa()%8Q1h zXl$)pJI!g4XBjEZIM>l#dlx-&?F_d=IQ}|f_R`{b?P`m693LWkz-*6_O(%FGga!EF zffbn_j=#07$of2DCXor5Poo`T@Ep4QDJ&2L9*!t;yWH;R2wn_G%g+Gt$x0y;1xg6Q z^o!Ul92T&vd$m-=3V9keD_6OX8yV?@`!32FNX1hrHt_;h3uuv*5{gnpNrHI6mTb5|28exvxR_v(@^H=Qre;ZnyFVIDn7ZS3BEq-MX6|GAm)X0-1A=LpQI$ zvry0$%Jm+W4U#dBD5}a6s<2Y;vGjDDY+!yR%Znz5fHt|AUE;f+Jei;yM5QA%zelgG zK|AWZ^xLLMteFj*B1NXj#?+_?KNjVYzJym~gMOxjHv`s8hwPvc^r%n=8C!eg&zedfR0(=_v$;hvNB9f_|D0 zHY%k9xgUS-!MQnC16Ms8Xe%BRsp|I;*eLtq@EQ?qbutz%2n8;Q+uYhGb)PUG+n!q2 zn4;S954_Op%XpKp?=vAFN*=oe*c_Zl{#=@sQbkp%ONSRR}6AQBaN;NC-?8HX{WGajP zpVwuA6I8?&ZNHBY<$jA{!0`5QSr{mXRcR%u0a~L)`M-Lqqrr{N`bkCAieRi{G2Mkr zxb0{%lM>|H+HNWQ4+Q~sfSyB~1ihD#p-Q>winhmb3;U~U#q;R zdomO;l$^ymc7bNno~8-HrfMesFoqA{p;N58Z-==%VlPC8+;aj9VubuI$Qgg|qKP!A zo!P0X7VSHA8#?qTVAtcLB2Ie3;%F<}$kS-$1QwOgmx1S4>0P9kjSO#0)~|+buzgqt zA47U=^S2iT9HDYV%K|#f{EAA=wQyebXOox)s$Nxhwz|1nD3*!Pucv@rfEl)K+jZig z3?f2(mSr}UlQMWWE7DeihF_4@s>QOLtaX1YPliQS5AZb)8mqu{Ppq28zo()&;#^n9 zCvDE@iZP8$@N~$)aPi6w_uRBJa8l^W-^3-PF=p)%+Yn1BWFw~&_+>?0j#@XTE5Yqn z-=PS2Iz+T2zKUdjZuBLtEc$?F#bu;qR??`0NL*nOOJ1;Z%{gmGCmFUr32SVpIXaDR z1hh73jAPvIPQsM2A|v?c5>qd1tDKrjY=A9dkp?HgNhgym%qfE2a@!-AJ^r6B_z zM}*0}+C|z*`0Nw1&;2Y0{`CeyM6ggzdU`TK%a1TXb|`DH2NE>PX?Ac$T#M-91Cy5A z%LTakK=gLCA6o}9L}3+~*o2DVi->G{8bumlG*b-=mls03C5kY!M0}^@ zYHbQxhZOwLYHrs=K%zvB%XJI>^A5XFmIe&EJDLg(1mciBK*|Q3roKqpG)&>4& zyg_^3J>t-x@dFf4=J{_s0ODKl2y=JNJasND+nhO72LB75N}D!tp|Bd1HI+%A+>0O{ z079_w;v4uLKyAwS;&@`OwsNi>E$hTJW-P%*U};N9$sb^GfnphyMgT@`4yy&rU`~V; z53``m77w@%>q%N!aN0zl)WH{cFpDC)mS>nnie7p;%`D}C;H#d$MYc}Y!A<2%Bo?eB z*tK7{xHuOwK*B)QGM&&K^*Tc$l0iTbaP21S`YU#A+h@&gYrP3|=qvSkp?|EbY%6!O z>AOC733Bj!c9AGyHVGvgU=tdfAsEfOGJhLduBlLOgUsU?=_PI z3~HUO%NB7@gI`Mz*d!mhw@6Swp3cN*Vbjrt4_y}Ve335 zZah#3HiC|bShZv=ge;#NiO5I@cRq`mZFmEjH$asXU>Q(^P=E2c;W@QBq2y;Yw5Ka+ zsje7F3yowiB~jT0_Hle`w*adUWmdGdaw zcoEJ4!NY$b1nknF{#_mPs}5G&CtftS0y}7MGQQ6GL2ua-G#r`SAHYVuJ+=3@P`MB! z_i2F#w|}*y84bdb2{s03@Q70&K2tR#`>b8}&cY!%SGWMSsHFMU?P&f)L+6k$N6*ZR z`GgeM5HP+GShjv#p{%UbYJf~wPx)@*T$Wu~EAPU!3yt6yj*z0oSM%yM;=~_>rcB!{ zh2~4tti`pYl8lEK=L05T9o*p(RBW_xWwPz1uE!Q=t*yqY({hT2uN3jD{1{+*eR^PM z4uI%H#Xo};98g$%>ps9nHT0?E6tnq05iJBEq6S?O(eJu1RES(plh5IG{)JgvIQd%Y zV5i*f{W4h>79(ksUIvw$DoAp6pnu+v9d`9q6kEMae%`9{z8cSxx9-n3g! zhb7qh>mid!IQ+qSVB?91y7Kze6k%bDo(!pKKfEEkn3tDt}AMK%}r2TOS&jHXhm)hz>B9SK>&WA2cfd`q~ zG`$Jk+auuwLf2p06t3N0s-;epVUw}MuC9=GM8Td0sP(@oW;#LpObp>s08<_x%;9Q4 zU`d_Oe0P3I5DbS1wr085whiNOJu_cNK9wK$ChRr%Dxe3@ziBqlWxYy@ta&VDL$6mF zqV(F+7${&Nc2%=;kSE=t93T=ka?sE1>At3weLFvsY_ik85omoM3MjW#Wo#q<7hPvP zn3s~@rwtf|Ou9AEgTuVa(1$0EdB&_GNaJL(b7mz1kr;nMbx(lfl7=Db8@ee~W_vaX zv^(N9Fo*q|%jMEtEE}X4>8NuTjW~QuCTXF}>3zQM)wO8RNRO2SZT;PQ z3uZ^{NrI$x^^OV1k{u9TBncSY!@OMRjs0!puN^3 z$VLTtgB1f#c!498tNQ(4v?Hu?Wu(ZvJ5ITvL1C+-GZBfo^e82r6?j%m6l5A61|pFm z6kqp7C#klVJzk8togp#wm}P^)N`BUF_U6v~!hqUX{N1Q5QP1!fpb=-T(;}%BFq<5M z-W3e|?CDcSSP;#wr>-`i{sezHpbgyjqbA zEzHD`;u&}VRcx?zqjJ+gppV&GWojU>eu*gkBMpmvcTB$7PwaoEM1#`=#`0=LDV_OUbXN`=|L zg3_z2v9|Gxw3%Hjib;( z8tc=;O_d5NJIHyx-jwS@{SYa${5{A$3sINk6l9^l*C<+MA-tR(x*LUgf301u&NbDt zkN>4MUj=;}_%x9l&$i52Mz9D_6HL#oUY0K$Kp#xy1g6_YGrnK~T8QRAQD6Jh(^okg zo48XU7QcsTg}S&{)(YgU34>qqXj7e`;_>hVO$_e8yV_IJBQ|UF;P? z2MaTatSwy}OzwXNnQq`(VGt7$KD1D>u7qv^k7Ew7*(Y^%dB>%w7gTn2zF9oK3Qt;T z4C#QBme9LI8^BrGhJuZ#5N^b8p?FT@os6n5lkXl9&E!WIwMaTS59)J6$;bfW2Z=D` zV3B$zZ^NY-`Mx8;o%D)tCG@S39{HFnbHcKF()@wxGg6sh;y>{K(uON#6u(8}S#{Ms zT(~amIuzYotMn#gqh1narGKpg73Ie@i93e{vK0-x%+LD6q!Xj-zHCam+zm#@o-L;f zYa!dRkh|j?Vj97Ny)kbAaUw2E46G@~a0*IsnzT8g6w7e`vM-s$Z+-oCC}0<^>t!M-01wU)4H|PeGeo=E1Raqp_#>ofPuuZ*J60Mc^=_H7*nH2bJtw zi~%yiUJsC)rfBl^Kt2MTpuNK1@2HvvS@#H^w;`#`Fc~9z;sT1(5s0L8<&zI2t;KL3 zCjx+Msy!xg5T!mTUUK}1{BCR0-%o8RWO`k(kimj38O0B7A-vwkuHeYTp!y^5opd=0 zfFvJzg%bkAeA+)=j zuv!QB9hj#eE8DxcD=b48*w={_+^*)3Oo~WY8~V(Fu^Kj{ZhrlT@>~b!q={xi=kG&~ zAAM3X!p^OKhZK`()TXs0DhMx44Jv2Y-s zv=h^A7S7+^>|injBI@Y}WBrz$qv zqk(bIDzTDfmT!)ph@&yJu`%NkyIECmTX{|k0mS9|b4T1G{TYbS(Vj-?`d*G7V>7L*P5@(lXuv-P%kDqN4<*|BM)f-+4aIMkV{gF8J!yD|$pc_zgh{@y^L5|#$1T0>xkrRN<> z$}jbX`CqH4hgqLPn6;-GaNLs|~7Xt3g01qfxQ09LNZes_99p z>ZB=VE7nCHgoX6|aJC7v+ojz=*kP)-q8vK_sCo8=M<=q~Ae?kT*hxPk zWgMm zF|VF3Z?si7wLJAi)?V1tR>#>fa|PMxUZ5G2x#$JN(UT(6wDSQKfYAo!+ddkbf!OnM zya9x-1Z#7OX3L7_8H=YLVRrQ4fEHuaoB){^oKIaTrx??nH1`$s78HbBF61BBPtqbi z5!A3g1}d>)YmMsFSVc~*O#BRwjSJBVht}Y^rND%0Ec7}MmQ$s_SyM(a~<7?f0tZYzt&7SN^rOr!`UGn5_ zF8hIHQ71@&0*7C4`#7t~*K&2dz+={>S68IH2t=9Zn+rraup?PS2f<8Le4NBy*Ngvg zt_8w?opPb&&(iG+;Zwlp-+L8GOUkyDFPU`{IFIz+NEEnOtas(zrNA2c5?89aTgN*v zvzIy5l_oy`pQCkUN=Z%)m&mqV@{g%CMEsF4o5EL)zuO<8KiF(w za4|bTRacV}e>R+yIG=l8O`G!|zM{)DBtq6gK@Aelw z7lF5Rasm5}p<*$1&xt$=$>j5clqTo;24*2MZU>JC=y;3tWhW;uXGbI!V^C9H<+yNs z6e&mLMWDEKn6-o>Uz)_B7QYC`-+Y|9KflK0cKK*{q-!oeITcBblIBy(g9^VMN>)D- zR-T?@QxG(d%JnYBqzqOi_>c}>{%p!L<0lUjaFx@7&a+)AKE8o9J1iPX*X_eJFOdyG zekwb7ndr{wZ&HF1KNEDw>)7g+djbtMJ$yD#O1x>J!e6n(Kl^ zTp?fbu5ron+F+q|kmi%9b=8oQ_2$MqrHsV<^1iBrRt5Nd{IN#QcX|p#%E3rs@*q%8 zS6`zr4tTvNj{&JJjq1U1Flvc8Bk{EZS7xw<>2&nV(srx6JjZ;ym-+f-9`?e=X=+st zf9%U_mf@B2t@+tftOffz-zFn1Fz`U6fa|{UN^3?DGx!kTHx6B$T9Vlml}oCn9|!Wcr_4UoLYBiAjbHmuAB`z+wD;16 zvA?y|h)NW77HldaTd2-YR#qz`zqeU_M3gla*$V()vmQCCoPE63FFsPX&Z8+#!QKk2 zqba~MNYQc(oZ>wkjafY#l3S|JL}#6IO3eCV_wHr}uKH?j!QLWf9yMrxHGGbTvj!4M z5tX)aVp%NGFZvjkHTT_tL-5btX|6{%v5~uvGe@od<)oaX_LmmxD}Ru5HgRiBrmP*g{IVQu&%O*JzWu}!pwD)NT+(NJgVGAAGb8v^_jyxN9+y}iAbVuyQ z;0>DH=epB*OOA)<^T!{I+Z%(x0E+Dw*dw|_knaTyL=Xrj5FJNw42Ba#F9@3x6JLZx z6ooMQmys`aMBs?%5$-JvNR(+4Od&dr;4}`i!BUJ%!`3I7>+2K z%y1BlNExkeAPh~cErq?0b%1q(b%Aw*^?>z)^%2e|3*uO|pJqQyNRGtOFdiW)%k4;% zh@6yNel$$#mK8#PGd~d_CmZZ&n2cPK&3=rBU3+ter9Nd@Qdzi86(tC@?IW!j(S(87UDh;d1cqA8KFJp zgLYVV#%(x_gY6^w3;rMNgaY`_AhO?={qKkWvk3ifwA0bVz}Uv*|H`JlI+o6V=Kmv` z|5LaVaSbTDDF6VprvU(@{x=^tH8F5@aWv60vU76&|AQ>Ibu^NWMjd>2^#;)UVmReB zUb86h;6f`(0<|aNA_-Ya$%SvIOw$^lZ*@|@Y%E?D#ys$9DbhuhsHe#Poh2 z9$)3?cfVHEzA$ga$bG(V#)M8MB=WwW-hRH{m3qBBzwV}{ZdP-+u3u+wclr9h@8#n7 zz8!zMPON@rPvL)iK5O~7d%o_TUWN|i(nlHp>?7*`~Zi%Y1*{_V2$J!M(>q zX|8h~mN)zOdb<5M?vyjyX?@-vUj>@8g=ah8Uu|x7QB*#^$6aYx8g)PC z_s1O@;W2Gd=HBmL4?jcryqoM&H;R5MRlB}F=UH-k0WI(+%skRxdhQ=HNBA&5Q*|;* zf1WU=9gQ=ikIg)IH<@o+-|EKv40wkwX6f@1m#kVj0(YSxH)iSlFwtpwHPP|69C^pP zbRK7(B>K%rctw9hPhfnX8hA~y|M&_yzRAE(sm`d>E4i09QYbXRIGZUNx=^B^q;L6a=*+XTQZ-1fK^B}9=(hMeQLAfzu74N|y=9xqXin-u!@$i9r5Q%u=9ha_Aj|#w6;s{WIA>pC=9>C{Wsgz}-a` zsBP~Oei?WqPi#C7v8`yEhpPf<3tEj`9=t{q&0v&;nn}y=L6ESCwAq{FmpNwV5Ri=T zuDt~4I0<1g2|=2!tOw3?{~0q=^QEN<%7JFyLUl*1Z$XHHio_|LyqB{Gq9#?1Em^Hp zO0onVj|0WpuRPBZF-tF{TXdm5pehm%7Rg_swMvIZI|q&T zD;UXQQyJc3XaWe#m+&u?w4O|44~jEj7zHtiGMqb8wPCQDLN9;~YF0Mp-55iBdBzQbu%7;Pe=}yT``*w8MMJz6K9B*l9@J0qq$8l7;u9PyN1l)=$kxm_D?qNE?c=UtSNE!|M7PUCqV!rTS zN|;iO8@02X^VvM=?$k$!Jzce}Qovsv78}U3-U%^KP7iM(h3w#+BX!<@qeU-O;DCK) zl<|i)5v4n4sM+Sh#d;{$Ot$CV{uZRpQD&Te=jO9(sK%oF%!(gTh=3)d>G3kC$YP!z zVq`;`dw*>XKIBT-3gaPl;NZZ@R!NVT%D6{0Y*K>jraK3;AH=C-H?*JH3UWP4ASbUN z-PjOe>qSr?#7I#JdCY_fBDERMdvCvPFHi%lIdV`+KZh0Zf1sZ9fx6(bE{ItkbW0-o z1_b#(0+2FD{Kl)4^&#Z8C?cX!btNd2<6)@$VyU_ois9Rab}SV=^ttG@AAzSHY0hfA z5&z7s@v?uok|0PBZNvBIGXRvl#l~A^Cx}G(RQV$e9 zjinLZ@~qfJw8cK~(yj-|OD<#`nhg{Y5)UN3e+a@%@!{ntg-mHLRKqu+0p<-JpUL?^ z%1iO&Q8!$^jwneMA@Q;MBsBJ(VK)KTeshI>`mqluYXY51oE+Ob8arGsKr$-g5l74X zvn4?zH&B+SwiHf+TcPs5mMg>t6*^Fw<`Q1huW3320*-k&o%c%3v)^fmnB$3U`ffFW;dQiE=^Q(&^<0@aUTG9 zlwMTRxaeHU4vl4k@AI`N=l~{dp+8d|XtD#q71|W8w(s0Sd1f%}k+wVkz%zFM7NqNL zFDhC*_7V&xn{|IKz}leF{jm3u*O?P|D7pO+$_V@fl8HbB>#6+kM@l2rO!O}b7ae(W zsPvZ&str|C0YRv@HpCw@-kjgp9iHi)o6vdZYHvO}fe$i8BZbVgmyR(Vst+aK*Jy_Z}_d0iKPPSQv`8y&}oBF!#0|8;I9d8 zOCG51b!7*nWX~ddX+zkW5Ni|-Z3!sxvKA<}RP7e0u%JlS^^YS@2eZQ_!kG2@IEA z8r}c#O{4#h#(CA>m<`?X0~KwVBtKKw0ZO$AizGqBIy!GjC+?_7>W~1HliDb5i6cQ} zv0BgiRq6&T4b%;QjHbj=aBgQsJ!rsF9}5BUys-h#o`{}4afY=`y3uITU!PtXZruSW z!s?^2CZ8@#K^IU{YZ+6edDKMVf98+sb~$XWDQ+9kJ}4YFK`klpET5NSQYM5^~@@ygz^G?;ok`q-RF7UFp#-=ZpyP-UbUp&H*LUqY0C=s~eo0h3-Eb3FjRu_tzMTvvP zib)rmI11Hf@*ZWqArLK)kwz*zKoHmCuEYKk_KOjqmFw1_6v22iHwHeVUS@9SvBgF z0<^14BY#$%&8_6t&*Mo|q{TPhDewa@g*852zCE=PNxdmJdy7fr4{h5Vn%;^@ECCp4 z$Ccc=IK-w|~e0R~)HMrP^}N$+XoLEm5oXH{6D4zkg<4$G)t;3C5I<;BF?=Qb%8S7 z25mH>KsyuG@+*(6N~_nIU1)}y@y>wh)(c0 zbine}v)k`{SNW^+lRre>nhXUPsa2poA@KRq=zpXpY{PcGDM3z)>m?acC<&ceP>iOT z$6f1?w7WI6m31(54CXmEwjpDHY44*2_B3Cg8)Qk1+JoApB1o?c;4nMX9I;CO0QTrCz4gX^>rB0_&4@yl z_?pqC9^-9&8VzTt5TkUquUwA)MKf7S+@uxQDQh!z9a47npuBDwT>3ct_Hg*=DImDI z`@*P1Mi{CfbM{_Fq7$*aZ|+SUd`AXfxZVmG@n^gxvs-)09ft4>oe3XJkjpHTjF8^q z_5^*h0QfE(7|UT&0KBCK(EW;8!-c6KZnq7;#G_ZxG_s4dYeOe`TU^aCR_s6tk$D zL=olv;x9$^p^1eAeO91KSB5Mc*V^n|C8lCj9~hAwgQhIzk~HeoSj018JCLW>68Q5~ zrM?@y#*2T6oj?be*I|JLXip=e1sW;-)4s-Pcr$$;%_^Rm^6~s? zZdaj=yV@?n$F@} zVqn+vTWh8yZRmJzzv^FHxdh0(fbka=Ei%^8Db2Ec9Lf^QF!2MiN0riJRGPw3v1@Cn zI=-|d&a{udF#FvC$z>=XiQCw72AN233GByi z+}%n*x~9TS=Sc^6Nt_z#knjGIS}q7ftF|~X9JtEjB0nAYiVFKC{N>7)&rR`jx3Cr?tU6`uaqtav=Vk}TOXeRmA$qs>qED6bSrW%{x~+R6p$=}b08?E z=+Bfu4>pr-fJl;{Y5!|N2U*ydfw8`}B3j4lM378R*y&@9y<3nc zbfTdkz%mz@D~-UXXttU_HhT>iH+IzKABwp?I2j+`p^gi$q{~oIZtdj=Sx!_6p{~(u z?-+23hDx^nGUE{&3|1N3;1QqR&~xYbCOC`l>~nmj?&=rwa-^VnQs_+k_qZAaRnHC3 zyb_MB6J>SdF|cm*FIZ9upr{P7v@{Xz1!!4VGR!D)MDeEYSN{B}hhN6CkaS91OBV1h z?p>rEUawdNW!wTRzD4w}VgL zSc`|fj*&gDTo-vWoR0}{B(1Pe#t1+25_xX1idB5n$dtj~+sZ->I4`U)!pQGf&eDDE z+xu0^7jvbH(@B%ncwOeCcxWTo1SH_WasGD#vIG<0L(+>P$K8VbMp+i!>qUDyo_9xu399T3X`xqz}{VFT^#v~dZrnD&HjFl9hp zbgTg^W%ZRsAg{EbP7TRb$!BrDLRQTmN7()v-2fYoG2k+Bp~;ImTER?;1jfWcJr=Y^ zUIF22f5@}v*^>9Q7_+74*0(M0e=dHkO92o@eDz0NTdo!lL%yPS4S$PvDDfa5ZFiWf zyOC3tu`=49YFmIK`<=(^!?Q!xw6R5cdKT`5(0aZn1HXDQUODEY3JSnxMK}Nn`2wY* z+KmYH+NK50($G1nd^W^p19;7$n;m7~BpLsy))tb~K=U9;UJIyP#*Ewa4vI4ue#fcX ztw@WIZ6@KHd{3+$TwI}EdyvUHl+&;&YI$U{+jo+9;;gG1Xr zDc=x^&@QI> zU<(_?l?E>RU-`A4bUy=@t4b{~`($pv|fvrSRID05?K3 z?Gt~%i|YlPg4P5lPbDCw^@eSlN96PIXwO#AtZAE8k%Ck^Ze?i?1OxRu00P9)$sO9< ziINGAwCkAyspJVZ08s_l+?@QYlqojJqCTyLb?I`o_FfTil`)CsbHPy*n1>(EUQyil z=uW+3?7%qN+$%Ri=3WEf|LBi0-qObZHSJ$w_UM}KR_q2Y8>JDCDZg9!_YW2$opFU! z2TOIZO{=o;-@4aHSJkv0j$F;^KWzppnv)vnGBplz7}2nYV41y;0^RJV8|@xB=c_NQ z)m_({V6P%p&P%qPLsx;lzvVq+(8e`aaDV$HFb5V%VDc_NAnwR-35AFYQ4sItWsoA;7$a6?QBWZ45~$x(kZH4)mNhZG~LDdYoVDS&h+*Xa3tnfQ>W#oSX=o3dK-e9|6v379IV7X=mvytN-e= zCHetYvOIOWfZLLGSNol@mHl=S47g02$+?%j!o`NVI4XKfDCwb&Az{rA&KCO+S;ff6 zO=+;eaZmRQx*BF;M>pP;vIx{smSE%$M_#njV45z|{70*IPkt;9R2qY*)e{Rn3Y1|c4t&|WRDn%manUNF$4C0Nh9|%OJuSQ44s?VKOX@}l4h6fR*##UULs{Ev z4BPzl+&djEZix5OC68#fq*b)Q@H579^y~-v&9G}Pk?xLLdWCbTAI;ype!2*OmW4WS z<|<0%a3_$O<>oAEgk7B6S^jqsOcHOs$V|)-{%_KBf;3{hs~flI!g&45zDdC%7#(7UlV}LEwpO|_IS_uZtIdclKDv_=BzUS8Dh-)jMV+?L9$}uf z#>NSunn5dss>>RlBWx_VtzK2uBnv6dl?mvHt_NS&+|${G{M;%-Y1{-D78 z@tnauy1z(Ut;rN|EWuhs$B0Kjlpd9c^C(6&kI6E}d!AT23gPZS9~ufi4RMQH8|bBP4m zEw%BfH!;%Y5xBsJ?6PFl8I{YXXpgP%bLiZ3{E99T-<~Vax$^M_ka%vG+~a6ccOAL3 zLGEwSt4MNADWhiAhizy9Y7#r?*}z2@`*q30R!bu;e4L__5r5d~=y`@}zE~(s-ed3=O24MEr@rNfKs!r;4~{E@@<967N=vibQjrT zHU|=_ES<)VpQq(hPX^fyqL5_P7!%nt^xgzLV38=EW)e0e%NA50rOlL#<{`($!D5sL zCTU59O;q_>!_sbC(KD{8A~6_CVasA#W!fdZ-Nid;m6B37M8%0#(NBp7jsya?7Oi*Q z)|+E<%5)8n&xV;7Vo(q5c}MeN(>f8VW%nUcr!@M1vG&fvnMC26Xfnyf&cwED+qP}n znoKe=zu=2)+qP}nnApkYxA)$yyIZTO-9P(})BSdzsy@#-?*pQBrJ#hiXcj$oFV+&4 z7Wa8#v-B zH(&1B$skj1+3U`I3s8bmsCKOJikbZ3m_u=rT4xUb4tQw7y$ zsO?f0{EeRMgem|J$v-~c-MDQF?CHfTZHq3kzw8JHQ_xsQ4{Xc9j}Gg&U|*8$=>4%3 z2<6}f^VR@Ddc*Kp2<+T@26%~@$7zu&5hTySb*VRUm_{~L_9GglclKGQJqg-&vTlo~ zK8v9h)TQp%{qhxg8Z?TNC}lv~=>NUCIn2VjmcbAdTWpWH;#;a;J(A0)1N5P3`n_&3 zD8a@OcsE){>3M+^N=KfleDod9CfW7qg{hN|(UzGC*@M5T>7?k#SKN09uycuyWSlNe z-L-O;u`b3QNlMJ~EiM=}3n&PNdV-^^BDEBTm%pV+0rYv)L5~2i?TdTD_i6A*ncq!P zK+kT4coZ3O!7B;V#DS~VTPvR;d%s^THG~;$uSd_~CSvvtdA&B+K~^H| zn9^nR0>k>Tmhm4z6OvX{vGF~VT;6m^|FE5w_==yxe!c-lCKO%U&r*eq;3f?I#=1nS zFH}G6rn{iAW|0Wi2NBztG`*p1+uPrkaAh}piRtBofn0uAURi-n-ssOBo~&fx*;%)B zRK~vqHtL#ZOBHKZDb^4nyX<%P%#@#<#?H}4Lc)y zCk)8&)@?#l-p^MY%9*qfW<(R7B7}vE_jE`WU!el;!T)fT37)C*G0HJfL#^9E!b#A? zExiQSmwbcN#j~b#UfwvgVk6*eywpUXIHvN=xQYH9U z1E9=dBEV6r>-!VNYPJS@vbq;zO9tpU1X{JdI(jLATuQMW?&Cql?feJ0Nrg$r4Mjpr0o4$W+VWxyc>9L>a9kn+Sa!@ux zB4MZOG$9!&ADT)NR9EGPPrJA`+3lN9!$WgBv%a>G9*#)wMZ@{VqutOy+|yB^T}FOU z3|Nu5IR+e6*l%9?3oQqGX`qQNUqhG6vY)bp!sFF-jx?3dxwNb$P3W^z-XH`zqz&vF z9|>|O9;%P=3WYpT?P&SWj@_%Jub;*qBZ3+M!3q6AtCt!x67Te(b@bIlc#L0iI%F!M$O9=agtXFTKN9eZOq){Ti?}*mF zmKxv*G`;LH_$^t;IlBR$=71SdI24^=d%&A7)=D>Q)>pwM-5IfwXJGimwd8!ftkmJs zY4D+^o!_=)cO!Yd{LtaXq5&&gKyF_I-IEZFQkWAJl~hLzY^~%|_o)fuTTpIh-9K^c zVKX(1w#_evK*QN`Psg1i!L$jxt;6iKFXvQqD43XM$MtURRX*)iT>yOun#-ibC6ZzY zbZ;#P#SN-b2NEK}nwf>QR~>CEHWDz2deWCP(hF|kZHN$ClZ?=hvt?g;U6j`HhC)(C zrbVT5JS+7?jEgC0oJ00B0XCWxXuRIOdK*`Sb=<2@ngv};q@x2ajOfN!EW<;)|Qo{X0WHOwp>09Dl@|Wc)yk z%RvS(uC6-YRAFGcJLzv}&}<~PQ>OF22yPg%mg38tu4~wmQw%_;6@g;gdo-lkR613k zg5X@6+i9Fc^H^oP7C=)F3oMk;mC*$ox0i|6xa~;!ZK9ab*@GecmE!fDDyRC_1A*|v z+rXWbvy(z!SV7zjy?gB9-p=3gqa(i%->>fgM5+jS19E5#$Wc!E-Wj!wH@?M*pZ&UVRlZ)250IRDQyby| z{gm9sgZZh2N)D$hu`$-(HA(fgBt5cb+|vVpjD@L39eAD4Dbfj8S=4>S-+mmv-+1_; zFhLI*+yGrO&3x9T5O{=!m9xu0KChV4lTML{BN(m~+g>p=P}|psvr)GfIsBay zjw9F<{q(`Ec&yd8IJP?nZ`){xeKODDw&T)y4t8}duEJ@#uk8T0JfDEE zYoke>C2XYGKxqu>Nkqyn$v_F34v94=4=_t;vELtVXWw%8+kw~+YJi!J*Rboo)fIvd zAA1})f?!WLWWy_sc`zcK3#e?wQQv5Sskjc(C(YMmpi~s`ZWTWy$JA5rPEzmS%5|j8 z^R#=7w3yy!a8(&X%B%dzScO>^Y)By^77`C(s!F)>g9?>_1TB(T@)r{(C;VphQ#>S{ zbaB?fwkIRW8fRU`-3PLQ2yqADx~XGJNk}}f{0xBREkORmOv|GNPR^Jq*8$*Uit7gw zZX**IQ2_ev#xTI@c06gN!5@1aQ?4_u?Z|fK)>x~L@p z%J-U~nYyP$EnTCUifzUwacOIPcQ}%_qE;S$si;5AAlvmTM2X%0fc|Zg<<^(%=Q0do zm2v+%_MIk_)Y}h70OxcPVZ`;yY7ORkMzUEm~?i^3zDi#a*+PIl9kEn-mCk`^9OsOx6!M#2EIsR905 zd8^aK_cXFbgoTr9vYTfZRBqNj>836@A}S?W=}dJ#Pm;~p9JSQ5k%gVM)4e}^Usjm(rmsCS)*Ad@Xs)qAJ|^&4v9 zPq$T$u$7tylReBCd$975k^vu0>GFF+2)lMX&J!?5fw|AozB;D%DQp8$N0YrTN=ckQ zLU~G>TWB4utdF@mz)3Z=cfq!QjG58| zTIMO8+bR$e-r+Ps;v_ulTPFjo2y%wx{y7j?KJrmf$XG0*F}Q2cCKIm5nPp|OI-2#{ zC$V6U)QNTV&>VTC&Cj`j{ILu{ma}kYj3-Cg28z{W-S}*N}K!c`pu>M zUy~>oGn5?mIDh@!enInE<#0#iDads&gNd4Hu=;s=VRESKV8a$pGwj-|eHoCQLWIX! z#!lxh@gxaV_9K*`>AH1!7m#pxVXI6_e=BKQ@!9D`S;+YC;LA=`m0W**sMbZjCZG}2 zJ6b@Q9I2o&NUQ8-BG#F_e8#Tb_%$JLEnsty-!Mk4C-JiQV%y5O8Nj}+$}y+=-ne4L zI@|qfgsG(_)mhtL0$6;!nH>=aD%`qbRz_Q1g~m5M&Am5QyA@pvkGW|V7h0wfO1n^9 z30EZ*MuVKW?HLM>7*S7ubBrCtq=!GYx3gVX)Y{C0u#|6 z5?G_*hSAg=ZMHDZ-P&1s=X=#p=T2~(@}uRo%gX}joHknmH#GHx!&U`6pEH((%l2dOip`=%I%clVs(RcpByOdVu=uX}T)N|o8!P?$YS7liUUa63aSkINN)U!%6-n?nf zJ&-685b@wv{CNvmED#dGi2{(%lph0bFi0#COyAeK1fT;C$bwm;ARQDkrz|m`9ig>w zptqPH0I(I#eGvRn&flP9RN<%Y?PpzY8v5*#d&nO`Q*+NL1C&q7bFtJC;Y|DYEbaLP z2E$y^D5IUrnfhLn$yxp%2b8DF5s5xsuwoop^bzs| z(KQii7Ho#(gp$Y^G1@`Fhl*&4OpqWeKR7JwmoY}Ru&LzlEFlSK6?!(rCCuUC6%FCX@UVp+)f(2s5Da#8^nLjEN9&8(T0Ww0b%?F0wVi=@?$izwQz8@ za<#NG_>W&By^*uI`TtB7WS64gpvQqQ`1JD2frW=7PEsn-{lWE*{BQQ3$`*DlS)oIc z#*j_**b^Sv1XQQok>F0B&$p+5&|_TX&zFz%ZTjl1&zmdPyUUl|;$nsDX^z;ZWnlNm z>+oB%#@FY^>qn?ZK$rK!-QnWmTl3eudsf4=?NUr`?Fz z^zzdl22+UJSq$<9QwDq2Z=uKM>D2}T+nZbO4==t>ZGIhIubxico}Zrn?*yk`=eK9K zNv2X5FKh-Fr;VDk`i)jgsuhQkp$gR%N4dyEq6hXxjT~0_%iLvNVF$LzxuPkW^8lSv zzC+r!sS-Nk2#G}f`Y3Yditt4=A`)V1WWR+9M0ze#u)= zdatn~M5&JESz56%5_9{G|fELT$=5@AVNoV$Lo-HZ6QE4(zzI6(X|` zO;&B{4a*?*B;Or}`q@^WNp%>_ds98jwyU)HvXS1&*84p&Ei5T*Sd{2IByY#km z%utPTY1FYpI<2m8vq8=-#&!$IP;lwfHnl{G%Yog){)W~#c-P(KDlhi$kTQ*QOYGLD zjVSZ#l#ViYUlzNHBMS~vFU2v_kf@Sy3{p!lIvwhE89VZPs+#h~bn|H`McK{O2@}@P zL}lkqjj2ECb%m_s>Jpl$(%_n;5<|*}t&@4I6GAk+B_Mic3x05rlO7`g7K*de}?^{X=6`kpa``-TC5 zex*E~G$XGDY=c!K>=)d84@Y|;H5$Q>6oxV`!Cvi+R;ORQ*M?UU%@8g=$3Oodgkf3h zJU5XmGd8mZ)r3?t9o&E|Et@~B6FNm*KU!fXYP$yIceXq}dfXFsu|3T)GqF^1S6*#>l)5Nl8s9Br zW42oS0OD7am@&{n=okx&pixzV{5$HF*Go*B$#Wm8djNtj9)$3b>q|G*N zUw$T~PewptE*!O=lJh3E+s+7gi$FR`Bo$*yZnT#234q~4Msdc;wk^4nL+^_0)_Fo6 z*geHWO@L+B1EQ6qXZNmhuoX+c{exUy0MS; zxva{puxEIuR+e4iSx#EPp0$zH*X;7!$~EF%)!k<5V2u)r1O5K8tq&~~H1z;igE%#r zZ|rg3%}^AiQ^2CWy_^x_(KlDR+R9+g<+xgn>I@iHnyQP~q`f2RXxS@4`#l$)40Xuo z!S>{dJNkQ#stB~Thh<@CoD23dz?MDg?_jtH?Z-^dHj`SD6e&1WI-sY;B0Ry?Z$gTy z!!##GfbpuE%%KbsDh)iFo(9IngOXTTiG1c16vb`%<8irr`h+z*i1nri%p~4WAQs|P zC-`^=PPPH{5}UPjE3J8=6RmH~9WI$fCRL_GBf|HX;K7^)-AzZhQAX^QO=(RSIw-!xu?M}&gdCvEPcQF`lvq#smHZn>%Uio^Vk>Q%@)G04nD zSEoJXF?<1e9DTSTK(zT|a4=U2m1Qb)8~&*?4b-+0d)M-Uzp@~@EBl5>dP9UUu!`En zQIfzfjpYKLfrF}rMxA0-zIY5F(j%1p_|6B-c@;gUDvU>{wGvLXJ_~*QrsRJInC)B~ zJ!Xn?m7NT`4>UtU=0=nJ^YPs@&9-dOXs8GmSn;fXf(+}P+SwNQ7`EMc5xZ8zU~K-G z{?mR^@eIB`=|ZUm9%{!(m9OLxKS{V}V{+-=R`Nw`6pGxy#lqra7V#6y*JzbscsMhDK@k(NKU zbUML`D|U77mwURqE4xE`FEMNp7Bp&|Yl0^c1|9H(fLAK}}Of!*yL>|BphMp$41y;d*#CqP=Q zgO-8#@M~7mQFpLO@+q6l5oSCvd{POBHesZ72W_`OxvA!z0}bbHqI)i%Ra5jSr}($p zazhA#MsIpbCMw|G3z6 zrN?4sNdEAvFoqG?=l~gGEhWHxx~mhQhAN}o{6p`7Na#?#P?%YtEcsm|LmuHXmIQmY zIkHP=HK!O^{oKL{9~M|Oj>qu^);F-=^9w*NbfsDnQT^MTzmh<<0gqTNWIkhHy1_p4 z2{aiTOrOu=!&v^96T*dA9^po9wfAR7*KY`s$S+68P|@eyv4D)D|5u^?|ELVh68#kZ z|0+Z7{{b9xHZ!qu`Bq*V7+cvJIeXEY*c!RGSeaXy82yJH+rY)q$l1k=;lF^dbhCs1 zxwz1+ey$7TyM(yF|1kvo-+QKsgT1+xg`4w#F2sf2#Krx;RPoO(W5J5exI*=Kn-e%rJ?E<3=O*a|YQM0Z|Vn~ADvRk;2Ri9x5T9&k=?-iJ_DdG6FGLVVcDJyyM8c0GIVUm2mmo zRJW;%k4Z2?YV$Q^fM(I8PN$5l5T_RUh%$e8l})kE$*96om~mWh=zH1dFxq$Y0c#D1 z=t8fYGVam7Hp+6B9_=KrT7`Mik!8lJ&>wpme7=Bd8j} z(Pk)Ss8t}iL(ZVght)wk%(-7_**N9QwoV+zjhHR~QmSdapOoJaM5(k-S})|AkRYSB z$SQ!Sbvaki1@|FCmTtGXI#c5G0DsXLs8Ld6ata}2*s?A@F}7C ziP+2ju;xf|h!D73veab8ZiY!gT^(d@ahmC{aD!>S*$-i<;Avjq1r5Ja z`j=IsmhRZ>-co7uUuhxUa$0xKqQ4*JB-e#ZT>l?o)PD@FogwNuqgU5dC-yLoDT>vZ ztO&W0B_r-gez#6qjdTg`GA&9=KZ0Zy58dJ)@yX6vXGvhno=zS-e3Uv68B`aLNk)xq zL=8*aH6fv?!)kIIcwAc;UJF4sW9Pgt9_F%EJYQw*N8(q(b{1yfm=hN~*}(tR%+GhDdrQSOfJpTky1^(D8ogDLqs z@qq=Srk?9hgE)^>?JO_7n!b7H8O7Qmv9RdTRI~2EcURF41)Z?tfaP8BCdbvZX^@+1 zX@+1S7p;cM%%(Vv2^pRMsEg7WT963XM0{+ zaVhFw={*OU%S7wW;@kOMKaDayEzzCg$StGRr}wBFNV`CHoZi~IlbMrIgKg!vgE5Cv zKjuaq8xPQ(C5S2GemObJ{-H{`2ja0Oc4sK|<|gAPk!IGV^`}k!pa7+(5AgDtXxFtGs|+p>>flK6AHfHow@iH2;O4d)8g#Ip{+aqQCT`RzAY&_ z_2D}%y~h%)bsO$s=)$WZgM_V8NLLGp+7GnR4VL-{Cc- zjV)2f=#&}XZlE^|F6#@f>*iOEi9xdG1A2}0O4u%QXp=4xN|j3^c6y3Dg!Czp>LI%> zRGrNRL%bjy-gueS7mPZsq1!uog9=*|ZO`-RcaaJA1itQ?m$_*<$`JNQb%YjpZSPnh zZpMYmy{{1y9?KU6`hFR&r>+~oA8a4$SF%fGReVA>YMcy;1k2EaqbJG?bnd$}^ksb> z_E#STNPg+-{-Xwy{z;zKb!DWd?&WkumPR!REQ7Ft8OPK-WoXSBloVk6>8mJo9n~}< zE1q^}Q5HB+e!p80h@vx>9tEjnY++0{e%{ zd_&^CfUyW|(84APkJTKpW1sru@5>MFBmVtgfjEYjAnB{;Kg+wW`jrQJ(OG{s-RoB= zZ&<<^iSlA{iBEt%crB!B23oL-Up*cw@fJtqhlRG5nC0^X2)Y7;PWZ2RtsP@Is;4Bo zr){l2^W`o*ca0y89Rl8Oo`3SI9}`fKIfwVNnbnrq58Iua1JPa4&#xjn!s4no3LNOlFxJh{euo2Mxf~)qBN5Y9s4((TsMII+Zp?n z?4V;spx0G!+k^|n!p*jP$vCfvFaf~J{^j%ZvL@a9vQtsKKwhzjDqRLTSdSn8nyBm7 z2ub4Hk+)*2zs^V5qT>l#Vj%Ar=gp2{U_8S@b(p~_TSnAobW5({uqjV&%&5EY*O#oj zug0K63JEbd?r1n6AMyFNBWbsKiaf*csE75Ti=*lhr!|GGQH=FEO$hMom~HCAUEV40 zEquW>Ny^9-Nv?X-Lxyw(mPGYc^ELRz)4eU-IU8vV%UD)YI@riGd#D0;@G_WA)`^1u zFYzlq{*W+=f}_DqDAfp1R9V4L>i7Ap<1^o>48F?=3mdy>`&G!vlh#hZ1Bt-$IC4Mi zsEWhiNgSr-5ApS2)~`3#rh_Y6*FKD{`Ww`auz{OckN%zcl>BiHk3PMwR#gAyH*|=< zjBrz<{ZUG!p7k08MFa}oJB0V+a$Uid{`GAkPmCGxfyx{35%MkLJRW`5@gDk=Zmt=l z{0f2|t85~5P#y=(sMksu$=N~}uoj5{e}aBLf$6h-vL2m`I8bDbi+b#}F28f%;O1Tq z_$)b;dk%OgdG%k^)MF}+1yyNoNI?cQla-LFDju9U|WFi&ldr@Zjma zS0Y$s;7?#r)el*p-*{h3s+SSjA#W2Tf7yO^?z-)y-K>xRw(xhH= z^REB4LnD}rBN=H60)m4M3L^J^gna)$j|+DrTQ{@+^-1Jqr%pZTs#@jCRP|2bHgUi2 zCQty+Rj|Pbo;!u~M55xiZ5v!EOgVL?{ik27!^9!-6%UDYpDc>G--uo-4c`VypP$~5 z+XU0s#j(K0dG`Bl_2=;P=Y9G1`@hTL>#qlm?C#gA^nmB_?9T&Wz|&p!XaDxsLG{;F zarbkAz)Su22%x}M`PZjM_xowK|HtH$fbYYkfZu0m_1B9+KrTkWV|Vtn|I^Dh|JT0< zM1z30$?VUI>(8U&?@evS9iw*?N`-bdlUlhuNDS=9|8hjPt~6vcTWQDi_;HZ4gnwQ zR~PF6FLrl1GXBF@^GO14lht20cOGB+U$5iS1|R>@ueUaK?&`X=eGU%YhQ$It7ZJ0+ z4%e@{K8vgUA4~||S~ApL_kr*91TMOF0sbHTUmqL>0bk!`zmIPRJbcf>bGm==v6t>W z!RyKPi}1%+J;eEN){Vp0J4L|Drp8zB{j8_o+j+p-WBT;>lzkZZzin=PK7N-_5Y+v3 z)?VE@%Hbf;G;QGjvH2wMewp3fdWP}!`ttR1^!*y%YQEq1xP1}eJUHg~92UQSpW61W z#dw?U`gmC0_I)Tf@NXY_EH~)vzHiwB4!7&;dBaWMtUhvnh&S;~btl_>f7!ZAjIYo0 zfcHlYhi;!tzo*6X`*eqxdV$Zu?&ryB0*kMM>CZii?)TB`17-WuuFa;mu4}J@)~AdJ z1RZOa#K~^1g>7z|F~8%dnO*lzV;Mhtzk`kAEPK0UoqMVME<2r5lcROar`ko`A${>q zKj|mGlm4TT&WmQ{p#grdZ`WcDEIuS%LMXH`%V`vZu+!r$nv_yCrsr z>m7Z!NSCG#yA3xQpKFur!bz_;)$8>Iwa$W#wM%c+E4$?6BmJT)qZ65@w&snwArJfJ zita+68rL{${jZIz>SbLY9Jwd$;md?8S_{9W6*f~|@k=$424K_jtLOQP!{w9ild|)} zgAR_0Lf2(Gzc5DtKhMMNYf%$L^kO;U{D%9qU8l$7C9MyhlbM`Vwa%8sI&VC$gZ0h& zaARY}_=2x<2^GCt0bg-#OtynVb19d7l-%+~LB>^u311O8Oa5ufmFk&0{hX}qU?axK1?kz4%frl?e&FShbJHC6J1sa0w}shN3C zz%OvCfNK8gYS1IKnZ3~@0zdgu_i<@$#j|F`-L_z%Y;d%SLq2l>;ttj3@WiG;sX4`n zR3Tk!Jv%aV2~ZT@ejMi`-+JLRZMA)S0_3#@I9R608ECooC zBeu4rQy1uGcafGdIo`|#KGh}ZDQs@ZdVcuHwV-d_B+C&{mudhCVoZM;7_Ej)r1>+C z>TNbR?OkN9Fa20bi2=~&qnCxW}PeSrJ63FFCK0!II{?Vl3x-19h>)#)M^_H=|-XP`mUAVv#Ac z3@)LpT1mMu@?_6>rD$vcp)Zq<=2xt@t?1|Z)Ob`cG@wc^Abxgh9?_>!n+6ZFej-dA zy8LOEpFZh!+2MFy+jZzJ&`z1Oy?~{TY^7znaD&bKkVnmXDIP~xo2m*9NsRO2(D$qk zp>#j==|+A%Zq3)sNHX@2BKf|GfkBbFu6uyrb{kW4!l%t(y4)9iI;r-uIn4<*{PsqJ zs{MD-%Oz7wW$+`wd^;Z94SO1SWh}a~ z;Ci!^g8XIJg_&~suLyi)xY|lx28?B68FtL9mRlTWN`= z{owe*L-HxC9Bs~;>bcmAiIDFNwIRefJl__s+)UVv2#?kuC^sKEDB<{DFIBr!t-iK} z*-v{Tf)@onPj#1XXX}9al@1)vY1)m-J^DwIgs0D1nnzn?n`iPeO?S|3%1t$JDDb1_ zT*=CEMj#{1i4!;{jPQwF=41Ntfd@*_mDCU1GvGvG4es2;O50{QQn!lQKgTUZZZ6mG zvg8)(Xsc~uRSk-+;}4e`zc_6%wYL0xHXMdp%aJIEBTPt(sW!|{kouYNQ z%9|eGqh9X1U|8MAHjaYSCbntkZ^e6xz)V1EMg}@zBK>|1me1|e>tRQWrO)ECZLLW* z;aaZ~U=rE5ole&zWnw5MGbTNBQ2Nm9My0_8hW%}oVpkD35@WP-&}Iks_ptFu4I(6q z+C@McRLnkfAx`aXYSZ$@;SsVR>fn1f_^S(jtu&{@IBRte%VQjk z8G-ew&Nf~Y*Dwz@I1hKB5e=hHK|skwFSc9>t2LbDqJ2VFuv`8@ZzJP#I5q^!P-#w@sTKLLK_I?{~(Xa-#_az*2;8@KN0I(uTEAQNaYK`cZMZ zKT_7ZP-oI|d*oLyWjO=iwx*tnu=&8P#cRV9X|86zaz8T;X_LpsQsC#T_?bh-iTX4s zidF$ml&%Mi7Et>m6;ZLszZ z6)Ef1zjEc!JSXIVMfUO z!69HptO*pPP}-~xZ3al==Dt@^BsJTmkV3ZWwwEXmeess&0U>7uKQ9xEe+sGo(_e>B zNvwV&l%JO%aZ8f3hK1%oHD;twQjXR)V`_Ip=bhu)#It{gK{k_nsYs3OlZM-LjfY>Q zL1NK)!|f^YIFaE(MrTLK^NA+NFJsS=Cx{W3A73u{Yn!6zCMn8?Ig^%lWMU;}nzc3s zfzNmSjc&I~5eu5VqS0Jp!+8evK9=*Ab^n`4%OVy`yE-uocTT{J8E>BDI?8e_3a#s) zOl*8HK*ol=z&Xxr5&{IfN`ffj@fMAf4GMm4jAp>nuQ>`imSbs?dvWH;rP?zN%F(j; z?LZJamHa5`DV1yea`2wGfhePM{AxwGd=B2_i=l=h*jHw?)?XI?mm+CN-=RA_1oBQA z76)(TfRaSGpsXHGF`PdNa#H4y{jtjKvfYO-*G~thSO(qjeUz&HP3j~M<+2|A2Ath* zaArjYRjnLf))>o|>GWHRQ*S2L4!whRv&SkP0K1ZIvXn)+Ohs8& z!I8pO*;Mw(%xxri?r;gD9lX1PoR){ZitX=x7aa;;_Yy2D*?m2oZLqwSg7AhjY`u*I z#%YdJy+JIqe2l$dJ*9%6(Zg$vd0%6{`oFQ6iZg%ds54-F z>CdV~n#|nw8E;_R{K6@k72W`^JhKyGpTHVCHWM$jR0q(ARnEeDL}>Gkz19TaKzrcC zMb2WJ#9o*}mbri)&)Ijr^-Nz7n=C4_trcl4RHp_lXbo#GDFv?%6R9Yy8)VR@3*hdL z0ZbpnCmfU7HP?>djmEiA@HmX+-Qy2XCbv>`*ZTWbv-1|Siiw@yy7P@~pp;49)=R~o zIVH2S3Yh&fnUA_ttOP320dkoy%Q0#SfVTU81a>?#X=QA_TKR8a=c!P2*72~2y{Sp8 zoMB5|6jJiR@7cj|bJe0j10T4yz-wMdrLRQI)=?^*k$D1@Y#xy+o%136MO_#rwNT&qN0OEVVXxtN?7blTLy<^8u3r~hHm^dN0fbFfDJ>TP#)(TuJ0(%}x@Cac zZr1xYGz%-vYdXQe5YuEnA}h+(P@P!cep2c@t!m`OY;EP3j9)%*%r_{?+5}s(LTTWu z+2G@Em)@??k_t`b{K^pPpBrwmhK27F?pc}sO~8f%R>HI`bo*92A)Xg(tz zw<#CwvQ;IqHq0DHdK?c^Csd#dL6cJ@$zaTmht71C+q5c%C9kd_1+0f?e}-Lc8zGJxiTJOc(X-G zj@}&_&UuFL2~3M6_F`Z}SS?9wB2?$G<|4u#If)Np*d)gZtoNjFBPcwbl#ZXd+s45pzl5@J1(0fe_al#!R}*dISBk~7rMVZ| z?T|3zm=>jMZLo)M1U#llVtSJ)3fB9t{4^B~$6Lj@Ds^vQjEGiPhXILQr--F{?S&S` zd5X2p(V0j@-JN7oYet9pTuX{b1kP2GTkQ+VBm_poNgXC+_t*e2=GL`qH*zaO2CEPk z&1jW6nS{c_YoUe&b?DSJ3yO>C@5mc1x+Z8iS5|PNs5_-rR9gIk?uL(Jy{eJ}N#IL1 zfyxJz=67gT5!ELX$*hJG2hAxTqQdq~ewyjP`3WgLiq-COvjVGk=`zGFeS(sX8>)qhy;|2lA?jyvZ7BpTMABjG#=Hey{u@<}S5#xb+9R z2-6D@qz28a}MB@xwysQ+vUA_N1+{3g6j#s z%^H8xuOwBgy(EYVqz>@^e<=W>% z5=>E=y4YKiK%stBo}-^8%k~tr|DkIR#FovHrT5xKIaNoWf zk}!1c0);l3hvl5o@~1Kc0!#} zCf2#ZHgQmxi5%QM9KwQ#g4h{oTu#>%;mSVLUMS6FD*`_^rV%Jhk2uIvS>KsTQ4+%k zObX&)>JUVx5fi)vLXwz9ejmq4{ySRdHgeQbG|5KpTyQV*q+a9H)~OTcX`&4nVT`Mu z3tj*Tl}xZffvk#-CHwPjn%7Y4Mz&L!f`p_#Ql~i^OG{3!O}A&{9IH z+18lObi@k;`D!Fv2yOt!{7AM#{l`-@S&U9EcJf99FSm_WMC^u^8+I`&)DMbA<0;bX zhSZlV50wDUrMUs-+6ECi1hxbewqPsb<<+bJ;U(K!cGw;WE>2Ls`9wo`klb*ZZ&ihR z1+7%-TGL_;L*XVx>q2zOU)6x2;U$RXnrm=*H*2K6o>P;02X?sG!k`J=y4>F4v5{rw zrxPn8vTmeYyBQz6=bUIuDWcJ_@NNgxc_AZTAtdaz5N~(CN&4ZV?-1A`oW}E-pV(Zb zDY>lI#lHE!K7r7~WNR$=&f_xoqboq;#D7}7NerK3ze|Eq;h{*q^CDU5xJ0RxjInUo zel+Ucu|mBSG}rrb>a8h=Y?smD^hl;^{XUikcjMQzS<7>_x-lkkRC3aGmu(2^l4d0( z&FsjMuZW?H$da=EbJY7x=&XP<?l|0s;=-SNca`bmm!lZG~S9hjABj5dRG=->! zgHsT)G9e-qYO3TJG*a;=Y$C*UjkH3M$h=Vb=}!5<=8iS(^Jwi+AN_8;{s(h1bYsIi zPrPDc#1?*`Jb4=kEtJ}zG?3~&!y~plZCIWb0(b8N6$YRoIpUnKu5DzX+rAOfHrU*EfANRD7p0&92>a9}cR zRBTe0FEPfOHINd?=ue%R19~+!TRN}Qn%@m#Y(4XjQYlXRfi9cbk5ahrSI7QKcUlFJ zOUWojR*Rvile7?~zXy+35`|M7g+ z@*B!P;KCaZEzi9G%hjSeYMlNY9nq)It?=f`GndP8n38L!P>{p{ffv==B%S-OD5iz+ zviHRStIgXOJSIO)I~GXQk}9}LDOx^-Cr zD;;ak!?JNl-U!D|#Me-lTn)M-_(V|iAhTAS;my#gO zfC=hT&|r^)O#VD!Ibg2FI}65jn6O4(E3EfTiBPd% z+*OD|P%W(M$I|>s$98wulnPV|n`lT*=Q{wZAj|km#_|?E7%$qY;OTl9;TcOmyj${Pr|+%Ndp6 zv`jCyG*(4Y0L%1sgCukk`7WzF;P-BQ0UaRCpDT2AOu|^lrfU*D*b2gtLzLAUWt#!P z!-2!5c}*jg0we}EOy6(*;2p)RdE$vjov5Qu;AUohakbMF-02Vf$Wi01lLBFjgJj1d znA3$7ED~UH>0toZgQ^s7krDJ=5?sMhP584PS)UZJ=@P(q(yVutd#0E|xAofSE=L+1qd7PVvbX z$*s-j@fEVd^ZiJ!x`g4BJEonyG^sHoKb>ype>WW-m zti9h}&{;#3C=lv!;cAU4$Z>?Y#GfiCPjmflMqP`Y5SgP-+EumnlAx@s$G4q-wP|lE zn=1Z%Nm$OvSoqV(Ldo zApu;$!8IU5PeuJeZTLyQz-=(BXw;#lU7ZkH(Mj@MVaUw>T7+|zMu04^HUL3ROhht6 zI2O~x>yM%=gq)8=*aFK?NCd=m?C+=ih)Wj$shTjBoR@u?R7i~W0NP2QBnf@+I8M&h z>m>xHHyqX>(bGWMmHqyRb{yUfU?>zjM(OfyO5e!>)xcU|s_U_Ka5g7LXIMH*#ow8_KTR;`x^-kzocd-I>F9-JlWh2dk zEQk=U!No=<-wwEiY>CzhJ(qWMFL}i% z3WrvQ>9k$0x-QsTp<-nA}ttAr#$*rFIF(Yh>+ za;IuRYH&KpPrO;0a>LGwyCQin>!^oqLnMG$SC~3d43)Cm&7r+XfELW?ux);MucjFb zsWA_7xUWZ7Lr4~#qm8rn1sQ%ALSdVxdYPcuSLf(ijxe>%=gnYt%{Rf4+}}Wurvuff zV@WCEAvn_10(r7XhXdZlC?MlN3=Q-2b-WLM>>&Xt!jfK<2DCZEbYqM3X-U`)A)QHo zvopr@C2)d`G?x~acOsg7)4VN z@jOgIr>{IBzONm$at%NgF@OX*(RPmk?ov4cHm3}b3@!PkkM9LtUV(||MZguOgO4Hn z%tIl9_Ge`25yF=d~^SeZf+n`%j7mMU%lZG{Y?K3f+?oi6b&yzsUy zh|d5|8b}<2&nBfJ64=H`I>j&T(!$f003);0qLA!(sK9Gfs%Wh<|w+%wm@GZ)%J1s&z>rxiry^)}z2*JYz#vJvr(jqA;^Nr>PAyvmAus7m-gvDFbnAPhg z+L$gkE5K52RokOIwz77WjWL&(Yq4qx0;K3v=vy0u1S0BBMUFS=bk2F~%ir) zS$4A5+ZJnK2X?g^{$e?V8DZ_v`p5U`%k-IsAyH;=J-QZY?^NkYA>bQ7qY}>=Z9UU@ z7au1`ny2`0_l!!mP2Vpbzy?^7?|kjL>=S*3DVVi85?L#)H<`!9LXVtR2T(>ONGELt zEM$LEtjp{k&S;`ugi^{DQf>lg29DYuhAcHVs|?I#HrGqYTl7bPw$xN(e$yy`G7Gph zwz_sGE~eJp3*#YbG6Xk`QwX8N z=-Z~$*4a+~2YZtD?Qzv%)9!KK6y6;5D@44tMCQU8EoD3bD@zuyusStkB+4@QUos{; zq3(qEB2ZLz>Uny3t%p4y{n%|Y84jp@>iGb{_N>42fJ@Ll5KT9yldmOuzrXpwo^=T| zIoAJm=jP$`5nuv!9d(wZY|KEiC|x`^`B~J=)LgFkudVWXnU2TTNmC5z`4V*t#@ms- z-j7^ZqT0zdDo)Q|&g)%cuyryKX+q{W;$>1WRLO+l3S{bQrP=O!nUb{ejS)oA_kUh0+1W0s)$b;6+_Uv6cd#CI@mB?mnSpF2jVMXgj3*}f`q0So>l@; z>wDR!WE5p1EdXdU4`3kxXcpX4PEFl)E3A0wOfuLbKm#&aC}Y*~DG6IpK4wJ&cbgfV zd?0~BlR8w&=^QBo)k(0cM?HwN;zkkHn4V+`wB&N+_<9NhvHx3GAeAIhV`2HNgp;Yy z!bc}&PLjQ`v2=geqr$xo$O%lVG=OC49*#&S_D0E%$w(J3BCp*-+6GRa^#~ayPv_H5EvdRyQIRlNNsPxk|zbi&SjI^B_=lmx2?sVNw!tP zQ!ek7dkAE^NEA$RP^W`yM4aRt@O&-*_AIt|1jKp7vvc^UV>AKGB&rM|gW;QymT1Y1 z`8r=Mr<58V-DI)nW(YP>su`qIU^kkqgl2jGIlWYlZtV@OhiQGdghbXmwN>F)fp4e; zNy{UH#e@R-mjUDO&h-+Y9~6uvs#&B(?`3=J_vI3jW|D^0D*%0yRQJB%2zY|z_y%Q3 zp;oCQpz<(1?q;^*=D;E}11ha$k;a4IblBGvChv2k$h=?P3k-#20|OdX!G|L)OKF`< zcKMA{UyB}*M z?VvI!II6D&fG79h5P%9PYPDL_;8z~gzt`ku0IU-TTTsEAuSlR7+J=qkbwC9eNlB1g zH>xh~IRA+w*0~v|h5@)N$WFvi|6!;S4}~xh&9)XQ3s=sY6PFInQMDH=2gT7#kOQwPQ$5|SV&H|j4aPaK>aNi4soQZCEKEs6?l-QbsRKKfFw=N^yLxxEUbhPPSo{q!Vhr3 z-T!EN8vw;{8ptrANfxmt6<@E+Ttvtz7si%)qs(zWfYFz z<-LD2X$}iYCbZR-e=&;fPpAAzICFJ>GeOQZZx&1jzYWwCzHg4?$Y#D4u!05^8g%Du zz`NPbN1l}mBIN!UD?OL)e5716Wo=-J z>*C6K*ky+|jrVHPtOOTDJzw%WY;NjONuyVU!v zwEDZP4HKi}F4GCN7#f%n6^6LqQ~N>6zz`n^$OgsP`@Q{O>YEfDmlWiiRA~}8ETg?( zGtgD0#{@6T2T4Q|kR6CNEN zE|?G@jc^e`-2*ndLzuSBD52DI1E-*Z{H?E@4_}+Yr45qRfb&f_EaUG1lp&FG{N=l^ zR{;E+$6v__A}LI5O83fbYJGsL0AX){Z}6?c!6*B!gSab0-N2-uP$f>U?yhVxLyt{s)%Z{qey zh{=>~usB+u9ez~}>F4P^e5>NcQ_ffX4(9iuW-S4nkYNmtr$FWR$zmAh-%2bL>8H(Y zCb`88Mb3>B&0Hiy=KR`%>l>aqhkm1SBV03$$QD(O9a?2%xHSy7Iw5x)W-0hj@viYe zDDwy28muC~E+Gls-aB0Qp+v@WKs^XoIuB<9yeG^u?-G}85_!DO>ew#l zp#iFx&c*ww@+7F<4&-wWz2)*uvoaFiMNASn&alvRkOMavKXN!>2@X0i((waU*cveq zsNO&zEX-#&b<@^YlNUg)Z4#Dp0ThD^=ymVwVRqvD+iJ2vH5c&vh?ZUs$K3&Evo@Q> z3bg8U)0pS-Zx60;J>I1V;y9C_m6Nfot)3yOrHV6A1ZHMRQ;leEyfIvAhntG}YqP{U zPMD})Z>Fkp6QpV5)wpD8Z}T$BUQhB7WvGsBk)#~K=LsDdyvNTVB8j9DLHCQKm0~6^3?!$iAUGXdD`ipv=?{u(5o28{Ws(uf=XP6hX3kS*9{7gHvqRcf zowz1v-9eSToM4$CPDyvl3dhcD)6Q&*b{>=ZwGBEUW8!yUI^AC2+xa~bxEqy{WDrWU zQGkA+LbI{WM=XId{!FTYP!&^ry+(Jj?;A<94&=Fab7?>bD3eN36WD-p_S$MhwL0%F z0lS#AR-me0+r5KAX@?Yx8{s+9Om?4-(r21&HQS>pY}1W2D99qq3y7PiBc!#z&*PUvOQW7U`z{(Db##>( zzxo4wqa8zx$GzsMetjc}zK=Tc)$kHe!s|%h{NuC*wFd!mYDY(VO%h`nxmcnwn7!Yl< zo7EP?jW9#r+zH`*i80yVHJLOp10NHnDAtT5s8+>(_>~oA_K6c1R0*dmVuPm|2p=*5 zlE7w8YKT;5C7?^9kn#8Fz2+&Tvy zGfVXioMojDBq(oFHVbJmp~k~t0(5sn1U^e8PIt}t`REz}oS0t?Av4=ERCG+LT&<50 zzysg1)7hkzAQ?*3#X3~^CK-?WNNXBWPzHE5T1$aB9YJEM{fdW|LyPY?)=>FvClK_< zX?@`1{AU){IU|8h0-JAGkkN|(`V+c2qy=<*Tv1l5%gt}69GQp>GZ?+3(0(!&s>@rv z7;z;uhv{IdMa}gEyn`^jQ$-NLJK94wpm}!t>0yz;J_e+UXyZ<$Yj9o`xEpD=5yW}M zNXb>2u{QCoD64n4l#UkJCIb=O>(PhGamXhdRq^voa`~7Q7V3GoZTCmrINcSfY=<_C zle7SY0~9Kmms7l%wY!r@we=8Pb|hsQmSBlA??&I8f*Z803hn`}nch;-Ugh;vKNvtJ zrW35}!T)c{0KL?=BNpgdVL0tY&I-mzAJyYqpxsfEYJOSAv6A?Ce2U^E% zw&R(I_*Pq7I}G@k>;;SKe00^m!z|ZQLnW7I3Ho?U^ktsMV;^sP`EbX=Z8f_gBT%+C z=@oxZ^zs1YPxl%E4pLn!{eh`SQ$Z~QatQ!x__X&0ul0!f(MV7Z2y4eno~jRg-P1O9 z&QJ!~+Q!c-RvceuNrDXuR$^CL6vT6v)8yC{IH&>2qQW^;$9@o2&>09GYfXBg;ur4u z;F_f!Nuh+zdv?IKPKT(FQiSRWV#BT*Y8R>y>iA3zK}{+Bs)Ephju+7`$y!$e1#V2F z!|i%>r6As6En>>I1;r1o`|yu)I$P4~^$PkPWlf?GCWZ_JhT{qCO>G=?(8rQ)*s?3Kx8ZUjkW`*hIX>XBYtX${ zV+RPUhD52!Nsk;Q>~yLI(jJTpP(}>;Y>k-H3hEu$30U9;+bJ!Hsm#A!k3M$4)3;y+ z)PU2*oIV;(Zx;{vNmW?27Qxx4fCLmkgsj^z@m~pK@H6ydFmvxB?7$G)vV(<~+k-VC z$02ye_(BE>L;Qh8~F9zUdjHB8L6 zGPs?3!*@&7Jxr-Y=Y&4ylUIU%AQg~W@Jei_D`HCF6m-o6i6w0wY|kJ!Ca1fH)#t;V zL`wt5>6NIdp+RQ~M2`&OM32=Tm>aU;wD`#W7Rv9FTOG7Wnc~btI_XF3o`AAiT~?4D z$t(NuL|Ep#`zFE5)&ntdyB>Wg7J6kRKZ=Dun91w)-Cc1$nWxCfixLSet$N~-8|}s3 z?c5Eo2mA0P=StE&$ldh$bHc&^}S1;8>SnBk!7GMG<{X=Hl3+LiKb=ryy3r z?&R+P*$jSm9<_S*hQ^~$I4$MRQ*)d_z;>(%He*KUo9}-&(4kURS17J|6PYw92ecd5TA0zBC^xyHbG#CXq2_5w^ax2lsN_Z(jruggxmnxZS^QIq1u!~e=)oD zdBtMuB5*~tYNL{5_EY!K*tqZ`R3kR9kSXokBQ0WbU{Wkdlt#j35*Cdha*NH6+v8iQ z25`+9-4MWeYEq~+vSCl$a))4T3!91_IuI&`7f{0hH{}&gwI=%=AVs-dkDv=NqY1G- zcz`BxX&B?4^7@$9+Rx+aawz{Tj+#M#A4yLT$!Q#{zhC@yzewDF+L+wj3!p&-6P2hO zQz!uWq4UavR$3%i(%Yi33a-V%P&*x3ua^Kxqpuw% zQFq6MC)K4n?qy=oFlD-q3a%p;)|>c(K7^2DZE2Hp|GHxdE|Lsd0JW9^8kG+Md2jP#;$;TkBoWijv0WZY_0LTA5x6kL3~6y*E4du;+S^{l1aOX+uj}7g6a3%a)%t$kCx%hYod>g~Igc z{?%5+r~zn^DAJa6o>VrlOn1zv^U+75Quxy$$SAZ#*Nwoa9g1rN# zv!#_!MJW-GNbj<~9#M2W<)~wIw1ldJYyxGl3!c1L;=|M!J06V7$m4ZYu%~#g{4Xkq z5sCl_4Pog>N)0oY4lKd=y*#D|p|XW(Lh1q%+gwS=%0m!BeS)iJkzQ(c6LC!-C zK+CJUgY^i$w`8)Zhs6ZmY0@el;mo;8D>aOhXrOcSDB!n`O=s4i%?mB)&a&*%@Hw>c z0*t5IMNrZ~Ob+E z3#Jhev_rBeGMQIR!LdE_h4%kAQ2-1RTyJ~CYshyZ-bReK1B*l`-S(+d8s9-?g z(h{Z;tONH7X_478u#kqwnvQdC^MB)NNfF(S-btt&nzd}(QwmQy56Z6Hm(YD&vHiFd zHXAn8N{1l@&-!L$1X38|kB;z>evl+A=9>??aQyhq)gF6n=~D3&(`sYa+%OjkWm{6X z#`>_d@_)EnWOSmpW(5+7fliyTKY|>l6~b=+GJ!;s=KwSTT&i&^Z$vTuuw5r}V8HP{ z41Xe27u|*v4>;*X5)Hd0Mrg%K$?V&SJ~~|iWOc|P`rMFuog`#RP=s>_fKaKaDWPs@ z>n>xEcrEzMfkjzB9=gH-G6NFMbJSbi-6>mIbURZ2!=+k0Pe@>heB6kGwqrMUG^sRw z2Bde9!0s2ayVpwOjGu=Szb(^>VFC!E%$Rm2dp~g!m-I*gNLHW5<8eYmLS-|)q?fn^ z0W_fmA!V4e5svPy4k z`AYay^fuIPG#0M}MCE#P&HGH~PDx5&5k7X~p>8#0XS4U{UF8Zmt%HU&w{{UdymtdJ zUmK}b(kwa{6i(2mekQIwLAQ?upsz#k?uo44~aeJ5~B~_`w zFel9_n}TZ1JT%;qHWpU=2+NT=K=Lbf30rs>VC?L{Ft@qcaL1VX_+EQ8h*YC3Z`Y%1 z(beu2$J5d5C-?ND>WXaAr8;AkT{Y80H}_+UcD2QQ|0>qPoBCJ^Lx@4Z4Q%cTs_AeC zlk-(ftXZ}oz@i_0N5O6H_wP|_Wlz3CDPa-Y;J3w9O8XtjZPw z58Rtn{+CsfjWF?WSJMuanmcvV$e_PbpU@*3h(9RDz<#R{`;4Blk{Phy?F=`0fGvX@ zYvVnxVmxsb_@$&Ff>>%$xltj|f8_M?H2whws9T7aZ|?a=3;WUEH2?fKvw3&J)NN(ypwH9NCJ#-N1OOE{s`1|LeK4v1 zS0B6omHODhyt!I?AZ_W?8QpwoQb*-Yespjq%}$$!@sP^oGC@s1q7+r%#CY|UfkT&H zHXkK(T7``s$yD_P2%FAk)g73FW6W^Nfp2P&EWKK$8i z?0zlo9Zo7;A084?d^60*7Mh`L6z@=U_GxGJf*r<|x?PVxboPb{Xv2IR3*a9i;z#~= z;HI55ah!P|0aNtnsIFPtB5_N|kT6u+)61P{8%HJ;*6Ro}Edf8F|2PcppPbzW6Z($j`+bmFzGok{B z3>Xkg=%=iip=8}n%gVw-n{83MV%wYWdO!ssEt0gDi(+W7NMXSXfbcZZV>dD6EpThV;MzN zvUmF)$Zm2A@1}RA6}zJHl2De_5NLL3!!cGQ%Wuyx0pm1otAj5U^w0qjAD~i(%0pV~ zMh$EN`gVU%choPk3O1OuRNoNsrA7zI^s~) zLl_M3^s(az^>l>lo7i%pXXkL|l4PBVecWt0hYf8ums(w`v=hm5?BVU4KD=Go8`B+t zEPUyj4b@xdFbm&}^vQI$dp6AV3OBcg1Q!;I?CE({GY(fNJJv5jqS-JF3}nMOh*q5G zrrBhM!XRoNG(lb69JCgotfx?&mm$l z>0#QEaTayiKYa4_2)L42_S%4;0KH>nJ5Gq2$IQaGa#pm*Sny3sb$h7jlx(aFhfu^zYt8um_{2plCwX zy1D|*LMQmk(bcGe5+73tnun)t{jjNI>|b*72PW>+jR%Vd(t5l&!@7XEBGF#sQg8MW zAPku~N`_2oW>T7!me-J>LRvs3WMN7qR{D>>b&BUW1u6>|(K-TsQ9;}W80Cmmx)!3$ zV6om#SFHY_nh-vKoSu}bjA~x3Ze~nLK$rzEufbj64i7d82cQRtzyp&7ohM`j`=aL4 zq&C~@3=uW#Fl9$vXwpW)`Rd?!q-kX_o;4Ug-K?f8)(vc`LQ={G3N^1_EXZ*I*g79& z_u&F)0!VI>#vaYG? z#NpZ#PjkKi=a%kiGvtdV8J|_p`MxdBJp3e*_?4na&#_!(MjCbof7b$g%x-G?vhLED zNwUs4xt}T&y(zjBqP)BWWOUs0H9(d-h%T>Y9AUa9xjx&FGH@BJ&^+uiFcPiO&`k#C zGhJXB9ZIKJ>5jgi4rRYGp}?OEOoa@bwt4+zD9tCD4g2dNDt+cXAxVZk#pt`Xdf09z zH`seXW-~Yx;@k$G78m!@oBAy>Vm=%eNH@ro1gZ}^6vmw~AB9>3^l6EcBlDBaS5VJE zMGU&S+n}vbM7VU84tu*Bzjn9OGyOR@-hh}_7?B+-q#T(x)MQ)J7yK{GVsb|9c0IaU zF7QX})3~keS$=bD0ojD5d>JJQ2Lq5dIN28D4tiW~+_As0O4HF*uEii#Xk2IjMtaKJ zpkRODx$^o(sDcoBkLuqq2~vuG_7RmD$<>CnJt0Ui=t${EcCK{zaYa8erd0IMl9BpN z$(=h?=WwFa4A7~XQeESTn;K1Nh|eW>q^sLV(v=gCQ~59%01gdZDbU?qkKh!e$5)DW zirBey5=?17>7Ym3PKUYABf8Ga>|F-6Ne>l}!L9-dc~i>>JdIWk_L^~g(XoSeGC`qI z-2I!P3PQDWx<&0sZ$mfxxmJ~-1~1{MFk5|dBl$*V`uuzAQt}T8qi5`f48aZw*r|Z3DeFnW3_YQfJ9yuBnyA>cjRfd*= zEuR zJMXg-CX7NfTKs7pu1e%V1v_f^jn;8j2}zo*1q_-yAi>t$wg*u}uhgx&#jG(rfO=;= zuxhX7_h6hHQds0#!o<TogEHeflp7G1hSXL3cJHDm4-?4#!X%s z93UIznfQg$J%Gn?7;kVUfJOp^Z98};J^*Wt1R52#9|L5em6sU zDg4zAYdN$A-k#g|+77y0{z*49anSV)^yNGqz_AVHdxNsbMGkt4=65dXM?kh6n-xOL z>-^Rq)mvKmcheruplv0(PF295%JKH;dRo3^&!m{sk5sko&kPtbb)qTWpLXW;5aa0v zkn1Ark;4smUXfP<`xPj+z%%7uo+HXm>9SBEqDre7C%{Gue&|C%wI*#xfWh7j!URMO z?lLMEDZR8_j$&ihtINfL(bg4xq}ZeCjq(R}vRy_w9O$KLpvxZ^llzg}!wE4Wfbi)B zy9~Y4hY`px=Q)!Kg;sqLS7-N+jQnGu#Tab*NUs1pq@=KWz6IFtM^=oSN2j2U3wN!IQGNYK!-I~}3%Ki8~>#iPlXEL$pd zJc{6cB-?UyoUl-XZHS?13pd6(Wm07@J`I^nCP%In6$;f|Vkro&E*o|02I$Oa&SL&6 z1a=`*^dNtb%B5`qV=E~oyd*&wE*u$6kW>aRD$AT_$ zs|AP#6D<~r9A%1LC=$c;i+*4Pj$3`(u|BPi^$q4NwhRzSuarXJeL1+vU`0EAVgqha zS0y@nMEPTOt~1$rsduuPMrs*XB)bT91{zJ%`f6Xkj-S|=O-yT2JAt*;o}k0DYzkZb z0IRZDChHYzTOlpUl`7+~3!jI{%+C^#>qcMmD z9b2&kKYEWz3EW_&HV%Oi@0)uMY9wkT&C;81lBy=sCqbOdf(mN{4)$E`b24Xesf*Mo zM`!s0+2UmmXyZ3sN}r<@=JRj|+CWkV@e=mNNYNZ?d0*ZeK+%t-p6Z2Nb)EVV z@igwH^sfuK097gctwfU_u_gOzX6mQFY_|Qap2(})HazT_d#Ft8_rmtG!)@;%u?MkX%AN{0)niZQg@Zg7&=3 znseO4S^jfO&r~%4i_xK_KJ>iy!1@q@nA9yLRgu&Dkp0Au9r|F0s)r zz-t;r;VW%+g3pK3O>Hq!=&8}~fRCX5?5hE1Ag}X-qHFw^bO<9d3>qY#lTfoU9QP1%&4+PoROjQC3hW`q>5Mc?rx z_iM*B4+}5W*zJ0xMNP{cu!o7f70t9sXl(5nGd$*22GMz37Y@jYSDBa8_)K?NW9^f| zP33i<JK_&9=?xR|};-?YSaics)CbL_$lF$GZx&{A~Za17sl)7xkkw!v^tHA9HK; z8HxIc1wXG?wNHaS%oH=V>?Ymq__;~7;;B*sOD>v-oEK3FT&`|XVm2EpnzFHPFx&;p zO0x?|0Ymtk;BM(P+k>DINF-`?1aN?WqT9?lHwEDqM5igb?!dPMIwa>7ZMqwPPhx|& zk4G?jLg)k#wUYq{Ax?ejV{YBF*bOeGt0#sDI;fF3PJ&J%rl~a}k%kokIvT^w9sVe> zqTx<->-fO&I-Me=zSzMe{R8e`t&>xAMBWLCV(V7>r)Z0TdB1v{P@w>JuDgDY28GlF z<@X}^(K8l8Sa3>`{jJ4LD%Zvtbh@x_`M#a zIGI_Ttc1yp&^57)>(Mo6jPl4)*;$_WgNN!I=a|%r89$Mp-ajz&&|T_afufeU6Xbcl z&zrpC4We^O-C2&*8xV_+C_5uuO?O>fL27S@sMC9!KRpo$U@9izhiK_j3>aD9_2Bb> zeMg3Tgu1uu6(pqTik|Jang@Lc)C(JNnzo;CYaMHG4%E|&n+$5iR6VkSSl%N#n!xn4 zktfI@yeFP4$drgp8C=*2WX1FXW2U#amj-%vmtnQon^5NnF};;kG)+=;@ARU$9xu4jN3>5sizmEabwmVy->-9>FuInCWz>u&^^6user0;i^Yrn>D?spUH*8>u6 z_8Y2s3BY}-uu@QUL-nT{d^7RAm$wMNyqZXYnIVx?_uUVYA*eUwm$h+(H$Hd|AVH`H zlxX(sQb~}2g>BQVB2QRVx!52W+zGduozDd>brmu4Do0h~zq)yYn}!Coem76VwzAk- zVJ$k3^}I3~u`~&?kX=gAtJuBS^rmdP7$<1Z65j2*u17FxQicJ!5#<6z!?u0FvqL05 zx|h^^-mmNR{tO{ov7$R58ISfq6@;aGAZ@Rwl+hsBM$>vG4FUrIQ}C*O3Jx70QU((h zppNi}rT&)QKR?<;!i!H1utn=E>B$PV+0T9K*Fo=A`qD&n@p;86f+~&!8oVd%3;X+l zmZ7a@f(a6k{SA!2yTWZGp`@S*rGtalLZk{2xaj~BYU9EzyLlC>p_TV(w%Mq218&DFQN{d++dgQJFCU+!Hn%?KhwCVjmT^B@z?}iUcSYmFaz1_9> z2!vTrV_*b#zqT__cz)f!boWtbE+MZ`kBX^HRYL?|pz1Hj6}JgCBtwCLaFuUp_Ivq5 zG#VfC4jDL|L8B=Vh6FVc?)Ob9jSS8nd=oY;WJztmE zBo7qFoDYER8A)GSv~bTLAh+$cbxi1##XtdJ5RHI3psXSyfYYsvn~5EF*dz>V?EnZ;=+oQ{!R3>bjWS4N9kEU-Wi8x>hxIK)^P7PIOD@s7{@3Na9T#zn*lZ*og>I37qIl zl$JqYK^?&ZsPcA~;@8CpDuYmhV>l=D+~BUVD)MZKsbnVOOl7uR&|!votar!Ch?=#< zGxvJB%L-^XI!<#)hrFl=rBUf2{4XfgxU~(2Gfc*aHa3Hopek*PR=m&?oObQdOX`-2 zjf!ANg$~>)k!DW`v(YA4h8|+tdCTr=o2b5bq(nM}3W-#sPaI4h^kCV7OJB288Q&S~ zONDWy63OGHt*qH}%~?Ah&Lx(#U7}J?<4j}P-8Jc_qif0XexFe@47+a3#k@ajmI<4k z@qqChU@1H<_@A;B0<$anpBTFgMc8^sCg&A4b@<6irx`9 zc$kRY$Rz>=(LTOa_E~a9j;?pOEa#(orzIT7JLK@VyDt;2%{+04AK4L{)YHhW*R_0) zKM^>A&%IM^x(BidX$RxtYbJjjfNX9>+0hZ2B4s9hvTqTD&=*kW4FV`097Z&_Pv6GH z9*qRG{=oz0)}k{g6onAX_DA;)8UuECtV@a3CzmTWJE(nkCMC&u=ufj7p6*N;vR2C0 zR~Nh;Ky9&(bZ;Sw9<*QKx+nJ_gAFEy=-)t&HSKl+9{0u=1n(BPP;&br$97XRLgw9< zYnJFdxw!Z*?}a3hQbNozrx~6H9}(S?Jy;=VeqpHfYuU|u{R!w^yj0o;0DV{dxHrVF z*>_(PHX*!GCUk>wygotoLxbED?cf@15@bC_6;<>VL{q-yOw5l?f}pB3_F6eXTqkF5 zlKFp7CPH@zs&WfJdA?p@xE1+K_}gWUXKfBrD4J#3%f>}O8Y$#pNqdU1V46vMFdEId zPQqgg#zVO#4xo4oV<}X_p@U!_AVVkuFPy zx9gN8=Li^iQtAGYV3&Ox6)ABie?Xq%>1aE#JFqtewjK&4R{i7##yI1|*di^glo}lb zC> zgtByClcTIs1~w**yrR?{xE4*yNhjT0ca=c$p@DA!dc!&(zUv8wQ=XVoZsm_K!F@4T z86^k&Jcbe0onn6c*FF{CO(=~iI;5R%f@e&6W|2f@S7m?CBU5^T3iH3dEP1@CynLgX8m)3*C_7d2vN1JnOUjM4*#6{QQA6nwF zs|Q}@YqQAx%ZoXWcGt+tZRswg8DnK0T_y${9;jmAcUxH;l`kjwNJ&f>&u|g95vC|k zip-HfPDOR(w%g3A2XEK;3LrhXMO#VyqfY+rU`gz0zXQ)mO-oJImn(xLb_|lG_8XpP zI;|zjFx>kgt!~UYx@7J_TiYyTjd<|phg2Ghi9pbjmg;V+bF-yw18GEwQ9MvLJ)ncv zBu;TgBYAT=y7CmiE>3^w+YiO*>-7%nbe!>oJFsIl;fUDbsec+^(!)|_F5&88O z#d6dhX`bCGCSV!hQHiR#UQ!=O6PC!jA-V>Qn1WzqIHd^_LC^9@6n}vbG#V zP)_0kQQU%ugw!2O34!y)L??T=MYp9taFLwpe`&!Ac~b;M8Wm-|y_trnUUZG_QEGb&nL z3iXjJ#>^J=o0hM$En*v13_z>B-w*|<_lm$#*hUIF0Yl)#OfyTz4V?hBwJRUQ(Bge-s$nd6UI1rhzg(P1b_l(EcH=ENJqnS<)#E*| z4x3s~C8^N^{43jwX$k^;anB+uXrkb#?Y0* zdPvSpHj(lml6VjZ!Pc99@MjRUIrE$Ig`>vWrDm+W3)h6P6dQrHJvFspkku86bw~yQ z7`Y|94lI);305N9k}^jk@HV_Rd2Pvg8+}R_U+~F1n(S7AVIC=F<>fr9j2D8xX7L`` zCPNoDjVp;*sET0UVd?7XQrHj)16kW_QfJKj0*Oc#0Y%WQhp>B2{MN4DhQrQg8|v6k z`p=cYsfvo7{N1+S=Fm0B(d)%klBD?*lw6=~SX`!14BwtBQusPV#v9rzFS3^F_Z#hn zA;~M{u<@ezY%(yYO@&>w^n-nJ)q;P5@)HB&$a~f0WQ70Eo6UENBHvJmE1A3Z?<~{otbq zdlw2+Bbr-ad4Og>S~cT{C#o&k0T{3k?ms6Fn9AdledRx*K>c~Ul44AlMd7{XbFh%` zn|H0doJL1%@|k(?Kq1%(IwRxMQ?wDXeRCzFq98o@EoXP(4P`$7Ra1dwK@q|NBo;;% z)a!*)UeVBAZe*mpW2G!LQ@oYMSQbGR@(A*2{p~1=cyBdak?bW`+n2ZTE#11(2#wBwc1y;5>b+4Ra=%Qy zM$`usW$)k=XlsC-^LPx%W?xy3${Qi`{1xAP;+V?@e)@py&sf3E7;6&VIoM)+Me zZotc9EV6GYXBC?=ce^Oa-IXT`6LF)1yP5?p5iAh0?Djc1KuJw!a*W6Y(6W%;=|>__ zAR8%wE=q+5nc6nH3p+R<;Q~T8*w_}SJ6x%wPLgGpwZg8cRB%GUo&l%}xGP~kL;I5y z%Bu*bGBK3P-H5=NHmUXL@{%YN0TE)udbMj8&gphxv59=HFyTYkXZT%64`6WDVv)yo zlN?q1T*i*xpgc_JeV{p5$V%*{Ztti-x<@%kBxdYrkk{LDODp$rc_GzoZ*V8r_Bk9_ zVW-B}PW-RB&Soe-wV+=IFdCV3XR;TEWu2iPPXhCTMOTQ%*>vy1S`;EF;f(5$0LL{Q zL(DI1Te{rtVhU(~)O~OP`!$c-wWmZbSSiX$_aO#x&!4;2z+Q$`-vwtKo0yh0@JZ}3zn-npRQfL$mvlC1^twe8w0>R_O7#+k#Q zu(LzjV4*#*bIw5e2qsRBBuaqzCDYxt7&pc)83{PuduS?j5w|h#{u_L-5AuLy)XCd6oZkIMCt~fBPd8$*T|$sx`7*u>iHMq8 z@v^Z#8wuLxr_T<|p8AU4YXNlx#?SP#Rp~BPbToh{6!q}HB_BK_VtsNUD!Gsmt*pBS z&xVPDOvB4SBsz@Z=h5UW-TrpKhq16XERG(#YFJby!1lw@(p69tSQm%CADu1c74Zf% z>cV|qEZqubn`_v&hJl|mbM6ESqSgJ<-DeM7B@VIPq9vFChL3f3mfdw+ph3XIPn2Nb zguLblca6)Z9mUYfLL4QLi3d>44qH!N^zPtLK!MF7Q4eBW><(SHA&75}L=+yL<|BEx z5UYTT$dCu0ZdEfO7l?VCz$)4+2iPtSj-*E@7EAtD99RIV>W60ZAljAOzWIls?`a&ve9B6VJ%7rA#S`kI`AEbQ+sine(uAD5@z zR#E<(jhpqQmU_m{Y8Mkz4=Tix&oJ#US68b#!Tb#o@M~TjstZ&+Uf$rzp~H{Qn{Uo^mu|gH zpm$Nnjx+A7gCgh<5oXbim8+wv!=GTYEnI61Vj{w)R!X+Dux;Q8%t3aCR)mdhNGfyBL zxH3kGdqm!KH?8BP+oJAcv4f2&A2N386)`sY_i9iv0bJAgOIRQ~vGD7HY$j&iSUrzb zGt$)_FglJLc|BN5xz?q;J(p0kNM7u%MN5b?2@zsoEqR7>P>S>9?MdZ0hR3%X;(V9>a=vq0k*CWtITVBmc*9ftEOK#^uWh_2ykT{0d^tBp??cC zC(ge+J8{&sb`)aUqmL0lIv!{(Q1a{O&`W>)9<7yLT8{6bO2t2=!jlN{Y3?65h? zBZ8B!>;vyNCNjmyw08UeY2N&vozyt6wdX_>n0EZYJWj9lqGwF#1z2gf;}VC<&A&B!zhEA70s#8s|4@$AdAM)7#=2fIL~DO{Zg6pxEF35-BuS6WC= zrj&5CG&}BlNY;*gYeW{DSk8BE$QZxk*o6XWHtGC|Iy|yZPGwt7y(OQdkSB9_(RYl!YHfDeS=tv{e>xqXB6?V%ieenq6^)+>e zL?s0`oPh6T$Ws6$`zk1&5g-=O20?xlFpGM^PS|bD7Bx`;6l=3c;bT~sz)V>qkY5|{ zqY|_(>x-Y#!3%}gIl}M3yaZd@J;dK&8NtB5Pp;v1w}fUZ02Nf$bVRkczzyJthmx`S6}jm_jR1I(G4b<=KETly(J2H3kJqG zuUIkS)CY@40P&`unf0)8{q4&+rG=1<5Qj0R4^HZW*8$0ikD4`!<(jxrJ_~2REJ&zq zzkG_g-`IbtX16;ToB*vBFI{E*;p~k(8CM@4Hz~EBR|B_G;Ib4%Tz$NB!aXsVg%~@Z zhUVKRmPY%##3p67C^Vy@wVO$5CE(GP$%}ioT;U{d7sCqhh^$CCr+(b)SOyx9t!7;Q zmJf!8Dj+;r3#we=QStI40K!pZDyFXvHdL6pEPc8<9t=^qV?2ySKz@|^Giv>lr*k0N zcmXyHzSl8B;^fm3s7Em0V27v5{oIoimrg1q+boJhouxLk*DJg)yQG_MTCx@30~97{ zWq7VV3|3Tj*}0KVbk3j?hX1aN1S-bmb~+44Km-v_H1qsD`#443s(+ z9p}c0i~y;Yk*uacnrgmgQ~X6(L_Yv$mpIR(Z>3IAE5@}+Lsy3#u68fRxd(un?_hLt zCf5VPMHh^n{3}|{X%=d`=DwV&uyMGij<5k}!T|p`8(8b8_>m2$_)O*PhRn`$Q`OMh zaUKm|n>L{%Vmp*t_)Z zS7b3OU-#Ep>&GJ(!~H@Ii@fAYjPS(TSa!gN^D#xtReNWhin7xtQuoS`J(TblAd{x+ zP!cru!gf=_)*v1vyP%LVj3Nki6McXG`t>D%RjPcTJ(Cn2Vxl>33w+z5w%i;l$&)M5>dZ`2G}oHTxXYDCOQ>hzM!gXtHK~`qCb6?R+5o_ zuo^jk#poK)X{VoM{d{$+z0$e$r8lbX%7L~f-kya!*jDcf&9K~6KQNx26q%-jAE*$F zHn_m<*~A>gfsgY8AYv^hl+apZ_H1_axiK=iPs`7=7cK712Pa}6%Fh`|o&c=b(kk!H=O7lu) zy0Q^71u&2oumX`Z^$u5aml-Gnfn;ZSFa-B1wB@MZ9MqL<9|A9@AXaga>Ae}&UHhh> z9DWe7#4Vh4|De^yae6!*X$*#8%kBYYu_lVb?6=*@WKpx1*8t!r=A9$Az&>1&=fN5= zy+v$$5&#HkG0SBc3M{*$wbR(zvpobrw2PHA$_KiJ#|9=P!^m)iSvCkwfqUZ^hmIr= zc%_u#R6M8jCBQ=N3(K-zhyn!;zwrKfUW>o==5&eIyj#D%SZ5iCGT$!`h;ndGs+bOf zg{tH9dI_Xt_lH#;046 zE$l76Ol`lOZ*cY?Yq~pKV(MiY)Gp(UFlmmSHc{kV@Z#;z?AI~TWo1ZUw@u zayb???OmQ5$5)AROhFWiN0&ugB=}j^3tvvj*Fgpv12PoeH{kdKF$kL(NZrTBamFv{2!1SQB3T|15v} zFAEsu9{~&j@E;bisR25J3J3te78(G6%BxH$IOnmq%aqipyd=&Zj#wY z%8Rfoe<7S^ZuOUu+K}loI&`uE)XUx9w2_0&pw&w# zs!yZjVj7b2wn$XQ`TW!cp)3P2)35CAZ4`Lk6$?I^%zL*>POII{$(5j`$WIs5xU{#Z zP>H*2PO`tvI12^_g1RcQU4NTVHBrXvN1PNTUDI3MT_ic?UL-g6UL-j#j)?{iC8kBT z;EDw%0#YS8g3xrFwKC)7<}UL`wTW%UZg;JRIIGBAMEez(wX&wxRbHkb%j$guq^kBk zpy_xkO5Ssg=c_+m>S31;Q&BuOA}u>8>O8wzKbg=_yw>6^_DAe}R>Ab)hDn`f)?{qj zdr-sZbZ)x#BW#y}N{US8aj;Bx+Wco8x*E=Sx-PIrZ8CwQYk=N$?`6KVpwe!&w2#)a z@wl|F^OO~R`oJO9zmTXJoP6cr?yzSrM5R3)w!Zo#&|HIaeE{CmScCJ4rttwLlXyNk z)8fQy7zO!DY`$b_saap#;rq;BZhy@k_RJ4zripDX|2uD8LSt2$rfhVKIF|tUg-@W zpKsxvhK}7*_~(k~{auPv@_sZ-M95-U^Bp=-(f(M?PI6 z!RIeVRryfId*qIX17d*)2*xx5I0!&TK^_Er5&%d+q67>R$o0T#Q=JYx9eg_g_7LpB z*h9KQ&Ibsd+NI$0Vd#U<2OD%^#4pW2MsLU|F~Q|paB3uet`i1 z{^w0W8W`lihH4V7A!QE*0D#WSe-}G9Gc|N^bu!gAws&^<|0|Qbx|+!+V~&3N`h)2G zv0Mt8@7WZ1aAB3DK{}K1QG~2zo_y0WUemc7Lw~z1R`MJFuehxo(x99i#8Q=GRyj9O{ z71#g!^!zSQzyH0i{*8GrPVW12H!gfWDVhKC^6~rgsodw|_5CnCeYc*=efvItzt7+Q z^C%zB|Ks%AeP;bTe-8h*_fH)^Pw)4`%iHjAynK3JbN@wj|6LLLRJ-mAY&d@_4_`0O zUkA9qpLYCy9$$~ToS%>7&gK4pG)xls-yX}^@Adlse16^@%Ws!2J+e=Gi--g14@A|nMKW`sh_~q>M@^Zqn ze*fq6^4jVw*VfbL{dq8X_Ih2I^mFs!JvF`G$Md^TYrg1W@8`SS-9DPi_wQ+U z`i*A&@8#oZ=T<~)d$fhm=l9d^Fh1WlhxDD2|629F-|uC%ynbLS{22?c%(uSB*X#*C z%@?nzn4ZnGi)Y5VW$sSxSC`8vvHky_el`1{J<5x&gd5m z4jim27MXmOx1Rx$3I#D&0jHZ z5!d!dA^=&FWL_}Iq5#v0UOXWo9P*7tpk(Gk3n1MSa-H0ndbtOv910z86bGJU4Le$) zCQ9>1VJBU>leTbrp;Q^Nfw}jeaU$Da(6N!oze?GPbj^-^L(rH+eRfP!jr92v5JG}w zod`VLbU``}t`XNkCkn(Six9g?cKNufkanOo*cBlgG%*au*{E5x0-gkk+eq7e$^Kd6 z_Ktxm_#QedfKF2orc)5487lhV%#VL!XKTN;)j&DXELy1^hz%?WQBaY%L{biNmqFB} zYp|til&d}ARlCEhM)%K&d*RR)pT|g6z!Pww*alPKTL0`n*SYYc556Fq?93LHT}45p0W$x>?^s-e&iWQUrUi};W} z<*Nbtqzlj^%U2WZD^@I468Z`*8)e&0K;RdahviVpAw&jkG||Et=A&ex4c2U_+;3M( zPA9Fu=|!wV<;N?U=7NNZP@4(az-j(2MwKt4Oe_Vrrb?pIKv{U2Ni-SzVl$RO!@fr? ziLqQP`d8tlT-Fg4-{TExh_yftO6l*oCh@z4p_~Xu6%bF;vs8-MHL_=|?4{2`ulou* z_e^(D=ZpNe^E6TZ&k_lS1ko}2jJW_n$zN`|XK{u|QfN~jL>Dl;z0MAsSpGv>9Vuz4 z;E=nZS1*5irJHW#UA@SDlFlXUSH#@%QcWCpm7*emIPk^hz{nU)wZPVN^}&d00^cF0 zw^!zg!mqh9%2$ycw~V&j4_?;&Bz4V=tV^?nB1+=pAtvgdMO@9H1Zna<(DRy~fG4d!Vt$^#&xP zA|7?JD!5n?GIj@Ljc!lnBDfc>n6p|VHmuZz(z1~Bo_SB#B@lGV$LV@hZkYg$sx*J! zS9GY@Xp%~9dY>tW0+Y@eCplCerIxj1m$41#=b^6K7A=hnQ>0%5d_Pd>o=CQJO6&c_ zBFmDi^mu;lpiGizqapqsG(Egiv4>u1`=`P@ z09cZ4y1%Jv^EyZ}m~PiIU4gYjW%y$sB5$%J@>22yAe0mM3#Jf(2sKdok5=5{nWx*I7BmbgVg= zR5Afr5ZE?hqW3B{QjH6i-&Aoi45^FVrOqp6q#JZfTYe0v#dwV+Od~#nrZWM(#mtEi zmNugHua)Lg4x00R+k6fgtQx|-x`HEbP&P6s7(~M$t7!aNB^yTt)UO2M?x@=iqmFI7 z;3!ZV(w;I{)91zkNXd~+_STNDGb!FA7STM%fVWrk1L19Uep~b%f#>q~kq9I;` zuj2B$a$+I7`ZOKe&MX;lw!csj{APC&+uzPp$FXmo>u8s!S?myf#kPPsFh2AFbF4z9 z+?QBiLlL&|`zxxXw<$#a#zX<*g>(KlJ>jt5q`)d&C6OI#1Dzo==?jh{4GYKoi{Z>3 zdg-Q?3suvx`PB>6cM&nT!m3{E@(&L zej!q~Su6bm^#~BUS!wDeD3SH@mIq+<@Mc7!f>#{a6FeM_E9kHnT!yo$DmSf3 z!U{){%5uGd?Yqn!SO%yE02xi0weZs3ntI5Pw;>J!|$b;~H}CGh!f+B3hM1HM`w19Dc#L28Ai#iRLeLMW_z3s##Ex zq_wC%4G-&K>W2cZcCs^75H9elj^?%>mWPo7tA7H*iDFIZIVcgh(1*5}TO8_3;!Zb; zx@D=O=9+0Yngj~fcFF-|gAoudkg;YO2S703CEEcJom#36vutqJO=+hqVeRTL z-%uyFTddoIO?S+p7mj2m0Kbyvz=`Qk&`H&%AwqtH8lib%xMBuT7n&7Sx3c34IuX}F ztA=2?UZW10age^?L@?L=4LOxu+cW&heE&3XWq$mSz4Ke(bMOjQ71 zaAjk9m9%x6pzGU7nN~RDb#a6xjuBLEic@KM{+5R^tSh@x*80tX5HADTvajg1DQUDv z){fw)Opka|vQna1KQ2uT7F#GMBs0?Tk4X*cDyHfe>&M-uxKcIv1=@#fX=lCKCk#yA zhYX&rW}K*-ji=lvz%?#uUljito{wTP<+Fxw&8UL>-1G#VabaSpME8vd84lN zo;7sae5zSqc@LN=N=H8}u$RT=!Vqg(^a0d16+uQ-AgB4U)~I#H7qDkn*}V_;M_1Zy zU1l`0+#hi{B3WQ5^LG8dm!Bsx*6$Ckddp-*J+rQ4m*(E!GKGW(5}yb%bmu-S;QMER^D zsYsa}9xu=rOMsuE!SP&XMZkM{0KMc>JG2PfjJ;zF6`C~o^MEAvRw*;c>@C)(0=$n{B9J~#MNUR` zaqp6PBe*@O8HKul$Oe=l`b?Q0x=DP#6Xhz#+;A;(bL&HMTZOh(e&&3fYhYlFS=rlSTq%J*W@wprefYvyTN?@ z)}Y_-YK{Gn4L_j@i{6>3>ABBG>nEW7msE`vrL3^7JEzwBvUk)|bBUL!l5&uvjyjkg0-`iezv*uT4y(7l26aBTImX-lM_n6XAF&kLF=&D~b z*EmT|nmW2uXy|&70qKiphYys8;h~7udWm12KZJScooK&01F~`!I0I!YM_k47VH=im zL(^SeNDAtH{bo)h1&bo7O5Xz@aR)3YRz%e^xC!MWrbk z6Th{As^?En=1TwS4|muvlv;K6uQF9=20kM06ktij*>1zLDKYBZ*5%^A>HzFuQx8%MT` z$P13oC@2~5VzX}(i9fdfUtqU~~U_$e+oNMoiqAa3W#9bzWI zC2$zO^KdT(>7I@-TO=LiBXMq`Lw=YewOSH^R%>-;IC7K2MSeN*6BF@E!KtWA_9E5J zs!y)Lnnn^NH?6m)4GJu2R~C3{4qg0>_xSkoT;H7JB zYv*GHD;4uf+qaW^q&!Z+dQ>fqrV?w*=fV)Gy)ue^X?vxejb#d-r+_MHfJ%EglK;vg zq^L^F>Qav5(Fd6}i7JYm>3JFVsFJAwv=(u1-<+5QmAkbn??<2 zb0nys7|4=D4w$qj|l&phxQ2T+RyN!O`LR9%A{SO}b1C_(!%T7-#Li=U4^Wg2` zL8IB@mMCY7G#7Qj8&$&lL~fzf@xUM(1VpkVO~;%m9b{2cCdTH*npi!XGeHVHVVAEB z_I_c$@R_EfAnQUQ3d|UCWXZPQcfsPir+?cLGF3&4v08lN##$X?v)sjXr(<#XwO%AoMhjXBdjZJq zkE3t@c&n#_uCW83d^dRuoUbWy6s?GG=BNP63VB|Ms&zv2=(OS7eN~YroHtfDVbo6? zSJ@%Y{o}gTn}zb#`IPB;f*wn90<guo{OS)wWMG3iyY)BaJg_=%AngSfjY zim~AYe2ZDbd!}j|ks^AiU!7@Z7_Q>H@@v9E_|e^wWg_(bz;Qtn261_}m8w8;Hp-~9 z+~zNc?4-ws;_5fhB>%9zLw1RQ-4Dc-9_$tduQyzrE{Ij3d|-Ksh@nnS`h+A{Y)4`b zmay@YK};txy(4B4g5! zJ}X)ipPV{ zg+k?%x~)i!y5=RWvaki|0(Qg}LwK#>yFC@)WZ8h}wpNm~Ad6rrK1--P#?1SSPKpax z0jKHvohZxDU1pKH0x#`BV7FN`J%w2%Tj=o$n~9vH2MqJp@(h~!utYjo$YU{j-eQf> zqhq@RX}?g4ux|N4JvhBBSUmR+w2EWmNz^|ipwJLR|GG(xpvRu;Ad|>tsUAoPJ%*U? zG3g;6t|>?VfV_3QZaG1<$Rp?Douc8zv1C>{{@80J0Mg(VbyLJh~& z_rVu(fGus6R2jM+eizjJq5BsDUH|B z47e4l<&g9XUeX}w9K0bkbuI}hqd#KXGOAF3M|-h`W<%S&jufocc`rwMBot)O2@oir zLGIY@L6kyxqSL?}M5REm1&Auh?(Q5=twOO)7X8P1M2{{{``{e`R|S(;ArBlyk!9rR z;vL1~fbQHU)*g(j-J@zNbm2V^p2=X0@t!te&TL?X#j|^+N2v$6e2hjSw&G#!UqFhH z&ZJVhleH$qwoS!kuKs<>O)b5bGf%6Asoii*Yf2Mcw$@P|BL?;uEUOPvu!rM(tHU$* za{Z01ru%jq>|NB_WyP**_$H|DZ$TqM)1MghxahRegfQeHFRNTfG zB2TD(bMXKk$X?<2+?%*Hc%n`TL?irk+bza(wGyi$PZH>RS8I}1a2=wwZgJhZfif+V zyMTTfcFRiZtEkkr+jF$pX+fP=4Mq!|#ouNEY@C_blqA?RD2AGbNN_Imn3yL_dn*?? zgLmH@u`jUF)#>{s+}8Amx}VIQoR6~*z*X8Tu7i{{Zg$k=F|kuZDNhXyNgDxh_PD30 zYDPvLO2Z}2N4i(g^>9;rx{2=8WuVUTMB@NB^5V5dvkci5Chfihh4Fk)84RK}FD&$M zAQ%Gm3o%LSSIYVa@Kv8OMRv*MW$VOVUzxwPyafZF={X(tprhv%)wA{2#Wtn7%ShlOZ=G0w5Dx4;E z`^MW7$Ce_u00ozW z?*zhPJPBKHOKnm!^C2gt_PX&o2r$xJ5#l`~yldL_!UG^WL`#a-8%;vbr+5Q;6_)qL zQYuMyPi=DULyWY20xmczw<=YALFKwF)@vv78n!T#u%?H^f8fS@sdBmnB#{>`|2&r5 zQ%^2qnD@8%T{I=Pj8QB5%Py=CHJO9-V(==QV$SRj0T>@riWKs*d4 z|4)Xej{PS)D3t^4lQ<||N~p}Bb9N+k20Jdepjo-$kKz^P20^CvEeY{O$ZP`cERrht zG|a8>>b1iF|C<%q=p1{N*OnzT2xi5O3^rylsscT!t%9jdO~2Cir%4kd>urR}t=)-mVR z(Q>pWCTVGP#)K@0PhYVsJ0*WoOHB^Cl6hRnIX}mkc`LwqkH{S1hwGnDvpAO#4qtv8+&LuQttpM_#7arapHBo$D?v za*ccV7`(%IDd1KnCiGlXK!9&d9OqjPWq+)Q4g+bhxOLUom8Ei^F>UTJZz@dY9L~4D^f#ua#vIDyc@M0r+ZrIjjdIo{^0wL@@ zq5iZ0kUY}=mZtX}eI$8`m?dbEDB`Cr!St!MvYSM=)egfOX7>(RWxWg7^s?-WWqydF zmNaD^H=+0lzmFOwh*#31?hFTP?v68aY~|1g$Cuk-Z1_~_)lU>MXs3Hqw*_pQkBYN0 z2R=_WQFz=SgwvAcDxLka(7@ zq^-)aCQ?(1e9B8EP5nzkAl_kU>PRevVC9~uGm?hfn;<8WuAuPoG=!Pa4~=(@Ra>cl zKg@PUWyvEJXbvW_Hg5Zax^HLqRKc0w?kTEU3=DAlV{vB*Jo~7(czUssgKKNm)l-}J z8Q7|0mM>YZRj1HE=;##VQZ1c?y9_GY#AYO>BksM)+Fp(cL*8|C5yy&8s$7RrXCvd% z3q9z)s{A|8LB-poas`;lZg8SIY@E;%>okA983y*avumb`9!1GyI9K3l%sTFb=#@Mo z&C|3CN%68=b1G}xL68$ma0wR?Ha*ZIQGSP<{sI!jStW3##>*hfNCmNN3l1Yrov`*9 z(p>Rxk-B=-md(SHfLd;tzL+RE6DS5~W{G%icGuND9$cLZE7Nu5raJwTtE?4?_FEw( zM#!s7x=uO7miFMda@n|5ybaauai}G}h9BUrd1O*&Y9C7;k!9MnQnI~7!XR0JhdBbs z6d{~GX?g#0!BEfIVn^EaX=FhUxdca}delTG$zMnzdf?TDAkm2S-pS`q1_2r|+(c*7 z2#BHT;|?RBWL5@`*EV0L2HKFc$@b-IqA_xtU8g*cq>igypw&JEzz}|==4D|JSXjp7k5&^~MA*yv1v zUEx%1ibpW)o$^4_Z*l5HHGBIyfkH~83Y06`q3ydNyT5q!F2m`EqIIJE5pWM;(9su4 zf!_l6Yfe;$2O^S96)3w&1l9#HU8y$9q&A#BTQUlDd+if=ZZ)>+6^~Y`yHe{&HI3jO z^kqaKx1%(_yyxLZV**KJL!qwo+m^r2hoo{jTZ??7CF0V1xK`5aC$+Cv<_0>0s{B=F zP;ZiybT0S-Se}d#j$Cey4dm|A0FPb#QCFZfVwxO*f~ec9uS?*1*rr|~cI<#Uyp`$i zk#2I^ryn3u%CWcun`Ms?RXP@%Y;(d>EZRvsZaP%MD%BgcQ>1V3&bj7vzOLBg+^hei zt5w{!XZt8|zy8|e%B&78Q$l7}204%%i&R<=6O-CRl-^m(tLEJh%)6r0&T@R=Fu-bJ z5NlIh35JTj=a!8#M~q<|@zjJdXjjdlYF{$5%!cFDKB#m#sImh18oZQCfkP-sAL!Ou z5{?sGrv>bz$P*4u5uFv2 z&HkY{5H&5TsD2GT(3Z5*CQt48=kH+ahLE;f{YATgONCTyVCG;^)U0O`7=5XcsOgaf za09qo($FD<3qJ}`!Rqj5t@XTOaIaeYV_m9MkaA*qTee?s_>T!{ouLCz+(ix$d_nD&!@nS0ZS#G^yRj?4H@|em}(gymYsV`mUXRT{UreQ zy_v20MJ%^v&Q}Q}IT8O#6>Sx5h*5WyScB_vCQg zD(f!2FD^%qnw-6Al}+5i&Xn zP&8u_Z?=?Uh+7ZiKY9j%nS$*7*l{eIc!qk`@`Z^Vj(lwQt($Vqy1Yqgg^!J4?vU?k zUECPIn#g1@I^sK1-F>r^f7hfYwu}Y_V9zlzbg2UGlY51GlQvd$zVPB*KAG6u5AQ?LahHRHQ$#zq>4hOl>_3w$`v{L1H zC;J`@#9JIqInO_c^1?(t1luMK9Tj1T>D5hUAV18Ewb_l$0xVGWal1MeDfn99#aXp%{i$o%B%ke!^{CZ8jb?%kJl_(S;I z)3;L%RH$aF*Hf}CSf{M*Z6A(D^VBrT!LF4JXX)p={0&oNvpb>tGt2zsLkhYMMNnrn zyp8p&0U`MZ$RUYiK9wNqeq*zZu*a*yw_Fj0%j~rs^T?~G%?!880z>S|1S8F6lC;dh zyQ|dl&giV9@B|;HUXXkiqNX@G1rzL+7NU)$>3j?*9F^-`*yW+D42ly3_CkqdL2co# zbh&Le(R3ou<{x2R!9kaJh*cID-CoYYUUYGotz;gDw3WK@hD6o0Xgd*+w3zAdw^y_| zU;aWZV@OasyCt)Gg-+>e<(=J`?RX19&#iZt6L_jS`1s3KLsML+;A`+NQ<|J5JAz%9 zZkB9;u$8A4GpHLiAU^G$^MWZ(Bt9y2eh@k~OJHcK1VDA@GzOk#p`tcG6{y$Jka)ST ze1@giHk#&c#xQ_cgpii>)0QoFGzNEQ%k4A+g%DWy5$mIEVwb@>B6&7D_^Fu60Tj+% z(cVF0Z)tVT*(0}_W~K|vSRSIK%n_}b8b1n4$e83yt^IW7AyEV)&z^_QCb7=z99;RAKvIIu)9u><7A^UoZ!lOJ);qXC+ zvCfj70Cn!>w4bN;L5eKBN5|!H>2Y#>c8FKVTYa|okr&zAkTC<~no%*k9;QIYoF`N3 zPSswtBb5S5(Pvz>j13$Q8Cht{)xv{5qKW>1_JUY^DCH1$e!@XHcXY#ge2$UP23X}G zRoKZN7TM!CL+mKD=+h*fUK8vD&ILLWT|MzrQ_4^-tUh|E-z6QX%aLbky*Zf|;GI%( zMB>P@d1{8Z(dFw@LiSb#C&N*CFvVSM{33_>Be5zz`%^FEP?$WMx>z|)N zh|5yNM(Za2E&)*m6i(lE-?@I6lze()qeMgZBwcraHrC=UJhAP4>ASt&wd`JK%2lhp)FO*O z%9-+JEQD(e!0NopQpNXOH6@z>{DbMnpUN9w>RTzRwWhWs8UF>4AO1po&_Bd){1>sO z5OZ7Ha)oQ4%NJ7C@HE$sRTNXel~OBxOE9Wwp|ehfR?2?#o@o@`5`MI@p(xRf`o^3% zFa_;3nI#rx997N1dJp~DwVQ=!c~I?g=>praSjseiUKLkK_8VDAHY&lP+UN?sruzI! zES;~oU-^HK_Kv~5L|>F>bb}k)wr$(VjcwbuZ=C#M+qP}ny0L9f{xdaGZ(a?m-uu@5 z;q*SIPxU^%vDUi5evliqs^muOA>1x|iMA%MFXIvEwvJkgzN2yALhXU_RJcvgLr3kQ zJTvAwJ`w!Vk)D~m6yZtnE7@!L;zpeTS{-|dzFICbZDM`9T_eBz`__zfv}?@;l?#A= zB;gM?>k;xJXG^kLYRo9vhJQaM^JzR=HK& zM9bvzSbE+}VSBWWQ_}JKwSr;%QsjfL=svZLDY}d25&2x`OrWlB%vn(eoJTVBBG!Gi zJMFB>geQN7V-F~b7)UI*4R66r1`~)_V6qVCD-~qW<1DmmNt^MK-Xg-m1I5Dedh@l&U`K;%XNE#YQP_#D{{ zh=k@z;NFnY|MBblhPahJl5jIcu$5l8)DSbsUQg_Mv$cREp4gL8k|a=93Mq6)8frHM zsAL|8RHW&*J=2te&x_^)%swK(KtOUngcxi3oHV z0+kbpB2N$}@Xm<{bD*=tClrK^@X>biUX+B(qyqTa1;Kx_Q4G;@gp4KpvjxSW5ch$6 zf?@z7R~{2;Z11xOeTjl#N@Dq&-p3$7CvGl@2pV{~^eYNNtrp-4k;WuEJt;{22O|h% z{A31qU5N<3oS`=pjKRorFI_`3Y?1n_h{mlPy9NUM23L1EAJEA5-_JNW2q**=2uSw- zmuEDvHnVfIa5lHm`;Tv=GjKFD{Xc~-=G4YfjPb0WT;`5Z@+7D-OV)iEnt4kk0$?OxvQraJB`B4hDY+3DZ6W@hxR+1Tab zSB|f@eLFi_-#$Md50Bwr;O%1leAanv zpEf>SpKIT4UOzJ?BK2bZy4v2Yo*TFP+P^ndb&NlJ2$qsKO9;#YHV|CvwJGlIzCgT7Wg5*Tkb3F8)OcwJd>6i zkrW#!?rC+FRx3$xH-ou8^Slw-io1faV(!1;4^e1%R^U zV|hBoq4g%i8i;`aLo*WPT9dk*>QOs@YmivqF&{vc5XmsHNx@KLP^u`{N~KA1ECVgZ zRoq-vZYI4p@+>n>Y$xS|d6XW>CD~EJ)h`$adpvh1F_=6*TtvvhdtL z4Q&2FdZQUV$-GSYl0GNWxd=&GBiev>)u3pUHOxTTGAZ*biAC09n28|Tun>R{D7W6pt@8sBC^eQLd&c$ML6SL5h!Dt3j4 zIYa|cZL)YCm5ZpE`cbTcfBSz&5DsH5-5~G! z=Oz51lUGG8qC`H-vxFLukQmkyD;qQM)TfYT3GA2u#~id*Jf@NGPm5~@au`~Wur~6j zo4G9jYTyPe1@S~9U6X9Lb{L4Y|JMYC2q4$?;X6Mb_|$nohDsTRKG-5bbT%#QuBoo- zYX~U4u!5)%evRU~!LNz%RxN~`nVwJz zYW(wE{v8(A>nz@z7Cd_p;SMHJB3dwTSjBj<^bcn+uueQIAD;U`jG#WCBT|J!JkVnL z3WppwHeeYgKyM0ZpeRKULC|YekX8b+djlcax*1iJF>}C2AV-x0{;itxG0_0*atm7D z0b_qr4gz7<@DJ$x4BQQg(1^Wl?^yyNmKi-a3Sact$##M)xu4IasS_|Izb(p(h<^U# z9GUn^zZFVpA%_*No<%lCC}`p^wIavYi=joI&uM2q>D=Pv^016H&A@GYwy&|!3nUTwKA-&G&u=P|fRB=UkMJV8xNB48^+8T+ z7W}!E*t^~MEL|?bk4vq)OJZJ;#PPFJ z5r_&^+iI*jy)ttTBw+{@MKZv*a8Bw@KK#1TYA+VY;bWU?IH%b{b(a#Njh3>W=TA^+=JghtL6dCe zKgf9abM+(}uwt#2IP%CPAEDOS|3r+-^PY?kxTK?ZlMPpCo_sbpcU(1Dvy)~a*l=0j zLJRLc@|U;+r`7eslk7G*-EEzCS}DryX(^;oQ#-sP8e`ue#<~TiuiZ2`&rOeG@yzO>MH4`C>B!(9KQ5y4Z7-Ymjq!@O?eP^W%8 zM7pno5#O~Ge=8l@e&6m~qqM)*(%$U1^a8hyuUFAx5%HOga`I;r)F97BweWl()*zsp?rDcj zK{S(3FL@0co1b4431X+uM%qfs92R$_ZRfZEI`g;MBV$T+mLmZLH7E zkR{iNPYSI0VWWjca%XP)#!5>k{{FPZdZljZ?_w^ZsBy;;BH}5_Cp^YCotJ#`DV+^t zjgDre6ha$7%w%(_Su|fRmlkR0qFVx{=3UXD@@jOe|FruBK@48{>86d|6FOr<4QBW5 z%V()&>c(Iv?quJ(V`k;@3lbk+arx7FcJ$;RJz->eV*6P5izo9UmB18@Ie}zmPm2JQRmU(?#(F6XTp}Zo6_&ZUt6RT!WA)1FiFg~ zVy=;|5Nf>{mvj@;laW*X{Ek?!#xkRekk7cBan7+N!0o+0P%{m%X~QomgAN&2^P_`Igkvd=kiO~OGTG2+1CvXs(rBH=IyWE9371eGGyPo53zvkU zetG$-`x-Owo15bMNJ#6yh*cpFTV%bxn~x#bM5Ed&rPrxF8r9YPFn%zrwqlk=cO4C8 z504}JS^E6>@Rz1&kcB1dNF91KHgsJJ=%`*TkpXAse+nLwNMw_Vg;{yM^ z;rLnbQx%}K$ef0bIzesK&UVG$LJ9O({HK9-E@!sL(k(P&eV<;X%K9F~xet5_{Olp> zIl2L>gScCdZx)9HW#clW?uW_pzU*!2rb7K)CY7A^P9)zzew3+w8(sGf0C}xK_fo_t%a@Mw=ka?W#H`mRwi{PLqG#= z8yCU}h4m54*M;}n*yLyx7g`}MRE#zZfm70H=t_TDd+;b>z;Zj~PMS&+PG zJoAnT)iDSbaDk6ztxAtsoP8OLMt^3LTruLQUgl8Q)@VYfQ=HHy>K6X#QmOzNO?P^d zme7sRG3>WK@pBzD&m3UnL1=pgd)q2DDX#sS(tTh=VD&F8NhC)6ziw(Vy6sZ@yedn5 zx?JgG`h6Rqiw^j$f1)JqBHfs$srz^qmKp6X_kPrd1-45)Lrg;} z_tg&`#`Hk7FIGTOYGgJxI~ya)}6BAI3K-eC^}o-v+bm%z9i zn;bJ?dN@LKA+Y=6z$gzqEf+Sz{3>+|4)UXG*g4f9C>aGBffauj!+s$v(VEZqX+Y@8 zr;UV8Qh8~qOHTA32-O!;&r=x@o~nA`l8Zc2;I7YTe35|1n1hzWs%iiQT11WrJDjXLRF&#GLa)r zvds!)5;xqWRQfi|bJKQ(Z7c>8E3wY;AqcUbUYd=fnI|Y>+{~+^>37LCzn%PJ9p!YJ zgdC$m2n^*`&cnf=8--bk=`c&!D8A+D3=P;o&JJ3mSg1Ae(Szwzf$*k>TAiyAS4GgR ziygmZ2nbnepIx0Hb>4RMXa?aUZStW$DpCL$lvR(jE~9_4vATc4kRSj{Hz#D)dFO)w zFP|>}mKML}HsysG;2-aPtW3YgZd!UFd_b8;h^+4X^$H?kKHV1IZ4qSV%UW`O25w^1 zsqU&>eUF+q?nXcdQeFmkK_26CZqO>`1xErZYhfSSi++Xqj-pk?z=XDMnh!AvR5|BV z0`tPo-faYK+K$RY?0d5=m^Ivo337M%i?SR@Me6vbjSb^%xh2u&>`$|zH|bT9<1};d zrYg|`+H;fJ$-k>bS!9GjnS4b#d?JtwI@@AI$m)!+Y#E+bP@SW74Ecy2iVh~ES@U}9 zRvlm>Tk9~Gcg?l=)N~j75J(+$P&~P|=rEj*zP@!1S2ee3i(gZFdf%_Hejn(E^o7TS zk-whuIuU?xikO{%EFTb9`S^%Q8q8^jVGk`xQp*VKhI$)Qjj)UotgN@V)eq2J4MkehIu zN*90OLm{lxtb?w|S%J}>tucX--rNv>e~`|##xAj9o_JV@M4qDP;ih$)%LoQK4X8MH z4#to02AH;SCF^ZV3>pCp8Zb`~6Y{;VmDzldmmH6z|M}rUxrjt>?T&9jM)!$O5?VvzAy$etsMje z-IeymXT|dFq}3yCK6*mTG>+K{U{#c+(l*GZ5!L*I5t+K}Xdb8|>n~sSlbh5U+)o^On<#RL~DvU{HH`e!9bg zLR@u5RT`A$H2UfBs$=7Pgpw&B>=RU)-ZZW4$W0R$#&sdy-1cJ?G|d-ZIU2sn> z2C5SnqfOKk__>^bL13nC5bB|=88}2BaLB588MQI;rPs!Jv*DS)MBd_EIz|!d3yju& zj0y*yW&Uc95J>sem4I>umdZxt(T6(6GOmlO0W?N$qknv zr0h@j-v}L&zg;bTTU&0USHT+#&;;EA4u2c@t3v5Gq&buc05`*72mYl>t^DLB#Q55O zFe>&G)1_DWB!Y=QLXk2d%7VljkX&OhPKu{F5>0p+q`X>Hz`%&1+dNsF=NsZu^8Phz z2$P>JPG(A|$!p0v6N9pXJ0Hz{IVk5Qt!dMfP9DUfgqyQ9;( z`*^uNTVk5s7(x*<_~jj~mg9*yNno86X!j+O2V9u^Js$T+cH|$Sb5q*e|LK@IT5QXRVG;yMDydjL334^K@7_B1J41I zl#jU@l#l-Nz0y(h6M0`mhrMTgjKk(Q#>$UGjdLCAiuZTqk2Ho>-PFZ&+#DHGI zzek1kEM5Z|6!1Z@EJzA;hr2MhK{;K`YL>E(Afc+l1VAaxppmZ5DJS>dQ=t85L$D~K z4L`}|q{BTMtr6OIq}k@Zw+>A?&#(!De_WZ$aMi-S_CBK`tQ10`2WP#T67($UuA6S{e`!pnQuGZ;^B20UbEoL5zE< zoLpxkRA0X~qTz4>FiXTJDRnQmrmTV@XfZ;OCOOK5fA8G}5&deIjujSg+g`ZK`#hGv zx|?$xB|$NxTrqo7o}`%VJ`u_evokArnl7{jA5Mj5I*-h*K;oxC)HU;EG7Riv<4KGg z{uJtRiXIM)>&GelDm_Dq$lMnzqnb8rbd+I~CS96Og@BbD!0Vbnw8UIk!3Z-wl1%F9 z1tGXMK?fRg64j-E;Vx@)Gf3$UPgsD1L(Ah=gHv#^66(tWATv7>6CZ2+6QcJnH^J$- z#;c++{<~>`2Ch93v(;79;}FyIBAy|9V>+fIVKki;$b5WPS0UM2Ho>r$!EWC7F$yz0 zplVKWSo)!)F&yxxTVv)6<^EDkuq9={(c)rDa^}5OkeQfjPZk>TCHOK7Ayuj<3zAtf zzsWTxM<|AK$&ydwlf&~I+RP$=P6;cz=vInmj2eh45VqdzCS#_$*ZuEzj&rI;V1`#& zP_3@QD`US}pC~W-tDVT5tlRc6;`{G_Qi&=S+GL>u4>P?yFCAXmJ9Opj(~u>7y(T1n zg@2n@+g7Mkm0^p%0L5JM?~^1lu-hdmWRGl8l6QOh71eYgRE>#XTSbro$^c+xft+?F zml3Dpg(_$*`1wjmT2)&hiKg14m`|pTj?*d451gIxQ($QWZpNxy*|*I3-x2f6x3+yz zc|MkDb(_ourA##qgv;T7!#(jpH|5RK&u5dV3yRl0k83|BN3PbRPP7LJ4s}Ffa!vs? zhzJ`cD)U2K>B)h2c*=jO0&W+Q(Vf!wAi6nvJI?4nI*iQaxvX(Lqh+LKBuz~v1l^u} z6S5URfE7SYT2QWSPe}gh1t9xF6V7$XWR%9Di@?t`U;?GSiPtzsjut{WC+RWE;@WFCRNCUH34Rv!#ka6(ub7IiZl6t`7) zv-%hGq4{!?q*3c-5B5ZT8ae@yVj5Knes<|?1$+4th>=V^#BvY3jOw>tZs5BfWj1bK zxY)R-JdWPn(Kp&{#KL7FxYMb>jm4Ol78Ujp%FG;h=j+Q^o7i}Z)))Dh&f2op;d1aw1!#F^G(Sg|Zy>A|Qqi+WY#bRGx zU^lXMYv~S!*`Is$4Q;Ec8@XOniH6JT}G5Rq=A&&i`dAeM%ecbJ@aCL0aD{b}+f)7Gu%Jh34@0b%Tlef=OhCHp$z zb@!!}=XPSEV`9i@%p7$*jC!6&x?-){>Dp|-{GD=qU7Bhp{ZM1WH$$=URX_epxpQzM znpj|3{PGR|KUe+)@~|Z$t$~2B(SU(O|34*t|9dZltAVwP$^X6LciE-YK(eM%^*UYi zuV{y8ztJ`u~fedlBv(AP8&7v zCUL)y&XLO`jgWy$sTqgqbS+f|0&%S6uCL5kn=ea_dw z&i6sh_f<*HO9KCE!_SBm{_l$KFSnkL(;VN=sb_xg$0>fFudtf$S9!lYbib#boEhKe z*B!p^n@4y(zxSz}uZ!!iqmrLd{QNne_j*2$pU?cCM`739pDliVUSD=S?@#zWZzDB+ zkB{)*U+j9HJv|F1IX#}=F30EHUs-hTL-gN!OD2A|OYnL=->+dgenll-E?+g>uQ@qA z?_c+J9T(p(Q*Qj;@1H9>Js!;?r|dHi9N%vmF$;FtyEQ#;M>&32u-tYQ4`PdWVGtk8S+Zaou+Js(&1$M_vrHQ#&W{H||idOn~0{NK+tU!V8S{H{wg zkKcBFpBq;f8-A}g_qo!(BbW z{l1pqbG{EZuDicVYJ4A!@ZVcARo?bfKIrhBwD0|VKL@@)+4cOsf2RF0vE%pnvkNa7 zz9q*V+7I||Cp)h~pWhAO=Ofv-cHbZ5ey>|<-@y-a?mqA5e(z5iGe2AQsptE?wf*(< zGljySp6|1cnzk`^JO1VwJ>So*Xa0}NoSwEb^zXOV@7JTBWq7K1|2*UFm7n9_nEh)+ z?BQd2$FmOoeWv^Kab?H*u|m(cWB92;udC;wbuVS4Ls!QWW)getiQ`kOnRmJ;+2-fX z)?cE3f1Ue%JgM3BcxCxKFP%SR*u^yPe+~7#Ox568lka9NG}96!(Qx^@{#``G#%Y#wLZ+N@|jNbYyrXq_4zZD2gt zEol$yigo!&J^P#t9F2Bev?v~Twa`Y-TdkB#tvxr#D&DiXta{DyZ!Evdbgz^@PbEGl za#q?bvw>g#({+h-YVNezbfNaTHo7jF@_1Li-dI%WD%@PZ^i;mGNlre}Exs~1k$!G( z*_muCF6^-K zlEhQ`6b{-cwr$wtd+bOq?beR8-lT(gQEhb|wI8D7$K6Ay} zaS?K>38Qw(C}Z_$Qadh&Sn3fgXYADbkbN#RC?h?Mch^m>!?Uo`Ed1(JUa-b0SM=y# zZ{!)_8;OejcFebW5=rqIOV_5YZg(=7H(ZD-K@`{SaH?J65_`TaE|cSpt-4N2llWw8 zliW{gVcO&O3D_>AT)4U#a!YGrYch(!OTN^8T3%muuU&PuE?g`h8mnfP%UT4#N47dV zv1(LkNi`sm&(PS&i40o?6vuZQ$9c)MT{z5G?A)EeDlEQim*JL27s1Xr^5ZvoVba3; zjGZW9YfdOY+gy3bIz&W!XYVhJ;*kTcyg%2vN*?12h*;RJZ=+dRS#NZlo*cU${Q<$@ z@8!vofFxNWOLJOP{sA^8DM_Q_t-O@y`Xn9st!)|iPaoMe{ie$x(Ofa5 zKB0o$*=$s{{h9Pi>9hukqLd%|Qdf8O+2dOrltFj}Y>@(BaC!3FWlLZM>4^B(IP1?clr0iM6m)T0#DJfFR)3f1kOjC&d${1YunT#%P4z7HeI{J@N;*X z)azlcp0?Z$WvInYhdEzC6iw2}4b~Emr=v0@yQuh)AI_v8I*>sgn_Edynp-d;S2Yp{ z(*UcDhH%CQhBrL4fZW2~>a4kelhu$A@j+h`T$J7Yeeuf0h}D4LXycJ$>#>sphVSiC zxhKuydq;@vv@arfNx=P7d*yDfK1H|6j@>a`vq`a6_eg@^^hHDcXq$BFOisG_9;#ij zxfTWqcI=!pSy9#iXp|{&68nS!HnH1uTsJ=8Kq0z{>XB;}j8L@Em5WGW#{^sQ?vLiL z2{Yl_%XQpr*~NOA8f$1}z2fWm!gJZ6EJ=)ulxqX*i7wSD!|-lV0D54uGY<@O+Gv_srqH{l$8~+4hD^o%ET9 zY13{`s>bCSPllhDYK8NneoYhW1QJ5K=$4JI1wn+*-7 zF0~sBPF{oXY+5mievS6GUOu0o6$eN;leiS#GCc_Szn($sy(3SF#1c zgj~z{vsC(o)GX8FsMVI^4-=Cke(VALsA9@tg_d_i__=#m z&%ri}9S-ef1m>42>qK!};{xc=0?dV4^lx2qdbKY#Q70r>5@0tA^*;>(AgI&WBx3t9Bi@dbA(;i_Ck)CL?lyxiG=dlbHFX6-&_{ zc_Zwar#~|TkusJ=T2tmbqbPk874*D2>N-k7rh|88Z;jI=dFlm<15DT?&2F2^0bg@s zXLgwc4xBq{66 z!c5LGX=@GwTj&mn?yyM}4Vt^6R$petegXD8mi3fz4N0V777eCZo1B9=$7jNbH_dh) zW4;!F(za71GQ1chWkp=%m|!vrO#!`10x#zF6p51w3VvydrpMH+JqkURWp0;!b>z;Y z+%pWy)iCq7!;hU#eiCt)%(Hww_(zPg>Ts z>&Xa(xR-*)#$7$2AQmbtZ@~Q%&KCtSC4I>DRBdzF;l-QhqlH}}ji&!GM%nN#d6JKG z*?@Kn#^y6LyDE*WQh_I9i0Mrl$dImMVM)nAB+p(p^WN&vmxcL{&Q7z%Z4DQIRYf~h z#;jPbq^K=#PwuT~EOTVyG8#O8xD3<**3(Hw!_8LB8uHLhi^SWr3=K{C&_HV)ETH?jVmGlLLG||2ixdBM+2hXNm!fteNMi;MO}^ zoe{u}=D>lIjM*@Wttgc=YY{D;qyK#Sg|09*Swwh8Bhpf^UIj|P653Wm5?1wh2{#d<%G|DSsnjkMKw8{aMT0wuE6 z1}-L%Cl#@UBXsGjd};yM0~;7lo=P-uz$522Snb=G)Rl<8*#d6<@dU4fpT`M>x)Hs=!(1KzuOe@xVfP^YvqXuy)M^iB-6U7UL=@v;* z6MuV7Fdb|y2W;Z~($hIwLcY0zPZ50MYttoGzvyGqH9O0z~ z;vC-Zi``DnD?^Kz$w}&%x!QLU57Gsid1P#$6$PXxHYiE0HLJBvsRT|^`Q>;P3qn}@ zpbQBDjg;}?Gxu+GZ$!XymwC}+TUfnZB!I4mlfAs=svztsiR>s{gP^Hx7jo+C_%fC+@;pW? zSU#Ctj)JPUK)NDVvhym>U|nWbmNwur)R|L0S9JMrrOKr(X1wCVh3-}%8@ZTbgL`}u zN%)8*ZD&wwZm6~^Z*{yW@3mVrNoewAcvhwy*#tu+x@)F~(8LmW9a}BRAHcmCvHwDS zRS|Di0zq|`QOv7hxFu#btYEgfyy7Atlj%UiGlh7IyIcgHnq7E9eP)eR9jbvx#D#m2 zQfx^>aQM8gkatG&=*|5SJ?*w2ZfF%sNHTTe#)q><)KeY4|E&qSl$RhUSJEVYpzke( ziomAbLW0&gQLS!<~4_R}O=-DeW=r*LO7SEQ%Hh=ZLej=0P)7hQ3JoR(E7p{d< zkH&-|FI5wU9C-s@lOsv`hU$yZCH$!3hi(IQ;KV;!v+B(j&VuS+6rqJo8N7kIHoU|yl`XS%12%PaH36&vr7B+w*lcQ%LpK` z`xL%xucOG!F#k`TV{{e*VNVySMPht|k)%!|(g!SnC{x?| zwF{YrKD|Y#lX|pLy>vp+;k966f+|$nx*7RJ&5!b%25mDGj5737J~0B)-MW2p zOBlOUQsI`LXu`lRA~1-s)tf+)OjU%G7b3oMrSRaa#O`+M@ou~kIDpY#lB?_2X9gyN zxTkZ)=n2jzcvkCt%_vFA7JFC1VAiU~^JDk>Ui`5Al*k^rX&pMTObCI|(OpJKTzw^| zi~Vqr9$9DU2LM{nhsv$l-%x$~jdpry#66h^q8hX!F-5+eQ1_h9T>+l6 z4AP+-Zc~q%ZGhRgR9@c8W;&NY5`FbiKR*Y*m1Lq;%*{Kb5 zU$96DE6YWe!l5~*6hcD^U)Z=#-7?XWZKZA^(-dg&WH-MhxBg)Z&^92#>9E#bd%mNC zX^#U-5;&LbSO`rpMr!V6YfS=%M5#JQJ5849Eny3!ZTZV7NAqI4RUMGzKVsqfJ47Ic zk0e@P`X9l*Z4CrL+7d=H>_uQD7D%n9>&5;#B4S?`*Gwty;x!4!U(H`A1{%9zBq{Q) zShMb|+ITjCU6MwYdBIk3kQj;VTwd%#0`LM@nW&r&*W}@fUQ`}PE#<5Hzcy#!D9Vo5 z$0AVF^>8l$4T5AZEzXbYy2_FLF`&|EqA9{=g`!u z7vpZG@f&4`tCvGlF>lQlD1dXOhy8jS7bbisf;41+MvwH0>G(r5+=V+lm^d^etd|p8ZC>Ls5>K$i z?-@k}w*-1Adh%jzNNYUm0fcxpnj;7^h;4c#)2Ryb98DUd)rXb58NtJ4r4bRkso{cE zf(!{l-efpUa^0Brn(e0K$FV#=$W+%TObf@FfW#VXLA0`#?I*Nsea8mf3(m;_%)5}N zF9(ztPW^*lbgiV3Oj~bWs--X5B5zxaP7P7^8y;B(Z>hZolXI~|=YOrI2nJWsK z)UMC#D;XbMVR}BXASCTU$g`RC!hOk&HkTwE8xQZXLtYRx@D@bCS`YPf^_ij@Ir`B; zFUD>S~JT`;(Iv@UybZvSnVfBP5^M|uDY`$|; zXz(>#4ix!_6h`;yY13Vd9)voO3qXL{)LPb7e3|knS_Uq1xbu!15PQIQZU7JLlor70 zHJPf+Vkzgf;XcWSc`SFg0}2@kXChh;<8mb7?2B3QikyZl4u(xGmah}5Ul|~SL4%vw z-H0dHk%u5SxY#V#shvlvHCu_w=#r7`UnQQsi%b^lOL%Q!aq=uBZa(H;oG@h=^xc!? z%fS2Kk18M4cyJ0tS}sV4L`9iAi%KH)j712)p_X1G9GM>`H`66I)Y7@Gc^<7f=B3+% zJMd^qie{*P?~YqS1mDUhm@j7quJNNJkq%U|r+>tnuL;fFitp-qphTadPX<3Pr0tlo zFQ*RR=xR)_gt{C)^1mE(8n{YmCYL?W%bL!x{Oeih`UlKW0U1#PE&vYDfv4+PIxLI7 zB#t>U1UN97F(@&rFAyK+$sSCNWbma*%LTp~pDSBXXe;OeGPIn%p-_m^e5B1`@}Usw zN9o*u?MbgBbSfPq&u-NhkyL&X%H=lFw-^B>UgMb@fduB_D^@(d8S^LQZBY<2LIDaz z49tK-&!aO&@oCS#EQL3t-^iE^)sFGu7Mf*}ZDedl0ARIJh7fRS;z__3Z!$K2j?NIL zJsdI(fV|wd`a|mRUwGo8=DQYRI-9jbP0*dA!Fv_C6y09A=W*JPP;mY$5+JsN<3aW` z%HRqS!7wvi@w_--v3ehe#o(jvzyzvZRt8h5LSgOXs2xrncRu%LwI<4p;i4+B9c4;0 za~F@&uFv*c?OcBuk%>FuY}6i|K$%az+hFwV)kTzbLiju+tG+AiOJ&_+ zptf7_YQXWH9pGq7BZbk8VEECQUXI5_ z0fen_=ow<2GUDa>XLq&jc1Kp&OFR$sYpO+|aI|T3O*r}TPKD$Y{8Y{mf4u=&JG`qa z+669PwEXG*ET^5L-+ZlS_HXAAf?7F^ zzkTlt1b+%AoCV1RRQ`5@EH9jN?(~HHOHLjW_$?K?+Y3&*qK4-H1Ee#*CGH^ErYq>~ z3-nAnmT1eQb|v4VP29ZEHkUYRU(s;@Q~K*pC1lnY0u5S z&z5?AC9?{QhVj*!+M-wzV4ks|mxM+v*KKj1@)NBuq5-7%@&vDri5coywT;4u+JM+| z3A6j6tTVy6*|AyGuc;+dQ;5LyGxl3PdB!kmpSk0aC+n#axR_X8oo&q6!SAW_0ogsKvqxW3c31=Gus;}2LFMuXO`9XiOw{k#c40FRwk5e zAS+>X8%#}{LTg+9vnRHlzzW5F;0d)vx5QU(t#OHT2C!Xxli8u-3=_i*x84M#w3ANf z$`V96Agm_M0=|jKH_I_QkrndldS_s%4HDRo8ovpy+kxX;rONZ$p$S`hoy?s;QnC!> zb9Uat9paNOlG|F&<11x^76uTUwF$y0{uy`iP^ZO={&Kim@NYgm$W88*U=h%RjGW|Y zfYvp2ek5JzU;ntfptXc7l_${R#L*a&mt_xiia(WCoZ<9uL0*rY6rQI`+Eupp5T~eb zz_VU>v+8IrpDy`&O<2jyTny}Buu(wJBZsB1lRh@Vj_dEx;xil3ZbazUNJ243)Nkx2 zk)ga6kzSv0{da(HoryN)0^udhF|_g8%J}pS%fnlQ6q=PxKl8P^OU}ghOPM|drR97M zwUH25yRVOxKv-V%Q^4j$vZvREq0)RYuSh2gL2FL(PRC$$@=G(=DM`$qrj`vzP)7{d0uwr(cWx}}yf%T-%ufMK1+pzi+DBNPFceYYHV<1btng-?Pb*}boaiM6bHb1f|a)SXAU6HT`|5EpugXJ8ZJ zsX&^)2-ST>t z{JGckv`Xk`7Tm_=7x$5oAS{k#3|HHKZLkN)l&%gP$a5=xyELYZZNaN!hfZ#5^SL=; zaUz?GV&EpnWvd9`@C4H4ouk$h191-RXyKOt0Z8KAiZ2xR28yNlqHx+MM4CQoW@d_8K4 zF`>&C;T>CR#MOi)(6S1m8=ih)`eFzfWE6X*<*uq7pJ>D|G)*2)>AOy`_W?F{1ma3S zmr9K!Dfb9D<`+Rc>LtQJw}&VQ_61;I?;l(`;r%WMfhqa{x=G-ToP*9rkq;J_H#;1t=Da;OX%atoDP9qHy7eX9yfRP@;RZ6c$3+Aru{og<;;D8ICe{x&l zR+9AAh4O=(kO%v-ac~RW%jMhWU(a;?+GSBs6GF+^~=zci~>z>njzBWIQarTBRf}?*5M1 zB_J@t3JaDR{);Di&3o|l(d6BPx~6cGd8M5heO>q$?%`z#s>tCZ+fMHOde$QcW7C_h z2K`PbkKK;iA-;Qt5G8g=lRxC@K?Qt=&bE78N zAqJ%3TEDV^W@P~3+JyaO$Qg`ja5?)n@_aV&kQPd)VCulzj%IPKd@4!!51YRLX$H%N zcfkW4pIjon=Qzn+Qf~dL0!e3HZmj>o2drzM~qa0 z+~yaJv7b`q#0B28dA@G2;y9Y3Er=O-%sO5u4J&&E%%ln1iu9<~gLpvmqOy_i0);;l zbSrCfyDJ4mBj>2~6eL_nnF!pFA z$h^UmjpD`SHMwsHFTnGq!;-GAzxsFk-3lpM+&=JR;i6VpYSn%#|<1?$GwJXmwKogPxx<^R{ z^{AcV2w1pNQbF_h8k+Tth7!!?Mv~x@LbUIeLuwTNZ(*vTEmE zZDH`q=^@+W0#|Zqp;uTO>I5!LAXoLrSSY#J^;AuKydhVvTm1!tA^=)%H_?TK<3Qw`h-B{Xq!rMvRf|HmF}l9Osm8KpiCMRMoV{;P+EWZOhrN6 zGBC5e2?B%?xRl3FaVSBTBx@_gdH*)p`(`D$^cPc0ay_0Cx8L+nKPkx)E8yfvQjnmZ|5}~`ON=9 z{`~(I`NK3HiA_w_h8vQjYG3<}Qt&5VTai}V_l`g3eDL)O0=|F?Jc$%>WHv1##Ph;> z!QI9+1hJo54|<8MOtW!KSUL#D>0xr@qba+e!JZHp9xA(}!nR0lZ@`i#1;Wl{liMXG zHv_k=#hyvFRl`#*@0EK9WV=WdOma}CgKI>bq*P!xnyiFodH^}SRE}=#4X%f2eYk`~ z);qOT;a7ogs02yNBZI|+0{WK$xHWl5n{sUx8BFg@;Ow&Ui&A~XXkt!0tMgWz=7*Ayo2bEL?;U)~E0g=GT+ z8dkxFBP~m5olJK5jY}v~<^IwoJWhOY3AMq>NoZr3C%{TlW<4Bt&(e(w7TAcCj#Qodej&jZU#zb4?x-TaF_ak(_z<0lI7hi z6JAOCoF;iM?J=+A3JZIjYGz76C*+V(#Kj<#>b6buBsZ>g^*TFZ9X^S`4}#v%lt4!) zz1)MX3$!KdRn>GGP+&AdN#$Lx1(PRcyTY(AiCN!*#g6oaem`P6W{m^BAH_BGe93Q{ zY(NTt6hYg8+akK<0M#eQQzv;S%KEq#SBR3Z*wR|llZu=id^c434~Q)y1l2~X#EI7A zo9&5;$;emh_MimQu0ELgNR^YI{iDsc7Agx@&YKgL4$V=u7c2+G(Mym6uPakM-K}Ea zg}DzT2ca}pG;fk{KmvBdVG_fysY$f|l(nPZr|6UW0bk#=Wx9|0Q z#c$u-^2uay>ozN~Wtfr(D%uaKPRFXnPv~#c&$PFri5nxnsxJ$k+PR&Xz2DlWX(lrs|uWKs}-U4`%h3yBe4bIYVk6-}^_KZ~2b|_YJ}@@H*RWjf_Kv#L6^suZaab-#hv;!(~goA>6f_ zi=iE>$6n?UyD1iCsErXY+Se0)Bv&(RY!aftq#2!Rv>Lx35vnzx{0{H+bjfd<@cr&O zd5{9tiB3XUlD+}mED$3CLAmX+$>nTh@YGh;*r~E-dax-*Ww#bX+@z~u;dR>LETX;> zIg8bRk*Z}Bj^E|Ie>7HK&NXiqOa{LV)D^yOj^xN@ zz80{81{E4~=WM{c+0I9vl?o!{{unDm2?5f82I&#IFX&1NV248&^?b>1pY4fbDlt@( zX)QYxw=QYIgSFchQ3EVB8T+r5ZhFZDd zWIUyUdx_*evD{9~D}tI3y^(wj@C-0UnI|oX&UcD{CHNsze(~OBW^&x9KccC2o$sz0 zg6OzJZFZq>qA#@iyR8iqqvS5r3APv-m=P6*xZhLzLCU}o9|_0?#o7D4{b1^w6djio z=Frh67OMHK&aioVC})F>XLMQkgI8$0ofSD z;ZPC>rciw!C2+&TYk=~k>(RCB6__tsVFI?#0{6q=1_5{)-FVr)5>Bf`roMhGepE_5 zHsEmY5QP&S9Ud;25Fw3l5kcJpHo8NYw#_J^)N=!;po09ZubmHHo5H0HlGT9oO*ky$ z?*Wt{k#qdzyRTOO{G7*M$q6DUOl?Z{%57?WfUE#vZ-8&`t-`@4`>unyD?{I!VY6>3 zF#l+v`1|D@O$7J)%b|r={N)fdo&SD(C+w=&26D!_@!;p9kDNka^Pr=er`oFrLS`%H z@jO!Npto`VcE>j;K_g7LqRTRLrtuGNu)HMOIubz}#B?n%J8S7DC^HDyk|KgrckYJs zP{ytumEUjT_DG1ylx?s$TAv+$RSoIq={AAi0i2Lw434Kj<@d>A z80OzfEEMUd&21*R#SKNyjTFsXBtz!>+JfsFo;inpqjDo$GmXd=RgN86Wn{QD47WNV zcN}IZ_)qb!@jxi^2i_X2BET*o3Ekd1T==0x#&bYD2v<4}X9K(^%rfs1mu?a~f+O?& zhCbZuMo=_EDH&P36(*yD$T8zbs4Uo8%lV4mA)X>h2(d?~XqVXM7=_&lDLqGlvKYKv z+Z@}pV3~00lx)gLJ75{e4i!v3q|j~KSIw)YiMkLA_DLO=lvoOOX~|A1S%~ibr2WeX=gO{@nf}YA@TU;5cv7Ulie1pju77mn*o2@`706)xIZz~_}HN0a` zhAdOIfi^XBSADRpRyd7vKN5APD_lcDFj*T(ml>E2wQ@h`qAw}_N2)&|q7zB;5=FCIsNN3ba}T}c@=UWb65d5j5;)GV&~=alHyJ;2IAIA6 zIxy1l16J4?F%YQUKp-s4XE$}z)>o4kK(1{PmU00UgA3?&@9SZ9;{4lcvOqN#@cW3C zUJl3I0cW!|o5c#W>U7hX=kjk4u5dlxr3m6Ulc1H8v8=70A*!W{Gf@O)W=d0yXm7kR zTxy4#iu!A_#5zuxs9$fUs&W&gY2($nWNL5oGRj_0@)2dIj&6~p9Kq)a9T~jG&mba+ zq!K~*n_1P)SI9;_ot7Re(OtoXK-BVMG2`Xm@(DmqAtZ0vHP9F0y z$&X@1$%%8icD??5as;Yo43jpZ{E3h_uJI!O`*MPbw?erBXl2}>Io)7_9c`;C#{lS| zo6Z+A57A7O<=env$q!c(W*DH95pRc71w70X@+py3An<`=?PmPQ^O8wV7UFc;3%d)R zk{l{$tAUwhI}P(~9b7AAQUU1?ifR#KT`Fag5z6OwTXAO2 zQ)eFdhRCx++E<;pCTHD2mA#x`nIKL{cghOK&TP}pY>Rdtllrv{Iw51?cVIf*Uf|pL zJrcMZm6Bu-O0-dcexO3LvCT&;finI~s)0}yQ+&Nfcd_pqNwf~+xp#AEKnN(4N>UTp zfN}QPYDBdw<*(Wlr{p-5-HKPr&# zkZSFS^)RdGZiY1K5EdGel3?Noy2YlDY~7fri1R^60zGz#@|tz4ZChNSa1T0w;OD?Z z$aHrIsJZkRmOB8O4{!bCjs(=Nli<8vk3N`6+VvRDWI`5^Z93UX*m&)utpuFqL=0<% zTB&CpIq0P%4=CFLxv;(QJGD%9pO4aKnr$`PqbY3DjWj68BFhVio2MhBwZG5fmqSaV zo;&+48Xk3Yl^MVK1AL<$LyX70=G<59e8u*$5mUq#Ql+fQeeyu_4Xw!1C?%^4o>Am% z!ksH?E@(j-^g9}-zCG_l_V53>`_H8ICX@B3dc9#l_3n zg%Fm)a=I80ZL^!z7Q~G(L*Cp8;eClQ+21vpG%y1n6Q(HEj3lU5#eVpe6=wE{6Btwp zrz>KEry2+!G69mnW=?8|RA?ojOX7nx`6yC|2wY!G?e~Bj74`&f)s#L58$^JegMEmK zCZfbqT9Mp32Ocv^^$nb5r4S@2Z&Nl4X)vM2!(ak*cS8g|OC?Ts&G`A~8UdV`UkxEM z+cQ*jOsZV1j}gEF-?G!$q?I5UO4P+VRQVN>_`E4f<^v7v^;N$#f7S=fJoYhz&Cs zy`<27G8U@KTf7)?B{YZWV5&vU^##0xFuYSm5WzdzLpGp!cKhjJk-$C%q>5}7TG2P5#8(2hstrtCmU7q^GtI2m=zZ4dADu% zN8LEx6{u{7HjIA1O(B(gjK z*&-7WHh2eG$8EObnTYsSTU8sYp{nEdz210BZQO_XV%@i2Bh;P!0%d z$4j274}9IzHg?WX2HD!i&ns3OUuH>y4GLCbS6URrbC=WP*cCXa0m`DnIaJ4f5LVC` z2p(%qdZFSM?)l)Fr5#D3gw1<)z_w0@sE|^G>Iq`Qt{Z9>su1eLXHhW%d9Y&mJl4?1l5W_tE3&uYav_jZ zo>DnJ;IeDby;oxg2&;xfsmV!?93|{@ss_>?j0;dk4Ek)1n9~aC9oPw2;0D_%Es3ej zzg>?$cE8iNU&ymiHa#Ip8xX}i;||I2pw*o#O2GQbQUhpxyB?Xi6*9m{H7s^k zrhoZpg zxb6=$J^?jM%(pVQoqEG}OVvG0sYK_5KIW5Gf_@+skXrCcY^N(?O5qfA%>{`iZ60jT zAU7tbyNA{1!<|G+1IOu=sHverX9`4*4B|wO)gG7|vf;G&$o>|}?~_{{v`Cra%tJcq zN9>+}vRYkMkRHh^`|(6r=DYhQ!OPYIF>$*deJB=sWhFm~g+7?c>-F7TaXy)+$jOTm z2`sI8;*lHe#oq1Q4X+3S?$TBlP*P%1YFRI*;;%^3`h zC-F;I!s%~>dzDr{C){j$R<5*vX76SYGkXB9DCVLk_2T%4yI{~1(t>@9x&sPL{N@5f zJ~ZLjZyCXX5~BI|?Rs?Wd)5gZG4l0@T_x9WK3fb+QJw{KyxvxiVMnTn>!cjG64>A? zF65i|MFs&t%}#Qk!px$hC8He^LU?*YeWKkRkQ{Qc#^U#QIl~Wo3ZEa8Q1dF2vVvHb z;Qj$)tmYmhgU}ai@Nq>j*KNdNa5+Im&~N3zhe)2=b(#(a%RYw)i}2mP>TIR4@9vwE zRsn=0J|%{W@pc&8MvE&e(qZ~Y>D&PFcOotT*vg0|9|O=nQJ~;hmtZ6Bnqox}eNE=# z*rP)AbV#QlR>AJ%?*Q2hes&(UdiI9Kqfa<3<c#fQdUXLD)95xqV2NmywWqgL2kn$O1^Ut=REvb%0NHKz zC^4bhltX_pyY+d+V(TJsMYL+8l4SN%_tDt6@FP?sHn5N>?b{l#}6C&YT z#=0$ne$<`TfTR~!DAx!>ve0po-EhkA2@+Pa0Rpp1Rni7?aEq3B+FjIV3_Fh3qcg)3 zs*8rpTdAko>_#7p5$Gpq31-P_YSYBe^?$eXzVu~VsWdEg%0hmG2vzDiIZS1eoWUC@e>#^!D< zby-@OUJ2!BR|3$1PBWq8alartS!f$d?SXl8W92Y3(IN7yJ&E0h3)IFzP}`g>Dv|Sr zq74BRyn+RdF1Mn66ezI6k|{~8KrG2BaTc0FVz=O=?2S|pEbzQ zp<0Iyb(Mv}^yvQ8R>i0RXptz=mUNy}Hn2>0%&7CxN1{^r(;>(&r?!^Iq{?SLdrAjU zsY~=^`wl`XbNRFA;N6ug`)lA5CKTD?iB0A*`AqX6)AmJAD!47kXhoF0m;<*$e)N71 zKTRxsH+&5Aq z*YB_c2`q#ABw=(jsWA451}R*iIqW6O=pjm_3}U$ld)J`*GoDt7_ZAuGa1t9!+n{zb zrF-jt@T`Kp1E#a3l}|+}5s*mlvc4WsbUfv#V|BEIs)TF;Wv~mLyjkMI)EGM+jLXR5 zbycvZc&_{}Du@w^00|9Y=}1ZqGnWo5!T7yArU#+2g=s?S0utL?Nyj}r0W*ZXbNmL0 z2n-w`!0JLsgbHX|U3~+f&&o9E>chqc30>0bE~|fH+jQ4t^I37xs^)n#;3&qU22Y5a zlV~9VHTZIKmf`*&N7%MA*yeGD?uQT;@Z0EefS85AIqnOA@MI#wmoo-92m1lYrVUx3 znwh@FvCcuxLk>X8tGk2s2)?&uvZ;r~1m0=VDjwm?xk@WFjFV`fbMz?Sw~tL{)}YM` zE$Gg&?9%W#wDAIrzK6I%BGctU3o23Mp*;=Pzrjq?Up!Z{S*=A-(m_lQI%VgrIR%o> zj1nEImT#5eKm%ePBM8k=Ur6y5B+;N9W@!r{20tS#a8Z)KLCIKXgXG>(`A%z*3c;cq zOP!Bs#!Njanhgu45fHRPvM4f{S53jOJ@bY3|2R>$d&FzVcOu?KjJE@eL@3{w zQ7l=SAM~hTK;Y66rV^|J_X=r|*)y<^hR2$Yb8qv1<7!C}-HzT#s2rNLY}-=`PdX3E zuHBc=eO$5qxD+-UHq}apAqCI+W@H3X7~_wQ@R5FyBrN8e54v#t_|4TGdu-`a@f6c) zW7pg;7Yb!tQn<$Yu(a}jxLag&qPJ!R5{ZFMo3THF9Hte*ZvQfYM3d(LGyz)fu$RYaNkb0dYWJ*wk za|eJ>si`TUZfWZ-V~}_)_{@PtSwJ4T!T~Y^63%nfTix9$TUvBGQvbuHT0BokV2FI& zh=aCcH+M9tG<^o7cagyE7qYw8O5}{6hZDao(~4mN2%^lGb|!m2aT1sGNB~GypT^^H zLPJ7jGrpvkxC8+-p#&k&41Lmrk6N48GI#(W2K}=I=vI4<@<++DJy^3VxH8xjO<}vq z=%ckndYcm%<-8RQafmRwNu^Dv{@95ox2MUvx6I@GR>Lc%_2cF??^riCx`Ese0Et`T zfj$|8CT>wcm7KmAtMRvN9>3Lh?jZQ28KdPVvNcLN0+bKZWf-OI4W|tVBb9HAH@R2S z+KphH_v*4rZ*2KW_*C>Z)NV8uuLMNpdUVbEOz2KYN?;K_cH^OLHDzbB_vl^a3OKEU zhBdc#5k0(j12JD4saDb~IvC^jWfdU_f(t3i5ivLDUa*o`iO$yUEc?E&ZX6|76FLZT z#1RLec$@YCbv}w_ZDCGqH#;%;dWC%&jj^;f@?Oi(5?6~_?){Jzi#Z?}Mzg9$ooKKU z1CDWfm?R}tslYHN%_^IMYRx<}+>tgGR{RLdkvc%~D|HE5co|^q?7=X%x!G{XnELo$ zdo_quqb+aOqifOC?iR<>(d;Mp^rPyEY|^DVW0hSs(?mD-V~cjR#eM%O*20_mSPDak zLBI`c?h2~ua0ip~RZOf|wjjWwAALu`ZSVK*QEO#SzC$Tt5!>Lm#Z^lCBc^DQ44N*4 zM}5754ebzA?UTqG9T9^xZP2XBCELL0X(!;4kqA%#Pu5zYvo`Qvh*3ch)F2hF8@ZDj z#0HQ>Zy@mVB0euRgrj!@Fm{5SEqa4OOGd$(Q8Q%vK}O5KggNlj0E0y8Q3LNl@#*NQ zL^Xt@jNN1HIj;}gn^gXnRg#S`@o-nu4wafab<@b8zfqsiBN~W5D96Bls}cK*p0bh| zu;1+rH+g_9gB)w)J+5LraTWNbq#=S>YEZdRA<%#1^ztL)6ymnO_T%x z6*#K#-tK)css2|VyZ@E?*ulKHT6-XE>C_qBd}&fgk9K%*Ym+p=}iJP;~ZbXZ3;|#+JHWk3Mwvh6-rI zd>sqmA0gsL{&wJ|oi%Zsc_0B(^yjFqS=%CUOURHgRNK?booO3KCKcA}2s14KKcW9P z4DO$t-3H{?S2>l&oEGt0;1piZ_ydNH?+1oI67fCC_5k}HL1d{=SSmUi(aB0*DM45KHK%teK%?-A&8N!b6*FQM+Q>oA7!-1tBexw3v%xXs}3O!3%)! zG}2=?G2|_9b+bDb5Fh1!Qh_b~L!&avSQW6q3$RC>90fHnKxXvUnb#29G!Nf{D5g`0 z3s!Oj)HP!nMOCtQ`yR+{atrUKccvA)qVkeZmemkwc4@;gRwT=B&oBYwG;XVdFBSCA z0TCadQijSyTI)s)Yy$dre^7YsFloZV0qKxV%w{KLM2L|1TEcEEz)0P|Ny~mboI0wy zyZxbO=cWNLP%Fr-tKx~eQ@AjlL#HnbRjUO84)6lkqid>`b~D9s)hLl#J?aj}w*Y!R zPve@!$4`7ZC1F|@ZaYToj%;=%rFz>J zfi^gD7sZd&q(_5B046#7ITj($g-;MOibhmpp%=HR4w}u247K`lZc~&zHS1CKzFF~T& zFbxc3!#Rjnoav_7WVu04c?ZF=3QZumBSf(U%Llo2j8R~Ama){8Xw62v5gte_O=1%P zj@+G@=z-56VlnAq+LCb=b=g0B^7RO~l3DiJfS>@qV`V!|h?>XD!nkr)w97dkUgJ`4_7WfrnK?>^OloFQnw6HK7gE_l&XwsUaf9sOi4hP1u(C{UEvN7HVOxz2Z+D} zlLeh8WCZ)7=F_A$+v^MwHS92DM_g#qM#A~(;CQ5IWig&L7(U&srYzPCY^p+1$_5HG zuV5_5aRJymA7%I90%!tAZj#0x&9db&&-vlZ?(LEjJSHlzWK|xmeJru;@+7nN6z5wTz?rAgRizXSLRnPgpEzdmsB$D`*qDarNTxCWYb_Rdf z0(;DEYWuS8(wIrI&N;cCDipmbx)h?kyaZ%)-1Id-mOF?puVx%!x+b|k+mJGF8LZGe z>@qMCtY4tr&;NazMl?dzcQh~pA1Ze44k%k{bVT3Cz=iW>mn+B<~<=v zhCRjTyS94RZYDR_dq8G0I27XC2A>ue_tKmCEiz(092Q77$dm-C4?7gboiQJUS_JfI ziIXGqlg?LA&p|~Dy1Lt-tx!a`be0Z#yBoiDx70KJIXK>cm{%B)9V?_9nKsm9ThkZ( zFU(?cM(uVzx>_#qN9@zMt?gNUb8G?Ggr$5LB?<=vkT*El7UT|kTyNa5zp+Zw(N(U+ zAXR8wXaGif%G{t}f8n|E`bMaN5PFa5-!BPLihuSIl^V&_hP6E*NHFL~=}302bog;a zKQg9N^w5%#`c285J5=XzqSFk}shUz< zaeUFSgLX1Op;Fxao1zLrwR5^f?MQD!H~YC(m7xYN;i)iNeRCuEN6`#;*~;1q3H7+5 zt{P-`^W*~4VMlVP-MxRN+m+?$OW?3jhmo!=elLk0h<+M5ZZ4m&OXa z!!DJEN%O`{UKt!98|9h!h0;BM$8i{Ma3+980)=fmcqcvpYmEdN6}IGUO3;{rv4nbg z1Ptl8M{s^OLwYIv)edVpv@wCz0-#g$S>zPlM01aeGyk@_m7PHW1z(tZ2CyA06V0luzS7**zZSH zjGRZOxHCXd1Z2Pnxq{`0sDgIdY(a;BJzudUcguxbq`^7oCVwm5Hb8BF2p^D~?GzNz zu2j;uMfBX{c~rtk-QVFkn8B-NjxKs?>?2Yt0A8`r7Lzk7cw$31r2;9j+{G3}=Cp01 z&+3Yz(G(>?3Y?-OM~YeUKqlOVK06=fJJrRKiJLhF9slWY?VL&0?g2>9(6T!nq47W0 ztcS&;$(Sr#Ds?=H;C>|Aa&(-qP=jrVp=k>@#yVwEWiUPsnM@`}t`!vu)m>sK2(B(0 zb?XM`%xKPH{woA_Ayo7x1Lt`k%O{{-M9!G}+UUwO;kucIx?uf37*ONsQ*&#pbnt&j z$C|PUP9JYmBs7e00pVgL?9NG1<^U^eqZt53JNBuGUC3O(|1kq^3t3LyjK%3EtB6~p z)0^&~N4ekSco4oet9$U|RX0AzXbuTy$eu*>V?Szm&im>(gYP17ac}0z->Q4&CfeA; zm-%K>W|zl;E^?~{hz1ia7Kt2Xie4xZ!}N=OU<8g^ecQ1D1|Ey?PQk4XvKV5T+>ff4VUdk<YXlDV zT<&u+XK<;D)F?-1`2yMEWe#ZLH(g4fqZQ`!a0l8zQU~NTo5nS4Kvct$GscP>%41*j zOJB!z>PH;tY!K)S0<1eciiivZv}OQe|6$f?qz`7iiPg$Gk1cNp(s5vG_hh)gBNz4f z%cWr*+60TgA)89Au0}Nje2$G2~N)8cG93O6fd3e3kn_1@-6oaL1IV0S@sJ_Qpuj9BX-B-Wx#C zkENdKg`VsLo?xysw3%LMQDg3QOlOM4q`)X$Dr@(Bs{jHwJtJ^j_?3#P1Ozii< z_Orun?;x=Ux^HNa&E=2Q93&VhI)Ipk%y)J(ie!;R2)+~=LCR(+M6qE50g#YfLOuKq ziv2C!Cv5}mwCqZLyy$K{ z9kK^}45-TB)&N;g5~8}^QC_O!(#Kh-Sq#7gyw&v=>l)43 zD%c?_0{(jKY+2b@?RUFU8`sXtV-7()RvcwSK!5L-48#J(mY`3V*g;lnJ*vErO#Eku8B~9B571l%3kV8N7`6 zO9f_x6tG3#@g($f&FEJPr9bVtB4T(wJBdU>OOnUC3bg!e|GEQYArTk# zqcp~|3A8x8DMffw@5vTaQ9;7&#S)8nd$&Ju8v5f1{HE4|T$Whr@p7?`@>Kx~o)QTBDk)GZ^F!Rt| z>R^GQmbeq-dA-k@yyFd`b4uM=j?^0vi;pNfBV0{)U0gwGZ-=PUdz(K!5eQ%^CgF!@ z=~E0CS>W~H^MHLvhJ1v&x9b%or0I&D?YEi-eFxMF8*!SppKxm(YjF*a>9C^a5k1x3`xDdUls#wb+|b=Lj*ql~goM zQgrY1qPQM0phWx35@qXXBcBWu{JFo60o1lTTcqptN{z1T9%sOiuuSsq<_e_mcb99w z#&GU;6YSRm5^nYzs(A^(eX6ihP<2D~ryG1T@x7O~2*13VNP?LmkyZEI50W9MH{+MJ zafCNMcn=^!s0Wm2_Uuwgkbs44)2$*;SXH^$AQ;>Ux0;>L1uk_JG4d)$RpP(8d4rpV z2DE-RPsFye*jiyNI*;|dG8(Zo39^t~O3|y>z1j4pY`YjIXwVYg?Ypi=FlthU0l5+7 z0z|{MeZjLsBtN>B)O_Br>-GK&AzQJcJ0Ka4_CFPbrF$T4ucwsJAlXLKdL|750{~O- zs(uO%9UxK$6BVG2@Q9`Umfk-<+C;*OPY$p}>n!QX3bxtLeeBmk?^gQKM0D|a#VUd- zjsqIJC+!RS`+=6Bt!IJ>5|I53jK90WZ6u+jpb4degV#c&3K6*J026BC!YsRa6|13@ z_i47-sB;5vkyiamZwiPV%(X1{ZYk@=%Zbt^p=@Bu|>&=gG9`{XSh6M1=2# z4@+2LZl%55wfP8ySx;kN1b4r-Gf;Sb-M)19QD-h8uThVRsZCWw1Yn@*FUJ+P2{t4{ zfq`(9Z)o;=`9m}sAM*|wIGsVGDG`PQH4*OjO)8BH&K`Ufz{!V8soHUm$$dzpfU6K8 zfKe$T@RYQ6)5h9$Gd>$~EW;KNAFvFgd60Hx+Qo8#-Sw!r9T;#fxC?Z<=2@e%{TWw# z1f1(LLBBm;m)Rr_6vvzofbJPdUs|+q&mbVT?X`7G=#<4k0bvl0fI6V8A|im(t&E$A z9e3Cy3(@WInkCcoeVE!6!~>hMEXM5tL&7XE;lb#i=~RR%(jyyqxnzSl$CZ4bIa}0` zr(qZanr8#7Ow}d`&Ka1Mv4O6pKB$0TC4We#+OVk#060vlD4Q|m;WU%}I*S6w9=R=E zVF=r!%bn!dPFQOT^pYjA+)nDcnB5IZr(|FBc0IaQHFiM2HhNBUOX;Xioo-0tO&q_T zbfwsd2vP~0=u4EAL0~~0!2_uBc9-JU#Rw{cP=aGPC-mIluCglfY>KI5CgV(Hwq4L+ zhIy=a$I6JBwZ${{db-OBXgE4fb4Z80s0gJ|=^^|tDAl;N4TdvJ#)vjHgO{KxZHrdC z&=Z_??a)i=mWqvvU`d4z+$oV}PYJWpCRm0ZV%mAj?rWQufWJ08v@mb6`>QcvSdW7^#{>8GP>$?|@mQ8Ns? zZp_8JKWml=o1O81@jA6$G>Gi9KO-%&nWc3PI~95(_;*`mHD3e{*@wySDdWkInsYi) zy!c%x9-*CCTFurrSpD7LFaz>7>cepI)qk5+$9LPK5@VL7#6RyoXaflz; z5uDW1$gbD5e2+g7IDyZ-Q*F8jvIuDh|U}G`LUS#>F0u1hxLb1LoGEGbj{=5X|;R_YN8Zc6h8yiPk5VD>gf*eRn1$$$02b zvm2i7Oc}CP%GOsGyd6Mov5s_aA&MTfU*WnZ_aK7}CWYwVK#n!-b^;#v#uxyd=nbs2EUiTkkHwCsH3ME$k! zOTjH;(@@S|R>p*~bYPRCtWpLxCXKwJ)E&4MP0C3p-CTE-K=Pr1ZvlG4Iv~F535HXi zm{M-#k1)Y~F;^KS2mCyS5!Rhze*D)y72r)MjVU^$oo|9?OnPRKL}piIfDB^t2ZqZw z-}vL6-beN#<2=woFh?OkbrYE~9*iPdD|9$2tJ840Ua`4fC=J<=i;3pFhmS3(cPE!E zPeIdE>D2?!HZOHd)nj7rW!db+Z5w?x!t;f`ZlK-ML2?S6MyG+gr+=fSXj>fjV9%2? z!K?RH-rT%AwJ6mh7wyXfbptTm@3>u$3So(cebr6c)svY%+o_kq;8_5w{VhC{BvZkw8vGb>z0&%&G@(*ZB${J-J0&N&KTu{_bE&>}kIP&qz&6P1cty zgCuqglBM<=o@hF)CCV_|`ys7v%sIMb?m=7IEM$#%@aBh98j6WP(2|zwZmV;%rELRg zM2S&6P&YlGgV!WZaYiF~b2_^66u&M`f9Tr}#p&zy4(sFa@9DgEah+v0%OOo@a++@@ zg^w7l01EhuJGnKO&@(yFPxcmRH%^HocK?j#7poxW^%U1<_?Ldd4sI}~*g}A-+k<`J zq?N;NeqIsz^%uo*)E;S`-6|$v8Q@Wgs<~cLA4n6H$hslA29214U}HF?2@^rk2W$x0 z6aXI5@o=)X97Iq~;sR0Jf`^3E9ZU&<^Tk9bd$>inr9W_yoaujQ!3ud(1VtJZWxc(b zhN$Ei1l}pG8^MuUs4U(cMRubukb;xcc&I{1MRePFf1h*;e*|COT=TZUnMMs#GdDw| z52GS(y|P8I3|f|AqSexcd`Y7jn?-)hPp)mEHp5YX7On5tMFg*Uh#XMc5{Bdk5IGr^ zW|tj>$OUa~r^b758Zwb%dF}9C0`jZDZ%cD0GDXoR8NxtXJ@&G!F`)C%2Q@e8(a4i< zh3WHlJ)-5}giRe@J>YAG%FtCLtX=~)A0-QV@berm=s*CWDW8DdaRDD`Jw!aZ&Gfp| z)oti=s)R{QT$jvYbtr4XPG(J!Dd34C!2%F%dq2Vb!4k|RQ@Am4D(>gwr3VGR{zmRBli1+kV-?>jdKU=<1S5XUYmrfo+D4nL>;{-gOd( z*9e&Q7WF+7T6O&PE$Cq+^(n(+(R1`lj+1>)_=*L8 zJ-U*NWHUt%de5915+AeOYr1m9#B854JRSuIHz2u*j#rdh5Sx9SNs5XHct2^)-49_+ z_yJXw15E`GhJZ-W#L!o-LzMLv7WC{)OS_FqX<3weQ$|kGILIGtcY$k#R5o(E>3W5m zcS2A*P`H#OGQZ zCO^8c!hBAp7935U{sFQc;8*$hhEIhOc+5facejs6XDkrpWg8d;gvE1$A2X~mIL~jq z-qPbIJBUFWMoMVh@5Z3XvAV;4IlaxzXvs?k!vh~<2Ap7zqE1dr*MUuky{O-k(3@|k zwm#DBbu2>1979rR{Iu8Y5y>(Tvzl()QMBt7o732nrJ3@LbmB8aT^+=w6&n_aR3_cj zyYsaRTimj%=cp*L{FO+Xpp64BU@rp;IRK23QZmA+ISLv-*?j|JtuK}*D9@mMiM1ew zMq#^2jQL*nDiJ&M%Jqn^fTEk+9H3iR2Io9@z)hR#X~j=s6FmP%+44Bfn*;Lr3BQ=s z8<@tnK!&7EY7Or+KgQ|d-Xtqu_*}N<#7Q^6unChSIe-Se4Ui%tyPq<>5}l+W_>@sY zfETyM=LWl^2Y&GiLZihK`=xEs!=TOLe zA--Y!p)UYn?zCv0Htds%t@EZg7qC;qDSO~q!J#!Hc2{q4P$k|e!6HUuabTXkyzMPc z`040Mvu|MTLALqBfv9d)F}EWXrPXZ0pOv7$Tmg#{CEUrq95mN3zaB^#=rmnIEOTYv z=xap?l91*q@gN*^ml#HVhTBW0Zs^Ja-^F*q&>QuhJ9WIIPJ>d4a$WF@5ybB%N@-L& zeDT&#(2(g%%C_qH&j;ur60lf6lRgy*6xmEq4Q;#2xd*63p8tc&xgO_1LBMW>GZJlR zBkOI}%@x6cLzw1p!9#O!mcgNK19P0fm&6$yoJkabN9i)|>qa{m-6kXlF876%3!*z? zJ>S+A=SL@0*n-^6wFV3E4iL&cF%NDqZrThzVN;ar6+9EbNs8q5+YVmu__-0S@_f4y zqi&FbL8sFnbRP%lLg(;!2qt7g|NXoQ+>X7{%YyPB0^syQGg{;m zo$lFtWwoE)??mRzue-o@8+;r za6w?!k^PN&`X?wqwmNa|4h$&Ih2q}{vQZCV)?qpiLF85Ju;Kp}eMM=<@i&I_J4>lPT6D&P2B1OX#et5ETOSksmJ{Zv6!$%j5)nTHlKsNn2w7Q_8 zfv%1p-;0|@dWiM{#dJI8qf500ZJlAh))*h0%;$0q1X{cFyS{G=sz(Udq@7c&XhFB7 zw{6?DZQHhO+qP}nwr$(S*)~p}ztidD_Ct4adnHv_wO(pHjZ|umIldMx!3;2btUqVj zUB?9)1Wf!y2?kEcYkqLoxP01C46Q80Q4*PW0M+cU_2flw4h{tr*enwDAlAk1(1ja< z`1VLd;o)gMl7ALr6>t$5^5E00Y9{0YF|QL?MSJA{+r`1LHk(LFEU|srr9kbYK1rl_ z6XLN;woMG4s08myz8vCaKa`7dfQ4k%*W>IGmT9xPS(TE*BH|a2M3NTLT(qN>7G^DS zk5>tDMq(I{q5%vQZjMhEmqLhaA~iQ>Mw+V>Rriqd`+O)jhX){1=LLF^dzYfG$tlRf z{@$W!n}_modFpKy<|Aq$ZeISii1VI)Xl$ z&JD`2i(!1l1hf>(g`&OOK}b*r@U$W2asbW21ko1{D*)HI2W71+o(h zzb?pTV%Ck-^H?<_UF`v*7GdH;jvqF`2rGu+ZOQ#%Q>w34!yvAoqY#F|4`h`Oee8`6Y zSLP957jhi>cTjWU{JXOQSDx!{SEoyTj;|NQb@4oGewil6-!^Avb)J90uanC!6lJ`+ z)8{eC(G9{5o1;7;IQhap@OEP&Q;bY&#}AO^&F|SsjRRYIPDFue#}CZo^g=Is1}1`3 zhTz^rDwR;o<(r-EE_QWroC6BRZVKFtOtY}k&Ra`d<+>BkzD#QrkLP}{3uKzY)puqYW-I-zSyad#oWc!9g#OyOvnW?qC4|oREX!+)s3Eqnt;i@B6Uy zcDSsu192h6`6xtkhRWF|lJ;_huQLHa4%Gp(1c-9KG#@#BWI>OO+21c6X=Hjm@z9~d zZds*I9$~z`rtXlaq~L}V@VyLq3V>u^1;sN0!~)tN$oB$fQBT+jyRF%xCMtkpZ5Anf z3=0#ODQg7sYXg2%g4Shy@l!f@q3}9K_&u2CU~9Yk_!}%E7}&STHQer&&@755SX=t6 z!SPylq#gl-$BH~hm*mM7LYJRo&R>GfoGcFccdHT4k7J4zSGw)$OWyFlj#DKhD@L69VDShb-t;rG9#*cueL1JJ5V8^CFy{2ZNnP+dAUW|-vnH`z z6F16d;p~?M36<@aj}dnp`_I+vb|-@qpw;4~tE@kqy^$y5>f_@krS|h`;C2dJmV$_@ z50_53CkC?+W9QS*eEY=GXn&X3q|6qDW>mCxGfAxkJlZmOanF`3oaF6dSOFf86)ES` zk9!@DIEqZg^#37@3saY+Pglo-Aqsblhp`CA zk5Yd|t$*@#4rCiIz=py1I%Y_md|Cqa2<98?@Km{lZdb9#jK z$JQIw(AI;2Qpcj>+&GaDAk{LG)ig*`&DU&-KM9NI2jJ`y=Xvz4)G2DkxHf6%>afGr z?!-9v08sNCj84wvdO*17g0Yi-MawzOLT%UFms1rs4%gHXHULc+;2&lKYaJEevjG*K zsJz{f*?DfN8hSgf z>{!)bED{HMm)`t}EN12F{yJ;@c;sTZU&vvRmt2Vvo>&{p4)}0Bq=>m{@2pc%cG^Vh zUKz5768-{Y(sUh4g2rChZc5l1#DioP6jFv!1fg!C@9$r~JO{8!l@GLMlA=RQH0N!B zZ#&eMn*&iLDHNdb=~VIx#U4MMqnTkmSPq18vu;Bo>Q~SJ`vr^Z?2^kwrvl6uRF!R2 z7=%sqr|;5AGV%{rBmaZrT?0Dp^s}s=uWq$hI=4ReM%7(8(ALD;vv3F7>Rq83mb>Z) z#?zA`({%6y6@t+Q7uY?Sn1eX*alQjYtcB=siRH*iXHe#j#2J!+{Ad;s3!PVSl2FgGn*;yV8!MzG?IqEkDb!FR!z{@F! zRa|6xYld~#z9}e&A4Dv13uoOwXmxR%9#2OagJIaRyN6kOo0QiY{ zw076>Ka#@A~%kF6HG`99^4*?MEVkM38fv(}Pfl0|QG8|!+ z4MJ1k-Z;jgBMAgvDP=en&nbNlu#o%2vaA=PK!L+AynCA0;%~hzL@W zGNeVq>yKNm2VE);*0p$yB|^0^>ahb?&)_>b%WY_ZmPh_9?4m)v7{J-KHmxkLj!R_M zA;n~71CelI!mjw88{p1F#DvWb1{b>rRDClw$+YFH%=PT{!P_Ct5I>s^x3{pz85PzR z?Vtpt!auSuH-=k~V@(J=a(}qwwG6zgn+Mo`3KfU3e@Wz7M5a&>tUR^QKR6GWc{g-A zNXJ)fAU8F2JwGbB9E+OvCeMxIt3)}bAPU8!%c3n3_1Y`}wfs#u@$T!~^YuM0zt2y@ zD^q*@#ic}QoV=J;5nS~BSi1h1xc2-kmx`cuQlWn}E^W9j$&YmO%Cs%pf}b)%z+FKL zy2yU5^z;ta;<#)iQ-284vO+cl`K99MZK^kGuuTa{{6f&FplhdB;RQ6*{P@)}CHby} z3V*|z@Z$fq{C@Wr_`j0SiVl|56+r+1meBwJ1piwkG!s*2V<$@o7fX9PIu{QYRTW48 z;DOFV-Tw?%4`={DkY8W`fd4t&YV6u=v!V38DW3rL0T%t%cGGGG7*%4k2zpyGu%#j- zipZCAdC2oiHBn8Ln6O(iz-+|MI=|=oKio@D7FSVa>ey3}F1gft%q2n7yX1&^wWK!d zq!*VcyMke1K_x z=BNG`suj=5~DBSGTh|ED}kuGDiS-PF`jh8E98(UIW zQJho>mK9T3rVWEBR*o?>U&N2HuSv0?F*IZoqxBhXQ$6HtlD+$G*|ZSb4#ys|rNp}c zp6}T5KLM2?^KE3nY!$fsQ%{374pyTkFR_RojpEBmSgP9+Vfm*g2W|+3nP_=_WtYz% zAye+yaM7e*dtLH+O|}j$L~W(M+NY*PokhiJe1-FJ-AyJr5YSMR_2Jc;yKE|1vi@&{ z6e)@Q-DR9b(nD@V^Fyx1(xPJ6Xkbuc8>C8Z7$9Sy6%u2~O$Qlja^5a)b3fEt*k~=ur1_ez<~;*}PAW53O3Q<3e#n1zYU$~b3WEiLeqoTUv` z92sqD8d&5swu>flpQ&Wp8DY9n_nJ8+cY#OFBHPS88%-%|nO1#cJ>Hsm9Yucmul_D6 z-swy2UEaJMf68A0&n~}~l_^la-?2x$T_Qnet;Sb+O~rcW4}^l_0}6>mG=ew^Ktw^1HrkEwYoQ><3N#1ar?OiNC^Z!T7paA~!5IP*n{m+;GHSzqn zw9LuW(8SjCf6JJi2A1xB#{VH>|5ZVga1AMYC;$L-W&i+0{#(16nHsvdI+^Ml+dI4b zzfqFAx|+!+V~&3N`h)2Gv0Mt8Z`l-haAB3DK{}K1QG~2zZk9A3`P`+uBtKOSBC+sF6u{M=p+ zKZT#W+w*(=jPLtD+^XlditGPA!$>C%6|+` zci-Xn`~O~k4DEvSV zh^a$ZiPL+uG9RDgB?Xc`kFUei-Thz2l+W>Y|BsJ{(|Y}XztK5dkDved_qluA--g14 z@A|16KW`sh`1$Pg@^Zqne*fq6^4jVw*VfbL?P)N1_Ih2I^keh>Ej7L0$Md^T zYrg1W@8`SS-9DPi_wQ+U`i*A&@8!d3=T<~)d$fhm$M@s!Fh1Wlhx8vM|F!CUzu(Jj zdHujv_%jw>nQwiM&)E}vnBVDoS!JeYj2S1Bte8`CPrhxIySDfGaeqU;;j4N2{G=7@ zHqM}ZD9EjOI)6-bT0Sjw{2eF0iEiDe*=NZCa}qwWzhP%Ee$S13X4p)A!cOn9a5cyF zXX85a?vo&1`GG5Xozc%2%7g3_J`b+etIpV$pEZ%ROlVu?2Wk+)PugkRVALV2(hI@B z4Ei$AnX2tX4_{gV&7U!^5!d!dA^=&FWL_}Iq5#v0UOXWo9P*7tpk(Gk3n1MSa-H0n zdbtOv910z;6bGJU4Le$)CQ9>1VJBU>leTbrp;Q^Nfw^}daU$EF(6N!oze?GPbj^-^ zL(rH+eRfP!jr92v5JG}wod`VLbU``}t`XNkCkn(Six9g?cKNufkanOo*cBlgG%*au z*{E5x0-gkk+eq7e$^Kd6_Ktxm_#QedfKF2orc)5487lhV%nx6&v$fyaYM`8G7OhnG z#0HjxD5ywWA}I&C%OL8~HP})$%GI9ms@>sLqxMuYPh%u2;0ZWTYy&EbtdaBd z(t5>L8iQ)031CqImD=lcXtWE^cz=bWSZ%8!T8&Hrf%y~v#ez1Fi5@_41&*K~22)1x zWT`a{)lldMvO~?wMZ8O&^3{NR&;{s`<*Nzy6)P4i34I2ajk0YgAn*&z!*VF)5F&#% znrPt+^HH+U25Yud?zbx?r<2y-^di=w^5Ydvb3sBysLh0I;52_1qso_2CYFL*Qzg-9 zpe#JjB$|wUvKh;uVc(&a#8@sC{mbc6u63t&k$1URMBShM40WKZu~QD5!(p|Byy%-0 z2j%kg5mw9z**nqT3p`o&Rs#+^R6&_|Y!_9&cY&I38Cq_Da?4_W=^JQ8>KbFg8E|R2 zxP@veF376<6@v&|F`k(yhl(oU?IlJwvV929;p9iIlB+Zs)&LF(s%n$+jID})QpYAG zxNW|7MEgaYPH{*3t*a#0rv!5L4%Ukc6|q?c1wxDxqfo$1oFr18^?LB}@9_pT#9AN+ zrSx}PllX_q$r!8;Des1u=S8<7qHjb{U=oCsMG`Psr)&r%w?h#Xi>@z4p_~Xu6%bF; zvs8-MHL_=|?4{2`ulo!-_e^(D=ZpMTUYaQX2Mh^@1ko}2h`9hj$zN`|V{wK^QfN~j zL>Dl;z0MAsSpK4|j+C@iaL8TItCzpM(oHw=u3lt6N#_#wD`IYWt|pGVN>LF&9Qb5& zU}TJ@T3~CsdS^s6f$xyh+bi=#;n!Rl<*Ue!TSi;%2QTY>l)C0d)}`4(5hd|N(*Fkm z%#s*cjaJN(@kTXz7anBU;`N0%5d^=F-o=CQJO6&c_BFmDi^muydpiGizqapqsG(G&IXc?$xAe-8~XR(R5J#$@( zH4K)VvTgpytJt-fY5}^})ja+a0FTm}Y6cgbTg9=dT+v_0&1+7nG~5V%r@ z!p-i3XE@&+rX$L3?;rZ*9>9`x)BROdo7X{-!F0Qx=?bhJD#IW95P6d&k(ZJu0HK_~ zUoeFTM5uwvAAhtgO5N1ps%Y7XHZ=vqb zR~PV6mROXqxz5Tdren>~q>>51g21*36TMftk!oD9{HBVFVMtx*iC)VAT-r)fF6hgR+r9!5|t2Sw-XD zD%m(HpnfF~cSqfJ7e>E4Bs9f$^btm}3<(<-WxF8j7%u-(OKBy-gwVS0)M=FP!tg=?RDZCIwdMDv9h^ z8|Vy~NuO{WX;?VspA2XA&`US9T&S9k%`aZ4zKe*#6;}0PmtTNbfwgaqAFg3R=2A4; zj2by@9iTUBP54CBHY)8U!Ck;aZM2725wxbsN@ERvAFQLx;mMR}C3x_Ah>5MIXz$7t z^~>8F6-4e4X$~JYMr+@Op?Z{i^~P&wXBjU)JT(1EVBRfL37d%l03IkW zK-A*Rz>*XIURs6kr8tFcnv}qBIb|^epFcDP|9F@;15G*5t-nw)R>=ypMV+8j+ptKI zL~LV=R&)|hN~Dg7P`PPM5>_~pRF>-vY~N+>z%oER0LW;{tc92M*3?6WybW;>ATL{6 z@El3#8Iu=SyQEu9rUMNbRS`CwfTC=^iW>?UaujrdwRKjp)mkUb6ag0jsP5Mz7FrT^ zfgM94@sk8&!5siDO4w#c)hI#hJcRWCZNdTIN!@U!`e<&f%$0LeF(mmnCNE&V9v*1w zpl&eG>d%!IhUjC~h=Vf1WmWnUl*)ADVP!nfHp42k8P|}59}xqA6w#_As@d(9;qVK_ zH7HE^PBee1D?)XMRn3BmB&|jDX?R!uv9(c7C7Es-rA_@XmoBfhlb8@d_NM zl}Q@Rz&Tn?qkd`I7tr+AOydZ^NIP%jHzgppg$su!`ov;iSTa7@CPyu2WRcy-)I7lD zyaoBpuE{WH;3(o~glu!D!OA)e8Hv_|Y$l((73RW=(gqoDVHsJdN2Ppbkca$27+lm~ zi8{%~+Mf-nXi7U>32Rq}`Gz{V-D2JDZMtI)y>KKm0r-_P2Tn|Xf=;S74H5Dq)CkQB z!xb}#y3nksx|JQD(TTVYS~UdA^%`~1jDze=*(z>4cdD%4X7`~PYA3n^XWFiuDErqQ zXx0O;L^dCY`N0)6VX6Z7f-4)-tE8>l1YKWG%Cy2EuZtrrag3mPQ=Cf6^S3;VVO`mk zves`7gm@XymVHI9O-Z9YvUUVVWqQP$l9dw8`f+J$u-HO5A(@eue@tpnS20yTS>OL` ziYrxvU!c9qmUh;weZauwQ$B9dD7DhyUUPJ% z0tID8R*p=o&6^ZSP7=a|(HfBh><*+J5TQ zO9HAIY1_duaT5CN0izC<$snOAtwvNvxElzmG^*|qIC4r0()6(E)21zMIS(IQxRlT1#+4n zYmHiGd;)uRmEHMZzjvkG)@4Q`OMcJlP>=JqJ&REkr&I8?31{6#ZeN!q3r+$(Q4 za~oE1^Q63O9a{N3{_%AD?JXp@x&OwfLPi*_By;guMWPe6dT8lO8~Q*7U%K519SvZ- zBeUOl&KrU73Y(1>OO($ll8Ths;qd}}wgmVo8XV7MRs_7G2hjVD-N1#vT9*LV`Dy)4 zsSuV^F^abDa)eLc!Ne8a3Tl>&7;j<7z+gwaHTZf<>O zZmZDN%FmpSa}5lvF^k(kJk#7_<#}EsF6&G5r&paR$>X4`Pg02}&C$K?$2QFL(kb$e zO2IJHw+F_%a^S{P^XTGe_#}2&C7B}H=QTi@>{AO13HqW?jjkM7B)+Z1r&?UqxFIMq zB^FIZ+%~@5bB&Yaq^YAn3JqQNG9Z2N?C^o|Fgz5|TF>#z^M^3^y%X(M zXFyiY0%xF%<%p|TK5WBMZfLs83rRuUFYj$xQnX`*5xblgR`GFI!tlDI3W7AsY z1vr$YR^bvy;!mn&r>HbVW8$|qQ1$%j$z185{oxM#g;J}|{#B+54Pjjuuvk?G8M5TV zbv)O$#o*F2yZs>*+zPUUz(7Xk2Qh5cZ}Z}~*1FooZi>_hpmRbcLp?>Ij5YY1zd#EQ zM~%i*tvLfa%hzkHY~#q55qZJ!83iQ+UTpR)gOiteDBoar;Ar7WmiwMB*Y_8Kw1|Mk zwTRFG!oA3Vk{&S-;f)Yxi*uBVxQ=p^3Izyif+WQFfq5bEsm$7f<+L@IG;p9uShQUZ z4nM`^25HRn2E^?gxkJn(xC9R4e>~hvLAs|S%oa%p`AD3b=#cN{NUfGcpw(KP8IIiK zaFL&n{KQ23QgABjlD$Z^v+9#;u%?j&$xZ9+X@dew+LZ-fn?o3zI*n)BUp(%4@@GHh zH@%*hraJlJJg#R3D|qRe+uHe9!Aixv()R5nA1IGgupU%Pqp8H&^0_dCYOjoLFI0( z%KOo+oZO2&NrK4W_wAlBet5+eSgSVz|*kVJdMbtG3X6Z#!)nL&-X52et1hy4&d2Eku=1(!cP? z@2MOPpLcp%5!x@Sod<6h4;sxLwnRBwq`9aIUa1n^CUOgwhXO9 zI~|M5uk|8%GFs45*b6{*e;j@L$6Gxebd4SO<>8RIL-D zN2d+v?y8D3;k>cJ38Q}ExXKQB?jF{yUM-Zb&ZkV*6ZBY;6QGS@6On+2CImhR$P!I~ zk4dkJo%WA{#ZQdv7{uLGQH%{I;9JZZ-ZE9&h!oL7{pw6R!*CVnm0uDT!jJwOStdf? z4ICFVVGx&hTd4{pXQPZt%WeLG$WD5^E3SS6P4W-hJ7kv_*!@6U>A`Mc@Os0=>4I1l z$_JLGh#2bRq)$kK#dak2fT;lLp<@kVsc5V%19_(hcWFwkOT9?=7qMymI>8Rq>IK?z zjsurV2v1!t&9e9W@d=9D20&iC%$I&_#G0?Xw7u`}{HvqKxfTLpB-DJ? zx94f|G88EJ)C#ofgb@z`()NVAc^Erqo2Z~M)z|@?IP5*;9A6x(rH?Pu)3fp{g*EU$ z8~Qhp@yWBCR8j!8D8T_pDikW8)NMs-)HN@0m4z)x7qBC?7{Y4}|JhRkPL>UrZfhk; z3$h57;yrq+ljIa-DMW}Q{bgN2<$eCrl&BgWD7lBVKb4FbdO=) zTAo2OAC^c53wbO?&s(f9dUR}eAng}Q5!Njqs0XLF1&inYj#hC@Jc;^60tyX5^e=$K z2zucmg=6A&|`@C4wD}8{+e?13*@!qWy=Yw4WYN>{$1jM#&C=j55?=SvTL-9 zLGggF$1v~FE-azw18O*~z7M{T18ixlq{`6s@VlVyi|%)@6?vL|Fb$`U(i^W^u|0&L zJfqTJ?O#ZMBD8rmq%>YfGvHRJmP67ncu9kxbMS`H)VU<2jQ)si%cw#D9__^%nhkC9 zI#RG&=baqwkx-C9CqSTh2DxLq2T=;)iB1D^5S0SK79gr1ySsBhwF<>HS@f6nh#p;@ z_Q4wht_mixLLNAZBFo6*#T$yp0o}PztUVZ4yGPYl=)zkdJd?o~;~j0noY}w%i)Z&t zk5UhC`528vY{mWBKNl1uok^v1Cu>cJZJUb8T>aaWn_7A=XP#CKQ@i1s)|4i?Y^|d_ zMhxsRSXLjTU=PRnR)=Tq<@zgIP513K*qf-e%Zgps@J&$P--_OGXp`C-xVZsI%)w<6 znEWdch$nkzA7{Kq`02X6{_blZxS{;E%JLD?^%^&bs#p)#1jp zd)~R$<1j^0028MUsJM+WM4nLn=HdZ7kiEk3xmR&(@I;*wh(`G7wp)y+Y9&@ho+Qw> zuGS>2;5tNU-Qv1+17%t!cLDt}?3R_*7g4Egx2I^c(}Fs$8jKb^i@(hT*f=vUDM_$t zPz*HFhBF)@#r_Es)(25-JQVxM58tJ8N&xUK2;bw8OqIqzp7fUC4wTn8y@-0Y~! zV`8U-Ql1(Zk~RY1>~W7#)r^chl!i;34|Fe}>*1#MbQ9gF%RrswiN*nNE7;Hy4mitLih%hrj#zA}Gnc?$+U(sMfO zK}XrKr2h!RpUHn478+9Kf z(cSaNtZ}Ujpau9e%oHQgvQh`l-bAY&?*&n_{<(-A<&YqEQTV$ICWW_IY%XpDKbJg{ zD1(^b=FTIwG*R#?5bP=2B$Sep_mX5x@Wq;iZ(6tvMu(W`EEWo_qn)8b4ul`#W;<$! zkM8UY37)2nN<$`JS+65}K$vf%xphXUZrBE)=DLCB1RDo#r(c~t#Y&2EV+wkv=gHr_ z@O*J)Fr(WHZ9K@UwBOo`KP0$#x?uQ#9w6FYXF5$BM=%w`2LsLFu+vp=*XUs`fsmw$ z-ME0=IKfRMQeBaL$M)EvXJt>v%)wA{2# zWtn7%ShlOZ=G0w5Dx4;E`^wuB$C{%I_^r=DEKFz;{in`laI8KYMAr(IYfYBC4u#o$#q$8G84PHPh` ze7usgu|W9x*kz_#fp{29{#S;lj{OHaD3t^4qc|vDN~p}Bb9N+k20Jdepjo-$kKz^P z20^CvEeY{O$ZP`cERrhtG|a8>>b1iF|C<%q=p1{N*OnzT1mC5O3^Lylssc zT!t%9jdO~2Cir%4ZzkCvL}97yab~hr=z~dmz+y2x ztz>LS)*YyRO515!tz*usqvdE%Ow!Uy+vtjo#+Ci};ul;qC1NnvqSoc~s`P7m`>PMq zYGvgfh{`kV;@?tF97zNoZCaoFop+~})R|gd-z{@*#Nb}q%g&bN=1n40tDa+|E*W&f zZN=c^u2^PWHcyrc<_^1zc4GG~IcVE(wRO8j(O+c=?2SLumMT+|R4enx)yiz%zqKev zw(sqL*5TI}Lq$qg&$U4w4CMya!x=BG{C7ueB|6P&W=^80GG6`3W^(%M?s_V&=RS^? zP78a1c(6`iVlC_mwYwir-K5|dk8DlXJ|#%O8I%WBxaCf`aH<@b)3+BKnf4!)Vp*Zg zUTvEH`dX&frapEAo$D?va*ccV7`(%IDd1KnCiGlXK!9&d9OqjPWq+)Q4g+bhxOLUo zm8Ei^F>U@}-c*>(IhqxzCD8QKqe3lyYn{np)4m?HW|x6_x&5g#SEKc(fhnyGw(ifF zEMWFF-*DTMwW17e&QU$2DhZ99?S&`-2`fH7Kiqlj3LF^3t?Y`eu)FPv1eRA{$qwu) zz>AIOxnWzA=@|st3xu%ug!*a$AbFtwEluw|dQb8cF-y=SQN&MMg6UIhWjBd#s~v_n z%1Ej$%X}9_EosU;ZbI=9ej7DR5U-?1-5Czp+#P4;*vg>~jxV>v*zl>; ztDh)j&`$TJZVT8p9~Eb14t$zyqVTvu2&W~>RXY0zNR#M$^~BIFM(@ha1RucL)NoYr zQ%VW zYu?4S1I545j8BC8D#$3mH0ZdNaRKddYNOk_wAqir=tnd-Lg#P_XnwU{= z>AuPoG=!Pa4~=(@{pa0yJIr=QWyvEJXbvW_Hg5Yu-M6!Qtl-RV_Y~DF1_rqOvG`*N zJo})xczUssgKKNm)l-}J5!k9@mM>YZRj1HE=;##VQZ1c?y9_GY#AYO>BksM)+Fp(c zL*8|C5yy&8s$7RrXCvd%3q9z)s{A|8LB-poas`;lZg8SIY@E;%>okA983y*Svumb` z9!1GyI9K3l%sTFb=#@Mo&C|3CN%6c~b1G}xL68$ma0wR?Ha*ZIQT_)x{TU>Pvr6Df zjh8`|kqTnl792*LI$`Z2q`BhXA$9etEt`iY0kzyPeKApTCQuB}%o6e3>`zzwcyM(t ztW4LHo9gsWuCi7n+HZxF7$L7R={n^QTiU(j%4OqL@itVq$Dx+^8h(Jg=8;LAseLSY zM3!mOO3C&T34>$>9_9!jQ-pB(q~+c71w%b+iydjxhmi$6>QNJ&B!3}==z&)o zfn-8=ZsJPI3X#PFOVboUH~p1I0^QWwOeq-1uc5*Djk~8**fLVQ=2weT0`Yl4p@f z-sxCZv>&%@WJu=;6!H;EQdbB6vl_eITR*||5Knbfk@Y*sY8kdmHb7Y1rrwFR$|dKH zt<)JkHi`$hK>MtbW1}+xc7;>5DIUSFH_8J|zs0F%)$Hx-1PUpUDp0O$hqmvA?Ed1> zKN(Ix6s;5O4}g0RgO0vX3j7whpL3!*JP?s=szBLIBCsxq=}NU(Cbi-8*^*JH+iM@d zbE~mkFL<<4-IZELs%ZrOXkJDHayv@%%X=PvG$xQlHWcbQzis*Zd`K#nv$e=KS|TpJ zhifIxep35-Wp1D|sLG#p2K6RMN#}y^faS>;;mGCI*g)<+4e;2-?{x)QBc{m_D2Tev z`nm+Jhi&Q=V#f}s!&{mD9_c2xefj|sr5uYpuvzvPQKe(C$u=iE#iE_G6gY&E^nq@jCE+;1b!zDZ@X)5F5#4oXJFBhuj3OR%6|Hmvd$>EoL{`KT zbknT)x1KkZjXdGt6wz5R+3fF%15wkWit5+k18qq=ZSvHfUw;Q%H-xm^>Mz;_Tq>ku z12YGUqGmml!01bjL`{z@fE&Qwl7%Af|L`>+p_(7 z!+%Ur>kJ)$;x2N4;1dcElNdHPogV7YG29&W_SC6&(z+?Kc|HVo3|K1hq%XJCZOF(+ zz*NfsvFzMivaD+z>n{PY@62q~FJieZb3RKT$%*(^s%WcdLyWqs#2Q==Bzm!Aa}sWrqbD~ zNxpky4l`I%QQLtLU6=F1P+52BeQ`N@)a2|{8^4lS1#qHXFlUmZP6T&dc9V2~UWQQF z_mXpvcqLndHwP%QM#$(SK+%jzyxCHYA#Od4zw`_OGX>fEvEx`a@eK8>wp<)&s(vvMKWDI1Jr(g?2YQ}fh zjEyu74Poyz9Yv4-BlS4e0CqSM%T{9xw8g&tuqm8t4y{fdF2UNg+G1TSGrJx*_g(}4 zxf4_7usGJThgn}vMnAOHpvn_6)M%kF0`wpxVUwVz0L%u*9F)|YPpegdO{fsuBKc;t49z!Uq1!btiXbLePmlh361T#?~*Z`tLrYA;? zW|BZ*#NdG4t$#}drzax}s51&Fkk4vZ=Re0QZEVR1d5wN&Sezl*EZp4W0fIa9JBTnJ^|I&Os>hEc52R^$-ZQ#&hBZtI4!mo`VJbfxp-Co5BJ-24 zLw0g*n|zK$x_4LB;Sb?+N8e60P@$TwUQfxoV4bqIw|zJs%~R7T2fJ1_oTZ=d@;6M8 z&F+NmYnJ)ZhZJ-jilEMDcpK|U148l($RUYiK9wNqZez2Ju*a*yw_Fj0%j~5c^T?~G z%?!880z>S|1S8F6lC;dhyQ|dlkI`94;R!xYy&(B4L``vW3MSYsEkqkh)A<-sI4akh zu*-c}85Ac7?1d7^g4)8L(&e_@MAL~pn}3}03J$u&L#(pM==O3B_M(f!Y$fwJq^;DI zS0t*YMcavpq{U2szrCW(`SNFK8AF27*)5sfD|AX%EAQ;iY{y#=dTzZxIf19TgAc!K zH8jPA3cdyvpkB#YvLo1q>1N3m2wQn-F@w5M1LD)}InS8lMB<}T=Lexh0SVl82^C(9UhxezO&O6>J@^vy92>|v^tPCX%y}}k?o{nXJ5nj26n(~3%hB0`H4`eqL^Is44-(*cUI2mI+>b*%GB3>Z z3&K6Xa@QD7Lshsj%~Zki!awi%l^p

{8mWmw(2^_|&2lx{Ih}F85w*Eexq<1i!Cm_QqYzVEVh&d^X+d>zP`ZC32z=uu} zOGUJ8Qhd(h#ui~`{8l+Np$3=NgB+s+_>$G&$VhKlR-)_V- z4%^-+d&U;QnH|~)eW)=#O(+Ea1`DK&oIPylItZi|506Bk!O~TF?#AI=3~XMkBUZzJ zUCyduDJmPKR?+0R_OTPPw#J{o8POEh{%DWBqAy+8ubQLq4?*!r1f$5m44p9(h;AVd zXp?^l7V8OsIxz>g=ZU!ude8?x=#}ae3%5mtQjX;_vW12pP~FIA^q$xSc_pd|QmtEi zs#P`knO>QquzG=1Qy=D0J?uV*wi!%g%m(;Gw1UIQy&r;!3b5@g+u`*5LCbr2{@x51 zUW>2<%(o9=x)e+H0@?^NN?DN3?VD;bhLUArX9f8G(3ZDp8TQkk#i2ljCgdHnd&l$g zDe?)>Mch3_eDXF3V(}eo2X~*`x%-g4;IfGl6z_h<*9B-KZ-0UsR~!ekvPum^zgi2+txuhXMjR}OpL|kulo>~{yUR|cgtAPfQ3s8ha+_++|OGMDp z&KkZ-ovef0x{)$~ERTj!5`j+(DCKNxBpZ-jr5TB%`p6ybF6WCpI7<~1tMy#&UxBP3 z__`7{dOn5><|}Z~A55t@We9h>R={u=OB|b{rtBn>*$ZHV8LQXe@621$~jg4f#4tsu~fr4zgnNK+T-Hnjq@O-1guxV01=m zFWz985B&Y8@-(yj@0v`NjH4!&=3M{dboxsdc~Al=XG^9AQZG#1ecJYLJ)Q zyU{>3EpqkQwF-CM-DtM?%n_e(ur6K0?4X5Dm|se8RRg_>yaZa+$smCIY640nx1nIF zg+lI26QgM+lh$asDl)9a^ydJ9pxEF0Yv#_>1tdEnNa&z>eNdx zqIJ^_zzzG+Ygjb3I#95B>0h%@PWBN=?yI`9Xz%P*F&RLvpS^}iPY(|&vd8%0tXLm>0LCnQuSaD%+)VL z%KVoDeppJCnlzU{+sNJ~j9Iz>fdXiaJOXPr$Y(bNXwTamytkT*HVLig`ZR}eyF9mN zRyHNOxo^p)vC)_WbiHPtwS@zpu(ue`602kDZi*W$5YQIBwu#%=ro~{Ju4JTElWN(J z7YQ3$RT=AaT|{v{<{t6`vL2_9OO>NW)@{7xZbmXs`FW0Hn6bDiX{cb-Ou$PTTB%g#{`VV_$WN!2`s3}5 zMA|p2Z8S=ZmECZ;LzWlWJCeCaafP4%=!eb46FO4Bn?-!@eUSy_=zKW65m^<>Yl_l( z?fA&18fas&*L%tMbZcSS^-M5QL~g5|73+sEp~l3`1~Gz)8dR6AYQSOX0^i6Domoqn z9>bw%baJ@QDaMaUqze&@TA;m_a^6_8)WV-IagOcL@MP9I)MU z#A#4trLO8a36-4r4oZw&8iLA#tieob;IrB_cl+kCc?l!OhWA{4)fBr|y)*s{L;VW^ zi)eTvC5_0Mj6~P-+`(4_om&@dA#w_awx$CZ;^zZp4eD@uhYE*5z$flL6_E;pGuPJj z0neElWji_T8-#+@ojqeH_cK~^Uiy8f=`d4v=)ty4J(v%r5SAWqP6EdM?C6xV^GPhZ z+{C(k=n!%lih^~$cI@b%65P#w3IFH%H|ck+6N(G^4Rp8&Q8OnkwR5m`Qg~7nNocIR zE${)Rv-x*jY3KmNrp4?}!kzIJXOpYqKA%RnoXawa+c8NPe;6{XG zFcV7pQa707GdTpN7Vsocf@E~|O297R5e@Ue{Y|A8d8;_Mo;*C6%oy`QS$>){MED|9 zlm^i{YL--Wxt?17yWqz!E+r5IbR9ddeyd?O(oKoTX7C6x>a7$dI?Xhq`w|18?OC)U zvZ0D%tLK*xf0yK+NJe*(jo9V*^)|ag;A3~La*wuTW4gA@gb8D=tI{#{ z(a`b`(4lMQTGVarQW+tN)>hiDAy|R+J5MX`D zV4CPj|27jOt}R5+H3ZB8it^iKS$AiLT35&QUsb?xsScl_lu@Mbc4SDKDW?ZpC7T~a ziAU1F4Q5mKn#@sbegtYi{Zh0s2vpHbraOg?pc6{QlN6AYM*i5LlZdE@Icr}@Mh6%$ zTOkh_?d=J{Db6VGrv|dZ3oLbn!IuMpCAG3Pzr#uP+CqO zcJ+O`9YOz!#|_b233l-@WfQv$4@fHFqB&OS4SYE~6lt16EN7?Y(q^1d?(0R9jDy{~ z%$wR1$1;EoqaIA`*xPmXQ|?SbTE+-j!2Pfgz;15Y{CcK;48v6$63*W4j?CZV0m^KM zTdA=~$AfkFVvbG>9Slo7h0M%(X3&tV9rb(lhVHYk0f0xYiOK`zJc0~a;F?ROx zVQ;Kp$H~k5?j7cb6W7?n{$MaYkD?i)bMu{u9>Yz9Ezw*hk!#g}0vhk+lSxLdUOB)- zIZKlrY%|Npww=U*q45PZox~&bKxpf2bv4M;-nrp8p84@+;KLrNR!r}(R&2Yl za)U9xv~WfF`c!-oZMRs;W16PVz9h!^Y8vC-I_meAW_2k0LkmVUQg{H~?gpmn+z~S6 zQC+S^yNzg|h|o`R(Ej%IR@v5Eu}_rR7L{?%jOPST|Ls(*ZAe8%V2`EJDLK*Z{=>GhyN{l_UBzDL2n!6rx zJ6r81Lp?c;>E2Ou2_Y?u*KKK{pkFzEo)jlikZOFuzZ&^7Hm@leyJ7Ddj?*iUJ_OJ9 zkiX7^&k6mlw1hZV%SNeFnRq_n=;?v&2NFBrHi{;+|nWiqI@bC5IR@Y{qBt)Q{|5) zVfWv(kM+TxP8}a2`}1Y&9ban|$zyqcS4WCjh3xSA#Hk||VR@k=H5WV<8L9u1ySz#r(S?Q353lmm9%Jha1Kwh+8@y!jIq17=#&yzi$Egb zu#jZW%4>cVn(fF>PYsD?+begQY-Qhh3xN~UC%0tAm{>JP84tn+-mxv3axx#$JGwRV zVjvM7`>O=ee~T8gOlhGv48S%>=Hh{~GYMHdt^Ha<+Ga!C3!y4gjfB;323uw~#THj@ z^7tf}064?mmU)z+cNaY*E2Xa>S-D#eGi#7dJIpZwAF=_eLm1JY=7Et33sl0VM0GAD z;O#&@+yj(6ubHfehJeo#6=p0b&BY=U-*kom^aD~m1Jcv}d^tQ-UESY>mZxRFOiHv9 zwbg&3x>P%uaz)D-Vb!bud4hK!|IT7nx;yKr(+X9R4*vzhcsr1{fZ}1+=(wUU54o`p zCV*GsD&Ed*%kZ-w{s*|gOChMGyOEtJJOQZ57MPm0csW@<@H7cA<@H$YWG~y2j96<@ zK^fz#q@B`_5 z&$HlHbkCSz_u`%=UE6&)W}aa<+rz;yF=DRHy);-6G^u%48HI~BnBd{ehPfj(a?Z;( z%sPb?y&wmhVC0E9NW~h#ry)DtVwH{D(;Ju5tY#&8kO`#dv10ZL#1V+4ywt z2im%k;o~*<=4eU6W5v^=0z(RE?+W>9qRd+AC_)et3ln(ZjjcX2(~)*3i!X~J(M?r> zEfS_Kj3PL@hI zwl3k2*{_()CH1TpC5GNF_B~6i&6)Gqu>^K%%IH?%?W#mnrXXP0>y79poPgJP2+dy) zK&(Kd0LfznvrWg$bMFT;H`mFid=xYdM{MQtE(Z&<6^+B~FI_X~{RQdN!onKw{AeQw zo2ZH|eL$z_H`c62F%;PO>T~aX^mBY)WXW%7MN)H(dgc;1?hpZMAU-nyo6n}3#Z2j@ z-`t(~G-$olU1&u;Jz!?8+fVF5>AaB)^%i)>T@;-HTZt@=*fsNfS1e2@wTtue5T~sA zMmYE~<=W@*efbFH{Vat8`EYV!a5-&W-)2;^lv(hfmn1ZPC;#Fy;ryd#-EH3M01D1^ zA0Z1?F0?<}5cwFGuZ!6q^eiR~EA=T2QYWB~rK2vjURvp4u)j z3OML|_>iawM)FR$*kgoNi4J%{PQ#g832)^mmSQ@SI+t>Ka9%8k++VVBlj$+-jZ5OLrqkDXTkb527~;+%T&S}B zom~VY((2MBel6u7%K6!;kyZ%7cu>it>*qrHF1Y~bxiPf~1O41lREJ~c`FI4fWGv}i zvC|t+wv~2#CcEFD6p>U%@6Q-T5b2M@4PYq0?6vTN=*4_R@Jb=z#zF5!C*o5NPY*sO zC!hDBGVx6p#_@f&33@=H$Sy&~vP~`P)kVt1M=%UhRLlZv${2&`9%e`jmwp_RH^9at z!ae@=7PiPAIPQey;7AcD0TO1<>q#j9WE=O7XWMpUFEgJR)EMgR1qOHCA?fJPDe6uA zwSr*F-2$lp=t3mqW-t5Q;D{=Nb)dp-yeFUHobL$V+WV zYG?0&J9DtwBA&t9=89B>2FRds4kLw?#?wTUs}zK~aeEX){G`RcXzn7qXaS8}7$&EP zOfp*gL5N~IT!5GGn0I9~IVzhQ0{`#1<8_Yo>J6~e{CpEHe&M~~T1~`g@nj~QE{z*s z^u9mYma}V4gHninWT7=I=NONNR4MTK`RhCsFJb8 zIszYHw>)up2viPFWqPTfCHgQy|&7ckGGO z$5HKbccjiKFm8naZ8?!A_GBt(UcqpS9gOZb zP<4|w#U;9}E;0RN8qHw?Y<|S#%*tp|nAsU()HM8|**Vjg3hqhJbJbRJ$T`4 zQ5E7qb_}R+t!kJgvXERT?}%Hh+H!=?VY-LEi-CQmbzzI@wNkKEVyZ^a`7&@!F>@q1 zzHTF!pm%~a^tMqM2SO0?ZMF}h4Vewu>|(YpZMz*hJ9sQ)3&u`PE#`v3HG5y5Z zGPMm?!Fp_T-0g3dC07bp@cbpiJdG03$bk!u-Q5QitwE6Mc>FS$6E=U7QJgc0H(PCK zP>^^-dmN#~t{xq2mKu%Es-fR}Wq%9KfnH|rI5@SaHoWn@C9pcmWF3)dFL!>HgANGi z!Nw9suG`ug-jvXTz2Vh@1U4$cG!^`6uZRyeHtBnr=4U)!$9}tYyw@VrJ}hDknZBE@ zRc8C_&{!x|9TFN=uF~a}wpCYRcVS)Vo7y4MKB71%SUOCO-A(Y{#o3bP z0XJbjQVqkA0hN$B=i~QH`xFL$KK+;QN-YOQxJP*zlUl&D^)lSW64SwmF z$#U5q*8Xvkn^R*4idiVkXOQFtp8{g`@CH+8N41$REF1lWsVl5v!_U7E_}EdedOMEK z(Mbhs9=e`#wS!P_2YlleAu;|0k$BxJ2|RS^Bq&E|4|?5lCF9?72H?kUg7k%}nA%?j z(pE$%>e^K{s5)F6*RIfp8vyIoYvhuSzY?~9iy=I+Dk;1S>UO`xncbjSC#D6dV`yAp z7_h|=JD|0951xxylDB78IjdZ_4NNHbxvB=XB&vT5DRS0Jy*~t_#@m!aW7;F562=V4 ziq65=&`AV(XAdvfqsI}hEMF;w*59b8e}X@pJ-iX?P8OQ$XRtH1SWJm7?LuB8r~-n_ z(IV781xQv3sgZ;0#zFG|JSu3vkXh|5poH$+7BpwG5QTiIP>v+|v2KrDFaVIoXKY5vMsev|XiF}+S1 zl0SC)C1N!cJKHvUrU_drc-={2;>ph&B_<-9Jl+k{@@4n83)GAl8S}ZAZ5ZNSozTVh z2|MZ|5?ruet5q>XUj9wi;#e@TwCabB@foU6j zTBaFR8d&r#dmnJlz7 zo*>EOBXS0Y)S3beNvz;^VG7u3S%+sRtELQx=BY^HRtQ!gDXU~BO$tW`=D``B!T+IZ zEILqdy?h(E=gB20?OPut`v$!+s!dUK6WfK3Ho4UJEVqgY^6K}_RVoB>uj}@YhX`8+ zJANg3TVTZy(Q=eYKE~jx?h`yNUV@fa&ngBpF36d4NC4e92KRS`DB10-;hc!RlbnS+v~*d%bgbtBV8Esp>ziZb}+i~K)S$%ztT8n5?;I5s|iFdf9oYE z;rgYWQxiHFH_zS}d%3`F75gxuyrG@<77}rRcbd5zjV+jzw=2l>IIn+U6jYSnfqLZC zJD1&a2w&tPv!flqjEfYLK*dX{C-0%JCv4*oQL*HcEy#}yWkf@95Mgz$3Wue=|GfVg z=qDNO8`{-=iB48>9-VjHo(1U#xeSZzl+~8+Xp3*_gs{96lMQDxqQ?I(54KCnlQr3Z8rJ@Vp`VTo&n+Qo?+a&o~?8W#oU{w#wM@gdizw=LBL!f z_Bn%MerZLT4~O>E*`ENs+{UY0a``M1S9eKZIgr>O_i%FpQ*!H(VcS3Ec;kV3wFZg; zc)_jmf*$azYEV&f6MZhn^<};9&1i?}@5z!1{zaUm{N9NF-bV znQHZNr6Qde7-qjzOQNWy-nmYMwGG{#l{*K{;D*lpKqIOCr@zZ9Bs(0U#h+7gq^)UP z$C#cc;kkzer;!c1DN$c$p?bY{wYEAf7<6}|G_1KMULvH$XdZ+Lk|DsbcVX_fpt*B5J>Gb(1?w9TfsLMv4pMDX1YK30$g;&5pwNSSN#BxW3OK zUwZV*+(aJ%&2FL_y9O2=qGLW7Jj*{<3#DEp#r5no3*j11^DW@kT}~=D#sbZ<1g()< zp@900otf4cR97ZY5okmnAXUEEPzeLXC09k2&3t^omh!_B12Rs~NpD~bwN*Twd$g{g zWefY1Oy0DuaM6{y120o(6s7U5|oNfzCOT zO-&az62>0{t$1+H__H<&MH8YFY|2IXw7p-~MDt)5U1>&fC}-ZLKW`lFRpT)}6`9pK z%KGZvH4S6M#iQUN712`xRjPbW{KZJM($NYZ&MG$+$-xnlQ9xrKqtP>un&W;wFME+5 z9T#mR9fq(?on)a1-Mm1d6;5EX={0?0n_bc8Q6k0@1{0-LtcWK^@MN?RENj@ThP{V< zB^%~cf--qnR$H^-I;+h~@S8lnMZZWz@leimbBF!Byx4Z~!9QP_6|NgFGuk6)n+~0w zdmnhu(`vvLdDS9FO5gOW)D4jr-JA5yL)VPm5DZ}>V)Bmqa56E=g-5yPO`nROFjre= zc9lcxduRXfI)_lBts5s8F~N+uphPAmoYM9>Tw<@#4=Pil*C03-yc36=GO8`H{0AcJ zpvcuTfrzuF!POm1(^MLPEMARoHT?1v?gXJomqd6)C`&DYHzbf~xx$Nb0Di^DK6)3K z+cPtAXM!9Ad_ka3{YBeZBEHU}gYLB0yPO}*dcp8eV#gy3}y3+xu7Q_1<0c-&o;P2^-J z!#4elue0Ng@ivSsNkwFaxg0wWk`Q?(g+>r`y2Yqphs;J639>UtAlf- ze#65Mm4moPnZh;1sYgKgSu|uD?Rp=_9>t7b0$u$HT}-v_SZ*dkA-JH5{Y8gcSdY@u zA^AN(i9*B!s>&Hw$g9u5xC8xN?rqCF-qIdHg(}N@b9zi%2#5N}NZKd5(I`vWmkDRK zQ(?#yc1*OB0-?YY)|6UpxG==DXu$wo(E?TgOJ}!hVV8s?tO@wCdXYd~e_q&r@vseT;n=CgK_$73 zR{Lct`6(2Zm&*B1l2L{qE;_XnWq?rLpK~kTM&1Ta@HPS%a*gjJh%v6ik#!RKnp7No zFe-d9kNNda+I}%$&V9}9aTSjh;?VPs(p6A;Z2m}n8vKhzH>jrX5~L1kDo zg`XO-$yiAl@@dI~M1SU2*Hm~yekfuD!fVZYB(CZ`Db}L4&YQ@Guogd!V4IJh!OBg8 zS=mt?S8F&(=O#ZIq0`xoSYB6%PSBBC^#VS^)CR0afFnC56yRW-*5p)W2kA|hlztm( zjD2j+-VXD2C0|Gm z`R0Y0B#DLGP=B!y#FFUHx^mG}FFAG@GPp!;Ka6h;(I!VJHdTzV#@xUp#QrV7$w?Hz`Qzf)XS`m0#s=f+6-%DNwPPD)-@el>$Ogv=c-x%34$ z&x)s(e2QuNleiWz!`)?eBdohcXBOvqcM_q76CEWqpPYUHsCH>CwFk8pi&ExO;^Z#~ zStC<$k|6bR;puun>t|31INL%TJ->WLxIDbOJ@4^w$~EVhCYHBy&T{xC(v4LE1oIZn z+sU3e&&s`^_<~{0sSri#{x|jnvzqAi8Ewt-EyI5Z0l-wgE)rP172>yFk~R$RH$Z-W z#vFBCCa})ip`kFs-dB#Q>NHRxltN9P=pycdh|3H%GUY0TMiriiw1e6}10qIIhCHx2ii5olJA_G5c0^A-@ zKIY?y!;)m`b$w?5@=!hKAnQWOBECS0JtU;V|I$>$T<|y#$NE#wb{*x)zgqV?JjnB=SgT!tOo9-M=NT0sVGd z0J|-iLx0)N3!`H-HGtyHmjA}kCD@Fo&(y=F3qG?cpx}0y2Aom%bcA(`AWj3J92)2S%V6tiUHt!=QiW$TH$YUp z`-a$|&}eUb+*`^0j$R=BCMeei=Wl*J386UyjnR{>{OS2r!H5JR+0?S47b@{W#6l{V z2Hx{Wj2Y~mFHnzcZh_|mnE`9njwhX{w&nz4!ri}ppWJ6Dk5BPae2WGT;O|O~HDeV= z^q$MXK_P73vF&mm9kI)2v@)We5-9a>#e*;!e1C<9y z3J;W;ADP#z7fpS}z<9cnlkJX^vDQxYQI$|w@FTs@yU~e^ zV#BTup*HD>{~m0K>+vzOLWN`F1Xx&C1l)5jkvMaRX6RHN8g?tW~A)!lBY zKCPf?{7RF`E{FqVG-3pWcw#w*a(f;i>#9FWhxupU)3LFM}^x$(gDI~(-j4Z1&b88>sRO(?uX z-Uj}bO>pe|V3&;6@J+AnbVQnSpaD9Cj5`pb7e0}Mq&uIb7bUlZ_$^ggG!V1>U4U1a z@VFT1XUVjIAdk7op{1N%V#?C}tRQzsks@5oodMxu7Q95bK*YM+_h=t2ErZ1=G8ah4 zN_M*+g-nTJqyV-k4H0Z=%i<<{f1iv81k-4JORVl-xsEnjo>Sfix296b84Y&^s4noP zg!KgDGdYZ38A5GhD3`Ypi9LN%=iT)wNhA^~)QaOYFq(r_;JxC&9>SUDH+jC8?@OFMC)9he$ zBi!~r98>|&U~VVduA&Rm7I7& zb5DflmVqVVAHF494mg_v*&Fp3oX366<8|vPQ3z3qcGka(MIO1Okg-3=+MytWWOxlrgDv7U<=#(DWH&ERu!UeTyLYl9l*&Yy(nA1 zqJ)N@t%_?B7_Q2O%TDZ6$UntyaZSsqwod;$%qml)FcQZp1fZBr<6Jpydz@FHd z$@0FCeD*@pyf7=pTd43IiJabfLQ$BB{Qd5M*^cjBoDO~<1lfNQVbwW8&Ar+1G4Eg` zJm$J7OZ(8(?G@1RNrJ^ELQ}CL_eE=PO^Bpgn3H)-Kn}Bc_MW`WXiNK}+LL}Z9=ClFqEdm_XT-cvI=q6=E<5goGMByX#tA>NW7)Ll2YADfncRY$ALt?f^XX4WH znK7#RtB4%fXsC4jOeEsNX#So}F0$<}`vO?=yTg*0aVy3}RYDx!TrFJ%ML~7(gnKdB z65f$7Afv9lr^T|Z5cavoeXCf6IWwou@L)RKPu+bEuvL;!Yb`p$nGl3HHz(O$hXqPc$3)h7S9Z*Oj zBGP@OZ|38a@R6DF5HoCQCKQ6OFB93tdli7&B_VM(n#fA5as4=Dz#XDL$Yl5v6L3qm z%#0psgl|he9ph);Rf}>!Mda4j;sJ?E^jY2PDkD?~XX zu}mm2K*mZ}hsO(xp(J)u+8Z+?%~i_kyQuknzSJAT15jymLcOTHi!qm!R21QVuhDcZ z!vuJ|3^t4Mf7`j+p6h7l9RE{oz6$yH?fvSD zJHxfxU<2e;+^OS)_hP>YHdKsNe0}-i&(y(ph{YzpEfy&W@k1*$$7=W%=mhp4r(;TY zw{Jq4R$*0l*PHe8tJsvS_OL!!St+AitTBQOARJ;;op>{G8_jz%|71*^jdJghWVRs6 zv{lB%b;yVhMqVD6Fhraw7l+(Cbq68C)c+j?;iOM$JE?z_{K(HrjR&5~n~nvp-&B2; zRp`VEL=T~iS?U&w@lXh8xgYmT*TtW!nJmDN31RxP{S&+@b zsvl?Ixne=S(gVT3m7{0?Z>`X}n78X1W)a1YySZQubs{B33aX>XbP7&&nzA*i8qak9 zav+l;)!S>}lBYJY7Ms?YD)^Y5tj@Uap`>wCT!rpXVf3 zHy9^;j_Qc;y?>YF)BSk5GdV8(5sUc55D%4gbEuV2+HUD%qV^WA%;?hyn}5F$1DVM4c1jF5FXyE#NNU3daNv{&jK{zq=(Yiz*u4o-u21yp|KC zN66@*BG1V+WwM3X_4|f|L! z#78(N)@9j-8LvJ>G7^M8S$bZeB7+mVNL@Qpu}S>jJ$Gx?u>tA zGz&F$It?qZM=Fc)cacNJVnJj^O=ss9xs8x#+b@3nlcfr0ML;Y&&^@X$^>59?UZ*nf zz-$fE@|S!F40IvU$y#vL63>dKZy_+QB6A5tO^Bhwv?bZ&mGKa$!fn%G93sj;Y2Tx^ z-+6lb@{MN@!;rfjGi1)bEkOoEa}59jb>7FGocIiK5&33uJlZUcq1|55J^4lbeDjjc zKwpq>X&d8H-C>BLvh&VmwU-8y0?D8TkopNDSV*K+GN9wG+J_4_QB&xg9+ADVwMGq$ zwGfcBv6y&wZd4>_jm#7cE%G$WRlDL3;v&WY1VGXppP`K=RjmZi1|35kZiL3I1otiw zdcLE{(TPG27!N}TZpx2%Ik!ca{i?@On$r5gs^&kOu^AKM{cKRJlk!_Ou<|2~k2@+S z-&IvZZ^vm2Lb=vq2sl%P3go3&1suwf3kl*QTT}S~U+()<2{+yCH5%$pyD0q&W6m(* zAD~}!T?f+OacA}$Quam(VA%zQ)ZtXY=o^@Odza5of$TEn1MR=aF`*`!^EM&39Ba!h zL1>bd3NQrpD)~j?4j)f3EU@ma2f}#Sx1f;?E9gM|LnQTfDCJ_(faVIS$~G&E!YBGO zcIc&<1qZ89^OsF7ft|Pe+15^1Hrp#*TAzBO>nV=#*q!}&;3ZhKCm`m==hIheX=V&3 zE&YXkg@s|4iv@oir|40hh-x_=gH<_j^~k``jh2C{aqz*Z;9+)Ti zAL22P1%WP?G963ils*MoDSY5q*Naf0ArKbcKF;X~wq6}C@>_Nr))(t7fl%lB=YdcU z?#dK1K(W!39H(&A_Yr=a>w+=ird{X;viJBy`4tKV_FaY3lXGqt$YkFHFQB|PkpwT5 z7+(2yD|3XuB$R3F)e8*H?q^MRXGl#wO@jk6Pl%I$GtwuCy$YYb99aA~CAlsS>5%aU z;FlY~mMTJYEga&A(X5YpZX?t)`AyF98e3uHQ9g;fYSAtPa<{Ect14>ZlK?toSS;+I z5|7L{mA`TWJy=Lsa5*6m;&wr*ucjthHeFPCp4{L0J7k#>W-}0W=XbfI!`osURY24P zN7fX^@C$Nmi9tv14i>$aKzH=>fcuYO;<5J5NxX_ElnO#rr{?(5z!|>^|>54_aG)uuOeGyN*`MLCbevK>c3DWV))n0z` zsF0hcETmV26n#CEu6-n}K0PUn&&BT$0lq9BD(&ecrDAPnUSz^X%%>=g|H|z428*qOo zMO|D)^GnZxhJ4Yr*7J8VETe0VxOYohvtFuC@447bQN;tKMxA(#a@V5E2u|FV%dnf0jadOB8yyT#vVePJzP9W{jYq19BOYx9zu z`Z-BSIdUgKMp}9d70vg#R*MVD;iEypNo~+- zx#7UfK{hDNC0893=qY(S{LTF^tKl29TUj1rs!#D&)&2z(X|1g{QP66t*Lj&jtdqTYfV-S0k8C*9FyROeS)54KTXxKFzsFb9Sj?ezc*z zE1-Rxq^}j}K}6Vo!P0y2a}`2(AfB=_mUi{pdFqoR@(oXQLikVM4o@XnMuk~U;CbcE zh>@=8mT6TwxH6#c<$Uu5A96yv{IvDq?o%SnS;l=?&ZeO{K(LCEmX&eOz*}c#^)s>S z82bkfCCKrmI~`t!K<_%sAF{vV7`Vt9EjuyW)ikgvXl)fu;y=;IwKKzYqwh9zOYeY= zoJ6%*dNrC;*Rrhm#d*Fq^E-+C2wwbMRK7Ko**(8{J^WC-0G(ZWDJxT=eY@q1d_6~j z&00yQ^qz|I$sY)VBm@=_i);jQ5`v0^{S)*?1S|!G5;Q_2+kv1*e>n8K|KSMQLA(tG zfb)Q!3zFQoP9xwWGyq}@#T<+|pg-WfgZ9PjYdaf8+>f{;ePjM22Z00@-Y2<3vWfr! zBMycVjx~y!7nVZmg5nPq5V>}UWD>z5)gy66LKb5nK}<@Z5-J&vLK=-skHjAVYm8YJ ztsz}BK)I&4zrb=!G?-m7wlR!yPq%W2a?g1C&E!;!&~0i?Gu2Z!#AB(}wr8!vvZ?f$ zn+H-lQPq`oDM+hBSTHJbL2`i-&Avy0*`o3X^F3!pafGCtk?%CXwldWs`T1nruuS@j z>ty$A>FK|@jxa#~d59ek6#jeie+K^loiTGZH#W03|G#z&poOFVpV!S376=&Z2NVeC zzlZ;6LzD51se7n^fb?d7fW-fIpS3VIc6D<$H#Bu{asB_(BzN?+Q;x=*{Pzq8G5h0q zl(b*7sR$6lD@%j*CKIBG*~=)!ZfGnrnx1d<(;$_!so3VnRbXgGuFoO5aKDLZAE%rB zKd&(j13pfw9Si~<-liiw`n~?H<>wI??*6<_GYa@Ujyv?fJzDPhzMgbz*86{+XLIiP zKX(iEz5hu$ZNC-d>-#=9pPLJKKk9z?a~a@}(8u?EeLnmcaq8h9==D9m7jS>AncpgD z`1j%QRgrP;YfbYD`&N?D@B3z4^mI}>|NH6f=lfl?&)56wZhHD=EtmKDb?$agu>bpB zF+uR#`KSBD_Gj)C@o(>EogiQD*WJ_0@L_^tMqhLPSxo;;5$9C9{u6wJU@MqEt@^2K}3X&;Gl|DU&m`|l-4 z-|=v|>)eNxt$xAYo&cUZX~;RT`@bg`T2StHXWTY3MRz=j59J4 z?)C=kF7i76TRa^;yv##%(Cfh4>w4%H_@3J;ua!+H){UGxfR{SH!zlChJzP{G>+}3P zINsU&VNU%RZx48Xzdx=w{P!E3!}Wyue}A63#{=xBTm>#4%L()LFh!qEj?d30yz2M9 zkIye{zVZxy=l+`Xn{^!s{!73$0to$Y>qwR^sx*|It0?K442m4FCo#g&gC$RY=_9gPt z;g1+l)+B{DT#7i*bdon;XegIrV-YxorN}&3_k=?I zrbe~e3sJp0qH1*Sl(ZKCW8ra(Y#A~U4~AnvZGk;%j#1X2_(E$?Lp%{8TBuTYjRAvx z9+u#*NHn{BRb;D)IWVYT(m!R;1`6?g7@nXJG~^KKNWLtM#-SQ2!yrzWIfckK*<*nk zuy=+)1B!eN;l5(!Vil2(kg`#Ztwbb2QAKzzl^kMJ@J2HoykP-qHu@0lmdd?$m6Qzf z`m0{#I&?vTqG=vzm`IJ8&~?1#uVQq?GU}vKNL!j@1}(JthnXa^u@4SYISkxe^paTX zg`$5_U8=Pnw62P-XA9_i(;s1u3^f4Npx=1x_Rwd2lak;(UcRErIib5pS^_~wOFkN) zK?iDR6A$g;s&}q1b1g$l4KVImoX>p&ttee%Ybz5zWx;Ku)G;o#H(PODP?XgQgK^`Yh6P;>m4 zHYALVNJ=ci(DEokW^2?9VUz$gafz7vQZ(v`2y`LIbOURZ$Q=_0_R3zyJj}X};8U** zS51Mae;KBU@_)>b5NI$xllRy&Ahi6Yrdu`_sAQ!!%|T2dkIu16QAk@&Z7(-QU4;=mZxgc_={9EA>@G%4o7C@7@B#Gri(XbbTh;b1%ur( zFEl~z&rGCh=?gyDmUQ~U$O*C;bFBHRn9KbB8k(C(bEIA)^lQ+>pwoQJ& zsfA$bOR3Z`4}8JS7#Vg^sqy<146fc04`KKLE9C*k@eda}BK=FeT)-Wj1HKP11r6z_ zvrWO-vWTe%ID1Tc8V}K}XvJ@vRZ`=5f~}d$Qk-Fkl+-QD8}DMbW}11}UN_5x z4haf`>-&b&GsAN;roepN&1VvBwj0y%ZuqY}T|5nMz(}47=fO$CSx5H`Tn$9~3 z)rPjG4%YO!a{*IxWmCMgBW+JgHc5oH29^5Q30K%?^hi-zQ)TK1E< zJ})0x$*(+2$F;Lc2cGQBSA@I(j^g^;`Rcg#EOVUzdD_K}F&7;3*aPE3Z*Yfd6smnm z^)*!C>pwr@Dh3-OlrJn)aNc;Qe=`yf`ppV#GSredan>=JekFe(aHZqmS$;5`IKVDm z)$*WgJ2gLhqx&r&hg8_qOPqfK;{?^dHom)s2V2U}=`w5Ov~_@Atu_&oRNJYwmxOeI zlC;qu;6&1!r>KlI_`kD{E=8nJW0Vje?jk3(9%Hor9CuE^T@OR}XJQCMkKxLp&fN;JJYhdn07Jm`iUQFT>Q9RDt}l z@i1BaG7dAK-mN!XJvqsI{^q0WSApktX37Td-k}a#kUCN`lEvZ<4aX zlcup=YvA}Q^8l3t=>bB;P-QPXcd(@$GUjiHhXQ-v+(hI`#>|{N!`UIjW0(@Kat_%2c3Y2&%2KiL2H*YNiT23q<$09I?`o0t9soi6u-DjfHdoxvJn={HaC@ zUgIOK2Wk@ygiP*6FgL_-XJ@VaEfY(Ye`WRz;pgdzp$YB|2dnv1d1j0`W{W&1Ct6lz zI6nls{UGg4312tGg(esubVW~l%OA(nM2S5h5$Q;|rt}n?1XAQp*TOv>{V8d?8%@)? z)Jc2Qycgm(dReh&#pK~se#*_q65q&(k&W>S0 zq*1U@GkF#Ir6R1GY=b~%t?iA}=J&&KWu)a7{t4(kD5VVnex3u3GHIO|BuBGp3` zQT5UQUw<37Ta4SSO-J;e*DuL*Abusy-b2%`pu>taL!{hrH6ru;FvT?Db_^@(4rRwj zOk%FxW(~noy?PxC;~;xewz5ml%?j(6sU29x>e2SVNx-=iRnPJr?MeWS$l4tVKcu22 zTtxt1aCv=dg|sz5(DnJSL@NyXqA=X@mk~@?vQu$s?z)FDyeqp>#>(Zc5HBOfg0JX> zDOr?9#-`wqOs9B5l2U?M4<2n54%;72Xl9g!FOw?tC2ZAq*4OJbaivPgQ;b*H;*?nwS)d>_R|s{3_XrDj^Z3y$``AVHa7Zk3R&w{TvI z$n+cPs5#0YCuzWCE2lSKd2R}qXUD&ZeY6L#YNmvVGU9l>}Bb(zZkR%}M09 z4T|1ZB7=gVv=m+*?rtCmPyKfcrpyOn-@6EGT8b*WX0d676OKg9o6oMU7|*42!uIHo zfYg@r_7ZUqE$s^7xB-tkKKA5_mfM%LONA>=Ye{u0EL06%z=q*PE9=C0PJYp&5TAZI zuz9_%^|m!EVCJt`PH88EDOzg}9jKSZ+H5~-O4KgQ1~p+?MIfj7f!2_9+B>Lcd&!Lt z?rVF>RZV&ns^sUC4$TN3;Bh#dv0R+W#i3#;`Uk^wF>!-VaJ#g{%xyr$&6Db?xqtEf z;LFqTyDOjY^7a#}0u^bXoZQ7{358zN>aM9frT+~Ta{g-b&rkr<4Y~d5W6mIySLjsu zaDseBfmDRdCXW~Rqb1N+LElI=vm)>f1CZWl%qkw@`HBRj&R6qya+x5#P#65SiSyh# z`tyGJ-V1h_sT8=|LAdp9I=mJe>NQ5VZQAw$mI`eO;z>ZFdb5-nbmltieIEX63^7EL6i4@(FWXSlGpC3fY6ZhTzMXL1<-M1t zn)|2w1BWpSDoK=4KFH;M{t9tS^OsN+mz2PHNQnH}I|oWJ?S(g2z40mGCBp z9@-UrbCsjHm7Il^B;C zx=1~Gd7CQj-OHiv@-UjEPvj43q-QI=${X8LZ`Rz(j2Gm%72+QY8fh8u6OVBn6|>%z z)ArhVbB)8K#PR)Wg}U}z8L;j+cEmt=I3CIY-Do!Jml<(9YhCR^H$@sG@M)o<{?39wOjQJH-ypMh`}M}u%~`!V3m40* zY$K?a;W@!^X?aDxUTpSFePd@iXrB-_kQiY~mOGwL7q_Q^bjZMk)yS{`!d@f0?xfOX;f4Xc53saOm0{9KH%m4N{mH3`kly zviq4y@dzD8u07m~!8#_w&F0AZ_(+`_=uvN{$*kr@VAYzP8TZ}f@K7K3{lrB4l7E%e zBzci(XVfND;Y^?iQkd4-(**?8veJbB#m!O@Z(p)ro&(!fRquKdV2fcl4P*6#dw5`*o^w0$j=~!#4%VM=`&VPaIWkE6O6DsGmaU+K<0ioDy{p0cRZ5nup%6g3DWj5YU z&}GCWP#T)u4o-n580h3{&r_bUArKWI^`7x*^w0jn(4nSC?y;8s$ze+8hf@FZ3dK(wZ=m z+4I1*z8rmfMw&exbd4SO8j5xjB2 zh$6pYxk~nUZthmBo-LHmPsUAG;`LaP;$e;96Hq|=M+M#p$rDUL56I37op$zv#Se|_ z7{%RH(Toj85u42FUeZ+o#EO`I{Ax_wLh%%5l%L{f!}hQDEfZjGdJpm%ut-WftW*V( zGSP;l<<`EzWXC*S6_-9i#`p*99Wsjy?7pDR_2AdBc)bx~b-}Fi4Y*U z6PObF^;t0*_yk3+0-#TyW{TfdW6T$y0I!=o|BljQUGqV(;;Y_kTXM8{8S|8Uss);L zLP`38={m#QJdB+)O;j*`SJ?p_I&42=9h@GhrH(8xFtGB>ht}~w8v56f^U1RumQw;Z zDIow!D^)~rWp)HKd>m4wbp=dmL<86s*8TyLv@Cdmd&0Gdftf-Hii_$*;^n9^_3 z+9*$51)L^sHX|+nY%z;m=Xq)Ofx1m$=qXGo*}{&L*^Fi--eQ?Im!{FqgeK6#Lm!AS z@D^$e?H}0fO8fnx4DFB))I-o)hsSq+#V9)<8AJad1&4(q{^vnr1Uvjt1D!}AOMOd5 zBPp#Sb`Mx9{jOZinp<&EE=*b>56npSSG{Ld#q5!SpC zS{lE#5qSNNmP6tcol2dcrNgumF=e;Q z|ADrfZH?`C*&K#|(-z|pATH~6Svel0ASkdqY5EE z>#d&IXDiQaRUKCw5HF(EE{k^U1D8SFKV@AbuqM@)2-Cfi*nJD6aJlDTP`4B}gvKP@ z5rO6zoKfN>gV`qYz|`(kjojJLhh+l|!EeWx=Z3UZZ8fiVssr^Yx4hHM2ce3hKqgMD zFtMw{$UJ}a8w-2!!M5{9rk};FAro|ppz0AP09RNKl}fCNJc-~h?ahf=!8ORzx`j0> z2Fi55-39bZaGMsJpG2iL+#aILj`C`}s<4{yEq)paaeqxdB`3nCz%W+TML=?yM@Qdd z+grKF8NB#zioHV=FHPLc<29$=)_kRJX1yMV058#HaP1~9bF-r_42vBRNqK5uN!kcN zvd7*>Rx&a1P#Mm1-qAmSuY{S}(~oxiT>xn-O)w5XpeS6fH%pUk`mNo)t1yxaE`vo3 z@WR0i1A!w{KNXX-exjL!^GQR98{qqJ%_W{G28iVweZ+VTpMJx>8g=X@(%UY|w{aY#_ODEuryNa3#)nu{ADPA5$!$RNkNx$}t4kLG;~ z1bfOh2qh=yJS7?vez0Z`nC34)&?Bcii~WJs(N0sL03ir*vmLS{z;t$ohD^~$rzMv! zuhkLWCCatYTt6mKHv~Ybxvt_n!N)?{=~rfsvy%O~GzCA_^W^WCeLOuknAB~AHSXh8 z+G*}0=og$jnl-$`3=nOpF`XcZB^;0DgM;O8*lf?csrN9KKuXlat)IoMALS+%sVqyq zVY_eDv$CgW=3p$gw?{%@?c@sn3nJQQG}pq;sd$<>58E8V!>{2#ZkcF^T(YIT?9@?4 zCY&O6^~~ED%bB?(y9>C9X6nA=KK`Ch$d&3FllAJ+lA(%CvlLS_ML}uTosRPHaFlQ#wj@)3xurl)&WxZ*W52V6})kl#j`nyh7!VJ6l89aeHzH;f;BSv#l~iNOJ|La!%Gxhup}n z?;Gq?ZLB}UJpO2UO^-Gu3(vYn9C zI^aCtUx@O=CMzztjVfEMU)+f+e8MwRB7tBnXkJLINWEaNKYt^uR95bUDnHgP{4VzV zC5gnNP3M!l`Rdg4ce0w-cir3@Ik=1NtgUIGag7+=s`CJ)T?UhALoqn1J%(AA&6A~! zxz#SMg~YvG4%RkIZN;u$^jld1clCP0Qe}LMdU58UQkl*ByBf{N_O%7rI_v_gzd-5y zu{y|uvDCnNAnnPO|7M@9NT*TF%t;ho#;YgEOirKOT~EdJ#K-Z>X?8mhAKvLhtcg9o zdh6|>gA6k5o~_}+rwAoDjcV5luha<-L6rl0;_8$m-TsYAEaMNe7eMo$v}Iy>{B2Xv zx#rX&+qjdD(L0Qn5@B(4RL?~P4D`~(ai)1s_RETRFOU|8TUU);St<(_+vXbkvdm=K z(X2o%p0sSVt?s>mCvjp7B?YhQXjn1DIuDCkbx+iNakJ;ON)oo4IiYmA< zOLdRB=uga47gP~gXyM7p-sXLKU~eB@d3#Km-Bo7?yGazFdH~Tdv#Z}K<3+%xi)BYF z{Z$mbs3HBJ0nJDFWymmIyqp1jb0A=CYlN9&J&PeYuG9`|)u&vqb~K++JJp*85U^oB zB+kkl_%PN$>2Zk^Mn|5lbo{SInnd@rCzf_0W_xxzWH0`jhNFTnZ)yKgqOEgG6yr>3 z`ktkmv{fn2=-=c5pVFc+Q~#n6s24ce8d3`(c)5F;w8Vb*2I$d59J|uqhyxlt(%+xP zX~5_Ag4_xW`C!$A8DgnxH`}YP#o?1Zk>n2$%sU26A^dBlG6|NHj*MXIb*kHOS(C#fyr3?!=CR=wvg3G zyC!s5eW~FC*em!?z)64CRB-S;{yKl?kOr}ymHCLB!G3=vj!r7LbeyLP8p2KL|A}*s z`N!UP*~@fBXUQQEXbdK{HU@m4@7UShmvQE{c#7&4f&yKASzKF!PTlFv9i6Ub;n`ZX zcUC971vcxLu-UGHhpT!0>ntU88{VdFR&kzms}wly?Y$_8Ef* zM#lo|3P)<=Jc6MwRJ)pfbK{SynHv}Jlu{xU;9QvwfX}+jp2DH)G^a1h=Fyfr;BBZr zM_(8Peha+!X;B>>s0cPykjw@Vco*bUrRofm>M({($w>5#wI+#)CxWlQrAb&}sHN7pAnrbOh`5EXHF;Wtrb*#w$hwXCx`eKK0QEAl0|)eh z^>lxaRFkW2{Q!w#j=4?v4127|;^CMin?s&L(Kfmf)BY+}sjkS)0)2xQ&Sj^Q6~#{H zF8wcEt-|(g+dGMym8VWuW_4JZB67PD=-#9ll;XVT=)VobscqG~YTk9hyo*XLEC;6! zy{sk%F*b$e5a_tuZkfNPNwBQL?;EiC>?%1_?TaQC*nWAn^eLV7sVo9N1*zQcfrZWcqc5eVL%w7&?H&o@Rj{ z#^)g?GOn#T-PK@XxjE`>tJ7?zbWmpUya{X?u$1FVpKYkykW&mosFncZ*ts`lSXVpN zo&n+BnAxhI#&BC^y%#}K5c4lq&{fcd7<;Pi3zp{p^a~OJn(q-U5p3 zx{wo!&br0mi^tKaCTFi&|B=Kh@GJ5Odon5VP;kp-D^UmJsUMwvJ1Gl=SF$;Ht(Pie zkepru9K)E@n=SbO>dM3TL(d>EU68#8HZrF{q-|P2zyL{j=I(FV31NnkNT5l#rGOj>`W?Vdn zVm*#kz^#rXvX!_3fS8vrHiZ+-{-yD~d3c*Kq|MjV4MXU=JcvHVFnw;7myDA-P1;q*l9wkq)+P`#+tCO=0?2nRrcmUfbQF z`0#Njfujg^gu~W6QdozhQu&G1&A4it0GO(qU_H`8T?R@8VXro^BXTTVwVuCfot*g& zv;`iv?@{J62MjLC!$<|y;EXj`4Iu^;(xRaW5GE>ws~}XU3?%4Lza`L^usGniYF`o{ z>7+_?4tG2lN!B?UvL3#W<%Nkm2{%j}TFXKcQY+6B(Y^S|K}>)>RKnDXrt9Zi1u z0!3I$2SyeFzq&FEvbdg1S*r8J-NaUE&uBTYo;y0_4_o^Ra-)~%!zTaBOtqi;wkA0X zcpC~!O(+z$=gJ>F_bPlZ_;|!2Nf^Hdcn95IuYm!Tn%6#SOkRP7dCY_*=ZQqo% z`a}8LFtkwjmZ@f{*HW?0S|=}WZ|sdk@l@5z!7rB#Wa#I*{Ddm9*&Wh1Y7XVucrlUDF|4NmYanL91;gm!~wUlzO7o6^8Dw)Tktp8nnMxkz)vmFgjoJ;rj z+b&p}DSf1oF(fRWT9?^6$E0$#^3H6_bi4v%;MTj&3Ov&7yZdIVqAko<@HHp{_e#o; z9mLH~HA}KUTF+67?$eFz6`yd=dc+nd79Wy2+5Iy-MQCWM1Vp{(Gz^(yp`zAH9jMn- zmvFYDe2k+A7)o(BW9-E)KuSsc0%XeV4pcMS;1P5oEm39ics9k=WY9u_XuegFUntKmlV>ksA0x}H4O0tpq*9;b74OZ8i z7@CBIjl>?l2g&8RSW~F%;R04oHq@f15_@e8Lu0f9d#GwL={yAGOJocal)mm|m0dTlHx zz&p8UpVX0M?Z^yywcXdLi2S(%L58Dvcbq#<$r_5qc*E#?Ve}NBrPaI{nA~P-aPI;6 znLfDS1IdvMqIWxDJzii_HL4O!JjwlhCjpt`1r*55ec#U`^TfK7MdRSX!GC{zDygW~b$f2W_5byeKP|D&Iu+W`Sd{^zKZPOf&&F8^4R z|8M%_QkJ6qJ_p*Loi}vPHFh_2?xK8qQ<&)4CQI4tt^Uoyz4ZrnaVbeIZuK3&1n@tn?3-Q1{Hs{FkhR&|WUbyx!6W&X3J*0tnDXxvq+wAdnpP|BI= zayW!*7|80R!cxWeMKw8-5c2i+r9ZVdfz+o`MsrnlYZAdDFhAm{c%OfW-^e#|XFm3Z zxaA^OZ@VwFtl?3%9jhp|fGd?&>bhWL!)#lP3Z0bw(9Q25L`%e>^16ZqH=0XxlE7q) zrzDmbxDj+U2kULj3)c=7o`pWOv-wk8$3iL7+!<9oCD~6@CE3V$hf1S!%&N+hbFoyu z!XD*Y+()@V>nd*K9-{5?*BBd$hH@T}Zky<(n0s1BF7zH~FU7lzd`$Ens&f;blT*QO zUFn&*D-oVl|I+=IZ*KG%pw+S0nCsp@tzGAbY3Ft6Dfa$d5@4k z1>0ZirKZf%ehF*^`jwXZ$*Zd(=)86t0#|gkq@y-@JnwVnq^ph-v8t_-X1d?5Ph}SX zO1tCrU!@%|Zkt4BJ|PR_i~)60NS`ss0(XYh z!vWu80VRU2avT8Rk8uP5lTk&Sxpka(f2ixRN$ewk2~N+yrVUcQD9y)Fi$^dWJTi9_ z7U_?0N}-H(tz_$YPNn4deH~Jst&nSu8-Wp+GJWZm6pGv`VkF(p316VP1Ci1`3p^Mx z1w4KK+>*31L=kPK3brx`mlnMcEmlt&Cavr-^0rmzMd|87z9>84Ss6woQy=DDNr?mEbx z&h{~j)R!y>rYu&l>2m@Cbn5P!jHHR5$FQO()M^Q#7-d4r)02wQe>j3f&QET5-<6Ey z#~FG%!4!fz_u4g-4=-$tg77pPn%||!Prj!K=^)&S6_azN4s)U|?kf$qXMyy!0axnp zO@Ol&${B-k7$|(X%8D;@| zgf#W{*Y3$;EDG^3Q3egCh%?!b>eOGqWM~n5dBQBH0$G&32LD);y+tN@!cUZ4;>cK( z0R|%K7UYmh;ifEN9j*n%M2>?MX=r3#pqTmtjuOX|rqo2gXl|)@?0LocI7e|w{$XvY zfQQuo=mL1UflCUC0;JU2XIe*jy?K9cox=z$z8#2`;NJS%B%(UmgEtCmmX9Sl$t!GK; zQBGJcdu|ky4~r-nNnDjad{N$BUq@Fk?YFb_a(p>|N+bFw=5x;i^KK8_ZSGN-1#4))%^AFj`jXQsq`JiedbS`a(zI|ioq%=~*e ze;&W5hSq9ldb+y5ynh^=U!7ipDSpQJz4rIUYPorM?Y`Iix9kwu-8;S(3_IF3ww`ug zJ_eSaTI^fcb8CNEw`|#S32F#>Y}h{rQnvnsjiCxH6TwL!+YPDEqBa*{K-=PDWQJTY^qN zV2aCaCFAZ!6oE%^x>s&!(m}EyHe>utLFpKfHC3ePd`z8aWog36$ic#xJsrBMMO|e8 zJL5dl9;=)+rIKMnVrU&1>5#K0(!2IHh%8kifosZ)#Bsqk?HJ^U*edMnEOZ=W&Iq~> zhj}z!UWVOZ&9ye6*t$tIU47iS-8}jGFhRwE76}nuI9&jRBLf91m2*NeSv3-xqSB1U z3|H%d1y|z6PI^pOiN3(`R-0R*!_QD&;{}S*a#^JtXi_2f~e}ZB{MDLD#;z z=*6Re_(1BgimmMePBI^&t@F%Pi?d9vJE5chIT;TbJdalhg>40mY4c}xj5)_qE*$&H z^qOlI7rvIXQ8BDuVwBT46q&?{g*n<7Tvd2_U&ovd20Xte1L^9X3noGG1m~%wUxBc_ z(!+f+2y6|-L>gT2!FL^2-Wlf>2auY^7?xeaGN-KotxdPU z%|#a^{Mny^sHCe0C`8zKP#S47G_b$ZtX@fmMo?|g5YtBW@CvCcKAN)_X~xaXowSOD z7RAifghM0ngV}S)Nyp)>E*YY`!I#EfR_=&s6jkC{S69 zFy?r1`dXfbf7(o!-X$wXLdO;|AsK?Cu~<8VDlB4GQ~EjoQGfEw(2XYng(nyuG$vrl ziY?7s0-g8OVAQIER8dL2%at^j(B{s@3Y~>F89Qy~3PEdL!g7xs7MnBhq8!xXvJlxY za(1k>iPsXt|BIe<{Uro%4O>{(&zJomDkjljf7E#D#k9T3P~C zHi9s4g_3DXkU1jQ(EQq7rz}2|3e0zZx*%KC&s^tV6wKkqw8_A@ z1#57#>fRZSjX%OB{0rS`%PX)<61P<}xDTTgBb1z7OD<{>D=V5HbN6GU|D{K}y2s*f$ zpgVwbUFIR=m$KXgiKiQ5pIl0!$1wlOoSaJPvwY_38{&UNoc|`9sz~;g|DR=3-2dl@ z<78@VXk(*q>uTd-X>4QY>`eb(ua;((#)khP{M2`LFm!S@W%wUqCe!rr|1O%6d`sGb z{1@VULxF%~|8t!h+uNC0n!7svS0w+CIlKK27`<(K|2vE;UG2vGR%E{!d!pp)R8MK! z#Fi;xw!iABadaFt;F--+Yn*RSme`=(6006?%#3Aj=9HDDlGWWlTZugOd*m{Oq`Ll8 z<2}NFq0yDCvX_E9T|U+l}|12AM@;Zawg7o>+e+Q(|oZ z3^KF!?a)!gnq=hmF$bS4(E}~CG~*0=!)}V*_Swz2sK-=~D68pQRDit|Lbi_kEzL}b z63Qf74KvR=%Bt_sZ+AYT0HE<-q3hW z!mwv{``XhJi(R6wNr-f|Jxj0P^w0KTb1Y+ih)Nm?9^jwwx;=f`yMIe}go`?ef(Amg zZq^2gT#!vS@H}#x^wm30B;UPT*|uTAB%7a10hYW{+iVediV8j=`hIunLm#(7!WT@5 zXeuNif=@}=5q}8?S8hF2NqH5|s8=#LcH{E=g1MMOBg7F; zQ#e%-{|?*iTJlS;-EX^w*-Dd~6-W zYfUSKUOJ&=ACojD0H@&U5xItK=Uo0;YVHDyTuvGJCytMWL&4gO*do8Hj$(EV*}C-J zW+I`FLR-3DDvAKkD$CpFHYNhIvJZ0KH6|y7`ojxpjfPe zrzP@L(;*JIY$6pDe_f426|HQknZ6Ht_70w?UODq>gSMwm;Mw5z{*0L2RZka>vo$?= zOJBAlWvyIMTdRvC(6slUQ2Vq;n9_~3_mULxx8>AdSyPKG9AC4^sm+hwPeGtIux^SP z148)%=5K9I?UY zWmDKj2}7mlm9jdd`f?E(j@hCHKKZJo5Sz8cjEN$DXbk61!XHp7%@{m9 zq(i-3!_#)FX=^b2!xOy37n22Ft<7N7dtJNJucTA<{G@n%3q`rV^ZoI{V47Adi#<5z z2CVmxcMK19mx^|1KR30)FpIJ`epMoO{&v!4gz@@=+?@S$Np&-xjh*N^ra+YacCrMGvt3XE9C zk*dB(hln003GEq?*DE6lM%M<|B6yF{|58x+kt~oNrnt$3GT) zV88ES6nqUwC?he5SS&JXFQ4F0FAPt^@O_6RZFGngf)Qnz7>acfRR@*7z!)LV506tQ z(Qpwtj`Sbs6?n_0NCq)y8JEY_Gq{mhM7sJ!m}sPt494jMy{dg57T-7=dQYFo0r`Xb+Z=$E}bAh6v(B>LBoaob}8eV!k?{INB(zFP46e zRMWxN5w;jm=>V2qoADwPlt_Umlq_~miaT6&8BoZ`by3>;mZkk#K>K+KN)Q5k8-U@r zDKMpY0BgWUq+*{Tr&w)(0v1(qxk@xDmF|G+13~inFYGPR(Y{^c@^S;XqEN~?Hdu{V z%zHZ>7G!zHezc*c`##?^hPBH5c#v2h>s2qfYZ?AQKKeSCU;dnzWB zaY4lHF9*b3m)`6r+IDl)&#$Wc!NCnicz`IYYVSYe4O)^!WmrXw`ZtMwmfumJ*Cj!O zaDBv!#;47FBohRaR^);U=czzI@_FFB9zkS|;1;Mb^}T$dhJB~=1A)=j8M)KtJb>ga zo5sADQ0>x}i+}mA=oBdjsQ$l$NG}#ck3_v3@{;UZ5Ad+*&fdF{V26Lar?x0ka zAFbP#$xcM~^oGm0Pi*=&_~?xB(O}~50s~F(QB?H`DgQh&A`zMhuzbQpwSI21*wt*> zaSr5Y>gcA&OB;RaChzfM@bdsFhB|-hM+}3W7~392Z`yY)m@3jaG$i!q0H|Z}vLYb~ zU>wpu*%$X}Y;>IXWa4prWaGziyEC}aAI^L6%Nh!~oP8UTfKzUKJ0FX^cID^wyR`^M zhLiRj<*ZddV&+Ygx;neWcbt$iLSo(DLVt&04uuZpfD)Xb>M3hObQJf)X-6)oc2Q`d zH1q`Mfs7Y-{ko;MC?Vp!~1n) zRD&ycKJ$T|$7^z7yiHf+|4hNP=AQGRD%T=atFLy&CagB1f~+Y+uRcIznynL$14OmT5Yj|5eCVnP<->+ zp`jL$iabXn_t`=bk&7y18HtvYhBM1#;Hct-;rr`bEA8#Sf5FS3N6S(w;6_G>xomzz z0JJN9fvHM5EM~mfdGh)|4e2Ya>AzOz-8(x<_JBm{A-`}%eL;1mVQc(=LHi2&W!0kd zKnk0;*LctgixYHQoOoSDkW^7Uf_bFRivcopaxc07;q;5S=K4;ncdS2#ArA>vA!sR;&{kOZ?POTKC)+Pe zM)l+I)9%Or=0^G)jmwK})2d%{p%yfQNml?dSu&RI=O{z~F(mgcZu5f2%Vn+^_%!_{ z@noY|^urPK!n@)?=X{upy?Og00(y)_qNdT4BwB)T;; zQYw0T4=H{dXT!Yog^;jw$z{=O5rSI7I1~h(#>!jLori#t;2c>_7&3N{(d$f4g{&fF zK@DEGE7_R+74T)YOHFx@73>MvSwxbc+#-lS$7jxUL49%ADC>@_jLk59kU4Csk?+ti zX>a@D_SH%<2xS=!h2R%HoN`%pCRp5lAtS6$R1@!$r>#ZM)6ZXViJTe?*Cy*5_nS*h zy3=>N^p%*-N1rifHve{*s9WAXib@UG1Ou)cv*{eo2+XLcU*dS)-425)!S^R#qTnFg zAJ-G~FR6W;Q`b9HVe};nl>?t~O!)CkbF7s&A*vz^@Ph^h1(Fz#(=MfZ@kGZ2!ksUM z5SL;N^uYS{9s!ImFNKhU)}AV146+AXKmA&vHv_(K9ec^D5Rg=i6m4L6(?`M`SuX+P z1Jx@E5s&jvTwKEtz!Ui9r%TL*{@D)jr++1Xc_v7~u8o_{fyw!3|Na5~Uz3gk`M45M zHb6kQ7{EYs|GC}Rf48!5GqiCv{eLGPue!7wN!L`X-==GKi+6|ze6~{g@mvI&4B@%b zNKeJ90&Lph%3&(0v+cfpcJKK-%k}%3dg1qZn&S8U4y*lnllRZZ^ndQjo$-5l+u{4UeL~dt|Cq}CzP$N9 zF8$YvpFj8OLErc3>xKX8IP9kTtHs~n``f9^GNW1y7MOV_0tG( zF_Lp<|MN-V|F)(66Y@Cc;rnsn|M8qT^KZ(&^!+}zw!feMK`09D`8n^XZ5v~^=LgK_ z`+aS_@PA(A_OzX2{=C2aydD1=hL@_(zjxff@pBxWuz!z;J$_E_c-3Qm%yfS}t?c+b zRqFe73_n-uclA8B?x&7)=;?aFP2#RSb9{*bc&B?(Z2x`PhAYgU?+gFWXLb7??`+?f zrHjW*``AYQ@1dU8sagW_pTn8&eTtsXvD`x?yR+^sz(@Cu=V9ARRwRP9m2>h`59i_z zm-V>s$;<4XTbGfvubuDV=1Gp7?TYrJ4D94)q9SAWBEg-d!^!KD)}Xuv&wdv4dP~3&o#;!&}qBr zO5=TFd{aE-`Jr;Nv8dWrw7GuerE+bXl5(t9a&34j{nFmDIX~=f*HYC}>|N&)Z>9IM znNz!>fjS% z_vhn&+IuenP{b@%A}(yY&DeIiPhHV^<2jnjTGncBn{V(W@Yq}3ZHzQGXH6{nIF(V+ zyB6`5*2m`B+qaZ++C|H*Toz@$^qD_$TFqnx)%1M~_7)XDU$}ZOrpwF9?;-O9Y7(rz zvxO3PpH6(}y<=GCr*2!(_{wiyud)EK+7{YXaaZRQby#~#;wyg%2k(^FHE!}fb)=Ma z>qOaX(nG%SiP=^4WL?NkxGvybd6n^&kTVyarCqC>yW#J+3c1&Y(Ku(8v-&oxpOio@ z^@vq4b?Sf0z7!djlbt1a=%v)-TUu)seRrxX*x*zsdG@b2@r>||M92L&6<9xuqLlry$JDuYJGHS-K5x(W=JZZskxCG6}FsM zlF)Gy?=9DM={RG#bAJl2xcItVj#m*=3_s(COvwFg*0on|Q@;jtjc;NHfX$qh0;Q${3da-AX;tNX^jk)w9p0fU-BYV2U~PpNcwdvI*uK+TaO?PnKDQJ3d*vLloPW+ z=7L9(#wIcBZR*+Vn&rL?BaYZ&lf2%O`S>sWX+rh=Urixtx^P)8DGpAE9(-&i=nNOEX z<2^fyj4iZ8uD@zI1D9$jTT9HxvTI? zQ>99@NPH!uzMy8QQUqUddV4d^?`<>BY~!2>*$kZyqp!VbGVjN^2D%D+w4oMzUFHHs zQ4C3EcX%s&o{p;2oZ^xvegxCPm>@=ZTyAA0X>P&DJhdnwY(tzjTA~?WINpfRLJCU< z>vKROC#w+=@}q$kgebem$KtiCF{>fr@x~M7)>9`X9N+tuN>94w&yEn=Szlzxl7PpV z&dU8Hq9khwH2OPv z68Dr5KDpa$TrVN;P%);O`iW~6j7YS}jf+@u#}rrcUO@|d!d&?7Y8@{}cCmrB)&^EZ zzvL$2Xr=jw!v;%p+t(*SZE2B58bR~Y&9@2TwAU}PBT>ax)*gR(9A$tjIk;Ez?1H}7 zR@Vl)Mm3pqrjen$Q$48?cs^FjXXa@4V6h?be0xKtUi#eAta+~|P4jAvC)3|stIH;yRD0c1(z0ff>_EBu4q(r!(Ndi=+Q{=+zlAi*@cpbQmt z=Ro`!cSAoeiuEuoA*sq#@$jIE`&WZ<5wwkqm95*avuoCZtKX)J(>b(u70=DxMc0il zaW`Kf!JLnKUe@ip2n`tjJhrnQl$wnx5-o%Yhn~eOuB=ˡU^Yo8Tn2BKuFinXUK zc1F?qC@UFwcQkaBh0F%;&EK1*N%J)dl?Hy}k^J|HB)+XwLm*Ml_jm({>WBB zbDxq2k|)#~1{Am1Nos3!sI7aggRJ14myNBQB9kRNCXh5WMNX>aG+Hdvf?QhZUdSb0DgM$ABUO=J0at?iYV;AsGnSRgzkW7Pw__d8M8D6mFhUZN)x=s0VJE1A!VuPSrXMk@X zkIt6%M0iP|YWd{+l-v(XTj%;|AlK^zzI+!|5-NZSA{zJ)wPD8>dSaVEoA`v-?Ttq7v=17V!i&eZ|n& zOA}SA9!z2xd?kV~nq6sXDKH{WH>uD2xN*Uq8Q?B&TDN!E9g-TYEzPMjQmRu@Tu*Q( zd{Sgg=4Exrgwo~K0J{Xgye2F=Hm)^=^u8?;J-^lq3nlckEo_8NSx-XTatpI-XBX+9 zNwZ&%GER#m#!$z^dQN*k%jWg^ux7%w!&dOh(5&iyN{s54v#ABH z)hnH$+t7NKkki!Z6=@yBJWeuk55hm%4H_{#2}!({VKhx=ddNZD`!=y-l5Z^JaXBV4 zVwE)!Y-q>q+FL*H48YC*D92|#KKZb(TDRtk$yLR^Di!d#L%!;X^A z+*Iz%G?sFO7gQwk?L`l zGr2!y@MF`A^H8mqQ7w|8GVQAuu%wr4i?p-qAst1lgYi z%lqwmbd5+)wyQo;2HXDJb&p`B{LFTqoqo2Lw4^j3n%)*}xou+P#r}G3Q7lROQtBn? z9d?@v53K>i?AKt)qte{(>_^MbNqC+R*t@+MF)J;atvmML!q<@TuIZG~9t!N?ygBY- zX^zayD~{2y`y_cK(=5^x8vxf)681I;Nn6=#+1yH#IVDflku+%`4IhJ>NFZ64%BJdg z|5$txlGeRwrS*`&(y6jW^liJhH5y3^M=v=gApYP4e2CJ$S$)%W^TO`n9$1B8h#Vf7>@*^CQhGGR(wF3 zQaYA&*gV9soSmIqlI@0cIZvQP)`+ZC?QR*7QuORz-J~`rMpCnQk(DHg$7(ISgP5R( zTy{OKk!C&bca0>5mhFk1oK7aoFsv7LoOwda)dsOPuoO^|^JgP~)3U9lJ)M&vuDxT9 z`C^G`8J-P6o>)&()RLSs#?Lg z&sygCcOn9J~eIlo1ESP#PbwF{^{AfKQ~Ws$?d`jLFxq4kzxJl+g8F-`i|nWdXv_>^z-sZ>oFx5pcs;6^{o23|w>e)dWhkMTS_~%> z+aFpul}qp{{vmaV=BtQWU6RvX+YqO0kP4Rhhc!8WBw0+>73Y@Olxt4dr$t&yI@P;v z5Ezg#)k$n?8pazxcg`b@!Imea+r#dCSWo54xkfpyT=5EFA=os>l9ayb52@n2rDtNI zP_c52oe&|vy(LN7VdbMa)=&^1a;`|;yVO!6A7RIpykjNw8j?jax!Uh_CTUYC(| zEqe5<-OvgN!Q-@VUlt?3oDe7ZA-j>?5wS&CEkur`e!L0uIdr{&deAUShB$Z4^;)@- z(E6A?J?*wGbx=sbvW#v?TMAQ6D@ix6HC%|%cO>Ot*JECih^=o|!@le^Nkddli>MA3 zbeE(H2t({`cFplJFDBiX+!HLLTRfh&`j)s*Hi2AWA{@JHT-o>XE)2Sm1eHMRO}o>E zlVlRMzBag(0SFYW>lW#jOu(l$f-Sd*IZiD2Zr!edm4L@-b_#_+jy@!fK+fPG<8H49 zAeq$|whioptQrEadUovN>k$#by*kgR9n|k6IEY#W6k%E6ha{Izb{f!lHC!HTLA^<1 zYN3Q{kO&x*H#&PaLO>5d8k`k5aqlOIQhO?S61S?@KLy;+4k)SSHzt`h{4uE#l!?S+ zq>u`-4nxlmlX`!Kl`+VG=a5M7=G!`9a9L1epAo&(cB*(P+BjNUvPL_$EPu!_kgw8& z)BuM_lWP(Ou8of+g7sE8w6S$1;H8)l5LU>tSkaFxCMH@Yq;|PHGw*|`o6>AHfoda$e%$B#2aSjOuSYC_l+fvTfrKZ+=@IpYgvkq+*x%RrxOSQC$(bEB=5AD z`JQ)Ee>m3C#4@q8LoE(9lM*$AX(HFzo_|8tZ1Oe>zy>)u0X@@{U`_y`i7W7?rgp3> zNtxQV)vzz6-6y%!jhKa0f5ykw2Dh;91Wt8o5W>LeW3z8I4AG^7%v`de!Kcc|*E09$ zX(1)Q5TR|*dmZ?ojc7?B$;yepZxho&VSGUl8rp_>c74q*$L4lDx~3bo>#zkHIjNYT zH1DT;qZIy^3CAVcGahowOZ3UumtvIgS;3Vr{>p%;gcA)2BzmHYGqO2EDN$k>8#My8 zUGp>u`=qzCd^la&Peb3TEgQf{nOT76N(6O2SZms%a%u5mBXCi1T6Rpdh`vfRBqW`@ zo=&WeD2kp*Zi42)_#kpm8_s+qYNCM{`ZO(c?a|_0v6Vmz1hNAkZ{Q8gxHqgFg7H2p z5j*6x^={W+Ho9&He=t-(ksC04+CCrOOR$=lA+F12GyHUP4We|6hJ^1np9v?&N*KQB z>9z8XUk6wKb&^0UW^7GvN7gCkjZ6-U#=P=7l8@vu>tPQ8bD4oTd(4&ta!y zvZF`JJf6gui!gpFnV6n(m$ z?WhlDFWZ~YhFp$)J3`PhO|&t#5Rw|WDTN?8S{_IZrra^M{m9QFne^o{!vgs8xPS;> zKkRxP6{2>e8FXj2jLROmc*Lv*a zG;90H4H!NvyBPqk)>H&jtrRu9IIzZ;%5>|1HEbfBjyfu*Zpbp3XLv`9T(X{j*u2{K z$4rpsdJgAtBJT+8Ika5J%M{X<^4l?fz+Mqea}fRICa&_S!I(AOFe`7@d$SyT(8D;n znI#NfiZBIuw!IQ*?62h{vzreM*<|^Xa&O)Yb66=2z>8~JC#oJgC;%H-<$8r|m&bc{ zP~q6Q@!w5Ddq<%I{>`^hq{y|%*+Z7?@>L}12I)>>AKJ4MT#3ECiEPyxsUr~313+FU zD;moH%>q=#vYVWz0q@!KJ{j9V{3Fu7=l6ndz|A1_TyGx^dJ51{?pBRjI)Lm}U@qz# zj=-d3(b;^7gXg2`i8Kv7sP<{@&QIy%&^d2%f6}meo(gowAh1qZh{5|&AW)>sbU`OT zRfxU<*3;;`-@k|8mX^st3QCXRzy>APR~~T;0A14EM{!BHUO{+%0QV)ylx{Lt?oYp5 zBevVQ(Uiz=tGnb3s0H#>h0uNn77I!?;|ylWisjWv7&OT-Et{=AZY$E)oUH^bG5KsO zX{Je%Xq($#l@=pTzG?H9kMI4|7LrOnp+WBFBQUZxTw{sFxd1nyA)djEY@7x=Hybrr z?<+}|mm&mUo8!0rJjoca_dAgj%(5zzIx;l*bZuBb;KPUgpjAAwNdPsF2R0cW(p(Eb zrav)rz%K+{2(HVZLUw>hGjL;4gir@?GfiN9jhtmI9J-@yR3S;TCZ{oX#F&)L7P;C` z;zr(+H3>K5dI{Ao)jqOpS?v<)l5W)cOy4RwhejQVY}}HUx+Tt_O(2VL5ZIN_lK&$o z+^QM86H%eDz|RdUVUsuJwErOQx&e@ON~)r|3n90_b7*q{mz`lxpWpk5agmnnmbtg< zk*rKY)58#STp)=k;AFi#E18bS1#$AzfVSo2pq9W$G(cYni_AQ;3tnb)^(ERr+WhkD zEoum;Pas=499hXvO*e*fk?BuT&pCj%6WWi=L((pl;Z$sSM<}wh9&FS4YrD3p&ZqwQ zlh>S_)PlP(ZBY^dp!ND~kLr+Eh zKyCO*zrbxUtZ3AsrCps6ThU4KU17+~{#t}{l}3Opur>fePE15HLpT=G!|RWtEQFko zMA!n$P)G#CbnNe^{D?~z0I8ZVmYkP;np8-P_5j*Rpd<->@HkG+)$1h$rZ*hcA<@%7 z+LitOh;|&_4PYn~J4Wg9Zc5+D0@c7;VXEt~c5pT)M`u_%OUAWq7}@#gig(=aWR`Rm zZ1H2`9f2e*$aZ#g_TR*iDw@r0oh298r21|r7(K5FG7QM6lO>KS(p>f?0i>`T`zgXC=3S(cBcX)lIHDt zbTxJpdYY<*z14Gx(8Ji(XkQBdjx&T+^>O-of}vVDV2?rlxVo=^JK0}br!Fkhcgj!H z;}a4GY358~*t!MVH-k*5*u~^eJ5v93w3o@<4_F(9%-&o+cXJv!6KF*k9hQ)uRYDv* zf-Rxz6|y4$ImO>wAEyA4B$0Qf>{8;xrQWqJbgP6ULD-@gCegYqjdG`IL27V1$WOdk znsUR=in}6tFYBm>Z9^o0SXY=jQVf-{+s&c9Nq`p2=&)^md9S7!3#l;=a=5QYS3^h^ zouiGj_5~S!7(!v2rh1v6*jMN1S&lHZ%;(KucFi}zlHA`wkf#IHsAEYf;vqQF)B<_3 zNQVR7#V8=-KnxA@^mV)sf9xRvD8iCnl?Jpq#B^he^l3@h4k4XMf3yhY*XuPKW+iZf zjWm}Qyd!A-BNZdJ@7^fr+V$SH4`hz)Pe=Q3(ks9Vsc~s{f*3_p5%D}sLZ`1hBEGL3 zv~mqV6)}JWI?;BI0q#;c0XC-$kPI#PrH}6gU0#8S=taO4r-P3n{LDikg7#-*=@G)0 zGjUGVtk>6b$Fs}qLO76;$G;)iDSNbOGrgYU3IJ6GlI(rpPTh%XNiS@qPsBM06#Kqn++A?8vD zJS;JN>-p|PSaQYTG+3EJ5}RsCVwNgy0BwZ~qCQ&}Mx8G4FTC)!Er`zmPZ~%ZgU=?V zA`;lfNjk+Z?b5>2mH;EO)1r{?WYw)<@Gk-pyS$|^*AxL`fgb---bMI-&f@yc$-2g z8>h8^2-wW@I_u+^e#RX2vC<+bD)Wuz1|e0)A+R^%e1yeY)R@)lCEA!SH!HwWZdKc( zJ+`uTm5ni%mus2%I{?91QqS4q1@09kgj*V`6rVFz}# z8~$QBgc)J&(E7*s>dW+*h9OaAaXq>gY423&Ng?1HKcf=Q8f`t(c^4liNSdekZ}*H! zwoTtJ9>4}zlJ9)&y6h8ug(;Y|I}%wdtv8v+#X^sqR|il=B}gZ21uSHLQ>@GE9?oc@ zUW8K07E*2kX9kYi9)>J6H>(WHWj5DK$XoPBfwt6CV}8>pfHDiXHMY8TC@!jmLMG#v z2$?ywC?#D8Y~|G2$6!60I+N=KseU=a@tZiLx(nkWYBB^jjZ+Ar#OT|m)YjQf{|9@L z_w8}jVbkt$-xS^)^(#cYwM6E^8ZBi!0V_)uudq5bVG=|M3&z`#z21*pSfbj=H7ZWe zU(V}YW3Y8H5oto^IO1heFjUEe;Rclg8x=#)x)c+X`a0M!UY92`#|PpoVT4oQnu3I;8J<=GQR{ozr(_gmBP{@E zGY?=P0B9E6Q%+6Ybt|lR=}a=%BR~T(Stw)G@+k>hP(Efw19zJloqQmHLX$dF%IO>_ z1Jy~et4BSEwBkk))|j4T3bf>MGSRj=oQDb5Gt%Q@Q&%#G1W=@j5v9WZ2 z*Q3I{4#){it2BUQ>K=|rC-z3kkI6_EFCwqqLfQP3E{QnmD!!#d>O-$B?8+5g!p>!r+a)G91GlZko=LVj&AJ@u7_!TxP(O3JGE8eSAlP+1WC&ygT;gb`j-LY z@XqxTpdS>BB&u1YMek*M?Dypol4g>I)hhsflT`P<;0Snv_< z6lOElX*gOm_K=ODI$2{?a8p zPJD0)wZX_q$nd};1b-w2Utg1WJUl{K7~P3(aC5SEbnnX}l)E2mCGDUxC^)LG1%M~_ z;1GZcDQdM^)ZkYh)4$i`W&o@c2wPCWoUcfr8QO-8>2*K_7)eQxTsNvN?l}L6Bi6YY zsD=T!EXYp8Q2$}55)XwiFE5}Ts|`adLWLyVb@5K<=rb2UP=3$CV4OIF|Xwc3wxYuW=cRO zJ-ij`W3oKVmy(jRU?P#WnSO$#0u%Knj2qLEC}bBD&=O)hEYOCwVB! z`nVQXh?20_(puA#ikuvLH&pr$h%F)n)kdqtiPq$s?TL!X$XDz3paj#dKA8DPm6M?T zqs_J!DhpT6n-iA~%~7=%EC4Zn34$OyJk~n z1VH^Q77lTwsU_Q@k`;K6rga=NPJkp$&-CRH`7Er25l+YJTFJ)!;&XVMJPHrJJ7vrC=5|_*-GI^vU{lEp|8Ma#Y*O*4QaN zomAIs;Rjp$SI8to>QL;UJ(?|^_l+8GT^l5wr~v9Z$6Tbl8kh_@Luw!2`$wB^`Huwm z4Z<++I@@lIj6;UR$~1DXi3L30JNh!iWlOyw+_jsFp&hHoUgikOI|-PC{9dz5(4V5F-LXx$Uya zG;heH?je93R0?TKS5F;tUjEjtvqGnPDnw^4APfnjy3&j9(v zs;eLlfliHeTk?JN0ePXz5E>+VkDHdkpVr)Ou#NSFUBA5ZKWnn3qo}oPQI}0J%rV~f z8uaPOrt+X*+jzF&7w!gOFE%(M><2u|Ot*$Hqaog^?tG+NGi7aHitFOade~)$H;wmd z)2sv+MLl2gJ8W+1Qc0s%uTmBX+%2D6(AIQ@TDjq5Jf(triR3=9+)m6Zf|?M$k$epB z3@}ESCoPE1cZz@|_#sn%@!n=;a@?ptqN#SB@2(kw=(t2}cA;>hFSPo*tql{SZbH>SzUp8({}(+nnBq?eSoaf z&Bc7|5(#(`?_pR#sNKL|?ZK()l5~BLt7)16*%-v(P!b2GP< zZA$mbZEAghtN>wefN$`v!oer|u7kKML*JTVvu`Od|7f82`{f-?1o!&Op@moclyx;OC={oI+sppre|n+N%dbW-I6MJW}hRw{ib=$2TZJBTTuX%QAGP z@egmXyd>K?5#pk^%roRDD*j;BE7_sL=y=HE&z6zQkUZ6>+J4Mon46wO>D zL+1S2g6kWeIfs6uawA+bjmQ>NjvZQMWVkg9w>lwr9A+u_Pw}qtKq&JE-WseTz%C&P z-QGJ~_@P9`b3i=^S2_=81H329GVcV2`6-+*) z&~4jS&8wz~x)2NYNgbDzSPFJ&$xbR+i0=NUnco}&SRvSH4ejO!X_7-IX~N;256?{SgVd>$ zWH~(?|9%hc{%`Nkc}R|&Jgg)W#_tj}VnCYUy?GY0)$B$EHR{+d=b-_rn9jxfsq!SK z-VWq*5548`OtUf)-bG9jIL@%pb&vx$89#D3VF?a8Fw*e@R@fRb5UAcjAS}#hH+9q2 zSCbb&u5A*Qasd>B3+Q$4>tS}{{M%}>Ks6We`-qla4#(XAXR|h&#R|0QbkmsU@^25W za6R6o2;w-Cpp}!ctgW6Qs-=oEQ3PgYN>hz!Z@e*FYKNPO`fIbqI!>6VUvH+WaucL! zvR5<&NyS=G*0$VNV$mL4n7UBQJw z)be97Br8@? zSVZVZ9!?D0{Nb(8g`2h!c}UIB_2^oxXpd}89`iBDk77m1iF3Mkz5aZ11gd5XlQyFK ziI6z1@go2Ga)ODsLb(EHW!#`S-C%+pZL2HC0O+Bc&KENe(M*-)+rVMT4_6ar7@(99 zZ--O`Jj@gFDUnqm@PT6OX8g$Wl1Wb%;&j>zy9=I@94cq4fth4G6EdG~o$f-ws(6a% z>o5@Q6Yg!T^Hwv?O^(c+_6R|yZEod!QxRo_szDFH(i+EE(nBG~rP}bxOZED@yp>`m zFbpK8sUSEVTq|W#0qGBlY7t{yDrJ%p%I9`lac0g_XCC;5$g@M*SDm;fXWc=Sy_{f~ zAWlhl$_mHMY}3wci*_E9`n3%@A!FiqU^?Ai;M@5<61W?cl4KA{v{8V5phB~;%||SO zGX6}eflw7we7#0@vF{s6v<~FCcXMe#2q=?EQWMyKarWA3M728aF9Exlv{s<1UfaEc zLTQH-iyPrN(&N4+WHP}Py;t*Vhl1T<6@g~H#rnXW0Zy?+N2c>zKN%#|T&%h;$kgLV z+qjUZPjHI@u4(KOa?a0beh}lq>3ZJA@%q8=>8F=p2y$TZDbl-xAY52j6jdvXw;22337$Xv-=No1G?R|K#)Pyp$9KEw1E>Yjr`S}XNN2x4Dv<#rClgQ^XchrL4+*@<8(qt;o_SC94abQRHmGohxfDXh9nEI~u3HJ?}&I@D|n2 zq;e@2j%r!Lg}GXfO9IN0f~W9Z8-UjxZo1S}C~2#mm`+5SGGnx)=~`vzyfx#EmdR-rNb{ zeTgyI-!+*uFasYGrYP2oB&b%!e)yFYX7-5_7*q+TD`JDE8VDaU0g}LGPHKo$XeFRa z;)68#C{l_kbJ~_5^O#ls*R=M1Y-xeTa%CqQp^Jk=!~59y3ez4V-1A5F{vX zQ#K1}Frmi7U;=b^Lj*ocB~EwE`1$A>0i2j$4IwkzGgNd;s$8v)5x@iAveVh5l^_{P z)Wtef`6d~U`$%gVQcwnXHd;%8IUPY_s{M+GmqUy1IMz`4Z6^@)$7y}w7S6szuH9 z1-yeWyi-LG!8_VRHlTTS`{`kkz&-|~ifH3brE73r7PuQ}w-Lm7#z@Iknz1(VtthK^ zxRj0-*(L)K-RseZ%5lgi8&&c1Omg{{6&C7ww{7=F-8kJ9sBDKejFYqggaZ^RnU_<% znYFuYy;ZB$Y_!C{?tCB5IBOP4`Oj3))UT#3EP7k?u_g;4&Z4} z*$H?w2vc{uJDAdEf$3oTraGVpC_Pi@xVe!evOEFVA`=ldcn4a?ZMNf?i1=1pTssW- znCu0M>wI+8zQZinQbQ$|X9@avO!Q@*$73IFeED$4!fiFXAtO+>H|Z6BPxSHtD^?s| zW=Vn#3RYrQS`@@{m(%3f6*#B?%A&$KRL6c0R?ry;9&1f{q2d?r`QVzR9Z8{t&3ksh zwoZqrkWz%|31Y*p8)_G-5bF3$4M9yQ{i=e{f{qu_F3DO~0tIeNq{HocbfqBPVJ%|H zw*|!yt^4qgaynbm>-7ry9%W6U5GIBU1%~4Z?M-bQa>HGrH+sN+a?X|3$~(09pGGzZ z)ui#;G~KLBl|d&nA?LjBF7MUQsUn<3X*lp7f9%m!^XJt0XO z5XC#=4$1JK)txI!!1~Ej189A_9+|imGQdeSEOu6=fBAllUtWJbk(Wquyy_>SAsdvs zX8S>`NgvWLx$JMSFy#7>tl3aiQ0K0sAqQ$HNjoRF?hiCR0X0m_w=%e$dc${1)jdq9 zMCXJ)=95=~ejpW)TJTD2rz>Jg;S_Yu1&JkX9&FDbHzudMht=o9okU9m$LW=*si8q< z3Pg_#;zWDBUB|nOVKA6et_1#@@KAES;$%_&REUkLtksIyB-tF8CuLuI}(pDEx zQev5|DWG=4H6Am%lkEFqDW$O$ypHcJsnz7J((SPWgpI z!GZ?qXajIMkMe}|Uy}7+sa&T)yNib=%cCzehl%AU@k?03>2HL4l~z9|+-!PQuC#w< z?`9A)djPK}=AtL{;`oQVV9*rOf_;m+0}4(2<^n@LG~w898Nq=PqWSpkdUWl3)(IUk z^7V*aCD(91TMSE4o&|Kg-d2xcN2-YHq#U>s*x)NJ@W}{sCjG<{l)2&=+g)aYZoKZNy@5 zIYC9xZ{@*f%7`W(1JFKEpx{`SU?cCEVnq>sP3Gd*qeAs`NT(oH!S3Ym0ND(Fb{@5Q z_J+ozPdF{*&r@@pLBMvb2sUFz=$r3mqPPv}&W0 zWcE|{(b%}~BUB?cu#hS3+aoPva$r&{NR&pxWfB&RAaaY%kK5y0sRnS(8r=}Ud1_Lq zHnL$)+;WFtZ3~-<9y$;zh8Ix705|0oO|>Tb9Uw)yU5}s(F{25wK6rp8acLOip7Q#b z*V@nH>T)RmEsmN&e;-Lt5XosAtiNCUb-zg5f7+Pb+zX&V1rwF198)L&`Jwa5gH~E3 zSJK<8cst^bG}N8ReTf3UU16Vu)&2-oQ3|fb!caRMTCbM?Nu#eFCQ*0Cg(uaeIqqd* z&@g4XjtZ_L7uK8jf%cx-Ei!)ScFVq!(8x*9b$h&~cL8 zaLVur5>~PS0<%h0(gt&IiGr?GOW{s4tMJ?1+tWE}H8#u0 zo4En8Q>k%z;2zwEjoO~RN>CqHEL-bc(2A1A=58%@Sz4K13FT;40?>g@Goj>hzaTqV zXd6oHfq8UeAB>}>iQC>9wYyp_ZkthJRFAK#i#*jktouZ zbe>c;uuON%sPoZBqEh(NA;>SMwwA}F%4a@%N(WJ?OY~&>4nit(`LpQY-IXi*Yv2+l z6xrg5P3AKBO!Fbr_C-%BxGl(NMU=gm1Ghqc^nMRNO)Pykd<`>DtFWe;!f!ky&$KI$ z7~H}mJYBH}yk2D#m_qWd_)xRQ76!{jXT}tSrnFSo@2~?2EQ9+bVRSR8F!qTCDO{jA z>?O?TAxfnTVz~!<*P#0|o>qzX78&Sp5*tg~pms8)d+UGjtb)A*rn9A$PemyakVx;c zz8+C@Jmsikb+m-4glqz3unV5NS>nUg7&{(}%gEz(Rj{XcuKX`5h!Kha2@PTCNJ+Z9>Ox3_3TRtheFLD+$~5Wf z!^Q>)UDE3=tAAqKbk}6_S#i;-=6N*WD8{1(Pl%h7XdwbM_;PcW;r<{;*tRp+=5dDZ zhY%O=+vswDn1#SO?hAqNWFo?sGX^*Z`vJ(N4OyU?nZCxc&Oy#Y4nWJRyMy%zzPDtu zsfWb`-f7Y*9^uTnN-H&tlW3rG^eEuBk4twm7MK}-)iW#_Fq1(MH<5*@3SZX$v6+KO-z~QIfww$yjKEWosVe7Og$-@4GX3b5VS+GC^DH> zO~J7}^M&^RI8gu$6I^e5#B0cRBHl)fw*!krDBqY-ELoZ#^r&D!;L;MN608IF3Tctq zGq8|`$C{3FZ}WfSYDp2@j^0VA9GbOk+fxcpIuFXO-IvgPT(SMQ6gC?+)k=pU1<(3s zWCT(eN>GGz2Y^tisVSjuY3nXyka#Wl%z;H& zKpwin0Wt#;&U4gT-Q6i$T68;7|HGwPJWohqhqRg0fCVM||5|{Ky07zD!#^Z59LqcUUzNDA91OYUm1R>B2ebR%E zTASB0cmN;<{j&w=R(p=}N6E82ShFj*GT0PNVY|ubqqRhOn-du2ycG>`h%mZIrA?>) z*oh{$r^&jv%;Wr4!z-rsW@waRqztwl{ zAo!#iqva>EHA*@Hln>Hn7^UtFrws@rm2ZqUxmVNLjbNSk>at32Z23y~RP;8~ZZsCJ z1VrU}bj|xr=uSyWU=coc9K8j{-VNPr}J2Cls zg?$>0v9vYvUdzxDSBqNi{g4%lIUpHEv#Lg&Xs{9kj&XaKBqdd;z%VDxDw~38%{(;R zkv0}q{0PgDIzaL(bqQN|8DQ+}!7#VE*>K00`uJXZHHcKBEpOMOYthy27RS@k>?im1 zqw0!m(xp0Mm0dN{L^t@&nHt=4EQ9%&YAQi70xsw{i29QN>An@}dJ})+eqjv)^c7mNP zdV@ksM!}jFBCNHH4&$-DB-JuMgatRQ{J$l8rF& za97g~m6|(s)5xH|QJ>Hw8i+qA$H0E85&MjuvXU9F-|Y-Hd4Mg09Bbn}u3|iK75Js3 zA%a+HP`Obd(0}Cg@-+Sd2B=$zm*a~1ADGl>{cDc*a6NXws1uf3X0_5MT_#ubT<8uW z>?L|}V+rn1axT7t0j7iSKv30mno*RX(Kk0u#+t3s!B%gCuc?p%{X7dWk(k2g0lmq}3II8j9?tL(+{#PHn|CRdK!MwRz zdmwG;)EV7;X;Me!O@4H6Ce2QphVhWf6AsiO0cJ3bL@UC?j24lT^}A2 zQhYPa$QGKRZ4~cNboOay^@1J7mbzV!K6Lhm3TVT89Sh(eA>v2=cHpL+HF2DIAOTbK z=cuk(+ahsG$dE8p+tbUPX&Xl-71rwrGc5r>q5n7x?w_392ISaRIhDqo7V%r)6kgBx z1BQ<82Zlcq@jc4+0Q(+6WT{YCDmohD=9;(j6>nh~yW1>Kk29hIhzuAIOX#PpnW1Fe zP0Px{Lz`_;yJFj$@OnT6AuW=$n2Ta)ut;IS3xM!6(qlI<{KT3-6|P zrWL!Q@{&-N)evZQX~QvAB+GBlFahHqZT10{V7;P5xv$W+!Dth>-YN!fq|VNZr6m%YHnZI;y(6{h?>)rU5WeE6A;@;)%LbxGJG=Z0D3-8^gDpl%sX5N z5)*d_a}ov=o&^2@6lwFLYec{fvNCc)5lf<_dfOL)HaKz@#gEmbM}tNHEtYK|z?@wI z$sw^=1WSa<2iR^f?E{Bw+)WKmhe}h4oR2=5TCY5#hpF|!F*@Q<)YLbdp=alC=aOWdihbN{Ifo5xHkVpmtF#lzbL`>moIbo=*&EXxfGm9Jnhn)k=r9Z4 zjr7TMw|h3s^$It)h6EQDi|pxnRx=J)DLd9LL893(4Gd(%Ifz!A>89Caxj|2P2f?xm zO(3`p z^$56W+c|!Cz8I0FZdzSy7!C6@JX`U=w!0Axj#fO+ zD!OGiV^aZUkIL(f1pP)f zEGST@N1^!E4@UJr-RtC>c?(m$xfgPh(QuRmFZA!;M6d^=rl4p-)w;R@%|a*m%hA=S zf)XE72%3kdZT+yRWb9vZ@&_jF)Qty=2hw`HIK#StxgybC<5F+-5+DqjIZB31YGzWJ zm6q3#p+Z_fCS+ksBv$&5zjcb|I0Y&T7|}WceNjQ&1{mdtRJskJV!>@a0V zTxilp!ujgpc%*4%F`hLTKHaRQEY=NdszOrA1`0K=U@XXS0oXbpW%uC%XaY!XlExm* zvgI+)`Qgm&?UEBbCMvMxG^b5-;nB^gvBS6ay3Cfppq42?L00)a6vW}$6Hjx#0OywO zX*1-DCK;bq&-uPB&piAjlK7RPNYAlcWkwoy27lKAd(3WX`?BuRm`SqEIk}%I6ul|B z6r#Mm1Y~sF^ff@1JBTi?W*lLS_kTP%?tk68{GB6UY($Gx?=QCYk8XZcfS?P|x zpAKceGNHhq3`~U#oVI!WWGKxinhpEwA}W36Jt0YkJ;ms|wtCoZCO6o7KxQ*I6yn?l zpB5MQ(wq7%GGaa)7DzY9lmw~|I~2y9F&~9m1oUZ%lOyw!&R0;+K}8I@y4#?wP(-+N zmJWNn8^3n9)HD4#INpGmR~V5UE2JEmHq>NW(--_N%wlpz?RGu7S}yQM?9;fd?OA?v zYysJXrFNOrDt_;E!)GNx4Y(2|k*P05`*ROfJ_(+tq5 zno?ckiJKZtX^77yc%-Y_NYa%PkW=|E82}CqT`ADrT#w)sqsLc@c8b`!bP`NyKk1-H z+fIkM&m+3d%Zkio733VBn@2t18e4)&UHe9^Ikb}~VsQr!KUq6$K_bGk+C zNN+_LmF4J5 z;IL4Kk*+O%FNq$Aei}J$E~o3gW_<>|7xxZ%lO8!56}uH6JXMC4g5@P8(0eFie3n5P zeD!L9@(?FIQq{0Tfhq}17|b6=FS#h~e00Tx4HI+|7;Pu!m&2%#9y{-|6DEv8G+O*= z9j;2`K?OT%_>I5@{Cq4jcjRYDMw&ZO}(3pX-gnD@d4C%N>aDF#KdMW(X4r@8I2Hu|A z_}UJ-T>eQnG;z@N4D{tZ9l)^-=6i#($VCo%i{^JO=|@1e9h((G%3UkeWzVFT(~nfO?avGtF?FIT-k)~n^$_Ff29WC_>yg6^cwUiL z0{ayxx4<*yUY;Y$PU*5xA)-pF87II-3V!HALA54rM}WcJ48jCN4el~37%9E9UXEg8 z)~n0Kg3;C$eWci<>W%UTcCuYYIUMMvYM{#>7?b;v+`|bmB7pGe1-lHr(}xkrFXuUv z3WZjE5m#sTkBt0dpv4$$`be(;JEWwrd%gwO??+aQoJXg)GeA%TWWWfyg5`*)f_B<$ zL5F}nU$G^3%Y|H|!8zw9e=FWLKy82sACR2w6co{}RMNLa^xWimRKiHz-{Co!!K-GD zE_!P0BT^~=Ua`*>lQSxKVnaBk0x7ZF#TG^8v~8l#>WZS#6eU3loT4O0idphNCftTT zJ0Imc)y0yDn>hv@|LJh;oJrR10Z7o$vO682@jutBhsC4Gm@Hc=bv%mTek9v+beynI zgKdbRX$v>TI%QI2Fg^{LOeROJ6%`8AU1BK+t}YvO>jvn|XwG8(D+G2SRP-hT=XoE? zC!k(L&Y1k#=*l$Vx|xQ$VEsQBP~+)Sb8D+~@PA0hnz9K_A8%76G>mWo;bJB1&Ph?` z04r;w830B*_Nj?o$XvkxF#~T4Sx(-J#px%jh+Cu6o9>`Tx!>h@5WY66d+_8{H$KN` z4hd(-o<#IxKWcc+`|3D@?;>$=Z|2J1s(a=p+StRF`DRmQm&bxGa;pW11`{n7i5z8$ zUMLd7^oxFA1ddyM+p#{aj`a=ZEw&60Nw1Vb;e9!{$zVl0eqsY|P*){7dPMnScCIto zd8v1@nnr3FS0uX#b_N-X3x2>0L+qP}nwr$(CZQHiJ z_Ofl;+-LtMH#a#i_dc8^ZJNHcPc!Wto#P8^UCApLZn1&V{sE|N(j>b=wbmx2olK=V zYJkoUpPX44O$;?ThmV|wJu`bjZs++_dZH<_jA^>t+Xs`QNB?KvPei#3%pncj4o!30L9gA1mX9JmxSClC}V>Q zM7+!PhPNWIBAH#xwoj@eTO=3EnF)fj27!CkxpihTcc_eBrVuy(0NOY-g|j$f*OkgI zMm>FU0N)f)0zYQS9%Ho;P#I2Q9*e@6GS+{V)*pAh6vsKYK{$s2s^R8|ihu!WF#|CA zYq)Az0&mt6xiZBaPiqgp=xAX5X4275jQ;XIU22HC2@>5;m@QS?a2=$>Lc`hqepPZU zcMZ#1GR)N|7KIqF(AeF5K-L-vzK+8yg+5{RHwnopgK)FenhFVlJEX?}Qsnx{!D^|| z@VpxG-ADQl{~YjD#*V#Xi*my|&wD(xgH+ZriRNohO@$vWoyox_6$iRMueeem@Cbgd%GSBKg{vGS0>uws=q zr=+#20;@CQk^mveQEOL!4a(FGiRLlcLBY~dQp|3=-!9gcBp0X=tE7o z#@&rM$|O+~q7Ma2aLSBQq|p!bEA>gT_Uioeum#vcm+B9T6`FaPg;2!YS zHC$}`87$$ZR~&HY%8_4&+!pwz@?*rKHHDzMw_3Tt+QYMqc8sE)32fo{MWOXPss)-7l*d`Z@p zQR%#L;VvM);P<*3(43(DDY(c6{ zu(Eum6jFDiqW&57X!htoH0B;R3sxD4D#>v^HYlGP4K2M9)w`B4oeb*?8vocaK_Drqc2?vF$& zlz*FU3S>JcZBQ{nB5j~0j)m+-O73P4W>3t!G^kk!N`vU$1Ie59I4e9f{p$LSSBa?X zG;Tw9WDn|Wi=>6k4JI?ssT$aVrw%&YM5KCV`trMsyO!}y@{sI_Yo?IJP|R%G?72F0 zDgR9;k&y>4cch4rbkcY?RLi%`-!5PiLPYeJVwPdB2UUD$>u1c!&j?WddW}~3V7?g? z=GJSnotyVg$=1V4B_L_KtcVB&}pe=Xh}ff_w0SU3)ZdR z0wf6%D_0PQfPkW{8BTW412mCx%C6gib`UO-Gc?;S>;QSA;P!ZN25+Hr5QNrbKyX4i z-%De=otAZ2y0U8WFi5V7L{7ONIij*kR^r4kR6s7Q;ThatipHV?IhU*Vfd{T!qSC(g zL6RTfTZ7tUWml11$S9*LwXbrE=s?eYuUv&f0Jpkszc{eaWss9MqW1-6G$9QKsiYG$ zj_N-Clj0>vS=Fp!5W|9;Is16vjT2Bm7qF7u?pmgrL<3)eG31_T4bTmVq*BF8@w~lG z+`im-{!rqDA#ZXwKtOwgYj=c8Y}jkH69&Px%e|Ta#PauEyb_K~&77K$$=G?;#+a)G zR*RU&3B?V~y!YVnOPsTeIXQ>^{xMggF z=y(clVjWp`T^&Iy`|ye-?`(cvLavV(Ao3uPD#&Hb1CrvP8^FrSdF_A6A9l8dOk zoAxYlU+`sUY{#s&dYe%@{rRZ!Js}U96e>&JMF;~{Wb8{m31Yr+(9?e8bge<1c zI;6?yGmvpvdwV*Vms`4F>w31r83bc*stSv&lFQw5Q3oDlfymbklIfKNaXt*ncV~Y* z;Bp(ccFEO?RBYW9p7}sRgUsXYDOAawd%AW1n8U3*^7R@plHDtIl_%tYZ&ibmf~)XL zIkpe;LvMOJM1N0~L{KJSqUzfYd=g;=`%!wUV+cE+2Rwmb1$c_Z>$Q?(LO`hPQZ13Z zhHB?J0me3DdsgloD4i=R<0F;0>S2GEXK;2HSc@OK{775Vx|Sg=SNuy46ILS&WK)8! z)I#-o@5XbPO*aO)#XQpPO?RC*gOySkU?<`UkjQYG?_jeq34d4mvd?b))yppop>|^t zHvwk+D?e&T>A(C?eR;|P!G_q97a1%NFo5!Xl^+xwE`bslGHOBGeMISR+Acr;rpQpV zJY0k570tyYRNKa!@AaDCo!aN3h|7cLC{++u957Z7`3>~fS72IUt$7e6Qo;{xOn>)~ zElFWX0c2@#@S0Ew6k^8(0J7E55KXryYIR}in`PTZg)^{yq85Y1V4PTRi@`h)1vs6* ze(%EEUCB9$3Ol+2m@8KYP-sCU80PS!pWP<;JC@(%)AvZE)0m#HJyZnlOe@Ws9p%;7 z%$-!UCc4*g9Uz%IiG0@X*<{Pb51%f&NXT6nytIaqU1{%5>jy-Lro6EMh)y~V5dhcF8k{u3M~N3LKoC1Mx| zE8^{&ELAMbA3Pt_fyr>`%1y`kNneTLfNBK707Xio`;->9rLj#{WBBFq$+L-J}T=#Vn=!KhyEYhVX-;7Pv5#a15nz0)| z;UOBvgTeFs3zZPcMPh7^PLp7c@l>AzPVMEyazhN@EOX!*nH4gKKbRS*je&J#d=&u( zr2Z1+n+=sv030$^6xoa?2Q10IT+u+|c%8I*h7eoD)49j%avIjq&q<_B+j5s(89T62 z{5K53a=h7)@f(C-PgTNJ!Zs~_> zqYzZV3PHvkptbxl-{Hc^!(WJfZlZMyS@VV*Va<5Lk?t)t9uUR~2r793o1&Jy80 z6%eJ07lhyR6e}IAuwl$HV-aj@!RZB5w$W-m^T;`FH}le$X;HCJ29lw0+mwlB@{r97 zWEx?3Mw_0~x7OJeeeNY9T%k~rD#h|RGI-Ah8$r_g%_^9CnAg&wjwMKwM`g7&8!oe& z+<1RT(^_^@CtJ^wPsh@G=6sWk8X1C)!MqT zg5cv#2n$N2lEcWYZ^Fd(3Vk6m#Cr9DazQ(>Sji*X63TzULJx{uJmLx1YZ_eKK-7&T z;Yi|Ccvi!%&R|aAinNIYR|K+D;<Ip3~Zxz5jj0FB6cQ7fj}4d`cz*v zoy6km+&gH_ioME7aVEHr?>oBbk?Ur893${I5ObE7ChFE&ed75MIRfW;m2JDgn;_j&f5VMF($$Xpa5kuzD70zIRDDZH^SUTkQ#zygCL0W$YflpV9m?=;~ z5oTV+-{7!--8^cfBUj1OsoA(IeBH@NCq2&Po3Zn5WiXr6rdHL_dvFD88{=H;38U~@ z^bfko-w1+X$wJTf1mak4+<@@j#xOwc&^i^Iz6i(Nlv#vMcharX&UrdJ-s$f`NfMQW zW*Eyc^T6>DcanKE2rc{!D_*QOX1#tO&-Hp#Ho*aW)xSH~N9s4+^^w^KdlboBf*pJK z1Yd-Mw^45Pv22k{c*RgvpHW4W`%YwL;^l%0s#ssOI0bdc&Fzyu{1wQA+##x*panep z^b9*tKV;swOykUL;gl${OgE>;L@J39 zlS&imv>g@YNT?PG`eKYgmsN}S>iY9S_lt+Es0+u=%nm9^tu)%NQb^ArFg%qmeiIGS zeX&s~9m)L#@(wR7xEr|}*g@Oyph(qzj={z_jz-prXlqiia6u?=N!;hxzo`2~0NMA| zyT_H>R}hOVZ0iGyH-`}g(Tsr9N1&pFgbyhnks2Sq@B_;*W(vR5q?0fb(`8eW1_=)5 zSJ#xdf`7@P`NC>Vd&I8mJ;>G~w=SAUhcFhu44|7&UO>u?gP2*79ad}Di039h8zIwJ z4Vd0m2u@KETXpIpk;V2D zE#r2e$xca<@94Ov3Op7D+5>tGa}o4iL53;kWhmL5#4`-~GOA;ef`9XbA+F{c`F*eP zq3+94#8PsVW=*kb## z4n2kT*%j<82|7XLij@a;mHQW$S!mdEC7oi}{vxcko8jy- zxfRsjqA`i}xIYb7!HS9$m`_T(w5xV*F0}==6p2*iQDEmS2wo$Ra}*=?bmr=MMCqrK z^FQB$AHBGGfx9}ozq{yhcg!_qn`@^kA?mW_>7VZcLXcuQt_t~QJe59U=t^NdBxfd@NVy+L zJP3qf>&-v-Gl<%p`OW#vQDf~=GgjV(YrI%g=B!d8q+!9^~mdTO? zD-mu0$`tZg=_Gvc~&}>(byvMa543 zcFS*L=nCZU<@_>9(tHX^F3>hCE>kFmZ`Tzmd<`Pw743x=Sh9>{SZgiY<+x4>^q=pDzGdlLRf&r{K&j|y>QA48rt)< zjC6Obl%-~hx3ZYrBF6;2jeDSVs0y1?_k~wzw!4r5_djcS(&M9W6)s;T3JRGP+?kDM zhy+otL7^jVwj%7Z;DJ3|^y^MG+-E*C_dKhECS zS&K<&uwh_)W3U{9_#!zu>9s)F@ZO5Or1@<7@;1K38&?{kaU5YK%kP%88^lQ_gyt-} zZN-)=)a<48az-p#_v>#?1ETedINf3 zXik8bBqgSyN=_&&{taJX<68POa*DYE-^f;iP%*=9$(RqlS1LsA=c$*7`hcSBZJYva z4X`sFj{({2OUqGtBV?Yx;v0_~f3kre-y!=mma#L&+62N%WUXNDSop^-4t7bX_1|^c z&PF8J2O1!gNjL+*dSMfYh`RHcdXaKV2;Wl_g#*yrKlr#6@lT2oewU0J@bVao>|4rN z#iq>N&I@vPETp&lkw_HCMhc*dQsF_S zw#;tB_V-D+fY1%rw?yg=mg}gKWZ7k{uxlz6oKUc50O|s6OPEj5zLG+D6~R;{hH|+Z z5m?hEwLV;)6NMrmLTp$sckIGB-OepGkk1q*diQUxg9TiA-DF=zfj2#W~dV6kY<=!vOrJC&xZUx&uh65|?)EL`||5?}B z4CSX5^y>gdBa?1V_TsRtG4$g}V4kz+3eh;5?w(tVLL?=eQauphxTa%>`Gsvsm)o6B z0qu>t56)x1h~)hZp6bLq6BHb<3uQ*KwIIH-U7bZ83>3~dbr=+Oc1Rm6v>zhhPP=4J^l&DZCUE4wstR{mUkjF zE&+;?4XF=GgG+n_#loD_Q#@j*)r;5UU3y#Up>j{!`FQLW)_UOjtAV^hYK$u29Pq75 z;658j$W^`qfqAUG@I$!=}5_1`P*6gVn^JSg%iV3jQ`O$2&L}in-9k zJ;)|Sc*9jgE_lIX)$4|Xzi7u;6)H$k_xD`#!9ya}$LFGw^BK{~x~uSPm?+3JybMI5 z!zg|pP0rHoulsx$^Si_1=&>t?MO6Z9KO8Mx1x0~%ark@D*YjJi7OSDM!vheha{|4{y^GOT

CKe{WE<%|rRPJoPq<^8eVlSzl(g`&O*pr@~P zF){U^LM-_R(++cWwW<@$Ul#$t;?lQm=1h0#*4qGj6Lsu3<-Xi6 zf({X37F}PyJe)fC2{zlrwZOKUFiX%7GdH;o zvqF`2rGu+ZOQ#%Q>w34!yuxQmY#F|6`h`Oee8`6YSLP957jhi>cTscV{JXskSDx!{ zSEoyThOZaIb^bhTevu}}-}cAM>MZ}9UniGeD9U(cyU$~iqZ@=BHb;3xaPozH;O*K( zrWl#ljvpY+o8PmO8V9!ajEDl$jvtuE>4jeO6ifuE48gsLR4SpE%QrjSUF>rICT=P2C|;Nx==r;JX>} z6adM-3W}!$hy}DkknaV|qMoo5cAK+BO;iBI+ALD|7#1clQ`QLNR|fp31g%T@;wNJFtBfvtGL}Qp;;7Bu(tGBgX6XANIe1uj}>{2F3FQEgf2fv zoWBGcIawU??^Yw8A4e3+u5??~7rfzp9VcvbgGr|Oz80sii9+6jficd@R*X3H!Qv4> zyy>TAJ*-@R`*KcbA!H-OVa(}+le*w_Kyu=vW=&$bCa#rF!`Uwi5-Qs-9wYA7_nxcS z?T!a0K&!<|S6F{Idn1p>)yKz8O6}*=!0i;cECmr)9xj}4j}2xa#?Gdp`SysV(f%&7 zNtrDO&8TSYWRhA5c(i5m;+`&5ILX_^umU_FD^mVZKk9WX0}aSlGcJG42SY;@5T2|B zRW9+Uc>WOp;V3c{(^m%@DokCHK3N$LhA7-N9>yXdKTQ1@wf@P|*_UlR2O9?8?U*5P z@@WawBbaNj!&Bvc>dA>qCl!)y7R8~?QXAUs72cCw)Xg_7*$nUj3X`-lJkuTqD=NF_ zTvmB)Fe(raYyhgCFn|U}Xe9wS>8gFabQLy+%;^!@8(V8sLt6_5N*#-ibK^utfKjB}S3&u|V z6)op93$+#HB1NudCZPp6VsDE8>- z49yJV!Ezv!n{^8kQNMx)*e_UIXNO!SIu&59psH-M!XRv-KYfQ*l97L~8aaR2=nBwj zyPsw4Y-O{((z*4yH>&Q^fwm^zo`pNuR__wcu-sKYFrJ6xIG?I}o^4O@o<1l3>}uo?>&lp=0hmZh40_HJznSyYn1psOp?%YzyUsv-$`C_)YA zDpF<3BTWM|_TXfZs&Egg@|liy=lq~gBX>tIN2ThIjRUPAtA#_9=B3PZWg}(^U?4AG z1tMwcEw1JcGf)Ns$@bD<2<~NQ%VEDcs4LqZ1YS--tl|RGTQjV?_H{uy{2*e9TR7|9 zeyfY)^msbb7!1Rv-95}gO%#RMZ@ZPrf@UwT0l-hp8%J(|eYhgey)|Nbi`dpA01(nb zmdg?pSawHir?ItXdkBDN7b|I$4|ENW4NOXgk>LolY!I3P_xceI9Z4YYaw)@!cuwha zfQ8&AmSw#V1qvK~;oZ}m7JuvY$s(_Lw|;%G&Jqx1zF!^?<>0PVF&zX8Rmn**M|~gu z=fyujBX;Vgb^vRSAB1lqe?Z@L7%eILc7as(ZO{VJM-x%dQi=YxPq!jl*lT>5+Fm{1 z;Ou_Zba%SM)bli`UB)S4(jR)-M3Fba^Vb8jU&ln3VHXYRg#gaB)oEpUbzCC54k;!x8;FEs6L!V#+yHkbA|`Bh zFu2%Vpz7=9BV@0k^6&1 zuO;9e-8{hl6R0?hy$d4GA~J=7VCAX#{=qrO%)6nJK{~!-1G%ZGtGQ9hrC8LoH+gOx zUnR;h1yLv-T^4PTsMlr*sHJbhiFaS;p0Dq5`8|FbUYXjfFD@lg;unHW1zkJ63NN6c=Etv=Dam&&RQPMwgctv>rT4qP!2c78)~g#d zuL1%9u!aTzAoagPqM4XF8#`G#{0Edx=i=e=|D;oE?AUFwq4d2e9|QIQ7X8+C(`p77 zRbsOUdRsEU~tjEqezvuct+)HSSxsqmT*;CQBZMB|) zPX7s7e?r>|t_?yJx}7OYe~)gWy3NpuzVU+Brk>4Hbwm4zCOorQ0pPdgPmx-38ipA$ zI=GhN(b#UpLnq=EtlQ$7t5g!c@(#oH?TqJb<{;MRJ}s&4%_~;I?^NpXc^7(`$}FkZ z;g4fz@W~F0Sr?}=spOo^wkBh*S$+)iD{Cbc2+b!5PF6|lFX$3Yth%TbG$SU?X)@#Q2k)p3cyyzR+> z971a*VxC)`t?NMk$9M8uFsavGle}7$t$PhoT&}M+s%cSYQMnvj=Db+5lSvK)JP=`h zc(rCPqiT|@|65Ul7S;adBF-}TF1Nh#F2{0lPBDEbFetGFQYAMWkRiYtiJ@etlN=>E zXP39GPij4Uy=ptlT}0_6-lDpv{Es)U^)>=hRp}-#Q?Tg;Pr+VS`<`MxTm09%veaYO zl=!j$dEv3I;PK+}aLq=dLy>x_zj5!p4zr3TPx~~;w&SpGW(-5#l=|5)zVH{ktcd_d;3yp!nCD-tY2aB zY7mmO!<&P?Q|0rs=fE{}`-z6S9P6FXo`xDc&lE0?)Op{N4LMe8k9oGFbD?w5$NBLJ z0&WlI`eWUU__BJ=|gil6C$V=T5Ep3GyK zHFj;=+GCbB@E-`ap>b$|2dBKKmNMEC4BQdw)R#~twDeNf<5x-8VfvsGA_r5y51mtKp+qa z#ez_z5#U1t-U$FDAdmt_2p}b(nnLad*_rsh0sJ8NgYbvt_sj3oA$UMQ0u?a{AP_|$ zj6fiRPzI3-!xIRe5wW5Phae1N7z8tjbch}i5JmWk01_Q3giwg35K1G`B6vqY8sj+x zaERD`$0CqNC=W@I;2{MMi6Ik0B#21JazG@bOd^+sr4l|RfJ?}Tjz}5pk_zG$Kroki zkjoxYG0Y<$(lFd5&tZ|-FB|4$>_TZSscN9bRKIe?n4I&T7Rq*cvn3TGd#b3rew&M4 z2(@TZC`%}dHJ-k%W7M?1i~d!6R%)O;hoXPm6StOj8udaS)i05}@Oaxldw$~oC$2#O z{HGB)9LW9mcXs*zgC=)$HIt9W9R2q62hsauxfC?tvMKQ3!YWIH zbSC4Y2wBU>MQ*9g(wkoHbW_0F@r2Ow;rEK8@S=zdxDp`MjNWtJnK|U1YQG`Mq@W_kA2DpS9od z^Yr~3T+Ge+e;ju|9$xv|$M^C4+*}Mlg`c_G^Lze`@A*I6sOPte>;HXxdXuN$`(9K3 z#=H|J_x-sY7e1Sm%>Q|Q|NZ$;?(^~bzMr1HUCZUZd7Hc2zu zga6z6RmacM`+fiXI(!r_pWfHpe;(a`Tf{!quKNrd&fm(z*UR(Q0q*aI9lxK)=YuZi z$3wYux&N1jNdo`tLpl4MUjNs}$IX%aR{7!s`?R-Mx!>>m!NboIxX*YP%}ws(%2q#r zZ;wCMy(IXY$iv^$aC|5vZE8^YkKyU=JN$nC-;0l7e!sswI=R(Lcz@sKE#>3G`EI+} z^X<3S?|vVi-tPRc^tkXhJB&EF`V=_39DRK}j+&0o>G|X1e#hzQ@ppUucNe*x{w~%f%^Zm?imDfrq7i&jM9l%PQ+@qEG_#7=Nko0+c9h~g!{W7L}j<@@N zd_0`g>;F4O=WsoK{@>r{?s0z`3Kzbsr*izfJ#^vc)02yf3D5eypOcF#tM5F$Ke@lQ zo<46+gUPd3Yr>=-8~1Oi>HR*Q--TLpMd!Oe-|g=9(Nw;FPrB2uHS2#b9!@$pBVya5 zEqp${AAg7O`L;NuZ^*ugkkMUuCr|V^vnVvCboJ_J} zPRu>|wpi}k-s{Kx4f%#I=jij3maW@3gZ7{xH|Oa5G0|!Hw9xUlo%klYb)RORB?rt& z_{9E(ox=D%H}aWbGx-TSz01PY9NC|a>&&@Nf_UWzF6(thKVv8lvQzjxxLU6`V_$sM zMA9;$ZJO_^K?px-r*VT(hpb4?2Lm(c%RpzUwi7*kX$3TY#=J&c*&m7kWKEKJ!6b_U zOecErgoJR&Hx_}CnG4N>bWg~2a%bx0?xS)jbi7jRdy+M5YlWI9%^ijvcj->r!s&%l zWyl8R-hIT0Y<)tG(JN6AhV-oe*F-B~ z5SuJO>?qmgd%~-BhgXg6 zoe}rKp)EX(kt~BJ;6Skrs4TEX&e2Qj6<=x$s);6mMF~`DuhF5=&O_t<6^dfDt%_(h zG6e+YPyA;Z+CV0{55*NYf`S-K8NrjK);Lr{p&!T&H76JGE`7pR1M)!^phuRkCfHZ3 zSga)U8C*8Xwv~XuFDwtsp_D_24BBX-g)_`Y$wC{f*;2XJu9TckT7TV(Scl4wS2WE9 z2^FC>6S9ue{9TMHUq+c&3T{o6M5lo=|2UIqGWN-4EQ5x9hguS2xlr`a#Y?%?o!Uj- z<$M8kZ~8OTfv(0*Iq(k-t1aYt-=sJwm#2@gVou2Ju?An@@shV1aNvOo%EV*4sPerF z)LhHZQUjD*7W+%zKr2$$7z@sTOUwBUR8w(5R^_i4MBuXV%tSde@jJUJ+(zvHUJKaftwV0}n=H^dw-x)l+9BZ2~xAfzmk zfXNzVLnyf&il|t0eJKj%L^!H|c$%K2QpAptJ!@qzeI9zwiW^y%W)nq}#1l#XA0jYIVq_&+F-yi9)#zP#kY$tCcWNPs@=79Q%pI4% zGg^vOSYrGk8I7ZN#9a_}z(R3=e*DwfhCufUC)e(t#va!jkc^6W)XA#gd|AlY9h5b? zJ(Y{#PPpQa)he-Jr7o0~g{1e)Te>cRpi@3h*MoA)1ZY&H`P-hNL&bWNRC3eXOgR*o zbj~=*f$}J|tR=gQZ9qQ{b={U|XFw8xGCFzFytEx7ygCv9L zRz1@tSUXgPKlTCg21_C@B~JiCIf1`m3K57<1C>AiXjzoHsljE@k`r$(mBET(jghJv zAPDvLro>a`yUWLh;|twOGdkaV-R)Nw@L`r%l(4zZ@(HG6&Ecey3BbI-mI)KRSGkdD zT(JCxii=@LUF;5ZUNIxxpi|n?LqILYODtg;@hLQ&3Fr-GPK27MLIPwN%BZGoLGz_we#=ljvaa2J4N+9lzy6rIP*v9jY0<|ISDT6hAZXAG= z9NA>A?FieG;!R>_jRW;x+gxE-%Z+7P2dk)3NQ$ zk^!fC^A*9bcE_>(?L2iHd*->0c6pk`4$+rv^OytUL+>z0DrCxiiS;!UVe7xYqDp!j zLgcSZ6fj;mXMfWZ4*E?BtkP8y*|FBq88VYT;W*N;aLhj$PVJ!=uWPwbH65E@yik1? z5Q8hM>cuX;0I>pV-x@z$!-CAEXtWtMa@smTuUDJ!iK=Z>+Dn4FfQi~@53nL=O_PF=pWfY8lK zQ!hY?td}-D0IP>LA`%t6;=mr^;c#3*hrQr3oJ}P+PL`o+OR9hlt=x@PzYRn6D0l0P zS5HqfUVeCJ`jx=ETc#2=5(5A{P+owj#hZa8DFD2*3g1g{3fnX(f#Gt>Vg^2cXbk?* zGOq`ka-ds(p<=9(6=sV%L8-Q2ktB)O#ulvTB%G8;9TTB))0!l#a3rZL*BaQq%iMuw zfO-Is(Ue&WFYK+UhYWce;vhg?HaFoplF&0I&#`t$H=9fc8ZxRPY&ro&*?bk(6*A-~ z=mKl&tYWLRj+-e0&I3^0uSP7iBjBz?1HhBI z;Y{_>+*p|_|47A<0GOH}sREgavvZ>@S4jy_jV08Wl^`skO90i`Gc1ZS3N~sbt3kb1gm#l{63DK% zy_4GheLAgBLE}qyq4dPfY=f) z9Gd78i+y3q_+*!=Qnqh@%m*&7lS>>o8;_S_`t7eC}5G z6JC@y$bbvW$U;3TvnI`9dqD? zBbf=nucSF}Z2A*)T(x0{kRPE&XkHkum_gKqW<}Ml?D&jM#I@h5Ay}^0sDox4WN*q= zaqYQXW&Jk02hC7B(G@t;cIiafzxqJ47JwzP@j%QEuBZu96~Gr<*_d7>ZQUm5`g&ZZ z6%Kh-9ASxL1l613R9c?D>0u1(%C3~PcD*me%Ye4zD|%&08tsv_EjTLEBi@v(lxWtE zOH+fz7Rm|9jI{J)QiHmJsrt$Ke!C&AR1JQP_AXo6S+Dj11Jm~|gJ-K5C+cS7Dfa7N9-Q5oTGAP7s9 zItf+b19#|M0`f!sGo*rjbO@vM;#Ay zW=+fO$J(XJ6|1AHwgVcX4mV)S@T!A#`ZBMur6*t4tb&IkLwEA6H(Ga6a)dsc^foUiS9 zEP|m@oYKXiY9;0`n(1=V7OmiJdAph0u!@@}2!vPIY{Xcid{&WEq|7#t7wEGkz)#WO zcrLRd;2k}H-goReF8t-11h~#m>u*YhAe~Sz?5~OQ!Y1nLVdlXbW`(H~sM}G5^?oL- z78~*{T7+%J?h%FxO&a`JK$3c^lo@39ChKDX-g_(&NFS#nCnLMKcS*ew+^*D&LR~;) z1IiG6rpyoBBtGAc9?d)<$1nCXR6P%xTG(#$L!eAO00~ zA{}6Uqdmrt!oMOWKZqVGOeGcNs zoPPSvvhA!WAH~lSwNp*RqEpVwW8SErDnI**b4it0mps}iJvw=t8twh-(cQ{$>XmQA zFDisrE4}JF+jDQ${OYVX#P~I$zZTTeGN5N3Q#vYU18e79^^4{j$H_@khqnq1UH38| zeevw@f$}gs6wzAG@k?_DF!#L^?U$!OR?Y&app4~+D_A~k!%}W&x=Zs(LESI!ZCO&Z zVH0@+YJYI$6Cv{hCtg{#$=JrGwaN={C`+xvB@V@(RLf3KX^O_gZ)~9I`O}lR(m(sd z9rg;PR-FB-Ocff!y3S#-s`fKv$%pHBu563JrDt~fLn^oxWC?+RjLi3A*sR~?#Br^4 zwTs;psS!Z`2$c-=6ooR@;BWi_%|9GA8dJ6A4CpLft+KL>BU?t~1;=L;lni*W*|!W% zUgV*CgWZ9ng)3R^dA?lTp9|6=0v6XILI()D~M zRTMeX^E~cRB~t-tE#lt3F)<4&cVkuFk8b7UUhGlwd16*2C{+ySNKipBkR^#8NLm52 zhO|ZFPE<_=OUW#9V*vicaH4jRK7rri{Q|KcYET5%1szz5P%5w3=NQ;_w^F?Nc z7EYMQ40MGD_mR>u$&>ISras?PUc@6zYRG9Y%{`JlUH9HL3c?k`jSdP^i9=bniSB#T zY0DT&)Dj}g$0~cOdkD;={#@h+9f~X8aL$lApDew#pm2Bg6 z)-x^ytSY3@Ga;j~_ulDUXb#`S_vBi`%|G__SW)Y=$c6ULNeu|9zB`~r6&!mP%G%~r zQ2p2+u;fxeFxAghX~REvRYjU`-dN#;Q9p59Wd}TW4{KJh7Rr}r zQ>JSPdMwEa(8jQdNWeoA0v`lqiKf6uq?g4`dxyc|$3}Jx;_j*_#)cE{EoKdGnW}9> zis+$!b*7zRxQcViFA4MEhqs58iO_cgM+Hq7#O2*qsshQ`D5KJH8^0j3lOFGiE8jqq z{KNJR*(C;cKM_)Du8k7LgnMS%}9;9=0&cu zuzBeMcElD#c&*{vT@~PD*?{S`R+6+Ji(n}}OQ<}?%)5+EigQ;1r|G-xD9g|tW|7+h zFYQ5Kw^=kjg;^zA=Zuvkx zIK53+Jok6BiX-Al)GrcHXb7Tz_9RBoV^4LEN#wFr_oRd#L(F%W^pN*gl%roDuN^O& zPEc(Ky(RbW5)U+nW2AT}UI&$3qg@P&`-DA)d5?Bs2}K`J!*TU}@P!;;i<>1?hOP(S z1$AF^zk{vF)AWOBICYfXc-@NaAq?dil?JQ-ECdvx&8s1$@j99TH$$}?l77KU8U&q# z*M+9eBq3$=M{HY06$v}P9os#KQV5TA8kmEq z6bLo}Q3cuEodc>>D7MI=zpO{}=<>Ap-w<$BFo_lNz)=)gMjp@KP(1eO&U|9+!MNHz zsy0LC-vZ&848|DmXcPXJ4J@;GcF*)E^#GTT(MZHr+^_!oBgIH(QYqcZS`%X1regA^ z{%y)lExngBPpgKh-EdWFN)uhS)=?fK2KERns}EAJhvRIs!!!3{?Uk*j`(_L5P1M?D z*{*B&I;ih&MejJYN$oYky@Ni|f`5lxdmV1@z0XTb5g2M5VUe zo}$f83hKOSFk0{|{x%a}Gcxi}8ZL4^(7k}J zg`3*bO?0O&0dW;!ES7E1;@x#(eWO~wm7@+$$*O= z=KFHZCz>m56Du_OiuD>h|Al@x>fTSHyXTQvCwV4O1~I|SokwhOqTp8`*i*JiC?zHDCCQlJi!}@1 zv~UTG4l&bNEEHNtJ41yW2tUNlcGM0Z-PsuuJWU&whD^S)UPpMJFyBUV^OR8Cunj`Z zbsf(MHV)iQzdC!0l@#aN6!cWjlfQfZ`TWvgMzp`D#m@jQDGx;2D{U&DXOGRY9JY)5<5sk?$yI8E&4mA5C3GkZmLzwIuz zV>9Mu`4#WkI>l~V>eeAJu)WC>Kgs|ujSRcP#`Bp4sU}AN3N8oV353OX61L!m+N5UY zT~12vW&L9iV5GYu#Ct|~$F%L42S9X)mK3iynuMND@f!3pEbo=2RFdqD+T_fK7-{Pm zTyRuwMXLIo%5_Vu*G}XmYx*sMHYRp5v2AbutyEa4=&2F&R4F^+}Jh^Y27T!@c0D5^+pYuSkNO2ZisDUZ2~?%MzH7i26sl2 zez+zM>xez2sPu_*4566g74$8Sdrt^~sM{Ea3QOcvcZdqtM7=bawanVyfewxO+C6+8 zbWBAioy`Cr+q=#*stYtRS^YRDL~1zwGrV zmxiQ?N&(=il`mRQC*yHz!(9Ce8K!N=E_$0hHtCv5P(oW2n*pacdkI^MV`?+GXPY8| zLzw20W39x8nl%3M#kh_7*eKon^lrHtr|(DQcTh6br&c`l)Hv@@~TwWy`xMC|eo^RVJVN!26 z>rVX&(1TKG|Jf22yAUF4aN~}jA91Gvo@pi1L)d(pbiPzCmsgIN7k8U9fB;r zVFEPB^CKgMZt75wXXeh+joS+Ewq)CZ40wD7np|qK83?%c7r197=2Om=d77~d-OQNq zGjF;F^7xDoJB^?sXdjP@Dl1LK00zXu${@S$jL{r6U#n%06$M+`p!y7rZMuSxsL4*) z0;tgZ!^7?Mo3_B7Uc%D0s1nEXjz5q}TJxa5wj83U-yLV1i}D@45SxLp0B`u$1~7_i z=8wXZ&Yh=tZ*j|59SRlVDpCKLGiLOr{O-qH zBDx1pNLo?HBl1deebuM<#c^EXCwYwU@ftreq3qUvk|tt;IAIhL;~J$oU;VfZa>ZoN zA{VX?BC|7Vdd1vwbi66y$!_+RFvy1lJAb#nuz{SsHk{c%TFxMJux;z8jC&4j)VIu* zDb}r0t|4`HiF7NMizl3emZ;-2mDHE=UEydh#zmxV+c}EnAf{BS!l|;Cck4vx^<7Z= znC7Mvs8c@yPvbJ)Gw3&qZHRK2K3oa?cD=q~sev6%%W5)}<88(K9L$@QJ1Xi^2~&*fj?jL@D~%#pmGqlCO`{ zg_9-_KYuJ{u}R8IoXkX^B)Fvw+M(q|Tk~*Gc_xy4+lhz9_X>ANa%m*~`81(Q*J3@k3VKq(LjUfsW3sOW$Iu8Cjad;ywk51fOl)TYt32=#JwoBP06!gjRD!Hx4~eCQ2i4IEOw zA3{R3D}m2zTVU#Qrzo||m=i8jns^>gZ}|PPho2G=XTp1Aa?)K-$U$EZjX;=k(1u~5 zxo;{>P+w6TKJMb(;B;)l_!E}fnf1AadVfH1C;mrZJjxyG-2;RH=Q{G8dccO#-6`Op z!g1r=Ut}rRTMJWs=@PD7p7WR!9Eq^5bEK(s*0p6dX~K|`<{CK=kUp?$dLYcDystUN zFB1Al`%lMzX6#M_w00UxBP(78%?omBdaD6-=MP?_y1dggjx=9`Z^HIE1L2j0tO;MH zB=q@V|8QzRfa=7N+|W`~E7Q#@!>c^9n7-zcTZk^kU1@->2TZTm*&=Z(yFIC3X=JD}| zB2VDO2{>UG#di1*o_;kb`1wy&Il2QD3BSLi8PppYkhpI*X_ZLsI$`#&rUiJVn4fnU z{gf``o>@mq2jGMk4n-x{@A2nLv@#A`^i^=kc80I#85uwFEV>*nsdTt@8oe9n=C^G+ zTuWar-FLXNX(7lLP&pRC^&~{07v@ApCe@Lpv{nje`ql&q%&RuD?;bhzaF`oM+2@yj z!^Ge801-@)9f zrjVc@SXlgSuR2&?Xe9n2?!{En$RxZ;xGqL!OFqIh&XIlYeO6k_9|l7cksb-+dQ#~L zAD2+kI)&+JieGP1qW6CN>}^~Y)$^!6Y8G}Yk&OyW>&*+F^p5|=Txcp`v11Kc1LG0j zw+-ti^qnkcq5rwkZdxU%Q#0neD#`YjT3m4x$iFk}-5j&Z#0et$C<6i|HU}-9Wo5aUP>f)yryMOMLqCMqX-<&(X%1luF|>s7!3c?(n0Ge ziq9tFr2vkaOlZE0v5Ya;w7pES#{HiRa0A_f(Ge0kM26pIs+{(!=NmGFkC6vEcPF)> zsFI`wR`=N1orAyAdq;jD5wLF$Or{8K9cE}Og{z$6tut~vl|2cw84}HXE-Mt1V}lt; z$lal-2+*i~P2dnF2*1ajNC@8(*>Ky4*N3?8!{pjb$Uqm6X$)HFp-ms4Vv>f&GNbV2 zOx%S%_cD7mG!9G?;p)bZW?vyPF|SFt_}5sdrWTQ!}-v` zphDD_S|4lgnxy?)lpR?$?dd@}#Kke73%pF|6zhy%UeJFb+IkqiTfc`;nqUGCZh)&9 z@}TJRVLfS63OvBX&DmzAno~~g0Z}L73;)rHX|EU>sO{^+U$5JV82-xfajpPwaVA$N z#}{sjdVJ?pKGf-37~7sjvTt<2JDOv2|L59y3VCrUsm5)+s|!F}noGdmw$rA|5;f6o zpfLsaBBkJzW~KoL!Qc)m##f~L)k&5iXfc?{F(X)|zyHADM%t);jDum6Eza88@?s%h z<*v)PeMeIgBkLepGk0n!35`oBKZ(cm5u$>y(DAH6R51OK3y60%Cjf%|v6Bl7F93gX zXC7d8Kb*AD5{$WwF4vpZb>cX2cF7&G1B&or78xNVew|FUod8=BT!np2#AL@+LAC*K zwc-Ex{}CRo3`hf{N9z)^Yd7Q2fAFkg&6S5${r3Lz_dym2<6UpxSaz2-vqN{k>Dp*0 zgsudkV{+IrxO#xsPYDCLVajcxH2GVHN`^xIJzJk@@6aLP5S?=KvZy5h*6)(JnXacq z1Ef_=%kkGPadC5PdpLr>qE-=Uv8X@YDBJBbRE5)VkLh)i?Z%H1dI_GS%CvtC?^YXD z<`u#zo_jipB>ZxDrHQn|rvzB6@}1Z6z8QDNr=!V|u+17r^28h`*?yF=$jP^@(B{JQ zpdfdTn4nsOdJ?uGKOqtK+c_g_6Gh$O5JVUz@1vO8RZ$T<4;0doD#c&TzZZpbO&f6* zBU$!eDdr{IOmW+IMSmiii@CY-jGTaUt2#0O?0@u&x?+gicb-AQI`1cp$yr&4HJ69-hT&I4D&(ozIfrrLFCyw~4&S z&rT#8LKXJraH=w4=Ot9Rs;ijmBb~THH3}(aztKE~NBw|RxIh2SuhviF^u&a-#GVoh zf9T<|m8JPai6OVi#OrnJwRd{3O_VE8ez5tJ^{2ijbppaYwPbQBRGEn_OTN;Rt~qaA zHW8Yp%dBh;A2J5+Pp$)R13&hF4)zt+9a{djtW)&i9yjgO&Kbw%&c$t@MZmUq} zpAP2 z_ASwt+kh?wR1amy^4x|0#`tnn?O@r>)=W?4Mvj_vbsN_M6I&gOZ@ggM(gx=IV7N0O z46lam#`5edM%07ICiot%rD3vszykUBZubuYXotaMAS#}ls#Y{hO1Orp~&!JGdxI4PKeX%M3-!#gF3>Cl* z_xF&kXH4%EE)Pt;f?R+FeB|E-8|dRRvwc;79mgN~VYg=8^MLGBQbP7JPDT%@M`_qH zh%n}+%hsjs_=Np4dsPOe8)^HBk4|rzLYDh`KTg`J@=ZWlvpK$}iq!AGr{9!{E$|*$wBTa-E)wjoD3mV$OefB=TV(H$8JM&Yu!c zywm)_hdB+lJo*%Mu~=%^$nkQ$LVf=Gr3w9D+nyILCm!LRLg$k=6aa{7ta%Hno*Cs+0d7<5cuSxVCrHO;!FIzf9;^jMs8k+DWAaf8I3r4p<~Pm}GD( z(VUe$E*QD+L;=`GD#U<0JSv;ikFRTeV(@``G~ujKunuauW437Uj<8xp@EaVkc*te$ zT`;0i?w{b4w1190+E2P(wG26>chKHNre>c~2WTEuXJhE3{`}azV{6YZFdF8OMIY^4 z$~5$zOwRIu-=jHRqS6^Ng(9~2@orF*FMg$hm2fp9c8cK%M#1nPd~5pS=l#d$6?qGD z1nGK;NDH%Ai79T7lab`-dP_b<9Jvpz48<=)S@h5?Ik@c>xtlsD|>eZH|xF8+mpZUN5Ut7&3;zo7EyppUFv6H%E!!WH9a;tw!KC~k?UGmwAD zk7!6;P@(`zzOOMRB}8R%)0v3s&l7qz{v#M; z=BaZq7fH+v9r=DJG`CYlh-zC0#!0LNCo1b)3;s|`3MhxTZH@ZAhFBcnON_S?$`ggP z7j!I>y84QRSUjTyyoyb@Y7aUFCkz#9m~;8olpSLOhF$DNj z9j`1bHAIVbV17aO2)BgB<`@+TbbB%Y$#^%|FK5%Gf|8*CT-L%ydwqep7pl{&+{B-dp57`B9 zJqDa-dxW)z5qEjFw|0Nd=qWLukMxhE?*cSpDy%2Im(=|mG%O;qVl-}WEh^W z=Eg(^Tad%!za1&B9}A)3gF~nGl;fo5RkY(* zn+wKHqKZkpS%!5BapRpisnW#I8yn;HHqASk{cU+gO%sgUGsXFXoFp!vHDOrS>Oq}} zeY&{T(~8rTQF9~LutswH89jA~K~c0R)D1*s3G*9S`eMzi@Yi=~;qTX~136-N^@ym^ zx`MYf_@ur{3pvy?s%f>DYYc9%x_Fgzf5A_V2HCu}SK_|oI5ebluey6r<&PbWOxZfT zQqjh;q$&xH6Oh_jE$G;(ibchGE$CRl`TpHpr90DwtIW0yv>nnRdRdrqiHA3#m3~O{ z#_Ibeaz1HDpkr%Mp15*))|NO0pKdz(Xk7W>T-R4!E*>GWQ z)e^Ue$}OjgO`=jpnPC#S*eLhc+#AT0g!3IQ3>I@m7+CYu^myTQNHNnYr{fm|8=!U} z@zAhefdOi=^7lAEAz>6fzDbhP+MGM5!Av0~hr--4)1Ea}dXT@lYS)4op^4EmJr)~r zG!8|*+t|yvkMS?oNoZwRZkWZd*^#R2D2u)6i&phocHe#&ii%$b#!hgSM@h8uFiqI$ ziUxD3BH_En#$}V%l7=E*_PutRN~6Jn^qkNL3(zGo<%w{dAX1Xw4T}CQ-x=NWw&QVZ zYZ&Bz7LCfd24^r-bVjxBs~oE2=FNK#RoRw4|JW?#ktEsdaZA5FwyIsLRcc0dq|Oof z_lXQ^{!sP(wtb8XFU)l?c77nZGSmO88KCYq=~aQ5z2B(e`jhHt9K{J_h#S0cX5&=6t+cnBI=Q(t#r^BS1Vi5SvU<6hc)QC z$9@hKFk~*lyM_^dm3vjvZQOL(%2JYtSrG^F#tPR0l|eyrarUcYG_k{k3JOq@KTULy zig+S$+U^wX4GBhn0b=ZM+eYX^lXIA+*jo!qKwL~}Yw!$x%KW2efB^#3i1eQ#IN7~Q zr7Mn*xtqJ;Goq-1xI?^XZ6r>R(!&9qq~3u?p4qIVA3jRia$m+w{x8?yxE5zKz{j-T zOHVV0E%JRn1WU+ArjlHDE`IE%0V!vn`7+g35{>@4gO}a^L-Gt=e@zn z#lpdW%{qJjirs5$$!W=jm#|^T`idZ`-T9r*!#cB zo|JhD4(D*ez=XeKPa6L@gPQ>yEp4paUH&VeFMzHd{~b-PT+=geT#Wmh`B^Rcb&du--g>aI#av@*Y5Eq7#uP3V3{F--M^b|>DHu|mn}0oM7mpE35>4gn!_RZaelK+^XDHnYDQ}ozj~X{ete}g< zr)0QkcA47r4~{gewSM25BcJ$ll!%49nw9f$DCQN%P9CO|h|w45<{Dsj6A{pWfNE{S zw4PpVqJG6R=8qzBJHx9(D4cowYDYQ6=$bugTF0-5l&%1J&utnzcTgr`6Fspoaag6;BM*;P&B_QcFvQ7+vEOvouRpA%Ss%PGXTCpP^){NV1<@FRl_L zp{34ipW1t!!p@){Zg%ox%#(^(FP-83YBup$DmAC`fn=_@L11cpJ}NAos{6qjWaS>v z^SUeN&t*}!MQz8gtPc^eTW;ipseiNkXjiai2lX6Gx1=*w{1m=o*%)%K!xeMB*LiA*nY5KqqRn}`GTD@od!iAnFJ z$Lvd636TL|O4uL;kt6l0<>~RwRefg8XvBMrMm$>av=!(~qk#Zq1^7$F2(wjGzRfYLPkT!X2=#pqLfCRD|=~v{`}G!$pgu%$EfPq4#kv#hKP+(sjK=*N#41 zEL3NppoyVgDVms586ownfb(96Q;B9aSY)}B^X3i6l-q$HQi$LX84afWMYaO(p>;58=>){oTZf(tMskbK< z%=>BGkVq7!Ka3Bib59+US;IjbDtpuDWz_y1&unW7ct&~&^WEu|$Iwa`ycqiJk|W*f zjS|0Xn{*DQ-EMAN)80p_fWWj5Wi=PLo6tJBzYTTlBB1J`eG}f^^`^|h*ft-i9;$QXD)%xOaC&I}BQa%;0x2&OaB1_U`uU-}0=0 z!0{C;4gar3rTzX41=8shTDEDsNRDE1s8Xv;JJFXp9v`Gv9p50QC(t>y(+X9{$uj#b z9$w@%gpO&bz~v}AlQzG3A*lu4rY&pXZm=y27hFker>=PMJ_Ez&M0WbA^rrWPTE34; z4|B{sCBEgGg6svJI9#vw-vaX*k$EQJRvfFf(U@b$9|qHP2iu9N%Z|xHuG5S>3A5XS zc=SHV49%&F-EHzv*EETTmYo#>kq|?Nt899m=C|H~f@(u8A#H~i2UYP#3e3`6heRv= zU@fXtzY8LgL0KfJh$L4c(te0gTjF2`%+;_d${bTrWFEudJ`@N|$2Q!yzO}dj@rdoc z-8i_4ZNq0i-*$~YUr-LxJ;(T(9z@_7tU-Dfj2cGwzPC<|AwJ|*WJ)d%iBize+gqk* zA!JB{Au@8%COuLGkwMm8aGm?4{0Df-rt~L;SF(R{LjGQ;$hFuA5e}fS(v8M)ij>q7 zoYHh=E;URqxwU_;_|s`hfk8lin?t0FT(aohOKO10*s!=A<>UTV`Dn(as|x-jq#+d? z&Y@Sb+v7Fqt=KIfiD019*_%}&P_qb~RnAiDDxsd*$W2b{U-tF1-U=@*z9OHt; zKKR3V;Z$~hH;W!(<1mxEy`nz@R-}&wRIy|D`742Fi()Z*Ycg7JQtW{q%i0SaNDs0oXt!Y|JcWR%G4$OoyIh z`vJ6U3y+>L&kmoR=y~^88o7n2!T{Xv6akgp0l-ABnLZdnr z2xub$xI-1z)&7P;Xnuql@8DMw4rLNz6_)H2Mh0+7-h7I%;outTSD&Q&Bpf<^`6S&|>Ah9tFn#p?{s76&?^Tuj;+iLR?5bYoXB`4V$+RZ?1d~7LzZpR@`vKs?Ng2!QFIo1UUMhEg&HiV5sUIs=|f3AzZ0Wy(n z&3z=4HRW0aXPBew`r`(3_m1zvlY5UjWvAYJe$WkKx7eB2w1g5Bx<^ z;aH&FJd>YL-=`g4UIgxrhaS^!%Fj9a z-?SUYxbyULyqShj>?Sl8;hlfHEbuRV^!$2EQB)d;sE9)qG8?Usj}vdQ0<%MS-EzrF zntG~Q{~qj#*j)x0my~zZ*3m;Uqa4GG8gVXIqD%?qtA|!6kKttPofX|dOa=WKc6=jg zh5xZQKx>Xt8e&i9qEgy7k6@YjpbA2@SdP2~VQyB&qo2i9HAD030duvjL zAC@3u!jVVlL@EXC)BC{hLGkWQ9jomtot6i@X5C;`8dz{Sq_tja0b}>fbE&E95@Uu) zP&2%irYyMdBmS5hilgQ5ipvkHlerHAu})FYXt0CR?6^zc!>n4|klt=VG7$u#)06ju z6hzUtb(^2{rqBy-iA0oxw*SQca+7bcVAj<C7sYo|a?e!{LLhk4t27YdGMID@$4uewl` z?WEU}DMgZcIKgh6&y22bk6#TPbdW^ZuaGQ=FrfEsg2452;AXUDW9opwj0rf9OaQ89 z*UI}gvBF&W+m?X4ojQglydk$!3oQIaXz3-U>R%oGSS74(YYjQe?1WS%E<0`7=uYe% zYeK~8uEXApFh|dUmdaU|G)AfMN&LGrUw~Ex-;r#XB$KqStSEsW)lQkYRF=JtsI@+w zen4Uc=#+zpm|*f_73(YUecHvDf<5#n#-5iI3V;$LkWzx@iAoWM1tJ6We2P)$$dXlA zWv?@|Y7Ag?#p1dxS>JqMnvQq!_q!2RfI6C*B2mTd6w61=VvpM8i^VE(O3KoZO)2J& zS){A`yCe5@{K-x18x>v19=I+_{Q$2Q*`&r$1{czQgkU9L5VxD^6-BhCNDeo5nXX=A zUVXaZU?6^FKc%H+CZrw*8|~U{9Wz@MoN^7+V{`zT){JxKKNMcleSb_feS7mBTinFB z4>hxpHnAilF)6tpzje2jaoK|wbYqdfFTkTlvgu!xEW_E&(HXSrt8LN=C&R^~Sc&!4 z()kWYLM;S&8*DixUY!0tjDKdI9Oe1Fa`!e&+e;+%>oHJAK^s;T|I##<68nxD+jI5m zCE(;Kk`Er2GcJcH^=`prh zM_4aE>JU{Zn>qEf}tw*;(}94Qa;|R&~&KwMX&L9bdVIv`8|q z=JbltPYDWQ+lW!$%b~2uzrDRFxnAV)^-$kzd+0Dbf=o&-&Qr{w&8<6~iH>5N`niOK z=)VQ~_mnyf5nTJD#~7JakPU^PkNx6va7r`2ie(`b>rdyZ#bu%fCqF7xNP3Tdm+b$Y zPe@-y?2PJ<5gC?_E8)GTN{mcRUuYWI9*WdUF!g?|C|#9>^hLMV6tvC^pvE!p9ys?x z-{fz3?YWmep>CuJRCr(=BFOytth_K`bTn1t6MBHgV}wKjyGs?s&gm#v!aj5w3MA4v z&py~J=0%91O+qXh8DMMYt%#vV-8WYOzOi5M~evY`)@!PcQ8bk?aM zr7GQr3sQ>liU(UfO~F6(8?S-nBP+#%Sw($JMHlgYFz$sSX4mJ-&G?l$?9#F+{bl=+ z!OSkg{dUOb!DG#ysw^__0yf2;gfBmG-T!#9mM7@A++;_{xy^LQC{i~-LajKOWL?KG z_>LM5Y}QWp+;X}{;^=>f1l=f;g0ox6nGh%&lhN%0=CM_r(q_{+2*X zmtw1rl7@|N9z*nUxU-}Oofa&u_m7dBSIdq^z*r3~zj3xFZxePMVd3N}4=Qs{5FhVK zRIE|8#x|8RW)>$pkGHZWJSds7+iDu5#V*g}S-Q|h1UrUQ5tQ{fg-Y#OK)Q9?zR}|y zc9{_8Y?SEUq63+xJP~Jvfkxi?h@us?+iZ{iPaxHdF$sl5F`lp2bAvobetjy~y*yK^ zuF|mFXa-Uk?Xd^E!=0+c>2ylX6!5cIb$2Qfp-X)()9QMg23E41z-*~6nf$0ueE@PG z9=m?f@fi0idv?=VzC0f&Z-meyX z=wXiU&Gb;4DeEu4Um>|NJ)j{rq2V3_5m;V*&Nd%y34I$!5!|c&Z6n2(3*hCyDXrwuflG#1+fxDW6&OM3C zIQo}xH&C{${9dR1YPSb+?XJLxCahdTwL7F2J*NZ#ezc2N!~kBUGhjNy+7lD0u_ENU z<-%m1!4}M?-8+}mQ-e>DuTe8>Yw|Bw>{%KECUPdjoFRdQ3aa1bORLEqN1^Cq<}{nf zc3!(MTv6<`Bgc!!no=xW)K?n5N4Kxym3>wnzbtYnhHWhSp}G5uJIxCx_1g2px30@2 zf9?C9I*C-+F$mFneYwZs{S@1gJ6}otQ>>hg=8Yz#SyI1<^TfOkfVfI_B4t4>d&AJDWUSFacf|&gz%24$ z5zc3Jb-gkN^PgJ{Pe_H2yr~4nYH2of7-Bski^g3hkJQ zFozJTF|G1){~0VjN^=-(LstS!rSRbQkz8ua{`Gd&N|z*1wc}8AG(M}g8HC4?`K1`c zM~+u>ta3{Gjb5Atd?K$?oRRD2XVFd2PzEJ%N-On_gu$u5WOiQ{GH1EktgBV+&Ds%PaoG@VGdRn!zp)6?>@F-^WxI*!IbvLNQrN)I z$l@+qk*hG<;pIuP!}j<)v?vf*8Ze{18^T}rLMtr^M^Hv%c@GW(Kh%RBNun3{$bG5{ z?=wWg`6^GViBGS6Y<%YJBrhB=2aGJ0RyKdxd)QcO7NR-O6F z2))hIgb><<1Atf>LZFChfHf(wjVYZ$=JaFMi$!2%gY~!IN%Ko5-gbXq>C;2r6js%m zs+X;Wyph%|W=f$-&7TD@?Nm3J#O%}BMp<*VDN|E_duw;se~&F(*A(KTCYgBVrDp5g)aeQy z_%jkRICiZc&!0FFeVg7>@f{&3#Rm7SLF;Hn&!mkgIAYRmvgui^NkC{(cw8^m^$Dc^ z>B)!1*v@IzxX_w9EPshb|(YV~mAeLkaBn%90&chQ@WuxV&|Wz?mv^r7Ps+FwV5tB2Jd1Kbw<`>2jh2 zTQPQ1$V7N7TX~cFk{tG9q=OlqrW@r;9}Q=D(PRF^zU%w{kYS8K>_X=$u8$wn$jDj` zAKhU8EA>G*7hgKU9t;d03mi=IKc~X<-wgIVOzhn){@)ac^G>~biWQB@=c(#{gE4{!vc^}Z= zzmq`)T&uP4&m{^v7NK*4x#2@#W{eR(AKx1t{QYJo{rWCE)Qk`=fvBbFcdI zqPY7hLFl>ut4E5^XZh!cXZPE2w*UL&qmbYIq!92Utorj=DIga+;GsKv+W+x+OYrmR z9>pl&bu#WTT@v9mvqPcJ#b zgkB%Ag+4hDc5Xep$M?J6E^ZHr+b^m=cc_ItUaX9O??OVKkJTUVw~s;|3)Avao+q!OD-@SeJVTpi`1(fX1 z{k6-kkK$_odo$wKmJE%T-IO;bVpsj!0RQ*?&v!1PfX^@3Z{u45_g}N{1oAIF^wz&4 zemUBD7JdJ$|8_c@bq)A@qYil9(E1F%oACm^o(8-=fTq8u?A^%!bz}46;R`}RQ1|Ca zdv)t57eJ_K+Q|QX<5B4CJiEK~1pD*l`SbbU>oI&Z{J!pS^DM-@cgXcIEP3}fwdGTb z{W{(Ce!sNkcVBMg-#+wEZq(U**Rqo`+-_*#gE)b|^1%Hr*(5O4o$T=SX6w$eKR-?b z-X637-M*Q?$A!~75Fol<=wq<^X|kHw>T_@UV~4u?Z8UpN)$zD%qv^Hl(tEG0 zGjxw|ZR&7Xcc=HgG`lRE^nO*pT$|VEELdMX_ffxaNKQU5EV?i`l6!1xUY{NEbZoBZ zF7&N&i?ucUT+gar()Yzzc+?#}Pq<*P0xm9dnDa}XYltqW3Sa6kL`5&ud3eHfu4dqxKnp8mNX>= zrEN6+Kpo=c7tT;(-=pzY!xt>O+>}jQdZ5z!#WFh>heN(&1z%-WLA#x=43XNqSkP9H zWBt0|eS30Amwtr(Iup#Zprm6(cgCs0xceO8xlf5e5fxkhaq5Noi3ib^yQpV%7`-c~ zlmpnPbyx(m&@EZ^qr>P;;jzG^l=3*v%P_f?$i_~$;G;u*&K|E!)w^%Cfqz(VI5Otb zCC~0bJjHi3U7xY4&DCPobS}0SO-jGrwPt}=^6|Q;R8b(N@-i(=`u)e3)o)TW>y8jG zaI=7R?&4z5Gp(7k(JY)O`CR{Dadp|NX4%8OV7_c{w2DhHbN<^chTZ;=U4u$ zNM|iOB5X0fD6ai5)>pCh%z4^o>*feaW&UZil&~zS5NX;)h`7-gmk|XxdZdQ0JFbG@ zaN(`s93JJDwL3RLL>+(O_rB6ud>@xj#?E&fGd}pwYgw{Pe3Am0 ztu>>jP(P=utc=;=MsCVuU6O&)#-_a2J5ZqoYvVduftapTE50Dw9NNfa<@ZFoKkKN$ zMsw57S?1az#A0f6JVQRlX@gy@eDKZRj0>OahCXe!Dqwtj6Q>miHF{M$6a@C^!2BxS zs&xBS`TH41HqX3AES8N8air#=%L*j|MZO#MQoc`$KCZ-=sNB;=WDFNZmmzTs8cmkb zIjl`91rK(f{3*W-y?uPx^W=l&1^Z1a)@eQ+A?-6gxXLpa&|%dx>Ns-4=zaz$_M<~z zF#Y_)7vzxrhp=m_4$BqJIg_C`ez7B4Wl7br3(-PRMx8nNC$1gQY6G z)-0o>c>ep$Lbd#RIFYJp@2^JLVq~Bc6Ts5@W7E<*(=2OTAzi=S^nH7Z()VtrjXfhFVWiDXN}-(HcDRakT{glv%?^@~LbpHEFyj!z^!M>7Hp*95PXfpi*( zDDWpVpW4RB?xd-nhr^T->eg8HWkU!P-5|#(f73qTtK9dUu-5=awkv zac_9=g0R=I{?g4%U5a5PfXgLaw^6mn@Iad6_(@0mV3Ts=L{YBk7OqXTsRj`pY4nsQ zSyjOVY=kv&0{@5wDY46P%rGu+PbI37?w^%|CeSZVb77ue4q4~S1K5Cd)Z-uC9K2K4mI&FtCTK(nzG3u4nG*&`Kx=pN*FFo8JGoJ6{)nvbiQ-D|g~;*|^=Es&l@= z4+`+rEO(nXu5RQQM@Maw*l_T-;Xg)ZC1$Xo1fMXIeY=Fr=k@LNbYQ?WWb@s!)ux>I zD!d1nMKo@K7@K6wj3wm8WQX=j@0;CewRj-$zI+-TDgsBMO_ulC91uhH8xJ(Tg=W#Y z3h9DNIEKx~YTQn3SYHDkUk1#%BGLY z{$-sQvDF==cMj*ZfcXq;=`tN^PJ3`K?;%!b|z484&->=U=S&5i`)AjFO7)hvNH z<3e1OsDhSLy+O%_t^$qc{xL3z4A zLf*DeZ_;{eAOT!dJu6Dj^KPx^(ljr(k;Kz*Q2_WN0a~d37 zr+_M7pCvGYebS_$=9k$SUs7Ag{a!B^!d6EaJ?(bQRi9OeCBl=E(WN6a%DB5Y@`i@+3C*l}>qQ2r zZwO+x#7Z*gZ8rOM0~E2dU%M!(hW%n_A;)FgbEK!CWJ~jahzl~%+YI|kDb0WSb00Q| z9XLYsaSj%{C_Q6bXbG+LSN16NU~MD1b~|j&CALj6`{!?%W-4zrnXz4pKXzT?e=gEt za2b6NcU1VCDG6YrvLh7*#1j;k@MfqIB*@AS&lml5&C&IflofxwP?UCL;wEQWv^E7n z&2@!DwL7Fr1kGH~YcF!(KY{xkD)`8Igd{SsNdz;jOw1sj60_pOS!TJ7vR#V9=>t^B zOwR@=Ind_0$63unQy?#rz7_HLh{wtY1wS=LG2KW~W6?K$E*If|m?X0km zb7bm`VptWU9fj*@l$>_5F%%6pUxt*22YeKLg~ku=UurD-8v8Y`#{O2E_{&C~q|}!} zS1r(I=C1wqNr|1CKgP5n9N<@Ebw=qET1CS9!4D_Xkz&HGVC6F+vhm7UYZeb+*mLHg zVlz$REKH@$oX3je?mOLlVk(G978l#niLe!^(|{AUMR1gsLDGbeP!`n>GV0Th=k1S< zpWcg0IHYiBt{ov9jdiEy1B~U}5e?8Jx6*dk`ukOL@)xs9NSxky2#l?xm&xAL%fy}h z7XW5JnZI$CGr2!y@MF`A^H8mqQ7w|8GVQAuu%wr4i?p z-qAst1lgYi%lqwmbd5+)wyQo;2HXDJb&p`B{LFTqoqo2Lw4^j3n%)*}xou+P#r}G3 zQ7lROQtBn?9d?@v53K>i?AKt)qte{(>_^MbNqC+R*t@+MF)J;atvmML!q<@TuIZG~ z9t!N?ygBY-X^zayD~{2y`y_cK(=5^x8vxf)681I;Nn6=#+1yH#IVDflku+%`4IhJ> zNFZ64%BJdg|5$txlGeRwrS*`&(y6jW^liJhH5y3^M=v=gApYP4e2CJ$S$)%W^TO`n9$1B8h#Vf7>@*^ zCQhGGR(wF3QaYA&*gV9soSmIqlI@0cIZvQP)`+ZC?QR*7QuORz-J~`rMpCnQk(DHg z$7(ISgP5R(Ty{OKk!C&bca0>5mhFk1oK7aoFsv7LoOwda)dsOPuoO^|^JgP~)3U9l zJ)M&vuDxT9`C^G`8J-P6o>)&()RLSs#?Lg&sygCcOn9J~eIlo1ESP#PbwF{^{AfKQ~Ws$?d`jLFxq4kzxJl+g8F-`i|nWdXv_>^z-sZ>oFx5pcs;6^{o23|w>e)d zWhkMTS_~%>+aFpul}qp{{vmaV=BtQWU6RvX+YqO0kP4Rhhc!8WBw0+>73Y@Olxt4d zr$t&yI@P;v5Ezg#)k$n?8pazxcg`b@!Imea+r#dCSWo54xkfpyT=5EFA=os>l9ayb z52@n2rDtNIP_c52oe&|vy(LN7VdbMa)=&^1a;`|;yVO!6A7RIpykjNw8j?jax!Uh_ zCTUYC(|Eqe5<-OvgN!Q-@VUlt?3oDe7ZA-j>?5wS&CEkur`e!L0uIdr{&deAUS zhB$Z4^;)@-(E6A?J?*wGbx=sbvW#v?TMAQ6D@ix6HC%|%cO>Ot*JECih^=o|!@le^ zNkddli>MA3beE(H2t({`cFplJFDBiX+!HLLTRfh&`j)s*Hi2AWA{@JHT-o>XE)2Sm z1eHMRO}o>ElVlRMzBag(0SFYW>lW#jOu(l$f-Sd*IZiD2Zr!edm4L@-b_#_+jy@!f zK+fPG<8H49Aeq$|whioptQrEadUovN>k$#by*kgR9n|k6IEY#W6k%E6ha{Izb{f!l zHC!HTLA^<1YN3Q{kO&x*H#&PaLO>5d8k`k5aqlOIQhO?S61S?@KLy;+4k)SSHzt`h z{4uE#l!?S+q>u`-4nxlmlX`!Kl`+VG=a5M7=G!`9a9L1epAo&(cB*(P+BjNUvPL_$ zEPu!_kgw8&)BuM_lWP(Ou8of+g7sE8w6S$1;H8)l5LU>tSkaFxCMH@Yq;|PHGw*|` zo6>AHfoda$e%$B#2aSjOuSYC_l+fvTfrKZ+=@IpYgvkq+*x%RrxOSQ zC$(bEB=5AD`JQ)Ee>m3C#4@q8LoE(9lM*$AX(HFzo_|8tZ1Oe>zy>)u0X@@{U`_y` zi7W7?rgp3>NtxQV)vzz6-6y%!jhKa0f5ykw2Dh;91Wt8o5W>LeW3z8I4AG^7%v`de z!Kcc|*E09$X(1)Q5TR|*dmZ?ojc7?B$;yepZxho&VSGUl8rp_>c74q*$L4lDx~3bo z>#zkHIjNYTH1DT;qZIy^3CAVcGahowOZ3UumtvIgS;3Vr{>p%;gcA)2BzmHYGqO2E zDN$k>8#My8UGp>u`=qzCd^la&Peb3TEgQf{nOT76N(6O2SZms%a%u5mBXCi1T6Rpd zh`vfRBqW`@o=&WeD2kp*Zi42)_#kpm8_s+qYNCM{`ZO(c?a|_0v6Vmz1hNAkZ{Q8g zxHqgFg7H2p5j*6x^={W+Ho9&He=t-(ksC04+CCrOOR$=lA+F12GyHUP4We|6hJ^1n zp9v?&N*KQB>9z8XUk6wKb&^0UW^7GvN7gCkjZ6-U#=P=7l8@vu>tPQ8bD4oTd(4&ta!yvZF`JJf6gui!gpFnV6n(m$?WhlDFWZ~YhFp$)J3`PhO|&t#5Rw|WDTN?8S{_IZrra^M{m9QFne^o{ z!vgs8xPS;>KkRxP6{2>e8FXj2jLROmc*Lv*aG;90H4H!NvyBPqk)>H&jtrRu9IIzZ;%5>|1HEbfBjyfu*Zpbp3XLv`9 zT(X{j*u2{K$4rpsdJgAtBJT+8Ika5J%M{X<^4l?fz+Mqea}fRICa&_S!I(AOFe`7@ zd$SyT(8D;nnI#NfiZBIuw!IQ*?62h{vzreM*<|^Xa&O)Yb66=2z>8~JC#oJgC;%H- z<$8r|m&bc{P~q6Q@!w5Ddq<%I{>`^hq{y|%*+Z7?@>L}12I)>>AKJ4MT#3ECiEPyx zsUr~313+FUD;moH%>q=#vYVWz0q@!KJ{j9V{3Fu7=l6ndz|A1_TyGx^dJ51{?pBRj zI)Lm}U@qz#j=-d3(b;^7gXg2`i8Kv7sP<{@&QIy%&^d2%f6}meo(gowAh1qZh{5|& zAW)>sbU`OTRfxU<*3;;`-@k|8mX^st3QCXRzy>APR~~T;0A14EM{!BHUO{+%0QV)y zlx{Lt?oYp5BevVQ(Uiz=tGnb3s0H#>h0uNn77I!?;|ylWisjWv7&OT-Et{=AZY$E) zoUH^bG5KsOX{Je%Xq($#l@=pTzG?H9kMI4|7LrOnp+WBFBQUZxTw{sFxd1nyA)djE zY@7x=Hybrr?<+}|mm&mUo8!0rJjoca_dAgj%(5zzIx;l*bZuBb;KPUgpjAAwNdPsF z2R0cW(p(Ebrav)rz%K+{2(HVZLUw>hGjL;4gir@?GfiN9jhtmI9J-@yR3S;TCZ{oX z#F&)L7P;C`;zr(+H3>K5dI{Ao)jqOpS?v<)l5W)cOy4RwhejQVY}}HUx+Tt_O(2VL z5ZIN_lK&$o+^QM86H%eDz|RdUVUsuJwErOQx&e@ON~)r|3n90_b7*q{mz`lxpWpk5 zagmnnmbtgS_)PlP(ZBY^dp z!ND~kLr+EhKyCO*zrbxUtZ3AsrCps6ThU4KU17+~{#t}{l}3Opur>fePE15HLpT=G z!|RWtEQFkoMA!n$P)G#CbnNe^{D?~z0I8ZVmYkP;np8-P_5j*Rpd<->@HkG+)$1h$ zrZ*hcA<@%7+LitOh;|&_4PYn~J4Wg9Zc5+D0@c7;VXEt~c5pT)M`u_%OUAWq7}@#g zig(=aWR`RmZ1H2`9f2e*$aZ#g_TR*iDw@r0oh298r21|r7(K5FG7QM6lO>KS(p>f?0i>`T`zgXC=3S( zcBcX)lIHDtbTxJpdYY<*z14Gx(8Ji(XkQBdjx&T+^>O-of}vVDV2?rlxVo=^JK0}b zr!Fkhcgj!H;}a4GY358~*t!MVH-k*5*u~^eJ5v93w3o@<4_F(9%-&o+cXJv!6KF*k z9hQ)uRYDv*f-Rxz6|y4$ImO>wAEyA4B$0Qf>{8;xrQWqJbgP6ULD-@gCegYqjdG`I zL27V1$WOdknsUR=in}6tFYBm>Z9^o0SXY=jQVf-{+s&c9Nq`p2=&)^md9S7!3#l;= za=5QYS3^h^ouiGj_5~S!7(!v2rh1v6*jMN1S&lHZ%;(KucFi}zlHA`wkf#IHsAEYf z;vqQF)B<_3NQVR7#V8=-KnxA@^mV)sf9xRvD8iCnl?Jpq#B^he^l3@h4k4XMf3yhY z*XuPKW+iZfjWm}Qyd!A-BNZdJ@7^fr+V$SH4`hz)Pe=Q3(ks9Vsc~s{f*3_p5%D}s zLZ`1hBEGL3v~mqV6)}JWI?;BI0q#;c0XC-$kPI#PrH}6gU0#8S=taO4r-P3n{LDik zg7#-*=@G)0GjUGVtk>6b$Fs}qLO76;$G;)iDSNbOGrgYU3IJ6GlI(rpPTh%XNiS@q zPsBM06# zKqn++A?8vDJS;JN>-p|PSaQYTG+3EJ5}RsCVwNgy0BwZ~qCQ&}Mx8G4FTC)!Er`zm zPZ~%ZgU=?VA`;lfNjk+Z?b5>2mH;EO)1r{?WYw)<@Gk-pyS$|^*AxL`fgb---bMI z-&f@yc$-2g8>h8^2-wW@I_u+^e#RX2vC<+bD)Wuz1|e0)A+R^%e1yeY)R@)lCEA!S zH!HwWZdKc(J+`uTm5ni%mus2%I{?91QqS4q1@09kgj z*V`6rVFz}#8~$QBgc)J&(E7*s>dW+*h9OaAaXq>gY423&Ng?1HKcf=Q8f`t(c^4li zNSdekZ}*H!woTtJ9>4}zlJ9)&y6h8ug(;Y|I}%wdtv8v+#X^sqR|il=B}gZ21uSHL zQ>@GE9?oc@UW8K07E*2kX9kYi9)>J6H>(WHWj5DK$XoPBfwt6CV}8>pfHDiXHMY8T zC@!jmLMG#v2$?ywC?#D8Y~|G2$6!60I+N=KseU=a@tZiLx(nkWYBB^jjZ+Ar#OT|m z)YjQf{|9@L_w8}jVbkt$-xS^)^(#cYwM6E^8ZBi!0V_)uudq5bVG=|M3&z`#z21*p zSfbj=H7ZWeU(V}YW3Y8H5oto^IO1heFjUEe;Rclg8x=#)x)c+X`a0M!UY92`#|PpoVT4oQnu3I;8J<=GQR{oz zr(_gmBP{@EGY?=P0B9E6Q%+6Ybt|lR=}a=%BR~T(Stw)G@+k>hP(Efw19zJloqQmH zLX$dF%IO>_1Jy~et4BSEwBkk))|j4T3bf>MGSRj=oQDb5Gt%Q@Q&%#G1 zW=@j5v9WZ2*Q3I{4#){it2BUQ>K=|rC-z3kkI6_EFCwqqLfQP3E{QnmD!!#d>O-$B? z8+5g!p>!r+a)G91GlZko=LVj&AJ@u7_!TxP(O3JGE8eSAlP+1WC&y zgT;gb`j-LY@XqxTpdS>BB&u1YMek*M?Dypol4g>I)hhsflT`P<;0Snv_<6lOElX*gOm_K= zODI$2{?a8pPJD0)wZX_q$nd};1b-w2Utg1WJUl{K7~P3(aC5SEbnnX}l)E2mCGDUx zC^)LG1%M~_;1GZcDQdM^)ZkYh)4$i`W&o@c2wPCWoUcfr8QO-8>2*K_7)eQxTsNvN z?l}L6Bi6YYsD=T!EXYp8Q2$}55)XwiFE5}Ts|`adLWLyVb@5K<=rb2UP=3$CV4OIF|Xwc z3wxYuW=cROJ-ij`W3oKVmy(jRU?P#WnSO$#0u%Knj2qLEC}bBD&=O z)hEYOCwVB!`nVQXh?20_(puA#ikuvLH&pr$h%F)n)kdqtiPq$s?TL!X$XDz3paj#d zKA8DPm6M?Tqs_J!DhpT6n-iA~%~7=%EC4Z zn34$OyJk~n1VH^Q77lTwsU_Q@k`;K6rga=NPJkp$&-CRH`7Er25l+YJTFJ)!;&XVMJPHrJJ7vrC=5|_*-GI^vU{lEp|8M za#Y*O*4QaNomAIs;Rjp$SI8to>QL;UJ(?|^_l+8GT^l5wr~v9Z$6Tbl8kh_@Luw!2 z`$wB^`Huwm4Z<++I@@lIj6;UR$~1DXi3L30JNh!iWlOyw+_jsFp&hHoUgikOI|-PC{9dz5(4V z5F-LXx$UyaG;heH?je93R0?TKS5F;tUjEjtvqGnPDnw^4AP zfnjy3&j9(vs;eLlfliHeTk?JN0ePXz5E>+VkDHdkpVr)Ou#NSFUBA5ZKWnn3qo}oP zQI}0J%rV~f8uaPOrt+X*+jzF&7w!gOFE%(M><2u|Ot*$Hqaog^?tG+NGi7aHitFOa zde~)$H;wmd)2sv+MLl2gJ8W+1Qc0s%uTmBX+%2D6(AIQ@TDjq5Jf(triR3=9+)m6Z zf|?M$k$epB3@}ESCoPE1cZz@|_#sn%@!n=;a@?ptqN#SB@2(kw=(t2}cA;>hFSPo* ztql{SZbH>SzUp8({}(+ znnBq?eSoaf&Bc7|5(#(`?_pR#sNKL|?ZK()l5~BLt7)16*%-v(P!b2GP<ZA$mbZEAghtN>wefN$`v!oer|u7kKML*JTVvu`Od|7f82`{f-?1o!&O zp@moclyx;OC={oI+sppre|n+N%dbW-I6MJW}hRw{ib=$2TZJ zBTTuX%QAGP@egmXyd>K?5#pk^%roRDD*j;BE7_sL=y=HE&z6zQkUZ6>+J z4Mon46wO>DL+1S2g6kWeIfs6uawA+bjmQ>NjvZQMWVkg9w>lwr9A+u_Pw}qtKq&JE z-WseTz%C&P-QGJ~_@P9`b3i=^S2_=81H329GVcV2`6-+*)&~4jS&8wz~x)2NYNgbDzSPFJ&$xbR+i0=NUnco}&SRvSH4ejO!X_7-IX~N;2 z56?{SgVd>$WH~(?|9%hc{%`Nkc}R|&Jgg)W#_tj}VnCYUy?GY0)$B$EHR{+d=b-_r zn9jxfsq!SK-VWq*5548`OtUf)-bG9jIL@%pb&vx$89#D3VF?a8Fw*e@R@fRb5UAcj zAS}#hH+9q2SCbb&u5A*Qasd>B3+Q$4>tS}{{M%}>Ks6We`-qla4#(XAXR|h&#R|0Q zbkmsU@^25Wa6R6o2;w-Cpp}!ctgW6Qs-=oEQ3PgYN>hz!Z@e*FYKNPO`fIbqI!>6V zUvH+WaucL!vR5<&NyS=G*0$VNV$ zmL4n7UBQJw)be97Br8@?SVZVZ9!?D0{Nb(8g`2h!c}UIB_2^oxXpd}89`iBDk77m1iF3Mkz5aZ1 z1gd5XlQyFKiI6z1@go2Ga)ODsLb(EHW!#`S-C%+pZL2HC0O+Bc&KENe(M*-)+rVMT z4_6ar7@(99Z--O`Jj@gFDUnqm@PT6OX8g$Wl1Wb%;&j>zy9=I@94cq4fth4G6EdG~ zo$f-ws(6a%>o5@Q6Yg!T^Hwv?O^(c+_6R|yZEod!QxRo_szDFH(i+EE(nBG~rP}bx zOZED@yp>`mFbpK8sUSEVTq|W#0qGBlY7t{yDrJ%p%I9`lac0g_XCC;5$g@M*SDm;f zXWc=Sy_{f~AWlhl$_mHMY}3wci*_E9`n3%@A!FiqU^?Ai;M@5<61W?cl4KA{v{8V5 zphB~;%||SOGX6}eflw7we7#0@vF{s6v<~FCcXMe#2q=?EQWMyKarWA3M728aF9Exl zv{s<1UfaEcLTQH-iyPrN(&N4+WHP}Py;t*Vhl1T<6@g~H#rnXW0Zy?+N2c>zKN%#| zT&%h;$kgLV+qjUZPjHI@u4(KOa?a0beh}lq>3ZJA@%q8=>8F=p2y$TZDbl-xAY52j6jdvXw;22337$Xv-=No1G?R|K#)Pyp$9KEw1E>Yjr`S}XNN2x4Dv<#rClgQ^XchrL4+*@<8(qt;o_SC94abQRHmGohxfDXh9nEI~u3H zJ?}&I@D|n2q;e@2j%r!Lg}GXfO9IN0f~W9Z8-UjxZo1S}C~2#mm`+5SGGnx)=~`vzyfx z#EmdR-rNb{eTgyI-!+*uFasYGrYP2oB&b%!e)yFYX7-5_7*q+TD`JDE8VDaU0g}LG zPHKo$XeFRa;)68#C{l_kbJ~_5^O#ls*R=M1Y-xeTa%CqQp^Jk=!~59y3ez z4V-1A5F{vXQ#K1}Frmi7U;=b^Lj*ocB~EwE`1$A>0i2j$4IwkzGgNd;s$8v)5x@iA zveVh5l^_{P)Wtef`6d~U`$%gVQcwnXHd;%8IUPY_s{M+GmqUy1IMz`4Z6^@)$7y}w z7S6szuH91-yeWyi-LG!8_VRHlTTS`{`kkz&-|~ifH3brE73r7PuQ}w-Lm7#z@Ik znz1(VtthK^xRj0-*(L)K-RseZ%5lgi8&&c1Omg{{6&C7ww{7=F-8kJ9sBDKejFYqg zgaZ^RnU_<%nYFuYy;ZB$Y_!C{?tCB5IBOP4`Oj3))UT#3EP7k z?u_g;4&Z4}*$H?w2vc{uJDAdEf$3oTraGVpC_Pi@xVe!evOEFVA`=ldcn4a?ZMNf? zi1=1pTssW-nCu0M>wI+8zQZinQbQ$|X9@avO!Q@*$73IFeED$4!fiFXAtO+>H|Z6B zPxSHtD^?s|W=Vn#3RYrQS`@@{m(%3f6*#B?%A&$KRL6c0R?ry;9&1f{q2d?r`QVzR z9Z8{t&3kshwoZqrkWz%|31Y*p8)_G-5bF3$4M9yQ{i=e{f{qu_F3DO~0tIeNq{Hoc zbfqBPVJ%|Hw*|!yt^4qgaynbm>-7ry9%W6U5GIBU1%~4Z?M-bQa>HGrH+sN+a?X|3 z$~(09pGGzZ)ui#;G~KLBl|d&nA?LjBF7MUQsUn<3X*lp7f9 z%m!^XJt0XO5XC#=4$1JK)txI!!1~Ej189A_9+|imGQdeSEOu6=fBAllUtWJbk(Wqu zyy_>SAsdvsX8S>`NgvWLx$JMSFy#7>tl3aiQ0K0sAqQ$HNjoRF?hiCR0X0m_w=%e$ zdc${1)jdq9MCXJ)=95=~ejpW)TJTD2rz>Jg;S_Yu1&JkX9&FDbHzudMht=o9okU9m z$LW=*si8q<3Pg_#;zWDBUB|nOVKA6et_1#@@KAES;$%_&REUkLtksIyB-tF8C zuLuI}(pDExQev5|DWG=4H6Am%lkEFqDW$O$ypHcJsnz7J((SPWgpI!GZ?qXajIMkMe}|Uy}7+sa&T)yNib=%cCzehl%AU@k?03>2HL4l~z9| z+-!PQuC#ws*x)NJ@W}{sCjG<{l)2&=+g) zaYZoKZNy@5IYC9xZ{@*f%7`W(1JFKEpx{`SU?cCEVnq>sP3Gd*qeAs`NT(oH!S3Ym z0ND(Fb{@5Q_J+ozPdF{*&r@@pLBMvb2sUFz=$r3mqPPv}&W0WcE|{(b%}~BUB?cu#hS3+aoPva$r&{NR&pxWfB&RAaaY%kK5y0sRnS( z8r=}Ud1_LqHnL$)+;WFtZ3~-<9y$;zh8Ix705|0oO|>Tb9Uw)yU5}s(F{25wK6rp8 zacLOip7Q#b*V@nH>T)RmEsmN&e;-Lt5XosAtiNCUb-zg5f7+Pb+zX&V1rwF198)L& z`Jwa5gH~E3SJK<8cst^bG}N8ReTf3UU16Vu)&2-oQ3|fb!caRMTCbM?Nu#eFCQ*0C zg(uaeIqqd*&@g4XjtZ_L7uK8jf%cx-Ei!)ScFVq!(8x z*9b$h&~cL8aLVur5>~PS0<%h0(gt&IiGr?GOW{s4tMJ?1 z+tWE}H8#u0o4En8Q>k%z;2zwEjoO~RN>CqHEL-bc(2A1A=58%@Sz4K13FT;40?>g@ zGoj>hzaTqVXd6oHfq8UeAB>}>iQC>9wYyp_ZkthJRFAK z#i#*jktouZbe>c;uuON%sPoZBqEh(NA;>SMwwA}F%4a@%N(WJ?OY~&>4nit(`LpQY z-IXi*Yv2+l6xrg5P3AKBO!Fbr_C-%BxGl(NMU=gm1Ghqc^nMRNO)Pykd<`>DtFWe; z!f!ky&$KI$7~H}mJYBH}yk2D#m_qWd_)xRQ76!{jXT}tSrnFSo@2~?2EQ9+bVRSR8 zF!qTCDO{jA>?O?TAxfnTVz~!<*P#0|o>qzX78&Sp5*tg~pms8)d+UGjtb)A*rn9A$ zPemyakVx;cz8+C@Jmsikb+m-4glqz3unV5NS>nUg7&{(}%gEz(Rj{XcuKX`5h!Kha z2@PTCNJ+Z9>Ox3_3TRth zeFLD+$~5Wf!^Q>)UDE3=tAAqKbk}6_S#i;-=6N*WD8{1(Pl%h7XdwbM_;PcW;r<{; z*tRp+=5dDZhY%O=+vswDn1#SO?hAqNWFo?sGX^*Z`vJ(N4OyU?nZCxc&Oy#Y4nWJR zyMy%zzPDtusfWb`-f7Y*9^uTnN-H&tlW3rG^eEuBk4twm7MK}-)iW#_Fq1(MH<5*@3SZX$v6+KO-z~QIfww$yjKEWosVe7Og$-@4GX3b z5VS+GC^DH>O~J7}^M&^RI8gu$6I^e5#B0cRBHl)fw*!krDBqY-ELoZ#^r&D!;L;MN z608IF3TctqGq8|`$C{3FZ}WfSYDp2@j^0VA9GbOk+fxcpIuFXO-IvgPT(SMQ6gC?+ z)k=pU1<(3sWCT(eN>GGz2Y^tisVSjuY3nXy zka#Wl%z;H&Kpwin0Wt#;&U4gT-Q6i$T68;7|HGwPJWohqhqRg0fCVM||5|{Ky07zD!#^Z59LqcUUzNDA91OYUm z1R>B2ebR%ETASB0cmN;<{j&w=R(p=}N6E82ShFj*GT0PNVY|ubqqRhOn-du2ycG>` zh%mZIrA?>)*oh{$r^&jv%;Wr4!z-rsW z@waRqztwl{Ao!#iqva>EHA*@Hln>Hn7^UtFrws@rm2ZqUxmVNLjbNSk>at32Z23y~ zRP;8~ZZsCJ1VrU}bj|xr=uSyWU=coc9K8j{- zVNPr}J2Clsg?$>0v9vYvUdzxDSBqNi{g4%lIUpHEv#Lg&Xs{9kj&XaKBqdd;z%VDx zDw~38%{(;Rkv0}q{0PgDIzaL(bqQN|8DQ+}!7#VE*>K00`uJXZHHcKBEpOMOYthy2 z7RS@k>?im1qw0!m(xp0Mm0dN{L^t@&nHt=4EQ9%&YAQi70xsw{i29QN>An@}dJ})+e zqjv)^c7mNPdV@ksM!}jFBCNHH4&$-DB-JuMgat zRQ{J$l8rF&a97g~m6|(s)5xH|QJ>Hw8i+qA$H0E85&MjuvXU9F-|Y-Hd4Mg09Bbn} zu3|iK75Js3A%a+HP`Obd(0}Cg@-+Sd2B=$zm*a~1ADGl>{cDc*a6NXws1uf3X0_5M zT_#ubT<8uW>?L|}V+rn1axT7t0j7iSKv30mno*RX(Kk0u#+t3s!B%gCuc?p%{X7dWk(k2g0lmq}3II8j9?tL(+{#PHn z|CRdK!MwRzdmwG;)EV7;X;Me!O@4H6Ce2QphVhWf6AsiO0cJ3bL@UC z?j24lT^}A2QhYPa$QGKRZ4~cNboOay^@1J7mbzV!K6Lhm3TVT89Sh(eA>v2=cHpL+ zHF2DIAOTbK=cuk(+ahsG$dE8p+tbUPX&Xl-71rwrGc5r>q5n7x?w_392ISaRIhDqo z7V%r)6kgBx1BQ<82Zlcq@jc4+0Q(+6WT{YCDmohD=9;(j6>nh~yW1>Kk29hIhzuAI zOX#PpnW1FeP0Px{Lz`_;yJFj$@OnT6AuW=$n2Ta)ut;IS3xM!6(qlI<{KT3-6|PrWL!Q@{&-N)evZQX~QvAB+GBlFahHqZT1 z0{V7;P5xv$W+!Dth>-YN!fq|VNZr6m%YHnZI;y(6{h?>)rU5WeE6A;@ z;)%LbxGJG=Z0D3-8 z^gDpl%sX5N5)*d_a}ov=o&^2@6lwFLYec{fvNCc)5lf<_dfOL)HaKz@#gEmbM}tNH zEtYK|z?@wI$sw^=1WSa<2iR^f?E{Bw+)WKmhe}h4oR2=5TCY5#hpF|!F*@Q<)YLbdp=alC=aOWdihbN{Ifo5xHkVpmtF#lzbL`>moIbo=*&EXxfGm9J znhn)k=r9Z4jr7TMw|h3s^$It)h6EQDi|pxnRx=J)DLd9LL893(4Gd(%Ifz!A>89Ca zxj|2P2f?xmO(3`p^$56W+c|!Cz8I0FZdzSy7!C6@JX`U= zw!0Axj#fO+D!OGiV^aZU zkIL(f1pP)fEGST@N1^!E4@UJr-RtC>c?(m$xfgPh(QuRmFZA!;M6d^=rl4p-)w;R@ z%|a*m%hA=Sf)XE72%3kdZT+yRWb9vZ@&_jF)Qty=2hw`HIK#StxgybC<5F+-5+Dqj zIZB31YGzWJm6q3#p+Z_fCS+ksBv$&5zjcb|I0Y&T7|}WceNjQ&1{mdtRJskJV!>@a0VTxilp!ujgpc%*4%F`hLTKHaRQEY=NdszOrA1`0K=U@XXS0oXbpW%uC% zXaY!XlExm*vgI+)`Qgm&?UEBbCMvMxG^b5-;nB^gvBS6ay3Cfppq42?L00)a6vW}$ z6Hjx#0OywOX*1-DCK;bq&-uPB&piAjlK7RPNYAlcWkwoy27lKAd(3WX`?BuRm`SqE zIk}%I6ul|B6r#Mm1Y~sF^ff@1JBTi?W*lLS_kTP%?tk68{GB6UY($Gx?=QCYk z8XZcfS?P|xpAKceGNHhq3`~U#oVI!WWGKxinhpEwA}W36Jt0YkJ;ms|wtCoZCO6o7 zKxQ*I6yn?lpB5MQ(wq7%GGaa)7DzY9lmw~|I~2y9F&~9m1oUZ%lOyw!&R0;+K}8I@ zy4#?wP(-+NmJWNn8^3n9)HD4#INpGmR~V5UE2JEmHq>NW(--_N%wlpz?RGu7S}yQM z?9;fd?OA?vYysJXrFNOrDt_;E!)GNx4Y(2|k*P05`* zROfJ_(+tq5no?ckiJKZtX^77yc%-Y_NYa%PkW=|E82}CqT`ADrT#w)sqsLc@c8b`! zbP`NyKk1-H+fIkM&m+3d%Zkio733VBn@2t18e4)&UHe9^Ikb}~VsQr!KU zq6$K_bGk+CNN+_LmF4J5;IL4Kk*+O%FNq$Aei}J$E~o3gW_<>|7xxZ%lO8!56}uH6JXMC4g5@P8 z(0eFie3n5PeD!L9@(?FIQq{0Tfhq}17|b6=FS#h~e00Tx4HI+|7;Pu!m&2%#9y{-| z6DEv8G+O*=9j;2`K?OT%_>I5@{Cq4jcjRYDMw&ZO}(3pX-gnD@d4C%N>aDF#KdMW(X z4r@8I2Hu|A_}UJ-T>eQnG;z@N4D{tZ9l)^-=6i#($VCo%i{^JO=|@1e9h((G%3UkeWzVFT(~nfO?avGtF?FIT-k)~n^$_Ff29WC_ z>yg6^cwUiL0{ayxx4<*yUY;Y$PU*5xA)-pF87II-3V!HALA54rM}WcJ48jCN4el~3 z7%9E9UXEg8)~n0Kg3;C$eWci<>W%UTcCuYYIUMMvYM{#>7?b;v+`|bmB7pGe1-lHr z(}xkrFXuUv3WZjE5m#sTkBt0dpv4$$`be(;JEWwrd%gwO??+aQoJXg)GeA%TWWWfy zg5`*)f_B<$L5F}nU$G^3%Y|H|!8zw9e=FWLKy82sACR2w6co{}RMNLa^xWimRKiHz z-{Co!!K-GDE_!P0BT^~=Ua`*>lQSxKVnaBk0x7ZF#TG^8v~8l#>WZS#6eU3loT4O0 zidphNCftTTJ0Imc)y0yDn>hv@|LJh;oJrR10Z7o$vO682@jutBhsC4Gm@Hc=bv%mT zek9v+beynIgKdbRX$v>TI%QI2Fg^{LOeROJ6%`8AU1BK+t}YvO>jvn|XwG8(D+G2S zRP-hT=XoE?C!k(L&Y1k#=*l$Vx|xQ$VEsQBP~+)Sb8D+~@PA0hnz9K_A8%76G>mWo z;bJB1&Ph?`04r;w830B*_Nj?o$XvkxF#~T4Sx(-J#px%jh+Cu6o9>`Tx!>h@5WY66 zd+_8{H$KN`4hd(-o<#IxKWcc+`|3D@?;>$=Z|2J1s(a=p+StRF`DRmQm&bxGa;pW1 z1`{n7i5z8$UMLd7^oxFA1ddyM+p#{aj`a=ZEw&60Nw1Vb;e9!{$zVl0eqsY|P*){7 zdPMnScCItod8v1@nnr3FS0uX#b_Nna4rt>yT}q#$73TAB2iibV2jn!H#x-m}RKt=p#)=%uV_)=3U&nRoM;z#E5a!j>X>iV=X(vAetBCbfYF*c?4d7szV=k`i*5A=VJiN}A*(S=71vrvt!2o(qg0 zh}dguU+$zW1o#}S0)Vkl3bLS?x2TSSVPd}a%RMjU*ctfmaPe3qS>vKFgUj&Ctx+`l zxK`1lQ^G*QQL0-xNo!S5HFGi3Kp`aKwYtBo6w2KsS>z_+pwr`$jNXra-WuCUIt5}e zC;$CRf54)GH9#z@f|Yk6PZ8$i*IrJm}AU3H!M5%Dzc zru451xd2ru{H;WjAF(C-YG&%Ez-+ett)9rM+crGxntP~B?DxXp{wr$(Crn{$Y+jh^Mf9K>R``nzHT}f3c zmAa^_H??ZjdLN+Zg~EIW2_En%AZB+jF!^>=>-oa6(M(JoA!TbmzJ-9tj(U~baeVep zDp<3SwdAX9gn~QZ8`p60@h6Cc>s|@qp-V>rSxP(5>y|5Nzn(JyAAS?04_w96-U^VG z0!mTWj*@=W;h%A>3N5$+uwLCpPN}#nA#=EB!XwL)!pp#Jw@aMab(%F|T97)1#s!7} z8ywMnT06I(x$s3fJ7(px%7xp2_=2CSYG4bZ`p4iRC*73$LojN*4M{YnT{0>m%;2o3 z9GrFS1fX}eu!3EB9HGkcCpfda!$9-ekbdL(m$;cJIkv zZN}LUVHsD}uDwdc&9 zM}9P#Ini}nyw!o*Q zn_;DZMc%Ua0OxF*!38LiWY(@=4gmp0n=@P-;QJV&<^QaE2in27$xbosx^M#&jDy?b zC78U0&%lsclYt?L<$W(q0NX8V@C;?ulwr`^6^UH(LGmPJm29MmVd%izIKwmeKU9rH z`|>WAZv*$-xg@21YlCFppf`rK$ttd*JJ3g+Oj~-F|Tpp-W)L zuOx2^EEvL?4$?`-7@XC80>{OR&~j>7#b8DSIdk^$pzFurel8FtJKeRvY7z~71;iFtX(;x?Ezecpu(bA`_Z|a$ zCBl3{y4o+%$x6c1XFi2A-M| zDJO_~DD&thQX=HAb=ROxN1s4U%G%r0A-vqujat{T6;GjSzQV9W}c8j$n3Yu!2YeZOE(Ct~dbKnfF=*$l^5^4wiU7o?&VGu2T910_CO>5dl z^xW~!J-=`oS)rQ}^rRQ6*Lv5V%51x_C@tqvcCNeYB$%v~!+<-Hk3mF-+k6L`MaTrY z(wBU8>Mx&v;D~h^i+G5z<6i{OLrVX>57n2aED)`WFM3hHgMb35+*SF(Am9@zL!hD; z#N9=d{-o^)2yBQBMJpgQh+Wd1Pr|gV&-q@h3f-!I{1J7z_Z+1OqKN~=>7l%a{rn6} zE3CB$f<{UBhL7p*92cd#w@HgmPn7b`GLsR9zRD^Kl?f?lbh=jl%e((cqP`=^#O+J2&L^_S> zi`c_N;Lo(uy#lB&$7XJ&qqQ)-j_QCZ+{qNOc1|Z-&cFHfFhxUd!w_XOjRB>-+pX_V zAzBJ1hG6dfZ7hl&e%pO6uSymS#Pv$?QI&FPB4CCE7mVZR?a=IGB7uPpsrHsH-tQu; zIE0S~*qpgS#ni}QU~I@YuW~f8aK8zB&<7^NWhyru<0pM3ivy~Why#?UN$yfw+!n_+ zTv_;Wo#=)!kO>>$iaitD%r-`y0=J(lI`FX^7&>5Gx=*xfzfNNo(PE90*SFeLYvEZ=CTgaP7|t)j|iKHg_d{^5=W87JtZ*EfRNES}CiT9enb zfqhCMZ`zW-=*rlJmo7Ioc={JjsGc35Q=n2gq8)fRO5Mq1BjL?JShj@mksTrc96L| zG9tDo$U(pt`1{mew4B7_>fAf%PK&+D$?+z5j_x|T8ByzIc^xAN*O7CU7ANXfTYcgM zkU0bAdR6SYAe*49AP*00DE#pPYyo2H3&>d{W)!|n_Q)agn2M+HAXEf+;;bDCyJMpS zv0$w~gP9p)T6~68iQgHk zm_7J{c8zf^_QX*HEe88tl&?g=@D!nEyMl3S*KQyLuVYwXw-}v@PM^f%ZYr$8C)?>Z zX=l8h9dC@cp=625!ZXa}*m;nI$lJ+$n#7iVMitLC>$6_p&}aHRsvD3%z8YU093%DX z?gpsr#63zBF2RmH{6fzn!CPq8dpLF|rhMY)YES5*Dt*VYGx7341yyV>+FU}qlos|$ z@BWGu!tPL2POyTWefmZn=_)T+aU!6rou`sfPgu>4rsJ6#JqBqZtkrraxE_q}Tei!mijm%DWm zgqbTSd-|`0et`4g*`(4$1|3Hwc`}+mM13*F;7e*n{B`|#p?k%{*0hCVr{?>WAtw=)Q*(?f_Vq$mOPC-4IJQY1Tf_4-$xK*oQET8B=j{YIQU>x z_+;+$YoE0JqQD$`8r|c{?#swUmUi`l#T&!OLKwy%8Y3`K!XgLM4=9cIpM-&BSTlv6 z>M}`KiRp5wNrOZO^DC>$+`&H-(fnbxWlq|n=C5+Hc%f6 zX>u`4F0N6AV5f-fDO$qsz>u4gqTJSXPZfM9473OK8s;YIyMzu?$;(g%9LF;a`Z8-^ zlS6*-h9R%y8vA{%@}uv`QN>bom*m<9S;%^sCyJVDm<7NYKR|{}v+uqg=IuzlkR0;P z3o%I$3%a6a{vwDW(WZ6bq^VwX=+bZOG@ycCPl%2@=?#ykuXLwOr&kbKR6Sn?oo8os zm0dP5zA;JYTb-D)J7*}tHZdd6qkzCCs4(7h*U`dDWu$zQl#<1ob3krKE~Ao< znn@It7jr#o+nlL_v|oLPA>!*4(~z?`n@jCLtwbZ0cojML3W8V3 zIHJ)OC`9?<$3EC^N_Ys>u5j(f9@Tc+VDgb z4Q8}58BH(Q~nZLpu`>$)aH9>s$nj8oQGuNIA54Zb_hLxbmJ>JJ_wP+ z*XP@_37eW%BdgH|{g-%y^}2h+V>}ZCDx}U2*meXawBi%x?V5e+T3WU{bFK>cXFQcY zW8_L@GbC>=mq@)ANjeCEWalj~_&tc;ocYD|#93qGQZrWGg>TASii^b7o|;-P$mR;e zHYAG#irNxh2a(B|1TPtGMV%uVcpcuGyz<9+6LU(BQ0T!Tn&MiKX$~c3>FG49j1Nkn zX5kjqHbW0TjXQ}{xQb}k;m^gzxrh-87OIZbPW z4>5YMcfLR^qPYd07i0#kRV$8kqS}fBhzWP^_I+~iS9x5rufkgtxIbT4Qj95!7^2r) z4h{-o^R`Ww)98q8J_|1aI1~p_XJnj4iVjk?Z?05S6qJX6)$BH+k=z@wS}LdvaKi95w!sB=% z%2r=3tJlbrzmS@<09#5em+0AxYssaV53w!>ETVe&Bc?^VehC&zC%4oB5vYnnAh8LO)vj;ekR>)Z+Iir z_C6d~0Z?adC;ew#XFHUiTF|cx9F0o8HQ9^Dy2{i~Ac=j(swYh6Y_@Y|BLz?j}tPsun8$8uXa4IA;00?D4v9lz-v|E`)9}E=9 zIB^&hadt=>EVKu8&KXD_!N$vxLJN>M|8;XI&V#j0K?ce28k!1Q#ACv@yMl2lL8-bH zv^Cob0eg>tG{wh^2w(zi!0{QWq+Fu&q7Fz!9ldNK=-<9|BG*3nbR!o75`u)v7YSX+ zMAhAj7flS<$uQR6eYW6sG@hi$+E(vaK&&}|F~yQWAw4ZCMBX^xMt|FblS_F1BbY=A z4nJEK*Tgeil?|7j*ea8Mir(U+WPR1oj+QzN%xpv^@&E2-h9&^D11l!Pwv&fFu{D+9 zc_I1ig`|05R*W-O<~6v0*enk0xiC_Ln_=tofZ`3C!4KqoOK7_HWLXu7aY#x;Vn!=xlMX zh!>Di7oO8%nN|q9T%*1fEW(_bQzv*Z?e3@UK6}_I38>W;ZJ`VZLY$kE?5@KCO`=}{ zBngI2s4IR5m-zfTQB19@q*0QY1VGgs@b#2MuMQ3cRJg2?^t=JVt&wy5&$am~_~XF0nbLHwb>2FXO^B9gW`6au;?Du>!E|m=1|Wv{ z1sm8(JQs%cI)IU}+Qrn&g9f?eJxnLe)z!LAD1S{9@{&)N<_w*Hk1u#~X#ef~>WeGg zrCWa;Ed8&|2x=x1K$RVl!W-9m70AeY!h?>dyvB+xx3pZ zK2@`@s=MpW>iJc4%0_Eg53H<|(KW^h!5R<-F{(zqk+6m4HJN`hrp8LSdq^@{5NXmX z?d&pS$O|JU2TT|&#*~Xg?v=8QkZ$7lj)HK~C%Ki_ze0ZGYpKc&&*???3$EWpZI(sw z#1ljpp^RDb7MX9=P5bcAby4@B_}*HT4+RJ9k~ll#Yc;sIAii1rIXnnJJp8gC`xlE| ztiH#xIr(xA1OsP|f109s^l7|gUv&%9t1xSH8CM2`>a=v~0rsvp>�+*2I?Ki>4n0%)tA6C`c7vK@MTZ zp??=OC$7I+TL|U3{(w3?+EYUPAnvoLVTV)y%UgXOlbqdP z9Pl|RBSMqU90RXcrn1GT^Z)^%G;aaVPFg(p+EWrLY=8hLuhTQ5*a?IvN*R)S6S;Ik zF}H7ay1V$r-eC?n1cw=DGb-KuQafKQX_f0%JjWuvaXf+h-ZqF?3U}uo)%`+E0yBvC zg*FO|88t#J-In_fij5=x3W+5bj`Ph6D%Ot#ZlR#MZMuMxF0b5!Q`tsSZ^=73^wICU z=$p!*5oTL_7XA+!`L|djRHD6J5O;0yl#2DgP`9ESj0TxCjc9>MNKq7#X79ggjklQa8ou& zl$VAA=tQlH1`@{%h{EA@j)*&OPr){TyZ9>{V>tNN$rb$Wme4G!D0n-@tikbG4wN22 z!-tAIN0;Qu7Gjt0L#`j9^_(nDg*WRF&-X*BC0B;c>T|yEzK&ydhQTDWd|%6xmqcN2 zp}-jDC2MB9`e2C&5We&i^By+tzkNB!^iXmU5^xrb!AV_+x?nl+QM0D8+!I$SC*d6D z1qqey=MNFLYr9X?9Dt+23Gix((q*=9uHML_agFhD(^C66bx43Bx0Mj`^8L9J{*mD< z)Y$1XEdMU4494F-?9%27!ZWJc+nMCnf*x&|eE27e6;29(7&f4LR3+-)8i&1(W#9qX z>L%qc`4AZBf+CZ(;3_2^6;I!SV4Ou};szQJLxrh}GRMo~!BB-;Cc`*Hln1Hbqc-1p zx_fesXAr}XI~_A*PChMx`b2XL00K3h$DW+HbaG+2W-&b4EcKzCUXfk7KYICQB^v=g zAYoG0MyEQ%5JhF@olB}O4aNl$fej$_6Na#mNUdZ*$6d7#7p@{E&^bNAyJM@3>KLoR zAgN=~ac*3wNYLsT$?BTqsTM1?#UI2)i~|UO#5rCAYYnPeaqe|GhC1AE^;>bS9U%04 z2jim?`5rKChG5*}AF*;S^H94L_r+Ajwfz+hq%~kuCdB*Mz*i#-we|zL&xu417P?lVX6Cc@_$PM^#-KU7V>TIpjPRlLdgcAP% zWzu!+OM%Cp*{w_38ODQU7Zg&5Q3au|WA5%=K0gJpNtX|_XOd$=O*H3iKyEtJmRo?( zBq5&fcyrqKZio8FqDr zdU?>oz*Ho`4n?TrT}G;IdZcNB$L=5hp()&jseEFf-#**#)6Cr#%2BQQZRt5vM7Fg!7=nKh+H%lu0q)Aa3q_Dq5UaHC>$Mr) zUFWKx9B~l2#4Vg{cdymOae6!*WekpK18@hoP!mOE{?l%4x}ep|X9)Bi^U9f9U>~l; zduM~3-Xgv^2?T<&kma%n1Cia)+G%3r*&YHU*2P91VQe(QA{T_A$g_5c z$3PYcx>U+^ERj?C6ksX;fn!xKOofI(Sa|z5r!CNWb^M3VqT8UpSa%VGI^Qo3gnDpC zx|jiqm8Rr4nX|r+@Z($uj0rdOLMMQ&#}CT4P#~c1DvX|-W2-{WH+!iX~ z$dp6rD>uOX7s)SN4hV$U9gym)smWg(&MMqbZtr{@GEDKa=?FXXJ6us=ZP5us^eenThMFHfTc)Jmw9yf-*b<)o zKNsI_|APKcG+HAoK1UB25Kt&45Rmf!4jRqW%-O`r%E86T9>Cz@;i9Gr4Fo#Sxv%%1 z?&<*x1Pt~A3Iz1u!?orn;E)~3_f_KvR32E_{k&EV$8e#BV-2i39)ff+5=qL%b$#sn zBaY05J~QP?j-3kTX>Dlilu?+iogO*Z-?~TjqnVEW;%>Rqz&xYim)e+ z9$SDJ@^lYMyb}UPoNu1o6U{E=`U2+bNDYP2VsaMr56s-ya?H$yjNT@pXSX=WM;yZ^{ij%F8Vw(`W7k%5pK3ALF^%vTB$=QWNbqD=Yu!rQI zk_}&3r!(n$V?}ABfSqjvY?3%otm3=|6h;Wzd#9l&W!8*Dx1BhO!fY#+Vm{aQX#!Pg z)*C|Ymygr1mY>dJYsQ4)Vdvb~Tmn4!+$?5z%5V#oGBZOWETwB7#5r@>T(M-Wze<>? zG|4x4H=$`Ux5C3??uDhr=`9hUkfipg6=7k4Oo7&sSkmUYGNY#E?sE_Ix^9bC`Q2={ zp>7M_g*wl@tmid4m%;ybX?zmN7IZxmNSf=mAE;!u>im7}p;>anj4dgaCVOzDh&s)# zHYhGM6sH$^ko%!~+Dwa4c%s~j3)$3E{pNj2k zDPoT3(-{*sv;giW)w3|?N^DHC%(b0Hvssk`xOvbyIF@!l7i{`}e1aQcP=G zzpcxU{FR%2suS3c3YUIL$vjc0d>zk>V0NZVx7Lgtwre4|N&2AKK*Hhcq~LXT%pLS= z=wbUA{9eVyN%&2w98>2kQIv^@v)IAN;rW4s!``LhW=i62RFY#~uN59Jw zA|v^&%M0T5he8$THNaa%$rA7D6?WuPH8NB(+fJL$Y@>J1XfO;ElfVZIY7mvvO$Cs4k6rR zurLAv4>JyOJREfZdjD4kULE*qm<)8M178RK77zd`e(>;)&V!v7lHmAq@NpmfPWTOu zKiqz>eV^w}?~N6o&<{vJbO6Z#39sI>*Tj=0XKuCQ*uj=0{azA*mnP?V@0#}8Tp z&xs@vH9E)gXoQrKgo}AHQd(Zusap$b2st zwrQ$_sykk4)3{$kLkk7dI;6@?=R!v&b!GNP=UXfKWP%Zc+>U3%#X7a77*W=oN)0k@PBj^2IxPH*kND(zbF4^ko(`!Q71Da zQ#-T&E1>{Q9KHYa|3^aqr<^C@8&UUA0RidG00F7}?;OM2%*e&n$;`mS-r42<54zme z(@H)XbM)Ia7{u(4s&88wi2&*g&(w&TtB4#V26uqG_Pj7m@)k}p`)S_aYA6JH< z9l1V-=)(ObrhS}l_WQiXH1Pj8skYbme|Vb?cklQ7yPBUzV6gM^KF!GQ`#5gj|MqCH z>+^cjtx@myd7jO&>-XF((D#0jeA<31z}xq|e?B+o|9;f{aB%5wAK%COeSJRs7=G$* zFW~t-zUzN~t&!gu!4b zW;K`R`gQJhSD^pWicT$jZqW6DK!UgHB268wFgw^E1?=fCOZ$hY5GyZyO;e7*I<(dQ=I=rHEu?o;IIa`g4_ zIBYsPV-$#w`x$3sB;4us-}%Gi^l$NW`0z3h(O$O$Z@25ApZ|Mqv%FR&xmYJ+Y9C(m z_zt7Y$LH{mB3Ylu=l=2b?hkXy$9TK{`}_TIy}`d@bPm@O=KuY9>K^yErE=lFd@Lu- z+r<=lIypW+pYW{T{XRawwEoJ||DF3|=jrqMIG8+pxhg{bzJB+bn%?i@`BkVrS9G@X z{nhSnA5G)?_qaR#N~`|o{QkIeBO)~gZkbjd?=0@3nrFz%z=R8}%Ag~qj zgq2VB%fRDf_J|PfXS!Za<=4|cro3rZ%&~6KQ<;kzcwb}mJ|O(x8CFIlhl9(8Nc}7uoF1Hr$&Br++ThoPH%DuHHY>mfTk0@ctb)s6&j1cDJ+EN!MZ2pJ9#qobNA3W z6+2$2_B<&XwzNY{mFEt^j=JnjQOwV6jR1 z0KcXh8S^EfgoVmFk$Afqf^;2RBQArE6iH1Npth9(`S@zk0Pq^zijXzB7$%c!^elQo zPol(4l+C_m|EzI)$G{Xq58WkTrzt42DJb#`RRc(t`_I_f+Ake-a4rnXR+>9fLn~r5 zbQEsUl)cYEMM9?(nM7-BZ$D1dN5pF|sAd1Uwk_0o4Vz$T>zC{o)JF zL3ObNh$z8IomB=5`gvG_zrs;$c2yCr#%92v0*U`jOB*P}_F%XJN6?UisUvu^)EkFt zs0;!*VCLi_-eivXYrx(a0`w{J)rIchXz|6G_EjGZoWpOh-3RD$37}TVSDFrMf`$ZDwMl!%R>ePR;F1$vH{Us8 z{2))KxMTd(RZ<#IgE)H!>&JzP+Ae~FAV-N)DPkv1l4#6&-TV0Wc!L|^%u|9>`#Y{k z{sZ}B4AzH~cSFtbVOoIzuHXwrLDv3L0Hs zW`|8Ie$rP*N?9p7p(QTlKk$IvR{6iFGNscT> zD`m-gqZ_}846<(U`A#haQC~`?jJe|rbVf_FiAav$Cu4B-j<^fK4_GPH^%2{#9+6DCU($;N?mBxiBF|Gi= z?x}W9B-=Tq_5R>cWXV^0JU(|&CrP%^k^T&t?cXR_1*#j$rFQRFt`ls|T$bVtLnNnc zTHJUQyEfCz!}hvb#D4%0Pyq5wPpz%+M2R^;pMFKRk`4pK~JoAtjgAlhLv{BieD*I5(!sCfgB%8C4iQb@pr z8)*CqN6Vr#%nUDz7M=KVX$+T*YK+y?fx&3EHY6W2-(23;9iJJVn=$$4>ux@~Ko7FS zqeLupmyWR=YYrxrO@ZbGH%)&rdX*ci#RV&@tGXD4)WvSo<`pwD3_7JP-UrlTJ;xHK zk)FUZn1Wwp=R}Ce7}NUK%J8cM{q}xYe+(I{8p6N0K%i_;F*Yn1#K5AcX#86x7e@oq zuMFnysMijsfon4FC|DcPo-$a|=f(+4&6!Q{(vGw>DbXYz)*4vqZ7Wn^t==O^Wkr>t zEwBW^#X+K~DN#eH>hipFWGT1&Fdf^@A{B75J6{p}0yv88Z|AM!+_lJc1mtNIJ49cw z&tnga552)1s#2)*CDzwag{}Skh$-u@3sb)QqJs0nJN=uUu-|W5V4bd-$bqwl$&{J& zfxwxDgJt1O(H(H1!;u#Ab2B1Gsv4Jt9%jD-PlT5dqH?eAo*i z!`Vz~{dftcwxkN=z}nq-<;y5kpL(a>WaZ=}pcF2gYAr1=cd1C{SGYK2^J;4A>${c^-oTM`i1F(ev4 zNi-JR0py~LYkp9T7PQJsTo2SH5&)UhjbLVg;l{>N`CB@MEdR>%8N%1Y149Gc4Gvc0 zsq)MSbIb;LP*$X@%3y+8g<(9bj2G5+Sd~8G5_<4GVjz$zT8&ICyWJ`rao(f`?N`1N z-Cx>@P+d|r^PnOr8!-bqUbg+zH${A%WM`Tne9&cGtxZ214`W3({{*BXrJB-Ha1u!2 zHyv}gIP|B)t!^|8t5Qd;6|-&(Ni>?xls)POV-R`}6Rk8(pkUy0_B|2?^;BIJx$d^< z88K~|4UjCaZRP@BBZoUTLu5)yVA_YDW>Wt`MWCq(vMR8dIDi{{xoS#)S}d{VtR!&> zLjt(&u2E5xaj(r!X`dO?A-@nN7j<}&PKvSi zCnFlV(oR?6+T~&Xp-vvRShqXd?wEZqJgH0|0cEX$BeU9)r_%EL4G$A|R}ST@)vG;W zJ|>JsU$ILw@@S8&Eum4_9*L%8(r(;D<;Y_$)zx0`hdtn7?Eu0pUB} z)L^G24bseL)Wj}qXeKkP6K?e=x;@&us(M&@Mho1V+tSlq!rtL1wV=m#ANuuDz-q=i z0EAy$#C|)V=!0dlC>YAi5tR|{hC=W(sgp1jJ_rZiC1Ag$sdMX=T2{H>NY%ah0S%@2 zF6Gm9$DxAK+s-@7B>i-Bt3*?Vycz`9GppKe-!?8)t~ecKwH>ffb@&0BMwcCI(-(P# zB~QZq29>}Tje0gaHn44Tspfg*JrHJS9sTs6UY6_gLu_f$doY_cL>X0qTo#Agqc#~I zpq^c2w?4RUU1`^KnbD|HU$eTj zsjpjymOc)@Jsp2~3yH4ozObrLk%lWNTzr;M7{sjaTl&(5-ccd{TyKSr1~A`J*sne1 zjX-&Y%|?tRDr6N&N6K#TdVxP#0eu$@j_0x{0pBtL>3_wp;UiwGNxBjG52r&rv z!vB~$FKnQ{9AxglVpo_+gS#C@*z9G(YqO)?U_{tu>>OgL(xoAu1|(^;N}EGxZ?HWS z5WK~bfc0@HaWQj9c$d^0BkV}eDAol;HlPhLX3BmuOcL@RsZ=rNhHG0`*z8-_DYmr= zu;k-if`V$!;x~}aG`CoLp4Ldn`I6l9YWzy_*emOkQYJ}rbg%oi3o|=+ioB&!Gz#_Y zf%C2$xH8i^I6D|Vid|Gqri%7?36P=q(8fW5Ju6gaC`T2IZ)@?XmQXWk2#QRJ#ZZ-S zO&;@ZD&`vn4CWiO2K{_hYwm`u@e^L+CNco$H`-%;EB-5@@`LK3K}~oE?`=Z1!XPJn z+Sgi*XkqN9TP3hiJziMN>n^gh-s2>F$mwU?DBH@4@=^LIQ9sc_E;`|=JmQP`uJW_5 zIFnM1b;+ZT(q~Yxt%dgIQMUG!3`D;llBMW}& zF{P_&KCpV$RsY9A^C&rK>flDPq3cc-tS_DeF;D@HmnvHODSmNoAMUPqqW$6o#M)W# z1f01Xc^SuteOTHJLvL|DDX9DTtu0HMK5QayK>atqLLzj2;KU1?4h8$zw03y`9(Ad8 zxa5JvqgvT9I$hD2#I-F8gSl1amPSsw99OZBw@1djWY_?a zUQ}Qyj~J-%Mkw=z-_#5Ej`Gxs1xV^bWTb?Fc_HzsEILBv^fl*n2;e9<^j!`P-^JyI zX)KI}r0tx!Lo8(YL=NLO9`2=J-O~}~3*>|RWX??tsCU1~t^bI^s<%2b9k|Kkqdpz@ ziHrKB;8oNmdy(s8)hE~BOrr=g12}xSScR;A3cRYv*T!C>8fg+XYD7Qy-?_+^dyF(}=g_b7KkDUKmF|wLR0%#{LSQ zqk<`EfJu8gQ259qrm9NJ>Qag0H2|A7jVg+q>3JIWsFJM!wh?u2U!Rx-m%p|y@5i)u zaxeBM`8YPO5|S=Pa3rdr8px8u3?#3BTSeKVb0?{$fv08>y*7mWW;#|sPoQ-m?F(NM z)h+Z93RcKc8&zj06JtI9WTrDlg&{AvfYO zoaPxxp00aq8wKNz;lTultHh(O+Q9U^?zCeLrRbm^)VZVTZev`v6jM1$|0JNiqjA`O z+UjXVYCo@b9=u-IYc#*#5aVi*;ifHkp-Fh1$SssU92jJWf=ZU6>-cTP0A19SiM77A zB3{q#Oq9Y%+~sSFyIYtqa-yXq#5Nz4CyOMYY_Xb1F?S6dKYr965Qe=zG?kFhsfmxM zqR&)WVe9P#T|rU?rK#2D;1qa@fljghGV2)^0#Oyx=$Vkw*n8*nCOk*z;(L6h>E<8% za-^hvQshGa`?v-S-M}5#vI>Et3vG4dF{pm*H$-wNu(%wltSkxr1$cRQ3fvfKWXY!A zSHZ%nr+?~Ki6dhGlZ3k(nu*Z_VvBjhYo=Noi4tb0U!7TJ7{1b+%5%be_`%JARU+)|z+piX z7HN66wVGgZHrl9+{Q3`=+@!~w(()I`q`OG|oH`dQMyh+X@~34WkfKhTbA9JE|g zWa?s`UO0;?kvVC|fDNOGUr6*i0Q&5CuJnB^)?(?o?QM(qUzR`4wGad=q2{B$Jx_;^ zsX*DMRE>AI4l&&KYJ8o*s;et z=p;%xnmcl0k0F*@Y)0t2OX|^2u$PYK4JVj3q~4OdH_3ZCqcL&2uF)`SG`u=$Z-Q>6_7JA>j7r0m ze-;Kxuol(OG6WsXz#F034oN?dB@IH(!E3@(r&7?e1|xPYqlyIt^k*v=w)D-bD8cHT zxAOD{!a;_eK!Fk&l#cBlBq_v4x(zHrG>SwU!0198?#=<#s#Kd4(VsRW`V4tGd#_0N zs@SB8d5~yItRoL+uV@~745vP^_7L3d9#tEm^RI!3zYNEiZ|M_$n-46pdUntBDEEMt zkI_lSR@|-p`=iCoU|K2D$yO6$*QRRvyZ&{`O+CGrD^I)TSG&=Q_LLT;T&<%5Rt)?h zL{=ZPP!H$nMu%ta`RWUMP51RC#H*N%%Mze#_$sLHZ$Acxcmz+ zs5{DAA`{ZS$UuuMu4sv}kzCV1z%=gE%{;l#M-{_O!S5$m7e;h7opo>bYQv3bcYMEF z55ttifJ~h_VB*%ska9Lhgo`)xoRS2e2E$a- z5DCd`5fk%(ZEx)&Z}{rFCH?_Xx;%aR2fsD_uI@W?E9dPb1bCS~i+e9+g@*%uaZLP} zSlUw)OUhOdk|XXRs+yUZm)htL*FD2C_-eSBJ;OwI>LN&Id7?=G0%h?^qj`p0%P*b2 zJ;m{Sa9J#pHZL5^a1b~mjWcm6n`i3!JIH09G9?bF#YLOMUSHY2wR{Bw@98-m_TZx& zI5IaP2xz#CEv}GQnX0=EbNYHgelYZ$dbsk^QbIKjt30t~9Nr`X8xuFSws^gRW-=bz3l z3}^J3VNC}4ly_Tu35SFhj^~Z;F$2Wf>&&J}3M5`;(Z`mI@^sVg~SU8zV?d_3J*m}5wQ$fTAjThQExRlPa|G>6}@Cs=9Pgx}y zA(w6ItT=U7kc*^=U%&A6#BpUW%k8z@#&&GPJTJWvJlUiGwxn+y0t4HdJPD%=5z;7d zJ8V6lSW#+n1YrmWAd-3E3XfySJ@u5bMtOgWU&T^#%b2yZKLBBc=*gVqXM-2voY$q3Tdhs_i1EtK zCW7IsW9OOb1rlL!`JWk{y7uoJ;4}{O4-()6DWS51&e@T)865bKLgwX0-%1zQYebng z*JPw;A+rhivnXnilWXT#BCnjULXX?7vvknzQVAykX35wIU22lrdE`+^$^2WbYElW= zuT0pX)-jFAxVWex?r1Hp^hU$bZETj&M+&i7GuNmB@#pI(yKc|!Abhcp@pd)l2pO&* zHO?tInUI^gje{dSYR!#DxLhdc@(fx#{$AEky_pm@$Rg6&<17@*uzQn?z{TPO+R3=k zY+Eq>)OOQy+J{^h2aD03*yN>^cF`4UjZ3@n#n1TW%A^o%MXihJRq2WtXF@sxx4{~yS}RHsgL8i)BH{# z0ld?vcne2D?e_a)H#ua+1AEh@PYFtJ2KAmbez_Aqf*L3G^z|8Mru{p$cvdKjSDV(q zs+;MRsrM}*=eje?T$7&vV(lA)Gl`-$lT2)LVjC0N$s60YZEIrNwr$%sC${a)w^jQs zYHRn$?w?!LU3L4O?pt;H)Hx4$89YOHDBu@IM|GT(K!05r+0QiXN`G1q?fTPTb7`xv zDN1C*U|C&ZU6dM4+neO8#M1Q8qe0JoXdFr5&_3-oWfX&XxLj46s?hq@nR9=2PhiOndYxoQAG zKclPPBK?`)s*8C?H0?zMt*}0AzaG^~=y^y#R;-L3ZF9hHZENHY`+6pQU`&Y(=Bihj zPR(c@gJy~+b+g}w>5v%9AOHKYdJ4A-#1LAtEQO;V`!@0JCwENE0*v;oG>BfjH8p!V zZ=RC=!+2}Qs0fCclC)iO7fFi}?9rsee6NziF=OAtAjoG}nrad=K{%N^>eTpt*LtYY zcx;=J-mraYToR@a!&Kn2I{_{^`aIAof^^Z8wd?KGFQGlZH#1d121~34$fJ)Xhc3Ab zZO#J14~BhA&sd=Y?`(G?EZxLE_mJ%EdoG;^!-)ulYZGC&M1bjgTPuLDRrc7;>5_J~ zvVS6G|FHX8qcubo;y)wW%)XS+0jw3gN8p5{H6?63w-M=x8Q7Vfy&$}6pXw2Ef{0)IbmWIvmXgfAGccmOTE$$-P1;2nUKh3Vp ze@)%$%pIPrX5v~~w0Bm;z4|w4ndAUUG^*ul3GE#sohl^baTmZu>RAm$wZuHvSXxT3 zV9DF}PNG@xNfoOxtF5G*x?uV|mlVHd*r|BxmCk_E*z^vx2Ml8yqa0?A*Mh-sH@8fb zF~TXC^rv&(4OvDUkUSCwCAsUjpeP;|Di5U%TM06w2u|TcgC}}B#Y?VGQXW79ILi6Y zRCpMq8L1#QtRY~v27H2 zXu5IzV&TR-2?L-M4{H#ZDO4zB%>4S{grSC|(T24C)xeA%Y95|Od9R)hz?VlM^3S6g zQM?ZQxsBJA3=%AKpq|dE4j5C}%N3Si!K4@guX(0g1*|rHjrGmjNNw;kqgrtWSru0^ zSEHpLh#_Q8jTl%jf6jwiTSD791@-tBNQW%&qM9+97JG_p>zonpDyhEf@ywQ2fEDz# zfG{2QGo78rW7}S2xDl;HN14W+ax%e>*O&o;%%;N3!nT_ajS&=)6@{wSS98t|FS63W0&rywnjnww)L&}^~z(X^B+|hsX{WFVyNDPDCDBt$jGF6qLj8O z9u?2pK%PZ~7UumEyIvL}y(p`KGH^7UZI_JS)5MsTp?CF|eKr;B%C?1*3#`9ATKW`D z`;-=e9|PyJD1H+H=>1*V3PXMeR;#2CAix+KhjvsSZ7wz8GYY%Wl{V4wZ{uzX5m^wA z(oL}BT)JPB)p3VFP(-9hX0W};_l8f1$g7@1^ft$DHp^1Gzkl~NtqN+o)SR^NJC#aA z`KR^ehflf3gVProh#2pg{i=mz;v7Ot-AEuQ>(6xi+y@J&EEn&wML{A}8WoET=7}4KnB`7p-;v2k_oPHm0@t z1^*Yo?J-?JdD9CH58|Qg!pz=9t|KTbYK-19e(_-KWB1uvP=x2*e*gq1hT4P}9#3Jb zAo=Ku+)ZUpLTmYj?~TjesUl;mT=$;9!v8z`5o;>nu04|(Zk_^zYppC-W8gA>iXhNrJ%DVrD9YR}7h z-Lxjtpv@DXoOfFn>|0~3g8R~tRb0rIZ@yB^!!!FX^n$Nr=6HUz_#+!B}vqY`=XRZTc5o6XRb*MT}D1={o!azY+$ zqK9Od+A2LsDxDm8b~O2J)^8D}GyC*Tio=NcRbUL&nDs$=N_1Vr}n<;sqmeMCEnp&6II>VrJw(!ho%do!$rRUPQ%Je_f?z{bB zt)wZ)lk?Uq1@lNqmmb8)OEF0>LtM{RiR{x3?-iSH&3wQTBN7{uIR5u{c#1&ZSOJJ? z*I^hU*-S~Lm&#wKu{Q2>NAU<-zIiCw)r6rJD<3gA{=5*pDz?3We{*Q)%Pl a|Roc=@V{2}4%+V>clx(66$5;}ip~xPgo)j|#N5~lOO|5x% zh}yLLN#XBt0=0tnU(l1o*VJSnP_9M^uYpVbr$5Jhyq(pHGM;*9b2$+5y?C_ z#dCNRBc$}>B`UY#0EOKP9p(yiN-XrTi^EQ~$_puqhul+$|`Ec?fpsm;LUZW4KIe8FLz_LIc+f^%N=k|~ve4iKDRgAo;@pOr-nB|@r0 zySnX?VcP83=9X(?*?yjhg?l9S%xi}xNUQDM4uxb-b znl&_Jg6XZ?)BuyR{7~^7*9=a80%ml zNd5mWl+wZ3#?k2qRr!C1Q!Zu7+wQTW{@r;+b6;a~LE|dSvo(f}oNY7*JH0SGRIs&V z38NlyYSFm#%}FK1WiDr>brE|Ohb#vPp>Mu!TiK0IIJ~e@prN}Hx2pW=a;GR_czpC? zrL0bFI6qYGrdsDy4eT2$q)3jCRUM*Ha5fa_N?ti*)oA*jAZYil)fkxnNdBnO%j%H9yaPwA45k zUki>qYm^k3r4vXvQeF%PaSj7n9G9Cbc|R*BW)MKUFkSdkdE!faD5N)4R<$PJKLGO~ zoQU=L2KkJ9A$8_qZHSpKa`v`+LrLo&X4$ZaVDURsYNV_SgxAluRV&d-*bZGY4I!8# z43*X9$GK2nm=gOZqCX}uN5PJusn}U=W1Ks8Fmo^TshrNA;Mf;P80X9=<0?phpeRU( z$J$jGoMBW}9G{7%@D}ta-rzjQ3|dxlA@vY$m%T(;k=K`T3wK*ZE=AqZ*mI)wKzYdD zrsrXx^-!J}aUY)ud}&M0%v}m|r}&oawR~}*%>b>Ay+mCtmzgxNyxp#mUxvOlBOmQr zaYE(B!#tAkg_-sU`jWFISuQnZl=O>Z$$>MsR{Ykj& zI2NtkDsG}>a(*m5Z>F$0TK`?r0sUIRFn%fg!CQ2n+Qt;s#r=qKE_lXY*Ei;*AO+41 z2)>AMTkTFet1{-!pJCqviX;XS4Qj(%FqOgrBIchg1o}z^8FYq5{3FKrbFGCBJot#j zpFIZDNiKQ%CmOgjqz)GN4ihN;*DCuy5Z)NOA22Cp*r`j$S@*lD4y*Vc(x<@m{7dQ} z#k0bE43$_IG7Gd}GE>?cxIA8+r7E zn>nF#6jvY;nkW8y14h5cukRb;R{99S%@l!FdZAJStUxCTKgRDS5#c(vTZT_}!EbjMqc1@ohHlCEp$%*3k7dp@RU>d@8UL zQ^!O^x(xox2_)esh!X^-M8vsYv&1J9gboN%wz8g-gv+G-_}K+Pq1mVg7&(GQ;(plz zVo*r?K;D7T@xoW`6RNE5vxt3(0-%bb`I}zHAV4Rs&WVWXc)9c|@`A1A;PMehB-}kI z$o&T+h-7?Z`gdK4h&~*_HxrCOD044eLwRsQhRE=b!@+r7@_b~w>JWB9t(Z~Sm#Wam zs$$-fu)Ai6pBu0xzMlBlYrz~*=m&wPfYkMObl8$PS?`W=x;6uczq~HIJ7o(Ahh6&TDIfNZazm=zwd=jCA@ni`yA@ik?ck6wl zk#`pvWeGlz{}F{pBM;CMQnerjRR}d^5@~TR$j7tquSh~6@c>2D?Xwr#CpV_V`$Td{ zykX7D&&Sw{0r-YBCH(GF{$mN?>3U8HNOF)?Gyj6Zp6gfIH=lR#{~2w5LC6g)00{(i zj{*dw^8e?E)VDIVbuf1_v)28OLy}J4!NlbML?DV%leYam2G{kjI!VfA+((68q1K{| zY-p&r0%LaAkV_HhrYFf%cuP6zJ45aB(1?yhu;3zjqOTek6B9%8>308he7=Fu2bgNbltPL<@NTm(c#Lcv$MVJ^)gYi@f@+^Q_ITg}#)zkI;R&$)P@OrKd$=$ix zKx6S)DAQdI<5+laFsU$xAjvS5&!kymX)9Dl~5Nr)(l9Ni46L z`M8inI9Zz3I2CH3@IfHKBcrQX=O|H4q&Zay%E(=oR6u-_;5B$CKHbzIeH;k&61G-(2B0b^zQA zBg`gcRpk2m33^MZCZ>li?p2qmQk~S)R>jM>gWAIh2Tz zO`ix_>U{5bd1z3m<2?gYFXs75Um>KP!%CyMO#@;fl&?GVt|hKxaxGSNmjNQWIZ!$p z1+_!YX#u1qHgr0u7QaC>+B>v++?R z0)^bwKX-c{<7yB&@$G zY!Vl}9sf$ZhNMpB1$VUtw8NPx0DQf0=~UvVE8|>~X?`PQ!=1=@`}s0qQAbH&VS4IW ziO)b~A}#vmQ1m&Oar(A$_C zhUtsXt&UfD!qpapXZcgE200W-XMwo0UIko2KP#hE6U7!PnTh@pC}oIMmxm;(T6&1( z4p(u3MoY7W5ivrO_@&}6L=g{%FDv>7AV(%ZGEX>QY~?Yizd+-b9LU6SpqRtVH?$S zfww={5Nxs#D#kV{0&brQ+7yl~1LeEnya~e9n#lE{R!@buCJBHC#fz>RgYKBvleT#D zqqN`24ZAX6#%*A11q#piFL=uZ#Zoi$jr zJinhr+9|I<3eQn10@C2>ONlq7VX!fnZ=$gw`b?*SAgJ2~BD>*<-6X0`hM}DEUAN=- zNDA4mp?hok+n{#E(CneA)SW<04MDd()I>ER%3@6b zhUI{{u*MQrN1K4Gt&BOY)dv{qoG^Yt-W;A?IiT%WW0uC=ewGv$v%>@f{8N{OsG9;D zNc|D|;eMgHEl$V&w#?GpRTSo3-3Yj-L6w!Aw)56j@NRQxz=rd{9G3$V$S`<-j;*%( ziz=f@{p*z6?joPUj(>}_E0T=o_oab|Y$$kA6exM@z$=3kF()VZkFgArQa3CG(#8WN z_^D_Lk(5g6Ycu*qA0^mdIrH+$3NLf&tyyesvjPkG$o3;($du^^a=CenvrvYv^rty%T<%=uf>wah-BV*GCBiMGjlmL9eemu0^`^el;nn6Ae|JYXL*0llhnWJk|sP z*S+ugH?RU=y`T?{p-}fNw15>-Yh6b zSeS&no?P3HkQuYfC|3ap_F4eCWYF=7_RMTH$T}+tgKys@@cX?xcu=Hf=tJRRCNy#R zUU8^`6o$V+=0KG4UN12JYxArM{w*R5DG+0c^dzML;T-yr{;#aEu_E&D&R__+V~35^M(uB)-RshN|pk*gVu1?@McK5&h&@O%OH(`eDuwbeaO`+be7701Em&XSG6gpU4|zW58RT#*o0B zp!RBKNLE#iJ(5*D`&2Yz@*L5$d9Fa5$Xp5-9KksDeszIc$C^KKCo-Ds4>7h|RR1h;R9yMW_Q(h;qSk!ctglVVk`dfYtz~*grA{4EK&3jX`T7ctY!x z(N5!Crs#Mg3epKccgnDJqYTzlZt-iHt4GOH%uxn6U$HMVPwO$+yszYnQ}(cpbUFU# z=3?{p8DHP)$V4;Sh?y@alr#lTfX_mO?g{0?=-IVnX@(=@2JljLgGTT}xJdhU>?2^^ z`qdMi+&9Qp-ZW6GsQrmZdx>s{zGC%}Vzh^@`(u5p@dHwJ^Mpn;GuDhIRK8=5Dv^L3 zM)KiG)OuW`%lWg8YA@I7N_1Tq&`QPIfwbN@+Ed*Xw5qFHI7fYMrYUM$D9wqz)BnhM zVfG$qWxKe9LS89z?8Iza(jh(O?P(ZFx)tw)o8Y_e_zU||M@(=3>kS)h7bmLaqd|sF z*7JpB9J}+Z@63n@!6t!+n<6Uj8!(w8V%tYH*`I!#^mmMjRE*A9;gqex8MOwfw7|{T z1!t(FW<$+Kd#jhLq-5hno3kCKp(tmg^w`PqGn%d|vxj!0Q(edEPkhI3>pM5)oR~WY zr23;K88XKxYCCi;8O@cB3-`5EJrUQb0*~>DQ(bxVMxBj+i?-Gc4}uK<&V*7I__O$t%0a+U)(bFggdV)yd&@(f zof=nePp}>s6>oA2R?^FtJ@Rs1Hq%j4aC~Yer)~NiOH0UM@SV!7KI+**o6=@6bex*F zU>VHUVr79aX!Jc(nF>^yQlugOyc0w+XtZOqWi0y~F5lt;@h-44*CC(ib*=C{W>-=+ zz2y#n2E8W9>uT8EA_Vq&I1HYCOIA8>g{ge4_t>hswg*ZbE~l{p3kO}#-eqeT)(E>} zbeE>9-viZ0Rm~R`TO1+UQ%#t8(Wn>^&!H!PFa0ZT09@X?080)>Da+?NP6I6d8_|lj z(@w{FpU4iEB+NF(EBnKV=gc@95KuHv>ofx4tvT(ln+f}GnYi0Nmwo_7YEwYF&z9?nr| z$n_o-5>Oh{ufb`%m|?jYp1OnA;E(lt7W_GXoz}m$(Opo=G`sQ;i<=|uSNr

G7uQ zzo4ZGFz(EbTiOhNjf1#V6#QVbJUTbEn$~-|z93Ft>W^8&l;eG(mDyY4+v-@;y871p zlZ!9Icepe8=?SgZ9Nlk}TqxGw`{)C702W0*nS9{+)o9G{4CG-T621l}eiLISBJ;~0 z-n}m8ea@1uGHz5ZFb-Neiz?S6;V+7MG9yxPPT%(9k(+2`ZZ|^#I~AB{g^()q=fgDs zDtZ7feacRY)ze5JGz`@<7KQRDmrUV8U}v1<^`tK|k0BFF*aXZ_a3`GK8YQnvAT~V^ zl#q7dS6>kEkB|jpLy=mG?N0aKslQ7P2IhYoQszUoAtvTJpeDXQARElEI#G&3Q@TwW z&=OG%dt39ff1!krmdItL`HPu|a*ZA0`AIjf{))U{<71|MDXfK{vg!pGuILN9{9fz( zLRyvg*|~7@fS#2g>K2?}Rx^>`OccYxg6IpHP1c+RZ*3HF)Pwl@$`8z+-m$3la17C9 z=#p7{wjsa@Q4poC5O63cVPaY{Vb$yw^NKR|{J9s1D|TI>W!m$7w_)p>^Buui8B3Bq zT3IxkI|kC8hA#08IEn<(!~Sr9;~0OKkI6m0C^%{JczU3Ka?jQnmriI7PmEhG%s5+Q z8bFQ_^1n6+$~ITb5lvEXAXzeQLL2>O)~vnrF6$D}9#i@7f@-VOD;CX$myWJz&;`P4 zyiEO%xMcadrFtdbzga`P-b4?LqVVL3A6X2+?@L(zm+GU}(U`?l8ko>@wsCIsBz3I- z-ZvDsD@T3^@_B?fjAQgN(3%RU%~%pj!+eka!M5D5Hi~r3A=9;q^9xh4#Hbn9UOD)!pwt%aiB3VkpYEk z$WIbbWcpyt8j7JaVGQDRwTiJSO!;I?`V?m^MKLtT58sg{D-M5$DN?QHmyT5`C$i

UGwV|X_;FaF91x%29X34ed9Jn za|O%~VN)=f9&CC|;6EMCzh*yJ90ES_%o*#6KYx*o-Rj35v_K~`1+~qV7DhzKE654! zLt;n)K?+3anW#DB=lcbji=<1*J&)~jysU0y|H}lySWL#jE>Myhg)uO#n<&F){kOP* zL21t{k5$R>rwM|*gE~i%i`K|>g%?;*LkA$;7Xtb1A^Q6;J*zdcg(~q{c@UN_oC)l12)Qwo~kW!$If2PGOb*$@phr2cVp=f^VyYZ-%UAV8= zY;xegdAo_Y1nYGK9C^vSR#Z_Ou=NHr{Uo3k?67f6CN&d1+XE$T1^-UT+fjCbwq3_+ ze{MU(VGV~qQ@V3KdCWeVx>%8eLD;CGbEZDMG%!hlP|d{OnHGupx;N5R8(c3wX4z>^ zRw@J~(iv!@WA0V_GSik|4h}lhatZeu{_T=QgY4ZQ=>`9WX;q=kix@W#xmPnCZK6!V zi4EG87s@jXrSf96pqP6ry9u6aUP9BZN_Ml#PTD{o7dvn>M&1dIK`=2;IW?d`5rh>7 zLdl;0_QrIKuJL-lNQB$Imo);PmO}zl8j%Yo1yX;P#lRmS_c}n=Py|32!oteimQxMk zmC%$Y)v-^W5fGKOB|_7nL>8w?E}MqGCOcu98?*_uuP{-*=04*+^0P7fTn-MBu}&8m zWctSjkO5Z>r=gXk_~#JAE4SR#iA{Mz%||;bFE66V5?Hj0?(dh%Q&wpm`X%rEW4=Jq z0!ad^hGVm#uffzvst~nn^>zl);njCuHHVp50e|blc=Ec58ys{#_ZNEhH}Mp4+zmck zJ2&YGY**cCp@iXSO%{enwNQQLDdkdh_-)bG?){A|8AqhklIs^-$7yqYI%ArYf=Ev1?*c!;^TJNj<>*bz65ed z738@8P0R9oYgrx3kYUMzdaW_1R>)2j0pwH^5UH?A_?5J&z)88i!m{u<+e8dnvGAQ!yJa8m+t)X<7witjUu~$V zsix+|FGna1y`?CRX>q^E?f2pEm$1*$3RS%6P zJ~ZLp#3zl&7Dm3doNGDl5^TgZPV$ z*J3(usa}zp-Hv@Vg$7zvoijSP$lb~ErQVsSYZaS3z@E^T&WLHDFT|L(^9!=h=6>$- zc!0T-i`DoskQrP6uO2`k@XF>$x7~wBj>Rgbd4e(TqcA=JSvgoz1G&^_r%L4=i86u` zZxV)lRD0ql%3-sVaTllR<);Z>{&WCpO`0av(l#Z>|KL5Gb6GviTyrTapgpvn6x?OrI&hUYC3oy0Z?UxuB*jO5?W=Ss9&|l&%l-qG zT04gJll>X=E)ck*U3?m79UF7w!2Lf)Z|!{$p78#O0ClRHF^+zl`;@B#%!BMh*fseb zwXQVrj(KZ0pT)|Ae(`!&b^{xZe=6qeI1mSaO!KLJuflSGfuRBQ=*r!2&%4Y2V*r@) z?a2TOgZVu}6M#BMdXX_VECZFl<9Q6D3Eg$09j9-qu*X`if)iox?4e>uWSNQXA z#3QVLfN;=(fn@(5s{;Q`!^cJ6%Gvn;-c`Bm(rh4EQ?7cQuK8EALp0#EmBNSX#NVh7 z$C*lUB39{V)ec(*T|t#)^W`05J9&t7#YHU9FNJL4J*wSC&9h0|@2!30JjwWdam@F5 zp7U{6^EEQ_^-!_%adTO6{r#w#)AM$f;rlX?^L3Eo`+T4CHL&x2Q1g9N(({tQ_uB9? zBZcp~;`__B=i@ZT=X2_r&+BoD&-*K+=KEFFHxI-2sV8T~=lOMq_xt7%LD%1ce>A013uR$M$Y%?`87)j-}_Sz-!}`) z-o0zj#9`0J)%`Jk$5qYu9yy=Oo2jn%=TG11x#sKh{+Z8ZY3A|U*7tMc>SDw9)%reH z(q{x~A&KvOs^dVBNVeSMFH=fR=#h=}jk5<<@R;l_3MS4oY} zqapr#Yo_wse#!?OzN6N?ug~Ye_a~dK@ApsG9}_#ik3YNclHpTw?5_2I|8}zTD){-` z0DeA_eQW#uLGJsyrTQK8Fz4p|e(wAJlri(OWuLk}?_1knPd_0P2KIcPb=0(tvDxx9 z&*=JmZawpTT;}w&ond^xy?(zQ{k(>Uve(Zu?q2!W502TsMnoSzrguE*Fy3dnKOa|i zydEobeL9ApDs;Pg9$NQOMmlt~Jzyts)}GiuMVoo1dy=hxzHI#^#`o8`@5hs>ZI5S` z_w&;ELxyd11K-zB&&yN|zUlYD%-0@y&&OEKfr8Cx_g3?J_qF>$+jC|(yrzX?;#3dE z;tr?fxcBk%?5<0ffuy&M_rd0IwvF|Q<^y2A+gkI~@Mr_`xo$~oSVy$WTjJUKWZ-DD z>!L;BxT}RWYTk0CWNPiXIY!~0)p^x(j&Ec6U8;Mf{CO(zIgz8%dYKjc`k#(-gky83 z^`vzwOnN|d-}_*1VLsHkvl~O2thDSd61TrP z{^}cRFuv!>#JkQLx@BI-fGZ2e)flVb>Rd!*Ar6&MS@lz;pr`V=pllQSB zxwKm=!fKNa;+0p_rm`pVTzbNJ0r%3Ql&6^NPr+&GmC~6D-j1`NYfT8XV@4T^ca!RI zF~m}jXgOo2?uYbqp?(?ZX`Gu*avh$zrAFabr_zEIcDaIk|9T_$2=7Q_%(p|n<&$uV z=UBQHZFRe&@w~x8YzdN>R)=Hl5~t|%ZE=|lPfXQyTAKJLV;f*UsfB5e&)a{ykaFSb zYREOMg|*2r954A&>uGs?)vb2b#j0?zd}yqiO(ts*{2s;f@WirFz9m(kL^eZxBPSwc zIlefq<2cq+rtQLh#(d}Q1WtbOWxEWwJgNw8#(@vN$rFnf!F%jP5l3S}9>)60UD`f8 z$}4++VHA%%{>tlft*hiQu7HS{_4+o7g@xrt+wsYv`_V5xDD1sF84#Z&O=Mw4tHL+H z>L>v)Jl@Jnd9F{=mfhNxa{Kg_Zbjd^O_s)|DpQRwj5Y$()n5ypO!r|L)81-n-n+=! zSO!^6jgF@&Ksj%;tdk15o6WrP$Z70X`%~>5-_gu!%0iA@-2n=MaXz@Xj=e74u~YGR z@l*0ybcsbbH^+}uS$0?>g&`?$!dNNrNYTO)85fj%*@}!|L+RGRk3piy*1d!@ZzJKr z$d@|j22fkYhrCWbnOrg7wV|IEP~lR(QUlAs0(o1nyGETxZs|VGc?&UiYVoFDetBkC z=Y-*Q@6@9^LfL20^u;fC=E$!|hv{w=1V5=g0J!X&EUPQo#4*lGLO!xP1v0$RXPS7m zIn~~>B1l<-i)H#N2Zw~}yC34cciI`F622^lGNixhGDtL5OsG$&;C40}m27^3UMZbc zCsB~_Wn1d%&OUp5i-pn;tAHz#j~`r~Ja^vWUjaReU+BQ4y=6@&tBOHa;NNHg$jV%X zUKlG>poZfq81w}+NtD2Qi_zJdxPNV%xMvw=Pe`Y0br^i^PLp~*%+=GD+oBG&*lPdD zmlr_?IJ&}F;Bj|UreqftKk~sF7eoax$l`D*DoAn(gy*V60AcB4x6u&Jc*F991s9N; z+gYA9H*l~R5F$P3X@HBcxxFu5IUBO*6C7T=_BEyZHb0jNB>jRB4 zB~Id;Fu)~tn~dwk`5(wfRZ%^1&Vmw(G`esS$?q8B0Pf^8z$Q$EZZFqyv!xg7X=DE(NB7P!aL%Xtfg)7mdB9?I1>YV)lbjq zimbJ*psJM}0>~poI!P5ETxnrS5I_cD)>1!C# zw39*G3^3FaksOy8J}7%^ai&z|_=Wu=1!Z07KN_vSdeCkS`}eTvNEtjho63<-16af+ zWHDCxetOI7*7gaaF!F%vgl*0;5uZFO5x2}kla}RkPy#-Lx2&iY_#0EPcjL z5!k5ykQ(AmupJr}V$_0&KIM)h0N8=dsoj8(n`nf-v0BhKV#ze3jgv2FO8p|K-S91NXS1g3GafE!FL&~T%exZo1^ioqf-%J(e_um z0*dQ|%%3c-PT#Mn%?6;U)}gZQu@16=dsaHOa)Lw}_YgUu z3qWo+KeQYqiJkxXiy|spEe98|T(`eQy6T9wwhRh5zv(i&*>GJfT_+(oTMIYY(>}YhAcS5wu|QYg+jEDxhn$3_esJmyC=e~(jl;DJz)3b zx$H@QLqz37%J2v$$gE(`ktK)_RUBU~`)C*;YbD9aggTIvb!K5DXBoFO2ZAni|BdRf zP8A89yP{TGX2E#@_BfXIkaGE(Nb^S|h-Pha4)z?M2{XFsES=h;;A81O?b5FfTzQmx27$Tirhc~gG1JLU z!ft>(i`RpX#7zWA&Eq$7f|Ya7ZZ8aFWd8ng^Ywx9xWD8{%R06_8Nm?u5->Qps|OUs zf`#P`xN>2~6bSgTq7K6KL}^Ykpkz>q#P&{_q_sLKj&I>0nonqr*h0vdE< zm}H`D_!}r>?e=m|WVE;6hUG>EJ!CxjCXOE8YEAl^22^gwXDiQqBqGmJ8p^<`m#DMy zHfB9iViy)q(adoNxn-E_5&HSo;jkIGp#YsJ`pnX%9-{(V@2qu(@wPMv_8eq?43b!j zQc1HG(c{?r&$nOb3S*Lmg?7{f;wisL7U%Er3Hk@tEeQ#70{_ARLw zR~^eIwIJm`zvPL3-B^XSP* z>gc)JcM^Bf1)6yj9H13>3pM~ojGlv(@D~LkE=k8F_WlOE!R8Yc{q>T`tZ3|(bdckA zwb=RyQ!L4G9CW=vp%yrGZnZeQ0V^&#<89;p?~!xb9KpsXw~`f%KHy|svXe!-Qe6vy z5jW~Mg@+5BtwL6EQH2J#xFnLW5ewSRz|`DeEf=2Z zI1`?0*C>+UVOn1SFCGa}dT2#6CdsAY+h5D)@o~#6d>Mq0RSA#GM ztZX=eY&BVh#rRC719guS;w`RnVSH*fp$)Z}HBvRG25wcovVM~{ey8bbeD6HF-&K~65fIBuZtErpYqqu99?Nid$k*O1=4 zD#TWkQMJgZWC`6s;0$yjPXF^{L#1EgEqbrPBVoDY;r`CL3C$=^(sdknt~klgqf^x( zUk@&UajDcsojq6Uov^&#=w=gn(X03n)-AL?;VSb;d}8-0LfKwNk*PtxT%AKy z79wF!7b##}|FD2#Sq>53p;~;qV^M(^Uw52CuNE@$*=_y7WLiXbGgpcL&O+>FyB_Dt6aG7X6b!Jseto8IJcxTbSB#P1bb@EO&fAQd zq-4H#B?M}vay&nFzwgNh*H4M!o}1R89m9ke5Ea#Bn8evvg0|QX59ywDmVOXV>+w*z zH46>Zx8G>1i%#5=iAerR*b%pdR41ZND;!Wb`GX=%>k2y(O#xv@M|=GBhtXTh;zaek0~C(0}=(c}b%9r~eV`+tff1q%C1K z!(9YaVuRFrxLoX?BOan6+DE?$$chiZVK>Z|XDkfg}EV9&a-XyI84bO8)4@`5a5 zAu$u#I6c_}`4RZBGtoHgugSv{JgMA~Tgq4Yz&2;#Dawx6$kLcU7|W0o!v+ltW1;Ke zg=P>EJbZ(b7)Sk%W5sWdHaPX|)a49wkh&IK%H62e*)=rlMY)=3d`B5#YvzL%fkMO+ zERi9qqhd${endeV>zqmd6{R8~s*KjF&Bf4=k!g?>y0mfsxehGLn73jL;QxK5i}QLM z8zOWkj69@|PLKSGe&+gEVe>W&qr7o39~m}enTPX;J2jQWQ)=2A%mNLz1S zs--X5B5zxaO8u+kJ3O)s-coxFD&uT{*x!3<*kH>FJ69Aqsa2oXS28}j!t{J%PDt8= zm}fofiTjcpWdbRA8X;Z(gXb_I`7pMcVvu;F-j_uGHSSj$gaQXi;*lTmr=CNYQr-XyhXtfb`<@x{ zy|AUhi(PwNR%oZ37N=J{P2KNU0@RsT&2l~8!Ti>M*iPPF!$qnwv|ECige0poTc$Fa zB0L*l6L8e`Lf{~aGwnrek?)WB#!TyUIrr1xNaBY9t^b8@`wwoim8}vt;FgyfiR+;8 z(Pl-LwcQ57mqCdy4u`>Oofm&cvNk=HuzJGe&xex}T)tCeaL_et4ix!_1ZMZ?Y13V_ zE`%D8b38w_iIudC*fQl&loWi#aOWLYe9Qskxjq80W12sQ=VYo9vxSW3hT9}B*0Idp z&M(LScw>=zSmz^gCm*bmSCljqF;EBfrZ* z$APPaW^(ECysYUAi%^e37dcP|c@(7o0#-n&zgZ4}4wAbbUV5m;O&`@q8OOzvwq09{ zdv;La*tzlFO+$M}p#%QSw^5|XwaD2+mhJLYBEJoTmZr+4DXb+d=#z(!S^Sf^WdhAoW~t9}ap7&{6JIjaoW@>{ehd z>Kl&0q-4?Ae2IhSqw9$@4Lqp!Y3|NX>EqBjZ*qUquzH>fbjKjDPFaY-`%xfJq|0?M=)B**hv1f$$v_H9kKw=uCD&ITaSQ-m(%naKNx5D@czyu)CCQX-GFR?T zzg#1>+quz{$Z)H>PY)kqjL$uTXPtv+rm($}1=1S~Q6 zY%6J|Ns?%r+h3IyBTl|)^OukB{nQqcN|iN(19H=rS&!HjI220J$! zHCXQ}Ntl-+1Yn!vxBWcH7_j#{krT|aDw8@gH2HLGSU}*zhy9>cJhDjuHIN5386VPI z3qYnnF>}B#1YHQO%b-GbfJZZMV^V}r2XHe@V1138Wi1@Kqij?mNwX%WF?Yn6l+6~o z+EC&~-jg*6H{^N=)h^XOvTRxH66%s})cQ={DmjNn9f@q*l9#$A&Y(>oi*XRxmC%y^ zBPZOd8N3rwp|QZv4J%=jH|DheAn&>XkakL{qPhzqx4?5~a{`y0VNajm`-yRpmh6_f zx9gFtOhVJc5OiE1i74P?y*w+Kj>rXZ^3#B}<>a82z(_PeUkHoLJhKa4W_0x>+CSR- z^6V{Y2&hjWTR9wA$xlr;hI5hWPg2i0fVUIckIX~TE|uX_YwyMsj z{`r&FoSf8xyD)805ML(^C~r&WWgC#fyIVeI$6T}!!daCRXc1tvz9dX3??g-5%XZ-8 z5bvB8$#n;NLpa6T`M5Fm`wcujr4ojnmM_o#*x@HKHU(5Kg%r2z(G|2I0bIesH6TMz zMg2f+_({LOZ7{58)S;zaoe*2mN%CD`$jts)gmaZffGn^!06|VnL^4A-7SqG)kD@Gu zoR37<0?SZH1jKag@2C8TOBVpCnlP4}mwlR4NR0LX+DV`!34QQ5PR`ZqB?P879M&Px z(?Hsl{r-q{9NrCJC=@$J>GEz$-^l{iz*=Fd>#=rlHYZ1CSUO9_wQLyK`RIyw-0x(T zbQWy!W8)oxBrV8xc69dN#mm{9vS?ddKo#HhPUu>9u>y532loMGBh7*=h!C#9#YQII z4!DJEiPi}{mxVJ2nTiBS5jslef}yrcG8}Pi`85+In1JBPo6tg#Q!@!GA369$HdAw> zFNjnesep-RA0+|VNRS%U1+t|udbBS>gLV{VNBmis6VB{>SC3sUdBrFU2MBhj0wj{= z?Rs=Ib`yG0dZGL&LrWp&VF%NRMuSZuy zNEV%=jkERz8GaZ-VVkCUnV{HL=jd6EFtyC*&0u!TH^GwJ-$0P31J$TwNh#tXIMUPt zd9p}{1K!0bAmcy`4fFJMybpiuApt1Dl3tYtv^m6dV~g}@N!Sh{ok@SR2<6x7H5+Cn zaDt6AmlnJuX#OJ=Be(C~DCpYt-nI{9j_glI`*6}LzzeByX?KDcMN<*+JWN8TuRJ2Y zuN|~<4L}t!fCM_xc8>w>QaJ%Orwot`E%~L7?*(06fr;ovz!j&1k0JcbLm`6pXJqLS z!k05~PS&i~*K^0S%j`lpkdnv0A=oKriEBwOY@|=bISAzA z(2x$(zSgSV>;BH6q4x7Ynvd)X*2sG{nO(!>p!rdXFDr%Q;1(9dcDiB(>p?&#BxNDy zQV2XOF@5X#?nGE}#o;tqnL-kqYDr?2DsBL6g$$xTTNg&1F7YqC@U|_8&j3#vNF0OD zCZ!@0*v3gZ#V_sB!qb)jBeT<@knDJX^qGbsQD$*Hx)y2gROv|};2S@q63-fKJ=1v?A16qfr}%I8 zj7qjm-!C4(23V5seC@jI6Mcm#n6*0+Su3qKna9OKkDONrP(~$4Cv62RWPekv%j_P` zXrf+(Qpy%mZUScpj@llEEHyW)49sOV*GtG-^hbfV)Kp`B(jkNPIl}RqIHbA@;~{D?1UHRS2%*I2+osgk*-rll zdy@CHSM7*^`=E52+Wjp~ZOBS!NIyGY?$};$0GA28r?u7UvP*is6 zd3t%Rhdm$t*ljZz4yb+V`2fN8tiSVsOVB+KO*f~LuO)iFzxlwPbqO^&*8g?q=Hc`a zU;=d=b(W-T%s{dzT|77WS=7wbT(0=9t@3-Bj>p$YQw-_(5_Jp4+mXHAk6c)y+Q~I4 zPS0P?>s@28butlYLgqN)Wl}Iy$%NqwWa?|B+3tFolC=^*7kQ_fE68tB#{`LUm7})G z**0UgBUwK&0txk0D+-@XpkiAbVyVc+F1COPPA?eJ@iaj8sjhUuTtf!T&c$x$qjo0? zLir6GE0a1}0CrgQj|onX+L&4&9>sP#@nF+orIHrFz9QBVaM>uAbg}MB_0%*cn;D&aAb~=YI#kN( z94Q0UNwBL&J&3g8MiJJSo@5HNnDH#@|iQCchnbU*RoPI^>61j9r?`vLjL^!7Wu<8ABjy&)`lCBqiSFK zjZ*L@Ut5t@+xLz?=X~(>3Ie`>3p|Mwabz|vBE<8;dcobsH3YGrS`T`OtxU6VO;|bz z$LV2m6wpGJZF7K6l2xPlR z6ijkZr-N%moa7wvd@cX>EVg(A#CgQCbNHxZGy%*csth87;hT__XvvNFI$tfPlo}r0 zWU=RF2sTlw8KhKTH=3-3W_kcQy;P2F?G3JnX??haMAkdCRpD2GZ>R)G%OiuugaZ1P z0psw_^%9^T6pSRQS)@hpWqa)Ror zC->kGfC?#UwOZ8RS02;9*W_jZtP==ZP{Ew9NT3zpavM zm$bUxf+qsv(0bGu8*T4n(3C(&D81Z+tqZgz>{Zot8&F_0LP_Oa zt_71PX1l_$Fo{{;g2j&Xg?>L`J7$doz8}Rk^?b>1n`}S|fD}R7f!iXw-L}o)2=?4`AC(M zp#7uGwiYT2SI(Ogmk!NQwHGW0#nDTU1FtJnJ>9Kh;DxylBnP21Ry1#ta6kff!{d+{ zmN>|GSn~R|xtH)mE5xXfCW~hx3FxuA> zek4~jY-|#uz@!oQ^oACYaI(d)+)rn3*S(3g1-7F9z0ztX$ zvdQIaWbo8h*4U}CXL_(HMrF4aL)@gRVBvMz;w+-R6FH03fRUq zwAGe>F^cU^r~FAcb9H|+LC!UA7EA`e4b&CBZ;s^1X1*4%f(8{DbmwfqyV=f1o|OtB z$Jb<@RaG!x;b*j$* z`NXQLAP#{}jdfe{ef0r(q00~&BzupWmcgIa+;6ar^@Ux(yz@V6vZbS_wQNzBO)|_e z-u4>w>B*+@pkUj0w&54<24OEYI3w%_Jj_hDhB2cd-m30=q+By)ZD5M);>vp1WrsJ7 z_iEFu1Q$g;U-CO_Zt7A=qgSs|775%fpIp$^bcR~F;bc6ef_sVNKC#?R%qxPL5WSIn z4DbvvMwurqh|YJ4fF<}LQ-1N@W@d8Ss6V2qcAf988G`7zL~VAVaH21?`n#RfqK(-08pAi*+G4P ztkccKeC!elcoOenSU{-Vz+mmcsp^t+eUPhZngQ7u#Nkj92c}ScA0=?Z!)t)@r0dbO z>=l?VSz!XU&jR z12(!tn6}L*q11B&r=WuTt*@OAUz@_E4U*M>^G!G`U2SR2m=kYvJ>!7!B|8~bWC_y7kxuVN5 zbf)nSZ?L>1+d2|K8^m-iFgt7MCnz%r*pecGQ+Mu$^H9dF9hKj2;`T_0$&_udI9i__ zepLqbyCLn#?qycH&+gUB)C zN2n~=TFd#0-yxnNNeHn=sA!ki=NN_E2`N2CfwCC9T-zMmv|yQV>XdBCNjqQ}$PN`u zKBUlX+gHu2rir=`3-(DJmy}ovc4^5@Dp`o`{->GW90FJ&*l7*z<_Bq8DT9}@4T7G^ zVOv}os2L?lrt)P=+j1wt+S^b60(^tyVaVaz7Gv zrz>1TLNHkyNtYRz4z+SW=%Ozv{zs}mA)*sW^Ab!T=OY`}urBOC2r_BH;hqoAOz?x$ zsgq87P8gsMg=wM*e>Ux0jikJ#rvu9 zB&gmF9kG7{cJOcFTGu+Vjo12-8zayVfL4mvQ>@dH-a8Zi*4-asHM%x5=s z)7Dp$7eKCU5|(lS6oU)sb?@t8cH;coYO+8z7x4RtmR=6W-2rE_Hk-u?wCZ%znCJ3u z53X=M-lYiQIFq22ld-I=o*}BGiZf9JW@buLjc9MYFTxMXT?^D@d_Px29EsE%%tq#VKL2^|@{$Il=liKG%i_nTSO&R57rKAn~xE74uS zg+SEuV=?39-||MTbsE|Uj(|cXlZQ&#BmF3&OVaX3y6J{8o zlo4--R0TZD6Y?pMRUq(zV(n)9$n%m(PZr{I+6%i2o{}6YXRCpkWIGcwpKqP+Lcpqc zisMxz@7n4u|-Fw^IJa|B-LE3x-ZDo z<4D`Mkf={^ivq4`>=Sa%&uM-TJSzhl9FKJ z2fD?kkZj$Ur-<`GNCG`}iSn9tt8H6cp>Pj6fZ*rAM96e^2&lRA8J0T$n-6dO@du@O_m7E-0G%6;-c^9`-Y(kLaX3!YKrY{H!@Yc6O(8uU9Fr@lS!L-z0% z)z74IDHo1vS;B?6TCclYLtY7vT`SxE@x9>X%#6Atd2iPv?EJ)ID=yf;UbjSL-0o*9lXAR<~RxW&cG*@Y06!g9J85N)%Y)fU8!Fhkzl z3E_Q-G1=cWnKUp19}}i1){G>mR>gk!l@(_8i4z!938yP!gQpq@A2I=wz-CTrh*W4L zpiAO|H2Ek}hzML?Ozro892NEiZq<}N2OC6yor8UdiYB7OQCgAQItLyzOZ5$$Wu*`# zC~s3X3u!Q+#=~F&baz7pK1(G|cg^_u=o$f>m|qPcGuty%bWEyTt&b7F1K+aK*`$>q z8A{Z}I#l^48ISu&YZ_8e26#4FOMy8ZL1L=?iiej&i|;trQ2A{q5cJ1sec#>elixS%Uiq{aV0c|>0qiw z&GiMmgD|{PMG(O|+Cw&=d3O8hVUfT-2BeB;<4&b(a9$R;8)>%@#CgU@$yJ)MHu0?} zt9Q7RjuzP_0}Up8hc=9pv;c$y6e^jQ zQ@okAyOT(@^$=ZlBxM?wV2L#EM&F!*8?>zo?g6fu-cr$C<@HoQ7(gbb6RhmP|8L3w zz0|iO7U)`GK>H@^PH zX;9e-cr*x8ce*>6(r1C`VEd*zpa&>DQ|Y+5ktDJ_0ofuG5jJ=STE}g+@#NwD$$C^@#e>NKg(4YsX8Tst8X_PzKrB#?LEO z9A9Qhf(;5*Vpm!e#B-O^$`@7pf5I_)HB!O)34Vg3yAF7tt=sT2}%EZcL=Z?Rs>jAl_ju zV#>D##Sg9f@Q-pjThi^N*xWOn_z-ZfCbhwWzqNW2oWK)h#Sl|ZRDJ_Yq%)ecaK6by;w_pX-fYZjDJ{nGM z7Z3PJRamta!P%#P1QbAotlKd0UkPOJGxTFHbMGSTz!2NAgN2yegEb<@A$Z34NZPaJ zH;l9NVB1dfO7FbYwy9yo6+jE=Ik7aYqNz>r9zU;m@G&`vPKzZOM>vJey_}3gTrCqJ zas>ho_lT>OWfMswOQ%&+%w^LR2Q@Uv$BZjbgCQJ?mkdRZcG8<6*ISeu80*XiZ8kk2 zNgEKwJL3+?@SxS5D@wrn$x;JoeY+l+xD_(MNi{5XR;GXXevDsUe?5_xNO8RCC!!%6 zl)7g7L99t1(l5E}Z?G`r`jD*IP*qUpuB0IcYAH!OC%EnpG(G_}Ow6}3xSe{#cT3eh zOsPcYgg)kzSAu>Z6_8r+N^GYqVoKo@bj<~cC2byT&mcD@r@M#M=fj;uO9RL0m8hwq zL1zj?j|}2OkJTQS8?xcF_{jbi%I}j~9kfW9;><%j=|}9IfU;U$R*)XaEBo<8SmwL? zCc(?r12J*C9(^bldSxX)iiJLy$?NsqU2#5{r^v~R5(zA=dg75A?Zw{h+zqb?0`AgQ z7f@1SnXW0IcEdFuGrE)P`(i1ju@$_I?=7j-#NB?uXH5K90F0g1b)n zg+#%E2I*)6a5|6jg!NyN^5 zNa@@F@^>OG0NBciCLaUPK2f0HSeIZU@0wyo5q(YO;@G1?^>j$5AXdTdxbP!XBQ~&*Dec=MEn;$DQY=W6M#5zh7L6csi_MSQ<6Ef)aLpRs5WsnA zQm8hvVNcw0hhS|Bn~EMf5GsZjP{RN>vnn8abNly^TX&kJ-U;K5yNZfzgnB3e8pg{!_m8cw3C;<7P^U8x( zS|nG}+pKsy;*K=boymQP0>52hpM=%^2vt!EuEoMoI~`iDmjFqluN@{)cgKY%)ulP^ zWn$1UWx9?Et|J%LoA`o0gpg$9+h}wsq#@x#4ih5bTgJLAf_~JU)_|lJS18vAL$c6u zlHG91@Cg!DvH=3KN>$PZb8w56c-mdmXAC=z*P}DT6RL}b%Uh|Z+U!R?_Os`=_lE&t4Ep=I1nO+IyXjcNzflf1_@og`y1s6}*BJ<>Jx0+3_DIXjO3> zh?uTcN;p2d$>ZJ#XPZ79PXVw_3^Shq{=g*x!TZw-d{%W^AuD>^&^BYdPC0s>pC&bE zeyHAKV#`Jcvst2SX>mM3d_+3eGjh7(n0E=1NxO;L-Ws*LS}tyzQDRoGnQFA6j0uOX zt&nr@rX}plc!m`Pvl*prDMa$E3<=-}OzEBkBU z5+)Sc;)zY>GWksNA=CCnPb#=A$Y@2By_f^HLVomq4?j&TeK&j!Gf=CrrkcWUJR{Gv zE07r6!XrFgu?W0gWfYh~@~-$$v&R+&%SC6#6ojUGpR84i3TZL zpgHU%%;+IXr3_-Z2Yc6``!k+aiT4&6=x`DnOWUA!GNpU#fAFk=y#uDRrIk-bDG`uJ z@3OugQFJ`zsAF}sgsOyW0%foZp1fJ&!_*i%9*oP#<8@WAr+BXXFDi%;iU0`>Vd+Rp z4KtSxEW!A_Jf;VsvW00v>H-qmTuH}0JOML=y>t8qi3kiFAi(NENQ4S#TU~tvpwG%Q z>FUGA1_@o#>n^K*V%v1rWb;{Z(W>ToG~g)4qXtiio0DiE0yX$@bC%)$AV=7?GuY;F zhVF+D7x3HYa)6kHz&Y*$ z79`Q29cF0@AqGDqEO1ehzd^}ZXoKY5QTa}5kqW`08%v#!XvR!EDVhxnrV$XdL$W9` znO9B0u|4yJ_Ww9h01OjcZ+paR$af;%MvS)ui$o~jm{BZQnjiG2U_ju~5~dQY1NRDP zk=Zk_kcP*aj&pDGf8%ON5#5g7NvIr}wQSo{3Qsx@%C6m)(0yF7{kRl18#dKSham;e z`etMVQW)cpj_{FwkR&YTn-98h{P@k)9(!!*Qt=eiYGc>jFc%7CTT-~j`mnU}f4Ey@ zbfULr1rmvYPMfhmf*ht5!fyXEfkcz%05kzys&OlCL^1uaT_m;mBxFiZgmVXgP^qaYp>ApGE@O~*E%?lV zMOi=|y21f60}{@2)LY%%DO*}}J5v9{rCK~sNMMM3+=zp=V>fp+sWg2Cq<4|P?iaGV z*GlA!pNA8_Ez^o&0tlkan06+6KXDS5^hf|mR-eY>aY92vWi!5{m$(E0G@%3`&^iCM0%SO80EYb4RMGtx=E!? zr~cT9Cby@_y0^^Z{8qy&ruF0IHt$$BH@bn`4*-c<;(?hU662qTqmj5oPg)7p(-o%iapN^flWO88XtHq>r3 z7Owv>(EIJtD z_GJ|z34#kL$`LU)=w7grS&7cp?kxMhux=bBR}(r2a>Nk_pm>}10d+o#W^G|kY&SbG z`Fe$Y8jZ2EHS%7|&=OaRTJHUj6^l6_8Ah|JMxAJ|5(AELdzd68RjI%*C(SCGf@;k? zG~AIk7FPTS%aJ-j@+)--TX-2@?CilXx4GGH$C&!~UVAl&RHH3#*Q0CE)$SI@)6wiF z_w=Lcifq!QI%AbxHPb{l_hXB8wZ(n^D%QfA`dA7>h(W*&Z0-uG>2L>=^Hof&S+*d+ zq91)n!ENvN?@?=IPrgGbVG-Nlx5ZUT`y-}kk_?(Ighze7f(`8uRPB?<8yyjYG;PqV z$|c*t=xHb5l932d08iFhp|dvdUWida5Y!+QuN%3O8pH;WMQQMvlK=JA5szfz}q>SBT?K!Ux+?!PXmsOID zF!69#(+-uIJ9X2@pubU{&?6d%KPbn*eyb7tjGnTR8L;2&3^#dzErT3u<2|loJaHBH zrKBN(SZYwYQ6bQOj*O~0Y9PtI1KKeoZSZG*jG7~#+(-MTi_I4 z&-ep|j_(JCKN9gh%Ju;J9zkTOP*^HD8sp}gxAPTmVHvyIEKrX#q5_Bv7!XV7r>vQw zWZg~6%ECjNZBe^o+nexuKm{Q!lC+qMVrZ~PVZjT4@HEn6H!L9? zokOQD3stKH0uJy3*Q0BymUc76an&f1T0QCx$F~4_K2PJC#m7&4IwfIR7j8R7?2c@8 z+h6;?4gLY3c}{_}yRkJOhK~TUYXK=Mqtud3faj49llSFR=HHuVBq3PIK0x$4fYZ!7 zTnG{qcL;M51{9tI{s9ze^P_7-zz(u9azYVHqNRG<7lAf7au>yq)ucy*MgT3AZ6Uy% zT>{A=u~-C4gvtllZZPcwhiu$U4NiwjQ;D39KAKvuJfnxH^}#VZ;!xH@7!2_AvEvB! zbcE`g*m9v~=WyqeWSxqA+-y0A4Q)1;T3xHO6UlSz;q9D0yj|HF(;a{;eCe7E)m!K= z3*U|O$#l1SHq7-3H@Aia7Z!``>3LQ&4p%8V)-OS#*)R$7IoP_ zeDd`OxRP1++JK+{y<=rNPKcVv%)+>GRROj0{et5nZk*021U27N(^E5nL@xZpb5e<%3 zJkKj4hKxm(iGYKlEAXK_tchf_t|o&HkfDz|4Pj{en3fRU$)oF{h!tZ~0cMZN>x~5c zMm8)cP^d?t_|^|b^*`O~T^ zQks>P*N~w?T0kabVM-)c`j5YLisv{5Dhn9VIs$!BLEHuy<%m?e7NX2xvEEKstp1^z z5I%sMo|LMLYF@2wW=u&ym<2Ge!Cm1F4>k%1pa+P+1Cs@vCu9WsqUO`2HrwkA5jE^E zWk+0S(niAh>fm^!X=O2^PV&8V@%xAwZsmcO8uDM3M2`8^cG;o1{VbG`uQ zmhNdYSM*8+RYZfg6o?$Ve^vd%fVpDGl+ zDY_J*yu1WtblmhcK$bg*F0W=BVY(){KHHEoa2c%7JnS+s60OqEO$O&PU0@m=N~c-r zj=rA`Wxq0^z@H3Eg$$gwdHrN4%_o`-`|BbqedawONrpYe=)1Oh*ls2_*n2=`GdL9D z+yfbL3Qi^}}5tSOr)rPe_AxJRjNa;v+u5|cuML#m8RP@l2k@`)^ojX+LaH7); z(5aeIUE_(H8ck`4&n0-ItJ_G@l@pLt`7jv(4h>x?(A`{*;1r|BSBiFu*tv8POld#q zphw$Ihq=!qy3Wk(T?VyD4;7HXt^x{qQ_BcEjaCl!nsI#5v4eIpL7`II{hOi+LbY?c zMeRs$LpS@mR+XU!FX5>$TYYmQ`A5+Vc-hL@3JLYNqOKZbc=O}}(_u$)sNKDPrrVX} z=u6%C@u2E7;e4tbLvIT{ta6(BrShL(cmB_+^%C}Mn; zK^lDZYJu_)Cp}Wtutb3>2}~HwA4V^^DD8Z7#e@wLbQ2hDC+3&KsE{5z@3Rvoj6yV8 z{AnGoO5{NWJ8Jli)^S$}Nt&$%44OM2!PeZi2T???)UCS3tT8-*dS^YbYOm(^V4NIM zSmav5#L?CzW=}n(Q*39h16vTC9S&cCPfwZzvX{mRyTdM(hDr0rOoc?1mUxJPh)H$!?U{M8O?IkX1e zp4<4^4!T_aNjEfc(De-TRJHBT3>YzWqAA{=cINdEmuus!wq;| zkyiry6)3mBGv!{MBg#(cvQQzSN~;+sz(xvw=tDuZCT&N6!QKqQ1Vjz)GAbA;y|iAA zVq?~;%f*7x))jrE*rV!=@&|UZT}C+^=%s3)%O4n%`;pwk2{9so@aYA+487Bb5y&s+ zIg<*7R(%myXZMec{9~ZS7;O4TuK+uwq_BIw1=#OLR*alSr?@jfPy}Sa2)Tmgh^T^g z+H66GfIVNaC3nk(T%^G{=O%wE-ZnsOfCwLuob418(XLd|w?*{aoTPk%tir{`E+j4ZA zuuy|-h@oilJHT zAuY+3D&w#VpNGlJ)y0v8Watlx%MIgX=`hAp8|H!as@tV>Wu1Dj1)@MivvNe zQ-wznKum-?#C42R)Q5ZHmUn)0UTvvqp|n%&P=aWXlvaW1)B%g5F^B~nTd@Q`dXGs7 z++e0Q4uKKxn|lvxBx)qh(wlFRswUDWL7dEj3Tp%o_FV3BGG}n9i_|DbXZZrz;$;qK z<2PMOpQ9D#^Kb{+KvDJc@`6 z1hi%VV*g>*X`~NkyouGyJC7}I2hwq1YxiWhzatm*_{*hX9ohtozag7St+4EZTr?~> zxA^O%>`v?rJ*2}ru||p!fYGtLyWu9afd|+eJxUkIYX6cFa+o37wQDL85Icmta0x{0 z^Kfd@v0><|3HW?V&Ogrs>zLhdakNvg_&oTJGjK|pDYinQb zq%8#a9IXO?u}})KpqaO*j)GxgzV^#KFXh-7`0sG>SR`5FqA!EX@XM`HH2b(#(W6ts zK*Le0TRBN-6UD$CgGsdK?U{a`EbXSrvVP}686SO(Hv`eU)~!)(T}B`>V;i(o%#{+H14MKuM4>V zRVnZibLw*9T1$gA5nJnWi#s7&nl!uGSnZSNql2fA-)k_?|05NxZ1WvaTYS5vk#hDjdK~icxJ<)*kxUW<1#~n7gC<|qT*puwt z4xsP>?sbPo$Mgn}?Y&0Jle!S>zJsq+Kd<>|{;MM?HPZFZK-ceqv zqJ%rzyJbz_$cCB-U*6g>sI@xXp4b)zj~cep#XNSyMB%ah13M)_agYwGZsQv za7vQp7C5WEKjov-2~Aa+qXc1~oX>BM1KH&Tesl(;-n*?btdU@SK*kfii&zEONRp*d z>PMd6xgEZp&_9JE(ZYKtb^!rzVC;7g>KhI0R^>23*XrM_fDxzoy&j}EnOU5ygvpK2 zHL;BA(KTp{^2kxyS)TZVhw2>XnAD0HKarl^KQQyqUFu+gqL#Q5mFyokg!bh?&b=l?{}ALzs7LxcN6T_0}^ib8>)E;zs5j%6wQ+x z3?W;wqB|fNkM=(mgr$2RZLg=4(IDAI(|RTi0s{b3@Tz_a4jmv;1``#aj_`=3{+8Z9 zKiWjXi%$-)Me8i-$qKgF&wcFILGM=j(nNIedBrM%DvkphyeI7o`}={Gp{-|v2@;U~ z4UE6L!fhm>q@W3QXIHR)k7es{bh7U_vVs53q-L?4$gjr8xU<7x+ zwlh$8e%-!w_fcmqA+J%7im6RiLj+);>MzF?w+S{RLxF*Cm2YVFd-+2&8Xxlx891Fm zqbU)F1T_)v_f0B|49*^W6~M`dOR3s%kI8*VqkyXrA%IaSBJh;7cGJe%bu&I2axB9Z z5g)J&qj`{aW!lAZf!+0}xE&a9F1QPHyXIM=vi%uXdjy>8GeN&SUzgb=4;06o4}k6& zNncvDaL*thx9zocOz4!wKmlP8jet6!tRf54w{yK{S#~!&YUSSB^qsyJ-*iKk$3-ppDvfNJU zx|rP!N~dIB^maYERyB4&z&3hLbW7=|PMvN@;!PaCo^++yi3m~&oajrGmO)@a9l-;r z@^+Wv*To1bgHVEFI4AVn;I6VN@@$H!WG3TGWwu?=VTO6EcgMAJQRyN4FDTWxwGDdHiMU-Ds78aywDSzcJ0th>XwR)ieO2F4%{h` zW={#T(I!}i9%9;g%kFENsJ?fkL^_2EiBzLc984bcVA+C8U$ayh-x=&ng>j@3$>XN2 ztl4zUSvwxiC6=^ZqEb)eOk>*JHR-3LYsvC{pHVXmyKc|L9 zb_7>rNe(2AR6N#(>*oyS4x(EkLDfK-RF6A@fFRVUc_+XP)nILk-Vr%?n26oTB?1M} zKE74u6MXB=c9V3B^<~*Apa4~hw&IJ(*sx~NL;vFvo5d|-Yh`->L0kPfA}UE;f+B89LU zRHY-VphvHsVLSS}?AxYkthp_MGF7JO#?+{oAP)7Bfs|KdgF&X0HxtfGhy0*1?5Icw z1wy_%tTDCfP(hG!;esBzf*CA7mex+^!VU>Z+P(?T%GZ6*+QDLU@zUjPtps7l3d)|| zE1@sod}t=IB!NNOK}nvB<_}SCv=R7{S|NXJUv9`=(U28w!RV>keg(PJ@3zZi@>3`* z59RZp1cNjmTy$y&NjH{4hLD9Yj6gJoVIqY^4yYec8ty*{14^-`3qI9l60s7} z)f;kRSRO-fO2>$s%|J`@Dl0ecSd5cOU{hpObJD+7+>m;lbYJ1wHhO<6Plm--4+ymm8ml1nPOO{8 zzo(+O<6KwACvDFeim{DN33MqS@CnL|_T02J@lqHm-z24EvF7ZN+mK7CfZ4zKZ03ZVaTZEc-y`B;{n}R?=vL$XwwPOI~nuEqH3kCz-ZB ziE9AU+?^&jLfV^jCNb`JCt<2Mkr9IPiK!QWDyOCrTTn~U2qj)cPQLu0RWf-8aZ(Q_ zp3VofJ_dQevrWX2^UG(1%fq|d^KLhX98-=dVmS-POgoB@IIL>Ui%n`>W0?XWO z8VUpKJ*9}s4n1W;Nz~NwPU238nDjscW3CctRH1oD8>n?OAc8;l_KmH0qKfhdAWB<- zVZlrIvd{sKBchaE?P6^u0`>{n=YEz0|8j$1VmRogJv|wr7dCo9?VEVf{kF5h4;_%9>oFc`DMI?ZpMzIDstyH7JpMM=hpGWP87E2>u?0%(K|w9Pm&R)5{Kt7ncFyyK@g)0@^G8>{!sCNrIeb07J?qfP zc{Q?XJ}@G2`2*}OFzkb}NT8_A zVYLt$Y>DuaVV2a{k^$FYJxMEnoHj8hbqR$Y%%do-6`AHxqL-ddGfVlP1gaNqQEk$7 z@l$ycNrfwkcJ2RMT%3y-qF|wFn@#A9dYz$=$RVK#xpotG%}QJY`fRuWHk&YqzA~Q| z2FI$Z0EL@P-}S*uu!HBbi$p2&Nf`M6+tAny;b{IH7nHD7sPq?%XFgOdm#-K4Gefdx z>LKF=ubCuJaGP{J&WL+D!dgZUhyGv=_4B?Zu(gy>Aj+P-gX~afv^QR^ zE#y8&FOWXtl&b^tHyKZYXpTUm^kmBi-Ji-B;XouCzb)tmi#-srkn*R1cYP6~272c6 z)xw*a;dw!(!CEw9Nyn=!If0mP_io=O_E^edlYA83BEkLmIuoN!Sj7-M=dy872%EO8 zI~_-cZSq)o3BaK^i8>-;HIlWFvV3x+A|s*P1uSQ_5e?XXIt)|g@lmFt zl5NJHUVnm$7vmWaKK#W|h+7)ezpIOR)xm-P#E;=tU#e@vR5GbKBM7BX(p}f4zYJgl=Px)@*e3o5VEB~Ks7dqiFJP~EfujbWj z=fxM7!6^t;{*4f3z2$>;Do z|H7;-ynOB75U0HE{c>3smLm#AsJwqA)*rZLvp^r-p!?F7aMMRy1w)JFtl)3i1V+#I zcgSe<-*j3}how0C>!Fj#xcwn|;NwY1y7E|hP;!ci-;$L?{4v|!`F|-99v30~EE?An zzcGP=t!QG9X;cfENqp3tM)1AMK%~q_H@J=KyJ0$ZYkYkSS6O z=ff7JAc9S9n%#u%?UC_-U>dA#iq`Hg)zT))amrcYR#zxGqTx;h)%xEQvz}mlCWi1S zL8y)o=KN|vVo#mWdUt+G5Dte5wqd{62840Do>{D;o+^%e6ZaZ^6)*xB+%#L{a$F@v z);yMSV%Dn+QG4xa4ivDHx@y=tD3b3`50Ho(I~e5lbYIiUznz~+H`y872(`Ws1(XBS zncGPJN!Qs7=B4EK=>SKel5b7);IXYT^$|#7pRwr*(>a;$oLP%OCB~o7+!Nusq+yBs zhHlD~0nR2tc1PR>=5e2Mf4OuQ%Lge(I_lmVPa4&HWoR*+FnBB-9TGVMVdEAicC*nL!^25J^9=9}sb} zPaP<*19i&oPaDR@%a%g(mpEs+xfK6}wM{_=$?zJI0$cdYm~VFl<5YrDbuDmfrUe4_ z9sy~Rj~Nlb1lWM%GgL{wMCV2Amx?@k*+kI0ed|E3dGPK+E&{{{3Y9GqI+Ka2yA~}P z>vND{tiOA2!R=@~Ns+a#-m!vMaRFnBC4oYESd@#ralMVq+JTcxcu=-{MFT}fZBo;5n|iQ!=Bif$ozUC`RswD zd0|$JHB;t25mDHtu^I9r91U(EM^mDEnw}%K*1m- zS`By(^hPz}6NHA9nmoc4-D3d*ME5V_%8K!hM&H&!C;B0U0V@ z9Ud?I2_~_L&|IG$ZmLvL+d<9i^`>4Q>W5016YN3l`4e?XNktL*_Zm&xJcOUeLvN!n zZ`Q`u`dmvR`}m(~^HtEtflm{q$z034RRp^bEz$J+>Sg(#1K5M9oWOKIH1i8Ku%&no z4DGc)BV(1biK#mca`AhpcBqSsRjp9onkeKYpAO9#IsqSF(8S>W+xyiQcbapT-a5#u zm_z%?uZz7x*kDmsv9+a(gUS8xAhQj8Yb;U{;)fP$j+M|&&~fYmPWz;;F7LRM-vyOj zoo|-UucDLInnSu^r6r6m(S`_CfKZ4LHR6r2bw5FLb4 zX31M*zExMP!#~%BU5Da(Yn9#3A!ZSLxEl+WP$!b2q@Y>~OsC*fr%9U=DzQxWFZ$*F)4VeQog4w-NaN+LUiS&EE-+5`Y?Wc5 ziD%CK*DDj*B2;>S08pxzfJX-{9(>Iy2^BU#0F>A9nNjQnLKLMG$*qxGI=+af*Z%_<^>h&M*_D%P~9d?KuL#J?!mEiqp_#>ogDgTHaF^~B5;`5 z2A`GxgGT->#t@ZguLs0UOFVgdARh@q*j{P&JF2EZ-aW$SZAfM_OwQPzw1Db#1S%lzuU&_@28FoDx;o6$Y4R2obm^+2tjXSS8!xv zQ2i0)PPzgWP?C?L(g_iAK7AncTRy9p2mCl-W2Uf?2B=7zO_~tP!US&88j14KKmeVn zWl>+^m;q5ZtkwZ>2kt4z8gLhPg<}K<|2naP-_;zFNfili%a}PZR>O(XEoksip6lS8 zG|^1#{C&v%L$scq$))gSHSF?ITs?X+aY zj8_*V5e~wac4F4e&hxi7`{>|MJaWtkeHfB;{H>VB>P~@=`LSDWz3n1LESor2}x zC6&SW`-el?Y(aQhRckwg+)B{BHG>cTWU<^)0T9g&bdRb;J*#op<4_9jpQUbG_L2vI zfi5UAQ3I}0>|Xx#EeOU{XezF+0WnyRvM6)BJQf61uw^`iLqvIy@;zexovX7a*Kh_g z1i8~bP3Gv`9H2)uR}Uai`}NqJ9h*ijEY~E4N1LfWxYHxDEB8k?&$M{M-y0-U%F6Im zdkCVi^t@w9^`+h@Um~C$q;A{*780q24CuJC=HbFc#27leTX=VLwLu+YH3%eSG%D7W z8x;v!Jv~YNH+hQricQf6aUo+r0w7_ISKms5sz#h=osOXvH%$FjoO=fdJQJgc^mWYL-OJ}Ee|G7z{ZseY^$fs8*LR%El)j>wHNmE)p2%gzk+P_E-(zsT=WCt7|BuT+66!gAn1eg z0goo;VD|jnZ$RNI!8$zR+47QkCX%T~*d2X%;Kf)qCm^PV=TldzDJBdj&3y&E1qC6O zfASCPC+Sh1h-x?<164S1b;!Wb4VHkbaPYyYVz=bjsu*W(R~J#mq=*bUJ3~C(X<=Z> z6JZC#)$uMPR5#sIe}l*DAOE2#*oCQhVxZqX+wc9Ivn`aZS~+WDPjAR>VIQe^Av;yk zfSn8+zz1B8OrCOsuer?%l8!{SwKx!re-YAr&}RsgIlPMq%!+yvocxG?BO#2`i_3(%E`A2Q{ug|Mow!M z-<$vfL0QOjUW9?jYH#T(3XOHMa39A=dZ#8CXTc8JG7767_b z!gMT=UGn5_A^(A6Stm?|hCoL z_&ABHu9xuRTpNrDH|0XxpS{}`%BMiUzxOJXo}6cOl!}4|J_(>*n#IfpD*niXQ|T+m z-;ITY1(y>7A!Y}p>S}U=Wy48@=gIY*uU&>IZYB+3XMTq}GPE_yUKvDHV0cx26hA-P znizEWZvT(xBIvemE^yy5Of1&!If+Lhg<^h?%H(|Cz#MeO?cnhM1Amc${N&{2+=$d- z40`IT!Y@1@W$IByF&JK5Hf_&~i*_z8w9%XXlq=nS- zpu(?*lGTrdm8U296eO*qa{Y@j8N*d60hEImmQA^4!sKD1U*+`R3!InAk8cpo4vR*z zb^CD5OB939pQ;XCrg}35o7CW>&qN)Hx&Xa$PmsZ;htK9osW&Zj#4GmrXTQ(Ix7)v< z|C5OpRisJY4F&`hfC&U7^S@-GnV32mJ6hWR2ieZx?C$*k#H9b;1RQc8`Mhepfyx6b zyPem_;TSAbbFP7HN(42Wi-wbxc3zEsf5ehmlV|*+`*KjhJdLct&FQ#HB1mjVDXDkR ztz=cIc5Qb?t6ds`={o%c+yTD9@_uZU6SMOmC{MHQHJSo!C< z5;hj1t&5|Gsu+6I5U^dZ#X%$I7_P$WlNC~hE+E3Neq1oUN-jn_UMDA2y!gs%`9w#N z%o6m57qcWjnd|V!GR-??1w=1Vs!KXK>2Ywf?2cEXAvz*(#`@&SJ<;q^t}kG|4p&nc zE+%C{|G>I-@u8Mh8gCpBC#|T!5D8JI9&K$pLtNL$z;v`RROAqX z_oQ!K*ynDQyZ&lkk1wnliaDUrcs<~kKXClZJeyBD5+y_(0_^GKW*N?fU=Zy&DBX=! zJv;?XE4^SKzTxpJGZ;`h6aBJu&EKN|z2NR{v38kj{ipLZv?@q2_T!WW|JOA>d`>1a zJLPv1_97D#Qgpdl?>|$eWWi*`Pt~I$vc0G5R{`;1mtxbsuEp6I8T~>3!1yLuWvL;N zY(5T2Xd?EiB6G(2b<$Vrglp!uQ`bq(Je?IQ7f+SC+D9L6iiC<^$7dsA&*^>0I{;{3WE}L5Btc?>jrvq};6+;>e zi`D71x)p6*$vA7qqD?GIoSB(Q zbz5UH_x72C(r8wbzJ&ePN&f5Zs2k|l@X331>fh>K$AcMzQp;gg&ae&&c$II=QZ3qP zPQ|x`*s*y)cMtP`cCBUiiQbSk06Ln6Av6R%bP(EDbEzdrE99Id)UajnAJsI_%7245 z@`aAE)seEY_^MaD`*S^)i{vliv)_67D^UJ5}H|27GSzAv9)e^$bC-{L+<*+c> zGY37P%Jt4^y}?ibEFwQh@E%-tkoF!&JLpZI*B-w+5W%2P{rG60>5!vAM?LRv{%Rm~ zsH}mL`iTXJDXgp~%(gqZMFoQ5+L0tTJ`8p8~*8M;@l6W*zVK6dfw4~uM45=2@KF$V| z2C4?DhKh!hMv_L8#-hZR14+C#dtWdq0(ZiAgqS>=<3R#yLU#I*C^;oVhb|-7ez?56 zw}=BfJC_CUs*oug2c)l zugg9fCBoN{!hCuOT=|+q+h#Pi3Sa8nM9;qr%J(Q`_q!%7s8YnAyra7d9{uV393SyN zi2re*FhKtqr1tyr|84xAMf`v1KpjmDO>9m7ukUpHjidXY>EaFx1Pt~A3Iz1u%l~xX ziTH-p-Bdt8I@3Tv^8d^8%}foQT^vpIjqRMA|Nl(OZC%ZzqfrOnUHt*fz8D@w&DSg{ z0))_tl0cn_xJY95QcBSq8nd*<=Ud$rNJUL5w)rt-7~0|MbBIpdZ(`cVsV3jgYfOE= zkCQ4pJ->&ysW7)bkH4#Vxdi$`!D z2T7-Gw*tJq-}~otbAInfT@MGBes*!ayx-U7LyuvnZgv75-($Oe_tzSEEfV^FA0A&7 z7boiAoNUv5f)5jD;pOk){cDf#_Z=YM z>;7@C%l&>|=2YhQ`P(F3;N`xI^H#6#^Zot$P+_y|&pqdqmw1`)&)fd}_adbCSSa0f z&cpI%pFmHyAJ3f>nTu$r(~h^>`OwGzJ-1m_Ba>959X`1aFL`{2QR?k|_(zef*Zp(In1x{ycS!`Poo8^Itxe5$5hdAG(-h%u8IdZsiW#g@N9fWAMYqr03VdB;0c3AMetA zoOzP!Hz(s4{~LM&=lj&aZ-&d_E8_SjhfsZJcQU3k=QaW6nHR96*AexEr82-t<$dpB zz3ha0{!tx4&w{aGzNZc)@~EBq3xYOyS!O;6lu2I}HbbqA7ytLFiGZ z?u0FZUIW^}k5<`xjMngy85l!C9Sp?INsm7JAR;ltp zRO9N9f3?Loj;cpN2Lf{e$5;pkUgV2_w%l+i1?_&uO577r0ASfRbjfI&YGOYm1X zlHIm4yv4{A7*rtPpFU|lh1ebpPrxu5au9VmZ>D;~U^SI~04L0xeE6HpF@H7KJA=O- zMV`7)Z;?`wvhYVx=?KSWJd%Kj0z8*;HZdxAgNYX25I;2=eUN5z#crE&QW|;PRS$A4 zx&T4p6c02^xcYSP8eY>^5xPPtbwUZGHBBPJZ?yS`=>(I}4-R8l4BT7v;%Li-!heEc zDm8Aj&I-JT; z4{c&9cg`?#&4Y{eFs_-L&%OOED4nBhc>T`JXV)-|MfsT(KjKgUOUBdVWiXM&d_AP7 zMz;66j=zRjVx|Hvgl&|o@7@6l&KXnBi`w`@*OiHfZn1DJw_*OytLORKAdcbbT9F80C#kD_+G#iG^8VrR{3X3 z!p3gk>``qgJVdu5<+D~Rq=pr`Fj^K;Uem8>xbd3c@oD$V2Ikrn2zyGr)uYmL%L zjjz*XFc31?V`Td(BeZgsoU*q5eY~`_n_?xgp-PM^z^{9%UE@i%j;TFAI24)k74DDE z?bL~qt#qV61E%{oN|piY268D~I~MB%o70yiI71Lg$(!ako<%NAH1n`MF6MC`Km^oY zG}HK)zf>I>%Y@$-YSA!(Oxr?#raUm@2S6)ys9XW>yhC~BaP5(Roqr6PJ0MH)b+;Ea zZ9aP`CezJ2mJ5hBm~=nfebjZf1U_nBf21-ZKcQq2FyVR{Kf;mHNDWhii^4@mz8o5Z zWy5MCHFaPx+N}-A$BZ}U_jQM7hUX?s{`uOQ&rZ;TOz}t&bDgDQY=`QD31t(YdBIH+ z7Dmr9BemEdg>_YD!{FMOZQ9%-W`+UB)Wv)M8m#9S;#ATTSOydDYwYZB5g8*|zZw~S zmB3lAm-WZsfyzPriwgwGdKDvs`~eIsit>iPm2$B(AbrYUZVtL_a2mMA^A3VF!EMO{ z)xEAq>6fbQ^TN4tE;-M`8C0;f{(&A7ZxfwPrTE=Y4Q7gCizxrstKGpYnV(Ki601DsW^D%A5159uz#* zD6J``%TqA~)YMwVRB0VGQTdf<0gnkgW7?dm2u4us?Y*gd5Pu7HJ;C04;{ncWKf6qt~7c<0z>+Bj9Mj8bfwNO-}Un#-5 z$~6jR)!E)kZ~Z);R7F~T6P$uRfKu5I66D*{s*u&2L2|X2M*h&Z&12}Vn8p%8kat`u ztV=>|iWCfv_ln0nv!#D_0O74m_${H$=({S0^?v2vbTY>BO+2=~8ic!X)9@Yxylyrq`f@ zVH{{@%29shu~ljPIind4};OSJF|Z{tgG%`zA|Zs~Ici zYU3gQ4s?l6-Ww@!Lg1~`ME$Tqr`$q^f63LE0uq=N=2`{Weh25dgv_w1ftsrVa+(fY zzIt~1o$so6b$-G^;;qG0fR$PW-V=hDFN?_{GXV(O`KAUtEv}blMx!QnW=AugW*c{{ zL(%Eh(oxmL(luP*+1!?%;uiJ_L#Y8hw*AnnlLA&V(gq-~a1;CPfT9nS%A#N>FNarz zyBP?<)1*wmlzSr_col=qN>k_5E;X-m!;z|c@dN5h@SV%1Y>z_(rMI1SmPz{P=vIj) z4R|#Ou%}nGT)(ZID_wBfOKaL;p=$B{Hw`b_*{3dY3yPnF`SmM+%^P&BcdTJs=Tgja z%eo;<(c1gyK|L+j=LgwSqxN7nX^7G*1Gvo(wMMMdKR`V?OK-h#-#SyTYcrxyrM_l# zXvg?lpGL!(DkP|#?JJj~|6-UfC2rCS?Uc2dxelqidQe}t3@&{fetS6l^b`uq5f5k30l?Hb` z47c9PfY;(cy}<~#P2V}hQl(2pJoQi1XpuI9&e~vq$R~Jr{&kEll*WXzELW|$!4KT@e=&I!{pH@Dt5w^eLy5n#>3y95ROJ%e9QI^EQ4<#Adq zA?HJK)1$$X=)PClE2T`5>fl!UZ5wKO?ig`Pqi7i7(+%fU(SK#Cd2n_xbQH6wnnV@l z{o*e}@u7u-0((}V&QOLb8rRzFT_vGrTpt+mFRi31;gU4!)mX$g0vO2CZwdVQs`|Yf zyv9#>iJQOxoY!E7^{x1?U&qgee!6S?q&yW)s1^1IU4uKY|&HO4uYK2ncC!KPY!?`mYH zB8+zV3;B}<>BUN~>elwmi#@L@^A$O6mE^Alt&A-Asr#gks#*W)S!dlJ^WR5FiIWF6 ziuIj$vS7V&oQMGmaJ*DeT2FC{bNg_2J>zW`Cm>c%f+yh2Wys4o-W)^Ht{A$D^NE37 z&u^`n()6L@x&7+1_zDToc>&`u?AjC@qf=UC`FPYNR$-C{5|3)7$LMs0qY~FPFm(cH zNjzyEePQ;y1=7n-ewC(*^`V_-@Hmxw>2j1qwY-e!*he;(({Y^ zJvr=}2PV#Q(Y_#VAu+;~Eq6VhFYnHT=#hboYLH?5MS4(yrQD;T!Wy8=7G|jz@Ezo- z74wnQg~&(=19F4oQdqTx%IK@l=@7tCaOgYj?Z1o43{qJc4M^L#at2w+@QLilZrt5U zz`CZw%@)W9_{p3a8Bp(L$*ul~!m778F&((dr(sWOk~= z^6G<4nM4*wOm{zxxmU`T16zx_wXKiOfXiQ7mGxm-Il2|O7k?a^RSHQLAvh3~Q}t&` zVFr+w!>yuh(z%gT(ZEx)ie4K)els1bpU2bMllF$KiRu)13k4};s*R{Kl!~#Pe=^gf zr(SiKe~W9(H95;5<<{yf^J~$a4-|-tCQALlbqTI&I5xShD6zaETuf1cy zDF!;l`pb+*Y%oM+aDzvDdPC2h7$`q|@ZFm!!4V2erw z&Q7$|jmN;c(OHP35@2yTQdwCN`U~)~uw=MV)QI9u->>|IRS&<6XA#+yxRxx?UHrRL zwVz?pWZwD&?@p}tvm~1GXk+!bhP9Pi<|g^G%MOR4vP-=P-t=aSWX^oB?QaL4zOfb$ zdtGCDeuXZ|W&|Ho(nxwyk&F>Rwk68kVm0gdsF5kd+1tuOO$0BTFyhGXSf0{--rM_C zs}~EEi_=Nd)p$L&qAc-R*0F#898k(`;IAXI| z{cDC=D~S?jh;OZFM<~A1oXT_jeAvOwfn@^hZU13@BNk~{mzA1eQWn~XjQsi!nB0W> zo6_s0B8XR7P^YH!s`Rs@Um=Iqk0X44jb4B)_ZVoIq{!sOJiTxxRRVM3pgucBBfpU7 zwLkRP^IXaMT8#P9bL-m{@4te6tV;n1R($nGU0bd;A5*@vca30+PAKUhFnxEJtGlsN zmWe6`OEm!K$bRQB`|#{gJ#B1}k&&JEPiVcslc8Td1-}B@Q3VxnvoZpZlwyI(QSC;= z@7kt6Jf)%YGWndy&4!3tLpM9Bph`szseu+j()^Y%xy%{2=^a#OE`p9zw_A~x zA=|8?H~F601E8)m7EliFa7$EoJF+bD;?g@X&|ijC@7EM-C1FdosQu zRH0o80eT2}8}I~fZy4o=q!Z|$WZ$99j9^C}YoQY<_FynUwa3yA5+60HN`P?=VBLb-joMTo8XY ziYpCW_P_FLKN)@oT2QAL2U78Bsl5ogl-h!s%F-(gR{p6KD8ZUnLCX-dHvw;iXxS(J zKo-{vIR&i=Po7Fa%jyr?HjgOg6VRWnVA#+%t)c{}cihU;9|#8;bN~fNq*FSyxsxOl zAL-Py2GS@JZ2+SSak@GASE*8MQbc`P59=}HYVW-w;j3bkD&|6>DX|SdoV}vC?=hTu z$Jjyew7FMqgv`GNAhH;YGT+k2&zkiwv3Yb&cPn>;mW|R$#+2W!{QHi@%wSR>)4^UH zY}=}8GF$gL>8hUA!=0;D&C+JLqBW_BDOcm5fE5jY2$9(fE!53*y3y{DbH4h*QQdXD z3Gpgs?Yso&9J&hZ{afBM25VAtg)rMMg*~uH2A6jM26abyOJq#i8xdfh$sHw8I-Fzj z2bjiK)%~%VDUJ7?6o$J521_C^Bz|ep69D z0oYE#*zAjhHDrQLF;oNMRO>a?W0f+y5^o~-YiCQMR!}XnjBZiws(}hUi<_W+DQ@#p z%d?pDrt4#r*>QfYXEjzcfyLh@B3!)b=j25AR2Zh}`UprK^XTXYY&$Dwd4pG2~^HLlB z;l5{h244*`wPP6XN?8QyC`&N*N1!ZPX)sHdYi7~z-BTRP1DC}jY4yax3+@`T3obF=<$*LTf>*%|DKlJZU;WXi6e6(f`EqG(Ch+5@k>SJEn8VDuT|IePX3`)1U&m&kC(E4#w8)Q{osT|ZrfM9)qeICB-H zcDNHr%YJhfHNquH>8$v75ki_^y~td`2yr%PIzbjW-qnp){Lgs)k6@68T%&Mua_)1Y zG0`V`CZTD;A_N0+hLd;*td4fNDkTVEu&eC|fDqHk2^un08=a0qp`uPlWREz{MswqY zSi`UtO5J6Rz!5$c5};p|HOWqncV!BGqURydHUD&WVKA-R1ZzCNr@Y(JLpUh3a6E5# zkLfSgR%<#%8cQ@8%?}65Wxv&#f7{@0E{T+=iQ6!b+c5r%M6{|r?Uv)AUC+vnft8D? z#Lf;0g}s|6Ckn*8Ft5OGze&qPL*&wJ?G?waa&nPW@#`19?pW@u zWx2i9+nDx^=;x&uf+y=_z?SrleLz54qX%K60YWMTZo7@g6B|l(wjc~bHlZUJoACsE z{xz*h_4J#(wEFYf`vA~zS9!44w8*w;>k}`K*dRSQK~EGJBfruW_(f>$3tNd4#VxJL zsW&Og<`JaOi2Sm2)ftV;rg#rP^f`2XI(|hDMPSdB?_BkG14J@6OyO}fsk@F+)-d;P z(W_W;PARii)(0T806mF|{A}PNjO)5&VymSQA2CkZ$yhLKb@V(#JzpXeF7GqlL&xr& z6P(7L{y_qqAUQ;Kz$q(&Hk}h6Qpl{#@LTBudyOc=`kIXNEO;g!e+ESja$@ZqOXQX7 zRp@csWrhygO)CDx-!ut3zEe$7E0;VnF^PYxMNKL`>y-&R#45TW2^SYN*bS}Oh2C%o zx|Q7`>PR6bbNU)pAntq}W!LrD4TLY|G0wKy3?bbGq}nN2I|Fhvr(s~YTdk?#2$vfL zU7kU6$IsL1sV9Tt23bTpYmAj*8Fp`i5x7X4Kr0CsntcnVkJ@%hPV12S;$Sh#1Dm|0 z!ZxaWtzl_5uIL%xOqmpdy|865tupPB(eC1%yh=r-8>-?&yXdFH15XNxSDW5DZ|lvm zIc2(r&u7Ej3puEV{=B1kv1y$I-KzT#rBfD@cvC4TsWXOEm&1dtoV6X0-bU)yDGzHK zroIYj5c^S)#9h0YvQ(X%pjnzbtWx3d`l&%PvVCgXDTzW9!h_9 z5x70zDAs9GH**w2m-XyRGLzTmbkkFHIrVlpcbwk|Ab@xL6mRB?ui1Wo>>`Iuf8c1m z^e#pTN~hkl!Y^~gM^NL!p1MBc%CLK<7S9Y}^=#Gr*I+ZXGWotGNbz*1@b$J)W+Q5glgX zOY1}ym;U9TC94$N)Agp-T%F#J4z8po$ht3kBA?aEe9d)T&WbvyDO+uyrZ^;KrU$AR zEVStKbbsriGoXI}zoIjy9B|zo0ji*}loimKPY@H{eZ{dN+dTlX698rB0rS}mM0U^k zQFsok*J|e-+8t^#LNacQo z6h=>xqkQs@)F#>c;(?`IgxQ&s0ohNm{@X#(hp%k#IMLQACW>jUEMwo&RmQ3eXFMgj z(7UX7!ql%g80r;{u9nO~7+(H?Ha&6Btr2=W5eHD#A8|;FPsZ|PoDO{PAoNR-u>h=w zC{sLb{dQ;VN94f&!$L!t$r`%}>f~$Lu}ASrm#2vMi|G){D_-QtC)dLSTR)lg5sItx zz_t5mG#Qb2eJbLfL~8cY&PGbqCU@fQd|5YFEg%_daMa_Y*%q<}Y0rc{dmt@*2z!;_ z88|6rT@{DGJ;mu$hb)l&yxd#-9QNlkaeP|Qx$7cB$PjK?KP1j6Mt!O7bwA4qoh_GC zuqlYd+PL)-eHQ?DDCf>=^AOW50tLGMwz#nbow?UrI6hm;#<#WV?5;_84`|Ub%abb8 zs#UBfc5sY#u98W_Uj!Fxk)Z!5!wqwG95i)AMySE)y#UVOG&s^7GLCPKahy9{4~4kj+BQ?ejHG5UoXz(zW*>7z_DmX<;ceW8rg~be zK9)0XC(4c?I!6c(o$BwFEW1HXdjbpOt`xjb=VOv%rh(eDg@lu!jbC{WZYuv5q%L2y zX7TdIqn8<`EhI=y2Z#fkSt6gB-E_8%1y$w1%XVJ4s!e_8sAxrC{FFt6_W0EQ-z#ax>2^UG5u)KXb zW2$3s29P(t8(A9;Am4p*8&`+^Dn8o8m{>D;z=pbgBdkSGJrD}%k(R#HC7R%jILtnF_|CaUMw ziX_!_`LXj}o^1|5D{4eoK~L`AU8Ls|vM139zNr`&jBnR0RA{GhG|FL1au<8QlWM^B zwXe`>u!jb^=;|$Wl`O|O2QWN-W6yYN#UH2kjg)CU4yt>E0K3fLL!%QxPQ_#ONnWAQ zSL!`Y--XF1wXDs{cq(bpN^qVm`_`}etiGa=n{>x-s+RG#d*B_Y0S6x#MF9)^k6AGt zUZ@BTHIS@EQFv$MH07F1lbSHbEU8HJ&6Ri1+2xqdX99ZZt_rOKwN#>iSTQ3a`7PzS z#T|EFIumFT8!8Q*pVquxeiYU7nHtn9Em7y5{gvV-U+G=FQdjV4bd`@} zBCM%tcvtPo)^ZCWv#2{mc?*N!4*r%1i52NM!xVeowZ~OO18*24Ra9nl7U!E%f8>;y zlEx)ue{14as{*aZ=ih;rHDPVny0bPx=W^+ofQ*5{$Qh4B2*wg4G1CJJ(0WMM#KCLWmTv=WG@AT|Wc@h^M|Q8&?mdp0I+r zDMsJq)swBS{dad!34zbx5s*|V^cLjkWEy7`*;h~WemYwUdK)OR%VKUQI{P-G4?b77 zy1boQ!)Fq^AYSA%_HHS%h$+&_MnsHs#qr(k@gScCb2T^1HkUHizkBW#KpPd+zjx62Qlu28<&Bh(@h> zrLm62r3(^kT5#33T4AbhgY?LXbQ!4>MLavik14Qp)%#M^ySWSO=?dL#KcdX%4jG+Q zMv)3@z?o{X8iNfeWyC@gAWT$=)s}Ke>7~nZk9OUeNH@3| zvmd{a6+}q7i8f8_+si`}(yA^J(LDtyKuk5=>fz*!m<#L@9Zc|ifFi7A0wRln-&`1n z*<4O%EH(J!Zey#o=d|qEFB}{TMy-8>exaA@!zTaBOm$xPv?n?@7$KQ`$2i%GPcq5m#bxI z)KRm~TPLsVZ0?Um@m4p;!>^PMW$NcS{|!~*030!V&aggslY_6q5Y-wDZQ?xshLZXO zu}|chOCgH9U0ZJ@?)EJADN{n@G5aspzA~ngAX*a`26q_T-QC^cqJz7;yIpjK!TsXy z?(XjHZi71v?y$T~_GNdo+5NHaPp3Pb({;Kksjlkt$$c}%p9sHNglZC|B0oL>2kL?Wrir}nXb2+Y2k)bp>%XERI37sEV`cIg z^_k1U`KHY{vym*@FT?Z_ZpOGB+@i3E=3;KHyyKm8Ws7LEwZw%7G}^jZyOB`PY^uNC zR^IA#@g1F<5pm(krNf`uyCVzJ?{=f$?eb z19-W~=JA#&Ygy`%y?Wt2QsW*OcQ{g{QiC!_e}jf5iH*#Z!Dx4!hoF-zRn>cFf9W^W z#GP!b9O5c94JLV*Gxy-+p(KIco6;3_hmdyc_?#!;P=4h;MfvEM+NW>~01hX6pOq51 zAwu{{np@}{tgMfCIusU@%=HjiivMV_mQaB;Ud=qF(d+7!2FhC?%Y3(I&S`F033hmyWQL5E;k1L|qLVSwg4GJi=b)$`!=qSWVY}xN}I290p&v(k{oy8-!Z-%Wg8fFlXc_M`H|x$ZSBU5UkR;thBuzj zZ>aayZN~C!D@IfUNhkOouBD-~fMCD)_-^|6_DxHc1=hl zuy?e8Iw?X?V~}3i%|xOzY59~>yYX{E=t{_DKfhs&R!{1A@!7VOd()qDTa{~0_pNco zihZ{G#RywV4bWNJUjkZuxSk!6Ojf*c$F7XBybOtJdYpS}u68TB5*>5XE-ti8BbITY zIUoANI|OEZRA!~>`>2+XP7M9TdhSo_O(gTCoYq)T*%D852QGwsEY<7($8Y!pwIdg2 zUCL^Kx2Me)R^I3!)1E^DN7#)_{Jg0mav+QX#0%iF(bY=PQSjTeXQ>=>eqf^Ob z!TfHOe|UEa12z?WsNE!6rB6||RCT5N;$60pi%~c9PP{+5VS!55X}MTGx@k^L`Hzl8 zKJ;X#XD`J0ll_Z#n?Lw|OoOeAK1E$Fm6|tlyj-tRU4*_gp&jnn^1|kT;P1(W!YsN) z{i!$;Z5A8Si~FQ;eljjM-%MOw6u{=R*%G<^P>(-oRV45}WsARPKa#B2ENWz6b-OP) zYofM4T*EJJhkGt(9=i~K6D+(^&pMUsI@{%Iwgw~)gDBNLt|0Q*RR7;u9_VUuF{y4EEEAGk*q&Kd>lpprddiw5rq zsYL+4!3G0;TjAaVBOK)p0H>e{J8^G6?RwSF=ak+>eHWRUdrBFgepH@|p_K|_*}G$F z&o3|-=8-`g?Oe{(_nJ(~@_XN>K3S&J8Z(9@GGlqyE6NxDr-T{*Z&vIK-2;rA{z3TG zm?hx;X0l|);tpp+s^$@#mJ(fam>Q7DBdjcz&< zQ2cm;|BbW!L7#o<9Lz-&GeJYT9}3RxR1%`x(SmjqYr&4ny3l|-(vb3%McA=Kd0$5; z_V*^jT@B`m!rTu$0i>+GVj>jJD*FD5jsMpkcmhrkEY>jZ9O|x(G^;R9f~&?O4o@9o z34VYwnTY@QXdxPnbciI4jz`>u;!|xh(Juj3oKTTC113)%ZKuvZ8f|BRMUnUo?JsF~ zG+I9s32ig#pK`H=3{qX*c_k3{-m)w#ssLD2?H+fLQ&K}R$S;yl<^^X?X)eY|3LrG3 zBNK3&{2xt#Pu20tz)(T7SpLc{=)QVpc=3CM`d{(o$nx^)M=)SuOc-EbivOQ;qmiwJ zgR_;ZrJccl%#Msk&gSO-HqeSA%p!USxMeSk36=>FK$DRc-L;>UMjW+>GV1t)S@t`Eot~zInTM zv8X8Y;rxDhXdtw$-|hW+R<5yau(_ez{XE>BzKn$&W1#2}b9LoF*X=lGJkyn}Oj82t^<2`6b6XoFIHrg@#o|p)4xB;0#?A zt~PBjHZACxbrMs9f6Yp!|C+xnGfMRoq;arnP=*h>lyjeesGkxUS8f_Q1U6(fXhi-> zRlzf){!T7AN6uH|ZE-8gM(5Q;2`E%j4$EnzEOMV?pt>(#fioQK*$9m#kI%ulVn| z;D}?96Y~!gHi<&_f_gCoOTj9@Xwe-=W|5ncZ`~AJ56gEReEj$uT7w920{L=rfPozD!^-M)w_vNLiz^xboWv@G=pSoq|bI5=7zS+ zNG1-!OvEX1M-i2^lg$lwfZDOZf;-Z1in`%ERzgn`GEe*0JQ!l$`5!%p9o7q38ZBMq zsFY|8z1So`=Ure&@kml0!x{XHD512x)OzDuz2Atv9!KaiVH%E6yE${{7KekF-V0sW z5RO&+4l2p0*cz#wHE*hDdIOLAS{}8(23u%qp0SNIb$Md_llR!$r4yBTxIy)AKj%APS47e_kxS0sVxLs;2WBT||2I9f9mXfu&&o3lt zA3!(dfMI&Ins0F7B7bv0V)}1tZ75ms^m8dNEO_@QgFEcY&Re}+<6(^Ab`72~QnHjk z*$C;8f*IG)7E$bajGs?ZoA>}45F?ahb&SLYKHxQ;{KnTwwCSQcNlOW|arpz~m?bcl z>61$^2X(6u&leO6jlCTxc}SS55uevVmrQ3}%bILKI~MJ(a(9%H=9-l`pYn$*tfv}Lww>iC{l)l`V`R1N5ij;+O)f~BPl{|Rw3Q>s%c%?wX zHS^0qSM=6Jaoda%To0x&LZ;=s6C)4is6(SDn|2DUR$1x>&9RpXF^in?bVtY_JVTcR zrFCnEMd~~*-u$rMl;YCi?m*Qk88y?D(-(3Xy_6| zFxzdDQ7CiP;P|3-+#`;^S@VVdH=2+50xWvrJE@+LDsvFPql)OVY?iDQ$K@@h{Rpy^DXFb@D5^#3lk@C^|iUL*WG4 z=hzA!1K$|?(>OT_Z!hg$GlE>IqbLj}IE~q3+Bo^6T`UBQl5C(sPWtArDUDaSL+B2A zw)5rH;_Ww2c4VC>BMT29tEHmeN>=5fC4AJ$O(cpD?f7Fr6&teV03TbXRgv-Jdj(Mtb)3!7;6AEyK@H2B(k&91`5^0{_@w?gY0O^bLUFSAVgO(7BzBY zp7tX(eu!6unAH2&JH1fu>{foY(C~-V>2K;JFb9$vO$q@Y6-8Q5MksFeS{nv61Fd|u z(krX(;|S`uzboko9*s@m%S6)|pHm3K)-Vez#qL;D@;7bscAVx7W}MIVL@n|nw=8un zZo6*GR|)f#F`L!eKgel@)niId9+0jAl#L_9F9_gSkGi&-|G-u;-|@rDrrp8NNWn1E6>plk2B9>yUm=dz&_5?(%xE$Z` z3<4V8=d$RB`%d@g|HLOOOYl|vpZSDh|L66VqqCWbmCF~)+Q7ri%EHps%+$cx%HGJ? zi_yf^$i>CV+{(o0KS>q+_CpsQO3dm@QHRrgI6?uT{{Mi$du&eG`< z`wb3Jqo-BxFNj7y;C~P~-IF)P)zsT6zzTkD{6KTKCe@#uTO4k-%Nq2$xi(?F?OxL2 zjlfc>ORGHU;s`g-K66bHS8DO?l*yZR^LzX9oSi#ZW(7{~YGqYv5HP*2Q7YZeNpx%$ zU0H$DUYy=IvxXhc?m0g(ncm25rp;>H;s#d3v(cX8pjeWwiDvm%_p^JK)DmoOU2%@i zY`w$(C7G7rFB`{6mUCV99kSWm`6}L8#uuKQW0+)3%rPLdkJXbubMAqYYuRn6B^Yw) z{LJ$x+HF1Zns_9i>aH{OCX6_FB&=Mzt@k{kl}S!#A{;=z?4g(4wR0x)#be@%FxA=U z!aH=Ym%hC%>83gvw($F#J2V+Dl(G$fcfpmlxl#XFgh;|oQ4G@yTT1pPRCgFQzU8-D zSdZI2=S|ZNN2rd}x!4+K-1Eq;S6anQNi03nvj{HNthO0$@}`<0i9G`sa_dcAjCfAUI1@Wj*g+xxs5wU;Jo-+n^m8_PreN^ll~p?Q z{juKdhEaR%a1`1mK4~b%vo%s@&ChH-z2SZUzI>U7VY27b zZQW%qWq(n~YHMCjEkgEF0`~NrQbyWdI#>Cd8%0baHt5!w%^6 z%HnWier*;X_qIH0p3YcytK^e8ocEze7ymr;-7_My3U9l4^)|0|DEy;6;FP9I|1--} zX|I+;;XU6fY0HyS%Yt>0k>mwg%?`CEtrDx9Cu+-&9&|NYIGnwE#6f{2>_FWPra%Yy z*1~6{z3((&vhFHc6{Zgp)9Ut+%6LdItuO3gvWRlZt5fIp+D$&@Z_5}pi7b?6aVFb; zQ&vub#^4#@`OMkyj5zfWZyYD zwW8~toI}udX#S(Mdc*LjNg`cKw=Kx7ve0B@sbfCr>%?VcNjD6&OxjBDRW2p#oX9>9 zxsv>-!~!rAc1KbE6=7?OoVla*Fzq@Y*~8m8HCU@l%yUdVZ>V;8jwU^4 z^_|E}Aw9&VKKD6fjJ`ecH91 z>N?tXM>uro@!*d&c7{fKW7Dsh@5$)bKaY7oMwGGm0n2KbNU2i@cRXb(LOITF*5}H$ zV&olzYf_1H&URDp>| zEwO@hg^T6*O-fVnLtE_tMZBA&1FrnUNb4dMV0200MgoL!NT!FACC|VRP4h-Ap&1*} zf-pnDkUoGUfTa}3Rka9TB^X>23p=DRrp?@erlacgQddtRpqIR&vVC3G$e_4Aj;9&y zxD!aH&&f#v`<3R6Bx+5WP3da{m6eLKNuBHs#xiG&%v_WHvCz;HLTkGq^GhlEo&$#L zqzbZ(hiN}!7D<{;c>wq-&5H4!ftAzAxVid#TJHW}d3W&ZqRfi#s{$OFHK2RvuXuGR{r zqeIU(U1~L4Ai`2z6pwOG5+<|EE$lCCX$%_UxNO8aD$9`6ZaEDB&>UIUn3Xa{Mt({F8UtTu<{KF-;bYzVWFN;mebNOBQCrWhZ`}Cdx#$d`O1@50_Ylvc|g~@jMUP6_;vt{dFHp zacNT}jwN=oR!P*xz0Wp6#TTh259I2=;;-nP>rJ!mOa#CE#Fvb1n3@Um z*9g7U4;VS0X=KLCBn_he!?6$oc@bbn&a9~34q6e!@_9qr$sw9JXZOtJg%V zSjXzx2bxCinlAbBr!=e$k91;d)U7y&rOLZxM>PE)ayW-d+u|7&Ldgp01#8!b42AJ7 z5R=c@?%Z>LexJazkA*i5N0BL&d1FJV7p7S9;$i@3n@)}su4pb+U67G-Py{uEggOW^ z7KGQUQ1VxN5T7LXy(sU`lyRv?xVn9vO@|YvDQNUY&K{|$tH->z-AH1sb8i7b3w%3C2R%J=? zk+Q0QUXdpIh}U*6;4l-I*wIma%W!6P(`@!7I@f_tw83d1x4V|a8{P(jpWeY>6mP+ z3B2mV%%Zz24nf-VL%hf%Y*4yi`a2#Yp8sjKIzlWZ%WoM%tfb{CJ(6TAWJ{V~AW2bK ziz{as)^7#tcL8c>wB53u-YOYR9812ueK6Uok4XkAD)|$K--UY;YgH71-H;PY73Yc@ zm-#K9Bi*&p0OF4IQkXj02C;7120KJ-GLc9@AN~m4J?qxBN#fEa<6kK&vbI642%i1U*^iErep_4q>9kSEoe2svze`cE5mG-<2ch40Yx~&+O^X zemf4qa6;JgKBfUQ_))0Q7FS4QN298UyDq^*iL26!amc*=jiLXd( z6+n1ai=1Nsu~{L?w(l>3w)P5zN=ZD%6sNYCFc>(qyA(Bpk!==aU{XknO~ zag?T~VqV@9S}-KHAVsWz#wI&FLzKH*nKy+WI{lV@gK60o>4EwuT%^CVn|>=$f7gK= zuQZgd*W;%B1mOi_i2p4yKm*M!AAeZ$csOC`F53(yq>TQ&H8{J4SC`-NR~++vKhYSx z6Tm+gVY3xPWnYx26oLipyRh*Kl20>8zJ^2Pn?_mrWQ8_-W5=xI5KLXVtp$QP@TqpA z?4v6MDXT&1@6KGb)*J;^tBvV4pYz8a^!jOWlxvF524LM5`xPVN>~)kMO!u}jaV<2f8cd?Zxa}0y zznO3|->c5Ck7VHp7Z#nf)t%RwOk^pZ&}bR^r|MDnU$(^|a#{HobW^`YBc6&-33;r*7ibdomwF#hV4isf3vlXoj3mdNgSkr04)@ z^QodJ5a_$yuw;@XqA}jK6nlfW_1*D)4?BW;zu_l6Z8}CzSfLs^1zS#w5dakfJD-`2 zvmh@pq;kRo=}Izm7i1COJYR^1H!4XB88rR!0#h$y*GUZq>l-}E1z~y7j;Ae*JvQ?= z!wJH_?Dw2J<<av{^^F=2Oc6D!Tm`)P{o{_r=ydm6^&OSDa1*d#qCM->jf*T7 z=4yqfl6Q#e+FZCQm`|%DkpfqLrsYbrJ_`H&aST_A!D-@P3`u&`XJw$g%=NFSX_?0r zD~j^YM@5Xv-uKsJ-UHP({mX>M};EfqNR1eRpI`hq4Dj(O<2vN^wzNBByV(7bcu+D|6 z$&WYi4bAWVU|j0GL;)<4T=iF}FAzg0HE?3A`_s-9vSx8&+z+6UolCIg_ygVZ?yeau zVT2RKZb_%_<9%fJm^}VI)ekhhhWa_LL#Bdf^x4+?1 z+q}#Mh;-Zw&L7-*JCs+qzGE+L4Kw-f@vJI14cd+?H19m1rx^i!ZELY$hVYr+s})%g zmwpA`LxDTf*$tlFG>GqP-G8MKKE5!tNxjgS5Vtv4Y}(!1JsJjeSRu{$@X(ISz;s%t zvfsOnLh1UZN<03IDsW~_gp57k4hlRQPXr$61i}Ik-nHkCD2MjS^umSJQE|K1r)n{t zat_i=*>rH`s5J?sM{_0gC^lN9F&-S42+k_(O*>fzeZaaIT*xJ1!M+ved{weK@Q#hD zM>BH&`3v(7D>3JB=&4wgG@`vhXI?|!cOODv^`uM-g8kJLbq2$JAxMjPA61<-Oa7%k{h^?6-V*NY)>(P6U*P-}31G&LSnru#dj#zs92koFsjlcI% zi)&p7$>4ioOm-`}SXp*D$uvH%_qhX;wex4B5Sc8>^Ss%UN63~*Eu5%iE@&i~?4@7L zTi~$(dUzS+&h+>pA=jALmyI_@n6piP;Xd5`M6ke*JQ)6Gm(O87=72ZT1;N)$?TJnL zWp6ipIqt69IA-?FmFyBb5FhMVi6xZWb;4o}=7>Jl@BG=JWR)~v<~;G0bb+k(y-bik!L~pf?Fw&7l5ho!Q*k8G1OubJ*zRjbPn^_S%)C9ITbq$FO*ucXoY`9e zu#sN^+n>j-Bf=JPk-E%Z@>M!D8Bl<*U0XExlH$e8qfaNPxgPj&Hm`$ z{@kzrye#g1iWhpW|B6T!`Yiwa@aTR!$@Y7nd=&DzpA_=_2&w*jR`k!s@_*>gp7wiu z-WL4)caLn~|2modaeno2So{?wB$WMrYv6nT{wVZ*7;@G1-t6!1{o&C4`asnEGFFD&+U`>5`E&d%^{@kS!a(}Tf@O>8&`h2YZc)xuVa$lUj z|8($wU%xzG_kXs#&5`vR#+gqPdY!EPyuS7L-1~ePpEh{^mwvUi@%Of_Tibj8z-?H< z|6>t3`}1J^s_Ub;+V9?k=(Qz7?PV|djgiPj_txLHB)-|N4+V{WWCo27a%bTOSW! zDHH^Df1b8iw~lf-2sKR`_`PpF3cX!qcekEmeZD+@J|BL)22joC>lruCLfrdDTpz=d zcW+bMz*?-=>8|(t?xB z?YBN=gd^!#yCh6@^DJ!h+Kl-gJ@}&m5(}`8KUNFmWwA>ADd#7Z#msoyk~{hmtN(%mdhR|6CM+I zD(sdxp|1YwyG6J(b=YmV(Rp8)Toq1wy{cZVFQ|1EY^+@XRWI$5k`DEYE{%?5AKRKY z=7v1%n=85ty=z=!t@S@QvZ|MLz47E9wTCa_FX=6OmsU7T`6VyZ#2S*DmR~&2o*gb8 zbsv?T@9%Z+R1~`|+66_q`~~^$cV3E`sG=6jk>@wur|mjDCNJo{37pL2t*UjlEY|ts z_#LdT*M}P$GsYKuoJ(jJ-3kPXYooIr9GXjc>?7ru&kHghdoAvGtf$j{SM|OQ^c3X5 zp1FB4rz*-T?x6Dj(jr=U;S477J|2J7f5EiLP2RGm^HtoqTww>}u*lEIeZ&6j*6fcyCut5Itb@gR@`k17Rm-ktGE<07ocv@Z4QoY z8kCw-jK~$!wbrvELY6>9aqUO3-U_YfPSaM~H^+!d3r}061Z7c$h||tOM2+4!49LEt z$0~T*<4W*$mtOKt;ZZ(Wd-EfNRG>?r_tnng`?!2kcFwEoC=L#ee|jzt&RzEbpg&=+ zWk~=~qCBa!C4;(9Kc|Zfz~pE%H~FzHQBQGmOV0D%SH1;v^EyeMh_+M%R1j_Y-N0xy zbRx}E)6sEplZUf3C&OuB_AhmsnV%uyVvgCD6dq#F^Ws@G|5fXuh3*;hHmz zvN8r!S$Mq}pr~*WdTyp%juB3%Y~1_1QKlHlSBlZW-0Ne@+$+;0Yg|4}x83-CXNto6 zZnlo0%mHJt*+GvjPe}q3;NpR3O~~I~k(^anbT5QtmLK(-SrLyN(#?d!i1={*o_h1XgBn5bD5s7l&<-Wf;|i3WFGQl)J(JiyIQ?eF6j zV%HaI1X=P6b@bJ?@TvwyS8)f+ji21M*jih@K5^=c3;eQ3TIcS*4VcG0e&OvPRXceH z!lf~^e%^$@9<7rz#zH$iTi7bK1oG*6rmhZ+_;T>MXc?dBgTH$Vb)eI&b-7yEQ!n$z zo$eH^i&g$~e{c12*9F7sMviebls1V?J3lM_6C_q5dNT^}2@{#Ot8e+d-n|}n^f>x# z-rLri6ceuXI{qdRjoaxAO#l-^3E453q5abPW;YrQo^QBcl4N!jzeb{sR`%QM5P}XG z57nT8vuIs}w816pLl$DyZl^XauN@ws3nKSvkApC}FxN_RI*hYccfXk((9C`$SrLyb zG@m`nq>anWuuhEFY&w7GFgX*&>@$rhCm)pS_|%7;d3HuDp8=Kh4d)>#BJ3u@NbLCc zx7Z!<87{(cKGZqJi((t*zYWeKoNGkE=~EF=voK05SHf!zC%R}KGZgHUKQr3MdLN7p z!84Uw7oKa=+xU&0Aal{OQJ4^pa~+rzVAViKKIDuc1GvGg=sdyD8tKJ8ahls~h|v#& zTp3?Y;)&wSBJ)bn(YN=c9`M!;Vj?*9L*n8q%#`-`D){j0R0?2iU9Iih@lP%}@-A4- z7N)Z3?aLk-y9zGrA7ifG{{-^f?Er1s^pNT>zpOSgZj~ENs6dut#Dfo#mKQecg+CQd z@TwnvPWMO1Sr_U|T5gYE^ir2I32bZXsfd~n+*rIcOp)hm<}3HJ;*mFbY%Klyn3X(r z$T(J?21nB>;7(D8(soZ{-gsUo6DG|SL_sn-5UQrG?RDzJNn!k+i<)4AC3F$2mamo6 zldb)&y+Z|H-5Mlc4$F5;`HM2H)+nH`)gGXu*{;3@T!UC9I4v1nK1QX8yNe@hXb7KB z&#JdvqKAZn7qcc(1faE99oP(z$Ig9yMN!mjmx2p9uG*d>J@h48ng>Ljk$k;Ou>L8g z`b~cxz$CK!j!=JGfWm@d3qSu< zgQ0I_;PD7n_NmE43(D#VeufD~LQl#ba6VMoU9@`(ta9?=w5<{r?{(Uu>GT;r6{`L z3}0_!fpwAts5gjVRfx71uBTRX+|5Q;(A#<$`Z+uRRPYuWKfHgbG4E^aSN}IQQ*r7i z6M33kU;4djkuEcLeFm5uJHK%9!-`;lUxC#Lxld>f5toG@7SNGw#4c|E91+=k<*YRU zIneJr@ldiECvq01P-HG(#&P$ZZ9Op-#3YG}ZEHnXi`1#X3R}b5O9K$q;Ua#D>ING0 z>4JFsqe0XAaq&mwcFnaT1f#KTRD2F&d3S^Z)Jd&0-L-x`)tvmr>=F`ZH|_#s8)#)R zH}!zHQ>P@hRw1*0Ci9WEKP!`!7(ltK7v)$r1re6|W+!_U*8>#P&t zkOFDRteoLXo)uH_A@4XL@pILpz<=HIY(dt%jLKY!o2{c&I-~OaQnGnKsdUbV_7itu zmey9qRm1`W3~6t&GDeV>1mdm{c(AN3CC%ST?Uhn*oJRRw*qPfyPNoM?0m_ zcDrShwcV`uZ0HtNoY!>zBnO)&36NM(uZHNv`1F(0=4n-hM`vJoBd}hhDQgpL z&5EQ!u4Y4yzg_@cqofs^$_167);~7fVhju4Cfu_!{hE>+3fPI$wlM8m?Tq&aKY;d1 z^nh38Q;HJ;_jEYqO7Q;kWy8_@xs9uHsqtxmXBNszdnXPH9Yb0r-@|s_a_n{0Ny024 z-1$xi&!w?d^uz>R^lZ&5xfjJe{TwDZ11Bs%XVqqPLnfYwLUAdM-HHT}C@@`` zSSxv~=+yI5(+3r_)M;6GAKNomS|@pD*WS#53xJnm;-Mh3W2wBfIKQy&{5mLM}hTSTAC3--(_wGq!~5hdzfL1DQvu zkgcTdBb=tpo8-E}KTw;InW+o92zKRB%#m0Mtx&tL$B9$EJJ;VV;G~jNuJ?>fBo7<5 zX6Oh^$qCkV7pRIe7r63>A`ecw2+Pb+pd4qez;e&<5*=TJs^zS~_zAkTAPbnUt1J}A zj3=(@G>LvT4ztF|LKMl;R8(F7Ww7pR0h7r#`O3tJ=(xnzHK$i8G-2!c#ohQ8Xe1Z4 zM26043k0UM4_`bluu`w{V+U7YMFA<}|9pA6C4id9eJ_phB?83RIRLY`{@#~lUNW8{ zw;EJY5V5}rlT~GigAR*Ep=t3Vrm@H=8^ng!K)^_R<>7DE#(f-5_uQRJ;sD>^-4L3hLZkzQ5F zzI5_)Hj&CZjOLe5Wf9E>3)!rO6Bpfy0J7rtb$*)Zz}Yc5A)3|#sljG>Vg@rDxqK2% zDLnLL*w6K7S!_n!ie|xB+hvNH)KYa=oCoPPvkuf%F$I$~^gf|M-)LdkF8yAGMeH3~ znK0{jOfm2$aRgMvstqszYb6QIxwzkK2{IH1nWw{AoQFU-K4|1SU}f#<)W~dr;AFN4 zE8g{(&}L1r2_sR}YWGqM(pLRwZuEA~TL`g_2Hh(swOucU73FtSRHsQIZ*TFBg+3%0 zuguf5eGmiiu3~cr4z_o%!NCBNtUCjR>Y1cHZWFauLW@B>y3nr!_Lj%B^A~W2SvHv4 zWAa|R6*TjbA|Pe^(!%8*vcI)NH|)MJW6=# zFY%szH8gSRB6btv`R@u`h#H{#`Q8~SYHug+bP2)26*+gP_IC^;t(_3^WJPz}8BY#f zLOYR8fQfbPADdVh>;x`eZ!S?`WMSNlA3RQ1RAI{Av|ebQ!OOAomyQ`z5G zO3@O+222WK;p&jYrjg@;{=tbXBLPRT(*F+Ed5s*kewt*Xb}qP=dD5 zk1)qp&;3~d3z3euL4&S}ilO-ZB~IH=>qhanFa-rgeWXrvHin*(Qk$~Cy_KKsDzG$t z&X)7HF#f3l-t$pxh}exd+Mp386WTM5^OwwUC&AD_!r-)+L3T`)WwqCE9Py$MFp?H& z@!N&O@sq7FgXxGD80zInwg|!ip826%hdRV#6h*X7FK*ICI6tqAR(Q;YmK$y{It&C= zqwy5^RYU4?mWPTz_tM+|Yi)xV0}@9(8pj_i(&g1Gf6*n|8&3EhC?0Nbf%ybO1+d&O zx-a&kdj&lpb**W!hN*Cqs&yeMB}mnOXm|;#x#kK|!Oa?_ujj<1-hmTgwlHu)w=TE0 zcx+^u_3_w>grXZI*KWp};3+4{56J}+YABZ7jv77TRvon#z7{PJBd!fQOM z`Hss|nv%e`shQOQZ$U9KUt zONO1CJhLN9p(2_(JPTm|`>^+k*jW*8%7@H4?-%w9JA>=R?5aRP-dtS~^IRi?|6Flq z!%_{)V0WFIG~PrTKx=}zfrRPIZRPEymS_&6iAJc%5pTID1Op7@UuJ9k6GX@ z;D=&l!~O{vMVSZ*8ZAxI%nx$OM_dx*b&a$_v5347h3QU(!RC%N?XxKDQE&Zjg8qAR z3QS|eTTg;wQsfpvkvs((C@r+wz%;PxUBg3;JZ*Ts79w}xz6w*aAtmyhsIGJRo`NQb zyR#v!0`_9)FyLaqrT;R%iAw$~H*+f8Iuuyo{u9z!2_01n=@$~HolqZGG9*v5D1|dT z2--K9HYzr$%aln0n=PGJYR&HkGq#@jN39gAeb121>Ps!!htaY3 z+?`fI;!-k7mDOS>4p4m%&EYdKv>N_Kw#q*-3^>;cF zRxYC{hHqQeMG3M6(|Y<$ux_+3pXdy$d;?1p3JAA_CWx3vn@}3DXoIEcePo&}_3nVR zAL8k@B>=`i=p0D!BhS46$JL@aa-8uD6WP1at?>HNGndD4n40Hrp)i>P5K zuE+P8>E~`ur9jXRXa4Tl)QM(wUVT7bpN@>UYWf`S?68I4qDh0;6Jeu6RYu554Th(6 z01UEC9`<(sxwF!Gy)7^1EtUK2W3pMPV5D(mRV?Y^MveRg>O{eqXssSyH>|TW%ytpC zlniC&o3K6=9qu^TB!uc%1tbWefl?5a;#;8Ffr7&{_=$%ED9l;X+W43C4e|6C0)3(j?zxJE%MwD(ns z_-Dbm>lbTDEwl?_Y5urlyF27>Qqrg}oJ`D44;00+2B8xInBLr`loOy;U&PZ7?2%$L z!Jbv)Qn6c?tZBV`A@L zMkORY%d;(=RS^JWnZ9n2h)Jf_nP1NN3m-j`Qp$g>S*J6S=pam?KFk|_J_RZsPWeUz&K)| zIdO>Qbm4`Igjig9m>~6Fs>X9f-2~h^7PEK^ zq$EsYwyyo$mE4NwfaTf;!Y(o{3f9?bU7($U>=$0-wrP1nBniT7H$cg46jM3!#1T#? zD~U6pPco{FGMo-{rQF({X+%1sc&@{SPvWaK@K|@5vb;8UlI9*4OBaabOe4kY?bk4; zxTN!>*5nhy4Jl6s`sg$-aLCiv^&_03)H zDb|G6-fqqrtYJzNiFJ7JwMG@?xq@BdP85}=c>S`6zpp!8`aVpyUYHgu88(Oikk zu8q6@-AB2~z#Mgh_7>wFTz_t1d3Z(S7brvv&P-yO{#ecOs!wDv$AScc$#*I1O ze-@KR|0g3xd-kmjTc|7+SZNw_Qbw|GnyPf<1K1iMhaA@TAGgBP;u!^G z5f}av$?UxFTH^ahs5|}^q9HW~pXAAnz0CF~#A1)OQQ29oyCWx+l!Yd*>`>zu-~ahI z++*X)G|3gy_S$5R;1@Z=h`YP%!}j3#YG$3Kj*jq;?R$k|ZI4 zp2Rs-T=JPhRq;y$!Yqg=z*nVXaTmYW6*2=Gm_!5JG0dgjv{KNg&V4ju^q9N`sS%)I z+>0T|L5E-aOb&N+U|LyF=b}s4O#jNFr2*V$G}|Awd#4xR?_6yj!x)kGmbU5uzEuOA zVn+ITk1@rCJ#;i^t3x^CmA3EeT!C^WD}(!re9E704auXM$eMV;6I;519xk{%=#~=L z1WB=3YNGi3zZr7Re$UCaNFi>%~fQ_i;0M>!W; ziIjwp6$r8)Ocg}0M>F`bo!*olz`XlkVJDkCJ;txx850s=56j%)1vysC?E;7eJM zom;AIRvL9s4S6kG_Yxi4C$fnWfic+tRb96oMrA}5Jzp*7$P_Oyr zM2oBWNxP~E8k`Y@f3O5V^3J-)fL|)}fd4lDVnCh0Hm3}b3@!PkkM9LtUV(||MZguO zgO4Hn%tIl9_Ge`25yF=d~^SeZf+n`%j7mMU%lZG{Y?K3f+?oi6b& zyzsUyh|d5|8b}<2&nBfJ64=H`I>j&T(!$f003);0qLA!(sK9Gfs%Wh<|w+%wm@GZ)%J1s&z>rxiry^)}z2*JYz#vJvr(jqA;^Nr>PAyvmAus7m-gvDFb znAPhg+L$gkE5K52RokOIwz77WjWL&(Yq4qx0;K3v=vy0u1S0BBMUFS=bk2F~%ir)< zNxMb>S$4A5+ZJnK2X?g^{$e?V8DZ_v`p5U`%k-IsAyH;=J-QZY?^NkYA>bQ7qY}>= zZ9UU@7au1`ny2`0_l!!mP2Vpbzy?^7?|kjL>=S*3DVVi85?L#)H<`!9LXVtR2T(>O zNGELtEM$LEtjp{k&S;`ugi^{DQf>lg29DYuhAcHVs|?I#HrGqYTl7bPw$xN(e$yy` zG7Gphwz_sGE~eJp3*#YbG6Xk` zQwX8N=-Z~$*4a+~2YZtD?Qzv%)9!KK6y6;5D@44tMCQU8EoD3bD@zuyusStkB+4@Q zUos{;q3(qEB2ZLz>Uny3t%p4y{n%|Y84jp@>iGb{_N>42fJ@Ll5KT9yldmOuzrXpw zo^=T|IoAJm=jP$`5nuv!9d(wZY|KEiC|x`^`B~J=)LgFkudVWXnU2TTNmC5z`4V*t z#@ms--j7^ZqT0zdDo)Q|&g)%cuyryKX+q{W;$>1WRLO+l3S{bQrP=O!nUb{ejS)oA_kUh0+1W0s)$b;6+_Uv6cd#CI@mB?mnSpF2jVMXgj3*}f`q0S zo>l@;>wDR!WE5p1EdXdU4`3kxXcpX4PEFl)E3A0wOfuLbKm#&aC}Y*~DG6IpK4wJ& zcbgfVd?0~BlR8w&=^QBo)k(0cM?HwN;zkkHn4V+`wB&N+_<9NhvHx3GAeAIhV`2HN zgp;Yy!bc}&PLjQ`v2=geqr$xo$O%lVG=OC49*#&S_D0E%$w(J3BCp*-+6GRa^#~ayPv_H5EvdRyQIRlNNsPxk|zbi&SjI^B_=lmx2?sV zNw!tPQ!ek7dkAE^NEA$RP^W`yM4aRt@O&-*_AIt|1jKp7vvc^UV>AKGB&rM|gW;Qy zmT1Y1`8r=Mr<58V-DI)nW(YP>su`qIU^kkqgl2jGIlWYlZtV@OhiQGdghbXmwN>F) zfp4e;Ny{UH#e@R-mjUDO&h-+Y9~6uvs#&B(?`3=J_vI3jW|D^0D*%0yRQJB%2zY|z z_y%Q3p;oCQpz<(1?q;^*=D;E}11ha$k;a4IblBGvChv2k$h=?P3k-#20|OdX!G|L) zOKF`{U zyB}*M?VvI!II6D&fG79h5P%9PYPDL_;8z~gzt`ku0IU-TTTsEAuSlR7+J=qkbwC9e zNlB1gH>xh~IRA+w*0~v|h5@)N$WFvi|6!;S4}~xh&9)XQ3s=sY6PFInQMDH=2gT7#kOQwPQ$5|SV&H|j4aPaK>aNi4soQZCEKEs6?l-QbsRKKfFw=N^yLxxEUbhPPSo{q z!Vhr3-T!EN8vw;{8ptrANfxmt6<@E+Ttvtz7si%)qs(z zWfYFz<-LD2X$}iYCbZR-e=&;fPpAAzICFJ>GeOQZZx&1jzYWwCzHg4?$Y#D4u!05^ z8g%Duz`NPbN1l}mBIN!UD?OL)e5716 zWo=-J>*C6K*ky+|jrVHPtOOTDJzw%WY;NjONuyVU!vwEDZP4HKi}F4GCN7#f%n6^6LqQ~N>6zz`n^$OgsP`@Q{O>YEfDmlWiiRA~}8 zETg?(GtgD0#{@6T2T4Q|kR z6CNENE|?G@jc^e`-2*ndLzuSBD52DI1E-*Z{H?E@4_}+Yr45qRfb&f_EaUG1lp&FG z{N=l^R{;E+$6v__A}LI5O83fbYJGsL0AX){Z}6?c!6*B!gSab0-N2-uP$f>U?yhVxLyt{s)% zZ{qeyh{=>~usB+u9ez~}>F4P^e5>NcQ_ffX4(9iuW-S4nkYNmtr$FWR$zmAh-%2bL z>8H(YCb`88Mb3>B&0Hiy=KR`%>l>aqhkm1SBV03$$QD(O9a?2%xHSy7Iw5x)W-0hj z@viYeDDwy28muC~E+Gls-aB0Qp+v@WKs^XoIuB<9yeG^u?-G}85_!DO z>ew#lp#iFx&c*ww@+7F<4&-wWz2)*uvoaFiMNASn&alvRkOMavKXN!>2@X0i((waU z*cveqsNO&zEX-#&b<@^YlNUg)Z4#Dp0ThD^=ymVwVRqvD+iJ2vH5c&vh?ZUs$K3&E zvo@Q>3bg8U)0pS-Zx60;J>I1V;y9C_m6Nfot)3yOrHV6A1ZHMRQ;leEyfIvAhntG} zYqP{UPMD})Z>Fkp6QpV5)wpD8Z}T$BUQhB7WvGsBk)#~K=LsDdyvNTVB8j9DLHCQKm0~6^3?!$iAUGXdD`ipv=?{u(5o28{Ws(uf=XP6hX3kS*9{7gH zvqRcfowz1v-9eSToM4$CPDyvl3dhcD)6Q&*b{>=ZwGBEUW8!yUI^AC2+xa~bxEqy{ zWDrWUQGkA+LbI{WM=XId{!FTYP!&^ry+(Jj?;A<94&=Fab7?>bD3eN36WD-p_S$Mh zwL0%F0lS#AR-me0+r5KAX@?Yx8{s+9Om?4-(r21&HQS>pY}1W2D99qq3y7PiBc!#z&*PUvOQW7U`z{(D zb##>(zxo4wqa8zx$GzsMetjc}zK=Tc)$kHe!s|%h{NuC*wFd!mYDY(VO%h`nxmcnwn z7!Yl(_>~oA_K6c1R0*dmVuPm| z2p=*5lE7w8YKT;5C7?^9kn#8Fz2 z+&TvyGfVXioMojDBq(oFHVbJmp~k~t0(5sn1U^e8PIt}t`REz}oS0t?Av4=ERCG+L zT&<50zysg1)7hkzAQ?*3#X3~^CK-?WNNXBWPzHE5T1$aB9YJEM{fdW|LyPY?)=>Fv zClK_GZ?+3(0(!& zs>@rv7;z;uhv{IdMa}gEyn`^jQ$-NLJK94wpm}!t>0yz;J_e+UXyZ<$Yj9o`xEpD= z5yW}MNXb>2u{QCoD64n4l#UkJCIb=O>(PhGamXhdRq^voa`~7Q7V3GoZTCmrINcSf zY=<_Cle7SY0~9Kmms7l%wY!r@we=8Pb|hsQmSBlA??&I8f*Z803hn`}nch;-Ugh;v zKNvtJrW35}!T)c{0KL?=BNpgdVL0tY&I-mzAJyYqpxsfEYJOSAv6A?Ce z2U^E%w&R(I_*Pq7I}G@k>;;SKe00^m!z|ZQLnW7I3Ho?U^ktsMV;^sP`EbX=Z8f_g zBT%+C=@oxZ^zs1YPxl%E4pLn!{eh`SQ$Z~QatQ!x__X&0ul0!f(MV7Z2y4eno~jRg z-P1O9&QJ!~+Q!c-RvceuNrDXuR$^CL6vT6v)8yC{IH&>2qQW^;$9@o2&>09GYfXBg z;ur4u;F_f!Nuh+zdv?IKPKT(FQiSRWV#BT*Y8R>y>iA3zK}{+Bs)Ephju+7`$y!$e z1#V2F!|i%>r6As6En>>I1;r1o`|yu)I$P4~^$PkPWlf?GCWZ_JhT{qCO>G=?(8rQ)*s?3Kx8ZUjkW`*hIX>XB zYtX${V+RPUhD52!Nsk;Q>~yLI(jJTpP(}>;Y>k-H3hEu$30U9;+bJ!Hsm#A!k3M$4 z)3;y+)PU2*oIV;(Zx;{vNmW?27Qxx4fCLmkgsj^z@m~pK@H6ydFmvxB?7$G)vV(<~ z+k-VC$02ye_(BE>L;Qh8~F9zUdj zHB8L6GPs?3!*@&7Jxr-Y=Y&4ylUIU%AQg~W@Jei_D`HCF6m-o6i6w0wY|kJ!Ca1fH z)#t;VL`wt5>6NIdp+RQ~M2`&OM32=Tm>aU;wD`#W7Rv9FTOG7Wnc~btI_XF3o`AAi zT~?4D$t(NuL|Ep#`zFE5)&ntdyB>Wg7J6kRKZ=Dun91w)-Cc1$nWxCfixLSet$N~- z8|}s3?c5Eo2mA0P=StE&$ldh$bHc&^}S1;8>SnBk!7GMG<{X=Hl3+LiKb= zryy3r?&R+P*$jSm9<_S*hQ^~$I4$MRQ*)d_z;>(%He*KUo9}-&(4kURS17J|6PYw9 z2ecd5TA0zBC^xyHbG#CXq2_5w^ax2lsN_Z(jruggxmnxZS^QIq1u!~ ze=)oDdBtMuB5*~tYNL{5_EY!K*tqZ`R3kR9kSXokBQ0WbU{Wkdlt#j35*Cdha*NH6 z+v8iQ25`+9-4MWeYEq~+vSCl$a))4T3!91_IuI&`7f{0hH{}&gwI=%=AVs-dkDv=N zqY1G-cz`BxX&B?4^7@$9+Rx+aawz{Tj+#M#A4yLT$!Q#{zhC@yzewDF+L+wj3!p&- z6P2hOQz!uWq4UavR$3%i(%Yi33a-V%P&*x3ua^Kx zqpuw%QFq6MC)K4n?qy=oFlD-q3a%p;)|>c(K7^2DZE2Hp|GHxdE|Lsd0JW9^8kG+Md2jP#;$;TkBoWijv0WZY_0LTA5x6 zkL3~6y*E4du;+S^{l1aOX+uj}7g6a3%a)%t$kCx%hYod> zg~Igc{?%5+r~zn^DAJa6o>VrlOn1zv^U+75Quxy$$SAZ#*Nwoa9 zg1rN#v!#_!MJW-GNbj<~9#M2W<)~wIw1ldJYyxGl3!c1L;=|M!J06V7$m4ZYu%~#g z{4Xkq5sCl_4Pog>N)0oY4lKd=y*#D|p|XW(Lh1q%+gwS=%0m!BeS)iJkzQ(c6 zLC!-CK+CJUgY^i$w`8)Zhs6ZmY0@el;mo;8D>aOhXrOcSDB!n`O=s4i%?mB)&a&*% z@Hw>c0*t5IMNrZ~Ob+E3#Jhev_rBeGMQIR!LdE_h4%kAQ2-1RTyJ~CYshyZ-bReK1B*l`-S(+d8 zs9-?g(h{Z;tONH7X_478u#kqwnvQdC^MB)NNfF(S-btt&nzd}(QwmQy56Z6Hm(YD& zvHiFdHXAn8N{1l@&-!L$1X38|kB;z>evl+A=9>??aQyhq)gF6n=~D3&(`sYa+%Ojk zWm{6X#`>_d@_)EnWOSmpW(5+7fliyTKY|>l6~b=+GJ!;s=KwSTT&i&^Z$vTuuw5r} zV8HP{41Xe27u|*v4>;*X5)Hd0Mrg%K$?V&SJ~~|iWOc|P`rMFuog`#RP=s>_fKaKa zDWPs@>n>xEcrEzMfkjzB9=gH-G6NFMbJSbi-6>mIbURZ2!=+k0Pe@>heB6kGwqrMU zG^sRw2Bde9!0s2ayVpwOjGu=Szb(^>VFC!E%$Rm2dp~g!m-I*gNLHW5<8eYmLS-|) zq?fn^0W_fmA!V4e5s zvPy4k`AYay^fuIPG#0M}MCE#P&HGH~PDx5&5k7X~p>8#0XS4U{UF8Zmt%HU&w{{Ud zymtdJUmK}b(kwa{6i(2mekQIwLAQ?upsz#k?uo44~aeJ5~ zB~_`wFel9_n}TZ1JT%;qHWpU=2+NT=K=Lbf30rs>VC?L{Ft@qcaL1VX_+EQ8h*YC3 zZ`Y%1(beu2$J5d5C-?ND>WXaAr8;AkT{Y80H}_+UcD2QQ|0>qPoBCJ^Lx@4Z4Q%cT zs_AeClk-(ftXZ}oz@i_0N5O6H_wP|_Wlz3CDPa-Y;J3w9O8XtjZPw58Rtn{+CsfjWF?WSJMuanmcvV$e_PbpU@*3h(9RDz<#R{`;4Blk{Phy?F=`0 zfGvX@YvVnxVmxsb_@$&Ff>>%$xltj|f8_M?H2whws9T7aZ|?a=3;WUEH2?fKvw3&J)NN(ypwH9NCJ#-N1OOE{s`1|L zeK4v1S0B6omHODhyt!I?AZ_W?8QpwoQb*-Yespjq%}$$!@sP^oGC@s1q7+r%#CY|U zfkT&HHXkK(T7``s$yD_P2%FAk)g73FW6W^Nfp2P&EWKK$8i?0zlo9Zo7;A084?d^60*7Mh`L6z@=U_GxGJf*r<|x?PVxboPb{Xv2IR3*a9i z;z#~=;HI55ah!P|0aNtnsIFPtB5_N|kT6u+)61P{8%HJ;*6Ro}Edf8F|2PcppPbzW z6Z($j`+bmFz zGok{B3>Xkg=%=iip=8}n%gVw-n{83MV%wYWdO!ssEt0gDi(+W7NMXSXfbcZZV>dD6 zEpTh zV;MzNvUmF)$Zm2A@1}RA6}zJHl2De_5NLL3!!cGQ%Wuyx0pm1otAj5U^w0qjAD~i( z%0pV~Mh$EN`gVU%choPk3O1OuRNoNsrA7z zI^s~)Ll_M3^s(az^>l>lo7i%pXXkL|l4PBVecWt0hYf8ums(w`v=hm5?BVU4KD=Go z8`B+tEPUyj4b@xdFbm&}^vQI$dp6AV3OBcg1Q!;I?CE({GY(fNJJv5jqS-JF3}nMO zh*q5GrrBhM!XRoNG(lb69JCgotfx? z&mm$l>0#QEaTayiKYa4_2)L42_S%4;0KH>nJ5Gq2$IQaGa#pm*Sny3sb$h7jlx(aFhfu^zYt8um_{2 zplCwXy1D|*LMQmk(bcGe5+73tnun)t{jjNI>|b*72PW>+jR%Vd(t5l&!@7XEBGF#s zQg8MWAPku~N`_2oW>T7!me-J>LRvs3WMN7qR{D>>b&BUW1u6>|(K-TsQ9;}W80Cmm zx)!3$V6om#SFHY_nh-vKoSu}bjA~x3Ze~nLK$rzEufbj64i7d82cQRtzyp&7ohM`j z`=aL4q&C~@3=uW#Fl9$vXwpW)`Rd?!q-kX_o;4Ug-K?f8)(vc`LQ={G3N^1_EXZ*I z*g79&_u&F)0!VI>#vaYG?#NpZ#PjkKi=a%kiGvtdV8J|_p`MxdBJp3e*_?4na&#_!(MjCbof7b$g%x-G? zvhLEDNwUs4xt}T&y(zjBqP)BWWOUs0H9(d-h%T>Y9AUa9xjx&FGH@BJ&^+uiFcPiO z&`k#CGhJXB9ZIKJ>5jgi4rRYGp}?OEOoa@bwt4+zD9tCD4g2dNDt+cXAxVZk#pt`X zdf09zH`seXW-~Yx;@k$G78m!@oBAy>Vm=%eNH@ro1gZ}^6vmw~AB9>3^l6EcBlDBa zS5VJEMGU&S+n}vbM7VU84tu*Bzjn9OGyOR@-hh}_7?B+-q#T(x)MQ)J7yK{GVsb|9 zc0IaUF7QX})3~keS$=bD0ojD5d>JJQ2Lq5dIN28D4tiW~+_As0O4HF*uEii#Xk2Ij zMtaKJpkRODx$^o(sDcoBkLuqq2~vuG_7RmD$<>CnJt0Ui=t${EcCK{zaYa8erd0IM zl9BpN$(=h?=WwFa4A7~XQeESTn;K1Nh|eW>q^sLV(v=gCQ~59%01gdZDbU?qkKh!e z$5)DWirBey5=?17>7Ym3PKUYABf8Ga>|F-6Ne>l}!L9-dc~i>>JdIWk_L^~g(XoSe zGC`qI-2I!P3PQDWx<&0sZ$mfxxmJ~-1~1{MFk5|dBl$*V`uuzAQt}T8qi5`f48aZw*r|Z3DeFnW3_YQfJ9yuBnyA>cj zRfd*=EuRJMXg-CX7NfTKs7pu1e%V1v_f^jn;8j2}zo*1q_-yAi>t$wg*u}uhgx&#jG(r zfO=;=uxhX7_h6hHQds0#!o<TogEHeflp7G1hSXL3cJHDm4-?4 z#!X%s93UIznfQg$J%Gn?7;kVUfJOp^Z98};J^*Wt1R52#9|L5 zem6sUDg4zAYdN$A-k#g|+77y0{z*49anSV)^yNGqz_AVHdxNsbMGkt4=65dXM?kh6 zn-xOL>-^Rq)mvKmcheruplv0(PF295%JKH;dRo3^&!m{sk5sko&kPtbb)qTWpLXW; z5aa0vkn1Ark;4smUXfP<`xPj+z%%7uo+HXm>9SBEqDre7C%{Gue&|C%wI*#xfWh7j z!URMO?lLMEDZR8_j$&ihtINfL(bg4xq}ZeCjq(R}vRy_w9O$KLpvxZ^llzg}!wE4W zfbi)By9~Y4hY`px=Q)!Kg;sqLS7-N+jQnGu#Tab*NUs1pq@=KWz6IFtM^=oSN2j2U3wN!IQGNYK!-I~}3%Ki8~>#iPlX zEL$pdJc{6cB-?UyoUl-XZHS?13pd6(Wm07@J`I^nCP%In6$;f|Vkro&E*o|02I$Oa z&SL&61a=`*^dNtb%B5`qV=E~oyd*&wE*u$6kW>aRD z$AT_$s|AP#6D<~r9A%1LC=$c;i+*4Pj$3`(u|BPi^$q4NwhRzSuarXJeL1+vU`0EA zVgqhaS0y@nMEPTOt~1$rsduuPMrs*XB)bT91{zJ%`f6Xkj-S|=O-yT2JAt*;o}k0D zYzkZb0IRZDChHYzTOlpUl`7+~3!jI{%+C^#> zqcMmD9b2&kKYEWz3EW_&HV%Oi@0)uMY9wkT&C;81lBy=sCqbOdf(mN{4)$E`b24Xe zsf*MoM`!s0+2UmmXyZ3sN}r<@=JRj|+CWkV@e=mNNYNZ?d0*ZeK+%t-p6Z2N zb)EVV@igwH^sfuK097gctwfU_u_gOzX6mQFY_|Qap2(})HazT_d#Ft8_rmtG!)@;% zu?MkX%AN{0)niZQg@Z zg7&=3nseO4S^jfO&r~%4i_xK_KJ>iy!1@q@nA9yLRgu&Dkp0Au9r| zF0s)rz-t;r;VW%+g3pK3O>Hq!=&8}~fRCX5?5hE1Ag}X-qHFw^bO<9d3>qY#lTfoU9QP1%&4+PoROjQC3hW`q>5 zMc?rx_iM*B4+}5W*zJ0xMNP{cu!o7f70t9sXl(5nGd$*22GMz37Y@jYSDBa8_)K?N zW9^f|P33i<JK_&9=?xR|};-?YSaics)CbL_$lF$GZx&{A~Za17sl)7xkkw!v^tH zA9HK;8HxIc1wXG?wNHaS%oH=V>?Ymq__;~7;;B*sOD>v-oEK3FT&`|XVm2EpnzFHP zFx&;pO0x?|0Ymtk;BM(P+k>DINF-`?1aN?WqT9?lHwEDqM5igb?!dPMIwa>7ZMqwP zPhx|&k4G?jLg)k#wUYq{Ax?ejV{YBF*bOeGt0#sDI;fF3PJ&J%rl~a}k%kokIvT^w z9sVe>qTx<->-fO&I-Me=zSzMe{R8e`t&>xAMBWLCV(V7>r)Z0TdB1v{P@w>JuDgDY z28GlF<@X}^(K8l8Sa3>`{jJ4LD%Zvtbh@x z_`M#aIGI_Ttc1yp&^57)>(Mo6jPl4)*;$_WgNN!I=a|%r89$Mp-ajz&&|T_afufeU z6Xbcl&zrpC4We^O-C2&*8xV_+C_5uuO?O>fL27S@sMC9!KRpo$U@9izhiK_j3>aD9 z_2Bb>eMg3Tgu1uu6(pqTik|Jang@Lc)C(JNnzo;CYaMHG4%E|&n+$5iR6VkSSl%N# zn!xn4ktfI@yeFP4$drgp8C=*2WX1FXW2U#amj-%vmtnQon^5NnF};;kG)+=;@ARU$ z9xu4jN3>5sizmEabwmVy->-9>FuInCWz>u&^^6user0;i^Yrn>D?spUH z*8>u6_8Y2s3BY}-uu@QUL-nT{d^7RAm$wMNyqZXYnIVx?_uUVYA*eUwm$h+(H$Hd| zAVH`HlxX(sQb~}2g>BQVB2QRVx!52W+zGduozDd>brmu4Do0h~zq)yYn}!Coem76V zwzAk-VJ$k3^}I3~u`~&?kX=gAtJuBS^rmdP7$<1Z65j2*u17FxQicJ!5#<6z!?u0F zvqL05x|h^^-mmNR{tO{ov7$R58ISfq6@;aGAZ@Rwl+hsBM$>vG4FUrIQ}C*O3Jx70 zQU((hppNi}rT&)QKR?<;!i!H1utn=E>B$PV+0T9K*Fo=A`qD&n@p;86f+~&!8oVd% z3;X+lmZ7a@f(a6k{SA!2yTWZGp`@S*rGtalLZk{2xaj~BYU9EzyLlC>p_TV(w%Mq2 z18&DFQN{d++dgQJFCU+!Hn%?KhwCVjmT^B@z?}iUcSYmFa zz1_9>2!vTrV_*b#zqT__cz)f!boWtbE+MZ`kBX^HRYL?|pz1Hj6}JgCBtwCLaFuUp z_Ivq5G#VfC4jDL|L8B=Vh6FVc?)Ob9jSS8nd=oY;W zJztmEBo7qFoDYER8A)GSv~bTLAh+$cbxi1##XtdJ5RHI3psXSyfYYsvn~5EF*dz>V?EnZ;=+oQ{! zR3>bjWS4N9kEU-Wi8x>hxIK)^P7PIOD@s7{@3Na9T#zn*lZ*og>I z37qIll$JqYK^?&ZsPcA~;@8CpDuYmhV>l=D+~BUVD)MZKsbnVOOl7uR&|!votar!C zh?=#)k!DW`v(YA4h8|+tdCTr=o2b5bq(nM}3W-#sPaI4h^kCV7OJB28 z8Q&S~ONDWy63OGHt*qH}%~?Ah&Lx(#U7}J?<4j}P-8Jc_qif0XexFe@47+a3#k@aj zmI<4k@qqChU@1H<_@A;B0<$anpBTFgMc8^sCg&A4b@<6 zirx`9c$kRY$Rz>=(LTOa_E~a9j;?pOEa#(orzIT7JLK@VyDt;2%{+04AK4L{)YHhW z*R_0)KM^>A&%IM^x(BidX$RxtYbJjjfNX9>+0hZ2B4s9hvTqTD&=*kW4FV`097Z&_ zPv6GH9*qRG{=oz0)}k{g6onAX_DA;)8UuECtV@a3CzmTWJE(nkCMC&u=ufj7p6*N; zvR2C0R~Nh;Ky9&(bZ;Sw9<*QKx+nJ_gAFEy=-)t&HSKl+9{0u=1n(BPP;&br$97XR zLgw9_(PHX*!GCUk>wygotoLxbED?cf@15@bC_6;<>VL{q-yOw5l?f}pB3_F6eX zTqkF5lKFp7CPH@zs&WfJdA?p@xE1+K_}gWUXKfBrD4J#3%f>}O8Y$#pNqdU1V46vM zFdEIdPQqgg#zVO#4xo4oV<}X_p@U!_AVVkuFPyx9gN8=Li^iQtAGYV3&Ox6)ABie?Xq%>1aE#JFqtewjK&4R{i7##yI1|*di^g zlo}lbC>gtByClcTIs1~w**yrR?{xE4*yNhjT0ca=c$p@DA!dc!&(zUv8wQ=XVoZsm_K z!F@4T86^k&Jcbe0onn6c*FF{CO(=~iI;5R%f@e&6W|2f@S7m?CBU5^T3iH3dEP1@CynLgX8m)3*C_7d2vN1JnOUjM4*#6{QQ zA6nwFs|Q}@YqQAx%ZoXWcGt+tZRswg8DnK0T_y${9;jmAcUxH;l`kjwNJ&f>&u|g9 z5vC|kip-HfPDOR(w%g3A2XEK;3LrhXMO#VyqfY+rU`gz0zXQ)mO-oJImn(xLb_|lG z_8XpPI;|zjFx>kgt!~UYx@7J_TiYyTjd<|phg2Ghi9pbjmg;V+bF-yw18GEwQ9MvL zJ)ncvBu;TgBYAT=y7CmiE>3^w+YiO*>-7%nbe!>oJFsIl;fUDbsec+^(!)|_F z5&88O#d6dhX`bCGCSV!hQHiR#UQ!=O6PC!jA-V>Qn1WzqIHd^_LC^9@6n} zvbG#VP)_0kQQU%ugw!2O34!y)L??T=MYp9taFLwpe`&!Ac~b;M8Wm-|y_trn0aP}>rQukz;x7@LmG)tHEzeb0;!I(I*+gKw3TavaB(n^Uw!1H|f#HlW>LU^L9O= z<>G`*9bP@)Ylh0uRV1ul12!Ke3wrSL953iV0HGAJi5*Fy42Ne=yR%s zNlaXq%wcsXYr;-uO_3?!i6g-R5N&%u!TrG%vzh!k^f;_*bgagwx*cROr5g}6x0RKk z!8LUXHNs2~0~54|t_GPjk_||QX(^mYf$fLAlh)F6+ZW1RA3^eGize+*FwYW<)b!_? zracHhtkLfiY?xggmO7FnLaHF&aMJ4P=|f=>7!zA&$y~;J=n^7L5EManydk^JN9}IE zY&dRg+X~};OZ)0zA%zj*p<3HfC2JB&6wE& zQhL6^H3$?d?t6B%C4J=f2YtvV*TK;4ne;#uasb9HB-P@)`%)H&03zC0Xe~gbc@G#6 zpvwW@eh-Vmz0jalh_kc}JOa!FwONiM$g62L05BWi@A=8#GN+D{eNXs`1%Exdl8j_C zMGtzc}KpEEoj1qe4FxrvTflv@y+eVs{)iU@c=Y0cdaVNLh}Rh0ux z1rUaSNYKR4SFb~q^%fTN>`Y6$jY?@*lzUS~PSQBYA8dDlYlT!ca=Ynzg`0OmP&-h# zlqKZjhg3R!QYa`)v>nXZ^azkeI>AEYJ2s*lrh~xWT^H=RHXZan7I&d<3kN1Yy0F50 zPNfzcO`iS%vL4`9`S^xUg%WtoLGpLEk4I-L5aeYW7zKpIbAlf;tT8yxZ@k{p<0m_a zK^sO&Xx#6{pvkei!+trv&CO`ZO9sOOA7cibV2`YwL#${)x2?Br+x9-&w(YZR+qP}n zwr$(CZR?%?=H(^#c3$UJQk7cOs@6zl%`wMUAS)}i>Ms-8UACJrmuXwt!n<(oOd~jo zBcy2l)wFtzIQ|ErF~ep{q3IGeb8#)PIQ=2o>3~UC8+WJ}6&o!~nQVKh^RXFPbE~29 zw2Y$uD_J}#cZB;cr!t;sD7tJ)Vt0L z6(ZNu#B*4!UqR*;PM+ppuv2cgKAFr5^I<{*WRW z3)ov`zLE3&9TIBYH?5Y_Aqlp=I>*A zq_%pINaV?e@}LWn;Xx)gO>RQ=_DDE^(Dl|fg=_YgYN!)r*kmlQtIFjaP_U-}YW!}B zm`>0>6M}gZz?8=ZvbpLJSW?C{-kqM}1;ZeMtXM9#Z9+L*&dk=4PvytF2z&Iu^63He zZko(;Sg#Tzsvk?((Cd^2DLwbp`}0|dUDRyt^x}zQo-u0+(l{FLoLP!MB*dLi z-4ozAr(%fugltNc+MG=Q?GC&4&tX63a5;As$p$J$IB4HRAr9S=Nt!Ejdfl(c&BdKd zO|)FR&w`&3Al3clPvk{~L|K=XgSGCQYzHqB&iM_TXvaGh5a_cBWlbpgOX<(I1$NBpOC7?*$&x_v6FdKNb1BM&u}ww-PWKv|3|+uw$g{hGb}B}$ zycV!E-3$hO4~HQcC9sLM)%w*Kz51+$~}Btg=$ddCE0!48Nnk_Zg$ZdNAr#{M=uV+%?u=1$)H z70y5SY*ttmOLtW=SaM>eNct&!il2W$;eh>vM43w>f` zB*pbY^w|wg^};A0W1`4?BzSt~20>;d`1{=jwH4F7Fd6WG5486r$fR+EoPD$IY1~GS zf6RVUlKi2m+0Cor84rz1fTCzc>V;D06dOh{KP&kdix_P2>@j|u+LC;r)Rl5J8oi0N z=D+r$C#RPjr2;q$e4`xx2}H$2NgD2g>OPMKr2R|3vSPTS*1L7kaC=hs*G@ohCH_0w zXBy z)FbQ#XxNGCv{0%U%sN}YX9WX4YwFYi7DS`-sk6rxxtK)29hFkY;QJ6P}_(P5=ox8SnQ%r zBfUo|f!m@_yO`;BrGhMALFv`i7@N37+Kf&X#l(=X*f}KOgt-(a&4`7$X|wFZWrD1s zC zU|vplosELr87mjda}Bkup-s}_H8Fz7kdTJLBdQTYfBdg6Z_wRCL6ex7{o+`56zUUDcI0dCRP23z;ieb2a z*_TY@w@sbIN%U4VHM$D1K$UW)gR4wQrR-zvc(X{q#Al9g8oX%ufkXGd&xHV2;^t=) zv>*8QP;=n;-P(dH&Gxma(WXAd*9qV}dm1!7Pm$$onK7|A%{}AO%H|V{FkIg1aT{mv z1Yv{CQW_E%e`f1@y)u$6M5eXj14!}Yb8n}{fvr9zqQJD_1Lk&krWZK@6GkdQaBU!! zj4R~y&P;U`z1Ta<0tI6;25v;AnOkb*sV1&)-il>gq&0}ebKTnpGEU-b-=nynuZm*? z61~tsf-jrYw5KY?d&qII{v{RV*j;yYibqn`?8TnzJeBml^w+G7$2QR#{QW{nU~|rq>Y*9?0*MQT*T* z!s}`142nnytUChVNt2@hNc5IhI3YmHqYZ$3%VQF8haI!om@a6b0w~mEmc+*}GlH40 zL?FM^<3lBAUepykrh^v@t+9vSfq4qFw7HAD!ZLt?eH~xH?Q9CppooCArqAdft!6{& z;@5j9%dvM#9B(3Y`ab0NAz07KV3&Kd7;=9K_BG6f0h4`R3>jKN?jV9W^SpomB<5k>@lQKwQ2*cfdW; zn}!%UorLDyC6+?_U0{_onHQW=*4R!bwcvMaN$0^mSuAsqvx#B>xJOo?oKZXMwl4wo z%TzTieaQtwL**A5uLe~raw~iK<_BRfFc#HS0~^RsUX(gs9u0)Z-!dG;A|O9V{vNjc z&e7VFsXqf71m9_!B609)^4B4lt+T;X;d<=Kib*9ElxY;fq0Uep*y$G9m08fvH7?rl z^8yNyu+Tr%90V&UId5N5eyKCa6Z5YFsvXmV21jTn0XXicezrXv6@2KkZ*_uke-$sPIVT>4MD0 zeN|D{-F6xYSNiub5R{=z5&TlPd>1Y41jfBtnlVeUL3bv~>mEy+F>We7z8>N@)F?&b57AB$vZUu!xkI>cCG&Ib6VU3IA` z5LJSF9vZJ!IgeoU;o~Wq3C6v7UoaQTCM2S684a*cpqSP+xpZVQz-(Sc$wrx8$XIXc zHmw9BUw}d1)6@Tv#x(EJt;Cx z8y`?U7;Rvl&7+Yih#fD-8$j4fkQS$CmaKTLk$B1xW_vFVXc0#B36Qb=`Q(*yvJu@$ zQ*VAxetz)fLf(Ph1TE4NK{e}RfD$XV76}Nd-V$IX7A`17%$5vuCH?g6>LRj;1c6>h zN3e$*H561?0`x$bD$ZrN@}^tLU(o3N;{~exU8wRWI@;~C{hq(s+X7k26*E?LwE8S& zb`k0q(v#)&m`Q;CJb-10q{%n9>f1~}X$T}+i~T{k7r{*jy{4efth*3+S$WY4^M76& zVO=$^@=D?R5sO?xS$6lDo$Mz^Q;|ks7&dI~VCJhLC`^7@EsW;XyLt2gzN22*v-502 z6}ay#5mTE)H^%{hkmfU-7NNj0+nU=AE!|s#07N=iNF%(UtGKOTlF|(HhnQpn(B!$+ z4sqy6{DGH>8IHxWil6+5yz}||dagofN!hmYBr|UU=8@hThyoUi zbg#TR6<9-FVoOwaYkB*p_cA6sQ^hBqCP8h|P6!ib=xO7HUj@!y_Dz25~t#J&fE@1L`X}dog$Dj z-_WY;2yR}MB_Z(8-Ts2dBJj3$4q)#wR1C)MIgxt-nS5TL(!^YE|14zs?Z9zA9dDtY z?8L<7?6AaQG-}GL92btaBISs@2o$$Av!-ywOQSf{;uqoAo3~@v=hvv*E*}k#boJ#Y zry{9g;(SV3V8Pcz@#;tX%F~lVv&AJW0gpG}!2{G=fQt}gW1VTeO-fMWXM%QlZ5y3351@g@htH-7i8l>Y_$!vU zXW!4ox7%Of|Bglzy2E?x1pxpELk9p*{NF*N85uhoI+)uzncLdXIk`EhDF0*O^|kM7 z|7SY8K?4AS`~U+0{MT~*chlyO6~X&e?Fd*FP|@|gS_VsRzKU%PWK%4#{#-bWq@&|% z`jP*{?c6@h0MIpczeQdbk15XTxVKLea=l0zoD?68pK#eAGue?Cf7P z;q4Ei2xF9weA@hKXuDX3Ns?u0mUQ#0=-kC>Peu;C@O zLWE!nEGw3TOfv>mlO$tHwutXo&qP<8A#`8^q{W2n5(HLeXxYr6*m z{J&O=&scjfZAu*&NlKWx&QEHgA$tJBC1lluVLehE*}n=AU`z`$oAV@2Ox7k%r8h6< zfs$b(gF=HkuUd&KJZxIHAP!4>YUgX_w`ONcQRN)xxzlMO{{DKxERWa!wp7t%jX#AG zQYu5&Jll}4n7hFl(YJy^V&vwC;BcZlWb!Pq!1_ST2#iTnZ6OikQ#U!st$3 zw?QWJT>0A1fz0Ps+Lt6k9 z3b(qFJLgxg*B^2fz|%IKN(yDTU$4+ZpUUCE;uqE$yu};cv-sPgRlEv^ug%nH>!glM`_;j)zOiYB_Z3Knx&eXT2Q>Q;>^tC`^Pa-PG-0#MJm_Pqk9X zT~;?p7E^4*N_2kJiK*=xeZ479Ohf#=ab4;_qbk3kZTqN1dM?+cf!c6yx?Q5$5k@bd zTBaX%RJ1)5q0%{E{>iLrA)++Ooq2QQgvVGM72RF@1^kbcLIM0|5Zdj_{@3RJE;9c+ zQtDu=Z)9!!|5ViGFP8Ry=KoO9|E}l>xcZb`6aWBPQvd)e|2x+(G1hl-b}-g8v~_g) z|FbT)wbc`kM(lldb^FnKqdDc(Uo$E2;6loa1GL6tBM4ba$c1mHOi~-3Z?%)b<<%*e z=SCHwsE4l4!8)+N38^0^8+|^n(RF=4PAYA6d>`H>LtT5_e^+yJ@N{>6-Y4mKy&p$y zd*2>ScfDRuI@M}@KF>4Rc72{Z`Fh?D5>H!i`M7(&_s?f%ecz8d9}X^kZDV`5zpu{+ zA45-FZTZ~4M|XYiuhnv!#dLok9$)3?cfVHEzA$ga$i2UBMukqtC33%?-hRH{m3q89 zzV0R`Z&tIpu3u+wclmn1@8x3oz8!u#Pb`0CPvL*NKWq57yT9(9UIq_ihylTzh589 zZI&+FvrT%6miqj>?caYdf_sgI&|GIfEN}Mmb$9u4-bsMZ3g7>pgyKUXX;OpAy$?=y z-s1QA{+z!L^7;I7Yh_n1;{CqPnajn7^4@f^<=Sqo-TvG^zTWy^>2Tt2v>9-4_Q-Q| z*n4}q9X1@D(euT|{EX7m16aRUy*%^}E;P)Lt+5uY8T! zg0r3PuU1#vNGk8&j$yynuKTjA_4n`SK$ENPQo6NT@Z?&Vo`n-b|v-G(MOO`Dh z0lQF;8?$u2nCP^;8tC|24!mQX+KjA*(kj3oh_FgvCluM!fF4YZJ6$z}z$A(QOvZa~2L-Xq)fa%0nF`K+eW-8D%+V>1V zV-oe){F$hy&lQId6ewv&;O?Xg(6Vz5y9_vzCpMag*jBX3#Z`f{0j*_n1C>zfFMm%)&*y}|BRll{?b$hC!+~P$Q=VrDpQV@5 zDZKdGuPPD;7QtVxxk`sdI|q&TD;U9IT@lu7UlfrA1nS|r`0D`Fqjut^E7 z8}IDVeh?>k;Jt2tdjp@f)pD)&-N>pooY@))u2sj)kJ~i>2t8 zD~4?w*s_#&)90Yqd<2}jr#h+ehX1R7jg|f*ngl|EXc@dmodKZaE;igUJ3=JLx2W}_ z^Xp$L{e$rNkOPI^sWzXr<%3WV*ry6)x&a)k*atiqrFf~0@631L5Df1)teX!aw zGDcF(u{K=1F`^p5x5?`4l(?htsV@!lmSx5)qAm7?;jZ%b2rCTl@8LQ`c;Y6vu=p z(60c#?kRVUC0aYAbpK$HWyqGhJwCTlCWyDt5dZWW@82kx`>X27BzNwZt>bM@T^3^v zf+Z$xn%;O6IyX|yL3cZw#(n_cQF>BM;i7XX+c%U7zR%a7paU4UhWt#rqsjIImupeD z*t~NO=9{Tom0q2*YA+Bc<)z@ z@f=NF@Sq<0Y_e^WT2PVkA^{3R{vWe z6GH{ms|ez1uiXlxhHW@!&tDzXn$%y_jB4(5saOe4<;^cEsBFn`MH^9ol^{{w(j~K8AcsLwq z&_NHlG)H5J_2VU|>Y@su0}EG!l`s8Z9m<_r!{x3HH&S8x)=zM=Zy_` z_5}2_@iVM#(v1e=zPhxEFspVz5ms-7HTg7I3OfJl8jI*kjiW{izcW8n*UKR@4RIU) zwgKVTae|S+HUK9@Y?FgZlz>%k!die9AwTehPB>#-G#3`8@)^k}lH4n!XE1L!H#9X+ z7Z_-@r}8s>^bt$MerchS3f(bECA!g&5^iX#L1o&sOUVBBus(l^NEH&5%vSSI_&LKW zls~x+G{4kk!CJ&BCIJN!mLj?|+${UaZ}PaBiH=kOxWLO=>YF}TZU*u!zHtag3RT6Y zphVz;Z<;19F{n@RTb(Fs=Ee5vE5@B@;wV&`Nqdxa20*kxhUzKo0D*w#tb0Uss>xbR zGMz1xQz9Bv8$cNz+l+bM`gV6NdWhuYfYc8^jl{nB@&FTKBo!c2F*Yu=rOHWuD$#_0 zr^N}2=;A=NcJ&J)3<3?B$*NGV6rf#X8u&A7t#2i_ejZOMBh0_?PJthQDXj4E^6aRU zNa{?$*_(|cerQ|g&~#UfV+g=V+ppx-#UVC@@(0FxM5CXX(>_?ohs~#CkX^`B-N0o% z1$a#^$uMZ(C}L;?t+S}XO4{@piB~7`WbLx8JVevCB3GQ2YiAUoK#_n z+Q~**pY*9{irbwDtCt6P2imz@qFwH+I-~YIa3s6(qPEPNT~$v4u;Q@M*kx< zZWFrmO$l;ZR42)ZLP_Yvf?_zuJmyl1q}8RNrL2vitv}DXxh*-#A?O*3R1JJ={h?DU z0jOf2X#@9%gV1LO7`4Ad8VOBtIjlU)RZjqxDtR2L%nRU`Z$9s(0q~X{K<6uZ4Hy1mRUBOFyZI-n zOn^?X8}`S@aef2!A(64d1|%yKUsR)ZD!1})4wZRZd}nI;AP)GtA;S<(bD zbA#m}5AQ9S2&9KYfrF7v%(JN00B%QeO1{P~ybfi6K3)2oZXBQYNU4G`J5k#8}hwxh} zdHrDTE*Q`9zAIz(gR_Idqv%EDM2bkS7e6Vo4-G6N=(BuPx>97}*p?=*N--6~x`6Pc zXf$Op=fn}uhC-fUoBmwg=768C%D=loYrOcE*zt6Lx%IXf-}3+ZsC*#0sF35{LAx6e z&Cy8lpZ3*P!`TfS#m2gUJ+wgiGIzf zrKCYm-6ph@P5M^PI%*e8{~jeKOdQa8pETJjE`~?!(-5kF{Q$ z09iQlpMWx!A}(Wju?|YQplL78B?NRnzqMpY(uR!X^r_C^%Ed$G`j5S^Xp*sxOlp+o z;ZPP^go+=CJ*t!(qtX;<3VpZ&=$&e4$ za9>&%f=f+p_Xd@5$;%J|0~wg^MX_4G&Whn$YHJp{C{QDS&IlF_bQJ_MR^hMz0L|SW z)EiPYXZ2|k~lWdA>YlAS}X`dt2R3_9Jt8h zB0nAYhzk28;gr=RdXQ>n)FxJ8O(F@98`s*>2KX1XD)GNG1~E3Y8&0=AyWMf;PQTBt zdps>nwDZQeT~76v@z6E4wDPim6^nYL?Al1&QywN^-K!KwQi-F_wLH^K zNB;?(rGP4`gGzZiko(9Wq^O9`=unE`)&-d~iYN%5>UtV=tB@`Ov=nx2T_2kUmA$qo z?M1h6a4mEz`ZzYJ5Rfc{vnME{=*y5m_a`lbSw-5UaV4sxf~8~6o8%fwoUD0k83y5u;z9?7DaWC#*g*HbZntI(CTpYZ*Sw?XY@uH@6Hz)!{lp`` zqq5t7+UjaXXg#lV?7yDht2epd5aDQ&;-t=dp^AGQ%g&cP?CWQRfJl^}X`3;ogDhxB z$5>xm5v^r)BuJts?C`e2-p$VyI#E{;V3`ZZkw)NCG+m7+o4p2%9X)FG3&C6;n23vO z|BDN+q{C2NX65MsSw>U>@mIaa&cXi_4V7&DW!gO^2&^Kg-aRg@zWdJMO>h?9$@}=~ zuZwT=%aMY{Nr4mX%yAV6s;(=bSp^(h2g>TkV?gc53|L|@pr{P7v@{Xz1!!q#63hs4 zc+sZMSKj=pyKnllkaTivb0+XE?p=z?&ya8ucU`vx4n1oXtTSWwxJ!bTqk)GoVPJ?1g)@8`Y=E95_wLMie+5n z@TC6CZAF1PoF`T&VZ?U~XURVI?ft67i<#2J>4foWoDOqh9JC>9JQDE082>u~S-dgu zA?Zb-!|p+#*pYz^gP5xdilP1(e3MDtYr0AckpgX-yrbX$BEm@=RaI#xfH^52z3AkWmm4t2>@$!Brj0#=P52iU%99e-<% zQQ%T>p^1w*TEPs8c*cYQT^6(kUIF22KghG^+2Z%LXw#+VmbWeLe}(=S=X@ZHxT=rZ z)*MY9hCD^DYW`-e5aIzq+OAL+H$%rvBW1KdRW<-eb~}$*hi8YXsiTYZ^eo&9A$5FD z`o48!ymHJ(dCj467}Ial+9}SQ`5h*2w<64gx0!@*@;o&AfnBE2bmXTMt)WNDtj4ku?l4T7 zOVem(L*nURArD3Ac?$my9~|23N%;g*gmlXJ>%i%3z~Z^Sp_LsHkE4E)fI>qM{YywO zfF60QflMHmp}Hd_bQ@s0#iWP4yQCcc1bJzD-f)0wLFg{JdlSE>(H|kjL-E)z?-=f2 zP}n2v($9IY35hFshZ>Bj?Sao{2V2-Es?c}d|H`ZRr2FY_MxLbaPr<37^u+5_Xboa0 zO)J-1`Ilm#0Bu?cDTUY82)GffVVCd&UQ{RG7`P@laVh~Rtvh7hG%TNoM|-w{W<}e$ ziWI2Yek)6RAQ+(64&X1AMsDBgMwCQ&q*cchKqXJG0f;KV=IZEIsZ6m+7Wrv8q(hgZ zx%Y~ItBgr3p97Afz&!MD_KM=RM|bKKZ41WP>Q=E4JooAk|3`0x@s>7j#-wkF*}ZeB zOR)>Mbc9Acy6kS{pG}LA&Zu0fouw+sx<%P&ruKEhMK!gXBS)j^Ppkfl#)LY$Otrln zMilHJSVj+|Ko|SzMw@%~`RWU6Rp<34*sF-8(~?cc;8j4+Z&~*!v{CgH+)SSYX8$4y zOzs5;#2xu9fgy2ExW8!zN2FNEP`1$mAeAd+BUd)$QQ2Ta;QPtdg+5JHd(GRu%3yuU z9nVbjVTghVfRRHRRLt54B6qNEV__d2$WH#~%!`;Mc)V5-L_Pdu%QePhr6P+0cLL~Z zM{|NkU=5;_c45t`o)YaJSAN|R?53sWXA#Lwm&Ztx#i$LwA@rHhI8jcKBGNG&VHcZ3_AA` z=2qvO)lMn5fap~ z>*uiR$GC`uE6Y-ESs&VTENtnR*cpm#Z4r=Ix;O)qfkgTZ=3Ch~6wWdipqqoZ`TqJ& zm?!8XmTYUTICPeg3Z;l%zwmU$aAYpa?6urRw{1i{FTLPBSti+RN#5A``?of@<45Sh zrI2B_S-C$kBUNSbL&0U?JAg17j>G0%QyW!Hy~#?dKCiv^0}OSR1$j;hZ5y{daRZ19 z(30YHN0QL_@} z?YZ!rD<5wFiRXmMJ&q)H)sjo==lmAFiX>&1FluCe*o5SxCbE;B^&D_mf% z5u{sQlMtT;O~>I*BdLIoubpEEy|TXwJZ?Kr(?GgP#GUvVCt}8RsEBLikVYgV@@_S& zNW^8nGGGQJLJ;u$V<2$wg;OT_f|wp06YAx;(oA@kBqyT34CC zr8xstIVNeQgKuWn_YZZcG}a$sb0DG0(y8zGdRRPlr<2_v3Q1;;GLbDq?~T&~7K-9& zBw|CdY(e!>T2IPo9CBP7EJnIxk`|X+N0zPCFYU$_KI57w5`(c6G%u!Bq+Zh7Uc8f5 zDk*hAl%Hr8{uI08NFZ=)(t71?y*V@`PgV1HZK>|t`nhJbR8mfNTU;O zDg-8WL^El#x-*wCwb`V#61#TDLR*KbuG-X#{3wZIuiZ?VD^HA5EzKTQDzSS0RHGPJ zzqJBdhF)R}6ewOiRR_2+lL@#(dfA^l z%CUZ1h2+rCqZW&|^N zw5b1UyqR2?c;6CmtT{8wHtgbM@C@alfLj_H({WM;0lqS_pKack`L-b1_ou<)(pF_t zlFWj}w7S8(Dl?j~Hz`n!qv@qbg_{4;IFZJteK}~(ECKa!xv4Q#rS+wODXtE*?9Cd_ zWAZdzb6JS ziw^6$VqKB$>Id5Khp=^r`fLIqxu^drPVGK;OK=x9iBl(0z)x9#=}~QFGm31f9)#D= z>>jYlc;&b1X5JM|e-lA1YDhn9K=BfK9oCN%E2l@@8uVM=9%W+R$f6I7Ew#Z|^D5V= z9m{9XO!cI0@!K>V7Gq)Ze;jY1aJxbXr6tQ&Jo!g(6YqI($IvW9@5oLE@55XFYcKE3 zQ#x>*VC@(k$uL`*zHja#Wl@SXmYh`JRa!J|>{}EB@d`szLt-WfEBip5mN4Mj06CU` zWmDQ0en^c=^5@Gi4e;VYfJ>e}AEcTfLo{{$c4zHJ=)mv8Oihr%60;HF7-N|^t2+MOzgf#9SE5v-M!t^F-XY4VQYry=5mcms)j(8B%yXTkwG1URmil;&O0x+FT??`*lFs>=uVfJ)A1nhom+e8ID zg7T04OrEheWPCO3B+YN>wee7wYzzbK%IIpV3wO-Ji!U}ZL} zbjO8@%H(&pl14b%Pno1BA&(O2D&+uc%ANhvdHqJ=CRC@}zJ}NezMrf5fl-aIZ8UjU zhH=AE(dGgPgG3n~<`CeYFrn0O^V_F0hFX>;8`6e%12cNa1vna&g9bVYzI+OiJ&zUy z@p`n^c3xL92+**>20E*HKnxWxR~UXplM;Bmmf0Fr(7J?m)(>x^zeCrVHA=HcYPgzt z8m$8W450^qi2?Nr<~^vjCAFi+-9|DK@Jbi@6P$%MR-0Tc@hri znT&Qu`*z7hhIAZ5As;d)b++?8sj}I=_7PYOa#uqYUcH5^lx96=1%$@6I= zNptw7XdY|52i$?^xA%sU=QG3om=V$9h6ra>0m^I;hIK+rRjkf1st%>kl!!pxTzLnc zS&r^_#-o+&EY~tI5+CGENLbLDX*4 z)h2M>Z&51~J+wm|+(`FzOEtRg(e)EAW}n}J&9KFYC?1JUv^wH06ltd&H6Ey9k?fAx zD$vz?SNeE+Vrjf$U3+Mk>yWib`%EN^P&^ zQT40~tiv}i?%8(2Sdf)ammD)A;z!_duYJux2a@Tu`QZfWX17l?N>bS zS6%{q4qV8lz#){N_jhS83dISmQB5U)hc-42>#RB1T5iT?6n3L4Yo_Dh!QB!fvLGI# zn`FtocE2jG=MDv@h|Gw}WP4NSi0Yf0E@k)wA1{OxaE6V!C6J!|E6DwB-% zPwy{?n08M9qc1iPF+MN@t^;>T7}$q!;zJ?ITONF`wwzT6>{gAvuSvEDP>L^Y$@J+C z{We0a(YFJNIm-fqkIO?$U|3&wxUWISaIx3fQKQ~U>7>Zye&^rPV=l*&I^R^YA|oFH zQz-$&vT<$7u&lPPJqN(PHL+GZi{>)V`Y3`VC*oVGpsk<{GU%)jt#jU!@ZLr;rnLcv z3zp#an5m?^>jQ%W^3Zi*X747~5tI`(M(drpdb0Mh`|c_%#`7LH0+1+y+yWn&NM)-e z`Rb0^Ph(C-Z3RYjUd#zWW!a|p#%1qPm9O65E>S?3t$gU6CByFzD-pJ;lV(rt-f{>=x=y zSXfW6gl5IOn}C1hU(*k(^GQ;E&r68-wkDu1dzuspH90BhB3jdixjZRRCT`Qd$4*ZO9&GHX{}`MN!H6ZdB*xL0WPU<*Gq&nh3slu@pbkl)Ha&&BkVm`d zF&T!oYHzY?7e~GwO@W*BN2KZOA-$8*2tq+MC_@cKLy#W1lt@TCn2|EU8W1HiJuzzJ zA8`~$40hP<+Shn+TFKI!qg^)!;tlqOtjBLeIU%Ahf=wg4wz81;)XIwlR1ZEfAY*mc zIv5!P#(diZdm|igfN)DG|A->MH)r}GX6Mssb2Z-B+n7qtSq(du3wwwB5le3YF4PiT z=%jytraCUX+Y%l5J@thoClxa-d?D*2_PF;ZNJ;*l6RHqaI;Lu07D#%2YDdYAIRfER$AtHupy(xvT1BVOL59GjwyEenS*k zZI0+ZrgCKE*5uC2Eac6pR}mnxuenmo5+9(Z)M znBaDpVTfKBVWe1%la|=dlemOfES>k|}DZ%A)npi??qcxJX|+FygvbLre<`5$Zd-~X^y z(G=#(d+U{fdL(AZ3}NS|nk1SbY~-j$^=n7;iA}m@Jz*#h>pgonR@n45zr7F!W&-AfzOGw`9s5jKJ+%b2&^wA^7LNMSE!) z*`%=yNt{ggzbhoO1BG&xwYJgNnp>Q5bjdEKm}tW?mIi4ku}7*W#}301GA4LaYd)N~ zi5I}gvE`t%im&pxMs4&BDhA@5VqxvD&`d)Xb;#{dj)rCR7gumZaqZ*%9Z3NO0}n;B zkZ7dBnZY&TEwWyhFB6&WUH;AZ;Y~I3sEU1SpcJW zjfiH1ka@mF;Z_=?uzRD!SY=L)gF1C_*v(OWBSn_lq2qKrcRRW~*~iQ0sXWBDNQt_iB(ztqODi=L8*!tQ`BUDrP7ZQXAga z?T`xBX3sIVTp!Qz^Gqr_AhBm&KQ=*J>+p6cB73QTlV&g8o8Zb*w1i+b+%&jY96M{# z&}iQBPinW;dvF8)N*`ME0%y+#(zzS8oG7rW8dDA=n&Nu77YEPr0PyGHdKloAerBSd z6Y2t%y~KDNsKAYC#A;nAnuq|6iYhv)By;ZbikR z{qAr!AHuC7-{|gXdw7!=<|NR9+|ZV-I$gOn$khGbB!Bw-pXPYd?uxs(f9CiL8vp?H z|6MDkgR_mJ(?3_`|KCozoTXrUz>X5U`;O|q&gO#3Rg`aQ3>7ukWDa_MWq7P;YsnH$ zJ?hk|aqXL%Mu^K?!Ak2Q_9_lh0TfE#a@)STpOAQbWu-_%_aJUn_0#Q6QOxlC?8Qo1 zlhSy3tkOfZ!KW73KVC$U5-F!POrz**DAJv>dcmsE{5!>W%V&91*fc?s)dx zIN_{OT5OgcLD<5-H%zTNnufrQsM*le5hD8LE-6yTfw$$wWwJ>+8Z|Onn4;Shzz}om*^vz1SNi)mG{W|$|*hdS}$-WgQ zWL^UFGYMa~X|JF!Icu`za#LpMfHN@)b(`{WL}3AzB8(7;&HniuIB|);&tb# zXw`N}GwmPe=d#Nd3Y(J+oYGFH_ezF|YvC{6;>Wc1KhfRX&&Zd87yR}8<4%gwVB8WR zSFvtuJ?R(K#@q$7><0i*!~mi}?Rbl((wG3m{8L2$KWRWi&QJ(UVvPU3weSIlo)P(T z#sRv>rOugR0J}o#VE`X601|-L*bf2l#@YPbq-bumjr+%ToA9GFYuyHT@_VTcXZKMkw1%MP#fu5N< zCLz#e@>fkF3crA#!8;`(%mdF6pHUDxz(?E4c~TOtkn-c_6b6OmpctU%3L1&~iHB_ogBZ z9E~E7@sa62bSEMBaD?1VG6o^fzjY7i!wMN9!99~*fRUJ{1?6{zXk#XcF3+5DJlEcmEDQ=iSXA8}SFuBK zV@kYlB(KaX&aComjDw`K;E;}t-(AXoO!4h+J&z1DC3vf)e?ei-)eHTr?;F(r$u`H- z?x){}1_KjD2Lsdi|FcCJ*;)V{t(+|Z2LG{1G8j3UoBvlDB9~NoU{)JK(9;XXrt=XZ zSzLbMMj?O+fhpk|-``bxRxC79Ld0`CXXX2zr#z~zr3fnl+dX~#uKk^zoj6dZ$KBo8 zT1!uNNBftr@B2U6@>I!NNsU|0KT7jbMoY`r`@!YmN5__a*Xzy0#l_@WPgjqR)5G<{ zi}%;Vz{%y#Q^l71&Bi|xxet#IZx-QSq!@#m&91M5$$zKzct3sK5ymJY9mN=Y?k8+z zD-aT7zie;xC?bKp`Eq=}8ql)4-}a`qa%w*Bo=;v+->(nupFg~P9uDr!e7|1ZyF0!f zFYX3H-atMAdImo3?VwM0p9l9(ch6Twm-oGgi?d(kDb87-?Jt*C;;71|C$)}p%cZkn z#TxBNQFE~y=#w(3zoVw`6-Dk+qivJW#3U6(iEE|Orkz}ctMJMqGCPqHk!A`r-6UhF zC@vJU&-Trg+ATV`m2&icoif$jA}u5tyf~io@4XWXhX?vB!lagl@m1ccK2s%>ArE zF|j*6Qc8XYp>)+>P+-=-F0o{^sadM}%2Tqh{`|Y`zH*ioeVnb|al<7EI-RLSKjC4F zI7!W|gYGtt?JjN+V;#-ZG}=usmL=fl0k|a2_gG>lu8t!rnG~3Y1?y@8Ko--d6bCx^{|S1)+Dq8AlPF952K%8JY@yO1@I=t6k_ z<~K6YU5*Q@Wg*EqtfJ5}j!`g@O82yRUf}pjPs1HYMnWHt6GIWz;pxxg&zRfT=tU^J zb~67Z0GYJTWskB`X;YfRbI?C(1!PKckTT7MRZRD5$Kf_wPQrYI`oWZ=OHLCKr=Kp* zHRF~dIu+ZfftQpDbo5qQMQ9wO?@ToIe>XY52x=_}ElX&Z^0JBK+%H)RQJKqyc1-jU z6k6Zs^~lMN;o%J6iA;JpXDK)4}Pv>-}ns+cDB#ZP~1I7B}ez8 zXZ%n+OT^aPmW|*@HtJ@*gPf@vZEuw6*g^qeGUGQtWJUAJkIfM&QHg|L+p5K5_bk&( ztyt_KXq5(YlUUb{gi)EV1SVM}Wf?=cZimpKuMQ!cYUs38p{>pn2*eax3CZ`!x)^ zSfG4FYsIXzF4c)HBRq?aAA?qwu}k)hXCzK+xq&n8rO8K0GE(!f8Jl> zJo;MmCe}FEJ9naLQ$^kDJozLhn=)QWrE0Qv4|NRynvHkH)wf^dEyf|Dp}Vh%#+gv~ z!ckIq?8_B~W%6#dE8Gi|OZ2pxZo~WERTyo)uvC<{J}Qp}?5b14nyvyRoK_}E3yv>s z5n|g5s){h4q&PIY)Nch!Yf_nPt2(eC#9Cr~6t5Ikig>=Rlba!&cRQxvk!!Wj> z3t7EkszlkKHz-GNptG*@uFFFILUFe*P>%F3_fH1@g$2D98^`1_Q|oCOo&zuoIPL*% z^T{>Yi5!s(q+PRZt;1B_axx7+x7pHT%urmn@_d<%MM%GuD&0Mpa1E3==J!Gj0dPeM@`==vlcU)Pf8fGXRH7S~S7qvdiS z)DI{(Yhvm(W>51wsNsbnt6vyBe29#69qr_pLQ6ii&nUlJPLmww0*D2Y^;yZ29Bbarmf zO~C^Y46cg2cKv6l8BX@xWnRXZs^H_kGf4|io#?m_NQrhwRt~%z$K`hVccn8PL@TsH zg3(>|fr~WGj4)bl*P(8vH-;PEojR^7ZL=pc@FyJy5Cmt)<=5<(g=sBGB8QA)+F1i< zV4Z#~QthMuLX_I1#sYBagzF}kjNEC5B*cwo@hE+YzRx*!aof2{a4h?3;E@~K@rR;l zUni4eAfuD8Xm-axg`SZ(!({a77b5KaAqCsYId)rWtfBPS@O4_hF25pCv)aax5(L7#PXAu3B6EtlFp8&l zbd=YP$;f;f%!(9vJ;P!lAd!U59q+tLS+5##2-PF8ZDqu0H1XX)`9RJ4IOuWw$-+Rq z(89jh~!%7*fmV1d>gCW;Ol z4Q8(_5olm+ zWoP8*!C+!**==Jq1J>F`&)H-GvFOGM&Ydft|(J$juqnTeV z8;nOQ*2%!bweLAulZQ2H2mk1Evqvo%-jD74**%(!oZ}B`o{ybVhr!aySs&~=`yG2Wx98@` zgwf+JuIrab>H#I*JuBT+8sjU6Frv#{=2;dwDZ?vvJ?obh=n>xCeA~hn!#>$x9K#U% zZ$nN?((wc5L}~ni5h<6$iBle~-fKr8<7(SmceV|Bx`jG+=Hm~S2uL?*5UE#p=dlJ9 zf~IF1G=hUUSCSsfRmsESg`mUnl%>56_$FVzFSuKRQKTLt*BH9be|oU}L^YUrv{^DK z<~HdBbBCx?+`x=Mz4MEF8|D^icQ~7lBXZa(u)5XH#(HeQcTlpuNx4RdOgev))PjlbzUCL|F`YTLN zK#up9ca*bRRIYpO>QyBsjvez>wOg$~#$(&SnQz+*zt_g0$N4M3MfdOSiCw;4_o&8j z&ITkv|EF(dLL`@u;MU<({@ywX$e-hc>m|NwNo%-m%gF1lI_}!oENjl8J@t} z*CSh+{rvDa|q#GS_?udazpqLt8{zQ#QHc zv2}5`;maG6y}6iqu=<4ICqS!MyPBG1C&1`?Us)5P>!WcXS}hQi)4on_$j`cIV>u-<^fg= zs`fM6u!0T+tf8WR2SptUZmjQ52M(XM3rU0FG08|`WLdKxp4%IdvHy=G+e;mG8*BN`%_|a zLC7jRCzT`@a@W6+623Nj4iYf2vz-a1-psl+EizVc{os1aqHguAPny)VX{snA-X-ha z$rS>B2Q;=?DajQ!vLt-)!FUQz>eYVyz^!$`-33i5eu5q*yf{v>t32dqs2ejjzS%i23Fo$ zV9I_@CDSDbe04e_FE{qy%fI$bII=N<*_Y@*Mb$wArHw8-8;2SHBxg)mQ9sRZYi=!tq=*#^&{FP{dYy&r>ptLhqxULb@m$6*cLCh;vaw)BmBhLtNwLq5Y|iKE5Rd z!Cn-=p$$X2Fnv@>=vhB>*E@6o>fE`ZSUR8Z(}7GE$$QNWh+{BB5XkXOm~y3!OfD9n zzqy08-K42I9l`x0qen&DnFpJ)AU%Vq>*j+vXL!X(Q)x<67EL~bb4D}Xr6db$09C1H zmZa=oo=oGYJcfoPUw50wR;Vf#P4p!vIA)~C^9*m}WiVb*78`B3Tp?+Y;8H%4`o1Li z`A`EgsrD%8G}(bAtDOKCt+1C#hN|4!BfQ{_q>Us5@Gleu7hbzrI_4Dy^9+b41TfXs zDGdp;@d;;mK*zK}H7}!jq3TnkG$OBJw1Ch_O06M&IQ))`XH&@_n%9ahSYhc(@@KSF zY(ZM{q1KQD3lIF5hnI_7Qt#VOat0&YJc?MM|1@uW&*beij zllbIBTMFrnHg6#hX!|SH=QD&aTrn$L@Ka}UDEyihmOlXJO58_;4Q|BL;3|>mZ`#SD zEhq!21~3qkF9q4CpxCH}G|lTo7Clql=gdPEb~2}D5gGB{OWl|r**@hmkCX+j%pmR# ziyX7-F}=z@+*EaC^p#s<0rXmw2m2DoxWe~Y7CIe0%>q$0n>k5 zww(obN@r*UjSqPB`EPFZht-$Agc*{R!RmvzlW61$oiVf;*=_ChaLe4^e;6vIgNLAX zdT-+)upWGy*o+3QSU0cp0n&3RcE+8?1fMkTht<~WAc_xJ&0kWj>!*`b2QfeyM_YN|T6Rd%)Wd?bTQNmzt z@5WtchUGC3WYE?AQ%10QQ_lMJdP@k$=xa2gBCp_x|71q*&qluh>@Zb&l;SON?T;J> zw|TPdiGtkWL3ixDB3J6jj!c}y`^@{zV|?STf4^H29n^M`x4mYaePegkNJpH@L&xGZt4uQ~% z;zFW5!!rRm+x2rHD#A)mw4WYk4lhf3ynX^BExMTnH5w>m=*Jjo=EB3G#;W{M2|}6M z+*V0w7lbcvA275k(Hb<$!Vgt0h!Kg%t)w6_ceDp4S_KnEA!w6-uNv8V-3i&-LSrX(Dx$W(}^Lv8JxlTPZfIxYTS z-3Q(+IQD5DC|c^i4=vm8KrD8{A?vz2=8l5F(n8bcH|Q*Och#8_(?j(h`&VdgUrOQO z8rw^*^k0N+bwCV2%2IsuODyai0-|=geLKRZ-1dV=(czH{is(S4f=(U9!B!6dU?XEu zJk^dAKVjz}=;Qny#i69UTOz+ndji0{r-Zl<^tnq_JoMt$mY!CI!qJr_K1FiTvsJ*c zYc-QwGVHe#A!&QXczheu*wn=BjT{vAO{{d#f#?~=>1JC+$Vmr7&19uu+)knbZ@(`v z^{Xu=r#$2@M6E9=jPS%L6=K?6$ZmvDmY75bVQH@kVfI^_p~uT8ggS8BLtaDj*(sH0 z?D(T1(R?doysDx5eL7%`oB)sdFaYn`S@;X`X<)TCy-rJP?;DfR%8qMnqp2S0y(4T3 zjg4%LtTmG8L8V%B!D+$IaDjmie3e#q&2%Vy>wV*X+Mzt}O$~9B`-n&!at3Tf`?wVd zrA>j|xP%`}$|!knexaoUbN#bULt-!|bRCS~)h<Q01{h-{-29TFo=S(c8!sPT&ZK zs0r<%1e(dpMzBXi2M7K98~*llHulF4DUD-?!HdKy*W$x3Luf3m_@cju!4CDedQ@DG z%TOC0uOXVfO3F_WOj}tr#c^^AOUDp&=ayLGzRy<1-Yjy>?i2Tdfe0-?&`fNGh2K8I zG3rn19{)AO@OALK@l^R8txtHxqm#bmunWk5a4^>VbkBzQciXF>|9e|q%8Hzza?EoqMP`=q*|9$?&N|u66&z0!sn4k!YIfP0zxvi z`}}<;u&+?3VaD@{C2$Tj^RyVW=FEk*g;UzUI?Xube_<9#<9H)+ABWS;2bwiKS{@{o z9HYv01#eJPbP@Lz6(&#xKL|a+pb1`2-=Y&di_6~a&<~QJ+{#!HMY{5Y{9Lm@GV}*E zl}lK=1cix{W8v{J-SwBUFT#-q~1yD|Lm20(gTGR3ARU6hysn^1S zEDtKyM$Ugg4!E#JI4KJK63U4U0ds%X+6b0t+8@Dxn`U_BYq^q-gVJZUZ-9oq^tVc0 z@4fceOeUfiuv{3i&S^-i4rMVe;(FvLL`O`ZzUN1C5^!$W8$>AAsPJaj0vP047yw2u7C47(> z2;kE^$})O4qPI7?5{hr$BR^*U={YOo7-Z=OES_q;5Y~q1h3O{Jb#XtGQE!4uYJ<5} z_#wwj)WBJuWFmNi00@uFa7s0su&*p3=T_lU;N-NA>IJY=E;B8;H&R9Nm;35)zs0o} zX;<^G7_xsi8v$9`Dn3y?*_>FS7L;Nj0)$yLtAgf*C7Qq?_u!ZKX&Ce`XK8%e)+;5E zV$E6u&TXjx);jDsFy%k)+S@K@Hi-9GBX47xwMk=(VTW~q^<>+D#$TK=p3Ul zQgm1Lbk~7{XJ6rp01pVANOGecyv+u|!E9sbF>B$>1K3(>gzM*P`jOrJok(^pwG22O zdXo!<0YxBPEK{A23%pZQYyU8hBeID)W(P(4#UmbflIP*ar5oX4T((Kdo+HpbzzqIa z;a1;Ws>x6g##O~SlylwTufa-!0MuMmvnd!W-_@CQiA3N+yew(rL*?;%Q~@)Rg2aZ| zfVP9M6lo_?FIY+BQTN12MQQAb^Y&W9+J#T z#87-hv}Rj+@ys0D110^h@tvH1_Dta22B%d-wCu-GtKs1ZP>W5RweEts1~AABf4|Qb zkNJ*m7C8*gC}5;SazXdN42e$4=!RPgB7zs9<$(@e=%28WXR&@_IM z)U!JVt+(t(ecDPs26}e&Qh0?M$x<2$sPTI&Z$UIql1-7$4&h2o+q1*t;60(uzal;k z2$16uB#zc09;QUh6p>I@olNYHP&3(q4Izj}kB;liXEvEgmcNn%E*qhtF#X~90vE@& zU)s1O_VKK~$?iB{8cy}=voVEdI%ddK*HcVx{J22Q*p?XgrLMqMj63+5e|-qA^x9Y+ zs#xg^Y1S8t| zyQn@KCS%aE5vH*=8*X&HeK9BOWsIK^z0(Nz4G!l? z$9&%gw`Kj`_n=%{Zyr1G3F6P_z%o&~Xh$*$>h^-ZQO$z2Y(hthb6E4eg2UN?2OPkC z;fynBvMWF+umlg(E6|ogfYqV4dTfG0h#j?pYVGoxMM$|yS*{}Y&yjIFY-DDlGDn^T zs0p5=ts?tyM}FVpypR(yvw45*q#tOA(h*$kWm<#xe@i5~T;E#SrbcTKGup5|!f;uL z-Dtdl03+f!{$}b*QOe}}RBnnC*kZzKM~>`W^tZDVC*ZEna@c-aA5I5hRJEu!R81=x zZ_6&%za!~dJ4Vg?GQCN6uQ}k0L1=Hl^&zHl@!;5UAYyGzR-2CG=y!0*MQyEmy8>@q zr~miIY&>1A$-GR>z?Y)$BcNFwf4eneM3*9ICratdB-NXVk20zw|LV39UX^t$TwWhE zjD5mPhG&f?z3vs-^MM4q^9V0H0jW)yT{pFvTKlv;{L(Q`VeK(%(9EtlGtws2f&39e zrqf$MtJPr86Z`U;)P6!xf^ghXyD_yf97MD$bxoL!CEeb@)a6tSyc<4rvJRJaWbU(lqqJK**F5Mo(h`@oyX)ksZP@Jto^UlJTR5o^k$k9i7XW|#7t-M%QInOc( zVuO+#t-~S)+Q#8JdfJyFIIpE_X{tGYzJM!H@ja9C8l-b>oj(+OY%1&kWv3xuo3<2Y zIJJgq2Iki*v|ucDl?3Dj0NzDt1Mk~yfxSu&AEolxCY{@XXC(MU{O@Vc;oU=U5?4$i zKGC_7#{%OR#hU}DP>exUi+C77?Vq2}J~+N!9pKfE$eABR1m6(_-HEJ0XVuEx$S`^Ta>?02{LJMSe1EwkJylC}#J-7LThtSZ-@1};8q~@Xg=?1S<;wl~DLOc(y(? z;-ob5L}=W*29R*RH(aI?1@T4tqNu4!tiFd;VeSabiD0QbQqudhXR9Mv$$m{|r)l?| z*Eas_&AjGOcEPYz&K7}8)Fs2{*>AZ6{j|`VF<2R9 z;le!}A=f&Un_m;S#aQJVJHPssvt_)o;>&)GtJyM&i{HtJXJ3R>{C03z#w=zSe2j9o8_u)-8F z+?PaJzZ97KbL97~0r;jIJW}zU>lA#P>Znc*#-9`Hfv`=dNS9VwU$sHT7TN@xaTf99 zb_v&H3e>%b`+%SWlzv{hQbIYxKCY;Y(*hq4}AcT3frAa=LG*2u0F=i}~h0zNK- zGPW9pLf-*2mR?&6^$ESLp}x5AGTU0q@o^Q~Ti)Q6l_bW6<;JC`YPTYalOMR=;NN~& zbRi6u%8$L@TM(nK86TFYk#1y`yt*Mjq6|zqJ= zsOG3fFvmAeP^^TRAIyjPnS@>jTLtxpRzGKffKlq$B-0oZp|qfMl7X7+I=R=JDqufI zM}{7D3|$3BY+{!gqK*=FzAku4AY7z(q*!g~2VBKbfXEz_nwpJ=?x7I7;`)}OO1$KR zeVO5YSf5+L^G;fV3J%L4{twOG1^TtOED!_biHH;XIaME;rfPIe1QGU|WqoqcVxnjn zHes^R@s8EBEQ|J#*A83Ecog?0L4Gp{%)cb4i?7&@CFfslUUKpidi3y2Fwu1t!e}zG zN=Xa$mktLLRBa3%Bx^XcOcs=EE=4+4w9ucQ7dZ+ak#Gn+*LkC~$P%Ssm>$O}o(Nv# z$_9x=AO{W4b3mL<`uEltdSi^3jsr}G1PQU>B4bYE@D4In-Xl8vk5w++nl$gQKbO?| zKY=Nk4d^D!`7C^dp$R`?x!3M!w}Q^~F+Qi6su}wLn#3*N$%A z4+IOIw&|%xumh?G7L%>A9X^IiuunP$0b@3>Mnd0tGfv8i>Ugf)0vds3UADyjRLle( zOQObSun`$r^hT-ESW>YHonNkCQnY@Cg<0XIHSyYS54;o(Q2GW2z~4u1wr}=-$J0un zpd+*}tT8#JeiBgX(-HU;!)N{+tQZnSwZSgr7L-|IhCLP6X+D>xwXl9mM9mau5F&5$ z97DUrLIp@$9%}Asu8Ld*D!Gw>k=(F?*m%kGdj^iRsT3>swF@|x6Q}l+@Lim422}-` z-iJhYhfpV;nwniT1mC7Phg~JUKw$qT-%KbEPb$I|3=9wR8<_0>LnrcoSem*T*}9nh z-$`Z{T{;b9E9zA*e`~gjwnzrNH&O)gorM~W5O`9_jwLJoY}?_>U@K^{>^^;BfD;EO zm%OAh{c@=0J|lWgma(Z4b zGeFPdIiLF}pr^Z>&w;J4{hF`KlAh-T!Iy@ABT@vvD!xA5dfrcRd_N|i1ic?71${n4 zYQA0+L3vo9$DW)i-=~)?fv@WaBm>afWX|XL)#qW!zfppMIUjchJ`W#Hf**$=SKS{i zAduH5u;=ZOu;+ES2K4ZN^!3SU@X^yVXO`3B`Q>tS*8Q2q@b-uCYj?ryUl$t5z~}2F zBnMPf;^p#L)BTc@)AROu2kbcidY*I>^nUwT+UoIW9zNlmy65_O)ry`2X7AMWydLI& zJ{H`*(tRHr2zfuSa=uQ^uGm8a-yU-Wzu4h+@7#LE4|?7&?~VvNE^EGaDFt0$Eew1< z1O>mIYCb>io&;SNrXIe4ppUi7^EJ>5;4W9zcNk|bN$_p5=IiFp?Q8Grb$rU;<2vJN zbA9`+zDL_@|G;Hf9Q3(>l=F44cGdk^QsetzLipC2ss6f`^3Fi$qy$6GHVA72enXT#Yyz^`{o(96cJuiy7G?mlm4ptr}2 zseeoMVc`3=vHAJ<4?3P1`6ZP_TK*!1rV0N$~w5r>E@{>+AL9>*es@ZFs7C z|GUQRiy+tj5$ETy#Qpooh4t+gO_zAp~ zN3IWvX8ylD$$)=Pw*CU^>+=ls{`d>ntpA$zkZZ$2Uya% zm)`3J=$x1wu3S_3WA@T`xTOC+*>jgiAULpT%W%Q$C3c zbmDbqdhsHhDqk zh3{Y{Z&jnSX|cu^#|N~&SsQL@${e5fb}Xf4a4F<3sf*450$a+s?IPuu&I>c2`Yi6b zt*6ohs{7ssdkgbn&RpD?(iG(tcTo8JwFsAAIf4njj>q5hUoma+QZ}t=eH7O(m)XGB z0R?uIyj9tS9X4Ll1gamRfm_9P4eJ6A9m%ELx)HYP3_o53Bi)rd?nLu)N3B4jbXIIiO;)=Q!7++oUU>-HEydH#8`48J_8 z2w}=mkg&-MhaSmi^jH;7dt4a~aOolM5FX{7y*D>PKpB7O{jt(j@(@=*!p3oR6UENX zey!*9=-B<>7ymo#tvp#eK1rU$+LB&FaDc-}M%v_PBQNEtK1ok;V^hxk!$-aqbK@pi zo{*;OSA1c#>30L8mC%WFUzSn5jh5!!^Q^T+@Ws^Vc)9|#vqqabx!Pk*2th17k_v}vL3?Iy?CILMj&DX35a*p6)h5pLHKce;B_whbk?aWaL zpEiFoZf?EZmXD*dZPrXmC4T%gbK;Yg>biosM7T5FM3RJaH|H&dxV4<}GD?h9y=DM9p+WB{6bd~TY1 zWSL}-%ctvh7=P^iCHK0Ysi!XoqW@_D>apf4i(^VVxgl5+@O4zCWET}b2qKylL`zs+B|n6MiW z9j-l4Z9H^R!3(@zsP&{-eQk+wob-kNUJ!CW(OtTosZY_b0&+U0Yd5L%>K{rGojhx4 z9&VCvoGQpR-@&x2G}pqTB8;AKC#%RCfsL>vPT(CgAtZL2kLkzx?<+@D(LC@>LlTQO zy7G`HZ<*mq-zsT;AGZ*_xmd-|mY=VutFeVsGbp}_J6LM^;R;Rtxc{7S z0hRtE(MHSr?Ev_ogQi1usNifGCqeCR;&vhPvFdk!H!N>}k3R|{_i2uU(7Q2L%W^x7 zvsZQ@%nqn$K9jA8#uZx5o@CO;Wu{psMr<}5|Ji{$62|N^j;N#@ROom&gq^u}MJ%0p zD(f50L6S!RCc=mTeEXYhz@PLN;W(cf?Bm6;jdKux=HSnNMZxM*5>hcUh%Z&aX$>bi zX&=)U?o_-m*vNVvj19ptmRT2_Ytz~Ij-4QJ(y)@75R7vkm=t2wf=fK+jv+~NfmzYI zgP}IjiGJa0%sMa&0pEnXY{lI3X@ zs0^^+ku|%mFZzGZNSp#Qk2R*gp=uR!rD{NFyQVX(zpRl8k>m*=BN`nD)=<^;IdtKq zGJMZNNwmQdya-k=&`R#j(T3>gRF$@F3zDyZ;XS7Cr--XF@+)eylh)Df&{*|c1z*BH zEgfAtMj?;8k0Wht44=@*Zm?XWgM@+;wI)=QMs2q`uo)zao&EO~MOL?63@&27YJZ7z z)0b#%85DLz^zkskx>ii{o%%X}PGa*Jq58Z4i(QnOF)T9w{%cz1IQ4LCBf4%UWX>_R zT_VRX^hXPYhpP109$A=8_juT4`VSm>PxxJBUI+4@KcaFX75K#x6qayjC=$d;DvmA| zeYH(db(547LLJG10mH~W+f`8n}z~SL9?^BVA6qYyOD}@O}{+N_K;CQSCTy%Kx=lSU1 zmB?ZmzK>Ejyh$JDqh2&%-avBr{Fz>sMN_XJkTb^dCJ$gr*R!&wW+GAKESq|3b?D2& z*=7K0x45m~$KzJfPnNN&l&h-fD%w+etC-3inz@Ypo;_Fu>wxU(q@d&FsAdnk@1{rP z?^%R{BfoE;xBac4r6{uQ2-jd^fpwBA-Cz*Iq7ZE-)Igc5eOz!wP?pPl3e&sb6pv0hgH% zM!GY_h)v$Yb3}OKjib&a9!R(Ez)itwoWxO-N}e^38OPOsw)xCZ7?Ug}x}_CiEnKe- zBV-L{Cnb%b0UMztq8n(?uN%)Z5FJ0YAD3`M256}p!5@uvq2vXQ<=+zwQYE)h_tg1% z*KqKau!)PG-MaFRt)rI9+%`zZojN45wh5YDo6JStDOIH?)5qtrTvTAy7N*$l1qg1t zXVJ;pe68#=7ka*INS~)R4`sV+@y$M*>P3pcS^8nGC#2u)k=s?jQ0Q;O}d8<&G{K zW?K66D&EJf{-xNP>XXD70@(ANPVP%%tLTXd+US|uH!=_MIl5UiJg_BY)JF~&X`L0D zm35f}ZgR!NI5sO{1j4`!DI%?uvEozrFHLWh_{A>EqKDYt*|K_xdq77EJFYaGBqKLD zF$5JcK3T^F?%o=r@%keTw z=9|X5pTlSLIUbA^*x;I6}6Xz5l+bDM;ID} z&F#9-Qm4ljaedKdv11_fDHL*)HN1t=Rd|w}m-zWlP|)uG8HJsnJTedGd)Dc z7oh4mYSESA?<`3D=IX18__Gp-s=G|0UyQ@7ak3GFvo#e}=HoM2_O(1yNH=)P#RzFR zMb|W^R>(DB8u-Lqc;~4l7PN$i&gu&Jr?d}W-7m1xZVF=mEW?OMr;cCyaQBFNY9jT& zHo=wh6XoPeo5cVQqKL#3flL^!szQJ|%)g3EOBOJVg-;>p;*36y*EIT7 zUZZy#JQEf>?(c2^O&F$mvTkGeGsQ{vo}IrO^9>Oam={Xz#36`rT9Z~qsn24}MMd0l z6CXlxNskiP?#bXsP%XjtT znPeZ%?)>GcAFI8HE!+llW>k5p+VGTUYlPZdNwU{8pTsUxbfmFZL3!<6jp|eR>4l1QL6Q_2}K82!i@GQ)tVnD0y}5)?bBJ8(75 zjX(Tn{K$9d<<+ZGBeOyLlbK?y1m|M{n^l2k^dvQ_-AhqOTaBaH(Yrk_L4_c0 z4!sx_21=c{ z7QI+>k#8r=9k+9rzvncQY%rJGUw!1i;s91vo>rQ(hFv#w z#*OU2aLGhJKC}w>YoPm_ObLO4qEga;5vE3!bq`^x{gLypu?Bl{F>aV z#Nn@NMDnx4R(Iv;mI1y!7BWfbOul0-IKdRPxtpUk=^He9)fwhVvRrQoM*w|GD7OOL zv)x9uf0Ey@l`CwJP_zJ9l+fR8qCLBsA4F*j*v$y%0hPGmwVtl$duJ#peO)|LrTFt# zWL%-z-_eb{ElL!t`FCEjO?|P zOma}V=3UF(X;!(kb?PN}o9RF!OtCeyzvsb1q!MgUe^f`skO%zBNNB8cA>S@aMMl;b zsn?u|p`)PCrYLl6<72%FEX$a+

b^d1`?7augdPdMk$d#|V=V^##ZAU)n_%{?K6J zpD9s;oS16M8js;Pq6I(-99al*^GEVfaBj^Fz5#4e+NZ@@Snt z+~oCeJ{}vb@R)Ti7u*swXmH9V z7#Vjp*wfW#l41DJmA}0huj#DzJ1%!wY98BFiFX0|X9~1ZZT?QV;nqo@Fu-GHt4s)mIiMwy;Vigt#W$2Ua2%Kzatq) z7XeM1)qF>*8)H&?We06nxyI0L88$Mqtj=tO%4n+aY-ziI!@g%CM@789-lW#~{@AZ< z^v)MEyLxp=LU54v5QM;Ogo#$pO8kHu0a_H^{l*8oC4+xCY(GFuYJzciOx~*kgj=$; z>8Zrk! z-txxB>@%MkA$@a7^XK-ONL6FAR`6PLpAf(~Qn=lMfDS-36R(GNIh1nt#VL6~OGA@{ z#G{nR*NZW%^q0e?!_Vw)BoOY%Llz#KZdEqD;=lL9P>gKcKLH~z7bZrfp-!I0Ad`5)B}Q8Nm0lzoksqQk z)ur&KrE^vLEJ}OSOTPzy;K7_6)7bFN9lwMGsZ~HYU%>`S3$-pV9js>8@Q^)U8;-Y? z(A9Hal`+MT0%=x6*D+&HK{KAKt1-P2=3?m3?_$tt;4-0^QvNJ2>u-j2sAr+85~QOt z8j2R8KVp0bfxc(ykUZgnB+l@k_KgaD|{2f(BF6j zidBxTNBzk8Ta+bC(7}RH0x}S>@)%6fecH1xN|7uW*D|Jqb)$WFMW$Kg8=0Gt<8fQ5 zgNV4b38WB;*O{9?My5#9?hjZ7z@P70{h$p5&pq)m@?8sYoGn@+#~IErk-Umrif%65 z^SJGYskpa`gh+vid}y8~89YH^*cQf1p6B~)Hg97H*aEa2IAGO_YLKc`=f5ruRQ3%fTHumb!%y$&0j{<*a(Ml4rtj{}U8q*)H3wu38Au2#rY{MO zz)g53O=^VR2pb*BasnPIFg&dT&mh~BVJ}yuo#nQhEqPHd$vlY9$rk0pk*1Lq(d3I; zb+Qwv69r?!)dn=(u&%B!+XdV*Qsij}A$>|(+;Ol;aJ4`68JQ*Du2${@aMpz1NoTu{ zo&tcqlSB`?Kte=M*lFcUjVNs%zjF;khEu%g-a^xv74NXXqk^+AHUz%XN2|-fiYI&1 z+(1XrY^`TDtn)BYt%6o)-Fr|=sXgB!c+;QhtPsnz1@~9B3Ow3L%6#3FG z0ta|7z1a;(2kAC_VRv7!C-TumJC<`VUj9zTivDn`&ysJjF%C} z6TUnmWvXY_H3|FE2F967oZS~`n+e6siN~&aMJt_}LIP=+vDfm!H;P^R#2be;QBRY= z!@~CB4A2zX9tio!Rp+ah24j!?!GS|Ks|zPoEXeHC%Lu6lT|J&F;_4^KYHy*Y9|Xv%qYy;BIZMhTpUjbB7p?cZWu zWyaq`oEw&moYmcU3fKwk)p%sjITUS@RlCE7pQ{POHAY6z%LAX_;cS;YO z=}%J6Ie@nl+K(LdoApu;$!8IU5PeuJeZTLyQz-=(BXw;#lU7ZkH z(Mj@MVaUw>T7+|zMu04^HUL3ROhht6I2O~x>yM%=gq)8=*aFK?NCd=m?C+=ih)Wj$ zshTjBoR@u?R7i~W0NP2QBnf@+I8M&h>m>xHHyqX>(bGWMmHqyRb{yUfU?>zjM(Ofy zO5e!>)xcU|s_U_Ka5g7LXIMH*# zow8_KTR;`x^-kzocd-I>F9-JlWh2dkEQk=U!No=<-wwEiY>CzhJ(qWMFL}i%3WrvQ>9 zk$0x-QsTp<-nA}ttAr#$*rFIF(Yh>+a;IuRYH&KpPrO;0a>LGwyCQin>!^oqLnMG$ zSC~3d43)Cm&7r+XfELW?ux);MucjFbsWA_7xUWZ7Lr4~#qm8rn1sQ%ALSdVxdYPcu zSLf(ijxe>%=gnYt%{Rf4+}}WurvuffV@WCEAvn_10(r7XhXdZlC?MlN3=Q-2b-WLM z>>&Xt!jfK<2DCZEbYqM3X-U`)A)QHovopr@C2)d`G?x~acOsg7)4VN@jOgIr>{IBzONm$at%NgF@OX*(RPmk?ov4c zHm3}b3@!PkkM9LtUV(||MZguOgO4Hn%tIl9_Ge`25yF=d~^SeZf+ zn`%j7mMU%lZG{Y?K3f+?oi6b&yzsUyh|d5|8b}<2&nBfJ64=H`I>j&T(!$f003);0 zqLA!(sK9Gfs%Wh<|w+%wm@GZ)%J z1s&z>rxiry^)}z2*JYz#vJvr z(jqA;^Nr>PAyvmAus7m-gvDFbnAPhg+L$gkE5K52RokOIwz77WjWL&(Yq4qx0;K3v z=vy0u1S0BBMUFS=bk2F~%ir)S$4A5+ZJnK2X?g^{$e?V8DZ_v`p5U`%k-Is zAyH;=J-QZY?^NkYA>bQ7qY}>=Z9UU@7au1`ny2`0_l!!mP2Vpbzy?^7?|kjL>=S*3 zDVVi85?L#)H<`!9LXVtR2T(>ONGELtEM$LEtjp{k&S;`ugi^{DQf>lg29DYuhAcHV zs|?I#HrGqYTl7bPw$xN(e$yy`G7Gphwz_sGE~eJp3*#YbG6Xk`QwX8N=-Z~$*4a+~2YZtD?Qzv%)9!KK6y6;5D@44t zMCQU8EoD3bD@zuyusStkB+4@QUos{;q3(qEB2ZLz>Uny3t%p4y{n%|Y84jp@>iGb{ z_N>42fJ@Ll5KT9yldmOuzrXpwo^=T|IoAJm=jP$`5nuv!9d(wZY|KEiC|x`^`B~J= z)LgFkudVWXnU2TTNmC5z`4V*t#@ms--j7^ZqT0zdDo)Q|&g)%cuyryKX+q{W;$>1W zRLO+l3S{bQrP=O!nUb{ejS)oA_kUh0+1W0s)$b;6+_Uv6cd#C zI@mB?mnSpF2jVMXgj3*}f`q0So>l@;>wDR!WE5p1EdXdU4`3kxXcpX4PEFl)E3A0w zOfuLbKm#&aC}Y*~DG6IpK4wJ&cbgfVd?0~BlR8w&=^QBo)k(0cM?HwN;zkkHn4V+` zwB&N+_<9NhvHx3GAeAIhV`2HNgp;Yy!bc}&PLjQ`v2=geqr$xo$O%lVG=OC49*#&S z_D0E%$w(J3BCp*-+6GRa^#~ayPv_H5EvdRyQIRl zNNsPxk|zbi&SjI^B_=lmx2?sVNw!tPQ!ek7dkAE^NEA$RP^W`yM4aRt@O&-*_AIt| z1jKp7vvc^UV>AKGB&rM|gW;QymT1Y1`8r=Mr<58V-DI)nW(YP>su`qIU^kkqgl2jG zIlWYlZtV@OhiQGdghbXmwN>F)fp4e;Ny{UH#e@R-mjUDO&h-+Y9~6uvs#&B(?`3=J z_vI3jW|D^0D*%0yRQJB%2zY|z_y%Q3p;oCQpz<(1?q;^*=D;E}11ha$k;a4IblBGv zChv2k$h=?P3k-#20|OdX!G|L)OKF`{UyB}*M?VvI!II6D&fG79h5P%9PYPDL_;8z~gzt`ku z0IU-TTTsEAuSlR7+J=qkbwC9eNlB1gH>xh~IRA+w*0~v|h5@)N$WFvi|6!;S4}~x< zFQ6W)4MQtJirFw;s%brqCh&9)XQ3s=sY6PFInQMDH= z2gT7#kOQwPQ$5|SV&H|j4aPaK>aNi4soQZCEKEs6?l-Q zbsRKKfFw=N^yLxxEUbhPPSo{q!Vhr3-T!EN8vw;{8ptrANfxmt6<@E+Ttvtz7si%)qs(zWfYFz<-LD2X$}iYCbZR-e=&;fPpAAzICFJ>GeOQZ zZx&1jzYWwCzHg4?$Y#D4u!05^8g%Duz`NPbN1l}mBIN!UD?OL)e5716Wo=-J>*C6K*ky+|jrVHPtOOTDJzw%WY;NjONuyV< zQWgo^EuUP_)^vtix#46yrGk5jU!vwEDZP4HKi}F4GCN7#f%n6^6LqQ~N>6zz`n^ z$OgsP`@Q{O>YEfDmlWiiRA~}8ETg?(GtgD0#{@6T2T4Q|kR6CNENE|?G@jc^e`-2*ndLzuSBD52DI1E-*Z{H?E@ z4_}+Yr45qRfb&f_EaUG1lp&FG{N=l^R{;E+$6v__A}LI5O83fbYJGsL0AX){Z}6?c z!6*B!gSab0-N2-uP$f>U?yhVxLyt{s)%Z{qeyh{=>~usB+u9ez~}>F4P^e5>NcQ_ffX4(9iu zW-S4nkYNmtr$FWR$zmAh-%2bL>8H(YCb`88Mb3>B&0Hiy=KR`%>l>aqhkm1SBV03$ z$QD(O9a?2%xHSy7Iw5x)W-0hj@viYeDDwy28muC~E+Gls-aB0Qp+v@WKs^XoIuB<9 zyeG^u?-G}85_!DO>ew#lp#iFx&c*ww@+7F<4&-wWz2)*uvoaFiMNASn z&alvRkOMavKXN!>2@X0i((waU*cveqsNO&zEX-#&b<@^YlNUg)Z4#Dp0ThD^=ymVw zVRqvD+iJ2vH5c&vh?ZUs$K3&Evo@Q>3bg8U)0pS-Zx60;J>I1V;y9C_m6Nfot)3yO zrHV6A1ZHMRQ;leEyfIvAhntG}YqP{UPMD})Z>Fkp6QpV5)wpD8Z}T$BUQhB7WvGsB zk)#~K=LsDdyvNTVB8j9DLHCQKm0~6^3?!$iAUGXdD`ipv=?{u( z5o28{Ws(uf=XP6hX3kS*9{7gHvqRcfowz1v-9eSToM4$CPDyvl3dhcD)6Q&*b{>=Z zwGBEUW8!yUI^AC2+xa~bxEqy{WDrWUQGkA+LbI{WM=XId{!FTYP!&^ry+(Jj?;A<9 z4&=Fab7?>bD3eN36WD-p_S$MhwL0%F0lS#AR-me0+r5KAX@?Yx8{s+9Om?4-(r21&HQS>pY}1W2D99qq z3y7PiBc!#z&*PUvOQW7U`z{(Db##>(zxo4wqa8zx$GzsMetjc}z zK=Tc)$kHe!s|%h{ zNuC*wFd!mYDY(VO%h`nxmcnwn7!Yl(_>~oA_K6c1R0*dmVuPm|2p=*5lE7w8YKT;5C7?^9kn#8Fz2+&TvyGfVXioMojDBq(oFHVbJmp~k~t0(5sn1U^e8 zPIt}t`REz}oS0t?Av4=ERCG+LT&<50zysg1)7hkzAQ?*3#X3~^CK-?WNNXBWPzHE5 zT1$aB9YJEM{fdW|LyPY?)=>FvClK_GZ?+3(0(!&s>@rv7;z;uhv{IdMa}gEyn`^jQ$-NLJK94wpm}!t z>0yz;J_e+UXyZ<$Yj9o`xEpD=5yW}MNXb>2u{QCoD64n4l#UkJCIb=O>(PhGamXhd zRq^voa`~7Q7V3GoZTCmrINcSfY=<_Cle7SY0~9Kmms7l%wY!r@we=8Pb|hsQmSBlA z??&I8f*Z803hn`}nch;-Ugh;vKNvtJrW35}!T)c{0KL?=BNpgdVL0tY& zI-mzAJyYqpxsfEYJOSAv6A?Ce2U^E%w&R(I_*Pq7I}G@k>;;SKe00^m!z|ZQLnW7I z3Ho?U^ktsMV;^sP`EbX=Z8f_gBT%+C=@oxZ^zs1YPxl%E4pLn!{eh`SQ$Z~QatQ!x z__X&0ul0!f(MV7Z2y4eno~jRg-P1O9&QJ!~+Q!c-RvceuNrDXuR$^CL6vT6v)8yC{ zIH&>2qQW^;$9@o2&>09GYfXBg;ur4u;F_f!Nuh+zdv?IKPKT(FQiSRWV#BT*Y8R>y z>iA3zK}{+Bs)Ephju+7`$y!$e1#V2F!|i%>r6As6En>>I1;r1o`|yu)I$P4~^$PkP zWlf?GCWZ_JhT{qCO>G=? z(8rQ)*s?3Kx8ZUjkW`*hIX>XBYtX${V+RPUhD52!Nsk;Q>~yLI(jJTpP(}>;Y>k-H z3hEu$30U9;+bJ!Hsm#A!k3M$4)3;y+)PU2*oIV;(Zx;{vNmW?27Qxx4fCLmkgsj^z z@m~pK@H6ydFmvxB?7$G)vV(<~+k-VC$02ye_(BE>L;Qh8~F9zUdjHB8L6GPs?3!*@&7Jxr-Y=Y&4ylUIU%AQg~W@Jei_ zD`HCF6m-o6i6w0wY|kJ!Ca1fH)#t;VL`wt5>6NIdp+RQ~M2`&OM32=Tm>aU;wD`#W z7Rv9FTOG7Wnc~btI_XF3o`AAiT~?4D$t(NuL|Ep#`zFE5)&ntdyB>Wg7J6kRKZ=Du zn91w)-Cc1$nWxCfixLSet$N~-8|}s3?c5Eo2mA0P=StE&$ldh$bHc&^}S1 z;8>SnBk!7GMG<{X=Hl3+LiKb=ryy3r?&R+P*$jSm9<_S*hQ^~$I4$MRQ*)d_z;>(% zHe*KUo9}-&(4kURS17J|6PYw92ecd5TA0zBC^xyHbG#CXq2_5w^ax2 zlsN_Z(jruggxmnxZS^QIq1u!~e=)oDdBtMuB5*~tYNL{5_EY!K*tqZ`R3kR9kSXok zBQ0WbU{Wkdlt#j35*Cdha*NH6+v8iQ25`+9-4MWeYEq~+vSCl$a))4T3!91_IuI&` z7f{0hH{}&gwI=%=AVs-dkDv=NqY1G-cz`BxX&B?4^7@$9+Rx+aawz{Tj+#M#A4yLT z$!Q#{zhC@yzewDF+L+wj3!p&-6P2hOQz!uWq4UavR$3%i(%Yi33a-V%P&*x3ua^Kxqpuw%QFq6MC)K4n?qy=oFlD-q3a%p;)|>c(K7^2D zZE2Hp|GHxdE|Lsd0JW9^8kG+Md2j zP#;$;TkBoWijv0WZY_0LTA5x6kL3~6y z*E4du;+S^{l1aOX+uj}7g6a3%a)%t$kCx%hYod>g~Igc{?%5+r~zn^DAJa6o>VrlOn1zv^U+75Quxy$ z$SAZ#*Nwoa9g1rN#v!#_!MJW-GNbj<~9#M2W<)~wIw1ldJYyxGl z3!c1L;=|M!J06V7$m4ZYu%~#g{4Xkq5sCl_4Pog>N)0oY4lKd=y*#D|p|XW(Lh1q% z+gwS=%0m!BeS)iJkzQ(c6LC!-CK+CJUgY^i$w`8)Zhs6ZmY0@el;mo;8D>aOh zXrOcSDB!n`O=s4i%?mB)&a&*%@Hw>c0*t5I zMNrZ~Ob+E3#Jhev_rBeGMQIR!LdE_h4%kAQ2-1RTyJ~C zYshyZ-bReK1B*l`-S(+d8s9-?g(h{Z;tONH7X_478u#kqwnvQdC^MB)NNfF(S z-btt&nzd}(QwmQy56Z6Hm(YD&vHiFdHXAn8N{1l@&-!L$1X38|kB;z>evl+A=9>?? zaQyhq)gF6n=~D3&(`sYa+%OjkWm{6X#`>_d@_)EnWOSmpW(5+7fliyTKY|>l6~b=+ zGJ!;s=KwSTT&i&^Z$vTuuw5r}V8HP{41Xe27u|*v4>;*X5)Hd0Mrg%K$?V&SJ~~|i zWOc|P`rMFuog`#RP=s>_fKaKaDWPs@>n>xEcrEzMfkjzB9=gH-G6NFMbJSbi-6>mI zbURZ2!=+k0Pe@>heB6kGwqrMUG^sRw2Bde9!0s2ayVpwOjGu=Szb(^>VFC!E%$Rm2 zdp~g!m-I*gNLHW5<8eYmLS-|)q?fn^0W_fmA!V4e5svPy4k`AYay^fuIPG#0M}MCE#P&HGH~PDx5&5k7X~ zp>8#0XS4U{UF8Zmt%HU&w{{UdymtdJUmK}b(kwa{6i(2me zkQIwLAQ?upsz#k?uo44~aeJ5~B~_`wFel9_n}TZ1JT%;qHWpU=2+NT=K=Lbf30rs> zVC?L{Ft@qcaL1VX_+EQ8h*YC3Z`Y%1(beu2$J5d5C-?ND>WXaAr8;AkT{Y80H}_+U zcD2QQ|0>qPoBCJ^Lx@4Z4Q%cTs_AeClk-(ftXZ}oz@i_0N5O6H_wP|_Wlz3CDPa-Y z;J3w9O8XtjZPw58Rtn{+CsfjWF?WSJMuanmcvV$e_PbpU@*3 zh(9RDz<#R{`;4Blk{Phy?F=`0fGvX@YvVnxVmxsb_@$&Ff>>%$xltj|f8_M?H2whw zs9T7aZ|?a=3;WUEH2?fKvw3&J z)NN(ypwH9NCJ#-N1OOE{s`1|LeK4v1S0B6omHODhyt!I?AZ_W?8QpwoQb*-Yespjq z%}$$!@sP^oGC@s1q7+r%#CY|UfkT&HHXkK(T7``s$yD_P2%FAk)g73FW6W^Nfp2P& zEWKK$8i?0zlo9Zo7;A084?d^60*7Mh`L6z@=U_GxGJ zf*r<|x?PVxboPb{Xv2IR3*a9i;z#~=;HI55ah!P|0aNtnsIFPtB5_N|kT6u+)61P{ z8%HJ;*6Ro}Edf8F|2PcppPbzW6Z($j`+bmFzGok{B3>Xkg=%=iip=8}n%gVw-n{83MV%wYWdO!ss zEt0gDi(+W7NMXSXfbcZZV>dD6EpThV;MzNvUmF)$Zm2A@1}RA6}zJHl2De_5NLL3!!cGQ z%Wuyx0pm1otAj5U^w0qjAD~i(%0pV~Mh$EN`gVU%choPk3O1OuRNoNsrA7zI^s~)Ll_M3^s(az^>l>lo7i%pXXkL|l4PBVecWt0 zhYf8ums(w`v=hm5?BVU4KD=Go8`B+tEPUyj4b@xdFbm&}^vQI$dp6AV3OBcg1Q!;I z?CE({GY(fNJJv5jqS-JF3}nMOh*q5GrrBhM!XRoNG(lb69JCgotfx?&mm$l>0#QEaTayiKYa4_2)L42_S%4;0KH>nJ5Gq2 z$IQaGa#pm*Sny z3sb$h7jlx(aFhfu^zYt8um_{2plCwXy1D|*LMQmk(bcGe5+73tnun)t{jjNI>|b*7 z2PW>+jR%Vd(t5l&!@7XEBGF#sQg8MWAPku~N`_2oW>T7!me-J>LRvs3WMN7qR{D>> zb&BUW1u6>|(K-TsQ9;}W80Cmmx)!3$V6om#SFHY_nh-vKoSu}bjA~x3Ze~nLK$rzE zufbj64i7d82cQRtzyp&7ohM`j`=aL4q&C~@3=uW#Fl9$vXwpW)`Rd?!q-kX_o;4Ug z-K?f8)(vc`LQ={G3N^1_EXZ*I*g79&_u&F)0!VI>#vaYG?#NpZ#PjkKi=a%kiGvtdV8J|_p`MxdBJp3e* z_?4na&#_!(MjCbof7b$g%x-G?vhLEDNwUs4xt}T&y(zjBqP)BWWOUs0H9(d-h%T>Y z9AUa9xjx&FGH@BJ&^+uiFcPiO&`k#CGhJXB9ZIKJ>5jgi4rRYGp}?OEOoa@bwt4+z zD9tCD4g2dNDt+cXAxVZk#pt`Xdf09zH`seXW-~Yx;@k$G78m!@oBAy>Vm=%eNH@ro z1gZ}^6vmw~AB9>3^l6EcBlDBaS5VJEMGU&S+n}vbM7VU84tu*Bzjn9OGyOR@-hh}_ z7?B+-q#T(x)MQ)J7yK{GVsb|9c0IaUF7QX})3~keS$=bD0ojD5d>JJQ2Lq5dIN28D z4tiW~+_As0O4HF*uEii#Xk2IjMtaKJpkRODx$^o(sDcoBkLuqq2~vuG_7RmD$<>Cn zJt0Ui=t${EcCK{zaYa8erd0IMl9BpN$(=h?=WwFa4A7~XQeESTn;K1Nh|eW>q^sLV z(v=gCQ~59%01gdZDbU?qkKh!e$5)DWirBey5=?17>7Ym3PKUYABf8Ga>|F-6Ne>l} z!L9-dc~i>>JdIWk_L^~g(XoSeGC`qI-2I!P3PQDWx<&0sZ$mfxxmJ~-1~1{MFk5|d zBl$*V`uuzAQt}T8qi5`f48aZw* zr|Z3DeFnW3_YQfJ9yuBnyA>cjRfd*=EuRJMXg-CX7NfTKs7pu1e%V1v_f^jn;8j2}zo* z1q_-yAi>t$wg*u}uhgx&#jG(rfO=;=uxhX7_h6hHQds0#!o<T zogEHeflp7G1hSXL3cJHDm4-?4#!X%s93UIznfQg$J%Gn?7;kVUfJOp^Z98};J^*Wt z1R52#9|L5em6sUDg4zAYdN$A-k#g|+77y0{z*49anSV)^yNGq zz_AVHdxNsbMGkt4=65dXM?kh6n-xOL>-^Rq)mvKmcheruplv0(PF295%JKH;dRo3^ z&!m{sk5sko&kPtbb)qTWpLXW;5aa0vkn1Ark;4smUXfP<`xPj+z%%7uo+HXm>9SBE zqDre7C%{Gue&|C%wI*#xfWh7j!URMO?lLMEDZR8_j$&ihtINfL(bg4xq}ZeCjq(R} zvRy_w9O$KLpvxZ^llzg}!wE4Wfbi)By9~Y4hY`px=Q)!Kg;sqLS7-N+jQnGu#Tab* zNUs1pq@=KWz6IFtM^=oSN2j2U3w zN!IQGNYK!-I~}3%Ki8~>#iPlXEL$pdJc{6cB-?UyoUl-XZHS?13pd6(Wm07@J`I^n zCP%In6$;f|Vkro&E*o|02I$Oa&SL&61a=`*^dNtb% zB5`qV=E~oyd*&wE*u$6kW>aRD$AT_$s|AP#6D<~r9A%1LC=$c;i+*4Pj$3`(u|BPi z^$q4NwhRzSuarXJeL1+vU`0EAVgqhaS0y@nMEPTOt~1$rsduuPMrs*XB)bT91{zJ% z`f6Xkj-S|=O-yT2JAt*;o}k0DYzkZb0IRZDChHYzTOlpUl`7+~3!jI{%+C^#>qcMmD9b2&kKYEWz3EW_&HV%Oi@0)uMY9wkT&C;81 zlBy=sCqbOdf(mN{4)$E`b24Xesf*MoM`!s0+2UmmXyZ3sN}r<@=JRj|+CWkV z@e=mNNYNZ?d0*ZeK+%t-p6Z2Nb)EVV@igwH^sfuK097gctwfU_u_gOzX6mQFY_|Qa zp2(})HazT_d#Ft8_rmtG!)@;%u?MkX%AN{0)niZQg@Zg7&=3nseO4S^jfO&r~%4i_xK_KJ> ziy!1@q@nA9yLRgu&Dkp0Au9r|F0s)rz-t;r;VW%+g3pK3O>Hq!=&8}~fRCX5?5hE1 zAg}X-qHFw^bO<9 zd3>qY#lT zfoU9QP1%&4+PoROjQC3hW`q>5Mc?rx_iM*B4+}5W*zJ0xMNP{cu!o7f70t9sXl(5n zGd$*22GMz37Y@jYSDBa8_)K?NW9^f|P33i<JK_&9=?xR|};-?YSaics)CbL_$lF z$GZx&{A~Za17sl)7xkkw!v^tHA9HK;8HxIc1wXG?wNHaS%oH=V>?Ymq__;~7;;B*s zOD>v-oEK3FT&`|XVm2EpnzFHPFx&;pO0x?|0Ymtk;BM(P+k>DINF-`?1aN?WqT9?l zHwEDqM5igb?!dPMIwa>7ZMqwPPhx|&k4G?jLg)k#wUYq{Ax?ejV{YBF*bOeGt0#sD zI;fF3PJ&J%rl~a}k%kokIvT^w9sVe>qTx<->-fO&I-Me=zSzMe{R8e`t&>xAMBWLC zV(V7>r)Z0TdB1v{P@w>JuDgDY28GlF<@X}^(K8l8Sa3>`{jJ4LD%Zvtbh@x_`M#aIGI_Ttc1yp&^57)>(Mo6jPl4)*;$_WgNN!I z=a|%r89$Mp-ajz&&|T_afufeU6Xbcl&zrpC4We^O-C2&*8xV_+C_5uuO?O>fL27S@ zsMC9!KRpo$U@9izhiK_j3>aD9_2Bb>eMg3Tgu1uu6(pqTik|Jang@Lc)C(JNnzo;C zYaMHG4%E|&n+$5iR6VkSSl%N#n!xn4ktfI@yeFP4$drgp8C=*2WX1FXW2U#amj-%v zmtnQon^5NnF};;kG)+=;@ARU$9xu4jN3>5sizmEabwmVy->-9>FuInCW zz>u&^^6user0;i^Yrn>D?spUH*8>u6_8Y2s3BY}-uu@QUL-nT{d^7RAm$wMNyqZXY znIVx?_uUVYA*eUwm$h+(H$Hd|AVH`HlxX(sQb~}2g>BQVB2QRVx!52W+zGduozDd> zbrmu4Do0h~zq)yYn}!Coem76VwzAk-VJ$k3^}I3~u`~&?kX=gAtJuBS^rmdP7$<1Z z65j2*u17FxQicJ!5#<6z!?u0FvqL05x|h^^-mmNR{tO{ov7$R58ISfq6@;aGAZ@Rw zl+hsBM$>vG4FUrIQ}C*O3Jx70QU((hppNi}rT&)QKR?<;!i!H1utn=E>B$PV+0T9K z*Fo=A`qD&n@p;86f+~&!8oVd%3;X+lmZ7a@f(a6k{SA!2yTWZGp`@S*rGtalLZk{2 zxaj~BYU9EzyLlC>p_TV(w%Mq218&DFQN{d++dgQJFCU+!H zn%?KhwCVjmT^B@z?}iUcSYmFaz1_9>2!vTrV_*b#zqT__cz)f!boWtbE+MZ`kBX^H zRYL?|pz1Hj6}JgCBtwCLaFuUp_Ivq5G#VfC4jDL|L8B=Vh6FVc?)Ob9jSS8nd=oY;WJztmEBo7qFoDYER8A)GSv~bTLAh+$cbxi1##XtdJ z5RHI3psXSyfYYsvn~5EF*dz>V?EnZ;=+oQ{!R3>bjWS4N9kEU-Wi8x>hxIK)^P7 zPIOD@s7{@3Na9T#zn*lZ*og>I37qIll$JqYK^?&ZsPcA~;@8CpDuYmhV>l=D+~BUV zD)MZKsbnVOOl7uR&|!votar!Ch?=#)k!DW`v(YA4h8|+tdCTr=o2b5b zq(nM}3W-#sPaI4h^kCV7OJB288Q&S~ONDWy63OGHt*qH}%~?Ah&Lx(#U7}J?<4j}P z-8Jc_qif0XexFe@47+a3#k@ajmI<4k@qqChU@1H<_@A; zB0<$anpBTFgMc8^sCg&A4b@<6irx`9c$kRY$Rz>=(LTOa_E~a9j;?pOEa#(orzIT7 zJLK@VyDt;2%{+04AK4L{)YHhW*R_0)KM^>A&%IM^x(BidX$RxtYbJjjfNX9>+0hZ2 zB4s9hvTqTD&=*kW4FV`097Z&_Pv6GH9*qRG{=oz0)}k{g6onAX_DA;)8UuECtV@a3 zCzmTWJE(nkCMC&u=ufj7p6*N;vR2C0R~Nh;Ky9&(bZ;Sw9<*QKx+nJ_gAFEy=-)t& zHSKl+9{0u=1n(BPP;&br$97XRLgw9_(PHX*!GCUk>wygotoLxbED?cf@15@bC_ z6;<>VL{q-yOw5l?f}pB3_F6eXTqkF5lKFp7CPH@zs&WfJdA?p@xE1+K_}gWUXKfBr zD4J#3%f>}O8Y$#pNqdU1V46vMFdEIdPQqgg#zVO#4xo4oV<}X_p@U!_AVVkuFPyx9gN8=Li^iQtAGYV3&Ox6)ABie?Xq%>1aE# zJFqtewjK&4R{i7##yI1|*di^glo}lbC>gtByClcTIs1~w**yrR?{xE4*yNhjT0ca=c$ zp@DA!dc!&(zUv8wQ=XVoZsm_K!F@4T86^k&Jcbe0onn6c*FF{CO(=~iI;5R%f@e&6 zW|2f@S7m?CBU5^T3iH3dEP1@CynLgX8 zm)3*C_7d2vN1JnOUjM4*#6{QQA6nwFs|Q}@YqQAx%ZoXWcGt+tZRswg8DnK0T_y${ z9;jmAcUxH;l`kjwNJ&f>&u|g95vC|kip-HfPDOR(w%g3A2XEK;3LrhXMO#VyqfY+r zU`gz0zXQ)mO-oJImn(xLb_|lG_8XpPI;|zjFx>kgt!~UYx@7J_TiYyTjd<|phg2Gh zi9pbjmg;V+bF-yw18GEwQ9MvLJ)ncvBu;TgBYAT=y7CmiE>3^w+YiO*>-7%nb ze!>oJFsIl;fUDbsec-H}L#${)n5~a(+qP}nwr$(CZQHhO+qQM@Ip^K(yu74)(u00G zsY+!}_8zTN)xZArm81^a`RO5kzM_vht>9Va?NX5$U>+z&R(0wr;Y%Q=O>_}unNkgwaYfDq39%^^q*b%og>VmanrdVjETrK&!pq5Cy6C zioj9WMhZItL*T?rGfT$}odC79`-kasz+y@?_Pl-21A3|+w3l`!XA)f`#~c#S;(hy< z+FtOy0M5#Oxj2#R5PJFS##?lH6e5eO$9rHMHnpHiQlkg_ukZ%#_3(^Ce<1)+NSPn7 z>j;Q%$t%LsHT%-FvTA$bTov*!cq)Cy(3Qe^NX|?)k@6stcn}D|)|-Fudl0ob^DpNM zM~$^h%~*LCt_fo)HUevVYHGnCt1A@ikPHGaa!Ys}SSCvntVFmaWsXGPZFq0;+LH4& z`jjrd;FEbY*{uS@JW|Zc%XwBAF9d(h;ytoWhAwUzR}!&M6~Vs4($&?auptr#vbNcz z&Y1TF5|JzdilAE$VfUQ)tzEwjhn>wf)UluRpDTk?6%{-AyKTSCp=*$%*NdwpN%JYF ze~inpxJ;oKzCBl@@O6lcH?&t?WG&afZ?qSNB(Idi#*5yw$-tmC8G7uIk2Lso^gxaS zA#7@w{VO0Fsa?GPnohpMnSbzgdgX1fb1a4U4Ake(cet8n=$QBcUV;LdEmKqQEA4GJA|vlU^N1rO}& zqTh6~;lA*pxfj|)gOl-hH4OR4m7?Lu=KTOR;T@=deuT+~B6-XRKDqxpOPbRloS0%` zfQF1Z2jMf_v|ellXFK1Vv9*x?A(tPOg456`*HTp&RR@LgAD`Y8-wK-#23lQ zNv{XWhWA$NCoN>#m$&gP-MZ2UjpGO_S^jNVzeSv6`iF6|+fi(}M$KN{NG{EMigh_+ z7SY8WDMiIbi%=okUFm*qh1T9_sy?rvX#AThkzEiAOm9FB49y7;lcdBnRLKd2#lPtb zY+Os9Mouwb;2YUW5GrQaEgAEv_eO=t{WA3$Q6Es0y@OMrtpRq<<1rwcePuZ+Z-mVA zE57-}F_#Ve^a0tQv4Wj3)+P{EB5MVE&%!@;dALVHt^c9Zc0MA>KF|P}Ou`ue)(e|J zMAV(n)Qgl`LimxQC>(&^{>jI!h<{p)@Uv{(fS1QuWZzQGDmG>Ac2SVKD^C_C;zkE| zH49oISRiEC?Q?Q~lA6xs7?BI0Wg)%Ok3^zCHc|jxlnM_rwQY76c5pz#1%z&}u`N<} zxKc-*B+D*qgBE*LE zYS%8D)9u1y6Zu?W!iTWW@NXeKfWcjhMIPHta#Zbe89RD|@-U_Mf#zT#E3uopy`uu@ z9_1jBn6aZlUT@DWt=z}ug;cY>!JS~+=Wt+!of>01@xSOgo1y&Ff_@#qXk^lz$zB|m zb%uUC3Cs%?T_GB0)4dC8QHZ33Gpa`d9M^OVF~6{F>2kY^DWLsP_rV41*F0|5o)WoW zr6?!ehZw|>don3Y6)vC0HTi{vOX;b$tIv7x3j(AD4S{4nR7jLf8F^US?&(hO3X#0u z;Hgf$b3wrYyHI8%TMOcA+qGHL!9d}RGlxN8XNR=GLVIB6oPqQaOq?7^lmPKdrn_q~ zZj4QAS(_) zbkSsBa8HX0;SY|F(K&liQgKi6*1wSg!><;_HSu&eWy57>HcF&_MDDRtvi{c3j+QzN z%xpy_@y&HJLgE42f)wLp+R8zn*_cRkzY+cE1*dvrRERTE;yDpIfAD}HGZFgz?t$8g z>s^`-dcp@fcoAaOIzi68+w?Z=pvOPuxGPKj($?evLW?BX>f^;pje!j zdX7g7wR-iMyiadSJyPyTyBLq%#@Yzncr%bUNR3eioCm&BiTndZ#Y{;W>4xgLhz6wl zOTV^eyr)&0`l zXAfN^4zb>%C71z*k9BvJ-E~}`LBPaMlwjb5yygdYjmxJU#n8$^93_#72T;upTTfo} z?%+^Bfz2XO4`N;H4qdn*h;NTX6ds=DBYC$FtALBhkO!Y`RWl(Mhk7|AT`0v!uoZ^(h>C0bZ$_FT@2$JCZMHQE)?}`06l%Ri;1ZR6=KO}n0A<}t5uy~ z{)PznHLniU1u7mdZ}8;M;m7CAU(R%wZoN&QcTvZVGw!Q{BIpniX3>q6tD~vI?_jen zTx$$sBEqLuO18DIZQu#aL3W4a?rxv>RE@%_?ye8Z*LRUAYt3O@kg`&G*BC=ME4whT zQB}gNgdG&G$^5f1RTlF7W1`uDNaI#1XO|%Z9w=E^K>T1)hFmOCuasT5bYs6yB)GFa ziJip$HPRDb3l%O{4lf!en0{l`S!RJVPaqw*GDeAeMBa5bt>dNJqV8j{gN-U5GIr_} zF*f@5YEUr&T+{eVSRgyG@auwXCT87OJ&#p0($yX?I*uH9Jy=V*)}_2Xmr%1vUhJ(! zONcWG5n^C1d4_XPiu2^{N#!_($G1bNWC8p1X`EzVHFJ}jFe_APS30=rv~#FpW!rXM);z{h+DaAh6=b|J^1e;YL?&flFKxbj?oyEGcXaPG6eS~QmKSuF5m2Qcd@I3;~Y>hc2nSHWSWJQcHUazD%YKO z_GMb5cs%!mT_DpGuFeCB$HkfiMj){(EhH#YO1N5@9rryXYe&8{A`4C|=esv#j306A zLIE|Kbbdu09@!_SvaP1xl21~|lexU;yUL&uMjKpazAq}dk61%wf`eWlcP+7$-N6C` zI3Wkcx$o%OMmdj2-;ZJG?QmIR2jW7C^HGT843)DlB<T=P2C|;Nx=;#;CmVJ6adM-3W{e0hy}DkkRJui zqMoo5c3ZPWO;iBI+ALD|7#1clQ`QLN*9QEk1g*>Z;-_@*Lg96e@Ov;X!Pa&U@i$mT zFtG2FYq;Glp;;7Bu(tGBgX6XANIe1uPZfENF3FQEgf8F5oIeDcIawU?A66rtpT`s{ zu5{bgm%QP99j9z`gGr|Oz7}V1i9+6jficc2R*X3H!Qv4>yy<6VJ*-^6eL1JJ5V8^C zFy{2ZNnP+dAUW|-vnH`z6F16d;p~?M36<@aPZ9SU`!Ch(b|-@qpw;4~tE}Igy^$y5 z>f_@krS|h`;C2dJmV$_@kC#rkCkC?+W9QS*eEY=GXunHrQf7-nGb&oUnWR<%9&MSt zxM#~1PV#m!tN@S5ij;He$GwhapaI!x#^rDMU}&fU!jrY2$|W8ZFW&+n97U#L`s!dq zg{jNZr>o<^5QRI&!&n65N2%YV*57$L2eOS9V8h^h9Wx|OJ}rTI1oI7cc&gmbJvnjd zq(ZXIqBzu9YD0Uy!uzsIy7{IhTLC^mVUkvc=i0+yMP-+rD=KddMg`)54M6o12GHOL ztt0@aUA0eFuENHUIXy!AW9yA-XzRg1sbkS`Zk)&nkZKvpY8s@e=4&>^UxY>U18{bU z^E~=i>J+tNT$?m>b=cu*_hOuT0I2y6Mki--Js@0k!Pv<^qUD@sp|)%8%c%+*himEx z8-OMZ@Q<^BwT_A(*?@}ARNijL>^wJB4ZR)b(QxG&$HAZs6-waOB9;4S8D}t_tul-` zN=^E6F+Pu2x+F}S;<3gKcC6~J7KwwsOYeR~7PInozs_3U9=RCq7jjtSC0AmEC)UQY z13sLODPpeLJL^=Goi>rWSBC7Ngg*e8G+l?1ps^RWn-aDL@gUg+g_L0wL8zPP`}^0g zF9EDlASR&jQoSu$oVTq*MLqt{VeO}t6S}r&aE%KQFT`i zv^DYeEZo7idRJ(M<*xdH@${s~G#&gvg77kIGS2EL;jhHEbfxLhfh@`1^xSG4nKp6-mJIjM1 zxL2VqNB!oYu59}dcsT{Jii=F|&9Ls;HwESJgNP+=;jH@ytuBt!n!UUR0N*k19JvMd;fg#D)`;mXV%w7dKuC*OF3V70*&VH&#@3$gApoLX ztfWys&^0_ZFew>Eh9k_fL1+rx8^<_wB!R#yr3|OyIi)WF7II%$mi0muC~)|N_s{cM z{H-^qOT6aY`t`*+%RrR*etAHYgL_iNbPz05C8x<8^?mqXm)al<*r`|A0jxcK5Wa={ z0ev@Nw504i1yb2}L5oPAO+-PG+Bb-kK$# zmj4n?eE2%|{P{aBzt2y@D^q*@hf9gnIC(LxBDm=9Q|bCw;@ZoLTq=UrNrnE^xU}KA zBtO#88`HLI3x3K70e1y0=py^I((?yci{rA9O#LBD%L>^LUBfLo+dT zHg>XfaIv(vqjT|aQB{Ei03PT()cud)>H!S^2=W6A0PvsFt;V+9F&l#KyZQ;R9H5f> zWvwih!D0>j21s{2Sn)z6f~1V==GgaF9Emk)X3C8$8wJ$M=o-wtj)w%C_?DF7YA6l6 zqi*G`n=)x;%$!YpVL6?s{A!V#q9Vn0oi20srtFf+M|%99|XOi35`@WINoxgx0*zIaKy zRc#Xqx+M#{LDQX>9k>e%J40(9LMnyns!?&|iduA|P&JD3w&+Xv-)tHw&Q=DB?EcX(RwZ)ahx-yNq`^ohj>9dqUK;NnJQ=CVT* z4>n^hFtH%S)j9Wn%PX3!*{}Gn3YAQkcNh8=6CZRewmxWD+};vV9L@}nY(Z9*m=H`A z=@AX4V=XE(=5Eg<|I?;yv)`RsPx_YVJ?P!4`%+EwT`l+_m@3^{!3;syYu==p4!hBF zChPW}dr#Ghb6u81II6^nduBx*UERM}v7pRWv`O|)?2XphRI=u6oe7RgWK{jMB{4Bu zrroVw>FP=-oir}biv^q0=3nZWi{Wf0Zv*Ss7Bo0I2j?T$-#TbSWlFirw*R7xQkQ3S z^{R+m-q%0S6-i`|L%??I=cY?2s;{5b*6UC7%0)R@2jESMML8#Em@ZhdgJ+6EH&e2` zH7A4YUP@|`K2FvVar!tfzS|#n0r(p|`6^HOE9-MSnKdl17}sYFD4MPhZhBVMzZ!6Rc`(g}rY9I1OD@(8nj>H#p zPgi?)qPS|m>Jjh#+RWo7e#O7_xvqw#C%JRK3w3`bT?=+??Wv$*gMa-3J@Tm&8!%{^8VxoZa5U&>;M3r#15k&| z8aQp7EQj3;x*K>m_-+8)Ai@U^9cVaE76Xrm9uGbofH(+sz|x0X54IdA3DN7o*Tb;~ zU=JBRaPmOs!_EhaeRw_idI0nw@(0f!Xg}C|!26){%Z`WP55gfZfZ_&(42T^NIv{vJ zShfKKBN7NtAZSC_gun}<7lbc}Ntm$MBH+Y)7>Gg;iDM`XM<9unFdBy<(8M~@*oIg~ zSZ7#QSa(=YSZ`Qg5dCu?PSl3!4wb-%kpUr8r zn)|d;TlOv5D`lPVb0fQy`cvdBn?2PtJtUh>?3=ctT2h3Omg6sY3}o=LeMEo3|3f*U z0RH14ayXRx&&B_pg#H)G>11kXVr%;UMAJb7OZPvgVZZzdyI=`u<;M)%JS+Pao6a?){#> z>-l+j`g=d0)AW44&*S#}AJ69dKJRDU>h*qqF0RD*`1$@+?(^~b`!GFyx1P&=`#yia&)@(3C?C)N?ex=qX8kjN4*%Qxr;eYe z_wU2Y+wgI`e0pDV|3!5FT@m|KyY35YIDab-UoX$E1KjVY9lxK)*P|}y=VQ5Zx&I#x zlLY>^$8z?2z5YL+pSQ>I+vQ7-?9<+2<$gaOhmYUO;6CGFG`G1=tK0qjy*>V150c>X zB9Fgk;rLKU+SH)(pTpDL_xSz(KbN1w{C>YYI=R)$c)#xpmh$o8e0SaK`Sv>-_dk!% z@ArOKdR+Kh9Y&m7eF~gij=nw~$4w^}^!)L0KjZZD_~}r&^L@{6m)A-s7i&jM9l}bSKA@HP_#7`Oko0-{IXvCn|6xq|8gKXi{CqsE*Z=n$ zox}C``M*Ce-Q)f?6fS(%&*k`e`{=?iXQ!8!6Q1?^-=~+?R)6#K=5l{*J$>Gv2a{*7 z*M&(xHy_?p)BAlq{}yV^7hUXq|7~}-kEZhdJ?&1v(X9Wud_3*kiimBGw($A<`}8x6 z&$rDXeW&EVR=w}{bD1r#AJ__i#=g1XS!Zindt>%#>pfr=G5GiZ=2=5 z?W2C&-;i(kYMwqnX~nvYGiV9}}IHPYWG?$BA#ETlabPMRLHLgiq`@>}_Ntf=VEu3B`RfcR}?)_(+ z$o3a>Y$WoJQnn&pvt!>7G$v7>9n(}JeZB;QkYHIS0#7$xkdA|E#C6b#0OG-QiWE`{%^HaA=Fq zV`$3#eaYy^9t0dQ_1akHc){6@ju~`NMLW~llP{2%_ zBvPOCdi3${@dh=-S|A6d^mkm7_y_697_1K|?}nJ?MYkfNZ$waF5`>gR5-?e(YzQT{ zLlG5=t}jKQoCrr15Kq&yREpR&vS+RArO!jJ`wBYuOm|V|i~LvEnkfH=DhY-J(J}gr zxd1@PUv9c*afV1zXj30V7cjiN&JLSc{zF?GDQT(Tkh`E)FMoTbn{MP?y~uu&&L!+u z#N6^yO&oWXq9TAe@Wtl9$QVtvz}9s2!H8-C-yx^BSLTVruembHSCJjJjJDhlUe^62 zbpQg=M0qWdGUkrU-x)2%Dl9Sn zn2g5JJK`<~J7A$WKtKNFY(t=XjgxElKx2>V4M;{sJnCdsaIqp}><-Er-JZ%ta4%dj zXSGIbSg8x8Wg+Q3^Pa9tAn25j)Agv_G65P@Y5u;i=uolIB$eFsK2r__CY>`*a;Q8? zEo;dxV;j)VLtVEmS{fIoNWTX7exTAlk!mbQsx?Rt71=bFg z;g5ZYyvdTtOUV;}P)^`4m_h_1)IjBrKUx;0ZfbBG(?b+KkS(P*aXwLg>^EqU&Y6$o0 z3XZ%%*~p+^5DkN@qVcy%HjWCYUkSwBQMVmN9ou-pQJ^-YJ!P<_&y53+k|Ue!tsP-! zQoKnltTnLI+eWa$O07qN!jd9Gi+=@-lbuLKL%arG#pQM7#6ouUX*#x@Su)^kf1x7y z&F&<&zn!O!W8Xa2(JoK3*dh9gZ2@y&eCPw_ScOcvFR{LcB5dR5M^s5~Q;7VHi2}w8 z=lnN4;jrJNz$#rOksWITogp*n3yvcV3&;G6;mjU->86$oRnxKg)eF^k5iz*Js$T5! z4=YgmxE6pc2cMowD?=*?OaK2f!eN_$Cg7cfy9?IBhKt!c8-ScBgu>*#WL zG9_9G9{e6+V(Tf|hcZR|@-{~Wk$Xg%!)F&IQCmZPAyT(lEByoY2oSniY3d~?k@fPH z2VnK^W<;WbR~*U=IIJlZAlf-k(ImA+F!#^J<7d$7fzh&;gGC(~5$Y{!} zg_ri$)I)~64RH`4uUlL297*UIlNVUKq+3m<0}UBf5jLHGqHMm38wwe66m)^Lbyl&} zS|`mE0T%(N?$;w0S`v1F9YZ4VlLTYI9RMy$*k(u7C_(Eyg!KSz!U5n(-EgM*Xl|^` zm2*-tB>6WcuVB6&9%$;IZZOd5FO?UD=wsH1gEGQpRr(W@%5>vlWjxR}!z#2H*N}ss z5d(n~(W)e>+3l9$@C(K@C`|cIG{4jpp*qB>W7dMnJSc z#+qpy0KtHlYzIViYNS}yNm_Ch7J#I28iV3fYeVv&BXqN3IJ0R zBvl|YadvLBiWaRgwboj3BE5)j+Mg+mj4VzI9* z8DDIZqn0zW$Zlk69^i7`f_!GzWEeDX6mc{{wmH;bWgUi$MC(B|lP}#0bKym4gABN^ zj4afnQa&@tLw+F)E^4quon&L}FNRb!rJb&XwX4H?L!I1iv2G7G-7$w=IFgwF{7RYw zC#K&)Csmt<2>B6egyx0eiWx**XjW9+%8oDSL|g~08iM6|jXG$?LH4F>6*rzcRo3sb z`_K%v6J3EbZC6f|{cDdj>j79In~%i&;EI|sRRMg#m5u3D($;N)u5Tx0TH%n_#SxY` zMo_&ePNn7fTOP)+uIx%#>o*5NybNf|zM|Krq|qK(JA$J!J>pHtN{MFuxHL6bY@wWx z%t*`MCN-$5n5ti_A9tJLO4Z;OXdkkro%L#;Ffe@|GI+L{aiVTEo^qc6*SMs8QT%6k zK8nqhPg^uftu(mT99^kELD}JM)!-cuFkUN&bld94dCEZN8Gse*7x&);q3{JV=uFa+cHw*9lpyCN4N{CKl!PvwZNygUwZYDfT~8?c5qCbgnoO#sDouPNN7r{5tR|{27<6usgqC@K5$3g zB_MNBl(}^)E$f^x#A@Drb`7PtF6GmjYByqwH?q9b+`fBhSwde(^q+gB`-pJ`jvp@jk?x**3fP9sb+cQJz%CN9sRVxUKX1R zL#%1h2T{$f-iBMeuPx%jLi(TQ3; zw)CY9eIkP|-R^{r1~A@}*>Ak$jX-#X%|?tR%4ZcxMat~(c!9oH0(=(@j^{Ef0^ZXD z=>3h|z=gkBmjKuKZv9EA5Tq08h5a#cUfe=`JIXwK$E+}w0(Co%us+Cy)nY@wLyNG@ z*gM8hp-F>34@gpPl`@0O-eP?!!25_L0_o#agtQK$=uY(N>J&y@M5 zo5bfkQLbXl4c9U^w>~ttRcLGFXU@mD1_suc#cd#-X>PIdJg*U#^(DINRcA`_I4J9r zR3b`qbg%oi4KuxTioB;%FbwtWf$^>!xG~i{x;Pp>iCtDnrik`=3y>!J(!xT5z9>|q zD@PWIZ)@?X7FRWH2#QRJMN<)XO&;@ZD&`%v8_d^l4f^?8t+5}n!H0j1ok$0m-)N8V zt?;iw$`7K43OV5uw6_V-5{(r9VSjzDW{))t86DL z%1805MD0uyvFMDm@`N|)yUNeL;zCj-)+LWNN{>$7rbheVW^}JIoO<;y;vXu6H!HpB zd)o_d*8J+Mcf|O0qF)PYX&KOSk0~7$vw`)CuKFc&jg#c0siQlEhOP%0kiK|!_&|9W z9*StKm-yxRLzsu&iT0~AAS-8qGf>8I#8oUGwqYqZG~MNeq@eECkG3o++OUbd0kt_? z`9#S4z==0jZ8Em8X|3`C9LiFwaET-FXVtP(RGOkO@mm|Hdj9leuJo_|aEJXusa0qH zDpQ4qu&xVOtg3?yS@PjJo@?7;aOs)d{*Vf81zAF1AS3gG7&hzod2w88UF~8wMQQ}l zIiZrFo}y638vM;4poPbyMq{ehoB^HX>or!kab(Mgyx{nZf|3C*Hv5*r$;&*HzhL*^ zXyHnh`<}1Y4;O;8h=9elh|mGTy~u!)9x)K%jSyywbCip?j&hU=1qf<_B*gfEc_Hzs z%-VwGv^AGBaG*$7v|SDk-^JwyY0UHn#O)lpL(C+&1P0YrD9%b`*xC#l*cJpkE*57 zRAO!UTo^*NS4PnOnBjsz7H16h*jfut2M>qy%)?nKp8 zu$0Uqw+7(f45wA-I)y%h!SY$EqiS?zqAZtx7-><{ZvT*(p@kFXF#}!W z!F{H5O!6dri>WU(l^5{{lNxdwOmmMUPuG34je>B+aHE65RN_!pZK3<#cG@zAl6BAy zYClkPx6!X#h$^3?|G^`FpmI2T+39ITXuqs>9=u&VXf%7=66I`>=Atfmqe^(6$SssQ z9vEbUfJm04>6kO6gDh&w#Ms?+tRO0b(9rC2a0)y}LnYgMoArzf0jmmW z^i0TT?0s4yqrU14}Li z6q6;Eks+eJ0xb_uff+-NEZO$^Td=t9>7V&3ER!1Fnhm^<`;eykGa{10(~#)Xh0$@5 zOjQwMtQOz6u~x^}EO&9;=~!HTtry9Y(Snx3UI4QD?daP--sQ%zOh)wIq33i}XFVL2A9JpLUczVve=)_mo) z?PG`MUj;wTwGap+q2{Z;Jx`mLp+L!}R-jcUjCcr;wkO=p!`M07L8ENnr#fE}^L z5MFEeZcha`SvFw0t(7D#$Rb#Z&k`z+G4np7lj6cvz-jt^C(1H(ms#Ylz)O1&*liX~ zPhnQc7J9tGW+Er)0mHntJcDLFERhZt@>qTz?67>%WC^Q7ozvvSq=&|QI$Ru)Ess~a+k0IuJOnS(NYs%3-Aa5P7TTW1I2)!i_ z9}}~qEaB(0z?&L zcXtk`R-xD?i~eIhqDPmfeejNetAa_akOz*U$TISD@s8qgKzHsFYY)cN?oqWBy6_$d z&tx#hcu$)!XEw0H;@LgZqtpXjK1L%ETk){=&ws^8XHqHM$yyU)+oobNSN}fcrk38z znWt65)NZ(@HKmCzTk9x~5d(V+memI-*u!zY)!~_Yx&Fph(|x-Q_AYAevSQaYd=u37 zThTiXZBly!H#Z=OIk-#$lYa#Q@j!l0U`*T>8EBrx87*Eml54UANaar1%$*B)QZd{V z{CReBWk^%gS@-d%I^3A{z&qD^9HuA=VB*vP6}K^l$P=pHTs(jWvR61h_a<%)o~Tm- z(Fi}?c8l>`t;DLxlLY$S)taOgT!$#FTU@tppiIl;E}&nA-Llg9Dk`<@_8e_?T2SXz zgVBO#@!L#*jWhF_k_4Lu#Zc1_3C?966Z3>=Z{;Fq@b0@K_61hDI(@%{+nWAR_nobz8 zu~29o?FWcJxwxDdjurb9 z>lC{ksXK?j!1g9j{3rvsG&1ZC8_yROq?#N7D7YMaClD6nN!Ws0YLl9o4>>8d*Nx9X zfRXNs5bqh`UDLJ~9stoHT2j2;XcBrp#T(G8u)H^xQc1FVYLjyxVx;X8aKTZzRjKL= zD%WkXUOSQ3u!Wg~H9aK$12^7FmD4RCiM(+6=dt9TdU6@Vyx-z?(UjaWMy>2GyRbsk zWDe4c!K-kN+tSIM)+Su|cqM0Jf$;UQ%S^Qb@i3VDKN+4n_MhyaR1UOH;-Gjbp)!Nc z*^$&4?6}~9X61(8idUE$1ew;iB*YgXvkADfNUGqI8DTo3SD&QS%E~voNzKgtr=8+X%| zDpQkGEAz+I%52_0wJ1imAMJqF;nx^LMM_sMwLu;XuGZ!b79?LR5SvO<}?+BE-l)J(5UeeMW4*IiiT8u#!qc!%>+z^zP7=((tX0N#DIUOXWag+T3B@RG7>;niZ)f(Dc)zLM{H)I+MYseLHH+E(7&) zyQ?!-qxGkODXk5*?$4PlVD>iOaNCr%q6}`%Q9Yz835}iYg(v|DD?UFz+92mr{ z?24_hyX}btmRDcN4(uwxi;d{HVOx{w83fu3gs}I7`qKhH@<{(vn%;Z#k>n|2mY_+Z zh@Z9u)2G(TZW7&AI}C4_-8*EJ^)6u3%d#()`5}s0(v*4JgyJLoK5CdCUP+I-GaRtF zJI>6pl|vsKUv7u7;ZvzsKT*h_o$gKD7O-tTD$dFr_&nJ};cpnk(yHEQ(iJ@>R%E9@eV^% zM`9raEB8d5ku>Dq1UZp}Wmi5Bc}$HBvVS z$+>5H8%YtHyvc{lRoy(*z!Z$3G0(3STku+h0~6Yu!Ssk>%yqn1z~t0T6)ZfDROdfB zBtfi~6+U8@&_92YCT0{|y00VL*t!e)mG}?53`+7S@MVlnuCd~jobd9?%UZt zRdD9Fdy4870|VTCTijU!&pzrco?dL^;M!Vs_0%SQ2Da*$D9+}jc+Q*VdWSKUtlx#1NFi2M5VU7SYMF^))THe20Fx0cQ*pW7U8d=ao zF2T{L9yQTP@)uHw9(c7ONHn6ock;QDL4ZaKH__QN0%EB8xWfo2nU%rgwawS5fi@&< zvVHlQXpG!u*D22;XqckvaHPw3 zESlhMkQ%z5&+qyK*+9(+iqK)b(>ZAEBo`prOxQFQ9Qy0+GmX%8=VQTE1arL@d$>!QyysgEl$0tW^Z37 zP)LbXfpTR#wEbAQ<$N^1uU7b?l)HCIjfFaQPzkumYUUCi+uVicR<^W~Z2pOFOD4H>eH(Sav#I1+%A3cM>OhNX3 z>^PQ9JVQNe`NG5wM?SXu)=jx)UEZX$!pFuicgXj&E^dq;O=L0{9r2y1?!H;d-!-X; zEu(<}*mFz_U8=zQWXbFTDM?@t2)fAo&?Oz>pTmhA}siAupz@F8RPSRLp`y zda`AOjDc+O6l{S=&G_z`v6051A?%%|qv-K}j3CDvzz#=Z*=lTow%GS?HidJ|q1CCw zC0LtQTda#^X4eDf-fQ5ydog7Wi(@T&nDymk^h0Y6syrb>jTQ7BzZqzb;=#+mqQ(afS9m&oD-iE?b(@NP^{*aAP2RsM37n>sz_7am# zDfx|iiAao`TWHJG;dK$7kqd89zTlqCHmy}JnTtnMuY1m|di+T8K$?cJ^Si_{? zz`I5qrt-59nq-nBGT-?+WGCmg$>&I{t!O*^zBpw6{^|l^^~j&)+uXy+lS-P zJT;ASuxn+*S^D`dzhR1Ob|-XyW|^OSNI}=32V2E9rV5Hehl9oAmca>V+8J(3Bp5Wuu3zE-5)D$PD zV1nJ!LbQ=IosR*9qjJ3qyF8SYL2-h>UMP_)s4d);F1PI_noi`|{3E1SaL^?lVwFWk zx0iFU7hN1?E1Ab3ZKbZfAyG9g+D=3yEoS=r?G+KK`)P&=eOc_!?AzdL?Jcj$jw2nu@EMwH=Lac}? zvDeqpH^(@zhpCp5EP+wHN5wKi$iCj9@F)*cIDF7yth1yiK%Kie?dPd|kRnU((Q$cP zdYoLJ9pV-8R-f&Cn0shbho8=gHK%Q?(cENTq;M^chzzV*|%SMi$y~ zweX;iXre!$y&x7JN;$-xpKwsl9o?`VpJQaS0akfP6?XE6MfNz(5IYJj`ZP(W*91F( zbAgUTS5JJ`lroeHtB)S)cS%R;a^zWBZ%*a~c&C&ckvOt!o|++Ubon}!kiAvG$#9e& zOmP<|SwpZGZyQ}LPh7NVX|?VIrgYjGJb8fs%^X?w0q4jC(t8-Uo+`4bnNSHPn&EzX zlmO530tn>hej4JDd1a0hW!-l~U$f3Ei{I{*N={}292Cs#XXmw#5u|Jg&inxklc#DNmJ|B33k$?k^AT~cUo z3Kg@^VhMVAV|=P)Z_OG>J?_%3b?cv>L5Ryz#YXEU{w@Jg1r$!-cHg;vn3Q~aW1~bv z_atFc^V943&*c63>cd7^m)3lJs@g}j#jhSbI9WoG7A>znN~7dz{C`OMrsztdCft)u zY)x$2wv!WkVw*FujY%@miEZ1~#I`lDZQuM4S8H9ZbswtN?uXsGx@-0Bs;>IJlIToY zI^ooA{2Uj$6tdaNZy2T3lX_lwwr%C!@Z;Q4nUzE2Atg!s419XWyEuU5YM6M_sgw3oX-#Wt?cv27`GA0oI3QR%$+v>Ivz@ z&`+#qezaahGH)trjTMzG@kDoELdZu_y?(*ILm#Lexj1W5R`a|)Z9cH_M*Ere91=Lf zE;QQ7t0EC~)2&r%3^EP_m#hQGR>%XTHFC`Jk$*#1k&lS|S#ErSRZ)I;BAG0h->rIucc;*AQ^AMYO|n_~ z6lF_QSIRHmW&39#>W2Oo@Aqz4Pvz^hT&(ZiG$*F~heskGda_e9=i>aye#JY@AAH}Z z0Lvp!Q5TD)=8YUL*DF-#zh9cr4z_K1VRJz6_vAw17TuzLRGf)63k~VTf2DDh7?+xF z#?Q|SU~}4RiCn&G#P7E%5_p}k#h*$XH8QZe+?Sj-QQIG^;up8WJ(n|&o{PT; z7T%_`vPO0C-=m+3o(R|WjyS8xLGc4a&tlw{yHZao&G_@CxOV`5$N-YTt%P$HayS4o z;qd~%M+$hq3mgiY6wBASE)iJ&J*sfl2%v*X_LwaitRt)z0qh1F0Q$Dfy$c{5;r0ik zpb0;AZ9nOH{h`k(y@UELGCBK{(og-UG8;oH70$AI$JU--U@*iZgErE+l&SAAk(A~8 zzDIq$M5#4u3_)bZ@~&5uFMg$r8Gkh+c8cx>Ag6y2zBOj?zyJ8WB5PraBw0@uX<-s8 zF~$k{X(0Kz-jYuqN9IWbBoEY=K?~iIh22gD6wl&Qh&RIhWSvkJeWD1vl1181e#3g+ z=NsMR;$QIV7U2B7nku#z2$4q%d1T>~fWnwATrq|!{s4W1?3{oy^KF{!h?>L^Im$uN zi-u&8LYOEkKlpbRhA~#QsHwDnmWUKA>Mp=1C>kVw;W74u^K}}fH$enKRWfhg`w$#( z3%RYw^Lb&a$5`fr&tSiRMz5!BImh3w+DJ1BV^91X$ z)5*rqe;9igv*h5{)#mYd`Wo4OSv}?V`S3jUzA-j;zAz>9?)iRku+_b;@8|xxmd+)l zzp<{{{W=tu-i(E;xTSxYCAhgI_;$F%)#d#;_v~PB(Y@I%*dfR-q}TCzHMVx@_R0Tw zzL6}HZ9u3mB=9j1@3*%$)E>h$<#)e6=Eiq-|Joe+Dd^YL;qK$!;rH?3>2dA;c_fsb zW#Dkx<@DP43dqn-D7tA)n|> z)E>={SBd*hIwzJ8OWHcQ8TotEHeV7#%2T{Py&~)jBYCT?08&A-i>I4ZTk|aI_Zm8; ziF2Q6D6UJ$Qx}&k<1M?0#qS|EW?x_*Oqd<4=v16a+Y{)?m93{hQ&N`yW*VKbsAEdR zh%06Bt-MJ*wD7y(g5dmZLQ@t?g-qEjIWnN3<6-}#EX^pfV6Tdy@bA|A!E)^7ET^BM zP2(^`3NP#y35O_xU~W-;bWy9zxrfDc{?C=|{6F)Bzsj|su!!iWD+|omr4Ut0uS!oD zs|$$3v2#2er72F6o{#*uS*)!4AqWs3v_t=_Q-mk0zL4gB)7Dv-b-_eCo;Nh$sY46m zUYd_U@^$WVkTTN1=@(WY3qYnpg*M}|n4|1st0RF?U`nu1<3Sl0VPuINfqdh8e3YvIhG&#N53}PfPXq2xg%}#HGSi~+S7_ka-9!Z ziYmVGheIrK8sVJEHF0Tft}zTemNL6!tI^c@K*lLCwC0nKB6xlMbIpb^RpbKOW7Ks73!v0k=6ib(Z|9hXoW5rXZo#y^)1^8!m21}& zoO_uFb!%ZN@uKi(K6GmVgPSYh7CRJ6{l^!_XI{C+0to|L5_P7qy!sA+2S25IeeqTsP9t0RWX!Bg)8L8p|#hlwGhqj z3$v_40uhx7X2-#@d8L$_aROG{8DZ$t*7E$@;FSQiMG3W9aeCCS{v6!2DR`N`6H3Kb zPY8@;PACn7nZ-)YLG!j^_>fDeZK}38AdVAV6e24=3#;qk(Sx%#BjJdl)XHe(3TP>#)zLYgNDFX@>~GmaXA@qSii=uC z6&zfvq#FJ~d*{DB5Md@n3e9diQdS-VEF{)uxGVTdA`GDg`k18@!m=UY+XCP?<>dv# zxvVx(!4%x&CgNBxrgPHeh6d0u|rxQ-fyw>sa`GlM}C{!h_ z6YN1WNmnX8jk~)&y|vXMtKC%v&*sFl?;7*rb9>i8PZLX{Kmk_4H}U!jO^>!)0^wwH zZ{~^uOmMYJd8QLPDk*;EX6-bH!ny(2bd#w3CD6OgSsFHe@f84Vk-w`Eu_o&#|Ju@9tuQ8(sC9 z{d6={4XiZVTB)frt>UDI8!fZR(%m82?j0tIWm{D@Z`zmO80N<}@dlf7Rz2%@KhjjL z+{R|pCNb@cF;9)o;vhBWO}Dae`WYt~#t5yxmKNJt$pMvS3rZzh@vV_1v{KYMfNe5P z_<`WjZ4umLUW0Q&+|G~fK-p>kc$v85{Aa|6)LAmZ*th$+1FG^E8;g$lEWepKe*PS{ zuEU|tW*NdZXyPBaWQGa47Sm0tn`Su#B_P4k`Mv83Hly;-`g)NGWhp`Yq78zDYpNvt zH>tV1aU&d?CWT{f*6I6w$4y%urCp$FXk~K)<%MaB@mU2&ncO)BErE}$#-B${cX2KY zb^mluRCb1x0;-uS1J%)qbcpd!;6zrMb$m`i01zX-(Vj3-Z}A?^~+e7Fa}Qk(gp+21hThNo{tWP3}LEQc~TTT$H&M*<_?4ucD1V zwSU5R!~=~VLSl87qjCu%>(2VU9~yfz``P}~$GdetMqf@=jD@IT;$$RH#D&oWl~xAjIL#Koce4asM9*U!G;GVw4lwz4;J^k6ixHF9#YGPg1@`VVf}!0D%vqmvoa zfAf^m&G!D!Fu?2vw9pA80N}3if9tOOzb$FvU~g_^@uk~;^l)M{adQ1{O8lPQmlh)jnn|CV%~^J@O+H5tcZq%E;|Lb$!)WFY3PL_+pjZH900Pj#yi$P84HLe` zOzQ=Rd4*BEV;g7AwAEyWnmjT#WV`(_66uUuoky;-C%c3z5xK;#N3Oi*FjY_PIWe{y z;F4B&+U#YO|rH9uAx-7Pg{s@mnLQ%}8;bj-_mYwR_8m4Ko3(WG6d{2;Ek+%ec$Ic0@o7s1Y? zz>_pWx-l!9UndQ6fX5Y9X&Brvy>_h+qwu7p$!gEu^;X!qsg!OyY?bz%Qchx;x+&C3 zsN!;9|BZaolPA4n=a}V#SIt#!s-x$Nedt;*Gj?0yM}9nJqjjouxHIB5`xxQ#8fD(> zeA0bva-}dr6XYhkiXceR?D0lw|5Fd`XL_q*hmSYGb`#Ff?d+pdvYr2Bv!sx~;ix{p`1(*v|Vd*P_liX@f|(_Y30`y>m*BA!lb>8sa1WfiN*W1 zGP(S+)@-YI)9bRgBa5R^X~I<$GE~EAr?BDfho@rRQ^=ss`wB*Kv4F2)L{gP!i)Z3+ z-us#GYd_?Yn)`$c_uRJZAO8McvbxOY)dQxsG@8 znR9aIl;oq6>zPyXJ;=^JYsMnEnQ5%bUIV&4Vh@Qb-LB1gQp%;}kJhVZHK=v)J9kXx zLFTT{M~Ag-X&&DJi@?J6TTH=k-ex+=3RGfOon3dqYQmOZGIFo^uy(IOnr#N)qz zLbIIwzll8)5L@C83l0zNk9c~a z0L6VwtUkZsq_d^Gx4X-lM_rk4LaHbG0PCF}6x|#+lf#6T5st#<6z{p5x~EXb7l9cQ zb#MKcEt@epcEH`pND8B=?lX*WJ^w1rNMqM;sBdKYsFuVNBp9#1usaX_;)Ww|V3$s_ z7lK>YXmW>}rGgAYdCq(s6fFATPT<)h@>L8QcwHstrmnKo_SnyI7Z8fo>!mP5VDh+Q z5xxZOOS%$eZ$^T6q>Q-PeviWq26rX;w=s(91QByU!-{H$N(I1DQS)dTn^V>L+HMJi zOoS{rn^=Y1CeQx%fi9nEhh6*lz{DD1^`rpCF?M3re^)~@>1oN!`i36SU#z5;5+J1^ ziMO~P;18*V4?{UPfy@9;i0~jCgosV=8c(S<1gNnI^#2k-txe#o(OG94uUf>U@B1lJ z_nXfL(@Bje>yAobl4uYm(+-GK^q2%T4hRForJ9^~Nc#NFFfytbzPqi;&df4@E(@7u zI=@=%CQ#*ArLRN5V9n5A=c&ua7|EIRM0kZX4yK_xBO@(C!y{u}*#wT0K@7F@xb2@y zwjs61PT1ol?`?ebHeCm!wUg#9T+4ACu6*{+S<1^gtpN|*M5q7{!6?I**x3VP)}^-I z!%UnK(5mFAH-vG~f;#E%41DEqOt!`+J;R*xRas^}L_=hb615x=E}H{*U}5yF^O(?Z zJBCb>Meb2ihHVC%292+Uz*s2i>=jgwj7#s?u?X^8e=#k&Mo_8))keYJZK70OMH=Y9 zUL9_A3q~9XEM`1UCE+rFm5plJj&{PgE36*Ht$O5q`xZ`p0AbSE&Zq@<@XfMDPfdan zx3dVVzDn^C9uvu3Y}7mSt-c#G(0LirP=;>lQiwP190DTOA46J@bioSjgXW~4dh?1d z0Jjn`J_8~M;}`F#1;tn23D~}nhVOtRrx!oTdK)VsZoXmD$Pc3W>!@$|Y)fD83c4}Fu)=S+@o zn`3?!rf3-JcxKb*aQ;{hGPnYV)ue#b&HL`8-8M3p5AZ`P2@#$As^)V8?%MBv!Zb}6)p$2+Ns9C>2J+%g*2gZ|q?5B8QyoTV`=@LtRe`*@) zJmz3{N`Ni`f}y#lRbHNdu$4rToYEug6+X*D6%}`J4`kzh=pglXbVoVE4yszk6e6PU zR!x`Ri~u%_a_6X~j}$hzmyOHWF2nY*i9b|SnRCH5CW157RlRi67T^DN7Hz~r1P|v+ z?3nM9l0BxbwlV`{zg6}jX~bS+{F#z?A-0F_f^1^nXR{KJEKk9ku;NffsL~FPj9W8D zMrITDzvBq!+n*gGbE}GjcT|K+*!%rAK1LP4@mEgns;r$wIS(KCUdzU{EM2*H4Tdht zIyT#&FWQMJY%7z|-*5KSz;!yX512Zbg;|afB?i6tjycuPY{cSqX$-edY)8C|9U|O@ zec~4O^)>-9r7$BhYZ{E?zn1yJUYraSXgxGLqGkv@^lWuptZJe27E z5J-PDWl_8j>X0x)P{%=VTH*?$O8f%ZgInr>y{zy_fhf&1JiH6k12zBB=UsnZy-)L^ zTW8yVavxeLae8dzS^byNOaav#r2u^!6}Qra6ezC}9yt4T*TPs0+&;a@pb4JCMav>? zQVlNwhAWZ3hHxQ!GA-b|<<-OQbb>}juVY6FC6-md5PDnr6LYO)!9G@x#&dfqHLGv4 zi8o*>>vDZ%rfToj7x{m6!8H|FkV0k*{{*mbN-hjc&dpQ0%t8&l?<$KbInjLAq)ZVX zq`<379e2x{gOU6doBaIh;k~$wjeIZ0ScG@G9P&ET&_k%DANIK z0OJ({-)`1BX(DuHVa4T#EWPnLXbTh9x{8P2k6MwnC<8{pmqPLK^|Gw?>=qzOmYTIL z7tj|AIvcM#GJKbPwsW8zv}ClK33;4}tX!Gdi>t%&JUP?*&OB7dF{^2aG`;Q0tl_ID z!oP*AVj0BHqBRcEHXE4ZH0zGcMQ~DO;0ckJu6;)`Df=bdqe};2o zQB)40kO`Z{2=Uz#4ka^zx+rxA4E3MNBrNCbs)SX=zpAw83v;DF3=K=0L`+dv0m5Dd zy8v))SjO&2ia#6LGw3~+)d7)r{o&hPnXTpLkquvH3stx50gFCNskW%pp%(hSh7|bd z!N6KwP3*H;B~Viyc@9;BNR@y9UI(=mL*UM8FO=ik0jurR3K2wzj1vF9O(p-AhMkGd zU=wkQ3zs=V!FP~)8iW}IWG?8}F5%PL_%185!C{fZ15`{`#b1gLn~u>Sa8t5IDEWsh zj)StY>W1b8PPTnEe*;LZwDj-h?g9Nj+>d&$h+cDp!Ze1$=k}8kmrY8uEK_S_tEY(; zO^z?xBU8)`q?1LTrQ{FHska;sXfiZpG{G$fX_36S5_D`uU?_Sz>jCAY0$^0H$b72S z2S`8LeNlcc1x9&RjNr>iRJpH(Re~H4af=i_$d4`Z?fBec!f6I7#oi^`PFOSOPJ{n$ zc;N)E1s(OTN+bM6s-yWa%E>v+X+DoP&nO5m7@#mAdCvw*c>AX)Dp+^jkc(MFiMJC{ zD?8V8YV14D;pxgaJf@v13}dRT@!pbNg_e}l+kO`CMQc>9-u&)ura48PpcJnfj|e6v z|HYQQgMJQ!FUh(Qosal^n`1=@RX_ee15_!L!2th|=EDVXZZ?=*;U3WeH+c+C2qSzL z&7Ncqu=b^I;c~eLk}MEp>#rYAcB(LyaPSlflJd1&s&dRTljcZ%g(x+y6QgQ03pNqt zgm+F%^xl{I!TuY^K}?S{MdG_M`+D0sN`L$g?)3m%4BxHbid&UR!WfN)tV*XH%nJAN zJRN9y2$q2NzxrUan>^+rS?|LTiFB|Z%O^@yKG9lTry{hpAsPEyihANY5NIcd@nL!; zhRD}jx=@h}NZ*87Ppb1h!FZM+w%J0Z0$=@xt_ZzivSWwT4Zsio{6G%TwrMTDnHJ;1 zX;lHu_^n$%in=%$O z+tqpT<>&p6?CzI~bib#u?2o-Lh29UsF1y~F{rtQ>9J*g0h`L{fs{QWokv~7U4BorD=ghLZJwIIzPrE)c8D9sO zK6e(({H_;}4SYVI!?OJfi@jVvs=J=Ev%6nEZXMdsKA$GsguGwhm$te+nudWTQ?v9dppPcJ#bgkB%Ag+4jpcW&Lf$M(D5E^ZHr z+Apd?Xf4 z5;^JK`uV>1eZF%U_D&Q#yX=rqpF8}GfL^?Hf*sZ(Jd0m=Z7EXA) zs$H(lt9KTxubz9VUDzch9q1Qb7#+zzwl%NM4!YSlS9BM8)i}pm>wm6iRWIp!;mJR0 z51q$f&|CN{E_0ajOP;HXH6%AJy||w~JDfl2KB_q0-|OJ1Dt4W>3yO023G&@$>2oYQ*|{4$fbs@B=GSmTf5cd))*8)|IK7@PNYETLg^DG(^Gjm~y( zXfEZk|0BP2R*>=7YjMY8J(U(%)%)7tQ;-LH>f+9vswl6xjmjUOMYR0F8A{}JH1?|h zf@zbRylGA6qqu&t%nsnN%eSxKtIR5BxA6iJs=kW_Z57$quM6I{CzW*RM%u13LO%;i z+E;XEoXU^6%n_V>mIxG4vgIGAT&SJ65^lMOx>bkKIi;6!_%!}FEP`I>mMmlGFnE)H zEHEmiIF57IPpT!fve7R1=un%p#Vu3u_`BM`KO{KxC+5>J&*niq*=r}p#uUp6pO#ifur4|R)fvwvjMpxm5dM6Q^wwU!+jwg@VUYd?(jQfNK< zHD$GRbA+fo|Fl_3P!?5)IOQlr)aZr7fb27Jq>86KrVMX);UWJkBFZ~!cW#)F3UuN9 zzS3EIAD2(c&Utwq#lgXGrRVhE*mds@3J!lQO9FxtnA88n3YIGtpGCWjlj$&Yo3 zdWsvHa_;Xw@-3Jf*Gck3w52~l1<|IE21YBt$J2aSNAxzDn|97J*A~GSQ=&oi`RJz& zHnnoWH`5sxp4kn5HQB0sK3czo9ofoD^5F&>`JoS*cR;?M&NfvQT+&#l#bIw*9U|#Im{W~{dc2x1nUNHnp+yRR zEBXh->bmYgK3i=pQSl!(1L^XgjOpasOXhS(w1`{l4QlpZN-vd6YLTnR_;D?Cc4eL1 zzr?~Cg_k21DuenL$4^~0gqI)=Ky&Q`4A-1#l$9}>ln;VHcxXPA)lZv~rg|O@V-nOm zLv1JtF89~@3l|d(BjSU#d+Lq*4r&C!mvgo5RIAS|QO@Juh~Ncb_ha3qo0+;~{YnQe z$29Fml^*>AY2xE2EzN^Xij5Nm*`{0AHkGCt1T@5vQ=TLhc_Y9uYr;6*5i?>!m-(oE zT)>`kR3+^_?=%F7M1w0Ysq&T?9`Hs<8*&(@s0rRNGH=-S+W+(4JxHyW|$D0t;qjh}BSZJqb3tOe0Kt5H^ z)Yb7Lz8q{eTE=^7|KIL>9q43pO|Dk<#KXLCyE{eee1$*V&r74+dET(Pkz))ErA=bP z&ew|n7>SjL-i!il+(hQ>@>@QySFf8LJ&rz`*Os*=#kh05j-N?n<5oID6VSv^LUvSU zaIf^f*@fl@&o|sJf+o9)fZ=GP<-ImLgpmEl19hm-ELtZaZ7>P@u=!Z^+sO^fYljEu zftcb@Hnol2P(#B+_S;vQMHXOfLX&i}S_LzoM zlK0DXyz9eH-8&zd8+M+^RI1P=PGPhzA}dEzfP(3zZa2@Twn_rurh~tP6D}EVqU+da28q1hzEw zR7K7EZ!BIKCdqR(^Hus-@yMIp))xalW+YD>GLAH+z|gb`xKlKsv|ZDf*PqwOgh_J+ zQIL%Gg{rA*dw+G}q%cC}q9)j237veo+cj1_SHYJEPD)0Wj!-G$?&8QA8Y0Fuvg$1t=^>!t#jJ@GfoN@3`!@aLv9n)a zQ51FC#n3{I%eLn~Zu*if&HW;dNIo7WSXYXvzEhw3Fp2Cw!_*(=fY?Rp8N)(z$RE=( zM=1wu8_~7fVRMeLZIapkzoDBcJye0CyX4_EU1Q-FY0x+fo(Mb2e7`90p`)_@CE{B|TS?a0JQ$~0?j3WAvH3W;jBOOXhgxuDZr!;vAo#!57H3?1rc995G z#OEm_WyC})i0O%cePre|eM z!%V8kRXX+B@~bx!=O3ekcC*_G0SLE}VWN~xrA$>tSMev6w~DFUftky2@a+B~pdF&S zgOZ+)vx+0+u8RRppnDM>p5m^a!8TYyOHp*)5x(BW0_!*jSZ@%+st|22Tu-g|b0-^J zL2vVAP-&>&Q^8AU?BM>T#{6$%pT^bbbj69U%%79w`clZM1-i`KwQ0}f*tz-R?^Xo; z{0gkUkpBv;BI2^}!vZ^!jo9TaJcmU#UO8(`Ko0bKzj!Fwj1xHvQz$a$G2^)Zo^C!d z7Q`fpi*0E|T8q@F!wOr&+e-rxHQ*wZM0JA<{_28w`=UWpdvWoHwID zqj`6P{nSaVG~Knn-qoD^#q1Iir#G$wqw8p8GB@?WxRYN=Y^_3OS0;0RZj~yNl^H;} ztmoxeH3iAGyMaRg+%xHAZ9ZECui@ut&~?@ba7aCA$*dgVOP&=|@*(axA@Fn6qrd|0 zc{U+xUPfdt#Ld>wDjiYz0+ekYP%0hsp?$@jn5DJVa22tD{)5^Ztc;Q5B|*5W1a3Re zNLOf5@u>A{5=-V)Xw#tI6IIGfMW8X#(vePSw4H9*WNjDgT^qXjWye*W;N(!#BmojD z>Xk5^81FuE+B~gl)P-zqmFNr%F9g4%^8t2h?Q)JvDb4?=O}5#rgA|QsI`xE zml(ssw{h33Oy8#Dh5~ltv`tL=Ry*Upfe%l6WqRPN$_d3WfonP(awS;b*^=Q%{_OhY znbg=6&^-%fxxEvIg^nSulJ8;b?^5h_)p5cMA>7$^2hWAERrL5cUGz-NE4c^79Q`ah z9$-ls?ST^(sIy|TvMv+PL!r1B$8JS}NEDPVO{|qXT6E(6sp*XhTI{qeypQdfEv=Kh zvukhWzy-ofG4W84e4{2IAa6g%-CaX6UVorvx?C`ymWNjU_*fgR2uM z(1oJQsghx({%9Rra^sgSL#;Vqn|!kgs0%->&|k(sFrIuCW` zQOuE8{9U1bZjTeEa(AY`QNT$hsZ#GAmq;EyWX;eKl#&yw>nczcXD)E*7DXPKbRM3W zp+GstT!H19;UPM<09DIbgP{bvwIK7KtE(&&$c!hh>NJUdHV(JO$wCy#(o|HL2W7DC zX?Z4-ZSa+e6VY*rt!YlJP-w!|^NYLi&C^IOXo(D-))oj%X&=0}pJSz7=f@5#!-@h^ z#;$yLx+Od{k^jCl!j}jTXXgOT;`(}Dl6lE^id#i_FPR0O1RI6rVhL>--i3BM6zo`*1)!}B?jW9!+ay_Z zZxU6(THl3_rsDost0YgQ?lqhd$uj#8D52{Zxpb$!(84%Rsn#(n6NRL^lLEMEw4cwj zsDwh~SS7vLKCePXWHgl6VM1|-3zA@MUA=Unv@&F}3U$(qQmvDXFWkQrX^7W=O&oQO#=SLJY!I<8XH5 zcGpV?@h=U!M^0+HUJNTrU{qA6Ng{7=@%Q<^NH89mCuw^i2G6^SjcGX8-rWWV15C2+ z3>2zolJ>X_)LIEG2Jz@Z-wxPY9_P*g&uM1aP;R%0d+k=x^h=6>zfB2oBMzI`q<-W08=i?byW3+@UHfWi;?xD~Cd9MA3S96SPuH{E zQ&iO6PTr{!g855w?%&#w7)Dy#VdTk*uDH|g9J+*dBAq}J>)c?QSQzXCE?zG#QDJ0Z z+>Gx$zb>i5RlI0D(3;DZg&^0bkf=)!xF}QE-&jh~62kjU3S!~vki@2t<30UC6Iq7+ z4`ZdT4%T>$erhS1WTSS@yOz1ru5xSZ)JgI+(fbWE$5zh<&jZ4w<89EOtD<5k0>7v_ z8){uB{uQR6plA%&Y0kvZQ&MVE7Pz+Zvt0(2rq9}P1`6Y!7~nl0#)gUAh@%Y{VKSjT z<2Zhiu67a(_9qNXi5X;XW%7|s?!=*KfZ zkn7L@e~hAt*6GDfT94r8wb6=*S=VyGEk=g{r)o5wB)@D(ea>=I_2XWg?PslR5Mw~% zh)3fHwjy0x$?_9jw7ubk?}6gs1{0V|FjN5KhSPmvxm+vgfvKxa3pGrI8&s|HQ7Iv6 zeuG1cP|Y=$5DG5VD1Up7P3j#u5oQX5#&zp*dy7YhmslTVTUMI*$5rS zWba0nlZ_Luw0aYnK1TgZg3%FS$UXBS+3I-2X_Sp|@HoI5^={c=UJIJ*y}9*P6~(s7 z81QjLWguJxHEmY&9IdX6$$l#T(sq?=_}wMLPEMZLk)==(O&yU1v=2P!eIj;L z#GCXcv(5{^eqm>DKA+jqt4$P!hp78T5G98cIoNT-2a4HaIWVV(m&rM*`b?Dp)+0aoy8?R%fC`8 zYa5+1#+T&M%K(%ajy!F6z7`@^&plP9WJ5~iSy5fb^j!r_5O-%oS_SO+;DP^nzf<2ud=r)YX>R6Z zy7h0*0#_vnM`d(WEu;V>P&?sQnem`J(Sj7t&;V%9WXh=6q%L1-ls~IKC6d{fHZ=$A zVsxf-PPsL|8(?fbeMPMtt9{Rq&FVuf`WK^P_qjW*g2bt0get4WP#mcCAezHxVrVt= zjckQ~d9jY7c!zVh; zD&N4;gaX2Cp$Q@8(I%8eELvx2dLN!5OTF7??E`&NvHdV>s)f7MAKkhvzvYhAry;r615c#GU!<2X=R6I% z!($k;NjGaOzCHS=@=hr4`;=8TrM)Q}8_aYLOJ4Q(-qU^Dt*I0U`Vq{K?oFL&R%g}w z4m}gZ_qq;5NS<)h$`=|@+PwZ}8itG~c+outrZX$v;X#M_r{U~~ z{3Q=o7n6#|yVE=lj()Q>o>_3tL&P--TEBZ=m5G%K#+*f{gw=m{fiKP-b!>Hq{Yy$3 z5r&hA+3ta&So%Tu3jv@vyCL-p*s3q$?hAON7)h{a{c)k#txMLl);g0g{Ik4$51|C| zMm=QO7a9{@hI1Fed?aFjK^mKVgoPo!QqZ1mb}6G0f}Z8smd>gO2(nCHGf2cFQ|Pj~ zP5z43=P^Mtg1I6Whh)rk9J(gq1FZnA9FnZwKeicAd|Y@OnwNCIlw?u}!}Q(ecm5IV znn%7k^zk~{cwScaXJXxHbwB_|EHoz$(X1}KaFGy;Qx6k_9!%9(j;O1@ z6x&Y|m0%_4pKS7mK2aHF4ESyNwrV86ddgxJxBirb3Cz}2r5(x5cn(;uJx|yL#s$GT zTdi}n6OjGWs}7p&+$tc8S{bd%y!Bs22_aD z4zh=4c(H%mbp$PjbQ@6qY9(S=q8c`Ik;~Csip#E!x>jxy-;Eae?*{;~rRhZee+N zMdTMKL<`OQs;K-}-lk&Zgj8b+!Dv36`QAVRrrX=gK`f>y@h)ul1nlmyW3I5A&n?u; zMA4Z6-sl+(kAG+fJ0(i`)7NkUL=@!2S;e?9=lV`#^5}yzV#Jrh$CRWY`kl`%i{F0B zVgX9in3FP+ebUsV|2zOzfjQ)`K3CidlMAO55Jg=0izG91!mEkzAHQAkHxUh~G592p zZ|r5ZM!*+(w2jJ6Yh8bGQb}28^2!c2exaYAk0RXGFHMu2F>S9+b_oK=8HQb5ogcOa z##S=xEOm5*zi-_u9BI4Ng1az5KZ2Q&O$5^aMycu@kfhu~Y6-CoUxXD%4q<~%jer26 z^2!_r<~GT);{URJ=0K-N@&7lHJ}nxn$_7hE#PcN1sp^!^^ji(T)IZ#UhyrXyIu>^U zvaXQXvw=x8$Q8p}>P;&Jee%rvM~ohm#{e|~G>mI8I63IxE6(I_MfXfA3+Y^N3ZL#< zUa&NP`;2A_R=@xC;u)B$&0`oN^4`)`?a#Mjpi|69Kj${8IKPXI25og9XT017xyBVF zSF${?r^u)B>C%uqvVp9L7dpPFE9mBg%Y$wyflZJUo24#_&mYK;d-}bO48Sw6r9)H< z0FlSJ6`iT<^c8M4GCG$BlZ!0pVN=eu(MLHJTZ>r#jNV7x2Jv$HXQ70YtZF7K{cvlz!@%GN8 z1KID41dRGGNIwy(fv4ZuB=XJ*xQs-S3G{1#*I4}9q_E4M&w302fQFiVo2V`#Yh4lEKHA4|0c8jQdq25T5=kyA3GD} zjs*>MlPuGCHCVBAX6?cO)S*CUKK~Rp!fk*|wgrm)JTQB^)3Hc%U5jO#ryrBKxevF( zX)$MTi^E==sd|0;InT-wOmrc8|1dH8UE7QyPep{O$jwL&RABh&S$s~PFT^z+2e#QY zQ$^9zt%1f#wDthI5QIEI9W2RA#%rdIt0souQvgL=Y5brm%0g1KkFQK5n(3hIa;2)Gl=~}Ar?BuC2O>mDI3$16ig*9PqBMBLjs7sp<9EELyQ#i| z`1iH5q%^d*Q3p^Y>O&+oTsz*u13L35UvKGZ3L_U?kBYteTE0M2ooiwgOLDSJtu-uo z!I9dL4qgxh>4~C5G}p27q{rFV*3}PSyCgu%wQgw*)5ZwewGr>bm?s#==zRKR=<#Ii zE;W=y+5DG48>ZEz+A)yiAD&P?+7zyxz?=sb5tVdW@sq>jADJfkEhfSqC{56%_j%hH zx=#pAN+Tbd7F{J*>d=5_OvVprB}octDmzGeQ$N*;u`|L+%RK#Hl~GI;dvFV==vBf{ zdJe8~gmE*@CD~z}JE?%Cc{LN?Ick4a_!f?)HdkuKvS3ll-DH?lo(5_PJs8{l`PCsq zfi&LuQ^-Ies|!j!?}sh&xV3oELX=mGbR znF_PoNbokC>4{C@%7tets1Z#V%cD4*ZrDL(2qN4mF~4bah0~;oOiPFb9QIy~4lY|R z$5;p7xO8}svB@7+feCx|nqByzw92_hR}5-=a=`vD-<1kp^cfMKE}mB#;Hr5a1FI0T znxaiaIN-{AZ8&FC2*T{?B3(rLxk8=nTzesVKhNPykfKTom1GMZi&tFsTM_TO*mN5P zdcq6uGP8a%726Q>4xLf3>c6zW;3AwX4RGaCua#m`3^OtYfF&EUAql_|b6r(Ie#3>X z!u?o|b(u`~8><$z$-)f{td5ePxx_cObev3Y;sDWjUbWFQ0BfI4eyo&A>b|8^n`!^)Mj`fr&Ru6H>qs7 zZu3}QBxEWa_YQ{~1kdB2q91Rcn9qIeMB!iUSVHkY`PK%nQ7Ttd20C#XYmd=Fhu&BB zaNu43@ciKzzf3t9Nc(U9diHJ%azNB(Ya@GxSIX7@lGo&g89Fv{+{>qF&+g4a!6u|_ zFZkOeZRgMfqJsQ8X%@dmLCN*4xpE*2*reX4%6OUR1!LMSjUnf#R2*a@p0S62<<0Q< zK;PsJ(eKe;@AOn+!VWf0UWPy#w4ENCWH}P`%xl0zp4F>&l zUS7hWymTrU?l9PdM+hqhPQe?JK4 zx|jCM0Tt*lI!e76)zUdQtD#ZHF2a!5KwzvcR~h34_h&fSVk=HbC|C>qutLAmr%?$S zatJdMQx0J{q8OcM#0s;1nUz2k^|uqQ2YlEG$+GyLQr(pxo8*SJ%|I&fooUZ`Y&?6Y z{{mD%tG`NS4?x-TaF_ak(_z<0lI7hi6JAOCoF;iM?J=+A3JZIjYGz76C*+V(#Kj<# z>b6buBsZ>g^*TFZ9X^S`4}#v%lt4!)z1)MX3$!KdRn>GGP+&AdN#$Lx1(PRcyTY(A ziCN!*#g6oaem`P6W{m^BAH_BGe93Q{Y(NTt6hYg8+akK<0M#eQQzv;S%KEq#SBR3Z z*wR|llZu=id^c434~Q)y1l2~X#EI7Ao9&5;$;emh_MimQu0ELgNR^YI{iDsc7Agx@ z&YKgL4$V=u7c2+G(Mym6uPakM-K}Eag}DzT2ca}pG;fk{KmvBdV zG_fysY$f|l(nPZr|6UW0bk#=Wx9|0Q#c$u-^2uay>ozN~Wtfr(D%uaKPRFXnPv~#c&$PFri5nxnsxJ z$k+PR&Xz2DlWX(lrs|uWKs}-U4`%h3yBe4bIYVk6-}^_KZ~2b|_YJ}@@H*RW zjf_Kv#L6^suZaab-#hv;!(~goA>6f_i=iE>$6n?UyD1iCsErXY+Se0)Bv&(RY!aft zq#2!Rv>Lx35vnzx{0{H+bjfd<@cr&Od5{9tiB3XUlD+}mED$3CLAmX+$>nTh@YGh; z*r~E-dax-*Ww#bX+@z~u;dR>LETX;>Ig8bRk*Z}Bj^E|Ie>7HK&NXiqOa{LV)D^yOj^xN@z80{81{E4~=WM{c+0I9vl?o!{{unDm2?5f8 z2I&#IFX&1NV248&^?b>1pY4fbDlt@(X)QYxw=QYIgSFchQ3EVB8T+r5ZhFZDdWIUyUdx_*evD{9~D}tI3y^(wj@C-0UnI|oX z&UcD{CHNsze(~OBW^&x9KccC2o$sz0g6OzJZFZq>qA#@iyR8iqqvS5r3APv-m=P6* zxZhLzLCU}o9|_0?#o7D4{b1^w6djio=Frh z67OMHK&aioVC})F>XLMQkgI8$0ofSD;ZPC>rciw!C2+&TYk=~k>(RCB6__tsVFI?# z0{6q=1_5{)-FVr)5>Bf`roMhGepE_5HsEmY5QP&S9Ud;25Fw3l5kcJpHo8NYw#_J^ z)N=!;po09ZubmHHo5H0HlGT9oO*ky$?*Wt{k#qdzyRTOO{G7*M$q6DUOl?Z{%57?W zfUE#vZ-8&`t-`@4`>unyD?{I!VY6>3F#l+v`1|D@O$7J)%b|r={N)fdo&SD(C+w=& z26D!_@!;p9kDNka^Pr=er`oFrLS`%H@jO!Npto`VcE>j;K_g7LqRTRLrtuGNu)HMO zIubz}#B?n%J8S7DC^HDyk|KgrckYJsP{ytumEUjT_DG1ylx?s$TAv+$RSoIq={AAi0i2Lw434Kj<@d>A80OzfEEMUd&21*R#SKNyjTFsXBtz!>+JfsF zo;inpqjDo$GmXd=RgN86Wn{QD47WNVcN}IZ_)qb!@jxi^2i_X2BET*o3Ekd1T==0x z#&bYD2v<4}X9K(^%rfs1mu?a~f+O?&hCbZuMo=_EDH&P36(*yD$T8zbs4Uo8%lV4m zA)X>h2(d?~XqVXM7=_&lDLqGlvKYKv+Z@}pV3~00lx)gLJ75{e4i!v3q|j~KSIw)Y ziMkLA_DLO=lvoOOX~|A1S%~ibr2WeX=gO{@nf}YA@TU;5cv7Uli ze1pju77mn*o2@`706)xIZz~_}HN0a`hAdOIfi^XBSADRpRyd7vKN5APD_lcDFj*T( zml>E2wQ@h`qAw}_N2)&|q7zB;5=FCIsNN3ba}T}c z@=UWb65d5j5;)GV&~=alHyJ;2IAIA6Ixy1l16J4?F%YQUKp-s4XE$}z)>o4kK(1{P zmU00UgA3?&@9SZ9;{4lcvOqN#@cW3CUJl3I0cW!|o5c#W>U7hX=kjk4u5dlxr3m6U zlc1H8v8=70A*!W{Gf@O)W=d0yXm7kRTxy4#iu!A_#5zuxs9$fUs&W&gY2($nWNL5o zGRj_0@)2dIj&6~p9Kq)a9T~jG&mba+q!K~*n_1P)SI9;_ot7Re(OtoXK-BVMG2`Xm z@(DmqAtZ0vHP9F0y$&X@1$%%8icD??5as;Yo43jpZ{E3h_uJI!O z`*MPbw?erBXl2}>Io)7_9c`;C#{lS|o6Z+A57A7O<=env$q!c(W*DH95pRc71w70X z@+py3An<`=?PmPQ^O8wV7UFc;3%d)Rk{l{$tAUwhI}P(~ z9b7AAQUU1?ifR#KT`Fag5z6OwTXAO2Q)eFdhRCx++E<;pCTHD2mA#x`nIKL{cghOK z&TP}pY>Rdtllrv{Iw51?cVIf*Uf|pLJrcMZm6Bu-O0-dcexO3LvCT&;finI~s)0}y zQ+&Nfcd_pqNwf~+xp#AEKnN(4N>UTpfN}QPYDBdw<*(Wlr{p-5-HKPr&#kZSFS^)RdGZiY1K5EdGel3?Noy2YlDY~7fr zi1R^60zGz#@|tz4ZChNSa1T0w;OD?Z$aHrIsJZkRmOB8O4{!bCjs(=Nli<8vk3N`6 z+VvRDWI`5^Z93UX*m&)utpuFqL=0<%TB&CpIq0P%4=CFLxv;(QJGD%9pO4aKnr$`P zqbY3DjWj68BFhVio2MhBwZG5fmqSaVo;&+48Xk3Yl^MVK1AL<$LyX70=G<59e8u*$ z5mUq#Ql+fQeeyu_4Xw!1C?%^4o>Am%!ksH?E@(j-^g9}-zCG_l_V53>`_H8ICX@B3dc9#l_3ng%Fm)a=I80ZL^!z7Q~G(L*Cp8;eClQ+21vp zG%y1n6Q(HEj3lU5#eVpe6=wE{6Btwprz>KEry2+!G69mnW=?8|RA?ojOX7nx`6yC| z2wY!G?e~Bj74`&f)s#L58$^JegMEmKCZfbqT9Mp32Ocv^^$nb5r4S@2Z&Nl4X)vM2 z!(ak*cS8g|OC?Ts&G`A~8UdV`UkxEM+cQ*jOsZV1j}gEF-?G!$q?I5UO4P+VRQVN>_`E4f<^v7v^;N$#f7S=fJoYhz&Csy`<27G8U@KTf7)?B{YZWV5&vU^##0xFuYSm z5WzdzLpGp!cKhjJk-$C%q>5}7TG2P z5#8(2hstrtCmU7q^GtI2m=zZ4dADu%N8LEx6{u{7HjIA1O(B(gjK*&-7WHh2eG$8EObnTYsSTU8 zsYp{nEdz210BZQO_XV%@i2Bh;P!0%d$4j274}9IzHg?WX2HD!i&ns3OUuH>y4GLCb zS6URrbC=WP*cCXa0m`DnIaJ4f5LVC`2p(%qdZFSM?)l)Fr5#D3gw1<)z_w0@sE|^G z>Iq`Qt{Z9>su1eLXHhW%d9Y&mJl4?1l5W_tE3&uYav_jZo>DnJ;IeDby;oxg2&;xfsmV!?93|{@ss_>? zj0;dk4Ek)1n9~aC9oPw2;0D_%Es3ejzg>?$cE8iNU&ymiHa#Ip8xX}i;||I2 zpw*o#O2GQbQUhpxyB?Xi6*9m{H7s^krhoZpgxb6=$J^?jM%(pVQoqEG}OVvG0sYK_5KIW5G zf_@+skXrCcY^N(?O5qfA%>{`iZ60jTAU7tbyNA{1!<|G+1IOu=sHverX9`4*4B|wO z)gG7|vf;G&$o>|}?~_{{v`Cra%tJcqN9>+}vRYkMkRHh^`|(6r=DYhQ!OPYIF>$*d zeJB=sWhFm~g+7?c>-F7TaXy)+$jOTm2`sI8;*lHe#oq1Q4X+3S?$TBlP*P%1YFR zI*;;%^3`hC-F;I!s%~>dzDr{C){j$R<5*vX76SYGkXB9 zDCVLk_2T%4yI{~1(t>@9x&sPL{N@5fJ~ZLjZyCXX5~BI|?Rs?Wd)5gZG4l0@T_x9W zK3fb+QJw{KyxvxiVMnTn>!cjG64>A?F65i|MFs&t%}#Qk!px$hC8He^LU?*YeWKkR zkQ{Qc#^U#QIl~Wo3ZEa8Q1dF2vVvHb;Qj$)tmYmhgU}ai@Nq>j*KNdNa5+Im&~N3z zhe)2=b(#(a%RYw)i}2mP>TIR4@9vwERsn=0J|%{W@pc&8MvE&e(qZ~Y>D&PFcOotT z*vg0|9|O=nQJ~;hmtZ6Bnqox}eNE=#*rP)AbV#QlR>AJ%?*Q2hes&(UdiI9Kqfa<3 z<c#fQdUXLD)95xq zV2NmywWqgL2kn$O1^Ut=REvb%0NHKzC^4bhltX_pyY+d+V(TJsMYL+8l4SN%_tDt6 z@FP?sHn5N>?b{l#}6C&YT#=0$ne$<`TfTR~!DAx!>ve0po-EhkA2@+Pa z0Rpp1Rni7?aEq3B+FjIV3_Fh3qcg)3s*8rpTdAko>_#7p5$Gpq31-P_YSYBe^?$eXzVu~VsW zdEg%0hmG2vzDiIZS1eoWUC@e>#^!DDv|Srq74BRyn+RdF1Mn66ezI6k|{ z~8KrG2BaTc0FVz=O=?2S|pEbzQp<0Iyb(Mv}^yvQ8R>i0RXptz=mUNy}Hn2>0 z%&7CxN1{^r(;>(&r?!^Iq{?SLdrAjUsY~=^`wl`XbNRFA;N6ug`)lA5CKTD?iB0A* z`AqX6)AmJAD!47kXhoF0m;<*$e)N71KTRxsH+&5Aq*YB_c2`q#ABw=(jsWA451}R*iIqW6O=pjm_ z3}U$ld)J`*GoDt7_ZAuGa1t9!+n{zbrF-jt@T`Kp1E#a3l}|+}5s*mlvc4WsbUfv# zV|BEIs)TF;Wv~mLyjkMI)EGM+jLXR5bycvZc&_{}Du@w^00|9Y=}1ZqGnWo5!T7yA zrU#+2g=s?S0utL?Nyj}r0W*ZXbNmL02n-w`!0JLsgbHX|U3~+f&&o9E>chqc30>0b zE~|fH+jQ4t^I37xs^)n#;3&qU22Y5alV~9VHTZIKmf`*&N7%MA*yeGD?uQT;@Z0Ee zfS85AIqnOA@MI#wmoo-92m1lYrVUx3nwh@FvCcuxLk>X8tGk2s2)?&uvZ;r~1m0=V zDjwm?xk@WFjFV`fbMz?Sw~tL{)}YM`E$Gg&?9%W#wDAIrzK6I%BGctU3o23Mp*;=P zzrjq?Up!Z{S*=A-(m_lQI%VgrIR%o>j1nEImT#5eKm%ePBM8k=Ur6y5B+;N9W@!r{ z20tS#a8Z)KLCIKXgXG>(`A%z*3c;cqOP!Bs#!Njanhgu45fHRPvM4f{S53jOJ@bY3 z|2R>$d&FzVcOu?KjJE@eL@3{wQ7l=SAM~hTK;Y66rV^|J_X=r|*)y<^hR2$Y zb8qv1<7!C}-HzT#s2rNLY}-=`PdX3EuHBc=eO$5qxD+-UHq}apAqCI+W@H3X7~_wQ z@R5FyBrN8e54v#t_|4TGdu-`a@f6c)W7pg;7Yb!tQn<$Yu(a}jxLag&qPJ!R5{ZFM zo3THF9Hte*ZvQfYM3d(LGyz)fu$RYaNkb0dYWJ*wka|eJ>si`TUZfWZ-V~}_)_{@PtSwJ4T!T~Y^ z63%nfTix9$TUvBGQvbuHT0BokV2FI&h=aCcH+M9tG<^o7cagyE7qYw8O5}{6hZDao z(~4mN2%^lGb|!m2aT1sGNB~GypT^^HLPJ7jGrpvkxC8+-p#&k&41Lmrk6N48GI#(W z2K}=I=vI4<@<++DJy^3VxH8xjO<}vq=%ckndYcm%<-8RQafmRwNu^Dv{@95ox2MUv zx6I@GR>Lc%_2cF??^riCx`Ese0Et`Tfj$|8CT>wcm7KmAtMRvN9>3Lh?jZQ28KdPV zvNcLN0+bKZWf-OI4W|tVBb9HAH@R2S+KphH_v*4rZ*2KW_*C>Z)NV8uuLMNpdUVbE zOz2KYN?;K_cH^OLHDzbB_vl^a3OKEUhBdc#5k0(j12JD4saDb~IvC^jWfdU_f(t3i z5ivLDUa*o`iO$yUEc?E&ZX6|76FLZT#1RLec$@YCbv}w_ZDCGqH#;%;dWC%&jj^;f z@?Oi(5?6~_?){Jzi#Z?}Mzg9$ooKKU1CDWfm?R}tslYHN%_^IMYRx<}+>tgGR{RLd zkvc%~D|HE5co|^q?7=X%x!G{XnELo$do_quqb+aOqifOC?iR<>(d;Mp^rPyEY|^DV zW0hSs(?mD-V~cjR#eM%O*20_mSPDakLBI`c?h2~ua0ip~RZOf|wjjWwAALu`ZSVK* zQEO#SzC$Tt5!>Lm#Z^lCBc^DQ44N*4M}5754ebzA?UTqG9T9^xZP2XBCELL0X(!;4 zkqA%#Pu5zYvo`Qvh*3ch)F2hF8@ZDj#0HQ>Zy@mVB0euRgrj!@Fm{5SEqa4OOGd$( zQ8Q%vK}O5KggNlj0E0y8Q3LNl@#*NQL^Xt@jNN1HIj;}gn^gXnRg#S`@o-nu4wafa zb<@b8zfqsiBN~W5D96Bls}cK*p0bh|u;1+rH+g_9gB)w)J+5LraTWNbq#=S>YEZdR zA<%#1^ztL)6ymnO_T%x6*#K#-tK)css2|VyZ@E?*ulKHT6-XE>C_qB zd}&fgk9K%*Ym+ zp=}iJP;~ZbXZ3;|#+JHWk3Mwvh6-rId>sqmA0gsL{&wJ|oi%Zsc_0B(^yjFqS=%CU zOURHgRNK?booO3KCKcA}2s14KKcW9P4DO$t-3H{?S2>l&oEGt0;1piZ_ydNH?+1oI z67fCC_5k}HL1d{=SSmUi(aB0*DM45KHK%teK%?-A&8N!b6*F zQM+Q>oA7!-1tBexw3v%xXs}3O!3%)!G}2=?G2|_9b+bDb5Fh1!Qh_b~L!&avSQW6q z3$RC>90fHnKxXvUnb#29G!Nf{D5g`03s!Oj)HP!nMOCtQ`yR+{atrUKccvA)qVkeZ zmemkwc4@;gRwT=B&oBYwG;XVdFBSCA0TCadQijSyTI)s)Yy$dre^7YsFloZV0qKxV z%w{KLM2L|1TEcEEz)0P|Ny~mboI0wyyZxbO=cWNLP%Fr-tKx~eQ@AjlL#HnbRjUO8 z4)6lkqid>`b~D9s)hLl#J?aj}w*Y!RPve@!$4`7ZC1F|@ZaYToj%;=%rFz>Jfi^gD7sZd&q(_5B046#7ITj($g-;MOibhmpp z%=HR4w}u247K`lZc~&zHS1CKzFF~T&Fbxc3!#Rjnoav_7WVu04c?ZF=3QZumBSf(U z%Llo2j8R~Ama){8Xw62v5gte_O=1%Pj@+G@=z-56VlnAq+LCb=b=g0B^7RO~l3DiJ zfS>@qV`V!|h?>XD!nkr)w97dkUgJ`4_7WfrnK?>^OloFQnw6HK7gE_l&Xws zUaf9sOi4hP1u(C{UEvN7HVOxz2Z+D}lLeh8WCZ)7=F_A$+v^MwHS92DM_g#qM#A~( z;CQ5IWig&L7(U&srYzPCY^p+1$_5HGuV5_5aRJymA7%I90%!tAZj#0x&9db&&-vlZ z?(LEjJSHlzWK|xmeJru;@+7nN6z5wTz?rAgRizXSL zRnPgpEzdmsB$D`*qDarNTxCWYb_Rdf0(;DEYWuS8(wIrI&N;cCDipmbx)h?kyaZ%) z-1Id-mOF?puVx%!x+b|k+mJGF8LZGe>@qMCtY4tr&;NazMl?dzcQh~ zpA1Ze44k%k{bVT3Cz=iW>mn+B<~<=vhCRjTyS94RZYDR_dq8G0I27XC2A>ue_tKmC zEiz(092Q77$dm-C4?7gboiQJUS_JfIiIXGqlg?LA&p|~Dy1Lt-tx!a`be0Z#yBoiD zx70KJIXK>cm{%B)9V?_9nKsm9ThkZ(FU(?cM(uVzx>_#qN9@zMt?gNUb8G?Ggr$5L zB?<=vkT*El7UT|kTyNa5zp+Zw(N(U+AXR8wXaGif%G{t}f8n|E`bMaN5PFa5-!BPL zihuSIl^V&_hP6E*NHFL~=}302bog;aKQg9N^w5%#`c285J5=XzqSFk}shUz<aeUFSgLX1Op;Fxao1zLrwR5^f?MQD!H~YC( zm7xYN;i)iNeRCuEN6`#;*~;1q3H7+5t{P-`^W*~4VMlVP-MxRN+m+?$OW?3jhmo!= zelLk0h<+M5ZZ4m&OXa!!DJEN%O`{UKt!98|9h!h0;BM$8i{Ma3+98 z0)=fmcqcvpYmEdN6}IGUO3;{rv4nbg1Ptl8M{s^OLwYIv)edVpv@wCz0-#g$S>zPlM01aeGyk@ z_m7PHW1z(tZ2CyA06V0luzS7**zZSHjGRZOxHCXd1Z2Pnxq{`0sDgIdY(a;BJzudU zcguxbq`^7oCVwm5Hb8BF2p^D~?GzNzu2j;uMfBX{c~rtk-QVFkn8B-NjxKs?>?2Yt z0A8`r7Lzk7cw$31r2;9j+{G3}=Cp01&+3Yz(G(>?3Y?-OM~YeUKqlOVK06=fJJrRK ziJLhF9slWY?VL&0?g2>9(6T!nq47W0tcS&;$(Sr#Ds?=H;C>|Aa&(-qP=jrVp=k>@ z#yVwEWiUPsnM@`}t`!vu)m>sK2(B(0b?XM`%xKPH{woA_Ayo7x1Lt`k%O{{-M9!G} z+UUwO;kucIx?uf37*ONsQ*&#pbnt&j$C|PUP9JYmBs7e00pVgL?9NG1<^U^eqZt53 zJNBuGUC3O(|1kq^3t3LyjK%3EtB6~p)0^&~N4ekSco4oet9$U|RX0AzXbuTy$eu*> zV?Szm&im>(gYP17ac}0z->Q4&CfeA;m-%K>W|zl;E^?~{hz1ia7Kt2Xie4xZ!}N=O zU<8g^ecQ1D1|Ey?PQk4XvKV5T+>ff4VU zdk<YXlDVT<&u+XK<;D)F?-1`2yMEWe#ZLH(g4fqZQ`! za0l8zQU~NTo5nS4Kvct$GscP>%41*jOJB!z>PH;tY!K)S0<1eciiivZv}OQe|6$f? zqz`7iiPg$Gk1cNp(s5vG_hh)gBNz4f%cWr*+60TgA)89Au0}Nje2$G2~N)8cG93O6fd3e3kn_ z1@-6oaL1IV0S@sJ_Qpuj9BX-B-Wx#CkENdKg`VsLo?xysw3%LMQDg3QOlOM4q z`)X$Dr@(Bs{jHwJtJ^j_?3#P1Ozii<_Orun?;x=Ux^HNa&E=2Q93&VhI)Ipk%y)J( zie!;R2)+~=LCR(+M6qE50g#YfLOuKqiv2C!Cv5}mwCqZLyy$K{9kK^}45-TB)&N;g5~8}^QC_O!(#Kh-Sq#7g zyw&v=>l)43D%c?_0{(jKY+2b@?RUFU8`sXtV-7()RvcwSK! z5L-48#J(mY`3V*g; zlnJ*vErO#Eku8B~9B571l%3kV8N7`6O9f_x6tG3#@g($f&FEJPr9bVt zB4T(wJBdU>OOnUC3bg!e|GEQYArTk#qcp~|3A8x8DMffw@5vTaQ9;7&#S)8nd$&Ju8v5f1{HE4|T z$Whr@p7?`@>Kx~o)QTBDk)GZ^F!Rt|>R^GQmbeq-dA-k@yyFd`b4uM=j?^0vi;pNf zBV0{)U0gwGZ-=PUdz(K!5eQ%^CgF!@=~E0CS>W~H^MHLvhJ1v&x9b%or0I&D?YEi- zeFxMF8*!SppKxm(YjF*a>9C z^a5k1x3`xDdUls#wb+|b=Lj*ql~goMQgrY1qPQM0phWx35@qXXBcBWu{JFo60o1lT zTcqptN{z1T9%sOiuuSsq<_e_mcb99w#&GU;6YSRm5^nYzs(A^(eX6ihP<2D~ryG1T z@x7O~2*13VNP?LmkyZEI50W9MH{+MJafCNMcn=^!s0Wm2_Uuwgkbs44)2$*;SXH^$ zAQ;>Ux0;>L1uk_JG4d)$RpP(8d4rpV2DE-RPsFye*jiyNI*;|dG8(Zo39^t~O3|y> zz1j4pY`YjIXwVYg?Ypi=FlthU0l5+70z|{MeZjLsBtN>B)O_Br>-GK&AzQJcJ0Ka4 z_CFPbrF$T4ucwsJAlXLKdL|750{~O-s(uO%9UxK$6BVG2@Q9`Umfk-<+C;*OPY$p} z>n!QX3bxtLeeBmk?^gQKM0D|a#VUd-jsqIJC+!RS`+=6Bt!IJ>5|I53jK90WZ6u+j zpb4degV#c&3K6*J026BC!YsRa6|13@_i47-sB;5vkyiamZwiPV%(X1{ZYk@=%Zbt^p=@Bu|>&=gG9`{XSh6M1=2#4@+2LZl%55wfP8ySx;kN1b4r-Gf;Sb-M)19 zQD-h8uThVRsZCWw1Yn@*FUJ+P2{t4{fq`(9Z)o;=`9m}sAM*|wIGsVGDG`PQH4*Oj zO)8BH&K`Ufz{!V8soHUm$$dzpfU6K8fKe$T@RYQ6)5h9$Gd>$~EW;KNAFvFgd60Hx z+Qo8#-Sw!r9T;#fxC?Z<=2@e%{TWw#1f1(LLBBm;m)Rr_6vvzofbJPdUs|+q&mbVT z?X`7G=#<4k0bvl0fI6V8A|im(t&E$A9e3Cy3(@WInkCcoeVE!6!~>hMEXM5tL&7XE z;lb#i=~RR%(jyyqxnzSl$CZ4bIa}0`r(qZanr8#7Ow}d`&Ka1Mv4O6pKB$0TC4We# z+OVk#060vlD4Q|m;WU%}I*S6w9=R=EVF=r!%bn!dPFQOT^pYjA+)nDcnB5IZr(|FB zc0IaQHFiM2HhNBUOX;Xioo-0tO&q_Tbfwsd2vP~0=u4EAL0~~0!2_uBc9-JU#Rw{c zP=aGPC-mIluCglfY>KI5CgV(Hwq4L+hIy=a$I6JBwZ${{db-OBXgE4fb4Z80s0gJ| z=^^|tDAl;N4TdvJ#)vjHgO{KxZHrdC&=Z_??a)i=mWqvvU`d4z+$oV}PYJWpCRm0Z zV%mAj?rWQufWJ08v@ zmb6`>QcvSdW7^#{>8GP>$?|@mQ8Ns?Zp_8JKWml=o1O81@jA6$G>Gi9KO-%&nWc3P zI~95(_;*`mHD3e{*@wySDdWkInsYi)y!c%x9-*CCTFurrSpD7LFaz>7> zcepI)qk5+$9LPK5@VL7#6RyoXaflz;5uDW1$gbD5e2+g7IDyZ-Q*F8jvIuDh|U}G`LUS#>F0u1hxLb1LoGEGbj{=5X|;R z_YN8Zc6h8yiPk5VD>gf*eRn1$$$02bvm2i7Oc}CP%GOsGyd6Mov5s_aA&MTfU*WnZ z_aK7}CWYwVK#n!-b^;#v#uxyd=nbs2EUiTkkHwCsH3ME$k!OTjH;(@@S|R>p*~bYPRCtWpLxCXKwJ)E&4M zP0C3p-CTE-K=Pr1ZvlG4Iv~F535HXim{M-#k1)Y~F;^KS2mCyS5!Rhze*D)y72r)M zjVU^$oo|9?OnPRKL}piIfDB^t2ZqZw-}vL6-beN#<2=woFh?OkbrYE~9*iPdD|9$2 ztJ840Ua`4fC=J<=i;3pFhmS3(cPE!EPeIdE>D2?!HZOHd)nj7rW!db+Z5w?x!t;f` zZlK-ML2?S6MyG+gr+=fSXj>fjV9%2?!K?RH-rT%AwJ6mh7wyXfbptTm@3>u$3So(c zebr6c)svY%+o_kq;8_5w{VhC{BvZkw8vGb>z0&%&G@(*ZB${ zJ-J0&N&KTu{_bE&>}kIP&qz&6P1ctygCuqglBM<=o@hF)CCV_|`ys7v%sIMb?m=7I zEM$#%@aBh98j6WP(2|zwZmV;%rELRgM2S&6P&YlGgV!WZaYiF~b2_^66u&M`f9Tr} z#p&zy4(sFa@9DgEah+v0%OOo@a++@@g^w7l01EhuJGnKO&@(yFPxcmRH%^HocK?j# z7poxW^%U1<_?Ldd4sI}~*g}A-+k<`Jq?N;NeqIsz^%uo*)E;S`-6|$v8Q@Wgs<~cL zA4n6H$hslA29214U}HF?2@^rk2W$x06aXI5@o=)X97Iq~;sR0Jf`^3E9ZU&<^Tk9b zd$>inr9W_yoaujQ!3ud(1VtJZWxc(bhN$Ei1l}pG8^MuUs4U(cMRubukb;xcc&I{1 zMRePFf1h*;e*|COT=TZUnMMs#GdDw|52GS(y|P8I3|f|AqSexcd`Y7jn?-)hPp)mE zHp5YX7On5tMFg*Uh#XMc5{Bdk5IGr^W|tj>$OUa~r^b758Zwb%dF}9C0`jZDZ%cD0 zGDXoR8NxtXJ@&G!F`)C%2Q@e8(a4iYAG%FtCLtX=~)A0-QV z@berm=s*CWDW8DdaRDD`Jw!aZ&Gfp|)oti=s)R{QT$jvYbtr4XPG(J!Dd34C!2%F% zdq2Vb!4k|RQ@Am4D( z>gwr3VGR{zmRBli1+kV-? z>jdKU=<1S5XUYmrfo+D4nL>;{-gOd(*9e&Q7WF+7T6O&PE$Cq+^(n(+(R1`lj+1>)_=*L8J-U*NWHUt%de5915+AeOYr1m9#B854JRSuI zHz2u*j#rdh5Sx9SNs5XHct2^)-49_+_yJXw15E`GhJZ-W#L!o-LzMLv7WC{)OS_Fq zX<3weQ$|kGILIGtcY$k#R5o(E>3W5mcS2A*P`H#OGQZCO^8c!hBAp7935U{sFQc;8*$hhEIhOc+5fa zcejs6XDkrpWg8d;gvE1$A2X~mIL~jq-qPbIJBUFWMoMVh@5Z3XvAV;4IlaxzXvs?k z!vh~<2Ap7zqE1dr*MUuky{O-k(3@|kwm#DBbu2>1979rR{Iu8Y5y>(Tvzl()QMBt7 zo732nrJ3@LbmB8aT^+=w6&n_aR3_cjyYsaRTimj%=cp*L{FO+Xpp64BU@rp;IRK23 zQZmA+ISLv-*?j|JtuK}*D9@mMiM1ewMq#^2jQL*nDiJ&M%Jqn^fTEk+9H3iR2Io9@ zz)hR#X~j=s6FmP%+44Bfn*;Lr3BQ=s8<@tnK!&7EY7Or+KgQ|d-Xtqu_*}N<#7Q^6 zunChSIe-Se4Ui%tyPq<>5}l+W_>@sYfETyM=LWl^2Y&GiLZihK`=xEs!=TOLeA--Y!p)UYn?zCv0Htds%t@EZg7qC;qDSO~q z!J#!Hc2{q4P$k|e!6HUuabTXkyzMPc`040Mvu|MTLALqBfv9d)F}EWXrPXZ0pOv7$ zTmg#{CEUrq95mN3zaB^#=rmnIEOTYv=xap?l91*q@gN*^ml#HVhTBW0Zs^Ja-^F*q z&>QuhJ9WIIPJ>d4a$WF@5ybB%N@-L&eDT&#(2(g%%C_qH&j;ur60lf6lRgy*6xmEq z4Q;#2xd*63p8tc&xgO_1LBMW>GZJlRBkOI}%@x6cLzw1p!9#O!mcgNK19P0fm&6$y zoJkabN9i)|>qa{m-6kXlF876%3!*z?J>S+A=SL@0*n-^6wFV3E4iL&cF%NDqZrThz zVN;ar6+9EbNs8q5+YVmu__-0S@_f4yqi&FbL8sFnbRP%lLg(;! z2qt7g|NXoQ+>X7{%YyPB0^syQGg{;mo$lFtWwoE)??mRzue-o@8+;ra6w?!k^PN&`X?wqwmNa|4h$&Ih2q}{vQZCV z)?qpiLF85Ju;Kp}eMM=<@i&I_J4>lPT6D&P2B1OX#et5ET zOSksmJ{Zv6!$%j5)nTHlKsNn2w7Q_8fv%1p-;0|@dWiM{#dJI8qf500ZJlAh))*h0 z%;$0q1X{cFyS{G=sz(Udv|2%!1|J&k=9{|XpjaR>KO&G|aue2m4(lC0TZ%BXG$V>g znH~VEHw~{RqW5rcpeP$ONUsEIqjw9T*g+p}5+a9(mV8O>(2YHK0VDl(m6Q90(DcQro2$j!3y%W9Ci|Z#SCWim*6kBJ7K00|`+M=J%Y<6quT34Lqh#V~J zgZkoqvnOQFwa{vbH9;#N%+S~CsM6vK;>(?am~MP?SL;Yt8`>#cq$R3 z`G#ADb#-d4L7&(}2kSjtD(Dp+Jw1cT!r}P&?EE>Gbh}>J0{2C6xaK?R;GzqJL^DO$ z)av5O;r)YV+8t{cBO)R4wJA2%hT8+k8NoMjle@cokCj-Vs=K=QY4!I+%4=D}T?D43 zFLjJz4r*?O2E|n&+K}87ddZ*W##J;Y-{T_9powF(N^^9=U_1&eP~oV4LEu%G7P_CRn0R%=6M2K4yG|k?-4!Mc3R`o?V`Km zM&Q`0d?q(5)J8Th_p1d)Kpka|=?wyIMu+R5n=&(9jb3=wW+m0U1}?0fQ>3NDQU(1Y%#cA#tpH?=XV1(iFR*%~_M<)n7Y?jiH!1yzJ-|Ll;bRjaW4Ie;? zC!c#eH4bd`DG>#x4IePK!!y0e379Za34&__sbpLsmv?5WtLVkvVHPMDyD@MhGR@pl zD^E3Xh4WS{`y#DDES~G$Hjr@=SNk5t{d`p%BarBY1`?DpC0sSlmg^3Zr9JNokr^kJ z$wotW(K{*R(M(R{O?ki&qZKYQ?+2CaTeLnh!Cp6z ztA=ROc7GlMoS>b;%y(pUy{ucf_uHV=l*)4D9Rp3T|gpa0W#LtTlZ`|7bNkQWwA8Ls^c!Q{s3Nq0{#v=MTYpRtAUM zo5hg(`ys`WGu>w8IZtR$+c6tme}ZwYx7o={yr8Fmf0W~r1tU&vpja3XPwI(D7c19q zPu4LlgiM$ij46F!LI=DSNLFmbv{5wI*p<>rDEoO{TzTvHL)h)w?o%bZ%~AgtXr);3 zGV3>Iclgn$+UTfJvF)rXxQ#rQxd7tw{ka3~k=``K$mt|B?=GFf| zM}e`Zt{T`te)6K!@$zUOME;iHAQl1nLGt&o<#&$Oo=p82*dX{$+Z2g|SChXE!EBuk zp30xcuB@0;QbCzU5gh6a)q$OEpiS5D5$YQ_VrJf|B$0CFPeogFG?+ zI-uGyJ!o)*W)gtoj_QXCXCXt#tS-Ubk=1%twADbMvB*Tt>%VnH(V@+m_o z0#Mh{cXuzJpZr)QOZ!^WNzoz38gn+lH|?rRO@XKq&k>Xy*}`vi(4l8-rf$(zFf#Vu$!PodLpQ1dkj!w z!`31JLDgFVti-|vrHI**VX35_zFl2J7Lg#(>*xsfaHEETDocPK2vfzm3|HQCOZf{L zy??wwmA?yB{zON+eYW58H+x$kOSxjk%8pi_)yytJ{X%-OydE7dsX)R}D;0xv5sT4A2)wGq};^D3_tz8|s31QDuTk~r`5t}UcH+~58ylMl_NXPHdKN8&Jr=TNpy1@00?P5!)Xx;EVHe- z-O$p#H3&eYgOxPG3%ZKi3MMJdK!1o?CIC(T&)Ojl9f?2iQZd7^SXS|qpPB3jmU*op z1qvK~{_W$e24C~l@dA%&r*3Vb)*=vPu1^jSW&e(3Asqw@Rnc)GM{N)O$GIj519tL- zrXOpU4}^CTWG>|MXtQWM``Q z#M30GP1*@z!VEoayzr~Q*~`AkkA1w;(trjDk1uYi4s@{`SjYS!mN3=Yu-g_~Era*? z^dEgQv>fs$A*a98^M0HyE0aocYPdu;ZIVnTRuFMVM(he-*?z7}L`>N1U~thpK$TY$ z<4hZlN?cDa?>uc%46)Oxa65B5oDm@{k#>qe%6vnsvLm>8S(b#rLwEZN9*e-++Btx| z$51gCyXQpi1!VGhfl3o|z5TP0>9+&N{dByAda@G}m$SnXi_xeluX2BIycH=&>c|My2#uCHRmIUYIszn(&i`2>z7Og3hyFDn7n}HQ6s3NZ0PeG%b-0Kz=IQdm8IZ z>upkk5Sa8V5#ANh=5dfDbwV zfcSq0h-PH$Xy{;W_aDGGos*l>|CKNPchlyO4Z-_W?Fd*FP|@|gS_VsRzKVSfq%#)m za4sA{LfUzKq!0h3Vdd-qab6|Ca2Cv$9`Yp{!Mx!zGSJS(^ax=wB0zn9rsoF*V zob2}+!Z@VM_nkXgg$0gxXSG$-SjXQ-x9sbTOk=i}Hsi1a&5My~Z0+ z8*nEkc82C2gyu4%ZG*({CDq9Kfie`Of6PPpVRm&@2Mc|L76Doh;+Exo$`7_%%71qM zGkq{(yX4DgP*vI3UhJMrxYwSu@XAC1l##9(UO!sba4wnf|vq^g882M@K>dt$}4) zEYO8M_gwc_6@Mgd8(5bvvA)jLKNs2d$x%ImHTfdIMk<-FGQj5MLNU3%w{xU7jA=O< znWb$vE7e?mdG)lqMt3}WCd|RY4}W4LtRYq1U>=jrGo2jVM5+45m{zJ|ez8J!mAp4b z?{~WYs<-+U>@9ryQG)hY!uxnCv){i2(Qz9Gj@3NvvX^01W8V%q82{Xj=6Ylk8@cN^ zW5n`bP|8V4U(tbq)?aYG+ ze4ET-N56$X^tjLa#YfpQKXWy|MO_OQ5(ff>UI61(vL>_luZh&0~Iw15RvwMzja9-%Vq(_7Bd!cv4Z;U`8e#CYNZ4lfb zNw$6j!U%+6h)h8^`GIpHXM|6P3r>PThyxgiLPLn0VOqj?MEDARVJ6sw5s1t~IQ4;R zB3Fbjh~QyDL&6~@foFO{;KYI$j00gvqFD??!HCpRX8J*v=EPZKvdpFBi(BRoES?#> z2eHWhTk~H?F&sz2MC53k^JC#+vce7q3CPJ=ZAUda5Ce$WS#L+e6y&0u3KQWNd*hm(>ju#bq6_5`yU9|J=_Zm)Fd!Rml@r-S?`#Y=XJZ!vzK3qMb6;3!V*D z7SB@8N$1m@W5ZGzrM`*FqDIL$328pUr-+6pYe&={eE$(nD1iSwgm(L~|8w%c2BH6s za5@<48(ACwZ_Tv%i>3Xa*Vzpk01)H{7y#gZ4*ylP6L9q@yC?tvw59+6r2adLn;7dm zIXf8Z8rnKK{l6)e+uG`hMaUp;cyJ-*#Q|F5u@Qu~oeu|> zzP7PF+~3#dgO8!7uC{#c-=n*}_t$E<&0@O0509^M^t)fHYG0VQV&vZ6H={zQ;}W^w zPj5fp?@B#h9$$BplQ*l`f39C=Z+H26zwhN@`Mw=~I!`QrW>4XNyFY9AxVyjZo?ZqI zW93qN8hg(odv6NZCt9_iU_<$uxp}*}f9>FY-);DO+&=ELIp6O~9ZP*b{~E>dz1)|w z-|F;!zQ11|%59b|+_O)5ikABPyzSqAFM@lGhR|GRKP+$d@^yFla@|RQ&kEoFo`m8< zA!$;B%DoRxcHZLm`u?215Ayl^a%*K*F5>;Z&Y8=_hVtHYvgg`vt=<0IKfd1jVCit- zZ?qY3a`nh_cG!D+xg9nfoze5f#{7)Z)8p@S`|d3KarpQ1bnx&p2i{hz4QIFGp_li2 zcC)lvDzQ*AY+@f){P+&7#LMe&L7t???Q{Qld-sPi>0`9j_x=6;xK{VyHrfYk@pFHF zo;pW;ttgy$FCR&&S+rcfXI%FD<@ubY`-Dtlhm{ANv!hFIR;~-`DS6 zlT&-W+`sZQW(&@CzQ0;sZ6m3?e~&v;uheUQ&hL-gH^QP@Bh9?tzaD-D@p(5nq;3>_ zS1NaXe$F%Hbp4y*Pgr=QzjWO`rjPJpekN;Wl$f3{rW}kiqK-}7c{f>ZTi$9%ef4<< zFJ|d;6P7GnI0JT}AU9^|d@<2!c{R}Sw;XuKI<+6CpCtNBNq9wnLr!3Pp6Yo`u$g>> z9NuK$st#>WMzv;L$3Z-D{g-swBcCvo`q?SG?wu`{9kI_ps={fR&^Ao>R3U^OHB@E8=t@JUtF#i`e`@$Oenh>5UD_T917wVodB7x!08GYva0dl($ki8sl9>w5 zfpm_^w*N`j$=*ZdkZ*gT*mEbV+tLU&Qk*>qIqJ|Jw}#URrb?6X&%S++5#IcOjt)ou zQOs1JYqakffW{>1v0<91r_U9K5ELkBN8s+H3(&H24!aCEk|#ErhuBuM$;DNHv;nQc zE(=}1W}W!!j`O3s&t1}=?twH-aRGmhC`cw93fc(kHdjt z>r`m&Jw+iH)&QFIdS5=^=P@zy!@FTEfm~Ar-flo*d zmP0X%5E-=INCRh(my(4xP`#;qw^cDQm9+M%8?gqJ53gX73lb_!bt-5Lr}3*0Rj!0G zz8Kt+DuM1V%G|?LywS)9o1rus_AP2rl=*zYzlboUYFBC}Ij6IE)ZNLCU^}`h8zui4 z99C<{vz~D=P%d{bA%(1Arfe<4^DdaKZ$BERYJ?_1H zyF5YlvF6A@DSho%#Q#xx()w$IN;@HDdC)D0=<5;WnFJtZkob*ODeHpCZBRr+BWsIM zD91uk`NdLn%oW474QyG^c*Flyw#G{Tu}T6VL9`6sqs{*A=>HgynW{3|hM=E4Ud!ib=3H7sV@OV$m2T)#$Cylt`^0h}wvI>ch-Y24QbPu@- z!1kFb^wE!gI9d^CU*crj+|k(LdIFMB5f3|9z8Xo zX_!fPPQ9jT69_ou;&j|AHI0Erl$*ZpD%h2+HAp5lyiS!ufk|bJlI$xDQ_GmMOI!Q( za#PoAiWJ9$DA2C}zV0b^jwM<mZJOSA6goFj%|UlNo5p?s;8A)~P2r;dQMPX=6?~tsK|u#FZVmaF zbVrlz2QJs5aItyk9?Ug`X^XJg`A40(1289DcYRUO;OK<5RDKr)ml2nw68iCS2O~cBhYTUU0{52}gYrp_s3 zr0aJ`S-khF#(0h@&6k}{z7t(M|d3YhVHS$_=buNc6+xPT+CQ!>!Y z>qo;NE35ylkcpuJ>Qw}BwbyQiQNuQzv*)i4YE9~|>T%%!q~ypXduc`38W(F24Qci- z_Oucxvrz34r!c2T)8JbI<76jN{wr36uk7@^bYv#8{4g2a$}Hh`vO8B6_+oPu-P_7t z!?A0cZEusKUT7D2!8V84H#+bJbEr(F)DvG@MG><0^CP0Dvo1*f!bAb%fphws8n@qT zlxLBu9M6umhR%?l@Bzn>f`w!H!Ej;=y>L~{g{p4f`0Rn|J&zbzW>G78{t1ZXU;SGD z?i>lBlIFHy^3fq?!JXdI$*Jq&WE;l*n>%!ws-Sb_q;LnHsK7$?6)oe~%>t0b!LB2j+2 zpdFNI6BbE=h;3xvf==8)k<>mODm$e?+yX~}%6zqs?W@EUSQ@Ac02xh*HUHe!l6pX& zr!EEpXT5+z`jo3Iw3MaU04p%czn7tMv0xqL=4iX`{S z=o!r0%?(Wr)CC4w?Wz1sAAQ6Uv0qxKq(XO$Qi*Ofq=XyVYEYRr?Gm#8J*>~4B2tA! zC9~B$6n@UI3WX`xf##RGELe+J#U!9W!cs(+hMRRi`Ar^IGtrSM02g>!OMTM^%gsQZ z)i(~|NTI6u6qE>D@J-XiB?k2=eybBj&AixNeZ{yFO&o=4Gii^q&H#uO$WT3n10WFa zoNbSYPBmGJS*EjPa!N#lY6B?4W1BJ0Ti@=^MGujj9FY3qr;*q)SjOap&rt@W+s*3aWfWrX=R-YM_{ zFohL9UY;Gb5=os2I7hQ_#1C!j9GdQmaSQ<%Y5SGjx;Vt9Q2xMJk7)EWOWFt9_^|nu z46+NEsvEehrvR_XB^d?{97PO`pmi2CSV@~cBhhMr)%a7V{7h&;NytheO>w(3VfFGL??C$>muQzetInu>4;+bf06s7C!t3|MeBcV|Fcp5hf#vn76;hTh0?sc-B^sfSmxW>GI0jJNNe;!O zxf^bVu+Hp?8LL-&f;M$*2ls8lG~0u%S632G^+#?dfaMwm{Y47F5i|;70y^~CDm=v z5H+}doBEe+tdkcx`9)8Hyt?IpruEvEJC@Kbv&kkorCnggC~dv8z#eAna|5g?k$X^^ zR0L@i{+yu)Q}Q)_;dLkj^y$*ybmREEM@kip z*`XSyrk4Ar*77aQe9XBxm%zY(r*Z3ury83q+)t~-WW0%Py49Ex-1bU(Bov8K>|JZV ztwW5@9l~#^u@D56%t-kD?cq6DcCSUi_rUJ~Xh9pwIGE=}M7>V_TZM zD#cU`>jJ`)qS2JaoD)Yp8wz=bZTfR{n*)BnD*x^Vt?}YtV#m_~=GNO{e9Qmqkn(}( zqC$>)2kmY^G)E)Ff7(}H4QrzBrCG%@RX(0y&FL(#w%Fq!e#q*j-zeG2i11SQC{jI9 zM=UtuEI;Cj_^$A=EjyD?j&{nSjnJW!v#QeEyBgjp52arILj0scc(Kr_ytO{_WX-M2 zctwm|CHgg^mXZcNb(_#qHtAbE>!@8Y{d<&{FmZ4rU)OOb4bl_K4(~4q!%Y#X@f5o_ zyAN~MJ=S`00%YOHe*(%_inxsB#WpDEf~LJVmk`kT{MM2oNgFbj)2BLvD;E!$>p%9w zs!7H+GO1CTheKIx5h{Kl_NY>Fj7n25B6e*BRm+!}$d&rh8)~w7_eui=$*It%VJ`X`vfP@&|KPMt3Zel1j8CmkUF% z`obXcspXk=I+`hTmIA7%4l3p8K<*=hkfI_!qeCf%TNh-~D54;Is_SXgtwOpC&{Ej7 zb$x6aRQB4Uv=`mN!L`t>=;PR=LO`+*&Yqx*qAx=N-Ji4!W)*3Z#+9g&3YL;t_*xJA zo8egXJdWCqxF>W?SS#O4AW$wtWmuK2M1DP??74Icnqi=nE^{I1{9ScmX;=> zy#Or@O@bLg4lmmD`O2GLb@xqw7LrbmZO#PV#l1^W`56*U;;xJL>cD6_OQb4`GE|MN zUt6hRY?M8_Y_~5gz0?WkPHRF-V$TEF{Ao@mFpyLg7Y>ej-VA5N+0HD zSt8FVQn8GS9G=vlxveNrhx5b=C5-rv;VRkZzP(?ycrjDDIGr$FjniRCjDt3WjYk3= z7~_8@Ad5E!J|w*;bl5!z6gx7oVGwgwK{3=HgKsjadrenqAyPmO_Ng&$55ZNKReFw_ z3q80wFpr16?K{kCz#uN|v{2zs%tRTMl3o7+kr{V;Q&|228s{6dwaYBhv-yU&(1G2+ z;PHfu(FU=|m-8=864uwsN*$8`i*AeW0#gRmLC5OHQvSQL2;`X>*r6`DD)}t#TfnCA z;{e-Nt>bUaISO1VE;Mm5M=O{?5zm+~pv#KZz$+kp?FV`GJX`#}7Hzup-14@?{jY={ zc$-=HCeK5&AJ}CYO-FuO(HeTR z%xWwv;SR&JxipPtHYA=77V=P(o~Q8d@WG+Yo|I28MM$TdzYd(v1}vWI8(P^R@i^)y z2`DrK(ZB2y1L%>*8ps548LB%{Lbn0tTTFV$yGzR9Pmq_k=M4v_7KHAiyEpNB8vPMc zJQR=p@{ZvS28BJsF8!Pbn~=DIcc{Ub+8+3P4zPudq6&TI{ja>5Pr9G}X5>lw{uG=V zN>98_h1MX3(zJ5Dm4EpJ3ecvNkWzSUjer}$8g>ak;6-%;j)7}}6Q>f8(z-*|O~dke zc(i9LXjZh1t4M*W?YFYD2Z8~5?EwB_Y2@~;ZbV6hM_P5v0aWq?8-S<+?5>V}mC6*G zWRahiLppRhntQJZxXPHs@;Trr3M@kpXRj!3dvvE>(Y9b*t!@+v@JxCmjJLFL zGbVjYEbg6CU5Z`6r6V-r(Peil{|s1+bVlV;?W|Qn)-B3LGqtZ1E~=^BoH-g*Os)DW z8WZa1GS&8S7*Vi?U>QA-0$m)Z8*T2{=c_MlRh`$HV6P&UPD?f&gI57Pzh&K{&_>l) za5H@pnEi_+Fu4~X5O?Ia1ct;t;r^x>oRMNBL)k_PfK;xOjeoKskIDuc0^d)rF7#=t z+H2nKRR-%*?s#UJ4?`400E`^kpkmfW5V?bO8w>mJKz8y+XI{iC!Q-`xAnM^KTdpx4 zD-~H4xD!BMJDL+T0&5VZv`OPt9tx1s!I`lDZLsL&2_Zat6mpSJv_z#Wp)V_ez6{9pwFV&Lx^HZV}Bl_>A@# zIs1WrGw9q)pu6LiUg28mL-X^hn<_-0Wu*?7zKT>i+zFs&y*Y~<<`5@$lK))u^zU;M|X6D1W(aKr6H3muhkOTBh0l@-#8&u({F)LbzZ}BfQHCHtgq7+->g09}t*7p3}ca_Y-NYF`gujA()8bg@NX<+v>=> zt#>mOM@Ue|uAjrMANxZjTv?WS%l6QwV_{3j%)wA>Ym0!y+Qk)^3?$NTFyG3~sc@FL z0NotK&G*-L!aPAAv1D6w#i6r|R47IC`h}+}hBI?nX0PQox@{xsdFci3$uh}iOY+9f z-@moN9X~=3E`P=Qs^?B{RA7H4nEXZ?8 zXxq5ui5oyr`_cP61%l4fel**3wK@1cxDOkGSF*BSxjU5+U zz@$|FTj2t8jUe6fnuPc)XgUse8c79weC-@V=#}GD;Bnh|ng-HUBJRY`I1w|hLq%L8 zhcqG~k$0sx@BC0+S8yh*u6{X3UR(}w(h1D$bNG>{K>Kd6Z_Iw>_*X7w2 zh$s3n*1F0BF3lOJ$}veZ9egvpzJI7orLq19n-d9DmQH=g*TdqeJDuzXQAjd#l$mT9 zdT*Q_uuv3FBM}>tbqlJO(t1)x>U@_7ileD&>Ald8(Snd&ATdF|eEVyuE3$ah(X& zqU#W;LmHiMQz0<1Bbr&8&7Gx;xy>f6mDsgI7TP*gb=9U`lU#QTDlun2wV&2=JAW{cQ8T%(n&6 zzCR7tA8l23CCMykOsgBrt1_b*dy@jyIGSF1RH*qcjT32X+Lwdo%o0!!mzx?>Ra##f znBwX{%igT$(; zcB9Ca>Opw@%HFp`QWm9HW64PcUZq9j#=b>C5U(&aH6&(&u(A)- zX$b?a4Ul6AST?17;fK_?Burn1X@D0G0)OP`^FgW!GDK6?Z+F&ygbw^Z%+v%KEHN7) zPQI2My5+C5xe5ut7!EN!nn@m$h?L{F5*SM%+J|tih`h_Kaw=`cuOOF<0@P0TYwgm9g;Lk{v&_NCH^T%e+L- zp?^LT#-`+*Ixo@%^kJrSgJT_|RhMdC_cI+)S#pT^8v}_f4O>1@cWrDQ$~bdd-9@wu zfdQ_+&2G$rr|)&m1;0*tfZZ~q5D0Tm40S9sCXKbF96fo^^UX$4da@k9cEA0 zL%{C0woO#fBPf~lXY$+)Sw|fZJrakcxEr=1DV`Rqj%5tn2(qFH&f&sBCi}X?OK*@< zpFjdQEBG%|c^G6EsUSA3!C}Ox<5u2-8q5A2QkO4UGP$|qP)qew=i?=&{6zsx%n?sb zZaP{=11qy(r8_QMR3^W(l{CW9e##_833-%AS1AYBQts@R&g(Y{H=#P+_BF&-@cmrX z4~%MzZKKJh;#Ou*s+j(8dAV9+g8|bX+0Wnm(Tw(YXO-kVLT4rlhLF*FM**?6D{tjJd z)+o&)so`qoX|xUiFoYibB?iXZXq`fH4;#gQu4HgANxMyl_6 zI=k%^UQe8I78BY8@Pjp828-!^0D(BOohUH({gf?FWum2ywrXMW;IC3EvKjzUtn0+cJ$ zuH~yPv$t^gCe7iSqIsm7#kOc_ zkqyVAwO{ePUwH}eIdCDH0*6q7-ruFYC=@5KMm3cH9@^MAth451Yq=SpQP_>HteK8~ z2X{+|$bxu`Zjv?k+Wo4$o;wtrA~GW?ll@JhFJe+eLG2Q}uO(rtMULA2^S8fwO;FRN z_NcJ}wV2fnj~!;l2hP!^K`_M~!+brIR9)`<;JFkEI+> z>U>kpii~^+Or-=6%f_`S!?N1G_8b8F*2G%vEc%am)<+Q}IT7Dd1#JawkU?jKXr1$( zg!eX*F|7?ST(AVU$4n*VT^|@6kcX}d3r9D(j-Z^VF8@r;eU~x&V7Gb_~lpp1zKy zTz-6;JulmB^SW%KHcvuI{(XIjE97fR#~+Lzb!0LaEwQbM&Yo$?-xbNR4THWu*i%dl zZ7ToU#BQPPgtcX@54_!%(Wk9vAh{_zz@R3`x)B$W0S~5&4mtl5RLs16da_0Nw7yL8 zBy9e0_1Mm;k)is50qm`YgUHc;kRbaiz&3kgnM!Q_mgv`SHu+Qaf#r$)1z4+QYpk4{MznZ!{TF*sniYhUBRX(dZ@j&|J`h&MPIvL3$?<%Ed32sVxE+R8%WQ!6hL zP(Ap_fQ;2$>tJLI81rot?2T}|0m3b%{3D70-<;`(Se#F%&DD5gZ(}MoXEp3tFYF!i zM=ZSs{-BoVLMQ#Znd-RkZcB9J_tY1XoK(!T@P(|8*yG;AJzF0Vw-q06NXo6>iAQ4O z+(28b46O-s51)IH^agitv}&$+N}oTVdfstt*5N~v15(#N?Hb-V!5Sn52i`W|FqWGR zQzw%kk^aurB0D;@PCP{--MKAk^M&xbrEjI`D^tl-tEFU}vrJmq+1wwE zW%AsLdEn92VuIUYh9P=kgpp!3PFiB;*->nMV{lTGe}s=yD?mOCQB|0jgb8*{3(-Q- za5@4MipupWuded!|S-r#g32yx6iGZ#lMUU0Ub zsc0I5w2{2@f<)CYZ#@>4FrV)0vs17>Tlz#Ttxr%qy&=7Qflldc;hEW)FUxNW(Ye!)g;jjVIxO1s$V;zPi)dP>j_hgNNia0bT4>h znn2%J5rAsnVFWzIOj)&$%3r6cF8+L1=>$umWjMvvgrN_!03jveyCqZhU<7X8`j5jD z6oP;LTeO#^kxd%wki^M!|GPpm2TDKaAq;!YcRUsMN!1fti<>6+=#C(M4LinjutU$vLTj46xnNQ=o_Q# z*h5r`Nfy8;UL&FzA!MGfQMi=`DeT_pFjiSo(ZuxoYTstS3~6Km~*77U8!0Nwj`54DS8Ym7O{b2A;a^nxth4qht$#U z(4G(r_a*IOPLDV!XAZ7d4^J`DS^z8DB=g((Lc+W3r-pi%Ef29vCdVzCf1L@q2T22&LRgEbJ5>5Sixfch|@c{7u z^XFlJTl$%qeom+hSoRX*ai9V>su8Pop=crkG%Bj-sFKXN&yycZ^_y4rLlXh3! z#r?CrU)TTu$o>B?P&zosDFy`ZH=L#=9Tk9!>LD|S~aeHbJGZMSt{6QUBq6+Au51E>055wSN9VVkFTs0Y3Lrrt*U;y z-6@J0o}azgC~Hz0FOOAvs5bc20{h2{C{iNj)P`vkoef30Q&un7G@5^>_-^?uj|!V6 zsID zTV5BwT5Fw4ZUrZtHA;)kG6*CcDX&I?xJCdhPAklnyHu zs@oFrp8)yb&&2wDgM3DR5WDg*H^t1CxcWN0A!YQBvu#*KF!`M+HBvVOA{yq}Ym{jv zZHI4}hT+ZOhs)~<;$5h(Oo{!I(4G@nqG3i+RqZTy&@Y`kS-2PbRnHgBuEk{4l z*mI%wLV76NXXK-!_EKILai5+E{Af$f&R+|2r}~y2wEp}-odsAMe~Z3ZDK}|m{kUHz zzYhCoK|0yD;)2XefPN<73pec*^d)CYwp?z?EFBQXl&4#5eVDqwE`rSKu)=pnRZTo@ zm&5hEU{1X5JQc0lE@`G^a(*tmY@x6@*}y67gnF-Jn79`H;w^qmYiEk?=6*)L6ujWC z?;m$klm_FL2)T-NTkA=`s5a&0Ni z_pOBwIP{FjpEC~7MJ{#D90S-DS`P#GfB}#IyvA_|fH%(J2S`d8e(utF(etUM!zO-! z_$@HA@Rl}2@v68GOC=W0c=*KJSy-ey$|;F7-o2Ww<362|uQ z=qLcBfC}`?)G-NxE|b4%5>faC{0!bH31J?1j`)m%&;dT$R?d@>aD|i~Kc_G#ECFOPmzL9opn zOd-;Ugu6EtY2auSfsBt#|Dihx!G|;CZjvzwdH$_?I3HHX5DD&iBqYCEfsbro9o$Z+ z4I?_|S`F${P0U*gX5S3qdlRPA*ApLWJ%lqF?I`eEB5mUn4W@Kn&igJd@vbxQ91u4| zsAGrX^@_fsueM)Qm845NQ-MxA%Wv?RSFW32OzrskfX#t zr71PRC+d&n2j+spLae=*1mB3Jq~Bxef0h88Y2cECAO~tQ^Divwy?v+s@c9J$ug>!E zE3P;(2mk;o6aWC}|Bf)~TbbHAm^+zS>;6X?>GT~;O#W{@LD6b5wm9Rk-Jfb_37HLg zD)r(k*4aaA7;Dc!I`38#5eU#fbAl=G0&%}NqeSb>jcXw3Et&&i+A_Snya~I#-p_}V znX7!g?H%7QUvHze_ntpt?Qp`(L$kuf6{w^|<-CecC-9Z%Wl{b${Q#YFl%E?%$s7zV;t) zx!pZoU$4(!ZgP8Hw-(ub&mUWB)qXv`+`ON^?%sdyHy@vuPd{frPg6%vUt4>BKF;@V zUTkx3YHM$DY9JuCHc^*PEo-=|m0EF_SM_wj zF=f?@;@eBVE#SzSWQHM>{$<7(jU<6zH;ajsYYm`6zO+-kU6Qb8@+0^AGm*)%;itnz ztjdPN21h{)!mvW}weD1%!iKeOjlB@!H{M5(NmJD=SQ=${Jr_;KD*@z&X;nB~Y|_Qw zW{hhZ0_v~0NhF^V48!4c*BN;?B9_>IZ4_%#LCAGJhKXLwdT^21EHh3BT(}Rk-oL>; z=KnILZp3y<_qH^d+qB-L(&A6kj{;!Vc9;b-^4#YRhsmuXPzM&^5R9jOjcvwFXMyaj zcR-3kMr*js;d*Ho7N1~KG?c7-t>0v?ld8r^vOkx1jc~YK7&ryPQJ&0aKVz5o$NjIy zD`RC6(OZ2y7+bXvq5KNjqY<)wH0+axA0{K^7E1Q0ExcF6zpJ+W5?>JdrZ|mq2*}N(iN@#_=v`g&s%+P(xfQAW9hAx8%_%7^)Zho2`jF zp|AtR-@JZJA}*LbNtutVscthdL@?Wr4#~5-DT3%cE?iYah(9%B%oKDK*ZoiixC1!v z`_;&7W<+g}*C9foX3H?JJfZvKj|EM;uD5Z4BI+G)w*Th_iKNrAKEDb$f>CAx0ku2rs>KdVG^XQb! zuZ_H`Jj72FX<`XG`QzK6)zuh^OSYI#!866Xi6elaO-ZUeC)r_Pt{cWvyKSx%Y=bVggoK# zaIWQ{C6buXA z?(XjH?l!o)4ZggM*tZfJyFc%vI_jsovio#LRmC}(DPF{C!0_i63@aZDLsZLhk<%Tx zdXA+7k@zQTskZtup7FpjZGV_Hha_!j)yxuA!=C_Isr4}PR-Q!lBT;-aV)Yq~m3Fyi zQ+T-(-Sp{{+ZDI(Kg6Z?=DJdkY;&O7g7GS{za?Ujjo;jQ(^XMPdQ8Eib7A5@!FNKZ zi_0ub?4%#d6I&f7w_56rOtS8(Ozk(+E+VLuS@%_2aWu{4Z&&P1g=JK5>=KmYd{{bJ zw!SDvSzK;xZKu?<87UiJv~36Wwt^{N0xcm^ng*Jo61H-izw65mLPuER^p$vcsga;U&jFNKZM1*fFjkevn%6^y8R0Pq;GPZ8?&JI*hYWr;e=(#LhPM5Rd^F zHn)HGmBf!xrRAV7{YIEIp4sUWa64SOm#8=3Sl7)wV|a_bB*#@2Xv+3FDMc~wN-In<*1WV4iZ+7+Kmz#%T<0AENn|jv`$jM@)&GK-O@a32Ru(Rf{~eAKlc|&Ie`4ur-T6PUT<9Anm$xAK zU0IACY9)PcHD}qqHu)Ss-Y51`OdwidjG&u8Dhm6Ufnvd^0uTW}^GZQ>H!Or2Gwl}u z)-`7Ju5Fw-^LCRNTJq@lutyz$LBl ztl8yWZy=IwvOb`=(=NWm>y&QGe*TDq7Vn*hC@T3zEekjfycyff^Vav$)dcAMbWR?L zS{Sd5?v@_^sn+GGTTio^bi&7UXW}(>oq(zR(WFzT@+hId(lOLoIczAe;FsNx24 z{6;zL$&=Z&bIkI=ujVfQsjKgcbL3htJAPN;M{zQ4qkX1)v^(lH_Y~pt8fD(>eA<0t zdaXE08{{UohA2eZ?D0ls@1T$Fklw1);p0uX(}X+xy9qejfqBEGP{&rLk)-=L{@5r}zrCAW(A;aPs_&Ir zjTbgo{NvOU({3Ak+xHTv(=}+J=uAXDX=%kEqw>0Hon$FTl(fG&y=H(tx%9AJCZAu{ znr#*T^QP?W*y4E1`v~Rd8|VEvDc%U$dRn=~I=iXWlABIzY93;P}H*2LWZ7%<$rn!}CRp@@lkj zJ^MD9k^IQ`3Wpy>sai&`qkXY~ZtwR{6YjuWQ!dujp4+u3LwMPvmxW>&>k49+UwW5D-SzXQhYB~0GDY0J8(&zWLVRqQX zuWAckw$t+}43&O9mvo&vT>F}2h7;SS;&lkxwT5wxVONmh+-4K9zw0ow@v9T`)sgx$ z^HX+w&_?W*!n@^RtDtwrFBir<8c#tO<*=eJEO$bISn$5W-huUgVk`*7ObAIZG|R~! zQv8{a#FAh{Xk_SM)U%rpK6}W;-P?27_?AX0Y`8dxu*esB&9rM%Rc=XX)Uh>;PH23y zf78@v+Tkg6s7q}MuLhjj192&}o(aDsEB+N}j!T;o0e)qJZe%RM+jTsNP**BTi*)+< znNinW$=Tkg{oDEbg-u!n`=rkk4FaD8!MNe)T{F;3uYq?Zn9S!1@Hs)dfJpn%y+?D9x#w4a2M8f$EPE03MIslHEhF8nPoVwQ6c3UuHGGx)& z)GFjIdG5E*xANI`xb=?@EbLJ>Pf9=>Qzv%)cXf2ro|e2UNQ{VqVrBi50BH>={H22c ze`sw2SgN5(6h;Ih#7CJRBpe3Ucq;W_uo{~{|E~sCYZLfteBK$yryeoo`+mmU{pRz* zd|D&MwyPSLBo;))ybB@|J0XRO1Hi&^t0m_hkv+dNj*cmX@9n5@u&^#%$U$fQTv#i1 z6RdKqGSH=Dv}SCu^VDN!isVZAM|6!m0j{AoD=Q;Q%PVVM*#v=`K?1YMH-dH1hB+PR41DEuOtvNXpge+Ay@ z6^uF(TFiQ$Nx^3TDjU^x9PLEzR@pp?TlFdU4=kMe!9>XBI-?fdA-2jIJ+%l++|DDc z`YRq_3w3u5D(_BKE@Wk&$&GR4(Gxg zY|#kz$?TTT(ZY#5bZ`YOn`r@=oA>=`yKQ7HKbWSD?yA(^a|s@?->1xsjBzl9iB5VZ z6Em-HKgLP*uQrKwx~UY!g&P>CVCMV+_0=1MADK=MaQ?-U;5P)f&XiaR_|wqR@&V2U|%d$typ>T@$cAR#Ed5_dquugbvYs$8eN4>Y%PwN+BlxZq;-J zX$)q=B!7YS^NG?1@2YVn+hxQ)Hc?YmjU^X+b22#dhnkmO+R}SpXVGRXRPac?U#$P$PYjSoL<-Gh9`z@O{a`fdAHJEy+8#wGk zzUZfFaIMV7eUSFmfDL-^57;``#W~JVWk&t@j(N4vY^3518BDiN97p_&U1Ge31Ckbw zjW$7XF*8J72KG8`Huca2iYrxIHU(w-e;GC;P{@5+ za;V;ib;wvDXcHiKZ3)FOWdXtLq3s`my=(|cfvC;2ynKr^gEfEC7hD~#-)HzRtg~%E zxsR<>xIH!sZ2rq>Kf$UwOTi3mRNYDwQeeDFc;W3gTnl40@cQ+qf+l&7mMn{S$TYkJ z8LvhAjNn7|Wm_Ql%Bx4->4l7oUdN9WODwAZAq=((r{>x#Lj7zWjTiRP>Q<0*iMQaY z8}j{SKh@u@FZ28KAhZ-&kwa#U{sgdcNi7ae%`Z^7%)t!5@2Q9>JJEjEqDqk%qQtLD zop8&Whn4yjoBUGjB+Z)u^=hP0f?0UG9P^IT&YGwEYk(s1jH)^ zzTIwg(njdc!AU3#TY3|4(iJAIcNLGkAGe}tQw5AcEQjLf>t|W*+bu$sEH`UkEn+Mc zbT(dhWcaT5?Bsj{YRl?06Y)9|Te-4u6jz7idvc}spL?iHU{%u+YkAw1StC?YhC_y| zVH?KKp*IfEH5;1aHtUVfM{rSR;0sffu75{1E&CS6eOLOHwObtt$L@Y5^L84woy8s9s zIHv9?%0HVrvlu;B)d7+B1K~SenXTm)kquvL2sO9tL5qGY>9(lU;TDGeh7^S8p}<-_ zEu8aOWl&QdMGkd?XqBKKeg};eW8m&uFO1{cA)D>>Dlt@utg^u0EoJ|ghTX}|U{eXo zOP6^gp?A=aG$=Dl=v>gRJ)(ba6MJkZhDSxfN0^wdiayE^n~t#{h@a$*Fba=ZoQGv) z)eX&yT6(*S`ZdPbjaS^3A(nTu#`QW^>g5cDzDEw;HhsX}? zzNik%fl;0nqXe>&RqpFyl^`GzUXkJl#fe3}9lu*lIPGAi_`6iwDO(2pS#aN`7jE#E zn9;zR4B~I(I$F&!F0L6a^9B3`CLu7xK}u87_iXTlx4%kaLUlI{xmZP1_`4ysa`R1R zCcX=to~}$I<2tz_us?M)-dobE(35g{+s^|yIsTOYXnyxL)0(D8P>xrNM*!~U z#khbakYd}6&PV#b!?~)AW)S}$9W-gwp#cAo=A%Uk9(LG0ksh%@Hw8>jC}RRxt)65~ z@b+cMaQWOrDOM=*jn|KVc51Md@CcL%QVO-)YVs_zQ|8Eig{U>IlVj?%i#8DyME6e2 z4Bl47q=>xgfSTpTb0fL%?kJPJb|=5 zgv)@3U;S{|O&$x-Y!6{b#JV_7<&!0?q)2)g*Re2FaD8EjF;HrLqt{Bx_z(i#-#R4K==OcGL2r2Qv_WEA zRaXIGqI!mp3n!k?T7yLWEg;|JYumv=|R?U&V` zyVSz2FBXPA@4~{LPt_mqcTd8ui_;IEK)?6(%ky==XS=%`Ip1O2`9$H@$?DIWJGalh z&zJFO!}sg-tF4W{cXi!5Ui$|w!;*d(}uq9n@_@T7unsdr`Vq_ z&!5kSUytGW!~5$Vx6i^n`$ya#!&3KeQ`?@k*ss%F?+?q{-Vf!5zU@Pg<%XTz_bt20 z!|ew8o`@6ptB*YIQcZ$W-AQ&|Z?^6N`}5<>@9j|&*zJ|+^R#$&pAL+!7ycOR{x?}o zZ1K52{jp2k{WhAtuWEnNwb}IAb>*?&`jioYtZVI*Fxkz!u+3*P=5zEkv*X%nBIjf8 zv%hhaWpB5vdk@&_veP{=JzU3ms$J9@GLY)@k$v(x?mryqJa1M#>TG6=nzLCho?Lxu zic!7ea#``36<%L@mG4?EdzwsmO5m-qTjGMb`fK14>D1I=x8XwXb!B>0IO*}Kezm^v zqqAUR?ZQ+2(k?0K(4gqj_*m|#t$AZ^$j!dFqPx(m#yQs7;BzCZdRfm4U*Sn-_#*z2 z!NO;0h4ZI?)Wr|+hUBK@7x%Mg;Kh^Pld9vxgD$?BQrAVhkQle05dXu@OHmVb)M7cx z{D$kaU8mdR1%nsiFEa(JYTYf1b%8hmp!LoAaARY}_=2}%2`!ULfnafMbT$y!T*__RXMLwygo1#)h!I;Xc)Kr;w)>gn?Vl&&Wuus5N0qy+d<)B+?GgqT&1X0q3-s95R zihIq9t8KwT+2Cjuw_@f3%pHc!!Ldz)N^^=ag;KiqdUj;k5~wJy{V3K;vGx4dwAJ?Q zF_Ox{zpYZjvZz9&X-8q=MlW1O6ra&!HGG|M6$HCW4~1V5QQld5^CLvmpiA%f)z0FF zxO_4WuB)3UPEO8ieWypqt_OcmaQJIk5&)E_KxS>ps3F|X<-bs=wLXw0oYpz67z95)ER=$2e=Ssg)1D zoyoZL%x>t@Vz2T6wKs8Da8jdJwL?K*pA9Um;jPKEZ7pBI0lU-%kTo$s+EElJ5T;h06=dG3VWV>G{5Ay zZN)mvrz50&rUzGf2J^97bBj9pvuXG+>m$zEp(m7f@!^$jmmN;nwOxnh1otbGp%=8& zk*%_<5N^1cANr_u58!ujwyCP%mcc$N4tvY$5KZ^Nnr;-*=hJ%0jHKiWEmG{O7#I|< z>$(T|Y`3vS#edigrYn3hrBmoEo6{fDA#HCosM~*mUM`u^rcjmj<6i9S$~t{`iG?!` zFGng=0SzoooVjd@EJGcF=GzGwZ@AK^Dr2xzMb?`EN{Sc1&&^cJF(ZgnO?m?xWs8x0 zq?v%`9v@rg9+{?D;|ghd?I!O#QT_F+>!dd#cu~atL~r?awl3MA63FeCrqig} zV{j-#a`I1G>u`&5^Hfo;=?<<bFoI4rLa)PP;HB#Zdh~`cd*>}$zzM7z2)N_ zr?I#oAcw4d?&{Nkb=>0{(GF6#QvebzjiLARB?R?opPVrj+UeWERsBeyn678;>d=fY z2cL_U^`1WXySGpWI^9~Aua!IXFmK%HPSL(t6-f8<(kORcFsg3k97jiOlialPwGuc% zW+P@WqXeHYm3_N{%;)p!b+co@HDLGJw$`GYaIV+&GmUKAPG@Wam>NmSjmZw}mp(MR z&}#BR;(h5T*;NFLL>sT{x7i_v95f#OfC`69lTy}0$kvL|bc|4$+;gE7hhqf zvcFfsPf({;0B7rLZRbjGa>MITDQ6bk*3wrKGatCM zcxjlT$kobM?PtTMXmZcmZ9g3d)tu)!9-2>p?-oz#=91KHl82C!}oQ7DJwKc)(ximNsDFKo33=xViV zta+|MEEArVj4mIeQO4cJkvB9%OlV})TP`s`!61lR6DtAG+pG?31}I|ZzE)AxAGS-O zg`8Jy&wtzuq*|H>L>-ZRJWR2#l~R4DKM!CNIebQFJ}$swmt~2D*KP3phUdlGTtmz zyd+uq(Z!Ol&QEl`L}kU_jufRGnYc-rX01&@Q1e|OQSEjql0mbV^jb@t`2WB?j}$!R zT|*KW*d>D*Rwrf=&xqM@;>@$0N7=6=;Pim1WG3eWl$>Y_JmYMpp~;Y!i7-X{o)WS0 zLBanTqnL3GY7Rq>6xiDoo*nsfX?IP6a>ubNePvc_{bg|>)QL+5!0z?0FY2*wptyVnXIA7eev}i*o8Wp=2C}5-TUpbxkSTGO zPQSMN>dnOc%LLSEc3UL`;Z-tDma?mssj2EIIZ%76{**s7a~TPqJ6Hm1hwAR2V&Lbh z;taX(Vni40UP3^iysu}p4OY}v65DV@sJF4eKFI;p8^*9HM%#fuB^8?57?uiCy zl3HoIYkj?|xde(iBqh&oT?NNB(92|R>j816zmnKnh0U%_=l|R(S0<}4f^yj|%CTz- zl5O__h5x!|GRWC{whG-K%+q4%t`p*tdD4+vIUSMB@)ov3W$Tbj<(eE8)ZEa%+^tj&cO6SWV=CE)gj)R6-|R$&4wC(z3_C7l2K|Z7gB{;|JZPeF)DnU zaLvl}ZAxw^;2=rc!m@9*Gua>f@U&N90KBT6Ql1dHro*FDg7=><8;$1AZCss8k52>K zvrt#sJ8@a*8Ph8HAGiCKV{fWX5@w0u&v!a_FHNkXCno5lXKP+5JSgWG<}mQVmQ~On zx!?f0t2V0}vhlo>N=tDZR-{P8LFqCi+R0-@r|zFx-e{nuPRqiF*q*u4I;nfR_GV5z z0D?3#FC{4?4Jjc-`vueBOUYAqWO$e?50An(^i%A+Av!z#Zer5ooIm`41G?O z46_LrAr|XR!yUo!8Dq9s!=rogGIlR`k|EXcfqbdZ6ElY^4ZiYU?v zg~A9^y{Nf;Cq~N5*b<&E#vD!zR34RLwu*+gNSZ2NlJkndKy5~5rXJ`b)R|W)M{?8XX%_tJ<^B1n>*12Bv0?|n(;Bj+u0sX-G1k@%T1TUCYuby+nFe-}+BJUvnJecuMmSrP=-}C*>6mAP6wkU?VlN3virbR7CQf@6 zV=gY{mXq-C8;|@bp5vYZaRi;elcqgai&D#$XNT_2N@60=4?|~x?8z%K=q9AGhRB}6 zK5yl&ZajnH-Pv8REaiQz2f3Nokim>5H$?}L8e^SUhbK|)n(l+tCH%1bOCb_8ufL93 zY#R%o^c=>?>qnt2zx4emX*Iz{aiv&NN0x7~-3}EemUR(eYlAn0FYGo&9^IQnU9jGN z>7%7|Fy1P~Td8*gZ%n$vF$_xRIzcJjZ7;Mi$y2U%jLJkM?e3%mtQjBV^DZf)5<6DO zY_%__k`o&bCw7=p-s6EJ*;?1GT&S#!n5{ycw4&7N41R@$z)dm=Vt&)`XT*7y@1O)-SHV5!(3iJT( zYEHf1vu+dzM$4uKpx>21&Hk=)4uH72Vq^(2mUD6e0<8GCo$KTc8#C2r$_55QzOF!4 z(@Ro|GFO0}F&RO-t={U>EfZo}EL5V%nL_(~X#7v~rY^3QL~vNl$}_B!B>A3Vu0Y1- z-@J+p|Lixb0uud)tz6+lM52W#qC}?tlI+=6e+R&5 zE+JgFqTumE(=I6g?q)z_cJ1z7AP@8YSM-FEo*L#Q!K(=K64* z&)7j**)$uibHTODooT5M=cCv#@mmS>L1Qdt^k-bhFKyIL z!l8kL!D(^B?3gOcYLDSKl0{+9KXk~8kQb82|7=Yde~x&7pF4v5{rAr(-Kp%5KzLyBROSe>qW>0MgO1@NOW+ zyr{9aC@S7ssHdyXB-8NWmpyb5e&bmUG#+nhN-oD$v3EY^M>5bGA_!1*ki@{hNE0pG6@-B|LiA|-`wb8F%Z6NS_TT(i)P>{lS-jVT zh_~fx(o#sP#?9H^oSl*Kod1LdUvXu_Q4h=Fbe)_u-bNdK(*kn=iO`$dD%eXe(H=(0 zBS#K(-138B_F2!2QNW#219-hAQq(!D6}{HoCxmd16mPd7VFQuPBIqmxnoV|EJ|n8%b=UE|G}IR%f#r;ov@e;rA0_IPtgWO8@)Ct4Xk?C=#Vo{ z2Z6tZ*wu4ijXBwf3S~}A&oO;ZQ47S=*^pKNcQJJ6e=*?He;MCIt#Fo`IhAhx+q1w` z8Ol)w15Fz_02$OyWZ+pcq(HnVjXOLD+BcmxE;g;pmmU+y8c2y`@uf@60lyrZEuB|s z&F=;?v7Wi6QHj-gV9aLop%LrD?AUwmPOBhwDjB8DYB7=ks6UG3@S7T04MUQz3QP>c zg7XO#sUBUA`cn!vt4Nt*f`y_5rXyqLGX2E#Y0J7OL9t+7PoD|Zi}v9cn_*LEU~NJL z;kD3)knrjd$siSNur|GqOp~YHAF%a9{JU%Mhcy&F_aywD=URa4Y|;E@oaqb;#jDVz z@aEDzm)BvKhWBrw2ssd00K?NXoi9WJ$HHXU^L(Gf=5-7SM~J>17p!VY9ZIbdle2@T zW+-LM`OKfwmMkNhkFMB$ge}#=UHXq+U6$WU$J)PP`Pf5G#V*#251USsP6|H~`UbQGl3 zpU?4*z%4{4En1|WNE==1G9o@2FnsL;&k)<>VJ}zZot4&`Z3S^J>0HQ<$!3*;k;ai# z@uZ8}9~37rCyFM-YxNj<;hmk~wu^YBC zgeM6*f^e@?X*Px_J_Ua{1B)EfN{otIcQ92A zR{rL|u3+R7m0`w6(3WqjP70`}DrR*XNJ*H)YF$&_mD-BugyY`#gj-}<6soh;zCb?( z*)P1vZ`1LHNfCzIZh(^8D5r81NFslst|ZQYKFO&!%5Xa{RB~&3rjh84V=ZPve2yt`N8Q##nx?Lv$aen4roE|bs`%qMemNs!A+VjrP6gGF8i@udcVvbi z+t;ouWHGGQfZC^>h-ryt)X+sCPkSXHw>IwjcOUgC18dafo0mAx;QDh5>*FhufM6kd zXy#X8<;ThnH5(VSI&%nS^V#h822yan-d;`;aV5!j5xakY?jAdq3d@DuLj6os-C4k` zzVXP!hfc6lqLe>F4HuZGqPzr~I1ko*|5;2PLvTin#0tc?vJBLK^Z8Zr+iy8+u+lV^ zq>N;rG$n911v}YaYd^#WPB%B5r~u(%E^DwZ!+2->w8(NJcc6{8A^k_Ojcf z5Q{xJ#${)eShO;*~V*SUk_OI8N= zmH1UZT^f={H&L|kLnpTMgxs9)crh#`aR`%QvwnyX2m~_bo_(()2jd;w)+H_m15w1e z6`iZ@_7`q7GC7wAQ;4qQ;ZV)CF+@2QTZ>vd@Qo;SXs0>$Pertoc`%=qgxQHx_-kpV z@Tnpu)~!$Gb^`pdCXQ?Dd_Ag*a1lybQ5;*Ur8UK6url+b>K~zTeR0H$(~CS)b5=Bu zjja(PO1(`Gx;MV|quzbdeZ%*kD7{!r{)j@9gLH?b*`D%;icxsvp&!yJ7NLg_Cnw?G_8HMAQ6d9mVZ0kW=Y!Ujhqk?*YjP<*qlG2oY~yx?5L zh_Q%_&xJ8p(E0`e;m49a-;0A{NZ%&JNdq4(ex9K9CA0rhT&h@Jb{ei9KNsVP1r2wT zuF!flSh06z?ZJcnfB~KR{8iixw*fHQ7AOty!tU?Q#3IjkEtPGZeN5%%KHiC>#hk-0 zjd*dT>h~YyJgY=7(}(Q;#ljkJZ8L#B6BVJRFe5!wg%x06^*MXKl+bb<+~Lql6+=(A z2ACw#*@M}IAm$0_VoPN*-7t4tH!=R622;Y5AqbkLDkMW+83OPPGGEN~%_o78*U#t0 zp1YUy_{v72{~VHAsZ>*vc7Mg~6cHKcM1o2QhZcxh^&U8RFncwluP)eNUv6W^UK58V zJh&+SE^+X{wVkuKmifTL+W2Cx&Ac5d0NmC*Aa+j|qs1?7^oL!UxaT|DOZ6Qhc&MEt zqouoxI)owpF-%&+z3Ux3sJoEz^_Q-tFmcoOsM`Nn&lhZ}b4`q5O-{C{wT6QzI95N_ z#SelaJ5`d5=00(r@;D#gzSab^OMz*-)-A7N*%*IwZN&dD;SI($zL>KK-)J%j~J^cqnp11EPm z;)EI3vfPO7y>vj+g1V{iJdHmaLJMb8n=1`dS+E$@UNUSdZvzdbKCJD*!rHKrU>aZi z8FZ;HZ{JNiPeB{P9;r1O6nB&wZ0^AEdeOqts^aH2FVNG)uSMhe%U|1=TLcgE5*>{a zU&W}5W&+^!a#+ zR&?L_%gqR)DsUIaS5ta+cR%-JxxjJqE`4R`4|nd-j38VHU6KlHCq(LK^aZ$|wHGE9 z^s4??=|_O7!%*Q)iTO=qt6Zi{Q-k(L`L5ImV$Vnf^znQ;V6IvZF>s18YbiR! zM1!t;H%9Zug&?e+F0v(bhgF(n=h{oThXqbw!W1Sp4F$-%9x3#b??uF%n+* zR#*&@so96W?a~_utNl$23@*aW(tuDo^I9$biD^d82xiHSVnhmNiM64osIcimU*Uct z&$dD?0?DRLW4d@t3#Y3rWG)HmmX4e0O%fm$&!;|iLO=_-2&gHS7X00?D}}a{lJyh5 zVc&88pC>$I?We95Ng{=csqu4VaD3@>pER|(BB-;r*4b}4zpoQ{1Wvx3!~f?gQM3&8 zqts~3s0=nUS@q@@YapNC7zdqU&#GSW@trdafhjjXX?+Y##SmNz^^Dpd{5F+6*KGm& zODjy3^8t9oN%%bRDfaR9iS^viK^*?&ni7f+%C|OrjZ(d)Hq?#NSbvHZKJvbPfQRVv zM-T|d{PmNIk!;|u&$D-Ph!d(dTL;B6yi&gYmx2}_?C^=P<9TsfEpX!_Bw#&nhF1^csI22Q zYQSTl-sxY7DF^rjMHwPl&`x@6lI3XBGoK-2aRMIE=Fi$Ab675wGTZ`nBkr_{U~^9} z-cI#s`wOJ1Fe{&yV2LIE#vePH0l*R{z&Lr(U@*i>GWg#0)B}1BG3FnNGVKD%D>JY2 zrvr45IVg0I9?U0R{i*9Otb5@8KNvGWu0|<&&Mk}JrFqldVGkJxBweLh+)&V8=apql zsw=00kq*Nx1jG-Vwmabq_g|9|g3b zVZ6J5fS5MUX1KkBQ=Oah)i`-VJFyMrMGhMcPgymzKlRgJRDXC{>qec0znVnz-OKyt zU=)*MA- z_0A#r#ed#-Sq5~lNsnOwc+H^lV7_3jK7dE?%CI(<6`|gbRT&s0NjPm%xTD~%a(Uq6 zHz5=X??DONnn@6mrs>=Wszhj@JpZxijIV6OAqF_muY!)JHgE5%VF}mX4jhcW5AhW? z;Wv+h>>?iIeDEFxrrV0Z`(ui(2dtB|9e^!T535mo(2TvbU}^pa)vV0v%*RJ$5O{N| zzo7hPB!gO%Djx`6+E2d-j~E@%JbTQ6ny(T{d6r3x&w0}pH-DnBPt z|NYYdBieaUYQOpF!Fv$6y>UDG)8UMZzSb-S1iRmEu4WD;^3I5b{Hvgpay<|9-95I* zg@8lRpF&~!{s8Tnp&}SfapA$C55=Kp^V8A`A8gP8Z-gZmfYoX`yfU)7vmjx~l#t#8o*fHKHSYB)`G5PTO5kpU=3T|Cm=Q1OpnYZEFU&FL~EzTGB1fjV_93q@TJgXx3xvj>nXs^v~IxbCPpjc z%zi_@dT{=V=k@y~pGPdjVz{E1HmD1HlL3_+LbUawZuG$R=ka7E<;`y25;n|e_WXvX7u(!ryqwEGJ-5s!(x&jYs7_8@f(20$;FRdbcz#* zB@fe$wD7$P7`;DY-i1{Y2=g7!`N+1XeRWDYD-oV-{{x3!91eWWALazDo%vW6-Y85Q z`#(Ok?2B+yx-a=PD@K0IX7(fj|7I13>;PkzvT3mAfwFQE2+XU;@Bi{YVXwhws(Ykxn4)!ICF*6qr z`S~}23Hjzw)_l^fvP-h>OY)L`GTYg*nQ&m8|X0<97 z+y-N|-prD7s7~w7Pl3Wfukd}L+6~1C`oJ{Q&Ny$xYeW{QRBFcGQ*IIdl>bQ8d~deQ z%^f#Ng!4*WGq0*}1CLuI>|Apl@|YW5+AEPT^7onM;hJ#pQqaDKwrs1tijMW562pza z46eR*z8jsUV-hA~?x(9;q4O~0cN($J{3sfRVxIU0kidwgc&r<-Ft1NdaO*d5#r8K( zF2q2`H*U66|AP~VO?p~%57gEV@SJx8Q%)0I74`A2n4cW?osuD^m6$rgZ4(aW1(h_)0ZnuhlB-QTrD>8Tcfkp=I4AI}vN{vR zFuku33AE^cr@+IocLkn68t3$O&|a+x_LebhI0{EbD{m+JG-hZ0g%Zpd=nuTE?CYg| ztqN+P7vD}Zt_RYxd|KxGdp#gjfO>iv&kfOhJ3!^g{PgvvJ}+B=aU5#9`FoTflObkw zmy@4XyjK2A%$)w{MnLr}r}@{X>o7NsD7k!V-Z-0c(G3c)rh{Ug4_y?0z?qSS-{ypx zh8=bVL>8##b{Tr7JXA%PxDDKWl=(fHU{}MVlTGOR1L@@Glw-~a zo)K$|7+oxK@HA?IbGR+bfO%3pX-CyMh$J}`g*mB5A%6I_7I=oA#T(|E??|r49yNmX zBbrdh>}LxnVlyA98-Y3PKc$qkXzEkA~jj--XmoTj8j6X+glV*_tU~8pIER$D` za6&fWXa%JM>To*r)(O@dj8Yv=6Kux-{6@GV$lv9K3TEuqH7Co9!rM?09sz{O30m;3 zR-=k^+P^P9ic_}+0c_)n)~1dk2l;Vj(B|Nv{;4 z0TZ6k1=16L*^`83Gj) z7(-X+oF*b^e>**}>K4zXpgB)hn2vJ6%TlHWUrTQ(O1@0moKQLsZsqIQ(6=Pz(Rrq7 zW?W(+xp9RZd5qzZ`B>&|AT?I-SW!#1L`WDWEVy0fmo($CMXK!3lN`Y@gAk5guFi~I zci+~n>qI!QuU~&B86XdDf?L#cr4Pb4oI1~^9Nhv@svHEj3SoD7;&T$?Ybz?VNXrT% z*j1p+&1Kc1EP)-Yde&j-5id6N1AK}pC5FJFGDnO8tG&gRpVq)TwlM>xTQswhX#2PV zg5D!ILfnqwX%e#V0(8-*bnV3{%@HY~%p@ZES^eG+k%e1R*1d!6Td67+yfwIJe=*AV zFxl1p9!sz^*!5CzWHuCjb2q1=kVt5X_AwvEMGATi6)lNl86ri*jklf3nUM4*G!L1{ znTKPY3d_(HIlS(Qv0W*Uk@bN2oG$#CWc}xA0h=svEM;SI5BcX0cWXOS6p} z-c6%GLc4PMVX))e^eJq1lv(-2gY_QcQ4~!KbDW$6G+>xIw7>+$Ep6NnlIsAI3-h;; zI}kt#Yu9-Vp&cO$L_lXz9+3KzAA>C$n0YDIQ|2CVe8z%$fpKY9d8ffXr4T)l`3Ngo zWM_Zit%hb6QYMPhGlw^XDWjjA*8|g|E|I&!W zM{2t3X2iE?@OS-L5WNLI5%d#Pwn4?;H%F|#c2b%lfbB!REI|3Yns=v^TkRD-paFTm z)y>ID1ePXVRuyB#|JUQ1wL~M|6*G8yN`ppDnZbHzkeF2%W+eU&@7!J&)larwtWM24 zo3Ox5TGblF~(Yy27{=i+UAp%p(hXGV3j|B}xvF5LH&^7>-lZR=%iv zy(gYqLpLwI*C)lvhOmxCx3r5Pc%un%&o1WrUJ$OlxM%nrQ&#_a|3tYF;{3${VBwIX z7aZZ6ywv=N|M#dzV#L`EwxsnQH;ErC{lBqx4&j+_!M0BF#kOtRwr$(Cla6gWUu@fU z(y?vZ=>AXN;hmh(neWLS?0R;sRkdCur#~kN;42NO2fYS~f?2X#uh_rMMKO~dcsFG? zyl+eD!1GSU;qAh8tE>okF3QM}sJFqo8v7pk+T=Y86PK)V95}X1XzZ8c*{LTZ)urhi`=|PaBWDb|c4H$uwB)X0j>a>K?d`xE21K)L?sH_bGXsdw%Bk1l$SXC+ z0zc9*EZ?Hhu+?EI=uAT^aG{AX!`tto(VN1^PW)T&X?`bP4%??Lz&Z{Uur zX@XFA;z9YL0Z}oO#iJ^^$veDIgluxJ2zeG|$mG;cYMgL3%thlP#$fJVV&H;u6pCJR zADOV1&nPRc`+-u684phi56E*<{$3Iq>YdmDZ3ShZT+DVJCAYW+RJf#rs{`xAK zH8XQg75Ldv2yEDWr3-{B5WS(yy=oH3Lws9eoIoGX?@N>9 zK|#pU80%xjGCOBO%uYO;4wO-!ap@_IoF>l4I_es)&a?!~O-caD?)75iw96zFp2p*| zyz6Hc2IiGdhr_$nF83}*rF|r4Txu&w7yz9%J5#MUyR9cxy6qL&z?0kpH^VrZ{a$iE zF9ff(4)zYTK6_7f(eU(DT?kv4vK+1UX6QesX^_5^4l>LOT@&GN3g0_}hQWS(d%)J< zq;>N1-xa|UWbTj>BvTjSHMHLmFx#PccX2lyhzB;xn=sz&&}v@Yd|69Y!I#G0<-9;B zY5}#&&aPzgW`593QZy7!Zz$afhxX&_s1Hp&eLnb)tQ&~fYk&9E{t^0hRWXW8E0`~D z`4@|V2{R%m|4-+MVF!*L5;Em>u1kzxN-s|!Crab@TmU3vAEu3url2rU$f zktrkKAtH`pZWSExT}KRxxO@i#U4N&qP4l&W6iRYJt(HD+v+vf*r;8u_)ZzV5TZ>5A zMl+<$OQIdk4hM7&QELraZ|l2vCejs`1NbaO6A(maY6VQN1}6|$Py{JAU9A`R*A^yH z95H{#7NTiXd=w;jr3m=0_tK z^9QyUGiJAY(wjl#?dH(%tnE+^(g_mp@ek>(mNTxnmKWn~#b??3-qrRhlQ9foxJ#ZU z_PXLKg$F{R2JZ(Jo+!mw3LGi!@VUOcI5B+&G!j<`FvRz$nhXmx88W#F4YgT|Weg|| z_Q{DjI^|Fj{Mbx*iA2w(T#|LYG(7CM|ace{ho01bAxq=}`hYMNb71N0o>AY|)Kz4rU;5a_DKh$D7lMVv=U6{A`j_N{0tW#3W2<|1~~I3K83v+rMwze=~syK z#70uilrXVJMxRnW%4rW@I)(YAYYI{A*!ybMxBjxcw?t<118JZ>E2erq`iSf?UO-<8 z35e|mz$*Nlfr$ySA8t6|3&}9D@Ny~z5bnKunu#V- z)lt`__?@U52Xu@lxgQdcpu%&Cu7$(1#U&Ia1Zbk(zM=uey97!2u1(`d@7_EkNPe(| zL}|*;w5iP@+L;G`LCtE<2+2biuFAbBzN9aAK%Trv89_FtqbP|03xX=Z1MN&> zvilrU36ubZv!jg?(N}lr3S!NnyOVp6P53}h!ZzQxi1897Zidr&HMcyGLC;!fPBY1i z-1}c#3q`7$TGaJEXxZ%4(C0ypYoRe#S@@Ll$c3(Bq|M70oFc~2)@f}O9U4%#X*>{L zOQh})zl@|TH3Dl<~vGYU&)^ehz1)lFewaM z{R^$30$V>etjtHC+mHz&Q_?^{sp2&ePPJObb7x_?z--Z-09#LnwU+x4A{drrezI-t zPF+fJD2j+idZ;(v%@7i}lD`&Iils%p4lUNa;F7ZIJpBNJs@V(*)*`F11nK4+o8i4> zuwn(zm#|*KHiA|i8!@9-Rk3v=CGghvaE~#xtT@tVlWHv{svV0MWx3zFn34iUq>5$$ zSH7%+r_qf8K*#;>@@-&L?og4f=3#qxdzr_UgL7sww+pmMt;)scfN&==eygBgR+&hP zUhmpqAtq(~%#p!EM>pFL=G0mXY#~^Yt?0yMfS6Cm8c+5%*vH-Ml$m{zd`n z_feJ?Oo~iT^!6TZTaPZIW3Gy+W@EN(YjGS*WPM$d+e10U&5U;h21E-^nFAeJf;<@0 zDwfJ6ZX}{ddie0m#otxUu}VlHwh)$PKHTq ziL|~fK!<4HOCXYv0(}6NKLVR_O=Ee^uoFMod@EfN_RcUFzd;jpub~BcmgrOchjPmV zCghBS)i`=sd8_*ReIo#dKJ=U1#GS2*`7IifMjyy?Los_nB3lM;+6Cpe4*X^-(2Rb^ z#JO-r#gp3xda{=JUH^Kh4e0}vx0IzTNzkAqN#8g36RNoLpH`ZAw1!oO*@Ia$!WH!D z9F44C^AX0)Odfk^G*)=m|1i-;p~Qf|AedZA%ObF4BGM0i^zoNN6*Y(3h+cuAZtKfT z2=Iflh4s1pKt@Bu;}Z{FiAsmTTI(4E0D$w;?3Y&~3wOVxQB$Ss# zp(cUn@VEcCRY2g;^qu@h?IyiQ z_ax&w0I}lK`&laVIypoymBzvcOQ_|jwSae6o8(Ql#-xaeoCG(g=U>ORGAO$Vdoq>y zIc@gaYOqSYNYnMZnF|5>O@BJkIq^7GWg|%-=D#e>!Bw%36B8bi1Cz+<&vKEvoqs;& zRtf#=FI6AWmu=0~cbKtYE%a17$G;j|pMG`fnY$NvSVEvi2P@__35DR7V+=d~#~Li2 zdJe)4s$cCw=OoVZr_q4Q&3N!p5dIgbI@WFk8WQ)V1S&!gg{1YE?9|P6v>>kmsJX`^ z$=6PjZ$7!Xta6weRFHC26-|+&%{8N0k~hQ0Y_NomFk$yFC@TQcc zDiEN_(w*YCy0n(J;*Im)ubO3@>_25c)L%GP9N94&z{O8}-RHjLFBD~DjZp+WkBb~R zEUa4IFAPp$x$8nBI66F01bV#yEJk=$noIP&*vD@c7$h(ua5U2>EL<0cjVU_Ozt`^Q zzxx{<@hP;>c%fZKQJ{d{xo@Bzf-#m3CW`i4d@P^7;Q_euO|2YHhBFJOTCuu!f08g_ zd5Cc&TdF1Vts7AR2`+wF{Cj;#}`$v7T*X z0sqmi4d;Go!-_?T3?kUy!B$^7L!~}z$k*z$6Au;>1xO7!+`r$e*jXs`i_w^%Y#(%t zX~=$)m0PF4=P-sz1@wg=+#;*rrcUA!poFqJEYwa@Y!6=8P#vTsLo0(Qt?3rEa6TAD zsfZx25Mwmpb*18tLMBN#5kEYW3Nmqqm3RS|Jwx&td_zU$B!_ZT*v!8~BxeT0cpjEP zLdCCC0QbU=mlxII9ATw#yJojM4M;n=>VGpeQsS8(owb$`({uPdmM4J&DuoMVxKTpX z6N7;@DBp1f%_%sI`?v61K0yqj_;$wv^``tT7$0S&Bq7?i%3UfX3%^cYp4k6_LRRWU zWgf?T5oN8|{IN@TcNXv*tVGMuvusy`k`RF@w2Nc4h1j0WA-G5LRAw z&rwMOE^=`xjOWxX4rw}tHB=>m{Si~5Q$i5&FZslb2RPk*O2K_E?z8{*!`?&os#=o1g!OlKLOkU5y_F%U(bW`d=8g9A7W6om-EVcoo!MVxydAE_3h=Ed>cF| zN{qE@Xf_5=p`!n|;qMCbb|FtTw0P3T^DrOv4$uwfKL8O4_fQc+sE-2EZy?``P``EGG=k9xZ&B$V8`ss=*9@#7bDFwJ{n8;Tom#@gX>vg)Lv! zv)7S#*pc=_sms-(;51#qS2)aYBs7}6z6mECUE%J_y~;6qil0)HGggtUJ#9u(^ z(PTr^qTyR(5-HwfMu0aT;fd1ByDZ-{?-Eh+ff{OtRUqyp6K{f;f$H*zQ!)0;Xj)0P zo|EiFA(UoNFhv7R*vZen2>!=1Ww|ujN^!yT*c9@AdWG&x(Rten0)q6JS@0=FD zce-k}&hYTEsR3+@f6)R(D~VD9d!-tuo0fw`Sl(L+j~$D5;Xbe=}05uB+{dULW}4giuh}z&0Fg! zLlKjT68R8JtiQ7|koTrau8X5E%+x?F6Q{4^!T(5d(o-u}N)>&qAxXXp?Ovbc`M|05 zbz!-tEIOxwGripJi8n%(lmo3rSG&8x=0vGJW)@c~Lr{s-pmQQC9R4&`nco<>K0#65 zt;9pcLM{3Ba*uQeb&)O_9;-UNp%TMq^T1$S%9 z>DLhKt3_3(Az?WhjOiy`KsR`aEZz^ntihxmQ^pDBnon8gKM!Z`Zd1_sDQO!|*eev= z4j1Ptn?^d`x@R*6fEhF*BATB9=%a^QXi9GVAmSnpK?p7zz%^q|!tjr&W0u~C8#Kpadd4lMx z@3hl2&?LLOs8K>YNKh4I=3~y#qAn?w;3s4%0oBz;@nBjQdF)vt zNv1C9TYvYlFa$JrG;9Z|v&@moBtpLX7BtE@QFh%Q8-jY-6A=hL+mHA)7cCf>oH_{j z96sWqwVie-74e3Zy6&-B7XBb#D}hGahO>9$JbYE{6W!s_#We~`C4n+Ed#ieY9&$bb zqEWDtQVw`?<~VH62xEGL%=5T{Ar3w<-f8wn_>w@-gbTKlGi8t@Shxe9H_S#>Q<$$0IQ(U&lye}Lm@m!GDxw`vE973m_Qw`txBg!lE_RoNSNNAxeuBX! zgfqX;=1B=e-`F!R#!D&Tc7j|C(b?;tHKJ8L-A}J&!rwp#if%=XLE!Y`+oi8=zr}oM z)^ehH+wD9&Hj76w&0eAB*(>t#C1lby>2>l5g12F^7T7lib;lsvsbK-xwBlv6GD)#) z4Gz_yBy8Y?m`EF?gH+Ja9{e!4IKmw(EjCSGt6_qv*;>uaSjgfUMnLIC$yNLllxMSn zBoF>YnJsXJ2s`yVd1nNMg*52l5~2sbZ#fcWlIff$6*gQm07c8{Ju&Xb3^;~u|0Uf3 z!dF__=+_UiaWADAmnT)x$tJ7}ZaxUz6>=I?8REHRFA^CfVAMq6H50C1I>d8M`HOCk zQ`jL0nkXsn79D-DUVW)U;$`Q=M}1g&cmI$lYpBLDfzj9YhD?HY#%>gweGIA_@|X0W%ukBF2y!b>t;B&=@>HFJQY#urA>h z;f$dj9$BU#P4qB;r*q0!f-le%;Ha!c!RssjY35naw&_#l)tn<~i=#!}1MLZktFZiK zYt)}mxRIqGZ>P!^+_qW(9BH+O)%ycd)2u^zjbW!p!Z4Lid)x?D7&$e&I+h%6et{G{ zgK%tq$vm!#cNY9wymJj0_^@vkniS#?p@CtM@ zbf~f^YE`>c4zW&3(+s{`0gWwTiGm=|YXTSYO_YJzF)rss3`V)n^+U2Hw#SWT zmsYjL5B`rtHfPzK6LOuEuI~!Kphy8+98uJUot@ELDFgTiejQj)lQL{GFuS8NA;iSA z|7}Kq>2w3<jw=h0l)dB^@K661fp|UtXLzMmfFeGk zzKu&S;@kNc?coV8CYBXkg&TO}f1y0G-<7{Kz;%IW(EQYirxG<$eA`-HRSep<|EVjO ztl8$|zsd2dl)rf&erp+VxF>Fc>UOeDo;yuk!zah4=KUu+&3bvV#h52cK=-5LicDM5 zh&P!4K$DX6MFoBkVly1}Le)avdS`Roc1Cpmy2;P0bAZGy78No|@j*<3uz2}`D|VpS zE)#mhiDe zC)TA!*TFrW*SK?=v>POJVD*emi;RPIxZ;O&4xSk*bQ-G8lu+%7zz&eK2@KwkJ*W^!eT_DY`>~>Xh*)#oyBDu)o?3(qKAx7(z;rlNo|- z@U0a#)AF}^aeCuEJQ8AIabCCODAbnASFKeM==2W(XQ1qkA#tT4k*5Z*kc8 z&om(yee|&qC!@8pGgm(3y>!iPQ->8!J+eiuhvVkj=PtD1%7kvaNKL&3_@c!{rlb~R|mavaNK57uT*}dRIe?>tEHE6dfg$mB1v$fq&?%sZM$+R6+se;Kd z=0xRTsle*jJF1#;;?d?bH#sryK+MRtz{!A$edHcEUUF=Q0#T*OZ9Tx9gMx~;XL-0F z4l%_mXu9tPJHUC#&oLdk@q(00Lpu_snEgaAz>(WhL7_+#1FrtX^|o#xFqYR)MZoY@ zCi5tUD3X>}agZfPV1V-Cj?5DLrEV%dRCK@o7<}Z-CoSvW7$W}zxHGOxRr3(vgNZS{ z*8Hxpjt%x1@Xc2)0`aWx2~2SCEgu=-dhyAELK|!62 zZ@iGN@DOe^Pnkv5ul8$$P%1w92ur!Mb@FP%rs5Ykn&Pe(IjrNJCRH|d3O+(3ukg>a zR$_33k_+~Pn4cF6u8f0=Gdj^ueEQ~ddyf%{-DG!l5?1h#V-u~8ogyoj z{c?o_P@s)zDGwv9FV*0&bq-z+o`V9UBK*U;JFYRvOD|&zZaZ?I0-#pl@LY1*3!Uu< z>|796mSc0_ZAaAw{`p{gWV|_pFD=PblO(-V1@x0?Q3^Qv8!+Z$FJNZn9UYmFzMh#T zZ5z4D=g=&D>FVqXs_yqM#hrvKK=JQcREukCvO;+DpRR#K(3N&Rz0&Jf+4%ZvLaV`~ zM){|^GuYC5uS~mvapyZPw3~GRs^c48wGYf-Ky{<4vWM7f1)e|KV_#+m^gwTpbVxQy zvc~%^QVK~W*KuZ>OBk1+HxjW(B~+UA+l{JBQc$?VaviCXwno8jM^64c1fvHA z%M-1X#?e5xPiSrgWNRRo(rA10hOP+%Z{lk&D{d1zOmmWf>|)JE-{woXeGfL3^#bbN zZBM-vv#n|bXcx*UnD|J0z)*`AxlniJivM22_3K}F61}EkK4P52HzAC$vVZzeLq*ym z@uuXmFC_vP07UJfIuI6-fLIk04Fj0)5LNa!;~#WtOMEz139(V)n*MSMwtaIx;AUOq zUh`{7-2KsKj5dTe0f5^}bqn|X9h^~AXB7g2n)C-DZlGt_fxM^`2vZgsx-MD@i_&=s zf@XU>OyA>!QB#!uZqvS5=>~90)@7C+N{|d~HC_OthGP6>)VDZ)UwVPA&V{86>A~9x z77mPt#2I-Cbljr)zzv*w{uzyS9XAwnf{h}WZKHp8q`4lSy_b#E!SX$+2c`5PSIXHt zpK87QBWQpn9(EsrB&TiaSk|}O_6Z%Pqhw|b?lsWPrtBTK+wcCaV#P?(ppqCZ zwdImH6(C&_RD({fJnx!KWI(_ubCVnieIgGj=nhB5NVnB_o|w4fw#`HSB5h0<)C4&` zh!VMD9J(mHR1c$BBE$3U zG7sgRNcRWw=&dAIm|z2PtN^w0tCY~cakA2zg6qo#D}#(Feo0qsHCDlbaLZRy=dzq0 zvZwy#jRl(^>|!u9f!;2e$v@dp)V70rNug-oQM~HT+C`A9FgJSn*G+12cnS}4`YO81 zT~;dQJ4zoZ;13kImu*BttHR`6$fae77>f`L0VNxv2H*(=FrKnTK<#n~2?sGbPguf{=S`CU-m5$}jwstu# zD@yF9o&pP1*x-8{XUBSl>@r|-^B;pAdD{%xqi$M-$QYWl%RG?yFnq~Ay!6aDjKC2$ zBc~o{j;E4x+;~-bKMbe|i}H1J=his2|Lh(d-{ui%w)fzMASIfU083?4BdF|dBP92W z0-&=b`wTye7A z3g@UN@`VNwuT=U_4I-=>JH+gv@OWoM?M_mFK`aXPYrN^WN+#5Mb<&@g_*PKhPx75S zboMZz)z9&}L=kPGv zaq?9Lcu`VJd0!~C;1%4-<1}kduW4cR5(qmqCAd40#1OU`9d=W_6Ne&DhF|OpCve<) zf)T!tV}svgb}74llT3K3v5TJVX4++3@OO27Fx`ifC##CivQ*#{KoOzrrV412SO=O^ zzS?ch`Tl{qFzi*|f&vN9`sw5vZP@fOLgOUqRiShbb?Frpc@+!YLBBn~bwD*2ki^h< z!4OyLKb4gYaNU(B1RHe?b z*qRxa5XPlBF_!U*X)?}{@n^=J>rxyxgBus?qC^CG!0<2O*YO9z{8+%q536UaCB+S*TmPz z1<_6jOQHGa1ah4Fcyxo5p*9Vd0GyhD+-qUun{GfHlZC#Z&^e<(sU__loujEaG zH|2Wt_GL50F!s{7Fi( zQP8N)Nkw>AmvwnHxgiF#W#!*Sn&V;3?#8JlwW^Sul<~dAD+HaG3ez%FyZT<~!cRrP zPN2Rcyu|(2FcE46S*nhwiOfR*ELu1eP(S<;C~Ntqfj{el7zYZ}@ie@p`A#8L^1hbI z;+9$#LGY$eP!Thn`ya;zds1(t$NUQ-%u*!69%$LDgmI+0bne`=HOtQ3hD}{Y)CikN zvC(IJk%a$Ua^&=wPqvjeY!!x^<$v-o(Jc*v`lhY2DjHNhc7K8?rkOYL4 zrUzbnI{4{KR3FkZ^4RmvC>e>z}a10$mb%GCw7XfA5TC zZfphs^U?}(3Tv5kq2!+MDP?bX1=hTE6w}N*-z0U8vpn7AcOrV*^ycy24`-3;xG~Yf z3n>{_jx}yAWexxv@n{u(WiA0==sLNgizJzk8*ld$`T(QiuZwM@vCHdM#OvdS`^#Q0 zmwXG(84?9+*BmFx@I>rd5ZI3xzAlce1vZ{Vr8i6)F2!gv&;R1iuxg0UU(wgCJ~9J` z5gnN;HbjGJwnGCBO4COi0}W9=U$MqqR|stic4;Y%aSl|XtGf(UiKNjoCc8*?` zjLo>qV9-PspzNVH(Lo589-W)p@x_%DkHA!RLL$Rf2;^acpU1?h{5mB%%7mPg^Dckc z4E@sv!6k4pEc*JgBPvg@!S`tD@rIIhE9v(LBs@zPlR{EgyembeCET}teigdI{sK4J z*MDvw%9cb>W#bYnMJge6>}!^2g4fM3EnZy;_m?Tb$(0D4QL1++<{VK8!mPXBkN|J+ zjlx&nMvXXuMCQTCu*@U~pM-F5tQhxm#$`=5^SOO61beC(a*}hSVwG5=!WkCU6?ki| zWdS}fKyh$gE>5O8hhIK>3KX9ng((mi3LMx)OfP7V*BS!;8EvhQN>={Dg-73}Lin|M0wU z*V?()j#qROn6s4OA#-%3rvryLJYYG7<&gnst&#PR+3YC@(vdbadD6kRk$tIaOK#g( z(*{H$Pgb#%x5~`(sBtSV=Q-s9&_cC~_h|N61_T+rDP*G6#QV-mS67!}CaBnGdX`iA z<9-*YqzcIBBA&e@J-?-H9S7{W9qqSaj|1htuZ&OC)g6`YwgWeZufdOAFRoH#tfpZV zgB>CgvPI(r_dHM|*P*li1>Xyx>3aOUF51x@z+48y zI5jT^R=_vXy9NF&rHagDZGx%#_78JFqto5_dA3mmoV-B=Oj4~6E!<_l2&21#j5Cn0 z9`$~!Vn%_GZfRRH2$%XGVIu=)K=%WYVu$(`fErOPtqA;Jv*2wy31pKsHe4Xgcn9~N zQwOXS38?`}A2ATW1iDk=%-JN6eCP9UQHff1?YdpZM(qpP_z5APxrn=>6SUIwkaGj_ zWnyBWy@hP%c9BdJK0r0n0SXYv5kb-mqYGLMVrj3Km@hZ-ay{{~Hacm3YLbde+>=E1 zUcq+Z>YT1Um%ib-UZTo;%AIKxC&!WMyaB4z)bgzavzsr_NfNw6qQ`uk#dzhRgZl

e?glG541i%B9y{Wy=O(9y#Do)R`kdx7I@eY zVPkF~L~OMz-1APodrQYud{H8JlCsu2_v65+rtXnIp1!#`>nSwb zF=bFu&jSOZ+la#@O?qVFJ`LY!QTSe_U!xj=igS1HfqL4I=ltG-3b|J{V@jrI{QpU9 zK5_rf1w4Jg3}mg~WsSEBN0chqBHXhJjb9$_k<%G{=(nGb%5V)f!laV(1VQ#8B$1N# z6tecA=9iLuq^XDnVRd{8@~IG=mLUIKHftm-U@3NLt>BQHw(`6H=I<&|Mu>YdB3{iw zl!^dFZF>Aq4$#vxSzV&?L3FL5#v9w>^@l6< zbSVm43buH)Rm!gDc(Wk&L3gEWXPDn9;Q}g<>XXCyd`-w48B@BS?k~xrQP5%b99O%J zkvyIk)|+VO%9H*i{U$#}Od!U0t=0vcH>ojo&*fZLjcOw_eg`^3MI2^SX#sz)7w+xCOmlv`vPR4g4?VlsTm5!P$9c2Ha>+FXM(}4r}ps{EaJ5zo5?CZ<} zgwi+{>;|IrZWenNb`sDjNoTZ=#P}YW*ph(}+j13-7t>(-V_riGc&`O~9=)ZCp{g;i z1`ly4qxY1uHtM|ok84T`Ntbfd?N^`kP#46gjoQMgf*3I9oAOEs4m~qnP?h2Z|AkI> z5uS^P3_6Chp*mQTT|2DJVGIR}Wt}+>iMcsv3>7&6-0}u9M{)4;WYB}8E?MucCHb&- zDaoN2-^0`4iuue0_SZ1arKr?5LU!ibAmJVnk*5V%kQ|vEx8Ma#)Y7go_|XPsVou(+ z5e@G@x=`w#{CiMJ9FsysDwc`d$;CB2OP0-yILR?LKmB*$_q1MQ$lKQ+*uZSLL9rxK z0Z=~Hm0};y_g-2ekOKKAtZ^}o?&+JtxzQylx({g?q=Ellg2WPjU zQv`qau)q)k9KcJ6a2yoj&g{+Q_})mr`=DswSd7)8A}FZ-N` z5^nqIJ9VGgo_?g(n{hD_zm2;QyzyqNWSkzS0Xh%3Q;+@zqh+I^i1x(rS;Pc0_>XCA z&1_F=VCSg${;W~kS;TlP`A@v>7ZtToJFm+Qnh7tG$ce^H;cCr}hY(kJ4E`$cE;&*3f zAT=JlbOX3ui5Fbah6s@pDrsb7hQG|+LcB5o3UdKcrfuz{Vld8i5{E>eB4~#c6z*m- zd8rLv0GBL;Q_L5+tUyvCUg@^E@iVQ+ed)Jz!rZ4?aUMWaetkW`F=?40r-wr|H6ki; z0aZL@A;VoSW@%y0I{$c;IBztL88sHfMEU0Uba5$+)IM5gb9S_)T18_It+3ypW^-f^ zI(=Ta53O%0_L_>CGUC5mbUmwZL0%ujt>VJp_MUc^x>|Xs|5BT8LcfjzTBywD+ZJr2 zIYj7)XBXD5E0>Poj%M;hvK-@B-f%!|B=ceEZiASZYTV5&ylGKNKO^)aJUndcMG7~> zp{@n=X)iDc1q4E;h7UhJZ+>_(-Fpl-!QLfYI?wp74vOKz#MvY^R<4ew5C4Q(ZV}jF zlaZ1Zf_K!*DfYD}cvTp9f#{94f3ZO)x63qFy6uxP@h?!=ApQwmu{n9(h z18Wo~0oLlg2;9E(tndS78gp#IXFg#1h~+HO_b3AEp1Q|Nx5Yijk_Q{r{*+vFE0UZ{ z?==vT!UPtHmk3~vl9AWITvj%Ncth`1ON!NANJj2FB|`)o#kQq_J@;_SXaT&fMH}cd zX>l@ut`hS(1oe69_LN!z^W)p0Y^tzR<_vynfTorCO@u9moChOfO-3foAZPc7ZT2-0 zdvfc@Rr6m&tl-B&Xec#)VJ=aZ;eQ`BSDycNb`UG_e>v70(47+*hVWjzj96V}C7v6&s5>X6 z#&Hw^@VmY;Nt{87qn0CkHB-nYmGB1SW_n3p9USLDKyq0CTF~ehRyqXg$f`Yd61kQc zOcM#c4tBvT(s;WLs2>+=lUTqcuXIsiEoczy=y$yKQ0-g<*GR2-aNX|S(6IkX;S~vM z+Gh%>=<_Q)xt4D=_mzH9z?}Rph`p-{8D+62U=#eJRs4uIK_fos1M||AOxqm-A|r}A ztNi{GTi2xM9Ubs7BDWo>VCGC#M14L6otmY7_Jyjq92MY343bBCz$Oi@HXtWRMHCI} zwYU85TVD>1$xteMxTr@#^^0GOu)nz{EG8wi@dRowONkmJH9%SAj2H#T5CZc7WRvhg zm~`BlD{iI*DbZt>CBn8gho81Xrn)v3!XR#2Hj+AJL=ugxcR|{Ne+jj7d`P^(HHAlb zpIRg6X${YzjzMr>${Cud<3jBfHh!usaB)waY9(?1bIkLXcr!1DTj|4g)aUb@8W;Pb4_bZN-)azadmA3QQpL%(9n*_rLzUQwC^-C@FX= zrqGmbBz^F_#F#nrc;3kywX;aBOJGt}$K_Mh{l@-F4VUA|&?H2SRM{%WAD+JGlL@Vf z3G*_ic}*xsWnLQ*l-0*eSArAcIq328892dxGC9oumN;cC7e!~)b$7EVY=ynsvjqsw zmMdMA9OF1Z9??{2erp}~xs*c$()j_D0dRM;u5eDZjwvaT^f*dYN zUzR&vod|_4+A$l!C8jz`|1)Oyr$GNeq3Hs01ZuBymfY39HQ11NzR{6TgYUUFFCmjc zRG~!zpDsspc&|@vUt!6h(4urJ$R8|1#@6IqZv?Wq{IY9B{jJdyC>7iY)-Y)d2Zh{5 z4szOE_jKhUW(JejE4n|v-lU1S9txH|9-H9FgN6*FnU$)kO_6T3W?%9}Qp_}n=$Jgu zZ)B@QT_?%ANzYi17pZwK$+HK7QRr-Xa;De|&dV5zm-<(tg2yu4Va;nfU3uehO$&Jg z)SMaVaW1&dMdc$GROOl0&l8P{|E9XJuk$<>u|oSe6oR=@73x~NY9BM}4Bn?ro+VGU z+30ti|0AveIqRlWyqU8jht{ig@=)K>dtkBkoKpRNZn}TG^Rc}y6mh9auOvxM?93Df z{dpeKBt7(Y)@fXpc_@tF zD!lb*zzV{Hb03;84;Zhq$ok%b;H7s1tUwwUL>-4`-g10^U#yLx zw*1>+Yrd${CtwWnC+?j)ALtaR!vA20lG!S`Jp}@Wx|rj>3=5gt+16!d=hG1eBGJu3 z5#tY6%Wn^#mSt)(%BB#4sm!-=jL%3O3|J{+K9$NVdkL~u{KB zyE$DFu<9{tDA8XAqbUq50HYb&lPzI{W~VJZP33OrC;Gb7182rdztRih=naGpC=v?l zzlmU=;MxJo=H7)YqJB1$hAfvF-T3#Ya7Mf(mTT@e2oB919JcKsaWdk)-@) zVn`N$7rA&lwEXLm?7lLrOD^z>pu!NYObN1k@fcT}c4N$Y2eE-UU}}!f#2T}J>P5_5 zn{F|Pr+sZkO-YM@)Ui{R)zTh1>BO8%>3n+4xGX!VDDd&`V>t7#+4;t`3>0h#ADuCq+yqT zNG3l5+$Ajn>cyOa$a z4J&=YhFhM#Tc>3{bTN=_IFep}eJ_99{|EU0xoA!71YEt~ARysbARtQr2V69B3pX=Y z8|VK=KWB9JcK=^#+Nk1chewhEwp&d^Xr#O&^+f&h z5;}+__ZXHhuYq^TM=>Osya-ua7~)=_Ld8zPcEG_S9=rXb`b<$hW5ono_2@ZAM}aRi!0}vPKK3GZ|SAd ztpIt~3)x3gWm%J;yu`)JdsedNUIJxNt_@p>fJes+p}H*l z9g*(a*Lg(i->y?zrlgV)xBU2gLVSe$92NwsNGrB-3u9tzm0N$Lc}w|xiBz5cRIt)% zQ|}7y!ZYITMMuWHipokdTcZF_WKL+65fQ=6!M4%ZvX=UCW9F7#^G^->p366dJskJp zo{N4(dar%#m$iD=q5n>4eG|(AyI%>VE%iH&)Uw<3{(JAGUGc<OpjLJ{%c;exI4yqq9-fx0!spbXZs$WLkvt>HMFgMI=U@IS9VftA z%o54J>wj2#r&!UVXiMLcDYKkH0rK()-VokF!)k!5r9pn;m-**nr3Mr4jf&b;?eP(&`Q zzOB>J;Y0e$NjuRF=tF@`J0Wip&tJNZV~jUFRiagGObXpGpV%OA(5NeF_jQu@x;x?u z{55p)UX}7&)#Gq5tyf|`q{J55P6n&^jb5TbJ;f&X79TS@XVcZq*sob*-gTlgXlVl( zMMW19j2bckX{f%`9H1<(#}X7%j{@~n4E}%aXef?M$4g79by16JL~O8h=N>%Lw+J$@uqJ7BBsW_nufJv zETg(%qKKk1R(#W-S5#FU3Ed*7!bS5!Q!;sF`bYCyJ@RCX9*xxc?}n2_a#bO;yy`{j z1Bdm?9T76Uc*>Ww3U++TL)_$>CMP_`;;0Dz;xFKTkSG+ue>{YC`!fHz_`i$9e*=j+ z80#BZ8~+!7+Nfb^|Htd>1`PlR@&gP2@So*>Ch`PaeabEh006Bi004#m1`H;~`cBRc z#=3^Kj!ysQe9LWZ^~9qQd!Jq1e)QgGPC510ObR@>kn-XHt?}3hLY5M8p&Kfb)Q0C< z?PPE{bqeOWQ3WXKq3d(74(xA2>c`1OpU-P_UEhzBN?RS@hquX4*IxJE)!ZCB-JPHJ zNqQdd$5Gqfw@1@muh)}K)mop=^Gvo~pXW~Ap7(>q)7D#FuAcAx^VwP7_oL2-gG*oA z*dDI$>+`|K&{J1iUia_OUElj_)!b%L-QS1DS6TYquT|AA%v(`%@9&#Y!P9Z^-0!Ei zpYM0Y9xsotyUEF$)$G65ud}ziyuIJ|va!714nLhImOrzn@W0)kHN0HiUw2P0gNL!Q zsXdLoXOX=(1#A2dqqKi=N`VNCiMZS{SBzdx?k{Rg9cuoge}_vfi| z)Ypo_iRbdM6hCJdUGVAT`22j#y>|Ee`25o1D@SK0`^Vbd>-Diear$yqko0~1?ln2J z*USAYUt_l5Z0Gx{)zvnV%KP`YGxbWn_UHWmxP2omx;4_w>;3EDXAqxflU?#g!FQ!{ z*XQRvQ&!i%8UBQsTk1>K?PK}~ALeJWR$7ti31iB^C?o3F)SYLO`L^Y)cGOp&XYgW{ zJ~v^>vV|jH7YcG?md+Owot8%f9e>M#XRK5Ear#NT&y<8mgY(fy}}U*kvAOW39Dfe=8(IGG1bqA2;&I|(ljMq@IUvT(bLsmnkt|i zXlBh+cf@+;gea&;oI*)^*^3~ml2zCeRf?7F@XDQ`6~nuy#NBXc^N%AWOW<)hP^^7Q z^DN=B^pZM-7i#?~!f{{`eC3*}bZE45(0IQB5iHgfVa*1{fWW-*|7MlekqPfXarzIT zAO=!~ab>8~4^&a;`m;gJ%7nd19`jUzywmyVkmaiI_Y}$(DhPZ8mJG9Q#v$+u%EGcM zWDz2R)*ETy4DwJi(*~+HmG8DHB&L$qUUehZpz`7sOmad(g{e#ht>H9&6{52XdN^sqHXOH%SIGN;%_ES?%u1g8z=ozRJ6D(x42nvK4AwnUC89z>>I_+`q<=f>6 zs*g2C4oc~3zasW8zbCD~HmI}{VwM}-f{4BzL5_(ZQW}ZRXqB=qnA`?MSR}Hx7=>~y z6qQdjMaNtrY}>$=rM#Oy2fgMa;M6_UNtGx3-<{W3>Aw_{Ku8cRgZHR20F>OthFfMw zhy=M7)qZq7{p-ujkg>&2+RAWob2+>0Ih|VB>kI8v1JBBNwxd)|L7##@O;44?F&9Zn ze29G?taglykyLZ64Hs{Ws7CN@GCDgY?kK$KOT#>6nK6rKi@o3_oevV1f04CmHc*5~ z+>vzuB@5ollHWZT@)*y4Hul2H*4J6PnMEeRO9g0e)mCUX+p3YN`StPty$YeQ+6 ziF;1HrfL)LJLKYY+$%PXfku>@zV6D~m8~^MBsRQGl|q3@W{r~UD-Kgjo3lw-`}J~B z*K7(G$Ark!uK>R8DRqt|T05k4|6q}2$dtQ1KDSXOh_%oV|MVO0-^iQ$tLRB5ckY<2 z<84k|7Gn*9B_?f}-gp!`H&V?(cRQQLegNQ6dQwf{qW@L0ZzvUbpRYkd2QY38`I&S_ zlj#R8*P?K-dFL9;HHB%5u-W;SM{@^YPP*>;qO8enC(dBJS<7?*)(Vy8i@lG$&K%E8 z$>oPoO5n?%L|g9@ z#b0Kj(j`V=PLZa;y9CC;Mx>-BT7|FV^t^OrCcXSH8QuCv-0x&}t}O7y<|w+im8*t* z*EHMSCP%%{F7kqP4zq7`;0@+biA=F4zP5@YWbNliSV3o9fc%At0>%U9^fxtbzt{R^5X-;%wf@~X zB*0XHMw3x3tECO}YNY|6sM1QQwJ5Lyn5czzA1jR3I8kAw&gY$Fcri4Q60Haieg`qW z`55g@k)n2Slf8_{H7v#My@Qgdr7kxgsnevH{*HPG2;HPO`5ctUa&f~AuySxcEMCqd z2J8VI4#ydE&;u^b(O7)_cnPYyr~>G~!qs5qOFvkLa;MgC<>Vyo`J0QTR{_kkX(Dbt z-VeYHDEf|E(A&zeSxA7%r!AM{mfRh5Y$w4Jbz$zDEEkKK)A9zA1oUtyN3(KGK8Hp&8+$*DJ zFmE?EG*wU+7--d}@-uz(5lh5=DZ!Eo-7!i6Ib6*|N2&l^;AJiKO&=^b134Do zID{kls^U{nB5;8>O%s zJt8`lWUW8aoh_47!WvW?Kp7s}jCtPrc6Tm%h~(se)DJ(6#J>4*025;*6(CbFHZHWK zN=bgo(S&N#VuVF>aiChe`UMdNfd@Z47jk2%+$jYUQ@^e zK0yplDzHTDWFxIl`cyQG;zP4h$L(}+6IET}pa?Vr$zIQN>>_)B%_wa^R$Y>ipVuH3gOEMKR0p&6>jI{c?v zE*vO(SMF(6{jh}A?}>TA<<(&-{CEP(>r*QvEnE1VUye#NLLn~;!_09Ept_SBic51h z+zerz*%UHXul5AE8PFEJg)fasBi%B#_=lysL>m$n;!S#SX{xYTgE=7oAT53yRiQ3p zDu1xN-K>i$RDz$Oy-63h*Q&h3!1TOH;aRK42)kIh%e(_z;*$16@Sfm#$v09yY|tn) z)8JmRcO(M^WQMv_g16nlcq}2(ZK@*YC<2|P0hXN5<0P<7)~*dxzr+Qb!liRX=7;X&vR~WOH6VIc!nZX z10P#|=+ue>DjR6pz%g+U`s@Is_LoQ@p(!kfm4~_N@xxLjk3*Gt!5w%Ofy_uyX4fn= zt#ZH+t9bI*)D`18l}=h82lGj6JMJtK_0rI+5=`iEsp4Tyt!lV@TRK%ZW3`o3w?RYH z;QDRqU$(JKUgYE#JqhsWmIIpBYg_JELbuE&o8**sff=K;_0j@+n61wZu%txpL2XhI zq*eHHm>y~jTc&*gyLXh_dSSnHq+HjeM0q)^0YjSgfWzhQaagHEJyvK880Pl z((><=wwkyMD!I5*UN;XceH?zf+y8Xu6I|VWVN@U^43?8Qc`YN+30vGZ^`s2EBZDto zZv_wgG2W8du07=pL3o5rhmFL`W)w(-OKov^fIgW6d>8bOX8(}~yrl=w`HEh{g}+!8 z1K0X){z)q1rxWOg{V{T!-#~piNZ)_OEHjn>bvX>P+)IbmU`4(`3$sq!ImA$+Nr6B0 zOHgf=FoDe6V0p;Hdy6Il>EV#)U}O{ZEUGnt+mV=(tMLo3Lm8k?m-?m~$LBdxtYFLz z)i5=++&8tBYiZ{FlZ$f+46HVdTSq+A*ks{;S|uv&O?1<(%9P-?SJES{K$K$dTJvok zVtnopeoG~%AMD))<5}K!WvqU1b})Dpy{ME(5$W~fCrS39frSKpmajrriYye{(&SYs zs%%&n5S|o`rX=c|IO5q*$USV+pR3y(@bguvwi~p@gMWz~PY0M=Z;SCQ_s=`!1JOl= z9QO{|-GFG0MvDKmuf7`AMBhuZif5{HJinULSzv9k$4>l^)l0uovXv3xCI3;Ra-xn{ zaKcf3#2xWn;bU8NCax6iltUY#LnmuhrMY)Cyi*=Zz5IpvNrmuYp;LKledfuMTbc2S z7`saJYep?81$yc>p`~Qfw|dr5yI`tzl$bDaa3fdOaVG`R6UzqgFAKv(5vlPMyEwZK zbJsoAdT|0|;mCIa%2oQJRNCS!@w1b|CtwTyl&`Q!pZWZ3R`!o0`a(`q3L|x0^4q?C4uzELRuOaR!T3 zv6m)IK3KzbX=qm?RKa}L{rU3l zjGq<}u&^2t+E1_>8Bp9U3L>-~!eoAiavs-ShEgsML4}`$7~el9C^q?zCVwey)j16u zC=wQJhn?MbVX0oqA9_9FR`%?HKP0#WcB40LuEijolVK+Fr2RZ3jtz9kcQd3G3qsH; z&5jHQE;6{tPX|6CLOw}2Wi^Q&q?#GEiB(vWNc`l+wYIbY{za{dd@qebj1BFE)2+{L zcU-yC@3ZS3PYV<6JTY#UQ~hP!bd4>oJSsX&Por)XQe}XaLawdr zW7D8A*A}I{=oSvHg>FS3$0im05`}Q~1Z5O`8RF>vq-8LxNSidSM3q#qlz)V-^}xRw zj#bX%sO^Y*Lf3?}^1b*2WiynARp?5Dna@8NX;D+IKgmqcLJ4#J0A1q2y(hJebH#m& zsLnN%7H|oY>T~E#{vAr3ta)o02H}kQiw+7?jzd|of$n|XZp|1>)<)Z}c}LONLceMz ztaz0AiAR1%Ww-yd)zys9dS2<+e?7lfZ*spO%+VytNuBpX756%poiB0N*Ut(8ktj~n zHe*Z&S>%fMpOc!rru-c;D3sSO1Azo?H&^ZRuNS19+y_%edq8d zFpKZxeSD?n;v4;PB(HH&;6yufTm^!v>k4RA0ms&XvbymYP&+aMmRJlZB26qMMMQf6 zS{j-JGlCpmwCVGeH^1ucoBk{)l^olg3A~GYm!kYLB$UKe7w^@9(RP+dRTgEa5?jBv zQp4CNb9UKoUs!sn6V8>^gqFmX2eSQb@7+7v>~5!RXvZVlN!|qKZA=_NDQc385cP`sXudDQJ@azi4{s1@g2iivd?vUziRPfrg(8WVZ0ir!<-lgZ3r8W1UxXt z_f9|-Zw!1$dQs@Gdk`pkWMIP}>Z*)ls6PhZWK#Ex{sSUC?)E0X`~@`5J7{Z{S)^z44RN6ZyMe*& z2^XUcVv#TFUz#MOua%WLCJq+e7T*P?1gL|K)sLm5wz3H1nHt!kF0m@{EaqFls`29h z+gGjQZ_P0ZTq-6waWO|LkUZ7(bN0XZ&Pr<92uURXEcmR;LE7Zl!&@t0U35}`B2H?nU=P~Q>>`*0jbdjE( zg=-?-PVe)n>!aR8UkI+q?hh{&p%QTvf+_Zu<^k|vY zSXROvhG}zY8qI7-JRL0Lp$I*9q1y1lq0OG8PcTJDr>wsYoX!R;p6eT0*&*>b>L&>( zGz8JVsVD}}BabzZ3FOjLccg@F1AlHY=^^hfDThBnUfP~F9H3edx{L1K#O`VIM@aEd zJod{whC3MK_XxZ6a~^C$;tJlO24iY_;PctR7B-41^qu#=@@hWme)^k{C+YiBaB3($ z@jB&OgBVKF%Jo+MO)ij!Hm!t|#A|B=+z8gNOZWjVs^fPIToaf$6^E449kOm3mdnGV zJzGJuqHSD73RG#om7zTl2+(T>@E1)Zw{LYLN+LYcs{0c_B}cFUh|16A>gZRgM6pR0 z`Dr<%Lzknu_lkh4gh?!y1CAokJoIq(isH6Mcj^^w3&z>%RH*W%!bwK&)q6|i1FU#?@^%5YIrXmQJAEtb?IZ|rnKpxS zFKOj38|vbS$T6XWyBdbL6(2ZT%tJ&aBO@23{sPB6-81NFsIe{GSZDGgPboA~0QWqh}{)NeI+ zUf+9aR+}y8FdLTSjUXHfc72mGI7YgXmggw8+3C4g8eHrk&!=-P(QI*xNWQ^mw8zNV z5A>Ts=UxKc9hcM!=TaY!Og7`c<&?;@B4-g=>_ zr~&*;;#9m8Vw{UBm&n3c-Va}(yL5v zevpgxunj)Cqa!4EiY6)znQVEjmf#*?u9f=638AWf3xtaE8lD4e47iPMW#$A6DbAHK z=!uRyZ|B_8*@fPeb|bW5Kexheb2t6~|NQZs{yn;%aBGe6BykMEL=+DUG`rnaN8W9{ zo2eK=f;x8n9CrQKUm~H(veaAFhc+DxTe?5&48^v#2uLhloPo(e!u7!Yw!I4L!D(oo>PL` z#w|}=0Kx;bqDN!cZg8krw9A^E6@?4)P?7oqIe z#p7Ge4Y=^J3XX<+p{pb3=_+}mAuze0Y3^FK?`)t{cC-(opm<5aQvHsZ;nZntxZwOI zrTX9U7no}V>6X_d#AiX%ak$e+%HZQ`=NN*o?63Tf+s@N8kgno!Cw|6>m~kD-Vj4N5 z5ebPrTg}Siahb0Sn86lN^@-Tn$U&|sP0qCXgODvOW|2p-(HT?M$h@)V>qxsU&#pk+ z(T}m#RVHw0&OlX;Nt)^4o7wgKLtV;^^+(toNT@P&>N~z37Ej&jWH*R{5}Bia$d;k^ z#_0hIMesBdu_0Nupn55-C#5wGIW7(sBi%7ci_5Je%hu|bc4G^laZMD6!B`5K7gH-z zFX?SB-bpJJ6}uqHPc#dEirsO<5x6vIy>hqS9Ga4+s=2*4Og#|;yJ^qcn-&|_iBK)N z4v{*f&1MlrB{YX!6ny~G$OP`G%i4sc^A)w3K-dv@l%Jzy=;YE&_C5Jr{q=uI?{(PeYh zQF1=@vOjm2+wsSPb@&u%VvDQZet+yF1y6fmZMgI*LJCZy+_S(fb-;yFX2+boK4VX} zeWw)32>#>IqW;fpGr2PHzQylYb7q!p*u}%(8Oluow=_1U)vd}U-m+q^IRZ9%l} zPlNSWTZK(gA`2SR>IU(L<*bs<)AsU1k}Ujrp8o-)|UpR zxH{0XH)}lakEiLH%eu4$Wng2L@;+5jaP)LHL=i|x;pyrA)xxuYKhTapgsnT&XA=O)J^fE{YWKlgg1eAOoH~g-e#!z&k4iI} zQDjT?AiREN_kcylE1y+2^R7txn=oooL;7I@ikIN)uzs9qIX&vupx^rT=pXitEc(FM zQX7mluX3H*v3v&2R8Q&_zfIF&QIk<_sv}-ElRP*l9LL&N{hygeT#x1USVizNX!IaWge*0 z5(ZowAjcB0Y)bpW52WCjEJYVDC6^={g!of+t*`48JE5pLw*k5*M<{ z8NWMU*3MD(Pr?`&asOzt2CqigGosDvPYoNyT*Z3^OiW%^!oqV)cKp;L31B%d^Ab6S z{`pK8o04IP%VZkR=tih{8pk&gY$#XYk8FfJPNF0*nYS@OPcv`GFmNsl7$ciR7hYJgt?CTOMy+KZW z0tw)#;JZ-aW{_s2g4nbMhY_WYTX_#^Ec@4_E?=}{a&g6>mg=X@$BR$-ivXIKBc7Vv zbhM5JR%XLWbzHb8Pkv`BYJ{Wxlu3vXax0RqQVy`D+}SUk*KZVVLUp?BYlyDk`?;zg z7}XftMw5qS7&j~xZ7z^7h?n7E4goTS38s#l-#(o&)Uq_$kT$#d?8{w{z>bsuK zZhHk-LCx?B(P6#P*=cMg<{@o`L_*J4tAR66Jhzl5Dy_?mp7(HWv)Z&Ehll2O=lt#> zJfDy}359b{MmwW@yJR9mI*y@`51ErX+xebU*=%3?@UI5BtD*|6-a=MNv7WO6!s0e` zkF}I9IJRvhPwB8y+{5|XW(*w~oba*99ji=m@rS%p?y39CPdq7SZeGSwNC;Jca%S4K zeAQ+477pK}Ieb$zkG0+d?m+b0dqc_bn&E!T2y1acgtICGWi|-GIw7ViRA(4fhtg+? zN1$%5yaUfHNBFmC0w>C+whZG>8*ZwC}}mIVYKmxq|Zu)gkaUxSX}Vz0BKO1+iRNs-C*&bOt{ntF}qX?3mh|ZSe(marjqim4-5{-L)V3wy_;M|Kvu*Ut#{(;$=b*6yQ{Dm&wJnqK)eKU z3w&fEm93KGt2=5xjX4>$6&TTZF((9-Wt-j`m%U3x##XuhGm(W4C*m1%Dly`Sf7@w0 zK?~@40F`YgF$;-Xyg6{ak1}J3j7|&`&5*>CHR%xI+RgA&N6$Z=YefP!nX`hzrSp2h&A|tp5orX5KzM*`i!pUnY4H zHea}UY-iQTQ2oFF_Ey6|G z&U=pCm%ul-B8u#0hZ?pptBZ-~`<7}{IRg4>O%w)zZiFPPV)PV%nc$eiG6}|sZ8nFa zovb^y!Cml8p}H8ExJ~;WJ3S$Iu(7B9V{kSEBbHne7)N6g`3cp{*eY8sP*t~qIwXbK z^b~S}9_=E>WEk2iy~!$F9Qk%M1#Z?Kk*2eU^iGN+2nE%k3^f=HL3-qp!XfcsMoI*0 zK$OVz#Hf)>VknFl?6BLlukqlt5~VpuyKW4`8|)2PkKc&0f<#>en?`nRWg+pYl@|%9 z9=v2g#_FzhFwzE$`L+r6MmXL8;g*vA5k-J+&h$gf&ZpDnsywl`F_oIL8g?uf_73?Y zmfrk-QA>28lm4AdbzFG2B|7qX>I+ItDr8#tLe@v@aqZ!rtq+OWij6lU<<{@SBQbJp zpeIK=@FK|qsq3G14R4%a4U&QbZyT^1%T9-> zlZlf^edlVC9i3Vyo+6R%+?KTYLU`TMw^H?$DQBwIQnJihCavsj?vF-tRn^PDu9OUB z=;k{8hRCzp9MOGF|9S8t1zm+As4*Da#ClYN5dQ?SOJJW(CWyFQTW=xk@+k8zl}F(; zd2Yo#@aSqW!R;`^5VDojkm1iPk%Xd!7h z9RUhO<$M)%x+^Jx;sAj?Qy`gBnY$@oY}t-C9?P-%7oJ|mP8YwART3WATFTB=aJHYR zU>bw8k-YSRMAa~FJr#hPU&pnnc1Fce+@$aSLY_n z|5&^K{)e@SrZ8X5Tdxe%BQZmI2s=O3B+(3EBS$5wUpt~tbka5J2~(6vbXekaFL-2{ zK;KvafNI}i1U$t|Nu`gV`Q0U*X);1bjbBj}sE}7*N6Kz<=(jW~*_DJ>Q*kM>g z#sqI_&4&{=u>u%bwj6X;u~lx@sExisg+QEBEUXb(Xg!k;tGzazx#M< zBPqaO;Gt+1;*C@|vl#F-7~St8C}L(-V*7Y*#8($0O(D`pix@T85KF=eY_&D?jZt=N zA zl_xuIIpK|UX;VPXX=T%^A#!woa-^zVsagxRB$7ZWdJHQTv4LYD!}F~lV-*T-}GJd=tJNbH%{k4+HQI=mf<$X+Voq}Yr1CjRCrSVAxx zZW>%Hj-9n=Xf$v6C$(GaJ-C5?r4KE7fwN}=>D-N4P83*GjVT2ZP5phj7X#1n0Pz3& z_hEob>iG}-oM0EQ%q7O-Km~48BUbA|(L@AjR8-MXC7E-dCm)u|zdH@%>6uM~{U=*R zWUsU5_J>19)b9>w^C8?S@{R7Ewud+IVNL=q$PI0os?(KQgG}A;P4cJT|5XrA+FfxM z_fHUiVFLi5^53YVba1wDbowW${6A|cm$T$;57<$Hci&On*V$Z9{}$!j8bd|RHJO8+ zUl|@N*jloLQ;#~eYFzu~rV-*YSFqB$h`x$JQ~-t2x7@a`?k6N3Us);8&^?G*RsD3k zQxr2iKYOuK)}%CE9xL}yZSblF_Kz1)q(sW94$~+&8wz))tX{BcH2+TV-tt-=6*f&! zX^XxuzgxAlZ~LG|aWvDA7vT z4&O2j!<)kom)8}?PbOe~Y#vZz$&y>amJij((uA z=S1y=^pL;L$VW%*rMxiWIz8k6(UzQ@zZT+3^({SU{rQVJ3$Ql+7Jai)Zqm&1alcM} z9rn?Jbh2;737MAw{Y=6eZrUs0OU{~Xx!jalIv|EAN4MJgFm-)h1ew=ih3|~2l6c%M zi|cvuC-J)TRHSOVq?wk<`MK<}g~H}!1E;hT>b;U-;#%m7r}#0gohiDT>lyh{;DWEd zf80qy3XDrU?^QF@Rm6^)P@B7yt>tYwU*rc;oDTfTWb+=PsQWJ)f#NtYQa< z-~2NRZ)rmmuL=vXRHEUGhfjYx3yX9|IV6zAyH~Sy+^17=e7=t;&R5CQCk#OFjTyhS zOA3YV(gFUicmj82z4qe(#9e=pzZYQu*8H1{Mb|oSAQvpgBa7cxkq3oEZQv_EW#m-;e)J?67ey`}T6PhHaz^ z9tD6DP=TJAIwm2|W%5-`A_~2LpTRpNA1NU%6d`~u8{KK=M)Bo<)9d# z=L#5!`Q`A7LLwdlcn8KL2;I0(s@{&%bpK=fesbBEdb6gyeV2^OEhWgWCzV zVMOO#t3sWsih4`J?3*EcZ^D%Ndg5cPhj2ur9R;3?r)_+q!IaL+df&w*-gO3^1LB4V zHZ3}YxoE)6%S;ktDRT%xQ-qoU9wSUAl&fC$Jx`N;4Br(&;;xnEdZdL(!`JHaO$3-JjGa37HLg z%JpI^*2zO_7;Dc!8t*g|69jl`3m6M;C`Y@GYBVoXAejV)CL#pNFrD(>LzFT)sUxvhcCHJ>J}0+&|Zs zKPInFA8#-3CazrGJH0>9{9nf}KQG#}$u<-4Z@z-OanUwQQbhNftp01f15i*Hji9`~CL7_@1Od`6aPU&PYi5em4YSnJp7UdmM7inbMvV>VO zCDPJqN}_2>aJoa@l-bzumx5I=H-$BZ5<*2kWXCJb$s786r=yg#Iz~Pd9~C40z`870 ztZjDaL6Xf)%#{8p?bCDedpi3C$?D`P&6;Xb43W(y8p%o@6r|mgiey2YWvQea@}rux;GoYie3FxzJrjbZ@wf!56JN>ShtY*hzS zbp&!28Xyx)ae$$4KB|a0Z)XKpAnS$a7GW|}4kP21(QOw-*76QxeP(TZE%h$AM|_?~ zGz*cA=w21)vkmE8$}J%@{U`u-ZHHMfBg=j5Z01|a{B=M94k38z)yQUCbQZYIdIzK! zWX8ih_BAT|2*jkzV&Q+vH3rQNxN2&h#0K(3770f>H9^xc?G(g)53)}9R3GY?Z>voy zh3~A1V67yA1@fw-Ki0_(ERfF_0sa^$v{7y>o+5%+Z~;GdpwX-Izf^*rb5vAv%;X8S zcQIAf>yZA@l@6EVz5_3oNWh@0U!eJ=k_V-gLyZgWg304+I$ss&k(TRs4g56$Llq#& z3;@OAA}tsIs8A|;nbAsgLD9b}s;vxSorPI|s8}>X^{+dO;0inlSg@%gSRtE=$Is!^ zkLC=xoU>tX3Rlr&P3)uDUhgl??}0WptJG){B?Oq2*F@*{Cr6)vm~#V))ClaABMf-m z5@SIeLtG&v(O;lsRthDM zouZkWkd8l4-qs&!EEI^8w?uG}_G!Iq6E&?YTbp0y&YoXRAai!M1Lo$_e8Lb@e4yOB zOzd!TViw61dM=t-ITFs*fMEsFp@67!ai4lYf_M#Q;3&m*3$bdPRBtmRRM$2|lM9~% zf1XK!`p4ga0(%Vvb9IGQwY}e85!19O|4EnMBH|iQ-QVHBfjQj*F#3+9t zQ9Tlw32}r5fEYwZ%ZfM`biOz{(_X!R2wDE2p~Aw99EMYzXAKBC+#c}b+8UDOXnaS> z_#MLmKqym|btW+HD#Ae_rNYIT9Pzv==1T0n0Fq*9=;}LX*49%0K8 zOHIMk*($pW_bl2>6Sm-rGs$5iWwEjOT?iPZ7#jqH@3ADe($Z2d+8#j;xxl}Huv=3W zjS#;bBv)abMQb{mz=T6%q37mClL6s%6aC9k<&Y5I={ex!2vX4XBX10SW@&a zV+9IyA>+6fY|=l=I#N?j{i%pc4B3mgQ!wJJ( zWhGeLXvNPDPE#P-InpJ)pRKYkf(8~H9hu?vBD3s20YRXkVEzKO{U-=W%d2x}F$#~x;R<1ioMl{4ahNy%1v~aYhyW{s$*_of% z$SUYC6NQ4Wk_>LN08KA!O@LCG2bw?ffNUclCo$uJDLWq!29FK5YGHH1#ch!Tsy6u2--&6NKu17*-Qz zT0Brz9JH`m@Zy7*BNTCCVUdmLoc+!!crAJ?gpn+_&CWzmJ2#mn>@1ylAR7Gg#f)CP9TOvlPqBc( zI?evd;Z7K&gi8cW56_%BCVAhl>i3EBmt$&_fr?<8bzFY(eM-VP)O=wb;wO!YnqJus zlNTvhM+Vey@NfP*Rjf8EQl7)qUfHD40047JzkO`>=(dem+?$k@PHagBpdM)4++Glx zA(M>$(%kH+{=FBEwD-BuBGyy8@~9JQm?m_wA%cD6HZzE~U~7lS=a1!VLJjnDMHE*} zSj%5jRUOOR%sMRkiujEk{a82Igk0H{{;7-f{r%6z=KnJBRU~=K{y$B8eE;u_jf1hF zzLk}(wYi~#t**7Rm6N%lmA<1R-G5ed6LUlT|L{HQ8X4O-8q@z*7n5mx^uM?6$#E8) zE@A=z@cn;V_x^wFY-np^Vs7f}@SkY@p>uTkFD!lS`~NGJEA4TIL$QSJ*Va1o{d&>u zjSUkU?$Gr1MGB+@v*GGCBjuUZ*+&2h1IRc4NC|0m5oN06AYdVOuB}}9eYf;~u^j6L z-!Gd#b(we#t+|Dmzs>8kCX2@;nzO8qJe=uMJ8Y5atm#FuwkzvL-j3|PbsAOZm4_A& z9hoz5k<%TA=A+)M<~bc%H?NVa!Y!3sqwkC7ULOtXSMa^ksb8p}ugVW+Oqn+;Zz+(o z%;NPKKP>5{n}ePYXC4XH@R7?;b?euYAoS}t-5hkOk>ekrt##hHEqLDAIAojK294B; zQ;q8evEedsoiv};A&^F$8(ozo0JSlsXeijx{c@iZIdsl<$b-Y;~sx{#ah*5 z(JHSI6W_wI)2EKeuf2YuFZ$sfREws2kp&#(ZUwik@ z@zlOrtwF{`70ASR;yC#u#+@^mBdL{EF|^2S3~jNs1KXVkc&e;5nQS}Hz2d9k6Wbcx zj>+A(NG!DnAs429CS1wzH8W_Q?-r8IMbq-SUkDaUo498tm!d;6$a-JQGEJR*`#ujP zeb|f}cF8Tdc&Zx>su zp7{idAMOWD)ZfkS8?^3lrC)eWdcPaL=f2px?T9p^hOs0K!T7DoKMB&W^!591T^2q% z%J8&tQ$vSI@NCF8OfJtK?i#c6#v{LDl@kz4XH7G&l$&reO>O7MXu)?&Dwms%*KX^& z)i<;642-iE9oT)hq-AnqR!cD6TXw3@`q(Hj0BQbQi?XKo==a>OQgtGYmhv`e zORFF&RSP8LRY!^j6tO<@5`1-DSvO?gl%O6pXV#oz1GK}|PBf)gCy+$*C6kmlW|B~P zZx=~iUSMsraBq{?F3w*k!G3R%beCjSnl_yue{XhCX5J5$5ZsThyY#B5CFgXX6Mdn1->J%C4RcaOv zuxL>~nRR-2a7_OmtMT?u|B+L4fP4sPkeq93VCK)PEjiZb?B8n14zrY`7X~-a?xs(9 z6fmicYYn%EaB9@-@A%eb<@nmt$;6iRW!kl_k&aaUH#WFwj<&Qi^_^wlz~7}!xMg|W zsB_)*a0yd@_QW?@jS|;Qx_Wlz(-u$nI-g(R5HO#NKeOk9E0Stm;8S>B@5!vd4wQ>|6Om{|C9$0GTH0 zgi&fj8<@1j4VDmOzu8=oQ62vh9bY?!>E?LsxecTCY{SWZlERuz3a{fVEl1+Q{xBA` z2OLdn+DdPS6v#bA(y2*z6?YWS$mzx(&2FPdcYhwmk+$HDVK1FLn?@JwQmrO&Tp5YA zJZMwxe{lxnt($4yEmJ0U{qDsz@X&pA8Sb3;=k5khWJner?D6p%J$L;Zv9)&;bU1Y0 z;0!V~#uIM0U;Fr@=02$iInQ_|Zm6ka=ZD`c3)A-iX&mYF1m2OUhEii$vg_75di@1C zhkSh?1y1K@2H&k;@(l`A3gI;nBTWIY0+4EZcCgxu8~$f88zh8 z0CZZ4*vH)F4;xg{5|{H_pYim*1Nw1ed}z8@D9Du6Q)j()j$SunBlz)9zJKw39sP^! zjRfLY)g-T97fk2OlBHJ*pG%4Y=w+TB*6`~{N;)~ZNPkSj@83e;b7Do3+xiKR-)GN} zvPK63Zl52AB*>lbL?DmHuq(gPhlhNv`VEl29v9;D7I9*2mtalMTpz&zTt6oA?CA$I z%kQqb8c#Lt-xj}RunebH``g%X3@pX6G|rZ*4J2c|6_fk$EIZW} zB>qYhxg85atV}*etk~y_cz_dK*4=f(DII{;>T%i&MtbpMGs$sLnH4586xn23IiJl# zJ^C+C{IDB%6k4VnhSWkhz@VN7LWOAcRd{e~C&rSw{}vgT|9lTCV2u?SraO)ZRC_@f z*C?Ryzz~5LC(HAvRKrmI@ROThp!+V?{gDwrCn3o`2giIV;+>6adTjq{bMVH(w{mx4 zVJo$WnE(Ac)cyT{Kgn>N_}lWr4@F~^scB5Bto!WwXxAL+q5!3JSWvYj71z z1UnooE(xmNCt4I0XyyjleN4!8f+c4_eNlWT5uO)3cOx&^X3mQ{gvdlS9>>aG)`)XS z(Ve~8G`s55`Md5zr6LLd1PrzaLlw1Z+<%nZ1fI2R{5bxX9jyA*kWE7YkTJL^4G_}0 zU!>A%(iNtX*y4Xsc23`!L|qyWJGO1xwr$%TzOl_tx}!I?ZCjnBW81dv;HG+cv#Q7GrJzDB668) zT)6lb_Gn%>m7U+!qL;`x!sLFxIC8*>P#ZI(jm5dEj>M!7E_F$GO>0_g4o~PR%L+%MSqyBU_mjQMVw= zzW2m#042x5y?4~3(|h+M>mQW)m+8|mwXi1HjtVWmv@?ZHBZi%$0j`DM2UM;C&yU^Y z>**i>`*44=B97> z)_rAXU61D+r*Tj>Ij#=u=>k)HV}^1(zJjYN_V2I#s=F@hxSGB_HHS8FwkgAuwyrD! zqk0uENF#ihBNf)w{>CCmez+O0&^KZZWnv-~=A0A;dQb}90`iEV&|0cD@1%z$Y+8A? zN_afnlfsz=Y+8Zzn>;?z(6I+W0pWL9VwO}x;=28kb@~>IL!EFgMs2i2m(?)Z(d*cQ zEPBgL7T@gOk_eTe&xt4`t+_kd$;zl(J|1!N*jq9oqfkdKx?W^SoWYs*iriIAr&+>u z!RL}V&VO}<9i3rco8r0Hbb*U~;N@b7vh7;_5FC3w>Yo{EnV_(s%nP-P-AQN*Bo&{E zO%x$g7~W%BgVk5g8oo_t&6?` zvJh;{y(N@2%0H1B>#$^$&bHlQYz}Ja4y4z*?XNh!WN)ZY(c6Mcu=| zG=d7p0@dcZ{J0{23%THO{7dJvQV12JFfr&4XkMu6s?Z|Rm{XLu_+~3-ev6F|@N`6d z#+_hdJy9x^i>-K~E0ksW{r0w(eaq&fkXpnapN+KoPH@x3NQ1 zADUle=l{GZ|2Q+dg8SMlQ)qi_lx-mo5qC??_#nI0j?%8+tUatu@$t-+Om*{w0#-Iw zW0Jr?b|f~1xrndQ0VjdZnRnRc)Gru{$296oRFuvQ>>KR!z5WwPqdlhG(W_X;ScrpR z+R3=vBdnELn}VjJBY9EtBUTWM1eS2*y)X3IOjavYDA#e2_ngX5I9QCW)cQE|;OK2h znZB5U@CnE6;p3^~G|wLczXrv7Hg&9au60@;aa(jlS*f8xro1<_{X|+V1p^-!h0ei#h}f$_%s*m0+J%Q00-bbCHccQf))lNyFzxi-PRMZf-yd$ z&1b>Fq&i(}M`02iN@=8~M_ zMwtIidbL4E;$o7bjE?)OC~Yu4=AsKqx$jFiDR{R~PJZ0BHJ54U=N<$M^-v~J>9o2~ zwatX*vnhF!dKCU%z4wf+Pp@w+Eo6v9d2m=ZScIR~U4p>POVDPFW>e~bz>Eo~AE^LD z@1B*{U1FuV@{cV6H(PZyO;|&2$5v?A%kZ)*4Ap5J{WvAeo}U_WmN^Njj9j+bG%;OR zyFUrwYr2p6G9w&323o6Tozobk#wPIY&wT(|m3+st5t5A3KC+_tzLdM==2F>qI-)=I zY4rmVD>KeGc!=;PK36fHW|pU%oygh41JU+9t&ji|Xh9SbJkOMhP|RSN$QP3gI>(l* z$}0O^;nkx6t7~SL9m$54L(_EJ(`j@g%m8&1HATY8yGiEH+QnYAt5=Iv#FUh!f7WFf zEVBsL5BJAz9e5L)Shp&=;Jq;26#4<4ud+!^;q=ZV+XNt`AYgZ!>XpSbX9)JU_gOBU zqn>|tqd+qFm3@_#S{M;}?X9)zc67{aRIn>FkWWwnC|Wa4UE4^!BnSQ&YWjBOy*48owh3Maz=k*vh} z>S%q2{y;2*co}RtCSIOR9|2$3Cq{UFt=zv4(ex2Y1wRGpC}=~g;$4~MQDEJ3V|lDz zzXqH>hrNv2%SDlEooG`L6v|`t{T7E?a@b<{ofbfy+Y#Z_YPJL(w1J}$SQ%3kdg2=F zV=S#nCMkoqjf@Ms4{7NG>m=5=L$@19_Ku*hi$p)nlIc(%d{Jw;>>{S$=j-Z(-D_;U z4!2%$+$pM1K6B=)#j3qUbNExRfbdGBj)7@t7G+&(RuJMjwm@4H27A=aXBNy#W@l;* zMhKM_>=#TkQf%teLPa-W%FKtUHb_U8nYI#xT2C`{6XM-AgQz`dv8-_C5|<;yqKvWm zSrrK*-6=9=3XAej6JS+*-N2Z)lWkLl6H@uK?7R`$ITpSZNv77~ka2M`s$wwvDt6Tx zvaZvE5^`5Bg$&86+)g4LK-pP>L0U=$pL-*5eIBS=47(X&F0*S-cbI6^ULd~)0Vkli z$|H>AiD$LN8%qdg=&8C6_9p@F-xAm?RdN>i>?~^NhO|Qpt9k~y+LL(ru8&+}+8S!>Zy8% zQ8)Qp-+CXUPpO)ygA^WF|KVo^z9=t@8y!#9dWRpP@E9SGL+??BuyZ;Hma_jl3-=?` zxX3xus0?{I7fipV{qlss~mh#v}#F%NYF^at~xQ|<}5;ro-O7pi@ zJN({`3IGJ@dw%7Bz;`+sHfPI~-BSSgU_z8)J%J#LXDN6`zGJoEe57TVP^-vKsi-1ekH&ovMC|%}d6~gkL(Z+6(%+Uf znM~{=-0w$x?mR!)Q#du%yAAaL|QR-IeGmP5YpeZ|zn=7o+8U`Vc-So}M$`5HJ(V%N?#&>0jV zKFWfGUSf&a+?59{(31mdg%66w)}E1x>TP`J2$|a0{p35o%fcf_S*s zqGC;|wKl1ov9s7Q`Mg!NQ6b5kJyug0TI}+S9%T#dgwUf1l_A+rlgLyqg(O>d9UHw~ z5myQEPDY7ttvcXo%H#1yXeeZDPe@u3do6aT+XAU(3`t1Lia@?Tj}5Y1`SrfYoZxNh~iEUTLxYG}y{e6yv$$z;d%>I2{d zK&*yAhZkII9hjO2VTz4vL#_wr?ZIe~{zTht{MVENs;ahr) zQ}CTo32RSM? zsj2<1cHu`eL&IyB8V?p6ZFhfN|{bf>PM`t{rxvS01j*xs$0+T}!f1U#CK7QZsQ zToe_D^>jOyBDkv73@1OJb{P=>LZrG`Qltx)2t(G}OP-qufPw%2uWQ>Dqregh&}`st})ryBCAz_|^;F#aM&o`fJ2z;7G;M{8bZT^Ta7sAT_P+HSz z)gbgne-#sb3oHG^wQrAF577|8eO7}=5^ZKH@asA~29*r^X-5H|-;tz0KR8tcIQ1qj zW9whR+(Ou}@_U~3tKA*Sb-4KbZpO?rRJ%ub)pJY`;77TPgAd?UI`>PbUwdXGF;)a$ zw_KRWH`s#uvi;zadT#VC_AzRKZc7e!!J4HuU?gKS%>5^@P)Yf#Vrezm{Wu&|%$$1j z#MW~UiYuDEZun&JL{o~Hi|Sg#=lJeTyy~x2XRt*s`H;0`KO}d5Ntb!igkDEM)YeUf zWblFinWIRhExiz(XA55oZlKt%+{H?2WQlSPiWiEIW@*DB_A}Euluxc@U!PFEf3e)Z z$Hi{&V6?g)nwZl=>g`GB8{Vy`y3p4KH*Yf)fcPqQLS;cMJHznjWXzF4H^oMgplq^G z5zZHOb-i+X^FTI814gixo+uJLhgRgbPbbJI@5?v@YJVOw`;Y;xE{Ox0)VpocB3mXx zjDK)77*_dt+XhQd(i}!RkW~OvDO}hC1n0W)?Y^!$>Cyzs4s6QKrWe(AgQ$2?-xOom zKVvluS~exrZDB z0-=?LbZ%=E328MFly9%n-3V~cv$jOnT-F3T^iFc@@63YKdkYKKIj$ml4rrHMh+`J`$h>Qc z9x_Fu_^Qroh|X@jt-a@MB`+PW9%~D!sD8gp+&TtkJ#T-cc$5$>%iU~*GsWW|N z2ef;b5I~x60N~5Q@D-5_Fee0dFr+g{9a&~QnFUrhSbqtgw!C)X?)3jHdw$HH#H?OZ z^|Y~&H`2PpNGVdOeNw%dlq@%vB(P5rDQTHP(m;2Dy8P7S z-dV~xTZ8)WcJq77o$~Sc(88?W>>~=8(&H+ObL#Pd7I8*NJJU@jHv9T%vhApMBx-wc zT=WShb_s_*+IRK!^*Cw4V}c!?FEqdmwAXpOy62%EEpFU0G7u{rD^QRoPCy5+$%~S% z0qHlud3NyEnmd_~7zKwJ2OO5=@pot*LfItdlO`xVj{l+MZLD0poy`?-@v*(n(vT!o zDQcje7Mg8vc_9;sL?=a6M{(8g0MqRcl|Mk(okTQB(C&&8^ePu~eqmgHPkcR{mCS2` zgyY=V6D!M=giBL+14xE_Y+Mn76h3i*slnpf4BGWnA>xEtvH|6V1HVZe5rqY z{s9k-TkFU5C;EfBL+7FR0T+^Djq~29bv&bI(oPuqd%|_1`9-Z+Kxk2TOfSymIYa-; zgAbFTgVU^Op)GYt{tCl%!^*oQO;8u^1Hv$dsG^--JVQdofYh0h-J3%S+Rib2Ji}dh z&x@pG8SKWPBxevIrdti{2{{98Jeo|8J1{gQ>cLQV{dJx>YaBE%i_{HqJJZ(tx5k{0 zv2*SHPt5%CKY6~RkAHK|k=`s{Ju@y6H@$~9z1D5k@EFD*$PWLshZ+A65VmE1+YxVt zWi=20!NuojKv$_2N;M23KiDg%&H>mxpO3f`x%7b%HPDX6&-B&3WxBJ_q(z{M%44E| z_H}ou9*Fxp#9G)kmO`UfmF}|AH?32|=dY6o%~Yc*T_Y|>aHho-b22AIZcZ?y%ZUzb z#oA6H65=v%JCS{J-R-|8|CSH?ecG_`ivri!Qwe@)eD$m&uy#qAk*X z-;ER@0$1Ti69nE=@?)vWK)ZJMGS~{*EQe3OSitxJ$|WC}>|c3QOTS@*HadZIvcG-? zhi>C6U*|_cA7?r5w>6(bQ=j)0TkqEwC0Ab$S~)$hml*-iV>zGuDFIJ+IiLMoU;8y* zmnA*V2|_Oo-z!ptzAC;xJ$l|xa{NCgo`ifKCWQPxBWk{0lmhaw0v>yEru?5?wgkVf zACQa!-X?NB&#yiYOTJeL3FUm;8TmbYJPCaqMqG7&v;+kBd;)sj9*KHhhiU>I9+191 zxr{z~dgd&0dc42fj?TJ2vl!n7nZ9-xECOy8kc|AkULtY=ib{OkK5M#Pa&mg!KJNe> z=U>kg9zwovA4^+3Ud=-%TvPYlU#~hbbAarfnx5CgoPdu7kFRw9#|9$453HQ8ld~(1 z2%)#f9HB1`xZOLCp0R_T_shE@qK?a&uU#r3_g5<;zYigyucw;NkGm%!_l2p4FF?S@ z+U5CLz>ED|uAKi6&Rmkv+eFRR&7H^B-q-8cl+nj^#?|Kf_Fa9CuFw8~+mJ-S=K@mB z*TLFV_h(6s|AQIPTWhAq>t4z`Baw^#U4Z{b|JMhXQNY)C+3#ap0T16@c+T)IIr7rK zCwe{JdJ+BjYWQ(BlzjvEdZ!9_+0gn5y`S;)dpiqwd(4>nZrO*C|J%mq=i_$?g&{p( zryVtIBU}KX<|!lpkBujx_lumKwo|OH*O#xC!|&Vh*6{tl#_fv`_x=&r=aA(6`{b5) z9oE}a_s7H1mhVG_k$=a(#|opap8M9_l%Wno18?|oyp>1p56NbM$)05U?D25LtcgPkruE^}LVd?5jGQbZ<1jbzgbyw>@P> zA?n$D|lhb=&Kmm>sTRKh-Vh|1*^A z@{@h?JMKRm?mBN#J?d& zakXve_S>bo(|+BJ&gaVPs%XONP5o+ZUZbmUef7dy{n9=;`OvWV(&Sj~sl8==_MeAC zOJz@yPpxa5jp5gNcFmH$51zu4?$AZTCB2p3;xdOhzvP96SYt}_(yQm$3*h2O|4G&P z;Xw~iO{x2$Lr|0}K#=cY=e4+*Dte&;X>Q$p%D&5E;)33Xz{x_vx<+r)YK=dhA7FE{ zHq_LVIX3U>TuQ^}Rwz(X7n1`3w3P8U{83msFU)-Ev%2T8nMx0??t2^PEzE~GbMs_Q zQ&LdcLE#V5AzFUr3@7qA9(yx<#k9>!*|eeaQ(C`VW(VQ0FL0>jtI96yu=SB4RQnJM z*(!EuSQmWgNG|Qx|82L<2=yW;=}_5|d8RPtHb-#bT`Eva$yRWZdZ~WuPPpYJ>QNIx z=aNyz;n$>fR1CGyBU#SUY4on}RA^F0aT4!om|RC_ZL3@O*{MEfhg+`d^>?+Ae@JlX zPwba-zU`xUiqA;8K0|f8i^Z(zTwDo?lzxXx?EjD9S_@~BSrlRNh5qB>>au6;vb$a3eEHx=HJ4)6{Es^{+k<1QAEAH}wFrzz{L+hYWk`RC0tg7WAhgehksq9z|41|+|cV>LY8 zF%>xbOD_edsA%8py}4mRD&VE>$4XbpLwo@#JLlC+GzSOAwSmi{bN53aFf{V5JXr>q zq(Ex(lR;CcpVLKF#_VV#FXgE|$v|miQ{MB#PoWia<0e^wh_*}%SQujtX=JkUYdqbb zb;Mw!rFr)}Yi$v1F*OEAUx0SjXj>;AdOMwY>7CQ~SDUTc57^PnX~jW>THOHwhIKYD zzlysm-LX~ian6y$Gw&XUX>CpPM|076g#wPez!ht$z&k}BM`Bb|?s?-+EEih0AyF&} zb+*w3v~?Rf4_3bX8NUpj9Wdf$;?eSw{k9GBtbmq)=7kPai`qagfI`(B36(bcxPl1m!vtR&(+ zyHg~?4|A$X(12I_HS0G8XLzyV-^zhOvHI?Npx;(IOLW4g?O=w&7h?vw?vf?lF)hN@ zdZW6-chO6wlRD(8vH@HRUESHI53g}BCXp2gMJm97#ql$@4dEq-L*QHo0mBVvI%QQX zrmFB-i;R-u#jkS<)e7_|LRHhg;3nA;L_aA;fTh>xrlnVwS@xJhx_*c0$Ic{$&;3k2 zLpcC_umxbimaig#DdXaSU_;2?QJIomRQw=>Xi*Ry%&dgRr=}{$ClZyb`5Oes1h2hY9iF+5`2*Lnk%7;Om8YPnz}D zmMG^*UsULVu;+>X((O!rieVLi%Q;=QNwwGTP@4GUSx5VDlVamkQLgz8rd_qU79JI0 zB5O6=xh zl^|PTzMj6u4o=;u_$vNjsp*T`4qIo_&o^FkVV++OQRm#D4(oV=S^au!E`ANF<+XVCwGFN~i#xjgj@8I@sQuuLquPuF2QQoqAa|?ewJT zT&(bC1o&uHxXv5bG;xffBDYIy*!x@apCGal(OXb}j+@E8Ux63!`t*6&)8iPj`E1!} zQ;dJB?g3`Mo3=6-nq|z4CFDkB|LvDOw7Ah|@qpug8;sgl1`WrUEbq75!-pL-9cugt z&!%+|(gl@ph?tMlxSQPgc>{QaD*UrgdmM(|jk#Ku+i9A;vI}l;Kr{21Y)w3-*mCwH zn?5Ex%{o48yW#xps_0AC9Wj(0C4l;HB#Ax${w6yB zpWz}3=TnnotT?W54t#J9{#+{>){u&bnuSqfsR~YKD9J_ln4xf|;)T&x&gWqC9~@Je zP0_h7y{-S~2@)4A8-*F+7}tSWAyzGzmnRV#I@wl0PqO*^88w%}(=Jf$XT>9L_E=vG`kawG1!Nv; zPJyE86mq9({?K(#XI_6%tJ}E#S*#**C^0Q?#xl)C_Q6bWC^J?Eqk1LxV8~fw-Yhv9M>+H6Zi|Nh0;q+W^|7{(zbgn z@-iI?hrt_uSB1}s0v{?m=Z~U*c!J^*?hIvu1Zl<5#iGBiIjVk=vf?ji^0Lk>oa8Kv zw&oCsx$dy&4*OJzkeN$5?L`i}XHf4W1#fxxuta(`iBS5L@fr9tB3A5p%WT&XwkvTM zeSj*d>G=Q!2g*G67^_)$3ixHxk77P=@i_UA(C4OTCLF`s!|)>owswUVXTCg|UDJ?U z9jib9QS4;$qqwI`p3Tetd*V8hoZiu^HSy9JM7J-N2CDGia_iOp^7t^Sq(wtOPewS@ zoh%$4!SX&enP_2o1A%g+;2)?7xdYC}YWs^0AAvkSJ-iY*Oyl zPQStFWjQpB3PO2P9AAoH=5zyV8yaR(C9blmw^paVESzmdfNqP&3IPzeieaLRO|@K2 zRbR=G%2(A~{?Nj0ICS=45u^j6r<0PNkF%O1?7o`;RiI}P4vyl!fx#|RQAbI1-5IXI z)(Yz+SEj)zmQ^vvLAZfh$#FLaP0?WU^`G+4fVZNL(AeR_Ypvzqrhd)q(do)lf7w5$ zDGgzp>>V^qM;@qhC0HgW$gag#cZ8SY~{=PMw{3YxX5@)yW0;B7w<+8U8 zGV!NQ$!u*x7T0ETf9{m4QdAg#d8`)|Sha;Ic6-4>+n!nUa<*S>f;Vt;G-!Hj1URJL zv}D%KaHTIwsRa=CoDleV8quIZ4?LR?wXY+xm*N&{s8!A=d_gLbOc+GJ*eeH&_{elb43zt`d0cz93$sN+qB)tVt|cR-;Y>e@#@YEENOCNXtgLq)~T! z(F_Ma~qj}*+VU!6;hP04s>BQJM!;jqv#q*w7hZv9<~yQw}&oFRlg z-|6JJG_{TyAE%3%seL2&qL`zfMZ*JGQbB#>gptu(v0Yi0P2izWT8wA6CP5$y$&e=2 zNf|9Z_59NIMFB2${VaNj>zysDm%O*{XyL$>fsLL6=)C&1A|+fXQ;xc!xi9#*ic0_~==(gw+R{ zY(#lH?^tSNBQoSk7q3x;3N-X`aa7V>7C|^6R~Tk&5V3UVLQ9<@x^@QhC)j~udL z=nP5C4cB)UsE)T3xblc54^O^`%*s@x9AmD;a?kV<9b5QO$61T647{@<3!JO3DiX*_ zAg=B*i+M4Pw867qNxMh~9!a zFI5+w3T=%@mpe)Bn)Z{#E%LD9+d3UMXRwA`ViyOS{1U;z6F{ybzld>^ypm|ExLhKk zE6cmkVULU*$Fd+}XN&s}Psn4EET%7+s&K9U(ob9IV607&r%L|@)`Vo4eF&J?eS%cB z+fig?ny*~v9G!(s($hsDvubisz_X}~OypcGz1cCZN=9Tdl+U~OBya-+01 zX0i@<(T-NDmrE!*xDshh(1b}_wW2z&`Oc5kVQ7YdcV&k#`E#ezhDJ|V*wgrNWKdnY zFP-v|L!|Zrt^J)qT}<=ILN=r2#6@=^fTXl_Q;=>xaCS^ih^jMBYP3<2l*tTBu8@pV z1_yN+sk{~=hs}sv*&-Ncw?t8!R;CGq^C;bJ(TTDmrf9Z`_E%`oFGg6d+pte@0egp5 zHqzz;Qw;Q393BOsdL2ZDwTgu1T-<-A6zK;CnI~X1-a{Y?A2+XbI0S_734k5EEmr0 zG4Y_=2AqCP6%e>+W^_}Q?yLU>@AY8S6zF8QYB=7%!O_0)JHjwEDZx*=5Ak z6@Ql^V!Eaj!a<9j+q+NPurg6@q^_gU7wGY4H@_ygDsu+un~>sn*y*o4-7>e*@+-eQF6zf_Tbw^<)M71CfHLnMT}k_Zt{8*Kd-G$RP4Hr8*T|2G#FKr z=_L78W7%CtU9j9&S*9xkO_{ki1B` zZ!ZG(N_v^J)#im-rlJk1w)yDPF!g|cLyJFJYOf#^-E5Hm_MVtE065`iibBTq>+||b zMu(SJpN_3bD0-0d?5BMQo^zvr%8-nVM)m;E=0r?}jcZIv_N^-8Dd1RlvkxCv_8uI4*i- zl?1^zf^;Z+`;89{OGcdS4p0Hi>O%2&%)YCFL|byT>8T{uW0q|1uC56Au7AQquQ+pH zsD@;*yH8GG>6ghh`;}J-tqxs_gT(NkU(A1f_Qw!Q`OmR z6n)k_#|3eY6mPe{p@R`EBNKLMdA7a>8VrAeN~AeVf?B|%!#N-q-oogbk%)ulMt(z&X87OgwtW7tE`|6oah zX=;4uNl-$H)G8>FuW0*22emFF9i(R0_>d!C7mlx$$lZHijVZ;L5@}Xc-#KGXQ5(qJ z)tFuhbMfym@M6HF|1zPOO5rRoYcj*;mv^DNGK8}V8j22L5F)UH(9pZ|p90Z>6wc5f zaNlgoq{OVgKx&jfdm!~Uvp;QGF6iaxOxc`DTR{(qsm=5?wMv}s149n0AGPRT^v=DP zp7cr*m(mfc>{erO8TCieTs|{n>mhKm75?!dXi#3kV%4MTkw6N878OY|bdYeA;0#2p zJVtYLzxM2lQY0&;wT$U-{TM$!(P>tNMwVt|AZ{y77%`9THy>5J&eHrbJVlmvf56%g z_I%eG2yG;E?oEJ^?_P-GYSr>*jPVQ;$*0Jz=;qQhkH>L{nrFL6m<)i(kLGQb!5b!y zZDqRTeZJ3b`!IXjx;ldm$t-Vm^IDHQ|ga? zeRjZd=j!v2eB7Zo;*k^S74!v9qyF$1`fT#;8jF9gA&PDi8c~qD7@}xM_GrEcxPAgZ$*jiJ~ddY6F^nWLH1?2gQ;Wp%Xla!R&^VlT4eTh^Ifu6U9iP z1FP1hQjb1a^IF?X;;>^y$3A>1nFLWula`^%m(A zpM0L&)^ZkKDK9$LkL;>X97(-x-o;Os7BdX#bUPQ=e6XLJ+$+s4Yy|y#oWB9i(9-pR zVpVAM{q~%}2D(&&zA2II1wU<_w$2SCifg;rKtYoIC&*dE|R!&HDrZDuDvl)y=5>WlV zJ`Q3rC5aDV`)8S+UVG-spYwS|23g2@GcvaZCd1>Ox}h#fl7aNKoFF2K^5U#w+?aFy zXR-P8p_#Gb%V1;5(hvi#=T{}~zvQq$%F>yWGgJK1)usPDf~?BqlEe62b1P0RoKZj& zbKx(N%*+X|CVhPVa>w69Fs4T5lRUX~klh*qTjp}5Y*P{;1jS1=r)PigxnEn@X zb>E;Q``!Dvm7hFlidpk8bxa0_DIIGXq*}w3>^{A zvpA=kO99g_b^NlxNGl==&=u)8+y%(`B4+PKCeaXgbW5psom8~Rb6>4k117IQYIrDU z_YyF2;Nkb2$>EOXon9W+wcrvt-M_r>(+Kt}hAmX%!RggII8T?yI9BANwY?^gZ^cNj zgpq#EV^nE=4-FN{`cU3=DTy?j8v!uUW`0YTPu}U6+WqcZ6dErCJ=M{xYpj^tELzSu8alA zxwS@0OH3Lwt021J5faBATht_@*gGwES?lOnCx*Fc{Aj}1eS)hGxV|MER|39JY9>Ro zOTs-n59U=b9Ra>MNKL#a43BVk@79SFa83eB{TFDM^rMkyz}4*cy|v6TB1x7^KqK_Q z(isPyC52;fGy`{)5~Xl_(~EaJ%mMdyQ^+$B zVJdP9l0#K!etH(avzJS8ZRf!qcI`A#)C?OL(_~r)5c@Frd_g@d$t=bjrq1hThF?=4 zO1RSaAybq^q^QgPWOxUeE@uDEC4-PP%;m?OdzSY4%l<|+|0lOxrKTk1`G(UaEIh`6 z0FfFA$sfJqJ8<$~@n%6+Q@GBy)Xs*rDh5e#a8ZIGe(=D#mAki^^}x;2^y;9)v=z<| z*wQ*6^2`vW!7FJBgkB!M=RMm?^Z$qcP&Z3TLwgr}_=7}ah@_Tl*Ee)fZ$9<=D_#8t zis^b)9W>So1e)vJlcHHtQf%vNV89BG)sOY?LLf*_l_X-gPFyFw&d0W{wPZRZL3G^f zm)0LUBwkreBAiPRH)k!bwytodnu3t*_KiWJtF0gbGlnaP0-=ys(I< zq|-~D0Z)Hqn-#X02z!5M1Fw89+RxGa!e~;P_)vA|s<_gI21R2twV;$GDWs|FA?eKn z)GEi$2`7K%8-}WmVyZfTS^bDvB@CzM;HrQhx8Pio8`is*3TmELH}jvP4rGOEaK=iR}&xV6o>Ua7A()U=|a~CK#Er6`RRgzFc;oZ~o(wprD!@;xo ztS}7ut;r*HattbG_)ykI(B@Ki5><`lQ7SEy<|CpS+;D!Tno{@7j#j z*Un!Z;hv-GMbfwJln?^iBgdp$cPpm1NKj15}3~ zB3+USnnzbS&6>${gjm2}9@ObzbL4YP_3%y0hW{}(2f`>aVb9*M3qO`syY}jf{TQDd zbT}$-r-BoGLBOX=;ME0j*M5kFQH)(p)g>YvbmzS>o--)|V)k~EE}}ZFP^Y-oUCKSo zbNCaas*(OkwgZd9D=Gh_gohzE-Hwiy_{zJ?Y?wmDHUzaxXA-KmogN%ojFYVirgG-9 zQeuv7LB;^`lMTt31mq{?x|*WGh8ta_=ZQS)GMO+qs}8l7EG z;dsCT8>6>q)oUsvy?D*Frx>9l-|Gihu`^0yapOvo=eTxLC49<;YmYwGUAedjzquk z8Zne4;u3C{*Bx0xbE=f%6sj9@rB{YpdV}zEsmC~6AXG}Ul6N+D#% z$U+7~!Cq59_pT=&P;-gU|B#pK6iQrK_?$l-paLy{;R|#iehKPN-P_Qf!TZlYEM)RD z%gAzXnFTH_oA(ZTN!h_^t1ROGg>1VnFQHRjxfBj}8g0VChgpDw<7)OrkTJ+vXUV(2 zM9WdD9emDG@GJT{!Zh^_%M4%ll0gKJLmC++c=Gd$>R@j~Iyk!2d$?YWktKEz*-~8O zvQl%G*FXkRJ#8Zg!qV6@>CSI!5zh52?OTFWqCx8^_hr^d=i;n}N1wO}Lt@Lw#OZUD zGhT9kMUpMH;gp7hw$cwP4k&+_l%gVqF*7mc5>_CH(TPT_FdLRz3q(_cpK?9o!%Rq) zCpb#=Q~_;M8rwI6slaxpz2~v<9Dc}JA_=eDIi|b@&Y3Jp0}nRn&KTF?1W(h(M8sR)=1kA zKo+Qm)Tq3uM_*epwSEC>muGe75~4EkeYwVbMbk&d9}q*U+g6Y*d+UhqWP*L*=n+vIBsQ z!eH+GdvQ3auy+h`e{g{Z>~&1;b`)>&v*HaHUxsKgt_za;Emse|gMh8|+YxieGfuiX zi&P-=eut%+C4}$?116$VVHw3*J`lq*uGkHqT``bcVe0+><%Pa76h(3V!SOGWWADb- zPaiyxK}XzS<~$k9HnXAS;icF4$z*z;1lzmA;?s;QVS&6)B&%8YSrE;H@DdC0gz%U7ns?11`j1%nz$m+O&G*i{#rMkEZz0!3vfh;OJCC4Z$nX%Qg9g*G zG#Z6@bqUR%T#L=l4mH2G5GTu~5x0j3rJN(@9r5bH^}C)%-Y}|7`U!ScD)@d zlTz79u%!E+*mPnrptFHLPEb0Sj%8tuBgC-&>qX1Ch%}@9_9eDv;6ra=OXd%3QE|)( zGI1-P0(luIuOLPYQjB)fyY+hj^~xJV#wGWO%}DOcSzHIyMn8lZ^mN_{+13{ols4I^ zq$seCvur-%-k*#V-{u;xc{cB0UH&j(;^ZbfhvFZXZwY59AlWRx^exn9v#>XgI@=xf zUbcwfihi=0tAxOk5HPysvvW9Al_}7wQN@H=XUNf?UUUuDZQC*DFADaF+!w4{R~)Ac zPDk#F_l3WPXO>K(V)!-b5fw=BOrqw0vuSDRyk07lU*?{5RgDvT+$wJGp68g))cD#_ zg@9JD&o~FujE$R$f)U=jrS>K=+KWsCGyG$4<*n<(_%s8ZAO(FtL){vco4%mSm~C34 zcnE@N97;wUEsp%Le%Q*gAuZ9P-_#u|&@!b64HeI%<)``|>|iXC)8c!8jzN(3oCk<< zy2y%%Uts0j#GvmvDvT%Um5H`+=|->dMPJz|(A6)l!a5yv1$$se85OEn*^h|AvA^Fo z@V4-5rs*6bl}X10ta%FTsek7Os3-1*=SnxKVug5dw?DnVx~u@_w&N9Y7#bk~A!8~s zNwg!1$ObrPyI5-T1a;oL3wTLx@OxEF7LrlM-y#IyLg1YOH~rofXd+3x%lkn`jTXpz z=8(|{3@MGgz3kJdz0G%(ASQr7z;)H%KB~9skXAaet#p$H01flk&)ny?0|Etzr`NH( zFs=6kWcI8te_yKe^5q}S|JrZ1j|yTlMUC%r3(`wgE1*Oz>5gvrRnKx;oW9(LxTr3u z5|h#B6Z+(0hwrPwXL#8>5&i|vWcqB;!6j3h7{n|%7udE)G_D7rBf8}1G*b?zS8ER9G8bt|FM7ZYXH!IMeBNkwdO7CO#nqWP367b3|=let5cCq5#>TdHL|7lvlF{=1Wn zG$nr>GGCTp&Le>T<&Sjn3Amc_l8(vbuOvtNcZJGfDxALE<%LL76;CSBjn6&0)xoQJbMGKxs1z#T)HVt zOh0bLs}}te(8;226Oj@1YTGcttC(79 z1Sl?dMk}=5TWB@60o<{U8Yc_A^PN%7&Eq*Lbs0hFQzEd?FmeGvjB|S0yaI9PTGuBR#@Q!69)>L97(xu(K>(kd6 zqTCWbfiwR-GLF0YhV<*rIXJRKv<>X1#d_|G@_qOYD5HeA>$(cp0hbNLr?sjGN;Bs}W6c3% zU5fUWdqy3fF(Y4~UD{XOX|hc!#EfS>LW>mJI~@3`p;&~Kiy-yR;tuJ|ijfQztzM7F zJ-l{wCYe$)!;_bk5pxsh(^;s3o*+fppqLrZS^!Z-kKFZ~EGN~h2%p1;8^l(%vRWyv zHbj!TGL6%S6=gg|VTUH|<|gfayAvk7HX-nmnC*HP^KKY!ue}JMw&EoL&5>mrRRaBU zMH}iSr09cKKjq7VlrhwNyCgm8uJB|U5%=3XT&#tmsS{*X(Ut?99#?I|n|QC7Ks!E=D%MFz72oLz;gB!r zSGF?^&+kdEwZ@eyIsOK#wmwI5o}9AwN8al@@!tIRAIiQVyb>;2Hpz)?+qQjT+qP|Y zY}+}p*|F_(Y}8hT;>viEs~aWwCK7(96yVEPvky`ZTRX6qMv)0AEB0uyC_?AIAQ9 zm;Z%SkaPPdtHsn2P3Fqf*u$Lg?zse1K`mBpXcWs>4~Wnw-zVGMM!&MwUN}W^{?lFJ zr>2@5!E}#RBs=V8S4KYrWgvvSVxDq6YaR6sid|6ngAdLJB_=(*VXL}c@G=F!@=%=r zoFqc7G^QQ&8YT&6%Wb{l{<09mPI2Vll-uyREv<*lKb1hVkI<{OCgQs&r$nLMhUjYQ zdlYC>@G44Lvd(qn+AgJYSWe)iog|+@h|a5|C~F&-Kh6@DFD(@KX2vJ>^vJ*)_yPo! z5{bu8^quW!-v*4mHKVDIVyl(cO$R=SJ|opnF40oeA{45gI1Cps*fZMz@m~b+EBl4+ z#or#VSLe&z=x`G4bcZ9l^cs5%#Esw*4H>roKK31)&c=6B_&Tps-25KR^_Yi?k)rz# zk<2U5%!&AG!^6dT#Mbd@x0gpcJpqwOwh%9om6TkMu5;`k>KB2UIqcSri|Y6@Zxwqq zfmM8O2ktO1hHZ17D~FR6K!Q<0yB1Gbr8O4xk%49P7K4GS0awXj7FLN5OTrGGI%uV2 z7p-AK1(H7soRy@oRii<0XB|*yF3IK>Kj($%pU}0nKXu#71R_$KCU;seC%ppuSAl1|A&$ zw#HuLqX1JHJ_7o(b*H_J!vv+uc(Gzl$Q9JCNZ4%Qv3O#1g-V(ci;^`0!xT4e*7C^{sSJU|;B&ihfi1-5E9x_7ge)wgx9{Ql9^=2$!PrhL)n3xstA7{ECFz4kNgW zzu`hYuvOWF^WlV5_wE+RUa}6kH2JRJ2f@$^sb6+>rBJpAf^U*zpmF=a=uJ4bALm4W zXz3dWAbw=uK*e1Lc&zr1Fs`eKQ)OAhefcQ7SQbv0lQ;)_x=ai^a`li=sI>E3V*OBl zc>+68e#eJERx@Py6H+as4rBtj!GYO*w7x?%zD9p7%BUd2+E2(A)}RFJE^~YRQi0yw z8lPv-A~d#=4Ml?2GSk+>9CgoHKH0$HtpcAe8RpZN2tw6bz=I{WSM)1B!nylA$So(5 zEe@aa_ZnK{N`cOauD$@O_30(+H#93sX($j{3a#AQ8`G+T$ zlkS-^nN4>n~lu!haE4Dwd#9Md)#Iug2#7#8#R4Fn) zl&GGPO(LYa@u-1bB92E;kwW}(NXTA>zarXz(>Z|Nrelh!o*nqtmAnR)J4~Qff|tkh zh4Gb~aZo;FHFesshc+vNAu&b6>$X`Rjo~aFI9|+I-S5e722r zWVU{u@x=dpG1*pnmb>p=ZLc;R!xBZf>&uT9 zH(}TcNX}K zc+0N}DO5|GhYO#jX@>@pVkPT6$T|FLPI8Bs9&=DJRF1?B^Q1h~l|50b=OYCY@_>oU zjH>Elpf$Wi65^3W(kgK_LCaz5$K+q^>H9Q&qE?xlqgiqOOX-`!;1)eYtz_aUhVMrS zowJ^A>YXWxIviuY-%$#|?bzP!4pJj5 z#NCn>j*6ODP6HjtYlMrKqdV>9+l;Qf8LKz(o~e5Eq+yvVI1<%21A?mbE^5(QII)W| zv`6VPLSiTs`qBpA!k_dM{%V~1YE*4d3G9iBqMj*b;f#torFoRs8NPH5_s`H0q1kcp z(`sn_VRdhX%Ha>zNPAX7^Lq3V)nl@NxfB{0*A0MI{Cx%?F3fqj;fyyFf?o3Z@vj(e z{ny}#*q&aa4XM^$g!K{SRSTg!diS)FOr>k0ugeHK(Kiklm{0OPB%#4Y=ak%vhG$Dk zsY;13#C-h30!wy@k_p|K$B*89_(o9t;fqMpRi5e7T0(WQ4*r6h*PRhlhArUOKe$S= zt~-vJx$prc7o1OrqGSrUJ#&$vXN03*;~sYVuN=yo@9*-`w7|nDg{dOr9=&jPq~aJD zmQ257E_cA5yvdotHm0MgNdODNs-Od%EE9_RTr)|uK*h78jZ(2!51C3*t)aV$*7vukn} z$yU44REszDf36;#4-^^{+)c@Jx7!pxOl~Jc`3F`)oe|rwd?EKiu-q}F_0iiH5stDC-!9I(h zr^B@LD7BZi>wE-`5;<22rUGLi1`JJ$!dL&oYO2CFj18*@5a~5$fytIO5>cyqPejnH zmh;_Nnk}$eb|=C&P~fcPeS`{!CtI9s+j!8HksXR5V~`)}k9RYL2CWpV1()Gy)2_pc zw=B4(?z&7rz@clkfJ6M0(_DgfcZtjN*)m+QMifX~FXb4)sELc5(XX!Dx{(%oYkRoI z8d_Ew>9b9<5f{^mLyorEZ(U4Hg&U%)E6P2)4*e|C-qRps(ZMYDdI)3KFJ7y_JzDJEd?cZ< z(R3w@&Ti0>;}!0iI^Kh|&J!k525(YARM~Ai=-R;nsbW?G2YI+Y__Bhu_wt$Ly4+YNpkcW7k>| z4;R%?pX~lnL3K0Z6Nv@&6R+Hnfg(`>f@Kv)^%8m}vxsYQu~;t(*)t|t26vN!_iWYkqI8Mc$1>R@OJ#fJMA@{pu)iFPYPI}}#W%7Tz5D0Si-X+^ zhHv`=bZX4aqZq|A_D_XpvgJ2PW%^F0NgK)ZzHC5;SkOxliijd(AWr}ihe~ZzMeeY( zAjNzuLo)8pFomFDGiIfuM>XqP z3^bhqi0_7K_JT~V9MP-`#(y33%~7Zo^Nx*o;et*ezYX$YFZaLx@lY4q2dMa2o}nyB zhn6gJ-_lR4>LGYqW$xJ;UK8#BVcCRG*sFUqvVto>96vL8?5WvQ>C^C=g+3ZB77Px_ z^ioC+i6aY{apEM-^OgOxazF{B$ zG(XLGc{MT)4QIG?&0PD#Y};eq;#t>qvF6N|^MH1|6k0htFU^Jwg6qT0J@fD{sq{uW z$Htj!Kduv+7v>v$vI0pLl$+B(-uw&rOB`8rrnV>S z6}G>_qNO@&406wQiJtgywo8tID?ZHzBQtF;9ixB zB7>U$va*0w!#z$)d`JmOrer+JL+N(;{V}&n?C)@?_K3M`XTH9}iUV({uhu#K)ztd* zqf_6)qol(M3Oy!7DX&>1l&}J8*y%s^5Q(&NFivoT8dnBq3AR5?hBWRbgO9?9Kgcz3 zb|W!RcrPWJSWM%c9Q)HC@thv!rftlRjRA$ik)n)naz`Z zm_BAhBy~lJyNAKq0qDQnSM+yx>GbtH{%r+}l_msPoAY>cR&xeImwBfQ z-r=B}(XWaKXJUrbYJ1Af=`bQzcc;VDes-hYB{dl#H z9S+0;tKN5{A7yx@r!*~Ga{LhKC`*>O<#$MxNU#(c&hb25+RIxBCI#1> zFWf6moY;+!5~qG1bKeRVO0se$Xu@8{#ZFw7)<54b3{T;C>%$_sI=s+?dc6T`#st+` zON{)u$8VNcWN@Jfbkk^TJQqeysk$-0*6tX;`4Trut`PCWe%4P>=`RRL38(<}bT{a_TW3PfEY#cCwzO2Z$8PL^~geRw7pX5kJm z^#-tdg%&XRg^9^a4dtqGSbPDcW(LFg9+ttvB(78e_o9!N7c~-G;brl==C^!}C_8x? zf3q}G6IdTzw3m@Has@n>CqVYlsd)d477%%SgPFnz?T>{T z%miJrKFZ6;LUrs^x>U&)ew@5KasCB|uF{XrI*$D!$zHMjW1sl$BIGq#g^{Um)wV=q zNUi;1aDhyIL*?gc4tJ;G#@rtYUJ>>nsGTR;xtWVdk?gGa=4)DU5x36$EM7=jq|K#Ux&3m+6vya!r*c?l(b9 zcSK6`h9t!3Y5^Rxli0b%j+pl<9&fhRuc1Wance>;C6BjAc$<*`B7_4HvLorgUI!Hg zT(0@Pq~Pi==T!~5+vKkDlgX?a+siWrwgl4DSZmj?98BOM#sBdl-WBEVLZ57C^JR?Z zV?XL2U>ePT03s9bVIoD)ABCphz`lur7S$>`^!)h8XSp5ESk9(+J==^bNgO2*(TKRH z@)wnLImH&ciZj#0;(3m$J!ac^4}PLhWK5~8*>PqzO){oK2*D4WOXfUmCrmD$KLxN+ zNlt^*LYV%Dm$FamU^NXQG)Wf_LUFN*TD`32tfTI5qU?v!R;WiKXt_bIaGB#tYPNWP z6Hhw1A>3DZS77y&Jf*5+uAY6kk+1NF4D*(uoio8I)>7fRK?))6qL00Q;oCzJZnlaohf0_ z7lGjASpfrBbPR7;cXVjXLdd`1n6}TQ_7a20E$nV$X!0fHe%m-9+`Mi$V@a!J(qU3y zOqq^kgZYLKQ)JCLkn-WvFdI&+w}2>z9YrLiC^_|*{3PLAy+*;(sOr}tHvS`(HE~?T zhnMDK*jJD6tGo@jf6sI|PqiqB4cu|yi$Zeb&eAiiZN->!zd5|k7w$FOBv>X2W(>H@ zMOxLrU`5zZc$xC(@aLX=h3Dyrgv7y)ySen$;y~7>6If#bmTa;jAzF;^MQi0>R%}{y z0hN7WhFahiNjoVdnxST3x;*1mO}sLjS2AqoqKrwM1`D2?-Nz964|+>{C@Jwtn3q-Rb+{l~%f z9yFvxEx~yP^2m6}jF{lCVupue!MYfW)&}Y@FWf@KT_O`v?^87#UE=ZQm-Pr*C+YD2f`V_((J>0@j zcJBu}&%CqcIEkaiE7F|*>}Q%6{7sqqkx?Qu-)vwhh3^IZV;#bO*2(U> zHmF`jqSGT2*}sQiz^h}0;}0M2UU0THZVC}Y8DS3hiiQ3%&Tp1kGdHQ*f6@v)<_GDU z<<9zB-5_@d@Xhe@06Z#q7(g-)?t)jx-PsTzMKjY4K^d4`G>VkN+SEkoq01~N{hA=H1oY63xulYG=km7uT=?%Qi}{5}shZS`4N52rtU*KrEJ&1kF zQD~Da=X`1K5n6$0+Scz$@xRSMW4I1qGL2vYWo1qN{ZJeC(pvHP(v_VYqAHLUgRotp zr_oiRURw^ps9+)EW-9NQ2!paAzH{my413(7j=``b$@#aKn2QY>OC6FgJ14#x!!o=3 zhkV&XwN{DDes(t$YV=@6&GXo49P~bB;=JV$bj>?s*pg?hjwSO~u_cR`R6zLr5=!Y< z-6v7%ok(E;l2d`zvDBD6K4_x&FEkoma(t=oZeQ#1@Iz)Iw*^j##95t^Gu{RK z(2VNlm#pd;7=wK&9+;HCTXl@#q`}~SYyf8jU8TMFu}hlTX#LXVygQI|*Z!#wYCm_a z|HFwUkMM*IGMx2fp~Q>1h@H=`?i*r%WUTg3JI;D%w>1 zeiEPNUJV?ZzSZ6>xl(p`+LS%8UeNf8%U^cJ{fR{z*@_DGY62l`tA(JERtI?fKVY>j zy42TL_WEQ@QyKKfO$bF%Q?sjMDG?SIC^0ig#}=2Y<7xzFA+I&t zgL!cPV7+>TG@7VFxWK4qyv9nD|8b^wkh_s%wQX^$`mIW+O>(+c$mI%nTq#>L6p?;2 zq=;XVEZmMs1vhdC+I^lsiXEjL<=j%9b7~Fs5)CkaHWc0-68X*G&Yj)br8;hfR@(X# zV)Mun+2)wXP_C#H>+IPDW=m8J=7ckEoXbH}V2>N6sz8t)oFnwCTEx>R}=epi_QTGoY#+;a#Nx$$hZNrJh}R& zn@|HzM!t@Z>#`fA8ziB!5&mYWShV29=ANEI>b4M=4MHI~oJqU?QqkSA$+p_;>CsUI z!h2odByOHv?3SBNFKXdF0_1;*%mc2ocb#2Z)tf#9Kax0H&iB(TlYX6=JYP5?g{lFX!$rbelIC#4}V*hAIgmF zL_Gpu4j?6WDJT?>@ePD}J^x2|rB{L@Kcc^l%PbPw`x@^Nh%P3T7hXjedKP@4J#yYv zyfh+ofoanHu9rw7X{P$Nv%0DrbZGxmUpQI2%`JG7>tCgC^FI9cbHwqUv>B$`*(PP~ zG--{H5|@_$ALume?Zpvmks=A(kBJYQwxW}0HU)quCmVuD?xIDo z4Btq**+m9DfZusWNlm;!C*Sr-gAZT3iYU-H0&ZJxy4U;+@UrVrM@bd}Ld zy7yF#YL1sCbgOg`2O;|mnt5dtZ-7>aaU>@;WyRMaJzm%NbDQ)VWDF1u%*~6;gZB6m zhYXHhq4QD8ijHh*7uAdR!AYRMH?`o_q>ayEC2j`kkH?U7gj+J0toxL-K%B7L*nIp= zy=1UYu1L^66F#uIaoM1?hP1_)elHRdjxU*}>duDJ{0V8*PcW1`H-&hs5yQ zNxE7@qZL;ioZZJr6&qEb8*Q|oS}wfEWoM)U#!T;JvDKE#3BmvZ(TL+o3o~QBH(Q$F zkfM^V_sx#Z%85 zF`MD|x%RmWZG>`>+b(i5A0dGl2{HN9i5~dY-wywE11!kUa9&F}N1z`yN!;yU@M6BA zp+p*W+LXhDXE8b2Zm4%}Ke}Ywj;hrl8B;J9ZNUE1QLNBuTUS zi(NpXwxxl?kSPUTnLF*aZXhvN)Y3%4@mHnrDTOMLS5$LRq(owY^W%@q68)uZE;&^4 zxc(S?rUYUl|{gpOE&Jbfqs zSmeMK({YhYJ;mm&?H4&MU4~QC%q@j91?A5>CjmB3p@KZ1%l3Ne+3QkF0!7C$dgFAU zHf2)FRj;HA_q&Mt3l>BoC>DqPX}rL}olS1MQLhM)ZnREWf$LZMb-`$rAAQ7Syg9o0 zb>UM93tY|d*Na>>@lTVgo4SP`VNq9v=h-WKs)ECs%8H?5g{~K6ME{yD4R=lL|om2*MsNaKPM6q-r0 zUYbJ2$@FMNJcA85i?J67^NNm+ENDNkEYr4)Je6};w!RDvPDM43`sPsihHGN$!Q>`|r@J%wvU~3=`+;$nJ8z7ebpX248$pdP z++bi$lbVXB_-iFW0LNorRtM}rZ>~&e4q1xk`z}f=Iu6N~MMURv{LpxZYrFA};nh(~#}y=6nsl!-s- zwU-^gnG>!h*-&n=cB60erNW^Hm&RrReebrXL7LT0EfTy7?G!>{q&;xxr#PiZch*Y4 zUgP!aUqmwf<`Mx?yree~tnl)G_)ueI`XcG3)UqEn5(EHD{h=lZ9+`+#4H^Rrl=u)` z{x@?^L}W{1I8GV4N%ETUatgkEb3X899eA(xwIt#3=sQLqN}mY8@1?m#`2G&bEUvc> zg+ovNgA_l|GweuNTn2(G4+~otD}zVtyadCrJ0522@x`hw&Um+N->h;6IH%~b$_yn+ zg|(V2K+wW4|1j=boWC!-z|`QuQGxd4?}Uf|#X#eYJOw#z(R|VV^>6b}h;$#AxL^Zyj*#3y-1 z#^WsjmeQd`LUN(qy(`iuApRl_z#5#2lCR!!O_~anDGjbgB@0%iBY#M5^;#a^^5hUE zaAO?7Mk8rLEcH$HvfLVT3)y|G>LkK-VeUlm=sD9ZZTW+z?GKrM%>f(`GS1qhM8TZM zM+v^elQq_Bb(tq6?YM38RJceV69qR#O$g=()sd42FW1NC#SnRKP$DeeKIT#_KL=)S zVT}UvS{TP~!NrFe*$#&;iY_(6>6R!6e7Y>ccqcLfKz#ZuDV3(UfLv=pox&@U3R zjOLJr3gN0?6RICFm0L~K@L;?OHMDtbr-z(rfBEAeCWyP3j7(v-OJ@pBHk5Si5nfWM zT6UDKy0dqYXprM8S$sxAdg8Ep&vfXgOG}5=udty2|w{48$-LHEF z-iDW7$z3L@w+gmg^^)v23+-xW8&V{P!gw?{cUTsP-Z4(AcL5{c>vlo@Dl;}A);`>RGF=YC2GpY3g%n8T$h!l_ES$mMXDT#Jx;S@y(0FRaCrrf!H@iH zMx4<%ts)dmEji_$C<0i16d&ID7F@=V$eU494|K;<$+_Ksiu4`lz$Qw-YMXpzWp!>5$n3nO42pZ_fGsfx9s3)!2do3)KGI$urux>1~X` zP1dVQ?GfhMD-3)U58J`KJ-~NFw-A!T(tN>^Q13rgm`zd&1=Vo9>G1&#Xsn%6KYyrD zi+RJ=xFLx8_8XaYVtp!nY+EK;J0h#m=2~vej7y5*)18>e`o}h#DvV1=jkhCrFkd^7lzAP z9XG}N)K@cHCYK5O`zq-f7FwIZkFN-gLEBOA=*~$cM0nSA1$Fr$Ci7*LU&dPF;Vm8} zX{B{)(A?Asy(KF|o!E-gvNXE}-Wj4##Uak%ek1&({nv1j>V?^APNzw%LxF7CcvLXI z1tZbc3e19juM1-xDAFd-@s|}ihgvK6S*1u=XC%Nm!ffCX+APlZ); zvpdVpn^8xCK(68Eh<+!~?lRB~zCfxnq`UG*X@!kG;k(Iv8plxWyVl-LuV5SPDml*W z48Rk>x^v%Ym;`Sc6B)RwxV4&=BfMRmwH7w|hQ3iJo#*ag^k2={sO0()iQZ95EsW8b z{m;oiGjV)LUh9+7j+e}3c;=SGhSbnR#8qYo-uk+P87wp(GO`M|^Dbx|Xce?dv9l?n zN|K%@?c1|8FwX0r@T5XrlKQg0OO^iKnaJMQ4glt56y+7yvKYcBy%1B&-v|nA`0J^r zS$Dq4>YZl!x-IU2`rC{a2|f>JQ5yKMF`^5pnO9D=?mx>N0k#q`s)8y!LZGm9N+nk* z3SW2r?kCIvW~Cn&+bCm~*RROe#}D_Hz22?`mfSOBiZ*V!&eRb}xOHIgAF%>mT-gg8 ze2dC&*tR@MF%(|^#h>BSlAga}u3LX(1q~xRu~u$~1=nte1s;@Tj5q}up?$vMjJd54 z+ZOK9Q=8x&sK(TE8L5%TU}R2qlXXKUWQUrV^OnP502g2!U^g+rh?gE+TG|OERFsY& zRChw7!dHkC;6k3qBxw9QB|FMRTvGBcf7lNF!v-NG@v$uX`f?(xPH-Xj=o$!yQuL}A z_lP9DN|}>G(^h<{#AGBrwtasTdBFdGG~PFOZXe2#LQ>=45idh2C3os;k!(iP%QP!l zU5W^hEyc@|44P4Ha4g{-Q4Pkfzu%CAZ1jsJRM|$4Jb^~#!^^bFB8r%Va&)R3_jkc( zPciqseK3M}svUBccc)>OT%^Gp7S$7aYpG)cJukp;@mwxWrny91K6?q3oF0WM5*Y~{ z*hfw;Xj0Z00sawh2!0RGge(`LV8wJr!Mm>DB(_2lg57g3-7Bk(7w$FT|BR=yW=%b5 z?T3}D6jSI9Vkm|nP#yh6hW-p;{ml8z_rhCe?@>2i*-d1@R!)G*)sc|_8shSV=NeW( z1z@yBH9+TZrXtBi+0y09gxp5;rL8TwZ{tiGk^rBq{Sm)8>S6K5-Y z9I_3GGWk;}#A->PBk=~l<&5KHixevk6tgXQe~~D;gv!hBNKDP;)VA-(WBO3v)`~^g)sCyf4?zZ zm{PvdjhHX`&!qvN9I}mgVjdYu8dxA)2gA9wE(ca1H!`|~{)tk7bJ?2^YJUC0T(Fo7 zcm7^&RDmaNFoBaa>q857IWMA^ZeZg~l&eR*-)h*=VB}jmHcX;rz9_h;pc(M}Ae6YF zz6FqG^v_l#L5Nw%Hr+&u$y!?;Fjj(t`_HKZ_R7SxK;@5Es2@Vzsqq#Zk|=)j`S|E0 zKX>iB-Nr^8iZ}#`pHg|cN=v+xBo5vo_7NJ~ZatTN5qaKXDgr8<=~O4jQ5yV# zYP7Tptwgh%FR;my{6k{L0^B786=8$>hB!A}+(a+J*xtp?2r$$l-A%&*O6AyuiiLl{ zTZj*|KR+UsBhY3nu96>g2Zfl0!|JvGqTuSqYJ?FY~*(a&?!}@ zM?nZB87Pp`+g5jx2M3h=5I80q+Y$|jD-8^(iad&T1a;LaZkPnKU=6`{WgKVN->DHo zs?Zvf!vzA(s9c#-dY>LIDPqyE;SO9^yG~JjUKci-80RXJ0c8EAzl&MGOzv833b}96 zV(XtPcyOB3N9g_tT~(;|=!VFp%w0_i`+9Ggls+ymRg91`NRCFsOE>`UpAKSqF$^@GdwF#TeZ!_b%)uVN;XO=pRW5J+pA7 zf+Dx&E1fQ;A@;|-hZYE43k5uT%ap>@V%-cM;?YL$spV`n_yZo-loygO<)_=PKIdUB zNYR^gMAL+^;4n88l#v{JX1ZXiBntlvo9-e$2LcD3A~?_;Z78lC*XFQ>Ld3JrT!zHm zT{4G?odNFogIS|^g!!_V!P1xPch^z^xVzMpFwE}}83-i;=0f{x*yqwT8XKWIb8XNF zkI1OgLTo5btWH~qLZ<5J*I0rWgR-$FZ`;U5_a9wo^-lpkXr)fcp}@*z5)VoVEw9pL zb7O8w?9I=B9mGBD7g@^o^#=|JJ6>=c$ut0ruT7Qs2k*z&FJ~w!Y2SbGB{8BSuQsK1 zNz6ADBNb;3YE<76_xS0#zZ>Vq%H0NMw_;L-fAz4z5d$0{OG)q?l@QJxEaU~=$iMqw z=-=2>60Ot(PsGk2d|;_9#Qyu!3%`@tw=@&_L;`W}BF3S2f>Cg{>2KM|LUPJ`SCR3h zuiq!E=bwT=M2e~AKox-5~MQOXG=ClQowTbMl41Mkbe zT@vR$)l2dLVhZc)iB8GOOu0Q=YH5+tNek!_sSBAN`msw3b2bIXtEBm(@vP`^V5TZJ z$ES-+;p7f6x|_43KWkJq_b`h31L!tK24OSiMf))Nmg25yXsILryT#PEju7VeHQFjE z`sLtdf2pUPfBMh0`6le^DDWqZ#eCa>T?`kHfpm6Z{km%D2;pd^AT--4p6v|}+*Yap zp5Zo_g{9WR!qSHxt?V;WKho3Ft^ruIApvtOWI%s`MJyx~HZ^?s@pE3T zuc!!45+=!7fKTO@zKfh?9`uQheAX|slQOVIbrNW!!H>l2$HJT!>}U7&GKFgB_&E^nRJN+;UpDx4a#HZFibG=S+$~C z?S*FM%~v);vQ=tZD%|sku!<2P*jluOJ(H230O%>Ro6`;CP{DF&012w0}Sh;J7IAyWN0IW3-(2td&7l|}LlS^~WS)w_jCF1eIHFfYqn z>gwP)9}1es67Um)abcxHsGg$6b0>*snaM1P*!y4?!ZMw|>wxxgu`ZbnLh4En9o~`- zxt?*ydk@{-Rd|iuh7aHU?hOO?uQWlisFp*Ph^m30;*(p&R!d*mCl%buufn*y>d;X( z2O{S!Zai?pk4>7|Go|6F<6YGBZiB66xF^2#fke{dctE< z!8w)Z zwq;}KQ)U#gs0LS*J;awVd#8t_8+T-sP9N0!{7iFzLNUQv^$szO(f zw5e7yk3YwJe@Qp_&Y*k7-vtnYU{%g`)a9Pq~?gQZ0)DZO-0O#QcFF@$M^j zY=n(r($Nq?S!Y(gT>Ss_=btjcDn?5qTC;?ucB2?T<|oC@StRgJ-l(5N@mzwEt2-{A zqVG5MUut=rPKG9-YNg9px&H9=#hgrNPfS>pJI`yuIH~a40?}3otecFR~JrL1d?tc|l`4ZQ6P%{!K>a27)N9dnvv>FsvdMvy(HW zUPylCFoLwdl9hZ`5squ#%NZ&ghilrX8{ihKD35a?^{%QPdElzg^!{ELJc2hhO?{o` zambZA$6-*cRcbKT64m?I*=LBpZ3=ApYAwdU;sYM>4Jp|-r4!6uoVc`KZBmB%mfnL( zZ03|3{&Uy+<5Pg^eW8R;Q+6dqc4BX?I2ge9m@eh1zq3wH*X0mncxB2HLG~9chq3!m z7AoPwaZ|?8BndJPR7@918;Z4wv%i1+`V!0~S2@^`Lxlr7`Ll2fX4|E{(i(z3RRx4C zY)~yEmT>%hj%|heXge4oz_ksBW?aPx2nv%n*rid3%K)1P)l_U%nM6(wWbHD^vWX1U zVic{IU4y&r3~;WWuWofzySKgc#Wq~IFx4eFa|(nx8eL(VR(cwTB(YFoFm{SS6hkwG zft;Q#tRY>5`98p+*TN0>rSg?ziY#O@Pw=`12%*Yw>(3x8O)qC|G%_rh&sqnH`-_Vs zu9rYZF4IitFQoO{&!Otv1O}9lSSBmrwfIC(w23>4oV6@-_v_0TlCq>G-Q5v>J`C{i zRjCNW(OQJpF&f)GnL1Djho?*Q#ryEpFU(B47l-{i1-rm}jhbH$E=;CeHZHNcR|+%L z&3NhHAwu9)XjB<@M7p~i5ZS1dJIh1iL{|~5M+4SSp4|Je#QC5E)kXIApGe;NH=s(C zA+$2DD6aj3HV@aCi7fPSMAj{*2gJp?SX!&U9d;Irx_v?>V1MG@c?&?!QL2Iu_GnqH zQrlBt5a^4!9?S61d7W)t=JviF;b4;8TvV|E2z7!Ei0Rp8rlTB+q1Y+{8^?sqlp%nX za@JGn{PLGz8>KIN+eR^3Ok|Sc`{#K*k+z%DB_Zn`Wph5_`p*^`$W>`-8 zvePu)#(t8oOMOUIf{ZKuV6NUE*uY|u;QpIPCMupCkX+tf=py=O3wh{rnek0Pk1BWM zTT+GAexvZv+(GV4PnOK|%M6rL_8D2~FBYa0iFe?|+o9E8*A$PHVLeKrA4HW#2<6Jq z-HXTg67(BmK0C;btbtQ=0;V?Dg)}eX9y$z*!F=s&GwRCPMC4ALa_m+Pu*oMDJgUD7 zg1ymL`K(@#)&}r z_GEz3hr=bmWx%dsA^5;4d?N1tCAn`2wF)RqeR^SFXdW)-e)x2VS-8|hX?ps4eoS^b z0W0%eS%5H5jc!~;5?;`dQ(q$X?WYX<@^7-qk3jd{@81*3`yz}&3ia3D{AyI@X^WXv zVI{wx%GbYA)?QweGEntSs*JBDlU6;)i z8V?a$SEz^KzBOF^Esf?(w&|cKUP-%D44sUs{2+#ZK7F@N%YNu#q1!1tlvdLhBULUF*rl)(tW94$Re9V|`VoNS$}jje1QEZtfDPq}ComhR?mwk{sF z&Q8o8J|3DHaA1JJu0zBBcRYO%z`!B@0>Hrj|8=Xg?R3nI8u+e#0#E{1^S-QC#5Y;2 zCHu)_sKdd)8{lj*uU`e zvm&Y0QdNT(ej$ruw$zOVsZ?MmfGVR@!Y4bRYKi#RKMc zyH`-GxtfP(r=|hUmC_ECt=g?HHJ?Yh4;yVctEjUR3nE%2c)V(oP7F?1h7X^aBpv>& zOpk+f+QM9G&SGKrrse;z_D(UvL}9jQ+xXkIZToNAwr$(CZQHhO+qT_({yDjsd(X+u z%*#|#m0k6+zo(Uz+6%6fB$%)8G@jp%f*QYD4$Wv|^LJXNilu>t^@>a-`$lf8h8kcZ z0SL=8uK$)7)EUzsu^nYf=}xasbWKKHXqc=$&@?#RMZ(w|X>OSUEX+|s7)p{uYK(^( zlqO7_?g_rf4VfmtTh;D#O_4j$+ZDIP>ZaQo@B=Uv+SdYU0?wB_2~%x0!>5dvtv|Q! z%4Mh8%<*uP@ng44^4!|mUzpLLOcpc=whwIemYI~&rY-Gp_6nqwy);EpQ5(jc%^j(# z3drr$PLK0>>yxG*s_FBgtVS<=tCwcf*jjt1Ls;KhsDvd7*^AcyB8`$4r?qt|2%TP6 z-_YcVr4NI^wrpo6i^wZ4A5~Usk910fIavDOjSGc2#;6(3m@@;X3WL{^GCkGD18km( zsuJFgRuQm!IL^LX@3#T?>fL$EkNL`Lv)q}~%rF^Nr}fLl2ep2K+gG3_VUc}`4jG!T zY;C3PQYK5pr8-|Z zsMqZF1_EON2-ty7_fXqlZ~Qv?jD(YQR@RvHD>58QidQ zKxacv`wG2y+<3bHbRcs3Pw!~nSiQh|pmR$O2jTZZA<%(h`u^$>+5KsQ;09sd1P};| zBRGPf31a;Vo*y|Uct%9bh{+lTC*nm<7=%C+MXo;tNu+>YKM;m0(w4$Hz%s-##j?b* z#d5@Q#qxyUn+0*CGDx={CMZMfU=WWKo#l2UL`+7?AwM22X~PCBz+RAul$!-{JVZ_= z!C^mv!>Y4A&T>Z`SxwnsQfb9VF>zz4Ku~^Glt~>9&El`rX=Q2eKM#rj0T6XC);F>?{!je0Q^VB$kNf|^&;Jw16L9n?y2t?lw59+66#fTh zFfrD5a&|D*HMDhf`tSQKx3$$1k4Ef$c6IyFdZRhz)L%2nap6MBivzUAVePc)cG-ZF}DyO?SOsPdZg=eLl}K*>-)NJNbIv4-!vXZ~3@;zW2{(XMNv~ zIv)-$eQjfVxWBK@2OmREU2XZ?zejg{@2^#Jn?-ehA0A(2>2|+XRlhK9MajIsZ$<@A z$HjBMpWc4H-xYhjJihKGCvR4>xvpPlZ+H26zwc#Z`Mw=~I!`QrW>4XNyFY9AxVyjZ zo?ZqIV`Wo&8hg(odv6NZCR(+hU_<$uxp}*}f9>FY-);DO+&=ELIo|I}9ZP*b)r{i! zUhYfTZgqM;-`}qfWj9L~?%5_iMM`~s-uCam7s0(oL#VH_AC@Y{S)^309A75{M zFm*WbHrfn0ID6zcI_$l@+zuO#&gl4JV}3^I=m#*8#^bsD+&t$E%BIDD)9(kjTsAE%i-c9D)mbcnb zUwz)ei&?tdgeA)sj(}Y#$cfQ)fc512$@fXR3d?w}xc+4=%d zQq#Y4Af00}?Of?P*?TDLa&0fjrKhQ&=`b0HjESXbh%;>e+5d~|8RHG258wihg}97$q^aNLu@P96sHCqabn$CGBM|f~ZPXVM$aeR=UG0cZOCB@17EM!=cVUju0<_ z$6-UU_9@M?gwN7R>J(n6^{WWSfkp6_Yp&9w(#%2Q{{D?%v91VfHZTSR=8ONARa!?X zya&bUKZJ}BND;=Jp;A9kMXu}51~n@a_9l7ETLtn?>!(ASt0K@-C|{`X_am@mm~}Jm z51*hcEW1J$0TO7vkp}i4F9kDApn6mJZmU9KDoO2CH$n{xA8x@UCnQvu%2d!AcH>te zifjo*d@;BsWdf}l^4!BzywS)9tDzJs)-6g=l=*zYzwj`{YF8>JS*Npkl- z8%6&aY!++Cvz~ELP)>I*LHVqpog+0~|D#1u6=45;CFHS(R$;|EC#cz`fyFu~mkhS& zp1x+pjuB?;KBuO$Yp908yo~Z65eWY!!>O@SsE8t-ZXzTD>w7;f4nCv`nR25+HQ=Ct ziWUj?=!)1!RV)(x>&82K)E|V&Bv;g*nsPE-3Lr<%K%JOiA*)4DAcP1Jayg9naYEH; zk9#lQE>BQ>%sDbp3Savbv48zNY5lc9rJWG7JZKh#boGDa7zH4u5&4Z)De8jBY>P<-v&){-sg=FH&`ve*tej^%O63&vDPU@P zsw9fJNK)cQ==)%`V_=A+oMUaccw;~@f^U=2*(q^H=2Kr9<}J&NSwvmz1uyA*khtVR z(x%=(7AAH_)cw~?m?1W_94Vh6<%weOCfLus!Q(wKA3$*_mNepu!`B`u!6GO&dY_2O z-aX_h0NZCK-$yt4;b?`geTkiIb4P89;|WMgNi^(Wk$1NA*U%M|C9*Y{6aQAQY{p`R zNWWYgO2bUtbLur!8(+X77rWzLv1tr6qTKX#SKh8{twAEO;dQDM3QRIsY18z^qtLmLat^xN*);Y80GGm(ata5HOUb^W^zZw84Kf;l zacjuWq&uoiKXADgxr@y^_h7CmOk0G_&c8mII{vAb;y9ees4%B2c;{+vK0AO9GDIQ-O|_PeG3=`j#ubbJ=J+>_80kDp4U}U7W!IIQ^n+@m zx2bXp8EE?*QWo$1s?ndL2~vnoplOXjuQ9U11SJiqe5)mS6$55GU)CRk`YQ%-E-v86 z>J$z1^7>KHNz3YgE2Lv6fqE4{TkUH zh=erz7kgR>lv${BiIJO=r)lslfpM@ADyfN9;VC&iFCCdlFF#C1w=#+Qo$St)1-{rE zMfbLH*Rb!JX4~83s2AErUa-z#^o4lX}}|_v{Gs<3hV$TY@yl545KklR2ZrAd1o133{9j! zEy9K0L5OcYMtxHxuU*_^FC%mfOR;(-TVU; zWsxX1UC<6nxe1FXPRKekZ$T^Opg>|D50#zLAZCFrPHDbc$NE*`3M>WG1%QOA$dZ3< zYe_Yr&r=rz0rI@D0neU*mNtHdxlOXsVBA-iRuN{^4k*m(Ex#t0CPPl^UtMDnU8!-@ zNbYy$hvIrUWTqizCF{%1uxU&?4vup3n(rtc&Wx!c;yZ z5k;JPW%LZ@?dFE63hDv_t@>1crjIsaiO?@4SW=-oMxjVM8dAayZ8fMwlXeN&{~p%o zPadgEten|u9tuBaScS}(>p=ZWRTiv8q-+vUAZ{tFOU=!)pZq3=qnYSP8Gr-4tfju` zgXv}<$Ko6J=SaS)_!N{7{O_BliAxO1Q~XvZvZ{Hpz50rAC#o1S2IU4&hQ~HTp0~c;or@j<85tne!%riTZ@wJB#29e} z$W)At3r(p~lAm%kf!eefK@n{nsMfB2L4-k|K{II;%9T8{i*y5jX07$D#MaN_No9oj zH|{C$12DN29&Vl;l_GJS2{?PRal{Wz>l~`?ig64+7)kq;?7A4lreOZSSdU2bGjrMp z>-ezwlr)kHsfru8jHdvv$t5W|H5_>i^uhYBG z^wnb>{!=X%4ivpB_tdL?m_qCKM10`#>M#|4yn*HQsTGozEdtIjMM z-AN9`rMVk!hOo|T3K^?cdw+T8Q5U_1FO5ke-7>ZWhNZeh8xj@bO?q*tt1wxEIUt!3 z7r%|FP?j;2KUm&w)I~YRo-D>dfudPt<__MU98+?-T^LgNO~gpPH?^C z8z~+(s1=&2aW2_Al7Rv;LtQGt+wNdImJnz+RgrQOflku^%T~{Bzw=z=uFg*w3B5Gv z^U+f(LA!(D^Q6!iCC6<-cfKh=PK)X!7?3FloLG(<2>;R+mmq;O^DlCVUhq>wrz)~iULzQ{K9e5Uj z%t%mV*DN)ya=;L&c=FoR72`OSPFf!a^Gj?y?kp4bQd6(uPv~*0;$lp#YPft`I#oDh zwv|-3K||Ew_-*Q6wy{iJYH{P zkM~Hif+0Ip!_?Gr-_%;JrJ0W@7yA+zSZx}oj%cc}$-@1#N>tjL@TOapF~M!Gq(@wV zFvZ@r=G!{N_}n4Uxl_5 zNhr3Z$*WRS*|07kJSiGgNz^%U#IvE0XV|7cSGPIf=c`g}H)xF)?-DDX7BIKo7X4f9 zpLfa!qKgtK?j5wd0l^%V1n+5IeKo9!u9tch*Hr0vel@4Fz}jMuo#-K}mu{nED-~vgroe3C*r%p$F}TDTq)WqhbBUYR@SOYbMI<+r#zHu`3vEb^3RKfPUWrj znI}tbWyUK)>?+}}8I`0I=&9R;mXb-|>RCtaf~ne3V#371ja*&FofJqCrWfm=gbS+n;#@*N=kr@jh6GK>SWchH432C( zWUl|%3yUTx>&T==X&yF3u|=rZf#{=h$uSCb!HDR!6;v%>Y9eRqM{lUzZob5_qi=<= zTwO@V87yYSUYa!7U=8=Bbs@Oq)OK%B8JC0T78pwtq_XqWcl+9UvT8oz}EUcqQ=3zO3v1xfleIBf~P5tBNImlmNx8SIu z3g)}+&zE;+0yGGKh1CeqeuCXdfZ}dZ5TW%DCi62C^Emc06mofgR0N2L@ceUvVw0IP z1xjhE&Z*%*5iw~x?Cib^OZ8Hi==6wM*|P_jh;i`kMsM6)i$OXk!%XH$`gw^R8)%X4 zW=Jd+grHTL9qA8TWN?t44tzv}e3G!sY7#w2G&5=wt1u@K1;~tRZD|7hi&_==UmAlL z8rlt~Tc6$TxO1o9XV*QR7AD$xW85yM`pbA|8(Ug=S-^@#JW_UT#P2B%lQ8dtj|5jfZL_W1V(@aM*hR%{h71cqdJRQh>WDt;7#AkFU#&GL`Od3TLgim!njk;Ax zl>u4`xwfv4O@qo@Ta@;qSva^Bx)psKn^Xu$6vEl#my!2nh@<(Fl)a&NY-4a0`;?bLdTS4JA(2ytNF2a7J;Vfx?tyQ&eoAd0)3%GX#^i(e!KH zk$1Mxt(pld9;JTblHF0-?LTdGHUDWnuXOCcp5LoCx!(}xXp-cl%6p-VdmYQpmpJU} zXN7=B6sK;RF{XtqXh=t2Ut1BWWp%_)q9f?=w!+%Y&lNmTmlt4}3&@fB!>3@n8c#ZV z4H!Fm)an<4u|6;n7uT+a1FxtS=kWG-7SGB1_)5*iH~QsBUgM;|iDu@w3Is*h70|2#j;#ZEb>lIhc4P)Du^3Q9 znn+5DkmdrkG&Bij1Sz~|)8{L1e%0MK{aH{dIkq_yco*j`Mfqn)D2cl+-m3$>?JSY9 zEXq(Nwtj7;hM`gB?6Tdyu=G+VoI9-vHHj?`Wc%CRyLYtN-A>!kj#svmtO?HBm?(lq zNHBevpLvNar%2f{E^>HMf9AHLKpoB#Gn63WJBG7lpZoTH)#AlW@#1vCcr{LkIWZ2} z5H=nWcwmhG9iKGb82FInqR?UYAW-zkz=mGbRT@1D(ecE=C)~B45_OG)YKbD=T$O94xvmz6(qVPzMdOA5%$fWf90THLycnVpZZ< z%(sA5yaJ8Do1 zjlr88CE!G9zsZ(n;*$=j_6^WbeJp_@Dp&3<5)X;dA# zX$5QO(K4&Ctb{vs)8^7N>e-NZT3E|UNVDgYoS$`ckoefxA*EiI% zL!xn%PhwDL2*Q7H1T!YbZQ%JLOx0=u6Ye^;Z5R7sx}KRzgbRwlxB71Z&tO{D2qL2{;C>{hc@!hm_JC zvThod%fqEPTS2v=X)pw|xIFPcVX-|9w~L~x{4#}q&*hra=cBEaVA z=vS#kzDXMSX*r}ro1?k+`Ugh|gGeq199f=u=;7=Y*=>*Z)GOK+jI-6PVk3C&)gPWw zZ-n8NCT_;0Z;9ExbE-?B3%GQIS}eNkZsngei-FdtT(X^|D#*G;$!Mnbb;3m@wVNYH zql&Rre??=xgUs3+XtG=n2jv}7pTXaSJYm7is|cbV zezN5n{jpMkMV>nW^tGcoK_jpRK~lT0W>rs-hS8N@w*;$csrgx0V$NzO^HU)~lsxBOy(=;mT0mIh9Nk;G0drRa4tay3yb^)h3^{(bSeJktj zBnWVsCWCV?X@!doWpPC0m_Wi^4PD%dADk`bA)=Cjftx~qf#aU`8FV$&*p_yzGkFoH zy)@p?500#GrQRe>x`|PC_6~g6t3;knd~wk- zzS~>sx0)xf?>#lE%@%Z+4O8+)5DpouzR4LJJzYu5a}>+$^xP{AE_RUj(>a%Lwzx$k z-{3RaW8~}y`puwoFM;-sTWW=KsSnl9t8S|B4-E@d!1Pt5^5ISZ70b<8N0#*GM2o>iwTnE?~a2wsq z%n23}>?>o?6CHQH&bgPyxV#=Q?Wk@>R9!2SoLFEghG{Nskf{TZ8{dVv`p;u#kRJ85Lvo71CxP-`wiw> z**N6SG8dqmgSh$Bd?(Bk^btz7HCG%u%SZ%MM6O?Wx?(som!FyaJwUk>3HR@KrcdaUYLu;NpGo)PQ8c_H;=#thGmu|D$gjL zH$}Q_gq}m@rs7s~5c&38c+QoMH-N-)LS-LE61!^2r1W!s3txqkvP&2=GCyoW@=+4m zNzVE&LfNm2$G4graNuJV91ZzHS4Ym%Rq{kbU~)gx+_h}q*+41nXdXmCag&0j`W-XF zsnXbRzy(Z7^}ppWFxK$XEw71*&Vr`naHbKJ!N=Fm(FI@GUj-hwou{cGUB%;0{EQPZ z;yRSYG;&BH5)yg0nw7=lGGFO2f-R!z6S1(6f?ScCoN4q2AzN6?B9COFGp4SQ_+ro3 z5qDjlU4eL_A7ibnOyJU-fvOynG}FO1v+MhZx|AF1kFYopQDkVQ8wa5WOKAX&DcdMT_Yr8N#YE)EtW-7!dt%dI2J*6NpbV+)^gOcaQ~ zSPGgKQ!7$0>1;3FNh%c;yCBL>Gz))<-Lb|0aBI?dG$l_}^LTHVdLjgN)10?A zEjF$bqF8htB6dij5p2o_CU!(KX|uXBmoc^3q_q;ccE~_mhpMdF)C>P8ieatYOqwfA zj8iVn9#$%{dj3=+8(6=!0$PS%q7M`(Ts&0=xY3vDSq`Q>JM-Nhuoh`Gs+c$kqeyx5 zCYs3TvbpLgIiGsjpF7O$_~XJle2O%&#Z_;=KX#IUr#-MXTzVBD2BuN$S>Tj9;J_)f zV@zJ3v8UUEmAG{^F3z@{J6U*bJ zEWq@rG_x5+wp0(o>t}WkSY*8NTXi$(q|r(`%-BQnmPPnhuMyF!?`@H;}ts{RyQZ%~m-1*V-o5^Wu)K zS%}t=oetiIyRK$0=gm_(aGYT67#&GJTbjOa?jmVXiaC~?RNz%wG;Zu$6a?`KLtR5` z_7_&>fhsLwz_kH#ECJJ|v@iUS3Wu2S%P zFTEv3BgDzqvO~Arl{RM~!594@x@Vl=k#~-}5r%FO(<20X$AL@N(MS?J!TMzQJ)!u_ zqpg*=kX6q3-TAV1jnymiRutAJf+-JbVu<9gbKvQFo%!RlwJaQKi;k}9_;>$iEt6dFQjHq9Is$u#D5pxv1e`@s z;RaR%5iL>Ab(Ypr3>dPGgR>YGJQBqk^cpKEr*7ze&t=7*S$0aE2BizYbT+*s?Lou1 zrf7%R)AbOr`>kygWwZziM*W#QcSDv@2LzAAAxZ9rZAkK`#j0az!#4b^X#8`yu#m~V zF0s-Zq|_&n0FDa&3l$!EX$DG&O>1x%QL4C=_n^kIeRYvkTFa!b=>^+>5RUXrOAe*;oZQD4srpG zTKS-XR-7-NTzJo;<&RiB>T5f%D=7qM*kA*#RXrfOvX?6izk*2#JZ{TujS6U8!aD1R zw~^Y=b!Ls?ETSroW}Zgt004dHff^B@UctNvm9~Vobt>{HFi@u~;F6j#iWYmSY}>pM z&Kiln>*?&aSAZ4NjDQd=<}0n8##Uk;;#No`^o+F{I6e7uOL?Nwy3FW#59c=hM|lR~kZ2-TNy=jXn~)IwdZkQz(ibHF~v12??hHZ-AwV=%GlZmRLZpUUl$T zg>N-^8bij3VaN#Djk?@mD1#g6zHX^T*FCy^V#Vz9Td*0n=n=&u(TP?^ z+=asJG^54?RV)(S5nBbidaoQS4yUW~U5?$l-`X059Xrx0a8 z?8>%9Q;V$F9Xf`k@CW3-bjx!3Mj<@Ma5;N+1RQJHLS@_iAL!t$z@;C(F#TP?Cw?w`N? z&1-)(U24x-`JKunqW#nR3nHf76Ts+-4TOyk%z*2_T@nWNA)NS-3GWTWP-^t;fMU+FfZ*fu5EAIu*B$O_(9m7%b#_##wo*FDGr8aS zxAd6HaV5_;Rjo+LhQO3d05NS`n=&k`?Q72gux?GPRnMZi%(FgA0xjkkoDen5f;D9`IU6|Rs$#nk8iWsBzPFy`%``CSV6&B-q z4;%rAmq2cTk4&VpRT6)7NA0IEC!@3iBRDVSgrKl&(|O~tcd5wOD%XD|vhZU^JY!5H zMjQ!jJ8dUu0X+|(uM`KJr8^^ zujA_LSjy(dx7qWu-ZrnxG-~rCq~zb%hqyw%rgU(j|EMF8!f1(ZO?385Q~a(-jBOb7 z^}(KEpleh5-zIhob|;K+GyeM-G??jxj8g zV4T=yb2!?`x?>yM1>Y2^i=K(owC}Og6M_pHd+I+1XM;at$t{6?G$xUsP~D8Bveg1r zbsMNdT&PV)E+^>GE^!I8$ni(unT|F$gZs{ zBtEtBA_2vNj}*vQ-L(!z+JGV7Ho@Kq+Z!OD^g-=C?D3-7i>M}ALzL5WF)ObcJg`iMR5J)E=kAu(IA@rI<_ z`ki=0296EX#mdl{F!%7e7YT20_eQJcil_AX1Ip(e$7UTqL|Gtp{nM`DjT6j45^&&c z19oHC=`eLtabl_OTrJY0Q|rW2M3SA`k~UumuUooS%Dyt?Ox0QnmO0C$m7UG~(May9 zdKuW2lEDn!T&LdPoXPawMl_St0oh}*UG7J@F1 zGVfA(WKNUkR*VCWt`-xV4l{I-3nTOtt8tPNJI{_{^BaScqWmK~>{1FJ+@%xx1;gPMS z>}&;R`$9a#R8so*#nT&7+ZSjQ&K91T?V0x1 zAaqo`Kcy}W`8zvRHFK|Bl<)qU9+ArL#SQ_Pf9`Y8Q%n(E@ucNI@COhNte&wq>d(loM3V;K@ZneKm=Pi6-S{PugQj35>{ZV zt)Xj-vSSNTE+$?8BY%yEW`K}>zDDL&93;1UqeWk3PK|>)b#d6uQF$XllH8%?bUSxD zx;)v(&F85+*?G$eZ>&q30&-3(n_dl(qh-pGs&=JpE!dJs0wwP;tXRYXj)e@*x8`i( zMjKK`yF-0KDBPE@i#a`FrUn;0Nysz6K8LrKqV{W-Vp5y14RCGXW&%AzYg0R-%?NCJeQUNE$Uc5KK zm8W0{!ECr`aIrXc)}o=&yyc(NZmsv=2L6>kwCDxSo(-gPH)=UiU{y7y6i7J5^>Qx; zp5p=F&&BmHz%BL6L^mhc1uS!k{y0#96V-^>x==I`0U8xmbW};|+~>)Usq*hk!+3gT z(_sI}RuReT?798n&=KXk!`XZYr;2Q&yQl5pO?;RWUkh?WTc+xC<<=lm_j{A<>G%H@ z#FKPa+{OJ9#9!C|0I2*A>L?wYZ5*BcNh<$)E#-2SyzK!ya`5guiu*d73kp|JzO6A- z)LfG}==qi5v4X87OE}f2Q>(_cZ*Cd^4s!)7jf?237(@k7C|%2K`|5r|;_;Q00yXV} zm{rwJxBEZ+_w%zCD@9F8#h>pg4S2_nmO})?}t9Ry0Un8{a{(b^2`1Fm`JMZgA00f zwAoE)e9P;?S8J_v$?e|>XN}TgvkZI*M~bVFAkGm0i_;2oCGS_|q)dG9H^wVpN>4nA zFNKWes_M2xyeB|D_%qRd-yomSAB3)aj7?GVCCB-H0b=4hBv6cszm9kfg5PG;`KewFiuGc5Z; z3FF*ZWgG?RFC+!&h&a1SgA25(%F_#xRNlf~#XGDgnIX$6E`(l!o$|M6E3$@iZlNBl zsO9JfYI{zUUPur5`;2@vlwOJpBkt2Pfgf$j+4*ZB?o{8>gVrA|lv#ka@we!km2#72 zmXG^&vg@#q7Q~Z%D^AF~1n6gCzHrmtzrJLw$(GAanWY0_7;?0$tq)Vz*F}(d9aebG zC@P7^?XozY7fgxQou?vI+a=93jLy$xmo4NrCmYzMolx(U^b^-YU%bVSY3+>B-Q3Se zmwzw#>-)!@6r{ko#Y3)Q-PU^2FRG2X3uf650HTNhM1tCJ7fq!w0EqafiU5AnfQFo* z{xFF${Cn5J0~~rr;LjNc=pvImXNm#r3ay6$d_V_C0A6E11i&3<_X8xM2tRk}yy*E< z)nOGoK=>AzS$InuB7ap_h@}(_XE=Of>MShM9p#Wf9PeJu)^VRs$?^F>qZ2GMzzDR{75Uw2 zDA_ZGsr$7K_uFgq+)N%9Z zR^|V;nS;qk8WD5%rXmg;js79!Bh`QCPWt1+5pp-l5QH@U);*jLD`V`Cq%Gm(IL!518!bsk^obgLkOBY)C}5X=c0T9`{Al2BmxgW zbp0WFiG4~_YJyJ`m&6Cgg8V|Py{I_fh^BL%&DY}Vt60`0Pc_g02KcR@KN8&)Yie=$;?{!KM;~u-@(M>zcLL)t4Z5pi^6q( zs!kc(ZuluvtJK@byF^#{4*?>-QAj`$d1{IR8Ee``^jK3qIy@wj z+|}*=K7aVwrn`l2)s-Ff^?84C^Zk7Aa(rKm?bYh;{F$9)Q^ni){WzI^O@!A zzP62hMGNgr-K4{#gBx8O{Y?EO2=LX_(d{WOUJ!nV`=vYM!fcEkZKX@195Bm$bf-)a@D49411)tX{A*9=_rJh)c1pr>i<`)ia4LxgK8POja=t z)@7HD5EQK2=P>c-Do&Qy#M(YUb%qwaZ654?jiO8`Z}&vm?WAtgP(;|15saMS^xnt4 zMc4Sc_@^5So1$qe15(6PQ%8yaV{d~i0)}q*&6$P_D0P#;I&F;_z`Rc&)Q#F-YiKR*@xapFDID` z;Kj&~kM6t)&CnJuu%WRJ2oOh@Z5;EoR#-7I&S(Y*)-wRS5S>^*6Of)%5x}1)Vy+Zf z#HDXog)NQ20VD*4>?xv?rMoPTdb0(Bj6WA#Y_UCW z14!zORMdpofdzTCia@@O=83m*SUP%0!&?)S8Drbh8X$BzD?~i!T@slE3}!HAgfXGE zb-u>l)CHLP zs;=*<-X&g*OHXox#XPSaRbt2rhQ=@d1V1L1FuIRaNTCC|Jby1oNm0%sj!)XDhleO{ z8aUuLG2VaiCsF_gP5_LbQp8kk;rOQPGEr^!?;yX&K13mIosPj32Abgwc|aZ(y%ElE z%f>*?PUb61VR7hSj~Lsqwx3hBfiiug0IG9Pr@6pk0N9TFA^uxc`kkIITiE=nK8S2N z$~Jqx%+n0lLuk;`i8kPyGCLc=*O)=i7@P>39Euu@F?(Jd|F#Q8e#>H&oz$2dBr9vP84B33(UUUW6}DUT)lNbrpT03fgKwsy@yCoC`uW6$r>60&JO zJ;k3z53^zwxfbL=OOJn?RkQi-bP`I+qlGOR4&_eC1GsyY8hWI@Y2X?#=b0N`DKh8p~ zfQeH;5`#?MVf^hgg`jaLrrnhQ9`@qAKa|qR&dGGv#aJ4J(AG*c5%R0t0Q(E+KOhns z(m3;1^o$4~>MQse7?4;m={Bj15ULj$fkn{QVAms31PuWN(qSx8L#60#gqz+T*CnIS z7CMRaV5(Ewz*8!+6^T-#x}^W&?~nxFuo8vV6pL#RrjRm%KLn7hHp6Odn8rUT+2`l8 zQ_ohd^DY<|&~icd>;*Pao;;}2yU=l8s&+X0LS&=?0FY~nMK7LN)}!Y#(pIk`_6tk3 zOpO-NxA$Ly*5J~u&lM+}sfl?{^4$H3SGwgf%7@_&nEwiDVu69hNiwKgKakbO07l27 zBoMqi9y;(B5%^l#jq2-%tBZ#hsF?~xGl7l>+gNS|E#jg*am^q%<-!x=evDNX=g@^Y zX$(Wigj|<-R%Z3zTcXogU1kJ0cUq<_!epWo$B8w)G{jl+ghrd)PO@=Dv;6If=%LB8 zvj-aOlC428yTjR~!q$?u$b@5Ri;MUU{cy=B$H@M`2dGv9jf$Q>yA;8hd5fC9yjocC zQ{CK76V8ow@gll~_8Srp`- zGNgFoWi?1IDSJ!qk{{z5U{k+D9IzZCqY}6TL+mZVQA(^sTHoD|&2IR_RlZ54X{7og z(B8)>FzLFI2^>1Z{P;>(RofaRA~t`k8C`zFyx-WR<-4>5GFN)e7Lx9?yGVRDy{>ZK zqiXZ;VY;+*oI7*Q2j3q*M0{adb3-+(^)DW0^G+zkdyFAeSU0*fbG+@h1Qw_~DZ znRdx*>1V{U0a{ll8@%f6$NtXu7ySQq?Nuas%l>~|dp!R&lhHSHu+=p%x6yZSr!}2^H#Mw@GK~f4b4_A&jl%`c2-qd zK>ETuznrE?ay76~Lh#rkQ|jKbMmjup5RZ3kpIv1SBPy1f3B4QTqM1d#;%-0Th9+W0 z=WO&9Uvk4Ot9`k|{$5;D&9~eW$c9JPxsjFNEsJ_4*9F9Pe~#7#TFd?86Yk_3uD6=E z;AygmoSQ~{N~M-7{JnGHolVzMYO8)29VT)7xp`dWMB$r$tW{|AX!vlAH{Du-cb*Qu zZg2z*6AcN{oPH&iV5O(ue`~UE(MGHuyZw z=!pXRTC$GSBtAIZ>clIHb>kg+J)L1O5SZ*NGkC4-y2|0+rfY7{-#YA&1H3z=$A9dD z3{mygil?5?lixjDYwRL?Q_9!iEUZGL6#W-DzdAxRsA%D7m*DI2iblQovMH!*<=mhH zBu?|8EGl!IQG5v^NxXowQwDKy)w!arre;oiVbS@46lYsXeR0-#L9(O$ODIuir%U>> zL0zi~8c}yfCDTjgCidWg6=m5n`tJyv=@2TH?n({Y`s0C#F!H;G&@*yss;|*y(aU;7 z(T_TtB;A?xnUvi%BEhf13MxXyxI@e3;?eOb^=G{H#}~zSOTiK05sNWezPXWkkPBOe z$tJVFrb=}fl^~r6xOsNhY2&$$30)jpxJ8sxd(L3Tw{41*Zw+m8bY`y9>ZIhf!Ll+I zBCJ!R(+^{}P}}VssIoXsoOY-S^V!D))E!M6LUW;!=(GhZ#VPcYnBb+Xaezqy2TJ1nO}m-8x35 zH{&0Q71I*_S&o&>M z1d*YuG%zK8zQU)WhEd^#NG!4`=>eZQ_GOgQi?gX!FkkRSiA+Va0Z=(<`9h;@B zM}r$dUd1Gm(i*36M+BI;`bF@asLsI4vqQ!`ay)1 zycH{tSL}AJ*)t~kJHCr*==dfa1>sBbgW3!I^Qx6U&n<~oIQ$@lW_Qh%G>v*KxvHo) z+j|WXrR&FrtIp5?2>nBp4am&z;Ah~At8rTdw?m$f?QWP%=NN2d*&Ev%n*}Ml|tN z>*qJo+_9B$yLQtC%x-gBrReqIZEV@17dr6Q0wZ<86Gp}iraG^Yl2PW`cng;F(1g0% zX+pAL0E9dDn{?`#r|$QJK>mM`c1`V>M9ns~lZlOqZ5wZ_iLHr~iEU2&#I~;3XO>JM+$~0PCUAs{3S^h~ucB1P{ zOHTvr-n{oOIm*-^mdOh#R5ZAkuEGMTzu)x8tpcmG3mYc@xy6rzd| zv_d;AbC{cF7#JVzRuvbv9h=p6O)64lYfbhjFqPP~ckJwJk4f87Xw4>L75W%MgbgYS ziUpC?ViJYCf#s6cW$9Tc(W6jU;Lm2S(W~lR7M;1`Q1>{drbNSqS#$f> z1f*m>AIt(4gnk`F!+}NP#e&RLKQSj31Zon-3KZHKyhBjEfEJ){3?>fj-xvjD% z8~blYk418P#9RK%o0dy3woYA3JHG-kD7#m?-A;^Mv+0Gk`iw7qa!#CO%U*`L3DGa3 z7HJzIUrdDTeRyt=oL4qOWHOA4rnmuFK9gAPaEf_Hq?Lf1I5)YMimVW2vy_3(oWbEL zUbcRaLAYhC-v*y^2Lj(nNSh2p!^n%V1v4-)!F3XztQQ-SGMk_ggueh}tufht0j|OJ z-*dOn6_iEdQ<6ma!TE2TR|o9r8u$7f)m&Pu#N!1VpP)bGS&sBWc~GEN$$PopL#XYT z3*h#QY?He1qElfXRqlpRyO8BBENa|)pveXiI=|1lSWn$%>*h!kt~WJO_7uvy;1S)u z{0p2Ju&3h$cA~fpqZfJIke5Sthk3?AWLzyg9F-PQL(VGSbjOxzGyz&*<{DW>w;~?Y zp%IE_Ih8-X&;<(3N;K2-FHmyT`1>zp@X>|2=COeaKTDWWq}W66O(jD<0!Tg-AlyAA zS8;HyiYpAKG;vzw>{Sae)1<7{$pvYYiFP{OC^Leq=1Ta8XDH#e7Q~`>t)iK()OPzdvtSCT9|i~IuSC}wk3DfcYa`z6k^$M_ zNt~CR^QGZ5mD6o;?>zK4OI>Q1MW;!YI2#w1nKAEjT=px5(s)g3XY> z->{_6VCV5(2q?nuMQ$K)I60>Vl@v)f9@EXkatk9@kq_iy5ST6 zYfM7TXb&QrX3eiUD6mo?VDUx#e2XfcnC!ZYZN}|s;U2Hu?>H_rhGc^Oi9%T!{@RxoSiTP#tyj=8*VT0z>c2@fvY?APOPY9ydZ45rz>EtxHw%O zhZp1<5NP6Y)z+#-o;pr0_u(Le%H8m(OSG~5UWaLT3&(QfmeAh8k9{QR;l*ag3YgW4 z0C7i@21x0dZh9vZLSqJw{Qa%UI|PWb#lq^rZY5=d6*R9*D&N~=K5GVrd_ zwKp7(g=e^2HCsLkD2qOsHftGpU#$H$R2wQ*v8Ow1C4AO6cjUjWT^4pweFc?wsc5+8 zQ$onJ>PW_yK{KW{y_VM~O`xd|qBm9o2{b3GRLc^xS069ccv-hCQafjb{m?ki0UV2p zW}kjj9JNNap~En#_D#(CT|@;RFOxSHU9Ue$`&}OzkRlK6<&=TLmm62R*$!vs)-Z&S zI-;=BF`vhI8|Qxlv+)k{AM&3OF$6vEklGI+vj;ZRt^t;yf!lwuJ~M+gODc}@m`igg zG^rbbqsvyRzTBFf`AfybCTRJ=OBOgg6UPrR=S~L$x@R5Q+h=LCsmh2 zCvKiC5Dq20{{2N0QxY-vtAk038w;BkG5`jVnt#b|h?9<-6Ar1!8KgLxr0B#n92p|i z)q>eSxtw>${V&+YP%Ib=f_c8dfXINKdrKQaoZ<5}uB88E>#m2CqI;yfqHBGMi$dFA zkav0!x9{)Sp~;-dUu-s5&=c^*L$R_%!=i&wmKWL7=;VqXhKwD+IL)3%aAk`?!@e<9 zNGSg&tYnKJrx;QMf@kaw`zN`iMh@yy!T|+I0?#SgKc@Rv!23+C8WT-_0FMP z+a%n%{g=&|=+&;N9~m)dOLv?yf#Cr#Bk5YGesv9#5PluG*cjVqG-qK;2;i(46YvJSf*%;sWZLJv-L2;$~xz5*9Z49^yP>UjrCf7(_0hPba7^=7XNF~ z08+a%BHBb&ye;8Nsr*gMIzZ^Ea?jgzU3EOpvnYs$wrBt@LJwEnc5jc)OV+aNq>4Mz z8RnS0EIX{_fesi;z=xv3Tb*>UFElp276^0FiHx2$oM>)9&S5oB zR3_RNVfO^Wq9E`!Kn3n@Gk;bm6>DeOE(~cA5;L7;m^4%)$eIlP>bOaj7k;uYiEoC0 z!1_CG4;rPb*}wdUdA8rYHvI(=dD|l7)P+TXL0q~-9-nUpG=)W#u+6V<|JiUS9}X`e zR(~8y>0^q%*MLvFKud_(B;$o-R#-~4+4%)tq0b#98Y$b*u^HUuy2fkYT2Xj4f>bS6 z%zkq8gY5|=SS-8tNk(tuLzo0@!8u<+bV%ofJG%%6K@*a40Zv@+jv;|_^KVTakoyOM zPaPNnZS-TwfYsW!t9V)LD`T314Y!C)?xMUGhVj9)lK7)e%`8kEJ!v(NlMba#123lm z2I}9@U%D>^7a~X7uA{XP&n7A2%q^=`sKj6c)Z&%eTos6;6>9I#>r#DJ|0$bj543K# zXYZEq33G?kBWD;M^U%}LYHMdnr@8q|7ROP_TISV!qb2niUmGTgQzI;cJWyn#{4C(f z6<=r6tH8`-S1L2U2qRmQ((DaQho6#A=&;OCkcy{K1YRbP5e`K|Q^DIrW(2{;`m7CZVQs0f&=R;@&SX1*}{xH}4~rhzs}``?uz+ zp*s}pJNWnOB?D>dXXIWv7$CZf7EujVB|Dg}$u?jL<$;zEgcG5WH*(vN#DMrk(U=4Q zA-YuwEral`)dQ(3=>8l#X+2Dr(ecUhKn~=YV-nlSh*mx?c>hIiF(Qmf$CaLr+iTzw zSvgQ!UBCA}84dj}g&S^+F43 zf8u`NB8#lS;;^lxeDijBE}9>UYtaU8HvQQcCFT>Y$tJ;Z4D@}0>b6S)PS)p!q&%VZ zYQ7&~li*Li8`fzmYh!z(!%U|~Zgz~h7 z2sKK#E_j#&%|mb?WK`}xo z^P&b&I@e?Cd%*-oNypXnd_v@_5cXZDWR%9R8W0(iA>tmwEPERM%ZW$tv_ms{q`$Ni zB~#W#f6E77k~yXq{VSIpGWNbAEKXByxgjdv0c@J<1rPhepuswk9d zZ)U^&K07$w?$eytZg=Bmi|b2Qkk$T=2>v8X<^Bu#|3jTZxp-0$)*v8wn4lnv|80Zt z|Maw646U6_|1-{c(WzZezVfT`WwLs^aEr9xYa>~Zz)7gV5RoT^{8+NW&$-hr#Q>;3wo z`0DFHJ-hq$GTrxiEcUht*h9OWY~POs*RM36$9f{(53KC3ld~)KP{FsyY{4&f_}x3#?y-aJ_shE@qV~(` zuU$$(m)GC=-XDU3Ur*JaA9qiJE(=o+Uv|D9YnSJ1zArX+IWj&&ICF`DZxhvDH+QaI zdta|(Q~Dp*=~tWU+jn){TAup{&O_q9p9{#@Uk7VfU7y9(J`cu3Z!HiT$C+VXlR*Y|1v_gJpq*?r%# zn>^I6r|W?*j<@p2^&!#3KiQpR^ZjJ&F0j77yD;yM>UQ0pnchzeXZPuL(e;9#gWb;) z)kMF)_NP8~DZAfCviFs3Pr5dm-ny>b_gkMb{vv5xIwnkXbI)(_SdDrgJx%YpbQ;Nc z+j{S>A7$CvENR~Z_PT7ePmB-Ou%BudbpGi{bb3oac^~&54tJh6D<5?>(?`u(Efr6! zJT=8A-*GrEd(H^1ExyTiEtNe@Bs?W>SJ*6aKwWL?IY&4)b=a&s(|TSRUlmTczo}lW z&Hw5wSYN&HP`$KCN;=dlx->kNd1`B3pZ(`*+g#CI=vm_wYpM6Oo>jf1_9-!_I3_6J^vw zIr7}P%al#0>%;|}C&3?6Ig4uT&EIQ$aeQ``H)}(UjTvL}UJfPH49*4o#kJAdc6QCB z+_sT&OXmd{PrbkIxh<#C0;+o726_teV9%W0m{Jww6n0Sg{56S|UpYdEJdelT^jkQB@0ur_r z-5F)* zODRs`-1L%a2`#L&3O+kj=d5wdl->JQ8~BCP z7C*%0ld^GK-9)jovtR2vK00(g_yL2$-^!8zz(hGxOLKZP!F~=$X@K$3MsD&`U81hS z#-^;>hqqh{=EhBu91%^aIkX>5-$5^xOlp!VOZ##zbarK(KD@@l z8ito67AgS;7RS$=H-wfT4uNy+1oSr?X+JAtFqMVYngI&(7h&h7%H{F#(RQh?azW?G?#c zg+&j7NT&Hw0Za;byei5vyuyET)FMD|3~^iONT$3I_`^f;DJ|@+PMhkv*^Nk0?+vt| z#5vvG<}aO%*$s&g*B+=g9y+KH1YR#xyHhQ`wnR8idjAG32)Ui;EZxr3CF@n%aXO@F zH7fV$9ZC_OJZowkZc=QV%F8s}!L}(k)gYiDj+}8PDa#px46`JR;~g_0CUlvN>c#o* zD@9e(Jn&3Ikcc<9@Q^BPnc@L%6}2G8ev95*tP*6&&DYUYTf?jB7hT02EH!>{Sz~K% zdV9sGEzI-DAZebvcsF1k_xSv62ddi0*%2;|qV@A61omj2oG}#I=vu>8{YoI8s%Px# zP>(MMosE|EnmX9to38_&Zm!AJ%AC5JHSTn$XkM)FrTcoSl{?KFR5!AZp`o;iZ`k-) z@SPyB5Yd@ZfQ}nWzh8mp^LX~U+R)+Xv3hPKAw(>DQ(4a8+erT^`h zJ~TU1t8;_ne#^4iRQL}^8!qp+*&qZTG#>tf3dy2z6x0G0w+)?-{dG6FVSZ!x2wf1l zPjeiM(S^BMn$uyFwXzFtdO$t%nPfpcCf|JaB%L-UJDp(e5n#%b12bK>zKY^r~HM%O2+eG^dCH9sb%507M+#P=m|0>4J(B);TY$EaRF8h zn8ahwC^CQx#Ddlh1g()y^b4oC&59WPFxZLV%{ZPY&NMQw1RZ^APx29OO+O}reLplV zzQR;#f3JcUzfPq9*4oL^#s&Z6l0EN&#dLl$i_W&}v9YV*vi>RN>LVzS`+mp6s!bQE z4)fc3B;!uG(U=lwE=oN3C}DnK#a5`OV2oG&s5sRhA!}KvJz>5zjL}O~&d9%|p{pWd zHgNml)in#kYvWAAg<7!#;=8JR?Q1GIbL<#`3Hj4wR0rJ?{?<$J& z%X%@Sko~IdCDK(-qNRC2*a6Ag-5BdyA=PK<>i{N^&3l;Y^8zGxQEJAZ&+^TCV{4xz1hu#BWSBduz1wM3CcBDMNSiJla z?#$14ankami$xzT6EvMfMforX^3sk>oTN8W@gTaD@fn0OA{Oj8vn;0()+;es9Xn-Gqw@g@cGP*UF&5*HWbn&Gs3Kku zu~^x_py$RYMjXAG!;m96);75p2i{!jU8BGp&EI}@L@|>|k790sT+5gJ_k?w18SSH2 z3*x0Sh%PUzUuZ&oWfrUbWpTljiHmx6-RU9Fchc~91k3wWWFiG+^#qFH0+G-YG6x)w zRW=vxp8UDq+IYn>m*=k7{nV}#i zNlbK0Gs051?iZ|(CA_T^08tGtLQzB~P`^(H$kQJUoZ63zKO(njt{o;AiFKyrwHwX5 zCmf(kYNhV3_3^6a;45Ym7eBjo;U8T`E0eyh2gIHJNn&jkG`%*Si@Z~;Oje=?=CWLr zW7QNSTki!3Zo6gD$yj~03f#cYQKM_G5#W$|(2!X;z?Zxzq~t@~b3owd{)z(if8gGP zsCgZcz7#WEL#uQ^<@HyxdPJ#o$cOe3b7YdzQpHuk0{H#Y+F)UbATJ5TT_teceL=cL zlZ;2LUlU(4t3sOwhD}r{EfoRBNJ~dLrOc$!YR5t5FxSwUnbXFgy`hZqSsqh&E@0(;!x|A;#V=Je;DW6q?Efl%du>*PUYw z3g5?FvNC;|k{b%xh|@MPZCh=O_6I*bY?bH$Z_1|>Cj>6(aLARQ{pU*tBl)xISLc#r zQvkOtl;!qL9A;Yjv`XH`t-ht$o2rw98A7=8oeu6xBa7(qaoXsanm2NHiaEMjbUctH zCA3ElSb+A5)ylebJU4~HVjP|)iLO|YrfxEYcWVHTB!+5n|HZ2jmAs6JhStYqT#1cz>6bDx)T%ZF* zn^PslXv9H)$$Zmrhd*>ipDohx=vKUh)eD-W|MU18Ezq|V9&)3N`&Ees)N^;VSI}A( zMm!;x8)m2%HnZ(SPnjNF#Pva+#g2i<`zfETq~;}*rp%M%w9Ge9n~|BR1H1@v;#SBJ zUkt1GbzzGWr+j~|w^6`BDWP2N7MDmKK4eMX5txz_qT|9}6=%kOR5+@5$I7>r8c^;U-vajip zOt!&WCPqZdDY~XHwL+l*ThAxv%sWplv7jmZ@2s|fe@g4{)$IZ+^(H@da2ZwvkTQ1d z&D|~Tp@H1@+6Z65Pn?|tFpcZ)eNE;e<1TWpK@|ZK`x-M^REFAVGpiSx6fa;J376BTjINq7juB|C~|yC+8&M&s?IYR}c6(6Hv(p}DgV8xQbB*P17N@{9<)32v+* zv?aIATfVCs%OL-7a^o*c`B?2iYUb9bGo{K+(L$g^UnA1uN|d>#`6O`;KP>+ioCVJ5 zuAvlL$HFDOgtBw{l55H?{y0inNwAV%E*964=2>XBLBWn?UI18I;r_!Dbe$xN?oFaB zSnI#^)=)SYYn9-x)VYB(Bw1z~0w#2wAeZj87ydTNQ>=A}%0waQ?xX;$8Xn|xFDjxC zIaEn)w$Ce*5g868b{JFK;{wH5T34@}e_9wYT7)=iM5)xt#1|f12{**6!KSYMraZ6y zcHPsYZ-PZ|VuLV@yi;mLrz0%rZumITtt#1EQu%<<`1b8AqW)win^FJ6NqfSN ztgv;HpJp;}c1%u)ra4clzfqo;!30MxmxNOa4}BS~xE3vg&463cED&qGL{XDkss@Ym zDAi`#fx03pZ@h}$CphRGEhN*W*DJq(y+b1%Zux;J3i>RDfQneP4gz4QB%wYR^O-3@ zhGHjkv-|Fv@&CmK4nqQ#SFcVDO$P{0W{R-losJ2uRt1_c5>+jBFGV4&)sALI?)E$d z5&Njo-E&ggztMe^fT*ZW<3yg`;ve&UNHFf1r)m2@dXM{xjcGX8-n|ApeN3|M3>3;2 zlJ>X_)LL;(da>w2pAOhNZl_Lvk7*{E5H8n=2d!4%^lJ(~|3wpnv!YaQUF7%A0i22) zEr7F{T_N-lth5k0qYnYdwL{JD{`j!u`a z&6m~mn$)7m;jd#zir;Rnv+{JyfY25TktlQ~*FF~#Z-Um;#nF-o3WHI3hIx`C+f&RD zK;In3El>AsyHVwz=r?5H0v9Y4EkGV6G`UT@XIl+ToVtMBgm@lMfeTjS;c~urhKkzT z$um_#Fn>kP6{ZD=VW_ziN}jCXf;;WTu0v=e+zBwY%nh=Ng~3kXZiycHUBRa6W`z_&neL#;E# zc3}z%irR3U#!L*|&!1X93tU?HSg!(0(`T(Y0)+5S_3>VgVnao5#n1)~F&WWba2&p+ zmOBal4I~UsiRx#^RGC-155*BL2zo@)AT5Ajh#x;&8_}B#yMv%!4rdD^4B(j^%66!M zJw;JOYxm+Nt^eiYvC{k-v##lkTZ|3^M%idINq*Ik`jX|U;>)!-JHS%gAWDzK9*@Qz zWI?*LlI1J1Xno58-vhfiP1TIIdHd+gm(3yu|W!Y(YZNjgo6K?Md*Q6J-t{85s@l zwnLv2HuMrk!CejUaPgjC7&`p+p)bN~Jgb4kk7Q`%LVhfH&zy zW|`-Y{mMq~bTPB5Tbn2Z4^anBWVekt-ojZy5O57&(UTJ?aQPp7>CE? zwJJcgB~z1@LQ*wm#`^B$gqY_P84`5Gkqt{ZB#qs5a?*Gktq-jM;tUj`HM5qpm0YAg zjFLr)_}6jE3yj%kJ~KoHbxif=_8d=9WwVs`Tyq;2z&Vn?-2#URKr$7tLvTKna`M3` zenC$~mxREhl*rSKF{tpD#ik?3=xQJoZqG##9++>E=+Mcf(4MaN!R(x#dsw}r^XV_$_a+2AMgtcZ?7`kuT7kgKyHtpfJq-=W{dfMfq(Q%c*?NYxGrCElrL)_C4$L^CN&52a&)G2 zPN_A&8^p+R`kG29R_lR2o5h<-qz|KG@1;Afg2b_8gfgqeKn$SzD3Zf#Y+x}2PPW1~ zJ_G~GBT%G#bUorn!QZSTVT=J1f*O#Hgq6!+g5lkkby0%+n{h3DIz%Vhn^$C-MXrIl z2?dDTLLE%Ztwkt>ShUXE^f5d|mU@4{(hv50*Ww4GFL>@j@FUNq0LSTfbL1Gq878u4 zp>yHQrCTny{SX!RcA*fN9TFe9hjBVjuo(7lqa}~?eKxDNQABJ3+IAd}szp@@l}Zfu z4z8MiDWgtje(cty8PPm6#kRvNslVMMBX#Pse3v^`pNC{)4?U2M{*Ye5TyQt&43A;V zCf%+v`}F9c$~mHZ9Q>@hE$vNV-(aG(Tk@>O_nPkKYE7j;(EG~->DJVVW^rD9Kwh7Y zjJRU*67OKQiQuR~jo1@mrA=8z$U_B!r+MHJY@Iyh>7ux^+Pm-iTPX^6N+UNfxsO^H~sV9ZIFQs`G$7ue$5amQA7=yp=lh!C7~ z%uWv!#gaPV9|REH*>8RnpjA)U%?IR(VkE(qMg3BtTZgP^t#u}0*uK1dAE5;D_E+$< z4>TscG{+u-*~s661u1N{5oY@IN&#Ei*`TJ(&M+~TdR@)>VFn9yAGs$(-B)&6?Y{z%Jnjw4s@m5+MX#yTElqG!-g;7 zt2WSB7wNLRHh7Ze9!GOWu;ffbh3u`j@IP@$=Si*2XK@v>B6IyHPCCTlRNE$f!awoE|7v1y|p1&*?2;N)(8-x$!ke6y!KV9OF(D zl&84;n$cHd#zkit5_eRs-6g5&>ItpqUai`j$|j3HU*eZCGUfx?nQW9$^eGXk>|~Bi z@nZYhwFQ0;=`^79X(nQrqZ%}Hk;_tFiOH;vxoq#FTxDR6I754iat*G%v@k!uA@cDT zqJ?B8eUq-2cPLpnAXOQIF`Cb2el(DP>h$)q6N@T{e+bz;1G;-`m@3TYa|?AdQM6|O zx4MSIMUt60q1D8X&oCGKO+*7K3|@(oTU+U^ z5wL|GEyJ?2S{Hi`O3B|EJTm_pzcmkDj{mx@UzsF1VOrl9?-BTu(+|72I6ZC+j;&l6z7&-9p`<%({g6@%47Tmeu7(U&m z_Z7_=^y}fzt4Bbt7Pmo+@JCBqwIA<_zIHJK-JI*F!u%dO8nnfstkH5CQka?fQT30II6l}ShUrBfsX5E) zN5`7cOpW7569z65oV~#HEuq+A@P!g%0QD{j*X%r)d!1A$_~sxL@tzO@;@!P-2eR)u z2`E(`P%jaxfqTHoIO5&{u#7~K3Gi)zIaoSl$1|g_4~Sw*#_S+7|0MfC)_k=|b-enp z*@SEoeXbz+WjH%VZkYYO=~$$>uEny=v(L%g+{Zhiw3u_a z#bHm5RNelAoEN3PjI_b~+nAUGE^S7TXTn01*sUm3U zmH?w98e0&XV1zsYZ7hjQh8xC?>n8fJDG&u*Dg3~xpM|7o%l`m8gNzrmeRD}5Wc71- zvFC0jJwDPAXeR$;mMc{hB;DR{I)#MB*byO8!Xf#hR=fsI9!%d%X{!s?S(n;au~tPP z2@Wobe~2ACaBSu5t!6%OF*m;2YBFwx@Y!vt9}u~vi%{bgH~PUWkKgm0?WOws!+)rq zC8eghi#miN`87mR!@27fG^jnF^1Vw}Qy4gDdz5W|t>yDK)wv`_F()Tm)mp-W6&$M` zYvTn%ke(`tM{}MyO}d|tZC$Ga+9g0VUFw$BFs%%sT^jK|jktqw3@@f%hn`Ny?o&fZ zl+6C{w_#dbsh$8xw($h>(WY>1_~+cQh$yAfil6PCBBh(;wipR}pfrG2UKefW=-$E9 zDUG~nnzWUisY8PzF&XO6iV_r3ls1rbCcY{aW9NjE=6QNS%A=UdwqU=ZqE`t+=-4^S z5ynk9mSl#t?=)zbZ%&!g^@Tc*_ zpFx)TaQEG$a}~6~?~z!tKyXH>z~l}buNBQNuE>8udjg*>{wx^IUH;j^*u;OJ73*je zJM6=yH{}DRmBFm8;bAu>A7y#gXZgvSdl~dhs;KI6>h9rQ8Ol%ev24SNgH+;u`U2AX zSkZmwCo|2DBF9-AUrpiB-Tl&&GE;DXwHw% z>r_2gSKfN2Mh`6kR1i*H(467_t;Kb2#^`9|t&XtM$WQ4{p6MFbIJ2Nyh#Ye5nkqA? zjs)$%nVeb`u3UPg0vpkUu-uE&X@~8Uhakco6Z4x!S2&ED$TS6+!C)U$Y2mVEbBwg{ zjY^0AF*NzX$}?im-mnQhmR33S=!im%PY&81<-1VAi@YG>)5h~?fw*Wq#K6kOtfpuY z5e~ZW+!)Lm76LJQx=0t%>{qCgooX*-9_HD72vSr?p^~h@V)2U0!W8g+h)%a*peMZY zEHmjPQ?d?0@6s9uscfeO1Qp?Asevh-d9Dn%WXK*R=AzW zvMiGcfwO2*886&Y!)hxEn2CeCrsHIK5&Mh8^QexV;8TMy0BXu5`NJA^B~TYrvP|F_ z_8s=0J>bA=zqB=o6Umi~4PPpQ;!CIcBq`10ft|IrPQFX|eVs_du(IWBekP}c(NdI; z5+gAqQdo?n)f*zK2>JNO*l2Wn7WMLvAMBy<47vG9Yoj1a`k)#pXOw=Rx2deTuJc&m z(u2zE4|YfF1TW)XBA@SHm@oZoMB(2iOd)u{d`tbeDCKKPeeF24wWnyoBd_ZRIIu21 zc)oCqKPDXXqyu+-9=#j?*dc1OwU9l+D`o5d$Z7Dv44oJ{?B`RrXZL2IU=z}`7xXnr z+1T{}DIxnNP2<-nesY30R}N+Yntt`GFkB^i!kDy6VaPft76)31W$fc$d(nSA(lxn3 z47d-}J3f~fvw@D2mm!b_?xe>inU6%h@aWSQC*TrpnA9Gb!Eh**;S{JEaHdrRnR$S4 zcdAC)ULaP5T6nhvi7oOr{@PLZw=01FjFANn27$dMgYI2VKA`0gVMLObX%>iInR=c- z9iRctfFTRCAl~t+PhHzEZUOtxP^N%fwNkR2TPFTXv!=bn9#S@Nno85Se}UUh%S#wP zuN(`8JM=f<5rR#@!Ex1kL&@l6EHY(XUZP~ER1Q98Dfr~Q>|q;whXKRaJ!BBR=luz3zesI*5jau{D>V$LMOZ#RZ z73eV9ioF@tQaL!QAyFsJLXg-1K&%dD8N(&lS2)>XD^5uWXbau2{D9(@VF?;?FcTwV z4q-W>D6Pof6(+qh3;rl7@Ker5eAo$zvUq#R?nDO__aU@-bV*hkQa z*DMOWi)fJj!E3}W-C79F4?}p(e~q;50Azu3NQKg!YV@@QQ#}k=vplOc7ymZ{--}cA z6*-Ka6k z;EYTxcr~pG|3;_r=XN#Ef4s^Ew;!R zpH1G6TyEC9vT)3uB;GAy#sHUqbJ~dElVBHoCY>Es&|YWW58e{iZ%lE_$kXp2MF; zpY;@lYp;t(o>>BN$}^->(2kpe!2J#5ZM{jR+r7j8hZyj{{vRNR(OaE+GNb+U=d!bPL4&ZWh_GJ~GWVgeV zh3ZC}|IuX^y+3T$g;^5-^#j-G$hxO}Wl}OL5sq~K6Ps2P7IfAR>IAi&@mLzpAXF6V ze_yohi*RF_Z#72?dR~lX)+9c^W+nS{@O^>Wb@_4HfHaiOI4^{21SW}8O8T%#*S~%g&m<~7H=AZ= z4(la?d8ICyS5-Ix$1P$uF1hx3j18~tm5Av1`wVliP1v|8s6Rqlwp8APM|)6+V27aw zSKc~53{KNA2$C`O(^W0dxajgb4Opjt6%9c!jza^)&|}FT>xO@u)u$%7_8Yli`I#jb zqNCv%Hk+$PVh3Q6oEF{NY3llW%(;RnrU|bId;3+)O$>USqrtkNT^VW!m2C7FT=bTn z0-d~L%PrDCSFi_m6w#oI6upTk?ECwz{B8@*W*X0bq%dk4f;CNnJ@xNEfx6*txUF=d z$(M^2b@|Zws7mv5Y};R<1fvrY5Hh5okVM&|3U7dOw27uPO;F{|JAxPI1iV*PXCmvT z_Z1=n7yRzzxajt-Kodyf9N!PxtJOi?Gluj>U`eTEZKR(@Z7jc|1TnPpv%9YB>!p0F z3T&Yj-AXg8x1(nIGS7K_J0OsQczPYn4OV|YKw-=L^6{cPFI$Fk_}6x`eUu-QA!2Zs zlb=?+S`IB@MtgL_r+k*v{O8MMh?7c~Otv*|jK!(w2HCErgM5t#O&D*$iJpnq>V%St z6=oSo8ld8O8FHuiuZkdX%Wn5kDr_XdriM!^o51H6;>pnozgPM0pvt8BJJR7(ju|~@ zMyw%1bg|IE(})rF;g&QV#!2yn&Ck|BMDfWej0qib(Zl!EfHS--?ogk62QnSjsA0@s z(F9tiCN1m;%{(No_-5443O7|ZLR5Fqo46IEJq#qU8=Jg3&$(j%9EJE{)OnD9gLhDl zvs|Gi+oJIWkllTR2&|t;D6)xC<7jsL_?aI#%)BRALbt3n_VRa&B#RUPQ!80wp1gd7 z9lQZc%`fR+huxvGhQHQekZOOLU_A=pHNY80`XMV&Fm1D@K2cs2-i8AI2p~vK(13HX z7?G#Z47>a+PTj%>(;!*4K)g7Fw$P>yOs+=^$Qm`&*1;*O%?i-;3LL?~L`sNEuN0yJ z5ggaHqb0gz?=V&jp;Kw{=U{hK_pYf_KKZHmjHaZT1t zJI8`^;s`o$8Nwp*FwNe8t1aU)qm*n45z~$T=5(H0RFB6JsNJ;fbZduPWzWA^2(!x-pOYA0TTz)sQdanv zRSCk(Oj;$%+^&OJ$1*hi@2ge)0FQi1iN0M?nFD%(#oj`TiKX2g%c!2hEvjirv~64g ze(w=10Zzxz6ftRd0h;hry5_>9`mlsRW)dOojBanR(EP12^WMSMtwfbG?ka4wp9n>K zsPxKyk2y#h%vz}^5(_e~nX6+_a0DcI`=~el0y(Xkl7`r^6rnuA`uk4hbZ~kTs=L(0 z^uw`Mg?Ws%JmDSlNQ{yANVsF0OXsJT14Nk_LOe&_dw48Y)eY&_n?pc&vq&qLx#@Zi z_l7|LflWE>5XfBF7r~Pr_Al|@fj1!1^T5;<((Sqq+IlP<|B-7 zk&W$vmkO$BaG5Z2&n)hc=By~mP~qzJh|I%ldq<+tPbLKN;!{8>I_%ls98sCacsxYl0b0UD6@ zTU{M3gkY%RrIpc_{r)_zT8cIDTrq;Sr!=VLlzMxal#yC8`C$-iRTcThe0aj&kj_xoyW#NOe z*L&iz`S0en_xhwbSs%v1;FfwJ7j%@d_9}O{I9lzb78zYb+z50MXX(ECdXT!51 zz3g_8UkQ6+HCD8xW?yitR&~3Ms2*&z zF74XqP3QK(cw`%yyWJAm`TIbc(-dsD1oamJiHo<9E#d33ll||03!Br84o=C0CxMd1Z?|m5m9U2)&4EF_OGQ zZP4dA@rT)CI6Af}Y%#4-U@Jjj&VUVQuGl?Sl3upon*^&Dq~Q4z_;q=Rtge{air%5c~oD1;>5g3S>Y-xLb-8h z$@p^<2pg4s{0&VOjkw}kRD7|aM7bZb@ZR2g|BboGP3A8LmTspJI}-u&ir&Uu0O{nV zqoh_oHepwcpBje9irrZ-?ZbjuHm2Qy{fB}r^u(Y{eBuGJBao>>MI5DVW${sgYl4wH!B3s__9u*M-~^H_0HJ z?Mh-RBLJ_V67CYTDs@wNM(^N3RS;X~XAyeEdGD9qC`jjgGa2TwmZ8uag~z3Cbx#+* zHTY`JuzB3w=e$5Z3Rgfrve6&nMfAxK*tI~s%gA$fq-_hu6<9Ym7!}tx-jvzj{wD@+ zMLapsRQxI@Ev<3nb$p;Jr0A#|ZqQmoHcfk}Vb5yXI=l$aDd&(8XI@SVUHx=R%Ayp> zzhPh9WbaM0hKz~qyk6{w`fS+SNyrqNI8T1~D%{_K>?=Isf*~pE(th}tN~s9bgFC>2 zSUxvAK~_J6z0OD}BEeV<$z+uwdu_~dxJ)WSt*ri=qE*A!HvvpSK_@> znF~u;hSLM5B+z%q`@CCvsnmPS*L0j(VrcVza2QNMcp9l(_}A>#U|b)s3NH%0t9TM*uPS6z5K5P_u-Q@QM-$uhvm1 zb8x2w%di^TP;1om;ico<-(Mw{?zv_I$a%lHe;ER}@^)Cr&wyVVjhMYF#eNVNLO~U4 zOgH+lCfBU@#!QY^q~|>-YjuIaDQki3q(dZb|DL5*>km02>+cQL%1y6KlM2hOrqmwv5f+0PHl_!~^1IT2Gf2oX{ z&i#O9XB!@hq*V+g!H-Br5RY-0O($8>PQb%H8;7#t;3om#p$ziwA&p$;EM(G3_xulO z-xQ+>&~4kcZQHhO+qP}nHomqwZDZQq)3$A!bLKy}xyiXN_u*7hl}bJBde|$qQEPon zsDN7ecN1iHptB4}v(tHKln1^C#2Fd{@+W6XIrm&p78B#9by<80*GdyEFm_%!HZmQ3 zB-O4fsVkk`RpO}Ddjwf}A6#4Rj7=Xg9ADOIrPo`V1} zB@hV}d$-+pVOQ34dy|{00USmqKpqi$?}4=?5lc(EVEiFU1zLv-@E~aQd5IV;QRJdOo>g_t6CUuW zh2k)kILo~aECZ?P#!F?&&conPy?BUK~iXiTEM z-Xx_raWPV%lS-2qGDpFPCvu6MN!&ctt%7ix!P!O{@U~H@cux ziNI7E2I4$Mmv5!`#3vH()K3J!GM7L%33vrjI9p>K7Cd}pt!~U7Ubs;GrBBEa4kVOT zM4GV1tL-=Za_^Vg3f#!r+JXfLgQKQb;;B*ry%#-BhHB`LX)bKmdhs44ur1|J_(y~E z>l+t_E&o8NE5p=}3@P#A>ojBnNtZO>Qz(0mg;Oq>!##Qe^2SvmAd;Ze@TXtW8wdQVfdvlT7hMi+-Jhv0LNwvz^dyil%GJd0= zPezGQlTPvwUN&@nicCnqcGoP@Z z7|$xJOV>e`Cv=K*cl72CPHVRggF~*eiAH0#O-pecbYy*9lIvYL`PGzH1UmRH>@s^= z(gZmW#$`<9bBL*oLiX|bBArMC_w?|gr?a1Bjgd-Vt7NK$47UO`jz&4p>B`4>$zqka zMdESBinffg(n(7JUl~Z{D%mHqPXrMf*Y{s%d)w)>pSF7_6lm)Q(efuO?{bgCi!b7e zG#&KgR^n;BS)lDA0Z##lg7S3!oPO}EN;Qq;IYW+oq_ZuwNmyG$q0eQ#xNkJ#4ShB!pd9JR&N_Yq3J?D$&B4ttC(J*A*l5L+*jn&XT&mPa3-CQzN>&w z)&h;_R}AbkClp-SO@IeWneS!bU2RA&X!)pz>M%PH z^G4W$9_@qSB`jWoxT*0&clE{!ulg@WnkeKLAZU2wb14~k)=UJt!M9$%a>%0QaBGnZ z5R^?l=`nsjVAil+*Dr`@C^$T#feR6-Fc>RceSc8E>?GUy#qcNuwEn^+LrtK`rrWC7 zqqg&W^@%sz9@S_Ggkn@~sueK^`5uRqQ3Ro>n(okNsJ?cy=BmgMupR3K8iM`lP8nME zxKt~E>l4*pd!oGM3Sb77{_Icl)xsTj%RNY3Uu1^}$>OmHERFI}5kLHD|DfEWA)n?i z?K*L#1z|Zv9`n;WZt#@ZI28Pv49E0+Ay5+nQm7eaL#cZVvUvc$g*7ZOgdi!MlX6PG z@Pw9C=+TbyyMk>p%wRFDY<{BEs2o3a4m@l*GE$3ZGZjm=ru<+N|6};eUoK@JI8;4H z-`}?5o+LXGaqXb7VpO|X%5>T}gin=5LVF7+#U7Q5o0+Cu8xm^hc?p4 z+X*|;m3TR=cAKg&iadyu_1l?q{(4Q{9cUc59LqA1#Ne|Z7G_{7Sci!TcgX=sWOOIF zh+R%!Z!^mTzINv-_h^eYrmI^_m@wwLDjlOAjV%v>9lB<2#qAd0DAB;X%w&|pTp@#$s;a1q9IP)HOq0Cm-=>4bwS@?}hJaZ>QU16t>27aR z>*~1u>k1ey)8SK;GK%usiVAHr<@98$V)KV6^-LbP!EEkcl{tzlj706HUyLyZfhwNP z@}Te)bVkX1k^+*_C>%X>77-OOXYDJ^>;MC1D*_{)gY#bqP1MO%4SWSW6luC+9A~HI;zqnt{_91vjHBJV%$wR1$C3jZMgy4Gv5(u#r`(x>w2Tq5 zfX88x1G~B9uh%pEV;HWw&0baQ;!0rMu7WT#pcAUJ-?>^!FIPpy_><v~}uU0n?_wRf&Njb(kj8ThhCsg=+>u9n!&FW+E{EzVz2zCM*)#MmvA@tCITvoDHq zzM95*w2t`yrCAxu{m_CDjS?P!x4VImL)ovpiC?@om9JIfEy;Zg~SL_p| zHb&muYadaU`6Mm3N`cE^2$S^h4S~N#QoBwa$HhkuWwW2Foh08JIJ2hQOG$=O0#aPj zDf-26XAq?0A-hkVgit`&HN!*d>?vX@*kt3|c6O_p#B%j_VR76%{Fh`ly>_b>$ zY9NgJZV@O{>_QpzR_OlxtXhmctTb-h^qRW?aVuBtCsREop6T96a}gmuhu3{^9MG>^ zI7f;TB}g?k5Kx2s8CTGpg59`#4aeynL?4Q0d&pmJ!sm?sR#r+JqGhAhsZ2ZU&=&$+ z9(pIFwCt9noCcWZ`NFuS-wfheB_287O4eZO<#&s6oJN!a~2{bOygr&Gt5$o_l@d)v=iMe_7)^cmx7TxeuxO?s2O+tuPIx@QgxWJWRPG;(W&EsA(`l!v zU`Lx|x?Y`f+|;bow6K~YLXXn(H1S@U#tI|Xs{r~Nd<7bPLIM;OKi27SjGQ8Sv|A;& z7i-Js5d6`Mu78n|+e-wD)vzBv>>eTUf#i?JUU@#dbDlR5ul@5|i5I z;#9sht|SHe$|V#lJ+NTWUoNc}h zPXb}HDkUu%9-PCIoc2d_M`P^nO**AS)?$z-I4mUDvx?g6BC~DzsmUSHTzlnilg->a zA0cpJ`jnQeSQD#8DdR!dpgXn&Q%>e1dMEc^yckG?#{nwA^xvW-ER$O3jRUZalKFVx z>`X!yPpjFhNLy@(yP;I&s!^~SE?`USrr6@@&7PkG;|?ycx8aOnZBFocqU?wHn@w%FW=q}X`rhL%~Mp*Tlf05uF$Uk4K z&hTIzabBiM*5SWE7;6Xe5l}qL85vXb;~_WJ!F1r2xJs~d-!%N}hyMWvcq;_AbT_dR zMI-_>+X7S5mMo>n2c0G(roJAlo$TgXk`Zf72nap#1}#qW>OrBPdP2FPK%f4QuVH)(;%hD}1QIWS#= zM%r!3!z)C}s7B`{3gqZC@RdYXJr6ST94M`bV!_=#H621dyMTF zmK}E_ygf&zj($xG>V)gn{&k)}kN!IDB7P8^-+2!FvhEoZ>~8$igj>5W$MiD{XL|$~ zCPwVlxwi%@f+jWZ3ZroGIuksc*)VsMM&5b(x>={NqBrDVGmJb@2dP*S_!MNPdz`Y7 zM@G|9y48$C4>Exiy}Su3aKcu8_F3>hX=LVeejUWI}ucrO%pah5A391h5t%8vfo5cH-faFvt-HwHA1U$yg1YE2X zc%IBM&pjGk?R1knz^e}{x!gfV-xdr0k&RFHexR)z89qUSZsVI>b-q`9hD+6g~qU5qT3f)u{*dlTAG9K((f`g7qsZz4&Z3R){S#bOE zD9;;KwYL+~C1u_z4UF;Wc1NrcvZNefC9>Sr1u`Q-`8GAbTp5Bws0x)6Sz-U(SY>u) z=<)zbakCT;84J1K)5A5~71T*GZ*ZvM_<}+Nm(48`A!lQul9QgXnp7mBBSc|lEJLR8 zk=XAZbIJuy1B*4!1LChNyaj=E9HrKUQ9Nzbw4WhfQ%XdO$E=VGz0rAK3Oc~*t~>8X1`)Gm(;UbkQjQu*z+p2HfJti z#}e4CEvH+7x2qOWnS_90Z!n@8cLrYLAvAy82eAT?a!45^m}x#{o_*h+zPV08<)ff! zJYuVmcio?#scafZ-j#{Q=xsH&{v3H-p^7rPzWa%4wu*F z{cT1yLzx5rc}YU!fATLc6Tv@n*4^f_=0L%@<||~u%7u2Y1yP8B`MQw%LC<0$fQKQ) zIi3bqjmV2On$Juat^@XD9i2-0_>Xm1X;xSquB&SkP^To;;g*5m+l|}r(Z0m`&CRtF zl%r%#kDlXJQ-71p;=FqO2$8y#o)wUU&*T=sC~&{?;X|S_1j#4yVwVwGB_{9%IUQ$W zIii)HSc>UP>RigB-tEmab*9W_byoe3KV0bZYaZd)&L53OWOGsYukt(BI+K18cD zB6`wbJqV1q>J$`TyavKcjX`vaFNQ^cWyC^NULj?__dUyDCcLVMtTth<9-#B zuD>hkyW~8a*ZSlJ4D@qHaXpTi*W(e$qOqh)<#umi`DXg@ne1MpQe<*HeE?%LK~w+^ zw*y1PWv_)lL@(wmf_EwbHx7C?IuW0GL`KLlIr*F~m5E=5Fpl4|P4EK}CBMpYqgXHv zv>etnp#)6|)nxEx-h!IiBxNM*5xrMj+>T4uf=A4~p5n}(BDqdYtnts|YG-}5Wv7K;33s&#N@&?#=M7YP}BS%f}6s8yuskkJ0zU~I7NM^zm^efxmzH13O3(15W4h!q&eA~=bqu7 zj`{Ehn&3}-Lz~CN5qx4#JQ>al77YY6V?8&~Eg z%}T_@GBwzg0}?O+XQINb6!wxqgFA3TU}Eq$EHqfuy)6a_CZ;PjQzIesE9m~EYb6)) z50D1|Z(j(njAt@U>e>jkr9?iVjvGB`~uAsLqh%sLBwJ4Ljk;Ab!Ipa?h!7z0yJM zQ;I;^9S$M;ASlA5ylXVH`Fgd5cJZgJBX6}KsqNi;?ySKYiv$KAn=4Wk8X$wFS&URx z8ZQ%3t}+nnrmYbS@sk$&;@OLs;(0W3VVJyPGRYY22O)~B2mxNgW8USFl;~V;2>ie2 zPS<(TD>uMWb92qS_(k`EtF@6MB@u$6Em_RH0Z4Z49(y76an$(U9jS8)j9DQ-TaFiqJ(&vHc>Vdj z;0nDZadvh*ncS`RJ)ziG8_srl1_x5h%NG*`2i9T%Wb)T|#kv&Hq9=NJ zk~fjw5pu!V$oAE&qn{M>`E9Dq7=HsSwx2jxrmpcSM30S*yZ!C5^h)6hp1*XMr%56P zIcUDAyL+FaH5hUYk6#9J+~#izic1#pMyo9i3KDNvj}x@m)uWTmVw3S%4fLCz>>t5d z(95iCN9PvR#y7sVL{=x6oFg*rrOwX^&;j8B*f_$dHCtQ5n^JnPH@rHKpe7}lWz~y%w4FVKHOq)ZJ8_GTUc|#(atDkkGJlwJx``t-2DsEAyfd zG1*~jSAQ+qCDj6J(d8Rr1Vn;tC!a78TFGq|>yg2WyO3vN%*9 z8jkR!1+`d{F(`0SlAZ|v7kq4n{Z6QA$V<<3j_cO2_K&OFtQtE|?0iungCsBbBoMQw z512wbs?A(c`A9aVuCR&?KmUBtV@HGP?HE2sCl#!D*jnnAF`E zc<9nuP>#|b^t$CrCZOlcfgisa(hsh3a&HAlTM?zWYe(6j`fy=PyHXo&0IXN9iAy@+ zO4tG}mhi}`wCFOp+v5^vW}Rk@m=>g-p=q9Bz!pbrpVrpoF zArn^Uv^87~X`=Hq-(t<~0HOy1XTbWPftEholO;*Jk6)cK8l~_@Djg=UO+O8~otr+S z1Sy$5*c#7DemyOJqX)Yu?oAflA`GojeD|L0)pm>>5teaf?b@eQTz(3_F*3RbeWpd) z%I+GQmG49YV%}R16Jb1BGdpwXP0mxt^g4A&{@6WR#A+yRrfud-6Shq7x|77ji=Q`I zOhh(itQ)4~%kFO%s2MRb=5q<#FvPt&p{wl^cJxOSxL|`;t753YG#YE`6~*@TTc>pE zL6r)KEJIFI9;PykUcG~g2?s7sPIHqZ(-!!Y%r97JV9~eSJ%@Al%}@Y}6q$`Xm{U+t z@#Zu)C-^>wSOs<0%|JUC57{Y(eHU(!qDg3bq9l{A$Qc+?Ybr1#v4a1Fsl#^58azXJ z4P^v0Ph~Q$Hr8e2fUuYDyCsqr5T`Hwa`9iW_w-diFe@-x*bbiQ} z(gPUS(dfz(=>ix2O5>PGc=ckpHVC=mt(TybD_c9SHf$n(j=d@Fa-Q8P?qOVcUAy2d zH1Y!PG;1jaTQIp`N08}pPXEFvxHzK&^~k$-Hn-;xzSvb}TRUM17b!N8ikDPR-cw&s z*v2uka?v+ekRKV!h=yW6(&}6l4oiFQdG9gEUoyfktgHPJovidcrr^3g2htyM2^QBm zr>)S*7T?wxVQDco7tZFl8vnmK*bXUA&cM^JWXf^k9?Al`@w6y;Y`ryTvymqd)AIKA zOb8#3OykzIT%}Ve=H7HQHhC4d+o$3V0%m~N=QN7>r4?x*9NJfBe8)p`ZU3m#jVJ2WDkzG>3vRVH^nibLql%Kd=yL_GAM1T@W;;}W zPmWYbHgU50>o#HvaV6(rW~*};r+^nCk#Hqsn$^pdigZ#?xcy=siK3Qz=Nb{#7Ib?~ z{wz3yJ38|NjimZPf0uV?ZUjV20H@;bw&pb*V|t#%=N=ZECN}8iBz>9rnzi2br*gY) zEJ~|6l%4DDdPyc5l?dQYNrCQTnTO+j^xdsAIAYlhj~>L}-iA90(O8L!e>r{OoP%8JZd= zrV@lZPX|aiAQ}RD_#wbygYpe0VB+!XceKl>fv6))6#jH8-Kzuj<>>USOsqDh&rv-v zg(sO}&d%vX%lWr}KBidMZ3LpMmWe}I?{@1uRG7A+sS%iGe;cckSHO0k+pDrA195|L zVoa61x+s_t;DT`sy&al^Of)#yDc#ZP#rIv54Tta%0h=pdxP%%x0*oE`=2f019_|l; zANs&Vq-@oObK-=*R7p?`5^<0+HOXCCi^t;VhC8bOt_$5T1~OqIT#0v*hxx{cOYrt{ zWd}Z%6GI2ATla}}N%J?hrY~6DC97~hzhUM&86w(P9%9f9wzQ#6i_ZZqBJHL&Oq9rOO+S>d@_ z81(`vu2-j7DA!oJAAnnTDY?QJ3pB?Pv{r7J0_qQTR(ex#eYrqokP&&HRK-SP6$}uU zTs2iL^YK1g>JLvW$QVHOdZ^zrd|*EgDJ%I`2$2EkoEy7=IA7^1&nXV08qFCR8cJl#B9d zYp=eU=D{we%8cSr&b&>3&N#xm)^lt!DyMaX_0^|q3dV|yN5NGpvZoTNO!=Jni;-%% zqZK}aRc7H{1^I;6nc^?pf*U#PW{t@eP0SvAA^WBTH%HO5F3MM;P) zvt6MC_G=Fig4acfAb-uT4$j{V>z;+gQK)J0yaBqR z1*`y;&Q90-4hc!-z8TNT*M0BW!D3wL(&cWwBw_Xn%AUb1VSvN=@JvcsGJ~$OvH}^+ z0#RR_3HXwFu|R!)LHJ(Dunld|=&8ki6}gR8`(+yWDHN8s%K1;SQKmocKgB%CK%s(z zb1U8^-bPOFHUb!Ojqf9fQLe+^Yb5lwX*l>`RQP0`b8DZp{bInJdz#&2DxS;8#a8wW z!6h5R$if&VAez5nVnjp_s2@<8?mr2G%dw`5J~d=hu#z+7(^CeC4(3)?Rd_;wC}IU7 z>dbp2t{S{3R--r1o5_c;7Cwz&e;q%ARhWjbvZFe!)N+u{PJA>$XRsTwye<=+pd+{H z1%8C94OssMj_Md!fP-;fl~a`+q&Hnu`eUdu8rJM)lv+}&0>MEM-&4GV-+>`NDNVVp z=b0|_P!#M4>@&F}Jq79x%CvRcp`Qq293A1mEWfdw znk}$dmAMl7`vqmKRx;Ps&fstL$#3!1145mH<{C(Y6Wd?o-&3*Ni5{!tllJEfrP!wC z1o{*Z_ym?=VFCo#MLE zUnL4ZH%8J|*8QNfQu4C$E19&RWFBxSWiPk|Ry=j&lT2Hm#B~nS++Aij!n&JuX7OHk zCy{D6G0{SEDH#_IHLkzP>_M%?qLulSIQapgt7Hn!5~SX)JY5fH{R|3$XPby4=a

2~nNJxUmoLQ!3=lx9@_@9CE!(9IYmnmOGq4go5h>pbTUkemlwkQq)V`K#RH}k>+OpTcJ>!5Vy`@RT-DB90)CxUn-VGV#O5!R;L?Mtz-dSdvY>ukQ>%9%=?1WnC#*#OEoo zhlF(aUz%%~0grQ#?40NG^7sb)d$tjibLwQZ2B7~6Z?Hahk9dq{ zLO?~-g+bfSz=YQPVtidQPhCsP_GhluVgHDyGN+B*sceT7Eaa1^_o7J$L6Gcy1qZ(e z(SK!saX)d@+Pc+_R&?Q;F_+;YvA3tE0|wdMVc3V{kU&veBI_Zt*;3%8BCVTEhh9vea}!x8?Y(DSK+5d}oDp=CucRO*e0g#?%a-VH#E9qgS0s7L*3f#(C625Z$$ARVu< z<^*EG-Mf9C*kh?kNcC5IivbVh?@Ea?V--jAna#sNA^f#%+vPIy+pdt6j{qEslc+N~ zK{HJkDc3(=Iwl6nOVD~|8_`()4Ol%LR302DB1mfP_nc;fXxcLd#?zIYYZP2z^*>#RVN4jlK_Tiks~Z5g0NKBXlYm zcMwD`d=d#scOgqJN`5KvTbiyi^zJQ952RxyyVZ|ErbO`@09%}n2sXKCaTBq(N5%t! zX|%Q}R=>YgPn#moDQ|;YTczZJhC2;ZA9Pd7dV=wp63(v-p*B90&)bB=o-v{G?)H={ z5(O1z$9}Qx5XtRvX0?ubsx%T; zd~2c?k8PEypFj%xj7?vJ&eeS9%vKyKCFz9bo(Ru96H6i>VpF!l;cOCQcf@mW4)?i$ z*S)7yAyg&CMgJ}i`S&e_jI|n%-~Ec>T++GhWZT92EaVvxN~4xgssK7P+Pa(~ynXjn zCuF5q!QaryPJ&Zm;Q@zmRup?H(o6f58T7$m(X11vK~YzyjKLyDP}jVH%-`5}dD3V> zlIJWpmlC{K+Z1Gw46otou*JNl{JSd{r;?OvYav@Rtq`#H2uPFs%!m$54jXX%#;R$T z=zORH(lJLbn+OKCZ=J|>4}RUqB@Rg;!WD~zZe(H_9wm#Wh8$!V>+gPBa66h$(qwI` zcdQ^bT)>#(si2VFR+XY}TyG?G@1RO@_rMLQ}CL_d{!R zON^qLpOtw`L=Lxk_L;cNY)d~-?a4SBi{Hdq3toFMQZ!1BQwN>}y-|z)1fgN2CXe<& z_nyZ9(f`Z1vSPZU*}rwre0$QUmgykg9Q zD14-T)wur`;|Qlx4JGF8jz=+cNX+)=Ok8>{D^^v11(5?A4V8|ci9~!DEx@bURkrCXEPi!z0#MI3z97 z=XA5Hq((#~&Y_5<%w@Rg#w^UuSmhrs6XpGmV?v1qGFG}eJf2?&Be9FtUZ4K`t6Euo z2eq)zk9vK004jY}s28<&A@-7ziX!6gHJYwvxB!p0!A5c6A3G1*a~;jRAW?ScMqS=LMiKgdPFDn-gU=OD9L$Vy=m|w7gttIkdXs?498Ef3k%)MxkOWz}O zBi!9>>V*r}#2_#E^=Qt}3HbR#Cx-Ul-mkv6Gu^rk)cs^76ohq2f&WIOINQ+X$Ja z0q-aXCw)>|$^9$jNB&l7Jn&pTbS!ZFrs^}SLMPrJdI;srQn$$bs~$Rs3)jWnhZ1{h z)qWJ5v`Z2kjITA|5<>XqiRbVj4ib@gqn;@ z>H&_fH=FEBLbl|V;fv-U1kB+3LMTX8J|RvK=b?WOH5cx`TU!Vf`GF4g`n0En1|d9W zPs5hy847}Je=KZH3(o}g@&!d=OqaL%ye7E1!8qabRDTOkJaZ1bUYW_2pwc@C0%iCL zdUw*|!PlLVP+>a=g7UdMGm4);h@q4tc{Y>FB$e>^=Vp3JTsH5BR+(EH*7FZ#%;>K~k zc|pbck;E+$(y+@ERMz8@e{d<^Xzne2Cx<@zQxJPo74nm?>_i0V>gDlOe>iGJ~76MWVbk5=19zT{M(DW|MReKe*yHfC1lII98apu}S>jJ$k*?t*`0Gy^qyIt44RODc=;cY#C3VqRogO=mls z+(yW&Et?`j6&euX8zgP_Bk)#Y-Us2D*^wL>;(lsaNIGw-6Xt zvAKkyCd5!t`l9Ud@>nQT(U$2j4iV)+`uB+KcY)rXeA5}kFyv0hG?|NEORxdaY@-8# zI`3moUP30hi2N^cJlY(Mp`BjQUHJw5Li5s%AU}`@X&d8H-C>C0^7GCmwU2AKS0@ZUHj7D@n`nyQuaoPV7Y)I>IkY3 z^mWYL-OJ~vAar-z`{e=^KZK5L^Z>YV&1%`2jyJ2u5BRMKvhagB11bryL;nBYkd%Wnkq9rwzW4{(fB=DV{+&TNs`oB#qrna^=sgn`KIXzet$ z^==OX67OOskMV=8<+FoJ%Q7+k%_<*)p~Sm(h{r${47yatbS#-y_7r5L@PT99AVP(P zKv;D9IIAPrdUd?OZ`p0wP@=a8LR}b8075;uBU8cv#YR(loXXYENBD8B3&w<-exV!0 z-V*@jUnCgRcNIZT&bbAU$-N1gM|p2130W*Pyz=W-=7@MnEZ5j=5Ez`<%bDuVl$v~+ z0(ZzdAx`7-t zD;D$eOA2Q3i+KFa-?iuSYfN!hkd9xj?(&mIh1@iCKBF?U`0Jr;^&@%Z=}93SN$052 z@M28XcvV^u<=};7Q@({T?Kcr`B|Z2&=cUTy8$^rqqKRC?K3vNZ#SrwTnzOID!Hm%+ zH8|-rQKyo=gF&Sa$k4Bc&z4E)Hyw1uEB2)4z|Y0E+rOayQxvUCeaWZ?39eWtEmC>biB`=&hq@l-g~ArOwaT>s1-Dz77c*5ElaQkWBOG!CaGY|Y zRy1a4x)-;JI5p0+RJWaE>fB6A_IyFty16F}InFC0z3;cHsLtOlmsZU2`8z#J)r!!P zCRNt*eKRi(QymD25ai`Qp8vKNbU8C0iCvXy*>10Hj4ft}+vi*lM!BwO9^yXe?N}y;A~@n{zGxZq<1+w#4qhZdc!yXt73J3AYmg}oM5#sPvTAT8Aw2F+^$9?wQe!mSQ*yJrxbu3ubkmt>+Wrf4MI%8ZVJ*@XP zymJL+3LY(>^pLp)*U?e#F2fe5p>y>vM;9bDCf3hbanK1rW^4pN-Vpw*9H~+gx|F5(=VB6l21|~lAZ$PkD zAx#sC^)LCFF}dznZn`z6y`j)VAYv}i(>;t1_?w{aJ%SEIf}qGoEV||^dXBzZaWw2VGpD3M*cN{D@XqeK!EXSzlmudr+x)|USk>tew@@e z8U#MPO+|Y4d;eW6EFdu4`FWpW6!3o>bL@Y6wA}T3J?YkL2>3kD<=hQ;?iTEOKS(`o zzZK-```$mFoeg|H>V7!540KHFh^U{vO*6yua2gY?UQ=ks+pHFdL^&wKqkd%G*x|9!8RDERI2(|uz5 zGkc2oxA(JNkgxaa?&)RtFi|nH?^pj>Z2wI$=VZJ76MUp#E1y6w-(M$$zwZu$0bU>X z`rPmL6|NP5pIT-~f-mE8ON;pXX6;_hXNyuJIwoc1x+ z9{B!#f81dB&y3FD2ExL>KTq9bfp%1G0+){!gax~pqE9Es=jY?z4ZGjR=a)8L1qOfe zf9$>eULOZjXD(Mo$=}!SUeh!C{k*@5bY_dscD}#bJso3d{Qn+zXI^PH{G8t(cWy+* zx5rxfy?;IY3=;}$a>?GP1g_NV2K=1oDjEj2BA&4E%Y7MoeasvY!u?D&$g8qEVNJW3 z<-{FZdJAl_-L}0oj0GAC3}4JL7N#uOwsD8-!a#4#G6Z5{(hKNd5^lK&jCbol&OAvE zSd#r0Yv&LwN*E>SYumPM+qP}neAl*Z+qP}nwte5c-t-_kdKNRAimKIrR)1vF$;^*W z>~H8P%%A55J~M2lKf+G$vT)T$_NQYybM6x$UU>n_dL2>E7|H|e6h05G)+^4~7oXJ; zv`lE5=KE?8!cW?%++ftfE7J2pzzq5_&>5<2L=RtD{!O3Jui;nrhav!(6J%a6NumH# z30^$G!5s1pg`i~SLh~S9<8mF`8G1SUs2mFIuN3>9WcAxxAtp+5hoQ%vx)Zi=dLdNl zvH>}FAF(1^pU^Q8$iGTiigZnmy@Sx0M7?%QlMVEF5)eXyr5y-7U37su4zA%>fyWBO zCJPWdN_Kg;s*rY|)!601>om~}##yMDv;v+430p{8y-9wVWA=^#$@m^R%YaUk5T=t5 zr0FX9;LHzSF*7ya+G?PjXcjG0_rwO4gea&;Tq4Q)IZGhw($&~f)yh?#@Ty&5l_Ps+ z#656m3s0jY%i!@iP;C7w3#<`y^wN4oml^|VqVZsn0u|b8bZE5m(0G4^B3W%K!&{6@ z0fG4w{#BILlZoy_aRm&cAO=x}^JJ#lg_7*7? zDG7ZBm5#7&#Ut;{X`swMPA8a*ezFT;5I8Z?ue{2&~zITC| zYaU#xhjPngf9dUSLFyc3!RdEtKEHu#EXvQU_!WZ)ST>#>FN2CK=ItRyHnM&2*Wu(x zu9T}V8PWg_4ym}Y*QW$>_72jE4H2mHN=`H2c`6LT$T8T?MWY~3oh$|nBzsaBBF0VP+$^-ltmIS zS);5EA-6*j6^p7XL7^NELlqED)w5Iz-!Zaht>~f8MX&t~Jo8L*QRj>Jx9S=%`v)%x zf&|eq`iMRUK*?Kbykl{ONK|N5A3zr{yt&E>9bfvQt%{JeRB*_d*Q=AixztTF@~&E7 zKThKk{!_@@{9HvGdzq{vfY|@Z=D^4pMK#aXc=^tVY69Obr?*?`iNdeBJi=F=6}yDC z)CXSL^(b}4jjT(vi6TnkiKPDzNSG-xyb`6DDdUZ5^e#NWvdQZ^xe!QsC6PSpj?3Q> zCB-T%G4_yz#?dqEE(qIiq1aD9_UUXxpnHXrV|PzukLwLcMnyd0WR-uuEM)8s${N*{ z!bNZ=Ts~{HN^Dr63#DZt={^0Hrb{5`l!w##pxitT8d+ifwx{S&zTPO6)c7`C1_dUa zJw|e%JVGsN$u48--^W8;yCqr@8>&dZ3i!6K(lwrB>y+B_i$#_xSK;yW(oUHu(Mm)7 zJ79Wnt7sXZW+0o=wQI3~w>5oLf;9w|l)Pnr>s931L^TiH<7yuF34llGO*M^+&aL9u zSSIwbP>X^NVA>Y?JLQQcHvn9rL*Zuk!84R+4$~fKxBCx6a}Quiy5at+s?F;l$zZxw z$8-tS29@rIeSo~dlE6#Jg6UX&IH6<$FfXuW!bI;?W~3S$ zB)_5JVi;T-vqPO*#7H;bl)CibUxV=yLzqf@3QcDMdV`rAE-YXL09vwv*x9p*@dOu09qu9_lr{r6W?NpC}l z{FR9U#tY}{Z(968pGm$|no0sY);c;vM&c(NM=BPM`6t7vJ@n#r4Hv4WW7CTls_z0~ zP`Oo|*u@thRzS^L!-s2Vpt%%{Hls#%Ydh%mY9l^Tm5oYUaZo2PQ7i2MRyeI`lG14X zpAXiNrLZJQv|>E?UBrZz6SQ|_in^sOj&dUR@KlG7PD-NI`n&?9F0&T;d+K2zbhDC_ z3s55KrA-gOs-cbW1O=~Hut#_}99PgGFSvAPQ^}2!WvH6sN}xk4ccayB!w@~n-8$pd z)6?{qA0C=MB{1*i$@q-~e*h1Z7a(f!CSXYl057eA_Y$0fR!vG^xa`vC{?8v8gMVz! z>;A@U=$2ooXsaZJnZgcGsx4R~Ng}q<1uHrUCnZwH1gMhU&VEWbU6yT zfSOvXm@2K~CJO&^e^mFYVGAt@yMXpVk+=zh(V%t!7bR@7!zz@(H6Fq`fL39D@Wd`S zQ++fyR_2OXsc4eCYm*l+Uk?v7bx=1LX!Ylcb3^n|Ys3K=;nGU|aY|*nvCvW;XqzDw z+Vm^PfsgS10E#G864k6W%P{zP<7yP9JSUpJ)a4;M#Hwb2g_72y`ZPSO2Py9gxY|k1 zRDrm_D>|B6f3Q4^6j=S@5snqBOU^)vz=htm&D>&9pA)vbP}D6;95q)>yU-+1sJ4># zDeH}ZXn~A1Q#k;F0590~iRjc)beLtkTBoK(wWv0MGQD;f^L-5+?%fO!$;kn!AAg&O z{R$KSCdWxCL8fEv+-S>GlKoX<2sLIT2#e|BL3Q>F3nPt!j9SR5QLh!D-DDdDvg&N_ zq_%&bPOBmTYOe243+vd^qS50FHz(_l;AHGt`WC228hJI#KqmKG3ZBV~K1$5c7j8YQj|d^95Bjq*Y2=w+gzx9+zr` zL0%PwTjCf&^&~r$l;v%D7{j`vS2Hzp}1nDyb( zRAaG)a6&R8E&Z5Oqpo18ezLybZip*YfuEzj%a(N1seQn}^uEjB*=oj$y4iTjeE?kH zlJ-XOpW^u_Hc>uq(kQji;9hZbrT_(Ig}GIMx8K8fEhExxsUznq1D&M)gkG0Yw4)y zV(1z!aBb~KO>qi&hauGfpV)rt)ky-X8fn|XF>w<9*#$-&D3w7%Q(6hH2zNISgr!QE zfGYQaJM=CFnU$iy6hlZ%d_1`kQYG<9g%q=K>7UI*d05osVwcfRcZk@2zS`48C}?9Wvt2ct>Wx{+v4u;T1X)KAIq(Stu1Dv(4iL`fLgCQ#de| z!>kB+M-QO)9kY%Lf4L?BuJhCKn_MnPC)5M`YvR1HiTZk&aqxy&ZYl-pb`);Cp8>1I zhJ1?_ZkxV)grP!{3V-IGsNN!F2AQ?V`k0UR9zz7u%c;o8$S&?(TxSHgD>bc9>mN~% zGDx2x^Fud*&v&d`$(R$SWo~YLU~a3>+QQGAhjRrCtTBUIPdwe!Y~^`YEiUUzblaoO zl<2Wv+AFC@At<@4$+Gphj1QEE3n+>{BJKYFr-}ksO1jBJP?r>fKnxJ7PDGr{5Cz`(3567rf4ge}$bu z2bkAjkMX1MFEaH9qMHgi{sXk95z!Kj6#w}^b1l4?zK>=N&s^nXVJ)|-(AH|7gZMGK zkAAasJ2TQp@v~U%R1>lAl(XWPH}a?QkA3;Mq)LoSE^VY9oxDx8_Wt$AZbcaN$~WQ{ z6~e2PUe%rLxi@QGRpuLF+#1ne3ul+|v?Q*y&%Q8+ zy#lEfXTM5Qh5FFWb6Bj({d8IKp<13R+ahr3>7Bmda&84#LSP^x^ZjTx>$f>^Tx(tJ zA~!{91khQb;=%615XNf!jbEVohrq8dbKf8icvKuM2ih_D6-vxQm81zbltN`-s`H9-<$ z{D9oxxD;k>!7|$F3mQ03BrMua2Zx`cGJ{lRdIRD%j+{Ye5?lg@v0D%K5|FN`aI*!{ z0X`DvMmpsCSyHP-5oom*XNE&JIb7uD!#`pof0A*^Ym>Z4wKMCIs}dl7 zirbV0UYmj$8#|0=+Fm^FdGcmH<~F>Z7biRTVm+>=2g-Trnp)fVSiwrfyi)hV0%qj(?ir^dx$|?FYCD8*&%VE}# zwrJdms;FQonMH04z<(G{)Gp$w9f*6w)Z%uJGVKlG`VE;(x@{=Nrokd4x#~ISr<`hm)pi-&;pOxT3kyL18LzC@VM7eQ!E! z8AHh0X$Q3LDY{ze*DOSpkJG;J$nU8f4xYEWTM*hVs+lOr*dQR1Bx%}bP3a&D8#6FA)>p;q*qjNH=?Oc1ZLs$W@`O({6$M%619N2% z_?67p63FIm0OQ7v+x$Z@HwGu;<2y8P;g$6mD#~rVogmAJN+C2fdmWqt&d^ZFHeP2u zV}rpegBv{K(;Is3o!*7!@LhaQt~K2JVqT9GwN49NXlGBVK~VMG0WB)w*gH|yHlG6P zMrXm2N&v-ViDhJnXfHv_!jfS|kt2$?{(R>ztayr#I137OPsvM~zGw&fZlPYQlMAg%L*n#B!A$@Z3GDS-n~)U!F~x zuEp!IB*jA;!zLgB4~`3b5RfI90w0lH7CG%528kaV*)fQ_tD+bij>9*b)xTw^wh}3# zhy1BE?FhwHoKt>@pAS2{J+w@KzUx2AZ^R%j>#|Z6NXkMPk(S%|1(BWbcvoEc2Abd> zvUkWTHn97FxYUE)#NhRYi`50ODv%E-OBONI$xa)W1dC};=mt{()I-M_z*5mzT>|n> z3+mLAT9bN_@GE4~`gMZsuh9#z)9Moq;YvdCYx$%cQf0--! zSdTGZerbK*=J~hNk992o!icZ_tZU2F=4HrN@~IJM(Fr9U1f=Z_bMr8E&N5L!W2&|T zICj{5%04U{1C*zZ6Ij*1pY*vB;kW?s8KCa!2(5P)%| z9sqWmLDN&1QL=>|E4LZXPQ1r3Zz)TsnF~#zgM~a2qvtKs7&$z$+n4?mLJ`^}AD{=P zw+V~q{*G3DL_C4|MFI*9LG-T-#Rz)zsTMMkT$bvdl+a_4`3{pF^8Si)A(JnN;@B?ZnwyqbxfCFrCv$)dG_24_d z_KWU!papq~ejpX6meL!qOR+7Op)9?^VD(>Nfg-ed6{Iv?dlTShh?Ya*FL-gipmWf= z(BzpUq>TQsZS#miJ|6A)Dw+*#(;8BcTF0Fn?V(VhK?gv9csjXbn+H)c;jvCVb0C!h z!6qQ8AiKM>f0YWw7FpDn^{^gYuJ-;L0u0L`?bp>OV&oBb`ZwbO&p7ux+b~$!y)* zq?=k=4`;4cHB+16s@9|?x@?W3JVrF^5m;s~q+mD4*=Dq|qL>W^^x6YPCt)N;&Y2BjQH3MZ@CU*h-QtamCmKRZ}Ew`sA zvy=QgQsT)-RNG_uwl& zrHbs5OH0-XJ-#x3Yk2efKhm<>?LkM_v7~Q>;ZU#}nq9#$GE{WD$FMEVE_~AA;)eLX zT=R(LN?OGVjJ{&LM$dnt-;KKV6Y1`GWLCMB`_cS;>ZgkkXj!QPXRf1Ek9Gs8S#Qsy zMmQwMT@?Nu9H|kOScdyV;J|;iEe{LxQJjqtcMcSJdeU?-SUr{a%|D-C8cgdpK^qV7D($uO;13EeoXi_Op!si^;F>^4K*xMr@v37F> zr2vTz7%jB1b1I%^Ekd^h^YCleLeG~`9 zOAe74aL$ULPG`pj7c?s~{87BbTqnq|z9At#51xs~ok3CspIE=Z5Psu$6MWinouPqr zmyAF4H%-Ef?^Ko0$|a3VOyb*aQI(9(dSk#0v5Ia;!p24pc1LM;r8OLaY-P2GI+l;g zoW4QkkGt4F+H-qx2jY!+inFaYgG+Y>s&-D+&H&%aX&4ypR&8oH#^yvqm7~+#_4BfN z?#Up#MHH6G8e=9~f!?2>2P_i9(@MgIWZj19qqLop)jHz5JY0(M#3U`Lu#GBTZ&==o zD|*2-Qz8aqEo@mzt4zD1x4--#tx{I*hNw8zF8VF;#F0ec(Wdpu+kSUyPMNOZ_1!f0 zMhxnqz36COYT6(|wdy`X>Xbnz+)@lm>WpF5W%FbyXKuGkZzFc^l!LYnQ(LoZ5dBq_ zz+S(dvQ(Lzpjw_gs#0e2{;feVvVCs@v<|z%7%Wt}e69)fU??-N9!h_4<-a>*E7oaJ zGjkF}mGSCJGLzG1ch^&KJ@awAaGKu@z=L)A5^H9Uui5!{>LLYCe`IUC@+n3NN~hen z!Yy;cg;V9goVq#Z$guyQ6w3@@_G;Dqm$8{zo&4Aqbgn(O$T9BbWAF~+rGQ%=AJ=nH z0Rg@?ahz*8ko~bDItZY_;?`ATSC-0##DHbu=qfi>K+MM}=DW);g8JrhPqZ z$tnf)a=WcHSEKc#fhnm8vhK^C$Y=I8Uw7M(wW17a%2qv~Dh`R6>47K)2`xH1JJ^2g z4Co)gt>}y?x4Y?%0G3x@&I;(v$BPN?zGhpM=^g;u4S=xsg!*a*AbFtwElKM+d{6Wg zF^ktEQN&MOgy~glVK<3ttr>zh%<37m%6t>B>0#Lu%Xk+>EpE&>YDDo7ej7227q6g4 z-5&Da*coHy*vzI6iYv3jSof*Ws~az1&`$HFZuQ?X9}#C|4tSbqr0}>#2%{y-Q9Atx zYm?}G^~BIFLhsDU0Pn}!&~Q}nnC1LtDP6xbv6y#Q*F94|_$P`Q4xZ7R- z6+ZO;v``mfu*PhHIQ?F6>QT7XAK7iG=!Pf4~cV*QCqHiJIHcIWyvKLXbK{-Hg5ew z-LtcMEa%K?^Ay!B0tUGGvADGao_Ww)I5}U>#=$tWHS=e5%=C;Z7aisA@4jqk7dOtRj$RTwUKe@fgbQ)QU0CdpyF**xdhB$ zH#pWEGLCPKahf~Z2nBoC-Z4`}kECQWoXz(%W*u`v^hz3*=4sr4q7rdx-rF3o6P|kpXy5s?uHB3=; zIMU?X7ff*1Ne$i4=5~AnZJ=faMd+~J=p3}Rlk$=|#ZSafdROqteWqj-P|u+JPmGCCDtS2$6dB^imjwfX@(yAsp+f=4UWRiSmLno97GEM`O?x2-g{wCnMQ#srec zhC*HEw>58%4@u=>rUv<1OT?w;V70jEkJO%CsT=4ts`6*8L7hor;+fz(U|AAI7;>34 zHjukdJv?^Ndu_hfuxU~_3Zib4zAl05L92SX*pUP3&}N38N1Dk^ufD%T3CF@VY^FU% zWXWhulFczsk!T0)nCW0Ot5i?qcA>t(8|SLi*_vXvbC3Ryu2xa!uI+=w-P%jHE3-PZ zOfi{VDP(_A3{pvcbaYB1QCde0ubOv#5bv^58_UtTLqDsDL5xjN1sE#!u3Hw)EHQ?4 z_+ul+fL#@bs(tbF5*vaTrcety&rZJhZ84cvtP| z_DTyrqlgDxc?+GuF7CE4krnYc-4tuyjpubm15X$@MO0>V7W=zmf8>;?qWTqhe{14) zt30*m*WZDbbs=rHy7M*xmvX6?fQ*5{$QjQ>F!~ZBQPV>U;CgVk#K8jy7k(6?{FR}P z8tXa5pdPijhuRdYK;?w8)~r80VLv9QwT2ErvFF)9@bUSGi3}SXP7k%{7;cVwyXw^2 zsa+IVJRbtv1}qhL(idCmHe}?(V5+5nSa$BsnbtLqbr%5GcV@Qg=P}%t*`LLblbOp&wXlP~{34YBWj>sjNCbiogjdii@+J|(*H;3tCWZ^a+cC#gu2zzyiosePZs`aI)b#oRt&=h*ueny$k9nrfek0KP-fHKr# zGzJ@xON)jkfSITetOHRZ(-WgcF-f2>VsOCj)V(Eu(@K@)9`AWD5N~ocW9 z6Kt6{w3mk_q*YxeqI&U@0hwyL*TcvfF&5Y-I-2140z_C#2SgSFzPr*7v$&qkSgP~I z-NjaE&uKZZUOGAzj9U8&a-){&Lnr_HGu3(N+n(es;B6=@HKmkgPW88QDVN8d)(U#^;^UPs9~Z=Jll zyLB)Y#Z%oN2fJE2l&PQR@;6kG&F+})YlivJhZJ-TilEkLXbbB}148l($RUwqE`=cS zZhfPbu-mKLw@eX*%j~5M^U$li)eN`O0z>T51S8dEg0$4ZyR*dd*66gj;20mLPLO;C zqPi$4858V=7NV7;@oW?*43+Cm*yX;o6p9lB_FRc%UTyxiWT|y0!E`*=<{vY?oP#dm z0IM`2s;!KJz3}`XOUXPIX)|T{6^W{G!FD`6aUsL+&u-zyT-h_Vj3GhE%%;rFB|4?6 zm3LN0mg5ZwJ-6O%cEE}5z{4+FHBC{0g0De2s8>>^>@apgnpu(s!e*{o^nh+;zxb4U z_A{n9k@$$z*?!3A41uAk5&+eK(L>qx4*8ACs2Awp{6 zPivOk;V9gJEw|G&6hc73dyJ2^iCsGDu;l5?z=vWA2T&Myd0RV;y`|L|XSdu+s+le< zV_C44GDnnVO56x6A!DL1wf5twheRQaJbNxWo5UKgd-P`ikWvuN85Y(qE6ogKai{z) zb02jl=<-2l4f#SaYcC@e)RD z4#cvk5_?@OeN(gpd#Gv&$s!oVTVxC)gzU==3Xk#-g~K}?#u`gnJk*(+(_XIHJ1MgC zE*+Q0g~##L=>c8=Z`JAUdu~Kiefl(zYkK+2TBrgYbFNH{J5^iZwp21GMXzz?5;kxg zWJG~2S2GX#uqOIF+B0I&fs{k+*)a#@?BO-*(HTa1D`2IER6z%SXhgT;G_j-5f={D# zT6K^UI2Y(}RMq%TbqPb6u=>b>ey4PVE=R7V^~OZ5zjt!+A&Dc)#)%o?dZ({bG1+S+ zoD4_F{v>z4k~IX2@s`o$()f9+mR8GlKyrtz!J`NGcgFCN4>(5-kly{6^<<$<^|(q9 z(KPq#g9Lc47eD|v_v0Xs%nLL9yl^+L+!e;tU?pyJ6IR<|@nj@ubae4?6`5QASuEvqX?DEe~`F}T3u4F6PA9A3C?0ukmZm_$d zau*lan?gm;H(P>UTpOP#*;}(lP>;E^Y2Encr4!<^RI<^!iN8reR04(3x88NE9V8~5 zT-zwo&^=1nRR8vP{Ea|xD88aeOvr)t8*>A6Pk3@Dl4(bB#?5ZydDka8U?UAtF%<{eN#=&A^?A9 zy7r^;#+Uk5%515wX-~p`2IPl77a#Bo{xkNA*j<3RC2qOQ)!*q0DQkF=W5+6rDd0+} zm9{Au**M=(t3oSfKXS)30&fXFQc+)+;6{CIP8^Vo_L9UB12cxI=3u>xe&yQ5!m~7> zcCmPl?N}sbnm4D4t0eo4tRx#5?@(oQiC$fGb}5#|SJbC`kNqq+Y+cQb*hjcq@g8GC z-dMpS(rXjF67xvo$c5Sm>81FPS%8k(M|o+&b9OHHt1CUXa3jK#=2v#u_REbr2e3Zz z9&@`|Vb;R>`LIEL6aLwXbb4UJ1(}}+{X)VYVcsX?N6waFz0#ajHYkCqK)2TRIDK64J7O*?40S53910WH2o#O}qZ-T=gkd!jw z!maDF_e))mP2v#oM{suWJ$;zsO=&TXN<4z`=$W~zs91lDQwnLKXDvt1b0#(S&(AT% z#TuFBq!9?dDdUfBX_3giB3jb@g76ixI{*pItH6^HqyNkA-+SVA`Y6KfG{JUy;c_F) zAP0T1zuWCaBniY`l#(QYdQwQCd(w~xX#i!5IHV#iP!3EpibC(CVfWH-2Wj8vA1BxkbU@xhO{Hc|s-<{<(tUkcdYBzCp2xBDbDX>TF;02m{H2Aj)Ee+dgMN0O#(m z$q1Tw`SfdwLhY7dicuycJbh_MgU4eCWc*}?k3Gog+$rx=5g7v6hD3Sfnek>FlN zLkoHo`NK4+V9FNc zeedIw?z@660C7Wwo0puz-L&8q(s<^PoSA4dSrHgZWpkOQ?_1QZqb z-F?u0{`ms?-yP;BX^vFg5C8!1C;$Ko|F4&$p^dq{lckG=t^R*pk93AkW@i6gP{=i1 z+J1WtcI53BrRhQr_BQCPC2A3?B48L0$CI|a5&?R3DE>rs;cgcn|Bb#wLcAz};c#0T z4-YT#^I>R9f0lmt=k?d+<>up|PM?2oM~9bJC-%rC2%FxTq$xWYJ-_z+=p8nsLi_@u@r<vzW$FL?zP>!)jm?>too#((joJTu`O5PC`nZ4h z@q9U%+PQgI;lHz+m0Ocn*XPg4!OP>{;rR)J`2F+8@wfCiarP13C$9wmy{Cm!TC*iZ zYm3AC``fI!yD|esh5>_eO2bleP;R;Sm}k21lCWsv+B0^ObcRU^XR$K_f~;g&r$miz zk`^|@P$@Nog>|B%HuKa*Hpa{~iRe~JU41e>`zzCR9P5RH)LTGUUQ<+w%5zf6^_sa- zpZO))qw2jOs?BQ3mE?ffIXwBNAQ87T7&14u_4AyxW~FREWzz*1lo%m(Fn616>gOUY@@T6rNutrM5IEV(4% z70%v$J$JScVW?D5T;BwHaZUn-qa#94XEl31DN|#c@?%`skvN1yncX*F>Kgel*>-6s zrxKxzsv=>uyHo+oxa(+4B}5}qVIQGqg@%4Z1ao^-AU6f(;33$&lf*uupmPoqE8Iw{TF10i1n_W{a3~j>(Gp-xD$ul6%SsCT){wY%xT???c)WDg zfPqv;O$PB+%UV9=+n^zKnZv+nGd_3{7(headm>i9yn|4S5@C^CGH-d~RNF)z{Bk<5knlEO=&WgJoWmiR1$uNhfzb9*#=3lSGXPKi3VYtHnk5YrL+ z(V1t}jsngRde;hExCRU>>CDZnQ>n!;Q$kb0tVSqu#m#u~5Yv9tB-7u*RRWtw7{z^i z)Hsrhb%r7(TxrH(4gTWdFRN_%DH<7!h;+kot*nwN>G8H57+8s@7L%2GT z_PvyK*c0)6_}pRLPzz$Aa>`>Nqlj5N0-ooOy#R#87D$$rzLm#$9ZvA$`cZTF~>?;&j^%Q&9;d{!;W&g-fA_$;wp_Dn>YoOY=eX;?_G_lsY&VBVL2PzX+|%$V;tt@rq0}?kZmw? zMeMbY_xcnmMqr{%WA8bSE%F~P%bI=-Z@9>=U@s6JFlMU&D+Vcsk$Om5 zyfO}PL$TtTnE=AYCZA&ih&Wv09;LD6Ls|R@m!`{3)`svJVKrr$1IYkX12qwd_V`ER zXc|M~8d-BpmpD`S{x^(3tFV|FOBH4ljrdSmTf5ua0vX<=Ub(piCWG5Ddk|OSU3oI3 zb8@93-uPWTz14bL4G9q?(h;fa$Sn+$gJ7 z(b&W3O$ux*Nqt;yatUjVh`3`KZ&_4KO0#C}NczGiU%13T)FEPL6ZtS@?ylC^f^{j{ zRpQ_5~$1Gt{vSKQV|ISVrMX zKXMv}xV!0ujkFE~B4_ie-UX|ov0X$6TL%y-jvT$y5TGy)wjt7N#LUqQX6GI|Y_dU9 zt-}jNnnQv}`pmT#UUZEG=v}*=vLNP{+erOdk@<0cJoWw0 zpkQUPul)ZS6y*4?Rg|Hzv8$7zv8TR~rJbRZC!Mj4p|i84nWeGee`KolO-${aP3iwr zEoGS=|M$s21+L<=B}@PSfq%(BssD!)jqUBsEY1H-_8(I?(-}Lv{pSq-MYQ``cf#pN zBC+SKt={sWL40R()69-HETdzI0x8jAq^8|Ob#`s;34p=~G9Ca@N>)=$l`16|ScHRT zJCFXrBjX?EW5ej@b?di23$L*)uL$#R%Lc94(g}(79Gf#ASH|=%dz2c+9J zGlyTjRyBInk=0{o)+}7qOy`m1m@k`UZfExGThy9pYt{DH$I^w*XXC~-e4lLEH)`0M z>fDJC zp06$r+18Fx6SeYm(}q!8gdAKK&6jN`q)FFicNNLRmJMM>cVJXnZycq5)5Ssi)N5Kr zf8W}K7jvIPn}$4E)eU0Odjxj+P2&Y$mrQS7ZR?Z`KK)*dQ=cv!T$<$HX*B!0FnCNW z9iA`fN4$x*zJm)qjqf&FkO^@mG6}wTF2TqN*G!fuYLzt%9dbKkdu-jH4%b1xYFlk4 z`>qS0gc|sy_C}8ra?foNYuzEp#hKqpcQSnKOq!Sb#pDa|^!%Pzf~B%%-r1?;n6ON; zzE_JZb63CquOsO{?52%-(O z>~S}H?olsyOKsIJ0zxH^4?|{}9~KXdI`_D;ul#0xKTSXL-yA&-MB33KSklH|g0|#e z1R2+ch68tQi=Un4c)GZ0VIw4XcI2C8R~L`>O*#1!Q9p62iHK!$=2_RO%{W=+_VZ-4 z;CrQ2E6peCclAA*TRHbera4Pa9Ddufa=Ec9k6wi%^5X` zBryUhBo$3rB$U28#Zp(7SUaq|J0$i?3pdHIKiecdrCC+x%@-#>Tiujd4@0E{4`Ul{ zeHv$N?G-sz#ZU#P{Q^)s9s`_>*}&=PqIK^Q~iraZSTsWs|md@%-Z2FzS3Q)J2mW z23OoZc9LK#^#hl*yfqW*_ygjct9s#_%)MbG#~EqIt+jvNI@IB4$*pB@(K>Vk^Viog zy~iAZ-@z>JI}u9oD};(}wAkr9FNdr!g& zz2b4;lU!zyOdEC5BrUNWOjha^O9XPjV!qge2mKItFKpS#=i7%T&i zWiCDUD4X8QT5@UtIvr)~6JE>5O)6Qbs|B8~1bV+g!}xIlG<_@-WXhW9^F9Y>pIeDh z`~)aJZhSxIfMQ1zp?Ee8>6^Dj^LdLD*|nmV(&9jRx#!1q{CbkoF3xV!U-O8E_fYuU zII)!W0RrTYxeKK1v7x}bm#1MVa@Tt?$dhsGs_%@EVLzJzBc$)A#dw1yoH+YsSTi*D zColl_&&hm8`a$iA`|IweGi}HBe@X$%cxG+jUHhAqbT3)@>7Hi}UD7hKbkbnMQ|+?B z2t!jjr613_OJh-rTb9VOXq>M3&7F?ZApoan0lz6Va_5VX#K%TYMWM-ZDyfrrYX zD21WeF2~08d;#hyU}f@`!^o@1I`t^D4#Eir^&$u=RHwhvi&r-(j?D9Kv61EP&xjJ% zc(HMY^Qcgb4}@v05(*y-5r}DuqF`zb4CSvNxfuq!-%>rZoa6-wNzMg0=4&zkTzvCW z$9KDvFBZOyrwc24nN{S%-|r*+zn}1@neLO^t*?SmG!|LfrnIX1FWyfMEg@dS=0x&N zugT(|pa*+K*C9l(BO#K~poab8#nC|)9*{jJggmEM3P#kICHGPh`62VSiqh?t{J6u2 zOjHx`Y=Y%YI9C)sIcv>xYc5@XH~vtmi30!ugDt^OMX#9-9H%scXYZIkO>lF7)w~(A zYbgOThBT)GLfQ_9RoP6r!&DJl)uZ??gAijTLVb&ox+c{9iThgKl@1viy>K{f5CX1iMF4_n{TosNxX681pwC@DR z;!elCA^1ZRtk?7sQ6E7*ic#$&-Zt~aYZJa_tjrc&3ibukURI?e?#z7a$lF!g>uz@B9~8JvO}FLvp~q~XsFgD6$7f7V*=9oO8hF8cYM!KMUnfvvaNPZj?F zyNBAYPw3urb$3Ib#}b=CKracFCe-B$eL{1VLIRGgixNiE-{YF69?ST;fg@$R4iV-# zy|j*=99;cIMIcap9LO_8rp=+|5>OuK1<&9w0#*eAd_{)5G+G)!Qm!JBu(9BJ@-MHH zmlO;t8RlvjY^;mog%u1c-pq#rZlU0*7d~ErZz%%CbUlKGE}NTS znwHBP0qVeOF-)gHEdd87sLz%Jc4n=_)dAp2VK}Ky^*4S<2afP=E|8^{c%p z$SZ_ZzjD)YyDo&=l_M_sdMU0%8Ukf&{7p(CJrz;FtAGjUXm*W3e6ayMHO*mDz;^-B zIu zxL`;dUWVsk6Spvs+7s;rTdIW@8Duq^lgnMs-KGwO}m zL_bG|8wt@!#oC`>s#f3SGn$+zh+Lj92cg3?gC^8}&?h(qG2)1=toN^j`T~tmO)jldt z#RGzIc=vo22Py^Iw)Af@0$uq|#v>iE+83E;XIg=m1LkG5|4vK&@d=}XM9r;|I_$Le zu-`zPUeMq+Wu#Y{FSQ{x3=AeST8keS*A_9R=IFH--k0*osTZ-bNmh(SEj}@#wY8%d zSq!1&w11XaLj5IFyvMDVR^@LM-KCQ4`5&a6V|OM|vxdWoZQHh;iEVRY+jb^=V%xTz zOl;e>oxFL^TIcimaDKqPZZmJ|A~T+*famHkcyn zvp4T_pax$8Zs9NS^(GCGlVOSiDt35L+E9GVWfz1(-?vs$;9jA$%!E~IF2k<*J{T0` za3(?NjEX?D<)r(I5owZ2B<_B_=d6}juXimqc+l_iz>sW^FdvWm1istXfUOv{rqn^c zSwmnSVm`3meKU{y#L7Pkzqa{YtW;3cp!GQITOpyZLd&kvlx8$_;^fhL%vGgLa}rYN zIjl6OV!AN)%<*7rx{v!Z!))CKTdU?A)9A#-Z&BW}LHe5#UaKt)V~9u1q^Rkg|sQqa3)KA=r_j1d#padLfsCUDfYGD^ojn?cn__ftVqmcy^SpJM!~UFuc7eluBv zPf1z+XHka6GzWL{_;BLVfit;cbZgd=Ai*)qqsOxi-op z!+7Ata9g{1^E-P9c^$KnjwDq-)u6yDltJtJD+;r0yG{2u&5ts-Bg~`Kco{Tc6HC>< zGNvf_)H%@0Kthc~OcG@W5gYmd+|&!&L8Nh)X3wAG15QT^fp&x`)3!eFvetCPNkpg5 z+t~rL*T7-}W~1VyQ&_fq_S{>YS!0{($eh0b|5~Vyj$wEXX+wODAM7QzKtmV`bIiqS z4#Z4ycX}O40GS#j9I6>1HuZV2q8mSD_R~lMsH4kRL!M5#rx~&d{(gr}*ao;*O0aX8 z!yar&(%{c|6%jqn86tWLlY+S+penv@aNN_us;R;Op?pSaK_BG;1J{fwQ+;XJpg0*> zE|7H%vuYht%i&QTv8$I%l4wnOH<89p!SOepgt#Iu=Vs!@0zj)6dMoUY44>Q@mmq>Wj@dSMEIx>yyV3?oR07VwWso_FA) z1?Hcg!Ap1KE#B7m-baZuiYCec*(c_IxLN+M3X2o^CsVbap+`tu`f#L>`{Y5a?6&-+ ztpCnKeehK;bB;iP$zjJ_KJ00|zv!%-($n1EeqodK3;*KV3vuSk1h4dK$EocHH zF*K?195ic*$jc6)g5+b}0YFRVDLBX8q545W z3c1lF^LnTY%Ofg$~GQb7e(g5|)A(5CojJrNzuYqw#*nc_Li*3^@oXWaR927kd zKbrZHUkOyyDV912X&5kQ-EQ2Fg$cO>@2ZTaX(ti)TNc zY*`2VIM_GBB27xQmZ|KqbC@yt+*P%aLCNerX44t!tTOa&WsB|jkYjL_LD|n!h!jqR zMBDcro4p=k*9q|s`iU;BnxJV46Y=^eNF;5~2X#O_E%h zjj3RliY)c|D!mH*Sx`Zw=U(7W7xGg3^C@K`JO37?{i!IJZk72gv)f%tNU;iBkMkRL!Fx*=DsK$0Os;(}ESBx2idvuBBrGJZF*$T$r#o=xa;S z6%J_Vwl@7VY$rs*`g7sm;-1*a8~um3RNsuwyr#y5jfQxy&BV-84J$u!t2Fw7aUSG( z3~YKI6xkm=*1>nX;FGbQ-VIcZ8@76CP?LsvlkMs5sAc>4{eXRDeyJ}N7cIC)F;FqJ)E5Yu~wEi@!}QSu*J@@`&LGt5kQfPq^lRn z1dq=xh4tGaTf~aIRsLY6_FoP@LWZ&u*jM4+CHmaHGCSH`G1`m6 zGdVkl-ozD5oolE&FiU1$_wxbe`y=TNC!fE~=y`g|4{&eV_6dBvNLO*Ne%$gGKIydU zFZ4tPa-bWgi<9}f+YsMYpB&;Zjh@9``Yn)c$$?H7bCkODB=mZ@|M(Uw$-^s_*OFaN zLXk!OP;Q-Cx$i@8M6=e7oGzWJi8FFg+^Bk;+`o%f^_z7Dn&grWTbK@ja}Jbr{VAH% z?kI@dzO4`oJoG)c7pk{gqWkyWHlFKMS}nJxEJWBoU5nE8_c?19+t8RU90pFBgeT(4czP??bB8 z`yJvUD+YYDe=s#@X8CzLy35ZJZ2G(4Rdz<=*wBY?j&=NFiS7(0T@EfETBsvAz)Dx6sK?&><@_i#=P%3X(J-0;+*-jQ4 z%GC$CM{I+9!IgS6F6$KuX*Ive-`^y<;b30otO#y6Ebw+|9i&-582Ksp7Z-1GoQ1S) zQLeg3Eg&eRu$Ih76dA0r^Tk*pdwp$MWpPb)8IV5ppl|!Y<(CEjlEt8V3=R2wsRTU} z#w_xXc-9m>W(q~}RGrrloZos{crI9pUD;kg)fQ4v{C%Cgv-i(>np@vXdKj$Y3tA)^ zl;xfxi%g18cGJ0-=!obj*^dG_Ih62?uUr-r z5sbc9VfZm4Xm>Nj12<%|gDnfel|$4;pXA#`lgK2tXPR?odF#U79q2E6 zdCH$cuU=Pjw=|K_SHDL~DN?L`R=S-M8@RfV{WO(yBnBdsvMR&rO1gH(vq=#uX_-b) zMRkC<`cmWEUCuaPhxqh#@p;dk_Hz4FN3Y-NBk-Hn<|vGF=<$LSazsiy*GeWd{x&z< zvDZEpwz@ni`T`NTf2otXZXg5K*c5&C6I~kAZ`A6snZC7M)cc~tOS$;1hO_IBv{6opxT)lihpD*Iz zVfmP&B1)=~Q$;>2G~VR!K*SS@PKvCKe)|`@RP(ky=I|U$w~uvv0NA zmqzwC&_4cApuutL1K7RS=AfEih(H0Q+YVyTI>c zT6z9eo%b?utbH&?&o7V4^B#NZ&%Ho+H+^%@xJ=yg9NF^Nuw2KX8wVpfifRur_{Aq^ z#rnQ0+6v9A%Ljyw%hrIZSS^sM7eIQrUr?Q6XZ><9>Pq0$2SU(5Jr+OPSNERj%0!hG zhAJ$Bjso1*-KBIW>T4ToV%1m*iCR^<$4uL_K^C9CK^ibyjVylyzY@lt7FW#9m=v)! zNtZ4yJh&ZeH3g54&A6REc__waJw`N?*=4j@vHaC|ksmYWOW?I}xC0NNpTR0{nd0>P zH4P81{`A%JKg7!)?GF$n(0?0Qf_XR+f31OlaL|E)r2ijnrT=A3x*A%$nEc;Lpvx}p z2GUj4s@JKSouX~x0q@Ne0X%2HMngF6RMHdiN`LEi*fQt}>MYwYpIEzzL!>Jn5~+R} zWK*9J-8LG&4U&Ez-6NL?rtgbmfzR`tkGq<$;pwl3itUe^%aZHwNA;YZx2p`lm+_ph zgA~8#`<$=hMyHF0^b$iUv51gr#Zf#lg|R)kCOsEUtu-hukwC* z7=BMZIn%z+uiO0JH;)MVe(#exUl-S3M0Dzc}?sKJmhq3091l}iWzHjf{zW2Z1#;5f^Z!)g8Hg@jo zd$ha`4qb+S`+Y4UD>GIehz$pa_IYg|AhT9 zzU}w;vkNa7z9q*VIu8VIC)=;WpWh9?&WE#a?Y=)K{9ZTJzk?rU-F@EA{obE4rhm5V zQ{VS}bL;ErCxpVFp6|1cnzm66JAvkDec#W`XMvB)oSwEbjPJMC@7JTB+wfHN{<+58 zs{rT0F~`@i*u%%vwr3s2`*io`)K@`?PKe6uyLGiYqPBVAi3Xd zqkU?8w2t{)x2W?^Ppr#F>e=UH;Ao`lqDASrtA#Fl&T6@2a`m}6R_UJIWyNb&V14Ob zrhB>kc{1@ik*m^XiT&60j-Jb3r{+$Z4Hp`(Yvb#pNso7x>-7cIuELGAOHY+6o8;sp zz2Ymw6Y1ynmW{c8ZniCzJw;x%&T*D{-y7LA%Q{{-vd>z>mkC$2W%Su zQ<|6G+|OU_E}wOtl^h=*wQ-c?yDvNVg*p8Ac^-G)ikm5-7b_6vH(aM}y4)r&X}$0q zOk^!;w71OGdEAm7rY%yspwq_`AX_ya_sC{%D8N!WS1`rGoSm+9=I&0 z(*vvf-UoXN^P$dN+!@p4W#xB~cmp&ER^He{3A|3m-}T;k1m3n zb`&6J^1`A+@EJW(#?cyAgt58ukadWR_Rij)AHk;pTzP-4c9lHF7Z9_uU*ATvv9aCg zIz2gdKl%fLBi_rCB>_pY#FplCY61i7PEwM_$D4U6&-F>V@|#;S?w>xgt>~M#$+860 zW$J*!m_Ok9hO6Nd>AuXPx|=P{dly;jOCU?BF#y^El=DWbI+@_RnanHCoW_0)mTDhB zM>D$_8wGN82N(#(`QXAD_L@Y;cE#rfTMpNPYaF_T1woYBlH)2F3~7Ng#&UsYiVoKA zF=6SK&8S!olx{tOSR~49{YyxTHc~E(e3^4zNg8WF*z4qz=@si;8~S+xH6GO~4Y1-X zkdMupTl8tvrvBrsj|fwz4uAUPmsf^OP6S@}c0IZiltUJ6A7H67M{!v;LVvR$^hx7E zlE>cJs=AUx0^__S>?6BVD8mPRx`|(xTjMS3FByAiv0Q)U;E+gt_XEIZyPYXI;mc|$ zL-w0KgH&tTl;(sQZhNCq#r7xY<WF-?6b$WI4Hx23b-Ogz~Iuvxyz>D zGT0Gdz5|c$mOY)kDi&QyaJ@xRUhXpd!bGV8H4;*Bg;5@ zTsB>&!{~E&ip=X_ww|us4t1!-PM0NL@i)4plN+2RK5s{5N_J84qX4`~L3ALaJPwbt zk~EJ{WUks@AS^@dHd>--A6UMK&;kkzd#khN1}-)uBBTcctzW-6+}{_jT#VTa36Itv zDK{TGDPj5FE>(KcEWWpe*-!f-gBJzePj!~>X6sY*s_Zx%)3utEdi9PZ2v1)$HIBB( zHqYdwoA068m6~f|k>N(qxssJ+4S`0O6DM#^7~vATO~>@&0}d3UtEeBjXTXSlH@b2Y zD{hzq)7DtSsOT48#~&^?eREo4YHs;>$Ez(a@JhpLUby-+ zqM!8oMs@&HY-H{5m&T9>xD$hVHBZm!i)?hQp{i9ANv9hax;xbqDuCx=q`apOclHBwlnCOC5;V!OOHwYJ1Bc>aiLP@0>%D8mfBPX zjKmnO9JJfOh8#8>ss0Mhrgjq00{(3qwh*U!KecIoYxe|E75`x-|zE+moX_URX z2WoOiHT#upK{zhga{erpJ}x!GJTYRm>G;EGbR>v9U>H$KIjqq3ZiqN{@A|uZ?y0C} zFb_ud*JdJu$cFb|i`5R7?lKbVOO0*3IIeLXbZ8#-LOmKzsQgkCl4Cp8mPOi==G!BveUudpeA^nj%EG3DcV=&mQ>1ws1xf?V zIHb*P8%qISvtnmARY$dfdI@H!Y*Fcu>&Pqp@ zPmsvsAL2Ohe6{`{ z>m(`2g*%d#b!K5DXPLA$2Z7CZheUVSr2Y_B@vLlyMD7q-FUXOuITU z3wut$j2Um5?L5kIEefS$r$lUYF-XRSw7@yeY#f>bdX@C6n8#BzP9`Y$r74;LORx4Q z^jMaqUG~+HCy#2+C@5Fc%-@b6b}IQv)Lk;q^7Y^&aRWhG`}oa*aQPgp+Z#g_S+Kv{ zVr`&2K7=A^Nzbk)BNXCZ3I+#n<$#hzxUjqdPa%Ro3Sv_Fko~FJ=CZ?!FV9CCr$idv z;A51k;a&11ANjHY{T7VfXJ}?c8b!4NU&aW_n=FtqUDv{riji2Jqip)U)uAs7Ylq%W ztHo^<4}e`oH(AD_RIaR~BX3XPt@KCc$i!tNckwuV)Dc zhU}q%&N^65Q(kz(5vIY)4C6FcvOzzVSuVy_uz^zEelG__PIv3=pTh8fC7fs35QD2cr&l`LxkJ)X1weCvh2Fg96KWLxvErBJ;pl%OSy zt%M|;8uVWUVVxlTejNb!Kn!5|AU@%k)TX6w1aCCXg@VU!EdK$2kTSWAs;AD^yM~>& zg!T9D^E+3*u?^&Msk;Wr_%nxOmNo&C8{_$?dxfeLMLIwp^JN7_ZDES_exSgPdls#< z)pr~JEzCR>iuO7l7O^KaiG?Ff>8pHd0oVgO7;c_wG;qKp*A`gq+o;r)sL48Vl_L^Q zfTGnCVwGb7gs-R*qlA_UwmgQU|39rwX8OORr9s$hcy4>I@Hfcf2}lj=zn4v`k!Jwm zlhumL#ei|*ve7OHs06fGCaeJh%U6~{I0;FQom$$Uf>l&fLdvEBou)cKk$JUa-u!$VyrSTeCvxV5>P`Y5vqU(E|v{O3+6ViFT}^ECEc?TS30_|m}uzI zt9YKa`lbwY}k@8;tX75M4*&J zc%&Vd*!%17MjKDm4A+aMGh%U@vcXPU)#7Wz%yFd0@zC``g*v}za;qg6jM(wenQj~J zafi?8a)cY7+)I`*`hb)5$xjySOZ6>(lGaHpiQBlHbIrnX%u zsWW3s*uE%pn6Y5_Zj<{Q8zFfEqa{stw&YE4=bp*oK>6%lsJO?(W; zCOJ-EeISJ$LFVbA?8ws~)3D~;rM|Zioe1I&YeJcqss&4dvQD7InIwHf{YB&waa8dGV+YLZt|OLM$3Z8*hOu$^ zk!s2;p&ci$CR)j@l>F9`;$G~qLBxz>T9mZ5!v2RN;5J1P)0a$9xIS>@qalAd-X_LX zrE?2yNVLK_3`p!gMJU_rC^9q3SEzH0&O#*W=^~R{GdwKdT2eqHaIBWt>R3=BAut?H z>NF;Mzy|zgZdsyNp~WxkY5Y9atu8%~NO{d6Q2vC}__4Asrut$cnN@e-pgHA3kl(&7NdGfMMQNW>N{JC@QaPa-EJ-3jV}@x zFaj>QvUYuDXflX*I$Mm9;CzB_wZ`9!nxtZ}cO?R5t#&*&dcW@_0M}22;*p!yp&QGL z7#JPhWt_y_SAw?C4-e^)b(Verp!0mF+?;`i>f3L$(?=)i$wZ`hCF+RZM5_C(Nhcap zX!B+_za_USum|WE z661DQ>#RQC(ZjaKfh7r^%XZ9%Cj3Ee?q+XI0{*Gmoui*7%k-A82hz2KbIH-Z*ltz_ zB>4|pxI%{r#_<1WRHt?b_ibw+2-6lZo8c}3E3rXpJzX#M&ykS&y11uH@fNO0Im5NU zQ4KYB!$?!)U9o4}*>v!2gt{b+E%Sn{;vg{-Ik>$zgar`&1AQY5hhR<7(!D7l6Vf60DFRs-t7c0)OE3jdd<$J4LC8h-xGC8ndyq zOF(7|wjf?!&Gr*svc6-7 z>HWpU3CuU2Xdnla7eVtwH*l?_l}uY}UaVy(+N5Y(h)xYr@%uNt^sA-z8cfc`60yJc z)VRTp9d@=TXhNqxudifmWSRN-#Da*d2QklP#tZKyH`-j1Xml*1#|~v)$k1B|5qmAv z)758^e)!0hue}(j>AV&ko2x7}kM+95y8!ho1!|aVjRoIvT>4>jCB-Q5Mzb%8;cLvl zG#CXAlGHQ*FH1d_D3ziS77iOolkPn$yKQqGh41QhB7i+(l+p@?}gA&9%ss%#4Gz-|X_qLrf(FYuP128rjO@zHi!pS|4{!jDl!ARdR&dySu9Te>zqm8g2$ zl;y+O87|*BDm3_-JqLD&+j*eNZ5%WEQ4h1F8dYu$Zf>J*>--gtISJ$ty}4iZ~b!g;>6B ztU+af3??mJW_KgLP)8o3(BML|Sf@@Nnf6R28k0*#wttm)_AUx}tS`~EiN(pYl(_k* ze{sU3QP6i!mMfES7Wnf^NMW+JwQg5GdGlq zaaxabIm|wk!u_b7`>#Fel|)XZqZHY#2BMNGPr|u8#s(I{pd_oj6T^_e-2BB#$2X(? zWPB}(V#cUIp-6!l@ECdYe^7nevoA{#%ox@)Wk5K&Asqh-&F16Hf|F*rLnF0noxhm|gwB2sYig=0VfqqT4C>D-1jjW0!U*4&bp8h(O zGa^`PK+%cl>WZ*l#4aO2oBpp%CwCkND zeAKZcfcJ!+QM^)%*5dZRP&1%E!-?rF{4=}i9T9X~a2~-5$6NYjaWy4>xzNJhJWNsE}hT4EQa*4A0qO3E2@o?afzB=1z z2<{Aoeden2)=L7h#X+!R5zOhp2o?)4IrTDt=|WbI=L);}i?i4pD+Mb+*t5tQ_(W%# z(BZZhSgR08HjtMvxecZ!PNKK1DeQ@DC9pwp9C$)4(l7GYTWek-p8;$a-ek6^xx&Qo zBCIz6DeYuaxw3?R9S~QNW&qzL6dUDOohXWVb-mMYG=>QrM~&Zv*X_V@u2SXs?Jz_w zy-wy%ASqdf@;Te@5f1Um7s+ic=kb*?!t(=&&N_q!VmdX=qbKz=^%FA+uI>n#LD@}9xx1g-WPKeCW zC+(_Odx%rkH{e^(zgcxOmrs>^y(TPYW-bJFFxn_0>Qlf`+DRXq;KcQJX!Dy5>og+v zYbK$ZBN;SylgdzCi%PGJyY3tyUT30@x{2kZ zgR3xvpthXPqBRl$>-6=p5sJwF{uH!%k?iTUVXQP?$Scy#Le!p>ywf!tnfTHQc1jZS zr>$iN5|Wbio1lG`^ZuZ?3H^h_?MGccSH1 z2jap2@eFK2G8sr4j#%9{Bu2gs-x^{au>{4J62bzJ76~Sa#4U9knAa@LjO$?i%7#Lg z>c2CZF(VwO%mPJ2!1W@^uIyC65Uzq-<{x23KnA=j5r@49USGuM*~lOqRq0qI%-LJBzg$urqpQ9RSjL%J57B4!3w7R~jczhhW}RUaMRJOlHz zxC~;2K3m&s{CQUOwM*z}=iSES7xqz*AuNt$j8@vg*Exb@N>_#s& zLMOI#_}!eaxlqi1W8x*pWvdF~@&?l7ouk#00C5d%YZH_J0Z8NBiZ7J*28yxWEQKuXxkuzXHPamjrXo9tJs8eQ!)!!I{Wa86xs_m(>enapx+MM4 zCr)Z@d_8K4v0%!W5FA@;#MMP4(6b7n8=k1brRC`36a|<9I^%7yATSJtD`+~4=_YW?e2!0ntz?A&}y`*1_T!YTW ze;+I)SKx`VB>fs850}r`a7@YU1EX0}&^t-YzevzXTCTS!Pu3o{ni2lQT*!;R+t1PT z!jw5!xCLVVRz|wzl@Ttf;U(^_!E1Dc6GUV32jiY~jRn3^;sRzbfsKQudo7B+2G`XK zh&cJ(i&hd8OY|`zLKOI9_U9C-KZV6XZmDv4*=e|8{6d&B4lvw9v_j?8Xu;Bzy$=nf z`U`O3vm>_|VI|36T_``u1$nSP69+%vy;Qz+{xy}C_jE6q9(w`3G~&gcraN$$`>Ghp zKofGXgN{Dv+HM4XE+j}nYC?3R1j$Rw za_o5r4{0x?{(PltKSE-fUL{-A^#Z=;de@|Arj!(`I!h>!!V{GfZJZ!5;xqZ*F&wAP zQyv%N+c)Zx9b!P5uJz07=vIahu1z>!MqI&IhLH?dTTQDyNb} zJ2(Ob$kW(1eDfX{1QZhKB`z)qO3FH4VTKZpl3 zFQ^#%&Qtm`!?dzBx4Tl(mj?@z@25bfaWzts=|Wl`F02h3@TGGnoP(G7a`oS4a2B@1 z>=RisgKAkNUCcOn8B5q|s|? zx!H_K$CzLAnaO$bu7Y2P6;xc$JUl(B!uSY2S8Q0Y;7fhZUV-|aDtqqzrDymMWjRU` zYREi$dR}|8U7*;wmVUbQN4pP51|TkYF3E+~6N2@Wy1X2(nhO&PI@K>VRd-%#F~iG} z%CM)e$SyGd*5kXjVs$j~*GAc><)-zg&UKAzU6_$9gpWA)Oq3W^MuT^u|D0JBtzLPi z0h*8nF+55#Xh!UmhQT77k_wu~R@sf4Ni+qRK%gE~XrOasa*eccjmk#;(Kq`;$uVHg z-LeWkl~p_U>WKWBm>RM@E^wuQ5q^ckrAgq{0&>-OjD?bmT}#y>z#nquzBQOPECQhS zb`vil+pkilIM-cCKQ6HO;-xAR|4Oz7iNh%=50}S56Pam8MM-?)USZTrpzl6qgEgQL?r|oG-j_PYh`(HTw^AhOo1j(G6C z-?MM?9~)R*juwJvM3qc~gRBNO=AXJ_Q%H8ja;!oX1CI2{U{g;Zt}c}r+e^6WFbkj7 zV9_O>Ce>Z_0J~B!$#IgPprJJaz3e%4MTE+;??ZBa!hY()@BOcRe&~Abuwny(# zy9{eVXn$0p^?-Ha_CugWieY6656ZE(R&@1nK<&z$)_g)_CayP!${RvB9WmIVSjAw( z(m}>$MC9m*`uS5X*nBmA>Z?RjLhfh5WHUN7jQUgtN(%g>ViN`}#L%pC9B2)VGT&yE z(N0Jk0x3ius~W2vz+MoU1C$Nrq1rp_c40C~`1s%#1-_(_Kj?@(Aqww@0_bB`-_!DW^6CzYLkI7E7- ztqewzTX?kZN3ieR{5JQ(0UEN$9%0OrL~k=5UKv?_TbN3w^-8e1KPo=U$P(ns`$90A zgPmhvPBLzg&IXnN;sJpy>Kt$4t0=J)oqP;`LeY_^hFVvj*jo1r(ky>zX?Z>cZ1ZpW z&p{ffRf?9LJe1P8avVCUtdA|NQ1NG}EL-nFXuqqEkcylsT;%el(`7$LW83TEkY<$v zopBHA6n0>z!gBqz@wVT^Gwfbt!Jgsz8E?4PZKI<^LI=lK9czA0=53xg<^dDwE~1U5 zjK6t!6~lT*NF7w@rlrwHjBCp%zNG3b*4D`Ry@gm=mW|jw1W4s#;%a7Fp9!841aWUDCDe_Xj?CO7gD*@P5h&m}|6iv_9SkQ@&<0^582^3IaxNo`seeev z7IZwQEiB2r{w<33IRS<)<nxtNiHCGCuCYenF@%u z%CEc&by!Sn3?k3>#ynR{!Z@OzE$1u2Fn;st-|<-6o+(KdsMjc=Lv7II=*%oRhibL$ z{^2bO^ols(uiKEDpb1Pz?27k>y@6#EOQWC*pK^=zCwn1M_PyOQHFex57054h&AP6} z3Os2QwQ!^Z5DL9~?hib;ePDMfsZQWLW7aHqDB!C+EHMIKP^=WXHfr^)c zdXS-Ffy_x;&}G0fqgp%+#xMaPDT)$D`cyw+X4;UJ=r&;Fis5gXQiOtxW7uM@5``Iv zL3CF9V5g}Y;5qLGq>wJOD&*r|IX^k%eSr++j(lyXAy~TEYjD|Db_Q_viL0#HKg z$G&5KjTnN0kB3j6ibxb~k0i7S%HA%L+B`{_x8MX?k{kF@Rg;CFpV40g2UzsKm*u40 zzXnbuig)@r?5I%(`p6vCAB7^OlChC`9<#Chi4w@r&fo5)s=troy*j9sMr1qPu)&Us z@!LH2<^2#(7VP)Y3x;-Y-zm*cTlSKGvqD?KQRt}!8D%|PuDW7-6-jMQ?`Um{cbFL{JaAuq#Y)pyZ;q#~w z=FzqkE$V5>qz!r75Zv#nXw*p^Qjw#NwZLq5w}O=S5L?)l#J%)HP@7vkIxl&m|C|K*Al11M%s@LSCYZ00Q*4oW14-^b zL-{w(#S~aYsIW9U(Z~yeMws@+OKDfM#$O}1iL!|$A?w6T%~Mv6F+(M=WY z)^XPx4btq-60OH1c?_^d;L&9G3ukQB)h8>8Bia#Ro+R;75;dS*Ek@<2HN&sIO47D* zK{SX~EZ{DWAS|@0f>Ijb0<*^qwRNz{>#_qiy@N)v(BTuKGO7eAfdnSB?Pv(D*gB0B zLTQzo1K8P|)O~8J)HtIZ1(01zXmmt`88vYvQ?OFun;ZpB@)@YU#LESU@=+x3k!1*v zNpO~H=`RE!nJxNvvk|6cZh{ud68>=U;ePufTz=VIPkTtjWb#&$qWrr?W-}5@-|6x| zuV1_n17|y1p+C+8E>E2rdM~>pE&cIBc0%Ynypw5QM&9}@gUmTqJL3`u%7G=|$Y}@# z&&@b@3#zt)&4gIGB}hm+VaDMyzoedkAy{pLoa_jU8U%CVa(!;-y7#eeSuehXT<(ZcXtBT8`p44lo_S19{tn5cG{!c*5V!9b3J&W^L~!BGm}&oT`%bLd1$zxD#$T8$AxvuZpw}EI9df-) z1fCgz$JEWKIOH!lX~&ok-6AQCnxcm2i3GkJ?8e7#)l5i6Gm?kIiZ%43lQ`iLd{Evt@&gxs@?{~+*h!){C5Ob4_T&_)nKs=iYnqi=m zyo@Of7KB-uq{H=I!%+lv6jRLHL?pW~6>#1Olsl?;Rl=J9qf66|k$ZroJo=vV8cYXF zHUO8}tRf)o4-X1+j$PK3aBsPL)UNl3X6 zLhl^*u;!cy(Qwh)&8YO_TSsS-5ji6)X-OF&C!P+Ci4yQBLZl^e!^Q595<2io|^&prryr_ zD<5(zP7>e`qEw@zzi+N^L*1k}Z2M{1*5Zn@riSCFtp80ue~U9P$PZt_3Oedt~4_f}znn}%osrrkqt zz1hHWb-%@n@3jT6NEdY~+Zjd{_9fO^<4Wc2|AJIoT%b5kO$^EE zNzsRNG`OQ$48a~vjDK}8)%5~#<-$70VV$yg>5GbX!^7OcltjlQK`uPTG5NUkaj#tn z$;!__5qc2pLBmYlycznTp?`tNNICwD)1qn!#<3-)oq0{{kl5bZ<7an_ruCE@o%5+Ho85qIe>6*k&BdG-k0{yWFL zq9e|3kfm)8Sjjv<83=Zt2Qgp^H3>VNy0QGJQfrTxQ-6ff<88QCrB>W83#z~}4@F_E zLbS@v@VHKjND(O4LE37&Zh4wyTytXQ%+hVx)(fbt=c8FEM~KH^!ZS)p3Y&VS_ftfq z3$l6M>9O%$oRhG6-ax$z@I<19yN)*0FTIDJ>X4KN(UpoS$9(Pt@8POO<|xT3V6qht ztoyTQtmw^!upfA^OS<{4MV@ZZ7pDsysW4(JHTy%_v}!tZMD(CybZOSVZo764C!*R( zJnWW9ECa20d1TlfUa7Z0}nagK?T(l=J+r>nF2pm!WtRSE8 zNb>waMXmS^ZU}r9=|{LslOjY?N;_qC7z@U{e**>}&Tb-L{L*A{Zj&#sC)mK5srqn4 zEbA;P&NAgXRk29DpxgEHtA$B~SW*i^+$yi+Sh4RCR@h1kzueiiqyxC{gpA5RBg0Ze z{$6t~DZJWHAU+IR`0VU|n4vFmkp#$sWY}rM&He>?LuuzK1b6noE4HghrrXP*m!)6y?serj7)Orbk3wS5gz~WP=FSYq9OjK%B-Z=qupr zv@NAsy~9Tp0Zc*iVwB2@zHhrRpss}$67&--L&0}4&nw-U-fmoL(6!zX^Z5I(1^xm= z&cFf$BL~7Il&Mgt^&p(9s0%ju9Sen3D0fy!Ww&;o)H$<&Q-k+n?pz2;KIPNaws_Kd zUf@+?6eM5Rr&z=sg3-fvXB2Xyl|JbQf|r;?V&cp6`F#MmJg3vbt1H!Ee5M0Y-_a6Tx_g~Ti)=m67VD0>tA zKCOL}>i;3_8-hgvmMpJr+qP}nwr$(CZQHhe-?eSqeEojM#7xX=X3>kxs9i-Z@5}VDZ>@m&H23vAZQg=NoZIyMJq_l%qo za?zxi;vAu~y*V+$I<&}yPM|<=ZxPjLrpS`S(&ehkGp0+Z;H)eY<1y3_6-cjp9wV^3aSccbtQ!0R+N(5Op=4s0mv4Qa1mTu69_COaeg!WByXm0iec~5w>MmIuw?!iJa7LKxLi{=} zph-(tEdg(m`s+bNo5`fW_0&v8@3(-=yp*JT|vnxvyQUBZI~60xhwhl%ZReH5w$1oj<#~;xNeCwAPm7X z6^OL>HhjUHFRqg$s9WwMRAev+?7{-rfh+zotWQOd;RDa}DHiN&q#F?5Wf_Sg`pTXjt``fv zxJZ-}s14p8-vMmE__^V=dcB5?70Ywbp3Esb=LinC)j+Tri=AiR`LoX#DygYc)OjLj zvQR*r1=y_yN112fkjWtAJB<=GEuFIo8bn&8w3fH4g5RWYf_^R%sYHit18ms7P%sv5 zrU?5pcE5Oz8eI`Nh_+Z2Cz-q||6}v$xF=R2W@|{GyxAhAHF7dgqLD}u8!|;gizRT1 zo{ir+(yjz^n8n&b81S@Gs$gp}l;3s=H?W1yj>6+YpbA4*7zSWJL6vPK`@$s@l3;SYW#K|mLU3+PO%0d+%)f)wBs;!4~eAK1PIhDrMd{_>=2#iv8lUk4#OR{R?Iw%TooNQ ztzB8Zc`eTS+IoM7I=Cb^+-sR^AuOa34Hsdy*Rqh51ca}ItP5SXq=~KCg$j&<_4nen ze?Tt0 zQxEdQLLF#6P>#9q*s-60Thjtt>L$?B#rTAQWr6Uqnm2MM40?G?P+=>FmMZZnvMW^2+${eJ7*DIgFVy_7l^)glcvh>QIK8BMJl|5nNBHc)|h0A0_SIn{q}fS^VNb`Xxj5X zP1lR}oGlMPF@513LPSd^LeNx-$A!GGYyE>VOZq(O&6>5M3X6g=@LZ;6wVa?yb1_J` z)oJ#r2LixGc*GF1iuw`{Xe0{-xMr5ngkb!{G!BYM{eqJk7Qx56iXU>;iBN+@*wVT2 z7GpBJlv%LQWrzqZ!c7!Rnd-8Gjl55xuYWld0br1I?7c^zauu-R6m!U;j=KFv%(lrWCsjv{N=J$YW- zD&m!mjB(O~3hNlb&?Q+aTmHKqN!DII+0{OC^sP2bUG{Qx%66y0&fXQc4G$2heHh4x z8A4{KD3F@dhgtp5ZiCmq;(kl;UV>SCLf*tG#RZfCzi5h4dJ9_)3qhP}AH&|MzO)%@ zko$JoBxP^&A@#2E%(iUDf>sYAa^mGO`z3QOCn;rs$me!cXvb=5-u!m1djiE#8yv>k z?uN+Q;||QIk6o#@NW+D3^lFMq2pI%TIfcl`ey-P$q#5~V^_J$VufYz7R2`WM!f6B% zyuc&-70{J0%FNbK&W4?v@xv?B7b~{0h4oQydLBtVTI=>F0X3SF08^~FN-Wo+0SP$H z(K~~LRJC$|fqa%KJIHF5hh;mF4NdJEVmgsa;E~!DQriR5;QD%yp}lj%VLapWUC)~} zLZz72ey!MgVdWNWd}-mD{O!5;GRkJLl*>3(mvu>m{mnSqt##D*FV*T$_NNAvaD?Ch ztj#S{)rA9i@{_7ejbr6JWFpKSc%@pa@z_}&)eo`WY0)YIgR$(*SyMYCU#mGS&!K zf~Y;g{Sy%%JzGezJ1~P=P%f=ku#mLaV3s1Y$)}*$^gsyb{SrX1$fY9ioxsDzd6fuj zNJ-3&@eOA^{C2j=Z@OwyEW?9?`Vw4f7PsrtL_xn|{yZ^O1V6?2fL}G@S4>`066QaG z1sHbE0NP+2>m%MeBOXW8_tFx=APp;pPDR3b|KsOJmR~@y3hjvWqo_~(jAhFo>$o=u zUble?qKSV5oS542cd``Zt3=&yx6mcQQ|&7SVV%eJN$1#?S_Wbm>nAONTw zRS&zjz6_PW>iFG%Q$N=Sdpfne32ZNxF?W0{mBdeE{ahT#X63TO9ulUGnFM78icnPd zMm%0p1BKJ|+3-o7wL)X@CRJWQA+rCNt>9(^8_zgQ2ijXD()4PTVJBytrG!)$;=7ex zq=@!PHIy4TU;EMC;>uC!;^QGH`7qB+pk)=>qTDICyjq&S1mTXQb^XhfTwcSVEQWn> zq4)6#55<4o_RDfv9dkSh0F|H5D(W=1h#aLS5*SprmZozpvBk+ySFa$LX#x2Q|8l_G z=I88y9j~i%rjF&HJ!tQv=*_(YhsE82hw&pl@J_t~eBlY2R4QmtabX>uX0<<|+8bha zZ_y~kGZz6xz+fOqpO@EU7MkqHPEQR9XWJ@v8*OFZdkKIN(k8WJL>pN&NEi-62i&tP z8nZJV(>k~|bE6^PpZF;S(*6h+GfiorHVi;Fi09&fvN8ymJ+Ec1A#Ah2?*&toDMvu7 zIe{#*8e@v8Ho1S{P1rd>-<7$Sp>`KNCMl$?AXvCr4l}BeOxw>f03NZ}RR`0fKF~sN)Jnq89HZ+;}^H7oYr5 z*66sr4;QJS7P=j`*mazZ>z4jkKkP4Hfu~$xOLrqHL0CLMlQkeEb@6hNY`|F}eDd3g z%IRLVISHZ0B%i=DcfisNw+;jnvO9z`5*SJz$S)v@$N*dulammlbWy3-7J48X zhZFi}{7Q*LnpuhI(^Z7{&X zm<)49sO4OgZJ2Zl%6ozjHbKb}bP$U)f=+{Xy2dCPxTQ5Nr&`R4^&sL&(8?Mi1IBIV zW}XMm5gIXG7;YvxqPcGH`#iotbR=oMZUz(p^ctIT%SB|3&kS`qskKaVaxp6dTNU|J z6%=E!IzZH-yqD9HV-k73;Srq)q}njCjDkkF7=enE04|W2=eS0Ks+?_c`g!zWB$nA} z=~`pJKC$ppMMylie=If=7cddi8J&cLj73 zE$AI7*}ozYz+`etg~?c%DP^UmttAu+X$g>-7)p_-ea82@MxAkhQbA)ZZ~^%$3T}g8 zoJ6Q}p%u*-H15i&*ZdY{A@Bv{V zOgI9rbK#r59ROJXN!TTg;mtOkFwT7(%-r52A@h(?H5{{)%Q_z{%vLlGx4(AHr1cl1 zQ3?vGeej};9Bd-XJNE&crQKRGA4ij6=Bv(q^wG}oe3K--rxr=fHR_m(VYz|%uK{__ z*jarw-7aQGHvQr3%%?)>rR+i}>gfS7aoK)m6-eWbpsP2-G3+AiLw{S${-k9x;=@6cV4p|yM`GWxy01j4{a(+iA zejlZHTs_JyYQ571Nyd`@qCk90F1&M&xa-f%wJunzBs^-uU>y*Yr}8uePplf;YnG$W zLSwKSlVG9J_D^;Zlu)a4m*|azy)gS%r&?+u82v#dgSM|T@rU>VjK{{*CKTjLM^PP? ziO171(2}9JQ^iiNf7w>*$+`4?gF<*>9jzaIBwmCc7N^}mZG~PlU$9>EH#pB^JWeds zZd3vu)v&an6H?N7Zwe!yG(jw%7puTW1ae-bl?IVODo7cO8GJGNPKp~Jm(dVdWEW%% zTk_NN;fe%2?z?7WvKS#2t#3=*I;mqpP;RUD*i}b$NXe{PP?#)kg2C18HgH9e`sLcL z(Hcgc055ai2CMqhH-j7;C5>aPp^=4EQZsqpQe_+SI$&*tTwFN)KzaErkf!u8sP199 z)G*1XFJ^<@j5G>TTV_T{DjKIbaFHaDd ziw$O}H+dUrBZr$N3kS=Mx^>fkxO<@8G5hQ8=&YQ+K)x zae+46Y*e9{tM66(6>Y5#_a(eP1-7JJa;gIbQxC5fURr&ob0rx|@hYu1b1)c;u0@o) z`EI8#2uBxS30Fi{iNmlSdWq^lU#JxA1I#CSdBjt47flL;MN-w6gI3pG2g}oqZl@y67>MwB_$2seW2@i;%YHD z;^iI80tz4|0}!1-ClM7vZkslO;Q_q*jim0=p*kgloM&YI)Vpi~wgC|M2{|_?C<}Ee zi|wM%+sB?NLlQfC2b>v$)n;*YURKw{N>l)PjdN(p%v2sm!W^YQl#Sb?XriYrwncN7 zQAG~_}6N} zM~f#jXtb$Zd7}3HNVc3@avBtZZ6gXTq1nf{OqEO-_+T?-$i(YVQK43JjVK}N=&)U{ zz|LnZ{uupIgSZi@Jd**RdE!r#7wC!n$@tzppJF-a$_(o=_~FM;IJ!`Fwyt)$_m>#Y zYJ!x*w^t@%iE#itFe1JFBdK@_Ky6)&2_VM(S{bQ3p)>H85!fDDQ*JkA^n$!PLc4e= z`?f&5bMM3hp^vTF`~Fy!op0O%4$^!gPvqH{-^%09*Cj{r9g(A>{pr+RmG>#x=K65@ z$G_RA^zk4%r)tm$Fl1V^NF38BJqo1Z=V(6zkT`AMtwz4uxEI({+BO&j$@&zb17FUL zNdr-CKOstx&r0{EEHNugb&_rfH!v*OrB5sUzPSAL3|Sd#CGNo1 zmAr!C78@w-AAss6O|mOgYi&Z>$yBPN2I&0o$(fbW#88uS_{eG4Ba;h;F(vHNz?Z76 zQWw))uX@nJ*`g}=f$V5t!CK`|aYO-`5bhDz7?tI4@1rz#KW9DLO3T6)8(T=wikQP+aXs5Pq+CNyu%3GB&tC#Jg;7cq3UTufpp8RQIEy28U8($H)YB&i@J#_F@MD(jF;*J^mEk1ju_&A=WBq4o z{c+bzah!7-gmW068g8Db2pEtSGXSH%hO3q(@Mb-cD^uL@wD#bOjt16mCLR66=r8Zn zrG~hhAkqDV*;2I)*FicgG@R}4S0&eS*RZ@L!(5GGQHTKxjosY`WUYbV>o~kp=o40d zlaQP;2sc};sgMx3LwXz_MXsM5td<%L&#NKdeWd^J&jDX$?ASZDC^x+GyvH*;NM#+9 zXfAhtl>-k5=0V5cN32_0>))2pg1qC@0tGZGKs6O)wpYXj85;GyPV>^AtYg04IXq~P zXdV^O2T$Km*DA7nb*L>AD-Q__D^_W9N?NNbusSm?2@sMTwRZK_piJ$MXdaUt6f7Mj z#q7rW?P6_7a)BB#9xI1pNdb#VUGVVwq<#s4zMTDQc%_sB!aX3pj!7)wSbOU4Vhb$9 zm*!oD>bmBBB0jL(mOa-)cLJzVe%Fd5<2RCiS(#my57@N*)aFf8Z?W-QXL(o1T)z#y zHV@n05j28#*;^#ep2V->kYG@7{VO`nxVte&nIwur^r2u0PMJ}PG#UZ}CM4(x@qWWb zr`hfXD+j&y%w#!l4{QE9%gm{;0z@wq=F^FDgH8c3x_g1hwIf^27nY4?qH7B(S@H5N z1Uz-rE8mUdvUO5Gn})0>U+=&b+ymaahKr3qgC*SbiUST^Ir7Vp+XCOTTub@&oZIo@ zHi7%VR7~x!0%*!36?N?@>Qx;rj%!wE!VG})>NIjl#$5}V!9?R9Ta*-D1$Mh#Va;w( ztrJoM)zLLB&<$8)i5yVdx&_UJFUi_6DxFs@+y%rJ{9acBniJGN1s6H#q&ysfP~vP# zpfK!_Pza(2XGP^;ZD=I`e6WTU?9pNgR+g`nLh5c*)IY->%^ux~bSDYS_0!oHn=K|s zm3AR6;#C2F=V;*Tp4o|4@~gC92m>;C=qaKhXtN=X^pQ!0_MGvk+iV6!DQw+RRde_)Io=vh*Zx^ zUw)Ty*D}6I9+EwA%@nd2ikWSjJy(Y=<-h49GV)uhA+W%r}F=+IxW=6j;O4Xl{hgB z6_5*Scn0^EqOs^e&gJTT;DIZbsI+f=kmLvW)}S_7*;Qm0GRo*m?W^1(I?%Jj(q<9HZRyC^_#IPV|&ORP^;{?>t1*~MZ zyOyaY(ZE+=47n#-19U?osZ{Y&Ja4ZPw=Z{|Ka_Z3$eY{^5YXP>+8yB%8}?f5gh6oa za<3);vHZOkuY@C0Gp8nGGIpM|G3IK4)gtC`LUBVg?>#vD66Y*qISP|MF>jZj;b~s? z(jc%XtpoYkvv)4L=LojQS!zc!ZW$XPI-Y`?SVz`fS4YswKD=VdJDZ;u5yF6q>>%9Y zLKy}_bN^-kDZp1e%qOI){R)+&r|3r~{9&K;&x%$@I#CI3EV(yR$zYaJh|JyX5LcDz@$l&wL=ELFV!H6sqLTJ>9y0 z%;DA@`Faf)$?g@q$`f+Hx2i!&!BzOB9NUNap*Ou9qQ56gA}EtEQT1&HK8dh`{V2WF zF@&AZ1D-&z0zAdy^;$_XAt2Otsg_7yL$!0A0Am}nJu7z(l+G2E@sUbg^{~IoGdMd8 zti_LAex$8wUCWS`EB>X239FF>vME7VYN2|)cjLLtrW=FYVjgMtrn^p@!AdC%uoLkF zNMyLpcd%KQgug3&*=M)@>g5-PP`j~+n*cNZl^->v^k03bzC2}tU_)%liwqVB7(n^H z$`1+-mp};&8MPqpKBDwDZI_>aQ)DPw9M=_j*n6PVIA1#O1+rlq!fS z4j8M4{092#D=@9F);tIjDd7h;roVg0mZY$x0J1bVculAT3bEq?0NLtjh^E^UwYo6% z&9ZHy!Wq~;QHw!hFitGE#b6$Y0-VlYzjtBouH+m=g&kc1%$2JHD6}9F40HI=&u)|a z9m{X>>3by7X-rSp9x4KNrj_Q+j`C`3=1wYF6W!~$4v@^9L_TZxY_jFzhffz>B;+m( zURuM*uC#Zj^#dYAQ{LDB#J#_bS;50^r_be0(VUL3UNJtZQdU(M#Gv4kejK$Ol8r<- zFwh~@-s08!Lzo2%{|OG0BUiAP5-|*f74h~>mMRwJ51tR|z+|{|<)&l&q_0GAKs5qk zfFdQ)eM*bl(%7aeGat4S%`h4wegjOgXM&sQ=BQKP&PzoHE`|eL2eeD~sb+E052m^| zNX`|rU_Y;Z`UVL+%6JZZz%8bvzE+FF905W5O{=TSdCI5&pdmtR09Qc`5mCTWZA^9~ zuKPL(^uo7>D=RWISp&*=OogmZMn;?j2&31aufaMf7zrOtDC?8o0pu6^hJeSuARi8EKYx( zOX+$fgc4-VxpYdJpn)LnAaKQ_Tl(SJCnQV^SJyO@1qYX$vqX4L1w^Uh1>rY6#Y#sjY#6i5SOgneaC!liZM0g? zJaUfP&AjwwT2yS5fn+G$Hf5rjJY@3%nMN3%(WdA0t#x)qpL>Z2S144ZO0hhS4BoTB zMv%0AvkK-O=CyRFV+qpaQCV%xhRdubH{Ku8v=-eWCHW&6je6YHi(ELGbY=gasv1$zkNyH(_FXg}x9OV!e7nxuBg` ztmKhx3FW_Fp$A1S9`OY1H4QFqApf+>;7HIp3~Zxz5jj0FB6cQ7fj}4d`cz*voy6km+&gH_ioME7aVEHr?>oBbk?Ur893${I z5ObE7ChFE&ed75MIRfW;m2JDgn;_j&f5VMF($$Xpa5kuzD70zIR zDDZH^SUTkQ#zygCL0W$YflpV9m?=;~5oTV+-{7!--8^cfBUj1OsoA(IeBH@NCq2&P zo3Zn5WiXr6rdHL_dvFD88{=H;38U~@^bfko-w1+X$wJTf1mak4+<@@j#xOwc&^i^I zz6i(Nlv#vMcharX&UrdJ-s$f`NfMQWW*Eyc^T6>DcanKE2rc{!D_*QOX1#tO&-Hp# zHo*aW)xSH~N9s4+^^w^KdlboBf*pJK1Yd-Mw^45Pv22k{c*RgvpHW4W`%YwL;^l%0 zs#ssOI0bdc&Fzyu{1wQA+##x*panep^b9*tKV;swOykUL;gl${OgE>;L@J39lS&imv>g@YNT?PG`eKYgmsN}S>iY9S_lt+E zs0+u=%nm9^tu)%NQb^ArFg%qmeiIGSeX&s~9m)L#@(wR7xEr|}*g@Oyph(qzj={z_ zjz-prXlqiia6u?=N!;hxzo`2~0NMA|yT_H>R}hOVZ0iGyH-`}g(Tsr9N1&pFgbyhn zks2Sq@B_;*W(vR5q?0fb(`8eW1_=)5SJ#xdf`7@P`NC>Vd&I8mJ;>G~w=SAUhcFhu z44|7&UO>u?gP2*79ad}Di039h8zIwJ4Vd0m2u@KETXpIpk;V2DE#r2e$xca<@94Ov3Op7D+5>tGa}o4iL53;k zWhmL5#4`-~GOA;ef`9XbA+F{c`F*ePq3+94#8PsVW=*kb##4n2kT*%j<82|7XLij@a;mHQW$S!mdEC7oi}{vxcko8jy-xfRsjqA`i}xIYb7!HS9$m`_T(w5xV*F0}== z6p2*iQDEmS2wo$Ra}*=?bmr=MMCqrK^FQB$AHBGGfx9}ozq{yhcg!_qn`@^kA?mW_>7VZcLXc~ER!V(RwCSzGDjltCcHO!b_&lM z4k>2&`7Eo97lOZL;SSj*Ll-xVD~VXBieS%S@$&LQ*boT=S=($ zu=|hrja|PDhn>w9)RCX`*QLRUii(~5?Uvuh&=ttx%lT!Jr1=z-T%c`OT&7SA->xfC z_!>mUE7}V$vX<-jEA6=<$qVJM@q+hkGBBu3h8}z50}Xy1J&@x-2%FkP|1!vWY8UUn zrBuP$j13?quf8Ex2o&mDZ?{%b-{V(s-wE=y!TH78 zT7eQzcnpMsX}~={_~^mj`2y95<`!5Upc#->%{bzTYD;zi2JHR2kI8+e^0;JQ`S&PL zf8MU77!zhuc(1t}EF}Eq9qTTq(Gi<`W*$6H2zG+b$T;;BZG>#!T*;^?2oHYC*&TR8 z*>^zIRA5<9gs=dK`H^|`df}88G_>by8R_m=DND^1Z)GvLMUDx48}~r#P!%?(?hCKb zY*f21@F<6d4e36`-^je^7cyGmC(tNgk zc^lv2jVq1NIF7KA<#)^44dNsdLUWegwqnZ_YWC84a%tvctji&@h%W9(DJnKvgbLZt za`#g!wDxvW^;rc)<9Dh=c0nvKy#YNiG$%kzk`mKUB_|XX|AsHHaV>osImKLoZ)7V$ zsF-25WXy-&D-|O5^VCa3eLzw6Hco-I2G|*o$AE11rRAu+5i-wT@r_50KiR;K?~wf& z%h(xXZ31B>vR1HnEc{~^2fHNH`tLezXCspA0}YVLB%A?Yy|4*HMBVvJy-2wwgzqVe z!U5>*AAH=3_$S2(ze~mqczKLP_ATYCVpHaB=LNYt@?>EmZgg;$v!Eq{1wxkHKF9kg zsp(9P5xD?b7Sh}ONF)kmBL&b!sqi3ETV}Um`}-tZKLjtZo^l!HWK#*PMgy*)Ry za_<-CQqA@Tw}NdS!+{lcYK-l~|ETM1hVoMj`gH)Kkx92FdvRFS82a%fFwa?Zg=m~j zch9XwA(9eKsU8S$T+=bc{KB@R%k9pmfc8e+2j{V0^0-}lO5}o-qMUT^V-QE~$fPV) zxO^T~<>wPFq^H_0Kjy&C36L5z1d{nsAyGDD;^au81c+ZS-Cl`tW9*QTfYZH&ra~8S z8}sh1qMeD8tE>lY&$fa=KfobO@iM~OG1zUw@ER(oT%qzH4@gEGziz?l-Mx1r);{`l zBNp2w1PPWe;k%HCsJRs{8SAr=ply8kY{TrTKTDFdt=%&NS#bcOizWkudsxo75DrnTM{WS{9;jD6Hj+tHe7aUqeS{8a)*_Y^<6(ZTIw_~vl*Gh_otf?5)arG zq!=I5Ru1~q#zdO?mFTM%oa&WPA>zhhPP=4J^l&DZCUE4wstR{mUkjFE&+;?4XF=GgG+n_#loD_Q#@j*)r;5UU3y#U zp>j{!`FQLW)_UOjtAV^hYK$u29Pq75;658j$W^`qfqAUG@I$!=}5_ z1`P*6gVn^JSg%iV3jQ`O$2&L}in-9kJ;)|Sc*9jgE_lIX)$4|Xzi7u;6)H$k_xD`# z!9ya}$LFGw^BK{~x~uSPm?+3JybMI5!zg|pP0rHoulsx$^Si_1=&>t?MO6Z9KO8Mx z1x0~%ark@D*YjJi7OSDM!vheha{|4{y^GOT z

CKe{WE<%|rRPJoPq<^8eVlSzl(g`&O*pr@~PF){U^LM-_R(++cWwW<@$Ul#$t;?lQm=1h0#*4qGj6Lsu3<-Xi6f({X37F}PyJe)fC2{zlrwZOKUFiX%7GdH;ovqF`2rGu+ZOQ#%Q>w34!yuxQmY#F|6`h`Oe ze8`6YSLP957jhi>_fd1={JXskSDx!{SEoyThOZaIb^bhTevu}}-}cAM>MZ}9UniGe zD9U(cyU$~iqZ@=BHb;3xaPozH;O*K(rWl#ljvpY+o8PmO8V9!ajEDl$jvtuE>4jeO z6ifuE48gsLR4SpE%QrjSUF>rICT=P2C|;Nx==r;JX>}6adM-3W}!$hy}DkknaV|qMoo5cAK+BO;iBI z+ALD|7#1clQ`QLNR|fp31g%T@;wNJFtBfvtGL}Qp;;7B zu(tGBgX6XANIe1uj}>{2F3FQEgf2fvoWBGcIawU??^Yw8A4e3+u5??~7rfzp9Vcvb zgGr|Oz80sii9+6jficd@R*X3H!Qv4>yy>TAJ*-@R`*KcbA!H-OVa(}+le*w_Kyu=v zW=&$bCa#rF!`Uwi5-Qs-9wYA7_nxcS?T!a0K&!<|S6F{Idn1p>)yKz8O6}*=!0i;c zECmr)9xj}4j}2xa#?Gdp`SysV(f%&7NtrDO&8TSYWRhA5c(i5m;+`&5ILX_^umU_F zD^mVZKk9WX0}aSlGcJG42SY;@5T2|BRW9+Uc>WOp;V3c{(^m%@DokCHK3N$LhA7-N z9>yXdKTQ1@wf@P|*_UlR2O9?8?U*5P@@WawBbaNj!&Bvc>dA>qCl!)y7R8~?QXAUs z72cCw)Xg_7*$nUj3X`-lJkuTqD=NF_TvmB)Fe(raYyhgCFn|U}Xe9wS>8gFabQLy+ z%;^!@8(V8sLt6_5N*#-ibK^utfKjB}S3&u|V6)op93$+#HB1NudCZPp6VsDE8>-49yJV!Ezv!n{^8kQNMx)*e_UIXNO!SIu&59 zpsH-M!XRv-KYfQ*l97L~8aaR2=nBwjyPsw4Y-O{((z*4yH>&Q^fwm^zo`pNuR__wc zu-sKYFrJ6xIG?I}o^4O@o<1l3>}uo?>&lp=0hmZh40 z_HJznSyYn1psOp?%YzyUsv-$`C_)YADpF<3BTWM|_TXfZs&Egg@|liy=lq~gBX>tI zN2ThIjRUPAtA#_9=B3PZWg}(^U?4AG1tMwcEw1JcGf)Ns$@bD<2<~NQ%VEDcs4LqZ z1YS--tl|RGTQjV?_H{uy{2*e9TR7|9eyfY)^msbb7!1Rv-95}gO%#RMZ@ZPrf@UwT z0l-hp8%J(|eYhgey)|Nbi`dpA01(nbmdg?pSawHir?ItXdkBDN7b|I$4|ENW4NOXg zk>LolY!I3P_xceI9Z4YYaw)@!cuwhafQ8&AmSw#V1qvK~;oZ}m7JuvY$s(_Lw|;%G z&Jqx1zF!^?<>0PVF&zX8Rmn**M|~gu=Y=*119s}Ab^vRSAB1lqe?Z@L7%eILc7as( zZO{VJM-x%dQi=YxPq!jl*lT>5+Fm{1;Ou_Zba%SM)bli`UB)S4(jR)-M3Fba^Vb8j zU&ln3VHXYRg#gaB)oEpU zbzCC54k;!x8;FEs6L!V#+yHkbA|`BhFu2%Vpz7=9BV@0k^6&1uO;9e-8{hl6R0?hy$d4GA~J=7VCAX#{=qrO z%)6nJK{~!-1G%ZGtGQ9hrC8LoH+gOxUnR;h1yLv-T^4PTsMlr*sHJbhiFaS;p0Dq5 z`8|FbUYXjfFD@lg;unHW1zkJ63NN6c=Etv= zDam&&RQPMwgctv>rT4qPzzWj9Apb`XjRT{utQ`aZzz-b&K>UB8hh}2xZ0uy|;9_ZS zN9W?VDb~GP}ASvUzG4}HbM`}%)nQ|k`MgjFax(YL=;~@bjzA2%|URtAeRi>6bTP5e5 znbQeq96h_MKC-VNFmVfTLd4f36o$Rr)R}g=mB#9U0REt?QNCGH@8pYu`}7bmbA`snPYFDzf2i4& z6}j6h>of8KPvRk@T9h&r)XUl1Fp>k-Al7R{sz;+{WEzoLYQacs zbJ&dqa%m<5^QJP})~}qY$nkByX^r1{@$)RLI#(br?2Ig*3l}Fgcat$I`Cuc?5)%Vb zO@({!KgUNCwk!UuCNQM?*&A)Msf5kjI}@&2q*NWXr7|^I!*9|q_4Fha z(VAFh#e?1G^Dp#I)bPg=c7XNik{cV`1M`vXpPe9?4(oqtAp%rFO^ao`@6^b zBbZiVky$$Sa?&j|SJuvI>+~mc|3o-h1>jGOMKq;r8ZBV5d1sP?n<>}cn$k*lEi6^Z zt&#V~8T_3cyzZ~P1AC90eUha8E9rYOlRX$%hUmP51IKEaan;YTrgh)|9EyM8L31;< zg^k>Ek~L=ik0<3cZJ^}PNJqnvGN&O;RGGUErh(ZymV=N3qtSyOi$mE#IE0?w{ zA6dM!_zvTd1Gg8xk>WUxMTp4JI2R@&#pOhtjFOO3b2^Ue^dN>1b93I0MJUL{I2ETN zm2Vp#F6HV1%{A>*GOuczrb;Wi;w6R9!T$|Ehh5z;v(}_eVf8+!`LhY-zl;_Y{fKqT z)-HNC*;u_uzocBuc2A5-XO;UWuZWqX;v{AGiJT!CpRONMfAaqaH=+LNNCE&5IvmLT z_u+pzq5lDHI++@p*qZ)t$h6bI(*2M9KalxID&SZQPA!`}A$Ssvwdeh6D zZYsEfCI!p<7xoV!_0x2--`5SgzW?WGwY{GI zX?i~2r*ZrK_b2l`pSRO)^?JXri){8izn5iBtjzwe)4hmYdr z)BBqH&!hWqi`b{yb)R9w`CEDTdU^gj!2SKO;L-rxH*#FDqnnHpY|3j_xpW6c=%ZY_ZbhPxygN8+3M%-?eXWjmjs^^dH8!8jt_;T zO${plF+AOUhu`o2d+{;M@AsESC%1YD@9*2ZrF?ui-)%R0zWw(4-S5NG+npbl9vA** zhY=@Np8{u>~}r&^Zm?km)A-s z7i&jM9l}bSKA@HP_#7`Oko0+cAD-^+|1ze0jko)Mem;EgGbGRNq|L^Zh_qe|e zg$v*Hb2)zAKDzMB+3DrwglGN!&*|m0)t@}Qx!hk{PoMYa!Q|QNbz#!a&4>5Y^nM@D zKZRQJMHhQNf7;#cqp5uVo_43-Xx9H;KAv`NMZ~s8TljqbdHNm3=iBCxzEkpFtKRqf zz08)^4{U`$W8szgqwn!Gdx8)1J6$iU%=GdP5pR+eb87C%x6N|j_EA6XZ^$=%HBX

GWvnsOVzo8ZK~c?MO{=3VDYk)dHW`e(Kdr0;(Em+rcq$ z68h}|qYjqIAfYL(L{vt&8wkQurA|Oq_`n@{mw?PlQRdbyx2$o(5UY9f*)^2nx|C1Z zo`ecW?KtnQ5cSj0tPxBa@TlWqPOoXX{aCwHxngyc)pkHb)Zqqf8D4d;PF>~|mOKmb z=~n`pH|kpNT0^(ZrJCiH_kfwAboA2#ds%GE53#02??Y`-5oA;aa+)7$jaX-V0(*9q z-T7d@cctCbWkw@Qe$VJokMXrVk47+5ic`8cRISAPMKfJa+M*TQEpIn-8&+}iq`YYz zTK+ux@pSy{EhMyx9&N31GY zHWM+LD4$g%6)CgL;|2O`3Gh=iIF`$-2zW;ip!XfSjthUeCIPPV)B2lIAxJ0G3;S#0 zys(M-dYF0ehFM`M1?qMbVZEOTtHp+Vixy#q~UotIm|NG#1LIve zaBZr2cz!s19J{2FOcCw#8X!&fsfC3EeO{4y(nmp>= zRLnbKH<+*A8ua^Jt+5xf&WC@6ok$0m-)N8VqwsHd$q%B33OV5ew6_V-5{(r9`9O0m zqJ_SnW)06=$7rbc`JdStgUoO7#&L4eCokkd?E*DJWw( z;tG}z+pv@yn(oqkQc(BHds~(iZP<9;fZ8msd?I9i;P@-6HW}OKlva5G4rQrTxWu9O zlWN%sDoxR-_>B!zJ%4&KSNdmvxWitd)QYozm8n8QSl2l$R@HunEctL9&y{U4xb*Z+ ze@F$lf-E60kdgU*44d`aoH(wvu6D7TA~gc&tWe2NPf;ji4gSV2(EP(;qcK%$&VbI+ z)ha977_wzVUT}OyLCJs@n|;gR#6=#;H`pCGTDX$sp6AQe{kb45B4BYXB6NUoFEXH{ zM+`)GBZS$)Ead{Oqa39|0fL$!2{C?PUPyc@v$kM4ZOsJ@94HbNZI^??PjR_H8Z*5C zaXUxu5Hkrbfy3CXhkGeV_f&-00_h+hiE|Sj^8GBS)uITrTB|d|p_?2o^7Emen228r zPDNd^7pZnueR2)f6p|phX}vvdP+&>BvcPL|2xC*H@l5-R$30K}%*Wh@*Yo0JCtsY$ z)%0KmFI{t6J0B}pshC&Vo}J_aA+Y9YXEK~R#1yo4` zRNC{Q{AU&+MO9)}mvS7BKFE|wR8iz~&-0i^l}rVowTOHB#`p}V+>KRvKf0BZd$C8! z=ZRUBpj0uOBS8hlK$awWAZZ268qyYxJ5e&&S~y`IGtdxf{UvvE%lDFwBji$%KSX4P1C-J%-8( z8*eAb3ZgOy4b46Wr@%8bRI-iN8PB*7u&R(o&xDM|-g~Eap*egP-;-+%H~-kzV@0jg zA{W}(lNu0IeRn{MDmeBol(o&Lp!(5Su;fxeFY9ydXYRC zEodq11t2>=j=ue4t)33L#twY)-Q+EBzNW-cv?9WpBLXbTis+$!b*7zRxQcViFA4MEhqs58iO_cgM+Hq7#O2*q zsshQ`C?nEx8^0j36CUr1E8jp9{KNJR*(C;cKM_)Du8k7LgnMS%}9;9=0&cuuzBeMcElD#c&*{vT@~PD*?_6GR+6+Ji(n}}OQ<}? z%)5+EigQ;1r>VQ`D9g|tW|7+hFYQ5Kw;41&g&8GV=&=f$@tmZ44D;6V44S#HL^@c= zBQbj3VvUi*BfEWRzfg*>ZuvkxIK53+Jok6BiX-9))GrcHXb7Tzu_Q*&qfd2^N#wFr z_oRd#L(F%W^pN*glp|jtuN^O&PEc(Ky(RbW5)U+nqojB!UI&$3BV7!N`-DA)d5?Bs z2}K`J!*TU}@P!;;i<>1?hOP(S1$AF^zk{vFQ}lyrICYfXc-@NaAq?dil?JQ-q5~A6 z&8s1$@j99TH$$}?l77KU8U&q#*M%m}Bq3$=|Jb&SC=}q)p0A?W&^E6j1*>)5$zmHpm`Q&w0PN{T$4pWDtF3e?p(;@is7cTSxb;y)o>2Yf;sHF6-NLciS8;3bM4b|dM);|=8;qxFC00eAB+$36)+DXqIz(yR z;<_~hWm+b80sS)UmgUwLQK>Drr)aa2f;z7nj21kLzs&^LIMXjFNw8^93^fgr;9TZ0 zF^`z`RxWY|Z@$}NpJ1gcQ+JEFt?Bo5KbhM(@24SvE3{c$`zfp3?5InlVkd-Bo*EdE zHUi-6agR~ejEp>#hKrmJbT6Q5;imR<ijrouXA?QUi+-|VBRLa%TM;JiZ8eKB_*@f z;oA@gp3Id9G6sHKqZ;V6pTMp?=QL}iS@msX_@X05_vvJ1` zkl}_`c8PnT7weaA?PLK8Jsa)ssf$Rp{jJ}$Y*#0dL!6S7u8Lpt(9(pf1r`#<-=`BM z<7H9f+&y^3=SFis1%tfg>V*>%vq3-;;y1Q*BD1`CXa>|Y7x6y`x;m+TWB9&PXvZ_68Lh<80(VF3i3i>3a!u z{Czk%Gn~|GKrrd!Q{HasBI*~KJ^X8Qi~UQirP^$QEQWYIiXRby({ZCc_qxv0LJ|e2 zgxjjfYAC<#KW&v>?lgG=coV-BGym{&kE zVB895gj&3*v*g@SN-mNte)-7P8N-#aD7Vvm9o@PX1zLC{e6UHh-;lm?3=C|k_acfg zL{6r_Z?*M$U`4CU6og04ByxsgH5o(7y`(j*oP3s-RtK%T^nnd_lm`1uifo!SKk$Ny z_0y9Rc0~dh`IRodorPpSvKC2ET+^B!`;wupAHWC=$uCM*oY1(fi+9v2Jbap_o+}Z8nDdtErR(s*@r}lj{!Zc>Vd5XzK9`Je+Efk#7$Nf#qYtGs z+!f+9n@a%MN$^x0!4#Sr%-G5)j>r?|lhFO9+Y}v~hg96rFS7*PxOO#3?QHT0U;_U} zlbTdq#uF3nAM2>P1blq-U=NH&H+rK1xMnuX$ODDw^vO$ff!Nbkv~72g2LxaAeXL!j zIdZBSM5RljP8!U5R$bpeZ0X`QRraXhzR)DwlLsuHb6{?7I#s~|=BErrXBY1&0 zp>_g39NPwb54GKdoc2D~+1`Al7cO~GnO$V*O5MVCYypVCT$v1-Ex&0#r99=F(c$cc zyh25#6SnL~r{J^5>xUEyuMWL$&c?HIW71?5pWm8=4{A^s{b^g{e8Va!rgi5&TDvSZ z$+}WdLVGle9=jK7DNCz;Y73c1yF7wjsQR*fo!F;}B>u|Pgq7;}7|p`WeuWCV&u0~e zvE6eExJ~FePJh1g*+bQDPo@$>n}Jl2o51xRd!cTFy1BC$rmS~Qg1Ni_hljqZ+p(|H zsq^2hKtd$vH}OV}xT?*U`wnuL)I0Y2bKgR=pj7G|Yl0GI0%SE#+=Tz^EjF|AV@7hPQ`1Fr^O&P`Cyxp&=E!61)=n#vlf^2#+ z$8uSGELPlC<*ccL8Zy;(X$t>DPj$f-LWL9@AMbA5wFmb05tOw@m)c);hC?c7EMx?> z=MqMTbzZP9$#(WZYz4wPc)`Clf&p$BKZ{bj_MU-WqULd0041X2ImB-DCJxic=Bk13 zMj2iG*6B}zwq2~-;%U!fn1%Ie`}G*UB2Pm`aS~;Wm>UDXRyRjjIM*^6gJMhUaaMfG z^lL`*m~>KnXq$hnTMS9Cu>{_a)l+$1poG#>WGNs0vrUuie)Pi8DZp;eN`vVoT-9_^ z^y4e(KLpyjL`O2sl%(xixyx9W;Eg6F=KGcuj+q4%2E#rf(p3X2g^}d%Xj6gx9`$ge zKs@`B-tc`|0s!;7Nhh;#jr^w!~S4$0HCL7!a*rWGF=Ptzy zJ?;XMccy(DpE!{Nzicm4T!TcGdsxo)J@?Lo;l%GGs}te3q*BxO4z^OFw%KDhr;B>o zYJrJ3{li|bjdn0qC_AR~nSCi?1GvkCAn=5wRaHDf&m@;OUBGX)(^6mYQ-seq;OL~H zYsXodkP+gf!Jk-{X!V7fr`-$}OxA2N!G<7G82P6Sfg&RPTgA8Q%I$II4&F%0oGmR^rTP;#1_Xkcr+-$&xGdln1EaT;+mi z>U>OcKWSjs?O+fkXycY%f*VTz9a0z1nlpHL<1kB%QfA|&CIiL6&8<+6&9B;9MuIA` zkYwA>+|?#NvQ)Igu|7+s#Yy;7$d{@6*^_Ua7EbHd3fAE}Ja@GvmWY0NXziI+n>j>N zhNYX;FBGoN0hpvp32_I(nZra<#;mR%PMB)g8tuvJUyLmo;pUL()b{Ebqy+M)#CE)! zQ6%fIp4#|5C}6*Z4b(H()`8=w`FbDKJU(hte)a6W3Xq`1BSRps^IG)+`{cQ_B zEhNf-_r&0+y^)ZMwhus+-~(BWK0F(?PE$lV+Rjwbpjp%jR5K z*ODgn*{N=k10B)__l=JPITR1o$9aW9o~U=U{Ab4>)H2r3gGc&39eokRC6etoM->x-O{Ig z+NZhz4hovfqWVE1#Te+`Rv7vts9HUR_&b7`Sy)H)(Z*sE(N9rNhSDYm!7YLf5mIZi zQHBY&oJ+5ZvO3;S7^=whs0@y0rQV1MF(r+2nBHdKMzaE~*V|WL(~7W;d(BCUplhjg zbYNOve#Dd)5Sp>bSj=qC60#P?9oWAM>neajn!7mgQe`uv6x5|2dt04k{aYozq&dUC zEA+z@v)afJBIYC$;(J^!Dv)V))%ms>8^_&Ae@laQBe{brgZD*n!;rO%Q08=9!rzvD+|K@p)}{{fg(G28~s@OTPG z1>n6aYB!ZN39|(f)ongI1e0x((T{+$Q(fLct?n&>P4Gtq2zN3e;y`HAbrYxy0qV!( z*he5g zZd#RZ(BlIp=iSzYc)&d+xAWk9YN1mg>Pl>kcXUrte=SLmt{M0CA|2!6=+OjTCv=H) z0aq4vUkSG#M;wZra!esAWpc7?zrOx)aEMv8gG)ZPrrM0~+;t=Nvr z;laB8e*BI4y~vS&{yk2W;H^$%auxW3&CyRE?25-){fpzfb4a#Lc6cZAEN(k4UFVQj z*WxOimiyWch|BW{*t<5GG}*#NnvGP(;GQG^c1cDm@C+E-A$g!#LaY7$NC*3t!=KLY zjiClO83c{H-do)vgh;W+fuqRw#KShc(l`gB(s{tDCVcgcX86kMAbmiA9wU{ah$UB0K?Yox`@KBKG3FiL*aH>PTw`d~v!8L^OfXj4_<6$ol{Ml#Gu zW=V{nIGjkEHBa#{^wK5S2iu-ZWNV!DnfD*43L>PP#OtPxt)(IHDHUfxOm6`S2s15@ zT0}YHpLq^IC(|E(VBt10ff0q^&u)x^tZv6sRvP@V*D)13Gun=9XHL#}!!~|GJeb7> z2#NnHQ|)JdtqCrIK1L$a6UrIZ0dRE@JG?stC#!>!4w7T_i8*y!@n}D}*0AO)LaW2P z!v8)>`@wiM*fy0vq|NTpfVNzk^aaoqAhe8*JBQYe@CL|XAUBOU%@n4>v?!zivL89R z6bHw43CC#UTi3;{0kFQ;j4d?1rD_=(HPmc>Z4#HZ)^|rDc`NJWk(P=F(hYK4ze1GQ z?GG5}5aiK5iVVeQ<$HNeznA}ey zt~bTS@LW(xC(3}o>VL0_=9@R;%|^3r|GAf!ax%p4;uVKSwv=#k*8 z=?d}`{S5yBN)yuM2J!P!%o8k8*0R;3`t%}tB_=#FA8;i|C5EJrcm51d5gVB)gVF3d z55pu|s;c+W1nM`|#-DDh9N{T74<&n;Gxg%;qa*`Anlt40hLLyec$_EUQ3CUxqkVNu z?NiwXrH-chUX+qJAwqddTUzNHtgMf@I^`FW&GnFemIQ07a7Jn+#SS5n`~>>Z>f9Z9 zO6DUfaAae%OD^+yM6LA>C3lUqnm;&Y**N#9tq3rD=O!T;@KtC98QLWh6%;8 zmTI8+F@y8H8mH?;97EF5R&tlnlkDP5yfH-XU>>JB3wA+FnWLtfu_4NlBSftTFb7Tb z6cPOsRt|KD!K*Sr<@n5iv&@ADyq&H7OpY$I#lY=(>UnT}v`d)BS8=rUoE_d! zn>q>MmRdTs9HPj;k}X^1LDQ1IA)WY*s@tS|9v?CmEjAZ3SK2Y= z_<)modhdd5{}?B=8NA$6IY+L6O1qzPA;n`kvMguO&Nxr5vJEV&$-42`{OC!u zwszA-U}Bq{;hiVUd)nZp)^176Eq;dsQisHnn&3JSMgA3;3ze?1Mesp)m&-3JF1bl($D+ugnk z=104m)gVD7)o@|5I3Nn!d97%;ZD-F^0Ui}s)z{$ z@@1#4PHs3qRO_Z$6VM3i8!Mzrj#SVXqEmJ=5$j4`K4aHz`kEBD7O*)eXdI`}lXzKt zv2Ekr3}D|@<(SibZ(6Zpo$Yxw!qHNb>Z%(k1uj0`&W?(wDBQW@R7G1}g~m5O&AqqO zxD{UukGpA?6j`PdOS@2C3C*!#p!z1km^pNxv(nH&s@(CJ;S< z3w%G3=nDw;ANfS>%)?!muv*~mZTEwdGdj$&XA{E}bfeZzSrdw=|JzorN-ym&bj>{U z-Rk>LS#5s2JMD!9Szsa-D1kK^aRgJ{(Pj(#+^vI^cfL>kbnXP-sX*E+XGV=cS?(QO zSuP^ZvBLNayRzc=Ogx3Zphx8f|3QAxrjiG>hh(elIog)8zKmD2+cs)3`i{1nc_7T$N>^ zd8I-wVm(*7)6S~Qc=KmC_rRjaz{G>w2KoU+7#cZSv>g5TkQ0U=j7_rVCqIDdhYQ-`0rcbs*{?xB7NP0u~2 z4pKcS&&AS6g#X-sVCg6*G#KHMMjPu|&eHdqO3wEGIG{RRrqCKUh9WZi`Jq=_AbO*O zm2fjFa*pl+2B3Qsyf^;&3-tMQL)OX|NwSe5)XFGQYK$A?XdwQz(OLkAC-bJ30{qsO zMhn@Nf!j?1E1CO2F4_d|$ULPa{7fEtBZIt~@{av-$TPmh!MhmH!^a-BmL_uW8!DgX z+lhrsA__x>VC4j==p)R@ch^LeS;)U+CsZWP-=iHAe5gs5$OVbA3xdP4F^sWugiR%X zWeZ8bq3(nE1;qeGue>HS*x&x5^d$;Gsfg!q_#Q)mop`tn@K6ee}A&HovA%liP^174+D0a1A97S4jqO&hG z;Ey#V{A3V!Em1z!5laGmi11cJxT3KRf=;DU*WR!YOJ)`PZsHPdI)YBY2|`2~=bgjc zwUKA#CrI$rxI_`CLM_1$QKpi9>>Mw|ppg!fq|X%c1So z2gIQ5F8ov=en;COjfg=TU?ibwK@F}DY0M1 z-*@+p4)4|;UZ1bW`InAJ0e;==fS2c&p~z(mZ0zG}{}1O|a~GE{&(`*yExoPxrkAKt zqU~(|x7xREo{b;&k53t~2K=2|0Z;3vEgCHW*F^Tn2G{EB06J9{XP(pN?LjkGIPy&|*$Y$K}!M?%D0RdxL=QCf|GHPJloM zr;kw6T5~tedOLlMGzunr!jXQY=jK&U7gN<5bTC+1zcM_GhR2*Sy3kLQ4b&fav(iQ4 zRbGd|D3qeFeCJi%gs0x3^hB(@x!R3PDKwZhXiZ*GiQsjg@=mdHnO))9s#+rygFJ)e z+Ds%c=IHvcxM8E2T4IU$JK`D3suPJexK9h^)<1^jf@Q1_)Hs#cl%iXg%!xy^@T~Hs zE%PmNg{zt=#Dx52&&ZrIVB8>N%t(C6bPMu%Woe6}+L7VabQB8JgzWp;rP;pYWm>}` zv(yQ*Ii5sV7(-80rRHlF{#G$n>ol)B%f$u9c086;91}U#Q?<|!vV?-dO436hvAtJ& z^jh6%_YUgiwJvR>s?U$1t~Je&-q%hSgUs-z(=Oz+jEiOsh6F$|toaUQk<8}eoz{xG zIg=s@oE3QfhF&c#Zjbk_C_XxqSm>;7*d#+cmsSoVYS$rAGUi@K+Yo6nSBF4qgqlE~ z5BZUVlx}Vjc&7d|d8SfdGd0>=m%hWt`rtyjesM6(d?Ly)t5C`y-wdB~duP}dgO|%f`lDz$iKD3I#!#z# zK%F?=o}l!+b5doN7vtX_IlCszeo%VVV1fW)1OUh<&`suv z`YU&p0kB5Hz{|;M6^2cAeC{Sv2_EHBbYSp6X=Gj(-U3D-R7l`mVvl>b&;t&hB)RVF z7Hz|UB+NDhzQk!Dgs|P%AZ18BHbM0D3GAfv*E)HBoawAJZ>_TI62^NXd zG*!uy>T@Ixo{E9sxjmg_Ilyrmj*Lw>1+jqql)3~~0cUQqX%fx9p_n}3 zc{#0j4KI_qJQaUEYgnxwW_N{@|v^P8yUYM1G^TD{hHe0cHi zQ0DYl$j{%_>&#uOY-XIPT-?`kHGZdasw-Hdc(CI=2(a9K*^qe3=NA*nz&3NRN_>Tz z%uI8?{G~JEhPvwG6o13!nXXJMJ&qvx&5V*KdueJewbbkKY?N=JGz_1|_x^`kONQvJ z)^%zJ1;-#ZQ{xkzDt(PYqCx)H>Iv1A3u*y3xz*0uDQB>Zkc1j zNN9~kw62jwU9Zpc%*0&i9e0iobTEK`Mp^Ryd<&N*Mi?pbcH1CdMv0rG{zk}IA0ys| z;EtGO$UB!cY!EXf%O~Z-2b^bpd2r#?${{&mKw7_h&uBPGk^B6F`TuBF<%xa@|0C^+ z>;FGRIXat}Sh@UTi5eJN*&8`~F__pIxwu%FTbUUB2Y1xK)Xd(+jPbuGqztoz|1;Yv z(UoIC4iyZHKK6f`760F+G;y#ux3X|^{;y%UFqpWw|Mwtz+jjrgATIT^T=rX0{kOOC z-FEqp=#!+kfT4hEI_-ShCFR28#7>?U(2L;N4!9%+{aXCQO1jqfS>C@V`{(vx{xJ=l z2E{KEvDO;H9S4WU#;2beiKood`2Yhf+GTvPiDgnf3Z;W<#yfyFz56jj2CD+cn_MHq z{viYXU3|-B1psd=eMl{-dcN?&cjU#oGa@H4f9)fTN3s-C2}2yD2Jha&JEU5i0V;Ml!g`XI$ygX^T@}G7l3e98`@Y}-8s)= z=g+)-@Iw8ea+24@9{;H$Jnxm}%~a-pXQRT?Y4Z-H@pgPDgn-i@JXM(eDbs(dS1 z;1lmk`*I(FgF806>NNz=O5*TNQ0jxl47*|Fe{CS`|Ud=^|8qv2%;PO3xw$hT6KOIveJM7B1VmJjKN6 zRqLyZtX482e;d=2Z&pipJ1*$%jFkG)Rvgu}5YHNskL&}*3DofT+4vGg_B1Yd<#H{B@GM^>iIpS)NkcaksbZx?TaqB28$4KH|Eg_6B3 z@$JUlor3G^Bv7AJXCz>(^ty{z&xji|Mn&?^$mqwBI@bddET3F_>}>6yXUplTktWir z1Erx(>OJo}+r>Ro|Dxuc_`0FlMOyzTnMR?}`)G3%w>oyE5rDZ~XI@PnLUV6FfttuV zr~gGrz@51o7Q<-3{+)0AoZ2Ht?j^BfCZ8R2l>nLg`YYKX^#QhKJ@vIPM#Ae_1kZv6 zM6EBtdT2z@b`nKX0zRP*GOTM~5h0SM?67Jhr;P~PvASGNV$zsz}d$-7MM z&vZuv`pGfIjtpg`a|V$teVsXo?_h_24O0d|=GTI;8E4uSw6x0Z`=%jw_=y|o zm%AG!N72z%ba+k2{1jNUrCMK`WogRvB-!Jo?HJh=%S z`O0YV4g@W_Pm@EChRqdiv3?b$Nn2pt@2Ng%(P3p_eCAs*-n1jUn&PmDhHnavKDQXo~BbngF?6oE@!cn}F98Z-7w1k_U0 zc-_c+h@>b5)e^sF%c;49A<$FRh2Yy%F(Y>R+f3$^woxR_5-YFaSO*tbEGf?)RS_5G zl>imTtKxp6OT7RH5%F|vnFvoj@7@Rpbadw%;A(0wq@2v@T7cFQa*_L%i^%K%1`Sxo zO#%F^C)&gqRM*=a=Nvk2xRj3CTz^~ohfv(Os+ToP19@MO87RJBS@n4S`z;_XYu z4A~;&6UJ}%fYKlAqRck9CE7}xK%K+S{DKz-{O+0Dj|(`gIHmF?)!+DLH{~D5yI{nh z?T#4yFkueZ!+x#_KHiQUxkpV1v6FZoN)!zhxmBi8U#KGI#i>QL2zbLnR6}+)mG-p@ zF@%?e<}t9a@pGd(hVj(=YTD8mwV{UfY4!T&5ZN$E#BXWx*g4BqF#HCXHQ6LB>hRDr z12{Liti{IyL!A)fQUm=aG1ZGr(o0$OY2O zdEesZ9PSCN6aDTf7EUI2`$g`>eZd?kB%aTi<D9}xOhzzmf4ySeC0kYDEOy`J&Ak_VFnE`X&EYAQaZxn|Da5#xqni#*q zNg-Iox`Vo$N!;IZfi@F!^ReVg&@`t>>>?KpxHKGZ zp3inM((^IRYuQ>a(8r3qGTo)o(W;jr{+j+=q+u%ek|hm=l9Gz*-6_J7f8Yd+(^v)P zC+>Q$=%X=k?aMm$QH}U%ylg*s1R)iKn<1h_z%3*<{f_1?nK{k^!$M}?kRGO!VMes{ ziB%MV9V!#LU_kArz4Ap}4iTEFnoY!$W~fUSOo;}j!}gj%%kn_kjf<5AU_sSlnLsyzdhQ_`Ig5{L0ocYKlx| z!A{nWu9JVg=Od?R*L0W|x??l@>?}qV4M=LFcw6ueRk0h0zMqH~peYY8!#0QdLv4y| zk3!=8IXY>Jbp7Ml(=4WMx+87#xUAzYyU{YoOIfkMN?0+dwj`^?`d^uivRYFh7q-rV z+EY)%gt4!;(i0pk;Lk_7z_3OiEQf+6wc&P|Dpg;^_vItriHx{1kqszzLmJ(E zRqltCt}=w0#R4TX2r>GUDVHUcq2$WNB_8vs=Fq}hVbn0+g*>l|}#QCjplT`!xgJ*DY z5K8XYJ$~ogwYO>efuTpyFB{HyYU)8{)I}jJ1kdLfL+tA>!qPiG?df!*o~$y zGE-KPX5B~I1G9-j$&lf{cl{B7b6LbbTqB&b0L7z* z#dyGa#*~5X=E846H^F;+8|4$&2*ckdC*gMZ_91sesGC|}OGK=|3pb2Zyv*F9Vt&!? z&v4$K_3O`U@mBwV9mflbee2PZBn5!=UGsOBpAU-LDY0B@Uy77nG`KBlYVKh2fZmoxvaA&~SU$5Oh!9AT{FW$iWAa*s_i8R`g zpp*zI>yGM=5V{0TQEU?|Jlnl@wk;uK>=AUMR>v6u%|s=;%0I1sc8K#Bz*4Sd6mc2V z0NM;^FSQCmO5$?6vq#+sSPs;$!NVK!(fhgG7c*>Yi3$27Lk<}B(stbJOe?ptK31)S;I^uVV1#%a>Z zBuE~~q zMz)X)G=3GzD{<$U4cTu}QQ~Kl(r<~E_F&wHe}Z`rL=$y%j~~>ft$v|j&n;v@{${LW zSM$?!BP9-x4Y-Oij&-}47KuDRCPe?XZw{NUq2W^-V$IhCfm|CXpzF9Y_=xwzXYy@Y z(^h3qxbCvE6x90rdC9HnlPnxik++l*wzrs-!tY1@?IW=ZSta>SfpD*|xOfDDR~NGL z+&(lArL}dvS6K;ZMAVcv#M+%|%GW*NECszAWVf)2Js@$(Stg=;_Z?a;A+BioHN|UJe;*x>issd=zl)?Ndxl8nsDhXgUQr4G6(JpBo21!CvZ(th!SkY`kH5_A)Z=v9#u>4iyc6aH7_S`<_s=#&K)kx;`EuOsCoBR;lht^g_Z~|K1NX0%g zOVsOVxf~7RO%O`wE%87W;xI01@K5NdKK^@M4i{}JZhCNAKtFcz#;I-U<}E@a+@Y>E zHnf z)qIjbZiw9Y`ACq4-(fm`@#NMrGDG<0GmR2LGlGc*95d1O>-YDMX_|~LThtT=hscw! zku(44=)DEZ$fl%1Ki|7(F5mIDp6e6769>g{*sPcp9vMqE_py#QL;uMxdH@|EAYI$z zBcvZ|GgnIFd9OA4PCxjc&11(V(Cp(d!_`J<-}biXW`x!8!g`kS-+p&k=x|YuZv@WkMo@OyPD6D zna_uc?f09@lIt&!Mo!P`RYt(`M9$|yO2E^7&ga1P*FnwKRY}itg1}3|zdKR{zAC;x zJ$l|xbNoN1o&r&0zZ>{{y@cij6qWe6eb#ipB%ot|H_g$leq<_LVTA?)3I^h_M~ zykFfP6LnnGeC<&RxW8H$_yV|cYl`D_=8M{-dZ!& zUiVYp8HikT?*sfl2EINx3ZesAXiZ;u%>|Ca2-!2fM?>+|tn2!%mCUuPXPZDSk`0?ji9{vVr9 z0`HeOJ#A;$U#~A;FGv4g!&}Yw-!txB1UL_lIX*|kAKs_8z3Z^wX1YH>%iF%73IqR+ z;l~PtuAYb1y_As-eLZi)N&MAE&JXcszUiK1yMJG{{u2A^^E}}FQNy9fC(G|?@%$mf zA*Mm#bExNes)oqo>tN<{kFw`|EayPk{!9r^GXh!1+9h$ShihS*+h*MF z`04MidzZ0{pS|C~#&Nd2-LlSu)PA>}&Z)`KI?hwwqVBN1c$c5_li$g}(P-C2i}G<- z3w`vQ&2q`q>Qi&9@;$rTiqEXT`qGI#@w)neM@Cekx#8_oVEVfMt04z zt`EN4llI7E!WEr`-_i=3DX;jYnn+_x^YW|b`HREllkStUGYF)EucFX>*}*T&5x~y_ z+I=ls(6B;8w_2 zQWuls;LuXWWgjKCd{LPB)MxR)Wj&MrySndfu(vQD?%d6jDNR96VHcG*P>X2gl|6*W z=Vand{}sz7FJ;S`)=y#MYK0Yy&91<{lBX)Wu*1ejicsZ4Bxt+XzF~tO)RA1;ts7~( z!2t8ZFK%DilX)&T;Wkfj>0QcKOuN+h=^5bWl)P7P6^PYfT;8r2^{MFTvM_LPe zlSu?&@}=(M(%On=?TWi?;X?V)ST%=y)&lfBy3OHC;WT5leRqPSwD7!DMo=DIgf!zUK-A=eOaI+(>_i1$dqN4p?#fHfDI(f8dw+hE zkP>+1`?1wGyU}xbbnXWI0tSb_l_yI9ljKOPE$P(-2H0JsrA&@D z^HQGblk^lew`4s({N!4(HgA*Vh-k_*fQ2!p-wcda!zR=HnaA`tTblPSveuU%mQrJY zbOq?=jW%_%!FPW%ue@^_`!!jr{eT_K>=ta4Xw@B15ZLE~3u}05k{#O>9~W#nTnp}T zSXNde2j)^{x%;ynu#)`h^x;=>^QsZp|b5G-}fTH0vkwvs0Hp{qoZ%!!9SBpnJO> z%LUFUi>?p2)S08SEEjIDSrGE5`5?vP=xS44$svh-UK0AA-6@pehc(m0ug9(VniWaT z9#SmdUpY7=Qs4am^xJO#8J+NHGn66s#gGBeUN)yap+VZ-XjHZT7xZ%Jv=%^FI)G!b zt2_G)^cn|e6kdT;qy!vXnml*g6kLWn0?v03(BHDBQ&h!bDGRQ*NGZr)hFzE`S71aC zDjWCxZjvrR_LE?6F!%c0GWW_d$)1o)*X=O=*qtW#d6=!IFL%HgYH`qG$yXA?l5+7t zvL@v1s7%Q&Dh3H4n-xU=W>UcCQBjuR5sJuFj|9Ut!fT@=nejv93lAxvv~sjLYi{6T zGbTZOFw}+?$ z^s5{=oYS?Nlza7$B#BR-wKR{m$T!dAWt#8d+LfDY5z&yw&bgA6<&3~anG+}RPneJr zyUoY-;{y+rqN`{?+<&1+#2VeXNtL$E@TKk)wZBbRh}>ST5oF6P)YH}2BB&Y^U&kLV zH+^y1;%II8`Npd+F7V1AYhAeeHDaCg`bTsCRqf;)2$#mu2DlT0dbLi^8H()mY~iZa z5&<&}jNP3Y2^HXTG19&>hdcWV^}w^Ob=f+ZGcWU|-JVpf%T?Zt03Y=V*9F6xCbkJQ zlyD=0Q5@Ge4>>fC zc%cyuuTM!t^^-wtxe7sRB*{hlguZaM;)TIR#^-Q+7=f|Oy68fi&c=WI^g9O)3%Lp5 z1jnICA$Bc<_+#$)cPUOVD_T!5v?e-{FWi=P8)EdMKduaKCJ99GW>NX2=;+(~5|8-n z2CQxHiY+bGG+<%;2vE^Sfn=MRd)7h6lHgy+XH9W;$e*_0{ zJ?whhwCf?)WBn8R&%9S|GNA-oiVzPyid$aVuofvQnBdnuD$Wc<%32rcOj&M^V)Ri} zF!F6{>Zu5u58he4HckWbGz*jmnDGJ49ve%6pR?j;4w)zFGvH`ig`BDC(Aw_lOdBuj zWP+r5{3ysqhXOTJb$w1q>L z1jNn#%c3Z1wo4&JY}f5CQ6Bo@tu2E>&d7dVCfGL$Y5p@`hpMmtb*AlCy?I z=HE2_N}r@2t#8KE?S{@f$F+;+{0f6cmIj*uuBySn!Tdc zTw=q22KPRe^Okl0lSs!R7EHG~Ig5Bs#EcVfp6xouaxDs{>!3_(d@)GQhPuEx!E6$e z0(q4LUCiSx8YdeR{M;1Hh^t?F6ml%b(k}Pn%#%mGXB?EPW%0{_D0VveQPfi^&-&%y zJ#pi^jLz|^74h;pRJSj-8k%5#xz*Z0dHf&Bq$Pcao{SKfduarGf|Ua*GU3AV1_H%! z{wSC!nM3x+YP-u0AHF<49sCj*EW`IP>V`L|lYF$x2CQ2scE6#&D>CS66@;?JxW44S znbP&Ftf`qu6*$Uf-ddgdvT%189JE_JRtbQ3RrFJ3EXw67%DM`Ul)lQQvPWiaqrr2B zOJE&PJ)IPEJnYqMe;&H&(fE3n5D>^88t83<<+T)qH=Gd~Y%H)(bEO&#VwvS*>;)UB z6dd<*(B<{EUWXM&2EFBd1SXC^ueIj=O#|vT<9{p9{H3GLQX0y>RWH(J<*onqPKlde zIK{Lg803{_cKY5gu!e;9lNU~^GsTEi&cb_CX!DJ|&IIT{ci_ZD!D5`mUX)6nwSX1R z*?+$E%upDcEGn|C6=^M0uLdV*jbJY+g`^H2sVJ-)WYDh*P891 z;@l{C9LDn>2nVT>+o*f${C#WKc}rNu#Ln;B`NlWU%BAlbq~gz@bRq+(CrG5=-Z!$AP0!oAM z)(AZIUXX9lBoa^?*2R|1tI_@f!=|d0mWzQCq-A4Wl4yH9GAY__*84WJ3oFiRI>9L+ zrpbIHR#dB@Ibg0!FsEM~rZ`Wu^h2{!= zW$5+K4YydsqW4Mn>@5H0l*U3<;`A*n`!+k{gP~7vdnG!lH{~<(Qv&x4`0rKV0~gDN zV+C^?*B25KGg6+}C@URZxIby>)2nzMxBHjlZmUldX9?jic00MQjIClOCuw75Yu^A~ z*sCb{g07z`W)fLN6(UF>^|^h1B#Oc$5I1pp%G8oc(p1t zpuU%jqk{H|5Yj0?Zj_-x$lSgQJ@xPS5}rT$98N4$K81XalDe;8x-xgN>k98+U1nC6 zF7PtMl}jO4Y$>c#?b04MUisldf3uLCQe3&gGd>9rK4MMZ8I+nEqU+9A9dFKe?GX(K zNxlrv%9N*=V5-D+&-4$`PuI@64c`**R#?3|&%GOl) zhm*==KG5<`A=~6B7bT+Q5LwrpStZwmYv2`i<5{24=^g!(+zTtwI-HxU$uM|PaR`T#&2MdRtB>d4b1*Rmo)zj9wckqP*4_2es0{aEWoZs9VZGo#8&)kdU5UnkP$Op>{w`6O`* zKdShrFbSO3TSqCeje}2q31#C70BFfBVIC*1Cfdlal!$3db1!z-q2R>*T$Hl4!5hXG z@R%lx=}V?8Tpzgd(^NQ|XcOnE(!GT@B3WS_0VZ~zelOeWD6%llSFCf6&O#yS=^~d} zGde8bT2e$Ia;}!#>R3=FBQhFE>NFvLzypdgx2;{fQCJx=T7|f1Myu4zBorN93pFOF z!=ssLrz0%vY5X|Wt1dl|OnJ#6Qu%<@{O6QcO#S(j zY*xdGgZ7l~yTbNuLAvSS`3Zm!O>2SFV6!4AlL;OmmyBD60CN?txE>>e!+=-W!XIb5 zOkSH-rVfYuDA{h-iMlExZ?cBoFEHd6BPi3Y-zUF_vr8i#ZvBBJ0{$$Dh>BFb0Vc&< zMM8Zc>OWih9h!~I(_t;%gD>I-aP*th%G&jrk=Y=@>1;7}g6j#P%^H6*Mv|)4-jxWH zt@`oY*!{kb08&3Sx>s&mhh8i*%J1mtE|Vngz7ouZeq>m$th4k3AieiP<>p^_xW4^H z2Lmj!o=g8aCaqSr5RW;j)Q7Fs#Z!W1#zjRZ2uvKC%=U%QdAC0aoO~&VA~Z znUP{MbpxHQK!-QG`8By!kv&k?i1bH?t?ug69Rp%}98{9vxm?G5NP;O^b2ocy5;!bI z)j8H_vTSb&`)~S|FfMtzXZy|Sz@%RzR_^eB1Y`IC(Sp-E#QXL&FvMw#IL$~Gzbo+| zYQ5br_RmpK`?|PiN(mOO0i0pl-!P1{c0&Ov{{><|oxe|Z8qjz(Tpn#fy-8zgp@eIY z2pE(%I(s)lKo39~oE157?DaLD5>T*CYdz+F{u)iiNs^1kP5O6 zL(dPBdVhwMG01@DkVx?6+d5%zSx{r25xvxQs(32eI9gk>Mmx4Ff5pjgKXQ^;S8wv2`WjrI-*9R>-ng(T^-9CR!$>cDXz=?}MqE(rh+@Kpg5{8~Nvu zhePs262)N_FB16~ayO(=y&cBHl*P|OU!RYzXs>z4ks#GSdW=201tZJ=2t6P5_~aEAXbKcC0K(ncBA1 zurHdtMx3KR7PIYP!!ocZcvu`#G(WQgTT(Y6Tr^?9JGWY0dAtk>M zp>5E69r&P)Xh|W-%89>k6VpLqd_fQz+J<^|ea$Y%=5{^0rW>{Eumu`9shFWO@27mD z6#kb9$0gb`9&*b|^vT$lVwCV%!Idxm%7CbZ6AcI?dZLRnvN=R4QDPYzH3GF=^E3$i zq_?wtI9=LLL*J?`8^B1JS%BwC1a&@GYuci6Y4Ktsa8Yquc1*O0zDhJCB%QpTPOOe7 zik?Ysg66^aAaYL|&U_@3k@jfdNJLI(W zZr5Knx^4%5FjPO08!&v@J|Evpu$q`5uFGaK{B(2;qI8Uggzq+=2`9%&7{2N0wepT% z2Uq}gl0YkFY)x)Q)+y$VOb(02yz)DekK{4yVGjXwnSnWb%$QU(YEFCGc+5T-=T7q9 z0|>KPei^6v6PXi71REztqFs+*tbt4zEFGM_v>rjbp%6jD)V4;vT%jdf z(5w|Pb(@}mtw)~W6DE&-A@5~r==VxTXvu)4lg(m-|G%AnFg^5u6;F(`y28;dtSdC!}Vq*7GhxYcpaxA54b)- zqE2fDSrV~>ngFljVCFTVS_?b4A9i}=Q!kfbCJ)j=TymS^PFRvSy|kLF3GK$_fbGO{ z!|s!|Cr#_2oz9zThxud@TVrtOvC{d7F^(~(=eJDc06ujg4znZKebEwHDp15E{ zNmTYipgUt=X~zR2**wh02mv}irc&qd$$%w2w^2r86aa=1f}0K(p)O<;eY&3Qs1Ii^ z+ndmaT#kJ^LeMfzv@y02k{Y-vg&;Xv9!L$Q+%dNO$j>90^yMvL=7-wSFdhFyhYx~L# z7(Oez833-zFy%K5cujM4On-2}yWcibFZ{7@ZSSby_i)&jasvbHh02^85dWCG4$9r~A;n=zH z-%Ue%N1+4$&9_mc$hFAXLzeCGRV3*K=}uxF+Orc}iM_pvY}Fd6BM{I7Kwc*+8p{C9 z0#wDao1CWs@7eP{8QVepBhtR-_kwT0%^>w$ZyyeN3eZvRR*hOZfb3RaF6tYOz@%i+ z*?ftE=cDV1Gz~nc_G#|UPwC^(Id5`*(y)4-3UtRHuufTs!TV7lP^8OrK_@^}h`s{W z)9Ae4zlY$KmdQX0N{`{d1|`>59&ro+UDDl0aY?yeL3n-u_a(`cZZcQyPrqCvw%fVU zl*n+ayW|Y01@cve(0&IN3raTQ3}(rS<<&?SG|4e7o2@=>E7I4TtpqGF`D`m`rb&`$ zo7-QN79&o+Y4ewl@BP#kl1e_ILGI@xFtRmVV~NGN05_l^p23W4oCZ5L8#P$(D@mA_ zA_QQYx}$7VAxX0)r!jZLn3T;Hx!O?TM&6S( z2{+_=3DqvuKC*0C?GoyeZq)ir-zqtWMjeT4+>)2NCC;EtAd7Jj*p<+d|05^dsu{c! zQK7NG&kZYKlQ-tH{~+(W0g!e|s-n6JA-BMDXmbLWoncR(-}{Mik(TV1xwq?)tV}}F z!w_^_Ac-j8WW78qnU2T>aq`oEw&moYmcU3fKwk)p%sjITUS@RlCE7pQ{POHAY6z%L zAX_;cS;YO=}%J6Ie@nl+K(LdoApu;$!8IU5PeuJeZTLyQ zz-=(BXw;#lU7ZkH(Mj@MVaUw>T7+|zMu04^HUL3ROhht6I2O~x>yM%=gq)8=*aFK? zNCd=m?C+=ih)Wj$shTjBoR@u?R7i~W0NP2QBnf@+I8M&h>m>xHHyqX>(bGWMmHqyR zb{yUfU?>zjM(OfyO5e!>)xcU|s_U_Ka5g7LXIMH*#ow8_KTR;`x^-kzocd-I>F9-JlWh2dkEQk=U!No=<-wwEiY>Czh zJ(qWMFL}i%3WrvQ>9k$0x-QsTp<-nA}ttAr#$*rFIF(Yh>+a;IuRYH&KpPrO;0a>LGw zyCQin>!^oqLnMG$SC~3d43)Cm&7r+XfELW?ux);MucjFbsWA_7xUWZ7Lr4~#qm8rn z1sQ%ALSdVxdYPcuSLf(ijxe>%=gnYt%{Rf4+}}WurvuffV@WCEAvn_10(r7XhXdZl zC?MlN3=Q-2b-WLM>>&Xt!jfK<2DCZEbYqM3X-U`)A)QHovopr@C2)d`G?x~< zBWV636(hIr-YDqW_1?A*WRC1lNBeNnE5Hk>acOsg7)4VN@jOgIr>{IBzONm$at%Ng zF@OX*(RPmk?ov4cHm3}b3@!PkkM9LtUV(||MZguOgO4Hn%tIl9_Ge`25yF=d~^SeZf+n`%j7mMU%lZG{Y?K3f+?oi6b&yzsUyh|d5|8b}<2&nBfJ64=H` zI>j&T(!$f003);0qLA!(sK9Gfs% zWh<|w+%wm@GZ)%J1s&z>rxiry^)}z2*JYz#vJvr(jqA;^Nr>PAyvmAus7m-gvDFbnAPhg+L$gkE5K52RokOIwz77W zjWL&(Yq4qx0;K3v=vy0u1S0BBMUFS=bk2F~%ir)S$4A5+ZJnK2X?g^{$e?V z8DZ_v`p5U`%k-IsAyH;=J-QZY?^NkYA>bQ7qY}>=Z9UU@7au1`ny2`0_l!!mP2Vpb zzy?^7?|kjL>=S*3DVVi85?L#)H<`!9LXVtR2T(>ONGELtEM$LEtjp{k&S;`ugi^{D zQf>lg29DYuhAcHVs|?I#HrGqYTl7bPw$xN(e$yy`G7Gphwz_sGE~eJp3*#YbG6Xk`QwX8N=-Z~$*4a+~2YZtD?Qzv% z)9!KK6y6;5D@44tMCQU8EoD3bD@zuyusStkB+4@QUos{;q3(qEB2ZLz>Uny3t%p4y z{n%|Y84jp@>iGb{_N>42fJ@Ll5KT9yldmOuzrXpwo^=T|IoAJm=jP$`5nuv!9d(wZ zY|KEiC|x`^`B~J=)LgFkudVWXnU2TTNmC5z`4V*t#@ms--j7^ZqT0zdDo)Q|&g)%c zuyryKX+q{W;$>1WRLO+l3S{bQrP=O!nUb{ejS)oA_kUh0+1W0 zs)$b;6+_Uv6cd#CI@mB?mnSpF2jVMXgj3*}f`q0So>l@;>wDR!WE5p1EdXdU4`3kx zXcpX4PEFl)E3A0wOfuLbKm#&aC}Y*~DG6IpK4wJ&cbgfVd?0~BlR8w&=^QBo)k(0c zM?HwN;zkkHn4V+`wB&N+_<9NhvHx3GAeAIhV`2HNgp;Yy!bc}&PLjQ`v2=geqr$xo z$O%lVG=OC49*#&S_D0E%$w(J3BCp*-+6GRa^#~a zyPv_H5EvdRyQIRlNNsPxk|zbi&SjI^B_=lmx2?sVNw!tPQ!ek7dkAE^NEA$RP^W`y zM4aRt@O&-*_AIt|1jKp7vvc^UV>AKGB&rM|gW;QymT1Y1`8r=Mr<58V-DI)nW(YP> zsu`qIU^kkqgl2jGIlWYlZtV@OhiQGdghbXmwN>F)fp4e;Ny{UH#e@R-mjUDO&h-+Y z9~6uvs#&B(?`3=J_vI3jW|D^0D*%0yRQJB%2zY|z_y%Q3p;oCQpz<(1?q;^*=D;E} z11ha$k;a4IblBGvChv2k$h=?P3k-#20|OdX!G|L)OKF`{UyB}*M?VvI!II6D&fG79h5P%9P zYPDL_;8z~gzt`ku0IU-TTTsEAuSlR7+J=qkbwC9eNlB1gH>xh~IRA+w*0~v|h5@)N z$WFvi|6!;S4}~xh&9)XQ z3s=sY6PFInQMDH=2gT7#kOQwPQ$5|SV&H|j4aPaK>aNi z4soQZCEKEs6?l-QbsRKKfFw=N^yLxxEUbhPPSo{q!Vhr3-T!EN8vw;{8ptrANfxmt6<@E+Ttvtz7si%)qs(zWfYFz<-LD2X$}iYCbZR-e=&;f zPpAAzICFJ>GeOQZZx&1jzYWwCzHg4?$Y#D4u!05^8g%Duz`NPbN1l}mBIN!UD?OL)e5716Wo=-J>*C6K*ky+|jrVHPtOOTD zJzw%WY;NjONuyVU!vwEDZP4HKi}F4GCN7#f%n z6^6LqQ~N>6zz`n^$OgsP`@Q{O>YEfDmlWiiRA~}8ETg?(GtgD0#{@6T2T4Q|kR6CNENE|?G@jc^e`-2*ndLzuSB zD52DI1E-*Z{H?E@4_}+Yr45qRfb&f_EaUG1lp&FG{N=l^R{;E+$6v__A}LI5O83fb zYJGsL0AX){Z}6?c!6*B!gSab0-N2-uP$f>U?yhVxLyt{s)%Z{qeyh{=>~usB+u9ez~}>F4P^ ze5>NcQ_ffX4(9iuW-S4nkYNmtr$FWR$zmAh-%2bL>8H(YCb`88Mb3>B&0Hiy=KR`% z>l>aqhkm1SBV03$$QD(O9a?2%xHSy7Iw5x)W-0hj@viYeDDwy28muC~E+Gls-aB0Q zp+v@WKs^XoIuB<9yeG^u?-G}85_!DO>ew#lp#iFx&c*ww@+7F<4&-wW zz2)*uvoaFiMNASn&alvRkOMavKXN!>2@X0i((waU*cveqsNO&zEX-#&b<@^YlNUg) zZ4#Dp0ThD^=ymVwVRqvD+iJ2vH5c&vh?ZUs$K3&Evo@Q>3bg8U)0pS-Zx60;J>I1V z;y9C_m6Nfot)3yOrHV6A1ZHMRQ;leEyfIvAhntG}YqP{UPMD})Z>Fkp6QpV5)wpD8 zZ}T$BUQhB7WvGsBk)#~K=LsDdyvNTVB8j9DLHCQKm0~6^3?!$i zAUGXdD`ipv=?{u(5o28{Ws(uf=XP6hX3kS*9{7gHvqRcfowz1v-9eSToM4$CPDyvl z3dhcD)6Q&*b{>=ZwGBEUW8!yUI^AC2+xa~bxEqy{WDrWUQGkA+LbI{WM=XId{!FTY zP!&^ry+(Jj?;A<94&=Fab7?>bD3eN36WD-p_S$MhwL0%F0lS#AR-me0+r5KAX@?Yx z8{s+9Om?4-(r21& zHQS>pY}1W2D99qq3y7PiBc!#z&*PUvOQW7U`z{(Db##>(zxo4wqa8zx$GzsMetjc}zK=Tc)$kHe!s|%h{NuC*wFd!mYDY(VO%h`nxmcnwn7!Yl(_>~oA_K6c1R0*dmVuPm|2p=*5lE7w8YKT;5C7?^9kn#8Fz2+&TvyGfVXioMojDBq(oFHVbJm zp~k~t0(5sn1U^e8PIt}t`REz}oS0t?Av4=ERCG+LT&<50zysg1)7hkzAQ?*3#X3~^ zCK-?WNNXBWPzHE5T1$aB9YJEM{fdW|LyPY?)=>FvClK_GZ?+3(0(!&s>@rv7;z;uhv{IdMa}gEyn`^j zQ$-NLJK94wpm}!t>0yz;J_e+UXyZ<$Yj9o`xEpD=5yW}MNXb>2u{QCoD64n4l#UkJ zCIb=O>(PhGamXhdRq^voa`~7Q7V3GoZTCmrINcSfY=<_Cle7SY0~9Kmms7l%wY!r@ zwe=8Pb|hsQmSBlA??&I8f*Z803hn`}nch;-Ugh;vKNvtJrW35}!T)c{0KL?=BNpgd zVL0tY&I-mzAJyYqpxsfEYJOSAv6A?Ce2U^E%w&R(I_*Pq7I}G@k>;;SK ze00^m!z|ZQLnW7I3Ho?U^ktsMV;^sP`EbX=Z8f_gBT%+C=@oxZ^zs1YPxl%E4pLn! z{eh`SQ$Z~QatQ!x__X&0ul0!f(MV7Z2y4eno~jRg-P1O9&QJ!~+Q!c-RvceuNrDXu zR$^CL6vT6v)8yC{IH&>2qQW^;$9@o2&>09GYfXBg;ur4u;F_f!Nuh+zdv?IKPKT(F zQiSRWV#BT*Y8R>y>iA3zK}{+Bs)Ephju+7`$y!$e1#V2F!|i%>r6As6En>>I1;r1o z`|yu)I$P4~^$PkPWlf?GCWZ_JhT{qCO>G=?(8rQ)*s?3Kx8ZUjkW`*hIX>XBYtX${V+RPUhD52!Nsk;Q>~yLI z(jJTpP(}>;Y>k-H3hEu$30U9;+bJ!Hsm#A!k3M$4)3;y+)PU2*oIV;(Zx;{vNmW?2 z7Qxx4fCLmkgsj^z@m~pK@H6ydFmvxB?7$G)vV(<~+k-VC$02ye_(BE>L;Qh8~F9zUdjHB8L6GPs?3!*@&7Jxr-Y=Y&4y zlUIU%AQg~W@Jei_D`HCF6m-o6i6w0wY|kJ!Ca1fH)#t;VL`wt5>6NIdp+RQ~M2`&O zM32=Tm>aU;wD`#W7Rv9FTOG7Wnc~btI_XF3o`AAiT~?4D$t(NuL|Ep#`zFE5)&ntd zyB>Wg7J6kRKZ=Dun91w)-Cc1$nWxCfixLSet$N~-8|}s3?c5Eo2mA0P=St zE&$ldh$bHc&^}S1;8>SnBk!7GMG<{X=Hl3+LiKb=ryy3r?&R+P*$jSm9<_S*hQ^~$ zI4$MRQ*)d_z;>(%He*KUo9}-&(4kURS17J|6PYw92ecd5TA0zBC^xy zHbG#CXq2_5w^ax2lsN_Z(jruggxmnxZS^QIq1u!~e=)oDdBtMuB5*~tYNL{5_EY!K z*tqZ`R3kR9kSXokBQ0WbU{Wkdlt#j35*Cdha*NH6+v8iQ25`+9-4MWeYEq~+vSCl$ za))4T3!91_IuI&`7f{0hH{}&gwI=%=AVs-dkDv=NqY1G-cz`BxX&B?4^7@$9+Rx+a zawz{Tj+#M#A4yLT$!Q#{zhC@yzewDF+L+wj3!p&-6P2hOQz!uWq4UavR$3%i(%Yi33a-V%P&*x3ua^Kxqpuw%QFq6MC)K4n?qy=oFlD-q z3a%p;)|>c(K7^2DZE2Hp|GHxdE|L zsd0JW9^8kG+Md2jP#;$;TkBoWijv0WZY_0LTA5x6kL3~6y*E4du;+S^{l1aOX+uj}7g6a3%a)%t$kCx%hYod>g~Igc{?%5+r~zn^DAJa6o>Vrl zOn1zv^U+75Quxy$$SAZ#*Nwoa9g1rN#v!#_!MJW-GNbj<~9#M2W z<)~wIw1ldJYyxGl3!c1L;=|M!J06V7$m4ZYu%~#g{4Xkq5sCl_4Pog>N)0oY4lKd= zy*#D|p|XW(Lh1q%+gwS=%0m!BeS)iJkzQ(c6LC!-CK+CJUgY^i$w`8)Zhs6Zm zY0@el;mo;8D>aOhXrOcSDB!n`O=s4i%?mB)&a&*%@Hw>c0*t5IMNrZ~Ob+E3#Jhev_rBeGMQIR!LdE_ zh4%kAQ2-1RTyJ~CYshyZ-bReK1B*l`-S(+d8s9-?g(h{Z;tONH7X_478u#kqw znvQdC^MB)NNfF(S-btt&nzd}(QwmQy56Z6Hm(YD&vHiFdHXAn8N{1l@&-!L$1X38| zkB;z>evl+A=9>??aQyhq)gF6n=~D3&(`sYa+%OjkWm{6X#`>_d@_)EnWOSmpW(5+7 zfliyTKY|>l6~b=+GJ!;s=KwSTT&i&^Z$vTuuw5r}V8HP{41Xe27u|*v4>;*X5)Hd0 zMrg%K$?V&SJ~~|iWOc|P`rMFuog`#RP=s>_fKaKaDWPs@>n>xEcrEzMfkjzB9=gH- zG6NFMbJSbi-6>mIbURZ2!=+k0Pe@>heB6kGwqrMUG^sRw2Bde9!0s2ayVpwOjGu=S zzb(^>VFC!E%$Rm2dp~g!m-I*gNLHW5<8eYmLS-|)q?fn^0W_fmA!V4e5svPy4k`AYay^fuIPG#0M}MCE#P z&HGH~PDx5&5k7X~p>8#0XS4U{UF8Zmt%HU&w{{UdymtdJUmK}b(kwa{6i(2mekQIwLAQ?upsz#k?uo44~aeJ5~B~_`wFel9_n}TZ1JT%;qHWpU= z2+NT=K=Lbf30rs>VC?L{Ft@qcaL1VX_+EQ8h*YC3Z`Y%1(beu2$J5d5C-?ND>WXaA zr8;AkT{Y80H}_+UcD2QQ|0>qPoBCJ^Lx@4Z4Q%cTs_AeClk-(ftXZ}oz@i_0N5O6H z_wP|_Wlz3CDPa-Y;J3w9O8XtjZPw58Rtn{+CsfjWF?WSJMua znmcvV$e_PbpU@*3h(9RDz<#R{`;4Blk{Phy?F=`0fGvX@YvVnxVmxsb_@$&Ff>>%$ zxltj|f8_M?H2whws9T7aZ|?a= z3;WUEH2?fKvw3&J)NN(ypwH9NCJ#-N1OOE{s`1|LeK4v1S0B6omHODhyt!I?AZ_W? z8QpwoQb*-Yespjq%}$$!@sP^oGC@s1q7+r%#CY|UfkT&HHXkK(T7``s$yD_P2%FAk z)g73FW6W^Nfp2P&EWKK$8i?0zlo9Zo7;A084?d^60* z7Mh`L6z@=U_GxGJf*r<|x?PVxboPb{Xv2IR3*a9i;z#~=;HI55ah!P|0aNtnsIFPt zB5_N|kT6u+)61P{8%HJ;*6Ro}Edf8F|2PcppPbzW6Z($j`+bmFzGok{B3>Xkg=%=iip=8}n%gVw- zn{83MV%wYWdO!ssEt0gDi(+W7NMXSXfbcZZV>dD6EpThV;MzNvUmF)$Zm2A@1}RA6}zJH zl2De_5NLL3!!cGQ%Wuyx0pm1otAj5U^w0qjAD~i(%0pV~Mh$EN`gVU%choPk3O1OuRNoNsrA7zI^s~)Ll_M3^s(az^>l>lo7i%p zXXkL|l4PBVecWt0hYf8ums(w`v=hm5?BVU4KD=Go8`B+tEPUyj4b@xdFbm&}^vQI$ zdp6AV3OBcg1Q!;I?CE({GY(fNJJv5jqS-JF3}nMOh*q5GrrBhM!XRoNG(lb69JCgotfx?&mm$l>0#QEaTayiKYa4_2)L42 z_S%4;0KH>nJ5Gq2$IQaGa#pm*Sny3sb$h7jlx(aFhfu^zYt8um_{2plCwXy1D|*LMQmk(bcGe5+73t znun)t{jjNI>|b*72PW>+jR%Vd(t5l&!@7XEBGF#sQg8MWAPku~N`_2oW>T7!me-J> zLRvs3WMN7qR{D>>b&BUW1u6>|(K-TsQ9;}W80Cmmx)!3$V6om#SFHY_nh-vKoSu}b zjA~x3Ze~nLK$rzEufbj64i7d82cQRtzyp&7ohM`j`=aL4q&C~@3=uW#Fl9$vXwpW) z`Rd?!q-kX_o;4Ug-K?f8)(vc`LQ={G3N^1_EXZ*I*g79&_u&F)0!VI>#vaYG?#NpZ#PjkKi=a%kiGvtdV z8J|_p`MxdBJp3e*_?4na&#_!(MjCbof7b$g%x-G?vhLEDNwUs4xt}T&y(zjBqP)BW zWOUs0H9(d-h%T>Y9AUa9xjx&FGH@BJ&^+uiFcPiO&`k#CGhJXB9ZIKJ>5jgi4rRYG zp}?OEOoa@bwt4+zD9tCD4g2dNDt+cXAxVZk#pt`Xdf09zH`seXW-~Yx;@k$G78m!@ zoBAy>Vm=%eNH@ro1gZ}^6vmw~AB9>3^l6EcBlDBaS5VJEMGU&S+n}vbM7VU84tu*B zzjn9OGyOR@-hh}_7?B+-q#T(x)MQ)J7yK{GVsb|9c0IaUF7QX})3~keS$=bD0ojD5 zd>JJQ2Lq5dIN28D4tiW~+_As0O4HF*uEii#Xk2IjMtaKJpkRODx$^o(sDcoBkLuqq z2~vuG_7RmD$<>CnJt0Ui=t${EcCK{zaYa8erd0IMl9BpN$(=h?=WwFa4A7~XQeEST zn;K1Nh|eW>q^sLV(v=gCQ~59%01gdZDbU?qkKh!e$5)DWirBey5=?17>7Ym3PKUYA zBf8Ga>|F-6Ne>l}!L9-dc~i>>JdIWk_L^~g(XoSeGC`qI-2I!P3PQDWx<&0sZ$mfx zxmJ~-1~1{MFk5|dBl$*V`uuzAQ zt}T8qi5`f48aZw*r|Z3DeFnW3_YQfJ9yuBnyA>cjRfd*=EuRJMXg-CX7NfTKs7pu1e%V z1v_f^jn;8j2}zo*1q_-yAi>t$wg*u}uhgx&#jG(rfO=;=uxhX7_h6hHQds0#!o<TogEHeflp7G1hSXL3cJHDm4-?4#!X%s93UIznfQg$J%Gn?7;kVU zfJOp^Z98};J^*Wt1R52#9|L5em6sUDg4zAYdN$A-k#g|+77y0 z{z*49anSV)^yNGqz_AVHdxNsbMGkt4=65dXM?kh6n-xOL>-^Rq)mvKmcheruplv0( zPF295%JKH;dRo3^&!m{sk5sko&kPtbb)qTWpLXW;5aa0vkn1Ark;4smUXfP<`xPj+ zz%%7uo+HXm>9SBEqDre7C%{Gue&|C%wI*#xfWh7j!URMO?lLMEDZR8_j$&ihtINfL z(bg4xq}ZeCjq(R}vRy_w9O$KLpvxZ^llzg}!wE4Wfbi)By9~Y4hY`px=Q)!Kg;sqL zS7-N+jQnGu#Tab*NUs1pq@=KWz6IFtM^=oSN2j2U3wN!IQGNYK!-I~}3%Ki8~>#iPlXEL$pdJc{6cB-?UyoUl-XZHS?1 z3pd6(Wm07@J`I^nCP%In6$;f|Vkro&E*o|02I$Oa&SL&61a=`*^dNtb%B5`qV=E~oyd*&wE*u$6kW>aRD$AT_$s|AP#6D<~r9A%1LC=$c; zi+*4Pj$3`(u|BPi^$q4NwhRzSuarXJeL1+vU`0EAVgqhaS0y@nMEPTOt~1$rsduuP zMrs*XB)bT91{zJ%`f6Xkj-S|=O-yT2JAt*;o}k0DYzkZb0IRZDChHYzTOlpUl`7+~ z3!jI{%+C^#>qcMmD9b2&kKYEWz3EW_&HV%Oi z@0)uMY9wkT&C;81lBy=sCqbOdf(mN{4)$E`b24Xesf*MoM`!s0+2UmmXyZ3sN}r<@ z=JRj|+CWkV@e=mNNYNZ?d0*ZeK+%t-p6Z2Nb)EVV@igwH^sfuK097gctwfU_ zu_gOzX6mQFY_|Qap2(})HazT_d#Ft8_rmtG!)@;%u?MkX%AN{0)niZQg@Zg7&=3nseO4S^jfO&r~%4i_xK< zE=M~B$^bEUdIV0l6Kc?*rp1{TTR~E4Jw4HY^SG~5@5dcBxhM-|gxHhp-43Ae0q%8& zM#uC9knO!k0mAEXKTIcW1MRfzN`AcPZap2c2Yd{u%HY-jSx*w8y53P_KJ>iy!1@q@nA9yLRgu&Dkp0Au9r|F0s)rz-t;r;VW%+g3pK3O>Hq! z=&8}~fRCX5?5hE1Ag}X-qHFw^bO<9d3>qY#lTfoU9QP1%&4+PoROjQC3hW`q>5Mc?rx_iM*B4+}5W*zJ0xMNP{c zu!o7f70t9sXl(5nGd$*22GMz37Y@jYSDBa8_)K?NW9^f|P33i<JK_&9=?xR|};- z?YSaics)CbL_$lF$GZx&{A~Za17sl)7xkkw!v^tHA9HK;8HxIc1wXG?wNHaS%oH=V z>?Ymq__;~7;;B*sOD>v-oEK3FT&`|XVm2EpnzFHPFx&;pO0x?|0Ymtk;BM(P+k>DI zNF-`?1aN?WqT9?lHwEDqM5igb?!dPMIwa>7ZMqwPPhx|&k4G?jLg)k#wUYq{Ax?ej zV{YBF*bOeGt0#sDI;fF3PJ&J%rl~a}k%kokIvT^w9sVe>qTx<->-fO&I-Me=zSzMe z{R8e`t&>xAMBWLCV(V7>r)Z0TdB1v{P@w>JuDgDY28GlF<@X}^(K8l8Sa3>`{jJ4LD%Zvtbh@x_`M#aIGI_Ttc1yp&^57)>(Mo6 zjPl4)*;$_WgNN!I=a|%r89$Mp-ajz&&|T_afufeU6Xbcl&zrpC4We^O-C2&*8xV_+ zC_5uuO?O>fL27S@sMC9!KRpo$U@9izhiK_j3>aD9_2Bb>eMg3Tgu1uu6(pqTik|Ja zng@Lc)C(JNnzo;CYaMHG4%E|&n+$5iR6VkSSl%N#n!xn4ktfI@yeFP4$drgp8C=*2 zWX1FXW2U#amj-%vmtnQon^5NnF};;kG)+=;@ARU$9xu4jN3>5sizmEab zwmVy->-9>FuInCWz>u&^^6user0;i^Yrn>D?spUH*8>u6_8Y2s3BY}-uu@QUL-nT{ zd^7RAm$wMNyqZXYnIVx?_uUVYA*eUwm$h+(H$Hd|AVH`HlxX(sQb~}2g>BQVB2QRV zx!52W+zGduozDd>brmu4Do0h~zq)yYn}!Coem76VwzAk-VJ$k3^}I3~u`~&?kX=gA ztJuBS^rmdP7$<1Z65j2*u17FxQicJ!5#<6z!?u0FvqL05x|h^^-mmNR{tO{ov7$R5 z8ISfq6@;aGAZ@Rwl+hsBM$>vG4FUrIQ}C*O3Jx70QU((hppNi}rT&)QKR?<;!i!H1 zutn=E>B$PV+0T9K*Fo=A`qD&n@p;86f+~&!8oVd%3;X+lmZ7a@f(a6k{SA!2yTWZG zp`@S*rGtalLZk{2xaj~BYU9EzyLlC>p_TV(w%Mq218&DFQ zN{d++dgQJFCU+!Hn%?KhwCVjmT^B@z?}iUcSYmFaz1_9>2!vTrV_*b#zqT__cz)f! zboWtbE+MZ`kBX^HRYL?|pz1Hj6}JgCBtwCLaFuUp_Ivq5G#VfC4jDL|L8B=Vh6FVc z?)Ob9jSS8nd=sC@{CBk8Rtwb&qY^wr$(C zZQHhO+x9uP?|a?VRd3Qg_*0csDwAZiY9~8;ea)^*qnj?wyx5L3LuiQj^)N*q@vbJD zBaQ((FXipH81{7S(9T__8bysim}*`i*;mX0eLQ+;8zk^3W7+Wjx0n)on$7le1O#n2 zEiTgM$s_!L1_&|!T=~^RME*-P(OD6=ZtEn_3pWp$q)Sge>6@s-z*&tnqc?y;gEWi> z1Lt`c%E6S2#Mtf~#z7ooDc<>Q>OtiKLC&vX`CdJFt>v#(K~H zZj-94uKfLMp0dtT7v-`!w&I5}IDNU!CF>CoijdjoQpu?T`U1EEz~zswX@_eg5L7|( zfkqtU&)fU8jZ}};krl>dN75#(I`amh9@TDRli`^yBg}7}ol{We99*(a;$hw85G4v1 zgx~ZOEA1_?q0G{w;cRR{Y57z(QL5eZ$l0zp^HP_okuj0_5+QKglnJJCkWC9@>Y;ds zn;uiQR$1k}ZpFe}Ay5&@MRGXOc+dJ9fl_)+%9wkY*HR%4#YhuJr8U(X&NCW+@McL< zn{^5m<&LC{Hg{Mr%8INe9)0r^m|?nXr$@T^tWqJfavuC2xLR~s!mpe8h-n)$N?hRo zpn4L&yJ;D->Vd#*giYL29!388UJ&9-`+)!Tsy<<5RSirn7zC-Uc1)f9mj{r5ir-IWYY=W2w?$!bZAB9 zhvRQ;E3&bGm`P+p=F@107(9Rv4su}Yps&Gx6<$Bm41!u?#X2|M>z8naP#a#anz8<*dvA=cTBFcPm>@1T?X zjUWh?EaZHTKbG~z6$tNb6a(ZAtwY}Ni*U?UiAC^qC(SDLoV%m_o&GL_BtcPdnz0Ns z7aSjPCy7U$(A?Lc{KaZx#`6dAT(?_!6CA)t?Yo_QxNgHu51EayTY=0u$f288;6*5C z8|7vn%Ld7qM-)}%8C6)R_e6R+PBt*VlJ!-SQ$U;C%r5c6Po7NB4WiN!n%|>W*PtEs zL;8KoB-YFZPLU$hWOHg%gddCYSYN^`vOzyn!kYnWrbBkn2zpeggA6X;9omRed8i=B zsBl3SRn8Qe7ejNmb77Z=DDA+QYxVn~XZ>&~x_J3&uT~5{V-;y%_YL3I_F`xzu_S>` z%RxbwgldtXH`)+%S*4J-wl6nizi7ykx?uFo^q_*&QoZdene+?-!$a}nH$gwm2OE{r zf!vQj_wd5}Ps5*jcFz!p+m|?q=ttt{D4x7>4Gm+sYHx~G?|pd0fNK%)ip(~ zpkJ~m-q0G8Zqe&Hce1sJt&2v|L5#&Oedwl>7mzZeKxS5C`_*bT;<<^>2FO%aeWte+ zf>Tt)7Hz-J5aoW0VZiYAaakBBhc#&>sR3G}CHYxB)zRQaXZ@t2YDF+MvY76|W!!c& znMn!q9c{N1{>Op z7)q|<9J@d>X;0GxVN*2|e;C6@@X#r?z4xQsU9nf9Bkp+t1~Ec@7vu~kyl5g#YG)3r zswMkQ-G&Z53fPVKsEE^^usGTZH}W)EIe{hRixuE`HhLH76(hr2la1?P8*Cre!KaX3 z+x(p+0Y|7D(XxQfGQXlyb1j@V{kbIOfvPvv-R*Af7K#-j^qVPQ7hs0%yLO#8D1(Sl zpB0(Sm81;bt%|hOpy5}f^=h#!Cu`lmm8ZiZYlrxnhmBR>x~Eo6<3CeToN=ye8aOHR~KWUxYQb)0~~gw*p#Q zG{!OR_ordXSdkI@^NFdKwpC6|B{sko!VwDG^6WhML2D$k4x+>!PF$UjD1CIYe&<{8 zBNtaMa92n7cNg7m4ml=lQ-m_+j+u63A#oVh08sCde>zyx=b1SdKk#CKq3pwgIhyvpa9@4KG-+5;t0#j9s((B2ZjYN<4QvYJdFsG zd$o(SmGIdoWMBAM4E%c=1QEePHR%-1iw{g%b}ttc z7jfS5@XK?C@&nP^(|&3l$Pk59WM&sChA$$r?P(NgfYD4fEL>R(@s=pU%o6dPlB=~T zWE)cON2|G87XhjB49Ah*LJBLz1$wL+u#<8k zXA)T;#~kF>Kj#N1pv?2%aR9`(;1TBToO$kCUa>iMsto>TJe4+W;6hH@_&D2)J&+#FU5mcfz;D;{P+ znJpf06V{Woy6CipKBu7cb*X=%tZXZHyXCtv zcm;C!a(@iuS^Ttm*RoN_%cV@%uc6%S}-Jk9>PF>G4sRGMA4c1%-4o?)1hpM7#*s zfZ)*|wnFUEp#D7_^y>~b+-F`iw*ot8a5BEm`ay5m5;Po{++V;(ynVHg_fWYIB=>27 zN4I}>Ni!ORV-su)(BKiLKz!zEMvghV?%l;B@;~7M*rJl=+jpb+6Ahh1zMMTXGv*Ug zU_-$8Mqt_cafPz7QfmP+VLj!0iSt=@Wv#r6H!d`SV>m*J7T?WlH;5BV2u+!`+X~HB zs98(vNhKMNG0um~!aBIaC8*eF;mTw?%Uw?`&|2G#RcGZC4c{r^S@|)*^!oI`(3}9# ziHb~v6`W95d>cN%Mm6-Q

xHJ`pVhA)*Fd644*JuT+SCo+n?z>--C|wsG>c)WOcU z-TP&-E-gmn43WA2ifue{%w_>UzC-q%D8Yo()T| z_t!%vk#PEh^}xmx5q0G;^&sUG6TT-a2>GM8eenKKz&|NM_+2up$IE3bv}-P76`eG5 zJ3VLyfqW)E?oHTh@LfO;pnuzJp38Qf6j}3B%8p*IG(_pO zuQ5=-O6;m;=O9nIOF2L!YUH4w+tYnREBk(NF4<(Ke=E@XF%(d4tIF6${Li}1dN3~~ zzfT)53Ym0!q6dd%jiC=u9P^w-N07$JWcS=k1R^p1lJ4 zZD1bzCHId@cd=}cVx*(aeKg|m9hszsGMD$ms@#11h16v0<;NWOIRR3=I)4%`DkRE= zv>dEW*Hj01xp3~^pvexrGXa5q+Yn|X8*}0-o7EZAfdHZOQ~LoSC;QZa0y|)*?EbW2 zOq^^96o0V`rrRsgKNve?B;a&!At}&>e~ftcR?*JH$d%Uvw`W?wpda86CV3d)Z5eDg zVR#IblCMy?k^3bgk6*Xobno6f5NjU2yAX?P;{yfCmhhcPgjHROmW=e+NYFMuytiR? z)t)6tTG#HGfh;)y(M6Jg!9C2&h2A;dM`rCnNyR+KTfQUshhEH!s^jRcONUBNtrbbX zgzvDDGr#L*MoJv}r#B-Kd1t#AA@P81K#K4&ZDgTOt&OGryb^u&fK$CP%Ey{2avuwx z-Md4O84LdX>4w^l?OB`(e8dOZe->ocJVwsB-S9GLr^i3xxGhcj)Y9tV)$~e$#w9>e zv?ldNsdtVGr&ySie2PO1v3&8IxJzqIIaKOSJs*qN!deelf7O@MPl;9moCCg9j`#wi zVx}aGa7Fc4Km*eGOTW5mw5!&)eb{(+TCZ*|pud{%6XW?wPQlm8iAqlVm>`eNoN(F4HE^KhKGSjWC+FA zz0pak?REbj%W-!|3_WJWpsqu?4mdyCkSx*${c?Wa?fJa6*&c2=-(R@Ewd0_E)U(!!n|2)SE~z6wd|9B zuFcm$pNBq8L?^Og~;0@MW4^J`b-i-*vMQ#pa@w$Y5Qn1B|dIZ)I${`B-!&c-J0 zREWhNp<1CXE|#?ddF#U9S3KHO=csr*JV6tK2k#%(-<)aAUAh}UZz2xur++T@3!#IB znMKx@FApaVeu7Llajh_ji3lHCDA`s+w}8hn2iWbCy1KmMQq&77yE@-3UfzT!tu%&o zKuSyKU7`)(ENw%fC-P25RanUPj)-RRBaB)koty{txuIla0P%xF7;>;k zJ(G9f(u{mRkl;>x#kUjsR!NV2%$2!dIXr2YVET+yW|;X;J%F^~N*TrP5P8;IHIEi= z3cHR(_tz`E$=IovMcL@zsz62gaZTbbV1aB!!>;nPn3#29blq1>NmshT=s2?FbYU%I zTNZP7okL6`c(6AYEFezBg^7VR z&+v5vxz3-5%q~)8`C4a9Ezk1K`LuKR1S5@BwtL+tIJ!XoanF>71twnD``@mOrHhbh zZTSFFz4$yjsBvIx&WI>5ZTWz?9bf20PQiqcN)g-|NhRZpxO}qG+(a+;kFr6**iC?& zkZIe( ziOe~%oNix{F@D9c3;0#7)A$s$xn&+5OE(*Pia$spk7skEZYu(Z8Le@dc|WOS-(w7r z3HEz{+%!d#cLwqi-~{azW`Ck;8f4ugeBOtow!&nL?1>8~&PE`T(v?p?k+hb=eVhmY zvZ?l&#X*$%qwtm1Yxhh$Pj!rIVh4vf{XBX#rZKbGe@I44as6FUDKasCo) zWM^{7y;}}@d>m0MyU=Y_UGRkUwx6)k4J4Z6`Iw)+CJ1^71VlS6TQcI*1&M_N@uZ!a zcC&K*?ae-+g^&psgE6BIO6-K!2FZ?#oH35!8oyRL4P(E^kFRLEcnrT=-+QiNw>=&h z2dxq-Sz-O*?1?xYQyUvIF0q?a1-F&wvJgOAdAM-IJ=ULr7(JVU=G`NfLi@YOCTY4L zIIXO?lR;|9@7|ihgL}GE?kHy)%?j{LDEsXIG1jZ{#kNd$*FQ+05+M`%xGQ76x&c+=k-C{)7I;7n@>tg!T=V_EsN z-Y{P*pdP4hTpt=7p@jtCq_gJn(nZJ!GP_%FZ*;9e6>TjDC}lJ%)|C?(0a7(RNmZRR z#cb8O=##LJz8}suVUAnRQjMZUlxu^At`<8?^-h#?7XUTS-thQTwi|?tE(km6SEP*7 zG{k1rZ7D^5{a{rMVI9zz0sdhopvFPrJqu9biOS0rnVtK(vc9MNEDEkn{U`{OpNz_C*~*o(*7mdptmgKS8kf>j6%CllfB`&!<%pyy zx40TR%s}Y~B-={^!MK+p&4+zvpe}5C5O~@7F$xPzZ%wdnTG#ny@B@g&u3@Zu`z_86 zQ)6jJqc9Acw)Zd#)sYmYzipPr3mQE<`T#%CZyY)Kc3}$K_g08$&7xZq06<6!na)d4 zU|H=g9Y$6jZNUH{ovfsh-q6+D)-cKGh6cmTGJ$CFf7Xw1=tu&9mrEE<#Ij4C{mo@R zu`KEYDNx|>3+|rgH2GStPZoL1y7cObw3mP=^L%rGCmj^XTc>Hk7bfHV+z&aO>u!O1BN8GpJ z>KJ?`X8stMqveu63puM(FZgq|u1+b*so@gYwo5XZT0_Ji8?!5X=lHuZ5iw!2gTck@ z0##j4PB3jcDRDi!e(M%gO@Df11l$&TXYXIl{h58odwdM*L) z=;Q+Soj}E6>|GFf6q3p32PsX?_YKTJX50;)4AAiw>B~+|Ud@e2EXAOvzRCT;@lm84 zl^22H)?v{Sj(lwrhg$k39Dnz5>i+s3liTB?;gPPn`r=X~HA-4YEe|UEek@u0Ojvz> zmQ6v>JTBL}9FsCwli))-d}Z2_X~s_;CiqiM3%bC5rTFv?*6grkC|!2|)4WVJ2>GS# z;ANsaqrXK7O8i35A+KYrTkZ)o*!1|-JSp+6i3)$s8vo+=we)`X7x;ey(MlaZr^y|aa#EuFKw^Z%1?ufAiu#fH-RrgRM03t0GD(?zS{Z&-oN zBH(4gz?1`#ARJfR=`P17*+?}}Y|L)K0J9!5V>I(?WMZi&Q(yBz(XX5OmG+|V>u zn|E;=9c9*D@;75#@#!ANMmk%*a-DdMAu|h$Cd-I6uVV6j0B+{)BNAOh;>p-@h-~L} z`|aG=(ul@K1T{sPb=_E)b;od=fonwF744_sdoYXJXTDAmmJFC(=9(?wLsL^-IZo1aMo2b+G2r(k5Sdhz)bB;`$^h_ut$i8DfJ zI%0-z>Fw(X@SF=4d=#12PN%G9o2{b@L35$64ysW}Phr77VB@SrU#n3j3=9NyWkj3a z7NbgnwC9f)DN34#mz}4ID~zvrPUa3rskqa#A><$rx)z`t$W| z#*a!P+qCV@YBzCap_{PQ3ovU%b&ZRhbbhAg+b~FF&6|JY(PpHa$13+{Uz+6o4lkyH zSWb9qR$$~=R+U~7p@CS9`D@IN=-Z6E$^A8x8qJLH=#bJV$3gz02z@c=`8)h%qMHZXbpLty+uj zQ{5peTgYfGy3l{RFbZj<#nKU^8*;%GYS1?5#YSG?1t6Da?v;v;-CCkj+gXQr=i5pi zJN{SfN000BM{43v&X2G5UkRUlkImg>6id**KVT2MI)(yIpNuMUAr5!Q?f3h{{NWIc zsRVEkfDQuO2>2ubkOD;U7{-um0aYg2?ReVwwgBwG*aI;KbO)UG5ZpD3L1#nI`yuy4 z?u^}}-r&3ecY|waKJS3v2)-bEp?$*p1OyP;Ab3DG^8N(E2!!E?G>BXfkb>w1Ve?|; zKd^|R5JtgL!f-@^<08j|kBFe*&K!t^7z_WLrqc;r5xv4)gg2!McSv+`i{KWkJh-H9 zs266DZfF;7k@hgjY?lq(q^&zBE~%=aMNPhPOv&L+@NcSqWZ{Dn&s21lU-D3E<`xVI zU1(iMKe)C?(3=&%(Lb`+;71Gd=`|95YD!vJ(yHGKzXLLdV~uWOdgcGOszCw#S0l1N zkp1t;{~3h-6IF9GF)+3<`M-i@ua2ejU){wW8UPUF7Z?EGzlZ-RsfoA-l-(2n0NT?4 z08;`H|_75TT(^Ql1*A2R!-{)zSovz>G`&5`) zpU2;|yj(oJ-QSNXdS0KWF}uF^C$l~8x6>}QI^VC0EcQL$moC2EkHe(1wmUxV-k*bu zxjDa&Q;)yLP}pSQ<%f#zJ{`R>nmo10w}mCxUkuC!~7y5Eb3la9^sn6@Z$?~m`t z-ywY7Ee@$$MZeXmJ>TDpEIGY^7Wh*Z9_epA_s^MQe3;*P*4s3H%LL@y zeZ&fHeL}}XApa_6DbO`J^bSH}67||LO*YWyi9-kqly)F+chLoE+q;Bc1s=;28!tfY zDB9-XszBO;R%4e3uhT>`7-gYm((-!{By1sV^(Ogcj@dZ`B;&hlF9SMGLYPcKkftl^ zfipjR#mv-vYpH^AqM5f)-4p9u5Tc+WaS12y=PZG!NmXM@Rx4F`z^inHRgUbP5%<8M zEj*2qEQ816K(X~JFR(_;(M#zTU8)bLio}CO@>giB(V@}KL*xAwjAXT`3~w@&Yh{+Fj!5Y7r+iRCma4Qb;4T>@4C6|OoR zypGfKU4$xEN|{gsZbg+yr;alJIGtcT`pIS_jfQ=PS{!Y$Q25WkOR2_<+F8!|d;xWD z>NCWiuG&^9U>1kf2J*ajLJXA4!&^upJ9zh4oj2fk$x9VD;6NE={IN|$>E0P?u6b~& z9?CV7{iV0R1*vnC1*hM+`TPc|u_!;Y;#U+RVA*JTybLO`n5Tyr+0f>}Uz?K;xl*>m zct{;MIIyx+(j%ra?nw=sl;Ec6-U00waVpsj?YFjqT#pjS$ty@VHbmHZ2^0u1Qj|g- zGhu>AZN~G#+ppUT)BtOq9F)?}VO9JeLMMHoF1V}e8>o%Z2oglw z@FV&h03~m!@s7m_B2m6oZ2+C$;N~hTbbRTHwkkrxLf$@SUbjx}=29ok(5q^J{Wy(F z$hVNW`MHWX_A*(SAF=rwLR53&x;CW;7&2a?`DIAEsu@Jf_IrnDET;k(cP%O;P{-i+n$1b`Ff*dQsdin85EdQ_87^5 z(g?MT1-rD3e;+q>?UqPMY^VbLD&X6`a@Tm0jbm!hFBVy*Y=!&NOFLzvcqs=iE0*RJ^n-q!S03Dyu;Qu3DBt!I%-6V*I)kBeE{CjcI$7u7T_`X6P7#xlW= zg<2GJ0F$=R-zg6?*#Y1RZ3B+ueUin0o*V(hav)6)hfn2?mp`I;Kmo zHmGzz>;vQtmINM3ZhwR_0zZLdA`roPDnI;@(nvKE{ma57N1hxi{S||1Llsp(5bEts z@u!S;=Z_7C7rK`wbl&;e+pkXG!%WdgAv5je6HJHd!wE%WfO-BcVufeB1XCa$JC_<{~C;!7{XNIQ)oJ4&>PI`a3Lu}YQGvOUZucUuh)&I;DO3P z+{;Ti@_Hph{rmwm46^cuzm+nvR6u=-AZ`viZ7^!sM)MB*HNkDk1J%8*9DtM@S!Azm z2-_24jiR9~0VQ760_B#f-QpA$6zQ6L%V3=BM9S)7)%eQJFU!Z~GAoZ$F>TBe{-=BM z%YGuin<$u-GI8w22%sv@T?VuO0Yq(G~9GYG{QGFH=gUT)I zL@&Mou>xw|8a`Y?1I;99v>4U1TiZdeR~zw(s;rgUii0|ViCSq7u)=9gk`zbleLq-7 zmco)K(TeflcM%g>PSD+=ebx=dT>@2Q7@&`nEHEe;?a-GHTmx&e^U zlvoQc?5wB<4S4EfAwXU>H{m%F(bFf+v35u|8%_G_(<{TRI{-!4d=%E@(`6~>0%~e4 zW2!Wdn<)Ix{ZZYnhRrp_Z3Ef|h2tg&MuXY`oE5Q6535iD*SHDm09u9o!4tdSO!UxP zS(z(lC8J65u8m*7eB9m9)IeQfpw*r$&JEB+#wnHP#zITEp{<9MY16MD z2R_3411O?YNK~@gEW+UDjjB<7y>2Q3c`x zuV`y*`C_>n%Cq{#BOEJKmz;qTfeXHCnYzZJJ|}E- z(E=H1q;dcR0ba1}6Va)rXfw-nwN6coXi{wgWqR%~=KC1f-@ED~l9K~cKmIlm`xVFo zOpcROf=tKSy3&>@C;O|!5US6J6Bg6OgKF;?6h;~b8McsBqh2dOyUH~3XVux zomNF!{NSAdKLS%&)D@Pa_Zd1~WLT!V-0mjkY}-P|=ihxDeK?4Dk+j{BezOy|?a)KJdhm$N=C| z)aXAp`3XF(+%Q1M3s)sHD+p6aC+bAAr0P;~ct$7U+HX-8DAR4wMl%YuGhr*g_Smkp zdYjpUW~dqO447`cbfoNCeV|$M#}eLnAm#&C(15A*=MAc8NUM~xY87yKJucM@gS;vV zx4PdDiDa+e*H-dFxSIk_y-WTLyKwI(=xiTS*a?jir7?JK4YfMs1Fzv&osm5Xp z;e=#HTKX}rMqR;F`DA^+-4Ii(0zXH4mnrF}Q~iK}>3x^Rv(bnZakci4{Q$VaCGCym zJH_)>Xrg@Fq)}|4!M)<>OaTha3UjRjZ@-7}Tt=kZQbW#F0y;|vEMGgn`^k5ezrHwS zBJ$Q`D8NXq0__Qb&zDALlA5p$+x?*gIV-N0WJIAPbY?{{nr0bytwYl8*3?$k!O$^S z;M&@eoZ=Mp3PY*^KC$`Kt&;#$G1Rh!W8x(A-33M+D3wM+Q(OtJ2zS#LfTc>AfGYQf zJM=0BnU$o>sa)qoG+NnAGQ1!^50j({%l@ za;|j2YA>y6hlZ%d_1`kMYG<9g%q=K>7Ub2d05ogRvD&qQZkz(=xdHdGzDq@Y_>BaDD%cQHhK&R6*wKy@Et1 zV)@YAn>zS`48C}?9Wvt2ct>Wp{+v4u;Tbv;KAIqxStuDHz0K_j`fLI4Q#de|!>j;! zM-QO;9kY%Lf4L?OuKm;Un_MnHC)fk~YwWbJiTZk&aqxy&ZXyZldK7N8p8>1MhJ1?_ zZj-)ygrQ863V-IGsMaEB3YoRZ`k0UR9zz7u%c;Q0$S&qpTxSTkD>*G+>mN~%GDx2x z{X;i_&wH#?$(R$SX=Y}1U}huV+QP@2hjRrCtUiNVPdwe!Z0T`UEhghbblaoGl<2-+ z+AE<*l+G zph{PUEF9O`>|G_MVpJa(ksO1jEas9l>eX1pGh#cCr`Hnr`(35J7rf4ke}$bu2bkAj zhw&r-uYAcDqMHgi{sXk95zzvT6#w}^V=cUyzK>=N&rJDbVJ)|-(8h9~gZMGKkAAas zJ2TQ-;j>uvR0FZ_l(XWPC-SG#*RK3rLOI4cmo`$DPR_boYyWy=w<3&s14@D)S97ZjI=#Ikl8D=$ZSZwz6se+IeT)qM7<}QsU&{t$cmwy);N~96Nk~ z91J%_l;(5X(%b>eeb0E?LSw0SBiDj7hq1cm3=?N-L;i%Y+HB=p6S`t^1LSP_6v;Al`tG78ZTq_-|B3A`! z1khQ*;=%615XNf!jbEVohrL=7EWeT$FFH zJ8-lxMTt<1WlYOExfpQ+Yrq(uIR*AOhSsvgv?H*SZ+O#DdWh(i0SU< zG51R8azHC#x3-P(8Bp09%d$RnOGmdN_u|hJ(@Fu!A~*+va*F;;3G@Kca+o!wEgCnX zDk@k?X5kxs@E?W~)r)v)d*a@(bz$uSZ-F4WOqCH;x>6CAi!Vl6)YO|VGE=lL!dzyc zD?GT5X~ zX$Q3KDY{ze*UUwfj?=#I$nUA_51zNXTM*hVs+lRA*qjKG=?Oc1tg-hB@`O$`6a-l319PPj_!Q07 z63FIm0OQ7v+x$Z@HwGu;<2%%G;gxh5D$1?B93jhzN+HxWdhHzp&d^ZFHeP2uVuQgd zgBv{J(;Is39p44#@SS~5uGL-rVqT9GG*1hiX=hKWK~VMF0L?4m*gH|yHlG6PMrXm2 zN&rP=h^3{8XfHv_!jfS|kt2$?e82M-);#<&UWBAm;##tR_i*o1Repzsley~?ygM=4 z&y%Riqm5MK8rD~98JlF!uR0ux%C2-HxYL`_lG*b?c77au`o>y3>~)OndF8suo8f#+ zh$Cr*g)&C?S(eFji&d=Rqei9-X74HsHQ>Ck!U!XOV!28WxbGg;EMLu)F3%=S*5Y+p zlH#F_U=xsl2gmt82*?slfR9KoiyZe3gT#&vZ5hPeR8Wiz#^IYy>)$d|T8R|ULwsvZ zIzn+3=9FII=fe(f4=oa)@A{AO8!?E>x-3=rld@1oq+~aKL1ZS}-xXHAfhPEd?Ci6O z^=*G3E_GoyF?hV-Vs$_)3*-XIl7$Vlv(v^Uz+&1Hy1|qIb#|f;u%M z*Cb!W{R-JMe;r}_Yjgu_ILCm?#Dyj==V=8qDH0eH2lZId8hHhTZ~P(8U*<|a)?>_; zUs~U{x&P(YV_gb>FygB}>)LX)co^~(y=(Yev_pvp0cpF#T-}YFvW%6{n5u07j_r4! zvX9P>RMWLx7enj$o(=r!$#~^hjw>htn-$>zB;*T}j%zm~)N7j-xk^LlrSjPk zn+@PKhi-S3fs5UHBcR?zST>LUx#iZ}UC127p~> z&~)Wz6m6i#%B{z<6Ynw1TFTOC=0X$bU?Go0>3NFOM-Go{_oaM8C_=mB0(9YYH(~MI z-qFgBh$m3LNI;<>i2fOq7($Oe)j}qc%TV2u61opE-(k{2-d|CUe1W{Szic`}wIcKs z-@l7L&=`!8;-PpRRCJDXGAQg5b{phA+J?p#en1Vy*7d>{aDXjt7FQa$9DL{3e$o97 zv>;E>52WJMQhMQaDYOMMl%-ebul_R+P=Ge8f|SB*Zvxy5(X>ze1uw1_a0*%%oII0& zl-3)zX&#Z!$D=)8MYE=DT0;s_?YNVrJroSo?*Ir8ODA_|b02QwmA|RC;Wz(M=$m8;% z#-NYW>q`Tg>WnwhkE_vOd(K&3H=T0ODQ!+>B2)XqgEtX?SV?!i~QOBL89 zmX@p%dVHk+*6`%_f23u%+kuX-V@cf#!J%L`G`oOfWGHKUjbWRgU3jO%#SQU(x#SVe zm9&Z$7=Fcgj-LNQzZ-V#C(_+>ORsV*_oMlH*H0HA(6Uko&Rj>S9PI{Dv)-OZjc|yQ zJInuF0+YnsC^8c>gr7~CPLM{7cXi_yT^!H<#5XBe0;5CB za1srH*49c_CI`X~cC{I?#YcB?f&@?1LZu;-tEkf!+9%Aj*4R8HR5NIWP<2_ybA*ir zx7Dl4nq(!#xi$ek)%D=(ntwjO)SuRAf;Jl9QQT|k!5?ziV(e6GuqYz;2kwZW#YVBwSUVcE|SEu4`#W$IQV{VrPed#M;djlma9& zV7So6&Z%&owFuo3%+06nH))Y*fLOYtwd&YaPAZftdh^QD9m|=uBD3Fm7t_8O{j&Uu z_iUAHyDfQZ9}v*i=z$-p50^@Y-EQsi%z{*%%?|~ajqeD;Vl)Ape?x6tJ^d~#srs`1 zF#s^!RUYg$Ewp3O`pgX=GDu5`*AqoT&#Q0^dKsGg%2Fahc1LY|=1q*Wbqp>rBD*44 zbx!57CE8;v{1Q4p9lxrJ#JBItbD?~)2_&8yCigU&)Llm|ZIJu7=uIR!r<74M>(e&0 z05yq&^nBnljN_(cV!Ndg7d}qW$%sE}ZS*2THD4?gChsfVL)-3y9hAzR_E8KJFF8bd zz$q(&I-MOCT)?!<;78#SbDbc=>V|~)Ja{G^cLqrXd}93qL+Fj;P2g$AWrhaQO(Oo( z-y{h$zEee9GnX_nF^PA(MMWY$>x}_3#4@@e2^$+Z*bSxGh1OsQvX#|5>R2u&bNU9E zFYaOkY0ve=4TvY^DbA+a6fWHbsM;x6D+7Ehr(s~YTcxSt7@HFbRhCX;*U!`PxhI3{ z7EwqtYmAv}1$uvi9b3o)pN z_M)SCscC}<)w25tsZ$!Aa7!U5sWXOIhs}efoVndLy^YwdQx@7LOm)q+LF89S9DDtC z%0hW^f@*p0s7i^=>$e8Q(B{1j&?@W-W3W*1^0_9^ouN$MYAF51h41c=tysHB)znc0 zRob&J$y8R4-Az~7<;>gR!f}2#01wvjOSG9izGmm+sf!dm{gJKl%DWgTD4lZO61U6| z7fyu(bL!@tBg5{4QZzG!*|SySU%kuJ>g30^fK%_ z+=|Yaa@(8k2w*w2<*b0te7uFxob-2ezX52&wZ0Fnp#-;%VR!}mlFVbgdG z5(WI!MVMaI7Ix#P)|w%BgRGuG%gi@^>mHUp(TsNy)Z)gBqec{Op|=r(c(Dq4)a@bv zjh!)Oj?HZPptv$yjCJn{-MaAt2CXzN>Q?_Pvk@^?=76V(Mhf?9gfLpN9L3Xr;56~x zR}T!WBJ|Fj4Df!u4Rr^3AD*(olSCV*m?(z1vWx=@S1HRftnrlOLhrKT2@}8KV2C#u znpzTbL0H*G>h#1xw?@eEL@e8~{)i)LToR^lqjbQ_M}a@`^aUU_1eu~~8+W_wze0!p zpXO?U3|5#;5U1ZOjy>|%I$TAB-wa0>UhzW5KDi#on0m>~PY@iPhpyenqsj1u8&eSv zL=v-4cGeQY*0~e+7b`lsDgntDgQFgw%{JgQ2>Zsg*#l|eLzruLFMvrY8_HOC?kP@R z+9ZLj7vzMZf?zKjyaSncQwzj1)7#Frv7o9PSn1A7SCy%s93{;NwBK?`Q9>Ri(lyFKw$yuv<%@>RqAjQ{ z_XAC_ReXOpjYH#F6T2Al@Jy4&<>IYH5(bHKJj`K0rf{LO35&bubA~$BW?Rz64?}Z$ z$VE6BmBU6l3BCdfk$ulr1n~y6w+>!6G6>M{p+-9E20#oIZ#NizMblDvyw+}Y!nZ00d|?gM~0{T?D8k7liUKKZm zzpZ(DyhzFyGd0N9n!?UK2dl+RzLI;orLLgUs7jx;`gO*MiDv@ufMrP-VaR1x*g$UH z_3+q5@3r}w!zM}LD2O^udO8Fy2d!%5qDS_qLz@|X?rFw1y?XxQB^(Reu$gukktL%s zN!G{QMIs%vVwW?_Z@X#hE;a#<-+bb>j zjKc18(1Nwoy#R-0x|{)BWFAk!RSj2MNAIOf$PCt69*3hmFYOLlI zf_hZr9%@r81CMj7V{}*fT9OO$Bwdan`*mK6VZJn`g z+qQMa)^F@Hwr$(CZCjb|-pW@=>fR(($)8+*0}6Q_-rDZ(At8+2TSmHPL)&M^nk;Ic-;`#I)07(=p&5~Qu_f+9Ub-8- z>+1Wb39xr#$FQv88R%Nc<;J%-{9(IpT$QcY;RPh;-qwb=K|Lq8abtXGppe08i*1ax zcTG}$FG-H98TRzR9b;nXQ2AdcbP9C>Ru;A2@V1|ZA2uFAS{Xlwx-WeXapH&7S?yAzVI ziPKX6XFy;M$^uLhT5R`++u644f;$izLiI2*a2s|#x4J^`;9`&cN8oJ%&=uE#x+M8J^c3|JUh52 zs{`V8;-htmIkj8y$c&t8X!GTvRbd|CGf$E}5FYi`jb#sMvwKvJTTYF-e8_Sj8V1K5 zgKI}v{iG0}n}!@Fa+6^iWD+FOpE=rO2gf!E$H=5x*F`OUklxqy%~U-lDj90kl&mvW ziA!7SyTg$@6}7T(OGW+ZdO6PDAqs4^2Xya~%y-_Tzm}m1sto(rvF_C&CEh{o0UXmw z1QFLOt4)L*o+UoT3ctBbADc1vJUg09aofx>M9+*dlC4Kci|oDH3N5Y-j|y@R@NudI z$R{By@)HsOQ3MzXDc{L4!? z=;C*=iozqCi#gcyPIfaC&0>((k`|tjsp@8JM#2EIX@0(2d8^aK57g2I1cj4p(wk@K zlrEND8Lb%(mtgeVx>uS0hdRBtUu+dL`ML5w`aeWzLb}WVc5aGkf;r+^wrW(bPDGE` zxLf7}rWldfpycsR@X#cIfr%mz)vn_ZM6$WEY7dpaZbMD{>9*1lmO|5DvYRPG4`v=> zGT^f*Lw0Wne%FTEaRM6AKldftTg%usm32VkXtMWJA&CPdl)I$4g~rap@|d$jb}`vh z2ad5gNK=U;QX?sL5RQ-$;6tr-cjPXf2P?;(jm{>%%%Ei>E%63K}3kmnh{dw@$xs1Qa^?L3mwKXOG+H{v8&^Dw(1Kh ziqsYzm;0&v!THfHUM_F>(bh|LczsRk1c*y&$>ef~JRNhkbfp_rbKZtz;xCFWqq2Ey z&{(MOTpO+i9`peX^c%DXr2Jh;`jkN zp32~*ISO~ixc@0yL9!UF8=lRNoHS`_Hg5PQw%X|5xkG%U4a|E(aAbk#-V9rf?o#n#FBho$^qLzIh|3U+%Ozk|15Q9V}K zT~WCUa_vl@qh=Z`ew|(z9V*&cv4&F*J2z`y`sJh&;(38Wx~SIp)B<})3n-E!<S1ed&yWb2jG*l!yYx_$8i%+++ zBcdsCcdi(f(dJj7@lDTjAI;S+Mc0C3E}F%K=II2IPLvlzL0m&Xmd9ll%0AC3i5UbC zFH9GHR9^U!AByRX6_qUs_z%E*2q$8_enGy&Uq~IfnCoH|3tT;IK2S0Shgr6)BAEOx zl$t4P0ugmHtyRjjl6HgFOoIp(2!o|HdGW5)7iPr%iD-`rEYYyTsH*l>Tj=L5?JPX= zy{e~kC)f`8k|sIRD!7U=A1I145pnkAhG*y%<;Q2DDSz_2m2R*fWCyG&xRJUEw@P24 zt;y?3d4#*HqZXs@XdJjuyP-T4ZqswoQM)P6jCqbv1io~nre`mOc~bm}_nN=BQKx}c zMqi?@mP$<J9~$uGm+nvjont+}B70bm|U_`=P)1^vj`lB^aRGK%}eG3Du&o9`wr zFAJdlwOQl4psFSuw#wmpoiQg|wjYaDY!)@rGPyjKoHtR}9Ulj1S002 zC;<9O1sQOGMr0Oa{5jUf2OfAt;?Euh>L8aoWsU*v2(5($zQX_lfUa=t1L2Kw`~@bZ z3_o>kKkIr|(`6IiL;4h$ntMqdpm`5s>5}+%I9I`D1wVMJ|JcmOn+z4&YG^rr?LK=D_1;3l}Lth_q zk8QE@Ec$ixvW2as2^|E0^{pq|4y17)KI*f;d5NPDGpqogqGz$u|*77*CEM6EaT*dAH6l26=aZQI6mPd50(>2DzV}kg6Fes9dNalSrFuUIDHm|&Xkcw-=V;+Grz+__TGpf3WxYy6sOHk1Xf=qoe2h`Ek6u+J0YNJ|3yp zt=p+*gP;A(j?kQOez{%F$N2i%e_Jh=F+AzpzI}zKn$zv;>F05~ep}7%_I;7v^ltae z=YD@{;neQ)H5w^LZ?pV4et)*T`TS_v-MM|C`*gnedDP{{ck#5$9vkbnzMj#2#df}U zIX`*d{Ak(y-Q@9xaQf}*>*AZ7>N9C)F=^!~m8K0%8+h<|^`2%nwPb{B*kYO65oJqE zvWQ0niD^d~PH96uO>w24wd5Y0tWxF?fL`A zO(!nu70Hg}^P#rJ0fwWQ%Pgz)=;QjO$DAgfg6(pw_V;l>y0MFe1rmiM0PWA)M-?c087{8ZwMY1_Ul6+5<_AdJqA}eccIVk2sua zQISOflz{oKaAHxh2so9GVM~A0ToguLWxX(Nms4bay-HO9CVE)KAaPLCa^}?ReWDVD ze;WvN{3@-UA4YS-@wE?vW^ws7U$D^PEP`8%xik7ue0S#Ox`njkU6iSO_MHJFFIPx-yp$7%|+HyI;mGN@s%eiFe zD+v)A=@9z{YKlT~94YHKK8}nVitU~T2r)tSPayvNZKm=C1v;G^ZOYna4<~dl}xYj%9h|;dNJg?ps3N(9>*yYi*W7O z>~{-yZNW9BH(7v|;W>iLF@drIMtKbTXfyR0p#`iG#*q_;PqFqQ0?kUmXb0uLBE?0; zcP5i+HZcq!Hz`cS`Pf^WhUiQBgCl>>7~{o|lvIO|65%QjZE$R!XTA$aSbhM##_+Jz8z(L zk@CV1Ty^)+=Vx4@O$36q3yu?Ncvyy*Cqdq2;y0+X5xXzfzph_pdY`8|t@I ziiwJjwQ=tFX>hk_tnLxROyX=ftOgR3S=mnCCQ`I;6LK|a+8>cw>A&K+aBH^5tUZqv z@=B1w<_SrT(KtK%kH^9^rT5oMI#3K)JM}+=8rI`M(g7u>P74^~!;oAxXQ~#GbUh=J zR?Q%`4grdov%mqcEq*B}%w?Qw#JoS)%a==k6X)ijiOjA)qa;zqa`T&5&~wi1<#%;T z&0zzcNE-iLrK`wciVkf}{!{rynV$kfySCdK$R4L4mLzo_g{HnquC~8r0TbFaum^N&T_A!L7=2O^Wl zkgKXhLYeg{1XZ}`@JqZ`|5PU0Pd%|ETUgo+XeS6fTDK4p*ZeeNcZsJ8J?q%>Z5ESA z4Bb{hPuQlJ8vcYNgV8lCP3gz6ly~jofs)ub&iw=J{=C#VwQin#8qwo3xhs2YT)Ey% z`l~LY`y2Q__JRLb-c*+8Blq9Rn|S`e_W?%}BLiz|JsSffM>{=33tIz64>}`j11Bd7 zQwt-5|4?b_8JpNTnb7}d-p?>O_@C>VAn2IJWC(zO)E0n%Wd9FmHL|lcwJ>vW{C^^G zqBC-G{m(dhT6h1)I5u=f0sG>Po#$E{4J#>KqeBJjCqK1fA;5^VdIDI6luJsN?ldSr zveaKNeg-eky!NuxAfx-6!&l^ZyHh$|>%0D8`r~oRPGfvNHSD)yj>8P+6ys2t+QNK^ zx>uSznPigN{Xs&hKat~?kv_g7&&xh{zLM-I9UM_Sm!4CAuhRAh@2Tzkiv`b^_2yGH zAL@wv!xU$+<`(a(d@(l-Pp%z0s7{MS!-zmeX3vK><725VIr_oV4HEZp+0ebV z^~2F!xLErSX^7aa z_w?NOV`A2|lE2^eXzt2Z;W&as7oYAeGT$vwhEj5N{rkbOv)_ivvHhz<;_*IR_XhbV z?>OHddOfCvcK0WU1KzkCzupU7@sC&`uvtnAWMT3GDMTa36tby3v_)cYxA4#vZsBDk z+Z20ft5D^m{Y2aQ3wwzG)A%DXj}3C{g_mr&-}4In4B_ z_k~W0=mfs>wJ(i*@)vI(>YO5pCo9LEpgrUl^k(0F75mZZ=~(gDSjaJSOC7Uj8HY%j zTE4z_oVVeroZ^ogtG9SKys%VdE?-L5%Idpj9a*`=Oh0Si*Y}&;OV&;&B9EvKpv4t_ z`OwiWaR4(9%?q#e+G6C>?5B&{Xo9_a>Paf@+~(migFp`YG5$LR@!2)I$kP;fj7>d) zdV+r-{;6*cJkC367dy5mvZ87oCur#S>6KI6pDmBUo@SxiX@5IIhJQ{x&|onSr(($k z`0m>`wUbpNw+~{jXCszDmhxdl4LY}Ban8D*?2zA-@8U71ls1IbN}eldK#spGOg1K) zB=Thc$sjkb`T$ZpuapLDTtctnv#t^lR!%26KG?3uFYvwDp2F2b zWk6N&5)~lWSRv)E{cd-ZO=!+(zYu#md6IdP8XgfD@fxBNWFDD=v?-NomrCE+jL8q< zEg*Xt(!y=}Y^>T0i9tVt1@8`SaFUvI*@6B4ppPrQt0BzE3cH7`1 zsiJW0A&sh;jn;C+?k~Uz9`0|(xE7kgE(P{QNp1*_h%awr|vjN_Y zGVb!&y?Bo=GAMJLo@5q|vOf=jj&%R+Vw>s^ov|J1mL4i(cZ#1{AL){wg&t{F6h2qN zbQA;K5?CEL?G0~Nhx!xK2H}&xexe|+@pdO-fpE7Q=46eL%WQAogC04+URFWliN@_= zx~bh!ZjwhS;ztJ9`vkacHJnN%X5ie)lK%LFYVMxo_7^%~a%mcz=Jxl)ezSI4|6vZp z=WvSIDiC4t&Q%SIZ-D>wVKE778bWR+oLs4Yt6f=P2HnAw+K5^h%2vuRu6+o~(bU87 z?IZoY+QmdxoI3DmZs@p7904gBOGXyRn`Zz_HORqD#y6*wjmM_(Z9hSQ6S+CfEZ&2| zqgmmU1S3H#^b))8?@m&Z5Uthv(5PiKigFsSj2G0n%<2hU{LZdgErSSA3*7%qG+X!a z6n_HJZYi3Zb_y4f{LV|ua7DGXiAHBa4Xr}y;Bq?ZqWXb@=-=3~Q%<@UbyZnQjvL-UCL zmUi3WA%m%1o=#6vrMPY|o^vdYc4kL&dNZOza}xF2#s;xoGkjCs*EfA+!3VN`r0wjh zXD|yI*xpzE5)P|-*y7lbJH#FHY|Lzsxl}lP}7tsYR!DlzRXqi>Ok<;ep*MZZuUGdGu`Nm zWQ8repR=;%b-wBBBE!XH!{C+DDSwdxZEub+&tlL7D3}2li$wy*>)0BYA8jXQaG$;s zpv!0Cl%)XXjkRzt^pk*r6udU-=!Ln+qGD+6gN~y-I#ztqKrPB3)HuI9J>%3h@NNCe z6DNrb$y3OfPR^mWiZ)`SN51c`SicVlhe?f78mJU>SW8>U>8c>_U<#4Y%aLX2DX{Fs z3yT;uK;LxDfwbcK}(ofdyu6G0rv;u8jQopkEUw2n~+do9lfM*G5R%ou}ExnNiKe;hAzB0a*~i@&Ak3YZCQDc8wzcGR1w zd^1(Kb$gY;#i%&V#{Nc&ru%z2i@b4S&-yoIdUvCNSz-RU4QZul{BdeYauVFDX)5!F z*Ca#l$t7xLU=XAJ}})IQfM!-RW(Uor3rH3k*u&u(Q*|Iw~nNj2fnu4E*n%@I79(AFsBWwzZn_#^_|B`Ta$*4Z;;MRck>g2D*yvr zB2`o+>2>@x_Ihg!P&gH;#m&!)+wQc;rj=Pj7$Nnr59%g;wEojb2}olS zMIKlHfbw$Ys$Q=v5fi*nH6p*VLt8~%|7O#FL#k89P%B&q=62f`aUmG-jESG3?&XAsA4p%3yPo zV{WE17#Te6QemUxX*XkC|Ev=Z&$irbjSBqYCPqSs79?}K)(0g0hKMfoD;UJIkQlk> zp{wz*r@=ll;LblPZEZm3h16yM(xy5nDkwumgP{Ee@w-$B2sCI+I&vI)*~e49VQmGebj}oWHx_shiti#dP}PHv#FRaIpLV@1cJm4uAVrTz zDH&;@fHkDg@;B};-Wm)Y#|~eg61ROQ#hj8Z=RB-Pz!>8vh6g?v^pDG_Y?9yc2jeqv*7F9dWwZU5@_iZ5&SOm%UQeaONCg#e4IfU9zai^+{j0r)&CI~{g#BxM+ z`W2}0&pr#~%U>^;t1x}uv+caS%T(wF2rU9h$Lin8VMs%v zi5QH5nDK-gM69P>g;{aOC-g~PcZCFF^2G3>3Tv1k*6AgU!e!nvx!??01%d>7(GsYL zyWKPv#j&mnrz)&qV|0eDzOdH;xdjo8_OJ>lAgGm_M5LrAJjG2w5T(J^lD0xzC?|iB z@_(mM;~T42-!dzWx!=t6=KmtJ{GA4w11e{Jmjj;Y$Rr(iAp%EX^N0^i|5DC0#QRWo zOqO<#vjF@Gi_sXN$P0-zGK=lU9Bp!8+@eGP&{ZO;S23xN)&}Pn!L+o9fs9nTI6`$2 zia#149Go+!TwAOWPg$|vO=CUi%Zk){x}p>Eyp&=f=UHee;a(+XBHN88w7cO2MJ$!Z z@D&PJ+Owf8jU+ZRf!nnV3Crk}1~s;~>BM6*S|Fg~xnP@Vb;G)GOnU;I>tHQ(4_e9u zih%b3v7*^&XPJNu7OPYw-e3kFhE>buea3rpaY85c6D%;<8PAMwVd{fG88zF)Rh&Nk zE=h5)`a>Cv4R_FqDjPMXW1t%3Dyk(c4G#jV_2P3S3Y6r@Q~JDtxpcAhd1i`n>W2m& z_Z7@@-p8ts-jCQRAp}+ z>VMFXJV?x=3If#1RAwXB_+@9PO72e*KyjMVa(oi!TRXt0*NHD@f`fZVSd*b6wjQRN z)b9Z><)kC`6H^u(4N2`4AN@i&k0iAk}#A9ctYlLwSc?e9i=$uUAv zwQ1=Io046iDA&cc%Q{?@2?H}e@=*Q#fJcqS+|(fT0m)3y0a~{W7Sf<*cdkW76yYA1TFoS8`A5qL$lu)niv zsw_2CnMFNgG%L~4z1S*N9FOWVN(#)5K&D0kC9O@sH<0nfAYKEviflH4CD7RR7-L3` zz)@Mror2yrxQTR9qf3rppNc`g`n~95+oe})b)*I_b)rQl^Fil~%|AbTb)+h#B?szZ|#5P|S8YoL&Jt+v;lE*iFJrOiQ zp#H@PQf+FAoGmM%QBGo&wG+{Fc?>5?C0Cc8BmTRnJW4vbsa{hKdd^m#%!L%RFcWI0 zX#7^kR%MU>;=7nE?n6q-sC1HaC^vnGgr1`%5SEkM(+U|{9t=RA3cErp&d!OitBzS@ zzB|-*r*K=KN4ic^Qj}506_BBjRyPU`rdigBiQoJ?%-d2DHMzW*gZGvsuqPjNXH6DB z0>>XSujw=ND?cu*jUZ{JW?I?$sEBP$zfD!t{IDXw(D-wY@)1VCDVQauA}iyCMblgD z%VA`IZkt7_(dX;|mQ@%SFFo!bWfoKtTIqlR3)s4OB?YQ7bArb|i6RoPY_uor&b8kV zo`q7y;jDq|-3J*$uCHgahYbyV*gL=XEie`A%wOxoH*Q#S;p}iwcUmo*YNEnFvwvHV zFS)x7RBLlyBlT1X(w8X5XhH`AgZ+-1kkyjk$$%K|eW=lwm4$n4NKY0Hq?3`&+YCQV z%;%MfQqUCGprU-U;dW)6*#t&0=;e~44mO_*!qe1R#MPu<{>0{TMT(o(G#+s=!!;^+ zU&eazJSVvf?bW&Iis^~VrNh=1o;uIX%+3(fK7lnAGnY;1e;eiOK)AHt&ArA_eFlet zJX=Vj1(V)QIEFk$^irq9vh;oCJ3JG!{}oY00@@@oI5ct!jlOv;W{AImONuD_Sv)Oo#@9b>I4Ke z$}Y{9M~9bb+7I{r2M8`05O5;8XXZpV;bwK0S#SWC+EVc9r7Z#!hro)u5n%eRSvpFQ zH37`_$)ONP$|SQ4KkjMQG}_8u`B1gV@Pz64E?Wx^Q^(Gb*iHIN(tW3b-5j*z4lfu0 z4qzpbs9%>zLX&Y@0)5Iio)JqFmS=gu#4mbnLcJX=ZZ}DL=1YJz)1C9Ar?uXRiU1zP zP!yB-!bF2nziNXlh0_GIHpSSQW~Tv#4)N(>blfkHx<>nyodJ`~kURvj0Xj0t#4Jds=S?{ zNq=a#pNnv5qpB}3Hg6RollM=rd@#BM3Y33!!8=9&9l$2cs7plE!O?1w>NJ1sOyq!@2J&ftmdtMnV!^kFDo$1 z)eE!?nuL=XAi-w0N-cY|U&VzLvqoyoh^yLvDnCKKK_vjC-AVW@9TJ_8joqY0rRfT@ zQuvJQ-zxZ5xRZkHV67eX108({QPbB7$EVoBW=a+r+B~)??k1P7ti45IhRt;8$2`hw z%bIj|(j}(`(S$BRb;hFw-l}xk^UO2M-=H070@? z zE4D)GnLXQ#}6^sur9Yp`fG2UVvX`kT;Zr;n( zXL|zC1aRnXc!BXkv{iC zy2Z4nC^e@lcgyxRn^Br5P${TxU{UQ&js)I*y`F@Q)&uG8EfRg)wu;H%@J%{wHr(LW zRIJGrMVwI*l(#9(OQ8SklJk-!V-$iSx9~9Ra9XX6@8GBFbKn5#e9a2H**G62P)Rak z&82W&?q=+|nK#_5wow8%Gb@_Vhe>zR{4d<2l|yPg7)hNW7g2~siMsAe)t!|1aR*Sk zo{ee{9Q&xp#o;LoeKO&w4W zBJ?*1&3pk(C+p9dg(L~TNu5RuaUe_($@wqDiQLSN-*EL<2+|W$SMJ)LSnoP=&;QO< zjX?dOWJS6SW^AfdwCWYxQ<&w~a0jpkH|lfp0tNsO^Q6{1RO-Wv%lHnrsj8ooY}e?mlZOWtPIV=alxSVsY40l<$l-`{9Bigy`Iq-UC*6w^w7l^X z`SZEW^69ZFByzkh`5U&X@N86ciJM5Wm0zngF;`xgw4$_XTSk5Ng!bX?0NKfP(hDw| zD-she#{L`%GWuJsmDh+yR7n-41A9G9LHk8j5A`1y(Z@oWF-x{CEd>VyK|2LBIj0Txad}W-D?lex0mJ75N5S4g`0)%ZZ1| zZoOlr$_2L?rH*K;EzK!1N~8Yh54s#2)m+BPza<{53GMQYN1(vx9~pc7f`RYHF0i5W z66bUAJ569iC&4<1M_93jLL!5XRc*=y8yhYU^L;d7lmGQ$Kw?9A-GrB$wJB_|ZR&O| z58_ZM24ypkTuZ0v-U@ob5lWc)v;#gn(N)8dFk?7cMR$(`IC09w-W8~AV%AB0dj@Kq zw2C?PpFmI=93Q4H$m3OgK_it*Gl)OsF8g7uv9>5q3o*p;&EPAHLgh*Q;SLa^ z{c2|ewpZMQIW00gxj={JkeKFHT8*~TuD$h=q3}X#`Z<{l9z7nPs{@Us&GCS@flqDW zfHZH2yZjxVX)&aYk1PAiv;)eEe@Y9d=@sN4x{!FB(gSoks#6QFT35#R`xEfT-X2n| z<$jRPjk>HvEh2Lt74Jl@CYm8a;@YG&C8?$(TjL^;?d6ffDjToY*7ALu=eOqX zPY`*i3PlU1?3+kqA5Tf1IVQLa&+D)41sE1$S<8JBRsEshT*^JMmq*Rq1my%QM-jx> zF!l5Qas7>yG%HzpZ$`O^)|XY2OpOIX{=B?wHjR$*rNd>lZcJP&zpE+{WI>ila%}r= zQA53W;b1DPyM|wAjGb77QTRSAvam(mev#+aDO~J9+gk*(!2g%#8TD>2%hnZl%1GnI zQc;&9uZYj0kd{vb+ewM<5^S6|jW95^cADeijII7k7nBXHb#kD|G>66|_`a{bjLg8B z>*&SJWe!2*Q6vnA8O6dx+o-k+a&3H%a*tt&8TnJxPx053u7BH+ibiICRAhKH99PcJ z+2QZ`i#3jbDGF(k?W6P_fe!&wjCdJSl`U)&8?uZxQP(t}^>(A*K7&3y(z|QinOi+{ zSU{t(rCqxK4kyX%eM9xpqECAIYVge2Z_1?B#u(G(gY@P(qOOv$5|>dawGenU)sU$3 zc1lXR7k9pL!^wH+_4g`t9;TP$AwpljjYGf@k*b4y?OL(HpzQEsnx?FWCh2f}AyL+T z<<(|7c*2PVY61!Lxa<*pF-)HPt&4LRC;%W{H zXZhG~%3x{CeWZJL!d!lGe`oLN0y~g~CY++4Y!kQw`O}mXzAw|AeuUeI>AQ6p z`~^L}eefv$QFXB&`v9b@gD7@G*f^ZpUtu3NQzJ{xQh{?5D;2npHnhepGQv&HTLsW5 zv}wcbFdm6M4=F@5@pP42x$Mq1==BM4R-&CSb#cWHr6#-ViaxF>VR-DRhQU%2$$`&d zp-@+uiA;AK8@xAy;`M}9A82=9pmw`bDz18V7dgQ6y-55(xYmM%mIe#POckd%D#(RT zEDo>J#t~JsLVvt^O@lFhyON1icW!8A?NNGGdDf$hV=;QFnI%hhG)Y&{GjSK~ET$;%HmtfKv1^%p&qh>qQv#BhnhNVz@eB%d@5y#ES*;;xF^^%SKHMLcHLcHFOTG8eRw=E)1@ZW1v zxw1^xo~5=4W|t!+m+uI z8#TVYq*%48caL*RSqH#Pv39@hnG+(PnaAJU*+2NfcBev@kH4 zB7( zOF~ofo2{-U&|Rqgy6<}%&O)rmTRgi>6d6TM$@aianE=kKYh#JD*zJOVi*w35XNT00 zgP+6bQIvdow3n4R=mjup!SOv#am>scbPfy)BegjQDb`|NxTH2pSNl!j6{}7-@QV=R zwux-s=~Q#lCBOOAXj{s^&y6nDN3IP7XPoEj$Qv?@BtB}8A;Z$D!gI=)Xtm_;azvBdvjeu+f13|5)SyZIt4__G@)Duy!%| zUsH9Lom%xIt16YRQ`I|#+eH1|n<;#_&ioAqa9pV*Ct?+Ut=nKrq06bVY`=VB?IsS9 zuDFRM`=pUgeMWRzsd+bu`+RhcTqYR5FOK;>&vQQRs=tP(zaGlBKW;9IufHGFa=PEH zGW=e~bG{Bz{GRV~zWTSn530Yfio0JD_+IOOMx^k4mw$h`b$^`Z_x@%=LC#^?S1xxC%&(KLL@KK;P){iYE!Z-J$~5zadm%;-+op7y+_XH`evr* z^U25e{apR^dH>Akx;Xv#ZRhv7es!_#_iA(hPs(=~b3TdheX{!d_TKG#|NCuxTJQ5F z<9cgj=f196)9c{SWmv@TYY`#m`*8ib>#Mlh_t6Ody(LrSZ9nCM4&O=p-p}{5|NE0& z&+q#u?2qwnzsH|dc**cBKK9Ulz<)d0eii)ut_MFK&c3z#{vh{z-BkMydYEX5@4EIlXnoF%fY-8gN}TNGT-fHa8uK}Rp4oNnG?enO^*Pu$&bGB# z)_RcG@3PT4H9A_yc&=U49?})<^pSk_Iq5$d>AYxGJnn3!jh?ewE}mR{Zi-dBXLDKc zn&n$xdYA56E_Ed<}|Wc*$C4b3VyWy_MPw7)AaJ$I1e&f$$dvZyacBJ(N9mMM&QQL~{%yXG>mwDVv z&l27uGUoi#)GOsPSG;W(LAUBqYNw1+R-Z<-<06Q~ZqYKv4!sYV=K_OL($jc%-Q-$4 z3oFfnuMXvTYpgOwkG{1Ap5Z^kQL*2Sc~(!tDPE)L+O$<|P9}4P^Kr#UV%qIaHH%!L z&$mUTvb?dC*J)|upNy>%`$^4Adwf3rTLqN!S672>Y0YenMiF?)m)cKDYb)+GE3VcB z3uS|&RqV1^3*h%CR);564GPVv1|)JB8tXZcp-X_G`1a#CFWJ@$hiQxLyAwEtg_o^T z+_LCGxM@c|{KlWpDg>X=6D4fTaRnHgD-Rimh-mNZ{rM3*a=?}M=W1v1V|+dl3)}T= zG%G9XjgHfkW7p$fKv4L5S+WEmNruSMoK}^upUp{9!svK2H|4o5Nk?vTOWOU@N2UdB z^EO!qpQ=<1P!MDMOV406Y$Dy4X;f#kxoPhrYkdi1DK!Q_laF%VU{xy}bT^ZE<(bpa zr_NmE188qzGh-!3u4)GZK|dc@Si@QqZ{IHeykO1YTyTv;v#`LAQeARfC50i$cSc{% z_e{~o6d4ngdfAMMWk>1K#g9dz$kw}rv}h&aM9-5x=aHbc286y&KAB#z+_j>e=TqTQ zzET4#yaM^ythq&>Ms4an&iV*3c4+@ezx?vbu*nI>?b@zGbAocnqUi-Jb>t{4%Y^G~ z=7&6~KS*%fJ6ly%u#2Og7l(dicL-$opiMXa(cw~m%Zemr3n`NAs~8v*s_S|H_-wZ^ zMkjn(4Q9xE(`AroE}K%HP{D0)G$`Bt1if4`r9q-7>Bqj<*_D0v_!b9c5MB;fr~nvP znmBjaoY3AV(YyymZQGojndHQa2Og*uvQYZzs{qU-p><;HIgYYdGoAMbe8#RVQI zc#R8Jp9Zv(9^Z&|fU=E@9p2Iyaz9sMV2{S>IbETRjx|)3N+QX0J$+Y)T0%MST#Tgm z^x@9_LLK02YhAil>deEmako2F<8qZJ!_P~#+<8I2x{-Ap8L>@d)5h0==Mj~ zc*02X;~F%d%d6MTh6YoY*=yTUopi#vUdzuYvT-|uwn@TBUqot5a_FG+vDt-EjT02> zr|qvzh5ty5!OB6K4Q%jX@geDSo*5e=5DX+t^AsG5n%fbsy8Y|ziQv`M@W>O=(arQ%_0`wXX(Wif72ofAX z7S!%Q$c;2Y-y-_0O@_pFx3~54)aLZ94FEXg}_9nfHo~ zM&tlhY0E;bN%QTI-@O#&^t{{ZI!c1319xU`4O1k! z>iLTOOxPq%ZW~MfU$dfTc9|!t)4<3Y1stiW;F_-K3>&ZO#Qa3Le-Pmf4*9AnYI_|z zF;nS&p%*+mgmtd~LxS+Cn( zqug{wTbc(19N~RDjL>i7(tM}C4q~t#8KE?uO1g z#Ci75|7~3Zax9S=XzeQCrTQ+*{FD`pCp(Bxvq%38)>cyMv5|o2`m9_@Rpy znYVih28Q&Zp4K`@Rzps3!x5(5$_)MVpG3W0ER$@EEq^_Qoc&%7imcAo+mQV5fTye% z-}uqvTa9U7W54Rn*i6NluVmC&N`2|CszvIo-1QmHl(_kYQ&bDw0UlW<2ZTPpH8?Cr z9w>>96ay9+GtUu$&3Cq1BY+*vfdeNQvtbfjVJd0X0$Myr-}%-HT|sQJu+X+fq@_Te z3KYL3jIFo?oGNsryr6cVUY|CAt3L)XeGs2;Ok&eqJAykJ=R(eHHs`&pQ_LbFa(?H^JGOybCV5vc5r5{8%-qUna$__fbuV9;qCgACWx6a!uPI2e-Vfm0 zanGWWviffQa|<(1iK4ZRi%H~3MQq^+Q}Qa8nh*BC28NTX5)JJC$hiep^EN7ZC2X>e zTxTm%>=Djn?#=Lzcnk64z`*DHvWF;=^QOC*Hr#T5q$k?!zEU~@MFR?JIl8z zrJ>-b6266I+iGKYF!<$Zt3V_1u6RazitCyIjZg{Pf3d7Tnm@O3eIYhJE#aPxxYFK< z$w*C`UdjEm-M1WfTXmW^iwAwN+rfEdXc03pK^-$&^G@PHI!`l)f(^8+fc(S;C84!y zwYnjhz)32%6whKo2!|h-Ax@x?GFEiv{;lqf1X$`cFMN#anJcXmeXwb7X2p_#5u@iM zB?P4)#3gCJ#M)nnH{5ulqQ71=oe_=OlnHX$suEipW{M*@j)$%jD9{F{{#PYVZ^(v= z#(3Lsk28EunQSwY6@v7|lETITwA?H8Hxqmaee2|`Jj%kX_U zzeRdj55oGj3#dIHpP)%)k;B3ImpUUkrgg9pLXseVV=rl~hi_XkSEXd9)fQqw>IBn~ zVg2aaR>D&Hj^eX=lh(WR^YU%A6=b1Hcgh{nrj>AdyjOCfU=5Qo)u?Vo10fl>mD)oo z>yKtbLU*8)^@tiH=aV$_Bn-$BJH05op;#qYY&hX6^JzrPf`1cQ)FStLi-YlnwXhy; zByOVE^RmmFB>i-FJ*t@f+Q1ICIbSSgD502I3?~!YA6ht-OYkcGA$5u7tB6`%lG9z= z5T|UA3YPhYH93DISxnXy=a$)&YfjjwMOsQa)w^vF7?3g5No;Ex#v4C(&LfV&mM5g! z!|r`pPvy(GMmeoq@d{xf*fhtIl)mW?sp7k(XJVpIv2u-_5Fx+4B}v+0<)b;)P!Jz- zu1May)KViKVaJucV+4u4;47!j6l|buFyVHh~WD>T%Hn^1m2o$aB7U`Buz^68X zEw_j{PAvFt-L8R^fX8We3WY$9J|vAm&fp;9Zm$O*nbjD!4eWxf8UnC-cI@Np5fQDaLD5>T*CYdz+F{u)iiNs^1kP5O6L(dPBdVhwMG01@DkVx?6 z+d5%zSx{r25xvxQs(32eI9gk>Mmx4Ff5pjgKXQ^;S8wv2`Wj zrI-*9R>-ng(T^-9CR!$>cDXz=?}MqE(rh+@Kpg5{8~NvuhePs262)N_FB16~ayO(= zy&cBHl*P|OU!RYzXs>z4ks#GSdW=201tZJ=2t6P5_~aEAXbKcC0K(ncBA1urHdtM zx3KR7PIYP!!ocZcvu`#G(WQgTT(Y6Tr^?9JGWY0dAtk>Mp>5E69r&P)Xh|W-%89>k z6VpLqd_fQz+J<^|ea$Y%=5{^0rW>{Eumu`9shFWO@27mD6#kb9$0gb`9&*b|^vT$l zVwCV%!Idxm%7CbZ6AcI?dZLRnvN=R4QDPYzH3GF=^E3$iq_?wtI9=LLL*J?`8^B1J zS%BwC1a&@GYuci6Y4Ktsa8Yquc1*O0zDhJCB%QpTPOOe7ik?Ysg66^aAaYL|&U_@3k@jfdNJLI(WZr5Knx^4%5FjPO08!&v@ zJ|Evpu$q`5uFGaK{B(2;qI8Uggzq+=2`9%&7{2N0wepT%2Uq}gl0YkFY)x)Q)+y$V zOb(02yz)DekK{4yVGjXwnSnWb%$QU(YEFCGc+5T-=T7q90|>KPei^6v z6PXi71REztqFs+*tbt4zEFGM_v>rjbp%6jD)V4;vT%jdf(5w|Pb(@}mtw)~W6DE&- zA@5~r==VxTXvu)4lg(m-|G%AnFg^5u6;F(`y28;dtSdC!}Vq*7GhxYcpaxA54b)-qE2fDSrV~>ngFljVCFTV zS_?b4A9i}=Q!kfbCJ)j=TymS^PFRvSy|kLF3GK$_fbGO{!|s!|Cr#_2oz9zTh zxud@TVrtOvC{d7F^(~(=eJDc06ujg4znZKebEwHDp15E{NmTYipgUt=X~zR2**wh0 z2mv}irc&qd$$%w2w^2r86aa=1f}0K(p)O<;eY&3Qs1Ii^+ndmaT#kJ^LeMfzv@y02 zk{Y-vg&;Xv9!L$Q+%dNO$j>90^yMvL=7-wSFdhFyhYx~L#7(Oez833-zFy%K5cujM4On-2}y zWcibFZ{7@ZSSby_i)&jasvbHh02^85dWCG4$9r~A;n=zH-%Ue%N1+4$&9_mc$hFAX zLzeCGRV3*K=}uxF+Orc}iM_pvY}Fd6BM{I7Kwc*+8p{C90#wDao1CWs@7eP{8QVep zBhtR-_kwT0%^>w$ZyyeN3eZvRR*hOZfb3RaF6tYOz@%i+*?ftE=cDV1Gz~nc_G#|U zPwC^(Id5`*(y)4-3UtRHuufTs!TV7lP^8OrK_@^}h`s{W)9Ae4zlY$KmdQX0N{`{d z1|`>59&ro+UDDl0aY?yeL3n-u_a(`cZZcQyPrqCvw%fVUl*n+ayW|Y01@cve(0&IN z3raTQ3}(rS<<&?SG|4e7o2@=>E7I4TtpqGF`D`m`rb&`$o7-QN79&o+Y4ewl@BP#k zl1e_ILGI@xFtRmVV~NGN05_l^p23W4oCZ5L8#P$(D@mA_A_QQYx}$7VAxX0)r!jZLn3T;Hx!O?TM&6S(2{+_=3DqvuKC*0C?Goye zZq)ir-zqtWMjeT4+>)2NCC;EtAd7Jj*p<+d|05^dsu{c!QK7NG&kZYKlQ-tH{~+(W z0g!e|s-n6JA-BMDXmbLWoncR(-}{Mik(TV1xwq?)tV}}F!w_^_Ac-j8WW78qnU2T> zaq`oEw&moYmcU3fKwk)p%sjITUS@RlCE7pQ{POHAY6z%LAX_;cS;YO=}%J6 zIe@nl+K(LdoApu;$!8IU5PeuJeZTLyQz-=(BXw;#lU7ZkH(Mj@M zVaUw>T7+|zMu04^HUL3ROhht6I2O~x>yM%=gq)8=*aFK?NCd=m?C+=ih)Wj$shTjB zoR@u?R7i~W0NP2QBnf@+I8M&h>m>xHHyqX>(bGWMmHqyRb{yUfU?>zjM(OfyO5e!> z)xcU|s_U_Ka5g7LXIMH*#ow8_K zTR;`x^-kzocd-I>F9-JlWh2dkEQk=U!No=<-wwEiY>CzhJ(qWMFL}i%3WrvQ>9k$0x- zQsTp<-nA}ttAr#$*rFIF(Yh>+a;IuRYH&KpPrO;0a>LGwyCQin>!^oqLnMG$SC~3d z43)Cm&7r+XfELW?ux);MucjFbsWA_7xUWZ7Lr4~#qm8rn1sQ%ALSdVxdYPcuSLf(i zjxe>%=gnYt%{Rf4+}}WurvuffV@WCEAvn_10(r7XhXdZlC?MlN3=Q-2b-WLM>>&Xt z!jfK<2DCZEbYqM3X-U`)A)QHovopr@C2)d`G?x~acOsg7)4VN@jOgIr>{IBzONm$at%NgF@OX*(RPmk?ov4cHm3}b z3@!PkkM9LtUV(||MZguOgO4Hn%tIl9_Ge`25yF=d~^SeZf+n`%j7 zmMU%lZG{Y?K3f+?oi6b&yzsUyh|d5|8b}<2&nBfJ64=H`I>j&T(!$f003);0qLA!( zsK9Gfs%Wh<|w+%wm@GZ)%J1s&z> zrxiry^)}z2*JYz#vJvr(jqA; z^Nr>PAyvmAus7m-gvDFbnAPhg+L$gkE5K52RokOIwz77WjWL&(Yq4qx0;K3v=vy0u z1S0BBMUFS=bk2F~%ir)S$4A5+ZJnK2X?g^{$e?V8DZ_v`p5U`%k-IsAyH;= zJ-QZY?^NkYA>bQ7qY}>=Z9UU@7au1`ny2`0_l!!mP2Vpbzy?^7?|kjL>=S*3DVVi8 z5?L#)H<`!9LXVtR2T(>ONGELtEM$LEtjp{k&S;`ugi^{DQf>lg29DYuhAcHVs|?I# zHrGqYTl7bPw$xN(e$yy`G7Gphwz_sGE~eJp3*#YbG6Xk`QwX8N=-Z~$*4a+~2YZtD?Qzv%)9!KK6y6;5D@44tMCQU8 zEoD3bD@zuyusStkB+4@QUos{;q3(qEB2ZLz>Uny3t%p4y{n%|Y84jp@>iGb{_N>42 zfJ@Ll5KT9yldmOuzrXpwo^=T|IoAJm=jP$`5nuv!9d(wZY|KEiC|x`^`B~J=)LgFk zudVWXnU2TTNmC5z`4V*t#@ms--j7^ZqT0zdDo)Q|&g)%cuyryKX+q{W;$>1WRLO+l z3S{bQrP=O!nUb{ejS)oA_kUh0+1W0s)$b;6+_Uv6cd#CI@mB? zmnSpF2jVMXgj3*}f`q0So>l@;>wDR!WE5p1EdXdU4`3kxXcpX4PEFl)E3A0wOfuLb zKm#&aC}Y*~DG6IpK4wJ&cbgfVd?0~BlR8w&=^QBo)k(0cM?HwN;zkkHn4V+`wB&N+ z_<9NhvHx3GAeAIhV`2HNgp;Yy!bc}&PLjQ`v2=geqr$xo$O%lVG=OC49*#&S_D0E% z$w(J3BCp*-+6GRa^#~ayPv_H5EvdRyQIRlNNsPx zk|zbi&SjI^B_=lmx2?sVNw!tPQ!ek7dkAE^NEA$RP^W`yM4aRt@O&-*_AIt|1jKp7 zvvc^UV>AKGB&rM|gW;QymT1Y1`8r=Mr<58V-DI)nW(YP>su`qIU^kkqgl2jGIlWYl zZtV@OhiQGdghbXmwN>F)fp4e;Ny{UH#e@R-mjUDO&h-+Y9~6uvs#&B(?`3=J_vI3j zW|D^0D*%0yRQJB%2zY|z_y%Q3p;oCQpz<(1?q;^*=D;E}11ha$k;a4IblBGvChv2k z$h=?P3k-#20|OdX!G|L)OKF`{UyB}*M?VvI!II6D&fG79h5P%9PYPDL_;8z~gzt`ku0IU-T zTTsEAuSlR7+J=qkbwC9eNlB1gH>xh~IRA+w*0~v|h5@)N$WFvi|6!;S4}~xh&9)XQ3s=sY6PFInQMDH=2gT7# zkOQwPQ$5|SV&H|j4aPaK>aNi4soQZCEKEs6?l-QbsRKK zfFw=N^yLxxEUbhPPSo{q!Vhr3-T!EN8vw;{8ptrANfxm zt6<@E+Ttvtz7si%)qs(zWfYFz<-LD2X$}iYCbZR-e=&;fPpAAzICFJ>GeOQZZx&1j zzYWwCzHg4?$Y#D4u!05^8g%Duz`NPbN1l}mBIN!UD?OL)e5716Wo=-J>*C6K*ky+|jrVHPtOOTDJzw%WY;NjONuyVU!vwEDZP4HKi}F4GCN7#f%n6^6LqQ~N>6zz`n^$OgsP z`@Q{O>YEfDmlWiiRA~}8ETg?(GtgD0#{@6T2T4Q|kR6CNENE|?G@jc^e`-2*ndLzuSBD52DI1E-*Z{H?E@4_}+Y zr45qRfb&f_EaUG1lp&FG{N=l^R{;E+$6v__A}LI5O83fbYJGsL0AX){Z}6?c!6*B! zgSab0-N z2-uP$f>U?yhVxLyt{s)%Z{qeyh{=>~usB+u9ez~}>F4P^e5>NcQ_ffX4(9iuW-S4n zkYNmtr$FWR$zmAh-%2bL>8H(YCb`88Mb3>B&0Hiy=KR`%>l>aqhkm1SBV03$$QD(O z9a?2%xHSy7Iw5x)W-0hj@viYeDDwy28muC~E+Gls-aB0Qp+v@WKs^XoIuB<9yeG^u z?-G}85_!DO>ew#lp#iFx&c*ww@+7F<4&-wWz2)*uvoaFiMNASn&alvR zkOMavKXN!>2@X0i((waU*cveqsNO&zEX-#&b<@^YlNUg)Z4#Dp0ThD^=ymVwVRqvD z+iJ2vH5c&vh?ZUs$K3&Evo@Q>3bg8U)0pS-Zx60;J>I1V;y9C_m6Nfot)3yOrHV6A z1ZHMRQ;leEyfIvAhntG}YqP{UPMD})Z>Fkp6QpV5)wpD8Z}T$BUQhB7WvGsBk)#~K z=LsDdyvNTVB8j9DLHCQKm0~6^3?!$iAUGXdD`ipv=?{u(5o28{ zWs(uf=XP6hX3kS*9{7gHvqRcfowz1v-9eSToM4$CPDyvl3dhcD)6Q&*b{>=ZwGBEU zW8!yUI^AC2+xa~bxEqy{WDrWUQGkA+LbI{WM=XId{!FTYP!&^ry+(Jj?;A<94&=Fa zb7?>bD3eN36WD-p_S$MhwL0%F0lS#AR-me0+r5KAX@?Yx8{s+9Om?4-(r21&HQS>pY}1W2D99qq3y7Pi zBc!#z&*PUvOQW7U`z{(Db##>(zxo4wqa8zx$GzsMetjc}zK=Tc) z$kHe!s|%h{NuC*w zFd!mYDY(VO%h`nxmcnwn7!Yl( z_>~oA_K6c1R0*dmVuPm|2p=*5lE7w8YKT;5C7?^9kn#8Fz2+&TvyGfVXioMojDBq(oFHVbJmp~k~t0(5sn1U^e8PIt}t z`REz}oS0t?Av4=ERCG+LT&<50zysg1)7hkzAQ?*3#X3~^CK-?WNNXBWPzHE5T1$aB z9YJEM{fdW|LyPY?)=>FvClK_GZ?+3(0(!&s>@rv7;z;uhv{IdMa}gEyn`^jQ$-NLJK94wpm}!t>0yz; zJ_e+UXyZ<$Yj9o`xEpD=5yW}MNXb>2u{QCoD64n4l#UkJCIb=O>(PhGamXhdRq^vo za`~7Q7V3GoZTCmrINcSfY=<_Cle7SY0~9Kmms7l%wY!r@we=8Pb|hsQmSBlA??&I8 zf*Z803hn`}nch;-Ugh;vKNvtJrW35}!T)c{0KL?=BNpgdVL0tY&I-mzA zJyYqpxsfEYJOSAv6A?Ce2U^E%w&R(I_*Pq7I}G@k>;;SKe00^m!z|ZQLnW7I3Ho?U z^ktsMV;^sP`EbX=Z8f_gBT%+C=@oxZ^zs1YPxl%E4pLn!{eh`SQ$Z~QatQ!x__X&0 zul0!f(MV7Z2y4eno~jRg-P1O9&QJ!~+Q!c-RvceuNrDXuR$^CL6vT6v)8yC{IH&>2 zqQW^;$9@o2&>09GYfXBg;ur4u;F_f!Nuh+zdv?IKPKT(FQiSRWV#BT*Y8R>y>iA3z zK}{+Bs)Ephju+7`$y!$e1#V2F!|i%>r6As6En>>I1;r1o`|yu)I$P4~^$PkPWlf?G zCWZ_JhT{qCO>G=?(8rQ) z*s?3Kx8ZUjkW`*hIX>XBYtX${V+RPUhD52!Nsk;Q>~yLI(jJTpP(}>;Y>k-H3hEu$ z30U9;+bJ!Hsm#A!k3M$4)3;y+)PU2*oIV;(Zx;{vNmW?27Qxx4fCLmkgsj^z@m~pK z@H6ydFmvxB?7$G)vV(<~+k-VC$02ye_(BE>L;Qh8~F9zUdjHB8L6GPs?3!*@&7Jxr-Y=Y&4ylUIU%AQg~W@Jei_D`HCF z6m-o6i6w0wY|kJ!Ca1fH)#t;VL`wt5>6NIdp+RQ~M2`&OM32=Tm>aU;wD`#W7Rv9F zTOG7Wnc~btI_XF3o`AAiT~?4D$t(NuL|Ep#`zFE5)&ntdyB>Wg7J6kRKZ=Dun91w) z-Cc1$nWxCfixLSet$N~-8|}s3?c5Eo2mA0P=StE&$ldh$bHc&^}S1;8>Sn zBk!7GMG<{X=Hl3+LiKb=ryy3r?&R+P*$jSm9<_S*hQ^~$I4$MRQ*)d_z;>(%He*KU zo9}-&(4kURS17J|6PYw92ecd5TA0zBC^xyHbG#CXq2_5w^ax2lsN_Z z(jruggxmnxZS^QIq1u!~e=)oDdBtMuB5*~tYNL{5_EY!K*tqZ`R3kR9kSXokBQ0Wb zU{Wkdlt#j35*Cdha*NH6+v8iQ25`+9-4MWeYEq~+vSCl$a))4T3!91_IuI&`7f{0h zH{}&gwI=%=AVs-dkDv=NqY1G-cz`BxX&B?4^7@$9+Rx+aawz{Tj+#M#A4yLT$!Q#{ zzhC@yzewDF+L+wj3!p&-6P2hOQz!uWq4UavR$3%i(%Yi33a-V%P&*x3ua^Kxqpuw%QFq6MC)K4n?qy=oFlD-q3a%p;)|>c(K7^2DZE2Hp|GHxdE|Lsd0JW9^8kG+Md2jP#;$; zTkBoWijv0WZY_0LTA5x6kL3~6y*E4du z;+S^{l1aOX+uj} z7g6a3%a)%t$kCx%hYod>g~Igc{?%5+r~zn^DAJa6o>VrlOn1zv^U+75Quxy$$SAZ#*Nwoa9g1rN#v!#_!MJW-GNbj<~9#M2W<)~wIw1ldJYyxGl3!c1L z;=|M!J06V7$m4ZYu%~#g{4Xkq5sCl_4Pog>N)0oY4lKd=y*#D|p|XW(Lh1q%+gwS< zJv;$3guQe828jp^93a5zLP&%PXj@%<1E9~!H0kQY#s&#p((5j(e`4Eo*JSfqanY*g zc{JcC#-j#Lh?|pWAp$k{a&wm9{vb!#wlmn~afa@P5Et;<=yHIVg}^!P3xV)tBEpw5 z1~>=%0m!BeS)iJkzQ(c6LC!-CK+CJUgY^i$w`8)Zhs6ZmY0@el;mo;8D>aOhXrOcS zDB!n`O=s4i%?mB)&a&*%@Hw>c0*t5IMNrZ~ zOb+E3#Jhev_rBeGMQIR!LdE_h4%kAQ2-1RTyJ~CYshyZ z-bReK1B*l`-S(+d8s9-?g(h{Z;tONH7X_478u#kqwnvQdC^MB)NNfF(S-btt& znzd}(QwmQy56Z6Hm(YD&vHiFdHXAn8N{1l@&-!L$1X38|kB;z>evl+A=9>??aQyhq z)gF6n=~D3&(`sYa+%OjkWm{6X#`>_d@_)EnWOSmpW(5+7fliyTKY|>l6~b=+GJ!;s z=KwSTT&i&^Z$vTuuw5r}V8HP{41Xe27u|*v4>;*X5)Hd0Mrg%K$?V&SJ~~|iWOc|P z`rMFuog`#RP=s>_fKaKaDWPs@>n>xEcrEzMfkjzB9=gH-G6NFMbJSbi-6>mIbURZ2 z!=+k0Pe@>heB6kGwqrMUG^sRw2Bde9!0s2ayVpwOjGu=Szb(^>VFC!E%$Rm2dp~g! zm-I*gNLHW5<8eYmLS-|)q?fn^0W_fmA!V4e5svPy4k`AYay^fuIPG#0M}MCE#P&HGH~PDx5&5k7X~p>8#0 zXS4U{UF8Zmt%HU&w{{UdymtdJUmK}b(kwa{6i(2mekQIwL zAQ?upsz#k?uo44~aeJ5~B~_`wFel9_n}TZ1JT%;qHWpU=2+NT=K=Lbf30rs>VC?L{ zFt@qcaL1VX_+EQ8h*YC3Z`Y%1(beu2$J5d5C-?ND>WXaAr8;AkT{Y80H}_+UcD2QQ z|0>qPoBCJ^Lx@4Z4Q%cTs_AeClk-(ftXZ}oz@i_0N5O6H_wP|_Wlz3CDPa-Y;J3w9 zO8XtjZPw58Rtn{+CsfjWF?WSJMuanmcvV$e_PbpU@*3h(9RD zz<#R{`;4Blk{Phy?F=`0fGvX@YvVnxVmxsb_@$&Ff>>%$xltj|f8_M?H2whws9T7a zZ|?a=3;WUEH2?fKvw3&J)NN(y zpwH9NCJ#-N1OOE{s`1|LeK4v1S0B6omHODhyt!I?AZ_W?8QpwoQb*-Yespjq%}$$! z@sP^oGC@s1q7+r%#CY|UfkT&HHXkK(T7``s$yD_P2%FAk)g73FW6W^Nfp2P&EWKK$ z8i?0zlo9Zo7;A084?d^60*7Mh`L6z@=U_GxGJf*r<| zx?PVxboPb{Xv2IR3*a9i;z#~=;HI55ah!P|0aNtnsIFPtB5_N|kT6u+)61P{8%HJ; z*6Ro}Edf8F|2PcppPbzW6Z($j`+bmFzGok{B3>Xkg=%=iip=8}n%gVw-n{83MV%wYWdO!ssEt0gD zi(+W7NMXSXfbcZZV>dD6EpThV;MzNvUmF)$Zm2A@1}RA6}zJHl2De_5NLL3!!cGQ%Wuyx z0pm1otAj5U^w0qjAD~i(%0pV~Mh$EN`gVU%choPk3O1OuRNoNsrA7zI^s~)Ll_M3^s(az^>l>lo7i%pXXkL|l4PBVecWt0hYf8u zms(w`v=hm5?BVU4KD=Go8`B+tEPUyj4b@xdFbm&}^vQI$dp6AV3OBcg1Q!;I?CE({ zGY(fNJJv5jqS-JF3}nMOh*q5GrrBh zM!XRoNG(lb69JCgotfx?&mm$l>0#QEaTayiKYa4_2)L42_S%4;0KH>nJ5Gq2$IQaG za#pm*Sny3sb$h z7jlx(aFhfu^zYt8um_{2plCwXy1D|*LMQmk(bcGe5+73tnun)t{jjNI>|b*72PW>+ zjR%Vd(t5l&!@7XEBGF#sQg8MWAPku~N`_2oW>T7!me-J>LRvs3WMN7qR{D>>b&BUW z1u6>|(K-TsQ9;}W80Cmmx)!3$V6om#SFHY_nh-vKoSu}bjA~x3Ze~nLK$rzEufbj6 z4i7d82cQRtzyp&7ohM`j`=aL4q&C~@3=uW#Fl9$vXwpW)`Rd?!q-kX_o;4Ug-K?f8 z)(vc`LQ={G3N^1_EXZ*I*g79&_u&F)0!VI>#vaYG?#NpZ#PjkKi=a%kiGvtdV8J|_p`MxdBJp3e*_?4na z&#_!(MjCbof7b$g%x-G?vhLEDNwUs4xt}T&y(zjBqP)BWWOUs0H9(d-h%T>Y9AUa9 zxjx&FGH@BJ&^+uiFcPiO&`k#CGhJXB9ZIKJ>5jgi4rRYGp}?OEOoa@bwt4+zD9tCD z4g2dNDt+cXAxVZk#pt`Xdf09zH`seXW-~Yx;@k$G78m!@oBAy>Vm=%eNH@ro1gZ}^ z6vmw~AB9>3^l6EcBlDBaS5VJEMGU&S+n}vbM7VU84tu*Bzjn9OGyOR@-hh}_7?B+- zq#T(x)MQ)J7yK{GVsb|9c0IaUF7QX})3~keS$=bD0ojD5d>JJQ2Lq5dIN28D4tiW~ z+_As0O4HF*uEii#Xk2IjMtaKJpkRODx$^o(sDcoBkLuqq2~vuG_7RmD$<>CnJt0Ui z=t${EcCK{zaYa8erd0IMl9BpN$(=h?=WwFa4A7~XQeESTn;K1Nh|eW>q^sLV(v=gC zQ~59%01gdZDbU?qkKh!e$5)DWirBey5=?17>7Ym3PKUYABf8Ga>|F-6Ne>l}!L9-d zc~i>>JdIWk_L^~g(XoSeGC`qI-2I!P3PQDWx<&0sZ$mfxxmJ~-1~1{MFk5|dBl$*V`uuzAQt}T8qi5`f48aZw*r|Z3D zeFnW3_YQfJ9yuBnyA>cjRfd*=EuRJMXg-CX7NfTKs7pu1e%V1v_f^jn;8j2}zo*1q_-y zAi>t$wg*u}uhgx&#jG(rfO=;=uxhX7_h6hHQds0#!o<TogEHe zflp7G1hSXL3cJHDm4-?4#!X%s93UIznfQg$J%Gn?7;kVUfJOp^Z98};J^*Wt1R52# z9|L5em6sUDg4zAYdN$A-k#g|+77y0{z*49anSV)^yNGqz_AVH zdxNsbMGkt4=65dXM?kh6n-xOL>-^Rq)mvKmcheruplv0(PF295%JKH;dRo3^&!m{s zk5sko&kPtbb)qTWpLXW;5aa0vkn1Ark;4smUXfP<`xPj+z%%7uo+HXm>9SBEqDre7 zC%{Gue&|C%wI*#xfWh7j!URMO?lLMEDZR8_j$&ihtINfL(bg4xq}ZeCjq(R}vRy_w z9O$KLpvxZ^llzg}!wE4Wfbi)By9~Y4hY`px=Q)!Kg;sqLS7-N+jQnGu#Tab*NUs1p zq@=KWz6IFtM^=oSN2j2U3wN!IQG zNYK!-I~}3%Ki8~>#iPlXEL$pdJc{6cB-?UyoUl-XZHS?13pd6(Wm07@J`I^nCP%In z6$;f|Vkro&E*o|02I$Oa&SL&61a=`*^dNtb%B5`qV z=E~oyd*&wE*u$6kW>aRD$AT_$s|AP#6D<~r9A%1LC=$c;i+*4Pj$3`(u|BPi^$q4N zwhRzSuarXJeL1+vU`0EAVgqhaS0y@nMEPTOt~1$rsduuPMrs*XB)bT91{zJ%`f6Xk zj-S|=O-yT2JAt*;o}k0DYzkZb0IRZDChHYzTOlpUl`7+~3!jI{%+C^#>qcMmD9b2&kKYEWz3EW_&HV%Oi@0)uMY9wkT&C;81lBy=s zCqbOdf(mN{4)$E`b24Xesf*MoM`!s0+2UmmXyZ3sN}r<@=JRj|+CWkV@e=mN zNYNZ?d0*ZeK+%t-p6Z2Nb)EVV@igwH^sfuK097gctwfU_u_gOzX6mQFY_|Qap2(}) zHazT_d#Ft8_rmtG!)@;%u?MkX%AN{0)niZQg@Zg7&=3nseO4S^jfO&r~%4i_xK_KJ>iy!1@ zq@nA9yLRgu&Dkp0Au9r|F0s)rz-t;r;VW%+g3pK3O>Hq!=&8}~fRCX5?5hE1Ag}X- zqHFw^bO<9d3>qY#lTfoU9Q zP1%&4+PoROjQC3hW`q>5Mc?rx_iM*B4+}5W*zJ0xMNP{cu!o7f70t9sXl(5nGd$*2 z2GMz37Y@jYSDBa8_)K?NW9^f|P33i<JK_&9=?xR|};-?YSaics)CbL_$lF$GZx& z{A~Za17sl)7xkkw!v^tHA9HK;8HxIc1wXG?wNHaS%oH=V>?Ymq__;~7;;B*sOD>v- zoEK3FT&`|XVm2EpnzFHPFx&;pO0x?|0Ymtk;BM(P+k>DINF-`?1aN?WqT9?lHwEDq zq@7ccWauOyuD@*Cwr$(Cx~jTt+jjNz%x-MN?8|P%WJG3U+?UL!6F2kZ z`99Hd>aLrCb}$~YQw+N<+yDjR;P!Y4CU4<0Fr?OGU`S$l-wPAKcFP((Ls>Ot7&K2s zBDZ{yJV{w4J85DVIxr8;@C^PBRb$bR> z`e(Uibf9OySFU0qkXv21UmQf}64>!8$=d=ehOnlCbkZ>fS9PDjaq%LwoLW{fm{CE_ zoP9j#`Z2hl3q;9IcP&dzqM@(g7-~R}K zQWUm8V&0Ab)8o9}g<)V(S_kTpXYX8g&mnw~v-GxB+!8KQbUf8BQe8QBJzXJd`|yfI z?`#1+WGF)#iv4iQa}_u&t-a^H#{gf6FrSdF_DgiKlJls%>-H>2U&tj`T*s`odk(DHe|fMSQl6}Vr{+Y;3F02gJi3XL2sv!sHE7e(ClHge_V#oLFSm4~ z*0pTKQz+)%R8=-PWtZEhq7DM)0@2SI6thc9(tJ3yug?B>;H9=-IwhCS(y?`y1Qr7c z4YCh6ComL>>MOTsMa$FzQ``+|+sQ#WT$)HT) zM77s##3bSh&cpOp#}H0_4@4rN3dj`8mn&tdgn&@H#aa>tO|{N7BCIXw_N?4Fa0XX& z=7)b+_k;c}&*1DZh!#Iig^{+VHEkn$p7`e;7Mw;l=%xfc>4oaG-u0(4+iomM%XyTY z>+U)UCTry|;7;UY5Ygc_-@#@PGJ&r2C7+%8%jX|BVx7jKUqsmPF9PTxrT^}S>dR9W zh}Ojyy(r*8K!H^5s{CLO@QIWmP|*wG?jlNm(sl#{HbjS_6%ZQ4F6quEVcOQ`e6Lo8 zZq+~jh`QW+j?x6t#DU`UP+r4+eg>u$)>;HXqa=L8$Mkm(*^w2N6hM~-2d@g3z#w;= z1EE?U4$*acqE{EDzFM`dS2%;(Cu%cE4#tTGw;0ZYP(d>I8}u&B-Iko8sd8c}Lb&pD zfP@xALSPR+_yIO3-*EgUAHPN-oyPP<>|rACXIklA0o0dcGq=*wT9{r(b-)zvWC~e3 zr;{z`-~4)*q9M0oh%%bSfYRRW)_157Ed>)pF!%m8Rz(lL?LL=RB?|`PdZqZNN;x$V zFvEfi#&Ps^Xbv)wz(9vod&?K^cM&!m!bb#bu3VvFYUD65cI2B^Iht6w-vmDB1C!x0 zl^c%nlfIJ00o6#v0ZP;)cPTAyi(?zEto*o6bi)|Pgbi@To(XPd8>3Ev+s_pp_*f1M z9k4FlCtAf#-`E=7U^$npLj8OO>FZ>OXyZAE0XNuE2HGtSb3{b#*R8IyXDOqCz(z>1 z0XzjYBqRZgwK3U|`0i_DunX7sS>%h4z8M?nBcRz$bYs`RB13e{`-5ls=c*ype@JmX zI!%MQ##4O?xOJ8i%Z;!=vn)VsWS1$Req(2(HU`#}@mB;GlKV@RZ!}cG0CCAyQDrk9 z@3STU@I-@*6Liw+8$oRrPv;)3$!prcJ|&SiZOLDBWo*Msmzx?q{X0#nv%3inaCphP z$edTm=K>@Tp_HL>&SX;3gbao72SFY8XC zY@;eoDGp`L+Vtj)!aQr-$EPB)T1Q!5y}G7hEV+2(oh8G2DxgZ0&WXPmsg^rh;lo&E z$09g5g3}9V?4s3s=23IpuIFVg(xPIc45dO5wx|=$6`-3JC^W+ej5j=|Z)~zF`rJ!I zc|u_#Rf`qyWC@-O*MnpXnpLrPv9Dx89ZOIq56fz6)?H?`ei8g8PixUDQdT&WHQCr^ zKQAw~nSAieS7L?h2F#512-u`SXXoAr-1D^RvqfCB2$0e@WtO@k{zCU6eRJ0_Wj6pr zSP!4PqduHW$a3aU>Uq3GXgG`+6S+c} zPRsGD!q=UGeA44gp&2*tMi#qCeQHGmvj<<$t})KVo;Zr2#bCdS@|7qUo+9*YS1^wK z+6{!@bqova7Nb+q>63WeO@&SPWINp^?Tojx}=nzysEVT6MTE*raGdA6>y5mLE%d zr)yz{gd~06lxOAZzIW|lF{Whca<@)`FmnZEPydzB4{$y_n^c;}pyQ|{Pe${Hs4vDC zd`Yc{zpg(obgy{WnznH4)O^2^+*-5!GKKsU3d>XZ{3p>c-4_>~+L6*k=p z22Suc0vK}j?<0sYuEUWv68f4H9DFb;d@}d>wNKi9QDDwJjqY(}_hsZFOS}5O;*DWs zAq-;>jS-kAVUYvs2b9M9Pr|@5teL`3b(tir#B{mTq(P#C`IS{=p5PyfX#TKTvmWuQ zdJl@#$j$R6@*%80pN6o_$IoEpCPA$1s17SN9HeuTAC1sy?1n6_%S0#W$gR5mAEBxP zRwKX>9TW0!FpjITDl&ugCX0%{4b;a%np_N%i))l2I4EL!ik9#@FyyABD7SUpQw1Li z1MPvmhIxqkE}_Fz@-mbG$MH;qzRVifs>B!|57LQE3Gg085UECewm+O#fQG}Vg^UHXlk22}9t z3DJ=!z2WinmF|@3^a?_Ys^`m~^Bjz>vdbpMH)iWsBX+pH>_d;CeSm`PMIk4cT=DY2 zu5$n4GD{u2SHrnv*1_sm^_{IA-d3t*63pvqP*+f{qt zmEe&Vl(ia(Y-d~jzf~tAVyg#)+6PV5koqS!%@g0#(cJNFs}oap=L{v-CT0YB6cG3X z6~=q+I$C(CjFfMZQnFZc4#@4uWmNJ}Gl_!oVy;JRn=@6A_N(tOM0}lMI#OT7@;^6* zQdd^}pmUOPGIA^Fw83OvxOtX5wd7MwTc5yrVd&r!!C216n_Wy#LuI;^_J1Gs5NJ-R*gg zyJM~y$275=rBjwYMQA)$4G_#*)UQtVjCoe>1%($3D^B@HQn$Y`Cz#blr_X4s7H{c( zLkIw-@-^Xr>dj!^y^_>nfS*3{`!nXK(-MJI-Zl+|A@-h9WL2lWGNB}D+C&#|7es7E zkf8}zDKx6kJftnuIvNncpL>Ut>c}RB7^M#3IhtTs!H@>3dgAh4Kw_y>+s&>l|Rm# zm{WR$LJt43%97Y8G86>JV~U&RYbcEe=aW0MT}6eP<6~F zb;rEVP)OvE(1hH2h`WDFTm$-Txd67CFo%9JpBIM5s;U5mn@zvDT^r> z`9Qm{xJ=;~{vB78@Kvac7mQ~ z0uLE=4kBc&Vdk2%@7eitNck&52v=O%a_e@iV6w4m*pIt+cGhxI24WbL&;%mKFuq7$ zUS>5=F1)v5H)%fGzPye9&$TO^@Hn1`vej41>NWBt3sQ3yU`wgx5Aq=leTkv<7dwZKoqroC6Ke$z*EU7# z_Lu5tljJz%tZ{296`jy>U-! zcc=$R#7!Ix^Ll%(>E++f&!n5}4R3_n-iHG#0P4)`r2o)$wnO=;1^v3f(WvBGlf8Is zt4#d_lGtZ#dct(hW;bBXGACsnu5vv=+J2EvI_8a-P4_r6{2~6 zgQq$PPKAU90HLfXc9x`P^zv4ZOyhqz}_PuP4O`!0+;|BaC}B8DVONHr~^__ zM=zTQ`nPYL$h8kX-N?m&gdm~vMM4)cQFXWCMH2%KGK}?ipDnl@jVCFxw$(dU5Nj@A zOtEB8NKeZOkvFcl(ckvq>?t$5g z>-{qw^gsx*_aw}!eT15Ov+ixy!AN+_byJr5p`+8wukD=(i%*26Y)kHg*5DE!LA5X^ z{TPoNYW?grd7IvrdZ5yib~YZniL(~C_F|}Dm>Q!7JO_HC8u4xsPfB~ZS zmvLpqWJjZa>!9iOq(Rd`$Z#d`JJ#!il1iYB$MF^cj%qISU>CYc3DIc9hzC*VNbRa& z|1ZW7PK7E;)ZHDALhz8N&C!{d)O<#?irxw$2R0fi9Ul{k*f5%(N0YNm`^z3b*8I+} z1ZM2AQBjp3$2V6?S3yx=T^!+Vbhfxx#0$u%%dgX7nN|q9T%*1fEW(_bQzv*Z?e3@U zK6}_I38>W;ZJ`VZLY$kE?5@KCO(GTnk_1C1)D=I3OMHHvD5h36(kRJH0-$P6_zWDqK?M;f8ql23=Y*>{zq98h7|)zvsa!XkZEH@k9j zSVa6hifGb&nu|`%#+3sdIw8sJ(xpFDa=g!v0>P=~#sF^LXlS6y^W6b+b9w*2p>jM{T|e{y6Y$rZk;v zowtr;7osJanP0uE_;UbzFr6Ee0f=FK!3MSx&xN7A4q#-gb}=>cpg}Hq57P;Ab+xV& z%3l+OyyVlRIYTGl;|rb~+JAe$`r=M^>DFHdc@=Z)IQez4R|Fd($||>h+}-UHpQ>3{)!p@G_53P2WurB$2Ub?f=o({$U=0X^ z7*!+QNZ3O2n#?~LQ)8ptJtUbeh%{-Hc6J#u{3rF1111a>W6H%L_e$ADNH_6&M?pC0 zliW(|Um-v8wN&MS=klUsf$KL>n`IR|@dVLDC}WnqMdn*|(?0xjUDSOjzPDE8L%~VA zB+kM3S`98Ph;J5u4i5ql55FwPW?|Kf)%RF7CtvP?VBpG8(1*8@Z~c?E;}U8f$%nhK zU6=sbtZY6Bv&^WCwz{|h|uIS=fLZgscbPSJwN~`&0D~;lNJxY z_LPJQ8z2D6>-5Yhb^;-aQikN-L@u3B%;TG#?k;|@cbEeX!D$BCj7m4Z)XrB+TIISG z&$&o%98ciBw+&*J!qd4&b-z%PzziaOp^XA#MvYKQx8=TrV&llaLSo5{<9zdiiuEIb zTPUb*n=YWF%PaTbRJPI7Tk=j0ee^po`ld2ygxMCKmH&fA{w>xBm1wUQ#9doFWqYsy z2|?IF>GyYZZKJ$Lr0?6X%x1Wpi34dN)#)fya)#>32a3*Ogs(FZP!7!=t0b68zYISm zVPrv%t@+{Lx<)xtjI#KJQfy6NbqHuVfBjOI+Q?L!-F8&I~ z7!Lk*as|J;B{YjF3f_(}YjC`l6QxJc@S!5l(It7Zh1li$ko$*dJtvDx;mvx)^Zk%& z$(3QV`kXJkuj815VKB)o-`DcwB~jR0C@{u($(k9jK3F0GgfIQXyoa6VZ(q(aJ(OI8 z1e^t9a8ehdE?7={)U0VN&%~9=NjT?uK|*Ew`9s9*+U`>|C*Wvs0=!zHbea8|yEpP^ zTw{FPwA6l19TK3(VE@QkYVb|$&CphsIK zAO6W=g_8mxh8^f0Rf+ny#$m5x8F)apx=Hyh@!Id&L!2C2IB&Wzy^@|2}4*& zq*gMZsrx@l~_f`X;zt}gSkqs*7 zK>dOxbhjyGqf>$A3aZLBDh$IW`qQ`RrI-Z+v#6m7?ryk1jaLxqtbNoz*Ho`4n?TrT}G;IdZcNB$L=5hp()&j zseEFf-#**#)6Cr#%2BQQZR~GX(mMdF9G2un$+_y|Y11ZxP>|1Oh=>$Z}bPfynM??KH9RY!3kv z>tZL5@`0`4wS`N`Fg6-tl?%d9{Izz7$3PYcx>U+^ERj?C6ksX;fn!xKOofI(Sa|z5 zr!CNWb^M3VqT8UpSa%VGI^Qo3gnDpCx|jiqji%%{nXA5!@Z($uj0rdOLMMQ|#}CT4 zP#~c1DvX|-bE`l)`zB}s<-Lg{XtBiL%BNe2BkU!uNMg@gr{69OT22c-IHYLaEcS%v4x z?VYbfhADnF9bspFhdU~)E!sgDL{(s9RelV=AjgInbmVUTkJlpTwq71^|1nG)*6ulp zXAy;BL9oiyeE;Aabmr~Q@gM_#v7!9b)aBf$)M6}p+N;7ZJYQw%F-0*LUOhG)(WsYZ zNtnei;)yq3=bq26afMw0IzHLj%TFFZFIyd_Jn8u&&9Xfzo7r!iB_t%WY_}+1Qdh`1SA852V`gF zVq|M(sws?b271D*SN|JhwVV1agqZT-wlJx%8SM)GGv^3Q6CSB_!9aT`!(MSM-d$w`ZCof-0U z4+^3aG-s@DmfRD~uEhER=Ica_xzS^?Hq;Ny+}Lu=%!SnjWm$K^0kqXZKV6ch052pc zE2XOHgAlT8Nf+es-qN&%61g>@L`l1KZ4()$6&t5v(~UR)(uIwaskIL&mC9_{xHxi2 zJ-Si28ck(W>?!;!n@*a$m5FMz0HPOT^Xv|7v(3#f%0$`4wL?u8^Fn@$+)DYHpMu+^ z)PtF-lu6JYzzBz24jiKtuNjFMoa(`CEJlqpBiU^$kvu=!lDUxAwQlB4Lyq%`Nbmja zETa8)%c&J}V&P8LLZv*oxKV|*Y~R#_!$ccGJP3LDxBI{81zpzcM|@X>YNpGp3uB9^ z4<-&rA1pn7Z;2QlcZNr{5Ib8;2)3%sh$hRSHjO!Jw`Y?7aZ|SW->q6t#+K+E*zKy@ zQZ0*ZZNwqCD!pr=3?bJ`zNDEB!00K9P5aNSr&`6S9$O*;P2$8Ys}irC-WN_RIIA^% zlKlf`qfIuAoJCt_f}=78O+S4}Ow5K^cWYO=hB8_woy+4w!TPktheqZ?IEU%W!0M$X z9iHyq=?Lz(E(S@Na_*wtKb%qO;;f#26^YC1>KmpKncQIr#FqW<=@P2y%SZLq`Xl{v zF>dw&M6+Ts?g=`UGq&vDnc~p(lx%OU$soW}Nlnt*(JC@tANSdJ`~5bMK%*yr<*`6z zeU2xqrX>#Z>a0zjD6| z_5Mn^7Hr$u)4;?A|Md&>$fs#Uv1*p99+&NY<)&M6*c%Ft2O{PIJ>A3TfWHao-XrKh zBnSv^z@lqvG+b}M(`2ASOhcp%Kp!%1;I?(L8g?`6Zs1$vzXEcD3Lo5epyNVY2s|9R z-+OZa;Udm;T*Nt%8T=R(GunoLHvIhK%LBtOzq76PwaHi z#L@fD{(r^J{~o}T@QtW@sDOZUXMlj@|A*_Fn;E&dI++=m*gL!YznzxbdRoaxV~&2i z27{RWu{?@euh~=t2w|0_LAsOiQN-+Jl%h8@=IKq(w|c3Nids}`^W(}ev?JH&5M8+6 z#I%po&3>QPmMK2n=?9-lrM)eILi|``;cdc70w?x;5(k zKF_l`cm1Bb1^V6(l26-j1$g_u_s{3%{NInd9}X`4?c@7+zpu}SAHz@G?FBr)$9Mhj zuQl>pB@F&PJiaP0?tZOmd|}^8Q2Kt~jEkI3O67k)z5RT@tMvJJeceq@->l~Tx_+Iz z-4*EnzE_AB_;&i~KC$_kJ4O84`&lQz+xvC*^fG)HuaMr?+_U;dJ%Ex%S|NHy>alOI6ZFCOT6XyT@dFmebx21C7zkDnw%-h8jc{({h zKcDcd-~B#5zqJ0!)Bm0OW9RAf`Z$<8d$}q?{=R*yOXfz|f2DfY@8>*Q!62{|@q~>}_RGNI zWA=y;?q|AQPKD(OYsSemE9Th3lYf)#w(YHc+~0_Q_+pMRKWWLPjXP);26|(T!5=6${;6|&%LY7vNP`aM@=L>3&w`Uo;sAsqfXi{2-=WknfYK)CIea6Otp5B`%mqF z=8u?{h)eqeQJ}0z3NN^1F`(&0FW!(4E``P-a0(0Id9dyY`OaUN`nh}PT#6kpRC}Hj z4O`lwrpj{%VMkqhlXeLDp)?tCfw{NuaiW_au(6S-Kg!uk49$*xL$KH+eE^oJM#g+e zC}E+pP9)xLh9F%B*NDrYBSliv1*mOhKt8@2GyuE?w<2VXE{4e@8$F9&(32=}6J@h6 z**|OC-Z3zR&_j0#*l7yNYzm4zL)8G1_5L$owtvkGGboZ3B7Xf48ag1yUG64^UV?cF*J#vmwM!)z%b5LC@0U}DU zQfHL`gMJ>C;ID8LyIoa8tFaj{s6gUBK4}Am*d7c|;0PLWFm(iPmU`n*4V6J4C(N9D z#GA}9e+}3>Lx4U-zPeCfu~MU}K`b@|gUh`KmxL1tIFF}~h*D0-&XFd6 z;L)PDI%wd&D%!+DyO_$I3(Q>0&|(9OTNdYY-#{x$*BBe#fJ@8SHB3`+L009DI8@-0 z$;?DKOjHS9FDa_A-F<*Aw*YFDe5L8ICTK`dRhzVDY*qZD1}-_#b@QDg#t-sziaW+n zT_vRfHHfo!uzp;qsO=&+2y&D-l_GZHB#FkX*S(K_k2kmx&O9YJwZG$vRdLkTMP$Es=N;zWN*q*(zmoX2s z?jz{bGu=glKk{FmYoh$0xg;1GOxO55<_rief3fM7%^505u}xzTQ_$%8GCOQy@sqwf zQp!rvA$MNCUg7#eFWuO?dV%vOokzs4h_&UZnl$brMO6@a;Df_~nK_zfo}=mFjTzk( zu|r;er_2*gKx=7~zal$s5o56*vaI_-`tld59^D3-7?~%E!9N>emgLBCv{II=H@fkg z$ROJWpYPN{5cQ>G%9uO8KxecxyNKlYeKH1D?})n){D7s>0ORKIbkxbZ;A~0Q#2uVHx;>SL=vJiSxAh9CQKcS?wxyK!%xk(Hk&sh9Ue~=!%LI5- zrN!&6l0(H>lXP;^>r6QegiOvj*}lpst(+C7tX)7qFKykXSZQ3C65|T+>z-=&M6#Vz zTJH}IMV5S}$K!Jcb&_No9qG@Y+5U}^RiL_|Tx$1@EU zJZ!J4Mf?X40kt>H3_j*BRmY}s;rE3)G)y3~_OPF6PYn4%&`Mn@H^4jZaJ~gxM-*V^ zpM&NO$clX3{Y6cO&q0dGY_p!_0-_xz!yk7ab)7AdkD50Cshr4PD1`(}xPiu>aI`E+ z!_4raXwiu;m&S0}sK!`L9T<#uYeVue^UdXb-SL^>xfzpxzV7C;3-lmMJW9kucj*}0 zvF2b>*%WAAaMP59(W~58EiPDLUDd@Xq%L-wHm{hOVbCdU@jjpy>p7MR)$|C=(qRF`eVpo)e!#01p;M*im_qAAO;piMdRNpxi}h-eq}IsN4<79 z4P29XN5R^V_LRYzJ~u94YOZXGmv*GBNr@)$u-3p*Z(E@XYxN#UDl4iCZGj~SZcY+a zO^F&pRhQ?bBTKpEhw0dMR;hrK-T8{(7r;?$e>-m-*RDmbBOp(!*dh9YV;*~8eCQ4C zP?bWZFR{LcDs1iNM@(6NU6}HPg$m9K@APka!hXMLfpxlSA}7uoCR1k82Le|b4xYsa z(}_LopQ~CPbS=l`XD@W$1?1og>w59?PhgzD+SkT+*RUW9X*wNd&78Il@T-+3LXv7* z)%KF$E>My-`hA=Tdb4EZu?D|)_R+=gWNM5O0>mBU#MWbsHx;V-#Z9gX68DHShxaaO zlD3BYLX>XvR>nKp5fDuC($sTs5}U;h58&$I^@v18uQ-SYLt{coZoTjqoN z+_WZ1Ydk3$tJMaMuQGQ~S&$weR16jN!gG5Y+94yphBzp&=Zy_St|ZKi$upd7@{K05 zfrgB#2-{9zF%Dm)HN^~hDu%$?I_ubK?W1O@fU^K}_sbDWZAn02$B=0JB+*!K2at<0 zuK7VVTF@#laXnC*NC0F~H-eb~h8sI;~^bg#Cek%G?siPy1%p)p}M4M z=0QbLHev>JyzKj_Z;JRj$<8!E_@K+WTAO}29>$97{s~A&N;RdY;3Sa3Z#w2~ap+Hp zTis|HR;7+wD`wpol4vxWDSOln#vt?{CR%A+K*7N09D5`T>Z!V{a@}pyGh*5_8z5O; z+sp;NMhAQC0IAPCc*4_yIbk4pU0ExD64ORQ_u%cDqBK=0tZ?ZvIcWVu2!?CANuxr z41*Q3I3fu0&MSp=NvKVc!l8*i@z`g!j1P{Eg6z#WDy}@Ys%&0ocVU@oC%OV>+Af@^`&aJiRs(QE*Y8OMAeFS> zssi|fD;v|RWNg}mTwjjLw8NnT{)GrR5AXvDH4<-)`0=l&c}nFy7=!JL}co z;o$n-WC`rF;>6r+J>}nlF7e6xq6AI|e3Y80A2#TeTj}sGxw=w8g0jQisv$e>;JlWQ z88$Ug^He}iGk_~r&u+g9+!U|QPgqENw3!OA(yGCGLlFyPF5II=k|$w)gGykF zMm?Jy8`!qFRP(&@9tbnEj(&PjFU$4$A@;QBJ(x`zqKv9QZi_?hQJahpP|vQiTOZuF zuC(jA%xF}puUTE%asIZau?VJ032GOIs^yr!7-maJoAg3EVqc)Yq*;OCN{d zo{m4gg+y0(UszSBNW+yBE7dFse z4l?&&u`A4^!QBocZ1ytYwK-64Fe2CzBS1ClgarOlzUH`pHv2;O2z!1}nA zxS2U6yi4kh5q6|!6zc*a8_giM{#w$?$bz4GOzEnc53HVb z)&H^3JW5WQI=E46=(>{y>x<_^3{-&QrHa;mieH@Dhr8>YXumiCv33?b0cS2pUdHj^ z7?yU!&|92O3hI7-Ys-?R51YsvQ2&jukO-Y0IPt=+L%}gNtzBM#M_p$-_dudk;DKoR(A5!s4 zQH~fC#MojlhQsD{P6FRXPp8;Ti53a`w{Xc&Pf;jy4dMC^$o&05qX|uG&VcUXo`kHe(1aK4_`Ys2D@8WX9G*(7K(sr)g zAyzVcB8TxC5BE~A?&%2g1@b|DGUp}+)Vts0)_+7{)mxpJ4&3DNQJ)U{#6|s5@G9z( zy~uU4>XU15rcs0_&Fbyxg91z1RRmv}LztU7O=jDlJ??n(XW!@6y`KI|b@In~T+R$u z@G&&Ewezz>l!|+$?E<9ksSi_d?$t`8X~f&|d9Z|QFN~v~+MelWV_CxIs9;JOVA7rr z6h5+usj3pQx>VwL4Zx;NqlzMDdY;BTs$?sGZA9JM*C%Gd<*%*F`!TJZ+>1R*K90?+ zgrtiR9EmEZ2C}3u1Ia7kR#7(T+)1iw;HgtHh(O+Q9U^?zCeLrRbm^)VZVTZev`v6jM1$|0JNiqjA`O+UjXVYCo@b z9=u-IYc#*#5aVu<;h`;fp-Fh1$SssU92n$)f=ZU6>-cTP0A19SiM77AB3{qoOq9Y% z+~sSFyIYtqa-yXq#6BOCCyOMYY_Xb1F?S6dKYr965Qe=zG?kFhsfmxMqR&)WVe9P# zT|rU?rK#2D;1qa@fljghGV2)^0#Oyx=$Vkw*n8*nCOk*z;(L6h>E<8%a-^hvQshGa z`?v-S-M}5#vI>E-3vG4dF{pm*H$-wNu(%wltSkxr1$cRQ3fvfKWXY!ASHZ%nr+?~Ki6dhG zlZ3k(nu*Z_VvBjhYo=Noi4tb0U!7TJ7{1b+%5%be_`%JARU+)|z+piX7HN66wVGgZ zHrl9+{Q3`=+@!~w(()I`q`OG|oH`dQMyh(r6w34WkfKhTbQ9JE|gWa?s`UO0;? zkvVC|fE}ZWUr6*i0Q&5CuJnB^)?(?o?QM(qUqV05wGad=q2{B$Jx_;^sX*DMReZI*C$_=8l}$ zV~F(@n-Ti%l6v$L?4{#*!wIGhskh|rP4b@3XpEcy&1=81YqX0=X^*(aDDMFfmQeH# zGaOgnhgir3@n@r?%E)#9tDx?a;b*WFb((Q74X=*co1j~%J%p(|qtbBYAFV(M)}k6( zhM=Pvcq3HXA?XLQq(R6zcujcfR0>+wV8pIvRIz}7{%i%qmcDrvC0M=lR-XPqILNRQ zC{QAU(y`rxB!&1$w}CZ?Mv-U(7+r|d-8rCIm1>hB`qO4apCM0Y?-dDO6`NEs4-!p@ zZRFwX70qLh;nXMA9)hRcqiQ2`{xuMh#c+)ImOkOP`M?sJXZK8xat~@^{FZK|ff>tCnb)YE&p^R#PN+KpDUr?fETY8@4@V&D%UvihKfdbmzE zIy`gFS6?`4y014OUd3!&mH=JDS3!M$D|*LaO>3_Zeh)}t4=$3ywKBUBZxZ-xS8I}Xa2>LYUUA*3 zp$a{VyP!cCZp%{ZvzYXz+her(aY3C|4OR<*<=}i$_^s)8b>Ep=Id3N+z{~VmJbNiCzc|qs$Hb3`r9CyVq-+Ht zIpZFps+pO2sg3?{-!nXeuZEl1GfZ@+E`oHHCz=ExP!_K=nrFzhu;}#dDURoZ%VLqV zdEsD&gTN7KoQX@>JX6=-K`#50DRD|IF4`pa`pW*TosL4G zvR+qYk2v2}YvY7i!>A2P-F1z?2|f-IU{IYs#ZHcQWd?qt?G|~4+qQTu+>#?+vs5-iIk*;+c=NgIPr@_w7Mew zmgAvA-`bvmm5Zs=-W~~sy@w|_6+~>%c%hw>Tj?zO4{U1)uYjiilvR=ua@n@dic@z5 zxk#G$^$TB59C!Az++N#lY{y2-^U@2!lT8X>OZvtkFtEMJlQ7BsMn%1;t=1pE&{dw(u5NM>kBE)+}WZSImi5Ez0h@PCFH=2x*U+D_` zA}sHPtyGHQme%ythZJS=2vTTNep$NujK+0SycZz)95z3bu%eG5u;<2iu6n!yBAFMi z@Hm#-Q%@;tl=rvzRV*d9j9EMT0}xh-p3FslHh2-vbzM5S)!KxQ7_aPXA{f3pcAlwT zAQ1+a|C!;bYyZv(PUArTAOTL05-L0BoE=GvXq!61mbB!txf4+{g>-Ovp!Wa7(Z&zcEkl_kat70ar};mKCP z+5yODCw1?VhqVh=Uj;OZ{isOduH8&qsZLGOEX^HOt8jS#)S?;Ny|n||gkNF}6)9gl z)dqPml^fa&XFR(K+#Yb0=r*gHJBgvodi5up%NuaI>#Mq+`Z%6D&F=&fz&m}4w{RxZ zZofZvlS5`aa5P=|l%ND>Q14mempkDjsBvLWU!QSh+P_naXN9tQwQ2n;u$f+&dfyUq zt~;~LHR<7J@($;tLRgxZ(05S<1HCeJoNL{e`?e<852VBSrKirRBAo+^ZF_@#Rbl$u z(Y#1Kfv%qs9cJN6`$QI({^g)GyA0gR?WWE`o!*}guCzATrax!0fYsY#&23%InmV{S zM{S>`Bs6xm7pep-toZbFf9s(ua9|L>vMaU%aNQFLs-Ury9oSVs5F62R#jzsWGYGO1 z2xadH^VtGKcF*`zn%;ZxmgFgFo}fjhM40vmu1~#{(=@uRb{NqpyLZSs>s8RUmu*)( z^GyuBq$%^T3C&03b<`+9qLLAPYdBzidz_VPBZo0Kz8rwH=2NL(KT*h}lkQF17O-hC zD#6Yg_&C`_<#B}+PEV1meDcrKCfWDmiKSDF*_E3KIY6+k>8R+-S3Y!{Wak_k%`{h@ zxo_nrV_l9jk(yHEQ(iJ@=3f#5^$JHDq1U-?211KMeJfy`ZWBD@4 z0KRw-`lZNN2v$p!C7!;1yR-HqauD!gsUgf{gWU{u^0n;Lt9YfyQ%wBDbcp4hAadlJ z=V^*L9%=4qg4zd<$ z&y+rAFg;=zdzIiBI5~A)6^Fnh)%jDGEQtNQ!bkiZ_UAKcVn)%W`yx}w2yVt8G~PK@ zeX0I+Kl?ved&l5RqUcXJ&cwEDOl;e>ojkFfi8HZn+qP{^Jh5#&$<06f=L0UtsfY>_V)MX+<9$Y;`&A4AXh-EYisbCJA;Lz z(?8h+b~c^eH3@HlEqWGt(q%feO7$d8&e5(_vWWyskm8LTCK7s*KI`mlWjOFuo%^S; z>_il*wb-?`a;`ma13oLNUvpfv{Eceopc$M-hx$XN@y#*Lb0_OzPv5JB9PLgyO%%4gg z9KNqb25MBe;7zA5qi>gnegY2Gr3kvLWsaf8m8RIfU`FtV!r0?vZreA=7Is!xj1l*R z(NSkBDIaw!EDCPcP79iu`l+=dNo`$W?5vk(o5Q{p?RR)VPww|F^3yT-qu6i$sTem* zpnDb?jLSGW)vz^%n`6Lnwf**$zwla!mnMeT+6_#V9LE_4C;~xa&v-$*~Zp(vij_pK#a<&0lT*g~>hz&w2Gk?@LMYx8x6&66U~QS_S(^@-i~ zS~bfh4jeItHZlS{)6A}V4Sz|Ma4l>hWIA9+mW;+E*&gy0iFYuJnGaU8%k)HU6&f17 zaIZR_tSNW9^cVv5b&5K7?Czv))}FfEST*6~iYe_&VfvF|P)qWoqf;75(>iMSHGJxW z`IlAN*bYt|``OKmVr+{lpfK=u+_Ug!$*=(t_l?*C_ElW!4#m?;9QfXC11e_&YRjNc z!HYT6_$1Oyf$kl};rPL|8fnBxaOUO_UA4zsD=kDn#XK3yTNs6Q2)0B?ZOFzMr`YqZ zye=ym_`;#7qcWqjIA4|fBd5fbH7}t1TNAfh73sV_z6Vh^=^Nq{kBof8SKFkL#6q= zXRBy#`=JoQybayixO%7zL=+{=G5aPjAMN}df!#$VgnomEAkw8UThODEX`EH$pFPog z>1-(&ZQv+wOSxef?AuI!1YF%33J&TGA4%*&_>oUI(@Bws!rQLfiF#m9gBYAUN!h6U z(k;R3{WO`wl#Ei4n5N`D9LWbit~^aY42%LZggN{0V%gRSjST>b1qtm=0vtCj>k3W! z{E4XrcMV}4FfXZ{yx3pbXq51Jl3SBqy)!i5t1{yoCjI>gCpg&pw1GECJ)%8{e^&I~ z33s2z9=D#r6sH+MLz-dgN8QN>y;;sX6$6hkaPs$3O=870=HB*~_dXkWHNHI}^W!Rh=hdcnea3nQMF0!^@le zEO1D4GQ;-+`3;Z_j4TFybz>T4b32)_))a`liLKI|({W@!cXBQm1^5Z`Vw4)fCI9;~ z)p_pMp5!9rV=O8&rIKY60Miipm+vpZ>H4sggVaQ0a$dtu0_soh4a}vg@Y)Ek-}BEh ze$ZY`wk?&984LTgPdhFx27;)HVA{qf-6I>vxI+}s;M*o#=87{B+LY4da=<)2%EJ@8 zq!Uz%otx73fFHg$Ol`FNi~n!cz& z$TyRB}Y-M^cz>qx4H1#>@6A#!RWur*6ze-v@gj@Nk_7CErsHO5!h%s!oc zSo(No;7vJ&3oM+syse$y!P@48yIWx;)j}WPXIY4jDp!%L#r0zAXT*3p7gZW)x$J5M*HLQ(wi}{FBv?t>jdveG{ zGrNDuesF@F-U?djDO1oP81}o{X`0MQWWl#lHmy3?8JY)jII3zKSY5(gCaO8IXV@wG zTc0b}8n8Z*`^zV}c%R&fZT-js^FYo;zpWG8G)4XUm zxWWbY!(b&rbQ5mdV)0}oWOQ`#VHKrYzmE{E#=oA1`ONI5$=;)bDw^-| zP&&KWySV;?Q~vL9lq=cF4*Oi_p}TJwUhACh7`(*=4(71Y^Ucel-*jWH zZ8}#0dFdnsY?T}g?vgK3KPth(nObi;*7g#UjxKFg=o#;&Y^%R|y#8UnpPqa(Stl1X&-i@)f)unqHhRPFHp6?dMCDIh{-LY$8tggcoT3;4F+v?m(Z$u{D zbjnJsGKpneXf8)Xct$~NPAaX{{9e?PvxuQzSuO);eTZZ}RWe(uYub~D9zg|>P9+Bd zLj1?RP`V3nHYKf>dHOs3VC0RDa_rf~afIAxbka72BOB*CYSkEI97b+fMv$zLMk?wH z6Wr-8Ey)6tF`tsyV&KOxG#mjtSQlgDls=N3iq{xhs>TXFv0mHgm6&^aCmxJG7;ojf%mOTo zKALkgzLQhoFMZj$g)1?>w1Bexwl7|cIgmdSuQAuF6&5Y*?|17|R}t^6sKUN{nl5_tRHb#W4AuwnT0i8c9bTiUdCAtVvg0Clb}$ zr7a9BZcpVGtBIiO40~4+)a!`EIVV7~9 ze|j^{Ys~oy=eYJkqRBucLOKYSEah-O$b_bgLB7(#hTUM1StWn|yVfHD9ezR)%AEk| zrjk8ljRoxvZ-58A#|B9R|HE|vLO8+o3zUN9_nCXwdGCj&0f*E+3Q%}<@il#z`bA|i zj#l#b&x1$SuA*YYF>V>uiJr9_1FxCXTz}vp_1PMw_M`~}k@-)cerb`|tukiP?Skk9 zng<9u{j<=6$UJZ6UDB*rZhP|=8*@Ig&|ZW3b%Yuz(7tt+>(*C3Gc7XA2kzZY7XJVNnMDHu zQT)F=8;xx(9h|LQt?Uf{<8)*+cDAtiUy+3D(q$d=BoRYjzA!eX70}Qx^t-58B}Rqz zK_a^`*>FiPtHVjAss=t^-d@YA5aUy~pgO&(#)pPPRRj%szmN9Mha8CJ?0fqJzJZ%* z;ayp2WfxI~{$E%3Lo0p$J-wfgcP}0{{@v}Jou9W$WmPYhFN(Vc4!QO>wKv}`&%8wb zpKp(wXOc^Y_xj#na4M9nwo1=eK`5uV9y7$J_JU=jGee z!}ip7#NRwDO{|;7Lsv$@+Z#S0v9yE^O-xEvGUdbP+f>(r83yY_dhx16B9Dd+`;mHO zrdfh9rZ^H#g?vWh25wdB`a%5aOtDGgTkW&9>XaYHgmFTeHXCnoWOHKi3Z=D_Qi!p!pN!dllo{icT}wL;mY`=Ze@YWk^JB#{K}oanQs(<#4isogW7QMtUo4Ua z4CZ&`o37xYj3wsEYnh|ii{{}yiW49ATc0+{^)?JehncDsUdt_)6L z5!EzWW;JsxP*exMmi<9+CAOLC2*@*|StjfSKCA3aUt{zl5A}E>)-;~Ehg7Mc@|%^> zqZeK)O2lo*#kHp9sZ}%;`RgEu-nSeN55*>6E9KfLRaW&7a)*UM&M2V(H61Mwkj&~E z*aBp)R_Um#gCzSltR9~m3O+{bTp5<(c3Y*l$a}|T_{BnH8EmDDa3Qy@K4_G%t`i{H zcVu(fmYHs<^KU6Jsc31CrH^Wn8iR;iE}0l7&F!X-kRuO4jmD%X?E~2~@HK>AE_)s%NSs!>7ED*I($6I2IHv5?ZPq9Id?ma?0^u=_2-3oQ0#RoZ2V9hpY;ROYsYT5-$cTBe2@ z$|tOtp^F*o&DDm@hjRHMX{pZ9t?MF5GUz?CxT^mNokAa1l$Sz#s+-O7V42CDDb?3k zq(0?JPQnUT_26sARhF0LEdpv-ojQAo#2fGnY$AWDVQOU~hsN6Q7!@o+6->vrl~iyf zG<3-FFRGWhIND%WLQRZJ#J{qzu$;F5CAqH`9@kf0i&bAG4dA z(hvO3)$g3wN-iu+uDHgOJ#oo$9t6(E*%#a8pf7oZD2SWxI$@6ellyTSEL~8?iO&)h z7!+mljXkSm?IBK}csC!mE*2k=J^SHd(-NC_t}N=5G6hJHFxBqiut`X;{5ViM3!`mI zNGLInR~I2zJ#Iq~#lXFY5V=^TG4H|`VY4kqtql6?H#|DlP=Fak5LK5&*HNiigbU^O zc)9x{d6x?EGthUR=W4ntB+?m8Q&UWV1_sPZYA1PWAv+uZ&L%8C^?5kz^%qS+7Yke> z8QdW~@^nd^1E-J|X)N=VH!xuo_82(6?#KhL8j-t1&lX!63O(0WEMW`9Ua42Hv~C1F zQ`C&`OOjLGgF7S;Yyf>_$B{SLf_AmuxzafZZTkzGAG|Pw;XSCYbgM_9(zlGv7XNJd zpTdYjPk;NBvrB|U%K}2fu&{XqM`evRtF13f5mI78C=}r3i81ywd~SPAc&N;MM$N-X z@=Y<#csV5#L!LV052eD1H8NF>gi;k-MIQUV{As&*LFY}15XGmj7~Fox7n^KF7qhwUC`C zvb)H|u|pi%a*=zZ!AEQj!zm*_R4z=t)Xj6JN3!`(#i1+DW>l(!CB*c!CL2n%3oo$2 zIr0inty7rOa5g%yH^xksV6?eT{Hss`V~7m)K?~ht*aU!Qu*6dO3|NU1#q!{>vpbMg zDS6OTg@5^xYI=czbflF)8)rFWT3!iYAqp= zxeZw-KWo=Fsha)C8Bt?hcqy))zK!m=?jcUzTTY^{gxL1EO)7j3X0W-G*eMH!-67>F zFuIF-`6P}245XEt4CWCli&N)#HS5B44j-GY)z>_EYhAhd1HY|CWhyQCI7!?~cH(2r z>FRclDx;FTet^ATl7g|hMooxH$pc^t1!@tsB_Iqo4^~`onn{}Zwgdti$gvZPGYWFu zNh-y>bd!gI;K5KiF$mJL+C16Ms3@m5t9^K27G0BUliu9pzzd0L+mKXw{hs_jD6^!; z@(efk8Nh%=v50f<2+VX6Z0r*bl)!d<1+Yz`m87P{JcF+jq!w!`1vEyx%)7~*nL`K5 zuaAKLD3<;U8L%?hPx1dt2ITu67fTjD4ehK=ogEDA+-zN~O>K=`Tp0iJXl-F_YWyGE zX+tw}dlz%2|5h1gnIHbIDS%2m#V1QRARt2ja|+=9@v5c{_7>KbZqENXl7Er8xc|2} z{s%PfOZ^Gw1F6KGm$rKAy#~qcjSUNX{_u>BC2G_}tC5;^GxgcExd#wx6PS1q7#VqO z33b|(5O6UrzO6i_J5%KNqfmdYx*lsso$*&a7F)sF}_K>oGqL>)g)l>zAlC@z$!XvA3l&-}lD# zOQb&ev`>ui7xnv7maOa5*HoA}HtB}UFZK+}%^{!rQ}4tpq^On0`t_?R2&Q%W9&X08 zsEK#TwtBz3Rzg30e9FyjlO{UV>85p)xZethUGyJzVK8Q08{Jjp6Pvaq8QnoqX}xhY zhD~RC?NiTb75#l{6W*+SQf*p_m{nIONw2^0(ytoN1iIvU^J-hCY>Ak5W1Rc+84=T@ zzo#)BZo-jpZ1ng(;O+@0Ui$XV2(>=j>>ws2RVbwd;(3H4C)_gGqUhAtu=S|yO&##` zgFD;?1*+|ISsc2~d=qMrlG+ z>@{-Tq9yc6n=|fe*CXobW~r_ENl2vR{%*)Z`_1aEQSX*O{#nqX57-2p|K#d%B-M=` z!Id?I61JoIAkMfnHXgWfUwrQ@C)6iM3m+jTw5QszxH!ANZOX}?hyuo`C!&{frcF1pIZi5c!~imyLPqmQR@FB zhnwda%Bs@d*a!Cm-P^@mSJutC);$iEaYPu717fsj2t4I$=4Lk^wq+w`g%uj#NnbHIj!tI_AAi30d*bz{|#{WA~eJfy6EpL#afm-L)fwrRU(k56t5 zqb`>sUG&-Eh{f$=M+tT^K*Xfw&6zMKAcRY<`k6~I@4ATscceY9&fnA4p$;c&ULB*e z)}br-Z$GE>9!q3FM~l4AL|EaEP+G>(V&~Jm9GarGm6Uc^>(1Pj$C1ul98g8g{l>D@ zr))IubD6oD$s*`#R+y?x>SbEPAT>qrpZ`Hdu9g%pSY~1yo1d zLpw)&4f5@q-0jNrnxzR8Wd7vCS?d3@LZE8fO!sS@wzwVeDyc<+>#xuB;vqWoGI6Cu zwdv$aNZ9DT9oUGgyQOBtXY_++R;V?f^t}1pBO0^zPeaaqBDCWRjR21BuwZ-h@B6s6E`ycQuyNl zZ?4m0aE&~dc?>+FYi-VXH9`FAU)svTYad%UE zS^mCz4MWO}lSpYFAVzzeJ44ML8w$F4dKi|Wa=Vp)IU2{S`pg&^4zL|CLH&GKj5k`s zk8@Z?u)y?qfCBM&pUih+8q}@0z3gr}(RF%V0xIB|&a4f*>V8sC>?X@T-15y~Nn0nD zP8zLyX`DBhU~8+U^b-QQv=(J}GfB7^Xn(YQF?tB zn^=DVM^tdfi%m0JMn!6Tf0);*pbNm0LYSv03#Zn=(|ie2Szu!YEY-6rNS~3D=bS;~ zJQoYj#Wz26e6~CL;S$+;xw3PX*+edUe;ycqzat%IdQ9@RJ`2OrTV?5*GpHLr`8+tb zgnE-%k}5hsCrd&??(LdfhLR$Tgi6ao8uv>UM+aMZ!t@-G@EzkSnb4h=+{*mU51qeO zmTk8dBp60vp`D255H4@Rzo72PS!cjCWjo~oUqxn9kN#^Jf($1S_EUnwEuqdo?qhjJHgst8 z%<;I9XK!&UL|OPc->7(X5cXtQD4mVZ)x4j`DBSq*u=Mw^CCN)MW&9*w(Rv`ts%YFH zE3ajxLnkyYZ#uyhu|Iu?LDSRkDoNgc1TAvYP?gEEh3g}?M-Lu;b6$>}A-X|rOj%XS z04zTQER0+w7DSz*9J~HA+hLSEbNBv9k8bb%vz$?Avu~4^acUtAvOQ&5J}GAk?Pd&H zM}1s#0U&g~JU7&S>g{}>1KV^mG&L__?9!20ga0cSE1&5{FS>FFCF@?D+~)LUWc<0=~Sf3U<`@Va-F2b$s2x zfvSCn80(xtT1QU~fnlRE7^ERS%!x9~#!zz!Bp=*@SI7r3yAm;xGIL%U13f4OPZ4?e zSV%q9hj+?T3O21AYc)I`?pg7|3N|f&=3N1=NXXQa0Kd?u4Dru&1LB6mvMu^H^JDEW zPDU-XWS5Ol+R59vqa1pRou9tBQ4$E%BCp9PB<=Zoxv5I1yFMQA%hs@b5h zpv)`vtGy{`D;v4MOw z&EZqvH-XVq-&-P|4mk)mX5QjT8nW#Ii*Nm2TRL(xr@u-H{`FDt>5MN>KMK6vt{pai zse>qDP2$G!z*96l4bLMev#(I?T*^%=@U@W({7HE0epL*nViY0C9)o9Soj{sK>Y$M64@9rF^xUKy-uhhyJj$ zcjADC&B7M^pp=Ub>DLln1kLst&NAH+PmTcun=!R{WY2F;bG#dGUEh8aB;{s8N}@3N zoEA&Ov*i2Sz`Rf%*8Y;e$+{U>n;?9bI<8Z)GdP+MAO<%+>&IE&vH`ij4z(95-w}iTgZO|poCOT(7V4G3DVJMu^sI5^^IybRxvn}`g z&!kNDne@hNV4Y$ij)ZEZ;{Kgrsn*;PFqxbvh+LkqgkU7FfFtjJqt{}xT&F_0jfZ^X zP=dz6Vr-|@!=VR9??}n^#T0-~I&}}5PA8{%1rA4yi1qDgTkhRzw?E^y>4dOQ{{WRm z*c`CpH*(9llAO&eH=>WuYJ=6(kO38X!JBkNvbPvpcm8R4zVxLp+9Q%R5n|`KIORO} zyr`Nms(VnJN(ct;`0Dj6304ZVW&Nwk1ajpwnUG?{=C9a1C(89CG5y`aTw{*y^%zSNe&I4FeDWG#MNQdi8Bj;q%}^skI(PQ93oZL)GK zM)8pegPlF~$YLl1x5J~{62=#a@-0EVth!*U_ztanPb8~i0j&@;0zPrgvji+-A^wP@ zzriGZ2vsz&zu;`Bj!+6=Ig0dVn~ucAI86y1H=-nCBq8>y2U=<1TPG!GzgSjo8ql84 zv}bhy0Yg2OO;kRwCRl4TM>GhCYZKedZ%kKtIL(_mY;BsvL0ec-*af6{UudNrah}mbQ zE^CpOl+MTr(4vX$!P>VXfUoO48ORQ|_ZV)kS#-`|ker&qd%W~<(5&V?l?j(%l=6`g z!S|)yuQZd)wbd4}(xcT2NUqMhVCN>npZVIrd|CK2=j=qz7WND6(9;shfdVa%LY(`R zQURJ7EF1Z1mO=Z}f<;OBuqUi`(!uhU*=0|nsqNS#6Zd=`-4HWC4MkOfu=-(^`Kx}l zU-jnQd;>8pZEe)L0^{c*!tK-JsaqG`%nsJQvJQAZOfQ9AfakkRN=q2MGs#~9ka7^P zhaI)*Qkn|{yZgr+7tcx0L7hmDEIuV)#kDp@gnm0~t%f~qQyXRMDs|*DR0kBz1*e|B zNIWD*{uruywr2e{ILQfLs-~fxqA7-=lCnP|boP{R*n(Gd;t|nTV3DI)^{&hRzyOwL z^*i)bcW8xDVG@xnMF$#aea508R)W3scO8?jFXm4Y-`Hj*xFgmdKgVbW2qlAF0=4C} zeyHHxm=sW8J#t}rY}~#FoWF*?P1?yul53u6Q4ti&VGKlx!L8ZvGDKwrQ0I4rd$pUc zfd_8msQ;>tEeSbu4e~LP(jb$NM*EA53wsD@;REX=+Pp`%|BLJsK~D#Xe*9;)ePhs7 zy~Q6FQN00QS10U#BkL`=t*X;*5&6o63tvqZtzDX9D}f@y8{q~9rm;noEy+azh}XCx zEfE;(NjINGFiYvZxlI^BR9dhIm{z2?^p};YUc$5mpotbpSC6TdB7yH-1hrbLW zcA%v)Lfvbeju5NTMrIc^B#d^q_?9Hu znyX_*rKzY2L2Mh?HJivfPEU%+J^d8YBpb4O$#f1%&f*MGlFIm8+sRwYi8`gQJK<*1 z`*w84$(Ef(a+?rv{0bY~LP(x?mb*N0gkT1qDqCRDNqD1cV2f0#IpB+ns3F@@_Gv6? zS?H=SVqyC}vdtOMq@Hb=)nVV_P&WOv7~R3f<=0nf zrjTYYS>DI#B+ZWfVW>*PGJHwBhM&y?59#3b!Ew`Hut2 zU^WhWfpWIdi!eVz^{c!S_3Gfaqe)kl#PQ5kHu#u8Cvax_aWt{q_i}DPc&w>9E2HQe zgZs4g25~d9j1*sIt$ozr$N+~xJtIL}0o)r&N|6W^(Na9+|1Oy~{dmvmHNuVV!d#yxyzbmqZ0SnE!|x!od?|Qx<99?1(rNWV6%RF7T9Cj|DP=bB4%*h=KgT0 z>br1K^*#PW$d^GS(a@&Z=pkib!Cl1>zMt%`=|X0NNa_A%Amh;laPu3f!W6YE_80E_ z*n(R*|7X9Jb0~m^dn+Q^qEc^@&JnkW9b3p#Qy&?e%F$;zm!-)j$LLY9(n@!7YlEp|r;TdE;KPYnO(XVixY*h-bx%SR+qDLqPt1RxmjV2~>Q)T;HqK3mTq$Y@ z;Ud1^Z*3*lc;I2Xx{P!1-Ox#!FU3)%eQ`6lhL7**{#o4xEzQeYO$k2R$=PRGfB;EA z2IJ5aFX|E&E~6is{6F;7;diIdv#Ei>Eli!qPhYeJ58S66B3g8(p1{Vf{8+Mx&RcBn zb`7mcq5=YL4M+2cEH4)Yg>hY-uC;K^noWaQAoM;Xf`cHbPL3q$$~D56_1`tmovZ?r zL}hNWo_-W_0)CG)wr}fPQ2=GT;^BP#XdWR_mZ}QGSMmN8=Hh|$Yi|zP`B0anTU$Gp z`SwL?_|F@Z)~&*zc09;2cJwwnh@G^p_lOU0&K!40upLO4mq0k?5AVyJ7aIbfmI*kw zI)9sJu|LJIa!`t!+U@Fu-stb5A|IjUU$}Oikz2v)!nm)h5Gf+9tVMo3=ck|&p;opO z4)l8x^p{8H3Jy;F$$zl*ZeZ>qY*_d_FNRbfj%B-C{GwVh3k*~r5#Dtjllb{iuH)eY zcoZ-FGU+#88A*&3z_%<`W(xIpp}zq@PRZA1?@}MbwjUj-K`vN})cTBMj0X9m{43Rz z5mjp&sqUv?sG?@nJ7)mT18B||wubSu)iVuAW=^VGb)VCR53!m-%kCiaeDX1Ciy=s^ zp|T#ck{R8uqR8F5Dv6*Y{|iUqY5=_;oo5?w8}2XBec7w^^xtJlc_>~ef*R#btJtqh zThKoF76Su}k;Mun$e|$MXMtWbzBT)Nuli39i zYxamA(WF28B`pCk5n_zO)nQl`7W~y;dy!%{+=HxfFpJ{(y1SrKI)ww`-Tjx6o%o0UE&G4L~Zc2}M!FVtS2? z_mfX6_w-bqG5aT9wa z<)2W!CLt;m^PtA`&v&5H!K4BCXXB@WwBZt37^Ay4=zML{c>2W%WRMLO!pY0}xky8jQlp@bdR}b0&FO_qARLntSsTe&$DNpIcdP^iVRI5zFGG7MP13Di z&5OXe{rAP!*ImuLDL^>Ql{>TkrV z$yi_U@QcbC7RKysv1ZVAyCjoKdEx;SqhQZ@dqKpLZI)G|dXO4~*6zW9{~>FnjtSUT zeJatyd;RW}iQ_G-->+!!koe6ZTz{fy)IB;61t45-nl;X6v*zi7u5l+}NYsq$OzWF! zE5G2X(3Ebx%WIb2w+AmKLl=i>%SuQ3nA{D9$+o3;TZVuR91y}FmZ++eUMx#oS)bII zkY=&E-GPYs&Xo=v%fZ5(>A-0~cyh6>ky$gmYxX zmvS(t{N9;i$dnZs-i-syA`;>NRh(b$?5CNr!y5(*y}zg_?~Kc+$yFC98kgcEVS{R?+6zfBD5ZOdp|K^ODI7 z%As2Ljq7*N@o$k0`sp9LP5=D9JP`z5g6d_QUC^}Szb1-wq> zeI2F+ygcN64efp()_q@>^}QwuzBT=uktXyJ5 zK5k!iy>EGWeIH*B4qcbuuQMKkz8}D~-9E3@u`|xON3QR8?bs!U+`YQK_v5?(;EKn0 zrvGyj5ibxc@B8fHhCN*H<2g_8n;mZd!J}{LsPFUo;e@E`y6$_QO3?k?($EhmDER$S z_XT`-5p-Xfd-`?=0B&AiZU(&BJ><*!kKrt(2!71eecwHJd>?$jPt6$uZ?kT8w*Ede z_UZT>9=VN)2Yjs{<$WJ*-t>Nz)%ib}5`DC1tG^$leKHcc=sg7Z1BbqWoQ474|HA&9 z+6{R6mxb3X|FRP=y+@+=)7>`_;CB<$#aQm0!}ljuz}vRwcgW*{r{Bj#z{hje+`p6o z4gEj1cfOwgg-{&a_kG?~*D=BAAlN!*=nvd}5&XQ$>+3kj`hI`=emnm67~bl>|L$@B zCdhSo!ud5O@%TBr>)n9$G1m)xTHEz~sxtKN8hx%Z?CE=K-%lIsGSK&ipT=8%<^oEz z^3V3A+Wp(v#w)DvuZw`sXHADbpB%rJm5awLhu9{;uaUmjnK~lN@58yTeX73CiM&G< z`?KEd){ovBufvX)>_|ji8<*snKJMjR9>ApE$;hRuMf4G&1LnT;;oGW*_Y0?t;JCf`?l)75}$h4cpHQ7t=zgbJs&*z7oD-Iq-%OhztunNW_%J?>Y~kQ zt!wX|7jF(%FM2O3&QDLec&duMS6u=koB;y7PkZmBtyD29RY*%)?sIlM9y3?;J_JtY z^44{_JC>V#349JVcbj7^E!k7czRu+|jBdsJWeu@;4i0S<-1gD(YnR2@F9Vj3+%|KW zLA3)P!~MmDFc)r~%o&REihC%0f!aiW-Z{dEd`_o64BjyT1!+4rbbg9k*MHbR*zJn! zt9fg3i@N|m(uAr&(cs-u`=%{{r>@lUUcD&WEk@`!0SWu+zU&M6DYqqpEAMjtQcBjM zv-E4Vb9cgBHxZAza5|T)3UXqK}>c$_>`akZr#mki=6SbTQ zIm=KFXn>?xs$l#S!&E{2_*{Z`-Q z*}3=WS7ONTkIGc(#1wf_8!HA4!66P88EMm#?SizI#uR9mk2*Yd{o-LY-@P^~1V@oExi9?t)L6&NeapZRXkHn(e*=^P-5BfaZ-3RQV0W z&u+sb<}7;K@M*zM^k=u8K<3q#PnKQYZ-U<4MobqNryTl$#MSORH(&-K}Od`+q^NmCtIEtH=a!uJrWgoLOscVtO$9Y>8;%_G^QEUIB+^=>a?iz8yrg!pS@~p9q&+V zpDW0=KEQOUwARC;B1~Lxr>e*sgN(BzPvf03BP91)Od2Ev9xBJw&_40ZLy(9!yYrAL z@0#OD-z(`rPFafHU2PEL$}cz4*V)3U8J6B89Idr{bJ=2R@A&y9Xsj&r$s%fBy8AU_ zp7#4kb|tFW$vY6PPNEL+BnS6vpItDP*y-EC)T$?w&owdic55b8fiA|%_|6^uJy>o` zJm1-rYmhznvS``sOV_?y=gSK4(Wr7=HmYl3pF&0M6yLVBLos=0ptaxg3qtWCB$NeW#YF8aN z9&7yPu+t7c^r+=n9V#rB)Gy?aPt@9lx3YoJ z5(Gt*-Slq~JHEpmHV1r$t4N$L4fd(h_~s?>ktO&`%@|k%DkAEijN)rGaN1)jE;^?S z#d}q6i~w1mqsdV?rV5*qOC5TE|Ku4GCoL<5Dd807k!dkjJ($FE{v?t#7l<{TCkSc_ zz34YiTPJ`R?Ksqx@xwHUD8W3supA9-_dxO)Z__X?iv2J=A*tG2`S75c7r#-p7{=Ds z#?Br8?3%ssip6|+HkaPM^0}qA_`2yO?gkhV%>B6M4d~QIY{dL$zLou;(qc-LXeCNK z@+@I>1z;;tQZ&V@dsdnoijuP_(Vel{9Y-Iau43Zf)zViLu^7I$d~cp5FVHGd8DhaB zZ}r$(4g6Y=ICscC)tCcC)h^~r*MQP-&t%?u+awbrEf7FPG(Hlnqiz^*>cL58ge*Ww z24D$Zg{c>5r}pRRfOmDPO51dV%2&bgo>B%LGKi=GqZP*K6a*pqm$omxm z-A3u9Dm{5X{u|Ic_4_i&CfeqWi_i2@%wCNElnUo1&s4R?VuNu0Fm!6Mgng7roWM$bWo)Z}uQf*oaa&=qbb-y=;b-y|lC18@yEXCJ1w^kemO84?V5RlO zP-Q|WRm!S?Ltj=H^n(l>9>JeOYBG`H$|eG(-vZIlGqOh<&$V_}T|WE;e!6&NvY1An z6Esa9(x-)}S526A5FCCZ^M7Q~)T;>POmKWDf|xV)t!-$SNfkLO=04h;26AxzGCJtA zd8`v8;?^+CRIsX4s;cNII#T(nn8_WRyN!n|9<73OLG*P~((`iEvWGtQGNAJJt-`@k zJT@`dhA3z&iflQRs1+Ue^UxIZciu;p#)iEWd<3VCpWf>& z23v+SZYSrf&;4bh&(oSJAZu6Xatb!*z0=~Cmd`M(35NL;Se%ds1ve0IfAYadcc&S% z$y<7l3vYjLG?*qj&>uQ+Q?i<*aFnD|$%3*}&OL zNh4^$Mk$Ht1se|PCGrf#Ce9ruB%P4kwKa?rOvJlU@j6TvJ`xU7r*_cvHTe71aqyL~ ziHl#{yYo+Op;pS=H%TX)JEgLA2%6uTE=4~m)ubsiBo?q-RbkZ^r`a9^3I6rWp_c`G zcL?0UEzzLqZW7>-def3wJHwT~DW(@eJaRzb7pTX820n4`K-9la$Xtt=Z=%*XqwoeQ z1D=sE>TI(xMv<2X<8Bam?7t!2qDm&AG;NBnS=6G=Cq~TF zDzB9$PLWnj^hlxZ_sOQ|xY-;4=$8LDZ|H`kg_))DlUP%)hwH}q4w2IqYS*ExtM8I^oayXWTkx2820vk_y9=^b{P3Jh>@5t7!uXr=X#z|yI*M)YmFw>26`3r8HJbl5z^v7DWqT$1gEbU9C;Mb?O{RqbvW zky7;RUfrZNCq`1Uc#)MPiN|U!yn~pahFo?%u90Rv?{|$PhL-J#ot#c4%P_1LcAR-a z%hd+4Hn0>>lJjRHfYY+Ar9GXKAg;Y*j`?DVY8jplL7rGoQq+>1GUQo$mm}Rfsze_v zH$&T4&)Fqd39~#zbvx84M%7tC#_O@9JoX|0EETQOIqWT({@VnONz(~)8Q=-XDp zQu>bKvwD-(yY%z&ZM79-p-Xql9nq$haC*E~a-v`jlQGq(Zbbtj8Mu|&Ln-TzW*0; z`G++*eyGP&CCbtY+GFKLEzSdCM%OOvJL??JSaRtcHfXD95h6{Xc#Ew%{^ zbTkBEjPp^s6D%I2zqI}5U9PC)NSFDaA5;AZSV>G=rYZh0BhgoJH!JBq4^Q9mqnc#I z>*ypN5?RzDU)`&foG=O`O_mv{3kU9rP}`167%m#Dw?2?;*Cnu-sa6RY_DH&Ayb;+$ zPGi~=!av1*jzCMkUcpY)7~v{PiE8~8Ljm|i3=s{k+yF^3tsyDwM1Ikz4+b?OcyHQ~ zcRh+6lE?)~*xK*vVP(V}>CvValXU1FYuY}x6qQtI;_gEPY*y#e$@AlTKMleu6M3MS zyIqem5Q2@3yFVImy6Y+Tv*@-7UzkOY-L>rK1UhLdCxw!SvFl>rD8t?L%)mQ28>Hi9j;h&fIy z_-@^uzGgvR%iA=a7d(@AAx z&qH6IkFIF1dB>3;)jxWSDh|~H?M3MLY+@~B$aw$}?#Q1(48$8|=1ja+0{4w2j$6SR zmfVUwI%`>qkK9>x8mAKo0w=X%&Lr=&nfab~Q-3(t(!?^cv_mZpHIouGglQtz*`9ww z)@<@N3%~|BH~~G=lweK(p@}Q-rlxkREJ>N#w$-pNrQIjF)Qy;hRDZ_D)&{q*?*vYD zY7oM}>0`5RHVo0FgUnpAp~0uh$k#IW=xHG(zYw8q(0d*Dpp9rrA<4>#zi$)ML1BDB z5E|NsdUk!yF309}J-VhFwd=428ab($p)~KOe4`ZpmkGxu+A|(<%S-gh*q36I@L9o? zFaFAasDu*@2qb!YbBvwS#R+D}8@sx2G9NSRrH=Sl>1 zK3HqoqH<~RVk2--aawjvw1~b+G$bUQyq-?1jwp(rNp6DX!T2C@PaDpBBWj|782U6V zbnVgNU9puw3k0$QA8+6d%(yqK9fI*bD-k>7wDoS+UpBgK2Y)bBKam?SeA+%A-%GHX zm?5sqW;6VBbPb|(jE02oHlGP6$4VH!>FKrdj$a2@0Ckc;D`sp>Zb#NB=8a4ai^jb2 zJCcv&G3#Lu0dtvwIeW~QR5WT%d)s)-J{jju^4|jpf(~UxvJ7?RNOXP~r}-0^6GsFa zCq|-Ok6^5UOc*R3oW8UkLA#+4LB!OyM!a01C0o#}6)|<2o`9`Kp5YTFkA5NVWohX5 zN=InPfTokpVuSy`oqjMq^nevljI`wG041tHArdMnme3U>M)w*a58GFor9+FNhfdGE zPQ|yp+FI+4TFHA}za7K%W+fJ4VDoq#ry>uyK0%^RYX(^ov4ffbui{|lHKJMzJGdWq zdgN0tmtZCj(n4Hvo8wMck~qDznyd-!#^!+S#B;;$leQ;K>!F>_n`(%9q;^mQa#0fz zSq^~?lDi&WdZ@-tAJs=0$HkK5W6xoyW3r=1$~>OLn2RueDw&`G?#j`r(^0vjzW`!t z(C{cxkXrREpE7+YLc$chq#1N)w~Wg!>lP1trFEt8?s%a& zaK|V);H5w#Zw@>YdSjb9g+v%_#=yg0DNn96kP zfHiC)oQ^swr*6nHnP+%Mj9jvwf7ra*_{U6;=6Vk2aU$;s?K!ku$jcPcmh#&%e!yN4 zO>+?aIw$}eS><|#Y?sG-c2MEix$)mkLwiS|1OCmoQKZPV$k{`d?ebM5 z=?3XeVjtSG6I_YCy@_nq8mS`?&;vkTCo3Au0L=na#j=~6rvdNT^FA5dLHr}qzUTLX zZ@|qU^;~Zs4tfgEQSMfaS~`I2R$wmb8;-!FWYO7tiG$~(>xncCJgD|*?#@r?0TqCyIxzUu!aI3rI45$V2RfW)g2NnxTHscIt z$%^IGNEkH9F)f>|K5i@0*PN{cEHU|PD`}=ll4zUTUzHXkPQGdLmyhrL)E1ITKA}PG z=OZw(HC$tf#kl}Cpdp^YjBK0+J2x9OSnn%In3p01V4LH&{XEGSu=hKW6U?$IlR7dq z`E+eqK;XlN{h(DmvPl3nkOwvyAJSY4K&C%2bHFbIT?nqrph9+lM>B9^QiMK%@(=ZP~t}3lQju9XL5M`b^&{ zIfq6aiEP}Gm%1g+piLl)aS+&*(31ZnC)}zTyc1EOvB1v_D`As2=CuDH@45kyc1o(E zx(gwyfNXLes+#bX*{bDBxthJS&-w$OUoo(}1?+ z@8{ts81kUIUHHZPfa(5bCKy!QqMVnw-efr z%tO*HmElxuc}FO+vmR{I`fIzks?MkW`IFb2oYaE5Fl|u~UndPHZ%gN88<4`gTRvyU zT(l6vS(OxM5n!~wBupvqL`&MscHra?@0=FNbq9MxIK|ufxH0zo4Lm)i5{8|YFVFti z;U_XS1ynDE6u0Zq6|^A%T*1LLAVW_@{XlK_Nx#5tFsx|Qp`~4&5L?kn@?Bxb%>G)0 zbCpJbEU-2JK~79WGDA2P)5GhJqAY}*k3`r4%TP!J#B}WMr~HUZ7XYc6FqWK`eVSBA zjP?N9NuVSNeegI=&eiKB1g19})*;c;K-!i4{)l!Q-VIXj@xA72owv z=vsHN0(CD3_W@-i&4Mh55U#<+Mke15xP@$q)(JhAg);}4iUdg!I!fn)p|(ph9C2*< zH4`P6fZ)lS&_a+?GYKmnIrv02Q*)y)h*TV@fQe@xB>~w;kQ&tmvZXM3v@b$~b`)ku z{8^Y2&g^_wk6kZ$#V8C12zI9eB$DRsdUQ2*6MCAeg}v2tiO|E?)o5P||Bf?+RrPWD zdV--^Ibe@L{kXcXfIHb=Tc<88(|5{G)Z-Hp2WjR^V%WL`+c$$usMy8iPdifob+nhs z-49qBhRohvK6i5(ITL6_7#)_7o>f8|Jc2Eu>lLyi06E3qTOX$Yk|dFLrtDJU!=>J} zE_ADeBth7s7$(uWERAxfYC&pnI>=AFS(OfetEB^84IZ~4|2G#M^{5g7M-Jwv-SlUei%Ywo2Gi1px9UE=vj_1wan+u zV0O(n!IIqHK#->c)u>}hDdHhG($oTZvPg#m-o+>&<3J1z^YnGR4}a_-0Vu+fUX=#4 zImC2hi}Yzp*bX6`Nq@8m<=5*q8)hYNf{iqn7Q7>9{v#D5x9{F4=-Tz(whv^E>`zDg zaMCNl3#oBwcY+v2QxWkzOhTuxJR-iY9kg-{Kov271Uk`nj{)vdIRQ4O43G>h`K6EV z1zlc&iReYZ6{mxbA^glkA%ga2Wa$yYmosrr)~wgpbH}sG>_Rw@lE=Rx*eQFoX*0c^ z;|c&(29oT3;7;9%Ye_F`q))^-2;}3?kPg$n)~ep?{?4JH_VYlRkL(K8$a^-KUBl&| z`B925D~06X78b;Ix?%5W>KtRhi4Fs5p1w4(}e8lPXW&33-ucX{F*Sa$o+Cv2$m}NlE;lQ{Qf^h-qdm5=c9o4WmzQg?Y6$|Q=v3%i8-oNQ>Q6MkPomZ3Qf3e^ac>>>kc&qF#hj$`(>?0%rz}+8%~1H8-ma%w;y$OUPUF zM}fA~RAYYAD1b5xxHY!Ab|@~YghD3cmk60Tv?wKA2yErl+Q(o$nmUu~1*v{H!tt9p zq`C{^A!;%NH;q#Wp~UFhrqtHiPX7mclK1U#)nU`_ao-f)9Q7+iytPE;!Wu1QJOL|9 z7O${6HDe^oGWcIICOe_-g!m#*RCelldU>sfJs}g&^-`M zH>Z=YC3?TV`M{oa2{k#^|8?i);q(z;0(BjAmZWUVK(Z)ZJU97S)XdaeuK2I5@_U(% z$Ja?y4C(n2bqmJZk-grJTv(#o$u%lY&tJ~#U1P9yG7)J)<~ZVIQZQ7>gy9Ng>T9Lh z?s}P$wGu!Vd8eBz$Zt}|1c`H%qqfS~Hegy>Pa73O(7F^8l=?c@FkY7@Gsg$w zD`A9F;F^MjrWu}A0#WOG*{5U_Wg{&BXfqFBApmF=+*3|X-E}Lhcu&YNsh_vEH5!RTVWD2z8a^(1W3InnK zTUa2KBvE5w`K^SLsn5bkCuUBPy|J-$f7heJy$;9;Osh12Wa=J{NGJA2$&blM7cU~O z-9p+7qa5fM5-j6svrhK?HHQl>p`(`D$^cPc0ay_0Cx8L+nKPkx)E8yfvQjnmZ|5}~ z`ON=9{`~(I`NK3HiA_w_h8vQjYG3<}Qt&5VTai}V_l`g3eDL)O0=|F?Jc$%>WHv1# z#Ph;>!QI9+1hJo54|<8MOtW!KSUL#D>0xr@qba+e!JZHp9xA(}!nR0lZ@`i#1;Wl{ zliMXGHv_k=#hyvFRl`#*@0EK9WV=WdOma}CgKI>bq*P!xnyiFodH^}SRE}=#4X%f2 zeYk`~);qOT;a7ogs02yNBZI|+0{WK$xHWl5n{sUx8BFg@;Ow&Ui&A~XXkt!0tMgWz=7*Ayo2bEL?;U)~E0 zg=GT+8dkxFBP~m5olJK5jY}v~<^IwoJWhOY3AMq>NoZr3C%{TlW<4Bt&(e(w7TAcCj#Qodej&jZU#zb4?x-TaF_ak(_z<0 zlI7hi6JAOCoF;iM?J=+A3JZIjYGz76C*+V(#Kj<#>b6buBsZ>g^*TFZ9X^S`4}#v% zlt4!)z1)MX3$!KdRn>GGP+&AdN#$Lx1(PRcyTY(AiCN!*#g6oaem`P6W{m^BAH_BG ze93Q{Y(NTt6hYg8+akK<0M#eQQzv;S%KEq#SBR3Z*wR|llZu=id^c434~Q)y1l2~X z#EI7Ao9&5;$;emh_MimQu0ELgNR^YI{iDsc7Agx@&YKgL4$V=u7c2+G(Mym6uPakM z-K}Eag}DzT2ca}pG;fk{KmvBdVG_fysY$f|l(nPZr|6UW0bk#=W zx9|0Q#c$u-^2uay>ozN~Wtfr(D%uaKPRFXnPv~#c&$PFri5nxnsxJ$k+PR&Xz2DlWX(lrs|uWKs}-U z4`%h3yBe4bIYVk6-}^_KZ~2b|_YJ}@@H*RWjf_Kv#L6^suZaab-#hv;!(~go zA>6f_i=iE>$6n?UyD1iCsErXY+Se0)Bv&(RY!aftq#2!Rv>Lx35vnzx{0{H+bjfd< z@cr&Od5{9tiB3XUlD+}mED$3CLAmX+$>nTh@YGh;*r~E-dax-*Ww#bX+@z~u;dR>L zETX;>Ig8bRk*Z}Bj^E|Ie>7HK&NXiqOa{LV)D^yO zj^xN@z80{81{E4~=WM{c+0I9vl?o!{{unDm2?5f82I&#IFX&1NV248&^?b>1pY4fb zDlt@(X)QYxw=QYIgSFchQ3EVB8T+r5Z zhFZDdWIUyUdx_*evD{9~D}tI3y^(wj@C-0UnI|oX&UcD{CHNsze(~OBW^&x9KccC2 zo$sz0g6OzJZFZq>qA#@iyR8iqqvS5r3APv-m=P6*xZhLzLCU}o9|_0?#o7D4{b1^w z6djio=Frh67OMHK&aioVC})F>XLMQkgI8$ z0ofSD;ZPC>rciw!C2+&TYk=~k>(RCB6__tsVFI?#0{6q=1_5{)-FVr)5>Bf`roMhG zepE_5HsEmY5QP&S9Ud;25Fw3l5kcJpHo8NYw#_J^)N=!;po09ZubmHHo5H0HlGT9o zO*ky$?*Wt{k#qdzyRTOO{G7*M$q6DUOl?Z{%57?WfUE#vZ-8&`t-`@4`>unyD?{I! zVY6>3F#l+v`1|D@O$7J)%b|r={N)fdo&SD(C+w=&26D!_@!;p9kDNka^Pr=er`oFr zLS`%H@jO!Npto`VcE>j;K_g7LqRTRLrtuGNu)HMOIubz}#B?n%J8S7DC^HDyk|Kgr zckYJsP{ytumEUjT_DG1ylx?s$TAv+$RSoIq={AAi0i2Lw434Kj z<@d>A80OzfEEMUd&21*R#SKNyjTFsXBtz!>+JfsFo;inpqjDo$GmXd=RgN86Wn{QD z47WNVcN}IZ_)qb!@jxi^2i_X2BET*o3Ekd1T==0x#&bYD2v<4}X9K(^%rfs1mu?a~ zf+O?&hCbZuMo=_EDH&P36(*yD$T8zbs4Uo8%lV4mA)X>h2(d?~XqVXM7=_&lDLqGl zvKYKv+Z@}pV3~00lx)gLJ75{e4i!v3q|j~KSIw)YiMkLA_DLO=lvoOOX~|A1S%~ib zr2WeX=gO{@nf}YA@TU;5cv7Ulie1pju77mn*o2@`706)xIZz~_} zHN0a`hAdOIfi^XBSADRpRyd7vKN5APD_lcDFj*T(ml>E2wQ@h`qAw}_N2)&|q7zB; z5=FCIsNN3ba}T}c@=UWb65d5j5;)GV&~=alHyJ;2 zIAIA6Ixy1l16J4?F%YQUKp-s4XE$}z)>o4kK(1{PmU00UgA3?&@9SZ9;{4lcvOqN# z@cW3CUJl3I0cW!|o5c#W>U7hX=kjk4u5dlxr3m6Ulc1H8v8=70A*!W{Gf@O)W=d0y zXm7kRTxy4#iu!A_#5zuxs9$fUs&W&gY2($nWNL5oGRj_0@)2dIj&6~p9Kq)a9T~jG z&mba+q!K~*n_1P)SI9;_ot7Re(OtoXK-BVMG2`Xm@(DmqAtZ0vH zP9F0y$&X@1$%%8icD??5as;Yo43jpZ{E3h_uJI!O`*MPbw?erBXl2}>Io)7_9c`;C z#{lS|o6Z+A57A7O<=env$q!c(W*DH95pRc71w70X@+py3An<`=?PmPQ^O8wV7UFc; z3%d)Rk{l{$tAUwhI}P(~9b7AAQUU1?ifR#KT`Fag5z6Ow zTXAO2Q)eFdhRCx++E<;pCTHD2mA#x`nIKL{cghOK&TP}pY>Rdtllrv{Iw51?cVIf* zUf|pLJrcMZm6Bu-O0-dcexO3LvCT&;finI~s)0}yQ+&Nfcd_pqNwf~+xp#AEKnN(4 zN>UTpfN}QPYDBdw<*(Wlr{p-5-H zKPr&#kZSFS^)RdGZiY1K5EdGel3?Noy2YlDY~7fri1R^60zGz#@|tz4ZChNSa1T0w z;OD?Z$aHrIsJZkRmOB8O4{!bCjs(=Nli<8vk3N`6+VvRDWI`5^Z93UX*m&)utpuFq zL=0<%TB&CpIq0P%4=CFLxv;(QJGD%9pO4aKnr$`PqbY3DjWj68BFhVio2MhBwZG5f zmqSaVo;&+48Xk3Yl^MVK1AL<$LyX70=G<59e8u*$5mUq#Ql+fQeeyu_4Xw!1C?%^4 zo>Am%!ksH?E@(j-^g9}-zCG_l_V53>`_H8ICX@B3dc9 z#l_3ng%Fm)a=I80ZL^!z7Q~G(L*Cp8;eClQ+21vpG%y1n6Q(HEj3lU5#eVpe6=wE{ z6Btwprz>KEry2+!G69mnW=?8|RA?ojOX7nx`6yC|2wY!G?e~Bj74`&f)s#L58$^Je zgMEmKCZfbqT9Mp32Ocv^^$nb5r4S@2Z&Nl4X)vM2!(ak*cS8g|OC?Ts&G`A~8UdV` zUkxEM+cQ*jOsZV1j}gEF-?G!$q?I5UO4P+VRQVN>_`E4f<^v7v^;N$#f7S=fJoY zhz&Csy`<27G8U@KTf7)?B{YZWV5&vU^##0xFuYSm5WzdzLpGp!cKhjJk-$C%q>5}7TG2P5#8(2hstrtCmU7q^GtI2m=zZ4 zdADu%N8LEx6{u{7HjIA1O( zB(gjK*&-7WHh2eG$8EObnTYsSTU8sYp{nEdz210BZQO_XV%@i2Bh; zP!0%d$4j274}9IzHg?WX2HD!i&ns3OUuH>y4GLCbS6URrbC=WP*cCXa0m`DnIaJ4f z5LVC`2p(%qdZFSM?)l)Fr5#D3gw1<)z_w0@sE|^G>Iq`Qt{Z9>su1eLXHhW%d9Y&mJl4?1l5W_tE3&uY zav_jZo>DnJ;IeDby;oxg2&;xfsmV!?93|{@ss_>?j0;dk4Ek)1n9~aC9oPw2;0D_% zEs3ejzg>?$cE8iNU&ymiHa#Ip8xX}i;||I2pw*o#O2GQbQUhpxyB?Xi6*9m{ zH7s^krhoZpgxb6=$J^?jM%(pVQoqEG}OVvG0sYK_5KIW5Gf_@+skXrCcY^N(?O5qfA%>{`i zZ60jTAU7tbyNA{1!<|G+1IOu=sHverX9`4*4B|wO)gG7|vf;G&$o>|}?~_{{v`Cra z%tJcqN9>+}vRYkMkRHh^`|(6r=DYhQ!OPYIF>$*deJB=sWhFm~g+7?c>-F7TaXy)+ z$jOTm2`sI8;*lHe#oq1Q4X+3S?$TBlP*P%1YFRI*;;%^3`hC-F;I!s%~>dzDr{C){j$R<5*vX76SYGkXB9DCVLk_2T%4yI{~1(t>@9x&sPL z{N@5fJ~ZLjZyCXX5~BI|?Rs?Wd)5gZG4l0@T_x9WK3fb+QJw{KyxvxiVMnTn>!cjG z64>A?F65i|MFs&t%}#Qk!px$hC8He^LU?*YeWKkRkQ{Qc#^U#QIl~Wo3ZEa8Q1dF2 zvVvHb;Qj$)tmYmhgU}ai@Nq>j*KNdNa5+Im&~N3zhe)2=b(#(a%RYw)i}2mP>TIR4 z@9vwERsn=0J|%{W@pc&8MvE&e(qZ~Y>D&PFcOotT*vg0|9|O=nQJ~;hmtZ6Bnqox} zeNE=#*rP)AbV#QlR>AJ%?*Q2hes&(UdiI9Kqfa<3<c#f zQdUXLD)95xqV2NmywWqgL2kn$O1^Ut=REvb% z0NHKzC^4bhltX_pyY+d+V(TJsMYL+8l4SN%_tDt6@FP?sHn5N>?b{l#} z6C&YT#=0$ne$<`TfTR~!DAx!>ve0po-EhkA2@+Pa0Rpp1Rni7?aEq3B+FjIV3_Fh3 zqcg)3s*8rpTdAko>_#7p5$Gpq31-P_YSYBe^?$eXzVu~VsWdEg%0hmG2vzDiIZS1eoWUC@e> z#^!D zDv|Srq74BRyn+RdF1Mn66ezI6k|{~8KrG2BaTc0FVz=O=?2S| zpEbzQp<0Iyb(Mv}^yvQ8R>i0RXptz=mUNy}Hn2>0%&7CxN1{^r(;>(&r?!^Iq{?SL zdrAjUsY~=^`wl`XbNRFA;N6ug`)lA5CKTD?iB0A*`AqX6)AmJAD!47kXhoF0m;<*$ ze)N71KTRxsH+&5Aq*YB_c2`q#ABw=(jsWA451}R*iIqW6O=pjm_3}U$ld)J`*GoDt7_ZAuGa1t9! z+n{zbrF-jt@T`Kp1E#a3l}|+}5s*mlvc4WsbUfv#V|BEIs)TF;Wv~mLyjkMI)EGM+ zjLXR5bycvZc&_{}Du@w^00|9Y=}1ZqGnWo5!T7yArU#+2g=s?S0utL?Nyj}r0W*ZX zbNmL02n-w`!0JLsgbHX|U3~+f&&o9E>chqc30>0bE~|fH+jQ4t^I37xs^)n#;3&qU z22Y5alV~9VHTZIKmf`*&N7%MA*yeGD?uQT;@Z0EefS85AIqnOA@MI#wmoo-92m1lY zrVUx3nwh@FvCcuxLk>X8tGk2s2)?&uvZ;r~1m0=VDjwm?xk@WFjFV`fbMz?Sw~tL{ z)}YM`E$Gg&?9%W#wDAIrzK6I%BGctU3o23Mp*;=Pzrjq?Up!Z{S*=A-(m_lQI%Vgr zIR%o>j1nEImT#5eKm%ePBM8k=Ur6y5B+;N9W@!r{20tS#a8Z)KLCIKXgXG>(`A%z* z3c;cqOP!Bs#!Njanhgu45fHRPvM4f{S53jOJ@bY3|2R>$d&FzVcOu?KjJE@e zL@3{wQ7l=SAM~hTK;Y66rV^|J_X=r|*)y<^hR2$Yb8qv1<7!C}-HzT#s2rNLY}-=` zPdX3EuHBc=eO$5qxD+-UHq}apAqCI+W@H3X7~_wQ@R5FyBrN8e54v#t_|4TGdu-`a z@f6c)W7pg;7Yb!tQn<$Yu(a}jxLag&qPJ!R5{ZFMo3THF9Hte*ZvQfYM3d(LGyz)fu$RYaNkb0dY zWJ*wka|eJ>si`TUZfWZ-V~}_)_{@PtSwJ4T!T~Y^63%nfTix9$TUvBGQvbuHT0Bok zV2FI&h=aCcH+M9tG<^o7cagyE7qYw8O5}{6hZDao(~4mN2%^lGb|!m2aT1sGNB~Gy zpT^^HLPJ7jGrpvkxC8+-p#&k&41Lmrk6N48GI#(W2K}=I=vI4<@<++DJy^3VxH8xj zO<}vq=%ckndYcm%<-8RQafmRwNu^Dv{@95ox2MUvx6I@GR>Lc%_2cF??^riCx`Ese z0Et`Tfj$|8CT>wcm7KmAtMRvN9>3Lh?jZQ28KdPVvNcLN0+bKZWf-OI4W|tVBb9HA zH@R2S+KphH_v*4rZ*2KW_*C>Z)NV8uuLMNpdUVbEOz2KYN?;K_cH^OLHDzbB_vl^a z3OKEUhBdc#5k0(j12JD4saDb~IvC^jWfdU_f(t3i5ivLDUa*o`iO$yUEc?E&ZX6|7 z6FLZT#1RLec$@YCbv}w_ZDCGqH#;%;dWC%&jj^;f@?Oi(5?6~_?){Jzi#Z?}Mzg9$ zooKKU1CDWfm?R}tslYHN%_^IMYRx<}+>tgGR{RLdkvc%~D|HE5co|^q?7=X%x!G{X znELo$do_quqb+aOqifOC?iR<>(d;Mp^rPyEY|^DVW0hSs(?mD-V~cjR#eM%O*20_m zSPDakLBI`c?h2~ua0ip~RZOf|wjjWwAALu`ZSVK*QEO#SzC$Tt5!>Lm#Z^lCBc^DQ z44N*4M}5754ebzA?UTqG9T9^xZP2XBCELL0X(!;4kqA%#Pu5zYvo`Qvh*3ch)F2hF z8@ZDj#0HQ>Zy@mVB0euRgrj!@Fm{5SEqa4OOGd$(Q8Q%vK}O5KggNlj0E0y8Q3LNl z@#*NQL^Xt@jNN1HIj;}gn^gXnRg#S`@o-nu4wafab<@b8zfqsiBN~W5D96Bls}cK* zp0bh|u;1+rH+g_9gB)w)J+5LraTWNbq#=S>YEZdRA<%#1^ztL)6ymn zO_T%x6*#K#-tK)css2|VyZ@E?*ulKHT6-XE>C_qBd}&fgk9K%*Ym+p=}iJP;~ZbXZ3;|#+JHWk3Mwv zh6-rId>sqmA0gsL{&wJ|oi%Zsc_0B(^yjFqS=%CUOURHgRNK?booO3KCKcA}2s14K zKcW9P4DO$t-3H{?S2>l&oEGt0;1piZ_ydNH?+1oI67fCC_5k}HL1d{=SSmUi(aB0*DM45KHK%teK%?-A&8N!b6*FQM+Q>oA7!-1tBexw3v%xXs}3O z!3%)!G}2=?G2|_9b+bDb5Fh1!Qh_b~L!&avSQW6q3$RC>90fHnKxXvUnb#29G!Nf{ zD5g`03s!Oj)HP!nMOCtQ`yR+{atrUKccvA)qVkeZmemkwc4@;gRwT=B&oBYwG;XVd zFBSCA0TCadQijSyTI)s)Yy$dre^7YsFloZV0qKxV%w{KLM2L|1TEcEEz)0P|Ny~mb zoI0wyyZxbO=cWNLP%Fr-tKx~eQ@AjlL#HnbRjUO84)6lkqid>`b~D9s)hLl#J?aj} zw*Y!RPve@!$4`7ZC1F|@ZaYToj%;=% zrFz>Jfi^gD7sZd&q(_5B046#7ITj($g-;MOibhmpp%=HR4w}u247K`lZc~&zHS1CKz zFF~T&Fbxc3!#Rjnoav_7WVu04c?ZF=3QZumBSf(U%Llo2j8R~Ama){8Xw62v5gte_ zO=1%Pj@+G@=z-56VlnAq+LCb=b=g0B^7RO~l3DiJfS>@qV`V!|h?>XD!nkr)w97d< zGgAXkUgJ`4_7WfrnK?>^OloFQnw6HK7gE_l&XwsUaf9sOi4hP1u(C{UEvN7HVOxz z2Z+D}lLeh8WCZ)7=F_A$+v^MwHS92DM_g#qM#A~(;CQ5IWig&L7(U&srYzPCY^p+1 z$_5HGuV5_5aRJymA7%I90%!tAZj#0x&9db&&-vlZ?(LEjJSHlzWK|xmeJru;@+7nN6z5wTz?rAgRizXSLRnPgpEzdmsB$D`*qDarNTxCWY zb_Rdf0(;DEYWuS8(wIrI&N;cCDipmbx)h?kyaZ%)-1Id-mOF?puVx%!x+b|k+mJGF z8LZGe>@qMCtY4tr&;NazMl?dzcQh~pA1Ze44k%k{bVT3Cz=iW>mn+B z<~<=vhCRjTyS94RZYDR_dq8G0I27XC2A>ue_tKmCEiz(092Q77$dm-C4?7gboiQJU zS_JfIiIXGqlg?LA&p|~Dy1Lt-tx!a`be0Z#yBoiDx70KJIXK>cm{%B)9V?_9nKsm9 zThkZ(FU(?cM(uVzx>_#qN9@zMt?gNUb8G?Ggr$5LB?<=vkT*El7UT|kTyNa5zp+Zw z(N(U+AXR8wXaGif%G{t}f8n|E`bMaN5PFa5-!BPLihuSIl^V&_hP6E*NHFL~=}302 zbog;aKQg9N^w5%#`c285J5=XzqSFk}shUz<aeUFSgLX1Op;Fxao1zLrwR5^f?MQD!H~YC(m7xYN;i)iNeRCuEN6`#;*~;1q z3H7+5t{P-`^W*~4VMlVP-MxRN+m+?$OW?3jhmo!=elLk0h<+M5ZZ4 zm&OXa!!DJEN%O`{UKt!98|9h!h0;BM$8i{Ma3+980)=fmcqcvpYmEdN6}IGUO3;{r zv4nbg1Ptl8M{s^OLwYIv)edVpv@wCz0-#g$S>zPlM01aeGyk@_m7PHW1z(tZ2CyA06V0luzS7* z*zZSHjGRZOxHCXd1Z2Pnxq{`0sDgIdY(a;BJzudUcguxbq`^7oCVwm5Hb8BF2p^D~ z?GzNzu2j;uMfBX{c~rtk-QVFkn8B-NjxKs?>?2Yt0A8`r7Lzk7cw$31r2;9j+{G3} z=Cp01&+3Yz(G(>?3Y?-OM~YeUKqlOVK06=fJJrRKiJLhF9slWY?VL&0?g2>9(6T!n zq47W0tcS&;$(Sr#Ds?=H;C>|Aa&(-qP=jrVp=k>@#yVwEWiUPsnM@`}t`!vu)m>sK z2(B(0b?XM`%xKPH{woA_Ayo7x1Lt`k%O{{-M9!G}+UUwO;kucIx?uf37*ONsQ*&#p zbnt&j$C|PUP9JYmBs7e00pVgL?9NG1<^U^eqZt53JNBuGUC3O(|1kq^3t3LyjK%3E ztB6~p)0^&~N4ekSco4oet9$U|RX0AzXbuTy$eu*>V?Szm&im>(gYP17ac}0z->Q4& zCfeA;m-%K>W|zl;E^?~{hz1ia7Kt2Xie4xZ!}N=OU<8g^ecQ1D1|Ey?PQk4XvKV5T+>ff4VUdk< zYXlDVT<&u+XK<;D)F?-1`2yMEWe#ZLH(g4fqZQ`!a0l8zQU~NTo5nS4Kvct$GscP> z%41*jOJB!z>PH;tY!K)S0<1eciiivZv}OQe|6$f?qz`7iiPg$Gk1cNp(s5vG_hh)g zBNz4f%cWr*+60TgA)89Au0}Nje2$G2~N)8cG93O6fd3e3kn_1@-6oaL1IV0S@sJ_Qpuj9BX-B z-Wx#CkENdKg`VsLo?xysw3%LMQDg3QOlOM4q`)X$Dr@(Bs{jHwJtJ^j_?3#P1 zOzii<_Orun?;x=Ux^HNa&E=2Q93&VhI)Ipk%y)J(ie!;R2)+~=LCR(+M6qE50g#Yf zLOuKqiv2C!Cv5}mwCqZL zyy$K{9kK^}45-TB)&N;g5~8}^QC_O!(#Kh-Sq#7gyw&v= z>l)43D%c?_0{(jKY+2b@?RUFU8`sXtVtol}f2QM;$xwr$(mZQHip|F&)0wr$(mZQHiH zclVzColGVz7htK4hQl`ABA*CzQgu#8KklH z^FoUiI{-u%1WupzQ5`LHt|vo+_5i;sX)Hqiw`3}ef16$kbUQbFP%%;>eV`SdrQAkJ z?q&~mPt3avxOoU#gV^2!*_+KcJ0dLO>iUgWiJ06penWU<5BhA2l%?$rHY@L`I>dsf zE+)c6q()}?^1H0Nw#iNMklcxDrm*Eu%xv52xdv>hz)dHKu?OF;NKs*#r15T;mOr+C zyFg8ekuhJ2*@ht=)Ciqzp0Oi8BftgfHCq*e`Df5rTdyg0Zr(ekS`RCgL1Y-RB62X5 zV07yM%ElbHG+9lJ_DtL0)6&hbQothb+53PC_O0Lo6iG7ce4OB29O%Q`$mSv6%CG*3k$w|tO1 zNm(U3X<`^UFb~e~4E_&QW6^=U%hmh915YkVY2W%F**EB|VQsRCtLQFtl<}4NSGi?$ zpl82Vu3{mOTV1zb97O0c*vT8o`vNP5u%?4_(g_Avb)UdV@e;J0T2?WbQ9;g}eLU#K z3AmpNM9FSDCxqGH>Ddeu)X26JJKaC z{I&WClhE4bUQGaU`Fk%x30J08PEE*U>^ys8%+&(BWz6G*(uP*vdvN$A-dVedJk>8!T{(9>T_J1x@QNkxYym!G zC_@^GgK*1#8b~ay{g?fx0AGnPpOCKhD|E7wi>SPt_AE$W$Yoeu$E>z|2OE4FM}+02 z=xjLa5mml_b+BDho~(iA=0wT~;vUL8x`~tsIc(i^Xw%VW5Rgcd|X zU=Kg~0X8Y$ar`Eq{)|LAjp>Wn!$jcEw9>r+sISIm?xdr&FujiJfGOO`6tZ^DCR;AP z`SmbGL+-*5Wi*WerM)|?A5bA$3MPhN?)`17iXMJDeJ*cG77WDoO7T&Za%v)Ah6R_5 zPYBptxkAO%$YEgY$hU8DG_i2M34G88Cc|YaHyz_A zeI<(ns*#8Tl&DGWQ(D}X#x`A9`Ei}-hB1%{8{mpP6Wq);N1Xz9UMf29u^bpWU|qUT zwThd*u{FHGa;{i~`uPmfH^>mt#&Zw@Zn32dv|AkJh=|&6T3uz&Q$_`WjgVpkcnWGr zNCK8>W3nUh-Pg%r7j7Q1$d{gcGd9sjK(m|Z#%_Q`hUl0N2G8>^R70p2NpU?oO@q0{ zQ+*1!b(Ry$jj%woEI?~yS16!4QC@5_ z`RJFg#0u99m>KO6ut|f?&V2}Y;Az!oi@0tPAf<20EOkZvh3-ZA?yh6XZUBa`5k7fO zeKeVn<;sN97bt#6DGb_=nIu0 z-m4#!3*L#tP8r#jQ2qlEdQjxz5l_Tf)8OI;_Rn92Ko+OUyBcKx|_HIg7-M!nesD zIbiU3cXtwUjNY?L4tto3IQ^mMg|l?p8sY35bp4FLz#&7(#pa+NZjmg852 zuR8_#q{q2JGj86kEOwLn)T#z%55AyXW1NdUaTGy|!9f@08&NPkMdy668wkPM z7#7$aMyI0F7xB283Y+lhPP$FnId5mjJL6p_S)#J=40Abl9wZ_1PBNb+v8A6;#f#0x ztk*a6xqgr8CM1xr#-9$(k@^jH15^&;9wiEwV8~SSV7M|eWMQa583xEvp5So1ZAo$v(4!- zF+m*aV?!zLs7AvqDIX@B*-rT(6WB44P6~tq4_Fgw)#1WmlcEKEbOm!*ek|?Xu7zC^ zlJo;pp4C4Oz3YcdF(u1advy|onX4%K`fr4OfQ#YTq|!tN9Y-a3GMYuAz8GWhWwj#y zy8gV-{o-M3+QP9j^MguqYt8nn6!J4DEKlW&pG3oSUtDx*M@oOeyu%C2UyZ*SIKkTp zV93?Kk0Hjmjz-o==xb7N@WH6?$=v7Hzi9hKfjRdzy2q8>SCES=?dk)IH;0jhFpNPo zMqr|ZMGmPSQ5qk<2m{NoW(vR5Wsdc?2mJt)>9 zw=SB_y69ad{NNarR$8==$K4O!k+h)&UwTXp?ELsbW?Mt~za zCgkB@9M@!3WCrO?mK1**sE>s-xfmuF*C<19P{j5WE#r4!$W2L6?&!Lw3O*JF+5>wH z^APo2L5Hd2Whetq;+Y10nKiJsa|sE(r@fEpn~5>h>kq% z4UeaHKyF7aqmqxBNfeY9b3Ja`nyG@cU;BU|;_DRCk@{0C|8r|7b#2uT zIwvV7Be$AP8%*W~msI+Sn`g;WOFqT4{Y6|0nBnd+y%o~gqBD*4xIYb7#fgd(oKH%- z1XMdWm)e0^iAE~%Dsu7_1h0|FJBpKfI`ecrqV+S#`=4(ij$T~7AY2{Y-(B>$JLZ~k zOcTplI%U~YgvMjl0KvRR{pw`Tm}li)P_}=LBwVR8JcjF zLZb@JL)t=ZpaBsqJ~%YB;fX5BAA%@t2Ze_$5(|u|i z%oK-LX5|zqK`bT#^frk#!fB@&7p*LY`bZUHXN&nwE7aK)aSSU3VAS5Ni-Fa9Mc^rJ zp@bbnAaP@-nWy82PJr71%E!DNa99#eJa6vxK_06I?PZ)PS;Q77v4;e;`Cgl9m72aUI?w{}&&jo=Bsq+JN9Dxa~ z_(XZTW}my3SM1K6t3v*Pr_yJPT&Zk^+@acL=;5dFB#{bN5$!oFUS3{^7@=UH>X=XJj(MM> zkjNpS3AyzUcmI~S0rcB)0c^Kmj{Ic4E)7pqRRIdOTYeiuS73)P=a)%R7E>_tfp%eW znZhyryRInVYfu@l7%zON+OB_I>CcVGUZ{sn7QAPZLBVY^^f@CR=m_f=K^zA{IMgrt zm%-LkyZHV!r3%evY=9_x^$oE@q0!!YyS0-09=}5RPEf85&fjJ}3!*szjnR{>9QJ%E zV?+RvY-(E43zm2yVj&ew1Mm4EMi2JR7pO%vx4`p)%z(9O#gR@_TX6z0;qKpkOzyLk z$0hqJyhnli^K~V~n6io?dd=nFpb$3i*mOCKj@ah2@)CeUaT0Y##%ZMJAZ7dJN<~FM zc?ej|?jRb;y#uSIg35s-g#}2?kIZY-i=@0@U_4*T%5=v{TWO_utBA`la!nB0x(C{X zs&Y7WUwDOPy9+D+QtU_}KRyaqeHFytd&ih(DW_XFHSu&?p)9;OhA;xQxi=>BgnX+ei{Y=(;k9y012 zM95mh%r$4UzDQnPW-U-I zytiU6X+GP&yp4bH#+6QZ98W~q>QBqs4e}%lQgaqyTdCy=J$q?Axis@J*5!~@R1bfo z6de~MLX~1?x%;UVR%g4Z`mBPg@lUE`c0nvCqahLz&PJp-2O6N0$+!a`df^jDNV@Y` zdQoyqh~HC`L;^6|Klpzs5uOwy{VbU@5aclz*|(Ili%(g&ofqWpC{Tonx-lSJ&VrW+ z6$o2(`yB71rKYnuM&tr%Tgq(rqmU_5j1<5Yr6Ph&ZJFPO?eCNEfM6P~Z;93&EZ5N{ z$#KeA^ zH*qw~>+QLrmw&%Fmu|Kmgqmpk=_TsUvG4&Hj zVxP0=3DY^7?Vj6+K_w-e(mW91xu#=@`-N@ElmpJEK=wx62j_8L@_xDYl*k7wM>*-; z#~_c~QAk^<^7uTgD$FNb$V|0ee#}9h6QML{3MTWTL!)iTD!|)yPj^CAi01tbp6Vnx z6A~H#gtDU8S(0Aat_gMJs-UEulRQT_A56pI4@8WdO zBO%ECvoNdnF>3DZhPPPZqf8@lHL28iBY#?@7m zU5)yIwd;n1zZl0j z6{;vv_xC&s!9$`p$LC^F^BK`9daH;W*l4J9d`u)_!)SgUP0ljyulxL1^Si?mn6WEH zMOA_v-&`$S1x0~%afEx(+2URiuOOo?zs`zfS|RLmjrvxx2y|v`U zpw?Qng)$%rac)nuyN(JpiC6?k5)7SCSN#yK@cDJ3m|EFLqa-s4fT}s+>nV%g92^R$ zaM>j5!EB1%VGGxV2<=fw!o$;iq;BV974eao@(|OlYbN9av9A)?#d_s|+a(}zHk!yv ztZ;ofrNQl^KFOr{65?@7woDD5XoT)cz8vCaKU9ixK!s)3*5UvOOY~XY?8?bu5%KdV zqDk{<*^Rt$@M=L}*BQZ=U(LhFu*GDG{iyN|@=LTf}VwhjCfvv=IVQ6mx7#XWwOwBxKkV`(obi!O+t?PvH*F_<( z_;hK`(FyqYf+vR#-aoGYaHqR;>u-R(i8*$h{<_>Rf(;R66Ni=AVwKu~F_Fk<1oEnzTwgy9^of!pO-169$Vh<>HWgrR*T2oA`a8Ae{C|ZYTDy zk{|n8s`9{ddC{@J^_!^8vI?Ghg6JZYF-zVd^R2mQA1&S#bsvfEuUGj{aMCV|b1=SD zgNqB|o5f$ig8; zKFdEB(9IPPjxt%-B>IUP4&rul>ntb6Lc)K>0Ek>mW2mqyd3wUE@T)`D#h4T({#nm*|b- z3EcO0K+IBjI`^p_7HSfhLBub$QDDrd5o+nS-FH!J9QjvCEV*%F|E2sH1K+c9Phj@NRc^avV0R^&OlBu};wyL=yU{}65DWN|6HTaS2t98oR1GHg{} z@P+qvoNzDNkl8calvtSHP z>O#~7%ZZPgHI3z&xK=q0=e#ILsBFJ@jJR9hd#>gL91l)_S4)(xuzz#+MjnrAjE|d^ z+Rv#&0u*_ygpgMrE}Za>4QHXo&Zc4c_efggh;}B6ErhbpweCO%z%Qc=u3`6dA%#b^a$^btu?A+tObLljz!10aibzZt7jyuYm%p0tlAcT5*INJAOI5Qcnz#I zsA|P|Hs~1YaKqK_#JP8Y(DNOPk5A=$z<3yfag%?<%DK%$?N;5FQWe(^RyB~;flZkZ zA7%q<9hKg*ft8+UyxmYad9SM)dOOad5y~}>g29<8lp(J~EB7!mPT@RTWtnr7n+$%( z_&ng~k+Ez@#F{t&*fm}(69;=2-~5U!XBF!HI%|J>%i~a%T#6GP+nC4=_;5d@ zh`Z`+uhCF<+D7VK8gYga{{Usubsb27$DZ45NZJ|3gJl;KQio9mp>JUB?OnY*2e3<* z542~JV?s?d=WRl6In2tglQ0C+OB0CV8yeg}$J4bkNh&yknRHI(JppoM{{NP-=TP{+H9RNeAO(*%z_I9a4A+=HonW}x3W zKj_oU-4V)Bt@>^2KySou=@6xLDLY-+h@Ao)$Ol}3OrCm+ueHMpl7U3Fy)+nte;L|x z*lz*u%CQGUkW&z=w7~M#4DYUUT~LlVh+N_p&c3(b>f$&(o{lmG$FvE!hg+zLqB8$! zw>DkS>g6*8`i^LwWOUW=c8ex?S!chFReuT$B76`gr%5)-;Q~DfWDgTLMRWD41hCo<&_cW(1(0YBc z$Y;@QP+zRO1VWwfmj^;UxGP=E0L4aAa+1td-$(d)p##Q*n|i4ez~18rk6*42TdDxjwQz(ZO0z!dv5ipAMU!?R zfV*vVT17zvp9Ih$&0=l~m2hmzsq`l|z@3GJ1(y>7A$Av}`g&@TWz$)O=h^LpuS13@ zel{IpcYc>UDy%KqK^a6UVSD&BdQ<1cfD-15jWsKIO1W*oN zS+?X_2vbIgepS$eFK}KdKfOb=I4&8>)*ryNEK>|Yf2lfpo9WLQZc&4iz7TaP>H+jC zyg-JUAHQ0rq~5jB5wF=3Ui`n7-tYc`{&y!DF0fYU30s;Nk zbfdWiIO0I^ebYDwl?PUKzo?bNFNR^yP|KK;E- zD#tq6s`0yw=bhX7g_(Mq%>SKaaZ_?}t;8$Gu;8Q(D6=BICgJq7#kS52dAbJ$(FvL} z);CM;nPyL7V*&GRqQ>0lDOnrp2WD<;C1&Q*>XNdoJK+%8YN4MlNmGCq5|ouvRrOH_ zS+=ALa(I7v+Cquknoy#o-MY4k4AY8@)3E7Q902LU#>v#$hm=ZXwqjfyxvU=DC|r%E zvL*H${wJGGn!A;WYO4UE7h~)E9&M}5%`eJC*~PU(O&9Z0ew*A%`G%i@+ojZlnW~gY z&>p}Dhg=REqZF?hi5Z;g!EG!?jWZ+JZ7Y#HKiiVIkk_?g=1xP7^M*+8{p~EG{de1` z6?0hi>VJL4o4p> zJ$`SA7#??qN45|=ub$o?oLF#HYx*SnN6to@ zY#KR>w$21cWeS>p`jVKKO|$OSu5=A$v`#vgr-g!zX^T&d%!P0c)7OEuD@!^&-Tku> z+;3eBk}~DoCA)t(qtvBYJ^d;Ym$$WdOeHe8qY#K~``^TC7K`sHHW>;s5q z#bVqObS&p=*}*f#p&KdL-ddAEfaj8$r1#@BWV}A^^Y8YD9Uy^5PyWgify(+EPgYG! z9OkuIqe`h^-M^uot1#2>XnrL}%q_U~_OkbBHaPX2Yxh|?AjwhDjz*z{4nvw4t~V9x zMtw0xy0!Otqm?B%1&0y~xhE^V+fh8VpY_Oher*N^(1A!05Z-`A z*VJgZ(SWDPK!=!yNE?7YWZuAS>tr?TX4u`px6XeJE7x;0r$vu`3} zST^n09JO?uPIXubln4{mG!60UTERUkr zTQ(H|LRe*KknUuB6ft`lrRXhKNiG40cIv)|VZ zrh)(GX|=t+|Ks~~xO>0n-?jWa0)yS3k7-7J-=}f={`V(~J)gJJZjE}suZwKXJ-?T3 zfxeH!g(djZey@jd^C8;$%{34_0n zPj3p0dwzFo`xb@MiNwFMluZUk8N0 z9{>SAkIx4^?vICZ=W_oqP16K{*N1Y>JN^EzkB^%pg{|_%2hM44@p8YP_k)M;B}kv~ zFuI%E$Ca&qf!-c}o_i_CInjr|r{RP!C_1#@3LnGM-FJlj{y!HV!vcPPd3AHEmk9p8 z&08tNhx6ZdbLQJ`uiyPVJiXod;pp=aZgv=R^YkflcRBj{cpNnypEC-?$Nh{mG7|3g z`tL6Oa{71kZ20IZ57Az?18=YEv7i5YZmYajCb?KAV(I{1^5h<)%*W?wQIV|A-@hl_>DOBI zKNk-totqJ{?a`J#AAcTyh6(w%xMXgX{a35^{C+O76$}Df5l`9pWd9g=e9j&d!u?Fw z%c-zD|C7d>X2qOXc=B(t-L<{fkNX?(4`0qP<|i%Nv~dUR!9Z`$G5BL+((`L$5^g*3 zPju@&%|1&FSdj6H{|!5Z^LuXOH^*i16LET%L#R2jKONVdbDsqB$`4%D?~H!NQW@l= z@_BHzS#id__^gSfXTjLC*jI-VdD2Py1wk9KA~PQh%48r5o2k}L^6;e{(EJ(m8gXTR zC<>G{N#O;TECw{4=*1fn!llqy1WsWgJP+1AA>a8cQ$KefolCLfm1^IUqG4M*)Kq!y zFzmQXZ_*AyKa?gzE-?4*BTjVd6E-#y^+!2diJ{rCZwMBfqz}L{)ySAH2_-C4)``U1 z%@CyP;2LoibgW2fx&XDK49Le{gPslk=5 zQK|MsRO=3}8r?f1?M1*?cp4*HhD^YN;TTX|V2_+*l+iD~)Erb7OMr+HtkhX!z@VRp zCHN~G#co#>(Q0f43@VWL4^G-ZA+`_06F7o~984X-o2A}3R6}JD$O$tiAMq}8!e0aS z!4RNNk*_Y)SFBX5Ec_WxY+p*|C`j@SIB7+s-^IjC1r>>IHfEvWv zJ6Jz1RMd6}90WN^oJtWpags!1*6YE?zsDQg2xp!WoZ8=URq~&(Cu6Waq`Vtyjt|qC zgs~Axkwpkv4n@#(jk+O}5`ZQq9$jCGMm-UZE+~1cuV{Fe}*~^%RS@#)q=9%uI z!5{gr$Td;^Pg@cU4W?`S5pxcNmcP_=$L0)`q}Zl0h$(1vbCn%7vGhe>9Vum{=#V?F zU$1a;sh4i-UA@40oX#WSSH#-#TumBxnW8F)Jn+fkz|0&?Gtbd<`Ob`Pir68qzgy;s zCZM%E%3qNkw}i3O4_VgzD1G${RgZ2HO^nPF#o(WcFiUb|C0Z#<)*IdUU1X4Llh1c* zA&B})GG)viU!XHunq5S4{2>{Gt9Qg*2!6m)X@GJ3)7h3t?+Py$a8GBC?+r{rLpthY zU2wiEY~l{i9^IbGLv$xn@!NWp)TmMqM%z-#d*&@&k4VTVAFt~{rDXy20PQ20|uhoa{hllvd7)Q`Rn^pO?07ORO|5Oo?$7_-$Xcdm`D+DXsShhayY9 z(&Oo+gE~pFjgItZ(Cpw=$tqCYP%gE5*K&hkYv!sHXBZ+mWy|8$tJt-fW*)ZJ)gt~A zh=AIgW(FVgm#Sk^x$wtA9U3N(S$o*ev?qrAAZVp7l^fuLcR1eyt|JPt`%ge~4`fBY z;r^dPdHi@rD0}xS+wND zmrG-~VpLk^yuxbeZ@)CivLB-gxU=Ra~qN4F{m0TPRNWU_eyQ5w^oCdDRyrW=k zNPEg)O`jVVFf~^;#cMm#_M}9Ucvx#-skg0Ag|&K*B$X9ahPJ>m1UDy%s-{E@p{mQv z^0B4d%HwowJF8T{>E3)r@GIarw!fXXj%&{%*AbAXRqPOb$uW;TFh2AScce<8(wA6Y zLlw6E^CPCLzadQd%0dO_g?IKhJ>j6=w7@!DHIWl%9g`_D=@Wq~4F}KSlj+nRcJaEF z2VKjt`Na#}cL6!L!n$7k;tLoju=cI-!!<0(LYhv8Su>}t1N?fmiIAk)R<*q(xC@k| zjs5^9g5E4yd91>y~*n7X~xSpFI~Sfgm=qS!bV~MkO$fe2(3gjs1y~Dmv-TMDPCcl7BwhB zPFc*r=Qo|kmwfb+Y1YQ71Uf7Ced+3CGxiHG`y+GPz?SOm13}q&1!t zjn!HM$DcBHP+5>3AXE$$_QDH$8`>cwzJ@p`u$RqEM6M*vjLCDH9rDd4vw?<;stDUo zU@;C~rFF#&c`Am$+B)mlYVG4@s(|wVboZ+fOKnL&V8@VX{3Ov>a0if!GOqbyHCoUb zFL6Ckn@9j;Qa6H`0frkpYvph07_$6p(-#O|4-X6ta5p$ujpxd9Bg`=y{R)I3C7|?EVQz$4WJ&XW%4|!tXlfZgJ?(iQC<18djx_ zTB~N=7?Nl-TPge04aOk!ASPOATtLCV7aaQ}4C<-6ta9CL(=%e)G@BqO6?VqR9>L{ykf-}%ZP%2wOf&vFx6|x3%NUm11s2}?Fc?^S9vp6CM z^3H384N0gik;0*gKJnNWwv11X$x*8rIaD_abq`2+Zy|p3D+(++1gbbXVY?h!h_VhN zW|Fla+sWr{#oysYX@gAo@XTzqqtZSzs6&1sOfKs1B%Ksv?axLubfulH#I-BK{6n3; z++yACZM$O*yzrzlfdrJb29C|XgN~~X#XF-ds#TQ!Bs z^&53DOoHsqI4Z6^x2tU4X7^y3YA3n^XWA~EsQXtR=+**oL^mEt1t68Q;Hm=ngDV@; zt7L52gj`>b%e2FxuZknA@Qh)4Q=Cf6^EW+A;9WVDv(~Qnh548;mVCvo%*dlXvbKdr zWqTx=l9dz9`|;^&a5zG_p;=LuzD;Y;SFqJS+23zBB$TTm&oSQRN;~V-Kj7f{-en2w zwBp3vY(3>afUfY#`=SI+34D~AsUJ7#lw0ZWueiEWL4vZw-Krrw?%}+akr}o$Q1etk z&N6^2*3R#~3)~d1FHTuVe6*PgvC^u+dqWWmWHDJ}CIR8Q-_&4dB@NQdXw<|m>}V!4 zY!hzvD7rn`x~h6udPWO8TRYOz+```BD7By`cAxt7Qow4)IsgO~ZeqV(Q1rnvSriQA zm59m+cS9j~n$$^{3Lk_+?-H=z($u+i%Pni%aHQ(q{D6j1e3$ZRyOU5s=^f|Y6_S2B zx;3IHLtYI6?3p!fw{IJlDp#D2vf2(?y3A-) zsXw#2wB!73&tnlxl@io04pl2Le=*FKleXxEcFWt%-G)`&JgIM5hn7E&zC9g(dJBoJ z@Bd&`p&|`eQn>i6pfHG8KeY6v4Sk?OF5Ya1js`H_QP{6P=Z!#lh0R8cB`RbUNk__V z^Ll|lTLFC+4UXrsDgoay0_p#WUB^efT$6;<{cin9sSsii?uGv`bzay+e?81Rc*CwR zlLmJ?im=(wgxBUky~T*I%h)}_Ql(2nJPSzDXq7gH&fa8yEFgG~B?0T>R^n#nl<+R8 zH%8c%o>8m|h-^R`V$78NW|$=8KUS$?&JEYLu&_C>uv2Vn6=2QBy8;E(oW*Y-ooQ~d z_B^YRkn<(E?bTpO^4KrylTs#0b9Ar!whJ@6aEiR6Q8Wtm?Sb>I9Jn^qIy^rdK8{^d zO{R+Wc@2=E_|(Qhfjuu&XDCM%jc;r5sg_VPX$Xo;iN#Qra7`ZbZYt&*1q|jJvUvVy_8talrAEnQrU|XZJ ze?7We8BV+M2lD5}l`p)j$n?1ie>kT=6jpVN-t&A-Ana7l_s`u||Ki6^zP6Lh+wF^L;n zn0kTqWS;cT{&0uALg^J}|0*-ZhOn-4c$}*J3^~f-I^HY0Vn~^po&J!DUy5?XpdiK; z`!O6gZ*vm(HhMb6Zc4OB;J<}ShI)!ZnQI6)en93Q4jWBqT5|?;m#$XXImS_~BJzUc zGYU!uyg2Mz1}87_(EdQ&L1Kg}TkUziT-~1w(IW#F*CN9Pi1eZYOL@dVg*QT(FZ`xn zz;~3VRxChL7a}7i49p9OPi55+DyOfxphEyh!J+SRaQH4RH%wz?G$d{3${k`Q!zXeW zzx8l01?!%UFkc`aVs?3aj4g%yj4`kB|C%=qE1fmx5POm+VEZlU1Kw zgENgHL}^xUPahOm(yk)-+8n~%)M+x?{^D`Zn?L(8x8e1?IMvA?=W#VNSi#58+}6&| z4pA!Zm9__vdZ0c^!FfTp{AM~)zeu2UAnglZ7u7BF5einwQX5rgC=+A5 z_+q96qkA_!ifgZz?b16(Kj`Hk|%7l004a-Zl!x6Y~ob z9Ig_Nx@r^C_oma1Ih3MD^qKZCKWreM`6LbYh8I-10 zpMz8283sDV#_OzSTnI!}NTX*$Mq}^2)4T8-p^NXywWgbY?CY_T_Gys|{qK_+FmwZV zV9P25&MvgI&8MLHvELBMrNH8Hq_VOk^q1h};VE!qsF5XGet!xU);#?)Uqob6<6E;q z_weu2)P6=pQ+OK^eY&tZ&XZ{>VocQI8`oFsn49I#uR0xz%dhk!c{5rtQaB61cD@~b z`^Q^79rR2b_!YV-TM&HBNTcXQMKVVP*_J8uO4Mu;qDQBVe&1CUX(4#ygcC=7$MKXM z@ZLSFS-)DUT%JvtttIHQB`3g|z$c=B4owJt5K$zWfgX`x7CY@721^_p1DGV-)zD0g zCJYdl=2Tu1=EDzf53Lem?*@(vny^UAyRFp(le5uAW#l)0 zz~m-9-j!DVfJ_Pu+dE{J7y`bbF7@FzvG}|Z;`G3*3l##(Q$&q)bJ8cIAYwZbdmvPS z^)Yb)Z2m_?QZmeQE_;b;C%9fa!a} z-8@X3vrSboSZV-3#}2zsIY;M5>gnT4jEwBOi(w4{&qn?Y6#NQo$CXsTEy@T$Qi_Et z$90>Lnsv>KJY`|?G6kH-Ek=mi!?(MtpviIp(`~I}X+f63()?C1dCZx28J$$;u7Xa} zciT}`p*yUiw*_80gP?A+82XB{%672h6}A&ON%vS5t>qbXb76@L@X$x%jC{qKqlZU; zeHp({s<3W_Kz#)LO?U$LcZ`Z7(n<6$GH_TZl7AQ!W7x5$I_M-yIhuQNVvix#J8VYi z`zz|vFR<5+mrW;_Hl*H?`*+C)I-@ai0yM9K%C6BaCZ&Di9;3WRKv+W22h4C>eIH^W z7sTRbNtKc7!JmSZzX5h_G zZHJ^E$dU#j=iqhWsWT~PS%VR~mQlq50{Zh+3|sokYE`N&is&z!5q*X{o&7f?d{t~x#XLwfCAN{r^EWh) zeTFliSbGSbc8{vf(D}DOL>9v_<~#a?-{u3$Y@XdSJ<2_x13}7v1?N`{ayby<))tA%bllP!_scFsy(HJDOc;LfE5FO1d-JTE!4wxw%OsCd$IP) zQPX|11@R_kv?xFEY>~i#uAP zY$Vro5tzoEy7^Zw^l`;-Q}D;>^`#MAO=sQvgW7Oo+CAUz)}t^bF(6Z?4w$(0F=XCQ zgXZD^0`@u7V%rt@9VxZw{zZ4Lx5N4vv~GXR)2A#FO7+x5KDV%VoBKwLUP7EMpZL2 z^HLivaz8M)?JJJwgUe!(w0YrRhJ(Nn zX`G8o*}PEK-$SnWlqqpaEiKt3_WH{Ht>r5i_(;#`um>OI#F4obK|sT8Y;lFe%2d_$ z9>=vjyYR_Ch#%(va?K~1D{T`mH2#Y98aw}ieK+pjPhz;|m0jgo9>56jX_zTSqGzWK zn!S!zJK7DRWxqX-9_5mxbW!}f1R+hZQEVY$jQBfwCQ%kS!Ofjld~u@SM=;n^u1Pp0 zCGRE4gy@Ssi_ola34#GR(^)(eR#zuOl@f$7#LaFLK#1w=3=Ns4gHA`GP+6}lvQM0E ztF?JbtYOp!rS7^;-~=BB2{5S6o?<7*yEX$q)%O(Wo_{{SG@Q|EhBX=FQ{HRsB^(l3 zIGHzkzzh&;uQQt_jU$?h;fI6ea@g)FxNG#VkVHz-!fl+#ZJhW;B3fOMe#i0Hp>J)^ zz{-q9=2apATM!bKR6qZnrk!BgQK`n+S%lja_7_7f6J`<$q;( z>e_#Bg3~zAKT3cTq=d>2I%h}HW^m#|3YnK1eJfpJuM=h3+>nu;hs-A6&!VV7POe{I ziM(;W2|ew&&eB1VF_hwSuB8y08kF!#&!0t~n0vC%DXeZ-Bvv0%nQ`=3;X&-T49xg?D zVw0Cv+C^8aH!knR7r)?}E0aR77qu>>SEXMu+FyQKvkaV6#tZZ;z=R#>d^b- zZ@)XWq|Vgx`EFWxBM0}=Uv#!CHE)ohTlXBHbje~8Zz%;Qcg3>mad@&-uyz14+DYBJ zPV>8g1n^E@;w_vBwL2eA-QLc_nf4#l;#r}rUTs?cx@)Far#`lYoa@dlb4_~q znY_dKs1TMXCiGoY!9cG~9p_pPxt-yROw*coT#+RR}LjxPsbt@~8!*H8Q}*4{C?vf%9(OwzHZx5nk}^}5%>4TJFQi@PIs z=?DR=ZzgHrCpRK|N=&(sl_VLGsVkS8%O7Gp0WX#sqRcjUb+89-3r-zMXL`K(q;Jf- zxZd$%d%oG8rg#QPY`3u7tvhb*dxJ?Rq$^_)*JQxSTL)X9xNY{x)$xK}wpw5kZttMy zOT8UTCF(y@hOC~{UwwE>MEBr{$tyqbi9C{>Uv&XN9LFU-lE(-iub|;^C6~67bP*%O zaf8q}=NR?*>c{O&XDs$?a^bpQG8>b|SFA02`s#oAR$HKaeCqFu^mL4tmdz4p+jUuh%!s)o>zdSdAwC zc$#nwIiYza_RI3uZopCB%~kBnn>3ST#gH5${|X!HZkH~;z(~D=4B{yhK2aB7mS>@b zU9*Ehl%k7Yd=9BA`ASk3P8u`$`Qx#QjZ$Y4fa8IZ;O16nhvpZpO+&%uzmeoxPu$eT z-hZoVM__-H$Vie3s8TG^^m3+L{hUAkyPCfS-{!HcEwxA-;I6e}T4m-CL-i}etaiR& zZ5F@`EFr?{2WR~ymO5f}d3VHI%~5YpQTuFc$pklxOs}?6%Lo+8rIz^T)rczn7yGeA z(47(%>Q`SaqwQaCTs0qeL}3;4A{3&=sVa4-8qf;oi?69>|9NJW>J+*Lq0S%erd}}S z@EuKZaKpSAFFHLLJ-bxQ!*396is18_W>~u1sfx`rri9BBM(&4G8$Lm{@RK6qjQEd? zj@s*qf6&*%q7WwSG+~&j?;A@Kf2=4B9e46>aN0LwM1<#dWPfg<-X8$Ka8VqCG` z-7+!YoQE-~`mHEj9sLg~>^IK+M3zE4HL%2&F5$}MIFC8OkqB!$h8s&~otsya$Mrd> zuaN^CGWvIo4}`gt_SHxEMZz9w{%QHmjNYkbuARnH%ZQgj@n$+UzSU%QIlWUi z3^!eaZ^HKc^o3UvvLt+&l+fjejo?&+$gCAdazRT~smw5~3}?y&Mq;fkK7X5Bh-tki zVvuPo)!tD{A^GwYGbT}3SDBjI^zfrMg(I`2*3kWE%-Iq||8YD~iE*Ya?$WWnSWxFD zv!!3;1~rbQ`ch?BZ3+S%iadcAC*p=<6x$F$xck(g5ad5q{n73>OZxk92n(ptBYI@#j5FlO1J+qFK;eZ=iI2e;?yT_j|(ZVof)?2|L(-FCzXJGiq zv*>iVq}=Y@Veqb}o!`1?cP)Lnbl>jErhy<=KxtnD*PR%HUic?EI=PlCwWU%(-Mc1O zU|yw(efP+*o5R#F#x}nc8jE1lE%W;%Ij+sGn_Ao+`*LnIhl24r&hK7LJu1gNKjy*j zgJ*wJe)mC+|zR*C-BJROh(!eOZNw_XXW=%fKIL484 z?s-=Fmp>eaIw~VNlj}*jJ911yS>qI@yAibBs7UAe`q|U4EUM#HebglEQX&%*nBJ2Y zIpGO{W-2t6Fx#>GRs-V(>fMHQ5yB+-JNBQ-f|jrHwk1|5h_IORhmXfL;TCE5i z-`>4G!>00Q^)VR1rMaEPQ4F6|)=L2#6`9a{8ABOEh;dt)WR2@Tpzj8{8H4>dW*W?~k) z0I(r=rJE+BpOR4;3flzW&6%_dd+uTKs&5#WF2dDC5X-(oWTbDSn48f2Q;_qrVMU=% zPXLsXd;K@e9quuul@Iqr3xg6-S89EakgzVX|qL*H0!C2!97R;oYG9x;F&OZ0}3Fs#Af^5p*GG zOv3tYug%UdBBZ#(z+q&2l0h4O8Qi^LnOsn11A+Q_BYeeWus$GPkBM4I%&SFmpAuJ3 zy(?M0ohR3kKF`DMCCXxIm&rwS5H+t7in$86HpGxhRw66`+Vls>G6W3<6FF8Ct28DH zE;rIf^`by&56#!-bP|FV=9@}{&0UI|MCALJX-0Oc90&aP0IPZnSjp1vx+@e9$xj!Gh+Hd z#uvu3&bFcKE`4T)_I}g3L0<@65kkx8uzg_l0Kbm{=G%rbx0&L^FD*(SK<+(9mvZmW zF7XhZV)L@7*&o*DlBtQdyF@Khqnd_e+9qjnb8UMliofEo0@7kpUxq=B%V(G}r~Mw| z>jc}44+Yc`JV}*t-x~g{CM@t3!V$zhl}r+OxxCUy+U`~2Tda)9YkuE^x8v2`XinH_ zi7Rwjw_<2@m?50k#oPdpHOYj>Y>Z=5ke3 z1kVGBbff~9R-e8ooNL@jFdNRc{c=k$;bu(O#xIJ9YAWXD$~)T5RI!LfUrnBWK&P#p zu^avcno0Nf+ss>;D!!wWGa@ORSe4s2!J%=r_Reg{{CN(^#HW9e6}Ye0bN#_tL7$(i zUAO~Ub(>H)rT<3NhLIdeB&9%>5cy)jc^XApVYj?ZZv9yKucDaJ>~)IN=) zA9yg)^Q@fA4H3>)($q}vU}b&C)2^_PVy=h8QXHbK${nSZ95;YO$^!DG)44hDkj_I? zyR?K|cXk(5kpeGxRH~r?8ACns1v(b1>x_G)y?QHL#BM z`xGup6>i6~B&M{bt@JjL2l?5FWPO5MuQ+7mMP1-nw zYg);~QkW7WTee)KJ8e_mx=a!jb*D+$9Kp9ZxQJXk-g^rplZ5hYd!#!@A z$(=Kf-9y~8M({EZncNnku!#1bsihZW*#RykR4cPIwuLQL4a6L0DnW}i>hFck9c@dt__&EF4RP9=HDLU$^ zU0i6HK_cT!b2b>lI|ycdSZ4LZ_fah=lLY38^~|5vn^@*eC8MFDvN@6X4qOQ3NUFy_ z#Bb;WtvwfSP0DJXx4YFBPTpw$w>^ghp0F#8cIv7~WbJfI)ei<4hk;Ah0Te5gfzq11 z1UI@f3-Z7u?E6Ia7{nngbw`^`oKx2}cK*2@_2bzif}i;^W;s)8gevlH7%K9S@s8!j zCpZ=5hbNM$g85ylR|Izo{WcYRXkDb6rB5-oRJEo2;+?k93o$qJKY6jb;JlQtGjegT zx@b;J`45joKJ;X#X3oX=Q~ir~nm+ijrofg*o?0FmA}qQ@{i!&UZ5HY?i+iQ6+IFD+cV;#A_vV63_FYS zSnfnvyj6BBNrYo0Q*RT=y!!jWs_q0y4NKJ@4rVA&K?14 zr;Z?Ml`}508T*@aqQN1()p^P&ndlw_AW9x`;^vC{ire< zM=KSPTDJn!?3ZgTN2_;(3#{#s2J+Y5rsqlG%Ma85#H z%oMH|Llb|1IYMzsLY?_GO@2g8>Vy*Gpy*9Qx=0~RoSh%?D;v`oCr8v&Iv`s_3Jz@- z%r`g|B!1yJros6-joOnW0;wvQx9)QY0e0l>nuMxF^oMClS+v;-S~c-2zj2n}Et^t3jA>}KJ zxNV90zJ^%r?@f%q62=pQy%&58Oj~`$Ml7CD^u3BtylM+R1}6*?tDkfF<))21qcBE_ zuf`*eKpk!gzK=SQ{QckId@MTIAZZ32kGM0%r`klaUlN=+ks?VJY@R&&cCCLb`u04F zBFP*2KeEVJ^gbq1+9tG+aGF{#|Wf1r7k}MpW09ef5UGAcvDfOu!zi2+07ra^J z*|?ulK%qe$nSk5W|8xO9S<5Q}O9jzv8JJ(tb@|Nj;`a*uKjX{q%}bZLV8Ou7Fu=eR z|2OAGBU=jxCo30AJA?nsj*Lc5=H~wuLnuZ=&p}rL(f6`?8D|qB124{k-vP4Z4>~4I z!IUy}prl=`TC1<2e;rx&ZfZISYp>wX83E9;*h`h#c`d!*UQ`S z)#3H~z_!b$qx+jrD>whTz;ajI(A>oNP#p2a*5=2{_QlTC!|}shTU6im-NnT*lgH=A z{{4QGx=u@p!S4? zPb3MAyZy#l3Jamh4zO!oqYg6bG0@h%svCaJI$kMOsU7rla8;Yu&u3CoO`hSY|n+#C_O>Rkv28xPQO zow!@swH_ShjS__(>JJ(xf?XJyK&ZtH*Og$}g{@1#pecjSx!MI9~nT$t37y*TQPDh`4v(S(6 zoY=O~z&1j|-k7VQsa$J{Qss#^z~t!l@`xjmnH*|7&UV3y474N>F5a{axH%-p_F)=< z@EDx<1{^!IH4+K92#GbG%|>|yrbWNN<0X9&mIlpa1DrT)JDy4DIv^}p!nwR@TS7RN z)*iZu1mmQ^_6Bn}F#*Q~r@0zS2`>>IYW9V(gYKN^N4T=7VaP0ZtyNb69xRpc9T9T>hFJ!QMugi>U^ z;{ba$8vEYCc?h8^?yC?&-LXG!wCZxjcfHZrkVKO$$M7vT3-8)a~eqYTYxq z>Dc?p$}3rM!I3qH%PpGh?5yLLG8JFHLo){6JNBep_-ICnBPTaJqd!82!!E+o_M~`3yKSI_GB%S~gpL2i` zGVA@QAK^fNb zW`djnqiMZCker}>M#4zWUf*D12-X6bxbP)|c!)clrbUXE^=Yq*{0p$BNab#eKx@7- zx`PRYPX}c#al&iUYZmuq3Q8xjq6qWd;H90e;rEuAO%ny>mGSUoW0Oq8pnsqAmtir) z*h7o_5}mDav@rh6a{11xR4o3?`U02EpqEH}P}KxU4GLWD=}8(KW0BDtqob0}JSTy< zBPr&q2HqO$94qfN3z$sqepJ-V!J6dv(WmIK)>aonhm&sYRnIkYiwZN&Vj~97$wycywirL;FA{PP;%{`+pKITI5rW-B~l8`*SW6x@V5MoXUyly)30{iHj?<0FQtUnQv<+R`|_~{hMxh1ZCZd z_Ta%zfDtr&m31sQu#uD=$3raD&c{OJP~1XJyI=du-XR!tKE`$!Svxy=992}NhZPi; zzFvrHG+6tOw(aF(VK!uv(y|E(W$19l^h|DeK&dO`{PZPmNjlM$ljhYE(hHYzge&vS z*Es+`h(_~u))WV70tkkgGPg1@`VWuUz}eBr$=QtQe`G3|W_$lN zB`>EQJ!}FQ4D7Dqzb91xmtC4T*qd8fxH|oxaX2%YIJ^CiApXAy)p`c0<;_U`mlh)j znkk=~P1$y@jlM?@cgcN#aYPG@VRZ8c1tDKEP&^oAAR^Ggyi$P89UGy>OzQ=ReT7-Q zW1C>kwAE;amO3&vWV`(_66Jzcod?j_lU>4>h+5*;11Rq~Ow|FrCdPIHT{8+#n_Ta7 z`=eMV>H>>9>=H}7PiQyo=MLFv@ZY(KVp6YFvmM49u17cWeDu6^G=RFME~&#Y^J9Nw zyQIcURXe?O>Zn&zj(HhxjlD;&k}$PC8np|RAH?;R+Xp);r>yYoBH5V~cv42lHfDwM zYo$RB2>8M(^@AIx*KT#;6ke1x*=;$yJ_>&C8}#l@an8+t&Z zRUNU4y_Egaq^lSd==#+TVZ+@IFU7p4&_SK|70lFP0YAmalq#=gujJvrHFXpeS(~G- z#2cIu+4_j6=D_wyZk+?jJ*Otxcl*YX9aji>?COb&+NU$u)G>#09iP-Qm(-3a$wz0m zGw0NMkez+@j74e_(^!?gI!s&S9x_#?U90t^lxuUe)~jYUsAUj~JMQ=~qJ--~D{2#3x$%Mg= z@(H$)J|b?s4v=FpyHsETt6bc5XoU8i%X)W~)zv(&mZM*n66>XGJ-z@NCdVCus@9Mt zJ6-R>FsbJ=3Af3E)vr#bIdE($UI(GvY8Y1OcLeCqY&O32bsqd~_^JedRV1UQO=ZRg zY(#GWKF#-=1wGULxzOgZ`0_$12Nk{Hx#RN00(TYmjx2ZMqroUPTj5l1?2xGw66I zy4d@+!JNIH+hjztP53@iBk+oUA2ay8ZE`TvZ3mdu%Pq0pWVD(21(AG!5ydu1q+g)KYzcbuJHz?hc%(;X=!ZN8xjd_gv0hQ)uIhz^sYdw|?yA&A2~y zz}=`Q3Ztp6Gt6W4CW;Z{+%DmLwBjEA&s?j)PwO2t*Ft(rNZW2yAoLifAw;(+-GC^q3Sb0SF7rrJ9;|NcQ~BFfytbvAeCx&df4@E(?`yI=@=%E>PuE zrLRN5V9ii(=cUWW7{!_LM0ABb4z8{`BO@(C!y{u}*$9D`MFPF_xE+v7z9F^9PSouz z?_+%RHeCy=wUgl?{Fmc8Liy~Svy_*0S{(tniC6&J~Ck|%cI*$nrw^QgOdDI>i zW%y>`Y4G@3D6EB|&R#*~$hh>L9g85p^_TjQTO_3#@W&{`yG@MBt4KW^_^ZRMZo!BX zp~Z~XsU&+Z{NbX4@{VBwj*Z217fqR!Ap~{#QiMNs;^Rf zgvUg37Z>dgW2^7R40K*bJd~wdx)kbzKZl6K6>Ufhk}g<*d(fQpS8H0)1;ejIO3Z=` z#{9{9YC-Yk1p(I=T8{;m;{4(-S!ZJf#LqWu9MK@I%gG{A7sypWuPeZsNF!6IH<5_4 zofUR4uza09#r_6e4R;yTgMvk8(z{h2?t--q`SiL?h!6cw4`Y+g_e_p&n`3?!wrCjV zcxKc0aQ;{hDx?CB)ue#T-RJJ4%{D5R4@^T_XGQYgnK-xTuM;K)h6L!sWM^ID@#$AM zwJ}n?%MD`fE=mP4p?Z2s=vn_Dy&v^L4~!@KxKD{B1oa^;(KLSm253DwBH&t@ecS)N8PVa1_}Sfw2im9S=xg2E;qaK{nB zw?8{X?p~FE;G_tjwD+qwF;4Y+!_PmttFm?$fJ+rTR(U1+rz{&1NaS8kSyZ2c zT4d}{v~du;mbk*G62Cys;FelY4=X}S5NZ<*5AOo?K+V6*c{j(a_i0`X>l_)4S(iDeZql-^eU#9V7xu#eTV;oM&8ht;>)lxgCF6a=;DALLGP#1$0;jz?S|D8G0vs979<6KfWqZJ%?F-4maEYa%XGjtfQgDhZ#U~5 zG?6;9aN_bqmOkG(XbY3qI*W(jk6KW)C<8|!mcj_~^|Gz@>=qzPmYTFK7cdqJIvTFp zv;3BQxBtL6Xvt_d5%IVXTe&f_7gt9RcyVU-oq4K`V^`AxgStC?YM0^Wf#W9GZ zMQ<3SZ89*&YtkK=i{zxrA`k+Uu3;gYl>HR$)}@0q&jP@l zP#uJQ$H3Ynzw3IBkNKJ)*9`fX<{1g)G2_}YB%@?=deTbI?5ksEDfHc^K~xU0kO`N@ z2nGZ@{M)qzoW{Sn)pzgx=Bqw2p{5324t0~USQQmrxRL(TMk^=SyPgF%0FHF3}W zDuEjF0Dq|JMXCe@2->Nw7=m_Id!U`(4p?okR)`@(Wt8~;Z7K!4)bC7mgqVm^T)55| z3ciEXG9b+;pmIS!cZr_f#&=m!3=WGN9-!koD|#tHZQ4hJAxy~|pyeO3IS$Ios_UB; zINA2u0t}$EGBUBuJp%hRJdV1rh+lJq!_|i(=Jr#OmQ6~tEz@gctEY(cGm$1i-0YQTSA?50D+({7@a2 zf?~WXM!w5PRC%n0SAran@QV~a0LK>jc6{z}5i|ppV(*fzC#+d?ry;!?-gqHv!AJe8 z(ulv1YiTq_IXR~}&F2Z`83n-%1}IEO-*do|-u@|y3f5lN=VBL866}Qjm7QxmHTIk5 z@N#1u9@EYhhBehze{asLLQnbA({>j4B{Nj6*7WXUra1*jQc6@!L;@F+|5C8t!8nKg zF3GwPn~#LG&9S0{rl0tq3Yrw^U|>LK)8T?RHyiA(aJT4yyF8{Bq|tX+&F)kV@V2FI z5puZ)k}Qzq>#rYAcB-(I@CX!1lJbAKROOgwCe4xk3sGy_CPshIEZ9T>i0+)3=zT8t zLjpFAgP9&_io|zi_Vu>^C`J1p-0OjHF<@E27q=*tgfkiqS(Q#Zm=*5jc{$K@6D|So zfA+!UG^AY4?0{KyUc)G{K^rRx+W8 z(;bxm9)m9oEL`=WF8fZtQ?7&E+Ca49jN=S59H&o@Ykw=bXcZ`KG5*z!d5AyVt*qQ8h z9_NXH^C#OJ;lZZ)Yw&b23OE zoB01*;l|C#*46C4KY=*!(5VBgs8v2sR{ty9BJ1CY9cTGGI~009&3U`2{urA2xGUd! zyE-qv{Jhu5>3X@y^nV)5`PfVKf4t54=-c|-tNy$w?s`fTdanCwkt+0A{`ukF^>&=& z_df9`(lk{wDaRPI_>p{MR0@ypTHv*`P09rWo?_O-+38e-HeCt>#6_iL*~@iki8rDy>4uNJbZ;v5Zv{7(pKFv!sQ^;IA!4XzVRsZcAnGK za)R^u^8ERH@bwyAYCd1jxOo=h-aF*_7?Qkuo80pHi}O0w`F_8&<#S(d;MX?zP;SuC zb=SO;I@G4G=Y=>>D&Q#JMDCiO%B#@AO9}s4(dyG_{u!`9`zjzcbqk;9CkD@#LU_( z6;G@@HpZ#ka=I>i&j_t8zRGnjl|4=*Jtpx~*e!BGU;fi~jdE^mw_A6m^S(5>ES&Is z{c*WAuhvnpzIyKUBZyp+2Q#2;Os-D;W?t(nHQ&$h>bVYf^Z8ZKsE#l=D&M;!{qp?^0 z7i^o{)Jm1>^SBXFoC0qV++Qp9(H=-?9QTOU_I_Jz%4&Mfi!y=f4F3B>Mc7r$h z#{#2LisJ+i{gl5%RyNuNAMHQpZ1KxfJbPE``G*9DqT@cD@@yW&Q@uwrbQ!8zoy}&A z=i-aeq;%VyYZiDVAFqo_6$IicFVoYd-&tCKyU9(gJ3_vJn*}s;7Z(HW=}nvsCXqxb z=eiGztIHlW%Wk#>^JN1gRa^?c=b>*gZ1#_A>Xn<)i~x$6T5CB`;ftW6gto(YZ-th# zpHo&_H%Cay^G}>>NK;Ni#0}nf3@E-MN2&zcW6B727oPGzBV&BBcjtzQs6ZD! z?<*a}_X+uA?3|a^F&rEmS9;D5PM!Awppb~yvJ@aFS)R<=l0jXlkJDKOXmYrboBCLr ztf#oKDd+L-E8mR0ah)PhOk1h}Du^|OGB8^CHJ;(eI-)=O5mgb~zD*om;io&Tv0} z)AxWD+jEqchzFVy%%RTWu2k;p&lQDAsR_$~CLQ@)4-5n=gMR*p6&jdeU6#-wO> zhT6~)Tpq9U7p^88MkEJo_tYEr?bL{ZFXunH(yczXL^+RpB10C0J&tvkZf0sz^(!5? zoHDcI?YG* z69V^?V=8Iyd8Z*sCF#>iz{UX~yKkVckh!#iD`*@RryS0u_84K<7Y~iZZk^ob6 zOr7l-iRIw4u`)hW`~P<5Ye6TQYjS^OPdv>Vw!6}_&R6&|{k_%8UFHp|8#u<$QClT8 z?EI|wkC9o4>CGs>$4zA3F2CjTdiS{7(c|f}d2d;3QjELQ>G+#OHEd-vGy+WwC1gis z2KP$un_Ou$c)sC(2_@N81P;dEuWz#wfX@g7HhtJ2W-A-;;UOPO% z6h!aQ9))6dVy~9|X*bSZ+4*L+Pc!q8Vns5h&~*AJlQAYU%{o48v*Glm4&y`|x5qTB zlDc26<5L%L>d_Ikbn2z7Z#V}@5oI?XL2Adpx5@7Co#8wZ??atqtSG*I?%Ti|;+aMa zygn5%H4CG}QYC`cP_nc35ktXt`7@)9toQ!tAOcgVb>W#dy^Y`KF$xzg8-)qc7}vf@ z0Zt8sTGawLTNi6Px9`Un9A8I^X7iKT^!8;B4V?uSb&qkE?;*iFciUbz zt$N6{*k9`ZS+^<;CR89xF_M7?Ny~E^_Ch5^6N2gorK!FsIqO253Cpcv%pU4;CV?$Y zJylWj{u_&z`bj{pX1+=vD*>RIU#Ob;Z_m#Tyfj9rT(l$`9HH|twS29V?i}rJZSAT+>y}XYayY&t%0SA5zeWLt zE%rbi%{KK_uT_X8!jqDbr6V+ogu4Xt`ufOm_3S#!MS4hR1Tkx3MId^s)xJ$XAb$31 z6-8CET?{MaxNLoncGs6|Zt53tLiY7E!MRdQ_nZ3MhfQYp9j5*`2a8{no-r&mhtimq zIZ8WN+lc+U9X{t2-zu3C@C&Ai(o+>Ux(kS~=^Tr=$bi9P@Iu^C=KD$U9VR9xT0uZO zQDF&xhB8rtto-nN(NEhHT{l@t;g=JjwEZ_;%5Srl#$d>~&d``PyEKX5nF~72MGk@| zaIZsoFFCi+BziW95c-wz8N^d!R@?;hY?l$XOK~_|2Ng2ovwjK=w0Z6^R+F&QZx_kX zMSNc3@p8c-PYp3lc=|O5VTbZ;t@6)Ke7Q6`#=(EIECL*e<0exc#65ty*3WxyN$V)G zI)^V-Bul4|ojy2f=)%2aR;ztw387TUi~0^-nPD)uG6)2O%X`%1q6KAjgh~;D(J< z`}yC)-+}36Sq!yuA~|C`ABrI63_UAr z8fG#@uF|R3=AS*k@%}M7Xg9g95Q6Y4874~ERLWFUbQK+`d{j*34$NGKLuU6E!P+3Z z+9~PzIIB29?>ZUK1-cdy5Gd~I7;Hlnv=l|xoe=75EO3th0P76mSQTRJh3lvl9d~jt z6!bP<29<{Ty%fBK#t!aZYRr2Z`qZyRrz=kUWTH<}>q?=j7U+KGu1$NT#?Q?kV_6aQ z^DD6aMClb;MZ#y{hXb~!8nMe;cnyneymJ0E0Xfj`{p6u!Gfw6#Or!WckDb8Xd%F3= zSP+*YF1Do=Wi3*x1}AKdU@r|sQiqRH64ebh=+y=B_Qisx_7V~g0d`G)hY3gGU8(pS zM)U57`l(Y|XuAIT`BZcA7qd%9oZh$zjIN`X$=uWd6Hb1nu(b%8U75^9-zrt6Dl>p` zSfCAV@yD0x;)%ZI$m5y{sqwjRdrfR!d@7mDKFFUR3grtUT3-^SgtfBQA2))%moWNc#Fx7Zo)4SaanE7JpCRZb|53EeW`Q7Xav&Xx>E@@LmC z&!ontfF9YX%WWNaEOZPRm3$9dy-V@eRmVv)MDS=) z0*G>KN~Xwiwsr=|}YXtBex@IJnKwzO9A z&aSPA10RSW#l%BF`i+{D5YTpxzq^KPy#7GTbh%(YEg8QdAL6`OCAB)l8V@*3fUgxP z(1oV^Qzgx0%t?sNa$SG>edv@SN3{OIqj(9Y2Ry}q@@U?%#K2l)$b&9Htr8ui@9FHQ zsJ$$LbPSLmW~>u2x9`A6n;u=n_rsXQjf2diRLD_Q_Yuxe;Z1Q_=I{TT_4~Ij=se7Y zNAZuu;;#y|b9=l5mAfqhd^1{`)W6G6ORGC8Fg-&HX;?q8nHHavg{S@2dQhsLHU;i zBxp`=4Yk-d9zNwcoP)<7pe476b(pe}WTUWLETJvKyU=EbiW|?e0JOEiA0!ZRpCpg% zNuerO>$~vPRNNnHk>sh=y@od;U1lEwC3PO7l=Jf+Rx`% zR6-?os*>Jpn^z$xHX2H9H=(%02T8EDtX{fOS{X7~g*j`+sMg9R7Vck))F-OLrLS60 zomGDsUuiKk!Xdh_LmEZjDz{+J6BTsTzaQ#VmF!8UKIagtzQbyMS?3kee6WzuX#C`& zI~G7u+`7)sFzr7*0uZ5V&662ylqY8~!vo|~@JbP2E+UlHVr6j|@hh4H<87BHYSK&9 z;qV@$Tg}?hR>TxcRxx^o27F_MWjpnI6c%u|X=Ng;-?7EOpTrT-kgC?ffUK3IG-u*| zGbJd{9ONDjs|oG`k>5eXP{8HY%M&BBe!}CKBAi5*BO;qs!A8vFA67dTVvx4#hqEKM zyWT=by)+n}f709Z;#g6GVq!W>l6iZIvF3Y`VLgAJWbA<$yzVMCrs3gwcIzDsu*ti! zP^q3t+Y&a={z_;uh{qQCwZq-=xO4=1O*6}eal234Yqx-=U(y5w&KntBm85%WqrZ{^ zcvX2iAXhbq?ynhlz`o&e2%)%5?TOmQwVjh<=lTc=%dmu_=^9FaNM)mf?WI zce-NauyE-_8+4eem^g}{FUidM zzpfPj3e!+g)rV^}XX5B7DYYpJ+*6t>A_D~kL2gI(Ta>)*K)-##(;&OYA~JzT-K*QXS=KVb1%;Jv;M6YV?gFe zMCS;xB3oL?_7`2Wz2QXYhUVc07nn;jQ~=A3p!?!fxmC~u(^nf8YM2T)s9NS@(n5du z4-PFtH`QE1D!5vs_I4ke)H!e>&J+fZ>(=J>6ps!su|6JIky3P_=Gskr6F&Wku>_Ki zj7D@hV9bda`G}z6uZDTK`A#qn9ei0q7ZEg^)Q#kR^A z2)d=ywE_-hAYBDDZC3M~tgem89hHA-yUEr6>Xcyz0DiY;D^$c%M`i=a(Rgic~wk9Kuv}(+p?ajpnDbFQ3EaZ|i2aak;2DkJ0xZx(&07etc6(me&ZYysu zwMcUiBZnL{*nYzYirZs3H9`S*P7mbq9#8whZmra>gZ8M%5o&2C~?9#__OP`AG5$+0G48O{oXMcMVSaGIxS7gG!{Ve5uX%g zO(UaFEGjQtVX8x6ps9UT`!q&-#9P0Musba{#P?(3uw@R-Rh{o{L3O^cdqQ zHi~zlYvJ{UM=p=!5H-)gLSb?TWPS`UlT6-Faa;@IC9ktRcAM8xBwRtdHaxJZ#UGHW zm6#mu+%InCTpryz9REO!slOq*Ea3M>0csGM~J~R!pB0og6k1oi%BYx}$7#sLF_Vslf=e_Ps)FQ-{3Wl(v^!uD9gHyrpu#eM~ed z7YsKHuZX3b->3nOp^p`eiC60|bR#-CB5W7%OUY5EzX|J8(czDQO+fq@sLjkO2ESan z6~tQ;c_W|gJbVmt=$;_?KLB1pp}+N9ZyyeN3eZvRR*hOZfb3RaF6tYOz@%i+*?ftE z=cDV1Gz~nc_G#|UPwC^(Id5`*(y)4-3UtRHuufTs!TV7lP^8OrK_@^}h`s{W)9Ae4 zzlY$KmdQX0N{`{d1|`>59&ro+UDDl0aY?yeL3n-u_a(`cZZcQyPrqCvw%fVUl*n+a zyW|Y01@cve(0&IN3raTQ3}(rS<<&?SG|4e7o2@=>E7I4TtpqGF`D`m`rb&`$o7-QN z79&o+Y4ewl@BP#kl1e_ILGI@xFtRmVV~NGN05_l^p23W4oCZ5L8#P$(D@mA_A_QQY zx}$7VAxX0)r!jZLn3T;Hx!O?TM&6S(2{+_= z3DqvuKC*0C?GoyeZq)ir-zqtWMjeT4+>)2NCC;EtAd7Jj*p<+d|05^dsu{c!QK7NG z&kZYKlQ-tH{~+(W0g!e|s-n6JA-BMDXmbLWoncR(-}{Mik(TV1xwq?)tV}}F!w_^_ zAc-j8WW78qnU2T>aq`oEw&moYmcU3fKwk)p%sjITUS@RlCE7pQ{POHAY6z%LAX_;c zS;YO=}%J6Ie@nl+K(LdoApu;$!8IU5PeuJeZTLyQz-=(B zXw;#lU7ZkH(Mj@MVaUw>T7+|zMu04^HUL3ROhht6I2O~x>yM%=gq)8=*aFK?NCd=m z?C+=ih)Wj$shTjBoR@u?R7i~W0NP2QBnf@+I8M&h>m>xHHyqX>(bGWMmHqyRb{yUf zU?>zjM(OfyO5e!>)xcU|s_U_Ka5g7LXIMH*#ow8_KTR;`x^-kzocd-I>F9-JlWh2dkEQk=U!No=<-wwEiY>CzhJ(qWMFL}i%3WrvQ>9k$0x-QsTp<-nA}ttAr#$*rFIF(Yh>+a;IuRYH&KpPrO;0a>LGwyCQin z>!^oqLnMG$SC~3d43)Cm&7r+XfELW?ux);MucjFbsWA_7xUWZ7Lr4~#qm8rn1sQ%A zLSdVxdYPcuSLf(ijxe>%=gnYt%{Rf4+}}WurvuffV@WCEAvn_10(r7XhXdZlC?MlN z3=Q-2b-WLM>>&Xt!jfK<2DCZEbYqM3X-U`)A)QHovopr@C2)d`G?x~acOsg7)4VN@jOgIr>{IBzONm$at%NgF@OX* z(RPmk?ov4cHm3}b3@!PkkM9LtUV(||MZguOgO4Hn%tIl9_Ge`25yF=d~^SeZf+n`%j7mMU%lZG{Y?K3f+?oi6b&yzsUyh|d5|8b}<2&nBfJ64=H`I>j&T z(!$f003);0qLA!(sK9Gfs%Wh<|w z+%wm@GZ)%J1s&z>rxiry^)}z2*JYz#vJvr(jqA;^Nr>PAyvmAus7m-gvDFbnAPhg+L$gkE5K52RokOIwz77WjWL&( zYq4qx0;K3v=vy0u1S0BBMUFS=bk2F~%ir)S$4A5+ZJnK2X?g^{$e?V8DZ_v z`p5U`%k-IsAyH;=J-QZY?^NkYA>bQ7qY}>=Z9UU@7au1`ny2`0_l!!mP2Vpbzy?^7 z?|kjL>=S*3DVVi85?L#)H<`!9LXVtR2T(>ONGELtEM$LEtjp{k&S;`ugi^{DQf>lg z29DYuhAcHVs|?I#HrGqYTl7bPw$xN(e$yy`G7Gphwz_sGE~eJp3*#YbG6Xk`QwX8N=-Z~$*4a+~2YZtD?Qzv%)9!KK z6y6;5D@44tMCQU8EoD3bD@zuyusStkB+4@QUos{;q3(qEB2ZLz>Uny3t%p4y{n%|Y z84jp@>iGb{_N>42fJ@Ll5KT9yldmOuzrXpwo^=T|IoAJm=jP$`5nuv!9d(wZY|KEi zC|x`^`B~J=)LgFkudVWXnU2TTNmC5z`4V*t#@ms--j7^ZqT0zdDo)Q|&g)%cuyryK zX+q{W;$>1WRLO+l3S{bQrP=O!nUb{ejS)oA_kUh0+1W0s)$b; z6+_Uv6cd#CI@mB?mnSpF2jVMXgj3*}f`q0So>l@;>wDR!WE5p1EdXdU4`3kxXcpX4 zPEFl)E3A0wOfuLbKm#&aC}Y*~DG6IpK4wJ&cbgfVd?0~BlR8w&=^QBo)k(0cM?HwN z;zkkHn4V+`wB&N+_<9NhvHx3GAeAIhV`2HNgp;Yy!bc}&PLjQ`v2=geqr$xo$O%lV zG=OC49*#&S_D0E%$w(J3BCp*-+6GRa^#~ayPv_H z5EvdRyQIRlNNsPxk|zbi&SjI^B_=lmx2?sVNw!tPQ!ek7dkAE^NEA$RP^W`yM4aRt z@O&-*_AIt|1jKp7vvc^UV>AKGB&rM|gW;QymT1Y1`8r=Mr<58V-DI)nW(YP>su`qI zU^kkqgl2jGIlWYlZtV@OhiQGdghbXmwN>F)fp4e;Ny{UH#e@R-mjUDO&h-+Y9~6uv zs#&B(?`3=J_vI3jW|D^0D*%0yRQJB%2zY|z_y%Q3p;oCQpz<(1?q;^*=D;E}11ha$ zk;a4IblBGvChv2k$h=?P3k-#20|OdX!G|L)OKF`{UyB}*M?VvI!II6D&fG79h5P%9PYPDL_ z;8z~gzt`ku0IU-TTTsEAuSlR7+J=qkbwC9eNlB1gH>xh~IRA+w*0~v|h5@)N$WFvi z|6!;S4}~xh&9)XQ3s=sY z6PFInQMDH=2gT7#kOQwPQ$5|SV&H|j4aPaK>aNi4soQZ zCEKEs6?l-QbsRKKfFw=N^yLxxEUbhPPSo{q!Vhr3-T!EN8vw;{8ptrANfxmt6<@E+Ttvtz7si%)qs(zWfYFz<-LD2X$}iYCbZR-e=&;fPpAAz zICFJ>GeOQZZx&1jzYWwCzHg4?$Y#D4u!05^8g%Duz`NPbN1l}mBIN!UD?OL)e5716Wo=-J>*C6K*ky+|jrVHPtOOTDJzw%W zY;NjONuyVU!vwEDZP4HKi}F4GCN7#f%n6^6Lq zQ~N>6zz`n^$OgsP`@Q{O>YEfDmlWiiRA~}8ETg?(GtgD0#{@6T2T4Q|kR6CNENE|?G@jc^e`-2*ndLzuSBD52DI z1E-*Z{H?E@4_}+Yr45qRfb&f_EaUG1lp&FG{N=l^R{;E+$6v__A}LI5O83fbYJGsL z0AX){Z}6?c!6*B!gSab0-N2-uP$f>U?yhVxLyt{s)%Z{qeyh{=>~usB+u9ez~}>F4P^e5>Nc zQ_ffX4(9iuW-S4nkYNmtr$FWR$zmAh-%2bL>8H(YCb`88Mb3>B&0Hiy=KR`%>l>aq zhkm1SBV03$$QD(O9a?2%xHSy7Iw5x)W-0hj@viYeDDwy28muC~E+Gls-aB0Qp+v@W zKs^XoIuB<9yeG^u?-G}85_!DO>ew#lp#iFx&c*ww@+7F<4&-wWz2)*u zvoaFiMNASn&alvRkOMavKXN!>2@X0i((waU*cveqsNO&zEX-#&b<@^YlNUg)Z4#Dp z0ThD^=ymVwVRqvD+iJ2vH5c&vh?ZUs$K3&Evo@Q>3bg8U)0pS-Zx60;J>I1V;y9C_ zm6Nfot)3yOrHV6A1ZHMRQ;leEyfIvAhntG}YqP{UPMD})Z>Fkp6QpV5)wpD8Z}T$B zUQhB7WvGsBk)#~K=LsDdyvNTVB8j9DLHCQKm0~6^3?!$iAUGXd zD`ipv=?{u(5o28{Ws(uf=XP6hX3kS*9{7gHvqRcfowz1v-9eSToM4$CPDyvl3dhcD z)6Q&*b{>=ZwGBEUW8!yUI^AC2+xa~bxEqy{WDrWUQGkA+LbI{WM=XId{!FTYP!&^r zy+(Jj?;A<94&=Fab7?>bD3eN36WD-p_S$MhwL0%F0lS#AR-me0+r5KAX@?Yx8{s+9 zOm?4-(r21&HQS>p zY}1W2D99qq3y7PiBc!#z&*PUvOQW7U`z{(Db##>(zxo4wqa8zx$GzsMetjc}zK=Tc)$kHe!s|%h{NuC*wFd!mYDY(VO%h`nxmcnwn7!Yl(_>~oA_K6c1R0*dmVuPm|2p=*5lE7w8YKT;5C7?^9kn#8Fz2+&TvyGfVXioMojDBq(oFHVbJmp~k~t z0(5sn1U^e8PIt}t`REz}oS0t?Av4=ERCG+LT&<50zysg1)7hkzAQ?*3#X3~^CK-?W zNNXBWPzHE5T1$aB9YJEM{fdW|LyPY?)=>FvClK_GZ?+3(0(!&s>@rv7;z;uhv{IdMa}gEyn`^jQ$-NL zJK94wpm}!t>0yz;J_e+UXyZ<$Yj9o`xEpD=5yW}MNXb>2u{QCoD64n4l#UkJCIb=O z>(PhGamXhdRq^voa`~7Q7V3GoZTCmrINcSfY=<_Cle7SY0~9Kmms7l%wY!r@we=8P zb|hsQmSBlA??&I8f*Z803hn`}nch;-Ugh;vKNvtJrW35}!T)c{0KL?=BNpgdVL0tY&I-mzAJyYqpxsfEYJOSAv6A?Ce2U^E%w&R(I_*Pq7I}G@k>;;SKe00^m z!z|ZQLnW7I3Ho?U^ktsMV;^sP`EbX=Z8f_gBT%+C=@oxZ^zs1YPxl%E4pLn!{eh`S zQ$Z~QatQ!x__X&0ul0!f(MV7Z2y4eno~jRg-P1O9&QJ!~+Q!c-RvceuNrDXuR$^CL z6vT6v)8yC{IH&>2qQW^;$9@o2&>09GYfXBg;ur4u;F_f!Nuh+zdv?IKPKT(FQiSRW zV#BT*Y8R>y>iA3zK}{+Bs)Ephju+7`$y!$e1#V2F!|i%>r6As6En>>I1;r1o`|yu) zI$P4~^$PkPWlf?GCWZ_JhT{qCO>G=?(8rQ)*s?3Kx8ZUjkW`*hIX>XBYtX${V+RPUhD52!Nsk;Q>~yLI(jJTp zP(}>;Y>k-H3hEu$30U9;+bJ!Hsm#A!k3M$4)3;y+)PU2*oIV;(Zx;{vNmW?27Qxx4 zfCLmkgsj^z@m~pK@H6ydFmvxB?7$G)vV(<~+k-VC$02ye_(BE>L;Qh8~F9zUdjHB8L6GPs?3!*@&7Jxr-Y=Y&4ylUIU% zAQg~W@Jei_D`HCF6m-o6i6w0wY|kJ!Ca1fH)#t;VL`wt5>6NIdp+RQ~M2`&OM32=T zm>aU;wD`#W7Rv9FTOG7Wnc~btI_XF3o`AAiT~?4D$t(NuL|Ep#`zFE5)&ntdyB>Wg z7J6kRKZ=Dun91w)-Cc1$nWxCfixLSet$N~-8|}s3?c5Eo2mA0P=StE&$ld zh$bHc&^}S1;8>SnBk!7GMG<{X=Hl3+LiKb=ryy3r?&R+P*$jSm9<_S*hQ^~$I4$MR zQ*)d_z;>(%He*KUo9}-&(4kURS17J|6PYw92ecd5TA0zBC^xyHbG#C zXq2_5w^ax2lsN_Z(jruggxmnxZS^QIq1u!~e=)oDdBtMuB5*~tYNL{5_EY!K*tqZ` zR3kR9kSXokBQ0WbU{Wkdlt#j35*Cdha*NH6+v8iQ25`+9-4MWeYEq~+vSCl$a))4T z3!91_IuI&`7f{0hH{}&gwI=%=AVs-dkDv=NqY1G-cz`BxX&B?4^7@$9+Rx+aawz{T zj+#M#A4yLT$!Q#{zhC@yzewDF+L+wj3!p&-6P2hOQz!uWq4UavR$3%i(%Yi33a-V%P&*x3ua^Kxqpuw%QFq6MC)K4n?qy=oFlD-q3a%p; z)|>c(K7^2DZE2Hp|GHxdE|Lsd0JW z9^8kG+Md2jP#;$;TkBoWijv0WZY_0LTA5x6kL3~6y*E4du;+S^{l1aOX+uj}7g6a3%a)%t$kCx%hYod>g~Igc{?%5+r~zn^DAJa6o>VrlOn1zv z^U+75Quxy$$SAZ#*Nwoa9g1rN#v!#_!MJW-GNbj<~9#M2W<)~wI zw1ldJYyxGl3!c1L;=|M!J06V7$m4ZYu%~#g{4Xkq5sCl_4Pog>N)0oY4lKd=y*#D| zp|XW(Lh1q%+gwS=%0m!BeS)iJkzQ(c6LC!-CK+CJUgY^i$w`8)Zhs6ZmY0@el z;mo;8D>aOhXrOcSDB!n`O=s4i%?mB)&a&*%@Hw>c0*t5IMNrZ~Ob+E3#Jhev_rBeGMQIR!LdE_h4%kA zQ2-1RTyJ~CYshyZ-bReK1B*l`-S(+d8s9-?g(h{Z;tONH7X_478u#kqwnvQdC z^MB)NNfF(S-btt&nzd}(QwmQy56Z6Hm(YD&vHiFdHXAn8N{1l@&-!L$1X38|kB;z> zevl+A=9>??aQyhq)gF6n=~D3&(`sYa+%OjkWm{6X#`>_d@_)EnWOSmpW(5+7fliyT zKY|>l6~b=+GJ!;s=KwSTT&i&^Z$vTuuw5r}V8HP{41Xe27u|*v4>;*X5)Hd0Mrg%K z$?V&SJ~~|iWOc|P`rMFuog`#RP=s>_fKaKaDWPs@>n>xEcrEzMfkjzB9=gH-G6NFM zbJSbi-6>mIbURZ2!=+k0Pe@>heB6kGwqrMUG^sRw2Bde9!0s2ayVpwOjGu=Szb(^> zVFC!E%$Rm2dp~g!m-I*gNLHW5<8eYmLS-|)q?fn^0W_fmA!V4e5svPy4k`AYay^fuIPG#0M}MCE#P&HGH~ zPDx5&5k7X~p>8#0XS4U{UF8Zmt%HU&w{{UdymtdJUmK}b(kwa{6i(2mekQIwLAQ?upsz#k?uo44~aeJ5~B~_`wFel9_n}TZ1JT%;qHWpU=2+NT= zK=Lbf30rs>VC?L{Ft@qcaL1VX_+EQ8h*YC3Z`Y%1(beu2$J5d5C-?ND>WXaAr8;Ak zT{Y80H}_+UcD2QQ|0>qPoBCJ^Lx@4Z4Q%cTs_AeClk-(ftXZ}oz@i_0N5O6H_wP|_ zWlz3CDPa-Y;J3w9O8XtjZPw58Rtn{+CsfjWF?WSJMuanmcvV z$e_PbpU@*3h(9RDz<#R{`;4Blk{Phy?F=`0fGvX@YvVnxVmxsb_@$&Ff>>%$xltj| zf8_M?H2whws9T7aZ|?a=3;WUE zH2?fKvw3&J)NN(ypwH9NCJ#-N1OOE{s`1|LeK4v1S0B6omHODhyt!I?AZ_W?8Qpwo zQb*-Yespjq%}$$!@sP^oGC@s1q7+r%#CY|UfkT&HHXkK(T7``s$yD_P2%FAk)g73F zW6W^Nfp2P&EWKK$8i?0zlo9Zo7;A084?d^60*7Mh`L z6z@=U_GxGJf*r<|x?PVxboPb{Xv2IR3*a9i;z#~=;HI55ah!P|0aNtnsIFPtB5_N| zkT6u+)61P{8%HJ;*6Ro}Edf8F|2PcppPbzW6Z($j`+bmFzGok{B3>Xkg=%=iip=8}n%gVw-n{83M zV%wYWdO!ssEt0gDi(+W7NMXSXfbcZZV>dD6EpThV;MzNvUmF)$Zm2A@1}RA6}zJHl2De_ z5NLL3!!cGQ%Wuyx0pm1otAj5U^w0qjAD~i(%0pV~Mh$EN`gVU%choPk3O1OuRNoNsrA7zI^s~)Ll_M3^s(az^>l>lo7i%pXXkL| zl4PBVecWt0hYf8ums(w`v=hm5?BVU4KD=Go8`B+tEPUyj4b@xdFbm&}^vQI$dp6AV z3OBcg1Q!;I?CE({GY(fNJJv5jqS-JF3}nMOh*q5GrrBhM!XRoNG(lb69JCgotfx?&mm$l>0#QEaTayiKYa4_2)L42_S%4; z0KH>nJ5Gq2$IQaGa#pm*Sny3sb$h7jlx(aFhfu^zYt8um_{2plCwXy1D|*LMQmk(bcGe5+73tnun)t z{jjNI>|b*72PW>+jR%Vd(t5l&!@7XEBGF#sQg8MWAPku~N`_2oW>T7!me-J>LRvs3 zWMN7qR{D>>b&BUW1u6>|(K-TsQ9;}W80Cmmx)!3$V6om#SFHY_nh-vKoSu}bjA~x3 zZe~nLK$rzEufbj64i7d82cQRtzyp&7ohM`j`=aL4q&C~@3=uW#Fl9$vXwpW)`Rd?! zq-kX_o;4Ug-K?f8)(vc`LQ={G3N^1_EXZ*I*g79&_u&F)0!VI>#vaYG?#NpZ#PjkKi=a%kiGvtdV8J|_p z`MxdBJp3e*_?4na&#_!(MjCbof7b$g%x-G?vhLEDNwUs4xt}T&y(zjBqP)BWWOUs0 zH9(d-h%T>Y9AUa9xjx&FGH@BJ&^+uiFcPiO&`k#CGhJXB9ZIKJ>5jgi4rRYGp}?OE zOoa@bwt4+zD9tCD4g2dNDt+cXAxVZk#pt`Xdf09zH`seXW-~Yx;@k$G78m!@oBAy> zVm=%eNH@ro1gZ}^6vmw~AB9>3^l6EcBlDBaS5VJEMGU&S+n}vbM7VU84tu*Bzjn9O zGyOR@-hh}_7?B+-q#T(x)MQ)J7yK{GVsb|9c0IaUF7QX})3~keS$=bD0ojD5d>JJQ z2Lq5dIN28D4tiW~+_As0O4HF*uEii#Xk2IjMtaKJpkRODx$^o(sDcoBkLuqq2~vuG z_7RmD$<>CnJt0Ui=t${EcCK{zaYa8erd0IMl9BpN$(=h?=WwFa4A7~XQeESTn;K1N zh|eW>q^sLV(v=gCQ~59%01gdZDbU?qkKh!e$5)DWirBey5=?17>7Ym3PKUYABf8Ga z>|F-6Ne>l}!L9-dc~i>>JdIWk_L^~g(XoSeGC`qI-2I!P3PQDWx<&0sZ$mfxxmJ~- z1~1{MFk5|dBl$*V`uuzAQt}T8q zi5`f48aZw*r|Z3DeFnW3_YQfJ9yuBnyA>cjRfd*=EuRJMXg-CX7NfTKs7pu1e%V1v_f^ zjn;8j2}zo*1q_-yAi>t$wg*u}uhgx&#jG(rfO=;=uxhX7_h6hHQds0#!o<TogEHeflp7G1hSXL3cJHDm4-?4#!X%s93UIznfQg$J%Gn?7;kVUfJOp^ zZ98};J^*Wt1R52#9|L5em6sUDg4zAYdN$A-k#g|+77y0{z*49 zanSV)^yNGqz_AVHdxNsbMGkt4=65dXM?kh6n-xOL>-^Rq)mvKmcheruplv0(PF295 z%JKH;dRo3^&!m{sk5sko&kPtbb)qTWpLXW;5aa0vkn1Ark;4smUXfP<`xPj+z%%7u zo+HXm>9SBEqDre7C%{Gue&|C%wI*#xfWh7j!URMO?lLMEDZR8_j$&ihtINfL(bg4x zq}ZeCjq(R}vRy_w9O$KLpvxZ^llzg}!wE4Wfbi)By9~Y4hY`px=Q)!Kg;sqLS7-N+ zjQnGu#Tab*NUs1pq@=KWz6IFtM^=oSN2j2U3wN!IQGNYK!-I~}3%Ki8~>#iPlXEL$pdJc{6cB-?UyoUl-XZHS?13pd6( zWm07@J`I^nCP%In6$;f|Vkro&E*o|02I$Oa&SL&61a=`*^dNtb%B5`qV=E~oyd*&wE*u$6kW>aRD$AT_$s|AP#6D<~r9A%1LC=$c;i+*4P zj$3`(u|BPi^$q4NwhRzSuarXJeL1+vU`0EAVgqhaS0y@nMEPTOt~1$rsduuPMrs*X zB)bT91{zJ%`f6Xkj-S|=O-yT2JAt*;o}k0DYzkZb0IRZDChHYzTOlpUl`7+~3!jI{ z%+C^#>qcMmD9b2&kKYEWz3EW_&HV%Oi@0)uM zY9wkT&C;81lBy=sCqbOdf(mN{4)$E`b24Xesf*MoM`!s0+2UmmXyZ3sN}r<@=JRj| z+CWkV@e=mNNYNZ?d0*ZeK+%t-p6Z2Nb)EVV@igwH^sfuK097gctwfU_u_gOz zX6mQFY_|Qap2(})HazT_d#Ft8_rmtG!)@;%u?MkX%AN{0)niZQg@Zg7&=3nseO4S^jfO&r~%4i_xK_KJ>iy!1@q@nA9yLRgu&Dkp0Au9r|F0s)rz-t;r;VW%+g3pK3O>Hq!=&8}~ zfRCX5?5hE1Ag}X-qHFw^bO<9d3>qY#lTfoU9QP1%&4+PoROjQC3hW`q>5Mc?rx_iM*B4+}5W*zJ0xMNP{cu!o7f z70t9sXl(5nGd$*22GMz37Y@jYSDBa8_)K?NW9^f|P33i<JK_&9=?xR|};-?YSai zcs)CbL_$lF$GZx&{A~Za17sl)7xkkw!v^tHA9HK;8HxIc1wXG?wNHaS%oH=V>?Ymq z__;~7;;B*sOD>v-oEK3FT&`|XVm2EpnzFHPFx&;pO0x?|0Ymtk;BM(P+k>DINF-`? z1aN?WqT9?lHwEDqM5igb?!dPMIwa>7ZMqwPPhx|&k4G?jLg)k#wUYq{Ax?ejV{YBF z*bOeGt0#sDI;fF3PJ&J%rl~a}k%kokIvT^w9sVe>qTx<->-fO&I-Me=zSzMe{R8e` zt&>xAMBWLCV(V7>r)Z0TdB1v{P@w>JuDgDY28GlF<@X}^(K8l8Sa3>`{jJ4LD%Zvtbh@x_`M#aIGI_Ttc1yp&^57)>(Mo6jPl4) z*;$_WgNN!I=a|%r89$Mp-ajz&&|T_afufeU6Xbcl&zrpC4We^O-C2&*8xV_+C_5uu zO?O>fL27S@sMC9!KRpo$U@9izhiK_j3>aD9_2Bb>eMg3Tgu1uu6(pqTik|Jang@Lc z)C(JNnzo;CYaMHG4%E|&n+$5iR6VkSSl%N#n!xn4ktfI@yeFP4$drgp8C=*2WX1FX zW2U#amj-%vmtnQon^5NnF};;kG)+=;@ARU$9xu4jN3>5sizmEabwmVy- z>-9>FuInCWz>u&^^6user0;i^Yrn>D?spUH*8>u6_8Y2s3BY}-uu@QUL-nT{d^7RA zm$wMNyqZXYnIVx?_uUVYA*eUwm$h+(H$Hd|AVH`HlxX(sQb~}2g>BQVB2QRVx!52W z+zGduozDd>brmu4Do0h~zq)yYn}!Coem76VwzAk-VJ$k3^}I3~u`~&?kX=gAtJuBS z^rmdP7$<1Z65j2*u17FxQicJ!5#<6z!?u0FvqL05x|h^^-mmNR{tO{ov7$R58ISfq z6@;aGAZ@Rwl+hsBM$>vG4FUrIQ}C*O3Jx70QU((hppNi}rT&)QKR?<;!i!H1utn=E z>B$PV+0T9K*Fo=A`qD&n@p;86f+~&!8oVd%3;X+lmZ7a@f(a6k{SA!2yTWZGp`@S* zrGtalLZk{2xaj~BYU9EzyLlC>p_TV(w%Mq218&DFQN{d++ zdgQJFCU+!Hn%?KhwCVjmT^B@z?}iUcSYmFaz1_9>2!vTrV_*b#zqT__cz)f!boWtb zE+MZ`kBX^HRYL?|pz1Hj6}JgCBtwCLaFuUp_Ivq5G#VfC4jDL|L8B=Vh6FVc?)Ob9 zjSS8nd=oY;WJztmEBo7qFoDYER8A)GSv~bTLAh+$c zbxi1##XtdJ5RHI3psXSyfYYsvn~5EF*dz>V?EnZ;=+oQ{!R3>bjWS4N9kEU-Wi8 zx>hxIK)^P7PIOD@s7{@3Na9T#zn*lZ*og>I37qIll$JqYK^?&ZsPcA~;@8CpDuYmh zV>l=D+~BUVD)MZKsbnVOOl7uR&|!votar!Ch?=#)k!DW`v(YA4h8|+t zdCTr=o2b5bq(nM}3W-#sPaI4h^kCV7OJB288Q&S~ONDWy63OGHt*qH}%~?Ah&Lx(# zU7}J?<4j}P-8Jc_qif0XexFe@47+a3#k@ajmI<4k@qqC zhU@1H<_@A;B0<$anpBTFgMc8^sCg&A4b@<6irx`9c$kRY$Rz>=(LTOa_E~a9j;?pO zEa#(orzIT7JLK@VyDt;2%{+04AK4L{)YHhW*R_0)KM^>A&%IM^x(BidX$RxtYbJjj zfNX9>+0hZ2B4s9hvTqTD&=*kW4FV`097Z&_Pv6GH9*qRG{=oz0)}k{g6onAX_DA;) z8UuECtV@a3CzmTWJE(nkCMC&u=ufj7p6*N;vR2C0R~Nh;Ky9&(bZ;Sw9<*QKx+nJ_ zgAFEy=-)t&HSKl+9{0u=1n(BPP;&br$97XRLgw9_(PHX*!GCUk>wygotoLxbED z?cf@15@bC_6;<>VL{q-yOw5l?f}pB3_F6eXTqkF5lKFp7CPH@zs&WfJdA?p@xE1+K z_}gWUXKfBrD4J#3%f>}O8Y$#pNqdU1V46vMFdEIdPQqgg#zVO#4xo4oV<}X_p@U!_AVVkuFPyx9gN8=Li^iQtAGYV3&Ox6)ABi ze?Xq%>1aE#JFqtewjK&4R{i7##yI1|*di^glo}lbC>gtByClcTIs1~w**yrR?{xE4*y zNhjT0ca=c$p@DA!dc!&(zUv8wQ=XVoZsm_K!F@4T86^k&Jcbe0onn6c*FF{CO(=~i zI;5R%f@e&6W|2f@S7m?3b|qd(j(F#Vm?Ve=T~RYx2x3UIXlFIGV3IT&4KS4@m=%{Hz_>~MYAhn_opSD&bIo0t4>G6)(#1^51XnX^-pb@C%&hnx#Qi|CZ_Bz z7)r2B%n0--An*w)jQ8DjwD3|HDc>cfWU=NPklT^VsN|z&5(VYOT#wtfW~w0V*FIo~ z_&UXOr2Z7k|J)i%U0d~o&PmG2$gQT+29vqLC6&J7=2`O8l20*he-YOLX1KddZ-sQW z=uBfh?oY#2aiSsx=abSd0oBgUrFNiJqLE6xiky4}!E0plj^d=A&OBX@X#EWG{^whW zqZd~%2vzYa0e5$E9iFd#oUS+5`vPKJt~AH z#ay;L{qtR5{J{+Nbf4M=GsWSRSvf^Y5Q|9wy-i|`aN23cMJtP;K2pWl*>)vIzSpK2=7OhrNOsPPg^6T`(2FNGzM_-E5IKB(zI~gpsd+WB8hy}zjW<}Y z`zJicb3vd&>imEmM_@uLK2hGT+2^k16}xlis*r!dQ|U8Cu2eQd^5$}h)ccX7gCIzD z-U5T)gXqnff4HByYHVC;#>%_!O_@t^k=WZ)Qws*!U18XVWRXBoTf*xgGTD;gCBv<# zb0hvgxq?FyMIgE0QzmY0Jd8&M}9J2mxd>*ssM%CEx(PS zE3m_t^UEYDizyiSK)bNGOyL;*U00OwHK>e#iT8Y{+OB_I>CcVGUZ{sn7QAPZLBVY^ z^f@CR=m_f=K^zA{IMgrtm%-LkyZHVcN)?*T*Z@)X>KkH*LZiL)c55Z~J${AsouFJB zoWIR{7DRIb8lxv$IqdmT#)tqS+0?Y87cB8a#6l{V2Hx{Sj2`TrFHnnUZh_|onE`9n ziX)wxdk0oa1(gFw3JZ{&ADP#v7fE@+z<9ovmFbR^ zw$e)RRuPw9KknYyS<6Wo zh+$Ad6Nntc_#%0EnYBQ<@ZO5Or1@<7@;3g(8&^8vaXb-at3NGkH^`GLNX=P*ZKakg z^z5beaz-}#y_c&*#)tnjE0P$u-rf~ zNy;okmE15m0vo=dCbf)dlvHyCzLBj&q2fl}QZXO;uQbTNo~K?S>H~_hxA6*eG$GD- zJqF~mFRexuj8S?2N^CrG{muq`e24DOSjNp5YZDABk+X)sV-py=IM^klHF(!;I~$SW z9B6<}CgTo(=!H)pA?eO%=|#ycA%0I$5(&U;|KR_nM0iq+^s{8rK#<2=WZzQGEc)U@ISXDQR3L2C?Q^`3mYUAu7?BI4Z7H+ek3yzMF;W0ql!^#8wPk)A zw!crt1A=L|z9m|Buv|x*B*!UdjayTx=!Awl15_7qTf%yZ@s$+Hrv#xoF_inO5s5u* zQv1W@IZ-$QD#Vukat9F3?RIXtfqJGm;X~YK^rw&!$ndtsGLPdrIjZ)lj1#j#WtiG~ zUu&?CozzXk-cgZ!mwJ#y+{Do^ueaxhUjF^!T)Nrb@K&hpV>qw^pw8S*`Y*c9b|^o! zpkEg_8kKx|vKNnSjj5kN68oG@PngcxZ1>zo3@Rz%l;(j5&ov!O+%IfPrW|lS1+q8l zJ~)s2lK0ECr$jzjIm$`zJ_dQ@jzZc>mB;5{Rbf8iLT0M%@?#G2oCu{sQ!tqy9U5&z zRsr6wd%6>{LNxDh@Kh(knUK%`Ae0rw&XV-XZgm!YFi<4p)L~G>*&%JP&>qw|XCQq9 z8!tx+EkNRe<@QSa7uF6186?A7Xew;cFB87KRg5zUO4aqC?b%ic*arlpDL!UI025#n zj?YLX1kOZ^3L@>`r96yT*8yG^-rYW@QY<}O+3SO*>KsZtupzS z=p9Z<)}Q*>(Nd>@na#)~{@>lq&;+1%V8w*kcJi>Nwx%+_UP-=sA!%Nj730j6d5?w9 z?meI=Oojh`_rPq&^)5~aJraWKKMS*JAEV~pZg`t@FcO|{-Ik?(>ge?HYkMcc;uE1M z+midBHMqn_P%X?!KgA=5TEBQr-leyt9;)=DosY+E;j9O)zZxnSrpBlN&w<{mMt*_N zuu_vpx}ke6V1VfTWn5h~+12RZK5V)>ZP0WOGF(mkj`jMaq!MW3alAu-qnZmn+=FgX zLNr=6;z1NTR=aLE_=|CjQ=y6yb$`#J5IiJmb9^o)HJ=f!qPL33fsKYr$Hzn>HjL)y z(c~=C{<_bPHNQJ7ff>7ER8%F%@y*rJRZtXI7e}}koh|Ma@d`5P^6RWvrWL|2*Qjq5 zi!f*A%n2S$yZgDj&mOi)0&1;ATPOp95a;$ZyX&YxlZZutB*D-Lb=42y3ZGvmim8>2 zG)gj)0H~T1zMit^&B3973YSf?9?YiL9ky^?h|nH|Bs@ILN9uMyRuLbWDGxE-x@JN? z5c?{DU949gxLpDgXQPR%#0uA!QySbp>XS^GFCiYcWXsg>iALzIBAPUx=Asj|I6rHdd$dB7GZMpu5)EXecztxTuoyyO8>zK1 zGtyk8q_&Hi-{(WUF+2d3Iw#nR+PfHiMM*^w_V)%&$0C%U$5Vf^DF3&uo6Uu`M$XB< z)aL8p&qLp4O4GU4dFx1aAzGrD`L(Nx#Y5P`>D-_UKn(LMHn5d=E)4BW03&0yi>a9h z4RXmxm`<3ht96}F{<LiB*c%c)Eui}Tc8uzgPab@-Q7O%shWjV-CgfiFK?n#Hd@1a zU}dF@t}#Xk)_^dGQ8nVtgl#mh$^6qXH8#q9=`AQE216PiMKD?ED z>tf!nOQ?AyAMWOY71XJuC@HA60@E2d)mie^q)Hsq!|Q=`vY>tXG+wf=x`pX=m^HeL zD+5AxS~~RrN7uV`<`p4ZV$1Ml(+>h>;6pwXqzbPfr?BJDzlE9;_uuVpgz{W}K%E}# z8KHg<&-wGP#YLLDK-+I~>$CiG0o_~y;V6@p?LLo5u5K_+_#BlHp~)A{fwyZ@*4j136haiG49UHTTsonc$2U9OUHo$YCqi2&P*B}AT|h~fSMJfNY_qAiWQR6xnT zib|(M$OZI4(C-DTVxI65fX&&WCK{k(9X4q~EK5_kDH|lpD?~ z#9g@OU>m@F{56g-9Q@nlDt>oMXckozyd7iK;CL-3N{^u7V?~~$OY&q3vCH=n_YcuV zP8OHKyY-0Y#}U=CE5lax1z&hy#|a0+V3JwBujT1$qOiA6V2ty!H8Wm)utWq1U;3$e z4?EA_zMK}(pAe~(lKodCb_ksM_VQz{^?SMlL8=y9q0j7iTbz3QLkeectEzgN%?C&1O~dG$Yd?JN{L6s z^S2-vSCN^xfd<4-Vd|31$;x;zRN=PCFb)yrVe0p&&3B&ezFgxu#4zM;#|)X1PfMUa z(Od(7K<(F4PflDqxv*Tb7#?kw`p|B#$e!GyUcOn$W`GY!n3T2Ana(goQQ1Z3vg&Jt zae+i&14#XZAuJ?PD;dy9SMB4atB477PLJ^3*jl4H##%5)>R5D~8#gKvw0cIex+Zz5 z#j0)bCvg$u00JO!j@Q6igQ`}XXM>KR4mVu=PMmue2tD7y`1n-52aJay7&rMxteo3C z)Na*%DOGX(U{wQY9oUo!@nJTw)=}v_8(8Uy#@h{*llQu+p||5K8lhbCC>WfnLK*T( zv~mw4;}p)bRhBtNxyj&njL!p(9vRDqM68JefL-IoGI6kX@y)Nua#o@4ue0{IM=qB8 zxjYVK$)z~)v5kq`fDiXWiny!J_8JX!r){L(r4eT+@efcYUDtsWclCbR7a9g%I??1%M}03or+M?suSw z)ev1C@f>-{d{fD^W9-g;Jn#~%+EWlSql@Wl)l^f4)0Y0izQV%LtHpvthbelLXQEn; zryvy$TwO9Sbi-xfY8-rUs<>@Awra-NyR{`$F)1R$uC7oo4_X+QiX_;f2z9)xNYyQm zG)?f>gOf#?!abPEX9oJ6^MgLk+#R7D)vDjN4)jLsmJU%`m$K89jo2x`fqcLf$mFTF z_*y%xAQ?zx+e?EX_?MwAhy51dt{i(%1UUtgUBUr|G41&Ru{+V z@pP0iIHpa&J={V~6qWf;yS3?pRxh6+(09xmS8joQxDxNZ4RU&m_|_y42+Bg1%MuJk zc1LTciH&D_2#{D8J9(53Yz?n1TuO$q(Fm(t5QgHf^&>n6vOv(~Ql=A$oYLn2OZiV6 zt9oH7Gz7xJyQevAf!6DjMLvsegZg6KB@pU-zdR7?!CmQM1}HX~l9Obv`aZ(X3mq^f z+|)~*0QMd~DBnVXfWGT6dUDR~0_p7ApaqnVCX%3~5`$}>ZY7Se*Z4B^y?Xw^+5N2P z?sUni=V@?2#wl^qZ$|n=(Kn&<*8}q($3&OqA#E}~fBbTN*ir?Eu7x8UQJVEpk8Ol{ zCf~`~Uq+S~d6ds0E}FCp0o-k?(<%xY_#}W1X%=%^sDxuvPNhG&0q!g$EV!Hy2(h~$ z)z?##ESt_MJkM?)d>t}O@w4d&yYsu;QDJS-4$2^^0wZhkWB3I*HpHMK_Xmq!OQ1V? zdBFWAFmYIW7bKoV6p97GDpT|QgLBZCcS9$G4E)80@>5e+bE8sAvFK@U3cv7tm8r)R z#b9{#*mOjrUYjLhmi`b=y!$%$eEk_$*b|`RldZk_;!!3yNnS{+2rl~bSi1I^xcdAo zpNgb?Tw!oIE@QMNC4h4H%CaTbLYOi_^s9m%e1Y>y`RN^^#c|14w*COFWtn0K`b*W( z+f0AfaElt8^o6KXQ4gSB;RQ0({P@)}CH1b2j(E+U@Z$fq^nUjj^gpWk{{hj6(F$@p z!GM7LFoA$1|2rU>shP8hla<4NpmPQn510R^FRr--IO0I^ebYDwl?PUKzo?bNFNR^yP|KK;E-D#tq6s`0yw=bhX7g_(Mq%>SKaaZ_?} zt;8$Gu;8Q(D6=BICgJq7#kS52dAbJ$(FvL});CM;nPyL7V*&GRqQ>0lDOnrp2WD<; zC1&Q*>XNdoJK+%8YN4MlNmGCq5|ouvRrOH_S+=ALa(I7v+Cquknoy#o-MY4k4AY8@ z)3E7Q902LU#>v#$hm=ZXwqjfyxvU=DC|r%EvL*H${wJGGn!A;WYO4UE7h~)E9&M}5 z%`eJC*~PU(O&9Z0ew*A%`G%i@+ojZlnW~gY&>p}Dhg=REqZF?hi5Z;g!EG!?jWZ+J zZ7Y#HKiiVIkk_?g=1xP7^M*+8{p~EG{de1`6?0hi>VJL4o4p>J$`SA7#??qN4C&ENihUlRc1t!x zoVD9C$^WD&+x+i#ttVqk^e*g9)m^EU#f~=O5L}hsjZlV=>lI(pOb1}}jK!w?=gw2D z;!KY%5rHOg;*M2`S5NN`PAoX9HGPu(BWI&cHjSJ`TW5l!G6hXPeMwBrrdfAuSGtBW zS|^>$(?Y?FdDSl_edX?*7>b?zb)mNttr)lHI>7qtvBYJ^d;Ym$$Wd zOeHe8qY#K~``^TC7K`sHHW>;s5q#bVqObS&p=*}*f#p&KdL-ddAEfaj8$ zr1#@BWV}A^^Y8YD9Uy^5PyWgify(+EPgYG!9OkuIqe`h^-M^uot1#2>XnrL}%q_U~ z_OkbBHaPX2Yxh|?AjwhDjz*z{4nvw4t~V9xMtw0xy0!Otqm?B%1&0y~xhE^V+fh8V zpY_Oher*N^(1A!05Z-`A*VJgZ(SWDPK!=!yNE?7YWZuAS>tr?T zX4u`px6XeJE7x;0r$vu`3}ST^n09JO?uPIXB z|F6ma8btm(rPImG$kfj4e^*T>O&q=dT&^ClK)_%>pg=(XHT>UoI|<*2x`zq~NOuMZ zNanxuaC0*w7gr}U0~32^m;b+Gxud6*d_3mpw`VYj*&oZJsP&djMSu`iSsJ7}86QQ= zUPdW;OJkni^m3<{3aO|?#Wp{#3`09|a{B@e+`j+)$zspv?X+8?-tX%on{&_arCXry<1qQG{Z4?l@B83l zZqEPXxcl+&%HKY|kN5lLV)!Zi%-vqV^Lu>H|KUa>zg5EE@8i>(0^{DFHH|;mcM_Do z-?!r;XOmL--_P$q-ybS{K3;$Br>Ae%a(~^t&E4$@^nX7n#0z{o{dAw&{LGyp{_Xv$ z6X5OrbN~E0d=#&c-q+lJ9^HRi#5vWj_Y5B{(8|l-%lp>>;qM1Pz|Z6JL67_6q1?IL z|4Y*}LE!bFobyh<|Lf!9=15_yeDQ&E+FQKb@8|vC;d=?vXFQDVCiih=t6!kE$Dik3 z3UW^L;qPfUAqbq0`YM_sUy2?Ye z*X_XD>w4_x|DM|_ua!wI)`^%pfR{YE$0+mhIa*XC>+|?JIN90zVNUrRZ}Z;xbiVujr`_E?n#T9OH@oi);mhz*fXlHa^)u1|FZY$AoY{)Ae#HEYDaoPNrEgCl;Rk zTWoi2@Ac#UM*PE@QUexb{NjJZ zPT~BX8~M#~S^Pwt-sKQ#j_gmzb?4kC!MySVm-RcNpRrU1IjMXeTy0jIaW6h=BI#K$ zHZAtmp+uf^(tbhEhOEfU2ZJ&h$iil-wUa!2X$Lfa#=J&c*&m7mWld6e!6l0UO(%Ns zhJs$P-ACtA?0BWx_oQgp)($mQo;wUX?$VpIL(mVU$&d@o zz59q0-TH)$jYR!X&Q@Y*cI+F1#U|+kuuL^F=1W2e3zc;u@pdx=={mSZTm>B~lA10+ z?I;8C@ztOK;5E1vA?tK8OeWdrS@eRQM2TA{TYbs?S>yJOfhmL@y34>$Q&478P~;h^ z29T@|U$L{be{|HrxiKtTY3@l4t%%XkQFugC_H&oOG-PUUrE65GJrUKq!>dO3&PaO^ zFczN1$d(}!@L)IwR2SGI=NM)5i!U_?)x{DZq68~-))+A8=V1x{3P-WqRYkNKn*oCg zB>u}JZJ-d_hv5kvK|>Cvj^NEwZyc(jG6>{^nUjxrmpS3D0sCMG(5J{(7wRikDpnT$ z3@#hx*h)YW5K(~VQqCbp1#dLf#vA6RW}^?*YN_07S58hRufOg^u0t0fD4OPhhKW$0 z30cQ${!@&uP)40t3TZ==#Gr{b|2UIqI`+w7B8!20hh7q6wNUggBuu5&oz_Lc<$M8s zZ~8OTfuRPV68IaB-46P^Z&CuB$J0kdDJNw2Sd%~Sc*$EGH1I$bZQ`+AOy%AMX0Byu zsR70s)VnX6xG=7AwZW~0JTcK z(sWo8G$g31P1-ZID*j0Wmz?OP`Q8!Z2YEWh9pk62lG1=0#MwJoKQ2_%b_pB=IZB*L z5j$~`L}S+L!NGyX-&e|h@{9O1TBXmXu3w- z5K0L^6BCcFFGZuC2uBx`NYl4cj@U7_XRqvK%)_kv3_9~nchTUF{MXx>DF3IG1Ve-A z8h^x`1EJ+FHQli}LnSG;X$)ct8r@uFhfOSf(N{-GSt&Z?&g<7J++6CV8+%tTa2}`g zi1-z;wmesp#$BeU3L+1DayT$EN7Kx6G+n+kqnjdj$m{Qxd7=quEsye7WXCOGEcHW{ zbw5g9{X*5F+e8y1^F%TDClF>yj;us0WyyM@8^4PTvTgGDPAvpcUrDBnx#J6TMoY7c zNRB@wV{rA3xC_A#SSk%Lj(M?D4&UDQHMXovaJamxWE-!P%qRQ+bH) zL@It;uaX*7>cMDRN_o$`rRxz1IpyPZJ*c!yfJaqYyzMDDRIE2iCpW##l*2&Cw6~Nw(3E{tTKO z+$vcGsvF9scJEql5Nyp{mEsITB&Tdy+bV z?JybsxCf{kY>9l-ya7n%ME*i4Bw)e~H2#F6WltK=XoIrYwwJ<;H4p!3rCy zE=D1Bu{*SR#mo$YPH9UI0kv2!vBYVlr?3pB;5XPg5h60iwEnd+{3=1eyvCa4GRV_uqY}T|5nMx(SYM0`29|o;3RPIE_efG%QDtZgEJJW}lBjA*)DWt=yeuDE%B?(3$F{Rd1)T29 zR|LNTj$`}VdF#0LEOH$Id0NE|(U%pwqY%K96^l&>sQ za9((4f725V`b`V0(^V5Wan>=JGLt?LxYBU&EIye|?O_+MYkAPM9GhRf(0vz>gDb4- z#V@{qaRO`K8b4gaf-I!zbeJ`B+B(3mSDOe)s%=%1ib^?oW_$sX{X2?@91lHDB z$5v|}H&X?i2cWxOjaX_+0s=dRMB^uk#)3P5T$FLm53A9F)_95Qf!ag@Ad|Wg%nUHx z*jX!oOUIDqUz@%__8Lo*Q9~*&q+fij-9uOi-&ZjE9x+!rBh2(q~*j z4}L@p1X4w-k*Q_3TZJReo7AAO#IDw47hGoa&TKS+I7#Meo7rU}9a zUD4Ir^26~kR%G{2Ksr{cDLn%xffRn%F?WkYe@@))M$@n=b<|ol>&B2oquEN?r*1F? zp$9S1O5*|w2EO3fCt*-e)n%3IZkwJF)27)3$@1D^F7P#SxOX!|rlbU>ef()A^)FNe znwlW10-K2gxY3uZrUa4)yv)`&IRIcBsi(wLEZ^lt^?YUiL z^ESH&%Tznj6*$v&=|tVX`ari9fFru`Kq>&Kqy<+Mz#m-Mm|i7g(qmvh;0QgT8{T_R0Q!yCI=m4SA07E?3%Ful@lC*Y_??L za7y5#)J*-jNvGUOhkwP@l?oD+9qv{Q*>Ml&wT#TLrGc8K0&|nPHo7t4Go8(biSf!_qTa z;Mv-dp5_+z4o9g4J+b@Lua^Q=Gu8nhuy7Om?Si5YmdT=ED6d3RMz|Xa!PBHp!c_Pm z9D0|4{g$TAty^wcY|#*9R0VQd9BGf*WPE~pc9q@v;J$aI-PC19qe}gm)ukQhZ+jk#V5*d$ zc5$d$iTR6Rww$y@FSJ|UZtga$>gGv((>k>LdGzh+_|scRbbbE^s|poqxRS!fX9a~p z%=)3FFKy@p6>{-rJ9IRF`HsSV{W)(0$}4O(Vk}W1t4KOhcAM7={MicVyJ&DcmsJV) zjuA-zPwYBA;^mqor0#d?PfCRlgK#hWkE!#*Ci?4P=D{0wg_$(C+fjtgekQy&2kI?G zgk8q&5tb@l8sb?%l18hvIdt|W`(pvYdn^f9AGZ=WGpB@iNxd<`uJnvzT|i_5+7M%= z>^H+CA^)*T6?1O5wuObwfrXu7TdM$TKHe25sOBtw1L;h2i?!!jjf9*p$!)I&OOnTa zS)Y_LNt&a3-M3ws*@aW&9gU($lw9jjR48^B54hrmf zp*ll3s%U&$i%+$Lnn^=YWJ)ZCs)TFun0He#-zZ=(-=H<<=TEifUdTE>;T3Ko18{z$ zJ=VA4zcW&PP(3uL2_N9SO~_Ul8hF!tetn&FIs3GCnrrE-YPb9-OGaY#d9JC zD!}nlMQcCDFU=jm-S%z z|HfBHgw79~cxBh2;24|ME-%2NF0~GqJd}7+D?34_D;kryv4yD@NKfWT|LhNU*ejG? zarUn=Q)~$9I)}%p+Ru=q9IoTNvMYv^nc3+NsraQRM+^#LY_T81Ve>X8fp4RyQ|zWh ziv<2#xMZlOD3rN|aN`GL{^79Egr+rTKzHeCm7QZ8)hZ${I6kAGWWbBVzGZOoA`k5k z#2q9?xU$ur=gZaoxez@vaB(d%Y=B5FDzKDC3{-d{l=;GM>IHm9d1}Q1By}M&Qo_Kz zkoZ(q9iejinhQDva1G}=l{j7luxZn%qR5$^ z=W&lJ*$QAAQTO(ZiCJ*@8|(6ZOlv3iVvmx~6Z0w|>0$&&q6(^kEGf)D@(Q>$lr1`U zl4=@wYF5!3L&$HY6ZMM(S_jg;@O4q$LLZ@Eg)FsEb%ruAwu>)jdi1oLFA8&vaN;~x zkShX&kCcu{-h^*)jrpeXB3==4BW}a#Un9xWb?m&FRGmfZx;3&%^xdd7u7 zRE0EpCS)}B-aEYu&k?%#o?L6X`NzHTl)OQ?D*#Ym}! zK3j4EtO~3e2{Y8M z&a5*GUujO|C1F1N@b=Iu5%zB2sGtdpw7lC|O)xndZB#~n;|EM`(&JreT;f5IEyNgIcdm%9ixd~Nc1KE`ut_C^kY5N zV)>=*eVg}R6F<(i5Ckis=Ci&%Plu1GK-s5OuvIsVbO@NfC)~}$#5vnk6@#S)0CeoI z`;>EZex#m0zQoAL&bt`aAnB;H03@YYsB&Dl8L3&Tsk&|?fWzkxmK{pqc$N&$0B+kfJtT}pk1lX7H3#AI{RtVHb(BFh7 zaDT_BI3k@y{~`m2g(CTveqsze_EZO*L@7sePfqMH#CnI#2z`G=J^BUq+VQgK1k;Ap zTXO#{`9NnhMoxg{bx_$g+Qp=_Puyda_Xr3}DEfdIj;rrOEaZY%+$^away|G{Q1`{~ zGuVnc%{Z8bS4Ztl(5=)S!c?A7X}J0?p+E`Nq8eI;praXhGgR9l=?Ai;LC86HU3ltD z3R>1+#I9vjv4DX7d=1J1_e_s+4`}%qon&mq{p!D8u$UQ4D`h&_YeMYWR84=^zfHNR zr}uK_Y1go{8?9ira-)up=iP^X;1G zL7i6(RttgU-)16QyqTAjB=|HKrkaLGNFIxrm`7}TYZrOLH{WgXPl(c$>AOYz*7W z6WysxAf4rjCIJYP#jB0x8FDQwI(_?!%n1ZbcB#a2s1(A+a)5b-l-NEzd4|G7#d2`M+HA zN#;u1#0!nTV!g)Be_-E@yZ4hA?s;Wbd6ow-0(=@~ijnBqX@h33qt%XfgJ{`r&!b1V zBq?1K|1LpD6KoV)NEjpjPM%4WMNV*Y=M`U^DEJW!_LOT9PD#moNire&V$UKpD_nwL zK+bd)4~5m$$xx*PAq;V|8wC(zIy*x{rs<&5Q7BZ_>x%3X=i6#+o)T*qwLz)7t`j)H z$3X%Nskuz)$r(1-j>-&o2#U^qOH!2KkitT6+nHgceTbjUF%q#M10W^7X`VXRpZZx822dY{tAS zzY;v#qyV<1Zyf>y+nYQIqYM$!C~!M$J)hZ7YH|c&5ON5ez}QSC;R|kPO>1V}<)zhM z);|V;M!G9Pyk|sq%-WuLfy9RB$q9O+$r$;SuE8(E@?P0Wr6}%bP0xHtQMQgDg+}F9 zq^r+qT(`t~0irKq^D_yn`X~bXZhRN2Cz~LWdEp9AW63@Bl(I&7e~aJ5QgX|fwX;6~ zVTI_)T;%72m*HGDrIXvOP56lM%FZT&;cH_Tnd$`+VQ~3h8J@cKADrMc4)l)_-~=h5 zvV+dqk+d0{_>e;8nKn0Mq~{^C3HY-pYLJub7g!>1TyH{8JFc^I(C$(R zrvYZk*a=-~lG=IXQAx@C+pTI+3E6K<*rC=jjmfyUs3GoXEw1!N!_aN)meI!wu~{=W zr~>g98z_5jFYX|Gu}|@KHRcEzt{^qeDLR>uTe*#cBRy)(jmNm$DCqJGTD$&U*3Z3} z6t~DC(%Ivz6f3a%lZ?Q{;sn~sxX|p|F#Xha({kEJ+?R(-(Vp1krImKk73+=5d-26D z_~y!_5bQ;*OX*eVSB&fQ)uh_bz!@yKwb2K%>}?iX`s( z?X;EZ)FjRF+)=d(hxbn{nz7w`JFrdo71mIZ^5t`FkOxz_q0Ml{i>tuhAxDXBv%0yH z7`m)if3mr}0jImZs_U7L^|n}d+mN%S4sOm-JD@2Ejh*d^*jWRgCYz`{u93p&DRPxh|LJU! zeXpKaI>ne>xtWjy1RI);ioSg1Lnldg&au%LyR*MCF~ z13oP^gqduxo1sqstT^>5UhDA`6aQg4!tzcKIrh!-G{rVZVSR$)>N<4mIUY+vB;J^g zd?1nf{bX+|C2E^Dd4I8@m!}q(f;BYe`PpIzS&Ou9N}n^B9x;r)M(_fhoVuZkL*S9> z{H044#C}oXBYpw<^OZC)qv+ConJHuhH)9YQ?;NYXT>o~E?TpTrM=IDHOk!iw_JzI& z06bQ3=eK)`=@o+l-F#c#T7k|!=r5d{ujk;~S$Fl+CVm9A>YC?Em21~2HV`{H#kf?< zB;hZCi#2f=i|a~wZ?LzQW5ZE)9iGRr6OyacVb$5ny7a;hdatPb%yH51HK|?#XL1@I z>kXSEw8T2ioo$3cJZ$fnt6@e_vl#s@@HAl`cS80`9+BZ~+JUBeUaC2fGwC48i6y!~ zhzOe==#ebHMNNMO3*xR4yj167l4GWU+OmU$lb}sl{RnBU__s)1xopekU{?1j=j>Pz>kQOKAQz2iY9^y#5cU-<`+$`RL z>Gn9#mRKbWaMwCCtuwQarHsfjYg#VZS|nqVsvy7~0cMF1NuRX3dp>8XXKw+JH+>je zGD0sR(5W3ZF-Qp%Qi<(*wINA1V!UlA3W4*@ZSA8L{U8x}2i(dtR-*`=eMfr4}^055Brq3d#` zD|9TF;;)k%xu4DL_ypO){1y^rz`@4tqa!U3r8p$^u>x%L1mW>MSJb^|zVny!i;D1^J*tzi&S_|>iKo?!R zgRYk4xZnVW$8YMLXscXw?$}J7(dVFgKnS$Y8aXmP735SrQJ>-!3VWm8*YaDKdREKc zx=Nsu7Oev3$#!V_(~#X?JbIhq^i9<|(f$Cu3pMEI3!^AtiU0XqOqUlbl0yw7yGaz@ z1vy>0Hp{d&oH1J}3Vmz!1N8SwY}X3`y>xe__MuuD(LYMem`HwGd2VUf!;j7sn#7h$ zL-(gGe~%wU^Hpn73H0lYZQ6sD@p-H)^}c!0?TG)#+?asmHn3;9F0-xNFz$LGo_xrN@<316H<# z0#F7$kQ|FrS`ZVH+C-AxS<9#H-4M*TtlZ9abnY;~ZfY27TU-f&j=SrYjrW@r%O>Kn z32P8g&822vGPA^i=hZ%_d@-oH4Ez$jm`jC6EX5e;)>#sc7hI>FPJ{?+W){(1ce=gO zO2{ng!BEl4Ah?UaEka^VI>9i_o`2(cUD?PR4oMZA6_d^Rt~3xeEvBS#1v$``wB4pa z>-qI}uytKn$F2UnUC^aMIyNwKuqbNQGYNvR)L6{y&=Rx((k*G|0Ln!Gjig{@_@mZl zPARxoJ^rCC)jCKevAiwYuQ&YL6ur*K0VM7`2Lv&p06B?iW5em84in4GQGZv1b~~+` zDx3F1aNCfrl0fESOT(6eas)!H3>XLC-jZcg>sWsQgnMUhr*R(p%PQxy1e%gWV7ZFE ziax}+yGp#lbzjPN2gQsY0E!SQ#q0IEn)-eK0s+L!z>STomr`F?LEH?Zf9m?#&d=ey zr?`~Bcjy>MstkG?a%?J{vzqKrZ_GgkTPk`xD6;EPUKl$24x=wVSC6{9y;|c}GP@vN z)C=}Za@4WVj>}GxF38IeI_GY34ho-CYw*SZb=C+4gCsbH37I!X$`RC!hsl?|VPK{Z zXFqNn+XjJ=zKud*VuvF?$6f1&e6t>3Qd;3dW0*ViTUysItRF2@3OHSf?Wyj*S?a&5 z(i5A;0|W49*jRcrfp^KhBE3oLE4rTqd#~fq+b3w{H%`Gp(VO zse?s$+g3ZA^Cec-edpdQ(Azt46)wvoZF{)2rDV(l8%>%#VI$2JDq~;|Vloa%Mk?TJ zNbFJhB(vlWz|nX&$F6;74`NHW0aiAC%YoN!Ul;*={8``x0)S}DhF2QvctW}`skRkY zeY*{&<}O&DtXPkcN>RkCQ~ZPiOHaK&RlSG1(1EVV!|pTMV(y61MP&@Bs1}^54y!4| zkWxl0ED^#~m1rG=8kLb0J(@)ljTwszey9E|5t3fIJnwkVgNbyLt10K{8(Bewq=#tB z)S;syEHSvJQVJpF75Ont=+nY>j$q_IaC>)!nUw z@o3(fMtS(vvf(U)e3!prN*sV=hOb%HM;~(VH5j5gMpcI-p1o;Gv*E=w%&OH-^g+ez{=2k)*@t6SsKlEPy`ym}$ZS*V)g z

a(8+xcVvZk{!kZ^RKHxZZnvN9NMF!*z2vU&CS+tQ`BokX*VJllUrdIc9l;sH)s zWOREu7iZD=LAJ6*9Li?u@+%5W(}LYZMAAa0zu#`r#$5R`t*jAI>FlQL&Lt+btF?D_ zXSU-F7~?Pf+nm4?y}^ecjvBh+LPcN03UIIFEV&Wf!gTXwOQg*_^_W4ur~!#-_nc>J z2@;7>>9hUNu~{M`Gi4x}1E(>_G)q>{MJr0=$D z`NJ`U1G`^NGcZVjh3~OGI;MaO_7SPm*})H`R4$P4Ulr{gboN%(XWTvVD{1C>@XX~Q z+A3VpTB-4)@WjkXzO*`zryi0;a0;Axm>iO8eC{!u1H;O}cxN~`yXT;o$ z#gsYg>lmA395}<&O34->sNSMtnW5xfZqRsDhN&Fh8L-ya(i33L+?@9E)ZfWbWp){O zJT5$ruTBpL3i+x}ci;0On;SA_KwL8_X4k?L8Cd@ZYv&lGNf@=~(eBtg-mz`lwr$(C zc5K_W@y{TPU<8u_R$VJH(;AKcOJl#bBl$hEXy9nB5!PA=Xhab#INFhgGH z^mQsGd#pr|;VAhx#+|QZ4aH)-Zge_7a@?w=)v^(o++k~Q>jC+mF);4~$&mx5cRg%9 zR%lZ_q7qCr!Toq60h#Ls63ESc`SmclB=;Hk< zGS^;j0c^GJnuh7*)Vk5my}dH3&+$XY?VdB*N2jah0B$w;8lb2B_E~a}i$Di@O;@h^ zXz9u*OaEh?{Qm2I2j58nmDlm#;QOf^2#Eau4}j9i)y~=Fo2C4JLzIiziuSu4ze2ZP z&^%Y!-O#v;3+zo{qGy^df1I2fA1K*dvqn%4yR>Ot_~)e);<8k-(YlF0NkCPCh10iQ zbu8~BCLNsHDACZ}O4w9?0zAJt?}rB;Hp<%6rn3Xp9;!8d_29nIVv5u#dG$dWC0Aon zKxNkqV{sXt(E2p@-d5*YdL=aGs#R8Ekx3xsOnE*O!Zie9byR7o;`^kU zoJ9cn%yjNg<&7`(u9VqQUDKX~e-Fx!cr4!MAL2LsiQHX)wJvVCz}4I73oUDSkYmRx ziY4Gmsg<@S7}+?}QL92LWj}buG>B-4I9O3%nBYczZcZGSjQ)_s5(77krsiP1g>mNE z#lkb+r*<-TjN@1&WtumwimN31j;bUZ8ShYKbc#`3b#yA0##aPXzQ(zi8?dhCMg|gY zRXoSokT+KFi1gS*FUH)`IC7x@p}iDuG7B)!fRv{uJV(cZpSsf1vlk*fY5rxqZJ*p| z(;zFO&oP%v6=p50uQ#jY7k^$`Q4V)(xS;bBVIN5NBg}z9{^V>a){D(qWq&2G6zG=Q zZYM4-ilOs6ZSY;u)RGQ5M`rOIv7}Tp!BMS}E)f*M66E!Ms#4j9rMl z^OfACcQC~OcpgyCgiZw-`bJ%pWPb8UhMmWGtn_4@)|m1XPIK&nL=%IEg>>M}o6BH< z5DQEcgM6lg4Y7xiY(gfS- zh0Bewf*th5zBby6ND_#>C?!dP^rTS2wxywW(m=}Qev^u{z&J2XDhfT5hF?n~?4-S8 zyc}?kZL#w#`U82{{;XvP?*~B?QvEnKcTPs4%Mz#_M;3X6JVta$Mw$hmAwH%cbV7`= zm-nV5Tp|^~&n*i1ll#jEBTvXgA|O{#92$8K#5XuLQRLEdT%GN02B|Mu5JFk3aKq;a z4CL6|H5o}0FQ0x{QK;SWr(%=|2@fz0qJvt;#6^OA&4XdVn;U^84S>LM#f=5Md@Y zr-(D@m+EASUox}^o;*P|RG}=&PNRP;%FY6#Ji$B4KcdK3lzw_bsy5`1D&gjAA|0-I z#YB$1Woc++UXYlEJ&sbx)aJBAzi4i$SFAb3xj090N&X>iserq*|F{L{R3n!Z6ggPC zMPN}e@al#3)$i@+|4NphA7r#jLV8kQ&a#}o zP-aE^u1}B8*Hb^MwvBv_?CWM-oV29XYy%g!vt$$Q62ii&Ros{?x~@g6j@2m)LAbG$ z&6UxRqj|~RslQ-!2fX1Q4klTia@7|~RoR|0?1c&Nd(p4h;VW|@%iB1h({Vb6o~ z?`)Wgmo2kL%5SjY`mcJ>{lGkQY|S{xQK&IFrx-jGOjcb1)Vmm13L*5lboWKiKUg;k=dUB|kYV^Oiq{weo9GsL`}#*URoR46i#Hj(`TEIjmBki|(`^&5 z?6=s6A>p%!2rclsCqsW*P3x+NJ3j^~>Ku)xPwFJCDw2nQfko_bPzO8zmWZp>4a1^X z3Mp}lYrGZ1wy&BD6iYwl#U!Lv-WIqs$re{Y%{3trXXOZDn>l+an<{e5m3!j}0&oXU zg0VP8^T=1vFAkDacHKPc^QvCjmQGLnG#^15al5!LlL>cLVFllL<&BKW8=l#k0z z;AjKP(GHHVs?#j$E*XLmFV6c_Yg4pO=+xPXnh~dh^+;*qlxwPA^H|sNAuC~OJr%hvC;|EBpb(?V1lrV&4C(&eV`SD*5pGp$ zf`rWMC$5NAc%sNiK9Zt#Bv-^AAsTDysG6|;Xm0c|)e3*{Tie>hp49Yvd+Ly!nC=mNL<0T79b>B zn!|g>(1!}|`tJ^LaAOxaOH-!R@00RH!nBbVzOuC!@i%(C5revnfw3WhOa65{0S9_| z$*I>^w{7sLXO3@aXr`DY4a5^Cyw#jjtj&72E@n)+7H;7=mx|Nc-Hdl*M+sXpjNR?_geIwuMU-uV5W^ z2sujZ*Z>Qzr69DGVH6*DGbLJPW|H$vS^0`wVjkH#V(z&a-9T-1iY#ge1sZLCEzF|3 zym+i%Bt&dINDX)LJ2;`4XVW=SS;$RnX`VsIkqidmR_KbXw65`3RPgQ%2Jqy~$Mx#y zmUWYdhd)&aEjYMkzINu#Se8$bNiZolCuz(I@eV1OwX>$zxUsR6@e>c$RHSlXZR0n~ z3@ZO1V#r17hJp9DoWRAA`>8WZzz9jdiq$qbs*Rv%)qnD)XCqCrq?y;YCucfkp#8G3 zpdUdcgQY_tR3HYh&t`DVjnLoa-pJS>1dCgaS_)fFlT_SeeNE=~xZ=&5;mUIkn3%>F zSD<7MXA)Dhx{hVP>^wfmLa}4C@3ZE=(mrQ>=H0%A5rWi}_(=-v;aX|0mMHKLGsYNe zsd;IqFb@6Ggt`x;@V=2wr3T8H0muL7<7al-V~PIF?xwAE%jfp|$fq?BZQdGzRoA(g zBEb-)$DcV>LwDyUhoc+A)V71zcMyOUf@KQ!qrhHD52EAB)*bI`YwnM?y_n}T^E&*= z)P2HF?^@f)5#ETyl<%SAdpe1x zj9F9jUAD-Mnc=9#h5@76c<9j)H+RyCXtj3z>wB!3+T{G6%iQYO56m;D+Z13qa3ng%-~QfH zqe$t#&~D%-e}jCr_LJ~Rt=3Kqc5XARcYZY#T4-oKL-7xkQP3d%wch+dwIe_6?9}Wp zhtPXs2m|CT-rOdk`(KGp``c4{+Mk>9qSf=>Wn`d(LXSv_HtTq9`XXHCydF$NOL$JH zLg57P7Suig^2jwsznX;0@_heNdrVNuSJv%+dDvzf#Zn9M@$S=O+#LD;M>_N0RC<-k zzViRGN{|2loX$9z8XMZ!=)0R*np?P-n&=x@+8H`|(iz(rIy+mMSsEMuhp1WK#MI8& zl>UEYFj=Pi|96F+0$1_TJQfIuz_&tA@jriKV|zO@OLJGJ{~C-low2jq{|KeGW9NSl z_0_UUYc_vCqCL&#%*T~6vBe&x$(B(Z zXTP+%@9WIrU$0e-QMG4v+nF_m5H;DkXF2T4W|`ZWefbo%EZSPNG5j)r;`7?LdX5N` zO?yWRe^R|YX3Dx;dQOF&W|3^j{AA5AU+?$6J@!hxK#W?vuV1|whoE1z18~x%MUB4x zXsh?lYsK@`{Y|#MY1BlmJkhjj6c-_f&_(lR8wPFCwborlGP-UNUU}Hq30$)$K1*AwKHiRuIG|Z#MAQuj|B5&&Ae0N z3o+rDWWYy@EOS@?-nTtzKX%i`ZE|ZK1syQ|y()bNG@aYMC+p&MYlxQ+s%6covggQE zvzE{YP4>8pZTF~$tNFI-2LYjy+natf%@>QCMxAS1*++ge;78NP%sU6bfk-=g5L?># zr=TtQ8$rgop<&;Z+uUnsIi4SAmm8*Jy=6cSxk!jAn6NmqXtXyvFaw+CZ>sB>-FFPfs0(UM1nD*y| zI9o=KVb9GnRTs)|8Goa$tQx9HjZkuaO_X?0G1~(_!F$)aZDY=5DcXKZR_zfENC$k~ zSaU{AB1w!u3Q0v%773;AX0g=SDfT8S?y!qtdW4)U)>!!by z;AVK$4XB}(C;tA-fc7x06&j657eV`IH2>T^nL8ZJs<=OmQc3RD{ldI1i>8JC zTUM3!!uoeN(5+pxb#c|CYt?;k0ZWMX&_70l64yhvW_s$)EzQAle0*LQr?T-EP;r`YvyjtK5>_Cu<5r&O9`a;QeVQX|=$J`pENyDqU= zxJ{?J@sb+DBL{3Cxcb+$B)&gyt#+&ml3(Wjw7Z1G$GeUd<~a>}l}+0A$^CbH#1nE4j>HGHtXmleEP4pR!Vy*dow<7Bj^r^@0m@0v(v<>!Wcec8t2yjfXqQ zN-K7${La&~oJn&#gV@ww2sCZ!OTbPUh#QLJBQroXZ#3A@(HgThz~s&|kWYE2J+yPs zM=#&5$<4M*r&$tLPHH6|)?D}7qkz0^J>9o;!tA=wv!oUgwzodhlMDaE)5wJk#j2Ad zAz`iOx^FG6?wW$`H=QpegIulYn8(%o4*syEUm8;G1D=@&TH46T-UsX4dpaKT?h_z5un-1z>^fyIs{Lh)=G(icy2<}(&4vdcverNu$?a`(3@`1K^EU7X#dpXL!a z&tZtUabhX$eFUg4(PGf zcr$eOyPqKLuVeX+^nbN0uFt!hjG>Y$w9&`yG3!gP8oy?Aw#;>bL|ij6Ek zKL(YsM~aOzoQH&Je4tEgm45NT5kZ)yC<><5z)^k*lAB>-_|Mlf%SoP)kmQ^|Vm%h~ zPscakb-cGb`C{YSc)GB%msv&5e!cJMf4w3eX1b4Yw>}EO&{$+?o6@T4KX~6cw1j#Q zn-j@9Jtm9)__4EXbRJ3sKNu=0{llHFksms9sVLoU z$&WjL%tSRB&n8&j^!tngkh9!8z3kHUwdzNuCJq7y{&OCVDtg(pZ$G6OGJDhXZj_tj zXU&r_yOt6tV`y_aD70;#Se4DVJ6sj9RsFAk1qfoSM3{FmQrCn!zqq%BE$Ptyp%aJ0 zMy{Q?jSxk_%Y1|4rN1yo3j*mZJT9ia_y*yI_xq(01LlNJ#bohgI7O?0$V_&#-U1MC$pCauJ`U-yr$f2Tm963HPNM&&HXWa z;W5y16_^mTigIjwPizK|^Gw}($K1QUc29DKAWgoEo<=AH)QEQ!sdyxuNHv?$Y#em4 zP5C|`^JO`I?xtSP1lqGqBtueg0%sT8LUb07T9{L zeN^%Hv3sa(`GjrXS9do6J?7aA0((fXHDOK{=@Xi>6cT>Rx+q~reeKuW0W9O|`u3FV zIz*VK_0l>3Ik@_bieNwVe?uQBGOhJDm;B&?o%IZPBVbh^z*l6*OQWR$CFLq22_Fur zCx7!wc}T&el3}if!@)i&o?XPG;?2A%;1&uQf8gU4c$XqzOxGi5*e~0lX)`_43}dI$ zKuvaD3#A&njyuSqG23GF$&C_&uNHbvMkZ{}-^oo?K-uz|;tXnhrIP+ryD&`1qVk9Yz(s?WA)hB(Dnq!Lz_I)<6M ze_nbIHWeH+Z1+yk0mHa7Kxu+l5o$x_q*(EH0oE+}Q7H?_bS3)6zMuvEU%^vqAyP^s z23#H}!Zp-C%wVO5))%RT=r@I*XU12sUwaj@9k0z&Z6v`WuIX7H#J3ue8dYrdht+A` z9{FPFt{#wy)y=i&gwQ{_l3PNZMb@YjrxKmA?=Vd$UeM%@DOHxq$()*4Hdz*W{U%e! zfJQwrYZ%8E2!o*-so4KUnX1*d_>9Iz3nCXr%^~P;&0tA-UuZPw%~#2huj7B*vnfDg zVbHZx=wQ)+|LRD|_Ce=^OFDK7n@A_2eEt~tGbjSw(lp<>)@*;oZqo{3qJRRGhF|Zq z;5Bf~xe%YqD>tBt&T4~ESCax2c)}TTL9{a)UUgzLKb`;76$S`pjfU7d%#Az!eVkKH z7}DM=PQ?R*b9nZA6bCE)xnUX5Wb|Y4JsFR5(CVMa3_H^zq8vCctNnXg>W5D_6*O9I zmDFyhwTJyG+Qh5|w<#mN(oCrhsbNqEnbC6mh`6?hF*Qezz3@LNkDPiDE1P7+ShV5; zBU)QKiov;1T2A|WnR&ENLd9#`dTCYuR?#ggSwJMSd;ygJB>ZoJnny7hy29Us;(mH# zG$G{CAH4-9{dIT}@C#8ySDVy?&W33Uzp($5WDF+6UH~8!`o6SMf_96gWhSiK^XYdi z_8_1shO_a@XH@uWttLI5jYv{dB7g5Sdd+Hi_xjXR{RkGV3<}K!3-|TBOX9tG3EYZR zYe^sAoizmYCE|tX-81*RORhFi__@vNYOR8*2BXK}&<+K28CG$HrZl6e6EBYrv{02c z%S%e9W4G3zj0IrqTHwOf^&ItOhugUiwAaizWzdR`PvYF4d)up5b015Ei_uAVO9}n< zA=|Aq5zn>J6td8v((zBO&N^e|#Q#0{xrY8UyE5(MNWu~pfV%Htj%ZJc8b~V2`AjAU z$pDs(bTLJ%d2Gg{ptuhRs~xj9zh-dW5o>BYG|I$2o%yAY?yrKZEQeQpH^uN-ztpRI z^=i6?ke0SQWLbg6I0t|IaDVLDg)_N@ajU2W-V5DBs^jnRDwWa_M&m^I4;Q2y1nh20 zrMi^z4BqzkKF8T(%;T?CBuEyIf{*-i8y$SFt))i8j;67dB4(8;(g}(^vihte;2$Cv z;ej8TvW|^OuN78u!l$xvXs2+BzOcA7;~%XZ1uT}}MXh**UyCqE(abuRilcSt}R`1`3Df{rmgPsC4Wi_Cb zaITCBNHOj?Fx=O!U;R&?Ltn;hr6WnyPc+DJi)GOIqC{Yq?Y3#7GW;p>yTU!&jhDd# zH?dR$s$)w+PF#Y#4J6cv#UxSxAz{Pp|1k50aTIReq23K3euvl5LZlgC%(iO`x~Ml> zaTeC;^Ko&+>@~35fZeD%?iP}*oIUeVXVTcFJhb2|!n+b|prs$4L*5Xd}YZ z`AH;f*IT+dBbvygEwei8OOyoPDr(gGYA82)rmrupz=Jrk3F4{=1N!GliG7j$Hbs1#UD=A=Ths43+mGc zSyBf|VrWv~I%?Jvl2sf)2g}EKB!VoRrQsa;jMsy66IGx?t|2|8qX>FF8uUTnv*>Ua zWC!I8JGE~~eD|lxre_i4ct7HH3=$1FXXdZX|={>+o3z6 z6>JnK5>2R|d35TRKraY*0A10OPh#$h&{s32rD4d$D7Bg+p1_ znVlSX|BbPi{Y;{yO0&{I%)o%Xh{Jn5+FjQEkr5)H{g0NIOWm52*FYJ%sAaCVa0_Y! zcJcJv7cFO>4+r~NNVr9*-YT6fZVofHkgKLXGB}kDXg-~#<tSUa{DT2Q>y?9i00# zg+%ULOt^j5wb|<#ewCEqsGscGt_hx@Fp;2-icH+`gs2|A*JgwAk2l?zHU*JEE|I&> zeUms}W@9SEwJJxwu|}^-e->N-`KcGQ+m)=`;cQCT$UdM=X>TeTwnt??$NXl80!pmv zxAF4dRN~`Cl>zX9M2x0EyBBOsP3XD@0n*J{J@yBNe~$~+ygsTHw7OPKO$Zz*D)8Y# zKHx8HC6_qhVcXht({SC8N$XF=QKi7R$!q=l*L1(E?t+%)g^i{J@6F`w6Af#BaqA4a z{&8-Uc?@hiUsTy|L~8%5Q{d59PwyJK&YjT*b=DpGVGEz?mm?ssaU(yL_)q6GrdPX~ zMkRg$E~lD<>7Oi5XF0hMZLO~5aQ2#Yy{Qk#T{?Jsej=?LaiYab_+iU`%N|=<1xAUA zoWy`$WK&#T_cWF-%Uoe=vUd6XnfjqTJj5(zC5X@B-3#=&J;~>u9MsePE^*hkb_~D5TWI+wwrywRMzE?N_OmiXicl+akuTu%7*s6O!iLnI zW=D+X{NPm1-my1%1ykn=`WC{9iO1utU-|A(y35%&sujIJPx&7HRofwnmk0SW9?qXj z{@gc{X8oCt&_E7+!)$S~Ph%ld;|{JGhy)LXv|sv|YX8Dow@myVcDFMo)4v6}2p z)$&@Z+i@6*unEQ1iM7WbBzp`?!^p|fiJCYAJNdP$_wn7ENX=jK?jX~Al3`1;{vRCu zWdM_sN$swp$nBddv7iILGY7$HYZ`uPk2dZ$>;U0i>5J9$h%$vdWKU#%weqGV%xC%y zNbh{JzCQj!zf$R;$E6DRYUne=DxE36Hoe+Ww4knaZ{B9B>=SBO@D%veZS=yP zQ_)8UUFDhu19OQ(1le9#RJ1E?O#+yI=+c3;0V4@-}`B7tUB#sSp0PoaL`L7SqAW@z~)`dyd z-SVQ;sT-L<1K^mOICL!N95)Q5MnghHk zq0e=;+)%FF&p%`x;0>wPqjp`dO3J7cC3}07=z)iQp0mckX1B!Mp>dREd1v6G*jrq@ z&T|pewnM!HkXS-eNMS9R6D!hNV;73CK=u0Bw#)uD)1^oL(1W?@`ysz95Jeh`?m0N< z`>7KAL=d~kP3%=y@{lbU$z5|+hkthCW$CqGEp};l^;lm_P9F6#dFv36^EkJ@n{q!; z!yCLvI3UY4MH-nBq3o`6KG7A?Rkjx?_%8NJD24R~Xn+qoZ6=0o8I3{lS+9b)ian6< zOQ>EJ6BdZMQ=$Jh1n6`(#QkB&Y7bWt`dbc37k!d<2TdZI$boUrgMoK-lj#rNY1=CR zd#C?z#q(p~6ngEtl82S4jK2CET3U%>{gcwolvw}ex$K9Tq!SScfs}Ox4j|>q1J^c9 zu&iwwQT3N28<4h}+ z!1&9;@SlVBk&yMpamgo`@Fnc8vA(OXug57xnd*;G8?Z*IT<8j_CMC=mzXoWPa~ZK7=rfE~HG7xgQUq7HqCw zyq(RLuyZrN&ruSl)X1r#oE95zvU?)o3dW>F)<&||aVBQk9x8l*us8~;mZ9F2CTUkM z<^4gs{{G_wc2_g3^WjZ!`=SQeZU5%Sz^66s~^v58+PJ_L`}L(w!SF0 z^71bUjBCd`KWFKDxpSk_cCi_^EOw+1%Uq!uZJK+vW$>JmB8 zv3RkHL)kclO=P(V?0FKlt$^LwmE{e>$Mz_LJt1YGPQ(yva|DE>MLy_hZM-fpXCJkyyZ2r<+)+Cjzc>RL3|k9 z8EWv8SHPO(ZAYXXhDnzf1p7B@(=WwZ{&c-Sl7qdX+B|!k=krlFeCIwe{3fcggxS7^ zw`?~?%8c+|LNe&6pnW|6r2`Q^yEs$p=5nZCHRZcZG%XvX356RZfwQ$J^4AC};cOZ4 zrECl-5nGeAnbJZ7+i})Y2zb~G+l7+{Vyre}goD`tqs^-2&*t;O*fBqR?~Q|h2$1?& zEc_Q~&QG7y2ngzrpFoiRmC_L?z>$cu0Rh252L)03&sUfJdlyJILmOAq|2M010nlzD zSyiohnX3C&vQ5mtx>2+x&Hax7jQVABa#0aHblWB2JBXFqX(e928L^;ZVP z%y&e$gPM1P_^+?-q3Zzxq5Z`#y`GKkwsC-}m9;iT~p;{Ho`p&EMbq(;oQt zh!1=nuJeC*K>Ye-*Z%+l=S}l~USF<9XFZ=ebZ>+7U%QK@{x^$=`o3Q;;d%ZgW!|oz zbv-Y6dBC^NJNvHlujfg3exJ9G+lKt^gYMdt7h!HeeO;j@bxgy|6|epE7R|> z37`7|Bk$|v?20v<|Lrl4|BDrN_s$(SegJ&GygS10x~%)!CFgg0HP`q3;OGB(s{8!7 zd*XLnoPPMS_y1VGJYV;JvAxTe_8Z2UPvL)?toypTbN|}=dL5tE|G3V&+S>Sc*9g?| z-al|17WMyJM9lj-SikD|EUWW-Fvfpt&sKfiOM9oochhpb#+ZTS0 z{Ui3zVX^!7sco+YjJN5YkB8-LpNA@azpkOjDt!R(zI``sxJysh3vL2u^^xO4td(~P zm}>j|W*aXszCO?V-yhZOf!;a3Pm5>wS@y9_{GWrs=gB&J^RNBs&s}oh`)J<2lHEzq zX6swemFIrPQ+6bRwv}`8B#?7qo6CC4_vmS6#|>a4?Q7?|zj2gnXS=L@FS*xat9@d8 zxQ_YMu&6VnCkF79dh$K)KO6y^w<#R~+Gu0ute49sSD#wrlK2GJ7bgsp`Weu@;_V#TRoOaQ&%jd<}PkrY1oL1ABLA8Bv z1HHwC&}Xh53>osW@;k^pftvU$uWVuX-pAu_davl#1!-GW)V}f?mn$qFthPmV)!a3? z#a-6kl6cA=!ol06c1;_64_&F{Jvvb~8+4E_d}4Oh!0a>Gao2g=3$JqCQZnYElk`iK zQ#ZVAS0VShaBAnQ3Rd41^`lbAMW9$EW4Hdh>{GE}1?fqGhhAy}o~5-`@n^TnybX4x zlIP#GW}acb;pn(8r$XyTku>koOdZ4qfoS(@v4QQ=F8r3qa}@!oPB=Z@2s+qcK?iVM$M6}Xi#CGgWu{P->2 zShR?~qsPiPTH}hawwIo=j*&4wxqI^?c;tzfJ|C-qvWJ8sA{Mr*n;2GB)@xnoN2i{L zfW(l9x5`w>#1vT~D+^jR{(d%RDM{m_&4RS2#uQ!o%`F*^4`11K^v#=8S$wJr^~B;> zlOOtqtA8dk{g_5|H``iw&vVw7z?RZu6KRT2&zh|pWI}FdvM;^zn*VAr*ZL-QwX&JB zlB3jiL4aYL4J@o-uSs-mSACqb=5a2##iLtV;zz43Ijxeyk`%dMEEjpD>0pVD2}wV1 zM#r(E_UPfqAyee)UqD%QkZ@uY%AD~?Qrjekzf3-wU9#MEpq~{{;ZnX(gDSp&_}Z?y z$DBlO>Oai-3Nv==@MT_ndS}_@Md0>qH=;X3JLb^zB`$U6DK5)K=x-Koxli=n?qrNf`m`R*lKrB~ zBGFnlqdumB-`;3evHK2sxqM2KL`lk@eG$-;d;0Ji4{aDx1z)0=IIuKv=DI1c3~`t^ z--Szi!BX_RiJaRQa;lM|MnId6Hm9+ zWg4VUJL5f8(f@?JU|>Nn-<1=`pFH{fdV+S4wqGaP049qPEq6Be8}n z`<=FMp$9F8sz1YWshs(>Kt=7s7vfd#rZz2Z>>nYEqxY$fLx1(4uT|uC8|ALIgbi$9@ryo&DJE+q3X^J@W07NaHc`51{%tMey z*-k_d+VbpgvDp8ny@#HO3ofKQJ!Fs0R~!%pXIPZUj!@YuXzpXOK(d4e!+??wJ4tPgF10nUHLzve)AG^fV`S2V z`vl_V=Ew=P+$M`98i=2;!dCe5k|>>)2i5~5@pIp^D3YqpQdkM=Rp(2zyPjBk+kl`G zg0H7B#wCVnUGktUU0jL+>WHbA!G8Q<#~dL7An$+(3k(=dyM(5z0(!kytW zVJ4X6x{NYki9qYvD-ju;50J7VFK~=A8Hc5TU#9#l<@OSZmkADeZi%7C(yKoVJCbGY zlznmHE}+~s3eMLw53t9Nn@W8Y@sKRAdf9(Z-awSrK6V0)~!z3B4hE%4RGDU(Jwcps%~ zdXqdZM7d}}zky)$9h_N_MpdoClQF{bAq`^4)U~vtWFV4fub6&ockIi-`bTH4)#kp6 zn}}UQJ6XZ3RH>|_Bkw@&qhum;XzDr=GIy{9(ggwRCZpkIt7Q$n@1aHE1untDlHNDb z+Jwky$_s5c!8Tc&W1QqmHtEMP$;H|UG*QSq?B=1$>2AFaDGU#I$$9gSA3nU+oBeI+ zSGyjYsXq0Siat$is`yd6NS#x#KI4@ZKfiE-W{Eq%Bgf>3_?LeT9-EN|TCzLMkVV$q zYeaDKjjh2r(Vk}Ck&}$sD21&goit|wJ%Quz+14{%aa^j1@U~`@m0+VPw15??orEO3 z8cdXekWR4vU!6p*{@BFn{e+|=65F#q*J z8`ybDRPA+KEFv!|VoN93@)!B^B8Yo7h~EXOF`$7DoLdm}ucK0zBBtvoHBQLffr{3T zNHtDHkbWZ03=&!@*zy>X0Yh4wOmtBs<-yo%xbC|z2-hg$Nyts>qRVErC^LzFCTkU! zOB2V5Dn`2&c(;4B|UPHR=NOKjMTK5HQbNe zf0yHLYEP19@nFt(x;Zb6EMq4osAFgA-$*=3=V|6paX^+8Q6AZ#CAC+rS2v`RI7#J~ z5?Cw=;qimBBnULq#!62;zBGK06PExMB@gkva}|wZ_qJVatk{yU;`E%Pgy0l}xFlT{ z*n8^;MjMY*^jC{!Gh*?ZvLVh}wc=~TOz|W~2{4U<#X3K!^J^vOjo5I}8E=~Jeh;6~ z<_R@FdXz0=^ns@8lN~QOlUlal$ZM?#!k>`Hj?gs;n%MzR(`Uw( zu>DZyFykN!$>j1B)qDgpmAFz}R(J*)vU75D5--ABIOX$2m;O|%Uf5wJDBYjyZ5FeU zizzjEB&3i;3|rB52dC$U>A3OMCYbSFxyO)%rCvnjWXq9_GgM=^WqS&ZFaB&`tN*2t zcxO%=FyB~H!kd#sPzx}QeKCr#!pemg%+-)rT1d=h+Sl|-Bi`h$6v3xv7hczxUM1Cl zZsHMfW*W>haRDO*H05NPcZyAURV38^cuU{O7#JE^)Ckjr8)t<5@OnDY(CM@KhpZxF#oA@Y+ z<(>p?1ce(w(N&;9s$s*iLv?2P@%yoCI^WS2q2YQGdw8bTv9;7cdC`x>RM1D#y1>J={6?4 z$4(Sw>R7vSC9^c3w+wUEh*55oPAWOL5^PRVgU(npCqJ+I_Jq}>ZH0z&VSzA=zEkW# zrNJu(Hh&!H)|T%}q`lyul=+@X?+u=+q327MNRLx!*20Fh*>A*4JP@tZA2{K-n}VZWB(&KvnVapZ^O z%G%Ybq3Hnb$!sY`lFKok^%`I6uM`!_-AiEz8?~dk(Yrlwe)zwXsGj*5UAl2hNI@|% z0OJ&{zB06hzX(vCIj5QXiL_q#)tfUg(0zN&_WJ0=z-%P)7s9TDP2>hqO>G0FYiSwovVUGUt{!lasZ<&OD)M>$Ex#Z)}7?QVA)tN z@lTDtdVkM3i=?oMTvRy%x=UIyER^uMt=r@c6Fu2x`UWaZkv30m>uYMe0$ZStA<^$H z8=cjsTROPTc!(5%Guf{Bup|?d)*iO@6wvR*yEF8YRGHo~wjkQJKb&$j&vu)&fhhsQ zmToYi0(-%vSjGplWQedM~&0y)$IwJ^_P&F0@>oQ_xI5lY@vo+xdVEBrq; zrV%JA4%x{vSl$^cP?94CjEm!88WDu25tF?9!%`SW0*>M(t`FC_3>`ETjPsBI3vQJj zRBIes+Kpn|tu+244DoeyAqybk5=qu5khL*!q(R?p<(nH^N&l6kBO$4cG-}Mo(U6g8 zkrlgj@GxHmS7goEumuVHKGny0Ig0<5)rp`C8luyqykI$f8w3DwhX#@dr-k+N;%Y7G zJcknq7WuuRsSp;yFGP=@ZH#D5Mm#}~FGunO;RbNb4rRL4z@B1AW3~IRQ#T@cxU4lJ z<2E#1vCB}Qz{p#Srbw=uGhTAtmHj!E<_4G=nuTc*Sd&m#LoA7wS9AS^mTYd>V0(XZ za)9#ACmYCt6hu&e_Z@Jnrjg88YhA3TFWDsTScpjvRq-DhUi#Tqe+41uYK8Q-_r$o# zo(*ocBzQunv7oPPY-E|~>DZEx6o^z{JL8S}oF8K$NjN$d0klV*7c}${M8aMR^K$c@ zq#HhT z9*V@PFp9a6Q-o5{2n&Z5tVQ>Z1?sK1t;vT&cTHY+yOI{CS0Y0*;7AI>l~2QZtvA!>tJ{zw z01MFwj&J`DexjYd8aL>Mj~bbKzxly#S)Z-b4$_}Ng+Bp@!Do#Re_OgfGo7$@+>H6% z#Rb05B|0qRiY*VCd{_#z=j5d2HdY@}1H?5^fZEJP)=qqh@-RjQA!?}mmOC+SpYhBP z5!5*&kkfl2U4_L;&U@Wsf)DFR?sgj-DhR<;v=PqrP{PFztLz0e163RXhg_^sH_o6s zPzI9*H@l}9Pq3>1NpN7HRjga5fK+>?8jaC4D>tA^ks3^?~+Cbg2wo)zRwWm*04Dt1$Va8g7?)&0JF`#%n#$ z<}vwF2>t!lz4rpltR{3WA0^LiHxQ9jc@)a$Ha4&v1}9$SnHYuw<>D(5{4Xp$Z;KwMJ3~kGE^#fnx%4RDbQq@K{8u7C zY>&W$>Sdh86)J*hZnW%mzRzO)HU^K$N8N=5QoE!ApVmUbXJq`>T8~JC#oJgC;%H-<$8r|m&bc{P~q6Q@!w5Ddq<%I{>`^hq{y|%*+Z7?@>L}1 z2I)>>AKJ4MT#3ECiEPyxsUr~313+FUD;moH%>q=#vYVWz0q@!KJ{j9V{3Fu7=l6nd zz|A1_TyGx^dJ51{?pBRjI)Lm}U@qz#j=-d3(b;^7gXg2`i8Kv7sP<{@&QIy%&^d2% zf6}meo(gowAh1qZh{5|&AW)>sbU`OTRfxU<*3;;`-@k|8mX^st3QCXRzy>APR~~T; z0A14EM{!BHUO{+%0QV)ylx{Lt?oYp5BevVQ(Uiz=tGnb3s0H#>h0uNn77I!?;|ylW zisjWv7&OT-Et{=AZY$E)oUH^bG5KsOX{Je%Xq($#l@=pTzG?H9kMI4|7LrOnp+WBF zBQUZxTw{sFxd1nyA)djEY@7x=Hybrr?<+}|mm&mUo8!0rJjoca_dAgj%(5zzIx;l* zbZuBb;KPUgpjAAwNdPsF2R0cW(p(Ebrav)rz%K+{2(HVZLUw>hGjL;4gir@?GfiN9 zjhtmI9J-@yR3S;TCZ{oX#F&)L7P;C`;zr(+H3>K5dI{Ao)jqOpS?v<)l5W)cOy4Rw zhejQVY}}HUx+Tt_O(2VL5ZIN_lK&$o+^QM86H%eDz|RdUVUsuJwErOQx&e@ON~)r| z3n90_b7*q{mz`lxpWpk5agmnnmbtgS_)PlP(ZBY^dp!ND~kLr+EhKyCO*zrbxUtZ3AsrCps6ThU4KU17+~{#t}{ zl}3Opur>fePE15HLpT=G!|RWtEQFkoMA!n$P)G#CbnNe^{D?~z0I8ZVmYkP;np8-P z_5j*Rpd<->@HkG+)$1h$rZ*hcA<@%7+LitOh;|&_4PYn~J4Wg9Zc5+D0@c7;VXEt~ zc5pT)M`u_%OUAWq7}@#gig(=aWR`RmZ1H2`9f2e*$aZ#g_TR*iDw@r0oh298r21|r7(K5FG7QM6lO>K zS(p>f?0i>`T`zgXC=3S(cBcX)lIHDtbTxJpdYY<*z14Gx(8Ji(XkQBdjx&T+^>O-o zf}vVDV2?rlxVo=^JK0}br!Fkhcgj!H;}a4GY358~*t!MVH-k*5*u~^eJ5v93w3o@< z4_F(9%-&o+cXJv!6KF*k9hQ)uRYDv*f-Rxz6|y4$ImO>wAEyA4B$0Qf>{8;xrQWqJ zbgP6ULD-@gCegYqjdG`IL27V1$WOdknsUR=in}6tFYBm>Z9^o0SXY=jQVf-{+s&c9 zNq`p2=&)^md9S7!3#l;=a=5QYS3^h^ouiGj_5~S!7(!v2rh1v6*jMN1S&lHZ%;(Ku zcFi}zlHA`wkf#IHsAEYf;vqQF)B<_3NQVR7#V8=-KnxA@^mV)sf9xRvD8iCnl?Jpq z#B^he^l3@h4k4XMf3yhY*XuPKW+iZfjWm}Qyd!A-BNZdJ@7^fr+V$SH4`hz)Pe=Q3 z(ks9Vsc~s{f*3_p5%D}sLZ`1hBEGL3v~mqV6)}JWI?;BI0q#;c0XC-$kPI#PrH}6g zU0#8S=taO4r-P3n{LDikg7#-*=@G)0GjUGVtk>6b$Fs}qLO76;$G;)iDSNbOGrgYU z3IJ6GlI(rpPTh%XNiS@qPsBM06#Kqn++A?8vDJS;JN>-p|PSaQYTG+3EJ5}RsCVwNgy0BwZ~ zqCQ&}Mx8G4FTC)!Er`zmPZ~%ZgU=?VA`;lfNjk+Z?b5>2mH;EO)1r{?WYw)<@Gk- zpyS$|^*AxL`fgb---bMI-&f@yc$-2g8>h8^2-wW@I_u+^e#RX2vC<+bD)Wuz1|e0) zA+R^%e1yeY)R@)lCEA!SH!HwWZdKc(J+`uTm5ni%mus2%I{?91QqS4q1@09kgj*V`6rVFz}#8~$QBgc)J&(E7*s>dW+*h9OaAaXq>gY423& zNg?1HKcf=Q8f`t(c^4liNSdekZ}*H!woTtJ9>4}zlJ9)&y6h8ug(;Y|I}%wdtv8v+ z#X^sqR|il=B}gZ21uSHLQ>@GE9?oc@UW8K07E*2kX9kYi9)>J6H>(WHWj5DK$XoPB zfwt6CV}8>pfHDiXHMY8TC@!jmLMG#v2$?ywC?#D8Y~|G2$6!60I+N=KseU=a@tZiL zx(nkWYBB^jjZ+Ar#OT|m)YjQf{|9@L_w8}jVbkt$-xS^)^(#cYwM6E^8ZBi!0V_)u zudq5bVG=|M3&z`#z21*pSfbj=H7ZWeU(V}YW3Y8H5oto^IO1heFjUEe;Rclg8x=#)x)c+X`a0M!UY92`#|Ppo zVT4oQnu3I;8J<=GQR{ozr(_gmBP{@EGY?=P0B9E6Q%+6Ybt|lR=}a=%BR~T(Stw)G z@+k>hP(Efw19zJloqQmHLX$dF%IO>_1Jy~et4BSEwBkk))|j4T3bf>MG zSRj=oQDb5Gt%Q@Q&%#G1W=@j5v9WZ2*Q3I{4#){it2BUQ>K=|rC-z3kkI6_EFCwqq zLfQP3E{QnmD!!#d>O-$B?8+5g!p>!r z+a)G91GlZko=LVj&AJ@u7_!T zxP(O3JGE8eSAlP+1WC&ygT;gb`j-LY@XqxTpdS>BB&u1YMek*M?Dypol4g>I)hhsf zlT`P<;0Snv_<6lOElX*gOm_K=ODI$2{?a8pPJD0)wZX_q$nd};1b-w2Utg1WJUl{K7~P3( zaC5SEbnnX}l)E2mCGDUxC^)LG1%M~_;1GZcDQdM^)ZkYh)4$i`W&o@c2wPCWoUcfr z8QO-8>2*K_7)eQxTsNvN?l}L6Bi6YYsD=T!EXYp8Q2$}55)XwiFE5}Ts|`adLWLyVb@5K z<=rb2UP=3$CV4OIF|Xwc3wxYuW=cROJ-ij`W3oKVmy(jRU?P#WnSO z$#0u%Knj2qLEC}bBD&=O)hEYOCwVB!`nVQXh?20_(puA#ikuvLH&pr$h%F)n)kdqt ziPq$s?TL!X$XDz3paj#dKA8DPm6M?Tqs_J!DhpT6n-iA~%~7=%EC4Zn34$OyJk~n1VH^Q77lTwsU_Q@k`;K6rga=NPJkp$&-CRH z`7Er25l+YJTFJ)!;& zXVMJPHrJJ7vrC=5|_* z-GI^vU{lEp|8Ma#Y*O*4QaNomAIs;Rjp$SI8to>QL;UJ(?|^_l+8GT^l5w zr~v9Z$6Tbl8kh_@Luw!2`$wB^`Huwm4Z<++I@@lIj6;UR$~1DXi3L30JNh!iWlOyw z+_jsFp&hHoUgikOI|-PC{9dz5(4V5F-LXx$UyaG;heH?je93R0?TKS5 zF;tUjEjtvqGnPDnw^4APfnjy3&j9(vs;eLlfliHeTk?JN0ePXz5E>+VkDHdkpVr)O zu#NSFUBA5ZKWnn3qo}oPQI}0J%rV~f8uaPOrt+X*+jzF&7w!gOFE%(M><2u|Ot*$H zqaog^?tG+NGi7aHitFOade~)$H;wmd)2sv+MLl2gJ8W+1Qc0s%uTmBX+%2D6(AIQ@ zTDjq5Jf(triR3=9+)m6Zf|?M$k$epB3@}ESCoPE1cZz@|_#sn%@!n=;a@?ptqN#SB z@2(kw=(t2}cA;>hFSPo*tql{SZbH>SzUp8({}(+nnBq?eSoaf&Bc7|5(#(`?_pR#sNKL|?ZK()l5~BLt7)16 z*%-v(P!b2GP<ZA$mbZEAghtN>wefN$`v!oer|u7kKML*JTV zvu`Od|7f82`{f-?1o!&Op@moclyx;OC={oI+sppre|n+N%db zW-I6MJW}hRw{ib=$2TZJBTTuX%QAGP@egmXyd>K?5#pk^%roRDD*j;BE7 z_sL=y=HE&z6zQkUZ6>+J4Mon46wO>DL+1S2g6kWeIfs6uawA+bjmQ>NjvZQMWVkg9 zw>lwr9A+u_Pw}qtKq&JE-WseTz%C&P-QGJ~_@P9`b3i=^S2_=81H329GVcV2`6-+*)&~4jS&8wz~x)2NYNgbDzSPFJ&$xbR+i0=NU znco}&SRvSH4ejO!X_7-IX~N;256?{SgVd>$WH~(?|9%hc{%`Nkc}R|&Jgg)W#_tj}VnCYU zy?GY0)$B$EHR{+d=b-_rn9jxfsq!SK-VWq*5548`OtUf)-bG9jIL@%pb&vx$89#D3 zVF?a8Fw*e@R@fRb5UAcjAS}#hH+9q2SCbb&u5A*Qasd>B3+Q$4>tS}{{M%}>Ks6We z`-qla4#(XAXR|h&#R|0QbkmsU@^25Wa6R6o2;w-Cpp}!ctgW6Qs-=oEQ3PgYN>hz! zZ@e*FYKNPO`fIbqI!>6VUvH+WaucL!vR5<&NyS=G*0$VNV$mL4n7UBQJw)be97Br8@?SVZVZ9!?D0{Nb(8g`2h!c}UIB_2^oxXpd}8 z9`iBDk77m1iF3Mkz5aZ11gd5XlQyFKiI6z1@go2Ga)ODsLb(EHW!#`S-C%+pZL2HC z0O+Bc&KENe(M*-)+rVMT4_6ar7@(99Z--O`Jj@gFDUnqm@PT6OX8g$Wl1Wb%;&j>z zy9=I@94cq4fth4G6EdG~o$f-ws(6a%>o5@Q6Yg!T^Hwv?O^(c+_6R|yZEod!QxRo_ zszDFH(i+EE(nBG~rP}bxOZED@yp>`mFbpK8sUSEVTq|W#0qGBlY7t{yDrJ%p%I9`l zac0g_XCC;5$g@M*SDm;fXWc=Sy_{f~AWlhl$_mHMY}3wci*_E9`n3%@A!FiqU^?Ai z;M@5<61W?cl4KA{v{8V5phB~;%||SOGX6}eflw7we7#0@vF{s6v<~FCcXMe#2q=?E zQWMyKarWA3M728aF9Exlv{s<1UfaEcLTQH-iyPrN(&N4+WHP}Py;t*Vhl1T<6@g~H z#rnXW0Zy?+N2c>zKN%#|T&%h;$kgLV+qjUZPjHI@u4(KOa?a0beh}lq>3ZJA@%q8= z>8F=p2y$TZDbl-xAY52j6jdvXw;22337$Xv-=No1G?R|K# z)Pyp$9KEw1E>Yjr`S}XNN2x4 zDv<#rClgQ^XchrL4+*@<8(qt;o_SC94ab zQRHmGohxfDXh9nEI~u3HJ?}&I@D|n2q;e@2j%r!Lg}GXfO9IN0f~W9Z8-UjxZo1S}C~2 z#mm`+5SGGnx)=~`vzyfx#EmdR-rNb{eTgyI-!+*uFasYGrYP2oB&b%!e)yFYX7-5_ z7*q+TD`JDE8VDaU0g}LGPHKo$XeFRa;)68#C{l_kbJ~_5^O#ls*R=M1Y-x zeTa%CqQp^Jk=!~59y3ez4V-1A5F{vXQ#K1}Frmi7U;=b^Lj*ocB~EwE`1$A>0i2j$ z4IwkzGgNd;s$8v)5x@iAveVh5l^_{P)Wtef`6d~U`$%gVQcwnXHd;%8IUPY_s{M+G zmqUy1IMz`4Z6^@)$7y}w7S6szuH91-yeWyi-LG!8_VRHlTTS`{`kkz&-|~ifH3b zrE73r7PuQ}w-Lm7#z@Iknz1(VtthK^xRj0-*(L)K-RseZ%5lgi8&&c1Omg{{6&C7w zw{7=F-8kJ9sBDKejFYqggaZ^RnU_<%nYFuYy;ZB$Y_!C{?tCB z5IBOP4`Oj3))UT#3EP7k?u_g;4&Z4}*$H?w2vc{uJDAdEf$3oTraGVpC_Pi@xVe!e zvOEFVA`=ldcn4a?ZMNf?i1=1pTssW-nCu0M>wI+8zQZinQbQ$|X9@avO!Q@*$73IF zeED$4!fiFXAtO+>H|Z6BPxSHtD^?s|W=Vn#3RYrQS`@@{m(%3f6*#B?%A&$KRL6c0 zR?ry;9&1f{q2d?r`QVzR9Z8{t&3kshwoZqrkWz%|31Y*p8)_G-5bF3$4M9yQ{i=e{ zf{qu_F3DO~0tIeNq{HocbfqBPVJ%|Hw*|!yt^4qgaynbm>-7ry9%W6U5GIBU1%~4Z z?M-bQa>HGrH+sN+a?X|3$~(09pGGzZ)ui#;G~KLBl|d&nA?LjBF7MUQsUn<3X*lp7f9%m!^XJt0XO5XC#=4$1JK)txI!!1~Ej189A_9+|imGQdeS zEOu6=fBAllUtWJbk(Wquyy_>SAsdvsX8S>`NgvWLx$JMSFy#7>tl3aiQ0K0sAqQ$H zNjoRF?hiCR0X0m_w=%e$dc${1)jdq9MCXJ)=95=~ejpW)TJTD2rz>Jg;S_Yu1&JkX z9&FDbHzudMht=o9okU9m$LW=*si8q<3Pg_#;zWDBUB|nOVKA6et_1#@@KAES; z$%_&REUkLtksIyB-tF8CuLuI}(pDExQev5|DWG=4H6Am%lkEFqDW$O$ypHcJsnz7J z((SPWgpI!GZ?qXajIMkMe}|Uy}7+sa&T)yNib=%cCze zhl%AU@k?03>2HL4l~z9|+-!PQuC#ws z*x)NJ@W}{sCjG<{l)2&=+g)aYZoKZNy@5IYC9xZ{@*f%7`W(1JFKEpx{`SU?cCEVnq>s zP3Gd*qeAs`NT(oH!S3Ym0ND(Fb{@5Q_J+ozPdF{*&r@@pLBMvb2sUFz=$r3mqPPv}&W0WcE|{(b%}~BUB?cu#hS3+aoPva$r&{NR&px zWfB&RAaaY%kK5y0sRnS(8r=}Ud1_LqHnL$)+;WFtZ3~-<9y$;zh8Ix705|0oO|>Tb z9Uw)yU5}s(F{25wK6rp8acLOip7Q#b*V@nH>T)RmEsmN&e;-Lt5XosAtiNCUb-zg5 zf7+Pb+zX&V1rwF198)L&`Jwa5gH~E3SJK<8cst^bG}N8ReTf3UU16Vu)&2-oQ3|fb z!caRMTCbM?Nu#eFCQ*0Cg(uaeIqqd*&@g4XjtZ_L7uK8jf%cx-Ei!)ScFVq!(8x*9b$h&~cL8aLVur5>~PS0<%h0(gt&IiGr?GOW{s4tMJ?1+tWE}H8#u0o4En8Q>k%z;2zwEjoO~RN>CqHEL-bc(2A1A z=58%@Sz4K13FT;40?>g@Goj>hzaTqVXd6oHfq8UeAB>}> ziQC>9wYyp_ZkthJRFAK#i#*jktouZbe>c;uuON%sPoZBqEh(NA;>SMwwA}F%4a@% zN(WJ?OY~&>4nit(`LpQY-IXi*Yv2+l6xrg5P3AKBO!Fbr_C-%BxGl(NMU=gm1Ghqc z^nMRNO)Pykd<`>DtFWe;!f!ky&$KI$7~H}mJYBH}yk2D#m_qWd_)xRQ76!{jXT}tS zrnFSo@2~?2EQ9+bVRSR8F!qTCDO{jA>?O?TAxfnTVz~!<*P#0|o>qzX78&Sp5*tg~ zpms8)d+UGjtb)A*rn9A$PemyakVx;cz8+C@Jmsikb+m-4glqz3unV5NS>nUg7&{(} z%gEz(Rj{XcuKX`5h!Kha2@PTCNJ+Z9>Ox3_3TRtheFLD+$~5Wf!^Q>)UDE3=tAAqKbk}6_S#i;-=6N*WD8{1( zPl%h7XdwbM_;PcW;r<{;*tRp+=5dDZhY%O=+vswDn1#SO?hAqNWFo?sGX^*Z`vJ(N z4OyU?nZCxc&Oy#Y4nWJRyMy%zzPDtusfWb`-f7Y*9^uTnN-H&tlW3rG^eEuBk4twm7MK}-)iW#_Fq z1(MH<5*@3SZX$v6+KO-z~QIfww$yjKEWosVe7Og$-@4GX3b5VS+GC^DH>O~J7}^M&^RI8gu$6I^e5#B0cRBHl)fw*!kr zDBqY-ELoZ#^r&D!;L;MN608IF3TctqGq8|`$C{3FZ}WfSYDp2@j^0VA9GbOk+fxcp zIuFXO-IvgPT(SMQ6gC?+)k=pU1<(3sWCT(e zN>GGz2Y^tisVSjuY3nXyka#Wl%z;H&Kpwin0Wt#;&U4gT-Q6i$T68;7|HGwPJWohq zhqRg0fCVM||5|{Ky07zD! z#^Z59LqcUUzNDA91OYUm1R>B2ebR%ETASB0cmN;<{j&w=R(p=}N6E82ShFj*GT0PN zVY|ubqqRhOn-du2ycG>`h%mZIrA?>)*oh{$r^&jv%;Wr4!z-rsW@waRqztwl{Ao!#iqva>EHA*@Hln>Hn7^UtFrws@rm2ZqU zxmVNLjbNSk>at32Z23y~RP;8~ZZsCJ1VrU}bj|xr=uSyWU=coc9K8j{-VNPr}J2Clsg?$>0v9vYvUdzxDSBqNi{g4%lIUpHEv#Lg& zXs{9kj&XaKBqdd;z%VDxDw~38%{(;Rkv0}q{0PgDIzaL(bqQN|8DQ+}!7#VE*>K00 z`uJXZHHcKBEpOMOYthy27RS@k>?im1qw0!m(xp0Mm0dN{L^t@&nHt=4EQ9%&YAQi70 zxsw{i29QN>An@}dJ})+eqjv)^c7mNPdV@ksM!}jFBCNHH4&$-DB-JuMgatRQ{J$l8rF&a97g~m6|(s)5xH|QJ>Hw8i+qA$H0E85&Mju zvXU9F-|Y-Hd4Mg09Bbn}u3|iK75Js3A%a+HP`Obd(0}Cg@-+Sd2B=$zm*a~1ADGl> z{cDc*a6NXws1uf3X0_5MT_#ubT<8uW>?L|}V+rn1axT7t0j7iSKv30mno*RX(Kk0u z#+t3s!B%gCuc?p%{X7dWk(k2g0 zlmq}3II8j9?tL(+{#PHn|CRdK!MwRzdmwG;)EV7;X;Me!O@4H6Ce2QphVhWf6AsiO0cJ3bL@UC?j24lT^}A2QhYPa$QGKRZ4~cNboOay^@1J7mbzV!K6Lhm z3TVT89Sh(eA>v2=cHpL+HF2DIAOTbK=cuk(+ahsG$dE8p+tbUPX&Xl-71rwrGc5r> zq5n7x?w_392ISaRIhDqo7V%r)6kgBx1BQ<82Zlcq@jc4+0Q(+6WT{YCDmohD=9;(j z6>nh~yW1>Kk29hIhzuAIOX#PpnW1FeP0Px{Lz`_;yJFj$@OnT6AuW=$n2Ta)ut;IS z3xM!6(qlI<{KT3-6|PrWL!Q@{&-N)evZQX~QvAB+GBlFahHqZT10{V7;P5xv$W+!Dth>-YN!fq|VNZr6m%YHnZ zI;y(6{h?>)rU5WeE6A;@;)%LbxGJG=Z z0D3-8^gDpl%sX5N5)*d_a}ov=o&^2@6lwFLYec{fvNCc)5lf<_ zdfOL)HaKz@#gEmbM}tNHEtYK|z?@wI$sw^=1WSa<2iR^f?E{Bw+)WKmhe}h4oR2=5 zTCY5#hpF|!F*@Q<)YLbdp=alC=aOWdihbN{Ifo5xHkVpmtF#lz zbL`>moIbo=*&EXxfGm9Jnhn)k=r9Z4jr7TMw|h3s^$It)h6EQDi|pxnRx=J)DLd9L zL893(4Gd(%Ifz!A>89Caxj|2P2f?xmO(3`p^$56W+c|!C zz8I0FZdzSy7!C6@JX`U=w!0Axj#fO+D!OGiV^aZUkIL(f1pP)fEGST@N1^!E4@UJr-RtC>c?(m$xfgPh(QuRm zFZA!;M6d^=rl4p-)w;R@%|a*m%hA=Sf)XE72%3kdZT+yRWb9vZ@&_jF)Qty=2hw`H zIK#StxgybC<5F+-5+DqjIZB31YGzWJm6q3#p+Z_fCS+ksBv$&5zjcb|I0Y&T7|}Wc zeNjQ&1{mdtRJskJV!>@a0VTxilp!ujgpc%*4%F`hLTKHaRQEY=NdszOrA z1`0K=U@XXS0oXbpW%uC%XaY!XlExm*vgI+)`Qgm&?UEBbCMvMxG^b5-;nB^gvBS6a zy3Cfppq42?L00)a6vW}$6Hjx#0OywOX*1-DCK;bq&-uPB&piAjlK7RPNYAlcWkwoy z27lKAd(3WX`?BuRm`SqEIk}%I6ul|B6r#Mm1Y~sF^ff@1JBTi?W*lLS_kTP%? ztk68{GB6UY($Gx?=QCYk8XZcfS?P|xpAKceGNHhq3`~U#oVI!WWGKxinhpEwA}W36 zJt0YkJ;ms|wtCoZCO6o7KxQ*I6yn?lpB5MQ(wq7%GGaa)7DzY9lmw~|I~2y9F&~9m z1oUZ%lOyw!&R0;+K}8I@y4#?wP(-+NmJWNn8^3n9)HD4#INpGmR~V5UE2JEmHq>NW z(--_N%wlpz?RGu7S}yQM?9;fd?OA?vYysJXrFNOrDt z_;E!)GNx4Y(2|k*P05`*ROfJ_(+tq5no?ckiJKZtX^77yc%-Y_NYa%PkW=|E82}Cq zT`ADrT#w)sqsLc@c8b`!bP`NyKk1-H+fIkM&m+3d%Zkio733VBn@2t18e z4)&UHe9^Ikb}~VsQr!KUq6$K_bGk+CNN+_LmF4J5;IL4Kk*+O%FNq$Aei}J$E~o3gW_<>|7xxZ% zlO8!56}uH6JXMC4g5@P8(0eFie3n5PeD!L9@(?FIQq{0Tfhq}17|b6=FS#h~e00Tx z4HI+|7;Pu!m&2%#9y{-|6DEv8G+O*=9j;2`K?OT%_>I5@{Cq4jcjRYDMw&ZO}(3pX- zgnD@d4C%N>aDF#KdMW(X4r@8I2Hu|A_}UJ-T>eQnG;z@N4D{tZ9l)^-=6i#($VCo% zi{^JO=|@1e9h((G%3UkeWzVFT(~nfO?avGt zF?FIT-k)~n^$_Ff29WC_>yg6^cwUiL0{ayxx4<*yUY;Y$PU*5xA)-pF87II-3V!HA zLA54rM}WcJ48jCN4el~37%9E9UXEg8)~n0Kg3;C$eWci<>W%UTcCuYYIUMMvYM{#> z7?b;v+`|bmB7pGe1-lHr(}xkrFXuUv3WZjE5m#sTkBt0dpv4$$`be(;JEWwrd%gwO z??+aQoJXg)GeA%TWWWfyg5`*)f_B<$L5F}nU$G^3%Y|H|!8zw9e=FWLKy82sACR2w z6co{}RMNLa^xWimRKiHz-{Co!!K-GDE_!P0BT^~=Ua`*>lQSxKVnaBk0x7ZF#TG^8 zv~8l#>WZS#6eU3loT4O0idphNCftTTJ0Imc)y0yDn>hv@|LJh;oJrR10Z7o$vO682 z@jutBhsC4Gm@Hc=bv%mTek9v+beynIgKdbRX$v>TI%QI2Fg^{LOeROJ6%`8AU1BK+ zt}YvO>jvn|XwG8(D+G2SRP-hT=XoE?C!k(L&Y1k#=*l$Vx|xQ$VEsQBP~+)Sb8D+~ z@PA0hnz9K_A8%76G>mWo;bJB1&Ph?`04r;w830B*_Nj?o$XvkxF#~T4Sx(-J#px%j zh+Cu6o9>`Tx!>h@5WY66d+_8{H$KN`4hd(-o<#IxKWcc+`|3D@?;>$=Z|2J1s(a=p z+StRF`DRmQm&bxGa;pW11`{n7i5z8$UMLd7^oxFA1ddyM+p#{aj`a=ZEw&60Nw1Vb z;e9!{$zVl0eqsY|P*){7dPMnScCItod8v1@nnr3FS0uX#b_Nna4rt>yT}q#$73TAB2iibV2jn!H#x-m}RKt=p#)=%u zV_)=3U&nRoM;z#E5a!j>X>iV=X(vAetBCbfYF*c?4d z7szV=k`i*5A=VJi zN}A*(S=71vrvt!2o(qg0h}dguU+$zW1o#}S0)Vkl3bLS?x2TSSVPd}a%RMjU*ctfm zaPe3qS>vKFgUj&Ctx+`lxK`1lQ^G*QQL0-xNo!S5HFGi3Kp`aKwYtBo6w2KsS>z_+ zpwr`$jNXra-WuCUIt5}eC;$CRf54)GH9#z@f|Yk6PZ z8$i*IrJm}AU3H!M5%Dzcru451xd2ru{H;WjAF(C-YG&%Ez-+ett)9rM+crGxntP~B z?DxX`H#T z=x#k7vIl$&sLJ5h09j8GqPpHuUaI5L$62UZ48R1vT(LMwj_g5Z42vJ+Xr!U*fxC9= z8qL`%*dZ$dt}e0BF2HLVMByuKc7o4`(@kwLQs}AC?|_e>{_Lv(XCSZhgQ9X=l=0&P zDID5J6foZ;C_xv4nu?qn*;K!uk;P$&ExJwyOKc9zbUd;&%BsNE8!iglE#Qm&PbICf3{te3AZ^df};?TErDqqXieFao!Yz^ zyo~ru1!ja4utneTB=>8_H4h6f*4XWOq(x239k7RqycNx~NoZ{C88bZQRtC{|To(?= ziC3AI)c8zyT4U{#!cFCMnL=p7jLo*q=vND+Kkd09Vt73}i9|w6lE=FWwES%Ux&ve( z5f}BNG{Xk*RUdO}^cjizhy_2dShY`sKFky|wd^L{?fAJ#wc@E#0!uEMh@2Nu3S6#k zQerk6Dw?vfZ!p{i%Sy8gNdZIno8WHgHQR%r5=bO!bp&vLfTG*XIX4C27DT5hy6(WY z1Ue+=7Hzs4fKOtBw~t3KdqU_05Vey52O&;<>SJ!*wAc+UrmH802|B2eIZlF3BBrS| zBawy`0XiDP%pLwHv7+HlbnE!Q@j9I%rM}p~B>e;KV6Br=bwu6?iel?l`=@A&fqB1r zolv0wcCNdAjs}I)1m*W4_|Y>KLRfH0lI0dStG++wqtgjZRhpv&VW6DPZ;u1niTUr}(`dq&S&boUDY&jnFl*jO)=gXpHj6QQ29Z_=AV)9OszSiWxtVp58w& z^Uz)DV1c5RxD(`gz0aGx;|-#7O5ItG)Ef|sk0?7MTupafTtRAYhp5wgn?F4f2w*BE z;fHAHQw$hc;Pv41fPF`Xe1y8U>lGxV>5881x0(li2h_=oI-0=rvymsrA-pG^EXb6IO&MI+31r3e0%NAPx0eQbc9&tb*qc!22r<2t zR5VRebno<{xE?W}MElGVW$S1op9~cIxxbGA)V4cYr0excjjrn+XTXrKO!DsL3Z(CM zmutVqaPD^#?AHSlZuT3hc?rOMs<2W}bwl;18+{3aPfQ4<-ts+lYRk_$87~BcBnw`%DE_D?#@+wDF;=j6i zgPVp1w0<{F#I~~7T460ZkM+DV8nHA9vXEU$(W}_K+4QDtyBH^E&=TJ5yRJttYEp&) zxe?_8M8mdy!LvgoKf0IHeBQ6?_5KVYTd|@$AQ_MLKNWR{GLJbn$t` zDuODG0~)+1?F;++ftI1IXMzb5ko^sezq`V1B%!3B38jOB*FvNU5xD696KdnaEW3FX ztD%+mX|~y@a|3UYS};h#jz)vDV9)|62QGhKz0uF_r05h>Hy2O_bvn2Lg`kNB8N>2^ zZrUgK8h*+1{KScJ#$H2j3Wy!dwJi5;DeK0}?@EhV7kcEb0Va1OPnzE6$+YSHK3x|? zgzttAOITuVrM=y?`3QtrPh(&NcfYnXP?X!d*gLo^y6^9~s}ok6215rzac5$^X*Dvb=z9()zR$%jj++HsG`eMqB# zs}Lc8Q7Izul(cr!#@cl=J{xi@!xj-AunePlkalI-#d3k&^{BWV7;rAQ3v|2YS);Q3 z8CQD*oa-||zdc`<*(47X$D9v+VGxagI-sl~B7oDa zjGKuaci1Eg(e3e?CDZeLnA#P@1Dmoe#_a(^!Ync2!RVjqRD>zgBO7?RWP>=zm3*K% zThx)KVHg9NX9KKE)g}ne8JLx^fv%=LsDNN4e@Lg=u&D|FI83T2n=$3#G?V^1ivq_U zxh-B{2-~B}o#fa~SZfRPk|na-PU^at-3>~oWMA}lJ-Svkc0j;3dQNmp>8MVfZb;%y z9KW7)rPzrGQVE>sOO%#DU_l+h1E}(Lm*UsO2r7e6f@3%*^xWXCvMTayim7BK<4k3? zUC?2Md8~KG%7~h^#WVMMy2}b^I66*qNQbA+Ih?FYn!OPccer*g$jvOqfZ=6 z9`s<@f=gesR2knH>`R4lq!P*Frmd{mbj?{i9?m6}v|XZ7PvcBu+TAtjr=x4h@_wID zGYq?K%*DJvYnBO{o$-M2I<;Oji0rgKBQ3I-rF9QG6?!B1cUxpNUjz=>hsp3M?VKN0w!0dF507LaprlHZh9_(7hDP6^eF&IFyZ1<^jfRrXnOMvks`xGd+RdZ#5E$UEflxVtYCuFX7g zh#%PzoYd3EuGh7Ek3SJOfzQ2DZMp}t2x$l7;%g><9Drjw29YAfdj&yG!iXOCI;kqaHAcGAih3MZvjy3Ie0v`9q7zFPY zxlnTZA;)%8G(zUxmur^jJh`~|FYkpUky1j;F{c@x2OkmLlRa1=XntX+^=sM9di@FL zUc6M=2LOFn{J1y7ui1BB6E-2dQ6_YQalAf3^+SW)6z$*|Z4zWXMio``6+~0Md@vf#xlY1k3&umaCJvx@3u7r%!=Zy?qS0OzPi6}~7+T)C(cU5=m*HeO z*8K6k*y7WSq}1!*u16o4))L@e_aA<4>BG&ErI9XMaZyerD$*dnjA8}URiZwwzn+EQ zqr++|p~mNC;i)BRShwqxCFckjc~a^Akzkj78x<*WCx1Ym;^}BRu{*Fg1-2dvC0709 z2F5t!#MmM&tdtrZ1SlOOchK1TE5AemH{e&h$5MCI5u#{quYsf5!x2FiVggsh3W`EQ z;wkbHvGMyKfuoZ#4*A6!ioB9z~I6+_KaoMRVgF6(KQAZn&z>JMl1 z02w;PvHNzIyCd;Ja>zR`#3Vs1=z^NTLJ&=&MeEE(Q?+Q{sn^hVr|KKK~Y3&`JI6mo>g5ibkqEb}WWwa~_U zHJD3c9jJO$-`VQsZJ}Bw!MvUVbpd7Cx^35ugE5Q<^;wqNSWe2|->gVm2^xMuS*w=F za08}|O zmDqw>ibg2$DsuAW2d$FHJBX8dIPr8op!G4x`<-nfj+|dUBU~Qd-JW;5IpmmfOcBdj zIA+>Wgv4Q01HrsS{_0>)pJ(M>P}j$`i!<}{+8xDhyY+JTNC!L z+6?m9D^3{#`063QKVyzKE)iJfZqra0VDBkKRCeeo6H20{j&~AwLd2v88W?kxK%)xH zL)t*CqX7~8xwmg@#S>MOKLAnM3JeQg!k2{(cpMR>^lBGtD-p0y$UgV89Qc_x z&OCK4E!&0U_A4oYPo#9(4QHSJyQ=EFL=!)fr8tl>v2Zh(-GD(f;jXCbEu#9ErG42 zbn^XcN)?((Uk6e4>>Xr>LZiL$a%~~^IeLNg8K+zwn7_$*5=3(Z8l@*&KIr~b#s~)@ z+4yZiFIen>h=r6t1-$Ev7&Xu{pRX3))C|uHG7Z+E8B01|WyuM|gu8e9KC#DA7MtXw z@D>T~$Jdz{ZNe&s=sA~-gF@J}ZQbcOGHjE_%1Zza#Yxl=5v!4`jg;k+BNZ75hijbB#x1})M3uv_ z>)bOW%S~AEmtuP|`O#sRDvysc6_so={`C42RJ<6^fbiijjzZkhp#EK5%&QI#{3m`4 zw*os@ND6_@`ay5`5)3@K+#ldZf<2A*w@`%;6!&SN2e*H3Npm`+BU4-~@Zb@rKtk4P zX0AEA?wvn}l)u7-aK)u9wr)rBCmK43e7SpOW-KOTAcjB*jUloP;tJ*EWmW^^!g|Vg z6X&z+%3AsVT)WT-kKu_ZTYfdKUL#MiAT?zIwv?JL(X$rUl1efjVw?|HMRoCqOVDvK z!c{4@m%1KXV70dzt4_|Asi_b?=wUy09EkFhb@1E3y8-HJb(c z@CMzNzJ!}T+A0`YEN2CO%O)^-zQ038tN*6cdO9q{*1WZno*QI1p2 z3b(pK(Gd-I8mQL)rkM2v<1;aYPYFVGd@$!%0}^}cgx0(BQ-W|fRIm;E#Wo;}+x5(1 z9raXk+?%-9@T-6k$l#{gBA4STDYE9VloPXFWr*5qPjjGvozzvs&OwoUhkAfS+}Ob& zx2OA>UjFU;OuEU=;6|wReJG$Dpw8Sz`p>$~W-u=$zfT7^3YC0oq6d#{m8p+F68nrz zSD4Ppbmz=k3@S1HgyxulHZYI-ocqhAyI4L*Inq)0E*g3G zmO|Q6mB;&jMPWYvTxPQM;(ZSCj0mOvw_p-KIyBn4tOC4k*Hi~&xoGa+pvew`Qz4;# zKnN>}tp(|&?aB=LK!8a4iT!|xlYQzyfgPw*c7NJ1HeR+An!m(3%gv?uFRX0}GDwEk zkQCU$U&eg9D;TE|l&WiiTQe;Xu=fZ^lYGpG04Bf&9G{^|@+CSiYQI$E(aR=+-tAil za?OKx7jh9GK2WG^kdkbzy<4KCFb@h%F#EJ_TQ!EJ-(!-)$ zB+U!6Vyu}m?~(B7ojVkTiSXa=ZkVmuo6l zPr|HPN2obB>t3eqjD*KrH>D{b+S)z*T3!jT_(W*RHss!D_0DnOR10&`k8#K$R?nUj zw`r{@2P)mEXJav&IBNlGF9r$*DbZ@cbD%e>5uYG5tkmQYuIL^M7$CZT8CO<}cQpF8 z4jOMy>VMk{8LT9H$9R5FQVF#3INTz@QO$)M>_RsxAsVh2@*oNwsa@6Y|HU}MDOW{_ zyu0I32pSZ%J~|VVnoo~X(Op60z(zx*<6|Nb8$$DSZ*-Drd)ed1n%^0cz>HZoEUXmd z_~vTv%r6Y6jV0WT$`bbse*qbB{&iX;(*j|eW7xZbMVLK(>Ie^})%DcXYX@5?0kzt! zC6o?9h;wt2)p?lzn}|h#B;LRgb;TFq5}#i?lBtD_G*U8y0H}%+zK*i+)!sgz3YSf? z4$Qj94YpuSh|ms&BrGh|Tk2*$MiC#GDHk!#s(M^L0Q)kYU93kQxJ?2QXT6cE*b>)= zQySbZ@`Fs8FFp>pc+Z~34XMV;a=Wv-QdpMd2B?`z;@#^q+;ZHD$O@!w9^l($9lG+YxUavRx`cOYq%A8;i zYR{jjOG+w=(7)Gc+U6nrJRW)*g?Y0!uGZ&T8rjGHT$`_gJ`Q}EC{5;C=B*;wg=mSU z=T|Sw{~W*`OyvZo1EQH??_<|+|_ut;HzPQtzyY$vUUd0^RPkvqO6~YFKvWl%OT^vmAe+QXu;9Fynk`O<% zP;;z=Zi0?u4{+Kib#-~irTi|a?CN~8e0~+3wALKb1uHFKbcr@ZumXfajHnTB#BZT_ zPUM}8s8g=jeR1%D5zCOK2Xt zX#7FI47ksOf>hxZ

%o{1>P>a{t}hLMY4e1Jvr$o)YQ>@|-;lnV+Z13$)IfS)Jyc z3Fzbq2uB((Z}qxQaCL!k!e^@t3r#$8_P<`4$QGf}0|bCly#zcuXz}1{PD!Y+0Ro`B zj?au@ClI12rATg#8d9mDB6qRK2AhH*))5sl3*%*GW?W;5&7LVW`93* zWKbFPBtixYy5y8Uctr?$8@qxd6NBoHAa~LgsDP4u6qQbhkn`ySq2KaZ#XR800UI-g zjWj?-+HBH`X3&H>+Wf_d}{B7lzHMbH1?N z_G1o)fke|hAB&Th1Ys|sfM};BD`vd9Ac=4gzO)mwZg!r(z1hd~P;%iCaOR9biJgc# zVA*kzGbS-S<5wytVVvjr@fB_758=0KyH8b|fTMwN@G6OtW%h6Go`|C{jj=J461zEd zNPr@br4aJ+{kbFlk--eq=;;(J|1PNv#@|02(q;?7)2dqA8RS-i?yVVo_$Q0yjtYQi zcA$GyCF)s?!yboHaQ`fI+f8hJ-LQ6h#|8~)xPp;A_cr`kghg{9{mOR6vRM)?u}^&oZQ2C$GwEo4B)oiz^^ zE+WRz+1c7cT%vWrRK8On$`w;*MbG-Ui8dNpn zJnM7}wYXvGx8mG8K`c$ci2SeUKnzQ5dQ#W&~@%hfybQLu1ne)#DQhy7f^>%1){HG?(SYbKl!sum-V-0 zkYhrPH|1_XZrazBnS;s0Uw#~ePMVwhpwTlRSJ3yUEXM`aB6w#iLAY_r>~B)WBV0k zt9OB6SmvT15XVT4O4lv`QUF08ln;0`F$c5f=Y9hUUkTRX5zm&F%rlWpJ;Ltj!vinI zsyP8MH9ViXQcW>oIBD)H=q)G+x%`uVU_VKZ@~b_@GR%?sJ7iU#at-~c}0a%A$98+^@eR*-ZgvaQ8|VEl`a=7TN@oi%b=v-su&5D3abrt=~UL{@uChq1LsTQHDVCp&qhH*7Vp z4P0`%k>N0_Tp)(xueC!w2C@Lqr4puNiR_Xme+&5!9LqXkDl`Pbg4@SAErFJ+<3D`n zUHWxJI*TCGdA_+I)B`)xMGR1EG{whBTy?#KALrU&Ot>i*+Wzd_zEC~|0{*>Mq4eaO zTlvyiH-QT%?~NpZi^cj^-d#!@p)YZz>brIP12cP>Q(bA2lTTCNfbGIwqfqnO}w$7`c>BBF?{Q7yP+f zSEf`HH1J6P?b0k}Hc;_LCY(xNIsR@eBrLd`5C}0lAXQhB6D%7}Dm+iF?|khtOmQ=5 z2s`sT+>xQJQTECpssh8S@}v0q+1A9M!*~0CJQqQ?b#sCHj$vZ4cF##X3MmxxgH$Hx z`v&HqGj0cu2N?K^4CE&#FXu+27Guy;Ulo4g`6yG5DvH7I>auBzM!qyj!YqCfkH7gi zb$@=1DeMZ+@yXU)e)1@j8z(KKmIoDnJ(R3|B&<9=$)_M`9hK`}jL8_TN(rDGys&J_ zH4`Qe6a6Zu2Vda4RDOJeXm(gMlC9f^YhI!lg#J`@@G{k#G1#OACw(UBP}BwJm3x8= zHa&bcPfERMp(9?g$3OdhF23FV1^w?>w8QSwq&_eppb$(TAo>3}7R|)e$=K1--r3R) zz~JoetfmSL1lr%Rult|r;tmT04E6&G1oU6a_3ur zp+^k?+x1!;G;)sND!e{fAyw!CA`I)t1=FkKVzlFRa#F>Mue_E|bQH-fL2r05OX8Ec z4u34uykk~C^b)1Iq?3~#2Pez!cr_ZLBLZivPp;e(%`WBo0_N**HHG0~QYQ2d%-ra5 z^z?CFv3eibM;m zev|Fkt>-ffD+3!JYH6kM#sP8CiV6&o5OwO&)}}MWb&U*6M;k*$4k36?`qqVg?pC?$ zujcjm!kVF&0}74T1Ah4f$G^<8`LrWZLewF^o?dR2;ams?(Vm0S-B{JbQ_!^13kKpF z9=|e!0i`q1FH6__JsQvp?(Pv+Jnv$AK|hk{;GSwlmsgQ_GySaiZpQK(4xCNMm8KI=xo6qOB_# zXU(`g2@+~bn|rELE<&^&zY3^RUr^-g>}L$;{OFh*36*vcZ!3|`QxoTOb0~S3=hHdV z7bRGYLAA1PU&Af88B*1N|C4d9P0WTixq;Fk?_^IjqVV)7_B)@fj`E>oJTn;d6>#3kxLiqUvKk%s>7AAYctQ3Agi za6R~9@L3PKMvMm$Hq@}EC<+-1I~H^}1icq*kFA5S8fZOK60FlsphE}%;tU?!b8yGt z#mxncdwAY^zXNe6_J+tCYTMhq!+&G&&WuGA@FyfOfZ_sy4T$L%+9SC`nzsQ3BNh&g zCv8UBfWi-E5JoJBiytpvC*r}n9|%Vhk7g)aEEGym_ic;+!w*2DMycSg+=TEzDb0%Oajf>$g{ogVQ1N5Ik zV!to{Uz`84Nd3=c)X~(?#MboxDyaQ$9NquS|3^XpyOJm38&Y>u0Ribu0|6=h&#z!+ zYUu3ZXsT~)=j8nV=UZ;;Y9<|xI{5DD4`BAi@F;4&W>FC!gjSRU>P*B%60?_5ir&ze zr8Pd^>ZU*{YErSyk14~@4qu-`bmD#!(>_i$`F>tw>id11RN3kIJ-khYx%GMcUCqlS z(BJuapJL?qc^tFrdwVqB^?p6+(x~(OJkR3X^?mLV=zTv(I&HfZ;O+h1KcAcPdq3)W zIJoq)i|gh6zCIs%3_EqR6Y%&R+x5G@*2rs-(Et1J_^QCT`?ad^g?%eQ>GORvCUQC< zmG}Mh_VfL&((CQ{bvHG2vzqhk`gQJhSD^3vULj84+wrIC#QJCM6!CA*XRQEl&)414 z%g|w*LRxQA-&s`OO(ExGo9+{Qm_Q3Je-H0pdxXF500Ce3k9%G2_xm!ZGQZE?Ch-C< z_hp>7dVQbo@7IS4n`M9QIj6kD%Y1*{_V2$JA-%^!>8^7gmN)wZdb<61?xY~+MDPEe zgb~7^Xw!l#ybn!v-4gcs{hYrK3Hbiy)yb(^B>4L}Z>bO$#(&ernP<1PcKdVx_EPq-e%N?)#wZXM`!mMKNVwDExAW(hE!tQeB7gM_xt$#(&{T$Z#L)0*2DYtaUf~ta#e)< zef{n=C9TifV~(E{ewI?{Qb!m1f<~`TcRnMtDqHl!f>E*Tc^cA^#?q z%#E_&O4Y9K&v}-Den1Q22^*j6m%jVQ%n>2n&s3e93d_?!jJ!!^^s%`I|0dgQ>s#HJ zpCSLy#T;W^;*xbMci=7z^u`>6A2udEzZNFpmLvaom+s@tlT^Ps8Nc}7&=WY{rv`pA zTozvu$2U2I>O;GeF`YTL2{6yRfF-?-s3$Cy0ZuCKdl&0vC*1Ro>Iixkj1BWWbtsWX z?bKfow86_V^Fg3Y`m(SYYHcLnBoukNsy-y^{b$Tf z&6l=1I5&nx3(Xy=fh92-Itq_y@?Op&n1)O>u5`6Zl?S3)S6Jo9?kQ;x0>;AQDA^KZ zJRS^3zv=>e#2llHUeU$x0d=u>h)BT-?NtU0`gvG_zrvC1ww2*6My9}^0tx@TO6w`a z_F#AdhS89NsKa?P)f)z@sq_OlVdmt+-(-&YtHItG{PigE)P;JBl!}yvKY~g}I5y*v z1Vj|zxshn2dgK7|UYd-l7*rTP_s-GY?a#aieusa6Vf=-<|phv1h0T zs07U7vD-qQ^-f5D^LTiRC}jul9R21GI9l{l2MyR)MH_!;6H~c!hM8*~T&#z2&E$OU z?QcQp9A(4ncWyqrhG{Iy&#d?nhYDCSo*plQi7e*pAw@N^z4zDQ7C^0(uP_<<4H_I+ z*(&W3QyKTDflE$w-E`-G@q;{-?1u4ETS2K$4dUb#q!$|^YO@Fqf*dJMrHGv{L839^ zdGGDl?FDX#GfxRl?dPx}`H$a|K2R52)&(`khiOH^*np(SA_OgmB51NoT^~XTKob*> zsw+XG9uGqolt|UHR1V)ZvSY94Va&y>{RlkuNORWUkN8*f8ZY}tF$sbO(=mFFJ_AC_ zTWq{#bAn1#Y}FXR6g0fP%nBV}{G_jnkg`;?&zaY&Q@Fm+O*8VUTHriN;}P*KWNm(` zB8|OBRux3<|KPA^W{#qn=V-imV@5YYY?s&DDfK`T&|DhfFVBiy#8~WuEbV%bzWjx% zOSge0M&^N{|BpLjFGQhUM=QFtwNPQ`pJnDuo&=DogE+RR0pM=5H zGwdb=-*2JR&p7tsWJ9ETiI)SoqqD>J0;ZrL9dWeEKU)$ub^~XRYD?iEx)mv(wOS!H ztk8wgvXJtceofOQ5^~JL>%3QK9tV%CFn`@uvM*n2lul}Voi2lckjWk++gBN(m9ylO zwe|1grLEl*D~SzNVq5`!-BaxvPqKAP?fJo>$ds>ee|&DIPLyn=BmEgL-M>+?3{W?a zOX=FNSSQ$=zAV8Rf=Eif1n>d;sw{Jv0&h6!Za z7Wy;gfgwKtTA@Sb3V7!o$}@*+j|A-eOIZE;{n%&=@QmRvW3Q1B20SZAd<5yg9$G zJ3KQyH(~P6*WP?~f*xdwM~ay1EFEJzR3A(zn*hxVZkn(#dX^cf#Re&?t2!G7*T!tq z<`ywC3^=AP-uu^JJ;xBIlAgdan1EknXNQZ(7}5IG$ndKK&U(G9KL!s}4&q;2AW+t; z7#ZXbU|>;{H~g)Xi=_eSQwDQ$&~1a$z%`zC5UdGqOCG51b>#x4=E|aYX+zqYkZ2SS zZ3!sxvJon`Qty_evZPAa5?F%Z<|I-5Em2LV>ioQPWFfcwFcs6rD&>E&J6|630yv84 zYvZlu+BMH{0OV>G*+*S)%wzYD4Zgu0s#2)*Ce&3^g|7Ylh$-u>3sb(ZP{DcPo&HUW z-|sWYw@On@;KW(OWXeeVK;TNn!88A0IUh4GGd7zER-B;_2O#CmbV9k^;} zJv>3tGZx|j5dqHye8>|a-N{sH{dftcrnnO1z{<^N<;yTck9w!hc;)0I{rQ`hu1^`l zt9dehJ;5Ky9qk!}R-y@1iVDb6tKh8!ub@?v8WbVBG`j!eo6g`Lr1Pr3F&nn!2PWDo zNpYsI1Ds|P9z}|TV|2laLDEr~+#vxbC$&-13Qvm0ap6+?x+;M~rd zcF>ToJ{Ah>d1C{SD-kn&;tXe-e528{zdpS(+@=FqjKfE1O)*`biXot;)+(k->!^v! z|I8oV?Q+;cOA-*!J}4SDK{OiF4&jAIckkOAS+T@sXtDw!Y~$E$_r~Vq)MND2|e&0-XA~} zrADTf)n*xnIB#5y#**hq_m{RjM2A$(EU-|@T1=mgmwi9wO%Y!^$%!TqA9PtqbJG{c z-AIw$FCOVgsk-D8oCH$%P20>h7X2wPEJw=CAuB&xwT1<;(10>UPn>pXd(EiTV0GW~!nD*hPiPWz^5omIptP*TG z7T`)>rkd=p7DN1dMv}OgAs$?3*RU|sD9EUVq8j~53D#AvQ825{_Evi9=kcT}((;?& z6!Za<%7&02-=0>5tlkWgtHm_(hrVqdLx06ImI#8p<4R#&5^7VVU~s%wJm#4#{exp- z#By2=)s;fs9a7#)h~Mmz0*elTDwa;zHk%frwB3-IWHr!c;;BnWt z+It#x&^MULSsk9FgJQJp$&iMwq{D@{W_gHzu;Z6&jO(3ESMT^qfU;))k?D8f zQRTWJQeL<^v3Wt5QaVW|h80bhio+8o3C~{3Z=o{11|1CJKs!^8@+*(6O6%8|U09}? z@y>wh)(c1KzLk5rRev1O^?OnQNF`0UN`L;KiiWgG8S7RdmzSebtuW}zqHs$*Bbc6K z$C9$V4R>RB7f$8O)vG;WJ|>JsAF)eQ@+kMrEuj(FZi&Vu1*c zhYdRA7CQV(uFe#Yz^pLWD#-RbIL{?yhD{CBTosVhbl~#Uv)k`{SH-LI6BZJ0Ev5pj z)GF|v5X5|0Oct35K-kVVHP~r!y)-i#HL){0n(;K-xN9AXPPdkhsxFqU;R4U*w)7OY zuvZvL4d}7$hhCi&u$qxJ0D*;@*mnmMeV|kp1w(l`ydvDqKnR{DWdf$$8{xpK7;ILW zI;VE2d6gTERNadoP+x-YTsCEU93m*a?Xd{$x>y7)?nR;ED5rr!CHKRj2#^3rh8qQQ9LG5f`xg7l$ z!*nTelU``2tj)}INY&MY`nqLs>ErO*!{Mi=favP(3#$?pX{dt2*?Spk(0Ad$SPrWa@GT>d-dD^T zKH|lyB&5!F%TIE-5QA_J{Evy#!Up=wLB{?ocDboExa(oK^9~P2Kz%k!CMRoSTDB{H#4V%S8<&Y!jAN`Vy%BfJ=!2+hU_=P1R?*C zN+oknn3lP@^}e~SVrz>4YaZSuDCqAQ{Cd*qre-UT(`pGhACj9M4VFarz0zJOWs+0} zx7u&pP}6hAh+7&(!w{ctIIoKSD^tybvxA|dm_^kjswnRle;JAoEgTfsvjTO7GE~vH z)@JW22{q&Tz=(hOB~=NRq*1TNBEAv8K%Ra};Llgp@7>@ve!@%K1P0)|20N^8#ecn1 zzEIsXsPXUMJ&nkg803Uc`L;4Wg(utR(e&pwr5`K zc~zOO$Z@MAe=TTbWWi6}Cv{ZK`d807>;9PkK1xcQJh)M;@4S-*>y6_?3{ZgMrHaye zid&r9hr8<;Z@V}Fv2qeT0cS2lUdHj}7?O6y&|REQ4D5P-Yt59V4;|0#SD(dKNPx}@ z7=K~crr;Qz(kjcxqb{)ulRS`kR4YA3rz;$lxVC|*6G%(qN&Dyvv)?U{UUu@UG*zq* z?L33WsoYDKqa3Q`y|gWYl$qY{3oieqC`SwmVr0G-&0+mICxLIRt6k)(M2iGID_lI- zT^Pb#O}PF8GJk*2U`*4J-LJEFxx&sdhH4p}8x)tGU)=A>Vb?q`ah{9z1#t_B5vFXp z>+yVfcP2!S3|v%$4C^n_g9aAWyBBkEAX{MoJiv8yuIysx4GT zUwux80FHt~-)V3ET~ua}%F1X!+QyYL$V!G!WIuM}?p6ZUH5G2QKt8}v=G4f5dN)gM z^+yy|y~T;?z*Qa}_36M@T+}xiue>(NlUzHqE~y%43Pp(0w9bw`Frc_iMewC5n7OgT zc&6>y{f;+p=6!D6^XboI2Y;;l<@7)~A45}X8$UZliMVI#E=T6=jpojiibOo|;wk z+5qyK=~(?dp4OhUH*8H*r@&h%NFh^gM4h2jjP3lBnI1j$`jf&8BaAqg737it;XS#1 zf;awKTw}hmtdLiP+>qN~>eq16RP9^q2pCWFFHCT_3Owq{4NRZw4qN6Bigx+|?K`Tj zR>oBeF_ojVPXfw28vFgHt?m}2w(}~df$N362DAGOG45s=9@_jDn)ui8oC4{?{s9gs zs3a-6_E}Q~=)%Sfto5}O@j4DCqGU$mP9GcG-GV%k6HO%{_W8hEStJ2v^VI~3xohCK zv7$?G4R3dP8qOEQ`2G)(vLL`*{i_4M9 z%97AufR}|O!;PXw6mR-|nc$fm@#WP$GD-=(Vk42vf7)+cy(Vzr+o(UeCU ztH(90t<*9%$)8P7ITH)AAo=7Vj2JNWdCwRqU;8r$Oc~DJRu*a^c;SQ*M}Eihl#-%p!y3aU zpnwjJ3%(OkB$$F8l3x@#?j8h592o(aB;3@{j19*To6YK9Gt^p1lrTelYfU>s@s;LO zp5y1k4sH%C6JT%q5Az$bNXxpc)C7~V&_-nB*MGp|CfwhYmcKwI1cvPFvx*G>-%uBN z@EcfsUI?+eU{(bR0cFXehC114<5CbY?Frows=#`fI0HDUzgHGPywZX?HKkXjpC$bY zIkbKp;rna!0&Ka*K+7aWCNJjcg)^xVm=g!}*)baVg+#CYq0gS@O5WFE%$J^9-?n)F z758IZ3P7;pt3T@6a<%!G@|C@71Y2}MNe6-HyTe@Fjh(VgR54hp0YFFgJCE6iXNT%( zV~dQ8?7V+M>jj<+{pus8IhlL{f=ZazkJNj4)ok%H1b4O0>KFE5D%?N#WNj>rj_R{{m;Rw@;)Kh%-CV5Y1 zI7&`{=DA5m0$bG@H5bYI>k7U zidReRMbM?x7R*$ZUSY8E&$&Pe*1QT@hM>I(cq2s1KJf>#xL(L9Xia$XR0>*Ff7rHp zL@}R${%i%qhQ4VPB}l#FR-XPqIMAR2C_o~e(xJ_rB$@a~r=B&CMv-U(7+r|d&B?z? zm1>hB>eG5yk0Dok?-dDO6`NEs7ZOd0ZTR8r70rE*;nX|E4uYr6y>cUD{xtxR#bA{A zmOg>bd8kqiecbxf`@>luk0H{BGsn*DPiRlM0y*_Ud5UR#lVPy4Oip^|T)DT&-%B zHp3OINli?-8V3ceX!t{j%wA}rZm!dfc8{F%)fbNHuIo*RS21hnB|zuURbcPm@}4nR zlbS1p*?uYPfkiU7ybCa>JIY%kW76J;0P{@lD2dYH9Fsr5G;Y*QzjC0D%7+?*-cPPB z4C$&nYTxeFh8j}u_-0!ULzTpUOdQ)`V%J8Ic|-J@iuwt_b_&L3UnHy{6LgB98W5*i zudyDhl-ZSd6Tx3QTN1T`YLR7hi)vR5ROngU1ocaCo0nRi#iTb~AEV5U^J_h;v6=}i z{x%We;!Qs%C&H(~Fjdz_K=PPJM?YZOSvkuay!vd3e?XKhPu>2(Z%MnW{m$6Reme;U zUZ&6F*-Kve#fiQ+Dt=5X?eQB+%0>{9Gxi~}ikX?0+VBteJ;O8jYM7}V!+2N9B1lJB zg0VjWWzkB5S-M;^i+1mx;#eNIEEY+tCk|#92po~dnYfhoGj-h^8<`PDTvq{qlvdHnSZoJ}u#`Av!gFNIKg_DzWpA(IVKG`z~O$!zw7?3lZ#6w_p zw9{27K?s9gZASovm`+a6kg3|}bQB5|bvhz@#CbNF8z;mXhOJQQE^7pi@Uf5p{i>`< zc5=KcQ}7c#4}q@vr?U%#Y27AR;{iV9-IgB0L7|1?dBb~5f3db&(<#zeqRD7}I9M+G ztKQzwmX(a%U~e?X}*B$LtqR1Hem9D@qLUUi(N~9=mX-!VONl`YBAcaQcm!+%DXk0eM zdjO)(q4U%6D|#pbd#-%vs>d53lDT0DkE2Q5b(FG(xqpjZ#gcPMnYFS$0HFowNnGS- z0~cXj*Ci8MEsgkyamr4{f?=zp=Nanx5}|N;pXnYtcJG|vH1_ll65s^MA+iHbSrN49 zocNGJW@U!oN*CB`L>boCWTa=oGx7K{C~A-sYv))ZuUxM}kJ~OYbkJ^6@hASKN!an7 zYLZ&H=sc+3Ne|}*Qf$<=j$lD zuFq~Dd@+x4w$)|`=`JAEPRZICkefLT1H;{FO$|r5+$iYs44ONBo>osi85B3jBGOr7 ztQ569nFhP z>m=w_-G?ZhvY5o1Nf{8? z(%fN{3WwKE4VsbdTN|)-*d^9rq4LF3O`tndnSu3C`m>9`?Ey!zPLsNsqZqoZXJ3+; zygsL!o~p~Kx5K&P{7wJ?yyK^MGiQ9w_WNTOIb`|+N8_b;F-lN6^_~@enIk@e8W;A| z^%+-&-8;2-W(ccitLDFEo2iw__bnl(+B1tB<8FQ?uP{C;gr)IuJ!e%g&?^&%xt4vo zZ!41h06Lssy6T)N(%G=sHaFN;+T3p1&yVwfX;k^nDFi^juqMN0g#;lC_4|B&t@R9d&ZxVw4Q^vL=RE3cug`T!qh)- zz3MHTCQ+?5Lx_f1J%d)6uYxu`Y`fwaZ(``hjTwiHXx<{PBZl!36^!UxL;maAW2{^o z*^EJPWdN)-?+U%T@d768G%wm#|4s7|33k?i$B9NN_ba3@dWsz7lYi7U$=(+aEbSu9 z&YTR$euDMi4vIc}WrN3wwoWloOmk%!`iGyy9(Bp|XfU^FGLt1)jV0*Jz23s{*;+QftyO1tO~QLXi;h{IRGC(-Vm+~gW3+RXOd|dwxL6~H zk+_b8*E)M!88#ec=fPPlJ0ZDBEmp0KtaA_SfY-9h&m0#GU!&>;a0aKrk?xRjd~=NB z-06BK#QoN`nHpv!HH+bFzK1dUm?N@h(y$C~<2E$a(_;0poN+r*b_~%uLU`y@f45}W z4QkpGSRi+$;DtILlN>V*)TS*YoCIzB%6o8A`M)4_`Jy$8mp2~0%rI>sL25cc9N5ef z`PA&Dvu!M>DhFP+^TJha>N`h8D+1%ETw0u%PlbGydXOXa&SB}iVWVghrptX_OJar4 z-%aztq}J3fhB7?Uv~j6;^A8!5R5=0mFfdEFNZN$u?b8`k9eXo?yz$-0f)V-;0-f4H zBZHJc0hQREXDgCq1IB9yzZ(SR#wm~4Ku!G;Ezy^g2p0v8sy0&R(r=TER3cyRhP0@9@(iGYkOz_vp z4c$)Xw!H&wU}l9x8E{@1?6tO%@=>-zqhM!ke?u}+J-1dQsjkb9o%ix=a{yXVBf<)L za{ulkJ)e+0iAL~E#kgR6yJn$6JB_1J4qKAD*!!JS1GcYyg;s+-G|)v?Z=tJXInFtN z;qe=L##<}?IJIx2OzUw_-6I6pWey)2od|L&9;;9C3WdH>?`irjOg^b)ZC=JxNsCs3 z^JLk#e${976^-1aJAPBOjJMqb??4SW_`oO%Sm1xmis|q|MR2HrWHpMyJ0qtl*JPU1 zgfV7GMWS!6yo1gz$8o)1@61nWRYLtr~+M^F`WcazKnOyhk`%9K^Eo{MO+F?bOjK(C{9Pt*3 zbKnXruQ;BrDs?;c=zr^K6?N{|-b>!DK6ksYYQV}CQvgb#`;%f& zO7f$lQyNLqI%@dTz3PMbmXzDr4$tiS*-Z>$Y>Fx%&~bNMv+!m~v8=-%8nFfdRa|Oz z#nX!%c%E$o%I5>BOTf=Te{!hsh@}_MEt|}UM!yu`mGNZFN-<0|zr^J*rE+P9{6SrCwXgxmv4z#QZ zYrEE+wFx?xOUDFc3=~GrcqBqFmKced9$0|ZL%Jpo?n5~XppoP+553n|&nX4*}c+?q43YaHs%fpBllY&FheepzOJ6hl*z2rO07SJDR?bybSj zyX;B%Y@?Xc13(c%qCRlOm6V zww<>VbwHj6(K&aLvQhY?T7uU5sWXQu7$m_ljLEz>k`JM--Hku>3<5HQIQwv8+13dR z^{f>N651X3Ic{6l<(qW*5>pHA8$#WnUsF4OVf|>LQo!j*Y)y9c&QSkdksjYL>hFg? z#m3U53Ajz_5$Q=>Th{p?*nJs$+Ij|2m}USDZicQObtN10WVz^62slB<&fjOGSX4~! z&!SAm6^zh~>#80dZWtWI-D*6D8vBP2a;OGwcOaFk!WC?ddHv>4Jk=arp4|TfZ_{Fn zbGFFpvgg!u33_uYuEJ$;sAUJYx|oEyZ~dDlSJ?1(GnEmrJ24rDBqJ4Y79{qFe4=Sm zJK%7vi(|(wq#Ln0Odl%?zj@ztr#F-UKJGMN905QyYRxN+bu=zrkXX}#tG?9=Q+*qx zM^>cENTn#^*&%*Rfu*b7m!jUyU0_dF=x+NFWj=Sv=&Uk|R9FMfREyOZY(Oa^7McKI zqDr&|LXFBuiXO!xiN=h@1;1VQngB^JU6y;a>&`^F!PS`k_>HU}LefpNX=2}A9-5F= zb&-hfDL?^Ys_9k_CuhW5V3+7%g69JiVJ#C7Sq%K9pcMtz;eOS^?a-uOguVE(v zg_(N;W3ei%Hryj({zcjc(xb_yrSd6b;eh6O$Eih607U^r)9|!=Wa9*9h#V4h+lb3l zVJ2LYLW)fGJ5Pt==+rjp6oq`}wzS<3%KMhFji$d`ElZ=0ntk3nd1YsFe=Lf(xsl8WciRF#aNpZmuAzqyj@U1=!{HyHexGTFTP{7uPX>vn?ac&^PqczQV(L&82zX+%_885d{a z*?yL?c`V9C%F+u8P2+;?czEJMhM(_F;rd+J6RoTvQOV4P?DhpFwTqQkR!5e@H5lVB zy_@WSW8H!KAC79eq5?%9gK}`sq)fSC+=4W-BnzaCT=nPy-N=54DYxt=YzY#H5$V&t zkkJ_;LsMlSntjJn$W#ke^?sTFz2^Fa^Ieq_9HrKgR5vrGe(XY|)Wq-BEct^`gnipz zj?*wm0R?X{-r6RBboODXlbL~cr4%lZuwUhE?R0jQR;S$E^2@1ay70_p!CER@QJN`n zBk=!=wRa58Bn-d2lZi30ZQHhO+qRP@#>BR5+Y{TG*tVT){=2nX@4mIDt=(_kRgJ5= ztM0zLuirU`kTC&3t$laoAyEJ;&z^(MCb7cn9<}y&Kq(047z=BQm1YX6s6&2>a^z1| zUvULz6!$Kk#&8Mb02jl?dV2l2(3SW~F%!2(82HsqqH z5_@e8ePfgZd#GwL$vim4b3`;Fr0nA*3Xk#tg~JOS#tKVn9Q3i9({_&93n{Yn79E$z zsmHAefq{bP)@7T^jGsr+{S(C{wDNn%H#IiCjU)T$sS z2rjU}$jZ^rs$zyxVfCS1{SN7HU5*?}>(#LwfA6HCJrYNj)k8DHe;oj)BC@9nI2n%O zopJ6wC2L3)<8`C6h0&81Ev@E_fTVU?gF6q1kMzL>9|(@@UwStq*5d^>Rii3FM3dZ4 zw-OLJUO)ld+;{yvGLOvkv%*~xe*NpL z|9>E!w7cRa?gxlJvjYNB{eOp1I=R|8yZjK9|9g~jDNE6Qj{_xS`xVu5mE8@MyC~n@ z6gq0Q$r9}J!uU|h-kLR>dc>tw>(VbbjS!cmf{oTq{8<9B;#U}b%XRz8ZbIVWg^dyo z-JOI@)mOJCMKQzUqYoQpO-keWp=uA+8ozo_-&hevN~FB{5RHbEO(qfAY0x4(8i{W6dVIZsH3QHBhvuaW%0mKW_g&&nS zzSM_OMsrnlTO$4gFhBf>c%NUe@5mQoS3c&txaA_(-wptjtl?p{9jhp&fGed|>Y89g z!)$wv3aym=&^6N#ye0fld0j!g8})@baX=EHG<{ zW1*C3?u;s~lI#bvl59krL#5FfdR67|nOG`cVXyKH_JiD@brm;aFX2}COSBDnLphH~ zk4@B4^c{^O7iuq*m*QXiI<(nVpW?Z&9qFek7egA6m~~zIHjG?uaykrmm(j0#rJ9LOwrvu zkI3gjX9D$oV=hWE;5?F{7qK4ydeYCTO?e7tIQD>|h=Ig{+wm66WiWw=1tyDtzS4dT zx)-F^nd*Nz9DX-k0jhk6>OsyE;GUma?lt1 z-e@Z%i6{1=lq3n%lR^sJmWJ9*1uC7#Ar)zcc3_%P6nY^IyOD<5P5nTBJ>(wWV&_@% z>*Zzpvz9J=5C~d81$JWYoP66 z2}%S{o*)adKo)7Y!7m1Bcac$^-~(xgC?W=FfS!=56*0I{xG9TBhigGGfn$F~8VZpY zD7t>1qr@?#DK)`2id*Upb6#;i)=^xNe^^_}|339UP60gKz$FDq{;SO*ps=X-`jz(0 z_Z|Gdr_3vt6H56Zfq?Fjfq<0$?}tZ28*_UnOBV}U{r?ys=?tCB%>GxJp=b?RdmK@O z?ql_fkW9fPUER$kc32Svs6dmfM$`TX4abYo)juG6z4sk_W+RX!ZVC0Q+6%gmXG@h< zet^fjk%@D4{$9QfUp`*n*VE6>AA{uGqq~u7zaB5w&jERMb$h+*w;hD_f;Axp01bJ-r-RSA015{JC^+_%ZkwKlt_O(bN9O zduMBF?-o8ioPDkxe|IMbPtF!@?w1h4wyvIjPg(K2=qvO${RuTzW9(R?H0AlT=k3ea zP#n{$%o&s6p<)^J6sILzys5G@rddA&C1AE_%dECKm6KF0-j3`yS@Ic;iZ^~CtK~+2 zksNb;Gqj=We4ns)A!C2)+D2-RPk3vDIK#4b@yd9_rso48?Y5D=#*BU6B%bVgWVtI< z^*D5|Lk3D%sKJ2qcSSbjv?AJw9qZ*Fz;(jRT^dIXR01|O^47|jgx zQuu&hO#B5H&RWoo9FT*Wn)^Y32t>Iia8B#RRihJ4XMe%^gg_KwlN)D)F;l971&~G0 zm!V0z_YZ4uW-vK{{z0L5it6I%tth0~XosQ@$b*rd809T>eKA`LgO(@+pr&OeL2fG4 z6eepWgQF^7hEM&iz$SIg)iqq$BqVb#GqkVCvjfRcna9@;i&Q;;wm!%v{n5>sgLEk4 z@8rr1OBE`C5948A&pZDz2W8zMmtw%pn8vr8&fQY95`aSZr6jFst0&XtFScRqXG%Nu z&k9(#LMR9{_}aN#)(At1rN!x759FfE$lL&$Enmk{xl|x0y3?SuAD|ji2}weUnlUGX zKm}`}h7u@Y&B{v>TacVBBzm+igs2%Z@2|Rp5MeSfk~k-Wlq87|?lS6tnThB4j|C>D z*!cG$6kT>YM)KU?k^)zCpiplk2%3^D0+H(urTiiW3$!cB zgh<5{%3aVEh5Pv`N=nuV0y1tt zd_=)CcnENEV$c#HS`Y?KAgsSi)O20Z#HQj3d3{evXuv~1q8P71_uxNfy5S8)P(gNs zQLYHv<`CX)_G@b~Y4|Weyj?^m&?(nYg@IWR%^9ToLf|mi@2bZ z$Q&i=4o9KvlWg|`c&O8<&R;i`4t7E>@k2iGxKVa_RCO5R4g!P$9k*;g4{H!b1~gtm z&h?-CCnmhv{HOK~t5bT&MhD<~?>!}9wc90SzRLP(^r_x`OVs8>dUn|o>DXj4X>Mv&g zgi314;3`-(G$Dd)s1jshMq;~Q-lQ`{s99u%kVIemy%wD=WCA9diC~@fN1o9^wB`L_ zO*R&Nv5U+QwkE9;BCR?{g*-jxTZT$$k1X_tUk1rgKDk4TLEZxG3_`Zfmbj;5isZU- zM?~CRuRy;wpkicD#|O=S1k6@__PpNY&dB3mqt`hQHWw=}kV0DuUir$33A>QBkzONp zXl#yCcD$^yOV~QH5w}T8u{`BUecVf0&=GKo*0Y#t5gPZO1<2pFwip-!bVDZ1W4VJ| z;4ETl5}^k((G$S&p&!+KxM3lrW~8JMM%iGj($ zjndj6a;F@rY?5eV-?Y9i19Jh2ITi;|R8Jl890tjsSt+G-{1w&0CLsvb@n+H1<%Ec? zj!#HUjWuL{Zo&16a!K!O$T@yNY6QE+TRm*TBFv#cXfhT93%iIvq*q54ML&15Rsmt+ zSg3tZx1gEtC=GLFWi&MiuOq3w1l%+7fLpPS#o1%!;SNX6IL0ohRVjb6%n7pNfW{GL zrFEfLPr?tu*;=#%{F*nYh;EJ7MdS{A^w}i5{I$E61pOEpjTC}9=&4B9gZ}#34Ya8* z<0C>6gCtX}>mvs$x;Y6~gP1mf*o38%ffboq)WD^g5$sA&GZLP%g&YEJ{YA~(7M)NbJ!58YZVkClD3YNOM`jneN=EEk~OWKw7oInOJ z@2?3xueXbx;OFC&Jz5QZ0)YZ$lu58-dmEVpMhdblie*_9?K5To8tD3`zI}2kqqLs< zx~kez?UsL3_{6W5sQYqv!DBG=Jk9VXWY-tEV@~@L3Ej`Fb(XvJ;+t!0 zW_G+`>Fo;yNC_50)omuK(4jAqU!P7%^Xv8f!=Oi}MxET{#uWe) zKV55POldaUX_e<06(>$g?SzYdd+bE%7?J0y9sJ040;=E8EdZCdLn`qTe4A-MU5s*h zOutdIgS$sO*3Rs7w@rt9cWaG8eB#Il!;Bamvjx*{(gEcBK=U z>=v1wVY_sUuv^YoV!2b*Ym{y~`mRo!Hg-kwTPD^1dITQRN{8nY<}N25-*;evx8db_ z4YDA%NCp8w;2MaLc+TR8re0>p*e16zyv5c5?tC5$&{%IV*?Jl?O1Fz?v@yLMkG*S? zT^F5zqn>qXPhc8Y2up2k& zoniCHYXkinR`1=RYC9OYZk2Fvg}j4Ot!dOEd5L&+>kfKcWr@YzdY+iN9@Ad)A}COL zeR1rp@M-eVq za^#$VYel}Cmu=wsvLUy)+q*HwH647te`N!p(vF~=VmC=n^tGCF%W1`Y!mVevY(mki zEpyU_-H&V@9K3qiMqqBUtv@2Ykb#3(jT8zPjP!m=n2%u}WqxXzsvmgV5pS-TunH|v zi6E|Zc!)??DSw0X_sVaZb(-X=#I5kH(zF0MY3n6BGHa8DV+Bx2s+hA&8ohN$qOL5l zc4&KbNN$xBu2W*awM%+Qv#ZZoEKG#`Xt3BC;WGmV@k=`goKyjLt0N;Ki~QE8Onhxb zE&nEL_wU)fb8xG|iTuzMc~?i)`4>K`Zkt&pKPxR$^ln)4nfscxgFmEIwMEK~)>Xe` z%<1{nPh%gJS5i-7SxM0e?I}7TWvQi!n_jK7a1$|)TFymuw7nR)B+fqo?OG^I-a%cm zD#k_YpIFP6-}9@t$5+c69&e39zTVbaRUKH;1tE6HRr+YHkdY8;yJ)LwrHz|Uv(d`v zkj*+DtcJxd2dvHQ9M_$Ij&=Lv*@g9pMxEC#&{^{#U^VQ<`_!5HNWF*0xJLR4pV3ux zMQpF9FklR?fCO6%m+-Px46ot{(e`Je_IgaOVVAUwL^6FFKd&eJF5)V%wp;}Ds-3{dMXx)g~3nLR8P z#&&B2QP4s`?5QG8dE&FF%LY9&SXSZ9USrTYxy^QYq7Pr6g4<>00;)T8q;x) z>$_e2Qj%V2$eAM`=C0|K(bxR}+WE^<$TUuE8bZKCs&YzPdsf4C&)+~(uNV+u=0Eez zg%eZTKXH~)^*JBuf65U8N8=l%tjq&bUrg_M-<7#6{dXnLJvQkVhPYNImw|NJO4!D| z=MS1yGm@4QMc;_^zk&w{;sa=USSZMpw9?1?c8^{*!Y6%AR9@C!FWAu~u*AtgErXgD zedz(!d%- z{TkX+kE22AqaJmu)6-k-nLdI_cP40nqv}5wAk{O8eVhDE>m?%ZMQJBn76e`!k z}U>lc21`g{fhhp6^@@2O~!3pq~iiap^8o16@VK6>&h}D^i}?~S5ZQLJ$r7GzwB1wu*rf$r>kTRoRY;rJ%ED-7m(N3 z&BB7}4h^e;^?=Ab$)nGyWZ>NQ2+~*BmAkztYkal+h zWZNQ*5?;ZLD<@&errmRsNtEWFf96WgHP_6&?yl=tYo(v-WbVL}MRgx**@M7=wa1yT zD4fGd2dt7gi7MP4YzNBU{N@P7LPb!4gW{Ynx+hYW?|5RR3Klio0=B;de6L(iFHKX455LG!++wc zr(WNceCcMwg$;tR1i60kJ=z-)j2( zYM=d+{lXP6RF(3Ra<-j`oZhSuKi3C24~HfMMbM^IkjU3y%jHgNi*PfUj7|qS@MMxi zc>Wp#+()!T9(5*z=Hh23`N1;yeNrbDL{29_Pz{Ab16*)|6lu~M7y2=(xsmwkDi9TL z4yJ=(B7M6^vpu^xU=f9jDvt^V#A4(6ZY2GXj}?l?Mu@ky-T!M5u>JEhQoM`X^cG9+F~x5+%GTw&bX$Bt$( z9|l#hSyYY~eJzW};u3(M7r45mv6>vP682D7beLgvAJiycO^Sbkzd@zHSCsW2gqgqN zip4MWM^}c`@n}JqiqISSi=A3F&8crf*}GhJ)MQcS);_=aw?_Mx_IPj+ipz+slgTd_ zeMlIorS%eOMdmHz`nPvbx}~7(=yRbz@0G9%oq;t1iJ?5OKs3R>t1!=1q4CNP%rd*; zOp0kqi7>2{DE<}O$idYw>BYOGB<<5YPzVFUuYb&WHY3ENiDZL9u~zNd4RIn~*`(O= z^lzGEN6+X$aAZR-$JfPWIJs2fg(g1Gm5{2=@C?(SxP+ zP5p^l6P!!>!yNA#r{mHSNJygz#Yiq-!1k*e9Zst1l=jtB>0(gYuvGfo7_i_qbkX1A z@5z-J(1xe9L#U~Xf#TkOn|7GnE-d&o>o7ce236$iS*}~;Fmjxla?W^MmOHpsIVwrR z1B1W3^Sl!S3xiy-{Kc#fwDN^?br(s()yDS0R{q>k8CJd@{@#;aSI|E;M~QgZYQX&$ zQtmb`6rLEUW$X;5O~8UE4&Ek3RLPpHwVUn023k|JjF^~LB8GXWp{z;wG-oD28B3S+ zW-4u%RRal+0JaS*qJpZb{ikxCuY%(q2RZQf3d88URoho!+|y3M3;_|S20UoknL5Yqm2hI0?5}w1qy&n*T_f@BzOoa7M)zefa>T& zA|&F?QB{6;fRd=ZC@^vws9f)15icE&_RzLYLbVQ>zv=E!iro9?9%K`)T5KbjI8VMP z49MfdByj0^$_8>;{iO^?!I0hXW7LLtOjim)4ZQmnVdTXinWCqrI;ca zPWik^)pDRqo=mz&4gFCB=a9Gi8j+fAyN>dz4yRQ+7ZP3Qd`Uxd^lh#eL+$yx${qmZ zO@}GBgtHS=vw`c|aa!M;xc~ic6&Vd^d9=G`bol)&ZD1jtoGJTVn}G+jUyEI zXv7{`($Z}2I8a*~mx)gkChJ~R&xjXV!yV0mC#JP~&u3W#a6(;4WsG5sWKBc89M_+E zDz+t2uQ<0(XU&uJSo>b@w6OO`-n^mgK4_ZI@+7UlcuvR=V_xF|2Ou9Z!U0`>ZDt5< zs&L!#ICdcYK(3beQJtXmb7*i_`Z9N-un}q)oCaxev7Ud`(%y&XhpF0%kGMx zdMk`hPL#WV)+VvIj|0i9~{A`e&hhHB4L*8xjGH8R*>p@!XF&J0Hc|q-Qyb zy28cswn~4C!u_5Z&x~j0*H>r72ub9#j{oba4n%$^P06%Lzh=6DtEy_Xrl;zq6a53J-#6gwJ$_tHVKb8Y`N0G@i$d8xghCl!w zIoJz5-W3G`laJ_BbbK0k(HeZ4Y>*z*(%Nw}_HfU@s)P?K=cYBj?Iy*M6+^@^lvUH- zqNB#QQXTWYF3E|S?d!YDzYrrSMT|;brb6usim0{2OH~GoGVgZ801?PSB@W~u zkbAL(!%MHn>oqfswmH{NoC?t9bP&|PiDSqfifJa>D`P|>=eaFq<9d>2TaO}+ajR&X z9}s+zWx%sCOBf?YKXmBQq)dKPD9`6%2ulp=`oUl-2M}`mm$}nEf3Zv%_6`PkAZ>lD zF>G^Ptk|acEvm(I@l5@C!=eKsCk?mpjtMCVrI6q#WT>%mEsu?9aJz6&YMmULqiqd{+iB!dj&|1t~iE=CN1CX)Vi_A1}_lcyiKAUJ?lY_;!paE1k` zL911u8lTVCK!N#uJNwH-&0=a!abek~`rP3$nmW1X-qt*X(!af##AZjKHz^&D0N-w< z&fVF%u=sxAdzu8JAcNMkoTZ0HnyF6~5F95GR>=66dLT=*rRi@4IE_ToY{QF)A*n^L z6v*-v-(f!h)=M;Y_*|421PrGnpAq3?rOX$W7H46}mRQ_%tmj7WTpGC5&wicc$JO|D ztHw$GX#xXfE^zitStw)_&;ZdW1jA7ycA!_GE?{ zo?w)y@flvd)(UH0J;j}0t{GG;z%gNitIJwj)3hO4L=&I6)}##CxK-`gEdM2Dl0GHMPyRB&sZO8}p8CY~fNhw6i|*D{l(gX|=MeDiiw z;uGPq<)Mz_rB%e|Kl5ZtcyW4fktIsgw9b=RrqwNo2x=r2y4Fxzg+gW zPyY}+)z^dDY?D~i7}%foxdJ$c?)^Tyljkv>m&pb$VZqURI_K|%O4qlL7Z2HXYoZ{) zyPj)V1mo(gEXT>=XH<@umL0v5e^3JY$Qy`=1x_aAtZV#$FEH))JPbI1;-zP8MEF z8&_I@nlORzE5RFUdrUu4qgcf~98Bf0`7V1Qa?Vje#XpM`J9HGZyDB5z{WWC~)N7k* zZLNQkhK?$9SuEX{f!n!^UUFC#d94|MlMm7-Z~d;_eLsNg4{`=ICZqWIK^7|S-7cCb z?kvvRxv$OxI>Gxhbe3Av@Kbw4wkwbSB zK2q@V41Z}N${Av5XcF!mC9$8QN87fMX+YlS-bz3*BX$$oA|BDo#;A02 zVE0S}fM)z?2FrJidr62M|Nfe)ko+g^VZy7LrYHqYZX1^q7!QwJ_M)xZ_?+u89pJdq znxLPB{`rK^;N`ebm%7iS9|`+#iMYCJL1Iw7!nb2T&m}xJC_8a8@B$QBV;4wp8rZU- zxb7`EzqM*)>Z2Yn(hX-BRUl_TXWFzYW|!vLU2^YDIamwOhUnc8u(Z>J4t;l?+xWo- z7d>l%mAgR~cdMr#*&QGMu0E8pJ8f)6TpGSgOz+=y4(z&fUlz9xNsokQ+JjdLk3Ow& zpqJB?{M`y*32nV!>!Fm~bC2P_k$t}OzRPwq$!BX<4tOqnB(4gHBk5&dv@Rsf8}_VN z@ze6nZ>{^-()V@#$*<#@M$knA4D6svWDz+q&U-NMu5L2@;XA8&>lWP^=r6x`RLZ8+ zX)V5498c@1QKQyQ5p(P=-uoR9y16QdYbfdT1xz4iU5>pO+ey777&L89gI}q)3%BuE z?a*Ji?e?`X`5NUiwZL>d(a4#L*w`J!|CNJLjq(u&)Eap3Mz_K$L0!!n*x1phc`Rf- zW!`*Jk-?1POq{C#=gZ&v^}FR954B(xA_#@<>6KN?1e0>}6_1!Y`evKV?d9Y-bz848 ztaWIX-Wt=2DtkwvC2<;a=5eZ}Yp(tX*U?xx{!v#=U`mZ+)NHmGxncT5$AR+qY*T9D z;St0;^!N4_>SfFCfxq}vu<=BqXYOoG0s+yYS-p5B#_Lq=(i?iBdKpZ&loHTXo$jgXesc(Xu3XjEgY5=+ zYO0a?ry>W_sb~yeTLQZ$dr(LJTir?4eNuUyj5YF=xG+?r2~;#n(epSQ6vfoz3}C`6 zvC~BE#i`!7A_o=iE_^NMKS~Ow2&h0x>{rz(MH$rg@;>_H`748QH2nOv9A?dIuF;*x zJXKM`!m(wqtchN=R4#EryK|jzH-r^v2mG%i=e#o87*To1vu7|+FW^ktlb5ypr~_Am z34)1QO%faX5x++zVe#COU;~Q@-x>l3gDnpRD;6p^V&*g|(yn}&M2TpVyv+l@7S;;P z*kvKY$$wRg2!~R=YS9f{K9mhMY^v2#q%2WTv!)#3f!BG9@4+|4FmxzuG#w8QQp-{_laC%WmyPl2z5}*Xi1w;%%Y*>+!mSo_IC#4B!MseTzGGr*{BJ2meHaX&!!$aRwO`{J1Y z^E~(CuJ&tW=Ifzy`{U-a^!oczJ-7GmD%0;}BKPYc)$jQ}_iJGL`=IvwsBXMUf@DSp6LSnc<#yk9=L-&1eyjPLX7HsANnBfP%f`&91N z#r4-w>CayL{JEd^`hdsJXa3KluG?Hl82|fIF8?b(^ZuFNZE5E5+urYU{pw=f@74A` zPuh0`b0L}keX92R_TK$_|NCuXM*s6B^LlGz=f0s=%lqKabwt$fYY9I0`*8ib=c}~V z_t6;ty)8@iZ9nyc4&Pbl-p}`Q;QNzZ-|zdU?2n0UzsH}a@RI3UdhDt5fd6)~{VMeN z-3Wd@l5=bS{Xy>cx~cvh{4nPMct7`hf6AQsd1RmZzVDk`Ur#?J6b1EupLN!@kFneH zx6J7Ker`VVe_ZDFwx6MYzrB9H9{pT~m#WXtG45XZIS!85zedC!KBl+5>e1h4dOjal zwtXHe^?f^spDOjcdmq~NQb#)VbiH6Fu~(lsKE+yir+ZUuf9`Cb{2|jmrjh?^sP|>67T^5)VCHL&y!T@)_dv<+v}d#Bz31BVp#3>30#4h?Icchw zb8(x?dK_^4JiF`GZ6pn_0~~A|=h)eL z-50G&$K9>8(eu_TrBkcVEwM`XY_9*j=lIu`-(`AMDxRm3o|8DMY?s-Ex_Rj$_;RlAEe)-JtNu543Mj`T{d3{Rw=J6bp9hu!U3t9pyQ>s;cj z^u9N8YFBi;v1OmNMlKVtXv_i2|5#0U#4c5Zn^Id=-aO7RyHYuG!`pTha<2`ecFrtk1vIN4 zmq0A_id8Un>3_&R7a5k5p2mCVrPSkDT5A=3b*U`aU{xr2_OCVZjPQ*_#eO>#SU-uR zdXHu3(AIP~o6Z|8#FZk7>vTHTEpdrG-9GNGPXbF(R0tQZ=rVVBEZ1iweNK0L8*Qfy5#B$3b5T+fXR zTTUp6?>vt4mTSLooUz=#JNd1+__9@wTM=FSd&Y?$zu6m;79KEmqKvIIp$KDpGlGGnwJbG^V@R+Ol_% zy}tZwIV~oErV#nO$+})9_-;1q$}6|2UxT>@kkHw}X3k2ERMQFi3;ld>aSdxtqI0|Q z^MW;(bI~mh&C(J-N^RL`l@x}g&;@;^&?{93Q*>NN`eidJmL0iA4?h->B1iuc(z2a| z6TLv@oJW${CL!!~>dEYi<*ps=ypRf)@|7A`@f8SQyXGE!8nvnaI0q1B?9$=Oxcu_Y zw9O62?b&WXbB1!vrs+#q?#fkMkqy`1EDU|pc#!0FaIvnbVwXTaFAe+1=@QHYpv^S% z>2hhjWk-^-g_g+mR}BscH}pIt0Jb|AqZ7ZZhcacq=`u;QR?MhRsD5v6G^yDA6unY5 ztx2LJ<;T9%-IH_n_!b9c7+(3iSTSL6dGg$KQ(y)3C}E)!m-dz|gRD9hO-W$ARZ?E= z^3R2-QYA_Ro{~{tV6#*y96+4T-puoB%gi&|IA=mOL#NZ|b9b85`(dtuw!$7|sMTJV zxj<1AP14!@w-p{wXH{xWamgb;oM~ZnAcH(Mx3ZEnw_rq`S|kvrAyzvL;S2zlH$1eE z+|t4Ntfi5Y)rb)B!9WXKl-=Wf@ygYh)sWz5{gGnxv5Nwh@9k2hH{J4kTZrwnFCutJ zz~fYBS?RCdkp#i%i>AiW7U|}hoOH`QREJVa9W2uCv2)H8C0RqD zQKqCx>=TCHNj+xcdhr1ViqX|nk6g2$grZGuTttf7rr45q3R++j=EAp^Yq&YGiw!ij zHZUssCD-wXE6v{=HW->)0H1iZr9~cTIL!+;Koi=@U*Cw%1QlCZd%Wdwqyes^pud`@ z=XAxkx;9WXs!1d>jr2WT>WP)W^D$CBGlx6-iwy~9TkA6Q(r2D#&AYv6nwP6QnSS1C zl`e}0wau&(NC+LGo3_4|Jg0C>_%x=Zz>~&OAJ-s-T;6@|wltV}%--8p8l;mhjoN<3 zkYN~0KklHmRRN&Hg29)!gwR+%au>{oH)G$=qd}D4c?i*HBFP`YZNLCFkzFlxNj^6e9eiS*=L=o%>W~57ICDh zfor*CFl@Z86AKXK^C7?)9`e^x)b}}dW2Vu8FiRLv}zBL zt%Tw}AqyajuQ&8BZnu-v*637Q^IH40f_qjrwsL|<8vhVa+|(2?sg~1du}lLB4kK)Z zFE5GIVR>jhND?>y^A<%=wOI}=X1(rsjdIr$Yik`8bb*~wF8gYkAn7D4$o+95Deub0OvyHFZwUfj=n0AL zv`rHYn!BRbSZ2k30ronU^^$Q5NupsE4W?P0oP#~bXTpd#%W)ZFz7~Pfu~#B8x)>y7 zMO@^VU@{I(1-VKFFX8qQiIWKmerb-T$JDDk3O$x(?vQek%?yROmx95@{dYh? zEL2p{h^r9J7X>jTeaQAyV|&@@&6^L<#x9jcGx!*zYUu?INS$W~*ThdFY`<;_Y3Afgycpq_qi_)07w5aDr*HHb+0rlWf$F zWs-}r6KJH6ci78CmebvO8&((@^pf-DpE!Dat266w9#Fd(pRGFcm5Mq`Z7c_?S)$I) zU!V0#jayheMYY5oLh7zjGXBglg}I&I z^u~B0>RzEbRgpF!pXss^y{;(LWsoDvVJ3!I;38C7l5W-i)nL$EJ1xp@X(tlWM zlZh^pq$~()4cC3|748N}JQ1;RU3A5)25C0o&s2@#N=d>5QTbT61kzrwbgGuC)xI_L z;y2tTEwMXEv1+&6mM9jTO=hd{H-~`4A9kF(24g; zFPCTu`IbsPCGhpH4cAzM;*UwUoNV8g)TSa9f{ZORyLMZngP|`kJ4G7FccnAZQ(U)9 zX!vU2fr}M`vBLR{>kILT8A*>Egnymgn2gl48P(iR+x;tXw>76pb9m4fyIq`DMwT&? zlhiSDb?+peqzg3j$k;$DibzjvP?FlK)~g#*iJYYJ%keChgun5FG9?H!Q^!lrJiax2 z5EGWWEs7uG{?3;-h&|YLwz6VL!idvzk`jVY5aN<_USjR9!x?QnQPE#7nazsDZOR5a zZ`FvejWETL9LGa92o~vpQ|Hx4&>OMgqA}h!-Q$d$)8+~_J$aO_p!Wf%=#!l+I+W>K z366MB$E#K&CFprNJIHJO6a0NjB0EagC}?Kajhr?+zKrFIJdY6zT0kb3tElE9kfFqt z;_{DYus$n0TPNW%)P++%PjvZDmFlG(X1vnFh2CZn8@ZTLqepx)N%)8rZC6lQUZ{>6 zZ%w=z@3nh0NodMtcy^W?*#tusx?7f~(8LmWJzE`$Lc+Z{vHwCtbun*tB0){Jam=ex zxD{s3Z^0Z5d8NgKET#iZuTMbVw2J(G$X+?(1m!z&y#hvexT0v1Y+tMI>R-}Ed{e^4g)Tc3}$WPOPB}ZPz z*WyT)zM=XebPYeM{P8_cSkPTZD7A@$PI(Pu<@6)blvzeSPFYQ|mit#KswKs>)M<-= z5y!YBX=9BwjLq*pO&rsgLjD6_UI8@Z4=38iIIDGTp$!TDv5X`n^_;?&?{yZN8x<(j zJ4I(B5cYPHO0F3m7IH2tAmBUINNja3DiPxwjwE*(lRjW2h%&XWUAvN58qiyYI%`BL zH%KQIA6^SKC8|NCubGoy)c%COYSOkq!Md=38b;kKwj6v|&agadk z^-#4r3k}t`-(;_kM%kYD8NZ2GFRDo^5>xEk1$EEq(jDM6%OD-f;Xd`K)t)f> zmd49_*+S>4Ako(l^)oquR+gofWUpn_`7`HEa%iw(te5bo+FpI2=YmC2SVbXwO~Y%^^GnWj*iC#U5trA>h?K*x{>r_)Af_4$qtwj&NSS>Rl@b0IX* z1gWKmtt}ZC5~cba?KDN^Zz)?KZR;OSIhq%{&6 z^d*dz-xq;ZSikDL+%ERd5fS^kxn|067q3Y;{%C=r7;5f@k)+DIVafqT5c1s#t z5aVv4@f&4`tDO&C z1PYT#v_^uciH;=={PBrzs&^&bDNaK`P#bN~n2V($Bhw-)a%<;dz78tSoVQ^M6u>#t z$9_GI3lqK*K^ihdqepthboz1p@5UV-Od6UI*3XTtv8eSNi6>a%_llx|TLQThJ$bP) zqBR-y1VX$T%@u?l#5OyU=~Dak98DUd-G`O35y8V{tr-!!q3McMiVXRSyxC})XbVZgvGMR;d*lT{Lmxo|thG=tH^3C#$dMawM+tWGc^w!QXL(va z%XO(wA<9=O)ClPsGoI6g^uyS{RHLLD&Aw#%uW|pfVC3JBBwhuP%nh6(l!``}*sQ;r zb?;dq--}useK>U2iF;m4rDACJx8v&M(NEnSz9Mp!dp#{A*p^1Hw#Dm3_-Ef

L<*zl z^tAadMjt{0$TdNL+RR4QPJEg2C|U+Ca=7b`J0bRf@!Sv|*f~9b(|a;ag~dwFd);G_ z5A#^=ZW{zL5YAMz0oL_M!o?S}^c6WBSsWCbT&zGh*5IcRj6s8&)zgG0*qM(YIJnp% z)}@nAsy$nU%IKPz<6kYFvx`g?>q~fTYI*W3C2le1Uy?Xw6!hJj?aRRX;EyUF)pT$Q zL|P$8h(tx1GK)$g_KZaczpkE9EF4)7CO6Y9H`Lm-rga{zHRi3?i#zaWMv7)+aPNUz zN(A4=Cs-h74X%k)ACv)9yJv93TA&5P-G=Yxb)ZcD(?W!w7t(Rc+?Ufx;OK73sDip2 zKJvdDbRM`$Y$2CD&(EIDwEE*!Y(za)-1GL&#&JY!gD z+)yY!&XY5k7RlgCm7WKDH9l9qpx9p63uI(9dqbfZr}api%LJeh>PP9?f9=hvB6Kbr zBhP6w5Rp`Q63XK?Hn1E4AztN~9DxMp;ww=)z8Ui;>8+ zT$aI`)30aFhU&xsxP@k!WSbaU5E8K3C_@N1weTcvL= z7-wSFdhFyhYx~L#7(Oez833-zFy%K5cujM4On-2}yWcibFZ{7@ZSSby_i)&jasvbHh02^85 zdWCG4$9r~A;n=zH-%Ue%N1+4$&9_mc$hFAXLzeCGRV3*K=}uxF+Orc}iM_pvY}Fd6 zBM{I7Kwc*+8p{C90#wDao1CWs@7eP{8QVepBhtR-_kwT0%^>w$ZyyeN3eZvRR*hOZ zfb3RaF6tYOz@%i+*?ftE=cDV1Gz~nc_G#|UPwC^(Id5`*(y)4-3UtRHuufTs!TV7l zP^8OrK_@^}h`s{W)9Ae4zlY$KmdQX0N{`{d1|`>59&ro+UDDl0aY?yeL3n-u_a(`c zZZcQyPrqCvw%fVUl*n+ayW|Y01@cve(0&IN3raTQ3}(rS<<&?SG|4e7o2@=>E7I4T ztpqGF`D`m`rb&`$o7-QN79&o+Y4ewl@BP#kl1e_ILGI@xFtRmVV~NGN05_l^p23W4 zoCZ5L8#P$(D@mA_A_QQYx}$7VAxX0)r!jZL zn3T;Hx!O?TM&6S(2{+_=3DqvuKC*0C?GoyeZq)ir-zqtWMjeT4+>)2NCC;EtAd7Jj z*p<+d|05^dsu{c!QK7NG&kZYKlQ-tH{~+(W0g!e|s-n6JA-BMDXmbLWoncR(-}{Mi zk(TV1xwq?)tV}}F!w_^_Ac-j8WW78qnU2T>aq`oEw&moYmcU3fKwk)p%sjITUS@Rl zCE7pQ{POHAY6z%LAX_;cS;YO=}%J6Ie@nl+K(LdoApu;$ z!8IU5PeuJeZTLyQz-=(BXw;#lU7ZkH(Mj@MVaUw>T7+|zMu04^HUL3ROhht6I2O~x z>yM%=gq)8=*aFK?NCd=m?C+=ih)Wj$shTjBoR@u?R7i~W0NP2QBnf@+I8M&h>m>xH zHyqX>(bGWMmHqyRb{yUfU?>zjM(OfyO5e!>)xcU|s_U_Ka5g7LXIMH*#ow8_KTR;`x^-kzocd-I>F9-JlWh2dkEQk=U z!No=<-wwEiY>CzhJ(qWMFL}i%3WrvQ>9k$0x-QsTp<-nA}ttAr#$*rFIF(Yh>+a;IuR zYH&KpPrO;0a>LGwyCQin>!^oqLnMG$SC~3d43)Cm&7r+XfELW?ux);MucjFbsWA_7 zxUWZ7Lr4~#qm8rn1sQ%ALSdVxdYPcuSLf(ijxe>%=gnYt%{Rf4+}}WurvuffV@WCE zAvn_10(r7XhXdZlC?MlN3=Q-2b-WLM>>&Xt!jfK<2DCZEbYqM3X-U`)A)QHovopr@C2)d`G?x~acOsg7)4VN@jOgI zr>{IBzONm$at%NgF@OX*(RPmk?ov4cHm3}b3@!PkkM9LtUV(||MZguOgO4Hn%tIl9 z_Ge`25yF=d~^SeZf+n`%j7mMU%lZG{Y?K3f+?oi6b&yzsUyh|d5| z8b}<2&nBfJ64=H`I>j&T(!$f003);0qLA!(sK9Gfs%Wh<|w+%wm@GZ)%J1s&z>rxiry^)}z2*JYz#vJvr(jqA;^Nr>PAyvmAus7m-gvDFbnAPhg+L$gk zE5K52RokOIwz77WjWL&(Yq4qx0;K3v=vy0u1S0BBMUFS=bk2F~%ir)S$4A5 z+ZJnK2X?g^{$e?V8DZ_v`p5U`%k-IsAyH;=J-QZY?^NkYA>bQ7qY}>=Z9UU@7au1` zny2`0_l!!mP2Vpbzy?^7?|kjL>=S*3DVVi85?L#)H<`!9LXVtR2T(>ONGELtEM$LE ztjp{k&S;`ugi^{DQf>lg29DYuhAcHVs|?I#HrGqYTl7bPw$xN(e$yy`G7Gphwz_sG zE~eJp3*#YbG6Xk`QwX8N=-Z~$ z*4a+~2YZtD?Qzv%)9!KK6y6;5D@44tMCQU8EoD3bD@zuyusStkB+4@QUos{;q3(qE zB2ZLz>Uny3t%p4y{n%|Y84jp@>iGb{_N>42fJ@Ll5KT9yldmOuzrXpwo^=T|IoAJm z=jP$`5nuv!9d(wZY|KEiC|x`^`B~J=)LgFkudVWXnU2TTNmC5z`4V*t#@ms--j7^Z zqT0zdDo)Q|&g)%cuyryKX+q{W;$>1WRLO+l3S{bQrP=O!nUb{ zejS)oA_kUh0+1W0s)$b;6+_Uv6cd#CI@mB?mnSpF2jVMXgj3*}f`q0So>l@;>wDR! zWE5p1EdXdU4`3kxXcpX4PEFl)E3A0wOfuLbKm#&aC}Y*~DG6IpK4wJ&cbgfVd?0~B zlR8w&=^QBo)k(0cM?HwN;zkkHn4V+`wB&N+_<9NhvHx3GAeAIhV`2HNgp;Yy!bc}& zPLjQ`v2=geqr$xo$O%lVG=OC49*#&S_D0E%$w(J3BCp*-+6GRa^#~ayPv_H5EvdRyQIRlNNsPxk|zbi&SjI^B_=lmx2?sVNw!tPQ!ek7 zdkAE^NEA$RP^W`yM4aRt@O&-*_AIt|1jKp7vvc^UV>AKGB&rM|gW;QymT1Y1`8r=M zr<58V-DI)nW(YP>su`qIU^kkqgl2jGIlWYlZtV@OhiQGdghbXmwN>F)fp4e;Ny{UH z#e@R-mjUDO&h-+Y9~6uvs#&B(?`3=J_vI3jW|D^0D*%0yRQJB%2zY|z_y%Q3p;oCQ zpz<(1?q;^*=D;E}11ha$k;a4IblBGvChv2k$h=?P3k-#20|OdX!G|L)OKF`{UyB}*M?VvI! zII6D&fG79h5P%9PYPDL_;8z~gzt`ku0IU-TTTsEAuSlR7+J=qkbwC9eNlB1gH>xh~ zIRA+w*0~v|h5@)N$WFvi|6!;S4}~xh&9)XQ3s=sY6PFInQMDH=2gT7#kOQwPQ$5|SV&H|j4aPaK>aNi4soQZCEKEs6?l-QbsRKKfFw=N^yLxxEUbhPPSo{q!Vhr3-T!EN z8vw;{8ptrANfxmt6<@E+Ttvtz7si%)qs(zWfYFz<-LD2 zX$}iYCbZR-e=&;fPpAAzICFJ>GeOQZZx&1jzYWwCzHg4?$Y#D4u!05^8g%Duz`NPb zN1l}mBIN!UD?OL)e5716Wo=-J>*C6K z*ky+|jrVHPtOOTDJzw%WY;NjONuyVU!vwEDZP z4HKi}F4GCN7#f%n6^6LqQ~N>6zz`n^$OgsP`@Q{O>YEfDmlWiiRA~}8ETg?(GtgD0 z#{@6T2T4Q|kR6CNENE|?G@ zjc^e`-2*ndLzuSBD52DI1E-*Z{H?E@4_}+Yr45qRfb&f_EaUG1lp&FG{N=l^R{;E+ z$6v__A}LI5O83fbYJGsL0AX){Z}6?c!6*B!gSab0-N2-uP$f>U?yhVxLyt{s)%Z{qeyh{=>~ zusB+u9ez~}>F4P^e5>NcQ_ffX4(9iuW-S4nkYNmtr$FWR$zmAh-%2bL>8H(YCb`88 zMb3>B&0Hiy=KR`%>l>aqhkm1SBV03$$QD(O9a?2%xHSy7Iw5x)W-0hj@viYeDDwy2 z8muC~E+Gls-aB0Qp+v@WKs^XoIuB<9yeG^u?-G}85_!DO>ew#lp#iFx z&c*ww@+7F<4&-wWz2)*uvoaFiMNASn&alvRkOMavKXN!>2@X0i((waU*cveqsNO&z zEX-#&b<@^YlNUg)Z4#Dp0ThD^=ymVwVRqvD+iJ2vH5c&vh?ZUs$K3&Evo@Q>3bg8U z)0pS-Zx60;J>I1V;y9C_m6Nfot)3yOrHV6A1ZHMRQ;leEyfIvAhntG}YqP{UPMD}) zZ>Fkp6QpV5)wpD8Z}T$BUQhB7WvGsBk)#~K=LsDdyvNTVB8j9DLHCQKm0~6^3?!$iAUGXdD`ipv=?{u(5o28{Ws(uf=XP6hX3kS*9{7gHvqRcfowz1v z-9eSToM4$CPDyvl3dhcD)6Q&*b{>=ZwGBEUW8!yUI^AC2+xa~bxEqy{WDrWUQGkA+ zLbI{WM=XId{!FTYP!&^ry+(Jj?;A<94&=Fab7?>bD3eN36WD-p_S$MhwL0%F0lS#A zR-me0+r5KAX@?Yx8{s+9Om?4-(r21&HQS>pY}1W2D99qq3y7PiBc!#z&*PUvOQW7U`z{(Db##>(zxo4w zqa8zx$GzsMetjc}zK=Tc)$kHe!s|%h{NuC*wFd!mYDY(VO%h`nxmcnwn7!Yl(_>~oA_K6c1R0*dmVuPm|2p=*5lE7w8 zYKT;5C7?^9kn#8Fz2+&TvyGfVXi zoMojDBq(oFHVbJmp~k~t0(5sn1U^e8PIt}t`REz}oS0t?Av4=ERCG+LT&<50zysg1 z)7hkzAQ?*3#X3~^CK-?WNNXBWPzHE5T1$aB9YJEM{fdW|LyPY?)=>FvClK_GZ?+3(0(!&s>@rv7;z;u zhv{IdMa}gEyn`^jQ$-NLJK94wpm}!t>0yz;J_e+UXyZ<$Yj9o`xEpD=5yW}MNXb>2 zu{QCoD64n4l#UkJCIb=O>(PhGamXhdRq^voa`~7Q7V3GoZTCmrINcSfY=<_Cle7SY z0~9Kmms7l%wY!r@we=8Pb|hsQmSBlA??&I8f*Z803hn`}nch;-Ugh;vKNvtJrW35} z!T)c{0KL?=BNpgdVL0tY&I-mzAJyYqpxsfEYJOSAv6A?Ce2U^E%w&R(I z_*Pq7I}G@k>;;SKe00^m!z|ZQLnW7I3Ho?U^ktsMV;^sP`EbX=Z8f_gBT%+C=@oxZ z^zs1YPxl%E4pLn!{eh`SQ$Z~QatQ!x__X&0ul0!f(MV7Z2y4eno~jRg-P1O9&QJ!~ z+Q!c-RvceuNrDXuR$^CL6vT6v)8yC{IH&>2qQW^;$9@o2&>09GYfXBg;ur4u;F_f! zNuh+zdv?IKPKT(FQiSRWV#BT*Y8R>y>iA3zK}{+Bs)Ephju+7`$y!$e1#V2F!|i%> zr6As6En>>I1;r1o`|yu)I$P4~^$PkPWlf?GCWZ_JhT{qCO>G=?(8rQ)*s?3Kx8ZUjkW`*hIX>XBYtX${V+RPU zhD52!Nsk;Q>~yLI(jJTpP(}>;Y>k-H3hEu$30U9;+bJ!Hsm#A!k3M$4)3;y+)PU2* zoIV;(Zx;{vNmW?27Qxx4fCLmkgsj^z@m~pK@H6ydFmvxB?7$G)vV(<~+k-VC$02ye z_(BE z>L;Qh8~F9zUdjHB8L6GPs?3 z!*@&7Jxr-Y=Y&4ylUIU%AQg~W@Jei_D`HCF6m-o6i6w0wY|kJ!Ca1fH)#t;VL`wt5 z>6NIdp+RQ~M2`&OM32=Tm>aU;wD`#W7Rv9FTOG7Wnc~btI_XF3o`AAiT~?4D$t(Nu zL|Ep#`zFE5)&ntdyB>Wg7J6kRKZ=Dun91w)-Cc1$nWxCfixLSet$N~-8|}s3?c5Eo z2mA0P=StE&$ldh$bHc&^}S1;8>SnBk!7GMG<{X=Hl3+LiKb=ryy3r?&R+P z*$jSm9<_S*hQ^~$I4$MRQ*)d_z;>(%He*KUo9}-&(4kURS17J|6PYw92ecd5TA0zBC^xyHbG#CXq2_5w^ax2lsN_Z(jruggxmnxZS^QIq1u!~e=)oDdBtMu zB5*~tYNL{5_EY!K*tqZ`R3kR9kSXokBQ0WbU{Wkdlt#j35*Cdha*NH6+v8iQ25`+9 z-4MWeYEq~+vSCl$a))4T3!91_IuI&`7f{0hH{}&gwI=%=AVs-dkDv=NqY1G-cz`Bx zX&B?4^7@$9+Rx+aawz{Tj+#M#A4yLT$!Q#{zhC@yzewDF+L+wj3!p&-6P2hOQz!uW zq4UavR$3%i(%Yi33a-V%P&*x3ua^Kxqpuw%QFq6M zC)K4n?qy=oFlD-q3a%p;)|>c(K7^2DZE2Hp|GHxdE|Lsd0JW9^8kG+Md2jP#;$;TkBoWijv0WZY_0LTA5x6kL3~6y*E4du;+S^{l1aOX+uj}7g6a3%a)%t$kCx%hYod>g~Igc{?%5+ zr~zn^DAJa6o>VrlOn1zv^U+75Quxy$$SA zZ#*Nwoa9g1rN#v!#_! zMJW-GNbj<~9#M2W<)~wIw1ldJYyxGl3!c1L;=|M!J06V7$m4ZYu%~#g{4Xkq5sCl_ z4Pog>N)0oY4lKd=y*#D|p|XW(Lh1q%+gwS=%0m!BeS)iJkzQ(c6LC!-CK+CJU zgY^i$w`8)Zhs6ZmY0@el;mo;8D>aOhXrOcSDB!n`O=s4i%?mB)&a&*%@Hw>c0*t5IMNrZ~Ob+E3#Jhe zv_rBeGMQIR!LdE_h4%kAQ2-1RTyJ~CYshyZ-bReK1B*l`-S(+d8s9-?g(h{Z; ztONH7X_478u#kqwnvQdC^MB)NNfF(S-btt&nzd}(QwmQy56Z6Hm(YD&vHiFdHXAn8 zN{1l@&-!L$1X38|kB;z>evl+A=9>??aQyhq)gF6n=~D3&(`sYa+%OjkWm{6X#`>_d z@_)EnWOSmpW(5+7fliyTKY|>l6~b=+GJ!;s=KwSTT&i&^Z$vTuuw5r}V8HP{41Xe2 z7u|*v4>;*X5)Hd0Mrg%K$?V&SJ~~|iWOc|P`rMFuog`#RP=s>_fKaKaDWPs@>n>xE zcrEzMfkjzB9=gH-G6NFMbJSbi-6>mIbURZ2!=+k0Pe@>heB6kGwqrMUG^sRw2Bde9 z!0s2ayVpwOjGu=Szb(^>VFC!E%$Rm2dp~g!m-I*gNLHW5<8eYmLS-|)q?fn^0W_fm zA!V4e5svPy4k`AYay z^fuIPG#0M}MCE#P&HGH~PDx5&5k7X~p>8#0XS4U{UF8Zmt%HU&w{{UdymtdJUmK}b z(kwa{6i(2mekQIwLAQ?upsz#k?uo44~aeJ5~B~_`wFel9_ zn}TZ1JT%;qHWpU=2+NT=K=Lbf30rs>VC?L{Ft@qcaL1VX_+EQ8h*YC3Z`Y%1(beu2 z$J5d5C-?ND>WXaAr8;AkT{Y80H}_+UcD2QQ|0>qPoBCJ^Lx@4Z4Q%cTs_AeClk-(f ztXZ}oz@i_0N5O6H_wP|_Wlz3CDPa-Y;J3w9O8XtjZPw58Rtn z{+CsfjWF?WSJMuanmcvV$e_PbpU@*3h(9RDz<#R{`;4Blk{Phy?F=`0fGvX@YvVnx zVmxsb_@$&Ff>>%$xltj|f8_M?H2whws9T7aZ|?a=3;WUEH2?fKvw3&J)NN(ypwH9NCJ#-N1OOE{s`1|LeK4v1S0B6o zmHODhyt!I?AZ_W?8QpwoQb*-Yespjq%}$$!@sP^oGC@s1q7+r%#CY|UfkT&HHXkK( zT7``s$yD_P2%FAk)g73FW6W^Nfp2P&EWKK$8i?0zlo z9Zo7;A084?d^60*7Mh`L6z@=U_GxGJf*r<|x?PVxboPb{Xv2IR3*a9i;z#~=;HI55 zah!P|0aNtnsIFPtB5_N|kT6u+)61P{8%HJ;*6Ro}Edf8F|2PcppPbzW6Z($j`+bmFzGok{B3>Xkg z=%=iip=8}n%gVw-n{83MV%wYWdO!ssEt0gDi(+W7NMXSXfbcZZV>dD6EpThV;MzNvUmF) z$Zm2A@1}RA6}zJHl2De_5NLL3!!cGQ%Wuyx0pm1otAj5U^w0qjAD~i(%0pV~Mh$EN z`gVU%choPk3O1OuRNoNsrA7zI^s~)Ll_M3 z^s(az^>l>lo7i%pXXkL|l4PBVecWt0hYf8ums(w`v=hm5?BVU4KD=Go8`B+tEPUyj z4b@xdFbm&}^vQI$dp6AV3OBcg1Q!;I?CE({GY(fNJJv5jqS-JF3}nMOh*q5GrrBh< zK~H%H!LkZXAh;t$u?5QqxpjM!XRoNG(lb69JCgotfx?&mm$l>0#QE zaTayiKYa4_2)L42_S%4;0KH>nJ5Gq2$IQaGa#pm*Sny3sb$h7jlx(aFhfu^zYt8um_{2plCwXy1D|* zLMQmk(bcGe5+73tnun)t{jjNI>|b*72PW>+jR%Vd(t5l&!@7XEBGF#sQg8MWAPku~ zN`_2oW>T7!me-J>LRvs3WMN7qR{D>>b&BUW1u6>|(K-TsQ9;}W80Cmmx)!3$V6om# zSFHY_nh-vKoSu}bjA~x3Ze~nLK$rzEufbj64i7d82cQRtzyp&7ohM`j`=aL4q&C~@ z3=uW#Fl9$vXwpW)`Rd?!q-kX_o;4Ug-K?f8)(vc`LQ={G3N^1_EXZ*I*g79&_u&F) z0!VI>#vaYG?#NpZ# zPjkKi=a%kiGvtdV8J|_p`MxdBJp3e*_?4na&#_!(MjCbof7b$g%x-G?vhLEDNwUs4 zxt}T&y(zjBqP)BWWOUs0H9(d-h%T>Y9AUa9xjx&FGH@BJ&^+uiFcPiO&`k#CGhJXB z9ZIKJ>5jgi4rRYGp}?OEOoa@bwt4+zD9tCD4g2dNDt+cXAxVZk#pt`Xdf09zH`seX zW-~Yx;@k$G78m!@oBAy>Vm=%eNH@ro1gZ}^6vmw~AB9>3^l6EcBlDBaS5VJEMGU&S z+n}vbM7VU84tu*Bzjn9OGyOR@-hh}_7?B+-q#T(x)MQ)J7yK{GVsb|9c0IaUF7QX} z)3~keS$=bD0ojD5d>JJQ2Lq5dIN28D4tiW~+_As0O4HF*uEii#Xk2IjMtaKJpkROD zx$^o(sDcoBkLuqq2~vuG_7RmD$<>CnJt0Ui=t${EcCK{zaYa8erd0IMl9BpN$(=h? z=WwFa4A7~XQeESTn;K1Nh|eW>q^sLV(v=gCQ~59%01gdZDbU?qkKh!e$5)DWirBey z5=?17>7Ym3PKUYABf8Ga>|F-6Ne>l}!L9-dc~i>>JdIWk_L^~g(XoSeGC`qI-2I!P z3PQDWx<&0sZ$mfxxmJ~-1~1{MFk5|dBl$*V`uuzAQt}T8qi5`f48aZw*r|Z3DeFnW3_YQfJ9yuBnyA>cjRfd*=EuRJMXg- zCX7NfTKs7pu1e%V1v_f^jn;8j2}zo*1q_-yAi>t$wg*u}uhgx&#jG(rfO=;=uxhX7 z_h6hHQds0#!o<TogEHeflp7G1hSXL3cJHDm4-?4#!X%s93UIz znfQg$J%Gn?7;kVUfJOp^Z98};J^*Wt1R52#9|L5em6sUDg4zA zYdN$A-k#g|+77y0{z*49anSV)^yNGqz_AVHdxNsbMGkt4=65dXM?kh6n-xOL>-^Rq z)mvKmcheruplv0(PF295%JKH;dRo3^&!m{sk5sko&kPtbb)qTWpLXW;5aa0vkn1Ar zk;4smUXfP<`xPj+z%%7uo+HXm>9SBEqDre7C%{Gue&|C%wI*#xfWh7j!URMO?lLME zDZR8_j$&ihtINfL(bg4xq}ZeCjq(R}vRy_w9O$KLpvxZ^llzg}!wE4Wfbi)By9~Y4 zhY`px=Q)!Kg;sqLS7-N+jQnGu#Tab*NUs1pq@=KWz6IFtM^=oSN2j2U3wN!IQGNYK!-I~}3%Ki8~>#iPlXEL$pdJc{6c zB-?UyoUl-XZHS?13pd6(Wm07@J`I^nCP%In6$;f|Vkro&E*o|02I$Oa&SL&61a=`* z^dNtb%B5`qV=E~oyd*&wE*u$6kW>aRD$AT_$s|AP# z6D<~r9A%1LC=$c;i+*4Pj$3`(u|BPi^$q4NwhRzSuarXJeL1+vU`0EAVgqhaS0y@n zMEPTOt~1$rsduuPMrs*XB)bT91{zJ%`f6Xkj-S|=O-yT2JAt*;o}k0DYzkZb0IRZD zChHYzTOlpUl`7+~3!jI{%+C^#>qcMmD9b2&k zKYEWz3EW_&HV%Oi@0)uMY9wkT&C;81lBy=sCqbOdf(mN{4)$E`b24Xesf*MoM`!s0 z+2UmmXyZ3sN}r<@=JRj|+CWkV@e=mNNYNZ?d0*ZeK+%t-p6Z2Nb)EVV@igwH z^sfuK097gctwfU_u_gOzX6mQFY_|Qap2(})HazT_d#Ft8_rmtG!)@;%u?MkX%AN{0)niZQg@Zg7&=3nseO4 zS^jfO&r~%4i_xK_KJ>iy!1@q@nA9yLRgu&Dkp0Au9r|F0s)rz-t;r z;VW%+g3pK3O>Hq!=&8}~fRCX5?5hE1Ag}X-qHFw^bO<9d3>qY#lTfoU9QP1%&4+PoROjQC3hW`q>5Mc?rx_iM*B z4+}5W*zJ0xMNP{cu!o7f70t9sXl(5nGd$*22GMz37Y@jYSDBa8_)K?NW9^f|P33i& zLTJK_&9=?xR|};-?YSaics)CbL_$lF$GZx&{A~Za17sl)7xkkw!v^tHA9HK;8HxIc z1wXG?wNHaS%oH=V>?Ymq__;~7;;B*sOD>v-oEK3FT&`|XVm2EpnzFHPFx&;pO0x?| z0Ymtk;BM(P+k>DINF-`?1aN?WqT9?lHwEDqM5igb?!dPMIwa>7ZMqwPPhx|&k4G?j zLg)k#wUYq{Ax?ejV{YBF*bOeGt0#sDI;fF3PJ&J%rl~a}k%kokIvT^w9sVe>qTx<- z>-fO&I-Me=zSzMe{R8e`t&>xAMBWLCV(V7>r)Z0TdB1v{P@w>JuDgDY28GlF<@X}^ z(K8l8Sa3>`{jJ4LD%Zvtbh@x_`M#aIGI_T ztc1yp&^57)>(Mo6jPl4)*;$_WgNN!I=a|%r89$Mp-ajz&&|T_afufeU6Xbcl&zrpC z4We^O-C2&*8xV_+C_5uuO?O>fL27S@sMC9!KRpo$U@9izhiK_j3>aD9_2Bb>eMg3T zgu1uu6(pqTik|Jang@Lc)C(JNnzo;CYaMHG4%E|&n+$5iR6VkSSl%N#n!xn4ktfI@ zyeFP4$drgp8C=*2WX1FXW2U#amj-%vmtnQon^5NnF};;kG)+=;@ARU$9xu4jN3>5sizmEabwmVy->-9>FuInCWz>u&^^6user0;i^Yrn>D?spUH*8>u6_8Y2s z3BY}-uu@QUL-nT{d^7RAm$wMNyqZXYnIVx?_uUVYA*eUwm$h+(H$Hd|AVH`HlxX(s zQb~}2g>BQVB2QRVx!52W+zGduozDd>brmu4Do0h~zq)yYn}!Coem76VwzAk-VJ$k3 z^}I3~u`~&?kX=gAtJuBS^rmdP7$<1Z65j2*u17FxQicJ!5#<6z!?u0FvqL05x|h^^ z-mmNR{tO{ov7$R58ISfq6@;aGAZ@Rwl+hsBM$>vG4FUrIQ}C*O3Jx70QU((hppNi} zrT&)QKR?<;!i!H1utn=E>B$PV+0T9K*Fo=A`qD&n@p;86f+~&!8oVd%3;X+lmZ7a@ zf(a6k{SA!2yTWZGp`@S*rGtalLZk{2xaj~BYU9EzyLlC>p_TV(w%Mq218&DFQN{d++dgQJFCU+!Hn%?KhwCVjmT^B@z?}iUcSYmFaz1_9>2!vTr zV_*b#zqT__cz)f!boWtbE+MZ`kBX^HRYL?|pz1Hj6}JgCBtwCLaFuUp_Ivq5G#VfC z4jDL|L8B=Vh6FVc?)Ob9jSS8nd=oY;WJztmEBo7qF zoDYER8A)GSv~bTLAh+$cbxi1##XtdJ5RHI3psXSyfYYsvn~5EF*dz>V?EnZ;=+oQ{!R3>bjWS4N9kEU-Wi8x>hxIK)^P7PIOD@s7{@3Na9T#zn*lZ*og>I37qIll$JqY zK^?&ZsPcA~;@8CpDuYmhV>l=D+~BUVD)MZKsbnVOOl7uR&|!votar!Ch?=#)k!DW`v(YA4h8|+tdCTr=o2b5bq(nM}3W-#sPaI4h^kCV7OJB288Q&S~ONDWy z63OGHt*qH}%~?Ah&Lx(#U7}J?<4j}P-8Jc_qif0XexFe@47+a3#k@ajmI<4k@qqC< zwO%xc?6f~4EwY)Vbq_lgdL#IETVyp~1PhU@1H<_@A;B0<$anpBTFgMc8^sCg&A4b@<6irx`9c$kRY z$Rz>=(LTOa_E~a9j;?pOEa#(orzIT7JLK@VyDt;2%{+04AK4L{)YHhW*R_0)KM^>A z&%IM^x(BidX$RxtYbJjjfNX9>+0hZ2B4s9hvTqTD&=*kW4FV`097Z&_Pv6GH9*qRG z{=oz0)}k{g6onAX_DA;)8UuECtV@a3CzmTWJE(nkCMC&u=ufj7p6*N;vR2C0R~Nh; zKy9&(bZ;Sw9<*QKx+nJ_gAFEy=-)t&HSKl+9{0u=1n(BPP;&br$97XRLgw9_(P zHX*!GCUk>wygotoLxbED?cf@15@bC_6;<>VL{q-yOw5l?f}pB3_F6eXTqkF5lKFp7 zCPH@zs&WfJdA?p@xE1+K_}gWUXKfBrD4J#3%f>}O8Y$#pNqdU1V46vMFdEIdPQqgg z#zVO#4xo4oV<}X_p@U!_AVVkuFPyx9gN8 z=Li^iQtAGYV3&Ox6)ABie?Xq%>7<=gj4nX8rrWk{+uCj0*loM}YumPM+qSLUwr!hx z_J1;&eRVPpJ{T1~ zncLjjCvBf7Fz22|*O;=~GIF7XZCyao#t^a)h7pLyFifPd$N}{OO2hppVL&O?bit>( zOd?i7np{fa0MWtR%BnI?&<{lve`t+qxA;|^JH=|m=6NIeAlAaC0c_LpGgz5%AS*kn z{Yo_l>FmTu19U380n6($(FrX)`%mBUdqT(NY_0ix) zXM?1oYGnuxikR-gCH!^_xk)L?Z5_81!H0qXJ7CWt9-`h$=unm1bY;MC9Mgagvj#Rf zHSeA@7_J zlLWD#3u*=nK{SaLtuq%*)uMf;UPFgI75sX9RK!V7SR8$Y8)X{3g3zMs`7-Dn2cwJZ zva!*P>H5{MEv^sy;A2QHAb)#N$Pp$-yey!z%&(}_LL2YZU^a<$pz2k9XRDjHg=(1u z^Lh%@1(a#)wp}+4#xNq(XIXAzIVppGvm$LJX!r$Xty&_>$wu#Q<;k$v>H(qFL1Pu9 z-idY7`1e#4cbx0$_@wPQLov3oDS<8p1U^Bz(Vm;OCSD37<(s6GEY_?&avO3fm3-uM zf}p&Z%Tep*bS0$S>N^Y(Ux%2s)K`)G&y9i9m1Q63tfZWb+)5g45Sc4nV#y0`t_4pG z`6ScUCvgp6n!D5FMo4>;&LqbD?j%eVCo)2CE;022Q03HAVhd_18ll9i$jO%6eEW);!tGuo>8Tbl160)VM(P1wI`GstJJIAsXntB3slj5*@C zL|~b_O+#UTy{8mW*`cRQD2bXn-bvgE5tANhV9ZqljVd$;X#=&621KxMZ{OI8C#ooa z0HU-N7#6&QFAE*;I3h~v)h^anB4D48eeP#D@b7OBObiFzw5KN{wEPGQY=^oQcOXHl zoNfnS%(aLiJ}_y?y=QFFT{23F@8j;FYZ5_$xI#EqS5mWCfP4sHu58}+is zVM#FdxW3Z^d8it&lX0SC5u2yP9u(B#dugm@&VQVPWam7eA5XFmIe&EJD?C03mc!TM z+p`XxoKqvK)&u<)c!Tx4d&FZr69g)t&hy`P04B8L6XorkdFotRwmoyI4E|?4l{RhY zLS;QDZzh*Oy%#|`0D@%eB{1+kfZmkx#r?!pZS7n=TGokg!d!xj#NL*Yl0U%i0>eHi ziv)_=999dF!IlUw8D>eHEg5hf)|0fd;IxT3sY@vIU>-$rt;jTs620_vnpw&RB~U$o zi)xdui=WDqNGe=Ov}?a`ad9qUh=PTxZ8o7Z>UD-fB8P+~d(9+)g4?9)aYo$J5!Nz-IP?c|sGs*Ofvu%<^8G7H6`Dz32T}Iy9b|_> zqrLHRZ6WtLdV%yAr(7MFyUBPGL~{fhr6*fH=>Am32nQnB_-#QiSnPp_g_J)9yz7e? zHPADcuNL0a49^QP4c4L=OFCX<$qB@SyLbCOvBy#to8+VL776ah*O?e?!YYR7Ih&1x zLfEuz-RU?oY?H^zO8^eVNz@S$tC6gYl;x8n6&VTTE?_ycjc6$M2CS9>DhG}f>MuDr zJf~47lKhN;@pL6C(-k9ashRAhA}+tcHBM;b7GNEs%Hh~`?irHhCam~Nu|1jm=rBx` z$48lpO12q)di@D1UW{iz`0y7;A#Q0<|E@0PRR;(D6F-JqfgLO)g+OQhptpPp2A*8* z4{#&Fp2quIs6q&e`?S!5+rPJ@IUUlGDJ~Xx@Q70&A!{`=*Q{Ol&cY$(uW%t;acPUK z+tK`qhRz{h?w*+$iwPNsAy7hNh-`znLV0=fxM7!6^t;{*4f3z2 z$>;Do|H7;-ynOB75U0HE{c>3smLm#AsJwqA)*rb3WPv`sLHDIE;iiwa3WgTTS;613 z35=fa?~u{zzv;A|4oh+N*Fz_far;B`z{iu2bmg)1pyU)2za=Y)_+z%c^Z!yJJT5}| zSv0OE$Yn0HYc69KpEP$p%g@OpWk9%?0WTKH7q;y3KH5V|Nn>#c&jHf1klE@( zAycFn&W9~bK?IxJG`k7i+au!v!8BOg6s_G~s-;bouf>0eF%=y)T#GX2#_3r$XARG=AY{P!B4G80QJ+oLxJyjg{Chj%-DqsXMxM{Y? z<+w_Uta&Wu#H?2tqW0R;94KHXb=9zQP$b`>9v~4nb}-28>At3ye>*>uZn87D5o&!O z3MdDtGq;icv#zrl%uC7d(*ce`CEuFp!DCxx>LZZEK4a4rrgJjgIkOgnN{m0DxhKMN zNy8HN4c(L}1Ds8Q?2fn%%;7%g{&MLqmJd>nbkx0zMjpPUkhWCi@xEVCn2SG`nQXmy zpM^XlLaF~Pn8c3`jkYeU0B_qh)d5*9n)^3svV-7MNT?qW!ir*RL3(MsGJ`%4Ad-G! zKOo{{pE^)r2kMmFpEit*mo0_nFLBOtb1D7{Yny@$lHoNZ1-9^)G2iYA#;F9Q>RRB| zObZ0;Jp$4sA2T9=39tdjXQ+~ViO!4KFBN(8vWcK~`__S6^WfcuTm*;@6e?RJbS4v3 zcP&~p*5@F@Sbz84g4@w}k|Jwey<-Kj;sVALO9F-TuqYRK<9ZwUV+T$y;X&E*6(KnE zY*ADl$8c3TRC;2gO#Uf)i<6xBRW~zI;@CgE5s}FMr;8by0Mr(&h!ERW9`?k>MCR8E z$!8BF%?q<)teGDVOFgp)SR1jFVl8L!eg$R z(v%Nv?H+zDuLM|pA~aPi=PY@bbYVrtIbdPxq5Z%9wD=Wr38hu*_jkhQDzwLz#RuaBrJU=L@ z1X_6{4T@SHory`!rAMjg zt{`$?qoLCAF_DN3q4~NuI?1%X?D1pG?F>m^#w;5aRtj=_b2WG77Y5YE67EK2iF<~> zfQ&f*IxUiEfw0Xn>|Mbk%$`1Vga^~=dg|)6gRPW+T5Z-6N{1lCxjD(|Jk0-1#3Dcv zZ{Uc!;)`&J&#xWH)WSv@DVae4RK*EjM_Kr4Z=X+v%O+U|W?ke4Td*cXXoo@)7MAKQ zbu$;Eh>y&ai4Q>W50173@LnS%|u%q@oD@dyS@T9>UM# zp|??(_s7Q7`dmvR`}m)0^HtEtflm{q$!yDSg)D0qnt4PGCA9n)wAA z*it+PhW6T@k+I6z#MGSzx%fR)JJiL+s#Yj(O%(EyPlx6Voq&%oXku{x?fvSDJI%RE zZyn@S%%T0{*Tr5TY_KS+*xJ&?!Q}pTkl6;lH5Mre@k0wW$4clX=s5NOr+rdamv>ys z?}Ey%&Ns{FSJ6pp%^_W|(h^3OXhQ@mKq$nB8u3Q_7MkZo-pQyM8|CgH$xMEPaf`H* z^PmAQjGP=WVUQS84i33z@-{-6vF|$y!bz{>Rzlwj`H_!>Di1uDCmjo1pRw8utKf+T zhz>$2v*ax@->R$D;lg!c*P-~{TBSDyC+(6r2jgoMxVRv`Y1}zH2tYjSGCzxjRX0Y@ zec6nBxf_CkD_cPi-cr70A$P|)#4LgjcVpfX>O@kM6jV!r=@gvmG--1}C6?*_WnVf; z&@OEXFUd#U+~g|M3SGv90ih~2jk=$s^UW&bl8`N-dFZ0?2LUtSJ`V~~g;$VM*kSPB zMa_}>@75MVS&ko|R+sjaP%n_@>}kmSJXKzx^^cj=Y2KNDPL6y?RY5h^`E04UW#t@ZguLs0UOFVgdARh@q*k0++cT`P-ynBSt+mOs=n4GaaX#v&g z2vkzK>d6O+_F}k?6A@50%^s^Hm`a}vKP6#Aez%R;-%lMGR7O3Ckimj3Ipq&t5rW>v zuHeYTp!y@oopc2%pd=qfr4u6LeELA>w|rJH5BPDw#!O)&4N#Fbn=~Pog$dlGH4^2e zfdD#D%c8!-F$1D-Sgix%4%}0aHQ+Ap3daZz{&ivnzpFVUlPVJ4mN9c+tcDY%ThQR4 zJlDZFX`-3f`TLOjhiE-JlS|>vYS`obkZQ?=VYBL-FRZuyn1f*;(KOG;;^ZYk*h?rN z+G)v(8Luu#A{>M-?Zm8`o#$_F_Axz_T(|_BIb%>_C!!8mc3k9)Nes{UmC8vN=Xrj7 zMces9`0d*6QxzxRXkZ+?N}^<${hPZd;%H1`Y|Ny@ZdM%guHxz?udV6FatGu zIt9zWODco$cY#CNY+iU;Rckwg+)B{BHG>cTWU<^)0T9g&bdRb;{YT@l$DtJ5KTF-X z>?IEZ16@#Lq6S>0*uDJeTM&$^&{SMs17ffsWl`pMc`OL3V9R(2hluhZ<$J{XJ6C5< zuHg(~2y&-=n#|F=IY5tSwjMyB_Uo}bJ2s76SguJ7k2X_%aHmIPS8hQ!&$M{M-y0-U z%F6ImdkCVi^t@w9^`+h@Um~C$q;A{*780q24CuJC=HbFc#27leTX=VLwLu+YH3%eS zG%D7W8x;v!Jv~YNH+hQricQf6aUo+r0w7_QSKms5sz#h=osOXvH%$FjoO=fdJQJgc^mWYL-OJ}Ee|G7z{ZseY^$fs8*LR%El)j>wHNmE)p2%gzk+P_E-(zsT=WCt7|BuT+66!g zAn1eg0goo;VD|jnZ$RNI!8$zR+47QkCX%T~*d2X%;Kf)qCm^PV=TldzDJBdj&3y&E z1qC6O3;753lk_N0L^T|bfhruhI%Htz21~$IIQZaHv0HL%Rg5#YtBa^&QbY!wogtp? zv@kH`iLis=>UftCs+;bqzrkbnj~8ePc3~=>80fdp_IrQlYzt+pR{pWEr#ED`u#eQd zke#Y%z)l7Z-~%p4CQrG+*W6|WNk<~vS{w+*zX)kQ=rad*;n;;D$j*;ZnrC@!f_Kxt z$}dA4KrVI-W8d9tadwy*OG6ojW7+`R!Od4kQkngcFiDe>M} zBd0ZsZ%zP#pv-4FFTy}%wYPK_TYIzx1BrFAlSg{PR`c4xC8rx14ztPyVkrJvJH%rk z3jkdzVLFz`E_w2|kpIB3tP`d}Lm(`;eVo-2Xt_FG;4|;iuPf471fkCJ%>|(z*pV(` zfMTO5K2GAQ>m~d+*9K$4O}WtaXYclf@+lDT@4X78C+FPCm(IEgoJVYh<*~^^jN|T&?ngR!;pAaYhVWdwGeHA);**E)fNN`>n)FR{a!!Of= zEm45zoIk`7rCA$s-$JNk@|l?VWoUtsOZg(pSyKsN<~2fp9Ih@&0=N)6@O&H zsq~fO@5VyHg3I}jKe+=^bu~G`vf-q{^W^%@*Dk{pH1 zil3itO$<7Gx4+=I2)eDC3*2`M6N|NbPU2BWp_m_}GC9{bFbkb=J9s?6z+Yq_KRJ0h zJ0i6hgP!`T@C(mJnR--F42D;iO zX+E_)sPOBdWc4Fq<>^U21xf3uT>oNB#&A_i0OjC?WmB%1FnO5hS2;cSJm;nI;~PY? z!=jOF-9B9N62&0&r>cXOsospiCN((eGf{`4EP-tB@rphE+3$1l z?e;I|f5)Ps_f8jdg8>1BU;+Wj1K|PLnmQZWm>N0)ECJ^FW|lUlPK^IQSTqw;Cu2uT zduK~K0E4r;vzjV25NLnLzV3gzi#se3FxU?$5YT@O*S|Lbha5;guNp_7^1#Y&=QVOT z2J_XNYhYb*5TuI{NK(!&>!aTvv1Hct8Oc|298@q*BP(#TI_{DP5*w0A%OP|c4!RXH zW@_Xa(F`_m4I?KOR7n{$u59Dp)(NZYL3HX?O&de%rsQp=&ih(X1t&)9AOg1hN2cTt zzfnY0_7BAQtTq}5QAv1)Y6*K~#WWF$Zqd!2o_%i;O0kbOXem|r-ncEFnW?A9{JuyQ zHY68Ti#>4+^N%?|u`3j+<4;byZ2+doQ{5#cDP4Ob&YH>Qh$U(MRl-c6NxI3s2}zB<6&@OOD<~;SYYqp6B(*~=4-E}q3b2a6 zk~Y(k88I<)n|-L$ab3L1>teqRah>-n(0=Y=JFnKh4EncAzw?;Zj_A+4kg>(zkDJ}G0kng)=ppYS04Jw4q#s@T>42Rvjm~Cbv#pondwrU8dGxE z&iSN9>4PSH3Hz^;{MX%4H_)%)llSV>ztz2t2Qvnxmcy!?VI36kD&Lr;TC~%gif;+A zV{?G+9_9h@>VauQwMp>eje`KDhTPmh? z2eOOSvp(t0w~Yc`!r!=$KIbJwM)F(d7sTrig-XzCfR~Dr1>V;y?C_^*M2KXTtrnl@ zM$fF_Kqx3Cfj1b`05%t7TfeP6ifXi|Bsgu!rh@n)vMmS*Hy;#$Mn zg4+FB;yS~+g1EOsQKB{+UuX$DN0J28sBDX)VNyyGZsv&yX?Y#TE={OGrx;t~jYpqdp0ZjBr?XVYM#$=b94fOS8WjG9KfmU-UVU(dWiRap!-5{-dEV zK>ukZ_WSbxHTmCz)c;&V9Zd~QY)$`VzvT13%XlKbA$2zu5RlF^5RlUU z{0L^IhR!aIruxQqPR{>txaGF4X4289gYT~X0A^ndkD}&l78Ln9zTd}5m7Sj7!`oDtTc5|@ z)x2B+{hgopDMo&u$1%IUw@33`@7I$qjXK}Y^DNF?-{&rY-uHu~)3#dy-rn#1^VwOy z_oJ?dgG)cVxL)4x>+_+7uL_L2U#l8l*tZgtKHoQE zBBv8ldEZZOKi}^vz22T*cT-b0t2w`}UuSQ31^T}472*WG9e=t`tbb-t5&!ml)(Y_U zeBC|03?0TPr1duSokjKC6mm|s={~`S3AFI?_wfF;NBH{=5b$;XxYy-=zb|tt^ZWd5 z5-;#_U&eW>*Z2AUetoF0S+;P`Iprl@=KJ%ufB(G*={*)ocb)UFyxAwv)9uG|Cj~hx zdjIz%j1UGzn-*N*eQ2udmaxz7=lp$0!1ph&PEOS#!Qa<8ONF>F{+lk&JiD#6+n@W# z*IQp4Js!f1b|Y?{UPbOs2On?u!^WdCMuE84pD{*8!kr$!orPbH{}xY&4li>N?R47l zb~_*X_`hd2%W7njinPNg_u(av?=VWey$=@@$$H&C_m8)Cf0&a$#@hVe-|vs>^#2{B zW2lZW@9)o3*O;FTl{5e4V;N!YE~d!S$?^I5xJTXY_wo6q)mN_GpPV0C5AWBo!?$6aYxnsqeQXEMbHbONtX-AxqXin z-TZ)!i9r2P&QfA%a_Aj|#U|+muuL{E=1D>c3zc>t@pdr;>e#!4Uj`m2lA6pzZ7T!v z@YSFJ;MKV0!E1EUOvYL0ne>7lLV`jD*m zpD{BvU)t*6+!z)uG8ZyXcf zt)~#%gW(AnMnevw4(H8OZy2nm(huN-nUxQJlR4(E2771l*Q3Z&7wRoiDpD5y2r3=n z*o;RK5K(~VQqCqu1#d9X!W-hJW}^?%Y_8aCQ%*`Fue<6&u0vhCbMx6XOk+`gX2p*@n?-OC;pa`?899eYI&V=iXxN8qVPnzIIf#J_;oc-cRMNf0!cj?sJc z84z0DV&g5F6I7yNtHuDPpyBmpR_OTRCw*0fl%=A5&YWJI!u5r2nvqx4Jm*mwkBDy} zYx7eTY3xO^svvUz2Zuc~a}>=SN8`mCGr9?4yS&~`sRx>X=F$j%c~HTFrA%2bbfY(s0k#c3pUL?^>PyMwQ8#>njwoq%5y`Rp zBn+;eVK*W8eha03#<3448zS9Hyd1zCogKaxFa-_ih@(~h*^;oa8#sGZTM7@+tw{MF zs})ki3SAg23n{PZ*EC%sA;&zt&U=;Saq!3r^VeM^`|`C$>7>Tj=`t7ynd~vLeU%Yf zIZIAiTmL>@+S*OAlGsos#ueb#J=L!9BwNSSo*x{FO!*4;$LDtHM9Ee<(w_m-{Tn6A z0CfYol&&3%b%M?5%MzR+h@|9A^Bd11mnNDy*d7=2xDOx#YA>2;e9T{}4vl5P@AI{2 zm_Vj&p+8d|81e(46*^R|fOp=ZJaf4ANWjiN7|k7!CHcDBi<&l{y%dw_W*y4~L>o-H zAMQTtI$HuCHLpKX8Ihk*G6|S)J&hmXNNJ>osli3zq9b1pjlr^EwUL@SFc|IDhU8<$ zoAdj+!!yHk6DI#$?agN==s~7oB(lNF}^}&R)3DBJ2rU?t9XPJ>&Y>>jbs39BZK?_3@nQBhQF0^u{0ok%3y8|x@~Y8xW;o1f;GWy$ph8Bu3W&>Tv-$^ZAe=a z5{=@aEdeE7HbUiA>fMr5mQ?9l0!t9woFuBhC8`Nkou8MEEaa9SrefMyrTkBJ=gNa# z07o%>ZM?NyyXH9#fLzTY`=|?!Iqd$i!8f==RSK2fgt}^~(6yf*F=f4VVagX4DmYKP z)4yr)`+X+)R%xmUoH%QkOc{wE2wbT+c;+8WCw8z4S2aB7nhs6Rp6EXF$U)^+b>ior zz&HUluMO`mp@HVoblS|nvs>H2uT~lfNvdpA+lqraK}lNa_i@7MO_P*I>wVwZM;619 zs49TH%2QX3_$@T6!gSL->xO5H$ZLArrZF;v(K z&h4ye2Mzh^W1+yFH#QKt5;4;!&TzKLHyTa*>(eX4Z90I(IDC}W6w~FY7y@c)tzxRQ zj+&_a&-~HdE{83&Bmn{KgQ9U0M596NK+ejzW(QShfvdd4bwI5m{*Z}X2&VcNuI#K8 zf25 z`va(=)X3Db+APBm=Zve-Sn?d{{?e9*=#Z+J1r|zKi|NzxvhSz7DdKAAm1p%LP?2$02r|7WCb+t}Si)qnpfMj}ZGw1sl+TXbvAX8ET(?0w(k@^)V0!@yS zRf0{&0$l0KRFnPHVu*jwND>z_#DnYX8Wu(x1sSzaRHI)h!Me&d3TD;W-b!!%Jf2iV zT7DCpfBgSL&|#z@ta*zV9_B^#nK7eX468Hwi_~&tOnXlJasAl2`fw;V8VxI zW}_XE_MS!^^bKZmR);6)pcrj?GNhp^>2M*gSsvma?D*vx<9cV)6}|6?CzSytpsd+{ zWcnR=RJm@5lozf}Y+ewilupu#VMWuW;_!q?!n4=%Tc}L0K?lP)(9V>j{K{ji()x8~ z7nZ4Jyfa|B^}><5Z{?nD)gMQ6{hm|+Qb`l8(w{%5q9LtP#=2F=<>jbUD-8OwDBKdy z2&O05v7{_-!`&F(g;P0m^=eO;j|pSZN9@v+Jjy+DOK3#4TcR;ZIl-(CpRO8*BZM28 z6=m_;q#Au0TkV7W?PgsUxBwF=lMm1A1)xp;spbtY)MQKw#k}_T2$RA1IYY!BAcfuLyTD5Q3*knSd$xMmX>) z2KysTom0Eiyvhwns_w-Ps4u~HE}ODF4iS{zcG_7c>7%1tC7Lwg)gZv0Ue$8_wsx*` z!D%n8X@`ZX#rNMdyliKmy2vdkeiG)_uK+f0(6!#NhHafqG0QFMhA>5I@1qCxv{;`T zWKWISgW04ZO0NvyHb2xFu}=R0_2?|U^~QbcOueqnh(eY6n$e*h<8OT$4QHy5pmw&e zT#o*WVY-yKNiVch)@J59r0VKHecdv+^l|v@;qcQ_Ky-EYg;j})G*m(1?7fV_AZB&n z+?zW1jtaSOy%jRz&wNW^xAv4f4CNU*6F!=tkXa}lA-l!v3I1dW^j$bGmcyz9e9H)= z_Z72-k9e^v390kl@{?RH#30-Q|6}4bzk&X8kg@-YU2ZB3?s^z*y_W&6#esT*5pJ8l zbBLu%mx_4mpQzCyZ3dmS!Tyj>@D@V?*2}HL&CDs`Ra|F;up>RKSnD5Ak2c7dA^XiR zLCAlkQpubXre$t!y>D)-*xDk%num7@3i^8nzn*lusoBcov|2*WhvcS5gC)^@ue4W6 znIzT0t@hhC)b!jj;+96yFvO=D&a0yT%2e~<>|p39W>GbXD$4uCUxwmC3kL=EtU#Ti z3{^C)wb{E$Le027Fd{hyLsh~hY1FH+h;IZikf+}g`14iudpCHEpYRelfdM$L!4B(N z@n7MTFH|=TYWzERPb0D=207u=zUFFpGh-j!DuKD`@%(CTSD~%d9vA6Db|2$L=~iZ> zx6((k`iUlT;R$!e5ntqYrLSH2nUrdbb1r?P9)p5Swf5fC$WBEV?eZ7$Ck@hzm0s1Y z?U@&QURCBRa@;D(Ukh3pS@2W$NgY+Q{?)V2x&`y!M@fm32RDlKop-Wey>Xm~0Sa)u zR8d+_af`G2aCbf9Z5JmXR!)K^;LK&n%Q)T~L(;Aox{Gs(fnCpUt(nsFq2syz>VNPR z5}@+}#$VXADL6)_w94}Fs7tKEBo8DW)k=@i=?X_Bu5DoI1k#dt(mwja>~{;Km!147 zO%>}yJI~;8D)-XmD2Hl!FKvq;Wu~|Lg3Es?$`ONt7@6-yb6CI5O5j`TY8SaG(ISEW z5iTC=E(~F=CS3mknY%w|Fs5nA?$=qoTw&)JL$wUg4T?+8FYfo`uxlQeIL}4^WhV>WeK?Rm_kA@0sfHIr^Lp_i0AWyBBkEAX{MoJiv8yuIy zsx4GTUwux80FHt~-)V3ET~ua}%F1X!+QyYL$V!G!WIuM}?p6ZUH5G0)Pd>m;=G4f5 zdiRIiYC#lMy~T;?z*Qa}_36M@T+}xiue>(NlUzHqE~y%43Pp(0w9bw`Frc_iMewC5 zn7OgTc&6>y{f;+p=6!bE^J!tSgFn{&a(bYgkD;lxjh`K&MBFoV7a(;{eVB}MuT~O8 zBi@?FgC$&ZVHEY$`b<9)!xA=21yfuPllpX^@R3PORhf|4sS?Yp4>n~ISr{?h{WRuY zDO(O~E$Y^`K0X63e{EIPhiT>LR^(p%acov8Bwd8yKvYiEpDBeIKwb{Fin2-PMp8us zPt7WNZ2!mLqRvn%#&-V6Opl&={Yhbl5k{QL3UWz+ z@Sfa0!5jZAt})kGR>&(tZpdvg^=mk3s`jmQ1dJ#87bZAd1s-+f2Byz-hb?mmMLYd~ z_8nDME90t#n95PwCjsRhjs5=9R(A_h+j*7K!1erIgW3It746Gad1Cdk$ zEG|bXD@#Iu0bUlC3^$4zQM~E0R?n$T=1QUBEb~&ko=;^arYod;>ZZVB;lrpW^6c)*lbq+nxWQ8qJ$aZTWi`8 zimx=Q@*F=Gc5riGnE-p+f0*BhMOxNnr6!n^g*GB1zy1RzH{t%KwEP7!AuwcTpH*xC z_=dXBgWtg7^FoN#1+ywp2q;SyHPp#Y8<&EJX;0{ePzBb*#2LU*{k^gX;*}QEsVTiG z{VeHM$f5P)2;X0$7huah23jU5GI=paFPur0z??X!&yLZ^FC=>H4}JDLTk^gZW4`p< z`nJXUFR~x&QUHP#U;RN5m0$bG@H5bY zI>k7UidReRMbM?x7R*$ZUSY8E&$d7b*1QT@hM>I(cq2s1KJf>#xL(L9Xia$XR0>*F zf7rHpL@}R${%i%qhQ4VPB}l#FR-XPqIMAR2C_o~e(xJ_rB$@a~r=B&CMv-U(7+r|d z&B?z?m1>hB>eG5yk0Dok?-dDO6`NEs7ZOd0ZTR8r70rE*;nX|E4uYr6y>cUD?ll0B z#bA{AmOlQES^pB7N7r<>ayMw%D4k?X`Q6IDyewu0lM0y*_Ud5UR#lTfb+41I>S;aP zxmwjMZH6malbV=vH4X|`(eQ^5nZ3|L-CU;|?H)Pjt1leYUDulsuVU8DOMuRytH9pB z7bp7SsQ59lw8w8ODH}mZ&e(^@DrRP0YQqKYdxmH5)i6^#hVibH zMUalN1Y>^$%A%D9vvj#;7VX|W#j!kaSuB!PPaMoJ5I7=@GjS>FXX?5;$Yt+RB~Gcu zMeBqfAKAY(eEI$FY1!>|;3J$kGB+X!Xt)i{E|6Fmsybd{xE819-suQ&L;RmEc_gzX zt>OhnpD~`JXFsrSMqPV}40pV;D?Cg682;Y%(?v-1?6iS1S5az*JAt(9H)l~JT#}T| zihma&qzTrG%q5Hv|0GQ($RfwPy77uHjOYId26@Oe3MVJ$J|`LzeX?f~niecVFd%0* ziHE@IXs4@Ef)EC~+KvDSF`b;CAyc){=_nK`>U2c*i1TbTH%^E(3|pbpUDgO3;bS2I z`c+wz?BsY?rr;-f9s*r+PiGef)4ENt#shrHyDdG0gF^GibB6br{$g#lrcAmCmvjU|WKD1%CTYS|%DImu_pXIChnji=>KQzwmX(a%U~e?X}*B$LtqR1Hem9D@qLUUi(N~9=mX-!VONl`YBAcaQcm!+%D zXk0eMdjO)(p>xymD|#pbd#-%vs>d53lDT0DkE2Q5b(FG(xqpjZ#gcPMnYFS$0HFow zNnGS-0~cXj*Ci8MEsgkyamr4{f?=zp=Nanx5}|N;pXnYtcJG|vH1_ll65s^MA+iHb zSrN49ocNGJW@U!oN*CB`L>boCWTa=oGx7K{C~A-sYv))ZuUxM}kJ~OYbkJ^6@hASK zN!an7YLZ&H=sc+3Ne|}*Qf$< z=j$lDuFq~Dd@+x4w$)|`=`JAEPRZICkefLT1H;{FO$|r5+$iYs44ONBo>osi85B3j zBGOr7tQ56 z9nFhP>m=w_-G?ZhvY5o1Nf{8?((GZC3WwKE4VsbdTN|)-*d^9rq4LF3O`tndnSu3C`m>9`?Ey!zPLsNsqZqoZ zXJ3+;ygsL!o~p~Kx5K&P+)e-iyyK^MGiQ9w_WNTOIb`|+N8_b;F-lN6^_~@enIk@e z8W;A|^%+-&-8;2-W(ccitLDE-o2iw__bnl(+B1tB<8FQ?uP{C;gr)IuJ!e%g&?^&% z*_M5|Z!41h06Lssy6T)N(%G=sHaFN;G~MaVdlTIPGoWEUk+NbO2Iu{ zZ)(le>HX;7N@{|v`?4qUS-s5HT-W8SsDqla)%IzMLtiGyy9(Bp|XfU^FGLt1ngM*)c@t2;req z{oRsfH>hb(V1e9~f*0z1OmfULP@A@pa1ylfEAPQg<^L|J%NMO#yu9(~Wrk_<2~yJm z;=pE>$fssEoo!=5RXOmoofocZQ{Oo%S`ipO<je`AS`x}yp>bbQdNp)R*?7Wv}n*-2_ z8WC2|llylU>G_20Ni>3QD#iul+cgUn+G!k(a@dmG#oq6v8nAusE3_Kyp@A;CdJA18 z%W=*D43FQ~Gu~RU;MBg6GOfo!b&n8WmpOcBbRx*9c&t9jD-`-ly{G9rKl!AVwRss& zB`sPB&XZ-|`c0P~2SMX_cm5*A3I+Mi2Q=vEDvLvi9)G}*a5I667 zMBJjc+I+2H)1+`TWZfoxT_TtLR*iD;Lwoe0jSN5cG?VLIeSgUkuK6waOgpT|lF^tX zn7K}~LVbf*?iI(=Ri$pH9{q1!t)k8y+k45|)#q*(Rt;F$VhTVh zbbnF|N=bfnbV?&hT1O3^x>tP=-;#10+u@mgKf8%Rj7?Dm1Ul}HYZl%gQY`E6heoUc zKoysoUGelH2cBo!fb#i(>JspC&_WIs93xnVe{+0-d73ny`6noya$5llGZx|$1RAzJ*=bKW0sh6s z9`(5U+7zomm4vd^EZ?56Zxi%dLwk_evuqH=_b&W&aIS}rxnXSfI%rDFAk78&_5`m>k`bzp> zqpnKvdY3&ZpKTOVdH^Uwh!n5qpDOCReh35*PkmQ5t{zG~VFhthjK0aMCtF|p@9v@! z0-wPnAgNO5Ey&TyG|norub$}rbhZ@qHc(`j#oSPI_H9NVe6DVFc{{a+&m?w1yvS$l z>7>Xbp>5~wL>-XlL3GZYq-+#Esg|Jie(KC&3I<7V3}Z4cj^snAYj@*MJ%fM@A*OqlY2zFn_p0=Jr6s8$~gPWo2M_tJVJy|X~6#`DsvGeyC zDHave`?DyMaRnnZ)k}hv{Qw;WzJl?(~Kdz{i~ij3WSuMy+|Jv5v;23leKuaMib3 zVXAL~^vH^I8L1RSJUhgXDX?_a`%=`qxeM&+3f*l#qReLx8J$%|kqT?TnQE~bgAFKU z#6lAwOjL>1K&Vj}NztQNB+;0$xZt{=sxA`IJq0L0Of}u=;pB{%3+xgdOz?bwBCKTsB8!3FTo{MhTux^!HTdIhW2>}h zwd~n192^Tqt$l=kp_l5zCja}H>b&r2PjV9UG8B=XQqHpSgKmi2v(${m#>&I6AdWIz=Jhxh-w?gYv#*Y@_KfSIg3++sIAZvmVSVr>2VaFDsx=zg#CiM;CG`nnpU5?vLKJzsw%$tI z?OEu(bW&V!M2J@>L^%UhU6hoJ z4RK8m)k@ZQItmho&hskbd{s9yc($LVY#xiUk+SrHLen^JJ06}mpW)}bQ@B1`_CzadNK`VjA-jEnN$q0gmDQ2u za1F-zOYbH-;8=Iy{)eNQuBbrK$DkbCGbvMU7`Gtpf3fzKL3Kn?n=S-`1eXw80t9z= zcPBt_4RWx9yF-u!J-EBOySux)%i-Xz!}nF)J9DS*ckfiq{OGRgAG`PJ?&{sU*M8q; znZ;V5uViS1ckBJ_k{WeQyTOzCF4Zr4xblGxG~ z*3#R=ZseyYl6Aofd$YI|=?HTYs$7*7Ots;5T*2!36f9lPy;54MT31xgP#9x}petCGe(IVTm3F9s_!vSrAZ zyV5r1tjWfGrtUB)nI(XULJZ5c;jQEU)~EID0_O%JcU#sj@^FuvW@6`*WA_j@sR6pg zO*Xq(C^)RmevI5+blR&%F0m}o0iO4B-@nqK*Rp)(La|@{+XgLiVS3yd0ITKU3_s7f zyd4UA_T_yujHMQDhrAzmC8+Y;`CB7=S*lhD>?W(mC$mFG4cglEYyNS~HilPj@Gr@I zvtIDr>7Vp32CYVNtjmVf0>6*(-Cat}!fWvx zXYxk=ehv@M+bjLy+~p~Zuko>_VLCpsYP@{|{E6jtblZHj>-hDx#Tn2?R7SPZ-qCdR zAk)uFs*AXyr&xBlaBiGx@VZKM^Zq}p;VIioE}}ow@F%uVQ0o8ZWRwohwvJ98ipu|g zDdl{c3UG%T`_Ja%SNCNum#=(z*+A1T;Zt>h&&Q`G`>H@Ijxf4Gr$%jvZ)Or15qk+I zgNxL?G(yRz5T=In=EZGL?Ea~>Dm~+sv~}5AyE}D0^X;t{Crw2{EqGtOgLXyeS77&W z9(BS$rCfR#I{N3gBkYu9Ll?!fpgvD7%OvC-mOJk*T!MW&&vvy&=MGC2`BhBeR z5bpq#pKi+`fhX)tqn)@S^0#KHxk8OW7TABz+K&c6>o2a#iE*JjH7ECv!?}%R zk3b&$s$plf@eSEVfFqF`?4 z&kKSZ#XhStK8#MXjpBz0YpR-Je(?_L@cD=zBt;<`Ibai(vkBQZs8rJO3>fSoB1=x_j73RUVFCCipbUZK)&qq}-H0XOepd zDx4fjGN_q&)?6MBid=Xs59%%HQ=jt}bT%oLk7HdD=)PMF;f!IZHY&Mewn*r(A2cOR*s)9NNyqaqeNO2ej8~C~nTMo4>U-6hC|ap7mfah+*4#XU zK^|Gm;r7LJefRN%44>CM>f^;9S|i4=B&IB{dIh=S7b-Zh7t>;3ELSKB`a9ukV-~;L zxAzP3CZ>O6Yl$LFOkzdGc!72XlJ9FxxfC(v9yBr(0s69-!JBf3+lf$xGlZ1l^fB87;t3*{Xc2@*eZAN|Go zJcZsJCj$FZGH1=}@DtRLt8*N>7I7BSqKaq}08ZuKZwmg-M9iMOLG&L&Ka8&0Wi8bLyWI%p>Is7H%Er+~qf&RLRTk#UqjXJG3N`|k_BaTEJVgbF6 zJ|0iFbvPG^`F(&ag^owuk@8)AJl-b`QJh$bGz}p~0du>?HxhGujzx*|1#|2B-$=|} zCNkPajG$7ny0q`Qyt67G?%hQ>L<|9_i0WPL0{euzM37H7pX?LfjLJ-uy_Ag5fR3!+ zb>e??0XSbb8Sek7GH3d^S(||X1qH!^f>Qn8A0CaY&4CU8 zCkq>c{}><{jU3F({=W=E-bo6;^g)zA_itZUoC@KtB__rgEv@q4FyWQ=>a$8j8J0lE zGWuU%&%LE5+FJfm{A&2UIqDkB&(BYBySx9K$y6cu@^pE6@C=?cFzE1VZ*})+>v(u9 zG++|aeZ5Wuk1CP)dfuN6{wpmVMe}`jx_vu+yWGD$xV$@idfPPbygV4pR21^=cy_t> zc=vd}al5;mWjbdPdU?H>O|5))_vr9_eYxOM^6~6wZTH>o28X1s9u{tTzC5!auyB1IouwV;CW^$FW~!hRUwx#ukbfn8q5$cNGIeuzA{#@x3->V ztSURIb%M((Cv#_)_Wk6?xICtL-bZEjHQ5yJ#y{9bskKxUoA-GVnDYNHS$32a zI*3m}%2_ULeybnn|1Ar#Oyk=irlp)nCcYcW{b!b}jc>3@0ndt<%4sH7zsMs=U{g_L z8uTYH@rUc3#s0sa+q=I91(u z1zFE>Y4doa{zgM}C`1to%I8nPzpscZ8j)3$GsEjq#w!mVRT=;N*GCGOX%5gv$J`uU zn+odPAuN|*$!?NVfZG!^yN$#5mZF&ebcK#2G=g9R5h0JSAIaE;ug6EmQ^ zq=b4Ai3tz(aAD1W{09+K%#lDZaIjerqs+a0eK?kBLk7C-`iJw+-kvh6Crp0Mscx|e zjIahkDtDF#kA*%Lx!3Pu78c3bSP}tON#k$s#3-c<%a&KgGLr+CrKlg8u{t!fQLotZ zw=BpE+=)}z0#3A*jyhq*(;F=$378|rIYpO$KnL^MsGyvzmBQX;q^>wN7l}YXm@O9T zk%Z$}kS}w9e-1nPEVK%t%&ROgWv!z61S5)x>ae&DB;K|tU+^9LN)$78uW3V_BMyif zZ;uQKVc!WVk_v3N6R>YjIe#~!=Cf&{ax#3|Z^@Mn{6uhqlK-P2_fh<)y&_VWUC%#k zqg}2+!odS6HOW0`y|kmsW0WOv z#M9tA55#f1H`Pz7c)i&9)45wXqPg6@ythh9rfIB=kzNd`_`BoLzt9aM*BZ)Kq3O`_)gUh2xSWf|V_{w51PHq0Do_x6;Wu zmogeNfg`<_rWPXO++R{gvOOpxj-|GsxX{a2l?s5kXMnOp-O3l#U+4 z=cz0N84Tsr+F4Y=az|uTN0=+jY_d#( zN>g)aBgOiQLvjA2tGzbIZ*#FvDWmki1I$3yY(`62I5nr;1*b>4pjZaV1~{i0VS8KT zlgLRZvnLJxq97JJ5*7kXj(1 z3pubHEw`&7uVomYum%Vy;&HDPuazMC=e=1kdMObRk!s!bgW$z{JW^MIK;K~QO`1RJ zw#LB?3!{6OR%6ce+|vEJSUht#M^Vc;CQRBIlt`pAhNYm`=`wOZaCY>4l9rPJbJB}I zY4Jy@i2=bK0agRWfD*yzprj!IgR?U325Hr+Mpsx^bQwo?7{D8SK&ziwS&E81_Q_0C z_Y^*k)I*5Er`7m}hq0*B&FQPRb?l{CkD~Y0m#gGqe)(ws*+g4cuG_c0I4?|$*pXu^ zsX8rUz7rBcN6cHF(scd-eXCA$?M?7yQd*zkY*%W;>@k!1<>C8(w*;2Nc`N;omO#G$ z_nDFd(8R#V*~Gxc+1d$UVr}H;$oQXofEmEV=syh92ER>h9Zi}3?E3^B0xbRL zG(!Cs_ZMs^D2rGqD24yMPECNeW&m?%hyM)ZBQQso{|uw&!^Zs|+yCT6gy_jQ?lyrs zCV?t^RZQNgZY<|_8%Abl$l5tJl5#oPa_WmOPmh?`l#E&8(|M&3YBlj?35stNn8^l= z!UBb0r`EB?%1)BV=bNPVH% zdK?hjb~?My!qfl*5MCwnuX1(ldT{9FPfFNxzhzV$e;sU8sB=xve`eSK zO-bisr`}Zlj)z17r#EasM3Ij_1aO|k(zke4f&XSb(y3O>y?V|VAQ122sA1d8Z_m8_ zv^ody$m~34wWs%#9~^j{U0aa2P;Cf>JM=eTdla=n&5hfNdC02T)}~wyg9g{}l?@2! zLWR5oFKF02IV0wh`VwvH0zO4277D+r(m5Pl&D*SfxrMmJWKZHlMdg7?Af7m$oTVc48QPACn_yF)Ybz*fcT_z%SGj-)mub1k<&V#YDK=Dqh zrax#0<00&q&@PSJ-u}I3UaKeUz90UiR;6m2v6i(_fn!E%&`rXq=z`OMH>q=hXHOIR3Oh$*0p=WIeQ+B^BZ((6; z+8HBN>DvN2%w|LX5BQF-Za)oQY=qOJMxYj`!?&@|gFSvv*b=&w?^pQgY-$x$JD`<2uuD3XSYn88gi&;MbOynuIb~sxy?ezK zMGCpx4@mYZX*}<2R7pEKCy1l1^RDw(o?&EAIdxHi$|pd2_P7JZbU`D(d>N!^hyV&U zl@rglnUK4FOpg`|?$HP^hhIh-pR^6d{Al97Sml$)*!nMZ!Gp8XqKNiqW2gbaEHeU0 zf$x@_1A@gyDjUzyGfUy#vU8{r?wTg+T*MckPkn)9IFn)booNjM0w_TwRF7|Ph&G!B zvMB!P&eg+r`-ufiOpiF%+(9nhF2r!rI|PeVH8c>WaMkm_0e0R2EKI34lp_i*xIe>k zo;@bHTPj=G8NJ)h>DL$6CohDqcCQ!$AN<)uj^yxRU(qa^aAk7AB!M)6jT~`Jh=AaY z`P=^B4Lqnx<(-y7&YO%c0x0=ZFLO;*uh6l|zf)^jmj@{-#IRypQB}m5dD&U;PSJ0X zlxYT*86OkQu0O_-mUWi=D!K(B*+~XokhW__|6hIR(S5-4yz}?aK`nQjRc9;K3FeNu zjO7C!o$`qzHnVWgBG0iyloLU#Mb}Y^5o2$OeEY;A~g zc`aT|Qrc;ee#yhMAvgPzg&(5QjT)v5qwau=Tw&o)DlPM=!1vrVS=GO*JD__ldK{v< z)*~AND4M&zKl`dkbou)u9%eYDr#j6g^H?zcMt`k3(4A?l8Yq;eH&& zV1qQw0l+!oHL>gQ+r7}tgK=!W4nEkK6^MG~0s|k`Z!yaPvM)^5Sa9f0zt~!ckEM)? z<5a|FNUqa%>$ghF>+DO88Ho>H6qPfIn@Q0!3&Q7HGQ-;1`(jXE#1y2W6mUwf?{cxX z=};Yt_}U{1ML3}PlxldF0Jk=w0k^^M=IT3w6R|Z)(Tfu0{rR*ksoYApk|mw(=~*Lo z_V3nqXm!E!*EYK+a|KkB7qV?uMP@3>>%S}ri}J|N<}HHkJZ9~&x;ot)zgF1XO${~Q zJ(%Iuhr4~!=6vaJq8cBdAYaQ#AajpmFyAb`qeXXlH^^>uu?#IWz8a6i9O9Z!NrM~< zke4j-j@_j^B?W7%PTc?8vegc}Xb+z24&FJI?+PLr{Hv@ZnU3MyXVDX-hLkxpKnEP< zHj|9CseI&GLd-cX;6AVt0~V9XmvBk)i6)lyRgvE^y+od)w5J053$X!ygWlE8Dkw;p zl@U%F%r+NN;Y4Re1IxxRxJZjk&(}d{I%?ak_~W#mT7{H&vaq!2 z0*zq$wYMqBBq#lUr!UNrXCni_a%bFCtjbk?gu3PP89s{sGXAP_$Yz%YBGA>fT%{}X zWTsx4Pg3HCTTk20Ky*BU9vG;vY9=D5LNn$Z+3aVS*Xs|88K^w(9?RhvuBxW(%)UbweFiD=;H>`oj@SLsy~wPD|RwyA1(M7!al@DCg8|9Voo;u-SP&N`%5N|$MHGxMi*NFexhs zPTVK^V_*@gBWdb3WaHS9#(wD(Anb=rxzxae2g4IFRhDEsS>-#SE^^pKHtJvDe_=O= z=SJ3w+ta7fbc2&xm*qZ<`W~Axp!e02C>s-@Fk$TYB3IxPHmp=0+Xk-*GSV6M&mN0l z-|pwGxK$ct8*;)TZb99iCDvU3>k8vTA<}53iRHp&Z_&{$YAj;7JcsW=`u^qN7m;js zL_+=SNa=LPMEZqIU*RJEoCyO9>2X-HJ_XM+c_?ng!G;sKJuEsj)Uc+-NKT*30ZJc_`xfgo3#Z3<dyyodv&Xy_)ud3Q8D+PxpXK z;Q^;|*VH45zlN}&qDwBhf6sgxQ6^^Ud)s}VB`Oh7O}0p_@)A1*MtER@O5?in1)6?# z|8J3b&%?6sk*Gv(y-Vp;9%}>`1+>@1*MW%)+;P7~QQX74gHL85zXV2&<@{cjMrz6= zn!?<*O-GEA*^okU-61f5dMmrCl{Bg{H)>(-zEWPLExv1WMSjFlqw41GL6k!;(6wDM z1xJm{#09C%R;=Y-faFP&sh~7Vq~T(pi*{AyPku>UnhHfoZpSbS^NW{~`yuM@9N(0x0*&Ofy6oby3wO0P~C)u7?_Nv0o=CQOat>lrR6rmFcU4KHXp!buRUaic)v8 ztC>YGySq!m*&+hXD|3o(mgpbRUVKKn7ZD4#znpQUCxt=Md8w;`PEd1c{EnI)Qcw8u zcEr>O)QK zG_x5WXVbQD)?BR8o|;(oiVeyA^7E%7d;XL|&BN^9mDP0mF3PUA792YIiE-Ft9@43= zmS)BcgrymCoPJ)(9F*3%6~(=NMIpAv@(b6Bj#Kkh#dbfOK-z!Yx}|m=ilQyR-l{&5 zt3tzSRbg>0;J-;$SCh6-BbY$0v?Q31!zZTWer{Y8?k3!CtmPq6`B||A>kiz4Rq?Cn za28QxDe#xmilQ;C(4_h5*@BxVVsoVWo};5b-$xMtW8#9A(ho(Cx>;2<$z?@K#|{QW zmB006!<|mX$3MD-BE;c)932Y$B3>#rBzMKs@h3KsoLsS$?PsKmA><<7g}Qft4Kgk= z7K&s$xQ(jdT{`jH1Gk_ah^AF9?EV_V2k}S^!Dqky(T}3gvM|_ZMxU>V2^&C`?9iRc zpZjoYdBjOei>|CLP5h+FDrQn6_ z`~{gWjGf{38S_n-^!1zU4qA<-sue9VbKN}@oZQNEHLG_uHx|9ugXs#CARf25+K$`$ z%Nu3G^l|1+{}_RvjTA>7=2SDWqXiMNLh02bw0@YY1ohd%rH>^~3Xl3#u{)c6^RgJ6 ziv7V?ND^r51bym}*#|><4rGW}78#Sfyttu#?TWSs^9N9wZRLkc<2lXv6W4ZFlH;|b zoqbWSC>zdk289FdnIJ$G85jNmM_V*}HMNtFIfCo2SW_`_H+yeEd^n;3FR{kYFRQ({ zQ?@b1x|}wf>g&J$9nP&T4(EWw9I4TYg`g2<;^b5#r#Au|bm!k?YaTM+Ulu#x=&;$k z^-!L*+xl|UbC6?$7^+ETomy9{z7)o*PNwj!Odn?ZdyfLVsqJnf1=12+UOmNDxr+lz z9mNyanlasTu|Gnv+Pln5=)>8R|-s*2#*Vy{2^mwf;|zeN%wQa zUHN$?z@V+a4dGS?0g1)jebWf$9C;HO5^3S?+zwq-P4!um>421!l!nvY(i)|kb?Vw# z9gl(EKLw1egrlQ=`JGd~VrKly)XgT)aAkTJD?tVUqfrcv^EiVBX1$Oh-KfjEyXy5h=JWUP_#p;rqRWV^2H`v@Ngt4$;E_y8C);xHC0Be7s zJu%H>wcjck!6JqKd>*Lx?cQJAUEx%=!#%3+3fD1}x2v1$n0 zTkm54!WDvNy&-fyXf~9G937)WDMfwjbH3!S0t1uR$s2UoaYW8_+r2zr66&XOrX^*v zK9tvKuD>ctrk#_>C;E|vH9j>m^M}*r4vct|YWaxp?dDjh2J2B#3b$=;uu*2hye*-X zqWmj?rG_;U@IqhtHIa;4kAX4}z5|lyE^^&pNwPTb{wS5zh0Kp)p%}y}R;>Nfb{WXJR}G) zq(9KwM!!01I|FOkNPfM3ydjqbZ%qBE$v+t~Q@GejQ0|i5cb5uRkDmJICAO2}7_Fh> z#nz2^t>rBw{&d+9%~?QgF=mK73r)B9o&)WGX~uN`9d5a17T=9e=Siw5sLG$VFwE?S z47emZK2_{h)1+li_OM*%DbhHmsA}tttyx?I+Un0G)J=8RoYpeI)XZ_Ppw*HMw1UF5rKs8T*czxdGhh`I%T`Iuc zt6zIXjrNapPIIddb5iME3`EvUWE8AJ&ThtBhp3PA$cT}W9>(aM4|W6_czzh zm*Y&I*YSHHuiJ4U@3)Z3_eUk)>~Fqzotcw9_m7)`?-#dd2ENbZnQy1(ZwG}Rt%QU! zU#|_kZ(r|)UJpXfJ6;=oeLdfRozHh9olk?6zPGn%?{8cNubrKpW*&gZvlVC(7o!?>%E*YoS*W~Y0@;4#8(o#pkY;gzxoR=KC@DoFhc&`7TrFodap-+O>0Zzw_nn z`jDjctnz(_O33BO+`#))Na+2(^6mBdUdUyB^7b9*`?`8|y6XFAbDbsUGl(}EFZ4WK z`F?rr`o8=AG&*VUdXaj*v9@(x)2Z#bx9>bC;rlj^mifNFdfxF?Sm|?XLh{^{rv9{> z_`*oysC(_}^V<9V%4OjD{t@=e=%(-O$5VJn^(j1b*S#TmI@){`eSNQm0}p0g0^eV# zd>_|;y$9V)yLmr@eV^}ACqEw9tAWq+`o`PcM+kX=o$n{DmCeIkK%s_71E1IRd!ZLd zW@q!sxA&*V_s4^e%kWV5`Z&kcqY(GrA=lfW(rbqR+=gE%O+r>?<+foCc)`7cH zgZ9pwrk%vWR((AW27!sr1e=dLTLby_{s#7ax%&m|^i21@p9kNh0wZgM z-ugQq#w$t8-}ffpcBndEhBNn6ZI3(F8=gDP-S?XB)Bd9BSUSdyck;|_@>-2}AKp)G zyR;k2dE0vLtsQ3A+AQka$n195=p36Itm58R&+888OSXH<-g_VQ9t^dgHmV-BH!?)b zSS=QgFW)yrsa|tBFL_Q2tu8#vcPy6NkH_7|@s!ytaKfE$={x^(Y-qDtbEfk=H#yHA zcYjtpU!7BL&s$r8c&MG(BqSW@7n~U#$=$a!uFVX%+BTMT=6hB-MO*5>uVqv&>Ut6= z+-na)V$bN!y%&}^e)CI0)Wzx&8y273z>h%4z3#oL!|kmO!B3?QNUNYIm#-k-?eo|nolgQpw0Z^&4VZO>A!wpbpKheF~UCz zO4^onrhyekoo9(49z_BLKiG1Qlg`vmT!=TFMO`aH=p0jvIlSwC9TvdPcS@G9v>Ci8 z+~*k;Qy#~-=_gbZ1FW?3-rCe=t?^4#-FsH*_y+|C!=v6Ea;)ye6FrAhbQvmI98G78 zXQK--q;y*ytLAwn?=K6A6$PTo&y$m-Us;-EcHdGR+ikVmRzj!=1Tg9E4UQX=isiftoDzr>Qow&j3|^+wN^9#g)D#yVpN~$4joJJVI9_6Z*15(Oe#HoaYVFF zr|$+Wv}LL+DufxV=LX+t-pKITIayVdaY=sz7lyoKw27p8<4o2I>hWqmrT?Sk3@%XY zDeLPOtLeA_d2hC`M8v*X^`|PlGp15#FPhOE(V}dw)v4Kj1ie@^p+%u8>&rFY-jQ)~ z`xK366jq9ouL9~@7y~=63opVRfM#2X7%n+eew0Vys0yz($|xy9LQhRqOR@hFs~UF) z)XNs4dP^|^&D`HM%-qvWGDa0rbX$#IwDP)N`Tn?jX*uN92E&18An$XOJe@k zvc!!1f?FX})7*#vW+ei?pQ>_vB7d_q{z2gx;WyKhO?o2>gazkP0qm?!8ftksjL9%= z47K4TxZIxS&YVp+j7SewZ>iUB+o+KRpCD?T$$j$*Qw zsiW;zY$^0iq^#HE{?_hX4d`TJRlZv8#NDiZyE91(vdo|A>#0%dG-p^@&oPRL-XgJX z;{)J7Mr9?TH>HFgGm(8chsovj>~^)G$J1x?+_coB9CNDG@iqBZznRL=AY)=EAvYpB zuvdKB=uGpA2L}HGY-dyEKNM-SwAW&T{Aa)ZKpiePgVs?<8(P9PWG-6$dScz;5_kun z7rsY(^ar~GXQeo+%{XIu2gY=tX8J7wKsu_}2)>t18I_%49UHP*cle-rIgmu{F%79E z?w9I#)rNuH+W#$rJyi4!XJIM-*^Gse+3@deumcGhAb;`RG&n{JqU&a1`e%_(e?@%J zry`+dVU$=bN75RMcho*&$lES`WVDj=+#eZ0Vk)-GKh>tU@)L15m zNK9;*smk7N86RQI&pbqHCrcX_!s9cJ90;rF+(ZVwZOL7IN8VZOebo7DP$19Ewue=V z9%>EFN9C5ZYt?!aDv*U3Y5$$11;mOyUs=h7pz=<6viG07Wxme1#pV!pH+3nKz^10& zPf@eJEAywi35smZT-9Dy0*VILwFUpTX~`2{+L6X2G^SP_cajF2wo3~0+T$v@@b_#% zbX22#p-SrNZu@q;B*xF#7;#qLgdoA{xmpQbnc6U|Z9io!oBt@3BJv&m@c$80ZRD5V zY%8Oq*{ZSPvGQq==%i?P@d$%5<|c-`uI}%cMnjT^6f$?*=O>7A0eLIdx-iC0u{X=J#Cn8_W9S8 z>`~Ie>Uw1LcF3$lbcVa1Ix%WxWII~PaP1*(2GKJBCAz?kD6Wi<&-ggJ|c zE8z1GkCqP%dZ>?J!qcxh2tHI`Yf*S~;LE1jF%HbqGWP?LL`@{ziMz>UTR!f+#I2#p z=^Q=*NEgAd9bVtmF@<|d04u#EF@LDy7xaOhslo8qvPc9(OMBGhqIo5?M9N`;;qc>f z`SILgbOZB(>fvNLQ(9He~=D- z6!kWq29yW;JQO{JMh|YEs?2)odo?abrpiuyWW!GqYl}Zu%+sZ3uTFU+M$gV2e+3Zr z@hh_0qxA@_py0FcBg(WT8nG*wdkl%JKXX=_fPnOS_B=n>jN>`;lPJ^YaALT7z#9*Y zc~J@CVw+n3EJbS65rr+0Y^7yTG`{>(7S#-gggLa)1L;(W6pQF6<2_EMc~K1hoGx z&jxJO)3EHBxalfpxdR5DzlzlzdbvX`ypOmev$VDvzS1`tzX9!aR>pr6MS=J$M6NrJ zs27-0u^6?h5{qUPm{Xw8@d}m20?_F9;^B5_%$-iTL~UowT`Rh|C5IKApv2(c2?AsQ z>g5ofD6d`$+8nJ)jQLD$)yOn#Ph{3hOjT`?jcJh-*yT*v(PxNx=%x5T^>7W$_9>YvyJgy|C@)c3cbv;>IvmBkxS|qv~uX)(?!GK+?loW zQ>oEO8Mh4drPg*l7CMHMa=yFGp2g_PisQIx;xDJ$Z9HejfXJ~iy2$COX9{=9S^60) z0;ok5%sWm*8J%UT|3Q1t9G^tkNNWqEQyQUWgXrbLA|2Dd7rnpA( z#-_EA178M7iiwAk42GJFh@usOzq^WRymm*+bUtr3B^kZ05ahT~A+<8d8clH+^QA^4 zPZy3Zt3sN|n3D*H<+ARYa1hLpDOz{uR=D`B8#=+@$I+Z!k%6Vipc`F`dO0RY-`&wp zNqb2I<(NWYh_P10%(fjXX=-Ev-v?_3HwrfAhhnCRhL>=PDsO_*5`SNHT6(%J2omhX zqm(7F5L%`VvBir~y*bri&*P+$RIPQ3iKhq~v}9-tOv(z@brGnDF%vju?wv?*H!IxsDJ`7sT6nwEc3(F+_=FnJ==HfFZ+c zieC|<0Y{mMiMnRR-G<_mAI7rZP#_Os^0ia9W@}PvT61sHUIWC(0(`Nw=f2;2{tLYP zQ(r}FOJSR{bX_x=M)B(8CQy>}y3&Q($YVfnN}ZjgjZB5LN}|mjFLy!vM&=xLQ2K%F z19mk6xVT#R=m@iZ*H8UTOkOdk zI<5RDTBya)fQanG4r>&Ct!tB&%wH%J67Y(V;LpO8S0m+c8S%>+1*5GODXWr; zH4yRcq+3keFqXv>O;)gag!;WBh2=W*yA|hgw`pa=EMIZNpdZAMF;FVjpk!Ff$!Jc+ zeWr`h;5f+LfGaVs0)GiXL!V`qR?bh1O#6tArwhKtIvo*Ptq3+?$EyK$&ctA?H4bNn zuXjC#P z*0AVvb@($Ho)VgrIsJ8wz7w`u>n`74F(S7_!^R7P6@% z@^Crb z1!G`zxARUG5zU=baEEGt#x~O04xva?a>1W+jc}MS zAMqSMVAbtJ1ATG*lVS#$Q56=I?t?L;^Fkisw5ani5Q(D)Yh#ArL+(%*XG57H$bAH6 z2l8zipY9_lBXzp*6W0Fn^IB>Bjat)k#xKM|_(WB2JV9|@m;9LF`qP(tVWy9@x=xG% zl_M6DBM9()aXG_Rbiw+H6R8W1hZ|a8HqKBHDm#qs!vpJ5MlX}R(lB4el)p~ZJQtDl zN6mL&Z~?Bd>Ks)50g$ca3iA2_C4ligi7GPKBge*_?->_pGDnerrh$cnI# zAsZeE>jYxWiWqr`pyRIud$@RyGY%fO2(%Os)Pt)&Z_7|Di=_UWA!8eG(EUK_ zphPg?MQ)knkNd>V-~^f8(W{OZMuM$@Apvfoj5TqU5d~Zd(qZuJ)!o`I8gRDQ!uv9- z3B?dFd#wnPY|2%oB#~8&nz6k&Iich@g$DCCJZ zY^4@x4kF}H{|&TV@qwcDSinYT(2mLeJf34oYV4MZo~v$Sf_R6DSDP>h0jQ=DHOS5f z(oQ~jg^yUtSW>VARFXM*QHEvy^0@RwX&rUMBCXlzB7JiWl5M)#lsZ#oUs;?}GyKY> zGPbdPMEQ`Nn*xsRWu+{J{R(2ojRW61(|wo)Zv4I~h1cyJLs6E9kYUo&Buss!ki5qy zLtFiok}vizCq!|wU9rEhZABX#p*`%W-$~SaYetD8!dhtyp~O%Qi`T}l}uWZ=LL(&yNF7TZ9j0M1UI zNVN?0$a7JKbx^^=&_eY`1+^0EdlU^QkjzWr4fccfOeT#AO=@zbM)))OlKwIK&?aX= zpN&ix&#E-%c0w6jPF+x|L~GwNWU_iwi}ql*?LKyJuh$$%80nKP}XP6g{mdh>}+ zu`1NDG@ygsav_k0f z8&o11l8lql**Y#gf_3=sfHor2|;96-tXyUudu+wTyPjW z3V+cZ;A}$acz22i=-@k3<&p8lX^^x^Q7g3jS%p+NZ`4VIN?1L#H|%Ee#A_Q+6rGwV|JGR^uTZaz@=l*4hhtiR5bI(5k#R-32ehU`jP_mGP| zU#b6@@`1-elI7e*HXHuCKQE2TKFq?9S}tfyH?x>l4olDSXiWzwkO5hwt{TMSkSlfo zt`k50*5`0QvVz$nXNTm>H5|GoVg1ceTv=op-Qm`0aC}?@9Gd5JGD(TwVGUDv8(;Z{ zajWk6Vz9<)Xk&R<*&m&3G=;Z%LteAg`D@knNLk%S9`SRA{UVD%6xMzcg+ z{G{0IOjLuE;qBNI480@LOc@AUa;??KWNLpDvbgpo#f{@MuPEvynFi$|Xb5HV{v^*h_L}Auzpu`r+i7W-ufA;80@l&98a;miwyf!SA?CP#b6gs0= zu7kRF((@MRXcyU%oE9Xq#x6$-$4`mrMoO8R&tdj438x9ojo_FvdC}QkbSGWXFzT(} z?fi7fkwc&DuV(!k_V=<9x}@2K4G{i~@z)~hn>pQ5t_ZEXT%9slA`~f+>hKV14J#>d z1v|zZE2&QM_%&j!M2(5fFvf4IS-VS7*VGbQ&puhTHk3>hzCFe+rlrjVv@+YMpc_!3 zPy^)-O$nlVT6F}?2X*Vvd$i)QEier0Iw<66&c)?cMqRe{(9hFwhMnO(#kl)dADdY2 zo>BM(@-c(c6PPC7mbR%_IX|m0{lRVoPk*f=gVyct<{%YQl6V!id64PsvSBW>n9I)B zOGnq4mbub18X9}k4swi_^rNrhgc4Dd7iSgY#+mH}N9E85rA3J^eHvAkhV64YJuiF- zmHP%&oWh)tmgt?LCLMkUwIY*6f#`j~tvE3cri3lvB3vMwo)un+e|-yeA>2SQq{ilx zJifA(-5mZj-=%F-0AwM5K&8JzgLV{VNBmis6VB{>SC3sUdBrFU2MBhj0wj{=?Rs=Ib`yG< zs)fDPbBWNy*wtuX3jdBXgjMx%`g($)S~*~kLH)S8uYfz*Ut6axEYo+&Pt@ZR5(jDK zOk&u&1=}}+OsLq!0dZGL&LrWp&VF%NRMuSZuyNEV%=jkERz z8GaZ-VVkCUnV{HL=jd6EFtyC*&0u!TH^GwJ-$0P31J$TwNh#tXIMUPtd9p}{1K!0b zAmcy`4fFJMybpiuApt1Dl3tYtv^m6dV~g}@N!Sh{ok@SR2<6x7H5+CnaDt6AmlnJu zX#OJ=Be(C~DCpYt-nI{9j_glI`*6}LzzeByX?KDcMN<*+JWN8TuRJ2YuN|~<4L}t! zfCM_xc8>w>QaJ%Orwot`E%~L7?*(06fr;ovz!j&1k0JcbLm`6pXJqLS!k05~PS&i~ z*K^0S%j`lpkdnv0A=oKriEBwOY@|=bISAzA(2x$(zSgSV z>;BH6q4x7Ynvd)X*2sG{nO(!>p!rdXFDr%Q;1(9dcDiB(>p?&#BxNDyQV2XOF@5X# z?nGE}#o;tqnL-kqYDr?2DsBL6g$$xTTNg&1F7YqC@U|_8&j3#vNF0ODCZ!@0*v3gZ z#V_sB!qb)jBeT<@knDJX^qGbsQD$*Hx)y2gROv|};2S@q63-fKJ=1v?A16qfr}%I8j7qjm-!C4( z23V5seC@jI6Mcm#n6*0+Su3qKna9OKkDONrP(~$4Cv62RWPekv%j_P`Xrf+(Qpy%m zZUScpj@llEEHyW)49sOV*GtG-^hbfV)Kp`B(jkNPIl}RqIHbA@;~{D?1UHRS2%*I2+osgk*-rlldy@CHSM7*^`=E52+Wjp~ZOBS!NIyGY?$};$0GA28r?u7UvP*is6d3t%Rhdm$t z*ljZz4yb+V`2fN8tiSVsOVB+KO*f~LuO)iFzxlwPbqO^&*8g?q=Hc`aU;=d=b(W-T z%s{dzT|77WS=7wbT(0=9t@3-Bj>p$YQw-_(5_Jp4+mXHAk6c)y+Q~I4PS0P?>s@28 zbutlYLgqN)Wl}Iy$%NqwWa?|B+3tFolC=^*7kQ_fE68tB#{`LUm7})G**0UgBUwK& z0txk0D+-@XpkiAbVyVc+F1COPPA?eJ@iaj8sjhUuTtf!T&c$x$qjo0?Lir6GE0a1} z0CrgQj|onX+L&4&9>sP#@nF+orIHrFz9QBVaM>uAbg}MB_0%*cn;D&aAb~=YI#kN(94Q0UNwBL& zJ&3g8MiJJSo@5HNnDH#@|iQCchnbU*RoPI^>61j9r?`vLjL^!7Wu<8ABjy&)`lCBqiSFKjZ*L@Ut5t@ z+xLz?=X~(>3Ie`>3p|Mwabz|vBE<8;dcobsH3YGrS`T`OtxU6VO;|bz$LV2m6wpGJZF7K6l2xPlR6ijkZr-N%m zoa7wvd@cX>EVg(A#CgQCbNHxZGy%*csth87;hT__XvvNFI$tfPlo}r0WU=RF2sTlw z8KhKTH=3-3W_kcQy;P2F?G3JnX??haMAkdCRpD2GZ>R)G%OiuugaZ1P0psw_^%9^T z6pSRQS)@hpWqa)RorC->kGfC?#U zwOZ8RS02;9*W_jZtP==ZP{Ew9NT3zpavMm$bUxf+qsv z(0bGu8*T4n(3C(&D81Z+tqZgz>{Zot8&F_0LP_Oat_71PX1l_$ zFo{{;g2j&Xg?>L`J7$doz8}Rk^?b>1n`}S|fD}R7f!iXw-L}o)2=?4`AC(Mp#7uGwiYT2 zSI(Ogmk!NQwHGW0#nDTU1FtJnJ>9Kh;DxylBnP21Ry1#ta6kff!{d+{mN>|GSn~R|xtH)mE5xXfCW~hx3FxuA>ek4~jY-|#u zz@!oQ^oACYaI(d)+)rn3*S(3g1-7F9z0ztX$vdQIaWbo8h z*4U}CXL_(HMrF4aL)@gRVBvMz;w+-R6FH03fRUqwAGe>F^cU^ zr~FAcb9H|+LC!UA7EA`e4b&CBZ;s^1X1*4%f(8{DbmwfqyV=f1o|OtB$Jb<@RaG!x;b*j$*`NXQLAP#{} zjdfe{ef0r(q00~&BzupWmcgIa+;6ar^@Ux(yz@V6vZbS_wQNzBO)|_e-u4>w>B*+@ zpkUj0w&54<24OEYI3w%_Jj_hDhB2cd-m30=q+By)ZD5M);>vp1WrsJ7_iEFu1Q$g; zU-CO_Zt7A=qgSs|775%fpIp$^bcR~F;bc6ef_sVNKC#?R%qxPL5WSIn4DbvvMwurq zh|YJ4fF<}LQ-1N@W@d8Ss6V2qcAf988G`7zL~VAVaH21?`n#RfqK(-08pAi*+G4PtkccKeC!el zcoOenSU{-Vz+mmcsp^t+eUPhZngQ7u#Nkj92c}ScA0=?Z!)t)@r0dbO>=l?VSz!XU z&jR12(!tn6}L* zq11B&r=WuTt*@OAUz@_E4U*M>^G!G`U2SR2m=kYvJ>!7!B|8~bWC_y7kxuVN5bf)nSZ?L>1 z+d2|K8^m-iFgt7MCnz%r*pecGQ+Mu$^H9dF9hKj2;`T_0$&_udI9i__epLqbyCLn#?qycH&+gUB)CN2n~=TFd#0 z-yxnNNeHn=sA!ki=NN_E2`N2CfwCC9T-zMmv|yQV>XdBCNjqQ}$PN`uKBUlX+gHu2 zrir=`3-(DJmy}ovc4^5@Dp`o`{->GW90FJ&*l7*z<_Bq8DT9}@4T7G^VOv}os2L?lrt)P=+j1wt+S^b60(^tyVaVaz7Gvrz>1TLNHky zNtYRz4z+SW=%Ozv{zs}mA)*sW^Ab!T=OY`}urBOC2r_BH;hqoAOz?x$sgq87P8gsMg=wM*e>Ux0jikJ#rvu9B&gmF9kG7{cJOcFTGu+Vjo12-8zayVfL4mvQ>@dH-a8Zi*4-asHM%x5=s)7Dp$7eKCU z5|(lS6oU)sb?@t8cH;coYO+8z7x4RtmR=6W-2rE_Hk-u?wCZ%znCJ3u53X=M-lYiQ zIFq22ld-I=o*}BGiZf9JW@buLjc9MYFTxMXT? z^D@d_Px29EsE%%tq#VKL2^|@{$Il=liKG%i_nTSO&R57rKAn~xE74uSg+SEuV=?39 z-||MTbsE|Uj(|cXlZQ&#BmF3&OVaX3y6J{8olo4--R0TZD z6Y?pMRUq(zV(n)9$n%m(PZr{I+6%i2o{}6YXRCpkWIGcwpKqP+LcpqcisMxz@7n4u|-Fw^IJa|B-LE3x-ZDo<4D`Mkf={^ zivq4`>=Sa%&uM-TJSzhl9FKJ2fD?kkZj$U zr-<`GNCG`}iSn9tt8H6cp>Pj6fZ*rAM96e^2&lRA8J0T$n-6dO@d zu@O_m7E-0G%6;-c^9`-Y(kLaX3!YKrY{H!@Yc6O(8uU9Fr@lS!L-z0%)z74IDHo1v zS;B?6TCclYLtY7vT`SxE@x9>X%#6Atd2iPv?EJ)ID=yf;UbjSL-0o*9lXAR<~RxW&cG*@Y06!g9J85N)%Y)fU8!Fhkzl3E_Q-G1=cW znKUp19}}i1){G>mR>gk!l@(_8i4z!938yP!gQpq@A2I=wz-CTrh*W4LpiAO|H2Ek} zhzML?Ozro892NEiZq<}N2OC6yor8UdiYB7OQCgAQItLyzOZ5$$Wu*`#C~s3X3u!Q+ z#=~F&baz7pK1(G|cg^_u=o$f>m|qPcGuty%bWEyTt&b7F1K+aK*`$>q8A{Z}I#l^4 z8ISu&YZ_8e26#4FOMy8ZL1L=?iiej&i|;trQ2A{q5cJ1sec#>elixS%Uiq{aV0c|>0qiw&GiMmgD|{P zMG(O|+Cw&=d3O8hVUfT-2BeB;<4&b(a9$R;8)>%@#CgU@$yJ)MHu0?}t9Q7RjuzP_ z0}Up8hc=9pv;c$y6e^jQQ@okAyOT(@ z^$=ZlBxM?wV2L#EM&F!*8?>zo?g6fu-cr$C<@HoQ7(gbb6RhmP|8L3wz0|iO7U)`G zK>H@^PHX;9e-cr*x8 zce*>6(r1C`VEd*zpa&>DQ|Y+5ktDJ_0ofuG5jJ=STE}g+@#NwD$$C^@#e>NKg(4YsX8Tst8X_PzKrB#?LEO9A9Qhf(;5* zVpm!e#B-O^$`@7pf5I_)HB!O)34Vg3yAF7tt=sT2}%EZcL=Z?Rs>jAl_juV#>D##Sg9f z@Q-pjThi^N*xWOn_z-ZfCbhwWzqNW2oWK)h#Sl|ZRDJ_Yq%)ecaK6by;w_pX-fYZjDJ{nGM7Z3PJRamta z!P%#P1QbAotlKd0UkPOJGxTFHbMGSTz!2NAgN2yegEb<@A$Z34NZPaJH;l9NVB1df zO7FbYwy9yo6+jE=Ik7aYqNz>r9zU;m@G&`vPKzZOM>vJey_}3gTrCqJas>ho_lT>O zWfMswOQ%&+%w^LR2Q@Uv$BZjbgCQJ?mkdRZcG8<6*ISeu80*XiZ8kk2NgEKwJL3+? z@SxS5D@wrn$x;JoeY+l+xD_(MNi{5XR;GXXevDsUe?5_xNO8RCC!!%6l)7g7L99t1 z(l5E}Z?G`r`jD*IP*qUpuB0IcYAH!OC%EnpG(G_}Ow6}3xSe{#cT3ehOsPcYgg)kz zSAu>Z6_8r+N^GYqVoKo@bj<~cC2byT&mcD@r@M#M=fj;uO9RL0m8hwqL1zj?j|}2O zkJTQS8?xcF_{jbi%I}j~9kfW9;><%j=|}9IfU;U$R*)XaEBo<8SmwL?Cc(?r12J*C z9(^bldSxX)iiJLy$?NsqU2#5{r^v~R5(zA=dg75A?Zw{h+zqb?0`AgQ7f@1SnXW0I zcEdFuGrE)P`(i1ju@$_I?=7j-#NB?uXH5K90F0g1b)ng+#%E2I*)6 za5|6jg!NyN^5Na@@F@^>OG z0NBciCLaUPK2f0HSeIZU@0wyo5q(YO;@G1?^>j$5AXdTd zxbP!XBQ~&*Dec=MEn;$DQY=W6M#5zh7L6csi_MSQ<6Ef)aLpRs5WsnAQm8hvVNcw0 zhhS|Bn~EMf5GsZjP{RN>vnn8abNly^TX&kJ-U;K5yNZfzgnB3e8pg{!_m8cw3C;<7P^U8x(S|nG}+pKsy z;*K=boymQP0>52hpM=%^2vt!EuEoMoI~`iDmjFqluN@{)cgKY%)ulP^Wn$1UWx9?E zt|J%LoA`o0gpg$9+h}wsq#@x#4ih5bTgJLAf_~JU)_|lJS18vAL$c6ulHG91@Cg!D zvH=3KN>$PZb8w56c-mdmXAC=z*P}DT6RL}b%Uh|Z+U!R?_Os`=_lE&t4Ep=I1nO+IyXjcNzflf1_@og`y1s6}*BJ<>Jx0+3_DIXjO3>h?uTcN;p2d z$>ZJ#XPZ79PXVw_3^Shq{=g*x!TZw-d{%W^AuD>^&^BYdPC0s>pC&bEeyHAKV#`Jc zvst2SX>mM3d_+3eGjh7(n0E=1NxO;L-Ws*LS}tyzQDRoGnQFA6j0uOXt&nr@rX}pl zc!m`Pvl*prDMa$E3<=-}OzEBkBU5+)Sc;)zY> zGWksNA=CCnPb#=A$Y@2By_f^HLVomq4?j&TeK&j!Gf=CrrkcWUJR{GvE07r6!XrFg zu?W0gWfYh~@~-$$v&R+&%SC6#6ojUGpR84i3TZLpgHU%%;+IX zr3_-Z2Yc6``!k+aiT4&6=x`DnOWUA!GNpU#fAFk=y#uDRrIk-bDG`uJ@3OugQFJ`z zsAF}sgsOyW0%foZp1fJ&!_*i%9*oP#<8@WAr+BXXFDi%;iU0`>Vd+Rp4KtSxEW!A_ zJf;VsvW00v>H-qmTuH}0JOML=y>t8qi3kiFAi(NENQ4S#TU~tvpwG%Q>FUGA1_@o# z>n^K*V%v1rWb;{Z(W>ToG~g)4qXtiio0DiE0yX$@bC%)$AV=7?GuY;FhVF+D7x3HY za)6kHz&Y*$79`Q29cF0@ zAqGDqEO1ehzd^}ZXoKY5QTa}5kqW`08%v#!XvR!EDVhxnrV$XdL$W9`nO9B0u|4yJ z_Ww9h01OjcZ+paR$af;%MvS)ui$o~jm{BZQnjiG2U_ju~5~dQY1NRDPk=Zk_kcP*a zj&pDGf8%ON5#5g7NvIr}wQSo{3Qsx@%C6m)(0yF7{kRl18#dKSham;e`etMVQW)cp zj_{FwkR&YTn-98h{P@k)9(!!*Qt=eiYGc>jFc%7CTT-~j`mnU}f4Ey@bfULr1rmvY zPMfhmf*ht5!fyXEfkcz%05kzys&OlCL^1uaT_m;mBxFiZgmVXgP^qaYp>ApGE@O~*E%?lVMOi=|y21f6 z0}{@2)LY%%DO*}}J5v9{rCK~sNMMM3+=zp=V>fp+sWg2Cq<4|P?iaGV*GlA!pNA8_ zEz^o&0tlkan06+6KXDS5^hf|mR-eY>aY92vWi!5{m$(E0G@%3`&^iCM0%SO80EYb4RMGtx=E!?r~cT9Cby@_ zy0^^Z{8qy&ruF0IHt$$BH@bn`4*-c<;(?hU662qTqmj5oPg)7p(-o%iapN^flWO88XtHq>r37Owv>(EIJtD_GJ|z34#kL z$`LU)=w7grS&7cp?kxMhux=bBR}(r2a>Nk_pm>}10d+o#W^G|kY&SbG`Fe$Y8jZ2E zHS%7|&=OaRTJHUj6^l6_8Ah|JMxAJ|5(AELdzd68RjI%*C(SCGf@;k?G~AIk7FPTS z%aJ-j@+)--TX-2@?CilXx4GGH$C&!~UVAl&RHH3#*Q0CE)$SI@)6wiF_w=Lcifq!Q zI%AbxHPb{l_hXB8wZ(n^D%QfA`dA7>h(W*&Z0-uG>2L>=^Hof&S+*d+q91)n!ENvN z?@?=IPrgGbVG-Nlx5ZUT`y-}kk_?(Ighze7f(`8uRPB?<8yyjYG;PqV$|c*t=xHb5 zl932d08iFhp|dvdUWida5Y!+QuN%3O8pH;WMQQMvlK=JA5szfz}q>SBT?K!Ux+?!PXmsOIDF!69#(+-uI zJ9X2@pubU{&?6d%KPbn*eyb7tjGnTR8L;2&3^#dzErT3u<2|loJaHBHrKBN(SZYwY zQ6bQOj*O~0Y9PtI1KKeoZSZG*jG7~#+(-MTi_I4&-ep|j_(JC zKN9gh%Ju;J9zkTOP*^HD8sp}gxAPTmVHvyIEKrX#q5_Bv7!XV7r>vQwWZg~6%ECjN zZBe^o+nexuKm{Q!lC+qMVrZ~PVZjT4@HEn6H!L9?okOQD3stKH z0uJy3*Q0BymUc76an&f1T0QCx$F~4_K2PJC#m7&4IwfIR7j8R7?2c@8+h6;?4gLY3 zc}{_}yRkJOhK~TUYXK=Mqtud3faj49llSFR=HHuVBq3PIK0x$4fYZ!7TnG{qcL;M5 z1{9tI{s9ze^P_7-zz(u9azYVHqNRG<7lAf7au>yq)ucy*MgT3AZ6Uy%T>{A=u~-C4 zgvtllZZPcwhiu$U4NiwjQ;D39KAKvuJfnxH^}#VZ;!xH@7!2_AvEvB!bcE`g*m9v~ z=WyqeWSxqA+-y0A4Q)1;T3xHO6UlSz;q9D0yj|HF(;a{;eCe7E)m!K=3*U|O$#l1S zHq7-3H@Aia7Z!``>3LQ&4p%8V)-OS#*)R$7IoP_eDd`OxRP1+ z+JK+{y<=rNPKcVv%)+>GRROj0{et5nZk*021U27N(^E5nL@xZpb5e<%3JkKj4hKxm( ziGYKlEAXK_tchf_t|o&HkfDz|4Pj{en3fRU$)oF{h!tZ~0cMZN>x~5cMm8)cP^d?t z_|^|b^*`O~T^Qks>P*N~w? zT0kabVM-)c`j5YLisv{5Dhn9VIs$!BLEHuy<%m?e7NX2xvEEKstp1^z5I%sMo|LML zYF@2wW=u&ym<2Ge!Cm1F4>k%1pa+P+1Cs@vCu9WsqUO`2HrwkA5jE^EWk+0S(niAh z>fm^!X=O2^PV&8V@%xAwZsmcO8uDM3M2`8^cG;o1{VbG`uQmhNdYSM*8+RYZfg6o?$Ve^vd%fVpDGl+DY_J*yu1Wt zblmhcK$bg*F0W=BVY(){KHHEoa2c%7JnS+s60OqEO$O&PU0@m=N~c-rj=rA`Wxq0^ zz@H3Eg$$gwdHrN4%_o`-`|BbqedawONrpYe=)1Oh*ls2_*n2=`GdL9D+yfbL3 zQi^}}5tSOr)rPe_AxJRjNa;v+u5|cuML#m8RP@l2k@`)^ojX+LaH7);(5aeIUE_(H z8ck`4&n0-ItJ_G@l@pLt`7jv(4h>x?(A`{*;1r|BSBiFu*tv8POld#qphw$Ihq=!q zy3Wk(T?VyD4;7HXt^x{qQ_BcEjaCl!nsI#5v4eIpL7`II{hOi+LbY?cMeRs$LpS@m zR+XU!FX5>$TYYmQ`A5+Vc-hL@3JLYNqOKZbc=O}}(_u$)sNKDPrrVX}=u6%C@u2E7;e4tbLvIT{ta6(BrShL(cmB_+^%C}Mn;K^lDZYJu_) zCp}Wtutb3>2}~HwA4V^^DD8Z7#e@wLbQ2hDC+3&KsE{5z@3Rvoj6yV8{AnGoO5{NW zJ8Jli)^S$}Nt&$%44OM2!PeZi2T???)UCS3tT8-*dS^YbYOm(^V4NIMSmav5#L?Cz zW=}n(Q*39h16vTC9S&cCPfwZzvX{mRyTdM(hDr0rOoc?1mUxJPh)H$!?U{M8O?IkX1ep4<4^4!T_a zNjEfc(De-TRJHBT3>YzWqAA{=cINdEmuus!wq;|kyiry6)3mB zGv!{MBg#(cvQQzSN~;+sz(xvw=tDuZCT&N6!QKqQ1Vjz)GAbA;y|iAAVq?~;%f*7x z))jrE*rV!=@&|UZT}C+^=%s3)%O4n%`;pwk2{9so@aYA+487Bb5y&s+Ig<*7R(%my zXZMec{9~ZS7;O4TuK+uwq_BIw1=#OLR*alSr?@jfPy}Sa2)Tmgh^T^g+H66GfIVNa zC3nk(T%^G{=O%wE-ZnsOfCwLuob418(XLd|w?*{aoTPk%tir{`E+j4ZAuuy|-h@oi< zH^w?;Qe`kc4Vg?PN3InW3e{a=DG07E8+Gdj=*(!&V*V=xb|F;sCIjbrAIm48UPR8A z{MzWsG~v3LhPq(=KNwKs=~HuSt90;xNXMG82~HnxQzSHuZ~@_BCG5^gQRVlJHTAuY+3D&w#V zpNGlJ)y0v8Watlx%MIgX=`hAp8|H!as@tV>Wu1Dj1)@MivvNeQ-wznKum-? z#C42R)Q5ZHmUn)0UTvvqp|n%&P=aWXlvaW1)B%g5F^B~nTd@Q`dXGs7++e0Q4uKKx zn|lvxBx)qh(wlFRswUDWL7dEj3Tp%o_FV3BGG}n9i_|DbXZZrz;$;qK<2PMOpQ9D# z^Kb{+KvDJc@`61hi%VV*g>* zX`~NkyouGyJC7}I2hwq1YxiWhzatm*_{*hX9ohtozag7St+4EZTr?~>xA^O%>`v?r zJ*2}ru||p!fYGtLyWu9afd|+eJxUkIYX6cFa+o37wQDL85Icmta0x{0^Kfd@v0><| z3HW?V&Ogrs>zLhdakNvg_&oTJGjK|pDYinQbq%8#a9IXO? zu}})KpqaO*j)GxgzV^#KFXh-7`0sG>SR`5FqA!EX@XM`HH2b(#(W6tsK*Le0TRBN< zRZul^G1EXHB;&QZzpNC>-6UD$CgGsd zK?U{a`EbXSrvVP}686SO(Hv`eU)~!)(T}B`>V;i(o%#{+H14MKuM4>VRVnZibLw*9T1$gA5nJnWi#s7&nl!uGSnZSNql2fA-)k_?|05NxZ1WvaTYS5vk#hDjdK~icxJ<)*kxUW<1#~n7gC<|qT*puwt4xsP>?sbPo z$Mgn}?Y&0Jle!S>zJsq+Kd<>|{;MM?HPZFZK-ceqvqJ%rzyJbz_$cCB-U*6g>sI@xXp4b)zj~cep#XNSyMB%ah13M)_agYwGZsQva7vQp7C5WE zKjov-2~Aa+qXc1~oX>BM1KH&Tesl(;-n*?btdU@SK*kfii&zEONRp*d>PMd6xgEZp z&_9JE(ZYKtb^!rzVC;7g>KhI0R^>23*XrM_fDxzoy&j}EnOU5ygvpK2HL;BA(KTp{ z^2kxyS)TZVhw2>XnAD0HKarl^KQQyqUFu+gqL#Q5mFyokg!bh?&b=l?{}ALzs7LxcN6T_0}^ib8>)E;zs5j%6wQ+x3?W;wqB|fN zkM=(mgr$2RZLg=4(IDAI(|RTi0s{b3@Tz_a4jmv;1``#aj_`=3{+8Z9KiWjXi%$-) zMe8i-$qKgF&wcFILGM=j(nNIedBrM%DvkphyeI7o`}={Gp{-|v2@;U~4UE6L!fhm> zq@W3QXIHR)k7es{bh7U_vVs53q-L?4$gjr8xU<7x+wlh$8e%-!w z_fcmqA+J%7im6RiLj+);>MzF?w+S{RLxF*Cm2YVFd-+2&8Xxlx891FmqbU)F1T_)v z_f0B|49*^W6~M`dOR3s%kI8*VqkyXrA%IaSBJh;7cGJe%bu&I2axB9Z5g)J&qj`{a zW!lAZf!+0}xE&a9F1QPHyXIM=vi%uXdjy>8GeN&SUzgb=4;06o4}k6&NncvDaL*th zx9zocOz4!wKmlP8jet6!tRf54w{yK{S#~!&YUSSB^qsyJ-*iKk$3-ppDvfNJUx|rP!N~dIB z^maYERyB4&z&3hLbW7=|PMvN@;!PaCo^++yi3m~&oajrGmO)@a9l-;r@^+Wv*To1b zgHVEFI4AVn;I6VN@@$H!WG3TGWwu?=VTO6EcgMAJ zQRyN4FDTWxwGDdHiMU-Ds78aywDSzcJ0th>XwR)ieO2F4%{h`W={#T(I!}i z9%9;g%kFENsJ?fkL^_2EiBzLc984bcVA+C8U$ayh-x=&ng>j@3$>XN2tl4zUSvwxi zC6=^ZqEb)eOk>*JHR-3LYsvC{pHVXmyKc|L9b_7>rNe(2A zR6N#(>*oyS4x(EkLDfK-RF6A@fFRVUc_+XP)nILk-Vr%?n26oTB?1M}KE74 zu6MXB=c9V3B^<~*nCueVx`F~I*LU#zNatlCtzFuLt z75Pl~+hvYtZ4Oc>nq}I{#zjCHDdb>Ddy27Onn`>x8qK**!ea}@L%Ajnpm+;oDOAIu zgJYu6UKLMf3qBZH-n!A=A|jXJWIERT@x9pM(~P9l>))h2W#ZYAd0}=VsxlC2Cl=>y#zu2pD-%>Hd*mmwg)* zDRC!%K%V01Xgjexur~#^9ttH^{p1G5IOD|FA}y?x8XW{E9VB}WxPi0uXrZciQDP2CBhie`(O*R<@E%oV}dg^6L8k7HY3l;`mqU?HDEIL z)ga~-5w%=@`h`@$X~Y4DxX4Zn3USv=Q%k`uW7AO1UslG1vUFgRqpVT}HYSa{qSPI@ z7EQ`YC*53kl|b^Lfo}nN!#W_o>j{Qao|sZ@<&QAIeKA)VB?tUGh7s1CVt)MBJ{90i zD2*vPq@8bqXH0r#kwj)!Wq=G~@&|^?HsAQ;p590HBI7*JK`=)lKy?$DG9HW~S}Sxo zDy!3Qx?ZulUnmXPkc)}ty@!u2sdp!rEl)wyRO!_N&^9l1Ox0sz?q%8R#BCdWHNx|S zzHXr1(?N0yokpjDx~G4mrf6Fn_h8SHGr_C(R^HsaJhdp*A{Xt;19by1-0!$uj|yRl zhJDpd+SQYpKHI66)`P_M64;+2VUlDv&j9+i#d;W*T~9k z=`N%hV`Uy)CI%fIsAAxETUi{HFDLj&NlX~ea1pl=rYKH|%#lD&MRnx1+svv5Z`b(> zAU(N7TS@$*PX6v-N$hFA1J6iJOHJ07D}y9<43eex8=h!7ttHAZ-1{M|Zp=BlWbQ#* z+bm>_c<|TauZv!!hVX+()pJWw}1po7;WPH{#fd2>3t@)W-=PJigz z55?*0^$zRf@bBrocX6F%Hp?MRXmXlwCWVg}tN;r5iaWVAn9wsh(NFdkX*W)ZBX<9c z<`=6V=k*lVXZV+X!VYdQr`SS(tJ{Np;G~tqZhl@7`SlmYa?~Dap4}=YU>V?1iK@9? zQXfbYmdLsxx(1Dyf?#7fr3n*3&x1~RDk(}v&X~7D4Qv^jC6=l7>nTDw37zExat{cITTBt1E9YuDdE|7wg)Oe^u zNJVtpd4HdD3V#G&-(2&y!I?%4QZqM0qz|JaZoRTau?$+4Vxra3g?ve)8Jk6Z%TKOt zqBg@(fEKOq*hK`ddWalQ+Y*N41`s(JmS&e7g~$bMZl}h3a2hg^V|nfHUIOx~!EZ}* zCo)CRCmF&(T0Qo%tTCYT&<8a)>CwoOaE0mfc0Hoy;)G2dUOnJzhRV=YB&=QoHXkJm zdhqicFX%u3p(&q$-EjdQX+1u)YWb1bEM+ zDV#`w?T5XS*3xs^7s_29LGovdChbr#&k~H(^yiwUJqSOn(eD#%m|Y!~I+7zosvzHR z((3BzLtzpa6I*7owsemx+?*lTdO2wxO{ZLeYG?u1H~P5b3XIFTBWFuHUb;=Y}LNltab~-ZM$Spf>4x z>=6$%__g#vj{U)GY8QRWAnPfey#Ja~1!vMXfRwy?2U#IdsBgX9T1b75U%`FH$=3$v zZ!?|+P@DipX-QTNyT6ps!U2djH7sZaiap^m5b~!0_x`{~4fM?CtA;l@$_cCi%+0M}qqCb|yxfFpI)_&1GXD;WzDAcRGy>+vG9x;DJK0 z6LdtxswZnBWclVuMn*z-@LSI8z#GcG1FEI~%Yq_=`b*3Y&#Tu7C%>SfJzvX6cg09q zY9@Oti^(l=jN{w52Uv%wusL;Ic!gxS3n_3bv?r4uABCxK`6^LR$TZ_lZ#+Z9i*gMJ z9dWZ2VwVQ>@9Cmncd+3;^P#yH*h7Pp@psk_`pA`_;mGFx0yg69tAD(Q%7-9%Obb4` z|2s>X(;yt1Vq<^?k2nY7GgmWm%-MJEE*_C{hYMnhNm*>)jpk1@bPoOD?3tOdn2-h= z0>(E6%QlEBl#`QQ3y=-#Dc?(+&$2IT$A@C7!mp-&~Jn9KK#Xdws@GwhO#{?L1+Lgap)dkmB-YBlv7Ojo~$VBkKXpd$E}EeQiSlkWL%Gz%UEdNT*fLkY3_EO zpR*%R7AoRK2X{FGS}d3^WZC6&ypNKS#^e~D1E6Igz1@dIqChsB4_%l74>GxBb{o3C zPr?O+Zm_;3QhTsmOPwgoE^CEdU7_HFf;|mT>wjC!e2Vs!7{aRvrZPU5!`*AkNx zP{2y;rf%=3K)OpgKqO}DXpq~}eM2kvesM0y3S@WFD1WE z2QUhmbbF!)hh>eS4^IN~oJCiN#@TfD+*%YOG5(b5fdI!f4MXft=$3Su-T5TY-iZ6a zJoZa2w`+H?T#!(xeEfyN=^%v#6DIky|$rtS3jCmgq>DWBTfJ$zc;3DCF%C`vY@ zJ}C7rap4pTb5c)nh#^)lUK4j|ttp4f-KpneF zq!DhYo(pI|x_{|cSB-bo`?e1o?@sGA90Uzk6Mka6KFKNgTe%$X;9w}`LJs#J8x`RV zR}Hz~1&>v)>ks~-9b=WNAVuEabIAt{idY|?i%QO?M=9&B!n0wbAk*+N5Qz?<{PAdX zmTr69=fjxa9TG>6SurfE6kz+|Xzt7}45*F8-;2r;^9p|j8gb!1E0S&jv&}K=UB$r9 zo<4Je1<~qy?&`INt`vt@Yt|A>2gAp@J&Icvt-85IggsT$l|kB(t^_ zYZt#no7u&xloT2sH;*KeIG^gG9l1C^W07;TLXbTi&43gIV5o3?bh5A*Ok@+GxiLN5 zRH>-Ci=5Z%L%A{350Nq_(1YBw7v@ zb}0tzh1|2>2DR4%Im-9xrdu#NfgE$MrX7noF172GE8L6T`Q8!HOn!uMiSZxD`nM`jF#%lDxC>YyJF&2<{46GB-55QO6*JP6ZZJBIY_${Qwb4bU@duuGf;}Nq^$|%ScZq!1F0kd`?M*XBwsailj~3`RB2Z_ zxT@4N%6_)acdLvme3pdfq07c!IP`#rJP2@Q9szbC$H9LaH7Cx$+uLwuIevDvy3}X* zdVyT$&qL-HsdD_Svu0LjdFT8(Is8JA#w**s9upj0AndT&%EN*aFYNtq*CsMW$h3C+ z0IA;mo*mRUur+5y6qt7Wz&uVb^rEL=B1okO?v12U@kLy|S!wQKm-|QApkVB#z)i?B z^UH0#HN=&!+i~nmv_^4w?)y7Hrpa6#`xFlg)$xo#VwYM-P^Oe{H8k7qyGYiKe5*tj zoLJ7cugDm`;@AZOYBp*7iaI>9k4~kVjXlL5q>#t6xly+jfy0b8xXgT?RC4bzhR6i_ zJwWbSV#zxL`3P`A4vMosQ8f*69udCpL(*Gevc?X?1r%o^5J~AOr=LjLOX0rG1OVAo z`^*v`%6-y&Gi}z1`E1ml|Ffd@p>D(f+G`y>W{&9)8#1ul6(~u zPYDq7X#*kO^O;3GVaM$@X9^ps0E)C(r0_8;OkgIh5y-C$_)!U3mh{C>=-`FIY8~Ns zVV;Am?e62Qv5a6~-zHXZyP88XDI#HQ=`#n$YS@vw1q>d`a~)lhCYlLdevUYQ2{y7b zIpp81hCM%yD3)F6wyG|8!+P6K*ysilP4j#$PG1v*yafZIotLc`aq5D^!-06yPR+Vm zx&HQMpU^_chKs|P(+4GX!s~!!$3@PV#BhyYE1!n3U*yMEv|T)g->vUGSFzh24~&CW ziI=Rf{&4n09FM7wjhU3#giDR5Z|BCb4KIN=@}%s`BuO+oYR5lf@}U1XCoTM(L7 z(b~x%wG!}X&EUm7T`G5yw~J;4ctBR9oK-*SaV!P(&r&lkd(8twLlqF7r~y?j_9%b; z5dh&RG!@fV2OBI%S&}|k84H3a*ft)*A|O9Z`5CeP$<^7HZ8!%T0^e<)CUNp<4$vc* ztGB~b<$mhUj!h#Kl5G;jq0Uqr-0czGlU>x!GcDfq_W=r(v@$%?9s(;Yz35n0d963f z7Z0chsv9?e21jTi0XXTbdAxKLHipdZ7TOzKYfwX53j#_Rjf!>SL`HyAOHWeMAWbn} zwJG`}ETr#;vrCxc(YI2ks1f7ZprNb94pX}m2We z*h5P{h4E~WVa!%))Sr#^dBD;oVcHOnF?O(HRe!Na80cAi`%`E!BVYU1S?k9m2gChb z4vW0_QjGA}+E}*Vhw~v>%vF1Pjf%3vCPMepkUfO(7a)VC^FR_b=G=Be!qy-TBrCsw zGL#|^bpw5G@9O2*pH-@?zb%6l9b&vGcN2Wep{C3nh$>MbAB|6^f>$W!=;;j24CBGF zKZKig3ldSkoCf$$khsncxlB|Fz+8T1>1Me>=y+e+4y`03|3DRT-m=jZpwo6A%i7t> zW?O}G%X3d;?WF^4b(}p5caW{#C7NNGtA0QnJt;CxJ3mkX7;R9#-IIwqhyx$zJ3#nq zunw14wwy$siA3r#W=9_mXfa03DUhk*#niP*iV59mb6-JkK|#pXV*a7SBrVc2K@Hne zpfVe_4haaV!7^YK7A`17?6xdR75&WJ+7hy;B!NL^XNZ>vH562NBJ^Om8qQUO%9cl} z258K|$s$$39#q9M9qrEfL9a&6j$pP*<*bbZts$$0L!{=V%v41KW-?#^FJL($Y053G z<_)w8gi{sQ- z8qz2X!=~Lm%tCb}h1qYLmC1r;53d2hPxKo{PQHDZBG0`wVp_A<)&u|$(n6-o5)@cg zdrOD0wP#x}fM_QxX`~NyHIEHUa=MY>FtcnRngaLw5e^+m0Pu1N!-;ry$+N$O+$WZ0 zoe%{I9Dc#w)0`H6%k{}3uX&e#U6IZb5M|z{m)p?_kZ2OGYwv2QbacWP^}jDvsW! zdNT%Fl%T{f1RV;xc6#MrK!Z(>U(J(}?^>wv*R1g`eqT%PcYlHZClaml9}-PYJ8DN2 z1OQ+i4FEt200+R<)Wy)o)X>S!(#~Ap%+kixng0J4iDqKzZ0uy|;9_ZSN9W?H!S^2=WUI0PtVajmD1M78^?MoANPWFJR$sO&6`EzflD?i=ek915*w} zf=FC(r-wYhR3p_yu?f2+1I&8NjPrYr-^0CxwwNkux=vZj;nt0oDX27E_q-$8R#8nL zs?POGDRM_tBNbk{#?#FwfVT9A8&e0w_DH;QgOz_?E51~PMFtZrqegrCK^&NS^;oFH zZ2VO#y))Ix{AYlnxIVs-Jk0F)S}f;TE(`06GU4|Eyp}{*FFlK|i z%D94aE_<6yaka{G!py;F>s*qgUdfk<+w@^>xD^r`?sD{lx<#uRb3@ZuZQjLgbd-5} z$={4|rKfuwTbXQy%5~y3hRiH1nk-}5yo$;90l1mFk4SV)$tM%5A+nv@?YDCiD`Og8 zQPdO})^!sR)*Yj92CfkeH?*IE@6lirV@T*GMymtt)*7g}1Y57OGM1I-R(SSk?E(CA z;Z$?0zl_v|Oczlh6Xl>@?tZ2X9Bc+HUP6(*8pY>Rkd!xtqB72BCoTx3>4+JBN^f6B zfahGX;G@XAcRJ;?+U%TM37QN4=%N~z^b{5-ahJ|Y_O%*k!oWaKS4OnyZ!xMS$awvT zlcJ<)dds_uBt_qe_)_N7VP@9<$Nisyu zw57q;RUHSIYjCdh!+RQQa6ZyB+{0uL&n0D89D5C+Ab*O@6;CcU>5DsjpBl{Wt-1q$ z4-J?B(5|)YKGhqtwu6l3 zq6__}45N_N+AJMGdLb8Vp@waP-fZL*-T?A>7T&4o*sUe1wVic{cYj*RW5@rB|LAjF z{zy-7=luN9`77z0@430VjA8})_Xq5OPuEEB>61}aKE&}3x&3~hSRfpNF_i!g0?<*A z2LYc108)@B9>WB3EuhL&ryWlR-wuF17<(Y*fbM|v9)hQ4G3aavdOzfz$b+$))CZg| z;BIgY&G#Mf8{rQKe`ue`J^=xQ4hSC5jel8f9@nWZ(p z-07x(D`--%%#SHSQ4il-fOTU35K=!)HU0UzLD%>DJgu_V^Lu=s3UlxC{JWNyi>JT) z`!Pk&=le8f-}nAxzUT9H+NECi=j$Sieecgp7k}@^VbWRK9Y0U+&%wpqoZrWB*W=-p zpM6{}&(F=p&{No%yFI_>&)A;d!;N}gi@5&Z$EP=W`n~Tp^>55OadO|E+cDv@3CX;l z=l9>A59MATukZV*soS+2?whx{yFLECp9lFk{vW5`u2bvZxik2`Jzur_JU!p{&#yyA zaq?-sO?~H4eYb_|lWn@suwncyJbXPoe;wfde%SH<@%Vhu<@|Ukb1w7y(lCkVe|;!p zztijc`uMmxlHV#@d|;pQ7AyPn`+o57vjpxl7D{uI^SH9r$KTWK$8|3WJ}2_<_cRP2 z3Q3z9RQ_XVs_PEF&+qr*V~GFHUml&DswKR?Z}XP&abbM7UF>=G+v|6~4^MA*f3Wnp z@Hg9yIJtTiI6EDEeLRjDkI(7(<6?iu=;`ryd;E46xt;z!JR3T?%7wSrX~)^?eC*@< zncFI>kxnYo4xc=Ll{mRaEA{a?T2vtE_4qnC+1dMLO#U2e^ZWRCIH}YB_Zb~Sb@+LI zf1kU?{A?&(_^zJH@N@Ujg`ZDPE-uDB>-K(5F0QP;bMNeE*(wrCn>*{a!qrbZmylv_)C?e0)Ft4&n1{aY)}P z`K?y%{rSDflGhJtfj?#8mHF29_?$V$hxwhVlT~JV#+Y_8$&5ZR_vG7Rxodr|8}l>d z8@imM&r4jkZsiQzgM!?gqw~W=r{&W^$KQ718}HJ6nt7J&Hz(l}`x|-+^XIvN&kURC zkFe9bEL`=G{ppy_ocjcbS6;xfUPsh3hVlSAh0lYl^@=n0#b6nNVd!zE?u0FzUIo`r{MX2(n zlnEu^)>Mgf8YuIR(+MV{pKQi5XxMkC#nF}vh5ur_lxy6nUF2QP7f|=6K0_Sns_m2m zW^q_;A3p472P32vJ19npRfr;^>#erqep^(ldzy@T{(Lqu$rK!Fe=#V8ao6DEk%XS^PK z{JOnC4YB6QK`H$lS0(#iHs; zP$XbcZ}>=D|+a2(Q7{g&pgvy)cGR*RiDPo{_#YDAVGADKBCV7Q1X@< z?^v865*1q22har!Z?3XJ$Ctint0E*V6&!Nr_3GqrE_Kt4ysH-2kJGq>{}eJeKUWdQ zUM8yuAohQFt(!qVQ`jkMNae#V(;O^?{dmJxX11BkR&^ zqKJ}sBI*BQ1ZGMMuS6+k%6Ow1y$cVpZ1Va}E(B6uNhFWD>NE3mLnEvPQL~a1q=Im(N7QbC^wITMpl@=?I}8xuQy61HNH)kL4iqUkC7ZGk5J26vdh@| z_wi8IZi$w}hAPsp0>165bd4w3I;HmfVv%LaRd_tTv{NQZw9*j&4wxR?Dq04p8OWw| z?OJT$ZB1X5U=4vKC2yJEdKI}gQO!g5xSGd(0^m`4Q%&QdbE`NumI-|<)S{pRn6`!f zPI;oq4FFf@P`KHB@C@ad!?Z`*?fxUf+yhvWZn(dyYV$fsGMH}FF1N_z5Nxfe6)8`QeY0Myi_{Tox`l@#at&tQb}ssj2~jP;YNaJY~GQd~7(r z(7iOF^Uc@ZesuyLW{O1$o9ir}U^-SGPAHiG%nNLpFwuLJ8L7qw$#1B*7zWqI>`>NF5T+8JLerUm-e6{j3ribO`_)MEDF@DazivDQ4^$4~US7hH*DD(t zkd-(5t(1+W0_sx&ad*^hgHgveo_7?e32sXssP1*+0Hox|B71E^*q#t?6bo$$ zDDk!tEVokYmY}etNY~>nF^hdELqQ|?WutELED|NRwJ(%TRse`TV8@xnR#n-+i2 zXOeG~rjo#pwT{k^k@yM6k&1<5{>gA^550I@!-cBp*!1Fs>brm#RBlx#cJT#>6;Sim z@ZlO7Xf8#g&8U&x+75cX+K5k7Wuwwo9MlO+)Jl7R6;5lKq%>Op=Yw@*DJ+Q+tr!n} z7crsb1npg!qHbx6qnyY+Jk{Z&lai>lKCb|&%dCa|o_ZJv-K-?#0+h&lY10F+YG@-o zLBT5)>=7Og#}#zQ3ohN+RC42F8LFnZ66ny%-DvgOFhq}Xx6XL=^fdkDhli$53Cz2B zGJYe$AHW0U1&CU_30RT>z)P#(y#%MARg)4JF1s|k|MQ2&;2$pYy1y|Sy5$!t+A2w5 zrmzE)Y6})gl89||!HQ18Nr}`k0V*f8QNjvGlFD+ep6$ET9asja8vq$inYG};-kN&Q zkheY-0_0_L6P_axJ$>RFYln2R(X_uly)xXU15lLBS8-h-U5vYe7t-IYGsmoGjNU;)5u@iws|!DRnu4kFw%}|`3(t(E#ZQ}@m{f*7nbx-wuuqT zX<1}9GBpoyId4Hevnw(T8aRqr8X?JcfQ zY2?8_!3-{HutXhXqixTIR5T?Wu7ou!LwtiB+-@;$_cmS82VOXm836oBn*GP7KY_=U z8-@sZ;cA5D1!0QmM4f0>R9(uB&*(&4`z;!RWqJ)dXvTr|rflWcp4*kyZ!>$)3^n7O z0n@FQPLzGC4>W83SRxw_#QfljnlP3Ad_ffrX_eB}t%9zv$E8|fkXJ?FmN-UGJ;_cb zWqF$(#;~sJN||fd`$D`7XiL7LSEi&<9+}&MBQo9MjY&!gW_`Fc)mUsHoRG{&OFt&n zs4JMNpRDh<8{$e;;OA)XvLzjLY9BB#z3(!3wwke`ZZ@8B9{^Xlq`i^+r+7Y!O_YzD zG)gTrxK|vVDL{c)VQy96?e{QV%ZPMa>d3juKxgTI?wA_BIT`FC%+DmKN zp&@E<{kIIS+F7SAa|?=}h4}O<0L>e8t#_@VTjx^Da?85GOi|kVXo0;fHs%LeQ=|5w zwx|fwD+4&qkF-Xt(?5YdJ4^3;u-`jVZ)!85kR`uobg0MpTAxS587jmnT^uS`qW_|q zE+=l$3htJ*nYj(AxOr0Ev7}hm80$-jUg_Kj#iZc!kb{k0!`x7D`3PZ1Z@5K3f9(6b_8#Fe?Jy(F5px$E@SR zU#>}j>-@C*CYKA+3H8AKnm8|PqP`wx9K2zcn@WMY9fe!(XTWN)A>X2f+otaxVW`lg z!k_sks<%j)L1t~TKIY@S#}I+^aw>8%vWt5c*BQa>N=+-&`bX5G4AN)F{LoF{^BpT! zGUkM7nVVZ5nA<9}w(v9O;amX&Ys}!*6HhlaTX~*Ui_7{F-S((6C3@_a_DU)dr8>IT z{@8|^UN}YEQ7IUP_;$m1SM*<-Y95{+4jsoVsU%TE`Mml|lYMGoAwi!PsL_=ni^R1y z`&5am8rKI#B*&nsh`T0@dN&sFj@S+4>9+*_ephMi1+VkrUtuTE0p>N>WBe%m>tgx? z(M^RM{{h<5h-isMivN6|xfb3`-$%2CXRdOxu$J3ZXlu34LHwBAN55IRof+w)_*txW zs)<;5%2{#D8~IcD$G-erQYFSEmo`$5PTreeb0E?5Sw0SBiB*`yq4<+(=?N-L;i&kH4OAU}S`t^R-o z1q~c15*BTzgTqfznL#Quy#a9>N6sKK2`+)d*sX_q2}svexY+{f03V5SBOUVnEUDF^ z2((&@GsB^q94_+n;U6)PKgl@dwMky2+L?7p)mT$Vg5;)k_OyWk#cj$0uT8;>jUC1_ zZ7&}8Jb5!8a~odIi<2FEu^v~`1LeGQO|5NwtY9T#Ua5O_k`I(e$yg7nB~esjt$AD+ zLN%90QO~U}v@OT37MVBu{`=9Qznsx5!2nzV;+?<<$%^A z?rj_6GoW%eR%LzYR!;6k9>t$0W|e|cMR1M;moV@K7v8=nW`gdbfuy!7hjCDsHrz!WM*h#gt^Q>S9owA$?X$7 z@jqhf^NnSNJi?@goCZ_e!%0)M@2w*sT+!U#JgQY|aG9^n{(hHrRUwdBUffih`{3fw?jW{7U9)31o9OfN^8T zZT_K{8-tVa@f{kt@XC4&73DVGPLSn9r4SmLy$((RXK1Ko8?Q5-vB6-K!401A=?y*i zPVYi<_%6OD*BWkqF|WspTBn6Bw6iDGAgKE8fEJZ-?42lUn@@ptqqAU1C4geG#4<8O zw3nb|VaYI~$PvX`f4=h<);#?(UW8>*;##tR_i*o1Rey&?l6mSAd^$1O&y%Riqm9+# z8rD~98JpzJuR0uy%C7Vxc+#8ElG*b?c77av`^H*49dwNy_~g6Do8f#-i6d!6gfm72 zSeD6ii&d@Tqei9-XYVQtHQ~Im!U!XOV!28Wc=?w|RZ)x$$KjjJ>fbU{TZt6WL;lp7c7)<8&MCjd z&xak}9$F?q-}N8mH)0T%by=wjBxRwDNXu>fg2+yIyeqDJ15NM`**jzv8`%9oTY-x|V5w-VE&+L`1$Amltx3H|_!Y8g z{W`(+*XRYK|*0tqo^D^Ws`P2xs=!6mv0@8Mexp^2nXPKy=F;&|E96RhjWgnd%siloA z(bKc?EQZ$eKO6ehlkv&399K{PHY>paNGcR4AJ=Y1Xw)_>a+QY8OXssAHXFig4c+dl z04K@%Pqns?qy}09N%2`i-EBu&hU_ql+~#{}4*?Mwd&p$P4g572|t+l0k)e@81nBA!6~ zA_0YlAo`b2Vgx<hzt>aFP_E0F$paUR4Je}OJ&4VbJ@K~pwIgm<$U=t8kklo$cze ze%7pinZ>hfx?8CmxNMY0BBuO)^`A+Kk>v zHQa2!B<8>p2~6H42*f@49f2`%Z$yB3CTEm*>2Qw8A|RDJWfOM}W6;Ox^`#+A zbw};{gX&O2>OJpl%TcJJD1eDmJ5=oYC?ZdYep69D9>{LN*zBvgHF$zfF+>CWRO=1K zQU^DR%R6%ZsSgmfKU5*-3t_S2ad6p2goL z0&JY=m*hm)R49h(`Ur3?^XTYDOnWOAIfFOfZLv?Vl9j2uMckIO``Vw3?dZ;v>lezpd+-&XQbl&jr6ucx9$%Ti zHN5%#A8Fa`_Mju|Skkw`a46Uf&92}W87ey7W7rmF7e485aYKAxu6aarC9PrwMqe>r zqvyZS??zqwiFEfoGOJw6{b>F^_0vTNw5-&DGuKh7N4tU4theV;BODUsE((8_z@+dt zip<50;AfMj6J!wM-Q0P^7RU2{1%fd z7NJ{$dH6N_CM^>U5leTpSDm`bNrh9zZeDr2V>z={WcOR|V%j&OUzT6-o~@JZwxwUYGr{CqI)Lzy<1^|Y;%7eY9 zg?CI_pLqa82Wd(1dZI|^`4q1~FGF)*SxO|y?x;=9e29^@j==>-ezp2|n$(&d@-*OU9r2nBNw-LK{%0b(Psjb;Hi2f=|V6Wd!S*lD3>GR~KGy_#Fq9cs52e4j^4}e@73(yqnK_A~%6Ro9naSz1 zyX&dAp7}UlIL+?{;K4e5i8Zsw*X(>eb&-OnKe9Dm`4l4srBm)(;g&h!!l`m#PTiby zWY~XDie-i{d$nr*YkQemo&4Aqbgn(O$T9BbWAF~+rGQ%=AJ=nH0Rg@?ahz*8ko~bD zItZY_;?`ATSC-0##DHbu=qfi>K+MM}=DW);g8JrhPqZ$tnf)a=WcHSEKc# zfhnm8vhK^C$Y=I8Uw7M(wW17a%2qv~Dh`R6>47K)2`xH1JJ^2g4Co)gt>}y?x4Y?% z0G3x@&I;(v$BPN?zGhpM=^g;u4S=xsg!*a*AbFtwElKM+d{6WgF^ktEQN&MOgy~gl zVK<3ttr>zh%<37m%6t>B>0#Lu%Xk+>EpE&>YDDo7ej7227q6g4-5&Da*coHy*vzI6 ziYv3jSof*Ws~az1&`$HFZuQ?X9}#C|4tSbqr0}>#2%{y-Q9Av{Pm}0<^~BIFLhsDU z0Pn}!&~Q}nnC1LtDP6xbv6y#Q*F94|_$P`Q4xZ7R-6+ZO;v``mfu*PhH zIQ?F6>QT7XAK7iG=!Pf4~cV*QCqHiJIHcIWyvKLXbK{-Hg5ew-LtcMEa%K?^Ay!B z0tUGGvADGao_Ww)I5}U>#=$tWHS=e z5%=C;Z7aisA@4jqk7dOtRj$RTwUKe@fgbQ)QU0CdpyF**xdhB$H#pWEGLCPKahf~Z z2nBoC-Z4`}kECQWoXz(%W*u`v^hz3*=4sr4qY&EnySM=di)>-Ig8&U5YNWGi0K`!BafcC5GAo70Yn`iA1FcWoVEgnn(HOqT zs#TstQpeTK*J>LCUXHXs)-XlY;YgEjUogR4CpC0G zo7?dTw1Jux6rsagwQkT5Y#;^G9$k*i#)JN`~!%4G;M$mmpnUExG+l1DJ~jdEY}&%)%hYSz|OJcX11_B=d7p>4@3l;Do|FV2&@ZYno>=sNlh4i zmSiOA*6IiF>`F}M3m&ahSB2K0YAV4$*q0H3+_uu((yqrJ8WTt&8wz!u-`2c6J|vZk znHuD4EfJTVgVo}uKT>;orEZ|psLG$U26ZNhiD!cEfMrP-VaR3H*g)<+_3+q5@3r|_ z!=_2$D2TdE`nm+J2d(PmVn+_BLz@|X9%&{wz54zVB^(Reu$lH4ktL%sNjAqkMWP+F zW2S@EtWrIZ+lBfDZ=9=6XKRYx&OQ1+x>`k@yS5J!cWW=*uFUGtGR0(erI7teF-Rr( z(a|Z5L}?v0ylUR{LA=XKZ7fIU4*jeq1~E286=0~?yKY%Hv&0zI;g5|N19nv$s`kax zOKdn^Z39Xd11igaFF}hr6gY&E^Z{-i#bG!>wQ6Yu@X)5F;a#<-+bb>jj3OR%dxB)T*{?l0x|{)BWFAl!RSkjL`@GZfa}5C5(f_;T=-Fl@>hmFYOLoJgL>5B9%@sp z0+kcWTC@K2g#DPH)*3nh#hzya!N=z#CNgYnI6c&&W4Jl$?W$96r*=_f@q7qu8?aR1 zNndQK+mMkDgQ=DRV%fPjXIj@d)?EN#-j}w=nWFVgUO(IZaro&jD#7y|JO+>~ zh1>=oolIk|BKhu#K1gRtL2Uy@bY02~MP=Qg_r>MtR+F<=ZTL!J6~Kvn!JJNtJQmz> z*-6v^dKpAz-%ZL!;+1R(+UTdu944ca07Ww<@n%atg1GT8{?aoD$Pi@j!;WRyz%$gd zmM=(XcjRNcYuS)%(&bG|EqG`Mb%%UQ?c~Pz)kG$P(GlOC?CPDN{JSbOzG>9o4||4* zp-UBTm((NNleoU3^NF|jI`+K%0wh082N>K8SwHGVGU&y0*(o1zii(+kKu@-$klvp~ zo{TLJp&8dzJv!VlIEcO7co;SI59H%m4cP8TEL(*w&>Hjh!=`YiIk+-;un23@VvBXY z#O%87+;atddncyMVR58o53{zEgnnSHL6s|HsL@Pe1n5CX!X`ma0hk4jIU<*6n$&K0 zG}gtoYah}L-yEiok%ilQ;I-QuiU%8a7BCKHM=)y5BZYB1E>)0N(}JzG-3nEG7op`x#|EcSP@^Jc>|Q1Ikc~(HLw%E-f0G0A`{> zunt6tOizp&#Uz2kh`|B7Q}>ntPAgTGd%WktK)lJ(nEmvFC@)OZO|WI+&|V&zkXCh> zi0Z{p24t$~UJoN{#8_aT=xBoD3lL!~9S~Uz`0h$S%;I`BW2w#;cNbfwJ*VZsdgBguTQ>V{%@@ZUPb` z=O)@xRakAfXT6U)yuAPiyl9cJdmd0S@+21Db^4vIPi`UhpGHb zxF(q-iOf%)4%zXUZPFPM>F!-=yB~zl9eo>Bf4ORwdL1R}ymj*G?$*Ir6i;=79PDc8 zP^NyK%imB%HoIfGuNme?A5zdYD1us}p)IT@4G76EAcsVbxfFuPyY-D$!fvl}-!erM zF0+?5%tNp4Rx{jA3kTQYcOk*mEV4dA0f5lBL$21k>?cn|~Pdat^wL1FX`BsJ1c=_QLan zEG6?;q|KD&S0t*&1>5oP#DxsMKf8q+b7jxeGKK^tGn+Cym*|wPR^C}1S&la#^xS&4 z*#RfI0}sD!)igx~3cd#aY^6z=vcuQ~X=X_l2%EWT(F3}X{o+&Z+0U5bMB*b-XZsoZWIOsb;#cjAg-E${bOeDRCpP zgp7&4)Y^}y9ukEx^6a_jY!Yj{?$Mk5LrOt7XINOftTZ!_#hvoIlw;x910|K5(cA}k z8l$PeVBleBR+3FrICB{AwHQ4gVki<8HWCMT9>mv|V$Gql$4eNsIS|XDO6+yD^i9zY z?4hb9B#U4aZ;>&K5V9{fC_KtT6b|on7;7wP@la=OPJ6j(@1)4myL4P07aqq~rw4ci zyj7>W@3|38_36_vtQUx9Sp%LAV)5MNK3qFm~Y1Kha;9Q`?QB~tV z)g=sN!s;Uj`km4dx*WNd)*BPK{@%&Oha`?H8z*Ln>z%$%#bmFQa55Yv`;*-HO4bl8 z##=_0OXKIQT3RjJ0m&V<29F-#-xe7iGB3>Z^TOT0a#t8ngO#|^O;~M<#gmbs(b2`nRb;OH-U3)^|7sehGqYPp2haA( z$Uf&U9gj!Os6U;qmczK!o$POf&&F8|z=|7RQJO17f?AqPsx-Uq7Z2D=+7cX5HeDOB`) zvnA-oweg9Py)|nD^_WYW){S3YIw3AgB^#}q_?rYoB~TcB>s`m%L1NO$wT%)D-J^s} z^>2?SMG3>piw_%RZEDliiE1y^Cck>nz(g@cYLvYC2#u1fv1m`~+9jJ-%in4KJAUiq zqUK2|UGa~V51S5-9Y3}`74}7)@0N8-mW94gLkvw-$)1Lx^2C+bhlO#mH2Ft2jG7pW z+pvVzx5e+aI@i)Wp-ES*vJ#6-0x4(8>(OAYQ2?v6N=p^rH`U}U0`PaHYd+A@Rsl+74?M)Zq(Q2!~w}@FG(yhFk`4{4%WNqSFT+wJWB&=7mMfEjzvyy3%tCHzGV~er1Pkzuc&E0P7R)F}JG~W-Y9r z4;$n+;h(KYrw2A%kok$wFC_dC=6ynbFK8kolc9_^zmG zNhclhxZan{NjF_*V%0mPEwoIoFXdOQ6n3YZIAvW>A5{#KHzMDBB~R%cOffw?FUVIy zmjVp~6D~?JU_6qc*Kr=}y&0D^raXmn9ESkW!~kN!9e7LTGME6w0@K9+zv)23u22Zf z;*9^+I{1LYFNgxU69C=h(ihCJfZbsYFo2I30Exis97h0n6CD14q?8dCZe5qXU+Q{n z5{HOCg0qY7>BAInN{ew+;t`BT&&*v##rpq^w08{7Eb9J5la4yJJL=fB?WAMd?%1}I zC$??dwr%4HJND$Asyp}or{>m7)qFVT+dg}pvrp}{SFImLIHgd=x>mAu-KUbXeLfB; z&Q{1Y#tp#KQV@D34ZV>@*h_iGd^zGC z-)84o^6lYe3tLYUJ`4cQr-C>&b4)~{%MhrXKo)s~Iz@C!M4AJeB|fDfazKo>llP<~ zS|$}B$Sw#9%SJQA%n>q{@XHnyhekdC@eYguh+Mf(sIk4xBK0K-f-8yTZ+e~l06BGa zPDIkc&!t~c5Nfr6RERVt;ps_1=|3DnBI74B_}7()DTVbVhqafVWMH@`UdwyT2o1PzLCUs9KPNDuf#|e`|9sC;&JP zR-~bkc|oG<4mgVKlN(b2K2h9Kuh{bn^Rf2glKjJ3Qhs+S|Cs`Gx}HnwC;5+7bN_74Xn-V94ws7ZS?-LIno(8n411~=1%`N zYv(`L|Acb)R+Y8W5{K(NQ9GT_K)C>D13V))AT9j7Kw=!(wAIX`I8=8>OY*dmqQgXSKec9}^!}$G3a0`jz4k?ryK$FJ@mR9h{rqTbDTq8&fM=E4N<;zOCIa zkC&fUkH<4_TP}`oZg(GdE)D!%+q|EVyS_cG?QUXC>&@L18|}385mqV=dBw7Y4K`1D z*%}qHP(dJN7td9XS6R5sK9WBlK=(k;s$6N45|z6ggu+mN+s}Da#E9GQ{+@!0fKryG zo-C3Yst~y&127R{T%$edE+Qwe09BUjpl{;=sSyYIR@EG)g2no+cbqg!Qr0v4<{m-Y$%M&5br7QUUHM{@0QL}uQ zZ-TKO9FaEeHN4%S$ci-<=>Cn9V4*EYV^#f<$VZLyt|7WBf{sW>nW;b$gddZDj^98TuXQOse{~6TjOR5gsef2s&haeNZOrN ztztIC@9Za*B2pdwrSNbBDso1`Zco^DvKEW{IgnS$egRpXbc_hDw@!L zxE>EY)7dN$%8S4W9&k2DllGSMgTss5C*(*Skb(EETVyXwY>yFIs-jH9Po}1>bVvJ! z3L$4EN8~>y!Cqz)45Uy0?rfrTMKG#Xd4V%2vx&bK4(Bey<0Q@K+OaKtQS$~bcF29?^wIT>vPg)*eKv0VBEdvRlfgEf)qSo{2l71 zLy3ud)rCC_NV$m{HcRZfWk;hdyScyLHrOn=&C@eRM%l(5PBJj*Hw+zhv_ zjTq?CqA?)q^Rb+Q+i)1^T}KBd3_kkJ%;nVcC2NT}(TW|U&Rw#o@)$Kcnct)Q4l%A} zyZ5=T4aa_6GDn}JzxTL^ip=ABV&M(Xn}oJ>tRl2FwWeSdmNp6!{$NJ_>VXXKCbfL6 zyN@eSi1GmMZ1&e6A&3ah^%&vZ#+uT*;Am3J&9({-;-GZV=grUrklq-tz~38!L31)% z_9%aIa5w!K@$>Xp8&-8;GT6703TNBU#&-=~>%3I_eo(*cv#v(-~PCI67LGS{NDphcc>XY+~zZLjON8 zKra*$c#he`nn!lEp6>>MOM~#gS4XB3f(#w59NLmGk8X{dr8B3`X33K=r*$9i2DxIp zjDg!gm3BF`>x#1pTQ>f}4Ck4R?WeIucc4S_P6@q5eMRTU?ZTSjW9QlhV$W3aJ9=n~ z3UJAUX`||vR_V%uddTwe#3|ky(XL8uWx?22LakxIw1pm(hrHwA*t<=p1ZdXT|7f%9 z{y@qr)6(J8K&?araBmkEFA3EFyt9cz0d%T%Q~=8>7ldiuevu{Jv6Om%wLR~Y3wb4f zU;2dyQ+oiNj{L&XBVN{XIA-cKV2zJcwmYY)dBT{0U? z<ErE!^{jI?U-9MDprO($k&j-S;^~MeYj|QyWW$(O3*W~DmBqd8tIpCyY(!B_Cvbqwgly)i{KYQ?Ta&j#Xvo1JuN+AKA1 z30_W8#nmA>UtE)+eA(VE<|-%8KGxyn#BL(Y&n`K0c6^SfmfP@Kvca`n(TVH7v;(KGAvP(X9;s?Yo@su|JZ*57=tFZDN)DXs}4mW=x+= z+g;}r`YbA^)hs>EFkvfRILVnf6nD4+PTYs41FVSbC^|6G<&v9cO&cuSQ)TB`Z0Psb z7p)2c8>e^XkAA1DF(2yWuZy*>Rq1biX2~G(x178VZ{^UrWH@daE-j^^;5kjKox9t| zFc8e**NirErP;E-`DAhll{(#?1SQS;pDezfcG|g)xwNDS%iB>+I~?7i2;>}Ns@up! z(&v~V@}2{wulUOUecvM1i(GxN{G;^m#( zK?x(^Xt4VLzz9ACQqc`JI-C)7Q5Li;rL@9WylyYu54~<yGJIp;(Gmm!iWsV)ZgmW3ckL*nb?u;*5pXH;^JB)(9el7c#NW+>=pW#8^y=ccECJ-reDaX(SG6t@zV z@bto@Lz4Sx)T|{_*oRy7+|#8QJn+o#Avf+JyUbxzn1VncS(W54%xTnxnJi^gyZvh| zwj@dTXF+Yg(O;(W7Ao-vsjgiv!588>$BKHsR&2Kgrw1P<52b6 z9_CrZ+`@;kOoEoAgy;c?k3L$~PoOMR?V0IzQdmMS$IDJJbhq z%6acQyvpx%8ZtY*L=1N$w)KTFmqLTov`+Td9E3UmZ&kGv@?3{y%fDPwc_^fLCTXlK zDvQPX*T`89-D=){+22|=AE77|&F}7a(h$P(J&*2FLPY*fdj{aR7LD0CS~Sf<>*uM6 zL6pfzYQLyBQV!9RC1{r-IY>#72dc^NOEswdRaQM}+SJ5e4FA(xwkqOSRJcd1*UTd#vn=#3GSHBAj|Fq*B85kOSm9*d^scb|Cnr z+Y3GgGU&!DBHFJ{^*fJt)ja9)IYw*pv)v1BpqSCB54L+a#iqM(sMW6ygpl=RDE z1gOU~LEMmG*|5~?r19=4Q8|y{AhnEBkHV2hRaf59tB7zG`U3WANS!Q{1kAck_i_o6 zv7Xc>f2U>I& zd!l@E-N^ce#LcZ9iw!d~zW5?vlF=IDxLdLg>0kQ@uAl^88Nk zp_IPRab4Wl8IeMtK0_r;Ke9G#ksZ}Q6IMJ14zq5*xIep%kcCWtZZF*X;~Xu_WcX7* z7K|b;Sk8kUT2tld_RO6yYk)q&XFAn4VmQZxCqP!Mw{WQSH$S&wsHc*)gCOhDS+H3odKtT= zkHd!d2CTA>i)uv%Jn0{P4lbN>Eu&;cgFs*u$AO#Ita*>Z^< z89Z@MDXFwpP|){hE+v=%WF}^Z%cisIoHRpi6KL(Iq&QONuJFBH@NM>8e%Mk>w2);E z+G0HPv#mHjGmq5R;q{0%jMxUwD^=}06TZ3-Vv-E0<*_%7tuLOu)YfVO4iR;QKW)}= z+|LX8cMq!pg*TJxxL_Xt?*=Nzs6jYu6fdlCnaN{DASG+^Dmqy9&DDIiTE?qHT1Q8$ zrd~fA=3r)TG)P=Qw5cD_6)sT8lBGroOyH*?n)+Ao^w3WScgcF5*cq+!J|ZP`B?xZ` z5flfq3ctCn)Mx@p8`?U>ZJ|zWGHE9ZZlOXJ^LjxKmzAuT?)KT#Kbbgi+h9fK4H%T_ z6|`DmgwZWGKd&KyM%m;yoqp7@byOu|!-X-Qk%Z&->MzDIp`{TxXKLgdYqRE%w`ra? z3c1?>#&B-@xZsXc&>>9V&ln6oOFYv=){O@^MfAqicAcvCvz@m&Fl7de!H$_P`F>U^ zt+)g21t<>c%0j_<+*FMDO}1LlBdMGWl9nf#p+p@26wnREa5b%HxnxI1n{lSGGbstfyH0>53?!z;Z zWMRkXR9tuv&5FeI3uZ(>O7}b{Y%+oHEn;S)@OEyE8$kLoIZ_FMCv)=<%|9)ui0%}x z$VQ=ZrAE;v&R2l2EWouZ+53Pf7)83r?g;Sca}NFt1WVofs|Y*EuxM+s$8x<(0&7C# ztinUFxoVrn2Nwm+xrPhp=%L9H5);nu&!O>4HRK`G;f5;{VH?>U#SGxk{+pzr#z4K4 zUAj{kqg4zh%~$@t1SWKq? z=Coou&&$t1Yb zt@q9TcQLrC=|+&c^ylDjTa+tf2Oz;?*GI6%GyCf#FvL&9pVzKl09g}ZdZe75PJ+-p z0ID>J*w5(z7%}E{Z5`#vH6c~W;*%5sMK^L3@w6cn6}9(qNL)rr%ts98Gy-q14Az26 z|DzHm)@JGLtR`-9bV3Wvp{#w?^$3Uhcqkze$^CJg=(e(+uq@)EL_f&rg$4`lfC0%Q zc#Ra4Qp7Pyg|AQJ~Xo32pi3v`M?C)vX|74Fqpj#VS4+}L`%2kMQTrq*#RPy zekcRiF5bg@+spD*Clu%;sPS#A3L8WZkIhk=p9VU`h;Lp^2r_I;QI(Yr$XcR1&?Y=% z?d?&5$xi&2;!vS?wA&smQqBiO z0>h7ErN8?OJz-B(Ztpk;MD{6WR@u;)5e|HA5?Ci`H+0y)4LbV{gJ8dAo40%v*|w7a zr=4|wqq0~F38NL|4e89An661<c&3WR9IH!AtEPx<3LQ=WxRmn{Ow$sK5Iv7hP1KU z*9X1GFec`%BiEcf@E#VKKDDrCtBMa)&NrD%iwD8egw?pnb<64!=I~EgnJIX@cWU_b z`uC^K!9iy#OTiwjPyGzXFFG6o zdKBw~)OLz|XAFb&9DohXMul+jtFN25sV!u!@lPSJ^bmC2B25>GxGB{x&8|KfC@z1c z#`S8S{T+KA*#ZT}kie+j`|v5$A?W6fCc@J{5%`L!GlTGxI{#toW~xBH-#8hd*XWU5 zfZEAILV~9+M2?rR4JHCUH@G)N(dS@Psc64}N!V9-#(@XMiKv~WCvJ=^;*ppc4q11m zJaz*s?1y&L=l9ByXZy3>vp&hx=jd+L%N}orZHU)ZM)V0QBEMeVER=|&lXO#49gW5- zHWbkBLyxlQ5d1SswHN8#@#;ftu|S_eO|0!YmKqUYXrN~vRx=-ZrP`axHu$5?fD=$D zs$5ZNKRH*XTWZj_p4fF&IiMn*6Y;AN_XCf>Lh7@dE|nv@qSH}^{b;adf9Utas_m3v zNLy_iRHUz{i8636S84)mARxlaP&$vF8HSRS$O9Qg6k$>atB1~ial3CP6Y`#%Nb;Qk z>lgZeV1m`AwxpM8kFjub9SzSJ?zp=0tyE)29cp1zv}HH!??ywNJF!8Mwgy!v&-;b* zjb3HhA~If!%f;ewT?alaTRy@t-jR;s+8peKHJHfuK^Q}m$tzh9%O9IRlczdNUGKSL zwlWBB+Us4M_w-{UE_ytVHmMl-Slr$n(54TG9JER-L%s6!Z+6=I*UgNMSy`{HFz5$d zrU!;pt&CpIR1k_W^pnGU!w%b;lf%U$B2^(^VYiqFBqBTO7Yf!=FDpeO%FWq}_f@f4 zn_IDhUzZnCNtc_vcd6aomt2))^WT=ma;q7iAZ=;EanM6booUPIrh))PaI~OSkE#h+ z&%8k-Y(6SjE-+F{!}#So1Rwb0azFVJm=7w_`u^xFKnw5%kneAjKpFJi zA}d)5dgq6pWeLZ$z!HdWoxx}r&HRkj{W1=iS#-r`w6Yw@vldC=7K8*$uEhZwVLqst zaT|$(ZvDImf9~Tk=2bZ7TXH{h;H34*;6hdfj{3lDAwtW>wdaOak|{$;Uz-C4`~_pK3CiyY7U z@W#P9yj?ew#vc2uSp$Z}4H;f#)Kh^_Wp)agIv zGQR1T*>3`ljx%N9tnMh1V@+XIj$b}4E`%M)_4Dizn7omLM*MQV_i@93RdDm6RGZ@8 z><7_J8y4NTw?^VW_yX|r}Ydi{^BL6+h*)+|24zm0qU^p;HYUart+5A=RO8luvQ`G0(DA7X-*#VuhJ}MRP44vgG zd*yH-CvPjF*RB_1!Q}MOP$|(Vid-grLT?K50xY^DBETz3w|+!|pQoD{`;hy>NHOp( zTsmL7NmN(MFkPOK?x)*1eE@LBkGScTF@L+mh3RJjfqO5;JO= zRKm;t@C|I0cEyQjdIZtlUx(GciKB5s8o;52;N%wmtAMrSE26Fg@g6;*u4-;fg;yJ}tL~phE)ma0 zFE8HAv&aSvXUA=vpV`#e94U8f+20tC;CZhBndIAzwdFuwXSt@*2!re_=)GKi8Jf5(}RF$60_f31W*zR|Zo-m1X3H z_;qo@{{hT-|5x)lGV^&~vGaa&S#tgLpqA6~dX?e(JdyKxnBx0%m-9KW^L1GBbyd>y zoWTFm@I4}h|EuEb)3xXQEXU_#>WSa$VT#}TGqmRGMcy|L)Az9_XU6C0Wry$U<^fU9 z_iZZY^WysRxa4~jKYz~0ou2o@#}og@ap-mTM~kno=cirI+ap2G>qw37!vo^iC%fK9 zPtSr$PLIcz^9iu~GmGwRi2iGT$;9_|2~p4c>m@YDx2VL^`Lm|`B`2rn?eosAi?PF!9$Gv&vjD6;wkN3!8p{9n zn8W|Y3cG*j+B0#~^L}-ALeOzl^R-XT@A7J<=l#LY|MgV!`EmEe@3J)W@MY)wv2k^= z;rn89mn-cvg1wN$|29?gb$jRfb@25%F{Af!lX1PhxqDaNqv?5g-%IwP=kvC;{rUJELSbOf*Lg=x z+ZekYfAfr<&&Spi|NCW5Pun@>*XzsI%klSXc&K=NKjYsEKgZz-`{#(*{rmKeM;+$d zO!vpb%8u7Vg`Q8x@MDEuSI>Rxe#%IPu8s%XB<|WH$A?%m?{rVH&G(nBzr_6d1p2-| zs@e5;W_dp?0q-;HVjB2AhkBl;Y6#4}4rf01$$Q?%at;-3&$_po-@32e58IwHBM`JK z9TTT|I2U)gtj4`ho@V!4x(ubgZM_dSPqJ-oRH zyiW&?N4qXs6i>QZXrt$?R!XMUo|mseShdBiSNgd05Ja(418zW6k znG=g%4yBZI&V{@sbul@1b}eO`wo$Sx7loNmeP;KZmNV%A)qQV+y@mPEKxa3GGXq`j3;vF?kZVmSwPQvZ zt9O&yNioz?k61Zlr{25lQ=vf_=~=v+ZgL&Ig_UOEXQ%RlHBPyrd;fYP&j{a0RP2{S zzSW~hisx9mHf?pgqshGCLR<;5xORtQ?Gl&R(`|8?9B*vZby}Ll2VsS>W{gaWM1 zmAkBcM6_4-!NMp$IpE6cW38*?A->=@3)}T=G%G9XjgI4^L-&IpASnE;JXsQuB>UUa zoK}^8fXz`#(&%I>FXgE|Nk@KbTgL6fTecNr>o!@IfT~OlP#9wjp=YobHkt0jG^VrF z(!76>wXytTIW-1AQ-BI=w5pQ{`Zt?-<&o3aug+ZU4d`fQGh-!3sqO&(fe9R3T*q0L z=-8?FxM0oUTy%-Uu&^MAQeAdfBZVa?aKcDSC5T0)$kx03 zY0*Z)iJ310$~p(-aGA#(FvbcLm9GPbQvU?E2h+^RPZ~Sjmoy) zL9difYmg{P`LZu{b!VSHyv9Krgjc{9DF6nSCxOme0xRIhfQ1e`+FQ1CvZ`1NMS+bL zNqM=;unQB#3bY7(MZ>;;CaDqxZ*e+1Q}@qpQ}-;R>i@#Rq%?+HahD6Bs`kIiU>~3$1SI$PP2872O4-{JuofL3V6TZD%$IF&o ztf#55hE>)pzK%ayY5L-@#?sjK_KH_sTI7*N(715%Zp1k4^@->JDBH-|;V+M)3~(g| z_G+8~>56Q0tf8w_5=mwn=({`B5-LFFW2C%hj&=_g>jCH68!~m$=kBIWdp)Tdmuoy3 zzMiTTPK)|AO{^0rNbRCqHa-?SX9!FLG$y2=lSWeS*I)%)o_(%1G}yY#o;#N6q?1k! zTE0e+O*E;#`OBc13<6|BZclB4D+ZQ)+U3!Rm;=h65uEPa_}(S;_B zmCyJYB0Ci`sS*AJ`;k#0X6+BL$J}v5Ne&PTYBvy+CK}-{?3Q*bLe%46C%QMI1cG>z zsQgk?)SUzIN8Am)*htpH(D;N(6NSTrO736vN`=tYPL?(*f&w}3_dKlHbr9+?zCC#|?-ZMi$N}cUghP*F=9gA1MStXtaBCj_%nU@z zSQcqbneU9E^-)yN^X{nYC<&Pk{xf@RoF>UrFHjs{!X;^T-CXwnoD(~@%RE(`0Y%X$ zE*Js~_ok|Chhn#4I$$uH5QlOFCo1+QV(WxY9 z*%mBY0nL3%=1&%1XW&=VW-Fc={ByRpYiz2C5FNYMdUbnwQx$26wwhRh7Ab7hQVcy86`OJJB{Y+x< z9;Nua1c_UgnA0yZg;1N7I!!&^*ovv!3te!CYZuG$3xjGQb61iaKOhOW>YfO{N{7Ox z^?=(~;I=3I1r?nWCC4k0Ah&`uN0uP^yW-?>*+bA`P=>5Y5`;92}5<2`k<-+i8sXS_E3#PVu+l z#ULpw@*>9slTk~!*@h?``d<;&rF;wGZB*2${{;R+DE+Y3_#MWDajVtt@IKA1ddS=X*7BLwPB3Kkb{ z^^k&CsIa^N?@u^i6x5XT5!++6&1HutZ=SanZizI8{`(kZ!<*!3KFVbS#w|FT_t5OB zG^$DkzKkKZ7ij=Px{ie|HuL%@)@+JOEA=?Nk}FV!4u{w)|gm zFGXXSV-x4mp!uU^kPh&kPBI#9wrbYk`)*nk-kxPxSkn6jTI(P=4SAtW2iOKHGt9GG z$p*bxCb<|}fd&fszxz3;ayr|u!+%BwJ>)$3CypOpYfbx`22^jxXDiQrq@vDK8p

cRhY;>LfV0P{n`Moff&Hd zVSK^~iA_u0DBf6{GdZ{2c>X>9AVqQ;Wlx=tR}C9a35%#G@Sh9s_$ErZ)V~JF_;dSY z<~Dwl8>5A&yFXPa3bcScrppS<+QJm;g8=?rw=5cItFJb`Ti69kRILp>?B5<##1;;) zr7!ZS1>pB=;J@-zqCx#1IJd!TU&o}bL`*hNsvMBH{S~Ypk*XXDpnOCe86-57apW;2 z{f0HSnCK!&N&|7$@m%*`5N=S!6ObD=L|06!QDy;QQ`HJ9#ej+5Wn*0uDEmFqDVolf z2UgUJs}Ad0K`9}|$-G1s6lDw5#Z8nC7L!TbD3N(^$isz(f zcrF<*h*h8i7c2T>1@oKN7vd8$l5W{Zs~ug~jMTL0Ross|{VQ>|)n|!w_%IiHot#&O z7BQ2P)G>3lZzS%d3pDenxF9PED35H=l3Ht4YnxIDoTT#0@hlcZ@C1Pw5`-El5lh<6z|`Cj zZ5Q6^cvIeM*JzTEMo<0 z7sGH%>}+_!Y;}3XML;Igp@v5a@fLTv2mv*_@P_)#8mT&T1CNL^_add(l7`?gu&$7I zM)Ua9?GiKXwjge36haGvq#iJ9kKtl3AU7%Feg{iBz~apHHC|qv)H*7SqMPr zYea8R6>6u&s8(cLvV>tMcn-c0Z}4%tq1vzb8nfTvk+9rxfA`O(3EenP+I1Xnt~lwh zN2i)YzCL^c<5H=uC>RlTYtp(fB{0@hSjaUu@gWR{_#}blo&;_bg}aNQBTt=F-I`;M z>dr!BGQbyAbMg0+XJp`Qa8oV5Es1UZ>RtUrCdr4B8*h2)$9gY93#T592}NG2CLB5H z27x9=lJpJLCy{gbamBa(Ghjhy1F6J14kq~}l$Fz$L_=m7{UmuU(MoQ$L{w9XYpKHq z2`i3qNz&R1XBd~?b(%P)FPXe>W8lhLUH)jIO^mZj`xeH4Xq9CIkl1~OShn9$WM-KE z-DHW*LL%zvB9&Y>I4a;={)0r|P%W|Dv8YH)U@(%@X+(OD0}y3uTfcTDv(Trv2ys-8 zR;rgyC_1_pY)nvvPFpu4zo_}Pqtu{nhK6%u0XK-cQ)ok_!7uD-{5a96EE;r)JMiea4F@zjIrVZafG)CBaA0Sp2QGRgsj*07D|1j9mr`brt?+ zBSspF4yUq(FV1>}v^K3w6&m|dqTQquc}-Z(XdShmf5m7)O>X_e=C5t=`&Wmx_S(}wI=J>Y@FW4CY{x=K zf-y>SH(P5G=ufmNAjVm;Om7KW0BuVcryR|*?N+salHZ7h3rw&;3?E6f!1ONRfo%;G zVcHT_GyFwBCC-mp50{GrATn}a7uQTF-r_Y0N0=rAnt{e%C`pRE3(l+?t2VxkV3(wk zWnPd~+)u1Tb}mnLApt}IoJ@31`)l%WMNcYsl$P>Ueu&K(1d6g_cCs{czO5X?#Z+;%erD z7C}NK60A_5s-t5`1HSdL8|$1&cZ*VykW@$O)#qYq$jCIw3SHWGn6CrNGUlz>0t9}Y z>*2nf#Dxm~6G0g=z@SHY!FKpoKJUUC9!wmX5!TCztv0W5ABiVi;`fN6LRbR36g_>m zHl#Hkbq7Jd8qE=e8^kp|mg!Xe@f1xOqt%C#ycxm6Wu*}jyQ$%fQ-b>Q2YHj>G|6>i z+Do>pk}t>d{2)_ZqcAN3YXS;ukj3wnwQOIZW$S-zu)UC+9H6`liTZLNdEwOGgld;c z8p*Wv=A~NtqAl{a#pu*vW#8eEWyqG=Yj8PdOQinZGouDOHn_Q>z)9`;yuOn0(G{kr zQwt)}9;7^*Sx>y@+-P%2qOtMt9y`PZE#(NalJ@5lRI^Y+TkK zO*(fhKi>*l8oW4k*5!qF%4u(nI)z=GF<5!mg*PqwmG;sxCD zQ6qC7Ha^&{=&`liLisW%^T*>dc&+mh>`2$9rxI09m@>aRIl<>UMTG=iv*kdOk4Ry4 zpPe=Ri_wEp2XO`nP@7uI+KMkz9!JX{L=JcU;|9bYG6D?{K^@clIXx#+m02w1JU84X z`LIvq{_TMM3_vgut%q|ymT>aHE_p#sLlp~z4t?xk7_(T z10gLJBtoI0OrAw25qrWRLflYGFA|Q-50#tgk{fF2T-O9fYmRy9_TUXXn37@`>fgEH zmHb9**X3f`xJxYgV36{jMM}`20Ml%K_ zM)d{a<2>1esgVpmRB5@OSL1VK3kq!oJs^gbvo{n9aheabIZWOZLj7o+2QNM8l|+uE zW8~Sb`XZ9bk3zZJM*0>bV8m-YlOsPtx%i3|Pj1HiNO@Zn#Ej5DLXZP85HR!TjM2Q? zvoA{#&FD8WW<#`Nyt##DnPeLon~?xGt(3upoSOI&@Wq>q%^#yP#A)|OOanij?^^wS z>hWKA;GyTc6kwM0$O0WlCgi=2yYuiWxD|Bg^_?iL9U+ad6vdKhJJ1&d&r8LoI- z9I{xwjl*N{QFma2R4*%oD^;Pfc5>7Xr;a-T{aCGkXU1?*mDr9lrJ1>jM`_n*e>3{l zpGRckjy(`g?0;YXyyR@u9-Tm&PyV;T=+moxoNfdIgj;hLip53E5lKS^BK(^1OM-*lHk_k6C46tBl@@t9J{JWDuEvo^uyx9a zr^}zc)wbImSz%A{Jh0EH7KOsmrqMOw!jD z&T(UU3ytU2yut%d3V`7(@I0lD7FW~qX9u&Kb`HMtwI10pP9uc1avEWMZwiEe3MZTd z$puuxx_>M$oObT?gzhFMj|sp?#qRY&lCG%X+rxqA%x{U?OSb6>y7_=Sk&Y$WGO1n3 z_h=J0Z?w%Nj{dFaID{*O_@@#)>jQ-WE5&vIXF3*fv?PJWGR8=oQN?FVJ-?D!1x~~G zVohyPED12r*w9PDAeQU4xJ&u2))z4VQha%WS0}^_^{m=P;X`d8?72kQeNon#klgIJ ztm@a)lBp@b!Syo^T0VHjuxg*U<54H;sS>!DSYDiL)CG12LO*gkXP3`15a&`z<%8LMR?r~8s{Qap5G2j)Y9u{?)W1m%RoM7=PlemKKUZK ztpylgDI>HnfaIi27*4Tk+{Htk7BdQA|8K#s`RFh=xmSWkK<{VdBu@jZuBp=l={o=V z`@aiX%b%t4gj$@xG{)p**+U%T&*T+nIQ?2s*JCGz=joF6l&#&xDe4>WtruRcI-1L; zOFmx`Rx&de13DON6p-}D;VJB-PfT#*`a87v%to{uk@_`~(9Dtb8@owlD6d7N*C$+d z50S1jF~*#sJcT)iHeOm8AK&14c#BX%vXbd%K3Dh1nb;td>4VW)fOF`LM4;MzeXNAS z@}eICHqVkhy*3P$=8JhnI$20sbCUmb3`QqEHG>?J#QbP#*+2y4WJH*RIWQImfU)^B zL7A~4t3M|GNPrJIU0j#Ehe>0Cl%+EyXQp_kD@#N@f~-sCl0bXkaL7$B0ZGA&*?%n) z%`FJ5Cw+W|x%}FO*QY?^7CZZAE44HBW2slupd47|@|TTV+)SNIdbsIZg5>2i!gcf7 zIN1rq`qtL)WyZ=ac5{^Evw#KOG^NK=RxjN)3xr0GdEN} z*$zT$uyyz{G;c~UGgMjxxFj-{)JZ^IvozB$d+QfgRMJ$x-LZ^Wp*SUGXleq^XAw3f z#{&8=KT}pnC z0FJ-wOjZ|EkM#23t|iCt*@4w1b3K@^80H|A2m4o#fILl3{aC?|*7h1d?lnEF5;~d% z*Kzs915^|!i(?tX)pm#t_CT4^)uBUqZpAO>#+0!wM0MPd$!%>uS4SL9RC7@*yyUoS z6`@}|0knBQ^m<|t&Y>MGf)Wq_NxWOUNLj*dr}>RqfP9Qz@0%m#ClPyhW;3)S+A%dPd_;42kW6-7D5CSMDKu%&p zbgcN3hlbG`_;Mwp?l82+qMjy%l3^)nm`r60ViOFP&!>ecmPL0<-+9wa8#V(Xk0bFb zaE7etH_Ga;B-arA<$V7_G6-?QLVnzZTWPP4R3wV=u=Hw`lDxRv8+Mm~zyvEicxpHV zPxPAC;Ms%8n+bJI;U@D+J2U3GFa+MwWeK{-(F5B~?!kK20|#T%tE~q8&VK`5K%u{e zJa6AuX^qGbsQD$*Hx)y2gROv|};2S@q63-fKJ=1v? zA16qfr}%I8j7qjm-!C4(23V5seC@jI6Mcm#n6*0+Su3qKna9OKkDONrP(~$4Cv62R zWPekv%j_P`Xrf+(Qpy%mZUScpj@llEEHyW)49sOV*GtG-^hbfV)Kp`B(jkNPIl}RqIHbA@;~{D?1UHRS2%*I2 z+osgk*-rlldy@CHSM7*^`=E52+Wjp~ZOBS!NIyGY?$};$0GA28r z?u7UvP*is6d3t%Rhdm$t*ljZz4yb+V`2fN8tiSVsOVB+KO*f~LuO)iFzxlwPbqO^& z*8g?q=Hc`aU;=d=b(W-T%s{dzT|77WS=7wbT(0=9t@3-Bj>p$YQw-_(5_Jp4+mXHA zk6c)y+Q~I4PS0P?>s@28butlYLgqN)Wl}Iy$%NqwWa?|B+3tFolC=^*7kQ_fE68tB z#{`LUm7})G**0UgBUwK&0txk0D+-@XpkiAbVyVc+F1COPPA?eJ@iaj8sjhUuTtf!T z&c$x$qjo0?Lir6GE0a1}0CrgQj|onX+L&4&9>sP#@nF+orIHrFz9QBVaM>uAbg}MB z_0%*cn;D&a zAb~=YI#kN(94Q0UNwBL&J&3g8MiJJSo@5HNnDH#@|iQCchnbU*RoPI^>61j9r?`vLjL^!7Wu<8ABjy& z)`lCBqiSFKjZ*L@Ut5t@+xLz?=X~(>3Ie`>3p|Mwabz|vBE<8;dcobsH3YGrS`T`O ztxU6VO;|bz$LV2m6wpGJZ zF7K6l2xPlR6ijkZr-N%moa7wvd@cX>EVg(A#CgQCbNHxZGy%*csth87;hT__XvvNF zI$tfPlo}r0WU=RF2sTlw8KhKTH=3-3W_kcQy;P2F?G3JnX??haMAkdCRpD2GZ>R)G z%OiuugaZ1P0psw_^%9^T6pSRQS)@hpWqa)RorC->kGfC?#UwOZ8RS02;9*W_jZtP==ZP{Ew9NT3zpavMm$bUxf+qsv(0bGu8*T4n(3C(&D81Z+tqZgz>{Zot z8&F_0LP_Oat_71PX1l_$Fo{{;g2j&Xg?>L`J7$doz8}Rk^?b>1n`}S|fD}R7f!iXw z-L}o z)2=?4`AC(Mp#7uGwiYT2SI(Ogmk!NQwHGW0#nDTU1FtJnJ>9Kh;DxylBnP21Ry1#t za6kff!{d+{mN>|GSn~R|xtH)mE5xXfC zW~hx3FxuA>ek4~jY-|#uz@!oQ^oACYaI(d)+)rn3*S(3g1 z-7F9z0ztX$vdQIaWbo8h*4U}CXL_(HMrF4aL)@gRVBvMz;w+-R6FH03fRUqwAGe>F^cU^r~FAcb9H|+LC!UA7EA`e4b&CBZ;s^1X1*4%f(8{Dbmwfq zyV=f1o|OtB$Jb<@R zaG!x;b*j$*`NXQLAP#{}jdfe{ef0r(q00~&BzupWmcgIa+;6ar^@Ux(yz@V6vZbS_ zwQNzBO)|_e-u4>w>B*+@pkUj0w&54<24OEYI3w%_Jj_hDhB2cd-m30=q+By)ZD5M) z;>vp1WrsJ7_iEFu1Q$g;U-CO_Zt7A=qgSs|775%fpIp$^bcR~F;bc6ef_sVNKC#?R z%qxPL5WSIn4DbvvMwurqh|YJ4fF<}LQ-1N@W@d8Ss6V2qcAf988G`7zL~VAVaH21? z`n#RfqK(- z08pAi*+G4PtkccKeC!elcoOenSU{-Vz+mmcsp^t+eUPhZngQ7u#Nkj92c}ScA0=?Z z!)t)@r0dbO>=l?VSz!XU&jR12(!tn6}L*q11B&r=WuTt*@OAUz@_E4U*M>^G!G`U2SR2m=kYvJ>!7!B|8~bW zC_y7kxuVN5bf)nSZ?L>1+d2|K8^m-iFgt7MCnz%r*pecGQ+Mu$^H9dF9hKj2;`T_0 z$&_udI9i__epLqbyCLn#?q zycH&+gUB)CN2n~=TFd#0-yxnNNeHn=sA!ki=NN_E2`N2CfwCC9T-zMmv|yQV>XdBC zNjqQ}$PN`uKBUlX+gHu2rir=`3-(DJmy}ovc4^5@Dp`o`{->GW90FJ&*l7*z<_Bq8 zDT9}@4T7G^VOv}os2L?lrt)P=+j1wt+S^b60(^ ztyVaVaz7Gvrz>1TLNHkyNtYRz4z+SW=%Ozv{zs}mA)*sW^Ab!T=OY`}urBOC2r_BH z;hqoAOz?x$sgq87P8gsMg=wM*e>Ux z0jikJ#rvu9B&gmF9kG7{cJOcFTGu+Vjo12-8zayVfL4mvQ>@dH-a8Zi*4 z-asHM%x5=s)7Dp$7eKCU5|(lS6oU)sb?@t8cH;coYO+8z7x4RtmR=6W-2rE_Hk-u? zwCZ%znCJ3u53X=M-lYiQIFq22ld-I=o*}BGiZf9JW@buLjc9MYFTxMXT?^D@d_Px29EsE%%tq#VKL2^|@{$Il=liKG%i_nTSO&R57r zKAn~xE74uSg+SEuV=?39-||MTbsE|Uj(|cXlZQ&#BmF3&O zVaX3y6J{8olo4--R0TZD6Y?pMRUq(zV(n)9$n%m(PZr{I+6%i2o{}6YXRCpkWIGcw zpKqP+LcpqcisMxz@7n4u|-Fw^IJa| zB-LE3x-ZDo<4D`Mkf={^ivq4`>=Sa%&uM-TJSzhl9FKJ2fD?kkZj$Ur-<`GNCG`}iSn9tt8H6cp>Pj6fZ*rAM96e^2&lRA8J0T$ zn-6dO@du@O_m7E-0G%6;-c^9`-Y(kLaX3!YKrY{H!@Yc6O(8uU9F zr@lS!L-z0%)z74IDHo1vS;B?6TCclYLtY7vT`SxE@x9>X%#6Atd2iPv?EJ)ID=yf;UbjSL-0o*9lXAR<~RxW&cG*@Y06!g9J85N)%Y z)fU8!Fhkzl3E_Q-G1=cWnKUp19}}i1){G>mR>gk!l@(_8i4z!938yP!gQpq@A2I=w zz-CTrh*W4LpiAO|H2Ek}hzML?Ozro892NEiZq<}N2OC6yor8UdiYB7OQCgAQItLyz zOZ5$$Wu*`#C~s3X3u!Q+#=~F&baz7pK1(G|cg^_u=o$f>m|qPcGuty%bWEyTt&b7F z1K+aK*`$>q8A{Z}I#l^48ISu&YZ_8e26#4FOMy8ZL1L=?iiej&i|;trQ2A{q5cJ1s zec#>elixS%Uiq{ zaV0c|>0qiw&GiMmgD|{PMG(O|+Cw&=d3O8hVUfT-2BeB;<4&b(a9$R;8)>%@#CgU@ z$yJ)MHu0?}t9Q7RjuzP_0}Up8hc=9p zv;c$y6e^jQQ@okAyOT(@^$=ZlBxM?wV2L#EM&F!*8?>zo?g6fu-cr$C<@HoQ7(gbb z6RhmP|8L3wz0|iO7U)`GK>H@^PHX;9e-cr*x8ce*>6(r1C`VEd*zpa&>DQ|Y+5ktDJ_0ofuG5jJ=STE}g+ z@#NwD$$C^@#e>NKg(4YsX8Tst8X_ zPzKrB#?LEO9A9Qhf(;5*Vpm!e#B-O^$`@7pf5I_)HB!O)34Vg3yAF7tt=sT2}%EZcL=Z z?Rs>jAl_juV#>D##Sg9f@Q-pjThi^N*xWOn_z-ZfCbhwWz zqNW2oWK)h#Sl|ZRDJ_Yq%)ecaK6by;w_pX- zfYZjDJ{nGM7Z3PJRamta!P%#P1QbAotlKd0UkPOJGxTFHbMGSTz!2NAgN2yegEb<@ zA$Z34NZPaJH;l9NVB1dfO7FbYwy9yo6+jE=Ik7aYqNz>r9zU;m@G&`vPKzZOM>vJe zy_}3gTrCqJas>ho_lT>OWfMswOQ%&+%w^LR2Q@Uv$BZjbgCQJ?mkdRZcG8<6*ISeu z80*XiZ8kk2NgEKwJL3+?@SxS5D@wrn$x;JoeY+l+xD_(MNi{5XR;GXXevDsUe?5_x zNO8RCC!!%6l)7g7L99t1(l5E}Z?G`r`jD*IP*qUpuB0IcYAH!OC%EnpG(G_}Ow6}3 zxSe{#cT3ehOsPcYgg)kzSAu>Z6_8r+N^GYqVoKo@bj<~cC2byT&mcD@r@M#M=fj;u zO9RL0m8hwqL1zj?j|}2OkJTQS8?xcF_{jbi%I}j~9kfW9;><%j=|}9IfU;U$R*)Xa zEBo<8SmwL?Cc(?r12J*C9(^bldSxX)iiJLy$?NsqU2#5{r^v~R5(zA=dg75A?Zw{h z+zqb?0`AgQ7f@1SnXW0IcEdFuGrE)P`(i1ju@$_I?=7j-#NB?uXH5 zK90F0g1b)ng+#%E2I*)6a5|6jg!NyN^5Na@@F@^>OG0NBciCLaUPK2f0HSeIZU@0wyo5q(YO;@G1?^>j$5AXdTd zxbP!XBQ~&*Dec=MEn;$DQY=W6M#5zh7L6csi_MSQ<6Ef) zaLpRs5WsnAQm8hvVNcw0hhS|Bn~EMf5GsZjP{RN>vnn8abNly^TX&kJ-U;K5yNZfzgnB3e8pg{!_m8cw3 zC;<7P^U8x(S|nG}+pKsy;*K=boymQP0>52hpM=%^2vt!EuEoMoI~`iDmjFqluN@{) zcgKY%)ulP^Wn$1UWx9?Et|J%LoA`o0gpg$9+h}wsq#@x#4ih5bTgJLAf_~JU)_|lJ zS18vAL$c6ulHG91@Cg!DvH=3KN>$PZb8w56c-mdmXAC=z*P}DT6RL}b%Uh|Z+U!R? z_Os`=_lE&t4Ep=I1nO+IyXjcNz zflf1_@og`y1s6}*BJ<>Jx0 z+3_DIXjO3>h?uTcN;p2d$>ZJ#XPZ79PXVw_3^Shq{=g*x!TZw-d{%W^AuD>^&^BYd zPC0s>pC&bEeyHAKV#`Jcvst2SX>mM3d_+3eGjh7(n0E=1NxO;L-Ws*LS}tyzQDRoG znQFA6j0uOXt&nr@rX}plc!m`Pvl*prDMa$E3< z=-}OzEBkBU5+)Sc;)zY>GWksNA=CCnPb#=A$Y@2By_f^HLVomq4?j&TeK&j!Gf=Cr zrkcWUJR{GvE07r6!XrFgu?W0gWfYh~@~-$$v&R+&%SC6#6ojU zGpR84i3TZLpgHU%%;+IXr3_-Z2Yc6``!k+aiT4&6=x`DnOWUA!GNpU#fAFk=y#uDR zrIk-bDG`uJ@3OugQFJ`zsAF}sgsOyW0%foZp1fJ&!_*i%9*oP#<8@WAr+BXXFDi%; ziU0`>Vd+Rp4KtSxEW!A_Jf;VsvW00v>H-qmTuH}0JOML=y>t8qi3kiFAi(NENQ4S# zTU~tvpwG%Q>FUGA1_@o#>n^K*V%v1rWb;{Z(W>ToG~g)4qXtiio0DiE0yX$@bC%)$ zAV=7?GuY;FhVF+D7x3HYa)6kHz&Y*$79`Q29cF0@AqGDqEO1ehzd^}ZXoKY5QTa}5kqW`08%v#!XvR!EDVhxn zrV$XdL$W9`nO9B0u|4yJ_Ww9h01OjcZ+paR$af;%MvS)ui$o~jm{BZQnjiG2U_ju~ z5~dQY1NRDPk=Zk_kcP*aj&pDGf8%ON5#5g7NvIr}wQSo{3Qsx@%C6m)(0yF7{kRl1 z8#dKSham;e`etMVQW)cpj_{FwkR&YTn-98h{P@k)9(!!*Qt=eiYGc>jFc%7CTT-~j z`mnU}f4Ey@bfULr1rmvYPMfhmf*ht5!fyXEfkcz%05kzys&OlCL^1uaT_m;mBxFiZgmVXgP^qaYp>ApG zE@O~*E%?lVMOi=|y21f60}{@2)LY%%DO*}}J5v9{rCK~sNMMM3+=zp=V>fp+sWg2C zq<4|P?iaGV*GlA!pNA8_Ez^o&0tlkan06+6KXDS5^hf|mR-eY>aY92vWi!5{m$(E0 zG@%3`&^iCM0%SO80EYb z4RMGtx=E!?r~cT9Cby@_y0^^Z{8qy&ruF0IHt$$BH@bn`4*-c<;(?hU662qTqmj5oPg)7p(-o%iapN^flW zO88XtHq>r37Owv>(EIJtD_GJ|z34#kL$`LU)=w7grS&7cp?kxMhux=bBR}(r2a>Nk_pm>}10d+o# zW^G|kY&SbG`Fe$Y8jZ2EHS%7|&=OaRTJHUj6^l6_8Ah|JMxAJ|5(AELdzd68RjI%* zC(SCGf@;k?G~AIk7FPTS%aJ-j@+)--TX-2@?CilXx4GGH$C&!~UVAl&RHH3#*Q0CE z)$SI@)6wiF_w=Lcifq!QI%AbxHPb{l_hXB8wZ(n^D%QfA`dA7>h(W*&Z0-uG>2L>= z^Hof&S+*d+q91)n!ENvN?@?=IPrgGbVG-Nlx5ZUT`y-}kk_?(Ighze7f(`8uRPB?< z8yyjYG;PqV$|c*t=xHb5l932d08iFhp|dvdUWida5Y!+QuN%3O8pH;WMQQMvlK=JA5szfz}q>SBT?K!Ux z+?!PXmsOIDF!69#(+-uIJ9X2@pubU{&?6d%KPbn*eyb7tjGnTR8L;2&3^#dzErT3u z<2|loJaHBHrKBN(SZYwYQ6bQOj*O~0Y9PtI1KKeoZSZG*jG7~ z#+(-MTi_I4&-ep|j_(JCKN9gh%Ju;J9zkTOP*^HD8sp}gxAPTmVHvyIEKrX#q5_Bv z7!XV7r>vQwWZg~6%ECjNZBe^o+nexuKm{Q!lC+qMVrZ~PVZjT4@HEn6H!L9?okOQD3stKH0uJy3*Q0BymUc76an&f1T0QCx$F~4_K2PJC#m7&4IwfIR z7j8R7?2c@8+h6;?4gLY3c}{_}yRkJOhK~TUYXK=Mqtud3faj49llSFR=HHuVBq3PI zK0x$4fYZ!7TnG{qcL;M51{9tI{s9ze^P_7-zz(u9azYVHqNRG<7lAf7au>yq)ucy* zMgT3AZ6Uy%T>{A=u~-C4gvtllZZPcwhiu$U4NiwjQ;D39KAKvuJfnxH^}#VZ;!xH@ z7!2_AvEvB!bcE`g*m9v~=WyqeWSxqA+-y0A4Q)1;T3xHO6UlSz;q9D0yj|HF(;a{; zeCe7E)m!K=3*U|O$#l1SHq7-3H@Aia7Z!``>3LQ&4p%8V)-OS#*)R$7IoP_eDd`OxRP1++JK+{y<=rNPKcVv%)+>GRROj0{et5nZk*021U27N(^E5nL z@xZpb5e<%3JkKj4hKxm(iGYKlEAXK_tchf_t|o&HkfDz|4Pj{en3fRU$)oF{h!tZ~ z0cMZN>x~5cMm8)cP^d?t_|^|b^*`O~T^Qks>P*N~w?T0kabVM-)c`j5YLisv{5Dhn9VIs$!BLEHuy<%m?e7NX2x zvEEKstp1^z5I%sMo|LMLYF@2wW=u&ym<2Ge!Cm1F4>k%1pa+P+1Cs@vCu9WsqUO`2 zHrwkA5jE^EWk+0S(niAh>fm^!X=O2^PV&8V@%xAwZsmcO8uDM3M2`8^cG z;o1{VbG`uQmhNdYSM*8+RYZfg6o?$Ve^ zvd%fVpDGl+DY_J*yu1WtblmhcK$bg*F0W=BVY(){KHHEoa2c%7JnS+s60OqEO$O&P zU0@m=N~c-rj=rA`Wxq0^z@H3Eg$$gwdHrN4%_o`-`|BbqedawONrpYe=)1Oh*ls2_ z*n2=`GdL9D+yfbL3Qi^}}5tSOr)rPe_AxJRjNa;v+u5|cuML#m8RP@l2k@`)^ zojX+LaH7);(5aeIUE_(H8ck`4&n0-ItJ_G@l@pLt`7jv(4h>x?(A`{*;1r|BSBiFu z*tv8POld#qphw$Ihq=!qy3Wk(T?VyD4;7HXt^x{qQ_BcEjaCl!nsI#5v4eIpL7`II z{hOi+LbY?cMeRs$LpS@mR+XU!FX5>$TYYmQ`A5+Vc-hL@3JLYNqOKZbc=O}}(_u$) zsNKDPrrVX}=u6%C@u2E7;e4tbLvIT{ta6(BrShL(cm zB_+^%C}Mn;K^lDZYJu_)Cp}Wtutb3>2}~HwA4V^^DD8Z7#e@wLbQ2hDC+3&KsE{5z z@3Rvoj6yV8{AnGoO5{NWJ8Jli)^S$}Nt&$%44OM2!PeZi2T???)UCS3tT8-*dS^Yb zYOm(^V4NIMSmav5#L?CzW=}n(Q*39h16vTC9S&cCPfwZzvX{mRyTdM(hDr0rOoc?1mUxJPh)H$!?U z{M8O?IkX1ep4<4^4!T_aNjEfc(De-TRJHBT3>YzWqAA{=cINdEmuus!wq;|kyiry6)3mBGv!{MBg#(cvQQzSN~;+sz(xvw=tDuZCT&N6!QKqQ1Vjz) zGAbA;y|iAAVq?~;%f*7x))jrE*rV!=@&|UZT}C+^=%s3)%O4n%`;pwk2{9so@aYA+ z487Bb5y&s+Ig<*7R(%myXZMec{9~ZS7;O4TuK+uwq_BIw1=#OLR*alSr?@jfPy}Sa z2)Tmgh^T^g+H66GfIVNaC3nk(T%^G{=O%wE-ZnsOfCwLuob418(XLd|w?*{aoTPk%t zir{`E+j4ZAuuy|-h@oilJHTAuY+3D&w#VpNGlJ)y0v8Watlx%MIgX=`hAp8|H!as@tV>Wu1Dj1)@MivvNeQ-wznKum-?#C42R)Q5ZHmUn)0UTvvqp|n%&P=aWXlvaW1)B%g5F^B~n zTd@Q`dXGs7++e0Q4uKKxn|lvxBx)qh(wlFRswUDWL7dEj3Tp%o_FV3BGG}n9i_|Db zXZZrz;$;qK<2PMOpQ9D#^Kb{+KvDJc@`61hi%VV*g>*X`~NkyouGyJC7}I2hwq1YxiWhzatm*_{*hX9ohtozag7S zt+4EZTr?~>xA^O%>`v?rJ*2}ru||p!fYGtLyWu9afd|+eJxUkIYX6cFa+o37wQDL8 z5Icmta0x{0^Kfd@v0><|3HW?V&Ogrs>zLhdakNvg_&oTJGjK|pDYinQbq%8#a9IXO?u}})KpqaO*j)GxgzV^#KFXh-7`0sG>SR`5FqA!EX@XM`H zH2b(#(W6tsK*Le0TRBN-6UD$CgGsdK?U{a`EbXSrvVP}686SO(Hv`eU)~!)(T}B`>V;i(o%#{+ zH14MKuM4>VRVnZibLw*9T1$gA5nJnWi#s7&nl!uGSnZSNql2fA-) zk_?|05NxZ1WvaTYS5vk#hDjdK~icxJ<)*kxUW<1#~n7g zC<|qT*puwt4xsP>?sbPo$Mgn}?Y&0Jle!S>zJsq+Kd<>|{;MM?H zPZFZK-ceqvqJ%rzyJbz_$cCB-U*6g>sI@xXp4b)zj~cep#XNSyMB%ah13M) z_agYwGZsQva7vQp7C5WEKjov-2~Aa+qXc1~oX>BM1KH&Tesl(;-n*?btdU@SK*kfi zi&zEONRp*d>PMd6xgEZp&_9JE(ZYKtb^!rzVC;7g>KhI0R^>23*XrM_fDxzoy&j}E znOU5ygvpK2HL;BA(KTp{^2kxyS)TZVhw2>XnAD0HKarl^KQQyqUFu+gqL#Q5mFyokg!bh?&b=l?{}ALzs7LxcN6T_0}^ib z8>)E;zs5j%6wQ+x3?W;wqB|fNkM=(mgr$2RZLg=4(IDAI(|RTi0s{b3@Tz_a4jmv;1``#a zj_`=3{+8Z9KiWjXi%$-)Me8i-$qKgF&wcFILGM=j(nNIedBrM%DvkphyeI7o`}={G zp{-|v2@;U~4UE6L!fhm>q@W3QXIHR)k7es{bh7U_vVs53q-L?4$ zgjr8xU<7x+wlh$8e%-!w_fcmqA+J%7im6RiLj+);>MzF?w+S{RLxF*Cm2YVFd-+2& z8Xxlx891FmqbU)F1T_)v_f0B|49*^W6~M`dOR3s%kI8*VqkyXrA%IaSBJh;7cGJe% zbu&I2axB9Z5g)J&qj`{aW!lAZf!+0}xE&a9F1QPHyXIM=vi%uXdjy>8GeN&SUzgb= z4;06o4}k6&NncvDaL*thx9zocOz4!wKmlP8jet6!tRf54w{yK{S#~!&YUSSB^qsyJ-*iKk$ z3-ppDvfNJUx|rP!N~dIB^maYERyB4&z&3hLbW7=|PMvN@;!PaCo^++yi3m~&oajrG zmO)@a9l-;r@^+Wv*To1bgHVEFI4AVn;I6VN@@$H!WG3TGWwu?=VTO6EcgMAJQRyN4FDTWxwGDdHiMU-Ds78aywDSzcJ0th>XwR) zieO2F4%{h`W={#T(I!}i9%9;g%kFENsJ?fkL^_2EiBzLc984bcVA+C8U$ayh-x=&n zg>j@3$>XN2tl4zUSvwxiC6=^ZqEb)eOk>*JHR-3LYsvC{pHVXmyKc|L9b_7>rNe(2AR6N#(>*oyS4x(EkLDfK-RF6A@fFRVUc_+XP)nILk-Vr%? zn26oTB?1M}KE74u6MXB=c9V3B^<~*nCueVx z`F~I*LU#zNatlCtzFuLt75Pl~+hvYtZ4Oc>nq}I{#zjCHDdb>Ddy27Onn`>x8qK** z!ea}@L%Ajnpm+;oDOAIugJYu6UKLMf3qBZH-n!A=A|jXJWIERT@x9pM(~P9l>))h2W#ZYAd0}=VsxlC2Cl= z>y#zu2pD-%>Hd*mmwg)*DRC!%K%V01Xgjexur~#^9ttH^{p1G5IOD|FA}y?x8XW{E z9VB}WxPi0uXrZciQDP2CBhie`(O*R<@E%oV}dg^ z6L8k7HY3l;`mqU?HDEIL)ga~-5w%=@`h`@$X~Y4DxX4Zn3USv=Q%k`uW7AO1UslG1 zvUFgRqpVT}HYSa{qSPI@7EQ`YC*53kl|b^Lfo}nN!#W_o>j{Qao|sZ@<&QAIeKA)V zB?tUGh7s1CVt)MBJ{90iD2*vPq@8bqXH0r#kwj)!Wq=G~@&|^?HsAQ;p590HBI7*J zK`=)lKy?$DG9HW~S}SxoDy!3Qx?ZulUnmXPkc)}ty@!u2sdp!rEl)wyRO!_N&^9l1 zOx0sz?q%8R#BCdWHNx|SzHXr1(?N0yokpjDx~G4mrf6Fn_h8SHGr_C(R^HsaJhdp* zA{Xt;19by1-0!$uj|yRlhJDpd+SQYpKHI66)`P_M64;+ z2VUlDv&j9+i#d;W*T~9k=`N%hV`Uy)CI%fIsAAxETUi{HFDLj&NlX~ea1pl=rYKH| z%#lD&MRnx1+svv5Z`b(>AU(N7TS@$*PX6v-N$hFA1J6iJOHJ07D}y9<43eex8=h!7 zttHAZ-1{M|Zp=BlWbQ#*+bm>_c<|TauZv!!hVX+()pJWw}1po7;W zPH{#fd2>3t@)W-=PJigz55?*0^$zRf@bBrocX6F%Hp?MRXmXlwCWVg}tN;r5iaWVA zn9wsh(NFdkX*W)ZBX<9c<`=6V=k*lVXZV+X!VYdQr`SS(tJ{Np;G~tqZhl@7`SlmY za?~Dap4}=YU>V?1iK@9?QXfbYmdLsxx(1Dyf?#7fr3n*3&x1~RDk(}v&X~7D4Qv^jC6=l7>nTDw37zExat{cIT zTBt1E9YuDdE|7wg)Oe^uNJVtpd4HdD3V#G&-(2&y!I?%4QZqM0qz|JaZoRTau?$+4 zVxra3g?ve)8Jk6Z%TKOtqBg@(fEKOq*hK`ddWalQ+Y*N41`s(JmS&e7g~$bMZl}h3 za2hg^V|nfHUIOx~!EZ}*Co)CRCmF&(T0Qo%tTCYT&<8a)>CwoOaE0mfc0Hoy;)G2d zUOnJzhRV=YB&=QoHXkJmdhqicFX%u3p(&q$-EjdQX+1u)YWb1bEM+DV#`w?T5XS*3xs^7s_29LGovdChbr#&k~H(^yiwUJqSOn z(eD#%m|Y!~I+7zosvzHR((3BzLtzpa6I*7>`ztjZSmAXBE0rEl3nArkSdcMLn z2ox*sdv>)YedP8BeaI)*!O-rR^gtAH0LCpO)#AMSQWl5+BHCDJEkLAs4;T=j%K_hh z4~xOQ(4bX_v$PF70?Y)pS&k#ft7$g?FdN|S`N`lir;d|-Pxy)je?7XAjAS!K4|>m> z8WJC~-D|pX#l&o%Gdvyz2sa?PiH=v4TM(Olok@y{2zWnf&D{@SP51#-l>2Ezj%V+NdHkD^XaOV@!-hrOuZlF*xPr?x)Q?R6|d#~eda zY5cU;?Gec`5VM+Y+)=da6`Rx8lckyRjdbEOL|q-kr4<_%h*T!s)VuSw3tQZ>tLLaF zvHX=to1l#YFJLbN3poIcl2S6lsW}Q7KiPc)W34ZiCn(RLeTlUog+^h!NsRek_9_uO z^vd;!uYjVP+#H}=SO(`jc)(4Y>S@JKViP?7N7?c?&YJ`B_zAz5)Ek(_wm^oYO==DA zG(X1a;oc-GU-(?M=fp`jz_1CEBsqWvy$z5eBDQALXME z{?lWy9-c9xZ?va1M#^V)=%Ah5PbP*$b}kO;%>|@Eph9W8eB|I1m6tMch^(DctSCX- zrMGR{wr$%u+qUhqZQHhO+qP}nd(ZhMlS%%$n7R0;ld5!gI;px^sr0+v^<)ERnoDo> zB9SPN4dp=>B*TMDY?|JL?Cp_o0io-!ZHmd$g;~?Vpo+bIHF)r0o3^26fvKm zeI^9+DuStu4P+eC5*v=-)J% z=dfKRMpQqRu%p*04^n#VY4qo_61%F~IVh0sQ1%mv89C_Zba!3T%DtVRNj2K(-w3w6 z5Bitcsxh__|BJ4(9>`73>(vH~L?+!D@5W(SW$49|z&vBo5u$N2**UWkg-D1yp}Hr) zaY@Ax`x~+;U21za0kk{p)<1{+oWt$XRU{Xv6yd0I7lk-*E-lW@Q?+-(NWG z#J*qH$v&k&-wxO*t1op36DLa&#ZUa4>E=?58)KV{1f1?QI2pQt+lY5}1?^OvTxBg_ zYq}W>`W_Bpf|n8Amce!dhSxwj=@OL(xlc0U=w%a5_x7zFvHHQg6S2@XEJRjc*YKtRNRBS`72yt z@Y%euDwgi5WU%DKT8Z>iZ>EzG5)ar0q!1s|Mh^PK+E|+V zh3K;zoa%*9A;wgR=Sb-E&K-ixSm^h=3u-H-dtox*0Uv1ZNr+kV2s!&^-OHqn9{-r* zrX=}8ORJkt(<>etmjFe{n$#Pm&N()WVt!WYF%~h{^4W9zHnk=BK)EaBY&3ckYt4V{ zMPFV&IZ7397WhUb{1b?ZnUXZz71d)N4M^vger3gIN4Q&wTFWM1SnF>Eh^1Wf!y zar%zPD}UiGarv|&7@Ap#BP7!C04mvGYsm{t$5a^O=ftH$K~F)!m-MZ4tyTgAb#)*DEQEUK1if^<6^OkHjVWk zsRVC}KJ8EyB8ua$tlP}ey>rq%!2v2 zJajh-a%ZevtaGL5iaNBNa9`{dKnIC1i>@tQ98Bzg2byl+T44|q5k53ivaN({0*_(#v)d%MGCy4(du$B`wk3u__QypXfw9BdlSi@h;#0dXQBLJX`a&u|J#ahkX}t{lT~|FSQY zC}5X5iIeD~W@da9Vu>p4LI+owl1kaf*70VUeu>W#-!yp9@B@eLf1e8huFNCAF61!q z@1W+$`Mb3RSDNi>TcbmLimw~Mb@nu9cAg@~-!fxrd7691ubs^=6k)Wy)#EWX_4@bn}9Y@gt6%FQ8_f%CD%+Bm3Z3veD38^iB$SG?No~ zQywtHXpPIv_dzB17Hxn`u-6UbrYV-R-Jgd5CuFZU^Bq}TFXtZa^EN2G87gaJPn=J2 zIt-DRrgHLuq_r64<3s?EMYYE)0ixV1%}0(Op4Vk<`unLZjZCjA9z2lWDXa9sBaGM6 z&>0kw5LkBvzLO?T0g&jUpm;)nm`57``Ig5l>H#}uyD?qRKm|~!#Uh1|VQvgFVTC|` zsn3r}(7dQ8eoO~16k6i|zXS6WXk~jBdxd2P1N%C@g4@{?oIw!*YeS#WKU&R>)Fq(* zP?qE1oH*V@==^=i`9rXtmBAtZW;x{Xen_$8Lbq9Y&Kug(cFacCpJ0;fV}9}yFXScY zALX=U$%s=MC>{pHn|fl}#me>DlXXlBAsZ$RV@4mC&;hRvk`)^X)fz zRQi$&hK4F2JYEf|T;yK%^eq6wQD7pbrw%rdpS&o2ygV8Rk-ud$h($ntko-Mt^_`== zCtH68HVD4cHbvs--Q=%JFk5Gfr^@}%885ssg{mVIU|&nG*P=Nck>W+6jzDvkYUFQiI-1l=nTB4hhq`c(jqdEvx#od3=BO z!t37x^J)2-Unk9P_iPNeGdV2sq6;y?BP%1>K5x$ZBrzAQtyL<@cI$AR3j_9G!XJQi znvQ)*(C9OpbqO2&Sdh%Te991t0MvE#-QCOQCqGuH(!SPoQgn#1#+(iCP5bInGa#x2 zg*-Gq?Q&kB=)=cTG*gUwi@soP)=fx6y)qi$zk%Y~+vGBl$pEu?6(t*G`XOVzsoS)Y zjQstT$hk{~mw=92y)3Jz%NwobPR&o<5j7X~v{kWoEZl)Mx)*2$r7n8@vGk^Vp(z$xyBMHN0{xsIG{xs)h9qE2IrGkD#^xlCr!Qi zJ^A^;mkW6Z_7k*7PXyI$j{(YT*xDo@sQOEQl~}l-6fs+}ES2=rx2ucDqLKvq9UZ}* z?$l6FWeLy&VQM&+;VPT%DStqt_m3B-@^_)ipXg|}&-Q!%WN!;*sZ`8Z+tV7bn%hTc zT*yq8*JCCD`tt&oA(AHF;A(6$1EnF5Y%TT&;a&tc9rT)ky0Gm+;AQ1SE6y{$Hp04T zUFDU+_ahd$hO+MNH9I>@j;11wz%Xps-oeaQMNpXjv|1X^YjpGK1AIrla%AV(g(~vg zSs|u2iEWMp03pq1I4?qhWwtf98(Dd@1_6k6u#!f2Ls#)w!z85{8VoVZ2B0Z$uN~sh zk@y2I6*C-*XB9vBnah1(S=0(qpupkh-#*T2@;6@{FYuam>eUu%F9K2K{>=fR?B9_p zq=R6gDmqT&sO`c3IM)JUz)rr<@?-7#3*nQ`@7Hq`LQBfNl_!;X6EKhT-ar(vSfqF5 z-KoeH@)BF3wp+{BKfRYR*_kRa@iYl)n|4B&FhfrpFY+pQ_OfsK;}Gw>G@wbs>x)~e z3tcP^)-ivGB|^0}?7jt8%iuFU&23Jnd`~*owrSzA$B?yZf9+@?=ewUwySEl;%lS_%z zC~-ceEU@6~p?LKpe&y*&E*U}ds7&u-RN7!wk{{{dg=tf^2|sCwfV+$qbe{cE>G2J$ z$zjn@rgk5uX^CtA@>9jZ%S3ltf0GiF_?e(xLC03N%oAv!@!_*+Lh?-$75<7f?%DTq z@$L2(_`mYdp5Q~c+CcyS{-Of_i2pZuXvQW^MvfNt&K7pIbk6S1sw$8Gzyt03I{z6i z?$7{$AV0tW0RMBk{jgr%`zJeUQa|#OY$Q_EtVK2YRi;>_&FYxX)7yb zx|wFRp=>BHYcSd<*EF$f;#JXgYM7U)QgZF7$TXmLpwWdDxzjW2Bm8`($X!VDiGI@A z1}`VjKOC8g>OBvFjA=2@#Qy5Iu}rBslDI*uS;a#$j3yncPQ#73)ytWQm7%2vA$hLB zvVL5`vT|6#KqaF7n&w0JUN%KDhL&c6txVKzsD+DXxTQKfcg0G{*`-ZYJM}_tt5`$R zhQ9<U{bX59$PRII*8x=8@ z8$7Mox0A5u@0MdT^4R=O`^>1^YfOmTby`T`cO&j169Yn3nQPCd8F$G{;&|0>*`(BY zQ)j7HKIviST;08n`S}?s@j+kz*d|Ctz5#(uo+eR90`963WBU4Z;#XzdHp}O!?X*|E z&Xew$nh&`w*X4>Qy-eQ5@(wg|nQb@tzi+GGR7|f|e!I)0p1Wkl76eI=hJENJN-HDv z67%)NX+=L|f5=*FlugdA*w7ubFH}%li<81tx$Rn2ULT|)8sdsA_%NEh-erENT0a;< zuJGO*(_w-Gp}(@+3v(xxs>)T+owX{yG*F$LE~QmRer~XSi6)vXvgZ6gr#;eBIy(65 zT)w4loW+uqfxi^k#F7H1kRoU5ILCO>)1!5_C#6!H3rjnsR>^x~_U@+puX?L*!QR5B zA0=sjC4G*EGXJ$Gg_YaFImu#`e9+4@tEJiH-4Wx)7H_+I83nXzFS<{6hpcWPVW?`u zKvUBMBd;`=TY@)3EtXbcA$@Eyb=`)^sik>WUv zg^S41I2Obs$7Y2c4Uv#jaXO4|n-W5ZJ3AgEBIjpe9E(tr%e9Y&RC0HK<(RZ9n(SC7 ztI`GE@R6BZ7$j3xiw`cpq+_X7H*~DKSMpoUXFU%KE&eUsE>S!8aU{Et`Yy()qqa6=2Fmj&7{DC3n?!S&>oMCAY?5e7rCJ_O>KC-)ky|d(4b(M8&!g$9=bjU z>%jgdq<)-i{QG&0uIKx4Qfa5_`|vgy>elP=yPBJWr?>O-K1t8#^EhhP`}SzI>-~Dt zsb2f{^E{J%_wREjf6x0t;%VzGKTps1{`u^z@B2~b!@;GmU2G4}_x1VUW9X@y9lyu- z=&tYmwR&!|xZdx>c)?^$H;O#%BvtIiW_ zD1S2#UpLRMJ>2iRE&pHlk9!@?_xn<(Qs2)%#&P^F_oeK&y1k$8@7IU&o23i)?2}$% zrGJ0k_V2$J!M#UAXs)v#mN$F(ySse3?j*ryMecu3Lh+%Hw5UPl-v=iQUhAaPaYVKWsQU zqvwx}`5C3B$KUDp-C5vv{P*&7@bEGR-cGv>XSd^_m+yOav$R?|u}~{)VjouG_ztba z+xu`qfuzU%bN_gI_lGg*W3<)x{r R`1_7+6Qa#bANxHI!Ar2DV+H(A4~CbcF~2O zPL9vd$2@9xzmLx^Ex&SfXR?26JiK2Y`xB=xSA|L6*Y94FQ+vHVzVbC^3(j`Fzgpew zBB^|Sk2_PZG-`j&?~mIz!lGLv&As2h9)1S#`8GMEZoM+1G`8UI#u<*)! z>A8PQAK}CNOxDUOGd*EUIT~j~9h-UZZL-|9yw#5S8t@HX%+lv3ELpX12JAvXZp_m8 zVxrUXX`O4+AN%onM@QM9~oWT5js^>GsX8J4a_$CWib!c}osy*vA4&s^X zzogq9`Gld|&race?_#y=gnj-|6;8{9wqdrX1|j^YmBI~19keVx7YNLtCj*_X+Ddf) zsp;4F5%m&wX?Gw3kTFi?36m%aFd6U36BNWDUta)9W+pTT(m5vA&YiBCy@$%7(Dp*H z=RsDtr5S9jG zpDO_&Bv{gpz|%<=pl$CGb{TM_Kx{k@v8`mAi>nH03tEL;7PLka#bA_)nn5ezK@h)* zwAqvBn=xwV;Gcx=uDt~4I00cY0YRFkq6f}={~0}9{iUS_%86#)Om#=BZ$XHHio_+7 zw3odIqAp#9Emfsl=>f0W8Co&CdrI65hc^E>Lb3!NhXcjdr!vnPK1(mHTX^xOUrjU) zEJC1MYn2X-b`BcvS15wjrXsA_&;$^eKmK1JX&ss99u$}V5DH=-Wf)I}TKzy3g`Ph< z)T~_CoAfbX705fCpDtOhnqW_%Vxf}IM_|b?+h!aBzpy+khf)?HGHAWACe9!qB@1n! zMpOB2t5RYrY3)@vVht)kUcn?6BvhE%RL~ku<5wZ7d%k_Tpr%SidjKB zM}PSIj~2bufc^JXP{tlwMV0TIp=O%~7VDr~GuWSd`kIkCMp$tAoSV+Bp&APFGRl9% zApDn%rp8L4B8qssiIEL$?)|hm`H?H+%8dvA00#wBv`BeGSHwQ5W0MkGH{Lm*{UA;z zxuN~kl#}aG0y%jF>c#|%STBMCAx4N%C}75q6RA&o-h2CYd4U>W&5?sr`Z}yg{DbtQ z_16ZKc0$bZqFWNt*CQw}2|~&u2^gqKJw`))u2sj)kHMh^OdUD1~hs+Od{* z)90Yqd<2|&q&lnfh5svTjg|gGl>|b9XdAvqodKZaE;ihL{ ze$rNkOIj${XV2-@%3ojTq#Alv&a)q-atZ$}U~YPoL|g0yFX?=cy5vUIq1iwYCGkMg`-dOQkQiEyRLqd^ zLN$C7?q}KH^_iFtpuChw8gawrZ;zB>6_yyiPekMB9&!_e?K4;GqaXcnvL?{E#L2e3 zqp`#F0wkj%9(J_MJ6jSmasy?JY)$4OxD_s&v0NcGDA$3~G?(<6dQH_K5OmDN>9|*J z8Uu|eH+$Vxv@ct0kVmbGA)vGMEWp|05!EshCMq+bDi-Balt zOSExJ>HfhY%aAK~e|&DEOps`yA^zz%*}qY=@K@89P43(=U&q^=x-7;T1WQcXG`sOE zbZMlTgYI@Qi~Rt=qx7Ph!bRs+acC$NdY`XBK?g8t4f&b$K$GhSF4v}TwSDIq%r%2) zi?H4K2c5YCupnJ`dr{TmwU=Zt*{o%{0BeOx^TpmrUT2BtrR4ELC?)U}Odvv39y!Wfdc#bAaAwGeo zGX}lJ%nB2hHl+5gmgZ9qnDKg9e+=rc7{I-_fFrL{Hq_7SN5dd1tN*Q#jiCbSRRVEy z&}oHH$2OXC5U37nP3o`eapeG{Fd5y-Ea`W$J69I?VtW+b+saeJv1^v?V4I^+XdijOHiy|a zI`9T_s6wXP6JJ|J5wiC4BdVmkE=2yqL;>T8bNZVax8G}=XPK%J&yKZ*&XAt)0mqSo zg=6-?aAF6&a8=EPs^QT1?1}0#j~G~HSu1w_35exi{aXL-5)xo0MWe;|C#$6m^lGI6 zpQzGWrL`!q1DL3Vb{{K@)+AABr0(xK>+oV|A|+Z89{dhseDg8dn=(c1;wDEKky}`b z{d)%`QA=HJK2oP?GyNU)5D>a)aq>ARk=5dcJ7DGDdRV-IXAIZ_JRFV-=%6QDnv;p- z`tcG}bx{S-fu)<_%9lZ~F6BLGnkLNJDNJED-5*yQ~8+z z`iK=`zl?B6h29vYGTmrM2@kaOpbBl;C1n45Sf4*dq$-JOW~)Ug{G3r03RA8l%`bIX zur{%(X+VLbm8c#K59@yNn*y#@q7zjBF7UFp#^zrvcS8kM-#COL#j4^{P$F=lH!V}w z7}Tfutxgnmi(&_j6_ZXh2^6Z$q&>)o8*$(-MS5ba9~C zy9NajhJl97WL2nFiqNjI4FZ|9Hn&n+KaVGs5fa5)hlh`2%A;V$shmX&-Fk!xmGr$gX5+?%;A>f_$czWEeDX z6frbHHd)kQC2aImGob}3{S3IUj4af{Qr=U@1Al`UoYi27+Q~**pA4vI zirZZXtCt7)2HLq@qh0T;JEQhJaU|0L_?0yJj!eD-jw;p-5OTxR2+i_C71M}1&@8Ds zl^veYiMaNf{|J`q)@!2~1=yLem0fvkRam`F??N+Fk9GJ@wOlw-_O9I1tomVztlty! zgDYylRQT})me;3NNL#fCy1X2fXof;w7KT~i7(#U?ITn}ZZnzu4y09x{tX}O2@iL$- z`iNedkVd*^YzYp_bcr`4D#e@j;?h)Mu?2HNG9xX18&{z&W2%0zzTK>gD^-G@p}ol# zx7Vt@!@%^s$>7;&#E80Dd&s>5T;h`UMDU;Bc`G(jK5Wn^HPhf;a&#mE1!RW0R)V+P z!FVnq(rv0E=O_c6rU90%p51=uxhh zISv+(+IHGmChDc3StXdz=TXPQoLbd%{kC$haKUOTscwUYsKNEyG`MVIoxI4&FM1N< z(<=uwtJksGv4U=yO*YLb?E*7FY3rp0_B3Cg8(>X|+=JSrB1o(7=QKOi9JWgP0QTr8 zx%I|=>qxn-NsmO9{F>IL9_4F!8VO@47pHW#uUL-yMKf7S*rXNQDQz`%9aM4kpuBD# zSo%2p_Hg*=&L_CK`@*O|Mi?w7bM{_Fq7${eZ|X@Ict-|bxZVmL_G7#yvs-)08G`T( znGPF?m(M7W3YXd9@dSOc0QfHGAI)Y~1iYmO(EW;D!-cpcNZvIIs6QmRBhW#;i zn%_WuIY{4s#Vj+C0(CtMv)W6C)nr4yK?}1<+d0Hgp-F*1^-EB1mNJFR++cmk!+VP+ z0_ovYv)VVaQD|xAXU@gB z1P1;yjax@N)!1a|aatuV>qB(Yt;rT zcV(h+aCR_w6uqdDND=A%;wMe^p^1eAeU`6ASBfkW+tTD+DXwZ%7Z9Eljiw^*k~rek zP{=!M+n=k~9Psm1`DZt1jSv44JDv_Ox84rpTj5`W)L)1$D&)9#(C!9A3p7&vr+tmp zuqOImnpHeAmE-x6WoMEq z(at%v5xR8p)>T@2SHnByq14M?h@VslFP6HMw>D>9thtpLuZXd$M8D?L(lVf@?i1Q7 zrhTht9kmN)e~uCpCJt^C>N@UZKzd@?;r-=dcqk$@pJEqh_hIh3$67B=fGnK^PCyw; z5tp&N*#@Ot(R3E)5&}A(-&!)HXhX(w`qXA{<>Mi9{l{KdwaC~;CN)d*a43r{LnRKx zA5}|^QE3WB#ILQPYWY(Wxl%uRL+y9-rIww1D@+vXLORZ1u`2e`WXT6>crI-U!KJ6R zdxOfj6=Vs4feg*|qS&lnXT@=?bhHXx6{!(GXM~Cdx(b3BtMJ!W!$Hv--3b zFIQOEMv*PTasp%1@{0OA+3cG7$Io+6zQAt5(L$9hc0Hai@6H5i5djOU5uyEryO9AU z-J>8v>mf|%XDH`!9poq#@(|PnNr>_NbAn=%nY9EtIw4e&2&RTg+@3}S3(H=1sJcE97voqnHP_k3EIXy=P@ zzntnXqfZq!Me%<-!oEzA%h@YI&xej%Es-rGP4` zgGzZikpIXaq^O9`=unQ~(F2(@jwlG9>UtV=uaGGNv=VV^T_2kUmAkeq?M1hAbSrc( z`ZzYN5R@u}b08?A=*y5q_a`lbSw-5UaU-gvf~8~@xz-2&W;j+mkE6CH?g?EJ(a!f4 z43y7M9af_&5oJ05WTZt+x&9c5`ft2e#h5an!==AzDfp^AGQ%g&cN?CWQPfJl_2X`3;jgDhxB$5>xm5vyf$ zB1obq?C`P1-p$VyKG9GVWStAhkwM^BGFy!&o4p2%9X)FG3&C6;n23vO|APy!tjkbd zX6@w&Sw>U>@kgV_-qHUQ4V7&DW!fVq2&^Kg-XkupzWdJcO=uS1+2{D`kE?I=%aNky zNr5x%%yAV6s-7F5c?BGM2g>TkV?gc53|L|@pqMPNj0_R&1!!q#63hs4c+uwHue|wH z58w1>VVUIE=1kyS+`AOjpCOSXp1OGN4ve<5M5?kVBemH2wUrviM!B=gc89{!OWkmu zv?jD9_B@d7ZwH^=(Pj^O9V2@_`A+gCI3E+@2wD;0^kD&(CGwmiRjatj;Yov;+lm4W zI4`VF!iet}u9AJ8+xu0^7jxx{(+QK+I9-;+IA|l-cqHI~F@bjivUn5VL(+>v$K8WK z@gqZ925~o46eEK%_$Jf3*L2kuB1QDzzcnW9A-IaO%Fl6gp$9hy7V*%xeTR7s7{sNW zmZ}1YnJB~3a_c`Jvg7V=ipyU>~|is4$ltNQb!l*=~;OeLhAUR41DXz_~coR z$|(Swl;8j)74nsjYBs|E)HE(|m4wVm=dmL;8Nh1}-t4FVC(8OwwltHZ1ega(@mWCS zFs9$8wNsqA2slpOZbeuGZ!?SB?oHyJ#$bdL55;r8ykoe7L2-|;%OK~$ zHYBd#9cnP9wg*0+18iZVsKUTy|0}QNlkTU#8F`YvKLw|T(hILsu{DUHG_72JYzBj84`rhUQ>cu}38Q{bA=#Hl2tjNXt<)38Dw9_`r*nl)|XDpH_Y`>h=9 zflz>cJAl7<8o5KOJ5dthk#-$(0F?s41|X^+yPK0=r3%F+S>&hHkS<-0*4`@ut_mix zLJl~JBFoUj*(-|s9^I*Tv>g~%t9!*p@Z76EJd^$i<1KC6jA`Ezi$~{Fmr@sS=?INP zblKg?KmQdYopHHzJ8M;tO^b^0OzrE0t6FL|XO3nSQ>(#>=7a{iY_)?tMilHJSVj+| zU>C>fMw>_W`RWT>Rp<34*sG|O^O9}H;8j4+Z&~*!v~l$n+)SS&X8$4yOzs5;#2xu9 zfe~>}xW8EjXQX(^P`2>`Ae9?sBX>6BQQ2Ta;QPtdg#k@fd(GRu>R^4!9q&x@VThtA zfU#p6RLt54B2TbhV__d2$WH#~%!{}cc)WHIL_Pdu%QePhr4p+mPXg#`M{|N^U=5tm$pabAsQ6-E=D`EMfuHqO*@QUYuW6hl>AI5?MC zRMZ2eou#v!{;SWH*aukg^5pFTZgc8g&3F1%*4s%C;4*Cn*Iv>JH#_R$h}bcql*bMXUI3ADQ23-n_o|)T}l;&|!8g=^J4< z6zuvY7jTSp6>YCkZ1dA|?=-mBLB3CyT%y_H7O{N8&uGt)vmfX;!_K_~x;q}36|SW| zG(YdUsX_!=R_cK1t4P(uod9aqo3qGa4heE+h2KRmDZKSUGjT)snZ&7h8N@hOHy*Kt zvAiFFKo8jlp`@go=L92yPu2{4ll(<6I>dA*v0!Lztuz&KAp9U#n_*jgbSEcB@Dwdn z8Z!CvT5aJy!dz>OjT1t3gBA!imo+>`*cfnIy~@l9R#KcR6VMY~5B|=%r?U(FDV;`W zqkdkc-R5rm0m1p>IfHw2Khf42lS$$jf{7?T7-$art&Y6gdUrDkgai%j`Z?_SF>WG} z%Cgj3wud%dOFKGd4u)boI|L-wF0R02Akluq`BrvL#k0%>=;k0E{y)AG76}H3CEHpn zj-6$s!YN|cFT7nboSDnAdo8!oZ5vU~OD}j&R!O#7QaASg{;drj_!0VWDP-7f)*eqR zNL5(^P;goJjvy>Xvrk0Xg)wd67eIlqOkqDk2$jGCDrwjuebi5#S7{THDe*Tv&o%?-Hlu}V%x0->uT z=jm#B;vq1(pJ^W2cJJ(uF?6}~9rlkhoiWit`1nE}SB*bSy z({Z@dNUGrDYv&lkuN<#}kJ~QOG>~qRaVLHziI{O6suG$xq!9^;d|S<`l5v@@44A=| zQT2(~*vLU{C`~T327{0-tmctN^3fSn*U0>_=j%wjuFq~jywQ)bHdUr@X)ZujPDxtn z;G5a?{X<=0~#E!cv)|%w)^Zd*k$gg<^P`iP(^=TTs1} zHj}cNhnyD&i;*6fq{Zbnk!5T3OS`d!&$y;a#9*ui&5NlOsh9M27w@E%%F103qMxQU57{=GU$Yxih+q8(abt* z9xP?dZMJEx#BLpO&^DoJtG4x`Kgtr=Yd4b?Dih;WOS6ZS%4}Xg)hLEGZ>@ki+wYH^q~K`}Yz>#*MM!~ZlzW!ArH;68svMY;*Jm8*cJGv88Ntk+EgJtiY9?1E z-nRsuYR=5Fjk@?4yh3>?;FiY5be&Z|fUk@lW}EkAzb%RO{b{hcb=26ErLv$gt#2@| z%8X|mObgWFXnN^Uq2|9dPh_xZUk;ixOF%tcZ)(icXnkp5imL;ydb7s!n7z!_T-RkS zDFYj`RQIWhf}^LqA&Njk3Qte>w;nqD`}%RqJEF^Mue-v5<<*xm{X6pTqQknb*j8k^ z`hj-*A?!S$KAQkY?&*JuQ@an|5YUInbX zS$4(J-$YT18qyCNP`rg-hYjMy%jr?K2L0BzN0~V`vgiY2OKmaMyvucK$MPApQoX2K z{5H*o#aWsCAIBRg+^-NqY00vcPX3|UBzj&vFtiHMJF?Tk`|#HPI4Jn=mJS>z*f>Q; zGR&5y?_0P^Tb5#tB_|blmllni_!b30yu#4bkeCa>$~{o0B@DPVK#nC~*_QT&A5!Cz zFnt-N0bV=^ax2j1gH#h_h^4OI?yUU?ANYNks|zt$VKzdXd@Vb6D_rSt6%u|i9AbFI z2_O07co<{qB{4rjaC98Fb{&l*!4s}ehTjuO&OF*#ONv*T2VCt(bXcziV3 zfL9~z8PjIkH*1^bN|tKYDAW-;I7T^FN+;khf{He<8H#C(d#$s!mSVz?cO0C>u;P;{ z*I?9G%Q$yK_j@fX|IBhw@iwSj0H(9+AL$Gl#Wh7c&YrG^fZcCxo2sHmP%;_J{mi;@VE?=}{^6bX>TqPJU-A zYlfr!lu3yZ@+yn$trrz|p84G|)-%=TnI8dA1-()T6z&^SO~h zfQAh=&{@|5VyJq%!3ZdsmcZk+%+{!Z)+Ma7efSvv8M@A_QJzIo$JNTyY#jh#2tD{i z45(i)?@6sArDKzdataL8DG#{x#{^ZIBUQd_-WYd{)WGd@cH29^8fr#RgbwSK&R%mX zF%M}gBocbY<_|an#dAw}qRP75=y?y*{?y;8g1*f)+I`T$~}$0^Ak_1nVXk!6jCA;pj?^uEnjt+y@kU!X^!6%&10?i zfIATV4n9x{{N}hHGoso&5aDd9K$#69u+E67O4S+0)uHs6k`btzEAPNF%h4Urc(hWT z<(dboDFpvGVul2ATS~KwJMMpJj3J4vDb%%pT5@;!kW|j6tC6oXMV!0$SBe_{O6}^F zxPnfhDu2}I*BU1zoC>}HmL_6^B9~fW1G#zE!DAP`)#PaonIwjxAnG*g=@7W=x2Tti z9onM~ZlwFVry5`P==n($bIfnSX4qjw6putFS|9Nginh~^nhaF2N_9tU73k@|a;`X@ zt}1ppb?bfWXcl(t*xXCpu0D6UFsnn$6p`7MK=vg@BNgXGMI|>7rM6e|s(IA~@-8X0 zvK*e-_puu5M_U(`gP~&YxMt$a5Mx+{Jv3nS+g5U@+7(SLvf+5P_A8zDt1JON2QFk& z;1EjE`@6Omh2jL(sHGCXLz|d{b=I70EjQycin!C2HPZ>~;BE;MSrU)YO|s@*dt8;* z^Mry^L}o-~vcD>uEf2m|Tg@s4cB{qS*Cbm8D94w!Wd7|A{WeCeF|Y@U zIm-fqkIO?$U|3&wystsWaCOk#QK#NY>7>Zyc^BByXDP>%KHpTgCLbJqN(PHMLPci{`e-`Y3`VC*ohKpsk<{GVH7nt8>|t^w~x-p|u5u3zp>ZoT;R| z>jQ%W^3-!>;pisU6_OV-LF=8kdb0Uz|J_wsjOR0O1Rz-gxdlElk;-04^3@%+pT?4m z+6s*5vX~Qs%DPSOgUivSCTFKw|Cz`tfD`eIIh7c3B)ILoouCc$Jb=o+lbD6XE7=^l z-ba}+L`Ek8ie^OO#g=pkaqVvOsjKgwF38@C9mBGYXP|2(pC8}mz{hsmye`+M!<&$j ze_tQs2Kk!O!Hw~wflLOYExt9;*)vV~yCOBVVc6FPdy0vnL*;*)*e%?hu(quIfw%iI z`n2^7BtJz57}Nw=H{wb%;K_8+A@6^JikY`hPqwI#)|W}1ge?%R5!+ccGE_e>fW6gl z5IOpf5#&$>*ycbiTZt{u68-wkrf{k;uspH90BhZBgLSsZ?6T+7eF=PXE2hk0eyC{& zv$~jwzHjx1Do4oRPZNb9pgSQ6n*==tU?w=`uv~&kVw>&ZXeZl_U2qqCQ>Y$BCT`Qd z=T1)u9&GHX{}`Mt!H5-)6vok*RDMErGq&1R3slu@pe{+F4n2i}uxGp2F&T!AT5qyi z7iYdbO@X`3N2J;8A-%Km2tq+MC_@cKLy$hXv}i~?n6V1M8W1HiJuzw|lLQJQ1_$hR z?Q1+ZtyF2w(XKlK@digj*5fy#yf9H0!KSf&TUkhaYUM=&swY1gkcoy{9gM6YW4>L2 zgE5W|K)98(e?$@Bn+yFAi_7V>g*spCZA_)stfoEdg@a@Mh?S2ZH)@FGKCv&pS@dy8KA;KpF<8UBeqESc9bCz}to#Ci2r^8f20rGT*t{WJjkqiKj@U zJGUimz7XEG^sQ8VWvZF#wUn%LR!J*6oBN}YJXQ5_uq!2l8G5=QUpgpI|Ar45zr6GW1~uz#b&SY=6#gF1C}+|5yYBSn_pq2qEtcR#v3*~iQ0tvuOz%L#9+OPd06Nh_OP z4N;(D&XK8hqiQYKl1c)l=rO8T#0HLq49~aWYT`j3(m=mMdqOPSm$HvJJ>sC8Ik;jy zJjFvytQ`BUDrP7ZRv+Hi>yQrD;mEPD zS|88x^GYf@AaP(>KQ={N>+o?bB73QTli?`do8ZnikP{qAtF7{aY0 z-{|gXdw7!^<|5FB+|ZG$I$gOn%+&kdB!Bw-KV9#n-4%Cn|6K1Ewg3Qf{~!F5jxM%N z&i|~G|8EcFa+adq0S8L(?mMc-I=d?>cTv8b2~^ZvlLhGcmC>=1ofT_1^{8{J=CyBb z8X+!A1skoa_^Sj&1yCq`%WeDWenR5$m9-KL-GhX6)latvMKQzkvo{-MO-keCv1$+1 z2ETe>|9BBaN~FB{FpZLnk!W|y>IIu-^Y0Y@Ex*-KVbcVaj`;iXyLCIqwlCYR3j2cg zSM!<$%Y5&L0fvUEWOw~wS;F$m{rs3%s{Dg1Ms>9LO=x_}>%v!StxL(R(1eR-X|Z_* zfs_;F)kqN62!Q2jg@uaGt7=jv0r(r!l`oYSzSNgeMsrnlTO$4wAV2(>c)xGZ-_ak$ zu6)c*af>Ccz78KqS%c$jTUJp_0T)Wm)D6LihPn0{6 zV*ezx=R}rhm{C+Ud#fGvOP5X-p2dE(^Mx~Phe9cn+*wszCD|`zCE18L`%1$L^s36! z3$awb!d~S&>?gS)t1529Uc#O7w`gnfhH@T}9_y&(=m#1HF4SH~PsRI;e00=a$_rzj z(=)*z9qHNmYZ0DQ-_nEDA8yoHfVJ_r=$n;t(`MF>`*rf`u#Xm`lYMJ0$h-vTXA=H! zvtA)za<*iv<)+Ni0SQb6y4BW)sq5<^$h;0~d>2%;#N&2(T(1k}#Ouyev8wHoW?Ckf z=d#Nd3fq$noYGFH_ezF|YmqO$;>Wahrs!^-XXHzv3xWFnac3nNFdoT}t62B7p7e`q z6P|)ujst)wVgRwAcDzM18B73TfvF;ZpERH$7bpZ~amIho+W3G&&xis!;{aXc(&x-E zfL)>WFn|vj013cr9ESjS;~ai~q?F<3uALV>pX$175(kLif-?(mX+spRN(-@6;^B;k zPt2W#MS7#0Qb^<7tJ%69(5L_%M39C?e)Zdx7rFx;)y*eB}oEwrI12)r6KoI0ZJEe zNJW~V?3tz&h2BU*@1)`OQ@_yPkGUsy*m;(HdwJQyHqwQU0ze9=K+nvak`U-J1*#?y zMP9(q;GL5Y=7Hyk&nO5T;iK*3y(kG+NCoh73WLIOPz=#?g^VTqas9mAIFbIUPAC6Of)v4%C&+>*kVV>W@Qp#*Ut*Le_(Iwv ziiklPq$i|mMGUGGZptFk=2}!t;5b~BhD78Ah^{~6C~-(>N=^71#Vz%LxuCcZ>mV-4 zKcXe&_n7*hB>-m{xTGM+f!fUd3yXSh-)TSoeuDj1UwO=*ipy#U0DuZ)008O#hAtXd zo7p*9IGfw({YM$;3>-~O|8E^ZE@`rM*`u(*uRo|8&ZSt7{qGy1HqlE02LKUWXe#Pq zppyxNI<*l$uY4q-x6eePNRaXFW@CecgUlc2AKUcm?6=l2f>Vux)#e*UiPt8Bro9l^KP zJ$K)KyT`1sNkYVaY@^`B{q+#< zDG!a}*a#d?Lka}a*5*jjufHJEZh_scxM!p}m)FzA{RPm%nq!YnvXQTZ#Ij%Ax>A znUY?DcoFPF)h%jKemPNt+XSDrFBFxQ?PW$$7%!L+rUq^?EM;qFy8!QLyClOs!t*sv zzp8((_kt^tal2EK8eBa#baZi>sifqWK*Sr-2`dW*hN5|QMhYG)`XU8J%%pkpzl|Hv z+(br2Oy${Mih4XeJb#BNi&-!ND*^drv2~=XOvCBA!--AknWc&K`5gG3bbL%@R#Wwo zbIR25Bgj^vK00r}LLQ@jJP`p44HiSTPo_?!ObYstio0 znJz5?@L@YbXov9Z z9%<6y3@)yoi6O?H$7SPNs$bCWLm+kMp%KN5({pYj8EoM(Kb|nEy z;_4N&1QqvSZYaph_+@g%7nDQ-c8ZF9rfDx4zbnDKry$&QVypJ3_V!>>c_Rq}5l`0gdL zkZ5Rpl3wjGiB{gwumxmIswz6=0I^1Y$49}!4;5YA8;vBWt7EjVeZa$Va z&lpk@<7d~xQn6YYqWsT+h0(dRy{xne1>qZ|rXmgZ;8L@hYbOef4E2B92xz1IFvr~j zHCn!vRAhaH{$obd^(`Os?g=kS64{(jPABL0eZQQc4lA&J`#-vJyD?d`H3ddG(~ia6 z;!zsEQj!tClX5R=y9}*Rx7lj3Ro}?urf5VJAi90kCmJy(D+RWv&ah-b+3jrU;IG$X6*Y-@va-@%|{7z9e7vj%- zy;6A2S{{r<31WJGV;}9{bYg5F~1L{%HoIVo`N$rZ1Ue@k?PaRpO6&#c99u zq%<+kZ9Rj9255#o0xwcSQuLSHp@!;B=?mqX zmLJx6p;|jGFV=sIFy==G$7l_&OVpoNHYdM zPE4_X9>p|KR|Yayi!dYz0tff1@);HQi0`A)<;YVZ_0e=gO1QHF(G?w`an7qf)tnRG zFXXu1ZC$_Ht6+3C<}?t-&WE$WUA+jJU|1IZ8RW@fxZ-0S?OF0;qot&%vGs%iX8Q}Ms}%o=q#%g1lu#l^J6 z8=cRhTOus=&5G^9KAmBGE_jzoP&J{^uQ&Ig5dvm5Gysft{p)JyM3*!T%iqnA3h z(|`K%<(sqHe>w6$!MF7cQYu=J{H`s=jx>`$w_CF9UYmVR9`BO|$R`mkFhpiTqzTJk9`RQSfz1z%k-~XLl^~+k|w(&XWv_4 z@3u<1`M6EmcSbpxY38;_E3ul(fju1MtT$hJ&(0~^2fv1^!c<4k7w6cmL1yBv(vSRf z!ba;{>3DC&9-~&&iSQecEHPu^u6R>{@C+s zy6|O%gbpe&ex8x?yK8rLd*XL`Baltp+b_zC`^Dh-tTjz2kf3pxr%Wq2NM9Hf3o=FRlE# zW}R#)OO$-DHM6ddJ+=I>Q7%_d-j-vPVEVWG?Zo0_-1xQo5q{6nJDzH$vcxW}Gmhib z$~$7^ytU+#>$VGEptx!5%2`t4$+e{iAX?WEt2{_PJWsrdMuBNu?-DlL{{SlH{|gz` zd0)d!DG~5hj7+WuwgQtz2R1ZNP-JaRdJ=B2$7CBLqFMtwBe`{spbnf`Xy5Id$M#$y z)%v*#>QT9_uP?KQr2L>?ehW!SY_ zPfNMB{?>ZctO2zRf9H z22VaLwGmKONDVHJ*nuya6xU-#8#%Wr3}nYf*EoDAiZ#*#ogGV!wEN-1&A5Y`+CV|U z`Q;&-cI_+IM+mQ+@)ylJWBKjN9)Oy!Eo1Z)l8`pmbCD6x!jcxcfOmz*dt>KdYtAgV zo=Z1x1n09`YGDj2t)6cVPk~xHM-pMslYG2wq_>D0uLIOX^gb1sz&aOq0~(=y_p08# zWlaswtL6AtS8BbIt;gqY!{oS!U)>(OVyEj>6e{(6A>lTCwEi{8EC;qtHIPTj5lFXbuGw66Iy4d@4e7ktRv`LR-oAP<0M&K1Em@xRfYjH5s z?IbsAlv`oDP46)C4gB!|ivO5ee}2KuU`u`P@Q^i+zA@p1R!{K(G`Kt}x;t>DgbA%8 zo`fwZK5#kr%%Dv!12U)T-v+T-w_|hd0Q*r<6h<>W7nqZJ{?(dM#%_>kZ)EytmOrLQ zFkgS+bRG5MMIdtEl+CgiL0H#naz~h@feb@=E_@smEC%7v5ZEFMR16z=-6R%fZnD)5 z*e~*y5KGh>q_9F@^SR>?zZ(2k>rRrr9Sh=-GU8?npM)O{?oRS=XB5>5`oZxHPE(c1Iv&DrCvU#46-2Wj@^JTg6-l+{VWT7WNn`kOC0T*oEEjT@Br& zw>3W-5+ic3L`g3-KuSXrfB7)LA6kn5mU4Isg#m#G@liSm35VV-fl_S*tkx#b|Eq!3 z*#y2CU3A6sszpxwzMnJoy!m`Eoz;r6?x_SOiw03L?SV)|Pf6h70kE)Ksww%$q|fgR zW8;bu`@5>_%q)wSve4P4i|Zxs0@Y5{`Z^R0)(nkyKwUP*D9+@6L^sHj;2NrPGSV_M zJTm50%@DYmKVVj#cK!3nwxpKXiF%#oy^U|)X6s?K_R>9s>p1>KC||sDmhrOAY9Ii% z5i21=Fv|%f_71?Ab*Zfnu##p3w5oU-3}IchV9o}+0$(|tQmhF`FR*5ORaTjg(UF*= zMJ>mK%NM{ru`vfWc}!@yokFI`q7JAi!?pv?gC;jZU@a7N4hpNrCZ!MTSOod4zj&71 zA}Q4YYU2>^Hqk1tB8_z5uMT&*g=0>H7IVOJN%%}aRg;>wlb!J08mnhXn;tpep@s7R zm@w&lSM-tx#CCZTP?NCK{UXw8ph|p<$3$`;2kjnXXW-ThbXiV3lBrv^65@@wfQZEP z+mIF{UAPAKs5$MY-m<0(hF67@kO>up`HT15g5pbc0&XCr@jF?d(UP9~9tK%N46L*e(SR8oaT6NxC>d0__w%h%a+EJ&Cdxa+_^l<#yV{W}$5F5h>d z{=M!J;=%mU!`Ptu#L*&1N*71 zvnKiHLY!MP{EUf#As(hE$ywKUa`qKYeS$>qdW%@Qhf+aIsF9u$X5KGQPpwhtk@4&h z=U>7P{Knw6*-}e>e`*@)eCA*TN-$kSL_>2;tNeWbU@M7aIi)AK8v>TcYAWuMUg)O7 z&|&KD7*2AAom6#-sl>$Jt(vbPjlgUer5s1&(7N?kmaam)7E>2>6Nhcs7yV2Xu8qm4AJV=Cut^900b38dG|w@n#GseZxu6=F zgH*C9jp_c0ng%na-?Bj1i<6-ey_aTB)C`e_o~@pX zRV{Rp{8|Nl_%@sdsbw`Fgx*&E z%v@_#aDdgb>C#?G%?ffp=@wjNQ*NN#RPEjRs-RyNLQ{bSIb_c8cK{2glQtAB%7Af* zl~DWwy=et$YlLcw2*{9iY=c-@^rm6j76WtK7TvLhNKUFud?E6(jqk`N<-de` zb?KnYGs$7w_x@ND1pK+z1KiT3e~c_N)dXVQGqCo_@4G(WVZO%8wL(3mdPYKf%(=FW z$SB#IpS9C7`{>wN3K4iWiOL}sG2ze{A-!9|qh=*i7pLukqy15thU1)Hm$0h*Q=J}j zWvsbOi8geB@GK-8yT7XYCR$JjGX@q0^q4x{(FCLrp5Fk-hmtF7WPs__eJq3WJ9 zWHEpx)gGNT(n>$jn2HcH99XBTiE~k>1ZvJF&!uVI0Vzs?l zBZdl*QR4rzt>pjGxHr`mY$8r^<+@-f_zqG}hccsp&IA40C;Im`xzCDXa9r&02ou{~ z*-sH-(>WdlVM^8nBmbDqaa3Mj)7Y}a$#%%*Zvd^8p7Gt>BVh2S$4T!E@oQdCn8rxN z!eI*1s!3V4Wm>Il%`EY<$>~)`RI0gwbc*P+l>Cu7^^U_4O{Rv7CWOT>EwVRPqK>Ty zEJbfu16T#A065hv3ZJU=5wc^4FRJ58U^K9Dj6g=B+G8WE3gm!g&VvPbTYUujTnwxPxEt}lXI5Sd=Y<h0z#{q{S0&;#RQ_-+MX z(xy}z#%MHRRW|EjR&tIm?~BIL~nDOiPX}D zW*lrS?v3w6q@5lmfbEkQA>VB6Mng6reG_Ust0@42^Q=JavV}?ozWR;a5P8Ms#EqyM zKpg-6i4vl1(^hdiE5?P}rUIJt+q8ZXb#*YB!uAIzLDI#ziG!(z>kmVWg;Mn!yHQla zhv4`A)+wP%d*G7;dc%XK2@>VBk_kba?V|ki6m(@^;i?aPJ#a=qxe0b>1JQ{+i9O12 znl?MB4Ox2CE>O~D9Nvq0j5ph>tPA{PQ`5zOf-u+*fpE-;r4fu|7UhtZNA0fkAaC6$ z`A#f~)565nogxKpl-N`{hdR#hITDZ->xqG#n{AHpXw&)?JY5V!|1yq%RGi}foy=o? z2KSPSO#A~n`Qsu6YDT}h<3$CCets6ZtgRx*G4C>N!IV9 zcjP+B@_BJA^nRZ6c3bl?GV^g?vGaCwS#tgP@H409aIWxXb&pU#j zHxDQVey>wG9~ajjMDDyZ1_Ff-Q~*qj^HjN3B69$eEz+2|J?t4nV2znzsb1X-u!b{-=pnyaOgTB;rFqG zlJj}EaoznfRH-oa{V{zJE5roR4Jxb@+Uv@_XL;`5Ama=i&2u?)UnbG4mC&cLU$ot?iG;FBb}f zdOpuOYTCxQ9E6%@41C|Wo`l{mb9&m&us>g(KcA1jZUd<9{dJAoXCdx`W3G=8$@{nI z9bg^y>rD6i!^)2LLxq8F$M9o?L08Xx>t4!8hrS*VaT0&+k^5b;Szx*++3xGf)?Z?O zew_QgJ^pm)@yhafS~|baaENIT`WWi@H&sJy@p&-wu}9VOHkNasVt?Ab)%@Ch?Rn7l zlo^SvW9^(c)x)#6!)r6{bNn>B>(*r~>tpY8uz8$qZ?~dz57_Uv(>XOc+Q50LThblY zm+bP9dGa|KI2!G`Xi+)tYGH_;w^=EfT6=1aRk`DIUGs~2;no4|1r!BtJtTKHRyh z2XmUDyy7kze}ES8>I-KmvDeAOtNsg?O8YfeI;L2c43E&7l26h zT`Xv)*uG&?@S!8Qv|BgIc9Zejv!JAXWl!e0{DkWQ;U%zCpqP@a;57A0?aYm6$5qt5 zCXCKGqm09+>F06rx1}D*a+XemH~FVRqcV!qcn|&LIwC6@?ZS^vwFO(eauv`1^+x^? z!I9swpHBHUkK!p_W9hmK)$Pt^^TrEtC1_H*9nQ5&ypm6Ui^~)QVymvx(xl&6+5r1W zEv$P&J^|Z>Gz(W(L+)uUoJ}T?M9G)BkIU<;9<{4(wuOu3Lu1uk3R#OVcNjK@CpL}B zEvZK2iWyoPIZy*yl@#%e8x^x z@wF$E5$vu!<$py+duQ)2j1p0SuDsvZx=J463rN{Hum47KaB$q{IX^meKlp=!BVNms z0iYy#Qfo^F4WR)}XBmLW@m5~SQ+<-2;?}mD$GeYwE7sQEWO-uRvY(*B7*l8iqqXqK zbYIpny{(q!y^E}kWr*d}7!Z8{#(ASnom}wkY~~d(r?Fp?t=b3F(adSVL4{u30R@45 zKDfA!w=UhWQ}KSmk;Aj-7KdeJMf_W1*=daefxN&4d!+!FqKhjrE-L$P>vt>{Mz=n3 zEE;vT!6mF!8#xblzT7!KfX)^a_B{1ye#L&rF2@0Tt&u@YpJU{`|RN*4$df|0;xzDG`Ku@?z$zs0(Ash=pbbH%b8AD z6^o@JywL(sRJaVkFjJ|(j3iPq?h9;^DM9v;VstR~{Ma`4%reQIkWbg`Fn-^irtrF- zt7j;8z#M9E&|}M2mcRlyyCYc>@pn|FWET}b2qBvlLye?k3ns6BXINEri-g@YyMihLxRO?Bz z`rHxaJnf4NUJ~{=)m^!rt54Cda^P}G*KShj)jyK{ar#e7^Jtr5>r6qm`3|mKrMVUn z9ck>GCs{?_2yB!!aT5Q887Z;bd|W?1;6VASK=;5q3q>N)=*CN`ykmwBxK+}Ip0E)6 zd$~@SEx%Y#Ut^1)W>9<`f4I{0$!&|Hwe8~_ud%eqFN>^o;pWqbb<*n_*#T0slXoCm z9!DSGO$_SQIz4ABveUDLt5#1WpJ`y~?);fh0X`oi<2`fuXMeFCbhf=AS0{VsY2LKk zld5&O#-HKmrBUIsXjs$4F@cWSF0p0jYsG(x%t}meMgcx)BJ*|)S-|Vn=Wa)jtIy`O zW35Rs>C&L%XA;%4lflpoFfo*n9hVtCD0^serTNJNiT6d(WLFt58e_D2&~Aqqa@cgF z4ilP9>nx-VE@2B4H4%aT~RCNKxKWy1t^LryU7R=JN|=h zb_W86%ShZ04UUQ8xW)y@p#{W?pV9F8RK(OQj1ntV2wEda&e|sog}W8cj5e}fhvUNt zOl8(Z7uxhTzT>ATT(oQyCPWilhbD#CwGfhzx#K7RZZIo44>0s5da+O3mUf#T7)K#4 zjISmM#PMdo^Gh)>cJ`$n@iz=&qc{%2;u9*(ln?eR`3UM&3*l^CtnJ(gPOmufFImkN zr?ctp%O9J%3$GfUVz1wWgLv+Dfi~@W$n{uXS_7GPDorL-AWN|yLywY{mp1H0N{S}< zHIGU&15t9;MLJWKJENF=)D=tuJDPf`qUM9Q7B7v{`h>RRPv*A@UV)d?%Cvl<{>&{zYx}03FQ^jdkEU#0ueA>DbB%8b$nlJXvF7_t&=kn4B$#48pm>~IQ1HK|XeM0!+N028dA4@>XD7Zqnmyy7 zTrCTK2jbZ2nbEs}_Y;|N5rV(JP01c|K33aZc6bTo`RL%6$YL43jnOo`0#5SLFB`D_ zLUH;G&92H~s8dV6Y!|0&h;=V=*!mDDKDq~YA zS5?tfbfofDF_k+qa~%zyKU@aufa>X_r03(T<_Nj(W9$sqA`L~n7g|TcW8sGbbfy@w%Ub|PMYdi!>r6ln z^asCqDA|mYIEzv#vKF!8x%h35oMtH-Yg@^m3Wo z20;ATuVl71A+sBkh2M8dRVm60pgh*g3hdg#6x;nkp+6p3^s+XeZGwLh7HBYZHVAP^ zfwW{+P6(yXim3%q_nc4!dFs*N0S`ReP_-{(GFRee8|YO|XnX<6Hjk)PP6gk5#hsa@ zwbk$xu>t=u!!24I2_G=GEx4pzx_`<&|R41Zmk=mo)lb zk8FyztM$GO-Qud#x=wIPsA;kQi52x)m`<$s06A^GRt?%xjtA#gZQ|`Y zk#wlF9H@!cOQ1`%v|@9GpbE^!$EItnVbR;9TXvRjb4p_&`;YW(Ec-S)};*`)W10JOceBffmaI9c{^ZG(+Vg}%mjk?;=h08+6kY2_2xYNH9_qX~qagGT7 zVz-m$%GfGqa*{4)uJ)DOlVX8>9s?h2MH&5(6AqxWX0x^_lfXlvxE#-JMS?^elp*~? zD`mX+%;Qtj8x6GFWm)tP*E?TUFL`g*(ZYcTK#*eMp&)^zCLtv6xWwDvKsMfdq-DBZ zGM|-<+ma7<-maEfA7PCnKaPj57b(<*q06n7W-{g^#A5l|ctBmmg(p5HYv!!bqJRU&iyrn8%5Q%BNJw zQP%JlPFLYgc3I^gtjo;G(gj_Hy6`CGN-T$0s$bgU#;e?4=x-HrQc0>bc*G}>M~qlA zbOxp7hU&TrRL7eOT)Rh;hbCV}WMwK)PB2$uyJdQcPAtLHan@ogf$l8G{1@u0iUhI} zepGjv#5@~ESmS0RiDYXksw{#sSr4>;DP&uG<>JJ2Tw)uVGiwx@a1H$8u6&C$l1o}5 z!{>E{0yEl2FCLfJX@3jihF0N30jU!=K0G}VKuwhXmnMW#fgd@!0JHdkzLykUGM-}B zS~OA64?hzotEw;u9hRR(rX@>Q#v*4>3-LzpCmS05Dlaj64Zwuuj{Cb?yQc4^d9v=~ zgmcA7j=;{JPWgsN2`o#c_7adJxUEU+Vl?Nm=3=7mxrqf&yR>&!;*)`X7}|@ZPhL?$e?yvTiR{Vk^H=ZcCo;+3T|5NJQ{UHnkz05S=*_6} zQneAOFgA#_xszmXXg^3?BaSM*$R|MydK;)EwsG*u&tV)qe&kwm%ioWa*Ai_MR!bzb zWq6l5>`-yySe5{`Hh9DMLhjRKF@4EYg&PA`KAMV$6K#?_Rl0xSjYwA6M?i_)rzmB6 z9Yq$#`AT(8(OIY@JzW%lb)&-qo@FIeVy9~9?T$qiGGe2Vq)roxdpwW?Yuoy@E2Who zlU1m*X0&R(Y(mlDwMb)v23*>@1=U5(m&}$HLo*zr3ppr-40@u%p2qiMz3S2f z>6GUjV%2w8%`c(8Vww*YvbmqXxadv=P!xCm7NnaFo}Z8tp=&LY8f;Z0WirE)%O~TO zA$+@vP}+!*#bLy&Y!QsJU7@H=E7O3(eUxrD>qJ`$Q3Io+yG)XJ`%1nq_9Mf3 zW}T%UfEa-Hm0Pp$aDDrY4hC3cJ(;Lf&mS`6YbMZTSIcRVg#0l-;i*-&ox zsR!*g(Cka9fWT!lqpOm1U;Xc|%>lfsJRN|mhC?@e&Yk?waK%I)6kg@KyAdS+2K)GmxProJWEFpZ!*KK$8E6l^cABaEu^%wD9zw zAN%$--+rVm;WQ&%1Xkif)B@cu_RrDK`nq^$N(mRQ$+^R|p)rlLcEiY16y5M_oZ%Cf0euHgT{xiCny1T%y7#!g!hAd463}MW}eudZM?KuL?nL&LC5l9dS{nvA?mD zp(jQRniR&t*CUI~pd`Wi8uJblLWn6QLJ|hZ|hr>kOa*SY8C( z7n;khk{*z@-n>-HRJ29awiulnqUJX|vJBHwdkv-FYK_|8dur0)z==3l6f~(@pVwD1 zKDxsCbYew9(Sw?2H|s_CFE`o}Kr%KS(c^%zAY$Y#f{M2u3Uu?CVjMa85}q!`Z#u7q z#^WhV&11hV@h-spNP!!nSZ5=0nvlI8TTL-eywU1QV)_{OFAc^(f+Yv$N3qrOh|?$= zN~dZ0AIm_w3ToP{=Q~;bH70XZ{-y0E*BIU{ z!%j|~)tRkO8ABbJ4X_V9>ihS@NfCeAo6I^t0Oy6B!R2ypPp>XX7y+stlGx!7(qt=F zC1K!SK{_8T{u6XtAhE-pyZ}~v62Q24CDB#X%0X$xlscP)j z3SJu?lY+R%3b#9uuz|>C67`6#N7637xFydRX&6#a_*9blda;I;0dhF>gqhuqL?Rt| zs3L=l&61tEc@#RcmET!hGqU}wq_TH0C}Vv|uFb4Yo@Asf$NY;Eri_C=d$N3)1@8U7 zEB8XrAeOsPA>U`M}o5PGrdSGDnCqNrb}U{rE^{TJX(9qOTUM3;K7^% z%h>SFgRq1YrBzTQU%>`O3%xEV9js>0@Q5Q{8-cHt*bR7~%9LVAi83#$>y)vtpb6sc zYD}+$yBt39zZ`TPxJqcIl0VPOn$EBe2Nt?1K{+X7plKloAcH!H^nsbU)r=>@T>8;vIXU~f*vqq>)9J>2qf7W|KtcPIIm!_%JI#ZKZQVxvZM(nSSVUx1~PUY zqba6Od-i21iUrd~#%!o=j1QmaEUSDYOEW46uazd`2ah(9G*a;?!>Qvg=l&eFq?s|iv?cbVtZ5b=QonWU zv;9^(*Z+;k#T@~WkAIO~!(Q?<>W)re&L`h)u=w`sqscp?z8_Ln-r>I;O@K{7s14O;WR`$muiXjaZiu{*&37L^1v>Oj{dmxIAVvnl&njPOL~HZ< zUuYOIp5e#z7Mjkjc}D~t7o111Bk`9$T3t;mp6<``I5_#u*8;QQT}FP?DrklGy(<4u zDx7c;p%PXP?}k`jIO*K!3Hy_rJSGe;6T90BL$UIc=occG-u#x-FF>2Vh=(uO6UA7f zJ?qaa#U5R<=8d+w#8JnJjswI}=v(!WS>JD12r``eh~{IFhfC5p>|-np8C8Pzbn`2j zRZ#RS&$e_{#Q>0H#)d%>7MVh~)m_S0w!Vl3k`c@kxjH6euIJD-i5O}FN7Zi^rI(r%m8xWq)?D(-i(Q5cZy{&R-7zUCJ&`Nw<}bzOXrdCV^v#h?-q0sH(~NPQkrgPw=hgMjAki5ma2+*%{Q{Y3X&gbcRUDGE&Uhd5!oL zpL~(r)^Z+SDJQxxfa;?ABZB&mX%|0TTFfZ)uiFLx=EH;B#>t!^NdNmYPOzI)b$NSwhJ#d z9nIy_B_GcTE18*#fgQ|t%BTiZNYoCp$7c9({T(`j79+ZisQp?=n3iaUjosvOG}q#? z>l1E&4p6T%vBq4#d5LikZ9KQKJia3F3lyP;W__Ki{8-(kV&#NZV+z4+IiLI9NCK|g z*T?ZgOi|)p*zO;or`L|T(sD7cNG}UjXAW?yXEZwbp&jg;Bs_a&cYq7A}hoR+i43oSEX2t|tBa5o{fhOAhCA!>usAbWQ

gMM1xHB}dmQ`=5qa*x%=Rx5_+r19LmFe3PxEa}0AbmJ$ zb>EOA<=$V4gOQVXVk|YkQ49-Xr~fu z5o<@@QN>QJbflUh4p&zfRfgfbQs zr`8&&pJLKjSq0GzkI=ZjIHE=w#lW=O)t|>FS~1K`lgCqrZc|)+pv@iOxDv>vQWF5p z9trpSB7|qXbQt9J5cQ9JVML_6d)H1BzY7v@>VA-Z5=Y=y%k^;nIsF~*9d#K za?XKoPT?3B&7OkQNoM&$_MNQddYk%W{b9Qq#WdzZQR>xko~{?6?3a~$AdZA8+FyP- z(UKZ|((W3rL6t*I408F-p zii13`2m7;e$P3-e<=f{U(|LK1cf#qh7x2rYUYu!q1BbcK%8^WTAqRi3um;`QjiJv) zgsI5QNRCus`RQ4F&Y!QuHJyfb*)`Kd(KD<8#>uqyV0Iyh`GPvwl39#@nL2Np8Nz44 z6!D}9f@Ua-NYPh^0lY&@m-GD#$zWs+3;A&u9;Lm$GEwNJ!?LSYs)|w`uee>p!V?@w zP^l5n{LyRPgQpK>uV!>Lg_~?E?QGcVV$g(#mnGlD4<9&pa`)G>9=KVWUhK7)c0&0b zc77fbdt`{x;FmP{!>&%=^Pcag`3@62)XkI9(B4HK!H}qrkkoSRc?S>aET(>arR%AT zTy(uE_Uao20?qYqNzp7RDK>T1a1ezjY9~7QK~SV;iV`tgr!Lc;7ZW=-KLH(*U|MeV zD;rogM&I0;@IQ=sf^m&5XJ1C1&L-~DLP?a(e+jf>SzW800!aSg3l*Tx;MoZ*cw!S% zNvD_mb9nkK(=5NkMAQqT3A*;aY`?(p386`C;zQS>tKv!<84``n{P|5ul0urw4w~N7 zPqlL5f@s<@Uq4u7981L>!U85{ohX!^gR25@(u{LOc2wtHDxi5$&BS+s+MgAnm7}@c zjheAMSd?-<1vZVRk(xpe*7k65eZ){8oj2hey3CiS|8E9&VLQS;i8U(}SF|c@-r&hb z@#6BD!sj-#WLt77vpAYKq#Mk zt(BN!nvpSpS+b!Rl7LxaZK^8BZ@JP{dYsC!u968uvT9MAEZx$;=_m=BOF+73;AVOM z2oO!+RU1Dgpn+Th)K*9dgg5RS+E*B3CvsdaepiD4Xe* zqB2(ib=B3m_^lN5cOj3$$yKoXo1PKHNK-vZj>V2iV>6M~Y<)on3J6Yc(CPQB8WbMi zIl>Sa^9qtT#=(>gz%^0Nsr;>Ki)pEo(I^8BfbP# zLh(Tb)&{T9DmPRHI`JACPccHr-Zu~M5Z(R={1KSHOgR}y2k-iUeOtpEP<1)lD8Ps+ zxrSfzn!KBQ5gbfNxF36Uup=?02`hY6q zb&?mXX@@kXoRd;Xkd1ie0l|$o!{;M?vj^0m=U{{Lzfu!+@JaG=MAD$$jJRaWvFK-B z1BQ}BJfbbrx?^)#PUUjkLN!CK^vYm!AQ(@VT8#ZAQgxV>PiwIFGGCMW?#}>+QYgR# zS}6QdM+{MZ}M`jLWyfLuZyQcbdWhHbcqhkCqeD0`wy%~;K4r_GeDk3 z8CmWvv%r;k^ZrpUDLW)>m0A37&>xr86->%&=fcrWgKY%F5HmZj{4YWVi(;rlScpB>_?ZrPoi57ZR4$Q$SF<^C+`Z8;zb8**0qfcFhp>Y6!I9;xC z#w+g62(sli+|p3+R{BweL8VWlQgoCMW+tXwq6#E2I?>2AX8m$2foN*TGp6~x{wa1jUJ9!AC-MR@JbG3CX7!DvMqbht%_p$~Y?r1WIEWT`oX zNASwDHkT2mI*3yq9Qu)X)+~QV&QYU;61Y8^C@e+YwHZ`}&`5EizT|?h zWXLWGIMl0#PM|XH=&xmt(Ao(cin$N*6*A^CkB00f9^!cL9&^aB6^8f66xj&aAZ31=XMT9T|7j95O%xQvJ#8~u6ym<%DD zVFHew_~P`=bUtP zW~m_9gAQ|5b130=1}x-Xg=G{Q`JnF}amB6#>12AZ1e?2~;|i6()H5^M3Phww)XUFmALjh~a-8(u+L<^Nh*o=!nKf?NLcu!b6y;-x3|r3`Ld zhfZo6~-Sw!7bSUVn~~i=8Q5nj+(IUDU zMuS=3lwEvvTx*3w9twP1;D7FHPUx~}ih=XYc9L@oKpmz!b6>>PIa zv0EV{$WS&eCHt{NUMiNn0l1xA{n$jNIUKO$V0w@izjr&t9E_TGW7P)2e8+P+w(ad$ zo0iH>f+s!rz@Za^1E2SYIYsMWI+1}l3=_ltj~6ZHGQx!Ri`mVJfe*8VEt%iHMcFYY zz{s_H2JCsTyy6FPfI_sZ&aKY_xM$u3DjvC4Y({cl&hn;1ZS+HkUQg$pkWGD2L1~k% za*90bB+K?A-u>xV@olc*hDY-*_7#i~6DK#>#W(&*xt36t0+Q|WEAK*GHZwcJ$n(8% z;Hp^|SM-zhLM0Togn+>qjlj8`2Wp2aMgY{moN~FwpUhS}fIm;{;-poE6_YXz2w27u>;= z(nZ!peEcgHriQ#P(BVALuZ=W?OSgIrFZ;^QKrTLU6;|osYdC|uO6cE;m3)XP90vw$ z{BH}-=bA3Qr!r|7K{U@mJPqu^fP3I=d8~D#D^!RVcl*-&s>uj&{&BoU4Z$EHBw|cO zC5d)K6WM~~Y!^#yo}$iMbcQU+4Sf4w082o$zpbyG4_}+Yr45qRfb&f_EaUG1lp&FG z{N=l^R{;E+$6v__A}LI5O83fbYJGsL0AX){Z}6?c!6*B!gSab0-N2-uP$f>U?yhVxLyt{s)% zZ{qeyh{=>~usB+u9ez~}>F4P^e5>NcQ_ffX4(9iuW-S4nkYNmtr$FWR$zmAh-%2bL z>8H(YCb`88Mb3>B&0Hiy=KR`%>l>aqhkm1SBV03$$QD(O9a?2%xHSy7Iw5x)W-0hj z@viYeDDwy28muC~E+Gls-aB0Qp+v@WKs^XoIuB<9yeG^u?-G}85_!DO z>ew#lp#iFx&c*ww@+7F<4&-wWz2)*uvoaFiMNASn&alvRkOMavKXN!>2@X0i((waU z*cveqsNO&zEX-#&b<@^YlNUg)Z4#Dp0ThD^=ymVwVRqvD+iJ2vH5c&vh?ZUs$K3&E zvo@Q>3bg8U)0pS-Zx60;J>I1V;y9C_m6Nfot)3yOrHV6A1ZHMRQ;leEyfIvAhntG} zYqP{UPMD})Z>Fkp6QpV5)wpD8Z}T$BUQhB7WvGsBk)#~K=LsDdyvNTVB8j9DLHCQKm0~6^3?!$iAUGXdD`ipv=?{u(5o28{Ws(uf=XP6hX3kS*9{7gH zvqRcfowz1v-9eSToM4$CPDyvl3dhcD)6Q&*b{>=ZwGBEUW8!yUI^AC2+xa~bxEqy{ zWDrWUQGkA+LbI{WM=XId{!FTYP!&^ry+(Jj?;A<94&=Fab7?>bD3eN36WD-p_S$Mh zwL0%F0lS#AR-me0+r5KAX@?Yx8{s+9Om?4-(r21&HQS>pY}1W2D99qq3y7PiBc!#z&*PUvOQW7U`z{(D zb##>(zxo4wqa8zx$GzsMetjc}zK=Tc)$kHe!s|%h{NuC*wFd!mYDY(VO%h`nxmcnwn z7!Yl(_>~oA_K6c1R0*dmVuPm| z2p=*5lE7w8YKT;5C7?^9kn#8Fz2 z+&TvyGfVXioMojDBq(oFHVbJmp~k~t0(5sn1U^e8PIt}t`REz}oS0t?Av4=ERCG+L zT&<50zysg1)7hkzAQ?*3#X3~^CK-?WNNXBWPzHE5T1$aB9YJEM{fdW|LyPY?)=>Fv zClK_GZ?+3(0(!& zs>@rv7;z;uhv{IdMa}gEyn`^jQ$-NLJK94wpm}!t>0yz;J_e+UXyZ<$Yj9o`xEpD= z5yW}MNXb>2u{QCoD64n4l#UkJCIb=O>(PhGamXhdRq^voa`~7Q7V3GoZTCmrINcSf zY=<_Cle7SY0~9Kmms7l%wY!r@we=8Pb|hsQmSBlA??&I8f*Z803hn`}nch;-Ugh;v zKNvtJrW35}!T)c{0KL?=BNpgdVL0tY&I-mzAJyYqpxsfEYJOSAv6A?Ce z2U^E%w&R(I_*Pq7I}G@k>;;SKe00^m!z|ZQLnW7I3Ho?U^ktsMV;^sP`EbX=Z8f_g zBT%+C=@oxZ^zs1YPxl%E4pLn!{eh`SQ$Z~QatQ!x__X&0ul0!f(MV7Z2y4eno~jRg z-P1O9&QJ!~+Q!c-RvceuNrDXuR$^CL6vT6v)8yC{IH&>2qQW^;$9@o2&>09GYfXBg z;ur4u;F_f!Nuh+zdv?IKPKT(FQiSRWV#BT*Y8R>y>iA3zK}{+Bs)Ephju+7`$y!$e z1#V2F!|i%>r6As6En>>I1;r1o`|yu)I$P4~^$PkPWlf?GCWZ_JhT{qCO>G=?(8rQ)*s?3Kx8ZUjkW`*hIX>XB zYtX${V+RPUhD52!Nsk;Q>~yLI(jJTpP(}>;Y>k-H3hEu$30U9;+bJ!Hsm#A!k3M$4 z)3;y+)PU2*oIV;(Zx;{vNmW?27Qxx4fCLmkgsj^z@m~pK@H6ydFmvxB?7$G)vV(<~ z+k-VC$02ye_(BE>L;Qh8~F9zUdj zHB8L6GPs?3!*@&7Jxr-Y=Y&4ylUIU%AQg~W@Jei_D`HCF6m-o6i6w0wY|kJ!Ca1fH z)#t;VL`wt5>6NIdp+RQ~M2`&OM32=Tm>aU;wD`#W7Rv9FTOG7Wnc~btI_XF3o`AAi zT~?4D$t(NuL|Ep#`zFE5)&ntdyB>Wg7J6kRKZ=Dun91w)-Cc1$nWxCfixLSet$N~- z8|}s3?c5Eo2mA0P=StE&$ldh$bHc&^}S1;8>SnBk!7GMG<{X=Hl3+LiKb= zryy3r?&R+P*$jSm9<_S*hQ^~$I4$MRQ*)d_z;>(%He*KUo9}-&(4kURS17J|6PYw9 z2ecd5TA0zBC^xyHbG#CXq2_5w^ax2lsN_Z(jruggxmnxZS^QIq1u!~ ze=)oDdBtMuB5*~tYNL{5_EY!K*tqZ`R3kR9kSXokBQ0WbU{Wkdlt#j35*Cdha*NH6 z+v8iQ25`+9-4MWeYEq~+vSCl$a))4T3!91_IuI&`7f{0hH{}&gwI=%=AVs-dkDv=N zqY1G-cz`BxX&B?4^7@$9+Rx+aawz{Tj+#M#A4yLT$!Q#{zhC@yzewDF+L+wj3!p&- z6P2hOQz!uWq4UavR$3%i(%Yi33a-V%P&*x3ua^Kx zqpuw%QFq6MC)K4n?qy=oFlD-q3a%p;)|>c(K7^2DZE2Hp|GHxdE|Lsd0JW9^8kG+Md2jP#;$;TkBoWijv0WZY_0LTA5x6 zkL3~6y*E4du;+S^{l1aOX+uj}7g6a3%a)%t$kCx%hYod> zg~Igc{?%5+r~zn^DAJa6o>VrlOn1zv^U+75Quxy$$SAZ#*Nwoa9 zg1rN#v!#_!MJW-GNbj<~9#M2W<)~wIw1ldJYyxGl3!c1L;=|M!J06V7$m4ZYu%~#g z{4Xkq5sCl_4Pog>N)0oY4lKd=y*#D|p|XW(Lh1q%+gwS=%0m!BeS)iJkzQ(c6 zLC!-CK+CJUgY^i$w`8)Zhs6ZmY0@el;mo;8D>aOhXrOcSDB!n`O=s4i%?mB)&a&*% z@Hw>c0*t5IMNrZ~Ob+E3#Jhev_rBeGMQIR!LdE_h4%kAQ2-1RTyJ~CYshyZ-bReK1B*l`-S(+d8 zs9-?g(h{Z;tONH7X_478u#kqwnvQdC^MB)NNfF(S-btt&nzd}(QwmQy56Z6Hm(YD& zvHiFdHXAn8N{1l@&-!L$1X38|kB;z>evl+A=9>??aQyhq)gF6n=~D3&(`sYa+%Ojk zWm{6X#`>_d@_)EnWOSmpW(5+7fliyTKY|>l6~b=+GJ!;s=KwSTT&i&^Z$vTuuw5r} zV8HP{41Xe27u|*v4>;*X5)Hd0Mrg%K$?V&SJ~~|iWOc|P`rMFuog`#RP=s>_fKaKa zDWPs@>n>xEcrEzMfkjzB9=gH-G6NFMbJSbi-6>mIbURZ2!=+k0Pe@>heB6kGwqrMU zG^sRw2Bde9!0s2ayVpwOjGu=Szb(^>VFC!E%$Rm2dp~g!m-I*gNLHW5<8eYmLS-|) zq?fn^0W_fmA!V4e5s zvPy4k`AYay^fuIPG#0M}MCE#P&HGH~PDx5&5k7X~p>8#0XS4U{UF8Zmt%HU&w{{Ud zymtdJUmK}b(kwa{6i(2mekQIwLAQ?upsz#k?uo44~aeJ5~ zB~_`wFel9_n}TZ1JT%;qHWpU=2+NT=K=Lbf30rs>VC?L{Ft@qcaL1VX_+EQ8h*YC3 zZ`Y%1(beu2$J5d5C-?ND>WXaAr8;AkT{Y80H}_+UcD2QQ|0>qPoBCJ^Lx@4Z4Q%cT zs_AeClk-(ftXZ}oz@i_0N5O6H_wP|_Wlz3CDPa-Y;J3w9O8XtjZPw58Rtn{+CsfjWF?WSJMuanmcvV$e_PbpU@*3h(9RDz<#R{`;4Blk{Phy?F=`0 zfGvX@YvVnxVmxsb_@$&Ff>>%$xltj|f8_M?H2whws9T7aZ|?a=3;WUEH2?fKvw3&J)NN(ypwH9NCJ#-N1OOE{s`1|L zeK4v1S0B6omHODhyt!I?AZ_W?8QpwoQb*-Yespjq%}$$!@sP^oGC@s1q7+r%#CY|U zfkT&HHXkK(T7``s$yD_P2%FAk)g73FW6W^Nfp2P&EWKK$8i?0zlo9Zo7;A084?d^60*7Mh`L6z@=U_GxGJf*r<|x?PVxboPb{Xv2IR3*a9i z;z#~=;HI55ah!P|0aNtnsIFPtB5_N|kT6u+)61P{8%HJ;*6Ro}Edf8F|2PcppPbzW z6Z($j`+bmFz zGok{B3>Xkg=%=iip=8}n%gVw-n{83MV%wYWdO!ssEt0gDi(+W7NMXSXfbcZZV>dD6 zEpTh zV;MzNvUmF)$Zm2A@1}RA6}zJHl2De_5NLL3!!cGQ%Wuyx0pm1otAj5U^w0qjAD~i( z%0pV~Mh$EN`gVU%choPk3O1OuRNoNsrA7z zI^s~)Ll_M3^s(az^>l>lo7i%pXXkL|l4PBVecWt0hYf8ums(w`v=hm5?BVU4KD=Go z8`B+tEPUyj4b@xdFbm&}^vQI$dp6AV3OBcg1Q!;I?CE({GY(fNJJv5jqS-JF3}nMO zh*q5GrrBhM!XRoNG(lb69JCgotfx? z&mm$l>0#QEaTayiKYa4_2)L42_S%4;0KH>nJ5Gq2$IQaGa#pm*Sny3sb$h7jlx(aFhfu^zYt8um_{2 zplCwXy1D|*LMQmk(bcGe5+73tnun)t{jjNI>|b*72PW>+jR%Vd(t5l&!@7XEBGF#s zQg8MWAPku~N`_2oW>T7!me-J>LRvs3WMN7qR{D>>b&BUW1u6>|(K-TsQ9;}W80Cmm zx)!3$V6om#SFHY_nh-vKoSu}bjA~x3Ze~nLK$rzEufbj64i7d82cQRtzyp&7ohM`j z`=aL4q&C~@3=uW#Fl9$vXwpW)`Rd?!q-kX_o;4Ug-K?f8)(vc`LQ={G3N^1_EXZ*I z*g79&_u&F)0!VI>#vaYG?#NpZ#PjkKi=a%kiGvtdV8J|_p`MxdBJp3e*_?4na&#_!(MjCbof7b$g%x-G? zvhLEDNwUs4xt}T&y(zjBqP)BWWOUs0H9(d-h%T>Y9AUa9xjx&FGH@BJ&^+uiFcPiO z&`k#CGhJXB9ZIKJ>5jgi4rRYGp}?OEOoa@bwt4+zD9tCD4g2dNDt+cXAxVZk#pt`X zdf09zH`seXW-~Yx;@k$G78m!@oBAy>Vm=%eNH@ro1gZ}^6vmw~AB9>3^l6EcBlDBa zS5VJEMGU&S+n}vbM7VU84tu*Bzjn9OGyOR@-hh}_7?B+-q#T(x)MQ)J7yK{GVsb|9 zc0IaUF7QX})3~keS$=bD0ojD5d>JJQ2Lq5dIN28D4tiW~+_As0O4HF*uEii#Xk2Ij zMtaKJpkRODx$^o(sDcoBkLuqq2~vuG_7RmD$<>CnJt0Ui=t${EcCK{zaYa8erd0IM zl9BpN$(=h?=WwFa4A7~XQeESTn;K1Nh|eW>q^sLV(v=gCQ~59%01gdZDbU?qkKh!e z$5)DWirBey5=?17>7Ym3PKUYABf8Ga>|F-6Ne>l}!L9-dc~i>>JdIWk_L^~g(XoSe zGC`qI-2I!P3PQDWx<&0sZ$mfxxmJ~-1~1{MFk5|dBl$*V`uuzAQt}T8qi5`f48aZw*r|Z3DeFnW3_YQfJ9yuBnyA>cj zRfd*=EuRJMXg-CX7NfTKs7pu1e%V1v_f^jn;8j2}zo*1q_-yAi>t$wg*u}uhgx&#jG(r zfO=;=uxhX7_h6hHQds0#!o<TogEHeflp7G1hSXL3cJHDm4-?4 z#!X%s93UIznfQg$J%Gn?7;kVUfJOp^Z98};J^*Wt1R52#9|L5 zem6sUDg4zAYdN$A-k#g|+77y0{z*49anSV)^yNGqz_AVHdxNsbMGkt4=65dXM?kh6 zn-xOL>-^Rq)mvKmcheruplv0(PF295%JKH;dRo3^&!m{sk5sko&kPtbb)qTWpLXW; z5aa0vkn1Ark;4smUXfP<`xPj+z%%7uo+HXm>9SBEqDre7C%{Gue&|C%wI*#xfWh7j z!URMO?lLMEDZR8_j$&ihtINfL(bg4xq}ZeCjq(R}vRy_w9O$KLpvxZ^llzg}!wE4W zfbi)By9~Y4hY`px=Q)!Kg;sqLS7-N+jQnGu#Tab*NUs1pq@=KWz6IFtM^=oSN2j2U3wN!IQGNYK!-I~}3%Ki8~>#iPlX zEL$pdJc{6cB-?UyoUl-XZHS?13pd6(Wm07@J`I^nCP%In6$;f|Vkro&E*o|02I$Oa z&SL&61a=`*^dNtb%B5`qV=E~oyd*&wE*u$6kW>aRD z$AT_$s|AP#6D<~r9A%1LC=$c;i+*4Pj$3`(u|BPi^$q4NwhRzSuarXJeL1+vU`0EA zVgqhaS0y@nMEPTOt~1$rsduuPMrs*XB)bT91{zJ%`f6Xkj-S|=O-yT2JAt*;o}k0D zYzkZb0IRZDChHYzTOlpUl`7+~3!jI{%+C^#> zqcMmD9b2&kKYEWz3EW_&HV%Oi@0)uMY9wkT&C;81lBy=sCqbOdf(mN{4)$E`b24Xe zsf*MoM`!s0+2UmmXyZ3sN}r<@=JRj|+CWkV@e=mNNYNZ?d0*ZeK+%t-p6Z2N zb)EVV@igwH^sfuK097gctwfU_u_gOzX6mQFY_|Qap2(})HazT_d#Ft8_rmtG!)@;% zu?MkX%AN{0)niZQg@Z zg7&=3nseO4S^jfO&r~%4i_xK_KJ>iy!1@q@nA9yLRgu&Dkp0Au9r| zF0s)rz-t;r;VW%+g3pK3O>Hq!=&8}~fRCX5?5hE1Ag}X-qHFw^bO<9d3>qY#lTfoU9QP1%&4+PoROjQC3hW`q>5 zMc?rx_iM*B4+}5W*zJ0xMNP{cu!o7f70t9sXl(5nGd$*22GMz37Y@jYSDBa8_)K?N zW9^f|P33i<JK_&9=?xR|};-?YSaics)CbL_$lF$GZx&{A~Za17sl)7xkkw!v^tH zA9HK;8HxIc1wXG?wNHaS%oH=V>?Ymq__;~7;;B*sOD>v-oEK3FT&`|XVm2EpnzFHP zFx&;pO0x?|0Ymtk;BM(P+k>DINF-`?1aN?WqT9?lHwEDqM5igb?!dPMIwa>7ZMqwP zPhx|&k4G?jLg)k#wUYq{Ax?ejV{YBF*bOeGt0#sDI;fF3PJ&J%rl~a}k%kokIvT^w z9sVe>qTx<->-fO&I-Me=zSzMe{R8e`t&>xAMBWLCV(V7>r)Z0TdB1v{P@w>JuDgDY z28GlF<@X}^(K8l8Sa3>`{jJ4LD%Zvtbh@x z_`M#aIGI_Ttc1yp&^57)>(Mo6jPl4)*;$_WgNN!I=a|%r89$Mp-ajz&&|T_afufeU z6Xbcl&zrpC4We^O-C2&*8xV_+C_5uuO?O>fL27S@sMC9!KRpo$U@9izhiK_j3>aD9 z_2Bb>eMg3Tgu1uu6(pqTik|Jang@Lc)C(JNnzo;CYaMHG4%E|&n+$5iR6VkSSl%N# zn!xn4ktfI@yeFP4$drgp8C=*2WX1FXW2U#amj-%vmtnQon^5NnF};;kG)+=;@ARU$ z9xu4jN3>5sizmEabwmVy->-9>FuInCWz>u&^^6user0;i^Yrn>D?spUH z*8>u6_8Y2s3BY}-uu@QUL-nT{d^7RAm$wMNyqZXYnIVx?_uUVYA*eUwm$h+(H$Hd| zAVH`HlxX(sQb~}2g>BQVB2QRVx!52W+zGduozDd>brmu4Do0h~zq)yYn}!Coem76V zwzAk-VJ$k3^}I3~u`~&?kX=gAtJuBS^rmdP7$<1Z65j2*u17FxQicJ!5#<6z!?u0F zvqL05x|h^^-mmNR{tO{ov7$R58ISfq6@;aGAZ@Rwl+hsBM$>vG4FUrIQ}C*O3Jx70 zQU((hppNi}rT&)QKR?<;!i!H1utn=E>B$PV+0T9K*Fo=A`qD&n@p;86f+~&!8oVd% z3;X+lmZ7a@f(a6k{SA!2yTWZGp`@S*rGtalLZk{2xaj~BYU9EzyLlC>p_TV(w%Mq2 z18&DFQN{d++dgQJFCU+!Hn%?KhwCVjmT^B@z?}iUcSYmFa zz1_9>2!vTrV_*b#zqT__cz)f!boWtbE+MZ`kBX^HRYL?|pz1Hj6}JgCBtwCLaFuUp z_Ivq5G#VfC4jDL|L8B=Vh6FVc?)Ob9jSS8nd=oY;W zJztmEBo7qFoDYER8A)GSv~bTLAh+$cbxi1##XtdJ5RHI3psXSyfYYsvn~5EF*dz>V?EnZ;=+oQ{! zR3>bjWS4N9kEU-Wi8x>hxIK)^P7PIOD@s7{@3Na9T#zn*lZ*og>I z37qIll$JqYK^?&ZsPcA~;@8CpDuYmhV>l=D+~BUVD)MZKsbnVOOl7uR&|!votar!C zh?=#)k!DW`v(YA4h8|+tdCTr=o2b5bq(nM}3W-#sPaI4h^kCV7OJB28 z8Q&S~ONDWy63OGHt*qH}%~?Ah&Lx(#U7}J?<4j}P-8Jc_qif0XexFe@47+a3#k@aj zmI<4k@qqChU@1H<_@A;B0<$anpBTFgMc8^sCg&A4b@<6 zirx`9c$kRY$Rz>=(LTOa_E~a9j;?pOEa#(orzIT7JLK@VyDt;2%{+04AK4L{)YHhW z*R_0)KM^>A&%IM^x(BidX$RxtYbJjjfNX9>+0hZ2B4s9hvTqTD&=*kW4FV`097Z&_ zPv6GH9*qRG{=oz0)}k{g6onAX_DA;)8UuECtV@a3CzmTWJE(nkCMC&u=ufj7p6*N; zvR2C0R~Nh;Ky9&(bZ;Sw9<*QKx+nJ_gAFEy=-)t&HSKl+9{0u=1n(BPP;&br$97XR zLgw9_(PHX*!GCUk>wygotoLxbED?cf@15@bC_6;<>VL{q-yOw5l?f}pB3_F6eX zTqkF5lKFp7CPH@zs&WfJdA?p@xE1+K_}gWUXKfBrD4J#3%f>}O8Y$#pNqdU1V46vM zFdEIdPQqgg#zVO#4xo4oV<}X_p@U!_AVVkuFPyx9gN8=Li^iQtAGYV3&Ox6)ABie?Xq%>1aE#JFqtewjK&4R{i7##yI1|*di^g zlo}lbC>gtByClcTIs1~w**yrR?{xE4*yNhjT0ca=c$p@DA!dc!&(zUv8wQ=XVoZsm_K z!F@4T86^k&Jcbe0onn6c*FF{CO(=~iI;5R%f@e&6W|2f@S7m?CBU5^T3iH3dEP1@CynLgX8m)3*C_7d2vN1JnOUjM4*#6{QQ zA6nwFs|Q}@YqQAx%ZoXWcGt+tZRswg8DnK0T_y${9;jmAcUxH;l`kjwNJ&f>&u|g9 z5vC|kip-HfPDOR(w%g3A2XEK;3LrhXMO#VyqfY+rU`gz0zXQ)mO-oJImn(xLb_|lG z_8XpPI;|zjFx>kgt!~UYx@7J_TiYyTjd<|phg2Ghi9pbjmg;V+bF-yw18GEwQ9MvL zJ)ncvBu;TgBYAT=y7CmiE>3^w+YiO*>-7%nbe!>oJFsIl;fUDbsec+^(!)|_F z5&88O#d6dhX`bCGCSV!hQHiR#UQ!=O6PC!jA-V>Qn1WzqIHd^_LC^9@6n} zvbG#VP)_0kQQU%ugw!2O34!y)L??T=MYp9taFLwpe`&!Ac~b;M8Wm-|y_trn0aP}>rQukz;x7@LmG)tHEzeb0;!I(I*+gKw3TavaB(n^Uw!1H|f#HlW>LU^L9O= z<>G`*9bP@)Ylh0uRV1ul12!Ke3wrSL953iV0HGAJi5*Fy42Ne=yR%s zNlaXq%wcsXYr;-uO_3?!i6g-R5N&%u!TrG%vzh!k^f;_*bgagwx*cROr5g}6x0RKk z!8LUXHNs2~0~54|t_GPjk_||QX(^mYf$fLAlh)F6+ZW1RA3^eGize+*FwYW<)b!_? zracHhtkLfiY?xggmO7FnLaHF&aMJ4P=|f=>7!zA&$y~;J=n^7L5EManydk^JN9}IE zY&dRg+X~};OZ)0zA%zj*p<3HfC2JB&6wE& zQhL6^H3$?d?t6B%C4J=f2YtvV*TK;4ne;#uasb9HB-P@)`%)H&03zC0Xe~gbc@G#6 zpvwW@eh-Vmz0jalh_kc}JOa!FwONiM$g62L05BWi@A=8#GN+D{eNXs`1%Exdl8j_C zMGtzc}KpEEoj1qe4FxrvTflv@y+eVs{)iU@c=Y0cdaVNLh}Rh0ux z1rUaSNYKR4SFb~q^%fTN>`Y6$jY?@*lzUS~PSQBYA8dDlYlT!ca=Ynzg`0OmP&-h# zlqKZjhg3R!QYa`)v>nXZ^azkeI>AEYJ2s*lrh~xWT^H=RHXZan7I&d<3kN1Yy0F50 zPNfzcO`iS%vL4`9`S^xUg%WtoLGpLEk4I-L5aeYW7zKpIbAlf;tT8yxZ@k{p<0m_a zK^sO&Xx#6{pvkei!+trv&CO`ZO9sOOA7cibV2`3sPD|H;O^3ax-;&UqZ>P3C((QFD zLdP6KQfd6O*XG7z(xZro9{>lK^R*psE1@{M%jGelh-#HAG*7Kl_P-PF7DwF_I^ zva9E)D6#yNNSmOI1214N0}D9-jFM6^!l^k58b8^617oc(mM19BpnZw8AcaO@yGe}s zUiK;xJM_x+h_8U6o7^0rTUZ9?Jb1uOo9b!BPht~1|3}&KIL?~`^7skAnA97X#EYfaD_{6rw&%o2H^8t7lO#ES2E7fCA|kt=GQAR=q#^i}QA2dghDL7+luyL{x}6qT1UafqD& zT4+n$zY-)+Cd8l%qLmK>%G+k{hTz~NIszA9*xN*|;nc1xl1(>FY8$MnP;wL-%mA){ z?xZv37WfLUKId*l z0ATL4Xr4CglZvhLrZ*R`Q^P5H;90?;H6wOcZ*fp1-YLN%Mq_bcp1r*7El&99=t{G1 zVD3S-`NM&zZdNh3BNe69Y{H+Fpub!JixVZ>$-Nvj*D${xNEzrfT|z8#W!~s(MF^6R z<|^?Z9Ceo%Mt+9dOQ&w=$^zfTcfrsb^`1L*yrfQpQi^h2@Qe|}?iN$H=pYiXSU{6L6$upCOivAMyUV!;s6?LsgUY!c=RrZhZiO=vZD=FwZPv{d z!GS}V=5WD7b8wcyp>G3ooWPgF862ER6o5zRGVbd}I~d(2BnK|{g_R4UJ7YcH))wbS zCsf#i+|9KH3-JyR$~`d;ZZK}z3_W2}l#YBo^8EeB2D)SM*6Fw%7180%|w`7e$i;2YG0yL-;uO#m;X9B}aKD zwfu=d!}VyRtdB12ro*P@Y*HorMDH4un*6WL#ieq<%-V^PKF+%_2_6G&1fw4rZB7g3 zY-CG2_9FYe2P*b4P>yC&Jmf;>@OTI&WJ3S_yb9coz0%8q@*e`=^g=UQWT9Uv4^Y$bdYuyl`zDACVK^N$gQYV~@_@0Yff;#0hq=*NxQ z8rXr@_Fzw7m5fyZ&japMiTeU7GbtsBb`^Qi76M)WFV@y$-dDfe;y?`M3T^!bX$tj(9LL_zLugJ#+tYZxOUA@a2;Hr9sQ1IQV{H*k}? zyL^w8SfQ%By7+1J_e9ETS;Jifrll`+jA0IHZiWWMRUz7t+!T7rpXSC@G$-HVBF&(Q zW3@_ibi!af3QbJ`AA?0Oof;*2l-&-OV}AJ(4(7f{+>yW5CFFf*R5}efdMq*wzhhO+ zGeG8f0$dKJF-Y$bJ=bXS*s6RcH!IXeHZS+91x7#}Wsm6%0&Yf!>!6!5 zGhK~dc-3Yl)w~8SIGj&j4QWob(w^RQg=UF88`{xn2mM|cw8QGM{tg#`@t+6>Q#?R7 zLUFXTr=5Rpu3anVA6|kw==8&9>6T7Ew$5g1=bz|5T%A8cieuH>zIe$v zy9754oKwU>$@MqD_v~a$qZ2J|KLD0{KY6(;91X4KA}ASdKLb2+^)E%{21F935O=aA zN|2*EeVdneM(W_>oCO9qWdpMlEYQ@qJ*^|Eb=;3P(=B3;9(Ul~0%epsx!@@A(X5a$ z0!Hdu5(;H04y`QQcis|faX!`}XgL~l?)DQH{zn_3Kvry*KT%vfP4aT4+OoZ*`6UVD z&Yp|zse;5YY#lQ``YKNNjbRfY;JpHOT1J%J!JrThLU2*e{fn)!PI!rZ_`^%vhfQN} zBcUkg#R!v_ROb2;ThoYrb07emD&R9n1XI6DJ|`cEpuB8m|NC4^6E9vzg~Fk`O;Y+i zLms}eyM&68gRtZW-k47)0F!-CQRW~Kpe=$4_@FaIc@4;J+RdV}DgdKfG)f;BXk-k^ zYY->vU_TWgwbNfm>6SW4fn~`9lNxJnkb45Z7-U^$E`OK zyg*>`sGf0jlgYFpbp7Kw{vg?$nmAARYQ%Z@<0#a1F59c=J%_%y{I54 zH|d~|skiC!i0|0n^s6^+2e+9 zV9f}|=gSK|-y=&F|I#)}X3;{-R9fAcC2Bx;wwXO0=F_NhPi~Ag0Pz!1Db82py>X@m zfSXohr}m!)78O84$*l!bq z*MkC;#*2=2ITH{GR+y7kSS6Kb)@-BtA)+t94sMapJYQ;8D6K|1*(@%u8;4f!MmgR9 z6`yco)>jbN0c0=_@y&s)aZ&i20a5cRdv+5y zJnX8ly}0L#4yRb-g9R|CQU~iqsoxfu<_vkYOfj5NvR}@OeDNAxBr@4Yjbm_bHCOd$ zk-@#v_kN;i%}=iXb6Wj)ofvoMP8uhq>P8{tYhz8od^z!yMs-`<*D5KwY>8d!VK;># z{s5UQy5UI$jp%LJNNr$`1e>6tDTXM571Lu6H zs;pf9zdru{O61%nqf0=itzMSZ)8&oUa_8o!?#P-82imGQdlv3sTfGZ3!%|oMz&LtR zWSTa9pnNde;5@rW6LSy;KF&9Q@Rbl9F0m{*iChzjlq1acUL4ROjOr60Q^WJgE0w<{ zbSF){`91mhe=ZmD4jd+Ek)8;u*&c(G*|2p;Ku`^q04uR@K`COlWLYZdr*Bsmkwqm5 z3_3dgczIAmL6s#!4}`1XTt=vDdZcK8#_S(2Q04DJl|RwZZlCS2-W~o%n*f`J{ zvRXJqYF@}pme*q@0|xQ}mLZbnP3g3@db7zg1+9b9)4giESpW(6y1(w;?+-_{`*%|^M+QCX1=>uKGV*`_%W@I?TEE|NT zz`b^eLq`$_yj09^ES^>T6ks9ufn`}MM1cZ_pMU!}tHs}Zb-ci9-l<<(sIv$}nd_GW zMA^S1RY(WHLREB}#8KOW|8cGj!hrqvLOX!9%MZdgpFg1IDwLL#eJf8Y^CoB>>Ais{ zXt7BD%BNG2E%YU>L~Xa0uYYj5KDw=ZP;TAu9m@fe45+P0xgI9N!Ue$dOm=&Wo1%XULBXnu1$)` z%myO<$b?<-D?7lQiHHfC9SkmJ2dMIDVw`EiS()p}?VYzxnjvmF6>ev4hchy?CCWhw zNQHlBRc-_~FUy(`c<63_!D|tCTQ>)=_ZTV`WA~iMvw%z?FIahEuD5>{GW~YoxSx)% z&_Hft;&OIaaxn%q=$5)ARL_rjaN0&ugB=V(E0&4M#aO}<3x$E<5RDPGAhF7Ng z@{>!6)HrEAr7XDM>!En{BVpy~N$xL#)=`=M#i+F5sw6+s!3)!-Y!iO+5CL}?E$BS^ zrPAXYSd-(TkxcDAOw$tC0OY5NqqnKvw817NDDg8vyMnHrUYQrrK;y$_(}d)k7ApJ| zYy7kS=i=M#FR+3%Fv$M}MI%JX%W4M!0PsTx0Fe2wLeWf2osFF=9sUD)PUqs`^1tMa zYi!ybvLX1ssviN%0V=tlSIc4<%vZ6mfpo@!70yKc`l30_bCtu03Q9wNn zufWXecu2sBZ%8RF|Dj=b)GfbuQzlK1p0SCmUw$#5NJ^)2V;S?Y{=2%KO~Yo@xjx8d zN}4-)Hr1?VJiXol>bK!HHYs&fPZC}-u>RL;u1T4nOvEKfm)|ERvH?eYmwM`K@_UO| zmU+BceWrx#jnnFxk#ds6|BYy2Lt|rp6R;vI_~`36ed= zH$(P`YFB)H9{qK!%FOUFNeki!YIbBfdg{XRg1n?N{s7W)zLzFZgP#W+n3-HfouRo0;V*^hvQc5gl3G-~P$i1; zrsz}HS0;@VXEOuEW*%5K+UD6E%4Un3U!<{;i))*z4*G@M7OADuH6IzLOR)zdMKObb zy&XLaVkuy>Vw^?8AAgoQ`!S9n_QZ)ahx-z}$R z^s)In9dqT<;KF)k=8}CA4>n^hFtH%S5sUMGL>|fR~Pyw6CZRewjO9& z-0mV#9L_Y4Ou>Id#Slyt=^+iKLoF&Z=1$K<|Ko;Cv)`?1Px_{)9q8?f+hR@gZ7ui# zmII4uPTV_QbUEMFN7*J*_+C=*Y_Im3~ zDp~WE_IO7nGOAwMqUh)i)6V9ORCOhkb{dz*`MmW>^AGj(`7kzQxZAyso~XE0V|_hJbC^&rB9kR9-%+t=1mtm5Oq*_Q9JL zigJ$8FrBev22U0KSx?UN)*KJAdn&3*d^=i2#OdKY`)yv{t9-pX*{!dYn7^J74!#(lu|} z)}9I~CivGc&?A?s9?7y%wsKUa^Ocik&0%jKI1Yf21Nd|gtqt}jpmPte4IVEbtPX>w zq26G<4o8EI20jIzIskRRtd7&h$#T%mptFv5jqeJ;4I-?6-+_h$Wj^q5;C}DT0f>W8 z2P}24d2hpkk`TQHd^HTa4|bo?11AS`HuSWw(1+K9uM0pABDeqaj`oes2fPP5x8!gT zelH9H11Ppn$bi@Zp$&ougk=*zFg%{{2!b|*O$a98Ih(g>8U!h;@o}iFJ$hi1mu~3DG|b;z(_fem`7Tj>O3*0XZhi<4A;r zoQzX(JVM%z9a@m1APG4)3*vZ)f?SHzaRQf3Z+o2ejwY&_s==(%hKX|G#&{vczJY{p z$)s(AS9wsYqREbJ=VVrk#oV`<%CdLfPC4_4j|t>C?&7(8SjCKQpJ32A1xB+^!zb z0DvGrzyJXMZTKJacOtGKWfuhifX);Efc$?o05elV7gr}!ePerPm;XJ}a$8q3>1f2! zZ&$w`y*GwSLGv|}0uL^WKJ)iI6sD1C-qxr7S>q)12 zt>5Q)Ci|}6b0>e#`$5ua>n%S|&-ecM?5zL$QRlw_syvA>9}O>_tV?Y_q%eBkJs1TnKTr49-P6n9VVrzwPh;;{RPRjz`$Vhm6KoiN zGY?-k&#wdA@4FqppU20&F6aAwsdK6Sr-n&9|I2+T`>kH@=llEhq5Nj)!ae(>w^*s) z&)fd}_aeB@XeiBf_QUdKFMoHJKi8ck_^inN?@1Uw6p}VIsQmljWalk@um8{a`yjvH zFON=kzt*0To~U?Cws2_*4pjQ{p0JcAC?{${zjV-Cs&UGXNRM&kH=xd(HT8| zT#6Z8*Cf550Wfvzw*W(n*Ed;S>9?632IFB|bif z3koDX9-sTi+q*xE$sePw{_pSi$F=(Z!e}3?#n1iydFmYXx1n(1yL>Fg&)G#6emXfm zKOgg~-Tgj3zqITk$5cri<#o491%!Wpy+1-UUx=Z}d_ z%cq5ozvaX?)~WkA{Uq6EPQoYl8+roc_f*ekhRx(B?DQrJS9NHAGO9D{J`UoQ8@Qy` z9`%Hw+|N$obMI=s?2LW>Q58YUgtlS6rv@SXsGY(MMjf&&Jr@kjpf3ZRuG&g;|EU$w z_!0dQerbOo0+2CI<^_`^3NV@A#S;?3AzxnrN@gxJ2huqv*Up`;m%WF|q0sh1vFAxv zx25&RL}~UQ^r%C3+!jvn4^^6MVD{~MtjOjEbW8;Dk5Z;0U87^q05m31j~&xQJ$!C6nu`=3E z;cwE%d{rRtbOCx~xoUzvg^GnrLLb2;!)%-J2>inGupCNRgvg-vCR#Xye3UG-!J19w zyRAw|sid`6-H0`){CEYET#!)VYEvO=IE`P0sPZM03B};nREcyND02@}2__>SY{oKZ z*te)f(U$WC{~CrVSG!ZY$h(})qwY?A{BfYGvQrM6!C|$9JnI=32j%kg5mw9!**VhS z3p`r%Rs#;)S3wzjXcbkybAg&|8d$7@a?4KI|c>2qm1yM}5g%*!bM5rYU^ zGM*YMg^DcV?IuPxvb_(`;p9iIkSjMC)Bp|%s%Vk&jH!rwRL3SIxNf|2MEgOUOm;{6 zsVOJdrv!5L4%UnPBVw}%3WOLbMxlV2FixaC?RD?t-{lQzh&4wJO6l*oBJq#elh$7w zQrZbI%ZqMBL|>1fz$6GMizHyON?G@Z+zv%lEULB`g>ozmRX{vN&r&IT+sK}^yqi7; zz2+n6)HBsZoiE~FoolT0AGstL5=6)7J^Bm)C3msmmcA6>xk`Z6Pi)g>FhgQ!IZ81@#v9e> zO}L+BgV%RrK8W&CB6-9em%lwqid9%*^gaoVqkG6*5Vp@kv5$WA!`X&F_Yx=D?vBPD z*Bg+Gig?(`D(`Gb$k-i}HLCS57s0J?*^Jc+v0=F`l$M30_ta~uE`gv^E>6e2a?==S zWV!k4uA)QPT7y(l!|PNj6qt0@D9OI^Ftw~DyNqo>FAsIirf6|&s3QFe;Om}B=U9@h zQ%d&_7FmW|xyR#k8)c$I3k~s4zv=#sqGh0(f$ZPT9gB6m&8f>`tU<7(swrG_ZWYIdQla0EAKkf5BuT5TQCMfBfN+ zNOe<#i-JWb-fSv^Wy2~XRW(2m>a7il$MiRs_jSi-y5~l8zPXy4&ko>&46#UIbDgDQ zOvkE&aU~OgIe|?RCVH<@Bh}bo`E?Z+!;qSoZR(stM!J5dl*RjiYK-R?!W7~YXgU+n zYs{>0VQC|3|7vMI<)9hwm-WYx{)z$IiwijNI%Ok+ynZwcvasvb8EKuV5GvX@qbt#R=NvC!tgVs9J4GAp$%2?|S!G%fxmFiv(N z6%Fw!d=;1Hr6UX3<%h|bR%Xe7lij(p;1|22nBG>N8jfA_Y)88s%|eH$3${7TzR`g< zm_rpZ<(`DvDvHpxpC3^ry>%h-7bXfAFPzig)cE~glRT?bl>~OIHFSpb#1A-*6f7L` z4~7$a=!L6lE>um&#%C{7-+9E~GOJp#^G`sm!0Olfch}G$b152aMvbhNHqfh;27IDQ z8up~;fB0TsV#DwN!v^Qmn+Qm(dG9vfz6o>Z? zN}`s!+LDO>v*N$!phVV-8yP;PST#gd1!i-z`UC#;@1-b06b8hfvCkBfh8#bytMM) zigEH=G%11MvPz=+KE7!T{((8K`WmvJn}499t&$X`3)(@cHerz@iP%Qwt>`42lt>*D zpt4gMB&={GsVrCP*uF~Kfn|WY0Fcp?S@X~Bt*HkLdFx^!K%O@?;5ib})5g!Rwn;Y{ zO#AB6D#C5r0Y%w-71tEfHO^g4+OG zl(5YXDp7(~c?fF(T7(0@6FcEd_0il|nagLSqDgYEOrF7fJv`9VLET`W)t}1G4ADod z5&LC?ODgooD3$3(LrZv|Z3b0n(=H+V-^2R?DWX(KR5M#G!{FzPt5BG7ooIfk%l_yP ztC|HBNLq{P)9|qF|9w-y)lPDz3c>|m*3sPb!}2gvVD*njI8v-CJ_RKL7kbk+bBjfN zO4#Z|QMW91)Lb#`M3X?F+DzV~tTO_l1v1u5;Q$B*JZIY@qEq{;!z|m`GC3uxMYREx z;kC_}=WFP2=VpLNP7X-@@Y6`_pRWKgF-B4WG8Jp*Mq8?q9H1IQs4*=;SVR{Os%p zI)|peVj4>TM%sQQzb*l>DV#qr)*}}4%#!xOHa=`QC5!Avrse@I=Pk%*c1eao14j`{ zBV?OJ4OY@-$VjvrWHbKMsW1~(kkZe93(LqtJuKxjg*@OF!r-C?OVmy_()wgbMN{1F zN?5%-$T!f=?H1#9XVV$I?}a0o4#2OZ*>`069duN&ZitW@u108{AEuZ_)PZJ2)v4_G zgige@*Q_B}s#mXrW*lU1%2syexm98PI=u_cP(9WWIMs6DMA^G?PqP|;C9-}`%nz=p z2~!ck7hGPSS|M%SBIx>ZRH79Id07~4iDLxSo$ORxn!Dj)4C~6Sl(BlXC&bHuw&*K* zX-XR9k+CH>EYl_4kffAg){9G1g~j%V6OtKe@!O;dbs1CjgZ1rZU0kUW{0!|)wz$1k z?HvZD=S>FBRx?)A&Bjyi9pDm|v?r4P1kXpYk@8`KMyZ(w_mZRIFHlfsm|G=y+Z~M8 z5+dEEI&zLO&}kZA+3MNtcb=QV)%gh%k&hNbK1NC2bTToo`Bz)1o>l zMifdy7giMGDV8y}S|pt=EgcnI3|+%{uFY+!Nlqc}Fr;eWW7`kCT1h}vBW*i4CQd@X z9bnY{5*Z{krRDJQaCZYiSgOC{P-Q-F2i`>>Gg6e{-h6g-#kek|leWiy z1f;f|cb18IX=qjnCJcDg@i3=WwcNg~T`F9$+DfY1pdo5-12zpW+gK+ra`KCwg!uH! z0nO`mt#_=UTW0^7<&<`TnWD7y(gJ%~tj`UwrbO*QZBh}WRRnUHA8HL-r+ol>c9h)u zV83;wT-T&WAxnNu>rjvKwLFc4Gn9){x;RuUNB^RkE+uZ#3htD)nz;?CxOq}uHxDd* z9DaK`{&eRPT-|+PR3IY^mXo>oEF;m0THQDGqzt?xgD+ff{TU8myd|?=d&(Jt@Cuy{ zA4!nUD3FSf+2Zj6eX<1jF6bZ4W>y5er3cXaidn;jzgU$3*ZFS#NiGwl6Y7TjF>#*X zKz%t#-+#p{GnE2$I}EqpONZ5BL%u-^w@uqQ#89D0fjgWEb}?sx^Y!k(yGd35cje8K6&>`KBAk=Q~oaV9XBFGB>y0H@8)2Y366n#km9q z)|kevBc5t(vhqBw5|{NQy6IMDO7z$(>5)_-N^x|r`L+!;J$H(@rBX2b+;uSQpjEE3n!~UdQyxaW{Dt^Qh45mfS9xoD=FOU0nemDkw@UPDK`ku|bH3P#4;928&g(mnKU-Si^H^TL>;awcQ(1 z#;qVr2n=Loz8B4A{W>d-YpttY=%z@G06HU7G|*M>hp`HO{Re37{-EBNsyVAqXYq1{ zm2DK+GCU_ZE-kO9&x_5zsek-D2jvUw791^1$#U28`SR{ekQNcJuo@9MK)4$jP|_nB zBCH<5Y<`Av9@kNhQXvmPO^}2bKQJdG?k}^pU@2|YISm{r5*BTTgTr@WsX+=ey#aA6 zNA>_S2`+)d=#7VaF-YfRxY;~uKOc#610C|+45`(E2(((WGsA(K94_+HfuER&UouWv zO_CR>c1CSd71kt@Ah~I+J#A26QLD1ROJfLQL%Z>G>$ArlPww>l?7G*}!bCe?tjFb4 ze;F@bV@oR^D_F6ZSIVxPsHWyJ=%?{&K^;~%m%+J5ajiq01LRSQw&qts74@;fSr{im(2W`x%B zO6UIT`Mr9x`wdafCTT9}yceqY*Rkw;sl&d0HVB9$Nt(79Q##0khIEYewH2{iHfMrl zdcqE08|>ZuT;UT7y2D-AdQn3p3(t&;*5 z+L_}j5LA74K#K}E_70TQjmMzckr}Y0Vn8ujVi_4C+6&Oquwn-Z#F4Zj!s){TEKB4$MXJ{EQNxplGq)85nsDA& zVT6(2v0NqlJh%6&RxcLH7pD`ZtMPg)N%7Feun9=O17ia31Y`-Oz=xz4g-*K%!Qw|o zb`0X~swl>WWAII8b+75FEkugwfBb4p+e2{`XO*Ah=fVzd4lEO(Z~G4O8Zd}UJFQd& zk}^?-rRCOtKxD@~-V~R=fX4X;?Hw|U4D7xkF7#kGFnGP;Vs$~R^5p|dlSK@5vQoz+ z!D8AHy1-Nb_0X~Uu~alx7JWdiV_oxsFygB|YFl%(c^UGQe5wVS zbwY^;0BO6z+&qk(Gfh;`n5ygmjvRI#vkuP=)lx?n>FHT{7DDUzpA7x$$oS-0j>;(j zo0Q-HBo*?Nk7_m|G-?_bxJp9jr1RJjn+)N#25)v$fRkhcCR>_GQi3dkrT8qNav0NZ z)7mM{Tm_saZ?_^X|7KUxlnKj1}mg3iHfLKCNwkTUv1woStdd3dyED`+;fjjKq(YVEgjv;g6!_j0hKBgn`BX+)-v(Hyw*s40OH^E*-tzDMvItH(TdVb5gN1;usui$3-Br*FJNnmm>Kp^hOZwZWv zdm;kOGdQEfONO#d767T-DI2-7A&<%i8-m|Yt}YB|s@iMb?o|itQ|@?Ynh!%2MFC8l z+Mr_BMi6=a=r1nH}fVcvWFE;aU7P5@6#@Jtrr^ra&=N)kT1FnMX%I zVA@-`$Qiu)Zi#(>6)#WTF5ot&-qn1kZ)Lrmga9tnW^nB#uW+-YE{=#D6H0k%U`W~s zfV0OwL{>5~@=zKsaNg5BgRX{|+S847{#^uWFHJBGfFmzlsW(fLZDP{y*;5$J1(m@d zYVpEC4+DZBP(Krsw0@?ny#rtNDN$sXTwJtH==PQQt>(?^dr!@3vj-h!$CADghC{)w zZ*m34NLSJE9>umeJ@-k2iyP$obj>B2Ep8FZH~Nh68aexcelzOaOQgHwky+td>O%|g zshcW9pk<{Fn!busJ=_VRX1zI!8s?B7cTxCV1e3yBFEkf7f}cs6N{~T}cXQ_vTNumx z5eW8_Z4gRM&UsEWCirB{z&Fib1fxStcNY5tt)rc$LJou<;$}N+hmY>;3<;j1jY>l% zUtX&tyhoU8qq%WHsBYK-q2{`V=L8!IZl_`V+j~*b}T4Op%97`|}%?AU`;jq<_cU$jaE`gA!iCsU3T|dT6BvM(H zddv3Ere|eO$IQV{Y;TW%#M;Fb{1-^H-)O#-om25Fa{;p^9WpUSZ-OW@{G!LQ>@!guu< zEl)fYCigSVQ^)?D9hAy}_CXvJFZqv5zjI~;bs9S^xS(07;kV)i<{Ck|^)(6cS;%xe z?lh7r`1sm6hVU!LtKj3d>og6dyJY-HfN2tDe21!pRt{-oViMn0v#Ml#<|_l{AFJs4 zBy4Qt5ObNY*W=UP{|Z zS*=6Pi-W}|PfXI{a@(k~wfd#qxWZ>#GbLg$)`I56)QZ$gdi#ra(n@9JE{O6I?ZTg8 zPaH`E9&K8m+^sjKroU6wyuKUe-iX27wCC+ji;e3)K z1f6TnEV7Ne_!zvycq!nP#>VtqR6u~QOdMyM_hrAWi1q_%u();A*p;QSpfPQ3Ft5r? zW*p55)Z%G+=~1EPzqC$duxVconlnp4z1(hU%++Z9X<&-0gROhB#`BoH&DY%4WvwWK z8?#jRsfzx@Om{;RfrJ*Gp6+iwbOiSG(!3sGiaxJQ?~?c znh%S!G6z16H&A$7A%xMAWh0}_^?nHVz9<+ggE(HcIsBR(&Z{7{9-u7@QxQg z^3Cxy!PHM?euUuYIB@Ga8cBvHT%U}%Cz70bw6~EIvB?>~J73n#Q4LJS7#Q*VXtD*b zM%Xi<&FW7LAH-b6dj?GUyRL$T=keG1Q->so^}NhS>>T>%GjVK6!KL#eUC>3S&G{noabDtaU(li^IBr!niO z6QWnrkTg%jHYCNu7LgHmpp?g`4W+ceb)t z1lmuTlo%neGU+Pi09(qPbT|Y(-}i8Ym*&m!@H3MJ>&u$jp{)Iog{xgh3KAF3xY&F+G{(XI~fFM z_+SH_O+6rns*gL2fRb4WJYLIejT&fO;yT-huZhOcb!Lt7ERs5|cAi%2002YSfd(<4 zLBYHiwXT$|Z7RwsFi@vF;F5+Zst!l0eA~PU?i#6~`|0eqPmm4NjGzb|)+?QZ)>cv; z(pG2`^o*?rI0MCVOL>yYy4>h_57#!ET?=wVSblfT?=Hgg3CWX41n*>wE84ePCNiY+ z7z+82C8?`}|4EhI_O+kjYKW&gs>tdsWTgz-IU67>ZbSE2OZkFx+s5B1JvNGaxIp`i zp+ln+0d|FBwFw@<&{xVmO~3hxC)Lc&%XkVYkqS_*Oox`Qy3F3f;hQw4Z;Ixz)_cGm zh<-<3CaSi9Uh1XHdUa^1`${n#8jo~43p|G`b^14)XkN5;F;x^j%Petsm^k( z1Jx9Qe^@ai0=X@v*~J|XKN=HAA{z>Iou8K6T|Ok0^XY2jD=iV1?){abMn9=ty%IOj zDOBZ;8iQJs#KcpmL08iDHiVE!YfujL71Vm?WDcogICTKr_)u% zF6VCjZ(Xgzjvd>3iQCoZE>~uCXqh51yAsI0q!^^)yy)n^4MeH!)x2unb-}z#O06u1 zXAXU=CI&G!h2>zV*gI~SI5Wf;*5MBg82xsY9IEz3Q;TdkUakE~=lv>6fX~4T*%Ua0 zlJtRY?L}cY!8K~B1n|(Nrs176CtJ(S_>3YRbY;zS0z0@{!bDcYV|0_Ox!0ao<@G#a z;1p3A(V6UTihYrjqKfL5;C(HLTP^a`o}a({&1*v1ZnbBv0xo4zF@fp*1(DO9iD2}_ zMxv$%7Ql7jZixf?5H9>EM0v}D@730`ioxA#arZTUt%8&jN?S7hy2HLrP-_ewfMU);$w^BMOGI`zwwhUOx@ubf;)osYghrm=z0I}@cn=-7c z9c#}4uy4(5)z4zMEwes~AjygNmnvv0XhV!TE5zzt_auF{kxXgrfZ_f~@_5ZuQr`7} z!2x;cyRmR|lj{k|iVYt-dUOv%5m z4|RupP3hpq_|ZfrgV7P+n&|AAruw`VT#L%S*yiMvB?oM1=*7?BOeHne) zdIpl8q5}+Rf~*^HBN^~wy6BJ(JVC|G+ova6R7mT~Bu~Z`h|rAdtQr}r9~i*iYB-1* z{f7*4tO9IvB$lnj7HEli{bo}*)f`x!*k6FPX|}~WTV!_KbMC$bzPS}s=CC-_vWHn+ zOhVtc)}YD}GSq0IFaq=-Bw>@FrvS_Z#~hYRG)-!=I~?s~+p+)C1>Y2=kCBPnwC}ak z6N(2LcN#bbXGbt%%_D_zG$xgwSlx`Rw$%bvbsMZlQm9K$p&;zlE_O_Yp{v&WSFMXP z-+`vU!}cS}eD;vuMR^3Fpc<5+2BRUwfLvNMGy%*+gNS~@VY2=L97eu%~OblOs#FYY$BQhQd*f%U@CDSyP;SCAXEL?1f&-^oPGu zvw*juu+*edrjzsA+%FgEgXcSLXy&UXH$zX$9a#)G~$y#nT%y+ZX7Ru2$Ze?U|0(AoSdNH(7zly8ZV*Y*jRc`3k-UWuRV3 z8L~sz`Ke|}76=q{5lSfUm*m zeiuWLu&|NX$MYb*x)5s$l|5R-sL6&{5>;Zat)Xv>c3=-xEhbq2qj-&sVT6!rZxC>2&8-q-Jtj?m@Ev9w+v z&k68OE;=A_WLZBpLtN|dbt)ozseqH=DBhdk&Qr36U@_h_x>y`LYthnb-U>`^w>5b1 z0RKuKTJ!v( z?rD2?lN{zE(1G00m8&{kxi!kv|K22j`u(3p@TA=pck%y3@E3Lf0LuS=i71_1?VMfy z=_&vBLdxYVMf(E|ls~)gsGjTWZm8Tv`Szwz(Q{3fpyyY{$4d6rtP#|sF0ESE{<&#{ zxGWWHv~J?B5)c(YVe~Dx?W_BVNyk?cRcvMHDGf^6JAhO0LGD-6^XVY+B8~Q~bC5)<=a+6I8n5@5}Et?Ht?wY`ZG#3p!uT zYnCkYy&r}cnyQlB^@C-J%P;ryV`8cD4{jLMF%~yr2`#S+U#+#SCAUHou3DwV78wLm z&XiXpAzUK>R;Lw~D!#9($(aP;Z%kMIRNnYfUrHIxRn=`t_)mcR@Mq%v{vm#&KZsrV zn4993OI&>&zL2tp$JutQqL>1%lv=49f{_h#?KLX2Quf2QOvCV&@WbVG1qp7{SLVcl z$!O0>EHN;nsA>+@JLs3Loh&?y{c7h6XV{K~Ql`1Hs<=wBU&u%-Ty^vmt_Zj)ifrClw`noBtx&_Jl1;BFRD#> z3T8PD0HTQj#6sHf7R_Za0f+^riU5AnfQDS55SYao|2^yA0}eeS3gnCfbdgJ+GsgmU zh1J6VK41VO0#<23AbtzZEWD)+QM@WG#8HVyFdjZJ zcNP}uk8(;Ojd!nR>v>M6y{LX+$o|Z-OUSMBD({S(7Xsd z8Zibu|NPz&x6wxtZlwyg(F>OuVFo+si~Vl36_O+ndr?Y~1nEg3h3-m2?xzBjF5r-g zG($NsO)Cn$k%rw#!|kVjp}!w(d5IVuf*vorU60VR6;O7*Egy*0bq2~&jNCe~vibEnE0{8~UCW_p6 zPO7tg&LQ+C3xX(%6>RyO0s)-4yCx%O;^on=Dhjn(f+( z`g`MJt%q{PpdAIDOQvmnqQR8T%lqENC*5@hp9A8C3O6k}g}Z6N&C5*^VySY9KvRTS z03IVu|HaumU5Z5_8X?S}<`i)z{Z*a*>z52Ef+tUq1yLZ2wBO(#i?qMQC{OT(v_}*f zi!?}2NY#oMQYqY&MWn;EsF=ucxGD{a$O{ltf5=hdn9`J*=oif`^?|vdxDe+kF3CTl zEfw&X`X5UG&NOgIL68HrSp*gq_1?bIe)xTY{f`RstK;+*NeBP{s(+>-h5u@DG_*0d zcd~S`u+{&M^hjsuWM=liatXz#_uJ`?!ft<3H=WDDa^S#mHbyOCl?M(1;&{@Q#cDyf z{5u>~7ya!H6MpC^B>=22*T2~{m4|Z0nqr1QA=i1%u?&|G+yWLaw_&grI+vD5! zZt-pNZu4&GcX_@(R<_puy8Ucs%K!R#ba{W=y~FQ(sdxVPy#Czp$L)B3on-R)etO;K zhQ+0#%e&{=iP71cl~zzdvx@?KO8S^e`fmob#!-m{Qi>@4jvB=E{1K6{vJH} z`+Gh=fU=iNzTp1oEx5C+rY)6P5r65^&U zuxb@GB#Ulp5vyZ%2!jxAtz>bf*Jo>9G7EcL$M07nsFhl7mq4XgLl{OhmgN@1o;RFm zaBU{mbEH}(B)rR!`D)nnAnaX^Qt`57^ho&)*WV4OXRqiim{2T`+I0(Q5s2b7+OXo( zN;0F>$DqbxB4SUmdFPIZl^8dxl~$^gyjI*HmQ_ zN-5fA@a7vNdr%fzE=sdaz_j0CBL;`f8X>g6>ze+9V>PR*BJTVWq^NT`o;IzMw5~`V z0t6Is$Uz41aw!s9N(^`LZtRBM-a+swVywkR%)qQ2OW-3^gQ|qxV$+du*se9SC+Es~;kOhV__Hq)kIS zA=KujLq_9uA}@N63s*5AoTH|U8KdUxnyw}_j7i+r1bX<2C4MWw<4_>X>N$G-3l(<; z&Jq`Pr%Y+mECJCMfCv3GpUV~%eFJLF1UO1<(34O&WKkM^eL>|7mU<>~S5N{x{I~$U z(gf1fjtt@H%VTWbQ4wZcYl?)-3=CI9D=blDEDu3ZJCZ9RT!_Y+I;uK!FxnkmIGPbQ z?@I0K#_<5LFlN5a`l!*uk~4|T)m9RmS3nyE(?a>4at}MR!^73JKTG5_dCuwH$c7ON z8;~9ZM5B{$#RoEER1BlZY7M5iAHan*>TpV-%CHmaOymq0>@Un*r4b1gA6q*4#>T3W z!Ira^%0}k6LhfVSvB;^0$hmx}MJ8aKoojro(*Oqq2{f)=vy9xC7={^uphQ;Dbg&O| zc+EFUjCN$CoyhoRq^(g7#97g^5j+jc7tqn>DUuh&aE@s5Uvn5vfP(Gnm`4JNOijB9 zq)i5X5~l5KMdEU{jQ}Cpl5E~fhJIvN*S$yh;jIJ2Oih^&urh4KN(_{G$5~>uvQCBu{N7sx)?EOTDS#Q zTq;f*k8|D)?ZtqRX)6c_0JLF#Cvz=*Sbhi!$PIxF`CX$DaBOQZ)R2qCEGWumEmo2< zTWT2C2oN1&S9%R>g;`|G-B_U{U$aD0C#uuAOw72}EHobo!q)lm-zL7uh2xogR})b4 ze}W8HaDsV_*_Lipyn=PuLF6d0VgoF=R)SF1Mv;7A&6H@FnMtlPWaZ0qhlwJ-`FbK@~lNQl^a5b7W0_p$$ET+U`oWg<4RrFsS-Mlu+LS)nPi z(z?cDQo*{{8^Dq`ozh5`>ZpToqF`>8WZKnY2Ih}ASYs*NFO)&20M zWg$$nq?*^XC1*ILqoUYY(2pULLDRtzDiC+G&!=-OjL|>l+{@U&1&dpbTMApxl9WGT z{!ZuhxZ*9E;mUIknV7~Gl_O=1W)M@ex=v)h@4r0DLa?K^9kJ$p(7t8<zxeO$Is}n#}pmN>Y}Z2 z%j5R^&Z9LDZQ2=wR@b?nAwd_V$Dg}ULv!aQhoPIm(6$3Pau9$Nf@TW#qrh583!>x7 z(w*#RZ5oWXyO@NuCX!6{6@L<~R$*iW+HN7BHv!Ki{^O~auHA?{-Dc53 z?^@Hq5!Qgsl;@%2dohiwj8R?vuY8deGsjVb1qDR4_1vQ)ZtkQN(PHlsaJKH!v_Zuj z!M%NvCRSQH>O$qfd4hMO(XW8w^yd1g5P8HL){T&A18yataZ)9O+W%5Lv(5SMTjo~B zer%pj-Kqf1fi2N7`8n`JjU=V}PP>Jl{7=wVV?Pb6)MD+#VCOd1^5|Dhp@oX-GZOzy z83hU8U*pZ+t9I(Aot2XH>+t7^7{mZ^hc~BD=m{m!>1cQ6Q2S?FUbJe_yOa!YSm*^o z(Pk6RO<#oTir0gwa0SmPMJS8_)`Hq6KpwHW5T#MLG}m{4+GC1RzM}T%*TXi;D3)4~ zkN1ck{r=SdKejag-L+Sl>?{9&ckK!OKW}M_3~dbUj7?4S4ULUmoeYgV^^Gj;44pjb zjBN~^oh{8QjSc_9=B#gGYUgZ9|DSu9Ow*(PwOHqat^Z(n@#`bn* zmgfIL{Eu*)>5QG-{xg_?_Wl1km@VD$#6tczNpFTC||#^*HzyKKPmRqE{sDVOy!iE#?PZu7ym8OQ-^oQ zoAp*!(_Y1D+kFSvxF0I&ny3qa^gwXnDhs zmwAy9dCZKN&!uk3m?Zv;jUUZJ@;4t}>f92^7i*`!kOQPQ^j5z?RfqA~**J-~IEV>! zD_!$eS;r{Zdj5e=oR86&+|sW*>yHH3|AV!2h^{PH+i;Rj$4)x7ZQHhOnK0jg{7TlM4>lCu15{oX!q^RL*t zT!=lR|AH*73Mz(=bxQ(ScxhkxWY?FXp65Q@+{Y3fJAEm)v9={ zqJud9v@+YAYLP0C&yz!GUh@T}bzQ3r*|~*ZCuCnIBCeepjpz7YC-OH;U9p5+-oKe_ zq)y$M8N{DnTzX8qU%f#Y7$`4)+AEHp?#3pLOh;|MhQVLT5tP@DUdw+SwrIucLK1%O|$xc3et4pFYdI%Z!YQjd~B$3$u*QL)n$f zb;xAwZp9Wx@Kj(F1~=LBKWw!C*B}o?3b0A&31{c! zgCJYZVEbMJnMsw|=~4BP#WQ+4S0d&KDmL zf(jKsOxc0&$C>v99Nv5FF8d=pwc&vzbRh&3Z=a|r>wG;)*kC*z#<|(!6mmN|4?m8b z;I3+5@WtczvD`K8skSJhl?kGP9DRe_x0}vnk}`4c`t_5ct#vb#m!=IqSsFX9ko<-ck0U1!<|{A)r5@tsAs3ie$-!sW`gWY8 z#EsgTVRnw{mO60s3lMh*Ji2f4z92^)EM(3ZGFnNGh8a#>X4&X5+pr7eH<|Vb@apy5 z&d0w8h?H(VNs-9^e$C^t;Ctr!L4o*@=F&339?>VIf|!cSSapzNFF~Gz859|jUL#Lh z1gJ5t+dJ@<9m*bv@di0aQZI>ou{Zbv$W4@n+~Q6<4t(ShKdqcd6b(IrrKQ`tNdEf< z|1*7qs&pS7pLUf?8k`iV*jwUZpeI>HO1xg@uU0*qNwmvEb%LCCUzxy9j2`Ic$B9SmOnSqzGik4<7tq5|xgaXdbE zKF!ug@aUci-?HwzyyUR;D>E6%>XbK4rt{9FF|HiwF7GDP=q}>^J2+sr>&EZu2ZrWP ztOOvAPjp@Vjf@r{gPUA7_Ho8{7Jm~ThXZRG+XpV7m33hlFj8dbs=zmro+dX12)d>vfv1^P=Is zZ7B(cE78>Obxv4f6C?7h`_4X*d3XKF9^>M7n;N5%A`NY+lD4dmoU43w?@mM?-RF&8 zwXI%<7UrA%QEYG(5A!zme6F`$-Q;+9?3jEqdX=woKRQ}}TV^wA1C=a*Or>JM6AkQ5 zEKl~6vv|*6i7=J3@v1UF%jSA`H-;(TU@Cq)P0ZqaR7oj}?qTO~0X-Xmc(4vt$gg<+ z0t3^ub%-6qt5X-LOzAVI*em|kWN!tXSC3%7;x71(la$7KB1Ii z;a8(8veV!>Ntaf!=)nG&xMuk37y-1+iXHo+UKd`63p= zJE{%x++EEUYQHRX9{oO52nlK~i}8RM@r*!kSFv|4oVmc3ET0~9a2qWCyRbIO=6}qi zWEbK6x|WJS1Z{GR-hAR#Mn+*O^R#AXBHasv3M}}Kk5wbzaMK@>f;nwW89040HZm=^ zbgNm(K)fjkfMw%_d8$KXhB86NB``Ulwqd?-m{S~h3wze2j>g1vVCXtt-j+Oka*KR! zzE_wOS_K^B7OSBy%V-d!bu?IKgvPB>FKvBU+VP-6F|W=R!3=AJd(<@ZH&N-rJvq?o zq6$E7z{zcEzOXJ4j|pe$=NK~42K&e9LQ*#-ZnFb{UI0Uh`>B>x`wiAhy$o^{6g;v{ z`N|53=H8{dpa&$_hAoIVqP@KxpHoRa>gVa~{$Utw>*f&_t|T33Q}>(=gT`+7j@+P$sZqQE%Hw_@=0-T` z6`L?k)5p88jO=RA^_|1^iup@1Kb`1j2fp+BasAk*hM=|XcXQr4T;beFPW~qGLA=i9 z_NciZ`C-@JD7j%e<&7eenZ6_!UYc9dU>!HT*=pk}<>-`|I??@OT zKSROHi%C#Q9=n^5dYc@hg6;#O)7O{fWdMD$1N-rF$m*6$cbc{4odj_f$Z~!qsWL3C zyZQ=2O){f)6Xw9mMtpQh?bB8V@2tCRzh$JQ%(yE0!Cja`SxI;mlR#}u2M<}V=R1T( z4suh=;FI9%0rq|eWso6LveA>ctA5_fOA*&Rv0i6&}ZM%6S!it_3);pp9s`)OhJ1lpxV8 zQIsO`HQwq=2y|?%lbF4t_GFiSwVUP1yV9PJoX~H!ui$sT#zS&V-^Qe=H$4f!VYW?w zo5CT^#bW0Q?e@;YQ2s=&HtXwXMfTW{@HnOD=P6zGi=?5I%6gLCv2wxvIO7~|l|@`;kzq2*nsYtdpo8AE%Rrvw$Gqa|i2?~qcc z=0$`4qo`8WE347ngBwBpW{K2p^o*yA06xG#wzH+^qBZ>zx_)jbOTZagle|RICx@Yd zEq0f5*t--qQ4-Q3T~g~j-;Q9D(!Y&NXceN^M~O3CoRqHs?iB7g&6B1vIxYeUmnaPF z7RUM9n=@|whkuaQt1jy`C}DL_2@S*yl>Lp46QmBI zv~w+*Y6S9dcoHU4FjfN5CNbMtcX4+7$tgp!_kA(pxFQLHxY9Znm~BQ`vuK5nTs{P2 zc9AgQevH&Fq`e+mtI{~Y;+Yy7_&B{Wzz^;wD8J};vm=}mD%h{;En+gVQ{K`R5XkaS zTWNa{Zq(BNCzQUhG)@bQaxjz&v?{guNoS9|gFU8;~?VbqW z7+x!xhxr~WPRP>_a~DD1;4qtiEAv5NkIv!vv&5KPn*LEC1nR30H>#O6#^^!_ieXt> z#X?1?Tps^&5lJ{6BpRBxq*`C9lT2N;-AiXX?9Yxec)q3=@w$>>q~KlrQ^vDK!c4xG zK;&@C_XDY17Sm58XnEg`t~`pw!VG@TIxHfyPxgnYqg@w1yU8LUJ?|y^Y@0jwt#kVG zkNHlvVvmsJERf#_o?te#yB(~PP@xjl$|RdC5F>CJ`Ft<kLww!hXBNNL}d}c;B;Pnuf;)c^~?dYXltZglP>F)Xe(*WF28lo371ShB2(nY7g1Ei_6rv6#KH~ z+kUC(lZezf+J*-nHl+-a@T!A>v@uuMDKvjM7^_nRk_J(prT#fNjrXe`WHRU?P%y*A zJ0hyf)RWkV&`)j;z|sCek1f{*Bb<~U3W5Z5`3+KjLfPb53tz|`m~RXr!8!jT=v-Z# zk^)zlzsyx^amb>2Bw~NzbKsf`?sB~a#f7<|y|}(m&E2T3D+uxPn;|-=oB7;d?e4T$1xk9D!LT5zP91fCDnzEu_>$X>() z>=RVw$`g`xD4!fH1G4$4Jq0puZecSTuj#2-sir?o*gCT( zv|K$}v!nDlk+{Z;?4(`U9!NA`Y5l4JPi@l3LVzON@F3_(t2sX{L~~F&%WII%eUp_e zq}E`Eq_X{@^V*5^<3N*7qN>l%LzEUm247dyrJ!lMyV-(4EGGK__=M z3Um}fStBA~PD7owt|qIbcbs-LMz#+}&4%+yb5=!(#TmrhB&e*d1@smwfdtHZ@J^ZC zF1QQ^#}RYf#2GX?JEcq5#||%vUS@3B8T?Z@#Lut~V|=IldcA=Rvu4Z(ORlzL7k|W+ z?gi!pwibzPk-50fdaoG$T8AajTC~=91_iby;x11#ZGW9|=+8YbCZfdF+hP+{`I{Fd z5qrwSmY)}*Rv2_%yfF2aw%GZK3Odykc0~s22-0YRF5D z=2SkEu$6^K2W9iOCXPBs!k571RB1nQa%Q!Qv{R+|V-(Ch9iga#!oE(}@XAmS#&pCr zdTCB>LPKrr63hLOt_P+2A_MYGx{9)#CZ3QSrL3k&XejN9UTnfvzzE-;vgoOmtz7)~ zWTAb<=zCl8AX0e2*adCh;h%-^5$%M@vvo77w#Oyx`;SMy%i)merKMR9O-|^Q1~h z!E?}`ak|#gAiauZN+a2VIeHE=MF4N-b4N{0{W!a52Ub{04wi2XlAE_|`S1?7zjoWK zTk4`CKXcHmD3(3k2Wxe?ZjgIxgc-_IW3^#ILBY}DC*^e%cQYX;`X1{H<>leunle&E zgX!hu3$`N9k_!3cqLs9THmRxJ?RWrevs<93Mt$5eG@+JLA^6%lOL*E0E1x*r0Oa@u zZPQUV3p|sej}`1!uM5(v@IJlU?%3Y=e0m&R(dmo)tei{{-BUPo2}}9Jf%h@4PQ)wQ zz5E+&^%n?OsPn~SI&j&p79ByUYBEW2Q)sQj*cjGEsu4|=U%Us4#Q;H>=;hTImc zrN`ySc3;GYQt*o4lXjM-!J!!&5LH)E@3X`~lSDmP>Lem*a!96?A%c(K8gRM!i@x6P z@M{nXF&g?V1C|cZG~AyI2S(L^9m3GLe$0MmA)sg_(6tNmGBSne3ceFcW7#X#>VPAg z!G&SmszFFttK!OXWo%@Hw&Um^aFFnl5fS%y&+NST7W|z43M($~N>>I!v;2<`$YK(?MrR9z5@V@fzAvMT8u<3A4J4Xv)KcOi6r3Ib6^q1*Q2lD#rB2D25e&R9u!d&G)ZIa|tgr$^ z?S4B;f`Rs(E3ryWPy+a3BC<|Y#Zu&{fFyKz%~xRsiAvU$3eI_mj*bc(Jj35GHg$2> z$O5v_N_CA+W2{?b$`61r!$rez&JmmS9yM+OBO540)bohiewMwkCX2tzk|B5^ZLi!>%9wCL0&+HBf6$;PD#vJ= ztI33Y;5V-VJE#!F4TBVzx`t`95&ot;Muw#p4C~Y;EqeIFJkcbAF5MwWfC^I0%=XFo z$axpT$X}dM9AO<&-cW%X(v7g%cGZ*(+m`rFVg-O*XE&s59T z_&PJC>rqi;k#7)e9Wn(kH%N-ZVUt$zj!7-g`71gx_3@zAlD z%>B5cEjEm{Om!$*VxCAPgp7H^iq2P0u#jfdGyByQTTs^`&&<-6X?ZD~u_i9{ z@mt{$$1v#5-*44kWFxD0hpBQI$@WC#Y5L&~hx@Ef6uzP=FUYt|#z8ql9L#WzUrJSL zQU867Oz1bQC0l24Hhw3IUg?FEyA`1UHDNCsTc{SRc56u0dgefFiy|X5P|1BBwQ(7< zWrOYop78dgLUXP+7+t|bfj?#C5pEf|ntW|)LCuwi1sUBPGQDj$ z&xM%%z?#NY>OHNtcMHpKt<_UozMKN*m4k33x|EJ7@j6nIJNi}C)(nXx((Jo7CAizN zyd9sb{d~y;Vk^8Cc{L95SPMOazAr1(d6p^yQ)U3&Q$GLl#z}QWLE5*&jjsC!dl2Pc z;qp2b9&hiA0x^bL#8!czmeY-wtYXqcG%}a5Vq8cwBnrVxNn&@4lXpA=R>F+LwAK6e zXSVy!{ELA3no($eDmLW1P^Ok@Wt%>UeWf`;Ee{}DXtN;~A8>F8Z5_~-x{m;85NQ0+ zVO#s-Ml)i z7yZjq^n!^~#f$l^%9-(NWD5Lk#as58$edpoQnyj$t3TIhW3Roj=)~zXwoUpUh#Vt5 zfbvrtWS87_*Q91TOar-;y^5;arF|AFa!^){_%2p{O_oiNc!krXZrFbX>4Qs}DR*=j>+BWRFUtD)G0 z&xPd4a69!>+iP^LR=ecUpwbg>v!^{nL2Wi1`=`kdjc%=A6Wo>z)rN8V#wSz~2#kum zdBr5~=MdUdd5!nIL`xUi)JwDt;T2V`qm;^|XVaKA!@+?s!1@?V+!B0y9F*Er-7w?h zVQYz4YM;KFFMvE!iACKipwQ84d9Z<5bcPnCIqO8oNdjm&6J?IXsOj&Mf+kJ7IRZf1 zC+A!=cV?kC$ZA;9@`OUt;RUe#z@Dxfi<+t3TEPOT_Bf8>Om)R^|ByhQ+z!1#ZZSEO zFIJy69_@lKIj(gz;dsYSTGF8)PzZHu4@+oor`PGa>^a&l8;dTcWn7TU;WOY10Gw#0 z?M?=LjC|{h2W9!fJQVNo%}b%|d;uJ*Gft?lfvJDE%&(z_FhnHdRUTm~f4Tet)#%Rr z{&)ucyT6ZIZ+#G=cdIEcRgc8dPt7-(uZ?bun6y4+OGT#b%-+02Y=3p^w8qZoy}k0# z?)9yWwh|1PLx=H~%KV8D1Nd)fOD7TaK(0C#lw*!zINqwU0!~HGH#F(&w?S7TO~Fop zN_B;shqU1KYTrd^A?iwynZ9aScBu*K+=;qvEfu ztsh*#m1x3PJLUmFegGPB@|<+}g9X(VdVh9Z3Jo?G#mmZy#S8}OmmasxhABzC;-0!x zh!uGf>4|;bl9p!a;^A~ePo1F1I0uOsljuW4RPi54$0go7mq>|6T^}*1qQIZp=QMkL ztlQT-siV!8%O%~;d}6*!B09dm*-y*-mf_-kXhlJ3bkm)WX6+4EyP@sqY*T{GW;nI3 zAP)Q-<>Ut60b`f9SGk1M$5F6g7L<#V?PI!bDD?@ws=dZ#78K9X{|7U}BJmWAU7Z3hUTtxO%u&fo?4M-!34IBfV!xF;Bk{JJ~q{#tomhVu7}QD{in_9?M$)U{*v9k{BEdbs>Wkd zNh=0jOEV_!x|^1f?ZaEB-gI$YenVS>DZuh}K0@ptuyYDJCRTS+tY0rR8j>GbO4pY6 z)FvBgEGEuAsJ`CHfJi*G`jtouGa-MBPzu{G58+P4!_rA*p#7V#U_%FjK#3IN#)Z(t z!*0j3$|gD78+BGA>Rwuqh#fUtTB$&6dln_~uFqS{%6GB+v(9|SDE1X$<->nCp$V* zRyolY*X<&hRJ!z$4p`45-^Wzq*#!FP?R*YbJB-G}cpLF9*oOGh$8xhh4rO0JY6Lz< znsKO%R7&tmL^$+yRuc0)=O*8+uw)~V&0qBUuOAM3GHL(=hs#`0hCXCL5IkF9B5R{X zQ|78OTs4&9XI7`T8Pnf2b0YlSedeK5ZOS|1|G(w-pJ0D!@>81fd^uLZT%NZHHUBR`i=TCHIyG z?K*(><yyZWSKZ8A+vHP?C4W8-{`!ON zRK@Za&WHtkw^Ex7Lay<+{j)2~>gUqKIPXAw4C|1lu(|S|qH?nCMS+2~ z1&03Q5`&YF$vFEk4~?wTPOeogD2C>A30t-hsE0==G7zX%|O9RZy`%bXO&W_wL?YCM$Kn zB;@9r_Q~BPbLJG}GKJ1V+nZ$j#+VjPf(t)@P%A}!okXH4M9o#aV%ce7^AQM zru2@}BpUolgn8FOzTk4EJ>^!|`ew2t6WH%gALpyk4u(6y`*rLC6+xO1J;ay^aIpQ* z$xdC|fYz#=g@@9kCT z&G(Z=ZqNI5rvK|i?$=?e|I0(}*TByAVeR*IY0qn-z+2 z0zOYu0={42wcl?F{`naG&po*_elKr3{NJ}vhz9;2Q@LN4H($r4|4Io6)poz-=JtGiJvek+e!ot+3;29|uI%)9wv3!{%sg^_ziY)V zIOOcr_PihG`hPCDe`okTHxlrCV&s0GUEHvR3w%813VgG{?mxKqOdR$6y?!_$=(w)^ z-lq@%yjvLfehLVDztnzxKD-D3mS;sEBxt}i$I-|QapWc)_37Lo-%rfR?M9^AhV zzTYQi3_fo&Z?-pg9~yeJy$_GvM#TKTmJoBlk2Y?)ze;QUo=gZn{$#1WAEf@JCveey z@b~*1`2OTD@c;f7_TPye|EGVw@S5pYdg7`3Nbr8T^CtZH-3W0pl5^+q{g=Z3ZA;@j zCu?{n)#;O|v#Pun@h z_xs!T+ws3@c&YjP`^Wv80O#Qe$JdDXD+neNZ0l^vg_N&~-+;pa+&uAav~ z`>7)x`g&e)lQ?V7oS)(?eA7KCcK=$o;R@sX>%#xBse^7ioimf;4a}GNCEa0t@h)HK7vIx?`_`(SV(&WFcx(Od&79g5U2hz@7wwU& z#A{j$-{n;{Q(o~aHIb&&mX&vpi#La>7u^?S=cgwf92JG`s}6o)4u5{0r@i-*7K)gq zO2matz>Hm&`_vV!H=dK3oK>yPw#5c-0Fny;~7qX?<+2gF|aM zmwmL{%4Jd3OP|Ffm-S3WP)*;*U~f?Y^o5%TW4eNz!X7ejpccXEJ9`*`_vyrk{yVx& ze(JV0jjzJy^(rd}n_Z!O6;E|eQHPDU6u!!*NbpXHed8woQ%6c!w{Dc}COzaEzqox> zPu7Lpgxdn%l~);G2{}vQS=zPgIRJmhP1wCQoW>=yoXxjc*~sYkqmsng)E+)I&B zIoVl)hki;uzLkx3(O0MHf-QE1vS`)IoA9^S*#@;C2z!!u9o#dwMH-v&nD#lq=om z<@HsMx>bN}(PG8WSPh4K_9Dass?E`|@R$pDK-4gW|OM@zo1vjrnN|wrTsaUy1H}DpWfr4 zjUp@Iin8*r zD#cV&KXK236NxnexQUf^%y6Xc6}5j(Scu$Rt>fj$EjG~B+QO%QAZT3ze4Efud;NZQ09Ebe9PpRNQ3kk^f_t^jF6fKx^lYJP)RIVN8X3Af zH4-a9=VPUPW{!3b78`))+Z(d=GUuM=&3ip*T32hlnf~7Dm9C41wasi3C`j#MTXud{ zyk`i^1hi&kppz!je{X&ia(nl=+tFg_vv}`VYm!a6HtP7BL^bbZ(zQsL7>db^OAjBG zKef71X>k3({%2}!R~0xKYqWaUZU+~7)O@T45tc*kBA^W_W*@#7ul6v#WqIfD3|SO? zNPQZL){VYip4Vxdv$p@k?1*aaE5(X%LcaClMLJ_bdX{-|)Mm^1pOddMLEIt3sB-F2 zrH)TyrI9Zh4WF(Ot@8LG911{awZ>%qMwuzGXriC9v3vibjF);cR z1e8qlVk_0KS|iCW+NX3ydzEkWHZtBv&BP3o6nG7uE#wun|3{f z2K0Zh#;gbBW)ljar3m5Bv$*Az4QsKYf(cITv*OG^l&p2J&XncOC|Vz7B?I4%rk;wh z`QW|9d($*&zGk8F05cA0i~Hts;Mbh^xkJ{e`V1(FRuN~KI)pYLgK_h1gG7)xpC1Xq z=t!WJvcAu$3oDKOXFhV04Tiu~m|CG$N^h?AkB&|iDeJaSxk_lBQ}RIagnFZZ;x>CJ z9nB8)b+2`>6}-WMJoa5WYa|0qETgg3Dq{a_OBW=1TBCj(bvFN?$Z#QJa--$>L=)v# zu;<7V#fU3Uu9p3@O;L1{73CwGNy|I4u~M?l+FF9a7rH}ZI_%QKg6FPjG?&?MUO~N1 z?3#+QR+Y{-k86U-)IsXwlhAxe0> zMB`7_ju~j>AsmSlZ>@oO$x8_Kkz{v@8M~2;!zwo<%*R@~z(v|0ZoB%IKWD zTM@2YfOq>~sG$hfn^hpd0=jqiXz+IxRrCYDB*SXZIbNU6nyqtHhTz#_}NxV$9I9vZi7r zR^TX~`S|11myNYc@1Wi4zJ>?HuBMwRXHl+DQPx#(r0`KTl|43d8x5I1S_bI=@989` z z*$J^zQti5FU`Gu4Py&cbU{XzPQ$-UR4Cd+5YP&SIR*UYtgjy@;N`*?+P9N?#P0 zA}X?@6=f~dpav~y4Qnqc1+NYhr6{Z$Y|yU@<{DAmr$Jb_9!&q&qIg^+%tE{u}es@Mt`QUSx-Tg>!Pq-DX_>v-<_ZwR+2 z5{bx-8)7TwH7K*dh^ZQ-l@j0tars!6B+7n|Osck<^??n|;;QqyPDpB)X$l{a73EsE zPMpsGDRqHXE%H*XwsLG1nl~Kt9g4Cx!SRGdw^h3}Q9tz~zeJSmFHU&85V| zjFd+X(rQN+784C!Mm5j#PX9{$UCmk296rqDUMJVJu~qEkBu(sG-3O^B*#hl6Dh|ks z63R0>w3N=8&Dy4PA{Uv$assOr5j;U~rX-Uhby$G4^rGH|)eviK>!cfPzq{L!wX zl?_`8R)T?xjOYg?5guvB752dfg7M}vHN(x4`K);SmRyL-c8$dP2y;B?NdioRP?0VK zOy&zZj`=J$lSgQHEni$8QTwa9y1QSfLuOTN!>>*P$r_A1p`$78l5nuz`H&9G&Bgt>WAW(fm*@2T7*TqSOG$ihHEe-j3)>Tm}gCXHg#(j|0bp>yzs z1f$Q>4fTHI_t^bLuf*k!$A^2n=3l1yGVbGeb0x`+UY#1w1%~j6OiN|)yc_5v{V00F%1U5X<*FiY<%_6ziR1vXO{-y2zx~jgAVrmKBi* zoNFYvI~J8m2#iLOJ59(Qv4LXDZR z3Ojd&8K#35r=<8OT8qR6Tb0RKj4-5fDOlyOkk^rl8?iE&^w?Fc{PDIcWOeD~>d;uv zlI>=l$ZI0Fueez3~d(_gA)}QDips%8E$nZ6rAX3cLL{yieesg7r z5NsqK4(kc-e7|vlqd%oq*Kf{^%m(q!=1MRUT~G0C*7;k|l2xttuSLLZ)lcTf9uB+( z;QOgiJ@e8#^x~M2f?{I2Op>|#N`Ec(BS3j(pJyBb>AW7Rwq{|V`wp5M4A4n>vXCg= zh&mFskn6>?=tN_S{W_r^xLmsey=EC@!Z_Whp0wM5v+rqqd{-^>ZiA z4sTA&d&(b0_CQ@DV%!c}-L;o{dbsv@@MOUYxsHXfL{pTOZuURPpipSl7wBgxvc0A3 zL3FJVT=KN9_FFZ9$pIr)0GLq0SbowN!RcMX1N&M?!t^D~7Wm7cDr~SiFTmx&1u}A9 z7xzpV-r@}@XN2}oG$XCOaMDx-0QRg0n=ZbcP?wa6b$*CVJQQXU2e&teuppu!cGfR0 zryGh$Wp8Rvl-7z>fuEZ*2$bc=9OUV&f0@crk|GC9isE4!5JYAW6TSSyl9@&WPU0nR zk2koD9JLfpa*?|h0Tmw9>zvv;4dOg4wEm-v@wM|Iiy+~Wi8d&ZH8F8yLI3b3P4#YM zyTxfpNa~{vnsafq2wPu$=#a zYP#@-2a|?oL=1A{YAkC#M-m8^1iYfD5te>jiJiXM8q=AMdV(NdkLC)&4dR#|%XX@R zy~L2k>hxi!Z2souw$b_>x2ffZU5W|?M$v3MO?uOm{+8pe;?KD}Kge9)BtnP4mWaX@ zVnw{Nmg6tHYOVZP z4AEM511|4ojnvy7uC7h@?!G&UaD zB)Y@fW6%w(w|XPAOVNb zXPuv5N2V?#ji_eAoaL{pD|~@#bXdp@doDD^h%{#R*;(^_tO2AZh#OFl#@trUUSgT* zI7SvBYPj>B2N-wAbYX-D>XIJFE z^bIu~RRSD`LcBmP&af&_7Lyh)tGfwbs3RXqXmGJbyi+%yOlP+07n56NPC&Ip&K@dx zoFCDRnbqlww1nkYKuO}1aqxFfwjU$kW56$k=%&Ln5V8s(A{1(>l-XaT;xE`lh#MLi z#UfD!;qo(G@)IDF+GF1OJ$M68=49x`h7TTirNoGT_=O7OZ6LHz>Vq>tYWEF~ z*$T8_dHxUpybe_uQVq!w=Y@5hGY{l7ft+1U8CB3%!^Z(vgDwNti7gaz7x~%Knbr|r zMF2%`XC+i*ErdV>UY zL8+~<2gKNV_Lfp9Ui*nIm)Vz6xF4xVgme0`{lJv(T<^izRhd%*O1_GB}c)tn&MOdyDtR8!v2v_#=UM+Oi z4-y?#n*9LIeC+qpk|ZYU7!zG)HNQR0{7P0eI4#qgEsa%)6woqr!yp-*M84bVA@yIj zzK9N#=Fbi?+>z;Niew)4ZXPN=qdMH_SX}{p20Ptb5@} zK%H!$PUL20eRH+b6xip>hFPE@7*`ra9M8lyyxh|@q5sD~?eG1yWO7-;GF;-bF<`?T_Bk9aZ z-Rl{RPJU^JxFm}Q(AKeo2+7NeGK+AcFAQA770`xc#fh$hO(;r&54v97l>Uv7!2l`G zU`)wM_03R~jD7}Lm&zlB_PynlpI*8k125sgT_&1a5L{3G{E7hJZo?Z=qVb5I-P=p= zjDaonY8zEt)B_yZDI_d3xn+i%|2f9Jo&I*;yfIC2MYp{(Ilv1fr5gnRT%UJ_Cf2eW zEOm4Qf9*WUpK80;gSjz4zJQvMOa;+KAl38@iIeXj{0X&(IV&8m!U9c0!1XH1uHsV25TS}&9uR3kKnA)d8IQg6v!R&L ztBFB47=UIj@mDJib^6jrBTkRObBGcS5(-cXMhZOsmuIp8P`xrLLc5k+B4-Cymn;on zzGGQJ)SjH)y@K+!xeVikKL51W2JoyI=#U-4qeD;%0whgv zFS%6SA1K~#rgyCjAr)FJz$Bk*r;TwgwHC5= z#;pvO+^{i~-z61*K6zSa=jT~lf(2X7gy{UIRzgEW5WAo(6lp$b>qYt>bBN$)$W2wwUifW&TbAA!bvq3Wa$Mz5<;ej9={KKPLC!+r) z5h!IpP(K->iEGf+BS*PH4ac0!F(`&L6}^+h@{8mbN$brv<>~s< zb_=3u?4^RlhvPg=FKoG!m3t7Tm^fR$N9JCoY9Kxa3^bvKyXfeHfOg}b7eay*q-I3N%22$tOuiRy*P@!vLwl^6>B1

Q= zdgo1gOm>BoEHg!fEZNM_4C@~<7?Eb4bjdUCLVwfNvj<&X)E>cuApZ`Q-eR+z(4z z=lW?N*Xsnnd>2&`K*FkN+QX`mg(sEPUT?pfh6nRX3mYBSDW_2hB_6 zHukP)C4w-TU1@45 zFd|Pksn7ekalxG#;4W`kw|Chck{Yco&8ag|s#8*2PjDxEQe;f#Wp&7e(&g3wy9B?y zCM-KPt~G`9zAX|xzt#&2CG@i`Y=ll(PeR;s3$try7wMo$vtN%gPL6IsvnWq+;-3>v zUEcb}P{+i2PJ2Jg=JoopX2P|@R`ANutm=MBjOv%OsRgdpE1jX+(0Z4U)70q|X&uBo zPBL*1!av##8ZkTxNxYX~G)-rE$U)uvHnC%pZ!F|-IVLk=l{TfAC7sk3kvPKZ-uN!0 zkCsG3-&u=lL9$f|KxzwaNJ$M=3X4!eT!UZ2T#`D&j*`#hk(1{nZMU(=9m$Y&Cp&M- zqVyibD3-Y?zp;LNuQoiVG(|=0@^(GS+!Crv^0P^i>T#Acxj$v_W7CZDP_36yEs~)! z?W-5Cq?c^tftpw`A?Xm1oSop9HCW>0fF^7 z-UP7v$x7}-W!n<1auGX$QET=Pt#YFWenfLHNLo}HPZvpm##-GnE{i0ngBsc$cjEaD z@DfLp5wqJx*JiE~(2|JFu2I*flFK5g$-PJt;=fCmT6JpUYb(^)a@t#jmxg4PJt1i* z*@s(?e8VIvqFJvI)t_2Yj+_*G3^MT&Qd%G0(LtI7*`EZ<`|WykjYvLuwNcAE+htpUUA*I>z`(%kRt zN6XJic%Bg0yS*7PD=nI>JNDng*O2k9>6FnP3hd#$IqqUQSwY6@v7|lETITwA?H8Hxqmaee2|`Jj z%kX_UzeRdj55oGj3#dIHpP)%)k;B3ImpUUkrgg9pLXseVV=rl~hi_XkSEXd9)fQqw z>IBn~Vg2aaR>D&Hj^eX=lh(WR^YU%A6=b1Hcgh{nrj>AdyjOCfU=5Qo)u?Vo10fl> zmD)oo>yKtbLU*8)^@tiH=aV$_Bn-$BJH05op;#qYY&hX6^JzrPf`1cQ)FStLi-Yln zwXhy;ByOVE^RmmFB>i-FJ*t@f+Q1ICIbSSgD502I3?~!YA6ht-OYkcGA$5u7tB6`% zlG9z=5T|UA3YPhYH93DISxnXy=a$)&YfjjwMOsQa)w^vF7?3g5No;Ex#v4C(&LfV& zmM5g!!|r`pPvy(GMmeoq@d{xf*fhtIl)mW?sp7k(XJVpIv2u-_5Fx+4B}v+0<)b;) zP!Jz-u1May)KViKVaJucV+4u4;47!j6l|buFyVHh~WD>T%Hn^1m2o$aB7U`Bu zz^68XEw_j{PAvFt-L8R^fX8We3WY$9J|vAm&fp;9Zm$O*nbjD!4eWxf8UnC-cI@Np z5fQDaLD5>T*CYdz+F{u)iiNs^1kP5O6L(dPBdVhwMG01@D zkVx?6+d5%zSx{r25xvxQs(32eI9gk>Mmx4Ff5pjgKXQ^;S8w zv2`WjrI-*9R>-ng(T^-9CR!$>cDXz=?}MqE(rh+@Kpg5{8~NvuhePs262)N_FB16~ zayO(=y&cBHl*P|OU!RYzXs>z4ks#GSdW=201tZJ=2t6P5_~aEAXbKcC0K(ncBA1urHdtMx3KR7PIYP!!ocZcvu`#G(WQgTT(Y6Tr^?9JGWY0dAtk>Mp>5E69r&P)Xh|W- z%89>k6VpLqd_fQz+J<^|ea$Y%=5{^0rW>{Eumu`9shFWO@27mD6#kb9$0gb`9&*b| z^vT$lVwCV%!Idxm%7CbZ6AcI?dZLRnvN=R4QDPYzH3GF=^E3$iq_?wtI9=LLL*J?` z8^B1JS%BwC1a&@GYuci6Y4Ktsa8Yquc1*O0zDhJCB%QpTPOOe7ik?Ysg66^aAaYL| z&U_@3k@jfdNJLI(WZr5Knx^4%5FjPO0 z8!&v@J|Evpu$q`5uFGaK{B(2;qI8Uggzq+=2`9%&7{2N0wepT%2Uq}gl0YkFY)x)Q z)+y$VOb(02yz)DekK{4yVGjXwnSnWb%$QU(YEFCGc+5T-=T7q90|>KP zei^6v6PXi71REztqFs+*tbt4zEFGM_v>rjbp%6jD)V4;vT%jdf(5w|Pb(@}mtw)~W z6DE&-A@5~r==VxTXvu)4lg(m-|G%AnFg^5u6;F(`y28;dtSdC!}Vq*7GhxYcpaxA54b)-qE2fDSrV~>ngFlj zVCFTVS_?b4A9i}=Q!kfbCJ)j=TymS^PFRvSy|kLF3GK$_fbGO{!|s!|Cr#_2oz9zT zhxud@TVrtOvC{d7F^(~(=eJDc06ujg4znZKebEwHDp15E{NmTYipgUt=X~zR2 z**wh02mv}irc&qd$$%w2w^2r86aa=1f}0K(p)O<;eY&3Qs1Ii^+ndmaT#kJ^LeMfz zv@y02k{Y-vg&;Xv9!L$Q+%dNO$j>90^yMvL=7-wSFdhFyhYx~L#7(Oez833-zFy%K5cujM4O zn-2}yWcibFZ{7@ZSSby_i)&jasvbHh02^85dWCG4$9r~A;n=zH-%Ue%N1+4$&9_mc z$hFAXLzeCGRV3*K=}uxF+Orc}iM_pvY}Fd6BM{I7Kwc*+8p{C90#wDao1CWs@7eP{ z8QVepBhtR-_kwT0%^>w$ZyyeN3eZvRR*hOZfb3RaF6tYOz@%i+*?ftE=cDV1Gz~nc z_G#|UPwC^(Id5`*(y)4-3UtRHuufTs!TV7lP^8OrK_@^}h`s{W)9Ae4zlY$KmdQX0 zN{`{d1|`>59&ro+UDDl0aY?yeL3n-u_a(`cZZcQyPrqCvw%fVUl*n+ayW|Y01@cve z(0&IN3raTQ3}(rS<<&?SG|4e7o2@=>E7I4TtpqGF`D`m`rb&`$o7-QN79&o+Y4ewl z@BP#kl1e_ILGI@xFtRmVV~NGN05_l^p23W4oCZ5L8#P$(D@mA_A_QQYx}$7VAxX0)r!jZLn3T;Hx!O?TM&6S(2{+_=3DqvuKC*0C z?GoyeZq)ir-zqtWMjeT4+>)2NCC;EtAd7Jj*p<+d|05^dsu{c!QK7NG&kZYKlQ-tH z{~+(W0g!e|s-n6JA-BMDXmbLWoncR(-}{Mik(TV1xwq?)tV}}F!w_^_Ac-j8WW78q znU2T>aq`oEw&moYmcU3fKwk)p%sjITUS@RlCE7pQ{POHAY6z%LAX_;cS;YO z=}%J6Ie@nl+K(LdoApu;$!8IU5PeuJeZTLyQz-=(BXw;#lU7ZkH z(Mj@MVaUw>T7+|zMu04^HUL3ROhht6I2O~x>yM%=gq)8=*aFK?NCd=m?C+=ih)Wj$ zshTjBoR@u?R7i~W0NP2QBnf@+I8M&h>m>xHHyqX>(bGWMmHqyRb{yUfU?>zjM(Ofy zO5e!>)xcU|s_U_Ka5g7LXIMH*# zow8_KTR;`x^-kzocd-I>F9-JlWh2dkEQk=U!No=<-wwEiY>CzhJ(qWMFL}i%3WrvQ>9 zk$0x-QsTp<-nA}ttAr#$*rFIF(Yh>+a;IuRYH&KpPrO;0a>LGwyCQin>!^oqLnMG$ zSC~3d43)Cm&7r+XfELW?ux);MucjFbsWA_7xUWZ7Lr4~#qm8rn1sQ%ALSdVxdYPcu zSLf(ijxe>%=gnYt%{Rf4+}}WurvuffV@WCEAvn_10(r7XhXdZlC?MlN3=Q-2b-WLM z>>&Xt!jfK<2DCZEbYqM3X-U`)A)QHovopr@C2)d`G?x~acOsg7)4VN@jOgIr>{IBzONm$at%NgF@OX*(RPmk?ov4c zHm3}b3@!PkkM9LtUV(||MZguOgO4Hn%tIl9_Ge`25yF=d~^SeZf+ zn`%j7mMU%lZG{Y?K3f+?oi6b&yzsUyh|d5|8b}<2&nBfJ64=H`I>j&T(!$f003);0 zqLA!(sK9Gfs%Wh<|w+%wm@GZ)%J z1s&z>rxiry^)}z2*JYz#vJvr z(jqA;^Nr>PAyvmAus7m-gvDFbnAPhg+L$gkE5K52RokOIwz77WjWL&(Yq4qx0;K3v z=vy0u1S0BBMUFS=bk2F~%ir)S$4A5+ZJnK2X?g^{$e?V8DZ_v`p5U`%k-Is zAyH;=J-QZY?^NkYA>bQ7qY}>=Z9UU@7au1`ny2`0_l!!mP2Vpbzy?^7?|kjL>=S*3 zDVVi85?L#)H<`!9LXVtR2T(>ONGELtEM$LEtjp{k&S;`ugi^{DQf>lg29DYuhAcHV zs|?I#HrGqYTl7bPw$xN(e$yy`G7Gphwz_sGE~eJp3*#YbG6Xk`QwX8N=-Z~$*4a+~2YZtD?Qzv%)9!KK6y6;5D@44t zMCQU8EoD3bD@zuyusStkB+4@QUos{;q3(qEB2ZLz>Uny3t%p4y{n%|Y84jp@>iGb{ z_N>42fJ@Ll5KT9yldmOuzrXpwo^=T|IoAJm=jP$`5nuv!9d(wZY|KEiC|x`^`B~J= z)LgFkudVWXnU2TTNmC5z`4V*t#@ms--j7^ZqT0zdDo)Q|&g)%cuyryKX+q{W;$>1W zRLO+l3S{bQrP=O!nUb{ejS)oA_kUh0+1W0s)$b;6+_Uv6cd#C zI@mB?mnSpF2jVMXgj3*}f`q0So>l@;>wDR!WE5p1EdXdU4`3kxXcpX4PEFl)E3A0w zOfuLbKm#&aC}Y*~DG6IpK4wJ&cbgfVd?0~BlR8w&=^QBo)k(0cM?HwN;zkkHn4V+` zwB&N+_<9NhvHx3GAeAIhV`2HNgp;Yy!bc}&PLjQ`v2=geqr$xo$O%lVG=OC49*#&S z_D0E%$w(J3BCp*-+6GRa^#~ayPv_H5EvdRyQIRl zNNsPxk|zbi&SjI^B_=lmx2?sVNw!tPQ!ek7dkAE^NEA$RP^W`yM4aRt@O&-*_AIt| z1jKp7vvc^UV>AKGB&rM|gW;QymT1Y1`8r=Mr<58V-DI)nW(YP>su`qIU^kkqgl2jG zIlWYlZtV@OhiQGdghbXmwN>F)fp4e;Ny{UH#e@R-mjUDO&h-+Y9~6uvs#&B(?`3=J z_vI3jW|D^0D*%0yRQJB%2zY|z_y%Q3p;oCQpz<(1?q;^*=D;E}11ha$k;a4IblBGv zChv2k$h=?P3k-#20|OdX!G|L)OKF`{UyB}*M?VvI!II6D&fG79h5P%9PYPDL_;8z~gzt`ku z0IU-TTTsEAuSlR7+J=qkbwC9eNlB1gH>xh~IRA+w*0~v|h5@)N$WFvi|6!;S4}~x< zFQ6W)4MQtJirFw;s%brqCh&9)XQ3s=sY6PFInQMDH= z2gT7#kOQwPQ$5|SV&H|j4aPaK>aNi4soQZCEKEs6?l-Q zbsRKKfFw=N^yLxxEUbhPPSo{q!Vhr3-T!EN8vw;{8ptrANfxmt6<@E+Ttvtz7si%)qs(zWfYFz<-LD2X$}iYCbZR-e=&;fPpAAzICFJ>GeOQZ zZx&1jzYWwCzHg4?$Y#D4u!05^8g%Duz`NPbN1l}mBIN!UD?OL)e5716Wo=-J>*C6K*ky+|jrVHPtOOTDJzw%WY;NjONuyV< zQWgo^EuUP_)^vtix#46yrGk5jU!vwEDZP4HKi}F4GCN7#f%n6^6LqQ~N>6zz`n^ z$OgsP`@Q{O>YEfDmlWiiRA~}8ETg?(GtgD0#{@6T2T4Q|kR6CNENE|?G@jc^e`-2*ndLzuSBD52DI1E-*Z{H?E@ z4_}+Yr45qRfb&f_EaUG1lp&FG{N=l^R{;E+$6v__A}LI5O83fbYJGsL0AX){Z}6?c z!6*B!gSab0-N2-uP$f>U?yhVxLyt{s)%Z{qeyh{=>~usB+u9ez~}>F4P^e5>NcQ_ffX4(9iu zW-S4nkYNmtr$FWR$zmAh-%2bL>8H(YCb`88Mb3>B&0Hiy=KR`%>l>aqhkm1SBV03$ z$QD(O9a?2%xHSy7Iw5x)W-0hj@viYeDDwy28muC~E+Gls-aB0Qp+v@WKs^XoIuB<9 zyeG^u?-G}85_!DO>ew#lp#iFx&c*ww@+7F<4&-wWz2)*uvoaFiMNASn z&alvRkOMavKXN!>2@X0i((waU*cveqsNO&zEX-#&b<@^YlNUg)Z4#Dp0ThD^=ymVw zVRqvD+iJ2vH5c&vh?ZUs$K3&Evo@Q>3bg8U)0pS-Zx60;J>I1V;y9C_m6Nfot)3yO zrHV6A1ZHMRQ;leEyfIvAhntG}YqP{UPMD})Z>Fkp6QpV5)wpD8Z}T$BUQhB7WvGsB zk)#~K=LsDdyvNTVB8j9DLHCQKm0~6^3?!$iAUGXdD`ipv=?{u( z5o28{Ws(uf=XP6hX3kS*9{7gHvqRcfowz1v-9eSToM4$CPDyvl3dhcD)6Q&*b{>=Z zwGBEUW8!yUI^AC2+xa~bxEqy{WDrWUQGkA+LbI{WM=XId{!FTYP!&^ry+(Jj?;A<9 z4&=Fab7?>bD3eN36WD-p_S$MhwL0%F0lS#AR-me0+r5KAX@?Yx8{s+9Om?4-(r21&HQS>pY}1W2D99qq z3y7PiBc!#z&*PUvOQW7U`z{(Db##>(zxo4wqa8zx$GzsMetjc}z zK=Tc)$kHe!s|%h{ zNuC*wFd!mYDY(VO%h`nxmcnwn7!Yl(_>~oA_K6c1R0*dmVuPm|2p=*5lE7w8YKT;5C7?^9kn#8Fz2+&TvyGfVXioMojDBq(oFHVbJmp~k~t0(5sn1U^e8 zPIt}t`REz}oS0t?Av4=ERCG+LT&<50zysg1)7hkzAQ?*3#X3~^CK-?WNNXBWPzHE5 zT1$aB9YJEM{fdW|LyPY?)=>FvClK_GZ?+3(0(!&s>@rv7;z;uhv{IdMa}gEyn`^jQ$-NLJK94wpm}!t z>0yz;J_e+UXyZ<$Yj9o`xEpD=5yW}MNXb>2u{QCoD64n4l#UkJCIb=O>(PhGamXhd zRq^voa`~7Q7V3GoZTCmrINcSfY=<_Cle7SY0~9Kmms7l%wY!r@we=8Pb|hsQmSBlA z??&I8f*Z803hn`}nch;-Ugh;vKNvtJrW35}!T)c{0KL?=BNpgdVL0tY& zI-mzAJyYqpxsfEYJOSAv6A?Ce2U^E%w&R(I_*Pq7I}G@k>;;SKe00^m!z|ZQLnW7I z3Ho?U^ktsMV;^sP`EbX=Z8f_gBT%+C=@oxZ^zs1YPxl%E4pLn!{eh`SQ$Z~QatQ!x z__X&0ul0!f(MV7Z2y4eno~jRg-P1O9&QJ!~+Q!c-RvceuNrDXuR$^CL6vT6v)8yC{ zIH&>2qQW^;$9@o2&>09GYfXBg;ur4u;F_f!Nuh+zdv?IKPKT(FQiSRWV#BT*Y8R>y z>iA3zK}{+Bs)Ephju+7`$y!$e1#V2F!|i%>r6As6En>>I1;r1o`|yu)I$P4~^$PkP zWlf?GCWZ_JhT{qCO>G=? z(8rQ)*s?3Kx8ZUjkW`*hIX>XBYtX${V+RPUhD52!Nsk;Q>~yLI(jJTpP(}>;Y>k-H z3hEu$30U9;+bJ!Hsm#A!k3M$4)3;y+)PU2*oIV;(Zx;{vNmW?27Qxx4fCLmkgsj^z z@m~pK@H6ydFmvxB?7$G)vV(<~+k-VC$02ye_(BE>L;Qh8~F9zUdjHB8L6GPs?3!*@&7Jxr-Y=Y&4ylUIU%AQg~W@Jei_ zD`HCF6m-o6i6w0wY|kJ!Ca1fH)#t;VL`wt5>6NIdp+RQ~M2`&OM32=Tm>aU;wD`#W z7Rv9FTOG7Wnc~btI_XF3o`AAiT~?4D$t(NuL|Ep#`zFE5)&ntdyB>Wg7J6kRKZ=Du zn91w)-Cc1$nWxCfixLSet$N~-8|}s3?c5Eo2mA0P=StE&$ldh$bHc&^}S1 z;8>SnBk!7GMG<{X=Hl3+LiKb=ryy3r?&R+P*$jSm9<_S*hQ^~$I4$MRQ*)d_z;>(% zHe*KUo9}-&(4kURS17J|6PYw92ecd5TA0zBC^xyHbG#CXq2_5w^ax2 zlsN_Z(jruggxmnxZS^QIq1u!~e=)oDdBtMuB5*~tYNL{5_EY!K*tqZ`R3kR9kSXok zBQ0WbU{Wkdlt#j35*Cdha*NH6+v8iQ25`+9-4MWeYEq~+vSCl$a))4T3!91_IuI&` z7f{0hH{}&gwI=%=AVs-dkDv=NqY1G-cz`BxX&B?4^7@$9+Rx+aawz{Tj+#M#A4yLT z$!Q#{zhC@yzewDF+L+wj3!p&-6P2hOQz!uWq4UavR$3%i(%Yi33a-V%P&*x3ua^Kxqpuw%QFq6MC)K4n?qy=oFlD-q3a%p;)|>c(K7^2D zZE2Hp|GHxdE|Lsd0JW9^8kG+Md2j zP#;$;TkBoWijv0WZY_0LTA5x6kL3~6y z*E4du;+S^{l1aOX+uj}7g6a3%a)%t$kCx%hYod>g~Igc{?%5+r~zn^DAJa6o>VrlOn1zv^U+75Quxy$ z$SAZ#*Nwoa9g1rN#v!#_!MJW-GNbj<~9#M2W<)~wIw1ldJYyxGl z3!c1L;=|M!J06V7$m4ZYu%~#g{4Xkq5sCl_4Pog>N)0oY4lKd=y*#D|p|XW(Lh1q% z+gwS=%0m!BeS)iJkzQ(c6LC!-CK+CJUgY^i$w`8)Zhs6ZmY0@el;mo;8D>aOh zXrOcSDB!n`O=s4i%?mB)&a&*%@Hw>c0*t5I zMNrZ~Ob+E3#Jhev_rBeGMQIR!LdE_h4%kAQ2-1RTyJ~C zYshyZ-bReK1B*l`-S(+d8s9-?g(h{Z;tONH7X_478u#kqwnvQdC^MB)NNfF(S z-btt&nzd}(QwmQy56Z6Hm(YD&vHiFdHXAn8N{1l@&-!L$1X38|kB;z>evl+A=9>?? zaQyhq)gF6n=~D3&(`sYa+%OjkWm{6X#`>_d@_)EnWOSmpW(5+7fliyTKY|>l6~b=+ zGJ!;s=KwSTT&i&^Z$vTuuw5r}V8HP{41Xe27u|*v4>;*X5)Hd0Mrg%K$?V&SJ~~|i zWOc|P`rMFuog`#RP=s>_fKaKaDWPs@>n>xEcrEzMfkjzB9=gH-G6NFMbJSbi-6>mI zbURZ2!=+k0Pe@>heB6kGwqrMUG^sRw2Bde9!0s2ayVpwOjGu=Szb(^>VFC!E%$Rm2 zdp~g!m-I*gNLHW5<8eYmLS-|)q?fn^0W_fmA!V4e5svPy4k`AYay^fuIPG#0M}MCE#P&HGH~PDx5&5k7X~ zp>8#0XS4U{UF8Zmt%HU&w{{UdymtdJUmK}b(kwa{6i(2me zkQIwLAQ?upsz#k?uo44~aeJ5~B~_`wFel9_n}TZ1JT%;qHWpU=2+NT=K=Lbf30rs> zVC?L{Ft@qcaL1VX_+EQ8h*YC3Z`Y%1(beu2$J5d5C-?ND>WXaAr8;AkT{Y80H}_+U zcD2QQ|0>qPoBCJ^Lx@4Z4Q%cTs_AeClk-(ftXZ}oz@i_0N5O6H_wP|_Wlz3CDPa-Y z;J3w9O8XtjZPw58Rtn{+CsfjWF?WSJMuanmcvV$e_PbpU@*3 zh(9RDz<#R{`;4Blk{Phy?F=`0fGvX@YvVnxVmxsb_@$&Ff>>%$xltj|f8_M?H2whw zs9T7aZ|?a=3;WUEH2?fKvw3&J z)NN(ypwH9NCJ#-N1OOE{s`1|LeK4v1S0B6omHODhyt!I?AZ_W?8QpwoQb*-Yespjq z%}$$!@sP^oGC@s1q7+r%#CY|UfkT&HHXkK(T7``s$yD_P2%FAk)g73FW6W^Nfp2P& zEWKK$8i?0zlo9Zo7;A084?d^60*7Mh`L6z@=U_GxGJ zf*r<|x?PVxboPb{Xv2IR3*a9i;z#~=;HI55ah!P|0aNtnsIFPtB5_N|kT6u+)61P{ z8%HJ;*6Ro}Edf8F|2PcppPbzW6Z($j`+bmFzGok{B3>Xkg=%=iip=8}n%gVw-n{83MV%wYWdO!ss zEt0gDi(+W7NMXSXfbcZZV>dD6EpThV;MzNvUmF)$Zm2A@1}RA6}zJHl2De_5NLL3!!cGQ z%Wuyx0pm1otAj5U^w0qjAD~i(%0pV~Mh$EN`gVU%choPk3O1OuRNoNsrA7zI^s~)Ll_M3^s(az^>l>lo7i%pXXkL|l4PBVecWt0 zhYf8ums(w`v=hm5?BVU4KD=Go8`B+tEPUyj4b@xdFbm&}^vQI$dp6AV3OBcg1Q!;I z?CE({GY(fNJJv5jqS-JF3}nMOh*q5GrrBhM!XRoNG(lb69JCgotfx?&mm$l>0#QEaTayiKYa4_2)L42_S%4;0KH>nJ5Gq2 z$IQaGa#pm*Sny z3sb$h7jlx(aFhfu^zYt8um_{2plCwXy1D|*LMQmk(bcGe5+73tnun)t{jjNI>|b*7 z2PW>+jR%Vd(t5l&!@7XEBGF#sQg8MWAPku~N`_2oW>T7!me-J>LRvs3WMN7qR{D>> zb&BUW1u6>|(K-TsQ9;}W80Cmmx)!3$V6om#SFHY_nh-vKoSu}bjA~x3Ze~nLK$rzE zufbj64i7d82cQRtzyp&7ohM`j`=aL4q&C~@3=uW#Fl9$vXwpW)`Rd?!q-kX_o;4Ug z-K?f8)(vc`LQ={G3N^1_EXZ*I*g79&_u&F)0!VI>#vaYG?#NpZ#PjkKi=a%kiGvtdV8J|_p`MxdBJp3e* z_?4na&#_!(MjCbof7b$g%x-G?vhLEDNwUs4xt}T&y(zjBqP)DUol}e`K(pt^wr$(C zZQHhO+cRfu+qP}nwsrP=H=9lFzHBzRom8jOPxVyks_OdxKulb=pIHUcxFhK5%y0}l z$vXJ95||t?tLJ&HnHZ327UyNb&X{!#uyCcyG%wmf*^6n_g&s3`NJfD>KDLawFt#am2xCAp_k*TD66q$u3YZ);|>QP*^C9#-_ zoS^){fC&Hxt4le*qZ7Z6QZ%L>ZtO1Qyu^8N-(RG<~Q%0gwBx37ITLh(+t$61P_BSP+!k>OFSVQ5{k;vj!9< zlbc{*b-NW@QKW9UrgNm4kte{*oVVVp?)1$d8%IgwSZi>2VU^TOp0`BV#=I6-8zBc5 zPCrmyJ`rb?iSV zeCx5T)O!D*lSL)`$BN-fX@tX{VfOfBxjd>xGwAujyQI z#!|dW%gr1N2BT{sNp1!xyc&p(mFar!gPWT;RzUqp5DxA>TE4 z0Pf=AdhcG)^*eF3m~8Q~c4h$u5R-n0j-Zo>@*uZO8^Q1ZUi}7A_o+~w;sMSxGJon_ zHUZlJ2>gWX8x)jW=I5|78TW zht`zajTyNhuZqwvTFSc37w_0R@j&QhtMa}-R%PcKvw(v%AI}weHs-hTnEkrs2)-k7 zbhJO6+^h6HCEHvdO8@xxH!6KBh|Z}BGy)8n7A+FTG)j*GY3MoH&j2J&+jpyhuO{vV z_LR0220^keMd-knvwgxql-th;*iJ#6l*>!x!`Q8sdBd~9y)jeF3R9h=3&ITyOLpng zO20QQZ#_d+##)Ixuw^ATf2i38O8W<(s!@~d3e{Shkai-K>Zl$%FMMKpWh61wtBv|y&N623nx8d$JKIaC}`KqiEH*fmCFIo$gw&E3yg z&$hy{pjr7wA;=;jMJ@1R88EttF#;4(?+y>zs2B?~wCn^F4q}dF>=&#|bWih;2cjU?>cRZ~< z_@bkM^_xk1A2IsN`&5Y`?j}fdA7PeMP5pI{4hs!u+xu1VwcIr?{^Ll8YG%Wh4jHw_fs{BEMM(v3q{I<0z-z}IzqhPu+eF@yTQsquiev`&f7zpzs@pqDy#s}3k7*};@qH<0F3TlAaZTU zR`Ug=BY)7f1(mFLc^3kn+Uu0>#&Fp>D4A{JPKWcySxSePGHb_g4Wl<&g?IcNO(2j~2%?%Qa#8L3(r=I3(k)1YjoNope$jjzB1JHYHFP_DCoM(StLivavR_5&%9}!}9lNu>>p1R!SgsH_GdtVUK2x zZbiD11m^naY>dqolcP#H5f||)0l>30@O97Z#4Gq!nlXd{nLP9q(GaxR5Qlroq(br> zwuef=jkKN@nk`xFfOLRh^q3#jP*Uf*GsLM6a4VBWBjjc!Qlb1?byFbQIBA255E5wv zEpaSlH&SvoyD__C-laj!LQv{O_Z~>ztjAd4q3KuGZ@h{{Wv6iK!y~&w;?jw)xu$Y(j{L{!+v;1oohc?`-{y8TlCj%3r6^A|K2*jl$e=O}2CM-XYm?SfKBMIHvp zRi4Nx7bHhiTER-37={YSg*7yd`%BSKcp&F;_1^!$l|xk0yFNhj1AJ>xldSA2vI`ky zbfxxHW)U6e+2@s`Pypao+vOJr7P<^_@<#N&z>FrO;UJZCg2qwR%YRa|1SzYUSp;I3 zpFL+E54>>#>gNJhyxUd7RGnzxD=>=O9jyVnA(2#~cqyK{*MZxcGtVDNyfElZ?gj{G zZ*c97aET3jt#-m7xOTZ$9e`N&-h)@n@kcYeI%Fbtp0y$7YJt@v=5btcLo@e1IQ$al zEMqwelRq(cm!IKjUiZ=M>B328zDNLf}2=J)?HUe(8@l% ze91eDpBE9rfQsxO+~PtR219fIW&bI_S3Jxoq_gb`m8AG0D)*)>6WkYk85-L$vo+7b z8rRwpZh0v>3&v_#h4)_{Y?qiTv;Vm%k$jx6n>?3hJS9RFQ)eC0WaJshxU{V;9n8xu z-LPdnOW_QHu_sl9MOMk>?zylXk1=25YZ}S)%7Qo#2Iae>FCK8Ym0P>`>P0HH_6pCu zKcQac@%9v|_|84ux^L9s)*bnJ4H(Jp6}!?CvfsC|UP-}K_@xZnhxwr=y$zzTJ5wU) z4`HI}+YWpZVLAIzdW&NSJD&$UfnYg!ipA@-l4L?asO?e>k-Ua#$2tMVHe_37&KxM6 zD=On7mAL9*U#DkqRv1{bAG`c;Yva0>AuU(@OE(i%0}Ethg09p;)q2mybE!=i2D!yN z((X-HtvG{~QW#(d;t7z*P^<4ilQ0Q?XZo_wZr#<(FASk}Lm@W-X8bEZYDmey`cPe2 z$^yZL*pe3+ED$h&@_nTr6dW#r5*RXSe%yUT$#2>&KmVr4V6;43z33Iq#ROFA#+>i< zn&6$<=c0(qgXaiU5LFy7RyX+#^w(ElT0xC@5F}E<4{S_d*Pty)L2*80NpSF*P%#u@ z`vm~9)zKhLmnUjfLF$`j>qfaVuzjKygTz3bSa7q!JP-vqoxgt1!rWc)If@E9x&oLh zS36K>ek2&?(4(K-Ciy#--^A1RaHP|yp0GVs1nzVT&6^$N)#&t{RJ10#*KsW%nLCMm z=I+@<^TiLJF1kp_T^PKyhLK%K&rZt+M2M!mu>pvCUn{eMhu==G%bTJ(9buhfd{l+3 zsxXK_{w4hwY8xaQiEv<`L#n;StM`X63l{zp941GOU=bx^7ziuk?VBuBEX*vP4{HBJ zxOBy)WBi1#L{UH$0%3q6CDDCKv)j_>rYkcawiC?|8X|r@Op#}To9X6=Q{c`^c{?tK z16@0`OV_DpQR5G$x;IGn6|-O;uYURl2|UVJHhjP>rlh`Bv%?$#LEBA>tIT=Ihyb7= zLTmt6el-zMz*0?2RwSm4WRHK4dcPUdESLe2<0L% zwnv9aFvnP`Pd=yia$=bw25_c1aJ9?|8N@7RMruP~Z7E-QfB~t$MA>G21rz{>OeIAY z@w%LbHS}{5Y2&usWoO0?tW=qa{`0?VQjOJ3pr6f4 z&PDp7TrS5>;!qZ+FW04HJrY6*GW%RQB~8#k5O)B${LwA_aBT#FDp(=Nn1lRzd%w1k z>d_{u!i4Nd#~o1S8&y#+Rm zS!OhXjV(AmpUO5`t$Q9h+wEpv`Z6slHp)OU6mFX`(M%q)X@N{549{rObL!SQtGw5} zScEGSDpI9L9!Cc6*<@FHeyfrps=6q?_M54Kgd|A>e_lMUN%ox|yGtw((DiD?B%<7xBBhwh5~~ z2;4^a#69KFL_(%Bmtyz3E(Kmej;7YkDx1d7&i>I&HojVG7gi8_ya{1`u~c#xx%Ew$ z*j|AzM21+8UQiBb2No-NWNSj%FIeb7p^Ha60ef}5iyMf#u_PQxoC?os*wq=#DO{m8 zk>H9zrb;|_Z~(z_xhHu)?23VH)Gi{YM@GcXI4Kb50$;D{tEQ7!T&;UM%~_FG87a;< z_wjvu7d>+A43A?3{sv<9^3r(iT8mFSKO#rqT#vGCCwL=-CHT>yHJLw7fQ_B##sXp{ zktvyPqdj8CJi5XeED!}Aju=b3{NCsYUMxt+_xqK6L?yU@FquS)EI(j#*fNevZi#=f!UbFr|C;1yeFf3W<`JO-= z>x~-_-rFb!$Q@dTg3}k_n42<-(CJRPb=o;kNBcYdT_{PSlF&3`8D=gxKH^R?uLhxo zpJDln^~Q|X59GOCx5_3sfUo*@JNt0mhPys88)3I1nM<%^H=p2(aPT(D%|4bbk_oRE zs_HYUh;r|V%yhh5P<|!rs}`rA4!OB~(ucnSnUFg~r4zJ(XRn@NJL-qb`<7{(xhFE!~TjKp-=)T9A|!}--UC9dFKvS_}r8q;pE z>pBmzwaBfDM$$oy#V-Tsrjr+tGUFg-R%D0OYBu7ziO&YeG*$zqw-tg@RKyk?|IbjB ze#>FNi1u+g7%0az8D;4KTH_^!S$(z9kVY4SZN%9>X_f&z$ zf9q2KODY#D!1HYMt}-jeMz^LL*Tc5hzN~{!p}ltbJ4=F2P&r~{ zft_XkMWq(nIBy1X$;<;)Z)*Q=vOFymD@5owQ^2mk4BL0@y75qkkzu|ovYRW(fB3d4 z(pQ6rUy;_U#j~7k^!`?!4vVfG;%gl?R)OoCS~rdVOht3XyRD5++FsBVV;Y;{>5_rr z;*}fiyK8IWq|%eWOGwIK%sC*oA(m3eMNKCP$cefhw{A^Wg4?fsKoRhEh-yoI7s>tJ z8c1GS_5sgH$V$turc(!#xWOcqykh5CaMh4bGHibl*4RyRcADG@YH!h)#CqJHhO1yj zMGDL(rCr)pIX9Kq0$YkiD)K0>^X3Pyk;plU5qmmwbv~l>(aHIrZ^4gTT)n_u9o^qu zbh|s|n6ga~%33&O+LMLGV^jk`y+?6(ux89Nb1ulgqFJ)bMH0LHjX6cHA~<_NSu=l6 z_Zx(>V<=k}3aHu&_T4W|9kTP&L;QF_A8}g7v&`M0A~V3;SB$Le&{M*fKu#O)B z%?L6u<|u(g7MusSf!II+z*~H9Xl%t1QII z@V6ZJR~rNo#X>dh>G=~@evARKOIeFOkf>Eovx_V0Rz#N&l)UUwE+ir9vgPTY=K|#q zqQ9r})H?7-3|5JmUAP#&h{&#|QM3U@E6u2IWiix8vIsLv)Ne|@*0zvsNHGAd=5AdS zq|Pe>M_~&o>=+Dz6En>$9XE6w)Yh(S)Y}1zDbd*T=3Wozv1-6x+L@e5bb%amP(X|K zwXvEp|7jkamHlF2JlP@i;>nG-@Z>N=7FUmV-#Tn^UX`R;5BOi>4chDe35Wh%0HA;} zFJQ+J5Z{tlgr{@nxpR5N_T0HL`xYSW)@ z&S#ElYnSTLvQAtR#u97<*0$8t`~g;1DAqw41YqRm@LI4xEJ?5u;g*!y5`j12J;|$! z&Rgh{y7+>R=Fwy~3Jh~dG0V?qnWelC{M8G0$Tk_exM^HT#6pz>dk%}2mlwi@NEpc4 zW)nK2-sea}vIr=GZry}kv*I^)eKs6+Hd|0fe$ro;1}7>icJjAdej9^VAcrsKmr0W5 zlTdPjwqbF9gkt!1U6I1qATnOjUU-qUT)$sw&kadlD2I#}yl0YuL2WYh*drfk@N4OT z9Q#Aq)Gqp#LDo|{dH)Ti3eIF~04aI(4zfa^P~UpHwUGKAzk>UYldlcT-~M?PKyd;X zr6pN8?EX?hivS?n)UcoxDE5TMK**m0-1CEv9_X3RSB+?DhUEd825HfZBOb4^WCvit z-oN{p*k>w>OZJt2j{^1Q?M#X>VHSn=n#;yQ!f)EK?sOU%w#j4W!2^Y0C+LWbQ%})G z$nwpRjEaKr;J2LFfj5+W2UJZ3mIXx!3y_!}o>#9EPI*B?d%l*D?uwPN)J*YK7L!}# z7{|A953~+dVRP!b@Cwax7gFF>Xip(MJ_=Xi@>QaskZH!9-gt&c5ak*WI^t$4#4Zi) z-_u3E?qI`x=0kHYu!jaG)jpk1@bPoA(_RP#!Oh|(b0plBkWgEm7 z%E?Ku1$A z@C7!mp-&^Hn9KK#Y#|60GwhO#`Otf%Lgap)e2J(FD9qZ%$=B8ZJLB=_m(9Ag9FaFd z=J_kW@yIco1^oC9*_W}5oiW-f5LPT}1$)QBKYDSnOG2&xuG4xpEXm$q51CBD835J; zn?OX=mB-YBlv7Ojo}wrmfZq1O$E}EeQiSlkWL%Gz%UEdNT*fLkY3_EOpR*%R7AE3G z2X{FGS}d3^WZC6&ypNKa&g2-81E6Igz1@dIqChsB4_%lF4>GxBb{n?8Pr?O+Zm_;3 zQhTsmOPwUkE^CEdU7_HFf;|mT8*p39e2Vs!6w0dzrZPU5!`*J;qsg)6af)p z!+N=67tZN+Zn1%UrZDb9*lYM*Ko4MW+ia1`cAXqm^Hj=?Uavev>AkNxP{2y;rf%=3 zK)OpgKqO}DXpq~}eM2kvesM0eS4^IN~oJCiN#@TfD+*%YODdCjrfdI!f9Yf47Y)iV#?tBtxZ^V6I9{VMi+qJt` zE?6nbN%uYmarll*%2I{P=V4WTKH)-ovi0&~4*Z+|sa``MnGY2bWkW_D*0yV^1H4=$ z_iylI2i}>WV82}`Gm@_fUvVe+CYIluyb~Q`YoO8Xtp>6geIR!Zh~-ONad zQ~&g4WD?(O7b7Gduq{XtKBlc4^r?-BH1{jfR}VPVE2Bc3nG(;j(Am8Q1euA@-=A)% z?YN%Bsh~%Ep#5hdX02o7oZAg=({_6N6OP-`)K6{g9zHGaL}*+B6eSx{AC!8R_y~%H zIjN_3#89gjuZg?#*3?7g?zHo<*e$H}!1Y%Hd4tp#RlqsmTb0N!ASz}`(nvQ{&jmCf z-M{p!tH!(PecOkPcc=9l4uS@&i9fMkpX3z$tz3?Ga4-~ep@(~rjf(JwtAY;z2IS26Ijr_Y>VLA1J_ zyL#=RE5#w!nzaNo!0@qdPqR9Y@-+yU_=yq>oRC-j;I44_w4)eWScsz}{@?*rvBTDp z7rr?-&8N9&M=j3JSmYe75M&R>Fd#((7%E&Joh&Sd5ZOd(ZcGn1RVu3PBIot` zP;LzML!{0L^dR>vMqiOrkcIudLD4o3<>T_y+bqnRwQ;k)&{EGn`KQ`^9sGId+eB_M z*D`Mv$tp-qFg?F^Rlax#eK?g9lwlXc_=*W=DV768eG@=WU*%$A>OqBA{1K)d=IUxy zE10(~0)EA-Lv@ad$IBZ$F?jI)asADi?$V{V0rV#7*nY}=xnBq!BEl@XzI=H&dGHf# zwux(vK}_kjd= z+AFc0*tbf0>}#RI1{EBVn({s4MxY2Ew2Y_Dc7=?yXz8a7Rigf zxnK!#Dj`A)tR>HI21;?3yfvX5$MEoaAeAg&pFV|??5k#Oavf%cD(y-KSCy7d+0WMb zZuRF1pCz$*=(6z_4n6Q84+317M}S?(aq!4F7+9{UJ%#$^N{&P znjC-YteMqW-Z{Tc4!=;8@yd3u#{@?g2s>=H^045<3w!_DwTVm-GOZmyK$Xmba1%1k{Bj#_4RNLG zc0BtMtx-Il`~D7)X$n`zKE=aAbpj)h*rgT{lqn@#4b8UuE|Rq)-zt#>CzkW=D>BBf zICg=6noT;tq7IMjqf_Z-V^8r1Ddh2NZuD(M&@iJ7E;HXJmE3!*Au_>!50JZ-Sjx^o zJ_4MOgW~K@bWMYtN2Krjkn~o#tg!=e0maz}L~@48=_iu*QiQKF0YEm@KC=Xfa-TFG zIeuh*w~g7~FCA%QdOh*b!GbPXrB5DVyxzvHkf@~K`eX3j40#HGWM2ivQv$?%+91gH zd}dKk*m1kfnZiaYfFf-cDSQkI6PQVB1oA5bepG^%C4KP|I(VV*T1WU@nCD2t*Q&&@ZRz~`)7L~HZ^6JA=VdEKoVsA~2q50{Q?qVXuD`w6C$tc< z5#liB^ubA;@H!yb@li7-v0UTV%BSJ%7x@VlZ5NLbck6r4RqS@h1LL4o;w3ArKb$?0 z$7AYaVx!GcDc>@Bs>wv@$%?9s(;Yz35n0d963f7Z0olsv9?e z21jTi0XXTbdAxKLHipdZ7TOzKYfwX53kFIZjgE8UL`HyA%Scw!AWb!2wJG`}ETr#; zvrC-g(YI2ks1f7ZprNb94p+Mq2We*h9-Wh4E~W zVa!%))Sr#dNW?k4z_Lrs}E5LJ>wJ{q4+1+P%-(bE~48ODQUe<(NW79^s6 zISsI1u(-|+xlD8_z+8T1>1Me>*mz(14y`03|3DRT-m=jZpwo6A%i7t>W?O}G%X3du z?WF^4b-X)w8gi{sQ-I?^Z%!=~Lm z%tCb(h1qYLmC1r;53d2hPs|%fPQHD(BG0`wVtTXK)&u|$(n6-o5)@cgdrOD0wP#xh zfM_QxX_OCiHIEHUN`{f)FtcnBngaLw5e^+mAnZNu7YquYS zZvlTm?{ye0Df@Q5RMu_K0@6n#QP5Jc{@HB%_2dN8rn55F zv)c!6yEH@mOgh}|{4QrySZlO{5|9f2@S5BxZhp2kA@K11!J^j^@Q!XSVBZN;9LC-S zk!K;9LVmFF~uFZIP(g zCJCseZ^H3+U+3)W=V!T81g+z8{mU_F z!!=2Mq{COHE!k%Llwktya$3*@_A8~Qcd%y1B_o--1DNJzvO&l%6-RGVy%~coN>Jh# zf(`{;JH2u*puwicujWa~cP&)-Yu1Dp|F5O@yT8Eyos72Pk0j6w0ss(>4gjDCfCFG_ z>SAbPYUpHVX=ko)W@%&UO#lB$Ml&&WHg>XfaIv(vqjT|aQB{Ei03Pf((EX3OdO!mJ zg8Tvl0Q}c>qp@Xo#D?JerhW`82dLzJQ6r0Guu#pu4$>75M!OV=ASpxYiT!rPol?_c zqSVN~kxwy?uFcHjd_c&FV@WI8RAJI1!5*tshc2r&emn?^kyR5P2BleUB^^xTVs=uh zF=8qI5?$o|+NN9SX{x~4O&y4u}X3My_hVhZ?MUg6}ihRlH7dz`ISH$N$5rRz(Fs`i>?C7LE4>sI_G zq*A4BjT9w~eD^o?w1^>q)pGLc;ph>m?(F(P6k4<5oYo@A471T`3;7+}=#t6E(I4R{ zy|-$~t6c5cS0GL<9{Z?*rQo7oWo|KNCy!QREikVj)Roz{dz&$vC=-?|em2#jOB;82 zcOmIfcfqCyU5krz5@Msdf&MKBsik>jF$G#g--$Tu3XGZSTghM5$sLxivz<(Lp|10s zdhM4UmP?zqRd7R)mD*Q948fb#F7ge>E>!%f>YdNNblH;Jwg~~QN|8bx)IzV$j!)JL zXt!k@lf7emqb)Youvt52(xvKI6;FN1KN>AdZq6-9>`E#n)iO`J`8!kQpX#A2;fyA4 z{bx_b^fBHDRI_g9vO1g`-6;bA@i!-|VRaCEUYwzfark##KVD4E@TveB+uZ=6N z)E#S>inFly!W$ThaZJ)QTtlZ6PbNleFGi%;nhUYHE3#?1KU_ga?BGp*TYIth$=T-2 zTfWU-Qi<)skY$NSJ2_=oEitb87*@3mI|B#fU3k*mj&5Qhcb#O9Sl_VpoTUvE92e-T z>rv!1rV1-=R;Xgq8K0|Acbhm?RDe}JDcsCGTa&CCCbDj=hr9A_5{V!GD*jE6`#dN) z&YAr!(ETgvn!jUfPX)C(;NK_EEuWeZsbjx%+Q4w@3kOce^A3SPBm^_t&)E)q8|1yu z=K+8hB)-px0~T(B!$F4w9|un!fIJwvKkR_11DCGT>A>60w+&zi!5)M?WPH!T1CA&9 zy#H|zLQ+ zUJyMmd`?_^5duLR!blVrLF|Ok5y2zER|tSO!zP46Y!<<31l|z6E__W4hY%hS2{Qw> zFc1MR7Q$c@f=C*}VJHGctcI~L2(h*(#vz++CZ}B1v2<+t%HlhWPY%>k^g)W_I36h? zN8?nOh#a3CaXd^yPQ~dsq1%HHLd?baFcGOB7vofzf?T$3{Ii0q13cHLRaH5!ZK^1t zc*jRZD_YB16*{jfRJ;XPyTPY?rd8!r}JC zZ64||5p`|fs1@Cv7H$)!zvMZb!O8ZX{rg{tNhpB-EJB9^x&J!+?@i`^B#=6p8k*Re z{+}3Xr-7yWAN#*z=zkCANw|iT-4p--I@16ED*q#1n3)>7xH_5Y8{0d({J%MuJGz?5 z$D@vZd-?QyKO-SbbJiq_`d?@$&czxebP2H~L zaNoSm-R<%B{XEFW^Zz*gcAZ-P&Yi*k?fI(Z=jr*re|{Z0ikDCCZR$IZ?z=5ypKQ~8 zh7IR$;o*M3*NPeqq@qvBH zTdd6Q_x<4EX9?VAER5zR=W%7LkH4qepX*){d`{%y?`b$b6p}VIsQky!RM#DTpa1X0 z#}L2YUml&DswKR?Z}XP&@!@>8UF>=G+v|6~4^MA*epq^3_?zuUoLs#MoSlxoJ|0Jn z$LI9?@o~Rn^z`_4&xVe!a^dZD+Hv+eAN%-z=C;aeq?3!ZBPI`EB~I?q zN_~8e78OW(J-!Z3cJ_W5Q$EMq{69V(PU`gkxzRCHhoATN_ql7#--g14@9L=xKX(sZ z`1$nY;$qyhZtv&h;>zkfS8q1w*VfbL?P(x+=4wrt^kd`xEj7K*$Md^DYp(Eo_vgFK z-9DPi_wPwp`n6`=@5RGO$7V!qTeOAG$M@s!5I)})hxDzI|7z8q-|t11ynbK{{3#2s z%(uSB=gcua%_2{zvl)%Gi)Y5VW)RlxauSO(=nYn_X!ZM zyuf9>j_79$=&+158CbUiSeKiQ-C+#$DFzS#M>G@z_27MXmKdNm+ z4_{gVO`kEZ5m)wyA^@2aWL_}Iq5xBgUOXWo9P$l?pk(Gk^B`U0avj`%^m6u5ITYGo zDfT_d>bJE*O_b&i!;U+3Cv4&LLa8!j19R>^;zYJSp<^SFf0eQn>6#pS2ca>EdhM7d z8|d>SAcO=1w087 zw~)4all?Qt>>UGB@I7>v0i7lxOeY~oGgS1!nIFDlXKKE+)j&DXELy1Ui480XQBaY% zL{j#1mO#{{tFfi3m8(4ARlC9~NA}K$d*IL(o<>QQ!4q(x*!oo#SR?1?rS*y~H3rl~ z6TqSbDzw+=&}iqO@%{=$vD#Kfv>2HJ0`n*S(=4qg6Wxd63LHj345p0W$y93?tftTp zWQUrQi+Gnl;j0GupbO9=%Tp8VEmAB}68a1-9bwx_K;RdahviVpCPW5pFww#p;-h4t z4c2U~*lSZtPA9Fq?m?_Y<;N?W;(~;VP@4`}$7%X5LX|J2Oe_Jnrb?pIK$(A>PBa<) zWHXjQ!@ff;jfEWaMN_}i1v#( zmEw-}TU$Y{PYLAg9jq4@Dq^z)3WOLXMxlV2I6iQq~1A$BS-7MBjj*z$6GMizHyOMp++9ZigZ&7F}0@LOC9eDj=SwXQ>piV`R@- z(L1;!wdxeu@ zcTZ!F>kUXoMLgnUm4Ci0Wb6*g8r_!4MQ|rvK5Ml~Y*?WSrDY-MJ^hxhOCac!htv6> z+&m5%Rbl?Nr|3|=-YAva_%>Yz1ty(6MslD$LM>~_E@KV~NCiJmTi-Hbd+7|XZ<%uRY09>I%;b!;2Gn8iz z(;j8F`;SX=4`4~U;r^%^yl5>W5e-k^ypmGrR@)C}`UfIYXe*g`Gti0iGrEDA(P@fWr zyQ6L!j5@aQyrV!(NL$K4b*~!-ASFi@*=rlZ_Jnw&SXfJ7iMNelxs_VC1cfCE3*K@T=W%Y+oBsEytdDj-y?!W|2emCEGk^ z|JdL=%#jM2a&KZ?HAUF^@2{wm-i8qQD-#8b7tYz=^n`;xlYFaml|*)|b##V5NuO{W zX;?VspA4t=(2LhKT&S9kO)p-kz6*%K0yHXz$7tbxT_uP&LJsK!;ZDMyub3p?Z|Nb;hfwrx`ClJT!euVBXD>2^)z403IkWK-A(*z>*XI zURnk3B{&7Gnv}qB*`+c4pFcDP|6rZh{f*hsEx%APR>=x8g&m+&Td+uyL~NrAR&)|h zN~Dg7P&sLh5>_~pRF-S?Y~Q8sz%oGH0LW;{tOXbL*3^TBy!CMqATOJn@El3#858GN zJEWV9rv3F9l@T@_fTC=^it7p)aujrdHMLf;Ra(bQ6anV}sP0$87FrT^f$f7L@e>52 z!R-JpO4w$HRVYDgJcM-st-=A|NnLQJ`e<&f%oVdzF(i4{CNE&V9v*1wpl&eG>dzJD zhUlZ#hyyahrIq^Ql*)8tVWm9KHbW}38CQ@49})e56w#_As#$H8;qddu)hJAPPBee1 z%R_aDRn3A5C9OsEX?R!*f>y3bDfs8fNH~@kHFWB~p=+shmm}R?Kr=~@< zs5XHzy>=M$eGMJ%-3$=P$pNV!f18N?3lsn*$4M$drsM3~XvTK_%wtt^atD-D_@XmlAfhlb8@$wz0l}YN&z&Tn> zqkd`I=F#+5P2&i_NIS0OHzXjogbN18d&Od3STa7@CPpl$Ws%*;)I7lDyaoBpuE;QG z;3(o~glw~^!Ajc=8Hv_{Y$l$&6lTK<(*_uDVHsJdN2GkFkq7-k7+lm~i8{ze+nx=n zXi7R<32Rn{_y#+;-D2JDZMtF(yl^D{0Prhm_8*)61RYmy7$W3Fs1ceMgeztcb)s2O zbtyYOqZ4uMw`d5K={4w}83);$vXx(ZZdY2r&Fn!l)QooqPPbk8R*p=o&6?ZS6=+aSC~dBh>((*naBONdl@GY1_du zaT5CN0;3L;${?XBtwdBrxElzS$2E;p}n!Vs%@^V!vx;JTDe z*`9<7NbNZ9t`POn(5w+m8t|y&VNS1Ux&2tXRJvldm)5jHL)791Y#Cm)vrb*+78E}V z@#$9pnm6cL?^;8*&ZU~=mUV-fqO|wX0()6(%n!1rMejpxQ4wTR26CDoX^mKCd;)uR zmfrbbzjvnH)c%P^mi(U4p&sLFeIAWqs1T=gaj0C0`HN<{oU}zNxLej{<~F3_=1F1HQ&Ep07YzgpFI53vOtO$5V51{uQyN(Ngxh4Uw^V9O1QZ7g*)C2o# z;=Hhl`g-{1;0?3fR0`DXD8hRG53Cj&@-13~ZN}~qh6+s@{8>PfdW)19WY#9@V?N${ zED=aAry?gKySR68oe|uw)U-lvKx942ApIYiAG!&AzGLM|#+-01b93thb6bVh7JlYD zoGV~pjTzi};_0SlE6=lPaamuY+a7hMB#-^lUP&dQG)MQ^AKNg~3#Z6CDh0z(-)ccwEVX-RrGi1q!YI&|~i@>F) zcltugxfNsyfq{(7_hZ5%VdNv#$|pw(KO84lg#aFL%6{lrB4 zQgF&^lf6i_GwYJ8v8Ip&$xZ9*X@dfb+mr=fn?e{HJB(-AUOeu3@@78fHoTq}Cp-A! zJg%k(%6aLUTHE+o!Aiuu()R2mA1IGfupU%PqN&7M^SCgCYA%hUpIcvOXJVPc=P00x z>!H$~59L2I2`MTQGdq>zc=SQ0Ori=Sr@NoWJSt_%0j)*c+cw5$K;>?%%KFf)oZO2% zia$@xDg~vA;2a6cDf%-d(E~}#Vb+kgXxxdas9-6XMQ#khe;7{GE)u96h$ z1cT)>RY%n5N<~>Nz8Gmy({8@V%+SIKbD4pz@ZdgD+9!Ate#F%08_NoLgh>rK4W_t< zlc#FmTSq{+Vz|*kVJdJaD>u=7Z#rxlL&@4{2ej`gx?1ViEJT%$)4%Y@@2MOPp0~SO z5ZW%PoCj_e_8ZI|Hbps`rMal{U#Sw_#&Zg!j`|1KARv+@Y1(H^=^zUm|6pvauZq>N zITNJN6L$LAVDA;=37={z3bM`z<;o!NE19n)lFi)!#*ZDh1%zR43{EB_bZFqhE9)^- zl-qbaL6#GhLTG6AIyeQMp`ns(yv}&Wg@9FtGH%g7MXUV@f|r@)LNM;34SedjN% zdHVl(5td1fZ^;7Q!@W;a{T&ub;i*sb>BMM1Po^r5F;_>ToP7yV8r~ z$!JDPVb2HI`Em5^8*A}&&^318lkXyLhVwNgj-nM2{xc%LvP_;^tZJPQJu+oDdskVg z3FnO!P8jtQ$5nd3bN8@j^=hGfc{XXfmY~OyoB(YMn}`HFI4pe*Tf0?T`}6)dxBK2xbxv3JgI7ZGc+(#T zWaovBJNrQrhemdc5^id!#)e~vO=fk^>1r*+N*I59YfRfi@RVj%fN_6A_pkRY<6&?5 z4)Pi>NlH7d)C3bVQHN#Z*S^5z#@%0(mOjA71qK23nMDS6pU~%e@avd-UI;O|;8ytx z0i{WzhB{fPV^WaOZSh@@s-Sup*!|e5n#&7dUa3JHTGA`hKuNy>cI_`m_`Yhr09&rn zAElBa6X$>Fgfl4PnGy!{+0YyKg+#CXVNQXw#jmT;=8M3VmrdS(L;e_-d@#(os<+zK zoZozmdCK0^g3UT1Bmrw8h(qYDfSY`pU!bpnru zes$#h3ap3al%P$@2q035`6`Ds>*1O;jq}_kA%A7^IFOqR5w!=ew^e^6%K1;WG?S(T zS_Db+TmH;pO20{Kr#y8Lbez1|jIjK(#Ugs0=lQ$;hwC)Dp5nB!E$nES%~)2#Ev9*M zX&UWpNIX3}%z-!qU!msk{(;@DjPD=HkWPgFJp{dVczm}P^s)nzakO_*2v}(1f9fbk zupA(7$e@!yRz$_=kId>BD28=(+yqRj9K3zt4HTl zmvYyS(h*w8=(5}8e-JGudXsXQcDAZu+ZI)mncC+GSM}6xt{m+u=2pXH?FlUmxoQUm z%qaK+$c!Esp)Sso^)`>}vy~_Is?Mto$Y(KY=S91Y!OOs&@3QVuSd;2Ygqc1mto{X3 zxZHDa=v#^#LSvGi@Bs4+u1JZJp=^_RP--`-MxJb#!?MAKpx5Keb3@vy_L`SFwZZz7 zTfUj*gAgS#5EI9?pE0W=$h?2_8w>mJ!MF2AXPzXiq2hIlpz9GQTdpu4DwWxkcoQI= zJDL-;gKCgvbPH=%3{>ct-30YZaGDmIfnw4dt`CuBM|m}#RhUiq7T=A8IJi^5qy+es zpNv&?;ZWS>QBn6;04ryCgJ+*j@i)lgrOBIlyyn!~n$Psjte4|p&?UMI?%kwi9uBmH z5%D7;X%9_IDH}m3j+pz1N+u>=D#LlMJ9;3*N~kG-eylTj0j#|=-q;_3qHww1EKRP7 z`FGE*;%F{}EGBV_CpJbX7#yL-skoFikgE0;YRS7qi9>2(!8*R%NA|m#FR$-4HLDE( zG0cH2b1i~^ic{a@0)?5bs^c|^V{vlkorVxQ$p7w=OFUcLBA##b9_=}D`UU%9)VZ5L zf6FVo%)Qu$?(bbURft5#MiV%F8L4)#9Z17=eHuB;DM{h1_`LurjlWiCE@6Z?lQMGer4ta8yW7UDa5g!hd}4w z$J29zDcwd`<9An;m&K_3q}9NC{dv z^?z~d$9RZEE6Y-E*zeo)tN`>ZoQ%Z)01^sY7k5xHm{`BjTq_5c(rM;AY;!QLfTrJs zWr87c$=2^>$Idb`kreT(C%uFNI5-Ikl^w)H6B;uHR(b&}nt^tF9JKx=~sL4*N9 z3OP=jjmIM^N>!HNPlPN2M{ri-arnF|8k4H27kO!Q;Oc8X$WUilu-BBxmTAi)FNoLx z9T|RiBq;;G(j~-sNX`>$u@w0Yjme2O3ChMHl+dvJl62)MwabQhx1A_3d`uwWKO8U!$@LRErqOM&UfLnSWD zVY>AdDamQ@bR6C^iW=1T>KUfUGv~9=!e&(=_OT!C(2e9;fFwpC^b zX)a(@PD#Jhp*FJX`-i&J8tV^nxKPmK>9w}~JgpwP)5))qMWi!FS;&`QcgGn(3&ruZ z6LDbJHh=a~*-px7A8?)TFGPA^krkKQMwYGCFYd$^0`bh0Ng&w@nio9!d$5+VwArP#lDKuq!`gmKsbfRpMHSSTrM5?1^e1|{8@dQQr10cqZ}Yw*psydVyd%2I?y4*Nhl0jpW2h^&p~Q zX7_+q#Pr-q5AOGG#wOu z_(}(k5^SBKBN=B))AuZ0WvoiE$C8r@yi1G5P5p|3p`YPsYe+4G;pOjX(h>&T8eql} zu~W zxeJLt7!NSL;zSO8ay(41^pjW~pgBACUAqoPk`Rg3Cd2QDrDh%gHd3NCIpeoyOS(B~ z0ZEtxBOY%}wouhbyC!s5{i$JtSS$EI(8T05Rcw6sWT$r>(m=MeGH>xS*su45u_;C8 z&hvC3L%1pZKe0~H>Wj6{dznsXtT`ltjX}iL#x3t?J9c*WWn8(f9%8zMKR~WNEv_wp zOyB9v9i6Ub;n`YsbXCW{1~luK@Ch&LD+Bc~&1w>PX zWtcWB7H!OvGD?--m14+}%*EdCxXNzp%2#M5*h2$Nbma!7QkMOU9TXm~p?j>QeBP;T zJ$Xuxo$?ML0FW_sV00|Vp?IV|!7CK4J@-k6y6y*Rk=FDq&k!#Qz`;&WBK*R z%u;j*5T8!Evs`;$Ersx()tC{X{HF5k!nV6FtqBaV4W)+8S4-{=KZ@$vboH-GZBgg$ zz2%}tU+Epa5?6>RG?lj+gIbe>gcG3`(9%TA&|jt2IACtxb%;2HFEx4EL#BygsK~mF z`nrTJdo3De;s^FsRcXRc+(lNF^dr*8dEUG2h-ZQDD^n-yS}3yTJUacX-t zpSo9F5Z|J5E9=3jeIJ{NL9|U_IV2j+wreKt3<;)n*nI(7BxztR+BAp7cnWOq;Ub&*OIW=qCn&E{@vfaD*W5E_Ow;dxlB4b zAicjJV%j4ClA+j0%yi%4M;(-F!oVK1vj8e_-qPS}we_r0P`7&QT}`r8ph|pcOQvsk z=%)!z)>{z_1+ltHw|$TZV4nJ}teo8xdcq3grs%yBmyfo-_Mcsa#rQr0hagfVFq=>#6R8}P zq#xZ;dugo6XsthxT^4de(Ac&ZeDFBC)a3zc_3w#ng18Yttf|C^L!m9_tppt~-~bxO zc48I^pHy?uS|3%$5IMah1iCS)7kknH^p(5uyPiQnx)4V%P7LcBzM-DALVkRk13&vs z^O}64E?+`Q{#|{D8_aV`2M^|#)-Q569f{3}&Yo$i?`7$+b)&vM_!BHlUFv|F#BP!9 zgw-XTH~gKa(Z@|7n8Fl2XmAru-H0pcfG6{LheE(H8dlyO1NnktT3;qb5{_WFR%~b0 z$WZ;j0M2H^e&pys8zF}(&^8AWxk?KtK1%_d4CPHLK1W*qg+mY-EOL3*Txx(t+xBA)Hy zN935g>b=S8U0nJ0v<2?AZ;|G+2Mo?CBS;0+5R5gL4Z#K!GGZa|kS3~xt6)^W7)a0} znI%z~FgfA3YM6-0=;2scdZ+sZ=XQ!CFC&^!gm!A!N> z>fq#znDPM$4koxhAmP?B0TD%@FD?v2tS%?hmKyxAH!+pJXSMCw&K(@{N34B>c+g7p zVUzxKraI1j+7g`vy$nU9CzUg;{9x)Mc6oR4PS=Jc0g~elNxAjg@hD7O>*x!Wp*3M1 z;eVf`eV{xVZJH|{)93c7f!j{adIBg4U|NPJUBm0g*n?zHKemiGO%{Gh#W7+R_O%G5G7YN^=%S|=@UZ|sdm@>bQ$!!MT%X6WZSe}^cs z+a1!sPqW;6lR>QfB&;zS+`xX&gqC^-vrpihO(u-ESzT)(>hdh}DOEz{HUqX|?R$2$ znBjF;V2YocV5ZoNla<(ebrf4(8yy$r9}?i!3Ql3Z0RG7tRZ3X^t$ZUIR=%Bl~-naro$CD1CQQyR=|;N|J@gR z6>VX@qK`otglA%g+z?KFs#&51(t3`1RKIRSpTwkF)+3e#vBa?S$?l(#X+lF&Wf1B; z#}TL$3sv<#>Hxi_y7;plm1AtBmf;jPGsZrw0;H6L&z4O2{SkycTOP-$pGX1uFVWt= zP3+RxhNOiaHdwsYb)H`im>LqImZ3HAhl@KthG0 zTS+xiNlbA+fBlg>j@ zK1W0|LCXQJPBN zb8^T@HM4)oc5s53)&g4LE}h>l5E9{ z_$$)&L;eEuVW0vpsu8<&zGxx>A}Xrru#()R&r1+n{ohQ(bb4mPXzvlA^2_@a*nWTD zg!b9tVmX9YMX}!9({}$NHOx(@1GBCxUv;v4W0a}?xk2&x{l5a^$+|0UD8v4 zbIXr?N0nn<=c9Sml69{4%@9*dO{%+ouq27XJJXPV|6|*|p;yN_G<$3<2wbrHN zMtH(SyR_IMgHYOu>T)EQdj!Plq{33w=UFW&lMw2K`O=Tti$MBAIitC%x-F655mW&2 zRHEN6*mv{`xho%QL&9>AyRX9sM$Ygk+m1~POVEW%J9S+sqTz3QjVhfqVEBf47|{}O zxV)|)-j(LkoFpI#9hk@(4L6FWZg0JfapBU*%Dd37el~xK<4`DVnmen8r!4pJOIa== z&c4#<9HXl87G!n&CHi`~+^m`H?QV_YD(tNV<#^AA z8zwIS7Dy@(Zr&^GN5P(Kz0{OhIv|OqNWap0KXr9g1e4ccL*Rm@o_N%*fai72l6cj5 zB3`vs(oDzf0xY{|p|m?*$1Uyr`C7?1aV7e}U;L2P&K%v%3;cBZOU6P%efB{MFJuo+>XCsE{g?1A~;n9@|6ZQd4wnQP~H&%1^myW~Fdr_jv&OWF|S zv+{f_wM016!6Qp&VUhkQmo&&$8<`L@8==q*$TPVgb_G_DbuHJNulVi5_;n8 zoXEv5HxN?VC&33JCV$}9_bo{qLnP5=s!$t)NSP5ZZ4TZgzg|A}u=RA2!$9x?YKT*F zrz9l$Ou?#2WYH(6Q$*(^q`4n|Nlqz=91)`d3SLx1%VdHCIfcPtIjBY$xxyxr{y9Pt zFvtfWK0z@FqSqdi8tm_Xk@}N_z*WQxHoZ^4Ku+CUl906U^B7i?gxf43l_E_@d3#e) z1`bD&$OXs^@4J(be7QnyCz*nO&AoIF=fjH_qaXlBLh`$n1jzTapzKB3Fr#y>G=83F zNchOW?O7mwZorlLc@bc*g>Xfq9|oOCrLDiC!@6}W5PqQS5=X?K3^EW=w;~5uiZo>r>u@h9C2$_B$iN`;fkf9IaF#fvG^Hl^M)63$ zVa+Se$2v$z35@)f_J2tIj}V|U4cyYu6ku%@0fj}qH?MSWzVDF#GuHf3br^F18UzFt z6$C`>|H}z!Xk!j=v~;$x)&Gw(lHSnK%>Gya`oxfMM3h4d=U>|+%y*PTl zy{{-Bx{aQUW=`h%ejJ@|0egLW+TU)!RyY7Xy4{cqsw_2kysm-F|dyZiUvNvp<~m>s?)QFTFCM|;>JA{LwUuFZ zbi?m^33Qgh^Zo4TVoyLJmmZEr%r@9XJhV{mJD@9Fgg zcrd(o_IP*w+8cZ~J9#n5K~-Xp+^*u@{FpO8Ms}pLKDb@BN^-14}ojR7qTo8ozl&V|f`Os`&6jJhvr)lmWEff<>D ziPqR6SvKw&Zqp@$Rl%B-)tKyr$VrodB)DM)hNX-t8jJ{tDueEu%@0+osO)civM}M# zPiT@o8mzQQ%I!_U%K5WwcY&^Akz56DHy=X^E}X;@yFurvETKL*@>lt#qFPaPnHhS5 zrkY?mZc#Z4QBy-#I{Qz@&Y^<}5BGD|UMy7PY}t^c&r*eIAQhZ(9&RwFdP zpS{bXa<+#HWr^SXP0YaDq;fbRYL6GjEzjicD_%ixm~SY*mRAoGNl}zE#?_^aZrQvu zZV;xGqO=dwb4+q#SAuM1it{2Dao^n2nz*3;xoL}g0O{I1DCn~Vjw_!{i>{%;EGa^v zy>CgDI21{pNlM6T+=p+)WwSlR1aORE$p|hTFvOe`wx3Y>O_p1cD1cJ}kK`%$&&^q# zn#L=}i+x$R<*#Bo5~a1MxmuVpQNI)jj5xA3iddT31zJlIlL&7 zoO;c?Odg1X`DERRxGZ)bQ=-8cNh$8Yh|hW$&E43k=8uLNHqLZrU3FRH>ntbu^bZ3j zS1VjbCR=FXBT$g4Nj9-jKt(%I1{KmjspZ_owf&8$9LllyDxpnv>iLWb`jP!`q(r^b zRK?18?1)qoGU7Hlm=UmGE;LcKqud*`o_1F<2I^=-npnP@s z=x`sd1>JIK7P%r?%EknuVaiG-vAZY?V`e$QChj8E92HcJ%@N}WZq#YY|_*xLPJ@t&HABPKM5eB4N@Gxo8Vu%$SGXL;LGUImHqWc9RCe9DDL! zlKpklV$ob>-}4mZ{@p=;un5&b$xkkK^&KNhCT^ z=p{;i6dl(pr;xJkIcA_p<){pq^R2kYi9lx=4y zC-<~sbiS$)d3zj0dwC^>$0=QBk|Rpo0p}8WmuP~!Yer?YV7&6bR(AP>^3z&rrpymz zwerfbHmDYy?6byb0}C)T*035P?eviD7iKRPr?N9)O-4=VBgu)B8P-w}mOo|i`>tu> zd5oynT&5#tiX3dLEs;Ol5qRWb%t@UX&m98s@RvFg@C8W#n1S+9+d5&g=~ zp~ZoT$L=cMDxyu~l*2tyY&JiVbr&UztQGo%mZJnL@1HF6GBI(m=Sg} zE^{VXW#_B$m4_6%%KzCOo7MQ}^}rN+PC*2X*3RJZul?Y7!;c;;KNhw0hpLdyFR`?O zr%w?syM%9!?S7=BbDEKP!Pr3+2kJpqH@a;GYkgfOdP3MXn6E6R%G+yokPI_0!KQ)# zl-Crw$ICK0y#5LIQ0)i63p>{k-obEjj@=Acc@-0Pe9H!BM4m5Q?1cG4eSFgFe4BB6 z7upOKjn+$7O#7IN9b83e;!xTkX&?QZeUB+k305^oDUd2e&f<=9$_dp>bbTEclbCN% z0wLtK-8fj|9CN~ZL4tX!s=`i=*2s9$Dow>*(EuV14H-tCE-S4h6OrU+VSbwiNYml+N8#)?~`y%>M4ndM_^sK-c6s{6WK zRN!=4!KZjZ`d)(-c4ZGQ8aA6b$(*)lX&s5K~rqW`#q zioqV2|4m8xd)}1pIi#khddeLY#wzQ68-g6h zHofq@!iKr0@Ruy1SEG*jh#8&}>y~V@4Y<*Y%Y?0-Icy5MGe1L^$Rm*+-Thi1vqrUe zmjw!IfvNH=IrZ>mAow4oXw$#?Sl;htlO|n`ha02JT0;wI-;bK;Clo7PNb) z{bWZ20ntqX0g?QFj%N(8GqW^zas012IMEwBx&CJc|2xXJu2kv)r(@51tHnJibd}FT zruohY#(T|ZQd+uqb#=3<%j?QJY(hetKZ=x+R`$_|tXgI8ZlEBoJ~-H4>jr7|NSlYR zjlDL7n6vlTwZQMjE%QhdHU*Bzs|2r;hv8B7-$f4|U4ETXCj=V@z(|*t@#RwBhixux z%$n=ML&E}5b}NG}S*XZUO7Z);mrk!-%J_w=7w1<>)eG`ytV@$yGWk1v4)aO8IMwoa z?t^IukFTf~$HSxj)bhAPr+_Mq-omGumv(i->oHnw-Mb{W*G|KMck67+JM}QLt_c*J zv+ri<82}cJaEoI^mm<8!3~KxXUZ)GlTjDK~cwm`}|Bq7S2Y8rv^+=@Om}&%G2Mc}l z!O}iiI!Xc(2@-&I$@uph&xO2~>T7rGXV0u&-<*0TWiAho3S(aKzAx~NUM6=tF36&# zMbb#wapXc#<8m3Ck=Clun46^6MgR=m$ac8_qN*$9^k<)jsrhM#9BXuI-=tC726?=g zI6FkQ9IWgXPA!}J{iHjpsK48pV=rX73m%yrY~>F3UMd?s72n_l^0mVos^h-70LJ__ zh_6VA-hKMEqoW7D`7ORcBf;kPsuURf`qdh@TmDSfojv1RTVI9kg_)QzsiUt0SG9L) zFJ03Q@rS|IVZit0wRTPd4o1W17`tLL1bB1ir7*KV-(cX*R*_tH*>jCTr7`<#XJ@$U z8MzjopYyLK4~LhQ80P(tPovD9@R%OcHw5eXx!0Vk6)B0Go7-;7mCU+^hUv3Y`hQ3N zxRal87}pN_h6|+kuOQIqFG{4O;xGF!iSRt; z8v)Xn&l~>^*?Un)*VDLJJ4D<@gZjvpO*ZK{_&KRk<`#MQ+6BAXraH&z_1YP`M{4wS ziOt#x`x)PcPH({^J;8RW?b=kY4J?XZ_BFy<1wX;-%BU!7?*%IJwT{%&zrHHaCskmU z2d7|cM@&}X3;5*Kk=u&D5w|F?VRwes2$Km@vqf@<=wgkWuWh3-oHe8GS2Yhf>mJoO zi-jk_AOc`@Ld#LekTm^auZ|hVHQoRc+m-1>A5!i7a9tJUna#k%c8sA_CZFr^Y(GX zw}{W#N4Cq4mGHZz_H0JB$<9MZHmgdWn{m3xK<)@D51sZu0i|*h3h;ZQQayfx!kH@= z%7X^x&AO%U`*Mk`tu{d(mm;0iL816X?xRcLKGF~Hg^Qh6DGr~w#(Ilqut_{ShO!V5 z*XMK&&KhoeIvt)ev|CGC)3-Z~2m2&{UIJJ`PerW2w>T>PU_NNC2zaR+&#CM0a~GX? zsqvs+c$p%&zoBT5H217z&8g^njwm!UunX*>w48OYlE0@7HCq@tt`J8+i${`^2hkPS zfl?1~ZcX=+z0JYmPzga?;t z*`!yLckuhtVER%Z5iQP`2K%(<<}D6UK7m<05pmx0V!UmQ`BOcuI}R~jRxu;^QC{+> zxVrAoweA&B^Z*3sYEAE2;BGSH3N0y(z zh{18|rwy40s<45lHJ!5q!$uwIEH?#dZ>CEBYF@R9cv+s>9VC!+M2`@il=BUz1QIf*P;6Gpcb_w*Lbn;n{xPAmJ5ZMn3)L^h$1Q|ujX$}XWt-7KQhRx zD6@tjw`eN)t#G5LRiN`Wdn#{wma=+v(Dq7awvIMp1Sv!gNGJ^FJh~ z!{D?>Sh#gCk86vcPv&P(=fmisZ>tasYf0ica;ZceT_9i13N)gDfoCShMe!|v+(X~* z8JBKXKyIld4pFu+;ibWK8S7{eiJgczw;>&4lruPuQ*_%y7`B-%O<&V)fD4j`25sB~ zo4>T0U{toBBXl$TAOChHn@-3X1&Kse+23r1tmoqHYLR<=V{tCTnwDmOorSdxE)w*5 z$xHa6#{p_#I9oOuYR&xaBhjV*EVel! z6--?CGa&R@P(ih-IUW8A5}|o{Kex&{S3u!>Hq6gZMx6Ah1JV2FwLuf9PlCvuOsi>t zAY;PJbp}H;HY&&;=__wpUV)C5_X7};ifPiwvV#=*jgQ?oLgqp1uFLW1V;+8^N3Jk0 z25rV{p%A3VM4D1dk2&9Pz1Fkkr6*D?tFGC=5+wr!VYX${OZyFdfFK17zz zu|b(ZPiAJ}O9+f`Bw0(azunEmM9Fu{{)(@8sG#1y8EMpXvWj|t2)qCIF0RBEUGb?| z?W48GbQ69b$XGWi=N8YoAZc!Bsp)wwt8o6VRt1T87Kf41p203l7dcUkxeJ$@TU0D; zhvvk_O7+LIDcJSb5AclUh|!MEPr*GSLYWT>_0OOQd#RHA2o>#D_@z_VuNj~?PtCGq zb@^Z%4+c6oLZ#VCXvmBtBmMJG+J2ff#WBovPb+Od0& z8woe^821dF0TF0wj)7Z8qiBO)Xk{8;XQ)QjgLti{EX4yR6HaKcO_G9-k3&K0@-v?3 zq^M}#1HA*QHy&=AL?;!UfK|<#W$7L>ANnv!1lowVH%)YvN@7N)u-viVN_4TLqp)S0 zQ!$Q6%zg^HfVRKTi?Z|-a9s3f0o+vvc3Pw3n!x1ricp;GXeQ(Vy=y#iZW!lKn+MGE)6cJl%YXJ9s;$7yMcF?`xw57o zvihlC%q>#cpckvUYpTI5QUw2&WDR^*r_;6IxYCV|G>CTuqK%uq8Bu`m3u>1Ck}0R4gyV}#G; z)WUPSnfx+G&I5Q6Ou*70P**T z!X^@qUBk-x5`iADE;nF^0*DbGnfI|~w3RUa#4uQLbaTU?=e1DB(&@$wtBvnEuUX|i zW)IaUC+Z{O><<^;XsWs&K9Zp|V7@a+L&)!18{_YpD$%nl-d!HT~1MF7VPU<^9A$jK0fjx?Y8(Z&>KUq^ zymlD5o99~4(+kjUX;17Bb+9>)Z0SkQ0ogPi~2*JK8kM^AX226>R zZi17*C_4-!%LY)*C7PS;XV23LUb!x5;DyP1^9$G7*SVI<>mRO>0JO$=n2+MI&H%1F zsEia9DBwz%imtSrJKdeDB18Lr)p>Dmlf`GzpQ5odolkrNVJ`9Qz_ijl0sy00Y^O9s zmhW8Fdn*2%P+T`28K6tW#K09VUH5#MddjTUJ%zw8_N^bpR%e+zAw3Kkl>+1Z2AsAtt@17bDn;F5IQ={;S0h;KL5E(+g1j31Yek5%N+0f!P!+u zOoa~Y;y`1#Q{BJSMU^O@S|#Eb=9`q|yOH#$-)@F)8@e6#gm>?lxFLk}i}; z7PcTLRXg=3u;>Vo>memjYNqUVsX%TUtIFkh!*pECT7NY z_8SOiT4bn3iSU)X$P!g|Uw9W?rzx3aOC{KhaU6M$_Ivg;_-juDTAn@aVX7Jg^Mw-@ zCXf!hwUkb!thejI>29?*e(s2fVa`JQnYl&rh871&i^uICUK%ENj+;Ga>=BvN>`^Q; z=BHU~FMstG4BR6lzG~kunNrO4YcYM=`TnZ)`;^d=xoAd~rX)1C4XSHbrMk+!2&bM7 zhU_f4m5Y#xb@xzD1YGxCwa&AD1lK?LD(D@Nj*4Ax^K4OLj6CDEa@OJkp6o7>xJKhA zFka@Zp)9~?`iUTT4=9(LGHg@ofwIgkNeHV_4)JgpY88Yuoo!ktAMVMKw9o^z*m_Sa zReMRLvB-%kyKSr%%N}p@@yVY}V^;HMDm4i|rpRjEnjP#yoK4BXN?~lLD z$T5%LZtyskZ`%=@;ok)zq@e=F{TZOWwWSXJX35wMp+Q15o-l>$oQH)os-&lf!|oQa zd+!QyMlWb`n!ww8rzu+5uzKE?*jMI)V|ECt#{z8=ys<;wi2T`h$HML(>ZdaTVD;z* zVF4335={;xBw?)4@T7<)79=xct004BV!kDD)0HZ?fuxh_aZ1S!_cNBlfq4Zfv3S5` zDz%Z0`e0#x$!$iXY*+loqFbA}b;B}h{O9m#cA3>2q-+m(#uGfo*rN(*d6?{&QWLgW zcEE4))65TS69jRuYo%#^vH@&OP|D;3I=aL0LAlBX3qR+8fppu{yw^RmOv5Eq3Lv7UlTDt1c*ws?=bbhIpZ9_KT*uA|^a1blUFn>jXE?C}NNCEt#zs-WD#8Y3G*&u? z=b#m&iR;3^ z#c!R!j}bs_ypL`Bv+LZV6M}_>fDT?hGr1Vg2xxF`J2d(nFVy%p;dT;~><+rUwilrbx_L*^%()j*;MNt>=+y z;T?{N4D2=$4&wF1NW1d-Q-n>bdLV@AAV7cUX}nxv^)-X<+o{XYiKgj&Xe<6!so6{Z+34~wf zr#zmhSiYhD=fS&RK8|Fh4G0JhIw*+L|8w8g?7s`UxfZm;LJ%gII0S2q3oFvIu$lBwU_5~9A(_iJdbUva6I%U5mBYi@4u z`_}`Y^YZ&;+D*Xw{c~lf*P~_hjAQnZ^ZQLJW&x11SKIq`oa^_wMf2D|^@);oFB`*HnnLeP0#`@K&g;QD5+@AD}j@cmr-_4)8D;JP#m z{08`aZd_k(_`TXbj7rTsCv;|Kh=3on_z zr6(Rbj|6Y0JFmi@-;IzLqd9kg?+*&U*Da0j;Kw<4pZ5#D_ovL+e_Qsc@B6;B{q^)O zg`%L|@AJ;u_Hhn?K+CMY@8{ODz{gc?Z~HmM_uK3D>+!$O@Kp2u_m2Bl0nWn{j;~Si z$B&sE&w7ma*`80}%8oa%Qs1|8yZ5neKXtTIPuCM}3TN$!^HaQqZ>BfJ_TQgv zxWf4Uy72pW(g5^&W&1oYT|8z2Vj2a$hI?P8YYEK14`;vjDSAJ~a}Sm6&U&_5-g|C5 z4%?rzA`pICIVDZ^axLy~TTl3$JpbKu?KYC}vGY0HJjt=MUHSbebf{&Z@Z;wJ?!A??P(&|PA}(yY&f0dnO<&P^;W?PfS=RpEHs9cl z=LJ~ZZHzWIXH718JC;$=yA<)2*2m-m0IlU*c2ROGmql66{pOEcR>&hRr<3n`Z|K(fsoPdGJ_?)HtE?bwwuN?8Jk>cxoz`Ab z_$r?wK|3XOjhp+4~+^j7v};|TneE1jq1^;P$}Racv$#fsta8V>pFMaYL=)<>t- zO-ik4hNKFaS{u2Mq00#+@tr4eUh?gi4zrd!_owhmi!a;dcoosb@UxBr1kGMpbcjCV zrz$wwlS;6**B){X5z*c`2Mc5P6baYfpKINv!1zL9R`#2_Xf`&sTV1Co#~z@6LU8zd zMT%5HvK+CM1)aLUAiI;al<~<{e(G~WvaZ6`wygW7k6au2)?JDm0d=`XLQ#wfgudZg z*i?ou^SJI-Ys>y+_Qo>Ua#~CRZQ-wrChL0H;QPN>*Pgje1DY%~J_(&I?B;9~C^enn zU>Fxei|g3ylASx1pOT%YhU zfpb10OkFzs8CPFknYOv%cs)A}=uR*W*|hx$%U!ujD{|rbTZJJ{nvYUE_RiKdRUDES z7p0*eIbA}TKIpT}{JPwlZ`qM#>>(xc164!AA`Lx{2|ha=Owoy7*29@{-}ITJ+AC%> zr_}H}n@y^A|B_xQo6#awmiFUV>h8%o2foF@7=~BE7b_(UEl*vzYzeM_A15qy;?do) zXOLINqALq-v`Q(+Uxi(oDp#UL;42&T2R2KWBKSzq1I#?Ww#_`UjdLdDGITnPKKEwG zydLKo=qdoH!>s^amI5U)bSWn{cq@F~&Z^X$;u4?$f@xuNAfo~fkBYJkk5ELOdL#&z zA$B`0(X0;~UwB9%g{8gqc}pV~n-LN6qk%T07>E1&;N| zgF}HIzu-zymNNtyV@{gFIc0=T>M@(piw`(dims*xa{mP<5^HkhCRW-p#gV#K)P|Te z7rDDy$IFphY@n^RfmPKnxrskoY5wN4!PMIJ@s3wtTI7{M(7JT>X+l5k^Nr|CP_>l< z;4e?0400z0^=X}5&==e4+Q8JPC6UfHGW2w5BvyjX$4Gn69_=11HYA*HZ^+imoO_rx z@Aal>U9ItE`gy5WIxia3HnUBlAa#gs+4@@Yo*^(3(3<`i09Zh$za;_8V@mw*1EV^7 z!FO#e8D2Ac-)dMT%yhF`e`AZa-C|OUg>d-S2U%SzBP%*boqa^_o+avzW6FUV4t<*QqKvx)0?y^GiDdQo0r(*z}p zZOn-wZ9L@OG;bU&?TQ)xRyE6}khIeS#nTMzSB(l@Cm$#=E=AX^3t7jNb6V&vq~WXi zE^AAB<;unjFR5y!>{={qe#+?&H!3qFV;;*lFzb6?J{N5&Ymo8s`jalILsE(M_xlxr71 z?S@sOS(d<`S_8YhQ%P#Mg-xpoJLo2XCXumWfTg)^Nn2RER@!>n0@oetsL9vp5haoI zkt4LUip*A?vuM^V1O^L3Y9CKY61r*SYs4gw((QUw5LIl}hNU&{y7`QEUPidL#6fZn zeR*RS@K2e3&;R8Jl{9_GDE{pLkk&}iV5MdSSI|o6nC9EvjB#CRj%Zcuq2N^AT4B}BcaU~oFjh~E8Jw%gE*Ba3#lSLw{=k3;hT4WMjl~7KI zawMs|of(#$Wx2M41k=8Sjk|4_MT61qD_GVw8~6cw=S_M{c7>EIGev|f+04-l>mM>0 zk!GHB$usXn30!YdB4X^sB{dP$ILk6)hL;2Gl?J9edPI;+gM|3CjV~Equ;zy6O*6Vp z`Eomjb`h7gZ8K!m4T7!>W;m zCzaM-Z@-&{2lGk`8y(o>C?i6osy?Xwo~1U7xe(AY~8RjVFMVi|lTf-st0X=*7jB2PD|&-=J>!JQf4E^k`5ciA11 z8m%qOsWVclQ&L<{a3_3HWK8B|b;yL$<<7YroUym|Qj&4A+C{J+WpA$}9-ulK+$HaP0dq2zO_4=@8!nMOz z@XFAv>V8U$>X);r1+LXAouS*%dY6#X)aeyz9mG6NGI0;WKiUl%F+2%Lyq94#O=o(@ zLEZZ{v15{NEaY)HCNpA{Hl>&)ozxbQIKu1R_%5W6mPA9}S&M2xvQ-H{Y71^iNexyC zi%>#bgI~g2k~+hVlF#IkljkIDx3S0_$&hs?J8#OO^d7`0mbodvv3`87Haw>^MMdlK zc0J165~@n_vq_Qaah5Z=KV|S^(~R>_t(Q?PlA$u~s~51Omu%yLKj3(sEK6(uxjyj= z(p;e6mf%Q4w1hD$aO728u+Y+sE5j%lVYxWSW za-#=+L~}4mT2vZO7fFD|THP`(izKLn8rmIq;`t8n5=WB}v)e`2X08&@l8DW&QP-xD z%Oa`Cy+{(`ze|@|b!y{lE7aI>+FOK|hGdpKA!#Vthg*+)!z3!AS+5b*pITCmoD_Qu zGVu~pS|8rgL7D{Fp9IVM?Rs>LNKdw_K2iqT{@itsV5R)bcAlMnwwJV|G$5MZ7H+w1 zV&ujCdTvoHN&8ajCFvb@n+gxD0mJOqV9BG>-0$p1%g;%8o)Fl(y%{kpEt;)6_TR$S zknyhRl+hjv?BTpQ?qX?<%*-o}(Xjg@c_q^<(i0m1*HIGoHVH{v*=yO{N|QMyPu7t% zX(0_CgPTYoS(nPD>UjTHd=Zk?y=bNNkigQZvPSf6ySFtONef3WIVB+jC?OpryX_j| z+YVyf_9`#$)n?E}kljs$bKb5;+Q%}GBn)!` zwDx$X*B8M7mR~05)NrU@YC*?%E0I;L5|Undb8t^u*g*~HBu&UJvq5HVy%U(w%GMfw z6Ve!u1fnKRpHWtPK$=oImUP%W#Ic;6om`UbhIBbkpheb*tX1u98Ie-->|WiZHYY|> zvv`q}B#FmrExd!6poUy_J+6^vJ@0poB!-sliJhEICd)9a7j~R^Ld(?#u{N+2P?Ga! zBY@Mgt))GklOV3WV~+V^iE0_14MCn*Pg2y9oHFEDdY2>JJE}wp;iSkKudSP8Q{ zM0GpVDMr;HOIwLuzb+8dak|2L$FKMlZZ(A`}rDUho7Ggo_1k;gW{pj0P!czK<;VuX++F|e-m2NBKLcXgYkv6upVwCZlc)pvdf$#{d9Oes+j%S zzz(-LUo2%Pp_p0>CllKrS~!(U@GAZxb&2Myh+18e(_PyTr)-c4midP@Ie#QsOx6|W zmf4hRPS~eKT1qBaXqAC#2iM?tNHK<;%H7Ijvmr3SlAG zG{=&ZzUdFC;=841Vxmy7a*dr3A-}yPN!nrMqdC@45Fc``NZz~DQX?N>$CbQeCG;AS zMKZbC?{y|=U@vKgb6Aa2vP+YtT&5}hF(c7eaW^aJJr7Uc@uQk##OvrJ9uis9B46FB zm7FjNBu$nXsS5}0iBQ{)OBgO1thYXpY}X~QnW_mRis1F7;BY1Dxk#{|c9FoWdN!Z%& z>S1NX9qG}g7n5}89&6e@wiK0AY2xlf1Z-C4(#iAVdp`}rDHD01nY&$&G7y4|jlE-) zI>DzE)WQx3d7bK-`3JJmEv~OP)Bfp#wC;1_}k=+roMOiIGj-`IQ3G+F0 zy@7hrFiVCwcg^)$xsuTOm_0r1wk~y0NWrp@-P3R8EVi4i}__<@iH$a-I&}HETdaIp0@gyxKK8MTwx*{yKG$9 z_wp_bx{w5wK6T2ur#6Buw}?4TEckBSu7Q<+$7yy7 zg+PuzB#l7M;2`5}uLmHR)fl!7?1HQs0L?<6>gS_KqgS>cBy zmrr&Y(0Dam9&JIrNn>iEglmup7?d|UdpANr4?r566*+P5Cy7#fDtQvOs@OjT+|Ldu zspdB(nKb+{sS=cl#ABq83bGDE&kvJ&e}~GP-CAFz0`K9cq-aB zT3fP4JGLx;$S{zv(uC9ihe(ra5(lo0k0pZjRynk>btT}Xm=F+F$g)_`k1Qr8S|+4+ zxjZxPgQ=U+Y&L>G9O_>i`R9;_L-Irt#bFjN68RZ&H>6R$9md3z#m_@upO3C+uX)Fj zAk{y5j4BS*1MNlV_-tY=WXO2{5$?#JK@7wjX68)1Rs#2pC5~Ic8kXFOJUVMxijUk` zbsDD=2m&XyV$LM*w3+#ycT;~j*3!f>v9v=i4mFb!HH2v**V&$bLe^~ZHVeQ8IXD45 z)0ALN0HKL1@TR7AtSm{H+P2lOFQwflxzvrAg;amW$JPe7uI~iN9|X(?MZ;K@b|+hI)2=%`V60 zc0IbL8@21O1sXZ2n4vW9r+lLn{+9{ICE7C{a?4Bf$=H`-l<-->l`sCvfT)BM4G1K9 zqKh-KIYcQ@Vi_AX0<~T9Gzj~ox3hdWUD{7W->NMez(|={fagjCbv{^Y+M;r4@nR!z zQE^&!Otgr;N;D)SoxGk-td1y(o=I+k=E3+Na!(u1d?RY2ff)KUEp+YC;$5+oKnn!2 z10QeT4a~SVtQ~^!J}VJB3(aC&x+{zUk?;@{V5zSO9gBKr3c!O>RflDdvq#4vWUT@;j1`T9 zzny+CJ@kMTPmHwW=>R3FK_L<xIRImPHP5P60w7t0I%X;<~5>P3p=wq09{dv;La*tzlFO+$M}p#%QSw^5|XwaD2+mhJLYBEJoTmZr+4DXb+d=#z(!S^Sf^WdhAoW~t9}ap7 z&{6JIjaoW@>{ehd>Kl&0q-4?Ae2IhSqw9$@4Lqp!Y3|NX>EqBjZ*qUquzH>fbjKjD zPFaY-`%xfJq|0?M=)B**hv1f$$v_H9kKw=uCD&ITaSQ-m(%naKNx5D@ zczyu)CCQX-GFR?Tzg#1>+quz{$Z)H>PY)kqjL$uTXP ztv+rm($}1=1S~Q6Y%6J|Ns?%r+h3IyBTl|)^OukB{nQqcN|iN(19 zH=rS&!HjI220J$!HCXQ}Ntl-+1Yn!vxBWcH7_j#{krT|aDw8@gH2HLGSU}*zhy9>c zJhDjuHIN5386VPI3qYnnF>}B#1YHQO%b-GbfJZZMV^V}r2XHe@V1138Wi1@Kqij?m zNwX%WF?Yn6l+6~o+EC&~-jg*6H{^N=)h^XOvTRxH66%s})cQ={DmjNn9f@q*l9#$A z&Y(>oi*XRxmC%y^BPZOd8N3rwp|QZv4J%=jH|DheAn&>XkakL{qPhzqx4?5~a{`y0 zVNajm`-yRpmh6_fx9gFtOhVJc5OiE1i74P?y*w+Kj>rXZ^3#B}<>a82z(_PeUkHoL zJhKa4W_0x>+CSR-^6V{Y2&hjWTR9wA$xlr;hI5hWPg2i0fVUIckIX~TE|uX_YwyMsj{`r&FoSf8xyD)805ML(^C~r&WWgC#fyIVeI$6T}!!daCRXc1tv zz9dX3??g-5%XZ-85bvB8$#n;NLpa6T`M5Fm`wcujr4ojnmM_o#*x@HKHU(5Kg%r2z z(G|2I0bIesH6TMzMg2f+_({LOZ7{58)S;zaoe*2mN%CD`$jts)gmaZffGn^!06|Vn zL^4A-7SqG)kD@GuoR37<0?SZH1jKag@2C8TOBVpCnlP4}mwlR4NR0LX+DV`!34QQ5 zPR`ZqB?P879M&Px(?Hsl{r-q{9NrCJC=@$J>GEz$-^l{iz*=Fd>#=rlHYZ1CSUO9_ zwQLyK`RIyw-0x(TbQWy!W8)oxBrV8xc69dN#mm{9vS?ddKo#HhPUu>9u>y532loMG zBh7*=h!C#9#YQII4!DJEiPi}{mxVJ2nTiBS5jslef}yrcG8}Pi`85+In1JBPo6tg# zQ!@!GA369$HdAw>FNjnesep-RA0+|VNRS%U1+t|udbBS>gLV{VNBmis6VB{>SC3sU zdBrFU2MBhj0wj{=?Rs=Ib`yG0dZGL&L zrWp&VF%NRMuSZuyNEV%=jkERz8GaZ-VVkCUnV{HL=jd6EFtyC*&0u!TH^GwJ-$0P3 z1J$TwNh#tXIMUPtd9p}{1K!0bAmcy`4fFJMybpiuApt1Dl3tYtv^m6dV~g}@N!Sh{ zok@SR2<6x7H5+CnaDt6AmlnJuX#OJ=Be(C~DCpYt-nI{9j_glI`*6}LzzeByX?KDc zMN<*+JWN8TuRJ2YuN|~<4L}t!fCM_xc8>w>QaJ%Orwot`E%~L7?*(06fr;ovz!j&1 zk0JcbLm`6pXJqLS!k05~PS&i~*K^0S%j`lpkdnv0A=oKr ziEBwOY@|=bISAzA(2x$(zSgSV>;BH6q4x7Ynvd)X*2sG{nO(!>p!rdXFDr%Q;1(9d zcDiB(>p?&#BxNDyQV2XOF@5X#?nGE}#o;tqnL-kqYDr?2DsBL6g$$xTTNg&1F7YqC z@U|_8&j3#vNF0ODCZ!@0*v3gZ#V_sB!qb)jBeT<@knDJX^qGbsQD$*Hx)y2gROv|};2S@q63-fK zJ=1v?A16qfr}%I8j7qjm-!C4(23V5seC@jI6Mcm#n6*0+Su3qKna9OKkDONrP(~$4 zCv62RWPekv%j_P`Xrf+(Qpy%mZUScpj@llEEHyW)49sOV*GtG-^hbfV)Kp`B(jkNPIl}RqIHbA@;~{D?1UHRS z2%*I2+osgk*-rlldy@CHSM7*^`=E52+Wjp~ZOBS!NIyGY?$};$0 zGA28r?u7UvP*is6d3t%Rhdm$t*ljZz4yb+V`2fN8tiSVsOVB+KO*f~LuO)iFzxlwP zbqO^&*8g?q=Hc`aU;=d=b(W-T%s{dzT|77WS=7wbT(0=9t@3-Bj>p$YQw-_(5_Jp4 z+mXHAk6c)y+Q~I4PS0P?>s@28butlYLgqN)Wl}Iy$%NqwWa?|B+3tFolC=^*7kQ_f zE68tB#{`LUm7})G**0UgBUwK&0txk0D+-@XpkiAbVyVc+F1COPPA?eJ@iaj8sjhUu zTtf!T&c$x$qjo0?Lir6GE0a1}0CrgQj|onX+L&4&9>sP#@nF+orIHrFz9QBVaM>uA zbg}MB_0%*c zn;D&aAb~=YI#kN(94Q0UNwBL&J&3g8MiJJSo@5HNnDH#@|iQCchnbU*RoPI^>61j9r?`vLjL^!7Wu<8 zABjy&)`lCBqiSFKjZ*L@Ut5t@+xLz?=X~(>3Ie`>3p|Mwabz|vBE<8;dcobsH3YGr zS`T`OtxU6VO;|bz$LV2m6 zwpGJZF7K6l2xPlR6ijkZr-N%moa7wvd@cX>EVg(A#CgQCbNHxZGy%*csth87;hT__ zXvvNFI$tfPlo}r0WU=RF2sTlw8KhKTH=3-3W_kcQy;P2F?G3JnX??haMAkdCRpD2G zZ>R)G%OiuugaZ1P0psw_^%9^T6pSRQS)@hpWqa)RorC->kGfC?#UwOZ8RS02;9*W_jZtP==ZP{Ew9NT3zpavMm$bUxf+qsv(0bGu8*T4n(3C(&D81Z+tqZgz z>{Zot8&F_0LP_Oat_71PX1l_$Fo{{;g2j&Xg?>L`J7$doz8}Rk^?b>1n`}S|fD}R7 zf!iXw-L}o)2=?4`AC(Mp#7uGwiYT2SI(Ogmk!NQwHGW0#nDTU1FtJnJ>9Kh;DxylBnP21 zRy1#ta6kff!{d+{mN>|GSn~R|xtH)mE z5xXfCW~hx3FxuA>ek4~jY-|#uz@!oQ^oACYaI(d)+)rn3* zS(3g1-7F9z0ztX$vdQIaWbo8h*4U}CXL_(HMrF4aL)@gRVBvMz;w+-R6FH03fRUqwAGe>F^cU^r~FAcb9H|+LC!UA7EA`e4b&CBZ;s^1X1*4%f(8{D zbmwfqyV=f1o|OtB$ zJb<@RaG!x;b*j$*`NXQLAP#{}jdfe{ef0r(q00~&BzupWmcgIa+;6ar^@Ux(yz@V6 zvZbS_wQNzBO)|_e-u4>w>B*+@pkUj0w&54<24OEYI3w%_Jj_hDhB2cd-m30=q+By) zZD5M);>vp1WrsJ7_iEFu1Q$g;U-CO_Zt7A=qgSs|775%fpIp$^bcR~F;bc6ef_sVN zKC#?R%qxPL5WSIn4DbvvMwurqh|YJ4fF<}LQ-1N@W@d8Ss6V2qcAf988G`7zL~VAV zaH21?`n#R zfqK(-08pAi*+G4PtkccKeC!elcoOenSU{-Vz+mmcsp^t+eUPhZngQ7u#Nkj92c}Sc zA0=?Z!)t)@r0dbO>=l?VSz!XU&jR12(!tn6}L*q11B&r=WuTt*@OAUz@_E4U*M>^G!G`U2SR2m=kYvJ>!7!B z|8~bWC_y7kxuVN5bf)nSZ?L>1+d2|K8^m-iFgt7MCnz%r*pecGQ+Mu$^H9dF9hKj2 z;`T_0$&_udI9i__epLqbyC zLn#?qycH&+gUB)CN2n~=TFd#0-yxnNNeHn=sA!ki=NN_E2`N2CfwCC9T-zMmv|yQV z>XdBCNjqQ}$PN`uKBUlX+gHu2rir=`3-(DJmy}ovc4^5@Dp`o`{->GW90FJ&*l7*z z<_Bq8DT9}@4T7G^VOv}os2L?lrt)P=+j1wt+S^ zb60(^tyVaVaz7Gvrz>1TLNHkyNtYRz4z+SW=%Ozv{zs}mA)*sW^Ab!T=OY`}urBOC z2r_BH;hqoAOz?x$sgq87P8gsMg=wM z*e>Ux0jikJ#rvu9B&gmF9kG7{cJOcFTGu+Vjo12-8zayVfL4mvQ>@dH-a z8Zi*4-asHM%x5=s)7Dp$7eKCU5|(lS6oU)sb?@t8cH;coYO+8z7x4RtmR=6W-2rE_ zHk-u?wCZ%znCJ3u53X=M-lYiQIFq22ld-I=o*}BGiZf9JW@buLjc9MYFTxMXT?^D@d_Px29EsE%%tq#VKL2^|@{$Il=liKG%i_nTSO z&R57rKAn~xE74uSg+SEuV=?39-||MTbsE|Uj(|cXlZQ&#B zmF3&OVaX3y6J{8olo4--R0TZD6Y?pMRUq(zV(n)9$n%m(PZr{I+6%i2o{}6YXRCpk zWIGcwpKqP+LcpqcisMxz@7n4u|-Fw z^IJa|B-LE3x-ZDo<4D`Mkf={^ivq4`>=Sa%&uM-TJSzhl9FKJ2fD?kkZj$Ur-<`GNCG`}iSn9tt8H6cp>Pj6fZ*rAM96e^2&lRA z8J0T$n-6dO@du@O_m7E-0G%6;-c^9`-Y(kLaX3!YKrY{H!@Yc6O( z8uU9Fr@lS!L-z0%)z74IDHo1vS;B?6TCclYLtY7vT`SxE@x9>X%#6Atd2iPv?EJ)< zbk7NiMk)UcA$EOYZXWvBbZ}?GX>ID=yf;UbjSL-0o*9lXAR<~RxW&cG*@Y06!g9J8 z5N)%Y)fU8!Fhkzl3E_Q-G1=cWnKUp19}}i1){G>mR>gk!l@(_8i4z!938yP!gQpq@ zA2I=wz-CTrh*W4LpiAO|H2Ek}hzML?Ozro892NEiZq<}N2OC6yor8UdiYB7OQCgAQ zItLyzOZ5$$Wu*`#C~s3X3u!Q+#=~F&baz7pK1(G|cg^_u=o$f>m|qPcGuty%bWEyT zt&b7F1K+aK*`$>q8A{Z}I#l^48ISu&YZ_8e26#4FOMy8ZL1L=?iiej&i|;trQ2A{q z5cJ1sec#>elixS z%Uiq{aV0c|>0qiw&GiMmgD|{PMG(O|+Cw&=d3O8hVUfT-2BeB;<4&b(a9$R;8)>%@ z#CgU@$yJ)MHu0?}t9Q7RjuzP_0}Up8 zhc=9pv;c$y6e^jQQ@okAyOT(@^$=ZlBxM?wV2L#EM&F!*8?>zo?g6fu-cr$C<@HoQ z7(gbb6RhmP|8L3wz0|iO7U)`GK>H@^PHX;9e-cr*x8ce*>6(r1C`VEd*zpa&>DQ|Y+5ktDJ_0ofuG5jJ=S zTE}g+@#NwD$$C^@#e>NKg(4YsX8Tst8X_PzKrB#?LEO9A9Qhf(;5*Vpm!e#B-O^$`@7pf5I_)HB!O)34Vg3yAF7tt=sT2}%E zZcL=Z?Rs>jAl_juV#>D##Sg9f@Q-pjThi^N*xWOn_z-ZfC zbhwWzqNW2oWK)h#Sl|ZRDJ_Yq%)ecaK6by; zw_pX-fYZjDJ{nGM7Z3PJRamta!P%#P1QbAotlKd0UkPOJGxTFHbMGSTz!2NAgN2ye zgEb<@A$Z34NZPaJH;l9NVB1dfO7FbYwy9yo6+jE=Ik7aYqNz>r9zU;m@G&`vPKzZO zM>vJey_}3gTrCqJas>ho_lT>OWfMswOQ%&+%w^LR2Q@Uv$BZjbgCQJ?mkdRZcG8<6 z*ISeu80*XiZ8kk2NgEKwJL3+?@SxS5D@wrn$x;JoeY+l+xD_(MNi{5XR;GXXevDsU ze?5_xNO8RCC!!%6l)7g7L99t1(l5E}Z?G`r`jD*IP*qUpuB0IcYAH!OC%EnpG(G_} zOw6}3xSe{#cT3ehOsPcYgg)kzSAu>Z6_8r+N^GYqVoKo@bj<~cC2byT&mcD@r@M#M z=fj;uO9RL0m8hwqL1zj?j|}2OkJTQS8?xcF_{jbi%I}j~9kfW9;><%j=|}9IfU;U$ zR*)XaEBo<8SmwL?Cc(?r12J*C9(^bldSxX)iiJLy$?NsqU2#5{r^v~R5(zA=dg75A z?Zw{h+zqb?0`AgQ7f@1SnXW0IcEdFuGrE)P`(i1ju@$_I?=7j-#NB z?uXH5K90F0g1b)ng+#%E2I*)6a5|6jg!NyN^5Na@@F@^>OG0NBciCLaUPK2f0HSeIZU@0wyo5q(YO;@G1?^>j$5 zAXdTdxbP!XBQ~&*Dec=MEn;$DQY=W6M#5zh7L6csi_MSQ z<6Ef)aLpRs5WsnAQm8hvVNcw0hhS|Bn~EMf5GsZjP{RN>vnn8abNly^TX&kJ-U;K5yNZfzgnB3e8pg{!_ zm8cw3C;<7P^U8x(S|nG}+pKsy;*K=boymQP0>52hpM=%^2vt!EuEoMoI~`iDmjFql zuN@{)cgKY%)ulP^Wn$1UWx9?Et|J%LoA`o0gpg$9+h}wsq#@x#4ih5bTgJLAf_~JU z)_|lJS18vAL$c6ulHG91@Cg!DvH=3KN>$PZb8w56c-mdmXAC=z*P}DT6RL}b%Uh|Z z+U!R?_Os`=_lE&t4Ep=I1nO+Iy zXjcNzflf1_@og`y1s6}*BJ z<>Jx0+3_DIXjO3>h?uTcN;p2d$>ZJ#XPZ79PXVw_3^Shq{=g*x!TZw-d{%W^AuD>^ z&^BYdPC0s>pC&bEeyHAKV#`Jcvst2SX>mM3d_+3eGjh7(n0E=1NxO;L-Ws*LS}tyz zQDRoGnQFA6j0uOXt&nr@rX}plc!m`Pvl*prDMa$E3<=-}OzEBkBU5+)Sc;)zY>GWksNA=CCnPb#=A$Y@2By_f^HLVomq4?j&TeK&j! zGf=CrrkcWUJR{GvE07r6!XrFgu?W0gWfYh~@~-$$v&R+&%SC6#6ojUGpR84i3TZLpgHU%%;+IXr3_-Z2Yc6``!k+aiT4&6=x`DnOWUA!GNpU#fAFk= zy#uDRrIk-bDG`uJ@3OugQFJ`zsAF}sgsOyW0%foZp1fJ&!_*i%9*oP#<8@WAr+BXX zFDi%;iU0`>Vd+Rp4KtSxEW!A_Jf;VsvW00v>H-qmTuH}0JOML=y>t8qi3kiFAi(NE zNQ4S#TU~tvpwG%Q>FUGA1_@o#>n^K*V%v1rWb;{Z(W>ToG~g)4qXtiio0DiE0yX$@ zbC%)$AV=7?GuY;FhVF+D7x3HYa)6kHz&Y*$79`Q29cF0@AqGDqEO1ehzd^}ZXoKY5QTa}5kqW`08%v#!XvR!E zDVhxnrV$XdL$W9`nO9B0u|4yJ_Ww9h01OjcZ+paR$af;%MvS)ui$o~jm{BZQnjiG2 zU_ju~5~dQY1NRDPk=Zk_kcP*aj&pDGf8%ON5#5g7NvIr}wQSo{3Qsx@%C6m)(0yF7 z{kRl18#dKSham;e`etMVQW)cpj_{FwkR&YTn-98h{P@k)9(!!*Qt=eiYGc>jFc%7C zTT-~j`mnU}f4Ey@bfULr1rmvYPMfhmf*ht5!fyXEfkcz%05kzys&OlCL^1uaT_m;mBxFiZgmVXgP^qaY zp>ApGE@O~*E%?lVMOi=|y21f60}{@2)LY%%DO*}}J5v9{rCK~sNMMM3+=zp=V>fp+ zsWg2Cq<4|P?iaGV*GlA!pNA8_Ez^o&0tlkan06+6KXDS5^hf|mR-eY>aY92vWi!5{ zm$(E0G@%3`&^iCM0%SO z80EYb4RMGtx=E!?r~cT9Cby@_y0^^Z{8qy&ruF0IHt$$BH@bn`4*-c<;(?hU662qTqmj5oPg)7p(-o%iap zN^flWO88XtHq>r37Owv>(EIJtD_GJ|z34#kL$`LU)=w7grS&7cp?kxMhux=bBR}(r2a>Nk_pm>}1 z0d+o#W^G|kY&SbG`Fe$Y8jZ2EHS%7|&=OaRTJHUj6^l6_8Ah|JMxAJ|5(AELdzd68 zRjI%*C(SCGf@;k?G~AIk7FPTS%aJ-j@+)--TX-2@?CilXx4GGH$C&!~UVAl&RHH3# z*Q0CE)$SI@)6wiF_w=Lcifq!QI%AbxHPb{l_hXB8wZ(n^D%QfA`dA7>h(W*&Z0-uG z>2L>=^Hof&S+*d+q91)n!ENvN?@?=IPrgGbVG-Nlx5ZUT`y-}kk_?(Ighze7f(`8u zRPB?<8yyjYG;PqV$|c*t=xHb5l932d08iFhp|dvdUWida5Y!+QuN%3O8pH;WMQQMvlK=JA5szfz}q>SBT z?K!Ux+?!PXmsOIDF!69#(+-uIJ9X2@pubU{&?6d%KPbn*eyb7tjGnTR8L;2&3^#dz zErT3u<2|loJaHBHrKBN(SZYwYQ6bQOj*O~0Y9PtI1KKeoZSZG z*jG7~#+(-MTi_I4&-ep|j_(JCKN9gh%Ju;J9zkTOP*^HD8sp}gxAPTmVHvyIEKrX# zq5_Bv7!XV7r>vQwWZg~6%ECjNZBe^o+nexuKm{Q!lC+qMVrZ~PVZjT4@HEn6H!L9?okOQD3stKH0uJy3*Q0BymUc76an&f1T0QCx$F~4_K2PJC#m7&4 zIwfIR7j8R7?2c@8+h6;?4gLY3c}{_}yRkJOhK~TUYXK=Mqtud3faj49llSFR=HHuV zBq3PIK0x$4fYZ!7TnG{qcL;M51{9tI{s9ze^P_7-zz(u9azYVHqNRG<7lAf7au>yq z)ucy*MgT3AZ6Uy%T>{A=u~-C4gvtllZZPcwhiu$U4NiwjQ;D39KAKvuJfnxH^}#VZ z;!xH@7!2_AvEvB!bcE`g*m9v~=WyqeWSxqA+-y0A4Q)1;T3xHO6UlSz;q9D0yj|HF z(;a{;eCe7E)m!K=3*U|O$#l1SHq7-3H@Aia7Z!``>3LQ&4p%8V)-OS#*)R$7IoP_eDd`OxRP1++JK+{y<=rNPKcVv%)+>GRROj0{et5nZk*021U27N( z^E5nL@xZpb5e<%3JkKj4hKxm(iGYKlEAXK_tchf_t|o&HkfDz|4Pj{en3fRU$)oF{ zh!tZ~0cMZN>x~5cMm8)cP^d?t_|^|b^*`O~T^Qks>P*N~w?T0kabVM-)c`j5YLisv{5Dhn9VIs$!BLEHuy<%m?e z7NX2xvEEKstp1^z5I%sMo|LMLYF@2wW=u&ym<2Ge!Cm1F4>k%1pa+P+1Cs@vCu9Ws zqUO`2HrwkA5jE^EWk+0S(niAh>fm^!X=O2^PV&8V@%xAwZsmcO8uDM3M2 z`8^cG;o1{VbG`uQmhNdYSM*8+RYZfg6o z?$Ve^vd%fVpDGl+DY_J*yu1WtblmhcK$bg*F0W=BVY(){KHHEoa2c%7JnS+s60OqE zO$O&PU0@m=N~c-rj=rA`Wxq0^z@H3Eg$$gwdHrN4%_o`-`|BbqedawONrpYe=)1Oh z*ls2_*n2=`GdL9D+yfbL3Qi^}}5tSOr)rPe_AxJRjNa;v+u5|cuML#m8RP@l2 zk@`)^ojX+LaH7);(5aeIUE_(H8ck`4&n0-ItJ_G@l@pLt`7jv(4h>x?(A`{*;1r|B zSBiFu*tv8POld#qphw$Ihq=!qy3Wk(T?VyD4;7HXt^x{qQ_BcEjaCl!nsI#5v4eIp zL7`II{hOi+LbY?cMeRs$LpS@mR+XU!FX5>$TYYmQ`A5+Vc-hL@3JLYNqOKZbc=O}} z(_u$)sNKDPrrVX}=u6%C@u2E7;e4tbLvIT{ta6(BrS zhL(cmB_+^%C}Mn;K^lDZYJu_)Cp}Wtutb3>2}~HwA4V^^DD8Z7#e@wLbQ2hDC+3&K zsE{5z@3Rvoj6yV8{AnGoO5{NWJ8Jli)^S$}Nt&$%44OM2!PeZi2T???)UCS3tT8-* zdS^YbYOm(^V4NIMSmav5#L?CzW=}n(Q*39h16vTC9S&cCPfwZzvX{mRyTdM(hDr0r zOoc?1mUxJPh) zH$!?U{M8O?IkX1ep4<4^4!T_aNjEfc(De-TRJHBT3>YzWqAA{=cINdE zmuus!wq;|kyiry6)3mBGv!{MBg#(cvYee$tSCXZt(R@vwr$(CZQHhO+qS*- zvTfVkcmF3RH#zs^Ca06Cbn2!0X{2k8n&VR-ERw3mBp(oi@;et6Y$dZ54;b8q83GZ3 zy=9`ppzLkYi!(A_t)3nYT3AK(Em<$VjC}-mYtj|t#5pguDwxK{Nd8LP9)@Hj3b?-j z?S|=H3P+rvKjTb>3RUw(R5yQ%kNGw(7{##pl&A;bE-7j7?gL%F6IYAL5ijpx7Ek~& z8Gz^vI*F(Va@({K3=iPdZzOe}4%I0c2kjeCJTrEP;jkgQJ;`WKPfF=-&m?PmmR zr=U*C{iFT;aTb4lqF_`sZP=j;Rc2!yYy+L-xrs^o*^q^t;8MJx{_Bg++qWz z{R2?lq)B##YOPI3JDEy#)Bv3yJ~^{8niy(w4j(xUdt`FKFs6ik8u(JRRqA4z>s1e0 zI9pT&Kad>_ELf`?Dvl^16T&^>8l$ot?tPTz?&qv$TWML?qI{zeWRZ}f7I?7?7+uU5 z0g9{L2*U3bFA2GAP{sxqhw~Z3Vp)rZxWJI2H|F_ zH5C#9cSw%|q{#J?gVj=_;dwRWyN~oA{yE^Qj2(N&7UhO_p7(fW2dS)M63yk#uX5l4 z!93^~{D^gHYyI02T99{~TA+YN1*oQi%=U`7AVZ_R*J)n*lXcAZJBJ4i63wF``rzsN z=~_jWuMV|^V&x%$VZ|zKPDyK31y*OqB>_T`qt>qe8kDIW63t_>gMy`_q?p}!zg?^? zNiI+$#$)ABEGb|ysS6%npVTiw(3i7+hF3~CAlw7e>zKp>j`DA84haSY*T16EjJq3ilu4o}L>~&4;FKAqNTVSzU_ye95brl^beipMuyW9A z&rFu{_ORx!v&@_dD?s!@VLqKWH|P`qqq`S~TsyMWd|}yWCc3tuk`*uSLcmi;z4F~S zE?XxBv}wqC^7Rf}!9C!uYq;3>Gg!h+uQ=e)l_S3lxh?Qb%e9nW&$%5hZWFi5gqF&X};<#poCd>dxuTCR}WZbo&8B8?(u|-MYRbaQ<71rzq)jA!zGqtbch!d*ao!S8i7pgBSPQ*e=!PRhd(2qn&@1Pa3*356hf za8^_f)`nIBz{fw{%^oe5U}gDADWvX3Mg24E(d^N!NOzLJTtA(SvDspBRB0FDB3=~$ zc#a0X?wOrReBTIQ0Q;Rnl05+#iWlDE~Iy6v%c?+Mr^DMA|@091Gcvl-$i8%$}HcX;8Belm^kg z2a-4IaaMR}`qlLtuM$z&Y21eJ$R5<$7D)@68%$=NQ#G&!PaSl)iAeR#^yPOMcP-iR1QvRDxA|nr8?nn_K>7?;)sFrV=zg@s4gox-b#Vo^M532ai z*3X!cpAn$^^%|}6!F)3)%&pgCJ2&s0lC6i8N0fjrI)N zpwm*#(2{_{@7eoy7pz;s1xOMkR<0lp0Rcr@Go0+82WTSYlwG$2?I2tvXK1!v*a7lJ z!R_(l4BkTLAPB9=fZ&93zL&;!J1y(5bY<1#VUS!EiJWplaztg7ti*|7sDNBp!!x+Q z6pcj(axPcz0}oueM5TS}gCsw|w+6Mz%B~{2kWofgYG36R(Se@*UbzZ|0B&{NesN%- z%OEFjMDGjCXhIqeQb{Lh9MygNC&f#UvZ`6dAch4wbN2DT8z-QCE?_0Q-L*_Li3Yv` zW5_+x8lW2zNu`RH;(2?WxP7_v{Gr4PL*C?WfPnS}*X{_H*s#}XCk%pXmwPnx;lbZ_Td#v-r4-Th!6%;WC!6E7s@agn)@&N zPXWH-VLl;U?N_KIB^Ob7H|<&AzTnHy*p69k`3}~&){bz?OVQadRwF9B|9r4rVy>)# z=jKH63Bn%oJerA=2w6;>bx4!ZXCULU_V#ozFSm5V*7a?UJh(so1(JJoABs2ARj(Q>cL>B1y|vha%>;whu-vdi2j}|iJ(lvMAf$)_$0y#_M`Mx#}IZt4|oE> z3h)$**J~xogn&@nrCK6+4b{$d0*r0Q_N?4FP&!vs#z!h~)x-WS&*1DZuogdd`H{A! zbuB|$uK1T8Cagvl$fg8csfFtG-i_xnn{Etpi+QBoo9;Ss1}mj7z)r*yAd%rV-@#^K z68^6AWuM*ptCwFGLhZ&PZUW5sSANux(tq`#`tpp3UE4q{oaMSyOMJh6?Sw5FjuY)pwNOyFwEgcKf6uxcPzijr|*$S zr!hTYd#DK9nO2%NJIbrEnLDXyO?0p0IzTda68Ws%v&oi=A3j}lk&wGEcxeqIyVBmB z)(?mfO?hJj5cmEzW(5zwoj#X0MRPjBdd2vtN?BE55QBnC`f=2DNH!ATz(9vody7}^ z4`CK8{3kd}j$FZFO2jY_R>a#kS*lo=KX^W<1C!y>m79+7lfDwg0o4eE0g9AF_bDxI zOJkd^%zW5RG{b0!_zf_{o(XQIo1;#FJ1-R-xEKy}9ndb_r<%o0KbY#?AURjeg8jVu z=^G^QDC0Ts0k@cv`dTdxa|8tKH?6KR=P9ECfQAUM0bB(&L_`5gwK3U|xbEvD&de=svr8w2ag_$mSnNc|-&Mj@zz6@rX8$e*|O>zb$@ZK5ho$c|)8+jQp*!#r!;$EPB) zT1T1Ryt<~LEI7F2oF&40Dj-S~F9^TsDONgKVZ)eZ#v<6*g3}AAY@^kB=8<#UZsw&g z)1qRd3?xJ0wkZ?M_T{`rJ!IxI&>KRf^?tWbmF1HiD$}n^iFP zFt4RU9ZQfVkIHImHe6;kx$*vxrnTr6Dajwn7;o;dUX&MGPd@tPD>B1$+s%yj@LQ)r zX6HTxJaDz@u|!JF;!JQK-*--aeG!hkDYFQj z?xb6%o%3{dywl%>k|Zh#%`lc@=7HlQ?j-YS5L);dR=ikm%zFJmp6m6fY=Q&$s(*K| zkJN9t>m#!f_9&9M1UvTd3BCviZ=>AoW7#5^@QR_TKBI~#_npYh#LER0RI$ElaSG~? zo7*RS_$!bJxkFSrK?`{H=^1vQe#pFUnZ}vh!YNT?nQl&xi3(s*9vet{M>QH`N%}Bg z&34KS8AFc=cap&sct9Idstgwf8y79;p~{;<^I>T1b}j4@5v3oPaIJnn^sXN+#gr^x z?bV6nXRadc>%HOo*rpu-#4H6vAudXR^1^<#o^M%!#_K02AdyuU~Ze28y4q+^Q89+CmynvJ& z2QjlEJFM2Q5zkG2HbSPc8Zf=B5S*eSw(9tQhN=u$jsQk-76fsjb4S+Fv1P_~L+j~FC+ZBH$ zI^vlZWDqA5a7E5!!iyo&qITh+s$O#F(rfJ0r-0o^h>kq%4Uea-bSF=zl^0x6xmW?7 zXQOwOSur-cHQl%#vBmaf9eN7wvn$wH5_E#f6)O+yD)%ogv(UzQGnh+e9;|*-+uiQr zX{A^pLcf^?b_HhGzU$D9hcb)|^IehMTuIL4+p0`o4IX(#TCWk$cDB*`TXi}jx^{@K zb=Xu5u6JtPJn=If%^B~uHZf&;L05ulY>KB#28N4QVYKhAt%;LLPyQ|;DT6WRfY^>$ zMj;n9lPDl3>U!L^HB$v{zxDw|z}qRRE%{w6_j_v~d2QJbJSQP5ExVdd9Zcc|lT`YO zooB&S`;Xqc{Y6-7H^bRwax194MPm}{aeo@Ff)y1hFrSomX;CDyjh|*6d=YPHhKYDTX0(W(Ee|OR2?wD)JHccpN;gn@h78;LH0|50N z#ofu8G0)7oApeSH$u1X3?DjY26up|@>;+}b{5{=o2+oe7d|fD@dMns>za(|o&QA~V z;{|=xX&KKlZ-8Leyo;(?8z@${$33Pv@y^FjEXxiJ4uv1iqNauD40F z5k@P`sAy#|)JL)yGh5VeTE5P~ER!V(RwCSzGDjltCcHO!b_&lM4k>2&`7Eo9 z7lOZL;SSj*Ll-xVD~VXBieS%S@$&LQ*boT=S=($u=|hrja|PD zhn>w9)RCX`*QLRUii(~5?Uvuh&=ttx%lT!Jr1=z-T%c`OT&7SA->xfC_!>mUE7}V$ zvX<-jEA6=<$qVJM@q+hkGBBu3h8}z50}Xy1J&@x-2%FkP|1!vWY8UUnrc}Y%j13?q zuf8Ex2o&mDZ?{%b-{V(s-wE=y!TH78T7eQzcnpMs zX}~={_~^mj`2y95<`!5Upc#->%{bzTYD;zi2JHR2kI8+e^0;JQ`S&PLf8MU77!zhu zc(1t}EF}Eq9qTTq(Gi<`W*$6H2zG+b$T;;BZG>#!T*;^?2oHYC*&TR8*>^zIRA5<9 zgs=dK`H^|`df}88G_>by8R_m=DND^1Z)GvLMUDx48}~r#P!%?(?hCKbYKhECSS&K<&uwh_)W3U{9_#!zu>9s)F@ZO5Or1@<7@;1K38&?{k zaU5YK%kP%88^lQ_gyt-}ZN-)=)a<48az-p#_v>#?1ETedINf3Xik8bBqgSyN=_&&{taJX<68POa*DYE-^f;iP%*=9$(Rql zS1LsA=c$*7`hcSBZJYva4X`sFj{({2OUqGtBV?Yx;v0_~f3kre-y!=mma#L&+62N% zWUXNDSop^-4t7bX_1|^c&PF8J2O1!gNjL+*dSMfYh`RHcdXaKV2;Wl_g#*yrKlr#6 z@lT2oewU0J@bVao>|4rN#iq>N&I@vP zETp&lkw_HCMhc*dQsF_Sw#;tB_V-D+fY1%rw?yg=mg}gKWZ7k{uxlz6oKUc50O|s6 zOPEj5zLG+D6~R;{hH|+Z5m?hEwLV;)6NMrmLTp$sckIGB-OepGkk1q*diQUxg9TiA-DF=zfj2#W~dV6kY<=!vOrJC&x zZUx&uh65|?)EL`||3%l?4CSX5^y>gdBa?1V_TsRtG4$g}V4kz+3eh;5?w(tVLL?=e zQauphxTa%>`Gsvsm)o6B0qu>t56)x1h~)hZp6bLq6BHb<3uQ*KwIIH-U7bZ8 z3>3~dbr=+Oc1Rm6v>zhhPP=4 zJ^l&DZCUE4wstR{mUkjFE&+;?4XF=GgG+n_#loD_Q#@j*)r;5UU3y#Up>j{!`FQLW z)_UOjtAV^hYK$u29Pq75;658j$W^`qfqAUG@I$!=}5_1`P*6gVn^J zSg%iV3jQ`O$2&L}in-9kJ;)|Sc*9jgE_lIX)$4|Xzi7u;6)H$k_xD`#!9ya}$LFGw z^BK{~x~uSPm?+3JybMI5!zg|pP0rHoulsx$^Si_1=&>t?MO6Z9KO8Mx1x0~%ark@D z*YjJi7OSDM!vheha{|4{y^GOT

CKe{WE< z%|rRPJoPq<^8eVlSzl(g`&O*pr@~PF){U^LM-_R(++cWwW<@$Ul#$t;?lQm z=1h0#*4qGj6Lsu3<-Xi6f({X37F}PyJe)fC2{zlrwZOKUFiX%7GdH;ovqF`2rGu+ZOQ#%Q>w34!yuxQmY#F|6`h`Oee8`6YSLP95 z7jhi>_oL>-`FDF8t~}S@u1=Tw3|}vZ>->4x{31<`zwM8i)mi>IzfLZ{P?Yh?cAv*2 zM>hyNY>x7X;N%PYz}vNnOffR89X~*tH@{~mH4bd;84(4h9X~LS(+j=mDVPXS8G?Hg zsZ>HSmv45uyV&LaQ4S~=yD4xpGR^#QJ8vy<_M#6yP)yJeL= zd4%!$nz}=xl7btK!FMy{DFBjv6%^5hMny3JZwOOR_F)U1A zrmPXjuMGH630jx*#ZTzqg~IC`;df!4gRSlEpVGKaMDtUFo)}FL=ZII!@T=29r$leJxI3 z6NS7517n<*tr&6YgT*6&c+*eKdRV#s_T`+=LdZsl!n=O$$u+Z_*1fL4o_uCV@a_C_9$tB;SHl-kd!f!ir?SqdVqJX|>8 z9vjR;jGawG^X(B!qy1fElQLTnno-f($t1NB@Mz2A#XViBaFVx+VFh?VR;2u+e$?w& z1{#p9W?cT74~B*+AUs(Ms$Akx@%$qI!ck-@rmqe*RG7LXeX=qh3{kjkJd8y^ewg|* zYWNVE_$|&`JVu(pCF-=_+gtnbRY*H@4QO zhPDR#u7mS_!D_YKJ7HYfdzLcu4ez2;JunuU#0RJ!>SnH_xo(-t@MCI*< z%+7ON)zI5<77bUfaTE;7P@x2VB~rPEmT?N>*($@BqtvATC&uRiOP7RcLp;{l!H!k^ z#UgRAck#`y$YNH$?ys}fk4G+s`?(wzdC8?1;jy)`?0^sFLyDNI_VyYTWv5M~?xi7n zDB&+aCQa9YBxvlp?S_P{K|DxyK_O)rMG)!+`rh8v%X0v$RQW)ACMi0^M04II_?AO$ zxj7J3l0pF*pH3yOQ0&pu8JZc!gXKUdH|rK8qJ9MpuwSsa&JMXubSl7HK~>pig+bUv zfBFusBqRS|HFEy4(G{T6c0bG7*~(^nrE}|ZZ&cl-18q&bJqvfRt==V?VY#b*U_3o3 zGEE0RP$3v?aDm;Ei8+V^ALlzj#A=8RmspORM81hc+A(HlKMrUKM(rt(so}-+wMwc9 z-DyjIVP9ck=+$Dup~Dm{(lbFV+f$G-8@3J!2&%y{U^NylC`H`1EK4>0?A_WDvZy41 zL04C(mj^WzR7Dc>P=p%JRiw(6N16s`?7_(*RpA~~TU^Z@W}plNlI^9z5ZueqmcxE?P*=7+2)vwvSj7dV zw`N#(?dyVa_(8-Hw{X_I{Z<#p>G5==F&KtTyL*_0nkWjh-*zjL17O|~K03f7=ESDuHu_YgALtq$8<>;~Bf}A9*&s9p z?)4)aI+8%(zAE&kT)lSN+hZvFaVoh2a3e7`&( z%E4W!Vmb&Gs*;mrj`}|Q&kJo32JF;J?EuyuKM3DK{(!#gFj`Xf?EF#uispn}>yNpx9q(Ahui6U=;=dTB5zmAD6%R^cu zy#Bc5deEivU|kDGSRz#GqaNFE^$fm~v)qOjXnEw%!Y&%r3jv&MtJBKz>bOL99a2nY zHV_HNChUsexdHA>L`>N1U~sX!K-Je%lT4e=%3RNGAG{sX4Dqw+aJ%!noKazI(GE&L zD*PjBa$~p!Io5>0Blic3UQ56`x_N;8Cs1)1dly8WMPv#E!OBzf{eyFmnRi1cgLHhw z269tVS97D1OR=bFZ}QwYzDks13ZhUvx-8lvQLoJsP)py06YsvxJzwAB@_YO=yfU>{ zUtCJ0#>oq56~RT{kELs$iL1}ga;XSf#})dQA@#Esje@GW7>AEz4v>kY6f}-llr923wS%#4iM$3c7ZB6<$C?&5vI#QXfaIv(vqjT|aQB{Ei03PZ* z(EZPF^?(Kd1o;I90Qj%zMq|friw&jkP5BtG53uOBwwqQnz^D?NMbO)l0WKFIaU`as z%R`=Ds)=f{#Dv|F0cJgR*7-fx|KVOjTg;U-Q_G%;wr#8R6mOG?%?bd&Eq{vClG8BEh|$5d6pzMsBOW>tw_x2C z-(01V@YO#yoo{D6Z!-t6KKE%!b#Gp=5`L#rkI%c%(^O_jy$*jILxWFtV9dHWl}RP% zY_>HSd(HB5;OxOzn|$)5Ug?*G+w~z{q!khy?sCk7x`mq>vvc!!UH2KyS z_SZd*jZBV0)kZJ_L)I1+PPQ>^e&wl=efVj+5h;}3B2V_FgJe5*J1Y&!;1jT~cIuBbmnKV#*(#?YWm4A*;_ZTXP0N%roCWiw+;8$5fAHRbI&?p)`t z?=h(jnXe-QrmKLx9X$@(I9QHayu<=}IEpVPk*SVL1m%*%xdl^-eWc}ZY611rH zHy3f1$#=QsjdwYgi*t(ULxDkwEs!d?;eZSQ)<_H`JDubx$vL~cb$wFn;p}%7O|7>Pkg7^Id6|MuFL(;}y4v>?^V#CR-j$^uyQajK1;`7JeFcvfmxpUM z5*>=vQ~iy5=XID>Gay^(cFtHg~rwr^RhE~5r%NC&@fKdEQ4;*8$?eJtC)2y*;+twblw1JA_qYaHi z3mgYF(QNMX)eJjh4A*KNbEhRT@Ce!Bn|UXryjzP@YddQZ?)+QG;>UlBf9Y{weoK#Y z|M>CO{Vm~}=drc7f@%%=>lf^iPuE!B`IB)uKGgLF=>r0RNGKMBB8>na67Ws{C;@>K zI6?p^0o4?8H^|P!_YL3&!5@S_EWclVpANwT0urc*NdSQ;0$~IK8H6&3R2ZH>@QjER zO*jN$7{ef#L8L?Uh=3@r|38uj1@NDT(BVMtzh3@(68K+B znvGMaWu4E^|pKduBt zJ#upa)`k5;Nc}Y3?DutpuJ8YOT5YfA|M)%~?%wbDcP&2;Pk;CKW161N_i5a||NY5) z&*$y5TfN@z>mr+d&+nz1zwhHP`KtOm)0;f~-uIgNH|CuIxbWGeWd6_d`|r<(a-Waa_x<$r z?OHDP&D-4F9)JJOgM2*ykJE4WsrB#N8T{YguR4C7-tYV8*Wsgh`SiZ#{`2Vm+amU< zcHL*#aQ;>vzFwZc4sd@z?D+jWJ|A>BKOV}R%l*GJOcMBCAIjP9^!mR(K5mZWx5^hE z*r&b4%Kd)d4<3G&z~U0hT}saX;XvBe+*A|-{JTB z|6Y6y^ZWhf(aEh|!u$I+Zz&%i&Uf3*o^QXse)s$E^mgZmrN@Q8*>3< zany8tPR}17_d8BckH6dNzq`oo^l$NO_~Ht>a z)>Q(@0T&W~5dMd}y z+d~(AK0Ud(nDDIM`#HI|vii=``;+@?>*@3MG?+YlwI)pZv2p*Fn%?i@`CX_rS9HGn z^WE-lA5G=^_oO@hTC@K5;^CxoGa|M<+QR4K`|)=epKpsp`c}z*wR+F*_aa+fKd=@4 zl!aI3Ti@ez_81@Lce-9yndup0#>pfr=EU5SZ;R!w?Y(~7-;i(ka*jSfY1z7sGiVPA za&wN(9}}IHPYWG?+lg7G$v7> z9n(}JeZB;QkYHIS0#7$xkdA|E#8uF-0I zpqgj`Sd>7e_8J`;?L0KzU!f>g+p36GBU3mip5GopTT9LY+DHk{KE3E97;Ka$e@iTS~$ae zlq|Htnk|)k?Mlh%r1jUmh;^v^ctz7(kWdk7Ga>6Z&ELhS@@15XrQp_7Npu=0^N%x$ zCS#v$#xiKwcc>*XmJ3DyV!M=U-KkyVUCtL!_ohEX9q4N8lmq|Zu-ZbN_f3j}a(Vg) zE9Qjk9&7Lg9xr*T0S6wapiDfriz?r{K+UxbEj2*7WwF2X4YVS4jj`YixU`(#Ks6N? zWL5r(K?E)v&rFmeQ5W0MlxG~YX- z{UT1MxTF2nRg&vd0y%pJ>&1nN*ernpAx4Q&C}1W|5~B`p;ka_9Bx%^(~Z2V7ub)}xrF_Sm|LE!iQ_I)R0I$QKG_@?8KbG@ z*_tlj8BtB(JLL3s%REu|HJ3;EDzf92(3bka%eo(>uDFqPX*N+rNj#DC|1klxBt}-E z6|-c#QH|b(2U#|GeWwMNN8ANr2P_l^=*K^uZ3uL) zaB}VLY3y;m0m-O{N1dz+&X5dRLE9^5Kg2C5mzrgraIY~XFpT$N%CgC(bI zncsR9yEaqJL-)Fx$A1FgQF>F&;G%P@I5w3FeJs?WpaYn;hy6}_qR9;cSL#r>*?sT~ z=bOWHMA_~BBf;DQSdwnIzp85UI!H2@Zq+kgg0(|s_+uX+Z?GitQt|{KloR+1rVxP$ zHBkBEkCsKLn;KjeEjjV#QW>lm))=X(0fJC(Z%RC6zPo&EIKI%mG^6v)*WG?~0Uu_G zMG2ehET3RH)*MbMnE=cSY?(08dzBlh#s$l7sJIx0)Wz;l=M^*34LYSQJp|NZyu=cw z5uZZSnSkD4=0pff8&Uh$O7kfP{qcU?cnTS;8p6H2gd=ZIHZmv}M8hDfX#86x8%G7y zuLR=msM`*sj%_^eC{P>Ho-$a|=f(j@$&pR=+K#Y2Dc&R&)*4vqZ6jD=rPd=sVM&pp z#lH;3$xfuAAzp*8;_|Y5Y$3byI33&0EE#aRH(wF_YIhvl-_BFVv1gv^XqTs1>=1p) zHjgSlL;>T4bM`kq;h^87z$#rOksWItogp*n z6OJPd3&;GE;nW^_@w%1^RnxKg#S7JU0WrA3s$T5k3lJ-?_O0>5H7v+nibk7JBd4tc z^m?@kpQzeKrM)D$3z(>l_5dq_)-+jZtikVtb#y5_nG&r84}KRhvGoM)U74bOX^W$R z$UP#>;iHR^sI4Kt5UJa&mHwW31PI-%H1z_M$a-nh1F(8{BO+13D-P@t9uCJ9bl3|n z!`W1F<764CwxkN^(8}Ft_1iF1k8-!(c=hx&5kre6unyJae2BQXHL1LXyXTD%!p zk^;a>tMI)Pr?5?v5*RM0EN0;IhsNL^9`kyjDF?ds7b?aoSz)%Q6O?KT7D$sUB;5-1;{c6NQOTsR&V@M=^ zl3*;j1HeTI+w8C!C1{O@upXdIH~>7U8_rZ8&5f10@{d#uN&dCT3z)Bm2bwyl8w|Ai zbLF`q`j|E1pp0-?mHq^!GTnGs84tA0unKL)73APY#6TcLv?_^ecDrRb{Je1u3RAul z&0p$@P#t1bv!EhLYf*g~9@c}@cLiMSWM`@%T;LTQ%`HDH41{+D9azbGvG&H3LAX90taekk_Izy zj#ksCU)uJ0H2qc6I07)z&TIJ%35YG>!l8*ivDg=uj8C@7QOg-wWH&N34{$keK|Zr9 zG7K6xiZ~h}+Z<}JvJOK=qO~BK$>(l`KjB4bgABN^j4afnQa&@tLw+F)E^4quon&L} z&xTYqrJb&XwJXDXL!I1iv2OP^-7yDVIFgwF{7RYw$EH6)$5k7K2>B6egyx0eiWx** zXjW9+%8t+IL|pr=8iM6|jXG$?LH4F>71y5IRn~8_d(aHE6J3EbZI@1z{i_c&YXMjy z8xO?%;EI|sRRMg#m5u3D($;N)uCK>sTH%ma#SxY`Mo_&ePNn7fn;yonuIx%#YuEci zybNedzM@yAq|qK(+k&GqJ>pHtN{MFuxHL6bY@wWx%t%W=CN-!ln5v(w@3$M`O4Z=! zXz#M6o%L!TFfe`ZGI+L{aiVTEo^l@mSGc5oQT(TPK8nqhkDD|~tu(k-99^kELD}JM z)!-fXFkZ`ubX)4kdCEX%8GsdQ=XXB^ZVJ~Ir%XgXS`394Y1N>;q3{JV=uFa+cHz4} zlptp%4N{CKl!PvwZJE~pL+F@fT~8? zc5qCbgnqlgsDouPNN7qc5tR|{27<6usgqC@K5&QLB_MyKD0AzUTh=&Xh}FFL>>5gO zUCO6zPeKKxcAR%ti27-0)(EByc+~MQXV$dbeym-pT(LUJYCE7I>TmMy)eG zfjzs*?tHM{yV7pzGNX|tzh`x*$NAcx$08Ui#VK7Js#aqDqM0ryZP5zumbaU^4Xe0$ zQr@%>gpL(4@hi1th7rN|`}sZ?ZlX z;JwEZf%I`Iax$`udzaK3!R<=TDAWZ+HlPgAXUhE0P2%$%D_1e*hHIIdTOXL)Dzvro zGw0)60RwBy;x-V^G`CoJp4EuU`V!stsxu{d?3eXPDiNhQy4U^KhM8VCMcz><7>4@x zz<5^n$p1VyIAqN#|x zCXabH74weT4d&~&2K|0lYwU%r^Wk4%C(;4tH`-(TDE#YM@`LE1LQePq?QKG|L?gw2 zKG0l?Xrb??S;I3|IayfC>n^gj+UFpC%;~4!EZfeC@=^ROQ9IQ{EIQ?^Jm!u1sq(Y0 zIG0q3b;+ZR(xa2NsnOoQ9^I`Br(XF+{Gvj5wbHA;vpx4_&9BaSLyTV|`fEWgEdzSy zF{PtoHn4WyRljJiah#kqb$F}L&~+~Z(ihJTA1DvQLlLd@9KSSo0CV3v(SCUfWaTVy z3d&fHxPs-wHZ0|arn@ws6x99l-j*dr8#a+Qp!Nq>J`plMaN?C!n~ZI2TC2POhqBZv zT;fptNww?*m8NJ+{Kf{VoV%2c5ttm_;Wt7<<(mVCI5=gPJi zTzY1wKcs?NL6#60$jE#@hRynIP8`=-SG(9vks1N?k5I`_Pf;ji4gSV2(EP(;qcK%$ z&VbI+)ha97II?9#UT}OyLCJs@n|;gRM>$G`0t7Wd5@P(oypZ@*W^KW8+L{X*I8Y=k+Aar&pW0*CQi5BE}#?&%1#1=2x266Yp50YrD9%bdv=l!lt(F852~fnRAO!UTo^*NmqyXgZ7;O5u}tA} z6i_7%P-)MH@}F6R6jg~?UCMDh`XJLLQALq6JOnBjsz7H16h*jfut2MYe-u(?nKp8u$0UqHwNH83@2(A3Dge6ec|gO zI)y%h!SY$EqiS?zqAV9*jI^j}H(z9CXyJr;%s^Loa33ihlRODOV(RlvRQZS-pvqRPkVUwGvAR1OEv z+dZua?HAR~gEtHNjb;y9e+&eAd8wZF*ep$ z#p>Cd2~y|@yL@f1_X_icPc;<Gd{3@5-27u- zj}^5}i(F{`oYa7z>bnD4RKc-#p{#8_1=WxJ0ZT3g6q6;Eks+eJ1T7Cwff+-NEZOq= zE?8Le^v`?|mPw6o%?94Xy-!p99T7?4X-M?x!ss|prmBcBR*P?3U#(+omOH=dbSy5v z(u?HDXhBP1F96y3arEsUZ}oK0HFn^W?45=ZzIk81)m!Rd&F0_poO5YN332Hf6e&pvRJ&0BsDLhy*+|A@D&!mS_rmM0#25 zw09URer#mNAnvY;Vr)18-(uGAmZ{oCq=+8sS7+K8hO0QI{E{#qet3ImnFxJ1a8%HQ zL0sN#r7Dn|jWQ}NxA6-iJL&PRxbh7&$vS6_XBaM2fK;E>kSvD3u09$A6T9u zVyKgoJ|PJf+mYACn$0g0D1m0SNgFYYrg!__P)*Y&sL9fEd;_ysQIjK&(r2* zC{XgL6=>B7BOU^z?Fo1DFm}#1Q9)y>u>&}E*nP@5IzLiNA77%UXXRN8Yv6x2^lu>J zlV>@uqyTJDf&-9LC{#YK+ly{7HgVWoD#dCj0t2iQ_MExQGg@z#dmq%g*J@!-w znM5v2bx%s@F~od_Ne_8{MLGHf^4jsT=>*k=&|7lP$bQE9OH zFEc<9+PoT48n2@na5GfPA?X*qq(RU*cwK1fOcGK?f5f(BRG|Qm_Iwr1hPHVPDOj!Z zPLB3aD9E4_AW%Gm+_Bw*D24D?r-3<$N`YV#5LJ-f-8rCIg<^{=`pbGmk1kJp{|y0G z1(R4I4;)32W#sYv4aH-h?#w6F9*nEqqiQpB{w)xm$zY7}jyBz`1#r^8POHqt;CY92itTiFFZ7L>z>fff^)Y5x7^R#N1+6`B=rZmxIYaQh=VqlNJ zvicwedpOQEJ3MnQ)?V3ax^K3?-bAfkmhHNRuY>yjR`iZTo77&z{TYzN99$xS$-e}F zxF^3OFedJc3^dQ;j215&$u(I7q;jWh=FWvYt{83#{y4q9G^DBNtb2b@9d1m!=l#=q z6s9N&VB*vP6}LWy$P=pHTs(jWvRgR*=T+PqJW;0vq7i<&?FQqiT8UMWCkgbet2Ie0 zxDHWTx43T2K$(`wT|mDKyJflcMO13b?J3&qq@d2L2BQVf;%_qnHqOjTN)l`u6hloz zBsiCOOw1#uy_Jic!JF^4*e6)&%JkhLZfp8|-B0Fr&iiQy;0o>kA?+=L>InKbQ8c&* zcXvr}x8Uv$!R6rY1PKIpJ-8Fx_290--QD%zvb?wI?!CLUwOh6SFI_bsdit5}s^83X zKa#<>m9)UmjXgUkxlb76 z?LBz(j`V2KzLmgoB_O}RH`k5x%eQ8{0G*kWF=*l}Qe$@`h>`Q+C~|;Dn$}hMWA=+I z$#Q|ElnL5o;&{9~Mx48cfaJ_@?z>2^mqMLbQc^ZJ!IbQUGlSGTZ}tldM!JjSZzO%a zG<8}i(hzsM0eezh7Z(J$6g_Mv8l|#oeeo^w99x~019C0nCU{M^B@$Qa^NT@ zHPM+l?17<|aQnZzqm!TG2K7j$J%Xy6jUduq(dqqv#@D#NB$}(t$0%dSMx%s~k$4=} z+Hx;zJuRit6Lbh`{}I*>^HcmNFHODVx@j@AabRKRVJmWQK*!|l;0sQMlISs+Zsz7y zIRefgHHHWXYX^*4Cm3TCuj?&1x0h0jr%0aP3wFft0_PRBnl7VTR-(Xj_at|KB>Oel z3&+5~<~lFZh@Yq_G=wd-UUwXr6`3N4sF|eBFdU{MD7oj1W)1Y7$T+6|0cS8SF`D_B}_D49z8@*`Y5b=IS1t=z!w9Z21v`+o~kU z{s%bp-+0`DA*S$_yWp|<{tA?Ic9_!bU}8r#t-NveN5O+cQdaRdUEs5QXdZSV5A{*c zNf^(0(a2h39Wh$0s*9;e*y7-Ex@N9aD00qAnwP%A6E`e_BlC?EEJ@OD`5qTwIAa<& zF`TGHiSe7t3H}mUI^dj&@+f2?j(7r718!vL7*G6x=Rx##-ED#i!9ynQ;FozKeq5V| zv~D(aL_(s_TBC+c9Poh+|F=z4Z6YBdR*1*f1~+EoK7=MttH?d2=#24mEaBMWWz0=? zum_Z2^lhwNg#~Jw8&ri$l3qI8YF2Gee}_hW?H(a7CblAr&PIT@%^fJ6<^n@p7C6LC zGmo@2!U|a+Nurxbh`_mq*hOzQrl7mad$Kbd>4i^SRAv`hx>P&28Cw7*wos+~!kOPV zoBB8Pl-1$niMm`(tpmR7K(FAv$csn@T|kf7H)rk9xgmMHQqXV3(g!0L#C+V^Fk8P& zfo;>Vi`gcROTMZSoY)r4ZouWmQOe$8pVmz2(WZ!G7pA#rUn}vhCQZ0>F=nkkI>InF zwOg*n)9gveaY_ zU_vgc3e`3a!I29bewT5$anNWZ3MzQcp<(tKu}$?z89r}b{-SFeptlmP^pln%piAa zHgcOqHdXea83RGRHW?2hwjhp8$@E7F?83VA-MX*7;tvDHaZ+Wh*lT^ime+^ac~&x6 zgJVnV@s@nc46BFp*z{6;7@K~rS`J8YvIpLd)X{mKp@%WkWT_r}=A}t@-h1Kc72vjI zrNecTENeR{`w5ox?kCu}L`Sksm85T5yUW>>5DX_L<@=Ttj+h4&hQL1{GgVPpiJ>Uo zFs3E+dek8dClJ_|bcgRU5>tJDHBEy&xe?`8X3c}CB+HOYUB28{dKcgM^=zdj#s?YMXB4JM(HFOP*^Q^-u-I@rqm zu+1L1I-WPk)(A|(>mBrZZm@%^MBg%F&g@D3(}%xE0)|XXUREa{@l1Ak(WeUHJTCQ> zJVttdNf;hic5OdN7d1v6H~Jmx60JE`{jd#m!RE-O6sZrU0GKwtU~k&n-<0y^GPz`7b2gXYBh$9+9K~>wQma+rRoTkBf{=QA=GES(co+oh)K4JOxqt2%^qIyrL_1F% zE{A@(UR$@&z>T2)ZakUmWy(3^jNzTwFDFp9jzD)eTd}WT+Cr8YO?Hg>Cv>d4L%QSw zEAkr;z&6&yUpklO~{mKpb|7aq4uu%y^(Aq=hxc zp~Xd8^H6Yk7K(h^iMz(wTb7z`IL>>itR%Uh8ucQ5FIUQy)7)|GO2H~(yXUs9)B@=* z51k#eDszWu+CLfQb#sNQGgNFcr6l*eY;_d>9R?Pya%{>-2DW;BpBuuJ`V2o2S)1UaW|j;+s=s?;)VM!ZCA z>~T1??i*x_I4Syrh2Vk3QFkpd7jrE%5^2&-8;*?*+*FpRzN|QO+{w4jW#5Dq9+n5n z{@6qZA5h)>2p1fScEfpd2Vx<(41cBVx2AS;3^=H;UqAO3T@3Nk!v3*%iBK-jb<72c zLR<$LZYrB`X<11gH{_zbMh$ex=-)Lt5aCwd*BliP4Sk^B((#`jz0&}$p2pG1{`d>a z2Xt(DtpRow3|yo+ztJ@gH(x_;!1p-$Au0=75kF5#=nKGyb7?>U>wciPVx+28W|&om zu>xfxuvZtJpeN^}+rT8uvh8KMI~plupW$96WQuF5Q?nbM{!C^F6t;9)`tMCSn?jiC z#}k!UXSzRJLE8(3_5QM(hQ;o%v^ zy8Y&ff4*WE)EgO)xotOTl}he9V)w122Y9BMop&1jk}l$zUPH-nz>6pvj83%O6DW{q zWgar`t>BaeMXco;{e0kEa6VjA>2LuVy&32hv~Ad3OJ6R6JKWf{kmL(#?28e)6QePU za-*V>>nKuND+M)uYJvslRGT?=j~u%>&3;DP7LW( zwh2;;FKGh$gTmg-u&az6p<<3Qq0r)TF%sC8mz}SxaPi!o3^%kG*HYT)fC5hSxLT{bQ&)*A>Wq~u-KhI$}8q_pwlz3X7nAnQx5jkb4A(0?q*4zHMWccUEQ z;~6jnUM7OXK?zIq`p+br_d|DUU?`<=7RZnWgqlHjs$TE!Cv8fB2iW+z+pILR%4yv| z+9X1eaGlupioyQc-d@7Bx}C_O&)^=X3dj~GN`-Plk*4T}H!kHvo!(vkOaLlqL*?XIwY?LcJb(yzs7)s(49b~I!jxD93@u}q} z3E1AkG*IR`9yQ1cCg1WL5}eG4{2;;sa)A+rkdJPx{TyzG6V_Tnv6nIBdQ-ZNoF`7s zd4m8yQGV=VBc!CyovF4Hzm`N75g%i5*)dh1O#nh|#FoGo@zHX>w1f0WT~bc%Mm**> z-W8nL^02BuUg7`lW&Pm1>TMhU-lb3PFn~8)8V!Xpm7sKt4?6}{4hZ_F;h@(|c+8b1 z{^-!iP|3gL=+o>S+9e)hQg2)qw*UMARYe5qj<5y`4d`c?^Uwc%x6pd1r;rjp4bE|->@$UD4C{Yq56 z@>zhJ@prsCnku<{#T zWCrdV^jyDlRWKFgDf|5_h4oI%Q0OPjOSMR}LSM<&jOsCn=$0Du$h^asqL3PpJ>2>| zI6-D?t_s1h?K}vVVx_Lx%@AnVP!oT=sdhl1(ln6bVZqjopO2oB@YV!W+!;jOw&Qmm zM???IdyMwgGqX?Q?3X#1=y_5}=79?1FKupNavM zLjEnmk5TXDz*9OOS&2IvmrHt4&?9Q4yH7Qk=#YS5gOh0jp|DM9gMR2wW>3*y-YEWU z679hh=r3?#I5sl%3`A3SXjOQiC&{nUR<_dHB%YLKCz1`J3VXA7Rax+J5~|$QRjl<< zj@+RdMN~6i=pG`Xzribj&%X+&_0c&#vfwRpq{bm0x;t-XYd%tA$!)Okc^-T2ogQqH zR%*Vm+tL%F4uPArBhv#@8&S9&ls=dZ~o!P0e_{+%U+jztL1v*T+J!0p$; zy~4S}DA<;Dj5*xnp`YA2*OOKXDs>nWSpDjXW#;WSR^BsT3^Czo0g>P7jD3lZ4Fo+$^PY&RJcRs#Id|@fw9!)=Uhm0{})xBr)X=GKUWn1 z&tbZ1axy!7)TFE1xE7eyYWMTT6Ye#=f7Ta{Ckx8(Y6vizZ(A{}9!xRLe}63vm+cJ^ z$j^V%Dw^8(=4RbbTtT}6>TJ1rlo{Y7(??h_P^>syxHJJ8y{*#Tef-Zh^zTP|jznIPCCz{k{2w?X}G9j?G_~=U|Q)^{>Gi z_V~E8vZX-7;DpI>*97o;{KVfDih0%v~9&Z z$cwIs4Gi|>qOVG+Ki${pWLOc_3ho&xq)UlZ(i&h=bu*O!r7WIs={A0h3ttKY_6izC z84RSJ=AUd^dDa8CHr2Uj^j{m7tU0E;o{jNzG-N=veWeNW_t(?IlBr5J?s%2aRu^IM zO%F4#&DCzjmtv!Cx+O(c8Dz39^k;)1e1i}+hkvcr{T?)ufMjrw-_HUVd`M+qRWlkZ zDq9js?;wTIj-+}5Li~r`F*@?_SEa1y_`2Kt5EP8}v+Ow~@I~C{byHVFBkKOOR;e?~ zIt*NXA3(E48z`&Ek9TK0v!o16!T~38L?aJjYdQipa8KRZIRs{VG>>PF2%QRK&2y$S zh*cF{u~Zcz;vCCOPH-#A4^Jdhg$laVt_bfG`vDdF7+vHWWslLev~^_yKRRur=A&PeCk=JVswElvy-#K3^}> zp8t7n!aUfvhcC`HB#A>PxV`rQ!G*`>aH zUh9)W_Jc7*vPU2~XyuOCV<0=iYLOvt@E{VPmw0v|NJe;mK~mF)AG^1obiQaAa!K!C zyopZEJf`*2J*dvaGDwAg+r49NFDNt`;+4f50WD@3dQGHc`@ikc9WT=8jGDlZntyvU zC@%PMrGk@qH7$OMaZ(0)a}$)+^2p1(G6~a`G77#u0Jd3 z;(I|b`3$f}mM%%?EI^TpF^nJga7SpaN$Ati|0s{>$eq!m9h7|N$rq?aNV5w<{$zhO z!Oamflm3-0DusZt3*i?WlknrhYfOvlOtWd54(Ar!=shg%Z54oNQSqKa6H z^%s>$Gb(|uRLtJJA#@sH8si&K61qQc=+)S_5UlA(&_EuFxG5$ocrY{%q#{hStpn#M z-hvmMeXfOgs3qkmhrDft{kwVmw444d<;n( zD&8>b{Ks7vby{(ZoIr#32NGSF734npL^9FV;am(R#UObGBkvCv>JN>HWd9_DA0$d- zneh1vnA>#$F__zP-;~H+F}ElpVlewy$r+k4LdwM(GAZ==W>pe+b{FLkFa#l@Yj=5y zol+W76a1t2WuNh9RAyqGq-2B#^<;nDrv7IL$jLfBS$JBg7OTL5!mi6F=4bzxFaIN_ zJQ-eS-xd}Ef(iu!LgxRvDjM5bIyhUqTG<)>r!KM>J6l-%??gJkHANkO+{mCqt;af` zRvd+dO0$LDG^~{xnZi_HDn8&J6$|De$642Y}jCc{oa|Cffhw)M^POZV9FF{R5*eS=r;S9h0>#fxM6kEiW0eh2P{2jLa)-Av3{ zmq7LVhM!LLy$9Or`j|h+dr9c7E4KY@;^1`grG0gR^kEfza&LY6>IGUk7k0>b7d`9B zGRn!JKBzm;YB=Bo<$rE0=Vu}%D(hT!W+sm>w@C!S>V4~QOO&r*`7W<|PBSB4*Rrym z-bW>qX2oxLvD2A!s9LAZ##HG1%-%ug7U_IOo$~4_^P+0!kH@Q`I3G@3_RK~f|5a^u zP>i#zP`@yx-)$)_1cg*zZK8JJADJ5uE5w)vj8CJb%3xFImwl5mw)V1wPz+1Mz4-!Q^XvyW`mhX)XW+k z%wfEykP_ni-Zs zbQHl8#oD+z!LS{?g7gOLj>6sGz`O6}aT765Dgmv#(ku~&@1CWDB$ zMIiO3WTG27;5I|8j0}4BpUwchHs7B~)%w!*B$(oHisB>B_*{~9^?!0M%wz*RW5L-o z$Kh-Y-ju;TAvEb;aQ-vL)^yav5SNX;{wtrx3Veews$Mq?62@FiUkVB-ncQ#c)ooy~ z=3M(BV4cGVn66OV;t9?q^7>U2f0(*8y(;sa_ft*jqAD3?Q146^WGj8=a#s%TXWR)( zvf?LOUE?bh<=6R?yF6EViKzy45)5M&>`%X8Bi8lD1*xpWQ%f|de}xJZyWNq3)@@{2 zlMarj?^PnZYS=c$%Bnszw02!-P_}S+#Tm&W!vEJX%MI9{fsR%`PvG(55SfDzB2)k>TXygC6*LysdEHN@ZW zpfCW$>UZq~>X1ZVD!%JTiQ)iCO)_|)nF^5K6*Q!Yc(qdv>5!7@CbZHfX{^#*6W6e3 zYlxX?{wR9}0_cjDzdwc{E)l8x!%Tv{p^LU~c`U)mlOh#M{<|Xy!IGknn@-ebPuxE$ zITFWqVBCwGEsyM;6iOlcODAcX$gcCC?AI=fDqj@dWC#%m2e95#4@Y^1*-M7UB4RFp zyWlccAkFp-iNdv^jN4zHLUOz)GDtIiP0~#BXBWpdM3<`>(oY#>u{9BOG zf`S@XE2V0SQaN_a3@=Z8n_TX&A=L&t+hYPpUGmbAeoltHUCknT-hOD=U%Z0EDA-pJ z_iTz#4zZEgp(Wc4p<}4{9r6*9%N=T8S=n_-P$I6mS|!}ltda_;7TwvqrR`N$qm(b4 zes6tP)mxBDHSfrVRS(Hb^y6U3*CtLiTm>h#s>i-%`}(FuQQU8JKFLRlKOWisBP6(J z!ONd;%_zK#v3T;A`4G{rT@QpN#K87?u|`N-JFe0y^QSK zY+bEQZH--ASpJ{Z+QQn@_&*v7Sth%O-F4tM4^HlDg)c0{oB0 zWoC=neJZ^vE*Bxb^zjYuNFA>9!dQoeYD+phJHAD4A@ zTjs?>!W4&0ZN@uiy5(x0&&`o{ z!Z}*x{9VoR`4|lAvOS2GB{g#78Me8`FQ44L`s!LS}2ZB zG-AXpgCmkreGyNe*51^C&>*}6)C0b%jo##HS*BTYTpbueY;(gSE zhEy+u34Aq^bS#yY3%VzpEol&(9Gi;{%b@AHw*p$a1$4jc%K3Af*KN`Q1eEom0(Q%d z9I^Fpb{_zRs{oj%5Qasa$&$y2WsAn&U>)w*^G%OP@a1fC1z1F^=;pf5Lg&fqx=#O! zSm9pSqU){x?cXa8$dN)XYJfn_^oyt+?F(7@nXz%trTfftTPcYFacbBA6^T9VipA;i z%~gF??nvZYtVRMx$&@AVOrwDaXzB2eh8b?NxO~20f9bLYq_diJWn!K+>&z3drl6P| zvsjGx)U;87)6Gqfr_7%X1Eu$VF2$AJY210e$k2{CR3cnwprDDRUMZH8TNx=8RLBJu zCVOo^v#ZOxD8}Av1XdjqLbRe(k2a)NCQwC-BvX~u1F7i!)(d4%PYBjI1=pz@W~a}S zP~O(4K*hjv%ZB6qx77}M;B{Xy+4az}dzZFmj@0WT8#Z`K_jeQ;OE~kr$?RjtMD|cH zr^?jwkk21p*_z#JR2vi(^D5bGS`}YrS4i+nW&= z@9UZ&kWeY$`=(u$LZrs0X=%eh=92Q%C(hoTK=&4jruk*F_GORVIeanZgMesldSXw7 z%Bjg0_f$I%ApYH5@eY%g5G&-JjMK0A!M(fv`qz9*KfkeMOAY_eMx`Bmh6e3;3M<>R zT`Wg6F<7bfS3AjvHGvSQSDnxx-mG6ycfpA3nFZA!T=8{EhBQF1N-sto-9KY*%0t@f z?N#3f?~IAN+%|Rd_zvt=JLqyI*3Og}hFaJ%v>$IL`-Yl0w>lB(^akUSt#Ry<#J_B! z$QxnLue){B)Ys}{&9D3OxT)_P`NPjC4P=Qf>}Zkmnt&+!@|%HWu+aG^CyTzIc|N%X z(Yh^r{%)Wx8y`|hYp1Sc0i22DeIh&cSa#F%Oe^0@qlZ0emYUG=MM3t0;0HpF)xScs z8qqlxkybp*)sfg^`)>wQbqCu?s!R4M!Y)(Hyood00|boTs7%di3tesUFxPZRhZdj; z!6>M~!xeTtklC$Qppe>N%kQ=UU&CDcdUv}L{RSCgMcJiXBuj%&Gj-bL)il4Raf_=S zuc9h6r0$vwFFw*^FB4Z9Oq({I`1qC1tDcqE>MJ@HA{IY5HpMFQQP0cQZPFoY|5WsB zFo}gHcIxo)?i=UK#5F`3Z#pZPUu3GO{BWkyvYp}2p8V`xk#3lL*W)9Tx8_%sI@Jo= z07(<35HjbziiDN)Eoy)Kx66yPXo8=fb9{y#k+z)~RkVx{Ec$AM`-0XtD+~&v-#?|lkD+MUv69IxJ!Du`M6nKYC>A&#w_hMUo=08C#%?1PoU#YK z;V4dPu0W{jJFwM1`JBWfeMJesD+@4K6(qA2+Gp9i9Ze(N2F{PZ^O$%S08(~CtKprI zv5$ihL-o7=dJ7sP#?p9w6q;DSzYVAo3>TWFy9|m|`of!6seTngrhqX|RuN69M5cci zrM1Ar4VbNASCl!XqRKjk!@n;So{DR@ZGCNV_9Gy*^>XFpF0qN2{&?Lr`gle=$nY5D zZ@L#nWU>P4nKNq`fqiZr8-IIKT2d%E-zQ1I!ftPxoc*Ri8Tc(D2W#9dRTvd)<%s~= zCl@#%P&Q#aDY}vk&;9-HLPf5{T9~*W<2%Dh9G7TmJ<%y0C~L7{YSFd*W7(fUQwjnK z`pYabL)4;q&t7r^T;{s@?Fc{5m&ylIZe3N#Z@(MTAQ9|(B+G5bJdn#NZEC*$nuDRl zPe6Q?q;`w1_K$s;+mQR+H+bxLP{+4DvlgNvdXf9HaG@9Ra84wRL%`L%oAhUx@!ei= zc)un2Lm^GvC}F{JAjX1t>>fM68rVzt=aCo^Ce>aLYmFaKQ(c|fh zeYZOgK0$MSu8ls%UL71cHOl~8KNMW-Y~}B$x&>Kw-N&~57&+$d-J>2I-aE%xgK%aa zCJ)1OBAS%jDhvYB&eS>$*tU)a1m;3-aJdS+Uv^Ti{slU4j3>d-@sq^N?wi&6ud_)o zg!}?7W?%A6Z5ZGlUH)8=7R--0%(EysPM_?ed)E`We_sMWBOtGS3>{ z091}AQgBtpi~QKDyaiduRrlj0%SmNsf~4jvpb8rbsiA%GP6j9AG03x5AQKWC7f#RPF$iW{=kbe$jDdv& zMP6mezNHzG)$Wz7F*TbX=!9~!Xk#V0to&vey^7t>VzSuy=9?WUiBciqVK&6_k0W$Wz&Pk|n|zbS8=K+^Z|%=#2Q>7|+eFo3PLW z{Z|}SwoS_)hI6+|{T-;535f{FHea*QnT$10UjD8)9=qX6zV>&(HPHE?6igP@u9QYz+D`FpBnLP3+Yn3&qCFTS8e=u0?3(soQH!SAOE? zS5e;Q9xxH3@hRp@p10eD!|E>$2qoN6f>=Ie>RNFBKNJ5)uEZae3dSbLH=bQ1Qml#V-du^>x zdlpTH!8NE!wA)DwsBI9Mm0$HncIi1RY@v6Hc?8gY%`*O>Uq8m5W1Qv7GKAqUrZbNK zg?pM4UV7{KcAKE7G?37chALz=TB05&-DC&m{N{7bqbP0au4?^vuqO(*4E|YK-ceh} z2+fLi3^!uLHE)47DO8{yTAebAm%VpZde?Z%5oIgyAtfI@xegaO2NLxPehV*H72NbBI~Vn${N>d266s^NOriC; zLAIGH_=j5>@Qw0DJ3_mhtLC60)yFeeGR@5sE}^2K3Wpp4wmqrwx66+e#)QcP=geC? zGrA{i#Upz4MOqr?dX9CDxo-c7?kRkQi_z#LjVM%(?e|MlF6&Z?`ao1Pa;l(d%9cs`$&A^{;vp*!kBa66yh) ztsnomzt5v7LJM*_yr!nS`GzqdU}u-h?z90s9hR}jr?vUbzp<+RE4HOJ4ho?$S&SQ& z()(e`$kXW{z9s9KRrABfHc2H0yKvux+0LGBVCFY7ufv`EEcQFO$`x^qoQ80d#0G-` zD1u!nk3j?ug@~;3UJ{WdpJ+hJ-*A*Egf{A}JMXx!nnW69E|TJMosrzdI92&8!Jne^ zf%uqn5S(()hi-DvPNAIqIG`n$b=zte27zuUleF}ox^R`vgy*9PRkC^n(Qcjhw60IL zZw&)%u*Bb>-`P-MeqOhUg4a)h8!?)VY5juJ#*ls#f-v2?mR`3>6=up`HU-@P>R6hH zhCGff@Q4?oWtZ5h|8(@@lyJJNG~_ID64O|?0owF2Alw}*V&v-1!=B7AdyoE>${FW$ zW~s3W!n-pc2dxVJBiS%X7HJ<@F(O}@oxf&M*|s`jR{9M30ZA3WQ%+t|qKWqvoX?2% zDQ71tj?iCNd!CkP4%AqI)DpapG>UL+P?_lGlgv6t7T=Xs_CTRkqYjo=Y%bf9_00z+ z83ZT)z8c{KsAH%pl2qJIvc1 zBXm;h2Y5cqCO3vMIg@V@LzF^5-EOE?6w{xg*xlS^xpP`d4` zwQIL^Ol?%~$~DlBF&!|prky}rXnf@R{@7~zwr1Tn_(}2aYNo&2#FLH0rR2W-(cM_l^hGW98~O;Nvwi2^iR}*|A>g^V@m>p!XtVqWqwsk=ZvtpFpz&_UhQGUO|A+ewTL z%FYtZ(o!l!JnKnoa|yb|h#O&MGCOvR2T7J~1@fyfNP>zhydr3xgqE9pu_RE2o~mn5 zQHg|ui%>JPDOu1nGngUk()Ov})q!8t9)5)G_{cS+M^Sh-XH*-0&^Q26uhxuaD&4AqzY+QPAo@xqfW(9I0If`}c;}#KS{1W2dUFJAQbnTBBYh+qM zG58yEG>G5MG5zON92>ECUj}zAJ_{`*{~2WZ$0xV#X>`Cv< zQdC;TB9qYeP?R2`$+v4o>8fm$PrAIOkX2RyEuL9d|G6jThCs_pH(2_FwvjGS;r{y| zQP!^~<@s@=qsbcY&;tx!BNQt5U7BDHE_aXWxD0 znK|NL*w@{Um^#F~kg`0a3EUELix(Z!sG?@{k33atT_Y z1L84xxHmmwK7C`($b&fq^Q}`$JZc6m+_YVHpX}a}FNyRFsW$p(>9|Peu_VujJBxa- z=^@g3Tg;SvS^!?bpK1sNjWgZ(8}MsL^CzDHPFZ_GgalV&;*F{`Hfdb3Gk7uie3dm3 z!6{r_mQz424tW-jviUX=_)(OK;OvJK@RH?3ri;BPlt*>y{m}ghxb*|}PXu^62-RQ_>h&r^ zZZO-{{Tx8hSHp_gz{a^Al_yypB}~j0`l-3-f)F}%Q;%f|xdSe7^`S7bxGQ$z%INMn z%^%p2*Vr((Rv+)Po|Jj44G53|q_gym@ng>760rDTDSR?V_CDG~?oAC1uMny|zWHKJ zdk}y(NEyC5fdcE+a$_j}v|ZtOw`gkrCCww|)pRuf1N3rHR2n%44zS_ol>IKMVC!fd3jJ^y)u=FW15 zh1!5ccnCydd+|Qoc(5V%X&gavtM<2v`mt1qDE~!iRi{OR#QW>BxY*0@(su&8wurT0 z4N-zeHJD_vCiVh9(8&>`Y!D61rVJ6~dg4&m`)gdTQf)Jch^O#P{fQVgR?WG>CG9(MaT4%>x<++I zsb7%IFiuAuoX91u)H580p#GfIbzQ`o?P9&AR<)OVz}YVtQenvGwpyN;UM)fM@+{qn zg7i28Aid(YCf;UplH+(~6QbLlpTEj+71guHx&TpG!_mp&FIZBlumTA3B{|@`{q0&5 zh%5|PG2RRjuX|vX7DXbdV{p6%2K?UDgCEFZ=J_eTtBb&yq7nR+r`4pV*WT9Na{$Q; z`^)>9LR#9$r->WKUs?Avt2@be{gr~j^W^;se3R4>$>C}q`e)&*ZZB zFQ8hKppynlq{fkGOyAXVn9G=bY5(|&MM-gy=v#HxPv`PB4`X6jV@?O;vfo6C=mt0w zg4@{AnG}xSW<1#hm)F1l5jtsp1`%xc^_D%}=TG8Pt*Uz3n9Cb!-D0N}snk5EUQbH) zU7RVrS;#n3K#<7-$_PQpm!8CSsiGy#Q)n7ro!~CsHF>rdfv2l*Z{BWxFS%1b9&cJW zbsIgT0aJS1g>g<@KJcQ>80n|FDP*P}R>oV7dWT|w^P{47DDexVucJMeA0PLV=Dfyu zq4~o7YzcNc_m_9POrymOn??rWrDFvO(qxHW9oXf?C{`1g)}eW~iB_9B*bZ5QhFSXU zm*k1I>A@lF5_8EDG#*ETSb6Kq=P#$TMcn-CuQT-I$(4#4m?wp%>)c-G#G=v35mgb~ z)w~HAb_dFD5FAdT8YNh_#ff?q3pszVuRj0yx;iS@R)t8$d9o*#mnujWrwIGe413tQ z!-S}P;(nzC{m^FCuA>e9L6XIQVlvcK*#Drqh>JZrS*R7X-Xg{7Qkt;)g+&;2(vtTF zYzwri(+Eq4^o$i|l%lH!ne5+n3}Fpu}{lK|P=Y zvBsk*^>}`Tq(*=Zb=RKf*s{hUe`Qg)p>AaYydyPceSSLE+*#q||BcG?9lh_(Jwto3 zc=iOICvA8SZ+NZQtP(Ph!B8GVwf+A2MNkC5@v{A+1@XIqAOrysXZ=@|D&aK4K&t)S zf~p(`+sCsJcT$%gDAIa{(fH|}+LuiCZ}jP5U&Z8cupoOnL8|*d{Ox1S0S%?_Un@&@ zzB4thQOD=6Q3Xy{VJcmrE`@QW#}#w2C5LZJFlWe#^>4-kCQ(TU*f#Sg_9Z!ON67~= zK_=_vi|-9*`7xvZq&{o=Tc~hGKn~&aRF{YMDO6Og`}Z!0|B=5Sl1C^VX$t{Chyw|s z^j|z$`hT7A+>LGB%>U0UhI5c!J=L;C<Y6pr>GJ+?J3Q@t&tiEQ zVEx#cHxIa;M>F#KcnZr2C@S%Bd#~<%%E{?^dB1gNKl^x`@DTQWd0X7<@@g77=AOFa z`FPfenRUqCuI_q1$O(9x_xQ;0zpp3df5XlBI6l4P3=@92&k_FMMB2Ia=o;JadcC+k zByGQ_{@9@vc7L`s@_Q2&{&=WeoZtzMk120Yo_=F0gG;m;-uzf4qr zT;Fr-G(Fr-sjPBKK55HJKsyH{lTWBFD;oG&%3Fw zETk^_w*mfdeIIY!Mgbq6uwTbE1HhlF@CfuTIrP%MBYi&Fd=h*6sQ+?0lzr{+@k$%; zw666LayRYi_i`HWau1yPT(UPK|CjZR_xn!>g~44PC+*d(Bis(cO;bkxZ|e`juje^k zttYr2&rcsu2cM7Ot>OE5kDDi9p1niv_aVu<*U3%qTHKeZ&NuMlrZ2eM$iIE?zT61Z zb=R_!I@E4x;Eg;^xO~s^CfOu7*_C4VIkR==xF7GQ0k8L34qZN3eh>4fcR+`jdg1qh zuE&XLQp=CMsrMb)uGf*AJyrYT&h@63&P%Vo)`!dpR6QG)q=_!xxlKO6sNdnkzioGr ziJYIk-zOU&+um+b?@ng7(@yW$^k5b5p>|$>&`=WOC;Q-c)ORopI%`%v1T`~9&j1!n zCYB$XVpVUs+?IT%g;y6|^8 zohM!}S^6z3aheH8o@K)is1&GqU~V`JvnoUd~!J&RkRU`cIEj)OyU z8Lxem!s1zB=0lI=9k0z)Mo?AHOMiD^KEkP+CtJFbg3>mIK%frk(lb{msn5~ai{Ud4 zATM>phS5)H?P7@og43?RzJk9pyRaSLBSWJ0CLX+5Y+t`71a40$?bMI7U1NcJ5|Xs9 z=*m1*7;~E?KKCvaET&;EI8M7zKXE77bQANa4r6oymT~$uY8@8C&38%u{nlahs_;-~ zTto5lA+IB)#hS8V=^08 zf+3~f?ou<)C;4z)T&5@(TX~tDF8%iH)0sE9`TLHrU*JX|{p`iXfJb^WSEFeJNy@qY z{ldzUXU&qkZQnbDA-g5>RI~Co*gyp*X(%FwRG@^~`C? zdh_N8MP=@Bqm1}(bP>vwvoL9+4?Z)R-^h^~q3)OplHG-uf>T7aZ}#r&FbQqKh40%k zs018eK*7Ovc^%Ek$$4eqa_`&;{*@3C{_;0PCLvjY!p4eOQ@D@IMOMc2a6K>ep)T1# zX?;W9^UY781!w&_MS+x|Oe>)<#thcTc=^wGhX3~wgZ1X7owKaf1*nCzm;|N*tkVWS zt$fJMzsw8ooQ7U)_A0-G_9iY%PFl>Wb{Ht!)Bd>?f)(lZ&GNT1&K%x3_c$DDYtksq z1?OdIB&q^e+{FU#RDFDjQ8Bs4^{7~GtWHDHSPZ&sqjPxcRw`cHeECxW8AjWLu&0T8 ziwlmMR-DrU24earMo5(>2tT_OkLcs5btCY!pZK>9eW8r=cORf#PB?MrW*v?Tf>Rb# zPr^b+j>@7!xY2q+=)Lxx48Nl*psIpf8uzp$>@~YX6zGRD)hJ}Zr~RB2NzD~ntk_%8 zKOkP$d6(d~+4e0u@f|P#RQO;4Qt2*QFdi|WY_2t^+kb*yES=P$Qk4zho(FYipMam^ z5RAjiQHoR&`WMDe-PT1GVGa^z+liU4xiV-fV{uePR-0v%6wm*hnX8t6jUZ7q=?Q9- zEkX5@VsWtWdf%|{$}-I!Q^?S7H+kEhr1rU+u4De|@O7Zs!GJwqMFL00#RJ8LM4-JQ zHM^)7ER1Si5FNy(M98nED#tGxk*gUAfp1LE%0xcphb$N#T0m><2smk~=jAjZ$GH2c z`$dA=^JVVB&6Lxa>|hm4w+`;0Ll$~ISMN%<{@4`bI_`-GnHTXq)?d7tu1htnbl`T* z&}~%hHaw6fJATyBKG>jMKT(uxxW2dO;J@ah8X^yG){QLhLY52F=`ke zxTg|b$pGg22SYB=;Lb;(vT05zbEB*aJ7y_pQi-wL4draI&!~Un_UwWzo3Zm8NsPEC3Af(JXhJ`&r${IfjYeCb4ek zZ!K_)`kj=?oEmc6RQB}}x`5B8$HR^Z-;mvB(?*+m{PV;;z%;UP6Uf{oWBOA3Xjc(99Amt+*Jg+Od%y8O<4b5ZgNv{(q=bFgT%5-3jp@2{OWD`mMICfUn7(B}K})9)$PWMhiWrw_6jW3vCgj}HUZoj+aWoJnK%Scg?p z_sjKs>%&hyL6M86-YSMaXJM!#?Z(5&?F9BVI2?$W&m-{PH95zM;~Hk62WFAaw4xCW zX-Vn6u}CadBIyhzyXYP<7jBn7u>jsr19oa`K4G`o4Zo?gsVoek(_&B z@rf1YDto&X{6uwXg$TB;Hg@hr#}}OW=ikleCbOCB|K2xt7GBgp#9qFI1oPf)djr}G zQ0s6$<9jl1RU1ud6RgC^2JR)T&H)@n%1Wk$)%VI%eUb7uMS2rfo5Npw=*n3IH?XCTuBNN)aRT9|vB2hGBmr=R&qFl|bW*x=bfMcj)MRX0e=C$D@E_3x(!|#q|0-&= zm(kO1*Ie;lfm$R!DIHln!k~`7i>GX8h#1$*uD4oXg870ZZbPahgV|=i59p_goB4c; zqHEYLgcfmLwmn687)rJ@_lr8C`gxh+UMZ#fPkrpeCv*4>)4iWV#4Siq|17eA)%quU zly$?7b1eKK0}h|r8+k{C--((CE;=VlQSe8i z;v&H`P2zt8VnCh0MIx)`?bd!;WD;DJP)>+)B&oce8J3-8xweA@)4qj`yKR_7gVF9Q zSk^Tg_yKz7O?pgrg_JBaMT9Kb%+U<%A2Jw`W}bA(Gw(zRTyIh$V(i2vH4)S}%Q9nz zmjmvV2BtfDM378_g!r|MFBx92=7#4@GrCRrayy|Z;$nlKS!aN6ACJzK_C$C|p=$Z$ z{FK}eOIzpqX&~3@1ipM1RT4nLs%hH8s*!~!mDXNwzng{!^GXXF9oXb3BSNIAvmH>0 zK8y#=OXW89u5InRdp)6jTN|fK7GV6zDYN@Y=%Nzsvlj6LHhsm=*h>>ts~${Z8GI#z zFq&OyYAG-xPdBO0`?ztzof+URZ(6r^*&UJ^tu4)|Gg7KkQe01PCwx+5Oy*^E$b{16 z)&RQ%zq}?aJ2tL0h4j8H5e|k1|eVl#ClG9Kg;Iz`mkogwZm5M%FwLpeoBn$m$Rt_uGK4@q1(`Umypxc z=@n@m#5_(iaSy^j+6@{pJPAp>mtizbXL`s%-TOANW0G$yU~V&*YJl=Ok^nvB(|C zkaZ_JZ_1+d9>ge?xhcP~etfSsJf}28MeFi*J<8k?s!H;+Ns;PtmNU6OW$f zmr*T}p)&2O7qFz4Y~zAI;CP)ZOKbnRKJg3FDHB`U9T_5eDkEuf3#j=|n4<*rHUu1@ zRgD3G^*P=Iu=>eL?nGtV60LF(JAqMa_7JUdqX&LOb1+C+R2okgNr1*$-7+qVB&dTL z+8uY|`3~?BN0Sk=+eO!At`g9ah|R81*QS!oBB{x}ND|_|OP5-8YU67w)Yx*`TZET} zWR^W4X(-u;TaSFhBr2j=uMyRsT2hXj6nhLZ@e)#6AKuYHngrRO1k3yFdUTCQPqwQ* zQU=@p+;xv&rTolxo}GTSm$albAe!D5Zny9=^b{P3Jh>@5t7!uXr=X# zz|yI*M)YmFw>26`3r8HJbl5z^v7DWq zT$1gEbU9C;Mb?O{RqbvWky7;RUfrZNCq`1Uc#)MPiN|U!yn~pahFo?%u90Rv?{|$P zhL-J#ot#c4%P_1LcAR-a%hd+4Hn0>>lJjRHfYY+Ar9GXKAg;Y*j`?DVY8jplL7rGo zQq+>1GUQo$mm}Rfsze_vH$&T4&)Fqd39~#zbvx84M%7tC#_O@9JoX|0EETQOIq zWT({@VnONz(~)8Q=-XDpQu>bKvwD-(yY%z&ZM79-p-Xql9nq$haC*E~a-v`jlQGq( zZbbtj8Mu|&Ln-TzW*0;`G++*eyGP&CCbtY+GFKLEzSdCM%OOvJL??JSa zRtcHfXD95h6{Xc#Ew%{^bTkBEjPp^s6D%I2zqI}5U9PC)NSFDaA5;AZSV>G=rYZh0 zBhgoJH!JBq4^Q9mqnc#I>*ypN5?RzDU)`&foG=O`O_mv{3kU9rP}`167%m#Dw?2?; z*Cnu-sa6RY_DH&Ayb;+$PGi~=!av1*jzCMkUcpY)7~v{PiE8~8Ljm|i3=s{k+yF^3 ztsyDwM1Ikz4+b?OcyHQ~cRh+6lE?)~*xK*vVP(V}>CvValXU1FYuY}x6qQtI;_gEP zY*y#e$@AlTKMleu6M3MSyIqem5Q2@3yFVImy6Y+Tv*@-7UzkOY-L>rK1UhLdCxw!SvFl>rD8 zt?L%)mQ28>Hi9j;h&fIy_-@^ zuzGgvR%iA=a7d(@AAx&qH6IkFIF1dB>3;)jxWSDh|~H?M3MLY+@~B$aw$}?#Q1( z48$8|=1ja+0{4w2j$6SRmfVUwI%`>qkK9>x8mAKo0w=X%&Lr=&nfab~Q-3(t(!?^c zv_mZpHIouGglQtz*`9ww)@<@N3%~|BH~~G=lweK(p@}Q-rlxkREJ>N#w$-pNrQIjF z)Qy;hRDZ_D)&{q*?*vYDY7oM}>0`5RHVo0FgUnpAp~0uh$k#IW=xHG(zYw8q(0d*D zpp9rrA<4>#zi$)ML1BDB5E|NsdUk!yF309}J-VhFwd=428ab($p)~KOe4`ZpmkGxu z+A|(<%S-gh*q36I@L9o?FaFAasDu*@2qb!YbBvwS#R z+D}8@sx2G9NSRrH=Sl>1K3HqoqH<~RVk2--aawjvw1~b+G$bUQyq-?1jwp(rNp6DX z!T2C@PaDpBBWj|782U6VbnVgNU9puw3k0$QA8+6d%(yqK9fI*bD-k>7wDoS+UpBgK z2Y)bBKam?SeA+%A-%GHXm?5sqW;6VBbPb|(jE02oHlGP6$4VH!>FKrdj$a2@0Ckc; zD`sp>Zb#NB=8a4ai^jb2JCcv&G3#Lu0dtvwIeW~QR5WT%d)s)-J{jju^4|jpf(~Ux zvJ7?RNOXP~r}-0^6GsFaCq|-Ok6^5UOc*R3oW8UkLA#+4LB!OyM!a01C0o#}6)|<2 zo`9`Kp5YTFkA5NVWohX5N=InPfTokpVuSy`oqjMq^nevljI`wG041tHArdMnme3U> zM)w*a58GFor9+FNhfdGEPQ|yp+FI+4TFHA}za7K%W+fJ4VDoq#ry>uyK0%^RYX(^o zv4ffbui{|lHKJMzJGdWqdgN0tmtZCj(n4Hvo8wMck~qDznyd-!#^!+S#B;;$leQ;K z>!F>_n`(%9q;^mQa#0fzSq^~?lDi&WdZ@-tAJs=0$HkK5W6xoyW3r=1$~>OLn2Rue zDw&`G?#j`r(^0vjzW`!t(C{cxkXrREpE7+YLc$chq#1N) zw~Wg!>lP1trFEt8?s%a&aK|V);H5w#Zw@>YdSjb9g+v%_#=yg0DNn96kPfHiC)oQ^swr*6nHnP+%Mj9jvwf7ra*_{U6;=6Vk2aU$;s z?K!ku$jcPcmh#&%e!yN4O>+?aIw$}eS><|#Y?sG-c2MEix$)mkLwiS| z1OCmoQKZPV$k{`d?ebM5=?3XeVjtSG6I_YCy@_nq8mS`?&;vkTCo3Au0L=na#j=~6 zrvdNT^FA5dLHr}qzUTLXZ@|qU^;~Zs4tfgEQSMfaS~`I2R$wmb8;-!FWYO7tiG$~( z>xncCJgD|*?#@r?0TqCyIxzUu!aI3rI z45$V2RfW)g2NnxTHscIt$%^IGNEkH9F)f>|K5i@0*PN{cEHU|PD`}=ll4zUTUzHXk zPQGdLmyhrL)E1ITKA}PG=OZw(HC$tf#kl}Cpdp^YjBK0+J2x9OSnn%In3p01V4LH& z{XEGSu=hKW6U?$IlR7dq`E+eqK;XlN{h(DmvPl3nkOwvyAJSY4K&C%2bHFbIT?nqr zph9+lM>B9^QiMK%@(=ZP~t}3lQju9XL5M`b^&{Ifq6aiEP}Gm%1g+piLl)aS+&*(31ZnC)}zTyc1EOvB1v_ zD`As2=CuDH@45kyc1o(Ex(gwyfNXLes+#bX*{b zDBxthJS&-w$OUoo(}1?+@8{ts81kUIUHHZ zPfa(5bCKy!QqMVnw-efr%tO*HmElxuc}FO+vmR{I`fIzks?MkW`IFb2oYaE5Fl|u~ zUndPHZ%gN88<4`gTRvyUT(l6vS(OxM5n!~wBupvqL`&MscHra?@0=FNbq9MxIK|uf zxH0zo4Lm)i5{8|YFVFti;U_XS1ynDE6u0Zq6|^A%T*1LLAVW_@{XlK_Nx#5tFsx|Q zp`~4&5L?kn@?Bxb%>G)0bCpJbEU-2JK~79WGDA2P)5GhJqAY}*k3`r4%TP!J#B}WM zr~HUZ7XYc6FqWK`eVSBAjP?N9NuVSNeegI=&eiKB1g19})*;c;K-!i4{)l!Q-VIXj@xA72owv=vsHN0(CD3_W@-i&4Mh55U#<+Mke15xP@$q)(JhAg);}4 ziUdg!I!fn)p|(ph9C2*~w; zkQ&tmvZXM3v@b$~b`)ku{8^Y2&g^_wk6kZ$#V8C12zI9eB$DRsdUQ2*6MCAeg}v2t ziO|E?)o5P||Bf?+RrPWDdV--^Ibe@L{kXcXfIHb=Tc<88(|5{G)Z-Hp2WjR^V%WL` z+c$$usMy8iPdifob+nhs-49qBhRohvK6i5(ITL6_7#)_7o>f8|Jc2Eu>lLyi06E3q zTOX$Yk|dFLrtDJU!=>J}E_ADeBth7s7$(uWERAxfYC&pnI>=AFS(OfetEB^84IZ~4|2G#M^{5g7M-Jwv-SlUei%Yw zo2Gi1px9UE=vj_1wan+uV0O(n!IIqHK#->c)u>}hDdHhG($oTZvPg#m-o+>&<3J1z z^YnGR4}a_-0Vu+fUX=#4ImC2hi}Yzp*bX6`Nq@8m<=5*q8)hYNf{iqn7Q7>9{v#D5 zx9{F4=-Tz(whv^E>`zDgaMCNl3#oBwcY+v2QxWkzOhTuxJR-iY9kg-{Kov271Uk`n zj{)vdIRQ4O43G>h`K6EV1zlc&iReYZ6{mxbA^glkA%ga2Wa$yYmosrr)~wgpbH}sG z>_Rw@lE=Rx*eQFoX*0c^;|c&(29oT3;7;9%Ye_F`q))^-2;}3?kPg$n)~ep?{?4JH z_VYlRkL(K8$a^-KUBl&|`B925D~06X78b;Ix?%5W>KtRhi4Fs5p1w4(}e8lPXW&33-ucX{F z*Sa$o+Cv2$m}NlE;lQ{Qf^h-qdm5=c9o4WmzQg? zY6$|Q=v3%i8-oNQ>Q6MkPomZ3Qf3e^ac>>>kc&qF#hj$`(>?0%rz} z+8%~1H8-ma%w;y$OUPUFM}fA~RAYYAD1b5xxHY!Ab|@~YghD3cmk60Tv?wKA2yErl z+Q(o$nmUu~1*v{H!tt9pq`C{^A!;%NH;q#Wp~UFhrqtHiPX7mclK1U#)nU`_ao-f) z9Q7+iytPE;!Wu1QJOL|97O${6HDe^oGWcIICOe_-g!m#*RCelldU>sfJs}g&^-`MH>Z=YC3?TV`M{oa2{k#^|8?i);q(z;0(BjAmZWUVK(Z)Z zJU97S)XdaeuK2I5@_U(%$Ja?y4C(n2bqmJZk-grJTv(#o$u%lY&tJ~#U1P9yG7)J) z<~ZVIQZQ7>gy9Ng>T9Lh?s}P$wGu!Vd8eBz$Zt}|1c`H%qqfS~Hegy>Pa73O z(7F^8l=?c@FkY7@Gsg$wD`A9F;F^MjrWu}A0#WOG*{5U_Wg{&BXfqFBApmF=+*3|X z-E}Lhcu&YNsh_vEH z5!RTVWD2z8a^(1W3InnKTUa2KBvE5w`K^SLsn5bkCuUBPy|J-$f7heJy$;9;Osh12 zWa=J{NGJA2$&blM7cU~O-9p+7qa5fM5-j6svrhK?HHQl>p`(`D$^cPc0ay_0Cx8L+ znKPkx)E8yfvQjnmZ|5}~`ON=9{`~(I`NK3HiA_w_h8vQjYG3<}Qt&5VTai}V_l`g3 zeDL)O0=|F?Jc$%>WHv1##Ph;>!QI9+1hJo54|<8MOtW!KSUL#D>0xr@qba+e!JZHp z9xA(}!nR0lZ@`i#1;Wl{liMXGHv_k=#hyvFRl`#*@0EK9WV=WdOma}CgKI>bq*P!x znyiFodH^}SRE}=#4X%f2eYk`~);qOT;a7ogs02yNBZI|+0{WK$xHWl5n{sUx8BFg@;Ow&Ui&A~XXkt!0tM zgWz=7*Ayo2bEL?;U)~E0g=GT+8dkxFBP~m5olJK5jY}v~<^IwoJWhOY3AMq>NoZr3C%{TlW<4Bt&(e(w7TAcCj#Qodej&j zZU#zb4?x-TaF_ak(_z<0lI7hi6JAOCoF;iM?J=+A3JZIjYGz76C*+V(#Kj<#>b6bu zBsZ>g^*TFZ9X^S`4}#v%lt4!)z1)MX3$!KdRn>GGP+&AdN#$Lx1(PRcyTY(AiCN!* z#g6oaem`P6W{m^BAH_BGe93Q{Y(NTt6hYg8+akK<0M#eQQzv;S%KEq#SBR3Z*wR|l zlZu=id^c434~Q)y1l2~X#EI7Ao9&5;$;emh_MimQu0ELgNR^YI{iDsc7Agx@&YKgL z4$V=u7c2+G(Mym6uPakM-K}Eag}DzT2ca}pG;fk{KmvBdVG_fys zY$f|l(nPZr|6UW0bk#=Wx9|0Q#c$u-^2uay>ozN~Wtfr(D%uaKPRFXnPv~#c&$PFri5nxnsxJ$k+PR z&Xz2DlWX(lrs|uWKs}-U4`%h3yBe4bIYVk6-}^_KZ~2b|_YJ}@@H*RWjf_Kv z#L6^suZaab-#hv;!(~goA>6f_i=iE>$6n?UyD1iCsErXY+Se0)Bv&(RY!aftq#2!R zv>Lx35vnzx{0{H+bjfd<@cr&Od5{9tiB3XUlD+}mED$3CLAmX+$>nTh@YGh;*r~E- zdax-*Ww#bX+@z~u;dR>LETX;>Ig8bRk*Z}Bj^E|Ie>7HK&NXiqOa{LV)D^yOj^xN@z80{81{E4~=WM{c+0I9vl?o!{{unDm2?5f82I&#I zFX&1NV248&^?b>1pY4fbDlt@(X)QYxw=QYIgSFchQ3EVB8T+r5ZhFZDdWIUyUdx_*evD{9~D}tI3y^(wj@C-0UnI|oX&UcD{ zCHNsze(~OBW^&x9KccC2o$sz0g6OzJZFZq>qA#@iyR8iqqvS5r3APv-m=P6*xZhLz zLCU}o9|_0?#o7D4{b1^w6djio=Frh67OMH zK&aioVC})F>XLMQkgI8$0ofSD;ZPC>rciw!C2+&TYk=~k>(RCB6__tsVFI?#0{6q= z1_5{)-FVr)5>Bf`roMhGepE_5HsEmY5QP&S9Ud;25Fw3l5kcJpHo8NYw#_J^)N=!; zpo09ZubmHHo5H0HlGT9oO*ky$?*Wt{k#qdzyRTOO{G7*M$q6DUOl?Z{%57?WfUE#v zZ-8&`t-`@4`>unyD?{I!VY6>3F#l+v`1|D@O$7J)%b|r={N)fdo&SD(C+w=&26D!_ z@!;p9kDNka^Pr=er`oFrLS`%H@jO!Npto`VcE>j;K_g7LqRTRLrtuGNu)HMOIubz} z#B?n%J8S7DC^HDyk|KgrckYJsP{ytumEUjT_DG1ylx?s$TAv+$RSoIq={AAi0i2Lw434Kj<@d>A80OzfEEMUd&21*R#SKNyjTFsXBtz!>+JfsFo;inp zqjDo$GmXd=RgN86Wn{QD47WNVcN}IZ_)qb!@jxi^2i_X2BET*o3Ekd1T==0x#&bYD z2v<4}X9K(^%rfs1mu?a~f+O?&hCbZuMo=_EDH&P36(*yD$T8zbs4Uo8%lV4mA)X>h z2(d?~XqVXM7=_&lDLqGlvKYKv+Z@}pV3~00lx)gLJ75{e4i!v3q|j~KSIw)YiMkLA z_DLO=lvoOOX~|A1S%~ibr2WeX=gO{@nf}YA@TU;5cv7Ulie1pju z77mn*o2@`706)xIZz~_}HN0a`hAdOIfi^XBSADRpRyd7vKN5APD_lcDFj*T(ml>E2 zwQ@h`qAw}_N2)&|q7zB;5=FCIsNN3ba}T}c@=UWb z65d5j5;)GV&~=alHyJ;2IAIA6Ixy1l16J4?F%YQUKp-s4XE$}z)>o4kK(1{PmU00U zgA3?&@9SZ9;{4lcvOqN#@cW3CUJl3I0cW!|o5c#W>U7hX=kjk4u5dlxr3m6Ulc1H8 zv8=70A*!W{Gf@O)W=d0yXm7kRTxy4#iu!A_#5zuxs9$fUs&W&gY2($nWNL5oGRj_0 z@)2dIj&6~p9Kq)a9T~jG&mba+q!K~*n_1P)SI9;_ot7Re(OtoXK-BVMG2`Xm@(DmqAtZ0vHP9F0y$&X@1$%%8icD??5as;Yo43jpZ{E3h_uJI!O`*MPb zw?erBXl2}>Io)7_9c`;C#{lS|o6Z+A57A7O<=env$q!c(W*DH95pRc71w70X@+py3 zAn<`=?PmPQ^O8wV7UFc;3%d)Rk{l{$tAUwhI}P(~9b7AA zQUU1?ifR#KT`Fag5z6OwTXAO2Q)eFdhRCx++E<;pCTHD2mA#x`nIKL{cghOK&TP}p zY>Rdtllrv{Iw51?cVIf*Uf|pLJrcMZm6Bu-O0-dcexO3LvCT&;finI~s)0}yQ+&Nf zcd_pqNwf~+xp#AEKnN(4N>UTpfN}QPYDBdw<*(Wlr{p-5-HKPr&#kZSFS^)RdGZiY1K5EdGel3?Noy2YlDY~7fri1R^6 z0zGz#@|tz4ZChNSa1T0w;OD?Z$aHrIsJZkRmOB8O4{!bCjs(=Nli<8vk3N`6+VvRD zWI`5^Z93UX*m&)utpuFqL=0<%TB&CpIq0P%4=CFLxv;(QJGD%9pO4aKnr$`PqbY3D zjWj68BFhVio2MhBwZG5fmqSaVo;&+48Xk3Yl^MVK1AL<$LyX70=G<59e8u*$5mUq# zQl+fQeeyu_4Xw!1C?%^4o>Am%!ksH?E@(j-^g9}-zCG_l_V53>`_H8ICX@B3dc9#l_3ng%Fm)a=I80ZL^!z7Q~G(L*Cp8;eClQ+21vpG%y1n z6Q(HEj3lU5#eVpe6=wE{6Btwprz>KEry2+!G69mnW=?8|RA?ojOX7nx`6yC|2wY!G z?e~Bj74`&f)s#L58$^JegMEmKCZfbqT9Mp32Ocv^^$nb5r4S@2Z&Nl4X)vM2!(ak* zcS8g|OC?Ts&G`A~8UdV`UkxEM+cQ*jOsZV1j}gEF-?G!$q?I5UO4P+VRQVN>_`E4f<^v7v^;N$#f7S=fJoYhz&Csy`<27G8U@KTf7)?B{YZWV5&vU^##0xFuYSm5Wzdz zLpGp!cKhjJk-$C%q>5}7TG2P5#8(2 zhstrtCmU7q^GtI2m=zZ4dADu%N8LEx6{u{7HjIA1O(B(gjK*&-7WHh2eG$8EObnTYsSTU8sYp{n zEdz210BZQO_XV%@i2Bh;P!0%d$4j274}9IzHg?WX2HD!i&ns3OUuH>y4GLCbS6URr zbC=WP*cCXa0m`DnIaJ4f5LVC`2p(%qdZFSM?)l)Fr5#D3gw1<)z_w0@sE|^G>Iq`Q zt{Z9>su1eLXHhW%d9Y&mJl4?1l5W_tE3&uYav_jZo>DnJ;IeDby;oxg2&;xfsmV!?93|{@ss_>?j0;dk z4Ek)1n9~aC9oPw2;0D_%Es3ejzg>?$cE8iNU&ymiHa#Ip8xX}i;||I2pw*o# zO2GQbQUhpxyB?Xi6*9m{H7s^krhoZpgxb6=$J^?jM%(pVQoqEG}OVvG0sYK_5KIW5Gf_@+s zkXrCcY^N(?O5qfA%>{`iZ60jTAU7tbyNA{1!<|G+1IOu=sHverX9`4*4B|wO)gG7| zvf;G&$o>|}?~_{{v`Cra%tJcqN9>+}vRYkMkRHh^`|(6r=DYhQ!OPYIF>$*deJB=s zWhFm~g+7?c>-F7TaXy)+$jOTm2`sI8;*lHe#oq1Q4X+3S?$TBlP*P%1YFRI*;;% z^3`hC-F;I!s%~>dzDr{C){j$R<5*vX76SYGkXB9DCVLk z_2T%4yI{~1(t>@9x&sPL{N@5fJ~ZLjZyCXX5~BI|?Rs?Wd)5gZG4l0@T_x9WK3fb+ zQJw{KyxvxiVMnTn>!cjG64>A?F65i|MFs&t%}#Qk!px$hC8He^LU?*YeWKkRkQ{Qc z#^U#QIl~Wo3ZEa8Q1dF2vVvHb;Qj$)tmYmhgU}ai@Nq>j*KNdNa5+Im&~N3zhe)2= zb(#(a%RYw)i}2mP>TIR4@9vwERsn=0J|%{W@pc&8MvE&e(qZ~Y>D&PFcOotT*vg0| z9|O=nQJ~;hmtZ6Bnqox}eNE=#*rP)AbV#QlR>AJ%?*Q2hes&(UdiI9Kqfa<3<c#fQdUXLD)95xqV2Nmy zwWqgL2kn$O1^Ut=REvb%0NHKzC^4bhltX_pyY+d+V(TJsMYL+8l4SN%_tDt6@FP?s zHn5N>?b{l#}6C&YT#=0$ne$<`TfTR~!DAx!>ve0po-EhkA2@+Pa0Rpp1 zRni7?aEq3B+FjIV3_Fh3qcg)3s*8rpTdAko>_#7p5$Gpq31-P_YSYBe^?$eXzVu~VsWdEg%0 zhmG2vzDiIZS1eoWUC@e>#^!DDv|Srq74BRyn+RdF1Mn66ezI6k|{~ z8KrG2BaTc0FVz=O=?2S|pEbzQp<0Iyb(Mv}^yvQ8R>i0RXptz=mUNy}Hn2>0%&7Cx zN1{^r(;>(&r?!^Iq{?SLdrAjUsY~=^`wl`XbNRFA;N6ug`)lA5CKTD?iB0A*`AqX6 z)AmJAD!47kXhoF0m;<*$e)N71KTRxsH+&5Aq*YB_c2`q#ABw=(jsWA451}R*iIqW6O=pjm_3}U$l zd)J`*GoDt7_ZAuGa1t9!+n{zbrF-jt@T`Kp1E#a3l}|+}5s*mlvc4WsbUfv#V|BEI zs)TF;Wv~mLyjkMI)EGM+jLXR5bycvZc&_{}Du@w^00|9Y=}1ZqGnWo5!T7yArU#+2 zg=s?S0utL?Nyj}r0W*ZXbNmL02n-w`!0JLsgbHX|U3~+f&&o9E>chqc30>0bE~|fH z+jQ4t^I37xs^)n#;3&qU22Y5alV~9VHTZIKmf`*&N7%MA*yeGD?uQT;@Z0EefS85A zIqnOA@MI#wmoo-92m1lYrVUx3nwh@FvCcuxLk>X8tGk2s2)?&uvZ;r~1m0=VDjwm? zxk@WFjFV`fbMz?Sw~tL{)}YM`E$Gg&?9%W#wDAIrzK6I%BGctU3o23Mp*;=Pzrjq? zUp!Z{S*=A-(m_lQI%VgrIR%o>j1nEImT#5eKm%ePBM8k=Ur6y5B+;N9W@!r{20tS# za8Z)KLCIKXgXG>(`A%z*3c;cqOP!Bs#!Njanhgu45fHRPvM4f{S53jOJ@bY3|2R>$d&FzVcOu?KjJE@eL@3{wQ7l=SAM~hTK;Y66rV^|J_X=r|*)y<^hR2$Yb8qv1 z<7!C}-HzT#s2rNLY}-=`PdX3EuHBc=eO$5qxD+-UHq}apAqCI+W@H3X7~_wQ@R5Fy zBrN8e54v#t_|4TGdu-`a@f6c)W7pg;7Yb!tQn<$Yu(a}jxLag&qPJ!R5{ZFMo3THF z9Hte*ZvQfYM3d(LGyz)fu$RYaNkb0dYWJ*wka|eJ>si`TUZfWZ-V~}_)_{@PtSwJ4T!T~Y^63%nf zTix9$TUvBGQvbuHT0BokV2FI&h=aCcH+M9tG<^o7cagyE7qYw8O5}{6hZDao(~4mN z2%^lGb|!m2aT1sGNB~GypT^^HLPJ7jGrpvkxC8+-p#&k&41Lmrk6N48GI#(W2K}=I z=vI4<@<++DJy^3VxH8xjO<}vq=%ckndYcm%<-8RQafmRwNu^Dv{@95ox2MUvx6I@G zR>Lc%_2cF??^riCx`Ese0Et`Tfj$|8CT>wcm7KmAtMRvN9>3Lh?jZQ28KdPVvNcLN z0+bKZWf-OI4W|tVBb9HAH@R2S+KphH_v*4rZ*2KW_*C>Z)NV8uuLMNpdUVbEOz2KY zN?;K_cH^OLHDzbB_vl^a3OKEUhBdc#5k0(j12JD4saDb~IvC^jWfdU_f(t3i5ivLD zUa*o`iO$yUEc?E&ZX6|76FLZT#1RLec$@YCbv}w_ZDCGqH#;%;dWC%&jj^;f@?Oi( z5?6~_?){Jzi#Z?}Mzg9$ooKKU1CDWfm?R}tslYHN%_^IMYRx<}+>tgGR{RLdkvc%~ zD|HE5co|^q?7=X%x!G{XnELo$do_quqb+aOqifOC?iR<>(d;Mp^rPyEY|^DVW0hSs z(?mD-V~cjR#eM%O*20_mSPDakLBI`c?h2~ua0ip~RZOf|wjjWwAALu`ZSVK*QEO#S zzC$Tt5!>Lm#Z^lCBc^DQ44N*4M}5754ebzA?UTqG9T9^xZP2XBCELL0X(!;4kqA%# zPu5zYvo`Qvh*3ch)F2hF8@ZDj#0HQ>Zy@mVB0euRgrj!@Fm{5SEqa4OOGd$(Q8Q%v zK}O5KggNlj0E0y8Q3LNl@#*NQL^Xt@jNN1HIj;}gn^gXnRg#S`@o-nu4wafab<@b8 zzfqsiBN~W5D96Bls}cK*p0bh|u;1+rH+g_9gB)w)J+5LraTWNbq#=S>YEZdRA<%#1 z^ztL)6ymnO_T%x6*#K#-tK)css2|VyZ@E?*ulKHT6-XE>C_qBd}&fg zk9K%*Ym+p=}iJ zP;~ZbXZ3;|#+JHWk3Mwvh6-rId>sqmA0gsL{&wJ|oi%Zsc_0B(^yjFqS=%CUOURHg zRNK?booO3KCKcA}2s14KKcW9P4DO$t-3H{?S2>l&oEGt0;1piZ_ydNH?+1oI67fCC z_5k}HL1d{=SSmUi(aB0*DM45KHK%teK%?-A&8N!b6*FQM+Q> zoA7!-1tBexw3v%xXs}3O!3%)!G}2=?G2|_9b+bDb5Fh1!Qh_b~L!&avSQW6q3$RC> z90fHnKxXvUnb#29G!Nf{D5g`03s!Oj)HP!nMOCtQ`yR+{atrUKccvA)qVkeZmemkw zc4@;gRwT=B&oBYwG;XVdFBSCA0TCadQijSyTI)s)Yy$dre^7YsFloZV0qKxV%w{KL zM2L|1TEcEEz)0P|Ny~mboI0wyyZxbO=cWNLP%Fr-tKx~eQ@AjlL#HnbRjUO84)6lk zqid>`b~D9s)hLl#J?aj}w*Y!RPve@!$4`7ZC1F|@ZaYToj%;=%rFz>Jfi^gD7sZd&q(_5B046#7ITj($g-;MOibhmpp%=HR4 zw}u247K`lZc~&zHS1CKzFF~T&Fbxc3!#Rjnoav_7WVu04c?ZF=3QZumBSf(U%Llo2 zj8R~Ama){8Xw62v5gte_O=1%Pj@+G@=z-56VlnAq+LCb=b=g0B^7RO~l3DiJfS>@q zV`V!|h?>XD!nkr)w97dkUgJ`4_7WfrnK?>^OloFQnw6HK7gE_l&XwsUaf9s zOi4hP1u(C{UEvN7HVOxz2Z+D}lLeh8WCZ)7=F_A$+v^MwHS92DM_g#qM#A~(;CQ5I zWig&L7(U&srYzPCY^p+1$_5HGuV5_5aRJymA7%I90%!tAZj#0x&9db&&-vlZ?(LEj zJSHlzWK|xmeJru;@+7nN6z5wTz?rAgRizXSLRnPgp zEzdmsB$D`*qDarNTxCWYb_Rdf0(;DEYWuS8(wIrI&N;cCDipmbx)h?kyaZ%)-1Id- zmOF?puVx%!x+b|k+mJGF8LZGe>@qMCtY4tr&;NazMl?dzcQh~pA1Ze z44k%k{bVT3Cz=iW>mn+B<~<=vhCRjTyS94RZYDR_dq8G0I27XC2A>ue_tKmCEiz(0 z92Q77$dm-C4?7gboiQJUS_JfIiIXGqlg?LA&p|~Dy1Lt-tx!a`be0Z#yBoiDx70KJ zIXK>cm{%B)9V?_9nKsm9ThkZ(FU(?cM(uVzx>_#qN9@zMt?gNUb8G?Ggr$5LB?<=v zkT*El7UT|kTyNa5zp+Zw(N(U+AXR8wXaGif%G{t}f8n|E`bMaN5PFa5-!BPLihuSI zl^V&_hP6E*NHFL~=}302bog;aKQg9N^w5%#`c285J5=XzqSFk}shUz<aeUFSgLX1Op;Fxao1zLrwR5^f?MQD!H~YC(m7xYN z;i)iNeRCuEN6`#;*~;1q3H7+5t{P-`^W*~4VMlVP-MxRN+m+?$OW?3jhmo!=elLk0 zh<+M5ZZ4m&OXa!!DJEN%O`{UKt!98|9h!h0;BM$8i{Ma3+980)=fm zcqcvpYmEdN6}IGUO3;{rv4nbg1Ptl8M{s^OLwYIv)edVpv@wCz0-#g$S>zPlM01aeGyk@_m7PH zW1z(tZ2CyA06V0luzS7**zZSHjGRZOxHCXd1Z2Pnxq{`0sDgIdY(a;BJzudUcguxb zq`^7oCVwm5Hb8BF2p^D~?GzNzu2j;uMfBX{c~rtk-QVFkn8B-NjxKs?>?2Yt0A8`r z7Lzk7cw$31r2;9j+{G3}=Cp01&+3Yz(G(>?3Y?-OM~YeUKqlOVK06=fJJrRKiJLhF z9slWY?VL&0?g2>9(6T!nq47W0tcS&;$(Sr#Ds?=H;C>|Aa&(-qP=jrVp=k>@#yVwE zWiUPsnM@`}t`!vu)m>sK2(B(0b?XM`%xKPH{woA_Ayo7x1Lt`k%O{{-M9!G}+UUwO z;kucIx?uf37*ONsQ*&#pbnt&j$C|PUP9JYmBs7e00pVgL?9NG1<^U^eqZt53JNBuG zUC3O(|1kq^3t3LyjK%3EtB6~p)0^&~N4ekSco4oet9$U|RX0AzXbuTy$eu*>V?Szm z&im>(gYP17ac}0z->Q4&CfeA;m-%K>W|zl;E^?~{hz1ia7Kt2Xie4xZ!}N=OU<8g^ zecQ1D1|EyT^BQ+0RjI7b zTCI_)IcAN|vam(@Mj^-|Aw@0lVi_>Hm@xtrSGy5}-z#1ca@(Mc4K5JzF54U4io}X! zb}`#NsfuioTrg)Q2+A4+?p5d3naSLtGJ2Up-24M*WD!&-@^vMByQ$Pv) zm?e9R)kZ*NIEi^I3TMh#|5;jp-1Sl%=iCP290sU{nn8`RrAEW^YRGpV=|B8) zz*iYN_Kq#e4evbf@yrfVS;r)r%bj24zypGL&@uQC>(MzU0gVb!O$C|l z6>&j^Mt!f-y!0pQnD2KE4;mzzM@96()A!T0iY#9pY7526LjuE!Roa}A)~X7u&WuX} zgd|6;UHvsEQ#&M@$7BZuOGimDyYYUzSX+`@phk?x%Ar_Nz+zGtJiI=sUxJ`7Xa5YZ zlyX402c*|Ai3J>MPyJnNfra?eyvtBs*W6FU2bSBi=X&T)09DHGT9IV@M$#`Uv&-@U zo3@|Yyou^9HlFJ&?+Tggx1rbOVcR=`M({3si^SQJ_*EPd3<|D)MW-2eH|8jlL{W%7 z6fD6hGfI(0Ltwy!1RWvXZ`kNG+udO0px2(6Ea&ZE&0lAkITcob=!L?3I&p5$DF8-y zFA%wQ^U8(0fcS#n z>uNxAg8HZ6A}5`cha(V5oJ|Q7hCLDrLG<9Ps2r>ftptD%*06#-S}eiJ@|99Z-HnR+ zXV|0Jqg#>gB!RhpIvZoN#pI~cF2qH=Dgf{t4Sd}*JMl_>l@<(PKqe18MKlC$HpG!W zGO3V!hwb4qa3ihfg%(RzJ0Kk(7(M1kHI&r3o(ys71Kg^lu?V?85~)!BZMrFt?VPkh z#R!SCftENHvKuM6n?0C4G4Il#W+5mIqI(Y{Z`R|i@X++D>o;B{qO#Mt4dIbJsIx7S z7B)AS%si)RU<;l)=x`H}>Y3@w?=tRM#y81BvL~*YLKZ_Yvu(5I>d>Y9H=RUA9=zO< zB0|zh6!6TIGZJW>A=0ugP|9-a92* z4=a^`r0KFEa?ll_bn5JsjM%WLvYHz08MZ;ErJA870fpbQ_w6oNw}K0hBuK1WK^y`C zineAr*+CD`M9TkV_71dzaFLv$*>+(E$QuQ>$BQ#~3!Q@?v?c?B6UzBs8r$u(ti#fk zRg;H7a#bX9$_2?0l~uA5Cx)Q{a$yb6;QmrH79GgBT)huGaODz}_N@<+`~cq?)Fvyt zitIv08C|J;m0LsydiHzeDii{^)ph&DfrT!EoV*deFEFDCX*ftFouF}4_wk<;FG0$x zW)*`N7Uay?#{+Mifcm+BmF#xcGSws+_zH|6_e5)eZb&4RDqf1`?RDbz<<9em5-$vS zle+-|+8bQEBV1y`UaOrj2(Df3)dV1xzxU#maAa!c)PzjN&a*bgTrIF##5_(YZfNGc z2ZvwcoMkLWVe%*D?ea4`&Ffwo1Qw-rARl}7&Sm!;!4^47?P$g=VCQ>3~F?H4#p$32ND`&9&b;fO77g#t^3CuZrzcu*MO1iUa_k@AqRY`8k7`Vg%J{Lt?9y~{>f~ewvv3kgFpufHX(+X?NgCLO-eqdwzyN7H^3QGzgOM`>g zgi4?gJ1zi_t&WChx;;^=3sc`L+cqkkf$bBu7$gSc#DZH4=7A`{>HPJ37v}Ct&QVm@ z(G|d4xjKMC3nIZVhadgyHp$^dv{ttAVM_djSWED``efmJp6Y0T;3GT=?Lo; zt}>N)!iFBM=5CQWD*#w74yeZMrh^VLQkiesi=fDTtVoK_3wK&WX5VYU4y2_lV zj0yl6BE$x871R(B1uWIZWJlt&1sF2IGlQ@*c>Cba1U5|uNg3LLWPDv9q5X2n>u6T4yKU^DypbAz9GUgzE z-rlckqI$H6sx%=xk}+-5oi_~gtZ^ToipXjmWq$MOnufCA;F5Ee2=A$YC{?^5{HCW^ z>1c%wW0o0I%<|>P7tS zu5H4q4+6IlK6y`hG?|d)%%#}#u1kSen5(HZyUM2Vv$KD6lY_6;){PYeA8$ffP$HEa zMs9r*Cbn1T3y~q#s~40D+KI(V9@&;q{tFg*P~_qfPrzQ&;Nk}Ik6#8y5~sqm8g_LC za|&0aO(eJ?kfjpO9UMTgT;WMR0J~yf8?}qb>5&n!GeHUjy1>_``l{(97FXxqL338@ zRZfaC!F_z+(M^wBH_PJ~fxm&6v%EA>x7O+t&yUCvIM=Ie+XdbPVF`Y8Xiesi6JTQ} zy0L(mMPy3m+hmUzGLNos1`9-iha<+)A-^{^iWdvg`a1}Gx?03cff9-^^D6!ZhXw5B zQ6n9>N}f*5#$Dm-PDVQEaW3DCop&pP*`zkLs*c`+D`49g=VDJ7h1a5g&_(`65DZHe zdcG$R$9m%ig!eXv0dj}dso?ZQIPRv*B6PZwZk=|{)7kM(e-}!Us3bJQSdN(oj*qyL z%&S3Y;b&O!V!biz^#ggX*Q2rt4&bZ)-N8Omzu~Tr%tqLwNahmk*uy9IA{@Mpa&7gSKi`l`h#s6%dUpY-9cKqll4QRM_J;Mu2V*n#>X^S)&o zXKo9pM3H5>IXxySfJJ$1An6^|Xpkl8!+ICf@sP)TZ~(SDUedIo{vsdVw1XpruUjY{cA?k|vc zcwxca$lbsW+J*;3s`hgXHpX!@vQ9)>lY)f{LV-)-KEM7&-7f;jzOUXruH?RgSY%;a zA6UFOj3|g^1f)I!6(uBmNco7=`0#}vScWlE_@ySDgprsoo0>F8a5%rZroG-F)%_Qf?f?%!=%=TEj*>H~HBJnZ|0s^tM89ii+5( zL@JC;5ETqZBAi20k35<;_wx?(rw*yUfN|Jm> z$30cxu`tjc&}*2BpzjJYOgS$@$?hbcVbGUR9g`INn_LpL7EFfz<{MRs!~ zIg@XzGJQ384@msA->jOQ#H8Wsde+j&vZ0ryxZEul}E?$MvzPq+2PAWb5yM&|+#+(CUJ7O7yT+~dWfSjo7aog5R6}bJ{2NVHsr>M5% zcd^{>t%2mVWk2wogsimeYC3f=i5pB(=__`g1y?QU6vOrxVXfT^XP3#Xp!ODxNvy~H zX}AhjRHVRsQre|mwR3Z+EwH6Xq#}<3J8wbo8i|~v7_p}_SJxv-Kb@Ta`4;@>#nlVk z)zSUkMUT5`4pyXwb3Lyzmmn~2K zd>1Hx5dA%!r?$aNF<2#LcHt8EVj{cVCecP1tu&*emBmmW$zsfGQNL;VI@=<)VZ{Kn z+Pig8kb18O9EB~UuwyU;PRum3bllJhP+Pn5F>ePfrbJ`Un|nQ=$Lc|QX=idK(FJnM zAptGk*QOfAf~R?KR`!d9iDZY+izheUqLafASzJBdee1BPc~z1cJ>Y-F8?@K`6At~k z06-ySe!z|+AigE92v67SbJy~U?YVPR$iLvJ^ch1}3hN;`GucGS{Yc_LAOu@){=uI? z)aJ}@&S#DqYnPg_@-AEx#!_qq*7nrYficJjAdej7tqAcrsK zmr0W5Q&4h&wqbFZLNR>1u1Mi)5E-v%FTBWFuHUb;=Y}LNl*7gg-m}TTpf(wL?2!*N z_;vI^jsqcVY8U;>AnU1Jy#Fqx3eIM104aI(4Y5L?P~UpHwUYWCzk>TtkgpBS-)24w zpf~}H(UPnj_IxR!MF0?OYFN+;lz75pAQVgk?)kw-5BAO%s75rm!14gifV67H5l>WG zvI8(+@85k)?lYCgCHu<1M}hkDb|uA_FpI)_&E;Sr;WzJCcR7uY*yJ@!usL;Kc!g%W3n_3bbfl0TABC%M`6^LR$h6?jY&=6Gh;j`I9dWZ2 zVV4CD?CGLkce3F=^P#yH+Czhr@pm;0`N)-`;mGFw0yg38tAD(Q$%i6&%m_ZZ|2s>X z(;yt1Vq<`Yj5-J5GuJS3%-Q$sE*_C{M+jnzNm*>)jTKBbb`AS+_Rh{)OiF_d1LGTm zmUKF%4C6 zLSgZ5_yQZ((x;JA%oX@Xwi1Mj8Fov?eCWMWA#y)Yy+qUp6lHJY6liOJo$+`K$Yx(! zj>;P$^ZXUxc;xt#4gB~H*`KkDoiWxX5LO~<1$)QBKX!4jOG2&xuG4llBFR3`0GUj} z835J`n?OX=ozK*Zlv_gho}wrmfZqPW$E}EeQjGAsWZZz4$5>?FQqC$iW$t!fkh>#K z7AE3G2X{FOS|V5=WZCU=ypNKa&g2-83!r5oz1@#QqChrM09}*{4>Gl7b{n?8Pr?O+ zZm_;3Qg^UiN1Y_gE^CEdQ>oyDf;|IJ7jRp`e2Vs!6w0dzrZO><%iV~;nl`ER;qsg) z6af)p!+N=67tZN+Zn1%UrZC|{*k|}%NDp9e+hUQ&cAXqm`&7n`-k>~8>AkNxSjbB3 zrf%=3K)OpgNF-+LXpq<2b3-flesM0eSA5Q}FoJCiN#@TfD+*%YODdCjrfdI!f9Yf47Y)iV_?tBVpZ`6Ho9{VMa z+qI`eE?6nbN%uYmapaCn%2I{P=V4WTKH)-os_pV)4*Z+|sX;>^nGY2bWkW_D*0y`P z6TCts?{Dx_C*GN$;DB8yGm@_ps=$;+F+qQuyf8p`UoaYjwDKe z_yyDLl^8e14jBnJ-CJlXbP=~P@7^lfnK-%1deHW4D;V?x9KsYYBfK4h-6jmLp>oO< zDi892WYqEN7M$MQdnaP;qfa+tv0XxtVEGch3yFxDTk(>yJ{t+z#)r=~%&z*gBuU%a zJu{FM2Ozp=GBCKOMTPJ?$NT6Xdr(qw&wuEWNP*!Oi{hGiy6dvxvQrx+(l3!atdy+p z`q|M^r-7Nx$Rxf$-HecUz_uX8_?WhG(5E&g(%i2^U%lW|uZ#+DW=cHALTC3L5M(Ao ze}8(Qw&Qvir-L5xf%cz;n6-|Pb8k1iO*`oEPdIMNQa`n|d-=4y6QOYlP?T&)eNY-) z;v*;)=A@qD5kswBye99`+fomed(zIwW4Ex@1J_>-g&yufHYvgzt{QT| z3m&UpHyr#$JI1O|L5jM+=aLT|60tr$7nPjPh*s8Jg=fP=L8jqlAQBx$@$+bMmTrID z=fjxa9TrEAT`?@G5@7q`Xz3~_3apF6-;2%`^NM%{8g=15E0%5rv&}W^TgAZ7nK^TU z1<~q$?(VaPt`dh>Yta(S0K><+JPq(U>kPF1TN?;Z3 zl>=-S2gllIA}O)N_GOm>wU7EFk>X8=$1d42F?ga9yes)~h@1UTF3JHGl381evrAZ_ z&FW@VN)C&NpGOi&noo1lj#`|bwa7hMA;=ktVL*xoFjTlcI$2l@A+m|o+?W|@u2NLp zMb7W@q1+fAfJmJa=tb^bjJ_hLAPf6@gQ9I7%E#rYw^@|`$HvY2LQ6g8v@b@1n* zZ!@{cT2DR4%Im-9xre36j`D`Q8!HY(b=PtCX|LkO2>rtSlgYuqZ<=7O7Xt4qUpi z-v<)hX`jS)V*e`Xv9E;+7c7St4HHbivFa?dz^NyY4qO?d#2q5W?QOX7Tz|VdUFtJ@y&$gh z=V9}UG&%mZKW0{E`RDvPx%@&=#w*)>9+Mp1AndR?$|Hi4FYE(v*CsN>$h3C+0BPR* zo}JV9k4|NqO}!-_q>#sd@}h4mgGLx_aGCi&spQ^c4Uq}f`e^w^mF{nC*}rq>e>9V+aWRr=%+#_Mb94v9(%Za4`8neJCD_Qx;*fv0 z8u9!%qF8pN+p50c4e#qXVWS&NGR^n3IDJhN@)it?abC7!#HkM!j{xFLKQ-%N<@(!~ zb3zLt8zBy3P9L1q1+N2=6CX8e63aDlt$Z5Jeo>H6*?#dDakswrT+MEGJU9VbEnd39 z`oq~9c|5K@K5kNKKc@z6r@&l^FbgquHVw_UM=Xu@cacrXY(Z#7MQbOM z)JnjkEt41bbg9Bg-Y$j}-~m~Y@{jsauVWc#K(?B3`D;EH8mfTsWG$$2iATlrj{pcq zk*S!zI@nNQ>XP)y%6KqD;kNNG76JKT>d&b4PoB=cY~wlDF!*lA42hFZOQ0UXT!S5+ zD)&=QPFy;vkZiLk4t18=&~C5rp6sG-zG=y3fDcfZq?O^B_Appc*+u8F%4>sBfp}m8 zQ2m4fG&n*l3BXBL?c=4ZurXv#kI>%OTB91;S};)RSah5lCo%%0T1K*(25GAKs!j1H zVG;cRoL%A^kG_>UMXeau1`S;ucDUM|80RhkYQBTf@u^%72p3&2cJi-iIj32u?W+4y zs>1rgsyf0tpa}!~!)#!!qvCrupyCsiw;M7$&vjKpZ^v0QT)D zs2k{edsi>d0jyHx1MQil=nxald7I!{4z=axKvYQz1!#OamApc+M^9&HW*85a1EJik zTabwQ6*Rzp!QwhQgo9&g( ztYfH$Yk^}}_U7=nc)KE|rNzg+PYB*PsDq9|D8lbTU zCyP{tdr+0nbhJC?2Ynj3JAyeXRex+8Xbo8{9HKNYWu_||F;f5oc>yaBNmFleHFubS zG7w0%mj**{FGE`n`^`aJ+4dmtatdM<7nt6fVcoT_3(DaK5lh^{S@-r^T^y&!(~-tt z7&h(hVHRqlD9nD_txOg)dwC52eq!D@atrLk6?yKh5z|}5wk839kQTCBmY~40J6b!9 ztv%aA07Sc3NuzwAYj|v6QZkGTN0?=U&=k1Wk8tQn0)dxH8BWA=N}mHPxC## z;P4CYp60apTdz+RdCj}^>x*@kfGG3*@_;A@ccqHyAXunMPLet5`|v+6v_TlKQ!ljx zSbO{+d<*#l`mV!hN!hmxq_S^=7LY!gh=P_%^sjxo71_dGiGs|_p_$E(

! zr$Oy9P6?C#(9?)Cb}#SX_4^yZE6c0n64`Y~F`3ywBpjQtD}Lt&xHAzkVY7q5#qI)CUr$Xk zZ8|G+J-dDIc1SbC&!)re&hK(Yg|$UHC;_SPkF3d!;TGgr69SLiA1rz;0q^MM0rsCj z#bNAS5P24nDHH@NPtEra&Ov704V?_q@f91$O-)_RjY=-XqNcscbL03bQI08yLhHcLP)eG^W+`#Sf0eUHoU@ze0i)LwmYDUli{FQioj7kxjLu6-u1K0nK)B4`~~ z=wFUY8?H(6BOSgnZOOLar;HGASI~kkuwN-Xy@RzlE*Z(xAHcLMlMO+BsW^I@>dhK# zQGyb`5OgZ&+UZqz0Sz@jezi_X(D_azE<`laOvd_nu@`Rye-p4aM~C&hjv`Bx`z{*_Zog z6)WQ{Rf~rQ(GytIlKM-ZjzNKmTYwWHz9ykC?B%A;w9~CLRu2U52W5@&&60X2UliP@ zhj^JQG&X)p;6eRE&91D--CkLrkr#Lp4XGZ~mSmD#p_ZmQ1snHsKlXj`6C#i_m#4;-$>_(q|p?{)=KbEiqtWTHR*x(+Rk8JDW_=zC5J{j8itfP4QZmv+*PU>^u`$K)IH{o zRTW?rFU9A79uM{GM3Ps2)*{^bx0uC`{}%t!d%);{lLs763Vjn;tf zp(BDvgs&J7VTMf@g~&XD(-^!idQJF>2p%CkA`)g6d~PrTPAr7MI23^-mcu|4ibx$} zVHjd^vdz3`0`$8jt|M2^O}FcB#(C*ow3gq)hwaa^Yd zF@%_#^L{KsK`zFrI2EaU+xT!PR~KlmX{VBTRogUGTG16RDTEIGuK_yj>V}!MCUpv{ z_d(5{O(_3mw5aGutXsBr(YwjU>P7k`{h0cb z|3BIZ1@NDT(BVMtzZd^!68m3jr<19niLL4Xl}$ShEZzT%|3^0er*864!Xi~7uk1Ih@kK9~M`+eP@>-&G6R@>|OKfX_g zyZ3wkUCYnI)8GC5n5O6ReHyp#e}6LH^Lab%R>-}8UCQO|D`*Z=$Y^d?Wg_r0e6jd>?d z?)!5)E_^mAng8?r{`>Qx+~?!YyOzs+^EP+4$KU_+ARo{F2cw2b{KJT^(k<6Ir{o|95o%E)APs2{f^Vq~o(`EGZ&kEZhdd(xeLty%wj@o>_)84=qaZQ=9r z{rEeK&$q=PeXHcZTD|A@dyy@#AJ__i%EBx2t?%(UdyEhBJ6$iU%=G*Z8E=vmb7Jnv zx5aYT_Fg~kZ^$=%IY*zLv~1nR8MFrlxj9GYkBLspr-hEc?Zh|Ht@||lEID9K!YB4O z>=efDxslHdo5@eu>0K7C=E(kZTxZUG62vP%a9OW2`WZucke$Nk!PR=j8T;b1CX$v3 zZPR>T4MO-yJB=HRI%Gw9J{XumUj{l;wVmkUODmxHGv+no%KlIUAZwD$3np0x@ zCnSVJzOe|D%v@+5q*LJ6^TnEWj}WbL|wWDTdGF6+7n*2JG^Ri?~J$? z4sGFSjAR)+0SAh0KxKh7a*keFulQ19P)#%eEJ~nKdyNi_b{-n+4 znUHmy=I>%u`7+AHQgCakBsvY0`Nx?=ld(@WV;MB;JJgaG%Y~wUSz*ex?$j>wF6RrV zd()qx4s~y%k2UxLkC(jFfCCRyP$nMRMV0SepypbJ zmKvbkve;kx23nE2##nF$Tw2a=pqh#cvMPVYAOe?-XC}&_qDpvsiII(L9|CkZ`H`#S zDoutpfJ1_++N3;VtKy&3u}KMTn(rOaei5fr+|hpPD#`UJft z6fhGfiPUGk9(??Jyg?1I=E*@R{T){&{^5Eu2J1u0yCLRy(XEK+8xa(k1R-US1WeW_ z8$!wLP(;O|>q}86C&Ez$#MAUFl_GYG>{%;&>GRO*K7-CY(_Pg0BL7)j6XpM)CBcv& zIz}Hc=Kv`AOHFqy&JaloZR&&Q0){tN*8ozYUP z!V=>T$!HwCBkqE*0~U${^y8n-HUzp?IJtKBH1@dOfMis}qfS-@=gUII?x3vE?WtS@ zcfu8atX7E)D|Ml?EF`^W-qLjm1fBA6x*n8UCP1Sq&ENJE9V*tFq>`K7X3C+!q;tke z4wOf!Wi8odYywOXI>6=~n^Y_Eowkl5L&RdVjIVvg9f~o?bdAlO)<`h<^u7 z4{jAL1Jw*aM8I{9Gl98 zJ{Iaw&;d-_!+xhd(c}h!D|IN`>^^vg^UYy8qU?76;b-mvEJ-)qUsbhv9V8h{x9XWL z!P=oR{IL&^H&_yRDR}}A$_e}hQ;0x>8mRp7N6VtrO${!KmYjHVsSH*OYm8LY070m? zHzl4j-(5a79AD^On$h{@>u$fgfDg08qJ+(LmQOGpYYr!sOaSHuwoI7ly~>SLSA}O^NJbi2A$HD9s+7HUSbK;h)<#EOh9iib0UPLji~)=rTLVD{&>G`JcSHa z4dGs1!jU&98yOS~qG6C#H2$rUjiUnUR|0W&)NO}R$2OjK6sQepPZ_M~bK?M{YYi;*wh^qbQtOeRu%yV);$H^iWG7P55U;^kad}xjwvb(UoQ`d0mJB%E zo399dwL6aOZ|AAw*fY;{w9C^hc8I=Yo5vg&A9{y5QXy0BORTS<2wVUC6;;yP5F&qN zqJZ(jIs2QQaL{j3V3n?t$d0v+&XAe(3CEF!g=7B7aB2^|cwNhds_EGL;)UwFfEZk1 zRWEk&1&9?``_}m38Wv-|YnrSy*5LQS zI=U2|Oo>*42fvG$*m{EYu1rzCw8c?DntB0B zWWBWM0a!h}5s|3i6$kbR4~OFlI_w3P;cP0oak30mTT%sdXytCS`fV7hN4Z;Xyn1?? z@$$n%)2{^P-7=N1kr)8rf${=GE#3?)Nde%cRrp?tQ`n|S2@IE07BleqLu2rd$$34{ zlmp%R3l(FPtT0>D2}-pEizG?JHnw0zC*h<->X-&pv1kdy8^CuvNKf> zF7S$u=9V9phmiuSe*(g>Vom88C=s~OySABI9O`r8b~lQ;WvQd)s%bZx1Paww%06X- z5fCkqv1S?vKrrA1+ddJUTB;7SYIP+NfpRUoShqOxk^faYAm6~tOQ{RT>_}io?%gxQLs@fSqx+Ld9xp-yhMShstd?wA8F9LY=o zekIL;W7D6Yb4Z(7~MjbTcAbV4`ifhm9 zD(kn|J!pp7iLStzwo50<{?!MXwE!%UjR#_Wa79g+ssO&=%Et66Y3nvY*Vp4Rt#HVz z;s{F|BdFdKr_%ELO%G#OS9YbWwd;K$UIw%!U(qX5(rAyYZNX8Q9`UARr9`uST$&mz zwopz;W~8MblN!_&Ow~`;_uCC|rE2hVw0GIk&U&>E7?{3y89ZCfI8iqnPq`0(D_qjP zDE?DCAH`UopzLtBYVeMG7_VhSx-E6&JY}G>48V%D^Shq{H-+nq zQzjxGErvpjv}(}aQ1}8FbSCLZyYSr~N|3XX1}R1qNyqwH?q9b+`drhF2Y|)0cULCC@^9`jvp@jk?ym*3fNpsb+cQJz%CN9sRVx zUKShkL#%1h`%qg{1Q}ILUnbF9S-?KW@<9uz;V-XCM z;*>59RVy)n(M*?J$mFe^-@K;4catoJivwb+nv z(IRX!c8@SrXwu-%0+Q5QrOY6+H(4JG@ZMvIK>9cpIT_i-y-Vti;C7{E6zT#Z8&HPm zGi840Ch_@>m8%$Y!?n!Ktq;s?71~<)ne%b3fPpn;aT|zdnp>W0zEtDWZK|1Ek45wXl$& z&kNP)%8^Cl+gg08#Z`?Pf+AC5(Nx4;lgGT9ig`!v2J`h>gMPoOHTFW*`S7o>6X^i+ z8|^WE6#iY6@`LE1LQePq?QKG|L?gw2KG0l?Xrb??S;I3|IayfC>n^gj+UFpC%;~4! zEZfeC@=^ROQ9IQ{EIQ?^Jm!u1sq(Y0IG0q3b;+ZR(xa2NsnOoQ9^I`Br(XF+{Gvj5 zwbHA;vpx4_&9BaSLyTV|`fEWgEdzSyF{PtoHn4WyRljJiah#kqb$F}L&~+~Z(ihJT zA1DvQLlLd@9KSSo0CV3v(SCUfWaTVy3d&fHxPs-wHZ0|arn@ws6x99l-j*dr8#a+Q zp!Nq>J`plMaN?C!n~ZI2TC2POhqBZvT;fptNww?*m8NJ+{Kf{VoV%2c5ttm_;Wt7<<(mVCI5=gPJiTzY1wKcs?NL6#60$jE#@hRynIP8`=-SG(9v zks1N?k5I`_Pf;ji4gSV2(EP(;qcK%$&VbI+)ha97II?9#UT}OyLCJs@n|;gRM>$G`0t7Wd5@P(o zypZ@*W^KW8+L{X*I8Y=k+Aar&pW0*CQi5BE}#?&%1#1=2x2 z66Yp50YrD9%bdv=l!lt(F8 z52~fnRAO!UTo^*NmqyXgZ7;O5u}tA}6i_7%P-)MH@}F6R6jg~?UCMDh`XJLLQALq6 zJOnBjsz7H16h*jfut2MYe-u( z?nKp8u$0UqHwNH83@2(A3Dge6ec|gOI)y%h!SY$EqiS?zqAVBx%Fn22H(z9CXyJr; z%s^Loa33ihlRODOV(RlvRQZS-pvqRPkVUwGvAR1OEv+dZua?HAR~gEtHNjb;y9e+&eAd8wZF*ep$#p>Cd2~y|@yL@f1_X_icPc;<Gd{3@5-27u-j}^5}i(F{`oYa7z>bnD4RKc-#p{#8_1=WxJ z0ZT3g6q6;Eks+eJ1T7Cwff+-NEZOq=E?8Le^v`?|mPw6o%?94Xy-!p99T7?4X-M?x z!ss|prmBcBR*P?3U#(+omOH=dbSy5v(u?HDXhBP1F96y3arEsUZ}oK0HFn^W?45=ZzIk81)m!Rd&F0_poO5YN332Hf6e& zpvRJ&0BsDLhy*+|A@D&!mS_rmM0#25w09URer#mNAnvY;Vr)18-(uGAmZ{oCq=+8s zS7+K8hO0QI{E{#qet3ImnFxJ1a8%HQL0sN#r7Dn|jWQ}NxA6-iJL&PRxbh7&$vS6_XBaM2fK;E>kSvD3u09$A6T9uVyKgoJ|PJf+mYACn$0g0D1m0SNgFY zYrg!__P)*Y&&!WB7BOU^z?Fo1DFm}#1Q9)y>u>&}E z*nP@5IzLiNA77%UXXRN8Yv6x2^lu>JlV>@uqyTJDf&-9LC{#YK+ly{7HgVWoD z#dCj0t2iQ_MExQGg@z#d7lL90J@!-wnM5v2bx%s@F~od_Ne_8{MLGHf^4jsT=>*k= z&|7lP$bQE9OHFQ`Bf+PoT48n2@na5GfPA?X*qq(RU*cwK1f zOcGK?f5f(BRG|Qm_Iwr1hPHVPDOj!ZPLB3aD9E4_AW%Gm+_Bw*D24D?r-3<$N`YV# z5LJ-f-8rCIg<^{=`pbGmk1kJp{|y0G1(R4I4;)32W#sYv4aH-h?#w6F9*nEqqiQpB z{w)xm$zY7}jyBz`1#r^8PPAo<`lS=7M)|wF8HWiaU^>0&d zYU#b4d0I70?S`vbQ<~_qwT|)_F|bEqS$&X#JsfA79iF)tYp-lI-8WlcZ=%*N%XVGE z*Fk-MD|*MFO=_><{tQTB4la?v_rC7!&tJ2AXGaMvIq?=UeXW%_Orw>AB~ z?k96Q=lwJUaD_IDYd>X`n;ms&OzebE%2NYF(nbKBJ?=58nvs!*(r}UUf$jx#E!@03ky9Q2!=rYTujpXg|hx0e8s0s zkzI0W$vUyuSLSamZ^6JvdQOKu=qNju^sO))3U*_QD>z1`ijMaXISFG3A`7iXlQTKil-93-YD%bJ=T7XZ(Ofdp2D|OK9b+qcyZV)x=?RoSl zhXlEc!rvt@DZGthb8#d1KglzRGKdLo?mS|P69vBl!Je{BLMbVEFGhUwxf3V=+4fN;Az^ZG-UFX^*X})g!wj_o2P{8hHVgPuIqSC zuyNpa`qkM}tfV;Crl6;Kp8VbO&*zs0GrG;t#)G^{d#%0rLxKw@^M()T0ix}7rqjf6 z1XD45Fwh(h+g$~BjUMI_2uYgQjq})z6Wl~1)fMS?Y>yp!R`ztv91NxQ_6SIK2>JEm>VJOH9Yw4`{w(IoVIir1i*VR^4CrIKWK)Fx*>#7JAm;DV!a zD^k_xRIXcMy>=omVe>Nyt9nTM`~St-TLxtkMcbj+AcMON?(XjH?hNkm;qE@Tez?24 zySokU?hb=Hg!`)AP4X%)NmcT%`^V|CyHD5Y)qC%?mMh<>>fsugWOk^+-Ecx@4TY>> z_E*7^SYlQ&lXk|dZAcz^0w>u?-&rWiL2t`BZte9?EYHkD=wsV-oZPKm$Mpw_eM`UX4I8tM*k zxlqvM>9w}}Jgx4#(#WrnMWi!ESjZP)cg7e%3&ioY6L4YJHh%U{*-Xf3?{l5)%}07* zlNFWOM3%19Eo{dYJm8rrlR&cNH_fM%r<^m`oxPA%sHk*8mmU2s_$>1HC56QMo6b9D zX|jsXXU*ITIjD>7w5@TzVU-x&s&gNuT^5sQT`4G`J(@+A-GjB1rPVgIg~Y90 z9@ZvQec857>{CS&cjaorQgwWcdSQCMLWSMyvkJ|~=D7tF5PFW)pRas&Ulr)iSYiMe zNPTb-xZYzg)M-#Ra}-0D_3TM7lh@~P(^GXh_I5aRoY@M%hj)AvZ{&!p+I+d|AcIQ1 zWv@T?E<_1RrP{H=D{;g_P~*g&xIE!ZvwNWuPyfT>*{t==t(jOFf7uXnsy?yEGVbJO z^a|ypL|7Od)pJ&b0KYJCm~PsY`>-P34WPy0(N*VAks7V6I-MUaTv1gJpeJ)Im&MC`#dTHAiYllfQ*D>J@K5wq z7jz*+NWt;(?#692})c~SlMpwU8`jen_7wfio+OrsXVSUUA6#(@9o<_HVtS|&qKY>6$_ig%e_&1fFu?-VbZX8(2bAqh5?fV;7J zO7{z-P&)D~<)d$tHp%Ws53Jt>nC)3qhXhPDeDh|GTlGB?GX&~Easkitk?B^SBbW+i|<19_c5N=ZcPpnh4`a;dqZiW*& zYc`2sLl7~*xcLoz+t&8Blq;vjLrk{-9OUxD;>r?y>PBz&@MI+u&&H~~vnu{2ph?Fp zN2)}-TCtYM!7<9YLI#L8|3j>v-AG(V!fTbSr34#}qJ8fqhK+zsr5dZ+TGqJ>w$E!( z<#U>ony+5<3^a|y;6Qi4IIc0;ar$^Q1oCEM(@YIBf{NMjZ?1PAEO)-HT1d-6r2Q2+|o;ML+Lk2 zT|8^f;N^`&FELD+jhC7X5C=80L_RjVYHt|{s>p(uZ9j8WoA}65(GJJ>ER_}~;!`18 zrs`);{^zi8TDMlP{T(M=HO*UwaPS#2Hx*n?UsHJ#?U=Y5>SKuSx*{WXz{vxMCoy!=q3e7I(y!Z?khQ4Cs=x!C(1 zRoZS|`U))vduX7GE?>h`$g-cZgTmw0ca1ie%{jHMB~9wFQ{Es1*rgBd8yyL9C?2Yh z^9qGLQSE5?&W_)!WvrjaQA&%J|KQHBZ+@@M=qVVwN_G68Y#MF30o{V`bMX18C}4s2 z`d3Vc7do6>4J@Nx6y6y*MY$^7q$-plLn;D&edz`K?_zZO13sN}N167XS~B6cqnHt) z{D$)M{Fb{ftqBaVHKm5mXLHUrKZ@$>v?Kv56tx6^3+Cosi-=qBT%D^uXqpg)hOFD5uS@8%+pJM4zHg5{u$JcMo?>#@ zt?w^c#5ubGpKga0Q8XN#V12+_Al61VV%lHHCfyaWk*{y?#I@vjysXse)TRHSt6k8( zWpg8Wz5LMW!lD5yTS#tO4AYwsjZ%~w6_r#^oYGdsr|wl7#J8Z_!n%KA-^*rV5N%yh z28oWl<(l#9F9{YP?6w}O&$fb7&8~2Ap8c0+OP}&-pXvhWL(p6n+8;eZ@Orq}erA_pLTX-8H#8xDu^b>43mmU{ob-bZal#%IC863|_ zy%7^)N*d=-z0JUlW(69Lx39jY72)5mH76~C&ZW}P0cm~t5mO#ONQNRKG1EN@@LDKW zVE-<(vj7@#?&82p6<}H^s7pQeraH+gP$j;kIm5Rr^uq+b+Rz>><|GpgF)kMw$hf-d zcvFpuecbGoi!O-?Zgsa6b%W9!zK4ybadIR(MJHnY(< ziRQ7)d@Y2bAQo6Crz@umHtHxBuXWjx^4UZ&rLzS`_#?&Z`L}}VUoRvAn5VugD`yvl zp0I+rDMruu#l4NM{YPg(5x!6V0f3dhyZYpaMdJ8zR%Y1eSI@>0L z4<2Wyy1boQ-CF{i;ID`W?8$_P1EEdlO`s0gLq9slRzfBUpHx%OYA;p#AUVC{4-8{c zFZRTJ=u3CwH$8)ZG$D>2+!)qXd_z5eLSB5U13&w9)2e)fE*~&C@1`!q4dyAiod@ev z3zZyBM`B~VqkD?#Ye{-^&8W8*{umoempb4&p-ZF-xU#78iog9ha=-BarZ7nl8r%p| zJM2o@@5y}Dt`Km9j-9*9Kt8XS+M7X+PKxeL&w&K5S* zY@{>-btfWamt>#>&49ulk_Vb5wA${Ebg*yP{pmz(4AsZVz-!#~-0BX&hmSoD7)7up z90u@8V;zi2=K-slaMd@Oe^y=x>5&%bGEgdtc(#cjl4I$r_av!za^>06=DXXxMw(CW zGdQaZBjs29V64Wf4>q8X5etcjG*Km70i!}?AVH60mPBL1;)LIvqFy!${_5Fzd)TsN_AEe(lJsW=0odkT<)nQFP!!pRvi<=FupOn&)*gac#(A__sD zT^I&gU5=+LHTYw%V=8`6YumG(IXLDG1AK&d(2Mn96TdT4?Por%2~L7uh9c4v${ALE zFm(|-ygPU&tAmnul4JFWIdxm{C`?>y81ogO)nOjtGmp|fP#z7|P38A#vwPGJTTV@S z0w@Y#T877+Lu*Gk17uL(n?{_b3R7WP4=vbxo}JBRcI@1ixy8D5jt*3lb8sAus8mn@Q`BhrvS8xt~Ow{}mVi_DV11q>talDGrZpDdFVEKiSPtHjhDBOImnDp{}2`84Ux@ruq49<*!bc+|$S! z5*AIZ$!?xuQn^@pWwd2DTtYDL=v`$79P0Mne6m;47UU`V7?l3-Oh}g-#LY`FORzv% z%T|x-(~anrm~hLy$CeS(I(MJb6BER?6TrIpss z((0J2Q+_eoOc$Q1Bv@O8Gg2!lb_kw`3Ft%f`}W9PG9ONXBO8-la+%L9YOQxbIq26h z4$c-E?G#L5yTTULNLXfHQ8`x>&n~{^a56X~R49g(R0H*|X)MHQtgaVvG)W6<$z6PR zl8ZC(#t^xKd93Oz=mjxlj+$zQhA4ZE5Va!GIY`QRWT-M*^xW>J?g!^byZCv06-Qgo+2IYfsgqzXsijlPA&T@Y*|Jq`)GhfN z(uqGPyN%1|alvC@!t-pn8+kDYwJ`r-+#?t4O54XAA8=Cr-Me7hKgLRJ1}%4&&TA70 z3GZ~6BykX)^{$smsSI+2;{GuhSuy%iS;SZ(qA|3q-!2ob%b9HnSRKpu_ew0>BXwY1 zJv2jJY4>p~B!4VNkmW4e8RyAW20*hKuN$4skDfGZYd38KB(~WY+`2=(rwz_~Lvdz- z>HQl4jOSZdj;aO`Px3t8NJ3?Mf&}pJ-1hUzKCm#%h;)L>pJUzim*Yh>;IzyYjz|26 ziYh#)Ab08Y62wveu4$M~{arWOy|+_A^*(uMyWMv}|7dry9K@@nSnKL;y?vG%;wIFA zS<{uTJYKps%FzEF8qXU?T+CFm z+v7z0v;BhZvC83!&Qq9YXZka0rqS}p>4ov3vK@deoMyziMf=h(CzS|~wVa*KRpLn! zx*RN&q4~OPc^8;)cwwzfOMfeAUHRGNL0QE3@Zil(Rh`^$eyG+>y(XX$)Hha0nH;I0 zF+{8EVl38`ynM#4-Sjmna4i5hC}=WS#AKHN?_V zlj^D)CqG@)X%h4tI|o^4P7%2AzC61mDT3QyV6{klLRDUJS4D2!;PS; z+XJ>R&s{oLdFT7oPv=f>9SWpPbEehsl;z%0mE|Jh>?@4UFe@vL&%{&s3wl)k;oi#+ z0xEfsdx*Bmo};ZP>dSaVyRD-Zqi<;)xY2uHJe6+J^DxnSsLo7yk57a?b!Db!FGYD% z{7Uv(K6%imK~~0|qpz0A%$nF1SbnYK2yO4U4A05NHBe$>kxnrJ|GKbkAZYj$egmofOdw~!GYdlfdIi* zIQK#D$2k2#$*96lT|3UY-!$~tCHIg&g#OMwrw&p+DbK}HON2A+-?MZS6zY#~Nu!K) zEobR@OeJUgejHGqE|Y7G8$l45GJWV47l{5-!btcxD{_wN20}{vD0pYY{RbmR)<^58{`Q4k)6L-M+m1ju)_pzK9jv7)mtHGUpzNchOW?OGsxtizS~c@f~OhHyn= z90Z+8rLMhUz?IA@`234Y_}3A13W^sZ(m3xJ=BkY_D?dSmqsAo)OBrebdWbZY^lRsM zAqItbm?)iwOVo+%OKmF2HxWh@Ux6?aI$sWDx85%XWp{x|f$$w=hd3ezWq^T*x&=A7 zLZmU1SciLF3COv>ECYkg2NGSk&spq{+?WFNjpC7h#hz1|i*=BY5*Yq1?SGf@A5(z- zt>>18rT}ZT2q-A*xqhL0^?if<@B7PHncF70pg}+`P(eTx{tx#?Lu+$8M@wf58~y*- z9qA1n&CLF1{Gez}IlEtCNL|Mo7k@H@Hg$D37dhZWkzfK%GMyT+A~mHhMzsi#_ImEl z0IpGjMikh;8`w70YV^9wAD1;4{5m^*AMVZvZcgw29Ueas*v7^_*)jONe;!w8EDOBf zKb}pU>+5wszuvySo-K0NRqNwlT?ypGJaM$xUHkI92Zq-4Z2P^rznwlhyS00@3UvD3 z%}-QlY+v>8`96Q_-dx>Z?e}zgbhdeT@@~D~*j@ASpB9&tOos{(Z0Y}!cO$SP$d<

Y-MRYxys{qW;mCeR$HxH>lrVlBF^9x!cf)0AmS6=QZB*xP{plH(s#u=a*%Bo z&p>7SF+>N~b&L?p#9Xdfe0UY@ zb6sx#KtI{rC0ka%cKb)7)7WCRK_MZSn7PD02~u`qaGW@-5N&*FU=)O;a+@4o2qf}b z5X22vtd{m!CK9$gxlJ8D<8-wG((n)=SIWPh@F}eu#gI-U$n+sx&9mZ91_X}%+?Z+< zwc0CKPI6;kd?na$>Yo7Z5wM*|5aEE?+AgG^46MQ~jdTzYDxli3Y%E{7S!QJerNIsj zFJ-+0^O90(WFk4?40Xff9CZngp>buVE;JXIC>WZAxL%h2l#&_F0yY|DF08}!C|`-q zs~K}Kqa=w4?4+Dkuq|cFDgdBtR+h_*k`chhA$`W*(Qno&qB@xu(^VGGj%Sga{`t(U zT1CuOndY`f`wKA}?nG%A;>#UbJ-y1r;2t6O74Zw0p{im}H7`zdZFRfa}AY-A0?IC+7vxq;6JTb$MRH-T|NxeXC~&Ih^W9z1H>2pFCDZ%eJTSmhzU*=6hj3X z0h&~ieJdnZ@68`AN=)H|UVXI4&!+H?`>iSkjL44*cX9|!VAgu0tQsWO|@A?Fx&I5ib_{M+q3vE@9u*A^qX z32{`}Q8ESPj|W(zu}-iQ&7CC_UwCsxttU#b3UPJI4eI+RTbc|I<)sy&lgNeh4}Uk$ z!B-)R;i^R%p=Ci4>eSEW2uh`wk)-2^7lR3z;fb=47>e-EUn)cHO6xksiOycl*iNEN z;;60$YxW2GLCGF3qc8Dg_ zo}pbpOgGpT_jbro>#PYx1>e*X>#+e}3=402r3HzlS)(slHXb_|y06u^TLNW|n!^SW z-1YP%)zgpMfUXLcG}nM5AUWhF`8|9B+JZUgdV5x`K($`P z>K35J#;mW+N}s9?hsapwODZqEc)lwvb^KR|^%xKLnfv1ID>aK)9D;P;P@t?-;Ml zW}~d5l5o)WT|)m~yMqTsD@MMht`;H_cyATQ>d4^)>llvx#~o{gr=YfX>QEm;gJDAO zXVt2bwvYiI8zBxgX9UPU#Y2>F?fm4R#aGARn-CIb5o+jS`u_gsxCKa$%7SUbIWu0xU-t7BPp@o{T16y98f27eUp*m^fRcnOj%S)T!Td2L z1p;@sH`C2DmsZ$JbJtK|`}bGYN*%c@6j#C2rQ*BkDCW$0BT`Y8Oj3_Y&@ zF@mwSGuOAab8^x*va~gH^q@DkHgs~bG_y1|{0|TlUt z0XN7)WvMkV=!DqMr3ym|d8c1qSt)^gVA6@QRK<|GhvowLb!<3YbwZuN>x_D0H|H(1 z3IiLRzO?F(8bI+1d)!p_q)Ho0q{=Ozg=t`~ZuTI#yUgu;kS?0Ux4*?o;VyM7(A}g- z#4ONv(I(Y&mYmrV@T;U`m)lzkNc)9hYlHc+{#UD=%@9X~At0*^)fn>=ddV!Ohf8L^ z*Tka{_KtKa9h!kWI$^*S_s0z^oZ%M+Lb7c>?@EahkezAC4BeD*mQW%3r8y8LwUOnT zJ;Es!oU#wH)V*3DvW>~mS5nRuANHgLX3xY2gCQcwqD7#Wl-~fPVj(znO2$H$V1 z*xf#3!w38Lbit=({&Gv_QJ&uZ5!64gfNYrkXjnK@B=dP&Sau><;;j6u3e z8Hg)l#CSL88i@`1J+?x;HRc-2))?7LsG-00Jlj(H{A)Y-uDRr^fB3T|j>MhlvT^6T zf)O@kU{9-=8#9mVyduxUdm0-*wD&PNc{+bd%!|I^57y?+K)i=k>*u0d)PKIAoPDD7 zI|x^~HlHK4Ym#;FS>&|pCwCPoz4Ljq1^qyXx0<*!S@%TivL@llC-lKK$T=P&6l^<6O zTF(fsu5|{oVpDpB8trmbO(T86=L z4Bt$$Wm^|_pblVy&Y9kL4#V2xaoRZHx7kVs8P!jU8UNY#vYT z8Ro=a7&lDk+j#LsjCh2#PeZ0nV%XL#t|-lU@fS`<_9M`&nIp`q@XCL(TF#s|50nYK zTAlt-M>Qc=yhO?WD4?VJrCDL(r8B>CXbjkzD!R&e_K+Cmn(^xY8^9u|40YweVROgm z-*zcMR!vNF_Q3;PlS2M`@*0JDN0Vz6h^2O<9o=|N6J~`o>y>3gYIR zCSbb-hcn%#&lf$}Y**3J5(@#Hpy=@X;1>dVi+3=)a$?OE+zjf)z~rhFtYK?j^3<=! z2w%KttyV#A-ire?Fez7b*$-Pk%8Y9|BQZZ>Fb#683~hw#;;4iXm!9mJ6# zH$GA*<9mA@-W3rXv)s8O9$U5sFQ#)B%koyzCy+<~O1>aIQk>B8OmqVhYA%SSH0_ih z3xbD+0f}iNpnzhdi1QEJGqb2UNUZ zQ4iN&FY7J5vifrPl?9t%jYkd{9JFCMMms%X9C|&1GQpT%@`z@$jsh4eg0mL@G>J&R zweDGsv{(WMhByz zk{5~p$Kp%+LlXfo(>ww|XOj=QY@XT23V_zok*1~62;&&mh=Vyl7k8(d8A70SixWMH z1*hFJf#W>x8ZF+8bRz%pR9GtLuB=LUT~w)aFR1>9G7zmuk|j8`6Eom-T*OL@DFwo5 zvQ_+El+g@kNJ2#xkBZ&;;i)2*Y#xRCC|yf#9GwPAb8?Gb^Kpboc8c2_TpE<2t0a<+ zo-%}y7)(3{8C8&9Xy^}mDhrF%bd&}&iFh{dI%6K5Kpl|)KszqfR*sl+1PEA3!;%ue zmd=eC5NS#lHm2r0L~}g}H#v~3xDhQ#ns6FpBH+-r< z(mW=6q#%URf$2Yo4MdR0)>!)-d6h<4rGcoBxWPz&1+ytdF){??%E(}qI03_XTO*bJ z&jvp5CVLhi`n@`Ydw>ks6uM>Kt2>Ia=#*fUKkmGcNX;v%Pz=l(6f=QW<$v`HE1<%eG*$b`kZ=aFnJ83nc`0%6BsI7flBOGkK1uBPRsDZ*IB=RGRLjfK%uR;MZy;EEoin3B|B zpZt_|!HI>-aRykDryJpGt8LjmP9a| zc{4$COOFy^G-6~aE91y@J7R=^f!@0~ugRKyOS%^cmm_=T6JaCpaM8$+V3NP72GI=z z<7q}jW()aGSyFEnB;EnJs1onyaZY@kBRlp(>ws(Lnz zzb+w4sTeT~eh~hM0k?Qg$g^);M!)wDk5hK)9jNTHF7304!VCHh1b~G9K{L?mB~}1y z#4YUEG)R32$>Ie2eivn}GxQlrvBBENupzS{0WoKx$QCPQ0k0*u99Ml8S*`3#(UNye-1af)VT z#vA{wO)`K5P#8uiy#~MOi>omz?{fK8m6m}xDRBreaElyoaoc{t;EGRVtYwA3$W$Ev zmB%AQ`OIX1utLai@(?4XM@1zaGop&N+!==LB%Uf4lmU_Yta@=pCED6D0x*b;NSY47 zKsE1~PQ&=+p{?3Wkofy{Y#DduuPD-+Ou!mp*t!%T5PNqAdv!TdQ+2*r)sY<9T$(z- zbpJtIh@*EV&@jKLP?$6X$8N5 zTgnD)m%BuW4`fps#uKnCiXbx;UDb$=0#u{bIlwoP23wHD6DH0Pe=zy1vd-K1V@WCw zha0fx$WH(q56Y`MPG_>+{3*BCtAyOw8zCJK*YC$QepQih1CeQyj#8CmEuV@&y06dS zm9$GZj-m}hkibrd!U|E0*Cc7XgO0@E_b-k?;($oQCtMmlx!1;oWhr1$$S2eQPFD*rpTEj*=h97^Te%l%`by~=`0 zryMP?K}o@=GkuNc)g2^VX2YNO@pIhgr6d8ufd`h4p3l9zq?q)nq?ntK0-^Ww2LiA3 z%Y!3JjyURTs~!BUL(XBwwuIYaV#!JNALE5hWsJ=J%$4gm1GuX^kO}#%CxF8xVhfVk zpTP)51bjNn85uP=PN31|&cClyr*AMEfI5c_bBq4ogq32Fl&FY0A%S6bfENdhP?s2bF2L*u={uBe1nqrV}E4~ z&+grtJx5QPx)mvRsWvxaoh8kgD&o#sRC_taX%2B=EvP+FyN#Tc=xnW#uc-I~YZYC; zg4*EGw(*t=hs`3VKq<(#QtT6M7hSH~)&1}S#Xoso)?k25wrrKSwJlSi}Ln# zgx^VZH&XSFZ{Y{@o+uqrps1pdAq_>WB2L7=4Wn~QDFwnvk+~m95{77UEOCC~a$t?s z+_cw(x&6JDMrVD=qQ2V-a+{cIeL_d8eitdXmgk(8WeylM@22=uH$8I#{+NQB4Wi)F zvD+Db)4C9D?xE&i@f)t^tRv8arI4cu(7Phx3Fv+5(D`?>YztEqVFD`-vDp3szq%Jp z)fGrx?xKC4kn%UMi;NWdmzRZ}av&8zPl(5@0$YHZGV0=KrfDXE#On~s0?xODmZBMA zT2n@o9!whjec|&I-NX(tF9J|o*><2~&(-Xrvx&+1FJO-tYIJKg3L^PiY-)s-Nb#=g zy%=FKC!@F<(tc~u{UT{s!B9ed8Pt=fK9CA~PM^(}PsI18WB7O~ZY#M46g8(=3EX!D zFR+0yL}8;8bY6nYmQk8D%$PiW>8Hxg42KO6T9isW5GlN@pTcqMkWJsRS7qbm=~qtH z$fs}*s)1j@Hq5^Tjn}CREdDi}AoRDV&-|4F)}Ln57II{2J5g-%8|Am=v|{p@dR|G) z4=$c}yoFMSh@I4|$;H%>6)2-+IG{^U2>Pf)+VM@T~GqmRGMZqr* z)99QuA=-(kN` zZ2LWYuflVNZ^^NT&OO2F$@Yuz$5#X7`AGJS-Pb#X-^-@PSMdF;yU*LX-`iux^!Ji| z==;8HZhby}hfo;Q^L5ry(>BIoC(t~t@B6X&B=CNj)6;f_`Stqp^>Xxm8=h+3-`BW( z5#T&H=J*^DzkmO`?OBKUHr@U4u)OX4P@(VJG5lDe-_>*9x|cH2p{MH!H;KFY$oV1O z%=fn^+2;Gn)?Z?NeV+ThKWffu`0<_3)W96!zMx^@}K_}Ka!Y#e9X+ARORm)h^P z`F(19w2t*ux2Q9$C*I{F{p52pa5UO=(V~3Z)j}6N2UsqdT77DcRlZ|)S@D_`SYLXR z?OrZ_no4|1xJiF zDrZ^qd&_*CH=ftd>SleUsVQ?}!P~Kvir%G=ucR&}$Ih;$jLSAkZuz1x^Qq7Lp37=F zJ+Qj(ZLqg6ALiV}oiR;8PGJ|BH$aPE<&`~z!0Tk-oXX?~{mwPHSEF(LOch^gHS&(|k3_|OIpzZ%MN_=S(sk&n+nr43jOOD?kR^0FoN5=j#h-49%jEfD ztFF`1BtMwir1q0qnD+#H0=5dN=C7`X+|pXun~WpylP`51m)2I?Ygb&Y3m3|V#;Q5w zvlbxlPyvT0fJUX3R6|mQ46XH?$j~KVaeT*doR@stg~PPv_U#G0(!%pr8D4pG5&X2H z06~)%HXWkR*og|R_Jk6w&6S6oLqxQ9_Wt}RJ_Yc~`(w4Mxh3oV;Um|Iv3Zj$M?hVs z0W6F$`Jr#P8aA2k%RHvL+0wjsk+r@Awv-wJq%A-_Zv@oI2H(zPUU}v;_G_|K`v5zd z+0EG~P^vp1z%b7T7uImrBs;b%J}%gDxE5UFFf1(zqSTihSIJ;W3!E{R3p`VFu*JrO zWu7;qVmVN|^$22-DYNx2p)K1;xiIr(&v~V2tbw5~Q;%j>tha3#=LOVwR4+83N-rQj zHfwItr%{{w53@cZOr1LX>6f2g88$iLc-`Ce7)~$_S+sq?rOq6sWw~(u&4Q3e&3h>x zduKp(C5I&Dc}eJdcBfE=55{y8zb?1tYgQx~dq}Z-f92qiNPYJ`&}X}yDLUa3Fq9$p zMV~>cy=+EvLJhyY(Wq+s9rSYPUoBE)X+Ms|uI}u!hu1h5!|)3DA|>G9(&V|zrrbGVH=sxdJT$U)iWHut~ZE!AFAL&dlR;%giIoID0}a zU8lq7WA`ta*ZpigUAZ0FP>Y=|OTLmAhLn>VycIrgM`cQOQSpNSf@wi?Afo~v3-as2tjKlqH;mXCB&5-bD{eg1x zp_3Ah|MgO}C(ZI}TbTW{FCutR(EU_r`F6HGMX$<^!!cdENx4_=8<3qoYiS;Bk!_yI z%QWA?v@18)!lA&AopU8C%Nc@%szK%a!Zu;W1#?spI@s3wtT;P>K(7JH-X~a0` z^^NENs@lle;V+G&3~(m~^=h4-(-+z3TEkSUC6Z1zFm!inBvgRT#YlTkAMWfg)C149 z)@AEt&OFSTc6(B_E?0Ro{JhjFoEHpgn%E{#klMvIZG0_xPZ5|2XidpLCyk}wufYqr zz53j2XtDKJytb`0$tIl}e)}0mHf?9nHA@*Ah{=pg4$lbr0P1 zkZSfb*^+QVzUBN$I(JowN&+=WK;Pdy3(N+x=-pD6p@3Ph0GX#g5%J*5VICc{4sYNQHm49lExhbrHNML z3%jKqK!|$u$C>`kIDsJEG%CLo6?J=G;t_XUKQ@x>AT&Oq(p2eSzmn%yy-FdBwX>Ct z>#x%*w){(G(}lm;w6^7sP2Gi84NtMxAHhLf_q(2ec3p&ejBk1T%sb^KV+x>!2;tD9 zxWy%awMbFH7`Nt8ae5$9)~e|Dl*RTaS|4Qv1K+l$u8OeP;H~*<<6qJ|%>v~CW?a%{ zw~eKM&sp&^yUY{yX;2icLe5lmNNv}2#*LSC5<%iTek26LLxCE~x;}?4>{R+6dB}+X zOo7V~wF0f=-W+Z4j!qRRtF}LK6)-#}$ZMIUsH9OSTJlDXM@y<%emQRq$ z;_u^08XF@f)w3HcmS`a$VMVM66r@nvEe`>Mq;YfKUr{7A>!pw)w(ItnC^tRv)|Npb zM+6@aW6XaFX};56htNr^KBJVMmmqOVlCuUyWy5LtfyVU^Wg(0l!LuEavePjgt)uer}3pz}Blh3OSZz zX_tF(i-pT#*X;pr^c-z{JtRYpq#- z(}4QF@tMjqU+Ji`l!me&)r&M)dFwNtDRJ`)r|6b=gS_(04v75%Yw$Qsyf9LoDTb_a z=ANTMn{VuO#y~sT0|zd07NaEgqExc11&nyk{`0M8`oh>`QITz}NGqXwH5frFSX)Uc zc=ex=io!ZU`u#dU?tvKK^g(>WF{w>U-6-BzoC^hy-FW^z{vc&?8&yx8uXhbQZwafI z*!it1-}nYfx%6#=RQ#DkGE19)=|AK7s5`}~6eT)f9`j`dW^G}L^?snhj(ZlZ4B)Gc z{|0uR3ibCo9yYNjHHoDoZ0Ue~yCNq5`X=xD78lKzU3&KAXi3H?^b+Ki$YLppZ*i^OB zaxrj%xNNLT5@oMPCPmxDY9Bzeu;RG(J2)l8B$c%Y#KhaBr*pKVLURSbGUWQ_hD)qL(fg!pc9w5*N@F1_Vfq$^ zZJUkJ!O*9ttrD%&oAMdiDV}S_PsA$Ffs18>v4Xjc>kEmAX({(?q?L{?Y$h7I^eUdm z?f&Jso9ffVS^S?DyPaHDMwT&?lQc22wQr;zWb?FhsJI}@N+^%)FjBu)0jnF*30!0f zOYy9hMDPSb8IpurDdWXw?q8bT$iSs8i=v0P-np`R@q3$&7B(CySP2F$G9qwFB0SQL zOPu|61fz{dYKH4Yvl;QYO}SvFt!jz25#~73fcfUr? z>2ib{AKgoqG5bK1^~p~b>`V2nght$H;?=59fO;NI_6pi7Lhz@ga-;MOLT0vIsHrpK zOE|u$b6Bwu`Q-9BO6uN%>B`*6&MUlwb(vXNI>5^iXD)?Yv8Av|wM$#) z3UTEI_xL2z@DVGz&Y;xX5FJ;(>UcB0Yqx09kmSqotW0_G3C2oH*Gv!LiABgd_F6PW z;GH>%|9pK_5nomUVRe^r%!^UD6?QhfP`0Lm@&YiE`9RAvg=CYbT$F%@Lu6fZdX-EQ zrh!+~g=c|Ed{Iki_`I%=Z(954)%_AP?WQ1ZXaz=CDs|$Y4_A+vrzT?mYZGiKA7M_e zlxh4x-)jmt30JX8EwV6>(9f8`vMSW>H z>pdylC<;#(Wk;SSnWi=8F7=(I=wzTDs`di$lUHQW&7Y=Pd|Oi6{FS@TDXLr8x z)Q`1ZgcdG+T2sorRBbp4)O7-F&Lo+C)SpBy;YStUDv7{(-F2iA>$soEFQIH)exzEm zOX$bRtBC;ll@c*+Y3{`i8ziharbQ`h0M0P3fZJb^n7(9+!u5eGA5DeBi8gVrDxI63 zhD0l@Bf!M&Q^c~pjv{lTe8oD)=qx0no-Q(}HN(RKt|dhz0>^5}t&Rm{5(2}Kq)ub9 zdmNw`bKBar3%R8MgJp=5X0%GZOhVD&wNPV%I!xM{ImJcIw=2QQz58L`XIgce}NCH@=8pz|kL4 zD{I$hhNgpfr?bVF3C<_@fHnSRv?Nu_y(It zF5@Kbz7q6>egtTbth4k3Af4xZ<>t&!n7;i+JADk2o=hZ)7ov{%P2@T;EjrPdBHvD! zJ1*z00M8jlnGjC5sR!*g;LK|(AKzs&y^Eq`Uwza!IQXd|MqdHLuxzXc z46Cx!80fxWl@d{vk1R#Na84aV$R40$Nc^kA zT4(j?mL9G>4kAhLT&`n2B*6rwxtqN;2^1Qw>Kx-VS+=)?J&>*?j7y&O*>eKhO-d zc0);16kKs;+}U*SZG^g{jIHv50dde+i5%Qs9KwQ#f;gGzTn^V1;mTgr9w;s4D*`_@ zrV%L1jyTBESl^k-P!hujjSJ&`)+2~aBPMwIg(NYJ`X9$h{ySRdHni7LG|oZpT5v6Q zr(WaK{#`H5(@g6(${1HO7rX!xDwzO4fvS#^t=F85r6niV zCNFet<7K%HD$AI&W)Bqnb*7K|avT>bax025WQf6l@`CO7tqd^MUQ1UHClb|l-W4)zpH7W2CgCwU`+mm8oJ z5xb$~f>VMD4Mx#q^q2IyG3_PWO~sFMX>O3Yu2F;zfh_@rE!dKHc{STlc***f9kv&e zixZS@KG8rPBrlxin|9<{Nh_7M*1TBDP_#+Wwh*2AN7Zk5WC^mR_8LOo#R{pv_tdz- zjva2cC}>irKCiE2d~})l>BN$VtOqI2X2uKeIXBuuifC*+yvGi8UdYf}2nlB`#M9Mh zihkti+bg*kx9Pn02M$+RY98x#iFX0oX9~;+*%}MJS8aBWzqYuqNF*R_M3FmIiN5-8BV~?Q%NYUdc2q|6^$g7k*8^TE3&@ zjS-2xl7qIZY-3orG%G1-R%f<+WejCRwv=t)QQtG6qXO<9w_ zL0E`-a00s>_{mm|O1!`uei~$+gT@EjWqtN`TPQz9Re^Y1M(;I#f^C`F^i-ni2{V>= zXJ`0)=cth2YxW!%iVqMB|4K!Mz(*IMD{K!d8{wdwW;OFleC1zn16A? zlu^)EPnItu-@QM&LR90yDF|7)5D^MBRq_lvsrVBP(SHF{K&!tG+gF;ULyMw^PS3qg z#kaiLTI-Ek$$MVE9mDlzB^F{}^LQPnA`iGeL84A;23ZoZgPH)Z;$Y@AqFM_(xF2?U z&ta!yvZF`JJf6gui!gpFnV6n(m$?WhlDFWZ~YhFp$)J3`PhO|&t#5Rw|WDTN?8 zS{_IZrra^M{m9QFne^o{!vgs8xPS;>KkRxP6{2>e8FXj2jLROmc*Lv*aG;90H4H!NvyBPqk)>H&jtrRu9IIzZ;%5>|1 zHEbfBjyfu*Zpbp3XLv`9T(X{j*u2{K$4rpsdJgAtBJT+8Ika5J%M{X<^4l?fz+Mqe za}fRICa&_S!I(AOFe`7@d$SyT(8D;nnI#NfiZBIuw!IQ*?62h{vzreM*<|^Xa&O)Y zb66=2z>8~JC#oJgC;%H-<$8r|m&bc{P~q6Q@!w5Ddq<%I{>`^hq{y|%*+Z7?@>L}1 z2I)>>AKJ4MT#3ECiEPyxsUr~313+FUD;moH%>q=#vYVWz0q@!KJ{j9V{3Fu7=l6nd zz|A1_TyGx^dJ51{?pBRjI)Lm}U@qz#j=-d3(b;^7gXg2`i8Kv7sP<{@&QIy%&^d2% zf6}meo(gowAh1qZh{5|&AW)>sbU`OTRfxU<*3;;`-@k|8mX^st3QCXRzy>APR~~T; z0A14EM{!BHUO{+%0QV)ylx{Lt?oYp5BevVQ(Uiz=tGnb3s0H#>h0uNn77I!?;|ylW zisjWv7&OT-Et{=AZY$E)oUH^bG5KsOX{Je%Xq($#l@=pTzG?H9kMI4|7LrOnp+WBF zBQUZxTw{sFxd1nyA)djEY@7x=Hybrr?<+}|mm&mUo8!0rJjoca_dAgj%(5zzIx;l* zbZuBb;KPUgpjAAwNdPsF2R0cW(p(Ebrav)rz%K+{2(HVZLUw>hGjL;4gir@?GfiN9 zjhtmI9J-@yR3S;TCZ{oX#F&)L7P;C`;zr(+H3>K5dI{Ao)jqOpS?v<)l5W)cOy4Rw zhejQVY}}HUx+Tt_O(2VL5ZIN_lK&$o+^QM86H%eDz|RdUVUsuJwErOQx&e@ON~)r| z3n90_b7*q{mz`lxpWpk5agmnnmbtgS_)PlP(ZBY^dp!ND~kLr+EhKyCO*zrbxUtZ3AsrCps6ThU4KU17+~{#t}{ zl}3Opur>fePE15HLpT=G!|RWtEQFkoMA!n$P)G#CbnNe^{D?~z0I8ZVmYkP;np8-P z_5j*Rpd<->@HkG+)$1h$rZ*hcA<@%7+LitOh;|&_4PYn~J4Wg9Zc5+D0@c7;VXEt~ zc5pT)M`u_%OUAWq7}@#gig(=aWR`RmZ1H2`9f2e*$aZ#g_TR*iDw@r0oh298r21|r7(K5FG7QM6lO>K zS(p>f?0i>`T`zgXC=3S(cBcX)lIHDtbTxJpdYY<*z14Gx(8Ji(XkQBdjx&T+^>O-o zf}vVDV2?rlxVo=^JK0}br!Fkhcgj!H;}a4GY358~*t!MVH-k*5*u~^eJ5v93w3o@< z4_F(9%-&o+cXJv!6KF*k9hQ)uRYDv*f-Rxz6|y4$ImO>wAEyA4B$0Qf>{8;xrQWqJ zbgP6ULD-@gCegYqjdG`IL27V1$WOdknsUR=in}6tFYBm>Z9^o0SXY=jQVf-{+s&c9 zNq`p2=&)^md9S7!3#l;=a=5QYS3^h^ouiGj_5~S!7(!v2rh1v6*jMN1S&lHZ%;(Ku zcFi}zlHA`wkf#IHsAEYf;vqQF)B<_3NQVR7#V8=-KnxA@^mV)sf9xRvD8iCnl?Jpq z#B^he^l3@h4k4XMf3yhY*XuPKW+iZfjWm}Qyd!A-BNZdJ@7^fr+V$SH4`hz)Pe=Q3 z(ks9Vsc~s{f*3_p5%D}sLZ`1hBEGL3v~mqV6)}JWI?;BI0q#;c0XC-$kPI#PrH}6g zU0#8S=taO4r-P3n{LDikg7#-*=@G)0GjUGVtk>6b$Fs}qLO76;$G;)iDSNbOGrgYU z3IJ6GlI(rpPTh%XNiS@qPsBM06#Kqn++A?8vDJS;JN>-p|PSaQYTG+3EJ5}RsCVwNgy0BwZ~ zqCQ&}Mx8G4FTC)!Er`zmPZ~%ZgU=?VA`;lfNjk+Z?b5>2mH;EO)1r{?WYw)<@Gk- zpyS$|^*AxL`fgb---bMI-&f@yc$-2g8>h8^2-wW@I_u+^e#RX2vC<+bD)Wuz1|e0) zA+R^%e1yeY)R@)lCEA!SH!HwWZdKc(J+`uTm5ni%mus2%I{?91QqS4q1@09kgj*V`6rVFz}#8~$QBgc)J&(E7*s>dW+*h9OaAaXq>gY423& zNg?1HKcf=Q8f`t(c^4liNSdekZ}*H!woTtJ9>4}zlJ9)&y6h8ug(;Y|I}%wdtv8v+ z#X^sqR|il=B}gZ21uSHLQ>@GE9?oc@UW8K07E*2kX9kYi9)>J6H>(WHWj5DK$XoPB zfwt6CV}8>pfHDiXHMY8TC@!jmLMG#v2$?ywC?#D8Y~|G2$6!60I+N=KseU=a@tZiL zx(nkWYBB^jjZ+Ar#OT|m)YjQf{|9@L_w8}jVbkt$-xS^)^(#cYwM6E^8ZBi!0V_)u zudq5bVG=|M3&z`#z21*pSfbj=H7ZWeU(V}YW3Y8H5oto^IO1heFjUEe;Rclg8x=#)x)c+X`a0M!UY92`#|Ppo zVT4oQnu3I;8J<=GQR{ozr(_gmBP{@EGY?=P0B9E6Q%+6Ybt|lR=}a=%BR~T(Stw)G z@+k>hP(Efw19zJloqQmHLX$dF%IO>_1Jy~et4BSEwBkk))|j4T3bf>MG zSRj=oQDb5Gt%Q@Q&%#G1W=@j5v9WZ2*Q3I{4#){it2BUQ>K=|rC-z3kkI6_EFCwqq zLfQP3E{QnmD!!#d>O-$B?8+5g!p>!r z+a)G91GlZko=LVj&AJ@u7_!T zxP(O3JGE8eSAlP+1WC&ygT;gb`j-LY@XqxTpdS>BB&u1YMek*M?Dypol4g>I)hhsf zlT`P<;0Snv_<6lOElX*gOm_K=ODI$2{?a8pPJD0)wZX_q$nd};1b-w2Utg1WJUl{K7~P3( zaC5SEbnnX}l)E2mCGDUxC^)LG1%M~_;1GZcDQdM^)ZkYh)4$i`W&o@c2wPCWoUcfr z8QO-8>2*K_7)eQxTsNvN?l}L6Bi6YYsD=T!EXYp8Q2$}55)XwiFE5}Ts|`adLWLyVb@5K z<=rb2UP=3$CV4OIF|Xwc3wxYuW=cROJ-ij`W3oKVmy(jRU?P#WnSO z$#0u%Knj2qLEC}bBD&=O)hEYOCwVB!`nVQXh?20_(puA#ikuvLH&pr$h%F)n)kdqt ziPq$s?TL!X$XDz3paj#dKA8DPm6M?Tqs_J!DhpT6n-iA~%~7=%EC4Zn34$OyJk~n1VH^Q77lTwsU_Q@k`;K6rga=NPJkp$&-CRH z`7Er25l+YJTFJ)!;& zXVMJPHrJJ7vrC=5|_* z-GI^vU{lEp|8Ma#Y*O*4QaNomAIs;Rjp$SI8to>QL;UJ(?|^_l+8GT^l5w zr~v9Z$6Tbl8kh_@Luw!2`$wB^`Huwm4Z<++I@@lIj6;UR$~1DXi3L30JNh!iWlOyw z+_jsFp&hHoUgikOI|-PC{9dz5(4V5F-LXx$UyaG;heH?je93R0?TKS5 zF;tUjEjtvqGnPDnw^4APfnjy3&j9(vs;eLlfliHeTk?JN0ePXz5E>+VkDHdkpVr)O zu#NSFUBA5ZKWnn3qo}oPQI}0J%rV~f8uaPOrt+X*+jzF&7w!gOFE%(M><2u|Ot*$H zqaog^?tG+NGi7aHitFOade~)$H;wmd)2sv+MLl2gJ8W+1Qc0s%uTmBX+%2D6(AIQ@ zTDjq5Jf(triR3=9+)m6Zf|?M$k$epB3@}ESCoPE1cZz@|_#sn%@!n=;a@?ptqN#SB z@2(kw=(t2}cA;>hFSPo*tql{SZbH>SzUp8({}(+nnBq?eSoaf&Bc7|5(#(`?_pR#sNKL|?ZK()l5~BLt7)16 z*%-v(P!b2GP<ZA$mbZEAghtN>wefN$`v!oer|u7kKML*JTV zvu`Od|7f82`{f-?1o!&Op@moclyx;OC={oI+sppre|n+N%db zW-I6MJW}hRw{ib=$2TZJBTTuX%QAGP@egmXyd>K?5#pk^%roRDD*j;BE7 z_sL=y=HE&z6zQkUZ6>+J4Mon46wO>DL+1S2g6kWeIfs6uawA+bjmQ>NjvZQMWVkg9 zw>lwr9A+u_Pw}qtKq&JE-WseTz%C&P-QGJ~_@P9`b3i=^S2_=81H329GVcV2`6-+*)&~4jS&8wz~x)2NYNgbDzSPFJ&$xbR+i0=NU znco}&SRvSH4ejO!X_7-IX~N;256?{SgVd>$WH~(?|9%hc{%`Nkc}R|&Jgg)W#_tj}VnCYU zy?GY0)$B$EHR{+d=b-_rn9jxfsq!SK-VWq*5548`OtUf)-bG9jIL@%pb&vx$89#D3 zVF?a8Fw*e@R@fRb5UAcjAS}#hH+9q2SCbb&u5A*Qasd>B3+Q$4>tS}{{M%}>Ks6We z`-qla4#(XAXR|h&#R|0QbkmsU@^25Wa6R6o2;w-Cpp}!ctgW6Qs-=oEQ3PgYN>hz! zZ@e*FYKNPO`fIbqI!>6VUvH+WaucL!vR5<&NyS=G*0$VNV$mL4n7UBQJw)be97Br8@?SVZVZ9!?D0{Nb(8g`2h!c}UIB_2^oxXpd}8 z9`iBDk77m1iF3Mkz5aZ11gd5XlQyFKiI6z1@go2Ga)ODsLb(EHW!#`S-C%+pZL2HC z0O+Bc&KENe(M*-)+rVMT4_6ar7@(99Z--O`Jj@gFDUnqm@PT6OX8g$Wl1Wb%;&j>z zy9=I@94cq4fth4G6EdG~o$f-ws(6a%>o5@Q6Yg!T^Hwv?O^(c+_6R|yZEod!QxRo_ zszDFH(i+EE(nBG~rP}bxOZED@yp>`mFbpK8sUSEVTq|W#0qGBlY7t{yDrJ%p%I9`l zac0g_XCC;5$g@M*SDm;fXWc=Sy_{f~AWlhl$_mHMY}3wci*_E9`n3%@A!FiqU^?Ai z;M@5<61W?cl4KA{v{8V5phB~;%||SOGX6}eflw7we7#0@vF{s6v<~FCcXMe#2q=?E zQWMyKarWA3M728aF9Exlv{s<1UfaEcLTQH-iyPrN(&N4+WHP}Py;t*Vhl1T<6@g~H z#rnXW0Zy?+N2c>zKN%#|T&%h;$kgLV+qjUZPjHI@u4(KOa?a0beh}lq>3ZJA@%q8= z>8F=p2y$TZDbl-xAY52j6jdvXw;22337$Xv-=No1G?R|K# z)Pyp$9KEw1E>Yjr`S}XNN2x4 zDv<#rClgQ^XchrL4+*@<8(qt;o_SC94ab zQRHmGohxfDXh9nEI~u3HJ?}&I@D|n2q;e@2j%r!Lg}GXfO9IN0f~W9Z8-UjxZo1S}C~2 z#mm`+5SGGnx)=~`vzyfx#EmdR-rNb{eTgyI-!+*uFasYGrYP2oB&b%!e)yFYX7-5_ z7*q+TD`JDE8VDaU0g}LGPHKo$XeFRa;)68#C{l_kbJ~_5^O#ls*R=M1Y-x zeTa%CqQp^Jk=!~59y3ez4V-1A5F{vXQ#K1}Frmi7U;=b^Lj*ocB~EwE`1$A>0i2j$ z4IwkzGgNd;s$8v)5x@iAveVh5l^_{P)Wtef`6d~U`$%gVQcwnXHd;%8IUPY_s{M+G zmqUy1IMz`4Z6^@)$7y}w7S6szuH91-yeWyi-LG!8_VRHlTTS`{`kkz&-|~ifH3b zrE73r7PuQ}w-Lm7#z@Iknz1(VtthK^xRj0-*(L)K-RseZ%5lgi8&&c1Omg{{6&C7w zw{7=F-8kJ9sBDKejFYqggaZ^RnU_<%nYFuYy;ZB$Y_!C{?tCB z5IBOP4`Oj3))UT#3EP7k?u_g;4&Z4}*$H?w2vc{uJDAdEf$3oTraGVpC_Pi@xVe!e zvOEFVA`=ldcn4a?ZMNf?i1=1pTssW-nCu0M>wI+8zQZinQbQ$|X9@avO!Q@*$73IF zeED$4!fiFXAtO+>H|Z6BPxSHtD^?s|W=Vn#3RYrQS`@@{m(%3f6*#B?%A&$KRL6c0 zR?ry;9&1f{q2d?r`QVzR9Z8{t&3kshwoZqrkWz%|31Y*p8)_G-5bF3$4M9yQ{i=e{ zf{qu_F3DO~0tIeNq{HocbfqBPVJ%|Hw*|!yt^4qgaynbm>-7ry9%W6U5GIBU1%~4Z z?M-bQa>HGrH+sN+a?X|3$~(09pGGzZ)ui#;G~KLBl|d&nA?LjBF7MUQsUn<3X*lp7f9%m!^XJt0XO5XC#=4$1JK)txI!!1~Ej189A_9+|imGQdeS zEOu6=fBAllUtWJbk(Wquyy_>SAsdvsX8S>`NgvWLx$JMSFy#7>tl3aiQ0K0sAqQ$H zNjoRF?hiCR0X0m_w=%e$dc${1)jdq9MCXJ)=95=~ejpW)TJTD2rz>Jg;S_Yu1&JkX z9&FDbHzudMht=o9okU9m$LW=*si8q<3Pg_#;zWDBUB|nOVKA6et_1#@@KAES; z$%_&REUkLtksIyB-tF8CuLuI}(pDExQev5|DWG=4H6Am%lkEFqDW$O$ypHcJsnz7J z((SPWgpI!GZ?qXajIMkMe}|Uy}7+sa&T)yNib=%cCze zhl%AU@k?03>2HL4l~z9|+-!PQuC#ws z*x)NJ@W}{sCjG<{l)2&=+g)aYZoKZNy@5IYC9xZ{@*f%7`W(1JFKEpx{`SU?cCEVnq>s zP3Gd*qeAs`NT(oH!S3Ym0ND(Fb{@5Q_J+ozPdF{*&r@@pLBMvb2sUFz=$r3mqPPv}&W0WcE|{(b%}~BUB?cu#hS3+aoPva$r&{NR&px zWfB&RAaaY%kK5y0sRnS(8r=}Ud1_LqHnL$)+;WFtZ3~-<9y$;zh8Ix705|0oO|>Tb z9Uw)yU5}s(F{25wK6rp8acLOip7Q#b*V@nH>T)RmEsmN&e;-Lt5XosAtiNCUb-zg5 zf7+Pb+zX&V1rwF198)L&`Jwa5gH~E3SJK<8cst^bG}N8ReTf3UU16Vu)&2-oQ3|fb z!caRMTCbM?Nu#eFCQ*0Cg(uaeIqqd*&@g4XjtZ_L7uK8jf%cx-Ei!)ScFVq!(8x*9b$h&~cL8aLVur5>~PS0<%h0(gt&IiGr?GOW{s4tMJ?1+tWE}H8#u0o4En8Q>k%z;2zwEjoO~RN>CqHEL-bc(2A1A z=58%@Sz4K13FT;40?>g@Goj>hzaTqVXd6oHfq8UeAB>}> ziQC>9wYyp_ZkthJRFAK#i#*jktouZbe>c;uuON%sPoZBqEh(NA;>SMwwA}F%4a@% zN(WJ?OY~&>4nit(`LpQY-IXi*Yv2+l6xrg5P3AKBO!Fbr_C-%BxGl(NMU=gm1Ghqc z^nMRNO)Pykd<`>DtFWe;!f!ky&$KI$7~H}mJYBH}yk2D#m_qWd_)xRQ76!{jXT}tS zrnFSo@2~?2EQ9+bVRSR8F!qTCDO{jA>?O?TAxfnTVz~!<*P#0|o>qzX78&Sp5*tg~ zpms8)d+UGjtb)A*rn9A$PemyakVx;cz8+C@Jmsikb+m-4glqz3unV5NS>nUg7&{(} z%gEz(Rj{XcuKX`5h!Kha2@PTCNJ+Z9>Ox3_3TRtheFLD+$~5Wf!^Q>)UDE3=tAAqKbk}6_S#i;-=6N*WD8{1( zPl%h7XdwbM_;PcW;r<{;*tRp+=5dDZhY%O=+vswDn1#SO?hAqNWFo?sGX^*Z`vJ(N z4OyU?nZCxc&Oy#Y4nWJRyMy%zzPDtusfWb`-f7Y*9^uTnN-H&tlW3rG^eEuBk4twm7MK}-)iW#_Fq z1(MH<5*@3SZX$v6+KO-z~QIfww$yjKEWosVe7Og$-@4GX3b5VS+GC^DH>O~J7}^M&^RI8gu$6I^e5#B0cRBHl)fw*!kr zDBqY-ELoZ#^r&D!;L;MN608IF3TctqGq8|`$C{3FZ}WfSYDp2@j^0VA9GbOk+fxcp zIuFXO-IvgPT(SMQ6gC?+)k=pU1<(3sWCT(e zN>GGz2Y^tisVSjuY3nXyka#Wl%z;H&Kpwin0Wt#;&U4gT-Q6i$T68;7|HGwPJWohq zhqRg0fCVM||5|{Ky07zD! z#^Z59LqcUUzNDA91OYUm1R>B2ebR%ETASB0cmN;<{j&w=R(p=}N6E82ShFj*GT0PN zVY|ubqqRhOn-du2ycG>`h%mZIrA?>)*oh{$r^&jv%;Wr4!z-rsW@waRqztwl{Ao!#iqva>EHA*@Hln>Hn7^UtFrws@rm2ZqU zxmVNLjbNSk>at32Z23y~RP;8~ZZsCJ1VrU}bj|xr=uSyWU=coc9K8j{-VNPr}J2Clsg?$>0v9vYvUdzxDSBqNi{g4%lIUpHEv#Lg& zXs{9kj&XaKBqdd;z%VDxDw~38%{(;Rkv0}q{0PgDIzaL(bqQN|8DQ+}!7#VE*>K00 z`uJXZHHcKBEpOMOYthy27RS@k>?im1qw0!m(xp0Mm0dN{L^t@&nHt=4EQ9%&YAQi70 zxsw{i29QN>An@}dJ})+eqjv)^c7mNPdV@ksM!}jFBCNHH4&$-DB-JuMgatRQ{J$l8rF&a97g~m6|(s)5xH|QJ>Hw8i+qA$H0E85&Mju zvXU9F-|Y-Hd4Mg09Bbn}u3|iK75Js3A%a+HP`Obd(0}Cg@-+Sd2B=$zm*a~1ADGl> z{cDc*a6NXws1uf3X0_5MT_#ubT<8uW>?L|}V+rn1axT7t0j7iSKv30mno*RX(Kk0u z#+t3s!B%gCuc?p%{X7dWk(k2g0 zlmq}3II8j9?tL(+{#PHn|CRdK!MwRzdmwG;)EV7;X;Me!O@4H6Ce2QphVhWf6AsiO0cJ3bL@UC?j24lT^}A2QhYPa$QGKRZ4~cNboOay^@1J7mbzV!K6Lhm z3TVT89Sh(eA>v2=cHpL+HF2DIAOTbK=cuk(+ahsG$dE8p+tbUPX&Xl-71rwrGc5r> zq5n7x?w_392ISaRIhDqo7V%r)6kgBx1BQ<82Zlcq@jc4+0Q(+6WT{YCDmohD=9;(j z6>nh~yW1>Kk29hIhzuAIOX#PpnW1FeP0Px{Lz`_;yJFj$@OnT6AuW=$n2Ta)ut;IS z3xM!6(qlI<{KT3-6|PrWL!Q@{&-N)evZQX~QvAB+GBlFahHqZT10{V7;P5xv$W+!Dth>-YN!fq|VNZr6m%YHnZ zI;y(6{h?>)rU5WeE6A;@;)%LbxGJG=Z z0D3-8^gDpl%sX5N5)*d_a}ov=o&^2@6lwFLYec{fvNCc)5lf<_ zdfOL)HaKz@#gEmbM}tNHEtYK|z?@wI$sw^=1WSa<2iR^f?E{Bw+)WKmhe}h4oR2=5 zTCY5#hpF|!F*@Q<)YLbdp=alC=aOWdihbN{Ifo5xHkVpmtF#lz zbL`>moIbo=*&EXxfGm9Jnhn)k=r9Z4jr7TMw|h3s^$It)h6EQDi|pxnRx=J)DLd9L zL893(4Gd(%Ifz!A>89Caxj|2P2f?xmO(3`p^$56W+c|!C zz8I0FZdzSy7!C6@JX`U=w!0Axj#fO+D!OGiV^aZUkIL(f1pP)fEGST@N1^!E4@UJr-RtC>c?(m$xfgPh(QuRm zFZA!;M6d^=rl4p-)w;R@%|a*m%hA=Sf)XE72%3kdZT+yRWb9vZ@&_jF)Qty=2hw`H zIK#StxgybC<5F+-5+DqjIZB31YGzWJm6q3#p+Z_fCS+ksBv$&5zjcb|I0Y&T7|}Wc zeNjQ&1{mdtRJskJV!>@a0VTxilp!ujgpc%*4%F`hLTKHaRQEY=NdszOrA z1`0K=U@XXS0oXbpW%uC%XaY!XlExm*vgI+)`Qgm&?UEBbCMvMxG^b5-;nB^gvBS6a zy3Cfppq42?L00)a6vW}$6Hjx#0OywOX*1-DCK;bq&-uPB&piAjlK7RPNYAlcWkwoy z27lKAd(3WX`?BuRm`SqEIk}%I6ul|B6r#Mm1Y~sF^ff@1JBTi?W*lLS_kTP%? ztk68{GB6UY($Gx?=QCYk8XZcfS?P|xpAKceGNHhq3`~U#oVI!WWGKxinhpEwA}W36 zJt0YkJ;ms|wtCoZCO6o7KxQ*I6yn?lpB5MQ(wq7%GGaa)7DzY9lmw~|I~2y9F&~9m z1oUZ%lOyw!&R0;+K}8I@y4#?wP(-+NmJWNn8^3n9)HD4#INpGmR~V5UE2JEmHq>NW z(--_N%wlpz?RGu7S}yQM?9;fd?OA?vYysJXrFNOrDt z_;E!)GNx4Y(2|k*P05`*ROfJ_(+tq5no?ckiJKZtX^77yc%-Y_NYa%PkW=|E82}Cq zT`ADrT#w)sqsLc@c8b`!bP`NyKk1-H+fIkM&m+3d%Zkio733VBn@2t18e z4)&UHe9^Ikb}~VsQr!KUq6$K_bGk+CNN+_LmF4J5;IL4Kk*+O%FNq$Aei}J$E~o3gW_<>|7xxZ% zlO8!56}uH6JXMC4g5@P8(0eFie3n5PeD!L9@(?FIQq{0Tfhq}17|b6=FS#h~e00Tx z4HI+|7;Pu!m&2%#9y{-|6DEv8G+O*=9j;2`K?OT%_>I5@{Cq4jcjRYDMw&ZO}(3pX- zgnD@d4C%N>aDF#KdMW(X4r@8I2Hu|A_}UJ-T>eQnG;z@N4D{tZ9l)^-=6i#($VCo% zi{^JO=|@1e9h((G%3UkeWzVFT(~nfO?avGt zF?FIT-k)~n^$_Ff29WC_>yg6^cwUiL0{ayxx4<*yUY;Y$PU*5xA)-pF87II-3V!HA zLA54rM}WcJ48jCN4el~37%9E9UXEg8)~n0Kg3;C$eWci<>W%UTcCuYYIUMMvYM{#> z7?b;v+`|bmB7pGe1-lHr(}xkrFXuUv3WZjE5m#sTkBt0dpv4$$`be(;JEWwrd%gwO z??+aQoJXg)GeA%TWWWfyg5`*)f_B<$L5F}nU$G^3%Y|H|!8zw9e=FWLKy82sACR2w z6co{}RMNLa^xWimRKiHz-{Co!!K-GDE_!P0BT^~=Ua`*>lQSxKVnaBk0x7ZF#TG^8 zv~8l#>WZS#6eU3loT4O0idphNCftTTJ0Imc)y0yDn>hv@|LJh;oJrR10Z7o$vO682 z@jutBhsC4Gm@Hc=bv%mTek9v+beynIgKdbRX$v>TI%QI2Fg^{LOeROJ6%`8AU1BK+ zt}YvO>jvn|XwG8(D+G2SRP-hT=XoE?C!k(L&Y1k#=*l$Vx|xQ$VEsQBP~+)Sb8D+~ z@PA0hnz9K_A8%76G>mWo;bJB1&Ph?`04r;w830B*_Nj?o$XvkxF#~T4Sx(-J#px%j zh+Cu6o9>`Tx!>h@5WY66d+_8{H$KN`4hd(-o<#IxKWcc+`|3D@?;>$=Z|2J1s(a=p z+StRF`DRmQm&bxGa;pW11`{n7i5z8$UMLd7^oxFA1ddyM+p#{aj`a=ZEw&60Nw1Vb z;e9!{$zVl0eqsY|P*){7dPMnScCItod8v1@nnr3FS0uX#b_Nna4rt>yT}q#$73TAB2iibV2jn!H#x-m}RKt=p#)=%u zV_)=3U&nRoM;z#E5a!j>X>iV=X(vAetBCbfYF*c?4d z7szV=k`i*5A=VJi zN}A*(S=71vrvt!2o(qg0h}dguU+$zW1o#}S0)Vkl3bLS?x2TSSVPd}a%RMjU*ctfm zaPe3qS>vKFgUj&Ctx+`lxK`1lQ^G*QQL0-xNo!S5HFGi3Kp`aKwYtBo6w2KsS>z_+ zpwr`$jNXra-WuCUIt5}eC;$CRf54)GH9#z@f|Yk6PZ z8$i*IrJm}AU3H!M5%Dzcru451xd2ru{H;WjAF(C-YG&%Ez-+ett)9rM+crGxntP~B z?DxX`H#T z=x#k7vIl$&sLJ5h09j8GqPpHuUaI5L$62UZ48R1vT(LMwj_g5Z42vJ+Xr!U*fxC9= z8qL`%*dZ$dt}e0BF2HLVMByuKc7o4`(@kwLQs}AC?|_e>{_Lv(XCSZhgQ9X=l=0&P zDID5J6foZ;C_xv4nu?qn*;K!uk;P$&ExJwyOKc9zbUd;&%BsNE8!iglE#Qm&PbICf3{te3AZ^df};?TErDqqXieFao!Yz^ zyo~ru1!ja4utneTB=>8_H4h6f*4XWOq(x239k7RqycNx~NoZ{C88bZQRtC{|To(?= ziC3AI)c8zyT4U{#!cFCMnL=p7jLo*q=vND+Kkd09Vt73}i9|w6lE=FWwES%Ux&ve( z5f}BNG{Xk*RUdO}^cjizhy_2dShY`sKFky|wd^L{?fAJ#wc@E#0!uEMh@2Nu3S6#k zQerk6Dw?vfZ!p{i%Sy8gNdZIno8WHgHQR%r5=bO!bp&vLfTG*XIX4C27DT5hy6(WY z1Ue+=7Hzs4fKOtBw~t3KdqU_05Vey52O&;<>SJ!*wAc+UrmH802|B2eIZlF3BBrS| zBawy`0XiDP%pLwHv7+HlbnE!Q@j9I%rM}p~B>e;KV6Br=bwu6?iel?l`=@A&fqB1r zolv0wcCNdAjs}I)1m*W4_|Y>KLRfH0lI0dStG++wqtgjZRhpv&VW6DPZ;u1niTUr}(`dq&S&boUDY&jnFl*jO)=gXpHj6QQ29Z_=AV)9OszSiWxtVp58w& z^Uz)DV1c5RxD(`gz0aGx;|-#7O5ItG)Ef|sk0?7MTupafTtRAYhp5wgn?F4f2w*BE z;fHAHQw$hc;Pv41fPF`Xe1y8U>lGxV>5881x0(li2h_=oI-0=rvymsrA-pG^EXb6IO&MI+31r3e0%NAPx0eQbc9&tb*qc!22r<2t zR5VRebno<{xE?W}MElGVW$S1op9~cIxxbGA)V4cYr0excjjrn+XTXrKO!DsL3Z(CM zmutVqaPD^#?AHSlZuT3hc?rOMs<2W}bwl;18+{3aPfQ4<-ts+lYRk_$87~BcBnw`%DE_D?#@+wDF;=j6i zgPVp1w0<{F#I~~7T460ZkM+DV8nHA9vXEU$(W}_K+4QDtyBH^E&=TJ5yRJttYEp&) zxe?_8M8mdy!LvgoKf0IHeBQ6?_5KVYTd|@$AQ_MLKNWR{GLJbn$t` zDuODG0~)+1?F;++ftI1IXMzb5ko^sezq`V1B%!3B38jOB*FvNU5xD696KdnaEW3FX ztD%+mX|~y@a|3UYS};h#jz)vDV9)|62QGhKz0uF_r05h>Hy2O_bvn2Lg`kNB8N>2^ zZrUgK8h*+1{KScJ#$H2j3Wy!dwJi5;DeK0}?@EhV7kcEb0Va1OPnzE6$+YSHK3x|? zgzttAOITuVrM=y?`3QtrPh(&NcfYnXP?X!d*gLo^y6^9~s}ok6215rzac5$^X*Dvb=z9()zR$%jj++HsG`eMqB# zs}Lc8Q7Izul(cr!#@cl=J{xi@!xj-AunePlkalI-#d3k&^{BWV7;rAQ3v|2YS);Q3 z8CQD*oa-||zdc`<*(47X$D9v+VGxagI-sl~B7oDa zjGKuaci1Eg(e3e?CDZeLnA#P@1Dmoe#_a(^!Ync2!RVjqRD>zgBO7?RWP>=zm3*K% zThx)KVHg9NX9KKE)g}ne8JLx^fv%=LsDNN4e@Lg=u&D|FI83T2n=$3#G?V^1ivq_U zxh-B{2-~B}o#fa~SZfRPk|na-PU^at-3>~oWMA}lJ-Svkc0j;3dQNmp>8MVfZb;%y z9KW7)rPzrGQVE>sOO%#DU_l+h1E}(Lm*UsO2r7e6f@3%*^xWXCvMTayim7BK<4k3? zUC?2Md8~KG%7~h^#WVMMy2}b^I66*qNQbA+Ih?FYn!OPccer*g$jvOqfZ=6 z9`s<@f=gesR2knH>`R4lq!P*Frmd{mbj?{i9?m6}v|XZ7PvcBu+TAtjr=x4h@_wID zGYq?K%*DJvYnBO{o$-M2I<;Oji0rgKBQ3I-rF9QG6?!B1cUxpNUjz=>hsp3M?VKN0w!0dF507LaprlHZh9_(7hDP6^eF&IFyZ1<^jfRrXnOMvks`xGd+RdZ#5E$UEflxVtYCuFX7g zh#%PzoYd3EuGh7Ek3SJOfzQ2DZMp}t2x$l7;%g><9Drjw29YAfdj&yINol~qRFthKMZQC~XvTbWG+qP}nwr$(CZQDBg zJGr^ZdAaxDG-=Z`P1>i)bf)wF5k}xO>F#%szY+w(k_Dgb^2e}Ty8z+6j$nY?qP5FA zd=iejDE$#U*-o`gIpc0`d!xS%CW%)RoMJ4+%mK$o+)m{AOK9e!U-oRdKJD=hd8X5) zya5j2t@hQ%HdMRrs*B7@*rh<`6lmYYEAT87xP@}Phh>dq#3PET@`Ng^)N?F76)PK% zSHbe4!6BeUZfcwG?ki6w=n7Hc0L|~-qod!3`Y!#pX&hr}4W~$vVZ1RpBEpYFd88-d z8Bwp7A>qYdUgDPhN&5NP2(=oq8M3lO3#JTcy z-@SIQ7*(`%xmzQKpT2^$r}K*MV{<+@olqQ4r)jSsOG33k&=X|fSL{3l*7)f*d?(w^LxKj+}wjH{ljjt#U04~kUv`v`1= z{cvcFh_)&T3m1d}m&A2$?UTA!7?5pGt#ee-bs4e1%(~XUaAOcr0L=hMZ3rqtQ0Rd2 z0jd7}6W_lCV=DhsRVo1^K2;_;p`YMjZe>-GGw_Ejk~gH0tZm3VgiqaHGaMUUxqncxH!v02OaBUri5dp5z*cq5%>$Xih7D_)n#Cut0V>vOMce6ZoC2;5kX{|~u)6q)j zx8h_-Wc2`FKfTvwVQ{t;o_UA@V;>x@icvf6SN@gXM zI*`N#CZYHRJI9Q(nskC;>yxnBW{RW3=te+ulg238?d~L087m^3e=Z^A!lu%(vDg~e zTsT~TTb_+4FL0Ga)?Sp@-I2570i~Bt*7s}^e)#; zEI1aU3IOUYf~%b+ZH}2^UhW0WoJ}^I*yT6s1ig~r^ciK<^exqA0M3S?bWPB&ax>6- zuPAxY#zzP7{TY4OVF}MXXPb&l4|7i;yrNx45nmiRWvqj+11vf%K+ll97!p}v4%`Z2 z9R&by;oh#H1xHw3_5es>D$`?d;SL?B*KV1}7k(o`X z2)>ZWrn^C;9!4X@pkR3+*h``iGgHK8Qm)3jfOSy853TxkO$4OYBMe7=6Di~f41ohP z#UvFscns9qrgX&94vQ(?(Ea*O2k4=)-&V?zoJnM!9CLtQgXg88iZSnT4xEMUe10s^ zF8KV>g{R>7AV>yRhiA_+WMWQ*q)G?)U*Zkg(K7Rl?BI5^a*Wzfd|t_vTJ#U zS){0?r_+oQ9tggw`CDYGG;Q1z&IDq?3W8m`g^P=GA$=qaWKENCtr5>NBqA9E6akkm z!p<47Ynxsxb{nfrs6!vA&kMa{Wn~+=n@ykfflH8s=d+6h3DXHES%2%0m~_D?-W_M8 z&{c@E7qn*{WDV!97uqv@l4r_6!+Fo?L|{;>G#$3^dm8*2dLaA0AXe4$-X)N=qt2 zX!(oW;V}^MCINSS;3NCH=kip-8k=Cbfu=y3)nkaqD$Usd7_j$l-^cfuN@Ehe<=!Gd zeR(<(qKueD;5}xuu#oT@w=FvyhKHIS@Ii_vgoa()0C@b=W+-$LYqk=&*P9$f#uB~57%j*PJ}K!b)I1Mr!v7};lS zyLJ{1$+^M=utg=!wr)rA#_Kxc5i3GxMT>>GkM=p*aAe5)_#R$~mC0_}0CF4Xf!>$SG#?yu+Ib zf<^T^C8FMSUZ@bco+h5dYW)f_w{Y?_|AL)zyY#KuIB;oJ_>xPXZBI?X#>PE^gB793y z5b{HBedpy;z&|cT_*pcp!^>eTux%=35uGq~Im^r5mLm%hcAF&Cw zm3=!ulWerryAf!4AM`J?QDtl;{wG~$HISQ}*Q*5>iA=gR-i`BTm7y0;9P{jtwjhn8 z@y?m02t-2M3DrFTj&mx8s87hIRH@C`1kmoVYyTYfa}JktSCMR>VuXYCT@>QbEt#ab zGN;%5irie>xzt3<#rrJy839t=U;ad1R7jL{X*pQy&dGN0GU1%xz=?LeQvrcKn_y-n zYct|Y>y>HLet)5~6T5yPN4u2%d|P10tiIGCOq?tU6hEK(6|_?^a^OZmeo6EAPaUtbdf}0aCfsZp*Qxo z;Tc;{QZaY(=C5%6!Dq9=s#vz;ajYvjIY}1;bMorsg3Xi-kDBDNIYO` zkV1S+YgyPMx@J}ErW=hg<7gYCoG$8F?`jr*K9kt%AgNECa zy1#Y;dMokY(H6Q zST`q`9fx^;37GhZ;`AJlSA5_uad|Z(7@GeOM~J860aUWV){+;z+S%n%VE+-X1+gr2 zh0b3Sz_&#r3Jp#1lDL_Rmd8b8$bnC_s2Y>?$GnVV5$To%Y!w5?T5ljJGRO92lLWPm z_#lzwiHpT9+BDL8q!PF-`m~Fgepf2U0v42BU5&AcTcpkCWKm2E35%UW5>A*)ang)f zn431sK3pcq8j4~-iUiP?zdAggUkD2ens9eb9X4y?Is^Fq#!Q?N-m5BA2qImC&$Ffp)(9K$In z#cAT^xKa$m{mZ^&BEN0wBu=8Ys;SXchy|*YGaX!IN-AX^YsZ^K`X&CK_@=>&h95X| z|NC4Ba3yYjHbMJ=e~y|1$M4n_TxqtiO^r77DZWks=h@Ss>3NDQU(1Y%#cA#tpH?=X zV1(iFR*%~_dnX7RY?jiH!1yy;-|Ll;bRjaW4Ie;?C!c#eH4bd`DG>#x4IePK!!y0e z379Za34&__sbpLsr*~$mtLVkvVHPMDn=x=BGR@plD^E3Xh4WS{+aj$&ES~G$Hjr@= zXZs$-{d`p%BarBY1`?DpC0sSlmg^3Zr9JNokr@Y; z$wotW(K{*R(M(R{O?ki&qZKYQ?+2CaTeLnh!Cp6ztA=ROc7GlMoS>b;%y(pUy{ucf z_uHVOFceRg62hCv12-T!O$9e_#K$1Kuepu*efgp7}(eG72M9I;0%fg zSZn%>{?TeSq%MBFhq4@dr^N9lLZ|OTjvs>ctPFO!H;W^yJ2RWN47Jz$X z1rFf>$tq48=^r6RYor*D1`_5x#3T{WD5utD&hwkZ+^uO@#Tg4sG7JQc3TuB@0;QbCzU5gh6a)q$OE zpiS5D5$YQ_VrJf|B$0CFPeogFG?+I-uGyJ!o)*W)gtoj_QXCXCXt# ztS-Ubk=1%twADbMvB*Tt>%VnH(V@+m_o0#Mh{cXuzJpZr)POZ!^WNzoz3 z8gn+lH|?rRO@XKq(zFf#Vu$!PodLpQ1eGE`y#nvJLLDgFVti-|vrHI**`BO z1YTBNw8A{oYa^_y=2c!Pd_Q85ODN0kUbB<^90hD|>dHZKwkGoh4#wlj!C+01(oAhSMSxSY}&uyP>6fYY>1)2McM07jzZ36--i^ zf&LJ)OaPiZ*V-Wt9f?2iQZd7^SXS|qpPB3jmU*op1qvK~{_W$e24C~l@dA%&r*3Vb z)*=vPu1^jSW&e(3AsxgYs-ojW_Szo&k8@2B2JGYuO+S_{9|-S!KEIx;5L!~Utvt!h zn}B(w_XeVX#UkA+uTBNlkeApJ)!ka&{^`Ap$<9>qiKj_Wo3sfs$A*a98^L`vHE0aoc zYPdu;ZIVnTRuFMVMr;aS*?z7}L`>LhU~thpK$TY$<4hZlN}NwF?>uc%46)Oxa65B5 z91$Tck#>qe%6vnsvLm>8S(b#rLwEZN9*e-++Btx|$51gCyXQpi1!VGhfl3o|z5TP0 z>9+&N{dByAda@G}m$SnXi_xeluX0>C-inkX@*+^&+J7{KBVHQCp%%Xg$KJdhyFR~0 z<#zdKc%-W@KRFdi4HM^6$^r|%9*S2#;#Z!YWRnpzj>>c|My2#uCHRmIUYIszn(&i` z2)N2U9LL^CpWG;}bx`wt16&dJT` z{|Vs!yJ>UCis1dKb_6U7sOWlLErX>uU&Xct(isbO_zyBCAuVE#{d&ojP}OXt*ub`) zM=^)4$;|DzPso8|PAsC5s#v??T+EuOl6=C%;Rrl}mKm6@byBglKmeUBqHyZkpjAF? z0j_y2$$NP;<%%XWz1{%ov*AVIL>kdd5|%%(p6tQUWXQ)V>=2;M=amCl9Xjs{#r5I9 z_Bu2d<#3&xSbp=3)BKTUk&OngE3B$1@sXh#OAHgWWkNvw9FNMhm6bBxOtachHWZjO z7;TtqlGruzs^Bs;%)?YEvG!DC63{!)=*)uL>5=sjem+y=CaCd5Kj~zRmlNn8j!Z@M zo(Dn3v>0e?cXixYrq~=w+@RU4?5+_;la5uV?n>P1>BPjs(9(mDJl9}RKQ3-jIV`WQ z98rHw^C5IElcEtrOEbY*CSp6(!pS|{Qk|W{d@v zT*q%~Lr;S^3RtBO`!^gtGTDv8K!j3bN}S79ERkV4Dt$huWBtUHiWtikp4Ri*QAp!= z%b^*0Z2qTxW>oeyCPel+EhO=~5qFV^0imkQrRUR>t7Il|yy~}XQu4g1v(z)6^ssZT z?q1vM{EU?Nps#;y6Qm+vpFlcKgD4~ccU6%weSJFdt1@o;&*!Plv}eBdlg^o{H@OVw z<%$Qrbl%4D4m5I^O*ibAF%G?&&EV9lW;A-;y^@qDe}? zU-GP?Nr6*Hku!B1W8CTK(K_3ck||Dwr5%#1z16p1Z{gFA612Y(-p50k z|7w)N%5C5rWw1&<=%t%g(`@tZh;d_!x7|Dq16s8f-KM)kR=1EaRJ35AscC|dR~pPL zz?-2KjbR2Yf?sK5Gc5cx_#$p-=x>fim(-ViqupK`dF+LM_%41I6n1-)x)#@WTQB*_ z;hH+HWkmAr?{?`CZk0&Uh%8&opVP2kc|(EG@w`AF5DCFd_i(lVUHf_L@VNot`-$%` z-~xqM;c(Dnz(+%o`yls)?F`yr=t8CFwA=8s@NEFtg0TCe_Zi)Aa)4urJnp>Q0J{)$ zKROt1+f5SfQ?>I2tAt_WWc!NY`xghdCz=KI6oMS~a& zf)Gig*!6{>h*dG>2Ow4#MB75zLfS(*LOMgbLb^kE4`Px1x6I#2aqP#!g=J|R3gVGt zv%-#sNXV%;?8moF2qDCr91aqZ^RqCHg(=Bp+ebqxxjMjdjN28AcdU|C=z?!}$&4@b zlPRmk2A5ybu~e%YI@aAP`7GzNo`(e&eG0crRL{K~$Sx$m3vwGw9_bwJ6Hd-|jhYeD zQ$9&X@aNr!(l}T@B7WfiV>+P#{?mx;_GSNj@_z=Q|BdN%FxEG+HvYd>)9x>p_J6vw z8#DkQ$PX|8z<&?_({3l=>Qi=6003xB0RTw-Zw@yx)^~DtFxEA+b#(gwb1b*D)f10K z?0t51`_X%&Ipx(~Gb!-kLduH+w8mp22w6(Vg>R@#QX8IcwUfc+)hYhWjVeM>4_%*w zbzpxJQa?^M`g~ra>-v72RNCtJKDF)fzPtx;xKaSe=zCD`mdcB@> zs@3{@o@cV{`aE~?^}HV>p0?idarb=hpU=+vz8`fy99;U^#`bW3U!M;?hMv0G^0|MH z?)u(etK~L}>Ha=EzRJ<>eyyr~Vcv?7dw<`I3Z0Hi}%F-Azv3tY&jv zzs}z7^7Ve-%f<42JN$H>SpLkO!vA)E*6?w6f89O33?9bHrS>%To<;WF6tGRSYCpk- z@-=hwc60yQ!TrA5@cFoX+-q~Z-HU0vzdn@PEM2%~oAeYd z_4#?*zyDqY_ZkhMxz2uA-t6V;?(*fllK`I;zW+T5#fL)Dqz09HADryG#qag~Ie#DI z^ZDi0%C20*`+c1=mx~SMz3F7jwcT2~{kea9z4gJ;;l$r)GvMItk>}{J_x5r-Y&bfj z=ZlT`8KtMk-|6<ST`w|F{uc$oultJQ|H+wsuL`#rl^S}m1Ws2Mh~4=a9rhgRa{ zb+{l;(&P5If4sf>!LZt8OcdyB*y_S0q%+mQ{ zqSNwfpyO{j@Q!tAKTbbM^qG?IivEV2!1z4X^O|5Y`3O0@$-q?|+MbMR&AN_*c;xyo z>9j{aVJP*pQFz@uTP{0dpMO+^(=wrLnC_`U2t8`1aDh<=ElbS>0yF4JL#L~>65W4l z_%(h+y@Xxb9tZ)!jrR!wxp|Z=jy-@7A zlhti$1RE*N9)uiqXpdXN=>$`y$@pjAzQ+h}en3ZuBmXF7D$q6B_Y6Q|67|?HP1Mup zibDtrl(ZvochUuD**S+@1{}!~8_h#(E867ZszBO+R$-R~tRw-4w!>e?LRt)c+5_iL)%|DKiEP==2K(Y2I&$EQj(o5+SUi|G>6^R3j z;4jx)r9-2ggU0(6j9{^@2x~Sl1_b7d|EEb>M<%id#pyqUf*42{#+{*BKTt)X>(2%? zD;xGEbTs3`amSTfAI8Hd0pBnQi`m_>*TT5qI*GssK%hc-~X zseHFpF)@|2_Np7P29*!5V3HFODok}MXbq?Fs}NPLgfhMu+>$DR?k~#R!&JP{$Oo&T zG#d6TYEhK=e8E4FFr{i&Y9~3Tvw76r$&X+=x+)tb{}~(>Ysj;naWPO%cP}A@te~Bv zzr6lOi=L{${`<-(V-Kw&N_S3BvrPkwbxz-kqxZx{j@mvkSk=%jRyY$2L)8LNV-Q?#6GHFlM-Av-r1x5AWkN^ zqW#pAlj~9fIeG@_!~_dlErJ3eMu<|#W5$mYsZD#_d--;Gg6d<jPe zq^%5>FqgN>p3|w7yS~s)HSnyQXFE#e6!IxxZhERDj=4xu=11)NV6|gpjHH@lZMb-2 zL^XnMlhxTNaYx}(UmE5u%Zyn>TkHid>3op9|l|1wj^li3d$1Mn#@UXD^xaPu|lk0t_`JOCgC~tnyO78;E;>caj(=g z1{zUr`ns!NSGLw5nb`0;RSE?rl{HGTuQW_8W6maR?bpjqU9%}t9225IzXJHWr`$P~ zXzh^F{ewl8AzSYD_}oUBAl^bl{L^o|f1_aTuc{}L+__`6j<-2=S&TIZmYB3@dgD>( z+(;qN2%TC&6I6S<7?*)(Vy8i@lG${wJP?lG_iVl)zUYi3minj>;E* zxFkZ&Snr}>(SawMN^e=e%0NXG5QKVbL;NxQ&FOvJ{+aH%5uJCg=H{~l_#i_xLdaBW z=@`?#>R?>a2w;wX(};=QqtrkpCQxo&*-1aBCVHDXr;w4Z-yvo3-me!tU)xS*}vG+N}$X_wM(4BoFYwwZwZWp zjY#>gSQWmq)AQ1inauLTWOOUDgx|^TTv_0Y%~5o3D|Ze1u4%TtO^$k@UE~Gp9A@9> zz#Gh=GMQ3Od~Fp)$lA}3h@#HAAo&Xu1&jyI>2GS>ey>rUMXGW<8`c^+Lwdpo9D52D zj_C)(i7oWPRW&E7x_#rb2deixVqlp?t?2nDAeMjiYyG=(NPwv%jV9yYtd=&=tCa?P zqDm{})}p`;V4@botK@YezM`MZg z<0Yu-q6(k`3s-}cFa2O0%AH!nm6MaS=WlMBUPUm^rir-qcs~F)lxHAnu|{AC3IGp{ z{I_D9{1$aeV7RQ3sJ@SH8ohro&a1wLEa>JRs3?m>`RRgoP^wK>Bncwck$DR`aR)_G z`*^7Alm>AN90@A()jHO%5?5eppe_JpG$oe&b6ZR50ezmj7zmK(jSYDA1oX7=GpudW zjRxbsy0nTgt9C#UR&Rwh`7~JyI{)e#i|9&?qecq9Ge1<<%ONujaU1`(0pZwjf|0;B z04GIklY>f>fK_h7T7VWIKk$T3IAdKj7Z&F78ObP;+$*DJFmE?EG&N8c7-+Sp@-uz( z5lh5=X`zw|-7!ify3vplZfL7PW!kh$$o}`RK7Wcx6%v)qR`XE!Im0Rxrd$V_U+S`8 zEn*dufC33i5nUQ?mi^>6d0fpzN2&l^;AJiKO&=^b19=wTID{jGs^U{nB5=VsO%sJt8{QWG!Zy&X&n35e=#h zpbU>~#yoF*yE_*>L~?RK>W803V&8mufQd1Z3XrK78yDJAuM7FAwq#v~#&cyWClIM(umxNTdVsDXRA!8Gi>HRjlhH*Euza1~g=VN8>+qjyxp1KDUAd=O^}`ZgzbEDc zS5Swk@Z$|EuTQOzvTPA>emN@92!*^X3^T_ufa*?iC@#(2a5IE;W>d^qz1kDxVL)5- z7P&MgjdaV{5*U{55^G3Qj5q1UrK!SV4d#GkMq2zfszP1HRQX_eyIB`gtOP$pdy^?{ zuT_1Af$4dZ#5mXObGX3Elao1UW6L zlVn7pBy?gyF`W7{=2DBK)uo}Otc{_qKhL?jEjh^{=oyMs4Sa0M0RvzZ6Cjd*8JPuXn1$W?C1TrH@nO(Egw8{ZPtm?^YQ&)`ZR61#W z9Lz7d?YOf{)JsFNN-&|vt%ip=wW{IrZRu3ujMY|B-3AR&gX_1cf7!+|d6AP}^d!iu zTMlSiuWh+w3EeWAY?4#j1!j!W)=LZQVYWUuz>*TV2enB>kXGT(VS1=BY?<}}?A}pw z>xKQ+k#b#=9*HdRHLXQG%G>fZ62?$2M(Jc%u^jb_X1tWJNh`2Z+G^r5sO;iSdEGp) z^l|v@ZvWGrPjGejg;9ZwFj!9JT(!nxt9*B!HRr?7G|BcbBLi#lLCM0m!Q@xX#$zK!SaxY_ZCeA z(!-&^!N?}&SyXEPw<9?vU*i{EhcZB)F8xh6j?a6fRKb`Xs$ptsxo>JM-_p#-oQrb_ z4E%Q*w~lzKvB|>yv`S3Io9L!njVZxxucSvpktoI9wdUJ8#Q5AH{FX{yKiInq#|pRHdQmx%BGT){Pm1hA0}BcIEMJwb6j?a7rOB&OOvSJ+AUr7=Og6xQPb!2L3!Tba>oZT5+{%ns#Mo7$Uo&bcY0y)*2`y!l zzSXmi+6B|UM~Mj&2RHI{9e2_oJ+W-?{&Fzf6pgQd8T# zL1kR>GK9cD2Bv#atd_5{Vz`#tnuRV3)Ciz6f<*&e1;LC}`0GDFbN2`JhE&a2eOil` zD=e&|$mU@=fw5_MMSUKuwoU!x=Q$`}V7K6Cp^E0a?$4KZX9Bc{fQ8kF(0)SQ$bb@V zQ4pc^5GM08l=HauvXt_92&w`k#Q6R>L9xlqngXS?Rp&Htph#G>9d>r#g{68a%=CK1 zt?bzY%p|x3cB40LuEijolVK+Fr2V`kjtz9kcQd3G3&PN<&5jHQF0#1DPX|7t!ahkj zWi^Q&q?#GEiB(vWNCM=>wYIbY{za`y{4b3`j1BFE)2+{Lcig$t@3ZS3PYV<6yfJQ< zQ~hN;bd4>oyeweFq8=%`HWK%ghe=rXD#ejhqAj_c7=qOo29ZxK&$QFgOrf(BP(^i6 zDNhG-9~p!c74aDzN-^BJAd^NB1>sX&Por)X(q({_!mh3BW7D9r*A}I{=oSvHg>FS3 z$0ij5l7(>g1Z5O`84~FJq-8LxNSidSM3q#ql+41{df?v-$ExRX)ON%@p=-ig`CbBn zav3Vas&pkHf6hM{X;D+IKgmqcLJ4!2fiCgj-jmwKx#PY?)#e&X3%G?y^*Qt=xrP!a zYu;LhK{%th&_Q9!aVRS`(7mtQtr>&K+GzVV?;=(KGFqD^B zc{)Ir5tTsvRqwHL@IOUEC0l=)c8>`Hs|c!hk4vlXzH@jJoW*zYKEC?v;v4;Pq@Zz9 z;6yufTm^!v>k4RA0ms&XvbymYP&+aMmRJlZDnl$SO+~5!RXvZtpN!|qKZA=_ND=d^g%>QSJJf}#-GA?p>Qh(;QqCg$a z6DyQ3;yZ@3WS{%?e%0c|OzGlu!gw`K=TBlBv>|Lf67awn|2qL$yfN@0=|!Q#?m?i~ zk%0|^n5znkq5c?rlS$ocx=IU?0(!7djd6PjuEMO+bKG3$!OekrJoIhfVO|3UacQT8 z3V&iI%CMB|`VWZAxZ9h;@)yuJ-=M8sW|5xFH^hYw>;?vpCtQp+h(*4fe`%7izE)Q1 zm;_jKTYMLoGN2ARRzH^V-<3rm&(y#Ub;(u9XK~*GR*fGA*uH8Ve`}6W;8Jm+iHkW} z!3>Ic#)JV~7PJOl0pV*u$g}6!;`g;^)1~K@w=M2}N&Fb+d?1Xts*l>%98DgEJVmc+ z{${NZ;sHR~u22^@L&r=bWi+NL8-OFboyV-hvqRO?(M5WC7Vd?RI=&};-#Rj0xj#qc z6o5^NZ~zkW`ASDM8{vOz8W%WALgu9M*btla;WY+tc9ek=W&9>vnn_Xu%mO8O&7pD_ z({Iz-DbAev9VTzLBFuxgnT2oiJT&`(U8d1=(|l^qg~qkfWrLPHS!Q$I0)9(k;ROdyw` zx+5iY8(_Z0q=&q_q#XVPd1-szaDZw-=q|c@6Thd?A0fp<@z^i#817(D*dy%H&v~#3 zi7R-A8jPv!fzM|LTi7V7(0AVd%B%UL`{{2+o}}+j!KtD2#OqXO4Pq!wE7x23r%<2( zZCVK_h1b>yxDl*jm+%8#R43pVxF$GpDgi02J7nE7ET4x*d$xjRMccTF6sX#MD@%JI z7@*e<;4hX&Zr|!gltg%>RmU7aB~P#ch$_J5>gZRgOtDE8`Dr<%Lzknu_lkh4j7cn? z1CFBbXXxSV6~%3j?$j&V7L2pitzsj1?$sZjNpFPlmNst2q;Khud*@V_Vi$1f2#t7j z+1<*&FIbFpM&(lNELB0)Ey_kSwXYK{s;S)^IT}?=t@51;+`+nyg?)G+JNctCFJhM9@mfU?_3)D|*BFnLiYyA;381eX%?TQTHHcE$ zg*B^sO0-O_{JJIBO-s$sB9fafkC7(Fc{LtY7)^L)zl{XgI8)C_39u7GGXLyc|e#yXQ1f!a&s4gKKA3s>q*(qx*LG<){sM{_}? zF^F0`u+T$+UexTnBI`3K3{or~{_2B2^A|0;pMT&LW4|#mSxIe;2_d@zx7X#SGwQ5~t#&5#wB3xkVSo z@_zUO-DMgClag|t6ATGHSu*gA^B2MB5Yrt+gQ2xF)0D}9@Pk~ehi&lD9UURTQ#4U& z$mGgvwS@KvbFI`jP6*ZXTOd@O*YF%*W58{6D>ElpNO7)=K~Hqt`8wyG&Mx$(v>Txf z`*{?1o4fG`1m=(D^zYIAL|SW%Cy8SSCZc#@pxN!VI`VGo-Au(164bHl=dkO?xQK)+ z%TjMyAKG**Z0VTU8H#Of5s+BAI0KV`MEVWpTiG}i&N3IEn}fLd{`yXsC+H)VY-_GK zbe53{rHEd?@N~s+WG>6>wcJLxZA3jUz2H4rCfRIB-q`v3w>G%rN9e(&kYTr3xj+3u zs>y!PG?Fw|KVMMF)v*&xPk)`FI0J zJSSA{aU`*;mRwpt=eO`xBq_UuQ6uxiCL|vqsg!n9I zIu3UlNdv`m<=GX8C;Bngy2=DD%^9f5F-bEW zd^5Ygf2d2PvHl2~0|`}@PJPGM!{Vtso$LltNHTMjnQR$)Z=4>mP!vxi5gU?a3#ymW zdQwK?kmKTDG148Aw7A?lvTUt>X*ag;8P`OS7>uQ$c`>yj^^)H9;+?coNvR8>{6w?x zr`R1w0)bnT)+=}G&7moIs+z}p!_*Tou$%V0y=k#=oe0&U>kz3!8l7-cAuzEcnpvCG z{ZAQln@w6Pv1^Acv~{TJs!hGfkCHg{+Rdc7^29jR((GZS607G=HHv}tTPvVt=q1KL zf#Steb$}Z~sh;Iv+Osp??E!0%R->wkg9xg$M{lButS+0YjSpUtiz{h z6I)#M_WNTeDR|lgYr~~i5mI0p<(>s@sRJ&Y3OnZH^%;A*?K`DtMliESi~7H!n#q-k z_bma(nlrO(!!BM1&rlu;xTUc%9VcZF;435h+2(zjZwsP*e;O<OA9n;KJ9T3;HN;_5)l-mLLFW>3>KmvtEn z%D~1fm3^wB;OOaYh$4`X!qe0Jt%nZ(zJA>Dj_5L*>#lHMIklxs|BgJo=&-IU))ncl zexMzH2wQik&n5tpd-|W^)b4|~1b1PRICT;Q{FDWl9@Sk<_sw0TEK0G)l9LL&N{hygeT#x1USVizNX!IbWgn>15(ZowAjcB0Y)bpW52J1Glb9bN*gFnfx{gMY;0f0!!|#bCW*%*=B!sPU#_!ITwR2SblQ0HG+&`ME z!K)GWjA*m^Q^N)^SMikH*1;XN|b8U$k!3tJ488EN+sYff{HY-8i;C%d9JgxmSVz?cO0C>u;7y_ z)nL?ENjr5z_j@iY{mimc@iZu30H(9)9cd35#x+Gd%$}}?fZcCxo2Z~iP%`Py{mi=>5moHi}xw+#|OZ8Ld<0Yp2MFCCB5l>BSI$B2qE3;vxJ1$&QCcm?l zG{Vt-$|OYzd6Y<3DF;|n?(CP&>o*EFp*r36HN;l%{an=#jB1Q+qshZEj2o7UHWx@3 zB+BqGhX9$vgi^=NZ=cQ>YFV0WNE_Y_%;+H(;Am718t5eW@+n02JX#RM>(O4@d0ojM zK*I(b=&b4iF;u)k`&kKfI0p4qa!~D9s|N;cDh-vs_%L_yX_TV1vMieOo#PKXQ#21 zn1{3#5(zzH{TG~p;<=?fQF&c<^t^|2o7JWTIXpDKJLh*7;rWE*NjRKmGTIsK+a(hj z(s2xhe8`;C+0OT*%4Yl8M_@I`T@6)u^%k;Hn)RF&5Ei$gd#t5=!Le;4c}ja!=i7e&R_bbMrEeLQ=Q_lrz(=<*P2Uw{Z9-&EcD(d93vw za0jB_-Wy7u&kXltMnsDnBAitPD6>Hr)(J6Hu{y)3I+Q+BA_8@DN%?%b8u>~?*r|JerKr(Ia#yFs z1#}8k>7z!k)+iz2RNxJ;G!Y{dxzrLH$knS39=q_ZCQoC?I57+bQM*xBo4|R$MXgNq z&<=HQBi+|6)#$oM*H65deSQlz!xkf=cqBT}>WI5gq@8xuc%X_!vO8j{Kv(aTW5wZg zRiVqVTlZUAqp)Mg`d<8Y^|{NLSq)mch|H!0vM(_jsW>kxD!G9uwY{20)w3>;XGyX3 z&*7O}AB&M*v{hj_7%KLTOD4_?F@|N>Ljy*?O(naEZPC;sD~?BNzv6kn@)F>4;6gS9 z4xt3Sze{^jC{AFFYAOLdw6SqmXU)mhax*@oup3=jGadg9?v@ad1@Rc&BunnK`&D^8 zcPKbTWJXjb+nYjP#H5IV+9h~jOTt!*9JTxBZ-4Wepr%XhSu4L&nPjwodVfL0w0i;= zeX)Uv@qrm|9k@%vz&?Z%9|}?4^5A>5<*Y(rw`%NtO|nIRQhaGkrcZb1w-IWMz8z4^ zSr!m{TpnTq!}_|zeGNK>i@nZ{8ueC6Cq*XrJO7s6pK?5@^G!7?GV&oXl@dTK8`q`` z%WC`Da{%mH6Kl1zXfE@tk0MBNBEF>x+6vkrgU$-kI_EtJ?`F6SRPy2T<8|60?waB$@-) z`zSMp$mqmD(F{pES(6SSuH6hjb@cqx1=xDAWB#n;>FZd^<;S<#^RnJHugf-S^CYC? z-`9t@LcXSSaAEwYBa^{siET}E_Doa$u1Jn;81(hQo?>EXQ~BQ}b_;bUtSxJO;O)MQ zK5ab%$xYD#1~oy}jku5ucraab$oZe3V&?7BlP$`p^<|PLVe^Np$97hY4Al<|U~e@X zM2`N$1ld;sw%HTQRATeDM8AHs%AcwaEKlq&z*;q1W1THBJMTGmUjpCUiYl?29ctLZ ztS%;^?_2(*$`RE6+eBdi=tfAwDo#%Um2Nm%20#R5Tr*gB@z-3W~5B821JQWPmCJLB#y#}!4A7! z`x*~UD_NRzwClz|yusd(_4th_Cq&dmuxVu1Ru&SUT6vLx>cK|_WUTI52P0#^m~We4 zZ-nCw5N;{uA5jGO=1f2I$N6;HT#YyOHl|W@R>O|v!rmc&#L`=U3$;WSI_Y0zs^h}D zEzyzRQ(s7OQZduQ7qUKLk9!aIY<)=FR(!l6DYt$n9*L1-18uP~v?k0weC|ck8{ECo zs=4AReg1&zdB?F?hYv{(NL~N5Yk1=XYmgNDzgT<6;7p>ZT{IKh&O{S?Vw-PlV`AI3 z{l>O!+qONi?MZU-ovQQIxnJFbs(b%*RsYz%SMOc5d#zp%z_tuHjOC`nG{^y@(jPh6 zJe75_2+PF->3TU%Um*%? zHV5?YQ_Oc>WDqN`MAZfZ8@TuC(13SPJ0QnQ5>dqU>RK~#r$?!Gi2^#8$zu!7Uysga z6a02FY_T&V>}0DkvSK^W_9F8ugQLQ{1Hzv*f)rEGl?4fjIFOfg(9NXv$HSnZ7+lZ7 zPB+EHu$q(1G zsMPgy)}vv-xinv&?fkWwk_Q@TeWIeNb?L1$EGlOU&y2PV`%7>JZr!U)|3jU=+fTMi z+JZcJ??0sw9tr6(gLru)EPNeL4}n;uEf!4>;n##D^r0cY}wgi1dvWL8$i} zhM|(plvR7F{dF5_<4<>#j&K#4hmu`Qe)r__#OM*0&I3hKYVuuij8G+t3T6afo z68Z3Q?Acgs5-Yr}QR}?}ih)0madEdYM#3`tipn{oxc3Ouhm*k|p+Ye& z01ebXXRwi~vAbTy&?U^QB=!j0NG{IA8bf3b7O<ABoa-44!=_6YKLD~`5bvcnr{ zQzt>4Q%k2-LgeY0v!$zCsax_lB@-biyA8`1@W5hW!t<=T8hNk=HLz|lA5aSRB<*63 z4>+i%|6Z`}A7iIB|0s8p%xmKh3GcL@B(WEo^QxChsSI?0;({2AtQh^MEc#s{tTwc# z*De*V!;x)nxi*&V=b2dem(-qR?a%~ewcXpHko>6}QJSM@cbq#{(Gr@)aKqqiVf3U~ zQ?qH)Ke5gF&z&38zqG*xFDQ;IP~Dpm%kg}x%2DOOUz6NVw-Qj<9w7eQ+;{yv(vQpx zv%;NVvgg?M{pI*k4Y)1yh2s$rQBj2l739vno&vb4-#rcEsp$=ay$4$*G_RA#w!3{t zjE{C_^FjPdiuJDU*1H$L5Eqd)%({+j-=hgePe}`$&qquL$r#{hN4}`D`#w)O<$Az*Zh_T1&!m>I^wTOuU2gw zTfS^N%Ix#n|C(0KS>}4)^szNm09|zhrNE`9+qqG(6uCPW?5b$9tI+u7=lOpvHO|G? zLgUVwB}HcGM3RnF7sEkZ!yp#N<>t!X&nk%-L{KkG7rxYX(}J`+pfE9g9&emioT<@=fdcL@ld!; z&%?s#p*l0-IX)5m)RCH*yA|4Tx!fH>6gHfr(bEgo4mX%gvo8UB6P-3O*m|m!}mO6PPpti z7OUJUZlYsyek?t2rnEU)|5?%j`&#jP{8Ho}U(tPP8&h-_&m-Eo(3wD8-wL9&s%9tmAhT|_t6bXn}P#eL5sWc7@ko|3aL}(m>->?b?`s#ut9)es~r0v1Y;b2Kgg)UPhC3By5H4w*(Cm=da7v<%b**ISx=$r%`+OWwp01EoxC}1Ys z%n6^Pxq^_=J_+0#F#0`ye%+9?GDH$@rU@XHny zhe6o~@eYguid?x*sIk4zBKIWXh=9t55OQrF)x;Y;S^yl>(XZaM-_f8d7*H!e7Yxo9HJ z$xaaCs&IK2rs3gO1gU)o#?3P6ti6)6}LUXbXzeU4)LRXxGI+#0|S?m4B(MYfFU}EyWV+C2K%E)DjqXs{JVpy5Dj+|Xztp@`t zqlyOOp>;|A3NkW5!rfS~e0gV=pLyu(lR>VvlwNT;PnTKvw7WfTydJ*u^JVJz`ue;a zFQjy2oR7R$-0%2&?En2Z?D6ese|g!PVYl_+>gnus`>Kgext_MU&dIL1=HJrZ`aHgn z`@9{fsp0qTe090^diQ#_^L^UezrLp5{`&W}aNf~FNMM`8@B7gwx6S*{+xhwC;h`YM z^Ivr+pRX_1`}IJ|!rJ}iN=E4QOi4oxyWZvd$H4v4V~ee8OHTDydk1Hhw@0VXn-`a- z+o#w43%||R#OvW}22hIYu;t&LF&oLDG%-Zkc;=)}6Pkwr4c+|Df}EB}pnqWg^>W2T%?LWb^<+&d8lmibpb7&xeQ z6+yGi41Ss;DTbi3P ze5~h6SKJa0Qxh?Ahf`Re6vPM;Q! z@QyGkcQ?u`%dJjd*n`{Sd$I=dr~o9(bPzZ1K){*@-r^+~lYnyqq1%^&jq*RC0ewX$ zw@)xiYZv&(WsRLX1wtZ!H?L=&J1-d!zl*aVt^Cw@0aK2&93u z;H!~$WN|yhur%|*FBtN6Rlq=s_xht^>*Dblq(sGR%I?rg0|+cz9%=(sGJ5mj6>km+gioA zyeYTDEikiFdave_Au#fDyYK+?(4`RksjGpVee*m~*WnRyEjE0l>G;X`ta3?WW5U`W9?+GY6cq)} z<03AvEDQfl%+sfl(}1%% z^#m^s>V`R-rSoDGbtguBHE0F!6r0SOB8_{@=vBYJ-U;&XezP>SsX7<|8cY~dlArux z+Ti+!Aia(nf`RdK%QeIr1r}Pxk1OP>h%ges4&xD6oRh$ta!O2)-EL#c$pPdAm=#zO6n9UvmuQxI|tD*AtXldpAkGltN;bMC|G#Y+(2oVoG$rl~);tyoS>Dp|GK6 z%$sM-hso`>1(!|TzV*;M??G&Jq352!MZ!Y<+N}{2ItpN<^)J5NF&S6O>q=5$?^Wb^Yf*n{}bFhI^Z~N;z7= z34btcAUjm~$yEoj!_yGkhfl7lxjGk8i>7xODYW+GdrRL;XeUPE#TH3Gm2=_1p}4%A z#RuKO&7K7M8BjGfMySri%$o1&-#~Y9#r--Z{f$9sZ}LkqQ3SbU^n20)lekucMa4fD zl8E35W?DfhS5hPcHYmP|`n2C7asD;6%b5>KRh@0kMhR@O|8$KutD)}u_P;jrrfVk2 z?@63@9$N4`%jnGk{o=U)`ud-^fE9_}a{n_Ikn{gNX)IJijfuIb zv%`On!;#+5(d9pb=x^KmuR&buXr&a4BKXYM-o7PDI%l$)#}i)+Evq4RY5t4+Ag*0adxO4>I*qx*5LWvOfJ~CHFR#5VzQwW zl0qppAG<`22*mREHb-7vf5W{FkijfjqZ(n-9Mn29tDVR{*$Ci5Hg57!)98#{>qqSb zqR(v2*msNVU6DjKAGb;P)CdCUXYPgo$TjSQi{Ww}exjJc8z(&PY}&S}b8J0c?9-Pf z7}wJcpD07N*By$XxLC2Dq~0@YEjwmTuCgEO*6`cJoW?hZ5?Or|2VAjwgBaCPpw-MR z;VscM#G71(gmcYllUkVc`;==CZN{7v&*QCQ%N7F!%TAtq+1vW}4X=|K}na6E-foG;YER9nQ?MOFI`^H}^C00EUFbI@70iVTMJ+mq&z{Belb@mkOC;W14 z(j#oObNKHH*`lY7pd6h%?fAtYtXb>CMk+%FT>~#G7%oOc7 zB(kF{n1!X6R%BAOML5X^Thp2?krwA~hE0)yoo!j>Xk>d9&ui1Oi9=8C>oj2lx1zXR z^~ue~4`SPeMYnX?H3P}bJ7!nk-lTKAKUQ}25=l6SMZ=LYmGq&g!(wqCz;m#-i| z@t!xHDWP{tdtPf)?A_M~t`$@lRFKpvP~F|m&-V1ooIhL@KWRPS*z_?~dP`- z`-*?H4a}L~)s*kY6K)4r`c$fnY45{sM&Xx4?_1xyZ>uYbx=!wU+zp%(DqynMXe})A zXDrOaMU%fu1J}vKo~4$w+*&bDE%=N3CXT7!2z1&%sJ^O?4tOpQOpo`>G0H++ZDlHS zu;q8yvQjOJkM~Kebvj2XjPn|>RyUNFM$%0z)3-kHAUbBIin`mbpeAU$7U_=LamnZU zTVPihCZwW<{9N2b*ww`Q$hTY)7Ey$TCiqRKsV*~R$VPU(6GSs;b>eblY5E)vUtk1~ zA|Q|10tTkA-XjS<9$8HZbzZ+ODwpdG6EM5$aG8?`YC8Sg*4my$<53RhqE87^O=`E( z!YkU92G2=w7y4SY8bJ`WR+e;r`datfTFp(EyR}tqs3gj1f4Bbf{&Gj<+w`zi)Hmyq zhhTI*2hni z+sFN;%3A(*M=$8xU+9BW>Hx(^wY9aFdn41Nhb#Size%@A*Zl+15B5wL)+F=PWj zO!1CPaC?$-wPi~Q)mw!>30;u;}8qQ!Rw-eaReWsU62jlr^T=VA& z)M-c&y@5MymvApa0L&aQZ*@laniXqV!l<-QBDk0uLq2P$A3!^i3cg_7PoI6{BvP4c zsxM}*3>&37B0l%9@Bwgez;*ex5mJp`Xz#W9hJlM)fSn>{fq+}2e|PZe!03PZ9OMmS zYAJMe5T3uoZ-xw|)XTV$?-oUuJqIkt<;MT93m<)?nd2mbneHWyAI}^8n=m|Gd{-5e zB9=(uw|**6g&Tai25u(b!a`jIuYvNRt8$tG>F9`TAWhIn^*8xL7x*HJu{A=#;!jd& za~uK)^2}T#)9|#e!FA#ABPuKU%3?ezn#~8=Xi9?L*IjtEm)C^)m~?@zK=jl3XRGD_ zZ3B30wTFY=pkKFC%2;B5CdbVvI(U$40hmw${`zmF#(fl_TMPPNvIHHHVCDN;^6&TM z1^?F1UX=aWO1VYXvCuJa65!{sEBru5gyfJQ3L5mj-X<7)bG|0(G-v(RC2V@3BbM)G z=g&N$2c&W>QO^oe!W^CONds9xptYTsrG76v2w){U2*2s`%q+{%;mrrP3Eg2R1|$ct z#08x@$(BM%uE3Z2rriCJ+b6zM=Q%(qBRdQMXpzxb&^1;`PHe8w-Ti8BC2k@uI-pEz zT8A~VV_GjBjs5ToC*u>PCcE_~Nm7^qrO@~phV+vuN)ywe5-tt_$E+f_a19>%mb$R@ zh7y(HTM=GJ0j$zXy^2%cQuxmdlRU+OW%tRPBSbwRGVgML9+_sUVz*k~Oz{clRPghvb1FMA z!M0>cscHl>J}t4Gu>;IGeTBG(Z^Qt$f4_P(#Y+rffa^2TqNmGHyIg}#2IoH678pPJtqQmJs!lI^Y7WDuh0A$EP>e#nc^CLwL))UXn%st04>ynH{h zNrt!iVWz3<1g+ch_#}sdI)AxfipqYx6w5bx#{Hc~fh|#3RpStYVsFWDuB&c7k>Va)cXz(hO7wxxIdhO1PeQNoN z6j15W18^tEkhYC0+m=mv^~uCyZ5QENx{w^WJ8w4;zp&|a!j_*bWKR{v2HR*l*H8V! zh7~o5kwyQt{Czs!OFQlTB|`JiP>&FwT$|l%niZK1YLA+~Z2=neoHFW9a^l1<2gQW- ze3xvG+R1>Tn?X9YCs`rMIXYU3;H#g>vFrG})K$EY8qy2_$)ptOTENUd)&PfqCbBY^ z<>W#FMPWwxCwWKo&DLOP;wqMq4lC#k$LXp$X?u}LHGfXKR|EG3`y z;~Wudl5k34_8Y?Z8=~lF8^|yOL{hTQV8N_1e_m2XL6qxjL!0+qH0ln2$vk9X1p|2D zpKS0XSNgAlb?#ItkO`{J{8fS|#@UgsvU-VkSqP;8Hy=QDj@X7K@usKWZU|ip8dj7E zxNsHvAWb?}%yiEzqyP;E<+tY>J4#l(y78)AjbS>qyT;*ZnB!?%motwk(Ht05@X02jKf{0kB4(FtB8Y$m*Tc-p zVqZ6cOAX|VZt)?fC3hX`!2FXjKk~2^X#*9o&QyM9hW6a$WkEVP=^Zt^Gg}tDD&*MU zn||E4IrLO6Dg|2BK^mIgqD(Xs(hagd(xW8;l8`VAXhHMfD@|s-g}g_Ln3QqJyLWHe z9xVL304Sw}yRg*f`CXGlC38@m9=nsTN>%z9#5KF4*t(;(Mi6S zD@0;dF<>R9ITQZjWB#n8Q7-&w*A5Vp;bE$P!V@Uyge4-4XZ+phcDi^n0e~oxN}CQS z^!?MMr9!G%B5LwYT3rO#PV6ghGEorfE}LR+r-OX&*xSH%Mta-8<`ritp=Pb_wPu=d zlH{clhp8O3WT#cOZ-qR2$Ne$D7NPZWv#X;Ry#?_F?-X{!uDTKp)-^*En#!GdQ_MiX zNmj0ny1l!unX${or{hJH@8KV;5QVijr1ssA-ptQ?TEh~iOyC7N(*vuF7Q@(UIh%(I ztyyxDkU#Qh<7Cdhe!HZ40N3?R3+rNZh03$8@r7C^`o;;VKW`DMt3QNfdbAvuxd(Ai z#c0Hj=3ZvlJA~3`)?SHQ3tRXnucBx3pGV4Hy1*{+k9PqYgwSmG7NB_v>@)B}c~2LZ zMZGEYD+WgHX{$QBZzbk&I`)TV%S4HTJ4Ef|UvGaSgo1;Lnq;k6NNCw?_yDOLmz|;% z5k;^BwdIP|a$a(>Z?6RMhK7|x;+ZENaz2%v6cIL-D*Hy%-<)&~EMWm<;+ODcaYFJ# z$WeChtaqW^m3cw)=nzV|HQ2AG9)DXr|3O#k?lCJry`}?!@jen^2%ltm_8Yrf5tamZ zhe2;b1@;V>txQq@PXCfGS3s^b)s--0m*jtX<-aB-l+EUhOOAD!|mHA(YhixemyMoc2Ouy2J!ZB?mtHWF0yBFq6r@5mm(#b#dxv>R{iCfwIz!-k|J zNz_K{H_Le80be)1`(44}c=x7lvZg(k$)>~i48ioOFz(Kh<{-AVf8;y|Azk%F6Z&QU zSL89{ZAE^p`SdCICKW6d%#DIVn1BsnCdgzNSKx8AYP;1y0$JC-8s@!ULHlDnGk}|)j+zYQuR7rvs<+DYrtzO zg&*HZpiv)zE0y#_ywcCA9li{vC7@(|^Un?$l-8r8&mRHu!Gcax;wTj$7m zlJV=}nE&HE=ign;=g7?GL&eU&o6C~xuSd01zFy^g^RRrMdU9rbo?mzPzHT0o z^nBl^ay~DvKaWbjNAdILeBA4KKYl#(e;kEgcYn0_`g(ra_Pjq4_PmYM_&z=&eSNa) zef0D!80YkOd^sPVcYkKlzYj5d{arHly3+@0>3RRWx9zz2 zdYN+N_j>i(FpYPy}h}6U*DtYd2r}FBI^6Ogp~7j zxN+V6SyJQkXh`_pnyK=(pYo5M&{6x|*XLv4>w{g-_v<_Ce-k^tkKeoSlHpTw?5_Pl z_;#}MD)jNy0C_%=eQW#mkHYtLOYJM@Vb0C_{oMEcDP!h)%Rcmc-nX_tpT0vV4D9(j z>!@iPW4GmRp3(F9*m~ywcbU`Ec82x!_WJdD^!*whDqi2uxO?U2I5=ki91(l?H@)Lg zhxI z@|Jw|J{dR~?Yd}DJnm|ti=MY!DVbV(ZjM#FXLDZloa5hEewXfEDSw_yd`{%7v|eU| zyx!Gyj&yA9wBB^4@w_&?E}C+GSH9j@ROu?*T)*^CzOqhEKGH3|(m#=UZg1I~A9l5A zsq888taXaB(EZxXu36Fc#FKf}9Jx%mqBZqiUS&1n5xZ0oZcJ%jd2>5|wY_}SepYmN zeAL2IlIyP+bdn<0Hh+e8dTG(`%vF>u6x}^2Qw>OqCuhH5z-QbDmv9-9} z7-?$CoLKa7D5auzF61q#i^;LIZ7JiliIQ2lD9n8BGkxH+m`M+)?t35XEzE~GcXs=o zCMP4ehr;8pLAd(H7DDKGGV!kahH06XvTZ@*Ew_2K$^yb_U0_qmU6ozfVd)7VQ2G!K z+$pwc*yMZcNG|QxjgS9&6SUxmX%`Tg@2zifYd3a*ksL+zC zPb!z8v5^xQx(qCi?>LV0lx@4PpE2LLJ3&xbeAzC;FOM!lm~r4IZ1TjRL-HOwQNq)l zP=K?(a+k4>i1y0fUl=8z0A6{0taX(<#uxlzVY|MKW@Tl)(Q$lo=zjDA28F+uCj)>< zGQTX$=v4U!*c>GRhR0iZDbMvuI&xdv(rzE#GOd_fx5+Yu)MaYG!Wbh6J^i(?$#fs4 zF`ccJ=D!zN8_S@}sWCv>0<`l+%R1?xyV=YukDSJSb>?bsU`I2XDJunPbq6>o*7@M# zI_|ne$4axQc860VW6V^(BM~XI%=(v#7%T`n@J6g9cVJr$| zw%#SQc^fGwR=)H(4}iuB82UQ(WOBuF*M@msK#foJO7lbE6~x)t8CdF(lwhDdv%W_h)!QL)Q`5ww$5awn(txS6`O0}Q4z+@ zIg=G-^g%|M5-0Iaej_Ayn~dwm`yVJoS5ZH5&4Lq)Ho9>AQrI!Z1Ki1LLQI$n-(Ifc zXUi!65V5(ITNoN`ux;xbpDt^qzNP5j2?(Q$v1JAZMr0b;4+)bMHdQvqm z*LX5~Jyk257XQ>Vu}+{Ow~KCB`~ zbe9o0pQ@}A#c_=bU_%S=7i!V4x)g+zjP#-_Rd5<3NsgK)bcK5ruk@Buo`>VZa13P@ zMHia1mOkUBNbJ(keUudpygTYTNi<9Lq^m;$C5|Nu`I#LrJv6;=c@0DbPsn&Cvww z=u`q&v<1slz;K_C`;*7l>H8J6*#NZEJ5<*_)-N_uS6#8zmO()WL~nOPtQ)yBpP8>i z=p+{JQOeItkho=uxj#iF5NflMC#gppTQPNep$iUi?P588VNfmP?n;31ebR8t?uqcL zbSNA;5BR?d-1cNYp`vr5WO+prWLI$K$P+|=RUBU~`)C@WYA4CdhB=Uyb!OotXBoFO z2ZAqj2S;~Ur-}y7UD2p7v*Nw{@Hm$7kah`9q-7QjqFtMugFh!^!j3n|b{b>87J<>W zRs3aeF-XRWvdA&PWEheHc9jHK%-f!_XyqKd+Y3ttRiMAze0`uiKA0kDS=Y8FBLwPR z5)Kc4^?;H@sIa^NUp|~K3TjH~knO43`m)25H_uxOuS5#-&%ZIMhIhb8KI&xy<}Emz z_t5OB6q-r}fwTdR7g@mXbRBaGs^7ol*vn?#TkZR@aCYf!HCtTQ@PW8hbW>%_ised* z+H!UjUW!K2N5;;hLGy>pARXX6o#eFKY}Ks658ZUAygkcsaAXe+bXGyK8gfFL4sZ>Y zrdX%BfCjx-CfOJpfd)!ByT3VTvO3#u!}2489Z$Ybs$t_PVG$KQzjNUo-$X5!ylVi& zpV=ofxA7a_7%oKJ%U7i+&;j$9E-SEV3sbE21Ne8{vS_6&zuNe2;TEXSv^MZ@etA%n zm^;9gzRIN*fIqN-|IAZ~{^9?~xeZ?XHYRx`V!VM`<$%KNuVDFvT;)&z|nEL?2078i>1&@A~%@@di~q0i|I>bj73^bru*lRjsg644n8?Hr6G9`nN|a zMbp`0-;!o=)nQ#LC?&)wnU~m{axGLV)@y*2I$xs(WhqBfF(wn;6Q1c7RZ)|0drmMN zd@Toj;{DRYDOy6Vxq?p-a^rK;IrdM{ze$(uET86-#zGdN^leO=Hfw`}p-&GR1zNzn z;u+Z~zDouyQq_-viGD(eIwI0@#ba zPR=U>^O(s=nwYuTcT#t<1=@KuJdhOy)F(C=fYzGj+NNXzCz;%GJc~Io0%2f=1d&F{ zc=4Irm%0}UaJkE@=rOK$zN}vC!MdY`6&C;}&cI1V3`R+ePug*byT5^Gu=zyIaJ^(Q zD;BpU6XdvEExta&6i0d-4_hx-s0~SzTP?w0z=n^>c-wgYbL5;ZN2u}1tz-qO??9Qcp2ivDVHm{99F4vX@e85_;8`SRmesmrr6*XpF|oyVnNp# zn3@}+?ZR6fZ^C=+8ciCKd>NjVDN8=_yAsPK(_Ls{39^o@7F`~AZ%X2~P+wKVo0ULR z-DMc_Y7lOLlZ_yltuCjy2+U+U(C|nh+2Sr2A*5j!-cX-eBU6WI;1O}=UZfIR(hwXz zuPfx8(L8!{yTnSnEr=Uhg%JXzPTY8N_K13@BlW*E!Ikn7<>Ug4;|KcQQn*Msi=Asx zgn&f8h79Iap|)C#YDGpROPB_NXW$F*`X477s{M*@F@GC85|%q2?(eLdFpTn~T*vX} zij(X-I@KKV{~#nVE|uDdf)V4iCanuooyVF83%TYdK8E3v94D|mkiw6ma(7X7ix5<1J79Snow_;nbrwrp!y# zgr`8;Ak^eYlDeV(Bz6uzs`wW51TN@oAeUIh!6v_kvU2*8YDh0*94D_OTFS1Lh-yl5 zEp=EUW5+Qr0jw->hw=Dbr%7V^k|_!|2Clr-#5k+8Z(;R`S6N1YiQT72Wq&)0 zObzno>l~u9kcoS`$N=m5hXtI=^2me^)e_qsi;5(K`Xfo5hGY-8KvAZ)^=oHx^FIvc zA&%SJ#tO?5+uA}wy4|&H3NOkM>$u42tf zd|(RycoBg|L8#sY0Weh&Q(cJo%#|WRvXZ#juE)FbM*IYhLI76Rug~<22k}qmim?)$ zP6#a5`I^y_l+FKM34>dy9?y^6?|bqi^i!d^=caYfVL`QcSCUNzZU@Z0{Lc3?3 zr5^z4JRT~yW?^Ca_8V>WFiCnckttq@JL0!c>O?i@L}H43I$`cPox1!zW`9eCaJWuA zYPJDq-%@#bFPrI|EWLhLtS84m95%9_XP_;SXnl* z6cN)Yr4SBU_`=#{>XwOtd@FSmjkZ9GC%gGAxmBLcUt9mz&kif?wdXr}`1UyPB!P38 zj)jl}Bh=<@w$`K{(CAg?n5W6oy(Me`bS+_=va~NYTh;zaek0~Cu)zW`e5BC=)4N3b zHZ@R0X-nA62p0jBxS+KjE*JafC@6hhTr;Kki`S$aVVV%=`Wkzoq$zSPxU+7o+62~u zT>wLiydcXsXzWCGE>CtL0VDz3Obkx@Yl?71PilA6mhx48h|L*9%CaMN@-&uzjAf{a z;e&>Saj^A>!ZSz-9=;(-jH7zv7Ui&Bx1RY&X9=VEEe$u-FfUD|k*d5&o7K3F#1k#?dqhzqE`eQ& zp1fEY&>4-ogP>fE<_N+M;+Y&tcdCLuN0Y^9_2DLOM(}W1YDC0tYB=MTph1IDG#N~j zUN@$_X1gl+axBjeGSxK-(;>1Zpt1&;|5{ng_7z&Tx?_Xuh2-S;!Ml+7M;0V6oaUP) z=Tb=vNLz1Ss%0qJqG(%;P7PM}9UfVRY^l8lmvy#4?(aP{Y_Mg6pDPNS)UMC#D;XbM zVR}9>CnoDb&aw%dgMnk*K>+cDH!13 zv4S?~+_ONx7q&EbapNC=l<%{B?-WR*Mkw-?jlUKvRC2< z-15<&a33^2+N|iYwc9}X{#NFX$NTNI&PTW-RhynlTs>jJ{Lje=A>Sz~B`a&JP)p~!=6ST{n5S+J{=lON8K%LX zdpG=&Ur4Qdg88zRkQ%6Uf$1PMfBzh@=4--nw-UN|94IlQ{2@o07t(gf*q2oYa&$GO zSHfHlANgGlIu2YVG*if&=VeW2ScG{Ly2yh&D4?NeAo?Q$I|y_=N{3|#m&9>KhJXi# zGx{Zl^#$VNJlTV(k-vSY({g`Yjn9=WD6|#yfEZZJ-cTyUX+F~BFnLo7^`m$0zxJe8 z5<8ZTQDnFN5dkPa3FUGd{xKf`BU$5_9D)A9#aFC&d^6@p#@nJGW{3_Ff)bE{h?Pfg zgznv*eOZcR%CM0!8=@WK%`G&`B-6;)j10tWr3xnE)FhBVDBfgj{urGhNqabC8UTH{ zZ}o%LUfEfOHHMdU&AFwEcz7Qr?( zSn;?xV6l83N5JNz>A(T0URDNIszPV&|LYq1lCx2JbOL=o`EG;Jr&kw6#u54Bki7b?tS^;y>o<+G8X>Bc)d=f*S0Iuv zoNyAP5Ksy023=k_>D=iF-Azs&6M&VB-Rp%UTTvshhX>J_-x9Y6wCM`E`G7o=jV0PJ zsa?tSXp=N=w9O@s+EsKMz?VYYsRYmZKw-j3vhBm0j71zSNno>#G16sJ@!8PKuVhw% z(=xtV(U=zlfMyvRdP$fhvfbwQDc{xlA|_ChFHi95nB;dotF~eIP#Xw)E^&5WlvO4q zH#;7y`ZWz8HRTuhpN##M51ujX+Gp-~w8?tv1TH3)S0`(Af!%@7k6aa=dH@J(926T4 z;k-7SKrug~V=n`^4s`WIu8@nLIJ2FhVvszP9ka|I@90cpx}WU@R?5VH2J#X{*TK}p zDa^KY`M+Y@39K;e2Ocm>^hd`(82(+lQvN}<*rc|4^3LkD1`mpf?xCDL2h!d1dD(kbmSyY1Dvjj(<9kB z|N6hX3pxwvQaK_m&Yv1%ax&~8j`62*iZh&kEokeplfv`#Nqfpx?&6g74FpyTZTE8HImTHQ2sP_lS)%vi%6|c zxa=MvUuR;DIYW60a|~^~wlY4wBk=GRp@w87Gt7Lh?olwYK`1i>qqm&TVKfr|(C+JF zB@&hs{SdHz0rd1*|E@G!%q!B#Le`oC-0A3#PJU_zIVOqu(blqo2+B%}FbQ*DE)1N< z=F79+_&KT%Yucm(ad7XJgD^(4Y| z^V%rc3DfGiLH;$y5Ms7;<&r zkQn(6Vr#Hf_%aM{N-#52S_C)%g-h}{Ag@`9>8HKbD=QjVs^9Kd#;j1B5;F`9A?J$- zo04MzLzwc;?{@Pvy>Mmr=B@$QqDm;YJkmlX zH9Wt%Yw#N#5Ckw7{lK~ATw;G*DRTZ`D}j%Lr+Y1my@t@y^$$N0?Zqewj3xdzDNG#j zWNLJZ(x1X?FS}g1vf?<>FmWNo5eFRUAzr2OY&2)?%HD?sQGo(sTw6b6*uA7-)hIb}=yrUD^#G&IJW1 zNR5e)6rp)&8NJV6uSC=xhW1$0(}YknEC2?{)HWd2!SMNfT3BLP^tTM1H_dcmGaz!f z5-aO8vt5BrN86ao%J{|wfb&Z zFW-hdZ{JttA9$NWDI2G?fC$*k^g8R~nSRC`^|8_-DJt`g<^~~E$04vc;(Ua~Thy4< z>m}NlE;lQ{Qf^h-qdm5=c9o4WmzQg?Y6$|Q=v3%i8-oNQ>Q6MkPom zZ3Qf3e^ac>>>kc&qF#hj$`(>?0%rz}+8%~1H8-ma%w;y$OUPUFM}fA~RAYYAD1b5x zxHY!Ab|@~YghD3cmk60Tv?wKA2yErl+Q(o$nmUu~1*v{H!tt9pq`C{^A!;%NH;q#W zp~UFhrqtHiPX7mclK1U#)nU`_ao-f)9Q7+iytPE;!Wu1QJOL|97O${6HDe^oGWcII zCOe_-g!m#*RCelldU>sfJs}g&^-`MH>Z=YC3?TV`M{oa z2{k#^|8?i);q(z;0(BjAmZWUVK(Z)ZJU97S)XdaeuK2I5@_U(%$Ja?y4C(n2bqmJZ zk-grJTv(#o$u%lY&tJ~#U1P9yG7)J)<~ZVIQZQ7>gy9Ng>T9Lh?s}P$wGu!Vd8eBz z$Zt}|1c`H%qqfS~Hegy>Pa73O(7F^8l=?c@FkY7@Gsg$wD`A9F;F^MjrWu}A z0#WOG*{5U_Wg{&BXfqFBApmF=+*3|X-E}Lhcu&YNsh_vEH5!RTVWD2z8a^(1W3InnKTUa2KBvE5w`K^SL zsn5bkCuUBPy|J-$f7heJy$;9;Osh12Wa=J{NGJA2$&blM7cU~O-9p+7qa5fM5-j6s zvrhK?HHQl>p`(`D$^cPc0ay_0Cx8L+nKPkx)E8yfvQjnmZ|5}~`ON=9{`~(I`NK3H ziA_w_h8vQjYG3<}Qt&5VTai}V_l`g3eDL)O0=|F?Jc$%>WHv1##Ph;>!QI9+1hJo5 z4|<8MOtW!KSUL#D>0xr@qba+e!JZHp9xA(}!nR0lZ@`i#1;Wl{liMXGHv_k=#hyvF zRl`#*@0EK9WV=WdOma}CgKI>bq*P!xnyiFodH^}SRE}=#4X%f2eYk`~);qOT;a7og zs02yNBZI|+0{WK$xH zWl5n{sUx8BFg@;Ow&Ui&A~XXkt!0tMgWz=7*Ayo2bEL?;U)~E0g=GT+8dkxFBP~m5 zolJK5jY}v~<^IwoJWhOY3AMq>NoZr z3C%{TlW<4Bt&(e(w7TAcCj#Qodej&jZU#zb4?x-TaF_ak(_z<0lI7hi6JAOCoF;iM z?J=+A3JZIjYGz76C*+V(#Kj<#>b6buBsZ>g^*TFZ9X^S`4}#v%lt4!)z1)MX3$!Kd zRn>GGP+&AdN#$Lx1(PRcyTY(AiCN!*#g6oaem`P6W{m^BAH_BGe93Q{Y(NTt6hYg8 z+akK<0M#eQQzv;S%KEq#SBR3Z*wR|llZu=id^c434~Q)y1l2~X#EI7Ao9&5;$;emh z_MimQu0ELgNR^YI{iDsc7Agx@&YKgL4$V=u7c2+G(Mym6uPakM-K}Eag}DzT2ca}p zG;fk{KmvBdVG_fysY$f|l(nPZr|6UW0bk#=Wx9|0Q#c$u-^2uay z>ozN~Wtfr(D%u zaKPRFXnPv~#c&$PFri5nxnsxJ$k+PR&Xz2DlWX(lrs|uWKs}-U4`%h3yBe4b zIYVk6-}^_KZ~2b|_YJ}@@H*RWjf_Kv#L6^suZaab-#hv;!(~goA>6f_i=iE>$6n?U zyD1iCsErXY+Se0)Bv&(RY!aftq#2!Rv>Lx35vnzx{0{H+bjfd<@cr&Od5{9tiB3XU zlD+}mED$3CLAmX+$>nTh@YGh;*r~E-dax-*Ww#bX+@z~u;dR>LETX;>Ig8bRk*Z}B zj^E|Ie>7HK&NXiqOa{LV)D^yOj^xN@z80{81{E4~ z=WM{c+0I9vl?o!{{unDm2?5f82I&#IFX&1NV248&^?b>1pY4fbDlt@(X)QYxw=QYIgSFchQ3EVB8T+r5ZhFZDdWIUyUdx_*e zvD{9~D}tI3y^(wj@C-0UnI|oX&UcD{CHNsze(~OBW^&x9KccC2o$sz0g6OzJZFZq> zqA#@iyR8iqqvS5r3APv-m=P6*xZhLzLCU}o9|_0?#o7D4{b1^w6djio=Frh67OMHK&aioVC})F>XLMQkgI8$0ofSD;ZPC>rciw! zC2+&TYk=~k>(RCB6__tsVFI?#0{6q=1_5{)-FVr)5>Bf`roMhGepE_5HsEmY5QP&S z9Ud;25Fw3l5kcJpHo8NYw#_J^)N=!;po09ZubmHHo5H0HlGT9oO*ky$?*Wt{k#qdz zyRTOO{G7*M$q6DUOl?Z{%57?WfUE#vZ-8&`t-`@4`>unyD?{I!VY6>3F#l+v`1|D@ zO$7J)%b|r={N)fdo&SD(C+w=&26D!_@!;p9kDNka^Pr=er`oFrLS`%H@jO!Npto`V zcE>j;K_g7LqRTRLrtuGNu)HMOIubz}#B?n%J8S7DC^HDyk|KgrckYJsP{ytumEUjT z_DG1ylx?s$TAv+$RSoIq={AAi0i2Lw434Kj<@d>A80OzfEEMUd z&21*R#SKNyjTFsXBtz!>+JfsFo;inpqjDo$GmXd=RgN86Wn{QD47WNVcN}IZ_)qb! z@jxi^2i_X2BET*o3Ekd1T==0x#&bYD2v<4}X9K(^%rfs1mu?a~f+O?&hCbZuMo=_E zDH&P36(*yD$T8zbs4Uo8%lV4mA)X>h2(d?~XqVXM7=_&lDLqGlvKYKv+Z@}pV3~00 zlx)gLJ75{e4i!v3q|j~KSIw)YiMkLA_DLO=lvoOOX~|A1S%~ibr z2WeX=gO{@nf}YA@TU;5cv7Ulie1pju77mn*o2@`706)xIZz~_}HN0a`hAdOIfi^XB zSADRpRyd7vKN5APD_lcDFj*T(ml>E2wQ@h`qAw}_N2)&|q7zB;5=FCIsNN3ba}T}c@=UWb65d5j5;)GV&~=alHyJ;2IAIA6Ixy1l16J4? zF%YQUKp-s4XE$}z)>o4kK(1{PmU00UgA3?&@9SZ9;{4lcvOqN#@cW3CUJl3I0cW!| zo5c#W>U7hX=kjk4u5dlxr3m6Ulc1H8v8=70A*!W{Gf@O)W=d0yXm7kRTxy4#iu!A_ z#5zuxs9$fUs&W&gY2($nWNL5oGRj_0@)2dIj&6~p9Kq)a9T~jG&mba+q!K~*n_1P) zSI9;_ot7Re(OtoXK-BVMG2`Xm@(DmqAtZ0vHP9F0y$&X@1$%%8i zcD??5as;Yo43jpZ{E3h_uJI!O`*MPbw?erBXl2}>Io)7_9c`;C#{lS|o6Z+A57A7O z<=env$q!c(W*DH95pRc71w70X@+py3An<`=?PmPQ^O8wV7UFc;3%d)Rk{l{$tAUwh zI}P(~9b7AAQUU1?ifR#KT`Fag5z6OwTXAO2Q)eFdhRCx+ z+E<;pCTHD2mA#x`nIKL{cghOK&TP}pY>Rdtllrv{Iw51?cVIf*Uf|pLJrcMZm6Bu- zO0-dcexO3LvCT&;finI~s)0}yQ+&Nfcd_pqNwf~+xp#AEKnN(4N>UTpfN}QPYDBd< z?=Jznn6y@)s$Sc@gFw<*(Wlr{p-5-HKPr&#kZSFS^)RdG zZiY1K5EdGel3?Noy2YlDY~7fri1R^60zGz#@|tz4ZChNSa1T0w;OD?Z$aHrIsJZkR zmOB8O4{!bCjs(=Nli<8vk3N`6+VvRDWI`5^Z93UX*m&)utpuFqL=0<%TB&CpIq0P% z4=CFLxv;(QJGD%9pO4aKnr$`PqbY3DjWj68BFhVio2MhBwZG5fmqSaVo;&+48Xk3Y zl^MVK1AL<$LyX70=G<59e8u*$5mUq#Ql+fQeeyu_4Xw!1C?%^4o>Am%!ksH?E@(j- z^g9}-zCG_l_V53>`_H8ICX@B3dc9#l_3ng%Fm)a=I80 zZL^!z7Q~G(L*Cp8;eClQ+21vpG%y1n6Q(HEj3lU5#eVpe6=wE{6Btwprz>KEry2+! zG69mnW=?8|RA?ojOX7nx`6yC|2wY!G?e~Bj74`&f)s#L58$^JegMEmKCZfbqT9Mp3 z2Ocv^^$nb5r4S@2Z&Nl4X)vM2!(ak*cS8g|OC?Ts&G`A~8UdV`UkxEM+cQ*jOsZV1 zj}gEF-?G!$q?I5UO4P+VRQVN>_`E4f< z^v7v^;N$#f7S=fJoYhz&Csy`<27G8U@K zTf7)?B{YZWV5&vU^##0xFuYSm5WzdzLpGp!cKhjJk-$C%q>5}7TG2P5#8(2hstrtCmU7q^GtI2m=zZ4dADu%N8LEx6{u{7 zHjIA1O(B(gjK*&-7WHh2eG z$8EObnTYsSTU8sYp{nEdz210BZQO_XV%@i2Bh;P!0%d$4j274}9Iz zHg?WX2HD!i&ns3OUuH>y4GLCbS6URrbC=WP*cCXa0m`DnIaJ4f5LVC`2p(%qdZFSM z?)l)Fr5#D3gw1<)z_w0@sE|^G>Iq`Qt{Z9>su1eLXHhW%d9Y&mJl4?1l5W_tE3&uYav_jZo>DnJ;IeDb zy;oxg2&;xfsmV!?93|{@ss_>?j0;dk4Ek)1n9~aC9oPw2;0D_%Es3ejzg>?$cE8iN zU&ymiHa#Ip8xX}i;||I2pw*o#O2GQbQUhpxyB?Xi6*9m{H7s^krhoZpgxb6=$J^?jM z%(pVQoqEG}OVvG0sYK_5KIW5Gf_@+skXrCcY^N(?O5qfA%>{`iZ60jTAU7tbyNA{1 z!<|G+1IOu=sHverX9`4*4B|wO)gG7|vf;G&$o>|}?~_{{v`Cra%tJcqN9>+}vRYkM zkRHh^`|(6r=DYhQ!OPYIF>$*deJB=sWhFm~g+7?c>-F7TaXy)+$jOTm2`sI8;*lHe z#oq1Q4X+3S?$TBlP*P%1YFRI*;;%^3`hC-F;I!s%~> zdzDr{C){j$R<5*vX76SYGkXB9DCVLk_2T%4yI{~1(t>@9x&sPL{N@5fJ~ZLjZyCXX z5~BI|?Rs?Wd)5gZG4l0@T_x9WK3fb+QJw{KyxvxiVMnTn>!cjG64>A?F65i|MFs&t z%}#Qk!px$hC8He^LU?*YeWKkRkQ{Qc#^U#QIl~Wo3ZEa8Q1dF2vVvHb;Qj$)tmYmh zgU}ai@Nq>j*KNdNa5+Im&~N3zhe)2=b(#(a%RYw)i}2mP>TIR4@9vwERsn=0J|%{W z@pc&8MvE&e(qZ~Y>D&PFcOotT*vg0|9|O=nQJ~;hmtZ6Bnqox}eNE=#*rP)AbV#Ql zR>AJ%?*Q2hes&(UdiI9Kqfa<3<c#fQdUXLD)95xqV2NmywWqgL2kn$O1^Ut=REvb%0NHKzC^4bhltX_p zyY+d+V(TJsMYL+8l4SN%_tDt6@FP?sHn5N>?b{l#}6C&YT#=0$ne$<`T zfTR~!DAx!>ve0po-EhkA2@+Pa0Rpp1Rni7?aEq3B+FjIV3_Fh3qcg)3s*8rpTdAko z>_#7p5$ zGpq31-P_YSYBe^?$eXzVu~VsWdEg%0hmG2vzDiIZS1eoWUC@e>#^!DDv|Srq74BRyn+RdF1Mn66ezI6k|{~8KrG2BaTc0FVz=O=?2S|pEbzQp<0Iyb(Mv} z^yvQ8R>i0RXptz=mUNy}Hn2>0%&7CxN1{^r(;>(&r?!^Iq{?SLdrAjUsY~=^`wl`X zbNRFA;N6ug`)lA5CKTD?iB0A*`AqX6)AmJAD!47kXhoF0m;<*$e)N71KTRxsH+&5< zP^++}n!;~9BhR!ekQm&;BRpNP2)tfp6qrKtuJ};1#})?5MQ6qogr>Aq*YB_c2`q#A zBw=(jsWA451}R*iIqW6O=pjm_3}U$ld)J`*GoDt7_ZAuGa1t9!+n{zbrF-jt@T`Kp z1E#a3l}|+}5s*mlvc4WsbUfv#V|BEIs)TF;Wv~mLyjkMI)EGM+jLXR5bycvZc&_{} zDu@w^00|9Y=}1ZqGnWo5!T7yArU#+2g=s?S0utL?Nyj}r0W*ZXbNmL02n-w`!0JLs zgbHX|U3~+f&&o9E>chqc30>0bE~|fH+jQ4t^I37xs^)n#;3&qU22Y5alV~9VHTZIK zmf`*&N7%MA*yeGD?uQT;@Z0EefS85AIqnOA@MI#wmoo-92m1lYrVUx3nwh@FvCcux zLk>X8tGk2s2)?&uvZ;r~1m0=VDjwm?xk@WFjFV`fbMz?Sw~tL{)}YM`E$Gg&?9%W# zwDAIrzK6I%BGctU3o23Mp*;=Pzrjq?Up!Z{S*=A-(m_lQI%VgrIR%o>j1nEImT#5e zKm%ePBM8k=Ur6y5B+;N9W@!r{20tS#a8Z)KLCIKXgXG>(`A%z*3c;cqOP!Bs#!Nja znhgu45fHRPvM4f{S53jOJ@bY3|2R>$d&FzVcOu?KjJE@eL@3{wQ7l=SAM~hT zK;Y66rV^|J_X=r|*)y<^hR2$Yb8qv1<7!C}-HzT#s2rNLY}-=`PdX3EuHBc=eO$5q zxD+-UHq}apAqCI+W@H3X7~_wQ@R5FyBrN8e54v#t_|4TGdu-`a@f6c)W7pg;7Yb!t zQn<$Yu(a}jxLag&qPJ!R5{ZFMo3THF9Hte*ZvQfYM3d(LGyz)fu$RYaNkb0dYWJ*wka|eJ>si`TU zZfWZ-V~}_)_{@PtSwJ4T!T~Y^63%nfTix9$TUvBGQvbuHT0BokV2FI&h=aCcH+M9t zG<^o7cagyE7qYw8O5}{6hZDao(~4mN2%^lGb|!m2aT1sGNB~GypT^^HLPJ7jGrpvk zxC8+-p#&k&41Lmrk6N48GI#(W2K}=I=vI4<@<++DJy^3VxH8xjO<}vq=%ckndYcm% z<-8RQafmRwNu^Dv{@95ox2MUvx6I@GR>Lc%_2cF??^riCx`Ese0Et`Tfj$|8CT>wc zm7KmAtMRvN9>3Lh?jZQ28KdPVvNcLN0+bKZWf-OI4W|tVBb9HAH@R2S+KphH_v*4r zZ*2KW_*C>Z)NV8uuLMNpdUVbEOz2KYN?;K_cH^OLHDzbB_vl^a3OKEUhBdc#5k0(j z12JD4saDb~IvC^jWfdU_f(t3i5ivLDUa*o`iO$yUEc?E&ZX6|76FLZT#1RLec$@YC zbv}w_ZDCGqH#;%;dWC%&jj^;f@?Oi(5?6~_?){Jzi#Z?}Mzg9$ooKKU1CDWfm?R}t zslYHN%_^IMYRx<}+>tgGR{RLdkvc%~D|HE5co|^q?7=X%x!G{XnELo$do_quqb+aO zqifOC?iR<>(d;Mp^rPyEY|^DVW0hSs(?mD-V~cjR#eM%O*20_mSPDakLBI`c?h2~u za0ip~RZOf|wjjWwAALu`ZSVK*QEO#SzC$Tt5!>Lm#Z^lCBc^DQ44N*4M}5754ebzA z?UTqG9T9^xZP2XBCELL0X(!;4kqA%#Pu5zYvo`Qvh*3ch)F2hF8@ZDj#0HQ>Zy@mV zB0euRgrj!@Fm{5SEqa4OOGd$(Q8Q%vK}O5KggNlj0E0y8Q3LNl@#*NQL^Xt@jNN1H zIj;}gn^gXnRg#S`@o-nu4wafab<@b8zfqsiBN~W5D96Bls}cK*p0bh|u;1+rH+g_9 zgB)w)J+5LraTWNbq#=S>YEZdRA<%#1^ztL)6ymnO_T%x6*#K#-tK)c zss2|VyZ@E?*ulKHT6-XE>C_qBd}&fgk9K%*Ym+p=}iJP;~ZbXZ3;|#+JHWk3Mwvh6-rId>sqmA0gsL z{&wJ|oi%Zsc_0B(^yjFqS=%CUOURHgRNK?booO3KCKcA}2s14KKcW9P4DO$t-3H{? zS2>l&oEGt0;1piZ_ydNH?+1oI67fCC_5k}HL1d{=SSmUi(aB z0*DM45KHK%teK%?-A&8N!b6*FQM+Q>oA7!-1tBexw3v%xXs}3O!3%)!G}2=?G2|_9 zb+bDb5Fh1!Qh_b~L!&avSQW6q3$RC>90fHnKxXvUnb#29G!Nf{D5g`03s!Oj)HP!n zMOCtQ`yR+{atrUKccvA)qVkeZmemkwc4@;gRwT=B&oBYwG;XVdFBSCA0TCadQijSy zTI)s)Yy$dre^7YsFloZV0qKxV%w{KLM2L|1TEcEEz)0P|Ny~mboI0wyyZxbO=cWNL zP%Fr-tKx~eQ@AjlL#HnbRjUO84)6lkqid>`b~D9s)hLl#J?aj}w*Y!RPve@!$4`7Z zC1F|@ZaYToj%;=%rFz>Jfi^gD7sZd& zq(_5B046#7ITj($g-;MOibhmpp%=HR4w}u247K`lZc~&zHS1CKzFF~T&Fbxc3!#Rjn zoav_7WVu04c?ZF=3QZumBSf(U%Llo2j8R~Ama){8Xw62v5gte_O=1%Pj@+G@=z-56 zVlnAq+LCb=b=g0B^7RO~l3DiJfS>@qV`V!|h?>XD!nkr)w97dkUgJ`4 z_7WfrnK?>^OloFQnw6HK7gE_l&XwsUaf9sOi4hP1u(C{UEvN7HVOxz2Z+D}lLeh8WCZ)7 z=F_A$+v^MwHS92DM_g#qM#A~(;CQ5IWig&L7(U&srYzPCY^p+1$_5HGuV5_5aRJym zA7%I90%!tAZj#0x&9db&&-vlZ?(LEjJSHlzWK|xme zJru;@+7nN6z5wTz?rAgRizXSLRnPgpEzdmsB$D`*qDarNTxCWYb_Rdf0(;DEYWuS8 z(wIrI&N;cCDipmbx)h?kyaZ%)-1Id-mOF?puVx%!x+b|k+mJGF8LZGe>@qMCtY4tr&;NazMl?dzcQh~pA1Ze44k%k{bVT3Cz=iW>mn+B<~<=vhCRjTyS94R zZYDR_dq8G0I27XC2A>ue_tKmCEiz(092Q77$dm-C4?7gboiQJUS_JfIiIXGqlg?LA z&p|~Dy1Lt-tx!a`be0Z#yBoiDx70KJIXK>cm{%B)9V?_9nKsm9ThkZ(FU(?cM(uVz zx>_#qN9@zMt?gNUb8G?Ggr$5LB?<=vkT*El7UT|kTyNa5zp+Zw(N(U+AXR8wXaGif z%G{t}f8n|E`bMaN5PFa5-!BPLihuSIl^V&_hP6E*NHFL~=}302bog;aKQg9N^w5%# z`c285J5=XzqSFk}shUz<aeUFSgLX1O zp;Fxao1zLrwR5^f?MQD!H~YC(m7xYN;i)iNeRCuEN6`#;*~;1q3H7+5t{P-`^W*~4 zVMlVP-MxRN+m+?$OW?3jhmo!=elLk0h<+M5ZZ4m&OXa!!DJEN%O`{ zUKt!98|9h!h0;BM$8i{Ma3+980)=fmcqcvpYmEdN6}IGUO3;{rv4nbg1Ptl8M{s^O zLwYIv)edVpv@wCz0-#g$S>zPlM01aeGyk@_m7PHW1z(tZ2CyA06V0luzS7**zZSHjGRZOxHCXd z1Z2Pnxq{`0sDgIdY(a;BJzudUcguxbq`^7oCVwm5Hb8BF2p^D~?GzNzu2j;uMfBX{ zc~rtk-QVFkn8B-NjxKs?>?2Yt0A8`r7Lzk7cw$31r2;9j+{G3}=Cp01&+3Yz(G(>? z3Y?-OM~YeUKqlOVK06=fJJrRKiJLhF9slWY?VL&0?g2>9(6T!nq47W0tcS&;$(Sr# zDs?=H;C>|Aa&(-qP=jrVp=k>@#yVwEWiUPsnM@`}t`!vu)m>sK2(B(0b?XM`%xKPH z{woA_Ayo7x1Lt`k%O{{-M9!G}+UUwO;kucIx?uf37*ONsQ*&#pbnt&j$C|PUP9JYm zBs7e00pVgL?9NG1<^U^eqZt53JNBuGUC3O(|1kq^3t3LyjK%3EtB6~p)0^&~N4ekS zco4oet9$U|RX0AzXbuTy$eu*>V?Szm&im>(gYP17ac}0z->Q4&CfeA;m-%K>W|zl; zE^?~{hz1ia7Kt2Xie4xZ!}N=OU<8g^ecQ1D1|Ey?PQk4XvKV5T+>ff4VUdk<YXlDVT<&u+XK<;D z)F?-1`2yMEWe#ZLH(g4fqZQ`!a0l8zQU~NTo5nS4Kvct$GscP>%41*jOJB!z>PH;t zY!K)S0<1eciiivZv}OQe|6$f?qz`7iiPg$Gk1cNp(s5vG_hh)gBNz4f%cWr*+60Tg zA)89Au0} zNje2$G2~N)8cG93O6fd3e3kn_1@-6oaL1IV0S@sJ_Qpuj9BX-B-Wx#CkENdKg z`VsLo?xysw3%LMQDg3QOlOM4q`)X$Dr@(Bs{jHwJtJ^j_?3#P1Ozii<_Orun?;x=U zx^HNa&E=2Q93&VhI)Ipk%y)J(ie!;R2)+~=LCR(+M6qE50g#YfLOuKqiv2C!Cv5}mwCqZLyy$K{9kK^}45-TB z)&N;g5~8}^QC_O!(#Kh-Sq#7gyw&v=>l)43D%c?_0{(jKY+ z2b@?RUFU8`sXtV-7()RvcwSK!5L-48#J(mY`3V*g;lnJ*vErO#Eku8B~9B571l%3kV8N7`6O9f_x6tG3# z@g($f&FEJPr9bVtB4T(wJBdU>OOnUC3bg!e|GEQYArTk#qcp~|3A8x8DMffw@5vTaQ z9;7&#S)8nd$&Ju8v5f1{HE4|T$Whr@p7?`@>Kx~o)QTBDk)GZ^F!Rt|>R^GQmbeq- zdA-k@yyFd`b4uM=j?^0vi;pNfBV0{)U0gwGZ-=PUdz(K!5eQ%^CgF!@=~E0CS>W~H z^MHLvhJ1v&x9b%or0I&D?YEi-eFxMF8*!SppKxm(YjF*a>9C^a5k1x3`xDdUls#wb+|b=Lj*ql~goMQgrY1qPQM0 zphWx35@qXXBcBWu{JFo60o1lTTcqptN{z1T9%sOiuuSsq<_e_mcb99w#&GU;6YSRm z5^nYzs(A^(eX6ihP<2D~ryG1T@x7O~2*13VNP?LmkyZEI50W9MH{+MJafCNMcn=^! zs0Wm2_Uuwgkbs44)2$*;SXH^$AQ;>Ux0;>L1uk_JG4d)$RpP(8d4rpV2DE-RPsFye z*jiyNI*;|dG8(Zo39^t~O3|y>z1j4pY`YjIXwVYg?Ypi=FlthU0l5+70z|{MeZjLs zBtN>B)O_Br>-GK&AzQJcJ0Ka4_CFPbrF$T4ucwsJAlXLKdL|750{~O-s(uO%9UxK$ z6BVG2@Q9`Umfk-<+C;*OPY$p}>n!QX3bxtLeeBmk?^gQKM0D|a#VUd-jsqIJC+!RS z`+=6Bt!IJ>5|I53jK90WZ6u+jpb4degV#c&3K6*J026BC!YsRa6|13@_i47-sB;5v zkyiamZwiPV%(X1{ZYk@=%Zbt^p=@Bu|>&=gG9`{XSh6M1=2#4@+2LZl%55 zwfP8ySx;kN1b4r-Gf;Sb-M)19QD-h8uThVRsZCWw1Yn@*FUJ+P2{t4{fq`(9Z)o;= z`9m}sAM*|wIGsVGDG`PQH4*OjO)8BH&K`Ufz{!V8soHUm$$dzpfU6K8fKe$T@RYQ6 z)5h9$Gd>$~EW;KNAFvFgd60Hx+Qo8#-Sw!r9T;#fxC?Z<=2@e%{TWw#1f1(LLBBm; zm)Rr_6vvzofbJPdUs|+q&mbVT?X`7G=#<4k0bvl0fI6V8A|im(t&E$A9e3Cy3(@WI znkCcoeVE!6!~>hMEXM5tL&7XE;lb#i=~RR%(jyyqxnzSl$CZ4bIa}0`r(qZanr8#7 zOw}d`&Ka1Mv4O6pKB$0TC4We#+OVk#060vlD4Q|m;WU%}I*S6w9=R=EVF=r!%bn!d zPFQOT^pYjA+)nDcnB5IZr(|FBc0IaQHFiM2HhNBUOX;Xioo-0tO&q_Tbfwsd2vP~0 z=u4EAL0~~0!2_uBc9-JU#Rw{cP=aGPC-mIluCglfY>KI5CgV(Hwq4L+hIy=a$I6JB zwZ${{db-OBXgE4fb4Z80s0gJ|=^^|tDAl;N4TdvJ#)vjHgO{KxZHrdC&=Z_??a)i= zmWqvvU`d4z+$oV}PYJWpCRm0ZV%mAj?rWQufWJ08v@mb6`>QcvSdW7^#{>8GP>$?|@mQ8Ns?Zp_8JKWml= zo1O81@jA6$G>Gi9KO-%&nWc3PI~95(_;*`mHD3e{*@wySDdWkInsYi)y!c%x9-*CC zTFurrSpD7LFaz>7>cepI)qk5+$9LPK5@VL7#6RyoXaflz;5uDW1$gbD5 ze2+g7IDyZ-Q*F8jvIuDh|U}G`LUS z#>F0u1hxLb1LoGEGbj{=5X|;R_YN8Zc6h8yiPk5VD>gf*eRn1$$$02bvm2i7Oc}CP z%GOsGyd6Mov5s_aA&MTfU*WnZ_aK7}CWYwVK#n!-b^;#v#uxyd=nbs2EUiTkkHwCsH3ME$k!OTjH;(@@S| zR>p*~bYPRCtWpLxCXKwJ)E&4MP0C3p-CTE-K=Pr1ZvlG4Iv~F535HXim{M-#k1)Y~ zF;^KS2mCyS5!Rhze*D)y72r)MjVU^$oo|9?OnPRKL}piIfDB^t2ZqZw-}vL6-beN# z<2=woFh?OkbrYE~9*iPdD|9$2tJ840Ua`4fC=J<=i;3pFhmS3(cPE!EPeIdE>D2?! zHZOHd)nj7rW!db+Z5w?x!t;f`ZlK-ML2?S6MyG+gr+=fSXj>fjV9%2?!K?RH-rT%A zwJ6mh7wyXfbptTm@3>u$3So(cebr6c)svY%+o_kq z;8_5w{Vh zC{BvZkw8vGb>z0&%&G@(*ZB${J-J0&N&KTu{_bE&>}kIP&qz&6P1ctygCuqglBM<= zo@hF)CCV_|`ys7v%sIMb?m=7IEM$#%@aBh98j6WP(2|zwZmV;%rELRgM2S&6P&YlG zgV!WZaYiF~b2_^66u&M`f9Tr}#p&zy4(sFa@9DgEah+v0%OOo@a++@@g^w7l01Ehu zJGnKO&@(yFPxcmRH%^HocK?j#7poxW^%U1<_?Ldd4sI}~*g}A-+k<`Jq?N;NeqIsz z^%uo*)E;S`-6|$v8Q@Wgs<~cLA4n6H$hslA29214U}HF?2@^rk2W$x06aXI5@o=)X z97Iq~;sR0Jf`^3E9ZU&<^Tk9bd$>inr9W_yoaujQ!3ud(1VtJZWxc(bhN$Ei1l}pG z8^MuUs4U(cMRubukb;xcc&I{1MRePFf1h*;e*|COT=TZUnMMs#GdDw|52GS(y|P8I z3|f|AqSexcd`Y7jn?-)hPp)mEHp5YX7On5tMFg*Uh#XMc5{Bdk5IGr^W|tj>$OUa~ zr^b758Zwb%dF}9C0`jZDZ%cD0GDXoR8NxtXJ@&G!F`)C%2Q@e8(a4iYAG%FtCLtX=~)A0-QV@berm=s*CWDW8DdaRDD`Jw!aZ&Gfp|)oti=s)R{Q zT$jvYbtr4XPG(J!Dd34C!2%F%dq2Vb!4k|RQ@Am4D(>gwr3VGR{zmRBli1+kV-?>jdKU=<1S5XUYmrfo+D4nL>;{-gOd(*9e&Q7WF+7 zT6O&PE$Cq+^(n(+(R1`lj+1>)_=*L8J-U*NWHUt% zde5915+AeOYr1m9#B854JRSuIHz2u*j#rdh5Sx9SNs5XHct2^)-49_+_yJXw15E`G zhJZ-W#L!o-LzMLv7WC{)OS_FqX<3weQ$|kGILIGtcY$k#R5o(E>3W5mcS2A*P`H#O zGQZCO^8c!hBAp z7935U{sFQc;8*$hhEIhOc+5facejs6XDkrpWg8d;gvE1$A2X~mIL~jq-qPbIJBUFW zMoMVh@5Z3XvAV;4IlaxzXvs?k!vh~<2Ap7zqE1dr*MUuky{O-k(3@|kwm#DBbu2>1 z979rR{Iu8Y5y>(Tvzl()QMBt7o732nrJ3@LbmB8aT^+=w6&n_aR3_cjyYsaRTimj% z=cp*L{FO+Xpp64BU@rp;IRK23QZmA+ISLv-*?j|JtuK}*D9@mMiM1ewMq#^2jQL*n zDiJ&M%Jqn^fTEk+9H3iR2Io9@z)hR#X~j=s6FmP%+44Bfn*;Lr3BQ=s8<@tnK!&7E zY7Or+KgQ|d-Xtqu_*}N<#7Q^6unChSIe-Se4Ui%tyPq<>5}l+W_>@sYfETy}UB(i-xlXy`fQ8uLIU~RglI>5_?bAN*-JMhi~1o~}5n2~JEiLY!{XHW+M zgwjv#2ZWsLQwIv{fSt1Y(}po|vL#Ub#V(j`uSEY~?2wUw)4heHKo|Zo;@Mk8I};;U zUJu-!X#scEvkj^jt>Z;MMTHp3z~PxroGk zdX$pRDm)t|3Nj531ChuOim!X4lT_R5J}<`n?vNOI%!)x_B|qCYM{{R>VL)vx{$5m; zsAu>q(1`P&vm&V$Fq<5M-c=0z?CCQ{SP;#w=dNBm=t?n&wPsC$bTE9Z+taMhqkMG& zCO)EgeMjU~U$`q=Uad%m78c@2@eDkGDt6d9^1?TJ`+N#)7V$a|t0Fh(f^`9WJ0zm8 zuvBk}+xZxITttRk_%zGvaoGUOt9VwC9$COPF>tJnMv`I+Y#(+>P`k)a5=ox;IPBsr zWBn&8fxF@_``DQerNV4rLFu)%Sljp|+RQFi#iY>ixOpVu#Q9Wbt;ogs8S|W@6@u*H zXa=Mx00a5!qmzZjU?S@Xjg9Hyrb-2sUF5u8Z_16Keu$Jg{vPC>#i%QC3bN4O8x$?G z5MC}1-Oa+hS!-9T3r)4`lYg$w*Fm3$K27Atb1n0h5v&5#1k>|tSLKU`(1%kwf$6r< zjIWr07NR*&)HnY0^i|HrChk;-#UG(sp)M|#wE}tT!r)gt+EnMLcsx8o6N3luAJ>05 z)113>H-O$m9NJI+T<#Y_2MaTctS?_4P9A&*nQr1*VGt7$KDJP@t%hy^k7Ew7+b4B( zdB>%w7gTn2zFWM!2~S#S4C#QBme9LI8^BrGhJuZ#5N^hAqj*l_osO!oknbH4&E!WI zwMaTS59)J6$;bfW2Z=D`V3B$z@4%%Q`F%{1~ub7gqbc4}xWXtKoTFAC6=I%O&m`3nmZ!TCsoQew*18d4LoPkoDC2dV8#WFm+ z9!Mtf+oet6B>AYC8DEE5qDr~Y!BwTEQTDTSzFTHo;j<((4_!9?z@Y~`0hs5x@}Zg0bt<@njw>QJBI>jrY2KM$E*q{{NO&YD`D<(>0s=kN(e8m(;ix=(O) zfw047D-8=wys-DbT^mamA=BFO0i=5Id2~?Yz}B1*QDEBg0dqUP(2Jac2_uyvxHXbW z#ust7i%rCd`)DTy?Y{#)L(Hh3#x$W-&nIv;{>{C1}RL3&{ ziC$_VL77m()zECa?IKw@@U9Y>b7DE&z9M7%h+!A-t6Hb=DQI)cJUW(cHue;MkU}2M z=0@FC1P(J=<1+JpQpvu@7$6hu_W-$RiYD(2$r#xa z7f_syKqRFrpMD}~Ert6y5ddUU?K6vmDD_G4lH*6@cUznOerZb~)9Z?b3>I|BD1LGa z;q^9l1xF?Z)gOcJrpr+PB>BiIoDv}B(*{Dm=QE3Vz>eE)&J;FM0TgMmNaACd8^cUm zA&_6`^Pv*7Ea{1z(7_9a)jGiM!aN6A+1|%pV;RE0zD=y+b~T4&QbfYq&}R;e)vzOV z^XosB=Q=niO*9iae;;xF5Nu>;a>%_~4tsnYQ7pUAZB;?xC+g#+=Votk#Da{cyZpU^_cgp0wL(FY}V!fS(M$3@N<$8e2bE1ib1 zU*yMEv|T)g->vUGSFzh34~&CWiIuFdeslIj9FM7ujTx8N&8dRh%5zx=Ag(-IIN~1b z&p?cxO+oYS5lf-{F0x6QE(lI5Ywl!_TJpQMX7J#iE|ojV*+#PhJRmDj&Z-^tIFy3= zXQ>*Mz2ZLj0HgyY#R+>5s)9Ie2-Xt z=W6fEG@OGCf$z3YlQ?=e2j~*a)!X8!{CVomj!h#KlxY&dq0Uqt-0czClUdZsGb!Hm z_XY};urxT+8UiaUz35n0eyumm7YnEdsvFmb21jTi0XXTbdAxKHGJ?$R7TgUL`HyAO;1u)CrvS1wJ!Q3ETr#;vrU-e*0WTjs1fDbprNb94pY4o<=h28 z&9gT=K9%hT;i3z|PWllk<1`JiS#?`VkzYSpRYO<@G-iN*m)SHd=e!$WpVcHOjF|xO1ReLc{ z80cAi^DQ)=k*oc6()@PM!Eig5#Ud}h6eT>iGLq@{=6pyNblyLx%{XO%4LZ_6M>hZt|l-2~sVuPHMF zqDqv{N8{D5;1P^DdOAZh#dxsj5BbBo1&OFvP6O;4B&NMXE*+HuFqdChx>>FtI^LJI zLo30^H&BI~w`_O?=(yd-vUawz*;e7y^4t?ydudNw9cRb#C&)(k63w8@MK2(Zo)np; zoe!u0j5a9W_Q}``#GaS)9Uy!)Ser{UTUI>JSUmL@v!f3Ov>2o26v)KjV(MBs#hC82 zxv!wNpdjRGG5^qhk{0QipoZ-!P>Bs&n*;<^e;Kd}3m23kc3Xy}ihky9Z3$UKf_PWRa?1531ssj&|q#pjSO-M<83da@N|O)_~RA zK2qaSda9xUGZ`>|2e2HGH02goV}}_i9f4$fX&@N)GNk#i&kWRsZ4Ux3J3mHYf$6OY z)=ld=zYKl=vDh_?b#K4L*?f8*ogf7Y9Dc#w)0`$>%k{}3k6D*qU6J+@5M`ckE)eCw zu4EA%1PfL1NfJk0FaGC+76=1&%B7Y+Yqu|iPXV8Q?{z3GDf@Q5WY%rq0@6n#QQ%Us z-nDm^0$b>7T&e0_9q+)*e&$qHn)u}N6sT?bDPiI)J#B*Uo51<&f$5J!g7fmACJB!p zZkaB0i5ytx!V#7*)%uA0He4Np&&13h19P-o@@FAub?OCw&eqi_B{?-*BHMOJCR1yO z_+w*sg}*ueZcIc>*z90%F}pxj*OL=Wn@&nx&#oUl?NSVJGih+U^Shjpp{-H&ia^SI z!)vmmxcS*uguui12aBFdz&kp*fPE)Wu^4+7L>`4?^7%nZlkT1YJqD*XFcvi6y<`ur@Lf}nX^u6H>mWw0i}hjjSLv?bGwpFB+Pr<@jaf&EJH z=^d=uVaZUs?f|BFnQRd9OWDE8M0ZAiixQOhg`h)T$5yx86KJsM@vC`K;$0IJ{+c!Z z#qVqB{qFw+qJ`u-Ky`os0QjN<0Eqv0sPV=oPDYLv_WuFN(>c34|6lp->RYx)YzRJY zYRAB`fQoJxH8NQG3)SrFAYE}_Me`8|64EXkqu-ygBvzyu$=5P$6j0A2t1xrg?&5G_ zo01AEAvEj`Iu$prN~9Ulv(|A9E3f(#Nf}hGEaTo*DQg=!G;Ee#8$)a+qD)jfS zJc-lowj*auR&76b9xCN$IxGorR0-pE%nIB(I)AZZK$$IR6YU<^8?3UZWXxJS;vE#p zsQPG&qoX%Xx>`EZ)D%%VXq=xG@;9c;KGiZ7!q|*o``50_X>hdn&xWzTwb6)56?2ws z{&^XtEY0ZXRuVbCt-YfwkjNYbgKgW*P8CyBT|KF;)g9}WiEy&^!|6t@HKewR-Euv)Ma}xtD9pnuFV)!NDOKJ zhIFh#O~Io079TM-W82wD-=|t()pe}hXKDc@MMgOogcR5hs-wBwl&c!_MjL3?+~R46n#GR)ihb*GT@Fc2{K@$)(D{{c$=|WDqk@VF`t=QP z&!cKUvS^a28k6pN=5gok2GE7b8#ueCeP{Cq?}g4QJsN`F4}-t}itQKF zC$>juhu{Wb*#Zy&dE7A7Gl<5ZZ4kg{cm z7T_pMLe9&EI2ooOm*jMq#AVannP9!AiK?M$G_A5`qMW=nT1>TLLPWP@(!R;9FrZb@ zWXHC9GNa9G>eWnX-ZO8jka5J%iRe`9OO~@}@=(igmuNh;Yut=%P8LF1iaY1lm&VWX z7WoC2mjVWX0{G8EY=0p8U%~%eg#J6F)6vAh*v904RZT~AES>+nF7D6(fFM7>0095B z{3kiezjP1)RmnbbiMR%o-4p--+S334QvaRgrX~i?E{-O8Ms`ll|8I`vj*dps@u-9E zp56d@UksPL##7_ z!;M;Ai@qIh~be&rL%$>pi_I%ayargYae|{Z0ijzz0ZR$IZ>bosupKQ~4h7IFu z;pXk({bk@4^ZU8@7~=E& z<<`!rTEhE%o41gQ3*)`*V$ZYNUcdW!czV0@#nR=%-)uMJ~!$)c0X!7KBwo4 zi~Sj+r^nyz@!MVe)xSyMRkUOkoJ=kB2kJ)fRj zT#S3v?R}qITv`6j)t$}xvGMSJdm2cZxmpt<{n)sFOG)eV_V`<%IaheT`~A1g%`S?{ z=l7&5?OLPm=i=d{V>3LaEy~>cyntoh zj;Lo0r2%#d?*|vF6({VA&*}(TCbUhneN_mdC#}>!VAR1YQu9H;40_Vg87gf=4_})8 zO`p-P;a7Ht!T^~QWS%fdA^=kfp4`E~9C8hXpk!u(^B`U0vK@aibaVDmIpo`4DfT_c z>bEsRj1}h&LytRkCT!qzL#Wba0&?y?VuiOpp<^PDe-yJ6=$agQ2ca>EdTp5|8|d@I zAp`|VI}o_L=mNFvUBa&dkL8Jt7a(>NZS!zdAZ>L7;@!hqT0UakHOeP^n)0OqWnIFDlW@`Rwse*E%nYU2g6YEGt8|4`j_jQg_rReoJdKhpgU91QvGpr2utv<$OX(I}st>4&#Dhih zS7@!#q0!Dm*+EyuG7Khaa^1OFK43x{mTSy^0c=uSH zH{f{5OBFcaKpAEHu}wtj-Wh7Hd2p#7$~BYyrMJHYsdJPCr{B5x{06GAC_l5}M-(Dp z*=Tya3@Wmir-vBX(B{Eko0AW@Qntc)NF6vhu(DOsBc?L$Ne!En;HK%`0qqBID%lO~ zr?!Gzj}pkqD@ZpsMA&)>6bLa=ltLaeVS-3)#`D43uiFdM0BfEcl+w>(Rs0{PCw-tU zxU36ejtAY6h`s?qo=E^w28rKzjj}$3+!jSdG^(xyg>pO$m0v7X*FrIT$IyQQgQNC4e0G;39<|-?6eCdm} zDni0S-acnuw@&WnQYX#Ot7?J$IE_olw~)E{xr#XUGFh1)vHz3Jo{=$%YM!m}@|_XY z7`|Orcem67g->I7gtt5^b_s2%54^PNQS#~!vJTBAiU^4ZlHNZ8VW#--N|ZvTv=^%3 zyU+m3CXdhLLLlXpc=D(lE?-BKB&(44*h3N;N6)aE0BpaxLO=c3r;{~-&J|9M?LCbh zt`{H~74e9pW&ZiHpphFWYgAhb7r~uS`K;wCu|b6nl%~0a*YsPO4uODU9!}?jQu8=y zWQEz=o`QY(dZT1g^e;M=}(*LaeR zV`|S27Fnijh5OS>J7uDHD-H3_fXTtFf<=I;zD!EjuK5Pu*7Q{g)(}`y@|M}HXOT-2 z)jV{Mi&@+!03M|m)if^pA7zKeGQp39S`>5uleW;GDGxN+0pJR43Rl|??x8$0nD)qj zD0x-RdjJd44YyYnEgpLb29vEirc1CksB}N<1LO^s1RhFme}pmuKY?T-5W#vXKm3u> zNHr7v%fcl`o*XLu6@zL+6;(hG>g`SOr;K;!j}3Rwk4KuV4*ve!0*?Fq3)(a@HF5-)3ka!b{2aS98HbWOfxFiv(N zWp%M?d}ZgCwVS0)M=Pn@&gwD^NQ<9y3BsPNrWa3Cp9RFAa?3i=i!VT|fSR|450}tDGf5gPM)mC0cF^n9Mtq_w zYvs1$piW?-R@wusa9Wcj#nF1-57v>Tup~;fVm$a=#Dta;w09+nx}`0Saw50zRQr!k zN}|^KyaJ>y(-!)B>R}*s(~^`6P$H|PO?SYmp^fkadCyp|M|e0K7tkS3xO68IiH(zG zsG8zRphHVH!_~hAA-a^ibw;bFr|B=>+%$cPU|!9W@f!*L0PZL+K-6MQz!DSyo|*;k zB{&7G8kE3r*`?9_pWih4|6rWg{f*hsEk97vmPzt6g&m+&Td+tHL~NrAmUQBdilhz+ zP&uiM;+8lPR2FOXY=2ALfTe-D0g%y@SPL%ftf&VKchxC!e3T7~?<6T9F{^w3;cnJZ=`qe=3vjbFfg+}+XCKwV*=)t)QP4bVre z5C^1%N-OooDV6BPLQA=!t%sCp)2|>0KEnG0D56wIRI=JE!r3%R{t@ zRZIg5C9FjBXt-GqQr_ipwUV5u0&#&?v^BPTvD^*iS^eS>juom)&OnL41>dzyU1L$7 z6Slih)GSIIG*(Tz(8N)wwvzWL>kWZufs8a#IRJtHFWB~p=u}g*nPs|Kr=~?TsWyQ! zJ$D%MeGKgHUG)*k$pNV!f0~H>3giJM$4M$drekegY0H$8{Z(QJ)n~*Bi|OJ)wf77P zBMpNLTga+WuN9zOWg7Xj>TK>Lw||~at0FDF@y>uBfhnx<@$&7dl}PGM!8uw?B7bPx z=F#+4O=1baNIS0OHpC&egbD`7dqrbjSkgb)CPpl#WsqITRNcX4y##nougEZH;3#5g z1Z}dZ!Ajc=7>U*bttXzl5NTX#htc;ZN80Prbl^dFmi2Od{$7$D?@s}hZUz{=#d22EhV5C-o_JqLaOQSPMP1uI*ep7;+71v8L zqEHe#v!WPHvy8jeA!&DOYAfqt=ol<;ZS6=-aSD2cA=Ln%*nH~NNdT%CYT3duaT5CO z0;3L;N+Y2uu7p>FyXgzSQl(5lm3zY-dKH7rN>b+3E;p}n!Vs%^@!Hmx;5wI0*_?#% zOYS)Bt`POn(5w+m>T|2%VNS1Ux_(Zy8**vrb*+78E}V^6FIp znle*$}Smfm?| zzjvnI)Mi8>OZ=VDrXJ&MeI5;Gs1T!cwy#`?{zWrcPTZmu*ez=_bsbW6^`N|I8C?E6 z`u1@6=_w$%zW<9+iHtB*LFVkefbHW&Fu;LYyt3HI53vOtN?gN51{)uW*ry)a!nju`@7{Qxm(p7twqulGHa9dF(2{Am93HrQ1m97j~IIgwXyGl&Os6H?vIR;Hx%q3~mtFee@ z#C9N0uO;y3Zx87W_gZYt#X573@QL<=-h{O1FWweV*8 zKAJT=Gv$+owcM^k8_RtT;>YYh`pweq%t&vA&tlb64aCAz&WdB6$nQ#DyYh1hzYcr$ClY{HLIdWr!&|EtXgDjqf?q?`8bp%mSN(DVoxfi zC#W=qqhdGKP<4E1NnB~4ePQ-{1(GXHew8Nj^`V{TuvnG*=`!R)wcJ-WMc`7?JAJ|B zf8=Edfq@Lo_M_RX-sZ$`t#q`CTotGhKxYMu2fGVH7_0F&et_m54jYW9TC)4Km#$V> z*~XA9!gGV-(({Y^J=yG<2PQ6ZQT~G6fun^fTI_keT-~1w&>{jB)gVIq3-uraO1MWu zgf&2zF3eIc;5x`s%I71f3Xl-v2jm9Fr7&v=l+jjS(7=HrVbONl+kY38>8CQ&>l3$e z_b{NgHy|~|V=goY~ZFoK}PImCdx?fEX zl=ILvwYKrHf|ZDRrtaBFJWw7bV?C&pL{W*h=5b*N)?6A!J-5El&crZ<%~3!V*F&W~ zAIg1Z5>iwqWOgdWa_fOi8AldIOm{zzxmQY;16m2YwQY>gfXd!jmi3`qI=U6P7k{3Z zRtiWK!8s6=Q}ky_pa+na!>l1~(YO&+QNdC&3*YF2e>0q@Uc^(|6ZeL#3u_m63k1n! zs*I@8m5Q)jd@<6Zrrvy!nWBXe<}w3a;lX_*w@+}#e~YTkHQDU{PMWHH zZyf>QivEKR3R8hYS-FYsbJJnN7(&)gJD_z>(bY=7W-g+1oc4uBeotk8@VwpKg3xwR zWzAiz2wm@AFIr)aj8KsI*+7&ms@<{yf=F*q3?-=U5RucXURQEu(! z2w6^43ZbsiYws9vhK5SE@jBxX8w^$%+~5(P-q3UJ_%1ky@9cAOt?ud<^LnhHd0Oa9 zJ9|7kE>`FI+JG~h# znLQt5=i9-jZ>+_`UdPCuSFVe^8P3OqIFeRaC}V`5Wtlv;Sj8$nYGle__O7x}1I`O8 zj4<*$maFuD`|e@Q^3`1F@@&#%Enb%;DIVGgHUSBEaGd{xfGoiT_=xnf$Z_v5NbK0q zmO;!-1;xl<9KPAK{w+hLl}G_S#JAR@BNSI*PU$6nKJ4)J&>{i)uKy^%5rep_%Tk3u zDGOypN_OK1L}tSMU18-f&;;L*oqblZzU?=}r7r9y29Fn9tPY4}fm}dYvao@6cG|cE zSWJ6DH<&V@E;`l#ma_Wl5|CF~P^X6En&gYPUm=_3k0Wet)jX&i1%UsFFdW_lfOY8eK_rE57tV;n9Mtt>WU0bde4@179cMX4w zb|~>6AZ>S;tGkg?ma#G#Q?)I?vHk8-_R;x~YTDQmJv}S;VrV_zvw>ed8Lu46aRmin zvmzXTgnWV0aqVV=dTrApS83?HR6aXmvjM#3(Cw}=aFUGwRBH=KYM^@dWA@2`DrK(LeVSL+H_`TF69l8LE3y zLia)DJ4|}W`zy+kFOb*vmrX~gR)n77`*-mN8iP?%JQUA^iq4Tv28DgXZiC!M+tB#J z52&Hox?cDK4zR_|;z|RTgTMK;UvxhMEyz>!1F1N*lwNpU3T?p*W$6|AtN$Dd6rjzj zAf@oyn*cXMH0=|Az>DhzoPyQ`C(k4xrS*nwnn&dG@o3Li(X45k){uf!JMLs@4+R7D zI{*U2(#aj#+=-G2kG1QW1F7T*HUUuu*xj7`tCT6W$fCZihIQ$3wf5f-aFsEM<#WMN z6j+8I&)-no_vy~OW9-1V+T1HQL+0NC;F5MC+I#{cNZCaI$XY1Z3T~*V1ICC|tnc56iH77ODWojJcFrs0Pz%qLw1-d!T zHrqXNF4kVzs=IEsz}`fxoR@7ohpq#Af6IHupp9#;;b!|KFb9@MVDc_OAnwWU2#kn( zBLd7aIiti%hjWY<0jb<5oBrfL9+wX_27R1fUmDOc9DHi++NYtW4c4;BQIKQFin?fX42~2mmkXmmBt`y z^~6FC1A-w?I~SF(dZDbl2Ve0nRbZD`TCz&$@sa+m;mPm+NXu@w107+lf>I7G7~d|pG}%hkVcGmb>kLY9MAvZ z5Au*{6iiOeeMvMT_+rh(Hz`;GqeIMa5)FaY)=F0<2f`0_wHdL+M|X091W(mMr6H56 zsM8kOC(N_f*gPdvGiZfSby>%AgpCEa)vL;yWF^J9HUT}=_2BE8e?Gs|pVn!DHX7hj z+-vE<9~4+PnKyVq_ZMlaHJKugC76ung@NX<-|o!6Yj8IcM@ZDbZkWez82>{gTveWS z$M)E+YiUQv%)wA%XNQ2q+RYV|0wgkExX{MVsc@dP2;CCQ&8O}+X_07vSh}ON>ey9I zDwHaE^UBj5%bB$zv)_6b)4mz~viyqoY?W-gEqQAn5YX1>fgh<4mr91+Ztd~Rf>fQ& z4+WQv?+C(TGy$7`Lv36={Vpr1`m+8p05IHD9_%$Ov}4lx%ncwiNK1;>6GcMLt8fi^ z8JhdbQX)ZiM{RuOO^md43@$JtyCPY2PUW&C+G8vH5;{K}zp9JGx9`exp?tClB%T{4 z_cWT+T}Li$ko#NoCX$>}%BY$3X&YLAn#4hRK5!YvaZ@s}-O`8)AE)SK#2>acdXb@; zFBS@u_m%FUZTG!A=lbFX#1r!rXH#tom+k^o?UbyQ0lt;f zFfiP$($sK_&5496OQ*5x=V|%elR3=x((GwX)`6G zdBk~nxD@4qNm^226IH(6u)G&n^nz=uNDRhW*s_#XnRZ2QclkkDrKHpiQE{qO^i$%2 zBZ0uJMeCin{qESDGF`*tvuWmq7}P_1(b2rrv_XVw*?ol6DUD9Jr4W?V8N;l@=D||V z+-{rRM(oxp3vCmox@OxT@}neXwvcX!BEtlgw)>L`LL?b(-PDyzrtrmO67=IwCdIKLZ!2kZDH+RPqb zv-9!PMGBt&$kuq}U5pfzPPuQ1Tjq!hr^10bb#u;#;>L`lMig(Mw-JMQu?l+B?IHh-oiS#P&20LhxH4Oeb?*w@y72-Atu!y{R{t%t z5iwTgfTxK@3ioS-Fj}%4#nXRuHu2t94-Bm$^v;|N@P51vbq9GLp0dG{L>s4=D2BPR zi~|c-Da$gf@s#93@3P_v6TjkMh&LFTS`u?XSlLJF^u$58M#%9*EZegFh$CuT5~jaK z>42Ay0)OP`3qWcJGDXui?snIIgbw{b&D8`MtT3A(PXDer_Q+rBa1{~$WjMm{iWfTe z$@MVC)JtZ5g5c;pbnQMKO@=4jn2LBHl9+w6vz8FH&YifwSkcK<2}s5m9QF8YwgIm} z*f*xl9!Lux!d%090ZdBSP{zV@PjULvCJAJ{DEAh#=$pWHS`i7W3L*Z7aisA@4jqk7dOtRjS3P zwU&17fgbQ$QTmzVpyFv%z68u**FV-7GKz1GahyBb2nBoC-Z51{kECQWn9cVvVjXit z^h_F-;%?l5qcM=di*TS$)>@Gg8&U5 zYNWGn0K`!7c7x$pG%bb4Yn`iA1+7oqVEgnjRv*5}s#TgpQp45C*K8XEUXHLoRyRS_=17xkUogg9CpB<8o7?dYw1%1$5T?U=qqEoCPRd8x z4vm7IwNVFWpm=GmNK)RA9lPk|+F`S8MUDt7=*j)vLwGqQc@~b~nTl~i`*zJjhIAT7 zAs@CNb+Pw5t+w5{@fBDL_E19=Ub};=l4iSL1BAtG>=|#ZSafRNOqteYqj-P|u*)1i zGCbvHmp@URIyoI zs`Oc_UuT?{cqZ@;SeAqlhFoTa4dmus5072+UYoBuY?2g?f~eD^r$gX!(5hB0dSs6} zw3*@Oo@RX0tLHCX!m+Ron`ws;Suz@vWPQwCB+@}UW-?gKD%lgcU8tx3#<}Wvwx-bS z)T8&Uqgm9sYx5v}xAxNQ!mI`@T})zai#ON?O^{@92yU|YqZVplx9#D?S9HlTPhpu7zD6112@fkP-k zAK=NcizVDTrL?CkTFmgIpdKCMqgqm zVsdB>To3M=ICucz%!fjhzcTbuV>PD`)T0{rP@7^IsFYCFn&sOQ_HB$>YhVu)d!7vh zAD@qy$gr{D_)v?E;p(8ft46(@+C`DY{lUMj&r*RWb+M&pO-4Qprcw%sW$V_QX;tG; zcL9KXXKJH%9`nZ{`?DC5oQQ9^lD3jI*s!ZowBBW3!et*Mn7fd zFd3aVD4G$87hCcX#ErYrm#%(5h5&mXb}Y*Vo`J5FTtPy+125ZM%Z6-|4o_lg!9zo+ z8{}JR=O2t84P-JHZL#giuHG5S-&M)+O~d|v*fUHF9jbu4q#mK3#Pt>JPrSX?vFGg< zAh~Hez~E-c`cYSsK~JX3PPu?nRLuMXda@F-; zm>`F0z;*{>nJR4l)|j_%Hu*D+!IjB_MOf<=8?5ssW|w`Zo-5$nJ5ePL^CL|=n6;%O z^aCq(s$4+>^=1k~KzBkCHgS3iz$|dg5!pnOq;}h*u`aeA4ONsQxh$%4e17Hrk+R;cQ`AYGCo9eN6RAR9raCWu+LN64 zy$pmTrxdd+{U946_qq3R&o_p}?ZhVYbor3vfHVxwx<@uov4%*&fp-i!Oyp+5HOM4Lq`&jD$&Sx#lFpDwckfEu z{UE&W=-a6J%T=<}>L^*~t&&%Fw+_alxT_mvVOL9sGWGJDe?t}6Y>(-_W|$wnNkP}3 z2x<+7wy>VmAtb(l>=QZWQV1gN);C%SyFJT&$`nwzOkdhC4?VkEO>sNTF+?woF;cB3 zNK5U#I!i2W4Nr>;j`4Bo1juI~s*946F~M$VAzDcq&qjg5P`TcOobO9Zp*TTc&lO4L zRp)O@mRffbOvZDq|3T8rIp`7&uu3DM+R8ZC3(pU-6wP9hHdB^gk*FFMY{tVA7c%^O zcMCV>%ATpE4G2nRHl=qi(J5Uly|Oy89Bx49|LERk2b|~(Jp8a#(-alR`{BBUmMw`R#6j=~+-{BfLyLI@~$kMY(rwoPXpmN=am_)tjU z01Eq4-qucIXJL89*)6-0YN`XvSQf0Q#1W;D5;p=%$e8Fut@U{7E?x*D$DWJMCcehw z7QNX&q!@&AhK04uN;3ml+$pz9IToHhP*TYm{pSEreKZvq3_J|YQlg0pXAT3t7Nh4w z6h++JTKoXdo%s4vv^iAfcnPC62Vz-7k-e^#zA4(CJyfNHWD$(wEi#4?LgwWLg7br|NX~JvXAMK7AUvY>-6G@{#Kn%F^b!Mjl^tvbjNoC|a~s%reZx`d%jNNwamuTv^Qha=a*YGWeT z-z&NJki>yy^htI0y^>T%^DqUk@c58~jto&W)V{yYwHOTRGF&kJ<}%U)qT4OZet zH(|9c7EeZkMn@MPSCP5&d+}qb{_AO&%*<{X9z5GAA$y;{bUYq8p?-I|SPbJHH)rm?k)h3@> z(7;47MQW6s+6axJi;+lA>e?lnX3Ot1-yNUTaZ&Rmm5$iQ%7=9a$BrM{o-+HQ_TQFu z3zmhxPXi1M6^WjPq4LC)*N261(KNY7SB#n%^V_h5*0;sKZFMfCcY>2Hnq?*CnFNwf zl-HxdT%!P%XO$MpK5r_?Sp?wkOxJ!?UigxK6*F6^Yuc0Wp8@&c&&3A(f_=w+5W5R7 zx5O-#x%xYOAY}|ra%@>eF!^05HPbc)A{*yBYL#gv?MCjHM&K>rM=I(I6I`jU&4>e% z(O!~RVqnHlRqd^I(XU*(Sh$x4R4*3Ku^oyeP4ebca1~|#A}h*7#@kmJUZPi5on4Bi z@fP(d-D5w?4qH|KLF^;kt$2^ICU30Z7Vfom@g8%#T4CD4`uVUyeiQ!LigbEl%>|jC2>n9B7h%>X z=ts_$VzttoRW>M&DNnc7_BefWQw*8kX^roKs+x4tA&2XA$((f4btYQ9Q`$nyL!W{|y}=Q;5Q^ z;OFqp$p{O;^Tg*AgpTkrc5+^ngsY_d__;;F;khV==y`(1;{Le;VvvYO06syniNd!Y zQ)+Bq^9TdU0w7AFh1=d|Kmg}%F3AWQc=_~e3WDtxU-5_fw3)$P4d1BL%QRMo4fkqoD;o3VdV-8sPRq?HDn+H)>F4YGOW8FbC!c-&-(c zeqQ)k8=;&rXvaYp66u>?XfS09az6L*N%vhr7l62-Ld{E#;jWr+3$jy$SSp;t&=g_j zfF}quDLDIQ%dtpAqlB5%oWf3|zbZ2+zR8fncya{U5QQ>G2aSHQNC(S|as+>o_K6~6 zk%s6AsoD^OtAv`fiL|+v6cRa()}$a2c>rP>jyOskQk&BfeWU+Keqt^vEXFyAN$`zo zN%}vf{U-(BY$KN>1UXQOOMmhsWQ`7(3RFF%$jGfLHZ0Oq$s@qZy7R;HKtAZvmYdnWPQ61=tg4IB( z@%Urqx!>JT9H&~;a(r!!lZWYe8ogfP&%@;necIZcua{rv_s`?=nq6Ms_6{%KuJ+I4 zlbzaK?f+oytAZ*Cq9uW0aCc{LcXyYIySqCK&c%Iz!7lFZI=H*LyA1AbgD>wz>|2SA z-Jkuaiu$Rp>^|MmRdp&eKX1+^7sH>ryS?uA4jvvB+j9i4KH(u6t9HdULH?3w+;NgUk|nwr=OT|h(7iw{YNMJFNTjwguJhw z+YMfB-`7us6mysqn4XY56t@i-6_7oUAD{N-$0skAYYZ=iwm$C0HwRDl?k<`Yu}ex) z6g{Rt2Vc7l4GgtM4~cNyo*c+M{a^VgR!si1*5IQ+i)x2_ zr)0>mzUb)R!54xSo;HKmiGs4#`;Fz>wx&o$8&=wvGBTKU-dXxAr#o)WMCPGd$@j{z z9q`0rySWYN>o{Yw=*sXBp!lEb*SNi!{Vg$lToB;+M+wUp4baJ$7G;=9f9=BOIi|aK z@S$mD%&Hg6U1=*MX&16;S(X%5Y6@#wcNZuN>lx_ALPQ~Qll^5j1HEPH`{<2Ujs^^o zj=1E=cR3T;E@RR|@d9udu3V$fCBs<%)+kH{xEI4T32na9j8;&KFw90e)F<~73zblh z%p{1CyIaeOtV-+$VyA?%*q(+cvOLb-HQm%k26mJ_#Ls-U;=Vl`biPfrF|HC(9);MK zlSe-CerThelu75$Rl+a~4>d2b8I*@&{XX?XD~z6{vz}3> zZ&5P+lL?BZ4lD!h88dTbkur~nLt8?FLM2Qx^v(Xo8(~pPw#>cITwXp~sUbW`N*QyW zA0D-MaULU;wR!S(L2Vw|P6JR;p2(iL)e0(LN91{ZmGIekEZs)|_U%G2UR<5TS#l?ixa7p@6Vn{<<07 z?5~cT**n^xrWeppj7o0cq!nLjt!TH-iyrW%7^l#x7@>jU+J5XJ-eY*)KQ*GBv%oVB z{!nGzx`0c+G#${->>00x#^iFOTYl7@#wkbM7AxAtlIlKz+8D3pRxpw03j}2nT)yB6 zynhY>7F(ltD}o_H0*7iF90U@7h;#X13t(u+_es2$mY&dG9c=hhd~MD72#YAcQgg=Y z3azRCBnQHikH7`PRn~;lg4e8vAd%bext``{t=-L$DVW-cD&ldLVn67qM+Z^iXcd;a;RTTW8$Z(fCuO5GRFhhg$nzZr&8yG?73M`9gn2U8(o4(G* z8BQiCrzLaX9$P{GM8Tyog#=R?9H&dDLP{~IQQIA!C|l>Bc$kpXHgjIyp_u?Hi-Nc< zy);uhqs;>*G|oXO0ZG=(SrL zn37F~)CKXdFk65{Ne-PhVDXHYZH>sB6HXL zA7#EW9vKIO8D%U%3-ye0YfiSZ89;OBlE0@TWNqouepn0~D$~_AdFR#g^VyLE zq(p%$FU7AIY%uWb5S2Y;s437bO`SKezxcgBXl5vt^IX1ufH4h7LQ4T|g*fh0-G z@UC>Z0o z&as^CZ)=oy%;3N7Ph;((+yn~|vp6F!asM{dtajXihnt)Y9~nExg!7ZhxD8s;d)XvR zb0R)Vz~cTzEj~FdqdZET_fs>>LWr@%p1;xHdzIpbk(_m?$r(!M&U`!a{~s*D{~+r9 z&n!Xl|MPUp+1%9F&d$)o+}hI0)!fX`-p$U{+SJb2#f9-dYikQ@Q{(?2RvWrF89Td} zGyT^zD#LvL|4tLkX+R5`L;?f5Zv+F=`2RGzsiT91wWXW$e?t0#=;Hogc=}p*{&zeV z`i3bLEr|YCmZOJS$)8)z+4iqZzQ>REN&Vy#2$tw0XcmtOLcZppI55gU1c0MOl>nOu z7JRL__6q>(8lz^{F5ZG^yU83iWpsSlZs%h($`!RHpImoeb{S71YMEc3TzTJdx}F?3 zIldR@mR@w$>~^m=5XCxKA6U|9pHS+3O1tGSf5c9M_s&feopPg^?KtjuGq#!UqwlS& z3D7fhO&N(^82=N~Ej4bY+6C0Dr(R7y;bput@gBQQ#L)g|(kW7Y6xUzr80xH=w#K!O zWM@*~NggHLoD(jnlLk4$;|Z%Y3~id-xYvhM04Zs*+jI7O6n1Z`q??XfrTwOrlbNP( zi?kD~xg6PlA)of-OYhn{XZzyUa8;P;>igjwx!22#-&OjPpN!jTpD7*fj(W^JMf$!* zTQs|#cAuDDD~!?vdx)+f2$D2=y^%UN>7zMiv?_M^`Vj6k;ST?5avbfzxM7vAV=dQ6 z)_pd>^;iWi*M6x(7_^jlti9QFjo!Jul+5&dTa&(*+{+(%T}~CgtTM^lKUMCacldg* zJsc)pG*cJA7RkmffPON<*md{#BF1T(x0fvej zCT^T1C0<;c`T(LeU9rmju6-ChP6&%qrDHHV*bC-A>H>?jFb`qKgG!8YG4a6 zX{3K$0~uM?_P9IY7JF2-AtI_Jup^RN_YiX5xtaFep=os24MHBPX7aM`-?>}LxMPK` zPs+J#O6RoXlZ*SgOUeVt-XVL|GNqYmyxKtnx;=6qi7Las&1Oow4&t5yxDbqJF? zHtR5J&-bIl#;z=%?~p}capx_z@E32hz4hr+wJtD!l{^EW(mrtfVWk6)yi96%amWsQ z(W1B-En3gHO<^EAGQPs$LsqPj7U*bSY@pryHPnPVu%QDK6r5Wcv~AP5bbEyG&MAM< zx-*gAy66U|`Pnf>Pa+EGU_BQZ^UN=5qY3y_c)mAu47B9Tfa|+<@kVeyyQdb$pwR03 zASfk$+pg*_W{MO%fnAP~z z2>xnF{+=#6AK;M)#;{(fPb9?3T8`$P@TD^4(O_<7gtXs*{mZr&ib%yygJZtfTK z;{z1`F}e2qf}6pX`rhs-YY}~I%K2SA#TQWT`l#sP$e9u@w1RLPKCk$|<FrdEqP!aNOR6vlJz>!@Hk0Cx({7FnQT)WGX5F+Y8st+vm8p0|ilqFyhB6$+Ej z9f$B$;J!*%lI-nhFprcmH`}iXxS^1)q<}U?QQhDl9MG_$I$=_QuvFAMS|%1$fBfvW z1wto77hO%QL+?`Ne)&RI%(la>e|%tJkFo+O0P&2S*!7rdXr?_a`PtvlBL_;9^iu<+ zG$iqt4gv$dYZJgw4oxC6z!M=nN(Uq2(7Pv4sttqH+6D!D6|g_HL9fQ=o$buiky>7K%WIBp`8&)+%Acv)vO-~n3*l@OsAy();!-g8VjLvP|xglTx@TqdBZQBB9$Uifa6)vKgcpPcW&(xo3vm~^f)deIYNtGp4Y zMOf-_9%`$$Mr=@nup0+aKD136|{g;xAcmYYo6FFlrjrB(BfNB+?MbQ$VXP z#GFheRcJ7kh_ag#b~LnlojJq$22}%l71WE2NoU%(T@mhzxdZv{b%ziS>aRZf7M<_8 z9N!Md!W>NT2=>YBmhaKRiQM;)N?cacLQ)T(`_p#2s60L}O$-n2~+@ilunHU)2 zp^B1R^h_pZUSZY8N%XHaiFLXu6~u%Z=qaJ*{Dbt>8iXDhPY-bZCH%l|2x*-uwc-z; zrlHPf4uPix(?dWove2^5&kqQ(mPnRUdV;+sV0o;j;x6g=-gpo;M2(5=EN9d~^+z$4 zm>AQ#>FS#?m@T8+1*+K-g)QDy<4TU(h(lbGrivDNS3;)w_f_vdtYbqW*lV5 zNP)zz#Q`bVQ`%Z9GeGuRRX>tO+-2tPX^EE~4)9&yo7fN7tOX=1Qt>CPIaCpFJpacJase@UZ;}}(9&`;=?R}ITSEZLC8@c6`W#?Ra( z#%nnE(ZasbCLpF1ZcJuFgOSo_RUqum$xw;bL$fPtj=)3DR>#Gv7PdfsrGm>UujKGA z)Ak1>QlFMAiqByk5>_bc1PD%BTwzR!Um#~_TRo_k6+Sr#rJ06@caeIq_HV|5yVLdi z3@^G(jx8wfv6T|H$5x&-U^(3ktcIfu%)nN~qbxBM%Da>Y&SAs7C{6>fUw^lQPO%>$;kV0E3!6a;26Ym7=D3%w)MXKB4p`u zv-Z^@`ch$M<8?=---_=}F0`Yzj7~EVk1MgYJ2QJpO$0uWGo%09OLYRPhK5+n$G+SK zzM3N9Tj(0LVJt0L;}C7Lp#^TU-spTJCsiiC5P8`;CX#9SPvIUtI!KF5a+tQ=zcvJc zfA94Hw{+5RM-CBoblx)vV+vu5nb?vQ$ z2z(kvu+KvJ$C_({{m8|Ef&Ea?Y(uSXch7PLH{?Pzu7(u(D0U5_K0K z>Q%50gwTOy?4F|dy{R*c-g8wG7@< zg^w8u`lF|XbN)vO)Ra%2OVuD!Eg*p3L2b|<@|QYYInU#3>hk;#Q%3oDd44H zcd|3YRGi|{ZQe-m9i*NPX-@Gy5A<`7=-=DK9xJlpQL*DARBTseA4RBb$5=3g8CfHg z{9`u9VR?B?L-Qgh+W}jE;dkxy3``5pzyVFq~i zK2@7TB&T*i6sP5&Xkg_ifs918=X!V*$Pp2*SmA^G#InGi&m%U1X0S@^U9#^G#rEsArq!R!h5hz@$lV*nwI z31GB(QaHfdm%l~Gb_f#hF=QtLiBrbe@9 z8%a)d@4`gyb9E3Buz3>9^h8rEz9(~_zmu!<+yC%EAB>9u(;BX%RjD+b(RkRpY{t>N zXg?q5NYg{O40!n251Z5EweX$wAsmrd7w4&BvQ*_0t<`-xQd{Rc<3LMsPkaXg?bHYX zOs~W+`9@0@Dv}}Tn^5a%O#u*`XBl#bEleut)qnV!$U8PCZdlzA;^?;~a;T1NYsKx1 z7#D7<3TW1U!{$lU&Cz%gI{=&nQ4jq(4yqcqFB~BjQq_O-T2Tofg5L+aLqe5y-!}*J zh6hIzEXrvu6N)g?N%{9F_|nkQ&EWf0|0x0G2H2e~L7g41lyL;6;uL=%|F4-6;XHilC_6APd@OJ< z&Hqn}>;Ept<8Ex{X8!+9np|}1)|0QQS3OVF{4Lrh?f2PC5h8RIZZJmVO(j2;stmAe zLnwo*pv`jl@QrnxI6%GRBa`WqL$mN5(Ql;_*dXik)jxEbVEH^h5_&(&dAqIo7@q#P zuh@ROz9_l+e9+A4e!0x>|2LlVv7h4qbeHqdzx}yi^Lbg){Vzf2x&Et1iqL1p=Z8o4 z+ewb!`{a|5&%>mU??-se=d+@J9=88uch0om)AP3A=k)`!q5tb-&d2%H$6?7=FCn3v z_d7%1hxaF;_rvh3uJ>ktfA0^+?$<}+?w8>j|AzmbwT?xMN>%_6d)@8@%Pj(<^!x7$Ze*K8PY(FqJCE-1gYLJ> zyCdTE%bL$!Dk1k5OGDpxA)(KwnveIpCn5L6>4#58|M&IF^L77c`@39OzhT_@B%#;I zn$Md%kI%i&m+@)C_v?(St&P8Tb=^AN`v-2r68;~H$T^<}>sMVLB{hBzro^u;nd&cl zDQ}F#E_!$Ve((LC?_7rdpI@-w#<%?+zM}9i!>{DXOYffe<#_v9^!>9Q>TEds#_{uw z%Kv#&^E2dr*3VT5n za=Keju|Hp)Kc5f3ZUd<9^L35eXCdzWBd(8O$@{mdZQvj5*XgeJhvjXbhYCZ#_MyiL z!_Myemfe)$b_0DN!UX>6Blo*xlfYDWvi;YSt-HYf{5bP}d(?F7_RjKsT0FbYaEz%J z`WWo~H(5h$`ME#+u}jtcHkz}q;&9Tn+4S0V<+b1Xlo^SnYvYnQ+0C=C&1*a6d-OE3 z+9gVzj2i9V85(;57_In*F7;kT*rC(v#2*@Ald0F^W=Nne>l>4-mG%e*~}0< zXS-Z7x%$);t8&Nbw&Fc2w7&Ez*R@>!G@1C6$Wv**#0hov*T5~xrK!Vy!;Q}S%Jiye z((6_2YJEYyvv6bW0;qOrpPYPXP<&~8Ec?{fyfHWA;m};!UF2Qs8fRnhxshG7tmlm{ z|D-c~k#I?G>ASSTVa6|cp)S^t(zN{IdG_ph@uc^p;{5QSi?6EKbNXei zIg|Og5>zR@c9+^kUdgAM;xYw+*s80vH0gJiR={3TGwZI9Z{SuT&HUx%phsFWXQOE( zQSyb}uSXYGo+UExCc;Al0MLe>J*9lGtov2BBLbE+}9Vutp5PE`03s5rj;D9&4< z_5A0w_4e&CqVmGOtun&$=pw{vXCdN7Z(Igs-_c`Le4TM+c>7B)`Ja)|KG}QoBSci7 zOP}}E&XR}t0#bI)tD9&J4vuSmmq+KWhX7DW#A|sn0F)$8YGcKqA=J<5A_Fiz+RRIN zs!P&W+}x7$eD{@a!P>k@mM5ky(*za9n0+@iUi~$Z?#DW+zuDZhd!Dtv1hJGF1EMcL zKWni4BNuWzlX(fuY3S2ptM&!8H*s2WP@z?~LqcGm4J@qTtx30USG=EdKp=;2C?iOT-l{2j}M-eo`>i%OkscmZSGO3s6wFL%ZdptA#o zKTkedT(aM`Vx1Mx64E@=fh#|Q`P#2}M4$ZLG<=x#6=Uhp6HLGO@XoN$i6HFSuETPH z{h3AI3tH;PQC^mhFx)H%d(^rI@Hx5KR#$RKW1p3Tzh!rbWcXrDHwxKKp9KJv=mriMutH}6sEp~Qg zpFX_A!5T+YAQmZu29_qy+%|=mArC?G?Su?BoavNRu~;g?>&*Z~g^OS3<|-8!kwhvc zy+MsKB}l$fjE)vwA6ph)S*F?J^67f*Cht2_6yEo7=z7@`fT~i5?BBi4@4Ux z{`Sh0?4sfaAtdvH=pbfAd_GkbSw4}-T#YC&Tw}aedXi~h1c8XK0xD}K+ta3c9u5-{ z)O#ZxC^I*lql28Yr=PX1|Y9d1!YwsSv2l+r)poU@@M#aYgD){7}Yd#jH98n zNo?BtS@WMDu@cjpQ-DvH%Di2DE8z9+^{}VMHDL4Jw$Y-PaIM$%H;roC&R}Q)m>NmQ zj>!z|mpwGQ(P;8~!~0Tovabvri7{T;Z?i`TJ!m{shYHK4brI45mv9JQh*Q6t+O)cH ze1tCiy-#}_iqVC&R+igglD)e7&HR97_9NN)$GAfC*^^BAxXcXe#E9*t^Ouy3GjZ%b z(}+sSL4~eQeZ-k(XVmf;P}#s}9+D!;ej) zW;C1u6)`mnqr`F*y!LRCi_S4a;ZDUfqphs>!PpQyQ<+WCxemRp-`EK<7cCowDbYCB zfoUOjErjG_?iezF8_b%{6AZ19UhEUMxy|+m`eCRmp zxv#${in`rWSP{on+w*S^1Id==0TE{;UoTVaYsECb>CXe0BzE5s>W>SsxFzXXqaus% znlm!TsfX*EF@JW#=bhu)By$3OK{r!+sRG9K$RljK#v?A%p>Y|22)oLBKPd>HqjP>M z2#6;rEaS~mCP6oGEB`GQVawae9$ihv|GH-1PhMeyTjc&J3l?a}_q|;jB z!2bshJdy{>xrZjwvq^-|uTIP&oDs9)#9L&$jo1QFrAk^daO}DcnggmKD^Xg^fmTtT#wCEp8CoBK251F`(C|BmzB3Z15AmVUpT?E zCLG{bVEu{QC$xr$$HET_=twbUm$w9th-|)c{xJnP((nJ|p=2{j;w(y~$XdXP=k7b( z`o~xpn=CH2tsP|}Ql}0pYyfrB z@i~s=-xCc`C%4jc|MByw;p8u2mykHSbr%@hKr5HItp~)P{!C_T6*9jzo&SBORF$I4 z0Lo*%sKBl*OtISw68h_zMK5dn*(!JgKTm_MyH1Ep3Zx~oc7`u~R!l8`yyt`@$WxC7 z4}9R+f~j)=|S##0CTm>1?txMv<2W^>u1qe&&8)~`z}TU4XXfPPI@D=!y=#!1UYJEhTfyJb^!+-&x2=@wR;*K|Wt!pxEd zNUW(>!*yeQ`pIeYwQEopb97WO(_k9>_5`CupC)2iqpu4k^S zPV(Nqy_o|K058SFLqYP5nuL(N{Q_@q9m!}+BagF<;i-C5zZDTIRQH~o%4+l6Id2Y9VEVy z;I<^KiP4(ee#Y9 zz6outC2}Bl$X~gu8_y(vcl8t~PkmqOL2BkPq&KI|OVvT3LSHA=;ZBmhru`ssi#V+K z(yIi`>#w7f*u}vmKZkSh_>*hPEnyxduO`|mtdvOT$nY+<+oRybu`B}YZ1INhg*>Ln zVtSLQ3fKEDeYF%1##<$Ms`PH)j7e75he3&5C&*>H?M0R*`AUDBqq9&*x;rTVYsLo! zJWEO_#Lm^yTkQ)fWW>h9NgbvX_jn))*4DKvH%e{NKqKD)D{EJ$#^wWrC$q)a39iRPwrhe-7)ff@yO&~+b{a=> zqj!7WLWq4d=w7*L?fS8-C_&NDou)~=y(O3neMm4~S*PjyAO_%l<>m|=Z0}x!qahYq zcP0weGf8{=Ch8vvZ3gj}BEJsUJ0920K;R6sY#6u4N^sCBIv%l+{9U!Kr5LJqV<(g6m4>3M%0#)D zx`9q#pv#}#^pf17#2Kh(OiIvhr?>ia%ZShx2bm;%Cf`0EmSBd~)WzA71P+5yb%u44 zEZ0-Q8N|^1i${U}pTlN#U{b)awL4s>aEu^%wD8p5AA1fp&_B`^ahed%gDUYLYJu+O zduOPqy`8+%rGyJtzGE0`?}U@5D7xd#cyj0w*^6`nOl|T)Y~x^X61jN2xkQDL zh4C^md466|MW}ewdZ9I!uLynLm`0*5JLIBFV}D~QLraVpFfELOt3wi-Mos|whb6I$ z1RTXlUmvdX8art#ndYE&F1VL_(ynpq=+;T{HPQQzFvr!*g)D%DODEW(L03n|QUrbJ zdp7)Wqxf5tih`msQl~W=OHWCuLs{tF%FlKcT$V9s#~CC{aB7JEd=wWhb}NoHXpF^# z_KfTNrPSI>q2yD zsG9%K@DfyW?G>bgn+-}|&xvWhBPYUaQSgLbU0!d=*vK;L)3G%PMK?;G{fsx^zuagm z0LkcBM7JaQyoj-n2nya>7|`8!l5zO(%dWZ@zwxa0J04G2Y99MliBAE>M+)pP#Tpxt z^SJE&=t_!7;o6N1R641Q(wJqEY{j9p<&Lx!#9ce@#(r zyPN^PM>bbgOEoNm({*yvcpGB~tp(-=5~j1T zlXs9>qB)F~Ly8*exa9-I?z5a3BZIr71@d@Lq^hyoD0r`XP6*;2Dco*AQEZMLlGHRXp-#E%cIbpsl;S)%g7F> zlFHser;PO@xiYstev*;08Vx8;m^2Cg?9TFI7Pt? zM}oYrnO-Col^?D!-Kj9x+_9!}7OgYtZO~2F|6oCZWny&aNmxRP+#)EFuV4$MjrJ!v z9js>8=#V2{2cEBm*d4g9%9LV6i99E&=bW*ppatUYY)G$!y%;(SxEOHhzf5SNl0VDK zn#!>G1uS$|f^=3!N7Y6OL;|%F830R%ImKHZ#}M%5gdm3^}a6)S`VD9edB+>6IidrK42YEk@!1wMWrhK2sy> z;csNC{1d}4;JkvxDo59&0Tcqw%95rSU}3018A#Z9jAj_VZP^#4$d*j&88cyeF}{4F zGpzCrEKMjNycU|!A3Qol(ulU<`%MfrOa(?uEFnmd(G% z8PBkgy^Gw6ZZ19Zc$|i*dHxm&lQ|;sqXSJdctgc;EKQby=lkrouVaWff^_Y;VAV@% zkg8P}939-XL#bn~X8{~`q?s|iv?UHBtZ9~>Qor@;vi(;&*8UC4#T^2Xj((C}!Cde( z=#7kH%q8Ehv-tHGpvt?TydO|j-Q)o83>Jt%sRB#r- zj>up7Xni@Qc(OOc_f`3aQsKC(2$itGO;^7P!!9WL_ZP0^yfCEegawzL_Gb#o+w5W9auFl6}$Dwn$}xq6GxmX+V>Gk zzu&5d&iFxN!OL*&Ay|w?9xO`Zu#d7ZWK;<{(9JDpRzcFUJloM(7Xv_68S92gSY!%a z)^{mitMvsekc?oS$mJ0ka~+4CX~bYF7*{SycJFVyOej7sd=9NEIzVa)DWp-xUh_Nu zC{FDYUp)Fm9c=6Qn*&^?0tR zdw>+1lc`FG60{SWypeBora1#aTY;S#380>`gvDbZHE|NFbxmnkaw~xYmTMmfyU4gG zSZAkwfp!XVSa^}!rsWBjB#f}z0Hw51Oy$b|i28}Lk~9PQB%|6W$L&B@&im6djYwyl zz;)R0`Qxe$JkDLFJiiT|q`Ak%$^{}N%UCgI`!(WceDZm6Yx7xrrJU$|KZ>i~j|l3& zW}WmHk z=L&O)KT%Yf<_Tyuc8)v>euJK-km5 z+b>ycu(EXK$nTyj|7Yi@<9#WM=XVlILulG%CTwWRltU+x53h(^>H ze3B=(4l>)L5Q{xJ#^q;!+?_b7q%5^~WrrHSDAUi!kscdYX34Htb~mPbgn{G?Bku05 zkK2Ret66ncy1K%c+YbuIIv#%@+?b%Bz|F}fgXn*uRQC=_Qf?!)gxWQcb;OO2o`Ai|QE0(@0E z4sY>$T@f>|fk`yj9m7KEO*<8R>fA>&R-eghkQxCR#=QiB9CY}#&g5`M2d0;Yb}qU^ z%=E7;S{cH9#;}E`Km2?F2IcAS7{!Xbx3tv+@U0r^mN3%KdyFYA?4hGUTOZ1qth9Y! z=L(i9T^Zb0l6%;~Cu6 zB`yI2k;i)!pR4Tl7i~2%x>kgci>&11P|mi|M?06;h}bysjwp6$r#tsgMYfZAF`bo$ z+l!G0XlbVMsvsoQtxx840s^omj%)4xylRSZ;mcT%om*<8G{vN`vI?T>AHU=J;fNY% z6a&+8S2T}~wPTnYCypkK+$XtuK^xn`aV6gtOHBbZyCmFm3lLs)(&68>2C0AS2_qoh z-Me)l`=675Q}=-klAs!R23$>}?yUhUNF-SR{|1qGi|sOp<9G!0ba2@&$FVC9@cBm^!YT7=BHIDdI^J1W!{Ik)o{(0eAUvWEyg~vG%AyXs1^GC1x44gceznat46mGCB zx3OWbiG3$LxG2FCKX~BW&fQzfdf;Yhd~wib+79D)+}1oG_RJ8a!7phHfLWQi=RMm? z^BW>~_%laJLwgr}2t}ekOj66W>k~4lyO8?zm9C{Sa?$muIH<1|2sG8XCq=WQq}cwk zfrThMRy)?k4~8T?Rg{R~I&q!yIv?M@)&#Umf@!otm*cM3izb1tyKBfto@e#_nKY zZP-X4oj2j^dzl|k-%SR0VH^A&i47|xSF|cj-oWvC@xs!o!Y8yh=;`9;qVfFY&uxq? zf(JVBjz;mX1E~z={NQx5ST(i09H!)Btp5yIDf#j)L;jH}skxte0llii1&H5Q?AdUU zN_|hC!FnGnyYB*IX9Q5>xk?ghD1hDF&pp|0upB%~Us?LYoqJRx2sc8vVGv=x87+n!^;3wgp+49H~69T_|C0ZJ+1t;Q4U&#X~U^AebZVuR#a=z zL+)L36=t>3kR3R)Q`@4|OJEwP5ltA|t0aSN#8G7!GQuUPplNKC)3k|9TZjb$_Cbvf zE=MlcM3=y%Y-EVBDF9Z131{wxUHGxA+OH)eh^!5-=;Q4OIpC zO*gtq&l5S;6*A#(tlHG3i?=kex=Ml;65l*BaI<`V1d1l`s*Rlx(0p42)K*9d{A$>h zL|sbFHiK)}ci#U8g!}gAQ&;Op61lRe@pDy3LfLem6qSVnsPoSsSO4XLzD}eOSh))J z0JBq~7-_0U$2Can$0ifKmoxq4jTQQb-lvlJ4ZM?V_re>`WTq9A-ERG8C3xI zZ5msi#{%}3a+wOpgX0kg;q%0&=*Qb9)^k5Qam1I0OBg<=z{c=3TIHI`P&Zy<{V7K1 z$mjY24x%dno<9QPrx_;$>A+nduy=Ea1M*Lf4l*#JO0NE=ycRFa@QJbWegRE;PH#2} z4iRm8VPBK9y<-oE>U-amdBQpcCD*s+s=;hPlX|}@<5iM3j9I%hhMcofNwBSW=03r- z55wmpeUm5TfY(61%fC`ncJK-Eas<-gos76-tI_CZUPFeGL_DHRvp+``Fr3QexP@v) zTZ^{q26+{KGI|E$6 z!FPa?u|O7Hy>m)=379utmIfVc(xDpwUNb4Zm@Zgq4&dOuGi@wngsJx9lm`ZXB%U_O z-;r}wyFKvonGgy_^dJXq%_Is-QFm?xSHU+>oU1Ro;wu@kivkYxtG_2uS+w`nGDm1{ z2MxyDhx!Sb@L5EE>mnZHc<>o@%&-%N3&0Rr4_qg0I{;gx8djz9q8@u`!P5K%s$H4W znNNt!B=F%0 zo`N)~+=xvNJvb{H_pOFbRbaEq$VnK&y?-wbHx=%VA?`OG=z#qX zle-K*5X^qNg{lRl@H+z*($B&& ziuHUDre|ES8v(mQ0J;41{Q>GTePsx$!oq`7AF@-==BJf6KG>iW-UxFZ0ISt>cx7by zWnn6r-aEne?y&eYBTHBy?*rL#4q=XSImxtMHXB?Hj1K~)sAIfQprXV^eDdMfBf6e+ zHSD_P#MZiZuy*;s=H{mp(6-=az#NQ`Mx}V^@qHcRCZp4ab}yk2n-ixCQ<+Mq7*O?nhE2$9x{y3qqWv+D(orIjx) zMqn+Cq~NE`5F%I#1MMKzzA5M$Gm>r$4(j61)s$!(y^OOXP)O$s2&% z#m%2hWQxNPOAe|VaRIZ-F=l_nq6@1w2nrL=^~kQLeRWDII|+_-{{x3k3>JJY0O|y_ zo#|Kx&L~_A`#)Z^oQnuk+AlFUYX&}yX0~MhfM#W{#!Q^tWarTQ6LQUAECnQ6<(EE%dTi$QMv-T`W55;jaIWYloB2veYzYCwTRuC7 zQx!mgW{nCK>;^-Q-prC~m`>}C8Glibcf`KnpACfxx}bEF&Uhb$YXoM=G%AK)Qy!54 z6#qz6{cg4_ESxt=h4RbXv#zRfgN|Fo?cMX7@|hZ5+N%)J3-%f3VViL9Qc*F(TDDbR zMaFtih+#*d23KD@-;GW)FbGpH_A}J1(YWagI*r(7)Qg89nI@nC;^=YYk98xK7WHX~ z9{ncn*Z~$PMd)bw#?4l0zj1=FNluIJ9kulXf%6_l+ko4`v)RUT%v2_AV~D0{h^PJ? zC~!}_P0!UXG=&QB;x51c21`J+zbt-KN6CNENE|?G@jc^e`-2*ndLzuSB zD52DI1E-*Z{H?E@4_}+Yr45qRfb&f_EaUG1lp&FG{N=l^R{;E+$6v__A}LI5O83fb zYJGsL0AX){Z}6?c!6*B!gSab0-N2-uP$f>U?yhVxLyt{s)%Z{qeyh{=>~usB+u9ez~}>F4P^ ze5>NcQ_ffX4(9iuW-S4nkYNmtr$FWR$zmAh-%2bL>8H(YCb`88Mb3>B&0Hiy=KR`% z>l>aqhkm1SBV03$$QD(O9a?2%xHSy7Iw5x)W-0hj@viYeDDwy28muC~E+Gls-aB0Q zp+v@WKs^XoIuB<9yeG^u?-G}85_!DO>ew#lp#iFx&c*ww@+7F<4&-wW zz2)*uvoaFiMNASn&alvRkOMavKXN!>2@X0i((waU*cveqsNO&zEX-#&b<@^YlNUg) zZ4#Dp0ThD^=ymVwVRqvD+iJ2vH5c&vh?ZUs$K3&Evo@Q>3bg8U)0pS-Zx60;J>I1V z;y9C_m6Nfot)3yOrHV6A1ZHMRQ;leEyfIvAhntG}YqP{UPMD})Z>Fkp6QpV5)wpD8 zZ}T$BUQhB7WvGsBk)#~K=LsDdyvNTVB8j9DLHCQKm0~6^3?!$i zAUGXdD`ipv=?{u(5o28{Ws(uf=XP6hX3kS*9{7gHvqRcfowz1v-9eSToM4$CPDyvl z3dhcD)6Q&*b{>=ZwGBEUW8!yUI^AC2+xa~bxEqy{WDrWUQGkA+LbI{WM=XId{!FTY zP!&^ry+(Jj?;A<94&=Fab7?>bD3eN36WD-p_S$MhwL0%F0lS#AR-me0+r5KAX@?Yx z8{s+9Om?4-(r21& zHQS>pY}1W2D99qq3y7PiBc!#z&*PUvOQW7U`z{(Db##>(zxo4wqa8zx$GzsMetjc}zK=Tc)$kHe!s|%h{NuC*wFd!mYDY(VO%h`nxmcnwn7!Yl(_>~oA_K6c1R0*dmVuPm|2p=*5lE7w8YKT;5C7?^9kn#8Fz2+&TvyGfVXioMojDBq(oFHVbJm zp~k~t0(5sn1U^e8PIt}t`REz}oS0t?Av4=ERCG+LT&<50zysg1)7hkzAQ?*3#X3~^ zCK-?WNNXBWPzHE5T1$aB9YJEM{fdW|LyPY?)=>FvClK_GZ?+3(0(!&s>@rv7;z;uhv{IdMa}gEyn`^j zQ$-NLJK94wpm}!t>0yz;J_e+UXyZ<$Yj9o`xEpD=5yW}MNXb>2u{QCoD64n4l#UkJ zCIb=O>(PhGamXhdRq^voa`~7Q7V3GoZTCmrINcSfY=<_Cle7SY0~9Kmms7l%wY!r@ zwe=8Pb|hsQmSBlA??&I8f*Z803hn`}nch;-Ugh;vKNvtJrW35}!T)c{0KL?=BNpgd zVL0tY&I-mzAJyYqpxsfEYJOSAv6A?Ce2U^E%w&R(I_*Pq7I}G@k>;;SK ze00^m!z|ZQLnW7I3Ho?U^ktsMV;^sP`EbX=Z8f_gBT%+C=@oxZ^zs1YPxl%E4pLn! z{eh`SQ$Z~QatQ!x__X&0ul0!f(MV7Z2y4eno~jRg-P1O9&QJ!~+Q!c-RvceuNrDXu zR$^CL6vT6v)8yC{IH&>2qQW^;$9@o2&>09GYfXBg;ur4u;F_f!Nuh+zdv?IKPKT(F zQiSRWV#BT*Y8R>y>iA3zK}{+Bs)Ephju+7`$y!$e1#V2F!|i%>r6As6En>>I1;r1o z`|yu)I$P4~^$PkPWlf?GCWZ_JhT{qCO>G=?(8rQ)*s?3Kx8ZUjkW`*hIX>XBYtX${V+RPUhD52!Nsk;Q>~yLI z(jJTpP(}>;Y>k-H3hEu$30U9;+bJ!Hsm#A!k3M$4)3;y+)PU2*oIV;(Zx;{vNmW?2 z7Qxx4fCLmkgsj^z@m~pK@H6ydFmvxB?7$G)vV(<~+k-VC$02ye_(BE>L;Qh8~F9zUdjHB8L6GPs?3!*@&7Jxr-Y=Y&4y zlUIU%AQg~W@Jei_D`HCF6m-o6i6w0wY|kJ!Ca1fH)#t;VL`wt5>6NIdp+RQ~M2`&O zM32=Tm>aU;wD`#W7Rv9FTOG7Wnc~btI_XF3o`AAiT~?4D$t(NuL|Ep#`zFE5)&ntd zyB>Wg7J6kRKZ=Dun91w)-Cc1$nWxCfixLSet$N~-8|}s3?c5Eo2mA0P=St zE&$ldh$bHc&^}S1;8>SnBk!7GMG<{X=Hl3+LiKb=ryy3r?&R+P*$jSm9<_S*hQ^~$ zI4$MRQ*)d_z;>(%He*KUo9}-&(4kURS17J|6PYw92ecd5TA0zBC^xy zHbG#CXq2_5w^ax2lsN_Z(jruggxmnxZS^QIq1u!~e=)oDdBtMuB5*~tYNL{5_EY!K z*tqZ`R3kR9kSXokBQ0WbU{Wkdlt#j35*Cdha*NH6+v8iQ25`+9-4MWeYEq~+vSCl$ za))4T3!91_IuI&`7f{0hH{}&gwI=%=AVs-dkDv=NqY1G-cz`BxX&B?4^7@$9+Rx+a zawz{Tj+#M#A4yLT$!Q#{zhC@yzewDF+L+wj3!p&-6P2hOQz!uWq4UavR$3%i(%Yi33a-V%P&*x3ua^Kxqpuw%QFq6MC)K4n?qy=oFlD-q z3a%p;)|>c(K7^2DZE2Hp|GHxdE|L zsd0JW9^8kG+Md2jP#;$;TkBoWijv0WZY_0LTA5x6kL3~6y*E4du;+S^{l1aOX+uj}7g6a3%a)%t$kCx%hYod>g~Igc{?%5+r~zn^DAJa6o>Vrl zOn1zv^U+75Quxy$$SAZ#*Nwoa9g1rN#v!#_!MJW-GNbj<~9#M2W z<)~wIw1ldJYyxGl3!c1L;=|M!J06V7$m4ZYu%~#g{4Xkq5sCl_4Pog>N)0oY4lKd= zy*#D|p|XW(Lh1q%+gwS=%0m!BeS)iJkzQ(c6LC!-CK+CJUgY^i$w`8)Zhs6Zm zY0@el;mo;8D>aOhXrOcSDB!n`O=s4i%?mB)&a&*%@Hw>c0*t5IMNrZ~Ob+E3#Jhev_rBeGMQIR!LdE_ zh4%kAQ2-1RTyJ~CYshyZ-bReK1B*l`-S(+d8s9-?g(h{Z;tONH7X_478u#kqw znvQdC^MB)NNfF(S-btt&nzd}(QwmQy56Z6Hm(YD&vHiFdHXAn8N{1l@&-!L$1X38| zkB;z>evl+A=9>??aQyhq)gF6n=~D3&(`sYa+%OjkWm{6X#`>_d@_)EnWOSmpW(5+7 zfliyTKY|>l6~b=+GJ!;s=KwSTT&i&^Z$vTuuw5r}V8HP{41Xe27u|*v4>;*X5)Hd0 zMrg%K$?V&SJ~~|iWOc|P`rMFuog`#RP=s>_fKaKaDWPs@>n>xEcrEzMfkjzB9=gH- zG6NFMbJSbi-6>mIbURZ2!=+k0Pe@>heB6kGwqrMUG^sRw2Bde9!0s2ayVpwOjGu=S zzb(^>VFC!E%$Rm2dp~g!m-I*gNLHW5<8eYmLS-|)q?fn^0W_fmA!V4e5svPy4k`AYay^fuIPG#0M}MCE#P z&HGH~PDx5&5k7X~p>8#0XS4U{UF8Zmt%HU&w{{UdymtdJUmK}b(kwa{6i(2mekQIwLAQ?upsz#k?uo44~aeJ5~B~_`wFel9_n}TZ1JT%;qHWpU= z2+NT=K=Lbf30rs>VC?L{Ft@qcaL1VX_+EQ8h*YC3Z`Y%1(beu2$J5d5C-?ND>WXaA zr8;AkT{Y80H}_+UcD2QQ|0>qPoBCJ^Lx@4Z4Q%cTs_AeClk-(ftXZ}oz@i_0N5O6H z_wP|_Wlz3CDPa-Y;J3w9O8XtjZPw58Rtn{+CsfjWF?WSJMua znmcvV$e_PbpU@*3h(9RDz<#R{`;4Blk{Phy?F=`0fGvX@YvVnxVmxsb_@$&Ff>>%$ zxltj|f8_M?H2whws9T7aZ|?a= z3;WUEH2?fKvw3&J)NN(ypwH9NCJ#-N1OOE{s`1|LeK4v1S0B6omHODhyt!I?AZ_W? z8QpwoQb*-Yespjq%}$$!@sP^oGC@s1q7+r%#CY|UfkT&HHXkK(T7``s$yD_P2%FAk z)g73FW6W^Nfp2P&EWKK$8i?0zlo9Zo7;A084?d^60* z7Mh`L6z@=U_GxGJf*r<|x?PVxboPb{Xv2IR3*a9i;z#~=;HI55ah!P|0aNtnsIFPt zB5_N|kT6u+)61P{8%HJ;*6Ro}Edf8F|2PcppPbzW6Z($j`+bmFzGok{B3>Xkg=%=iip=8}n%gVw- zn{83MV%wYWdO!ssEt0gDi(+W7NMXSXfbcZZV>dD6EpThV;MzNvUmF)$Zm2A@1}RA6}zJH zl2De_5NLL3!!cGQ%Wuyx0pm1otAj5U^w0qjAD~i(%0pV~Mh$EN`gVU%choPk3O1OuRNoNsrA7zI^s~)Ll_M3^s(az^>l>lo7i%p zXXkL|l4PBVecWt0hYf8ums(w`v=hm5?BVU4KD=Go8`B+tEPUyj4b@xdFbm&}^vQI$ zdp6AV3OBcg1Q!;I?CE({GY(fNJJv5jqS-JF3}nMOh*q5GrrBhM!XRoNG(lb69JCgotfx?&mm$l>0#QEaTayiKYa4_2)L42 z_S%4;0KH>nJ5Gq2$IQaGa#pm*Sny3sb$h7jlx(aFhfu^zYt8um_{2plCwXy1D|*LMQmk(bcGe5+73t znun)t{jjNI>|b*72PW>+jR%Vd(t5l&!@7XEBGF#sQg8MWAPku~N`_2oW>T7!me-J> zLRvs3WMN7qR{D>>b&BUW1u6>|(K-TsQ9;}W80Cmmx)!3$V6om#SFHY_nh-vKoSu}b zjA~x3Ze~nLK$rzEufbj64i7d82cQRtzyp&7ohM`j`=aL4q&C~@3=uW#Fl9$vXwpW) z`Rd?!q-kX_o;4Ug-K?f8)(vc`LQ={G3N^1_EXZ*I*g79&_u&F)0!VI>#vaYG?#NpZ#PjkKi=a%kiGvtdV z8J|_p`MxdBJp3e*_?4na&#_!(MjCbof7b$g%x-G?vhLEDNwUs4xt}T&y(zjBqP)BW zWOUs0H9(d-h%T>Y9AUa9xjx&FGH@BJ&^+uiFcPiO&`k#CGhJXB9ZIKJ>5jgi4rRYG zp}?OEOoa@bwt4+zD9tCD4g2dNDt+cXAxVZk#pt`Xdf09zH`seXW-~Yx;@k$G78m!@ zoBAy>Vm=%eNH@ro1gZ}^6vmw~AB9>3^l6EcBlDBaS5VJEMGU&S+n}vbM7VU84tu*B zzjn9OGyOR@-hh}_7?B+-q#T(x)MQ)J7yK{GVsb|9c0IaUF7QX})3~keS$=bD0ojD5 zd>JJQ2Lq5dIN28D4tiW~+_As0O4HF*uEii#Xk2IjMtaKJpkRODx$^o(sDcoBkLuqq z2~vuG_7RmD$<>CnJt0Ui=t${EcCK{zaYa8erd0IMl9BpN$(=h?=WwFa4A7~XQeEST zn;K1Nh|eW>q^sLV(v=gCQ~59%01gdZDbU?qkKh!e$5)DWirBey5=?17>7Ym3PKUYA zBf8Ga>|F-6Ne>l}!L9-dc~i>>JdIWk_L^~g(XoSeGC`qI-2I!P3PQDWx<&0sZ$mfx zxmJ~-1~1{MFk5|dBl$*V`uuzAQ zt}T8qi5`f48aZw*r|Z3DeFnW3_YQfJ9yuBnyA>cjRfd*=EuRJMXg-CX7NfTKs7pu1e%V z1v_f^jn;8j2}zo*1q_-yAi>t$wg*u}uhgx&#jG(rfO=;=uxhX7_h6hHQds0#!o<TogEHeflp7G1hSXL3cJHDm4-?4#!X%s93UIznfQg$J%Gn?7;kVU zfJOp^Z98};J^*Wt1R52#9|L5em6sUDg4zAYdN$A-k#g|+77y0 z{z*49anSV)^yNGqz_AVHdxNsbMGkt4=65dXM?kh6n-xOL>-^Rq)mvKmcheruplv0( zPF295%JKH;dRo3^&!m{sk5sko&kPtbb)qTWpLXW;5aa0vkn1Ark;4smUXfP<`xPj+ zz%%7uo+HXm>9SBEqDre7C%{Gue&|C%wI*#xfWh7j!URMO?lLMEDZR8_j$&ihtINfL z(bg4xq}ZeCjq(R}vRy_w9O$KLpvxZ^llzg}!wE4Wfbi)By9~Y4hY`px=Q)!Kg;sqL zS7-N+jQnGu#Tab*NUs1pq@=KWz6IFtM^=oSN2j2U3wN!IQGNYK!-I~}3%Ki8~>#iPlXEL$pdJc{6cB-?UyoUl-XZHS?1 z3pd6(Wm07@J`I^nCP%In6$;f|Vkro&E*o|02I$Oa&SL&61a=`*^dNtb%B5`qV=E~oyd*&wE*u$6kW>aRD$AT_$s|AP#6D<~r9A%1LC=$c; zi+*4Pj$3`(u|BPi^$q4NwhRzSuarXJeL1+vU`0EAVgqhaS0y@nMEPTOt~1$rsduuP zMrs*XB)bT91{zJ%`f6Xkj-S|=O-yT2JAt*;o}k0DYzkZb0IRZDChHYzTOlpUl`7+~ z3!jI{%+C^#>qcMmD9b2&kKYEWz3EW_&HV%Oi z@0)uMY9wkT&C;81lBy=sCqbOdf(mN{4)$E`b24Xesf*MoM`!s0+2UmmXyZ3sN}r<@ z=JRj|+CWkV@e=mNNYNZ?d0*ZeK+%t-p6Z2Nb)EVV@igwH^sfuK097gctwfU_ zu_gOzX6mQFY_|Qap2(})HazT_d#Ft8_rmtG!)@;%u?MkX%AN{0)niZQg@Zg7&=3nseO4S^jfO&r~%4i_xK< zE=M~B$^bEUdIV0l6Kc?*rp1{TTR~E4Jw4HY^SG~5@5dcBxhM-|gxHhp-43Ae0q%8& zM#uC9knO!k0mAEXKTIcW1MRfzN`AcPZap2c2Yd{u%HY-jSx*w8y53P_KJ>iy!1@q@nA9yLRgu&Dkp0Au9r|F0s)rz-t;r;VW%+g3pK3O>Hq! z=&8}~fRCX5?5hE1Ag}X-qHFw^bO<9d3>qY#lTfoU9QP1%&4+PoROjQC3hW`q>5Mc?rx_iM*B4+}5W*zJ0xMNP{c zu!o7f70t9sXl(5nGd$*22GMz37Y@jYSDBa8_)K?NW9^f|P33i<JK_&9=?xR|};- z?YSaics)CbL_$lF$GZx&{A~Za17sl)7xkkw!v^tHA9HK;8HxIc1wXG?wNHaS%oH=V z>?Ymq__;~7;;B*sOD>v-oEK3FT&`|XVm2EpnzFHPFx&;pO0x?|0Ymtk;BM(P+k>DI zNF-`?1aN?WqT9?lHwEDqM5igb?!dPMIwa>7ZMqwPPhx|&k4G?jLg)k#wUYq{Ax?ej zV{YBF*bOeGt0#sDI;fF3PJ&J%rl~a}k%kokIvT^w9sVe>qTx<->-fO&I-Me=zSzMe z{R8e`t&>xAMBWLCV(V7>r)Z0TdB1v{P@w>JuDgDY28GlF<@X}^(K8l8Sa3>`{jJ4LD%Zvtbh@x_`M#aIGI_Ttc1yp&^57)>(Mo6 zjPl4)*;$_WgNN!I=a|%r89$Mp-ajz&&|T_afufeU6Xbcl&zrpC4We^O-C2&*8xV_+ zC_5uuO?O>fL27S@sMC9!KRpo$U@9izhiK_j3>aD9_2Bb>eMg3Tgu1uu6(pqTik|Ja zng@Lc)C(JNnzo;CYaMHG4%E|&n+$5iR6VkSSl%N#n!xn4ktfI@yeFP4$drgp8C=*2 zWX1FXW2U#amj-%vmtnQon^5NnF};;kG)+=;@ARU$9xu4jN3>5sizmEab zwmVy->-9>FuInCWz>u&^^6user0;i^Yrn>D?spUH*8>u6_8Y2s3BY}-uu@QUL-nT{ zd^7RAm$wMNyqZXYnIVx?_uUVYA*eUwm$h+(H$Hd|AVH`HlxX(sQb~}2g>BQVB2QRV zx!52W+zGduozDd>brmu4Do0h~zq)yYn}!Coem76VwzAk-VJ$k3^}I3~u`~&?kX=gA ztJuBS^rmdP7$<1Z65j2*u17FxQicJ!5#<6z!?u0FvqL05x|h^^-mmNR{tO{ov7$R5 z8ISfq6@;aGAZ@Rwl+hsBM$>vG4FUrIQ}C*O3Jx70QU((hppNi}rT&)QKR?<;!i!H1 zutn=E>B$PV+0T9K*Fo=A`qD&n@p;86f+~&!8oVd%3;X+lmZ7a@f(a6k{SA!2yTWZG zp`@S*rGtalLZk{2xaj~BYU9EzyLlC>p_TV(w%Mq218&DFQ zN{d++dgQJFCU+!Hn%?KhwCVjmT^B@z?}iUcSYmFaz1_9>2!vTrV_*b#zqT__cz)f! zboWtbE+MZ`kBX^HRYL?|pz1Hj6}JgCBtwCLaFuUp_Ivq5G#VfC4jDL|L8B=Vh6FVc z?)Ob9jSS8nd=oY;WJztmEBo7qFoDYER8A)GSv~bTL zAh+$cbxi1##XtdJ5RHI3psXSyfYYsvn~5EF*dz>V?EnZ;=+oQ{!R3>bjWS4N9kE zU-Wi8x>hxIK)^P7PIOD@s7{@3Na9T#zn*lZ*og>I37qIll$JqYK^?&ZsPcA~;@8Cp zDuYmhV>l=D+~BUVD)MZKsbnVOOl7uR&|!votar!Ch?=#)k!DW`v(YA4 zh8|+tdCTr=o2b5bq(nM}3W-#sPaI4h^kCV7OJB288Q&S~ONDWy63OGHt*qH}%~?Ah z&Lx(#U7}J?<4j}P-8Jc_qif0XexFe@47+a3#k@ajmI<4k@qqChU@1H<_@A;B0<$anpBTFgMc8^sCg&A4b@<6irx`9c$kRY$Rz>=(LTOa_E~a9 zj;?pOEa#(orzIT7JLK@VyDt;2%{+04AK4L{)YHhW*R_0)KM^>A&%IM^x(BidX$Rxt zYbJjjfNX9>+0hZ2B4s9hvTqTD&=*kW4FV`097Z&_Pv6GH9*qRG{=oz0)}k{g6onAX z_DA;)8UuECtV@a3CzmTWJE(nkCMC&u=ufj7p6*N;vR2C0R~Nh;Ky9&(bZ;Sw9<*QK zx+nJ_gAFEy=-)t&HSKl+9{0u=1n(BPP;&br$97XRLgw9_(PHX*!GCUk>wygoto zLxbED?cf@15@bC_6;<>VL{q-yOw5l?f}pB3_F6eXTqkF5lKFp7CPH@zs&WfJdA?p@ zxE1+K_}gWUXKfBrD4J#3%f>}O8Y$#pNqdU1V46vMFdEIdPQqgg#zVO#4xo4oV<}X_ zp@U!_AVV zkuFPyx9gN8=Li^iQtAGYV3&Ox z6)ABie?Xq%>1aE#JFqtewjK&4R{i7##yI1|*di^glo}lbC>gtByClcTIs1~w**yrR?{ zxE4*yNhjT0ca=c$p@DA!dc!&(zUv8wQ=XVoZsm_K!F@4T86^k&Jcbe0onn6c*FF{C zO(=~iI;5R%f@e&6W|2f@S7m?CBU5^T3 ziH3dEP1@CynLgX8m)3*C_7d2vN1JnOUjM4*#6{QQA6nwFs|Q}@YqQAx%ZoXWcGt+t zZRswg8DnK0T_y${9;jmAcUxH;l`kjwNJ&f>&u|g95vC|kip-HfPDOR(w%g3A2XEK; z3LrhXMO#VyqfY+rU`gz0zXQ)mO-oJImn(xLb_|lG_8XpPI;|zjFx>kgt!~UYx@7J_ zTiYyTjd<|phg2Ghi9pbjmg;V+bF-yw18GEwQ9MvLJ)ncvBu;TgBYAT=y7CmiE>3^w z+YiO*>-7%nbe!>oJFsIl;fUDbsec+^(!)|_F5&88O#d6dhX`bCGCSV!hQHiR# zUQ!=O6PC!jA-V>Qn1WzqIHd^_LC^9@6n}vbG#VP)_0kQQU%ugw!2O34!y) zL??T=MYp9taFLwpe`&!Ac~b;M8Wm-|y_trn0aP}>rQukz;x7@LmG)tHEze zb0;!I(I*+gKw3TavaB(n^Uw!1H|f#HlW>LU^L9O=<>G`*9bP@)Ylh0uRV1ul12!Ke z3wrSL953iV0HGAJi5*Fy42Ne=yR%sNlaXq%wcsXYr;-uO_3?!i6g-R z5N&%u!TrG%vzh!k^f;_*bgagwx*cROr5g}6x0RKk!8LUXHNs2~0~54|t_GPjk_||Q zX(^mYf$fLAlh)F6+ZW1RA3^eGize+*FwYW<)b!_?racHhtkLfiY?xggmO7FnLaHF& zaMJ4P=|f=>7!zA&$y~;J=n^7L5EManydk^JN9}IEY&dRg+X~};OZ)0zA%zj*p<3HfC2JB&6wE&QhL6^H3$?d?t6B%C4J=f2YtvV z*TK;4ne;#uasb9HB-P@)`%)H&03zC0Xe~gbc@G#6pvwW@eh-Vmz0jalh_kc}JOa!F zwONiM$g62L05BWi@A=8#GN+D{eNXs`1%Exdl8j_CMGtzc}KpEEoj z1qe4FxrvTflv@y+eVs{)iU@c=Y0cdaVNLh}Rh0ux1rUaSNYKR4SFb~q^%fTN>`Y6$ zjY?@*lzUS~PSQBYA8dDlYlT!ca=Ynzg`0OmP&-h#lqKZjhg3R!QYa`)v>nXZ^azke zI>AEYJ2s*lrh~xWT^H=RHXZan7I&d<3kN1Yy0F50PNfzcO`iS%vL4`9`S^xUg%Wto zLGpLEk4I-L5aeYW7zKpIbAlf;tT8yxZ@k{p<0m_aK^sO&Xx#6{pvkei!+trv&CO`Z zO9sOOA7cibV2`3sPD|H;O^3ax-;&UqZ>P3C((QFDLdP6KQfd6O*XG7z(xZro9{ z>lK^R*psE1@{M%jGelh-#HAG*7Kl_P-PF7DwF_I^va9E)D6#yNNSmOI1214N0}D9- zjFM6^!l^k58b8^617oc(mM19BpnZw8AcaO@yGe}sUiK;xJM_x+h_8U6o7^0rTUZ9? zJb1uOo9b!BPht~1|3}&KIL?~`^7skAnA97X#EYfaD_{6rw&%o2 zH^8t7lO#ES2E7fCA|kt=GQAR=q#^i}QA2dghDL7+luyL{x}6qT1UafqD&T4+n$zY-)+Cd8l%qLmK>%G+k{ zhTz~NIszA9*xN*|;nc1xl1(>FY8$MnP;wL-%mA){?xZv37WfLUKId*l0ATL4Xr4CglZvhLrZ*R`Q^P5H z;90?;H6wOcZ*fp1-YLN%Mq_bcp1r*7El&99=t{G1VD3S-`NM&zZdNh3BNe69Y{H+F zpub!JixVZ>$-Nvj*D${xNEzrfT|z8#W!~s(MF^6R<|^?Z9Ceo%Mt+9dOQ&w=$^zfT zcfrsb^`1L*yrfQpQi^h2@Qe|}?iN$H=pYiXSU{6L6$upC zOivAMyUV!;s6?LsgUY!c=RrZhZiO=vZD=FwZPv{d!GS}V=5WD7b8wcyp>G3ooWPgF z862ER6o5zRGVbd}I~d(2BnK|{g_R4UJ7YcH))wbSCsf#i+|9KH3-JyR$~`d;ZZK}z z3_W2}l#YBo^8EeB2D) zSM*6Fw%7180%|w`7e$i;2YG0yL-;uO#m;X9B}aKDwfu=d!}VyRtdB12ro*P@Y*Hor zMDH4un*6WL#ieq<%-V^PKF+%_2_6G&1fw4rZB7g3Y-CG2_9FYe2P*b4P>yC&Jmf;> z@OTI&WJ3S_yb9coz0%8q@*e`=^g=UQWT9Uv4^ zY$bdYuyl`zDACVK^N$gQYV~@_@0Yff;#0hq=*NxQ8rXr@_Fzw7m5fyZ&japMiTeU7 zGbtsBb`^Qi76M)WFV@y$-dDfe;y?`M3T^!bX$tj(9LL_zLugJ#+tYZxOUA@a2;Hr9sQ1IQV{H*k}?yL^w8SfQ%By7+1J_e9ETS;Jif zrll`+jA0IHZiWWMRUz7t+!T7rpXSC@G$-HVBF&(QW3@_ibi!af3QbJ`AA?0Oof;*2 zl-&-OV}AJ(4(7f{+>yW5CFFf*R5}efdMq*wzhhO+GeG8f0$dKJF-Y$bJ=bXS*s6RcH!IXeHZS+91x7#}Wsm6%0&Yf!>!6!5GhK~dc-3Yl)w~8SIGj&j4QWob z(w^RQg=UF88`{xn2mM|cw8QGM{tg#`@t+6>Q#?R7LUFXTr=5Rpu3anV zA6|kw==8&9>6T7Ew$5g1=bz|5T%A8cieuH>zIe$vy9754oKwU>$@MqD_v~a$qZ2J| zKLD0{KY6(;91X4KA}ASdKLb2+^)E%{21F935O=aAN|2*EeVdneM(W_>oCO9qWdpMl zEYQ@qJ*^|Eb=;3P(=B3;9(Ul~0%epsx!@@A(X5a$0!Hdu5(;H04y`QQcis|faX!`} zXgL~l?)DQH{zn_3Kvry*KT%vfP4aT4+OoZ*`6UVD&Yp|zse;5YY#lQ``YKNNjbRfY z;JpHOT1J%J!JrThLU2*e{fn)!PI!rZ_`^%vhfQN}BcUkg#R!v_ROb2;ThoYrb07em zD&R9n1XI6DJ|`cEpuB8m|NC4^6E9vzg~Fk`O;Y+iLms}eyM&68gRtZW-k47)0F!-C zQRW~Kpe=$4_@FaIc@4;J+RdV}DgdKfG)f;BXk-k^YY->vU_TWgwbNfm>6SW4fn~`9lNxJnkb45Z7-U^$E`OKyg*>`sGf0jlgYFpbp7Kw{vg?$ znmAARYQ%Z@<0#a1F59c=J%_%y{I54H|d~|skiC!i0|0n^s6^+2e+9V9f}|=gSK|-y=&F|I#)}X3;{- zR9fAcC2Bx;wwXO0=F_NhPi~Ag0Pz!1Db82py>X@mfSXohr}m!)78O84$*l!bq*MkC;#*2=2ITH{GR+y7kSS6Kb z)@-BtA)+t94sMapJYQ;8D6K|1*(@%u8;4f!MmgR96`yco)>jbN0c0=_@y&s)aZ&i20a5cRdv+5yJnX8ly}0L#4yRb-g9R|CQU~iq zsoxfu<_vkYOfj5NvR}@OeDNAxBr@4Yjbm_bHCOd$k-@#v_kN;i%}=iXb6Wj)ofvoM zP8uhq>P8{tYhz8od^z!yMs-`<*D5KwY>8d!VK;>#{s5UQy5UI$jp%LJNNr$`1e>6t zDTXM5715hrGJ7Q<#-Y>bHR;1N#JuX>E~7NB#qt$t^ElFVzbf?Md09kznBKt3b|KG`IkC*z93m zIayk7DRXRk?24#9x1*_ywPofCwAMLC(=Tz>^^c_^L8fly1Ih!V3Cy*5Ffs+PzK>-hs(h*5P6WUPNSajE>zi1xU#C$Bp%FZg0Uci(QD2I&#M ziuEBti4|Lm7z9;s5wHRa2b4T!Q--;MZt7-b0a-)>U$4DA*u#wq3aT^#x<5?y&qcWM zhFkJq(CEFRdCI&UsIo^|nyu5l?!Q@E0-4I?(^hsg`YdL45$fmC6J>RnNr3)5fTf5e z|E_V=x0rxZ5r{Vz`hsxIgB$mIOhKJlcOY;xbE6gJ7+)G-T{SOrOW^wu3tfK6!MjaP z_7fv1NW(Dn>o&JAbCnU~Cf_XKsj?kzz8BJC_B5nj-h+*UA2sRsIkOfmsz@?5J2e`tyQfftMDkHj*I9{tQ@ z-?7YV1j$k0@bYdRW;FPkE|2DUOgnUI3bYo0C~|zVfhhX6B@1XFm?;a764`6I@!rog zLFlpnooo8BboxMe=kfVm@b*pZ zrcZRFh>t%`fZC)U6C_O2(ZmbC2%J9enS9&FJ1zEW5cBxrl;}Vg$$_=c9bgGlt`50v z!qw1wk4^Xvp!Pq$?bk8T1&ka->pY7?JflRyUKkB39Ezpx4AHSFxl30jFO@5K%`s1xg zF)S|v#jVY(DID?KAP%+gNih2A?b!M8IU={iN6jN$b@9QeNMe{cms}c{|9M}u@*cnZ z_$d1iLF2Gg_k2W3e?@{1Y5$pVL#7cgX%L^Qlm>K;?LzV46|B*I!9coZ52kUEv>)a64kIpZza0L0k0G`sNlUtvVI_xp99o8%(Q*@q6I+*1P;H}L&L~&z*lLeb z!lbc|tsA;FBmY-6{=z)!Wf`w>)-}0e(ZbuiEJBeJX?CU&nP<{w;g{orSD%lvCW<7i zr0-sZ%Y3yO3yGMezXGdEoKGGy2M@*ceop@)z7XYbm6TZa>?5n;9TiD9joTeo$e8eG zs>K^aKkJa;AGJuPD(>j0!^+0EGg^rP?*PXZ-KG!pO5bfg@14b%aN`4ebm@4iBAJSKsGLs**L=Yo>y(n2^Pnuw6 z(QC97v-xytW}#>0O(Cf;THhy1R91#26s$@y+}v;qzowRk?qH=a&nf`xLDM|9$I&cv z_1U-xx*7v<3}*@;m(H~~o|Ij1MO z?#`7SWK%p9^}Kk++oc9M=jLX%dXa58-+mHO5y&6&enN@Eb%g_)mCnFI_SJy7Ku?bl zRcg}xJ5?&0Gg|glb}x%)?JD8QBRJ@gZ@AMoJ3S?)+3)Qe-2f>s*2kC5#Uczzz+91M zNL#x~{7fEmN#AnpIL?}-_8@Dl@QEYMYO>sdMlLmJ^3(@fs(#^TJXlA~8LwP>?=dy5 z&VHE_=PVZ{+$bjWZSVeM!GbbblpNGOvNhOYR!N_>a-?9hN36J{O<`oTIJr`_psFbv zWlFm^4isoenR%>HEP%Hjz4Wh9os(y8@1+Z4dv6;b3XyacZ7Gt>Q4wWxwJ*A#<>Gec2gy1^yg7eydFWsqA*xpVBKfA5>-wZ6k$M`a&<(pqgZpe~phBnYHQc zV(8PXHt#&v8L+g0jH09s2}TX+hcr}QYzou}K4T2gZ|e6%D+#dhTj!2=rlxClAS*At z>=y0(Sj%Q7{K0$jJu7{oBe`*U{&4vsTkSey2$3vGTz?RXNCRUJYaK!jS&d0eNzG9$ zQLRC3LG06>Fiw-DCx{4+BYreoM3&iMKOQ+gGxboIgp9ULn+{|zOjg#*fiMZVAd~!P zkivD<&RJ}ww_(ziQtqOvUbcvQJyxQvFh8-fXY6&aE@=a?e3MUUN29c`{AK68MsiWi zr;glgW3E`~!VQ~7M5SCeqNH$pAX2$=(EO84{YqGIlsDzB#R-?bI5wKM_JFxzxc}-^kkdztX7PUo7qac%9v#0RTb1fdK&ibNHWgdIF9%dQUW`y!uN9IWAmCSy6!2 zSZo9VOEH=7HKj>P{nL&1KX7?@NbUhlvKCk1KRt-}mDRTOHr~ z*NITq9`~P>oNQd(?eDh(^lQD^$ugBN#uQ#P`FOSdLiHYl#EUv4UnVTKHp07K(SiUcZ?~Y^3@0k<$ zpRSK;KJKp1+sEgDgIKwg?uMSz$e!zbw(%D2N7zulCT`v??jJk2pEnymAGh~AZH~9Q z62}tXkH1E7e9w0!Y&SYRA8&702XY%F^LK0$o}wi_->-XjUkl(~BO%mRS@%mDJ$zlA zzMQub;4{K^KgXeXP)M3opmJ{m6CF2rJ-**(Zv%WjKipbb6$`jOFSF)yv7x-z9c($a zo2xh9cMmT&K3F=Ocv-5Ryc46_EtuvkVZSC&$ z^3az!b+IBu^0s#S@-L;w%l$J?VMv3teW75GWJ?hBR zop*!zrunsI#8;np;CzNICt=aDnIm8a3UYmh))y0QSQCl$cla zC*&B$=dq601e?)E$l+B6uJXY4ctmT)bqvHK$A3|$E%FgVsgI4^>(1G7$r1bPy)vAJ z5pCUcR~16&K{J^Pj4EhJYBms(Kmg>*4{q}&rNF)&|w_HcpQQx zRaqCD>Fy(Xs_Iiy6_f+btcmiLNY9)A1r>=?IB7R)0Ypuz5?iuTslpvzr6aU_Xy=5e z3l44WVVHOkJnjz^Yp?PgOZW_(lup6<-#%55IIsx*GR+lQG@4mx+#kUR7VGk`CIe$Y zV7~a@)}^(iBD+wW{(~rpffQlf>8f@8mE^kqY)~_@VXsn0ypQ+IC@Qu{FTpI)cN(<`X@g5317Z&3*UMZ?L_5~zqmo-QI}1M53KEe<~9 za@jJYfxo~(0p-n-?$PD34{F#X_*V_L_GsUT6G^UU-_>Pgx)eZ;o`E_s!NOJxpg@Qb zqU7?J@neK)QyzC-zMY<+`dG7MpcKCL%i_N{pVYpZppp)V86I>CLb^Hxc}4+986G36ZaG+HQw;wE3na$U#LkS zB#4&5Thu84O3p(44YMOef_$@DA3DGO)kQ|g=)wn0MYx2yyj|9;PL15vxpsqlQvARJqnx~C}*L&oK0ij0dOfiDJOBzxs>he zO9bEMs!`AZj9Ws!C*09w`+&=|$X#sSxCe4fVOk?>wtw+7w*clOYp%~Knml$A^u`-C zjOSo2P^rGyd&q0d@jMjVeh4M_z5+>vAcD1&zIa2$5o*SI=lKf`JXw@_OZt@tDyo1W zRGaJK4{5JXZ)^5Xv`-D_ytCEUAML>V>7o%rrdo?fnD&+XV~R!qv-}%IjC39)1}ZUu za%;*?`a#vvTU6Nv477a?$qRRWRTxjv1j$6l(6mONSD2Y$LQ)1)zEx7ZN&(ZJ&ub4s zedYZ)=jU)_wMqthxqWCDq@{H~t0O%74Ww@sypO77xv2mhLB_TbLyL zj(28D1D|aUqkCGotJ!xgq{Qv@80A`|D95v5t)kPXCA`D2Cu99FeWyRRg`U5x;zU)qZ+P-R^`1iv zEVZZ+J^KK}@~?WSdvgv6FqNd%WcZue+zNWRT#rXsVWr$s7}yR>*i5sB6-Hy6s5o5f z^Tsl?5SmDVR)`C~jTql_g!ZaLUbC>lUP|a1mTdRdPC?jQo0EssVbVl*OEn0DZc_B` z43yAvVciX|Vqh&SUfv@H>>eKOk2C0i2VAP7vBcWZB2-mjIncg^tHJW8ey|S3c8%fk z@p0H1TT7~beV*DF2#}}sb$Ip!^whCatSyrDdgI>O)bcQ^Hb4=n9R)26DesKUCL?K{E|;8~@gR;n*?!;lNe^Cq-fqWfMy{-@PrOH zV_h^C7N)Xk$tdESOQR<+Z#OqIHBc8AXtl?(Q+@PdOT<2Dq2hAgQ3@s6k&t3;XsZEb zn$!!(zPGSmfAUBbVwH>*^HBI%!%7s!90%$js?uOBA{CQ>dc$;bey?!Oy| zeDmZ1#z%?EK_+8tTxd#^ll)Yo3I0xr6BN?MfokpO=SLU>8Z?nsqFyRMyU5h@XVh5V zNN#>V99Kk`f8m}0-vg6d;o;`mQ7IAEnt-!68Ap86w9KOEE*r<-gORjd%B_h*YzXD` zk9LbjKQX7ivyKg!Ps$*>kgB?Y%X$j%np}`#P{WbOPzzdTQh^n>>N60o1XztdcF0eM z<|p^jRaO(OUE1kpRG!V zNCV(gRPQ}B{t7rOU(-j(2~#C7%?njXC2U8tpzKhxe?%wb+->?RP@+?(g=QFFYs^}D z>AqQR`7*TwOH%s3d3I%@Ed^O)-7ta#xUt9&N!}cU`V=CtUggO-lPYI zx)O^um;;gtY2nML5_Jhv<(=jAdQD8R0{j&1Ri>z|M)eH_ru$VI*IGSB#Kp>8_6^_y zhon1#?-p(BcYcDeI7<~1!z|=e6BP)qtuv9==K){$Vp+XBm)WsffEag;Ux2@OAV4%r-qiY zHiowT9OuTCU$Hb2n&MJeS(vMy04(Ld zF{n~6xP8w;kZDPZtm?(a6%H68RZm`<+9DjMk_qdhV1CIh$L%G;9%||p{Bb>QHC)We z6%Cg!OQ&*Ytk&YHR%nQ79KQ|yi&mD2^X$CBM?qfQGCip zVf1BU6i#;KOHn^)#)}CXGy>ZtEha7l$}a8{S55tk?+0J*_TOE3_?Nez80E+a17)O6 zUQ0-{A{KXz-O2rL$l&u=o54eV3^$~G_i3(wp2KppWJNU-^9_ zSxgFmH*^3xpV6y0@aHSy;96fz-$|tcw1Qo*-$sse>!{EBX?ri2rN)w=E(c+jyJ@f* ztjO1BVb-bJ2N=rK$?zwB32IG}CXgBHEcdy%uhE1c-5d%W3~XYag*66n+me&=)qdf% zDE)M4(qFV=c)W*7WbR)1NC7@s+W-%!fy2YYwIc$W2E8msT0?hhPBFDNIHM|wT`Ns+#5 zU?D-D=Bd(_APdJfH+ofwsTkG)pAe1BH_-3eOd#k;_c zrv=QZv&Hz5|Fu&2Ky*?f$Gw4e)gzjtk>EYBQ&8=j25B z_Ry^tZ>C3hDZCe|9;+kfA9Iu)@ zP$E2A=v3TTpL(+7RHVNk#;y?lm{CbdgPypJYbl%buAH{l%$xo_OiUQxzm~6Uzm*2* zj%9=QmxJLZkJNaKU6|Q}x$PQlIX?!naO6J*Whg;h!t!DrkaR)QUYJb?=y-ZU2Tg#bD&SlHj0AIwmRxAqM*d$(U_NZFLxtF>^k%)&Z?Y#x>!7@L|~*z3V++t@dD zmW}cWb_0$Us%XCB{&aDBDnNq>SWtxs?I+ZQ3@G6i1rb^YVKO&OF^6L>OCg_&pejI2 zgy)|f6#I`!Q=o*V@{Ae|6bXx_-Olc-phPd3iB6BGg*~gEi5LgpZsgj{wFsnRBFtot zq>q=_v7Q$BcACUuUKm=n$&r5FMHUD7aoL=GXmbuHhG5mXLF8le6U|gKW9SSyRADVt^5eeTdpZGmd3<`iQVh2)$b?Zu ze)wePvuxrcO=oF~zl|@Mpx`l&lfm`AGkx99LWC5H#ekplxx&*pENh!<< z(gw9FVFe{D1(WcV9{3mik?L6-l^s!c=&G<*o|izNT)N7TDs8a{^VtUj4Qle$2dN2K zC_y$8&;>5sTT<&7cifk#+H8GEKDQ8wK8M}}*I?pA^=tDG2xk-*Iw(xpABysIbnmM+ zYldLbR+>J|Tk?)(x)n1KrNfjDT(VnAyS>NF&L)JGvkJ$)tGV4ele={hjz%d?s@!MF zxR=qaJjsLJK2`{bL<#EFX=7T*{Q5MEwbff5Rt@HwHc+IWKV7*$)&uG_au;b5%hx)E|X!G^u?_Q)wnt zKo9n*Hf{^SQJ7JBikl7Hzuq^Ghra1O$gRg9D(SFL;ZMv!8IqD+`v#F2b9+@-`UD!| z8?d#@DAcq0f;iWKUB}?@gp1JzvB;D2FG&*C*UC&8l>m!wjqe0g2Gl{v>cdk0ySxD8 znG)EpF1aH4B<`Efs`2dr+gqjMZ_P0RTp}(sem+Yhm`)zgkkGHof>zHfAbjNqdHOU{ z^tKvpy7<)my2<@}*B|4Y2ZRw<`CikKt;s{5tLRn5-=q~n)DKA08S3I@=$K)ojK)}L z18``!{g8QZdZ3yzvOq`2!aW~S%lD}7TT99-$9z~u4%ny&2OuGzr*v4o9{#twVV<)% zWL7Gd4Y5%lUSr^TTNyY}#&4pzi8wjHEKriy94eb3?IyL2{M4D>Vd7>p!aR73N%%U~ zL$eRqWeQD4eoE0AdZg59G&A8A!?dX+m3k&5o)#AJK$MQB;P24>fz7UzPcV5%hn&9- zoX$EduInpW=>gFg>IX3>Gz8&qlN1B!;fHF-1Tq=QTM`1dex@5tI>_4#ilGmX=hmln z2dHL*uEN__@jGh$VG>*vkG-}$OU4l;i?0cJ#xcoP$ftZ?Z_&j#7`SrqbedoQ; z-0Bb7@4hDF3A(=IKh+eTxE%^DLG&f5WqQlMjSLi^O)DU!a9bMy*Ml|e628F;YXuww zR|UsUBp{`A2dx{2|5LjlL!vAYMBBk zgY06_Hr0eum@o2-H-yE>?iB3?pbFm&#aXlR~uk2B9=~z zHthqK0o^~PT_ey&RhMwny%Lyx3&b!v=O7TbWHoqNy2aRyi%m}=k{d1$ zktRpE)gF}?jksn%4fxo9CZCcLV3VQfD{I5SIZdOY?lEmGoMiQ0yf;PP!HSk9Zsu{C zQf{li(l#?+kAnc0Xwo@%la{&IP#1hJH2Z*iE3l<(6LNT{pJsJm#G&_O3HppFvS00NyjtJ zTL7a)Omh?shSt(dRVD+%3v#g@vcW@lbc6&?)hCFn2Voe~0cT(o$_a zK@@{O9>og-&2G2Zo_ka0W-5-5ppIQPi(NO$MJQZRnsUQ>->PF_OUuMgUu0{GfW*?t z8Tb!Kq|ackg^feuG-DpRDTtfzukW~df<9vLmgcfUM=6O=vgp+_PiG8A#*)l#^G$T? zdeqb6Gw!2hlFg>%wVl6zOT9Z@gdSWnDR!%s`y(?_WhOrqTqd3a2(#fBZ0;46QRU>T ztfcDG>RTVcU`J_?=cLew^cWW_0^^M+`bjqp>*>}1@s4iew43(uMI(K?WLcBtILaAIc-nY4cPPr-{wQdTj8 zM#j5MNFHh;JIQI^c_{l;(b#5FJq~=VqN5>y=*sX}nrg0C2u#jLs=Jo$8yhI49nHNM zC~i`)bf05JI8`be4!D3xiT;&Ny zbo%5KGGFZ38q$u-lPeHU^h2z5r3qZBGf<^tl4ctCMpj+lV5dq$-61vy5~?h%`nIo! z#bZ|*={2H|WX1>+=@Rtr7#(1ND6U2#HYCd?R1byqgp9@k$NBz3q&p@_QJHmQ>1y5L zPHe#wj)@`>7)yTBLP~kc1)c5r8%c$dQYS>&v1Y+{k^3JB1a3_lubjcgp z4XP#%BB;_HJ&7i=x@@jG%FZWV_Gb>W+y1z)4j-b8Y;jdvZx0c8HbiRJOPO##R1Q?o3?PF{M?P#$u)#nDk6 zCuI=eOC$T4rahT23&K5rYAh~oRW>EbOlVB2Ys|}1qiK7SeAPJW9y(O0xlfH_X>6M3 z{icj!P!E^uYExAjUuu}5szA%0%&}Z1Pt#SGH5m(vz=lkfJ<7u1=&3GWtuKm{p zcVUw_bz%j)Rp7ev9-Meyc9#9nrK`5!Ax^w1avSFQJzq{W!5Q zI@HYpzqPFqCieAAy1>{H8;n)2GM$>yJbKL(PpW3W4bvep7AF6Pv3hd1ON3Aw(k#W} zUx=G{_p>{OW&wJ8RvLIO?%H2_d2gPQ{-XqI$LL7wSg=5CP zg+UN6Fx1t=W`eM?_f)9~{jT+pqX}3xCB5MXR5-+ppN6S`=l23!@^pD1Rru+mDQh>| ztKUNVe(z>#g7lV{4G_nlOAcM~m)e{K1fTQ=7@l!Lhu+!lMwq%uOb-z3?fWjBhr>zm z1ZxxFcZ3qt54Khk!dBU1w`WV**(&}?82!WU?~T^rRS3IAG?{%VVFQ>exKDtI|JIbT zaNYhnerOQ~u$+~8iJn1!eC7FSu4dv`TeNpp#lQJCX_@3mlxS4T*Amz} zL^)MRCEzT8iqx|jh-!&>uCcU~V8W2K@1Mr7;E^a*V^mv7J9R<#c`hk^&#+VS)GMC@ zrm^WAY7ZF3HAXwkoUDa_-ED4}sGvtsFzQd|x*M{LI3Ri?4oY#?Z$XkjE>s@L7`EbP zM&qBsg@sJ?c8Zr=Bd0uq1aOq|pR4lF%P>$vY*>TCh*8BYzXdgv{uZfA=gk@1+;ONS z`YChq5|jR-fF|aMCnneJEhB*yS+LUW=PoJ}Us*~T;b`BblA;7WN+c^3{jABi_KRnA z>jfK79d3IXV#|1buIl?n)yB5bWMS#X^^1iY^ThNLrMQ@bfQ(^6DP!h0kEirCER8lK z^=}4dbdd9K)GGV+v=V%IgCUQP-#nQTc@C$00VW%0WSVE zM%7|Zk!zhZ!dWHJcRiWe@(QqmnideI#d@K&)7VVRMcNFBgr2tk3rdd3O}i&Nh<+FUm8*t-62QiqlN4$j{;eel5Gn4eAlNOhcBAmoK&SKViB z{81%i<06h+Qn(zHGsCX=vo@orVCXv4;fuU!wB-(P8=}wN8%mzf4Cj4XM2j0DoK*!V zqh1)+2{A>nD&43mlrBRe0(E2g4S0Gfy8Q{4MzW(!V_zj1|CcysfG@kLIJ2QxJmUGQ3+t1)Pt7>0tV-Jq+D@4VNnRw{a6hdQvH=IfSXbk(ivCtk!pw+WkW zixE*Y9Gz%&$Xy`PMl)jEU&$ib6|tGGtM|gO>~ON8(COHv`=zZ>(7tVbCw{Z?)alHm z1}$AkYEul^n;4B$lp7WGubwcat%^t0vo?@tQL%;j;MA^{#YivOs-O%E6?@wy8ASaPo1FU#tUWsN6q(`kGb+HC<{>Tlk$yC8Pb*`tl>D+!Mg)iVQ@I_sxK7!Ceyi_aL13PzZCE z2HvVHXA}avRAcX||5*em#g{Z^_;iJS8KG9|+X2O#W&**-u;sLspat~QkIJ^4n2E$A(G zJw@@eEIGPv(Ax`pf{CF`>3@^hCDfI$x}^1vyYoEqxcLMmH%SW^)CgHS>_Xh{!Fb*- z=YNcfnY%|vx*(t0n?aU@%^$8F+fg|@Sl8c=y;;8>Ir2*rvabYewI`CPz~*m`e)(dR zKT+>r8sD3TwQ91)I$dCL-gWG{0KUEvRbn?g(6EJBSx7|Rv;0e$EvWyuk=y{#jewX{ zoQ@nY0~~WmHo-Wt)#hNNgLT_BxD&oHR2L%yr*Y3?yE_CIHul7S6wU^J*pgck<8V|m zFQKXlTXnM;s`4gKhqyqSj$B^IqfPXP6hm9J=bvgPN1h#ZzMJ)Xr0L87os-fqLVgt} zeKkgXkRF+oNJu=Gkuv@&5Ct+F5o#o(I0^#>JM31?OFTG@WJ&hnjvGDEI(vQQ!xy5Q z5Md|&hLK%sX-IrZ#d!j%2OlYrvASz5jEn(8o^67?(I0Psa7!uwh(f?uXSzXV=aVUO zHQv~pmVI zwLx)P@v-`(oVx9JBnFOkw1tY$>M-~4*=I>_aQ6nQrt-(MxqZr~ZO0}ZJ|sCHb^Vjh zq4i^|0TOWFEdzFAxv4O9QVC+|uN*DX!xQVo6C{%Do8neq2(KHu7RugIl?=5S3YJ;R zq~-06y^%=n$~sxt<>G;K-5jT%5CvA7L)woirh6|E&=n~BYJ-6dtcSl45+6Wz3G6ff z@FQ+k*P01BJxaYx6i_%#o?0;XJvy6BaN5lio+vYO4!-*PxmquO=FPO|1CZvQP$5{kA@}8rTO}7=daC_JW@&P;}=b>OK+W{ zQ#f0AX0&D4UxCnZ>0D>}A8Gg9eX~|l7v#x%>y?6fB&N#@V&|opB$^?tXRAi_X-D*m zO}J)0Vu}%p4N0Et1`ki+>l-TqQ0_SlgD0CQtM*d*>onHJpY13eV<|KbCA*r?_hRNF zBqw||XUOgk!|hpfIZQ$!_~*SwdubZkq_PZ398dMVDg0vx3gs$oX{EL`w>aVGlwC?T z(S~Ix3DQtvk5vB`I|NI>kl;5S1e0c`)*qh-d}~nWrlhZlwWoyH{F_73P#Us1p~5oov-t z5@e}uT28k!x5JC$J={E=isS9q?C^%#)JY)c)Y7Sy5P4drZ0Raj%9i|1$s|znZo~2g zY~WbP@H}hIMsD;$b@W@bN5q0XNxPVnLw1Vk{Y#dE6O7bmz;ZXqyf(g&@J{Ua-66Jtd{x0@d(hUsKUbvQs-Vz zek|4BI}PKh=?#OuM_VOiuhXZt`vXVRuXbniL7Ymm^{(#L`&Wq}PJAuMb#2+oljR$O z4Bf8{vd5qQRUc2%Rel@ytB*go0RYhWZ`M*eINLZn{mLr;&w9$GOa$;`OlUb=i(c|ac7N^BC~XSNk@vy;ULao0E?4yb7k)rm81-O z@K?r5UrJ9r$xp@frpl_;M7&2pKKN6yKHngpk#EG#Jj@L-^F_|yc5g@-{i7@!77%Wd=v=MHA> zg+A4@`BQBB0!ibX85JBwnNMUznTR;M3WIa>%8HY7(G=c-9;I9CN7+HkN-o46g6*=` zXe+Y%GH&5+tEi>udun@5)E-C=g}d}TbkrV-b0hAPQ-N=7shPPeVeS;)lKqx%F4P%- z)v?#;>*X?&CYJZRHL|O)_hzKyJu6Pg+yv++V!m+G9zkC+)_;~ujTt5V;+XQZD=qhv zS679Qx$RbX&Zw%1M{ROAp65)7R~;v!m0QJ4G>pzqr5DZQHplCKN;;t4D(J_rgg<$U z9#Y#Fqr14DkS_$!`Rn?|oD`+OxFtd^W8GG})6T1mx$|e(_W`1a07Qe@a2HIaF#(A9 zCkp|-Q-KDZp%9qF7=FLC@Bjy&5c#vm06NK}&X{5VJ45SW0PisX5`b6P4*+n-*!=)W zD8kQNI?lU4)O1+I_YuDYrsrQ%2gzR)=VK|w!Wj-8nK}vzbw@ZPk;b}KvUJ?1lCyoj z4$03}NY%#;K=6zizO;)Agl`ql5^v{(E|6UTh^e3X9}F1$p1yx>iCXC*2{uy%TIqyJ z4KM@kbVYwQTMLNei99GIhy!#akwSK)Aoo%LO6LEN2sc65F-|E6zLJFAO2O@=e4@V{ zagA@YaWDDy@UVuhrwJVffaFtxo|-x)A<$;A8b`9mh3K=57 zJq?HCbt&+X?x};@3AJKGXJ4s7ov4X4PQ@YY zNb;jH_0J~>QW#ebKNBKf25GO}HwI~MkwFgs6KR()A_i%Ij)1ZSF{nbQF_Tb>b3q}2 z{a{535|IZWy6%9z*gm;2CBY|(OY$9aUSU4gUQB{-SX0vPA>}`o0GzJpl!PDyYBlpO zDD1g;qj~rF0Q;Zy=JZBf@sbb#01PMq0BZkDIa1%s)Yie=$;?{!Ke8mPzJrO$|ISAg ztu|+)B?9Apqjm{x3+Qgb-F#97Sgr^efiizWo`f2mPWnqvf)DdOapUgph=ek*^+dMz zI$0kX8F_xFK3M&9x_*AxyStFPxp`@kgJ(;8N@UwVS)+tTWC|C(qpouR9# zsnPNNIDQ|xzTR8dd)~YHKE7_*qI|Jg{v^?kp*;r04%{CK$g8oIW7(f@gUv#a~4{h{~e z<;J!7)r)ubczS+Ze0>|dJfA%;bVP0)>}`FY(Sh839X%a2Xi#b=+1~yHbQ552m8OXk zeA1%F-sG6m705)MqfMowAr&xy!Cq}yTTpPyU`a=i!y@4-s*huMXU!X@(@R@mniw?g zJC_%oGnN^qJMjhEai+1d=quvrD{^=tz7#pxa96_DF}B9G$50@~zh@+AuBcjs`sd)4 zG`hvVQCw7Dfx~+(nXT^jYa`Gu&CeHtC?2v3iTQg5N2;1+{av!rXXv9|PB&LkJS#|g zq?JpvQ^a;wwl-onY+T<@y)9zToLojEkgYSIk3k>fG|FF_=3PQ6|MxdE(V|E)A`y@q za5H(Ex&ZRDjmf}#K>xsRo*%&4sXs?%gnUAjsKZ!L1A!hB!ze4b$>g+gAB)MUpkam( zuoI?v?og2d7k{%hf@vUtzswwwe1aeB59h14h}(WaWIeJ`Y;gqvXW1wwSq;m6d18~u z7y)p>9?;rvU%)uvtM~?Sx@qm(vSPjolY7-Qta=b7fZn|rM@9@a;FIk_do|ELD2Q=P z52Gg8+`HCN=SBbU6us0W)Q`b7jbNduBxNF!=|T(Gg763So_PYuP%N2Hj2jzNBa>bU zlCN;bbYShVf%(ps>>`xDmT=};3REcnde$?sjIaus+!27uFp0flGr1fwgwi{h$qS86 zBha-Pw8CE$$1!^#+|lEB?XbtcNiP->!KYrlBuWW`)osYh1}9VSl#qAYq`WGNJ=|abZZT_Z5YTKb zaw7f%NNNk%Z^2-QZAa*(1d>~1)z^p~0Wq$yqjdC=N|bsTQiQ`#w+WPIaWFZG>cR1H zC`{qM8F4`pCA0E40o)7?{8Vfr5o*q%m@_d@G@?}kt1NF@{Rg;NRrv;Dx-U+mYEtQo zOMS490M>Jc_%dT9el>FE`*U*`)<{c(+z3WE|8&de2Wc`PkHoKVh zQ?#agF~LU8S?c^Gf_Sqft${k`1MzpfV^-vrd{Tb)no~pr+0w@zE@u{}EO3bLE2J9K z0Lf)=o4>FRSeP-vV6p}@G9$t$bpTO_%+^&=Ug&%&F4lv3VG+{&Lt~YtSy>FH1g~09 zG^7Kd$Bhjni_yfcvhjO{Lx3>W9IH%lfi={lLTZ((b6LW9b)2>Mdl6*$((tn}o-D|0 z@lqk4u2JfCk51`=x~2uHBSVTrS#yLrK%ZXqUe0C$^Ew$rpSYMop#b_?J=O8@64Vx1 zd7kRT3Fbv+#R7uX6Xx2&=krxgcdl9Nx#nN-oHO}RBTb2^Q&K67JLM!U$J?@8>7b7;BsjQY51Kf zLb{_YwewgM$1?ffMS}EYe!>m=du3dz_JU8%L3Lm}6tQ*2NDJxdp-C^l4W2M3jK#3pCoq;lja)(DBC6)uqQ_1jkr@Mn zf`$gs30*u$y5q^Qe;CS9pbHqrv|y8djqKcoOlg#sg>bAw#SA(yQTAXA>tNK;ts{kw zcYw;dW_GwjKdfcy$}492gQpZzq3X;c5@pk~!^Dk^A(EmSp(btYMangSq~pA$xmC5U zDHa~5(->CcW?H;ZRvfxiRup|Ex9}1Jn8V}+*3DE@H^#7o2sY-Z1#;Hpj} z^r7NNQcrC8^!J60;iRX5(EEZ;7*6aAx?GMF9);>M=~GqH&S*a3Pw_D1HXCxh9Qp7C z&Bh(zp=u;aSQ8#;$@;KuL$>EX#Q$y1yj!E8kEBd_>0%e#QrJ$nNX6+Xr{2x+q z4%MGn2Y5*%Vy2h2Lu7?2RgnR8>jE3H6D(C`g(|a{+RGc%8USEUX*Ul|9$mH(3%d() zGKtM;095@A8(Z_jF{Dz_pPC!pRX?wVL+re+m5B7zEU(0<|Fg3C|G&JUt&NGfsk6g>hV&bvqsxB<)Ze!E zzXo)ry^&BMTJN1>8_#v_vt~<1I^8+Noyl|qLNm7&ggBbuvEkY6qZ$y`ih`6cu(7r0 z5VVE%E3B$#Tp1syKaWvtzy^VT`hKR_udhu`*8D?j`TXTz6m8h-$@?|6?#t~{aE(Y;*Dmzb5!^fv$G%7 zz5m(_t2340mUEyyP9TQFyzL%g$m%3pu;*g=EjH3b-d3aZih>`nZZFgWt!?a)dK z`qd@uUe`vMa;(t4)m4DZcMEjkjf9tQu^;NzF_9qj_G+IHs-28m6ZxH8SYtcV0Q-cS z{R!wvHw3>!-!8`ZvlYj0C!P+}2Y3b*l(hK|vZ9s5S?ZU!L+}JQ_LQ`N+9}o~q;%|z zXFE^KCwerQpN6++uWMxo2QC~r5Pbs!r}>wf*-d}!76Hu27S`xBId89%b_;v4_O&YS zYKO(g5B_FmX@hylC)xpq9M}J6zar-j<^N*soVqgsmNgvPwr$(~Vohw@_Qc6d^u@OA ziEZ1-#I}9rT%3z@wQu$h=w8)TU8`4Dbw97b-N*C4_%Qyg5U)Nq%fvCPXKLY>$n6Zg zylGLoH{4$_yAkUxo_{n=Xcm&qZA{vx;qx;Le1E?wp7QlOl#0f&jxDbb&qw-|Em;6g z#_%A>u%3;5S-J6iktTI`Xq>MP99g>^sS`28j=0{)BRiW}k-Y1Ns<8)ZdL{n+ReS4! zkF58OS!xQ24aNxYeG4n9OU%VhLaf@_neP1QO z@3;1IqbPadFWl0|Y^<)Ch&^xbdei6!?cT>EV(cF3_p;|^Lb=aE;*=cLjGOe?bw<&* z!b?Ul)pN4SVGf_K1j?O6UBjIuVUH|5kBZ6QYo*c>RgL3-6H z#R7e2oYHdmP+9K4s!>`(YsEwCyY3C@4*&Y_TqYf}wnuO9Jn2&S1FnL%iIpXB<}aV| zEo@Dp!>eEA3SI96ASFV1sQ6TZWH?+ULgkn$LT~YOtMdM`EJzC8TDIG*L^o*QgW^>Q zGq8q%-ZRiOO(+3c2hZGER?lDKHZT`vLtJZ`j(9ztMZCk#zwJE8kjW)(W7qhHlCJQf ziajMo{|JfYuGqiA9ZK;(l#9)YYbx>hoVZ}>h1cZNJ6(UZ#9LhG`!jJpO{3lX#&lq7u76%Uv91!>EnS9wMGg%mTg%BB z6EbraOpo7s3(6(ezWa+-k8GGVgvr{}_$M_9h1l@m(d75vrt6V)%BF#VPG_o%G!-j_CQ$8P?pOm@cnP?dx1`J4eK$ zz$~09l^M>q(9xcN^z0Yc#a7VZQ(fn=ZZey<*~N6U0)9~{MyuDTc4Fzk6(%iDvXL(? zPPb$RQE_`!{%|Fe_d&C>d~5(u43rz9Gkc(i2X{J=8{*Et$l}}7uV!#>eS7{A)NTec zH@da@UCauNx%2vkJ!>%cF{hYq(6k7KcmRs^ zM~Q0=p92d2z1z^dbCF;lfe7F-o%9ngh68+Ojxb@yuIjB6uhIyQoPxtr${MFyXIX2v z_<87RMB93uEuXZ>gy}2r!yBX!S%GJ;UnGyup+e@V2FhkN$fjzFcc?7BPQl1OuD~;5|MI z6f4z~btgES1~@w!&>=H3gw9XXS?Z=ms!x4dp=U1YnWvKIkUIvUAHx-%vyyk^muo+k zFUZEVzM(@Fc8g13Gt9NvD7{DH^kX9KMqzP{fuNP_n=DF;LA*Qp zDEsv|!mJg}fFlq(h>~Dw<2)*)fH2O$gg-a?Hqcl#5@Sx<_aT>$x zBUL5uis(rzd#WB3<*s+{IlN2DGpx7+3Y^$k3@v5}|Dakk&^Hn=_fR8p%yS#pLefNn z$E8m8U&kYgV~#tgv9Y9iY$CQlK;zUF;!;*&*V^B9+3>DI3n{Bb(x zHHk~P+aZ83Fk`;sqcZgzvwp^gWe}IV>1<7_*Qdme(yvz639FW|3~<7S$l>X%gXzNl zTz|*eDjHT2VEL1`nh>_sIV8>V7qdFYYz&oYb^(Ddm!$&IdM{N04phLuqG~pow0z0U z6K>n~xS9q)Q+JjkLKVcT?3X}jsbPr8OaFvs)(>_$bP7oT-hBGH;W9o2L zJe(#AxiMlV!Bnp{-m@@cpl0GAzjhI&*ih37!C8o@Rpv=QlFo0V1oa=8=78{=ON+np zkK}xJiy0`<*IVH#O2@8euo;@ zgxMAKMCnOOh^`&?v~~TpUd*%_S{1Sa*7gzS9HXWloKYm*Y4I0z-J0GPL&v#bFz>h7 z{a;eTj!L!MpkPc&P<0lfG|;RK7(sM66q{{Y;Za(OU=9L0u_kD7jeB$c<~@0z5CFJ- zdpMTd$jDNELWSI%)X@&z_~I!XNe=2X+>484^(jth=5``$yd8t0)f~^zE(%2*82gPL zBEdOvoeh~Hhl8_*{6t*%UjdYSbn|w!*;$xtBOleh9uuNcQQH6yKsZTa_Ao3xqW%M<~Ms98LImTU6M1|?n#TO zn=|$k;ogqTBU(P?VVN24*^4*s`?n4noi)D(@I_o++49|ccCki>=mC(%QnYEro9y1U z4Q>h((!lrxqTVj_^somVysI!(4Y|B*i;V0^4W=sFojLbE5_dV$8e5B;C>1HcU&q|W z%{11)+MkPT960DxbL3{kqD_-Dm$#%3@*>I3?5 zvl{0RNRrkJb@)RQaBn6g>i)j%boY6l=qIc!qC9Ek8o8et`Y}()EP~85tg*dP%^>s9 ziUJM$3eHCrV-B%KGSaCFC{+G2qFUI~x=06C&E$9@vk)mUFIBml5ivqLu#WUwI*{MLkye z5_nXg;y6wDCk?uqhCT+x<9?8p4{;1?3!)W;rsEWq$+xeJ#ezbTV3}f*CB>xkY;#@| zp$W`F2iPqyC%pQRh_jafgn4&J!|w^7wTV?sPk%Vf8P7I;lFdydiSMnIK zf}u;m!G(_df{RI(cOJ!}IU6b}oYD^2xW`J-J-Nkt7Orb4-AQs&RUSS?PSr~kC4oG+ zncJrW``xf3o5Hw^yj9_9dLQ`Z?kz(r=$&7Lz~<(itg;cOhxs${R{5Xu!R)QA`?7TV zWFO3>)U9mS5ycwgQ+*NA%=&RzY!T5IB`Sq{gN3huf`UgJH8nS3kE+c1Sk9o`6> zEz(}{{3Aagc>ei&dIA;lxFUQ;9^yfX6fi;M+5tc>hp?|3#LIj;I-oKqKLX zPZ4e=WbC}SD|csiP1w)}MSYm2U)QHfUo!7LF@7V9FQe>3lOQCguB_GFe@cyc%*;vW zW{?_yz{F)i;$h?%<-d0DcNL)}$|5?F4TCAkwyee^^OK}xKxxw;y2|EipOoxx`cyVI zB*&h!drm6EzfsHhqkb!`9Wip)hUf=zoT8Qk9xCZkaQJdYb7>)AJWo>rXwXo*tlteX z&a|bL1dMPd4lj|d0X@yq}}0=ElpzoIn*?LP<1z}u@I^;*zjuC?kKVje9S)k z>iyD|v!wG9*R?A5sPbgVV3(g)1eGT^@7ljS(Ci|WWcQFgpvm(^Jd%HmaJ_V7w~Jb8 zVE&0Y)t|L58#OI`M)o(1Mve(Wvq?V}I5Mb%8NsW%)fv9Dwl2zc)CSfpMWdES%(6C* zwhoD&gKrvk-?W>OP78@{apay=$6lP6m4i2OC1_&Z9tn&??_pl z527TJP^H3aW6P{JT(y9OHO>OFZU$X?`B%hZoyyUoM99$Wb?HNXncXbZGlhy?a$%ir z0)#rtaXGq!ue8hPH+Oruaw}J!%S~0sLZ~5m8iTywk*-7b<`7_tq2Ksw=SBZpe*f(I z`#AUeW2f|6d?&^XsXHw#6LJA%sT_QmDL>qZU5@d`Zvo$+pWk4Z%E?988ti{(7Vw51 z$o8Jn^egc!1dmUsJyu@YS{1X#q}y_}>d4q|KwmKdpE;0U2x2Y66hEiKLsqaHW8cm8_&&E=+?bC`n* zsqdkbQo&6ct(FjCs`wWxJi-}rTIdJurNt{R$4;~NpAIp7*U$Km)gknZQ5JGOL%h%x)z2bJHxsC! zwo+C2ZAMAZk^LKVCv32bcJEP@AJb}&2ASZLx5 zv?Dk-t=Jrt%9;^9&E*4`MOi<$gofGL*@X@0Y#F`q$=JH_{1wb{g;97=?v#Rc{&6Y~ zGJ+94#2N#NX5lVl*ecl$Dsn`csAVwI&w785?a>q*#O-d;lmjb}TpE?fZdB#ze_7mi z&rIWAR&zmK-ah|eP25(X5Usvtw`u|n6TUKETuh9RAB>Xke=oA_n@FY@FzCuc2~$be z37g$LHbk>Rnv0MD?9a)oZNX^ddo&0xhGRmY6a@jd%3>w zBrx3iI~@VfF#hwz6YE0;sp&=|>uCmW7NCy?@x#aQMg?d{+&B1`nr=H@gd9;Ild%+Gn zXjOh;fO{+3Cwat2*sH*jypVg~@fGWj*GnB1SyV6k6dShc>QUU0(PVJU@p+hL3%JWM{=sVw`+`FDYjd3|qlQ`^Qb^Gmg}fM|LLHul_7f zB~^I7qzzU+8R^)UFkXj^A&uOPr!U!jL@uG{HBLi0UU=#`p=K8hT@BuE$p7Dx7Rkq# z`DF(Jf{z6XqV?aZ+x{N|&BNHv-TXiIq%XU48!6U)RKHHw{w>}i8}Qpo6()2OX);FS zOQSfEt_reiM<|D@q|0{r@{eqEdArICe3{7oI!FzCzR&#{*!e!F{k|&gc}Wz0ZTvSP zRrtH|`^&TE<1{zmbLv^x?{P}l|0|;Q`&B70A3N}=CwC^``E^I=`{oh(U-@q;_v_;N z>!|eKC}H8;&wE4v$IoZs&!dRz?$6f1K;JLNp7$r>p0|l7U}K$hqH#8`s@mrL_T% zro``mvVOenr+zRIyXxHs27C^De{vfJe*ZK3V`3-p@m~~PG6PDFz4ac5-%fU3#Xi3q zq0UEgZXLfrr~_ZOG`~Y1=Dhsh&ja6|GH3pU?9(vdeQW#c>7NNjAwAz`9kp#^+>XL6 zGll`5ThGEDm$^M{XV~9wuivjn|CZtN!|&f5cdx=c2glrBBT^3^(>p%(*zYslpN}g$ zevg%g0Ug6nm4;nC4}bPjM>-7jeGn$`*PeJjrCJ21ds6KGdA8va`}^xW@Z(9-vBx*t z|9R>BA=5FoQTS`9=Vhvv*z)^e=4+3-=VL7QK-J;2d#mNW``Y`U?KvwNN!P|TX{v{J zafi=#-2eD_cGsiJM9$yA|6ubt$H9I@_W`iqZLfQ3dbEM_T)(6@Y#`O;FZ=9&GH^87 zb z{iZv;@3rZ5@s#(w`t`=*kFKK4^-CZ1EBlm`BZHDF;}f~(_SVh$VNZwFs-9xsI=6Tm zgYV6p+7&%te1&J7k;}v@2220tRZcSjsmmYYO{py_Z(irGj+f7R&#EqukGlA3O5K+o zLSo#3Li~@rZzV0%F-w)m3!5G@_FbM+mkhpy&gKf%wYuAu8v+RejyAU&BhAfO6N`Q> zWwcE0MS`XEvAK?pt>wH9zZF(4in5;jEFXAnW-@|n`rZe7iwa=R-Mv`Sl@yeAQ3ZZ# z6R*B;g%kUpOuQSsVcF)VZrjlND{Wq_a)5B!7dlk&SLYOU*!lv9)IP;Sc1j!?H-#QM zQp&pZe%Wm@LB9$~IaKvzohwYZFA!e(lnItlu@|1EU8$dW5bd~&dDceIyJnVi`ZsGH zmq0J|NL8?Q8h$7|7a5mRo+fx1q|_5x+v*g3b*eAe;Z>-5_pdhzj0lbVj{9~guziw9 z^&QL5W2|X+HJ>+Gh%ZHz*6VPsTjG;?zAY(N6pX9BPEVKlWNicNC%3Zg3H$%tE}~ty zx*GCKZ{=z>jV4OD)O%W9U-hb6^{^{itQZ=r;a1FEgt|w!Jv^~(QfW;yrclb%-pKtG zu?#Fp=s1q|RcyO(p0VD!J3&-geAzB1tcWQ_oN*B*ZuZ4xMD`y$QN!1nP=U9<@>Xz; zj`7RcUl=8#244Anu630@CKQr!a9!WVaB_0q=(|3-bUy|GL!;g+QUJhY1u`2eMh)Qs zE>~HA>G4*6>T^S~zS7pVyw|6{!XK=y+Y|+2x^himQLGuHq48SeWJUnnnEqC4%icxy z#xmG)S}c&E5dFN#wq8E;ZZ_-6C%36zi@nAl*wMme$w`e?(*Xg7eLlFjj<+t;u~YeZ z!I{gu=n;=)ZB6`JW7%bm5}u;a4SS`~Cshwua$HRAW$SkwH+r`LaU3d5j^QPYbsGgQ zc7go40D#^O81XvwWO2oD*M@aoNJmKfN)M{?3gU0S?iq9Xd&}@~&R?9hQ%@-4^2;~V zJ~xW6d#3@*71lYMp%1v+nX9s*5M{Vk82+U70N{6Wv#qJ(mcc$Rjrhpv6wUO>R(ER}P-)U!!N&K=M%2fDf%B0X)v7kSpL)_VHQg`@g^h(*Z zHifEeAoo&Nch1@4TRg0BR3&1u3UF|F^4xt(WCh{~xX?k!c*~VRRUL<=DzecEP*S{% zyf9a-#E2$RHR%g(mMul{mu7Ob@c!Di@Xj{PnNY~k>oED;ou>4Cm}_9HaKspDb<}4s zP?5v}xOyVm5D9ctrREfuJPIS37sdp$DB<&~smk$-M(1h#0>L%LYhxgt@kbDh3NNI# zcCtNdY2@WJAw_*K(t(oX_Ih8uayR8PCOO)8q}h7xq(KmRyHxK54cm-ft;`uzr9>1%u!fuV5qf& zS2rxVPB>g?{^qg6(cbp=OVC(a6p%yGzVPsG!aC^5{q&aAt>Y7P(eDGglQ4{Kto8D|PLrIqJ#h0w+PSY3Ymy1Y*7IlCj0xFUw#iZ3 zEth}%Fc;#u1LjfH)Wb?$zs9I@udZJ!=RPV1Mhg&>zw9TYNbLmjl*Uzu#>d=JNm;hD>AiZ65+Yy-wm zk-6#EDNTtcxDQQ>umxN_{Zo(0x>s#Br3PAwlMFpcSzX$46e}y4;@3VY&kX#Mw<*@0 zvf3HN=%cA*7TnR&R}-@syt903nx@FtDpVa{!>4HR++60-Z-eZ%>kgR)uFNOvktaGcvd#Ha)L^k@Q^^>)D%6bk<)0k%m4ueFK$Du z1VC%IKC~UAh@by=ilY3mTMjSgyl#K}?P(zOr*%-&1v>*=!3AQ z!tYE;03DP2TTxIVQE>%tjw(@-tn&DBIY7q@O)ptlG17&iyfYg&CEL8MB?MxjJ1nNd zK20)Y?uuS(nG^p7)aO{iN8TeWiGf`*lwoah4&j`b4JW}O$8C)LS^`$jQI*W(Vvv#( zb&+R+%``j}{3;o$gx^OZUOpuBr8$Nf*P!kw{8)j#UE$S*Kc9BbBqUGUGRToQZaU>j z!V8dZ^Lp@+w23UId;DfivT_d5?T7sXO{BlVdVQcGA&feC*}$lT8`e`t194*f?Z zk-Q16A7wC0hQ758Een|vclpfwALqVo+`mkYI<1~-gh0G%#;J04)e1FLJtZeVu=2>DkD{;e#L?qhokf51fX2=EY}HwS?C-PG#&XD-CHn0A zjai@6_=Uw&Ol!hH0Yx@vg|2N?jO2HY=YF})DOemx-HKrC?PAR+OX z!oIbBlyEHGotocqyx@UokS3*#wx>S8ua--oltWVT{LVvgd=srg_O20-aORxC-X?5* zW4iGBUb#9|g%Oy~c3FvCSCneEA1wUWE1N;i_Pb5!7Jh*iU3Y^Jm&}Kb+}Z`c>{ThP z5aNLgf*}7#4Cv2C-ff7ww=vl(3G)rKY8O=gpDMObDAg{7&;b&zEHXOkcuLrSpkbXY zHl|+`Wg&R$gr0k^NH=KGiKvYmk}DQ9XtTh`sT!4)65s?``B;|>+Fp-bs*bzOzAgRY zs>`}=Xll4wiXf>q&02(RoZkQiU4eEj>Qb(bYHSvUF9O>wnyL=*_MB)2#9A)I#QUX> zTa1iSOQn!1)W+AQdz?}6$D~J2c0fyNQxOMA#x|Bio4v`w(3g*c3IpI>^^EeA&?6HL zxf*ofV#R2zaDMapLV98b;FW{2+R=r}O3#>4&HuF1zY>32bDA_q1b4C9$$MpD9XmNm zA3Im~PT@_tz%Y-F53-_y_QVAX&|R}#+mub@rBqr@;IJk|Bo4`xA<<49FFEu2*78FI zE_YcKKgRdYmp4c~*mtyY;sM~LnRzKm!D&bdDLO9k_BW7BHlOI2ua_)lrQ){~LS46O zq}NB-;wg?3;2K1W^q}bTYGjyAxCpUWZ=3E3M$Q>?#hRYHN>{M^KvN8yu~J#pz68mFqDD!mgGST4b{bh*@+}IU8b?GCQ&xH zIf$Y;T1u*mz$~@{ZJ$)~E&d7#VtQ`z4Xv3qN-fw%0SR~hMOvvPZPDTL`Xa#@oufCe zOYHR9!uX+8STR7_#En01kED+la{pU1e3>9gZXUoqVW96Vm5-da#Jvtx3`i1a%4}U7 z;i${1S!`Cigk>Un2CROTQFPKx^{d0m|LJkCN~%rh_PF%pmbIFaLl0$~)5zl)|LUyD-9j%Szd-dbWZ zI1pWDk?h&`SIBKxa~+Wbg+syWeZxc+#iyH>U`5*JdM{EduOWjuO@5jV0yX*uu?|nN z+zs6qse9B>OMs--|Hy0G$~N7cZtbH zA?@j+1gsk$7V<7DqY%5)$ZU5ks*)2Mk0f`RQa<1TCE426uidGvjhL;&UA1D=8srj- z53fa=5;b7c*Da|pYX1o*YBRRLBDirt82`RkX+viqD(Y$aJl3x%JCI3z%_Ua*gwgsZ zy;?&1#Y#S>>C8=kDu}GKb6c2UHh6wQL4>BgNM^WInViJ}N1>2{TMiF>6{WlpD~H2` zSJf&MZ?{5OmtL*`i~A(gZr+KyCa!3@j@~ak{*KN?JxP-GyCmUt+i6svDB7uO4 zShEQNV5=sjy^si)D?^6jB=>S$Pw*6sCIF5?0#?_r&y39n2~X!ruoK-*h-}w|S}>B; zt@p0PA?!4c=g02%eT5PGY0;U zaIk&*O^$|G(cbJf|LY+JLifX@Y{6 zEllppGJOsI-j?dZsVUF{xNAA}BIi6Q4vkh!4SKT&}blB;wJ>M}Qw8ujvi<~QTEQBYTp|x~# z{YeIe!KgmRI!%%9E#(SkY>nhqWO#Ads`;55G-B-m7bX%bL=hu0{g-6lp%$7XeF>)p z@glei53J6|<6{3D6}7L6Z>EfJ@tT4sQU?;lSbH~uB2~!)Z`O-bkH}uM3t(!KA8H#9 zgOkL~=gTc7f-HiUg~{uDO&z7`OXrQ&TCpk&xjBPGQ+~uvmCo_OT8@?!HE3EC57&Ss zK7*X-6BwS%IvR8wFLQIW!DsBGt!$c$+O_CW;YGL3qodm(#oxjZILZ=VJ0H3T5+Rdl ziw0d26Gs{R531Z$?@sx*I1L3wW3)kQE{=hUN{6b*qfLPQI;1>v-i|9+gy75&|MfUN zLi|nwZO9ml8SNF<bAw0v@rF+;gMyi*1Bs5MRyyN{@zp5Mn^7$x#EyXy@ve0((%z1w&xRTQpz5b zeEV5n!k4@lD*)-(cvO!g`huvjpC}66dbp2={}j{6k%wS=34Zf=9V8xad0IZlb*Wz= z##bur2<19Ek;{bK!`N!7Nz#pWUo!L8cu-j=IwA~(Pr)zt23`qT6%$;1POxVEdk&cQ zqSi(~9{qJC@tq1r{9c)K?Vw{>2zMbZ+w}q$>su3YClzNM5Ba9ZZdncritNrD#j04E z=p2AU@KN6jiHj2cv>&-m!B3nw4o0`jxjp^*WD$6X25@4>zlf86xT^?*Z-wYl`45^N z9aapv+8v++S=5CS@LBxUg@||L>N3(uYbGq%Kiu393*3H(hhB5#!cvdO;&h*$Hs8e> zLTiDz14Za9>=YcNmuZh;;|^HQjgdiJ(|_{%PNu1I*eLpLcufl79xLAM zfWrhMnM*bxxF5;51>lyxqNk%vL*P?O73jwqRsEF5VIa)vZXy!x$VU+!Tx^l*)XS&T zovp%Tb1jAiXxXK6#dvwi*j6Nt`kX`R>UMU=e%>!c_X*bZ`nnSs_Y_ zMn{`6i%B8%j7N&Rp_x%E{;MEDai&XgsI_xl=R8Jd%-5iYaNyB`63fKs-ixr54Ec|c zXn~?Flr~y@NCrslp3xC!fet+XA7T%m12yJUBP!&1F+G>eeMK!GPghe$73}5kQPAa} z>%diF3$?;|e)e>xO{7nehcbkV3OcGb(oZB{2a$nK*{}lfk~Hqf5b(fs#<6YY4UI~?&Ld+kn?H?MKSt;NYfnZMscYF7bOSRTKrk@W~T`I^Aw2n;BnP>JgC%~%knV5^FhDF#S5YH%hJc0Q9ChJSm`Wf`(1 z^G4=uxL&M3zt}9BLKABX3J~uPZ5Ro!4v`FE$tG*d=jaT1`okgH0NBg@pCA}R;R_!^ z%mR-hTsOow#Au6C}!jL63y z`5+xTlU>7H@;2#>PGHQZ+-Qq|m*_oZ=evCuoN_%;&w%?|LirBfmp zM6*D8wRE9bU(_B_G-e_ru9>|ix;SnlxN6ZN_WrWfrLG|2qXEI!KJ*E*OC9m`P~Kf_ zyWLR`_m$2E|C(x5DH?4aT@z2ay!%0M3U#VzLcHFHt{2tS6=k=CS5A&H3oc?nO^-JL zG6kkS)R37~3VOYEFNC`x`awS5ef%8k*gHk?sOLzG?m79>swuQ1)bfHLY~IKg;Xr5;$MylLO~ALQ()l1M90Dy>$ry zXqCBPn2bfP*lm5E`tP>Bhy|1t$``#lCTD5j)H975Y6IcUBhBgiZI=bb&yCNibxjXQ zOC^Ia%G_`L6d1#)d*)9-pKPE@ru@PS=oS`uoo)4oJI13E0e$?wqdMo1Ax*=+(-+bO5>6i9wKqpT*+0>8GqmyghxO?lQ9!U5Ej&AgdFE?%mzs?WQk^*rqxF0avxyW`udk1jL|jSoQ^fuS z(9>(rQf0N6U#y>vqB{q;(>ETS{L%?^O_mB`sN(_=Rg{-t6X(HN7&wnBUANy$p}&rp~7{RFZO$fJPuzu{4wUOK0QDB&hpCY@UlSx^4_ ziu54ZMl_A~x$~%aqT^W)=FSZL3~Ek370eKcQqwmiMYV(UC(JHt z8CEbgj2${X8UldICwm;6-y+9G;B5EGiB6do^mi;yyYW zwDpm^$!a^~26u>j+3L`N62I!VdsFJz7P1z8_~f>pkf$piFS?Z^4q-}s&JQsHfndh` zbIb;E5Z<93UE)#@AVq>_$%X3PK=F1nlUrpdh3IMl4%J*cLySwQji`+i->6cjc81Hq zbaV%qH}iQ}guOULkd|f|pDIFf!^Tv87a$00@}$l_z`M2t7rvYo+2v2Iw5GTWR(4@b z;}axq0FIb(W{FRF-m2#DiFPbY^W^cAk;fEwA8>O=B)$}Usmv5WyGP11zX;~tAQJ(; zJw!vYFM@!0|KQ$<9C$$rO4AQCNQP?S9dtAO^KpuwI+5(fEDebx{g@Od4SupTJ4NkJWp`Fw zu3A}f9ci4n5aWplj`WbO()u=8vv=j}!-4#O0$%w4RosfQ1u)wcDGl<%9PH1=BQ10< zS8ShuP3Px7-HT+zUBE4m`f{b~4;<#bszfu>haLRI!W#5wH-S7C6``guCp}Vy5ny2T zKYzWF&~h2t<5fY*MP?HB0&VYF$@{Ak+r)!gYLLt=4Rn$XHplrq%zkPK#lYE=^#MAKFU2BE6s zSgHEag4p2yaJIC2&@femic#&S!ld&y z(NOBc*c~pej~EGN@Fku@mIv_m-)8a@wZrd|+OR=z$Ed;N51wq4EH1Apena~LpD&%4 zj2Et)cQCdI9_b}InRD|{LfxN`ktzK?t|oJ1yK~ZOA~7;eR_Icdvn}jIeC}=b?J|GA5e|J+zH)N zitHvu8ff$dxL>swCl~c|)4@WUGkUE6VbS_SK4oOFsahSTTz zrgiRYsMcafJbUJ_Kx(P5^RAXr6aocUW0k*D$+w_ZJQsLAOehvPyIYIw0%L<0Im zJ{=Gbt;aZ6#kln}9b%#(558NY1><5MR&O`iGMdvGO{!b{mE7YZX8>WE8W~iI9aubm zX+@+GKBo9=I|h2v8{aC6K`J%-2=pGkaj4qgjNs4`+#C%sm2=;Wg!bmaL-KKY(J8pVu^g}B!radXt<+L0#xTXV_gBKq-@cM6EEs|sk6;tEa>d?gUnSN<%3q@d8eZ5=YN@0H& z(kQHaB}b6i8BweZ^^??C+?WhDGg9Vo z7Rotw5a?Yxd%ou)_CH*tD(9o)F(={cJr9(M)P70-7R`Ena>Svcq) zhEc^qZG7#i?1?k57+DXEG?C+VJQplxbX`*tuj z*xszqA`ZR-l&l4^$lASA>RZr)@rn%aaEl(@0Pvng<;{G_T6+iw@0(>~AuB?C5U(;g zM3QvYqHs^aUE}`9$8SO?6xE9yygi#FB2Ck^8Bz`3M0xRJ$qirGh(io;s9yt_NNv&4 zU&j)qy%RhX`w$i&Y{G951Kv$M#QEqq=9px%*^SWi@x4G4G444VWDOLA@a$H zh2&gRPPtJ4#Po_UaVOwV45Cn&c{oIUWvB{8Ra|^@>PL3!-TJoj#RnO3!W(7D2Vk|C zj;xNZye&?rF!(0g-XE2mWoC;A=6@ku&Lhlott6W^%H@E{gYbjF6n9QE3s#oeNK8FO zKB4Q$)WB|NPHu1bhG^LiM=8Y5lrQoGGZ=H8WAL2x@hP&)K+gC^^olz0(hzw6wefb|r86C0;~<_9`&n-I zHXLGpi-!-6bGX$0JDGR*-`EDs<+?~Wn=^mq6IPBG9HDm5Vp){Mpt7v5pa)QBvfJ6A z74#P2X4^F3^$??0aOQp>T|c`0i|5Vf6`yxJ*iw|Dm^QE*bc+Fn989$BvSIAd&g^DU zV|lf_!P2LWR?55;OMJ7a(rDLxeUG7_8=d#pQcyyLIY#WxVOJoBH4?lmRnt;RAZzrc zQt1bP$JITMU38k$5lbGb2XPUz+cEZF)S?@!E*J_E&+XW*w_|NuIwu*9?BENBUK|#5 zJ_za*wS)OY7S1R_9Q%JaTJB|(DcwH;r!^x#Mk{-YKv1iSQ|?b=_lg;i*TITP5~QDs zG48r|{*R#E`4cF36uxnpDSf%in~rrck74>fo%h1F4aJ3J&2}oO3T%_C+fR58r(-2| zc}5#vExXuPP{zz$JmeS90+aHs;jD$E+Z9)SMSAS!_D0d?d*eQ<<`LX6&o&EH5ZID} zhIjmS4ri)>Ld{xLEZ9xPT)o+4w{V@dT{D5=VBe?%q54h5N&4Unl&%Cngc}4FsdQ?_ z$Z5~$Aj%g~wSe1g3k#RcGU0-9kL>Fj+~AWx680YXP6fMnDV`ce zEiD?;lRO7ZJg|c-Qj5{i@Qquo)PLgyW0RhhJUD9W|MXe#1X0cqT@&>Ws#=&D^1DET z^+LNg))Fb(>NUFTD?bCe`NvmUXMnEZ4DKqUL6<1|6H_`34A=(U6`jvDUtp#&Ya4^L z%z!-)>_UNh;ca=Xb)zX(N|ba5Fa)T}3Ud8*x<&~@Cn6+bN<$%yaY7Z{0_SQMPivW? z$zOB@FU<@7sIJXMHq7iVMg%Se-7D}g>|cW>ktVo)9Cp-df_!9+7>>b`(aPJ)K9Aem z{Ie3o*fGfQrnno?xDBws>phoNMX0O`So7% zC5Bx^1YtD!kS)PGsVCX4(NZ1I1cJ%$Kf{GK&!v<(#A$K0J29yWLq=KmrOOyrbtYb; zcSv){05J8^Wmc)H$2ehIu(X2GKO1m5^)?7LnvBw&&XVlL0sKa|qez(YLPfLo8=6y< zB~k4t@J|53)Fdr959={SI_=1-uhR4#0x&JoRcpk{BWP<~+K|*n#NeE9V_iMmiu#;j zZNHE)Tr8xd-B*#aOiQ z0jaoYNX;(7Ck4#3U(yxAqy^}Jdo+2HV{-hJI;IN|7&hzv-5lf@`J0f%@ltsE*ero63iRP?G)@zdjK5vpSPe@TQjnZyt4zoFpcQG;L+|Bx6lMQ(I$e>w z4)5d}+0eEn<t_EEe7JFiU3iRPk@#5VZ^1QI@mNvHwna!7CM~($7nU^>u|;a^ z(NbJMF+$)^+^^4#J@!5}Y#Ky3v2WfZQw)$swm>Z#xiW{~n$Fx7(vI&Ok!zd;w~Jx+ zcoOrH6YHz0b4V+SquEs;EG%TzVyqlHS@mopGNa#Y8wdFm)5;7TODbH@i>&vT{+QV~ z-m{GxDBYo&m&H0H6cO|t!xG|lj?9pdMHQimK4)q#O>2%y31z1c(a-7kg^4WQnX>L5 z?%YY$xZ|zE#s-N|CPv7v9rRj(WWa2cizBfi^ILejmW2I+r05v;XI!G7*HF=tIFTVz zMA-b;t)30bY(e#wnVNk((W$bEvr{CxXB~?(ksOP1ZTIN<@^gWxus}%UD)@+s=c&0R z`+j!`j%pQa1G6&U%;ViM3MRC#q#pq}$)8x|PWAq*fP{P`CTZiv}&jAw9Syui`H{(ZV%XQ4Y66>w-iat4KLAgY~ zvai0^V4qfqoy>lM5iPNIIP_CPH4m#0Med!)8_}K@CmktXzZsKzeCy~;HlbocpeQXT z;UUzcH&+EcMUJ*XHPxpx2cnMu4{PTTUI`bh>tx5aZQIU{ZFFqgPRF*L9ox1#PCB-2 z8{PluduL~U25ZgNpz2xmRn_~QdFr`X{#zR$evg=F5#Q9#@1(Zc0jBrm8E2C!$$Cw} zkN>t``fVTUNu2uGhQv>1cIaiyzhk(+^&y1bNsyjmjw;ux8Wac;YiXL5W(;Qkt56-R zf~D@?C*{?2MI)UKiNuU0 zIN)16>ZdkmAHwd_ezJ=U*)hQiw(KAM94r7=YLb*Lf6^1dqg>Ii?Pi%+K9bq$OsZ6N zjs?`)Tw}QYp0f!=JsP_3-5q=Q9J;@#$TWg=HGZU93CEvIOaAb%H1Gp>^5I<(aQ?P= zABv0jBEsFrmBq%TKrcNbu=u_S@NQfV&o9o!06vQjVBu!%+>idF^!|lZkaJC|&Bgp7 zn#7f%v5PtG)pG$+3AIqMu2CdoH6TKtbeCj%6ZOJcbM6?yd5MaRpORvH2-7`Uo@Bq1 zRT1?BlmQ5NMnC3w)Hvwr7dfNw2koB?N=&$W!B%!X<7Ehd<)S$KIZl9FZb;qlHAocB zlG}X2{cSFWo$SEBA-C>*Q&I<+cOrpk7p_}nMZ|YrMu|eZ1?Xz*dk|<-@GMMRw90Yd z+A5*5UyA3Xogkk^h{~;?C~X^pVrzVRP@ zZ9-H3g{@XrI~DLK`h-+Bu}Di*gHWJ)Y(JdOV8?6?#D5mRujm)L6MwzOUYRR(rNc?E z)fo=&(rxI`7dM1UFksmF`_Q+4G85NL;p4PMasB-($9)biT8i#JL^96+Q%B;@b$4g0 z5gUigon9X4w0J}!*#f)-R#I|Zy3Wyms$V#2#;|KQE~-OI?h5v=cvkV+804eIl_O~C%qw-xp(A4RYtAlK}h^^|W3ut_u}sqizA_N7mU!<+OD?+#X_eI$2WYAaY61SWlUrh0F7TTiM? z+Y73JC#3~`hH*6Kozz}l2tjKd{4IEW_O9B3;mM1J2(Bn~IY#aE(0@)-V13IS6xipw zCZbv$_Xb~D&%7!4p zYnf{6V*YZ=TsmII;;jUqDjw$37!O3%n#Y4Bwo~*iI>fpC*v}~=k}V3G_46ETSBk7m zQVY9;GaacXu*IoHy?j?d0xHt|sScSBNx)fPp48O_KshRuj{MV;%Sd-km`tZSphi*Z zzU~uU`SljBUf@58EfkAUsUr}fB97p06ded%M+}O1d*<-=f_hV*P9udI}TjQk~9j`_=NYH?dJQm&0t__A|$FNWfKXhuHCDl z7m4E#RHP6;?c=kS;4g{R;ePBxZ_zPDSIrFk%SxUDOC82g%Rx(HdBXTgPB(P!)mu!AB^D4?>U~$Sl#Z(uLn`LnnT00wnDkc$H~0M-eophPI=;5o{hJZ zp5*R&SK6ygMzKT@F8G=_>x!!s?}N6HgSTJ0i6XS6VDxqYAv6+aHiJptOWNUh91h`i-&<@-}WU>UPqXI|B zllP#Ndwq5O>`pB9L0K+FPp$HwAAxa3hChXpGi6+QFKLR&2-3POzks!} zB#SIv*G`SB$Dc{H8!DPAXZ59IFt&8E0yLesD4fFJTnaNC?49|(BVO_=LJCz9XJNvp zsoEidq*zJ1_j30Cl9Sv4ru!^ZG?fFf{TwL|bwy9)%GpT1ggnIfMS5jb5zq==A`$UW zB5{Q{i=g?S^?l+m_S9Xf9#N}I_Th{;|Aq8*K~S@%O`pcBAtO=_(;f|?l<+y%{DkuWgCmldlavQl|C7jSj8PcQl5iT(l0)1f( z;mn`-81`b6@?uz}UjgiijijC~Vd0F7KB0M#|1o^w6y}$%B|@`p@2gec`qT2x5|zUb ztbz8lnC9j1J+jAm9&<4yAhsI#u2G5qpnH`JJHwo8JLfA-zA~JMQ4><3x{WlOQ=eSFvPrl#R7_V zh>{3ho5l{`y!l2@{NM{o(o~-4Q=3D6WbXe3H>*7*rVO3OvAcJXWL`u z8H$uC*z(9hhMpFVgpIx5>A$ouYr4D5P1OPqqZFo!h<)(H-Ij`FU|2Nyl)2ajd-Nh_ z23wzsq9%cu7gh!B|Hw3^xXUq>L<>+nJzOsldvTYkAk`YWJ-!3mKn(OGZu5PO7%Nfc zWjdKtcgq6~de*{nn@OGL-u>j6FH+OermgqE$mXPlJqvPN4UMtNBBWM8Ep#0vZ(cg* z1{z0Or?pjdXu{m2@j-nql550bb2T{A&;2sq`erBz0&K+I4 z(*DPkkOTZlBCCu#X+u~$VD#-VAhZ2*Gi!Sr0R)7?NUtPRrv>{cdYTH=(xud1+^Y2! zI85MN&Yuj31`HUO6o#$*h1FDruOA&&5g^iS$OMxuX&|Cj^%@VSSt;YYwJ@D$wdhWO zucyFS&3z9M4ofmW-m-S5EhRe;L&hLK&>QP!3JF}!Ukxh7(WYI46>pw*N!f9px`#v8 zY6b_i$Z0M@yE(^ZcyAgkTOkT0td($#U{uFOOzTxuY+g$Xy|&%oVGS)Qjr7^1T8oSQ zh((UF+-qG(Ndb_kVHhBkFX<5cqt-%T;{SK?I`B*WK#8N~ersoIiO-gcdwL?b3%p6a z%Ef1&csnwFv!Gv2g+!ZC@5*34CS~l@k;y|xH`@^I#9A9LAFRYtbnG%fDxhOcAa@h& z<8F4s%DF)HSR)j@6M?Wi22|O~W1>%f3NJMO;IHMON5Ym z_Kv`UYQZaWV4z4)0I;m!s9r!%XB2WxEEMTRB73BV4?my(UC|n?1h-D6UCeMV(BN)V z@S3T7T97VQdtV}(V5w-&7%!c&684jWQLU1HHvd8vqj&pgIp5z&XZW()N2kWzIE+?2 zW&cokB3pWuRHpA_ny{8k>&t@Z5DR<`L=jPB4B+uc;!vq+EYBHs5~P@GWk|x^9;Og9 zXoBrEw7|>~d#ry~X&J|co|d#4!wf5L)mXc0gn(lV{h~B+=cr}7seKksWp`XI_%%F>l3>Clp7?wb3FRow+o zD$P7v!>YsV0Tzu21-&|lBg?n~#BtLTM;@Au72fsVEc8)mF<@{=CKobtNF15Sj6?5z zg5@wp&EYm;mjLuFec5p#L2!<+KDTe^Xjnu-(!onHnJ{>3J%a!U(A*T~#pTEtG@QZW z6?4r`(=GQk^Cumbh3Zou&VAak5@_Y9+*E5a0N1;#TgJg(Qt9<}j`dTQh}%e1ocpbF z*PyKMLB3mr;CplOf4xpILSkrIdZ*gSN>6)I<7ufy|^X96^QOI4J zMS9|cnJzg7uDDcdklQouen+CB)hcKff$98r^Y!9AU+V)Pu0OI>kk3!F9#asa%RxdYM?-BDZ>D9>)>a&95_$@M z{f}1_3=u=$$?uolgctd)R9pu{tOV^|mMWu84#{(+vFQFHdO3P6#2dU#@&-p^Qp9*p zf}7LR&m&t|w4H=q*-FBkHv27gcx8T+srsGF`2hW)`%XD0#!h4(@d9jS*( z%6e38@_H*;nBM^0++%|5b34g5pVC}TCCm*bNTsTZuE^2mirFm5o9TTfSW-unxO*6! z9RmHg+p^xy4xOH^`@gAxkuqIDWf|ir|Lv&Ib~7F?&MM9T=u)rb!CRc>o;A7SxWY(` z0j8xG698=SOqM6LpNI>3=Ccf#j8@^;k&BqPm<30FX=W!RIA;+g**s#vVrZgn?i#x{ z7%B9h7N~PAzl2BW$rhm>EexIP>zIlrJ(K4pZ4$n@7`P|BSV%5WtlhF@iWPs?qp3S8 zCc8Ukj=r@<7|LIcPPy-X@bGj8Z=--k>m3FQs*0xP_2bn%bl4LQta#m$zL(;a zoX|9L$?*fyP!=t4%Wjb>kYFj&o#J@9w3jyHjq~3wo8_GBKjhvup1GGDIk6icB~Ep~;AIy}*Ydc7dnj0mc<78&_*k6taX$lyW{ z=%&!vc+L$QQ*@$#uii3#^*1^aQfXuG!@B-Lg8_NxzJhxQ$5=X;DB1G}uzmQ32jIsy zwQ@ZgPS2xj$LikxNy3WdBgK5;CVBP^=umr_>X>dIQR1hym%CF5Xt@)zWTx$CiO{Ep;o7zbg-BtKzhjG z?(I(1&O*6goX!MoYrkVuQ|^nR+&Tp@hdE3-pf3dJ236xGb%KBhEtJz?zIKXgYw+BL zWLzV|K&WfU=#d@i$X5C7$)cS$hdN zJx9P}X#zB$S~yREA0jMA8};{Wh9_~$p>~kgwySZ zG{V=y9_N4Gye&RnBg+mJ7ofSNMQRkqG)OQk-s*?l+W^+8zuF`{|D}Jf5A}BG`jI+Z zEaUC?+o(yOCt_kMZ5qz8*<8gY|Qx#@<-6HjWqK*8qz zwp<~~3N@Q?o(^`hO=j%VEhk9LI!g_6Cv!AkR3|@_1rJd=W@yOAq7`|{s$V@ z*&=t5pGacW*jk#-w;_y( z4kkhb{XuBz73_-`XkMlAgPtG%=rpI}3Cqa@uV;%MaGoUniXqm-6&%+gb;MkxoF12cFg4L(ISA2N^%mY7R>ZVyo7z~ z2Ug=CLZfs(Aru#@sO9ro_8RIoC(2$ZZMk|Bf|e`fGM5>iq-L|%7x9FnE5cp5S2*vZA}Z-!ci53>P-bUPb~;cnkH5OR=TDK^-)Oz6Ox~ zf`fdNf?IoFl2KCMPBc#Ua;+%IeoP9BJ_`gb%?RkjqGNc$ zx}ig37C`<5$FzAWv6C1?Zf18CLz6Eq^WDM;=H_+98BJU%l@65xW6E$K8_Y8ROp-PI zfRqoLf?0QDy@80d-&RCYij-52&Px=|(QObciL81VV&gwlSrx}cynk*wf_-rhyUbm8 z{r61gvlR3En1F42zDOho?o3^SnpTWSx9fwOJmFr04T2@2AV&X-9HbS!b5?}C_~%LY z4nOXh7kHkI2uK|4*y{@)Ee>RDI)POdVDSbk5~BGCUzAqfMfrw#7f{&;W~donk+hRS zq6ummrpqHv)z~wmX*u0`R;m|`ScXZ_6azeAJ3sq8_@A9M+l9$yiVL>KhKT>;3v6eK z&g*6n2&~V{LO>}hXMAS3(^ac=nvb7D9m2NwCq1YHpUWAx4)eW&g$9q@=Z%>BOf=n| zopTf_#@!T3q7;0A(kjm*8cO4Ai!ac-A2+$&QP;o@7wL&pQ14-|y$1~`K}&FsfjlBk zGCevdw20xpNU%2AytV!x*ej_hQ2@!r`YS5~b$7DlsyGVEOdZ@Zaq21_@{bfZBdtoM zbkX}Niqwn9&ed_A4}yAM7mjPnf^!-q%k$l?L?cW|ImlXUrMnw2D@OA^y|7Xlf=;3i zn-f{#@TalL{MyL%5r+D9IUXh!X3@8occeS0i+sWGNX_XLofI*fPcA~i)>17eJ!37Y zNK99h+T28rQtLBuz$5027m6MMcYz-epbFfE#yyGB=*BLdF>cyb(yskml!Gc9lvj{e z9cOD;=V+-QE1Ctx+VGD-jEuq!4+DaU8iYuN%BL}h{3nkA9{(!Eefx_w-_!xh8!JDSS`ppKAcW8Asc%=G(tyx=`s;Qqvt*CzUz+EJnMdVTl8D{iTcA1>iB9(nWWOGQ z0nd(Qjz4_7yFppnxXDBerG(ks%jSAZINvNYrmj*q|5ht>neU~qmpbcibOPP%!8gLn z{PC#ZVIY!tcsH<*k$v@@wwnf;qkj7t@W+JqB1x*k8JDnn3x6EF8z z;M8J%zM`e$Pp(9?36aULp37Xwc-FhWyGV+#muo!FrzC$WPUZ_}oH-XSA?1lUvpgi7n(vn*?~oB+kbM4;lzM*sqKTsd@%bE?E3W!11EBkG-jID#iTUucday_ZH6t=YPN=Bn{b)`7Gl2O1FSfyt zs5C-qD~%Gt^l%EeGbBm;?xSHy815Lj4m78kBb7W8R zC;{8|_*EBeIN6*!D8w8A(xKJub{JKOhUL2M(ONdaAYUt?M%#wdH{(1)wI9d2!@m|* zsVtR+%GB+x>LK({^NEm+f|ZqXAe%GC;Cn`x(<5Y`#uN?l2uTS}vfslOg@VRiaGji~ zgQNiA4g%gZ!eDmskoZ{b#}0DyS;0+VzCMtM7oF11fjr{Abl)q;c6_bSy9HbCo5sLKg`TG`s7DvjNmt}o$sr{0*Wc6i)IzFV!0YTnn5Y}z;kh68?}RU(9ka8Fr)<1Ejv9fU03Yd!{3DEKvw;*J;dz-YXqp5!^(%RM1dfe7=>7t#2eEG{5^aLzj4u^FTq^)g z+v+Vb?%NDBifjKV(*Pz=TH5H>54C~2-_8M5>*-ExoHoK3=%SG zqVk#!*DoF7JEQ)|u*)s#5Clt-ly`%PxlpgM*dh76ee9z#EVHwBz?U^tW0}D0YkN(h zMh|A#G>4taLGNuU&RYhcYuX;gmOO2BD4x5FDPF*&0>bANQ%c9^Jc?3pM+gg$oCvIp zrbOrRK@U z*7xT56z4&Ac0{-F9}!%YqYHI6yBhcVpEBb)&2UO2PU@7Lan9ferc~FzWmQkX80<>$ zz@!9TtD+4i^auZAgK$F7QQD0gy`ZUy(kofYy#+~k?VWg|_H)UZLtWyt{MiW&C z6Zqv3r?DLAca-53=w|3pWmD9uexnj%os^~(e6b83Tf!CvMWojRDdL+b3%6}t&W#+5 zc9-jiVoPaDIlGwalu}K-NCV8934ynRM1Ix3bz`@3u8LizmA3i-Y#dr3TOaWl$Q71g zojy6kY>KME9CPN5aoLM%jHGak#Sl)J89mDzjeA^56Q0|no+AQk1q5TF5FuMF!A$=% zS+yxevFwdrnG#53a)McOF}8a%?;N1Od3m2MGa=f9j2$4$m8)yK4$^ zMiMF=;ct?PK?_=F>ghS4ZVQH4Clr#ynXvsY1>H4^Y_rXd9vxL6tk)S%;`+(icB#qa zyaw*wU;ekq9K==Dj+0BPdgHs`dm@LkT+T72&T`jR1;ik*03nVfYTeGx=(dyz@|~~_ z5Y(gs-wev`s7wekG3|ey7GgPB$9unXe)vJDb5zU{I(@Q~Rr6I?Cw9-MgxW)klltI+Z$z zgOGjtO}w%R*Fa0eSdwGw(xR*29?vWM*$w)2G6q0BbJGIzpdG%%0fU2Q$Xw)-q63@S zdDX&QP$KB>bq%-`X~R=!v8#UC!x1DM;ie2G>mDU75GOPzCJ%o@Hwo;6D+08~gb%E$ zSSf=u*r;rHMmm~3x|Qfj5uF=gwl}j}OpPh)Mq4DV27}4_L1J*`C|xC@(TXb$&hBlf zijAtrjrOabS}v^6d3&TB##Hxtq1A@V5ugu1G~{^H!c3p*&5~v~AgWFoi&FY6lMes0 z-5?FFgO4es1T~o{*oM$raU(5%vlp*7?p+?rG7Pg(a_@oi&2F3v1%YLC{l>RcQgNE7 zF*3RrYqnL^+WrQQL-14!dcj8@2YDh|J3DjvUBOG&>?UULQFnoya&GZ+y1|92y-$toR<>L5$Fd^5;wbNyy(v;D3OLAZOWm-GngE0 z*VH>V?_IKOhgE6-dFGs`JRDVc{dz|=Q*HwKoaQDc)@`V1xfTRjaPjxtea8!~txyoU z45h6Hq;pVE@zx9<57Ys+LTPipVnNq-| znd46DIudhv4NU|be`PYCQiu|Hc@-B$as(DQKmN!J(O=r8;sYi3tM|bN{(SPX{`Dcs zKM=RZb*bte61#9QrdL{D71puAJ_Ek_Dn($P^*w3#b z=A+L5v+|CPOlV)vOp~_tT$M9ew!U-?PDM5MyXWFgVm6S(*9^MFl{G~nBIbA3KqB~Z zyMSKl)r(ww{S~p*U{a&P77@m-N2ahtry1i8U(uID?zmn++aX;qne6`_)7(W zKgUB~W(Vv*Z;nhzHd(Uf+YU+!StZX=W}8bGkFYliDX#uDdo;}0TPZzESB0;gqb)Dl?QI720Y%51@r%{%O>= zFn3paj;X9`8k@P3 zi`BvLJ+23*_M%kI**%+Vz4#++fFluh7l9)G!_={?Z>Q}8HcUs^%ox&Zpq)d-J8-Ap z{Y}-1nXExIF{VmUQykJ5bjdP~)d9y%DIOf`obF`(>h~eeiBIx`jK`Y~ETKb-faF5E zeN&{5NBm9fk2N?MDPOhek~kS4Qxa5zN*1I_M}D8y>bdmK+mAz-z?E?X8;zt9vBW3I z(_(YfHF)QxvXcncnYk0ez2{V?r1=k?wjX5P6$fxY$S8Az5(RTS4<+apPu57c)p?GT zwBx4DL*YDaR219Te^c0Y_iS-L2x0!M523&lY zk?mmUyzoLJjBb&Fz`M&lly^McAH=7(oLpgo3z1_5QLC^*4f`7}E4?YWzFfF6$e8M< zOvPqn6+9TPLN#qJ+sOfE>R^e!@w(CvJA~&Hs^)E_%kHclB)JN6 zqvwCsq!yQ_=peVRlDqsxrBc45%%LLTK!JPNdNiyWT;92SS_aSxHbw) zAF2{!#!K_OyoqYc~P^Sqgtzs!h`FN8YAOwp4~|S)l$A zL2SC|Gkt59TiNebD#0HPAFWZMOsGKoY`hU7Z`7iJw}*EvAMR3$K6zALSG(apt0O@C zn<}H#pjb`$NWpA#hwGxE#BTC2uuzo)vBz;{v{%F~11>lJA?Sg>&5$$dx>baNsX4pM z14RJKm*U+^&z#E$5_uzX@}BN!GAYN6U$yt$fR?x@Uq^R#mHWq^o&BSmJQA(;9{doL zL~}Awsa$FVjonRz)Lu~lY?f4?VMsny7d{tFbbC_8U+D0IVt4OEQl8pIcTY%dGg)NH z1P#H}h^sTiQ{-Yja^Q++jz*$DXb|agr4P*@(u%P|%q|+AcUIKS1Qh^kLAYP@RmW8- zq28;L@vOwRf{JiL;P}3?hXtd4R?sDiWCJa4d1<14t<67C1dTU%u20>e8>Si72IlC{ zj`}BIkiDbi#sXRnxdnAVvlCj_JdVm45`dPNP>Qotd2ei#I3BX??-0c4YB2{bW;p81 ztMnT(K7^-tt$g$~ts7F5_IVtMi@ZE}Sx1O>Bm( z0RLcwU`%dwJU@RGZH~p}^q8b5KHaggtY1u%agMA%EBu zDAcTY!4ON?5*k+eU~6lz(sSh#+mAbh`M#Fn9G?9B3@ipus!$5k5D87!`k+Qr{w}&Uo zt9h-U60ouRdW5mP{=^oLW9==F|%W-FlzO>|1aFa6?(^G~>59e3c z)c8aHQpXBM)LHaOT{n1BuSIWNG*b=ZE`Av!w4A&^R+xowaA7#F)^bzKO@21PWpEj@ zzpap-VxhI^|NIQs7_|8X9@ROagb44lrl2lA#ALRl^4myjEUekxIJKl!4Vs%ezPEUp zs1sXpN|t6v-z#18u_)LH+;@bZwEqe&LcJhM&G96Wbtr&M8;=U+TQCA`HQzMwdrcT? zUy(MRj=wbDDa1;_*D_hcQrjX3(ex1}Vw!vJ{itA9`jz}ha307iO(yDrk4WecBh9$W-M;lgXqET++B$ftcrJ@laSXGrhIgxc=op5WqG3 z6yEO$+F1g+!skm>1b0{bEGf6vBYZQSOXV1%iJof{ zQw|r*rFdo*#0J#RM8uV*`(AoFgy}3a?=rFqxO2{E9cbmWN-;CZqDqn;$L(7))i6$L zAMm6?U6OjT-z7?aZ;fTIZ3ZCbWEABUS2GzxDLoNW%3cWytoiGxrdYSX$m$$t_`1z+ zfqGkv=JDS5r;!@?G0~#)DH)fJHEu0s4iGjH(W-(fJVKz*HA*EHDGDDq{_aQ20cNG2 z=UXVF7gsOHS4a1E7rkCC`4-&MWQx|VIZo8!iMX|3@b57KU0hl79DEDPuh=#`O3@Ua z|HYl+)R3ONV6IucX9f-|r-B!H5?hoSWMTB~+9S0jk>}kzvb33UI+s zqY^ZJosu18BF@Qq7e8%={%M1dlK5B_eSO&xmB+Y{yL9ygL&>_8jJrgVo+ZplA*svW zm0~iI?pr=T3*F&=LK^MqKeZ2KOChOo@Q9b9l#o02HA^-j>SmZ0uPlc9%a-8fN(N3V z*E zNE^C5nc$nqzSPx4w=JA00}|k)RV?+53hNwt-175TPPq`QNbSNMhJBU+Q3ihsg;+J| zp7Y}6<%PHjIxdEu<)r?Y-#I$DA}S`(vzM&rxAcwUfIY9H{TBRDp#0aR@rj0pqw?)m z;KuM3#2YTchY}PCGix7sc$M-AKxe4V9-H6$O-)t%b zlzo;VPxJ#LNj(d|WiX6e>tbLTay`9U=-*N*a5ifLpyt~@%ms_daO>yUMip@U3KK9v zvoBP~SXAGpeN(Nf0mt z*`|{~F;Qc~1I9|QfA=xD&t8#`8le0h1NBp=J0;GXLlVVzE)O4_q-Dph+jaDpeIbV+ zF%&EhX;*ZDcA6e)Za}_lObo2Ih|TN{iizSoxMn(pA{1&wkj(tAdF=-Av=?mb=W7M| zo_IMMoismnDWyf;2@-p+V7qV)Zr7d*-|$>7F%- zN&X?RBLVJWg7VP8Jp-KUE^eY{VQjA=Cj=O3k?zJ}f2A^PLdAl=;LXJQ+8^%`%HinV zGr&i$f3>6)BkHjQ0WMV7s9OjLM=cxgoKx@a;t`EN6p%nl&U*W93^du)Jrc;*H#=)R zDGxmYL1G4-XPj89q$IxxtB7Z>r~M$ym;fmC^k+gu4ePyx^{y$$&T8R z7MG&NK3v5Ik1w(j0AW_gl0ic|2MUO8BMp}_>5+~5Fnpy)6L_9_iE0Qc&fO*i>HUB{ z6Z9Tb%)PW3RW`*C{7-u0k@t5l#N#{MK-Mxr)>yk}M5&@J(jBMB*u}vvC4(`)y@7G2GT~3wZ}MHl0%m;MYF)s6of=d3RL+Cbs6ImH zx34o)#6{t$?c}0DwM#cdE@kFoT+r8h!=&_naW2>5WPA&3{}>6bbkt(&p!kQbvmY)@ z2My?h$6`=zPxcXVuCWdf%ix`J8i+BvS?r$MNy4Tiozg#$5_)9fN(Dx2$yYd@PXYEu zy@uuqUJ3*}dP|i;)nZ%??&HvY-BHWgXz=?#tSZkZUC2+hUw+KNoRgwA{t!(S#)8A# zP*6s4=$Y<hZ_ zi6~^Eo_2*Lh%qP|bNsr6YKZ7%lTpI-Rwgucb;kVg{0{<9c|?lDIG?S`L4Ckx35?`?Vdr=DJ)u&!S+0ud>u znmv_2W}|yz6z#&C+*2Z2xb2JYs7XEth`m3?BaeAC4_#DKoM)Vhe zo`a4m+7ruX0UKcOAIs{h*{=4$_F?ngY2y!Ppz&(*pLpL-8d{NdewRCBMB2IV!#%iW zRTPs|6MhunvF3H-!GGAt_>~&yG57cU%Avy&cE{(Evh!K7>ISPQ+<2H6j6$sBk|UUb z-py|E9k2VsxbwRs(m3%eCdJjF+<$mmyFtal^$8?HLr^$2hiEJa8MSe0YYNAQqMycXJDG zdbHAy2)zgo58HZR;kpFOm5@IDITo>yQ0V0F!TZPcH(#cEkKqR3P12?FRN!*I7$Ho8 zLvnrj@^I?lPpIW4ksU4tIoV?y9rtR)7Q_VJ5RY?ePmh0M`j4XOp6+*>mp6$iJDm{& z$nr84k2n)#TgM3KQBAVVq-{*!$->hyO-`D+oBOb_AiSa?I7z4^Yd$`e zZ`uxWrdi+zI`V11%y#m?D%Ej-wFW;DuP-Az;((dvEQjc+4?rKeoK5BqO=!(i_h|8^ zxaUY}f4$nDnulRoiks!F21-hl$RhCq3E(Idc?HU4=P-yj^j@)~TIq#m=FL+!M6yw8 zTP)ah54VgKBG_E8fjyOxpn%X-W<7(VJxkr1R8L@ics-Cy6?Mv-CQJ>`vNFGpu*H)1 zU`DRV$fO(O?tZt;z9QjFZXLO7{)>zg{7?uBqb?}QBjz&v@1y3*_uuw5az*}6$9e;X zGZMoP{`2P%tBVXJk@nw~wr7RsBKr9vVlieb+x^~?ygiUSNO|hNfRisggKyX73MCj! zjv`x7F9YS>!)gIf4JWEWbiNs#}I{=F`{;qx6hlScCHh|QnE;_sg9daGxw%0DYor~}) zxiufY+wCg`?q6wwB2g{-Oc7OmLB&Vc^3CSH(hn-Qb(q-{$^(kIL)Vv+SO zD7%Qyp>~e3?x~ZlWbS{C`2Lb^ zymV9!jw&46r_ZO&0S52FJNA+p-Zhgi1#NgfdSp zd%5`k>(4u3f>n%?MzmrHP3cC_hs;ZinKh5+pSV^(jpVriB~^7?JVxEE?>*P>I35p8 zK-EZ>t#JL}>x(`f*B&1?FLRpHf^k&gw*jK9JY2XE9UITWj-5>-2=7tIWB<3vEoZqP zHlv}tlTBqS>fN3#M0C1T>8k7)#|8F)p-T5#`>4;Q94aVR%dFzH5E>gxRD7}yO1;#( z^7)S_ByX{Wl#w>{a8de_{K?9AC~VQT*$6%<&0+eVQM*3{`umDa=g=cCyPY$XuKumT zhNN?ij>MV*PrZ2wnN(tmEs}%`Ia$=;HE=u4Rqa zMpKYrRSq7YwV=+4T5SsTY!;Ig}vvuVe+E zWw^tt*HXI5`oXF;>N>bNE6T%caGi_ldoH-@6TP1&29Mx%bz@)WSuAqJkE2j1)=D*) zD~YN-?5tBnpEd=yJhf({-*NsA_y&~h8`AM+&W>E#FV@LJeT#2_#n!XR_5Zo){_)Po z^*UF=rzyRZB0IJ-Qylc?dq|V=(A!?4r|YthHn=q52`Bptmd)6GAPW_L?yw=_V4Mh< z3o4?Epbf#=z}efodU+1wlB*c($fm-9ooFf8gxPYgtFQvlr>KCih4rh1#Nv;h&af?U zA8ZE01-Q20(2Ob>Ap%3C^>=6#V$;FqK-J}&mBtYh1DQKavTPzlH5i4Rc>w1eKGZy&P=t5PMiXv4u+T5CKVn=!HFzX7>u1FfFfw7P>|!3xfP_dFyA{^ z)M}VMzf_)*Org0<#xY*k03lQ0kO`uN+iDkSDUV~2rMVqj#$XUZOd$+cPAt_60+}$1S>&*ZUUzvh19Hm8g6|J%5 zo$&)I{@`ShzGx4=>Y15o=lr1mNB#~lPow&`y)%;um$h?@&ZWY1RTEwsc(4$7B^p)w zEs@R+2OtZTa(ihgjOa4F^>Dxn%7c3kmN*X-ue!kg)`H}vcMYmQ8A2=djO5zeZ*zB< z9?wJ{LuB1_yhmK9jiI&t+hJ?IpwlO04E87PjW-|U6sanBZ-Fj4VrgXD5=Ll)3Kn#n_!N{z1ldsMk2UK7i;_8NqTX7_WZ zdopFFo~NN4vrfrUezPzoOS}QkUk@z*x+J?V59?A2{UoX|L?}~+?p`>;m!MxC_1;Eq zU=5g@6)>^JE}(f9cmKh#5X9HMI<2m(O+@b4DaUSU51Vvs&ZGLBALPYO&Q8DsjU2xV zsJWh+WZ!gC=YRJ65bBg?O`Od{-ksm&i-~BDbyfpti2Pbp8Y2Sb*^xo~x<6R-U4qy# zC;%Tgflt8QyCC-|rd9!ks!z=i49&r1-wmG(F$DGLw=sL_q7 zNWu#maOz3Kytc@|FMX3uya%}TetnNC?};!9Db!tk@vBjpr7mPth8BN6maTmzuRcF3 zrK9Q|R~lW8%bTppil869vTrH2lBE426{uu_THv`-dwPd%by+f1XgEM@U8Wv}`_gdn zvoM@B-lBt|cp>dlF>o}j^aTvJJbtxK$-e7ipl z9b$ulY5ZTAqnTT{nYr3HyW2Q9GP`@bYihuOK@4{t82rEE;f(+W4*3@X4DA13H$Qe9 zx41F;-_(!6`@xI<*7Y#y1esP5a0314Sh@3IlO+;MyS)hV+ ze?H#J=t*f(WhtzsA8t8RH-}K+7@qUS-YlpM!85=5E<|rj>R_QLl=|lq8&_%A)1<}@ z8X)rF)Ih)6d=;g|WZ0FHK_lSsd@t-LysV>6;<@fTS}2tf>WZ*lpI?bzM-SnBY_ic9 z+`QzaeP+pKrzaX&8|tJ!bEzv+AR_Krl2W`z!{gchemc#F=3>AKFn8;0x_L5B(Y_GQ zCTi)VDgGbU-Z@6Jp!*VS+qP}nwr$(CZ`<9sZQHhO+qR9l-{fW9o5^G*dHJ2BPO45$ zQv0t}*lX>T5h4rDYW#(oQN1j4bya_9-obr%fN6K`Pu9@v=@E-YCP$%qm0*oAEeo46 zT%R(lV)|tecJk^o99321$<%C+WT$@P?ZVW|kkV5SDOG}H!&H!A$7q6)ZA8Nz^`qct zEYwgR@@E~R`3`nd?a%2LTleFlS2dTvD$#i0y=b(BJ|VE&xDbq zO`p|s2#RmL#C$5U(uQbM+R4o1Ie`o#F;jQx&C4+GtSc5gyzEQIDVOE8b5bR6KI-$V zDx=y4H5OH~@kQ3tteZ?yAfTZr>;22Wt}02A-mfC$u<6RqBJMoNA-AIWA@_VyVIfQu zFetGtav2vauo2KQiIK#*qbwB}Z>P7J|KBRuMsGXo9YooA?tcZ4wv#+xBT1|Bl)z-i!CXV7aW)59lW1hteQxyDN<+n8u!loGOJ+nbWWja zn=>6AcN@^uJDnb@`3%}&ppqh&ycx{b95?=){!tC9H(3=>t2B|o(cMesvi~^MnrpUO zF7BcAtUoO7X+LH~pWJhZ@y{o!1SenGvvbxn7pmBt3|&=y=wq(Rvfc~pVywdPOwsTN zok}q^ ze}`87M_RDORoNs71W>Yax#v`xXDz=eqco9_P;f_S5;5^vzqhwYP$54*K;A z_Q<8H&-3h-T~;>S`UIild4oV85{f1b2Nn|0L4Y3uhZGQshadsd9B|#U!bGnDUk$_V zgWVT%Fm99ffa3v@uYB5jzXS3h?1IpPv4`9Zl;7*TYZk@#1K(P#-M|ezMAeQ~s1{Y83I7Zt5Iz4~@WfUBgAzri<)=vg|Lj>0MOt zC2%E{bU&zr72{~$JP456(dW{O2Kb*q8h7o&V=0|6hf3PNs$?wx<6B;_NiA zbpO--7l`|x0XGrXkg|&c06=F706_h}DiAYMLl;*kQ+;E5XP5uG8RfRFX427!qu;K6 zKYDKrmxAVNCIuc`XnApv&UjoTA!`Y_$PJZQYQyub?q6^PO$wH|Q6(tqq3d(74(xA2 z>c`1Ozt3xQegBV>N_##3hquWv_g>H6)!ZCB{hgopNqRot$5H#K)7D#lo}Tah^VwPd_oL2-gG+z=xE`MG>+`|Kuv2$?e$VgGUH|)Q z_1tE0{ojYjS9$u~uT}Lg%v*7C-|w4I;nQ)+-0!EipYM0&9v`o-yUEF$)okwT*V)@$ z{@(9<`8fV>r=QLf>z~em{?odtJ`=`%>pp|4$8*c>b6BQubTD-p}{<>qGg?(uI5WNpG=Izn{1L z`|m|?pV3g7>+FZ+&0hZQE`P2&N$^>b``?o=d?+MsYEb$2!O6~B{9gZ`^Y=l1zh54m z?8-&F-`6=y`M5B?n@;vz`>nOxpZmwxTR$v4F8qx)BTlX!1Tg5g!gu*til4KKF8p+Ie11OW zS-bmve12*5m7_P4{bTFt^ZMAIG<~@$O!~fl_xd-r*T?fKUu(ADZ0Gx{)!jad%J=uU zGxbWd_UHWmxP2o$rZvjK=l$#9XAqxnlSBGO$$zDC*YD>%Q(ix?8UBQYSLRFK<74^= zALeJWR#utm31iC1BqRFR+>>vU<+kOmcGTaHZ}4K4J~wg6x`i`n7YcG?md+m&ot94v z9e>M-Z>&@Iar#NJ&zyu$>^JlT#_y?~&kUQ%PuS^A7Ov{h{$x~V)_okrD>raSuRZDs zL%E-w!sp)Adf6HK{G%#@mI-acd`}HR_)$BB8;m++S$ZxQm_c6#I$gDu=>AhHpz$O6 zCH&ItZqx| zkBQRkLFiG3?zkMY=}Eo&jh~q8>Y@iF*262?!y< zl6C~1PP!l+2iNe+pd$rhlX-}3CA(Z)RY*I~D(te5HJWG!<4n{HS^-ajgiWN)o+ST_ zQG3V0WPA^uB|xVM2-67&(lix)aOV5ZnCa>-Z8cC%G>c}cJ7NP%LKIXaE|KKD>_rfD z=_+igD&(fpYCt zIyBljXuMycNLJg5@Ma@ZKw$oae}zTs$VB&`xB`bz5Q8bhc{0@M2dXIa1KFWw<-*^j zkNK)V-suAL$a2*LdkPf`m4rTmONQAt;}Q6UrJ$92KgviXoEGI%6D6p zl2S=)ueuRyQ2Fr+Cb=M?!quii)^HlX3Q^@tC=-got*H{}G*IRqrV>m>KG=+9(6Db& zi=r*(3;tF5Qm%HVc9C~En@8Q9{P^QQS7oOhID^A#3whQvE)L4&=_9O|6|!@r!54V6 z=&c4ExUYgT_RuP-eCGl++cdCP2j!N*{@l~ojMOp0g45^HbaoBZP?(od{v!qvxMVyv zRtgna#M@1bY-D>Mpu@?JTp?F(GN=I@5>(M5>aEZ`$xoP5flh9Qj9_YGhv)aecJ2Z$G^)P)DUZq9F)@EaYf>v*-l!2ZAfV+ z#4Inm6%ltA96bsQL(7nVid};FjN8Y6g^9&@NFY|*79!p9Q2xx zpi|FO7j?dfe+jCw(tnmC!H^(2M(@#Q04TYO4Yw@L5Qz#c>iy^fhS!&wp<|1mw3QK( zmI@Bpb9%M%*B82}M&6b4>_@3w!hQwJO;44?u@}iI0*HMdYz~Z!QB-qm4Hs{Ws3!1j za(X)@o+$j9OT&C+nX!v#i@o3_oexr%+{n5#8z`bAo=E!tYy>kThL)ogGi1C`joyU& zSvGimC+34FFC~&k+;REaqoi1cB}VU)&^Wq>+y!C#EEN0bM?aix2y`!TvhD6@>~XyT z$*72joviZCmV}JmL0O|(|8f!B3YX1Ttq>cQ>q2Q+NP17brs@(1I_2VY+$%SYfku{_ zzwRnJl&v*LB{jTGl|q3@XN{8VD-To4TC&U72K4e!*KCRw$A&7>uK>R8sdSDd**c|k z|6q}2$d!9MKDSXOO0>`r|MZ*g-zZuJsu{@s?cA|g$J?B`EXEoHOG@4}zws(`ZKRrm z?sheg`vAbB^ro7^Mdwy=Y$z3apRYkd2QY07{h9Pclj{dA*P(E;d*>Og7Ajx35S<7?*)(Vy8kG+q)&XT}O$rFH3O5iV;OavlSN9B({ToS2n zYH(4o=){{%Ww2~mWu&SG2tvKJA@P|0=JLMo_)PcQh|V`xbMx5&e2^g)DQvE@bd2d( zbug}E0x&1AX~IPBRcfRf8!W%B;$j$56SGa7Q^-iy@07B5A5e|)97C8wd;(2p0(y;^ z6)r4oMD1TK&8HkR#sNslkxBN_im){<-XIp*99ZmaBUomo)+IqYR~GzYcNEjx%2UI!Yo6_Bm!nze5Ou*ehuJqe@CI|J zLZ;l4P+LV2y7u!Us-(9rME=4=0po>p`kNZR-)oX*m8z1!jJf$B}}CWB$Q# zVh_D=Rn3K}>Dc(}h3Y$x7+hvmD|Y?~h!t4ZVj}=a9nxr&R=l9M!ycm{5iB^OMzk`_2e2n&{Oi{bI$x%k+9-iXx-a$#! zQkR>L)M?gCe@8t8gl<;+_Z*bSdU3-8uySxcJVC)L7VH5Y4#yRA&G~%H3$?%kYmLJRsA#Jsh3SHJP^wK>BuOH+k$EdR2`42|#{{VC zlm-ba97!t6)jGDX5_e!3pe_JpG-cNOb9-y*0Yl!pSO}2kjSYB?MD(=rGpudWjRw=c zy0nULn|44^Hebaxg)}(|y1?ohtC&iyqehB=vj9~0%OMLb3A@0y0gMpj95iT7VYe0Pw_4I8%KzH&*8I8L4QJ+$)o3FkcT3G<8rn7-;pU@-sv95o^SL z8R3!&{V_^qy3x=Q9%!3E7232*$o}{6zCem7RT9IpZo6rd%hQU+S_yI>f4G zK?Rc5qWUyEtowi86mYeZoT-9vftPhOH~p|Yj1*Y?;}MP&tBOxSiNJ;4w9VXNQJ)gF zI#JXuiybvrOgqseP^dPO_bBU(fM|h?HB&eMf&tIj_K4`z{^~HxcD77TiE2@80A+Y> zGv@gkI^4M#Ad-^>uOAr>(#e?eX8Wuzv z1sgS!RiR!fLc7T}2xQjU-b!u#Jf2iWT7Ki50zUv#*x=*kIZ!K;)R}>EG@C~L(6-K@ z>93f^5`dAmU&*gaKx_)<4~+GQ#XPg5eXxxWTTaO$yOF7RfXjIc@|j(dVbH))#L@`a zW>JHcv>7rItp?eQKXoe1gcYRpGvLB9vQQ68`Ai`X_=PaIsKFAolZ~`K8B);{x4RNn zFAwq!v~#<~xZT-wM(=y!NTvhuD{1x}nSKWyRjeB#!2A2*_*PJU3qR*Sies1LNip4bp%eeTsTqouH4hC24IP--xKqLD{8`2 z1n>oy*QZuUTek?hz8sZkg+X2xhFjtoL3JlP6_@62co@UFvMXh*UhN6-GN3K`ie8$M zMtS@@;10`li8mxEC7AW%(o|uw{o#aUMq2zfsX|@GRQ+ImyIB`kssukndy_40uT^`8 zf$4dZ!L!wj6?L=mlzRub#3k*CJPM58Q!w5y*@bWp>R{(<&znv6?rZU0pG*OX;NT@gD)H zZRee3qFx%BRe}iv9(6p-sZ}kvZ)=wdSFE;@>NaSI8r*UKZ5970c1TXr@bvo3w&ErLAUegDP&Gl-JDzOCN{d zo{m4=`2<&YUlQDyg(`CNt#_{=%lq(ps!?euJt@q7s6e0#DRB7*B4eykPQ7?ZXeo`U4Sm{;X+Mao{=2m9BBF3!}{aR2<%YdGGOz5bX^{t+D z)GnB793>@A9NZ|>b==8-^u)2l2g<|nP(*1x#VyY6!`yX`wO*V6Svd=wfHIaME@Szy z4NAG8=`PMC26aBawPZ-qhK}X*smPUN>eE@gTw!G! zMYasj364w4E9&!Nvv2AjKhHt=0=orA3sbV(^?bg(I}@Zu1T3sZgbonyMh2Ahh=vHO zhcKI;p`6Ecl%rJ0Lr@bWA;u5P35om5tSwkdTXjwY2a1G6+u`8wU07<6!c1>K+{%$X zz)XTm;4pgQ;a&{VIT>y?PukB%;@m)od^baCwIBkm*6hr1;3kKQ{B+2amSN8{XVD;ISr9SR^)%{HAyWouE#ls~J~j<1cWqVLi*DuQUg%Nuacov0 zC{+mONKi)6mm!HBNLmK7inK}NPE<(+OUW#9Z2+rcCwUW`uPJdPt%z{?umH;vc}|h4b$rzDq~XkMMS&)qH&z&7 zgPQ}(1nAqo!@LFz;?hnlRe_{T zlwoPP^&b$~agR5}_p@6_N9O{rC>X9@oTHmx5g*uHALKwHjH;8F?UiHkW}p$v)y z#>4@AR%9Bp2PJSCrMfo7dh z;sHR~t}r(bW9Lj06*Q(QJAfmHoyV-hvqQDi(M5WCR-T2>I{qg^|2i^0d6uJc3cw~M zH~>k7eC4B>jR=jJ#s#jD&^hTmcElz_c&)*k9Tngt*?`HGW|EX3i(n}}OQ;;i^xL#{ ziZfRMr^(x`NXtLl%px~=UfTV@ZqsOb3e!rq(4%EGV_AuJ80O8TX*9E;33RZKhhp@+ zg&M;Lhjx3?et#%JJLLoQ;Pf_N@!a3g$_|OgQ9nsQp&^L=l`t`a9(k;ROeB}3x+5j@ z7+}7|q=&q_q#XVPd1-szaDr+<=q|c@lenib93jO+@!Bu%817(D+#~EV%z3a2jW2kI z8jP*&fzRgvTi7V7Fm&Dj%B%UL`{{2+o}}+j!KtD2#_LpU4PhuvD>qpAS7tyF+Po4{ z8n3MpaO01bL*fs3QJtW3@S4!XsU)O~{*Z0cutFXl?b!;N4Q=BpQm|V4tsL!vP>?}8 zK%jUUxnrvbQ8M9?P91X)l>)&AAgUm{yK_LL3dJT_)Ti~39$k+1-YWvG3MR2a4mgS; z%h1EwD~iV+-KkHEJs4N3N5#gUxz|8=CW8^iTiW;;v%V!3&(5hXr7qyo5gLh@vb&Xk zJSs*ylXB^H)~XQO78R43+Sds;wbX9T9IYy*R>Kvo2~Bj_YDamDXxKxrj2=kAE{@ZU zHqY$y)fcv^&g)IES5a%1CA*HntDv6WvhGo6ljg*B@N z%Ct=G0{SJ`O-s$sqEee~k5OjFc{N^D7)^K(GQsR zRxWY|ufAJiA7I7HleY`F&8c@a-|1UfZzmyu%d{C>d&w)@?5K+)V#kD1o*EdEHUi-6 zu@8}zjEp>#h6|kcbkCrxVW#$UW1W8&f!a$Gj051v3s>sR(qx;Mw0rgxMsq=BFo;^b zu+YPRU#f{)+lBN=55aZq4dBhgR@_qz@ zJ!Kn&l9O|u6O9QzSu^lW^B2MB5YwH-{y^(!r>T$w;fJ`{4%^|QJ3B*yr)ZIm-<=Gtg(oDixTwm_)4uHiYs#)8}FS7uJIlHy#Mf}ZGk@^{WXon07A={72+kkR8Q!A@h_=?4P7=owOhog+Kyx^3b>!XFdzecgBx+*U&tcb(aTAGDmZjdZ zJ+$dr+0!v|FcjO{BOtMMaRvVc674sdZ)N9HJj+~wZVut$*YKaPOf*C++16fh>MSD_ zP7%9);q8j$%v_e;Yq^bS+lYQ%dck|LPPW^Ux^V~$Y;Ewwk2HWwA;WI7@qA)Is>%|8 zg3H2p0%0*8ht0dDHmRC=lao?=UVHBc80stw@tzXiHf?$00T3OaCB^HGBBAF~yaK%l z&3R!dmL$8SHaYbnM%p|A7aW#bma06Xa@`c`wi9^{otuhZ(L>_jbK^Z%Io<%0$O)5w z97*b`C6_VG`7L}EP0lW1)XMy@3(ZGO;vhZizX;>FE*{@%Zoq|)Q*t&I2wNRFPglzm z4~5D7O!L&Se`g1!a-e+>2gOVNBh&Ak89|-KjteemR%-aIc!9Y_kZyfVLVOl79gjPW zqzXR1c8($Z%JC}rxa~Sk1L-aqe-dDtgc;wVDxsA_8kv~Hx7DmF8K3#efceKNx;_aT z8#%-srOB1na1gSE)gtOhJ|<)88ks-td>v`m?b#iOH|8mPz+Bi2^*4i3#ymWc2ZXB zkn`eTG0GE@w7A?hs%))(X*aI$8P`mS7>u=`c`>yj^^)HH;+?coS-A_M{6xF(r`Qum z5`jmX)+cxC&8g|{R5h>fhPgLla5wFFd(&d$IuWW>*CA4e3_9VaVsKJN46`nqCrcS~ zn_XHfv3rLcv~8H$s$IS4kFo^z+Rdb;%EUO;((GZSGMo2LHHwk#TPvV-*d@k5fzriO zb&v-`se$!i+OsSF?EzbnPNSNclPIc;S8tM;oIbm|o{HPLKQYtY5Je!Ng{POfgO2xG2vZTY%4Nd{Xjc` z5cZx>pG^QH_w+x-soe)}iJl^6@tP!x_$do8J!;MDCQ&WbgYbr#-2+w`uL3sREW2Xq zZ=$G04e5ssC_cik!-nzV<@Bgqg8}Q?qs$x|S@gkirFIx=KIM9~WBCl)sovBr0h{K- z;;hVpkK+v#9#;rqv}D;zC;u$gBzj&vF|-TOJF?Tk`|#E^92IUvZd@Vb5D_rSv6%u|i9AbFK3m^IB zc$#49Co?}naC98Fbsdc)!xOGgM%)ug&OF-NNQ&6xjNhFv>*lBiCSweYcz!h5f>$H# znb2nSr-lz=uHrodCjDJk!NT+S>-?!h62y95<|B3v{qvbPHl^Uwd66z?2s5SsC(b!W zZK?KkKhqhNC5Kp`F__5OxaAXd*Us*tj5D{@Q&hJQ7~uNb;>Hqq`d)AT_-ri;*Vd|| zt2*I5uvy0}SF%*AMxl<-(J9)cQaTZL5mdB+%}7i~+m2!YB<<4>Gyndr_6ROi=UrT%iKfqn{z@*01K88Fz!?ay^7iSBp_aADjMp ze8`g2)xrOy%5MAGPjEHFQyo=g^%k;HhV7gU5Ei$gd#t5=!MSbY@01=J#XVf0ea6tC z(TM=N!m-)}k6`F4<({VB{KS)L=H_KQg_KAIC|9OK%U4}yZ{hGwn$tH$^H}RW;0{E; zqc4;KzXk5cjHnI|LSd z3c)`=Fe3uFEv4DT9S=Vm6G$Q(3U!^ImfT%FB$e~&YUC>|5tr`$m7+#Jsa?GiH_$0m z<&PSJT9d@YQ^7aD(j<&9{tr7WFc*LkHBs zjdXvHRFmr-{Q!w#j`=Ov410{o;*ppnn0EQeWg;ldQSdo>%4dJYnDz zQ5n&h>~D&Fk&~i|>X+bsEs0w#^3HP(f)1HZ7^uMD^1N#sz{3t|u%Y*OL*0YMi-D+|7HGi#wloLu@GX1*4zD-bT3>|=C&$58v z-F-UYS{SjzFF&o|X=$jFDlR7(J{?A)6&tg9Vs z&jGM+&1}`rVz@1{K8hg8iTIZ)Xe($#j5;gC>Rk6EeYcTJY3+dF{z&q8%~Vp}^?|_w zdFi{caCDRF3CW9@qV-N(J=yv>e0LQVPz=?duoJxv365Mv#PSgQ<9zbQ^NyK5)!TwB)pz}tNpecE~k zlAodj3~7R_8*w8U@M5~?kPkdT#mw8MCtFlV>&qlh#ukXsjO(l#8LA%`z}{*&h#LLp z_2XCt*yczqTZt{u67%}arf{k`uspH90Bh51i*>fh?7HXNeF=PXE2hk0aj0bvv$~js zzHhBTl_O-R(L`Yc=s`%rCP7aDms;Ja173lV8oh73gc)@DnGHh8Cz|u1*+;cSdXMom!3jF*sERam<&T#t@p257iYc$ zO@W8)N0j;OA-#+82tq+MC_@cKLx=&nv}kAon28F(8W1HiJuzw&lLQJQ1_$hR?P~%! ztyF2w(XIyr@digj*5fy#yf9H0!KR5rTUlsAYUM>Dsuw>Qkg2A79gM6IW4?W&qX~{L zK!mk)U}O>Cn=Abgi|gsMr8-~SZEU6Xtd;}og`-pch_$aEH)@GKbn?HOsg4WZwj^f( zZ$n|JNu^9Hf5`gCJ)S+>v-Kefdx`Oe*ST4&Hd3Ro~n8|*p-sO4E zf*Pa2O{_-^2+2<%heVFqzXXxDYwIn9U0!9rrHUwAX3wpd2VPw*X1E;|7-APD7%4X6 zq$LjC9mSS6Mkhu2NBB6kg5=W>RfS2(m|)km5G^DPrz1dNs9dkYE_Wp*P@EvJXG$b< zYI8Tmi!Iv;reira|D4XtIOr1gu}UJMT1z?D3(od4mCR$2HvTTXAW=2U+m3}N&ZqnP z?G&ufmOfF-7!njuZ^&$4pi{b9d1tm~I$nd&bL-t?1s?16-~X^x(G=z@_!^XfdL?DZ z4q@k~nk88vY~-j#_v=RXiBGy`Jzm= z0Tjkv*4jp6Z)tVP*(JA}Vx|krSQ?_G%n_yeH*Oe~kTKDhTKnO|L!tmio;?ShO=6YT zJ$j>WP$?Ma6boyIm1Y{Us6&2-ax^@vzqo=kntLBlVatODIe51Rk z?cq&wn2SIMazj_H>U8DSC{zD?ll+Ei*+(r5JrclvyO_reNSH{Om_SUQs)T1t~TG#%$X@s~e z6>PL_;;#}A6+mJ1Ew}Bf`-w@%S2jvCbPp0XRX^RH6vYhB&pvFFH7Sjk$ErP48~p0Q z{o_RxDN*w3!!$~+#-iORs~2oq&A(IpxBS*eg-sJwy5jH4?>6lm+x~33D(nk7U(IWl zEc3k|h8UWvlHK)#Wr@o#_w!?7sqznQ7}YTrH(?1auM1zTwXP+%LKCi9rNtH*1X9kF zS0f=@BLG&X6_zT#ud2zJ1mJH>SN>Gq_)=d=8O>GIZAth~fc)@h;{E<1expB#UHO=s z;+9KXeI34#vWCamcC4b90GJd6Em=L=`pj)hXDxwERcO0r+bO0tph4wXh1=v9@c7hYhyy@WgEZ!tFH4dpx{JvPzHF%LA3T&TT}UW)e_`RJ&;U6tXC;K*Bka>yF&m{a2=DkAx zui`w`deSedO?e7t zISv4#i2=ky+VK|6WiSDV1*VDse$s%3T%iz{#ToyNb?^a)o)HCd#sRv>rO%mT0lUKL zVE`X601|=MI1T~u#yJ82Nhu@F-8wINKGpTuBn}Y21!orC(uOErl@{Wt#3L9FpO`xf zi}Xi1rI5zESF`mzr&DtLzKB8NYQ)3PtV|(UR`wg)foa0Z3?G1RjkT z1D=0=?}*#zqX@TB1>5L_%ZxCC9rVS1x7rFx5{SJhB}szxq>w^)r6KoI0ZJEeNJW~V z9GIpRh2BWR?xf-NQ@_yPkGUsy*m;)ydwJQyH`0ZVfj%Xg-j#@asHa^i{O6TQ$@8Xm0I)l#vaYKch7M;S~wBY9DCJC`rIYpo;!Ylxf z5vKp*?42&fA`y)cW>9mAIFtUWPXF~wh7`e*C&+>*kVV>W@Q+2>Ut*Le_(Iwvii|}X zq$i|mMGUDFZptFk;aXHog6sy-Hz_g&W_Ia*YV4@)$5bf7SFc*t($+1-)QD4{tQ2_O>XPxwqvWmmahN% z)6dC8>Z3e6y#8z0_WkDJuaEQN>Py{wN4Gbh&8<5{?1_MzLa zmrgBSYuxv5>G!?e6e$Mx2GWIs;gh-if8r? znUyNExzsY&@q}ylub(1mlp73SQbde1rDnMbnhz3!6|NEhqNHpkRI!ScG#Ug|pn?C~ zm(q>>xK9+zjzX8>TA3O$a{0)BX~_N(Ve@GeO;Lps)ryi7vp!TbCm24ekFI7h%XG#B zh$A>_fQI&bQHu3)vI&t@a<+0@63VfZ?G!8eUozl70l2IfDDXrv73pM9GC*y{T*0K| zwK3GJ=AGzz;s~eorhm68>PQ%+xF~B99NfU1tt8qVMKS2Ar^R9zvgfxrmc^#2tgNb- zQv)3nMV*P}Qnl$|CF2-4wflj0s+MzTYP%xTvD(I zYNG+IQbc-(Su~AzYR;M4Dxn9)x+#;V zVWKj#0;xl?z@IS1k#P6s4MdADBEA)sP(neLCoM{28V4APs%bG4GGC&k9UQS0g_H`E zVX9c>K*rp=nnhUM98iLIo}2@Ps~$-cVA3=oO;?GLkO#GsqDfmIxyW_+HZHOIfD(_}3&w^4#_>%b!>9_c<0M<# z5(paNOz#CP!V25a2m<$OBS_9+OIW_d20Ps(e|qd?Zwc^e6iTY%*YAMz&%o3YBSi!qCz_rs|JzlVA>40~3ss z5zP{bAX|}jokn0leOZY-au~z#I2bF#B-n3%0=ql>4KxVrjpd>PBwNfN8W-Ikt!`FTftSLOV`X_4~ zOD`oMtj{v6yfI);5H1j5g|K=OfQ)=3Q72E!qa>@TIn zBW@P@Nva~tc51NJAbQO?Abb0Ny7eb)(C;y|}qbnM@kJ()1EVlsx$kJXc<R)(_jOMPht#35ES9)1x9IG(ggfN?gPGEtz&%mgD*R|j0{M84$z5lO(t1{VF z{{JiBV*lT-VovtP`i8E?`bL&^hEASz#x{n|&X#7D#)kh9qUxKN+Buuj|Bpo~)AZ>7 zEYyloRp)?00RXT_0sxTw|39Izy`7n*xvSHE55k$w*xBuWgz&$U!|F;pAGRg-%-Sn* z*U|fCdNAI;>=>Hez-SfNib)q}Nvp1YeY_)~kkaLe%okN&Q>cini;|#|Af@Zm3GkL) zx^zyp*7oAZem}p}oHL9giEg%SHd>8m7`d z0f%AXylK38CH+@XTl>&1QG~xSjKmWV-M%}HLcih3W5YabQ)O>gx`9@W{rtf;(cX<5 z<^Y_}pnY~h*e&xb-p=jv3IUHl`k_wybab<5xAE)w+9M%t<0k$2Lw{2(y{Gv=B#OW+$agXG+N#;#Qr>UW^*Z9GA3y^bK^#Lko?WTi>|js?nS&| zIOG8FEkd3DkkaGm_|?0p(;MVC0BcsWR;kNK)5fiiZ_{pf-!|9&S38<7K8~$lH+DxAF>gqp0+eb=zhkr-$FbyRnBpe^`pEw6rtp zhK{`QX9WRxw`G_NyeFbRK+PWo;_9r`UNf=BsB3?ftdr>bywzqmaT!9j7D3cv^8k_1 z682jj;+M}`*6p0nmY~+RLl+gm3AB2mE!{RzBt{^Kq@uBtgwc1qIP&rWZJU>OoAh>Z z{yQ1>dyBNYG&9rv>HK)`vVuBu-$*_F%&CR@-wlUP$mD2pbL}$}rfUtS=2h33&TZpq zej{n53vGed!0~~1i#%7Yt>lHiZsSw*ddLjO>J8!oy{s)_z82c0lU&j%{^gan+wHUK zhq3gy=ot3|ozUXs0)%aQ#>yy5|C(EHxVALC7`UZw&++B5rrB-$YlKyF)0(}(_7A;u zEWg|9h}fXK+ULPeorJ4viU|BR*EUb>4Pp+tOCNb+^-Q9!BgRNMb;yLeKdl5MuBEJP z?aZfL?)A$sl2?ZX$7IYUYd}sGw}dUp@?`34dBshH^x~ zr`Ie;M{Oqyoo}}}&Gzc{>>dBZ;R9{(TOf1TnKTy2H@sCFhGgL-elTTlD{FEaq-E&N z^7C-$4ko~?!a+wV+fxn{FKjXS*HT;EH(-*2NoFI{`Y1__Fmh5SoT3N=7Yifi6~Z&L z0_Dg$-FwQ-<3B9v*k&xQqh8^cnx*AR)N=0`j?@F3JhuE?b|nfQ)$~N$bhT!hVdz=P zSwG>_uAVt#HjDNy^PW0|z2LK6b)S^YWZ5eThwX4&_03##cJ7VgG`X~a#LrT@cu4~e zZJt_eKJCX2W=2$8JjI4A$DQHR@c#uhNh3*UE|wNFS#Pdpe#i{YUrPX`s(B><`K*6c7MX>{!mU|1HdtQi|L>NPN|#LBnn8 zJhR0HpnMtnbF2hSU3@&@WrkLHrQKe!j0_xT7-?-UP+DcQMgIev9dK#W9}%!AFpMS_ zs-J~4VtI@6i06L(^UA;QW|UN{jdkhDWDj@a1`HlCY_Z6g=vbPrw&K*EeYdyrH;FVZ znN1FhHoZy9W7s;YsCOtmVd8J}s*to{@|d7++)Eh3B7V=EjjPtnf)fz@W3j zcKoR>#ee7DFmu*Buqih65sTUMd$2T_RAZ~=x>)pFAHmN!t-0XgQWVR8QQ!JcMObL0 z+87r#2HQLFXxy8!k#!?r4Cqz*&&LpWZI%5_tZ6E5%~I3iwR6r&F~&C5u4K@NJY+5E zU_;H#n=^KtZCWX2`{QWY;wEyf=>C~H?@>wM8A*h==9Kzr9; zd|wQvNQxGswfAWnr6@hsV&Fe>wXER8=K<6lsPj#0e$4Oa^Ns%QNqK_5<&#ue5+JGt z@X&)2PVQDc$w}WbvS+xg20tz?zO5%@^!=KE4F)DAj5Huvd%Wk+>Qx9Ri4c3z0ubPI zS&q{*AxTnc*~=4G&?QpSeubP21&qwfUCC1ZdAQ4vOYff&oE$% zNeS(4&41(UoVqg&nl&8Twlhg4w#_%Tor!JRGvOQCw(VqM+nLxlclNl>aBYtz@m?ds{P5oC}gsaCi>ayR-J z6%~W0p3(wQ(FxSxHFcZwu}K)N!HDpa~gk%JW>Ti(HVF*&Sk9^l$Ot z*vz515VR8a^=ULcpd>e?xz1zB60-m5VR{qhp}~|SPn?{F{K0p}ziR?bA&O>e1^;oh zhNBSG!?K=817e|Cgm_*Fvc}mQ+DdDnh36cGtJ_zDy##WuBK8aTS8#&<9DP@kP)w#9 zZC>BGHr9F_V>ayrgSl6@uz%r!N5|w>2keF@jk~{bLGw|)M-bkF2UNG07U0`BZn-eW@~NJe4!%`c(-HA`CFsnXqj z-ToJ4hrWPgYoQDgu zi!lGbx8B5c8U$N%f>JI4-ToDpg220K<0COb}RBE^^WTR=w7>r9#%MK z?!+~7Y+Da%R~`i>C2kRkpJG{|5nztJNS%U`+ZFi{7k08}dCtLt&$LNZdr1i~@`6|Q zkW}s&t7gyCE0(XGpt!nMCT(cnd=?fEKSS2}G-!oHNLZgI+@QF^Mu8d?+^p2Hsd$a7 zpEvYNc+valve?C#8NnwYq=GSZus*@{8s&7BNXjhMs{Vprj-9kQ9mN@TI4-7fkt81! z%VSmjK}<9K^y9icJstg*MrOYa47eQfN6N?Fscf9dfEE#)qryWlvv3XmabuZ)_m#2w zO7Yf6Pi@mN<1|+I2prD~aLXoZhuXDFs!9)PA+Es+9)%s=J9BwH*m0w}HnI@Pk$2FB zUO@423lmXsMw>NDm5*iGw8>0JrWO2XncsDnG6GmYDyODgDZIxC_|oF?jl@B?+7~;v z4SCDU41TM-0+t^T9BObk7@UQqk0$6WtO&t|PNZ{zi-UuaY*F^#loWNOXrIIL{sfOD z!9P%V1;Sb>MJ_I-Y{PgY9~@Bzlm7J`*6 zYsna}Ndh@w!L6BQxFXWBvTYFBB@Tk_>H49y2qx__=nsK@{w1MR!81?z8)#ER3B5UIk9^Ytzp_iGL$AjoHs3*K&rXbRI--?miHSY|sOuwBiy~{gp91kEY-cUUnX+T}NVn7Gvzx z9m~`rqjxAVyBG7_r>Ut*S*YSqA=FsmFDBv<(Q>`DEDQA!9JDlW6Dp}x??8Ck??5O8 zRCl`yt1=e{%4o&Xm{x1jV0yRX6bjoOD}Uwd=r0c9C;gbZ{z(~ts7KwVteon$CaL29 z4x}pDf!p$=1^5NVwNr#UktNb1LNDWHP+t_RnL4Xr5X#7uS=&LvUymS^@+>ue1*(&B zk}^}IIY4dIhV9Xc6&!km^j~ON4?rAj(6{v3s>1jkbiD*nlvtI7`OWF`wlZQ^lBPNJ zW$+a|KUf`e{QQZcs3t}9s>~v4S~sRN5(LXmhBIq7hV0N>wgV^hy1R*B7tdEzpvV^m zdY^Pn>JRQ<`0tYGzE|q*Q+gM|COZa?I>jkdyA>mvsGe&e!{L&nGPg zYo8wCn|9}5zFIy)LI`~w@w`jNnl)TWlJayq@7nB9UZC%|r7yL^LkxdrvfGEZ=sH(f zP=&KtGOMg|bXU+`81liSp;}Xo2&)@XCv~nPqC!eJNXc5U=?cL~31i^J#DqY9G)wB! z0#R>Kp^1IS&hO?Z>%*XwGOoc{7<2Z*DRuG8C{M0uyxT&I4af+axDYd?prgN<|M;@@ z1}0MTAy^%aDYS8^%W6N%+(6c$ZBa17+-m!x!@C}88DYnXc21{7AC$Z+Je7hUKLt?!n}L=7Rq6IRFp!U+gq%ZIpCiw z$Q6ix$|0yBjQD*})-WsWw}b-57tsG4t>8xhk6nC5G~|GSVWuTc|9oj z3ks~;II^XGdUWhVbd!c>2ehMHkDZynCz|rAt#;lXwG*DvUgfbEf;5@Ak9YFlvWWcvw@XT_TLRM8Jas3!Y}IYbNzR&(htK^t!0} zF^y>s1oT@|e|uMiWVl;vcI1m$jVGFI?NVtp6W$VcvoDN35#&rFU=!3EQbtx`yasL> zC2q4%z{`hYeKTkdz{{lsB2JdPOewNh=qru#larA6%m+OEMpGwF2{kYJ?f#Hj_M~wZ zEJUNVX*o>pv;$(3R2abvEVi%piaMP?#~>)tg5`|Ccy)Wk^OHdND`mUE>{h3-jips0AQ-kl_3Hdf|kL>;6^rG4thi02Um<$rjIy>^HnbsHd^17YOJj8&x;`?Ss_9j- z-@Db=FKuyrp> zI8km!Hm;`sIcs;(rPDyMs#f(fRkKsHO*Y`OnIcH&BGhPv$dgKOELj;~(~eLESMf8; z?$bBce&PW2l9yb%Ulz^GcSNs^mVbl1-&gO@b%Ocp{7CTQEa&~U=5u)Z^S)yH{raNh z>gz!xr|0!D!~c0a=W{>B|LHF0b71>xzvk<*q~|$7@TK9qMT+28#n-2M&-+P^-^b*W zpwGjkpzmjR&DV>9e;&5~V^7Ys-_y&sz}NKyvVs5GWX|XL)#qW!cPl}`oR2#L--nMU z!H>i6tL~2$e}C^!`<}N);-1&x8vlm}c*1K+Qg@Erf55^vYfn(mjJoSwJOJNu6Fujfg3L7%se3goPSFM#Ll{R z{(c_=Umu(X{$Jl_zmISGKYV-PIm55y$W!;8`1N@EMdahF0qSfx`^Nt3oyz}ZQ{yY- ze%8bH?acq}F=P7MWgiB9Z<|}6kKZN~2KRiOcGR?ua@q?vPaF7sY(5FTU*z<(onn8z zzI?qLehLekB8-LpN9$qzmC6;6$V{B_pQ4r z!yWp1UI-KTtB+hC63zTmJ;}D;U$*`N`|I<}|NT+JzQ;Su_i6F$KEpnyLGW{^=XtV* z*!*jM`g516=Y2G1U)k=Yd$aki`^s~_?J4s&l8%*g;$#o^!ZwffnD5ck%s;m-V;NsN z-~ElFY&+X!oqNDux2?{J$>BQAQ{AHOUww%#U+E{`CAnXR@i^>NnEIjHl{Q$zj~a#*k3&9J}EmrJm}!7D0E+R2#9d{3-CVtdo6CJie9Wh zp5Jhrw(WACyrA*`(ptOlvr~262CrP%vwy9TZ&+YBD)!4M-}+H3#d|bem%h5)*>ui$KCT2+ zQn$moc9BQo>87|$oh(aMlYdz;r_!6)srw-S7!vtbo(upc$&p!E(5nj$a5zf?OpZ45Ql9FQ^b|I? zWIaB7c4+HCd~DfgQ~p=Im5x)g2IE*k^+aYj|r?9orQj=j=J$3vO{(mX^d(-;v8z zN_dI_7wqK%uM}Ne@i7sZ=gp{CPV{bl;#gGbY=a9J%Qgyb?0nfXJ^-x^F#Kim(d?4# zwhim7;3px?3oWS93y81nntSv~)TY71tgk3@r>;Qy#iw_MZB8U%_jWy&Gpu74T_13% zGe>DzF4ADLAnZ}|9>DA1VqIOyDTRGj68@guDV*VpHQgkj$D{e0^@oxptXRIka&Snr zzWW~NyWP$lo$zTrlp*)UkU^onY({(h6LEW^QPu9-=;hKWEed66f6m3O?(EZt*Em?C z$O^K4vTa(j%S7JEI`d?j%#fU`TI6%k)YWlDBY z@q-|eX+d-llL9`kin0u^@b6spKOne9cx`l~)4mA&kzoZ?mJZgZ%?;e_#-ynChT2f# zoE~orm#!x4MkI&p57e6vozw^duNSI4X_jBxA{-}uze5&gj51;8hKZui_7uo4&YgaJ063 zed5&@7x-k5w9egp8?lai{eE`aE5Y+$R^5-Fw|7`r<) z5-LFFVx)bh4|etz>Vc#}t+r=Dg_|9VojE>`(6{JqsHTow##n%KwDP};>eZT&3y zPLNoL=}aj>CrqT@ufPj2UQ~y|=A2DJNVSbo@>JG;L?lHv>!z#bw5%|L&JP zw7Ak}aD(H0 zJPyU^##$@O?KI9_-32#2pqc$lwj>#sZ#jFCP9K+^VVM}Q-gNqAEIAR!?lX=kryNx1 z_%uYGd360*KJ!x2H=Ku{{9`*2Novctzr|)xK!5QY_fwsHyg06L9(-sX;anpcPM?aH znwdd-xe8uuILTT2n7;5|#S4SAjQ7FVUwFnctD;)2RGXboA{#$w&NkgV;ap`{D5k zm8MGjdzHKd^(uw1HZE4SZUiTn?D-cgrVCTqbav&BP2Gi;4NtLGA0ffq_y4@C+x3v@ zvA*>$GVhd|OsIesq9j9)5*8QMY(cApGz*jmSnw&D-8Ys3KW8OQ?K6+nr$N!Q3b|6%p|su7nKoY5$%V-B1W=HS z4g_nc>-rqKa8nr|^H3A5u>~)})C#ncdvmnGJ33VWR&Akj6|lU=KLUTm*BJ#Awb=o5 zG&|JSyw<>$2~SH$myb~?-x2E13y`=asaeA!Gf0gY>EqPH z_05>Nf8p~^aqSX00TIwGKRi_cV|x^l*4^Wgm+8>B^j-+NO1zGg1klkrQS$s^3G&N$ zvp*8V$tsR6mi)ATq3I?m%11a+lyzp|CTE$pH3vh?cZWuI*rtjH&tB4MF0tc3gL)mw zdC9tkCepErhtRD~%p#l-v*5&=WxI^BUWvi#+AEV8pAS;9qb_ibvzUaXfL|s-74v$D z#mNSTJU2x%;_BBPh8@YVw#&UZ@#fL&8VBcUnFrVt$4(_big^I?tX}rt6E~1$bdFvv zNtVwbx_z+K(1iNSE!PIh<3p*Emh|m=GQyzmq~Y-iSN5sNMGDIs2o)m*qM#>b4mcjG zZ7({!`SW~r@JnQ{4Btm-8r}fM`DhmnST_(HzC$xBGU#d*M6$-XK9oUB>3Wt{G)!a) zoMqE*t&V+JxH}B?+AZ#@gh0G1`pGg@<#H8eT?Gd!ALU=Nho-J0A#(>yARQ1roj>S! zIjY%1@4M;I_1)oZuU*&9P5%0SyMREb=jSLJiak4!b$%@_JjZ ze-(!Zz2vD#U;&*eMr?BCUL(SrZya?dKzq7TrJ)MRbD=`gMUk12Mqq{rH3<3fq>t5yH_pS1MlnvHW|Y zLF(i-nw~m8pBfIn5;k%1vs*X*u?@6x>DvZC{HbFyYn!0ywaI+conlpr55D>Y0mMBA1VNr!G-%)h_ZCF$ z>!|dlnCUuNl@ls&ppx|?N|jRqw4azWla#h9o&q)?;IH;33&S6Z(qOzbLigPlq-!+E z1k{Fg@ny4Wv>9N;WVOsu$Y_v-XZMR1zMcdVC&zg2&#c53^Bqi)uGC!#$^=i0I ztj_?&&wQ;K)WsZa<(NziZv>VbG-Yk#ty$r8h}9g3@wW>vmuM-4<_ZC2sP)ec*I2`% z_X)S`EWhTI#zHod^ersAHe2KUp-(S6B|5;H@+svBp<4zVauw*n`Lf|?!Q95xx#ajX zz#|)FrK1a%nU+4iiuZB5e>v`^`Xq6d2=4q}C-EP2byf~<9NZL)WAx3*n>7+tqKjO@9FHIpuHlD zctRmJ!q6aWX4i$DIy1I}=Z8Lr6AO|5Lq11I-A5>0nJ3w0g>SGfGb>9McoF8pt&l6e z6j7;mVTT*9e1ERLS;#>pq1@mRpF|NkY(?K0oSGY^>&9OlZ^nP+9!(LJd=Z(IDgR@f zsS?{Q(^F)85vq=(7DEwuXHFh4Utd+kpOrvT-DMKeO(`H$+TeUM#f}2PegCO-l^e2Bm!KT2S`r$87%q^UhLiBFGW(OQ0`L|j{%XR*T;1t*Sq5nyAD_ZMH#eTqD$FPW-v zec;kpQ{iB|O@g~h_Xf_0bcJmgnAm-ST(;X$WNw_VSmzX-g+kiXMG06lIw;^?QbZwk zs+QX7SWqS>HX2UqG@-o51B$b>tzEhPury?}40G0uR;iasC_1zO<7LP2Z`D7-a)o|pbJ>f@I z*uE)9|224aOhJUEwLoUDS&@{<1Vx8}IcIgWAnqiU&<8q&T&~5|Hyr%N=Uoc0(CG?v z__CW{lUo%z0(FhZ2s&(ZSD$Vf5ZdD)l7!CWI_AR?exWsYbF?Ob!eCULVVxw)_Lgu2 z(YHi!%hNsEZB_>+1q@rd!G#LN2v9@|P3@5E+0{Ukq%Gn!Bc2CU;(^tAxt;Hwp`!M6 z@l2NzE?iM?MQB4}7-{_rr$|w7!<+G7*CnzQ?gE%t<%L+s!Qdov@_2KK2q6pMWnywW zUQtCVd;j!AYbjq5gxr`$qAok+{E^1?&Rm9;7&&NC7zbC6Bsz_p;N>5d#5@vk6eo3k zxXxqbprvS%gW9#=R_^h0jZ0gnUV^uo&VPg{u4XP|0VG^1!5R&^Iy#mz=o>uSSm#Q) zQYX;>XW6#`M2NJV8(|M{^ZBn%@M5l(T`u`nX zf@-O~f{=H$Lh0{4F=?>pK$tBGp3tq&>nj->S!Q`Uwj`zOLCLe7@g{uEjkW-gj*dn4 z*rU%28~F&M;H`yux%o~q3?F`rp%>#foz+6(ahIj$v0at;6kvR&zz$Qcu@X6r%iNEy zq!=e&YxN~DevSo{hM*(DPunRiudnR#Gz@PFV zx5^L1d1a$_xtQJ6t4k7sho}c9w%~16y?#M$C9$aXa=+w=l)S0QoWOmKS4ycmM{)he} z){pec)bjXATGC=Ppg3XDIQXk4%a4iwJ^)i8s&W4WgtAReKGfFID!#fYc5;Ejg0pWakYbY(Wy5Mw>nq9*~ z_Iz!4-d18auYDE96vH3Lb0WG<8GG`YK(4OF^h(%^zlQ-AgU$n&3C&b;XL(su8CDTq zg>H%vPD<#gT1bINzz!mPuhPGA#EX)+!$ZJ*lWC(8lllV5F~01<)IUsqKhttSFUMxf z=9StCdO(b=X0EA~;i|JOahaN7thPl>9A95+)cRVW>eFNZ5G{zc75;voA`K%^BA-X2Nu1e0fD?SmYX+ zn^Ayxtu&z|+}cD^h{YSs%^xGv2agqZnmg}5%}Em7kPXIRMI zMXp6RmmYcC4#U*kJ4Hg|_DFo_UM3klp<+1Z#>-yk`)t;4V~98cv>mu0)k~@nDpeTl zom{nlQ^#D+0@!WHGGlmtme`H3q?vn2M(NgP`>%AaJrB#q9eN=hIg(w$TyQt)j*Mf> zCEu%hE$d5V-(;e-U-oVw@R=FlYD=R;(ErT@>CxPUW_ez7K+%wa zjJW#iCBeyl3&B~F2C?^#wGLG|5f3#8zSemA0F0IZVmNx$dK&snZ%QPJh2t*5R6=SI-C#@e$DP|f;XBF6qe5`f zvHyCZD3>*e91%eD<~AiA0d4xi9)2KCl%t7uEE<;zJ-X!0>us}%BMucE`v|3ww`!p? ze$ZI((j0pTW~09k7Nv05Mw#g|ss!w4=aw_8An2H1Y-lZu0YHn4b%P`9*UV53S3X!udW>^_*9IEmG^rnoDymB0?mx$gzL$gn6-Z=-dA zb_%pxc$MA$$sI017-_QsOlhZ_%9SJe2ph~_MF}drc{AMhnqlaR6&k2%sKu< zL3x@xpap#`c0zQHA?cs0ji)4aeFKrr{Ht|GbNN)s=S#wJX68ar2a~N5iUAcOwY|)d zDSljkhmL^xux=wtzg7~41*&0VH-#+Cm6*)hxZBP?%2g)Ts4KL$DA&;XODpr^8zLWn z5n5PQGUN2;%0DU=4oFqTP>hzdSwC>5Kec#92~o^RutYjLFSM5Q@fqPpu!U$yjlnB%a%(5O zJqotit8G+%R_ErxK_zLf$s_Z(>ARNy<@mSz#?`N67c83_lRd&f3i=T@HiIuRG1J8&;r&hYtz|`*!GEc^{(r{Z*iU3WG zR32r7r26&Aye>ch*2HnGt)FL2F)n-=GqO`_jiiRC6joM2bi*Sgt{;wwQAV*>TJDO* z(XmzxQ`5xJq@mj+XCH85TPUsse6iF7K(kBAHMaofSuYh1zBNQmvL}Rqcz5sGiR^z) z3QFA%)K7wH^$5selEfl2ORDp zU7_)Av}End-h%^Cg94uW?#OROS_2qu3Ka&qVfOcC;*jRMm&&)!KBw~X9`A(GW6$B1 zM!Y%F^ac)cUzC0`(uVHuU||iqwHrg82@6qCn35hU!|>5D`<}gAifK9x{bSQi6G6+c z0vIR%v;(mXMaUP>!IsEkxMA$PZl;fz22sG1A_$)TQACEe@)y7}#CS2+Kc5Uj-Y}mZ zckWTz>nHsO?blzKl`0hlNsl+&E+L_Dc0`EONJzfuRiD9=2h%rG+M2=**5!6q>@`tH z!h?$vOtFIpj_usNwX6p&=B8IWEynFIKKpHr17eR15gPoGrU00giF=;2y)?hS1P^s{ zWHdkTq7R`+)rLuHId^?RhIAHEzgOv6Dg!5NudQ_O%9}Ljpw0t$ukO%i0LqtqK3rm^%d5=wjw|_~~@~J}rz? z$;^?z9n11c^#nk=gD+TsHjQV?Kktc6OeK|G@@)SUCEYBy%}CS>r3t+9xoAH}_YI{< zZQ@1KqOIag8y*sg&D4Nal%SNNvW2Ak<*!mXeoi!Hk*^=3Jcgxg2WAcxvqls~$Ie-S zFk#BEEHk2WFB#applaebPaVJl-^$+H?ncc}9wPE%F9jxzyOEkw560$TVQtuuKb9~!tMesl?*4ZR> z*pEkV$_Gj-gH=<@!)`(`#`0{y@`E?;GUS;|QPu6#)626eoS*n(#g-KpsnqxM1*Gq> zvga;9W`-X{j6_%ZQ={rk*`0pOo5X_a(HM!7cLa3fvkB{?3YhhwR zxB9uJ>ef3gW_TH(f^hPJ<_iCJJ-%xzR#!8BZIu0|{ItQ;nVw0lD+{Wn$RXFRsWOx5 zXvjafU#HeZtCwDBz$P>yY|oMm+7Wx@VTefQq=M$LRSuJ8axFn-FxUrGTDTn9Tw@&q zU0S0Mm7VmUkYe0ybugtf@70oD7^dX(AQr61hNK`CSQ{$xa+|KSl^!Ru zEGy(f;4E6yCX2T;usVtYX5!%P8Ms+KB!MCcJgQ?S1T^4_fZ7U4{)on13Dl+3>|bz= z`%e4MUU1-bUpks3Nfb&ZMlV$%31!p$l2m5$z^=MF7yspg{w|~uSlJ4;fM2IXF;Y~I z65n-QQrL`SHJc)92n7VkIB0ZxmJRZcAMD}q40#2~>ti5F2B4ZKXH)^8w`r_-?hDx8 zNIhlt2m2#-!k39Jkd4tp)h=4ftA5qwDL8Tflj>o`csVHk=1Q1+Q?p!Rk96^a+*9a!zV^g`vo)|IepnEI7B}?3j3R- zZ0&o2RFM5srU~nmKRCf#s)n)w&1wTG3|C3sFuyvaFl3z+OM6$$t z20aHGoS#cg*gz*J$`Qzd|7FA_TZ~4(@EFjSe817${8e{k2E(CLj$5c|$eCUlV&(87T zsYHj-QS8gCk;=tg3yVH+6@tV80OE8x%NZ`Yz9Pw&+Hgz5KwIfX?a&5(sw z?;KKI1LlpErGN*UwCMVPx6B`&j2Fx`2XOG-nO0`fLR9;4N`pfriKoqScNCn}t`9uC z#)JZqy~sgZGl@cy)Lk3FRq&0J=W2^C_=<*XB7g(EYRCjCvyT2+rbw;rprM%iP(MLq zUbATMZsH;K2cJ><3>zW101V;vz;&|r1CT|kVHGM*>ao{WER6_Y?aG|?e8TTc0v}G* zSL6tKGKfWqiowXG{fvvq-=iZMXOFoM^VI^WFH%VfxgQ0S%~(I-HKsDqQ;;T=ny~4h zhh}Bsz-wq#_&2MJcS75cDWLP%)Y>^tEwC9d2VaQXJ<^~WWTXB^mKq`b&?Nh9bc9SS*^aN9-w=z-0z>jm|tm9lzsuUZ-j(-thz zjlv4Uf39o0bot%r+%4jVzv*u{Mjs#;H7^wE++dk|GrQtc?WPgyZW;VPqEu$ z$wKuYE?{=s$Lx=obz{{AL1E NF}CtWHU0C&7{Jf8x-J!h+5PK%Jm=Fdj?88HS5u z|L;W0xrj9R`AsRbr02zGVNK=>Xi;*=2{dvop9XmuEUzFz3Y3p_)w%V30QJlpN5P}; zj?GB!%URm6uZ?~P)$8fJ6SS@`DkyETQA&|xnPA>}#JfKkExye)T=!`HhkXfU#K^%# zeh$qyA=?thTtK>2e(6)F%W7(C`1@>k%xlFooHP2#YQ7QzTb$qEme6@h%4bp)znd*H zGpCJG!Td6}tgC9=pyO6CTem!ie8$Gtjw(d-f_;X0*k&BOR8-8c)@_wH;jvy6V%QO= zq1Csp55v<848jzQ{R~x0G%mV=E<@HCwc=q2#tCSE71px3y_Q~}*StH3V!H6Eux~)+{N#|&IU1}7+Le)}Q0Zo`;YDBB zDbU3?uEH`MbQNdtpCTG`v7#?ArNh90b--=m*=*A}W-6nW5m@sy*wes2C{Pc)O^?-X zH2Dg#;%+}WKUHadjva?9lu&dcLL!D#6w+u1RN+l|9bTO7aKhB>K)$z|K}$5~v8Z;-omRS>t!H?-Q42Uea;j6>ny|%qrro04&F&M z!E%L`Vu!{TM1J=XCa`fPp~xmmgR9kv`J*6sgn3W0lx{_P{N?vHX*L-ErcScdB4yzU9;Z`xonXDuFwNmK(Pj+5Ylu67gefafIAgo6F<4llD16%rb)VDiFk1cZK*>OoYH_8ls#soql;T!mmQ?#6FiEGg_IbTQ6)qT zA~>OAPfL8s-f5y3MyJvo$ieQc;agj!&K2z>h~`>Ct1BwPq=gSi!A(VKauPhwXQcU* zEEgorM+e-Y$&wtA<1g1ToD0FQSoZ(RMxK_v4qhlr_{Ggn@a2bm@o9fG?I{(L$yZ5% z{`U%v-B>7nr^^$oe(_ualKpgr;V2KZJaua5t?ZVf^jpi&8M*7=RUhiUExTzv(P8Krbfh=gv!oYQrFNh1MUsM;1S*$EUQ82;Gx>deS( z_kG=}UWgt0`Yj?^A8B|K)VzTsV+gMC)MY;P=++*&+JS$o2xgZnAvYaEMxi#x2UG2F?R8V z1bs)agt(o<(vnI7b$4fl{CeUrHJGaHs1eL&4gw&qk2kB z&O97zS6al{$P?W$kH#8{k48GTyLEl~I6;(~AtZ3*zemP#Ro{?(y*UL%wurQWS(t9* za&H<25!zPJ4uc%$WlUkSBG1Yu9jx~njUsEHo8japqS}Y6Lh?_s-!BYt4z04i~LmkIFo}c625g|6oF(C@CZ1BGjcdRR%pl{%wV7 zqW9Ajh&p!UrsHTfrD9I>95&J*x~7rU`onxvIH@bsFpWeW;64U7JY_RKW%I|4DB-mU zk%!b|*WHk3Q-5dug&(aIKMD8?MY>Tbz%N&%p>9%=E|BF@wmeV~Q^luC!oBVaAJB-j z-{$UYDFj2EAgzqP65#l_W+m3dbHxbSk=m%9Tdu#}6)a*Ij`0WoANSl|_m5vJeOO%@ zch=!Sn?{&{X5B-tz1g7hb>fo6cRGT&6pOl*?TjM}ds6GIait0lf556O&(WQxrY-$Y z_WDk|w*KC{_FbQpq!_?B8Q#(?hT@GT#=p3l>3M^>apRuhvrSn(_eDj!6XNXP0I+b# z(F%|7P2VqkJ!%(1v+^_0h3|!WFmX~huZO-#rk@b9(oTt$85o*EaqP*eTNuOc9Vg(W zkh3K#s(Di8J%T^uuHvjNA|9A3j_tzOPEoM%fI!1t==P!FIGfGXl88HDDKLJw$lG++ z3R@lBJO^Z+fSsdWu@M({n9{a;++<#m3}pL{{TPUanuMKBy;y-%>9q%(sb3;k@wPmx z(kmVp1yvB42Vw};q1t8UgxtqP6v));U~RQsH@r=jgA6^U-Y7BV^<7 z5g8@qg-t!vdnuwa1=+lB40uGYE=jmOui!ofgrZTyU56VQ7d}Ieb*L(XSjt6}W4^aS zcZgLZbJUa-@YzcHHvL(&)(jTHc=x<`CEfg2qL0^Di_?WpG}tj#TK%DI+BF@zqWZ8g zdbI1GH(fgi6H)Etp7zV+=kJ5*F4M4)5;R{3q;9^(c0{i$E)M1cmbRyxot%=1(FlZq zY}^_6W~=GATXG&Ux1V)SC_r6`J0 z^&!9KBuul%NOT-k*b-Xf;1WC-Vir(fubI46gsKH)uFP&Q+Y7aF1dOhM?8Q?60p=rw z-D08uG`?6sRq`yLV0j# z%LH;03LBSy{0>hM{d2{&r1)Y>g>pY^>ASP{ZjQCYO&%x*mSL|MH~RY(ResZq7`I*ywv>I(PP#2BpAAK<8)XS3mBjw4j~ zYBzVXPA{pLSf`#Xwoj6qT$`AsHsKsm35_5j;21yCQdBzAnmQ7snjTQ}+$c=(k_{u- zt|YcIgYX-x;4VR{)3%gn^$#9Y1#yIa6r)$3_kG!qfpjgjkYgQd8wtHpdR^+(^mY^2 zfUotASj69bE(jE$a0L}08#|ILp-+Xut_R~^MxC=G?N}LeOvpeHF_=9bzNFxY4eN0dCf#(5Jko-`Hir9hY^`k1%9kLS z;Z6xxU^RE3)@kS?%O-lhcO|!;`4&USg@F0LnF4t7_SneJ05#1ftiIKf00<1BkjizY z8w1#1*R1y@OwLzi=e;QF^}(U3>%r_~!=xU6pQY9s4mqP5?hV)F@1(Cf7n{qBhA@TT zPq^#Ys`AU^u89O|Jg%9!Bjo>3;!1LbPIhHRiR#j$lR80yAw5M@B%7iG$Yn~@R3=R4 zF`?PnMuwwk6@y6${-hv?$GXjBkS^;a;^Ul6K-qHelLGKjhIsdoMz3=hvuLGzUq_X} zEqptO(poWDdI9WoZtBJUFMd%5`u_a!DN>G|r&Rf*_=)XiZ^D%T;yK2)bNgDR{nxn4 zRYf($qv}FZXe$~3FIDR$GMfM>yX-^@OKXSnMKEDt`UH#^;ew ztg)*E9l}PbwA~3&o)gLQ?Em5Hn__%{-EGIVZQHhO+qP{Rd#vBswzbDIdu-b__u2oG zo12{ba+A~KYnr~aedtO*bgiH!9es@?f(hEsdoit(?oW!rsTELW=WYUjDhwZUa>1Bz zfy{}^=WTswfvocJiC7e9x>}!uWZdhKo$D?5dMJ>a1r1Ck6_czwYJkTuI{|A~>eZ(S zb!j7dPr@yIP8B6&~*l6M*idGSr;yrn=wCs|0h!h5*bUw2hyH=h8TO@an}q zjOYtfK=@1PktVe%L@Q(O2iUacgn%q`4%7C|S)6Ir{$>){^Ve-SmfDFs+>q;@aE_iXdN!VgP_&tC1SK#k&FI#R@F6Ec)+6u zio;amEax_mbFNTDLzAk`8#RlK3hFe-VI?%iG82zN4ynLpgrsTlj6>Kc+Ui$pdAmB~ z^)GJlj|CF7*sv|2b^B*3=E99%qJhla&)y>@mn2T&EjGm|W-qF*^J^VKylqq}IGT(Vw_GBP>|t|a2zZca zA~2PPfjE!RTFfUGyOVOL;3nC77C2t5Bj%bIvK-PLp@gC>l+`Ak8xB3lOeO zvFTnL`b(B@ya}tttiz~Pu@O_cmE{{(lKd~Ncem(+iweWN)~QyaB3iKsQ5L%`^C>AH zges`|uw{$dxawW#plCRM&tLj~%kC?%R^M%IZ!U7%uyag}=X3%$s#ZFC?-6W8#&6{J z$tV$O(&=96&&8ySo;c9EYwKhgKp$Iag3JXguofOU_Y?7ITj9!F2Yb7j9y77c6F*e* zM{h^KE{zH+ZRXO`BtM21!2_fBgrFTC%y+Hd6H;2KJL5%V)@jIa33N{!?m$`Q2;eIM zHz>fd76APWkOL2XF2ee0xa$(r`fSZ{nD!`e`{Wl;uv-OG?3kKNNkBGh7ui`m^9lQj z@vO4CbRA@RLZ?V~M{n-nw07$-IOHgsXf$Nmv=qfbN7mIQx!#qLUrl*Mpo2GKm)g^k zCdh#>E@LX6LrkR?u#e9d>O>;Ar-ctao&79pj8p(yB~vY=yXC8KG{|{QS3J&37OA`~ z5|1;Mx22DjOj-)~%0Mbt%08KWB8bqqzBixkZKu(G+U}uHpsgQ7%b&2k%RLe=zKAQ* zbkL7miT~=&1Z@`ycnUxil&ACO^n+(rs%|LD9&+R(oo%5_!rB@l<M<}!%M^L2 zdsAv2!+@9)w;V+aD{E0(y=?%6rVITfGj?OGWO|8)pwt^z(; z^EIMhF|f~^P;g~80Uj)+zL$Y_H6gvAWzD5&isIDBNmBo4cLL<~-CM${!t6lI z8({N$v=4@tuy_gLrp6E5)f>va>b@9hqL5>Npy7?rrDWh)GZ5$o-+KAVAPbwqtwkUCeZ!WIs`#(gIYi+D+C(aQ!kw}<#n`bUTnc)N6H?YBX zW+eY|oe*4*pTOf~@Y;avtiI94Kf-@R5rn3yyF;I$`r6H!DkDe0cB~g@2==EtWoX&s zQmp{4PgHyDiSm{!fEif&vp-E&3wPWt_aJe7ksTr=i^n3cG|ESX{P3&&gK~?8e45SL zwc<((!g7c_=BKsX;3>0lDEQUsj%oWspe6*QP&3MgQui2S^8kDcYgl3kK~g#=<&=Km z2`#J8qaEdU1>0np!6IDQyhN)}IezMFc-S&zq!!U8DwZrw`N2m1$MBcGT*^RjsCtgR zzir1oNp>XS+CgK*sCF}z>9n&6pDK)m_7+geP-;M5VXTwaSsRif#+=uUDF zyPUq>W|j$j?ao#1(H3n?SGSljVa#<^Iz~SlS{?#Bbj{p~+AY9QqJtH38U;h}%Fu@# z{;~v%rJe$@0qa#c(>jVVe>dn;x*84K3%~`Es9|nLpdxaei=)7IlS^2Q$V^;qMho!h z1Dm;z6Mt+a`Q(wA$tZ=nLIx>SR#F!_SYI-jCVA1nO$Uo>3lVe;0keRj{Bd2<-QK3w z)p7e*6);??!>1@^6y>)S720OX>B&~f<_}TgnLKcV+0?x%a}-w)iP}%U7-I|qRWzOH zLE$UtjFRyr1tg_WFnZ`LA}V6e+Ey@EPx$G;h-#(mfMG2ecx_J(7)_?L-baHU3^U0#4f`F@(Xdn z9INyOz8oHkG|e%Nvr}_%Bi<~9Gw_C1eSUdnVIvc=1Xo9nECKqzvF-fw4aV5w{1xTvQ}IQN-9jmkX_`L!q8R6^ zX{<-(p^veB@9z`?;D)^38!WYs$TpWGE#d#TA{x zW{x|9C}m-!B_i~C+|E>--w;XSjzo8lBm#^aVa1-HOdcV5^gf{?vJ!*Y%B*G|!V*&h zVcd6%K%rt6%AmJG_vdF-V(eihaoeWX-1UfCIch%{>M8L|_fDFN2x-~8?u+Aqe&vEW zQk*D3sLQV;<>=1rw6tl zV2BFcsEosy55mkP>u=kHS0{dtfeO@gU5nNQN_`5=C%rQSk}Gl_XH)1KB^QRi5a6=V zJ0YcIw`}EKfO$?%`O)m!`9TfGu=>g*kZ(~%T19wazv6d{cu>cicS+dK`CYcZUtG=J zp2JJ_=I4Of#RUpP`Cni_=v-CzJ2(DJl|Pz<-G9?Q)&_ezb$p5J&zG>b{j60akL3g0 zoG4}#aw6`NCXZNz<%NpS)OUY-J*NeWW*Bl1lDq1J#}iDbJ%dB#{IOWZ&kQx4cA5%y zv`MDx)hWYG%{=`TR$WNwQF8uEyjP~7+{pDRfc^$wfkvN@07b=*b$T2lyU-r(R>|$f z+VVLBe>A=8pQq&Z5&>g1?1vA#M@W1i`Qx!ymdEa#>rDi#`gB@Rr@cwyEIXdWq_(*@ zm1m7BNrAp{3B^heELixL3+|>McN^kpO_MuqG#BGucMr{A_6;;5;TAGN5aphK@&)LV zK-jEONsERD=kO%E{Sn>K7`uCuPAQSK2qX#)3rY5@ye6yAY+HV6a!54CUb)+3Gw04n z2%MNcr6n`g#HvBccn~(|j%~q|llh3=$-S8u1BviBKqZ*|TeO&EQVYFd0JcFg4-cH3 zNyy@9HER`Viw$u%l&VZM3Rc4fY>C|zTU@=#^OIoQ!3FlV%(D!=yXYZBDSa8q%ENk? zS%YlKah3`Akj5vb&K&15|^1bm*PFl|9;E*6#a zrZeO~KOnU|AUzepm&;St)%{&)d0Ga{q(nPjTYV7SrP{%iCtA)3t6u%j6TAcYcNeSD zJy=Jam#LC<_%9H~+JSro6c4jU#uWW{$c=R{9e5?K671YJ4L|$ge}Dnr3c)SijqF4b zi9k)Zz|^$GODXa}r^$$^ug7X9yE&F*#99*qLQlLwi_^S%P$;OLP_8HtX!&42z-ZQw z#kOJt2u&<5BFM5ur9PXO!5myJm?MeHC6b{MK=kSM!~?ndAQQxmT41talaOl;OxK{1 zc3blB3Xw9Z(Yc8N**f)n#gUcIgUmb!N-LsRaCc9QhfvRMVHY`TF8_|{bedw87vsNW z#~lf8&yk^{U)_Q_;d-@yoh#6zzmB_zA4KPOo(;dOd&UI28~-%n*6zzO{S3p|9s!1l z5qov+t-*?*NzJ>$C|tD81P^C6%pIkXdtSC~)+wy$4LR5ZBTv*pD%J=-1=;Byr)=br z-nf)zH6zi3Odv%sZ-NS(u$7l}7W_~0m-*ayBgGlReO=J+;TftUMf+tV2msV;YR;<= zl|42+)ZwDhGR4iqstRgT6i5Ro#$k7YszrM%XQad?@qQ&BITcE?V__Qsk8v{r7b^jt zC$r3Tj|Nve-Q*7N>cdJdbI{SZ#e#oiLi&rI8RTXDrQ0rhJCoPl- z&Qw3BzmI^(0Rstuj2Zw<1<#{21N$sGSuEk$yo5t$zhW|%)U#TU7<#|h^D416XU=EG z64z5DNEs!VX*y<}eczwHxlTdlqo8Rx zVk?(--JhSSXdG^T>6%XO2c%OA3v0adqy66BKvi_@13FE=v1UDrrNAywpMCG6pXK`^ zOL@+SAbyN&rEm?m)qw3 zZALXinGOGWNkZd)@-HqE!9Q}=-R86AK*71@D`dgSg?6w7QGkK@x{&ih&tf8ghatr| z{tK)Mkr!<=kC`xB2kgl@I+gS>^^c~~tgtv-SJx(>R!OYGEgiwP8@J!1eTns(n`j7Q8`ryGi#wct5Ga#JC)bC$I0&-9f(M5{C+ zdeUGW2#mMt6ck^)2Et3Wv)_DUs0WL1p~}{GP7#bqt816|wUnbM=VzxzS|J4EekGHx zzbomx(Y>|TRXWO5yS0An;kQ~(aQ z14H>`uZ2HEFXk(PcParl4th5_5ubWQddM+3`J6A6iC?-fj^DFQ@BBmud18h7Z+~cgb@CE*$F=s4CCyF2mkZ^lmFG>L* z+xUNS;%!IvGIN>1jbT3CU~uOhl1>4fqCV7L%LumIEf72Tn{OKkU3x##oNUf>&u~x2 zeE0*6@F%{ZP2=JSKCvgB4Cj(UZTLCpBGZ>&D}>9sI`5u~1b+bbYW0QlV_yEbBLr%lFQ^_crSxwO^{D?s`dfaV}pFsNrm$! zCE_BPYHZ2@37CL0QQ=k!d&!`|9k?MdG58x68Z7GG7J~#6)0OI}k&yWnbpMjI;*0nP zNRK9c32wZzQX9Y&Ay(>V^42gkGfB|hIe0f*?_wnKIO8dIDonVBKeDFfYhv7&DPRQ4 z?n9~`h_|Gq!M6{5?N(AFE?2U=gH=cg%xnOvGvqj`BE)0EPB=1%->{L~b1Gb~WRUxm zB9L~6L&!b|iZChn8Vzl}PHmxG{AugRTWv^cdv~8ZbFkVXfx*Y-id2OL$e?i+BbAlL z%S4o`6ok5QYXn35q{Y5y_9CWe9*tZWCbx)8GDiDBh+->3fS2%?cX=cwI)@tq|L?ie zb*}Wv4Y1VQToW&T;l1E$P2@=NL?)dsjXPh=UI5vqi(77kQmB1Yp*1Y$D37^{Ig7Z`z6HLwACMzA6hUEVznm<&{J>1DN2BW*q_X=jk8I%{jRKt zE~9TjETzM9Rae_;x4S^8vFs*j1wuzvGPXD;;C&PFt3T4pCqT5e#n?a+yf2l}`r~>7 zf0;oYVYL-@;zrIXtD|&_7jtd^lAXK9UPyf$)xLK}>YM^&RtV6RG zkzf3d3b9K1r4f9-1RPt;90iWA+XyD;lPC?nWmLw25R81Ag=^=mfbTqyl-w zmNUw3C!{uW`}eJ2U5e!7ZU{s)?xu<^4EC9x&+apCwh63 zH<8{Ea>3ci_SLMTpA_@?ZK~85e*-MGpEyURw&5y7kByGI{q3^kO5qBgzhs!FQ6dI8 zXuh$#d!M2;7;+7dUj}pB=5Gp$OD6F~t1S%*5^q?K6SUaXqm#{Iqw!fa^qZgTAHiAB z%gk*@=N8q5H@>$-RwtS4BQou!&d+kt0pWbuIKrqkTU*1M5_+&VyjqZ;MkSagKvsK2 zLWr?R-^&y~85o74o-BhhI+h>Qye6i|~(6DlqF1NI;x)Qr9^P&(j z*(;RLkE`6Q8aq(zd|?5DBro_R5VNNb zm_j?M&0JyGNEW89u!;>o|9sG6N4@Ip7(PcQ6|8yKTI$s{0^kn##ywJE?CGDzLa!w7 z(517W9Hl+zb<34ZK+l;2KYkOWA6&)c-U^VmB1%!$jBH-t$ z8rYJk{xP)3Meo=BAs98@h7=mpE*X_DW@vUyF3!4663{z)1YnmQN4T0qcVXTH0(+rX=k?epSk7l)@jWG?>6P{a?`S-1H&E zNXhiU)_7L(>%a0gda!%q-ekco!q6JTckjtwZO7OVVHsD}u6;_x<)`o)BBOiIXIiAK z?5?p{`A#$-=DqbW5yqo6voe<6Yi)&|MGL2ry|Q&ru? zcA#TSE;T;Otzv_{`+f413V}T8x&sm*!k55~UrFBPSusSkoMcjtF}SMx1dodsq2<-H zi@}TmxwDRmpzFur0d5c_JKeP`HOWT)LZhfXv0C8kQYod%7n1qAo%nrubAsWd^FzLr z9>BnkMpvFl7r5|O8plk+s~5X9LCEEAy#ys(S=zZZVH5Fl?2U1k^Xyh}597+~+WBvx zkr#NUnM*O)g30+if=rKd`WHsQMd=-=N8Y`&IX#E)MXoa2+6hazNU@1jyrg>ap89&i zHja@Mi@rI6{K!y7G!*-hR_Cg4SlWBfdyhf>k`aDkUG10XWF_Y@`Pc2)kp7TMu(;0I zZ3RxY__odnON+5Na5lfy`2Xd>c1U@$2cDXfDaVO>DD&yYe?`e->#aeXjXZ&vmbJHM zK=^oM7`LwFD4jwv_ok__$*Z{CJ{5HkFayLsr%}u=tw;;t(7rnR6M>i7cy&uIpJn3f zE(t6Lk{aY5ZcboIZap(>`$wH_JW;P!K~WrDaI3tb2mGrVRFvFBpUZLmSnqo?+M)V; zvZX?@h?CV{w-Hl_D>x4`TAjl<1-uZ6gexF_{UZ=nkxmK1O`jfsRD7Yau6sw5PAbv@AJ^|CV zKI?zADtxQ)u^{Gl?>#~jLX!ZB(?fX;`}rB1URY}x0*#XN4IkIvJ!DT-SOS194Gmot zDS<)mI0r(tIUJ(v_C~KROnbF%Td!~hbxhV_k{V2q2yHQ%1EGRs2sG@SpS>+PLsR3# zRDy8l=>Q1_L_=T?KLj{zP`=>=Ogw)5j&>O}5Osu!!k=!Xdv&0`9G$+EiPgsRIjRGu z@FY{r-Z`CUIsX>W#}o^@jX;#uGI1#F-EMt{3e#3JH3IYOZ(~*R3fS&*dsVh%Ag)(V zjH#4Y7X>o{TriHIw?lJ~i3SHdr8!!?_`Zv>;SfF|U~}aO7gHlgfUzUryvozW!~G%f zLm!xkl&#!wPMq+UDh{efA`VifCb|37;;}fo;m#_6>q0k-flSx{SL~hSVZJfq61@Ff z(SeWU#Lxlj)_tN~-1LpD=?j*7$tv8>Z77e8WoqHzxD_z(~7=IA7;=v>1V08qFCR8cJl#B9d zYp3&N#xm#&c{kD!X-r_0^|q3dV|yN5NGpvZn&7RQa6vi;-%% zqZK}aRcv26NdyyU!A7dmPj<7|YY@rCe3hdjMSzeq*# zP|kE?oBh1J*mmL}pg@@wuG?XHq({&;9XcoPKIopO)qpMPszs2LzA3BJ9g!E^hxE-; z*Noi|3}HQT;*R=oA}QOIN4e)spNgO`Pg`eZg+uFmd++c%mr$dv8z%%Y(To^SB9j_H zX?q5MOBD^e=t(M3e8bq{I;Y~RJzieb5vxCg-l^L}?P7VS-FVLs{qU|D)Q0Lh}cUtUI zPL4Oudvw>)&4^kz!{;1DxQ?8=v^ZY3+Ul1mh|Co{+pB8d1=$2;4S9HAOA&|{Wal8h zK98JDVou@T8=Ap_aa-O~`2gSbbT!Y$OfM?m;lG;|B?dJo4Q z#f)D9UHu7NOttS=ZaPsR1W?8PqQfn$M``Jp@*b!}A>s*Df+3A|!AtA}wH{)6Py6;^(Sd1%Ky4uwxKN?J+;`cB)8FOzx+jh3WepZa{iNSl;MwyPVGz?D3pJ2 zZpGWk+rSClMgT*u@qGj_%60gAjfB4D7Y;rc6+W5g+}bB?zZfv*o@V!$isv$Nk(GUY zaPh`4vM`1Th~{sY7!lC}>Iam@`%l8)GOX#sPYu}=tmF*&w3I=jgSnMe6`s%^idcb& zTJs)>t9mbr)#%OhCh{Syg-;{c=Hq9ua?=o2c2uX88V=IgiH}C;bao?_*JYv;bmUgO zz>jdX0qftuQ61w7a4^oRa;ma}^rnkSe+)H7!;Uq}x5=7gCfiG|!zvseh?NOWl3xM->uow^Jf zI}NGe*OOwSPkJL0=_@@cGw2nC7uC*}LFYIa-Q|`|O>WHBuYTL(`m+x`hW9xDwikt6 zVDco&gS*NDi_5HZ@m`H)Q&|VAUp02NdiYwYmPs(Lr$F67nYL~_^b=u>qa*y6zo4wuNancO8T_p}`7OSBK&W%jR1IlxV%t3aJr&EH=&?FJX@Aa8f^BL} zpicpTPf%g9=c%iWm&Qo>CM7M0HS2`jj$B5i5Hp=Dq#*8o)V4WY1?jl@4nxG>DXuI1 zRjlxHV5Cl78V(?b=*w4{9wIt<0yy$qxu!B~x&gAoX_T>3TrxXHW<{+e92WzkEix zJiNO-@9}ibGv}BhmbY@rcBBYT#Hs;;d5hugWY3&q<(^l3!La63h$i*;8+U?PO?3K< zwrcs75io?{z*N2_5>&kz>c3Z#HtY~!fc*Z9IpVTJV4c5BLt%uyryO0?X`n(Vg_=Iz zMcf4upBZ9g%2f)DDm({i2epm{M6hu0)YOJ2rlfEHqP!In8McHk2Oa!4B1Y-kA>Ljp z=#-p$9%w!IFE$7!j)QLA+nW_pafAi7LtTeEn5?< zd%GqMR__yqr?iO@aRhg$BVl5FaIeP;mjP(A1<>q^NYK2M1~ zB&5Ut(p19?c$|Y|=RBVuPjw1EfArulIz9-K$2Z{LvyGUXQzxr20R5MEgY~(4#A7@Y z0xF~~2-RMX1KXa`L`$s&LF>UNlWjmx`A)idW7fm_{f@JS2IQTt? z-kkNt{lryc>sB*b-i2?*T#Adt-kz2Q7-V;cVIPu10!3|!tb@p6OM#b)w5HCL3cil) zOwae7UPv=P?6{#ZHby~Q%I2ScW z!9vxwn9v*bJwqXpM?w?!=ppX@BYEx6Z^z|ew+V9?Ap3b?bgZW4pm?(xus(DNcJO?5 zks@t538N5fACZtH5+|_ZjuN>FmHC44%#W(${`EqCW=!@>J#0GfJCh0uZkK7m8GTPj zSjPzBJP^jAao)cKwwBh#|L;?(@J!}9h>B0&5IYna?TxQTE4lyC3#9)z&!-AT6cEXVmKD8Fi8mq^5?~5=Hvlnquy+oi9@X3e&j&IM)~cOAI$mwf z3B-iEcl$oE$5Nh<>aX|~10KlVl@e#hDvszgn~Q@&*t~7qws53f2 z^Or7Cj(?tXObnElp!Li)qOtrNuzDJ(JUCKBkks7oIn8>}U(XmAPgioX-SINk+P{2N zB@`C8#tH2_gKfjrI9$5VeZq4*MU;4zI)0HK9Y(70_^VJ+$+h55uRlR0iSrDK9P)A$ z;g*FC?CN7)b#mZ831D~@I>JIy2zE6L`6-lQ;K}Fz05=irX}-TjD2AhWO$$GG{_`a* z>5z`hak0R|MqEP(S!Fnl=#RGZ#6wl(S1rT6&xT^0pNz zBE&ox5H4oGON0R;*4=(bduVAHEY4ARKsr{kTm2|xN)*2VutjNzV3V5`HxYY#WIQ04 zMr)g5b^A+ov?=nO@;10Nl}avXxYIy&K{q9=Cm5e8;rz-FYU4wByp2fg=@UBdZcoV~ zQBYxa>=)Y(k=!0+FUK(g6K>z_F<0 zTNAx_Y^zND1X9>%Z2BT}uI4*uw&GAJNhdV-M0oBQSP}sdo3iB&XOkeiBc6kExX<~# z?mZ<6p(-&h`gd{2zi%mItkrn@?pGA&lFnr(+b-T`A5}XPP4>*LgqS#xJUfQqBpbrL%W}Y|=in=AZf0l#P6|t&t6EH7iOgd3l+X2k<&XbC<-%? zzu!GDTM4}jQy~w8AbU?DtU5=ic{l66<{gZL$6PmMX&<_}y#hME$*}lDXexH(erOGD ziBVMZvoeo~$l*56J`=YYZD|LpJ?Up-@tZhn!D}x@ibiR1>cF$0H)_$JAT+Gh>5lE349J}9XK+jyLB5#Xq1!w+_$o0JiaSB!ZO zg^$#)8utHU9N|=`p~T$X@hFB4iP;{ViA&FA#;WSCAaY=%q0;d)k%$kY1$Z^N%C^7k z31H3b3`=6hFB=zC32}ULwR8cBg6k3pcVlxTe4<`JM%;K$i)C9O?DLHKRXf+R^sE~qO32$%Q*x-m?xY@{(#Sp-1U zobdIOMXydy04iKIsd_NmVo%t@HDN+W6q3lubU*2vxp*afWTt$?44ay9g<$Nkl`NSyU1vJz`te@+>2$CwW?8UCb1+>%W*qemLy+mcVGgqe5MqFhiBxz*JKhonXN z>~40I)QG6WITW#!xpX((n1#6+tGvTyqTJtcOenEH#!6R*$MXweBzDo->(jrRtCZDu zPz(C}sMm)FpwecAdQp29VlOGFC?ft|qv=|P3-EXwY!nszvGcG!*U`*9{>R#U75Z`D z-%M#X+d5|x%`Qw!G(ER^S+Q^cdoYz3lIak~{DKW^Es+O9dmY5cSnXzJ?nQ%K@*bfZ z;qGozCtR>526@S^M{|Zwz|S8#F|_~oe)Ywj;nr=i4)QAQ+;PHtu~!5eCdMkhwsdhY zx&Iw%v4L-kMM^^a&`Qm*60r$7jy=ffl-k|xmzbtiSk>M2X8rssHfgIptPfUJ%IF?v zj9}vs0WqRZypgnp<}*=nGOEr-xqC=51BfY;PEa9z}WD6zLz z9$YEjCk2mmIwjf{bfneauRWyLNR%l(w-*F4K zh~~%Ln74*HkrE>X)lpID!qdsP`a<6 zcPA|#eC;U-6}E#QD4)wSqxcDg7)lwEXA`+hQZbKzPKKw%#ol2qI0UCTXfrC^+)_J# zEoqheRwCyjy-6a0=iWAm`7fT%J*xZpnj~fri3=SR7;|cbTDmRI9TZzZA-|dJ@L2^uXcXS!jZumAdOs6D`DU--p~kMC-ZPT#9ct zzrEiNsg~RsHmlG1Bl|jzIT!|0%nSUjPF|8le1(JKT$gN^@#;e*qd@pGPAq!ZdH(k0 z9@9g~M@hn2GKQvfA?ktUCdSN|#q*3`sh&h~o&%C9+s_}OZr667syQ8w2FJmxB}`3`XEh)llz6O#k(ck!UGR^LW}rq-r(gwkNo6tqE^x?L%!^E`>1=0_ z+X#8JW%1*mELONEI>fO9-J>c~|Is|`buI%B%F!?_e<^^#Ko=67s0CLo@v3wYjnuf6;NAg3FK{wBI#K8W<6#KJP5lus=e7v9U-4W_ zQ(D_!(L`DUHe*7(p9!vYR({I?R(_=M^+4t1yQ*sF?Kq7^DAzg+1!t;IfxHx}+{MT| zf%9&aW6o7+GW-+gcaNh_#MALkNf_YguCw6Dh+j~U9|p%F=sgO4^S3e*S<7({F(i_l)X_RSPr0&I)W+$ zeI0Xm_wxBEh+U?9pgoHm6KcFUe*<#UskYn_geFA^fFYn)$uAOr_;`w8fpu>^5YEfK z35{%6K?fQTDyg?kDHodtGz+LI+o&*#81K*6rk7?G9IQqySTeZ;cG>D@TRmOgXs>i_ zed>*=yKthfNpxi64YfD8z%VX%Hw;c>BuAy|5CkcNpbrH&Jepa8ISFvT0Y$BZ>G4S9 zDo7QWNu?iQclP6fmtfVNfS4PfPhF{{nK7KS^cVIO7KUFg01lic=~14DYB?T5R5@_< z$iUEzmVm2q@WH7Pw&dBW8E0-+7g5EfiHy3s!hO7GVPGm!V27eK@Ghg(Hoeldz~lFi z7ibE1VJe>(=(o@I`?T`5g>%)a{@6Lu8?#$E#b{s1O;t8x{{jx?2d+RSPrJd_-ev{K zL?YW-91O$12yZ#)w*+_R*o7j<1;i`Qv%EILd+J^R$`J>VOFSakclTP|oTtVzP)6aH zHXQEY=4)c8EPmQ;%;vRw`Hg_S<6gP)0FIH$e0R3U87&f<6F?v+^Vx2TFc3K%t(~T} z-tA#P;$7_IF@CT$e0FfZGEIzsv&x5HDDkcx;xUi~gD#aa9ZTkxJ_T7ReBfBui%_8< z5EkA(&guxZUL7y+TXq}P7wav8P!|N`gHR9d$P_a`vC)(qr*hTz5q_NOf-&KyUFZg} z_XI%s7YYXTT}9B7b8Z1-a&AKAQQn(KLKaI5ul%}|IU-&X%QSZD1qNsKvZuN;q$Z!H zz#TGAh*SPB(kF|(3ZK2~Tl_dDyDbgrknsoNmm9#ADnfM4AL5A7tc`eWA=ESZPt5Qd zTVdo=K8d<%(ar~Px2;U6Dr(}BICRLcSlB@&9hq?|f8_;vvXHRgazY@)?|@WaO-`_E zxT^9zdA##?$TB6)WFYL!?Qq9Lw8c89fT#)nUR4;y2jtokgZ{qTU+`H3-PX?s?mvb} zz}h`0@h+lJ0)(ng&h-z@LTB9$9S<@H6dNf_PF~KANH4~tr@t!l;`yskk1C17@aePZ zip9J%OTjFD5s$z5yY_s3jVbO5((%jHUVieZkejB?r&oj)eLa+}ek89vJt?Fi=^RxU zUW~~auSyG|9K5h>%C`{y`c1@JK@UF9d8zXF2GQcYXd+j?57)9pF$Ddo=Im>3Fk`ey z4Nm$@)TyNJU{K)$GSvL=*)l2prh|@n#h&yW___FY`xo?o%AqOf#%`;F0RgRH00Bw- z@8!_U%w0`gtexDf9UU0lyxi2)pn*V#I`{SeWA0wCK)_%>pg=(X-Cb*KJ8W{G^}VVd z0rvqH{nU2TYX_NB;<5?*S~IcaK_!bNmUMY33d%ImOq7^$S~J0|#m~6DzISfGW5(jV{8`JhM?}{c_LMxcKiW2%Ds52uY34C~kRN4( z!hydOcdu#Hror0SJXTk5eiIjK*-`p8Yh2~=4$odLSE*`^bd@PP2Zt`ll)j*H@@)`d z=Jq`XQ(OAc%x0Kk`)2F)%*@7=&R-loO^$udOpJZoWSog-M9Tx?yYOo?%*+%Twwc*x zAGfU*W;WU0=d_${Ij#+nGfsDq@Juw_(&jHStuf1aZ1_Y4xQ}O`c_SBxQLB$gOrKWC z*%UPOb&*=u@QduT)RzTU!%LwSKI}Tzjh3|Hkf=;`Gi#t(EQGh|zy%vz z6tr4u6tVdjdu8U+)lJs7dK1UA!}dxKX?BsPnC>$udu2_nyP_N*+vfE*SXJ$7P}AW? zjH34n-$#Fj%-yyCwz6biRC-QG%xO-wVJfk)WUbXp{I|sGjFS1?6^ka_jM?av@1T~+ z@t>*Mw}@>f8W{??`@u5(Da-F!*lGmRsk-1Ajfo_#t^r2(ou}DWK&8WSX&=3J<6&uE z=P^6x)Sgp(U?E90B<0fH&3@0HFtzp!`1_&EKf5cQpkKfD-)oZp)^xfY%otf%{a#{=>?DR)eZ(x2pq)lk zdP|NTV{z#5XByP4v+g-D7`AnQj^km7_=gIk(6+j4ouLNd=Nu8n?L)pCl$E|fiUn4_ z>6o}}rRsHE^~kpYZIlV)e zt1E^(-=cQh?U4#aK{2NjAwU5;3-cink^w;rlO$o8L9Yf^o9lHD=n*;qafaaz#_u!i zbKgPn)h>jd4#Vz+-;sDR_mKNR3IyK{tz!7U0e>L{fC)zQi|r8+LFs`JfLtpD5s4uY zMAs1oI|Kpj?B=SJ^jdB;; zkS*FKGr%uKSg7{qk-Mgy|ATT(KYxR=i%sFMWb7$t*GY9jQv)ku{+TCn=O$n6)>8e# z%MT-yujwwm8mQ4CARZgOl(yu4bZM8WHz#v%c;spzh!GpoXD0sAp1!f5*R&IPe`<}w zo6yPmA@Gg(A5en<`cETq+E@7R!T;%m|M#HgVs30^Z~lMr%t;GJ|3CKs;MxDg(-eGT z>K-Z}AiZfIAnE`8(iY~%ZtgDThNh0LZvTIE$!&e@)T2@7fL+5u%>H;DCGFQ7DguOv z%F+>X6&Tvz*XIyjxZlLIk5kP7pVyd%fgdN; zjs}4bZ&Q(;{oa393-SpJcYfZd7zO+v#~l0L9xZqMUQfC;>jOT|b2xVcp1TG6-Vahw z+iwN=`o8ziXJ-T7kGdZYE(0AC`}n@E&xao)Pdyz4y}!qH1Mjaj3tA-&|2{mvDl+bV zt!jQ@-%3*Yf8UIWo=!*?d_TSYe7~#q`T2a^O-+{t{_Xv&6Xfgtx_f#VK1@{1=xgpji|xND;+$;Pe}az`Y~>T^<@@V|@b}$8Fu?2M zUZ4B@zTCAu@KehyN$};qob%S8|MUI*`cQGReBqvR%2%R1;OA}s{(BM9Z!CiDI`3h5 zvtO{cCy?h(8gf?b{_ja7Aq zCQpYCFY^%{^*Zo&yB_)lzGpYfYh_c5b)zQt;iZo6Fv|S=4i}Wj`n*2(kGFS!n16kY zwFkbx-yhc-{@X_9a6Mtc-=C-Mu|PX2H-XE?a>D#wOwp&4E=`MrNV{0tKcY;wuos06N5?*{yw=O`Kmw<4af@ymS~dVS0s5yJgU)yu20JYh|{ zm}SQuTY3v@vfZ}5)sF=l3k+Y(G8UvP*|u?q?7~2A%rXRGW6}%gU=nV*2#j~@Kh8W! z4_J~3Nc@dBfeUzQ6tKW$2@rL8lSimIbUYc;oAsOk^C<{kGU$wb!craNr1HCWw_SF{ zJ^!eQrf0#}u-wyt5`EN7=Y^mRTb7**1!XdngUwQJC%OOB32Od`dx^SqJP-rQo}lo7 zOBDy2O7`Ik3*%C3ECQ#n6qy6-9#`n(%`(W_L+4WJc%j6keNPbE{D6&*M*UIAQD$g%?i+%|Ch2ovnQUY%kb)8sF6%_%>t+bib8?Tm z3^`IFHJgXpR&gl6SBG{0ufeSdTce9(GR;BHrWf)iO5Q}->`M*I9&>aK{zd4ew*>4m z31vPBMV_f<2+4Z?89!6|rKGJ8RI2cR zSLp~VAKEz~?t(*`dl)8J1dqdkV(V3&V~v=hm(ndbSMO65i35w|FVk9~L!+IA#`_VB zWVI;|Z!$Ci1m=tXRSK;o6WN903K&E|45AF@&QPuEucXimV27HK4S$t7;;jUEqx07# z%TX2RE>I{?6nqaV9%9>wL*NsVgXK`nB18tQGuFfz;H6}t4bo^V+i6iuOeL+k>_V(Y z<-^OL;DUq-SDg%A#cB8~K$R<|j4uMWqDr7sN145!j5i*BXETyU!@fZ+jJBA||CR1i zs&b=tmUBLxL*1Ep53#4Kv{ee2#$mOAJnbG61LgAY7E;Ix-ab_44LDrzQUwm!Q$`uR zZxK|dyba?N0W>h5hqY9D67>2+>Ay@IMQ$jvDG7KI2{G@2YOfr>2T=^{op zw7K)w=Hx>zmn}0MPzMeUEN_rN z&m;gTgT!yVLRlL^Zi^x!8dXz-LOB|S$}g6pYoQpvWoXA*)md>Z38HQI7JUkUlCw~M!{P*yAm6Okht6+sb&(l5y6{0;5g}nAZ~t#rw?^*j zTqo7gt74A*FqKQlH=nujv4S}EJV}`!vG<+Lo{=$%YL>13{FM>a7`|0jce~gFg->H~ zh_^H|b^&dn2fVoBUh?7(vJTBUiU^4ZlHRW`V21eMQj|i5v=^%3t56@yI*-rzTp;Cz zc+#*NE?--eB&(44$Xy~DN7ta60BoT+)q2Uq z`j^QPC@`t45t2QnA!->5c4-^`9&YOD4UwYQPzCyBz?WU+j?qLL$CR#bEV2yQGWUn4 zR>}nNW*Xw}K9jv`1&aVxeVOErZSys}jme85tO2mZqz$uc&jObQs#)kR7qh?b0C<#M zRFk;qf0P~SO9bEMs!`AZOj<&}Cp^$(`+&=|DO_#exCe5~U|J(>w}0JWZUHPv*W8{} zw0P_#7)&;5n9jjkpwj%X_mJ0E;&~{!{SitC`~;GSKm=>4{P2g0Bh^gw&+``?dHzx9 zFBw!Cs;B~jP;ah_Kcv4pzpXhu(LFVw^UhXZf3ySdXNX1$nQ1Q`VLDXqk0}}h%<^v- zGtqmN7^=hu$*n0n8w6L!Y*A+yFw*rorYzj~S7AKG5T+0xL(>_9USVd13rQJL`&CKt zDg{n^J+D0k_m%hKo}a^!*D4w6=k}ptkd@Z`l*`0Y0retW5i-tA@6nR+-lv=8Gic?rnq-pXkf^o7FDXWWB;ww8pEgqW7 zEZtAUv@lEfAMebT20hyz#`LssS99!`{d2I*)+n%#I%k{3>>cTUg*i|rQ|gYdsiX*9 z{r(nF)Lj!Ke`cb9@x(d#NsZg{CA9CIH(oCSrOZGzXBz2g`2Pj zpjpTtJfQ>5L=VlCmAPzMGMXgk()bC?$K4%G4b&9|TJ5py)Bt_h3b9XGsJL8jlv0Uq zB(#_t+Im2lHthnk?=8GHfFeqTL?yGuA`E`ks1k)K$C2iTx->+aSj99jU&2a6kA|Cd zFZop-S1ZwpDi9ZVNn2yX7t7sHp4Bf7;ZUKn=meAqT<}%P)HN3MF@CcHMa`nfL1WpZ z15F%-Y9ncvvepoY7RX2=g##c6@QiJjh)y+Gn^~r#d16vTlWH9(!*h!<*T=yA)>R*o zoE(t){=0$LFHas|e3YadWHQ#)m9|7V$zLUgP<=|Au#heeRC~uDKhiMBu!*b^^-=-a zRi>Ulv&QB|a`XG)xFXWx3-1K@9+<)!A1~LQT8X6A6r7{UB=VcKWfo0u*(8<#jI`}i zZcQ9wLnyC*v|BXhi6!lwZEVP5QU=+TOw}D+)=Plb^nwh7296?@M$jgU8mze0fRSh= z(0c5#Lw-6eKc$ZW7nYHQdPvfH61m?un88^UmZ*(vxaHA+il(T|g|KRAfVaQxk86zU zt#wEAo+pk(Isl)dM(?4?SKwj!ngK#ixGJGpUYJ4}Q9GI?Rfm$pBRUb+Zj-t|iEf=X zno*#g30vu<$7Z?J%hV1uL)B<|z-05eBW2I>9nFeAmhjpgF(0^s228m>Z%|oXYPpnE zvw+LJp~PJL~K9nwVk*_$k_}Oi^2n>KhD9_p3CXjYh19 ztF?#h8^8rFX?G;wF`l=i>n)7uA|l;}8gjN0&`BC# z>B{NNSFWr4<=HV4k+&v89!5$9Xjce)t~5H6)R=A9_7^3{Nnx!dBMK#mu{08z;!=27 zxSPHJELHLtRH--IzE>g0v?S%f>cz$tP8ecUFJ9Z)B3$Q^37ex3e#tGT?Ioff8k!Y? zaeZzzJj}@zP1i3g=W-XU*5ayGXozZD{|$qSR@RC0?7YH9L0-KwK(jg>t8FXj=9y&E z?2=9}6O`5-T3}D}wb_2wl&D>(4Jv}P@&Hb=1I;0;w0B^S_Tn3F?AP{`tLpS9WQory zZR!!;=Evc1hB7fqXZ!M{=pQtb#e@x7f$fqOQ`Z4yR}adorvAnEgD($<@2)(8%iB+k za%6;oGBRiHB_ui#%e%(zl>Rql@cFCFkRgA@8#24q$Lv7}&(Nvx;dr@>e8~vuO>R%n zM+<e_F{`-n=PTmi+FwoINu>gGf?crR#!hqVsL%W9doP%!CX%48 z2jNz`>9Cq?$k%A$Hfh@j7|Jv$@F)HWYE6=+keTbO_qlklF+?EUoC=(b>|$PpHHL88 zl9Te){t>k({q*V5Uvy*myoXBVjQ_$k&CIO!%xvVFoA{V>a4vv>)u(W4i6;u z#bkVluDjHj65Mx-yCoEfQXJf>zidKH&Kx6dsN@Yod^%yg%6czNH1<#T2M%KvloKhU zyr2E0$lf)vkf2ZVROw2Ph5t4;dRK_47}W+wB*ma9i@78Yd({{44B7VO=rsj?e^#jP z1h4YqUtq`60p`@%VSLH|GM9WII;oK3-axzR5iQV2@gMgzR>B+UduUeh%#@GjR=i0`v{=+}!kGa|he-V0TaH4yWUIm-@tBEQOg?MhE2lw+K;X(M&%Tb}xsv z%fhIaJ`q2t5S}e{D{gE~y;yT9GF}k>t`Pm0Q%gyMp16-|E1UMNoVM4@o2efrCXDZ2 z%h$HwN`rL&Wrq)tgW;x#(tP~8FtZ19+cny9ehg&k#D5IRSc15O<;^xA>58VaFq;tA z@$}l9AxRrLn%%29jVl)qnG-Pj%&JAkHawwOl8ZxGWEm#DFZQ5Pe1uAqKP+}-4OPRJ zn#h&<-VSaa@*GfmLz*T`70d!ihu)i}ugs~ET?Hg$JZokfmswt~id*Nc4m2CvsB0M|jZ(43) zuP2*bW8c_WHp(a14LDkuqQ#EK)5Yzn04*Y5K@}pjzfc!4poDugL|7e!>D)Bs9Ik^b zrF<@essITwen58c-(+SjffCxvGa5KhBrMu?d;70~68#irdVS&+j(`2kB)A0jBiHV3 zMIap$;ihw>eY_-2^>oO$)1;R3!qBQsP7M35vbe~P`@W*WzDYQx)rp>@S{XHol~@x< z0^}w&cC>*3g)K__&kezh^=(E|El=*Z+&NQkGi#oY^W$y2vF;a>eWg5f4b3gQtYAf= zo+&%F5_gmbNmzF(MNw3u%{g2cf>q~+QIE|}v{NxmVKWp^g|$#AkNa}(8H5z&@fqz( zvD|tf6ULGG5tE&dBktwWrGQq#ZY^u0Q=qa}mL)ytmX2-(?uGA1rsV>X1#k`or4+px z66gV>r7$Z<8#Hc26;!a4%)(dt;9m?!s%LT3_Qc&`tHRoO-U2~#87f1nbj2bpXCI8T zs3}(;WTt3ggxSnM7kF@QNv&htabKcpv-KtU+(M)Voca@g1`{W$Uz>+OxT61{gTj>I zP?oQw`&_lzFouw|()MZHQgk%aub7J{9j1QZk>66;?>%mIHX*c}RXFus&F$8i-mQyp zHcD|(=RQ-#y^Q|LlRW6{V}pQ5l%Q#yHlc&euTRHVTU{2dVRIr#q9<(kvBuuX%Mm)( zP!M394a}BC;8QePi6@)60{lC2*y10Gxz;}(7uTka3$LWhP*!T~-Z`-gYWEfbgAy@7xR3mpn06{Ognv434*HU254Rm z$KH;zvi=ZQGdvBJSOh34Lo6*#M0*Ze5|#urj2uz8;rp38x8mWK{v;%w{I@9+cn9}3 zMdf=?IElM9-n$*6^)!*HG}=h@Z{6y0HDiP9=|!7ELCJ+~1b12^S`vFM$kvyGPtQn` zhrN!GJ+E8`c_W;U32`K?uu%FCKg%L{cA<(@T-4Bn!Sqdez6P8ZRv2OAS1ecY9{0`N zisiGp()r1_$x56qOJW?f5o|mXaQ`U(8v$9o3Ge~wd4c23evsIqp)G@$n+l4N!6^W+-%tX^}aVpzC(sz*fSrA2p}y@G#JMi)ItGsyT&xa=Wu9C>Ns_REc2?@B1XxUKd?%PPpe{OA zAC|KE@&b@oYEZj|nkZT0O6T z@RdL0>C;Tn+iHy2;#2eMCim~vdaO$x5Jp_(dreEW77s(NqIVU4lXfU^KOk*qn5(;y zQ>L*p8dIe$z@h#2L)O9Rfokf=0zEw|_k3tA-=l$FEg7#I%V8M>V51@&fP{RW(qZ*_ zgnD(uJXdk(tW+*LVxs}P=D_u~GH{}d|3q^WNlKu3kR-1KR5oM!OI5l%fsvNU8N`R>CcYSyM?G%}i)K9W3O5C_PVs`q2J??XHw> z2t{azT!1c|?m8@<+bde>0r42>2MH)N1ktZ1i6QjxLp5XqxeV1UDWQ8m^9?3F(ja;R5L-DISXFURnE4JA=Y5VW&a%y=`b*{u|UlY)vh^#|Q|UlZ~KeP0SrHKi9`heAs*LrGeh{_?MGfC98x1*8;SYXjhVh^BqQ zH+W&KfK$+_;P{CIq_p0kP2-S!E*|aaGMY7Q!wOQ6YTJ!0?Y>~3ej7l5SQ@!Qi#t&g z;h}adb0C#G!8#zS0K1!$e}yu|23gdH)u1k2w$|$aLF)@cueW-@-Hxok$QlOLLWWCkn-`UDDTV==9 z2H1;;mGh!)`@m&j_fKip2()q4CERqc1ZLj?2~5s82*fS<4S^AHcSL|$24|F5@!&t> zc|a;R%7#DxAP-9i>Vw{nFV78VD%+}G?^FirQf_&sn+`%1L;#E(TcKiChY`6$^co6! z@j$loMy8*|tia>73nA*@Cz`J?9x4=B6}S^XU)q}zG=r)UrF06aSM-%=ncVpGim@9P zo1R1@H(Vd0OpkJ_Ju5L9@yvf32(WP`pOO+_Q=k|sYa_t9%%Y?3G3_j!W%XZtHbvjT zik2pB=5d=+Z>zu3H?v-kg8`RlGq`q>mjAG$E)0tv5lVWfV@O!@gR{rpM^-R0a#I@2 zbKcQCfv$v^*wKx4BrgE9mBbtQ!;u#(*O{itG%{&*@5+zlfJ$Q!HG5*AhXKJ5sGW*R zSUpkJ+=4H87b~z!EG$^Xclk*FRPp5YzNKch+JO$SV@X{L!J%N+HM)Rfq$_KCjbNLf zoO!3g{T<-_aLFN>DQXtYGyI7096tSqel_gaO`yBwmR{ys>_zkUuAMAEpk<{FoVtus zIoJ-QX1zX*8sZQqcb5NI0F%U9D=-r?gr82FjF(1?b9LhuogdBp<`43asTWL2%6>{P zBKTmCI`X~cC{I@#YcB?f&@>|LZu;-E345K+9k}f)>uC# zR5NIXP<2_wbA*irx7Dl29A_oPxikSi*7e})n0-7w*PqmBfHvymQQT?j!tWQDJDN4P zL-!YHsWzD)jwKk6=7oXgu-|OYy{U6I6GuqUz^Ue$bwXv#SaCSh3^Q$Vl)Pu zdqr(rIr%Cpsrt0~)(0@yQ5x(uDYRwM{KyR;(oai@*A+!V&#Q0=dLEkn%u*ylc0+A^ z;!TXSaR@FjB)cS8aZ2T~A=+gt{1iGn8Mmy9#JB6pbEbT>4kVr(CigI$*jYm^ZIJy_ z@FJ4*ub5FY^W8Qy4>gg4^tA6hjN__kY_q8z7yhrJlM#Q|%J5mbYOYu)OwLD|hqm1t zJ1CVs?Y$T%UQ&p3pHpT8bs9S^xPWPi!I#20<|;wD)fEZxY4B7W?i7*=_}JGlZr%K<_iO6h-GwLA~rU1up3IF3$4Kb zWHYOI)S+BV#^e<;-`}$}q#f5MHz1yvhrc$Jrf_L4K$T8OTIt{$|LXb%J5?I$4zW3r zP-W>fw*5RUAG^}Yt`UVKGe?-omY{dX=m85v@iY^$Az3$}dMIrsWHb*r&-WLiJTOU% z%50)aSL+sc{uVspnko{5vF0}|q?V^%(A%BAkya=vbwZRKYZZJKdEiJOaBI#%vSlrp#4rnL~ewaY@= zgsHCB)`@&8iDR!`Pgp3Ak5Mho98@T=d3{%*7}~tH09u7zVD#rJoJ2jIaveuy@*$5m~;J#>(Qr`@yF zUw9WH1*K8$TH=;C;=-wLU`||}a-`e6QHo}SFncy@{8C*emdD>V1)Qo+&Hov7@-ldZ z@le1mj*jX&D}w-E8avE1?a6#u672=hVExfiWml5Sg2uGI#=I;wo^~+JSB<0Tp+|+9 z`_w#^#-@GVZ^|qN^>n?iHdCebqk$=^3bN|S8p~z&GFx?Bld+@>YRFRAqbdxEnd*Wl z1PLuTIoaF1Zx870!!2u%DYd=oi~yEXTg(h-&&7)g@4RGNmhS8W+75uQ^MLwj1R%Mi z|1L`H+J8;(5H^j|AW^_inTP3CZDKc$YOWf9H^}Vjx6F9qx9(!u5lw#;K`pFLKd49X z7J3;nh!ZQLN8KFoU)vgC=2*|75BghTi?Qlmrdu)4AE-OFw)dr+IV~O{It9Q0S6}AW7QjFoN4Is-;=jWLchCu^Z#cbi+LD(I1vOa{}r9!9Jqj)mtru)Sb-3?oiY?>&yJ_qjSDVgUgSQtL?S*rcMI z00VW%0WPYWplWlZ%C*iJ zZe95btOR?gp$f0uKvqbzov{JJ;?{SKHkZviwXP>m>atPX!3Ef53?3LB^Rvqzsg83C zguYPjYWU8LKdNMIT*OgG3YUX&W!g7?)@Jq;3|*%=eo-`yw%h@3L-aZLK*{r&`=Wf)h5(Pv6TqHZj|0Z%W*v_Ij|N_LcK?yIB_{2F{263A{U z&Ma)Z`_dRg5?NEIX@588?C>HfpG{REUup_FckL|~Huy^J=oY(zPNFKkSL@dpCnTH* zyaJXaVuT@=SYZRXdDp^Y7ra*IY7UwthNB?rH0bFNxa>8nm5LtNqYkX6`?;qYUv=yG zix+XsZNg^QVMG=U$0S-GauA!F;JD#j4bUJnEed%Zxv~Szo ziQlX|b-FOCK}#2s*%m|gCdMEYm3oZ9!Y8tcbc7nFga zVsE=<;!G1`ScTu$WAxcpaH!Z7PA;(Fc((K@p7kj&0zL)J|D(Vml%NlAZ7U4J39432 zC4h%EF$wReKHgkv!eruJ6e6-D9;KUL&AIZpEUV)V1E+|}h|XkxRp^bJ z5K&OO0Pk&1*ld=g_W1beYg!f5a;-US;dd^Tj0s5Z%a5G$NC2ZRG88e{HwUf-cTMQu zgK*|UAcg4=Vtg7UT(3=YUs&y|Ivi(FSw zPSgagXZ-Tf#@GIX!&-RMJCKOkLzy$3s%Wev|j*GeugzSV)3?WSo>wn2v{AtmpwF4PV3C8hlj#_S|y8eFb&HDYQkzey3hf2U!2V$8D zZ2sn$moGN?6OI0*@x6Ii>n0nl(*EmVY6hkHcKK1=$5do)-#l|QD>?NdE%J2mO@ zA;|%07@Twttsi3zkb(nm8FHA&O@(WaNsvf?sQ1zCo zWUAFrvd&s1EpKn^jYM%**2%&y7Y}6UJ*Y!S zd;r-eaLgnVMBc2fH4}Dvmim+^pm3Q!wP5ahb~c;hwwq&!o*QGNSdWnw+k3SaSzH?) z7v>$}P#UiaIFFqqt)z8_Ch9}IW`}uC?ug#P^QcD{U z6iuy5Z=Iu4x>$N;wq-h8fzbcaz0L|a(&@YVW~-zr$dmWc|Amt#X2=X;=cSq^nj@@d zt48~z{2Jgcv$oHP2*9-jJe3NRRW7@DO-0~O8;27EO}*PAGcxVg3X9-ce# z<+*5MsLbI4M)g04MG-~znrixnXnXchl_HXPFp8JR7)A)0rz;e0r2z{2S2~Opmee?? z6IaKbY}Hp%WT|aBF84F{!;9lRygZ(Ymi$f0 zBv6WOqw)o8;J=U&c{W^)+~|WE=(lK(hy{C+_OU029F)`hm#haT7-`LbDpo38rqhFOp3?)KpLwkDdQV}{F*%nr7W7+;*Nrn3)4lHX& zriiQUK8}TC&*gB^97Vh1e{vPAAXtnx49^!vPn$J0n>GWI+HCai-N8T82N%4-IsO6Z z-i}y}=UZ2fDhClw{&~I=2ha8d2>A2ozMotAiJ5*@s1sQB0^^~-95=cFt7X1$JQ6fI zy6~`q%%#_hA4~Q3PQzqsdc$z<(M}23`}C>p{=f+nfO7h5OiUnJ%K8A7>~rC_(u zffBOwhU&4#?uz=SFwf2eDtflj0`%}A@S(aT9JnCUfjC!yUT;3h~eqUn~kzMrQza8rJHJ}~1Ix{YJYk8MYpeO~*sY1M*duIJqVLqkQP zt8Sn)Vd?p9Zd5c??%oxnD#rXeEWY_={4ORsj{jy5&sd85B^lF&o9__!j0&!z%qOy*%GJdm0BW)E-Drg}aPAbkrWob7St4Q-NoF)5c#vm06NK~&X{8XJHzT=0PisX5`b4Z z4gm1RIQ#)gDI?BYJI=d5)O6Xz_YuDYrsrSN1}RWy$pB8_#e z{L}TAO3C*9I;1#TA=4N)1i?39{L(2d5WZDFOT3*ExfsEXPQzFd?gLL zm4e$#{X~B|`ZK=G&b{QgJM!pn;c5zoH=6Y5}GYWlX}|lZw=TID$aNM`m!}m4x8S8G1Xx7>qpk z+BK91D`bQO_cR=u*QLNmwxGdo8MXLB39|16Aj z29Bnt|2w9RkE)EFmKaRuiQ4IWCemesc7j*b2AGAvH$bcto3@&HG>7W$Xi5H0*T7MA zA%Fm=5Lou{Rl>kt96h~WS68d&!`|2N@ma0k_t(VN_3_=_`~KAhe5zgbx4WCq+u7HM z*^I8PwvJBs+tb2Q#njfo%fQsY)%E*z&6F6o_s{$1%E(sB*50*!i{EPJ&*SydQRdFH zUsv0Y*N?l~>*tf1-L+kfUAC>?^ZJ3(ric4`_nX;|NeAbq&(>8A+{V<(*2>+FfnRI) z+w;};_4Dz}$Cj(phx^0VgKGny_cqUW)UIDoYrDH>(|U6^#YQ_VeWaC&V_vaLVS~+! zUbaSs40td=+2w21^YuS$W?u=2C%`?xvnn^*d7L>p$g##vIHgqv>TKc-9^MimJ5~TI>@{DgtW*5eXD8?QvqXsPkt`Od7DM(>c-@F zlli9Ts7N&-LusN`YXc~9)3~sfh&1Z>N8Q58a4EZp{DAYR^716GbEyj6`(_XMTQ$q4 z`6eg}1r>%rz~gZj1hoz}O|F*LRmsgztbZ|s6&X8?kksj?HlWM`!N#UAH*>S5gqvyx zEtkvTQi_C7ie4-?v9$h$lrmQdFjI-_4lFMf>y-^ zapk)qn2i8XF!ohHMq(9_Kn&JclM9PQ$0Q+Y1=Kda*Ts%Okha*)N(p$Q?Nl5XhjO$I zb*OeCl%EZoER`0GQ>GOuqC_i*EaX>=bCr;U2CZc{Lg*lN)YiD0UVzo0M%Ou_4kYhR zt5z|a;&t{DOA@M%;wU^F0ScdyaJ!>|*vx03$b+!XOmK@#QW^@xcjaQv{uZuwk_n?i zLvfr9`AxU0aYy62O3d78rYLw+P+Pk$8sIWlclsFy$jZV75b~>KhRnIjjK+`=ZX${O zhwJedXF8iDf_afxApl^!U+ z5Fx~@l*s(&Waz7G{DF*VqRu8tH#nnel{XlZGMo5Ep@=_4*c|$Q-VLB+6P%b@s|l}T zNn%z660#T@z?*eGD0pgg?kRerxu{8U^&ydCPM zL-C16)rCDYFxiP)HcQO9WhbM5c60W>me(w~&C|YE!=$7IeKchT(n4-fisu=P6qbUa zd_pPL;dFz!WLDKCP*{bAEG!|vevaLxY2D>PN*08e;W*Q~8ABQml8FkF&MYQD$oW!? zi&lDB({Voi@>teHs*G~fIWXp;xRp#Ox$?GzNryzLrxk8;&Er>!=rrgU|jmb2;_ADO#e=w4w)Tb5|^?+(ymL=8s53p~kgr zkG_w!5t#2w=BShOkDiy&QF&Z1EIbi;laQ89RRq?i))cHlQbxf-U(AR zk8uSG(Vifk%>f!D_>m#Go+CWl7*l$e98HS3*;XOJ9F(s5Jeisaq_@T^u#bkokerN` zJ<7j)+syz*ygWVDhE<)v9V>${nCzpn;RR8X2ActQ2n!a3BOfr^3N*kZX5##>Ac!z# zX{3a#!Z|RY!g2^GU}|2FPSIVW=)*IyXMEo70`HLXzMqxuW_cs;xqQCMlutCIt!jVg zsG#VT5>Rg!l@0ab$ba^%cY3pqy70y{{HITH4#uvcq2#OkynRm^-%JwyegOY(dR0Y| zkKF&0Ud8kOu^X8f8CYBE*;p7k+UXfu*cv!`&>2}9I5}CES{NDphbF4$WN+Z;WJ3Qx zHlR$C!~Z?CDyI=CbP5gt;IRn+K<>Yq*2vD*)WXcg@xPD7iO$H$^?wZHf5lnV(Mzpp zh4s5J8#`7{`PpvCw)trGIemUi9w3>7F+(0jGJTfi^D#+?10V~4k+3tZ;$e0}gRV8v zc$YxCMXA}hjyI*>X*NMj9h(@j-uoVlaz?DlC($~TTEP;DTH)3qQ8=`lX&~{Onm7n> z$tb#Lae34pjAEK<2q@{YNi6j`r`on%IANj0`r^QgNxf6bwwtiK8{f+J*74F(lh8JH zP92R|oT!WK5t}ep>h{!Xpjb;eo^cufSLecnc)+|zZ7S>tq9PX-`vB0#6WTBVk zOc^8En&&U57f-N*#^P6O9Nsd%b8QGC^(3Rr?#MatmfgRv5^p|f6Zf4_NTHv(FVaY= zX18Mrhd=Ml7vHyW%=W>qVXrXO((%POacz*Cc&PLvIh(N3xR5{DA9I_3iS+r1F>P@^ z?>RHNl^vrDauZyK;U#SG_$0Em*Fmz+Y?JHs@y6Y2#vBQ6wj1k2xnq*4XDU}s(R$Ox zbX)UWsr{w6>$aA-t$$i~k3BfOm&^`$SrC1dJj$GSTu&FiuhGlcyj1QXclvm(KOHAs zwNMm57D>(aIA4&rL+R9ie;{2PBMsQ{$6QpVFm4&f*RUV@)Z1=6H;Fgw?57sC^cgAY zc%|21h0T|!oO`0!?4a-XUfXGQ51Gk2;}K7pThNFre5_ffm`mZM9B$35>!MFBKW&st z7nHZ_QDtX#B~T(RGGN$AOK8oIER zlz6aj=}6$MYY9~zrW{=)-NwL!HLiE@>mPi3%H_X?3~PO@p`@1Z_{v45RC~61CXWtm zsKUcbS)KMI-lLC6HAY0W26RSpXdQzbI<`=K**1^uy8y|c)l6O2zh1hePS{mwd8b}F zr*_SVzBsvFI;B1(*w|*znWeVSPgL8gf_Fq7!jWg%v|CP#xwJ-We5ltXv<;(j{QY|p1i#C%R^S}n%6GRKwdfJ@9GbR zGTT=@63V{TbTLz~e41!)MFyM;OBzT#-WBd&jh%z7IkSK|&fQ!QY;Ue+Bp2h`6@ht2&S7H8q?c=HtJQQp=TWoj?9o z^!EGM)$PG6HrifAp<-{BBCgZN>%U26S<$U3KZZeFYiZYM_IYS7t+s#%x{v=g{RTn5 zfoSxsvE;;%mEb*zck9!3Vc)D@9++t?mJA>Kab%o#co4h^FDy@J zwv#`w&>Jp+InF5W=DU;s@+Jn+7Q~)aoYH=y;O1>qH8!mT0y* z;mqk9t(Lo-v#n1D_~qA?RYoN9l+OzVG?y^Wgx=3Xi=ByfCy7a;^a}HRMu&-SAi;M+ z{P)!Q+dF0^bJ|yjyOe3ntq~ijN~(`UgY&bTn;lzf7~d+)Y1o3?6T4H-4C3UnMAlUO z=O9|^_TO9^iG!#pQiGYEOO#0+|7!IpLswwLPhwp}bAl;Cln)1tuH$~J2pCq3vRRfQ zAj?{HjtGC!vOeyOaIf=>)Zc2w1@(1vNv(0wBpLIMoeJ$?JTrcX&dk zLYAD3EJ7Yq=fi!#E9N>NH@?5o(8riONhRXxy3iX?m642kTl2Glks}97l#8gGG zmyZJcK{aq7$cCrjX`%68p2dS;F=$*9$&^O`YOMnOe;YuZRp5ugWmi0xa^$q{*9Cpg zr_VS2d95JRzG7gCU=SJoegcu;86jl61Ox=TQfmGQ(c2g8*tlH8!JZNe1LNYA6lk{b z;(Cc2PqkyUt`;e+C2gaPr#3TP6kEzG-YwiDpsLcGq_`v{r=)3BGZ1DL0ocmRo_`+k zme?{2Uaymkx8d#QY(0d=eug`L9qV0$!sQoR85h&6DzwBlOeIhVN;!_m{vjZPHihLO zTJj8!Mipm+K7@+~*!f^r;0LQ?swEE5CEARy;wr-l5-dZEp!pbo`2v6kI?BK%rx7KG zW5_gd)FC-p*ml50(Bwu4gqfVyVPVzSr1+r?BQLk*ueOY9B$CkgsXD=ku2@9l@M>N1sGWNXnm>#@xnF8XZ2}6m6kPa z0IVw5#4L~?6bG&gGtytBeaL~3MpS?lr*}Wm1}h5*tOEV!F*W>#oGd(5o;+EkhCAi)YfH!IhXyMukLf-jC-U)=_zX0MsCTTZUK0th8-v?sOU=3cDJUuO z8G@n70JLFX^i9<*^7H+JEkshJ0Al2jozRi*tSMulN#_Fq8?&to^?!~RwuD@K`Ar`wq#Qr#q9^f5j$%iAFJ_* zpp|8_okvJM%z)UE5+$|Yynx?}jkXf0mvUdw1csA_xt^U#IdqZaMiG-qM&9-{%ZdO5 zu3udW!TY!#4lM+6G670MSaw{Vn=fnO?a3T zyFPu=Eq?(*)FH^Z+9Vq7i2WzD8jzh)}NsSLx6pfFd0zrw+E=&ftD1`(!Q3t&qk zQ7-WFezS`*QfnSkSZ2iB8;6yuD0!p1Wc2H_4PJvRU>s;A6uUqt+v3n>38ZwTMdM}( zdAYEw>9#Y=chzSv7u-%mQnLk*(;45wm4T(CCIZ`&Epy<~LunGNh7w=h+os$Sx|%cs zIAk4N?=KZn(=b(wo+)OF_Sixs8+jHsA4%B;Dx6Wd1AnhJHHc{z2}Jw;t|d;u?xT*x zJ$1(S$U;+1Alf4>Q=iO%%M%vLN4#_^$V-|>B&hqGOWTN~yw$~dI}L-6mW??dj(3xw zG)xga2BiV)mpK%|za)y{^nF0YUBzigw)u4ti^|>VjM!^a`9Ks^bE{-DL02BUK3SUp zAWcZRo@vtPEzLRP-kX|$sK>#Gz3zW)6<1M>zc6+sx11rf0W`7pnDmiWnt{eN=-A=F zI&F1~%R2dl=6sS|@)SPakPu0E?%i#9|M$lIsjgrn zVbW`t1%2MH1eFXB6H?H;1cw8>*U!lVCV0J*V!LOszulGnq#;(F<3T{i#7$r_&)KZU z<>fVvElX_7N6h|upc)yOsHW}#gKF-ly|?%uc|l>SBM}Qnsj#a?W!dKGwNf>+_{&CT z*Bw!5rh4M3f^T9n$EFlJcE^-is*>tJX2Vo)-t0+Q)&dZuyVlq`?o z>^poB>{kL~JS)d=Bt@#-H^QnC>|n8qWxq+z%nEG&xc!Zw9I6uf5^XQp!&8$XB$?fa73}9u6u25XLcIC${c58GQ3VA}nj|Vy$F3yJFgI-q=U0SK>pC^AOu1wgNrLz2 zL{H;=a}?~qbrwYbLRl<)AbF&-mn$Fbcl@LSz)p*50ael_Um8YdFk(?QYiCk)nD1#v z*^9d(@#HW7nbYjC2+H&n28*wS@lr8Ws`!J{<~kFpp$SSi*jn5h-w8uCJ&FU-Co)2^ z+1iZ=r$_Y3*LGf0;0egN0NmFh!WMIPfo%o78GTW zAe)6`2+V93+3riwwVs)aF6hm`IS$z-z=IV~C;BA%DD7GL?4%}e>2*6#NtG>-be;PR&g1G?n-x(){PQWd_jyBdiL&AF+hW)rqVft zac+;1fb_o}$mqG*rqIt;t-q6}jjZoq#u|`@QT+YiwNg-iYj_BN{}l85dD!An)&Kz5 zXn+9n|JADc|B@|nHL!Lu`9B{pUv+6UkgTaxy-nBb7VQuXcyFch;X3m-8o+X;k(`QE z`dhcdltEQc{j>e{`D-_Mgn0djSh8Ol$<$|5r;VCtlephU=h$VE@#pe{@9QGx^Sb;m`2{?!kk|K;27zW%4L ze)VG?`?#D{`s0`UE+2DT^M8K*!~gv^Uw{Ao*Wdih-+%S1-~9NSey8VoT=XCM=->H5 z{r|HD>&Kq)Z~y!!fBD^4|Ah3>czyfpfBW{E&)@v;{a=3j-QNE8^LPKGgTMdkyRW&) zH*Y_-&tHE1-SKFg|`TzOm^Y=e`Rear7|MK}iIOOLy{>Mi2{LbHcu^0Q# zzWw#r-+c9}Uw*sB_N!mLzx24`m&dzaU3v`68`<_a{YJJu&cBvz$LTk+?HB+4>wo-3v%jAu~z`@Zja`eYs!7+_t4>Xy<$s!Zhhq3=bmnD*IV>S0{>G827Q&T*h8Ty?sje zedxpH$i3{hQs=$5EsfG^*Qd?d_qLBx^EP$Zd(l7J*85Dp*Q)o;l=qZ5tZmjd2Jhfr zb&GShylvcdD|_!_@1@Oo`&94S)K$Ht+}iDWRPJq-mgZij?qTRl_qw;-(#Cggx2(UV zd$4qnYF_``p0C$jdmBynTF32^?ks10*4Q;vecAnNLkm z;SoH6Ss&Q?HijR2=*#eCeNWu(*fan&ZKH0iJFT9iyK8$%9#j28gWslZ zv)n%QyOyZFT#IbnE(iHOMsBRXoa;@?b<-W~dZ<07CNrbynC?{Sb{^k#LU*r+D|4Hv zHGQ>L=cWhMzecJtykGoH_oQK|CFzlPUY4;QX=_@f{=8JvY#ORkdBWPXJjXuAjF10v zqHFdz^^v0@G&b+?jWsnj@LhBEa=!I|l7xx-s+LKTl}#dQ zXf0Mh!!~nDNn_{Tp_livm0eHW-b{G?eNDI)-SL)9A1bL=lBAAg1z%y=h|HRPGRa-t zx3=Q!o!iy|)|ifxETa?av}>_Ug!0gw?s}iJ!dNq|eUiJjHfJ>_60W-h0vGGV)Y=-_ zNW0&w{p>ZLIn;KL7HMf8j8@ii*(D1kqjVS7qk5NI8AZxMOZeT4k2e#(ULTJUD4t*K z2x++_ITxZ#>pV#-Y?6oh&Gu&QH1fF?>!T_iDfue_QTYIUZQ6H@>5Sc9_0fGpF}z$p zn(h93n{A(o9lqbP7IO)4oh-qU*1Vrl*G-9E-J^!~SoBFdaCB>~tT#v(>!*kOp1eVu zeHPEPK3zIk`kjjh=1N31Nw=4W&_%#MaE~b=^SM1m=>{yB#g@Hku}_j}}ru z+qX$iPVI>7Wm2mYiXKv8!Gg6)rw)BbE^lUe{@!MJonxNMO`2T0V*TOHC42PIvMs7_ z6vel1T{EIlMHWeOcMWPDJiDxyo~5SsKMrN1je;;w8#_}{OFKb|pjL|j8DSc^EFsT* z3_Xd4qbF%_YwEVMIW=M-5%geM21Pe``_%4rV>MwQ=G*ls-SxaE3_kkpRKJ;N|KCD3 z>A{MG)j)XZT-Wl^vX@@1Z#Qz9TD4NZUgk(3>G)Y#=H4aU>P}0x^9j0Awy+Em4aw^{ zmQqb&0LU_w%p2%14V1oS%3hIyZ97$`Zpm zl!L%o>FX|~ZCz{$u2qyI&$BPSyjPQ}0n(02e9z_JfYN-u}kWCX0_qJm|5-F zJez-eR;zT>V6U|`%MuW}Mcr+FX*}r;G9N5uB>~K1O8oBwqdI%RcWo>gUNd{&YFH)A zbhBH3V~e%ln=Q6UV_-!~%1XxMsr9#YDOWiI8W%}bZLERFj$zp3x@`=F<+bKj28Nz0 zb3a-EMQ(@GkX7@}-DvS|_6MYlIrQOrYr0(yvKELOES&RFwPPoeXhR^y_C{#!Yc!=$Ph%Ue_E68m zi%e>zTg_X2kMm`n1j@p;)6*!!jErws+jtf&S4(>TjIrR?h(v zS)@3aRt8#jnlRk?+ao|Cp*|1}VdX!sD6zqDy&0G;1)&j?YZpK5hE=0kmcXA{1G~Ia zNou);O{)nz=q7+)B&oce8J3-8xweA@ z)4qj`yKR_7gVF9QSk^Tg_yKz7O?pgrg_JBaMT9Kb%+U<%A2Jw`W}bA(Gw(zRTyIh$ zV(i2vH4)S}%Q9nzmjmvV2BtfDM378_g!r|MFBx92=7#4@GrCRrayy|Z;$nlKS!aN6 zACJzK_C$C|p=$Z${FK}eOIzpqX&~3@1ipM1RT4nLs%hH8s*!~!mDXNwzng{!^GXXF z9oXb3BSNIAvmH>0K8y#=OXW89u5InRdp)6jTN|fK7GV6zDYN@Y=%Nzsvlj6LHhsm= z*h>>ts~${Z8GI#zFq&OyYAG-xPdBO0`?ztzof+URZ(6r^*&UJ^tu4)|Gg7KkQe01P zCwx+5Oy*^E$b{16)&RQ%zq}?aJ2tL0h4j8H5e|k1|eVl#ClG9Kg;Iz`mkogwZm5M%FwLpeoBn$m$Rt_ zuGK4@q1(`Umypxc=@n@m#5_(iaSy^j+6@{pJPAp>mtizbXL`s%-TOANW0G$yU~V z&*YJl=Ok^nvB(|CkaZ_JZ_1+d9>ge?xhcP~etfSsJf}28MeFi*J<8k?s!H;+Ns;Pt zmNU6OW$fmr*T}p)&2O7qFz4Y~zAI;CP)ZOKbnRKJg3FDHB`U9T_5eDkEuf z3#j=|n4<*rHUu1@RgD3G^*P=Iu=>eL?nGtV60LF(JAqMa_7JUdqX&LOb1+C+R2okg zNr1*$-7+qVB&dTL+8uY|`3~?BN0Sk=+eO!At`g9ah|R81*QS!oBB{x}ND|_|OP5-8 zYU67w)Yx*`TZET}WR^W4X(-u;TaSFhBr2j=uMyRsT2hXj6nhLZ@e)#6AKuYHngrRO z1k3yFdUTCQPqwQ*QU=@p+;xv&rTolxo}GTSm$albAe!D5Zny9 z=^b{P3Jh>@5t7!uXr=X#z|yI*M)YmFw>26`3r8HJbl5z^v7DWqT$1gEbU9C;Mb?O{RqbvWky7;RUfrZNCq`1Uc#)MPiN|U!yn~pa zhFo?%u90Rv?{|$PhL-J#ot#c4%P_1LcAR-a%hd+4Hn0>>lJjRHfYY+Ar9GXKAg;Y* zj`?DVY8jplL7rGoQq+>1GUQo$mm}Rfsze_vH$&T4&)Fqd39~#zbvx84M%7tC#_O@9 zJoX|0EETQOIqWT({@VnONz(~)8Q=-XDpQu>bKvwD-(yY%z&ZM79-p-Xql9nq$h zaC*E~a-v`jlQGq(Zbbtj8Mu|&Ln-TzW*0;`G++*eyGP&CCbtY+GFKLEz zSdCM%OOvJL??JSaRtcHfXD95h6{Xc#Ew%{^bTkBEjPp^s6D%I2zqI}5U9PC)NSFDa zA5;AZSV>G=rYZh0BhgoJH!JBq4^Q9mqnc#I>*ypN5?RzDU)`&foG=O`O_mv{3kU9r zP}`167%m#Dw?2?;*Cnu-sa6RY_DH&Ayb;+$PGi~=!av1*jzCMkUcpY)7~v{PiE8~8 zLjm|i3=s{k+yF^3tsyDwM1Ikz4+b?OcyHQ~cRh+6lE?)~*xK*vVP(V}>CvValXU1F zYuY}x6qQtI;_gEPY*y#e$@AlTKMleu6M3MSyIqem5Q2@3yFVImy6Y+Tv*@-7UzkOY-L>rK1U zhLdCxw!SvFl>rD8t?L%)mQ28>Hi9j;h&fIy_-@^uzGgvR%iA=a7d(@AAx&qH6IkFIF1dB>3;)jxWSDh|~H?M3ML zY+@~B$aw$}?#Q1(48$8|=1ja+0{4w2j$6SRmfVUwI%`>qkK9>x8mAKo0w=X%&Lr=& znfab~Q-3(t(!?^cv_mZpHIouGglQtz*`9ww)@<@N3%~|BH~~G=lweK(p@}Q-rlxkR zEJ>N#w$-pNrQIjF)Qy;hRDZ_D)&{q*?*vYDY7oM}>0`5RHVo0FgUnpAp~0uh$k#IW z=xHG(zYw8q(0d*Dpp9rrA<4>#zi$)ML1BDB5E|NsdUk!yF309}J-VhFwd=428ab($ zp)~KOe4`ZpmkGxu+A|(<%S-gh*q36I@L9o?FaFAasDu*@2qb!YbBvwS#R+D}8@sx2G9NSRrH=Sl>1K3HqoqH<~RVk2--aawjvw1~b+G$bUQ zyq-?1jwp(rNp6DX!T2C@PaDpBBWj|782U6VbnVgNU9puw3k0$QA8+6d%(yqK9fI*b zD-k>7wDoS+UpBgK2Y)bBKam?SeA+%A-%GHXm?5sqW;6VBbPb|(jE02oHlGP6$4VH! z>FKrdj$a2@0Ckc;D`sp>Zb#NB=8a4ai^jb2JCcv&G3#Lu0dtvwIeW~QR5WT%d)s)- zJ{jju^4|jpf(~UxvJ7?RNOXP~r}-0^6GsFaCq|-Ok6^5UOc*R3oW8UkLA#+4LB!Oy zM!a01C0o#}6)|<2o`9`Kp5YTFkA5NVWohX5N=InPfTokpVuSy`oqjMq^nevljI`wG z041tHArdMnme3U>M)w*a58GFor9+FNhfdGEPQ|yp+FI+4TFHA}za7K%W+fJ4VDoq# zry>uyK0%^RYX(^ov4ffbui{|lHKJMzJGdWqdgN0tmtZCj(n4Hvo8wMck~qDznyd-! z#^!+S#B;;$leQ;K>!F>_n`(%9q;^mQa#0fzSq^~?lDi&WdZ@-tAJs=0$HkK5W6xoy zW3r=1$~>OLn2RueDw&`G?#j`r(^0vjzW`!t(C{cxkXrREpE7+YLc$chq#1N)w~Wg!>lP1trFEt8?s%a&aK|V);H5w#Zw@>YdSjb9g+v%_#=yg0DNn96kPfHiC)oQ^swr*6nHnP+%Mj9jvwf7ra* z_{U6;=6Vk2aU$;s?K!ku$jcPcmh#&%e!yN4O>+?aIw$}eS><|#Y?sG- zc2MEix$)mkLwiS|1OCmoQKZPV$k{`d?ebM5=?3XeVjtSG6I_YCy@_nq8mS`?&;vkT zCo3Au0L=na#j=~6rvdNT^FA5dLHr}qzUTLXZ@|qU^;~Zs4tfgEQSMfaS~`I2R$wmb z8;-!FWYO7tiG$~(>xncCJgD|*?#@r?0 zTqCyIxzUu!aI3rI45$V2RfW)g2NnxTHscIt$%^IGNEkH9F)f>|K5i@0*PN{cEHU|P zD`}=ll4zUTUzHXkPQGdLmyhrL)E1ITKA}PG=OZw(HC$tf#kl}Cpdp^YjBK0+J2x9O zSnn%In3p01V4LH&{XEGSu=hKW6U?$IlR7dq`E+eqK;XlN{h(DmvPl3nkOwvyAJSY4 zK&C%2bHFbIT?nqrph9+lM>B9^QiMK%@(=Z zP~t}3lQju9XL5M`b^&{Ifq6aiEP}Gm%1g+piLl)aS+&*(31Zn zC)}zTyc1EOvB1v_D`As2=CuDH@45kyc1o(Ex(gwyfNXLes+#bX*{bDBxthJS&-w$OUoo(}1?+@8{ts81kUIUHHZPfa(5bCKy!QqMVnw-efr%tO*HmElxuc}FO+vmR{I`fIzks?MkW z`IFb2oYaE5Fl|u~UndPHZ%gN88<4`gTRvyUT(l6vS(OxM5n!~wBupvqL`&MscHra? z@0=FNbq9MxIK|ufxH0zo4Lm)i5{8|YFVFti;U_XS1ynDE6u0Zq6|^A%T*1LLAVW_@ z{XlK_Nx#5tFsx|Qp`~4&5L?kn@?Bxb%>G)0bCpJbEU-2JK~79WGDA2P)5GhJqAY}* zk3`r4%TP!J#B}WMr~HUZ7XYc6FqWK`eVSBAjP?N9NuVSNeegI=&eiKB1g19})*;c; zK-!i4{)l!Q-VIXj@xA72owv=vsHN0(CD3_W@-i&4Mh55U#<+Mke15 zxP@$q)(JhAg);}4iUdg!I!fn)p|(ph9C2*~w;kQ&tmvZXM3v@b$~b`)ku{8^Y2&g^_wk6kZ$#V8C12zI9eB$DRs zdUQ2*6MCAeg}v2tiO|E?)o5P||Bf?+RrPWDdV--^Ibe@L{kXcXfIHb=Tc<88(|5{G z)Z-Hp2WjR^V%WL`+c$$usMy8iPdifob+nhs-49qBhRohvK6i5(ITL6_7#)_7o>f8| zJc2Eu>lLyi06E3qTOX$Yk|dFLrtDJU!=>J}E_ADeBth7s7$(uWERAxfYC&pnI>=AF zS(OfetEB^84IZ~4|2G#M^{5g z7M-Jwv-SlUei%Ywo2Gi1px9UE=vj_1wan+uV0O(n!IIqHK#->c)u>}hDdHhG($oTZ zvPg#m-o+>&<3J1z^YnGR4}a_-0Vu+fUX=#4ImC2hi}Yzp*bX6`Nq@8m<=5*q8)hYN zf{iqn7Q7>9{v#D5x9{F4=-Tz(whv^E>`zDgaMCNl3#oBwcY+v2QxWkzOhTuxJR-iY z9kg-{Kov271Uk`nj{)vdIRQ4O43G>h`K6EV1zlc&iReYZ6{mxbA^glkA%ga2Wa$yY zmosrr)~wgpbH}sG>_Rw@lE=Rx*eQFoX*0c^;|c&(29oT3;7;9%Ye_F`q))^-2;}3? zkPg$n)~ep?{?4JH_VYlRkL(K8$a^-KUBl&|`B925D~06X78b;Ix?%5W>KtRhi4Fs5p1w4(} ze8lPXW&33-ucX{F*Sa$o+Cv2$m}NlE;lQ{Qf^h- zqdm5=c9o4WmzQg?Y6$|Q=v3%i8-oNQ>Q6MkPomZ3Qf3e^ac>>>kc& zqF#hj$`(>?0%rz}+8%~1H8-ma%w;y$OUPUFM}fA~RAYYAD1b5xxHY!Ab|@~YghD3c zmk60Tv?wKA2yErl+Q(o$nmUu~1*v{H!tt9pq`C{^A!;%NH;q#Wp~UFhrqtHiPX7mc zlK1U#)nU`_ao-f)9Q7+iytPE;!Wu1QJOL|97O${6HDe^oGWcIICOe_-g!m#*RCell zdU>sfJs}g&^-`MH>Z=YC3?TV`M{oa2{k#^|8?i);q(z; z0(BjAmZWUVK(Z)ZJU97S)XdaeuK2I5@_U(%$Ja?y4C(n2bqmJZk-grJTv(#o$u%lY z&tJ~#U1P9yG7)J)<~ZVIQZQ7>gy9Ng>T9Lh?s}P$wGu!Vd8eBz$Zt}|1c`H%qqfS~ zHegy>Pa73O(7F^8l=?c@FkY7@Gsg$wD`A9F;F^MjrWu}A0#WOG*{5U_Wg{&B zXfqFBApmF=+*3|X-E}Lhcu&YNsh_vEH5!RTVWD2z8a^(1W3InnKTUa2KBvE5w`K^SLsn5bkCuUBPy|J-$ zf7heJy$;9;Osh12Wa=J{NGJA2$&blM7cU~O-9p+7qa5fM5-j6svrhK?HHQl>p`(`D z$^cPc0ay_0Cx8L+nKPkx)E8yfvQjnmZ|5}~`ON=9{`~(I`NK3HiA_w_h8vQjYG3<} zQt&5VTai}V_l`g3eDL)O0=|F?Jc$%>WHv1##Ph;>!QI9+1hJo54|<8MOtW!KSUL#D z>0xr@qba+e!JZHp9xA(}!nR0lZ@`i#1;Wl{liMXGHv_k=#hyvFRl`#*@0EK9WV=Wd zOma}CgKI>bq*P!xnyiFodH^}SRE}=#4X%f2eYk`~);qOT;a7ogs02yNBZI|+0{WK$ zxHWl5n{sUx8BFg@;O zw&Ui&A~XXkt!0tMgWz=7*Ayo2bEL?;U)~E0g=GT+8dkxFBP~m5olJK5jY}v~<^Iwo zJWhOY3AMq>NoZr3C%{TlW<4Bt&(e( zw7TAcCj#Qodej&jZU#zb4?x-TaF_ak(_z<0lI7hi6JAOCoF;iM?J=+A3JZIjYGz76 zC*+V(#Kj<#>b6buBsZ>g^*TFZ9X^S`4}#v%lt4!)z1)MX3$!KdRn>GGP+&AdN#$Lx z1(PRcyTY(AiCN!*#g6oaem`P6W{m^BAH_BGe93Q{Y(NTt6hYg8+akK<0M#eQQzv;S z%KEq#SBR3Z*wR|llZu=id^c434~Q)y1l2~X#EI7Ao9&5;$;emh_MimQu0ELgNR^YI z{iDsc7Agx@&YKgL4$V=u7c2+G(Mym6uPakM-K}Eag}DzT2ca}pG;fk{KmvBdVG_fysY$f|l(nPZr|6UW0bk#=Wx9|0Q#c$u-^2uay>ozN~Wtfr(D%uaKPRFXnPv~#c&$P zFri5nxnsxJ$k+PR&Xz2DlWX(lrs|uWKs}-U4`%h3yBe4bIYVk6-}^_KZ~2b| z_YJ}@@H*RWjf_Kv#L6^suZaab-#hv;!(~goA>6f_i=iE>$6n?UyD1iCsErXY+Se0) zBv&(RY!aftq#2!Rv>Lx35vnzx{0{H+bjfd<@cr&Od5{9tiB3XUlD+}mED$3CLAmX+ z$>nTh@YGh;*r~E-dax-*Ww#bX+@z~u;dR>LETX;>Ig8bRk*Z}Bj^E|Ie>7HK&NXiqOa{LV)D^yOj^xN@z80{81{E4~=WM{c+0I9vl?o!{ z{unDm2?5f82I&#IFX&1NV248&^?b>1pY4fbDlt@(X)QYxw=QYIgSFchQ3EVB8T+r5ZhFZDdWIUyUdx_*evD{9~D}tI3y^(wj z@C-0UnI|oX&UcD{CHNsze(~OBW^&x9KccC2o$sz0g6OzJZFZq>qA#@iyR8iqqvS5r z3APv-m=P6*xZhLzLCU}o9|_0?#o7D4{b1^w6djio=Frh67OMHK&aioVC})F>XLMQkgI8$0ofSD;ZPC>rciw!C2+&TYk=~k>(RCB z6__tsVFI?#0{6q=1_5{)-FVr)5>Bf`roMhGepE_5HsEmY5QP&S9Ud;25Fw3l5kcJp zHo8NYw#_J^)N=!;po09ZubmHHo5H0HlGT9oO*ky$?*Wt{k#qdzyRTOO{G7*M$q6DU zOl?Z{%57?WfUE#vZ-8&`t-`@4`>unyD?{I!VY6>3F#l+v`1|D@O$7J)%b|r={N)fd zo&SD(C+w=&26D!_@!;p9kDNka^Pr=er`oFrLS`%H@jO!Npto`VcE>j;K_g7LqRTRL zrtuGNu)HMOIubz}#B?n%J8S7DC^HDyk|KgrckYJsP{ytumEUjT_DG1ylx?s$TAv+$ zRSoIq={AAi0i2Lw434Kj<@d>A80OzfEEMUd&21*R#SKNyjTFsX zBtz!>+JfsFo;inpqjDo$GmXd=RgN86Wn{QD47WNVcN}IZ_)qb!@jxi^2i_X2BET*o z3Ekd1T==0x#&bYD2v<4}X9K(^%rfs1mu?a~f+O?&hCbZuMo=_EDH&P36(*yD$T8zb zs4Uo8%lV4mA)X>h2(d?~XqVXM7=_&lDLqGlvKYKv+Z@}pV3~00lx)gLJ75{e4i!v3 zq|j~KSIw)YiMkLA_DLO=lvoOOX~|A1S%~ibr2WeX=gO{@nf}YA@ zTU;5cv7Ulie1pju77mn*o2@`706)xIZz~_}HN0a`hAdOIfi^XBSADRpRyd7vKN5AP zD_lcDFj*T(ml>E2wQ@h`qAw}_N2)&|q7zB;5=FCI zsNN3ba}T}c@=UWb65d5j5;)GV&~=alHyJ;2IAIA6Ixy1l16J4?F%YQUKp-s4XE$}z z)>o4kK(1{PmU00UgA3?&@9SZ9;{4lcvOqN#@cW3CUJl3I0cW!|o5c#W>U7hX=kjk4 zu5dlxr3m6Ulc1H8v8=70A*!W{Gf@O)W=d0yXm7kRTxy4#iu!A_#5zuxs9$fUs&W&g zY2($nWNL5oGRj_0@)2dIj&6~p9Kq)a9T~jG&mba+q!K~*n_1P)SI9;_ot7Re(OtoX zK-BVMG2`Xm@(DmqAtZ0vHP9F0y$&X@1$%%8icD??5as;Yo43jpZ z{E3h_uJI!O`*MPbw?erBXl2}>Io)7_9c`;C#{lS|o6Z+A57A7O<=env$q!c(W*DH9 z5pRc71w70X@+py3An<`=?PmPQ^O8wV7UFc;3%d)Rk{l{$tAUwhI}P(~9b7AAQUU1?ifR#KT`Fag5z6OwTXAO2Q)eFdhRCx++E<;pCTHD2mA#x` znIKL{cghOK&TP}pY>Rdtllrv{Iw51?cVIf*Uf|pLJrcMZm6Bu-O0-dcexO3LvCT&; zfinI~s)0}yQ+&Nfcd_pqNwf~+xp#AEKnN(4N>UTpfN}QPYDBdw<*(Wlr{p-5-HKPr&#kZSFS^)RdGZiY1K5EdGel3?No zy2YlDY~7fri1R^60zGz#@|tz4ZChNSa1T0w;OD?Z$aHrIsJZkRmOB8O4{!bCjs(=N zli<8vk3N`6+VvRDWI`5^Z93UX*m&)utpuFqL=0<%TB&CpIq0P%4=CFLxv;(QJGD%9 zpO4aKnr$`PqbY3DjWj68BFhVio2MhBwZG5fmqSaVo;&+48Xk3Yl^MVK1AL<$LyX70 z=G<59e8u*$5mUq#Ql+fQeeyu_4Xw!1C?%^4o>Am%!ksH?E@(j-^g9}-zCG_l_V53>`_H8ICX@B3dc9#l_3ng%Fm)a=I80ZL^!z7Q~G(L*Cp8 z;eClQ+21vpG%y1n6Q(HEj3lU5#eVpe6=wE{6Btwprz>KEry2+!G69mnW=?8|RA?oj zOX7nx`6yC|2wY!G?e~Bj74`&f)s#L58$^JegMEmKCZfbqT9Mp32Ocv^^$nb5r4S@2 zZ&Nl4X)vM2!(ak*cS8g|OC?Ts&G`A~8UdV`UkxEM+cQ*jOsZV1j}gEF-?G!$q?I5U zO4P+VRQVN>_`E4f<^v7v^;N$#f7S=f< zflUINZ&;Agivao)x;dl;bbMSJoYhz&Csy`<27G8U@KTf7)?B{YZWV5&vU z^##0xFuYSm5WzdzLpGp!cKhjJk-$C%q>5}7TG2P5#8(2hstrtCmU7q^GtI2m=zZ4dADu%N8LEx6{u{7HjIA1O(B(gjK*&-7WHh2eG$8EObnTYsSTU8sYp{nEdz210BZQO_XV%@i2Bh;P!0%d$4j274}9IzHg?WX2HD!i&ns3O zUuH>y4GLCbS6URrbC=WP*cCXa0m`DnIaJ4f5LVC`2p(%qdZFSM?)l)Fr5#D3gw1<) zz_w0@sE|^G>Iq`Qt{Z9>su1eLXHhW z%d9Y&mJl4?1l5W_tE3&uYav_jZo>DnJ;IeDby;oxg2&;xfsmV!? z93|{@ss_>?j0;dk4Ek)1n9~aC9oPw2;0D_%Es3ejzg>?$cE8iNU&ymiHa#Ip z8xX}i;||I2pw*o#O2GQbQUhpxyB?Xi6*9m{H7s^krhoZpgxb6=$J^?jM%(pVQoqEG}OVvG0 zsYK_5KIW5Gf_@+skXrCcY^N(?O5qfA%>{`iZ60jTAU7tbyNA{1!<|G+1IOu=sHver zX9`4*4B|wO)gG7|vf;G&$o>|}?~_{{v`Cra%tJcqN9>+}vRYkMkRHh^`|(6r=DYhQ z!OPYIF>$*deJB=sWhFm~g+7?c>-F7TaXy)+$jOTm2`sI8;*lHe#oq1Q4X+3S?$TBl zP*P%1YFRI*;;%^3`hC-F;I!s%~>dzDr{C){j$R<5*v zX76SYGkXB9DCVLk_2T%4yI{~1(t>@9x&sPL{N@5fJ~ZLjZyCXX5~BI|?Rs?Wd)5gZ zG4l0@T_x9WK3fb+QJw{KyxvxiVMnTn>!cjG64>A?F65i|MFs&t%}#Qk!px$hC8He^ zLU?*YeWKkRkQ{Qc#^U#QIl~Wo3ZEa8Q1dF2vVvHb;Qj$)tmYmhgU}ai@Nq>j*KNdN za5+Im&~N3zhe)2=b(#(a%RYw)i}2mP>TIR4@9vwERsn=0J|%{W@pc&8MvE&e(qZ~Y z>D&PFcOotT*vg0|9|O=nQJ~;hmtZ6Bnqox}eNE=#*rP)AbV#QlR>AJ%?*Q2hes&(U zdiI9Kqfa<3<c#fQdUXLD)95xqV2NmywWqgL2kn$O1^Ut=REvb%0NHKzC^4bhltX_pyY+d+V(TJsMYL+8 zl4SN%_tDt6@FP?sHn5N>?b{l#}6C&YT#=0$ne$<`TfTR~!DAx!>ve0po z-EhkA2@+Pa0Rpp1Rni7?aEq3B+FjIV3_Fh3qcg)3s*8rpTdAko>_#7p5$Gpq31-P_YSYBe^? z$eXzVu~VsWdEg%0hmG2vzDiIZS1eoWUC@e>#^!DDv|Srq74BRyn+RdF1M zn66ezI6k|{~8KrG2BaTc0FVz=O=?2S|pEbzQp<0Iyb(Mv}^yvQ8R>i0RXptz= zmUNy}Hn2>0%&7CxN1{^r(;>(&r?!^Iq{?SLdrAjUsY~=^`wl`XbNRFA;N6ug`)lA5 zCKTD?iB0A*`AqX6)AmJAD!47kXhoF0m;<*$e)N71KTRxsH+&5Aq*YB_c2`q#ABw=(jsWA451}R*i zIqW6O=pjm_3}U$ld)J`*GoDt7_ZAuGa1t9!+n{zbrF-jt@T`Kp1E#a3l}|+}5s*ml zvc4WsbUfv#V|BEIs)TF;Wv~mLyjkMI)EGM+jLXR5bycvZc&_{}Du@w^00|9Y=}1Zq zGnWo5!T7yArU#+2g=s?S0utL?Nyj}r0W*ZXbNmL02n-w`!0JLsgbHX|U3~+f&&o9E z>chqc30>0bE~|fH+jQ4t^I37xs^)n#;3&qU22Y5alV~9VHTZIKmf`*&N7%MA*yeGD z?uQT;@Z0EefS85AIqnOA@MI#wmoo-92m1lYrVUx3nwh@FvCcuxLk>X8tGk2s2)?&u zvZ;r~1m0=VDjwm?xk@WFjFV`fbMz?Sw~tL{)}YM`E$Gg&?9%W#wDAIrzK6I%BGctU z3o23Mp*;=Pzrjq?Up!Z{S*=A-(m_lQI%VgrIR%o>j1nEImT#5eKm%ePBM8k=Ur6y5 zB+;N9W@!r{20tS#a8Z)KLCIKXgXG>(`A%z*3c;cqOP!Bs#!Njanhgu45fHRPvM4f{ zS53jOJ@bY3|2R>$d&FzVcOu?KjJE@eL@3{wQ7l=SAM~hTK;Y66rV^|J_X=r| z*)y<^hR2$Yb8qv1<7!C}-HzT#s2rNLY}-=`PdX3EuHBc=eO$5qxD+-UHq}apAqCI+ zW@H3X7~_wQ@R5FyBrN8e54v#t_|4TGdu-`a@f6c)W7pg;7Yb!tQn<$Yu(a}jxLag& zqPJ!R5{ZFMo3THF9Hte*ZvQfYM3d(LGyz)fu$RYaNkb0dYWJ*wka|eJ>si`TUZfWZ-V~}_)_{@Pt zSwJ4T!T~Y^63%nfTix9$TUvBGQvbuHT0BokV2FI&h=aCcH+M9tG<^o7cagyE7qYw8 zO5}{6hZDao(~4mN2%^lGb|!m2aT1sGNB~GypT^^HLPJ7jGrpvkxC8+-p#&k&41Lmr zk6N48GI#(W2K}=I=vI4<@<++DJy^3VxH8xjO<}vq=%ckndYcm%<-8RQafmRwNu^Dv z{@95ox2MUvx6I@GR>Lc%_2cF??^riCx`Ese0Et`Tfj$|8CT>wcm7KmAtMRvN9>3Lh z?jZQ28KdPVvNcLN0+bKZWf-OI4W|tVBb9HAH@R2S+KphH_v*4rZ*2KW_*C>Z)NV8u zuLMNpdUVbEOz2KYN?;K_cH^OLHDzbB_vl^a3OKEUhBdc#5k0(j12JD4saDb~IvC^j zWfdU_f(t3i5ivLDUa*o`iO$yUEc?E&ZX6|76FLZT#1RLec$@YCbv}w_ZDCGqH#;%; zdWC%&jj^;f@?Oi(5?6~_?){Jzi#Z?}Mzg9$ooKKU1CDWfm?R}tslYHN%_^IMYRx<} z+>tgGR{RLdkvc%~D|HE5co|^q?7=X%x!G{XnELo$do_quqb+aOqifOC?iR<>(d;Mp z^rPyEY|^DVW0hSs(?mD-V~cjR#eM%O*20_mSPDakLBI`c?h2~ua0ip~RZOf|wjjWw zAALu`ZSVK*QEO#SzC$Tt5!>Lm#Z^lCBc^DQ44N*4M}5754ebzA?UTqG9T9^xZP2XB zCELL0X(!;4kqA%#Pu5zYvo`Qvh*3ch)F2hF8@ZDj#0HQ>Zy@mVB0euRgrj!@Fm{5S zEqa4OOGd$(Q8Q%vK}O5KggNlj0E0y8Q3LNl@#*NQL^Xt@jNN1HIj;}gn^gXnRg#S` z@o-nu4wafab<@b8zfqsiBN~W5D96Bls}cK*p0bh|u;1+rH+g_9gB)w)J+5LraTWNb zq#=S>YEZdRA<%#1^ztL)6ymnO_T%x6*#K#-tK)css2|VyZ@E?*ulKH zT6-XE>C_qBd}&fgk9K%*Ym+p=}iJP;~ZbXZ3;|#+JHWk3Mwvh6-rId>sqmA0gsL{&wJ|oi%Zsc_0B( z^yjFqS=%CUOURHgRNK?booO3KCKcA}2s14KKcW9P4DO$t-3H{?S2>l&oEGt0;1piZ z_ydNH?+1oI67fCC_5k}HL1d{=SSmUi(aB0*DM45KHK%teK%? z-A&8N!b6*FQM+Q>oA7!-1tBexw3v%xXs}3O!3%)!G}2=?G2|_9b+bDb5Fh1!Qh_b~ zL!&avSQW6q3$RC>90fHnKxXvUnb#29G!Nf{D5g`03s!Oj)HP!nMOCtQ`yR+{atrUK zccvA)qVkeZmemkwc4@;gRwT=B&oBYwG;XVdFBSCA0TCadQijSyTI)s)Yy$dre^7Ys zFloZV0qKxV%w{KLM2L|1TEcEEz)0P|Ny~mboI0wyyZxbO=cc5cLy#xHv*&+p+qP}n zwr$(CZQI7QjcIpJ+qP}aoBxZA-Pp_S;T* zL@F3y)N1}kf_K3G?qYSiJIjdEGG&rB-v#_wJHVS?;V^q-Ou?6%%t#x4FT7nh*K|glhFzVk0^L-^}bRj3iTB=Dmp##LnrAF_N%cDiIc)tb(}@KKw97PY`A5eGe(%* zxTgu%b|3cXXK0T0a8L~Nn5%OybryIHDxMVvq2hH$I9StRu1NLV^YV4mP9X&^h{0xP zIl>MS(I&7dh)%ayB}4c0rlmB?8Sx$@d`UVvV-%qHt-P$Wpnn8PrgNi>WG8gDbphXp zXUL9Zt(T3!LO`#H8IOEq_Sp1LhqHRi6c;y(3W#-a0CiyrHk%`49qL;J0|geb*DF5p zsbHEdGwTRgw5u_gXerP6L-=0|fcaQXE7K#DaGZ=WthGC-J=gp|ZmZG9hdhmvp$SwYhS3 zTKZ~Yk+8NPxv7yfsrpAkzgzSvCm1yh<~%pBpOVlPIOcJrY8QI(v|-bZyhiO$Q8prf zU~YbDb*#01oxQoNlwc+pW5b}{K0E?DG(;gpA?>&m&>A;^+3P;AC9tG@@+kgH z^D)!x`~LLJbutPsId$U^YlWQ4{`^d3({TGs*K~S+VLFwNkor3x>hJvx6a|+)z-ju8 z70Xc!IaYz%?0X;GEbkX-@>^Q5?6q3i3KN^a&LSnF8U7LkF_??IQ{PJODx}9oI8P;8kk9VG^My%>`SIPix^)F)5gx?*Z*28wCV38t`{i?DN5 z*~)r{Q90OhR*|!0o~1RLk_%}li7M}mSO9o9J*v5F-Gn{Vk}-{F_vp1wS7cc$f%8I% zP5Fq~^RS-llN-=b&mG0}*ruM3N5G3l63&&|y#eK$X~$M47_abNDfnF2Xx(UpylUa;!N+7|b3T;DzUe~P zzR%V{4~P_e%FB(SLDW#Pn9~H}44stMzOEyoa44=Qn06E=8N-!{_&m42P{?D2S+&2c z@am%d^Mc?btrw%Q<+L>rdsx<+c5cmusH_!_MnPF@Xjag{ZX zw1(mj6!!0)2D??J&wxhW8o6w9A~|SE${`7 zIbk|Dk_U&ys5sH;cd8Dz<2UD z-!|a8bbq8cSe@pcVV{n9@dldUPJBX|$Hm~iV@^Ei&m{!g@N&?Er!T)&2$prU-#r%b z{}kGhb;+v@6i(j1oO^5cna!4FE+(k9UeCf}GPxB~?G(75JR=^SgC|}RUnUL1dFm%= z0Dq!Uwhyoz>*te9%AYqY5*16=U{MT+L;IhJ2(^;iNdyk=zz%_m!rd@aV^a0D=*JtI ztkg`61kbOa`IWAfT*N&UOA!%5=Cd7W36pmopen>U|cuGqfefq%GZY9)Xb0sP|SOgV8O$Q)5gO4LCgWWf5 zg(3p^44TM1rowbf2f0qk188>G1?>VM2@-R!QBmjXRTtXDp0REH$DclWt62W!mZ z>AkJ5NR+7o`c1RwDJ;~U#v+_$z*J3JBj{o$Eq2AT7tzJ@sANLWxy7Us(OM6J*;aaF2T>HE&mw*(15xVsXmc|oO%&V zQ55Qn{>l8>IGbeM@5&1AGW-_6R6IOaaj~g!y$g^W%Wj5}CvZ?9WsP+N+BYV<`Xi-u z0-&)e!2*c$yi`T$jq48lWdd=4(URYZ9XY3{iPR}s%(*F)=-fT_MC@a)@wq!v%TkUf~zOgo(@&4~`RK{2^y>ks%BsdBkdK9i% zv_2*B@KcPxA!xjg-)0kkUHmiL30)g3qEthw@V*~c$AqC6kH0a9y`lygx3}oKiF-ZE zx>uD)bGEoOmIi4zq&qma+~S9|L0^2qTBe+=jWSPA>vDeKaEmRp&NrZ@S&RG<%|?fa zZX%8Puo0#pVq$uEBq_}F3?XU??$Gp{aa0-SBQH(!}Q0<$2OncEIdEh>#~yl)9Cj?&pjq*_azpA{ejLisSU1d(etHU>AP zbf9mzb-;m5iqOr4S?!ha!A8b?FH?LB$7@(`w~qIkq*{l?3?Wl@Q*}zLpB?J+B`QOL z!%EdUTv9e_ifk@Si-JU?hpk=xwWyQZq*_Ph`-O{#$+0^L{yW&4Qruw1Oh+nV*wP^4 z(&xNGsg)lxTKhr z-2akJ(;n_D(WXgakbS7wLX+lHqD@91K#7UE!hBzFG3jRcy z6aixP)K@}B)L}>Z-A67RTHv@fTn=HZ{WRZV#bytz3k<8z@}Q2IHrtaaL9>rnoje*T z|3@+nI-pH26{?+!F1Q3Si7vFDst0YRMat6l8jFSZL>+wIOBVxvJW3-gW9d!SL)+v!Wk~MWElb#PD0Zf8 z=1c>oOyIhc*w~YgCrVUUCV8wIy5-CEZx@Iu5fa983F|QUy&8dw%@bDCM&!HOY0T+_Vrt*RO>;NGO!GNc4RJw60~l;y|OVo4s~{OlLO-x*p&1y7%3o;x12ru zbGFTpLS#u&Yd28Gz`)|oX)X@1eRRd7DsG}XP|?Ph>Yo*sF+pDa-g$~e0QdTC|9J4QCD7wn;EvT{&YC`fV@9FXi{08lq>8s*{8G*=t=!tsiMTno zrr67QHp|$Daiw*w{I`&Z3*6JprD!aHr2HKL#>Y9m3&Wt|^bV9Guin|5o-KC2KZqq59H;EI0!JG>8z=as#h4se>))z;|LR~nB;45pPrs5V#)*0;@@dCY zBjvDk*Pu*Co`6lt+uJk1z1=g6TGw(EPa&Cl(^OgIlwEJ1iaYR`3PnGskS?t3%ZA^Ur>C4;kwlGI+e5t4~2ISw;gokBVIJrM|nDj`xWU#^s;5(C5R z7VC%=G}SuS2r;*y+OzX!!RX!4m>#Gl)DHT)yh3uq!CU+}6n?iguW1|6aVI?YFk?5d zLNzDqNzd1;^{zja+je78Sk58uTzA(?Fj^~z19c)D1B(u~`3?ROArW`dzIG-Ku{qh`Qc;jZgr%)ma2XAt!#r z#rAg(*^w5N7DAPUgscjeLL+sY15m6FhiJRK&}xd(Uai{JD_uYwlC&8m2jj&0D^Igjd#I6z0@O}Em% z+EZPQPTxw$XkmCC)dP`xkSb*FoKCcyfAi~Mh=$&VBgkkP+n4ojx4uJ$YAKi)f_n6~ zu_$`_Z}+*rDp}AIH7F%SSIMb~fEpHFFpQzKL$Q;J1O+*!Iat2cYsoHQ#nDCP<39LaR3RI#ZzDsR!UmV?VW8uefrX5B{ zB4~sy@k(?z+Zb^U+J3I=z{7N;?|^abKG7;^{>IYq0nNQ+5$fkN$XF*uKpo3P2)x0P zGSF^uoFyb|zixGtJxd)C1TsR53*;`WB_uJXLA=EVE2}Hl{v4J&$E|2ki+fIcP(3sf>egeJ(EdI7cvyW8w9C* zaL+hc9f70{Q4BWWqIz${hwBrCOqZD~tbRxE?HH@JkhI7wbwU%&8Q4;;_7~ zcHMPGiwFM?S$d0Jv9iLUtjWeU+j&Ka&BTL$ff5UBxBc`;kAO`&R8HP~;5~P%K5OJv zivS5-b5@xf0uP!u$(x6cDVqT({CdR19o6APVzvvnQqP+nC4Nz!miEjFyXN=y-r;pF zfqGjvb}&MMDN$jmbV@jd&2_l=Zjm2krg*P@a2{AEHXB7$TVllzc-Vfit7ifsM{T34 zJE(?<6g+9XD(_18r#pt-aX#JW@8X8M$Ax15)T5hT+-U!fLh`Z7v~Q3JZti_W(t5VGqb^ zXBa`RK7FGOw0GIJO|y6lJ9uTvY_pB2Q87Vmsv|=wpXesTY$;zx?3qsaArqKUkxp{> zLQfbID%Ihl5R>A0eKZAg7=BFcov!&EV&aT_Q|^_o``)#K#n{rN%iVejf~*zfJ^fb# zfBW;{ndGu0dL1Vvc~a^H!oFBzuqCx({`&s>u)UIDYnr0bQ}g{QGHcED%T%&cNK7x~ z^PeQc3_lz+Dkq8n!Tf`BOP(g4Mh>txd}uQD?<4S0&cokp#B{Z(*m$6nc%&Y4Yo9dz zqCgyb8r@^c9?M9@mUaz6B^$#?Lg>c88o!~Vg+&gi9*~>vKM8`$F{g_@)n$?~lQQJe zk_QP7=2ljfxkG-)WB9}C%zDJH8a&BYqc+c*$%Zf&J`G`h9Y2Fsm;|%1p*XJ8vXjhC zd^AC&vl%kKE)$-hA+_oTe1xeESp5cy>=>7ag?3t%RgoE_Gg(ypW1v17+U#nWQc|l7 z&Q2cJQ@n)Nfi5>GMX{~xktX<16yyNpJig=bvx=e3`bT(;C<2>(9B2=-XqXg%Y(=Y66Tz-|=fmn$~De)?D@D+xvlFB=YlX$ss zcRir?)5`~(Z6b`EUp~WM9^T!a_jowvnXyk1$yqvQJCKJZVAcYl-=cXs*)r!?xaJjJ z(5*P+qe$HU#-3o*5S~7xu3Efh_z%I`Gghn#2i9zc`0bUZ4cq(cBfURkj5sghTjg(4 zlN(~~DMeLx>MIjSqNI;^5p{vbWd<9XaF#)#2+cv*Law6%@E7hKo7-?j73B|rm9~N- zLYMGlp@JSqL@9ha#M;XQ9Fua-1FQ!B*W0Zr8*>8@wZN6*rN?kH8VRu+q&l@WRHy?CdK>eH^iwlT5s> z@AQEmY6cx-Tqu~u<|(j-1hx5InroQ~ALk(0IL_zCQyjz2AKm$ij}JoS@bvliY{Dn! z)JSXfLH-rqV7%`haT(480Yy{=f!j_%1Xg^ayj?R-T}#V$XD-#D|AeP9rj6VvZHDB{ z<&voOqDTgT5$${g2EPZ1W%&xfMz_Vq${W<>f- zHEc5PGm`=WW}B(c5p_>XP|pDDG!V+Je%`+Xx|Y_(_pd2cXeM(VSlPR8hz$~z=Eldp zmCWzx1;THfVs&utChJKM)fq5KN4k8_^Qnv;2_W9kw4@U(^+LczESv(`^+$*q?42uA zi~Q9B#|u0S+Nu>#GG1fF0bs=0yM3S7W3Gr#@l$w<1`FWpN{%&U5kv5v&BaD0__b}* zN7g})0M1Dcbv49R;rJRxcma=IDxH4kWH8>yL0!scUX>xup*CQM=IITVT3BTpE4z- zYzyA>`V(ZL826y?ArE^oPI<_{t{%o!Cp+E~Ke|Ve0}KSYKv(0CuY4Igu3Y{PP&59X z#`{~iLKw2=w9tdczgW_O7V*do2NNuG#3h)3rIv|v)}d!-;gEtSQV2&}+H&i5v~Z%S zYuKNwcV@}-tRIr9P~(4^6jPW$5t4ft>&83O3J3MG|8O8 zI1mOy1`rr7Ky0!y^H3ERG`7IHABagELplZJY@uIND`A+pQMXj=yZ#F`63^4*b7Vtc zan2TQp^hf_DX-^%T+W5nh=MT+?_Y`a2hKk^AP;X){h3QRnWJrj;iYocaJQ@iqv!iO zq%;O^x^1Vwr8ou}p;Ab>0>OLX5{Zes3z&P6^Gb={Qk6skG1}kxd6Wo_OAvn+O&am@ znTj1+D%iv)E!@uv^R^Yp!$sZc;V)*uN`(rAt-5`W_E6I@n4Kc?0NR!^Tm8tSisZiw zVT#ibKqoiNZ^HNXNV$PA4A(YA>-U%HX_DnQJWYsf=@Z)Tu1`tAk&vObY!}=15nS$Pmg^{|isQaSeMVnJ3;@HM7R!A0tCZ-v z$8rvgMwMYIpFOR?A~q6t4F@MhvK^{HVsR5E!~EW!YdZP2^E2sR4u&^EZSTWDmGuiS#(hB=^fnrd|wkCRUSyvhR@g=d&SoMTyUCegQY{Vdw6Hln`331&rFvb1D zH)Sg9&nAI)M?41SaGvvd+LYxsI zH);x|@S{PYuFERG*>z8KLR5<8{|%Y!#6J}h8n6#zLAJ9bxwKoEK^qJb$vkl!6mfA( zA1rbJamgLX_>F~|D}@>;an5{mDb9nrO->3y{~DGCQ_N$+x4VLVDnX&T7Q8jn3J!A* zk2uN4gkaBTzX8i_4x#)~o_6@B!w39oSt;_y`8M*$0gOz-i=y=_N^tnuvZOYF z{;GVq{KQt7>{Ij>J2m^OVP>Svd0=`YDw+RJHxm>-h#hDN0hXOS%!#e349^SkXD!U}_dBvM6^nuX%J}y}t}AD<(S{{aXjkwjr)JmkFYCMk)!YKxD`T%L~V}F#H8jjV^s835ZJL$ zQE2%XiN%Id{XLsqWZGZ$_%Y{ph9xlKmW_(51=+tjTe=F1gX-f6c4KnHy(3?MM_hSM zOJrKX?edKJRxk;2r%#>XK()J{y89epswE&-TeO8T!3nT$PI9^q3pELu1&9+3ol#c& z;V<#{b)p$tSxKTLv+w~m9B>U3#jlQzg_Jm~k`16XB_1$EYeEDL$ixv5>Aq4ob8(7z zNR0Ui8P>Jq@tIHjAWhL6-jx22zs z@iXr##knBDva74{_KAyh+1+f)DdCX`bI78}bLp-+(F=1kmU)NEgt@(jq~RV%6OpcM4^QmqdUK&H(K_M-GI#9UHPl867jM%A$h3g_NxSN}Gy->lLD8wb7F7+82J|ADm#L)iR`_&g$hHJO}I`FHQ zQ^yI<#a=N?s3?or+S0|r&z%u7W==GCd*rWo)etipd4_yD!+v$ zo;e0yuS{i2Q0VLh0O>vgUY#_!aCN7|lvwrxAiU1c3}PqXqR8cl9?fLZi6z{AIT;?} z7kh`fVBj2PAiq#(=a$;}>PV{Hwh}lN>5LQbJ@&SN%~H8L_bBh@YZIA(#V@pxq0Ok^ z>u9$;c93nH_*aN6xv*VsUQjT9ByfrZ)on8blyrIJ9-PZJntMy%$)Jw@jn1E7Ej$CEJTDCc2xTF9aGmN?-}LyHY~FlA!p)9Qbc(=0-2Jj zdh&s+vl!{;LI}vE-eZvjRq2=Eryz(b?6Ecf`>88~!k{k^HdNFtr~JVyg5THN9U7e+ z(s%^1lc_)nNbyrtIw3?Vqzi_6D`XM#f*ZHrm?>_i29)TqN)upOn!--nAW~c!3ZN0T zE*eN2(<2B+)H@;Uz&?f8*xw~wVH?B3y-uv)b+?3NQ%1wtF=P*p)o~#A2pT?A<~zBj zOtcWWejjrE5U%HDb1J-9|Mq%6q+D{N->f<3i|Fe(W~U!aHY@P6Jb6hH_7Mt-by>1z z!fgnVhy>=#I5F>GugGmBgtUBr?u>V2I0HF)It9bO zOCp2*cY$5nd|r53ReL*&%v#X1EsGEDWUrl?R&)LJ70HCuIUVX z7-FYmn$+31B}kuew$UD6jpwl^H$H<*Snih?E={)j&`z(&uH1rNfm!KBpf7N^l(o^R z&MPw?>p+rz4aKpGE3)UW3~?!1wTqs40_)W(%ap6!Z15-6_a0l1lzCku&cxB4P2<@zX|Q+U)xX$s zMxp+%i}tr?9;U~cJT^t?g*efXjfvcVFV}slxSP(_Dm7K7ZIs@H5l0x&4Fm%^)a&feeMVLjbr4oGzr$ z{?XI|)RCX-4G_5!s>>~&D=%4KDw%$S)!B~=R*G470&HeU2JfH2l2o0Xo(b{QZopTdBkNn;AvyUp1!kXlLX`Z6fi6k(Rp{(x-~7iZ$#rQ+o0KmAuRHTXkI;59nt8QUy{&^Uqs_?el9(qUta z_F-F=$cLalRh@jy^k)n=slZ5{2|E?_?DZ?XfroxQe6~zVy=kK%T(KoS2YfER-Tnpn z58?d(K+%A9!Crbm0f1l(06_MC3Pm$Db1`wYa{LeUoZi*b_5aBk*W9!}WJmOS)i?r? z2U7MpuamgH>w4UZY*P5X@u!^ojm!F( ziE4^8;Ei};LvmrY)H~O(@VE_-RhdwmcyiKWTW^Lm)q{-S48;-Wmo4{1y(_UkkMTNQ zYi{(Iq7C^2Jv+J_JAGkwK~dhFcmQQJ-%p#YDZmQ>!a|{{`XGcPTiOLNytg!Ep+sg) zAW_XzIk?sy4mLLA8n%S>eivAi*X^pMP{XZ%}>tdTIR__S;i>nV9x-HQ~?yLl%N@f z5t8P~Wh_R8JuTU7E0Hoc(~>ox-?eV$K~0AJia_V{?INQ6cgwjIV|@Nj*FvQtq@+oO zrF`GilifrcTs#=`}%tu03rD~Szt1Cl`sV@dLdmjuPUT>)wE?1^!j?h1% zVknlX%x_KRLv3nvmTs@)faB&I^S@hlUJNZUJ22bTw`E!u+u8_2u+@6kLYYEtmwd_7 z9rh!q%r@;mw_a+Mr+TbO@YG4;w=7D$dU{{jabPUgbjc1698ESk)N&SWorzA$>Wekft-H$~sVc=T zJdl{rJ6`VHisr8SXh6F4Z?lLW|10rrz_F66I&kY@pP z6SCDWxtcNA?pH3_HOIZ7kOTk`C&=j@dI#K1VD}z=2LgUzL?b3`bCcnEBd#VrEkZg1 zO(5Ekc_Wvtv(>P>VRs|n8vhl*9Wr8Y-;tIRbw22D=zj0b5tx%m7d&IQb#KFwiU^|~ zVl@J10B)bj6E`1hHvDv;#Fx*LzXzZXSul8dNB73=3(*HtP<}X!uonS|2^>ElY)ImW z*a684%DM>*jM+34i7wue z&OXHUn{AqHiEWGRi0z8)2`L~K@<@G{VLwttp48bm2_-Jq^GKAGf}BffB1*=d14f9m zI0dC37xMTwC51GX(otcGA3nUQELe9UA)1J-oI>=5+B~EA`BpCtLQ#v7b#6=AxxFm~7X3f{PGn<>l|?{p$$- z_uXE=-}B>MkL&%u!lfeMQ`0n2;N`x8<5s`_^Zot$P+_xT;htm4N4&!S=WYM~dlABS zES&Z_?_qhfU!b=qfcs7gVpjD2?@0s!G_np2n8N$;RQD}Gf56ZA`>=rjUtZn3nnnD- zuX9!k2@(7^-5doDTWhyJ_m8i){@D851REX3T-<$%TwP9nzMhB8M`sKI3GqK;3=9N2 zy#YH5JkI|XPlpdL^AQ|$J8*Zq9{TyeXE!VAWKv3WA}9CZB#-aV%YA(h7ZgeRJU{o3 zw|9S-Qa{Gp1K!{7j~fjBZKHFzfuP{;&r|nUfGwpf|K(!^LH;g=$kWO3`T4k4!|wO- z`K9$&zW$%QA3HDK*T=z>nafoXviJ47*R+g&U$3ts?b+h9o$s%94~H0PzrV-b8CO~j zKj-(yog0yH?J<_V?_Uo;!vy@BoH94c0V_4T{y*nA3I;)~2q&z3vR?+CA2UY;us>4` zaw^PEnA6Ut*|EnKUi_P^w{33?V*y6|!xys*1<6Y`ZCt^-&`=w*^Z{5Hbo|;F1Y6Gh z*f5T5;{hyln&2gChMV#N{;A;;ZPR4X+JtjcC3xbyPJ7b4SA0-6Ai8j}%Ev=OMS1?F;bKpzOhFaVkUCXk!^oa?rBr1ic88Hjy{`QUbEa9Grqu z2|RU|fSe~G%_bqqGF1&ASnfaLX6n9l)WNvWEnBJYNDQrrP|=XNMN{|k7C|*+YH_4% zRcgEt)Vd?8M|Mw1dg0OMA4f@-AQEw**#}hT*`j6{Wb{ieGzZni62YSdt8`ZB(dp)3 z@c#-&v)NTgwi=rOfe0l1lPGN@7u$p84*HFX6halro2}k7R7+_P!~s1kANeM8%wG%o zP9LaGUZ5`2SE5v+Ec_8tKEl44h$tYU0LQ7EON0W}WU7rj%umHi7oyctwcD~hOgWW_H9p@IUG}daq z_@8E&N}UIdtAgvh;4KlN1<22{W< zJ|X(?VWPH+V8BSx;*^S5NfX2xGv4>U0X;roM%Z%{U{nE4E0X`PJ(+_Ip%vYbvwRrV z#0*V{ip)Y#a>#y`c}%3+r|!TRlN-P81)~)r(PMZ z8vIfJN?qd>|KKGdP@uZT@3Ch9)Plw4TUHmyWW_d(K@35o>&u+*@x@QNnkXqNMaR53 z{RV~W3%v|upPG4&qYQ2l|6-Pwry7#@i&RxXq=651M<%8i>N)o2i#H}TQ-ltA{he|z zQ~|A}5&p`Y_(k-^eu(n!2kA>56g}DvR54O7WP^V|!feUk%P~sXvOZ|WZz6-N8+?9~ z^TAY?lBuH}cmka<(rhAmDy^I(&{&jAok+%opJ8(3t@^`zY(INqkhifru4^0Ya(7pu&yR0R$uE(Hdrk7sZRt ze0kJ{%SN@vYU)6sG+P^zk6CZ7@9R#_^v}OA_~+_xKD$5;vc;oCEOeKSv7BlTCX`J9 zbAp?u%naTY#%l2)3hSz_Mxpg_+cfzlO!R}!>5KP)b(qg_MCl|aF!ZKi*I2ocA~MD_ z0d+F`D#3qzUe+H&2djthE-v6H8dZ!93kT6L$t#=wR?Edx1NSR~dN}E|!)oA|%sC0x zg|?>-*7mt`0#R}1kiWDeZcRuui-)%cmHF5TRa&d}NK#r+W@-y8fpc*Xt7=Nr5~#X9 zFCAIREk8`fwX;YCp6t$5hP>Dx#r3!I)^qM!#qw_yf9P3dgGq{%}CtuH!ZZzP)*{%Uc+F_O8$W7OvlEx_+UJ7fLXYz z<3`hR`t|IM<~NTNQfb{Fe*OuB9aQ()^zIfOY#~jn!=#zp)&X|4(o8^HW2@R;8qx(q z+(x&L9Z6@FqCDE@|IRkD7?DDSUW$*fgOt>IjQ*xV*|50DSxM{>neO=BMMd1!SWtxA zZQjaoNAnvP!@Ml*9E{jzal;d+W_Ud^NzpqV`~d+T*9~mg8$Q#;Oltjj3A(Pd8u-B4 z!+7P(C`_Mfr@>_9awbHPD<3)A|D)Yn`GvQ``wgy$OdbMa(`rZ%r@htW4&V1f7@OENP7^ zMQyd($o^IC0U`_B13*DnVJkX!u%Q_;;%khD1byDvK;TTq$ecLC-X_~;6=jn;A0p<=1qw!RAW`r?ngES~BQeJH^ zPNhOW7GBN^V>_%$mw5>__#QbBL>Z$-s+QAk6@f5kQj5x5;7t3MrZP;IM9n<7SjtAs zfR>kSKkZEsPbbBNIv5XRSyyY*AKTMdku4w*@kpt*>=cX`LikO`+&v!cDQT-4Rl};x zNo&Qd8(k8WdNXy8s?ivj4%kF1of8lObk4ppT?<2%-eo2l=3r`tOD{Plb176-? z#6-LrY&-GPt@tORIDL>24~~hIW<=U|8fC~ol+jfkj<}P2wEfA5nzpRdji_#Un185~ z$34#d&bB*t-y2sd3m~AZHE?A19eh;1ZiHA6sZL~36rq$!+=XsU-L2yEgh9-`*QzO0 zq2Hv7ZW8QZ#$I{lwN-8NIVRIVKXby*T=g=-Amo9bLvQLy1@0_Vn|oV|LrC(Or)zUU`*X+{>~nY|@6 zBHJU;oT8j$-j7FHi_IR!1;v8A_-$H?wv46r!S;5uE}>ilafbdTSJv5}{tgS<_a=*P zrxh>eZtErg4!Fc4>x&jR!S_}AMfI>jtK3S9cgfk61{|Cd;a&sLaR=+Yghaoofs(HR ze3}VVxq5c{UFfcOb$-H3?5oXKgqdCg)*FUUD2u@?GhrXG^GyYMTG}Yhgi1x^%7$t( z%{uPhfUMi2t*fessb@6Ly}2zt#U<<$fm{c2Z10u}YN1ZkSUFi#d;8P0vN17_HeyL@Z3zkIPhu^-j49~S<%I-K!PCXPLO4 zmUfkJ(vVjJA8UG5+x^?dwb~84qr9#I2C^P6aMS3rgKg>}zo_&{nBSlZ$f8NlX2%Am zZ8pt3zoG}+47H=54#eAXeQt;?J!TJjlbSHII*7~SP`ItNhj%=dCOKx;`rg zMe1utmu8H=?P)ZUu}Xr<)v(J82;kTF5Pj3<7 z)!i3nH45Tz6}hYLGBUlG^?gfU`p`QH#KQGf*hnDLExE(mQ~qyA@9>$((IkcJV(BQ^ zEnaW1Co8~r@!(h>PZVeCNVpS4C_q+8cwNi*)xEJon)Mb7H?d2eA{}rp! zOd8DnFw$l(3r?FI{E_3G)QzSY8<+%gj>p}Pjd<1i2Y!JL2K~O zSB>Uw=o&x4B~B7OP(hOe=C|U%mMMS89%_`tcd*`OBr9|>f~S40)yNixe%e)h3)SQK z)%@;aJL^48l84-WhK=&A>}X%5k5cs$Eu`WTuBs!x=0Jk zy{nO(stB6pFQiXu#20J*np?XwAGU&;>{q0ORpP&vG%~Var=F9#s^$Z$XI%{o7Me#X z$&&{+ij7@&vY>ql90)-Qu)LHp+D{3Kv-_}jz2of{C&1P&f+t{16-dk2zU;%&?&x}p zbIHNo&u?wn(sbeD`2*^I@D!4u3WCO8*mTI*N2jza3UR5*tRo~3Bp%hukI`t0Mxb0;v=EjN+4vWxBEjYc@*V{K!A-c z_F~y>{)@G9iV-!4wshMzPTTH2ZQHhO+qP}nwr$(CZO^%r3^SQ|xbsp;JydExRrdey zZ>{w@D~e;Oty$;8Oscg9bH2v}H+2<<1>jSML676lPn4`DJtLottI zFGC@hhoHhwOoZp36BL`wsL5YSQ*}-a2a1G6(_v@#U0AA@!bqn_)XJ7Uz(|aPZ#R15 z=2{HWIT>a$Ptwmr?ASnyd^ba4u^rK?gG4i_-Lxc}09i zhhhwuF36-&L_zpe*VCw5g;W`!rI2gu`q(t6%(X>nFS><;YoS}w$FWHTzeFLNJ$@N^ zUxqlkKS>$PD$*vkD`6!iECr*`wI294{jth<9F-kWPw1MER=yX1plpWnunKL7Fw^-b z0}X1*^(UzbS|~vdBhV!-+$-TYj^ z6Looh=DC0zDFj{x)75y=*=xYq(W6$s5X|*~iMY6SH5_IAZ)2he8X>{-VLqlMvYaAi%ectlN&T7IiUM^wPpnXai0>GVl6|h* z`&Ek3?bmbO8dGuhP8sqj59Qj$r=eW7hgPQ~Mc<9@{!@LFzqS8(aWxm8rlwnDk^&b%F zakn@5$cyw)ieEB|sf? ztbQydwUtF6&(y#Ub%|ApXEEOb7L6YV*uH8Ve{1$p;8HQciHkWJfei9^hJ*oKX0!$# zexYkW$g}6!;`g;^)1~K@w=J%JGyNFnd?1Xts*l>%98GTeJO!_6zGkfuq5(jfu22^@ zL&r=bCA7a)HULLtcXqe@EU_RJ4(Qb(teXI&BQ4IW`Pns=1@5d>9=X^b+6Ud|~???#T1{iNK=^*be zDTY5mUfP~F9H3edx{L1K#O|r}M@Vo{Jod{whCArx_XxW5a~^C$;tJlO24iY_;PctQ z7B-41^qu#=@@hV5fBKt|C+YfA{?t%-;&#fn2GN(MmFuniGc1sYHm!t|#BFN?+z8gN zOZWjVs^fPIToaf$6^E449kOm3mdnGXIa@)qqG?=33RG#om7zHh2+(T>@E1)Zvu|}H zOd>eas$&eGl*8WuMCE67b@Z!LBHtv9{Ind>q0Q0Udqu!e!X%Q*0Y{N%8hSW;MRD7s zJ@tyV1> z*oO!>R`JC}%lK|@so!euyuSC;tTtQFVOA{38$mb}?D{5WaEx>%EzePG zv(t00G`QG7o=@jo!r9^$k$i*CXpfPzALuuO&b&E0qd{PV|i z`uFI5!mTyNlSDE26Hz=c&}?>F9eKC)Zl+=g3F_GObJ+D`oP7!Yw!I4L!D(oo>PL`#w|}=0Kx+_B)Hv?#B@CJSD+UmIWJ7b;-t4! zMyFmxNSjCC{KGQK5|w9^&YL3LHbT!Kb5n6EI!L^GF5Krz#~VOmIia$TBZ*zLWK#M$ zzlE>DN!cX~8krw9A^E6@Y$Rv>7olv|#p7Ge4LI6X{TL}x+MaX8aR%HZQ`=NN*o zY_I%}+s@O}kgno!Cw|6>m~kD-Vj4Ll5ebPrTg}Siahb35n86lN^@-Tn$U&|sP0lp> zgODxEW|2p-(HT?M$h@)V>qxsU&#pk+(T}m#RVHw0&OlX;Nt)^4o7wgKLtV;^^+(w3 zNT@Qj>N~z37Ej&jq&J9y5}Binq|4BI<8*+9BDflf*pSRyP`wn^lhPW8>=y@%k?xoz z#pTwKWoz|IyRn7OI3@~2V9W*0i>Vc0~0%<8MRs5naUX3Y|>hZTsvf- ztwU8-ZR&-86veRDZYIr@CdMh3W)CYBSv-HLQ4Fl#S^+IXFEIuR6fT~s1Kj9K^(+U| zo}GDb4_Jz{8dXdjgi)nDdJ|1#bXi?>l$=k!?9UzMcKmT+9X>^xSmUa<-yb_kz|$UB z8ZNzxkOI>v_AGEp9dO{3*)S)s&)Cv!-zh{gf*Czp)c3)h+yAR$H+=Wcy)QRQsQWju(RGL|hB3r5l;q^1S2P`sP`K-E`c16=7L6PG76n1P!cf-`n+d?mJW!=247fHxjwN8(l=g)mQsEH){W44gym;W}l%vZB zsm9L`NnO9)S^E(@@cS@R6`;4oY=k)ZT6XA`yVB+;B>18~#PEz0Jo3(QH^S6SVtj;P z>o{=fIvPoWCs>~hzb6!*d9<|>7qZG3zdK*n&QbPH!WbBF|7fxXuSVE2qRHw{4I9K< z#eD`$OkP*Q!gWh_{L~^2U_LMN5;=$d`Ais_l5^_3NaxpwnbHl8b&OV7s(szhbVOy! zA>wNcB(yYa`9$5dv3V$C&uw)V)-D7Fxc)Y~F$bQ$*O@;)Tg$?+w&>`pj(_)W)-uTz zFV(1#t0S;?h;piwOu$(L6>eZL5YZC#TxV`A#e^a2I5>-8#v@Uz!Kks4a_WZe_gq%| znPsEoZcw@aOlQ?Q(jGL7Yl?Q5JzWn0yWiS2QAUrT_^UsY=WfV6>VW8xI3&r{unkH6 zv{-d4ZPfFU>#+v1ttsBT5yw@*dP!_OD4@ zzG%th;)+8p)lZ#|7oYMM0W>j3JTU7)J5M9CZb5%busxh{WCJW0jZdfYXTp*?wFT=$g0{j~$m^yBL z`*cQM%iLr`((rCzMhCe7N3DF&Kr7ChPcFRY(SjgWkM`Qm<4Otv8aCKKYgG@3q3q=f z!>3?U0*~7=TcZM6m$1(A;ccWgbe&nFIE$o;qnW4CIsiZ)dZ0!Gs8=xWL8UFBZJmm8 z3JlaK3%I0ajH<i__3O)>6LU*tU^8rNcsg59e>2F?48f!pACitTMsH zAM#4Er|vU9@uZx&c^OA8Ayfg%k!jcRRhQXYIDC`l@J-%4)_M=P1JQ5q4JF5GhVwBa zti=Tp&Y}#I*&qb#gqW&OoncfRN|z}ffx5Z!4m`6Q-SLb|Bhgu|aiE-n|1Vk00AFTH zVRmuH&4=0ulF*7=RqLlEcb5lA>3q5x`AS2`se6B=sL@AaSEs}UbP84RqeidRC?VmL z{|&G-5hE12)Dj!W)vFF3yYQ_hPh-e9F$@J!yHQsg-+8}9wM^vD4s~!N-PbME=($Hxq&dXy_#Favo4T(NuibL@XW4{*+?(is<0dk z6??}e^Un+shGp1814h41C7ZHs(bOW#ACK04h4X%;CBWyvg>3Ra1mbl5F6~93e*$Y% zQt{!Tjg7-PYfiS7oADTg+-S?1Y58_=wgd?+h{k9qnRBn*ugdGWLcz%+GomtC-{kuu zCWYlyFTwj-61G}osoXz*`@@(>f~*Vi5H zYtS)V>~(fjskTx&$uqg$`L^_!%5f#nH&v}j$%ep`O8~KKT$?g1tL^iXh1dd6z0^DrkZXIx9r#ocF}Nw~>r#Y=Gf{#ko9YDk<*zz~F#9bX}O(y2*3| zWJQe8dMB=)tbOdhy9$eOy$6l}#7iKzz(*!hSu2UZx})~fn37Rjff1b-b3#y=x9Plb z*t%3?Y?bRj6Pfw`L_A|oB}N?aZ#!)#XaPMBpt9~HW+8ElHwUiwQDh8}(u#qi84`Q4 zBppIryBU7!==rDfv-V=gFssZR>$G6$@u-rDU%QR|pC#2-x*N3=5zNU0=V*IEh zlfr0;ZcTLdOjG=>NQ`Y5^!34>Vq$1h`rjsY3w9^0Eo*(??!JsZZ9N0YPSFAeH9^*m zxDXF`{JrRq^*=$y%-g3UU6f1f%Op#}<_lMk?W`IZsvj7@-fB3A9Q~IXWM2i?W=|wt ziOts%{rb%!cd9dI^BcQL= zL~a1+MnKFWMn?{q3641|lVF_KW^*{&$+BY`+y&nhs*90{)3oog(-VRV8++$x91;&^q=df)M1f34gc|u*421!M4R*WsH6ENsqBQ4d*NvWN zgRLR!@f%TAkgyAX)5xx^EF?a)@*)A%gO?P@SlzV_M%sWO-!{SC=#Mu*xTU0jL=oVd zGu;rA^Xas?Do^ZfOr_?mh8^>Ty+i(pr8hq(YKbm%(!W1b9T(nhiH>}p`hpUZ3YiwZ zko6IJTzfcY>qBC;V&e@-x%E5oNDS;7Xp5DhHDT`Ib1xF!;O>o9%@t4S^9PjAJC4md zyhyS@>iVZ$!y6}9gCyX<+XigLveRMeq~gR<-?>_(N2k__r${6_w>>`=gOuRrNBkDn#Lb9%bI8@+cf8&#jmT9$hUaI2~pfA{Ry&DOTeoC3c=2#pX8# zCq?;3czw?n+9a*g;^=6o}_k=5C4?Tejnk z$8xOxWu}+0(Z=s%m4rvOma?%Hob6{Sn8qM&Brm-nQ8vt5kA)@7r~CTs6s*sdK2b^O z;}=hFNNr!BQ#f0AX0~VAUxUzb>fB`cA8YsD|FBe17v{@(>y?3eBxXntVdtlsB$^>? zVQ>l-ToQ0_a7fTx%#sq|6$>onEHpYJN3V9B=( zr?{HX_hA+wq$GT|WXc?j!0lUeI!r+!_~*YxdubZkq%jYPpG^0^%O|q|g>sg)wo%)f zTb#0Y$t0P)iveGQ(fuxhB4%bKwvX#Z zbaf%p6e4}Jh*6Ubu_Ua(T3bWc7-h#AqFhY807m{A5zPQ0{d|qWr8r1#_eP7c%9I)h zb?V}4IB#@o^Q?3#DzYjj(&&sgjl#QVHb0H#6~f5aK(Ihijmd=Sm7p--_9En z-eo^UWG^u9)gYN#73cuY0Xh^}Ird#uOkXOfI=rvjAsMdCmSb+YKAz*}nN)N@Y|pfQ zY=XGf;q6dF`ceTW#a6sG!I`IE3BhEzX>hSPcGjYy(Y)oK)NZZ!;0FGcKD6is&Xx_N zb2n-^QD9XyrW8mx#rbkC2A<;q;LpkVFu*1C%t$vU*aa+eiSam4ffLn;)w)nL5dj(% zRdiHI>fGnaho$mwreQoivuUvZWUGkmb@tr;aOjBo-QjFLgi}Sf(cRPb@FqUYfv*L* zp)FH&x^iofsr$W2_VoL|bMPeH6?bv}IQR=20071RUl65(vyG$EKc4dc4N@*=$=e>V zp#<-~qq?uNx}b6v<=YxVMa?ytgPva*9xK>dGKW)*I<;zC`{t$*;4oFN(71@cia}HW zh0?X$wy*9dBpzQ`DNxfsh*?$rbi4nj%pcmS67Y$3#+PA6zi1qs?wY<6B-AzFKRYOKt@woHa^|%`)&M94W3wf;dJ1EKV!T zmAqe-lQQwa-~L|tQhMS^d?{o!S5>zq;ynTK!k>xu`v&=p{vdYcV{VF?FLCsBctc9- zA7|Sz3uE#*Q)r}a@JBSvwbv-oNZ1bF{vC!lhaWDlD~NZYx-up5PeOZ6WQv9vMOCr0 z+(Ex|?quRx>{mHoIK#FtlrYYnRmM?}{z6uej)=3XG`K*osyw|AN#!Z*RlLJ~k{PnB z;zaBv*eQRDwjygN=Mw6%idv3-ptk2g?S=G^zt6}=NA0D!FycBr&XhAyJx8i`zOMreR<_$OP74RivNw!>W$}Ala z!<3_4ZGD)!zAl2y>#)LeMpa2XZkNUJykJbc?mQK#+Ae9P`Rn{#cG*I1bF%TLv=i#R zl78Y^=!>WLF|GY?bT`*C@}29^lY3B45rpKo^5&8KlsrrNg2%a&+w{}UP(49P5;@!O9C9*33G4%`IqXC28^Uv=cQ5#(( z!B#4N8=YX80cN0`uE_6JTOn~gkq3o1ae$5lQpm0(B1ipp=Kz%zti#pZzQ32 zl5qQ}U+C}0oD(~&T+6<_+$>=m>4HZAAO)15XQqxx2(+1eRg;K9FW_hJPDu#!z;i@r zbQAytMUSE=3w%XM#NmbsYnAyqX?wDr1}rtNeDjdA$OAuLCEuO z-NX5?f`&+N&m$rE-SWJo`|99!f^8VlIoGOCr>dgfk}&&b2;ZA9rM{kcSnDC|(P&43 z=i+G_pJ*_p^RnJ|afx@Gf#-lYA%abd4q+}DaPu;g1X#-KLeS))W`M^C)5(AKPM2bk z2uBDqsMv)ZNq&{5lYNpPg>Yr@vmgqjk@g#WW03Zj7-aFkkoE{8Vvq*u2q;?-gDM4^ zvIw;}7UdJz4p${15xD`P>krvV>{FUj6MUjLB|b10X}XzY(3|6@>r*p#7&ClKam-j`~)nwhrb_X4bm@@jTM% zJD8aK--JTZs{J;9#$mfZshq~8)$1zMh%Z}Y46Iub4Cf* z8Ii35q&8{vhiXXi@bJd*c6&bFkEbvHIm&GN`1*Vuw#vf0w#w@6^nSgU>hAXRykFAY z?)G>-ZbqW>_H?zoe}2f`+URI+`@X+;eI5^Pe}4Areuw;{#+`mvOFL^_XJ2PuV|8nG zx;~GWH&=amelM>Fd2w@Za(R7zw#j(6c(}TLy?*!4?_YDgKW^xJetxbma$|FAYIAOR zce#ChdwM>+UfqAc9^SrwUM^QdKjB+_J=;2aKJJ`8uMeM>44~iQ;FFVKWM=^hd84jeL*!|rU3St}1=x5U*b{`XQ)bi>; zRZ3S2P4p<0iAV(G0^E|{DVhg+TEMJt*srtaGbiw?C*7Ydv21RdAG6tvV*!x@16w!J zzeeRS*0Kz>ak>{r>YLQVwx(r11RbEcWMia;NZLU2Z=g2n%MaYu{^!}4k+hBP$)pH7 zFD^RM=2nfUx?5!}0f&hJi#`Uoa>5VSBsxRb$nM%-s`FDceNx>3YXn&W;5UeKYD8ZH zKI1sHR}K}xjF3e2G-k2OcW5u4Sq6+u*H3Sb`8m?A87df+-du8D?pfw+b{BCca;C zMxCpQV0H&Lb*a&11iF5UR`f4uW1lS$?)s~uPNhe}r6&X_-(wG6BDsiOs(yj;msTE> zNDehFxCtE`o2(UO0!}p5Gi^1>q9mWDMRmyN(~ZU)qfI4~LLt(KIRjq?3yscG;~6s)h>m zEIG(t`$rlf`Q3qlCLorrp1kE9I;GHEj%Zm*QJ<~aKGhExPBwmurA}4?h0&%I{@DAu z3Pn~t%#OlVc(O9;->^D30?0)1{QM3OcLNiD72F7v+6*|x9BmwpD2>26%X`nDQAQ3; z(czl(R|k*2`uW(+HkE;$6f>j$-K z5`-LVeFqu%JD7Eo)JD5efrgSB>O7=;_%lT={@RB9v9~QFmZaufl0G-1$Fe%s#CBb+ z4A|~b0)gM=a29Ak<4R!oB4kHk!3Meg;i{t`s~`sGJ_vtgR?aa~ekaUQlPv`$BO(bM z6>Zq-AbzrPa8W%123o$}ZkqfMuJzE0qF4fOKBqUMLoSF3lcdasCFcsxa4}bBCD96W zYrcHa64O@)Qa8eUxbH8*#swh@9&%|3=>%=tnVz z%`@nk4|TI-XTs+J2?su6e8h@R7j&#Z5Q)fQ@=O&_NQgTJx3J_J!zlgT;9;8lr;p=+ z-yr|Y3uqbG(eA)eRFl}<@ES=P?Wruo;=oFMndUMDrkNvI();Er%R*3(Nj+7xXQr5N zREew0@57fnqgneSP+$VLD`fg1#@IV0d?;WOgBa^TS{hxg6Q-by>?2)RpN#u$g5%0_ zWK@iJsy2KlnkhKB>}tW~c>3})60{5&_>eL8jRrFV>gV7qh~3vj(nFH>1lL`Cra(z+ zCiQrhin_C6%=A}^2IPk?h5M6a3U#-nk&f{STL?K<%cP#((>8J&E{IGPe+jfb;vlo# ze%Qv9v}4Re8mM}uLG31sVJ&IC%?pLiA&1J!vftzmUVIR9guKwO#WMXh1(+d7GlYFy zY39T~ss{7~LEPZ{M+8MATZv^J#M_QvD-13W4*jH^v9*LO^~NYSptdX5orms;PSkupsiM?71R}c%_4RE>_016;o;N z<;)Ag2v9J7?ELeD0CoHn7Z|M5j8hJI!ayaQGT?f6=F~CC`*BsjkAnXkRig}41lz3R z@>AfG3eKVW3+oUsX;jqo%66KpsA{E1ugYbv<p;4;pPusZi8CssSGnv$Z!pKB;h8m!7J%s`rSZM zv~rikMg*`YGXYHo2siY2hvG0C4sZM5uJ&mPnTp!CUpK29tC(#3TbqZ_%C}eB|DoBd zO!AifpEY~D|L2Cr&cWEw-0`21S=ZX!(7{&Mz}!aP!JXF7O5f4Z+{D~a|36gDx<dHVy?@CxKKj3F_2f8;P8Tr&0QmlC_2mEa7aQ8zn3$V7JN(yT9BB<5UH*G11MU0& zdns4i;|_;n3Ei))b>{o^qT3rACN|um>FtZ;NC{@c)on(~GpnHh*a)(yU2Hh=0eaT{853o(D2*J(@^kBK#BSsZyd(x-Mwsw5uYv?56F3W!YYiKWnO zJl}7dd`T_u>s=l9VC)rZRg*=lyhcoX3&&2oZaC-ZlUbquY&k=+&l$OBMf} zLbJUMg~zne;`)Ssz#V_>-9N`w`)aiY85dO`72}EH;Ex!0&R~k9Qd-5(BC|2H#nujN zcOKxWvex`-+j;I4Uk#tw*5Gzb=DtO2sXYj}F#R*(N{XkMLH&HUkaRAZme>7)zgXJD zJu|r!9hyPf`(l=9>g?P1c_`__YTU3(X2~U|1>}2JscVO-^>FxVS+r>h@*YIFsy@#8hgF#8u@&?*jn|>Cs6!wKWL){s2Mek zC20u8Z%y`zpMIsU-+$|}@X=9*tBsQyI!ugfL$+aZdH!(Mn4LEs`5mjAfLJx#@WAwys-!GyBfKID657&38*$CMRaK1mnGBrwXl)l>$SKGY14n z^XFQWCA~+#=YEy46KS-Rw?SK41zD+DAStgpQZ%55<(U`%tMkgbA^WBT^{6?s<`f&C z9kzC&DZM&@IGQh+xV$lwn8JIzNaFGWYnz#So7i@7{yGWvdyBZcB(u`A>HPS6vx_40 zey{}pestZXS4|~X^y`ft^?6n!I0~LNoaV(~@vUn*XEcym{%97dg3PDuopDndOII^- zsW-#q98?yW16iw6WUN=ISvbI=Mg3&f>EXdK{d=s&+dKV7PSFAKA*4ZauBm~EKeM*v zSf8VRt0g+&sIRF6B|cq&BWK+#0A!d%xw8qo?D>uL4tV@5yyjWuJeGeCP8OJh0s*s6@+ti6rs#NVRH(T9PMS0(Pcm zZ9Bw8RaT5M;mET#->7{}L@11lQ@oy#9AJ-srJ9%0QR;u?79zPb!|4!m=B11C}> ziw?H<_>G>s{*Bn$J964TwBF$KGBw5%Znt0ic%$Y%sR%jGxF&9>sblAd-^>fs_W)_^ z>2&zsk*S7KV_CB6);fCq1v!U&eINx+=Vu1rtzYsD3RPsjA_mk!_zp)^2^;BqBtDqm zw>N3gSbAzX46xCB_ zy>^aXH)11r@ld{;c)pJQMfOGlaV%<**RKnvb7slXtA)=cMFDg&PY-K&b;Ko|>|G>3 zrs4N*A@Dh|BFSz2_{i_G=SW$jg8{eCk3$k<&UYe^$79%)U+Kd`zE=GPNMDZ&ae9k? zVr`dTP0(B)!2n!8Ci3j*1~kj>uDTjeHSOOPzh$rtr&s&mG`~nlc9SF@@3>~s#my5+ zCiK?bRW9lcFw_;3`*1Bg)fOZ;r3u}R1tC@@A0t-mb4EPC2`}sJy5W=#Kx_3l>;)se z__6=WaZ#ETCes($WLr6(%|kuGjtEqHK^WI4pzy#Df*2>u^QTn9Q2g+dnP8y%F4i&1h@TS^XP<*( zz7+A!#x*^*f3-PyW8qo3J2A7CT13qMejV!me!!n(xK41kyzoO&n`LSm(i3BjMFpCeTtW?n9{}3IGHQwg^KRwQAgdl-vZKwQc-3 z&dCN={c6anp#aDb+>{0gY27bUX*KBzQ%Ph|hvK&cLWG$B^(8{$9AE1b`?<6u89X?0 zZg=m&U~9WZZ|R7pnhsR1!X9O7L1l8aIJmxbBa*A{cwb z$Z1+(+X0TnnTB(X??WA=)A$@-DazRgt43@XtT1ste{<~o#Ob2aCdV|GUy{p?n6LySA$ z+EeAblDD7LO=ZU`WcR76tG?H5kwwqHhZsv8>SCELz9~~K{*Sbi0!HNTQT1cDd0cJ( zp`uN@5aX;)YI}D!j&6fI5UB1S$W!^h8-q>7pj^=N?m?gU%yRg6^7Of>G}M439EHT8 zqd|3KpB~B2$rzMUj8!n$Sm#Ca%NUg08Ta{|0zs3{JluR=68H>hI{5WRrCZdk#wY3_ zth8##Nsb%AloNNc$Jx{-I}Bbqks`2F0&hu(1Z{cyIVo~TyPmFbOBlQ2ffL}TjvDSn z$t(d`Pcm%P%@^5xQ~_5am<|IPe0B~{AI2Mjpa)Dw1tH3-5jITN+Z+XMV-`|3)5vsPr$9KJz`CZ)`XH zlmTSXC$M5UU`Xnphvs1AnU~3SuB4`9xLS#M*5cp0-ed#GX!-B~!5}!m@2Z1}31co0 zKjT|W9k`6Q1A#N(v}pGO@HGX<WTjNr?IttcJj%LkUAO_E*|G7*f|qV8?;n_>6zUsDPY zk{i%raslD)Aoef<6`ot(C6=N-<-jftZ=rt=%ca{tnj~6@1BIN^GQWu))FRX>S?W%z zQa#=BMADqyz!R#PYS0KEK|7P0gB^u7s1jxp9I_rUjL6?nWzHy+R>?>m8kx44mim0A zlP7u&dZIVb&(Pt9gVj>7_QwBKsqXL?OpND8ERCCj(Bhat6ZgGStI?URlOf*4fj+Uw zfn%c6wvlULQUjy3Cuez~@xUaWxr9um5mUT<4}}d2_3o&f?%%1my^I}qbI!gNoyjfJqmIgKg;G_K0OWhco^XPdhZJSXphf^o1%-bToLn+-I!6=(g$p;Sm2fzA71d6uc&#xLp6QMsmPGk)+g=Wm_KI zzS$uNB>89-UfG-yZ;i#Y+nWJ#vQosK!v>Fe4bMKWI!e$$;fjFZ9H3Bd_s2x;`*;7H z7?tL-nA)Dx{;W_N*P*uR1&4GR(aCA-rz=le)hf<2iBJ(*F;5AB zKVGB<6-J^tR_X#~T9jJ8NmZGb%sT8b%)eU*qYwaJMN1#ww@SdU|r0i=%V>5Au7t?Rz1$p|J1GY zDc*h;qguC~&`_F0>&)$gktTF7HmDkGdD*&?dW+jsB5 za}XT+pekxv8TDCUCdL0K8U}X=ChH1{N-~6L?8{*?1ukpE!J#ZeAw)51-IT3CS}sy* zw`(cxQ1YcfCcv8t_SaK-jz)nj2fAzT+9lmw&YdQ_Gfj`Phpj(-jZ*aEiUz#;t4pgv zC}7_j-qYrT4l8ZSrOMWx~wLkrMf^sOJ$HV(@&_5uOFK9u(xckw1=;llUUM4zC!_(Od}>L|A%cmX=^D#qXcRv)JXim zn(8FUw4+dJ69k%DW`ms%-VNJymm?MzNXJcK3n(fPdt?=8fh;8(cwqr4Xj{xC^{-MU zisGwK$bqM1Q+gDkTWdyD$geOlo<-!i=j}*N)LegmYQ7s$LL&&6MIR+J?>eo-5@I;FBMpjPhYv+$e(*3q*z_UD#DRqq=^7dYrAy4yEu9r!NCmHdQ4g} zK%%p9>E!Q(Z;`>ULL90ZJbMHmaH{ zDm-S{zGN6iy+hY-NIP6@pMB3_7i7)k{?adhNB(5{y~{06>7LEhd4!xGvg^VULmZL@ zGO^h3lrfE5hIr#DU+11GR|UQwPdF(gjAg7a!9@Gp1Jm1#Aq(Yvl(Ace#uzFy(h9!Q zxJ;RE;5X4rh;en)*hKC{_}cnwx&7t>z;xN`wB$&ZKatpSN=8ScD#V>k*X2-YiSp~x z1(;L$i=(Sk;@GR#5s;Q2Lk7ymx+MUtT&7~5dQH{=a}t)Lfo~wZrXlgWzv%UY;4x`& z=4SU-1Rv+faKg|Y(SY?RWIgkP@)Rat64H*`U$wz$MMflQe}z{KJ#4i?+T%_$q)CRSmr3C4 zcikq+liHdIa<0r)ZK&3%)SU~bb8v%8#8G_dt+RXCi9g6>gT%r?E>Cx;NJ z{A0K}kV16Upfm(Llz`qiZ1awVp$=L5%tx|aqr>`4zxT3a$?c_VMx$-v& z;065NT6}{I9I~rPI}6hVp1Ap16j{<6JAJ47^pWP1*_Geiw6xV2@420nb*^UVD{7fe zJ2=USw1|#H>y0e^50pCk?cjSc)X}+vtaWAZLY{ZUdfvgKM6vJoZ`jImaHO~>WByE#DnyVYh#f8sQQ~>!YI=Dt#I23>D$ws~y>=bowZ9_NS zzHA9)xJ7K)DhO!91|4NaX{7?$N!|Jg`vhjqb^!(20fl_^ho=AZxY~KO!0~Jzhjy;@ zv4|2{D}s^&ligHrQ^xf``4ANN3@-b@vhIl33RLFDdQ${R7HDBC^zObm0~851vm&vj z-WQ?1I=+yxweL$>!_c~gd;qcd%jI@CsQ7py+3DyV*@Bj@qxb~-p=p=M&4qXq2jj~j zd*z)$z4=BTtymI zxw?_!au$LlXhgnqZs~Ri&Kk{BKX$%yt|Cg$N_MC0dG`1zR6StY6=0l4JZf$-2+B5C z+HF)kt=U-^v3p-B5^(HuX~$n>NzF^;*2>w6rh{@*WX<)R+dQGi9y=c z{I1ZU9T89Hm8u67HCfBN!6N20eskgb3%5mlMWj`jLpeoG5}3#$Cfh$23aRvw-Fsh5 zm*Z%@rC4*6cfvfx9aN=5<-A#$m|iPP`uQQ&0}K7OV2O9fYL2r{Z7<36MbATixV(Iq z>%^~VgM8CXYz|H?fw^KzBu{6FRUpCy(dT2`CjG}mn-1|?2kO2bRCbjwk|YMreR$aW zM=9_XKW3Se$fLISIg3Anv-+|Y@ABTm++)d7Y74yZn}Elhdv9~lH~v}x{rRIw~VJB9@}dhYe@zhOKVtsmW3 z`-21JZ!ZNiXf>M(ZWhK;x~h+;sm1bjuL}1wB7-+q(%&ZH4uk;s5|-uI-O0CZIM%8B zrLD8@$|&~WH$N(D`>UCko8aFb&fcGSv!1Tss%Q;6{dm5!nyf`}_Pw4E{0@leml`Sf zhQDU|dv=qZ?9i} zUM?21ax#7`P!J?n%P1pV6d7)_x+CE5M<++rM6lMfCuCTk$bADa+4C!xB0rWSYF4e} zhN0g5`{L{Es-oZI!JT5unO z@?aK)u(Au8%5>p7bSG$C1G={<%^ik~=}`oFMaV>+iYC%z^9xFic-GO_`dFgRo&xmC zCUl0|%d+%{R9^JdbEtbVLo29=%J-Uh8OXbW|1|k<%e+q7@fh22-?G@mrkMmGI*IBC z)&t|_vt;_*7ixq0tIZ97^@q6;MZShNO~;@3_^_}h*VgLoYTO0Su^$Mpk#Zt_zQ6u6 z%Y}g=JrqSi3Jn>szo%Q_SjfjF*4VPC3<9OP?BFkT^A<^b!4|Rqd<~N99o$+dOL|-h z3w?6<&NNMiq`=T_tmO(4A4oa)O@FTnrG>G0)ai$z)i0AQm50x10Fr%V6M3(`g3%Gvn;P3l~CYc>+E zD_6hI)b17U5)OK8r}E-B@ipnga-74ac>a~cx#_HPci&no$`KP=6*fY{*2E4JXP*~-CdX7{ywYb_I})C`o2x({v4(H zzCPyu4DS9O)&Aa;_P!-9X%>-`g2`};2In~(1M z(wjT$^ZLHa^LzIUuj~6co%?fj`*TwI?-Va@?)Rgv_w)BF@ApaQZO?xJZa|U0{kMOA zd;V{~`}QBd`p2(-_4N<`{x@I!zrWLEpTBzlb?588{+%xS#V`Nj)n$M2)em2N|M`cX zf2F5;b^fou`JbP^`~LH9yX^n?&38Y1`~BD7zj?OD={L{xIRE-tj?-_R;jh2?=AVE4 z)p!4ze)h8;JN*}*|LN=RHHSa@_MbohH4EbNcfZT!zx?j==O4T7um9pFKX&?OUw!>Q zKmYfy{{8d!KX%p6_5W{w{oC&B+kg3O^Po$9#~IU=Kl{zE|MBy8|KtDq%gwo+9 zo6q0;@cmzY``zCD_VaiDq=Ucz>btMG$v1C5w$ERF{oVIJ{OzB=x#}1H`uYF)=JWSI zdR2VgSO4<)KRD#)H~z;)^!(1>da)P#&%XWj*WY~gt6zS*#`ddUy}$Ih;+My}UR`<& z%p2MEIQ>SpJ8ue&95J4|K!j9 z;wQiS$)Ds>Zt1?=w)?*CdE~kGoQe)xYIBs$zd6+3I%~>(=l9UzcD-UteQtf^+~=Nd zZP#1$N#nk4Tj^uw+Zgw;)m+A2M!kJX_kHNY=E%M5w^HZ5w=Ip*YuBgE+4r`OQu8)- z*n81G+t&L`zSpYv&6M|)Ijn8gHU{tDUUiFew!CfJbt`-CWACNSdHYoF+tgLPq}-lf(_gwcwVcb;d~&EME_I|mr?HNoZ*RA$Ic|(i*X*R6 z_rYiMIcm?Eg08{)#K5GY3F~!uFquzHPvH?ffmt8e`Zk6id+5viUiubmp_krjD}7Jg z?$|T{HEpAAtUIlqq`PZ-Ngh-ELxbO@ZnNAz^}Cj+zFdoJ+%56q?R>UJLAbwYQqhbwcNsWpAISLdb&)xSonF}z>=P4}c>sU_)=cwUyV z9%*Y@r2f2A(`*{5QhCDKv^>W?$Bd8va-wVYM3;NXnp`cex^rdHV$+bP5l39RbFkGq zM)&cisZKqQt?!wcNc}OnN#m8bGU7jdf!?Gk)9&uYcbT^~wPT7NmhD{j*4o&3u-JBN zq|~a#$*wm}ozw>N6Kmz@YqU|fm|-MOn_1hRi-*>drjfhnkb6$K>~YU&-}2}UQPlX} zsU50~r47$=KOePw87&We$>>uXTFX%jZSHwZaf*$6p5xQV9w(CSeEr$Ir}dGeA~ZJd z@r^Y#HSk??_Hw@UfRcoX`>K{nl9f#&YG^H1Kf^Y2N=akq-JzHFvXxy=-QG-i{e4Zi z7TxidO&=<$SCXWTWCdSg*@(=Velp2j-M6;l?48@z0@j$0k}RVW>$GdJOoZ~#obGy` zw8B_3u6>fbwl-%qClao^1OgZ9#MIgv+DNZtU-i*_LovKuKAP?Rdz)>ai5-`uoRZr7uF*Qm}}BvML$H`Tqqp6d1bkO^Uls|}@5 zlEl``>vi2g*97L0)4Lrl@iv+!t&bK`K-;%TPfqQK>}67`6p9{FV!?v7N~aEeM=oz> zdH&vJd7WdP%T1bGyJG#}&Lw;F(XuV7ZxqG1Z(TE@QAHL>b9W7D9z46Om!74j^*;_} zqm6C1u zbz?PQAm-ckDBbnEC=5RO?Nq;+Y5(6sHtE5Ngw;TJ>0H%I5|2S+IS>Anokyh2z zJWCE)>~?*$7U;l!io23jZB1_;*2)sYI+TOJS?TL8rEOho39eO?B+s)izPwkHs{zuE zN_@}d;N#S?lIq^uOtDMqd1kfYznEF=**u$ndseG-)L^f*HOmqZx<%b>erY`E4l*As zWhDX3V@mw*1EV^7!FO#e8D2Ac-)dMT%yhF`e`AZa-C|OUg>d-S2U%SzBP%*boqa^_o+avzW6FUV4t<*QqKvx)0? zy^GiDdQo0r(*z}pZOn-wZ9L@OG;bU&?TQ)xRyE6}khIeS#nTMzSB(l@Cm$#=E=AX^ z3t7jNb6V&vq~WXiE^AAB<;unjFR5y!>{={qe#+?&H!3qFV;;*lFzb6?J{N5&Ymo8s`j zalILsE(M_xlxr71?S@sOS(d<`S_8YhQ%P#Mg-xpoJLo2XCXumWfTg)^Nn2RER@!>n z0@oetsL9vp5haoIkt4LUip*A?vuM^V1O^L3Y9CKY61r*SYs4gw((QUw5LIl}hNU&{ zy7`QEUPidL#6fZneR*RS@K2e3&;R8Jl{9_GDE{pLkk&}iV5MdSSI|o6nC9EvjB#CRj%Zcuq2N^AT4B}BcaU~oFjh~E8Jw%gE*Ba3#lSLw{ z=k3;hT4WMjl~7KIawMs|of(#$Wx2M41k=8Sjk|4_MT61qD_GVw8~6cw=S_M{c7>EI zGev|f+04-l>mM>0k!GHB$usXn30!YdB4X^sB{dP$ILk6)hL;2Gl?J9edPI;+gM|3C zjV~Equ;zy6O*6Vp`Eomjb`h z7gZ8K!m4T7!>W;mCzaM-Z@-&{2lGk`8y(o>C?i6osy?Xwo~1U7xe(AY~8RjVFMVi|lTf-st0X=*7jB2PD|&-=J> z!JQf4E^k`5ciA118m%qOsWVclQ&L<{a3_3HWK8B|b;yL$<<7YroUym|Qj&4A+C{J+WpA$}9-ulK+$HaP0 zdq2zO_4=@8!nMOz@XFAv>V8U$>X);r1+LXAouS*%dY6#X)aeyz9mG6NGI0;WKiUl% zF+2%Lyq94#O=o(@LEZZ{v15{NEaY)HCNpA{Hl>&)ozxbQIKu1R_%5W6mPA9}S&M2x zvQ-H{Y71^iNexyCi%>#bgI~g2k~+hVlF#IkljkIDx3S0_$&hs?J8#OO^d7`0mbodv zv3`87Haw>^MMdlKc0J165~@n_vq_Qaah5Z=KV|S^(~R>_t(Q?PlA$u~s~51Omu%yL zKj3(sEK6(uxjyj=(p;e6mf%Q4w1hD$aO728u z+Y+sE5j%lVYxWSWa-#=+L~}4mT2vZO7fFD|THP`(izKLn8rmIq;`t8n5=WB}v)e`2 zX08&@l8DW&QP-xD%Oa`Cy+{(`ze|@|b!y{lE7aI>+FOK|hGdpKA!#Vthg*+)!z3!A zS+5b*pITCmoD_QuGVu~pS|8rgL7D{Fp9IVM?Rs>LNKdw_K2iqT{@itsV5R)bcAlMn zwwJV|G$5MZ7H+w1V&ujCdTvoHN&8ajCFvb@n+gxD0mJOqV9BG>-0$p1%g;%8o)Fl( zy%{kpEt;)6_TR$SknyhRl+hjv?BTpQ?qX?<%*-o}(Xjg@c_q^<(i0m1*HIGoHVH{v z*=yO{N|QMyPu7t%X(0_CgPTYoS(nPD>UjTHd=Zk?y=bNNkigQZvPSf6ySFtONef3W zIVB+jC?OpryX_j|+YVyf_9`#$)n?E}kljs$bKb5;+Q%}GBn)!`wDx$X*B8M7mR~05)NrU@YC*?%E0I;L5|Undb8t^u*g*~HBu&UJ zvq5HVy%U(w%GMfw6Ve!u1fnKRpHWtPK$=oImUP%W#Ic;6om`UbhIBbkpheb*tX1u9 z8Ie-->|WiZHYY|>vv`q}B#FmrExd!6poUy_J+6^vJ@0poB!-sliJhEICd)9a7j~R^ zLd(?#u{N+2P?Ga!BY@Mgt))GklOV3WV~+V^iE0_14MCn*Pg2y9oHFEDdY2>JJE}w< zD>p;iSkKudSP8Q{M0GpVDMr;HOIwLuzb+8dak|2L$FKMlZZ(A`}rDUho7Ggo_1k;gW{pj0P z!czK<;VuX++F|e-m2NBKLcXgYkv6upVwCZlc)p zvdf$#{d9Oes+j%Szz(-LUo2%Pp_p0>CllKrS~!(U@GAZxb&2Myh+18e(_PyTr)-c4 zmidP@Ie#QsOx6|Wmf4hRPS~eKT1qBaXqAC#2iM?tNHK z<;%H7Ijvmr3SlAGG{=&ZzUdFC;=841Vxmy7a*dr3A-}yPN!nrMqdC@45Fc``NZz~D zQX?N>$CbQeCG;ASMKZbC?{y|=U@vKgb6Aa2vP+YtT&5}hF(c7eaW^aJJr7Uc@uQk# z#OvrJ9uis9B46FBm7FjNBu$nXsS5}0iBQ{)OBgO1thYXpY}X~QnW_mRis1F7;BY1Dx zk#{|c9FoWdN!Z%&>S1NX9qG}g7n5}89&6e@wiK0AY2xlf1Z-C4(#iAVdp`}rDHD01 znY&$&G7y4|jlE-)I>DzE)WQx3d7bK-`3JJmEv~OP)Bfp#wC;1_}k=+ro zMOiIGj-`IQ3G+F0y@7hrFiVCwcg^)$xsuTOm_0r1wk~y0NWrp@-P3R8EVi4i}__<@iH$a-I&}HETdaIp0@gy zxKK8MTwx*{yKG$9_wp_bx{w5wK6T2ur#6Buw}?4T zEckBSu7Q<+$7yy7g+PuzB#l7M;2`5}uLmHR)fl!7?1HQs0L z?<6>gS_KqgS>cBymrr&Y(0Dam9&JIrNn>iEglmup7?d|UdpANr4?r566*+P5Cy7#f zDtQvOs@OjT+|LduspdB(nKb+{sS=cl#ABq83bGDE&kvJ&e}~G zP-CAFz0`K9cq-aBT3fP4JGLx;$S{zv(uC9ihe(ra5(lo0k0pZjRynk>btT}Xm=F+F z$g)_`k1Qr8S|+4+xjZxPgQ=U+Y&L>G9O_>i`R9;_L-Irt#bFjN68RZ&H>6R$9md3z z#m_@upO3C+uX)FjAk{y5j4BS*1MNlV_-tY=WXO2{5$?#JK@7wjX68)1Rs#2pC5~Ic z8kXFOJUVMxijUk`bsDD=2m&XyV$LM*w3+#ycT;~j*3!f>v9v=i4mFb!HH2v**V&$b zLe^~ZHVeQ8IXD45)0ALN0HKL1@TR7AtSm{H+P2lOFQwflxzvrAg;amW$JPe7uI~iN9|X(?MZ; zK@b|+hI)2=%`V60c0IbL8@21O1sXZ2n4vW9r+lLn{+9{ICE7C{a?4Bf$=H`-l<--> zl`sCvfT)BM4G1K9qKh-KIYcQ@Vi_AX0<~T9Gzj~ox3hdWUD{7W->NMez(|={fagjC zbv{^Y+M;r4@nR!zQE^&!Otgr;N;D)SoxGk-td1y(o=I+k=E3+Na!(u1d?RY2ff)KU zEp+YC;$5+oKnn!210QeT4a~SVtQ~^!J}VJB3(aC&x+{zUk?;@{V5zSO9gBKr3c!O>RflDdvq#4vWUT z@;j1`T9zny+CJ@kMTPmHwW=>R3FK_L<xIRImPHP5P60w7t0I%X;<~5>P3p=wq09{dv;La*tzlFO+$M}p#%QSw^5|XwaD2+mhJLY zBEJoTmZr+4DXb+d=#z(!S^S zf^WdhAoW~t9}ap7&{6JIjaoW@>{ehd>Kl&0q-4?Ae2IhSqw9$@4Lqp!Y3|NX>EqBj zZ*qUquzH>fbjKjDPFaY-`%xfJq|0?M=)B**hv1f$$v_H9kKw=uCD&IT zaSQ-m(%naKNx5D@czyu)CCQX-GFR?Tzg#1>+quz{$Z)H>PY)kqjL$uTXPtv+rm($}1=1S~Q6Y%6J|Ns?%r+h3IyBTl|)^OukB{nQqcN|iN(19H=rS&!HjI220J$!HCXQ}Ntl-+1Yn!vxBWcH7_j#{krT|aDw8@g zH2HLGSU}*zhy9>cJhDjuHIN5386VPI3qYnnF>}B#1YHQO%b-GbfJZZMV^V}r2XHe@ zV1138Wi1@Kqij?mNwX%WF?Yn6l+6~o+EC&~-jg*6H{^N=)h^XOvTRxH66%s})cQ={ zDmjNn9f@q*l9#$A&Y(>oi*XRxmC%y^BPZOd8N3rwp|QZv4J%=jH|DheAn&>XkakL{ zqPhzqx4?5~a{`y0VNajm`-yRpmh6_fx9gFtOhVJc5OiE1i74P?y*w+Kj>rXZ^3#B} z<>a82z(_PeUkHoLJhKa4W_0x>+CSR-^6V{Y2&hjWTR9wA$xlr;hI5hWPg2i0fVUIc zkIX~TE|uX_YwyMsj{`r&FoSf8xyD)805ML(^C~r&WWgC#fyIVeI z$6T}!!daCRXc1tvz9dX3??g-5%XZ-85bvB8$#n;NLpa6T`M5Fm`wcujr4ojnmM_o# z*x@HKHU(5Kg%r2z(G|2I0bIesH6TMzMg2f+_({LOZ7{58)S;zaoe*2mN%CD`$jts) zgmaZffGn^!06|VnL^4A-7SqG)kD@GuoR37<0?SZH1jKag@2C8TOBVpCnlP4}mwlR4 zNR0LX+DV`!34QQ5PR`ZqB?P879M&Px(?Hsl{r-q{9NrCJC=@$J>GEz$-^l{iz*=Fd z>#=rlHYZ1CSUO9_wQLyK`RIyw-0x(TbQWy!W8)oxBrV8xc69dN#mm{9vS?ddKo#Hh zPUu>9u>y532loMGBh7*=h!C#9#YQII4!DJEiPi}{mxVJ2nTiBS5jslef}yrcG8}Pi z`85+In1JBPo6tg#Q!@!GA369$HdAw>FNjnesep-RA0+|VNRS%U1+t|udbBS>gLV{V zNBmis6VB{>SC3sUdBrFU2MBhj0wj{=?Rs=Ib`yG0dZGL&LrWp&VF%NRMuSZuyNEV%=jkERz8GaZ-VVkCUnV{HL=jd6EFtyC* z&0u!TH^GwJ-$0P31J$TwNh#tXIMUPtd9p}{1K!0bAmcy`4fFJMybpiuApt1Dl3tYt zv^m6dV~g}@N!Sh{ok@SR2<6x7H5+CnaDt6AmlnJuX#OJ=Be(C~DCpYt-nI{9j_glI z`*6}LzzeByX?KDcMN<*+JWN8TuRJ2YuN|~<4L}t!fCM_xc8>w>QaJ%Orwot`E%~L7 z?*(06fr;ovz!j&1k0JcbLm`6pXJqLS!k05~PS&i~*K^0S%j`lpkdnv0A=oKriEBwOY@|=bISAzA(2x$(zSgSV>;BH6q4x7Ynvd)X*2sG{nO(!> zp!rdXFDr%Q;1(9dcDiB(>p?&#BxNDyQV2XOF@5X#?nGE}#o;tqnL-kqYDr?2DsBL6 zg$$xTTNg&1F7YqC@U|_8&j3#vNF0ODCZ!@0*v3gZ#V_sB!qb)jBeT<@knDJX^qGbsQD$*Hx)y2g zROv|};2S@q63-fKJ=1v?A16qfr}%I8j7qjm-!C4(23V5seC@jI6Mcm#n6*0+Su3qK zna9OKkDONrP(~$4Cv62RWPekv%j_P`Xrf+(Qpy%mZUScpj@llEEHyW)49sOV*GtG- z^hbfV)Kp`B(jkNPIl}Rq zIHbA@;~{D?1UHRS2%*I2+osgk*-rlldy@CHSM7*^`=E52+Wjp~Z zOBS!NIyGY?$};$0GA28r?u7UvP*is6d3t%Rhdm$t*ljZz4yb+V`2fN8tiSVsOVB+K zO*f~LuO)iFzxlwPbqO^&*8g?q=Hc`aU;=d=b(W-T%s{dzT|77WS=7wbT(0=9t@3-B zj>p$YQw-_(5_Jp4+mXHAk6c)y+Q~I4PS0P?>s@28butlYLgqN)Wl}Iy$%NqwWa?|B z+3tFolC=^*7kQ_fE68tB#{`LUm7})G**0UgBUwK&0txk0D+-@XpkiAbVyVc+F1COP zPA?eJ@iaj8sjhUuTtf!T&c$x$qjo0?Lir6GE0a1}0CrgQj|onX+L&4&9>sP#@nF+o zrIHrFz9QBVaM>uAbg}MB_0%*cn;D&aAb~=YI#kN(94Q0UNwBL&J&3g8MiJJSo@5HNnDH#@|iQCchnbU*RoPI^>61j z9r?`vLjL^!7Wu<8ABjy&)`lCBqiSFKjZ*L@Ut5t@+xLz?=X~(>3Ie`>3p|Mwabz|v zBE<8;dcobsH3YGrS`T`OtxU6VO;|bz$LV2m6wpGJZF7K6l2xPlR6ijkZr-N%moa7wvd@cX>EVg(A#CgQCbNHxZ zGy%*csth87;hT__XvvNFI$tfPlo}r0WU=RF2sTlw8KhKTH=3-3W_kcQy;P2F?G3Jn zX??haMAkdCRpD2GZ>R)G%OiuugaZ1P0psw_^%9^T6pSRQS)@hpWqa)RorC->kGfC?#UwOZ8RS02;9*W_jZtP==ZP{Ew9 zNT3zpavMm$bUxf+qsv(0bGu8*T4n z(3C(&D81Z+tqZgz>{Zot8&F_0LP_Oat_71PX1l_$Fo{{;g2j&Xg?>L`J7$doz8}Rk z^?b>1n`}S|fD}R7f!iXw-L}o)2=?4`AC(Mp#7uGwiYT2SI(Ogmk!NQwHGW0#nDTU1FtJn zJ>9Kh;DxylBnP21Ry1#ta6kff!{d+{mN>|GSn~R|xtH)mE5xXfCW~hx3FxuA>ek4~jY-|#uz@!oQ^ zoACYaI(d)+)rn3*S(3g1-7F9z0ztX$vdQIaWbo8h*4U}CXL_(HMrF4aL)@gRVBvMz z;w+-R6FH03fRUqwAGe>F^cU^r~FAcb9H|+LC!UA7EA`e4b&CB zZ;s^1X1*4%f(8{DbmwfqyV=f1o|OtB$Jb<@RaG!x;b*j$*`NXQLAP#{}jdfe{ef0r(q00~&BzupWmcgIa z+;6ar^@Ux(yz@V6vZbS_wQNzBO)|_e-u4>w>B*+@pkUj0w&54<24OEYI3w%_Jj_hD zhB2cd-m30=q+By)ZD5M);>vp1WrsJ7_iEFu1Q$g;U-CO_Zt7A=qgSs|775%fpIp$^ zbcR~F;bc6ef_sVNKC#?R%qxPL5WSIn4DbvvMwurqh|YJ4fF<}LQ-1N@W@d8Ss6V2q zcAf988G`7zL~VAVaH21?`n#RfqK(-08pAi*+G4PtkccKeC!elcoOenSU{-Vz+mmcsp^t+eUPhZ zngQ7u#Nkj92c}ScA0=?Z!)t)@r0dbO>=l?VSz!XU&jR12(!tn6}L*q11B&r=WuTt*@OAUz@_E4U*M> z^G!G`U z2SR2m=kYvJ>!7!B|8~bWC_y7kxuVN5bf)nSZ?L>1+d2|K8^m-iFgt7MCnz%r*pecG zQ+Mu$^H9dF9hKj2;`T_0$&_udI9i__epLqbyCLn#?qycH&+gUB)CN2n~=TFd#0-yxnNNeHn=sA!ki=NN_E2`N2C zfwCC9T-zMmv|yQV>XdBCNjqQ}$PN`uKBUlX+gHu2rir=`3-(DJmy}ovc4^5@Dp`o` z{->GW90FJ&*l7*z<_Bq8DT9}@4T7G^VOv}os2L z?lrt)P=+j1wt+S^b60(^tyVaVaz7Gvrz>1TLNHkyNtYRz4z+SW=%Ozv{zs}mA)*sW z^Ab!T=OY`}urBOC2r_BH;hqoAOz?x$sgq87P8gsMg=wM*e>Ux0jikJ#rvu9B&gmF9kG7{cJOcFTGu+Vjo12-8z zayVfL4mvQ>@dH-a8Zi*4-asHM%x5=s)7Dp$7eKCU5|(lS6oU)sb?@t8cH;coYO+8z z7x4RtmR=6W-2rE_Hk-u?wCZ%znCJ3u53X=M-lYiQIFq22ld-I=o*}BGiZf9JW@buL zjc9MYFTxMXT?^D@d_Px29EsE%%tq#VKL2^|@{ z$Il=liKG%i_nTSO&R57rKAn~xE74uSg+SEuV=?39-||MTbsE|Uj(|cXlZQ&#BmF3&OVaX3y6J{8olo4--R0TZD6Y?pMRUq(zV(n)9$n%m(PZr{I z+6%i2o{}6YXRCpkWIGcwpKqP+LcpqcisMxz@7n4u|-Fw^IJa|B-LE3x-ZDo<4D`Mkf={^ivq4`>=Sa%&uM-TJSzhl9FKJ2fD?kkZj$Ur-<`GNCG`}iSn9tt8H6cp>Pj6 zfZ*rAM96e^2&lRA8J0T$n-6dO@du@O_m7E-0G%6;-c^9`-Y(kLaX z3!YKrY{H!@Yc6O(8uU9Fr@lS!L-z0%)z74IDHo1vS;B?6TCclYLtY7vT`SxE@x9>X z%#6Atd2iPv?EJ)ID=yf;UbjSL-0o*9lXAR<~R zxW&cG*@Y06!g9J85N)%Y)fU8!Fhkzl3E_Q-G1=cWnKUp19}}i1){G>mR>gk!l@(_8 zi4z!938yP!gQpq@A2I=wz-CTrh*W4LpiAO|H2Ek}hzML?Ozro892NEiZq<}N2OC6y zor8UdiYB7OQCgAQItLyzOZ5$$Wu*`#C~s3X3u!Q+#=~F&baz7pK1(G|cg^_u=o$f> zm|qPcGuty%bWEyTt&b7F1K+aK*`$>q8A{Z}I#l^48ISu&YZ_8e26#4FOMy8ZL1L=? ziiej&i|;trQ2A{q5cJ1sec#>elixS%Uiq{aV0c|>0qiw&GiMmgD|{PMG(O|+Cw&=d3O8hVUfT-2BeB; z<4&b(a9$R;8)>%@#CgU@$yJ)MHu0?}t9Q7RjuzP_0}Up8hc=9pv;c$y6e^jQQ@okAyOT(@^$=ZlBxM?wV2L#EM&F!*8?>zo z?g6fu-cr$C<@HoQ7(gbb6RhmP|8L3wz0|iO7U)`GK>H@^PHX;9e-cr*x8ce*>6(r1C`VEd*zpa&>DQ|Y+5 zktDJ_0ofuG5jJ=STE}g+@#NwD$$C^@#e> zNKg(4YsX8Tst8X_PzKrB#?LEO9A9Qhf(;5*Vpm!e#B-O^$`@7pf5I_)HB!O)34V zg3yAF7tt=sT2}%EZcL=Z?Rs>jAl_juV#>D##Sg9f@Q-pjThi^N*xWOn_z-ZfCbhwWzqNW2oWK)h#Sl|ZR zDJ_Yq%)ecaK6by;w_pX-fYZjDJ{nGM7Z3PJRamta!P%#P1QbAotlKd0UkPOJGxTFH zbMGSTz!2NAgN2yegEb<@A$Z34NZPaJH;l9NVB1dfO7FbYwy9yo6+jE=Ik7aYqNz>r z9zU;m@G&`vPKzZOM>vJey_}3gTrCqJas>ho_lT>OWfMswOQ%&+%w^LR2Q@Uv$BZjb zgCQJ?mkdRZcG8<6*ISeu80*XiZ8kk2NgEKwJL3+?@SxS5D@wrn$x;JoeY+l+xD_(M zNi{5XR;GXXevDsUe?5_xNO8RCC!!%6l)7g7L99t1(l5E}Z?G`r`jD*IP*qUpuB0Ic zYAH!OC%EnpG(G_}Ow6}3xSe{#cT3ehOsPcYgg)kzSAu>Z6_8r+N^GYqVoKo@bj<~c zC2byT&mcD@r@M#M=fj;uO9RL0m8hwqL1zj?j|}2OkJTQS8?xcF_{jbi%I}j~9kfW9 z;><%j=|}9IfU;U$R*)XaEBo<8SmwL?Cc(?r12J*C9(^bldSxX)iiJLy$?NsqU2#5{ zr^v~R5(zA=dg75A?Zw{h+zqb?0`AgQ7f@1SnXW0IcEdFuGrE)P`(i1ju@$_I?=7j- z#NB?uXH5K90F0g1b)ng+#%E2I*)6a5|6jg!NyN^5Na@@F@^>OG0NBciCLaUPK2f0HSeIZU@0wyo z5q(YO;@G1?^>j$5AXdTdxbP!XBQ~&*Dec=MEn;$DQY=W6 zM#5zh7L6csi_MSQ<6Ef)aLpRs5WsnAQm8hvVNcw0hhS|Bn~EMf5GsZjP{RN>vnn8abNly^TX&kJ-U;K5y zNZfzgnB3e8pg{!_m8cw3C;<7P^U8x(S|nG}+pKsy;*K=boymQP0>52hpM=%^2vt!E zuEoMoI~`iDmjFqluN@{)cgKY%)ulP^Wn$1UWx9?Et|J%LoA`o0gpg$9+h}wsq#@x# z4ih5bTgJLAf_~JU)_|lJS18vAL$c6ulHG91@Cg!DvH=3KN>$PZb8w56c-mdmXAC=z z*P}DT6RL}b%Uh|Z+U!R?_Os`=_ zlE&t4Ep=I1nO+IyXjcNzflf1_@og`y1s6}*BJ<>Jx0+3_DIXjO3>h?uTcN;p2d$>ZJ#XPZ79PXVw_3^Shq{=g*x z!TZw-d{%W^AuD>^&^BYdPC0s>pC&bEeyHAKV#`Jcvst2SX>mM3d_+3eGjh7(n0E=1 zNxO;L-Ws*LS}tyzQDRoGnQFA6j0uOXt&nr@rX}plc!m`Pvl*prDMa$E3<< zK6^?BQK?JxWcv<6Ds%a>=-}OzEBkBU5+)Sc;)zY>GWksNA=CCnPb#=A$Y@2By_f^H zLVomq4?j&TeK&j!Gf=CrrkcWUJR{GvE07r6!XrFgu?W0gWfYh~@~-$$v&R+&%SC6# z6ojUGpR84i3TZLpgHU%%;+IXr3_-Z2Yc6``!k+aiT4&6=x`Dn zOWUA!GNpU#fAFk=y#uDRrIk-bDG`uJ@3OugQFJ`zsAF}sgsOyW0%foZp1fJ&!_*i% z9*oP#<8@WAr+BXXFDi%;iU0`>Vd+Rp4KtSxEW!A_Jf;VsvW00v>H-qmTuH}0JOML= zy>t8qi3kiFAi(NENQ4S#TU~tvpwG%Q>FUGA1_@o#>n^K*V%v1rWb;{Z(W>ToG~g)4 zqXtiio0DiE0yX$@bC%)$AV=7?GuY;FhVF+D7x3HYa)6kHz&Y*$79`Q29cF0@AqGDqEO1ehzd^}ZXoKY5QTa}5 zkqW`08%v#!XvR!EDVhxnrV$XdL$W9`nO9B0u|4yJ_Ww9h01OjcZ+paR$af;%MvS)u zi$o~jm{BZQnjiG2U_ju~5~dQY1NRDPk=Zk_kcP*aj&pDGf8%ON5#5g7NvIr}wQSo{ z3Qsx@%C6m)(0yF7{kRl18#dKSham;e`etMVQW)cpj_{FwkR&YTn-98h{P@k)9(!!* zQt=eiYGc>jFc%7CTT-~j`mnU}f4Ey@bfULr1rmvYPMfhmf*ht5!fyXEfkcz%05kzy zs&OlCL^1uaT_m;m zBxFiZgmVXgP^qaYp>ApGE@O~*E%?lVMOi=|y21f60}{@2)LY%%DO*}}J5v9{rCK~s zNMMM3+=zp=V>fp+sWg2Cq<4|P?iaGV*GlA!pNA8_Ez^o&0tlkan06+6KXDS5^hf|m zR-eY>aY92vWi!5{m$(E0G@%3`&^iCM0%SO80EYb4RMGtx=E!?r~cT9Cby@_y0^^Z{8qy&ruF0IHt$$BH@bn` z4*-c<;(?hU662qTqm zj5oPg)7p(-o%iapN^flWO88XtHq>r37Owv>(EIJtD_GJ|z34#kL$`LU)=w7grS&7cp?kxMhux=bB zR}(r2a>Nk_pm>}10d+o#W^G|kY&SbG`Fe$Y8jZ2EHS%7|&=OaRTJHUj6^l6_8Ah|J zMxAJ|5(AELdzd68RjI%*C(SCGf@;k?G~AIk7FPTS%aJ-j@+)--TX-2@?CilXx4GGH z$C&!~UVAl&RHH3#*Q0CE)$SI@)6wiF_w=Lcifq!QI%AbxHPb{l_hXB8wZ(n^D%QfA z`dA7>h(W*&Z0-uG>2L>=^Hof&S+*d+q91)n!ENvN?@?=IPrgGbVG-Nlx5ZUT`y-}k zk_?(Ighze7f(`8uRPB?<8yyjYG;PqV$|c*t=xHb5l932d08iFhp|dvdUWida5Y!+Q zuN%3O8pH;WMQQMvl zK=JA5szfz}q>SBT?K!Ux+?!PXmsOIDF!69#(+-uIJ9X2@pubU{&?6d%KPbn*eyb7t zjGnTR8L;2&3^#dzErT3u<2|loJaHBHrKBN(SZYwYQ6bQOj*O~ z0Y9PtI1KKeoZSZG*jG7~#+(-MTi_I4&-ep|j_(JCKN9gh%Ju;J9zkTOP*^HD8sp}g zxAPTmVHvyIEKrX#q5_Bv7!XV7r>vQwWZg~6%ECjNZBe^o+nexuKm{Q!lC+qMVrZ~P zVZjT4@HC{IQ;;S>x31f^ZQHhO+wQMz+qP}@w2f(ZPusR_&YpkAi4(EU&AHeWkr@>g zb&+{dZ`4{V*ZVA(axx#$JG%Yi#XurF_E!m_{}wG_nbbmW9Dr?<%)4Juj96XCd}u?8_7->ZtH@+56@5?$=WX)frUW5rsli~k=bL@Lmkc< zEmPb)tg4_k#Q`*hB{=MkP<3c;6^xYFBwnusB&R}Yb}Vco;L)xo;9{k~^JJE}Zc*TB zr<>gVo_$zJp%^yCS~YD6^J&iV%dv zq6A)eW2?{1bfle$lFQ;qbW>Gei-gI`IIwRC4mv8OO39+P6-0?=!R^bVTrXJF-cC&C z#!~kQoumx2gH%%3u^iRjBNU3j6QIDzhs?=LblNo259& z7{~?h9$zmzT<|Q05`xTS9q@LA+#L)Z2o@c4GIdeWcmcVvxIo%4p zUA2hHBm@k5gAv`h6Yv@jq511Rh!uzwAbFHvrumq8_I-c)<~kXbkAkN0h^<22Wq*FA zvT3;erE5C9zc8IzSXkqoAMN-42CAYI&UOHg9V;(7e%MQ zW+ICtcI_PB6$=we-NKwa#3`%35e~jgh4y)TUjc%7KTFX-0i0YITyC4!w;9z8Wj6fh zB?*n+$-lfzIRD65cboSbfP!<)N63Pe3+-SFq5uQ)bs^`2p2b7}4?~J`JQb`Okr!<= zkC`w`2kgl@Duwhh<&UP)tgtv-SJ!4?osw9GYdV5YH*UXs`x5InH`h*JrY2SrK5Z#p z7RQpA?jq(F$5BZ5aUbR!l;zhW*s%#bg!>Ak_dF#koa?jG5O{s-6)I`;H zCTt)Cczx=*ZM}p&w30E+X!q!~PFEB;Yr*qE$xVfb&RNo~KhszG5UtXP=!t{%ATVC4 zQ&4`&CT3elDc%lJjt$ z>ysNW(9a#k^*Cmpk4GSj#*)sJ+r0thn`y^qvU`n65lQv*{)|xsk^VT`fPaXEUJE~n zUd&enuM`4q9Q1B&J>*}O!9 zD_d=l%3=*mbzLL1%zS~~mi&!24JWTgxp*pCM><2l=U2!r6#2_k?JVm-b&>M$5e$PA z6|=#bGe%*$hZ)ksr5{J-4Y2WuaF4Uz!WQ@g$DFVn94P`NK*H>KJt+l%Y~#Ri(A$pe zW#%%2nnJz3z~Ih1Bpv-ZMZKxNmJw{ZTOfAwH{Ui8y7YdeIoX`%p5dO3`S1st;7@!) zn#aWvykky07|tby+VFGGMW!#mRtT4Mb>2M|3H}t?lXoeo4-`(`znpvP^qJ3=W-cbE zwO-G{VKKWEQ|}bGpFATSoH% zNWl1?i3+z;*h>Zu?!XO!iNW8n&|p#bwiv{ln6A`JjReoHp!=1sm0ZL5K02wsRVx+Lrc$$cEm4Q$< zZH-`vpS0K)&t60q&!dqG!{io|Nk(fw2vKZ>3-A&i^Dd7hN9Axs;Qu{$yv~(gxdE1% zn``F9FS-|8t&JEdnaHHmrE%kn-t#Bhbau^cR0^?=EV72>9OW@rF=rBh&yu5%tVc(O zS=Kk9hN@@4b-RQ(o3{F6@FZ3#Az1n)~#l>WHhz+Yxi2Uu-| zo!F6c%9=>slEs{xLdnkEV^5?$jvAl4BXv%JF)IXU%kg}%CsRQi&p)3RTp_n4PEHOd zle^VEClniN!x``Y&PHX71v5C;fJZ{0(qly7nMWH?p$tF8_#1)6>-udr3Dm_u!=KQ% z!68XEq>AkOad%7@iSzoKfC7{>$$7lR-c8->S=YU)Jesp5Y_K)Sx}n@5aO4+1Yz+J2 z3)V8_8*zs}TosVnBuKRKp~Zh2%ncf4jx1Ek*borhE9i7}!@? z7qzHfD+OC6rfLM8F9FAtFh_#p>o$Q2dM8LjZyA+yAOs=b=J+7mklB#UEaW&OS5qud z3g=D-!`Ol$yy)Gyuvj`)#Vk=tTE2s=Pw=RX_AOW4xDf5?%t

H z@r~~-fz?qa`-n_?sq?b}bU-*CHkL4Q&DPfNrj#D+4X+L)ut^D~xiG7}GCtVYr0->l zpYeDN`|Z~8UW-iou$VDq>Taq|neDSfW4=UnNN8BOT9;eeR$YnRg?UkknC!5%tG^a) za+^&1h+@BR@h~}dC&7OQXH%L7+=Tf^H4H}vR6^#QkKZ@#QyBdD^k2a%wE`I79_3|J zY97zl%Wwx*Xg;AV{~}D^E$;*Qp6#amsR6bVNS*qtPArA6iTu;X;-X@}uI;-nf4pXs zgYPQar&8|fb?D{Su>CDj6J(czRnp9H!U`T478TFGq|>yAJ8QIAk~mZ!8jkR!1+`d{ zF(`0iqMiu<7ko^*{Z5E#@Jr8hw#(MA_K%C)tQtE|%zRM+gCsBbBoMQQH<&^@s?A(c z`A8O~uCR&?KmUB-V@HGP?HE2sCl#!D=vvCvHbUVY@Qqu9#Ml!=;&rbi@X)1`pd6(= z=yl7LjDOD=fFHjZ(ig6Ba&HAlTM?zWYe(6j`fy=PyHXo&0IXN9iAy^EO4tG}hVaO$ zwCFOZ+x-$}W}Rk@m=>g-p=q9Bz!pbrpVr>5teaf?b^FkTz(3_F(Rr5eWpd)%I+GQmG49Y zV%|#+6Jb0`Gb>~1P0mBd^g3lo{@5)`#A+yZrfud-6Shq7x|77jlb<(AOhh($tQ)4~ z%kFO%s2MRb=5q<#FvPt&p^NPkcGO2CxL|`;t73@2G#YE`6~*@TTc>pEL6r)KEJJo= zE~YY!UOhm?gael*ySd4MX$yQx<`=9qu;^RP9^jmPGo%njip<6h%rP*qcypSY6MP>- ztm2<-??5{k57{Y(eHU(^qDe@5f+UlV$Qc+?YYH$Vv4Y=)DPX&04W6OAhB6$Qr!t9K zAy|Q=yo#MPDI6V`2WNO1|A(rncwfQw@@?RrCy%78Z*7q58}!DgE=AQ%YzI2p(Pw=>85n5h7y9CU*Fn87= z0d)Ns+}{$Hr8ddPPiPdiCq@f=T`IXu`9d;(w-diFZ%#0bbbiQ((j6Gs!RX2Z z=>ix2O5>PGc=ckpHW0but(TybD@!}KHgqCxj=d@Na-Q8P_F-IkT|56RB;o?^G;=8$ zTQDhqN08}pPXEFvs5rd?^~kGtHmBzhzSu=(TRVOU7bzxzikDPR-a}tc*v28Ea?vM8 zkRKV!h=yW6!s=WV4oiFQdG9gMPcqy$w5$CRovidcI{&&o8`2MQ2^QBWyRE>{7T?wh zVQDcY2hQfV8vnmK*bXUA_Q2DxB+7B(9?E>W@zh9pY`ryTvymqd)AIKA3IJB?M{siEqHeTJ*%V(Lm`bz@Kfy73+ zhno|a(p!%V+x}6<8xPd0RZtYb3vRU+^nhP=ql%K7=yL_GFYA48Mmto0PqtKW7IBjL z>o#ICaV6(rMyu05UZy7^k#Hqss@2PtigaRNnEhfMiK3Qz=Nb{#7Ib@d-YhtS8#?m? zjimZPf0tKCPB=u1Kd0jFw&pb*V|t#1=N=ZECN}8iM17h0nzi2br*gY)EJ~|6l%4DD zdPyc5m2lur$84WtHQS$ z9}8lx_g*73!8GxpI6aitu%Dkn=|y#x!O$p)-|(^h-9z?dMWuz%Wg#J}BBe0M9p^x( zHituW-CpQ5MQN|rZR?dTpbkkoOj3jK5+N-{b0AcZ3;~9{^Ru_5XJ~4im`V_CJRKlm zg;5aL!w>#|4azqh|B1)1-%-w^2BHoyk@(ZCbguyF%hBmunHX(M@1uHP3J)^H?48qz zmh*1`eN3^?+i*l#EfYXl?{@1uRH(M1sS%h*e;cckr~h`J>#MRQ195|LLUfh9x+s`Y z;RWLudOI`+nP^auW1552i_g0#8xG+k0ybBka0xYXI2b$f&8s|39NZrQU-W^A2-&I) zr-TVVsgl4NB;r72YLdIu7Wc)`4L4Q+TxYsr3}nJaxDu~Kck_)A=b-K9$_{)iM}`hq z*X|STlICx0O&_q_OIG22e#4A)GDNhoT*SZ|Y-vNC7ROm4qW0@nH@UOa5g}k>q_{wy z!deoNz{R@QoG5&cH8R-w>-%i-#Yex)4fNljIn8vV*TAAfbjRSfJUKptW+#6i|P#Gt-)a>dOTx1C7W7q$)NVt6+e*R6Q!dJ{hIrBFCIpc7zT92{G$n4e;)>rSYDHtm*9t9Vvh@MKQGUapPFGi~6j#l_^R=Lqg z4vvtFLK^!Rjh;EwT=(la*^Bh(xM(BkFoZ4YBnw68U-J}N;RGfdUQ;)=IhB1LrD8l` zFi~nHig({>uT3sXS8_<{*b4)=ohOf9?F?+Y_p$N zl-N!@_!lU%!gT|tM|uQp)1h6^34+z@%uy-D9Zbj{ce!4TFX zChn*YCla$=c$9nI^r;An^0akkRyef2xAzXOa|t!tx^aRL6U>MUOJ!2RDQ&OAC3cJa zpfV+T4TAH)J8{@4quLTHejvj3i(Nevh&XE-UERU{A$H_s!=LP!IU$mVi;_E#+=uS(#E6DN2d5`Wo zx*1XHXZV~V3D=QxmlntCS6h7(1d+LdW_wlbyC9pPtRW8%Y$*cp0__0e>+{IjB<2); z%?`++bC^n}@E}wKcoJ+Kio2sD1aV-kKZBqrE5)o-XkkdxFOshaIH2yHwX#twlo_-f zyp?_)6yy`0XNtdY^Kay^n>8j^G%GNZ^OuvR79qkE3oq+36Zx`__c_w{EaK0 zZP#bKzoE|zdek-`f&4VTIyiqfta})uauD|@Q@DmW^#}+*i-v5WUGL%8qnPnapsPQj zi>dY<%S|UJ1Q%Abzvyra>rq-dB)cc8z^y=|JuTiPS2P-UBM zOpS^Q;ZPqLN&7@M8D&fRGU3d0Dh!#zj*50tAQXDSno_F`7loJ>&l{jCTEGfm>FjjP z?~ssW?3?kdeBJl19W2I{E?w@{OA=5rktXlS#mQA$az3Iqp5Tu<>5eg}s9q%`HWo=2L{Ls5_e zu=g+zQQswWxN3f;3g9?_Y0!^Z6Pq0Ji!U5`CC|kFYgGV!Po64{nx{0+A=pyR+agKK zLeo4D&g20ye2QcD?J$2w@`dD(Z%&v=l32(MHH(EHmPCivm5Zik(Xq>*snd`OemyZJ z>ZCU!fxgOvGJ{@Gcv0Lq7q=I4az4S$<USze*H-Zj7X_ ztouP{rQ~JhS2Acr$lT$Q%U*Evt$6ClCz-ZBiR%E<++Aij!n&JuW^tZ(ClP8m(NRKk z$>|q>8kb*X_Mp~cQObNuocx6$t7HmJ5~N-(JY5fH{R|2LXPby4=a@S z08ABYB7rrVA%1(MX~O`21LXH-%n|1$0_*&38VV!qJ>{tCP6HJ}Db)1wF5)hTxXfT9 zQ?4>-RN*;DJE(OuAcBQ^$L2OXF(riq5aq4lh|nc`Iq0Cr5iv@i4)OLfLC2)r^8o9? zf4M<0aU68>-rlV6$|EeW9qM}A!6cnZx*dFR_Y#K0;FKlLN)ah>*G;d00#}#-FvDHF z$F{*N33wG&PSH}t5)wdfvv?DnPP$3)@-I1^q=um#z;NbTl`md}n?kBEV zTi4priY|OJ<}zF)_V%>2!a;U781^AKBv90rh}%NUAlC8k-F*rlh_>~el6!P@y-RJ%-l{B)jVQjuz+UB`usi*r$96f9I-iwV6^ zpEDE^c_cJp_a5TzKa$sgemgFJ-6qVTzwGCQ(XpBuK=EeNe|_i@?BMzAB3asU5=J4& zK0H24BvxR@4JBd~D)R;7nIBch?dyg9%$V$%df0T{XC?&{+%D6AGwPm>u$~dbX&{tC zCaL5IYna?TwFnE4km%3#8vT z&!-ATBoN7lmKD8FsTU#^QsETvu0LYTVDDU^dgQMbcs`J6uvYDO((xK=P9P@Sz1#PR zJ(i026hFncXz&33uH;xVR&hk{*<2hH!e86AUCtxF?Fv}=2*9B@i8`a=HB)twa{Th7 zqobib1+8bc5sl^FfYsAL<-w7{1EuDE&uKP@raog}JYC7jcE`zBYp43CN+>LFjT72= z1lfkEaX5FMdxzzCh$!(Yb)=FX9Y(0}_^D7)$+h55uRlR0it`ML9P)A$Ilw|v2zE6N`6`rQ;K}Fz05=otX}-UOD~6$XP76PH{EH5z`hak0Qd zM_hslS!CS)OoK?zMEa*Yy-6%=Gw zgXAN6D|eIUavUn!1QxE{=tRcwL{+T6T2`--Cs>evWdpX9TQ1Ra7S~eBvL51G4_L+Y z@qd@0<6=bq#F@nN!1H~q*unbjk!{7+6`+=I) zF{V>e%@+DawGxF%7pGh6kaNC1AogVIC{RnLq==(rq_1*TbgsA5jus8I}oB5K9Pi^ zyMUz^C9jnDEmc`G5VQSVfLEFDxCH5E(X^2upSjqfrGi~z($f8`FmGFtB3#U!0pVf> zyi~YQ#Jbz}Xb&wdgT*N_4@k#KcB>zSOo`%mA#8CPBG}}n#ZCC$9vKe^rqSA_SpEJ| zJ#Df)r@RerZIzNU8tybuec(+g>j}nZau~ldgxdH}9&Zy8d-{aVyX#YuNF-FK9s9*L zAcEWd%xWF=RB7CoxX<{jh!M!>ro}3s<0>V(?y;N`vr%=J+GkIDu!x=1UDLryiF}88 zkVL}N$tb_K=bB#O?fgvUmxIxbaNGNEP$fWvxt;W%b)DT%L0VzI9&ijQ`PM`)9@{EY zKY_@-GxD~i1p>81V34EkV@Xy%FIps0&u`e2a*s7vlZ#&2xATxqmG$#a&QO9@`A zZ3;3-hS#t(*kWE&{@oRfQ%Op-wcxFpRtVU81f)rRW<&rJU;~cdST*$$oeymR)&N@)1ms-!l7;i`PN{KQU${8Q`}CpG)4VP>Svd0=`Y zDp}x9H#0N=s6AK-A-25&?1`P3Ebj}+XD=kp3$s$Zg$m!1$myLY6or|{-|rrnt@z%B zso)1fki91nR-Gf%yqk3&^A1MBW3HR>v=3d~UI87SBv^bRG!;8?U$jQogh;CSS((QK zb>Lah8?~rU5E@o$@+fz7uXzj*{lAPW zE2cY|{aXjkw1MON#AkaACy#rZ9Go52yj%hVF$a=&B}?jr)Hw zj&Lf~P@?bdcoaj1#B7hw#HHslV^sB55IL~XQ0e%YNW_QH{5_jpWZPf%1hD3Ih9xoM zmW_+6g*d*sTDl60gX-f6cVluSydz&gMqGJMOJrLi?DLHKRX!F0Nxy89ep zt0kdUTXcjoAqa79PI9^q3$=(?1W6K&oKaW&5iaosbfcMC*+`?MvIu}`IN=*8i(efb z3#o9~q#D3%OFUqU)`STiP)H&o(tV|G=His_k(u%lGi++d6@suY6WPUk6@c3%A#v85 z$x5wp{WxX79il(TWcU*ka7#DMj2>x(Z%aQN<7eJgi*rFm0aDdCX` zb0}iTbLprCuK% zfJ&Pc>P78ch`FSsq6q(cjizfECcxumuu)v_$IjjMTt_qa_@8U@RmjJI-!DqD+15Fm zD0X35qUpKS%gTiV*n_FO;7mX)^9wexwL~5a?R6j{V~wksxhD;B>3g_txSN|zy>P*r z8000t9?cm#0Y87p#L)iR`_&hBhHJOMI>@WIQ^yJK#a=ONs2HpG+S0|rpYRDFh3=)?;|522h{>K2)Q)m`Us;kvl{P-1Vb+LwZpc1ePR z@wEnALI~eH;T#?WAQ5p{n8U)VA7|jXY(c)<1Hr(Rt7rgkteiZ zA|*x&s-wts3Ql#JvN@p|&vgH?FOwqVkTHdq;-_J0b`@@eF6+jCP?MfPJ;2fRW|MVE z$d=SHe9`=afEjdO00pVaC&Ve@H1zLB&6)e}))qoVUI3t8pZ1i{AeiUuY1r~ST|uzz zkA=-?!I_|5o}fsy>GD>e=LATltRXU>7wD>JzgRC<6QP`ZzxS0^nVeBCJt z6*fQ+l+XE@QTzl#45b{&qnTVLv4qDjC&NSHV(%~)9D>sv^cO1K+)_J#9ci`ORs!cD zy-5Or$KE!Gc`8rm9@YJPZ6Y&>#DxwDj5#$z9o?444vMXlzzT^KH;&8A3o6!+ByN$A zhFylBvL2uOgLC;tb8qQ8IrPz={Fs}n;NQ%4_^bjSGzxEV#;8Ply&xVs5~><&W9pg|Jfr;HhGjP+OXVpi(l`PCig{7bE>#h=6iw_E@FBRQqKGC<&tq zd+aR!e(K4hG8#yR4Hb3Et9fJ$`PWC*dW%-|+%kti>X1ks6F7Y!wk84yJx>YWgG;GRNk0e1;kI3{rL zuM;cy-7R6+RMGJEjM;-@b(|a^{ z#>~nbW;GxIN<7xW$jkTV&iF@0Gf<4w!oyx%jb2LmVUJ4*E(1k=N>cCY?Ju9ESg}}Iq%_R&qA%=?5 z7G;l@$3mcrwoHd{h$s)zzDI1o^Y!-Ro6aDHA$K~a$((&#f((de8vz9BypKJ(@fqYI z^1sCKXtOnjc6vp3`Fd}ix~$H07*B+!kT>D;|q!N^AQonn-KF zW=x3pGeLDu%5OQq%8xWY?x>u6SJjQZ9j7q}64@^iuHe8biO_FusqHba41VJB#4h}P2~rC zx$jdY+;q2AX{bBxqVz9}Im3v5fU@Yi_NBq&&g|Ev?2Qt@ate#6!>NMN*D-f@FQ1T-7&?GArVhHF}@r%SAKAvJ&VBK2}gz>U(LL(bi(t-Mi zNa}4<%EhDs%@$UdZ&VtEkN0P6(@Qf84%VO+ESX#aJ8$)~t)4D#v{$*bKJ`Y|UpUg& zCOEM1hS(ciU>H}p83rXVlB3df2!a$r(1#QP9?dMl90j=FfFf5y^>`$56{HHxq|%SD zJNxm#OR?%sK+KKLr>@k}%ot8u`iuICioz}z3J)A7=~14D>Np;QRXK3=$iUEzmVj$; z@WH9#x8&Js7-w!*7g5EfiHy3s!n{3cVPGngVTU3$@Ghg&Ha*j|z~lCh7ifxhVXB@O z=(o@I`?T`5g>%)a|JXUw8?#$EMr&WlO;t5vrveA@16LxGr`_OdZ?l4AB9Uz^4u;}i zgtZ*>TY|fB>_QRb7RD*hv%LO-_t3p6tUw$@E_IJ!-`#6KpBN&+5p_a&DTa# zS^Tuyn9XbV@*4qt$G&pq6*@#H^WE7ZXS7IcP5^dbNiFiFdJ+ zNBhFo^4Y$9guP}fuQ-tW6Kg1EESsU@(LTF&}o0#D>w!+A#d=hol zqMZ-qZd;jBRn){M0d&Z)SlB@&9+`0}f8_;wu#m9eazY@)?SRx=O-`_ExTx|xxxe#w z$TB6&WFYL!?Qlnjx5YTBfT#)nUR4;yFU++i2K{}vzu>(Hx~-oN+7-tD;E9oOA2Q3 zi+KFa&!y+{YfN!hkd9xj?(&mIh1@h{KD{!e`0Jr;^&@HJ=}93CN$052@M28XcvV^u z<=};7Q@({T^*0f3B|Z2&=cUTy8$^rKqKRC?K3vNZ#SrwTnv;*Y!Hm%+H8|-rQKynV zz@X9_Wa!t!XUnAYn+`hS6?@`yz~|!I?O)LUiA8H-!{_V)0|E-e1Oig{-(%6t%w0^K ztsPyh9RLijp04U@&_JL=o%{O#8E&4iK)_%>pg=(XJzZ;U0uDKl{9ZMWKox*hJkIOn zag63`IoH6t6Cg+zqmZOs-PT9HKjO)3>9bO=&SX7xO!5F|IGl$XQkG@bOT zW-Qdnvtk+S5}HO%tf*45Xx!PxeQlFg*F)$uY<_JFYnYR_o4fAo#1x&Ftb+*J3mln~ zKWL$dsqP<0@Y`%O5u%dt4A&F($%$(tl-#0QJU#p0B$ZU`K@RV&O<5|F+Ym~YzS>kZ zlVMu3u^TntNZcTvS=kub`H|BoPgajekXF@Uh=yrUkGB0fLwx7dPIb03R%#Q5_oi=~ z-{)?#zy3lSD?Ph#tm&X%3h|U$DBbXrb3T)~H&K!?4&2!`#3qRc#VW~fL}7%Wy>}jp zR$QPiylF6Rc%XVI?dl~ZYEzM6Nxx%hz0x1i<_5;=hGaTEDDoi$FE_jNy@wq z?P|7Oq~b|aBAkS9*}cBy0cqHNR-zV&zMZk*L-XMN(mnIDZp0=u%iP=PG@I4AfSU&$ z^rG(m(f&vhYaB|py?^J4D8;q64cfZ=$zOTsCp&@tsc`8hl`WElE7tMM31+6t_3F&Y zVY}v2nq>}t8A>{SofN+Aj(UK8{XTiGP5)cl=X@|@RBruSjWeQ?0$%kSvs{OEnp5d5 zDSm7Y(9_F2s9SH{b7C-T3xJNLVF(XH4-vIt{h?S5dJXVVRkp(WdWHS{sTLI`m1D2N zZ@$qxYdjbZib>!L1~rJy1=&7e?+CdC{yN}y2gDaTVw4mIJ{5jE;B4&G=&J=>i^LH! zW1J$3+XlKBc0KrF4B05k1&0t}I#?8mfQK0mITnFB0KL!BfmaW{8ZHan>B!$9umuEw zN*Fx6qx0n8gCsb<9DLjdzY}?b6NqpaY~Sa-(|=>bC-esr6dOQt1j7Kq4GI|$-y<$u zhkz0Zg(H@TUmsLQ$5B0=UDrHWO4Xepy=l@fsi}>EX%kxQu6v;?o3=9Zqx-EL zb283|L2l2x;cAmsTLP=Bd6DtJZU1sdg32hJ{w1%0o0R^LIQ6E>gNU^_CPBFP7x+IK z3Ip_?i_~#n;lFSG&m{f7*HCA3V>5g6|0|!)S~&Xu8UK%b{!bZC#y6(!p#lQZn+5_> z{@*Xb!ra)^&Dq@0)WOB|{|~p^*4Ius8g=sDH5|n3kK<9&e$AmGKnSlY3)Y)Rh$dz) zrxd%PvB+qCzSU2IRMMtmn;TPsq5XY*4$+1CO-%bZ^~?YB8q+Y~+fnoK7rxR&-)akfZyYoL;u^O<*x7RNw;Q$|L1uQ=dS;Aw_xA$@2|V5shib2-s{)d+g-u_?|a1r!EfiE?i1Ue*;B;7y`S}h ze7#?HPcOrV35pqgzxvN&`frLkC)@R(;3EWE`2>3T{yHN3eFq5odw$&ObHCqLxKspu zYMCVpzT8)E-Wv3OzQ11|DsEOR+;dL(NL2X$yzSqAFGBi`h0|Tb@oH5BNEM9~Sig%cqxDvqd)Q_?DfcN+N;|9Zj z%jg_#AT0R%^VB^SU`ORDaQRq4n7@lD`gC%9em?Hiu={;{erfZSZ}2DY$KK2L^>Hv| z=5ke({C)lIH7%px*XyfDXSVok=liSO!y$&o@9%MU#+7!%&-wjv=SE~)dyJLu``5$I zFrmOEm+Xy7z)H=o|Ic}jqG3=g;t3nS+?S!}$IKBS+|N{lyeiAnKZd+ncI>gGm%t|5 zZQEPJSb(v>@Wm`+LGqGq8+Y(74D`k#gxW)glQF$nj|njEf}kaX&X^}G)j>`w-+MRPWf$D@kJ>1D7K{zc zJq;+)N8NN@2-?tP*|`u;YVHi6ZQxOVKkZYL3y|D z@nV}FuyIkSKPoxO48NTEhG4Nt`T#7GO^gLnP$I(Rok)D$48eMiZjqP4M@pn-^HAF= zfC7AVXaIOEZe{2iT`ZGn4th4dkQY(XCdy`CN-}fkOx>5R1~@l{RV&RMsgX4?8afJ(Sn6KhBABLZEv`(hYK<47dUr(i$nGg=F9OE= z<0#n@WFj66$AH>Ad(1!}?G83GL`3N(cKN|Z}fL_R{w zM>sYUkpx8*;ki_DiBZ9u%yjUE1*qBRL$q6}cH31_GRPaQdXekV1qq6$c%We-HKs$? z@P2)jpevSBCzV0k(j+rzq0K!^Cz*|YaG1(r;NGH_##+x8|8ovgt@EIDRdhX@N8g?L z2ySfHwtp5l;^~!M76o~p)@EWi9hcF3&2GcWn zk39oID_Cs4WpjZ_R%+86#1t~VzRU?9U;L!6iITQfa?G1EXi&Vq(9baOshQ_I%HR?8 zFJ^6dsv(WPNL3R;9{AvJWM+<`nd4}_cwjBHhay{{%JcELgF0ENjgItZ(0u49Cela~KsZt!t!Zv_QM~BPpGRY~Y+P%it^o{2yR{+p znDyrRzV7tQ@cau?V6Oh=vkUYfTOwN2Qg7)P+o|?oLd6VdPH5ANh0(jhL_I!4ab3;T zIJ7=)n>N3MnPJd5eepi94(mCNIGywamcb1C8ap>qRMvzxpiWjmHTaLu%lc#JVD%9G z#RURoqpFEf;UESUMP<|9YWa8?kbV^~4=4S0I89vBIVYjI(Du~9+CFzKU}~-$ikEhz ztqI9yiSX8-G9NqPN*j$HDJpBKOdY``2yRXiH7&_nLN(Xtr6Vi(<%g-bc2?=Ylij(> zkQcyFTz@-XJ=d;fo)aKnyTmc(f@2PQU~K3O?of?FwJ)immMVPh=SN({U|odrg@p>v z8}IaQM&f?IS)omaS`sJD8YWX#@&^J}Iu4%Y2h)iI?7~$Y54yI~uV-&`zj@@4N}C3W z^G{%$pt{$lcen6hOBp&{X06<|4)Cj$WNP6=WmC;83clMFR zh!kp!QUb&s;N)nIAbjSBDYLd3bf+Cb|i&n-v+TS3U7G-JY;3T$- z8=k;5!|RbrO5X7h4~PhOZs5b-2$?SC((A`dFmgsLc zYIKf%Q3aj_qI+Eaw$hOT1a%CFB}@>FhI9bAs^D52)Sv~g@)0)xwTT8oCU+y48)CS# zvsV3)i6twzGJA&b^Yp~f1b2sn)qJWtGsYaXMIMwBEw45lr&eVc3oqw`wHsEW&%A^l ze2*LmqKZ)`Q_pF)jzF9heO07_*?NKoiVt4h{r0m;>B9{oe#K8InrVjfQfLEd?#xGn{?DOxl%-X{_F z%$E7VF)?C2EsyF>q2URs;3F(xaY=zihd>oiCt{yV3sK%-%uKQxY&Y@Lt@J0NIDL=_ zAD)?wc0|T^8g;xcU=bIYrw6sx%8I79Ql^xA=nr+;@0Y$GzM^8;3OW$~& zXLDO-id)1d0;LZ0*#5(yK^j=yL>GX-!cFYI1ByOaE{B4lvK(0z>0u-cPm?wQQ|XIv z;8P0rM}|7DeyL@Z8;(@NM*z@RhVNQ2Wq%wdB(v?ZvrN)YN4H8eX~d^VfIYpc`_`3yU7r<$D*ZL1M>{6a_B0yFR3%C6 z>R7!T`xnD}DS4A#c&DP>!hKlH-HZCVb!h41@Y~Djr?-gc>h24x8Wm}{io(@*8HGXI z=Dwvbedrw(a^ZR_Y$TBRmcn7}DgQT=clb=?Xp&-fu}qZQ7N0lxlQqzH@!(hJ3Ju zedf*~mKt3;;%Q*AW~+<^bj}9*Lm|Oi90^z-w=y>~r=(A5g9*Zp%(PN{U{oX85M!3y zH^T&>z>#V-b6$jwrKRn@rM*&Hs~~Fu-X$og)(n0l>GZD_8?Vz^NqIk#n_f+pWY4|w zK4}$_bSIDcZ~JibbLXgA8YSZ}zaBWBs(~wW?Sr#};iI@kwG^rt-}X%*k5Y{jZRFw;?y4jH={sN3Rg%9}w6b#Gr=FAl#o9RqX95LnI<{@wnb^t1wr$(C?M#x1ZGN$B z+qUhUe=koI;|W)JSRYqaJ37{tn1@EDxA6nP29hkZcG4MTTfHYup%=@pPEMISbr)2lX( zFP{jV8#wyRrcJ>!+T*{2q`_e)e}<2qaaHR3Swlw8^dn>G9!*}t*c$=rbvqf zKK;9>zq25exr%V@8)WuwzuuUpIlEV9;bNJMeFW7qA}2ULBd@5}i`~AdZ|p1w?Gxe# z5+hv6a>w)O;`UUC9vQf>8W}b~xC<3n(jx{cydKJIZkl=y-%*ZQArDDSh>VmlFefBF zl|@^ql)mbW4gnkmhrZpx;j6IJAdQ95fV744Z$AqeK9R%7wTF8#SjR+!*&KNvKbdm_ z1M2NGxz)S~tXi`()4rP=KI-GXpO}bW3SL=FvKP5_W^Hm6&IF1OrD?4_eNbRgi?ZNz zV+eCYoAFf3lgBM@?$q1Nn%Cp}cpHD5$Hin{86QIHB`rIh`5%5Gm75giEh6<*r4kqn!U;aIln5rT%vt2okS08M`B&r~Cvh#7o zqe7+(*jmKBWo>i{T<*%Mv5p7LP)9*!I7wpsy9;-GmyLtZUtq7&Yh%^ z2A-NlX6v^#uNL?_=zC|EvIbx4h&M3nXHgP9&Z?dpTV3?rO4hXv$< z0O2jAb&NOROH6&Xp|pTknB0)dV1j2bd7|bOFa*XO!-EM9SB^(rv5x6`)n>~aO3_N+ zr+rJ+0bpFQ5LG@*{~(~erE%DM-0W;dYB{TP?z@`Xtv9<{7v*Y_=BCYirb&1i{hKd! z(A&oj1(hsG*E(&=0A0}V2WxG0S*(`bnJ9&kxZT$VcPBqr_*hd>h;23~M+Qki$$TY| zV&)1se&nzvAPjr0e>@?fO#>fMS&yl_%*NXZx{Ra*N<*{T!71+i|^5;hMRxv^P!^Fae)i{^idTUy1qNGMFj#!JKDVS zu$U~Vj0_3=Ie2M!3fwSiWYLD-XWrb3r~jWPVVTtU<}A=1{M$6u??I6i-nvAecC6OZ zWSX)VW3~AD)#V!IM!D0AHpjx!3%y9*j3$f}jy$lfFGt^=k!DW^U1JA+`3}k^1Yc9q zD0&g$KSP47iy+$DRwH+L&m&lbw(C*!6o33{x_ z39!cSi724`qk?Zl6p5yw2ju64PCNU-;)h0dOycgUXvT)4h)rg7FMm`4B#M}!el@0T zVfczO%1;Ti;rrM7mWi-8y$5*>Sfr&LR;q%@S!hGja%7$Ymv8{=n5GufWm^ghnDjLfRAl~W0?V3_6 zQcn{81?*biPVl|edV#iFBcP=c!sF+&^uIHy5}A|w_1Q2Q_=QBS0-#TyW{TfdW6c+z z0I!?8|0er!uK6HX303d4EjikJOnFK^)q>4BVWj=Q^qt{u9>&gDCMp=es_cLc9kw5` z4^9u%(nl5;8QFN}!|DVc4gKpV_~ls-%c+2yln{U<74nr2Yt|z*Y8vOcOTuQQ^Ei;3 z3=y>kuD4Y{lVt-Y0L^4+K^DPM{FX2|%zth&+Ne%l1)U~tHlr*z+s_C{;fnYf*pRSfli{7rMV?1_ULE1!DfWMy`UcY0DEqIT6cm0Aaxbp zzDnHD84i;Zpn2_;w-2>5Dee+?8s^;Fg(Vcc!3@OJb|dCrhRT#SNedg7CFnsql zqfRjPrQy|3dlPghwuCU1W|SK&|65z22y0#mEltqc2)rJu<&g9ZSyU(F9K8B_{6rF3 zMt{(@X-FZDfc|tD!-l?b1tnOm?M9A%|96l<8&IHl2Bl+*2T2O?p-vr35RC%SIxxBr zhr4q?r3%#sMf8XDpdLex_U;Q3z6v&}LJlOFBJ1G&=?j|2F2jjWtUUyGi$}$J=Hnyq|TY!qmbnVNyn_7Ao zSB_TIuNK2)t#M6E*=k35tQhzMh|F$ip-#?|^;XZnXDiR_RUKCw5HF(EE{k^U1D8SF zKV@AbuqM@)2-Cfi*nJCRaJlDTP`8vfM8>4uk%8u!T+!ksgMUrtfoa^S8+rahAC?U? z1iu|$o*U9twbi`dsSebq-SSO0AA~820+~3q!o;l(BlCvpHx~91fNkfGOh1cTLni7J zLDeHp0IskeDwWt2d6U3j+MAQKf@_eabqi}&43z1AxeMx-;5IEbKZ#0hxIILh9p%+{ zRbe#|So|~+;o?m`r6j?p!7x?TMM83$$Hd%Y+grKF8NB#zioHV=FHPLc<2R?@)_nch z%zix%0bZidxn-O*9Tbpe$UjH_MQ1`la2yt1yxaE`vn^@WR0i2Z19}KNXX-exk0ug|5s#R%}Jn=C}4XQK_8x{Ovm*bbs)yFQH`;*_9t zQTSPakRn(sG#58QoKBuhltE5#bLSPCAI|7>5`-|s&34F+5YyQi8Zu2AosL4jyjDkempIo(bN!fD-4Fn!=DJGY1Rn=! zr(c;h&PI-RX$pR<=PA%J`*?b8Fsa)JYuv}DwA0*0*e^79G;4T=86etHV>&?^M>HP8 z4+qQXu-Tq>Q}1Cefs~|)TR)3iKgvTQQdyRM!+zhYXJya8!pT%@Z;yn+*2x{53L@HP zG}pqxrFfb(58E8VE1=;&Zkc3=T(YIT?9@?4E}SNI^~~29$Cb4ty9>C9ZC#IfT6`vW zv`(?xl)82Z3~XueB#bgZNTa}QwefspMXAabgh9wAbOK{F9)r)jqBW_Se3g?@ds=<# z0~+io3-O*5-ZBL|@&bwW(~}c)MUye|D_(-1hvhu87E4mx(3+h1kfLlHLJAGZElE|L z(ztGjb=iqLh0RVTEbE~N?7H!tsT{3?NaTdeKMW^#)>6tC=KK`Ch^G84Vb;oew+qWh zPv#^)?K=A9E0Yzzn9fx*B^>& zWMQeS5f+Li*xfNk;6gD1tz=wiwoRBGYTF4}tpl#}{e@^xZ1UoA+vu{@`o*32!Y6z) zB~l2sg64(viu4Oc`|~&QN@e9vsPbd&!tY{FJV_*8ZF-;F%~z+U)X8c--*t0u+lP#{sN`*$Lb&trcwjzfs7|tft!8yBArGxGbd4W z8LysXGdX<@cRdx?6CcMjr`hd50(hqnu_lg$>aDkj4sythd-jG4pCXjt4C-Ag{8A@; z1XWJ#iK|o2KlX3bVws^VUI5L1q|L6m*)J=Sy+Aq~9$hsKWvOgfY@2KB%QBN`N3#O81iBtZbeOqMtz#Kn`se-TtP*f9 zx9b{nHF|$KxZ>(y>z?efJQi>BRkt-+E9&6JY}GxQqR`l>E~p}~u)>p*z0Ldfz}`Om z^7hy=yQ|JfPg4l@8OZH`%&OVUsKqz}pn2#nPvOC7_;`FZl*CbC7vjj~t zMZ&asxNfy(4wGm=^#Gz_R#(4O=8K?B7weALpI1@zqJ}>Q4QM{XFGGe2;^mC!n*#xB zTO%x->)DLK@uhZHt3Ks=wWIk=+Uef3fPfA2A#paAz=yF0DvwK~aC(ZrO2_{!+a$W5 zJ+ZV4G28$Cf$Sw%({NPq+RgUrxA1Hir!Iv{UG75SPo@Jb?*!pP-yBa9Z2c6L2Pn?=eYeiT;S@yTwTZ|( z63OWYdmBj+o1C%RvnAae)xZ?2{$bDeCR@mAq+JvG?7sAf0qhlmC*b7NH5D8Jk5uOm z9kL*{voasCGuZEsq|r$QmyYv4LWXdY`l0d8v1*I8FMC*2aJj^c_39 z`!cTF7Ee*#LQtTqFNo{vs}qitr~?oVn?SKmrCg* z`~`5)26iJc9dYk9ww6+CILh|@(>OLla^)JV8XFmxF4#WrCFSoKP8z-jm2==f90rHF z1I7tWu}(85Yhe&~n_Fh8m{HWf45#xvjoC(=kiC)zrFk2+ps5}gs*YrhTZyt`iOvur z!X|nJX|@G|Y^ZmJVsf0eZ&F}};B#EAKn$yccR+0$+v7tiX~3pZdo zJodE2mk9&hHTO+wOzmSSBQi}J7K=9K$(SU|2(Sl%e?VdITeca&$mCQ;I2>>%SYT$KA zYwYj7CK`iRSvATtDC+pyd0H*~KuqEL8l=Dm1#@1sx>CBf>1ZdQARY3+iyEfrI-Ke9 zt#c;$tK^36Co@|hOFDDuS?{*2T(5)J8(cBSpVbik#2I;tsfv!%sICSpJ|U3RXiM< zY;(w4DB4CpV%lHDCe;Q| zXIIIoYF{+Dz>eqD(x-IRr?LqA6g>Zz3XfQlG0?58C>$@iMlGEP5!Tc+qNC<`bE%n- zS;T{(teHV@8-G)n#ENv3VS+99%JZ_ko;MtlDmpVJi{n+XH)=vuQT+n47m&0Gkf-(h z_~~n2{jKd*d)gxCQYIA}_@}QRYRWSSg0a|0)O6nhv<}iOsecd3MF5Q?Z)xDI+ImJY zxJxbmt|rwgNI9_-kmc7E{$+w*W9R@9cbW}?n2?8@#I&~NbXS9k<>sijtxmg{)^1 zV$@L~R_D4a>AQtuN^b{>5Gu**HC;)4+Y5mJ;-&A#%GpJ!_gh}f6r*SS^3m4M;j6Q- zn83IH5J<8FdJ}SZJe{MG?6WIoFM~A|y#*B6bs;AVoo$QJ7oW3JP0n7m{v(-95HIQp zdonrdP-x3#D@h0BsUMwVJ2@MLPqI09t(Q7;kb*%19K)E*n?2dM3TL(d@aj}S)> zZXD|xfuWwYe12l9BR~61^O{_vE?-hw{#|{TJM>FhI}g^kCMpG-j`-$yNB0!<&$86$ zx>0X0{0TOeE=}M~a+h#d((01VJHgKL$m8Y{i2Ni2a7Yt$-LM;3zt^wxcKN_#bnLu6 zMv4W6jNUBD6kNeb&G?S0;lcX;e%#H5{pgW@-a(F4z^#s?vX!`kfY_HWc7+qo{-yD~ zd3c*=KMrz*&&k zLvl%`$*pz=BOUD9_Mx4KP2u`jS@=zRUfbPa1n}`Efujg^M8np+QdozhQu#^M&A4it z0GO(qU_G)zT}CPeVXro^BMK~CwVqV9POf|hx&jZ|_h|E(14b9+VWfg;aHblph7bcv zY0vYL>nd! ztz}_}>6Pb6=w1R8Af}q`b#Ssq%=z|7jwX1%K#|tcfl)=kuda-Ptga_hmg@ZRH*uBP zGg=O8=Z;SK!`8k+Jm@9*uqppKQ|;%zt;x=U-iE?b6G~ZD{?PSNyS%&jr)z@}_7Ylh1_;WZJSk+aWIzL1`cHq8}}f9CdSp0=Hv^#oAlK{O3dI)~PeaR$gC zLAQ)JP35N|G$|y>WWI8BC=O3-lTT2{w{J>X{h@qr7+Yw1%T%+}YpL00ty7k_H}*!N zd8_K>;Fn7VGWBy^e!>*l?G70}rdaNM$iY`&h-!=mHgFy^pd>#)9FjO^Qi-B&R@VT; zonB?WrHW|WW=}2H`(B*@GyHZ7EU|MFtTdZ3@)8H{_F~IxqvN9dLqfb-A<8MJs>0+H zY=|p*C;(Z*$uLMbI`@mP%WX*s3>O&usS??&+U#}l0$?lAbTr52pL2Q{Cqv>MPDx~R zODQKu!RcO>l6f4;dg|gc3QfbD?Px^O+#i3x?Si$L(nneuL!#oTb(yVmOlnsv@2s{g z$15;K9=+@Az$4whyKnX?y25+~UxPAmujEYGLEQXwvt$dT^&GXBKHaEZ@d@|rM{IEt z@gb>`-O%AFB12OpAeud=VaPNK6}4WPK)t5A#IqgcV;n`mP@20LQ!jP_Qd-g%AWLq4 z7-7$r$7vDn>eh8kJImwq+`~KKNq5w{wBL|aRVujB=X1#YnDH!hr2WOj&ZVI}nU4EN-BqF=7 zxPmK&XOBQ*I1Lm6G91H7vXKUF1`DwUtLse+O~S%PVvoRs^zvM+DNOco0juUO)S{>o zM{NybV~hhwm})WEJOtHCR4g-;?9&w*ukrwu!z%;U3Tt`-%!!-RPLA3uIjZzF1GmSS z$Kl2C9zi}|*&cF;l6q`p}+!yL6;3XO5-y+E`A2cS_MdnIr4kks0!8yRTCb#d8IM3}^A~ zI8UCEH59AyhSB-L=qW%;t9dgprOnph-UIUU&)|X&B!ZXzA%bzQltljR$uEd8$y%EzDo zQ4LStRdJi}PYr)=2Lz=0|4&Bga@=sCuf0j}%Wh>h6bE1XryrFxpak!!L6y@8S z!oxhzPmiBikY6CeAuaL(i$(0RJ&=`1=NH4#)_!YqUF_x=#*TIMZ3~g&e^q^eRb~Bpa3BP-%3ISyg#*E|$(;*rR-l`zSYPUB!dkL%d!78f!z@P|hpT zZ4Ud$)0Mx)Ra})FM+MVu+nlrd399;o!4$d z=!&kEeAFh7?|sgaeARIxR<%{qO#jREsq6wkWp})eSK0ydR>?GeCGyE%{E*T1E4GXG z3H9RlxnOQ{I9Z&V8U5QXsLAHi89n8Eha@!O0?^?+lPZ zR~RG~apr$-9YWy2CuG5#F`!OL=`)r%;Lh-RIN*CMpd`>$&I2HVG0p&Ba_Y!4w~q7f z4|P3uiGAcRq3QY8j6tdwrTKUo@kr)_N0yGlBK;9ADU`9UmA`tPQ)xMVUx!p@D-@dJ zMqq@d%wM`Cg(A0#7|FMD!WXFSKxA~!f)7T_0Z-pQx1_C%(ZrkSLamI#Wk%S+4*Ft0 zo2`XpiKJfCl4L=8QYc|N($IV9K&A6|x&i*lr)R!UzrYu&l>2m@Cbn5P!f}}~1 z$GD>SyVVjxG1`QTwtEpEWb-p zfMQP*(m}WtD>mm!9p*$`+*cZI&jRUd1FqEHn-FI$j4Kx7F!)R|WBmgIu5?b`_ckH< zwj=lq7(Yz7X~8MNO$%X8Zh{y`l}iMcD%=A22x%%6Z}(&|4uxcxIFpu3#F_j@bt=^_ z1zLnao+ultKo(`M!9NaVZ;@G^=o4j^Bq|PNfRUJ{1v#WrxG9@NhkHRWiSuAZ8XB1o zD7OB9v&1p2DLu(AhDYihdtPxq-cejqU|3r!;354#z5t$X;Ff}-1ZlMhEG+7|d82># z`+)fGRpyt(yf8vgKtKP~!Kl<|QAIH}HdukRIBLwJ3idFN{#A5GJ?cM0>-X4%flIXDCVgtCo){k= zPxs~V@O^Tzf!O26yXh|=@bmUGv2yS=v9R?tBk<+*u=jk^v$^H-{<_iOE}*xwz3uZl zQM&OGzOZod_IfY0yue)KgJ-gDj4_48hPlD_cv za6s7kc6IdX;58KO9$y-sej?!Yb@Z|R@%i<1dvn659{zB4_xkze-|-eCZ&D^`PoxIXfer8F~d+?wR5uc zknS!#Zo*PN5t1sNU&DG*#3hm@OK+M1Jy7&0lmwJ6;hH65;&7^s5)^Vx_ML%D`!*$j7ttpRH@^&n2o!*JM(12KLZHefmnHbkUr$pi+ z+!m*1rHZ2>XiCNoT4|TE=<6IR>mHslnIG2G7Ss-Jp(N?=gU_IrNL!WYo<@g%VR)e@_lFr3{t+o7VOf4~unAn%KdJ3R++{uf)He$tZWNrnDD)1K!OX5Yy#ge_FDT|8>SCW*hmIPs5 z1OG^f;KoIRP?1AW3)g@%a)9~;q{PfUki_6FuA4jfj)4O)4=axK6!3&%JkyHhERGDe z6+nDJ1z5s(e%$V?#ir-Odlqe{z6L3}K&uS;1K&_ax+x2bgUNauhXdJXJ{1B<+b$H- zjX>%lRed@P?ONczohU$7#Bl@DTif3Ty(f!+k_;KJs~-)0L+6umb^OJ3}kC#%5|eTz|7!+2?}+4bbjrGzGI73mT>n~ zT2jIZ8zvc;u`Ep69OOhEh}aJgite#Eoq%WkS9?!cgnxA-=&}|~UVhraS69im&8ZOw z-V1A70bD4{=n*EN#wHX^PMa3=jMCwuzl6GqrN7tgzN-?JW7#J#j z`jJv$-s(KUEIcuiXtV)hZc<{kZpNh}MW|J21&>VEg2RHt3?>mB&GvVdAqoFbFZ$Z` zf;9)9p299Hb56MtWbVwz$y25jb=3+sViASY^yO4)C+ zW#&BQhTp%){OxoQB3KXY;jcOA=$L7#711u}${b62TSskUvmDkBg8WAQ1U)|l4hj=` zHZM+qC(2b~^^MixyV|F+aQeX)hT7^X*V(yGIb|B627{qQF6&`5RG-u#U-`b7mDY4qcOfV0yQ|E5waMh`ARb{g*vgVh4dMFHf3yUEdUK@kwYyEMi%$b)?}V z%c2|8^ZvTB3B_>Sl6=u6n)CD_3-*#!MXMCvm<2?XaE?Y%!($cj6wKJ@lG%3NR2zbi z?g}=(y91gzLDPRR>@60h;;*OG^&@7-?=dS>N{0C?09`TZ`NVl=1C0KjmqsGA?-2#y z?F}9lYZ&`cxmgHLTzym?sUb%aZcsRpq<=IB&4X^9S0lVfMA)Zz=cI3)oCf?XvhGDgztis>b*8hP z@u0e~>BPHw>-fE#S^i?AXd;v#iZD!aX`B;?LrB1qA@wa9V}KzD2F5xb>V6lKH&0rx znY6EIux*BABW(UTU|S1iw4Vl5^YQ0??fC)uuhd>;im&{COYQOg&x;u|W_?>jV<&rk zTUQ$wOJf^DXJ>~0d|H}W8XNux6}sOFqebtb9y>Q0*%c2_dCuH88XA*^mgKH8lL{@ln~Q);7(0b9%Vn*lV4Mdz+XU z%l>KEVmw*1O0z%S0MGq9bzHk~^iP{Mf6|iW^W4st)1$%2G4ZtF_1rmgJaXi=@X*jA z%x|sVK8_H6^zdtEW!UkXUE4%{<7)3!8?dj9r@u1RSjoF#?~slyV3}m}_lr&Yu;ak` z{?aUsFmA%tZSxvYHK^35XRW(hZDQ>hT6nd~?5}xl`pBAH&*pU{TC`6$@2-&fh;Pmh z`v~aa$FTE?WYWMT(I39x==AH6lo?MqpN*6736a>xGBLCxw^p%4SxMn}zAJ}_>F~lB2w|Khmf3VeIiVAcR z>O7eQbDLzcnPcoZPH@(s?&Vd1HFJxk2ds5R+T;YkHr>ONBZfLg?I&6MhRI)RPN8-_ zoi=l)^?8%>F=^$JQYIm9Tatxz7ugfj^W~zFv07Q%kBV9-d!Satx=hz|QeZ!NDepe<1rA=UYE8v%_Ka(G zO8FDqc&s(l7F`-B+o9`RkhSPPHWE2*gezZ zu1jprPT0@bwsd+5q!|hIFl^Ui^a0?JJ=5;tM-}{pcNfMKnLDm9*tt5AfYk}xiw_2G z0(o|k#2)FgoY$x486N@bEdX)3fAiElsu?Obs!oU4DB<}!i4faPO%&H(zAv34fqUzg z+Id1;DF&f!B{%3yxikuk=%prZGZx;B8cavyv*J^t>W7shE5-3k^m_(H*+jb+YmJtl zG4qk!4(plWcmeIpmKB0%Rds7gm)Yuur-gkSL#}+juu%Ya?iC>1FPmGq>}bChnzop< zwtQ;MbLZ+|%a12KXL~vOX#E8}fS*>rZaw3#9Y3SrV^wXqj<4F0a1DR-`fTM6eD5q_ z*=m@OKq4!yuz$Ik;`LWz|0>*9Y7supy>YXv{zu>m91(#@s)yYB=fg(Hfeid!spPj$ zXb5{PV^LV&v{lFS%aBCl$;lDK!)myjDmNmZ1oQ9`?ncTnRPtiSRhr`$+zfB+0w$F^ zV1Naks{O(yvamxAbGZ25rMN@Ro#pf8$niT<__Ozq@I3`v&^2L28&>0bx&L!X%(j~T zK6lZXx9SGwg|{gfuWw{!j@FK~+-W%@-$Cg*W>zi}^oEZvO|q}lz7{KWn>8{ySfOZg zQXht53lO?d_Klf9!`H8k$%p5&6s1*LbphBJ`Zu!Fg$a1IM-UCWKWz58Ou3k49aXw3dZQ9o3hxG6Co36Iu%)ywK*Q8cGUzvddp-~7X~m%T z*6<9%RvaUce3*7?m}jlw1iqi?``igJ5{8qei2(F_wfB3}u;O_-AAEk555bFhCiu^&?ebE;s@x(;qQ(@=T9Sv{K|49k7hl#t)AMv+kCgHyW?Sv%%MN5}ZP zSM?Zha>2dWngq+%=LI&hLn3~2n6F?Ei`GXwgLqXwCT!dVn&_&4st2jLM_lXfsr9Xe zHnAfC%2pi2t_4lHG<#A-=+!X%&^vtO-??{3v2r;XpbeJ!JO4c|D3QScoNpcQB$K4lU!Qh6Grpknw*Ee!wMbbcj_5~5VBAoi`xVaGTYsXmJR#0vwNhL^*|lOc z<8wve`>EF7xW==z(|8YttZvfQaE+~8JVNQ-0sakV^v`H=5Z_W^SkbM!g;Rb}825m1 zau8Epy~3~%E3Z(NCsceJ1Yi}#8%2*Axd~|_EoYiMK;St!mCmptfLK)t##BzpwHnR18#6V`;G##t)X z-@KPkTUhR|9K=9Kz7lG!glw%G-n^(CQ~XNxn7as8)XALrm&lOsQR2?z#O5WBd9*xu zZ4P07)fU7T`{Pe=oW zOptKYPM=*|c$OpJ$?Z7%H7md-ul*kq^swID<`YFs?A{BIFMOFS75xc-Z}#g*xP^qk z2L=+=E1R4)uj^K2dbhv^4{tq|CoZcF-1K{%B$#2gxsQP7H%qODzGNaLDV_Y{hS}K8 zCt;%J1E0xvko6`AJPyHMiuqxlD_#09RAb}4`keYE@Co;asuT1K`|@h=eYCoHy=2P| zr{p5t%P2CRgj!P%uVloH{j5ybB4eXnzZ;0DKpT9fju7L!VHyM1fkEI%a?G zaY|^+-TlPd?8tluf-Jhaf7k(w59NG7uaB5eyq;P!3ep<3C>}Ffe-7G}?H*H=XBqA? z=i%sSSeqB?fv~>^oc{wG&+i*mBqt^g;zQ;`z&P)O+cdZZ(NSHu@Yd!d$5Su@O>U?8 zH*u&ck$DlEN-U@ea)S|r=jB(m>4iiXZg-iq45D0w>wQ=X`)}@hInL_lb}mrOWBiz3 z^U&6TLq*E7wbg@#b7aj31R3y73UghL1aqbKH}*7yALOM8%kCDGswE%u(g3&$ll?q{Vmh{!RLd=z8&n8hLOqC<#jH zN}b=jd{`mB`}1N8`IV>5Ul_@=K|5)CTPt!R}^VhtJIfLdj;KVBAfm z0%?CNGzq9HA*Vd%D?(`~EsFL+FB4?iUCL>KRur2;^T*WM5X9)WGDS<0l8bQUasayp znu_Sdl^{o&8*iHIRIudT-$R?Xfn|y zeRP6pp|O^(m9|0@KB`iQD?Bg6i{c;Xz*lNz)5wItw>mWHryb7s*;W%pevFR6CTGAx za7bJORoLd=PfUhnQbf-G2#6>fSm>X39u|Q+2!BysSvO41tjZ6KJ!73!PhlRd1W&=J;8?Uo7EcqGJ(+0g! z@R51=w?>&)1&jWkmU5=Uckh-0J$ORvn{I659un>okgPG1#>E z+2Sa*^b|#=D{PCRvWvK{xG0$_>`Cwi8dczS_8yJkRaE+6kA9E@`CiJBD8`LD9B;!M z(IC*jxkAj!H8fI`9QP3y&saw=KyUIbaoqOl!0II?{Dw?2F^F=8vqH0-!-|eqx5l7O zLbVRY-{PopWAqXdY`~Qz+F4#uT`)Hx9LVESb1O`&`EV5fW0v8ax8+7Q5kil}p%Dt^ zI?yt0v-j3>JB^5*-(qRhD!1`ZO~fywV$NswA~b|#>PJ3QXMUHKgF*NTwMrj0Os7@lkvgvq;2dQl8m;hzYibpcuHJo@0j2jUzy&F5BW4lYp?H5 z?Grvp4FvIOocuNXG^BShyb(+S9FU)~;d#wVIfYsT*q6+-UI}S|_Cj|P>9~3vOQ|+P zq_#od%0bHT5H)gCq#6sH!P`c~WI1P;PC8VTl5;8X%5iWw#P-_$s#;}Q@o1uo;j8e| z=K4r%G1RKz{$;=hZaV63VI%)S^TaO5WBJR6B;;a1VF8QD88~2x{C)DEkPejvD^<^_G5g|8Osc z4O1lxmYd%AN^U^jo-To@-q#h*IkvTbgxd+pSQVp#qW$U_mn+rl`0Lu8@HjEYIQ_uM z-Xq8q?o{qx&qJcwKmgiJ$tr?l({WmVEtwx;Ar@c)#KL=XVO1&?yp$wOn(|b2`Waiu zj3_6zWjdhcC?rAJiP#HN8gtSx9)|e3`-M_+2I?mTb0hcmz)DWJ7btZl901E5d6Xg8 zptB^7p%6>IB9^4sJrc(nRWAlxXbnG9L4PqctEF*3pyHBRc9b^WOAevuPT)p0 zEX;=d38%qe+_ap|whkGgw@XuGP=vmD-mA*N)7J1=SV|R_Z72m2XW|5n09Q|Thk@n1 z?&%_q+yaf^rDJsxG}tuO=&c7v)C{Slz8N3;KswA}v`(4!*LWR>0m)5x)7`AfkEP^n zGG^!{E})rJ3MAChl1+z``Ya!y;dQf>a$)G|;1F(t2jy7MCkU@m9OTXf_e25Bs;UQm z;VI6nKn%f4L~FXEo5ak{HBj3Bp47=PbYKkUJ~*rNThn0zr3Ma;0Hws(Me`wya{!&p zAmnqtWZZ9jyV!AXP7Xaih7+m>dRTZ$rhwPU6;Zq>RmsuWvyxle;XT1!m1ah^DlCH3 zm!|2Pq=C&TbhBkY_S;7MIoPYKm%=;BP@2+!Uxm+ebq5qMMK(h^Ka3+W>%azwjr)SS z_>S;Az)y}#kTO<}aGV}JS4=`(eKvVGO3h>kGz>2qH#VWYnB8nFUhz(Dd))*Dfe{GH z8@xQe`_{%Kc8F^UEWPK5VKCFL$I29y?UW@`(?Bt`1$l*(wJSE^M_q{}pLm2s>`pKQ2 z9h5kt)w(^8J?WHf&nm?EdEgC@ws5Ub5E^_3nbuL|QJsC<%lfv}q~Wl8Xx~PT5Zzg( z(<$v z$7@kZ>Z1B`oQg-wfuF_PZoJd+@xz#OkTQBnmU+n;eO28c^i8&lz9wbgSxmp(_4z}< zr+>UiI_~!|xGNp_c>v++cK6(gPmpv$2b7K6MLU{JP=62#Of?VGvJDj@%5KH`4gzZj z5_AOngFV5d!6pYP_bY6mL5{Wz6sR7h)pHvNROF=9zs@cn@SBvg?AJ}q;UyBbr?u2v zZ1(6YKQ+ONq-9JW&S=Oz_8U17GpoM2G%=JtyoI5^Lnl`6a z3}8L)2XK#SFD|oTZP`cVl!ki)q(tc!^0-><9W2cV&v-Z?`jF*S3^%;OI(P=^2+@w^lw=I3EHbYIb;{hi2RGF6{W4cP;#`<-npb@NlR~* zb&iWHf`rgiC#%Tlfwl?Qj-K|FXpUP68=4vpyf+X9D&ALe9{o&?oy(`fuWh+Kd+AxQ z_vRhBIS$R?+JVK*N=<07OEdD3hq^(k6gbOa|cHej`tj0Ty5>mH=g zd7KSIyr z6UZgMj1(PU3%b=S-KkIFlwekD?Wy-G=1BQqCY7sCsMs)yiatn*=3GTqhB&&eViW@e z1DG`nK!|F*bSyXI?Ko(K0U~Yc*^h~t0j`$o;rn461-bASCWbmyb%NcAf^uGtwncb`Hk*qtfB*NT5q!4NL2pMVpi`9ieM7 z7-5a6-kxFiOd=*(4Kw^Kd@?E==`mf2H0Z@PC5l{8TGaS~(c}YQvU(;uURlZjtOjZL zHKy>9zEskNmEbhI(U5(8kZl<_#F7WM8Ms8{G3{LRp);(3$Zh8s*H&pil|jZ9+GOjA zU!rO4Vs2>^C7X)uM z2DTp;UkL$a@L?VH7RKvoBt@pEWg42Mt#8SWD%z(Xb4_me`}=e}4w#Ags367!bhMWm z$@_=$UNYm?lSVm(Q&^z}W;#1ePFV%}=h6EZ2({-jaL|H~aV>TYcQX zQjDPX4%eR43gr4F3W$_4^8xu%zmm{PVJV?NYW8y!@*8H1Pce-%5lRY3rs}ImZ<2d2 zDBB)}YD>|>jH4-Gi%jk_gVs~REY^pu@JIdb9W7B=frPC*3Hnc@ol|=zLAQou+cqb* z%{SJ>wrx+GiEZ1qZQGgHHh1>HKG;X!!S@5IuhqS}s=C&*`hJ8LAXI)}soF<^Z1S5s zj!N+ollEo$2jP8g1uy^75|pr+2k{}*dza`o-n0DZDNlu+*eb zs}Mq!l2%AsvcGaToTO@__aNE8o@X?pWOXUhvY>(b`nt@K`;3Hz=f24sqd^ic1;X$+ zQSyZMB3IN)Eb?FK3rp$9pi~qUKsa=!i z9X@nLeSl}T6m5)6L*GD5T)M7keUb#p9aRBzoTMAU1n%SRpdR_2ztR^=g(;diqSFFV ziB&tc`!En9aMq@y9KmK&J+Pc?k?rs~T!MAlF=#Vx1!Eux&XaLkR#eA*?H15zSJq`s z{D+E(z+*+k@Ej&0LxavBbrw@HR<85g6-biCU%xQxuW?Ph=KCWLg#(1Ho}OLMv77a~ z{q#gy2?SJx2D&8%`wSidr7kUje=%Gp-eASB2#OU}A(w#E1{2Jgm{#)zfX2-7JrN~S zj9!qu&2t>}3KPW!V1A^&uf8sP9jM?&0z`7l;>XHErrR@cqDduRd7xRqv6?uuuYm93 zbUUcb-}EsovNwz}`P|g(sw(h4%Q50A{_O|!{~Njl^Kd02tbu@VF@S;O|4R+@|Cph! z2G%Yn|GAs%vP-LhZ0&c|>vYX-(GJOg_g0DkzO!JX0X%mq*@;A@zjZrY8BE2mEZZ-i z7`w?s%FiJY&46u;;DoUehM?}M7}tCF6V1cBFvpAjhn-xc3qZap8TIliA$&jQ|$ zQvyC;p*7#H@_u=keos9)GrrHSJN(}_kBEAH?^8Kn7uR1$B|oDC1ady_^?V*bp9MaT zLa)0&Tm1aIzU+G5p9p*2Mr!;X9}&O5IP^YydKOG_dOW{fj?cTlvgqH37{2$GO#E(_ z5cPb%Uqf^Jib}j(zG}K(b8>p#zwYfiF1}x;+yuPeKUa2oJeo&NIc6R>zuz>X7woeC z)%3g_<@kLrxqYYmJ~a^Xd}8K&pPpZ{g$les?LHcYl@C_&ypDzPD!ne%nv^peJdm;ZH&WCpm|2m_jBu6;Nvo5_j#1^HaQ;Z@MSh=I6=QUt)fLo%?+}soC{-W%)cWoj+vQMK=h14fVWC)exF} zAIyC1QTBX{(Y{ z>x@m8+tekk7rujutVNC1w%G=69Iu__?Z!w`Q|82?w__Amu;l% z%0*%3bD!A*m*q@)Ky}~yU~geQ^tp>WW176I{6A#gKN^IqZ|ot2UMCapx^Ea(c`4hL zG(Pg1SF5Z*Y&He9l{{71g&kI200QMtk-(i|+lEd4$ByLEZtV!`O?t>zesSB%p3HOE z36}-@OV3iiVhWam)6^@KGgpEg7h$)WP#ULRI9@it476^R0A^k42_MPh|uNu;<%3ESTDJ@3x^qtox2lw z#l@HHGW_zWBKR3c0m3FPY&t}ru@hxn%?U+Vn=21lhrdzY+4~El1eEbt-k)n-C693h zB&_V$w^3|tY&SYiPmbM>{_#QK@8!vW_#{~pOLIC^fdO_WDS*-OR$j_;eUgs+*0zlM zr;lta#@21JEa9&*wfMqlV{ko#wXn%_U*<8Lt(NA!i>!@hkmc0qc-jKg^G2&WnV`Gb z%q!2F#(s5{YM=OyW_B|+N|fpjFc8f1!Nql)b;*vMiq8wS9Ii#zSPTmb!bsI+$2D?T zvI1w!l>*NcZEUe|Vd(rC!73*Ca z#(BXneCk&kV8vG;ADeZzsME+Ty~jBp5vERU{`AW)uMC@1btQ)+=6OlzM|P)Bh7ZO}6Tc3(`dd~6IeSR4Tz}=@kVt*^ zL%h#UJ5yA`m(@^)>^FS|ndXWq&B-tLoy|rS+n=CUN~bl*l%)JPmb$vL&mP}mp$)<- z;ENRF2bU+$UA6>Qz>eY9FK0ieUhp+~6$G@ae0)LqjVH@DI(Gt)2!10BL6i`~&Tb(sGaIqN@BR}YCLWps= zzb{_77_k`;9c?^PZ9R5U!STOcs`R8;eD4UepZ5I?S`u_W)n2)qt54Cbvg2?}*KAVi z)jg6VI(^YlKiVeWI+K%bzK3pCYOaMtfgd~PN>-9J02*aZoWwn0giq`?9oLQfbD$Vi z_3M#)7K~V|(UqG-amNG~aHpUNK4B(ud%2FEExTAxTVoBYqE~z!cev8@&1sFLvF+m> zr@FMrD~+IW;p)?fankGiwYy?HQo$AFVza?Mg5v4wh0uZcCjrRUklz- z1ZF~76LR25BdL#T&;o9+J~ta$Y+V+w9ZPlcN#_PFKck4Ioea8WfRVnK^tjaULD^%A z3$+>-D9#Uh)~52$XtcrVLAwoH@L|)@Z-|iWUrqv=z+$$ci?P4&r?<>+?VcbDBM*L^ z1fz9hte53>8fLHUftnmr&wV9Z5KYLnoIgvYPe{!&PmWq`IsWiA9SLI&7)F&+4lA_0 z8^X`syCPQ3Jr#BJ7r@9PY$n5rZFmp1S?%!XF8^YGsj^KJ$2Km24lTf4s71l(QW8=z z(TlBA!D@^oIcc8I75=MurMHsyIvgK{Whk>Oy3nMx@*O`#~E`+vrwzP4@JH2AdzhpL9oX)1TEq`k2F1%`Zj=BB}3gr4Jr&+b@Ak<_0 zc&laJD>WHW#+!=}4LymQUs|yiDaae));uZ93`EFS7HLhH?~J1LQB^ST?WpT03!4t! znY}emljW%wC=D>CD;--oK_-uTh$C%m{5z?d-C({<3kCrzVo4|uKxwx)v>GIfo&VWIk$zh* zhZM10x4%ZZ>58|u3<^0S_;?s$-pHr<&U_z2C9(R9Qhi+l#V$+E=@*%TtIbNCq#kW- zMc4fcU2u$T7tiqzgKVMjPzH?elZ9J#PlR8kLt@i;!tE*YIFRE(M&(4x@rfqLt>Da2 zB#4nz9A7T`Y8sl zxCSTEvWNxIu1(Itof9%+#hGS1kFi{fLTlS8kr-YKlCvQ%a!xQCg`|L9B|#MPc#6i# z1O~k{MKNIO)*gi%%d)h~zB=;cQSTWB=4zPv+Y!c0CqIe01M)0i4?Yq%5v8?`-z~83mN(!lg!4y2PDvlKKULdY zc6jmS`Do#mNMq=Kj8Qkd15WZ$E*mgz!PtF=+Ae^(I57-D;q2Qa4VSXfdslE`zE z&Ahid^krf1(%WgaxUJ#G<5ba2m9Z$5D=TTs+f#Zg8Ot1*xQqtPA1(uRfc11z(DJZX zvjsnN)1mP7EW^T*KQz!;2gzy33vW8YHdvWqp5_7?^kSIhqHP5msO0VUa!}=Tw%>*o zMg~3QyaXnW9^YzB`m%N z1=itln0TQ9ohb&avSyy6LR;_bbw=@av_w^MS&JBPoc-t9FZ6{m$)X}V z8WEO4^}nG7En#gX0r09Y5emZEfqMPg@!SK^@iPZ;3CCnMEp?;#W3eujJa*&x4+Mi$ z$!*jqN05~U;;iGl?Y$!0phzSj zH*AQlm{z0A#)nN+E3OpBPmq+2bxES^^+=~^x>)X8(JZbyu4@IQgcv9D5nE8Lg=)ok z50L%J*Qh~W%F$Gc&P4NqW4=XE(j?rT6G{hL%K@8szw~sDl9X?*;8%j!_}X-d(J%U# zbj{B4ZBA({WF<=9#;|R(F+3Rh^0ZZ?1-vVrk)PtbX22j;0S{cP=#Lf5Z(d(WOw0h> zvyoOiy0DpO=+diro_6|IVsEQY6XytEF8+0LT^U+LPfpTA&(*$@d5|yA&ZFW2ttg^A zu|os2)~wbxr4qQv<(K1FEr{U>12ZIvG*ZTk&)mP&y^-UWyUdFoV|(Yz>ct;yI$GFp z0I(7aT;#-{RK)mX9hW%!8wiG*Prn$hmrQ5HW4C03oVKea)<>9Q$&TY->V*omA!u@| zB^eCa@iCZg8}IQ(&gpW58=u@uRxta3ll3T07VS&*EQLngY2toYp~UNYIN8fhB$M{=ZYYxX|4yWTzBYYH*KBA`2g}r0Wb!%?;6Z<*SY}<-2x^A`3~r4A07xqnKc< z#B|N{5T00qsAI21Q;5GeBlTaXuPWlpN+7E4GKzjR47bG2h8N0KmseVh&tyK(@Ju1y z;wcv;q~Q?RP@h>NSBGxk6?NfRq!wS&5E?$OE99HeJbH7##7w&_h#gvm76znF-1uy`*e2aD^LAg??q_g(xWw@%1hORqeR^x)Z|Q(zWMb<>=J%d@dMS5U(ne= zDzT1*Nq!Awaan=ie*{?SXC zCAT{kl}HH$RBXx#hBaHuU#lC^cgHdoYbR~>!lNl4zGn86I7wo*3Bp{YJO~m zHRzh5;hb5)3?lCp+fZo<3VRwqk9Dd`4snK2&ba!a(=!H`?i8koIIEQN9v) z#BCwhiD}S@Mi=>ZLf>;acm464Wt0x#benqAY>S_LOXcIcY^HZnknF3E{P{S5QI@3v zaMZAAht0W>9qO+b>Bfgu*{Kb5U$6p1ROBK`5ip!n3Spr{E^J(z4;gOZln*_~r}(RoM{-MH=e|QyEHP_@Gf?EKEIu$P8kFr(Z}C)2RP(tmMtn z2DgE|hJsNJa@V43x%;nmPED%g*%d299nLA*0P+}GpSP?0-PlpzBQ29#H9#~(Y1 zF8txa#Gx4xy_}e8^BRwlIHDy1&&Xd0OQ4rxCok59bjG6|K*(34IYMxQxTZ%kovI+u zQRLBDeK^URe|fpBH2%hHYPjH(phAIAHW^NnT{ot^X1gi-aW2mfGS@YV&>^rTps)p5 zkgTj_`w1^w-?78?LU3^c^DQLm%K_zu)BI2>Tq|h-Y3t2PwG2gDlx>Sqslh6K!z0TO zEw$HRaxRug{k^9~4R-8sb47uZ+Vy#TCF7$j%+Dtl#N<6lc{a0N_%FFp<^bZc@$eoy z)CC~}Zy_X{^$<^2pDFs0BUirmV%(k{t*w67HC5%P5w0>=sIhq2WZ z!^9hnz9fdPasSdFRCp*d&-@6MdM;6FMMG>{HjpNrdse9T!j=YaPMvjmk)3in++N8v z4gX^)Fc*GxtMzU@? z{6AQ4taQ$ob9*{O?~Y$Wg4oJ0lrLump@C8tm=094r+>tj zuL;Z3O6cl&pv;h>Pk}futnHYwFQ*>Q+0~d{34J+yEO%@CPBjgFx4_bXb;fNdkLhDE`1`#-PNgzCdD}H+wKOg3&IG4vr-(m!mbd7g% z1PYj&zgX$`X3U?QuSHSZ2n{F%IUoZ8GmqXF&8I#4vJ}ybVIyNUL_6AtM|hT5wvnkB zDITYlI+%z{lRy%_c$2C5b99C@?ctDl0OaMq)gMYv;KCChJ>Rtu+u5uoa)SOG1JSF< zrRes`J&((Ngo;Pw58abTqUq*2E<}MVAi<(2Sh73gb zHRIO=N4sq}Cv|H0-Uur#%5nm3Dj-~qL(gFAlo2mig@3DUw>z>TUJ`krUsElLg`-WQ zYa+>)cfZL_Ax`BC3D+A?wZpr*!mXEZ%1DuBK?QXwX>cZhra)AN>N7G+fUno?`LQ>I zK1k=gkDmkVdZ&mUwe1KIJYi-PuT-Nnx&1Fx_36)Wqk9XD=hnQ#1CI;N!&%{ZOP?&R zrsYrfXSwVg{pM>uvtgV^h-&3D!usA7i4+PaoP{U_e}{F0EH9jN?(~H2CMS;x!brvZ z>xCd+Q6q4G1Jaq_l5haD=?b~~0zH$DCE7BpUCH-olQwU(%_WZ7S9Bb}m4e^>4xaUe z#DJAz--k0D`+K+~iN!j`M3+&;Z%Z@3l34{t%k*kZV^Iu$bR0`8lmG zV#G`F=LuaMlQPz`X&Z$PwE=PD5@+{CT4zG=aNx43U(*0mQ%Jz{Gxl3PdB?D7pLya? zC+mMDa5J;MI@_oV?hb^0=Kkib2LQ3fLb78M&TGR877H*r^)i6zKvhrV3cLDCu-F?Z z1t~zz22&HKFxu7?_Qban*q}KMJfWB9m-y?gH7-%k z;%yh-WOjaWg^J^cTW`jvw3AQg$`VC5Agw0N#($GiZkA(rqAKRq_0GW47$k5UHGUIa zw*$wzN|oog!xFdjI+;6xq+}V$=j^Ah2F|v+8IrpDy`&O<2jyTny-7v{6LTqlBljlRh@V zjqUHy;x`-7Zba(WNJ2A5)^F@4lcByAm0q84-913M&cqmVf%FpL9NKtoWqNvt=jAIx z3CT)knE6`$N6E|%uEG$E)^a|F-bf6r-PgxPBqA^NDQNQo=;^g#tTbQDE7HkA(wYO@ z=@^VoerX0dC5ijf*0KW$$;pT^i*RBr44lX0(*|Y6h^~T6C`f`0I$vCue1u730+ppR zCTFJjq^n3qJ^`%*a><~5ZaC$pm(Iz-iaGF>iRTss*ONZK!d&sT;q|G|c*IZdY^8R_ zK$dzn4a(2!ThrH7k-xK6Jpf892(jgy@*tZ$9>@&Ay~jk>xzKkW=ntYy`k zYiS9h?>x$#Xu8#bxG+FI1DlXe1<-~eRrd{vQ|utL23v+OC1N~ zHA^$&Iat54p^~Ti?~Y~63dbt5K+_O%y@;|aI~6d5so<6Qhno?S1FuQO;w*vJ7cqJ^ zG6)B{qM1s3Xr!V}UwEs<=rDK;QNck%xt4&C#UK5gGud2GJ=4pByOx~7X9rf7%=KWt zqgjG}KRUd52IOgS>Bk6twzk*!^Q`GyO*b zh{n+u@)Ga%^EAD%Weyf@0a#+n$hW*Q!X-7lB;7UmjgIhw=uG}#+;grmz*kCK!0aV( zv2b*+MKRalI=X+tPsDoBO9EqvKPE+p1D?!`Pm%jmSRCY*D_2&WMj9qAggIm5M|y}? zsl6I4Sh}+JVSs)^#9#RA%58;P0T`?c_&N=2X;4@<9BDa%W^zhid^3Qn-WgQbRp^G2z?Wi3Rx@QPeg0KxPXWvGi&n6zyLWmVj z9r)TYEUr~f0mQqw0tF~DI5vC>9+-rblIbNccF&Pg&9XZT1icXI@z>s$?H8y%!PKcu zJSZA8RUBy}L&7ncYLE)zjExcU{Fz}}*_zv3sp!jtgemq@pwhS+smOJptPdC0NA&s9xf9O8 z%Y3=|Z!K~&cF?x*9%)27n?#TL zap+8VfoY^MYHGRJjL62BU-XzMc=E1-UPu&FT+ci_J*qEi`kpF# z?){}_`H*BeN)l?wJ$rgyd$V1j*|?T}RQjWT56Jo;F8D6Vh1QdT^;9~%9IqOSlZ)Eb zFEv$nUTM)ID*$D<(^nK1*x`-1uI(6Y_5AfQwqJ5Hdei4RMzt=?$QHs!oO>opj4ES6 z|6q*ItcupIJk#QvPy{hON-}6h?UY8q!kv-|n#b4Jjhaa{1eideA5~~za%6H1weSqf zMu+K}{h{R;u;y=B1)s{QoqM%KASS1WY>x|EDPe_Q;qhn^xHW-X)gNP^f1?x>-)6!=ZWK;1I1v%HD^2q$o>jGy9BgDwGTD)QOVF6pajCtAY~BX8I*4P37Xd>gt^RRtoyN5JsV8Dp>uE&j_L=DWAm0 zV#Xvf8AxijgxBE;@J_H$X!k7|ajF~7(E`WbH;*tN-Ttt= z;b;!V>~ti9_x+xITf=N%bvc@dp5aw84GyyE+)yK@295^>)Ezl}*+^Ifzd8#0n44vl1qpdNmi~=D0Xe)b@6Bh9^I!%dC;i#a<7II&~2Q#{vLiwK>Y^uQXuvZMNxsCaXjHcPl=Z&*h}Yeg6z$DE@I)@LP(h+3a;JT zr@Z+u7_3OfA8yg0>H^*~DLfc1nQ9JUV7)ReO{D}W4`LMuhlmo-nq}|FII3M9xp@rn z`NMk=1GZ-q1tqAuHUq0*8_6$zFFE5X=(7p~4t1)*6DUnP`fC}(HFg4qq91~N1q^vi zqd>a}hu9vy$Lun!1!4Trgf{+ckhC8HEm4jrQ+iO1zqMkhg~ivd&TB3t{LRGk=1_S< z45K3fTN1Ar3|~ITxD5Y0HmY|1lnb^{&7b-znUs+GSuoX%@e5XMIs-KYVM?(HlNNGl zPC6E}hDMohtIBXUxDAmEGLKc2)h^y%5X5z0KNdR`=AJG#5-0wUwT{8nn*3d6UbF%I zTNgFjc}e`B<@(Wk$ZluzZp_&JoSml5BsCuDpu<$z6io1w4gX&QzvVvU2T+1qo}hd6&qrdyN5mhVN&*;oh)~iWCVM9A|Z``PrFw zc;1)?Or*PrH=8md^6)E0bdQibs4+}SqmUWbS5SS))L5*oQSy5Wv9l~2ae4@m%h_^1 z5UwAcfAV?rdByD!3$he0C#(_Q4ZKB*L<%C*c3D4mXl;D6sJgsbR&VB6OD%5Ff+4b5 zSfT&VWqpq}zZ;b+V#zF(S|U3MhUDN2i$(+*c-|l46uE=pL<&YfR0Q*X zU$mUdaHC&8#A*vV9<&ygWM2OkMf;pT1}^0@K(B-46+{SsMJmTr~;hh)^0u-p#C}BWv z(&cE+E<1;4w*52aEeh}oKj5$1l$)dpNJr|5^M<>DV-!!LqzjvN`|D5sLagk2yKQRf zxLGQYU*?*1U5y=Z(kg1>nrENS(D>F-1&>;AK)(RpjD?emj2_auqx>#3-it&CJqj_j z_TKfWf0luUpMrLfp<;o;Nn6mR&ocYFcm#}L5)vSa8cX(6KWb*$ke28+VCahJZ<6R{UV6q4URc!3{_uU1&|n$G>u6YRLNn1==0u+CW{fbgNhYvajqc z-q|O%!Xh1b4Que90t#fYf)63N{lI{g|6SqvT+;=5DuadrNb?NH^T0m{V0WA?_qA>m zxeC$ZZeLno6)8UUUHfaKU{nHp0{T=W;wXD$p)FAMc9GQPDXP3hC(x4IfRC!0EJVGG z{v!DJCI5R_PTKuz;6&m$r;o#q8a1Gg%n`jYXcB4}8>#1U8_S<4feh^Y?QW|2`zYV5 z16yfCcG3+R?5G*P&2wMg5AkKep5G?&g4I3_kyx|7eZ46!%2y#Ahud#=j|*Zlh4t@q z3(`y0DB>^5FL}^uGG?B2tqMi^#997oRPT+Tr5j4=HkQq_C>t>vb&z~kc`gctt3Mo zzD8j)6inak^1!HHx)29vJ6okc&I2w_ogR8GyCW<8@wIkB>^i)YX<$a#7L!5YoUWa9 zi3R1r7I5S=fJWeEoWBKCUBzKSD%}<&qMbD3a9LPZOTZMYwn0gD1V#&lJ#o1{H*nqi z*s!b@WW&694@=fX7})|gYhce9f@wT+UPwK@vqP-5=i4rV+T%>fO-iV%tjZ=XFZ#=( z2xe+3r5t5$*U6-98Jh9;&8lILTQ0R!&#t)K5w*}_f2q~j((aylTvz@M*`zeuHm(q_ z?-&{%yK`iQh$OraMd&$0V`*A#RGdF6nSf?ar!QD=@y>{8|8VC{yxIk49Xi@ym^>j= zYVDxc94H-Xqf7*W8Ii}-&8avz0-UU4+=p(7j7C*aUGzkfKn`y6<6qTma7Ht-hvd}k zY=vaW=C%iaH;oHHU_qF&il-K(-p%SKOTJwXW- z+t?m@D6Fv5|Qlo4^_Ytxu00iPoNwL~`3 z`DGH1Jbvt|u* z=H_G}2t}13rG&cb@9?y4DcZz+%>dky+NhdauDj6{C~Ogm7J>VZYkt3*!kD=aqf71H zDl}lr0R4|?_t0B!Hn3csm_+ftmH;-{l6GY~!|39^6 z->K*J@a|RI`gwHknF5RWV zMoT--9+BIB_qbPd)Y%QHwCw>qnFlBX(eCpg8f>v9VYgE!hCfwm{Sj;0SQsPDhI>tF z)%~)d3M}(b6wWGGv&;;i>!gSbk!k~^t+wlyr%A>&CuZI(-G*(WfZBQ?ij``VWCAuU zqlC1usb^+CMMSzFo9CS#hrq=-3A^VF)Vly*ByyzdXjARdd+4bSS$Pmcsi<}tIrqGcZGul$4Ke$b^rbAmq7dl#p zX5;I&Yxi(6vYph!ZiV#XV=&!$20C1v`Wuee)yL45;BD2}-fY0a=4`8zLn1K>4j+(> z9nDBYqDkF4^i%ZfX~r^R+1`Zy=M&Dfw!@aj$_Pq`T1>SZNmi;l z@EUDrXs{Voshby%)&(N~IJArL8A(`RtE}{sd;b zl*kW>E9##W=o9vrB7aa(Gj5X`l7L0}5kAwT2#Ji!PMICnf-!G+Kp(`}O$3Zznq1Cp z>gDwW2RJiT51yE1gGI$zrd+!!2Duk>r+#6rFo_6TYH^5L<&^?E=3UYXM@a#~on2G< z4;Q|WVfp9Z&=iq~YtChbR~t&ChY<^(-TeT6xj;Z8r|owb(+6aiVD;_)hM5rBl<}jb{zI z-aBd@cmK7>Ux36JP=ILYK(vfH9Rj@(hybHt{3KP~J z<1={8W7ylAqyhqz)rfRs+-@C=PAo}MWn03JrwP)!qU&?#l%U=^LS z6nH$ppG#%L7fE#%8;Zb6)-!Jp^Xhdsy$BzW7c}MN+S@D_S{kbqbOvoWP>pMeS&nq} zA_FfZOZ{0MFdGn!IY&RHsRjaWD^ob|Q%^1;+BTpw9B%<1LachfiE-rCnm>Ppc_T29 z%}1gLfddcOhrX1x!E+qY$>Z?s^>v(1S{~SW-C*6Slt~iZ{iwqIpfVQ{ zw+yF?pAkpho9y>#?W0obHQ&&7ZjGVIF9zi?6^@1%o~YzA!0a7?XF}$uSaUR=SrG=SUo@lC8wWzgurL+ofg%2~`myS~doV!6UzLE{B*Wh=f%ThkLV*N?m|C z!(W9~--XzqrVB5d?D<)hTzeK;^uZVX7lt$WapdeU5nlkm)tfN-)=K=r&;)}jHyCgA zpp9=>9*h{Bu1PL>kv8fBgHtyG*+@o+-G^T!*Bg#FA{!p`H{|Z6t~-~S%M6Cl1z}IQ z>R7At%Vci|1Zv!Gn7G2^BFM2NID@CUG9yK_=}?Is!9ft7!zz+ZPywXUCBKy?O%~80 z*;q$MB54!?iSZ&*;KX8F=QD^`v=VW#&L<&k*!hS7xJW}h`v_wlGp^KQNojWD9d^mgj(*^b(&;ndos4|51WkfG-hxtmciB_0QrY z^$*Q?3Otm+@QO7$)-^4N~lS2oA4;6-|3Aw=EVE)vx z^T#b62mZ8@(Xj{=Xxb{zy*S*vOy0WE%>IHKD5G0Qz5b}bn&^a^y=r*!|4!PH0(Y^qPQ*%&z?wGrvN4@w*-7@VG zp^k`9iirXS*3j@n%3Epm{!_akuOw9g%1vue)#^q+(`!>C7B8S0s-t|$$Gw-(4*glQ z`2e4Yc2F4Ek0THfe%61hcG!J?Q1V}2ekQ}E*D^Fd)7@j34*7~bzZRUdVm7#Q=eBB$ zfkb)Oc_Cga%IYo+{Xxcy7zD83l$>Ku|73mvc>zAEu)C+QPyQBuES_WC@ZPgK*8rjy zOb%h9!u_v=`T+Hm-7jFH$|C}@z*$VI8wWAQ6`MgrdoCfdS-Zo&P|55KmvjV(3I0&1 zh?}kMGwY(d>#K|eRp1~pe)5orTUV@2@dz54d4o5}(@mf|HxhcF)$uS2LeN=$#oS%> z6n)a`bVD&@AK9b5)dJy1XUP&G)xN8PYoIkaUsr-A&!>>#0(nll!x?3#Orc)aN+@-5Y7f*jRw}4tf7|82zh0*=u8|FE>c( z3?22AXeATH`%O?VqKF(LreZeswaOsur*O8AdOa+aO4;iTq+RwgwOI=x?**|ln`lgFc7xE(n$Rmwe z;8phMeYo^UZu+gKZf?Tn3W1|0mEtK=0lntkj|Zx1lBvyaRJ!x*MYAqujroLw^ynDm z1TB3*{Z@pj9_m-%!Pl%w0Rj}%;8Q5NjRaFJ6>(je7|t@9v`52KlVUDsyaezE#Tp-M zm^)Jy68{rMKqdL7HQY`Y;JcW$>|cnfMzsPZQa|euzhysu1C63u4-C>E^?M$|$vz_4 zeO+hK42~yyrGRAswLBtZLaVG~{X&B8sp;k#y>CHopvxk`Ttrws0wK(Fr*SSm9)wT{ zRR^|sK?C=9J31&D&iCn4&!F@_IhKl>jjfFZE=xA{iP4NU;5y|p2ajEX&Csaz>~1Lq zLN!{gbDi10@xw>9bS@g2X}Zvd=4v3bf8<#54jg)jcr?s$r7r(?I2#=@vd$6TRq%yx zg}^Qj3n^@5(*26N56*`NM(+$jJKURVU%MrwH2>{@7nWMBD#gj)F}A-AWtPE@FArQR z2gi~F^wmoa-1oi!>#gddMNH$lIm>R;DaYlRokPK9?pwNTWH=!X*`SecZQ;Nx4NX}s!oy*eT2dV<1vZbPnooAlR$;G|ahoi?pOeT}dRZVI zWhiM*9w{6*JDI9`YJD2N%eDZ`?stC@o4bp2R_njqM zHT)R^`@|jvS9&AYmAT04-0!9`pbNCPp(sgSj2bys^18m8K+&1^u++$H7ZHasJ2g9#0}257u3(;Q|Qxu#5zAVi2|$N2la}UqlkC%`B@&4k1^eNSHSpCyxH9!M$8puzzlh zNq)3W2u{d%;L&1ujokFK?%}#=p=l8W!HJ5Fz(=U=7UTM|&>^sGi}_y!|0dg{XxJhX z%yXR{sCHXoWX+ayr(o$$rt2@}Z@3z7K_a_DTZKs$4uxTVDIVnU!LRi6$t>vcsx@d- zi7Ctr$slr@99MCH$InEf;8!HuCH)fwH6$Q~no`u0yu~1!%f&affF*_yAf>fejPDT| zQ#TJh*j9X%vxJ6I*%ps%0gVu)+DTQU)kr6NDAgC-okuA~sRVt7v4~q^sf`U8Nso52yZ76-1R!rk zZv#s4(wnR|lwst#5yz{yQf7U$>pol2*m2pHq(X_oXWmSW!IZG}W1?^3d}GOIk1`P3 z?LS_omI%D8PnB-b7A%ccHW@KtOth3*hu>-&@BCV|jGgmaOu zGyf4yI0j+`)-HFTu@hzbtkt1(((k?Hhw~#*!Q2W#MdUmcLxJxg7dIc08oSsC=jYY| zHg*{$e%p-o%pxHO0bFi@n)D=(=Z=Disf*lfh%##+Ye15xM}*L#Io-?1Wf5SbH-+C#VS*8l`6e=^mT z!b`vbCFMa9NK!Rtc;7)-MA(F-yD+5{445?!jCdB#XFf1SGh>C>9f%0xvjO}>)hqf= zVyuBry@95cc@<68pkwT$ph?s-0~PDA6CK_mjIkY1BwO;e8cxwtGS=QQx&NU$KvVp< zf5d*R&cV_CXA==9Tx;(SKXwp>t)gI3W;b@lZHqNQ&yw2}(MutA!69Wmn-n)lBI3LW zR?!uF2|N^Ol3gT6o7%#9lz!IpS-q5<^{dp2$^-kNEh|P1nCPLW^VGY{i5x&mADQ2E zKhKuU#H``@MCTBOvnnuzt;H3YuhR{bNe{P7b)J?RYyZgvoftX*mUy}Q;Hk6aCv8_IDI88X*B;|bV>|F5Zyft)o557Vn>un$gDT_f9_?!+vLT7>4+ zXDoUI7ZJ92L%DdCc`XWPw1ZbF8Ts$BUPj8PUl{?GQ@pI3aqJkXA5ar<+=6#BF3|r& z+BpSj7IbU+E8Dhh+qP}nwr$%sx@>g0s>^nlZR_-ZVqzlB+|0$vh|I{?H#_qp-`H!d zo$sULiDh_sImp=Fx$ZcY`TnZ!!xpJpLg%nrVl%&djXt(Ge@XH3SaKe1yHLh$lBUPD zD9Z6-664-F;`c+nGL-YK2`v&SGyrFN4PAZa2$AxjCR?l3MmSJR;3qL?ck^-qxzkpmK?`;=cus=pO($zwm4qc zuT(Hcf*mP9IX2*5gY*%b-<*uqxO)Z5;T1?1f@^cYS8vSgg!WoiN))VVt=OqVG#7CA zc+dI`1YW5VnQ;*PPLR1|^<@+P;>hPdP>GVRW8S(zp+~OqsCx=ed`af*WCC@q=uF=i z3{)O+E2y~anyr*tIM2Z$H=12HKd9~y+EA4U{3W75qW~x5Tk?h;2jX!3CIRy?zsvgb z$@$C6V|dBV?5uEhae*8`E)^7j##w#0bM41i^{qkB{gd{-HrUgt?L%mH_77&@Yo#o4 zBTCw)@-jDJ@7O!+@QD%tbpa4u3-R2@EplkNGlQW{AnO z<5ZA?brNl_b~#Q;=1FR3O%Z{6=~=2+uXJODq06N|-8G&(wH^UJva&DB^f-EUksa!d zqU*Dj#ZxfeXnNPbNXhj%9NKc&7Y}BSfatHpxBFgs9-C9H7a@?!<4I+`)+Vu&%y=TB z>gM89o)wM+IoiqvBnusoK+z8;>~%rzHu&M123Oi>F8ZC$9;*KAD@b_!4MeyA@*Usg zGvEVX$h1mPlbRd*;5fVe0nNb(t9z4HF@dERI1&~UQRcLwHmk^VTW)G{NF>KjsoQun z=hj;gjEF9|B{Rm@vQg4#5GL@Jb-{##>5$IR?H3O^BEgZrauD5@ND1?#CR*bFOrt~| zE*Kl5p!wr!)+*u_E5dFFWw}ZujJh-E5}OH@m|C;P2mZLdGt5o7M>$$|@qMyl`ZA)W zyVWq0I_Z?dEF;hXt9?xf1KQ&p5E4P5a`>c(_Juf{E%3X0fTHIWqt(z5&{?AVv^j;D zXk_B6_K-c@faLao)RaGOE_ZcT_g9g{NjVUsBF%VR&EKdll@7){kqQPFwVHpC;4SdK zvsj((&NAY(Oqrz3cMd<+4)EqzILICuQ}E>`Gt$Pe=Mle*w{_bz_~?iG1}gND4{GUd zVj~Ps05sbGQPGqvCCde#Bq5}{9H}1f=2(ytX-@D9KJo-EPV?wOBBOXfx*&t2=7W9% zp;|qZ*oY3mH#0j6Bgqt(d2eC_v2!|Oj3g|VN`#05=+fjj_qL@>>knRNs#+SM3L zv=nHb)FRg{3QYB6lgr<;4>PIUUR%!w6YhbPSLbe^ts4n0UY&QAhB!P{EG;T9q=@FW zh_^P%w56Ur1U|7Sfd|gW@*^`Hac83BqBs)GL-q-Z2%F;qhiiv=O4DywFm3xvL9!MwsGtYJRyg7@0r?GCQKe?yIrN z^wPlj9)kRODGnkAV!^wIbGR$8lXzbLK-u9LnGiOMOFCTE+FUt1EqyhyNLX8t+|)># zRQ)}n-!1xt6O0-LbDkU6Pf2JC9P=nrwF|v?+OTOyUZeKAC>xPKFgHK7I@Vgh&fZ*B zN-z_Qv0+eeA0B}n8ln&)asVU+ERVtz^rQHAv6OxD0v3twlF>{;*K$F8=}4O=5?Incc@%%9`G{%uZGZauDj9{B zoVxLlwL;Eie}1O2X}JBlYdXEZFr7+BNd1iu_4ob;ih@fY;3WOpisdkd9IHTW_N|X@ zmiLo1`8BOra<)m=OdQ)CJYW^rXWHKSqxpIvQ>yt7S7!k=YA;n6YH?2wsHyALBb#74 zPb7VVIj&I`c_;s7BC{h_?HunVGb3`{!kirV35%W~HlB2a)>(XC0lZm1bJ0KntZW!; zZkyMaDdh}BHr&SrF}2_Ezr0L1-^giqoA;VMImeohpg9XC>fbH!0(6X*g`9UfW@CO_ zbV-i!RM2V!9@No1CW0_+&_}DN6q1LOKN^a&LSnF8U7LkF_??IQ{PJODtbp zoI8P;8kk9VG^My%>`SIPix{5_)5gx?SO2b7Xw%r&1w+amd_B-$2#Vdmmy%WxN8K9J-1D=u6 zuqT$oTlt728BZn8B;D&>U!BARn9Efk=aQ4&7h&hBvX%7?qH?h1tRiR0 zJWFdfB^T0A5>?(9u>kOJdQ@}Ux(R!zC1V=V?$K+VuE?@h0%wI1oAMEzvm{-ArZ4rt zTcr@t5(n#np}kb5AbI1|;h(deeCL}&+?j=nl()WeilK#DUAn}sBppOJK04LYiohB6 zs~C0sTu9y|=3zb8CpVy>o;r%_u}wW64uKbqB%CX^djrZh(~eGM_8JuEbHa3RBo7n^4zuI& zq~HhG#DQX?wH?|?&t(QRg?f8|!k%?VIQnylcvF2Y!`pDRfbZmQzHY#G>3&ObusY2> z!9E`G;te#x9s7hdkBh;3#~gdmpGgR|;pLzSPhWhl5G?Cxzj-X;|0%R1>ylR+D4e`| zKJ(V@Gn*~VTue}Hy_$u^WO6H}+9_~9enLDr15dmlzDOE|^VCn$0RBLuY#(4b($6QE zls{`$Br2A!!J-%thxR`e5o#s3lL#E#fgJ)Bg}Y{^#-!?P(T_JaS*e*C37%g;^DA8| zIgh)CaBtQV=fXWLvo4$>z)bl_+8TypA`ZMg1M7zEU5r2)XE@*;aaF2GNxE&mvQ(}21YsXmf}oOlsTQ55Qn{>l8@IGtqO z@5&1AGW-(2R6ICSaj~g!y$z5Y%Wj5}CvZ?9WsP+N+BYV<{3E4w44|!b4e%=Zij?2v~dm z`8ekcxgmCPayXvct@b%4-&h;Yc>DKnRK{2^y>ks%BsdBkdK9i%v_2*B@MDa>A!xjg z-)0kkUHlW=FuD)bGEoOmIi4z zq&qma+~T{nL0^2qTBe+=jWSPA>vDeKaEmRp&KIDjS&RGv%|?faZX%8Ppb@4ZVq$uE zBq_}F6d`H~?!fenaa0-SIOwT*v&_{j&$|JvXr{OtVIU_4M5sqY6M{3RS;=`eSb zcr;Srd{cM#K6z^p#2PN2G{(5~PcpJ|CecQ#4K*?%PiT)Ll<4JyqxE8w(P<6TtFO!- zfmx7?%xwp!7L~?V-q!>cN9pWCQmv)Vj|z|hp?sKFg2*)+8-wdoI?z|#I^e)2Md;?j ztoF+IU?bzc=P5phqcyD88^=3MQmunxhLEY-sX8Urj}G@tcceSk>+;72m`;Ei)n}b(3PBUuhqd{6 z#ei+wS6%*i%_ckVWwuYH?B&bQ^RHpM8^R`tE(gn`nWKaiTvAL*?te+AX%BanXwxJy z$UanTp-FQp(Iz7hpu|L7VZKkem~^|H5S8HPp6P6ttzoTi7ui`=HbBgLQ31UK57;Dt z$-^5|z8%GSuBdz@3qwap*_w}UKJcNVLFHx)kG+!;#w>I#<#HRo@D}LWEkb#zq+floFs4!RtJ}mW z6@MvY4jV&oXjxix5!CH|fjzTMy+%X>Tu#8u_$N9qQ5K`={n|gNuN`<>2iORT3N-2a9k{z9ky{?@Icw-AM+@-@7s;XEngVI~C zY?M@b)?WeajF<(fDF6%;Cz^?{CMNlm>aZjI z?jx5DEpXf#E{8DIew=TyVzUR<1%}mUxmQO`o9)SzpxMW(P9BYv|09_O9nhwi3f0a< z7hHmvL>FX*Ybm#$nzzw|)f4+F17;qI+91-NG9-88mL+UC6g$&4bE*MTCUDhBZ0yO$ z6D2AvlRVZ9-STPs(*;tqVKLeYA3m}e2am^M%nx8W9ZTwj|`jsQO9c!l*?5RWcz2FYA>h(zv@P1MK_VB3LIaSyWWg; z$o`&e$>1!aB(;}qgk+*hj)RO=r%(=lPXt1tN{Cd;=SyX&#K17S#X4dIO|{N7Ld-3w z_Uyb_FnTvMrh94$wZHvcULiT*;4S_f3cuT$*R+l3xD%dwn6aB!p_&u*q~~kadeMVkxkQ2Y)V*9&?>`04B3!%zF zLRN)Kp^-Yy04UZ6L$uvqXf;J?FIH{ql`bF-N!pB(gYn`aErxTzlo0d*2EFsMH>Ibj zsvH=K;BMRw(BUNENboP9|E;zWDVpL_=@F5o9!t?aO+%Ti+lPYK|T80SQI_|xBFaQlq~3p z8k7>EtK`%~Kn)Ae8OG4sq1Z`9f`S~=94w!G-b7fj2_E3FIP-)`sF1=z*^sVZFOF`wvGC(K(+;B}5j4V@*GG982>p{DYO5))Z7<&R-d5NERSjvC&us z4d9fmrp#eF+GkDq=8gd#!|$ZiH-g+OnaVp{lh?F?c}ym2-jYA>%G`#Nt}r!t{1;8C zv$+cnuzSn9%A8fo=h;jCmBa1NcP(3sf>egeJ(WpK7cvyW8w9DmchC5{Is!=@q8Mz# zN%6R~SKmy1ZyQ}@N`4?~)}}XS6z)~)F*X^Q-8#bZ;@vd`ZOO?k?;;t|Qwdq7bVl^a zK)Kw}3Kz~II~vK(9+FW=Z5N~7Gl!Dvel;g^o*o?+Z73B6zeSZ~t^oCGo?J5=-+04o z>e?o!vd^PblsgPMO0`4*R~G-#a6MSY;Fl`aF4m0wosMZu{wx9s!$lsGPjJz&q|%eb&g!76B5v=BzR|1RgYR zl2;EMQ#J!o`1Od1TdISJ#B3LCrJh$kO8lZcE$x{VcFnKty@RV<0`<0T>|lfhQ=-CB z>6CB^o2zi~-6B88Oz~d*;5@KSY&MFhw#15W@UZ=2SI-1Oj@m|7cTf!zDR|O&Ro<2G zixb#m_+lMmp=H5r)dZf9K*FU;FNy)UWkb8@9V9N#%*gF=GGMTI{yw#5EobrgdXEm; zlM?R=GTd>V!`qH-29)|4UZ+Tcb)?*-#qs*pR^J2xB+j7OUKP78h-OGDh=ad2&W zw)SG{^GMmmX5@a&4oIPM7>Xxwz?As7;;bDCyQ3rcaiFc=gCNH%#VnMlVTjYu5-;%B zAnu;EGEplO88qxXm3|)NWD}mJ3cqmjuVt~C)hAapFnaI=?V93U9f+dwTMYKQC|(Fd z;K;*HcLn3wuH1p~Uq&%OZ_qmxoj-`i+*MeGkGC^y(ocCiJ6;)X!bp>pg{PS+u<{`Y zkhW9!G>I(zjVhmP)@QuGpicFBR5u_1ej1-09KRdZJq%FTiF%aCT|=CD_=TQCLbgz^ z_OR`cP5H#p)E?19Rr-!(rxWCZ3#-|lwYh|JDJ&e4-vSiLg*_mvonZvM`t*%D(B5QU zH_hTL?BJCtv&}Z9M#TiNsSXXLe4?8Sv!#3)v1dBvhfH8bMLNmh3q4^>s8olGLQIP1 z_0bf}VfZn%ce>_xh>0`yO}SS-?|Rq%F2SV+TJj*_-?^1lW$ut~ z@)-W`I>1>9~FUy3- zXh^NP0qOa77X>*0c@J|F_FX`QtK?@Y+aDz`4*D@^V39$5@`fX=fvprTqef2ngVeHVcfdu&`W?eiVF8z zmfKiP$>QIv%2)~c{fxX;E0N=3tN&Ad{9A1GFM;;o<{Ak7W1C;&UsEw$3GS=olXhqH zrC26r_U&uaZ)-n6r*Z?MUU6^3l^tg7RW+hi#kF)esJ=Z_tE% zonkstpC$6&*M?G;R{bEel5#R~D;YE)r0%fEWzRVImfUq@lZ;y*M0NJlTwSKuLOPqY zrg5IP#}TU7(NThP$?50zH7>u(>_DtUqm+0RIrs`gR!QZZ#7VqdxV!FA`|0HaPB#%o z&MuzdFAi>R&U!qY^32$$h~zAtvmMC85-@84(67-vootzNEL`&n&*)Yh@=+x2Ke5Lc zHH0TmsH+yQ8U91?_KX#4!htoLA%1(MX~XvZ`bck27$eS0_*VJb)Z~U(drDE&o%+fI zk|^opT|`~rahbt}CY)tZC_-}(wvg+n0Q`kJ$L2O%QAPQ`z)D-e5urO z9b)Ze0**JgGt(zv^#iW?j`hz!6{3gmBNx@ zuA5!~1+LHmpa#3T4{d{4;&93=93rI%CB*i<&0)SV z!%BhZbvJ8bpbg%UxQd&|;fLUeTv+Mm8F*piV0QNZP<@Wr%tVy0%yuoD=qW#MHIXQe>P(j$L{@$u8%E6sVoQL9xx$!%OE*CK0YC+;vAya7 z0s8mp^gLP0ViNiv=Q2D#OE{K)#|=4R6*BV~{fQ4n+wJq2?$n6%iE7wn-e)ES1k5&5 zpCjsymY|*i*l8e?UHz`nY-<}U;^hf~lhNdN*V5t`ZCSu_f(5^p1%wX?ap<3jx7C2tuY0y@! zc#`oND-Hl7&fd-2#2#}+e2SmKYcyB@UsrOhDT^3__iQdUGQqEHn=a>(-?jxTy!c>{ z9E6=w@fxW*h&g_FQqj?no&r`g+XzN-uRv;PAaY=c;enEKzvna>L{gv7(H}2mWxC^} zt+Y~oRK(>MIL8TWJ%Vh)RN0-o&%DEOJcJc_6gyJM4i6$!x&4$WDP>#mrq>@K6UDd( zg%5bxi*d?B26pu@E<4%r9{JHdiX31d$OXC@hkWJB&~fGRzk!4I z!yp7E;JJng#q#npt3h%Ry_LJkb2$zbZTt&YZnVNKM`~C}#`(qFM>V#ErV8V&C+isgZadC!Zo40*iCDa0_)b!B2QS z2jp_jtwt1#QFwnO*6%t0UNmXM&u1!jXsKWmpR{m4 zEzH|iAP*OHr-whE0V@?M6t?R2J={Z0%V2hj%mZj!%53!`lPZ$`E`%vgLjaxJG`|kt z+au)$#xPvl6s_N1s;5bomJHEFd9{cseJad z28-B8+%+7W6v=j|28qQ@oDB1Od#>o@U(ZgZe>oUl3$?us2UXguGqscav#zrpDo88r z*9D3}A={eh#bsS(?8leHI%U-prgbsfIkgdkOinzez9YnS%fJ-(58srjus@vy-W~B6 zoWps_=W**Pl@C#lcGkO%Mf!b1E^VdC?R&SPFqe2HGud|jHVbh|h}@_tn8J?+g}N@Q z0B6@d)d^84n*S3r*@=H5Bs5?j#)52TNpfMgGJ`f4B$9dTI4I)cm_AtK0OFE6kntM} zH&+TZP~wdF`a+xsbDNwLg8n5e4W^jKgl~5R{X~L7buD;nrWG9K4jyrmj|st^(S8G# z&qyWp0*x1CKq~t1c@tj$=Cu>4?%uZ>sl+}pSg2x=z?D=~-MwVd#DJX?ef`aM3wB52 zQHr!}^_B(LniB{^ECmF@%d%4BmGgDvj{_K)gcn8YXO!UZlVwS50{vzAaQU&VGTDdd z4R&hwXT!`$ne)K(MpQEYpKc~7d=NX(5&|qcd6;8cQyHFT;*VYk>Srd!cyndmL*bKK zPe^i8;h(P_=&ktPg{k0s0^q$zVHWK}l)US8AF~byf+NoB^0apyonC%zpClMOLR4j2 zGGEk2*MvyQ`B~|Q1f($QC+~@yjJCADDn03^V{x0;n{SRAH64WvSCYQsyx%D(1=_fsZs1`lXT$#PLNzNP z7_As_BM2R;T{iCjpdVsasv<|<-f}C142jwto{CA$WyYxJtst;tp`y_8F%pXnqxyR` zyU4UZ@9|^K?F>s`#4Q^YR|~R#akg|776;YG6YR$1huC{0kWr7o6Umxdm9TaL3G7AtV8akt__`_e|@#{o0wz85$ zOJ?B%YB=B;D2iVk9SbROSS1@kZAv^~iq?b(9FU14BGP@OuIJ(u@sJqv5i+c6$K`{t zE)vFhe9OGx+REl#!gk@J( z9V`olvBbZ6XuXbljqW1b)pyMW-Rj#mI-rz$1) z`7-4Fuiq~U)7jQJ>nJuM8p7$h)r-o7zc7EN@`5w%W0{_@fULyxplPlG85n9@P0c*1 zkxJjfb;8};tm}me)gdCvEWVM0Y& z#MYM1|4#0Ig_v*P*ZlQWl6daGLu~O_F5YH4wnY2o~xDFZeLd(ek5rl{_=3$e0 zr*6Y%nE1aT!yoraZYA}vkRAG2s&d0|debt)_M529um~P|0qeq-GfCbc@vXXRA1quI zcOQuFtyTMybI>e_vopNZfQbv@nI)XT0o#j5TomRov*^X?doG)kE%$)abLJ}O!&%9< zF68gHhM7n4;cU!XK^{wrl7MI{FrI)>o}_F}sKhhgJ?~4W2s&g;;imYhTbN#kTcgRi z(ZkoIXHX5WcfDF?T@bJ)wG5v(f5T%0-4#GWsPGDM2s;h^`%rV{`q|oougD9quh*kF zAp{KO!33@(aXj_A=$CY@Nq?U$3`A%4DhkP8ORVFvOG zg?4VKov)6h+HEU=W0B4{0pDY98`vzByK|57ZoW2=30VAG8yVV+3cikZ%VP)G#)*H0 z*pds|<@y-~^IHO^NKoB2LqJKFSMJ`qe51Ly^omey_tan-Ov*jwD5tCnJz4nX1R{$U2LWelCQ7 zT1l*{l-jjGc}+@hgF&Y)6x`n(gu;@!cYK>uyxTu;)otWIHKMO zVF&gx#K!(M;S$>z7Vc$Y1+Tj$ESoYK&W<5_aIB64xku3OzB1p*HD#iO$o1=h>zi;r zH=9%8)%v&B+X3a08~tX@8DB(S#}PaIV6s_(pXKp$lCY0ZP^`<6H4|<_h(shXU&gU{ z4;%MSU+xhdq+Fx~tOY|zauPXR(>Mfd=?cQQD%+(ehXbWYLz%Fg78@-?Xn0o3DJ`J-Mb+@L`CZj%iY7-O(udBD-=6dIe^s8-c#S;ZoK{CpyF6#pP$6ORCR} z#)T3=jld1#hAHrJyPa%i8vESdp3{yDl)g_}W80s|QeZHEAG5jb5)q5XrY1*juG*DD}$ zB~+JNJXc<_z*I8*5UaBv7pxSs?ikq2=xpjzHO-X%xTU|Suc#>OVxjP_<0KvOBVir; zL$C@vjxH%En&A>q4K^MaW&D;LYYoH9&FUhGm=vL5S67&~Ck-@oWirfAq&n_Jlr>3*MP-nLM#YV{vmM>-=mOUG!fbJ?k?CahGTAU>c8~O7qMwzu-J{E(!}Lg}3A;CbY?X5!$*QiDt1ZYB2c=Y(?g-3I=_nZ4|( z?hMJv$0;!T%wwYDKMZt9qAx=zPD!pyL)xT#0eBVqFl7qhUGoRnqSR|6o?Gw@ zjD8a{JVuu2`4o>Lu9`ISfn04XQz{A?c*OP{(#+;gfga4n%s3q2@Nql9 zHJ6hU%o{E$+>h>Wd>t~32{Re+J99f+(cx_|j>^EQ0>4+~NAU`CZHPdA-|jDXFM@3A z5)fAw?e`S=`D*cG7VldZe>;8rFxNtsWt3@QG+FI#<2T6uhw zPear`tTZ?ulQCMA5OR;!W@_eQ;%w#UYUN;0@9OEQrV0fB8S32E`%icCgaH77euDr2|2LLDSMpN$zdDL*K(|Zb|-+B%tawe$-1qNe!a(&+K^?XUdpjkLO+hIz|QJ= zO2SKQNGmOe(Q-KHRb9EOkY&aGu}x@Nem0~`$)a{=9rv|KTV2niWw-8LA7(crE0{W+ zZdEs#S?>b#-|!!wl0IxAjVvEpPxGE@Q4t^)bq&@N@Xe2I#+BHmnLeHR+9Hu-nP}Dc zQ_lU$W&OlNHANcmO1!Wkxv*O5ooiTl)CS0^OsGvfK5ntCH$$50K}K+f;)wIhmV2b$ zl~|w0cp0xXH+o3XhWv(}9bJx{KDRojDDO`E3uQImPn)bMzzYGwLZPa9FN7pp+66JZ zw=`v;L}pDOQQB@@*G!6G#mZsWd@XJd;mXRv*xHAfMrpQeToSdU9@8XTgQ~JA_89S* zLo3bI%1F6c2;Pgnd3uYw+2-yaZKCYz)}f}0aW20_W~F?^PtN69=E+1^#wh4u&j5>5 z0Tip0pc#b`lIF=}EJlSrE!k}=kuo>ak~N>-wQlA?O@{q~KbNi6iZd+whMsvco+`lT8k_oJCt_ zqLVT?bw6EcZ0v?vcWYOMhB9g=t?R>l;rf)tyGGW01iR_;!0Lr1Ew1j~$#0x5U3B7d z<-A3^e|AP`i!*xq)x@qZtFIVJq;dzL;9CxVrb;PmE*{iZ8xHj=#JJc75X?%%xW;Lj zPg!$9rc1)sQ*(T@CW7rBOKX!~4_A?J`?yZO+V8dj0!?20RYw9<4Y^({nwHp1t20Jb zQp37GVVx_`Q*fyMr3XwcI1Uc7x9K+64V|mE**d@}(J@X&VMUHZn&@s&81)dV5jX>I`%Ip=`Czl*Cj%wEe4hM00DZ`U!IN9MS9V{BKA3{?gJFcd2uMue z_yJ)<5=X=iNM2CZO@L5jBGDlvT`0RSL{ZF~$SDaaGd6o9ytpqTaVQc=ETz$JC=zAN zrlCl5@s4!%A-3Oa(`-v@TWp7Hmu!zn0lAQe>cb5Cks|V>&c;b7ak-v{qNEh$TuKvB zGWHxWLY&1ZC8b+7WP zjOf&~IdB{vEa{_;0$vG3`MRh6nrz%+e^3urilxjY7Xx@l!Nfkj}OgQB= zlqJaV75jnsk9tA_{?kYt_vQb4@_z>L|D}35n;Dtfnf+hkbk@Yy`%nLWg!6x@cQT$4 zRSzWqpgRo!$p0_jZ*FGf>gH@_VB+B7`u_)7ZtH2K9F98q?-~qZ^v7{4YQ5x8;=_km zl?CfgBt#Rjl~agbQ=4ZrKi%l1K`3fbvd)buL(}}eIs@;*`68lunEK`aafM+J@P1t5 zpdWDmIu+s3@Ab11b+MSw#>AMe-I+3-WeiHC!L*Vou?z}=NbL92wp&;7%T0>kd- zs>UbQjRb|?*Y%jl$%ItF*W>H=*PBY8ulMKe)YSEA9?#Xw?9Hw~|JR*Dg20#aclWW) z_v{J6Pwz*)0B`T-?c?+CL4ra?->?4DnEvZxj>&etN4N-qR$l&I-XBN!pEr8}f6we{6kjf{hMiF77@>t}Z7(U(bW)!&3%}g0z?RaL|Kg#7Ab%G_}=etVfX9k?85ppU;j_u zx1E>o%fn#G%*Cn*+1vWle%%jwR z1u4JyPxvvc|6>!sIS#YGi1Vu)eC>h5@tE$c#{{T%LC}(ZXUrp}${+`&@12{?vJ1}H zduA$r((x5<(?OL>2|IZG zFzQUXpuC&6c+t&wn7AmEZ{-{%`d?0cLoisxefG?gO$-H+kitUcort{M^ufB0Zjl$k zhl(Vo^N`!h_62xqQ1)Q8IF+Gmw6TmPIcV8*f?kA4o5-7eDFNAI4o*R-1fIG}K+cnp zW|NR)nW_d5EO#GqGj*Rj>R?>xmaWvcB!*T*sA$OCqN#g%i=Y}ZwK&qXDm7jRYTXgl zBfBRgz3}Ms52K_@5Q(_Z>;tOvY*DieGWsRwnuF?MiQv(KRXVHm=yY>1_&>tYY^HZ_Xg=n=@?Y1kYWRNvn_9E4z3E&q` zaYI2zs!xZm;r{w8K~pHFN-Be}p-!gPM4h{zPBI;RXE%{W$GJf(jkTID{^u5^Qs+VA zs^EG$kG4DY9_C12Yp)XY2bawb>a=e{0*u?sS41f{bmvf$Kj?7LM;#<+Uln!yzFkb^ z))jiTWoWSx+C7`&sc)baxoeaacfhsf^a{GUq%gbcTO2ZI$z*!G0y?^sua^YH*zPV+ zmrDSpTE5D3SQ8{PxVlZ+E3P`>K?8@3@aosC6Z$vORH_I1cYPIw0Tr-|Pl$een5gX{ z7%)<_IHe+1(gd-_jQ5>yK#vcY5%wGf7*&ANisV0FPv&4lXhk>VEFXq7F+&rgBC`;b z9I~M4Dpg|`g*~d6cuYeXD%E%dnxI6wzLj$1wy^_SRWCz6M*Vy6iC2cJ27lDQBG-7u zKWs?|6sWH8TkI(SwP3ONhSdc!S+PxH5JS-D>LMq6eDQ;>CQ8am(J^mMzd_;ZTrb1e zr)HkxFoRpfznG=vv4$l6JXKW?Y2cmRk%=jWdXByM{FMpK6rn?2f2Z6FRX}TLgugN; zei41KAELbbUiyLuMUQp^RgBaN+29|FFkABXa*R^8tPh&;tH>bh2A|*Ld@$98Wa_8~ zo%!9|VG|E9 zwwU%bZo(Up%0JdCBt}(w(At(#KGQE5dW1sG1-M;zDlOw+(Nz{NyGo9gYt7Or%`ejx z(BLw;W2E~kBQ$bW9I|$S{k$~wn_^}0;YtiEKref$-Qy{C&gs41*yP#rRh|z|9aPDZ zZL}ocgJ%2JN>)MYhH`1$JC^JCo6{F%*u&r{shbwp-X(6osOMmM-7FH`0r*rt)YEtv zJgQF36~b@x^{5yCv-a@sDKB*SL69n4N_YD=-r)iZ*p6uXoqqtDTYweWy2rDc4xghG zquFKy^Er4sbY=j~KFT_45+45F%Pb(l|aMCl~QF!ZKiS6I1`A~MD_0d+F`D#3qz zp4T5j2djth&d=c~8dZ!93kT6L$t#p6EV@|^7RwMrag&e`X%2F8Y7VGmTvRr-<|YAM6lzQ4tk z_1A?do|!3Oy>U-|G7|UuO$)6vRFgQc*Dx5flHcJu)3I?a-WiV_U=}XxxY4wnem!}k z`OPDRR9ZKPpM3yf2i3hay}5-4TS(LDFlpwtb%0&2G!qcl*s8XdhID}tx6$omN79+4 zD33P!zp;%hMx;=om*OMrASJaPp}(q7HY{#(RuX$eraQiMQ4zN_78D_Oo3}FD()kg` z{72=y9B9skY5j(dwN6o-DeeTL-h@MzB4!_*x2BhLRwi>wg3e2CmbAu|qPALXWdAJp z0Fedm0id9(uoay-*w73a@ioRnf_%$mw5p-_!c=3L>Z$-s+QAk6@f5kQj5x5;7t2NQyHd9qGldkEM+5RK+DUvpZ2PV zr<3AB9gGLEtgE%@kL_u!$QF=@c&Jocb^=BWA^fUi?jDc!n6%Z6s$o^;q_tw!jV_5w zy_vd4)o2V%2W+C1&It$sI%D4>rdLnXWs&P{o0=BWrrrR~_TFYH^fPk2bvHz!pa7z| z|Ncc1P^1W$94D;?osPG6r>js+4OELG(wvbbDy2^Z)7>>Hjy4W4ZY8fpyHtX4munWx zX|TJI-uiwxu8Fq#!ao7I2cfhjz%O*9Q6X(Khu~~Ai~go-pF=lTF^eYzC+oaaSeJy{ z6e$`S?-P%EV$FPKpBS;4mP2tTSNDXF_YvYZzaYn?g{O?C6}HQz0Wa?`Vj^A*ww-wF zR{RrDoIc2i2gk%pGa~IfjWXmP%IK;NN8Cw1+Wu%nOW z?~N;!1rSiy8aOoj3O=k}H$p6kR41}1icrcV?n1Yw?pASn#31J0YtVxB$hQ)2Rtl2HHC!E#bY)HAwtp{ zPMZs4UG(%zB%=m=8bo;GOWLj<0Ov9n{N|#{W(2q@LjN_xvu5_O)2!Tr`(Fb3rQqhZ zx`0gp0%+=wSypj7j45Vw7XzfH#maOqdvetFw>3JF)Up6>^F8eWKsht*S&DSb{dj42gW6KxJ~Nj9cb|~^u#gZicZlm#HSt6tF-&vRBQKm zw{JgYPBoDx%KOn@hU!%tA06R1SDmpKLo^Q5;9V}EW?T~(kraciD&dki=v9}`KVaLF zt=|~<`ChKM6}&7!d`6JK2%cSQhx?)UPb}pN*G`8K{{r1nhh~XQNqoPfwG`gK)J4BU zWUhKJvy|1AXJfU^MSh#v#k5+qo*wC~^je^PsD+ky$X&Y6ANf({YgckCr5fX$#Sp2- zs9;_3d;5G~vo!3-!aLd<9qOZ%Uip>Hu@`%GdHNGt+!EQB#Sa--=p*-09aXdLrQ_D> zS#!<(MBwP|rD9F%wJcO;94AVE0wNzxl=gkx+|&-@b;oei=^=!blh7eFOEKC4zBfmo zv@5pm+%zz-?co`eF3k`+oYk#9NvMzjpB*s#$o`v(V{lBnIOjWUp;egVuEd>M(E%2H z-k`*V^|xxlltiAC*RC-8tz78^C%-aN#hTF8V>>NWF zmf=}Jaj7{4-JTqF4Lu_#S(xuISFqS&%9dLm4`X>cSM{!~t2saer8U3l}p~oX{gfqvJEQ+S`BR7aJtAG8vFJasBOO zr644+AG&mRD}-tr3pbmg>=B@Fs$;~so}{#z6-7{QbYk9hl_$iw-}Mz2^-cO-QkCdQ z`8&Nju>yY#U6|Um+KwSGprA=b=&?SSrLM(zqUpi?nlF3eWopIqes;7)AlChCyr+bp zu^!YUzz$O=?wP!0D|JJ=mxO<#Rv1Mm4$9`i{Z)Bt6m<`JV3>&c88$`pt)S*x^8K#D zYdR@SSwecNN-Uo~)R;+RUc`9&{g8W^Yza6()U9b{cmi7f!m79n$I8(y-@V}Vz^qJI zIv?4Aq=cqBT?!|FvIKDneU09Yteg&smR0n^0QQ6VK>Z~Chdp^`*s`cjuD5WILb}?3 zI%AO-+sPXX16K0I8TJ8ju2j?YDl4NC}BGeghsYnnC?(~^al%6`fl5%o2l{m%V*dn0PoNx4(c z#msiC+0CjLcY_SikDN!k_^08&xzc;xJsfawiBj~rih(=4$d`Q{bC;Xm9!7@oEat$ zDxk3R-M}r%kU3j1msalrs|P1x5(~k_<;Z1a$rw(di^Gx-2QeZF)_mV{W|lns(jG)) z|HL(BKyDFUC#!w-ize~aBzU*tHXkR_l|&n>$JH(`RD~Q2(ismXBDUc;-dz}3@5M3^0bh>@WV(W zKVo@`cKEJtmaHBvR8EgZO_$>J*b?IrjFA%1A$x~~UP!1COdeHi@zY_um%Ek;2v^;EId!JTPGIXPPAJ8C;y7?I%j#F_Z> zH3xS0Y`0~6Luf+V6aw^+^;VIH+@7&Z_Q*%D-YB3E;K=^jpco+x-c`W^spaUdDM{UX zS+DSz;IGeU2i~9_n;%vkzkyIY3a+0eZ|DsNDTy#WcS>6aTA7u$N!tyxZf!&3^IpF7 z#a4Hs z|MvI^(|w!q$UDXkhNsEBY&B&1DFEfC!63^OL;R#!_dJ_N+jzTjJ7n=7y<|+u_2NG+ z77L?EsZ0xdMX(J>)nu~zY1CCcrGq<5yW(e);iC4a7LHt{g92_e(jH8DC%kYw*U@UT z$KR8sM~;fNi#3=hF@W>DZEN3oVCPp!#}I-^=DqIt6gGC}W@t+`DpRb|pR_^iyjiP&=p!O-47rYRN!_ z;isFBei1>#eB*aq%i?wE{at%L!cWx^VWkbv2l7T$& zal#-FNi>edr2r4K)z`2K-bG5BQgd^Fgbp9suS))$?w6FzW;^HsPJEe55oAn)+6EU` z+%#1kuOR}9qZ98`=PoW?W zxw>CTNm&m-W0E)abYj!oIT%K?G$-*81fAchs?-p~!LBv~w!}D2PVlhFzp?146iTag zM7Bw@t+iGUNi_^XaOy70M2<+Yu(tZ;8Kdl!-_K2<5A{3*+otc2PYuR(>k*85_?5RB zJBWLQXAY(fZ*cs@nyO64$YV)HqXiHVxa`+kbFOOL%_UKRS_HMz1hvDwWTNFIDOVh~ z&3aaLjI3PDg?4tR=e8IJuRMGiDJQgZTtC{YEW;hG<0_zZV_bN+?B= z#V;QD+he&i7UZ@;S24}2(GT;FME8Is+jZ$n`+$I^IuGJV1LR~Xf@W)vdp7inOrdYc znZ%AzY{nx*BSOae;h(5InU zk8FifR98Ptj=agy*Y;tB2jmx|%a7??*2Ft(MIS<^$Kx0E&;_?$`A<|2Rv{#_!W8ZX z6Wgn)Weu~w@}I<#{uZ%lXS~{m=3*ssQ6Bf4hH+gKj;uG<5u(H?I~fawEe)Qespm+9 zB4)p(dg$1_a6;4BGu%o*6D5Vn_Bdrk{7B^_gcUX`HvCXJ#akvx16)v$9|upw6HcJ3 z!Hz7S;EFtPJqh1!xJ=N)yGg|#`kN-=#kZEjm|f^jD+=v%H2ZV>!2cX2iqX2_{75EV{IztdpX{?_*N zx2x6H?h|mMW63jWZTfjy-FKu>U80FdXAH4YEg)=!P(8lhfYg zKHZ&*^1!1kEVYR$S+1Smipzf>G*c#rVb5!vODRh^W3oGap)6NXX@@I4{GI<<=lw zZ1LB)U4YpujGqR1et1~VSrrQM+{9t3aYydMifku<9-miNol`|R69Lcq67RgkWYWPb zPd%Q#iwW!7%)9oXECIvgZevCfw5RK3mAN{DA3b7WWe}h%b0mk=%Y4~&Mb3&gs6JC| zhpr$bW}*YG04g;9=xAsCwl$!;hp@CYro{H5JpxieV?HCGHHRoBy#1VGQMSDYVlx2F z&g0u#0~o~((`R8y$L=%GL)0u@i$aMwc^0u#y^+%-3RKyLVwlmaWZl8GC7$*y zhE-6PwpWMgE%G#A7%x%Egtgx1zp^pJ%C(xw6ckr%i@WSys#iUn%ltdV>j%hx&3r(D zoi*TYq>je@95sxA>aX(Qzcy`>osS;4zw>cg|E9rq6Rl`EDEjah_Z|RkoMNJwr;5{d zEL~-+it&g4B;|P*7mS$t6$HaQA<|b-So}hgzx|O4>~*Vy9|q#v7I#PN{UD_H`EHyF zetIj+tH_iKRY{UAp0aYax%?@z>;Gz@@rxONR}XjizTnuQc&^KnPx{WhhwBwDvhS1S zVS=Zh#CiwE)w=82zCW0RLb@^*aYH6Gd1q%WC2E~Da(%L(o23?zgxfpl@!DVmTZy`D z!jRdM65fZmMDzfj_-92GpUC}>)0+-OAp1#)xA+Od=NoW%T+zAhG)>qLaa=zn&M8KH zzWQk=!wHKmi(IHahzwv1dc)eXwY@Fj&TjG$)6ItjyZErUw1k|v(VICqUd|-6v1)Cv zOn3=s)G^DJD%P%2tRZ!9jCL-U0TRwZi`8)$iR(yst*|#0;~`SF?jFao6H}^G;Z|A8 zI(H!ScrB=WPI1xk*QuU@r*RtW>-HJPH^ewj9j%1I+^lbyso_M@{xqD-@i1l|azyh? z?3dxI+kmIJpQ|{KGj1lyj3GHe4i6pcZkH^+#7Mb^3gj*mI#uUqmSdrVTeE>hl=u<9 z_!3-S@(-jgoPsj=_~NmO4O3@?d3?mc9=h@UCm$n*5i(Fyn!Ul6vhKymf(d>WnO<$Tj!{Z5mqu*c6ND;Ri~ZCh;6?=p9o|>R zXk81AtLE*7D5PvwghB+Gs#1rp0j_Yo`j}|;Uu0CNOrdKK{?5^E>IGvC+tnloH^`gu z{GltYYm2JLbyz6=yo)<;T>rGZBkg25&wzNUVA+;2Yo#> z3SrVl6PB6g0aTi(x*|Vx(#f;IVGF{D2+Qrr`r1N$IHb52jo=@Pal!s@&A@@82^z z6yj7oP#@(J4t=8C*7BVhy;sXvJBz207A=G3$*>2#*JO0%4_u}?e$X@yH{F15!u2@# zd{Y#(Abgz^)8T`Q;826es1rqUMoUqyOgE_vW6F?<#9CW?ft*~3X?-AKkZvp0-c?H` z`PWm-h(vx}d1`Lc-Iv}3p3Is?L+2Bey(NIIdNNUoajq@u+_AG*Q12_frB~z%J&vXF zT4hje0t6ljKZ6%1;)Y=q0|+48ylYSh@}H}6wEImH!!glx>-BX>Ty{VjCE|PbSbeK$ ze(otI7oGb4l7(C|>qzN#xRHf}F^SgueEDK63`3^973|U-k?VQ-22b3Jjz>#M?M@x~ zAG+H4t(!JCl2=O)?Jleu2(krKwngyWi81JfInmL7>c~=BD*4sDYJ&LZm7Cc1j_teI zO$=hJ^Gji{2sT|azE6_l0>W?WaC>aaxzy|m#^*S`dp7kbpY*8CgFgh#{-ya&D#aAw z+ENhqJ*Y}Og#-n`)HJ-U>TrFbk(fo)ow1~mQD~EJU4+bve3)^JJ^RArytI}t43;J; zJvxK)S*bg6OiW4R47M8tTn8!q@Ob;`X)ZXe4I3 zYXMmU>k91MfpZqbB+FUod#MCWDFt&6!gsP%p+h?`<}jh^4z_}YK8=NA(B^zMU66~V8=4vwa9mQ%cUMDL`s{lRL2M01(T z3dLgIVDcg4YFC%HQ>%STWEc7#`G7Z`7`ZRJ;k*IVfq3Y};@nKkMCX@k3|i@?P4A~- zl!V4MrtsoO+Jn1rH-6JI2uKs=>>`L|TOl&k11RJsG&=}zTs5x9*X!~FlXGusL*3w? zl3RIkKeaHZ5OpNhN836lXulSvhgXfdyOECYaCPYdt`a*$I)KXyIvp4t{`CuT zr~q$vAeSpA5CX+KeQ+oqY4t9Q?#v=tH`?GI&#}5}J9V5vUS5f-a9QkW+aWH^CF1M= zH0iQ_8EQ7r7=gQ!QgBEz(ST>b;tj|HO%t1K_lDXyHtj;%Q5wSZaWe=Tc04ybLy3^$ zjsk{}ZAk_JeA2l4!_v9H%0>e9bh0XQ_Vjt~ zHm_0UQ+rI#DubwbmC(#pxOKq>)G}hB2{0zAB+C%A7)<0?Q9mUyS#Y_KHmaWzU>T%~ zv-Y>#naNkV>N4*>&=f?-+DX<-?3+tM6H>}gfmoh`R1l_GZZ(K7VP32veT1ZcJ5#NvKFx_vLSBX< z(qqaQR(|lck=uORgvTrWl6I0KbxGN^n+fPF+^g7gr_12AL_h~b` zbPtTG-6d)n8r8Jy(}1MK&9$AOD87nXd8EanzI6R;=dVyD4%>akw+Yr;Z%XK;ZzNSl zeQWr4ns8EY5cWW>sXruG8`_Tn0WOrGXoBEdu~2CD(Lfb6@3g!pgj}Q<@yP7 zQ_K=AP*=0mqkD8CyCuflGVk#u$Rq}&kG4YwCrAuUmBHwC90y^OEmYOJ=>qf`Y7$Pi zR1Wc#Km*BcX3X7qd8o<24^W2u?jZ7x4X@+)H`IXK=NRwbCbp^U{ZfY$JugasxFEuK zOPZSL?JTX1xZC9ylFf9HSc-$SRk)(G{=^L+k+J}Ne*C^YbeGISRN&0Q;gDS7cZ*)_ z?o$r>euR&|$xc53U(l+sNjnst*;81?9nHH#q&b)j2?HC3Z6#Gt_k9W%r3$y>MI2Mo z!dh~N$esNBRJMz{9m@;Q|6;pk*J!hy|A;l~V%~NCy3!L1;1ty9nN3C>_Y?R1v%?*tT>kRI&F8F$`kK^n2$$57iKS3QM%FCZ zN;kTuymjd$XqryrvN;0CIQWQM8=eL}oPI5wYwUZp{2gig*rR_PrzAR1kQX zyL4`gU}!|U!#KIauNm(;nUsnkM_3-{{;2ZdkBUO(ViApj9sO3B2wko$OTfxVmcLh0 z!7haZ+sc6%+H$LpV*%A;8L})_;r1wRjxqp_&3Mh|bZ+<xV{q#Z`<~W6 z=MBsC7eeoP2r!ywT`{a0L^jU*cq0j$1 zTQC|49UWb;Ury!H?Inb-{;#HCIx)Fsv~zE#g5iDq&~m%yg!R$tV%bkvLA~11*?jvf zHNZon1HY;(Uvac}Wt5@+u||FW_5WnRQ+AYH$Nyu%pW1?fDgXb(p>%Yyb#nekr~E(j zC>Ju7>~^^@L$+S9JXSbev3Lt|?M%N#Pd8XXpPU;XDBA(pBYq4yH)&t^Wv7x7vXyZ# zxJo=p!j(aUF@dgHmUe)N2j|wx^o+NX))k)}9{&j64-eiPv{lLVX9sGXbgP0IK|Lb{ zG|5p48UysoF2-UV$xEjk+Kpf1f>(lo{rrYeI$epEg%|4er}?c5$IaI*GIs?fGCZ z&mfr9QJJNx&y!kG1_|u*&vQRIFJkF;<@Cmi%H~AkdvHONV~HNWVBeunwDw%QH3`dk zp6*s3csav^zqagRctS3;+9|8Tk#*B8RjLfqb^}*G2T&|g21;x45?p_ro0A75VLv3Y z#UKu0soMiKan4-Y*!bpp)K6xQ2^{jJO|z%e2$kjDF_h&Z!3o*C!4m?<0@SaLH>A5&qU9_hrd`HK^pSm(r zGZ&(KDSpMfO`p72Q((&@&oP&arDl!nuQw~y7vZlU^urx%9{3y}!UKh1gn8F5KWdIY zfQ5#P;$BHSMaHG3+wqHw0{EO(Yho8H^~8e~1wyY=*2IgpBk_ujqDF?FE)OMVAR61l z)$he^-(Jd@M=wO*1q$y{TYkoL@I7Fh{W=w@?HO@amWAPy3O$c=U+zphtu*D!o8sC9 zizWvX4{jlvGnd5!BNrMk0Q*dZ=y&;s$|}L~?^=f#y#E1BC~E|)om%FEH5R-*tQHac z78eW%xy-c(Ml{0Z4^BxNapKx`+WDrT$050k_8~ku`<&WO^Q1f*M<)@%vUktgmS3Ph z#4U|J(y{bc&toDv%lBiS=46RVYt#se*p%f%w>gNuje77W}&SIl@=dMD_!r^5~$C&7G1^88d_`#?V9`VUJOq zlTc?Mr^%0LNF7mP>=e9cNf#-Fh_mv8!?Q4raI$}yNcv|9OTeS;f%yc*0!1%9#xywI zrcry6grQW#^VYqOAi$2@T#`_=h;o>glzuf^!YDo(U1)qe(va|xLEN!G{a8aR_VXgf zUkT-o!QKx#kxE^C!$vHgQSiBrPrPmmIsqpP6=|4r40qK=o{=9T#aH7NMW6|@06##T z`15`HXg(I5Y>+hl2e+sb<(JyTAKxT+Q6dGBOt?Hb^qo4tSoEEF76p=b^lh@pSoA(7 zQo1Iz;Bt|MOfnswIVB+1-jWPF8b4S}?H*T=Lvlk3&^MY_`W0_hX*SM5LP~J(x3vFV z%6~ipe6o&58jc#G*&-mnpzG>|;nnvI=6~jw3&QsYG{J#^&0&CnDg0lqjfU3dc8-?L z7B>3-u{tsuI+~gN?-W8dsS0+wLr4QppO~x8#W1XyJMSxEHZk);yI_&+*q9m-(7%DC zW94~U9fCmCrw*~`XLyg@4pu(Cqxk!{B|E>)R*$E%x2U$O>M6gk$Ct5>jj_3lg(<-g z&yUN)t*&)FKliV-3{FA4jdh)_x1q3%CL9!nExoHOfz2&}_oH3TPVcX|7d!pSuFWoi zb^$&?-S)5Rv9&X|FTSsfjTFIbeIh+U{?CC#zx}nLwm7CKzlZfPH{Sb)x2C8s0l&_6 zcOUn5zt2xkj~n-|W5Mh!eY>kp-;dkto69GrrxYAw{mV|D)^>N_59hC2LEEqD@{Dp$ zAf@gh@v~Q&ucTAM?4c~fl-}9&*wK_->!B8(D18An18d4=;(Q(ty{O|Qky1FSRLdV0 z&qex{luo_836Zi8iiwUt+M}6r$_ZFxb0SIcWG$1MQQ@Q3`QlI#o}%>`6=DB$lXq(J zKxK5B#2=HYYo2BOUPC9e3GOou#dS$}YNFz0JY|>h-@7S{*cR9ZlV%4iIuxeT_xZbX zW$G!>6_w;5O=2?_f1409;!BuAmV-n?3$Y9q1m^FOKv^sm(q*$0C}0ikj|Zn^>4tv_ z_Ny2Qd$;Bfm*cNyIs6m=jl)o>Jn&nj?7t8Na*OI?i&|VRJj|!_?N@g4qvs19%C%r{ zh=0&j7MQI|ASst#m!2_J7m!5Y=6E~`lI25c z|6Z7N!A3usH_+#Gb?&s2FjU9u7m_CrK%qs0HRUv)qv~X>BZZS^N-|gF zMjaPsWQiYve&>C9oNBks$jd>en5c}&wW?n7F&+8gAffdt``5_KeSMCLjuZ`}q7V+x zR1w;uO=feKV}Mz{kjmnZT;+A^_oj#?%)VyXZ}VprTEzi%SQrs0@qDlv{o1Sp{uw_g z9O+_j=o=qYpI4Mq>U_vkRlXa2+Qp-!6U`~#kd)@;8o?ppD6xsR7*4GZWS)`0YCiiY zU^K%q_*ma3f8C3iGBoQWD&*r+{OZ=#=1%Z4)XQu@s%F6}(gL^PmDdwjO3XmVwB&;3 z@Q`FFmdrh5Rndm7-mUwYCZ||%NG+JD6mrVJT(eP2->MXI{Db0x83{ zWSYN17KOIioucy zSHV>JB~&3d4y(OZt%YRzP?%*E5{RTkI6DrW%_E`Igcq>l&Im`JzLw|Til7KqTa;9* zm7q%l@6XOfmrC$AJgHQ4^_0+1`jpBbm|3LM3^;Es@*R2!tyRT32grV^gGy}4Yi?Pe z$94+ODjlUOn zX|Xwq_mJYkW_Qo*CnVWe-AEWy3WYhrU9OiJ`m@$Ud-U+U)levMD6KM9sRCBQaCLN! zJIWkFEE_U==zPKpTVYYlu!5a)l}z0~X#XPI0|{jJ$2wu zzmHi$J}eswp*7$ehn$>11gGUD8o0ch>_o!P%juj9IawRGzr*b*7SP)gdwGjxBDIAjwL_=W%zp=l9lH6xI8x;Mtr+wml=>?_Az>u+t=x zs4!rw5Ss-3L?*{O&4J%!a&PB~0*vvsOSz~2v{zEv=V_X6o=Dh|f5-X3IIPWEW^%9d zq{qRU7eXqD2($1L>f-(`&?3BCh}xr%_{`Mf?T0HK3#Crnxf(ww4~>QOORI zZVgN$U-7MxA+l7^I)n!qC21gfbeRW(%xdsXN!s{u?Wj6zpRWEaIY*E9kU5J-8u@m; zw8K=M;NtvXKF@DrPMp8MuWPq!1uR2Z2Tg2Kh-VsOXffTUxoMU|QGq2nI)8Lt!)I1T zudf#wQqEUDL!7ASLGR#|`lSAo&yTpVJQqj+@rM75AiELo1sas4h*Kjm|6B z%VaMw=?Hyf)T5s`+(kLf)%-I!(C8^AurQ*a3X1xAZ$gW$RW($aywZ};(_N*1`i01x zYfal82=H1nAEZRX9xSuxJVky>+^!J^>`g9u43-M;vN!FS><%{K@A3QWZmX?~$PuGi zkPo;jryB?qg)cbPUzrbxOMDvSapZ+)I#(*ac>i2$Q=>OwF3ykx|1R=cbiVr230XfU ztp*#cWkxENDp|wBN6vUL^xs~TH7>nBY~rBw04IBnd5wWV z*fiW414#1@tpT)jPJqARnjWSkQQz)a&=q9#iB%$(fGhQ6z#2cuGf|1-WMD*Fp#}}+ z9F{~5C5m8{U@60;CM$qFGf74VD7XJ>DA#!z?!XhupRm{^803ciQ-M@!>Z^gUPV4BN z$14oUh<85;<<=ms%*p4F{Aj{{2&ZY_sb42U$>uV?KJ!WA;e3MLcGU*P=(GI9U3yA6 zCq(#CX>Tszc}?v7$?w1G1Iv?q6#hqjApidwKUv$E>s#A7IqBQDSUX!9TN^q#G5+V# z(#+D>@ISQF`cC$Sj!vdb{|zl=nC}1YWr4EuVKA3SU|>9;|5w=d|NXJXcD81g<}Qx^ z8Oy)eoLv7~Apa|2H{pomS@Vs+R-Tff{-%u;aGfelmQDkkL73xAsvxw0Z|dchjRv?2 zA)O>kTLhzfU@n+f%Z}esE8HHu#;hlHbJj$!(6`?1ORxT@0TeH{CrEYwQ(`&Z`&cv)yW3-~|KJ#%%Kx;?TWV@Q%+=dFguV6( z$nptVm|%Fj3Pa75nH9BXl*H)~+h>f;=x3;qfx09^NpOR!mRM)pWiKFH{aJ0<94ng% zJ1|+tyCtx_8txw zZ~J$NIng(w{+gU=sQ1t+{TwWdy3aTC(@*qXhhLShpfl7~P0BWYi|l6on6cYbg7 zAZX+StFb$iHBZbAYjWN^5-)w9HPzjR2!tHc-vOJQ8|i*90fPXYVH@&w;#%f!&uUr||Q)JBCl|#4#nJ&Wwh>a^wnO zD#vZtmA-)4v{&Np_fij}M?-uNeKlM356_rP<{H+h&P!N>7N`NDapd zcq!EOu>|!p^f|kJejntLu9KdOrQ5;4;@^bi#&jkVeh?Uoo42q)wM@OjCH+^n#Nxx) zFhtq*N!8nN^Qm;Dc;wr@^Q>d???f#1Iswn zYH;PSAxyL+gi;;8wSAX!5@QRhC831Fg7`rzFOqQ0>PrEwi42$q4FRy&<%2Fj(|fo9 za2h(&^t2k`oP!$i@MmY@?hMld$nLE0Nn1zz8!Kv*y0k5MXR$?qEP)_5`;`gG=X7B?NDzb#M9M%s{e-Gj)e47EUMQdF08=E(#->qX0!o@{XkzR4aM?pEm>-S^Ki zyRHwP7be`AoSHp^m|lwHoUwChR?;i95C;f#j6pL-v8pWb$&+e^qxH4h&}LH9Xqos( z9i==I)~b=wY+i9BsT1M#?|(Y9*f6iZt0Sg)jCV;v{lWsKe;YIqL8V+}>v7~$8fKFQ zV!#jtqfQEC(THMa2+EaGA}DbI26H!uD*T@fe2`3bEk5+Sbx3vr83-v1OTL$P)TJ>g z!73pM1>2AI!7Uc@S6r0sP(vb4;xuSUO2KI_csI5Ds9GUhtsWHS<(23LX7!4hz{|2p z{ep7X2o_D%o>El&KD)|b8tNIj{qL7%iQ;A*2zv%8D``f7%}GFn5qPd)ApPPY;i9W) zSxL$-T;sDYm7<1%m3OD4^bRRI*3mJ8oS`6f~9x^T*(uh3OR3Cy^f+=+#`5+ zIbjl#B7-4G=D^rs{=b9Gmdn#WandbG;kk0BgJu^WCBlD*QKl@9qSbDTkpu>M@8G{C zYxXSao+n-m?V68;4${-*86G7L=kF(fimAaKH(dNVKa4#>ffcsG|ruq#{= zBF3-ri?m=l&?nPI#4cwC7ha+X4gRI7XT$vc0;+_T8QTDw1UeSd;yE$bzF`UL-ajH< z*{Qp)qQ|DJpw&&L0MS5D(6wQZ`Vjh;3*z6uC~KXe&rE>_(L#v_ zlLZ5aJq<&%ST3bn;$FB%7~EC>9h#=RvP1^bFn2|`xIl?(-huL($&CGTNB;^CB3T(H zWwGYI{!$^7C?=G!_30_7Q2bZPY1#(TQL@kntIIM)it*aC7%4Yo&JzY0UgbLbZ!n+~ zPmq}F>2oWJG%GUR1a57T0jz+6a1!ZNqzzvJjbVA0i?_71{~S)V*tnfvZ}U|=`1&YnoW*MVfVEL z7zdQK`%#VWmE=4?H2Ob>sYOs(Ri=mFRY9`%!!ep=FM@PNRsO@i7O7dg9~@h>wd* z@AX{@WR0azzCN=pIQii?kjlTC6Jnyh!ivG593!|+L(Qx+bw%ja86;h5Barm*ZPe$b zI1$Q$7lEIV-@UWAh~lZZh=+t4x%(3unNRxV!I3pv9OJdw4(Zk*`ygXW!fhd`_&6)X zcz#0}JF_=)`RdI8@iG@|On&nT;BbM`gen%&AIXeN#9%osqlU-@G}_p{{yK5`2ZjSM zhW=#Bn6WU`d**m^h$5Z#401g;|6;DJRn$h+$!f}8udNRd?P1=16#31;6!Ql#V}Cvm zWbF=gpz(4>xBq*l!qvkBu`l}l@6)VRnaL?2O?Kc z#uDBJv3R7E0^y}7-47*6LNz%TxjqRvaYw3eTC2m|Chw)O*k1n9-E9WBjmY@=qz;}_z1k#ncXx}HMOa^vPQow!pvd~iwqy^{+ z^SYJe3DVI-pFd4EPDhe^9l%;3`WDkugP^7~Wi%Nfq_N)TKTohs>`-ze0W}pZ`#SdA zAQznt9IoqtT{76=&6Q}Vm}fG5|p;g(rn?zR0)gURBom@ zZGdp1wBmuN5v9G&H*uv;Pi#{_4Uo@XOzVzn<54oyfoySk(!_dVBiJ zTh3?uW+rVRN2#_Q%`U%Qc56;ACXb`%_2=hk@b21+oC6Q z7HTj=;z^}AmZe)ZR7ao2OA z;7k3#5h;RS5_i20Rr@_WpnQFD>VI@~ z&6#F*d49PZoppZxWqcc8`r2JE^}AU>(f9d!3Cs2?EcSBwtnPft&hC2qyt8XN|9YNq z6ZC%jSla6H01cgRPTg~Ty=ukI*=6licfB5F`+Y39eWm+8))Vu7;ADTDoL#Yp3BEmM z3x2U9?B2O`jU9BoU)~)Nw_R3$?NST6zMAX%dF z)%o$TwB`L!uJ7A6_*kyr(RJUvn=;g3EkFsoSmVVz$?RDDxJ~2LA!+ok<&>7Sd@9>d+ z@;UB19PT)8QaJnRy=Da07T;t$m&%?dlAe;dD{K}yV6L|H zT%w#n?KbN!KfJDtuL>tT-c+yF=G8h1)>kh)RWEIllMnTZE)9=mo?4sMX9wMEn<}~r zy=t7}t@OUuv#OVLya?o;w1+MdFX_#F7MIyg_{1;NL>f{+ORw%{FLoDCI#0@u4-dZy zR1`Wd+5~=a`U&tp?7S9%sACq&QRdcNr))ahCNAi`2pvr2EUSNSny>LC@Yz}2tPM3b zW{%B!JC@Kgx)ksi*T!bs*)^4N+eXVRofl+2^_bstTTP`0R`tB~cNgTrpSid*rzyxO z?4a=lXb~^Ja)c6l9gn@~y(>Py+LB8;b)u}-8DU=p#BD3OGSB43T;>QbJWKeCs95t)QZH3cU5U0_ez{eL{cy@C zW%p^+I4Xi&=n^kuY1e<3dnzz2r94S+*GsM?vIJ-se7393S>u-}d-Se0@C^wJMaO+P z<^di>Q@lpfbr`BzolIwq=HiRdBy`%GY8H6JpKgju<@w_(uhP;aKUi9%_Wm^e+!gc* z*esx(ySyB5OKak2G>#-nzR-DGTwQjrS$4H9m@gX`sp6FXI}dY*0XR4YG$=Kt8d4}^ zXsu;Og)IV$655X9z2sZY9i}X|ZjX_a=ASo93Cm&%k)|94i5tD}7*Kphj#UV>$CMCk zE_U49(sDYQ>A1fWj4+;5XY#diNG3@N@*Sb!Rj-3zwz~G3tvScaXA2~8B z3kG$;J`N{oDdVG!+?1!fKe`GVo3id7K61_28#l>v#B`+^z=Bv4Xnn(#@bPrtpCh^( zO`zTLziW#Si>a|d`h1MD20*QB@a=TwrDt|SuO@4i53miyVa`sCUeyK#fpgYBzly&q z*|t^wan7F2J?|QiZD~myt-k2ELWw|;?~JpQ@0p^5CpP*^=6NGJjuWF(k2nsECQJVU z&a#Dq8z)cpj8E!^H8AXD;?eAq?Y0H`ET4{$_T>k-(hHc6&8l0>N%V&P!;FszOS_Ih z`o*VLhD~+^VdqvIwiCR=U-};4VtcmIl3axTMtRRbtT^mFt6ez5 z2YaefK$l1J^=}jlUfwY(tex^9i3UH53lj?h7sjRg-XEw#ql$j4WT8dL*QH+A;S$vI#p#HwzAM# zlazw|Mfka?aye!sk+M-wV54*~vX2C#otekyrkTfIlepK zPh{|dko$?w((O!Lie9B1r(?QyqjI<2KTvk^tfhIlNx5+Afh9UoN*^B z%Nc?V|4bSuIA%sl>NFeGO9D(iAye8iC6KyR)P^227rD7uCCrkW zucNQFMo`r+x=J`$YW(7|#?{*N@lH@*nCFv0);f3fX}~`2_Kj=`$5 z{Jhl5o#zdz8`;OuQCr0}Y z)CL!`4V#ZwyPMpwxUqYLEr{NyI}X9@{4W4rK%u`D+Nq$tVxHOJ17+nY(f*ccAj?j- z>-S2U%SzBP%*boqa^_o+avzW6FUV4t<*QqKvx)0?y^GiDdQo0r(*z}pZOn-wZ9L@O zG;bU&?TQ)xRyE6}khIeS#nTMzSB(l@Cm$#=E=AX^3t7jNb6V&vq~WXiE^AAB<;unj zFR5y!>{={qe#+?&H!3qFV;;*lFzb6?J{N5 z&Ymo8s`jalILsE(M_xlxr71?S@sOS(d<` zS_8YhQ%P#Mg-xpoJLo2XCXumWfTg)^Nn2RER@!>n0@oetsL9vp5haoIkt4LUip*A? zvuM^V1O^L3Y9CKY61r*SYs4gw((QUw5LIl}hNU&{y7`QEUPidL#6fZneR*RS@K2e3 z&;R8Jl{9_GDE{pLkk&}iV5MdSSI|o6nC9EvjB#CRj z%Zcuq2N^AT4B}BcaU~oFjh~E8Jw%gE*Ba3#lSLw{=k3;hT4WMjl~7KIawMs|of(#$ zWx2M41k=8Sjk|4_MT61qD_GVw8~6cw=S_M{c7>EIGev|f+04-l>mM>0k!GHB$usXn z30!YdB4X^sB{dP$ILk6)hL;2Gl?J9edPI;+gM|3CjV~Equ;zy6O*6Vp`Eomjb`h7gZ8K!m4T7!>W;mCzaM-Z@-&{ z2lGk`8y(o>C?i6osy?Xwo~ z1U7xe(AY~8RjVFMVi|lTf-st0X=*7jB2PD|&-=J>!JQf4E^k`5ciA118m%qOsWVcl zQ&L<{a3_3HWK8B|b;yL$<<7YroUym|Qj&4A+C{J+WpA$}9-ulK+$HaP0dq2zO_4=@8!nMOz@XFAv>V8U$ z>X);r1+LXAouS*%dY6#X)aeyz9mG6NGI0;WKiUl%F+2%Lyq94#O=o(@LEZZ{v15{N zEaY)HCNpA{Hl>&)ozxbQIKu1R_%5W6mPA9}S&M2xvQ-H{Y71^iNexyCi%>#bgI~g2 zk~+hVlF#IkljkIDx3S0_$&hs?J8#OO^d7`0mbodvv3`87Haw>^MMdlKc0J165~@n_ zvq_Qaah5Z=KV|S^(~R>_t(Q?PlA$u~s~51Omu%yLKj3(sEK6(uxjyj=(p;e6mf%Q4w1hD$aO728u+Y+sE5j%lVYxWSWa-#=+L~}4m zT2vZO7fFD|THP`(izKLn8rmIq;`t8n5=WB}v)e`2X08&@l8DW&QP-xD%Oa`Cy+{(` zze|@|b!y{lE7aI>+FOK|hGdpKA!#Vthg*+)!z3!AS+5b*pITCmoD_QuGVu~pS|8rg zL7D{Fp9IVM?Rs>LNKdw_K2iqT{@itsV5R)bcAlMnwwJV|G$5MZ7H+w1V&ujCdTvoH zN&8ajCFvb@n+gxD0mJOqV9BG>-0$p1%g;%8o)Fl(y%{kpEt;)6_TR$SknyhRl+hjv z?BTpQ?qX?<%*-o}(Xjg@c_q^<(i0m1*HIGoHVH{v*=yO{N|QMyPu7t%X(0_CgPTYo zS(nPD>UjTHd=Zk?y=bNNkigQZvPSf6ySFtONef3WIVB+jC?OpryX_j|+YVyf_9`#$ z)n?E}kljs$bKb5;+Q%}GBn)!`wDx$X*B8M7 zmR~05)NrU@YC*?%E0I;L5|Undb8t^u*g*~HBu&UJvq5HVy%U(w%GMfw6Ve!u1fnKR zpHWtPK$=oImUP%W#Ic;6om`UbhIBbkpheb*tX1u98Ie-->|WiZHYY|>vv`q}B#Fmr zExd!6poUy_J+6^vJ@0poB!-sliJhEICd)9a7j~R^Ld(?#u{N+2P?Ga!BY@Mgt))Gk zlOV3WV~+V^iE0_14MCn*Pg2y9oHFEDdY2>JJE}wp;iSkKudSP8Q{M0GpVDMr;< zLB{K`q&?4C=K6T;7n$*+kj2;uLP?m*@O?SIMS55d!uqufs68N`ph;zs!@>HOIwLuz zb+8dak|2L$FKMlZZ(A`}rDUho7Ggo_1k;gW{pj0P!czK<;VuX++F|e-m2NBKLcXgYkv6upVwCZlc)pvdf$#{d9Oes+j%Szz(-LUo2%P zp_p0>CllKrS~!(U@GAZxb&2Myh+18e(_PyTr)-c4midP@Ie#QsOx6|Wmf4hRPS~eK zT1qBaXqAC#2iM?tNHK<;%H7Ijvmr3SlAGG{=&ZzUdFC z;=841Vxmy7a*dr3A-}yPN!nrMqdC@45Fc``NZz~DQX?N>$CbQeCG;ASMKZbC?{y|= zU@vKgb6Aa2vP+YtT&5}hF(c7eaW^aJJr7Uc@uQk##OvrJ9uis9B46FBm7FjNBu$nX zsS5}0iBQ{)OBgO1thYXpY}X~QnW_mRis1F7;BY1Dxk#{|c9FoWdN!Z%&>S1NX9qG}g z7n5}89&6e@wiK0AY2xlf1Z-C4(#iAVdp`}rDHD01nY&$&G7y4|jlE-)I>DzE)WQx3 zd7bK-`3JJmEv~OP)Bfp#wC;1_}k=+roMOiIGj-`IQ3G+F0y@7hrFiVCw zcg^)$xsuTOm_0r1wk~y0NWrp@-P3 zR8EVi4i}__<@iH$a-I&}HETdaIp0@gyxKK8MTwx*{yKG$9_wp_bx{w5w zK6T2ur#6Buw}?4TEckBSu7Q<+$7yy7g+PuzB#l7M z;2`5}uLmHR)fl!7?1HQs0L?<6>gS_KqgS>cBymrr&Y(0Dam z9&JIrNn>iEglmup7?d|UdpANr4?r566*+P5Cy7#fDtQvOs@OjT+|LduspdB(nKb+{ zsS=cl#ABq83bGDE&kvJ&e}~GP-CAFz0`K9cq-aBT3fP4JGLx; z$S{zv(uC9ihe(ra5(lo0k0pZjRynk>btT}Xm=F+F$g)_`k1Qr8S|+4+xjZxPgQ=U+ zY&L>G9O_>i`R9;_L-Irt#bFjN68RZ&H>6R$9md3z#m_@upO3C+uX)FjAk{y5j4BS* z1MNlV_-tY=WXO2{5$?#JK@7wjX68)1Rs#2pC5~Ic8kXFOJUVMxijUk`bsDD=2m&Xy zV$LM*w3+#ycT;~j*3!f>v9v=i4mFb!HH2v**V&$bLe^~ZHVeQ8IXD45)0ALN0HKL1 z@TR7AtSm{H+P2lOFQwflxzvrAg;amW$JPe7uI~iN9|X(?MZ;K@b|+hI)2=%`V60c0IbL8@21O z1sXZ2n4vW9r+lLn{+9{ICE7C{a?4Bf$=H`-l<-->l`sCvfT)BM4G1K9qKh-KIYcQ@ zVi_AX0<~T9Gzj~ox3hdWUD{7W->NMez(|={fagjCbv{^Y+M;r4@nR!zQE^&!Otgr; zN;D)SoxGk-td1y(o=I+k=E3+Na!(u1d?RY2ff)KUEp+YC;$5+oKnn!210QeT4a~SV ztQ~^!J}VJB3(a zC&x+{zUk?;@{V5zSO9gBKr3c!O>RflDdvq#4vWUT@;j1`T9zny+CJ@kMT zPmHwW=>R3FK_L<xIRImPHP5P60w7t0I%X;<~5>P3p=wq09{dv;La*tzlFO+$M}p#%QSw^5|XwaD2+mhJLYBEJoTmZr+4DXb+d=#z(!S^Sf^WdhAoW~t9}ap7&{6JIjaoW@ z>{ehd>Kl&0q-4?Ae2IhSqw9$@4Lqp!Y3|NX>EqBjZ*qUquzH>fbjKjDPFaY-`%xfJ zq|0?M=)B**hv1f$$v_H9kKw=uCD&ITaSQ-m(%naKNx5D@czyu)CCQX- zGFR?Tzg#1>+quz{$Z)H>PY)kqjL$uTXPtv+rm($}1= z1S~Q6Y%6J|Ns?%r+h3IyBTl|)^OukB{nQqcN|iN(19H=rS&!HjI2 z20J$!HCXQ}Ntl-+1Yn!vxBWcH7_j#{krT|aDw8@gH2HLGSU}*zhy9>cJhDjuHIN53 z86VPI3qYnnF>}B#1YHQO%b-GbfJZZMV^V}r2XHe@V1138Wi1@Kqij?mNwX%WF?Yn6 zl+6~o+EC&~-jg*6H{^N=)h^XOvTRxH66%s})cQ={DmjNn9f@q*l9#$A&Y(>oi*XRx zmC%y^BPZOd8N3rwp|QZv4J%=jH|DheAn&>XkakL{qPhzqx4?5~a{`y0VNajm`-yRp zmh6_fx9gFtOhVJc5OiE1i74P?y*w+Kj>rXZ^3#B}<>a82z(_PeUkHoLJhKa4W_0x> z+CSR-^6V{Y2&hjWTR9wA$xlr;hI5hWPg2i0fVUIckIX~TE|uX_Y zwyMsj{`r&FoSf8xyD)805ML(^C~r&WWgC#fyIVeI$6T}!!daCRXc1tvz9dX3??g-5 z%XZ-85bvB8$#n;NLpa6T`M5Fm`wcujr4ojnmM_o#*x@HKHU(5Kg%r2z(G|2I0bIes zH6TMzMg2f+_({LOZ7{58)S;zaoe*2mN%CD`$jts)gmaZffGn^!06|VnL^4A-7SqG) zkD@GuoR37<0?SZH1jKag@2C8TOBVpCnlP4}mwlR4NR0LX+DV`!34QQ5PR`ZqB?P87 z9M&Px(?Hsl{r-q{9NrCJC=@$J>GEz$-^l{iz*=Fd>#=rlHYZ1CSUO9_wQLyK`RIyw z-0x(TbQWy!W8)oxBrV8xc69dN#mm{9vS?ddKo#HhPUu>9u>y532loMGBh7*=h!C#9 z#YQII4!DJEiPi}{mxVJ2nTiBS5jslef}yrcG8}Pi`85+In1JBPo6tg#Q!@!GA369$ zHdAw>FNjnesep-RA0+|VNRS%U1+t|udbBS>gLV{VNBmis6VB{>SC3sUdBrFU2MBhj z0wj{=?Rs=Ib`yG0dZGL&LrWp&VF%NRM zuSZuyNEV%=jkERz8GaZ-VVkCUnV{HL=jd6EFtyC*&0u!TH^GwJ-$0P31J$TwNh#tX zIMUPtd9p}{1K!0bAmcy`4fFJMybpiuApt1Dl3tYtv^m6dV~g}@N!Sh{ok@SR2<6x7 zH5+CnaDt6AmlnJuX#OJ=Be(C~DCpYt-nI{9j_glI`*6}LzzeByX?KDcMN<*+JWN8T zuRJ2YuN|~<4L}t!fCM_xc8>w>QaJ%Orwot`E%~L7?*(06fr;ovz!j&1k0JcbLm`6p zXJqLS!k05~PS&i~*K^0S%j`lpkdnv0A=oKriEBwOY@|=b zISAzA(2x$(zSgSV>;BH6q4x7Ynvd)X*2sG{nO(!>p!rdXFDr%Q;1(9dcDiB(>p?&# zBxNDyQV2XOF@5X#?nGE}#o;tqnL-kqYDr?2DsBL6g$$xTTNg&1F7YqC@U|_8&j3#v zNF0ODCZ!@0*v3gZ#V_sB!qb)jBeT<@knDJX^qGbsQD$*Hx)y2gROv|};2S@q63-fKJ=1v?A16qf zr}%I8j7qjm-!C4(23V5seC@jI6Mcm#n6*0+Su3qKna9OKkDONrP(~$4Cv62RWPekv z%j_P`Xrf+(Qpy%mZUScpj@llEEHyW)49sOV*GtG-^hbfV)Kp`B(jkNPIl}RqIHbA@;~{D?1UHRS2%*I2+osgk z*-rlldy@CHSM7*^`=E52+Wjp~ZOBS!NIyGY?$};$0GA28r?u7Uv zP*is6d3t%Rhdm$t*ljZz4yb+V`2fN8tiSVsOVB+KO*f~LuO)iFzxlwPbqO^&*8g?q z=Hc`aU;=d=b(W-T%s{dzT|77WS=7wbT(0=9t@3-Bj>p$YQw-_(5_Jp4+mXHAk6c)y z+Q~I4PS0P?>s@28butlYLgqN)Wl}Iy$%NqwWa?|B+3tFolC=^*7kQ_fE68tB#{`LU zm7})G**0UgBUwK&0txk0D+-@XpkiAbVyVc+F1COPPA?eJ@iaj8sjhUuTtf!T&c$x$ zqjo0?Lir6GE0a1}0CrgQj|onX+L&4&9>sP#@nF+orIHrFz9QBVaM>uAbg}MB_0%*cn;D&aAb~=Y zI#kN(94Q0UNwBL&J&3g8MiJJSo@5HNnDH#@|iQCchnbU*RoPI^>61j9r?`vLjL^!7Wu<8ABjy&)`lCB zqiSFKjZ*L@Ut5t@+xLz?=X~(>3Ie`>3p|Mwabz|vBE<8;dcobsH3YGrS`T`OtxU6V zO;|bz$LV2m6wpGJZF7K6l z2xPlR6ijkZr-N%moa7wvd@cX>EVg(A#CgQCbNHxZGy%*csth87;hT__XvvNFI$tfP zlo}r0WU=RF2sTlw8KhKTH=3-3W_kcQy;P2F?G3JnX??haMAkdCRpD2GZ>R)G%Oiuu zgaZ1P0psw_^%9^T6pSRQS)@hpWqa)RorC->kGfC?#UwOZ8RS02;9*W_jZtP==ZP{Ew9NT3 zzpavMm$bUxf+qsv(0bGu8*T4n(3C(&D81Z+tqZgz>{Zot8&F_0 zLP_Oat_71PX1l_$Fo{{;g2j&Xg?>L`J7$doz8}Rk^?b>1n`}S|fD}R7f!iXw-L}o)2=?4 z`AC(Mp#7uGwiYT2SI(Ogmk!NQwHGW0#nDTU1FtJnJ>9Kh;DxylBnP21Ry1#ta6kff z!{d+{mN>|GSn~R|xtH)mE5xXfCW~hx3 zFxuA>ek4~jY-|#uz@!oQ^oACYaI(d)+)rn3*S(3g1-7F9z z0ztX$vdQIaWbo8h*4U}CXL_(HMrF4aL)@gRVBvMz;w+-R6FH03fRUqwAGe>F^cU^r~FAcb9H|+LC!UA7EA`e4b&CBZ;s^1X1*4%f(8{DbmwfqyV=f1 zo|OtB$Jb<@RaG!x; zb*j$*`NXQLAP#{}jdfe{ef0r(q00~&BzupWmcgIa+;6ar^@Ux(yz@V6vZbS_wQNzB zO)|_e-u4>w>B*+@pkUj0w&54<24OEYI3w%_Jj_hDhB2cd-m30=q+By)ZD5M);>vp1 zWrsJ7_iEFu1Q$g;U-CO_Zt7A=qgSs|775%fpIp$^bcR~F;bc6ef_sVNKC#?R%qxPL z5WSIn4DbvvMwurqh|YJ4fF<}LQ-1N@W@d8Ss6V2qcAf988G`7zL~VAVaH21?`n#RfqK(-08pAi z*+G4PtkccKeC!elcoOenSU{-Vz+mmcsp^t+eUPhZngQ7u#Nkj92c}ScA0=?Z!)t)@ zr0dbO>=l?VSz!XU&jR12(!tn6}L*q11B&r=WuTt*@OAUz@_E4U*M>^G!G`U2SR2m=kYvJ>!7!B|8~bWC_y7k zxuVN5bf)nSZ?L>1+d2|K8^m-iFgt7MCnz%r*pecGQ+Mu$^H9dF9hKj2;`T_0$&_ud zI9i__epLqbyCLn#?qycH&+ zgUB)CN2n~=TFd#0-yxnNNeHn=sA!ki=NN_E2`N2CfwCC9T-zMmv|yQV>XdBCNjqQ} z$PN`uKBUlX+gHu2rir=`3-(DJmy}ovc4^5@Dp`o`{->GW90FJ&*l7*z<_Bq8DT9}@ z4T7G^VOv}os2L?lrt)P=+j1wt+S^b60(^tyVaV zaz7Gvrz>1TLNHkyNtYRz4z+SW=%Ozv{zs}mA)*sW^Ab!T=OY`}urBOC2r_BH;hqoA zOz?x$sgq87P8gsMg=wM*e>Ux0jikJ z#rvu9B&gmF9kG7{cJOcFTGu+Vjo12-8zayVfL4mvQ>@dH-a8Zi*4-asHM z%x5=s)7Dp$7eKCU5|(lS6oU)sb?@t8cH;coYO+8z7x4RtmR=6W-2rE_Hk-u?wCZ%z znCJ3u53X=M-lYiQIFq22ld-I=o*}BGiZf9JW@buLjc9MYFTxMXT?^D@d_Px29EsE%%tq#VKL2^|@{$Il=liKG%i_nTSO&R57rKAn~x zE74uSg+SEuV=?39-||MTbsE|Uj(|cXlZQ&#BmF3&OVaX3y z6J{8olo4--R0TZD6Y?pMRUq(zV(n)9$n%m(PZr{I+6%i2o{}6YXRCpkWIGcwpKqP+ zLcpqcisMxz@7n4u|-Fw^IJa|B-LE3 zx-ZDo<4D`Mkf={^ivq4`>=Sa%&uM-TJSzh zl9FKJ2fD?kkZj$Ur-<`GNCG`}iSn9tt8H6cp>Pj6fZ*rAM96e^2&lRA8J0T$n-6dO z@du@O_m7E-0G%6;-c^9`-Y(kLaX3!YKrY{H!@Yc6O(8uU9Fr@lS! zL-z0%)z74IDHo1vS;B?6TCclYLtY7vT`SxE@x9>X%#6Atd2iPv?EJ)ID=yf;UbjSL-0o*9lXAR<~RxW&cG*@Y06!g9J85N)%Y)fU8! zFhkzl3E_Q-G1=cWnKUp19}}i1){G>mR>gk!l@(_8i4z!938yP!gQpq@A2I=wz-CTr zh*W4LpiAO|H2Ek}hzML?Ozro892NEiZq<}N2OC6yor8UdiYB7OQCgAQItLyzOZ5$$ zWu*`#C~s3X3u!Q+#=~F&baz7pK1(G|cg^_u=o$f>m|qPcGuty%bWEyTt&b7F1K+aK z*`$>q8A{Z}I#l^48ISu&YZ_8e26#4FOMy8ZL1L=?iiej&i|;trQ2A{q5cJ1sec#>elixS%Uiq{aV0c| z>0qiw&GiMmgD|{PMG(O|+Cw&=d3O8hVUfT-2BeB;<4&b(a9$R;8)>%@#CgU@$yJ)M zHu0?}t9Q7RjuzP_0}Up8hc=9pv;c$y z6e^jQQ@okAyOT(@^$=ZlBxM?wV2L#EM&F!*8?>zo?g6fu-cr$C<@HoQ7(gbb6RhmP z|8L3wz0|iO7U)`GK>H@^PHX;9e-cr*x8ce*>6(r1C`VEd*zpa&>DQ|Y+5ktDJ_0ofuG5jJ=STE}g+@#NwD$$C^@#e>NKg(4YsX8Tst8X_PzKrB z#?LEO9A9Qhf(;5*Vpm!e#B-O^$`@7pf5I_)HB!O)34Vg3yAF7tt=sT2}%EZcL=Z?Rs>j zAl_juV#>D##Sg9f@Q-pjThi^N*xWOn_z-ZfCbhwWzqNW2o zWK)h#Sl|ZRDJ_Yq%)ecaK6by;w_pX-fYZjD zJ{nGM7Z3PJRamta!P%#P1QbAotlKd0UkPOJGxTFHbMGSTz!2NAgN2yegEb<@A$Z34 zNZPaJH;l9NVB1dfO7FbYwy9yo6+jE=Ik7aYqNz>r9zU;m@G&`vPKzZOM>vJey_}3g zTrCqJas>ho_lT>OWfMswOQ%&+%w^LR2Q@Uv$BZjbgCQJ?mkdRZcG8<6*ISeu80*Xi zZ8kk2NgEKwJL3+?@SxS5D@wrn$x;JoeY+l+xD_(MNi{5XR;GXXevDsUe?5_xNO8RC zC!!%6l)7g7L99t1(l5E}Z?G`r`jD*IP*qUpuB0IcYAH!OC%EnpG(G_}Ow6}3xSe{# zcT3ehOsPcYgg)kzSAu>Z6_8r+N^GYqVoKo@bj<~cC2byT&mcD@r@M#M=fj;uO9RL0 zm8hwqL1zj?j|}2OkJTQS8?xcF_{jbi%I}j~9kfW9;><%j=|}9IfU;U$R*)XaEBo<8 zSmwL?Cc(?r12J*C9(^bldSxX)iiJLy$?NsqU2#5{r^v~R5(zA=dg75A?Zw{h+zqb? z0`AgQ7f@1SnXW0IcEdFuGrE)P`(i1ju@$_I?=7j-#NB?uXH5K90F0 zg1b)ng+#%E2I*)6a5|6jg!NyN^5Na@@F@^>OG0NBciCLaUPK2f0HSeIZU@0wyo5q(YO;@G1?^>j$5AXdTdxbP!XBQ~&*Dec=MEn;$DQY=W6M#5zh7L6csi_MSQ<6Ef)aLpRs z5WsnAQm8hvVNcw0hhS|Bn~EMf5GsZjP{RN>vnn8abNly^TX&kJ-U;K5yNZfzgnB3e8pg{!_m8cw3C;<7P z^U8x(S|nG}+pKsy;*K=boymQP0>52hpM=%^2vt!EuEoMoI~`iDmjFqluN@{)cgKY% z)ulP^Wn$1UWx9?Et|J%LoA`o0gpg$9+h}wsq#@x#4ih5bTgJLAf_~JU)_|lJS18vA zL$c6ulHG91@Cg!DvH=3KN>$PZb8w56c-mdmXAC=z*P}DT6RL}b%Uh|Z+U!R?_Os`=_lE&t4Ep=I1nO+IyXjcNzflf1_ z@og`y1s6}*BJ<>Jx0+3_DI zXjO3>h?uTcN;p2d$>ZJ#XPZ79PXVw_3^Shq{=g*x!TZw-d{%W^AuD>^&^BYdPC0s> zpC&bEeyHAKV#`Jcvst2SX>mM3d_+3eGjh7(n0E=1NxO;L-Ws*LS}tyzQDRoGnQFA6 zj0uOXt&nr@rX}plc!m`Pvl*prDMa$E3<=-}Oz zEBkBU5+)Sc;)zY>GWksNA=CCnPb#=A$Y@2By_f^HLVomq4?j&TeK&j!Gf=CrrkcWU zJR{GvE07r6!XrFgu?W0gWfYh~@~-$$v&R+&%SC6#6ojUGpR84 zi3TZLpgHU%%;+IXr3_-Z2Yc6``!k+aiT4&6=x`DnOWUA!GNpU#fAFk=y#uDRrIk-b zDG`uJ@3OugQFJ`zsAF}sgsOyW0%foZp1fJ&!_*i%9*oP#<8@WAr+BXXFDi%;iU0`> zVd+Rp4KtSxEW!A_Jf;VsvW00v>H-qmTuH}0JOML=y>t8qi3kiFAi(NENQ4S#TU~tv zpwG%Q>FUGA1_@o#>n^K*V%v1rWb;{Z(W>ToG~g)4qXtiio0DiE0yX$@bC%)$AV=7? zGuY;FhVF+D7x3HYa)6kHz&Y*$79`Q29cF0@AqGDqEO1ehzd^}ZXoKY5QTa}5kqW`08%v#!XvR!EDVhxnrV$Xd zL$W9`nO9B0u|4yJ_Ww9h01OjcZ+paR$af;%MvS)ui$o~jm{BZQnjiG2U_ju~5~dQY z1NRDPk=Zk_kcP*aj&pDGf8%ON5#5g7NvIr}wQSo{3Qsx@%C6m)(0yF7{kRl18#dKS zham;e`etMVQW)cpj_{FwkR&YTn-98h{P@k)9(!!*Qt=eiYGc>jFc%7CTT-~j`mnU} zf4Ey@bfULr1rmvYPMfhmf*ht5!fyXEfkcz%05kzys&OlCL^1uaT_m;mBxFiZgmVXgP^qaYp>ApGE@O~* zE%?lVMOi=|y21f60}{@2)LY%%DO*}}J5v9{rCK~sNMMM3+=zp=V>fp+sWg2Cq<4|P z?iaGV*GlA!pNA8_Ez^o&0tlkan06+6KXDS5^hf|mR-eY>aY92vWi!5{m$(E0G@%3` z&^iCM0%SO80EYb4RMGt zx=E!?r~cT9Cby@_y0^^Z{8qy&ruF0IHt$$BH@bn`4*-c<;(?hU662qTqmj5oPg)7p(-o%iapN^flWO88Xt zHq>r37Owv>( zEIJtD_GJ|z34#kL$`LU)=w7grS&7cp?kxMhux=bBR}(r2a>Nk_pm>}10d+o#W^G|k zY&SbG`Fe$Y8jZ2EHS%7|&=OaRTJHUj6^l6_8Ah|JMxAJ|5(AELdzd68RjI%*C(SCG zf@;k?G~AIk7FPTS%aJ-j@+)--TX-2@?CilXx4GGH$C&!~UVAl&RHH3#*Q0CE)$SI@ z)6wiF_w=Lcifq!QI%AbxHPb{l_hXB8wZ(n^D%QfA`dA7>h(W*&Z0-uG>2L>=^Hof& zS+*d+q91)n!ENvN?@?=IPrgGbVG-Nlx5ZUT`y-}kk_?(Ighze7f(`8uRPB?<8yyjY zG;PqV$|c*t=xHb5l932d08iFhp|dvdUWida5Y!+QuN%3O8pH;WMQQMvlK=JA5szfz}q>SBT?K!Ux+?!PX zmsOIDF!69#(+-uIJ9X2@pubU{&?6d%KPbn*eyb7tjGnTR8L;2&3^#dzErT3u<2|lo zJaHBHrKBN(SZYwYQ6bQOj*O~0Y9PtI1KKeoZSZG*jG7~#+(-M zTi_I4&-ep|j_(JCKN9gh%Ju;J9zkTOP*^HD8sp}gxAPTmVHvyIEKrX#q5_Bv7!XV7 zr>vQwWZg~6%ECjNZBe^o+nexuKm{Q!lC+qMVrZ~PVZjT4@HEn6H!L9?okOQD3stKH0uJy3*Q0BymUc76an&f1T0QCx$F~4_K2PJC#m7&4IwfIR7j8R7 z?2c@8+h6;?4gLY3c}{_}yRkJOhK~TUYXK=Mqtud3faj49llSFR=HHuVBq3PIK0x$4 zfYZ!7TnG{qcL;M51{9tI{s9ze^P_7-zz(u9azYVHqNRG<7lAf7au>yq)ucy*MgT3A zZ6Uy%T>{A=u~-C4gvtllZZPcwhiu$U4NiwjQ;D39KAKvuJfnxH^}#VZ;!xH@7!2_A zvEvB!bcE`g*m9v~=WyqeWSxqA+-y0A4Q)1;T3xHO6UlSz;q9D0yj|HF(;a{;eCe7E z)m!K=3*U|O$#l1SHq7-3H@Aia7Z!``>3LQ&4p%8V)-OS#*)R$ z7IoP_eDd`OxRP1++JK+{y<=rNPKcVv%)+>GRROj0{et5nZk*021U27N(^E5nL@xZpb z5e<%3JkKj4hKxm(iGYKlEAXK_tchf_t|o&HkfDz|4Pj{en3fRU$)oF{h!tZ~0cMZN z>x~5cMm8)cP^d?t_|^|b^*`O~T^Qks>P*N~w?T0kabVM-)c`j5YLisv{5Dhn9VIs$!BLEHuy<%m?e7NX2xvEEKs ztp1^z5I%sMo|LMLYF@2wW=u&ym<2Ge!Cm1F4>k%1pa+P+1Cs@vCu9WsqUO`2HrwkA z5jE^EWk+0S(niAh>fm^!X=O2^PV&8V@%xAwZsmcO8uDM3M2`8^cG;o1{V zbG`uQmhNdYSM*8+RYZfg6o?$Ve^vd%fV zpDGl+DY_J*yu1WtblmhcK$bg*F0W=BVY(){KHHEoa2c%7JnS+s60OqEO$O&PU0@m= zN~c-rj=rA`Wxq0^z@H3Eg$$gwdHrN4%_o`-`|BbqedawONrpYe=)1Oh*ls2_*n2=` zGdL9D+yfbL3Qi^}}5tSOr)rPe_AxJRjNa;v+u5|cuML#m8RP@l2k@`)^ojX+L zaH7);(5aeIUE_(H8ck`4&n0-ItJ_G@l@pLt`7jv(4h>x?(A`{*;1r|BSBiFu*tv8P zOld#qphw$Ihq=!qy3Wk(T?VyD4;7HXt^x{qQ_BcEjaCl!nsI#5v4eIpL7`II{hOi+ zLbY?cMeRs$LpS@mR+XU!FX5>$TYYmQ`A5+Vc-hL@3JLYNqOKZbc=O}}(_u$)sNKDP zrrVX}=u6%C@u2E7;e4tbLvIT{ta6(BrShL(cmB_+^% zC}Mn;K^lDZYJu_)Cp}Wtutb3>2}~HwA4V^^DD8Z7#e@wLbQ2hDC+3&KsE{5z@3Rvo zj6yV8{AnGoO5{NWJ8Jli)^S$}Nt&$%44OM2!PeZi2T???)UCS3tT8-*dS^YbYOm(^ zV4NIMSmav5#L?CzW=}n(Q*39h16vTC9S&cCPfwZzvX{mRyTdM(hDr0rOoc?1mUxJPh)H$!?U{M8O? zIkX1ep4<4^4!T_aNjEfc(De-TRJHBT3>YzWqAA{=cINdEmuus z!wq;|kyiry6)3mBGv!{MBg#(cvQQzSN~;+sz(xvw=tDuZCT&N6!QKqQ1Vjz)GAbA; zy|iAAVq?~;%f*7x))jrE*rV!=@&|UZT}C+^=%s3)%O4n%`;pwk2{9so@aYA+487Bb z5y&s+Ig<*7R(%myXZMec{9~ZS7;O4TucVzrkSIX2rQ5b`+qUiQd)u~c+qP}nwr$(C z-u+)p%*1SF7H?4%6&1CpRYql=%JTtm7Zufc_JFS4imOFsiI=u93n+jX^+B`;9EX(! zxNKMnhWhd9)|0wU25S}dbDom2(EVPO~Z5_F*3`lJ6?sKN~ zSDM7od01Q#D^UUH)X$j&|y1Yf}Ks7%^3VpgSZf?JdpvP zy5moh=jn*dqdB)W_=bQI_z_Fy2&P?sH~ z$kn5r#bUkwStaC~m-Gz(EbwL8wyk}Wa@`xxTP(AkRK^jB`cnI6DR7@)4s;ZL*qWuK z?oAOb$Qw>IkYBw5R6|~RYgtTyzCq8+BrpB(8s^)r-Thw@^}_=Cz{$JGYDJdMHnsUe zHu;BjsQ$ zDPS?Fa~@u=%}pH<6ub?^6e-tTRR1`anM@g z)izw-9pH^~sMzQeSlo5DIN-pgJ--aOHSl%Qm6T7{nGG*)1GpDV*~H!ofVwQycMe zew8K+VL&D~9Yr(*O*X`#9x|z*T)VBo5^w{Jr}-vxRvREKAQ&Cy2Q`%B*{(Ek>V4db zgpn}08Hr>l-xlp8$W~6;fI@_LT7PpKGuic|?2RtWuBbO@P?I2(I?>&Gl2^-7R(NRo zm9=Y+B2n2%+`7>4F4XBJNi(Z!OlF=FHL!ViEp)iC@IUFPOK&o+8iv=21G2}?=|W}$ zQPVBcXMdoJ`LElF4BU9R!$pLo6Gl6sn!c=lJAjP{5z(IuSq8!GRq-7ypD@Ee!a(_J z|2E49@=c*IH(!x$U%$0WHXoEL0ZG$kgk_;CLTT05C>gL}Q)M*N+cIo{PD(XGO9BeN zW$xLWvu+0FAxV%}ID^>v`4wzVak7K%qluJKcHH!}f^d z-E(CV75A+5lY9f;=v5~wJB#cdhz z9zDleA9Xp;Y8LhI&skziU zQEFQ~W(gZ1GM0jy_#bFRTT9TwHneQfGn1be5kilOY(LcOTp0#KeeZei(a&2v#4D(y z^%9k&=sY6lx-|pb8+-{G+diWu*Ul2x(jIPUF)|azVn~JepANP|%$3pi)EG}bM%YE3 zLo=2XCX1=H25B_>1Y}s!+L{XH;gYJ~yq2kO3c=W&timFze*22UVZ2A*X0a-}30 z=ND|fSWP7VSG9eO0AmZXH6wc#l+GEI@qtQQ^`N)IJuou_tjUL6eyF8kO+%lSEB3jI z39Ft3vLQ}eYQA!(n*cNRg&#Gj_+Ndfwlry;U|np{gA5i37(n^1!UqZtmp};&88t8FF0A+` zWrv@CLu4RQ9Q*XJ9o^%o29V5^L_TBZbiC>On@<~EBy^j^E*V4y1bzt zh-+^Pvx1w?c8}AmqA49=tzv9MxvZ)%h+f_W{U~ZHBpZpazrS6wt=Ws`yD$qD{v#YF zN48)gC1MB&E8@+oELAkj44xNi-*~8W`G$S$xVJ>1UnK&epCTpET~d?F;>dt<(}v!r1GKz)R0Kd!tgA|k)V>Zr_cT-P-c==tmW4AR9%@3aln zA>hmgnvrWj;Q<=P{r<1!8QscB4R!(PXbYPR*tGQhf~I3{&7LnPoDF8O*fg zdjFadzA`^OQeTPEjk|FTInRu_RjHV-)`>GLwVY#WIKS)AS+r{cA62qnm@GwGxhK|Mj- ze&DhPm(+vRVF;=~g#bei@~5r6ng*%|tB7(VvO^i;7VSCx5cewA(TT8(=3(YnkB&(w zGY&2}M~Tp`GKgZubHXorisiOu*bru!kuWy4z|=e{>qxb(Ipi#t>pAI*l!)jEJ;`9W zEy{QkdC10jvcDmC1{>~^HjBcbjVhSCm{-!l_C-kJ zhb7fj>rT_^+;}sjDNWi1O7e#?h8x?g=cR>~;}1T$ip(&bHdDi0{FW(@nc4S#_gu|7 zEMZqo{KT{k>BY|Q+^8PJZ?2j~thyj@>!IU!l!xPS8ID|vU2obHc=_4t8q+Imf4{f) z4zIKD)ml2S0^nnf2=j`h5<|!>uS3Lk^SvR`#JY6?vO(LiSjod%;!1zOg7*uY++qpX ztLmIwK>ip?!jZ(N@T`Pfp2D2K6=)I(E(>I+#BvAv5iFItllQ?c>sd$aAac5;g>8?K z0)fu+^{Bq6JBY>9xVF)p7J8JD;*4=0-L-YnBiBsx*oWb-BW5ivj@7I-d&Tl2a`?}7 zD_eJfH$a$!A0Ake`QrFl*@&*sBW4g8lX*ATA_mQ&E1bdtQQ+Z-v9!tWjtt{PgEas2 z1D~uEFjJreBTT)BzrtYwySP zd!xS%CW%)Pnqn-)%mK$o+)m{EOK9e!U-oRdKJD=hd8X5)vH=d@{pYKVeW-TbRTr6! zuuGB5DbT))Pw-hda0}&n56c?Kh*u0%^$Ar(x#w7BDpoEauY&bOgHuq8+|)MV-B*E3 z$Q7c(0b0PlM@PR6^TV|8cvBK!+2wIL{tEa@<>n8GooHEL(+=@Yr0)-zz}*w zxSb3x&kfp;Qe`kd(6C@$2UXq#nh!%`r(=GHh$wa6h->BRzI*LpF{)_ka<@htKYayh zPv;fi$L4%+I-xk8PSajdj)ZD~peM=zbV;>4GwWLa!i_;hK{NxPKSNLvLc#}>4@mX*pZNYI7*qM5YSIZ9 z@u{-O3H<~Ib1SP#T!BAik$fT5#$94pwQgjq;hX0TqyrcWpL)=Z$Il?8h5^j1$aX7L zY{awUAN7zata?nZ%LFH=h|OBQAHgbp=0kvCZDVpUQ1+`b%F_L`hKmX_x@sdq4NiKA zg;h#mY-G`01xvVXXtEQM<`B3*{DWWO4in473Ol3Sw;zdmU82iB(Jb;HxvhBVd=In^S5FPT&2{MQi z3OFODGvP%MX;3?HP*pD4b?DT$>r%k3$3=#pbce>$mb;Rt(#i`is+=za&#}=v%Pboj z+!(K44OwG*vkp84_t@lZFA6$9Ws8;ica-`TmY8Ycyz0#+GWS=$s_krb@ibE`6QN&E z0y_gUY~8kL$3p3chj=f`ZY(FJ^KF)=t^^LfAgxu2XF6Kx{8pR{iLM^tYaBFGg6o`E zHjaHyMsmiwtd31spVJj#8XDtilY!yll^N{0YO3QT)04kRNXlT$+99?gmQct=OvMYx zi8>#(Y)(~x+pfMt5%9K)YD#_;%KhBvNnV-v0?$gwO3SXKQU{W_z$6sEVCR@|Rg+FI zY<&_|+e~qG7~KeJZqgV0$0EhF7%fDB(*Wr;K$Fc7R2v1?U-a6hk5l&VgG&tfK(nE!^8RwBU#+$Q=MF zZUuw}E#b;Q`acefkbAa?wiff-#b=%Sn)m;!4T6Ybp&EC0r-zgsVSwyV)?oL?Yn0LK z;EK8w(!~WNF1eKnNr*aay8GrjLHUB{?rJ@@^r!!$_A#>y7r_@2*>pFE*28F|7!)io z1bayqVrGi^Ov=|-7qAT~`k__du8D%wdW7L9Y$Ammfgx~WrkJGS29JSS+mw!Y+F>!p z8@gZL=>R=c_S;H3k~4|UlVc7DXz;!?R59i~&VjSCpU;ma+6A9Ky6_ep9|Xzb>hSJa zhD^+%pyfCJ)LHh@Ivrc&EFzh zrD@})a3v56RS@jjEnHlj3+p3cAZwb8YmIoGArZ+Upa{Bj5q8dqU)%IraoAXGLLK@@ ze_rSvtEkw>-)#D<4_tyAJfB@8NSaPS$@yD{#H0&F@$EPxg|0%Ry`VkwB5OE*z0jWN zlRQ%n8qRx8Cjx_7rRlJT-_zjN&;!}`1+l4}_b!2~C3o=tYf2TIPFn|3^5_|0g+QUc z@pNe>^*(w5_Z}l(?Vr0ze-c1(02rYqSw86cR6+{_AlmqAMk`R{4v&G5Hwn1w10UJn zJ(s5%*4PBg12hHFtR6!=R%y--z<|AX`#!$MR2q}$E&mn)>dV`a5M{(H3hyzSg@uIQ zxNX_tFg#?H%gloZ3c*g$9v<^2NfRN{J6kd$0>X{oe0m#RU-k`9H5phI6d}Ye zPpxp$GaA~{m5g*}w3NAelBcqm+yciKzLl%LWv~jHL+80iaHgw}0=Gh266w)ls0x?2 z5(R}!6YkXd6GWUSSHI99H(LRANnqcuHu_aN8}1Vynrpr-G&mW5N8NyzTrnDsY|am0 z1K!@B_qP!FU?jIG!3WoWxuhu#!jUmH258W*V*oyL6(h&2ZP(7iAvt%LAhwv4+1Bky z-gte-pbuyF^t9QyG}s_8z9CqaUTlG!ob;-{Y-o4cZo*uqZD|YN!nHGv&?t_ulKEHD z>NVmx6GCH#&6Z-*C2Hp4T4HhfL$uQYvxqkCP%$btT9^vi_EP6#GqmPbL*;21Mg3Q@ zL}p$zFufi`pgU-9(^j+sp0hd0RHv?c7ckrsiFB3TRATNeJ2 z^ZgwXYTY-jmeV0g_P#pEL=sLvux{8mBBIV*rf#I{BEq*MMPWbm)^|Q`Mf~GJgr7yj zI=mdl0^6oiR<>P4KwMjUL+C) zvY|Zaf@FA*iA|H6ki9(;E+BNhwM~(l{iPb}1X*@j3+$?L1qT%DDS#TknvfpF)zX%{jYp_0KJF5YLs z&j^s}{t6`Wp+cgp%gDo8cTTp0mx<*322Ql&oeB!}*#t8qS(_1GTCYr__WKK`o!IpY zJKCl6=i351X7!~GVd7*-qWFoQGu>Q@abs+gk$}^^1}8%oa2xXOuArTYldG%+Y)vD<1xBUV3nbs`qp#03bJF5)|ph^V;~ zE*k2xk)W-=du_q&{CScjX<5Bv2D0D)L>Em226s0r6Mo}(8=kQRB^7rkZ~h7w7<@J> ztcs<(Dj6&}u~H)a6uHGp%J{0C9xit1o7xCZ;G5}Wgv0~31}VhHw3dTDu`-h8ej)no z2B&&qRERNA;yDsJy>o*gGZOm!?t0X!&c)$nRdlF*SI6}_8S@$$evLf|DsdI`AqnMwSdW=O3ws`g!zfEmPK2YvTIU9}M#9H%Td(o5E zOO8?noCUs73I7D5Vx}YwcR_WZM+4ISrC(Vw-1*bHbCAM5ng{qp9@~_NcF02{}=5Dt4swc;_i-1K5#(9^5{%daxN`WS$hSZ z4HE^KhL?dzbP&bIt-(>c^<|F_V{T_q96fqjzo0^Z?VF>iBd@@}CI){uGE>YW>;-7p ziTkurx*5zmTfb)o13zo()BzSmqw}e=#}>Lm9AdReLof{tAM55Mv*R%DF98!jQJkIw z@`?}KB`%+41Vb|mafC!V9zZ2KY%O`gtDRjQ1vZOBEr?~ID|G&vAigaUQD|t2m*mY{ zv;r<7Lk@hZMb(&`Kjvi|t7x|zV5>Md)_Ma;kvXS~Nl+#+p8C#zCoNLcI~l1Rc_ij!uCI$nQ0ZrfD!Am%GkJ zLGFx|i{-h-pRD75uFY409|zuzc7Y7sj-+?9@xRw~iM1&8`lx!;@o4{k3{p@y$ot<8>$$#@J zIy>IXpI=2LEY%0KK}w40oul;OENnu+hE)kS;E~xB(~yvS4fY%%~ZHxIXq~XV0sNzr5qVc#G!7T83px+Q_SP!A$k?fu#MtOxD?!BsaE)WnVS#MKLND_&nV7Ysb=;Os zNSC|7=s2?Ebzsfqniq0*oPtfld9gR<%^^-CM2LYkw|d;hIXXeuVY8Hn z1jnD*`(Ce%WD1dKZTJCFJo(+*sc~SdPl+fnZTNwC9G>Y#PryWwN)TKdNTuQmxx6z| zUBxc;4zobP*o}c3k!j|ZT6wF9E1b7t*%xUIV)0z}wt*d_Sz263<@$#DfR&J7twVc!cqK8ajg_ z5(4Xvz<1K*DF70^6%&h>d$NvcA!NhEVNB@*6FT6vK(b;Zrj4Sx#;%l4LfOyr;>ugk zAHr_ecAqNQZI1fKKr6+Ims!6#yTgx0|BQ|r72D3Lf!io>nF}H=-=90+9_dX(jGRtF z^X(E#qx~+hNtw(GO{r*Xr;}O;xV5D7;+`y)Imp{Yu>#y9D^kw5FW1vRW5QXd-@gt;V3W`)BOWBke|FLeY`vx2$8>KIEY0+evte6uo;ML@>Loi!sgQv><*p(HNN-8AVD2hX!p*FD7Exaqcpq*=6wBhFk6e4M% zf2uhMR#0-@zNGR}XOJiEUk6k>rUwm<&`bhw+)@2-;Vf(jnbjqmM*>uIK@Pn{`z7NhOewIhq;txf&7}q)tT@7}q+N~Jp4ghMdox#zG zTo(uzT_ASik7y~UNwD>b>teFP+WyKPgf&1T2Kf7F|7v^1w@g6AM=DPjWOkmbin{K$ z(@40|zlVXK3}s5-mm=l6XlW-f?#(icSxODMGf`gmSlT2^>*CRdb~dbkp3UO>yBA)4 z3e2YEYknOyzTL7hT+igN$crw-2#+icW&6B1?~}xwHMdr&DBG>VwJ-G9g9(2C(rG&O zB|)Rltk)&1^}acEZCSVjt#vNY^h=#}{bT7#k!jlaf%3s< z1M_SijZ8u8_&DDH!d8N`xWuyLByx=;QjRd&dvQREFse_0jP=hauT+wa=uVn?^Lz61 zgD)5I4(ujqk)8;u*&YLw*|4=pKv4CT04uR@K`COkWLYZdr*Bsmkwqm5^g23%J>00F zpvn@U2g1~FF2hwe-BSL7M(-alQ04DJl|RwZZlCS<{LS7L%u=bCv9hDpXEn2nP`{9w zEU(8*0`%tvEJGwszQI-BW(G<_AlX{%55m0&ZaU~S1$AcIg}}?oi&mUxdToSt)x63p zh3`i!atUSK-D`HTpBzm^8i8Thu(^YouZo~B`DwK!04snCwiIn0T55wMjc6OqijkjTd zE8EYNiHHfC9Skmd2dMIDVw`EiQJL$><(;=pnjv;N6>ev4hchCiCDKj_NQHlBRc-_~ zFUyh;c<63_!DA74TRR7^_ZTV$WA~iMy?{(1FHm`6uD5>{GW~YoxSx)%P)}}R;&OIa zaxoe;=$6JYVL_rjaN1H`cB;ut}0&4M#aO}<7vFr0|RDPGAhF7Ng@{>!6)G%>A zr7W=E>!En{BYx%SNiG>dZwh2FJh=99{7IdEdQt9yxtjT`S zK&EydrfG?60P<7C-qToTT5ppQl=zvTT|wJMr_2Lrpz-0eX+rW%0~P*?HSXE>bMfu= z7x+I2@c#ovdx8()Y6k%T@IeOvkoliN(Tt284IRww{zC<)b8>U~e}cIGZrU8OA$Y(3 zIRcggRB}D9mc`PWuVP;V>5K(C{0Ezplo2(@e!b*QsA@J+YG7Z_qnJb2Wae?)C*;I2 zCl*yrRjOTaE@sPAO+I1bbOatj%M8rdI;q%NAb`#mRXlZV&?=v{0M|U1;=4SWazzuK zUT*;P+3=!pB8_M!3CkZ?PxfGFGUR6yaR|`n_sW5+4xM*};{I@8e;t~Oa=1=TEWi22 zY5qvF$VP+L6;aca{K!y?C5DOGG9e&-jz?wM%1W7Trde$$8w$)Cj5f?QN$i?OWu$ImzMeR(=!fhNS&Ox@@!1s{x#P!e&Ra}xIkexI2`mC@X=7@ zKFEC$JA*bDx=?94?KV6ud>a7vAng9=eMUE&9N<_Yk2^0nz%B$G5c-hWJ;ygVFLXZA zqe1w+&^zKcMxYQsVmpL32p*6mTR#F31i~;xrXZaBz&X(~!Y9N9C!rw30SrXpAwk9jjzjy5Ji=GUE&VWXfvs!R429EVb%} zj&-+6e#`l+=V75mpTg}DwR3L=vJ0v2g4_m^M>>c5gp>1Kqh`ePluwco{CW4GG)}gU zh#&a>giok{Oi2I$BD;OL|6csBN&J5rJ{^qpjjWCTcjUDDi>3Xa;p_$t00{B}3;^)o z)Bhd66L9q@yC?tvw59+6eP`Me)TZF}DyO?SOsPdfk9`h1>evhVsl zck=hVA0(c(-tzPGeD9yn&icL|bv_(i`r5|!@O)pN4?c#Ty4v!)e~<3^-e3R8Z5G%4 zeRzD8r{Dcr{qu!+D^BkHeKRV2Ixd;}{q*+p{jS{OR9Ug`PV3p|K+}v{Z^;<^Zot$P=2#?;hufcQ>@hI=WYM~dlB4g zG=%0l`(b&rm%qEqm+MXvd{*TC_aqb_3Q3b1RQ`Q%vhxEPjI4!o^a8_sUWLoeU=>}F}TbYh`q*u*}p#PJ;mf&xj8+vooA z_U;d3(#L44@B91xajovZYqSs6;^+SUJavxxT2VOhT|SoL=j@^jKb;((pO3lM?tUMi zUs`-smtpRn-CeCfJ-&D^KG)+w!GDj`s(uyUd+3G%sX+)oYNl|5Q3owc&jkWA=*mE+tF{u|e`@$Oenh>5UD_Uq z0A!4ldB7x!0!+qx@B{^M$k!Kul9>w4fpm_^wR5NIWbdJJD73v$?75TGZD|A>Da{^) z9Cc`qTf^xDQ>DrJXWzcZh-`j9M~5T-C}k?rHQM(KKw}d1*f34h)8|S+2nm+7Bk*+6 z1!&nhhg}97DG(dYLu@PAzNaxpdxXJB<*D{g8Y%L!j`I1 zu5^c2?F_9L-aRGmhC`cw93fc(kHdjt>rHpR|rlbPtNle+UIJkTQ%XL#=+GibB_)9corC>`nTZuL|Uy z&QFIdS52^|P_a-+=p(RXm~Ar-fnQi2mP09v5E-=INCRh(kCKHpP`#;qw^b=Im9+M% z8?gqJAFp7N3lb_!Z7OIDr}3*0RlbBWz8Kt+DuM1V%G|?LywS)9o1qLE_AP2rl=*zY zKf^HPYFBC}d8e~^)ZNLCU^}`h8)g3)99C<{vz~ErP%d{bVa2SVouj{e{zr?RYQX;c zDkx(Qt)j|zPEfN=1B-P~E*b34J$=nc9V09_eNIhh*H8_Gc^TzDVi5jIhErpuP!UDE z-NeWS*7tr|oczcYa^*&Ye}RJnDq5u6qbp(`|6r36TsPj?qx~RGCb^>h)RdF!QUW=8 z2I|BFi&!m!0wG3-Q7B->j}!fw_PF=*?eYZG$C@JtrS!F5k@&~#N$al-D(!@rxh1p&ScE6%bF+F;@!PHn3$a@21Z|ulWc#bx(Er z!x#Qf=Nc>hM=lA31ko~hk2(WD$z5!?WpRW^P-ywnk1n8peVG|Dw)ja~87^tAV3$3o zQ!9Ucp`B{rSvk*sl*%RSQ^4HxR7o6jk)$Gk*!RI^$H*8-HOJO)@y3X11m7m7vs2=Z z!mqwG%vY8fvxv6X3trOsAa%)&tWC3lB1+6p%ikU;#VRZ@dY_2K(LLlU2-|0-*hfG5;b=vmeTkE8b4O!~>j_9kMLg_a zk$1KvWatXY8rho6MQ|%zHe<0utY5ATrC}!NIrW;VO(5uyi_>wh+%yInQEvLWt7uoY z)*zMG@H$lr1ty&}O0us!Of759E@SQ2%R^nWDOwy8qDa32_`0XkIhJVckkb8wMV28~ z?)LcHMwuYdLPPx1Z@hn_Xzs73C!5^4W44aBIdxf#H3*iNv}t5eAX4_vNA;bQa7Gni`%(-vW~^N&Gu2VhRR z?)svt$!jOcV7yt&bOF{1mFA1RkG#$j&r8YUhfqr3E0{zCB2-7^i$7cv@yA&2qF~X1 zH=9atS-;9aRSgh?dTT@CG5yWyeck?%eqF^$Kd2^pn>weEk*?n%W%1sx8sj;dFopO8n$8IH8Z#?QSlWQv zw_2J{Ibg=~W&JUzzhVIQ;sTDmPT4>&uOAJAtgQaGLNv~<#%B*y?|H<)GK*TV^G`r5|LWKJcju4*Qz;rv#=luDZJ<{x4fsTrRw}JUfgQj^ zEwuYsVYJ4HN+WeX@2ta%p^21eMR@Q#i1E$GXm83CwTqh^WkjxFDR%E2lte9cx%o() zCe8GB)I&h%CdJ9;phT978*YG=gX>}O3LY_F5Abj}&Y*)HaA}UllIzDyP}M~hKnE7C z1}k6s!8(*XwT3GvCuz^$JT$#ZV4h7AaqIDZ0B$JHK-A)mz>*XI9vb;?#W?vb>Xg86 zStU_@AKx^3|G=DAeGOUA%|B347KsYe1?`|zo3KcdL~JAT7IYF0N~HGjP}wOB5*9d; zROYL7Y+og=z%oEx0LW;{toi4*med3Kymc`UAkP~c@Ei%~Y2#;D+oT%}#(i~Z6=7EG zfTC>PifamKaujs_)ioB;l^REl6n7$QWBKFG&msIGE zQ7Y4ohLrF?TMeqvrd>kzzlZhtQ$(tgsAjgBhr-VpR-rKEI?()5mj!DPtC|E9NLq^O z((thEC%-A+Y9=~T1>gcNYpHMgV7VD6u=>U!94S^6pMnyB3%zNYxWu47#cy?@{4p=K zS6?yiM3X?F+DzJ`tTO7i_{5vf{SVR{GsDV#qr)*}}E%#!xOHa=`VC5!Arrsf7N=PAf%a!H0k14j`*nG9j3yMFR;8m zwL;pmMbP=>s6-TEn`b? zSf)$7AyFybq!*W_3X3h66OtKe@!O~hbs1CjgZ1rZU0kUW{0!|)wz$1k?HvZD=S>FB zT0KV8#mZgo9pDm|v?qf91kX#ck@8`KMyZ(w_mZO{87Lq#)TI)Dto(wvZ^tesv&Nn5vGs>ett6nTfu;={6DOh14lrtei3}2& z(sEdNn5&*3ELHM2RGAmtfoBoOj1*;d%~I1UCk(NgC!bASF|JeTr1f#IfYi3*&N5Li z4b3XSgdWcyJj|(84VQ0ArwV7Rwvy^LXownIzfJwiHrC0Docy9EAwJ!5K+}3{%Ne^|=Aol*m1(O)7%43V%-1Lycj}v=3nSj*?q1?6;1T>zedP zWXZ2-E$UIemZy<0hH`OAC%cN}s9!YWrG!md!JX1p6PH017kA3*=7FV;!*6%{pYD8u ztGh3Z3S@-Aaxy2cWh6RLi~FXYl!13-@P+HG;9)<;TQb|Vr<@@OkC5rGk$Cxx0;zDB zEglchCv$-Bg8tELW<|hTdH|iT=rvsUi&Y75t?%Zaq%uJ|p>Eh8Bggp-)R%+w{a4H~ zV<}LV!!XOebXW~G$IIi3>BIb_*1`xKh07mkeM5-4|#ZR(L^9UoQj-`?Bbq9 zwFYoIQd0^we&KZ}1N7-K-*n^nd`HR^jMNW@b zd{zG44O-*Fzr>EG1I(?r#rRhE*DvJ*(M5$E_YT_KfM||JivP5)z8cm<-%GQKXR2~M zznaroU~RF-LHv-_OTSUFl@Z~k_)(;GqK;T_!dZUA8}VJ?V_SA6sS@p!LmQz(CvR1y zxpy_ZQyxma{Dt^Qh45mbQ+aEB=E<5{nemDkyGrzHMlCG^dg?ZzrDD>zde%|9VEXqc zF=681Mxn0bP6nhWmL1+-9)^b^QsXIhadsc(u6wNY;snUTQQ!oWu@rF`%ZqJL$^}h( zaV{aC^ZBhMLy9(JET>Ox23I~FGS`3Xg;kS`ZDdlTG!KWe*dkQoK>Sg)K?mFHM$wu!iT-x)5A?YP&b6j9WpL5E#h7 zbT5j{@^w}m*HT-v&_$6N0dz*FXrQYgn6V0f{Re37{-EBFsyVAqYw>c0m2DK+JS-S8a4j1|9z(-8PCkdylCeedbGov=K z3TqNckleV|mNvk@s8w0ur7?)Hq1|x0_1W!?CwKaNcHQG?VWOQc#_e*dzl@izv89!d z6|7jyBW2e{@}BZA3F}_9IFd@NC6^0BsQSVn@~P#Sb~>6Vbe00Ds17RS=|KJ?gOH*k zKBGf9hDR4<(kP-Je5&hd)U86M4A4@U zjG`|?65XG)3}zK+lg5>(k_wiRS>##|{F~ue?L3a!j<_duO++i-OE6GALv>hsHWyJ=%_jS89V=!48ZNKInMQ02Bs+p+rQR*ii`5l$r{?k@hGeYZmrDOl~{9e7u z{e~!KlQb7~-V0US>sWTa)L~yg8w5n6Bu(3lF&$(D>Kcx4@i@-iz=2gov_5{SR* zJ$4TMr)a2T>o3#pF+pGzLG|u&Y4zQA4sSxU_)gx(SASi6qhF2`HBJhgXlIVAKu~pE z0nIAl*gH^GHy#6OM`pkhivh)CiDhJnXfHrZLz7@eki&~MeZKPMSKWQnpM_(^Fl7#rozF5B%3OD}c8dD5EDlGyV=w!iJY zdqzIEY#78{RZ$G} z$Kabx>R!`TTZk0VgMDg@+e2^_XO*Ah=0Xo{4$R}BZ~G4O8Zd}UJ1tZN5;IYTrRCOt zKxD_=-V~R=fX4X;ZS69P^lZK%E_7fwFnB%TVzfal^5y+YlSK5jvQoz+!J^yZyTDWc zbC+uJFHT{7DDRypY(m}$oS-0j>;(jo0Q-HBo*?N zk7_o;|JF1vaFvA2N$0U6HtEA_4BqUh04K`&O|~?Xqy(4+O7WRPd; z*^~AOrU>bj_t$~b*?`4!eM2idBpyfoBmsqnAo}NoVgNnzSOb|rE=zSsO6WGge2YmB zd3Q-U{0Z{X_PpT$)q>DnboVB4PoqCViihH{U*0j?!JxQD*rlKIU=tEo@D4Q?Q`-Zd z&jGfuQBgtktr2h| zSi>&i2fV0G&@pgLXyQ~7Qbu>kx@lM;50Cb212}ttAnO(tqnX;*2^Y20Zq6KyDyCNb6^#jXblGZqd5kF7L$Hh=4VllIlWi! zEwK-<;^oQP1>EM;yPEIxt*p0`Ai!nX46ePT6>fIa#SyXpV(qJ9>k6Xn%*@Qp?8MB> z%xVnVG41mHO0HRr~S!Gt&8(Gizo`qkYa^Yp(-xIUgN7 zS$h#!?%3PNa&~qBdXrh+8+6 znP`+ts}01r$+PWsR}aaxO`71eJeEmZkz-*Ujmtq}oYX|;7SM-AKEmyP?~hM^Oc>T9 zn)M2*Z8dh1_KD6M{5843{VCa8Wid_}OEwlQgoMcByxx{`RqJgfg9gwgto=(^JHk&P zQCF9kJPLNi@`4r=x0|kFT2`YU<{wG!?UEeV+BC-P7q_nt=ZTojJ3H`WoO#;LiPi9{?7 zoup~yNQWb3zoq&ZIK6N~GdMHfN<)(*g(>v9f&MV2audUfS{9ppsGj04lcm{RP*EO- zPR0{YVrasSE}!6uKk+v94ahPQavCl+>-Ru zVAnEhdj~o+>udK3c`>k+Sadgod~NSL(`YWy#pOW5-)R;Qw?|pQ^QB1i5((is*Acqu z9mW;)_IOWs=c0V@sS8UTqDq!)=eOeWABZj0C}BAB8s~szz%y2-(--P;b@dLo(nJ0H z&q5y}Su_ECX8-KP7RxS%T;&gUw{#kO22JOT}6flt?Jl1)2u^?%`ZZb4rP91#K>I(3i90CWx8=k@xzv4Czy$V-%yKNT`mI=-~JN+ zyu^IU#WGJTo~fG^8)4>M?@)n|`Ej=qR0Qqod0Azp#T>+hR9G2e*PSt%^WD#C*>gqF zmOi9DLvx3rAS`CG6RrR%JpbruXZ^M zM=!)?Fr1SQ!dn9v)eY-sA+U4z8Q>#f8LvyFN}4i@)TPzPZ64KB*^g=h>g=;ke-g3p zP@405@ZD$~o%cCf1T#&h+TlN^HkqzRA3Xhh+_ua#*dCG<9T$}V!Q#FHfP-61 z6x(!h+K!Ef5_nD){NGD8CA8E>tC1x)gBbYIFHheD~+8wYC_W9ezFB;roJXr^>k@Uq1Oe+a8`@ zy!d`VmXA5UankoYIG(m$&yM|}BvkU1@jo{dvQu|X_OcT8S)23RKGY+O8lix5I^4lBNHm_?Wbv?CP)*; zVR3FTTJzOUJ0Le~jx0)%`Va~`v!*xfEl0=O65i})A4$V}NU)0!>q{HR$s41YgX854 zVh7u{j>?3W;6?+>Y}sPHDwP^?7uRU_a(Mvp9JFK|m#LJ2wBHJ6b1^;=ZQJf~EGH?o zdKF%ky@GouVz1wV`sXwcgJ7M;DR>(9kA1^_v-pM>*Xg5`aG0C*4NFbjNcwLkQ#n3n zoWri@zKH|!0(BekboX-=2a0AbWEnAJCn&$e$9p5->r4J6sSBq~AOV4R>|zt(OoHr0uoSqZ4f>JgWn1%bNO>l*LffgQ=J-dZ zy51k0&k{K)azST&$<*RJwF-IB zz(N1+uVb;V1;^9$_d*g;vn)a-X_~Img02CqaJ>eY>kM3gs??`3w2Ad|^qTv?*dlgy zD8YZ^&G<4J${9KUv5p`i+LgiQbu6$Ac!0_+Gv>t0)Fxg>(;NQ3>=C9!C0GcaSe*6N z6LT=u!=n(V9CTpW=pLF%6E#+phEKZqHn<#{F#km4c4mETp*EtBJp!q<~P46|J?)<^aRM!u>#*yY5 z@J+a0mjDD6VQb>oDM{Ej^#X>P6ZQlTtvRjy=o`D8uQ=}A+wou zMC7up!Je%J5kw(XT0k;XL<@`G?Nx{C3yq}g65cE&jVvOY#OvY|wv;0*N(En0tH?11dsTFrqm2CS6xbQ@VUAs8;tUac zoB@FvpMwryTUl|vslvtcbTQi0W?WBcrvnMRh^+tMC?%0US<|+sp&fwHECMHR^lC`A zt8}S80VBM!bkIJI;kU_nEr6$`5S}k%E@KWgZ7-9m@z|CP*ub!0c7#L;lNIotDyP5h zfkA=rHTLA->7+FhQxAGcW{Y8$(ul z=+g&iSY)7a%&7dhlJ?*(yv^Q>egvn9a(5HPa;%V;7}+W3CbYN+aa}d8DAgMZ0#b5s zYQw$YpHkZR@ji7iX^;%0*T>qsCh5Nxkkmh6D;7N4e&KXo>YCl-%i_yE`y+C>fXYTft@S2^imMPKs(eFMIu&u>R;dFTJVUMSWUUU~ zTxX^{Z->_?tLZ&fcl9B(yh>=cD!jVTAGGq4;R!J28f425^q8!a*iqkPu-NfMN{@%CxDAN zQ2^K*W4ky08xJIB4fm)e}AUhP6JvJ z-9-FM#O21-K(;~fwUOHb+r-B!12RrBqjgEywVMeT?7XWubLA0LzkUAveUuA;^{KaS zEW1ye*=2axbZayc#!!aPH96`STsyOgh9P| zRn!s$=YPf8%+OP!3DT~n=lpAzw79vpGaMyQQLBW!Sk#|xobCPnXMI|)~jpO}OXbHNPPL{)b*1QCJF_ayFqT~vg?3x#~F zM)g7xs zw4lJvyp`$VdqxElvckz#g^g2OdJkJaP%Fsg0*aO2=rSYtz_9n`ldFO$KUXE-M+vlV zV!Gl0VJ^@z(HdVaIg{HpN;)tA`=j zsG%m|WJ~>!K(%Qw#mkbd2R{!j1@O@XQraCt*>T`^oj^bf&V7#Y*Ee@eu<9T1c@pL}o7z)l=t*(oK#VL?&kk1TgB~9(v2Kb2|-SKOb&tIC9%msI1fu41i^cIFP(ETo!+p?ivqVTV(ExWE!n z@27KqX2DzH0LCL6dAe?8X+2Y8%5Sppd7pUipB?Uy47mk$!OZ4To0M`PV$1)TJp2F=h{v0(sI1aNl)6=<;<0d`5#`WN&R)-(A z-mvd!19SecJed$i*TZ&WdG-|}8X*)D{Es&>uvxxf!TkKUeF6#(-&y~PcR(tg;obF> z5l7b(G|v`{MM6hM7wnhQc=Y&*5NQ3YX;@57t(orJJE>#(A3wC-?zv%qw0YPJ5LeKy zc6PPgKFbdBkr}|R8Y)#BEnb;|j6c?B@4x(Is68xzn7?}G1FA}AzhohVO zKR)IEn@G8kq3X2DgB7;*g6*@y?TO7_kn3cD5dF8o2KwaO>_E-Qj`I)WuzR!KMNoDs zIWb2W7qh4IlMGxLLr>Kv(tyJknQ2YpNqaKrT*+dvx{L> zSUaS5w16%pN?Ci5NzKDdvNL7rluNJiYeM)+*ls_+VT{30`eot8zLjSqh-*uOd)DB+ zaoL7rru)?dPghg6v$nqku<&>@Ga?04zV*bbjIq9qNN9SReQ&PzD7q3G^Uy0Uv`#0J zbE7{W3gsIDvpp)a(Fl0bOahU?K7Tt8V(=rCdsjQTj7#Z(eZhDGaQGRqP z1r*BfR=*~^R~oRZ;79K!-z2w(V^4!Ek3Pp-E|yw0a=zZI&|dt0ZNfO*vFC%&0U$n53IDO` z77L=~O14{Q02TMi;H$7KHQ!ELTol0PwAqt-U~45Fv?>$(oqkWeXg`vw*eGgb{^s#e za@IuWc(_Vb+>Y>4&Ng--@h((&m)iO*rc>Yn^Gxhiq_%g|T}=T-KsNk5&U?8l?X=QD zAa9yy7c80*Oe(aMWX?(fAB<9Dq5$kO6=J{x0qwgq`@d%cQt*KXbdju4unt=Jlkc(M z9TBxi;J0{S0LW#YJus3{o}b{<^nXq~+fTdRw2ioAcF{jXr)Hm12k4&EX5$#7|FG}f ze{at(FdpWW!x-&c$~5wsOvwuT*rz*LqR|~Qg(9_J|1d1dm$+8NNxYsBKg09_qhfj# zxie+|`SAI5P1(X4MZOLcZDAEJF~tvYHkSHYZ^@@hp!B7erTS$ghY`Ld55EHhE1o5y zmS{w9{x+#9_DmgdEswGTe8+t`;2+!M7FY=C7UcT9nkK&g3o4HR`q;`X35^9LQZbG$ z@d$g2>Yju)1NoQon2y{PHO5KVkDh#yT7)z!KlFDNmMLzwn7Pc)EKzBA^gXbEkXV4k zrO&uF*V|vT-Xu{db*a2{|04*nV=s>+G+mM$)+JT378@AVD03=-ZXibA{xBMiFpbG= zXA)W<$OxA@q!jZOgfIQNUHQL7- zQgM(UDZxrOZw$_U$cb#~>KhJH@r-i7b$sG=d&mhmakzNHoa=8-J(L-xadHApUI|3H z2y5^Iw8>hK!cnK0^vJAL9MU0)gpjeEZd3I&8cZ_X{ z$XJYiR&s`B^w4tgh71Y=zByF@&)$+eJh~uQOzj>|kxNPg5D*y6FZYT+t2!I!A}uRC zq%ZgL4)`BSfKS!&$-&V=v{(n{7j$2}Fuw-A!ThfbbHl%#MAdL$U|pDCU@HIB%hAN% z%E{Hn-P*zUKdwg>6IV;i|C>t4AyrX1QyL@e=@Z-D(rfti>T)$qwj#Q4AOX&Za#xi7 z8w}#LEyq_tR@teqi3v5#N^{92pT|_GZD5=C)7s0yo3K!ZVNg)u^U)lz9dtJQR(`h? z__4S9anK#q-S+&vGtKQ3$k*M`;r&$|2fUhcyvok1z7pOr*!Vm;SN^=|udWsj=y>tG z^MCVya}Ijk*}J-8+5CEcojYsqCM9vo77qI8Ro)bQ5Ab-pzQ51U_It025DE(7d%Nlf z&aK>CEP*1fri<&Vxs5O0KKkz#9-5uJnzO6E+S++D1AIFIU;X*~yg&W#o`oI1#$OIz zKmd8ZgXZ@gyOZ+R)#CZIRqKoU>x0R2-7-n6uEUhM5|8~ww^9dNiBTWDpWzFdvl)tG z3?37Fo{>UKc{hzG!)CF)va(u4Y6BE)hsTu_9MY4OBRUI|a(8w*G_t&tW8`8fvtF_1 zg~tvB4IFaWHqPF_%)D3f@vc{4?CCO68Aav?nt&vDgzpq}kWdf?m0Ss%w_|2S_6J_d zK;&Mn)e6j!x&m_5zycLGb(k$0*`mOc4Yz0Jy1;yMX5r~Y{p`<|Wq2eJ`uu5#(D_t7 zcyCWqY*9Xd-DJIaS9Iep0lx(KghxS^_6gKHmu)BqYMRzWdO;Yn8B+pW+UB^uMKhOq zC5M*KF~E~KoYO$pVaDA zPuXZ9Elq3@QrRHV-{+e;{g~;qQxpv5n4CPO_D=B{9Vg`3zOZ)d0k~*r2uLtR$!HNU z?-y`9#_xEQA~=N5+9}zTB+Czpj@D~*V(`)kgw;b7i^&%H&MeGKnmyFvXW~Q60!9*ZZ*;0xQ;OXY{d~8K355QgiD{2jtq#jEb}qXD9x!# zpW8v)5qfk02x!X6l^UX~--1Cl^}i;_uqOj%L?SjXL>p9y;ADFXk8d8~)mP4mk4hW7 zDqum^)t{_u|FhMOx-BQPIks~n9 zhCO4eI9CEfXy57%iyVr^rcoR49wNmT!(G}KOgWmU9csjzKK|8u?7}4JupoPtJ!3Zn z-WI?0$@ywmeG}*VaX}6tX{q{lfG1E&T0#?7Bk~FOMlc}KP6U-2h}2N-R67VQA75}C z08JFQWTeoK=R6uUBRUgd|7c(8xghFfgAjCl4Tr&NYREna#jvSgL?8&fA#aBNJ8Ar4 zGZ7B2GP8{&ixsgDPMosAIF>AyFYmqQDt~GTSs6Ptpj-N5J|ckYs!9%^9lMk2IZ9BtY-Vk*Pd%GXeB z!a2SofRvgfWgF=mXp!afHd0f^KW7V9rV<&=GqSP65nZn1jjjhL+4JyspzET>MYYw! z1hMzm9L{sq()O?*UaYt-*!s*C&!B(^GKns^(vWeiZVeEUTct=sE}#QJutQtu|H{EP&!(2_Wk+QnUx$JjU?bHt)e4C$I_l@ zPH!wKP?}akTddu-q8nXPU(i%z6<$0Q+!Gjq$Me0Mv>bmP=-U=z`i)otos0KSi*fJo z;41n?`&_)&0)6|CmX*Q6NF+Sl{Wf0NwWx7Z7qjw=4?@g}gL%7~&6I|^b#@z>Mu|fE z7<*&~vpkb{?S-_)0c-j6Et>2hx};?38f24Z{(LR2vA+J2xt-Ohdf6jUqwk)w5Wt-y zU-@;V5!@v{Y6wX6Y}9C~f{(%fR3$y3>rz+s9rYWgkm97j%==6=fb3S5-MP#f9Q z;;H!xdW9AjvmE>q?K1K=rR)~_A!NL}$g2i0Hq?2$q4{_pX1q%bF*naXnUTpNg07_v z-~G{jCO4!&oWIV@9$&@KW0S37Z(ZQ_T=rtA&mo)-Qz<&KBd+%HLjL4VddI}cgi@#} zmTpWYx1H}1R=cf>GEZJLDN`nH`2iv1&ld_XCePl#6kA1Cee*!v%rExUFZRRoX496> zv3Ad4@J(<(uByOqN8~(lE^p<={2La!>~PEdhQ#0BAPA>Hwm}Ouwsh*4tsPFTB;3@|(G4lAIbE7Czy^!7j zv+dkR4E0sbZ@N(d2<&A)R4YsLxWHll^~Y6{ryuLJ5piZa>rU#~`eBJba2#RUwS;N3 z2Jrr*p|lMhUor4?%%v7iZL`zr@2T{Zyjn7i4<|^?Q$lFuAVR_N1$amgc>l3}6zOBd z(9<2JKl^RnbX)fdu}vWTvqLV}0)pu}uLK8Oluyy5J6(2yzWy|GQ%W~~p*wn_+#MOtz%-+P!&BoHk%;Y~b z)W+r(j&2sL|Cu0xEcXBJVn7wXf}=TnFffsSVnDh7!ir{2j+Qo79yo49TI85Y5#mZR!l!0*FI}NBz4W%`S@RrE6kR22UPmgTy8>qX%m~= zQMz1d1#wP`EBgU%JV7;j6}aVlwzqAdDU_(mwmqBS04|%Xwv5ZCs3pm!^7Y}DxfB1_ zx|MU(ZbjfbcEpqB?eRCz<>GS+{4|GbZTcr?n$=pr-|ew4-~u&j;l5_&VjPNf#j%r@ z1sFB@3f){2kljQQU`Rx>wqaV&s6J7@VjA~H38kIs%^@7#ynVH!oN9E-HA}ULRWTG)3`k;>sE|ww;>A( zQ1)vA$LT5p72noC;0^JXWb~_qHc@UPC&%~B51GN*csd4cJIlqV9gHdC6r-F zcdGa~a>cSS>_L}1?qbU;>fvgxx#B@Ytnl`x-%|I*`lim{npp8s*s}Yh{^RdEPp315 ze)J%LycvwB1MM4G+PR5I@0I85Yg-A4Au%vwkcz~ScGdFiC|1zyf(GSoO9&~T31xcid`zgduiIN!0F+p$5Y|Y zf`ZWhypZNf>oV!OSz>6%7%mpBGgQ>V)Tk6o%BhT!{#C&BAWZh&e(q40d0B+L-w3KY zA_Qwit{!Vhs{~NRh$K^$)`O_%12zie&Q1w7I0ZMToaSaOl8`^vsXB{5v6fgLa3*Axki z^%Lj2(=AV+l`;7+4cetfx-}Xlf7v$a9!|U8-nylIj8p{#qelP3tb75%z{-FLn&nh5be8oyL@ZX@?{&0>8>3jw)*R)e4Pc=DIsa>X%%G$ei z#fuLan7*fS)6b>1y|1(ieKh*mW9C3W3yh-NC4mHdul3&o^BU247LiswtF_U%6GwK# z>AJ(6B(-J76k)e%X5Pfvok0RdUlgY1)Wz;L1*jXkq$A7D3c+ZIp`+FB`km%?KEXoj zLoH!#gZ@T2j`f}n#Rd(s#7c6@Ifzz<|NIbWo7YkUnkFo-dwmM4P!W4-(tY?yPkc<> zX)tWtcoGs;yRLgzRm2Y7D^-?|D8WuPFPj^eS3LFct%K!UI8@+f@cdxw9F8w*=OpK9l$n5gcOzEKK^! ziQ{f(H~&kSA<_hdAb!#yx8MR7bFp|X9r=r=S*yR+$%;$)4@CvPSe5Q?mq}}=irRTQ zs6Va#+&qV)X2nS*xAc->zD%EBWDNKJx_Y=9kfZgumV!SRAuNAS8ypC-?={7EznhK! zF-H{VG>>eF<8=oE=Jh(3W+mx#*HLvf*e8#vZ@yj1UU)T}D-!le2tv3)PnoZvVPYJO z&sTw|&F9CU8o@|`S(@9BSfxLlMU@(s5E2EHMY5`BN+lBgrzou@9&XTF&37f)6Dq3A z6IlGm0^#ZShP&4H7S{j*QhOhFPVQpc$eFMAJ>#!e)WdYIG5)4UQ3NJykiG@8rtyQ{ zopWQDFQpZQvg>1#G&JS^14VHjEmH2`zkSWxt{^3u#G=C4W4wYG$+Stj=<$=e&7oVU7Ki6hIW1lXL zyt-8oZU8bac9zOF6utaRhn^Gr0rYGO&z>=_4&U9A%pq9wFVm+HIuR|(9aRPa8CPoE z25ft0Ljnt-57-<%YNH91}^fbo?Z-a|h94$5%gA zWdw5_{IovGg?qWZwKo03x9%%CYP-GXxPAn8Q4#1OoG!2?G=NkRh!ow`@S?u{_BRwl3n0$;guan+s*sVYvSkCAnZT*} z@~I+*Lu+W?e3KuN@fZ}oS0E7*oD|F~;4uiM-{kU(g^oW635vYSk+G*5k=5=OuQN4U z9O{O1v*=(ZxvhpVj9teaWHMQ9vioO6Ng-E=Jtv`)x8&?(rKn(R`FX|9<88@?jKLnc z>G@D5bN$M=SK_H`Jk1ng{B+L*xoT@SF>3t1+PBG;xJ2*tVAt?>y` z&j3dNXIrRQ>`KO5ATR$^nuyzUCtoicbk8wL_Mp%aDP9t;SCi;0je=YRPr$`-tP2r} z4Hl?uh!}^w3XZ1zS{HkF%0#v`_mx!9l5Y{3ed+O8*Hf50{#ls&ub4u_XmW<}mh09qD!6HFO*j3AB=iMsY-;4iW&=K}5Kxx$2!Kr@xla>7f;vvLS6iwGGw3>+Wqb!BKF zMeHg1TSAkStANEu2xK~n0n5%WGJP>x)r+kJ(kt|3rv0|omwn5oqmUYuB-)*%MU*x$ ztx7EY(LH(&OMA$@A|3(MpL2}A={HXB=NaetGL4`(Oz12kJO6lF5MKG}`}dflsy2|& zkcKN}Hd>*aB;95OXNU2*=Tek3^;EU~J=_VJwCLCI z(Dz&_u=uzvEp!I>OpsWu$r=7QLP&|np5YUzRP@gu1HT6)x;J&LcCK|>9toQDLch_$ zfy*PW^;!%5@W{N7p2{xy!4wT@M$p!h0~dKB9CJr?v>aY>Ww$z=`!p2q6a$ThIyldc zyY@ZKswWKT?-itwKp;6k`#efR6v3?9{H!;HUU*L;p&qo|miWv4Z2?sYQjpW>9hmar zAHe{RomDQk+h*tOw1Pb`qr-2(&Z_pe$e!BdS166?Qv8UtzJwVgPnVPUww!lnjfAaz zl4>k=!GS5WgCpJGY#1}I)4jqR_9waOHF1r+rf`$wCWB&U`!l5*g(AD2l5MMshb3pb8ej@4~dfgxHHtSe4!{z2slJ1@a0L zb}c!qJJx$p@N~l&q$PhfgsW^Py`N2~k~JcU_Ue3R^!$4KYZ#zIB+GtQ>dlC7^crZXoOMlOmL8uZyg&DI(yrh?mWz;L zk@1rgBl4%&Ei;$Svey-}HefIaN~!>zaq^N9O@6N8Jk2amySh+ug#X0c_qIZHqQ(rS zmgIe=QG#WI$UwW8V%9yj{HCJ1-x*#t=45rv=C&hM-+XAAPH_4c%NQp}16^H-q~dOh z?XzaFNB!#6Vig4lTpF?|#b%#HzJ9nr_Gl-Z+{C?A)r0JT@1iyc@_v;|ZVYE~CEq3n zD*=PJ+tjEiqCZ1+xV_JG^B(i=(~ATH38?rhFEz6u_c++-)b8k-*{b4|YoeWCIH7CL zxO8r#@{u0|Vyhe2oA=n_CnbEUn}xNBCmV}P%d`L1+fl*i2wBjJN5NV^K#Trva9Oep zZ#Tza*lM7@$sm#f4?wjN@2zF<8;*ur2=Ou8a!$HD`+Ed<;g}rd{k?MkK1|Os2>Z~Meh4n&4LgjxKPlgl4fIf%M%XhP z>waC-ST4JX8}$0SyWsWwuvtf3FF)=OQ!JY~^Vj~SvqgVsEtF4kC0fhOIy{TMEGbOsE|g_jR#rEO$XI`ztqZjCqbr zWJR8#y*T`%CwLM$-B`K(7d4 zGs0YU*Madc$*L`1VGRmVP-&G{1l60+YKt$91j5K$Z5<*yk#J}UVwN@~6LNMIBXmQ? z5%^65gr)u@5x(mu-;fqf;oY2G5&k7fMQR&0>UTAi75%rj7nti!38;sHvF%~N?g%j{ zxx4_HL7Q85x{w^lIrnpm2s6Tj2KJOX4-wr2V#FDnRZt9tVT}FacW_SoaUIV_EZ(2a zU5n2`3r=}ls+jx%_>k)VolneIMe2eUh#4J`jxXu6uSSYO%UEdouWb}^FVWP;jgm}N z7VBmeOlNPdb^9N1N~r&J74pT{4) zP^26N{J9ywGKXDTHf8?xzsX?b5aoG4;`ijW=15f$9e4$s5=bUg7`f?xI$g^Xa$Ih* zBj(y+Ibs&A8z7@q8cnvY;~e}ziwtPiN%7ipen95zf2=&WKq!NL-}{QKL&yysAHbGc zHL(~vul^c1OyU4N%i?5ijCMUXON{^r6#2W%Re1DfzVRcm;o`!{;~7 z_T+8Cts^d+{_|kV+!rDwxE2#{RI9N~<%*ldi_PP!tceUs;p(=U25ECBuy~a&w2{D# zAy(4rm0LM7-OPSwmzY1N9;A*V{8kinlUG% zvMB-hd%ZR&a}?I6LOseewd*R4%8h3rMbMvmz&ku>N}SK8)J>g!HmmJTMI&};%w<~L z?9jnUl@pmQ^`%fA*J%tu4ghfL2OVDs@O0s;A4I4(s*JcF*tQ?%?F9Wbt(gsNUF%VJ zk~NSc#QY&&nhP%pA;Y)yS*DRXU=!D#3Zja-<0h|-?_W~`K^?h`4fE^u34R+%87De+ zLDF_9*9OiyTtO=$S1U_`bz-}B8UTVlV) zQACgGK-*}EJdaGi0G;G0GVd zT&SS=UB0xM;&~j7A#P5$d1B|i2g@D9Q9E+7c%miE#!Y*z>34khCQ;dE)$z+Bhicfy zvLBkKzqr%9a8kcLKXU7)T>sUHOZZ)IY^4+2}s#!dfNu zi+Inh>#%+~mc6~gd4WapLywDHkiRf%VYG1m4r{a}V{Q1hVCcbJ8{WLllshFc=cFQKua6-GI{I#GxKqF zZ8!OTpi(eofqX!bZ;CoH`H#An!TCh{pZ4OtNYQtxS8_T0x6WGRU#AU}h>fE$82)SJ zP*<@BGJy#dOH$$@F?SlQ|I7#4yiACpO*oy9O2deh&PAHE&|Z#mO`ULV>xb(_7ULDTx&1@SK3esH3$ z=xJwqDP(3})+XD|`bT1R7srL45aO4JSYy3cUtf=i zEn`FRlJR^+8L~tyr|$}46l(yc4M^S{qP3iqU>q)@n#GuR zMTzt$OVBkq@2^V{6szCeKMvFA7TM1wfhDEsZv?cfVgFVQ)PV<)rl+M=PUf}-s zNIg@C&zcm{nb(@F3sdK7gn*yXkfHHw{RDxe(HJ|7UP>Q`AwV1a_Xh3b8GVyBlF+D0 z_sOOg^(H~#MUip+c(-Se!Iu|54s$z~S>r-$>afBUw&{kIZ*!WE9^wa-Q7mbB8cm4(BXQyR|RIeY@-DYEB7-n zXUm)b|CveQfwG-p=NqLt=l8?4=H417uPi#(f9$a@=N$FT^3@x3k+kVMvgxyKyGF=7 z4n=tw-4^x(Mo`3#<84Qx1>u{aAQ%A=XFZl`m2j$2FxA0cepR-U{qy;#C#hR61Zh3P zSi($i?OTQ?JAGONmY4z#CU|dGr`mx;pkthcT|)^RR%OZVH>SpQ>V&*?s^FO_4CQN- zwrd9h=Gq<-rM z+bFQcAP(URpxe{uGzyCL<7YS6|H=Ro$t9GDvIher!~qAB|1Xr4{zrRBPZN6&i~l$I z=b}@;o@zz2@@1-eyKsx5-+u!rOzbYwV1mq-N_8w<@zcHysT84{A=B|QAkJyx0R57m zQm#({!!lsRu$578ow6^$@X%v|{p43ceCz%CqWJ3TK|8zq^%4~H zJf8iz4-9&`%l_=&`r5Dlx-9N~P85Er|2G3D{8j$->DB#yk{$Rl`6TTBFex1H8BzW9 zq8yZq8}!(nJstS;vL*C&{eWs5^fsCOd4BbISp096uyFRropHd!$CL2KVZ>F}M{`h+ z-=|ae+aqcB>u`0@!vpHqC%5rOclVq{cDL`B$I)5WXC}+rAnVuef<@5H0;+Mq*Goip zP+_s3$7gldOLlhm+vlBA`}x=Nq?fS&+sD#Yw@=gX3HS6p&)2JN?3`29PIdR|VRq2R zg4b7i;A1^0{|9dN*U8xxXN2(EW47=YC*tm%SNHfq_xt7D5o!Bn_17+~u;;6lalnVL z@YhrI=f~ZXu;;?`!R*WTCb__XoIHRx({ zefzGiThDL*z++f4=yL%z`|Duss_V13I`F}a^sOaB^K}pS&O+*Da2FK#(f{?qZ5;IV z5BB@`R?x%0C_IA#i;sK^?nz&dw_e0PzUpDlhO=&*zTRnrUN*G9Lhon11K!Sp-X1~I z|3da*9Qd}e`T6({LP1FP*J*oo>nOLAaMQGL;K#<3@cTt}ck3zc*XzsI%i+Im_-gw9 zd&ljIFwg!G_vf(G{rl9GZ!PZIbl1nj(w6^2xp83o&||rAXZL-}E^xTr$j}#Qf^g-L z=R>MVaH>1S;or&DUEqFwo&~)>YCCoNWd=MgoZW+*V(W!J2fLpqt4XcC_NPC0X}jM? zv-i~;Pr5dm-ny=Q_FJDaB2n~h-I6A|dFQwI?8X9)p8oE5cACluI0o#mA7wc@Ea~6N z?sYlnpO_u4;XTzZ7z`Onbq2^i1swMuj&zZ0dadqx zZKuyj?Na)p!n9gn{mz2eyA0yk|L1C-Y>(_-I+EYrp45IAUSzup;q#P@{GtLypJ?4lnd`kq2 zXujv4q+V*AdXj8;hamlY>1MpZi$1n$|u{3^k!O#4>($2n&<@4RO`j*SgzwAP~Q3N<2CzB}$xzAw-K zUvf-L{&^!hjvKSfh%^qJF3b1=&Zd=$7dKDgOhA^=9uV;|`Dl5`aodV>md`*;|H25a z`T`c)Ke+_hDQ;|A}N$~2?8!7YP(Rvhu3)gcNBz?p6oGUU^F&5WYv3NKRXs~8v*uj{%81Z=gj$0UB* z4T2QESU^;IOO}ks49HvS4H}OBKrfX{=~Ai51#vHQc4eJDyvDrWtEjKexF;Ym19McsG0WuYLqKR36N%Svh?}fwDid| z%NkcqH)uEg*qNgCyPv6JE_1>fY<4pIo~J5_BkSgcY)c~0UIEN1EP4<|vB;14#imTi zudXJ~FB+Mn6$OTGLeR=YJ{^E0_$NG{*2dZHw5gt#)07%I5|2S+IS>Anokyh2z zJWCE)>~?*$7U;l!io23jZB1_;*2)sYI+TOJS?TL8rEOho39eO?B+s)izPwkHs{zuE zN_@}d;N#S?lIq^uOtDMqd1kfYznEF=**u$ndseG-)L^f*HOmqZx<%b>erY`E4l*As zWhDX3V@mw*1EV^7!FO#e8D2Ac-)dMT%yhF`e`AZa-C|OUg>d-S2U%SzBP%*boqa^_o+avzW6FUV4t<*QqKvx)0? zy^GiDdQo0r(*z}pZOn-wZ9L@OG;bU&?TQ)xRyE6}khIeS#nTMzSB(l@Cm$#=E=AX^ z3t7jNb6V&vq~WXiE^AAB<;unjFR5y!>{={qe#+?&H!3qFV;;*lFzb6?J{N5&Ymo8s`j zalILsE(M_xlxr71?S@sOS(d<`S_8YhQ%P#Mg-xpoJLo2XCXumWfTg)^Nn2RER@!>n z0@oetsL9vp5haoIkt4LUip*A?vuM^V1O^L3Y9CKY61r*SYs4gw((QUw5LIl}hNU&{ zy7`QEUPidL#6fZneR*RS@K2e3&;R8Jl{9_GDE{pLkk&}iV5MdSSI|o6nC9EvjB#CRj%Zcuq2N^AT4B}BcaU~oFjh~E8Jw%gE*Ba3#lSLw{ z=k3;hT4WMjl~7KIawMs|of(#$Wx2M41k=8Sjk|4_MT61qD_GVw8~6cw=S_M{c7>EI zGev|f+04-l>mM>0k!GHB$usXn30!YdB4X^sB{dP$ILk6)hL;2Gl?J9edPI;+gM|3C zjV~Equ;zy6O*6Vp`Eomjb`h z7gZ8K!m4T7!>W;mCzaM-Z@-&{2lGk`8y(o>C?i6osy?Xwo~1U7xe(AY~8RjVFMVi|lTf-st0X=*7jB2PD|&-=J> z!JQf4E^k`5ciA118m%qOsWVclQ&L<{a3_3HWK8B|b;yL$<<7YroUym|Qj&4A+C{J+WpA$}9-ulK+$HaP0 zdq2zO_4=@8!nMOz@XFAv>V8U$>X);r1+LXAouS*%dY6#X)aeyz9mG6NGI0;WKiUl% zF+2%Lyq94#O=o(@LEZZ{v15{NEaY)HCNpA{Hl>&)ozxbQIKu1R_%5W6mPA9}S&M2x zvQ-H{Y71^iNexyCi%>#bgI~g2k~+hVlF#IkljkIDx3S0_$&hs?J8#OO^d7`0mbodv zv3`87Haw>^MMdlKc0J165~@n_vq_Qaah5Z=KV|S^(~R>_t(Q?PlA$u~s~51Omu%yL zKj3(sEK6(uxjyj=(p;e6mf%Q4w1hD$aO728u z+Y+sE5j%lVYxWSWa-#=+L~}4mT2vZO7fFD|THP`(izKLn8rmIq;`t8n5=WB}v)e`2 zX08&@l8DW&QP-xD%Oa`Cy+{(`ze|@|b!y{lE7aI>+FOK|hGdpKA!#Vthg*+)!z3!A zS+5b*pITCmoD_QuGVu~pS|8rgL7D{Fp9IVM?Rs>LNKdw_K2iqT{@itsV5R)bcAlMn zwwJV|G$5MZ7H+w1V&ujCdTvoHN&8ajCFvb@n+gxD0mJOqV9BG>-0$p1%g;%8o)Fl( zy%{kpEt;)6_TR$SknyhRl+hjv?BTpQ?qX?<%*-o}(Xjg@c_q^<(i0m1*HIGoHVH{v z*=yO{N|QMyPu7t%X(0_CgPTYoS(nPD>UjTHd=Zk?y=bNNkigQZvPSf6ySFtONef3W zIVB+jC?OpryX_j|+YVyf_9`#$)n?E}kljs$bKb5;+Q%}GBn)!`wDx$X*B8M7mR~05)NrU@YC*?%E0I;L5|Undb8t^u*g*~HBu&UJ zvq5HVy%U(w%GMfw6Ve!u1fnKRpHWtPK$=oImUP%W#Ic;6om`UbhIBbkpheb*tX1u9 z8Ie-->|WiZHYY|>vv`q}B#FmrExd!6poUy_J+6^vJ@0poB!-sliJhEICd)9a7j~R^ zLd(?#u{N+2P?Ga!BY@Mgt))GklOV3WV~+V^iE0_14MCn*Pg2y9oHFEDdY2>JJE}w< zD>p;iSkKudSP8Q{M0GpVDMr;HOIwLuzb+8dak|2L$FKMlZZ(A`}rDUho7Ggo_1k;gW{pj0P z!czK<;VuX++F|e-m2NBKLcXgYkv6upVwCZlc)p zvdf$#{d9Oes+j%Szz(-LUo2%Pp_p0>CllKrS~!(U@GAZxb&2Myh+18e(_PyTr)-c4 zmidP@Ie#QsOx6|Wmf4hRPS~eKT1qBaXqAC#2iM?tNHK z<;%H7Ijvmr3SlAGG{=&ZzUdFC;=841Vxmy7a*dr3A-}yPN!nrMqdC@45Fc``NZz~D zQX?N>$CbQeCG;ASMKZbC?{y|=U@vKgb6Aa2vP+YtT&5}hF(c7eaW^aJJr7Uc@uQk# z#OvrJ9uis9B46FBm7FjNBu$nXsS5}0iBQ{)OBgO1thYXpY}X~QnW_mRis1F7;BY1Dx zk#{|c9FoWdN!Z%&>S1NX9qG}g7n5}89&6e@wiK0AY2xlf1Z-C4(#iAVdp`}rDHD01 znY&$&G7y4|jlE-)I>DzE)WQx3d7bK-`3JJmEv~OP)Bfp#wC;1_}k=+ro zMOiIGj-`IQ3G+F0y@7hrFiVCwcg^)$xsuTOm_0r1wk~y0NWrp@-P3R8EVi4i}__<@iH$a-I&}HETdaIp0@gy zxKK8MTwx*{yKG$9_wp_bx{w5wK6T2ur#6Buw}?4T zEckBSu7Q<+$7yy7g+PuzB#l7M;2`5}uLmHR)fl!7?1HQs0L z?<6>gS_KqgS>cBymrr&Y(0Dam9&JIrNn>iEglmup7?d|UdpANr4?r566*+P5Cy7#f zDtQvOs@OjT+|LduspdB(nKb+{sS=cl#ABq83bGDE&kvJ&e}~G zP-CAFz0`K9cq-aBT3fP4JGLx;$S{zv(uC9ihe(ra5(lo0k0pZjRynk>btT}Xm=F+F z$g)_`k1Qr8S|+4+xjZxPgQ=U+Y&L>G9O_>i`R9;_L-Irt#bFjN68RZ&H>6R$9md3z z#m_@upO3C+uX)FjAk{y5j4BS*1MNlV_-tY=WXO2{5$?#JK@7wjX68)1Rs#2pC5~Ic z8kXFOJUVMxijUk`bsDD=2m&XyV$LM*w3+#ycT;~j*3!f>v9v=i4mFb!HH2v**V&$b zLe^~ZHVeQ8IXD45)0ALN0HKL1@TR7AtSm{H+P2lOFQwflxzvrAg;amW$JPe7uI~iN9|X(?MZ; zK@b|+hI)2=%`V60c0IbL8@21O1sXZ2n4vW9r+lLn{+9{ICE7C{a?4Bf$=H`-l<--> zl`sCvfT)BM4G1K9qKh-KIYcQ@Vi_AX0<~T9Gzj~ox3hdWUD{7W->NMez(|={fagjC zbv{^Y+M;r4@nR!zQE^&!Otgr;N;D)SoxGk-td1y(o=I+k=E3+Na!(u1d?RY2ff)KU zEp+YC;$5+oKnn!210QeT4a~SVtQ~^!J}VJB3(aC&x+{zUk?;@{V5zSO9gBKr3c!O>RflDdvq#4vWUT z@;j1`T9zny+CJ@kMTPmHwW=>R3FK_L<xIRImPHP5P60w7t0I%X;<~5>P3p=wq09{dv;La*tzlFO+$M}p#%QSw^5|XwaD2+mhJLY zBEJoTmZr+4DXb+d=#z(!S^S zf^WdhAoW~t9}ap7&{6JIjaoW@>{ehd>Kl&0q-4?Ae2IhSqw9$@4Lqp!Y3|NX>EqBj zZ*qUquzH>fbjKjDPFaY-`%xfJq|0?M=)B**hv1f$$v_H9kKw=uCD&IT zaSQ-m(%naKNx5D@czyu)CCQX-GFR?Tzg#1>+quz{$Z)H>PY)kqjL$uTXPtv+rm($}1=1S~Q6Y%6J|Ns?%r+h3IyBTl|)^OukB{nQqcN|iN(19H=rS&!HjI220J$!HCXQ}Ntl-+1Yn!vxBWcH7_j#{krT|aDw8@g zH2HLGSU}*zhy9>cJhDjuHIN5386VPI3qYnnF>}B#1YHQO%b-GbfJZZMV^V}r2XHe@ zV1138Wi1@Kqij?mNwX%WF?Yn6l+6~o+EC&~-jg*6H{^N=)h^XOvTRxH66%s})cQ={ zDmjNn9f@q*l9#$A&Y(>oi*XRxmC%y^BPZOd8N3rwp|QZv4J%=jH|DheAn&>XkakL{ zqPhzqx4?5~a{`y0VNajm`-yRpmh6_fx9gFtOhVJc5OiE1i74P?y*w+Kj>rXZ^3#B} z<>a82z(_PeUkHoLJhKa4W_0x>+CSR-^6V{Y2&hjWTR9wA$xlr;hI5hWPg2i0fVUIc zkIX~TE|uX_YwyMsj{`r&FoSf8xyD)805ML(^C~r&WWgC#fyIVeI z$6T}!!daCRXc1tvz9dX3??g-5%XZ-85bvB8$#n;NLpa6T`M5Fm`wcujr4ojnmM_o# z*x@HKHU(5Kg%r2z(G|2I0bIesH6TMzMg2f+_({LOZ7{58)S;zaoe*2mN%CD`$jts) zgmaZffGn^!06|VnL^4A-7SqG)kD@GuoR37<0?SZH1jKag@2C8TOBVpCnlP4}mwlR4 zNR0LX+DV`!34QQ5PR`ZqB?P879M&Px(?Hsl{r-q{9NrCJC=@$J>GEz$-^l{iz*=Fd z>#=rlHYZ1CSUO9_wQLyK`RIyw-0x(TbQWy!W8)oxBrV8xc69dN#mm{9vS?ddKo#Hh zPUu>9u>y532loMGBh7*=h!C#9#YQII4!DJEiPi}{mxVJ2nTiBS5jslef}yrcG8}Pi z`85+In1JBPo6tg#Q!@!GA369$HdAw>FNjnesep-RA0+|VNRS%U1+t|udbBS>gLV{V zNBmis6VB{>SC3sUdBrFU2MBhj0wj{=?Rs=Ib`yG0dZGL&LrWp&VF%NRMuSZuyNEV%=jkERz8GaZ-VVkCUnV{HL=jd6EFtyC* z&0u!TH^GwJ-$0P31J$TwNh#tXIMUPtd9p}{1K!0bAmcy`4fFJMybpiuApt1Dl3tYt zv^m6dV~g}@N!Sh{ok@SR2<6x7H5+CnaDt6AmlnJuX#OJ=Be(C~DCpYt-nI{9j_glI z`*6}LzzeByX?KDcMN<*+JWN8TuRJ2YuN|~<4L}t!fCM_xc8>w>QaJ%Orwot`E%~L7 z?*(06fr;ovz!j&1k0JcbLm`6pXJqLS!k05~PS&i~*K^0S%j`lpkdnv0A=oKriEBwOY@|=bISAzA(2x$(zSgSV>;BH6q4x7Ynvd)X*2sG{nO(!> zp!rdXFDr%Q;1(9dcDiB(>p?&#BxNDyQV2XOF@5X#?nGE}#o;tqnL-kqYDr?2DsBL6 zg$$xTTNg&1F7YqC@U|_8&j3#vNF0ODCZ!@0*v3gZ#V_sB!qb)jBeT<@knDJX^qGbsQD$*Hx)y2g zROv|};2S@q63-fKJ=1v?A16qfr}%I8j7qjm-!C4(23V5seC@jI6Mcm#n6*0+Su3qK zna9OKkDONrP(~$4Cv62RWPekv%j_P`Xrf+(Qpy%mZUScpj@llEEHyW)49sOV*GtG- z^hbfV)Kp`B(jkNPIl}Rq zIHbA@;~{D?1UHRS2%*I2+osgk*-rlldy@CHSM7*^`=E52+Wjp~Z zOBS!NIyGY?$};$0GA28r?u7UvP*is6d3t%Rhdm$t*ljZz4yb+V`2fN8tiSVsOVB+K zO*f~LuO)iFzxlwPbqO^&*8g?q=Hc`aU;=d=b(W-T%s{dzT|77WS=7wbT(0=9t@3-B zj>p$YQw-_(5_Jp4+mXHAk6c)y+Q~I4PS0P?>s@28butlYLgqN)Wl}Iy$%NqwWa?|B z+3tFolC=^*7kQ_fE68tB#{`LUm7})G**0UgBUwK&0txk0D+-@XpkiAbVyVc+F1COP zPA?eJ@iaj8sjhUuTtf!T&c$x$qjo0?Lir6GE0a1}0CrgQj|onX+L&4&9>sP#@nF+o zrIHrFz9QBVaM>uAbg}MB_0%*cn;D&aAb~=YI#kN(94Q0UNwBL&J&3g8MiJJSo@5HNnDH#@|iQCchnbU*RoPI^>61j z9r?`vLjL^!7Wu<8ABjy&)`lCBqiSFKjZ*L@Ut5t@+xLz?=X~(>3Ie`>3p|Mwabz|v zBE<8;dcobsH3YGrS`T`OtxU6VO;|bz$LV2m6wpGJZF7K6l2xPlR6ijkZr-N%moa7wvd@cX>EVg(A#CgQCbNHxZ zGy%*csth87;hT__XvvNFI$tfPlo}r0WU=RF2sTlw8KhKTH=3-3W_kcQy;P2F?G3Jn zX??haMAkdCRpD2GZ>R)G%OiuugaZ1P0psw_^%9^T6pSRQS)@hpWqa)RorC->kGfC?#UwOZ8RS02;9*W_jZtP==ZP{Ew9 zNT3zpavMm$bUxf+qsv(0bGu8*T4n z(3C(&D81Z+tqZgz>{Zot8&F_0LP_Oat_71PX1l_$Fo{{;g2j&Xg?>L`J7$doz8}Rk z^?b>1n`}S|fD}R7f!iXw-L}o)2=?4`AC(Mp#7uGwiYT2SI(Ogmk!NQwHGW0#nDTU1FtJn zJ>9Kh;DxylBnP21Ry1#ta6kff!{d+{mN>|GSn~R|xtH)mE5xXfCW~hx3FxuA>ek4~jY-|#uz@!oQ^ zoACYaI(d)+)rn3*S(3g1-7F9z0ztX$vdQIaWbo8h*4U}CXL_(HMrF4aL)@gRVBvMz z;w+-R6FH03fRUqwAGe>F^cU^r~FAcb9H|+LC!UA7EA`e4b&CB zZ;s^1X1*4%f(8{DbmwfqyV=f1o|OtB$Jb<@RaG!x;b*j$*`NXQLAP#{}jdfe{ef0r(q00~&BzupWmcgIa z+;6ar^@Ux(yz@V6vZbS_wQNzBO)|_e-u4>w>B*+@pkUj0w&54<24OEYI3w%_Jj_hD zhB2cd-m30=q+By)ZD5M);>vp1WrsJ7_iEFu1Q$g;U-CO_Zt7A=qgSs|775%fpIp$^ zbcR~F;bc6ef_sVNKC#?R%qxPL5WSIn4DbvvMwurqh|YJ4fF<}LQ-1N@W@d8Ss6V2q zcAf988G`7zL~VAVaH21?`n#RfqK(-08pAi*+G4PtkccKeC!elcoOenSU{-Vz+mmcsp^t+eUPhZ zngQ7u#Nkj92c}ScA0=?Z!)t)@r0dbO>=l?VSz!XU&jR12(!tn6}L*q11B&r=WuTt*@OAUz@_E4U*M> z^G!G`U z2SR2m=kYvJ>!7!B|8~bWC_y7kxuVN5bf)nSZ?L>1+d2|K8^m-iFgt7MCnz%r*pecG zQ+Mu$^H9dF9hKj2;`T_0$&_udI9i__epLqbyCLn#?qycH&+gUB)CN2n~=TFd#0-yxnNNeHn=sA!ki=NN_E2`N2C zfwCC9T-zMmv|yQV>XdBCNjqQ}$PN`uKBUlX+gHu2rir=`3-(DJmy}ovc4^5@Dp`o` z{->GW90FJ&*l7*z<_Bq8DT9}@4T7G^VOv}os2L z?lrt)P=+j1wt+S^b60(^tyVaVaz7Gvrz>1TLNHkyNtYRz4z+SW=%Ozv{zs}mA)*sW z^Ab!T=OY`}urBOC2r_BH;hqoAOz?x$sgq87P8gsMg=wM*e>Ux0jikJ#rvu9B&gmF9kG7{cJOcFTGu+Vjo12-8z zayVfL4mvQ>@dH-a8Zi*4-asHM%x5=s)7Dp$7eKCU5|(lS6oU)sb?@t8cH;coYO+8z z7x4RtmR=6W-2rE_Hk-u?wCZ%znCJ3u53X=M-lYiQIFq22ld-I=o*}BGiZf9JW@buL zjc9MYFTxMXT?^D@d_Px29EsE%%tq#VKL2^|@{ z$Il=liKG%i_nTSO&R57rKAn~xE74uSg+SEuV=?39-||MTbsE|Uj(|cXlZQ&#BmF3&OVaX3y6J{8olo4--R0TZD6Y?pMRUq(zV(n)9$n%m(PZr{I z+6%i2o{}6YXRCpkWIGcwpKqP+LcpqcisMxz@7n4u|-Fw^IJa|B-LE3x-ZDo<4D`Mkf={^ivq4`>=Sa%&uM-TJSzhl9FKJ2fD?kkZj$Ur-<`GNCG`}iSn9tt8H6cp>Pj6 zfZ*rAM96e^2&lRA8J0T$n-6dO@du@O_m7E-0G%6;-c^9`-Y(kLaX z3!YKrY{H!@Yc6O(8uU9Fr@lS!L-z0%)z74IDHo1vS;B?6TCclYLtY7vT`SxE@x9>X z%#6Atd2iPv?EJ)ID=yf;UbjSL-0o*9lXAR<~R zxW&cG*@Y06!g9J85N)%Y)fU8!Fhkzl3E_Q-G1=cWnKUp19}}i1){G>mR>gk!l@(_8 zi4z!938yP!gQpq@A2I=wz-CTrh*W4LpiAO|H2Ek}hzML?Ozro892NEiZq<}N2OC6y zor8UdiYB7OQCgAQItLyzOZ5$$Wu*`#C~s3X3u!Q+#=~F&baz7pK1(G|cg^_u=o$f> zm|qPcGuty%bWEyTt&b7F1K+aK*`$>q8A{Z}I#l^48ISu&YZ_8e26#4FOMy8ZL1L=? ziiej&i|;trQ2A{q5cJ1sec#>elixS%Uiq{aV0c|>0qiw&GiMmgD|{PMG(O|+Cw&=d3O8hVUfT-2BeB; z<4&b(a9$R;8)>%@#CgU@$yJ)MHu0?}t9Q7RjuzP_0}Up8hc=9pv;c$y6e^jQQ@okAyOT(@^$=ZlBxM?wV2L#EM&F!*8?>zo z?g6fu-cr$C<@HoQ7(gbb6RhmP|8L3wz0|iO7U)`GK>H@^PHX;9e-cr*x8ce*>6(r1C`VEd*zpa&>DQ|Y+5 zktDJ_0ofuG5jJ=STE}g+@#NwD$$C^@#e> zNKg(4YsX8Tst8X_PzKrB#?LEO9A9Qhf(;5*Vpm!e#B-O^$`@7pf5I_)HB!O)34V zg3yAF7tt=sT2}%EZcL=Z?Rs>jAl_juV#>D##Sg9f@Q-pjThi^N*xWOn_z-ZfCbhwWzqNW2oWK)h#Sl|ZR zDJ_Yq%)ecaK6by;w_pX-fYZjDJ{nGM7Z3PJRamta!P%#P1QbAotlKd0UkPOJGxTFH zbMGSTz!2NAgN2yegEb<@A$Z34NZPaJH;l9NVB1dfO7FbYwy9yo6+jE=Ik7aYqNz>r z9zU;m@G&`vPKzZOM>vJey_}3gTrCqJas>ho_lT>OWfMswOQ%&+%w^LR2Q@Uv$BZjb zgCQJ?mkdRZcG8<6*ISeu80*XiZ8kk2NgEKwJL3+?@SxS5D@wrn$x;JoeY+l+xD_(M zNi{5XR;GXXevDsUe?5_xNO8RCC!!%6l)7g7L99t1(l5E}Z?G`r`jD*IP*qUpuB0Ic zYAH!OC%EnpG(G_}Ow6}3xSe{#cT3ehOsPcYgg)kzSAu>Z6_8r+N^GYqVoKo@bj<~c zC2byT&mcD@r@M#M=fj;uO9RL0m8hwqL1zj?j|}2OkJTQS8?xcF_{jbi%I}j~9kfW9 z;><%j=|}9IfU;U$R*)XaEBo<8SmwL?Cc(?r12J*C9(^bldSxX)iiJLy$?NsqU2#5{ zr^v~R5(zA=dg75A?Zw{h+zqb?0`AgQ7f@1SnXW0IcEdFuGrE)P`(i1ju@$_I?=7j- z#NB?uXH5K90F0g1b)ng+#%E2I*)6a5|6jg!NyN^5Na@@F@^>OG0NBciCLaUPK2f0HSeIZU@0wyo z5q(YO;@G1?^>j$5AXdTdxbP!XBQ~&*Dec=MEn;$DQY=W6 zM#5zh7L6csi_MSQ<6Ef)aLpRs5WsnAQm8hvVNcw0hhS|Bn~EMf5GsZjP{RN>vnn8abNly^TX&kJ-U;K5y zNZfzgnB3e8pg{!_m8cw3C;<7P^U8x(S|nG}+pKsy;*K=boymQP0>52hpM=%^2vt!E zuEoMoI~`iDmjFqluN@{)cgKY%)ulP^Wn$1UWx9?Et|J%LoA`o0gpg$9+h}wsq#@x# z4ih5bTgJLAf_~JU)_|lJS18vAL$c6ulHG91@Cg!DvH=3KN>$PZb8w56c-mdmXAC=z z*P}DT6RL}b%Uh|Z+U!R?_Os`=_ zlE&t4Ep=I1nO+IyXjcNzflf1_@og`y1s6}*BJ<>Jx0+3_DIXjO3>h?uTcN;p2d$>ZJ#XPZ79PXVw_3^Shq{=g*x z!TZw-d{%W^AuD>^&^BYdPC0s>pC&bEeyHAKV#`Jcvst2SX>mM3d_+3eGjh7(n0E=1 zNxO;L-Ws*LS}tyzQDRoGnQFA6j0uOXt&nr@rX}plc!m`Pvl*prDMa$E3<< zK6^?BQK?JxWcv<6Ds%a>=-}OzEBkBU5+)Sc;)zY>GWksNA=CCnPb#=A$Y@2By_f^H zLVomq4?j&TeK&j!Gf=CrrkcWUJR{GvE07r6!XrFgu?W0gWfYh~@~-$$v&R+&%SC6# z6ojUGpR84i3TZLpgHU%%;+IXr3_-Z2Yc6``!k+aiT4&6=x`Dn zOWUA!GNpU#fAFk=y#uDRrIk-bDG`uJ@3OugQFJ`zsAF}sgsOyW0%foZp1fJ&!_*i% z9*oP#<8@WAr+BXXFDi%;iU0`>Vd+Rp4KtSxEW!A_Jf;VsvW00v>H-qmTuH}0JOML= zy>t8qi3kiFAi(NENQ4S#TU~tvpwG%Q>FUGA1_@o#>n^K*V%v1rWb;{Z(W>ToG~g)4 zqXtiio0DiE0yX$@bC%)$AV=7?GuY;FhVF+D7x3HYa)6kHz&Y*$79`Q29cF0@AqGDqEO1ehzd^}ZXoKY5QTa}5 zkqW`08%v#!XvR!EDVhxnrV$XdL$W9`nO9B0u|4yJ_Ww9h01OjcZ+paR$af;%MvS)u zi$o~jm{BZQnjiG2U_ju~5~dQY1NRDPk=Zk_kcP*aj&pDGf8%ON5#5g7NvIr}wQSo{ z3Qsx@%C6m)(0yF7{kRl18#dKSham;e`etMVQW)cpj_{FwkR&YTn-98h{P@k)9(!!* zQt=eiYGc>jFc%7CTT-~j`mnU}f4Ey@bfULr1rmvYPMfhmf*ht5!fyXEfkcz%05kzy zs&OlCL^1uaT_m;m zBxFiZgmVXgP^qaYp>ApGE@O~*E%?lVMOi=|y21f60}{@2)LY%%DO*}}J5v9{rCK~s zNMMM3+=zp=V>fp+sWg2Cq<4|P?iaGV*GlA!pNA8_Ez^o&0tlkan06+6KXDS5^hf|m zR-eY>aY92vWi!5{m$(E0G@%3`&^iCM0%SO80EYb4RMGtx=E!?r~cT9Cby@_y0^^Z{8qy&ruF0IHt$$BH@bn` z4*-c<;(?hU662qTqm zj5oPg)7p(-o%iapN^flWO88XtHq>r37Owv>(EIJtD_GJ|z34#kL$`LU)=w7grS&7cp?kxMhux=bB zR}(r2a>Nk_pm>}10d+o#W^G|kY&SbG`Fe$Y8jZ2EHS%7|&=OaRTJHUj6^l6_8Ah|J zMxAJ|5(AELdzd68RjI%*C(SCGf@;k?G~AIk7FPTS%aJ-j@+)--TX-2@?CilXx4GGH z$C&!~UVAl&RHH3#*Q0CE)$SI@)6wiF_w=Lcifq!QI%AbxHPb{l_hXB8wZ(n^D%QfA z`dA7>h(W*&Z0-uG>2L>=^Hof&S+*d+q91)n!ENvN?@?=IPrgGbVG-Nlx5ZUT`y-}k zk_?(Ighze7f(`8uRPB?<8yyjYG;PqV$|c*t=xHb5l932d08iFhp|dvdUWida5Y!+Q zuN%3O8pH;WMQQMvl zK=JA5szfz}q>SBT?K!Ux+?!PXmsOIDF!69#(+-uIJ9X2@pubU{&?6d%KPbn*eyb7t zjGnTR8L;2&3^#dzErT3u<2|loJaHBHrKBN(SZYwYQ6bQOj*O~ z0Y9PtI1KKeoZSZG*jG7~#+(-MTi_I4&-ep|j_(JCKN9gh%Ju;J9zkTOP*^HD8sp}g zxAPTmVHvyIEKrX#q5_Bv7!XV7r>vQwWZg~6%ECjNZBe^o+nexuKm{Q!lC+qMVrZ~P zVZjT4@HEn6H!L9?okOQD3stKH0uJy3*Q0BymUc76an&f1T0QCx z$F~4_K2PJC#m7&4IwfIR7j8R7?2c@8+h6;?4gLY3c}{_}yRkJOhK~TUYXK=Mqtud3 zfaj49llSFR=HHuVBq3PIK0x$4fYZ!7TnG{qcL;M51{9tI{s9ze^P_7-zz(u9azYVH zqNRG<7lAf7au>yq)ucy*MgT3AZ6Uy%T>{A=u~-C4gvtllZZPcwhiu$U4NiwjQ;D39 zKAKvuJfnxH^}#VZ;!xH@7!2_AvEvB!bcE`g*m9v~=WyqeWSxqA+-y0A4Q)1;T3xHO z6UlSz;q9D0yj|HF(;a{;eCe7E)m!K=3*U|O$#l1SHq7-3H@Aia7Z!``>3LQ&4p%8V z)-OS#*)R$7IoP_eDd`OxRP1++JK+{y<=rNPKcVv%)+>GRROj0{ zet5nZk*021U27N(^E5nL@xZpb5e<%3JkKj4hKxm(iGYKlEAXK_tchf_t|o&HkfDz| z4Pj{en3fRU$)oF{h!tZ~0cMZN>x~5cMm8)cP^d?t_|^|b^*`O~T^Qks>P*N~w?T0kabVM-)c`j5YLisv{5Dhn9V zIs$!BLEHuy<%m?e7NX2xvEEKstp1^z5I%sMo|LMLYF@2wW=u&ym<2Ge!Cm1F4>k%1 zpa+P+1Cs@vCu9WsqUO`2HrwkA5jE^EWk+0S(niAh>fm^!X=O2^PV&8V@% zxAwZsmcO8uDM3M2`8^cG;o1{VbG`uQmhNdYSM*8+RYZfg6o?$Ve^vd%fVpDGl+DY_J*yu1WtblmhcK$bg*F0W=BVY(){KHHEo za2c%7JnS+s60OqEO$O&PU0@m=N~c-rj=rA`Wxq0^z@H3Eg$$gwdHrN4%_o`-`|Bbq zedawONrpYe=)1Oh*ls2_*n2=`GdL9D+yfbL3Qi^}}5tSOr)rPe_AxJRjNa;v+ zu5|cuML#m8RP@l2k@`)^ojX+LaH7);(5aeIUE_(H8ck`4&n0-ItJ_G@l@pLt`7jv( z4h>x?(A`{*;1r|BSBiFu*tv8POld#qphw$Ihq=!qy3Wk(T?VyD4;7HXt^x{qQ_BcE zjaCl!nsI#5v4eIpL7`II{hOi+LbY?cMeRs$LpS@mR+XU!FX5>$TYYmQ`A5+Vc-hL@ z3JLYNqOKZbc=O}}(_u$)sNKDPrrVX}=u6%C@u2E7;e z4tbLvIT{ta6(BrShL(cmB_+^%C}Mn;K^lDZYJu_)Cp}Wtutb3>2}~HwA4V^^DD8Z7 z#e@wLbQ2hDC+3&KsE{5z@3Rvoj6yV8{AnGoO5{NWJ8Jli)^S$}Nt&$%44OM2!PeZi z2T???)UCS3tT8-*dS^YbYOm(^V4NIMSmav5#L?CzW=}n(Q*39h16vTC9S&cCPfwZz zvX{mRyTdM(hDr0rO0`4zByJ32l!VxFv&p4BzLe+c`)y?1HW4?_GMlozYCF%jVOG+BN`#{(4 z#MNSQ#LGLF1r$I`1|T|vP9iFT+%|0l!vlEr8%f=#Lv>09InT)asdw1~Yy%+h6LM}) zP!{S`7TZOiw~swlh9q|O4mdLgtIgu*ysWN?m8byp8t2fGnW;RCggHuqC>ys&(L_&M zY>VbDqly+#NClyCib%wxG#>@Xw!`?i@lUu{Mw245Il*xMUO3$3NUq)jO3cqUapM*~ z@UPW`j}}j6&}dV+@xLbH!?nJSqw@WE!vkcro!qC&0c8c{;j z(P6t@ft}A-{4x5a25}=)c_srs^TeMfFVGYDlkvTIKE-m-l^ND$@WYRxaCD*UY+dbg z?=LZ))dVSrZ?8#_HBW9=iZ42LLXbT_x-UdJKwkk9HjX~p2)K?zm>sOtB5_Qk^eB*qpQHT@K;pE0w;K6s<6dA-Y1?2BB=XN#)f2ePAq1#6W<#SsN$LbyjZk?IT9V(-jDa6e`fHn?I;Vh2Wb*1u)QBR*7z&8byz>it7$5?FyRECq7$D(ki zjP;+T^~YT=#c|GU5YAzMYPfl#B49vT%m9r38m?NFz?=0%u1s;q)7pbCIvQBNnRN6M zqrbdQmm1=3f<*TdW=qvJTnFi}&~UcDUzJ?TUBmL040AP#MIi<(Go3v19MpqTKM#^B&La zAeD7YqPg7pRSrBLmCQ2Au+6boT<0Ye%-4FDx6)MAsHn zvf|}k2zctKSH2s^W$UDXHVs)%zTSZ=xCgv-4Hp}K21~f<6$c!;a^#mGw*|gwxt8+l zIk)4*Z36d!shHYd1<;g7D(c!*)T=sL9M`PSgc$(o)oJ9AjJp;zgNepJwkRpQ3hZ{f z!kXQnS|_9is-tUMpc}Bp5;>r@bqkscUy`+DR64I*xC@9c_`R+MG$*Kk3NCWeNqIN| zp~TsgKw;P;p%6q5&Wg&x+R#b>_+Sky*rUY~tSnzCh1A`ssDFk%nmxJ|=}r=u>!-6Z zHd{=ND(ymC#H#`T&(XlwJ+l+9WuE(15xdR}O; zWVHj*0fNzEepEwAo$JXEr#`@~N*arh`y-JG<=>{80@=<<8&r&tNE>L0VIWaPoi9VsFtoiyGJ)$(ogw+q;W z5E1>Qm}MC3K^5QG`WZ9wGXj*qUZYh$m~RG!x%HZC=jOdrvh}c12}qhQD7WWb8a^W6ae8t3}M?gyM!~-g|KPCC*vKaugr|3r~{9&K;&x%$@I#CI3EV(yR$zYaJh|JyX5LcDz@$l&wL=ELFV!H6sqLT zJ>9y0%;DA@`Faf)$?g@q$`f+Hx2i!&!BzOB9NUNap*Ou9qQ56gA}EtEQT1&HK8dh` z{V2WFF@&AZ1D-&z0zAdy^;$_XAt2Otsg_7yL$!0A0Am}nJu7z(l+G2E@sUbg^{~Io zGdMd8ti_LAex$8wUCWS`EB>X239FF>vME7VYN2|)cjLLtrW=FYVjgMtrn^p@!AdC% zuoLkFNMyLpcd%KQgug3&*=M)@>g5-PP`j~+n*cNZl^->v^k03bzC2}tU_)%liwqVB z7(n^H$`1+-mp};&8MPqpKBDwDZI_>aQ)DPw9M=_j*n6PVIA1#O1+r zlq!fS4j8M4{092#D=@9F);tIjDd7h;roVg0mZY$x0J1bVculAT3bEq?0NLtjh^E^U zwYo6%&9ZHy!Wq~;QHw!hFitGE#b6$Y0-VlYzjtBouH+m=g&kc1%$2JHD6}9F40HI= z&u)|a9m{X>>3by7X-rSp9x4KNrj_Q+j`C`3=1wYF6W!~$4v@^9L_TZxY_jFzhffz> zB;+m(URuM*uC#Zj^#dYAQ{LDB#J#_bS;50^r_be0(VUL3UNJtZQdU(M#Gv4kejK$O zl8r<-Fwh~@-s08!Lzo2%{|OG0BUiAP5-|*f74h~>mMRwJ51tR|z+|{|<)&l&q_0GA zKs5qkfFdQ)eM*bl(%7aeGat4S%`h4wegjOgXM&sQ=BQKP&PzoHE`|eL2eeD~sb+E0 z52m^|NX`|rU_Y;Z`UVL+%6JZZz%8bvzE+FF905W5O{=TSdCI5&pdmtR09Qc`5mCTW zZA^9~uKPL(^uo7>D=RWISp&*=OogmZMn;?j2&31aufaMf2T<`RyTnGHZM6B>5B@vTsw(F zS)Benm(uk}2qnmzbLo^cK?6bDLEwrZoEIFX)U@%O7cfC#+y5=7v;s)laGG+ zip((Gb~B?r{MKoZ*|`q^4_vK!ED_f&{KT|PnWe7q+^Al}@9x?rtok5u8{w1plt+^Z zSje6YHi(ELGbY=gasv1$zkNyH(_FXg}x9OV!e7n zxuBg`tmKhx3FW_Fp$A1S9`OY1H4QFqAph89a3pamJgZ?>XE3L5McPDyD*{<6@!Y`y z1j`kk*IeJ-7n4jd3pagi&}c`UhR)Zv?@xWTEGK0&%Q2Za{c%V;CTJ zXq^g9Uxeds$}B>sJL%SG=RBPq@AP+}B#BBwGmPb!dEoenJITBngcg2=6))BsvtB=t z=XyOVo8SPx>fasgBlR2Z`p9gAJ&I&5!HzwAf-l0s+bB2tShh$eyke-T&!{5GeJ3(A z@p3^0RjjXCoPs*!=JrV+{t9G5?hsW@&;p))dWIdSA2RP-rg7%Ba7q+erkm4aq5@cy z#|Dz#QH=&!l0FPrvz>B7#?WKJon&wY9?-^=D#L}r#zhNysPbmed>C50T?@NJMCk`6 zT&v#?z3YcdF(u1adv)UYnX5?qdT;oCb{E65Nu`N&+K!5HBvgw8eKAI$%c@0ub^Upv z`^Cdn)P-YbW(Sp|RvPVBDWqo*7@kTOzljFvzSyXgj^zFVd50Gk+>P7~?4WIUP^4-< z$6#X|M~@GlgGj(n%PJ>9VOwg9L~3t7}SJ!M|kDd||bwJ!04O9%O5gTNh2F zLl}!+2GGqXFCgW{LCmbk4y!e6#B-CMjgV=q225`&1gEHotvddnp(+EGBY+Vd6LK(6 zj%zZ?(u1_dOA3GV)y6`aTnv(nYm~s)$YOhnmT^1KWTzy_cXZrS1s)3n?E$@pxd{5M zAj6dNGL-C2;u!{g8PzdK!M}OJ5La`J{Jz)tQ1@jiVkx;wa_xi6WxUK1Ma3cEw+bj(Fw;8N>+%T#+-G@M4Ixs9iXys+SzP^cp+$DPT7eq9adx!{ccy z-O1Bw?i&+2~zmR*a2qO*gJbY_WY=hn_cnjp$sF#d{<;QSCTXNwkp$CgGXMG)@#JG zoo)2~R-KNBt{viQ9X3^i>z!IRPy9?rbH=-^O-$Kd(3M~so8sw`f#Kp+811`jYvQER zlfO$y%3#bnAhsixQOHHjBnrrhx*oS}%~XNguYEuf@OFx7OMVy2{oWc#UR(A9&q>Hi z%dVzV2a~wLB$d8m=UH&ol1?#fe-YN&&2V;^+zM)M(U`<~+@FT4U`0g=%qOK?+EqI@ zm)ZhbibN{%D6sPu1h0|EIf@Z`I&*bBqV&_r`JZpWk6v88z+D~P-(B>$JLa0QO%uvm zIAz(Bg~nsl06@J*ad)z2%rkQ?$iJdlvdcvhyZwzhMXx3}dqG(R!Y_wEpFFXeYg%(?Kl9Q;=s1QEqT zHSO)q468WC0NJIi!yZi3s-W4$6?H47O9)C{_NWk&5Ovw|^v`#J@(0o1(|Kwe%oKxF zVrCaEfiEVq>unNkgwaYfDq2|#^^q*b%og>VmanrdVjETrK&!o57X_*Jioj9WLJB(u zL*T?rGfT$}odC79D2WMr! zSeQt52)%f6<1IQl43Wjvbp9=sKQsxKjI0E8Z@`~_u z%|3T6uh^bDSB3lwo=TrFbfvH!k~5P{q}-1r9t1+L_2wV^8ANT){N{Y-sIhjb87uF? zHDN5pMqq7EO)VH?b%kObl0g7QZV9gg%VbG{l?b<_%#jGZ3GYo_U3A_;pVGw_d^C?H zyHQ}6LyB2`KFccOh2XDQxI?zd(8W#TN+K4jBG_|Syu7>+HblZe);62e8S_3zB9cWw z5p?Sz?EWKuW7luPVP~@ib>t`gb!l*-qGBh1yXChrbOmzwa(<+x4>^q=pDzGdlLRf&r{K&j|y>QA48rt)>G4sx3YV`E1%*rt?##wBM1m;SpwJOFTM>3y@W7rf`gJE8?lT{n zd!aovI2nIe!;p_$DH@J!-Y;Mi-oE*f21@F<6d4e36`-^je^7cyGmC z(tNgkc^lv2jVq1NIF7KA<#)^44dNsdLUWegwqnZ_YWC84a%tvctji&@h%W9(DJnKv zgbLZta`#g!wDxvW^;rc)<9Dh=c0nvKy#YNiG$%kzk`mKUB_|XX|AsHHaV>osImKLo zZ)7V$sF-25WXy-&D-|O5^VCa3eLzw6Hco-I2G|*o$AE11rRAu+5i-wT@r_50KiR;K z?~wf&%h(xXZ31B>vR1HnEc{~^2fHNH`tLezXCspA0}YVLB%A?Yy|4*HMBVvJy-2ww zgzqVe!U5>*AAH=3_$S2(ze~mqczKLP_ATYCVpHaB=LNYt@?>EmZgg;$v!Eq{1wxkH zKF9kgsp(9P5xD?b7Sh}ONF)kmBL&b!sqi3ETV}Um`}-tZKLjtZo^l!HWK#*PMg zy*)Rya_<-CQqA@Tw}NdS!+{lcYK-l~|Dx+`hVoMj`gH)Kkx92FdvRFS82a%fFwa?Z zg=m~jch9XwA(9eKsU8S$T+=bc{KB@R%k9pmfc8e+2j{V0^0-}lO5}o-qMUT^V-QE~ z$fPV)xO^T~<>wPFq^H_0Kjy&C36L5z1d{nsAyGDD;^au81c+ZS-Cl`tW9*QTfYZH& zra~8S8}sh1qMeD8tE>lY&$fa=KfobO@iM~OG1zUw@ER(oT%qzH4@gEGziz?l-Mx1r z);{`lBNp2w1PPWe;k%HCsJRs{8SAr=ply8kY{TrTKTDFdt=%&NS#bcOizWkudsxo75DsyEr}Euez7R7iKn|R8!kJwQ6l{kxx-4y`mUcHEp-~0*^Er$`_s(` zi3e;8QjCvjD+hgQVub1Zar?*Tz(BJ}sC2WmU6cX2xC5g%y( zS%_Kd7&-TL!`rlj9{+^nwk-8iTf3J}%R3PomjFe{hSUe8!6iO|Vqs3|DIPJ@>cwmF zF1;=FP`M}Vd^~mwYdvuN)j-}LHAWS14)|6j@(YNHnUXZp4b^i24M_Jd{pzamu6qCW zVbk4dgNB2k!D`}9tk)+w1%Dft;~g9f#a!s&9%Pduyy2=L7rfxH>UG1xU$kSa3KgWN z`+F|=;2{y~<8x8T`HW~~-BoxtOcZ1qUIrr3VH7`)CTHpP*L^;W`Q2f0^w<@{qACHl zAC8u;f}+5>IQ+fnY%#BhSD;ZB?z3X)RxsOK!@gAv{G6FHCs+`z?&t14d*~{0h_x0i z!3;2btlQJ4C0w#W<1Oq4JRX?~ZTt4k6hE^8hD2YrwfNFNwdh((-2ZsU*Y!-=n z5bI)h=)!eDe0wCK@bEMr$=mr@1zbdiJot30nhCi;%&P=e(Ox;gc5!g5jV6*3OKe|u zDNy^UPZBBKgm~yi1>LVk)-)F z7wxFU`B{tHqZNXjkr)P~XaGZn>!Xu}#SkK!NX?Czk>)Bz)m`NLJ|D`B;Q@%$Ie}i} z-o@xEatgArzc(n_=AnFCo_d=_`G0KOtS_|Gb58!HHeUyS9{M(uo6NP&TSc-8QWMO~ zuU%Cv9zq{Z=LTii#W22N0$PgYLQ&rY(9>7Dn3#G{A(niEX@|MGTGa{WuZw_R@#;{W zqvG-M22TzhynkGObEdm=>umtNi8^+ia$oKjL5GMii>@zU9!?$n1eKh z=AVwKvXJi`5zQ7v8n;S0y9^odK*`Di;s=W|0^8~c4A!JYO=Y$x`wk{apcJB!CK0-F6QmJgqlV2Vs9>3LYzv75Cdz; zGn|1^oF#8fD#tNAydFp;3)rVm<0Sj4nVVdPS)oe1(!o`yrBe>Db-i0fuzC>#jkLSL> z17w=Q)wxgcuuzl02qbo?g#=|v30F(A?Y@g-?Z~%EWWkB$eEW)w@hgs9D4=GO&abG$ zBm3x7w%OEM@<9rD{3kE^wlZjh(FT{9?~_XIJ=PGJV80j0T}v!wXRrVPPRK#=&rfu1 zqnt;i@B6UyR=BLO192h6*(gMEhRW$DlJ-)BuQLHa4%I%h1c-9KG#@#BWI>OO+21c6 zX=Hjm@z9~dZds*I9$~z`rtXlaq~L~Q@ZAh~3V>u^1;tYW!~)tN$oB$fQBT+jyUp36 zCMtkpZ5Anf3=0#ODQg7sD+7L1g4QK{@e?|Dq3}9K_+6OiU~9Yk_-ia97}&STRow2D z&@755SX=t6!SPylq#gl-$BH~hm*mM7LYJQ-&R>F!oGcFccdHT4k0Xj@SGuk03*PX) zjuSSz!6egsUyIY%L?Lg%z!>LcD@L69VDShb-t<$m9#*cueK{wz5V8^CFy{2ZNnP+d zAUW|-vnH`z6W7Y8;p`U$36<>^j}dq4d(YMEcE^Jgpw;4~E37}9y^+V`>f_@krS@}b z;C2dJmV$^Y4;N0j#|EmCxGD)ojJlZmOaZi^joaF6d zSOFf86)FFyAN4wxfd*u&8JEB2gQ1}c2v636DwlXvJpTxQa1@z}>8pbc6{apppR9}r zLlkZs4`UIKAEy3{TL0wf?8`QugAIf4cFd4C`LqP;5zIB%;i+;z_2k5*lM2Z;i{emc zsSWM+3h&7->gJo4YzFuMg-Kc&o@oz*6_s6dE~~sY7!`;IHUQO67(jy~w2}avbk#my zx(XXZ=JW{djjc7Rp{)f2rH)0%xp5*RK&oXVt7(v?ny=ave-aka55UB z(j{Tq5RWx>uwzw!u}B>3U3~K^vY3^x`|GUrpyr&Gx*6npe^hGvHGU^x)V&AJ7Ns9!+?>=!JqvqLTu zoeD5lP*t{BVGuUapT0vY$;dxgjhw%1bOq?N-OsXiwzAn?>D>C<8&!AdKwA@U&%zyS zt9OZJSnjGH7*9`%Ow+*+R0u{JTwwQPVh-ZK$N3Hru^OVoC6*&6k#8cAc8uBCj{{nQ zQF{txYIreyt&(a&ciPfl*jHE>dbL<^=rBc#^h{98_7tSdhOI*af@-h~SdE1XN)fj$ z%Ti50d$+cPEGkK0(A5>{_F0N^L)jU%_fK3tLK-WoBzMQm#l00?Oz%Vh})EW4w%)7aXxJp@3si5VJg4+Iz(Vd5%d%dG0tF7g@a}0&i@){yWRcgr zTfe?oX9@~hjZLgkhaCSdyx;tHB>UkQ}F5{Fi=?^_^qR5-z z`Rjq%uVbRi@{kq@uRm_N9(1WZSl7Z4mI&4QsK+*3J%jJ$EVrQrS|0hcu!{!uLI7vm z>a?=FIxdl2hZK{U4Mf7R3A^HVZh$)z5fe5$7+mZwQ1$iHB-5s|GS{=)2XBWoL;P$y z-0u7?XH-~Qw1X0m3jfHO+!$^_jx{0h$o;{h*AnoKZXRI&2~-@$-UX3o5t%|ku=3P= z|KJ>C=H1ZAARS+^f!x&8)!eA$QY>oPn>;s;uM*{$f+!S^E{nEE)N8W@)Y3QM#JjI^ z&)4_3{2o6IuT1UL7nc&Laq>c1MR3vgW9iyw;_CCWTq=UrafSZnxU}J#BtO#OE7O*2 z3x3K70e1y0=mPtd($hOwi{p}!O#J~&%QD#zD*QET!i)dc()-!1)@Iwax5dUBB&`eC7jh!qVTrBPF z=v+KpR8=4WfQLE{bpO*_J)i*qL4JV&0RFqX(b%#(VngtKQ$Gfl15|RqsFlSsSg2uN z2kDLnE1r);kd$%V82kB*Be5pUOu3e2qkwuIU4@y`@sNNM-;`2Z38i6o)UCX6Qzp%f z`C}8`xbkX1k(^28#xmh!ow~M>OT%W>y)n#YN}4}?KGUjZJiE~a>bL1PF)el6ND@&t zw2|sH-=fS`x(5l~36ee5H%s=JYEOJ)0sU>F#?0_3SqtJ9YHn;L zX6DlJlDw=t;Skbtp`Rv6gP#W+n3-HfouRc4A(g^(#i%%PSuMIzs2W9iOY}MXJDWy|vz38js{pJQZR`9VWvk82 zFUnZS#kE6K2mMlRo77V2hL4QXrPPCwqLe|v-i{syu^cc)FB32b-}Lm{<_v z${+WC!%Ld1+0Xc{3YAQkHy8RA6CZRewmxWD+};vV9L@}nY(Z9*m=H`A=@AX4BP}X3 z=5Eg<|C6R{v%lN5p7brzyU;sTccq%FP=- zoir{_3k4g~=AY`B3*l@guLEmW7Bo0I`)4EAKRRecWlFhAw*R7xQkQ0R^{R+m-qzmH z6-i`|Lcq4||4f%qR9`)*t<@jvm5Xw+4#1lhi*ioTFrBkx2hS9TZlq*;Yfc8)J(tuZ zy&tb3;`DKz|Fl2s0Pr_@@>QPjSJvlvGHY01F|N%TR!R=*{0;3~g_?#%@hdrEY{9m- zm$^@~#;WgJyU)@FN{)(lGz=|t7}7vQ>gn_(zRg6)}9I~Hu$e!phrGcBa&saZ1uQI_Zuh8 zy2JiZa6AAZ2k_ZGS_kZHK=(dg2RvRtcmoDaQ=`E~1C9nA4SX6rbpYy+Sp%nyljX3R zL3ac1I^Q*b8$|fvfddT(%0l4L(8K<_0}uzH4p{nd>;9$#B_Vnp_*yvj0PF#y2TmU7 zT-ez_u@A2YUk`vDME>B}J?%T24|pGRe%aA5{C+qD22k99kO8p+LI(s72+J0LU_=7p zF$8T0n-F+m^t|vnF$ohETLhe#4+BvMB5@3b;RqzL5=P@t1e#b!8ru--2`YvPs7#ukx@~Rf`?l?&+Ksi@9$rm1X~e zopSau9~ZJ~sUKzjqS;d&(?gQU864!Xi~7uk1Ih@kK9~M`+eP@>-&G6R@>|OKfX_gyZ3wkUCYnI)8GC5 zn5O6ReHyp#e}6LH^Lab%R>-}8UCQO|D`*Z=$Y^d?Wg_r0e6jd>?d?)!5)E_^mAng8?r z{`>Qx+~?!YyOzs+^EP+4$KU_+ARo{F2cw2 zb{KJT^(k<6Ir{o|95o%E)APs2{f^Vqw+3R}j=lhx4 zDzBAJF4m5iI)IfpxkoGW@i|&lAnEh?Iyl+c`(;e|9B=pk_;@&}*Z=n$ox}C``G0?( zyT|=)C|vlip33p__Rxi&Pfso`COqr+eoii~tiJQ~{^b7JdiuOQ4JOZCtqGHUY}~)4 zruX}Jeiv%Z6`k+?e7C#XM^pL!J?T!r)~x@%csS|YjEHTIw($A*e*7KA=iB0tzE$#H zt={wdy~vi=4{U`$W#N_i*7x|FJ;sOmovxQvW_tbyi8slLIWhO-+hVzEd#@k&H{=_> zoTJZATDETE4BCT&+?=EH$3&;)(?ZAJcH*1p)_t0NmK-oA;S>8Cb_(P7+{kB!&EzNS z^ezimb7X%yt~2L83F4I>xUAP1{fwbJ$WGz&;A*|%jD7K06G_X2wrRev1|j^UoyHAD z9kL=l9}LW(F9V&a+D`QFr4`Wp8S@%(Wq&9FkTpr>1(PfaFrDbd6B5E9-&h1nW-c@j z(mf&9$(^Z}yN}AD(D6#K?@89MtrcpbG$& z5wcDb!(g0^nnf$%NszdOwAGjFpEYjp7?^_Zp|cF=GzDQg1wopjq7Tme@D)2-`>m}8 z%86#tN_9_cU`dFAio_+7vY)#IqAp#7Emfmj?Fp~i9bPrMcShU`hqmxEMzRc^fCI%g zpt8UkIY%$8SA3~4s3w{K79~)ry+(&dI}eTbS15|rwko34$P^HmKk;86X#<()J``8r z2nu2_Wdu)_TH{a+g?=DA)SO(zyYvZP4af&wfF4=CnqXhCVzH9YXK>jl+g1Vszpy+k zhf)q9GH9cT7S1pqB@1n^W=rK>yHavGY5jFCVjU_!UePocBvgdjOvpM;^LH_-d>Lh8 zDY!LN5}gLh{Nqfb$=D~Gu?!ma9coF8#~OTr$4lO7z<~!UC=-wEqRRI!P;)IqOASzNS?n);1FcA1 zV=OoWE-mLbP))@JS(U$H5P{3aGZW=dQ6;>+#K=ar4*@!y{K!>ul_tX)z#&0ZZBm}G zRq;>i*rWtE&G(LIzlhT*?r6VtmE`)AK+fL5dU2s5HcOyDh*4q`3YdwLMC!9%4?g}q z-k^q9^W>nE{*J2>{~$dXgY_Zh-4JuU=vGAZjR*=%f{?OE0w!ye4WZ9ixw!a{!e5rKUR; zXNV+)HuXVt0mGZC?68TYFWTxzNlOKX+M7Pc{ce#%QW}wx-K>MpP5{4mrKuGEWqK&E-+PitM;0w55LVvhGKzD{f?6noSf@ z5>F)kfB3;HiIJ6P#Vi?bRHJv{L6%Kk->HQl$}5SKF?U@4&S)uCVTtjFWHgT65qCk@ z0Smo_Eau{JK>5yR;$E@mAX(` z7LwjGZ|S-Of=>B3T@T7F6QEI*=5Kq74i)Q7QprtkGv!cV(mCTK2g;+=vX<;JwgLS- z)OA~;rEy`3^s9hx`zqZN$+k{uy}wvwS#p&gPcI#mNfK=|#J_{42e*orfocY_solF4 z8+cnYSEX3PV96<4=C@wOuFX{Q(7mqa@t**Al-^V`xaiy}j!or49}9IT=m4heVZYO! zXmW$Vl{yq|b{{;$`Q|VkQFgokpfmRXmZTf*ud3R-4w4L}TlGwrVC_&D{@4e|8!U;u zlso|l>bAqEV;j#q3e<+QrwrEgxp4qea%7Xewj*p$iZ_XcwFZ`Y z+Xz-zsr5)uSW;wY@h^jMvJlceeQW%1 z4GS`tqS0p5$Z6{UyNs0IVL~h)7iM ziUWIuhr@9N9rl9Da5k0PI9Z0OEvW)Jv~o9E{Wc8Mqui}GUOhd{c=_R>=~n{tZkbBh zNDKh*KzRY87H2^{!jYu1Tx($aE^`N#0qOxjMpI@jys)>X9x~)@ zh=Txm+1!NZNJ7t;JjdE0-E1-)XvnCFu;~O8W%E^BSICf~pbM<6vx=?OI&P*2I1fN| zzZ$X7lCTTx7!rw}Bp3_s0B}*lHao0F30mVJtOsZl4ggQ;hBMVib7N(${38`Zl7DUT z0_N-Cfu;`X1_Q1BTzPJYK4y(LC?i}}r9VNbOgA1@#sh6LtU{Y{1v&T;F%U=*txBSr z-EJ8UKW|)v!j$hs^Ow3JREJpAET~A*T2!BghxH)!T>)1+*_kQ`7kEWSbIT9Q!$^VE zKLO!bv8MD4ln7ktUE9np4)r;4yBkH_veZ#?)wCN;0)=WTWuLOa2#6NQSTl_SAQlmG#@~9yCMkL|5QU z+ocm_|LOzHS^$>F#se`wxS}RZRRCXbWn+4kv~`=H>+5luRygEUafBs~5maxAQ)zkr zriU@CE4xzG+V#E=F9X_=ujrL2X|zYyw&18tk9bqEQleQuE=>& zrs^l_`|XCfQZ@KF+PiFNXT9173{2m<44$oKoT!_Pr`!j?6)tIC6#prnk76_B<0g$# zD-G@yM^`FPPR_1+5}MLVL}i4#fgmhZ>LgT!58R=53CJHQ%G|o; zmNiZoVl{6*yM|I+m-1=blTZPv9p~K@qJA2hHG(Mv9(6p-nKdo9A8VH?SFDb*+74)l zI^2LQ!>bO~>C3#rl4l`4{YpUdMqTS&Yv{JQRI|MD9xzjsj(%ETFN=-&A=b3$eW)!e zf{dy_PV*zJQR|FPV9&0yJ0I-#uC$xF%xGlE?^zw{alW?au?U7raY`45s+E|(Xr{|a zTeO0^xYvkuuvnUZBsG06#^8TT5QO-Xc4vc> z0ZHnuQf83Zo2-uoc<-@9Abp&QoQ&+^-X--$aJy173UvXI4JbqOnKD0gllXkc%2kZH z;acYA)(7Uc3T>_Y%=tK1z`z=_xDCWJ%`H}*XEoxozC^dZ>P$%<`(=HSN=ZC|`u}dn+6wyAf0n%ijT3ATX=Y?u?<;WuOZ7n|4 z;;P0CL6Ir3Xe#2a$z$G4#k`|-gZcWcLBHSC8hat@eE3(`iFAPZjrJHn3jZER`9bth zAt!u*_BJ6}qLJc1A84*cw9xm{tl^ofoGh&6br;!M?Q;-6=JeBVmThN6`6zytsGVve z7M*fd9`i>1RQcIgoJ*?2y5!MD>CwsC)M)QtkM356Q?Gm@eo-O3TIp5a*`9l|=2vIE zA;zx}{k5Q$mH|EUn9@-(8(2H5FHF50r=Dp@`Ocj$fKP zfVuCTXumuKvT_zU1!XKpT*2~T8el@eq#ew&!3*mmHycu?yy%Vwc_kwWvb8+)^!ex zRkfcXOFmr3b7fl$E_F%iEMoQk?+FH-HS`s5m{X(T~%(|UW_pum!LWr5e`5XPoXsI=!p`OhpuimJq{F6B5LeUNFBsG`W3p679oDwzsEYZ3SM zjfq)Mxf`qUesn7*_hOHd&l9sML8)RmM}i89fhaW}quPxQ~>MNuGos zG4=VT@**B#QbSIIY3`Ba>ALr}Q4p>eZgfzXN*v0nO?2OzPFu!MvJToo?R$#uHu^OS zQRUFbBmTdWb7c8uK`e(if%cRD)W&`ix-lwVlj)iIQsUFw|YA08awdGcayik`I-_((TWIXjta0W zljoJFS|>!0P8OBgWbg7 z^@fYn1+glW4=hg+G1SRPpO6HL?MUnaQvuXN#~Q>^(O6vq@=g!#(v(`0dXexiV$=F{ zf*q*U3$*1N2QHTop1Pc;70RMWWK0^;XGLq`6BM}#fINShEB#oHHD7*dd*9~y=itY= z76M@;)O^;r=V|jY6e#)B3bg8k5f1^<_Jq567&~X1sGu>`*Z~|n>^|ijogb;Ck1x^F zv+^v4HSj+h`ZtjA$+H|+QUJCn!2w7r6e=IrZANO;H7|0Nh0RMBup_n@!fOrR?y3MM z%LYuhwUVR-Sp-Y*SwiJ8X5M9VQk=UAI8EPeM_GpMFpJz4cxev;yUn8MDa-0RU8pdqJEKp zLPHS!i#{=e9($^TOd^-1x+f*{7-GJ|q=&q}q8$AKdF^=Fbb@L_=q)gD>O&Tih(EGITxoE~xvW`yFgWo~9p6!>Ob6 z#_LvW4`C?Js5Dsp7f+xFZC(v2jn~l(xEZSDkn{^)(je#@ye>3#CJ8B{KVsW5s!)JO zd%lWhL)*ND6s*>HCr5iI6lBl|5GbBO?%3`@ltOr{)4&`=r9iL=h$_hL?i^69La{{_ z{bfC(N0+C)|Av68f=R592acl1GV*x-hT^eLcjgmo560E*QMDO5{}u?(WH82fN1O1+ zY+#wivwNmTsRy`xj7B21;(qnt{VPT~lS=7M)|wF8HWiaU^>0&dYX8OBHw9-FMO`N8 zxa01)V>{ikZQC}#*tTukwrzH7^NaPxnEt1xMm06_@XyPwx)0~>b_ppo{%vz0Exvxbnza)p@xA{Ul=Lv}oHgcoER^ zRn|TF-MIP!X{JvCyMKWcA@>Xt_J-n`(1@fb+}|vNBT}qnDBE}*oZ6MDkt-YSuxzj) z@a6d8%z(D4z2^B=Ww1WwhG(YvAVfg~%-Ep~K4x_Ul{;9kv9J#xayx%?=26TFCSJP; zwjO!1t5>8_3?Va*T27IPO-`TI9FmBBp6Ejn$-q?8fL?hx)MVAiCf>~41<-ftnE39YkmUsN<)erWy;pbI1Rfs~zLK8535vg*p9YDi!bs9O$E>7Vj z|Fr-uiN988CT55{lQF5XtlcI%AOD)p)6Q4%z8>;K}`k8u$TSC*w-v);AoS_0^q*cpof02DNqF3!MY2$6on zxmGp~h11OW@6AEneCoav76}HZC0km{4xMFWLMft`k33y59GOcpyDitzZR=5>#Yg-H zt0db^$tyd5|JDX~f(U)26mr})Yxf6cw5lwAc%&=>2S{e4am2h!8sn;|XIV*A(CSM+ z*idI#kmr=pmPyM4H<-u(9T|RiBq;;0!Ufb>NX{d3u>|=wjq!;W3EIXXjKHw$l4Rv6 zweyB(x2-TJ|5+&eW%2lCa|0f7tfHe4f9T2xFkLlIECeC#jNJN7e4 z#E$Dw5!cKii%3Z1-E3Boh|7Fp#16KMs!znl{SoB)v&osxU=Xf_#XRy*E;?iC@&{im za1Cw81>_3B6MY|RQ)P;j<_uBgn52~svyoljKh&ktSbvDifrc(iud(gxVfoOVPJV?d zB$+wNM85QWcboydP!wM?5f_eS6TX+qW>QA;fa7d`A<`Y2thn4JvTU_}aVNGAglDQq z0?ks;ypURvdd>hidm*b-QtE;&Kh`SzEO!4Tfx@jt=aswp?9h}vRn6nQZsv&^*i8p) zZ(3+vBSyFEIza1?#w6NM2u$pVX3}AGXD(xEvrTIyaqW=(ZWF4yVp}irsU(iOdNpaG zJTXqaID1g3#OnE3{nOCqxfR?h^c-uTK=JIMI>3#wRNrba4dl#sz0X>t-Kc8nAc8LK z(VJ*0tHb*^>W`y29tKj+Hmewgcg`awQGr2>VSu& z!j3(8dCHy+c%c%_2xjtV(fFsRnOvTD*%WZBIW^BV>f&Ye4CSFjS{xhGby9}>c42Hk z+q@_9VM)B_Pm9B)qspcvne`pp`U?A^%y`D$v_LhEwwD1Ne(qiKSQ?k^albjU1j@tZ zs>V!}&X*RUxH{0PH)}kP$#}rLKg6~_EWjQ9tqF|umf^EFwR`_L!ClxiPJ>i| zAY~q*N41&FII^XB5ZNHJd%!Z|iQl@Lc}Fz;Sp>bPA^o7?rg77DHfcsV&y3SGjKOSU#gxswYj0--g++7z>mC{dfbV+XYG}9eK9m@jq)e@t#L_ zEUiM!j_h=pKKwOxdwFl3(t)D{8^`EK#@W*JJqs5p%Tk=N**luFgaOwExUmEr+tR-90~$QiKkr6q;AeLNT=ES0kky14qN!`w+pC{K`+l$H zYJ!Yb*p0Bq?@JEd@)tUsg+%X+2UwnQLWkZt?#9@9Nlf>!>>c|qU56t{$V6+C;kU#R zGxq>%31RD;@f+ZhPL7Iy64tlm%NSo^e>>4?spL&Dz}NNi=)@`k=+YkOD5 zk=yDnqEq+{?DE6>%HrGft?t~>>1r09jb%qyb^MEev$kojM5$(td>xU!LzGjcR07@t zlt=@sp{TZ)=Ne0EDK-K{$Np&y3jvu@4OWe{v{U!@e$OSP&slbAo(APJ@N_o)L!CjR zxTa`_*^{*p=-bULQx(hzsy_xZdG1CmqYkJZi9=G{4O?)O4+~XCGDdBLSj55EeVK;1G5X5NWmS2Jz%l;*)OJ^;a+}v^Kr3R^U@e))1 zqTr?$s3)dZ9j&8*mDz~W9cL~olONein&B9qWs;&qJW6CMR0FIjH};Fb`t`yM_)fPy zO|fMHKUa-?;~En{G(}j3NyB2%#ylybL>WHz5cr=kq117U>xWauT9zhTvW6E!a|XD1 zBwCgI26_p;d`gjBj}{d1dW@%bURQEhsIb8Xdh2>{EEO+T1b#)+5@h_A*&0=-x`Z{> zS8rqWq07t~rCBsJJgq#<)&Vfa(0z3haQ%Wg4;mdw9h=mjC*L4C<-ix!P0+R3Q{~#` zjPX{<3|voUx4Z(Z;b#Pd>2aRu?KC$N^UyX!BEQersKYQ)f?CQGmDglPfjyjCthOyb z!b9`BbG~*^K*yvH!r?rV(asnjE}1{z9LIiA3|Wvl+xZ?>*=}9>2&@FTtDy_8T*FmL zvjSPc5%C(j$6Ctg9oyEEr*v5nlv?3JxO&wg z;}$;GB!>M&)oIkzA#~nrQ7aQYutOhQPxp08HNNc8^Aj&-pW8&t0ANKFk3=V0 zA95Fpw9}263{wV~G7Itjg+=^eXfV!NS)V@m>k=vHQ z^(98573W1oB{vYKwpa70de#Ni4% ztG{_wP|KzEw3XkfOfuR(y}ux0+C2f9q1aHwWZ(Q-9gIuDz#gm<-%sMarNNhKt67D> zZq?Y^nqFdpCuypq!`)M(@PMgN={fM^|An zzW2Z(m_!NOCd|l0DqAJ#dw0}c8gnvw>o-*Ag`5y{mMsQvJoYYCS%6CYTOtepuLuzK zRAR)Tz?Rcif;I$b0G(|+F$;}HqB(G_k1At`oL(FX!-&+AHR%BM(#_~iSKmKffUOrd zhItL&K-WqxKfcYLm-V`NO}0^oCm|*Owm!ra?kS~%3+q$k2RVYa*ycoM&otH7vgFvh zVP7BO2{x7vwf}Wuw@`P&>XP;={?6m*!zKtqZi*f}s0pra#D#Rg`4f$SrHQ?H zMC)c7oYMs+=UvC{^KVzzqDt)M2butcm4!siJu7wU96IPS$Nea2Ik@s2)}(Uelh(c25XCV(f|k7?LgFh!wXa*5R0BenNFKuIgqB zeARWJE@`0-1Esu>N4w||IhKxUZ?b9^N4_0xft$^1q}l8NgOkz-N|@TGGWB^>RJm? zmq(d*slrcAQ&21RzDHM!DPD&;mgt!=R*LmFS&5xzN3q3~;c-#^A;GU&0g7qZs=~x1 zZ0Jim*cQ@;lM#qebj~Lsr<;-zcn(O!Q$^Chs(-JF7h1OBO~!Jp|2d?WvD3%z;gp0& zwwAK96`by6Dw@ThttT%&qER=@*^GrH%%%JKY!|G}mOju(8xR&xuS;*8VNy9;dS*d+V1$c_e1Y4B_UdnkJf~tmmjk_3K3RiA}m@Jz$Fw ziw#Sj>;{ia6B?K(f>G}|jKHLrE35WV`|CE<#RGSgj&T%PhErTk8T+scP*M^;S~6w# zN09bxxE!Y7QT+3tqrJ3@ZPQqWB#x*1UlfwrAws#zTH9y=7M3R*U9w9lraFkfOM^6( z*dsNPV}}umekXX-Xx$yVi5DQqvE^X0im&jvMy>Y^DhB>K!NJ*Pp`C^+>X6%}8V$?p zFRtK-;@ZPkA4&NJ4HJrCDbYy%YZeQ+2CMr;^ryJFwfG*s8_C6)Xj6#H;R04oHteE^ zB3o?@Lt~U3TZl?A={z*$Q$+M{SQ*gePj00_O1o!ztQF?eIQSD6hn*bNXR;qs+w`1n zK)1v5<30R*p339x=bZ4yy0j?>=d`lvl@NJ)rX1;NSL)V+P01uE${wSN1>A43aN+qj zoK4)ALmHSj7!Rn0dy;lBCx`4*Gy4}T2Par*E#MVylKJg?A>m#2QzZ6+b6yQnsa1gv zFq}|Bk(FZ~RmF^@LTbZ%dL2^XI_xgN`gd+H@99GeUNG$05V|*`Rucu*Rb$G5#8X_4 zx8g849$@}lTz3Q9(jX>=zd~K#WY4kg2P*KQ8gW|ZizXtVqN0ipE6JVvJo#}{|J`Yr zOwVi>?mYmMet4aN+V2h=(LXwzEr#%_DAv1s+U}kuhB*nf;nsCzt4@}$4KwvVHYgsx z{&&_pS$D-v+&|X)nJpNY?EeS;q=U1qqtibs<^P*Qxs;^<*k}J4yz_$YzQ*Q)&Q+8T zFoBQy+hhR+yf8Xa1X!_z(~LT`YF_&0rV-&WSFqB#h&_qJRzQR@v|P8Z>?I@~U05s9 z(%*?&SABN7|08*WKwhj=H7SkfM=Cwk>wIc~{o_THDUot&!?cRdMk3uQD`%{l&0kY| z*L+rog-sLGI$|$NFV^ksTfVG2%53x6@6D?g%yYf323Q&@65aKKWeH1O3m)8}?#eZ`UX;!(Lm^j`yrN;qnr`gGl+p&3XlWDOi)OmYOn42gI@E=~r6srYE@I23$5-&SXM60$+n(6*HgUZfZC~c3|f0cH^zf>|#TnfMQ7T>3}|B3GA z2K_h}JmatLA9qrehUS(CxrlXJ?MXkYHsLOqW#0#jA^{T(YR6wNlg0)k;h!o3`%Hrv za)w7?68rt{U7G-W2!zU?GY-~8Aq8ZL0q+W}M*zRW0!#R|%6NP8*_pQk;*a77PD<@W9krSfn?~A&EBLy^^i#KAn=|^KnQCTp`z( zFoYy9`Te0&QYd_*fRT7JCv^V96^xYjk^kQCw;$;9>xQI_A(Ch_RiKSQsLT*M&`wYE zYqPD8G@itRN`f>%R}w8`M+$B)6|8jr7nyJ~yxpH^1;J;s&>JbFz0`Nimm{u;Z8q*D z-(DWpu=RAI!vM$vYN%5)$0QW`O#Z4#RN+UMQ)H(kl(}zzNlqz=9FU^{a-LK~%Vhin zIfX%CIX?|Ca|MmX{c;4v;7||1yaQtrgs3D?}QTa`&d94IGZ5kn@on+;u0R_;7^WO#Tk~G56d(oR27EgoXqf3CZtP z;3MDDfUy&5!-~$iRD(ZJ6Z4ir*fU4@*gz=t^(4Sq3*m^yI1B_zq^-YUAe7F@dEdk( z-gE{6!SOz+PgX(v+Iu6U8O@iaoC|A8Rir!8f8M>35&{A0fbJ8aO3kDInU+{R@kFuV3h1 zecqt|S6(^Dw0xBf77Xm-2N;;te_~%Wur>oYSU8#6=>5meNN?a^YWjau1VyXq0JKFA zysv9lF}EQyuw%`*0gz>RXg{fnW)&#?MQs{XI=ltnU1NH$wh7}_ZCaOVuA{uYysUk_ zL3g8*=W2Ytyqi9}c)jlrpMzVM3kUz2h3n7$m1CAiwz{(RlL z+P`@OJ}z`d4&FXoT>%-~zP64)N0F*p?PYp<*CH zlQLUjD=urA$e1=;G|Ms(r7ixRq%N$Tdo8oF&l(t%=9Jd7s7zrU$?9hM43f|?9q^Fs z2a1r(V0TYp_cUGgAb`knD)9RkOV7-sAzK%IC3^CT_|#?P2Bdp8U(i!~C#~aJJtxl> zaWX@(HXT>kl|FK6T|268(%KrF^0>N1#VZW|rUL*jT-AarT@9eGOeB++QV6*Bv4tXY zx-&?B#&ZN$GV5!S%VG$S7R&46i$Y`Vy|b3XLZ~y7+H`DC2AK5gX=>jzj=f|7*D9e6 zj*#*s{2BCj!bYy3PO(zgc9S@9Q=an5VwGUul{4jQ0>t1 z;~&ppEjW(MkF?L;_+klr`IlLVK(;^t#6VL{zNg~GL#MH{L7ixbYFg%E*rvX1JTpYl|0OlP@&C!YR5^v&pOz9m_?!t^`o%*5^LxE;QUG)f%i zUeTH)v?x9n_FpS-Q%IJ*lQdlZNjTvxIA&-|L_%-@LQ8C`t;+D9Wq#^ktj+L!@BK$Gbw$LSnKhB!1?|zOY#$!5Rw$-A`WBZd93Nm+42?m1K zhDhq0t4kqAip`zN77*X-C1Uyh^$^s{B6vK<&%gy6ejzX`(Ap3HlvR+TP&7>L4v#Gh#g z!naRe=tySv?LTkkg+CfHXIK7c1B)o|=MPQbMk2tKxuqMPdz2Lo~E2+y7GpT`iD2xrLfa(J~So zIk>xv#) zz{Ck0nV}Z%2;tVLB2*!INKf@QPdmi`!7^r4G`gLJWLxuS%JnkMpM|IFV0#J4pOBLC z(s*+hj7;dTf6hrWu;Fm;@@-T9z^a{P1d>L$gt{0N=`ZI^kPo4eUkD*+?_D~&UzUu* zvj8ch#?b?xARC7h&qxzrawSA0H-}I@LnVkW%F$L}3!tV2Kl0!nZALI#HHP1)*?<4d zR(aT9m5atoubhRrX)Cz=zIbQREke-COso6*$tQ|6O>0isk6Rf{! z8bsJxU$)vvVWfw06t33asZG}=_J2a*&_J1roN-zWn8f}w0HqOGlY{xC_u56%oV{aW z)k;QwV>mX`(kdA~;yWn*&zu-y^08fJg~r-2iXUfgwQ_%5ECy$8bBRrB#6zGdpk|t| z4jC^0{45QQzQo|2-cDX?krmg}j_Bu`8urGYg+H9PzriH)k0K-IjyFVqO*}_Uc62!5 z0nYj~*WEWr%&JUSOAP>oKPlGP^Fz8V*&i!Aa_1d@cQP`r;$SqKe^2XD;L#M+PHg=h zSZ(~VYdx2CrTL&4a`RRwIy~F^+7DKAFfg4IFfi%=R7@j)t*M2X zv%`PI@eh}y%YR4lf09t^NTeRHJM_G@n%{xLR(aoNn(d5WzSWE-rKNjSS2rs=zpT7{ zPe^FvDYRS1AL2J3;1f>GK(~3kz~f zwR>e!#?M_m**{aNo>50*ots>f$=?uj{+z^%QZ0|?-kWrA`3QTm-9Ok(EsxuG@+rgV zE_|4JYF0PA9HZCPy@_*r?lc^Dwa&J@Q4jyoF@`~K^4Tl}0=^>Ko zYIlLSOT45K4=i%=|7mx80r%4`?uqmpQ;ndjAc6P3@3fB=4&s1Bf&`FlGXCAhQz7?- z>e?+U@QK;;i(S{a%=!L7Zp>5G=NYll)A)AB8CAHnND?JGj$9yWTqc7((n|RWYm@ZK z5P+!@*)B6cRCTGC{^Z>-H9u{iV})VmlQe46Ad43hXN&BbgPq;Nu3>$*pL9zV^>;gS z?3qkw!9BBsrQH6`Q)#28;tP^Mwsv?!dE6%#z?k0#^%*JFyHDSCbac-%zr_<^$lv@{ zl>&!fzgpvZ!<*@{vuBiR<0H4dFcT9darAlMqVh)VsblgkdOz4Y4EVaZ(#%P~#cUWI zW0jAFf^5#b5d6c}HyE(9RV340_EaNRX~a6)*%|HvB-g<6b^6idZvWg8^JoA4!!WZa zJf_Fw70GIT?j@&cMM7-n`lj1rC9|%fVH$W!U(z`}?&xb2#<9b?;SBBdBM?0LlM-e1 zwiE#5b{}=Sk9Bqtc})ZWdx$DgD`bfx{<02}2+m`@5}1ly@LYg4_} z-_d-tuaMR%cnMxsMunMs&wu{8(w2DqSHJ{$r}E8m;TDYTh)BzQf*w6Pa$E5?;uiTf zY=P*FaG7v5TO^0b&Q_@Tn$~K=Su=XRRr3H~_ox~$7Lf##2!P!QD?=eg(iF^E9W(yR zXahuSTc#U@G~e+&raGx;dL8E)ZW-m&sW;g90pR5OT;0OOMfUC41Y9FV%BV?^ah|G< zzdF0e)@A+0uN~{*%)V|3^~vfGEVF(&iHe?&ceZpy+;)9)YTcpNFKunsZ-4ZL%A1`; zV#U3`AH*D%seSaAT2fQuJwB@}i|&5i2OHVW+s75%B0ggs*)Bg;!0(pWvmV(dI}07z ztSWhG#_b{lyCtkVblm>{l*)+7A?}Sz^!V}%X0Bi=4(gjV>y*Cj%Otk8S_isc2z62i zhT<2wjV^_IOWq?EE_Pm~*nj*o(p?08PvY7!kcNu5I-|39QghwY?(mSJ-CEk3zS(I! z*e3~o_Gbz`6|#ifVyg(oy4PIc^He&XQ`Ot&ECPC}aABN#njpEoqN$NI_pD^iDd~BP z$Tc&t^6jFv06Un;-_nMf%?%w^h$CP{Bgx4F=?ZMYsfXCNrhCcWXxC`xU0kOqFIp-I zL=G|VB#OB<_zP3}95m*=oRVSp6>w%RO_$Q5&_*zqnRI$fCQZV5jb_>cI|jYC^RDh( zbW%4TBzWi}p7U_kww^ffFkRo$ggPWK!}>Lp*hUc;tM+ngc)l@A1%`*E(=!+sxlkI_ zU462Y?2Eb{VfV2ICztPcpQrj}CG67?ac6$pyx~-fzh4RUEZSH=WpwPBBJ^H6r_zgS z*KOG883;UbiTMHU7&c$MZG8{Gr97@`zhe5qAUN}B<|}E|%;>@@G6{G?g5iB*@M&@T zeS&OAek-;o_o;ES)0&Z0ywNiP(*MNI=V1-r$H zMq)Yucv_wPV$&_zvNuDY+5oPXU-x{-DErODu$0`Ia8R5m>T~Elm7|Mt*)i+)(Tt{u=^`T^l;q9?&A>(gcM|w-#ge(8#Md$e4M8 zbIdhR5!8=BHF#!8*!0>y08df3bLFF*W|}3x4#&{EJkSo0tf6Y+OTTW>uKv5|3%P0Q zqx!OS!)g7>H=`x>{tkOeAq8R&ISH5GBU#NQO)>P=0hos;+E>ADLzr*tr@pRFH16ga zsYU64Cl_W21#Q{(Vv%@@*$mg8q<3PdI?luj_|=>w&+tu^bJZ&HtGxb>twQ3r*0(m2 z5ScIby<_D7zJDIwGO@A7#>haN0dA+$xC6rN!4f^CPhHe)Q0% zRe*`PB=HQjRIH9JfG1}K7Fl24BNOwy_=Y#`zE5k$x!VPhTPluAlr2bjp?_7zJQ_%1 zD_W2!^b*R<>JjN-0F8#lq?C#k|8mF?>Q+l(0O*Z$kO6M9BY zEKylni=~kHOw>&!a<6YJ&Y4)AGifmrk~KrM)1!y-kkp5L8>b0dYr z@PRjMAz6*hp<@H=ox(`w-3tkfQ(B`gFAZk0$q}mf&4E7yMy~-ASgV}V;U^~+nwJN^ zRo1xzj^Mpv21FZi)TIu<=%d#JPozExBzH8arU8MC3I3_mAEL2VLVZtPdCl?+aIm-= zfD)HalSGvsq|j@8=)M*(3tV?uj!z$R_Z>ZQfqT|(GhzvaB1I)qmsonp`9kQmnk_Fq zk@%PJ12;XisKO~zt-q&%U+)@(64B^!&c|Pw8E5sv0&;#q`&euT<)))#j`@H`!+nE6 zfwe>e)r9t4wJ%G7U{~6!VE`|wt)EC-AqiB ze7o!?|C09;+{-5;jhaqcUN@MqJJ@G&CBEpAN5yg*H6#iBbqHnGd@4% z7mx^TJ}lHPgC^{`O8h-kxL@vvc3r=^zx+Hk(~{-Iy-_>__}~bYdMly99~1$Xh#gOd zf$xNMZgwor>Pkl*^UYm!lH|Iudp#%u@yOsEyLZ1L5k?;3o?tVe0!+*>f7Q{*TjLj6 zn)urqsF3v_U+F4Ja6w3g6Pj<6q~PP@QqVfX#}l0t70tV2bYS!$FP3x^wrsP@#}SFxP2m*K_7{3mmYxERi-H#* zU8KHGt94uv7@uAein1Kdgxq6vjVI0x;~ol~{yP?H?~5Gcmc_rP_BRM1jr9X}vy*h; z9Rm*(?Q-CzFeY{(%EfMf^`XI1nIn1u{j@lPo;0|h*xY<{z|)|WAS9_}Qw6~T*;Ek^ zpHitD74{hPPUbx?E1+yhO0)x6$mKByHPaVo%!_lOs)lcp5PmG!{F&40o58JQr zvCd9EJ{vCnnOxnDk>$ZN!1YyILYfJ)evEQtP2XqrQ$L%Tr?S84y+XomDLqdE&i)p{`$>TBbE z&lG*&(eg6p=$NV=y3kGE53E$v1*Z|;OI}1M*QbdbQ}>jmqQMPIx>+f%+Rq=h#8F2o zAu*g`F;*v8`hIJG1|CXO2RrF$t_cADgcD&z%w^ZWLzr76dOtQh>xIULaRZ?}Z@aJj z^*xFvQdOZ)@yq3@)ydG$T5uN?CMmgZo6te*t69ZsFk{f0^BEW^>jGs zXE+ph!qBItF0?ulV}?K(?|(pHN4FYBfujuk=_{)lMy_igv1DaoUIve^KZ?tZEPdnu z0tfm=4@MhBzr<&bkG0dIt{TDLSLNQGv)_OvmeNgd5)fsJiDJQ#%N7&}5AWz(c0vR!mHT#~k~24Z7dbu9J@pp^N-HEg(e_3zS@z%rFNKf~XBqV8vCoo0N)-a=Na6O}y+}v!y%is0J^$ zh@sRWZqe&EZWQ51(besOQczr}zmYejUJtS5CY6#IwMq8f)qn*J9pcb;_-bUKnwfF+ z^McXQVGW-X9`g9jUD&iTs3Q5m^;+b3-3`vJI$+6l;1mZKA)M;`tuCrW`_L#6#WdTb zEbsMXA3SN8^e`f8`in)uqaycLesH(J0-AK8M7*#CNuk=YKY>Y`k6afefl@tXw@V3n z+eld^&kL@DoixbtabT1hV)9-%M-$EbA!u1fL*iw`2g>KxcDdEWVk^W}KYEpq4oaQ8 znS4*W=jCl-Dj%eZYuFa#H#cv7zPc%T?>I3t&b8k_IMX6UHA;l9*hQA8y!*_(=rT?D zTe?(?#R%7d>uA4cPmQFnycLW&1Wi8?6!!t;a#My)Nu-|awIM604wJ9jN7I<) zJi1a%!nY~1nzwiXDc_nhlyw>)=^mi4n+UU9C9nk&s2|GcjZ@cfK9y5rs=}mxO08za z;)VdG(c+VX3~4_xdLCG?OUOv?0rU>rICy)|G9|}4Lb%3bTfS*WZbp3LhmwTxANON` z_0p6$_?snVJA@7m(|GbbT>C66oKYz~J^cG_0jt-p0DJU;I=eBVomZN?r8Tq1U5Q;~ zE+ke5ziKSVCcz6Q)RoAOWp^y>?!JCH!~eT3-Qah?1g=<<{Rl}Ib2K6;vavbI%-AZ} zps9#YN!)a$(ysv0N!2)oWc#}r3&DWAf|OW1&@z?ENJo8;Ag}l~qhYoSeq+&%_1wBa z88!Y>_%y53Y7R=aJ0jx=9%JlLg`_N8c1)=;%PcEEi~Kb69mg0+)bmPVnwM+6{IAnUq)l8eRu|5LAr>wUIjbT+wOkX5HB3M|AQD@ zH)0M)TMsx@RBRju21hVn=`Myg-2}+dODA{>Gih>^iFNK6@t`TX@wWklT zvMt6w_UH{{`#YRUbTTr+WGl?kc9ClTtt4>>#+oxtL4=!w^#WCdt>2~8XK z-qPMHm7&8sQM3K-yen?8=j}dgb%#2V5hB%qHy(gqm-m=I>qsBSQy-qDDppuBY@~$O zv0yj5W9a6y%JliiqWgUA^+q4{AWg9V$&L6yQzm>dhvXbK89qGJ=mXx0t7D!Ch<1jGd zBQT5ZKzBr3fPDYDef+3;q`-5xF$M_pkD>lWy;A(v3H%s7)W*Bmw%}c-X5QIe1S;mZ z>K|Sul-%H^0oaYQShFzAPRd>@&Alo~2UTO=SN03!0l?3En}!MBVApKCo2R10qizsVqO5)q(YP7F0GuRerWB&r8O zs1E$~h91Yu)MMbJVS8Jen6Ksx@Ltgrufm*^PM=b&;;TmnoBeEbBPH+)lfKK zhB&2R{NwC`Cqxq74~k&b#s2CjGSzv)`eCsc*{mr638#9CyaZy6zwwSyqunFkCE>iU z>m7n7p=bWmD(A{5>h*=rZ~Pas_@z1R{*XZMd49_Mk&5XH=6}8G=Fi6!kF*8@!^Hpx zllf28ZcYDN7d00HYiE=H_iguOw^k$Rno9M{Ozm#*4)LJZRw^H!6MvHdB4--usaTbt zbq7K@d?j_Z?WcDvVCo3YKpM@|drYUDhG&yxz+30odFuDq#R=cXdG7mN?dRz1 z=R@Vr`^{zP^%qDjxA*lb)AxBY_wz8-_vt?Ob8zSDu=eY!wD&oY@1^nIh*Z9>%CAq? z-gjWG&&Tu=pBHGF&-*j9_UlE?Hy_jYu{U?t=jmmK_v;3PtmpeSo%?xl{drvaZxkP2 z?#I2JH|XPu@8dZ1y62k9%Qe|@s)ef0J&nB?|)d^w+-_k3p4 zzYQ~d?Jt@5-Yy~Qd4Iix=K2;yxu-mc6!}gMuBXz z5A0vB8ZirioW0uK*W+B@k0sZy44=nF0@So#P@RvIrr;m0iU zd|$TIzJea++`Qk;ecv84Xa6nPhn~;d*7oP)KL|yEy>3%!W8b>Bm0MF3(rh%ip{^7ZMekz`aJi2e^dkXdS-h+EuBAP0%98Z zK8Jgsr)vq!z7A(U_bGbc$8!%AZGk;oEpI*7?uYG9SrJHDmX1l&y&Q`>oK_RwCr^L( zT)GXVyluS?H&1eGZC11%Bo2CPw17s(8(2^EOWGs4qTSw-Pu{14$79_Wt%@hzt#r}z zRx72`Yfmk)iubI}tDbXw8_RFfJu4MY(@9TB991^UtkBoHy3Ub~EnPO7&NQCaM%TsD z?r+N18;dI4MVsrF9?DlXDJjRgC07QgQcoSNoAV>CwyjmY#h!IeahAGYn>n>B+Mc*F zPnx5biC45{-pi{j#@wQpDnd=EEi12X=P!WEC+#Oi2M|aLS4pntvXfVk&6k%8wD(%l zLJ_@GiM+7sGHcWAI(7t&hnC z09wmAY@=jWE{d|A`pq6VEN3$UYWm-X`ictR&Yj&D)8%C3_E5R~H3(K;SwjdsPbc4W zUooumQ@1T?yyZ5pR++(AYzl3wxT#9%Vcw-2Q-kKgSQ2g$8}_W8X1w~MG2uC9h%(_2}ajUw<< zF0~(**H_)@R$Z)%7AuCwYuIG77oqQeSRI{OH7T^F8Ia0lYHZ|2hAt8E0 zl6B;^wx!)Zyk*)jwr*2o2&l`|5{hDsq4W&a!lp8O{*3EvwYKbEWN$1(ET_dJ&=&qU zZ?dYF4!ZlBb>)%UG@#B@k1jzhDrJt|C9Fj|DFad3^t(%&H{uomQT;)J%g)KFrix7*^Sm_lJ*P_`(;H*9nOBEX{WUw1j5VZ0 zcA#o#Sg4`rA;Ej6<9Br8r`2$#%olwospg6)%_%kF&SsOc?LW{fWiuM2ijux;OWi#= zXQ0)(rCMSPVt}jaCUc*~_pC6UEA(5%`LR z{Q=FArAXdl^Z--$&uvrpY@?h>nGEet!;ifgGS7#(2D%Es&*4^p4pV`G2!@2CE21Sn zcV|^w>a3-agT;^t z^+8_~T7=E*ZSl(4h{b^LcmqVa1?r+i;C;PR?oGG&+7V<0_D2LQ@w)-FSMKH-Qgy2V zYz`Tk&5C`x$Kr&*XASk^ZL+O1S*ezLxDLgZIs`Pt@pFz8MHvIIu|G*uxTlPWNj;_$ zy7B&p3enZnAkM##L?TTtoWu${Cb$xJ@|sYSWv%abiw(53*58%&O0MINR+_)q zt+6z=y}ja9mlnCDkTfn_yqhpi`+Oog6O?Ua0Qk!jXoH+dfqfdlbNXT%9c#E6l_b*H zMuwg)wZuyB`4~yB*`wWq#fF5l?G5RAsWW%e=DprDjmtIeOkYpcN~cBr+GdtXG?WgJ zEgK&TZXnVh0$LL?@F^q7_v>$koSyxzHniBfOrATI>SR++jat4&k8B$dz*s1zr6GNU}(qsc<42?!#?nz%RPX2<%T)mdTR1riE|A3kVl# z(eSzy1eCw&MOLc6Ym6p4YM#;+?Nz?eTS<8yO^keJD7P%W(4@8UnE)cQQ8SSl;ZL$1 z85LpHL5M!)O(09KgIUnHfuS|i3VmU>c32VqI1YBAe=|xXh&PEUDEska=RoWccSA2W zlI1WoKC#L~;qah}>sNzP5uCM?rH#uk;1$b18jH!|Ob)GW#ba|%(N*J9?Da=bAjiX= zhgF9TQUk_6SDCDP#bzUl1al$6;YU&PODpDLc{wB8+DG}>!ANP#Vy$WOow1+&l$8uT zJL)=0f~G@vX0J^%r1|QFii3Y}Nn2bum;FEIM9%e8yXc|TAX{ykgE*XrQFB>HM z#QD4^NCroIwUqV!_TAWN^icVzNmiJAmmw;J8Yz9bn%_FRlq4+MgJmk=xK7Fa$>Zw{ z{EFLcCA8E#Ro6Y%Ay)9t%EnhtQOV*T;z^pCBBoSx8qJq!A)&tuSrW)epmkUrSq+iK z&Hp<^QBi01l*!L*XQD@jZokcM0JOom@&z+lsPAnYq}*^~W(iO!9Z zkzIFa#?b_q_RWfBRZU7MOiI4Agn6>pm3G|qG_45tlHBsRPlB4a^aWS{(F6q5Sw zDjB+j%R@L$Ixy(DIhp}mx9&LPM24wD=EZ?4pK9MQFi*qG4?qw*lkzC+CXsLXa`>LK zi7cgc@@hf2at_(!g{gwZKTu(@K3EYSOp&~-3+T-Zfw`Cbj*ItS09HV$zu4p`BSNIA zvmH>0K8y#=OXW89u5InRdp)6jTN|fK7GV6zDYN@Y=%Nzsvlj6LHhsm=*h>>ts~${Z z8GI#zFq&OyYAG-xPdBO0`?ztzof+URZ(6r^*&UJ^tu4)|Gg7KkQe01PCwx+5Oy*^E z$b{16)&RQ%zq}?aJ2tL0h4j8H5e|k1|e< zZa}jrPjKR&6HZ;;`o>Vl#ClG9Kg;Iz`mkogwZm5M%FwLpeoBn$m$Rt_uGK4@q1(`U zmypxc=@n@m#5_(iaSy^j+6@{pJPAp>mtizbXL`s%-TOANW0G$yU~V&*YJl=Ok^n zvB(|CkaZ_JZ_1+d9>ge?xhcP~etfSsJf}28MeFi*J<8k?s!H;+Ns;PtmNU6OW$fmr*T}p)&2O7qFz4Y~zAI;CP)ZOKbnRKJg3FDHB`U9T_5eDkEuf3#j=|n4<*r zHUu1@RgD3G^*P=Iu=>eL?nGtV60LF(JAqMa_7JUdqX&LOb1+C+R2okgNr1*$-7+qV zB&dTL+8uY|`3~?BN0Sk=+eO!At`g9ah|R81*QS!oBB{x}ND|_|OP5-8YU67w)Yx*` zTZET}WR^W4X(-u;TaSFhBr2j=uMyRsT2hXj6nhLZ@e)#6AKuYHngrRO1k3yFdUTCQ zPqwQ*QU=@p+;xv&rTolxo}GTSm$albAe!D5Zny9=^b{P3Jh>@5t7!u zXr=X#z|yI*M)YmFw>26`3r8HJbl5z^ zv7DWqT$1gEbU9C;Mb?O{RqbvWky7;RUfrZNCq`1Uc#)MPiN|U!yn~pahFo?%u90Rv z?{|$PhL-J#ot#c4%P_1LcAR-a%hd+4Hn0>>lJjRHfYY+Ar9GXKAg;Y*j`?DVY8jpl zL7rGoQq+>1GUQo$mm}Rfsze_vH$&T4&)Fqd39~#zbvx84M%7tC#_O@9JoX|0EE zTQOIqWT({@VnONz(~)8Q=-XDpQu>bKvwD-(yY%z&ZM79-p-Xql9nq$haC*E~a-v`j zlQGq(Zbbtj8Mu|&Ln-TzW*0;`G++*eyGP&CCbtY+GFKLEzSdCM%OOvJL z??JSaRtcHfXD95h6{Xc#Ew%{^bTkBEjPp^s6D%I2zqI}5U9PC)NSFDaA5;AZSV>G= zrYZh0BhgoJH!JBq4^Q9mqnc#I>*ypN5?RzDU)`&foG=O`O_mv{3kU9rP}`167%m#D zw?2?;*Cnu-sa6RY_DH&Ayb;+$PGi~=!av1*jzCMkUcpY)7~v{PiE8~8Ljm|i3=s{k z+yF^3tsyDwM1Ikz4+b?OcyHQ~cRh+6lE?)~*xK*vVP(V}>CvValXU1FYuY}x6qQtI z;_gEPY*y#e$@AlTKMleu6M3MSyIqem5Q2@3yFVImy6Y+Tv*@-7UzkOY-L>rK1UhLdCxw!SvF zl>rD8t?L%)mQ28>Hi9j;h&fIy_-@^uzGgvR%iA=a7d( z@AAx&qH6IkFIF1dB>3;)jxWSDh|~H?M3MLY+@~B$aw$} z?#Q1(48$8|=1ja+0{4w2j$6SRmfVUwI%`>qkK9>x8mAKo0w=X%&Lr=&nfab~Q-3(t z(!?^cv_mZpHIouGglQtz*`9ww)@<@N3%~|BH~~G=lweK(p@}Q-rlxkREJ>N#w$-pN zrQIjF)Qy;hRDZ_D)&{q*?*vYDY7oM}>0`5RHVo0FgUnpAp~0uh$k#IW=xHG(zYw8q z(0d*Dpp9rrA<4>#zi$)ML1BDB5E|NsdUk!yF309}J-VhFwd=428ab($p)~KOe4`Zp zmkGxu+A|(<%S-gh*q36I@L9o?FaFAasDu*@2qb!YbB zvwS#R+D}8@sx2G9NSRrH=Sl>1K3HqoqH<~RVk2--aawjvw1~b+G$bUQyq-?1jwp(r zNp6DX!T2C@PaDpBBWj|782U6VbnVgNU9puw3k0$QA8+6d%(yqK9fI*bD-k>7wDoS+ zUpBgK2Y)bBKam?SeA+%A-%GHXm?5sqW;6VBbPb|(jE02oHlGP6$4VH!>FKrdj$a2@ z0Ckc;D`sp>Zb#NB=8a4ai^jb2JCcv&G3#Lu0dtvwIeW~QR5WT%d)s)-J{jju^4|jp zf(~UxvJ7?RNOXP~r}-0^6GsFaCq|-Ok6^5UOc*R3oW8UkLA#+4LB!OyM!a01C0o#} z6)|<2o`9`Kp5YTFkA5NVWohX5N=InPfTokpVuSy`oqjMq^nevljI`wG041tHArdMn zme3U>M)w*a58GFor9+FNhfdGEPQ|yp+FI+4TFHA}za7K%W+fJ4VDoq#ry>uyK0%^R zYX(^ov4ffbui{|lHKJMzJGdWqdgN0tmtZCj(n4Hvo8wMck~qDznyd-!#^!+S#B;;$ zleQ;K>!F>_n`(%9q;^mQa#0fzSq^~?lDi&WdZ@-tAJs=0$HkK5W6xoyW3r=1$~>OL zn2RueDw&`G?#j`r(^0vjzW`!t(C{cxkXrREpE7+YLc$ch zq#1N)w~Wg!>lP1trFEt8?s%a&aK|V);H5w#Zw@>YdSjb9g+v%_#=yg0DNn96kPfHiC)oQ^swr*6nHnP+%Mj9jvwf7ra*_{U6;=6Vk2 zaU$;s?K!ku$jcPcmh#&%e!yN4O>+?aIw$}eS><|#Y?sG-c2MEix$)mk zLwiS|1OCmoQKZPV$k{`d?ebM5=?3XeVjtSG6I_YCy@_nq8mS`?&;vkTCo3Au0L=na z#j=~6rvdNT^FA5dLHr}qzUTLXZ@|qU^;~Zs4tfgEQSMfaS~`I2R$wmb8;-!FWYO7t ziG$~(>xncCJgD|*?#@r?0TqCyIxzUu! zaI3rI45$V2RfW)g2NnxTHscIt$%^IGNEkH9F)f>|K5i@0*PN{cEHU|PD`}=ll4zUT zUzHXkPQGdLmyhrL)E1ITKA}PG=OZw(HC$tf#kl}Cpdp^YjBK0+J2x9OSnn%In3p01 zV4LH&{XEGSu=hKW6U?$IlR7dq`E+eqK;XlN{h(DmvPl3nkOwvyAJSY4K&C%2bHFbI zT?nqrph9+lM>B9^QiMK%@(=ZP~t}3lQju9 zXL5M`b^&{Ifq6aiEP}Gm%1g+piLl)aS+&*(31ZnC)}zTyc1EO zvB1v_D`As2=CuDH@45kyc1o(Ex(gwyfNXLes+# zbX*{bDBxthJS&-w$OUoo(}1?+@8{ts81kU zIUHHZPfa(5bCKy!QqMVnw-efr%tO*HmElxuc}FO+vmR{I`fIzks?MkW`IFb2oYaE5 zFl|u~UndPHZ%gN88<4`gTRvyUT(l6vS(OxM5n!~wBupvqL`&MscHra?@0=FNbq9Mx zIK|ufxH0zo4Lm)i5{8|YFVFti;U_XS1ynDE6u0Zq6|^A%T*1LLAVW_@{XlK_Nx#5t zFsx|Qp`~4&5L?kn@?Bxb%>G)0bCpJbEU-2JK~79WGDA2P)5GhJqAY}*k3`r4%TP!J z#B}WMr~HUZ7XYc6FqWK`eVSBAjP?N9NuVSNeegI=&eiKB1g19})*;c;K-!i4{)l!Q z-VIXj@xA72owv=vsHN0(CD3_W@-i&4Mh55U#<+Mke15xP@$q)(JhA zg);}4iUdg!I!fn)p|(ph9C2*~w;kQ&tmvZXM3v@b$~b`)ku{8^Y2&g^_wk6kZ$#V8C12zI9eB$DRsdUQ2*6MCAe zg}v2tiO|E?)o5P||Bf?+RrPWDdV--^Ibe@L{kXcXfIHb=Tc<88(|5{G)Z-Hp2WjR^ zV%WL`+c$$usMy8iPdifob+nhs-49qBhRohvK6i5(ITL6_7#)_7o>f8|Jc2Eu>lLyi z06E3qTOX$Yk|dFLrtDJU!=>J}E_ADeBth7s7$(uWERAxfYC&pnI>=AFS(OfetEB^84IZ~4|2G#M^{5g7M-Jwv-SlU zei%Ywo2Gi1px9UE=vj_1wan+uV0O(n!IIqHK#->c)u>}hDdHhG($oTZvPg#m-o+>& z<3J1z^YnGR4}a_-0Vu+fUX=#4ImC2hi}Yzp*bX6`Nq@8m<=5*q8)hYNf{iqn7Q7>9 z{v#D5x9{F4=-Tz(whv^E>`zDgaMCNl3#oBwcY+v2QxWkzOhTuxJR-iY9kg-{Kov27 z1Uk`nj{)vdIRQ4O43G>h`K6EV1zlc&iReYZ6{mxbA^glkA%ga2Wa$yYmosrr)~wgp zbH}sG>_Rw@lE=Rx*eQFoX*0c^;|c&(29oT3;7;9%Ye_F`q))^-2;}3?kPg$n)~ep? z{?4JH_VYlRkL(K8$a^-KUBl&|`B925D~06X78b;Ix?%5W>KtRhi4Fs5p1w4(}e8lPXW&33- zucX{F*Sa$o+Cv2$m}NlE;lQ{Qf^h-qdm5=c9o4W zmzQg?Y6$|Q=v3%i8-oNQ>Q6MkPomZ3Qf3e^ac>>>kc&qF#hj$`(>? z0%rz}+8%~1H8-ma%w;y$OUPUFM}fA~RAYYAD1b5xxHY!Ab|@~YghD3cmk60Tv?wKA z2yErl+Q(o$nmUu~1*v{H!tt9pq`C{^A!;%NH;q#Wp~UFhrqtHiPX7mclK1U#)nU`_ zao-f)9Q7+iytPE;!Wu1QJOL|97O${6HDe^oGWcIICOe_-g!m#*RCelldU>sfJs}g&^-`MH>Z=YC3?TV`M{oa2{k#^|8?i);q(z;0(BjAmZWUV zK(Z)ZJU97S)XdaeuK2I5@_U(%$Ja?y4C(n2bqmJZk-grJTv(#o$u%lY&tJ~#U1P9y zG7)J)<~ZVIQZQ7>gy9Ng>T9Lh?s}P$wGu!Vd8eBz$Zt}|1c`H%qqfS~Hegy> zPa73O(7F^8l=?c@FkY7@Gsg$wD`A9F;F^MjrWu}A0#WOG*{5U_Wg{&BXfqFBApmF= z+*3|X-E}Lhcu&YNs zh_vEH5!RTVWD2z8a^(1W3InnKTUa2KBvE5w`K^SLsn5bkCuUBPy|J-$f7heJy$;9; zOsh12Wa=J{NGJA2$&blM7cU~O-9p+7qa5fM5-j6svrhK?HHQl>p`(`D$^cPc0ay_0 zCx8L+nKPkx)E8yfvQjnmZ|5}~`ON=9{`~(I`NK3HiA_w_h8vQjYG3<}Qt&5VTai}V z_l`g3eDL)O0=|F?Jc$%>WHv1##Ph;>!QI9+1hJo54|<8MOtW!KSUL#D>0xr@qba+e z!JZHp9xA(}!nR0lZ@`i#1;Wl{liMXGHv_k=#hyvFRl`#*@0EK9WV=WdOma}CgKI>b z zq*P!xnyiFodH^}SRE}=#4X%f2eYk`~);qOT;a7ogs02yNBZI|+0{WK$xHWl5n{sUx8BFg@;Ow&Ui&A~XXk zt!0tMgWz=7*Ayo2bEL?;U)~E0g=GT+8dkxFBP~m5olJK5jY}v~<^IwoJWhOY3AMq< zNyzZPBLsgW1z%s2csx8pSs2}kZg6w5cXaQ|Bb2)zYbEWVGAKBzuLXc7_uvqK3Mp!} zTGZfI9@D?q>NoZr3C%{TlW<4Bt&(e(w7TAcCj#Qo zdej&jZU#zb4?x-TaF_ak(_z<0lI7hi6JAOCoF;iM?J=+A3JZIjYGz76C*+V(#Kj<# z>b6buBsZ>g^*TFZ9X^S`4}#v%lt4!)z1)MX3$!KdRn>GGP+&AdN#$Lx1(PRcyTY(A ziCN!*#g6oaem`P6W{m^BAH_BGe93Q{Y(NTt6hYg8+akK<0M#eQQzv;S%KEq#SBR3Z z*wR|llZu=id^c434~Q)y1l2~X#EI7Ao9&5;$;emh_MimQu0ELgNR^YI{iDsc7Agx@ z&YKgL4$V=u7c2+G(Mym6uPakM-K}Eag}DzT2ca}pG;fk{KmvBdV zG_fysY$f|l(nPZr|6UW0bk#=Wx9|0Q#c$u-^2uay>ozN~Wtfr(D%uaKPRFXnPv~#c&$PFri5nxnsxJ z$k+PR&Xz2DlWX(lrs|uWKs}-U4`%h3yBe4bIYVk6-}^_KZ~2b|_YJ}@@H*RW zjf_Kv#L6^suZaab-#hv;!(~goA>6f_i=iE>$6n?UyD1iCsErXY+Se0)Bv&(RY!aft zq#2!Rv>Lx35vnzx{0{H+bjfd<@cr&Od5{9tiB3XUlD+}mED$3CLAmX+$>nTh@YGh; z*r~E-dax-*Ww#bX+@z~u;dR>LETX;>Ig8bRk*Z}Bj^E|Ie>7HK&NXiqOa{LV)D^yOj^xN@z80{81{E4~=WM{c+0I9vl?o!{{unDm2?5f8 z2I&#IFX&1NV248&^?b>1pY4fbDlt@(X)QYxw=QYIgSFchQ3EVB8T+r5ZhFZDdWIUyUdx_*evD{9~D}tI3y^(wj@C-0UnI|oX z&UcD{CHNsze(~OBW^&x9KccC2o$sz0g6OzJZFZq>qA#@iyR8iqqvS5r3APv-m=P6* zxZhLzLCU}o9|_0?#o7D4{b1^w6djio=Frh z67OMHK&aioVC})F>XLMQkgI8$0ofSD;ZPC>rciw!C2+&TYk=~k>(RCB6__tsVFI?# z0{6q=1_5{)-FVr)5>Bf`roMhGepE_5HsEmY5QP&S9Ud;25Fw3l5kcJpHo8NYw#_J^ z)N=!;po09ZubmHHo5H0HlGT9oO*ky$?*Wt{k#qdzyRTOO{G7*M$q6DUOl?Z{%57?W zfUE#vZ-8&`t-`@4`>unyD?{I!VY6>3F#l+v`1|D@O$7J)%b|r={N)fdo&SD(C+w=& z26D!_@!;p9kDNka^Pr=er`oFrLS`%H@jO!Npto`VcE>j;K_g7LqRTRLrtuGNu)HMO zIubz}#B?n%J8S7DC^HDyk|KgrckYJsP{ytumEUjT_DG1ylx?s$TAv+$RSoIq={AAi0i2Lw434Kj<@d>A80OzfEEMUd&21*R#SKNyjTFsXBtz!>+JfsF zo;inpqjDo$GmXd=RgN86Wn{QD47WNVcN}IZ_)qb!@jxi^2i_X2BET*o3Ekd1T==0x z#&bYD2v<4}X9K(^%rfs1mu?a~f+O?&hCbZuMo=_EDH&P36(*yD$T8zbs4Uo8%lV4m zA)X>h2(d?~XqVXM7=_&lDLqGlvKYKv+Z@}pV3~00lx)gLJ75{e4i!v3q|j~KSIw)Y ziMkLA_DLO=lvoOOX~|A1S%~ibr2WeX=gO{@nf}YA@TU;5cv7Uli ze1pju77mn*o2@`706)xIZz~_}HN0a`hAdOIfi^XBSADRpRyd7vKN5APD_lcDFj*T( zml>E2wQ@h`qAw}_N2)&|q7zB;5=FCIsNN3ba}T}c z@=UWb65d5j5;)GV&~=alHyJ;2IAIA6Ixy1l16J4?F%YQUKp-s4XE$}z)>o4kK(1{P zmU00UgA3?&@9SZ9;{4lcvOqN#@cW3CUJl3I0cW!|o5c#W>U7hX=kjk4u5dlxr3m6U zlc1H8v8=70A*!W{Gf@O)W=d0yXm7kRTxy4#iu!A_#5zuxs9$fUs&W&gY2($nWNL5o zGRj_0@)2dIj&6~p9Kq)a9T~jG&mba+q!K~*n_1P)SI9;_ot7Re(OtoXK-BVMG2`Xm z@(DmqAtZ0vHP9F0y$&X@1$%%8icD??5as;Yo43jpZ{E3h_uJI!O z`*MPbw?erBXl2}>Io)7_9c`;C#{lS|o6Z+A57A7O<=env$q!c(W*DH95pRc71w70X z@+py3An<`=?PmPQ^O8wV7UFc;3%d)Rk{l{$tAUwhI}P(~ z9b7AAQUU1?ifR#KT`Fag5z6OwTXAO2Q)eFdhRCx++E<;pCTHD2mA#x`nIKL{cghOK z&TP}pY>Rdtllrv{Iw51?cVIf*Uf|pLJrcMZm6Bu-O0-dcexO3LvCT&;finI~s)0}y zQ+&Nfcd_pqNwf~+xp#AEKnN(4N>UTpfN}QPYDBdw<*(Wlr{p-5-HKPr&#kZSFS^)RdGZiY1K5EdGel3?Noy2YlDY~7fr zi1R^60zGz#@|tz4ZChNSa1T0w;OD?Z$aHrIsJZkRmOB8O4{!bCjs(=Nli<8vk3N`6 z+VvRDWI`5^Z93UX*m&)utpuFqL=0<%TB&CpIq0P%4=CFLxv;(QJGD%9pO4aKnr$`P zqbY3DjWj68BFhVio2MhBwZG5fmqSaVo;&+48Xk3Yl^MVK1AL<$LyX70=G<59e8u*$ z5mUq#Ql+fQeeyu_4Xw!1C?%^4o>Am%!ksH?E@(j-^g9}-zCG_l_V53>`_H8ICX@B3dc9#l_3ng%Fm)a=I80ZL^!z7Q~G(L*Cp8;eClQ+21vp zG%y1n6Q(HEj3lU5#eVpe6=wE{6Btwprz>KEry2+!G69mnW=?8|RA?ojOX7nx`6yC| z2wY!G?e~Bj74`&f)s#L58$^JegMEmKCZfbqT9Mp32Ocv^^$nb5r4S@2Z&Nl4X)vM2 z!(ak*cS8g|OC?Ts&G`A~8UdV`UkxEM+cQ*jOsZV1j}gEF-?G!$q?I5UO4P+VRQVN>_`E4f<^v7v^;N$#f7S=fJoYhz&Csy`<27G8U@KTf7)?B{YZWV5&vU^##0xFuYSm z5WzdzLpGp!cKhjJk-$C%q>5}7TG2P z5#8(2hstrtCmU7q^GtI2m=zZ4dADu%N8LEx6{u{7HjIA1O(B(gjK*&-7WHh2eG$8EObnTYsSTU8 zsYp{nEdz210BZQO_XV%@i2Bh;P!0%d$4j274}9IzHg?WX2HD!i&ns3OUuH>y4GLCb zS6URrbC=WP*cCXa0m`DnIaJ4f5LVC`2p(%qdZFSM?)l)Fr5#D3gw1<)z_w0@sE|^G z>Iq`Qt{Z9>su1eLXHhW%d9Y&mJl4?1l5W_tE3&uYav_jZo>DnJ;IeDby;oxg2&;xfsmV!?93|{@ss_>? zj0;dk4Ek)1n9~aC9oPw2;0D_%Es3ejzg>?$cE8iNU&ymiHa#Ip8xX}i;||I2 zpw*o#O2GQbQUhpxyB?Xi6*9m{H7s^krhoZpgxb6=$J^?jM%(pVQoqEG}OVvG0sYK_5KIW5G zf_@+skXrCcY^N(?O5qfA%>{`iZ60jTAU7tbyNA{1!<|G+1IOu=sHverX9`4*4B|wO z)gG7|vf;G&$o>|}?~_{{v`Cra%tJcqN9>+}vRYkMkRHh^`|(6r=DYhQ!OPYIF>$*d zeJB=sWhFm~g+7?c>-F7TaXy)+$jOTm2`sI8;*lHe#oq1Q4X+3S?$TBlP*P%1YFR zI*;;%^3`hC-F;I!s%~>dzDr{C){j$R<5*vX76SYGkXB9 zDCVLk_2T%4yI{~1(t>@9x&sPL{N@5fJ~ZLjZyCXX5~BI|?Rs?Wd)5gZG4l0@T_x9W zK3fb+QJw{KyxvxiVMnTn>!cjG64>A?F65i|MFs&t%}#Qk!px$hC8He^LU?*YeWKkR zkQ{Qc#^U#QIl~Wo3ZEa8Q1dF2vVvHb;Qj$)tmYmhgU}ai@Nq>j*KNdNa5+Im&~N3z zhe)2=b(#(a%RYw)i}2mP>TIR4@9vwERsn=0J|%{W@pc&8MvE&e(qZ~Y>D&PFcOotT z*vg0|9|O=nQJ~;hmtZ6Bnqox}eNE=#*rP)AbV#QlR>AJ%?*Q2hes&(UdiI9Kqfa<3 z<c#fQdUXLD)95xq zV2NmywWqgL2kn$O1^Ut=REvb%0NHKzC^4bhltX_pyY+d+V(TJsMYL+8l4SN%_tDt6 z@FP?sHn5N>?b{l#}6C&YT#=0$ne$<`TfTR~!DAx!>ve0po-EhkA2@+Pa z0Rpp1Rni7?aEq3B+FjIV3_Fh3qcg)3s*8rpTdAko>_#7p5$Gpq31-P_YSYBe^?$eXzVu~VsW zdEg%0hmG2vzDiIZS1eoWUC@e>#^!DDv|Srq74BRyn+RdF1Mn66ezI6k|{ z~8KrG2BaTc0FVz=O=?2S|pEbzQp<0Iyb(Mv}^yvQ8R>i0RXptz=mUNy}Hn2>0 z%&7CxN1{^r(;>(&r?!^Iq{?SLdrAjUsY~=^`wl`XbNRFA;N6ug`)lA5CKTD?iB0A* z`AqX6)AmJAD!47kXhoF0m;<*$e)N71KTRxsH+&5Aq*YB_c2`q#ABw=(jsWA451}R*iIqW6O=pjm_ z3}U$ld)J`*GoDt7_ZAuGa1t9!+n{zbrF-jt@T`Kp1E#a3l}|+}5s*mlvc4WsbUfv# zV|BEIs)TF;Wv~mLyjkMI)EGM+jLXR5bycvZc&_{}Du@w^00|9Y=}1ZqGnWo5!T7yA zrU#+2g=s?S0utL?Nyj}r0W*ZXbNmL02n-w`!0JLsgbHX|U3~+f&&o9E>chqc30>0b zE~|fH+jQ4t^I37xs^)n#;3&qU22Y5alV~9VHTZIKmf`*&N7%MA*yeGD?uQT;@Z0Ee zfS85AIqnOA@MI#wmoo-92m1lYrVUx3nwh@FvCcuxLk>X8tGk2s2)?&uvZ;r~1m0=V zDjwm?xk@WFjFV`fbMz?Sw~tL{)}YM`E$Gg&?9%W#wDAIrzK6I%BGctU3o23Mp*;=P zzrjq?Up!Z{S*=A-(m_lQI%VgrIR%o>j1nEImT#5eKm%ePBM8k=Ur6y5B+;N9W@!r{ z20tS#a8Z)KLCIKXgXG>(`A%z*3c;cqOP!Bs#!Njanhgu45fHRPvM4f{S53jOJ@bY3 z|2R>$d&FzVcOu?KjJE@eL@3{wQ7l=SAM~hTK;Y66rV^|J_X=r|*)y<^hR2$Y zb8qv1<7!C}-HzT#s2rNLY}-=`PdX3EuHBc=eO$5qxD+-UHq}apAqCI+W@H3X7~_wQ z@R5FyBrN8e54v#t_|4TGdu-`a@f6c)W7pg;7Yb!tQn<$Yu(a}jxLag&qPJ!R5{ZFM zo3THF9Hte*ZvQfYM3d(LGyz)fu$RYaNkb0dYWJ*wka|eJ>si`TUZfWZ-V~}_)_{@PtSwJ4T!T~Y^ z63%nfTix9$TUvBGQvbuHT0BokV2FI&h=aCcH+M9tG<^o7cagyE7qYw8O5}{6hZDao z(~4mN2%^lGb|!m2aT1sGNB~GypT^^HLPJ7jGrpvkxC8+-p#&k&41Lmrk6N48GI#(W z2K}=I=vI4<@<++DJy^3VxH8xjO<}vq=%ckndYcm%<-8RQafmRwNu^Dv{@95ox2MUv zx6I@GR>Lc%_2cF??^riCx`Ese0Et`Tfj$|8CT>wcm7KmAtMRvN9>3Lh?jZQ28KdPV zvNcLN0+bKZWf-OI4W|tVBb9HAH@R2S+KphH_v*4rZ*2KW_*C>Z)NV8uuLMNpdUVbE zOz2KYN?;K_cH^OLHDzbB_vl^a3OKEUhBdc#5k0(j12JD4saDb~IvC^jWfdU_f(t3i z5ivLDUa*o`iO$yUEc?E&ZX6|76FLZT#1RLec$@YCbv}w_ZDCGqH#;%;dWC%&jj^;f z@?Oi(5?6~_?){Jzi#Z?}Mzg9$ooKKU1CDWfm?R}tslYHN%_^IMYRx<}+>tgGR{RLd zkvc%~D|HE5co|^q?7=X%x!G{XnELo$do_quqb+aOqifOC?iR<>(d;Mp^rPyEY|^DV zW0hSs(?mD-V~cjR#eM%O*20_mSPDakLBI`c?h2~ua0ip~RZOf|wjjWwAALu`ZSVK* zQEO#SzC$Tt5!>Lm#Z^lCBc^DQ44N*4M}5754ebzA?UTqG9T9^xZP2XBCELL0X(!;4 zkqA%#Pu5zYvo`Qvh*3ch)F2hF8@ZDj#0HQ>Zy@mVB0euRgrj!@Fm{5SEqa4OOGd$( zQ8Q%vK}O5KggNlj0E0y8Q3LNl@#*NQL^Xt@jNN1HIj;}gn^gXnRg#S`@o-nu4wafa zb<@b8zfqsiBN~W5D96Bls}cK*p0bh|u;1+rH+g_9gB)w)J+5LraTWNbq#=S>YEZdR zA<%#1^ztL)6ymnO_T%x6*#K#-tK)css2|VyZ@E?*ulKHT6-XE>C_qB zd}&fgk9K%*Ym+ zp=}iJP;~ZbXZ3;|#+JHWk3Mwvh6-rId>sqmA0gsL{&wJ|oi%Zsc_0B(^yjFqS=%CU zOURHgRNK?booO3KCKcA}2s14KKcW9P4DO$t-3H{?S2>l&oEGt0;1piZ_ydNH?+1oI z67fCC_5k}HL1d{=SSmUi(aB0*DM45KHK%teK%?-A&8N!b6*F zQM+Q>oA7!-1tBexw3v%xXs}3O!3%)!G}2=?G2|_9b+bDb5Fh1!Qh_b~L!&avSQW6q z3$RC>90fHnKxXvUnb#29G!Nf{D5g`03s!Oj)HP!nMOCtQ`yR+{atrUKccvA)qVkeZ zmemkwc4@;gRwT=B&oBYwG;XVdFBSCA0TCadQijSyTI)s)Yy$dre^7YsFloZV0qKxV z%w{KLM2L|1TEcEEz)0P|Ny~mboI0wyyZxbO=cWNLP%Fr-tKx~eQ@AjlL#HnbRjUO8 z4)6lkqid>`b~D9s)hLl#J?aj}w*Y!RPve@!$4`7ZC1F|@ZaYToj%;=%rFz>Jfi^gD7sZd&q(_5B046#7ITj($g-;MOibhmpp z%=HR4w}u247K`lZc~&zHS1CKzFF~T&Fbxc3!#Rjnoav_7WVu04c?ZF=3QZumBSf(U z%Llo2j8R~Ama){8Xw62v5gte_O=1%Pj@+G@=z-56VlnAq+LCb=b=g0B^7RO~l3DiJ zfS>@qV`V!|h?>XD!nkr)w97dkUgJ`4_7WfrnK?>^OloFQnw6HK7gE_l&Xws zUaf9sOi4hP1u(C{UEvN7HVOxz2Z+D}lLeh8WCZ)7=F_A$+v^MwHS92DM_g#qM#A~( z;CQ5IWig&L7(U&srYzPCY^p+1$_5HGuV5_5aRJymA7%I90%!tAZj#0x&9db&&-vlZ z?(LEjJSHlzWK|xmeJru;@+7nN6z5wTz?rAgRizXSL zRnPgpEzdmsB$D`*qDarNTxCWYb_Rdf0(;DEYWuS8(wIrI&N;cCDipmbx)h?kyaZ%) z-1Id-mOF?puVx%!x+b|k+mJGF8LZGe>@qMCtY4tr&;NazMl?dzcQh~ zpA1Ze44k%k{bVT3Cz=iW>mn+B<~<=vhCRjTyS94RZYDR_dq8G0I27XC2A>ue_tKmC zEiz(092Q77$dm-C4?7gboiQJUS_JfIiIXGqlg?LA&p|~Dy1Lt-tx!a`be0Z#yBoiD zx70KJIXK>cm{%B)9V?_9nKsm9ThkZ(FU(?cM(uVzx>_#qN9@zMt?gNUb8G?Ggr$5L zB?<=vkT*El7UT|kTyNa5zp+Zw(N(U+AXR8wXaGif%G{t}f8n|E`bMaN5PFa5-!BPL zihuSIl^V&_hP6E*NHFL~=}302bog;aKQg9N^w5%#`c285J5=XzqSFk}shUz<aeUFSgLX1Op;Fxao1zLrwR5^f?MQD!H~YC( zm7xYN;i)iNeRCuEN6`#;*~;1q3H7+5t{P-`^W*~4VMlVP-MxRN+m+?$OW?3jhmo!= zelLk0h<+M5ZZ4m&OXa!!DJEN%O`{UKt!98|9h!h0;BM$8i{Ma3+98 z0)=fmcqcvpYmEdN6}IGUO3;{rv4nbg1Ptl8M{s^OLwYIv)edVpv@wCz0-#g$S>zPlM01aeGyk@ z_m7PHW1z(tZ2CyA06V0luzS7**zZSHjGRZOxHCXd1Z2Pnxq{`0sDgIdY(a;BJzudU zcguxbq`^7oCVwm5Hb8BF2p^D~?GzNzu2j;uMfBX{c~rtk-QVFkn8B-NjxKs?>?2Yt z0A8`r7Lzk7cw$31r2;9j+{G3}=Cp01&+3Yz(G(>?3Y?-OM~YeUKqlOVK06=fJJrRK ziJLhF9slWY?VL&0?g2>9(6T!nq47W0tcS&;$(Sr#Ds?=H;C>|Aa&(-qP=jrVp=k>@ z#yVwEWiUPsnM@`}t`!vu)m>sK2(B(0b?XM`%xKPH{woA_Ayo7x1Lt`k%O{{-M9!G} z+UUwO;kucIx?uf37*ONsQ*&#pbnt&j$C|PUP9JYmBs7e00pVgL?9NG1<^U^eqZt53 zJNBuGUC3O(|1kq^3t3LyjK%3EtB6~p)0^&~N4ekSco4oet9$U|RX0AzXbuTy$eu*> zV?Szm&im>(gYP17ac}0z->Q4&CfeA;m-%K>W|zl;E^?~{hz1ia7Kt2Xie4xZ!}N=O zU<8g^ecQ1D1|Ey?PQk4XvKV5T+>ff4VU zdk<YXlDVT<&u+XK<;D)F?-1`2yMEWe#ZLH(g4fqZQ`! za0l8zQU~NTo5nS4Kvct$GscP>%41*jOJB!z>PH;tY!K)S0<1eciiivZv}OQe|6$f? zqz`7iiPg$Gk1cNp(s5vG_hh)gBNz4f%cWr*+60TFokOf>LAR~9ZQHhO+qP}nwr$(C zz0bC7+dS|5H#aZ2uk#vrB~@8jom#DtsyS+oub(hms zt5GZpF<_ywyZeBwH4uCqhgS-H!s>4ll2ZoZW~((75(0Nfj{~I0^^=3uQlsH{HRQXG z^dJ5?;H!)sd&d^#hIgL#cxDHwtYZ?*<<75i-~qur=otKnb!%(=+Y(xkcbrHFzgMV7A)wS{8kA%S7VDs4_lYgGkS zXT~J~LXxA_uKpU7sT~r{W3q#SrK6;n-FUxUtSw0{P$R};}ioj5n> z6ab^U7l>RtvekTH*=Q!ZwxE&~FYiLYQ%Ak>-8e2=Ck3==$a?bi4qU-K;H_)8*!VM8 z!cDI@;Lw#LzYMu8@J-9LlwZ%e9WQPZxDQOl)cz`fraV$n*RG;o)zRX(W`!oq07$P+ zBZp+%wV)YHH2$$gN#Rvsx7!ug>;~03AvI7PUE>1XfHjuL0ky4L&|LVEtSzI`dF8@g zKzzaPbv2+lLH$#3k&{l!!x0E2&ZYzk!yXBRAbN0CR1VgLRsz5WYgoY^EtX(q`AR9I z?nXuZGwjjq(XB{#lE7R)osF^CVscbz7vdsb6##gS2EOi@op>d`N(+WCAd`olA{v4= z8{$YGnN&!=!}f3)xRKWLLW?D<9gq$Xj2`o&8cOP1Plh=40d7^&ScKdkiBu^6Hr*7+ zc23%$VuVE6Kua78*^QLk%^u92n0IMVvk;U9(Y*(fH|ud$cxd|7^&77eQQ2wShVaN9 z)Y%qE3!58EW}Z_uumw*YbhwE~_006;cNupr#%fX)#PE2Tos9&azS!LWtFVNiD9UKTv)?1xW5#QMF(;&SMLK4T)9N0ed~iHKft#J zwaLn^BD;`LMptTIvWf@_z1H35j_@4a{>9GRLqH6fF+^Q?_AR|~8bF^?09 z8=86V!Qq!UXBo>;nEZ)(yZj7K^SYM?fkkN@$j6?&bJ;ydutm;NJDPFJ*a*?_6x_r* zvhKP%f>!q76-(aP{Je+|22^AR;T9LlFc_NqFZ)jczT#m%AzkfPs3av9QF%A*S>V3l z%h1@4S#9|a*0|P=aLY^4*)UckD!l)Euw7!Vtbym|MDhv39`ZbziIfOgOr3Q|lhJ1& zy5Y=tuj#@LUHg4^b zs~4%*x+^^MfrJK`$J;5r^TX*E^HDDyWSL`ZJ$N}G~1|KLK6pPnuCCP+S5(GFDsk1r{w~kp>@ctvKX&<%wx)G0Lt3u*mmVgpMi$7X1YN0x>h<1@ z=Q5ja404Nkq}`kDI&lUor7*xw#1kNq;WppFW?>TkuJmP}-TJGSUl>B|#v*P4%=lM+ z)R5AD^`ZLmlm&tfu_Z4uSRh~k<@+i>C^%dKB`{>vg1GyL(%-aQe*R67p=f!y2GJ{; zi%F=qjXB@zHNiWz&qWcJ2hUNeAgVZEtRC_k=&!H9w8C2RAV{QyAJ~}w?jc)}!jb~W z(%|4Vp%N&>jtc-}tD_;BZco(e!qhj*wv7sBVEaTZ28qEqvEUYic_0dKI)DA%g}J+u za}*VJbOkV1t`4Bkf=DpT;YUBaP4agvzsaZXkw~X8Jz;yO2;7-gnm0SjtFf6osc21f zuj4vEGItXBtlhK8mWv-gU38I55Jv0mp4Uo zI>LIz_^3)*Rbdc=f=l{w)OJWV65+r=hg5ruSMLvD7A*WHI82UQ!D33pFc4P6+c#ON zSeQR}KBxne;nJ0xj`5Se62$@42!sKOltlL_EpAI=o36}!*iJOVXo&a?FvXq;Zl;@~ zPJufw6&<)34s;#RF5Rb^#Z5n$>fRtZSImO_y!z=IB=9KXIq(6un3DQhEe>-81noDi zt}^E-qXK}22(bZN1vNxO0ZX+p*^#*J>m<+%HxF5)OHaNTo2Vne*-bQKH-N%JG>iv> z=lK^ZA(V^6*dCoG!5rhMJ_VfG%ZcTN7{FQPz%?=}WDtKaGg2D^>&o~l0t`s~CCWD& zDxm;4WU45#8BY#al7G3PfyVJVY4r>twu+~7kJsfitf8NiNSn6hF1s>zV5Q1U^q>Eo zCe>Ko1P0i=VxtTsL*ce56V2oyn-|D5!tjhX zJ*RK2vn%@COGLOrp(0g^<#A;2o((pFr1hIsF!wO8r9&M{kS34HYHK!JW;MC-{*b1% z=oTr-AITVR?yz2z7h6w0`sFJ!!*tutjP~$br$J`tJ_I~)wd%1%T(|HO(>7(6y25j# zdJ(_7Yn!m@gTQTsPu^1=O(tYHb1C+`>r&tq=4xuquCi(T?Cc-iILP3c4D!TN46!D|AK`c6uEfB6R_7bxVV9+8%x5G#HsMC zhFzV(oWd1p6A7*eWU0h+2L})=S9p>Sz^)kBM(rYUdSpcGOppSBF7WlKzG^y&#nriY z(3}-}m6PI3a39}ybkif(&GI-#;BO%2EH6#et+o2Z^CNNu&h;wWc7ZoRSb`rNTK@y% z1=!e$ZY&^X5t)+tHrXSF%%dxu!2(g>;fS$x$nTAf;>Cir{tg14t`;#X4h;Cw=%UkO{d%R5?Klc=qWTcA$R9 zyl=F^BADD2hen0fCA1=j|EMM)_iQ{LkBJJzF;rrQL49_N&Cemp; zD$0>iEfVy_7=bRU7V*{f=Y{SU4_i?ej-8nuRFYb0v|pu=oA zk|f{JaZeR^EDW><^cvFhWGD{ROQ#TEOF?s|Kn`Ya4Kg!z`e%4+Pp6d^TvEAM0iI{0ca>Q&Ho7(4xE`^^_GKM<3hlEi z*jW;Eg31*u59})UFD|pt#(6WCOJ*LdepB1s?%`>rSRq2cnFe+RX4t;#(2a*Oj12Q# zk=P*mu7qb=b~N5Aov#ebi|g&oXa^ip&6WUoo<(Q%?zB z0y%A>i?9nUHY3Qun4=UDS#TcQ24VvR0B`ZZp{Wf=L_zKlNO3zTJY*SH1~TwzRD|5S zL$tk=-yt#Q!ryZ6Uu_UX6bsd~w>LAa;ur&Dm$D9fFj1?5W*1k~t(Yz$D0$hVLP$c? zWy{k)-v!DaM1N1`sckS*3|5JmUAP3kn8>cTNwg70E6u2AWiix8vKTX4)Nfk8&bEkc zSTO*t_HJDiq~0q6M_~&o>=+Dz6En>$9XE6W)Yh(i%-aErDbd*T=3Wozv3k&6+L@e5 zbb%amNI;ACwW)@&;AtM5mHlF2BH1DI;>nG-=;Sa&7FUmV-#Tn+UX`Rq5BQ()2JLnK zghPKW08mJoAF$&Hh;PX&!qYYT+_k)7d+uBn@-KKQea6t0!g@%~Og52nKazM52*K8y zfAD7zwK?;f^O>W@+NEZ!ybITau@oDDwLLYpV35@nigic^0T{U@ybdgrB?(p{+>$a! zBJd`>H+gl@c?*3?7hmwvJeurAfng3QX8HLntBebM*(O65H;pTaSg49%&tdWM z@V-^S1t z$l=TRWs;=%6qH<`ZCG5UPz>L$D^mCxM8+%H3oo*k>-Q_|xgp65<*@OB_iQpSs7;0* zd*lNRejPoK<3I?T+C~2|$a-oQ@4u#0!P$%rASJK9AyxRWHOR#M;NS8(46^0mSF z+stPH6eoZ&T9TE+o-ZY|2mqo@4GUU<5>I#xgo0_nJwN#9!QS}-)rjU6SRSAmkXFq& z;)!ZYb^r$K{kxCJeWvobWMBFBC{TaiuA~?fW>I*rxg0Dc{N^3&E~n8En|x*-JWvRB zg3ib|^%QM{Y~NhTs3-^ze#_Y%cthEDK-E-WSx|(q0EzjLdG&hXlovF#=W7}1?pP^H z%@l8CF}X#K349y(Ke+9E12GIXUUIK-uu#ioK-yZ2R&yzQr3?8liCugLY_td!vYqAbr&eg~?WXFp3W~<>REg|@SYUbsdSGZyfS4pD zrlCqsC@lUBUtr@}`ZRKixdPwFR)SD5!*0o#54~3^MDFLQmx%g+qU>#)0&NYjGaiot z+3ZWpQF$X|p1n#%%2~yx%-zll za(Cp(!bIHY;4WuDO9TsqEW3S<_fb;QnH(c>0kkZnxBHPu6v##jpo>!BL8i9MZo~HX zNw|Q}4c50r>JFCcsFP&bWv#GlDixeiux9}30&Yu~Ptm@TLU|RzR3?UUxf>B!(ZIGTP*U}u9Kr`pUT+L8ZfND+FV3Z!?G0`P+dhT^E9}%5+ll{0*Vzo^rxx_< z07fH|Zcp~&u&gok<4Itiv*-%ZIGgUCTZ=*@C7e<{5a76`V~F{MZAq8golgPnjk*uc zW548ayY`gG1uI24>E6d6j@*$+S*mdPJgmykCtOHRwOxM9fu9o~HE0MV^PxhbY{(&-{SBV##5)rd9Iy*zMzXaazOr4NMI8(j&Ny`#6n1t<8!WU3cFq|{AHl@Q zkwgg)zhJt(6640$AtM2&dkam4F5))k-CIRF6DL<$589q>1%rNoLzv=agtud`+l1jY zR8F}<bZg44Tu??kM9^yx+{wo3>SEMLNRArVn?D_%0zXCpz|`0&|=*;Rj* zBxzf_X9lw307Mr}1_t-Es1SbVcpv>^4@xTTN#6P$DKPwEQCt&GcU?AIc50(U`XzFQ zm6G*cKRa6LG%&LnnZ);}n-LNZ*cPN1AJbM2`qaimn){XLs~4Q=l~EziOo``M=Zi7LFQ1loA~Y@mijobf z4@!eed<4b9oYYf1VyM-N*W_J#Tk4^5Pulr->=xE~;QFh9yg_P=D&QROtxDt<5EU~e zX`~yf=K>m#?qB-VRpVXt{_Vr2yVC{@2SJ0?#GhENPjU+WHZI3II2ek#(8E2*CPjF| zRYNX#!DH3yhJ(Lo$5<6ANKyCqT=Ky~BG$*}qLT9&(aO53@NAeU$TYkRM54neejZKE z((SMNd>He)!{X?%D~3f?0&G7VEnNjgfpu~Cd(qiqUJ zGiOe)AX?qe-F^1ZRpJn9En0#ZVE9w@_9NJQb`X+Dy-^RWuJhzxn~=~gupa)Fpv z39O>Ma)9mP;8+_?Bqf&EzU)$<_EDcCQoITA*d<#g22WIicO_pAakC%FMLEDiGHYvb zb_q+gS>3Ek$zc)k^GG5|^Jy;HQH%4l7P&_&1UVxy3`o%ch6>k5Cku-qL^hF{8#5!# zRf?*+$oYLflpDhX5UFzly~w?b(O2XYWMO}AP_)fM`M5mwHjDEA*tl6=XsPF%{7Y@V z4*op!Z6-IFYn``>WEG?)n3-R@s#rXPKAg@C%CL)Je8mK`6w8I8z6qeGuXZsp^`Js5 z`3Tbvb9J?<6U<*10l(tap*ly!^SAV+%JL-5n&cx zU%ot?I`|1T+r+iTASNPwY^7vd4ch{qz#L?ENbc_TiBHuitm^K1w|sdMnX=X#)&(gm zrFV@ngtM{>0~=K(+)UU;@tVv(9aCi?-#a3jEr>L3m2!3>?!dl?B8P7G=oABK1ny zflD{``#^#_?UUF}>|Z54_O(#qg5~g{VS?#5R-I)QIQ0b5fh%K_xI^S!bJIFnyeaBF z65C&|@*!iVUKV4cf2#%+6Tmf%zkmg@6AQm8$Yx^Jjn(s5F(X~+0i)x{k=KK@lxtng z+jR*wi{!=LT(E>Vl@K8Y){R&9AN8ux5~W2XGv@szHIu1Ll1n&hX7aR5nvZ`9QwCWbK?BFy$x5M>u*=5OMQl~ z7sPe`JZyfECdc3Q$IR+1|D0bZmtQE#cxAiKW0IpAgdH|Vc|>sXg?-@d+C-)pnbwXU zAkCZKvy&PJw)TvO0@IEkn8)dbUi1`91gQ+cy@^yRp_t1zJKbIEa{nj?6pY;zxEYyd zez~2umbl7wJDz=s)+ip&eSZhYG=-~kpW%1>{1H}%9IkWmS)?17s=X@Z! zFOa*ISjx^|0Ro(mgW{i`=-Ng(k4WG5Vd<@KSz`y{LW;9dh~x~F(@!Mrr3hbV0)QN< zeP#&|<$h^Ca{S1G9vid2Upmsr^m^i}`Z-hzQK&dXMeIQ7Be5kS1@r)E8@ zTz~s=PG})yBgA3M>4THH;B`Q9;-h9wV!0-+l~2RjFA5SW+bi(1kH^)=$4yG@=hVRM6u2w}5mz2AoN$i~W+BGTrlI-vh^5i~F0x6PEeOr1 zXzgT@S_ycxW%A;lE>$?m+r_W~JRmDl{!u^bbu0r7$W}8hf6WI&LlqF7tOZpr@u+zI z5dh&RG8NNT2OBC(U6MXo84rdi+%_J@A|O9Z{Ta3X$x!H!ax=@Bs>wv@$%?9tJBayXah2d2KK% z5D#nss-G}`21jTm0XXTZeY|uPHipdU5!xGDYg9v93kFIZi;i>SL`HyA%Scw!AWb!2 zwJH82ETSKPvrC-g(YI2ks1@VdprNb74p+Mq2We z+(XMah4E~aVa!o#(*G0V^MIvG!n7eCYwTdhs{Ue;IM}=R=2v7fD_{55S?k9m7sLHr z4vW0xQjGA}+E{kLhw~vt%vF1Pjf%3p&7T_S|+u!qy-jB)g!H zGK?Y!bpw5G@9O0_fK{q|pgofm9b%$6ZxejWp|;!{h$=~;0F6(ll2<79=;;j24CBFa zAe5VR3ldSkf(FUF)3m23kZd;b6ntt|fZ3$UalE9#=E7Z$_8Vaf+33@0(4d*ISWy>Q? z12p#FWRa?H532H+j&|q#pid)rM=(dF>W_^Bts$$0LzL#F%yeZVW(r^+FJJ{CY3ePm z<_)w8=i{tco zI?@;n!=~Lm%tB2Rh1qYrmC1r;FRuZ>Ps|%fZh?KcBG0`wVtR|%)+7KB(n6NY5)@c= zM{B3CwP$+>fM^#hX_OCi4UY{>N`{f)2(xSungaLw5e^+mAn2rXE+$WZ0 zy$}To9Dd>5)0`H6>-EVZuX(qAeX-6G5M{n!9uVc=u2eA{1PfKkNis)$AO7csHV6ZD z>ZNu7YmXmTH$e%5q% zy2RA;G^kz1DPhtddfG&hH^KAQ1G8VpM3?0uEfQXT+;TnWQhBhhg(EBxs`XKiZMb>{ z-^p2SLkqM#@@HWe4eEse&bHNQWqEa6BD)SLCNmp|gkuwS#qZnzcP1hxY<4iX*j=FN z>#0emO=o4UXSWaD4rzw?*>t$w`CZPau(oIiB_I|4ku|w7+=3iyLg116gGH|;;2qsO z!2T1cIE=juBF`c+g@Rz^srmlFImpbrp_4&6zG4Hpsi~{EQOTuP)U-EwZX91F$}t5| zC>~uFZIP(gW(laJZ^DUpU+12$?{WD(ei~kx+N&=vB~s($g|v#`qVLDjwa>)W=V!T8 z1g+x={mXG_!!=2Mq{COHE!h_Qlo0~%3R=(w_A8~Qcd!=6B_o;o1DKX&vLVPX6-RGV zy;*}TN>Jh#f=&fpJG}}oprPi+ua+svcP&)-Yu1Dp|F5O@yT8EylZe)<8#J#10sydv z1^^)WzapZUm^vFfSvt5_+S}2&c(|ylKmq^{bsp&cXSjMm0|0{j0s{d2*L0(?W4FbI z()Xr(4A=))^jq6as~KQaiOnMDZOH(ai;y@HQ_|%j&o9+PHCbZ9Zpi?%9y{y&p6mZ` zFQF~wN}8!Ps32iI5HV9SdcBU--J-UhNHbW=+#tUAXdNxnh4ecYE z@XTfffZvusMQX`u7-q!i;981DW4jR#orqhoZi{cOQc3vgAEVB zuUHAcQ>n-2UFc~lv!q^!KaQcnCp$1^U7X6Kl5;lOnvA_>`8ja*V606(c~Y+!|N1!Z$g~z^v$BWCuH5-WzMe3>k#=Y}8%qp5Z?b9IJj>Dp! z)EHB_+n#Ny?Ph8*)5#KAzI7Mt51U`ngJnb8PS%7KDr_Wh^!Lum?2pbA7h^3~OFL;r zp6JEx?MtNz)0Y0Reuc@aK}gmPZw~rSmCw(f1J~5;CmQNyf-sC>5X>ObA$mkW6yYlZNOYtS zLLrtyD2+&q;2i;JjOP%*A!7R-i$ETsJS0VehZH;{hD->NAR-~l0g;R{iCh+zO8Arj zE+Hd2B4xBoDu`PE!CdA+E_+DDFpqpl!*G{8hec+;Y?za=3#GZFs(}_${mK<%a?X2N zDBI=DmQ;xBsiN-sZ7zBt)S^kDETJscc>21IQPcV^`q%7Pse$qwivDd++*;ac)C+x7 zzeMuF<8A-!`HBA@S%U)j&qL&JAopJ{|2>KPuVl^1)X>D%^#3ZFg9euFf5!i#X#Z1G zlW+|wdnf<^bY=hmWd2vHoBcOt(%9bF<^NBa+|kucJ|1)Q+tVLJ?~mnD(0t3Lz=I2` zEDh3`jE^E@Eh87Xr7}xzdb!h01y|6dV3{9Rf}$R|xd7|J{vo7(nr`;{x z*YkgTpAL8L_x!t-pNFTv`};9X&*%FzZr}g@WWML~cG|68@Aq|)&A#XN(#_xZahQD8 ze#g(#_j7PDH|PIx-2HfXL~$MbV@G5i#M=5EjL`7^%f|8S$8-zu*E_wngXo__Cp zP5m45PMqBL=XPB9Y*I4+=lT8j=R>*A$Lsrkdir)Pm;2^z?rx92|K~wIp8v<`xBJxk zckT@SZ|_$fKTq%X{qyVaQM`P5UvvL?bpLG;`&7H`Gi*42D-T~U&tC_)zaMt|ejc9> zx||;m<<8~)Um7L}{I3t??00(oUmqVgNAg?cix2G6-eTo`zwZYRKTF^~<6$&6xsNMb z{rtT>{#^Hx;Bz7me^0~lp^&txLFGS&r@Qa)`~80}K8E@I{_^PLRxjcGeVezGj}Pa& z?Pkxn-(J7_eRz7i^TX2P!r$yL;^gX6;Ouhr_3=1rIzFf8kB|Eur>Do??e*VX#?8jXKt&!Ryw&@J7VepR^sFyt<1;gXi5^{eA8p_qU;N;k$Y&$IshC7k)lHxwx3{tl#@Nxwx|W&eQvo z`)ljz^Y%2DJbSe!O!~2L|CXBG@8kJhs5Mt~zWejt?rtAV<@@)fJN;U-{`cbHq;oSO zwmsUy=i~eFcNm{>vM<*x0$e%#-XZ}@VKK0j&Mx{Whv4+?T~j?Nzwot94v9e>-2Z=zfGY4%xi zz?_6n>~Gj9jNfx3pBXljpRm)rEL_cz{pq;Qockn*SAO8KUT5?(hVmdgh0lYl^@=n0 z#b-?tR3m-91cZ=aStkNdH(ij9gKNZ9 z(6Ius$pXZVl3hNoDx@7~4R%GyI!z3NaW-let$-&%;ug|YU$TGJxV>Xw3ciQVGN98G zgy|FnX@-hEIP=3->}>6~wi+lWnnf$sJ+Xl$AqpxImq^Nf?h=T)bPcvtjdHaoylQuN z)#% zeZ`8!Np0Ed#i;USl!>L_)>KJ!8YuIRGl?c+pKQi5XxMkCB{7x@MgJnalxy9o zUF2QP7f|=6KSLepYV4E)|KPCNLZ0_cii2`_`UorLgzO$`@C6<(d8+{j9;l#9JhqD} z-@8D~wG1saK)GeHzw`~XB6W?i;0(C5oZmn-6&GYx{)#~aE*sBGltV?8@b(fT8`(Yt z=y38QSIJeH3~K;~1XZ<3dB#@7KdEDr65KT3JEHv}PN%q|{nk~I>r(qg^JiL zfdU~$iBTwECQcHm&w4%h`1g2&8e+|pgHrlCu1fr)=wuAmhm?0i%<-aI5z#jyC@={^ z$|4DvtWh?ElG~w(ibdC#qEJqRqY8+p=~*g8>=@azR`$~8q1Sx|oq49asPjesbDk#3 z|FJ}ZAwhJEK4Q)RQ1X|W?pT~5k`&t12hjx#Z?3Y#CYHWvt0N^X6&!Ns_3GtsE_Kt5 zysH=3kJGt?{fd}do~w!DE>lzl5C=Zl92gm+spi?5F5ekZP2fA^^mfZUQTR2NNBJtU zs|bgyu7?e1ypalHY_sE9|MtP0MTg^b-n zS)kPccD7Q?2Mpc@>?I}7`tT#y|H@(f2 zLxD->jFTKFk5bE8vdh>8^z%^HZHbn~g(=dn0>165bWbGPI;HjgVv%LZReC(VbWkQq zw9yd%4w@d^Dq04r8OWw~?^}+yhvWZn(dyYV$fs zGMH}FGhKqULuL45A0ThAB=S=71R#_X_zR{Gfe1BF`Qwk4MX8$_Tox@k@#az)tQgi9 zsj2~jP;YNaJY~MSd~7(r(7iOH^Uc@YesuvKW{E`!o9ir}U^>z+x%WtT-7>3lv?oj6yGtvz@r7b-K)MC8E5~dNKLerUm-eBfL2umAL``1eIDF^-W ze%*Kq8LS$@y}X1YZ%{TeC>TV;AggHnTO}Ju1=Ozu;_j&14x^53Jntw_8`7RKSkvdm z0Z7S_P4?Q3ustc>Bo@{hSn6#fSYf5sBSB$Fk)g%E493Y$q@p2SgRkQ9vV3eIyYe_4 z+s-T*aJn~N5&UX*9NXW{Q^&Dqp6h6rr&;U}eaSYDIWRu-4s)bJrreiUUqcbL{`)Jc zq_-hN{>nrFn^7aDtpoIWwF#f7+D4_lB)AKhsEzgjD}vTE zS!t}n?}K%8DLk1HtppE#7csH*1npg!qJC+Mqk_miBF*8Wi;}3VA-@o*+pLxTo_Yib z-K;eA0+h&lY10F+dUzuuQNb$?>=7Og#}#zg3ogUiRC42F8LGCV3h2y7d<-#wuB1wx|=7Y6})gl89|=!HQ18Nr}`k5h^#WNx}+8lFD+ef$h7@ z9asja2LKsOnYHl3-kN&IkhdWY0_0_L6P_apJ!A45Yln2R$#kG0qbkCt6Ht`RS8-h- zLym$ju(r-Bwp#1BnIhmk0M-3!#6nBLF0f-rBz}@$EVu)}MG4#Nuo@+3jfb!vpiMXc zJgFPbR3FWamAUedR18V}waE*ZuZIVkI;a~AwEA=9xgq+PHR7O*a9Nf91f??Fcvu+^ zw9T*zZN?Sk;77zjAVst)iE4JcWjOr2aSaMnz7x$~>WWYuVpX%CB1vmeeHtFtgVc8g zTvYe7ph&YGslJGjNVp)2LtC_IWh@Rns^EFw)Lz`3(t( zE#bnUi9WH|7nY1qw#iY;8ChgEGBpoyId4Hevnw(T8aRqL8X?;pYOt~nLq?*tAe+hO zZiPSLMQMW!xUh^Y)T2^9Gsr`JAq*~Rutc3?W9`p|R5YcXu7tHK!+b-X+-|XM_cq-z z2VOXmnE?DsnghqCKS9S;8-@t^5o(0yh2e@BL|tfBRNcys&*(&4`>h&+<$8@eXvRVI zrfe10p4(N{Z?k*Q47C$ofirEFPL%zt4>W55SRxw_#QfljnlM!Xe8H8C=~dF!ZGx_^ z$7NdKkXOYKmN-UGy(vzm<@uW)#;~sJN?B{y`$D`7XiL7LSEi)V9$DLhqcT0>P031$ zX8pJ{HCSw+oRG{&OFt$xs4JMNpRDh<8{$gU;OA)XvZbB%Y9BB#eeW`OwwiIGZZ@8B z9{^XlqBxK|uqsX#&5;cnI79rrL^%ZPMa>d1M@KxY|%6>H~r zKLu_I*B7TuL_S&!g&1kopuM5+1v2PN(vx=KyFZj5XC)0%j3|_ZF03fVGb|Hs^+-BB zS~@Da7`lcFTw6O*)0{%y;YhW>C$^t@^^$<9M%s38Oq_&%yTGV}Wim);N-GhS5$*

y}&AIAMs@y!q@JN^xDvr)^I{1*CSIcUOq|X=v66rVM!0 z@i1rBwA_BIU8-EMI?8H0pdspT1GWsWI#{PK^9oCzh4}O<0nHnAt#_@V+vZZu^2&R_ zOi?=eX@R{gHs*&|)1vpGwx|d)sscI9kF-XuGd_VmyUOl-u;07VZt60lktM%pb*RVr z+MdTE7%IgnT^y=bV*a9;E+=i#3htJ-o4F0ExOr0Ev<@wQ9{qSa{`M9UT;G3VR3Re_ zSCYB-tRT^eT0OM%r44-`gD>7}hmHm?-jUg_Kj)1=c!kYIj3vrv6-h}j>-@C-rc?;h3H8GMnm8|PqP`wx9=u^zm`Z`V9Yt90 zXToZ+A>X1!*krskcg*L1u5VJ{I7;#}a|`aVl~$vWt6{)EmL=O3f(L z1w=NW4AE!G{LoF}^BpT!G3JJAnVVZ5nA<9}wemCP<6Hp)Ys}&{5YIHXSb3h+h|BsC z-S(<8C3)$e8|ephSkg{<@8 zUtuTG0p>T_WBe%m>tOPO=%GSR_yFx~LbOC9#eY7~T#IO-@26SAGgmoTSj+1!vbEah zAb!m0r{65w&WiF;{47yB)kG{h<*YpBjrytbv#&UpREc%Tqm9y|leekS-oGB*tqiAL z`9}PrLU^^(tG=^6_h!wn&U!;#pjXiWUZ2CAMvJ((;0 zvp?KnuTW~m*}uwEp&_j692To;KSP#$xQ^$_wisM`W~V=-f?GkB5E#hFd_RWG`fW}e z*IHM**iDfd0rZbh$xu&GC}R!&#xKzP!(pQ_Rcp?G&eGK?E894-WkgLUTJ%F zk`I(eDOeAxrO{MkZTVanLbaDh(a&u!w6n2H;d2yFB@Iw%&xi7#S%ef-iCJCBaXk7U z(tbx#c+-U6%+$mlIVe? z6)moXZK7zsWS*oLIbY-F}7hjCDsA)G} zWM*jLgn7(BS9owADIJqM2|r@$^G)SNJi?@goCed}BgxZs?`@+XTru3}pfHsIZxgwNQbz-WY!DF1k~AHEOz9wtnldpq)>p;q*_;Vd=n1=gZLs$W^My|}6$M%6 zgYsk$_?67p63OOn0OQAx+XKQdH-@GX5;`?-;g$6mDl2TfoggcS${;i}`y8AC&(KiG zHeP2v<3hlyLK;02G8%jDo!*7!@LhaQt~K2JV_%OIwN8s%X#bqlfS~HT16owUv3H@Y zZ9WCnkNp8lE(H{mC6F2B-?^9_m+T+8KtcIH&xQFdu$+duW*meK&Aa(1bx;-fg8SkerP&DlND13nDw|@vgY? z4K&F=Z10d=Vqo_Jaj6HpiNWg)7pDtiRVW`=o+4tXlaoFn2^QOt*aM~lsE3X>q!bqt3tZ&cL=4B{Q@~IVQ)d?dW0;KH;ck?iI&Nfj& zW2&(OICj{5$~iheQcE9SqNiu&Sqy97e>U`QAmfu~Ij*DtY*B&(kW?sCKCau0)TnD- zc0z8jC3ZI(w(d|A+~KQ zCV%SRrrgxhdpYy8YM9y$SGA@z(Pe8Lzvki;BZB7w=j1cA6Gzauav?u!gG&*F?0FB{1hpZVLW5y}mT0sp+hHe^4E6OuOg((|Q!9C<s^45ZfCsW$ zIR58V+!{Plrv#!Ae!A@je86l389py28N`K062TxV^lRGBM+tFBIg6$ z3+P(7sXg69cj^*QXL+J=033PoYNJ_(Yzvci-@d|lKBx=^QJWVQdN>daf%>_ar1cAB z{XO`KPnjaS+fsgc@4tvm1b}Z>zVK@}*#uis_j7$|B?{RF4vkRXL zxcFhdFV}pcxzaYVLZh!(ud(xA=y#*;{Uo}39+_3H?z~RLFtwL)>gf?eNi^ogu-~v{7luSV)$U7IUKgT3ho*`%q0+#G_f1!u^T72iA1U^((l+FJM^sV>6kefO6~0t zkXU=Tf>VJ+2aOim**O)@vlpRTLwNW#{HH9F3=zwAv{#+FD@cXY#BN@Bd*V2=S7i6w z?qWMOV_ueD@t&_KO4B*npusdu#pIMMd$?m94&U}cG zwvNFCN99(es?Vuhx5RqwL|($?XA)NRkofoAcrR2=Hi0Da!sVaFl6&gOWeoHF7Qcz6 z+~suhTb!Q_8scVy+WpTHlZmpNGsQ;Lakcf={krUPCn89VZ0@XODXlH_NVsY}>Xr-&QSaYxiQm%em<3cc!a)rn{ft^Vm(uY43BL?afDdVUZP= z+eMYF)Gutu6+YsbE0aL56*SMMRivFWIGnwbRjR0T{VG4wDf}w-!j(eg)uHpv+jwzq zN|~(Y^INmi< zql8}=9o2VL1p~b>b((43mHV_N-VLI~=FwB9s%>J?~0#rd`AuFikFMdpT*9H5MY*!!1R?sg8FX;CsAktfg zui`Yo-bhFW1Wy%OIKM{ugJ* zo_p89a54hX>O{mXvDEavgRPXPZSL63>7rh)T2L}(|FG9vlO1F=;*Kd@c3)cf0M;`8 zBXCm6swy_VXNt?aF6nQ!(=uQ2Q<$&!#L-Db*UqyHAtTsHgFkUDG3pDo&%0SJXso#; zf{nq%HYP3aXxsMocV%38tzKe!g`hx}pO#lvpi{T{vxg@u*?4x=9bMH4uR+ba=6O=3 z+BJ%GL{84pu9Y&0c=O<54eZ9^x)MICY^|kOuoNA8C$Ve4#NYz0h9Qq80VSe)li7rjZJekj7TbGqv^k1CTt_lNZv_``Z?JT1b>0`y&s{F`lH}@vHeG5uNSUw>4dmHibi1a};f^Qww5xf&rW#@ALNiZxNm$U$f0_T<=FC3m(^1^bd~P>N!dKwdJDYutIx>~T2a6f?`>L4 zm-kl$yBbJVgD9LUQkrsgrfGE;LzYw|+WOKf==5St$0I(SbZ5Eto?0s5Ptcb!q5OvO z%>0(8KdmVgu`Q*B?pI6RHb1iJ=~Ok!g|?_GV0Wpg(O-I7zr-DU5>4f;#<12jG4WXF z1-LW`GYqBF1_#8$w+;cP@TKOj_Mll(I4Y7}qk$fw+ir_SnfSgV+Q3>yfM=TNWw$|~ zWHIOL23)2CW@PbjOp@&ZZ=qN_-H2I#6`M35a-+b&@R@7L`FL5W%LQQYsi$4ov1NBF zdA;1d0c|};qz4oM4(6vlDCMct?Aacqp5rU!ESj=qC60{D|J+XiH zm#Y9O@!!RP*J_&?rC@-1+-*&Y^>3Ag(v~cLK-i}#T8)t-NbE^A2txc{q(sKmRp;9p z3`}oNIGCjguH2 ztL(QTC<?yQ^u3VmS( zaWnLu@rwsLf5*?R!eV^C{sSPX5~vNx;qf$%O41KN^lmz93R){DlG}W4C>q-)gC8De zm%6-zTK#(xn;>rFBi3Y6t>=Z$YVbm$5v7{GM`j)@M*iJYMm@g7)co7}P!Fi*)D9lZFD(>u zSY3&Y@y_lks_!M~(KX}VUbtf{Og-wL>m+~(AaP|;_YHshY2;z!5kz5<9yp{4s&3ew zwBMWgtV1E_2o3A+E(7_zVtQ{DMKX?HgjQT<)$m|_e?QJf!(PysqiCiU) zU`x#NC%fXYR{!Go?i`$LvmN%yJd4|o3*a2|>RMcd({f+i0d{#l31inrlR8(}NVAF3 z7}%4DlwFd65;zMIYe+uPEUC?Yf25Or%i&KKLQ|LlW)@!4uJ=}VC_Y@=anLBdJ>jqo zuQcYtsC0f}bu*6oMhkS+b+A5Zp&kRJqKJ39_#ru_o_bG;dKXu|BW;1F-CLB!%szvw z$}nOM%(6!hm6@h>c=gYW_8h}1~LuuT)#t=*zFJK-=|pae96F)(TNf{i-TVO2&e?r2ExE+HgD!5{Ab8EaG}oQUc8d26v)N`cM7eRq=ewW`fyh zuI*2Rc^M~t!Y+16L{w`jCr81_ZkDn|Eb>~)!V@xe!>rwCc;akEfd5v(>P+bajjR!2 z@zk2^<{1W+o3&3?dzRBB7z2;~Rd&#!Uf=B(dlhYAzM`LD8Mt>+rraP-ewulbCE{AH zdUT&&WUs`8NA?4j1hK@B^zqK0;VD8RGi4y^UFTuQR7+L$Ug{wIrn-dFZIvTzrIw*o z4|B#|tOCT;#Lt#2`MqKIT{|AMvv{GF$ZAo~ND%=SREv`Fxc}TQ9j0jdkghAa3bpQ_G=>^enlu)gIKX1sl@I z;FR4a74tZtaZnNYcHB+87=v0EH|P&Yg}c&@vBw9TRMUGGZ2QNU=`Fw&p3?d40-+II zPLm`~!n3{&GHF%8&XC;TgHe^EpH;<-r6L+by9OOH5qg}tRyM0+xq&{(MSG-9tgDCS zNGlzF&PC);74Wj0#XI9Xf0b>1v6`$KpUsb+v}kKLZv-W`+Zo<@LVjcn&ig`g=78wm zjM$79*jA0I1`|*6Jl#q{=6VAK@$lUB^U6N5F#Hqg0+m0-7=DR{uHEFq@iQH{N}4P(krMd2GMicR~B?aI+f3tD;x~bhq8TNDXll>O!sQ z$yXgOT^nZ^e6CYGeE;vVbTUB2P5h56{mdQ+Nbx^KLFw#f@8W9Y>h}LuQ7&dHIqY$w z{@H#-^IGL_N8>5VcQAvF{?}v$etKbYsO(_F7C|%O+NymSke5z`$6CQo=PvOq`KtmX zjG^VaeR(%A>F~l}DbcNL@Z-*KYov6u1_!IVfxzr`D5rU3|4|=iCfn-&Wv!wAQ+nTnmr8X_ppTW)e!fP+bg%a1R4nA6HnZ z`aP>9XAwfaFkb{v`w&QfC}%cTRktM(JOB$IoJjNqg!qqqA$8?rtxH%faQAljLCF~% z=Ge1|VF|iXX{W6TMK=6vuTiCwb{M*59zw7}7%HzTNN}gQupkLaMt@9Vje#9OQ+Kr4 z!Z>&9WaXXjQ$L+M!Eq{-Hp`n)!&8>~Kv9;9jCZUwKEtT0JU$ao<1g${xxsmmAGE3B zLFyseDu0QwrD!PU745c-UW~b;b>c?rf$~!CU`;PMj3w!^^H{uUv!t1h+3m6HyoJ*KXbrct6Z*B1 zar{#BgTMGby`4D*!25`DE_^0f-#6x}EDOOa6?zfpxze3+R&B;xFvGbA6ioso9@36K zZy}2XL?Sp@1oV{-GUx`4$Rfe?bFE7NJotztm^%j4MIm#_5)0fFRu2n&hY6Gjy27~+ zgg?d^2uwy5aq8ZA*8Q%b&n~%#^eHqw_mVzH`K&w_M=cS-wEw`;Sy*H+!X=G723XF~ z_nJ!0_5VDeJY6Q&8aDn+8-mhf5~f4DHA~r6l}97Iq^8 zznk`f@p{NJzQw`27|_GV9=?_#a_}3hfExV7!X+7zK1;A_0!j1<@&v&(8F3c$AIS+N zkuyS!gMtqg(Gr;;L2hA4crL0jMxL;#WMHn41QgOfkY8|YqUe>^ga-TjKg7OdAutv3 zf(_qe5TFwew`4>u{J#v#O2Tbc5K2*|q`W<8$o&T+h~xs~Mt6W@M1QW(n+c{6l-U=+ zP(GZ92{QcSaA-b2Nq~G;3(`@f4KpV9QUm%}L&8r6cGnW|a~-xcz=r^PHIyp`{UG>M zDt+x89kz583OI6c{g5q-owc++7=fR(^sATa8N;hBC|&_z-a_1$XCoAr_f< zm?)EmOVowzTWu=EKN(6CUx6_DSAiVzZbLvU^6mnY0^tYp4sm2G@&E%7bt_UxrASjY zu`c(#QX=R6vJ4awA5cvFK4*zjYExRGe>9Ku8`hlCT%41Hl)$i#bl`p3e{=yn-M}sV zivpz0GN`bq=lYfI&Ho+Zf7O!wj;Fc5-3FFM6(}lKI!| z!nkfRLVyetM}pk;qLOi ze06I7{kj{*Bqo9e>CCOINGs`MplF!LDw5{!C43J8tTk=?3?R6&~D>rB|(iS?}`A`E4@tibTx8i zSqOuhS~8};B$N~*WT`(n5ym(@JQ_%udrbo0#<`2#m+c@?yI%u!L5Yy}Ra0e!^#nNx zL0+4NTIW)5)W{@fIrgl`RKFakt5`b0;m1rhJe!#=F9>Rl&=%V<`pBvd=`T2cm@ZQ?@54aH`8xO1}9MUD-hl^?HZxv)E*(4wN;Fl^|FqWbcWKW>H*d3 z1Q8ys%Ja*D=pE!N{zB_FKiC#ifEaS2s3Q)5MJW?_^QnN0L?*}6sR&)xLh+u7ly?{I z>@SPUQiT#^aSapd{-dT;xDiC(OCb^orgT2{OPBay;~0)yZYoSd`wn{P0G@Rtl2~71 zMb^wc8RFG>L@PtN<+e!MVi`_=%vKN+RXA&*p>)-4mVUKRzwC9^CDr&PqR)*ObiN3T zR~!%j<9bWA@|0oZWelkfj|me5$p%mLp%Ysb7#E_7SxZ6)-KNYqlM9r#5cW$>O%j(& zGfkfoCb?lsBYm3OGTk+418u$zkB&TC(KE<~vSgL{B@B86SLq*eGUy#;jJeB8DN?=! zfpAL2o;VPFsxC$bZihYI;JEl$JmZm3KWwfntb1}Ov2>tT@+_`h_fdJ^KD{z`H0E>& zE&$zTv#$}7$5R+cej2C0cWuZ`#kqL&_wW*xLUzS*WvN-Cudcuadsk&hg>n>1_ zt0|13l%nv4peh23??&u}qBnM7ovWont$CRY3v#BHr>^y1j<6BsCQ92=Y)5UPvavxn z>VtjtVk)jdQ0YJ}K0G+I8=xmfc@T%VD74VhUFy&Yc&skjNb%J!6;uWmZG5=MTlRJ_ zw{*+oBGP4dmR?DF<@IRAE2lp@-CwbbJKe9cQ6euVw*PRjui7vRrd%_Nu4Bz#{iVJIGJQE|tRLs8{lCHg2h3EN?5FTQ z!%V{e=PBRO+04Ys9PG`lEZm&`YdtRXCNA#(y_)|6Dte(e zmblNE2zYKSGXui4*&JRox93Z{>Wn60A~r3qZZb8SUY>D@hjs!ci!30fnZs7Y!lL39 zo7;5^=jp73hPJj{A^?~|}-e0_K4nz3knxVxa^j$A3dG`#dmezIDU0-;T7z5;PuEM^~%B{ZPakQU3*NwW1ERB_499& z2mb<0v#$@SbsUlXdv(dbS#6dG zo=@Cg$3IK@zrZ#6nBH!?A(56ANylf$lM6+T$z`%e*{HT+ZjfFXJ7DNVwafMUXs$P# zZau9UXE?+*wl#PjQh05UpX&}l&0X#evn9=^lFP{HXEO8sfm+i+$>1-x;jUF_C1;cK zx3ONgc+A@wI4y3=qF%jjgd;~a2TG!t<}1IxICWO|HTi1My~dh{Ioxl5&H7n7jn`z4qMNk$&83Sdc8>8& znnP-8-gIU)wbP$TESD}iaYco7M!3I{>)-{rqBMCqy|l z=(Olt;Eni)T~2jgg<#N}XU7fj1UW)s8o1R40+f z263Or?!!1eG5;{^xgUZzdvRWTQIc^g{Dvb6|G$nd&39p%&|p#P+lR zRJChks~Ac+-cMn`m~Y@{>x?HfiRLCJ9E-H$g$Bx)S^3+EO(G)QRV`OG z1~-|&gTYnEGg1Tre0JQi3yA)i2Qwa$miHgR)|eMnE$6-l8&*$m9UF%D@BeC~kMkj^ zuAJPAv*x-6NFVJx&2$9@SZ2;2z{hhgZ)CwEuei1MPHG1JIGu898=uw;Y{Gu~d1dz; zBMLYk#dk@@{C$tIrXQ|yKOq266}B#>wZU7xQs*ym1aj0|5O5icQIAL=fT^-)-2d$Ba=L`R4Q8Kgx7#CZDjFMe~&LOdg}1(c3J$ zjjLrX>=%W=bvQ5CWz9P|x5jWCRQkdbWNVxqwSmFfq?b-4@F+x2iweu7Q7|j>B=c)N z-?EkbSPqS|1v){~sSNp~A$)V14e1GcvO}ity6F-h$04*zww96@9@*B1JkS})GZDFP znf3Sufk%1Af&#OI&oviLOl|+z{1&gse#s81{vs+)nogXX2CKc9IM{wy;kOFfl{)s^ zWVj#VUY}e7(tVM)8~wqvms6Wat0|&Bbm~9I;DbfMt(;~BWkoY54+5NAe6K`@2oj(J zcnAVqgsAt6poCTpt1r6Kd_uFU?rm9CZE2`q&Vuy`!bnm@TMT-2_5#!~<7j+ue3{FK z;KaS+zsJ;fc*$Y9m*ZGya_0JQkPnJDQa}C-C4R3Y4k_Qv*oXwl!N_hG5lqrvAHe}# z`HuZ{V(8ZqOXs=1I8?{|dgw`7TDf_at+{!Fr=kzhpr#n3lrYS47g~wBIP=U?w;as} z&vDltRWzbOk<{RiDSe4r9`~lDWf1PwI_f??$7ZcL5SFXM?|acXI0@!O1zVCt;`Q>k ztj*+QUE)F1f@Irui$}x1^G-WHtQD*Oh+$P!L2a5PjV{Wc(IW6}7{2cvJ`Bjd@l4Wj zH}56;ohh9E_5;OZ)Ik``Y>!EFqKV6>AT7eJJWe$#FdxT! zp#^DRXm{=6#fCPA1n1-Sz@~K^89mnXb)NVA;c%4caexP-p$Iaufu5>I;~h##7G^WL zEI2Kii+WKWi=EitlRpM71?LtTrVeIgXd*N$#MnD1;)0X>`B^_7^w+y4J|DI zow&>z6w)aqn%b&}UFe@!Y~d<1ASH;wR-qMP-H%#b0w^;0a{*R1dReXSAjK7kbn7^3 zm4`D`p=J9iDTUUkB9MrT>Q9SdA=$c9Oo^mG2?_Q;^@dB1NTcdrhlT?B$i-`?yfq&& zPTTC7zrmT~;Et4Ic&O2H+M2t#F(EbsDjMQq-&^dOc|@x~$nh3d)d}iS=jVxX0C1g1 z;fAEyUI;t?%-U3#lB0hk)B;D~J2cI%wX<;vv>%aOOqqP-MtsTgOl&{V1Bm>_?OZFA zmxPtH_438E0(vZ@=;m;;=x@*r4fYjfjgYfuM9ikz2vl(Bi(qdMiqRE~S5u3|p_T zBA|M3=Qb?bwtH?dd;h0tSv1~*yVe1BeAt=8ogw`ca!*pQ7 zU_YoSsw^Rk$ciAJjM4CU>0Qvk_eoTwjKMUfC_~(7#pj2=sXkPcOTelIye{OSX}+2X zrLr2pn89gY+g)kF!$6-p+Qkeh!&MK)t9jm8_i$?6R~>VuJ4g6{qoUS@>(k7;H;_x= zEc{9LpAAR5*lZM54>qecvaQqQls`*mME@?&KXdpOXjd8h67ub{Ta&o~(<(wRVk;Kc z9NZ9B0S5fk9z1;`JN8wqAk_xfx$ROv`c{mD^2G=%(E=--`5pN?kuX@f4T*pQutE>| z*$>}El+J(0u^UeH0*6_L8HUs!bY|RPr`_A-yOW!1cDLNI^+;z3Mp4iU60Uj$g@UY9 z64r)yTxrceO%n&9qW^+l;c;C3wSj3>84LaG`B#4DOZI%APYoP~4A@q(qF3Aw8P5f{ z2N!Zj2h>5wEusotL+!6(ukduQCiTe&aJ|}LE;`-={ z8cuXrlQse@u#6N2xQ?MH@imr|wxukFb%U)8qwc7i5Ps7W{yoTr?3N~5AXY^F2c^|o zi}`0bfv=Sqw!gKeqGO`E7qCO{&K{0{G&;DAwln#L2sM!psop|m-P7-&swe3zM6(6i zTZjB+xN%|4>S$r*u}a1*2!0^>KnghNj`fQqPvxnhT9m931)4V*X32#xtP4dc1!(&} z%|UD?jn+e;oc#?0If`@C=h#FkE`%vnbO5SyKG>ATL|n*839KUj&}Hr-W$QXtIBZtu z$1S*hMSh3M5XiSbCLToUClGDj(HCRu7QD{RiW%f@(UPSD^Zj!!%poDJshh~aY1)Dg zplXu){b&q-qKec4iCZJ*_xi(}ly9bcJyN9;nZ=ojnV1lFsv2)ztLmip)hAZMV1+3~ z>hNG!K-HIr{82w5pojb0ykuU?i9tfU9pbT+L7le7Z4dP7{}q+uXWvz1N_Vr>j(Wr`t(>RPhvu-f42{ss3jmu_!OCjz*_)anoU>uU)tt?r| zLu(A=%CwePa*PLATNuvnLf4k2@85mP(!Aqs{*3~{4n}{!3?(C~A`*&ABROGax(55p z^kgno_o=JobR2|4`uY(xvaYv`RN6YJoOB?*;ZxCqm;ud9J^WP-S!^glEEE54I zOD}ki4>6nbT|ZGH4MkM!@Zy{&rPxaVq+;K9W`!2Go{_PV9)us11P%qIAlK1{Zs*=qGy>xQ*%p_%UO%g9Gaz0-fAIpBrx{7DDiRuq6HHK>I7 zoMe)H7d#*qvy->548`3bFv~a4) z5Bee+Xsz@1!cT+_(j1upHjMi<`YWj@j3|M#@Nzylyffj3M;ZgWo0z5Qeoi*762>aHErgE5{+ zKZHwt=|90ZXx=6!R~1Z@P?2n~dWhe4F|)X&KXIR6JF@vuAT#UR)ftnmIQ~T@pzDWq zM@%J>bv$;*|q$u&BxkrUMTxLy3Rma(Yg~f_g{+BDEgeU7ri!ckCW(44x zOVZR}5F!_~{D#0l=)~1B)E|E|L#}lE)|6Sm^xxU%Kg)HgSMardhlrhC&YRn5M^mFx zmECL!&6=Nj8aq36DuLHaIwUDX+a$)d#<27Ku_C)eXUr$z`OR&^eC3?9N;33gy^Em) zJG`4*E<^${pwWOh&Qhi zXDvA;jTw556>MWA1-@u%ax@1q>{qd?f7t4zTE0Bn*)bpUB#A`9gs|>K2ER7MJcC18 zNaoEZb;!m5%52*OUZsDZfv8r=G2&aBBOfMI#&gceiT-@fD~K_&QxEiV4%#~x z5uH>Ow2ym`VH-#^?mm^fUcKZ9b2og-RtrTFHJEVEz$PIVlM+9D&{xdwPOwfb;Yfe& zm;7dCzw#+#Bkiheo=;*HUF!RAp6wzfD`v~VBIW`ghU;z1AJy~nipy+tzt~wW1%rX5 z1;HqQs2yrSoaxtRWsJ;r{+6wQ(8ydzUL#`fi%C@@k%=x%KHMm} zIRa69YZ?z%kmdf}U??J+Tgd`6BcCq*_iSq7uMZCU#i+hyg+?%YVyAP2VD6>{V;>}3 zk9<4{rdQRUbwF+(?TD8wO2|M!w}t%QMXKQ6;chV5NR{s6iu0u$cy(r*TWxxm-V%i9 zemVn=Opjbsi0yGwF!5UXY$3T(DFiq*8Mx(u#r*RsM(~!_)b+(SOw@31!8gawtDb-! z?p>2bf-LNw{c*`9u8q}68~lic=lD(1jXmWH45DJ(v-vF8#FyC8*D-`RO$*?szeI23^x63JJYl=G^nrC8AkV}& zZsdMKzc^@T`}&rjNb_rlttQK(cM<8A_^{Gp)qeBNWO)aE;c^fy(S>MWdSZ}N5~`npuMS|KYVK0+6@ z*{R^d1kvNgj?eA9M1sLZ%l|(2z)x=;cbxKWaC#r(h&k2JJ6E_;b#y>_FvN4DXTw4G z^HQ?xq);n+wS| z>1p%gS}6p~#aLa)2Ul%2&25k-t%I`>JO^Fz`h{vBn9jUAozmFQejmsV8WhfddtTSQ zgm`>oc1O67tpWhnJ6}mErtKtz65JDen?3~!niT}Q6l`RaRAY8)Tuzh=@g<*AyK=`Z z`7+<3Z4*Q5aicod31yHs;?$v{efL}Fkr|545Zss&qui?itE(huA=9>V;ku2?xp6^z zN3EzGMf}Ilfn*LtovUx6_C-iYiSTuyek;WdjmS>6R=xlbUoP5&mEsz+l!;m z=8!oO!XZ!zRkeq+C~@fB8DWWkoRt(~h|Y#1!&EjChW$=cf>g-!9bU{>_`)2Pb;UAv?!AmM3^_ByFkeIdoq^I4Q%wRZU%EZlVC!O$5vj&qM=%zt;4G-pF@f zKC?1?6bEM6Z`Jwb`0ae@PzA|*MEmCqo$T1*U$_AFzk0>6@QTPOeYt3jYebz2v|6@CMgW&pKOUa?HafdLWxooc z4{b`|uAO&}a>&(P;Y{3J!0r`2OGy^q&P~<(OU=}gVU^H`6N8SGz z*UIfUUpG@fnamvnu2^}ETKzVHjM`KnKGbF$bSp}Qt}7sY4UdEOHKxEO@E)9KmYUV; z(KfmQc_bY9gp1elXZpGBEJf>YfnoUUm%@yXf0zpMhW#9xy@? zJ90E)3hjMi)yEVb_`4gttAT%SZ!KQ6*$wt$NJo=BN4>L(1(yT`>4H4Oqx-#2R@vr} zPPFh}4swD^ejQAwHcj9q4q$!6g-spOsZ2-!4bRW~23P;BS(vIoqpUJUswo5OsbWzG zuX_mZ#Kl90gp|Ejsv@xQ*sh$5~M=$(dAi5$8f(^`|k+uS8bqp7BYrQ~2HuewEum0db{BC4S(L?H49EINPd6^0tx3$EyM7-FaQqta$5OJvm8Fi&c1lk! zJ~~DTnzjYL13GxbWoqcLvB?Ymcu~4rOQW?`h=J>`3yk_rZHOX*Pd-24SERZxYk&&B zv`e4-GsI18ffYFUT!*hEE;5ek;1H1&^kf9yRmTiW?%Hr^^KWOc)`j}cYZZdx~sN7VvqN6@1*-vk#2RcM2%(l#xUY%mZRRXT{jOw9|?-6j%Bu}pt{pck!a zP+CtTo-|tT>CrfcS$ul*SB>3Kc^9ccVeqQNkbknT2#t%PVm?-R%qna<%C!H|<=(Um zBB?wf!L)Rtm+-%=B84S@Dk0hQiXoZ3X?Lwc4+o9_B9cuj5hhF_BK-T@B>+MY7JzhzYhDcCe+a@nFF^3kX!#A?Oa(K zicIQ#?w&&mALSp)`9!tiefxtxyM7 z*JWqnHG8yMk;dwR77geK71mjDKQ4GQF+3T)BcIF}&O)^b2g{1XTY$0&C0X% z*m#5+Gx(NDdL;-iFOSr2jbFyqKDp#$`56doBFgNS!lpNm@b2)eG`V4M z?XT$mt+r_L5X?S1T?q<6BWkG>kjV{S`bp>+V zY~SqB-$^8jw7n6va!G+x7oBav?Srka05RJ4kGqlcJX%yeFT9a+63DdR!clG@#t7?T zqf*5W5>W?Ua}xetx9`Kl_b&!Q0jx;>`Pp&R5+PC1(V<2=c-(}wnx^Nv9^|RQGKiS_ zMf)?+9ZjWF3q}ewz2p|Yxts^0;54jOY~q3_M^Sm7>?o*#Q%;%dRh!#iv0_d%t8A^gz3Q1ssB4LD}JHdh5SxoxE zlsLMDO-zv{i2Z!Tw1i-2kBBAC5y#nsw;6da{>ZIheflc>S|ZZQXGKCRlLQ{aVzhrY zq65JJ{j@)RgVuZ2suXdn^Qq#>^vLIJuW%?mfvnxxko{LEIz8U=*y~B-%$UnTuCq=M>5J52nYuq7)b3u<^1@+Sz@{y*}9qizhz680G&qC zRkiBZ>Dry*ZQ=pn%`^c#SHUJDIPP@P6N#!o+YZ=r=t}Aw`!ByZhsi^vD;^T*epzI5 zzfrw*8omvZem}h+eU6yq>qKtbmt^ysv|_ zfam+XuYv9FgWB(_(w>(jf!D^L7HIhk{2o7_1wN0$uDd^50|I=$9D3fL z2zuT|Y6Bi05x&1T3_g2$=FReYyuaO!&%3{J=--DJzV{Z*0&W)(4E(-d!}0=(OMTqF zYP(9#E%Z!2l=jOA($7Nnm`x(af+w1r1(a&RetNH%ix%PI;IuBC&-F7;s zCP(X-&-IJC!}{U?Kj~+`lYygAz(uR_F`$($X3l20bZYgvB~JOC-EGBZR$zVUUAB9< z;(03hIhm`|x(p)l=+K=NfOV|Gkl0yR7SjBloO5a+!2RYvH%F!e+`VeyJwXl-9ER z=6U|=aQUqJtnB>wsDq=T(0$p-FU%3Z&-1wZR?r1imbGLy5a z)!DLG=S|>su)bX%X>QJ*Snzc&qoQ{!;w!C>&2w;QE$6b2mRr6k%6{&%c;K?0$^2c@ z_deKLQ~-7E=E<0$Ag8d4#2chVu=2(pO5k%c@vi@dZj+z3WliI!uyM7*3dCktXkW!s zomRen&7FQT90pKqh0jXr8;km zU7_sNzt+S%!aoun_w8I@^CX()GnT1KSJUBQHfKB^Uy3B5+v!rb$SwYSTT(917gv3q zks^(5mO8|<19eX z?1M#z;5T-nf}=g51Y>vQCFc|w+_5AdcYeV0>O_d{{F4ssb ziZul{Fj@_t%=Bj-)7xxq*}KSDUjkW5k4>a4L^*G=sh178`)=z|~OP4?M^2;a7E-wPFd%FSM1H0&d{ODM|^eWsaTk6ZICCyIgX8d6JB0>u(tQ>95S{k|7jER1>L$o2pI6U7MuG~!6j0lg`A1OB< zyC`A#-!4^qGOWJ0h1pO0B10AhJx_I)?`9j)^s5~>oHMnXm3#G%BneMnv^0;l$TrX9 zWm@i`I+R=LV3Fa*&bd;R<&1zvnUg1RP8i{myUoY-6M_zuVydYhx&MI?i8Z-%6Dw_- z;Yi&nYJ*Q$h}>ST;pNILG|<-C!l)XQTqhhZH-B^5Vrp&q`6j3@F7V30YhAeeHKCvM z`bTyqs@lmp;4h6M4{#?3_iCM<(-+(6*+SK*C6mrHGIV!oBvk^>#Y+3m9PaEdG$fvF zt;^QSoOziy@Ajl?U9R$G1^B2}x-J;jHnUA2BX)>w+WA}Yp29N|(3+6}Pnt-7T!R*J z`}BF((PHVd_-tEil1;ic>I9fXHE(CpwMdy5iph*i44_k;=yPw{)ymfejEQ&s$KKX;%jlNd?x63$pbr00+ zkZSfT)rxRJzV-ZBI&(t$AM@m>&8G8@ouo5C+yTR=a@t{~j&EbcxhEiM`P^Gc-*6s` zEXr;&g2;~dV2jlOm+mqW>r0(&q9nd)9&~6P_Cg~DTAzY|l8Ihyxf(`mB*jJhgsy0} z@|E63#^-Q+7>1$Ty7)qy*2aJQ6oG@9h0Fwhg5%Jn2%`=}{ORvFf)po^6^$nlax<;S zH&$zh4I#?WA6NQ!lO%!!v*>~{6qN0Ki6@+OgSaTRgRq38Dl?^n{VE>Z29+WxTUTp4 zciht}wt`D$vxVtgTKkHp=I)}a#^<=}&yZlQhh1-*4n6n=^q&l`?0e;A6N*Gj5yGJ- zamz~^)?!5k6P(&7#hHO9S?gk*Da-9q)IQ2e2EJ`gJr!Z|!8?n$rfJfA%|hh?W*pKM zkBz0EuUYXkhwKye8DM0sBF=Pm2yORF#*No?5<%j8enfbqLxEb#`aUNBRysX+K2ov` zhQMX0TA@~IZ=N=2XP1hUb^9N=N+_Na@*whrdZWPNc6%uu%}(_-@3sE|RzRu00@oet zsL9vp5haoIkt4LUip*A?vuM^V1O^L3Y9CKY61r*SYs4gw((QUw5LIl}hNU&{y7`QE zUPidL#6fZneR*RS@K2e3&;R8Jl{9_GDE{pLkk&}iV5MdSSI|o6nC9EvjB#CRj%Zcuq2N^AT4B}BcaU~oFjh~E8Jw%gE*Ba3#lSLw{=k3;h zT4WMjl~7KIawMs|of(#$Wx2M41k=8Sjk|4_MT61qD_GVw8~6cw=S_M{c7>EIGev|f z+04-l>mM>0k!GHB$usXn30!YdB4X^sB{dP$ILk6)hL;2Gl?J9edPI;+gM|3CjV~Eq zu;zy6O*6Vp`Eomjb`h7gZ8K z!m4T7!>W;mCzaM-Z@-&{2lGk`8y(o>C?i6osy?Xwo~1U7xe(AY~8RjVFMVi|lTf-st0X=*7jB2PD|&-=J>!JQf4 zE^k`5ciA118m%qOsWVclQ&L<{a3_3HWK8B|b;yL$<<7YroUym|Qj&4A+C{J+WpA$}9-ulK+$HaP0dq2zO z_4=@8!nMOz@XFAv>V8U$>X);r1+LXAouS*%dY6#X)aeyz9mG6NGI0;WKiUl%F+2%L zyq94#O=o(@LEZZ{v15{NEaY)HCNpA{Hl>&)ozxbQIKu1R_%5W6mPA9}S&M2xvQ-H{ zY71^iNexyCi%>#bgI~g2k~+hVlF#IkljkIDx3S0_$&hs?J8#OO^d7`0mbodvv3`87 zHaw>^MMdlKc0J165~@n_vq_Qaah5Z=KV|S^(~R>_t(Q?PlA$u~s~51Omu%yLKj3(s zEK6(uxjyj=(p;e6mf%Q4w1hD$aO728u+Y+sE z5j%lVYxWSWa-#=+L~}4mT2vZO7fFD|THP`(izKLn8rmIq;`t8n5=WB}v)e`2X08&@ zl8DW&QP-xD%Oa`Cy+{(`ze|@|b!y{lE7aI>+FOK|hGdpKA!#Vthg*+)!z3!AS+5b* zpITCmoD_QuGVu~pS|8rgL7D{Fp9IVM?Rs>LNKdw_K2iqT{@itsV5R)bcAlMnwwJV| zG$5MZ7H+w1V&ujCdTvoHN&8ajCFvb@n+gxD0mJOqV9BG>-0$p1%g;%8o)Fl(y%{kp zEt;)6_TR$SknyhRl+hjv?BTpQ?qX?<%*-o}(Xjg@c_q^<(i0m1*HIGoHVH{v*=yO{ zN|QMyPu7t%X(0_CgPTYoS(nPD>UjTHd=Zk?y=bNNkigQZvPSf6ySFtONef3WIVB+j zC?OpryX_j|+YVyf_9`#$)n?E}kljs$bKb5;+Q%}GBn)!`wDx$X*B8M7mR~05)NrU@YC*?%E0I;L5|Undb8t^u*g*~HBu&UJvq5HV zy%U(w%GMfw6Ve!u1fnKRpHWtPK$=oImUP%W#Ic;6om`UbhIBbkpheb*tX1u98Ie-- z>|WiZHYY|>vv`q}B#FmrExd!6poUy_J+6^vJ@0poB!-sliJhEICd)9a7j~R^Ld(?# zu{N+2P?Ga!BY@Mgt))GklOV3WV~+V^iE0_14MCn*Pg2y9oHFEDdY2>JJE}wp;i zSkKudSP8Q{M0GpVDMr;HOIwLuzb+8dak|2L$FKMlZZ(A`}rDUho7Ggo_1k;gW{pj0P!czK< z;VuX++F|e-m2NBKLcXgYkv6upVwCZlc)pvdf$# z{d9Oes+j%Szz(-LUo2%Pp_p0>CllKrS~!(U@GAZxb&2Myh+18e(_PyTr)-c4midP@ zIe#QsOx6|Wmf4hRPS~eKT1qBaXqAC#2iM?tNHK<;%H7 zIjvmr3SlAGG{=&ZzUdFC;=841Vxmy7a*dr3A-}yPN!nrMqdC@45Fc``NZz~DQX?N> z$CbQeCG;ASMKZbC?{y|=U@vKgb6Aa2vP+YtT&5}hF(c7eaW^aJJr7Uc@uQk##OvrJ z9uis9B46FBm7FjNBu$nXsS5}0iBQ{)OBgO1thYXpY}X~QnW_mRis1F7;BY1Dxk#{|c z9FoWdN!Z%&>S1NX9qG}g7n5}89&6e@wiK0AY2xlf1Z-C4(#iAVdp`}rDHD01nY&$& zG7y4|jlE-)I>DzE)WQx3d7bK-`3JJmEv~OP)Bfp#wC;1_}k=+roMOiIG zj-`IQ3G+F0y@7hrFiVCwcg^)$xsuTOm_0r1wk~y0NWrp z@-P3R8EVi4i}__<@iH$a-I&}HETdaIp0@gyxKK8M zTwx*{yKG$9_wp_bx{w5wK6T2ur#6Buw}?4TEckBS zu7Q<+$7yy7g+PuzB#l7M;2`5}uLmHR)fl!7?1HQs0L?<6>g zS_KqgS>cBymrr&Y(0Dam9&JIrNn>iEglmup7?d|UdpANr4?r566*+P5Cy7#fDtQvO zs@OjT+|LduspdB(nKb+{sS=cl#ABq83bGDE&kvJ&e}~GP-CAF zz0`K9cq-aBT3fP4JGLx;$S{zv(uC9ihe(ra5(lo0k0pZjRynk>btT}Xm=F+F$g)_` zk1Qr8S|+4+xjZxPgQ=U+Y&L>G9O_>i`R9;_L-Irt#bFjN68RZ&H>6R$9md3z#m_@u zpO3C+uX)FjAk{y5j4BS*1MNlV_-tY=WXO2{5$?#JK@7wjX68)1Rs#2pC5~Ic8kXFO zJUVMxijUk`bsDD=2m&XyV$LM*w3+#ycT;~j*3!f>v9v=i4mFb!HH2v**V&$bLe^~Z zHVeQ8IXD45)0ALN0HKL1@TR7AtSm{H+P2lOFQwflxzvrAg;amW$JPe7uI~iN9|X(?MZ;K@b|+ zhI)2=%`V60c0IbL8@21O1sXZ2n4vW9r+lLn{+9{ICE7C{a?4Bf$=H`-l<-->l`sCv zfT)BM4G1K9qKh-KIYcQ@Vi_AX0<~T9Gzj~ox3hdWUD{7W->NMez(|={fagjCbv{^Y z+M;r4@nR!zQE^&!Otgr;N;D)SoxGk-td1y(o=I+k=E3+Na!(u1d?RY2ff)KUEp+YC z;$5+oKnn!210QeT4a~SVtQ~^!J}VJB3(aC&x+{zUk?;@{V5zSO9gBKr3c!O>RflDdvq#4vWUT@;j1` zT9zny+CJ@kMTPmHwW=>R3FK_L<xIRImPHP5P60w7t0I%X;<~5>P3p=wq09{dv;La*tzlFO+$M}p#%QSw^5|XwaD2+mhJLYBEJoTmZr+4DXb+d=#z(!S^Sf^Wdh zAoW~t9}ap7&{6JIjaoW@>{ehd>Kl&0q-4?Ae2IhSqw9$@4Lqp!Y3|NX>EqBjZ*qUq zuzH>fbjKjDPFaY-`%xfJq|0?M=)B**hv1f$$v_H9kKw=uCD&ITaSQ-m z(%naKNx5D@czyu)CCQX-GFR?Tzg#1>+quz{$Z)H>PY z)kqjL$uTXPtv+rm($}1=1S~Q6Y%6J|Ns?%r+h3IyBTl|)^OukB{nQqcN|iN(19H=rS&!HjI220J$!HCXQ}Ntl-+1Yn!vxBWcH7_j#{krT|aDw8@gH2HLG zSU}*zhy9>cJhDjuHIN5386VPI3qYnnF>}B#1YHQO%b-GbfJZZMV^V}r2XHe@V1138 zWi1@Kqij?mNwX%WF?Yn6l+6~o+EC&~-jg*6H{^N=)h^XOvTRxH66%s})cQ={DmjNn z9f@q*l9#$A&Y(>oi*XRxmC%y^BPZOd8N3rwp|QZv4J%=jH|DheAn&>XkakL{qPhzq zx4?5~a{`y0VNajm`-yRpmh6_fx9gFtOhVJc5OiE1i74P?y*w+Kj>rXZ^3#B}<>a82 zz(_PeUkHoLJhKa4W_0x>+CSR-^6V{Y2&hjWTR9wA$xlr;hI5hWPg2i0fVUIckIX~T zE|uX_YwyMsj{`r&FoSf8xyD)805ML(^C~r&WWgC#fyIVeI$6T}! z!daCRXc1tvz9dX3??g-5%XZ-85bvB8$#n;NLpa6T`M5Fm`wcujr4ojnmM_o#*x@HK zHU(5Kg%r2z(G|2I0bIesH6TMzMg2f+_({LOZ7{58)S;zaoe*2mN%CD`$jts)gmaZf zfGn^!06|VnL^4A-7SqG)kD@GuoR37<0?SZH1jKag@2C8TOBVpCnlP4}mwlR4NR0LX z+DV`!34QQ5PR`ZqB?P879M&Px(?Hsl{r-q{9NrCJC=@$J>GEz$-^l{iz*=Fd>#=rl zHYZ1CSUO9_wQLyK`RIyw-0x(TbQWy!W8)oxBrV8xc69dN#mm{9vS?ddKo#HhPUu>9 zu>y532loMGBh7*=h!C#9#YQII4!DJEiPi}{mxVJ2nTiBS5jslef}yrcG8}Pi`85+I zn1JBPo6tg#Q!@!GA369$HdAw>FNjnesep-RA0+|VNRS%U1+t|udbBS>gLV{VNBmis z6VB{>SC3sUdBrFU2MBhj0wj{=?Rs=Ib`yG0dZGL&LrWp&VF%NRMuSZuyNEV%=jkERz8GaZ-VVkCUnV{HL=jd6EFtyC*&0u!T zH^GwJ-$0P31J$TwNh#tXIMUPtd9p}{1K!0bAmcy`4fFJMybpiuApt1Dl3tYtv^m6d zV~g}@N!Sh{ok@SR2<6x7H5+CnaDt6AmlnJuX#OJ=Be(C~DCpYt-nI{9j_glI`*6}L zzzeByX?KDcMN<*+JWN8TuRJ2YuN|~<4L}t!fCM_xc8>w>QaJ%Orwot`E%~L7?*(06 zfr;ovz!j&1k0JcbLm`6pXJqLS!k05~PS&i~*K^0S%j`lpkdnv0A=oKriEBwOY@|=bISAzA(2x$(zSgSV>;BH6q4x7Ynvd)X*2sG{nO(!>p!rdX zFDr%Q;1(9dcDiB(>p?&#BxNDyQV2XOF@5X#?nGE}#o;tqnL-kqYDr?2DsBL6g$$xT zTNg&1F7YqC@U|_8&j3#vNF0ODCZ!@0*v3gZ#V_sB!qb)jBeT<@knDJX^qGbsQD$*Hx)y2gROv|} z;2S@q63-fKJ=1v?A16qfr}%I8j7qjm-!C4(23V5seC@jI6Mcm#n6*0+Su3qKna9OK zkDONrP(~$4Cv62RWPekv%j_P`Xrf+(Qpy%mZUScpj@llEEHyW)49sOV*GtG-^hbfV z)Kp`B(jkNPIl}RqIHbA@ z;~{D?1UHRS2%*I2+osgk*-rlldy@CHSM7*^`=E52+Wjp~ZOBS!N zIyGY?$};$0GA28r?u7UvP*is6d3t%Rhdm$t*ljZz4yb+V`2fN8tiSVsOVB+KO*f~L zuO)iFzxlwPbqO^&*8g?q=Hc`aU;=d=b(W-T%s{dzT|77WS=7wbT(0=9t@3-Bj>p$Y zQw-_(5_Jp4+mXHAk6c)y+Q~I4PS0P?>s@28butlYLgqN)Wl}Iy$%NqwWa?|B+3tFo zlC=^*7kQ_fE68tB#{`LUm7})G**0UgBUwK&0txk0D+-@XpkiAbVyVc+F1COPPA?eJ z@iaj8sjhUuTtf!T&c$x$qjo0?Lir6GE0a1}0CrgQj|onX+L&4&9>sP#@nF+orIHrF zz9QBVaM>uAbg}MB_0%*cn;D&aAb~=YI#kN(94Q0UNwBL&J&3g8MiJJSo@5HNnDH#@|iQCchnbU*RoPI^>61j9r?`v zLjL^!7Wu<8ABjy&)`lCBqiSFKjZ*L@Ut5t@+xLz?=X~(>3Ie`>3p|Mwabz|vBE<8; zdcobsH3YGrS`T`OtxU6VO;|bz$LV2m6wpGJZF7K6l2xPlR6ijkZr-N%moa7wvd@cX>EVg(A#CgQCbNHxZGy%*c zsth87;hT__XvvNFI$tfPlo}r0WU=RF2sTlw8KhKTH=3-3W_kcQy;P2F?G3JnX??ha zMAkdCRpD2GZ>R)G%OiuugaZ1P0psw_^%9^T6pSRQS)@hpWqa)RorC->kGfC?#UwOZ8RS02;9*W_jZtP==ZP{Ew9NT3zpavMm$bUxf+qsv(0bGu8*T4n(3C(& zD81Z+tqZgz>{Zot8&F_0LP_Oat_71PX1l_$Fo{{;g2j&Xg?>L`J7$doz8}Rk^?b>1 zn`}S|fD}R7f!iXw-L}o)2=?4`AC(Mp#7uGwiYT2SI(Ogmk!NQwHGW0#nDTU1FtJnJ>9Kh z;DxylBnP21Ry1#ta6kff!{d+{mN>|GS zn~R|xtH)mE5xXfCW~hx3FxuA>ek4~jY-|#uz@!oQ^oACYa zI(d)+)rn3*S(3g1-7F9z0ztX$vdQIaWbo8h*4U}CXL_(HMrF4aL)@gRVBvMz;w+-R z6FH03fRUqwAGe>F^cU^r~FAcb9H|+LC!UA7EA`e4b&CBZ;s^1 zX1*4%f(8{DbmwfqyV=f1o|OtB$Jb<@RaG!x;b*j$*`NXQLAP#{}jdfe{ef0r(q00~&BzupWmcgIa+;6ar z^@Ux(yz@V6vZbS_wQNzBO)|_e-u4>w>B*+@pkUj0w&54<24OEYI3w%_Jj_hDhB2cd z-m30=q+By)ZD5M);>vp1WrsJ7_iEFu1Q$g;U-CO_Zt7A=qgSs|775%fpIp$^bcR~F z;bc6ef_sVNKC#?R%qxPL5WSIn4DbvvMwurqh|YJ4fF<}LQ-1N@W@d8Ss6V2qcAf98 z8G`7zL~VAVaH21?`n#RfqK(-08pAi*+G4PtkccKeC!elcoOenSU{-Vz+mmcsp^t+eUPhZngQ7u z#Nkj92c}ScA0=?Z!)t)@r0dbO>=l?VSz!XU&jR12(!tn6}L*q11B&r=WuTt*@OAUz@_E4U*M>^G!G` zU2SR2m z=kYvJ>!7!B|8~bWC_y7kxuVN5bf)nSZ?L>1+d2|K8^m-iFgt7MCnz%r*pecGQ+Mu$ z^H9dF9hKj2;`T_0$&_udI9i__epLqbyCLn#?qycH&+gUB)CN2n~=TFd#0-yxnNNeHn=sA!ki=NN_E2`N2CfwCC9 zT-zMmv|yQV>XdBCNjqQ}$PN`uKBUlX+gHu2rir=`3-(DJmy}ovc4^5@Dp`o`{->GW z90FJ&*l7*z<_Bq8DT9}@4T7G^VOv}os2L?lrt) zP=+j1wt+S^b60(^tyVaVaz7Gvrz>1TLNHkyNtYRz4z+SW=%Ozv{zs}mA)*sW^Ab!T z=OY`}urBOC2r_BH;hqoAOz?x$sgq8 z7P8gsMg=wM*e>Ux0jikJ#rvu9B&gmF9kG7{cJOcFTGu+Vjo12-8zayVfL z4mvQ>@dH-a8Zi*4-asHM%x5=s)7Dp$7eKCU5|(lS6oU)sb?@t8cH;coYO+8z7x4Rt zmR=6W-2rE_Hk-u?wCZ%znCJ3u53X=M-lYiQIFq22ld-I=o*}BGiZf9JW@buLjc9MY zFTxMXT?^D@d_Px29EsE%%tq#VKL2^|@{$Il=l ziKG%i_nTSO&R57rKAn~xE74uSg+SEuV=?39-||MTbsE|Uj(|cXlZQ&#BmF3&OVaX3y6J{8olo4--R0TZD6Y?pMRUq(zV(n)9$n%m(PZr{I+6%i2 zo{}6YXRCpkWIGcwpKqP+LcpqcisMx zz@7n4u|-Fw^IJa|B-LE3x-ZDo<4D`Mkf={^ivq4`>=Sa%&uM-TJSzhl9FKJ2fD?kkZj$Ur-<`GNCG`}iSn9tt8H6cp>Pj6fZ*rA zM96e^2&lRA8J0T$n-6dO@du@O_m7E-0G%6;-c^9`-Y(kLaX3!YKr zY{H!@Yc6O(8uU9Fr@lS!L-z0%)z74IDHo1vS;B?6TCclYLtY7vT`SxE@x9>X%#6At zd2iPv?EJ)ID=yf;UbjSL-0o*9lXAR<~RxW&cG z*@Y06!g9J85N)%Y)fU8!Fhkzl3E_Q-G1=cWnKUp19}}i1){G>mR>gk!l@(_8i4z!9 z38yP!gQpq@A2I=wz-CTrh*W4LpiAO|H2Ek}hzML?Ozro892NEiZq<}N2OC6yor8Ud ziYB7OQCgAQItLyzOZ5$$Wu*`#C~s3X3u!Q+#=~F&baz7pK1(G|cg^_u=o$f>m|qPc zGuty%bWEyTt&b7F1K+aK*`$>q8A{Z}I#l^48ISu&YZ_8e26#4FOMy8ZL1L=?iiej& zi|;trQ2A{q5cJ1sec#>elixS%Uiq{aV0c|>0qiw&GiMmgD|{PMG(O|+Cw&=d3O8hVUfT-2BeB;<4&b( za9$R;8)>%@#CgU@$yJ)MHu0?}t9Q7RjuzP_0}Up8hc=9pv;c$y6e^jQQ@okAyOT(@^$=ZlBxM?wV2L#EM&F!*8?>zo?g6fu z-cr$C<@HoQ7(gbb6RhmP|8L3wz0|iO7U)`GK>H@^PHX;9e-cr*x8ce*>6(r1C`VEd*zpa&>DQ|Y+5ktDJ_ z0ofuG5jJ=STE}g+@#NwD$$C^@#e>NKg(4 zYsX8Tst8X_PzKrB#?LEO9A9Qhf(;5*Vpm!e#B-O^$`@7pf5I_)HB!O)34Vg3yAF z7tt=sT2}%EZcL=Z?Rs>jAl_juV#>D##Sg9f@Q-pjThi^N* zxWOn_z-ZfCbhwWzqNW2oWK)h#Sl|ZRDJ_Yq z%)ecaK6by;w_pX-fYZjDJ{nGM7Z3PJRamta!P%#P1QbAotlKd0UkPOJGxTFHbMGST zz!2NAgN2yegEb<@A$Z34NZPaJH;l9NVB1dfO7FbYwy9yo6+jE=Ik7aYqNz>r9zU;m z@G&`vPKzZOM>vJey_}3gTrCqJas>ho_lT>OWfMswOQ%&+%w^LR2Q@Uv$BZjbgCQJ? zmkdRZcG8<6*ISeu80*XiZ8kk2NgEKwJL3+?@SxS5D@wrn$x;JoeY+l+xD_(MNi{5X zR;GXXevDsUe?5_xNO8RCC!!%6l)7g7L99t1(l5E}Z?G`r`jD*IP*qUpuB0IcYAH!O zC%EnpG(G_}Ow6}3xSe{#cT3ehOsPcYgg)kzSAu>Z6_8r+N^GYqVoKo@bj<~cC2byT z&mcD@r@M#M=fj;uO9RL0m8hwqL1zj?j|}2OkJTQS8?xcF_{jbi%I}j~9kfW9;><%j z=|}9IfU;U$R*)XaEBo<8SmwL?Cc(?r12J*C9(^bldSxX)iiJLy$?NsqU2#5{r^v~R z5(zA=dg75A?Zw{h+zqb?0`AgQ7f@1SnXW0IcEdFuGrE)P`(i1ju@$_I?=7j-#NB?uXH5K90F0g1b)ng+#%E2I*)6a5|6jg!NyN^5Na@@F@^>OG0NBciCLaUPK2f0HSeIZU@0wyo5q(YO z;@G1?^>j$5AXdTdxbP!XBQ~&*Dec=MEn;$DQY=W6M#5zh z7L6csi_MSQ<6Ef)aLpRs5WsnAQm8hvVNcw0hhS|Bn~EMf5GsZjP{RN>vnn8abNly^TX&kJ-U;K5yNZfzg znB3e8pg{!_m8cw3C;<7P^U8x(S|nG}+pKsy;*K=boymQP0>52hpM=%^2vt!EuEoMo zI~`iDmjFqluN@{)cgKY%)ulP^Wn$1UWx9?Et|J%LoA`o0gpg$9+h}wsq#@x#4ih5b zTgJLAf_~JU)_|lJS18vAL$c6ulHG91@Cg!DvH=3KN>$PZb8w56c-mdmXAC=z*P}DT z6RL}b%Uh|Z+U!R?_Os`=_lE&t4 zEp=I1nO+IyXjcNzflf1_@o zg`y1s6}*BJ<>Jx0+3_DIXjO3>h?uTcN;p2d$>ZJ#XPZ79PXVw_3^Shq{=g*x!TZw- zd{%W^AuD>^&^BYdPC0s>pC&bEeyHAKV#`Jcvst2SX>mM3d_+3eGjh7(n0E=1NxO;L z-Ws*LS}tyzQDRoGnQFA6j0uOXt&nr@rX}plc!m`Pvl*prDMa$E3<=-}OzEBkBU5+)Sc;)zY>GWksNA=CCnPb#=A$Y@2By_f^HLVomq z4?j&TeK&j!Gf=CrrkcWUJR{GvE07r6!XrFgu?W0gWfYh~@~-$$v&R+&%SC6#6ojU< zRM+pY0|_jH`y^p>GpR84i3TZLpgHU%%;+IXr3_-Z2Yc6``!k+aiT4&6=x`DnOWUA! zGNpU#fAFk=y#uDRrIk-bDG`uJ@3OugQFJ`zsAF}sgsOyW0%foZp1fJ&!_*i%9*oP# z<8@WAr+BXXFDi%;iU0`>Vd+Rp4KtSxEW!A_Jf;VsvW00v>H-qmTuH}0JOML=y>t8q zi3kiFAi(NENQ4S#TU~tvpwG%Q>FUGA1_@o#>n^K*V%v1rWb;{Z(W>ToG~g)4qXtii zo0DiE0yX$@bC%)$AV=7?GuY;FhVF+D7x3HYa)6kHz&Y*$79`Q29cF0@AqGDqEO1ehzd^}ZXoKY5QTa}5kqW`0 z8%v#!XvR!EDVhxnrV$XdL$W9`nO9B0u|4yJ_Ww9h01OjcZ+paR$af;%MvS)ui$o~j zm{BZQnjiG2U_ju~5~dQY1NRDPk=Zk_kcP*aj&pDGf8%ON5#5g7NvIr}wQSo{3Qsx@ z%C6m)(0yF7{kRl18#dKSham;e`etMVQW)cpj_{FwkR&YTn-98h{P@k)9(!!*Qt=ei zYGc>jFc%7CTT-~j`mnU}f4Ey@bfULr1rmvYPMfhmf*ht5!fyXEfkcz%05kzys&OlC zL^1uaT_m;mBxFiZ zgmVXgP^qaYp>ApGE@O~*E%?lVMOi=|y21f60}{@2)LY%%DO*}}J5v9{rCK~sNMMM3 z+=zp=V>fp+sWg2Cq<4|P?iaGV*GlA!pNA8_Ez^o&0tlkan06+6KXDS5^hf|mR-eY> zaY92vWi!5{m$(E0G@%3`&^iCM0%SO80EYb4RMGtx=E!?r~cT9Cby@_y0^^Z{8qy&ruF0IHt$$BH@bn`4*-c< z;(?hU662qTqmj5oPg z)7p(-o%iapN^flWO88XtHq>r37Owv>(EIJtD_GJ|z34#kL$`LU)=w7grS&7cp?kxMhux=bBR}(r2 za>Nk_pm>}10d+o#W^G|kY&SbG`Fe$Y8jZ2EHS%7|&=OaRTJHUj6^l6_8Ah|JMxAJ| z5(AELdzd68RjI%*C(SCGf@;k?G~AIk7FPTS%aJ-j@+)--TX-2@?CilXx4GGH$C&!~ zUVAl&RHH3#*Q0CE)$SI@)6wiF_w=Lcifq!QI%AbxHPb{l_hXB8wZ(n^D%QfA`dA7> zh(W*&Z0-uG>2L>=^Hof&S+*d+q91)n!ENvN?@?=IPrgGbVG-Nlx5ZUT`y-}kk_?(I zghze7f(`8uRPB?<8yyjYG;PqV$|c*t=xHb5l932d08iFhp|dvdUWida5Y!+QuN%3O z8pH;WMQQMvlK=JA5 zszfz}q>SBT?K!Ux+?!PXmsOIDF!69#(+-uIJ9X2@pubU{&?6d%KPbn*eyb7tjGnTR z8L;2&3^#dzErT3u<2|loJaHBHrKBN(SZYwYQ6bQOj*O~0Y9Pt zI1KKeoZSZG*jG7~#+(-MTi_I4&-ep|j_(JCKN9gh%Ju;J9zkTOP*^HD8sp}gxAPTm zVHvyIEKrX#q5_Bv7!XV7r>vQwWZg~6%ECjNZBe^o+nexuKm{Q!lC+qMVrZ~PVZjT4 z@HEn6H!L9?okOQD3stKH0uJy3*Q0BymUc76an&f1T0QCx$F~4_ zK2PJC#m7&4IwfIR7j8R7?2c@8+h6;?4gLY3c}{_}yRkJOhK~TUYXK=Mqtud3faj49 zllSFR=HHuVBq3PIK0x$4fYZ!7TnG{qcL;M51{9tI{s9ze^P_7-zz(u9azYVHqNRG< z7lAf7au>yq)ucy*MgT3AZ6Uy%T>{A=u~-C4gvtllZZPcwhiu$U4NiwjQ;D39KAKvu zJfnxH^}#VZ;!xH@7!2_AvEvB!bcE`g*m9v~=WyqeWSxqA+-y0A4Q)1;T3xHO6UlSz z;q9D0yj|HF(;a{;eCe7E)m!K=3*U|O$#l1SHq7-3H@Aia7Z!``>3LQ&4p%8V)-OS# z*)R$7IoP_eDd`OxRP1++JK+{y<=rNPKcVv%)+>GRROj0{et5nZ zk*021U27N(^E5nL@xZpb5e<%3JkKj4hKxm(iGYKlEAXK_tchf_t|o&HkfDz|4Pj{e zn3fRU$)oF{h!tZ~0cMZN>x~5cMm8)cP^d?t_|^|b^*`O~T^Qks>P*N~w?T0kabVM-)c`j5YLisv{5Dhn9VIs$!B zLEHuy<%m?e7NX2xvEEKstp1^z5I%sMo|LMLYF@2wW=u&ym<2Ge!Cm1F4>k%1pa+P+ z1Cs@vCu9WsqUO`2HrwkA5jE^EWk+0S(niAh>fm^!X=O2^PV&8V@%xAwZs zmcO8uDM3M2`8^cG;o1{VbG`uQmhNdYSM z*8+RYZfg6o?$Ve^vd%fVpDGl+DY_J*yu1WtblmhcK$bg*F0W=BVY(){KHHEoa2c%7 zJnS+s60OqEO$O&PU0@m=N~c-rj=rA`Wxq0^z@H3Eg$$gwdHrN4%_o`-`|BbqedawO zNrpYe=)1Oh*ls2_*n2=`GdL9D+yfbL3Qi^}}5tSOr)rPe_AxJRjNa;v+u5|cu zML#m8RP@l2k@`)^ojX+LaH7);(5aeIUE_(H8ck`4&n0-ItJ_G@l@pLt`7jv(4h>x? z(A`{*;1r|BSBiFu*tv8POld#qphw$Ihq=!qy3Wk(T?VyD4;7HXt^x{qQ_BcEjaCl! znsI#5v4eIpL7`II{hOi+LbY?cMeRs$LpS@mR+XU!FX5>$TYYmQ`A5+Vc-hL@3JLYN zqOKZbc=O}}(_u$)sNKDPrrVX}=u6%C@u2E7;e4tbLv zIT{ta6(BrShL(cmB_+^%C}Mn;K^lDZYJu_)Cp}Wtutb3>2}~HwA4V^^DD8Z7#e@wL zbQ2hDC+3&KsE{5z@3Rvoj6yV8{AnGoO5{NWJ8Jli)^S$}Nt&$%44OM2!PeZi2T??? z)UCS3tT8-*dS^YbYOm(^V4NIMSmav5#L?CzW=}n(Q*39h16vTC9S&cCPfwZzvX{mR zyTdM(hDr0rOo zc?1mUxJPh)H$!?U{M8O?IkX1ep4<4^4!T_aNjEfc(De-TRJHBT3>YzW zqAA{=cINdEmuus!wq;|kyiry6)3mBGv!{MBg#(cvQQzSN~;+sz(xvw=tDuZ zCT&N6!QKqQ1Vjz)GAbA;y|iAAVq?~;%f*7x))jrE*rV!=@&|UZT}C+^=%s3)%O4n% z`;pwk2{9so@aYA+487Bb5y&s+Ig<*7R(%myXZMec{9~ZS7;O4TuK+uwq_BIw1=#OL zR*alSr?@jfPy}Sa2)Tmgh^T^g+H66GfIVNaC3nk(T%^G{=O%wE-ZnsOfCwLuob418 z(XLd|w?*{aoTPk%tir{`E+j4ZAuuy|-h@oiT^E$VZs#I31S|h9Gs5!o^M!wp(7uZwUHW&oS`V^r9U(Sw6 z15s{2BVaoPby6-bkq=|HI_3?}O82HLF)K`Ul5Pk$Ff7@nPb>Ytxcv1DSs803?!eZS zyn^8t8z}7`fa)eqvMW?;Z9>|~RH~x}==|`>nU&GRP?K}`$Z6OklM9A1CG69{m#VE& z7t>s?deFkzqAK`->}X)YTIEo2L;;x)?h)4*mE~~nqcnFvXFc0W%fc4r8-*Z?gcP;F zi)Fy*V#WwiTey?~*$Zdl%Hn>21OLASi1PxL2K9 zXC`xp%IIYZaq|zLjYCs7iz9Yjsr+Kp(;#YA-I$|H5=9~UP_P82%qT?~4S@j@5_E)kzhR@( zYBPA~rvMn;y+GvJk^jL0%SJQNwFQ-|czG8B zo;vE4@5XW2Iw_z{L)Me8ci;-{0dHNy#m1k(5^j3M0f(*}`DMs$fp1!_rTlu%?RasU zzH0620(gs8aX86t_96tqVbO{N(!$6yWOs^W;dwT z38{hV=o%O32CT6}4ybM2g66`PWNjIh&MO!00^$pPud4yg3F@DMi=1>)9*#gLaW*AT z81_gg1kr=DqH?e{v=RV5Si=hTXt4w<%U4PvbvG*NpJ9(?k8VY}lLY4a>1>S67L%h& zyAT)gssO-qH1KuL?8GbiRa!8F0hv7X6wwg0*$_wi$fQE@9kz$dz>Tz?7g{V??SOQE zVDy+D)lgFBdNRbR4{)oJ#vs%QSg zpUAjt8Q&xi$)31o3Rw)r%(l&*t3#La-*gfgdGK;aiU>(3jdw$}eB1o(0yZH;M1Lt} z83ub$#do%T#*F-o0OhaOXq6ALxq2UX;L0T`?OPut`2oH)s7+RO71@Q1GP+XxDz}IZ^z8S_RVV~- ztLyfQ0}EXSIe8;`UtmTP(r}PUIzi*8?&CixUV@ZW%_;^lEXbL&j|bj30rhhME7|R? zWvWRu@D&(C?upg_-H=EsRlF3>+v~*b%bn*BC0-cvCU*k_v^ThRN4Ug>y;eJ65L~<5 zs|i3XfA7UB;mFj?sR@~koo8)~xmsYgh4-UV?Im=j%!sJiP+vR6?n%BKF z2rNqLKtA^Doy+byf-Q2E+R=<##zu&ar{E^mk#*PA5wx-ouUPWV=I2F(FrXqk2)DRU zhQZL>f7yQu@D&g93F&ITLM17=h|0TZ&jR-aUxvnZ%xcSbu*S7^gj-&U&W5oXQQ`gP zgY6P?Weq$xCz4MP_K@e%Or%7}V(P3znv6aJ8JD%Ur-OO9r5m=cXDggRF!rXZu*fR8 z+&vd{;4v16e9a)4URe<5!=QY3_QwM*w{dHiT)jxe)?MM54u{mE5_fTlbGS z+`1!QuK^?3y<%5+LJs&=H7F^#3cr+N`!GNBrnf`%_hd-~WfCT;zU{y#5mvAtrMEhU zu=9Dq69`s-r&zpRD@i5Vme^7C(s3`NVsHHco(Tuef>ZOr*zuL<6%eJ+Z)Ja~>$1yRKT zWA%{VK!1G&rWMwj2SFkw{J_TacMsW;6qXb~mIeo}36(%0c3c1;TOAG2bbF#!7pA^h zwrx~61KTHRF-Q!?i3PV9%mYz?)A{T7F3jDPoTI3)qbq>9a&-WO7DR$!4nO+YZIZuZ z`At53k3>3+=?U9IMc~e~(!AMGUX9J%Nkwa-dmYySlDU(}XYHO%wp{%1>7t8-+=anQ zYZ%#;_U^QPK!j+@8ykSQ_qQ=Cc=+w~xx6Ww(-GDy#z$4kstSV`6kO7eqqak`kq8F{ zI;7fLyn25KvtZ#r!C`Xb3Kml$hJmmm-oD9F#lrl-^FbY$441Clbc~<$l_(CVMj#AO zq$IjeX>nT`+jM2-!*-$>MnlAJfGPG&a5LQ;bqd^hsp!DPaG>jecIiIVEN=S2RQCqS zxndUV=haW&Ac032&w&rP#gx?7YH^q&AZWj7b(J|!85ICDM2HRGDyShM3RtR*$&SQz zUnhZHxOvDTU3&7(*hC!x&TgU^y8#p)qG3E3JkP&S387ph#`fqm3Fa72^(o-gUQR4G z!~o7R2dA%pmXnUUHUSXahZ5nw>-FHyePPzeRVAyY+>&3JOalKjgR4K$9|NvmfF zu~j^sd%P~EVGaG9MB211ciEM(11nW-qW}EwHmSzyCNRL}CFdf2Q6ZOWCvhl?)1T*3 zx*iFk1etR#osuSKAc#8%T=D3Zez-OYK^3eJWXwVSyuDx7MD=L%56MP$BxBm9J8u}~ zS>rxF6_M3C%KYZlH4SCK!6oM`5#Cb)QL1=B_)Sl-($NYV#w;@y!NwMxUO;6Vt=2P- zoa1&gFMXL76&qzB849;enP?^t*}Ooe5r${9={bFCon6uAULwL33KgkRERQ3D_iV5c zB(2}9g1LuzEgkAuf;4$lR$H^-GONjr_lGpCMYl*v{z%4nbBFb!yx4m3(Jx<-8K&EA zX0(UjIt?;A_aWect5uIB;<|;On6@di)D@l^)rPg8TTsqnjSNZkER}0)GQB zXL)I&ZmrcPo*$7TaIRO`whO!o!V>)G(3;F2C&0!|bYlTAi^!DBx5*wcWFB4N3>Jt2 z4@ZopLw;{;6fYK}^>+~XbhU_?0wok-=2iR+4hz`LqeeP%l{}rAjl06vos4wS<6OQO zJMUHovq^1gRUN$tSHQM0&c&WE3a>@~po{#CAQ+Y`^n6bsj`hY32=8qS1LO{^Q^Dzr zaNJFqMd)-V-8$`@r?cao{w|axQAucqu^clG93OEfnOB3*!q2ec#d>4b>j&~&uSaDQ z9KcuoyMukCe#2cKnT@bVk<2C7v4>CaML2jH0;yKIy|>flSC9qRI(cz_U-!umkl&=6%aF&fFGGi6YB%b9ziv0E_b2K+-#^ z(I89GhXHH0Q*Ou@dQ7;J46eWf+L%&hxG>nbXh9EE-VB-#LugT6=`4Z4d2i1Vt6*GG?7l*QBjVBYLTEX#t3v-wTQ2-KQDB@c-V@% zaO}+Nppw)|qx~v{^b7*SQ|aP2(IDLy8eY* zWSxk%CIt%@gaVhueSZCmx?cp4eP6wMT*-X}vB<)1~DJ6cw>m$Nw`_Wx#R-Frs5Z4hG6`O-5OI zkk)uf;g7!BSV)tLL2_}85*Qm!=NvtIwmRjH%}PiYOayr_ZlDSzAQy7C09wVeUQ0~msz5SnYw8JjL{=_*fiVT z`%&Jm_$$#7&%7XmIH7_$R#)LJY6y{T)YaSeRpk5oK$-9cL_-u zj5!CycEmCYxu}^$0Xb3E7tDscO?4=4iOPEl>i?_#;%TLZ~!%YNWF30Y~`)pY7$ z5;vHn(pT&}3$9wyDTeJY!dkl-&MuQ%LG3LXlUR@Y({L56s7QhNq_j)BYUk!sTVP9( zNJSn6cHV;EH4-^TF=9_=uC7OvemXh-^DX$%i>nv7tE2n7iyn8!TvN7bLRkx^EPJxh zc#IkVsP`!DPS%WhX3hoqS2Rm@xkzHSzcHui)dXiRC~M~L>3&0Sb`0g~LIKrV!M^(? zsl#@DdWat{=%Y@{c$RrPRAdI2`-+iOoq9_663A&2U4&g=u^B-I#vG-P$b$3WHV_*q z0CZpCy7LCMP=6+#lCE?b`d`7TiYAo_bcPi=#lVz5fg?7}7R z#YA?!O`?r3T4_c_D~q8%lEs+WqJGozb+$!p!-@fDwRh{HAoX4mI0{=xVaH$yoS125 z>A0a2ptg49W8Mx}Oo_&xH}`r#kJW?r($3^eq6_4hLjqd7uT3?K1yA$ftn3#H6Uh#t z7f)`yMJICmi~70f0iv{D2)tKzvJH5uUEu=dR@y z+jHltkbl8b=`)6|6xKs>X0nNt`;o+hKnS+p{DVJ(sLh$*oX;FJ)-E+;R=Fwy~3Jh~d zG0V?qS!KKs{51=A$Tk_exM^HT#6ndBdk%}2mlwi@NEpc4W|KN&-sea}vIr=GZaswE zf5dO>`fWJuY__0|{G`7w4Ng>4?Bs8^{5FQJKn`EdFOwwAr=a8lZNuU+g<|-2U6I1q zATnOjUU-qUT)$sw&kadlD2I&~yl0bvL2WYh*drfk@ayP-90x+!)Gqp$LDo~dc>gt} z3eIM104aI(4Y5L?P~UpHwUYWCzk>TtkgpBS-)24wpf~}H(UPnj_IxR!MF0?OYFN+; zlz75pAQVgk?)kw-5BAO%s75rm!14gifV67H5l>WGvI8(+@85k)?lYCgCHu<1M}hkD zb|uA_FpI)_&E;Sr;WzJCcR7uY*yJ@!usL;Kc!g%W z3n_3bbfl0TABC%M`6^LR$h6?jY&=6Gh;j`I9dWZ2VV4CD?CGLkce3F=^P#yH+Czhr z@pm;0`N)-`;mGFw0yg38tAD(Q$%i6&%m_ZZ|GP_?(;yt1Vq<`Yj5-J5GuJS3%-Q$s zE*_C{M+jnzNm*>)jTKBbb`AS+_Rh{)OiF_d1LGTmmUKF%4C6LSgZ5_yQZ((x;JA%oX@Xwi1Mj z8Fov?eCWMWA#y)Yy+qUp6lHJY6liOJo$+`K$Yx(!j>;P$^ZXUxc;xt#4gB~H*`KkD zoiWxX5LO~<1$)QBKX!4jOG2&xuG4llBFR3`0GUj}835J`n?OX=ozK*Zlv_gho}wrm zfZqPW$E}EeQjGAsWZZz4$5>?FQqC$iW$t!fkh>#K7AE3G2X{FOS|V5=WZCU=ypNKa z&g2-83!r5oz1@#QqChrM09}*{4>Gl7b{n?8Pr?O+Zm_;3Qg^UiN1Y_gE^CEdQ>oyD zf;|IJ7jRp`e2Vs!6w0dzrZO><%iV~;nl`ER;qsg)6af)p!+N=67tZN+Zn1%UrZC|{ z*k|}%NDp9e+hUQ&cAXqm`&7n`-k>~8>AkNxSjbB3rf%=3K)OpgNF-+LXpq<2b3-fl zesM0eSA5Q}FoJCiN#@TfD z+*%YODdCjrfdI!f9Yf47Y)iV_?tBVpZ`6Ho9{VMa+qI`eE?6nbN%uYmapaCn%2I{P z=V4WTKH)-os_pV)4*Z+|sX;>^nGY2bWkW_D*0y`P6TCts?{Dx_C*GN$;DB8yGm@_ps=$;+F+qQuyf8p`UoaYjwDKe_yyDLl^8e14jBnJ-CJlXbP=~P z@7^lfnK-%1deHW4D;V?x9KsYYBfK4h-6jmLp>oOyJ{t+z#)r=~%&z*gBuU%aJu{FM2Ozp=GBCKOMTPJ?$NT6X zdr(qwPx999NP*!Oi{hGiy6dvxvQrx+(l3!atdy+p`q|M^r-7Nx$Rxf$-HecUz_uX8 z_?WhG(5E&g(%i2^U%lW|uZ#+DW=cHALTC3L5M(Aoe}8(Qw&Qvir-L5xf%cz;n6-|P zb8k1iO*`oEPdIMNQa`n|d-=4y6QOYlP?T&)eNY-);v*;)=A@qD5kswBye99`+fome zd(zIwW4Ex@1J_>-g&yufHYvgzt{QT|3m&UpHyr#$JI1O|L5jM+=aLT| z60tr$7nPjPh*s8Jg=fP=L8jqlAQBx$@$+bMmTrID=fjxa9TrEAT`?@G5@7q`Xz3~_ z3apF6-;2%`^NM%{8g=15E0%5rv&}W^TgAZ7nK^TU1<~q$?(VaPt`dh>Yta(S0K><+ zJPq(U>kPF1TN?;Z3l>=-S2gllIA}O)N_GOm>wU7EF zk>X8=$1d42F?ga9yes)~h@1UTF3JHGl381evrAZ_&FW@VN)C&NpGOi&noo1lj#`|b zwa7hMA;=ktVL*xoFjTlcI$2l@A+m|o+?W|@u2NLpMb7W@q1+fAfJmJa=tb^bjJ_hL zAPf6@gQ9I7%E#rYw^@|`$HvY2LQ6g8v@b@1n*Z!@{cT2DR4%Im-9xre3 z6j`D z`Q8!HY(b=PtCX|LkO2>rtSlgYuqZ<=7O7Xt4qUpi-v<)hX`jS)V*e`Xv9E;+7c7St z4HHbivFa?dz^NyY4qO?d#2q5yX>$B+f6T1T^3VBoa`}a# zj90e%JSI81LD*q)lt%<7U)TrUu1#c$k!kJt0n)trJv*s!U~A8aC@}5#fq9%>=tWP# zM3Bl5+?zUy(6>#jy(o)NIoE6?J%IADzlJ zn|ez=NFk5^>T`=uj|Os^*% zI#k##tMthujMvxH9TJrk+;9xOn;}mDknF3VcuIg+KpO=4UcfBs2|Ho8Ia}031yHQb zB887(VFEK{jX-{7z>iAMx}-0DLI*DtUgrqE3-cUoZFe7kjb#J_`!>0X+uahHMG*yS zOP@73UdxWuBVh1Yk>}`=JlR6%@^i%bOR$lX#UcN0HRAbkM6v8jw^e0KRY#N$xk60S*?;@L&*@DoFiq=jhsg;07TP82==~9K0yj=_{zyq=( z~OU^G0t58)O-h{<5RgF5H7l4?Brk3a!#{Q+g10aRE70}Rds}QKobV|huOedN5%JS zK*c92Z#QIip6jZH-j1_qxN?o7U{Hn%CGabe%00A+0F2x9st&L>|d^jIc#9Xzv*Qh8v zZ6b9q4cS8pe*rRSx(*~kW6y0jBy0`hL9z=9DZ?m&P&d%`_O4!@16ZZX2ih}9(IF<9 z^ESb^9BRwWfvA!c3efm;DtU!skDkuZ%rG7-2ST}7w;&PqD`0Hrp$mTc3NQ>Mk81cP(5BfB6cLZ}( zs{YtG&>FH@I7De)%1l=_Vx|BF@&Z;MlBV9`YVI%tWgw7jFAav^UWT?D_M3ycvh6|O zX#+Rw>)$;~VluOVNH{iOSNzTmaAzW7!e$49i`@mPzMh(7+H_XtdUpHZ?T}`OpG}9`o!{k* z3TumYPy$lnA6b(d!!5|MCIlY2KUnly0^ZTh1MEM6io@8uAo46CQz!^lo|^9;oP*4~ z8#)=J<103ho0__s8q8w8Yh2qg=(H4n%ZI*yq`X-!s_jT_1`W~0x z>3E+LCR-PZ=TL zuAl{7V82p&dIxK9Tr!fWKY(djCL4nMQgQS))tfcgq68&=A?Q@lwbQHc0vc+5{A!t! zeAhySzh+H%@&8(SzxxaPKgnpq_jvF9AOHXn=l}qU|0`rP6H{knCrbwxOM5#y7Y`Rz z6-WT!q0R%{|4dg8XaGQvUtj=$|6Xo1w(O4B5PaX%kAdX?mE13CWw8tvYS`C7w#0)Q zFGM0py1K5%em>(!tVuIdu4UOMpq@upVdiu^B;dq1r4-dVY1FQ&RJLxaVw5p;*YS*_ zZx$4&lvD346Fydnsu#gC>zCbJ18Sp`ZllhJTG51ON1FisHvJ}|luWE85oAN7v0o}I z$Ngj?&i|PS?@WlIj4^%+84GLSo#I($Dc0pVGOcgUi=Atr%1-QjBf6qW%+oQ#}dB(7{cRR`b@4v2b&ZwpM55FIgzI zxOOP*on8p=|2i?g;vK;r9081%k<|`I4M_FmG!&!2m>2(SEs(e{ z+nTkM-@aZ1OGS(g3lHnP=_If6vg_c2I4$$5U#wf+S)4D&RB~SA&t`-L1{#R4KHdD= zQpJ)r{}xY4tB&08?m)s~?uBN>-U$hdlUpK!!-?&ZDX_u<8v?B$Fs96Oghfrx+~%FM z%d{2BuvYItBUiZff+u0F)xE2j&946CrFL%J5+}Z> zK#H_Uj{5#MyX(Y;Yawg~@6!HJT6ielEx`{VZC zX9e&2WADJ;BWIs9Y5!{ao{VJwYgUS=w1ab&#VY%xmuXeYurIhL#*HuB@$fbdYS&rv znC%T)+eX4r)q#Pgh6zO;YpSpYZ-rVig&DRAeWQ^BT>)tIL%Pw_vpH5%k=*c&^>}OM zbrkvKzx=zXc&9J5dvX1C^C@2iJZtBztXP5j{RTbqr4kt`acQf?SGw6dXE+!JjE?66 z0x^im0p31f>j1tD`ZnNu55N;LYLFNUIvsX0;AH68;G+RpgTNL%YnUvJ-3GiBdNcTH z2;Lyf0RtCqJXjb3hl3smJ|2!d0C~XFfl~*%7A6hZ>A>5;w+&zi5kGizPvgPH1CDoc zHTZM@dN1@2!xwHp*nYr$ulvq|i|+@(FEW7O0D=aD9T+?yx=&cJ0R|xy0z)WnLD+)G z1)&>)CxlO!sMsv%!f+UjNEm}*C;~|=hmklGfhyL*FvQw|SVvZ8R##ScT1QrIR$mtX zek4NBZsi9lj^jj>h#Z|`aXdmyPQ=ML87U>FG9k@;UyQYTYG6I;{&E24H9Si1k2|Bs0NPd!hUpa-DsiBLj zlc~P3y|c^zA9T5+tC@T}=IFPlKZxER%cY?CmQ8^N7gkvsq%#>GMaWu4E^|pKduBtJ#upa)`k5;Nc}Y3?DutpuJ8YOT5YfA|M)%~?%wbDcP&2;Pk;CK zW161N_i5a||NY5)&*$y5TfN@z>mr+d&+nz1zwhHP`KtOm)0;f~-uIgNH|CuIxbWGeWd6_d z`|r<(a-Waa_x<$r?OHDP&D-4F9)JJOgM2*ykJE4WsrB#N8T{YguR4C7-tYV8*Wsgh z`SiZ#{`2Vm+amUvzFwZc4sd@z?D+jWJ|A>BKOV}R%l*GJOcMBCAIjP9 z^!mR(K5mZWx5^hE*r&b4%Kd)d4<3G&z~U0hT}sa zX;XvBe+*A|-{JTB|6Y6y|AVpe=;T%};r)G^x0H_$=ezA@&$r)Rzx#c7db{(((&NJ4 z>@ecw>QmtCa`g4_IBGgRr{|B4`yHpJ$KUPs-(BQ(`uFi{_~Ht>a)>Q(@0T&~o(`EGZ&kEZhdd(xeLty%wj@o>_)84=qaZQ=9r{rEeK&$q=PeXHcZ zTD|A@dyy@#AJ__i%EBx2t?%(UdyEhBJ6$iU%=C;g<7AQ*b7Jnvx5aYT_Fg~kZ^$=% zIY*zLv~1nR8MFrlxj9GYkBLspr-hEc?Zh|Ht@||lEID9K!YB4O>=efDxslHdo5@eu z>0K7C=E(kZTxZUG62vP%a9OW2`WZucke$Nk!PR=j8T;b1CX$v3ZPR>T4MO-yJB=HR zI%Gw9J{XumUj{l;wVmkUODmxHGv+no%KlIUAZwD$3np0x@CnSVJzOe|D%v@+5 zq*LJ6^TnEWj}WbL|wWDTdGF6+7n*2JG^Ri?~J$?4sGFSjAR)+0SAh0 zKxKh7a*keFulQ19P)#%eEJ~nKdyNi_b{-n+4nUHmy=I>%u`7+AH zQgCakBsvY0`Nx?=ld(@WV;MB;JJgaG%Y~wUDPqdC?$j>wF6RrVd()qx4s~y%k2UxLkC(jFfCCRyP$nMRMV0SepypbJmKvbkve;kx23nE2 z##nF$Tw2a=pqh#cvMPVYAOe?-XC}&_qDpvsiII(L9|CkZ`H`#SDoutpfJ1_++N3;V ztKy&3u}KMTn(rOaei5fr+|hpPD#`UJft6fhGfiPUGk9(??J zyg?1I=E*@R{T){&{(*cl2J1u0yCLRy(XEK+8xa(k1R-US1WeW_8$!wLP(;O|>q}86 zC&Ez$#MAUFl_GYG>{%;&>GRO*K7-CY(_Pg0BLA6R6XpMKCc%&(Iz}Hc=Kv`AOHFqy z&JaloZR&&Q0){tN*)5+f_oidiz=s7CL?gDji8zEcZ9lvffdWA3>8ozYUP!V=>T$!HwCBkqE* z0~U${^y8n-HUzp?IJtKBH1@dOfMis}qfS-@=gUII?x3vE?WtS@cfu8atX7E)D|Ml? zEF`^W-qLjm1fBA6x*n8UCP1Sq&ENJE9V*tFq>`K7X3C+!q;tke4wOf!Wi8odYywOXI>6=~n^Y_Eowkl5L&RdVjIVvg9f~o?bdAlO)<`h<^u74{jAL1Jw*aM8I{9Gl98J{Iaw&;d-_!+xhd z(c}h!D|IN`>^^vg^UYy8qU?76fobjmEJ-)qUsbhv9V8h{x9XWL!P=oR{IL&^H&_yR zDR}}A$_e}hQ;0x>8mRp7N6VtrO${!KmYjHVsSH*OYm8LY070m?Hzl4j-(5a79AD^O zn$h{@>u$fgfDg08qJ+(LmQOGpYYr!sOaSHuwoI7ly~>SLSA}O^NJbi z2A$HD9s+7HUSbK;h)<#EOh9iib0UPLji~)=rTLVD{&>G`JcSHa4dGs1!jU&98yOS~ zqG6C#H2$rUjiUnUR|0W&)NO}R$2OjK6sQepPZ_M~bK?M{YYi;* zwh^qbQtOeRu%yV);$H^iWG7P55U;^kad}xjwvb(UoQ`d0mJB%Eo399dwL6aOZ|AAw z*fY;{w9C^hc8I=Yo5vg&A9{y5QXy0BORTS<2wVUC6;;yP5F&qNqJZ(jIs2QQaL{j3 zV3n?t$d0v+&XAe(3CEF!g=7B7aB2^|cwNhds_EGL;)UwFfEZk1RWEk&1&9?``_}m3 z8Wv-|YnrSy*5LQSI=U2|Oo>*42fvG$ z*m{EYu1rzCw8c?DntB0BWWBWM0a!h}5s|3i z6$kbR4~OFlI_w3P;cP0oak30mTT%sdXytCS`fV7hN4Z;Xyn1??@$$n%)2{^P-7=N1 zkr)8rf${=GE#3?)Nde%cRrp?tQ`n|S2@IE07BleqLu2rd)_Fb9lmp%R3l(FPtT0>D z2}-pEizG?JHnw0zC*h<->X-&pv z1kdy8^CuvNKf>F7S$u=9V9phmiuS ze*(g>Vom88C=s~OySABI9O`r8b~lQ;WvQd)s%bZx1Paww%06X-5fCkqv1S?vKrrA1 z+ddJUTB;7SYIP+NfpRUoShqO zxk^faYAm6~tOQ{RT>_}io?%gxQLs@fSqx+Ld9xp-yhMShstd?wA8F9LY=oekIL;W7D6Yb4Z(7~MjbTcAbV4`ifhm9D(kn|J!pp7iLStz zwo50<{?!MXwE!%UjR#_Wa79g+ssO&=%Et66Y3nvY*Vp4Rt#HVz;s{F|BdFdKr_%EL zO%G#OS9YbWwd;K$UIw%!U(qX5(rAyYZNX8Q9`UARr9`uST$&mzwopz;W~8MblN!_& zOw~`;_uCC|rE2hVw0GIk&U&>E7?{3y89ZCfI8iqnPq`0(D_qjPDE?DCAH`UopzLtBYVeMG7_VhSx-E6&JY}G>48V%D^Shq{H-+nqQzjxGErvpjv}(}a zQ1}8FbSCLZyYSr~N|3XX1}R1qNyqwH?q9 zb+`drhF2Y|)0cULCC@^9`jvp@jk?ym*3fNpsb+cQJz%CN9sRVxUKShkL#%1h`%qg{ z1Q}ILUnbF9S-?KW@<9uz;V-XCM;*>59RVy)n(M*?< zwrB-+%iGP|hE?1=DQ{YbmOqbvJRN^~3kk08zcH$i5r!+tTzpoL=tQj^TKdw4K9Io| zZ?;260~qhf?AM?3Mj*VxW+TQD<+F;UB4xIDyg;8V0e*@G$8(t#0q^Jm^uA-)ap5o5 zB*1lkT7Oe21nGo&VSi1W7dBB}4>J$mFe^-@K;4catoJivwb+nv(IRX!c8@SrXwu-% z0+Q5QrOY6+H(4JG@ZMvIK>9cpIT_i-y-Vti;C7{E6zT#Z8&HPmGi840Ch_@>m8%$Y z!?n!Ktq;s?71~<)ne%b3fPpn;aT|zdnp>W0zEtDWZK|1Ek45wXl$&&kNP)%8^Cl+gg08 z#Z`?Pf+AC5(Nx4;lgGT9ig`!v2J`h>gMPoOHTFW*`S7o>6X^i+8|^WE6#g|)`9bth zAt!u*_BJ6}qLJc1A84*cw9xm{tl^ofoGh&6br;!M?Q;-6=JeBVmThN6`6zytsGVve z7M*fd9`i>1RQcIgoJ*?2y5!MD>CwsC)M)QtkM356Q?Gm@eo-O3TIp5a*`9l|=2vIE zA;zx}{k5Q$mH|EUn9@-(8(2H5FHF50r=Dp@`Ocj$fKP zfVuCTXumuKvT_zU1!XKpT*2~T8el@eq#ew&!3*mmHycu?yy%Vwc_kwWvb8+)^!ex zRkfcXOFmr3b7fl$E_F%iEMoQk?+FH-HS`s5m{X(T~%(|UW_pum!LWr5e`5XPoXsI=!p`OhpuimJq{F6B5LeUNFBsG`W3p679oDwzsEYZ3SM zjfq)Mxf`qUesn7*_hOHd&l9sML8)RmM}i89fhaW}quPxQ~>MNuGos zG4=VT@**B#QbSIIY3`Ba>ALr}Q4p>eZgfzXN*v0nO?2OzPFu!MvJToo?R$#uHu^OS zQRUFbBmTdWb7c8uK`e(if%cRD)W&`ix-lwVlj)iIQsUFw|YA08awdGcayik`I-_((TWIXjta0W zljoJFS|>!0P8OBgWbg7 z^@fYn1+glW4=hg+G1SRPpO6HL?MUnaQvuXN#~Q>^(O6vq@=g!#(v(`0dXexiV$=F{ zf*q*U3$*1N2QHTop1Pc;70RMWWK0^;XGLq`6BM}#fINShEB#oHHD7*dd*9~y=kmw7 z76M@;)O^;r=V|jY6e#)B3bg8k5f1^<_Jq567&~X1sGu>`*Z~|n>^|ijogb;Ck1x^F zv+^v4HSj+h`ZtjA$+H|+QUJCn!2w7r6e=IrZANO;H7|0Nh0RMBup_n@!fOrR?y3MM z%LYuhwUVR-Sp-Y*SwiJ8X5M9VQk=UAI8EPeM_GpMFpJz4cxev;yUn8MDa-0RU8pdqJEKp zLPHS!i$^hn9($^TOd^-1x+f*{7-GJ|q=&q}q8$AKdF^=Fbb@L_=q)gD>O&Tih(EGITxoE~xvW`yFgWo~9p6!>Ob6 z#_LvW4`C?Js5Dsp7h#|XZC(v2jn~l(xEZSDkn{^)(je#@ye>3#CJ8B{KVsW5s!)JO zd%lWhL)*ND6s*>HCr5iI6lBl|5GbBO?%3`@ltOr{)4&`=r9iL=h$_hL?i^69La{{_ z{bfC(N0+C)|Av68f=R592acl1GV*x-hT^eLcjgmo560E*QMDO5{}u?(WH82fN1O1+ zY+#wivwNmTsRy`xj7B21;(qnt8!bjUlS=7M)|wF8HWiaU^>0&dYU#b4d0I70?S`vb zQ<~_qwT|)_F|bEqS$&X#JsfA79iF)tYp-lI-8WlcZ=%*N%XVGE*Fk-MD|*MFO=_>< z{tQTB4la?v_rC7!&tJ2AXGaMvIq?=UeXW%_Orw>AB~?k96Q=lwJUaD_ID zYd>X`n;ms&OzebE%2NYF(nbKBJ?=58nvs!*(r}UUf$jx#E!@03ky9Q2!=rYTujpXg|hx0e8s0skzI0W$vUyuSLSam zZ^6JvdQOKu=qNju^sO))3U*_QD>z1`ijMaXISFG3A z`7iXlQTKil-93-YD%bJ=T7XZ(Ofdp2D|OK9b+qcyZV)x=?RoSlhXlEc!rvt@DZGth zb8#d1KglzRGKdLo?mS|P69vBl!Je{BLMbVEFGhUwxf3V=+4fN;Az^ZG-UFX^*X})g!wj_o2P{8hHVgPuIqSCuyNpa`qkM}tfV;C zrl6;Kp8VbO&*zs0GrG;t#)G^{d#%0rLxKw@^M()T0ix}7rqjf61XD45Fwh(h+g$~B zjUMI_2uYgQjq})z6Wl~1)fMS?Y>yp!R`ztv91NxQ_6SIx~i+EKbuEt4&xWn4jQ*fGC?oh(pz@zET*vx!|{*v-aJ@-OMlt0tcnWi>&o zFZ~dMo#kPElVV#IEf4$<5`P&fiMpdHm<3fXV9&yHAK6P}sIKYFPyER-HV)xLh7^}% zD^KY>HYB?p#X;e-lL^a)7(%(mG5i=pOK#?Jq?8;8B)TI7FAP)-UeF7l8;Z zRms0_6gDrU|4Bb*b~<~ZtW;C$g0DE%EBYw$`7ZO7Uysp0Z}Zu;DRr`1AYk3f4?VP- z5!l|e(6~m1W!rUt(IJmRx}g%9+!4!W!0E$Y&erCb(Ms;sp@`%Vp}FE%FY%!!O|W`3 zVWU1iMz=U~P^rf0_fh@L)Zw`m(k|j0_iv%<*+X@RH%pnZ-9QG&L+E;+vsk}T)6!J} zOWwCP*;3Jn%ga#R)R_ki{=TfChDObkxvY9R~Ws z+-0VDPXTO8winER&u5^?r6!w=glB(+cTsLW?P6J|nZVG?jD6u%V+bmTJ>C0u%!)c%+}bWEB+Nb)eT<^6JB(3vbTBH5!}~L zSkVz%?s(Z139Y2H2ng=TCyM>ub-}qT-_;Mb9SrZ}gZS12L2=9cQIg)h|D5C_ZkeD% zp+cNCkKCi#%w-}kj(L8^ioYjfb| z+SUjg_j)#SXndI??y7%x(>~C#3lCZk7Rgb|=E8%v=CdO_C*Y6$}k$0-w zn?6Md2e8~<5{UHimNYu4?B02nDPn>=Y4j`JEmm`}_Gu5`hQ*#sF5DPOW@py&hPC79 zcvsGo*Xko-Py`Ke3AVnnfu6cGoI5&Q%_elP?dYmbd#Q03xVcgq3TG%tc@f)mIR3U zJA9(AOSAj~bwM_ieOK0D_#=0>t652q}(98Hdt4KJqF z%n0+S3>y0lOfo_RG!nbMEnlVUv7g!ny{O<}e-AV;+1EqjYWRC03#(d|q7k*s)M&!i zC9QG32AFFPUIJ>=W-zn}_40LF|3a`t>}!)l8W+y_(i_McIHZ3&frjc-f?U+Lz|!YV zS8AIxCtRg8@j98=@(-~`oE8yh!hd3N*4<3b$Jh*yMw)iehGU@twNxaluPKfIdw91v z9a}IXBMQ26KX<-@jwv3*BLya6J+Q%^089k8(Qnj)Hk2ODLB~~&TbF?%D`7rbSmG;~B_#aqafOE@x9iX>p=qke%Ow&BtdJDM?-|rHDs4Qen_&P12&krBTsR0FO5Jz!G zPgkwZGOvzc2FOHVZ7jb)PcOxGfQT4nJ1cbeHPT4_WsRAVC~m6GENpuRGMFQf+0$t0 zf3)Q72x6!Mr>Ze8bj97f_m+zr17&v%OFdyHvD98`jBCx4l1@aPA(B^_o@?@T2Q89+e?vEDG%_IZ*lW=$mppLB8d%Q^@=iCu>@oT&UBW%LiIU}n8&xtK zn{0o`UnJ4aIAZa)ibJ+LYO~PD_=#uP^<+h*%dOi8Y@l1zvF&gxeZ2ze@?g_Kk}sxm zEJf%`j>Ra+kBLcbAWLtr7SQyo3l&&YZDl_=b?)OZH;%P0s`!FMuK245m_xr8^ zx8JdnTf?b%a)I-^Z)?9QuwQ)<5)?Y0L-UpihBMCIGX6J7y#F^N?+83go@TVTwhDlW{VQN z&)a8z^Qx$xXYFaLuzR^|Y;b0OVbqjQ(ii3uQwfWGYv?*S&!oS5@a{t2$nuv4UaIY8 zR6@Ho<8NzHZ9~)&%US?|-4S4OtQr$%sJPQ?D71up^dy$GHP_o399&Nq!)-14&9qJ$ z0RM~drZIa3ksNSC%btpQ@QX$%B)+3pQok_nCi}G*ftpNc@eku4 z#xT>)KazDGyD|Y=7#56<(5Sy;_ab^`V^L;Tq5dUnh5qbtkPZ>Aw=~JdQkUf}oTpnIOZO5bB0KDgOHYIO|XfKE}e! z-(#j)P|oNBP^S3>PRwd$1jU}I4WC^RNYLV zx!HnPbscI*QDneOqb%mzE_p{;&EhaNLR`d_)Z>+f7C|k8piExavWx0nt-Yn$n+zr|HV00xhvM!Ph zbLY15@Wk}Wvm`8EAu1>f9j`iM1yj}nrz98i?*R~zc5=Z{#gNY)%!BM6CsQ_Bg7MdJ zm3lL}&Kzeht_8z(0U~@@rAA08|4ybl&H~z!-Gu#2#AGK_0k%O1^-;V0yM(7}gVIjY zV+|>J_1lRUtUT-33zZQyzkMQSA7umJd>ZYW|2$;Q?bCs_-I@)BFqEKlOisFn){pTA zDB+;DOt~$Trhe;C$xz6H^Yp0>PaKj@FetaLOWT6r{jZr@>H5kw09v)Q9J6*Q%i9}! zBhma-^@=FVr2|<;dG4R#Dx8joOm9Z&1!j+%u^pQP-<$ zEu>w(%MDzO-da16>EPxZg3q;f+=@Rhf=F*w=Sp73-jE~Qx-ps<#O z>8f!@>!ijHp^&mB1<>o=9eYa`A}ev_;&4i@2zbS;_YJ6qem}v--{xSLLMZM~+NK@( zo!wvZhbM+_k4Sqs4f+dQ1h%bABi;8IT(lb8?ia~#($@CUdqm#k7iW@9;R=TfxHUQO zixR3_wKdF*G0t4!8YL9-Uud49Vp-u8K$qY6)dpyspP6u1*wYgbPdr_Bay6eRG3BW`7n4pyLrD z3mkZx_;ChxaBi?4(2MqDo#RdpxoM~OFE|cPa5GvU|9Hz5vw;E1$G!jihcKoQzjeNNJPI}@rueXu%A?Ou7+>=r zreQHPye+p{9J8;7ScX-$g5>`>KclWg2J8z@$tEMJ=fjKCYz z51;=lGoG^h&rQNVX8f5W1cdhgS0<&ahohVOKT_rYolUuvt>U!L{q5Jz3zp9smnRlq zae`2wgjw6zO#JyGbGAJ*Dl#u-oC!?p-lQjGvs0ikk>-Lqsq~xOu zdsPOeJ8Ao>k8U5D5*851pOdyGt?~Rwqla!?NGr5|te7S(T1jh&LDj=dqC0Knj8nJy zb5iJ9$nLPHX`IeL>SgK0zMXq3h;v7sYhM4odDVt}uJ_dhS4TspyMCZNY3cEHZd5W| z>CO|kI@bCsBC+LZ{=K! z5S<^f?7M1Kb5(U)GVueX5Zb9!e^6N9$On2?0p5m`%_47KM*xC?$x)6YhXkIm2d!@U zx=2*RYQ{y23{=ymVeTW!x2+6Q^q6I5?JP2~($zq6) z45&d5#II~ptpA?%i6I9;=)$>U5M9)AK(;u@u84YM$U9t!BC}L(9sG#BS zf^HQdsy!VzXR$Wi*xXAk#1k#406FA6>#yJq=U|P@qi4`WrTK*_=|q zO+xZbXDASoFkGx@!S%PNF6x}(1S!4-k2n%dgf-;R*QwO+yC;ir7-YkwS@b;OZj_%I zQ>lR|2;xLaB-!wV3K)A0L2(#+i>yi{?-;veQE?ap%%pU!=wX#&P1$7nybCHx+y^Ui z2bc=!+Nqm@6-RI3&`mPURii*s5a~1qT=4` z7sl7Xw=e%Q*SvK_mZ%RN0^$P`0z%{e%L-{?Z{_4_<8JL>^dD;^lZmUP<^M@PRLkb^&&kiF-O)5@U=9SKcyr>mS3p?wp948$||2EG=ch|sDPCeg!w@V-Vy z`*(p4rT}?HJ3U~~))M&X{=g_tC_r#aD4^R9e6Q5I-PH{~VfNy>-T^&@t&0TKA^sO%~4)7#&d zJLvt6S}D+v_aBRCIMnM?P~iRc*8deevwich6)zMN)bl!XPok1hM`UJhW2Y~i~A1?=>rGu9PAh37zfVkGlsJBq@vg7k-@5*IF3w~nx zV*#gckqh`4m>TE!o%HaZoZ_lmffEoqDpu|l3oj{-lO=Xy{H}4-{3tgZ^Hl_%W0+3| zn@uIz@FYQE^mpZ?O^jSjDMir?GU-^QJldKPNM0CxA-IgY3D9whm3UOkW;SG2LO+V0 z%(LCZ&aG>4eiGFB^&nnKDv4j=p;KBs&nvZVW+806@;y$lY+j#VqC9Ecsem_Il4y&N zk@J$5iVO1VI_7jcF+!ENyfZb?g1iD!gh6v(lyVynfnoC(MdrcE21sn>FH8X*Y=!|u z339{M#&HigB-m^Xk+$YBmPfvQD-rG-VX*~B5?eS>;dti3LlAuHrhj!7h0_Ag)7VL+ zzRlEF4z91&^75Ev#l|2g2gg2^w)xvFQf`ZN0C9gux!1$uqnU{io=<1uz>;@1kug0U`+$76Ki71QeWEel80a}^EtLE0r56@nJ98JeK-Ndb&TNH{#kvu9ycPV{WN8yuj zgS5vfT|28}0_MDT!oP@QAQebRM2qabLyx7OxcVFqa3d`+=kYt!*cyK@K3660UXA6up)L?y| ztGkgHX99{sUyC?gn6kC|WEj*;FlV~$g8`$9`do)Xcyd+5m8KcxjcFpM>sid>K=mkU z(+~5e6r`W1_{?7vg3k*Sv#d}?zR5zkzDjFD8k;105%FX@qcr6gBKUv78tgccomD55 zx3=fV4inKLFQooS?#(;9$0@?>9Qb(n0}Dygnu(iEoEueMTOmF(kt801=NYDItLtdkN{>^IS@^mr z19l&!c~?1pTP=)tIBLElyz(OC{1AujtK|@K*c6OPJ{p?)%Nz-Pq~J@)yPj~Vd1Sii zO+Ew(lef>;V9c0IF(bfRJZry|gzV5}z=VB}Ii9O!+l{nDQM($-ap6kNB?L6at_8!N ziy~FDzER2yCf>Ozf9a_fL^cq_ED|oih`^7$C)VD3mMPlsc#)Fuc-U$k#=0eGF_f;u z=WogVP|Z;?KiPl69*|T+9}>1L+jf6ZKCbC13)z63Bw@!YPaRBQNGKiLZ$iZkX{`-( z$GE($f^0qCyxp$H3? zmH!?ONp98#pdTyD(IG(*lDu+d5r^6L&Ip!waLD}G$;O3^xWz+K$jDV0!HZP+M0kuO zrhHZxJ6AX>Rkyhys=3fvdRA}7eYqKe6nl25jAFJcmLx&ytTES);-)0@^8<2W&>x0qswlHf z1yzmJxba)&0Pwqq(VEGy-?;F*3!%#MpC7ELnI-r|;xsSF_#FleW82J#8TASf5Tgi9 z5U}(S4IH!c&}NE}EME|^XOjltuAYi{#LzHbq1`>wJ!2ALXJygD(q1kGLqhj7Bb&iQ z5m{Ra_u5xj%AM@IW`N6OCV{`Jy~xI6D2;lSt#8_ibk=$|X=KRNxaBDBL8GL}U*8D_ z>$z}CX-hdCzO`ulDXbJDrV_V3e{sV~!-}#2Qx|agMDGSRQic8DDGJP5r#s1mINVJfXE71*!ht;+G z^VOBPc_c*J2q*iE6VLVM*H08-^}g~oH6E2m6lTgUZg_!1cQ}6e2yT1Sll`<@SflM4 z?#I(iGK~wz?Wt9Ux^_-t6W3EQi*%@9gkP1SMg_$L5VgM5H^JA^VDX?mi%Xi5Z3n+A zC+I)Ff141R5hGy3V>R7AGLAk;915c!khLjAZ`)mY%;tUxk-b7D`pZnJy_JB!9L4eg zjNmu#3F~WrPWnx_;h{ip3N{)X%2Ss_t0#vs{keo31^4fGkr|%$>L@ohz_U#=^m_4} z5llwCR&02YV;XThK{Z}G_j9t4E&Kf$2l|6{LaPL7r~RvxbZS&18ynVaW-tK)y8 z{=P66b3Kqw>V9giv)QYc+FDAfIyH{(2>-j zOAUh-=jPwcW8U-5{Feu^W(t1X_^1OAHMHgx;e9r*Fu_ck z$2%>r9R|2@2i56T;Zz>j-gN+`QKP3i4s1pOIBjw}vag<^S0q|0H%DF;fc~!yYZqv} z3hD1y5lDGwd?;SsxskRvQC;cc;EdmuS&T4|Qvo6EMtcj@>*=>Ct1a zu&s3gc`ZZ%2H&YRwoDu8)g~L)OyeUJQ9Bvl9KsRIJJ-7^DaJPJNi(}bqSJfgX^k3z zdu5kFv8UcWAd&Was{_oKlq!|9U;?j5)R;#Wdo;cJ3a&o2qnQ(dL1??j zU%@H|y&q1UK>x&QwB)u1?;~oTO$s}M0fhOfk8v+5V!bQ|(9L`bP%0z8`;la!tVv*c zVlg%%i>mk08eruS)c1BE7szGNutROfudELhbWmyJjHQ2f@MKrKVF&XPMz^9fUG^Nc zX4(7;q{9_|x#JZLx?X6l0tt(j+}#dX>bzLrHt63FDm)5V_JSM1v+vy9&SZKqL-=xL zUql?J-$*hqOicQ(J?CFL%83jJ(<6o`h#aZcEzf~>H;p;@W6|JvjU@E487shrM$>nI zmD4O0Biv4DUCYW&PL9SsYT9$D|gVQf?{smN-6G3%XSrZ9~UjIGG8tXl-|ds z6lZ3SNzd&HT_?s!nNWj)f+nVVwP;Fyb+lASF(*ifR}Wpr8%dDwaJz!{6c%*rIS+n7d3t0dG=tmjWuSnt(^(jV@Zi?LaBX z?Z}#EueN5M)cZ3F7HCHIR}30cB;%v$!gJSD?no$y%Ha&gAL_ua7q$%rd?UT&#ojDS zAgmH5FQ$H{mBp|?Wy%=DXQx`P)0@8O|zpFvBI8S+8K z<>%7y;lokmd!bc8z{HxBM&Q4jN+;+74chq}R<>oYRE}b5xLWIvc8VW!5&=N3HmOOh zRllm?iXO*12Wl|1>f4MAagacbUYz>Zz^ua=FKKJ=yS^>%1p`;5efkdY0pw9X>~7dyPoFd&=$2WrM4m3bmT5Q40Yt}& zI;IJ^%`oyL&+iT4)BB<_v}P>#cF4or(xjYNc2^0+KnKO7j z%y(?`bSTqrk|9)-UCl?bGWeIHK;62L5zsPedDHJxQiF!nSC{3(OAPceb*I9x?ch#K zT<^K*UyrZ7p<(*Y6adGfSYt8nef_>iJYo}={xugwWa*8SJ_uu7_1g>zTWhfq3BStBhED zWA8;?L$7H2o~#;bdI%wtkZaKWpe(h=jU>X+C_&|W=Fnh}eZMKj`~7@^@xu3br$rP? zY_Iz-5MHn2`7X?V^(t;Ix*AXPT%H%eiuh(zEB()U@064~DRTEW{4+Q*Hc6%9#%n&B zXZ5DII%=tXM0TCp^Rj#jWZp+&@XHhTQOhp5!@h82=XE#TsOtN$wZ^8y)VS&AKV>^)BB5buMR#y_}CeT(eU4#I2UU6HYXp@`x7eSdrf43guid_Id!Z9c$5 zs`#VDW|?loqSgNJ7B#Bh1d+*LEK*fO(yEbZKSZc4adCnc>ev)zfD{xtKsda|VxgIY zru+8yHrD`rVtXHV4z4oWsJYMg1EbGZwBszValV#E5kv-SfSv`Th7ri`-nsdgFS!+& zlIvrN6fErCj_JiOGL)fTGIFpceNx3Sq1N6A-AAPS$N0*o^k*eEvXS|}X0KG_+H8af z2ho4fjU{l3lsA4qr|HgFX_{Ga@BCZ~q|=mwfP((AfJ_&&V$pw?+60%qWpO{o$Ni=H z$&5=^6_WK=QwAi0L%(FD{e%~CCAn?gx1WnJLBj5l~CWe{GH1TusfHi9B6rTF?#v?O_q%c)rf;RK-I zVC5?RK-DeEap(iu52EK;c=nBZb@}cCbB5u}KTV%TX@oV&_f+WkrClj?ny~Dh4e%`l z!EpHsJYV+HZf1j>*e6rqX!wZY7LLs81GiWt=)!&iR&uX-XSNM+POpvEr3DHc{4~Lp zLj7Fcn%n;2I}cS|^}XH;oW{XD6!t3AOzPYL4yV zY%_-G?cF(qMhz-ZutwhzPE>xZ4>Xm)@*~apguRh)D3cJYu;isPGC)%D7Ewfugw;{M z`KE$Wap~mQs*nlrfyHx6xO4(pw*`EnVG|%h0pWL964neulKR84O@>yBW1Vm=CT+|V zxAk9i<2Ug~ISiKDtp2&tk|NjLutx#t_EdXQ-gm#qjjs)~14L_;q_CgNZ_HiQbs1@l)oMNB|n z2gguD;<)GM_`${Nl%C!m3zx4TR>dH@@{wyi@hqrvEH#x_6EAaKWa@zQ*0ilF5 zjvvp9Oj!>aoJCRLSfbv(ke^iKZ>11iO?>HkRtlwN5+;HC0?!L~Qyo@97I%jJme^wD z%5Sk13Y~?j&$Jgpq9;nNa=DX8e2u=!aM;oQa%kCd5?Y6vLcN!=jM@RAS^Z6K?0}Zt z(jNMtl$#&z=K}q2+ASd7BK-nyjv)-Y35`Wmccixk!L_fRf1fFuN)r(kakxTGvlS{Z zNq2eAH05JKTc9eD{XWemCx}SHx zWQK<~TvAn24K^tPY-dXIFE{aZ`lRV3*X(;-bD9?{#Zy}K6>2KiM)ocC#lFC))bU=^ zp4fGqQykQxU)pK-yJJ79w6+CJ$Hxky7RRh$m`V@;qY*n z+GzCg7@)tkr)K+O3nC|s|OhW(&{hm=Fv=(iRy z_Q<)En$9aXW{3f_B5G;MLJB_-jJu;bT8^x_vRa)jd>Dvzivq^N9GvGTT>n1Kt0fNW z9TcY#K_NRo`#efPm44Z@`Ppa+yY!wyL^));D?ZEhV+lq7sKyd}PY# z;7Btx|BI2w=|O%0>w{F~hOkafL#Rb!n@*uSicP72P8bg5J4yAUBqCGc_aUi3!*PZ% z>KJfe0dSy}NE&4^n(TUup481GUHKdS?~=@+#JJ0DIOYCN-PDl%VmbLqyS9AhJ?jG) z1e%d-;__K_p&HvM?`KnrRQ0Iu2MxY+x_*8Bb#$enJa(U5%926$J9hLCcXE^crldo_Eb;l$w|#c)0L$(yHP+m5q>OlJ=7o{q9e- z|HoV^*Iq}|TAxloD5VN;&cQ?ced=Q!`)O`<#?^&_J^Ux;p|=&96D4Lar3BA2l_DGq zRQA`)X-1t>%OA=rhuz^d<4#sLEN**}jjhL~S@>tO-;A(>)X~)xiK_0WSw8BP`_!&q zE!I)f(^rOVDzI4RQEosFryiXIQ`+UJzv4<|{CZK*>Li`%TrhiqwieR@uXV9*% zwoNCThLD72CDvb0=Qk1qvlQxMu;ZL^bv}EN^uj(h#`Ama;eCX*pGYd?DOg8A8(x** z+O&WY=Ybo?YyIXm=O zPvd7*TuB(vJ;cvgT9aH-26Oi-KH?#)r5~b;SkoT;{!j9E6n$MZhEdjR$A*y0I?Giz zG5vmjcNg3~W1CH+&C1g*QH4Kq=l)thw0CHatp$sSu0`q@nMdZ)H>KtUVV>iQv_%nc z$36V!p{!)~W;PInFzBFuBebB!XFM%c_7J7dflaj`I=juZl^E4}Ti}~f?{^tR9Ux0( zg}YX`oMDz_jLpxhNtx)+zGA1dDqEW*RVUUDPWZamHCMWz{h5_rG{U^V`EEs;t+hO2 zT$+ZV7{b1eTfOmB*9D~XwY!f}hICzSFNNMo*;Rs3T1w?R_g2d0Vv=qt;&z0&%)SHt zaf($(k^BY>l7Qkmk1(1yfz=LgJQ0+kx9TQTOftdn3e-GxS`PI5JVw}-v}5`Yb-*{Z zC-LxoKe?vN7&7nHtg7%&2?}D{=rO1MFz z*4-{dr}54MT*5;1U%~?WDx8PE-v(mD8<|y+4gbOz58-og&NRMBU?CJ6$l|KUW1@y6 zKdn$m1t)o zhCWyZM~9BkMW>FGs^SPCR4Lv&31ay?o#4cOq7IsmtO6Tu{p(W(hKSFjaX$<(yFOn* zc1X^MYumQ;KZ~4fW_A(o_Y*!(9&7dtWs$*Gh-v;*0{PL~fv2;LLP5vX7CS=DJ*E>z zk%mDMYQ?cs>jsV?@Ykq-R_!#e9cK^<$G~Itg$3dt*!P3axCX?6u!%t|$#oNp;fvbO z!Q)hR?OX$$ArX?399Z}j*0}9`dB`F?xg@RdA+fjuoV$Kezkvx?9+c4nK($7@kFmD`zv~|nPJj;yNu+#T6R1F#%c&f&GUVQ+whx6 zOK1PG+j0&C3Gi=3#hO*?Y%@6H=W*i-d8_N9Len^Vt!4mP?D9-r6-ymN@Z%^|p}9}f zU#Z=SNq6o$xB7e{u9Fj8j8Z(>bf7bpClie@(aGDN(6k~BTJ14*1v1PSQ_)xyllc0* zw#f74H>blqDs!|NstqfR=AebqpZXxXJgCZ@&!^Q)oqo2e9!$p|^{6l8Sl#Z?z)Mzs zH(U9eMt<6$J_tRSgwr_W_=1nCgHQ_+rrfGA2|I}a8+*@PJ`k0nNXaB$aHh0$d;~9Mr?Lhyte@brb#M1 zFl32xng!yZAyo;b;Ef?XaF@;n2+N9OYHdrndhDy%(H<`DUa4R9E+{< zmI&5s^p?%SkTwF?5sq)I^f24$o3Fp$pt*89VWGBR5uSpPSl)auwx4VX{hG&+JZb}N zW5idB5#_%qZRoUV5cz(46%&2?RsMnR&=Iv6sv&~^tOk=R+QL>8(0z6aDf!FVp3;e7 zPm-c z?6Dya#U_#9T=Fmx&KGudy+01-KiOann4nsFqeuxH+rGYixWGpFUL~N=1oDtOgbr$T zOB~T=-0zZ=*fA4f4I|ZJSrr!S8mv4?a~SQxRy&zW;UgZQxYqyK?eDIaE>EWF#HH$L zeo^f(h)N{$Pd7%4nW*Jh=alvzyE+T{MBSvgAlEO-rJJOo3{B>gR_Y&(Ku~|p>Afvs z&ULfdRI546Kjs(|2&*!r_t>aR&a9Q7dV7`bK|y++wE$yM7-^XRay~_ri{b(85#=s zP!D|~iCf|$_pL1fWs5}dRiD=qpWpi0_%7N>UO8Sr))iAzN54$nIseRgoZr|_eHg43 z2wfr_RN$Saj7p7E^U}YV?2POzJBSi_mwY9a#e3_nM+rG=B1dW-i^cHYsD!zWJCY7e ztXh#26OO%CXZ{yj(BWl52y4ROgk16KyW&>^>?wggEa_}AXV!Ud7J;>`AHN09T3@^I z_Xhq}JUK*c2_4c! z%KlLRG`_jOU43YB@2vpNH{ig&9szIpGk#uRE$oKve&V1RJ+9&emtH@35m)rgbKNu& zvrlW2U1z-$QM=32k`E}cE2MAZ{nwwLkJA=BCb;2+LW3+x4myw54?GOxrA<3V24dwC zMGDd+$={sV6KRt7_`yNGC%VcwS}>s2l1 z{l>ca_s7@URmHL)NHocvJGHi2MYJ+QFogGGb2P?n^C)gI#iq}hYrPb zq_=qRNp%GWYkIm^D`cxpirK9^>EH{KQ1@9|!EcZ~z`8*rL>)$Z=inglkS$8r6zZcc zjd$!o~o8AaM-F9=(?$7%4Q}2JgK|>vT@fq=P6dddhvO7?^@`h02deoqvz5*~OW6AZne}<(;fedvwUl&<&CLw?3 zka?i)X50BjYb^K~yVgBeV;BC3DexbE{F{G)_GbC&4Y*9%_8r~!*|gmtV4Q#^&_0 z@64dfZoNi|HI3?*>Dt|r9kK!ct#lzmci|=z6y6MqQ>m(-_8rI-h?R6XjvoQ>PLoII zSA68Mf8{YO14a$n=>;~){{|QwdrY!^UYrPl&-32zYClG1J{~G}-fu3;u0KIqdA+Y! zfS~7zypO~5pr`x1kAa=f!`jcQvfk%pp_j&gEz*TPD?dNHdf$P0f#9hpA%D=6P{2n- z?dOY9PytTRV{hI};M2>F;O7kp%_!(?D(~as`s29lUn?P@Jn+3y00{gf1U`vo|nA5-nWl?r_PJd=P55C|2Oc;POneP2#{;$f&24SCvL$hcdxeh^*ApG zyyW$n75Lal%m>EF`vjg}b3_QeJ?060av<&Bd-YBn^}b)-pAdIm)qd_%3wgd;83lla zgg&2YKfw1-LY_-ApiieD@W$1}M$n7HeZE}a2;M@f(A!k)=k2}M=fUUe#EcR625`N- zxqIKxtLt}oUVsAa||5WMvy^nRJw+kS@g`TFwta{O-?z8e1j z-f{OL#C>?e^)Vv(@IJlcTaWWL^N%gIvf~e`Gz#n-eylX=?tN(6Pao+tH1I{9Bv^ao z21~XGO!uZa{QI&EmpGpv=Rxm}T28%wIRQ^g=MMmfZOU zyhBw-V9!>|ThF!6Vf#~d6sn%BTgp@~&*BcR-FU#s)9jvSx2ar!W5D6&Nv@;Air$0F zL63tT(Cl~v_o;qKf7np6J3#g+;B?@4wELn}^`yI%F?QZ=rEF^LsU=?Zp3`I1Z%$}q z`AxoO<yjy-H}&g{MUC#_&Gk!P^(%+8 zv}424E0a^Xr;gUm`C%`|)~enTzdH8>Tf@)I+}ag=KLUj(-I2@WD+a58 zv8MEvl~?cc7pKc7{U=pd5J-(XawZWgr?__(sG1A36!-E%8b|-Ii7; z3dC1mXJ$%+S=(g}Qd@uQ3k3vk7t=0WT@86W4>OU^8uX@+5dfFE+{uvsp z;Zn?5{Bn^w>EQ*6I*nX%cqJ4I1heBQ1g{1aP( zGUF;l-0X+Ph!!w*sz#tYp@QUa<)h#d73-gSurNwQopj|7Uh6IcB^Hsfb6(%Za&U0m z7`Q#U_JDpSg+;#oNs~!RRUosqX4DiK;B=FfF+15RNPlWbHBj2xmiGn+D70a3-KHrJ z(^Y6C6~~#w8kwy9p3DmTF=nvU+OmI^SDURS zAgQy3(~5%{qoxxE3g>)qaUFkMx^t%ze8G{&v*?+CZDT_mqq*$5Mu|jG{_DcIgXdU4Hlh9P%Owdv+SI-4I-I82XczyYf_46e5kbiozeY zA7uEP-R)|sxTJB;%Oc)$yF>s1*fY(72E5v@Ink7y;iZays|JU}8hRd*0(LrBW0ODZ zh5!nmOaKbq6-)Y4I+UHwCUwVuMX!`k>rkl525~KQ_vD^|UK0>ZA}di!RFVdlC(k{$ zgjZmWlNLG&8E-kWsH)?!RfRWNWt0>ze_vRrR(^{jQZ?-lX_hTR4Ul4Tvh?}bw)Dv{ z%bifj((g0{?@d$sJ#jE2B7!Ea|Eoe4k|hzu{HwsE#QYiZ=+FeOEQFxLGd!R7t7c;#WnVM21e z0ixLgbrV9qr?7 z%B?d+xt4o`4%L=AWDJzCbDlI+1rvzTA1RXrrz|KbJ(lB!iNS{|vDI`S-dPw@i6&28 zGL;<*0+~ByUDydLvD?da!d!*L28LRDBz2?G>%^m#=1*>WT%GLz|3uBDMSeL{oeR%^ zChXI`z^Kk7bq56}qUCXn0p66*J{{mWQ;CCtJwlB}3dKw#b5EC6awX(^oUH%M(eA-w zL(m5cLN!g9Rnzq?zpd zHFOcLU%!_F1D+w9-;S*|<>WuaJ;*G&c?ZDQB4cJOAvZ2Nd{_Z$^`O<_fyV!5X6jHC zJQ`=Rdf4HB{OhRsSmR50E}ff@E~JEG#A1TR{q&agt921+*y|PfU8cEf z`_L9gv~wS6HY5{@t>;g&Srf9eKPE@*wp{-?IJy$YA2N@srXN-6`8P(Mdv`~#ocpR6 z8ZW?5MmtPKk~;7oZnHamXS|HU`_SZ=C{1WufF4>vzR-$AG^8e`VP%q7sYcQnNp;gb zWi0+L0AfI$zu~L-E^AAB<;unjFR5y!>{={qe#+?&H!3qFV;;*lFzb6?J{N5&Ymo8s`j zalILsE(M_xlxr71?S@sOS(d<`S_8YhQ%P#Mg-xpoJLo2XCXumWfTg)^Nn2RER@!>n z0@oetsL9vp5haoIkt4LUip*A?vuM^V1O^L3Y9CKY61r*SYs4gw((QUw5LIl}hNU&{ zy7`QEUPidL#6fZneR*RS@K2e3&;R8Jl{9_GDE{pLkk&}iV5MdSSI|o6nC9EvjB#CRj%Zcuq2N^AT4B}BcaU~oFjh~E8Jw%gE*Ba3#lSLw{ z=k3;hT4WMjl~7KIawMs|of(#$Wx2M41k=8Sjk|4_MT61qD_GVw8~6cw=S_M{c7>EI zGev|f+04-l>mM>0k!GHB$usXn30!YdB4X^sB{dP$ILk6)hL;2Gl?J9edPI;+gM|3C zjV~Equ;zy6O*6Vp`Eomjb`h z7gZ8K!m4T7!>W;mCzaM-Z@-&{2lGk`8y(o>C?i6osy?Xwo~1U7xe(AY~8RjVFMVi|lTf-st0X=*7jB2PD|&-=J> z!JQf4E^k`5ciA118m%qOsWVclQ&L<{a3_3HWK8B|b;yL$<<7YroUym|Qj&4A+C{J+WpA$}9-ulK+$HaP0 zdq2zO_4=@8!nMOz@XFAv>V8U$>X);r1+LXAouS*%dY6#X)aeyz9mG6NGI0;WKiUl% zF+2%Lyq94#O=o(@LEZZ{v15{NEaY)HCNpA{Hl>&)ozxbQIKu1R_%5W6mPA9}S&M2x zvQ-H{Y71^iNexyCi%>#bgI~g2k~+hVlF#IkljkIDx3S0_$&hs?J8#OO^d7`0mbodv zv3`87Haw>^MMdlKc0J165~@n_vq_Qaah5Z=KV|S^(~R>_t(Q?PlA$u~s~51Omu%yL zKj3(sEK6(uxjyj=(p;e6mf%Q4w1hD$aO728u z+Y+sE5j%lVYxWSWa-#=+L~}4mT2vZO7fFD|THP`(izKLn8rmIq;`t8n5=WB}v)e`2 zX08&@l8DW&QP-xD%Oa`Cy+{(`ze|@|b!y{lE7aI>+FOK|hGdpKA!#Vthg*+)!z3!A zS+5b*pITCmoD_QuGVu~pS|8rgL7D{Fp9IVM?Rs>LNKdw_K2iqT{@itsV5R)bcAlMn zwwJV|G$5MZ7H+w1V&ujCdTvoHN&8ajCFvb@n+gxD0mJOqV9BG>-0$p1%g;%8o)Fl( zy%{kpEt;)6_TR$SknyhRl+hjv?BTpQ?qX?<%*-o}(Xjg@c_q^<(i0m1*HIGoHVH{v z*=yO{N|QMyPu7t%X(0_CgPTYoS(nPD>UjTHd=Zk?y=bNNkigQZvPSf6ySFtONef3W zIVB+jC?OpryX_j|+YVyf_9`#$)n?E}kljs$bKb5;+Q%}GBn)!`wDx$X*B8M7mR~05)NrU@YC*?%E0I;L5|Undb8t^u*g*~HBu&UJ zvq5HVy%U(w%GMfw6Ve!u1fnKRpHWtPK$=oImUP%W#Ic;6om`UbhIBbkpheb*tX1u9 z8Ie-->|WiZHYY|>vv`q}B#FmrExd!6poUy_J+6^vJ@0poB!-sliJhEICd)9a7j~R^ zLd(?#u{N+2P?Ga!BY@Mgt))GklOV3WV~+V^iE0_14MCn*Pg2y9oHFEDdY2>JJE}w< zD>p;iSkKudSP8Q{M0GpVDMr;HOIwLuzb+8dak|2L$FKMlZZ(A`}rDUho7Ggo_1k;gW{pj0P z!czK<;VuX++F|e-m2NBKLcXgYkv6upVwCZlc)p zvdf$#{d9Oes+j%Szz(-LUo2%Pp_p0>CllKrS~!(U@GAZxb&2Myh+18e(_PyTr)-c4 zmidP@Ie#QsOx6|Wmf4hRPS~eKT1qBaXqAC#2iM?tNHK z<;%H7Ijvmr3SlAGG{=&ZzUdFC;=841Vxmy7a*dr3A-}yPN!nrMqdC@45Fc``NZz~D zQX?N>$CbQeCG;ASMKZbC?{y|=U@vKgb6Aa2vP+YtT&5}hF(c7eaW^aJJr7Uc@uQk# z#OvrJ9uis9B46FBm7FjNBu$nXsS5}0iBQ{)OBgO1thYXpY}X~QnW_mRis1F7;BY1Dx zk#{|c9FoWdN!Z%&>S1NX9qG}g7n5}89&6e@wiK0AY2xlf1Z-C4(#iAVdp`}rDHD01 znY&$&G7y4|jlE-)I>DzE)WQx3d7bK-`3JJmEv~OP)Bfp#wC;1_}k=+ro zMOiIGj-`IQ3G+F0y@7hrFiVCwcg^)$xsuTOm_0r1wk~y0NWrp@-P3R8EVi4i}__<@iH$a-I&}HETdaIp0@gy zxKK8MTwx*{yKG$9_wp_bx{w5wK6T2ur#6Buw}?4T zEckBSu7Q<+$7yy7g+PuzB#l7M;2`5}uLmHR)fl!7?1HQs0L z?<6>gS_KqgS>cBymrr&Y(0Dam9&JIrNn>iEglmup7?d|UdpANr4?r566*+P5Cy7#f zDtQvOs@OjT+|LduspdB(nKb+{sS=cl#ABq83bGDE&kvJ&e}~G zP-CAFz0`K9cq-aBT3fP4JGLx;$S{zv(uC9ihe(ra5(lo0k0pZjRynk>btT}Xm=F+F z$g)_`k1Qr8S|+4+xjZxPgQ=U+Y&L>G9O_>i`R9;_L-Irt#bFjN68RZ&H>6R$9md3z z#m_@upO3C+uX)FjAk{y5j4BS*1MNlV_-tY=WXO2{5$?#JK@7wjX68)1Rs#2pC5~Ic z8kXFOJUVMxijUk`bsDD=2m&XyV$LM*w3+#ycT;~j*3!f>v9v=i4mFb!HH2v**V&$b zLe^~ZHVeQ8IXD45)0ALN0HKL1@TR7AtSm{H+P2lOFQwflxzvrAg;amW$JPe7uI~iN9|X(?MZ; zK@b|+hI)2=%`V60c0IbL8@21O1sXZ2n4vW9r+lLn{+9{ICE7C{a?4Bf$=H`-l<--> zl`sCvfT)BM4G1K9qKh-KIYcQ@Vi_AX0<~T9Gzj~ox3hdWUD{7W->NMez(|={fagjC zbv{^Y+M;r4@nR!zQE^&!Otgr;N;D)SoxGk-td1y(o=I+k=E3+Na!(u1d?RY2ff)KU zEp+YC;$5+oKnn!210QeT4a~SVtQ~^!J}VJB3(aC&x+{zUk?;@{V5zSO9gBKr3c!O>RflDdvq#4vWUT z@;j1`T9zny+CJ@kMTPmHwW=>R3FK_L<xIRImPHP5P60w7t0I%X;<~5>P3p=wq09{dv;La*tzlFO+$M}p#%QSw^5|XwaD2+mhJLY zBEJoTmZr+4DXb+d=#z(!S^S zf^WdhAoW~t9}ap7&{6JIjaoW@>{ehd>Kl&0q-4?Ae2IhSqw9$@4Lqp!Y3|NX>EqBj zZ*qUquzH>fbjKjDPFaY-`%xfJq|0?M=)B**hv1f$$v_H9kKw=uCD&IT zaSQ-m(%naKNx5D@czyu)CCQX-GFR?Tzg#1>+quz{$Z)H>PY)kqjL$uTXPtv+rm($}1=1S~Q6Y%6J|Ns?%r+h3IyBTl|)^OukB{nQqcN|iN(19H=rS&!HjI220J$!HCXQ}Ntl-+1Yn!vxBWcH7_j#{krT|aDw8@g zH2HLGSU}*zhy9>cJhDjuHIN5386VPI3qYnnF>}B#1YHQO%b-GbfJZZMV^V}r2XHe@ zV1138Wi1@Kqij?mNwX%WF?Yn6l+6~o+EC&~-jg*6H{^N=)h^XOvTRxH66%s})cQ={ zDmjNn9f@q*l9#$A&Y(>oi*XRxmC%y^BPZOd8N3rwp|QZv4J%=jH|DheAn&>XkakL{ zqPhzqx4?5~a{`y0VNajm`-yRpmh6_fx9gFtOhVJc5OiE1i74P?y*w+Kj>rXZ^3#B} z<>a82z(_PeUkHoLJhKa4W_0x>+CSR-^6V{Y2&hjWTR9wA$xlr;hI5hWPg2i0fVUIc zkIX~TE|uX_YwyMsj{`r&FoSf8xyD)805ML(^C~r&WWgC#fyIVeI z$6T}!!daCRXc1tvz9dX3??g-5%XZ-85bvB8$#n;NLpa6T`M5Fm`wcujr4ojnmM_o# z*x@HKHU(5Kg%r2z(G|2I0bIesH6TMzMg2f+_({LOZ7{58)S;zaoe*2mN%CD`$jts) zgmaZffGn^!06|VnL^4A-7SqG)kD@GuoR37<0?SZH1jKag@2C8TOBVpCnlP4}mwlR4 zNR0LX+DV`!34QQ5PR`ZqB?P879M&Px(?Hsl{r-q{9NrCJC=@$J>GEz$-^l{iz*=Fd z>#=rlHYZ1CSUO9_wQLyK`RIyw-0x(TbQWy!W8)oxBrV8xc69dN#mm{9vS?ddKo#Hh zPUu>9u>y532loMGBh7*=h!C#9#YQII4!DJEiPi}{mxVJ2nTiBS5jslef}yrcG8}Pi z`85+In1JBPo6tg#Q!@!GA369$HdAw>FNjnesep-RA0+|VNRS%U1+t|udbBS>gLV{V zNBmis6VB{>SC3sUdBrFU2MBhj0wj{=?Rs=Ib`yG0dZGL&LrWp&VF%NRMuSZuyNEV%=jkERz8GaZ-VVkCUnV{HL=jd6EFtyC* z&0u!TH^GwJ-$0P31J$TwNh#tXIMUPtd9p}{1K!0bAmcy`4fFJMybpiuApt1Dl3tYt zv^m6dV~g}@N!Sh{ok@SR2<6x7H5+CnaDt6AmlnJuX#OJ=Be(C~DCpYt-nI{9j_glI z`*6}LzzeByX?KDcMN<*+JWN8TuRJ2YuN|~<4L}t!fCM_xc8>w>QaJ%Orwot`E%~L7 z?*(06fr;ovz!j&1k0JcbLm`6pXJqLS!k05~PS&i~*K^0S%j`lpkdnv0A=oKriEBwOY@|=bISAzA(2x$(zSgSV>;BH6q4x7Ynvd)X*2sG{nO(!> zp!rdXFDr%Q;1(9dcDiB(>p?&#BxNDyQV2XOF@5X#?nGE}#o;tqnL-kqYDr?2DsBL6 zg$$xTTNg&1F7YqC@U|_8&j3#vNF0ODCZ!@0*v3gZ#V_sB!qb)jBeT<@knDJX^qGbsQD$*Hx)y2g zROv|};2S@q63-fKJ=1v?A16qfr}%I8j7qjm-!C4(23V5seC@jI6Mcm#n6*0+Su3qK zna9OKkDONrP(~$4Cv62RWPekv%j_P`Xrf+(Qpy%mZUScpj@llEEHyW)49sOV*GtG- z^hbfV)Kp`B(jkNPIl}Rq zIHbA@;~{D?1UHRS2%*I2+osgk*-rlldy@CHSM7*^`=E52+Wjp~Z zOBS!NIyGY?$};$0GA28r?u7UvP*is6d3t%Rhdm$t*ljZz4yb+V`2fN8tiSVsOVB+K zO*f~LuO)iFzxlwPbqO^&*8g?q=Hc`aU;=d=b(W-T%s{dzT|77WS=7wbT(0=9t@3-B zj>p$YQw-_(5_Jp4+mXHAk6c)y+Q~I4PS0P?>s@28butlYLgqN)Wl}Iy$%NqwWa?|B z+3tFolC=^*7kQ_fE68tB#{`LUm7})G**0UgBUwK&0txk0D+-@XpkiAbVyVc+F1COP zPA?eJ@iaj8sjhUuTtf!T&c$x$qjo0?Lir6GE0a1}0CrgQj|onX+L&4&9>sP#@nF+o zrIHrFz9QBVaM>uAbg}MB_0%*cn;D&aAb~=YI#kN(94Q0UNwBL&J&3g8MiJJSo@5HNnDH#@|iQCchnbU*RoPI^>61j z9r?`vLjL^!7Wu<8ABjy&)`lCBqiSFKjZ*L@Ut5t@+xLz?=X~(>3Ie`>3p|Mwabz|v zBE<8;dcobsH3YGrS`T`OtxU6VO;|bz$LV2m6wpGJZF7K6l2xPlR6ijkZr-N%moa7wvd@cX>EVg(A#CgQCbNHxZ zGy%*csth87;hT__XvvNFI$tfPlo}r0WU=RF2sTlw8KhKTH=3-3W_kcQy;P2F?G3Jn zX??haMAkdCRpD2GZ>R)G%OiuugaZ1P0psw_^%9^T6pSRQS)@hpWqa)RorC->kGfC?#UwOZ8RS02;9*W_jZtP==ZP{Ew9 zNT3zpavMm$bUxf+qsv(0bGu8*T4n z(3C(&D81Z+tqZgz>{Zot8&F_0LP_Oat_71PX1l_$Fo{{;g2j&Xg?>L`J7$doz8}Rk z^?b>1n`}S|fD}R7f!iXw-L}o)2=?4`AC(Mp#7uGwiYT2SI(Ogmk!NQwHGW0#nDTU1FtJn zJ>9Kh;DxylBnP21Ry1#ta6kff!{d+{mN>|GSn~R|xtH)mE5xXfCW~hx3FxuA>ek4~jY-|#uz@!oQ^ zoACYaI(d)+)rn3*S(3g1-7F9z0ztX$vdQIaWbo8h*4U}CXL_(HMrF4aL)@gRVBvMz z;w+-R6FH03fRUqwAGe>F^cU^r~FAcb9H|+LC!UA7EA`e4b&CB zZ;s^1X1*4%f(8{DbmwfqyV=f1o|OtB$Jb<@RaG!x;b*j$*`NXQLAP#{}jdfe{ef0r(q00~&BzupWmcgIa z+;6ar^@Ux(yz@V6vZbS_wQNzBO)|_e-u4>w>B*+@pkUj0w&54<24OEYI3w%_Jj_hD zhB2cd-m30=q+By)ZD5M);>vp1WrsJ7_iEFu1Q$g;U-CO_Zt7A=qgSs|775%fpIp$^ zbcR~F;bc6ef_sVNKC#?R%qxPL5WSIn4DbvvMwurqh|YJ4fF<}LQ-1N@W@d8Ss6V2q zcAf988G`7zL~VAVaH21?`n#RfqK(-08pAi*+G4PtkccKeC!elcoOenSU{-Vz+mmcsp^t+eUPhZ zngQ7u#Nkj92c}ScA0=?Z!)t)@r0dbO>=l?VSz!XU&jR12(!tn6}L*q11B&r=WuTt*@OAUz@_E4U*M> z^G!G`U z2SR2m=kYvJ>!7!B|8~bWC_y7kxuVN5bf)nSZ?L>1+d2|K8^m-iFgt7MCnz%r*pecG zQ+Mu$^H9dF9hKj2;`T_0$&_udI9i__epLqbyCLn#?qycH&+gUB)CN2n~=TFd#0-yxnNNeHn=sA!ki=NN_E2`N2C zfwCC9T-zMmv|yQV>XdBCNjqQ}$PN`uKBUlX+gHu2rir=`3-(DJmy}ovc4^5@Dp`o` z{->GW90FJ&*l7*z<_Bq8DT9}@4T7G^VOv}os2L z?lrt)P=+j1wt+S^b60(^tyVaVaz7Gvrz>1TLNHkyNtYRz4z+SW=%Ozv{zs}mA)*sW z^Ab!T=OY`}urBOC2r_BH;hqoAOz?x$sgq87P8gsMg=wM*e>Ux0jikJ#rvu9B&gmF9kG7{cJOcFTGu+Vjo12-8z zayVfL4mvQ>@dH-a8Zi*4-asHM%x5=s)7Dp$7eKCU5|(lS6oU)sb?@t8cH;coYO+8z z7x4RtmR=6W-2rE_Hk-u?wCZ%znCJ3u53X=M-lYiQIFq22ld-I=o*}BGiZf9JW@buL zjc9MYFTxMXT?^D@d_Px29EsE%%tq#VKL2^|@{ z$Il=liKG%i_nTSO&R57rKAn~xE74uSg+SEuV=?39-||MTbsE|Uj(|cXlZQ&#BmF3&OVaX3y6J{8olo4--R0TZD6Y?pMRUq(zV(n)9$n%m(PZr{I z+6%i2o{}6YXRCpkWIGcwpKqP+LcpqcisMxz@7n4u|-Fw^IJa|B-LE3x-ZDo<4D`Mkf={^ivq4`>=Sa%&uM-TJSzhl9FKJ2fD?kkZj$Ur-<`GNCG`}iSn9tt8H6cp>Pj6 zfZ*rAM96e^2&lRA8J0T$n-6dO@du@O_m7E-0G%6;-c^9`-Y(kLaX z3!YKrY{H!@Yc6O(8uU9Fr@lS!L-z0%)z74IDHo1vS;B?6TCclYLtY7vT`SxE@x9>X z%#6Atd2iPv?EJ)ID=yf;UbjSL-0o*9lXAR<~R zxW&cG*@Y06!g9J85N)%Y)fU8!Fhkzl3E_Q-G1=cWnKUp19}}i1){G>mR>gk!l@(_8 zi4z!938yP!gQpq@A2I=wz-CTrh*W4LpiAO|H2Ek}hzML?Ozro892NEiZq<}N2OC6y zor8UdiYB7OQCgAQItLyzOZ5$$Wu*`#C~s3X3u!Q+#=~F&baz7pK1(G|cg^_u=o$f> zm|qPcGuty%bWEyTt&b7F1K+aK*`$>q8A{Z}I#l^48ISu&YZ_8e26#4FOMy8ZL1L=? ziiej&i|;trQ2A{q5cJ1sec#>elixS%Uiq{aV0c|>0qiw&GiMmgD|{PMG(O|+Cw&=d3O8hVUfT-2BeB; z<4&b(a9$R;8)>%@#CgU@$yJ)MHu0?}t9Q7RjuzP_0}Up8hc=9pv;c$y6e^jQQ@okAyOT(@^$=ZlBxM?wV2L#EM&F!*8?>zo z?g6fu-cr$C<@HoQ7(gbb6RhmP|8L3wz0|iO7U)`GK>H@^PHX;9e-cr*x8ce*>6(r1C`VEd*zpa&>DQ|Y+5 zktDJ_0ofuG5jJ=STE}g+@#NwD$$C^@#e> zNKg(4YsX8Tst8X_PzKrB#?LEO9A9Qhf(;5*Vpm!e#B-O^$`@7pf5I_)HB!O)34V zg3yAF7tt=sT2}%EZcL=Z?Rs>jAl_juV#>D##Sg9f@Q-pjThi^N*xWOn_z-ZfCbhwWzqNW2oWK)h#Sl|ZR zDJ_Yq%)ecaK6by;w_pX-fYZjDJ{nGM7Z3PJRamta!P%#P1QbAotlKd0UkPOJGxTFH zbMGSTz!2NAgN2yegEb<@A$Z34NZPaJH;l9NVB1dfO7FbYwy9yo6+jE=Ik7aYqNz>r z9zU;m@G&`vPKzZOM>vJey_}3gTrCqJas>ho_lT>OWfMswOQ%&+%w^LR2Q@Uv$BZjb zgCQJ?mkdRZcG8<6*ISeu80*XiZ8kk2NgEKwJL3+?@SxS5D@wrn$x;JoeY+l+xD_(M zNi{5XR;GXXevDsUe?5_xNO8RCC!!%6l)7g7L99t1(l5E}Z?G`r`jD*IP*qUpuB0Ic zYAH!OC%EnpG(G_}Ow6}3xSe{#cT3ehOsPcYgg)kzSAu>Z6_8r+N^GYqVoKo@bj<~c zC2byT&mcD@r@M#M=fj;uO9RL0m8hwqL1zj?j|}2OkJTQS8?xcF_{jbi%I}j~9kfW9 z;><%j=|}9IfU;U$R*)XaEBo<8SmwL?Cc(?r12J*C9(^bldSxX)iiJLy$?NsqU2#5{ zr^v~R5(zA=dg75A?Zw{h+zqb?0`AgQ7f@1SnXW0IcEdFuGrE)P`(i1ju@$_I?=7j- z#NB?uXH5K90F0g1b)ng+#%E2I*)6a5|6jg!NyN^5Na@@F@^>OG0NBciCLaUPK2f0HSeIZU@0wyo z5q(YO;@G1?^>j$5AXdTdxbP!XBQ~&*Dec=MEn;$DQY=W6 zM#5zh7L6csi_MSQ<6Ef)aLpRs5WsnAQm8hvVNcw0hhS|Bn~EMf5GsZjP{RN>vnn8abNly^TX&kJ-U;K5y zNZfzgnB3e8pg{!_m8cw3C;<7P^U8x(S|nG}+pKsy;*K=boymQP0>52hpM=%^2vt!E zuEoMoI~`iDmjFqluN@{)cgKY%)ulP^Wn$1UWx9?Et|J%LoA`o0gpg$9+h}wsq#@x# z4ih5bTgJLAf_~JU)_|lJS18vAL$c6ulHG91@Cg!DvH=3KN>$PZb8w56c-mdmXAC=z z*P}DT6RL}b%Uh|Z+U!R?_Os`=_ zlE&t4Ep=I1nO+IyXjcNzflf1_@og`y1s6}*BJ<>Jx0+3_DIXjO3>h?uTcN;p2d$>ZJ#XPZ79PXVw_3^Shq{=g*x z!TZw-d{%W^AuD>^&^BYdPC0s>pC&bEeyHAKV#`Jcvst2SX>mM3d_+3eGjh7(n0E=1 zNxO;L-Ws*LS}tyzQDRoGnQFA6j0uOXt&nr@rX}plc!m`Pvl*prDMa$E3<< zK6^?BQK?JxWcv<6Ds%a>=-}OzEBkBU5+)Sc;)zY>GWksNA=CCnPb#=A$Y@2By_f^H zLVomq4?j&TeK&j!Gf=CrrkcWUJR{GvE07r6!XrFgu?W0gWfYh~@~-$$v&R+&%SC6# z6ojUGpR84i3TZLpgHU%%;+IXr3_-Z2Yc6``!k+aiT4&6=x`Dn zOWUA!GNpU#fAFk=y#uDRrIk-bDG`uJ@3OugQFJ`zsAF}sgsOyW0%foZp1fJ&!_*i% z9*oP#<8@WAr+BXXFDi%;iU0`>Vd+Rp4KtSxEW!A_Jf;VsvW00v>H-qmTuH}0JOML= zy>t8qi3kiFAi(NENQ4S#TU~tvpwG%Q>FUGA1_@o#>n^K*V%v1rWb;{Z(W>ToG~g)4 zqXtiio0DiE0yX$@bC%)$AV=7?GuY;FhVF+D7x3HYa)6kHz&Y*$79`Q29cF0@AqGDqEO1ehzd^}ZXoKY5QTa}5 zkqW`08%v#!XvR!EDVhxnrV$XdL$W9`nO9B0u|4yJ_Ww9h01OjcZ+paR$af;%MvS)u zi$o~jm{BZQnjiG2U_ju~5~dQY1NRDPk=Zk_kcP*aj&pDGf8%ON5#5g7NvIr}wQSo{ z3Qsx@%C6m)(0yF7{kRl18#dKSham;e`etMVQW)cpj_{FwkR&YTn-98h{P@k)9(!!* zQt=eiYGc>jFc%7CTT-~j`mnU}f4Ey@bfULr1rmvYPMfhmf*ht5!fyXEfkcz%05kzy zs&OlCL^1uaT_m;m zBxFiZgmVXgP^qaYp>ApGE@O~*E%?lVMOi=|y21f60}{@2)LY%%DO*}}J5v9{rCK~s zNMMM3+=zp=V>fp+sWg2Cq<4|P?iaGV*GlA!pNA8_Ez^o&0tlkan06+6KXDS5^hf|m zR-eY>aY92vWi!5{m$(E0G@%3`&^iCM0%SO80EYb4RMGtx=E!?r~cT9Cby@_y0^^Z{8qy&ruF0IHt$$BH@bn` z4*-c<;(?hU662qTqm zj5oPg)7p(-o%iapN^flWO88XtHq>r37Owv>(EIJtD_GJ|z34#kL$`LU)=w7grS&7cp?kxMhux=bB zR}(r2a>Nk_pm>}10d+o#W^G|kY&SbG`Fe$Y8jZ2EHS%7|&=OaRTJHUj6^l6_8Ah|J zMxAJ|5(AELdzd68RjI%*C(SCGf@;k?G~AIk7FPTS%aJ-j@+)--TX-2@?CilXx4GGH z$C&!~UVAl&RHH3#*Q0CE)$SI@)6wiF_w=Lcifq!QI%AbxHPb{l_hXB8wZ(n^D%QfA z`dA7>h(W*&Z0-uG>2L>=^Hof&S+*d+q91)n!ENvN?@?=IPrgGbVG-Nlx5ZUT`y-}k zk_?(Ighze7f(`8uRPB?<8yyjYG;PqV$|c*t=xHb5l932d08iFhp|dvdUWida5Y!+Q zuN%3O8pH;WMQQMvl zK=JA5szfz}q>SBT?K!Ux+?!PXmsOIDF!69#(+-uIJ9X2@pubU{&?6d%KPbn*eyb7t zjGnTR8L;2&3^#dzErT3u<2|loJaHBHrKBN(SZYwYQ6bQOj*O~ z0Y9PtI1KKeoZSZG*jG7~#+(-MTi_I4&-ep|j_(JCKN9gh%Ju;J9zkTOP*^HD8sp}g zxAPTmVHvyIEKrX#q5_Bv7!XV7r>vQwWZg~6%ECjNZBe^o+nexuKm{Q!lC+qMVrZ~P zVZjT4@HEn6H!L9?okOQD3stKH0uJy3*Q0BymUc76an&f1T0QCx z$F~4_K2PJC#m7&4IwfIR7j8R7?2c@8+h6;?4gLY3c}{_}yRkJOhK~TUYXK=Mqtud3 zfaj49llSFR=HHuVBq3PIK0x$4fYZ!7TnG{qcL;M51{9tI{s9ze^P_7-zz(u9azYVH zqNRG<7lAf7au>yq)ucy*MgT3AZ6Uy%T>{A=u~-C4gvtllZZPcwhiu$U4NiwjQ;D39 zKAKvuJfnxH^}#VZ;!xH@7!2_AvEvB!bcE`g*m9v~=WyqeWSxqA+-y0A4Q)1;T3xHO z6UlSz;q9D0yj|HF(;a{;eCe7E)m!K=3*U|O$#l1SHq7-3H@Aia7Z!``>3LQ&4p%8V z)-OS#*)R$7IoP_eDd`OxRP1++JK+{y<=rNPKcVv%)+>GRROj0{ zet5nZk*021U27N(^E5nL@xZpb5e<%3JkKj4hKxm(iGYKlEAXK_tchf_t|o&HkfDz| z4Pj{en3fRU$)oF{h!tZ~0cMZN>x~5cMm8)cP^d?t_|^|b^*`O~T^Qks>P*N~w?T0kabVM-)c`j5YLisv{5Dhn9V zIs$!BLEHuy<%m?e7NX2xvEEKstp1^z5I%sMo|LMLYF@2wW=u&ym<2Ge!Cm1F4>k%1 zpa+P+1Cs@vCu9WsqUO`2HrwkA5jE^EWk+0S(niAh>fm^!X=O2^PV&8V@% zxAwZsmcO8uDM3M2`8^cG;o1{VbG`uQmhNdYSM*8+RYZfg6o?$Ve^vd%fVpDGl+DY_J*yu1WtblmhcK$bg*F0W=BVY(){KHHEo za2c%7JnS+s60OqEO$O&PU0@m=N~c-rj=rA`Wxq0^z@H3Eg$$gwdHrN4%_o`-`|Bbq zedawONrpYe=)1Oh*ls2_*n2=`GdL9D+yfbL3Qi^}}5tSOr)rPe_AxJRjNa;v+ zu5|cuML#m8RP@l2k@`)^ojX+LaH7);(5aeIUE_(H8ck`4&n0-ItJ_G@l@pLt`7jv( z4h>x?(A`{*;1r|BSBiFu*tv8POld#qphw$Ihq=!qy3Wk(T?VyD4;7HXt^x{qQ_BcE zjaCl!nsI#5v4eIpL7`II{hOi+LbY?cMeRs$LpS@mR+XU!FX5>$TYYmQ`A5+Vc-hL@ z3JLYNqOKZbc=O}}(_u$)sNKDPrrVX}=u6%C@u2E7;e z4tbLvIT{ta6(BrShL(cmB_+^%C}Mn;K^lDZYJu_)Cp}Wtutb3>2}~HwA4V^^DD8Z7 z#e@wLbQ2hDC+3&KsE{5z@3Rvoj6yV8{AnGoO5{NWJ8Jli)^S$}Nt&$%44OM2!PeZi z2T???)UCS3tT8-*dS^YbYOm(^V4NIMSmav5#L?CzW=}n(Q*39h16vTC9S&cCPfwZz zvX{mRyTdM(hDr0rOoc?1mUxJPh)H$!?U{M8O?IkX1ep4<4^4!T_aNjEfc(De-TRJHBT z3>YzWqAA{=cINdEmuus!wq;|kyiry6)3mBGv!{MBg#(cvQQzSN~;+sz(xvw z=tDuZCT&N6!QKqQ1Vjz)GAbA;y|iAAVq?~;%f*7x))jrE*rV!=@&|UZT}C+^=%s3) z%O4n%`;pwk2{9so@aYA+487Bb5y&s+Ig<*7R(%myXZMec{9~ZS7;O4TuK+uwq_BIw z1=#OLR*alSr?@jfPy}Sa2)Tmgh^T^g+H66GfIVNaC3nk(T%^G{=O%wE-ZnsOfCwLu zob418(XLd|w?*{atSXaR*(5GtpLL_A9KQGjeajE@`tgnMN)DKeWA4EOJa!%dFl>Mfwe{CpEPZs7y} zT21(9@ni;#HkB(+)V?3dmXk|PgF>)vM4=@#`xuw0k|_fpY^DsEcpWM#)QYYVB}5$^ zw(Awx`HaOMqhD$eH$s(XGT<{${Auz6J&`{d-<#)CEC*egVO<74{1^&H7s}4o)h_q` z660A-kaGC;$|Ni?4uA(nq}P8W6;A=Et&1@M#JFE8BXuWq2L3Vv+e2&0?Z%8=kXJ`& z7cXVs7KnH5op>Phu~mEDAFHzSja$G$nos13JR9>{dHnghoS1 zJFs;nuVA>v21@$}pt?zu>Hm@xtrSGy5}-z#1ca@(Mc4K5JzF54U4io}X!b}`#NsfuioTrg)Q2+A4+ z?p5d3naSLtGJ2Up-24M*WD!&-@^vMByQ$Pv)m?e9R)kZ*NIEi^I3TMh# z|5;jp-1Sl%=iCP290sU{nn8`RrAEW^YRGpV=|B8)z*iYN_Kq#e4evbf@yrfV zS;r)r%bj24zypGL&@uQC>(MzU0gVb!O$C|l6>&j^Mt!f-y!0pQnD2KE z4;mzzM@96()A!T0iY#9pY7526LjuE!Roa}A)~X7u&WuX}gd|6;UHvsEQ#&M@$7BZu zOGimDyYYUzSX+`@phk?x%Ar_Nz+zGtJiI=sUxJ`7Xa5YZlyX402c*|Ai3J>MPyJnN zfra?eyvtBs*W6FU2bSBi=X&T)09DHGT9IV@M$#`Uv&-@Uo3@|Yyou^9HlFJ&?+Tgg zx1rbOVcR=`M({3si^SQJ_*EPd3<|D)MW-2eH|8jlL{W%76fD6hGfI(0Ltwy!1RWvX zZ`kNG+udO0px2(6Ea&ZE&0lAkITcob=!L?3I&p5$DF8-yFA%wQWUKkYve8U*Z9yd~ zUfzX(r;d8%yK!8$P6}w#koDy29k_yfz+2aFvGHfHgqvP*z@aNgei?FG;G33fDZid` zJ6_x-a37e8sr^*|O?jlEu3bgFs-wkm%?eGJ0gzsuMh?liYe6%ZX#8V~lESONZnrC} z*$t|7LTaEoy2b^%0c$Lg18Q5hpt^U8(0fcS#n>uNxAg8HZ6A}5`cha(V5 zoJ|Q7hCLDrLG<9Ps2r>ftptD%*06#-S}eiJ@|99Z-HnR+XV|0Jqg#>gB!RhpIvZoN z#pI~cF2qH=Dgf{t4Sd}*JMl_>l@<(PKqe18MKlC$HpG!WGO3V!hwb4qa3ihfg%(Rz zJ0Kk(7(M1kHI&r3o(ys71Kg^lu?V?85~)!BZMrFt?VPkh#R!SCftENHvKuM6n?0C4 zG4Il#W+5mIqI(Y{Z`R|i@X++D>o;B{qO#Mt4dIbJsIx7S7B)AS%si)RU<;l)=x`H} z>Y3@w?=tRM#y81BvL~*YLKZ_Yvu(5I>d>Y9H=RUA9=zO6!6TIGZJW>A=0ugP|9-a92*4=a^`r0KFEa?ll_bn5Js zjM%WLvYHz08MZ;ErJA870fpbQ_w6oNw}K0hBuK1WK^y`CineAr*+CD`M9TkV_71dz zaFLv$*>+(E$QuQ>$BQ#~3!Q@?v?c?B6UzBs8r$u(ti#fkRg;H7a#bX9$_2?0l~uA5 zCx)Q{a$yb6;QmrH79GgBT)huGaODz}_N@<+`~cq?)FvytitIv08C|J;m0LsydiHze zDii{^)ph&DfrT!EoV*deFEFDCX*ftFouF}4_wk<;FG0$xW)*`N7Uay?#{+Mifcm+B zmF#xcGSws+_zH|6_e5)eZb&4RDqf1`?RDbz<<9em5-$vSle+-|+8bQEBV1y`UaOrj z2(Df3)dV1xzxU#maAa!c)PzjN&a*bgTrIF##5_(YZfNGc2ZvwcoMkLWVe%*D?ea4` z&Ffwo1Qw-rARl}7&Sm!;!4^47?P$g=VCQ>3~F?H4#p$32ND`&9&b;fO77g# zt^3CuZrzcu*MO1iUa_k@AqRY`8k7`Vg%J{Lt?9y~{> zf~ewvv3kgFpufHX(+X?NgCLO-eqdwzyN7H^3QGzgOM`>ggi4?gJ1zi_t&WChx;;^= z3sc`L+cqkkf$bBu7$gSc#DZH4=7A`{>HPJ37v}Ct&QVm@(G|d4xjKMC3nIZVhadgy zHp$^dv{ttAVM_djSWED``efmJp6Y0T;3GT=?Lo;t}>N)!iF zBM=5CQWD*#w74yeZMrh^VLQkiesi=fDTtVoK_3wK&WX5VYU4y2_lVj0yl6BE$x871R(B1uWIZ zWJlt&1sF2IGlQ@*c z>Cba1U5|uNg3LLWPDv9q5X2n>u6T4yKU^DypbAz9GUgzE-rlckqI$H6sx%=xk}+-5 zoi_~gtZ^ToipXjmWq$MOnufCA;F5Ee2=A$YC{?^5{HCW^>1c%wW0o0I%<|>P7tSu5H4q4+6IlK6y`hG?|d) z%%#}#u1kSen5(HZyUM2Vv$KD6lY_6;){PYeA8$ffP$HEaMs9r*Cbn1T3y~q#s~40D z+KI(V9@&;q{tFg*P~_qfPrzQ&;Nk}Ik6#8y5~sqm8g_LCa|&0aO(eJ?kfjpO9UMTg zT;WMR0J~yf8?}qb>5&n!GeHUjy1>_``l{(97FXxqL338@RZfaC!F_z+(M^wBH_PJ~ zfxm&6v%EA>x7O+t&yUCvIM=Ie+XdbPVF`Y8Xiesi6JTQ}y0L(mMPy3m+hmUzGLNos z1`9-iha<+)A-^{^iWdvg`a1}Gx?03cff9-^^D6!ZhXw5BQ6n9>N}f*5#$Dm-PDVQE zaW3DCop&pP*`zkLs*c`+D`49g=VDJ7h1a5g&_(`65DZHedcG$R$9m%ig!eXv0dj}d zso?ZQIPRv*B6PZwZk=|{)7kM(e-}!Us3bJQSdN(oj*qyL%&S3Y;b&O!V!biz^#ggX z*Q2rt4&bZ)-N8Omzu~Tr%tqLwNahmk*uy9IA{@Mpa& z7gSKi`l`h#s6%dUpY-9cKqll4QRM_J;Mu2V*n#>X^S)&oXKo9pM3H5>IXxySfJJ$1 zAn6^|Xpkl8!+ICf@sP)TZ~(SDUedIo{vsdVw1XpruUjY{cA?k|vccwxca$lbsW+J*;3s`hgX zHpX!@vQ9)>lY)f{LV-)-KEM7&-7f;jzOUXruH?RgSY%;aA6UFOj3|g^1f)I!6(uBm zNco7=`0#}vScWlE_@ySDgprsoo0>F8a5%rZroG-F)%_Qf?f?%!=%=TEj*>H~HBJnZ|0s^tM89ii+5(L@JC;5ETqZBAi20k35<;_wx?(rw*yUfN|Jm>$30cxu`tjc&}*2BpzjJY zOgS$@$?hbcVbGUR9g`INn_LpL7EFfz<{MRs!~Ig@XzGJQ384@msA->jOQ#H8Wsde+j&vZ0ryxZEul}E?$MvzPq+2PAWb5 zyM&|+#+(CUJ7O7yT+~dWfSjo7aog5R6}bJ{2NVHsr>M5%cd^{>t%2mVWk2wogsime zYC3f=i5pB(=__`g1y?QU6vOrxVXfT^XP3#Xp!ODxNvy~HX}AhjRHVRsQre|mwR3Z+ zEwH6Xq#}<3J8wbo8i|~v7_p}_SJxv-Kb@Ta`4;@>#nlVk)zSUkMUT5`4pyXwb3Lyzmmn~2Kd>1Hx5dA%!r?$aNF<2#L zcHt8EVj{cVCecP1tu&*emBmmW$zsfGQNL;VI@=<)VZ{Kn+Pig8kb18O9EB~UuwyU; zPRum3bllJhP+Pn5F>ePfrbJ`Un|nQ=$Lc|QX=idK(FJnMAptGk*QOfAf~R?KR`!d9 ziDZY+izheUqLafASzJBdee1BPc~z1cJ>Y-F8?@K`6At~k06-ySe!z|+AigE92v67S zbJy~U?YVPR$iLvJ^ch1}3hN;`GucGS{Yc_LAOu@){=uI?)aJ}@&S#DqYnPg_@-AEx z#!_qq*7nrYficJjAdej7tqAcrsKmr0W5Q&4h&wqbFZLNR>1 zu1Mi)5E-v%FTBWFuHUb;=Y}LNl*7gg-m}TTpf(wL?2!*N_;vI^jsqcVY8U;>AnU1J zy#GF>3eIM104aI(4Y5L?P~UpHwUYWCzk>TtkgpBS-)24wpf~}H(UPnj_IxR!MF0?O zYFN+;lz75pAQVgk?)kw-5BAO%s75rm!14gifV67H5l>WGvI8(+@85k)?lYCgCHu<1 zM}hkDb|uA_FpI)_&E;Sr;WzJCcR7uY*yJ@!usL;K zc!g%W3n_3bbfl0TABC%M`6^LR$h6?jY&=6Gh;j`I9dWZ2VV4CD?CGLkce3F=^P#yH z+Czhr@pm;0`N)-`;mGFw0yg38tAD(Q$%i6&%m_ZZ|GP_?(;yt1Vq<`Yj5-J5GuJS3 z%-Q$sE*_C{M+jnzNm*>)jTKBbb`AS+_Rh{)OiF_d1LGTmmUKF%4C6LSgZ5_yQZ((x;JA%oX@X zwi1Mj8Fov?eCWMWA#y)Yy+qUp6lHJY6liOJo$+`K$Yx(!j>;P$^ZXUxc;xt#4gB~H z*`KkDoiWxX5LO~<1$)QBKX!4jOG2&xuG4llBFR3`0GUj}835J`n?OX=ozK*Zlv_gh zo}wrmfZqPW$E}EeQjGAsWZZz4$5>?FQqC$iW$t!fkh>#K7AE3G2X{FOS|V5=WZCU= zypNKa&g2-83!r5oz1@#QqChrM09}*{4>Gl7b{n?8Pr?O+Zm_;3Qg^UiN1Y_gE^CEd zQ>oyDf;|IJ7jRp`e2Vs!6w0dzrZO><%iV~;nl`ER;qsg)6af)p!+N=67tZN+Zn1%U zrZC|{*k|}%NDp9e+hUQ&cAXqm`&7n`-k>~8>AkNxSjbB3rf%=3K)OpgNF-+LXpq<2 zb3-flesM0eSA5Q}FoJCiN z#@TfD+*%YODdCjrfdI!f9Yf47Y)iV_?tBVpZ`6Ho9{VMa+qI`eE?6nbN%uYmapaCn z%2I{P=V4WTKH)-os_pV)4*Z+|sX;>^nGY2bWkW_D*0y`P6TCts?{Dx_C*GN$;DB8y zGm@_ps=$;+F+qQuyf8p`UoaYjwDKe_yyDLl^8e14jBnJ-CJlX zbP=~P@7^lfnK-%1deHW4D;V?x9KsYYBfK4h-6jmLp>oOyJ{t+z#)r=~%&z*gBuU%aJu{FM2Ozp=GBCKOMTPJ? z$NT6Xdr(qw&wuEWNP*!Oi{hGiy6dvxvQrx+(l3!atdy+p`q|M^r-7Nx$Rxf$-HecU zz_uX8_?WhG(5E&g(%i2^U%lW|uZ#+DW=cHALTC3L5M(Aoe}8(Qw&Qvir-L5xf%cz; zn6-|Pb8k1iO*`oEPdIMNQa`n|d-=4y6QOYlP?T&)eNY-);v*;)=A@qD5kswBye99` z+fomed(zIwW4Ex@1J_>-g&yufHYvgzt{QT|3m&UpHyr#$JI1O|L5jM+ z=aLT|60tr$7nPjPh*s8Jg=fP=L8jqlAQBx$@$+bMmTrID=fjxa9TrEAT`?@G5@7q` zXz3~_3apF6-;2%`^NM%{8g=15E0%5rv&}W^TgAZ7nK^TU1<~q$?(VaPt`dh>Yta(S z0K><+JPq(U>kPF1TN?;Z3l>=-S2gllIA}O)N_GOm> zwU7EFk>X8=$1d42F?ga9yes)~h@1UTF3JHGl381evrAZ_&FW@VN)C&NpGOi&noo1l zj#`|bwa7hMA;=ktVL*xoFjTlcI$2l@A+m|o+?W|@u2NLpMb7W@q1+fAfJmJa=tb^b zjJ_hLAPf6@gQ9I7%E#rYw^@|`$HvY2LQ6g8v@b@1n*Z!@{cT2DR4%Im- z9xre36j`D`Q8!HY(b=PtCX|LkO2>rtSlgYuqZ<=7O7Xt4qUpi-v<)hX`jS)V*e`Xv9E;+ z7c7St4HHbivFa?dz^NyY4qO?d#2q5W?QOX7Tz|VdUFtJ@y&$gh=V9}UG&%mZKW0{E`RDvP zx%@&=#w*)>9+Mp1AndR?$|Hi4FYE(v*CsN>$h3C+0BPR*o}JV9 zk4|NqO}!-_q>#sd@}h4mgGLx_aGCi&spQ^c4Uq}f`e^w^mF{nC*} zrq>e>9V+aWRr=%+#_Mb94v9(%Za4`8neJCD_Qx;*fv08u9!%qF8pN+p50c4e#qX zVWS&NGR^n3IDJhN@)it?abC7!#HkM!j{xFLKQ-%N<@(!~b3zLt8zBy3P9L1q1+N2= z6CX8e63aDlt$Z5Jeo>H6*?#dDakswrT+MEGJU9VbEnd39`oq~9c|5K@K5kNKKc@z6 zr@&l^FbgquHVw_UM=Xu@cacrXY(Z#7MQbOM)JnjkEt41bbg9Bg-Y$j} z-~m~Y@{jsauVWc#K(?B3`D;EH8mfTsWG$$2iATlrj{pcqk*S!zI@nNQ>XP)y%6KqD z;kNNG76JKT>d&b4PoB=cY~wlDF!*lA42hFZOQ0UXT!S5+D)&=QPFy;vkZiLk4t18= z&~C5rp6sG-zG=y3fDcfZq?O^B_Appc*+u8F%4>sBfp}m8Q2m4fG&n*l3BXBL?c=4Z zurXv#kI>%OTB91;S};)RSah5lCo%%0T1K*(25GAKs!j1HVG;cRoL%A^kG_>UMXeau z1`S;ucDUM|80RhkYQBTf@u^%72p3&2cJi-iIj32u?W+4ys>1rgsyf0tpa}!~!)#!! zqvCrupyCsiw;M7$&vjKpZ^v0QT)Ds2k{edsi>d0jyHx1MQil z=nxald7I!{4z=axKvYQz1!#OamApc+M^9&HW*85a1EJikTabwQ6*Rzp!QwhQgo9&g(tYfH$Yk^}}_U7=nc)KE|rNzg+PYB*PsDq9|D8lbTUCyP{tdr+0nbhJC?2Ynj3 zJAyeXRex+8Xbo8{9HKNYWu_||F;f5oc>yaBNmFleHFubSG7w0%mj**{FGE`n`^`aJ z+4dmtatdM<7nt6fVcoT_3(DaK5lh^{S@-r^T^y&!(~-tt7&h(hVHRqlD9nD_txOg) zdwC52eq!D@atrLk6?yKh5z|}5wk839kQTCBmY~40J6b!9tv%aA07Sc3NuzwAYj|v6 zQZkGTN0?=U&=k1Wk8tQn0)dxH8BWA=N}mHPxC##;P4CYp60apTdz+RdCj}^ z>x*@kfGG3*@_;A@ccqHyAXunMPLet5`|v+6v_TlKQ!ljxSbO{+d<*#l`mV!hN!hmx zq_S^=7LY!gh=P_%^sjxo71_dGiGs|_p_$E(

!r$Oy9P6?C#(9?)Cb}#SX_4^yZ zE6c0n64`Y~F`3ywBpjQtD}Lt&xHAzkVY7q5#qI)CUr$XkZ8|G+J-dDIc1SbC&!)re z&hK(Yg|$UHC;_SPkF3d!;TGgr69SLiA1rz;0q^MM0rsCj#bNAS5P24nDHH@NPtEra z&Ov704V?_q@f91$O-)_RjY=-XqNcscbL03bQI08yLhHcLP)eG^W+`#Sf0 zeUHoU@ze0i)LwmYDUli{FQioj7kxjLu6-u1K0nK)B4`~~=wFUY8?H(6BOSgnZOOLa zr;HGASI~kkuwN-Xy@RzlE*Z(xAHcLMlMO+BsW^I@>dhK#QGyb`5OgZ&+UZqz0Sz@j zezi_41Qpb%X z5oJRgsb2Ff%KT&^Eu;%gF4Pg`v2OcAGhkl>vl*<*dPWS^<_#5Weu-zI9z44;y;7H6qc2Q$0A1L@BXm zB)VHwqLC0e2gQmspEfK}x%<#w-WMzp7!BmkR(O^2# zqB3Ld_Du3WY05VHyIt!^-x9qGy;F5ps%gHX1wRB+rF$coA?SL=n>5p5H+sfo-Tr&$ zsakQS%aRC3l{j(7tjMFQ`;8R~%4|iOWdF$CXq`7$k|V|z zY^SFt>;y?Oam&4MN+_^u6x_>2I3wCVnsi0zm|M~@b<PPpbnWeaN0Oo z4!aq2H}J0WT?4p5gbyA#&~TtE1Rf1N?7uqzaS-Z&r4P66Z#qyCqSt}1g<}uE9x!^~ z{bemggNvE#Po^E|>MxXZ7 ztC8MtZaP{eZ;P83)h*MXC~w;AtCHy@(R^grv=PysBuqRDbHQUQjg#pw{ssJxfI|I) zN&)~7I~>UU_uzkZV*f({buu+Hu{HhQ+UcZ$rTd@u|7hp`E#OJGhLk-N0025O0046T z!}ZNf4P9KFO!bZJon8L_Ov@cz&E(@TN54J&LG=DuE(OiEYzjQMu*%XPoyqtpLe?^J zky|RW^rn|P-BfS|O$wIzaV03~k(&#!F6Zj>uzpoo~egDtXYI{BZ$M@-Q_kPd6 zYx#M2`n$g$)AW44PviFe?@#7?K5wVp>h*qK7uoE4elOkpeIJL(XYF_VJbgb07jtv| zAIIH~hgbgg@qIi$Hy6WC;b-pl{GLDKd;SkM>iMnW`hOpv-sI``zSq>hG4I65eSdDp zh0i7>^M9V-e}6ud`+U5<@2974*K)aU-sbN1`1^kzpsJV^SAQw_4536fcyJl$M5Iy`Jl`B@lftu?*FA> zlEDA^P|kj**Z=kLadRZURlfMZKJ6`5?)UqC@bI$)?lT@nbCdhHvenPu+vCr5F9|*; z^6>XG93Kivn;KO9V|cpz4!__3_u^xi-|sJvPHy!Q-ru)*OZoV4zT0m0eEaS7yWfYW zw>v*9Judvs4kJ#kJ_XJ$M_(V0qo(6?dj9yh-*I|+{M}yv-9>Jvf0Jj!M^}09_BtIn zdtHzHd_Qwr<+aku#o7^52e1++_h@B4K1YiRBz+!V2PZpwzl`v11k zIb4sQ|M&N~d)(iK!iDeZsT@CV4_)~A^yK1V!n1zw=j7tb>N`*GPwua+r_bBdVDjwM znlS0d#{FArdcTk7ccIo?(fRJrce}fNG?nk)lkW6u&HCSqhm+3Dh}iaM3!jhg$KPRm zzAX;vTP6S1>OH^Ti)?xQz*hKE7G9ZeeUH!CV|3UgZrssd$c$2J{6LU|#Etb2s z_xf>vL%!k5Ir{vhW$QN1pgkza%{e-MOmtd4Ep+^CC%%bp-KW`S$pLc`KC!=Hr!aoc zjeKU=Zr^uGTBg*cYEQk+e)`o96p! z5W-K|Y20AcAuH1J!N3gqGSHc-?L-e>S^>?UF|QF<_J<+>S(9X5Fv+3-(}`X@At4;{ zjYXhj=0fuz-4k-1+?jg0`=}fW9j_Goo@5Q%TA?ONbBAHaUAmLDaC)Is8M1-7cOP*g zTc6Oek;uPF*@|?{j(tPWm_&VcOjC{Y`4SL9f@PfuJl%9bIu5Q8S3$=L#3l<6J4$x> zxT=tLpf%VPA?q|T493~0S+oM41c_TnTYbs?S>yJOfhqVNI?I4gQxK+85TqF@`rynD zU$L{b-`Z-RoM;xURQJRNmV_v%NL(T*`?*UX>e4mXQZ>rep75&O;Z>u1XT-g5XbVqc zB+K9lI8bZ@DhsTUbM(@B#g`g`YN82XQ393PYjkL|^U!#Ig`!w(t0G#BOaX!U6aVFt zHjs(#LvaO;pdbcQM(|{*H4fEK=m)Yx&B;Z)OP}!7fPBye=#k~C3HB8$7ApyT2A7Sp zZ6zS^3(LcDDCH0$gEpFI;SBRpvd{)=wp8x5D3{$Rkr*@HdIbT5CoBj-S zpsTS{4*Y||Y72SZHz^Lv<>@1=m=m<icy}yyUG09C)CDGV$0hs(kMPHP$< z91>L3CgmAh75}7;O-gXneD8?%i#VO)j`mwuNv=-`wc8F z;zrh`*+dZ~@kG-9Ml8b+RfrUluZU2W5?JPvs)G6R!AU zwMuMQsSBlLA?ZEymaa=6=#-Ds^`P7`0UA|l{@~UAHA#8W*NWzY6%auhKn{Z0nTP`-?@EC0FV3^wL3@B+*7g{5xoRaI0t; zsAeFW+P!PBfwwhtRf;tXmYlL>e(P22+DtVM-Ro)|{|SIc=}k3*i_Wd$*iJ$rFH3PT((?LIfhzK;@4=S{9{lYH(S!0?5 zj=VwH$e>^l4TG$r@o$xE92HQ%5{SE_Zaa)Rw(-29Ky65S%3w{O8wVgIM>g4OJHqy) zc#~LIYhbCjjbMe9T8{*UB}Il7|1uaSJCTZpcn!Xa%ggeyh3v}XbZk4bWWed(d`0l9 z-EnMxJ5L?Qo_VgLU7lvKL-ZxvJm$do&^yeL3Yl_WVtoxo*!u6UsFL1>5cw+;1&kNY z+28bpgMO0&t8|q_cC2-DhRmc-IF2+d9P>|xQ+w#e>sl^UO~>XJFI3+J#NZ06da;Wy zK&-&px5f|Gupo0O8f`|6oVE_o>(wTFqG}tJ_LAT(V4^nK1FQ&I(`2Qw2EPy1(WUTY zO0*I@_+7-r))TaMWs3TxEshEz_lPuyk1k50wubydq;9iT`g`gTAat|R)C*7|>!nQ( z!0O?Rh(raiIIu@}I2>2dVK2B0XH&_IlVzydk}9A>D|e&SZ^KYM%H4Y7)zj0AmmeOQ zekCyPmZ^k|!~g&flouds@n&F23IH#y!uL{~!ZuAxV7Q#Jn1Rn98iRje&g+4u9O%|x zs2Hnch1sG`P^v9hBuOH+u>~tS2`42|$3&>yv?d8F97!t6wFb8DGIwAZpdJ8ZG-cMp z3wvwoAw%AVI0%rJ%}scYB=n5QbF3ZG%_h@znaovDIwfmd`i zxBRd?j1*Y?6A+FSYf8^RiNJ;4wawh(P@faGyHV6FOC2>=O}o(~P^h+2_9+{TfM|h? zHPbi%f&nks_KE1!QgxVRyW6H`M75|kfwH`I7z=z29q!!>5Xs2_sULrviTw)|0H!8L zsz7Gq?A&O}RZ;>}V+l28B?wFC5OVqaJ? zKG`NmEoWqr-N@8Dz~#IJ`OL1!FlgW?;%J0ybEv_}It&?!)`Dy%pSu`GqjJsKF9-l8v=L8&c7fcDfSQt_<@Hb#lAKy4~A!#~gU!NM-`?D`^fK zoBjkHS8W&~J3gZmaqYKi2$t(L>Yy11*_*OeTzhU;S-;Kh zK{M1&bOp||T{=phN zBQ5=y)S#|ls(!M*-)@L2RfC_Sy~~z%)~kKM!1TS#;Mr=%iMrW%%6$M_;ga@6@t@-P zC^l0*Zqg{V(%@ckbfp3XWrw>}gLmA+cr7E+ZK)&YDFdBl09LG>-~AN0DO_KiG7eWjEsv2qA!7*_X`t1Uv4wlIvp((9IR7SWP2*OgOPC`}qz#V#* zfc%l7%&l8)S>uEuR`cewYbeEaDWA4I2^Emqao$}a>ZhSuBbYMaQOCoaS<`a+v399) z#p)=l?SO`;!wuLnyy{?`zRW8uc^2Z+uLLx2)V1EVhHjfnHOnjS0W(GE=%)qtve=j( zVoi(QhuWed$fyeBG(XZBwa)ki_UtOV^TB@aO1r7cj7FCHp4Fiq=WBZ&i(se}r*v_s zT8a6KX1biTMJu>l-freLtm5WLdDA+y{CV`_>G<1QNN|1sjZuY+FkDII;$fegNQvmH7bz<5Vyzy6#z0^t=l8!?tBpH(CkDYMPv1^R3W@KZE6p3AHVct;PQ z_Z_>A3xByL0j~4Y`kPWANGH?_`)lI7u!;J5n0fGqSz#&#>UI=iy`KrI#fE%~7GayQ zdxW7vlLmhlkfh!!Wd@nO$@*A;_Z~|G(#NUD$;dA5T~coZw<|THP!|x{fHFj%Df2@& ziO+YeT*a6hu4Qg+ePC{@(ALV&oR4z_46HGW+dw?i++yWAemHy_yQGp#5$*FDAWinEg@putUZ_S_ zjw}-2*5Xqwu4>#66qyo>rXuc|Jm%e0%sXl~n6KX&^!r_{u@|z=hku2gNC%kTXpixu z@bABrA4Croa>55_Zxf;=8Y%wsf#zC73w=M$8lJhz$--J*cag2tJ_qq*PCxx-*>+Zx zkK$*E+NmaD(J5!;F>lmQm7jgZxui<0OCD{M9-X{RjrRWa=x${=^~yKm7Zt**m0tCo z?YTE=es$IxV*DD>Ukhq!8PGG2DIFEFfwl9l`bBe%-KF`YpzfFVwk#>yu!+0@wLiG> ziIDk$6R)h=WNc&8TIB^el%-bT5{KeXs%0mrG(}_LH#Si9{OQSD>7V`K4ts@CE6)B^ zrV0&VUFWb^Rr?vT{|vWFY-{n!S2A( z!j&xdJYTNv&jo1_0gG!9p#y|_kpU$=Vj#jBAa zYYUds)?CoQfg)kib~!lw6qg&MG1D6mw{zqUF_YjDIE>$VxR-)-Pe+(7kPh;ZI5*KD z-~S=CS`>j+YjtKgbd$qHem?XQ6Y)#Isi;f#BGt~SPp-k5MiL}9t+%HQ3M^??7IU$1d!e0;WeT68fGTN#N_#$(|I8w!s7lQ0QjX)%2bngBDvF%xc^>zu zlBoc+7IAOin3x5XyRjhlq!aEB&eVm$dW`4B&~p1L)xNoC#t4` zrDPVlF#!KzI8nPupmrec3ttz}DfAHxmd{chRii5tWx4obq(x1;`64qz3n$ED2D-w7 z`$*}SuU}MhAtd#G$O(MEAYvv}Ft>>!2Of zzNhGJqhGTSRX$Gt!Xv+@ayWS2?rBA6zo>Q|yjj?9G<(<-feWv!$52^elqgURu$6d znUK-gd++ovG>7lvdvdMe<{$fdtf+Nb+rcH+c)3uPJd9 zt%z{ur~u0{d0vUCbwc#$wBet-sv=D|Z>(^_sGm5lvICyGhc&BL3+2nRDbuwCJ(lDI zXk*w!B;cV5fe!+*L{s1+(#v9}y~AMfVE9jb9MiNso8Mm2aR){$YEE>=FaJ zABamm*i8&xZ@4&J5UWD@!15FkL!F%T2}!Wnj>H}?6+k_7tU)XljnyR}@ATj?hv2(VG3K~<59l)`}?o-au z`H@=s_!2!mE6-wB1OKz3e*+nxJj-z<1z?L39Dt-kq4IIvW~4@4^CDMS*t~QBJ7S9= zyw>pTt_pCnY`}C|D@j_AMX(f~B~%_`=3Pc7#ks41)AZeTlx64+v&e0Mm-ZmA+bo)% z!mN@l^mv8ML{8E@hIwmw2F+YpA{{K`kr+L1vBv1(k=?$uUnoUbw|t-;oZcoZp8Gpm z#S!r&>K6$pGz8JVBorg)v8Ou7Byw4*ds0G=A?77*N&G>C#W`r-je%w zi3b|PF;YAfuY=02(JltXeZn5YyhppRgrX0q;kf!f_(Be_#m$l`L)U}vg1Rrd-@#Vo zY5Ku5oH|Nxyl%zz5Qg%MN`uvZX$6YV=GBnWcpc4vo1t0`Nx$GF4T8?W>q1j!l8`d` zBepH03I%wy=c{Now9RWs!D^j%aqDTK#54a`AQ3Iv;gsDkY7 z&H>db6kBA`U)Cdfba~qQZwR<5n8XTs;3$eLBai2AC?5NCXFjp^U|j7URhyynZ-MYk z24jqOvnM*A1A7FP)dwlq!*RCR;hB4}_R3b%eX|AjCTi`nY}Yk>9n|-? zqIVqHr1l!_&wwQ6;1UT;{v`;+J^3AhF>zmHpm`Q&w0PM_uE`=Gl{;lKcP`{{#c)&b z$LaN@Ax%wZ-TQ;;aAVp%@1NGAFhx-S6Q>TSxb-nao>2Yf;sHF6-NNxduj1C=i8>_^ zjquZLHyBUVO00@JNuY0Ctw~zJb%@fs#dT{2%Ct=G0{UgxEz7MhqEcIKPtj&41$ACE z7%g}ff13%gab{jpl3>%I7-||K!MV(1VjeN=tz6^`-h8*kKEX;?rtcPUThs6BeloXn z-cLgSS7@`i_ET25*-@9q#7+pMJT)*RZ3MvC;~t}`85wyf4Hr2d=w3kA!cFbzCc0CX zfI7<)jRWAwi&q=XGGtqrwEOlI#`8gCFo@c`u+YPSUGiRT9_d$yt*dRN$^p!9?Do$;K3j}8BA8oH;9P{H-?-cl$e!h2? zY@+GHCb3+@_bAVylP{PT!}c8j-3^b-64ycxy1#eLL_Q)dEA`*WiwKqdt-sW)S0@pJ z91`Tt3g7eKQh2NRX5xkjQ}GjVGDxwmZaiXhBRO9JfgZATLJ0|3j{qZrch)p~liYc5 zI;2!5v41eyS}A|XLGXiIZ3b=eF`S&BAd=2PzJGcT9 zK}35EXPenM6;IOVU>bvX_|^T!EC2>bMVnenj_oC+!bxJ6PrMz`oau|QJ5AS7Eo+gF z3r~0tRtdHnQdjl?0nK$D_~H8SNn|)J)*cTm$Q2m^(C``fj-V_?qi{Kw)W#JPFLF|< zk1MaeKm+Y1L0%KWnx*xvcB`5MH4cM7&X)1 zY=d*r;yFl9de1^QE(=FD8tZToVw9YW1VWaFPE%EL#Dih8-%~ua?OxfzsO)L)#KG_q z{>k(@rH4_cu;W4qnid;;DxP7j5TsgNk`SK+O~&F*BC9}*uAE{DKXW__-fy~0(m=UM z#vb{b#AC&_sYqyMk%j}}`8FC=BxBQ`8L<9YM%KpT;GhJ#p*FbC8uUXov6@F5$Va73 zT%z#DoUS5oyFR*s@J8Lo*i@Lpr?`MrI3;MMLab-j_6~HY)Yl&1a3Z70(P?b?d0IYn zrjlJD2}`99Gm|aC?2OU_=ZoQK#^XS-Za{Za+KkI;?sJ~)%}01(krtNPM3k)5E^No- zKjNAy5reblHO?oOC7;vVoxPHlD=T+EmL6&4e-(QCmPF*yqV>++cyVk^T*m#1|0Q2E!Lp;+IlKjqPd|9X$DK)YVm z)KL^o#vA4siu>sJ1IoU9b4VGcL8!>3gwO^ zZm}aSyb1@__~i*ls@*H4SlT~k&nAr@fz9~R*z1O%Q`L!ircnnUgI5SI1^mLuh_3S= z&|eqE4%3ahvY(bjy8$%V+&Zf4%2F9HSk_lq7bV714yJjku{7QEXwb7CnnyA?v`>4D z=|y0ku2)rNs)e#xsXqzAO+;6;UYT(B+4bo7F31wh()K)*Ktk=)XM6()D?y#PE! zOk*`j6!DYhV7pWs*^MKbD*F)((mVSs)1C#aJ6X2HQeQ;T3hGk#>rlOgp9c+M#Y^eY zHv0WnH;0)y)-vb=V~TAtSG-Ggt4DGfw35B3oBY?!2E|#K1MWxbDBLd)Lukn|m5zRr z+9bN3JTSHLG1@XyA$ssu)g2Ukc#HcE0X9xi5e(DCsk;`g(w4>8BZ&!l-o*u@CVmA$ zkk7C*RV3y@aB_FlDS$q=I;ar#TAh!a2E@&k|nppDc_14Ol z@Sgvhxtb7z6;?gu(Z`}=r^1B}S3cnf!#<{0tnh(PmWMHxUIOzyBuCqxYsbM*0s`Uc zc-SqGLwWNr3*67XYqE41dKmumpkjGnt4MZj4jxlXUZ*piq)-v8BaD3wGA8b7L zM5lLclE18{CEjAEFkkP0kqHIo_Onz$1K0_@e=$x`steW6yXj77ELp??^?^iIMosT% z+qSlMC7jvK9-=z=zkn`3&95weP2TFx9-gdZ;M!QWbyUW^1~h7$W=j@pRw>jFIygo; zmrDb1=fOnl*bK$A#l2Qpn~Skv$=mi$qFM1tm8&qTtYw@#VS2q5mA|GrsCeuCoB^k@ z>mTU!8^tz6IZhw127}*jY?`WIgi|sZOyzhOu?{;TdBzV&^VDrZQ9R679LgHC5M)FV zoWh3&kN0#)6knkvKY;$_EE70W&5UnNpvguKe6%ana=NjDA)r?qSO z>(K4)yPD!l`2KDhd&X5Jc2VS^X(n|G1?zJp43Z^ySOdUJp~A_d7S|6a4Ara+wxo5h zhUWB8bMQ1Odv$b@{J9jOJDyF561C{ht$c1|kYJ(xb#&IXz?drDZmLzH~ z9Le%6v&Oh9qy}!s)0^IZt)Zs`Md+}f>FhN(;&YHUf+Jw2Y}6qbC?1uEfpMkVH+|HkcjpgYr8s_4G>$ai0&hX~I`}{<@SEelO^IsrK!&lYfTY)n zz&Rr&D^;c$SBB81ONOJZFTMVnT8wIY#G{pJFV);rNh0`(6*DA|+fbUG-*We*F@_?t zrcl%VYRcZ`L;iC*S&4F?DdOC@yHrr`E48g#G?|(a?EbPrP*PI z7Y;?mTOaV`i?-4ZoAgz%N_B>Bo%E;y0lreSm9gLCEjGD%E5H1Q-*O(DtgM zjm1WMMiF*Q@U?w$fq${m4Q7-^hpK?LTnBafMIpj@wN&B)73$DOO1LX zshuL7=T%@spQRK}`gC2*nv8q^T%`yY+t#fi&8pI&`V8r_u_off4+J-zHv!rpk9}zDTk#plypoN9t38xy17vg(VCY69UTg{bkeBX8@4ET{ zse4t#9ajjM9?I=q0S+}qkl^NHg3!>4HPn1ZSjq<_O40F z?4g@0**kKMofFf&_HcpAI?VIUc~TEWRODzaEk-6Y&niVQ`GY(o5Hb1 z-{RQr9GrEd4fe@Av&)WC=lQRzYcXXG^Lbliqr&#kUtJh+(SfDw3Gf*~s&Da?Zrsa!y1 zBaZ4u6LiIOpe{+i4n2i}uxG2-AsMEQYImY)2WPH5O`f~WTZGy4KE1Q@5Mo{>7(*3i zU64Mxv}kY~xbYu?6%a}kdSbK)CJ9tVOb)os>gPBJTB+i!gKc*P;x&%CjQdX{d10as zf^}p2mXhGO3<<4XmsoW3C;*!T7fiP?(i;KzIT0iwpe#i_7t(g&JSX zb#%Gbw5C1lnS*2Qkd==hH(HS%Ou|oRs_o3DCEiKE%RpFaTq)hs52`kNhi3=(WOYEo zPGYn!A-i@f4w;d24Sl{mq$<=SZ01SI2g0M?y0Ppbb#{;Ham%SummgUkM8n{?V{q*V zyPp)|*QOzdiTq@!2AL#@%xAVX*}<_*{4p}=)^$;fAEfs+eKS>0iAuU!H6`neRl?HN z`tEQ9PerX9+)`10nqIc^cd#Ox?E&5UB=em&DcCYJL6u?uI`+Lfq~tq@J%D36ks$nf zWwnX0!?VPvSP_-W^syOh&$FY+6t~SBQ|!zbGs$|Cw8-A8tdaN)eA8x}$w-#r+fS_=4O=j~9gVM#) zE4?+{;S!XdTlXp>;83Uc_KU58CO=ofN52HjGd@jr04F!uG~OI>ElV}BS0}tjeB3SL z0ZW`nd{F9m=iksIfq{t<5Y?{Z5JZysAJrbJ0NsX~xYKRrBW%T{!6Y|Rh90au#3aCH zQ@Y&V5d5wUx8npfVnFUol(&|#Z3^pvgBfy7R>+Z;1A`e!cJqv?PVwu-1a;>LdDe(6(Hue@P%_LMooBS5# zaA-zvVHsy6_b#6LP|`1Oh!Av3$$F~a)0hZVn4Pa;s1oMZ61#Zr#205`4Z*Sp^O#kc zkPD(p?A2BD^^x}M!77C$bKn%u;Zcl`vX7UjJj(qP_Ahjp%Ph&U(8sQh+gYkFq$tu` zbX@MI?g!^byLh?0%~N_AXfWk1!#W%$h#iDxz3Zft zD*_!MxWEP?%11sc3K@!p)dqL<+N8sDII=9PR!6h^y%GxcNE}#J4^5F)+I$=f$ezmJ zWjG3V#<+8otRPv8)(y|*M^2hFH5)eq5?XEa@7y6iQU~U}AviKYbZ>^O#`3HyM*aj6 zO>jTmNa#F+h0&psofT^s^{{iZ=A~bD3L!2_85^yu__G9L8Au3y({=0eE+GE! z!di)j?oPtG;;Yl+hx7gT=*>o1l~jLzsM1BX#;+FGJ6b@I6d|uRNTcLpB-)v@e8#5P z_&vdY&2M#(-!Mj{BmTPhYTe4Q>BqMHhkZ`_qjAN8Ww!gx08>Lnva`0o1hDvYJ3Atl zEPv;USs7)16%yC=Opyw@+tclZmbBNuC3++u;Nr_BdS*5EMHmQ@r>z=cvX zc}+09Zl<;B53Q8l;5E}Af(620X-!_7EA@pLaX@b?Dz10@Rxl20>&wQ`y z>D&p9L%x(r_OuGFlI#bHl5BXaeYxQoMn(DYnOHJkez)=s&V$^5RRuRvH{n+4OO!Qv zT`7-9mv!V~)E$ij7g{%zr{Zl|E(TgR<(V5U-4e^7dP58(8}mb z)YVd{X(Q{~?JD_Y=vx!=(XKTYR1N^FZvz(fJi zR|?303p66LIOET~Ha_scBa%SYC{PEv^eJ;Ra7RcjEbtvB5a8De$376=D2G2VDP`EH zYx`N(yP7VW#2(V8;MCkp$^gZ)(p(Icco^gU19N+Rf!;8u6!K{2a;C1wWKx#z=K;m( zGMUDhAt=5H3-@HvVb5DCqbz`Y@(|Kr#94RH&71mQ-qU<*B*|Z0Ddga7X{g;~pyIjTq#}*b_DqwCLNBBtH`4IC$sZW6humXZ z>^zHp-Mnm}YpKEqe?jx8z)sAZ5)kRq1uDjoM4li{5S$YbXMfEQpHL7wB1GBAdr=ZD zkqY2vVT=8|@Ud5eIit`I0#7AV*51)!i)ZD1 zZerta+5=C4af5{$<{d*_HQ{IF#tE@iI7MJ6Ld<~=5hoLW?;I~gBNGh~rcrZ>IFWv< zOeXp!K#AbV6J$W<$s+I8`9&k|E-=ayd?4=-g-0Xz(-TrPBL$TUH)IfLbImIPIQEyN zp^$iiqH6a!iX4&}k^#Pv+){5?bBc2@4&svhLt0Y)_sRb;1@KfImlPy9NQ-$uenI#3 zEA5-_JNSRkFQ=9@j^>2~0=hu~0+RoKJR1$H&Fma4oXu_Y{^N9{GjKFD{a=ZMqSR&W zev89*zN<}`*sb}i(5TkhD!N8h1PlP9zS2;HBf=!H1oQCGK6PJH`#v;8f{rw7B73f= z9PIBCOK9Vwtr1ev#a56eZL(|J|{0P zt6lrP*nd9V+*eOi5AUhd-$ zCx^@r7xVkvUp?L49-Z!99ln@dcX{jJWOw;;emuXN?%8{~xAFV(>io{S%DyoahS~w^A3ja-i!i`-YGg>cAdH(F- z@bO?Ok!nrml3H^|v5<+`XRc&dzq~Y_fm|-Z9uFQb<$_+;88e&4 zVr!sOoI0ZUuZHV%C%1DJb648tN?NaLKx>c~^SpA-%4G10=PfqLu9m*yxLMycrs#5T zfh$qf5Ntj+}7B z6qnB~-ZiG?$Jq~GEOdglr36G7TU`S!4$;9DPYfK>=#wi26oB2{0tYEeadO}tz z$d+PxR;p4e5V|CCz*K)R3eZ+vRdafU7|*#-!>*>#3@l4^8dKdrNagsC^==CAZ@;%5 zc(WAYMxM;5P?7SN5ke%~DQ5ulU+fzc(iC{vV+8hN`D+T+98gH$(!vThdNN%B;;Y*3 zx+GKUMt?e$TtSe&=hg)>Mp#O0tyY(6VAnsj%nV@Z3RMlI$^_FxIyE}GU1fbLpnwGE zNpmttG;k(rXu*QUG(3R#?3i3G(Y;M>XoZkP50yQnXx;Aqs5u$b7)huImvKv^Yz&8W z8rbw)ZSWmv@{~;EnE9S1MW(7?uCCURk4k7NW^mn01GG6)%fbpUOeq^=9M??(xg{+2 zKdx{SA|0Dt&7FxeaF6jR!f0=;5>=ZYh*rTO(3~2Z9?F=w1EC9%RTF51m0(8-GoKqJ zG853#&&Z}>;HJ?bS&7F$dWFL>s}osd#0HKbp!k3=q5v4)Maies1zViGRiL6QWtAi# zYuCp|Rx}Ij^B)`SJwuETgoPIb7oZX|Q=dD$Dmjl=-xlf@@Z5#W$E(pbyuiXRx}x}- zgTr8qJJ__=m$jAt%$i>q(%&V)KB(jGoN1`SP%nt?64Y)Xxc?V?OL3pzr6To4Uz9y` zcG&<_z7%bfBUkQWiu*1k=;25Q_(g?-o$zDCuxkWfj9mdu9oB>+Cstt79f!~J@|O|= zI&W^PI$`oxV;uS(r4i>SCY3%pu?I3vD|${;CCxFf701xOD?=b)KD|wys=ao&UuaBS z-wO+3o9(n1_&eH1lC>{?LXiF7p8G z&ZT~VN@>dC&R#GuA%SWv5u{;3;XI|oJCP8J9VwW?73?2&afY9=jZ z`67HbzgXMMcph_e_bF%vKGo(_dCY}|gzqTF!@qE;QxU6l5PpwENLUL896VN@m`0BLpC{RG?XJtQZU=XGu8J4GRCK$yGHY93mwGlLrhw;EYh1!sfK!W!+ zQeK!vAL^_*2q_nQS>jcm(R*u!No#$c_SdD|Drp`z9g`$hqT#77)`mAE%KUnqojZzE zs3W|KHpkupWVl1V3eEfmcbodRwyb43JabEI_-DwQYg#E*<{JS}r8-z-)a=Q*81B?d z*aC*Hob_;NPrY1pcPIvwi|OHfMf#r!5J*x(R>rVd;2`z1#=fSd zgosW?C+KBGIh0{X0LH0V7|%LXG!O{7fldLo5Bo0>mO(*P8H*moO(Y-Fb0Uju!f;n>r1kbXkuhQhf3)+NxoL6IbIDOOtfuOcjo7l4zDM4POEhd;ah;E|hR zgIvQ<Fu?2H{0{c&f3 zFSW zJ5zVtSkofig7?DLkX0Rwo^B>Y#mkq&jo&xKe^cp|C-}(!AE@*UTumGe%uMv`9ZifZ zoPIRSdNwZB&K5@222M_N|M_ZRYGGvXAL?d3Cwl`&ClmVr3SQDp4*u6Fy_(5Pu-hNe zU}+!_kox}|Mg3pQZ)9g{YGLN$_@9gVDbmUHzgF}=(_Cw7IPJF}`EGCbIqdR%B(0Iy zrH5EkRnvqAnJnGR#3qX; z)0*1f$QN~~&(>UXs62(yuEt28{axpS0Hdy84)P_m3}X(KcF~n>sFP{9B2%&~mn1SH zs(nNq$~V|j;afSk=heWn1rj3Ncqp6th*uav(?|?`-u&|F=BHQ}9sQ>PiI}#B#Zci7 zzt%AF5~6|NmhV!o&V%&5%Tj@X)`gj7D$!V)8-Kq#`lHqr>cEwUb{fV3rSt@HM_92v z)}hA%Z)Z*K3`CHT0VZcTrLea`tCB;4JO%x0`>{zCNzDajyDuZU9_jIo)oQI!Q@U6ns1q(H56nuXjGPZ}A5In#R& zt48R_-FQx|K9By_3E6n{@7@jBP6ye;6~*38-NDoJ%j3os>6iVrM61K5D#aRpylca< zLBZ1D>*2J!w(OK_%9$FjL0^2-16Z6VU$tbQk<*QD(*3tARqUM6=n z+uIMCG-=G+-)5zNu7hPVh~SjVwosj0 z0M>*{SYFZ8ixsh-lGRZLV>umZE`ywVT1?tl5?*}_z;L%L-Sr;9y*y#dy|)O;l` zJIi!5+hKr`+3XQ<=q&rN*ywCZF%`Hkue{)_^|e~U^J!@w5^V!= ztKIi$eG0YgPpGdhk=|r1P`a^W$#?qKRwZM4w-peWLpt-e$(@?kLrNz;F?k!8+?^O3 zO>ugQnec6SiTnd&fAP!3}+$0)DWngjGtiVL$g(mP` z>U>61(X@TD;|%riWx- z5C`mVN;TK<@zx-E`SW27*8|oOz#p}3+?7|-gaNr<{*<`4s`F}zXM~*EBSzLS%L7WEq&q4O! zyK02U9Um-$A8CMUeGzN6JltI@ZMPsZXCJz@q3qwf{0&@Xt1NCo)sg@3HPAsfCDHY5 zAe{tBQz($3XDdUnJCmNDvXw#}H_Y|rqf})RMim%i4#C^Tp0!hE zZr{e=|K=ymp1yT!WsI@eQseXTGuP0@Dt&Kq7oS>b3b7YePAq@kgV<~1Y!s0(azH3(J~42R4kx!~GW4i6AlIG#M%eGWbt^YRZ#`j=r+OUx8_8$B`! zUqtS8IBSeZq^=}`37!Kb0KgNHXxd!anjxLvWo+c`KN6w7Y_JBk7>+)jf?4lwzyR9PdJZx?lN=>o5&*tkHk8AwP!9nKbAA6g&1 zkyn$a)&T!EuM$m(erV4~Y|-5fs!G-~+g_bX%?_ue;5*%va*!xu8qCTQQ62+Ea+;tt zf}iVHRb-ERhX^qnJCaf8v5+I}=_Y6au%H5AEfLTl>&4{rol?6R5DF<9=)Tj?#W2gw z7{`~i&Z}Yn!v&VVzrvMsl23U>m4U=zhEf?=u_g?xpbMgMF*(4oAtE~2B$kP$1t@_K zR0Uc<`lha|OX|n52+3RAn=kDubXt5K_cp!ImIJhpVNOPWhyu_dOVgI_9fTaIjI(Bd zLvk{>hh5}6!38Rm8vhfGc#q^F?kzWT5;~naiyP);BF6uhrYchL8zfm{fPBK@tDF}2 zb3^)O2G2ka{ZwLN8IK#8dF)DJ;}htW`RnL!%aP=5fA&*Y2rrRqm$M3+e6R%>;Xz!C zwocsghe>qxp1^_kPtcK2GE(QMzT9o__q!mtyHB%8c zBD?#TVs2TN*;-by3`DY8fWYrI{{CMNlG*Bk;_wn@zF{QmU@hYae9$*rL5@=;O90%X zsFmM)r_U}oCr7U)ub!ZJHJ@NoVJ$==K84P_GRMNH(Kp`7-tz8UGF{|6+hp4)UDGai z;z&c@_H#&C^byxk+XqcA{m@2JgXfU-)a3i0`2T$)Qy>>dBElL72nQV)NaOz-1phCV z8LkG_E++r`mgZ%rRz1n8O6BWR^-ke7QNQ=uh)ydt?*?(7kIs?H1mpL`G5_az_QzfI*YNb$L;3c{&1LcR z_oG^N_uEyv-^+OR*Fm!1^L_SL|MvGm_4ieA_e(tgYyHoNWd85+?=QFRkJD`5&&g+g z@5f1gpRbVW?^k)hTnxXb?(Avb=htn%@0&*iJ-_$K?5~UKucP9hQT+VbpZ9t`kDt%{ zpGP6rU7yW8=i4;fiu_io+ehut4n_s96{SJmHp z(^ah#nrx#M)>b787go4$sct1PTKc= zzMuWypX_>m-#=x4jBoor{_Mg_x^MBZhxP;h+sXE;(C2qO`1x?wt=;zrx!>!i+IP^y zth>+qx!?O!`t;A1ed_tXZ*F}({ghA;*!_LhUfnv%ZpYs=t>^o>`ON=uncdxbhVlLO z`u%$J^BSHi-apT{d*$aiIA;GE7JK-Z+V-r)c%Sb2d|cl4ek|AXZ6A6n*X!(lXxU31 zZr9cEgq^@yed72OYvP^iPO|w~*}6-N@2_*ek0&*|Zm&$A=f(4fbi3$!{;$FAm&s~; zv+slHuRZeakJ0P{CEL@k&8GLRYmbB0=ZtW8Ela0_$!^YtZ7!=ZpX2A5UDr-SX&+mk zgN@@XTbpIA2g&^|8?95Lqjk*Z+C}XlU9nCdsb`;){-cr3i)N+c&Su)EIjiO3$<^nk z7^QnQmldyB{`IAInXcus=gEZU1kMVZB{uNu9bK0Qr=|{@4Hs&!YoqJJNssqG*Xs)^ zodp|fm!5yFY?6|Wbc?PGPNbjPnm6W#+-#dGx(mH(oMSC@zc;d~m$kicWS=#MFXOLh z%zTzsSdDqaE>(mZlAD&_+|OU_E}ylZl^h=*wQ!W>yDr=LgxLM~xF2`likiry7RwRl zH(aM}I^8BOX}oY9Ok^#pwYJRGdE$8NEN|C`8yhpm7rY%yDCt}Zc#CVJv+eAfOF3;L zWtT4sGM;`VpD`V`?`;dJuFeoKG zjdRycs>QRg(k%Gu_%m;fU8dyGx7NTj%r_hv^X-^t^(2z)HJYYPTh-=dGG{m+TZ|;G z-R@Mg$R+lCTU08?8&i3mnkw3Yo*fag1SpDYKaTa1YrSxow%EQq zfm2*~*(${?izxgE$ExKNwWA!bCnOEy@}0?l^nUM9TWuPd|+V>drhK!yZrNlHJfw6H5T2%0zXo9$#Inw zh9ut^V>#b5SsP1qOi21=Gctx9rArq-28kj|?-J6Ym4p)`Pv)FQlG+*&@;dosdc|_r zihiC?g-iKL4XpSIgAY}_KlIyD&7!+JC@7Rn&3 z9Ij9iFt9Xn?y@Pc40;5ZZ^xy*WlJNgj6qitSZ|h;m%9wTFi|Q;4aZY5?ETv)RSfSV zPG@K8@wH{@k!h4QE}N#^Zuq%7Me6l1TSr@FhdS77r^B45D2guWJ`q38Y=9!#y(>+w1Qd12qGTi7nXOfbv0ni9j!UWC<16)Fv>6mU@z=2{^ zCDkL>3@D*!gDV%2;h*tnVS?d2M7mh3_uO|>=5AHAaMxWnbfZw_lrjV&MV zIMu}k9%*=u3s;{8^phUn@OHo-8(BNNr7`4wu7toIjni|wLK_`xs4A5NlIeQ-t`4>M za^SgWDevjSo&AM6z}eQiOs({phiT((cZ$a4Do?tfmuk85f_`-)>o_uEo9L#EuLaL3 zJQF^R2`TV|k<`cauY4}AUN;*WEL~==ZA*313Fmq(Kck4o?R45ENh5tx=`pFHgVM)l z7fLnGU)Vp+D>fAYBhdyc2W>X6{|+0ERKSC?sGRsUfkkaY7GhQIr#8)R?VcbCA`hre z{-JiEua)L>7-p^R{W3YEoc&6&AQ+cxK7W=<8<(14ni#R#bo{ZKaKw)}pdV36J}lSr zt`9qR?~GVJ_f*u?p9dw4u$c%WwBb3}VzK*8dl`=PrOG;96x%TWYj7U+LM;kfmmHsh zkxq2E5=LV<(Mj`!wqUpXmCj1q>u_uchQ8FY@IsTu%6IG(ft`w()Cg~!{m`fYqXtCm zDQ65pk^{(s+8qeFkw*9%tGUgJ0OjbPGu^vUJbs)>WL^mh%J#na6VAF`Oa$veNL+k{ ziQ>V21^4edHY{A%R;S5^X(DTUW#&h-feXqWg*jn zJF~ZjDUw|Ee5HOS9FiutjirFES+O&_j1$#qU}TK~jucgJP1iJrjn{Qz0is+!M0kTk z{%VTaUWZPs6gse6qy#Gr{>xyMe2t`@Y|UTo9m#mt@ID?!7&r2%zSG}_kclilBNShkK(R{_v-*XmU}`f`Cn-nk zo6)tqA@h#0ZDQH}p%Bev9?Ftq`y^pjUE^U_X%JYnp0In0+zzC_A)>M)<#K$c%8xIXd^L@cwG$QOLLEs;J2J77GEG{W0zv1y{zbLhq=*L2UQw$rvEsY{dmhVr z%DDbZpkWpbqFJ4og+0e-!i+P`avo*A7J<^XQzA0F7$9XuTHqLGG73)qb(IKS#O)~( zD-#&>(ilaLrCW0pd@Re{Cj08holCiA7?`7B=5L1|GnMot;x3tM`Fik?uz?`0b^K;Q zuzU{M<&B|&EYMeGvDRM}_m4brN!PABJs9F%3I+#v<$!`%sGzJKS0Riq5@J&NknO3; z=Ca+3H`hlCr&t zd+GFhi$iZF)()MWX0zKWE&#idcCwUNsZ3c(Ti%}BTgh1F$i!tNXzp+cs2#MsgN%lo zt%~*ELl-SFZ}$=m4CzBXt#y!`hP=>*BTT)O8OCXjWW8PtlU%f|Ks|-L{a!YToX*zU zkizhQr<@o6_|fBAjcH$Fzv|7{OvRb6RODH5eJNPgB6Vi&`iy6C?EJzhng#9vj~tT& zLLdJc95y2llw?P;0gJ4e=ZN6uJ6o+0z>em?fs>5cFp;e=g*0;kJ&vRAeCvg-ASOvf zcv~aFQm{@1O287vRzea^6*@veNIOukPaDA19}Sp3h>Jfav1zUy!5xitA?LOm%X`2Z zph#+^?5_3ou4dyYW)T%VzjNgs+dwXpx~rFrJ99{4Zsj+*F`AFOSEx)@qy^+MU6y0i z6eL^k|K;Ct&!myI`flaBg_);B(OSpFBJ!jnws3?gd6iGe2Yp}z{hg~41sw3mxdmGD zHY#-`VzQ20>4?M~plJ1kSm~G#;Va_AAffpOTOLEwe@Jtai7tYqBoKQI*KO|={svh* z9;tp^blJ2Dc?J+VS*5sK1Q;hO9qp7r-s_f5)^xGlx1wHHaa_|1N)9$o;w7}8SPjvN z@$M&~%G0PuTFll|iq1gwf@QizR?@`ZniWg~UCjm^f4}r}j*^gXD(6!IU;o;0iP10o zm~hR?^leIRC}1H-+d{W(wJ|&x{PMI_q>+4AIwL*Bbxnsxs08l6Sk@oSpWC>;5FekG zbk9OuY45~hq^3=) zeqw`?)LOM#-H?jsB$Z!^W3eEF!w*cCAkau2D>`%kR`*5%EOnX}KF0RUmDY(p*t9pZ zVoSn^({qv%{-PknC27CJ-d~3|+<2m*zg{$*5sTfF4RYG55?>o;iX}OYgRT=S&<3Z@ zsgj^KWWz;gyluGuJ$z1^E!6PjUc8La3!J1!cCuh!qGu^M>`oo0Qi%-E^>DJ6*IW^V zJ0+1Fp{o}(we3VnnHgKc_C=Y)i~-Fflgn0A^%h7|;!1K};TfpS$jsCRTn0OH%IAnK zg;uCs+G52iJzVH+7O;_vDb>5jC6a^naO*Gvf)WI*p=V4Z|$4vfu=>)a8{H02xdN8lK6- zo7`n0_|)ve>*~|1r0P)hJR&aK3zTAu8iGUTwFSJ>nn!Q$ml&zH`LTm5P(qR^<2OE> z-J+iA2z_sjFeSVM**TIXas9n-$y~&oMJ_c+LI46kBYKO<5IZeKwL;_KMRY^KGtl`s zgU^$7)jp-S=)HQ+_@(xT`#YOPG~--pw=vw=qC|Vo4mHO-eYkkW#S&Z5UxZjKiEF}? z=P{%Y|^g}hxwdK3W)fQ zRT5k63rfWJ2E&ORMx+ne08ysawQCnL3w?TvU?=q`oWtB0o>EsB8+(F6FjRmz9!VfKNfpe!l2fw$8)3i`(FHT zeUvC3IjQYBF-(YmqoO*E61jSd(H8pPAw4qB(hdN$o(~n9Gtf}I`wez_=)~O_h~%$? z?Qxq(wW1odBGHAu9Z>h2&Yc0CGYrzf9Bz}3nyrADw-jF9%O*M(1&Q9e$RBV3tt?9| z$zIK>9Xjhqa;U#-qzeeGv{UQvx?qtM{v#Jr0*~&TTmS zlhyQ=)S|!^plv|(yWLuQ_4$qtwk;MkQQ%y*eLgte7`ds7ttAl{61DOi{WM9Yrr9<|FDHC^gn@UK9VSbsU3oS+iD1c)J4oDxQo9P*dR5Yt{3~~NJzb% zT+=1E3)dtZp_*W*1{%8|B+2rw*fZ{|+ITjCosveDxj|O3keCVVTwd%#0tf=w8EBji z*W_VJUQ`~)&1Eb6U>no$6s1S(WT`A4jHSp4VFN}5vCwt!!qW)xo_@iJj3fTXu@W~& z>s$u*8VW|)NSzC=W$sjK9GY5nV%$wMej^OA)pJ1$Kp_(GR>%-lQ8A={e?0gaYF$Wo z3R4gfRY&U7XJcr{$TZ0cTw8gVuLDcd=d9WO3j99P!+AZ94H3Q*K^`Ipa{O^q z?8F@!NEnQa@~;nn&qbK$FVdwz*O5HObgE%kIWim zLA1P@ z*)ZWoqc@TMYs|kS2n7z3#4|5~xsFqWQqd3#hZUqz=bi=fy`Z_?n?q+!UU<8V7NgORgfCB0Njd_U}>e3xT6N&XhN? zWnKX08w;)Tehh#3<8T+TbLSjTdA+rJ?H!kdWJ!MYqtIQwE1 zzoMj~h=bygi{a9w^f%>yshO328f~@5`wJI652BDxfZhj{GkNocgcgo5*F)b2F#XEkivE zTopha6;Y5h-~-?R?RdJLB}20Ki{e;x_t35o~(hC2nJuO)EwZevDwmj z#n$|8AVbTU8w$l(%}3g7CLao+KGcr=*Y30mLZ^~Z@~jqp5y?MKLOI+<`WC~#h*xlDE|>~YT`-26>Tv7CjeGJslT@U$j>90^yMvL=7-wSFdhFyhYx~L#7(Oez833-< zR0LD46g9j!u*R6mbnAdMY$BYFIx44b$TFE{ct?y}vYvm~yxRE3OpxY!4(D+q?+EQV zv|Px`6w;RR+cAE?UJ*@m5dGyQuJWnDm^IxnD{t3(vmAWT!#KH_B@AAQFa>zFy%K5c zujM4On-2}yWcibFZ{7@ZSSby_i)&jasvbHh02^85dWCG4$9r~A;n=zH-%Ue%N1+4$ z&9_mc$hFAXLzeCGRV3*K=}uxF+Orc}iM_pvY}Fd6BM{I7Kwc*+8p{C90#wDao1CWs z@7eP{8QVepBhtR-_kwT0%^>w$ZyyeN3eZvRR*hOZfb3RaF6tYOz@%i+*?ftE=cDV1 zGz~nc_G#|UPwC^(Id5`*(y)4-3UtRHuufTs!TV7lP^8OrK_@^}h`s{W)9Ae4zlY$K zmdQX0N{`{d1|`>59&ro+UDDl0aY?yeL3n-u_a(`cZZcQyPrqCvw%fVUl*n+ayW|Y0 z1@cve(0&IN3raTQ3}(rS<<&?SG|4e7o2@=>E7I4TtpqGF`D`m`rb&`$o7-QN79&o+ zY4ewl@BP#kl1e_ILGI@xFtRmVV~NGN05_l^p23W4oCZ5L8#P$(D@mA_A_QQYx}$7VAxX0)r!jZLn3T;Hx!O?TM&6S(2{+_=3Dqvu zKC*0C?GoyeZq)ir-zqtWMjeT4+>)2NCC;EtAd7Jj*p<+d|05^dsu{c!QK7NG&kZYK zlQ-tH{~+(W0g!e|s-n6JA-BMDXmbLWoncR(-}{Mik(TV1xwq?)tV}}F!w_^_Ac-j8 zWW78qnU2T>aq`oEw&moYmcU3fKwk)p%sjITUS@RlCE7pQ{POHAY6z%LAX_;cS;

YO=}%J6Ie@nl+K(LdoApu;$!8IU5PeuJeZTLyQz-=(BXw;#l zU7ZkH(Mj@MVaUw>T7+|zMu04^HUL3ROhht6I2O~x>yM%=gq)8=*aFK?NCd=m?C+=i zh)Wj$shTjBoR@u?R7i~W0NP2QBnf@+I8M&h>m>xHHyqX>(bGWMmHqyRb{yUfU?>zj zM(OfyO5e!>)xcU|s_U_Ka5g7LXIMH*#ow8_KTR;`x^-kzocd-I>F9-JlWh2dkEQk=U!No=<-wwEiY>CzhJ(qWMFL}i%3W zrvQ>9k$0x-QsTp<-nA}ttAr#$*rFIF(Yh>+a;IuRYH&KpPrO;0a>LGwyCQin>!^oq zLnMG$SC~3d43)Cm&7r+XfELW?ux);MucjFbsWA_7xUWZ7Lr4~#qm8rn1sQ%ALSdVx zdYPcuSLf(ijxe>%=gnYt%{Rf4+}}WurvuffV@WCEAvn_10(r7XhXdZlC?MlN3=Q-2 zb-WLM>>&Xt!jfK<2DCZEbYqM3X-U`)A)QHovopr@C2)d`G?x~acOsg7)4VN@jOgIr>{IBzONm$at%NgF@OX*(RPmk z?ov4cHm3}b3@!PkkM9LtUV(||MZguOgO4Hn%tIl9_Ge`25yF=d~^ zSeZf+n`%j7mMU%lZG{Y?K3f+?oi6b&yzsUyh|d5|8b}<2&nBfJ64=H`I>j&T(!$f0 z03);0qLA!(sK9Gfs%Wh<|w+%wm@ zGZ)%J1s&z>rxiry^)}z2*JYz z#vJvr(jqA;^Nr>PAyvmAus7m-gvDFbnAPhg+L$gkE5K52RokOIwz77WjWL&(Yq4qx z0;K3v=vy0u1S0BBMUFS=bk2F~%ir)S$4A5+ZJnK2X?g^{$e?V8DZ_v`p5U` z%k-IsAyH;=J-QZY?^NkYA>bQ7qY}>=Z9UU@7au1`ny2`0_l!!mP2Vpbzy?^7?|kjL z>=S*3DVVi85?L#)H<`!9LXVtR2T(>ONGELtEM$LEtjp{k&S;`ugi^{DQf>lg29DYu zhAcHVs|?I#HrGqYTl7bPw$xN(e$yy`G7Gphwz_sGE~eJp3*#YbG6Xk`QwX8N=-Z~$*4a+~2YZtD?Qzv%)9!KK6y6;5 zD@44tMCQU8EoD3bD@zuyusStkB+4@QUos{;q3(qEB2ZLz>Uny3t%p4y{n%|Y84jp@ z>iGb{_N>42fJ@Ll5KT9yldmOuzrXpwo^=T|IoAJm=jP$`5nuv!9d(wZY|KEiC|x`^ z`B~J=)LgFkudVWXnU2TTNmC5z`4V*t#@ms--j7^ZqT0zdDo)Q|&g)%cuyryKX+q{W z;$>1WRLO+l3S{bQrP=O!nUb{ejS)oA_kUh0+1W0s)$b;6+_Uv z6cd#CI@mB?mnSpF2jVMXgj3*}f`q0So>l@;>wDR!WE5p1EdXdU4`3kxXcpX4PEFl) zE3A0wOfuLbKm#&aC}Y*~DG6IpK4wJ&cbgfVd?0~BlR8w&=^QBo)k(0cM?HwN;zkkH zn4V+`wB&N+_<9NhvHx3GAeAIhV`2HNgp;Yy!bc}&PLjQ`v2=geqr$xo$O%lVG=OC4 z9*#&S_D0E%$w(J3BCp*-+6GRa^#~ayPv_H5EvdR zyQIRlNNsPxk|zbi&SjI^B_=lmx2?sVNw!tPQ!ek7dkAE^NEA$RP^W`yM4aRt@O&-* z_AIt|1jKp7vvc^UV>AKGB&rM|gW;QymT1Y1`8r=Mr<58V-DI)nW(YP>su`qIU^kkq zgl2jGIlWYlZtV@OhiQGdghbXmwN>F)fp4e;Ny{UH#e@R-mjUDO&h-+Y9~6uvs#&B( z?`3=J_vI3jW|D^0D*%0yRQJB%2zY|z_y%Q3p;oCQpz<(1?q;^*=D;E}11ha$k;a4I zblBGvChv2k$h=?P3k-#20|OdX!G|L)OKF`{UyB}*M?VvI!II6D&fG79h5P%9PYPDL_;8z~g zzt`ku0IU-TTTsEAuSlR7+J=qkbwC9eNlB1gH>xh~IRA+w*0~v|h5@)N$WFvi|6!;S z4}~xh&9)XQ3s=sY6PFIn zQMDH=2gT7#kOQwPQ$5|SV&H|j4aPaK>aNi4soQZCEKEs z6?l-QbsRKKfFw=N^yLxxEUbhPPSo{q!Vhr3-T!EN8vw;{8ptrANfxmt6<@E+Ttvtz7si%)qs(zWfYFz<-LD2X$}iYCbZR-e=&;fPpAAzICFJ> zGeOQZZx&1jzYWwCzHg4?$Y#D4u!05^8g%Duz`NPbN1l}mBIN!UD?OL)e5716Wo=-J>*C6K*ky+|jrVHPtOOTDJzw%WY;NjO zNuyVU!vwEDZP4HKi}F4GCN7#f%n6^6LqQ~N>6 zzz`n^$OgsP`@Q{O>YEfDmlWiiRA~}8ETg?(GtgD0#{@6T2T4Q|kR6CNENE|?G@jc^e`-2*ndLzuSBD52DI1E-*Z z{H?E@4_}+Yr45qRfb&f_EaUG1lp&FG{N=l^R{;E+$6v__A}LI5O83fbYJGsL0AX){ zZ}6?c!6*B!gSab0-N2-uP$f>U?yhVxLyt{s)%Z{qeyh{=>~usB+u9ez~}>F4P^e5>NcQ_ffX z4(9iuW-S4nkYNmtr$FWR$zmAh-%2bL>8H(YCb`88Mb3>B&0Hiy=KR`%>l>aqhkm1S zBV03$$QD(O9a?2%xHSy7Iw5x)W-0hj@viYeDDwy28muC~E+Gls-aB0Qp+v@WKs^Xo zIuB<9yeG^u?-G}85_!DO>ew#lp#iFx&c*ww@+7F<4&-wWz2)*uvoaFi zMNASn&alvRkOMavKXN!>2@X0i((waU*cveqsNO&zEX-#&b<@^YlNUg)Z4#Dp0ThD^ z=ymVwVRqvD+iJ2vH5c&vh?ZUs$K3&Evo@Q>3bg8U)0pS-Zx60;J>I1V;y9C_m6Nfo zt)3yOrHV6A1ZHMRQ;leEyfIvAhntG}YqP{UPMD})Z>Fkp6QpV5)wpD8Z}T$BUQhB7 zWvGsBk)#~K=LsDdyvNTVB8j9DLHCQKm0~6^3?!$iAUGXdD`ipv z=?{u(5o28{Ws(uf=XP6hX3kS*9{7gHvqRcfowz1v-9eSToM4$CPDyvl3dhcD)6Q&* zb{>=ZwGBEUW8!yUI^AC2+xa~bxEqy{WDrWUQGkA+LbI{WM=XId{!FTYP!&^ry+(Jj z?;A<94&=Fab7?>bD3eN36WD-p_S$MhwL0%F0lS#AR-me0+r5KAX@?Yx8{s+9Om?4-(r21&HQS>pY}1W2 zD99qq3y7PiBc!#z&*PUvOQW7U`z{(Db##>(zxo4wqa8zx$GzsMe ztjc}zK=Tc)$kHe!s|%h{NuC*wFd!mYDY(VO%h`nxmcnwn7!Yl(_>~oA_K6c1R0*dmVuPm|2p=*5lE7w8YKT;5C7?^9kn#8Fz2+&TvyGfVXioMojDBq(oFHVbJmp~k~t0(5sn z1U^e8PIt}t`REz}oS0t?Av4=ERCG+LT&<50zysg1)7hkzAQ?*3#X3~^CK-?WNNXBW zPzHE5T1$aB9YJEM{fdW|LyPY?)=>FvClK_GZ?+3(0(!&s>@rv7;z;uhv{IdMa}gEyn`^jQ$-NLJK94w zpm}!t>0yz;J_e+UXyZ<$Yj9o`xEpD=5yW}MNXb>2u{QCoD64n4l#UkJCIb=O>(PhG zamXhdRq^voa`~7Q7V3GoZTCmrINcSfY=<_Cle7SY0~9Kmms7l%wY!r@we=8Pb|hsQ zmSBlA??&I8f*Z803hn`}nch;-Ugh;vKNvtJrW35}!T)c{0KL?=BNpgdVL0tY&I-mzAJyYqpxsfEYJOSAv6A?Ce2U^E%w&R(I_*Pq7I}G@k>;;SKe00^m!z|ZQ zLnW7I3Ho?U^ktsMV;^sP`EbX=Z8f_gBT%+C=@oxZ^zs1YPxl%E4pLn!{eh`SQ$Z~Q zatQ!x__X&0ul0!f(MV7Z2y4eno~jRg-P1O9&QJ!~+Q!c-RvceuNrDXuR$^CL6vT6v z)8yC{IH&>2qQW^;$9@o2&>09GYfXBg;ur4u;F_f!Nuh+zdv?IKPKT(FQiSRWV#BT* zY8R>y>iA3zK}{+Bs)Ephju+7`$y!$e1#V2F!|i%>r6As6En>>I1;r1o`|yu)I$P4~ z^$PkPWlf?GCWZ_JhT{qCO>G=?(8rQ)*s?3Kx8ZUjkW`*hIX>XBYtX${V+RPUhD52!Nsk;Q>~yLI(jJTpP(}>; zY>k-H3hEu$30U9;+bJ!Hsm#A!k3M$4)3;y+)PU2*oIV;(Zx;{vNmW?27Qxx4fCLmk zgsj^z@m~pK@H6ydFmvxB?7$G)vV(<~+k-VC$02ye_(BE>L;Qh8~F9z zUdjHB8L6GPs?3!*@&7Jxr-Y=Y&4ylUIU%AQg~W z@Jei_D`HCF6m-o6i6w0wY|kJ!Ca1fH)#t;VL`wt5>6NIdp+RQ~M2`&OM32=Tm>aU; zwD`#W7Rv9FTOG7Wnc~btI_XF3o`AAiT~?4D$t(NuL|Ep#`zFE5)&ntdyB>Wg7J6kR zKZ=Dun91w)-Cc1$nWxCfixLSet$N~-8|}s3?c5Eo2mA0P=StE&$ldh$bHc z&^}S1;8>SnBk!7GMG<{X=Hl3+LiKb=ryy3r?&R+P*$jSm9<_S*hQ^~$I4$MRQ*)d_ zz;>(%He*KUo9}-&(4kURS17J|6PYw92ecd5TA0zBC^xyHbG#CXq2_5 zw^ax2lsN_Z(jruggxmnxZS^QIq1u!~e=)oDdBtMuB5*~tYNL{5_EY!K*tqZ`R3kR9 zkSXokBQ0WbU{Wkdlt#j35*Cdha*NH6+v8iQ25`+9-4MWeYEq~+vSCl$a))4T3!91_ zIuI&`7f{0hH{}&gwI=%=AVs-dkDv=NqY1G-cz`BxX&B?4^7@$9+Rx+aawz{Tj+#M# zA4yLT$!Q#{zhC@yzewDF+L+wj3!p&-6P2hOQz!uWq4UavR$3%i(%Yi33a-V%P&*x3ua^Kxqpuw%QFq6MC)K4n?qy=oFlD-q3a%p;)|>c( zK7^2DZE2Hp|GHxdE|Lsd0JW9^8kG z+Md2jP#;$;TkBoWijv0WZY_0LTA5x6k zL3~6y*E4du;+S^{l1aOX+uj}7g6a3%a)%t$kCx%hYod>g~Igc{?%5+r~zn^DAJa6o>VrlOn1zv^U+75 zQuxy$$SAZ#*Nwoa9g1rN#v!#_!MJW-GNbj<~9#M2W<)~wIw1ldJ zYyxGl3!c1L;=|M!J06V7$m4ZYu%~#g{4Xkq5sCl_4Pog>N)0oY4lKd=y*#D|p|XW( zLh1q%+gwS=%0m!BeS)iJkzQ(c6LC!-CK+CJUgY^i$w`8)Zhs6ZmY0@el;mo;8 zD>aOhXrOcSDB!n`O=s4i%?mB)&a&*%@Hw>c0*t5IMNrZ~Ob+E3#Jhev_rBeGMQIR!LdE_h4%kAQ2-1R zTyJ~CYshyZ-bReK1B*l`-S(+d8s9-?g(h{Z;tONH7X_478u#kqwnvQdC^MB)N zNfF(S-btt&nzd}(QwmQy56Z6Hm(YD&vHiFdHXAn8N{1l@&-!L$1X38|kB;z>evl+A z=9>??aQyhq)gF6n=~D3&(`sYa+%OjkWm{6X#`>_d@_)EnWOSmpW(5+7fliyTKY|>l z6~b=+GJ!;s=KwSTT&i&^Z$vTuuw5r}V8HP{41Xe27u|*v4>;*X5)Hd0Mrg%K$?V&S zJ~~|iWOc|P`rMFuog`#RP=s>_fKaKaDWPs@>n>xEcrEzMfkjzB9=gH-G6NFMbJSbi z-6>mIbURZ2!=+k0Pe@>heB6kGwqrMUG^sRw2Bde9!0s2ayVpwOjGu=Szb(^>VFC!E z%$Rm2dp~g!m-I*gNLHW5<8eYmLS-|)q?fn^0W_fmA!V4e5svPy4k`AYay^fuIPG#0M}MCE#P&HGH~PDx5& z5k7X~p>8#0XS4U{UF8Zmt%HU&w{{UdymtdJUmK}b(kwa{6 zi(2mekQIwLAQ?upsz#k?uo44~aeJ5~B~_`wFel9_n}TZ1JT%;qHWpU=2+NT=K=Lbf z30rs>VC?L{Ft@qcaL1VX_+EQ8h*YC3Z`Y%1(beu2$J5d5C-?ND>WXaAr8;AkT{Y80 zH}_+UcD2QQ|0>qPoBCJ^Lx@4Z4Q%cTs_AeClk-(ftXZ}oz@i_0N5O6H_wP|_Wlz3C zDPa-Y;J3w9O8XtjZPw58Rtn{+CsfjWF?WSJMuanmcvV$e_Pb zpU@*3h(9RDz<#R{`;4Blk{Phy?F=`0fGvX@YvVnxVmxsb_@$&Ff>>%$xltj|f8_M? zH2whws9T7aZ|?a=3;WUEH2?fK zvw3&J)NN(ypwH9NCJ#-N1OOE{s`1|LeK4v1S0B6omHODhyt!I?AZ_W?8QpwoQb*-Y zespjq%}$$!@sP^oGC@s1q7+r%#CY|UfkT&HHXkK(T7``s$yD_P2%FAk)g73FW6W^N zfp2P&EWKK$8i?0zlo9Zo7;A084?d^60*7Mh`L6z@=U z_GxGJf*r<|x?PVxboPb{Xv2IR3*a9i;z#~=;HI55ah!P|0aNtnsIFPtB5_N|kT6u+ z)61P{8%HJ;*6Ro}Edf8F|2PcppPbzW6Z($j`+bmFzGok{B3>Xkg=%=iip=8}n%gVw-n{83MV%wYW zdO!ssEt0gDi(+W7NMXSXfbcZZV>dD6EpThV;MzNvUmF)$Zm2A@1}RA6}zJHl2De_5NLL3 z!!cGQ%Wuyx0pm1otAj5U^w0qjAD~i(%0pV~Mh$EN`gVU%choPk3O1OuRNoNsrA7zI^s~)Ll_M3^s(az^>l>lo7i%pXXkL|l4PBV zecWt0hYf8ums(w`v=hm5?BVU4KD=Go8`B+tEPUyj4b@xdFbm&}^vQI$dp6AV3OBcg z1Q!;I?CE({GY(fNJJv5jqS-JF3}nMOh*q5GrrBhM!XRoNG(lb69JCgotfx?&mm$l>0#QEaTayiKYa4_2)L42_S%4;0KH>n zJ5Gq2$IQaGa#pm*Sny3sb$h7jlx(aFhfu^zYt8um_{2plCwXy1D|*LMQmk(bcGe5+73tnun)t{jjNI z>|b*72PW>+jR%Vd(t5l&!@7XEBGF#sQg8MWAPku~N`_2oW>T7!me-J>LRvs3WMN7q zR{D>>b&BUW1u6>|(K-TsQ9;}W80Cmmx)!3$V6om#SFHY_nh-vKoSu}bjA~x3Ze~nL zK$rzEufbj64i7d82cQRtzyp&7ohM`j`=aL4q&C~@3=uW#Fl9$vXwpW)`Rd?!q-kX_ zo;4Ug-K?f8)(vc`LQ={G3N^1_EXZ*I*g79&_u&F)0!VI>#vaYG?#NpZ#PjkKi=a%kiGvtdV8J|_p`MxdB zJp3e*_?4na&#_!(MjCbof7b$g%x-G?vhLEDNwUs4xt}T&y(zjBqP)BWWOUs0H9(d- zh%T>Y9AUa9xjx&FGH@BJ&^+uiFcPiO&`k#CGhJXB9ZIKJ>5jgi4rRYGp}?OEOoa@b zwt4+zD9tCD4g2dNDt+cXAxVZk#pt`Xdf09zH`seXW-~Yx;@k$G78m!@oBAy>Vm=%e zNH@ro1gZ}^6vmw~AB9>3^l6EcBlDBaS5VJEMGU&S+n}vbM7VU84tu*Bzjn9OGyOR@ z-hh}_7?B+-q#T(x)MQ)J7yK{GVsb|9c0IaUF7QX})3~keS$=bD0ojD5d>JJQ2Lq5d zIN28D4tiW~+_As0O4HF*uEii#Xk2IjMtaKJpkRODx$^o(sDcoBkLuqq2~vuG_7RmD z$<>CnJt0Ui=t${EcCK{zaYa8erd0IMl9BpN$(=h?=WwFa4A7~XQeESTn;K1Nh|eW> zq^sLV(v=gCQ~59%01gdZDbU?qkKh!e$5)DWirBey5=?17>7Ym3PKUYABf8Ga>|F-6 zNe>l}!L9-dc~i>>JdIWk_L^~g(XoSeGC`qI-2I!P3PQDWx<&0sZ$mfxxmJ~-1~1{M zFk5|dBl$*V`uuzAQt}T8qi5`f4 z8aZw*r|Z3DeFnW3_YQfJ9yuBnyA>cjRfd*=EuRJMXg-CX7NfTKs7pu1e%V1v_f^jn;8j z2}zo*1q_-yAi>t$wg*u}uhgx&#jG(rfO=;=uxhX7_h6hHQds0#!o<TogEHeflp7G1hSXL3cJHDm4-?4#!X%s93UIznfQg$J%Gn?7;kVUfJOp^Z98}; zJ^*Wt1R52#9|L5em6sUDg4zAYdN$A-k#g|+77y0{z*49anSV) z^yNGqz_AVHdxNsbMGkt4=65dXM?kh6n-xOL>-^Rq)mvKmcheruplv0(PF295%JKH; zdRo3^&!m{sk5sko&kPtbb)qTWpLXW;5aa0vkn1Ark;4smUXfP<`xPj+z%%7uo+HXm z>9SBEqDre7C%{Gue&|C%wI*#xfWh7j!URMO?lLMEDZR8_j$&ihtINfL(bg4xq}ZeC zjq(R}vRy_w9O$KLpvxZ^llzg}!wE4Wfbi)By9~Y4hY`px=Q)!Kg;sqLS7-N+jQnGu z#Tab*NUs1pq@=KWz6IFtM^=oSN2j2U3wN!IQGNYK!-I~}3%Ki8~>#iPlXEL$pdJc{6cB-?UyoUl-XZHS?13pd6(Wm07@ zJ`I^nCP%In6$;f|Vkro&E*o|02I$Oa&SL&61a=`*^dNtb%B5`qV=E~oyd*&wE*u$6kW>aRD$AT_$s|AP#6D<~r9A%1LC=$c;i+*4Pj$3`( zu|BPi^$q4NwhRzSuarXJeL1+vU`0EAVgqhaS0y@nMEPTOt~1$rsduuPMrs*XB)bT9 z1{zJ%`f6Xkj-S|=O-yT2JAt*;o}k0DYzkZb0IRZDChHYzTOlpUl`7+~3!jI{%+C^#>qcMmD9b2&kKYEWz3EW_&HV%Oi@0)uMY9wkT z&C;81lBy=sCqbOdf(mN{4)$E`b24Xesf*MoM`!s0+2UmmXyZ3sN}r<@=JRj|+CWkV z@e=mNNYNZ?d0*ZeK+%t-p6Z2Nb)EVV@igwH^sfuK097gctwfU_u_gOzX6mQF zY_|Qap2(})HazT_d#Ft8_rmtG!)@;%u?MkX%AN{0)niZQg@Zg7&=3nseO4S^jfO&r~%4i_xK_KJ>iy!1@q@nA9yLRgu&Dkp0Au9r|F0s)rz-t;r;VW%+g3pK3O>Hq!=&8}~fRCX5 z?5hE1Ag}X-qHFw z^bO<9d3> zqY#lTfoU9QP1%&4+PoROjQC3hW`q>5Mc?rx_iM*B4+}5W*zJ0xMNP{cu!o7f70t9s zXl(5nGd$*22GMz37Y@jYSDBa8_)K?NW9^f|P33i<JK_&9=?xR|};-?YSaics)Cb zL_$lF$GZx&{A~Za17sl)7xkkw!v^tHA9HK;8HxIc1wXG?wNHaS%oH=V>?Ymq__;~7 z;;B*sOD>v-oEK3FT&`|XVm2EpnzFHPFx&;pO0x?|0Ymtk;BM(P+k>DINF-`?1aN?W zqT9?lHwEDqM5igb?!dPMIwa>7ZMqwPPhx|&k4G?jLg)k#wUYq{Ax?ejV{YBF*bOeG zt0#sDI;fF3PJ&J%rl~a}k%kokIvT^w9sVe>qTx<->-fO&I-Me=zSzMe{R8e`t&>xA zMBWLCV(V7>r)Z0TdB1v{P@w>JuDgDY28GlF<@X}^(K8l8Sa3>`{jJ4LD%Zvtbh@x_`M#aIGI_Ttc1yp&^57)>(Mo6jPl4)*;$_W zgNN!I=a|%r89$Mp-ajz&&|T_afufeU6Xbcl&zrpC4We^O-C2&*8xV_+C_5uuO?O>f zL27S@sMC9!KRpo$U@9izhiK_j3>aD9_2Bb>eMg3Tgu1uu6(pqTik|Jang@Lc)C(JN znzo;CYaMHG4%E|&n+$5iR6VkSSl%N#n!xn4ktfI@yeFP4$drgp8C=*2WX1FXW2U#a zmj-%vmtnQon^5NnF};;kG)+=;@ARU$9xu4jN3>5sizmEabwmVy->-9>F zuInCWz>u&^^6user0;i^Yrn>D?spUH*8>u6_8Y2s3BY}-uu@QUL-nT{d^7RAm$wMN zyqZXYnIVx?_uUVYA*eUwm$h+(H$Hd|AVH`HlxX(sQb~}2g>BQVB2QRVx!52W+zGdu zozDd>brmu4Do0h~zq)yYn}!Coem76VwzAk-VJ$k3^}I3~u`~&?kX=gAtJuBS^rmdP z7$<1Z65j2*u17FxQicJ!5#<6z!?u0FvqL05x|h^^-mmNR{tO{ov7$R58ISfq6@;aG zAZ@Rwl+hsBM$>vG4FUrIQ}C*O3Jx70QU((hppNi}rT&)QKR?<;!i!H1utn=E>B$PV z+0T9K*Fo=A`qD&n@p;86f+~&!8oVd%3;X+lmZ7a@f(a6k{SA!2yTWZGp`@S*rGtal zLZk{2xaj~BYU9EzyLlC>p_TV(w%Mq218&DFQN{d++dgQJF zCU+!Hn%?KhwCVjmT^B@z?}iUcSYmFaz1_9>2!vTrV_*b#zqT__cz)f!boWtbE+MZ` zkBX^HRYL?|pz1Hj6}JgCBtwCLaFuUp_Ivq5G#VfC4jDL|L8B=Vh6FVc?)Ob9jSS8n zd=oY;WJztmEBo7qFoDYER8A)GSv~bTLAh+$cbxi1# z#XtdJ5RHI3psXSyfYYsvn~5EF*dz>V?EnZ;=+oQ{!R3>bjWS4N9kEU-Wi8x>hxI zK)^P7PIOD@s7{@3Na9T#zn*lZ*og>I37qIll$JqYK^?&ZsPcA~;@8CpDuYmhV>l=D z+~BUVD)MZKsbnVOOl7uR&|!votar!Ch?=#)k!DW`v(YA4h8|+tdCTr= zo2b5bq(nM}3W-#sPaI4h^kCV7OJB288Q&S~ONDWy63OGHt*qH}%~?Ah&Lx(#U7}J? z<4j}P-8Jc_qif0XexFe@47+a3#k@ajmI<4k@qqChU@1H z<_@A;B0<$anpBTFgMc8^sCg&A4b@<6irx`9c$kRY$Rz>=(LTOa_E~a9j;?pOEa#(o zrzIT7JLK@VyDt;2%{+04AK4L{)YHhW*R_0)KM^>A&%IM^x(BidX$RxtYbJjjfNX9> z+0hZ2B4s9hvTqTD&=*kW4FV`097Z&_Pv6GH9*qRG{=oz0)}k{g6onAX_DA;)8UuEC ztV@a3CzmTWJE(nkCMC&u=ufj7p6*N;vR2C0R~Nh;Ky9&(bZ;Sw9<*QKx+nJ_gAFEy z=-)t&HSKl+9{0u=1n(BPP;&br$97XRLgw9_(PHX*!GCUk>wygotoLxbED?cf@1 z5@bC_6;<>VL{q-yOw5l?f}pB3_F6eXTqkF5lKFp7CPH@zs&WfJdA?p@xE1+K_}gWU zXKfBrD4J#3%f>}O8Y$#pNqdU1V46vMFdEIdPQqgg#zVO#4xo4oV<}X_p@U!_AVVkuF!t&+g2y(*tYHD_MOGd+|A79T~wVq zb!t(o=lt}1KZOFdeYv5#MMKuK1*0eCdllr?zuGR6$xooLJXOxV6AaUQasS~sl>S1w z`)8KC4ZQW7;H?BOc6p)yX*cU{v^I?sKajw0&a0oV%J`V=C@T$c2`6 zb%90eL&(Ay#vq!*Fi|3+`_%U+4R;@efu&f}1s@u+iC76~@+pY}MEi5gD=Iv}-xSdT zVKrvm5|?!z6f2P%XN}~8SbskZVVjPgz{*U5SlLk>ma92PXD8kppi|imSzeZij?s}@ zbp78$)%vZ5fg{?-72seTSL9S>2k1=}l>QiKjD|G27$y}}t3Ys2#B>)f;AI%~-4_Jf1A7he5cOU_hpFbKs{oGTmM`?3NNUgErHH)FuKYunHXQ2tz8b=;rg--sFp}Duckm=Aok8PU9zow$O{y! znwwQ|usW{@Jf#hkutNwWZtPU^H2lzUa63TRsJ8=AV_xJf&*Uz z=uH`)+>cz}@G2`2*~(FzkbJNT8_A;k6JMY>Dtv;a1eyQh`_D zJxR-doi{Ki^$3OUEutx|l$d5welI?rWR~(n30BYFpxUPE;ivK>l8RIk?Ku2BKR**S zLcv1SF`v*K^*%)*kw-!kcIzhY`XhM-=(FVl*lxfa_{n~p8y=~t0Tiz{{MH69!1kX` z&l9CBCSeo;?ZRR+M1BiwyP|}zK&3xpJn^GyyM8{?pBj-pQ4g8Sd(R|+g4?F+b4K3L z5!Nz-IQEBdXq@#ef~}@>^8af}6`o0715xqn9b|_>qrLWaYa#bNe1`NLr(7AByUutN zLURHdr6*h3@BUE1hyWs4|7A%pRP2d}g_J)9yyJ%$JkFN?9m0UCa^x7j-yg1K*$N?`$ zA#Q1K|BfE!Wd{fTqX33`fjulFgppQZc2A+KGH*h1tuIAfom|`f3$F%Uh`@gxQ z1s&3%87>xh$cS?gA!{`=*Q|Z__TK|a-Uwk_2^q`Ho6-D*$P+9`O__jA<>m|YtcBI2 zl8pNpmwi?-J^bMkbX<%GHHxjpu7?&_oz2FolX9wt&lIVw{1{M1Lq@$z+kL7eb;^vh?R zTa73hqw@VouHAF}$pXE9h3-pV#7!S<6$&esw}!uA6C6F;+a{wmc-3t^8J6bkuZK<| z;|_r6fsZF4>B?j2LCGm5eoa;u4Zv)B6W~=QJSsx^UNEUA$Yn0HZ!Tk(n6z*^&Cl6V zqzDspV?a2c0WTKL7qRN{Iow4{Nn>%0$N|!}l-=w@Ayc9l&W9~bK?IxJFuxAl-6i7z z!8Bam5Ubr=tfftq=ajd`t*%gVLc^T~stvd9ja$F`w)jX7PV%Dn;QG4%d4HU4G zx@p=wDv@tf50FThI2z{mbYIacyq=xPG}#+o3%9-v1(pLenA=GIMc3I5=B4EK=>kWi zl5bA*;IXYR^$|#6pR(zR&^eoJpW28+CB`4q+!5iqreR6=g>A@|0Zu1Dc1GL>=5U{K zd0o4U6@pcwob+ygBM;wD$XKcI_}nck&c&a}PPU%E%|f0Mq168pN)kYaMq86pgtzOO z>VPa4%l!$S>>xN17VZaxvZB~ol3v&?&!7(kil!es42U{Aqz)9=gF0vTrwwD{WlN(4 zNS?7=Ur6v`ZBdXxGQ5PQz!vhF@b4^RoJdlttp;t*v_Qb#As|iiGa~|+0PAr4Mykmd z=zOUC(ou)c8wmP0uN}xW_dZ?7MS%Dq;j#rn7cwyox1t3T0}e8bwKtzlxNXfxX|mRp zTUHQjE?`XYBv43C%W~0IuGf)2_Tc1_o|G-0kwQaHmPOTZ440)trN_1^#cbfL2ChCEDjKHzRtKI1y;h6-0HI-}CXaMO z_ngN7(feUsUN+g*?AzRLyg9D_j9<8dkjL3nFhDyiJL?S+f=I7DqEZg?H zD}Xh(JtT=4vt(3QDa7%`)!dn17+4!ixD%Zv;T7==GUCE}QY6~~VV7gnyNpGcJ$>Q? z52oGq*wt$fTPX>((yT3<4nc@>eVo;KkpGK_MUW)k&|~dM-u@ADJl^G0nPqTp3yoT$KOahuxpb2}%e2W`4#7wvxz!p}h)VWUO*AHS?fBE`AHs z33GL|t`*K(6@$Fs*QGf{C*bD~o*3MFeY^bRPIKwfUjum&cWgiAJ>M;a4H07%UtK)k zpWOQjHebiL!6GFgzHg!CSPt6&9mgKvbV%yz@`+3NRZ!X0`D*p_A~tEGHKYetTEgi1 z+X%rL5C$=#PP`t!iRLwtcRZ@jM!9o9GLs)^(jw#RGHA#LBQFn37%a||gG26>yoHcv z;`fGvaNH}knb5aPe&}nd#skmgMaKfyXQDpCDs=1#qKi<p)_6 zwbF-zlXg*pgYl&bTtW!nEba^*1RxQ9k)OrFsu!d0v1CrZ)D6MFm93}`Z>7-kH+S15 z)I5?OcYWRp>R3vQ6jWQ0=>(kWBxz$pHJ0h_c~2%u$UbcfFUeQK!t^rC8eP_v0ih~2 zjk=$s^VK@zf{-ntdFZ_H8v!%$E)NP)l~0IM#BuQ7K+TE!XLA#wEXN;Et4Di6s2{|0 z`Z#29mZ~7w`p4Y*B=1yEH%Cw;%4BJ?*JFaK3yc#!TXk4?;)%2W<u%}R5ZINv?U28ZA@18qX3n_Fz- zuOY2;-HhX0pf`>qaNpemF-zv@*rmFgua0L1kvP{zfia^-sG-|*-$t=<6j&y)Hn zen!Rmmc%U((y&bvRMzE_zjrEKZ|o_4BZofxlN)_q5j4zfi_a?XPNVP|V}wey+XLdR zEs?x6kdK5Q;-LKJE4rpZ!6VZ5bx3w2T;9Ziw1Dbl1S%6&KW6T^FtKmfH7Bajq&vkT3nrJ3=`8weKCR)qRA0Y{6!5Ey_iKq*f9TzoY8pAVwsd^mFd6pkv(ROwpakIMfSj7oA92f_$k}O$b z|Kje6JRH*;8#67jpVfc_DDhYcBQM>ZIpH4~&OnWxOu-85kji5G{N<1_pBI@{)85J; zw-)kf&EUsBUMP1`1pH{0&sB?QJ* zXeMEx2{BlZvLJi3G!_h1uxT=cLqxfs@-<@fm8-ie-*5^s1i9TlP3Gj&9H>t;TMr;m z=Y8nTj!h#Mk#7>mqs`P9-0l(Gk^ifgXI8u(-~$pSZEbX-GXzmsde*V1_FQkAFBw=5 zQa5f03yIW126WU}bARqCY66|zEwVGZ(x8E{5)6_u8XfD#jfw=Vk)EXSi#)|**|zAN zxR9|Q0gy1uXJD;KRU^T(M#oT#8?JF9!MzQHp66hEc&yM3#={VdoAfPS#%&&Ix9q-< zqO`iVtckP=Y|4aqHxpRnsQj7*to%UZ?S{(9cUf8A(|!_-Q1M*Jx z^fk<#or|Z(0Ct(O{NtBg-e5cZa}1+0SA)PfMsifTc0rH=2>RfBz=Np;n1cZKD^SF8h%S#r zwt`fisZ{DAc1Ir`crjMZF^HMb+0><4iYdc!b6-JkK|$!n-~4@tNqUq=q8g5eAXN@r zT{19q!$sgK9DH!9*iCu1D#n?cl?7CBX(GeU&QLE8S{Rt}MA*Ry4ZMp;wGEHdU*Iu& zM}KJwc3>(V8R)l8_j-TjYzb$pRsOMcpf_T-bcoVAmz%0+z)l7Z*V&|ZOHMa78fKLb!cgK}J-}li3j|#(VLFn`E_n>FRCvd+suQ6? zLm(`;d6?A}Y`Hx8%Wu(TP*m__Y(*a|`O*z*I zVDI*W@+}Yy=)DZ1C+FPEm&v*gnn!tSBnet5Hn{ZZQsxMIjw{vJsS_BO+0C5lN|Ty= zoB{`=9}_43VWdwGdl5c;-ZTGpOmJBo)F$Ki$1l@|Em4H%oIk)3qgfsC*hHvf@|~FB zHL}FWrF;~1`9(V)z}>n$rK+fjPXcI{VKKLbia#{vRQ}8faAzT5!R3TNh}j0Ix}2O~ zS$9_Dd31Z@ZSAqE}3-TUjc0J^1@3*2`E z6N|NTM&emWp_Cu2Iyu)jFbkb=Gk7$>AW&qeFgbZKJ0iUhgP!`L$cyKzLOrS^4#TI% zrXv>h+$06F@JT%W>g(M7@j0frBS^ovD~ya7!c4t1`v?? ze@sU+HFGv`vU2!uGnv7~!{z^}6aU%*Y;d6UzNj7o_W~Dw*L2Zq1sGT0vI%=rGjZfV zC5XipcX}ua$~4kU6q|BdF~P0I)HuK9_}|}3=}4%Pr^~IR>~GjrHU^Pn>Ys8&U(Krw zz%V>}%|~vEYoQ~`75iin7*?p+QYJ%nnjRzbX4eH0`|rP`DdL%?Eke$Ma4KCL26 zU^{Kyn<T4xEva2eR!olsB6Oleg z!eCqfc{omuWT!>4qX7kjd_7$yNUuf1JZT9{IM z3!`L8aco%%GXcz|SUJXYywTrEekLM~4I!axSS`2k8ycY};~YH?3tlzYc7y<|wjjZ| zaJt#WZxf9{)4!OoNy@N~*WZ)Ij<)^QPa$Z&4N~)Is4DB?(doy3rp}1um`PcB%C4VB zKxbWX5E12{J5PA5ww#lyKnu{HX4RQB)@gBQT1?KfA7|a@BqBXg5;EXVdz9Q-b2trYNzfOj&qTn!o>c ze7<5TwW>^;>1)zA@5`!&&DS-Ju4BP+aMWW+*Wh$=q~S9Jz(OZSEqyhduRUt|J_%J1 zZ!ldE(x@_(!qeMLk#hMtGo9F9bOHPA z-})?%`!DNq-PT(`&G1}1@z zIW!6*dl1q>pSdYqH|T;pRIjtgpOv)8A5fQP>79y++ghSt+gXQv z>$XLl&<{v3gm55kj}e&*3JL@>FkFZijR*=^49fW55po2PpgHkV;zwj_f6)-+L99KL zgy0AwCuGkEA2DDOe1#AyiDt1&B8mvTVFF|Pgy%#{&PdI%^5947Tm;n*ndT-jZ`tOq zVfU~|T-Q~6R2_z>P8h3!a@)QIC0`;}Qfc@7dUy%0mYuT#rTgy6=&Wz8`FB2g43!l% z&E&E*iK=q6A8~<`CkQRHM{?T6zLv$*tUACwACV_`fB1 zrTcltkR!ke2J zxwtx+8JO5RyZrw>$}K&uq{C51za4`C%)S^NC9Rh%DguPCijpARiMS|Y_EJi*YZ~*k z#-|&-6i6j4Dz>>X6&Tv#t22mB+%IC;@N z3xE5#UcRrZv!RFZ6L)(-&#$o^|GO*AycS7=pZkXwMaG@a70plV8%avvuj?_r&l^C{&*S}0kNfSe%(=|} z=Z${f$J^W0f#OEl-#gAJZ;3L$@7KM%uLVe-u`s%;ocpDXKEa-Df1X=u$XT(wpW|>s z7!(~^aK*QwsjeHsKL78tw;@5lA3oijss)0dmpLoNxNw2%F3vpr&DERlyN8z>KOB7? z!u57zZk}Ex?oLNvACH5^!&64VxY+M8Mn=Nz9{=sXyiWgKo(vsaVMPJSP2^M8B0JE}AI_ZuBUb%c38 z-;Z5m{TaXDz{Dd9D`8_rWnB%he zi8{T?BUB&QACKwIx=(<4b`P{kMEIH$zy;n!lvtX=S>}o)XKIo+K zLePdR$<76XG8xFhW~jH3+@P%HZbN%L5T>Lb|CR}F$C#4xJFzA9V(HU&O>df0P^tFp#k94 zxaA?MbibKQvd}Z>g*=H8Hc&Qtll(Ks>>UG>2|aWdft@Cy%qF48)71;92 zIQrG**&}BeW%Y~Be+_7e$3sL3Rp_iRV9?LO68wlnvD;Nfv>2NKg9;}6%NDJt5Z{I2 z2^>a44yKOa%hYHXtfn#u_M(Y7bGa0;(>;V(3lQc#cTR3LRT!M zPAGx2p-E);g*JCTonSip&S4^lfqR2q{M%~2@L$-MYK=Rsi=xZvJo?Vmd#D3LH9$4+ z4<5T6^l9&eBshju_v_Q_1ca-?bH#2Gk(V-og5@p<=cR;2_9R5>!gq2@@omGhTN- z{@vc-MmTen;MD$(%ToWmo%Dgakg_hQS$<4w62=B3B^F_5c@!bj73%s>N&uR;M08yV z8ufTMx{ze5zLiSEma#p1MGs>xX6<{>iD#ONraP&tTyevUv{uWD?V zKY)QnQQq)VDIZG%(x(FE?x@!Wr-^Ga=O|PY(w00>-Rs5$OwE-=@!W>AIU(675!Mn| z;%zHjZmrQRMP)^mt}VC-!Ocmc_DiywP|f9O@z7F!>3%Avja53}cxSFW_!)2*)7QpV z%e7;X;|R#rDsqTE=a|Fp9~*pyJ5Zxg?MoeoWy2f-2=F4Xe}Z^ z$txD(9uWc06@17GA>G+bdhKWtrlzB#4eg*2 ze|;0K<))wc?M=Z?e2g z(Lw}}2gs%UjG z^{h6laKt&2YBZKSC%PZn@=#q;b@QM?X&Z3^IzINjlvgEuog`*!L*?p{6L1npkyjmaw^;PYgv~BAO{)?|t!1+=3@J34jpSYGdSeiJ5EHFbE}&rG zGmc#n28|S5R{5^hscCU-nsty&uPx?$Un7TGH$!AfN?_Xi?0OJ!I$`9`6vI=dU0&F_cfswk^3f)mht zP%2wOf_w*BRkC_>NUj#MsBikVIShkkvsfYs@{UWzH7TeK(SpJ8UWu3|w)A(7i4m)5 zc~mzF4G%~KZ(#xR3kobc1gcm%5xZv`VY;%f(|R! zjF9poG>9zOw>F-drKTYd?b={M+Nm;~9Iag<+rZdTg7%|z`Js)WUgH9itsaGEcl9Fn2|?&{F~yA$aPCLCaENt_uL$jhRe3@3G zFJY^{v%g-iNvc#qo?^Vpmvq!=yurcszRD5UX~l}W*?KCx0bSse_eKdG6Zj}MQQxoA zskG4HUvPD%fCOcQyH!E9-@lWT{I8t1;vjd0tOYp77cnf+cvPRvnl4eW!(^FXzhLUpk9`1bA#-u(Yr7k zG(_o@f!r1c+9NjU@1UNYr8hpfubrt^wHeW<(w{TBv||FTkE0Px6_V604wXy4e=y7z z6F2CEx69hh-Gz)fHP&TFv8`cnGW>*NR3 zO@kW$2Hw+%Y=uEi__(LF64A`qN4G*?p>{OClG{~iXT8fsdY|3LxL&%M8Ret=UaWDf zgdU2(`C^;PL-Uw$gB7UPmjAEnQrXj`qbdpWXQ5l*}GiTpu>^lYtPbz^tx&7N14 z`GOp`Lh@rtD=P>k{0&v@JUF^ILZ&@ni38S)a2566&<8;0J(Tw+ky(`##{41L&mZokGKe8mLlyuk5i zb{z_i(JAe+d_3wB>u{-k$p`h)BXqjLQOPS?m^#6AP!MB_-QOHGFSC;PHhMZmZpySs;D1Dl2fGVH znX3uczCq^h_8UxSTC)3f7cQ3BImS?}B65S{(({Y^y*TWf2PV#P(LNz=ATh#Ktadz~ zE^beS>5+kpYLH`myziekO(`e3lBPxE?0;abNL0lbEV9A+u98md^le$~3Ala=QC*%%f7S9N0$8 zy=`rL23+CFx~vb=+R44hqxk*Eyi!=E2*Hu4oT@)l8Z(f*9Bu_=gU+3#iUyvVRqVtq9Q+-5(p;Vmh?1PyeJ@x8?!W<)%vy?^TB#b0|eS{eaFbRaYzH zilw;fVcG`)fF-s5I>3sT!zmGi*W{BDE!-MTn;vn&s7{xePd%Xm(K%t8ME2NYD2 zG+p~2GY06w#tf{r)n$n~4riidM&eFiTil(3JketN!rh^qQb73H?xPSE8frBJ`LdL5hsPcYCa)}CiPV?!V+LmE8e(;Iqj zonA#|30-`TE`PcC$2=b@YabW7(EmBA217S+2ezz4;Os3R01p^Pbw!z zLVpfk7M=_@iW*tG;rE$8zvAhi@gypj64#Oix`Th4s{TDJmdsb5;M0lKewsv6{@X+& zu3>e#mbppc^rFMDsO&;Nk}tg(BbhTFZ0pO>w{NV)(?QR~K|ryKvKhhGj5LZ~Of+Le zh;5NFw^-dKK6+%z=+8}Mp%#KSPB?MYS1eEI9^cK~iuJRl>iNl}*-E@VTT(o%348(y z=-{}}8xci<8R!A|d6Co3ez4@BF@QM; zj>Yed5UU4fU7#3PmMmtZo1Hc;4H46x&<&vmtdEH^fTQ+nc>%;bEx1!lW<};n%D<39 z``Zb=zeYdMj(ZHWOiFa}e2!iulPZBZanOJrqftOu>?#2I^l7%_Z8gSX@u~H7lkeYV ze5`8$2v&UcdtFa$J%xZG+hGM2aI*>mkhD^P>S67A6?FuBYbH|ZTzr>;UyQ#YGYR-s$0V%PazIs>3?GZ^|xGb(nl zW97Ev*@?GU7AHp!M?S!w+n?5*U|NxSif><~?&yq0 z$qCTB_9{9@I+>JriMx$*?*U=)g>NuJv30$O1zZq+*NZEST=zcnYd;vi2U<|47za}E zYN@>mx|G{On99;C443~U4JgA}R6)xUv^N2-hiW?{enS@53p)p|icFqJL(3Tq+cl3U z=`8Fl~#N~Tjfwt0{w6CdiEG3v;?V2UMw1 zZBRsi*bM74fG3&Gb**%cPq|S`T-wb~Q_z z(X#fW7N&fSqaxOC_ydT{UTEQNu9Nk4&z!TBXO8Nws||=3aT}LKKaOBx(oOBFpL()vg$-(zCb=8Iu--eUEK#?V@1#;=3vF4pFi+b@La$CGEEMD`PYJ^*980 zi9VBOH+h+t6MbP+;)qzr^B0!1tq>$R6h9~fqa5H;`@vf8wkdCqh zlK=$DqU8qjbopi$o!(ugu{>}&ERt3)9L#VKI3mqc32B=r>bhIVC7)7dPU(dOn}i-; zxt|*T{QkGJ>~?$b5l$T0Yf%I=+=gaXNURJsUGFhm%ab#obcDDefe+U_lG&10i2~z~ z7_ZUOZ`fDkuH8h2TRypEp2dEQ0H6BlA|!ft+Mt=sX!V2bAX@h8)94W{DM}Zmp9KgR zg0&(GNn^x6Nz(~($nkFOd=h`h^S_0HJ>?rkl9O|v5>1Fc*fR;u3Kk$3kTaYmLSc1v z($y$I2t(ZLMgWAE&d$(~sXFL%6p9sfx}v+pdA3^X$HbaOtxy`Ss{~H)v5)|Rs;o(N za=c43@MC>X!LGT-({saVy(UZJgZ7 zr&)htTSE8*fB8>ZB^n`@Zs{yLb(NEgrb=8r^LNK`XD!L^w%)|Fum65pd?t9bNd|1n zTss5?wl#VZMj0ZcQsB1RdOotDRA&ppAY>Cdfw7rPz~^7lnpRK0D#&O&t-cKa4R@7? zcu$LNnYBLh0f`UNlN0nrlQ9Y?UxJ^9a>ErLMw7bhDCLZDe~MnjlXFU$wX@y%1jrfRhD$XWC;VYwO85;SLVQ_gL>7KgwZ=B#X4)phu-~`E`as$p;k+kWY z_>jWpWkz4h=h&-688%mBq^BV>@%S?+>W~wwXIP>yTra{8Tdp&7(C*Ul#{p(Z*zuj} zQrfxXQHetBV;nSU>$qAap z*@G%o4)5<8G-JEhHej3Z3#`FHmGj4%AP=T8Lz|)WCs)CneU4(?CJl2ZadbJaz9e%6 z15S5+HP;g#$1|t7?LY!}rw@r{&iIhtg2km zHCi}Lqg!i+5RI~W2CXw+glv1*b|f-h#nFo!GY%Tjd_-SHjN&CL7|}O}0@k+1Sh?1- z8H3}>09dO&75a7K1xz|=-n6X&8x|vy?5u$g6OB|Jmq_9C6geu#|CBYU-e*rNog&Q6 zoD9f*g0)|cO1}JMgGY&W&N0zUvt=23R&KJ^WjNz0$%Q^;#S>=!#UW5HaCEg~mLl*9 z_q6GWgYJ#c}TaZ z5@)dAABp4BN-kaJ8NxBsM0kALu&(zJ{`}GDYBs)|b!T@?!dqaAu6dqxnRcyGJ+Y(HZLUPqwtXf++mmb&w?MzwR`3{JyCy&;qM<`}2h zleI92yUi_gb<8Mg7NbA;o+j*LPRL$K!?JvhThLUG3)M&RChbJoF+^tw5n)sP-BM-O zsA-R2LEM!>=NkM>^2{_)8+MRzlC<&5Zy`1j*Djv>qQ$dT^@VdlFNhv z?pph%wPyA)lo6R`jf=$_f617n%L%ZDfmtF%(-5jL?4(=+yTc z8KeaZsKj@@T9Kq0FkU(Y+$o^IBZe9oY#V^F)P3CHgjCE+5eZsnYc;^@6W2K2eNBH2 zUuD&*&Z21I>*Q;<4FWNR@BbnNHY}X?qScepvr9ue0R`z&1YZ1QhOWz%rr17jioZ&3 z1|?ytU%5bNhPAv_1#b9YUae=J0{>u@I-yk;WvSaM%m=u9n~YNT)JttKm!7@l;wC?t9sN=_@M(0__gcd`)5OFR;aA|YB&=}MG8ILL%t&M z4*D^(!D@Dyo~X@21H%{YWv7!BwnY^X=(yW%S$Ka)v1}sl8?goeRb1-! z#nTHMcwTJ-DrW;~i@;C8e{-nth@}|=-8zcH@q%kL(ufdY&CDXYYL7RUS_qlNJQ&Jb z7=*U*H$_RTNyizc*z>MDFDn}O!Xc@mGk<4szAE=eO^GXOUO@J@CT_MW(t3XU4799@ z=(yFLwh6hE%ftj`3=~GqcqT$HmKckh?OTG@L%Jmn?m@W-qLJh;4ZYRa%qj=>XvE#s zrdS85CX}^i`SpZm%fN*cj?KDqgc&)PEi=in=1Q#pmE9pawyDBB> zU3aB@w@}RJ0iXz>(tKWjs;F=KArL^k4BXhbdMNcp6eY|s`X(ViBCqH}I1Wux#*w*;^CQ)doSFi3%8n2>pMBp*Osd6<0Y8wO?wbN1oJvaJyq z>Dwq4B(ysUaNM-4DKzQvC#DwMHH5iCzod5ZVts3&Qo!j-ZccXf&QSj>%Z#rZ_xHn} zU}Ndg1l}a|i1s9|F6q7#>^zS>Za#r1PBQ?9G(*>qx{(cfv7C1*1|Fkh=kGC6EGVV- zXHh2O3PozgbybfJHw+HqZZ_^mkNx9*9IJub9ZBV@aD`f9UcNY#PP7J>Cinir+qT%@ zoG!4s?mG8efL`B7sB&2zXxqcBEF@v>+5Do(6*2nNOl1u0K}^OW#YhF51&KYPkZ6|F z4mcR=;@Gwi?M7@4H^9omZ{G9T?hPY=k2?t*M*t9w+VII>9gfQsB-XUxYHYT`RNn;a zlNISPQYndgbx0ghVCiY}rD$|>7dX%rdf2^3Tg)CXx~PsK71n?=)nYY<7*fiLhb2Im zsu8V%P@^)EqDQkxp)q4|!Ee>QBtX*3l;s}ocrcN!b2Vl^d?71}l5`Vom^!qVhb5#{ zohPDu2~vQVX}QX!8`{YbEaXD)%i@ zM&mJmYQx_5>TWg1@3h2{I5)*gwVfa@b@1*ivAQ-sE-pAE#H$mgoPnw?N=n9txT1$@ zC2Kqx1qnyzc@cHFEiHxN27^CUA)C{fyDnL1-AXVU&$a!BotJYlB<$goMn<=lad8%& z?q#W1#Gf6&Otn~=`+fhBnQEnYcbvI|~$1X%lP5f%jQrI6w*t6qx znub9NEO?Fa(J=+2vkyxj&kVdNr*MIU{}*fT7~D$~?TIEQwr!gyc1~>DwrwXTwoYu@ zwr$(^$L8eTs+oB+_1;Xqs{5s@`@`}=l zaYSk)#}2^}F(vrWXx$yVi|512v1enjiLdawMXmP^C6Uj)-Q0lz9Z8@+b{Z+P~0auCS!WL7%uf?qsXJkfBIz({s6> zxgTB}@8Re1Rvd4?WQRA@rcM5GNh_UN36-b+oh@DEM%|LXDVYRD*=6`p6q z)yRV}sDW{V{(w}lCutvZa>zk7y?@DiaDtiE3|#Imnb*c28s6zJN#gKl&bwYJwKB*N zf(vXgvSReJvWT%nNNs3OuU#r!ha=m6}UYet5 zcbq#{(F&5qXv6S)Vf3_FQ?qF^AgRqp|IQuaBYkke8-gS2m+sAo)p)*j<*0HH@g(=t ztvE!sCr|)4_gz1a^y6=aS)oo4*$d43{&Kvi2JDvk!tn^OsHnoj3UZfTF9B@TpPGir z)bxho-h-VIiudVb+ueZ^+Go3q#UNfK#d=qF>)ngQ5Er2~)Vhvr<;n83VTRu42F1ho z{|;a$>ngvA`vI_@+x{Rb|I2Wl{~b-~=wj>S`~$4~zauJ_G8OFhIZ#7(UeP?(*j>@M z3-jzuprd9REx^t$jgA!UtXRWoMx0wT0e(4YM0hOaY;>+-&*G5fzrq-ruiIAk5)zLt ztrcnM@5HSuzq&jqix?jtz1gU$QyMOgRJy6x`PG8@#tJD@BIVSEXcb+IM7mN|&e=4Z zz9;#w`K=BM8po-1#9o(Pt=l-Z{MdGs+2^%CnpQ1X=6c=?Ff~*py6Of>6PBKC=SD?S z^f%#Uy?*5?=OJMx9pnTNF@X0gcC+wKD;V{nvZ%PN8;;6kOD zx-J+|KigKVOebkKbj>`3V1Y1HR+}I1N^@yO5|D)cn8*?hJA$TaZ?%na;nKmvv(Tq{ zHh+raP#|fNGoym1DD#1$C=(H9UtxHTQCV?vE}F_$(4%yN^B_BDRmqLiL$qD?5^YUU zU&bTcZ5_1~eMjrSh1LV*sc`!*4+E`->fD&;%tv^w?@ zeYIR>+Qj;HyG8-{`__znyl2e?m74(bNXj2>*7L`Yf-TuDZ5&$TK z%5AqMa6wZ|JZh7}^E&^X2EWK!^v^`$OE$M)MtzaAn2!HSu-KVuN zM|bf&qFnqr7pUtSb5@iF=aC4#jCEh_PCu_Q;mM!j*awOt0TK;v!(T9y#sVS{m@EYP zO8YhF0*&}vjOpiDn*ez55lJ9>45*Vr>g;z6aA#N@EbtvBPy)y*#{m%j7>7SF8CCe1 zYsY!_yP7VW_&(C7;Pm`U+92h#;(RQ%SUA(c!|#rQLcI}AN#wDvl`LJ4sg!Kr&qK^PnLr?+}rca&X0^u74^u(Jvp$il@AX3^VfqO$H|HrTI8sUH}xN8IDv z>^w_;J-lpx*VBa#13~kt!A{Mbk`U=L1S%(xgr6Wz5uB3{=RjsjPAQ2T5u)wnyr_tl z$pi?p3xfY`M{^RT9nL^gj6obn51k zgs6d^%dn#Gr_};nA<~$XrzaJ;|8NA6oS)p_t}6-AmoxNcf+-kf?xkxe4^GGk8UArN zG_Om6pL|aP!d|EqGddff27RI?<|75WXO8%}0bAndMS#5)${CG*7<49)w*HO|TQVo- za}$?%(-CwAj29}@xZwELRTF+rc7h07g;N-YGRz$K2yrSIclTs52AO!6=pPNIuoKz0 z%2cv%5|l8$9APG8z6|nSy{CM z6ciFKP;}h^N3lanV`_qL6u0CX*1W=etb>>Y|FD*%|9$FzbOAhF&m{>-@vGH5prEkl z`jzg@_Z|Fy#hkBc93%380|9}+0|9CLmt{!Y>S7iV9m*CWd&58kVtu1$Vx_OOzTdpUw0ZN2S}+t;VJjir@_280uK_LbBg-;OqQ zK(=m7Mo9+%a8F2t@@))0pYQ?EbdZpJWQ3QjR1pnoRyT3Mv7?>OY8zX%uWlcLH3h;3CT_T_0hY zNOd$Hu|~_|kQ>;OP*n`>sUGKoCvlJn1Qw)=q$0>1Qq&{rBJA*`SWTK>MUE`lhL!~- zGQ37L*=A1{%6yX>kh)fmRO+OO8I@3)N4vLHu?RkFu{obtLzPxGKW2{!b8z<9ak7m{l!2@1NXD}1A*F2%QLp{xt;YKNY>DxN zaUI1_JqE6(voxveK*{omsW(g?)qSJNIGL8<*pkL(Sa{~6U~+ZbYx9-yvZ*Yg(wk_r z(L2CTKLMVLbqvn>B?$D`OQGEPB7BFZgYGdyhQ0JD4W9$uOSyocuq9;u39Q){L97!O5c8OBi$q#h)fcbrX6dj3 zL5?!wQ`h>XaM_&v?nKDvTqVmPj9}pq0EMwX^oNRaUX~pjg2pzq+rVBcOJf7b#US<4 zvEmRe*l0$8?5$X5EvjjM12&ViPg}N_!~!54S?k3Xr&78YUKf{et`CV0s|FF0FW%h?IrR5H_#JGv z)*tJgkA}{Q@{~$vB|;eOqJnK;x1Z9Jh6S1a)p)Jsmes7u;1Nw7g=Ho8g{lnIkxI6l zNl=CojKaSdO0WauM->5bfE>*V$}+vN1f4ADYV3ynS;8IiZKIe0j)E>rUYuZHLrFoL zrV$lDN3^Moh}IS24~3F##>{fp#42Q8ZO%roo>*kKORhKY9jO$tCZdA24|eOp#(|Ji zDNAMMW&_LM-OiFO#4KB|P6cQPAd9vl+ZzfI2ye8q=D)3ii0B2*bW6X&ohsuIn1x@2 z3wQ%-I%Q6%&N73AA_~nwewuf*iqc)I)?pWq;zPAK4WXmx=U5rfK%Q6sw=@=FwhJAY zZ-9HMeO2i2FJyH_4B`$!+WnUzY7Q@ANEbVImRKaMG^Sdz_~Ql$M&``qsgd}CqZ;f7 zm=ePCP~OOdZ~X2KWhRK2_P|m21TsXLjHu$nT&AW%r8_^Sy$bWDc^epn=zgGzT}ys# zunB$#TBPeHJ}13xAs{iF3{cER^x@hvkj7NBEvem(?PxE~8IX%BhohSF$&-cX6||er z^+^I#+-1;Ne$<2f8)DSl3ErrW@cx4n*beL!?ytz-z3L_8SdvjD=fD6vp@Rc6nuc?4 z#m5hE+4+fXU21x9y}?G|WfaIs>i7PBf%h5RBZi8XcY^;(=Bh~Yk^6s=xrF}Tr!#v; z6C(?!9|EeLje(J)ot}-0wX=nhwSkip{eNC9Of8HI{)3>ZXKZ5YWWw-2Caw&V!~ZjX z>*zI|Gam~G$aw7klQR4Nw{s&qTT=@&7svk$=qErY*Z&btf7{;w9?qrCnB#$XLf3Ok zt;JrQ*w*^GsV#3#uH|>4h~~U+)eHv+Iq6qo7B} z2JM{Xh7})Xe)^V-n362`vr3P1YEImgnh9sUw%Cc1F=CH3TZECF1T?>)TOT~$cFDv~ z@EzuZbWy71F}((ncJ6MmSX%A?M=0T6k#TB#*nYy01XP*pU+z%x9HrlhzN^)ui(QfUmQJ<19znpe)aLnwxyz0x z@Eus-t$(>*hs=*Hlt%nv90p=0Ua&ZztCiX^waRV|Z?m<7J6#0(sBbhIZ$FJ0rrX9e zSex9A$KJI{EVqY}6mASeUq|sZb7)-d4N)D$GXC+p5FD1X@XA~%!$Dw`^}U#5n>qjT zM<_}CupKwzhv#lHVBP6E}rWSHJ1V zYV4*j7h5S=a^ReRZ$Saf%hYpySyPzb?cW&TnGC+(zq0wD(T$*=;WSE2^tBjw$!f-Y z!f#|WZ$UArE_2d_-H&V?9=^KUL}2Z(Z9F2skb{F*juZ&!kMw+sn~mWdXMAd!s2#fB zk!-CPv;JA47DifccNZ47RQd+#?~&Uv?J&+!j$7edqiqIq)Y46M_^m}2jvYWPp=`!3 zVffZAfwr>5+OFl175pKk zq9t5wwYC4TYY z)24~a>=o20qij^T@rk_*_?}<8J-J%mbbo6Y^7XRPtZc`Y$`7$kuGB+sfsBM$-$P$h zEos<#nvGV%fNawKU^OUmK4fib-+P28Js$sKo#@DlO_~_Es=XCj}gE}(|zOZ#(RhY@PS zS%)&wM|vNdg{jR7Q3SL=5GAn>T%iN#$T0KDlt1BKFwnh3}Plr6v9d+EPLt_bF6!^dikY24fKa?+!!(_DiXUmYUM!rR(qgknXy%3&4 z#CkTl!FD4vC2_-Ml1Qh>>1DT5k$r?rUww*4C+2~N$%&lUvYjJ7em(D|Z#}l=ma-a` z-UotFw%TOe{rYZ?poFwX3UcNc=y&II%INFC0Np&`3^I*VieH>8$fRo{*uLCV%(&>{JdPs{~ zow-ckO9~q#QGJ=!_y6uQ3RZi%pEO2EnuXV9Urux^AN2mXhS^Ts%~=V}tR%vgM?NE< z@E$6}ft@PR>?k5kuGf|LC}|9a_1#3?flr@)Uc}7Gf#yBOI-G!Ve+v3eT~m-)wUd+e z3GCh9C(urRnEN%fuNFs(+($F&TC1zO+&z5^mF`4X4^Q2HAwZ^U9Q!s2N9QQW672Ga z+Pol{u`pD&naf$m*={WjSP$tCb$k&54Yn=!xvWO*4Vubl5%H@eT zi7lh3KTI$t?Ki2rmF_Mm=;g*HJ+kqb-RBt6fm3LZw3-d-;e#vR~I&tI** zVTRDma}L`@7)Y%)KFij!BR}iYbhEo>3FAU&#+-e4 zNfisN+ut*^2qS_W0h1Ui!3<3h&kUcxNA{2sc9W(R7r~R1iKTp0sY3El`!NSOrOP6b6?QwZXJ&8IWD-N$6!Mesfr z0nJEFst*)Di*DUG-t0Zn%9RhU53D1@n9x_zYp1M;@p}H;DhKFN=CIC$$DptH9XKU} ziFODN4K5(3zL$v&)fF064eJhnsVSYk$rA5gY15tuRd_dS?&<<6WRo~^nXrKGjKJc; z$h|K3p$^$)dXZR#4*+7r=q$;6!PhAYuTaea$s7f;GMO_lWnR_C zTKXVxXytw`Bm!@L+77E|Mydk858IB~o7WVfP@n)Ra9EVxN&iH~@*PiNa(A+v@)k)< zJky&2;l;T^2bC$FD^U?>4=N_YORJBnHL0mj1pyU1lK5j8N)7VWvZY~up!>`lnOIBQ z#TXj6IQ%!RYU=e}(U)P*DTmBEtI2m!`PVHDOsWF3(XAMK)^#p0jS^ao(|((9gSehr zTXYAcU&<)HI?l7{nOxm}OJ@(nvM13AOi!Zy_BNdtSt$kjJi>FH6(RAGI*n%8 zW%A=*-nO)hc`#^#O(L=+80(ol<^Uf=-N3bN^|j=Hm9U3`!lQqd_dyMERb&L01e?@) z`-Pbgf3Wg)U9kBj|LRDyIvg(uQ4@JVf3efZq&fCYD0!91j2bUW-`eFh!Kt@xYmEmN zqB@VrI2!+g(SwAMT-qq6QTV-WRQL8HNwE}^6@BsN?|TL8LPub=Kw>BlED&ulTqV|p z3N(HxqG?8FoN*By88N1nBIRn4wJdzyl5V_nO40%C1Er8p__ZTE`&NWlG_g!jDE69N zn*nabE1M)+uHH?f%;-5i2(C;hV0=SNnv+X8Ug#xiH)Fus;H+&BC~ZvRi8B~@IdB`i zobaF@4I@}e&%~d&CBdndK5q)?{D};uo2q^9y&ZOPUW?{j*Nt^M}Bd9!2*J8swn~CGhgmcCn zP-gE^>7Xcu01W=}&ht(JECh1J(u-9WXz2^-;wGGgr-kET}gxf zY0gxDDwaO!%|yx|vlu<$8UpdD;E=nNW3ghU!W$Ra9RB7?Asi^O3d)naZ3W`BrB7m&&K{E<6ZgWUl|aj89E?;Pif=ZT*W#h2oxM z^HQPzF3Q>64UuYu5qltqI!zA_6Ehd?cM=XM7D_! z{4|V%N(G5rGc6H|kGDz=TMG;iAIHD1CYq~)I)eT4zvi&Oc7wOmh}#jQzoa4@?U6I# z^+7p^Y;B;-_r6Zsz+oOuC7>z51VbkFz3K^;^1Yhz18KC15~%(+Fo1ruX` zML?WE_mZ``-gNGn)2J+?)r+(C{tzX@@N!wChgAR7H_wvK#Bhq;jHC5NK-LbDgseFG z3`{_>I?mT}HH4NF8_-kK_!SquEx8-ijxJAIG#nyu?H%a$ah6{0!m85+G0*#1mdd(O zqFR}oQi&>%(e(aUs)(&KK7=Mc?Ll zGS*ydDDV4#ylFG%6mxcfYSeRmJ51}Dk@Ua!R+7_#mPNa1RAt|SmL>vbb|3wOVzst! z5<^03ZycbwM+fg}J+Q3Yx<5-JfD>wqD`E_) zC93P|WV!ypm^5za@^+D5ymL+NS;ya>1jCqa=9D;mE z3k7ucTF($!SK_tia_mC-fm|&gpgBV8Wz*ua^kwWu;UHEsIu6p|VL$(>p}P;y3sbQX z8*z)sNS`S!@sb~%oG5b!tw~~W8wZl!4Z}!!(srYu^Up;6s-FOm7!U)E>1*Hq^*D$* zzZk{ZVqiIry28WuvP^%A!uy^X&xrrcucyX}8Is6n72oTj21IcrMa8_uux_%6r=nuH zuB+m*V2*y`9f$}$hJm4!`qcQ)F=!bOwF%nglZKamA@LzT|Z z&x@DfhCl=!IouCD*%JW*lZ)t3aCjPc(Hwl6td|FJ(ff;JGbf<9d=} z+lV5GaV>A19}s+zVZ^sIO&BA=II{25ph|v}FU#X$3`-2^{6R)k3?Sz8FLS4T{$iOj z=ot)fN8bKeXWZesT(L>>TU3qdQhD{GdK^AW96%$e%N-54!z*ueVQWhIi?|Nyk z*fKeub-pQ(<)mOTyQz=-k&fz}s3|bzg$t(GApgYbFQ{~NHYmBwZRBLo{ z#z7XrXJT@H3x?!*@rC<p)!tQ-(X6g6~MHkfYg`By>V!o#bH8~e zQyaxm9OdTirr0HD!eqj)`R6z{7D(*`Y3_a7-v3AX??eZadIFu=tPq1Ezp~&7pp&Ca zUI1&ih_GDR+oyK~p6ct)ZMsFGVFc_?_goH~P5*wM)xq-^&&zBLpRnNIHJ#l%q1^fX z$CHPArzKI)$Njm~7UlV>vVA2TL1~-Xy3GXC!3{FTmC_agl0m?chWLte!}+L9h{Y~6 zSg8*z(dW}$&lrm9inw#lIiwxN)5tsWN&QRheRCPfw`s-(Z|^^E@o{H*BF0{hT-Otp zB{>qiHcuB`Od3|2ff})Z2r9rEYPwB6QlnVK-0e-|aQFZ{5ZM=~pkkjz3hmnRTAdXU z@BSJxh-x)WbT(FSWTB(-o#sn7rr@^DqW}(zLeF)5aEd{O#v(^Ro zt;kJ64R+n%o=-6hzDZ+u9oEDHulVKM2DwQ^9qI8>=_j-c2a0?TuWdr62M|NfdPmsk~ZH|AAIQ;-Cwu!+kKj7LBzebLfs zc+PR34scj$Nzlv0_Kk@a@{oa|z82%1qo0yZ}X3 z+XfPz1vYOgY6l;+NQa572kVN4c7Q*LG)}2SlDVn zhrT<_ZT=w17CmZ!mAXI|_o}8J*&QBx*B(mQ9XGck00yt((+77Q1AA`VfTETmsgdvu zJMapj(Wi9|j57M--t7RE(3bo4ZYqg=w;28#nP-64U6!kH9$TAIz;nSPNo7bJX%G9N zRRK}%ut)idpQdkKOYO(Dp0Cr71sT^gq7J&xz%H72Cb2!!ygMWB+7|O)zVqt0E|J}V z{<6zQ#Vk7QmZF=*@wD!0RT`}nQHQRgeYl9wtu;YB0}013U_wc&GMug04w@ywplLf= zf(qR|_|4BM`~HF**RRdV*C^+y1?H292F@I$hOQv~uWZaJ)Q>QrmcYX|`W04jnkv@7 zhW0*tq3`|`JZ!8Kptqvg*+1fkMDy|RiLV^M9r;*-!s-|UdP zzMMX%?&wy8wG7QNSYcUGXYI;2Cr)F{JWe%t&ea{`IT$I$KkBFoOsR5=n$8xX)K8yk z+f%{KHl`*X9YefBfA4Ig0h-|kdI_juWa06e{o9D+IyT@Y*BZ9m^m{5bwY|DFE0v3qw!LD_iaF=CAfib%Z$ z7B~4n(ioEadi9Gt=N4N~BLUf8XD1LgHo4t@PUcBLidUz2_*RU|83p)wufz=U&cL~7 z-$Nboe=ILgW{ffMy)+2@fgJR7B>v^>=KCU?{{R)L35c5-AT81-a}zJ9#G)<(gwGID z_KDRTi!*WfL75Z{!AB#!0&@fil#Nn9d!ad{gZ3DF!^a%%m8bU1sMqdz)`FK^HFLJw zt6teI3!Qrr43lH!FY>u-OXgM77>0IBFOQ&4$8C=KCOg(J2fs8vJF0mGTPUY>&6n3! zuIlQ*_JTY#RLT5PQG)4})rYSwfZdYaX`)xxc9V6UlwT)f4SmHg4HRhu6%12!-H!%E zuyi>Cn6Zj&HBfqRt2VDFKt;L=Ui15plY%J&%8?WMRkTY`2emxCjz4*Nr7@3(pTCyF ztbUuRcjf+`DlcZ?*tS#Bz^Gg*6ThU}y-v6r!Va_rUTx35sK_!xQX2B;9t_kCIG6I^ zWi2~y#}j9UV5U)(z`=PW=vGcxys#kL#AYV2g22UO%Z0&?g$jqZQ>oT(a+k1If@q^*NEpBrF`;g5&lTLQyacnWX*v0{Fj_V(AcE)>i6krZ1nJr z@xOvE1oCjiBdvjeaL|E)RR3$LRsS8I=4xQ=V)B1SUI4nZ8c5evs$QpSc8hk12fVjZ z`SF|u8V%sM(nwFmD*dh7VauQ^sIzRpd}8e;kC3joNhJHFkxhL@b=qinH%a<^bdFsn znZ7Si_&+alKJIG1MrOVqDt10@043Mok7_wRZ&w+9FB3UmhpB$g_c>n!JKu*j-&Z9) zFNyrG4L>bX`M)c^zubC0&T@P|r=Iz}AE)?zzQSt0U*-JrF#Mi+a%Oy=Uw8PvZypi! z{NAT>zAmr7j!S-8@$=_=-s|~1em?Vm9*13bf42Dfd41XSygw22yp7cOJw76Qf3fR* z_Vg^65f2FPZq=E+Odoe7}a}_!X6SxqQ`hzvkrhyno%>bzFYG zOu6xUzkjam^msInoUzY5aD2aM#4Oll@746Y9q0IcF1daG^L=U{;QqwO`98b2W)0(i zf6C$iW`)_mck7ur>iM|3KOyM2s`=ih;CFp9)ARY{=l_1L`TD$n=67A1dHlBX``oy? z-0*v~xzCmI9l=^i=6|26`M$k(`#$)7o0!r2yvexU-rT*f@6q%+JaQQk@%vgr$oW3n zxbFTcsquX@B6x4jRCzl{{h%js(!Tfe{T%rIWY_ci{t5eIV#n|CXB1vCd`nI|v>yoG zPIq4ae1128UyNkm+I@de_`PnaeFs0xx%<3d_`N@6%=`@5r=IWo*7n!aPY8uUJ>TaY zHEm<;cKppVdcL1q&-@>NoSwFGjPJMC@7Lp>&+t_7{&~mUD?i8K3H#TG=)=eKj%OXl z`%L%ewfA;hpvt%>?F?G6UV1$Gw*ayip|fTtp{Lye_i-} zJgNQc8MAzzmo6SM>|z@DzlM5VrfLYxz7J=<_9=Qk#&Ql7ZO^*5n%}#xJr3KRGb7-& zES-|3dN>z%xU9x~PM&A?T)PaVd~AIVH&3!{ZC11%Bo4Z5w9br;mb8a;MZ0_? zpM6dTjz_yLTNF>aTIizZtyW5=)}EVV74O+xR=wuVBHx^a83OCmQp2}A?DJjRg#a9NWQqS!zoAbkNwk?%C zMP9YeahAH@o7puh+Fm#^&zd8E#4B1epXF6nV;)g}icn)}^U9n1#j73QS^HVh@$pd$ zM@g<5(82eI-H(s^aqq3TnId|r0%2j(b;hR4Z3;l^h38-*V^O2EZMMM^&tqqKyD`$# zlsU2J?N~}h?^4KHQWukBXV+53X&WW8a#@)9+-LT{X*u&Ru)6Pku(vQD>cYjHFvZB>_YK`DFLm3J#z$`RYLx|u)uzC-uC-XvP!es#u;91IBO#ZvzEbU79+!cSv<&RrU7>!d# z8LLl|+DS3QQjcgkQ>WgC%yXeZ8QEF9yKYJyzJ--$;a8{ff;D!zqDTLFBhLumNL1{% zW4_gsaH`kXKW)0|b|;g0!-cpKBr)v{r`jbh(dXOZGFjf(s_XQ0@lU2UiG$=8=6!yj zfbBx6g{!L}xAYdaCZh=a6oB^A^7^WK?W(JF;bQsFST(zB)*|>liq+AnRii>nngOX? zhQ>xtWY}^-aeT)~oR@6drNfNH&fO`T!s5$z8D4pG5!{R;KS7fh79E1m*r^hZ=7a)_ z&6S6YLqxQ9_QAp^K1ITn_vczy$zyy0F$>%EZ8R$@>y3`nlVkUze?oBhdwGgPLb438 zr8%7{{{Wklq=eDQR$l6JeX@?+*0!|!r;ki4`qpiV3;}hST0&urF_@mg+TY24zRY7f zTP@A|msuOjzn0Tt5@-uhE*h=sq=WBfGp{^z8vE6MSNkM%G_#qpQXp4%fd0a`7+hS( zUKj7!srbBP&EZ^hjYGGvAc#_3c3dNaAuVvmSSj#K)y5JT|0DIX6&1^l(ydDni$s~N z2Y|F_Bjv=%m%iYUps`K}d!2eRy<)j*L%%4X#-n%0XOIYsAQCN`)*V`%xeNumr;I?eBx4ILmhG<`91^PUen{}yX=jQ~{IVL#kol(1Ak|zkr8%XB+u3YX zw*3iurF2?@R8i88eW|NE`~2}O4$2_B0gT$Je&0N0w3cgv>wf4#UsAX)>>exq7;C zJJg{TJDuP83L@wdPHu3P_&gnzso6!vkNof^1<`?wayZ;dic;Ky5xJ_7Kv)LYZL~x) zKCryup#>Bc_EzW34VjZRd(!-|1_Hvdv%Y+3C~_M)Q`8xw$5dxn(v|76`N~ek>SQJ zI8zj541h+NlO}Oa8R3$;O~-ZP0}d6UtEeBjWBD5giH2HZpej%j3uc zTuDK_8fO>uMK(ItP}M3)q%#c+-JNQQ6~OZ`lHM~%y9bN)3Fq4z(sfej9;QuuJ!u+% zHJ%JVFVza?Mg5v4)(K?9c9AU`UkjcycxD1x6EfgQBgv0zkOD5RJ~ta$EZyH;JC^EX zlgi~E>voqAlN^mkv5e9qcH}nhwV17AxBNeD&V2n z)K2`Gz#_I`i*YLV(_7}Zc25w6QHRv0A*kKx>t(r}hS_WTASOptb6+VIgcGtY7tfOa zCM0K>Cr7Qe9DkJZ90_6%8AcUTk1Djh8^SN#yCPREJQZ~H7eL7(Z6?EsY z01;SUs;m>mag7TgLkqB%YSGZT6ajPNJekBka2YJZ77<&GmrZ~$4*xC0?K(F%QIwX|Ciq8x`f)4v-f z62zNC<(HzM>>P+a;cV!|MzS7;#V1ypC>$PCa^u!36+&4%TiUqdo?Wr#1DH(~r?Y8o z%b%LM3$GfUW3NAhgE$}dJgwSw;Oo(UjEpkx6`PDG63m4Nhn_^u0ah$U@^VHvHBa(0 z1Ci2}MOstlJEN$5lobrTJL)=0e@qAO%-$NON%PbT6bG1bNSoa@mjk}$M9=LqPgQ4t zku?fA(p14UUH>s|zHX2R5a;nB!W$g%*HG5=IdoyA(Szk7C0Swc142~_G*WtVG(kE# zl_V_NLS!nSxKGIg$m8n_{EOObCA8E#RM$P%f34u1myWHRB9X;E#FI2OMog+^H<&Ne zf`Y>cSrW)eAh%l_Sq+lL&Hv1zh$`00p+&6M?XOX8x}vQugMyCmJ|0FGH*)E|Gv7y$ z$t*sjlwSa#xMlG<{UTE^wOPs2wBwDfn7X~N1;@B{(H#H35G~{$N)qD-q~TWG6X93? zAh75>VfPid9msGYqI05Td4&^YSFq>E6GezCP5{fkn#Rc5$?~#)9ZAbNv#?UKOxl`* zKo`10qC0HTM1tn7Xw;WkabAEuPh>o$T|<&+e~Sduu1(ItUJx*2#+zn4kNv(DhSIiE zBsRPpBx6Ndcfy1Erk(eh!MP&i31|{K%lEtQY^p@#9;qX@Apz>dp9U<+-n9)Ol({ z8CdlaO;+BQ6!r~d41>PW!EVBbbKmR%$HWLq&L}#i2i;S7)sNmK+Tb)sY9qpk5 zC;4x~WVWI-vaCh)c#i&y?HBsO*c4%*9gRp!!Fm-a0ZSNLaS1q8=t%iL+Ch5#+6i0( zF$pt=@rfs-HZ66dcw=!c6x?>>`49MmlqqdgJ$1g`HEcX3EFvNocdoqSo56DEku#=69j_j{yL zHC-$ZtY{Wj9oMyjQ$vkYc!?}1*TS@7y$49C^EGOamU1)|V=_^_V3}``6*URA=LG+O zuH}GEyaPO)qs8T#EBF+_H@-GqV)ctYCS9|$e4A4n3t0&NZKK<^*%%%UeR2(W8fqs0-+?rBkcfSA8f!IZaz^nTrZi< zipFip1Uqe4i>;3^$B~}IL)QxyYJ=0{R*N$jvf-gK-8SCij$F{?{Aql0FImCp15VK+ zKV7sh)w2{Fai@t_sX|WB^>DJ6(_9sVJ0q1DrEd^4we3Pln;l=q_C=Y;j0Mdnm(5X7 z^%nT2$d%%}$}?D(nU$rT00?#Fl*<)a{#&U6u*Hg3e7MxzDrBP&RcvsNPbLi?v83w^ zO3Mw^cIB;(H|4!{izW?C0fc8|%92koR${njdi02VkV%7Q_v$Lj93Qo4E1e>=E%)N9ccRf+^)C%*mB7i67{DOXVWrEOx0y z`jbHDXT)Gp6=tW!q*i2HvV?9Zcn-P{Z}54#q1vzb7PH^rnYi5XaDQjhgl3#4G3Z%$w?y111an%GItkcN1i&F zx;4ih^}U7gWS}34<|6U4S7gv_NK-AoEvaq(>V5r0Ch4cMJ8yZ~=Xx)E3#T5f31wcI zCM*TY27x9=veXUr7m-W&am9}ucEWWhhFPR)Ux=8P9XZs)e(pYh$|Bz3NfK<0`pX?Im9<^k+ zdE}r{ec%hTt5|xM{atDPAoHGV03hsQ8HIw3EE;mJfug~ z`M<*iI?sp7tyyTOzJo?PJ#>E05yK)RN{oU*hpwp-N!$^Ii2uFxR@F?^)a0@J&M z2evg3gy~C|&2X22mDs;(JzXykE|8G=y0~Ua@fNR1IsR&bp&DrHg^{Mpxnj?{vufkp z2zE&rS>^>>#X({wv2%H`{}DhCz|KVDbhxGnSM;LxKyE2t$3;dA}Z>)16+bv2%L{uHESD%ZeB`4P;FLZ6=`F$N!mN9S5 z7ASywu7~q_5*H?PCyYE~fX;yYiskqt1mA@>JeV{zBczuTTWwzBF%nO>#P1nJ4Zj2e z5IKFZHl#Bi^#DS;8qE=e9mFv`mhM#j^&CwWqt%C8Ac8N8+T8dTQB60yJc%&5VR4R)?5Xi~dAudif$ zbcOl()Pjht2QklP)(h_?H`-i+Xly*Z#|~vd(7;;|5qmw<)758+e&pDdx4jsr>7o`4 zo3kt}kL9|=y8!ho6>5ZR{Wrejgw(^>YN}zLB&lb9p4c;6&>vBRn<#af`;^`XxCz7BpeCk%~`HmL1h9ve14w|mgjeol( zSx8BJ5sg((uk@jOn-kkJHzEWM}-Dov*kch zj7Va3pPe<`#ppq(1Gyv!(3o1w*orMv9Y;&UM-F%1aVNwcGF=!T06V1zaC%LqDYID0 zdTqE*@?o9G-tB-u2Ev<&)WfAO1#OnVPgE49GGP@h`1v~N( z1qTOVvV0kNANfgKLl@KGe@(Je4T7hdI*9H9ps@c~+X3f`x;cg{x^*mH!_$ebI%>U7L%s7x$PvGck z{8tGD7(Vs~3_1;5B{oyYT;yd(trR8|TR$Op9dnrB2TUz8aq^TTo~#=m9dcoV}q`h|_$e%VGAR{L_!xdGOlv zuad~Abc`aqRbN;_`RPwCw~@ZZ2nfj<&*TUsFc)93;>pdJKN)X}f~XNHP$*Jh20TU{ zy)mj!dp4jH!Hi)eV>VPf#)tdQEVE1_Q!`=$b}Ll~A*Uw3I9%~2Q}gHO3`zRK5%a*W zm-|+KNIm{bPdv1I*Fr33vzDj{`U`XfuOgSC+bj1xPWur`&fOvb5<7Sv6i=fJt`K2N zGs6|n%R?5c_i;E(KAH|Jpz391P^BtV)=rMv;k0q*3x8H?;>;K>>Jr;g=5#Z6u_*2O zY`@jc^_LOpxMNTF69?jJNC0P}_UHuae9GMhlW(ssl8h7L=Mj1JU0Gim>lP!8-HKNO zuJ`N!M_W1>tZoD&n0s>)gJ{Khrj>eH^ zh;{0Sm#h5VYTNCOjF6XD9>~{Ji$dXO)99K|3gAwK^bGt=){tPm0Yy8!t1H}k3A>C0 zaTY{Cmx2a+0%+=&@=$$7W(n~1+C3lEhTsRueD}$7pk412;iI-40lX*ltiqLQv?iDT zrK&#tIZjM(q4C_BcX-fA!9_R=98c+!#nrUj*}*KQoul7;t!Fm0^9W(Btj6EIcLhTE z!U<PCIvc!gfi%eId|cB-svNO~)dRmc%hx#+c|bs`zYa=2tSS zKxvs?t!XTZB@)atHuRFwNo2b%?o)qu>x<|Kl6-lBS0^Nl^{m=P;X`de?72kQeNon# z{|!_?tG@<2Hybrr?<+}|mm&mUo8!0rJjoca_dAgj%(5zzIx;l*bZuBb;KPUgpjAAw zNdPsF2R0cW(p(Ebrav)rz%K+{2(HVZLUw>hGjL;4gir@?GfiN9jhtmI9J-@yR3S;T zCZ{oX#F&)L7P;C`;zr(+H3>K5dI{Ao)jqOpS?v<)l5W)cOy4RwhejQVY}}HUx+Tt_ zO(2VL5ZIN_lK&$o+^QM86H%eDz|RdUVUsuJwErOQx&e@ON~)r|3n90_b7*q{mz`lx zpWpk5agmnnmbtgS_)PlP(ZBY^dp!ND~kLr+EhKyCO*zrbxUtZ3AsrCps6ThU4KU17+~{#t}{l}3Opur>fePE15H zLpT=G!|RWtEQFkoMA!n$P)G#CbnNe^{D?~z0I8ZVmYkP;np8-P_5j*Rpd<->@HkG+ z)$1h$rZ*hcA<@%7+LitOh;|&_4PYn~J4Wg9Zc5+D0@c7;VXEt~c5pT)M`u_%OUAWq z7}@#gig(=aWR`RmZ1H2`9f2e*$aZ#g_TR*iDw@r0oh298r21|r7(K5FG7QM6lO>KS(p>f?0i>`T`zgX zC=3S(cBcX)lIHDtbTxJpdYY<*z14Gx(8Ji(XkQBdjx&T+^>O-of}vVDV2?rlxVo=^ zJK0}br!Fkhcgj!H;}a4GY358~*t!MVH-k*5*u~^eJ5v93w3o@<4_F(9%-&o+cXJv! z6KF*k9hQ)uRYDv*f-Rxz6|y4$ImO>wAEyA4B$0Qf>{8;xrQWqJbgP6ULD-@gCegYq zjdG`IL27V1$WOdknsUR=in}6tFYBm>Z9^o0SXY=jQVf-{+s&c9Nq`p2=&)^md9S7! z3#l;=a=5QYS3^h^ouiGj_5~S!7(!v2rh1v6*jMN1S&lHZ%;(KucFi}zlHA`wkf#IH zsAEYf;vqQF)B<_3NQVR7#V8=-KnxA@^mV)sf9xRvD8iCnl?Jpq#B^he^l3@h4k4XM zf3yhY*XuPKW+iZfjWm}Qyd!A-BNZdJ@7^fr+V$SH4`hz)Pe=Q3(ks9Vsc~s{f*3_p z5%D}sLZ`1hBEGL3v~mqV6)}JWI?;BI0q#;c0XC-$kPI#PrH}6gU0#8S=taO4r-P3n z{LDikg7#-*=@G)0GjUGVtk>6b$Fs}qLO76;$G;)iDSNbOGrgYU3IJ6GlI(rpPTh%X zNiS@qPsBM06#Kqn++A?8vDJS;JN>-p|PSaQYTG+3EJ5}RsCVwNgy0BwZ~qCQ&}Mx8G4FTC)! zEr`zmPZ~%ZgU=?VA`;lfNjk+Z?b5>2mH;EO)1r{?WYw)<@Gk-pyS$|^*AxL`fgb- z--bMI-&f@yc$-2g8>h8^2-wW@I_u+^e#RX2vC<+bD)Wuz1|e0)A+R^%e1yeY)R@)l zCEA!SH!HwWZdKc(J+`uTm5ni%mus2%I{?91QqS4q1@ z09kgj*V`6rVFz}#8~$QBgc)J&(E7*s>dW+*h9OaAaXq>gY423&Ng?1HKcf=Q8f`t( zc^4liNSdekZ}*H!woTtJ9>4}zlJ9)&y6h8ug(;Y|I}%wdtv8v+#X^sqR|il=B}gZ2 z1uSHLQ>@GE9?oc@UW8K07E*2kX9kYi9)>J6H>(WHWj5DK$XoPBfwt6CV}8>pfHDiX zHMY8TC@!jmLMG#v2$?ywC?#D8Y~|G2$6!60I+N=KseU=a@tZiLx(nkWYBB^jjZ+Ar z#OT|m)YjQf{|9@L_w8}jVbkt$-xS^)^(#cYwM6E^8ZBi!0V_)uudq5bVG=|M3&z`# zz21*pSfbj=H7ZWeU(V}YW3Y8H5oto^IO1heFjUEe;Rclg8x=#)x)c+X`a0M!UY92`#|PpoVT4oQnu3I;8J<=G zQR{ozr(_gmBP{@EGY?=P0B9E6Q%+6Ybt|lR=}a=%BR~T(Stw)G@+k>hP(Efw19zJl zoqQmHLX$dF%IO>_1Jy~et4BSEwBkk))|j4T3bf>MGSRj=oQDb5Gt%Q@Q z&%#G1W=@j5v9WZ2*Q3I{4#){it2BUQ>K=|rC-z3kkI6_EFCwqqLfQP3E{QnmD!!#d> zO-$B?8+5g!p>!r+a)G91GlZko=LV< z!&5Hrm3s(eyGRsFa!{v(Yebyn9PoTC|Mo1lcm%|G#ItkwsADt%%p|G|B7@j&AJ@u7_!TxP(O3JGE8eSAlP+ z1WC&ygT;gb`j-LY@XqxTpdS>BB&u1YMek*M?Dypol4g>I)hhsflT`P<;0Snv_<6lOElX*g zOm_K=ODI$2{?a8pPJD0)wZX_q$nd};1b-w2Utg1WJUl{K7~P3(aC5SEbnnX}l)E2m zCGDUxC^)LG1%M~_;1GZcDQdM^)ZkYh)4$i`W&o@c2wPCWoUcfr8QO-8>2*K_7)eQx zTsNvN?l}L6Bi6YYsD=T!EXYp8Q2$}55)XwiFE5}Ts|`adLWLyVb@5K<=rb2UP=3$CV4OI zF|Xwc3wxYuW=cROJ-ij`W3oKVmy(jRU?P#WnSO$#0u%Knj2qLEC}b zBD&=O)hEYOCwVB!`nVQXh?20_(puA#ikuvLH&pr$h%F)n)kdqtiPq$s?TL!X$XDz3 zpaj#dKA8DPm6M?Tqs_J!DhpT6n-iA~%~7=%EC4Zn34$OyJk~n1VH^Q77lTwsU_Q@k`;K6rga=NPJkp$&-CRH`7Er25l+YJTFJ)!;&XVMJPHrJJ7vrC=< zOaMCq2&KHswLPn+YDCTTi1rg)NUjOnSIpkqdxKf3__w$B>5|_*-GI^vU{l zEp|8Ma#Y*O*4QaNomAIs;Rjp$SI8to>QL;UJ(?|^_l+8GT^l5wr~v9Z$6Tbl8kh_@ zLuw!2`$wB^`Huwm4Z<++I@@lIj6;UR$~1DXi3L30JNh!iWlOyw+_jsFp&hHoUgikOI|-PC{9d zz5(4V5F-LXx$UyaG;heH?je93R0?TKS5F;tUjEjtvqGnPDn zw^4APfnjy3&j9(vs;eLlfliHeTk?JN0ePXz5E>+VkDHdkpVr)Ou#NSFUBA5ZKWnn3 zqo}oPQI}0J%rV~f8uaPOrt+X*+jzF&7w!gOFE%(M><2u|Ot*$Hqaog^?tG+NGi7aH zitFOade~)$H;wmd)2sv+MLl2gJ8W+1Qc0s%uTmBX+%2D6(AIQ@TDjq5Jf(triR3=9 z+)m6Zf|?M$k$epB3@}ESCoPE1cZz@|_#sn%@!n=;a@?ptqN#SB@2(kw=(t2}cA;>h zFSPo*tql{SZbH>SzUp8 z({}(+nnBq?eSoaf&Bc7|5(#(`?_pR#sNKL|?ZK()l5~BLt7)16*%-v(P!b2GP<ZA$mbZEAghtN>wefN$`v!oer|u7kKML*JTVvu`Od|7f82`{f-? z1o!&Op@moclyx;OC={oI+sppre|n+N%dbW-I6MJW}hRw{ib= z$2TZJBTTuX%QAGP@egmXyd>K?5#pk^%roRDD*j;BE7_sL=y=HE&z6zQkU zZ6>+J4Mon46wO>DL+1S2g6kWeIfs6uawA+bjmQ>NjvZQMWVkg9w>lwr9A+u_Pw}qt zKq&JE-WseTz%C&P-QGJ~_@P9`b3i=^S2_=81H329GVcV2`6-+*)&~4jS&8wz~x)2NYNgbDzSPFJ&$xbR+i0=NUnco}&SRvSH4ejO! zX_7-I zX~N;256?{SgVd>$WH~(?|9%hc{%`Nkc}R|&Jgg)W#_tj}VnCYUy?GY0)$B$EHR{+d z=b-_rn9jxfsq!SK-VWq*5548`OtUf)-bG9jIL@%pb&vx$89#D3VF?a8Fw*e@R@fRb z5UAcjAS}#hH+9q2SCbb&u5A*Qasd>B3+Q$4>tS}{{M%}>Ks6We`-qla4#(XAXR|h& z#R|0QbkmsU@^25Wa6R6o2;w-Cpp}!ctgW6Qs-=oEQ3PgYN>hz!Z@e*FYKNPO`fIbq zI!>6VUvH+WaucL!vR5<&NyS=G*0 z$VNV$mL4n7UBQJw)be97Br8@?SVZVZ9!?D0{Nb(8g`2h!c}UIB_2^oxXpd}89`iBDk77m1iF3Mk zz5aZ11gd5XlQyFKiI6z1@go2Ga)ODsLb(EHW!#`S-C%+pZL2HC0O+Bc&KENe(M*-) z+rVMT4_6ar7@(99Z--O`Jj@gFDUnqm@PT6OX8g$Wl1Wb%;&j>zy9=I@94cq4fth4G z6EdG~o$f-ws(6a%>o5@Q6Yg!T^Hwv?O^(c+_6R|yZEod!QxRo_szDFH(i+EE(nBG~ zrP}bxOZED@yp>`mFbpK8sUSEVTq|W#0qGBlY7t{yDrJ%p%I9`lac0g_XCC;5$g@M* zSDm;fXWc=Sy_{f~AWlhl$_mHMY}3wci*_E9`n3%@A!FiqU^?Ai;M@5<61W?cl4KA{ zv{8V5phB~;%||SOGX6}eflw7we7#0@vF{s6v<~FCcXMe#2q=?EQWMyKarWA3M728a zF9Exlv{s<1UfaEcLTQH-iyPrN(&N4+WHP}Py;t*Vhl1T<6@g~H#rnXW0Zy?+N2c>z zKN%#|T&%h;$kgLV+qjUZPjHI@u4(KOa?a0beh}lq>3ZJA@%q8=>8F=p2y$TZDbl-xAY52j6jdvXw;22337$Xv-=No1G?R|K#)Pyp$9KEw1E>Yjr`S}XNN2x4Dv<#rClgQ^XchrL4+*@<8(qt;o_SC94abQRHmGohxfDXh9nE zI~u3HJ?}&I@D|n2q;e@2j%r!Lg}GXfO9IN0f~W9Z8-UjxZo1S}C~2#mm`+5SGGnx)=~` zvzyfx#EmdR-rNb{eTgyI-!+*uFasYGrYP2oB&b%!e)yFYX7-5_7*q+TD`JDE8VDaU z0g}LGPHKo$XeFRa;)68#C{l_kbJ~_5^O#ls*R=M1Y-xeTa%CqQp^Jk=!~5 z9y3ez4V-1A5F{vXQ#K1}Frmi7U;=b^Lj*ocB~EwE`1$A>0i2j$4IwkzGgNd;s$8v) z5x@iAveVh5l^_{P)Wtef`6d~U`$%gVQcwnXHd;%8IUPY_s{M+GmqUy1IMz`4Z6^@) z$7y}w7S6szuH91-yeWyi-LG!8_VRHlTTS`{`kkz&-|~ifH3brE73r7PuQ}w-Lm7 z#z@Iknz1(VtthK^xRj0-*(L)K-RseZ%5lgi8&&c1Omg{{6&C7ww{7=F-8kJ9sBDKe zjFYqggaZ^RnU_<%nYFuYy;ZB$Y_!C{?tCB5IBOP4`Oj3))UT# z3EP7k?u_g;4&Z4}*$H?w2vc{uJDAdEf$3oTraGVpC_Pi@xVe!evOEFVA`=ldcn4a? zZMNf?i1=1pTssW-nCu0M>wI+8zQZinQbQ$|X9@avO!Q@*$73IFeED$4!fiFXAtO+> zH|Z6BPxSHtD^?s|W=Vn#3RYrQS`@@{m(%3f6*#B?%A&$KRL6c0R?ry;9&1f{q2d?r z`QVzR9Z8{t&3kshwoZqrkWz%|31Y*p8)_G-5bF3$4M9yQ{i=e{f{qu_F3DO~0tIeN zq{HocbfqBPVJ%|Hw*|!yt^4qgaynbm>-7ry9%W6U5GIBU1%~4Z?M-bQa>HGrH+sN+ za?X|3$~(09pGGzZ)ui#;G~KLBl|d&nA?Lj zBF7MUQsUn<3X* zlp7f9%m!^XJt0XO5XC#=4$1JK)txI!!1~Ej189A_9+|imGQdeSEOu6=fBAllUtWJb zk(Wquyy_>SAsdvsX8S>`NgvWLx$JMSFy#7>tl3aiQ0K0sAqQ$HNjoRF?hiCR0X0m_ zw=%e$dc${1)jdq9MCXJ)=95=~ejpW)TJTD2rz>Jg;S_Yu1&JkX9&FDbHzudMht=o9 zokU9m$LW=*si8q<3Pg_#;zWDBUB|nOVKA6et_1#@@KAES;$%_&REUkLtksIyB z-tF8CuLuI}(pDExQev5|DWG=4H6Am%lkEFqDW$O$ypHcJsnz7J((SPWgpI!GZ?qXajIMkMe}|Uy}7+sa&T)yNib=%cCzehl%AU@k?03>2HL4 zl~z9|+-!PQuC#ws*x)NJ@W}{sCjG<{l)2 z&=+g)aYZoKZNy@5IYC9xZ{@*f%7`W(1JFKEpx{`SU?cCEVnq>sP3Gd*qeAs`NT(oH z!S3Ym0ND(Fb{@5Q_J+ozPdF{*&r@@pLBMvb2sUFz=$r3mqPPv}&W0WcE|{(b%}~BUB?cu#hS3+aoPva$r&{NR&pxWfB&RAaaY%kK5y0 zsRnS(8r=}Ud1_LqHnL$)+;WFtZ3~-<9y$;zh8Ix705|0oO|>Tb9Uw)yU5}s(F{25w zK6rp8acLOip7Q#b*V@nH>T)RmEsmN&e;-Lt5XosAtiNCUb-zg5f7+Pb+zX&V1rwF1 z98)L&`Jwa5gH~E3SJK<8cst^bG}N8ReTf3UU16Vu)&2-oQ3|fb!caRMTCbM?Nu#eF zCQ*0Cg(uaeIqqd*&@g4XjtZ_L7uK8jf%cx-Ei!)ScFV zq!(8x*9b$h&~cL8aLVur5>~PS0<%h0(gt&IiGr?GOW{s4 ztMJ?1+tWE}H8#u0o4En8Q>k%z;2zwEjoO~RN>CqHEL-bc(2A1A=58%@Sz4K13FT;4 z0?>g@Goj>hzaTqVXd6oHfq8UeAB>}>iQC>9wYyp_ZkthJ zRFAK#i#*jktouZbe>c;uuON%sPoZBqEh(NA;>SMwwA}F%4a@%N(WJ?OY~&>4nit( z`LpQY-IXi*Yv2+l6xrg5P3AKBO!Fbr_C-%BxGl(NMU=gm1Ghqc^nMRNO)Pykd<`>D ztFWe;!f!ky&$KI$7~H}mJYBH}yk2D#m_qWd_)xRQ76!{jXT}tSrnFSo@2~?2EQ9+b zVRSR8F!qTCDO{jA>?O?TAxfnTVz~!<*P#0|o>qzX78&Sp5*tg~pms8)d+UGjtb)A* zrn9A$PemyakVx;cz8+C@Jmsikb+m-4glqz3unV5NS>nUg7&{(}%gEz(Rj{XcuKX`5 zh!Kha2@PTCNJ+Z9>Ox3_ z3TRtheFLD+$~5Wf!^Q>)UDE3=tAAqKbk}6_S#i;-=6N*WD8{1(Pl%h7XdwbM_;PcW z;r<{;*tRp+=5dDZhY%O=+vswDn1#SO?hAqNWFo?sGX^*Z`vJ(N4OyU?nZCxc&Oy#Y z4nWJRyMy%zzPDtusfWb`-f7Y*9^uTnN-H&tlW3rG^eEuBk4twm7MK}-)iW#_Fq1(MH<5*@3SZX$v6+KO-z~QIfww$yjKEWosVe7Og$-@ z4GX3b5VS+GC^DH>O~J7}^M&^RI8gu$6I^e5#B0cRBHl)fw*!krDBqY-ELoZ#^r&D! z;L;MN608IF3TctqGq8|`$C{3FZ}WfSYDp2@j^0VA9GbOk+fxcpIuFXO-IvgPT(SMQ z6gC?+)k=pU1<(3sWCT(eN>GGz2Y^tisVSju zY3nXyka#Wl%z;H&Kpwin0Wt#;&U4gT-Q6i$T68;7|HGwPJWohqhqRg0fCVM||5|{Ky07zD!#^Z59LqcUUzNDA9 z1OYUm1R>B2ebR%ETASB0cmN;<{j&w=R(p=}N6E82ShFj*GT0PNVY|ubqqRhOn-du2 zycG>`h%mZIrA?>)*oh{$r^&jv%;Wr4!z-rsW@waRqztwl{Ao!#iqva>EHA*@Hln>Hn7^UtFrws@rm2ZqUxmVNLjbNSk>at32 zZ23y~RP;8~ZZsCJ1VrU}bj|xr=uSyWU=coc9 zK8j{-VNPr}J2Clsg?$>0v9vYvUdzxDSBqNi{g4%lIUpHEv#Lg&Xs{9kj&XaKBqdd; zz%VDxDw~38%{(;Rkv0}q{0PgDIzaL(bqQN|8DQ+}!7#VE*>K00`uJXZHHcKBEpOMO zYthy27RS@k>?im1qw0!m(xp0Mm0dN{L^t@&nHt=4EQ9%&YAQi70xsw{i29QN>An@}d zJ})+eqjv)^c7mNPdV@ksM!}jFBCNHH4&$-DB-J zuMgatRQ{J$l8rF&a97g~m6|(s)5xH|QJ>Hw8i+qA$H0E85&MjuvXU9F-|Y-Hd4Mg0 z9Bbn}u3|iK75Js3A%a+HP`Obd(0}Cg@-+Sd2B=$zm*a~1ADGl>{cDc*a6NXws1uf3 zX0_5MT_#ubT<8uW>?L|}V+rn1axT7t0j7iSKv30mno*RX(Kk0u#+t3s!B%gCuc?p% z{X7dWk(k2g0lmq}3II8j9?tL(+ z{#PHn|CRdK!MwRzdmwG;)EV7;X;Me!O@4H6Ce2QphVhWf6AsiO0cJ3 zbL@UC?j24lT^}A2QhYPa$QGKRZ4~cNboOay^@1J7mbzV!K6Lhm3TVT89Sh(eA>v2= zcHpL+HF2DIAOTbK=cuk(+ahsG$dE8p+tbUPX&Xl-71rwrGc5r>q5n7x?w_392ISaR zIhDqo7V%r)6kgBx1BQ<82Zlcq@jc4+0Q(+6WT{YCDmohD=9;(j6>nh~yW1>Kk29hI zhzuAIOX#PpnW1FeP0Px{Lz`_;yJFj$@OnT6AuW=$n2Ta)ut;IS3xM!6(qlI<{KT3-6|PrWL!Q@{&-N)evZQX~QvAB+GBlFahHqZT10{V7;P5xv$W+!Dth>-YN!fq|VNZr6m%YHnZI;y(6{h?>)rU5We zE6A;@;)%LbxGJG=Z0D3-8^gDpl%sX5N5)*d_a}ov=o&^2@6lwFLYec{fvNCc)5lf<_dfOL)HaKz@#gEmb zM}tNHEtYK|z?@wI$sw^=1WSa<2iR^f?E{Bw+)WKmhe}h4oR2=5TCY5#hpF|!F*@Q< z)YLbdp=alC=aOWdihbN{Ifo5xHkVpmtF#lzbL`>moIbo=*&EXx zfGm9Jnhn)k=r9Z4jr7TMw|h3s^$It)h6EQDi|pxnRx=J)DLd9LL893(4Gd(%Ifz!A z>89Caxj|2P2f?xmO(3`p^$56W+c|!Cz8I0FZdzSy7!C6@ zJX`U=w!0Axj#fO+D!OGi zV^aZUkIL(f1pP)fEGST@N1^!E4@UJr-RtC>c?(m$xfgPh(QuRmFZA!;M6d^=rl4p- z)w;R@%|a*m%hA=Sf)XE72%3kdZT+yRWb9vZ@&_jF)Qty=2hw`HIK#StxgybC<5F+- z5+DqjIZB31YGzWJm6q3#p+Z_fCS+ksBv$&5zjcb|I0Y&T7|}WceNjQ&1{mdtRJskJV!>@a0VTxilp!ujgpc%*4%F`hLTKHaRQEY=NdszOrA1`0K=U@XXS0oXbp zW%uC%XaY!XlExm*vgI+)`Qgm&?UEBbCMvMxG^b5-;nB^gvBS6ay3Cfppq42?L00)a z6vW}$6Hjx#0OywOX*1-DCK;bq&-uPB&piAjlK7RPNYAlcWkwoy27lKAd(3WX`?BuR zm`SqEIk}%I6ul|B6r#Mm1Y~sF^ff@1JBTi?W*lLS_kTP%?tk68{GB6UY($Gx? z=QCYk8XZcfS?P|xpAKceGNHhq3`~U#oVI!WWGKxinhpEwA}W36Jt0YkJ;ms|wtCoZ zCO6o7KxQ*I6yn?lpB5MQ(wq7%GGaa)7DzY9lmw~|I~2y9F&~9m1oUZ%lOyw!&R0;+ zK}8I@y4#?wP(-+NmJWNn8^3n9)HD4#INpGmR~V5UE2JEmHq>NW(--_N%wlpz?RGu7 zS}yQM?9;fd?OA?vYysJXrFNOrDt_;E!)GNx4Y(2|k* zP05`*ROfJ_(+tq5no?ckiJKZtX^77yc%-Y_NYa%PkW=|E82}CqT`ADrT#w)sqsLc@ zc8b`!bP`NyKk1-H+fIkM&m+3d%Zkio733VBn@2t18e4)&UHe9^Ikb}~Vs zQr!KUq6$K_bGk+CNN+_LmF4J5;IL4Kk*+O%FNq$Aei}J$E~o3gW_<>|7xxZ%lO8!56}uH6JXMC4 zg5@P8(0eFie3n5PeD!L9@(?FIQq{0Tfhq}17|b6=FS#h~e00Tx4HI+|7;Pu!m&2%# z9y{-|6DEv8G+O*=9j;2`K?OT%_>I5@{Cq4jcjRYDMw&ZO}(3pX-gnD@d4C%N>aDF#K zdMW(X4r@8I2Hu|A_}UJ-T>eQnG;z@N4D{tZ9l)^-=6i#($VCo%i{^JO=|@1e9h((G z%3UkeWzVFT(~nfO?avGtF?FIT-k)~n^$_Ff z29WC_>yg6^cwUiL0{ayxx4<*yUY;Y$PU*5xA)-pF87II-3V!HALA54rM}WcJ48jCN z4el~37%9E9UXEg8)~n0Kg3;C$eWci<>W%UTcCuYYIUMMvYM{#>7?b;v+`|bmB7pGe z1-lHr(}xkrFXuUv3WZjE5m#sTkBt0dpv4$$`be(;JEWwrd%gwO??+aQoJXg)GeA%T zWWWfyg5`*)f_B<$L5F}nU$G^3%Y|H|!8zw9e=FWLKy82sACR2w6co{}RMNLa^xWim zRKiHz-{Co!!K-GDE_!P0BT^~=Ua`*>lQSxKVnaBk0x7ZF#TG^8v~8l#>WZS#6eU3l zoT4O0idphNCftTTJ0Imc)y0yDn>hv@|LJh;oJrR10Z7o$vO682@jutBhsC4Gm@Hc= zbv%mTek9v+beynIgKdbRX$v>TI%QI2Fg^{LOeROJ6%`8AU1BK+t}YvO>jvn|XwG8( zD+G2SRP-hT=XoE?C!k(L&Y1k#=*l$Vx|xQ$VEsQBP~+)Sb8D+~@PA0hnz9K_A8%76 zG>mWo;bJB1&Ph?`04r;w830B*_Nj?o$XvkxF#~T4Sx(-J#px%jh+Cu6o9>`Tx!>h@ z5WY66d+_8{H$KN`4hd(-o<#IxKWcc+`|3D@?;>$=Z|2J1s(a=p+StRF`DRmQm&bxG za;pW11`{n7i5z8$UMLd7^oxFA1ddyM+p#{aj`a=ZEw&60Nw1Vb;e9!{$zVl0eqsY| zP*){7dPMnScCItod8v1@nnr3FS0uX#b_Nna4rt>yT}q#$73TAB2iibV2jn!H#x-m}RKt=p#)=%uV_)=3U&nRoM;z#E z5a!j>X>iV=X(vAetBCbfYF*c?4d7szV=k`i*5A=VJiN}A*(S=71vrvt!2 zo(qg0h}dguU+$zW1o#}S0)Vkl3bLS?x2TSSVPd}a%RMjU*ctfmaPe3qS>vKFgUj&C ztx+`lxK`1lQ^G*QQL0-xNo!S5HFGi3Kp`aKwYtBo6w2KsS>z_+pwr`$jNXra-WuCU zIt5}eC;$CRf54)GH9#z@f|Yk6PZ8$i*IrJm}AU3H!M z5%Dzcru451xd2ru{H;WjAF(C-YG&%Ez-+ett)9rM+crGxntP~B?DxX`H#T=x#k7vIl$&sLJ5h z09j8GqPpHuUaI5L$62UZ48R1vT(LMwj_g5Z42vJ+Xr!U*fxC9=8qL`%*dZ$dt}e0B zF2HLVMByuKc7o4`(@kwLQs}AC?|_e>{_Lv(XCSZhgQ9X=l=0&PDID5J6foZ;C_xv4 znu?qn*;K!uk;P$&ExJwyOKc9zbUd; z&%BsNE8!iglE#Qm&PbICf3{te3AZ^df};?TErDqqXieFao!Yz^yo~ru1!ja4utneT zB=>8_H4h6f*4XWOq(x239k7RqycNx~NoZ{C88bZQRtC{|To(?=iC3AI)c8zyT4U{# z!cFCMnL=p7jLo*q=vND+Kkd09Vt73}i9|w6lE=FWwES%Ux&ve(5f}BNG{Xk*RUdO} z^cjizhy_2dShY`sKFky|wd^L{?fAJ#wc@E#0!uEMh@2Nu3S6#kQerk6Dw?vfZ!p{i z%Sy8gNdZIno8WHgHQR%r5=bO!bp&vLfTG*XIX4C27DT5hy6(WY1Ue+=7Hzs4fKOtB zw~t3KdqU_05Vey52O&;<>SJ!*wAc+UrmH802|B2eIZlF3BBrS|Bawy`0XiDP%pLwH zv7+HlbnE!Q@j9I%rM}p~B>e;KV6Br=bwu6?iel?l`=@A&fqB1rolv0wcCNdAjs}I) z1m*W4_|Y>KLRfH0lI0dStG++wqtgjZRhpv&VW6DPZ;u1niTUr}(`d zq&S&boUDY&jnFl*jO)=gXpHj6QQ29Z_=AV)9OszSiWxtVp58w&^Uz)DV1c5RxD(`g zz0aGx;|-#7O5ItG)Ef|sk0?7MTupafTtRAYhp5wgn?F4f2w*BE;fHAHQw$hc;Pv41 zfPF`Xe1y8U>lGxV>5881x0(li2h_=oI-0=r zvymsrA-pG^EXb6IO&MI+31r3e0%NAPx0eQbc9&tb*qc!22r<2tR5VRebno<{xE?W} zMElGVW$S1op9~cIxxbGA)V4cYr0excjjrn+XTXrKO!DsL3Z(CMmutVqaPD^#?AHSl zZuT3hc?rOMs<2W}bwl;18+{3aPfQ4<-ts+lYRk_$87~BcBnw`%DE_D?#@+wDF;=j6igPVp1w0<{F#I~~7 zT460ZkM+DV8nHA9vXEU$(W}_K+4QDtyBH^E&=TJ5yRJttYEp&)xe?_8M8mdy!Lvgo zKf0IHeBQ6?_5KVYTd|@$AQ_MLKNWR{GLJbn$t`DuODG0~)+1?F;++ zftI1IXMzb5ko^sezq`V1B%!3B38jOB*FvNU5xD696KdnaEW3FXtD%+mX|~y@a|3UY zS};h#jz)vDV9)|62QGhKz0uF_r05h>Hy2O_bvn2Lg`kNB8N>2^ZrUgK8h*+1{KScJ z#$H2j3Wy!dwJi5;DeK0}?@EhV7kcEb0Va1OPnzE6$+YSHK3x|?gzttAOITuVrM=y? z`3QtrPh(&NcfYnXP?X!d*g zLo^y6^9~s}ok6215rzac5$^X*Dvb=z9()zR$%jj++HsG`eMqB#s}Lc8Q7Izul(cr! z#@cl=J{xi@!xj-AunePlkalI-#d3k&^{BWV7;rAQ3v|2YS);Q38CQD*oa-||zdc`< z*(47X$D9v+VGxagI-sl~B7oDajGKuaci1Eg(e3e? zCDZeLnA#P@1Dmoe#_a(^!Ync2!RVjqRD>zgBO7?RWP>=zm3*K%Thx)KVHg9NX9KKE z)g}ne8JLx^fv%=LsDNN4e@Lg=u&D|FI83T2n=$3#G?V^1ivq_Uxh-B{2-~B}o#fa~ zSZfRPk|na-PU^at-3>~oWMA}lJ-Svkc0j;3dQNmp>8MVfZb;%y9KW7)rPzrGQVE>s zOO%#DU_l+h1E}(Lm*UsO2r7e6f@3%*^xWXCvMTayim7BK<4k3?UC?2Md8~KG%7~h^ z#WVMMy2}b^I66*qNQbA+Ih?FYn!OPccer*g$jvOqfZ=69`s<@f=gesR2knH z>`R4lq!P*Frmd{mbj?{i9?m6}v|XZ7PvcBu+TAtjr=x4h@_wIDGYq?K%*DJvYnBO{ zo$-M2I<;Oji0rgKBQ3I-rF9QG6?!B1cUxpNUjz=>hsp3M?VKN0w!0dF507LaprlHZh9_(7hDP6^eF&IFyZ1<^jfRrXnOMvks`xGd+RdZ#5E$UEflxVtYCuFX7gh#%PzoYd3EuGh7E zk3SJOfzQ2DZMp}t2x$l7;%g><9Drjw29YAfdj&yG!iXOCI;kqaHAcGAih3MZvjy3Ie0v`9q7zFPYxlnTZA;)%8G(zUx zmur^jJh`~|FYkpUky1j;F{c@x2OkmLlRa1=XntX+^=sM9di@FLUc6M=2LOFn{J1y7 zui1BB6E-2dQ6_YQalAf3^+SW)6z$*|Z4zWXMio``6+~0Md@vf# zxlY1k3&umaCJvx@3u7r%!=Zy?qS0OzPi6}~7+T)C(cU5=m*HeO*8K6k*y7WSq}1!* zu16o4))L@e_aA<4>BG&ErI9XMaZyerD$*dnjA8}URiZwwzn+EQqr++|p~mNC;i)BR zShwqxCFckjc~a^Akzkj78x<*WCx1Ym;^}BRu{*Fg1-2dvC07092F5t!#MmM&tdtrZ z1SlOOchK1TE5AemH{e&h$5MCI5u#{quYsf5!x2FiVggsh3W`EQ;wkbHvGMyKfu9Qrl7}EP-3$x|*1gB$y zGc^-%)~q%o&&m3+36?crGWXRW<`ogOTz~q7RKRJ(0f@NBP7Dfh*GyAO!7XFcP|jag z#)PtTV3VV)QU*3Ajl81N9k>=v%1I~PTz8c~@}Yrm0eZtaAinDfhEtxHQf}psFu{E> zR~aP-{5*yc)}3O0{MSAe;7urvDLSN`Z-Qq`dS;PCW>;l^3}W&JhRZhJ_~V}5NA@D) zJkUWfMoTO9Xb&yzF3tM^vk+`K%s zDAght?aKpo12Ek0xLuD5VTp!))lJ&flbJr-sh8G+#P$-{tVf%3Y+nDW=EOzU;vZV# zva1JP=4-Ra{mY9vk9OC{%5CW`q#0vn9$h8|9UiD+;CEYD9F;F8_(-gsQ;a4+x31f^ zZM&y!+n%=F{k3h|wr$(iv~Anw?D=<2PO{I}M{Ywd zrIL@FP7stAb2)0=oUVklTYZNi;_DF8mij7^|G6=cy0Yv8ot2c6ky}Zl4I*=eODuW8 z&9&gJA)jR0`XsIaOmlUb+z4rJ(wW4#-<^c1;zULW&LyT^0IHmtN^C(bMI)4W6*>6w zgI3Ap9mGjJoVYt5(E1qU{mwQKN6s&w5iSq!ZqK{j9CA$Ar-GKBKLgzoq#OA^@1m)`b15HiLZjic^LFzIw>- z&zK{QO9Ym=+cXpg*n3J5l^uG@gp#PKu> zaR(B#%IS9S#axRR;scYG+{=X}#hf=i{PLV({J`{gbsk#>GQ{DPSvW+B5sOFwJ&j@w za9XKGh06;e-cm)_Sz^9Z3bnR{>_bZa7&W(RVqkTi;dqLhD4|CXNL<*dW@-2#pX9@lqzAP-dob}~+szr^M#u?GdU_+A>Tne!j#AlW$1=f{)mL(U&v`3jE@ zg5~h_`1Y(rC+F12s`Wts1>Rsi?;i0O&jf)AsPp`{9e@cf`9yg;XP!EjmTk|RDue$S zPo+&8x=>jU%A3h0Q13;M4uBxpdI=1C51=*A+!Cz1+R67AYATwI)s7@}aIYMV{yjC!4+kjNpS3AuI?cm0vL2K3o* z0&F&64t-@lFAR=VRRIb&o4)IVmtY6aXBUZ5=94h;0k)yB8N$*0J1!_;t5E4L7|(pD zS}tEN^k;@-&(uT4^IkJapx`#?dK?k=bcD5xAP)V(?CR%zOJHj$oqYd_QiW#H*Fls$ zdk5K|&}eVGTwBO}j$R;r#wk|^=58{c1koIUM(N3x54t~s3l@7IVj<;E z0q^=EMh*1L<*S7^HN*3QOoO#(#*&U#S#kg|;qKkOPwf3Fi%s%Tc#8!0fhDHyy{@bf8xh*E3kuwq!8$=AM}(=A5Wyx*g4*Xy_dB0UhR8OEE0mX)Sq+d2>nYz&oXfH+Yvo_KcA*m< z!xK@q{AymkMxOYE)RYO>Qfj_L&stndD#>_=aXw%X)x{q!LC3`iSEblq>UwN})!u5X zIxVMa_)3w?%8vnMG++dU9C> zIV<>ER)Nv;{T(t|{WqP~(_tx&{(9&nGA@6J9{6|?lCHd8Jt#TF#Ba$;BL0|d@BBPU zgvUilKa0lo1i8$GcFkpM;*;jCXZbnX3KXHDt_%nlGvLKS`NEc6-bZ_ADQUkP!gGMM zEM&I&P{H_dKB_x8xRK`;&0HbraqmuhJfXn z1~<(Xx$IX-ku{H{9GLYgL)2b-nga!Fq^=rv4vOSE)B_~q#tsI#J>A#z@^9y7(oJ>- zH$tuNLjmOgb>=qGf7W$2gLx_WeLBEVsN`D{J$S6EOnn5B*k`P|!gNliJ7?BnP>Jy; zH1|Y!E@@cezM-2kWq`9uklhitfjQjgTppM1V)-EDNJrhfXyoBr3TaDKZtwdQg}L~1 znaS3R_gTm@B9!{yf=T@7&}i$j3h=gFQyq}yqPc&ACOZgDg@pP6AuK4i7NnQ9D>LW= z0V3%q_5&hL_NfB}cA!q#{b|G4c-c~D{u1ZEZZ5@nu(m14AQ@gmQeX>tjQMs~Fis^X zRo4QyW?CR%?-7tD`Ir#_On?nIK0}q{OLSh;eyPZ#mrVq{+qVwnng{PLKzM+6(=yJSQ03thef%_8|T}|A3Jbz2@lGa zuL!}RXN#ihIEJgzq0$o@W%5taTb$&~uezC$6371Ojfh14KV8hw1faHHMTFS4@~|g1 zCNexPB%eKyG%w7Gv1ZD=N5ZFf?obpa!hgTJVYXs>7N!Cp2toFqgjuwXP;+k9y-eE~ z36D8%N>e_xwR`xryb@sXiO`g7$i30(o#Vo(=4YiJF zmmZ~}yMoA$jfP6c$3!AFgy!qs=p@tjvd51#w=*Px8MACySSiT<&Dq?UUl>puOSl`A zCGHvi0y5&vb6O#7m_2>!2oI*!_0-jC2U{rtwc4yDlnz0Nb90i_d6@s3 z=$8OVyn!R?iZ8+?KEHM(Qwu9;q+|vGP!$J!9cAIGy?s6vE~{i6n01jGY{8lkp&bfI zSXipJ)XiLsB0e%xE@GNh^|*Wh_GLVqSdTn#n*=1zdLvn}C9V&LG`L;l2bnZqd>n4^ zrisBLjnHlJr+w_qyGmg;sIctnYAhgrkv_AFO*ttvJZ=s}G;uD~SvzuJZpI?#aG5B3 zIGPD13dm6L>hO4eA(+G_LUVn3xT#V}Z3i{4*PD8Ms2?h2RvJuQ?Bjo~%~wGm2R==dCbKPbRuODMv_#W$tC!^q2e1cIIf3bb zXyzAeU`z2F7}{%pM#d^<6H|8@*aRF^BdOo{POg*kDl>v9+a(gUS8xAhQj8Yb;U{;)fP$_Lb00&~fYm4*R68 zF7LRM-vyOjoo|-UucDLInnSu^r6r6m(S`_CfKZ4LHR6r~OsC*fr%9U=DzQxW zFZ$LbaDiQBaN50dfg{D zyTCZ$vsH$LCZ0L^U$0DLi%{tS0zj!=0v;W-cG&dUpR6=D@r%8~Y;Xt;Q_v<m#sLCMS7z+0=KN1&3@RZl)pv=_sDoQQz3Y4%to!BqNW_$dh^^1E%!{(kDnpfc)7gbWsR$ti#E ziV*ZRb_GW!2Gt)y?xZVF0VVk;DxDA^=hFv5zvZ)tdBBeYHf9PNX@H8fS)~cFEKJ}g zt&u1%4Fu4MS{C&sju{Yz!)hH6ci^6atO0j%S2#v+@UIgq_+8B*nN*SRwv3qrV>KKo z-GT-W<+%>dNfXV)&fkYzKSb-L0ehg3^044YNwd||!q$LtIPiKclz7AG$W z!d^lF(N0TN%y@M{65$|xX(wjgY}|i)vybVa`%zb4UCm zgBhsN(jyCyV8d3V>)fpnFs$>OUHXJr1Sd z{#okAWiNRU80dl`6E)x}#qQ-#--2MAg{I>A8W4j8DT^}4%VR-M1zW~LI7E~ODc>X3 z-?=(_at&t?Ly$Y|(`1g`%>jBuv-JQ1HJ-=r?ASDNVYwzTJlah4!JQtFUAYC_Jk#P0 ze{YabDJ#QM?IDQ5(({fb)t7pse2IX1kh*aLSV*K6GN9wmnuiM)5o74=ZsFb0)dqEp z)gX|R(WqEgE>t9F_4Fk5-{dLgD>g+R#D$Fg2!MoHUVSSKsv2?bbvlMx+%WZ9ajqR8 z^gMf`qZ9dVFm8q*+@v3|GA^?a+ZDIP6vegu6%C{{U=t?9`UHKUx*VQSsTmsdvo0BeJPC!fz&!?_bQ%o36 zn)?cR3kpIm7xEA6C+Sh1h-%m$169~@b;!Wb4VHkbaPYyYVz=a2s~Bf)R~J#mq=*bU zJ3~C(X<=Z>6JZC#)$uMPR5#sIe}l*DA1}}p?7~z$G0<?h z3EoZnD!&YI0J+#TjBR(X#o1wMEDdE8j%fpM2RC0GNoDraW@R$3*~4c5^d0@mnUil9 zro?+^jhxmjzBvH|f-;}!ya)r4)!x!!Z0*q&3?$acMjq)6Tg__&mz-{7ILsm!h@r@{ zc8JG7767_b!gMT=UGn5_A^(A6Stm?|hCo0L4mEe4NBt*Gu?ut_{Y7n{uJ;&(`e=(pR09cN<~2fp9Ih@{maY- zD*niXL+LBW-|ZL4FIi8FqxkvR*2JL0cl!&Ti=f-OxxjtLFtJ#>=OiA56pHylDwA`41GCT>w}Zz64E#j~ z@{^O7vm;WAG3cqU3Osl|%G9HZVlceAtlFZHFHMp#i(ka!Z$3`lpI>7Ny8?84vNe~V z+{)y}N%N`YL4{urC959^D^E}IDM(sJ<@y(6GKQ;C0w@PBzc%HX36qD3c*^O)=Q%Ew zAKxIF9Ttsb>-OQAmna6IKUE#PO!a0AHmSi$pNTpYbpd+io*;uw51-AGQg2%5h*xa! z&wihaZ?}Iz|0fy^y?45x8w?021QQ5I`F{tEW@74O>}YB4Y-tBzaCUcAQ-uZs9qicG z{ZDsshXn!#`vC<4`tRZT_a@+w9m(fa;|Nq9SlR8oMh?edzM5kVtSb(JbTI-+%GqUo z^!p>0%$hzU`AUwR3g&5K1#VWyT@pcJLsDrugigajw_?UjjXWco!6vR@Fg0im1x|fjFPlMgt)#3HMMf zVXv&1CPL9Iy4lmS?@dA}_VETSr3&90m*q1v^%R-k7s-;`ftL^m{+F0?~g?&{!{Zf#- zx7I6&Q%qi#^EwP`KKvl$=kqYy6U*6g_?oNlsAWv1?Pc_8=8Ym=fk7gv;#lF7->n z_OTE#L-g*54jr5a_m%3NpLHQNqFLtJPNmtb%m&;%XrUK$^^Nw0lUU+VvhMxMDWVk9 z+}3OD^d*1grl0Hp_NBt5pHwnS5Gq^8GbNarF4d_qC5P>tPimAtXwsLk|2oNk-5qrU z{Te=buTK42-RpQTV^C^2tjZDAK>@GwjajNiJI$f^mJmBO2k7o$9?-6}>^{*OvIam$ z(=ddFpob1Z8*47L1Zjnw{|Ys18T7&^OSJNj%oBA>#kB4~cF}s)C*AqBQNT<18~4%Y zyoAU|e(U^#c>SSJ33?6iQc<$N`+9{P{#1+L9%rw~2j9g1xYgk)QyI)IOXINJd_jV{s)P~~= zErI7ql7JeOZE-YAN=d@SJP{!+ujAOI2{nkEll^uiN<}Hor8F6#a#O#55nCHjL&sh@ zo>|j4SxnUxC$(wRC!vuM4$CI2)YdSyPOoc3Af zJ)7OjJs~2EOy-B8Dn?SqW8CzMJ_j=T+_)(2{4dadbQA{YKaIqGU;e)*|7Vc<-_cP= zQ$rJ5)Bh`>_P=p-|I_~;3H_gPo``Qq-Ax4qq%#c!r1rma3^P+hXBS6PePcT(=l?(G za$8q3>1fo!cUOM^voD5QQS&v6iU1+Bq9jmfA}*4ct&~#qhQ=(d@%dIa1yWIyigj*G z8HRTF`W&JY_nVmZajMDp^BPm%@8hJ(PS5Y*Z7R&I&*SfEUM_+D&d>W4Bfrn%m|fr7 zqxr7)>q(bJo$u#)7RRpdbC*Ex`$5ua+pPd^@Av-s?5yAWQP;!4rJr3~FYovD`Ost7 zshgdE$M@K--~F{lUWgf+1p)#zVCa5IDv1+pRN<@pV?EyzdfI|0=zw6cTX=vhj9vNy-j^*QGGXs z9FuLjPw-&^Exi0aynpQx{=NeQeBD3pb-CW}%bd#mK7X6U3%uNyaop1*=R7QL_6hWK`*Gh%LC%Wa|2+vKghA1!1y^_N>dk*m|e z$J_m|@#u_EATIW2jFFLWr^jz+fyeRR;_1-gWiFzfPCMRi=R+U=_v~g_jZ9LJcKGBz zyyWp6Mya>=;esMrulwiz@%HWybMnVno8SBU{c)ZCzhiU^)e+|X{dwvd^RuCH=D&O_ zBh1~!6nQ#1K0hD#sN4NMKEJg3%GLXm^JDAb{rWhNG;_HsLjJyf_nMN{=k4)Tpfy`~ zw)6ef=4KZ~9z4*Z1cuu{>-I$*t|Io!OV_xEtbt_lk zE)4X>EQ235COy9vCgGMN|9F?~{$Mc_3(mmu2RHK$-Mq zVKda)NbWzi{F^?aU&1f#4n%=6Cn!AOlEi?f5gESRbth~Q^g?LT# zNqPalCL0*@B%y?bN;{BvyBGp>>|Mey1CJC*P3EDtl>vG9YR~}iYTWYRHM(df<1F+{ zdO;7OgiVyq-Xy=wF*}EVWI}hHC1A%%DAP$O@^n>wNS6E0n3f)(1U3>frtumpdFBiU>#!&{6@fk6cl{+X86Q;6-sa0d*dAqP>1^Jc0y z3|3R=2XMg5%7?$n9P?L$y)*dhQRJx$^%f}=DGPrDm5#7)#v=)cD8O?nXA`4>H<)PQ z4e?X6(g$faSM0VaC#8|sUG*T>q6-idPH{uSgsV>nui-U)6`?DXQYVx^TGJ#l{6?F5 zm`*Sm{a`ni#lXEqFOIgHFZ|~src&cZ>#X2>HjlnL^$}vvPz_KC_=Cr03w_o*Apy?q z;Vq(+9lUe&n?K-a(MugPU|$t&{Gm-u<<1#qws~-|9>z73H@L#!qbpr9L%?lUI;lY>24MA~*_xJwAaef)yFD{= z6wMrajt0Cqcw~k7>#mZ0`C6lNQse7%84QF>_88f|$_TBTC5No7e;+Sx?WR~sY^W0B z3h?WmYS(y@tz&A>4-Q49e1-etb31jSWGfx%&w%Ovjgn=6x`A9u*N(+H!RGX33C<8i zQu3zxjc1Wd6U`iKkBfQS2M__Z7tJ(2CXcE^W0~;#d@UL#kZD`!&y)v-`~YZ$4wWn5 zop&hD9IibQu=5X0a|dKezV7y-rp;$B#bml!_v-?p4JO?WcOP|~HGz+s*B_~j$WJJl z1WdS|#*c8MG*ZLV;G%HRkuQhFVA-(RNKG9WjCN~7@-gGh`F-8tnc=w!lYg%E=Cc#@ zAX7Y2#9U|T7~7%xU_#jhXijj`wVxj`WxaJ_%9meMaGrRlf79ak`%Ln!(o_>TaMm!H zG7>)!I8$-(%s-e;>|htJYPiug9h#m!(S7ETgUYSy#Lqv0aRO>y8{S<)1I?xBw3&Zr zx3+^{tuzvnRN1Ju6$f>KlC;wA?{NRhCQ z&Ra1^Ix3SpB*5gPHcDFINzquY*0X<=x`E1qbOWJcsIV2B+gZ~N8uHc0LV-PRY#?$b zVx~`=;cSy{G@AC;r&osCbO4L7`zWm`rpr??1k}`8#Z+k>HBtGW`J=mC4qIqR0s`6x zMdK!jMuXacoRx9S4yw=sS9yu+fLcZTArre0O!YBb*;p$6NJo?9U70*X__(`cXn?!I z!D>8JoEc({S|bn0ij-FBk5j8KjD?o+!rBa}(x+cS54?x>2T(<+k*Q_1S%x9b8CRqI z%5$XqOIseIL#k#LSSV#JrccMqwx9B*h_9XGL=%V)x~!wQ>5JoTq{!wMk94F|U2+Oe z0xA5aZRQ$_{*lIgk4obO|3f9Gm|Oi2k$`|#65>Q|r$G&xRI2{s)IaHTI(P4-udA^trh zNnFej53aLoSQu#(WYj`YjeeyB>nhhMm{n(cE4}secv2N<`Au*N`T$C0Lr9QsPpd*! zZwATPVjB5F-!_M#zhW9o1VP?$rLZmuwJB0CINmEB^URw5!9Fo!IW33kN}=u!Deoo3 zZ+1z6MTbBYODAlbO$$-lZpcis8fY`|)TQ_*tT1(e2_K%Bm3Bnhdm44nH<-y;9iF6v zVzlkakcO_L!-cqJd5C|ogU2<-_0Fa%dfyXIDg#JBS+oDh^gHmVa@`OqFI=72ydX>| zoum`Pil$4&;R%z3d#~lUP?=tX4u)}{ohf_ymB&`4_3O+oEK|*RXTWspg(G#}%01nx zKaS}7J*fbsk|tcGKYvg~Lt3Sbb*qre%TcLT81!XPxFwzuOi!|7Nm<^8yD_{AhjQlX z)t)dP6UL&C*rh3XlzZlu(1>ieL}QY2f>|FvT{RAS2p2R9%Hp?4HTp8P+6UX)&ANng z733Mln_Njpo%%Z*T<@DKfvsk&n5&J4{5#MkK6!7XzzKo3QWN#V2Ay&X9sVU}X9`GQ zR+wuQWcwYQ=Mpl*rUq)R3dm_XaQW)l?RUPb;??=dFA{GprUI>ET`XP0dG5_^=_xK@uP~Gv&|}*Vy*epi zH6v{R!Y?jj-yKl&fl^r%4CUqUif}grA$XdU37B$kgafZ)us_n&Ikij8t6XrT>R$YS z`VxHSvMJl+5JBl}r=4YzK03NpqDcc@4Fc@xRV~+VYv)QAoc7Y1c37xdeE&_u%XYS@ zi`;_ZCt-g53SjdFUF#id*w)z;v)r<72vfB7K6+43i}krdw$!LSm`xg@^vVD(^Fyr> z>+}y$kIvFtZ``-e)a%-eC{(Gh86Da&{?@0_aHa|gYG?b(<>q)1Znyoxet0m-oNN##GekHo^mG(+0lcYMh)qdNCnw~pG+|no- zhWK>Dc~$gZnQ9)K9Sj}CEUG3^MR~vY%TRo1;h?~t6{s_mp^C<}HhWh|s2SG>MkL2z zs7kmbje0c}@r?im^7LB*f4-`I?*^~&6JFvbFaYN@*kOGu{wt#Lh3ck3jeiI4X+*Zf zASZm<*IW&6X6&O|B`{Y#o?p%FDzvrQ<0O5^?ql32-O7ygR{AJbKhZ=kJmIQ1;*0#Q z^tCHLlTwXw&ZUpkV^FZE*511s*{KMlUH(G;q(OSI(yO|)J@aDAtIB*uj$0-9Ye6d` z3x4W8siSJvzk1eLw_yJJC@FFB;6|~&^G+75H;w}_Kmm@IDoX1qZgF-W?yhIN?cxN) z%1Q79oVg5n8ONJ_NZJ)ccX2K;usbYO-=NUXs;(kwdyXJw3^IWtqh+9aEFlEbKkLSy~Ga-6p;G!C2 zSbvcoRA4FhXsECTD6{!L)bscb^3;m?Na{jlq=W&v!Eq@p+CpXY)#r2w;3zoso%Z(M zMP&x5EQ|)EZJarSEM)jZ_G35hZY5w{Q{iUw+I+Q1B%;J1YeqhnHxKdXWE|K?|AcO-e=c6 zpB5%N_+#BKrw7XU7@At!_}L&z#644Y0aEwWhsikiY9&!L;;nhySi&_IMo~|#&-61f zzrtpzV2bNuQlAbKJ~D}^Dibn0RbqMd!KO?i3nQkxpT^uPWy^uBMcvxg$7jIhudT}Z zFs&TjirkAoj?F5Cq>B(7h{~z@Go>&C$jjkYQ8ww^NUCVysaZs?4Isanj@8fOY3)gS z!`4J~3cQ7a6f)ID)EP>}SkFJ1>CscKKPk*G!iaNOKrRUo-jmxWc;mmtHRc-23VB7y z4Y>@ac!ravYTsH%z__D%Fu~y}@Te;{Fnz8&Y?(tS+UWK zt*@aR3 z#z$1qW2z{(@p6PNCn<&ct=Vhu7;uV#PO<(n;}IJSQ5oFe5ue`BbLaRbJWJ^8bA0vN z)i37dNJ;CY(3$?vaWxpaz8kPbB?3n$+Umw*VBP2+h@=u=aXC_1SrYmS@UpOExKY%I z;!WSL{P|T6zl>)Q*_61JEYMy2yHvHGVbNsX`ULMztoE}cn(}C4^|*$$m0IQ|`LoLo zhoZ7ey$Ig)W{hNxe6a0r2cN#N77u$}V|#vuF3M&EA5+pudQp*#5kb}^%G_c#>-eaV zDZ@Xvm4%uJUN~XIk>9c0rTe_M_p4Sf7AhB~lcuZjdaOzDu*UESD4>Jmg6~8W38tWj zgPQ}(1lZgD z!~8}p(y}fqHNm7Tv=JHk^&c>~3HLXpW{j% zTx~w4d}Z$%!4{oR(m`PQ?l4z(W2Y<=Rg7QN0H7oLoyY9MvqSZ?u|-BkHr|EMdVwcH zzj_LO1=gbqD&S^i1RyEJ0+plMjfmg1O$*$mp>s0%9LUXvh+0E8JF1{ba{g1TEo7;I z7D3YdmN2=@8Mo;jRA(-Nj#IZ=k(MFbETT90p4tPTt}_^ViZjZ#uw&&m2$N92@LSihvJNUMZZT54gq^Iz9CehT?zqu2zndv1a5B_<%gsb=$~ZZuuvrb>`{zh zM;~jU6Dj3r?#PMV2U%{h8KLhksYgD+UfQ2G9AR3KdW!GfB=6}AN687$JohU)M>?65 z_K3R;a~}Yq@rCa&L$P(ehy|Pw3me6ihA#VG`L&-6KLahOQ;Y+tc(v4C1YJsP!Axc8 z6$UH+EDV%j&8whg2-=%~H$t@R6MrC!>xG1<>?QE0}VQW0wmHY9opPUl8KLW>RAG56p1!~(Sjrhab*f(cJeKPQ7F7Ah_GyD>p*sUIP$+8H_UD(#QWX>tAB^=$h_U z?glLzrIU;)zgzj2qs7c%QX$j9Rvm2Hs%rA5?sd{tJ*|f;SF8G0o8gMqq$Z|Zje`PK zH2fh%W-qi*H|Oa_yGPFX>I-{y*YzgEtC+R(5}JIY%kW76J;0P{?)D2dYH9FqlL8aL`Do*d|-@}b6{_mis&L%QmY+P8bPp@!5u zzCSI8p-N&vCXVeev1_BqydnBcMg0U|I|XBZUL>p`6LgB98W5*iudyDhl-ZPc6Tx3Q zTN1T`YLR7hi)vR5ROo-X3F?>PHZQe2i%D;~K1P`x=hu2xV>J_4{B0t_#hZRkPJ~Z| zVXCf=faErhj()(lvvQU2}(K?~WNA_F8g4_g3nW&Cs*cwfuEpuOcRE7c5dWu39?5J;t9XIYXN>3Q*$?cSQP*A~ z!yT{e3inb!hQD|HbP*Cg8*SjsRg~J{P9QDY%~{k4rzEAb;@?FGX@d14a|t8FKS|RG zvdHnSZoJ|PaRs(plC5Y)de&z;C}v z%S1!u(rxV($F6d6kyP>P7ryRTuB>Ibz1G{9_KoQ0r5A!H>tw)|^o@N$KwF~+VWa^< zDg|!4jmHx!N_Dm%3_>=cBN(gk1bqHAtx5Ito4mC8^V<6W&~R6Iu-CN6wrT4VFOb+E zJvl*76d5DG(iQkcXzmMZi4?^xt;wl3Daz&%q|k``vUJrMjmxHZ4?y%ebZ$C+MGr+_ z&z0|7^>_nBGB-@&aWtvBj#Abz_ixdwSaMD&vsTszAhZBIiIemI= zD8u@ijPxvcCLVtVMGbOd?Ho(wmGf2Taoc5v4%$sB{>0xj2|K=1O;RhDJTftff2&1J zDn9F#2|L6px*-V{7d6-ot=Wa%a0t4U%_8bZAtrPB8dV_fd>v)i_1O)CFXl1Mw%QCK z-36rDDOo!Max%;+?!o zMWq|6;zYaXr^EwK3W-;n-aBvW&9OOUx`xkZ!`urwsE7W%qj|Auodn&g`w*p57L#~W zDJZEkhDDd%gSDKc9gyBe>eeX_Ya6D%3TP1fQIW)5yP2|7ot&Uqnmw#iVfXr}K{K*_ zYXi0pyTlqSRK9qs33O*FGq4^?e|8bLJzy`^X;L?H6hoKw>`O9}*XMB4Q*}A@b~tyO z+X*0mcl;D@=7_J^et+yDhfIH9Z@ly_MhQx%-m}6lbHqnb8C+<)9_26x`GGrq*1Y-j5Ehq$bF^FMA@N z#mjunbzRPiI;bgIZJ(w%Bxa@usu(P^==5}d>!CBCe*nLtGo~DH-5mj{ps|z{(3wvV z6W)Erz9QQ_0J0MRW#<9&*$hN>&-hc4)^qTd=pkwruSupvn7RPhtKPz464hEWglL%6 zGia6hDrnQgx+|XXCWc#=^Og%@`C{2EbbLuF$I+ zFJRJ6^P+9_-!vbQU}Fh*oM@zSzd{P5r^r!0`G;?l?0xaT(k{a6%*lZ4Cs_aOpyl72kG+UOjZ|N#yRfaR3l3eIrRy<+qR~!uW3P)E_nq{S!u^<|t6eDNT}qsUkQRzs92p0<9wv-Tr$;QwKvA=wd~lVc%{o- zMEu2ch~*V8a^#ciVS=rn%<>4u*?HjFeKeYkNW4B3aZe)k=h4naO4KHI;_iG|H&-nn z8EbIV(1hrm6>=~IU+knOzOTl^gM=QDA9TG6@d zB16a!ZdyMi&M8KHsqS?@%L$z|msGGRh{W2s^%H#;0C*_p%4_ow(=7r8y8gDfu>_sD z*PA~+Tg%3`wd(AyNq7%v(J{-DD$}Y}tS5GGjCQV)NyJ|S7i(lU64#ONT4!r3!-k{m zJUEMGBP3U;#j3TDb?$*3@LE>+ndPM6YgD}e&fqXO(j796Z;o-CJzWolxZm0~Q^Sm; z{$=}L25cc9N5ef`PA&Dvu!M>DhFP+ z^TJha>N`h8D+1%ETw0u%PlbGydXPQ!&SB}iVWVghrptX_OJar4-%aztq}J3fhB7?U zv~j6;bAgOWs+<6O82DGXNZN$u?b8`k9a}Siyz$-0f)RQFfllq9kwHqJfJ$u7vlU6Q z0pqoU-;DwaJbb8;!KMKiOU>I2PEgsb6p^5HwpJayK5?D>!^hmLMrsz7HX$tN0CirXQhHj^`+ungTFn@$Z z8E{@1?6tO%@=>-zqhSBo{)S|tdTy;qQeBrHJMZP*W(TyQMuZjgc9JVBPvG+Tv25evZ3ativXrPO(-a=Q&vY)d9!{ayhjJH-SIJIx2 zOzW{z-6I6pWey)2od|L$9;;9C3WdH>?`itZPd=$-ZC=JxNsCs3b7$GNe${976^-1a zJAPBOjJMqb??4SW_`oO%Sm1yB5!2y?ieOg*$!ZjZcScTAuE{j131iHXibUUBc?bQo z9P=;Th+ev@LhC>+mFOR9%!o*SOL=y2$K99C1e(N#N<-(THE)+6MfH592K7oy)VXJW zrMSsgdRMR16?__9<)hZ1&LlDMROk)3ED0+Nwagk9#Lc@N5x3~AHeYMlG$|YnS+_}F zm&j$mRij+|&>nqgBg4-<&E&dQ-(RwXbAAgx(+(@LWHctp=7_gQtb=~cbg-IDx+ik0 zP~YH{YsK+&RjJ#lNB>(_tEh9w_FnRK^|{-HMFUp0m;z7=-JcYLQj#AXozh5>)=|T! z?o}Vex1`+0dU$5v&t_r}V^dTCfsVW5nuYg=6w5mNp%H5UP{pZcS3JGQj_27npnN`{ zx&-_jw2(uEM=Zq{;M!3fh8I+;o<@WSYib(aReQ3v+(O7K>dsK!!XUVVza>IqMLNzf z#g=#NaaGa48wN=gl^LDI@ut)tIVGl~aS7Sqnz+@fK7evBXHs^uPkN9?~^&a39K90F5MndFZ{ydR8f@M?LPoHpMDXC84Y}%eN=& z+XTJV&>kfAEE@zdJ|8)eX?@-Cz7`Y9)j@AZgLW&mizd6IQ$>B(4}k#Usqf0l*+Z!(tRQZR(KmVZWb14H-Ca~d;4^pxBvlH%1vxsI#!*G~ z)f2s+&YFVW28!&mm>Y`Dw$12+&)KamZ>QGqnZzcD7x|1mofLT_wC%i|r~~pmh|aN- zl#RkC)e^McPn|hT!5|5aVNB-5o_q*(?QZ<3XAqDf#LB)Rk<|^VdbELcj?+cK$vi#iC+*e->pju3&^_Tvzqz zaKqpr?pEVL)Yw0ckV7?ay923Q6|P`w%S7Y+zV&aKTw%lC%~VFf?!;v5l8jWqS&-Nx@`9X9TU3VtZ4bH~w$8Tf>5t44AO%wa}^3a5|s*6N)PXP)LQ%$#e zI5{Kc0=q;96FeWF2y2;u$YS6(7sg>$m(v+b4gR>>*edN=Eqk^L2gia@YabyV^iqA; z%)?Ek`s-|c?~-WD9l_N7>iY5wc#ES zb1%|9kRDAoEtO9h^9MA~J5DWn0w@X~nue#{BO50;L*$U4+eVzG3NzuF6jEfe-+4L| zN2j()rzqq*x25fVP~NwUZ8ZJmYFQd})NFIs$tydX`(shO)eZ9SE2Tr3`gzWOLzUP8 zM+~1cEDzq~;Hxl1wMIjmIFG-fq&`9H6FFy7h$3&-)?10YJqRvO2OHuE7|2^lq{Pj&%p_f7q+( ziV75c49dYhlQQLoaSPJSk}Qxma@C^;bR+vErrffhuq8+&Mx;;oLPlqZ3{91RX!adP zAyX|>)%$4z^qT7v&UaN#aFkj{Qr*m$`mqa1@MNCo==@N-3NmVLat+?R0jQR;OIu^2@1ay70_p!CESuQJN`nBk;t`i9WR24=3)D zg>VWSxtQ#dt9)+J8~sDdL3pP)I6G`~GtkAI3Om$e;n@Qvm0Zz0`vkv7Q$Zmh!!WF* znrQH5u@Gyqdfvs+BrR+t_X*rduP(%!L*S`IAqU||C)k??~AgEp= zW0;}jp0Ck(RfeeS-x#n~S<~WSPF)>$bJgF-QDt@*xZTg)k1kL42@3eCPIlgMBbw^d zr$JoO%V$PvrW0B^Mu%Ik2uDn<1}t`ZyL-yi_8{a+d5(^5iR9L$Mlf8eJ@opS5af zwQL0>ci0*{xI=zr3@>^^a^`^O-HlmK7TQ#gs|Jxw^Ss;BLX)-QE4-5Zv7*xJv@T_2TaC?(XhxAvl){!5x-YwO(7hKlc5a znyKmOGt<>Q=Tv|4MG1s^Gf+Vk-H6}1P&5(wH9ETJppx3X&sP**^K(zbVtQuNbnn*b zJEq^sUHkREEB0H5yUh?$70pI>Puuk)V3?m&AAZ9?sp@#;(lpcPZIkBq<9|oSQ*~GT zi~mH%pE*K6==|qGDP7zhUEMxWmH%%z<#Lv)(_db!-@8xP-s?Ob*aAiQP8JB!b4@m1 zPtVN`)tv0OA{a*9T6MvJxoKoXoE6+m9x@NIa1~HtEG?Jqt9!u2!*hEzM&@f-`>OYD zZ`xwkyE{K_x|)FcQovM88fm z^04sQTI*hNDL&z@TUum25A&q^M~EeH;{WLr{H40=IO!-fpejpMeeKy zk(%NwrkY}8{Etf0vu{xn)^TX!>5$`BPBCLAF18KOE?UtJ|O9y1}RG3#=ucyG^BKW)xdlGkS&BViY zWg_1*jzn`&}8Rh1T(CgRry{;i-~!0xbC|Tzr$(&K}b(c!voRKNGF* zA9qtzfDr_Qp2vBu^`xIwTL>1+^8SU0rht$RX(wK^Qow_t5S=Q5cu#{Gaz{YpkYW2g z*C&A-x$V zCN{J1m^MWFptcZ4FB8GGf6LKXSY$NHCx6?C=OK2lfhahi{}y z9Q=YVp#OSe<(h=XoGDs0i7t5$dxGkggf{ZN!gcQ1ZfiQj7a#4y`{)4Enot!Is}K6vT1OP;U2mu^GcIs_!@kYh_qqWkcVi~$%KE7mtrxssefUSLNZ+g&<<;_jya4 zQ<_qN0nq|-&v*-}3vtde0Fe-{Lfh;P3*0lTx{H|9gO~Ckz_V;v9$cZ5s3WL6rHk1k$*qDV{f>X z!rVyBOflQq6~SP@svI=uRfsdK0g(WP*l(BqvNK&BQIuLO=DU-gp@M>flz01wFS#r= z!mrO)=SMG~MI)mgzwS=&fUcg$r&1#p5&gHDRM4a{Nuck;#b{Jz)gsF!i^%KS?P6x_hqq5p;M?mz0p$SS zp3d&Ty?#(w=H_wfuJ7wp=l0w4+u?byveC?};U&pMPET)6uF#gyRe7>%c z&skQCOqT^QB?~i%_)c$3l^kqs7n!QbP3oNCvdaVRe`JxLQBNyiSQUM`wQtE~_@^hG zQkzn9Ow#J8DYYN+rLYwLX0h$5Ds_>Z16Q+MIhbpl7DrZv*=7ms5Yto5q!Ztd6-HU6 z>*5=2Qo^#sXL4K0H?Q(Z6FAh=S%mx!PNnv|*L<;Ww>gth0i`uKv0!*wNmMrL+UAI| zF-{#Ze>0fn)z)o}W?xov0K-*O58>&WhhP>V{TUd4_IP4v@3N(%SY0UAD5$c?Y+_}5 zm0PB7T#>7ijBizp+p>^hMbu6Z0%l^eITbbyxG3BxXp}Z_`fzckEYf+YA$^~z$9m8$ zK`Q4{2x3zpT%rbqwM$s)=KfSN@^h1fbX=Kl0y_2|f}JHbkoNVl)j0RJNLO7kU5FO5 zcq8;I(W?oy4u4p#LsKmVZ9=bb4brW@B6?sC^3F!Y#6zHkMmvlW!80kTl?nMOCkr0* z#Qg-Bu#@LXjZyxwp2&zRlUw5*swli;Tq2wGSa&bkQ{oDTmn*kQ7H7KWy!N1(DGz1) z?HCQ<*Wio+JI9Pw25%**Fa885ec!+fYcts=^{glrN5P1l)Z_ofyUfNcO&LG)<>F*Y zDRQA8yh1`>%r$-#5VQ9jYf}?N_?H|9p+~#F+2v0wWf_TkiP$^;D;)$0TCb9t0ctjqLRE981HpXD|V#ZVZKn}qC(-+uuzj3IUSw;__>_XpC*462_?W4b*TPj*vzqg zc63b?Y3M(Se_>+JKUsHnEi4l*ZRB}nr_&el$3@0N^eDy=3M&-P!G3H=Dw&YgRI|bw zP$jF3p46E}Mh%gI7utX6qG9Y#Zq0=Z{w1uIV#{xnR)jeaw!BNi_m`oYvFQt+NNELr z5r(ItHK4l-nXqZBxP@uuC7oFS7Nw;$i%HG+a7K%2|0;eIL&2B`_5+1lhA;yj6q=(k zEn0HW9JfE2fX>cTc>@s&OKwf8Z6LTU8^mgN1<+LZOPP1`0Y+(=yn`)~(+M*=={zvPl1-G$Uqd!}#cri3E7dvfw&mPUu&9hu?%w z`o}FeX+ctjLGGXq6ccY4Zb?LUS^r`?B59eg^apodKb9HW{)8Fbyoy{?Y8(;RL*@RP zyO71L^1UGV?;@w?Bl;%XI@1(YMH2Bm=-vl@`p3H(lp5Y3R`Gn{9~|*Q-a!64Wo3&j z_SRTGrcC_(>3H+)SU}NXa3ux=vIPD`{((QMlX~N}Mx6qo6=g~|tv?JFOcpJT<-%e8 z^LgpCG>LF+ha-LEqf9vKlIWdmdchSyb0K(Q@XEqkY?>D#Vt zpJ!cWZ3+vFol-uuC-mWfkw~|D#yS2}~X?6nKMh5=xhMbE)1= zj{R(&U%+Y~U9DHCa-fdz{IZx1NKtKT4{xR1esw7?e)4qIU!r9}4r&HXCpP~$BK#X)ZNy<_CR%=P7OT&99}}aJhaRMsUPTCw zpRG3?XDgi+#&8$!v7{H+$(|+r4qe&ImP$uVw`S(9!{9XY#+21Scc`b^BHqgPdvD@s zuqC}NHyRsi03U3R#Hk=rgadALvgtz#u0}2FYboj&$0uxXvMJ;9Zj@}4B8&3hZ5F?h ziiydz@1rJowVICAS0perT6&ii%)4uF@xny!AE(z`vbePMzA2Z?-7iqmaZiYlwFjmW z=}loOD)o9yJd9kNkk8U{Ghxj75h$-xXPOxi+!J87P>v`QOpZz$6EL}};O>yty=nGE zM8sEd^+(wFqmAecv#Q9@a3(%mitC@lCXxDxPzH3GQu~;SyWO6@`P(O6Sq>=qUn5+n zj|(cq|6ELU^yImFKS=V!Ku?@FwUcSmAr?3zA#}yK3#iN%95J-(wbI=MU8Q9W887x_ z#w?z)SX~`|{11v?Ws<-0|4b2-{Qu3CTz{CjxLUCM_uMGc;^6GL{^yTeDt6;W59FaOF>7Wjd-5T#ge&dKK%bNjcW zPGHYaN9WKFdlisyrn^F%_TeUxcW#wYOCZz2`NJa>9^=0>b*+Fgp6c3#QpGb{EO2ja z&l|gbQ$UnR+njeaXJLLlxfGFQrA~p6w>I#B)y`ADNxs!7QvyGEcu=8}GKwec!6dYY z@qV|}P%Nv*=y05afL4!&#as~v!RRlTo0#?`+s}zz9g{~pdm4{c*u&EZSbD95Dc^4K z#7*V59}Jo8!h*KeuDqK_8<2mWLbCbyI%#w_v7oS$xJHcX`k%-0;NXJIGfB~_)HMe9 zFS#RGE&0p}A7uMhj9hP~L!(!=Xy&k8;|rs(@AmQa<{8Q^mB6+YNyGeLme9+VRkwNO zrWVrr;oD?KZRTOBzm$s+O{af{&4bL-9hv3!B~0=Zwqu|7Ip?Ln$ieL7KrDU7rC>?v zEB^-vUBQl-K*n?7WlByN&$uOUiJ7ef)xmCWPxmm>ELU3jGV{RK<@Djj|;>{X~@1kVE8FMQ~HSIcfTdj^iLLBBDv9;=clr=$xN({@`@u}*FU)C7!F-Kdg<8{Lp9jNC%q{z3o>)a|>{>`FP;}5J&{9R0?qw1oSa{rOf7m_O ze&_#nEo`!N37Z59RcK#jPHU+C9lRU(<`Ev1dVp5F2>#V|5dGGdnfaS$*930Lj z6dV=BWio4`$~sc4s=&^=;zHJj-ik4^yN}!tzd$$8df6Pr$e#aIt{f?EMA}eE)3T}S z(NsP>QCj7Clt9RoZ6=>2bE6!17(PK-+DprOT_RV*Pb#x&p!W>l#v$>=&&P zWiYK$(dV(~7<(9o11STxK6Opv?Vr>ZxIOdDO+?0~H?{BHPOYsl?oKa8N#PMwCw5GXh{CpRF0z_e(bKAb6a z*CR%qhPL5?xB*e>yc0gP5Qopgz8rBjdmKKZ8PC*jZHBu}IQctiiaBRhiff@K+GQKn zMT_<($+Y7Ud~6i>wt1baZt@#?Kz@;#)7pi;ru6rMSqZ?BjFrg$DVjHnU$8|9`alm8 zb*z<|INnLo&+_19t^w|Q7uOYpzTID*FEwtXFq&HuFdMdH$xX)Cv_&UH=n;vqeO~!x z-)|q%lc}ruhWkQUffjk^I__(fd9OdC!~W@s$lNCmgi@ti)8@%QeE@%|AKxU5^2usr zytXVe1mF3GCU1vL+%6rko)YHw4oMNBkN;NaY1O+o@tE)ag*GzS}L zI7IeC#;jo7yI>%x`JGhdCP%kB(;!I=cyuR0nyDPvUY)($fthLx32Co@#2 z4AvAVGT5ZDtV<(t)~ig`wbF10gZk|TI*tE*e+KMVcRrl3TpDZ_Rl-^I?V?`Ju|iyC z{4-p~o@v4~lwgW6j`T~&Ip2;`%C1ot9m^o&kfwJTjnNWe4khq``cd4RYydqo6G9O} zD`I04!AV=4wU6;VLs(%gTdQZMQ=}k zQqX(8DM1kbMxO#A=twF}s0a$>%76}`GAwWye=|BVIbS2?W9^H(t8LEbKTCiV*5~8M_0HeIVGua#yusQ2Qa}8B6uR9jhm^CM<7aD zuB*^ltD9#LAnIp3Ls)->9c~wmRWmRPV$k>9o)K3sYb)qA|DfV zCJBJ15=l$D)Qvek!(_>9pJO*h=WU`i9gA}Z?qe5J8b8e#^$8reMpW3ivNfh5y!}DX zhC(I|6$-zPw;r8meM0DjAdfBY zi>foFJLCSbA_VxN>1WBhZL+D@fl#iTt*o~+2|8r%4XV*N%+?VF7(%#i3+cpN)zo+= zM-cv~F~hgoT=OiTJ(tZziopYoqgym>=7Z^Fwn~{)BcHXgcVYUhq}Ujv&W2T})vs@G(uXGNt?~tT$$)kraBaV@)cmrk&&vLUI9@ zub+qk%o67K^V#vUw=!&N7mF>q(umbyu8Sj zVJ0PGeU(G_u_Zk|YtP~BH|6%=wom7cIYAW9&|HG1j=yqN$xgJeqq#Yi<$K(N(7*|4 zv1%?pAfae^6tO>V0uHuV+w-<{uK zLz22k&^rg=(TVERnD^~$dF?CU&niv4M!EYQ*;Ez=zc^vK@Yptxc@|O={Bc*j&yk@> z?5r36i8j{6sO;98={Hu65W&3GJVN`)7u??V6$_K4B@6q!`8vw3p>>QU#|G+U3HXFF zWa37Ki-NV$nW`i zT^)*>X5;5*UwG~dwXj~K8moquVzhhcL1!6T#OJVdpbv7*Juu$mnVz(Tmwb5?b;_t=FDh@+SGvaB#zAjE1Q`R zH;-uiT?(~ia88SMa08%Q{9pfsyF*F4RQ}`Ak{#SpK=FGql)XQ!#dkR?(*3uG_+J zLeua{-oO2?TP33T1hP@~5D)}7kPuq`NyYzvQ|9q7v3IxlfA5umyY(6=*EOo2W@`Tw z?~)JtZKsM5xrsKJAoHhDp2$=M*>@n7BUI97JHGqJIZYj+UkFgh4Jcq(`i~j3GYD-_ z4EP%yxlgfuoF9w4fpTB3Yu`s_-)}2-U;lwiFF)?Ia(kaIG6NqcbKehA10Qa3-v@U; z4r)IxN_!s@MV=ZzTcnD7RDQgB_P(Cx2E0u_i1^)2i}=5X)qXrF2j+hZyzkAO4S0Ck z75?~lhiVk~GM)Q=e))b>`q@fEB=_yc$p7x`LFDZy?6T*rH89Zk-KqEGo}~AAv^MbW z4)x=m$LOuMchMrZ*XP6i7}WEg&HOUV^6_`sBJgS%)yV(jDJ(azxYXDEy|(8mH@ElY z{l=;D{Nr)jQ^fD(ZFRTTyJhs0XZDu&<5?$m(J5!Iw)gocH}GxQ^CKhRzL7-W?OX20 zDd>_bOyuQ0SLA~W@$Zdi@8n_c>&4A6N#{lF$6p!|k7p|*|2GklkB8d#x0?qMkLB6B z52w#`^x}Lo@X6sOPd;E2Z!uZqWxDp`>c;b9|KoXb*68hD=H<@TpPPnWUEhO4_fe_9 z_hr=FkHgK&p7+w)fIBmim$od8=l#@IW)fHZo4|m#!H+i{qri{PuwN&41MfaZ;W0Cy z^w?YfmgM-Gu+~!`z-nx2e3pqyNFyagL+Is@^SNzsEuE)a+;z_o04S zf5cF_+h6X%|77rJtoyuG?YO&@DQ3ZLwRC#@p(Rf3hTDD3cV1+37}mq77BcNCm>!D!{bvc_dDC=J$-Xi9Baef9!9Ie{Pa zAJkm#?(_)0EBAmqg~fRSg$3^Ro=aM2VwNjW7q>iS9lAZI!Hm8{&K8O`wR$^Nn}P{~ zPPSK@qs`4(lS_UsWpvE$MM9t`<(Tc0*MOhF1R=0e%vl+i?`d^0niVEOC z?q00v%8JT+=z>3WNY6Nl3-vP(;$3%f z&)P5s*UWM*|7NY@64>Ql=?bHk{Z7}qWq#?0tCDghp}6YH^mN%bwsydN zax42^5&xe%MRbc77sHW#H!xOtE z)z&lRYZpiZ$;2-d%c^P)N?neR&nb#l`i{!1dmx=Pn2s67f=z z0stl}lG|D{X^ISTyUGE~j<@qu9~zPkl(%;jyx#m3+ixnqsrq}mSm@`Cfa9S2lMPek{`0IB)};qS2F8FL!FZFD#9 zFTvKOFPs5>_sw+3jUejTZNPDbcg|+)2d;GGs;(+V7;P7Z-fQ0i1b(>L)l~7wegl<; zz2d4QE`WsC=FB%4NEli+yeb~JJ@0p-|dDo6+f6WDRoyZ8BXYt zcek3<9Y2F!Et}DyRFezjS?=!1IlFs~hc}6+L@rhZ4y{aq+_yznp^t!zokUDm+!@r> zaX4zCo2>w4C2;t;g<2(6B(a)l|F34bQWSp~W+zMU_Z>^`Y_pt6#SHyU)3?1DD&O1r z2Br!ptl?HC1C9b!DI9>SC$cTEU}sfoPI1Yd2#Q5v%r91D0)g*p@&aO!d74oWcqaJm zjAXO^NJ0^zg)}xl?9N&m`M6BU&~J@(VWfDxUY0K0&A3cRk2ddUx9_@Wk%XVY>b>bU zAG_k*r~Q#3%c5SV`m5LT4XK9JPCPCdy3J~RhDWlbr;j??M>|y8XG-!dH}D;5EpZ2#ia(uT6;uwqms*n!`0>wUVB`f9e=+B&E+LQc@&*<5C0~dlfHn+PN2Gjq7(7T z1jZnLQgENnDTuk)!N4BAMk9%GwvnZ$ODnMwav@gEZ}#xd{!#<*Y-dxUUjEG6vU#sJ zO$WR#m>KA+S?RW9T-(ewiGkK3we1jKBY29!PQqwG1vzCV_j>uIkl(l8(}5Aski&P^ zR-0uI@#_YCTK2>d8Na7Ay#?(>|EA{*uBS2o=QL7*yRYT)NXsRfOsR%L$!Gj%6 zCqgE0B;LCw*JMe2)8d!mMWl1B7z9Ha5?VH9snu#kozY}h-4mvwy~-zMJ9*#3i4jDW za@*o_T}HcriBnV_dJZZx;z^!Ev!ZWxP}28#6Q}@Q2pa}32#jV%i4VNi4m(oJqu*}K zFJ_4(2^P@>Wtf<|`!e?gn?`X_TnAwZiB%S=2m4h5gbm+|;O*UP9Xtq6FSrW8>=sKi zIgE}K_su;;7mW{bmv14#e7Ac(b{z&N4LF}}23a?1&1N(}YYEcfdueO19cQtMvKc|` zy~^xhl!9%s-n8}Z7*;=RC5zCmw!wFC%b{zl=cXCTeCA;8Ky+QM>1X^IOMT8h6T^C?kMzFk)uQL$weTa?yqTZ zo{m+J6G_}m%Dto)Am8@s;5BIrRbKD-*@ko#1l{BJO#?%8pu%Qjup;3%P4bGNQ*UM{ z?2Q~E0nyq4ErobdMI(_)gm5(MwEQ9WeT@US(^n|pUyq@4~oUZW&a8iH)Zp68Q z=Jp?+Ta(Ass3cY}#q*>3#hPJYV@tp|4qzQodw86 zpQSdIf2~<&$j;xK^GS_gTsp3%2V50$j zApc-2aP}Y}@tD$~wSJ6fJl>r~z-gl3mUxIZrJb&~KESV*TdkxSy;YE>{l3Sx+FXE zVj0luxzLj@U>~;_S>={WVKtb|_bvB0C@$)hUrjCIV_T3Y~{iSAj+`tQIUP2B_pEj1mo{-qL^;o)3aPI zTh2+xZ!3nl?$pR^jIzg59w#6)h!yF>Fyz(9vY2uc;jmpb-4Kp~m~zFN?!8J^zx6|= z7*U@r{U|fC6&v+pNYJRp02+F`{!rFk6GJ|wR2*Y&6ti^f#!Q==Siuj#T)>TkE}&M* zRn_zp%~0b{aa$7{s?W;K)(3(^-T0LAq*lVKG{BB{32L|JhTBElG}3B~UJ1#R5u>(D zUBPL2q52*|H3^nNm!2_{p()^q>?|efN!F@w9$DVvlglvm+;vzgz#A)ypv8vjVxjCr z(wc6w*eBBnTf7`(u^eq>wIyH{`+<&6D#f-yg(L|BkHn_->^hY;e50VGyTB5i^s=*$ezF{i-m2cnw}0kT&_xpRZTSM;mqExf!uch%`43V39D`|D4KC!B^s5 zhb|5z4K!o1sSb0}W78@&FI~nl6+44oOfY#n+0-0Rdyf6v=##k8d3$s1(2Q-KFYh@) zG+&bZ!>3EjrN9_Dk!`umQR)jBUR&~p1RW^OQbOD_FX=8EpW--?^Oh263`3xswliOw zO52`ykN(C+a_UzgrtT8?gKt#u)$isyVn<5Hg0-85$t=n@H!q=zw6~2ulvX|?Mhn{f zG+iVb%uNzq-emcI^zUTu5l5Au(v`qPgH5zj`*?(ur!X$QKuR5j73|}b^&~r`wNfcv zIsWBN2Q=Jxwq=059sUS`i02GNY<~(((dOWVzqaz>WVZ#NZS!{o4#Z$$--#HB`dr*lb-g2ZGrxyy{|79S|Z-oA0^PHkh% zViW4B9rL|GKC$@lQmiRa6Fz;zisrob)5uGQsRbU%jT71=`bM=KlaaWnx9RQJpr-6V zHuWi&Qe$!r62KxEiIR#d*nuwQ8zcaL5! z!uAbE0`gH32_3m+3j)AiO-6Sv88Bal3d2R=<+PFDDHKTv9Qz7b+qgV4u^1vcoiF*8 z=ypPEw;|kum8@>__d){NUh{Zi{AS-*1bKiC(>pJ{(;$u=?N>}pw^=fOe<}9T01BLU z_F2XOkjdw^YI_a=zJI^T$q0v{Hw%sCiL5hW8@*miheHy z^qp=yaPB!xNC@1*?5-l)-w^$o93XsGWB~Bga_NW9dr}@6ubLSG!>gUN27Asq0TSv; zQDrDNZmC6xa1!SZ9@AIsEY#a+TbPW6dV)DE&nayx+&}eA$O${`_17P+nUOl;p_4^H zik*w0iRKtBJ=|@{kZ@SlAe_?_g}zemUrepxd`gUuj@vaqlY>TWJP>}1#tKu$h|c^W z-FK{oB~4$(Z9zW&RfP{#=i_m{4?;)p@8+K^BU-wokH+p72~gMO(XxMuw7xXC;hs<7%vHy+;#BmqmP{=~0%y zfTd0z?M<1?$GjoXFUE4kkcJ2>j}*Ezp&nwWV)gp*Q??=n`R#NfR7y;=UEz5N*#oILPOEGD`)dNRHS72J}E}@m&ZP5n$PR$yfxRK_IgQxTx^7~6C z##Y%MPHf1ideQP7=6s1B^J1(4WaASNy-t{mVkUlKX!skUJ|6zl%%ex2*48Bi&7iuk z_7jt%0e)a;V69yqBt7(Bvz#C_&uas$u*HK6tKWt&$*z1|MjBboq$S6zn;UY0TXbm1C3h}7 z&8Qr1&*^FNb*vGrHiSD+l)=(o(NSiF?kGk9C2FMWS^yY#zy>lwg>+5-$>%$jrp{@r zbL;1OADKm2RRw*@t~5#Y1813-s}KYq1zyA z<>;n^QwXXGF)|E#x|BI=O6doDGSp42jADtXf-t4oZl&SYt_@vKjPAIvVK33(oh22H zsqu{$Q7JiUo3L1ck{yf=MtyJwMD1VWBd!8nM1eLE51)hYEUCuSs0-ryE}8pE+Cbj! zri?0h@W@dRc*u3|BC&-=5tN@jlW807Q{cT6Z@5d-D-=AVp3MUjnOdqpM{@;?g zR;H^y=Lej2FB8bP!VH~w5H&06(BG@ExVm`jM$#tSKtWvg+=3Iu5DHZr&ZrZ zLch5|-u84VB*RG7uU;+P7&hm%hm?((sL1Q)Pl+y0J4mkDbjW>Cc6u}w#Qd}n1UiR4 zzwJ{;eLYn6*4nRj6(xLS^1r-Kx2hJ6HIJ=Jq=2tAC{JNdl}t%C8Zq@Fy1OInm+{Lf z(B{5~8qzS}PeM#XsSh_~W|cx-uHOjbZHm28Ec6^d{Br7>CcV>lB0=#%m{YybjM3!} zI@dI2J|l?jD>9#7_lpQVE(AqzA`6z?+g!{jpYG4`Ik^Nb)cNEfxQ&w5Dd~jwzo?R` z6ivE`(THk<_du;Io^m-! zvTI!^_v%x$Y_`uQjs2+XJU}Y@dadz$E&vt>QI2~b$#OjMa9I|YbDWJSvs&1ZVPQ3^ z8k&*q$)3Tc1OT+o+%!tYp-}3vxk>%3)|YUAa>Dsy7snK=4P5$W5yR~eJb7d}{n7SW zFakUTT-uimfV5O{XyeTN);Ga%+`0#W1k9-h`b2(q&L=kqZP7o2VQ+aFf(-x&u6S5( zJdy={M9~rvHrGBDXal&K$vkn7AQ_GyW@;fSus=8yjs0V?ESLy83hmX&0FBh8Y@S1D zNz*v(8!CULcM`eac@BKwmzkG^8|-z!7-vAorDug*dcH7eq6qsfU}^`|Or9cXlr!2| z@*MDkf@Z4%uM1N(zrJr4nZYEH=cws}^s)mo-b1dUpaYStwa?Ys6)H8`L^*f&CBiu& zP3^M;BkPi<`XFUW#I#TcR4DJ-+^YkM^8++WpMeq*(Q=CPZ|AoctE zxkx3HrQSpx9s#|54y;wyOZmkH*=Tz6fNKMjv8i|65Z7esAjUdw2r(rENp=ZdoW(&< zTmfT9R-EJ-)TD|m^pM;6W$A0U{5Od54AzvaRR2#%L-akw1|W|T-v1x3(#$f53c7@c zaD{AsQFJ5u?LFLsa0l6#7E3_-^x9EwcN}WDPuHXZRPXVFn?}Y;n_qsU`I9~UbQ0;g zb!ndBhGTzawomkvl4;Dt!|i@|cyc|v!CFsG6npnh=|tDF9?G2s_5sp@V)_?jI9g5r zur&29O51Pyh!uFD)ZZMi>5Qa?1awtPh6N(X+eL+Gv~zPzjMGdknlZ9 za({O%WC>R%EDwsXBB6p@myO3?{@PH?>eIv`9_)c-Df6n6hBn_yGG0nt}R;|A$-JgglOD3Kl}X3*X1*g6MJjxs0|WWH_|I* zW?b}~P+r={#DKLqQZQZX_`1mxtWdT#e4s3#_Tk=?I=+pnO%OV@qc7~~iqD5>Erm;z z5}%_XPAK?`DIbL0KmoxwysJl23IU`{@GLo3`#V^?)6DEv8A2(xR)9-A-@zE;Qfez^ z`-6W>xl1R*WpF04liZsHR2JqSK^dg2mBz1zl-#g6o!<=z!kId$a|rOREx|)9XG3*q ztCi7`kj2R^jA^|8iWh(@ZjxEzlb*Mxb$p@|%i273JZUjS8g$(O0rG3uQjWlK+ zhCVe%jm<)#mIjaH7`D_@yLwxAsG<|~6Fq`pX!0{I)JQMKT^XS9b<@uRA;v7egV3V)Hxe4z zjM7J^hiJJL#a$NSJ{WD_r60KC9Mk_dU0SmMh7LnDPx|PvcwCkitco<1ERDlgM)Sb$ zRg>q$Gu8!$A!-viYK~A=FtHoNp^RKSl}J+-+^h0qdbct^Tb9(#0v2h5*b&>fS~@&v znJYrXsrOUi()pTbsSM!k50^GZjfFD!6G30g1Na86GI@(S5ckP!*`ax2zQg4YootpY zt*k43!1@9oz|PAiix9M1$fbU4BPZ;iqBby1`J8}Bj z1smgB^h&cvGav)AI(K$-8}TFFzZPn&>f<4M2A+?T(Qn?RnG9o2YNOB* zuE~Wh6YJb&EfhK;Y*6rb>I?|E3VEh_gr?-wj34W?sc!Wb@^?#I z0Yquv$zf9Lq2dWjE5eltuqEa?urQOJ`PWztQ)xIxVgE9igna*#@hhYRFGmwf738~K zYK~<=!31H=foeRDpesLi7u}4z1bpH66U3im;{B7tc(*Y(LVU z;)(p~6Q_i9UzP!Nl`=x%O@F1)SJHCK5tXs+yTRRfi;&&ko4Y zSSkU#>+9VDR|^NaQO4jEDmjD9&xm7XY3`-RB$ z5&#Qrjb38Z{?QoeC1`Fw#EKmI{kube>Ip&=jKFd>=Vl@wx*724-yY$DuFus)^@*rf zXmnQ8=7$?SHE}s8r0dM>&q2c_rtd5oXpwbr>I2ez9hk95+@zxB`O;cFoC9dl82rwB znd}Q^-YJWv;G$9*Y$us@K={v(>EoWU#S41Kd#KU%vCNDUa*DD7i9C2OGd{(7Jm!ht zh^aIQpLpB6{@4q*= z5j-3M^_&X1|8M3FBaZ|tnzBNtNb1tU_x#}y184~hU1osrPgH;C`2*+m>);W_0+6p+ zPLX%bDs*AlvVYV^{$Bu6K&`(t11ha$k;a4IblBGvChv2k$h=?P3k-#20|OdX!G|L) zOKF`{U zyB}*M?VvI!II6D&fG79h5P%9PYPDL_;8z~gzt`ku0IU-TTTsEAuSlR7+J=qkbwC9e zNlB1gH>xh~IRA+w*0~v|h5@)N$WFvi|6!;S4}~xh&9)XQ3s=sY6PFInQMDH=2gT7#kOQwPQ$5|SV&H|j4aPaK>aNi4soQZCEKEs6?l-QbsRKKfFw=N^yLxxEUbhPPSo{q z!Vhr3-T!EN8vw;{8ptrANfxmt6<@E+Ttvtz7si%)qs(z zWfYFz<-LD2X$}iYCbZR-e=&;fPpAAzICFJ>GeOQZZx&1jzYWwCzHg4?$Y#D4u!05^ z8g%Duz`NPbN1l}mBIN!UD?OL)e5716 zWo=-J>*C6K*ky+|jrVHPtOOTDJzw%WY;NjONuyVU!vwEDZP4HKi}F4GCN7#f%n6^6LqQ~N>6zz`n^$OgsP`@Q{O>YEfDmlWiiRA~}8 zETg?(GtgD0#{@6T2T4Q|kR z6CNENE|?G@jc^e`-2*ndLzuSBD52DI1E-*Z{H?E@4_}+Yr45qRfb&f_EaUG1lp&FG z{N=l^R{;E+$6v__A}LI5O83fbYJGsL0AX){Z}6?c!6*B!gSab0-N2-uP$f>U?yhVxLyt{s)% zZ{qeyh{=>~usB+u9ez~}>F4P^e5>NcQ_ffX4(9iuW-S4nkYNmtr$FWR$zmAh-%2bL z>8H(YCb`88Mb3>B&0Hiy=KR`%>l>aqhkm1SBV03$$QD(O9a?2%xHSy7Iw5x)W-0hj z@viYeDDwy28muC~E+Gls-aB0Qp+v@WKs^XoIuB<9yeG^u?-G}85_!DO z>ew#lp#iFx&c*ww@+7F<4&-wWz2)*uvoaFiMNASn&alvRkOMavKXN!>2@X0i((waU z*cveqsNO&zEX-#&b<@^YlNUg)Z4#Dp0ThD^=ymVwVRqvD+iJ2vH5c&vh?ZUs$K3&E zvo@Q>3bg8U)0pS-Zx60;J>I1V;y9C_m6Nfot)3yOrHV6A1ZHMRQ;leEyfIvAhntG} zYqP{UPMD})Z>Fkp6QpV5)wpD8Z}T$BUQhB7WvGsBk)#~K=LsDdyvNTVB8j9DLHCQKm0~6^3?!$iAUGXdD`ipv=?{u(5o28{Ws(uf=XP6hX3kS*9{7gH zvqRcfowz1v-9eSToM4$CPDyvl3dhcD)6Q&*b{>=ZwGBEUW8!yUI^AC2+xa~bxEqy{ zWDrWUQGkA+LbI{WM=XId{!FTYP!&^ry+(Jj?;A<94&=Fab7?>bD3eN36WD-p_S$Mh zwL0%F0lS#AR-me0+r5KAX@?Yx8{s+9Om?4-(r21&HQS>pY}1W2D99qq3y7PiBc!#z&*PUvOQW7U`z{(D zb##>(zxo4wqa8zx$GzsMetjc}zK=Tc)$kHe!s|%h{NuC*wFd!mYDY(VO%h`nxmcnwn z7!Yl(_>~oA_K6c1R0*dmVuPm| z2p=*5lE7w8YKT;5C7?^9kn#8Fz2 z+&TvyGfVXioMojDBq(oFHVbJmp~k~t0(5sn1U^e8PIt}t`REz}oS0t?Av4=ERCG+L zT&<50zysg1)7hkzAQ?*3#X3~^CK-?WNNXBWPzHE5T1$aB9YJEM{fdW|LyPY?)=>Fv zClK_GZ?+3(0(!& zs>@rv7;z;uhv{IdMa}gEyn`^jQ$-NLJK94wpm}!t>0yz;J_e+UXyZ<$Yj9o`xEpD= z5yW}MNXb>2u{QCoD64n4l#UkJCIb=O>(PhGamXhdRq^voa`~7Q7V3GoZTCmrINcSf zY=<_Cle7SY0~9Kmms7l%wY!r@we=8Pb|hsQmSBlA??&I8f*Z803hn`}nch;-Ugh;v zKNvtJrW35}!T)c{0KL?=BNpgdVL0tY&I-mzAJyYqpxsfEYJOSAv6A?Ce z2U^E%w&R(I_*Pq7I}G@k>;;SKe00^m!z|ZQLnW7I3Ho?U^ktsMV;^sP`EbX=Z8f_g zBT%+C=@oxZ^zs1YPxl%E4pLn!{eh`SQ$Z~QatQ!x__X&0ul0!f(MV7Z2y4eno~jRg z-P1O9&QJ!~+Q!c-RvceuNrDXuR$^CL6vT6v)8yC{IH&>2qQW^;$9@o2&>09GYfXBg z;ur4u;F_f!Nuh+zdv?IKPKT(FQiSRWV#BT*Y8R>y>iA3zK}{+Bs)Ephju+7`$y!$e z1#V2F!|i%>r6As6En>>I1;r1o`|yu)I$P4~^$PkPWlf?GCWZ_JhT{qCO>G=?(8rQ)*s?3Kx8ZUjkW`*hIX>XB zYtX${V+RPUhD52!Nsk;Q>~yLI(jJTpP(}>;Y>k-H3hEu$30U9;+bJ!Hsm#A!k3M$4 z)3;y+)PU2*oIV;(Zx;{vNmW?27Qxx4fCLmkgsj^z@m~pK@H6ydFmvxB?7$G)vV(<~ z+k-VC$02ye_(BE>L;Qh8~F9zUdj zHB8L6GPs?3!*@&7Jxr-Y=Y&4ylUIU%AQg~W@Jei_D`HCF6m-o6i6w0wY|kJ!Ca1fH z)#t;VL`wt5>6NIdp+RQ~M2`&OM32=Tm>aU;wD`#W7Rv9FTOG7Wnc~btI_XF3o`AAi zT~?4D$t(NuL|Ep#`zFE5)&ntdyB>Wg7J6kRKZ=Dun91w)-Cc1$nWxCfixLSet$N~- z8|}s3?c5Eo2mA0P=StE&$ldh$bHc&^}S1;8>SnBk!7GMG<{X=Hl3+LiKb= zryy3r?&R+P*$jSm9<_S*hQ^~$I4$MRQ*)d_z;>(%He*KUo9}-&(4kURS17J|6PYw9 z2ecd5TA0zBC^xyHbG#CXq2_5w^ax2lsN_Z(jruggxmnxZS^QIq1u!~ ze=)oDdBtMuB5*~tYNL{5_EY!K*tqZ`R3kR9kSXokBQ0WbU{Wkdlt#j35*Cdha*NH6 z+v8iQ25`+9-4MWeYEq~+vSCl$a))4T3!91_IuI&`7f{0hH{}&gwI=%=AVs-dkDv=N zqY1G-cz`BxX&B?4^7@$9+Rx+aawz{Tj+#M#A4yLT$!Q#{zhC@yzewDF+L+wj3!p&- z6P2hOQz!uWq4UavR$3%i(%Yi33a-V%P&*x3ua^Kx zqpuw%QFq6MC)K4n?qy=oFlD-q3a%p;)|>c(K7^2DZE2Hp|GHxdE|Lsd0JW9^8kG+Md2jP#;$;TkBoWijv0WZY_0LTA5x6 zkL3~6y*E4du;+S^{l1aOX+uj}7g6a3%a)%t$kCx%hYod> zg~Igc{?%5+r~zn^DAJa6o>VrlOn1zv^U+75Quxy$$SAZ#*Nwoa9 zg1rN#v!#_!MJW-GNbj<~9#M2W<)~wIw1ldJYyxGl3!c1L;=|M!J06V7$m4ZYu%~#g z{4Xkq5sCl_4Pog>N)0oY4lKd=y*#D|p|XW(Lh1q%+gwS=%0m!BeS)iJkzQ(c6 zLC!-CK+CJUgY^i$w`8)Zhs6ZmY0@el;mo;8D>aOhXrOcSDB!n`O=s4i%?mB)&a&*% z@Hw>c0*t5IMNrZ~Ob+E3#Jhev_rBeGMQIR!LdE_h4%kAQ2-1RTyJ~CYshyZ-bReK1B*l`-S(+d8 zs9-?g(h{Z;tONH7X_478u#kqwnvQdC^MB)NNfF(S-btt&nzd}(QwmQy56Z6Hm(YD& zvHiFdHXAn8N{1l@&-!L$1X38|kB;z>evl+A=9>??aQyhq)gF6n=~D3&(`sYa+%Ojk zWm{6X#`>_d@_)EnWOSmpW(5+7fliyTKY|>l6~b=+GJ!;s=KwSTT&i&^Z$vTuuw5r} zV8HP{41Xe27u|*v4>;*X5)Hd0Mrg%K$?V&SJ~~|iWOc|P`rMFuog`#RP=s>_fKaKa zDWPs@>n>xEcrEzMfkjzB9=gH-G6NFMbJSbi-6>mIbURZ2!=+k0Pe@>heB6kGwqrMU zG^sRw2Bde9!0s2ayVpwOjGu=Szb(^>VFC!E%$Rm2dp~g!m-I*gNLHW5<8eYmLS-|) zq?fn^0W_fmA!V4e5s zvPy4k`AYay^fuIPG#0M}MCE#P&HGH~PDx5&5k7X~p>8#0XS4U{UF8Zmt%HU&w{{Ud zymtdJUmK}b(kwa{6i(2mekQIwLAQ?upsz#k?uo44~aeJ5~ zB~_`wFel9_n}TZ1JT%;qHWpU=2+NT=K=Lbf30rs>VC?L{Ft@qcaL1VX_+EQ8h*YC3 zZ`Y%1(beu2$J5d5C-?ND>WXaAr8;AkT{Y80H}_+UcD2QQ|0>qPoBCJ^Lx@4Z4Q%cT zs_AeClk-(ftXZ}oz@i_0N5O6H_wP|_Wlz3CDPa-Y;J3w9O8XtjZPw58Rtn{+CsfjWF?WSJMuanmcvV$e_PbpU@*3h(9RDz<#R{`;4Blk{Phy?F=`0 zfGvX@YvVnxVmxsb_@$&Ff>>%$xltj|f8_M?H2whws9T7aZ|?a=3;WUEH2?fKvw3&J)NN(ypwH9NCJ#-N1OOE{s`1|L zeK4v1S0B6omHODhyt!I?AZ_W?8QpwoQb*-Yespjq%}$$!@sP^oGC@s1q7+r%#CY|U zfkT&HHXkK(T7``s$yD_P2%FAk)g73FW6W^Nfp2P&EWKK$8i?0zlo9Zo7;A084?d^60*7Mh`L6z@=U_GxGJf*r<|x?PVxboPb{Xv2IR3*a9i z;z#~=;HI55ah!P|0aNtnsIFPtB5_N|kT6u+)61P{8%HJ;*6Ro}Edf8F|2PcppPbzW z6Z($j`+bmFz zGok{B3>Xkg=%=iip=8}n%gVw-n{83MV%wYWdO!ssEt0gDi(+W7NMXSXfbcZZV>dD6 zEpTh zV;MzNvUmF)$Zm2A@1}RA6}zJHl2De_5NLL3!!cGQ%Wuyx0pm1otAj5U^w0qjAD~i( z%0pV~Mh$EN`gVU%choPk3O1OuRNoNsrA7z zI^s~)Ll_M3^s(az^>l>lo7i%pXXkL|l4PBVecWt0hYf8ums(w`v=hm5?BVU4KD=Go z8`B+tEPUyj4b@xdFbm&}^vQI$dp6AV3OBcg1Q!;I?CE({GY(fNJJv5jqS-JF3}nMO zh*q5GrrBhM!XRoNG(lb69JCgotfx? z&mm$l>0#QEaTayiKYa4_2)L42_S%4;0KH>nJ5Gq2$IQaGa#pm*Sny3sb$h7jlx(aFhfu^zYt8um_{2 zplCwXy1D|*LMQmk(bcGe5+73tnun)t{jjNI>|b*72PW>+jR%Vd(t5l&!@7XEBGF#s zQg8MWAPku~N`_2oW>T7!me-J>LRvs3WMN7qR{D>>b&BUW1u6>|(K-TsQ9;}W80Cmm zx)!3$V6om#SFHY_nh-vKoSu}bjA~x3Ze~nLK$rzEufbj64i7d82cQRtzyp&7ohM`j z`=aL4q&C~@3=uW#Fl9$vXwpW)`Rd?!q-kX_o;4Ug-K?f8)(vc`LQ={G3N^1_EXZ*I z*g79&_u&F)0!VI>#vaYG?#NpZ#PjkKi=a%kiGvtdV8J|_p`MxdBJp3e*_?4na&#_!(MjCbof7b$g%x-G? zvhLEDNwUs4xt}T&y(zjBqP)BWWOUs0H9(d-h%T>Y9AUa9xjx&FGH@BJ&^+uiFcPiO z&`k#CGhJXB9ZIKJ>5jgi4rRYGp}?OEOoa@bwt4+zD9tCD4g2dNDt+cXAxVZk#pt`X zdf09zH`seXW-~Yx;@k$G78m!@oBAy>Vm=%eNH@ro1gZ}^6vmw~AB9>3^l6EcBlDBa zS5VJEMGU&S+n}vbM7VU84tu*Bzjn9OGyOR@-hh}_7?B+-q#T(x)MQ)J7yK{GVsb|9 zc0IaUF7QX})3~keS$=bD0ojD5d>JJQ2Lq5dIN28D4tiW~+_As0O4HF*uEii#Xk2Ij zMtaKJpkRODx$^o(sDcoBkLuqq2~vuG_7RmD$<>CnJt0Ui=t${EcCK{zaYa8erd0IM zl9BpN$(=h?=WwFa4A7~XQeESTn;K1Nh|eW>q^sLV(v=gCQ~59%01gdZDbU?qkKh!e z$5)DWirBey5=?17>7Ym3PKUYABf8Ga>|F-6Ne>l}!L9-dc~i>>JdIWk_L^~g(XoSe zGC`qI-2I!P3PQDWx<&0sZ$mfxxmJ~-1~1{MFk5|dBl$*V`uuzAQt}T8qi5`f48aZw*r|Z3DeFnW3_YQfJ9yuBnyA>cj zRfd*=EuRJMXg-CX7NfTKs7pu1e%V1v_f^jn;8j2}zo*1q_-yAi>t$wg*u}uhgx&#jG(r zfO=;=uxhX7_h6hHQds0#!o<TogEHeflp7G1hSXL3cJHDm4-?4 z#!X%s93UIznfQg$J%Gn?7;kVUfJOp^Z98};J^*Wt1R52#9|L5 zem6sUDg4zAYdN$A-k#g|+77y0{z*49anSV)^yNGqz_AVHdxNsbMGkt4=65dXM?kh6 zn-xOL>-^Rq)mvKmcheruplv0(PF295%JKH;dRo3^&!m{sk5sko&kPtbb)qTWpLXW; z5aa0vkn1Ark;4smUXfP<`xPj+z%%7uo+HXm>9SBEqDre7C%{Gue&|C%wI*#xfWh7j z!URMO?lLMEDZR8_j$&ihtINfL(bg4xq}ZeCjq(R}vRy_w9O$KLpvxZ^llzg}!wE4W zfbi)By9~Y4hY`px=Q)!Kg;sqLS7-N+jQnGu#Tab*NUs1pq@=KWz6IFtM^=oSN2j2U3wN!IQGNYK!-I~}3%Ki8~>#iPlX zEL$pdJc{6cB-?UyoUl-XZHS?13pd6(Wm07@J`I^nCP%In6$;f|Vkro&E*o|02I$Oa z&SL&61a=`*^dNtb%B5`qV=E~oyd*&wE*u$6kW>aRD z$AT_$s|AP#6D<~r9A%1LC=$c;i+*4Pj$3`(u|BPi^$q4NwhRzSuarXJeL1+vU`0EA zVgqhaS0y@nMEPTOt~1$rsduuPMrs*XB)bT91{zJ%`f6Xkj-S|=O-yT2JAt*;o}k0D zYzkZb0IRZDChHYzTOlpUl`7+~3!jI{%+C^#> zqcMmD9b2&kKYEWz3EW_&HV%Oi@0)uMY9wkT&C;81lBy=sCqbOdf(mN{4)$E`b24Xe zsf*MoM`!s0+2UmmXyZ3sN}r<@=JRj|+CWkV@e=mNNYNZ?d0*ZeK+%t-p6Z2N zb)EVV@igwH^sfuK097gctwfU_u_gOzX6mQFY_|Qap2(})HazT_d#Ft8_rmtG!)@;% zu?MkX%AN{0)niZQg@Z zg7&=3nseO4S^jfO&r~%4i_xK_KJ>iy!1@q@nA9yLRgu&Dkp0Au9r| zF0s)rz-t;r;VW%+g3pK3O>Hq!=&8}~fRCX5?5hE1Ag}X-qHFw^bO<9d3>qY#lTfoU9QP1%&4+PoROjQC3hW`q>5 zMc?rx_iM*B4+}5W*zJ0xMNP{cu!o7f70t9sXl(5nGd$*22GMz37Y@jYSDBa8_)K?N zW9^f|P33i<JK_&9=?xR|};-?YSaics)CbL_$lF$GZx&{A~Za17sl)7xkkw!v^tH zA9HK;8HxIc1wXG?wNHaS%oH=V>?Ymq__;~7;;B*sOD>v-oEK3FT&`|XVm2EpnzFHP zFx&;pO0x?|0Ymtk;BM(P+k>DINF-`?1aN?WqT9?lHwEDqM5igb?!dPMIwa>7ZMqwP zPhx|&k4G?jLg)k#wUYq{Ax?ejV{YBF*bOeGt0#sDI;fF3PJ&J%rl~a}k%kokIvT^w z9sVe>qTx<->-fO&I-Me=zSzMe{R8e`t&>xAMBWLCV(V7>r)Z0TdB1v{P@w>JuDgDY z28GlF<@X}^(K8l8Sa3>`{jJ4LD%Zvtbh@x z_`M#aIGI_Ttc1yp&^57)>(Mo6jPl4)*;$_WgNN!I=a|%r89$Mp-ajz&&|T_afufeU z6Xbcl&zrpC4We^O-C2&*8xV_+C_5uuO?O>fL27S@sMC9!KRpo$U@9izhiK_j3>aD9 z_2Bb>eMg3Tgu1uu6(pqTik|Jang@Lc)C(JNnzo;CYaMHG4%E|&n+$5iR6VkSSl%N# zn!xn4ktfI@yeFP4$drgp8C=*2WX1FXW2U#amj-%vmtnQon^5NnF};;kG)+=;@ARU$ z9xu4jN3>5sizmEabwmVy->-9>FuInCWz>u&^^6user0;i^Yrn>D?spUH z*8>u6_8Y2s3BY}-uu@QUL-nT{d^7RAm$wMNyqZXYnIVx?_uUVYA*eUwm$h+(H$Hd| zAVH`HlxX(sQb~}2g>BQVB2QRVx!52W+zGduozDd>brmu4Do0h~zq)yYn}!Coem76V zwzAk-VJ$k3^}I3~u`~&?kX=gAtJuBS^rmdP7$<1Z65j2*u17FxQicJ!5#<6z!?u0F zvqL05x|h^^-mmNR{tO{ov7$R58ISfq6@;aGAZ@Rwl+hsBM$>vG4FUrIQ}C*O3Jx70 zQU((hppNi}rT&)QKR?<;!i!H1utn=E>B$PV+0T9K*Fo=A`qD&n@p;86f+~&!8oVd% z3;X+lmZ7a@f(a6k{SA!2yTWZGp`@S*rGtalLZk{2xaj~BYU9EzyLlC>p_TV(w%Mq2 z18&DFQN{d++dgQJFCU+!Hn%?KhwCVjmT^B@z?}iUcSYmFa zz1_9>2!vTrV_*b#zqT__cz)f!boWtbE+MZ`kBX^HRYL?|pz1Hj6}JgCBtwCLaFuUp z_Ivq5G#VfC4jDL|L8B=Vh6FVc?)Ob9jSS8nd=oY;W zJztmEBo7qFoDYER8A)GSv~bTLAh+$cbxi1##XtdJ5RHI3psXSyfYYsvn~5EF*dz>V?EnZ;=+oQ{! zR3>bjWS4N9kEU-Wi8x>hxIK)^P7PIOD@s7{@3Na9T#zn*lZ*og>I z37qIll$JqYK^?&ZsPcA~;@8CpDuYmhV>l=D+~BUVD)MZKsbnVOOl7uR&|!votar!C zh?=#)k!DW`v(YA4h8|+tdCTr=o2b5bq(nM}3W-#sPaI4h^kCV7OJB28 z8Q&S~ONDWy63OGHt*qH}%~?Ah&Lx(#U7}J?<4j}P-8Jc_qif0XexFe@47+a3#k@aj zmI<4k@qqChU@1H<_@A;B0<$anpBTFgMc8^sCg&A4b@<6 zirx`9c$kRY$Rz>=(LTOa_E~a9j;?pOEa#(orzIT7JLK@VyDt;2%{+04AK4L{)YHhW z*R_0)KM^>A&%IM^x(BidX$RxtYbJjjfNX9>+0hZ2B4s9hvTqTD&=*kW4FV`097Z&_ zPv6GH9*qRG{=oz0)}k{g6onAX_DA;)8UuECtV@a3CzmTWJE(nkCMC&u=ufj7p6*N; zvR2C0R~Nh;Ky9&(bZ;Sw9<*QKx+nJ_gAFEy=-)t&HSKl+9{0u=1n(BPP;&br$97XR zLgw9_(PHX*!GCUk>wygotoLxbED?cf@15@bC_6;<>VL{q-yOw5l?f}pB3_F6eX zTqkF5lKFp7CPH@zs&WfJdA?p@xE1+K_}gWUXKfBrD4J#3%f>}O8Y$#pNqdU1V46vM zFdEIdPQqgg#zVO#4xo4oV<}X_p@U!_AVVkuFPyx9gN8=Li^iQtAGYV3&Ox6)ABie?Xq%>1aE#JFqtewjK&4R{i7##yI1|*di^g zlo}lbC>gtByClcTIs1~w**yrR?{xE4*yNhjT0ca=c$p@DA!dc!&(zUv8wQ=XVoZsm_K z!F@4T86^k&Jcbe0onn6c*FF{CO(=~iI;5R%f@e&6W|2f@S7m?CBU5^T3iH3dEP1@CynLgX8m)3*C_7d2vN1JnOUjM4*#6{QQ zA6nwFs|Q}@YqQAx%ZoXWcGt+tZRswg8DnK0T_y${9;jmAcUxH;l`kjwNJ&f>&u|g9 z5vC|kip-HfPDOR(w%g3A2XEK;3LrhXMO#VyqfY+rU`gz0zXQ)mO-oJImn(xLb_|lG z_8XpPI;|zjFx>kgt!~UYx@7J_TiYyTjd<|phg2Ghi9pbjmg;V+bF-yw18GEwQ9MvL zJ)ncvBu;TgBYAT=y7CmiE>3^w+YiO*>-7%nbe!>oJFsIl;fUDbsec+^(!)|_F z5&88O#d6dhX`bCGCSV!hQHiR#UQ!=O6PC!jA-V>Qn1WzqIHd^_LC^9@6n} zvbG#VP)_0kQQU%ugw!2O34!y)L??T=MYp9taFLwpe`&!Ac~b;M8Wm-|y_trn0aP}>rQukz;x7@LmG)tHEzeb0;!I(I*+gKw3TavaB(n^Uw!1H|f#HlW>LU^L9O= z<>G`*9bP@)Ylh0uRV1ul12!Ke3wrSL953iV0HGAJi5*Fy42Ne=yR%s zNlaXq%wcsXYr;-uO_3?!i6g-R5N&%u!TrG%vzh!k^f;_*bgagwx*cROr5g}6x0RKk z!8LUXHNs2~0~54|t_GPjk_||QX(^mYf$fLAlh)F6+ZW1RA3^eGize+*FwYW<)b!_? zracHhtkLfiY?xggmO7FnLaHF&aMJ4P=|f=>7!zA&$y~;J=n^7L5EManydk^JN9}IE zY&dRg+X~};OZ)0zA%zj*p<3HfC2JB&6wE& zQhL6^H3$?d?t6B%C4J=f2YtvV*TK;4ne;#uasb9HB-P@)`%)H&03zC0Xe~gbc@G#6 zpvwW@eh-Vmz0jalh_kc}JOa!FwONiM$g62L05BWi@A=8#GN+D{eNXs`1%Exdl8j_C zMGtzc}KpEEoj1qe4FxrvTflv@y+eVs{)iU@c=Y0cdaVNLh}Rh0ux z1rUaSNYKR4SFb~q^%fTN>`Y6$jY?@*lzUS~PSQBYA8dDlYlT!ca=Ynzg`0OmP&-h# zlqKZjhg3R!QYa`)v>nXZ^azkeI>AEYJ2s*lrh~xWT^H=RHXZan7I&d<3kN1Yy0F50 zPNfzcO`iS%vL4`9`S^xUg%WtoLGpLEk4I-L5aeYW7zKpIbAlf;tT8yxZ@k{p<0m_a zK^sO&Xx#6{pvkei!+trv&CO`ZO9sOOA7cibV2`3sPD|H;O^3ax-;&UqZ>P3C((QFD zLdP6KQfd6O*XG7z(xZro9{>lK^R*psE1@{M%jGelh-#HAG*7Kl_P-PF7DwF_I^ zva9E)D6#yNNSmOI1214N0}D9-jFM6^!l^k58b8^617oc(mM19BpnZw8AcaO@yGe}s zUiK;xJM_x+h_8U6o7^0rTUZ9?Jb1uOo9b!BPht~1|3}&KIL?~`^7skAnA97X#EYfaD_{6rw&%o2H^8t7lO#ES2E7fCA|kt=GQAR=q#^i}QA2dghDL7+luyL{x}6qT1UafqD& zT4+n$zY-)+Cd8l%qLmK>%G+k{hTz~NIszA9*xN*|;nc1xl1(>FY8$MnP;wL-%mA){ z?xZv37WfLUKId*l z0ATL4Xr4CglZvhLrZ*R`Q^P5H;90?;H6wOcZ*fp1-YLN%Mq_bcp1r*7El&99=t{G1 zVD3S-`NM&zZdNh3BNe69Y{H+Fpub!JixVZ>$-Nvj*D${xNEzrfT|z8#W!~s(MF^6R z<|^?Z9Ceo%Mt+9dOQ&w=$^zfTcfrsb^`1L*yrfQpQi^h2@Qe|}?iN$H=pYiXSU{6L6$upCOivAMyUV!;s6?LsgUY!c=RrZhZiO=vZD=FwZPv{d z!GS}V=5WD7b8wcyp>G3ooWPgF862ER6o5zRGVbd}I~d(2BnK|{g_R4UJ7YcH))wbS zCsf#i+|9KH3-JyR$~`d;ZZK}z3_W2}l#YBo^8EeB2D)SM*6Fw%7180%|w`7e$i;2YG0yL-;uO#m;X9B}aKD zwfu=d!}VyRtdB12ro*P@Y*HorMDH4un*6WL#ieq<%-V^PKF+%_2_6G&1fw4rZB7g3 zY-CG2_9FYe2P*b4P>yC&Jmf;>@OTI&WJ3S_yb9coz0%8q@*e`=^g=UQWT9Uv4^Y$bdYuyl`zDACVK^N$gQYV~@_@0Yff;#0hq=*NxQ z8rXr@_Fzw7m5fyZ&japMiTeU7GbtsBb`^Qi76M)WFV@y$-dDfe;y?`M3T^!bX$tj(9LL_(a4qWk8pq$;Z_m8`lNsni^E zKB-QfI_p5MBKB=3To-!<&_TjXB5O+*2NV0>fhHTcmKelDgb&S>tScd#z+;&GY<7vA zonEoYfAcFkJKoHnUxg7qx9h{Y(l_>RR}lYwop9Ab5BN8n8|k!iKg?y z4Vxt$od)!{p=4wL@dHH|vav`#lD6Se4Sn8`;7)qPx8i$ONRPbDlsRG9J!t;G^ct#6 zGx49e18KpPFpA$I@~paO94=fJbRLTCtyOrDu~9FHveLg+f{OCv8poc)0@;X$Ugl-~ zVbYG)aa%SaUG4&-W6zS)fi;(HUdY*T3N{Jn!QPlRhd2=zCI;4!V>kt+I8EFfSBhb{ zf7zEzTw%q z?*w6k%~Bc?7=LE#d%ZG}E<~oa;R8tV*!_z}a-=U25#`Tf+ALZ;Ud3m(Ytlu`WP z7Q*Xk=nRTT2&_8--$|3B07&$fS2!U+%%cr}e9L1JafcnV*_bY9paLk=WR}FoFf)Rg zutXrg)Z;@XXkOG6JEnsd46U(;-+_4ww6wX4y}~kpfqflc!R>4c&Y*~ZwWiPLAFXCX z>f+aXD9f>TN*r$@boxHz_#s%&%3zm!vlw!JKcrZ4rrWGM=Lzj;J7%TpPcY8)HamHV z7xWbHk8)hHV8p2n6bl35Nj)*?V&VMl$vUQmkO>omF{KYo=z!M($%>7bHj3sPyHYv{ zWjoJ{D{no22)kX|eX3-$IqDw+trRO>X8Gpm4nG=I8yz(&ww+Z4w~^;G7eHLTKX<@A z(wl}DIh};&-6fVn`(0p_G?^EiQr6f`C$->rYf0z9Jy|Sskh6(m0k}t2pqx=V?6xlf z^~+Q>EPcrZLqp{k8m|UbDsn4(`sN2=FEAF>RRbHyPhONdULFmE$lo#?#3CR+Nd6wS z{LazZlc_%g8wB5Jn<8=WYVy}1n60zHQ{j5-%8E%P6_jZd!J*Dj9oXp>+Lc+*&NVLD z@bdx+k+9G|)f@yXC^>ImQhup3$P@Fg1F9XJr=?S*=$^ zTMYzC9*K-`;Xp=!R831%{Y#o`x?)xML0CZF2WJyM%dKmnMo}%wxlTh@gB_}RE6TA0 zfSPM(aC9Qu1;R-eh@JQ&Qp#ZxY`x;Tm@L1xzoLe)257_pe?RSCZLjc_38?T$<>`XV z#(h;$*WGp+30L~}Fc6fXOcDH2xO^8a?F7cXS(-6Ru|anx%Ih9Wo8-^BShS&?4U5{d zS$u!@!mCe#*|c2EucOAdTQ-L4nJgB0(S<1Ck)@$bpBKk{lBl!h)+!ZcyH&XMg+5y_ z;SWGMO~<|jX!M!&y12DoEJ$WvK4l0+0O~sW?(XIDlOKy@X$IFRymJg^x@+vnhD0ed0#LW%O)hEZW#@*PoS9AHo0_UGQezJMaf2)UdUK) z>Nc$eBVT_da_*ABC7{DrFZ1f@@Cpy~gv;CgG+1mnH$`vzKcC`8| zW_A(k7t)jE^_WS3{yczXh@{Ckxa!+XKxqghTZ{ccxEH}q2fe1C&aAr-cv*ST3iE$n z8)02FukuRa`w@#=LRohAnw{(?M^lkTU>G)R?qKGtA}CCLS}ly`)w_B00KTJM*|YO( zLlwC1ED=+iL^sC)fRN@hoED+LGTWNl4K3YUg8)Q2SV$whpsTp8V3N`d^oN*a0?_2S z)(&y#Nc@49iW!c@vWlPl%w#{X%xeWHP~hpeR6;( z`*$P@=^&V?ijEW6YkTlN&NV?8u#+z|{aCtuAiVSW{CciJXi3?&@+32F0_Kt48;AlH zi*&ENIu%$$USdmBcWZh3r}r`@J5$9co+d$U(oP5yX6R|-g$>Aoc>bJ`*F0aOe)E#;S$-jN&Ydh zf`~gZVpI6a_H+G1^aq;_3@&;HsPbxJ{LhA?66cf4J5QSwL+o@a+|Jw%M?^?Vq@5y= zGT+du>DqmmrX{ig$WLW^Ph*{Fy-i9`;%9<(d2Jh=G7q4E#)r?Q35hoiRQM~F zxM$zb#kbpE;Qz@+L+zQ$?*ah;2u240Q2H;i(Tt284IRww{sShbb8>U~zjDd{ZrU8O zB6z>59RbS%D!QIm%V6ovSFx>ubjE@aFNPyXI61G6e1F7{Skk5^UCFRgKs^nwz|3m7 ziNlF)h$}1y)2P{Nmrt9hkfuk`S;f{5pO{f3rc=2vk9k?fudWBus9H2`45}KFwi-L_ zYeeRs7_0;FS@RwllRo@K5?0zj5aqGhsK-Yp;vB5O?~xW!hbz2AHF!t zSK@i&Fn?yGoFwu6B3jrGUsx^jz|zkD|2Kx%<6R`hC7-avwG&dj3Ma3gvHe`aQ(XXQ;wr7%%3EJ|EnjV2VVN;%Tfcn1H@ zrk>ukrG68q)OBer!P>S2*aH z9i5Gb1Dl<}2umJn%2Z;kM}VPl?FBz;BAqRgsQ#;fo=lZ^lXDZC5_Ky$IO3XLT$tJv z1`JMYi(D2G;?Ll35so2gq9rwKWa2veP^;y#c$M49avSV2@0qXp+|7JmrFj|nZp(M$JGg-uGW~FXnzMd$p$b-xenTxej@%a?1 z+8@~o;p5lfq<94`+g4>ucVdzFNkKMzn2c_(65nJ@Z!;kic(3-Tkb!wnABnE{S!Y56 zs%4Jt6spaNESsAL4b=S3-jUugB6BQq=DmNWB61P+ZJm}5AJSJ&+KF~R9|~;R2?djQ zfzovxW4!6960K@uQs|EP#0JTOMqM$xuamsj-4R#duc4Fos+8ZV9*2Wzy%O^wWwy|E zGFYW=^b!r~DK`1H_?Xc-o33uge$5*5t`nU>OB={2D!Pzh)Q|y4L-nQRK#kz@KOy?f z1D&Kl3U#5>sPgRlEv^ug}A=VJq=-1@e?AH+1>euGSz8wk^v|{-{is3jA#Un>%nH>!glM`_;j)zOi zYB_YOLku8hXT2Q>Q;>^tC`^Pa-PG-0#MJm#)38>IWmGpz6j5}>N^BbRim9cCL9+;| zbkaUo7fW86{8s-|i8vmiM<%r3T5~o_uFMCQQ9nz)XR&&^#fPVoNd1siMvF_ij~Rc_ zW`jqa8y3c%{{s9+NTC4!(}?W$W&hX5|3Aq7mxR>8Sl`In_tM&Oj z&t%*6dG6%vc|S-zZN25=?)lz7pPlu6Kk9rqxb(G+?cx5uJ|BDxJ$1F^bN?RQ^}WAV z%WW3Z{e5_Rm80MNT2=ePycHw&{=OL%IvtnD{eF7;`F>aG@$&e(o1DB^&E~p(oxR=V z>;1l$i{<-v_~|^c{Fyz4|Ly*);p6W9x_f#VJdBk~?P=^ii|oBAV4G;weu53w~4kiNDciz`@xg&(UG;?d5jZaCAn`7aQ|4 zN>7i!)9t&nz~%67@pSO;G6&vPs|{zj$5S}L(nGi+iXR{Z!5t;Eaga6z7= z$L(|fczgGUG3jHp)%X4V{e`f1Wx=eXS^*crPDI@pE?3g`Q51&(Fu) zYj?ko&o3>$a&%_0f2`fTULX4tr!QB9NZ;4*UXxRMz1+X@HD(LWcD}z_U2P+&ynl~7 zQ?Jx(f6niZ+c(0ZTO-Z9-oGAx2Jv|}*`;n2eOD@XeSXd}<#hd<;ZK-(q`!3CKBkZG zVSXlSWt9FrVN5v~WkemDy7O)_-?qHfj{55J4qnXC=O!#!wr~XOLP2iK()nVd)ADMd z<8L|ej&*83PCrTXnUe5|{)U{u_&n9~nqdF&5psBwfvY;SJsH)SbsY!s$n{^+X^(uu zQ0ix+@Va-lTz14h|ELP5{e!k)x~B>u^r)G_1x6jTEHxJh%%Cd`ovzYKbpNU0*Z2|j z5_V~OAPkT(PUZoVC;~7U@4+1u#4cA~07_;mI0w=>Cfm-Hu9Llo$}ZpbLb2ygR=1@Q zY@|4Q5OUO^J#G!B6HJvRXdYr)(IyvH1=0qz3cD<5jV6l0FcUR{mfxKqeiLc4C($=! z)YjfV3Exd?3D98z!gvCLG)-9-oaz2Edb;{cQx%j0&8(T~j#$r}5Cs*9Q#ffadl5uU zstQ}ON~zKvUZpd%VtDtIxEl^_{&9q42|Nx5inUL9o+W&iUP`C%;%~pINE}!Mf4Sx= z9UAQ%G~TaZ1dDY=ShImKATVG2zq+M$WFmV|oc=>7h=G)0+!?C%1635d{%laQvSDvh z$GlY_?{t1TWVxyWJ%tK|ih>`3CBv+naR_`uaYdDQxg{X2Rl<~#jmQ)FJe^KThrs9o8K3EN<(Xek( zi=xcu3;q=nQ>u2Qc9L^Cn@8Q9{0O$AtFlq@pTS|VhCJ&T7X#&V_YzXb3fejP%j^bqzn?j(JEzKFu4tih-hSOF$(2aC@Q~LijKKr*tUT!OL;ea4tmW; zz^QwxlNxXMzxdZ!=|7@LAS8&E!F$vh07~v+!!5HTM1p*aT0c6!{`F;M$k^g1ZDqKG zxx8KWoKCIW^@Vn-foJ7B+fgc~kWT?q(^DmJ%tewiKVshps~sa_B-I>i!^ImTsu6sf ztj#l-b*;<2SV#DiHDHNDg)+ouo(lE7*Ih(Y#UoSUx&8A3kOo#&g3gGLWa_3m0wL?nx z4;ER5Y`NRxa~oxXcnb~jPrvd0je@zqs-8@8=Z@Jr-saS0G1eehV$!DRjYpw#Bh?&q zx3g*N2LK+WC)E@#I+wD2L#g2Vd<_aZfN^Wc&!jt=Y(H?h7KMw=JNIC&DNI|0&CWkI z%^iR_>ALHSiYAYp1cUKr?Vk&0~J+35bCWB@yGNxr}uUHXS(M`bl$m|o6ip5gACCKAyciTV@&(1 zgKr3NZ7fpY80PWnML(c9EHg^YCl4k?TGe$^Py(S#|)C(v|8px2mL zVM0;{)V|eHyh;Hxo-gZtuSiX zhI97()j_RE{Z&0K?0}T)nPe}m2wUS~4Wc2<{>7eF0%aDeUE&nx6lofKOJE#qM9P1~ zs_>Pao|lfyWR@Q$qg$CI{7!b~$^u_(j-q>8xog;WO|$K7a?}g$A}?6yF#ARa-e3-u z$&`BHYpW}qNrWmWG^Ff4NI|m@1P`V zsmskr>NIJlzoQ-kLN_T+J_jYTT-3^ngoqG?rLDUV^GF zssK8$a5Y%@(ht_5+^IEOIXOvt{^q9XRRr^FnuuGE_XBW4c?O~uYXp{{0PxVre=Ekx zZ&9ZNhRZ66>ihVn(fbGNyy|Pnf^PnSin2(QpDt(zrP_o=k|1IonYW-5cTgm?kB7=m zX%M%-k)SePtz-QvaRrtJ>H4W8FAkX3(hj64&ReTCc1TOfdY2p%t`V_y_ ziK1p+Y_Gmz+=(WRLbaK+M_Fe8LeL*)lmLqCvF*l;N?> znCGo;cjuyqNKOt&{qWOB?3*tSFfm3_0WuY1<3d}ioaCnxP55_OoUn*44peJbzaYXO z(4d*D3iV0>+C`>;KeN{QR&wj-@uV`s{2T8S_yL&03Lh`ej#`PN&IFvj**M~dwsj6o zcf~k{0F1Q#N^V^oVpAx8V5~>IQ0T-5$nR-~#YYKV5Cy2pG6_%)-Y^3!`pNgiq-I=g@ zd60LYoy#TK<<6=zYTpA#A{~HFQN8cT_&eaJVqG60H%yh#G(S`!ji>|7f~r%={t2Ck zbFcZYK&ei>7MfvztubramHSqO^1KOgu$fYr9 zq+7<8z_4_eSVN*>yh$%EO%)bvFb5MlG>^s0EE@@8$-wB?VLL=qF2907f4elj-M>0@AW~fUgc-tL}#}Xpl zrW$gN63}THVA<-~?RTDw{MGr%A0jUehJ1{aO3?0L_&jO!KT_j1p*!D{Ag4ujl8h*n zgib6dhEvRAF11KnT^d@-+8EmU^PHR8l9L>Qo}oz9z{l1fI<*pjDh8T1aDO-meRhCR z`%9#e&=i-$%EMgs1YoI>$Dzu+;0`>CKxQNWXolN++$4gZU-5 z9e0+AdTD4@2`2Qo)$lN#=Rz>HDadTD_@%+}`ySW+VQpf;%p(klEpOb<1NEz>@L-8)Kdy|CXp zQm$*#BatP(rnRU?d0U=F!WhcMD4pyomZN^rjF%ENX$5vlTTNUBm0jE^ubT&!J`TU# z?SH!S39jzGFe;D{2FuBuyq1yZL@e%`dQt}7k--dN>p~ z7}>-;i)szvb|k0dYy86NPzLDJrN8OM@p+GwDj2gvHB3z{_f4(kTblWpa&az!f&Wh9 z))7xNHd(lzR*A`Y6Ww&H{Yh}!E9sF?BucS&t@*YNF+O()zonAb5BBbY@htDVGFCr0 zI~Y8QUQ|w`i1d2#lOp@jz(RsP%U7i%|F>g(Lq7C}Sz&GL{$X zpri|$_TpSZKI|-2JY=r_*b9p$8SBWTMrj@nWwAx5_<`7?O35)Q zO~HuRwG~t?Uuq&}>PK&=-EO|*vZHT>v3y-f#~CbE#a@~W`CtwArF9{=)YNuwP#KrJ z3?VR(f$3fptL5vg7_OzZW}%A$H3I02V9`KVK`>(#{`wEl-2FklAyso$pVs2#3JdEf zvUyleU~F1mQJ)8^ZBzgFc@D}K*ey6(sG|9<`}5`9nE)*!U|}^Pw4YEnGN6Q86hvq} zgvtC2)bnCO*9e3{Z`|P^M)51hMZ;adJRDT%{ zU1LitFAG?)s7K1Kjl@0WVG`E8N^vBWXiF|9hG6xDLF7}*GwpQrpU_zfsG>Tkl&1r^ zj|@VJiujBUr5J8qkV&J6g7B%Xr%|^G=`uh|Vb|96v1w4*Ym3rebPEU9LbsxiW0ML2 z$wD}Lf-;J}3<-39(lVG;q)i%EqDm@QN+#iJJ@9XaW7YFGYCGbd&^2MLd@q4OxeS$I zRk{)p=JQWRTGW*5Pcjp-oKUllu)3jwUHi>bw`KxYx1le96PUepU#GL>ZTfuW&9A!qraucw zC&xBt0`KD9rKtQ22`6#a#d~#Nw4EhVl|>n<#@4T`)G#*6o?W)v7nWY?gmb4gp(U~9 zfoy-d)L(6sW^_Vucb$ ze8+H>>~r7VuUfpADP5dS7_Y|ZFek=A8^XpT0S}DvzY~zf8v`GbUKBd)9t4UV8Q3t0 zxvHQT>W{%Unbf_etF#a)pa=WZ7`KPuD$FW9$IXQv+#Hz4L*Mot<~3jtmv&mH@F!-X z3`@zb|A5GhyS*tae*um24cgje7U|i1LtN;B*L2KX@5We<Y~TSvw#$9z;y0obGn z2OuGzuXI$i5&pNPae=cWWKJrN4Y5fdUSsfPM;SO##&5EvnIt8^EKriy94dz~{Wh(g z;>?-fVe)n>!aR7JN%$tuL$e>)Wg1OKep=BQdbG@HEGyv-!?d|Hjb=6^o(>lBP?Vmh z@bB=!q0OF@PcTJDr<}hIoX!R;p6eT0*&*>b>L&>(Gz8JV3MmHABabzZ3FIkA;`cQABcymJ9{c4T!yOC?dxTy3IS)1=aRu*C zgE6%|@cHau3mZih`p)}bc{QJOKmEc%2HZK@6p7<$5dssu(Chn^r04rU@0{vV>;f(wp%ITRyIc8Z z(_*AEDwk?!sS2`gQ8t>XeVuSoP3`8$(Wv^H*W% z!bw)|)q6|y1FU#?@^%5YIrXmQJAEtb?IZ|rnKpxSFKLB~4Rvuu^q5f6{V#@u6+bvz z%tJ&aBO^DZ{sPB6-81NFsIe{GSZDGgPziHi^YW%lK|@>ECLeyuSC;tTtQFVKywO8zDFp?D{5W zaEx?iEzePGv(t00G`QG7-cRRTqS@jW(R_o?XpfPzALuuO&bJ8tO}&ZRyyKd-u} zLIheC>VWC1NR`8#0BV+-v&dm~adId5-$gJM~G#w}0W03rjlqiS)l9gvv&YPm$Hp0&#b5n6EI!JtbE%zlE8g2R zAuze0Y3^FK?`)t{cC-&-pm<5a(*2H^;nZntxZnaNrTX6r7no}V>6X_d#AiX%ak$e+ zD&XU5=NLk-?5_fk+s@N8kggJOCw|6>m~kB{;u<-m5ebRBTg@sGahb0Sn86lN^@-Tn z$U&|sP0qCXgODvOW|2p7(HT?M$b7Nq>qxsU&#pi`(T}m#RVHw0&OlX;Nt)^4o7wgK zLtQG3^+(toNT{-O>N~z37Ej&jWH*RHl9{7SWXsTdVcqUb$Ou4o%5Z)jZxCrk;p_-L&WJO^c1|M5q>Bhe#dL=!BaJfr%Z_OxmpO%w!Hmly*DiWg7S z0d5SXdX|G}&(3_e2dqU}jjARNBB;_Hy@@8Wx@@jG%Fd@=_U8_BJN|gE4xge;Y;o1w z?~k3N;As!64VPX;NP%gTdltB*4!CeC?3k0+XYA>=@06k$!Au@4>i-gNCRZljw*(w( z&djn6yLcHqLwP9Rmd3_(oRmR;uZ-+xoA+hDEr|C0X|TAoRoRpzv!F4pZZNOPjAraj z3RL50dg)Q2=D##fq_JsV4w^GdKs{V;YD`sWeQ98ds{<{2v&Qq7JWbbJ)@3Xx0~@nc z_Nj`3qo=zeia+JM!?N!@90mSERf8fp+{M zY~7(gn*d1e>3@n-yAR$H+=Wfz)JYWZQx;%)RGZn1B3r5l;q^1S2P`sP`K`K{cSY0R zL{N(w(hnO@yo6qd_2b0K=~1@^{nocfnb=7L6PG76n1P!qC)^m@cS@R6J)T&Y=k)ZT6XA`ztZL`B>ZAH#PEz0I`YnO zH^S6SVtRyN?>KPjIvPoWCtRNlzbBHId9<~X5Vp!0zdK*n&QbAC!WbBF|7fxXuSVE2 zqRr}04I9K<#d`)!OkP*U!gEV@{L~@|U^y@I5`p zj(_)W)-uVJDAlNuuOqZ~h;piwO2AzN6=`5K5Y-a%TxV%5#e^a6I5>-8!6#L!!Kks4 zcIt-i_gq%`nPsQqX;8iZOlQ+O(jGL7Yl?Q5JzWn0yWiS2Q9+NO{G&gU=WfU{>VW8x zI3&g0unkG^v{-d4W7tNJ6-{ss7Zx(v*Ck$hgPi&V62MWxf1%35Aj3!nv1ttsBSsy! z@*dP!_V1Fqe9@B0%^inYs-HR^FEQmW3TR@EcxrOf(K;GfnGGx5ap9se`JJt#5svm# zCMinDqeQw&Il!87XTNk_zfrge)#i_^l=)qrNK)r%_ z4{B{mZR=E&Q(&M@Il!gA#;98Csd8=eM!0LF`mU$5+g<@yP%{F;bXc!+b{boWc}QC! zk-G8=?poe)zMt22zML+LXmB2YJ1-hpS9qyHI>XeB$#H4ao# z2>#*53=01>OKl6EQ-OOD(a1T)pbxu?yd7@-&8w6T?stwHtM{37q#^)XGE;?NA3d z(tX`hjjnri{ltse=eJ-pY%wB=N1_w0j<^d&+G$6P2dY>kyCb#=boE|2Rvb=O6}lX| zb-%SW3OjbJ@5OIdpSzry)S#t{$ZSd=`x2v(iu0nPk{gIp+pBq0J?jE_mK0l=56|rS zSd8?dtqRM*P_cJhGI3^zF)YI#8Zi28D%n+Ri>4M?aXec470>&XmjIsw7qTgE2qozK zUD}I6aRO^pQwiXqjg7-PYfiS7oADWi-RR1i>G*eWw}gl+h{xzAS#q!4ugdGWL%}H` zGomut-W2*GCPfs~F2Va+61H08sNFw*`AxD2j8nLXB7gwRb%gKk}U$1;!9gHeY!)xjZka!?SNv=vVh>@ z@(>dk*4G{GYtS)V>~(h3sJBu&DKfd=`M30#%kiYnH`T1j$cMmGN&vBJT$?g1tLr#ZGho|CAd9iDk<;!z~F#9 zbX}O)yUBF~oQ->qA^2 zUsF1`Fn-jL$zZg^wkA4zrYV0{B*!)k`ubo`F)_5M{BIMxg}M{gmbE_cc3(!Hww{6H zrsx2Jnjq^&Tu25y{#ps?>Tl~0^i(P*4rZP=nDBq(?3#5)uz)q)e~|M2SpKj2ihz9EA~s9d^6+H6EN+ zvNY#t*NuUAgS{c^@f%T2h^UKT)5xx^EF?a)@*)A%gO3czSlzV_M#g|K-!{SC2*(>B z+)~Ovq6qNKnSO}b`E=S`jW_l-rc!fO!;a;`-XVX)(p!KFwL}*>>EFv#$Ax!Wq9ebj zzL4akVy1;JWPQXQ_a5%q`jEJ-_;^E7Zv9R?5+lb3+G1sBO_+Q5+>4|)xO<~jbH!8o z`~lVTj$^Y9ACer9y8da`@Wu(&ASpQTwgJ1b+;o^anFNXScdi!M(W!OfDH7?Fo=2N@okt%=S$CYY=)aotrHGW9|O?AJ!_G!hCsey)sab#0;4s z?EF-dL^Fhq9M!0P?T9|HN!P3=Ofe#{Vae0I;E`zpePcxcs(ptM@Dwv;)jleJou<0@ z^IfGAEQOZg6ju|5KFk7yl!Wh=Oxc4GxP5Cbhbbro|NOUTFHIwxG?pQWlj;6mc7&X}tOCpMFwKepOQFd%0D#auVU=*(r(Tor>&(|p2N`n-3 zZ*&-|%&Bour!Ee=IjV1@$Wl9WoNniCN0%r2c=NuU%xh82t0z_F0w`PQ6G+~`B<=yzyOh=u!-b}^?%?36PHS1gC87-=nl z6>gIG?R+8OUG`JN_JZ?X4N|F9fezrDphJ~1{aHCXDu2U&0GFS?bdn^Zs1?( zLyKPE?Abs%ccYdQ1y)sK%7H{vTrc@%LxKWK* ztqVmH5ui~~MMsrn&V8QzSgQYG8phKzn+E$&wo1reXV2{qhmNS<9nR)MxK-pE-92p& zZxX|t1X_?A+Ok!rE4K!jy5F1RPrv`CIi9q;;x6u=IsU>106^ov?4@*YwsCa&XRG{w z`ze>R6l@RJQG$2hQQg!Yj}>h#S;DDDomw@neRI+gJA!5|6K}6lv%l#I34+y4@*?8J?fLSSf2#8ZVDkdZ;$|)B^j* zizree<Hs!ST&k|r}%F9ERPDCCaAQ<-k0C4+S#{#S$CD$7PP*a*UXvc zdq4Cs)Kw(9>j%pcmS67Y$3#=*9$YZ0qs?wY<6B-AzFKRYOKt@xoHa^|%`yli9VxFy zf;dM2EKV!TmAzk8k}?Uv-~L?rQhDM_eko=&S5>zq;y(fM!Jmos`v&=p{vdYcV{VF> zFLCyDctgtQA7|UJh+y(NQ);Ac2t+i@wbv-qO4<(J{uzchhaWDlD~NZYzA`2DPeOZ6 zWR8XzMOC%4+(Ex|?qud(>{mTsIK#Ftlr+wrRl!x1`9fBdiHNhSG`K*osyw|AP30}@ zRl37|k{z#)LiMpaEbZkNOLykJVa?mQK(+Ae9P{p0*xcG*H< zbFzU`+6nbu$uMy({KZ@RnAZL$x|{nM`BLzLzrKImNl_Y%TO#Bt)@`jP{i52KyI_|6 z03eDOKs2ZwZ_!j56M&e1stDjG4QR+23V}(C@!wnvA8_aykw0e~po?7UoGAvdE3_U4 z@BsrL0eFr55CCtS-4BqIGW^`7^P=ZdO@~$d0P$O3X5lSuh~iaoA(l!kobm99sk5+1 zca%dCX}o(iTgQDmCCBIch~j*eOnt%t1mBqPTf3xC_)Y;W@orw|64@1igyx0+(SXtK z`RDhJxQ#xNa4S`yjb5nC05i}|SM+zQt&k+1*n?7nBtSBwC;}NDnf^m}5`qs$ z$lWAk5c2$6_i#R}kRcM>^GHa3w*nv8zB;&_P#Z>c&b1oUshXI#6wJOE!uKXjsjnwK z)_Mp>G}=+%xkTE=CmKxYyqx!4T;g44;5i^}h)~m_Lzs&O+`Q~0A(jeJ|8Q?L( zbTZD~=~4_5(FkD%HHWYx>95LkvQHAEFrFMi7DRyz(td+)4ATA*qa48((jHMn4ALMy zAyq44P^C~)7LgX`qCx`u;i?oQA`d`x{ULjaeM(bmf=?8eVJL#IMcu>2|*6jX69d5)O-6*`{DBm_CNjR7U#qx9S{HjlqdiIYX2pT)VDIVbuf1_ zv)28OO48{&n3(+El8BuD3fSt5!ghVCoh@Wy(YTJ2zM>VgC<2EAvp=ZIs}Q1DNraH( zM*O_OjUO+f6a1h|h7Yyca^S#)zTXdS>eA8ee!u)&eQZu1?&k9Ows-q@v~|24pYHN@ zb$+eZs^RnY{C-bo;?vX7^L2fmA0BTuc7~p&UWR6FR(AeoeaGPU{JuZ6%C+j+>fY$w z;Nj2g_hk>fGGe;`erEVFDA}Gq0smyR;+I8}S~}OWwne@?j?UUDn?*yMLzoXtR9|AfX@bkVq{$W`{y|va zLLO>=skneMCt|96$_Mr5m{gE6}Wr>md^6Hj4jCZoUNJ&&pKfxMTdG9cBePzank# zIK1c%V=Q?wy4M@BfKg(kjOcw7IVHEwcj10F5DTVbb3{?KY$Y!uPqd6M(9#BrI~f(d z8X`bLOkraxBQpx%Q#7Q-aiat@y@LJNP)r5O#xiaAKy_r+Fs8+7wa3D2b{u)aOVXER z=yGX^B$q{|D1JS_nNfyusJ0D2PRFBs$;xFWy0LBMgev;%uKdhpD&EI&f}m_o@LD2? zTBxoG#V?`Y3S!_As0?Y;VMQU%G;8h`EH$aIRzh4;9)qmD4DO%H@)3?jG~c zfkNs!O?3Zq#tOuhCD z=s=_l-jb1e@iG$yLkegCQ+SO?RL>5rFcUp9&Jk-I_g;LA$+F4ta|$@DSUa$Qe91J6 zF>57P?36di6v128mH_NixCAcKpIStQ%M5Skr{n`qZ4**QuuDe9 z7T}lFSY;$lt~JD9vH-vuE=%d9lC@k3ySbM74GSG1i0QNuG3Ane9zA?^@bvt|u2<^C zDwt4qjGe&G0^lYgiP(iJj2TwN*~wz4rbSLuS4b&>k~{#Nl`LG6nFa|Ct(m@Ch)y10Gnzk-zX*;;I2kKKdBTM~zzA^BL>1hNu=E;o|k_mHjCU}kE^V5u@O-VqVj3m#s zOF`rUnpd0)-m!H;(DG374}w4}Jri#(MncfENvqG=qS7fDPyVdriOfB!n`=@fn^CBk z`r?6tdQ_%HIId_4VtY;P52m?MC_5d*yVRvY7SxxxB4lVd-$0)dHhQ~SC6M+d7D#qL zTnktW_HI_lZY8Dp-#KjejT^{6kPh=mWz61l`kGIU!_2V8t&?$w_@$|u#{?OG8Rrw8 z!HOor*16*980#_y>%FWn+^S*i+qP}n z>e#j??^Vqk)YMGX{JHt+UhgPG_G#jq zS;nLHA~+{Gs&2%7^lVD&*$DCux-^A-AFcYPR@&k$VU;!mXd{!@OgPd8%GY;y6AJYZ zdFlF3R-3Ss6*mNupO7I>t4K0lFHdj)|Zf--Q<6UM*y z?Mo1?TAW}@c4V2vXX}q0|2~(!vv4_86qj?pW1O-veT_fcWY!v73y&j6BA4af{ceGe zd-yFiq1MO4=tLMP(LWmvT|g8yw{h5;&WLB7C^~|Bni4rf!5O<#y9mi+qRmQ)?2X!h z_%3l~cC-|Q_d5i;73&AX5N&fy`ZnuT09A;dS&O*y(HarJ4A#GYD2IexhqR#bdQxgt z4a&xFb2B3xO`gtJZQKDmPMDhrRa>;^8^+_tU2ni*HBZUEK z!g3IA{7`GutVO2>R=To^N-4y_yr~cB=(Ded4=hBa{D}*w6@@y(mBU+c#2v)Rq|kmmn*>eJcu$jlMWkao8Y1Vy$!H;WdeUiFLxQ3R zWt@GZGIPW@{zD|Z(7%Wn(xJHab?1DoX@~9uED}mSgS8QlnRVq zN*nC9Em>IJc5N|wqlI`H2{2l0Eu`6!;gJI?0#q7Y1;}(C%D~O89`O-ZWHg$Lr&OV{ zUe(?Wonf;^)G#`P@zy?F<|MEc7S<@pD7Ak>=udoje@wR(Gtjd(B4uPeU44I1v3I^^ zdGz4B_B0be=L7~OYNx?w*x#yt8|w1cK;)*`Ju@17n0TMFB|D}%<%b-AaqUek#gJFz zpuyZw|B%S8YV8V)@Y08>eTakV>W$cmN)wqC8A1;5_F1)0N8}>ol1r^7x|Dyt;ggLF zT>*?_^N;=+VF^Wz!!`$YU76vFZZANl{8LdfxdPNmG4^le2O8Vinpv2; zIQ}P!Z&XgM{{^A1b?1MFaG|RmU(Sx=yS*)})22VAP2K*MpbZReYoc}`P5f=y?Y=4h9FRuXkcJY4OuZJ!JJs5&d5oljmstMtgI76&v; z+||9JS6(13o(-+Gu`9;)Qs(NG#v7yhAfnb+>Sb1H{vM;=6jjwrkPRuMs`GBvD>iHj zqO#T}u{)F23SK9kj3AdV+HD954pB9nXvRc02%l(`2Q*)J!m>X}fIUlKLPYU!_+bkMLk? z+ZEWge15>OvN=}vwKV*114tg|B+>>)PvqvYL2T=wi)x>|N})%fFW;g&#FY1tI#Ko) zxL~5Hp`Fy|T^#vlt%u`Ub9j)*l(~Lr=46wVKHnLPuw{jFa=E)cHNMd)>r$`3zUEuK zgQIhq^tQ^n>Wj#-qR;~I4lBa2LvKGmzrC!{yo~t>Yq@EjDat6CVb_G@Q|GJwqE)bZ zH9O{xgG;~Ir2f+8LZw@!%#&Z^9nt-C_NQ9Yu<5hWLx^9%5-uEl>ftUN zTT^kB+iDJ8MfZU7<+dXmuRMeI$H&L6Sci%U9DYmB`gox0oUVFL+A>#dZ+xNx+3*Y$ zw{AH>pQ>><%hW4j_ zHVH1~m>sClneZb5KRri!pbHv()Y(nTebw;3LL`2SG^Ce|F9f$iS7&MJ6@&eoPJZ>c7$kd6e7~XYvCF2A2lpGElEo5vNYJE~kXj*9Jx|y926LTp6mNgB zE;-yS8;mq7|9^10wq^9utN#B+$}he zAv{Q_Buy}XuU>(tFcuPzklvl7!j!7b=BIT*m(y{Fl1xztpJA?eP`e+nlsOY?!jR0c?x0OX}=s z=ztxj(y$?=KUAk7?3T~Sfn?+X@cV22coPIXn8d$_W^?aEU@Hog{yJ4a@8O>OBclp< zW+M~Ga2Q{0S!d03D3`!~(l3h)qJ~fdPnx8BPKsvU?M0bKR2J_d43{i}LjP-ZZ09Td z{s_+*=|L+B?fdq!tp8Q}%n`{zX${SZJ(@az62G>{M05q3m=UBnZ0svaP|4+MCYCWq zW5nA&$S#|5CY!Ot+emv%8$n{oKE5mYoEru@Q7rcE|F0Sk*_p5_Nz|z5*D$6?5)pQU z`Xff?l@KVpSL$X8GC0G!G0aSu<3(URWJ%Nl&;hhY6c&keWl%HL1UIM8F<>5*j!X!f zMvxBMaMs`Lp?CCpqq2y>I1jb|tvD$-%hZ810G|~0tceLj<;R?;Fp($7T4x#f7iW5% z9#LYSr!{yfjDcI;ck?fYJ$5bhW2|N`R0p=Ky48_aTeq8>E&16F+$sdn&WU!agg!lCj=iC}!k zfA)||#PS)DHBxw6MxnBta-zi5(?*M$nfMYjGckAf(mbIDBJ~1|`p&sPbEMV=;?F@` zE;SWr_>kSyadcayJ82z63D}Mahc9xAEwYW@9*Q9ks8ihw-Gw-Tk)T1Q-y6GPA1V1+ zZHbKY0Z6@JXaP<`!7CiAbaR*v10|$LHl)|!4q2LFCES=Zf-7ZnoGhyL-~pJZVT~Z~ z3=I&|AK#t3@z{13i#1{}9Y|XJN@xMrU@YF8gV6LLErAo$)v%kS24d(nYEa`TRFy>Z zkZLe#%#tarWWvfY9-<1g`T^M$X6KNV2L(a{geH>Pmav!-_x_jDi!~RIoRB1tl?fTT zwK>(G{3ioP#{z-@l1Ft}2(l&7s07>F^TA;drlPC!-~8DswAz^7*#M-xM9}~R1;_Fo zJc6Hnf&t8O;H0XU7AGG5zG1(cQ_RJ!2@|kJv;S^^jW+)|gRC4DE)VXBjkkia57TM@ zJ0nq1A}RoZ1tJAb4>=8}N5a4}Lf^V;hYcN%7@mOF_#&}Z>NcYwT_MHcuEJ>L{^ZBE zF7sE@Qm|*mMuz`g7xrAKA&##bNwy^RZXAG5I2MeCrjV5g{w&4KdCU}karvWVNTbh> zv*=M(CUKJNox%vQY{$)8-gsrQmA`@taKTyIx^+NZdl~nPiK84_NROE-Wn2h7?_Lw8 zh+u(n;Nnr6HxOro0Qm@FUQ@%s>3{I!we|8yC$s?#tjQFr&IW-h$2yB++yUyN$;N;r z`}xU-%{pKvHjz5|Z=cSFD1jYW!~M>xl%NsK zAP`ElLUwAKwuY1Do&)e<*|9*4eX-VsNuY(-g3FQmVQWgxsW!As;+(KWFfyfH@uAZ` z%PYekdjmB)V6n@i_R#$w2*5$lkzQ6w$+9_DZc97CtO|1^S`%mi{xDk%XwYx zYc~G;S-wiY88=q`Yh+?=#$dMKxP+txc7&zTXXBGv(zX&m`e%$7FNud21i?X_wK+85ZbyG6IibfZR)CO7(8@zRtQM zu}AY7dh^w80?1aODC}e}r5*Js2!lL?kO5XCsOP!Xa3&4&{!Zk_p=V_JQyIm1v0BH= z9U}$MNp>u3e}IiD=DYqMqY1_&QZmS;6TR>?Hsw}gTEG3P7|Jg@dq$WtQ+ zC#q0XE}KQ}uj}A;)*YT&4ta#B-#c+~#C}(-Hq3R$iCh%pc|T5g1j%NMVo}NX>xpgb zG7JsCqr>c3LKr^Y|vSbhmG1xKri$)V?(CJW#> z3pN6x0_HTdfSASg=cR$Aq zywrb>NEY}i|N3<6em}|f{g`|b@P3#S@c9g_{(6!3%f;|}?9QI{eR|pE|GIua)c1Rv z%>F#T`aCTD9wi`<{c)%7^YHN`@NpP=)%DTr=jZil*ZuZL(EU1G?f3A2`1Q%5|Iyt& zZ<^ih`Q>tS*7cc5|2D|*wYzBQce9A7@ALH%n(bFu?B()V-Sv{4-Tn4?XV-rI^*re& z;QjWoyxr~5GsJ_axxDIQ{Tt=l8LGdA{!V zVsn=x?K_M$pD6G)S^af$=k~St^*TPS|8bpuwYBl@uC80lYyZGySj_Kp5i$GgVEwA= zv$)##!Iw`nz@9P`(`}nrs!*>*((|wDNJap~}UXQn5 zgg?IOf1V9z-PnD-Q~14Xs(%ID&$|1(o%y{zrcZx|>_gx8ZFB4M@f$)xVE5N)dv)t5 zhn+yvw7&1h=99qtMRs@VDaP09%h$`{_iK2ndVk;J_C3w#cCKTlQ@n1Ah0f9_IrzmI0`E7_iOZ8p7iU3u)cK4pX>Xj?iZ zOm=fEY;#+U`5ZmX?6`IsN&DFP>~9=p+1e~?-vjo#Y_v~|57#lDY8Q2e^u#-Tq@H|^ z`wvGt&zqHwI-BXD=B$>BCs&`EVwCRKT~@qi1=g3|WV)8io+cBX61XaCme_w@{nK-a zaBAwX*>Iupx-z~hob-58xmsUP?JU?>yYN)Gv`I=j)GN9)JeGcHYu=a}a2#aCp!LFYFqO5a*4{E-=Z)jFv%Fa!ZfwjL zU+{J;p`v#w;47|;&bG5_F6FX~lwCeA$av~Czvr@?P7A2&eH-X0$b&v}ac4}GmzCc^ z=KZ5du=2_tLf~~g{-*beZk3z7Wl7^Bzj3+33c_ZSZ(G4rnN`qk4DeBl?jt?14;lO1=N$Gh+>;VU9%$v;WCR5^9U-*ypps}7}cN-t&e zX;eQdf?VttFJtP^f0unKFf1iIiF4OWs>Qdk(kl4uP?@*JE>rU8TWjDQ<{yrX`Ety& zdK69e8coxot7>yHoimz`Ek>5mX?LnwbUyO_ls$Y6a{iHZ$)E`25)_ zpqjtD9CS-o zo*fao1T2bcKZ^B|Ydv?Ew%EQshF4s8-YUf_iz@~^u z?edRvwrs8i*I0B53xY_sCC61VSkioFjOBdKWF0KAF=6TF&Bz!I)Gj@O7-Y&U{R=3I zR#GmEJef0I0F5;;^kwqV?2`4i75yxq8jtFQ22}9{#K&gME$Sq4Q~zPsM}(MJg*FT;hc8qF4lGTaxoiq9gC7Ft+wtgb*we@>W6+fZ*P8+I zaug!ts@idIK7ziV=Jy=*&ht&<30B zbXoEg#n1sxZt#})yzLdqS%pOp0tlx0Q2~tdI6TTq(mX=pIcgCgSccfGv_#WBaC~7Q z`4krRR;Nw%Tx>=}$oB?XKgBrQ-xe-ijM)qc57!?kHy=7E;rL%KRJv0wzP5$gPkO_H z76si;be3;t>yq^KIq^bREnPo6b34!6iQPvxYW?x5S0nrh%s;78B6l9Xf( zK}MJpCUA}!;S;*d#`NO;>?=l9Qa^CdfD?%|xN;LKZkyr&ZWXj3#?3`;F4pj}WEbja ztF2*G^oy?I4wf6gIIS@?w|u+kq-JvUd1OV<`RH34uMD zCuj79HoDf(RjLW3)AbBp9qRGrpmWhu-qQ#F_7>`Zr(5eXwbG{^W{o@DDVi6nyy<>k zYUR!g2Gxyh<0wdNVw*O;7Q812%mlQiWS|qqQtww_`P^Q;ZZ@=7dMsYsmKtOe&h^@U z#u1I%>2ys1V*@eiF{z>b(uZajDs?U}>~EzVn~Fap(S|GgZ8mVh2aSiSKSQ#podmQ% z#cV?tVpZ>^Hh|QlJxQgFOU*D( zj96_te#?tE62$B?j3^}^lxus}hn=~1Ml7FsD(V@`gOf$rOoS2H@a}K1+Tqe&gkycG zv5gnSHq3(!&cmInN5SY(5KuDFi!E2eY7QqlX&uuQ?3BOITS_YFgP=6hihN-;w^t)Z(dY&LLf{@O4_nyZno)X$7V+T^ye^yhO{{M0VvSZx{QbK7Fc_SGQhGm zShgIR=a~Etd0efbe_^XFKwG0-ZOwD-$1>h&$>{PiGFjYx97#h%_=H+k{qH4O@Sm_E zmIU$uls1b4s{zv3x$jjJN!5BOq>$~Z?IqGpPrRjhK*$ln$HN%oT0Yfx`s)BHk=18} z^78^Dc1d#9pwJ9LeMagy<#2s7x^^dY-Z8dKJlp>-WHY&kGGJ_vG|Z}NJnS+J5{u3g zZdZ}VfeaTiDmzk+Pc&X`8GDvIUW~Z>=wiuN%LGLyQ9jm8oi&NHazk*ueTYj6TBi&zlt>clME838k9oLQFhD9e>7w2qw;vC;Ve z85{Bf=Qy)*NHW-E;?E);PtjPJz@X>GC{(Hg(qO|tWs|Dfm8F-gBhAN6+Uzx>Pe_32GMdFg4U3Yp2 zC5doBSv{UY7=I+>r1Sy%W0lQCyBA-sk2X%RG`hk2C{_I%;5ZNEq8|MQoZV+|WMIp_np|HBQME{u znY%vYnH)R6aPrFnZ-7^h*#WUnU=1Fdi5D8sk!;8+YwkHBwE4zfYYeob-FM(3XE91- zFH9lJTtJWG>^s|frZ0#|5*69jjIb1{Q-v0^gte6fz^lPTCi6jYx%;Dm)BADp zN2E5*wIg_=u`U!mc4K+>_yd$ltyJB$zTVaByv3|yVrRFmd}A9ZWm32GfVfkKB$ieI z(`)1T$UB9~WJNk)F7rh>MomGo^tf4hRVXvSzmrvp%SFI(;?mJhNtE4g>0~Vz%RMWa zg%!s&?V#iklO#SO3(D0{?HKQVQtCX-YUIUiEv4uTG%q;j8x$oif~{GhH1O4I@bR|` zPvFdu6Fk>+ z7{p4@{_|yn(fqlMt8G(daRYIQ>@o{LO=DUQ{G2%aD?U6N2Ud93Kv{Y%3e z8MxH>yYL~lXRfqP{NAR$nGG8NE5X1;Mg&Glgh$$bfxWkmV6^c_&2Y77HX|OpDI4Up zRVA@D%p6O46bDl$RG{;dCZ|f0!H69Xo$03G4tMyBE?cNwtaCM7$Y9RK#Ho}(h5oYHAOyl}{Uz52>xQbkAkcEMSe#Q(I zm7#XpOzMRu#f#`hLZ{&KafTnq>uP;UuhF~pp7BfV_jk88jlWECrQOExW{VQ-Jv-DL z^96pLv|aWA&pAYsNb zEds2qu!nF2+@?sPdy^;%*84AgG~^G)TgAC5b#7n`iB?#LfeBqFh^4#jh2}k0N4r8)=Y@ir(>)MqIxrG6PMTnC|lyaSP zeBr^BP(!>Lbn2Qp#d-C&sgfpL6EvJNE4X3gonk90Eq+0F!^e?sRmr|&@=G>>@&}a0 zw=G){)h83lthxgS%?Tf({Ps_cJuuG;JS9=XqbT zIRgXTyVqc+k51B^fkg2_)E>8qTq~wYCmLPo+W~#Y<=pwlbB0kmgwt*EL8}!w^P0lP zchN-eq9EB@7x|qWz$nYo0641IbpFn|kscT<8|wl8R@$lecb&5WL{#J=N)XVUlM7&> zM9ytoCvTV;$Tw3qP-*kEd9#{clUfwm|L7PJ!h1j^Dw4)RpiccxO5gs=hQf>@Y3 z1d(aPcu&8OM5Yn{qgcu7!*y;$drbx7Y~;=b*D`nNHBK$8nZF92ucX0fM-v`ZzB~ zv7sWjq9}ug=nNjp$59 zJV1~yN3wY3CcI0U?2yQ8%Fa@%5klr1*EPu zE!Hp;Zc?-^M5P3)_zev&{cNte0+(~KMC$7~F|M~`hnp=7oY1Mu?JXV~S!RAZwjd(w zM#{CB@xpt~iTVv78XXJkwnLp4GV~Tg!d?sUboH5}A3prH87{(UJgb4g<|<9eWxXo) z&PV%9h8`wcW5IVEm%bleNj6Hj*6dAW_#E>u2||U3BK6FRV5#F0rBXD)!eRT-sC&l> z^;Xba@6D;ZCNHvGMu*cQnX2i3Bn9rmuVJ;8=V)k7Q`%LI4k2B>>Vwv{`^Ocp(`C@igw>D7_7Q7CO!0sRXL<>g+Uce1M4KmMu z!-MUzK6{%jq#vV-KpYOE_ZmOJwscKe3Q^U#8Oyt~Gkl(NWJu5zdp0!1uoPz3$w}jF zv_7N;hzn4V#>`sQR$__jFiHj?VyNSm2N<)@bY_SM>XiD2%WEPZ6^caUh00BL$_+MmtZAJ^X^nd6b>sCvn316y8Qi(!6%!-2 z@C)V1S^d;RsSQj6sopg>WXscns_1srxNH!oxoDEO1o-cT_}0)EK?9))vwj14S?!AMqlCx)Rwx%rEfj;=@j$@rQT#f{NGLXZQ}5ioM;P0)PWvMx#x z%^B9yXF_zMeRzatm}MK7nvj6lEmXmTTw3^&@I@O;O&=rEB&qiY%>6%}?^^t!^aaj6 z@qXpG7GOD>H%E@spP?gq6}lAOT)O9S*$-24{VNnCu|wcR^)ycB4i?2UH(K^Q-)FUY z8-vH>r)kFmsajG2SFS{3>)@;zN*QxL^JlXr&WPrwF18(EPBnLzh}5ae@>}UxdmfgF zJ@iC4av;8fy5MTi85u{LOS)ZW^6k+>mUTka*%@ZNh+Rs8Gy^86M?r%< z4l?;eWw0(iqZssR^^PBFUFe-;uIuP2z^-SK@Il9p0KpSxM)6WDN{ie7T+M*~6eqf; zz+`sSJ1p=h|169Zp10)D;&Mv~&2M(oSm_u)z)ZdHS4d?C?crP%l2%tpfx79}xRN15o-EBS3{=9V)m!D*RZtZ6KY z0Knhr>-vf4BywFAcgf$?`T{yoia%HA@`!}7j!nlnY_JuCBZnxfH_|%eCl3b>o5mFl zASIa?+#r3g`Ga>9v*w8>4t1iAI-Z-E^~KpnL-1dJ=tqt!Zyf-HEf$g;i(pO%RgtkZzhrqAn-1Xl+(p3if zs0*Z*2)C&D6+(ikA6X^cr3$v$Z+l97)fYk(Y5XrF6Nxv9l7GVme}+$EyfdBL^B zkI%oZxLfcBlxRHSC%3jz+oL}gd$bJ8&T3ul*(oHwar%3;x=EkUQ3LtNr1^0Qm%JN-`Nh`xmLIcTk*s8=)oG zI&2A=FFBY6GBq3=fXpp*6p-5_&5Y|{{lbPymg4_!G<`-mR+$BwhJfo?lwH{=pW&|x zZmEBmIRP2ys$?woB1Bywqh|wyaG)!inZ&zh3hLCkw|a~&gU28x93+%$@efkq;dhRuC2@dd2|y|11Dr+ONZah37ZS`w-_c~Qf!v0FfMNZ zUGCYhIua1B!EJ4VVh|u{oLkYk(r$m@RwKQ0c@U}4N**TpY#VKqW3i=>r9JnEe1~S5 zWB*imJFy4DSxKmk2&un@dJ4A^Tw>k&WNs(GAARDu#>Us9x(ExllnK$XrCLH=L=rtS zKdSx_0?QXu*f71wGc{*L{peUTnz3=>XwtxSlA{;6u`L)|47ONe44~R2;+$Lf;ZY|U z3br*!Nw_Bn2Y+|((t+rAP6SHX2h>aa*}yg6Y#ecK0a!sG$^`f|KpiZfvEi7J*#|_i zCZl(d{Qe~QMbdn=MR~mTu+@ZU5`8W&@n%0q(*s-TVBr>kDW;5k!z&|PT+K_|RgKr+ z2ru}H$se41)-?w7Qi%(cy%;VQj_#!}<_bbr?@!pVSkJHGz!;+U2@#@zM{|=CLn!J!0 zWn~D!J;-n|*EgR8LQ+4U7klnr(&H-?fnqWwy;7+xFX8@%)hQ@A&IS*j5(dE=wdy@^ z@?iRAN>g31!Lr=Og0Ut7fp>6G{7dxUfqgq?Z!PnIld195R+C{ngx79c{eZwdU6=}| zxX~YKW#XRuY%kS!2=}3Oj+l!2F6!_nk?Jr}4acr`(4h80%J*NomO{@#)1zdox}MM1 zROgx)#gv?ERci_Tqu^NOSQ{r0ocL5;ESlrQdCKE_eEV7*&@K+5=~}nEj&5ZL>Dq|% zX~Y$TWq2|3I{b7xexDjbq-f^A*M@F!rE&rw`iCQsk1~yI!#D4NK|mpyR{U)D6e-mt zyUl>#^HT$O<$ck1j_MOkmD0$AqDfQ9kvcpm9Fw6AsUS`!Nnry)YvQL|F@BCe^*c{5 zNNEgR$@Yi&&*(M$5Lz~la<~ao_GRf2?R$woO$#cM4}^kqT96`^^uzN(7%-|eR%22rj$LmE4ORI8UkY2#2 z3x`F+`Adgwv@P5R8qtnM(ZfD$I#XUy8fo|eP(i=+{>V6Vg(h~Qx8v% z%1}Onj};phEQAuD(-)B5$BOPdf9V-MBw3E)_-ZoG?(UbKEEi}tuBGo&`oo=jWP=|r zcrHl=))Rttl)AheFPaMz3p!QL)s?qisnNsB0A;w77ZexRq4l`Vtr#7RytPp_YPo6s zsWV;U8W(0{3*ke~T~j4SmC>Lb7?V@0!qrR9RA3{DAcjYAI?afk(lB_KQ(}J8*ebhm z6N#n((+}te6&jdqnH(c+T%*#FA^IkNXgLPVxf@o&$I>e29vzXN6H|k>NBOQ4u);6! zxHR$HS|F|(4>8bkF>5JW1o(rl+&2dEhJ`@%o-X1g6#G@mWarvT>4yb2U%V7$;-5*@ zKVor;%l^va{1TaILqkn?E9OmD(J!tvZHV<)&& zV2gm7atXe_4ZGsVODS0+C7VUxyKK-P+0oh{G|0U z5Ji1Z4Wu)QZ|n6`mRz?5jBhn5CAJ5F9epZ68Z(ofN9ALhs{#%sN zHHE%*oZ9+Rw7`+~^#jb0E`M0wFf<1fb~@sLyFSm}%^^1M+H5UE&#+3FdIwnzZm8iC zL&yDms`l*OEF?^P>h^-ZCP^E+9v}ro-;`k zQK2}{N;G31_u8B8>yfs}9eluJpx)`Z#F!Oyg0u{dIB+LDHtF|h)C;#hU2y_7{-#Oo zkr@=bVi{I}iUCJjMUa^%2v?^{wCx3aRj7qeOOWUiPowIN`X9RzaKJc8;9$^?*JRMW z>!}Bn90IgR(lX5gu`5%r^QQw8pcyb^kp{#kUgfFlAC!B*{_{^$K(1OTNzN@J-=$g8 z-eC_hD;Ra9Y1~lYKj)QYH1aE_f{_mWEm*iLOX#K*+mXBC1+VRgg?d8 zKO}z`D$7Q#g@5Y!^WDq)W*`-)P}&N;8P$?GSZg6sCoX~zm;gYm4o4aNCFfTd$xp1 zZ7YD{$L@^h0w#{_Pbo7*!PPtalxg9VVeIM*AV8mk<1=d9{$oAkpYL{*;2;+|?wEkzExa|OBkz!bx!h>?` zwFO=MFR*51PHR3sJOkI8L**6mFC8)XqImg0*wTLbMOgUgi2B)M4)}Z(f69wwVtmd= z{$vw6HLUtnI%+b)q+%lmE#%;=bSzjkjWXY6rP06ORzy60KLf)!kvyX?mt0U+yQO`5fFF`*Najy>u3+3<%E;sKSo%M!xc5 zOVP=PzmKRol2y>_>JwY*UV)lr&&|zGC%|p~P5(J41GNg#lH>alI#-SZN0s%lr4=gv zOqFHp9Z2mr^$}8$Q-$*!zBIb5rzmWDT^!QP5|C5wVV#0@>=Zbz?_<2}H;Ht+ml*IT z_&&yK?seO!NRg0%F;>Uw@0EF*=asqNRJxOBqcJ@q7q5I+?-0423f-(E3Yl?j8P%6m zoyFQ3C9kIdE7P(8yPE*Hj4k^e;p)NpJD%4cm)st)KNiE}gf)R(pqsQvBtL{&FX~1Q ztWB;L)RtCC>&!iCsKia1(M2{2$_;j0)^=(0x=^_y7X9POOwppZ4?6u=Ef8R($Qu@u z{Jwiu<%{0|oK7x&EJ9OkcIYxcyWtmpb=gJlkC=6#*983hh3$M~-P680C6Sc~L%jcq zNh1OcI_LlM1i78zSPI4}S`9=YR4*rZ-D=}EoWOB;4IQ4hho-5qxVR&|B>C5_gK$+FB7Ok0oG z_a~!8w>bvu?oB%wmp=^|*f~kgA$cccnnRfKiMGlvy$f_$Ol=Ip&vwT=S4=}WqMj`0 zE5I?t`1EgitZh$~0Qu_GO6brVblEyHOU@x$tve>Xg#lh+`~0;VauYNGX-J)M-f-7& zjN+*jbbqJZ!u`pfiIjbBw#>{NH%bKZN?kLrs;~l%TSRSKbM5mO8eZEg;ZgJV>F1%F zFtJmRe}%McE58Yi^&k;IkNg~5ee3)%I88^xOGevISFu3hq|NU%V3|=Z8U|;WfCPx5 z#*#kPjhLI&rzW`d8@XcmnbqTm=d|5k~_48R~dExNbU)cxZ*?*^ifCbTN#<6kj9 zIp}?k0_~1+WvC%ove{#B(OY^7boPlYw@3qB#T?jCK!Ge$@F5_x@9($rzb!bMZ9M;# z!k}sRqiOobQ~%CSPGxpL8>E?-(-6)8UUfA&{M!KnCn`1C1AL{aw0LYrXh zZ6Ya6la#p&PGH430q>R7nTY!7eTDGAMgKclPTIXI&;+75r}u;QYITtJjA8vzXkscE z8>y!;8_RE$Acl7ScGs1Cy%cX%fh{y5+i8aNc2taCzjL174)A2bpI*mvgVo;;kXSRn ze7z~o%T|6m4z=CI-#Isfm#6)2PnH;hTJI(RpBLW+wDF|{vA!Qso~Vh#`9H$ zKRG(#^Df^VRGw0QM>u@SF{1;`h&6tF<|cB(HKTf#zp1(rq`ZUN!mc3hp(ldg z+~U!B&J`VU66AwY=SDOK>!6rmzCua1Md1w~x%&v=-#8OjU=^Xl((L#}o*y{Ev?ozQ zyP`Gz62488MGSzdl_>e0ymEvYya`RkC-J8avqNVccfG+N)&4ZWdJMp0fHi{fONPH- z#%5i8vb-p)4GH!UfR~)00pn^hDo3sP_wutibsP7G2GNQI{KX-pg*H`Say@)N)|jET z4pv!hR)D5=;3yV4LPBJEr647Uz=XCP4Z$T_hp|Ekt#Z>Jb~YzhB>vlc7A$0f|r@W#k1$V zhsAPM-4K7hIR=C^3%CCGZMu=ewP_H5XH!ly404>CK83-8I4hHQu-;=hil~lihMALq zY!|8m!8?I^OBJU|c>Txd!t8zI4hWD(-*sMtZHLVQ;!>NJ|4B9BL1oUi%e)lsDRU1$ zK4V0>K)tl7yi;SDl8v6oe1sAzvavnzRz@}rE)zoRnZq8|oD(4$E?m1Fm40|_??^Nv zXM`gyE+yo|)1fg{0zE+tw?sD9r8Wg3j~%&cJD5!=o8v!+jMR&)sb{v3n{Nswc4io) z63PMG#$bl0tmmh!BV6(0UmM}MiHvvM47fM-{;j|8p|s#60!@&l8WjC~bA;<_CnaeA zFn`LF{ZaU(?AQbJwvcX(X06m8_bVgPMVX;8~4(_8Nh z6t)OOi@@38n%nClH(~BY?^M6D3Jusa{PoAIYw)!v3skOFOrq#cTL6o6QKzDfVPs)X za=j(CMBYB)N0r4ns^iqOg)h=x?}_Ku(9LV_^+|EEK9r-uE!AQ$_Gm)fi;J1A*AG`N ztTP4pWrf5 zjtLd%Xc~gCY)L9xXv1#pCtxKIv&AbaxsvAH0@Sfrv6dHq9~jGzZ9`a3k&=wnzjs2CKloh3Zqr=KZM1cA?Gd^C{~h&+jyStPm9*YtCGmiyBieoJM}sd^ z$N%fljp0v`T6@5pG7(0Pv*BKqT5-R~uLRFH5QVb})+#l}<2o)RMWkH+(OT1a!_z3^ znjJG|o@T?go=;^xAH_;JLOc%pH@%pops{;;FIhx7Ka1y$9vk1qIT5S-70f#yPb6}< z^Ke7`!h7(s7Fl@!U8%5Q%;#3{4!&|^j*_e#HcN5ex-XN)ivG6{_B{`FaTni}$m2Eo z;&g!{6-KnBW?yitR&~3Mh#qvbF3tMqP3OOZiO4n*54&ZO^Y?)?=V|CLajGvkB3BlcokG3?rnjqK2pyNWp)&n&jL19KP< z&9b@Akumux+!cyUSPW*CnD10`{AiH%rrrab#D< zc-22CaT33!?Qm6^|=Lb&_ut7z8D%sExyXGbBhVfU3T1hQgQ&*=4HyHK!4UK&?R%)LVN zhBEi6NgxmLZHaLLeLTM}O_B!%AxmSdj}^=8oDDHM@oYL!Mt#Pmr!;b!I3Me%YrHzs z5->L@0VuoIi;>eVlT>&bkI(Y1pII1~S3VsM?^3(myBL-Bk(_ab|!S7ZZEatquH<7oDK$^E<#yw*C{JJ9;k{)Gj-_lF6I-K{rX!P&mDzbSE6zkF%pbH1+iP;6Ji% zAY!lm-B6JY?j!hqO*w7&m~FNpeDlvYNBwwsVEs7CVNUFP)stqi%jH9k+LiEm^n z6M_J%X{x1*KI)dae6oSTT?sl}Jj|m$5s0k0fCEivr{G(3gmw3MkW)q=QxrDm=Q-G} z7+IO58g>O^I#Q2ki&c$$^&yWSqCoSfI%FXv0c(+QN>>XE!co3-LFBED8T*AD^`hdquDTqkRCmO~VjfH9PQcD|rqqcNjyg1TBx}3F0a`VIh6U zXlS=#4sBKhfujqD)owFC8o`)9u)UZuyWNxC3?gqghlXcuhjNfkka&-ONN=^AamBT~ z7;h^+%ii~{wpW>qVF<%r@-(s66;~-d5DGPTKd|sbDaKOZNO6bH_2tEh=`)~_xI%y- zzDLz$SfI&}$yI2m%~~vDKyk27PQ=kEhmzpOX2MG(dM@RXtm~!WVPDTeI&cb*$lxK5 z@f{;hJpfnp>0}4Lrc?pe0X>A-ofxcxG92`tn&rPf1LKSge+eXKO1t!4Q5BQmrFC0= zi`2>xEirZ7I5je#d?nRxDr+d8*O!t&+0w}HQFY!Su?vE7$j^4LcINwzc*(5_$XAJ< zhY6mgYJ~(6VA z!wF|77`6EGPuHiu|u9{dG0t34wm4_&~rdvK9pUUwKZb>=Yee}fMk&LCITQd2QzT5$M z@*-sf*_e)^Bmyi5ssInPGmXjab4(>r0u;`UHcCWa-K8stHHYp_?m;%;13d}beBUC* zOO&`7PUqF!@g=yfNVOvQnM6orHTDw>3Q`TGrcBtmT0vtNily@ zdtcn>d>~UN<7`Z#x!opXFm*LnrI${V9JWBkj3;)DolD$4)~kYap2OWm9`vzQt>kPr zQQC2hGCD2Aic(y{2zk?K@L*L7A{+b?oBy_$z3!G-Xiafprg_IoDft7n^ zrZbXIEjq>aHZJ@k{^GprY_8o$Sza(HGCk4Td$?^qx{QvwDyEu^*|x34aWIkfbxCdy z8RI21&%)b#uY27|sE)I6n>Avj_P$|(j zkD?XMSU=^TNtWLvlxRB{CaopX`mz8WqJb}gNJ0wq0bKqFY|1r_g)H702unvZ*miNwkqbgXh<4;AkPiO z>;;Kz8N6v1l;1k=o2@`I`W+MJ!Wk7$ZX4*yTIP5C>!CKJ4^ZAxmaZg0gOVhD-`r2A z;?93sY39)yRvl&!X3+>&(5rJavVzS=7&kL{?4i+E;a&g3L>q+?0|J9!aw#o~z?O+f zKlIVZUk+8&9Bw0e1%|qQg68k7}2$a4o5{9sP{PwRx_hWZAbEJx4*=4AJeH~kj*EruvOQ_~ar3fyPXdC0#xhg`-tD zDH1?f9~hEfHsaT6(W#SAUJ{W<;$#6M5eT@Lr?ZpJ{9efVTX$G5WQ3y3m+_@mZR1J-eGN$H`yAK zA|`SY+?<|&9ox#F>?Z8VRO08f*>9`CD)Ays*Y9R71n4*Y=|tzm<6M=EB!QU!vNQ)* z#Xe32f?tj??D!vRuz2b@2s@~LwF{k-ILn_#11dM;!AC*(U!>|-yAfze+?Nul z2t5>%)?>0$H`~#Iyau4=9+M5<0?! z-NT@)0My@ZD|)-TGZ?%5?daq>ZEeccMbu&A7bStJnh|OTCf@@35MC)@4uP z3M0`57?xv9z@Up~vpgyNgj`TFU!*~#H4Dd&T|~u1E!g@?GdsaS*^9tQ7T^PxLKAg! z*IB(mh#~*9KwN10B|J$@wFqdp&~>tIpeva4OkI?;iTmcFVW0M5Ah<*^cgvJ1RQ%nD zrtGMg>h735{?QzvD}Oyc~@^I|>%W38QpW5l+wR$EkVjuqPZ?^|~Ye zD8(r`rE2DoJbD;aqWK$7}!> zKlOE=`l^#ZUM;Z)-eENn5 z;Knz#ay%K%ETC$|>fZfH!ieP|#*u8Pmdv+qL=y~H9-`or_cXVw!Ph@?482WKUsh2P~t(Vv>uHIozEHB;E@tUO@a4d^+y_>~)wv7e+N4qwh`=t#l79}!>V1EZ&ed!F9`m7;etJ6+ASWFZk zHRN#rey?I@q0}!%V}i1M&@rYV`%PAEodTc37$z0a7lLq$tbUt1iAR7E%I>gGJ58}Y zcws|zkdh3o45GBAThzk&U>Kz$g1kbE(SX;LiaQFKB;iE-@JuSm#2HrN1z`3J$!G8l z6_t}5%28o6{}Pd$84TljSOy6dzfu9*3qM|7REu+jmB#Iw-SRXb?c}Qe&D2PVXMS|n zT1HII;qzFY1P-VaE|B3y2~kfB2G*c_#}zcE;56>v!gKiqF@)mV9ShW(^1EPsl$DZ% zXxl1xsgNxEI(d0w{|gFPsTY-b9P>q#wPN$fF5%r-z;mz?Ekn<;ZHdZ&QtL(k0+IBF z!q>$N=1$p_u|EW~JoG_WdDT5fB@MX9#icNwQ@1#z=@iybl?3)jOo>hjLBzl06Ehy* zboVI*_r18!{@)LGi;vgHii5=^aDI7-5=kKq90Y^A`f=|rfVt|g7E#ZC>0cW|yHBoc@lvzS)52{I>(C?Mq-)yX2LkPw*y8j_1_qPaG>k)qfxC0`RBdNch2j%%3E_pu0plUDY zmGwH?q%LxkNzCfo%QN{lcv6%YYuC_h44^_q|8c|L73S?io@{9Gq>txeKI$Ey8_s_K zA`z5f*#nH%(z%i7@R#?_%M-)P6Jhg8UBcs zuuf}ZG!DWwO6B82a4-v7zN}}jBk!;y?T1pAt3|^d*3qK z3=e}|CMnKZQdx*cC4J})J2DJP?+(h$1oG$cR(JRODY7~*2W3{Ko2;ujitbkHWX>0> zV1m=A`G*7_!2Zo*b%rO~n9C}4vaY}t;zS3Cub|R#&e(*KKQD!eE|w#oKfFvqD{Ho9Ny*$_8M&BEfWOM`(NfDtm<7b!|lhv zOu2XXan8QNa&<(2V`0YLT>5CTA!^a^tucudZ!#mmn~(5BY35y)Z<=?BDEUAQHNz?p zcan)WLCipPdBmw0duBAPq+8EP_M#9R)<0!wf?oT2N`KPs50a7caL2}#d| z)9qQ=#~@nTGKlZq1g5KOGUvoetPrb@1hqcF_WKrIueuj9f0NOICs zD_2SteXJo#z6$MLpXB+#sr7YXxuz^Sr-3uQ-0z7uLY0&QtwmS6yTRr}sXk^FS1UtM ziPWHTA}bvJG*+447`Z+{QQob@L&ZWZ`Sx;;bO&{jE*c)II=!J1!)NozM#$S*s^+9; ztS1$T>Izeuo5+%DekBfg#GG?O(86Ia@`44Zi0nXOpGK*7V;0XEH|;5E)&4EYK^6?k zD@d!3vo)-9w3L?-&VpcW7&16SMB;>n0zyR%0#YIJsm#H@i_exzIk&IjQ8;dxEu{3V zmn4TjuMWIRZ7f&{IIsnGYs=}^5bUc(Ri`0gIU0=VCtW}{c!@0D55cU#q#aYn3Fn$m zS>`_vXYX!P(D*578&B9P6xBZ9XP5Ks+xSo)|*1`N{9c{mx@0PM;nt$_j71E;j(R8C1 z_x6IDyYIYk2xss`F*aD@n{-ol32rB`I^)zX@ZYd9qt-1gC_tXG85rXd$X4iFCiE8~ zS`4rj4Hm-7hr{Q!`}{Gdo}sy?LZb{ zV!bcrelf6`3gTl*b4{j!S0nMEkLR-xh3kU9*hHt2J*WQGQl1x)fbZ_!2G%Kyce-aF z`t{%qcy_F?{o&!>3(C^MP9~r$#n0wkG1pth`eB+ib(OsRXRXj>e2}_X?yS4j33Rgu z-3%-9$Dx3Q0wi&9Z(^Px`szFFGz~P#E-z}-j}dEF>sy0J`cCfvjei|>J$*@5h9LVU zUhOl%sK)$yLrKS-T8(HEAdzOikiL}mtatx#kq}}nSASVZN&Zrp%I8x*cP?IrU#QAa zH8_sW#Z|D0ny2tCt=X1dN=Hjl`((xeK}0a1S=iN2JU}m*(2DVl+30ddmA4VP1WIiy zMt04Ub^o5dF@S87LBdEHst1GhQJaD0kJm(e%W?5vYzp;c6)95P`IB1=E86DPEpaRD zB*yjKrI}s?$#hu7tQX)$_9?Xp@4Y#_1q<`qSzM26?)`iMwrnEhTDjZzt9(2C^g`~S zQ8_ZXo*|GanlLI5m&cK@;=0c=0J0D39nmM1kOvo|2ZNYjBO)W@l!9WxkJ{8fLj>3V z)i(GEnMy!)wNX5n7DgU>mPnGRi~82zeJl(C%^eNff$A)Cq%w(+@4f|%GES6T_s52y zUiL%;g3tCNe$7P-Mkc2Y0zQY2cxY{>9ZE&KVWqBntd@m8$k$4s(YE32-8c_lRr^GD zcyw`%!cs|~OwHb^9-xPuPk?9?tfZ6!-kdoO+cUzL9wGBQu3(6RPmFh({Sm$-5H#U} z?c_`uBncMoz~@aR2x1riuSvK4#6fl;E4V4l*9RQ_vQx@AkW0*$=4Tbrj;9rJuVDLQ z3$a`OuM8Kv%fc)C%PBv>U=zZbUug5B1fp;3nHS@wlyEyiE{5pr_0Jm7s-Etr*D~R6 zpaVs>qQ)R_`tj}3SGV6{zBFq&QN8VU9v+*;qnKu|(DUpS`S=nt>6-L9c?7}RFj))i z8-u!IknPm4fNWavvRRp=Shfa-YETk3@Ip+ajnY9XXlM_97+f6Tj+GXhrmxj7!PIQ6 zW@ap8aSbD&bfe@d{t3#n*+7y9|DwzmI75V;`klNp0>eTY^l%B$1K+nCi89G_&XWoo zt{H%$W%ZsI_hSYe!?ypDZUEscEp7DchuFB6(u~WKs_0}BRt7g8gzgGCjj9as+_D#m z3=%MEqVSpt*DoF7Ij8(Zx5p{$5Clz>ly{4czF4om)FJV*bK;{uEWNvb$dffxW0}C{ zYkNbcN(*AxG>@6eM(b@V#$5(R)3h^&DRI{7P&|JXQ@n^yAp)CMOfD6p^CV2U6Cub) zbjr6nmJ*%I14;1TrSol`%-S8O^uj_jA3@Qh(0Xm;Sjkity&kOxf6PH3`L?TjUZZlT zLsXFs9M?Fng{lR!AVRi0rBpoz2JEVVDGhWzBc8`KrmY4*eJ?1*mTKSJ2b$Cqkub~Ww~zoaK}nqd@)oYctKJR?M0&s%UQQV6gyQHd#(kofcy#q>h?Voxh_jA_xJ)CH82~Joe z!dOifh`*Q#*?Rx}zTytOCv|agI-B0F_B*58+8D|F{C69bIT6C>S_2UUiN=5#jc*ZS zNR2x35*ug?o}d@7-6U9-@QQH8&<>9*(~u^57{JpxWh}uLXbNyt)}r9`75_BztY_Qw zsq$*hk+j9pBJY9rgv3=?{<1adPbl2TQjoV(053;8C>!0Z^8 zb0P+#+~@is*^=9m&n@LTrBqWcQHkWuhQQi^Bfjb1xv^R~SH-SSOIdw_Z5~-5S|4*6 z$QG7hoIN{3Z3(MFov`PQbJz>3kEF1T$KX$!8NJ9EO?X^M;a}JzU%-Ra^6|$+!GpJ2 zf|&kivSw3?WZ4_NI?b2J-~_egVr=(r-Z?;q_4+YWWsiD_hri6Qa*f&(ra7 zU3#NkEh;i!Sg$jT_|3Dk z?Q)aJMGee{zua%3dBAnnu9HivTH^=*Mb3%CtTs}^T+e-}G!`+tQg)n6~Q47bF1xU(X z@(cKcmrtnkc?)Ew3sD?c4v<6-?G_bMoKh_*Kf^ybr&%j5ypAH$infStrk(Ca&R=V^j0~ zBb{cwJlSH*lO>@0(Q!qlEosD?OaP!sN&2D!KM1iI4tt?$A#c62Ic_^6I)B~d=hZnt zVi$`F8Kw9jra@S|e8Ckv&}4P;89VOJ_?mZR5>f@ye zok|_}LGV8PCT^L88xc$RSfUf_(xU6&9?xssxlP&)5<0MY#->HaK|5UWLple~kom}E z1qT+@i>k%@phV!`n;K9n;)dtYVpsjN$766B{4Hs8=6!N%5v)l&S8^Vyg{@BbYuIydm3@CVKjOZ2%m%?FMNu9Xt#nCCJGP!8Z8T3Y%&9TfI2FaUXIJmSO0P68n$j?{*U$NN`MR8@Ilt z5(+Z}jgis47;~*M*7mnJZ2V`Mkc&S0ScsF+TG^Q^AM#$hX1A%s3a1{~qSnK4bM12% zT5x4Tw_T*B-U58l;-YdX6Fso4KlcB11I$TKuwF~pM<5?Hh}`U6aH7AWAcPvU+mu2D zXVKZ(ZYXzeKe}Yvj;d6_r&M`#P?ugOs_S+E39LKeFl8w5wdAj4O{Pv1#D7TGXGwVh>C zPBFP_`h`wQmSGe$a!SBWfO+#yiGa;hh(LG9(!HKK*4kv_0O4`8-dJsjP3e>}l`E-& z{Vsz3`~{(Kvc+LPDo;>QC*vD0qQRhxTi^#O`U>|(8w$N^Q@H^9HHcbJt5}j1%oT&;Npxolmdy`c*c*{+iHgFsV`g>Fx}+^xiAeZeZN`&I|2k9f0cihF9$aGZ;|asH*HC z_F94G&-U1t*#SM!nB1SIMow?$_*Kqy%7oJ3~ zshE!#C-F@PBdqKnKh#i>wn)4wx$H}c00sb2d#DbCMI<0rg+#*uCOkxy{ms}D650|U zj#WZzl(?q9oPurNoDaBJ7rEE`S`v4E^ckZKp-lkb_EO!#eSZgM6xCUUz@R4mL5LgZ z8FnBqDh0xng@&$+mcpWRUV@<69uL#^_+Zo&rN7&>Z&tbioRW2!rH2wELtBj(z^I`Z ze;M^H&fk|_psRCXDMNbjc7lZiqakrdo&p`Ws6KE5r=EXCqg}@h#hhTH2xi;p-yLbL z$7kv>D?Y4bFhv_Jp8H0Nbw6iID z2k!Q}zpGd=k~F9!##AY2h=Cgeub3tA3%2jQ-Bu)iLmju-ylLV>IkUpffdM=M|d9VrMxzdkdq7XI0 zm-r-kT5OHE2JgOBb`oGaGj_tc_nhgJH2=ZT@&nJiW)m3@Fv{E{M?#;-LkhaXkulP3 zb)F|C?znC9kiSS969zRwjt}Am){>G2E!V~6MiY2#ki#wBKITv?KL=!OVT=NDo9V}I zLB)pYSq_IT3NO{eXqL$Eyt~XpxhK;7fjoLE$rUEpfE+78t^6t_^lzN3^rqnYa>2?V zV~StW6)zrBxr-$sRe|clUCJ4J23{9Z7OJ?# zWGl>#UjCs;O%6}tK~7&qce%?-#e7HUBL)0{0{60wXlPZKybHOs3=v}yf+0ZVlV|49 z`WQ5AsB(xIH`U9|L47mrlYLB;IpwjuMZ3X*NrX?W*Ti&GPTLsUyKnaltTi{UqMLMN zZzXh@$|cDU6ZL9m8$tw|{CE^6XJ{sn)*)82cL6QW^L9b*DkCO7##kmCafc?^QVFJI zky1N?&~(dZ=FTp+vfryzoHracTD?RGU!L&Acr!%Ks6`!TALm9c+@%zC>bShFcGG=M zhmY_#MMkSZv8vLsyxG<+$7Mx{-PBWHp$Z#(kK^oEuaI2^Om6;T&?9e~A$!zKs}LDO zb9R{r5+8;y*@u^&IfoHA;%4O31I_VNQjQz1O7DjOHDOV{j_%wVr}m%SgX7ygBF*+5 z+z_Nha}r>wY-$9R-ED;Aeo+8)mSmq{NIpatE(cX~ds4+;$ne8rcke`EuG&U-PjD?W z8AS2~b^f)8>vQ-s#9}>CkyYUw^+dkVAmWuuAF4rwRbz*kJro}Atf<{d3NVO8!G4W5 z9aqVOdaq9U^Ag_*3j9gFlZVb8CbarFewQesO_aQq<;nW>HvdE+6z<^pJ~fAKsAgyz zsN*9$%3t_F_Kp&pizqpy7L)8lI)#I`{QGT@!)NLhX7}5#cb5* z;mEUZQtybk08j5)x#%^jOgc`!$^b7)iYf04r53z`J9(UD&FM8QtX=|Pho%H~Cz2S# zR-?mis(0d01j_J>ec=R-TTd{;_i=3Sd(19n*Kd*uPc?SYv)xR)j0^s*&JU*haPnkT z(OH%XoB}8!l-*PTZ4&E1lgd}S%{kvcFc*fs>RV7C0a`zuT%!$}UPfq~B)uw>?x8Nd zf+DYCp*!fe2e=NX<^qx!8ZQ{)YW=73vx$l!z-o>+T^fVqKJoKyNrR z8ugK)P_yDiLkuNLI6-XPz3#<5Qqs&rbKbR|$G(lD<+#$7>-~BuqU<%)1H*TsK*!6G zxsOTe_n0bIZdj3R~@t>okgUXlx(V%fHFS znE`khG%i%XgbR)?t@)bx8o40a31KNT|C~UMb03dxkTTS!;SzvT6Oel?Y<$xVh=Xz+ zX!T5}daa@qTRSuamu!uoh+vw6X^q0hh>9K2JfSu{eiH?kW6u_SYs#fyCub_8rwkDv zEv&7p@`nDUj1`Qiv*?w)Y4E08kKVp)rWnRv`Zk7ZIei7MFbiShKyzNJIdV79FM+emXftl8Z-wWL-Rl9MvN zw|Ir16H{SYhH6*eD_!`hDA)`zx!bU*%ZM6bGbuLutS>T=p~{OYlR-&jS^aVqu)xXWA-`&7dS|hDGwOgB zz%l$B-tP$9T^4bL&6lhQ?ymS%Qf{q>|86{=$~IKEVl<+|A${#SiADzSbrJPzZW;R(^QNrV-eS5YV z%4z)*mRO)mLQm$WMDg#PvCNIl0AOBPK~7;UlP;9p6F#Nv4X?nOw~k_(dFPv?&T*Ef z+x$*MZ=2pc-uvM!QXMxYT6iHP*bPf!8t>sVC|aY^e?CzyA}lYBZjYwBWr<;XHn@5(}qhi zn#}XRxHGI8;`3MZb*qoez+ps3=86r`pqlN_fP>QX5ywD7l+RbJG1nDBn}S_hN@JV@ zmFVg&LscSaw2aAal5WWOtPo=}?lKrOkp(Dw=uLDG!lg&&=5~B>WyK>fm7S2tuoVJ% znBeCzaVoz~iHoJpu{O62_#E z)D`baQE3VHZJ%F-?y$eWjrR4Q+lR6x5mec@#7dD$NFDo{C7R%MGfazDm%{yJN^o)| z0%w%!9f~?nqnrm5r&kIl-T$hWJsm|e-&z=Isr$=E51cm|!b`jGH8sxQx zfPcapobSUkKGTITND)n8(5?$8k&S>jfA`!=_sXing9NSrZRxyJ1C3g=Ct8 zXtE(NWCuT?p+7?yE!jUjFWj|u?zQ6;-2~<=Wq8OO9qH-7Ar22%j$wIZ09tEgJ!Cd} z3W9W`4NabO@NHyY>e`aqHrBKOk;s!(Eaj~-^E_(Y%FB69xd60K?czO}eU<@125$

Bfzt<>Q zX2HCi9=aO^xii+TR_B^(S;zmV%~ydR2R@DD#futScd#z+;&GZ1#zr zo!+s@>iHF&9d8!Tufh{n8iP6@CB^hEQ3i09wjp4{Duf$xTPU96xhEqk%;dX=MALcU zM$M8=&I9_~P%<)r_<kt+I8EFfSBhb{ zf7zEz>Tw@u z?*w6k%~Bc?7=LE#d%ZH2E<~oa4c&Y*~ZwV}`GAFXCX z>f+abD9dqhP8@F{bpAf%_#s%&%3zm!vmEkxKcrZ4q1&uH=Lzj;J7%TpPcX^#F+X{U z7xWVFk8)bFWW=cr6bl35Nj)*`V&VMl$vUQmkO>omF{2Mm=z!M-$%>7bHjd^TyHYv{ zWjoJ{D{no22)kX|eX3-$J?b9=trRO>X8Gpm4nG=I8yz(+wwqN2x0UC#5I|hMKX=4E z(w~MHIh};&-6fVn`(0p_G@TcmQr6r~C$;2vZ%OCDJy|Ssl(UUu0k}t2pqx=V>~<&t z^~+Q>Dt*ZXLqp{k8m|UbDsnG-`sN2=FEA0+Qv(~wPhONdULFmE$lo#=#3CR+Nd6wS z`p(hblc_%g8wB5Jn<8=aZt~Y9n60zLQ{j5-%8E%P6_jZd!J*Dj9oXp>+Lc+*$u%k3 z@bd-=k+3v4)fxmVC^>ImQhup3%oFpk1F9XJr=?S*=$^ zTMYzC9*K-`P&R831%RVPh0Td^+uAS|HogR_mF<<_%Qqo@|;T&JO{!46fu73J6g zK+Ux`JUWr>0^y_!#7_JXDdjK?wpnpoOqO5UUr|F?12krUzn}K6c2IcB1XOsW@^VFH z>ux)Zgez4)3{w68Ut6dhu$F=qpO)4saY z42UX0J`asoyPQWb`tb1-%@pI_qA!?>WfKxnuZ#xxZ=jg=Ho0_UGQezJMaf2)e#lsF z>Nc$eBVT_da_*AhC7|P0FZ1f@@b0bJ*@$Y zxqXDjh4f^3J!TT1KM!CTB5CpsuEsVKP#OZs)?$AU?nQ9ZL9ZF83+pZfURGYT!u+4t zMp!qktGrV9e#9czP?p`jW@m@V(Nv@n7={hoJDB;Z2ny4mR!ie~jcy)&fbXbR_Ut^n zPzCNgE5y_$(amuHAf)*W=S3*6%(musBP);AAOMjL7SafB=qhe&n4~mAgCQoF05o~7 zwL=^_5`W;OVuoX}tl}p>bJ-6pi&{Yn6gd3++s9c=zUHgr1s=0bz1l+UMIg%Dzd1mZ z{X3F{bP&u`MaPNkwLSPB=UN~P*vS`Kek@&oA$;=r{CciJXi3?&@+32F0_Kt48;AlH zi}bF%I~7<%USdmBcWZh3r}r`@J5$9co+d$U(@qEzX6R|-g$>AoYkr4{Ww}yCY9vWaEWZ&B>$LN zL&O~!vnhOK`?>ug`h(2|1{b{pRCzTq{%6BUiSxYVnJ3?9Ioi>+@?=ZkLaSN4om* zlT(q@C~-ceEU@6~p?LKpe&y*&HW@+ls7&u-RLWpgf)DB7<2RClBNGj>A&{D-1S|`v=yqN$gQY)T#kL0084E_d7>*#}EaHLvddZbg)oiTT!nU4A zF^8_j#O<_C$bn-)D$-qEq+67!S*;q$Np0-74;D483N92(qug99fX3PAsKh;HnXej& zYn^Y^zA_$sI~9@<=>h%K^dWztnN&^^mOn5Y?aAC^#K$V^7^1`Hoeo_cI`0O>_2J3( zIy4vMc%7VBe)FBx^pR$fogAwt+*6mRj5t~zG#q|4gcAZelzDdqvg z7UPp6^FXyrzRrmLI#fkwu$ah+^o5#JU75bNe7H0#{VE<|RwVMC9g0k zCc+S(iANJ?M4u~?U9N)@zgyW()u4DZzT`LEf!)4am{{&#|D0SFoZ=EGBdVxH8v<3R z7-bV%z?ZaZTztGXP+%RX`6OyxIp}1)x$#aZBQd|cuj-&x4t^hZtkM0QmducRFkFx{ z0My;t+aQbsUMI?9K&nfldT<T-Qq6KG2iB8zJ8gDz3_3G zQkl&k^D>)hc;$R+ezp{2!M@J7$w&(f+#g|i+UeV3#47S9{TV;ALY4FK=3Li&{Ed#; z+8bSy({ntG-Jaor)!)(_0|L1$B`n`~v{_l&)b*9-m&$}~`lns%QP&*38@)qiS0Zty zqj?7!xy-fsbpWJ7>(<|R)P_aVhIh4`(weR%Nm;y(8 zFLem}TU(8&SW#!eraYpV>ilG7wIbqso8?DDS!0nsAMiEvk+ag-$7}uKBYEpQisBUP zE#Eqd0z91*E!)5;&co4|)w4dSx#~=G);YV_tT$%wZo2=fxB3?BEqwY>gZ5X$=XfZy zKfVM}X&Wbo#UkyZmtk3R-yJv@|J% zqG%GsK@cKkl)8a1G_kf6_CD4D)(O@H)(zGJ)(h507@sVNW9fdX{SYBJ5=Z@5xTq|* zBT)i!VpiGF5UE>cFagf|c(|NwkfUJ|a&Z>>F&=i!%^8;Zq-AkM!8%o>Ak?;xv}UAp zzLLuLynDBf(rATBkA>^b!ECfS+^2Pk#o)ZFQlxddc9=}{-}zaOMlZ8q_wk4;y9Uj0 z?MWZB!`d@$!zmnWAJJd%|IkT7{VSdT07Ultvj2JWzZT*DR`GN+F)+3<`QLTZUL8y4 zKQ0${XaGQvA7B80|6Kl0nPmd50c95j0D$%s0D$y=>+Pl{2F@;yCVED8PR{?e%W_*s zBk^d&;qR_qKYDL8r@Y2%CIuc`NO^I9_IPXrAxjCl@C}t|YQyubPBOT>1_krns3H{g z(DgZ32lh81_2XpY-_L7wJ>QR$N;_TOhquX4w_cCm)!ZCBy`7);NqSzN$5Ff9w@0&G z@7I$~wc5X*=b3D~f1f+~dfpEbPg`&KxO=|$&u3?S-;X*U4laG|Vtcs1ug?b`Lr>l8 z_&mNxcYW`#)pDD~^nM>6U*+g`zgE@0FmJ`keZFr-g-*vMa=)M6e!kz8db~Zq?j|R1 zR-w4K3I#N`}_0MIqGXo;mmvaSc;#s zi!StZa(sS1=25%*eSCgt`IVzPll^1k;r;s9pE!NFDn$Cee)pQ3+UxD{m9IHlaJKXP z)#_#!N#*l<+?jf%QTua#f84$i7Tp?Y?*0Dt@H2?dyU8wfqv*R*x%>C$JX223zZw37 znMe9d&;4Wi2p{HWvQ|dv&lAR!qj5&mv6%<&Ci88}TkWW?0q@|&EPZaml2r>wz%CTz z#w?vLCOR#zCOZC>Bkx$J&g1lxM4uT6ujp^c3C!Q8dR|lPKYxWB-(=vb4((1xwP)SN zK|FK)mvq}BpD>jA*(kj4U96U!u+KlL!fF4YZJ6z;LI^!-rEq~!2Q5p@1p+hZNkgZr zv=ZHaYWg*PM7@Mv+8qc3WQ>z}!X%0SOvZb12L-Xq)fa%0nF-E;bdJfkbEWHM@1e5G zx4ls8d63m@X$BiB&K`sub?A)S!0854rOEhb-@eBPZ+<{Wha>+eW-8D%I`j-cV-oe) z{+Xz!&lQId6ewv&;O?Xg(6)C8y9_vzCpMmk*jBX7#Z`f{1+Bs^3tFRzVlc`?&7kG? zAc)^Y+U!a6%^0C!+~P$Q=VrDpQV@5Exb_g zR~3l^i{LNUTBSpyorA{v6^vl9sR(N}Gyw$Wi~q+Zts@iJgW~ibLO~3q4CBsFtskhO z(DP@5nw1TElRDnA6@}I$Bv4K4685aZP^zar^ z$O_syQs?zQTJ%x{_TN`V8GC3IQMz-6nr#|btb=mRV0-TAYewoAVaDlmZaTY$YADRh zDE|?K@Lw{T8Y_j0DB|fRMmDs$_tWO!L#~i5Hy%_64hpDfk@SeJh<#MUCMCFTymLVN zL7Yr-L;I;IC)c9{a`Fn)jR_XEUIYa~j1Z-e$BZ8*Qk(X?_xA1b0yV&zBL}7QbyyMq z=juu8uMH~ggqY<)wa4~a{;$6^R{GCa5(o*RZTKE_27r>g*l^421d$-$qSlYjZ*YB? z88Wu`Nn05%VIgmyJ*QhMcYUFgYUout&vul`DfG91sp+YbIOZZrnIEz5gVmmqF_LPI zwc+B85!D#JO;&fO!~=y-V`-STEHh>iZLt@;r1L@Yk_%afW&=fp!~;q1pM@|(d}ujR zAw${=)$mQIpLv7FXJS5p@=`o$#0{6PJyMcINPP4@5skfj$V~vY&s?F8e)Pl1nn33g zC)@Up#tzpDkc^6W*wHfYY)R0_4U{FaHJOv(R;X;oa)sESTn9?iT*7PWHC2Z|z%du6 z<6fz03^bzL>~&YczHF^QGO^)xsuT)LDr=NvUul?H#)3`S#;=!~x@J?PI3`4aeg*J# zPq}j}(Z(^Q`v;3GL$=)g@wtsMLA-^A_^01w|3<;WUsYcwxpT*S9dC2$vKVU+EHP=* z?8dXurIBh5y4%Gp_5%Qq(u-;e7oAJlp`ldpeZB?-9l)eDmHx6pm7$6%APDu=hWKOpoAdj+!!zA;BRcO~&CO>A@Ii)X zgpir`(lMq()xo%;F~A)Ertu$o&r(B`m_WI8WoLt+n&@rnoI*yre#ex>d%tRo=V-zd z;uB~(W6*2NtS})dLu%h@DPE<38LyZ1$Dsa-0o;oVIPy9rL;bvdGz_w``ritf7%HG% zMG!X!omLn%Y@;~`{_3FCr2eWNS9U;3_Dr&uR)no_u?EqQX8&R@Yk@LL)h=-g3yL&N zz9ldYHX>zpu_}CJ=jWv(bD8Cb$>>%l3BQxwxw60)+oR~-R_+@1U9)Tl+Z>HT`^XE{ zIn2J%fj5{#Wiq9n_}VIpkhPy55k=i~LGqVB6fmARr@yIj`@P0_mZ{3|Y*=gP4Cx6U zaO^2qIA$LVCw9;aSJj-T8V-%mo~S}q-#aLYTIzE1kvdJA>F=nAfY42glg~kktQI%i0V@aB!{X&VW56EZ;c#3)2R-4^ zoJ=IvkC&jTiz?`0f#I@BqWV6*Y4raIIj{N}vY?xPprR}j<);hUL8&%jktB#%N9HZ* z#2pn$9pa&~QyRoAaU`fLR_j>5O5A{@fw};Y(Ue&7&+V+J2Ml=XVjw`CH#Xqe6VTJf z&#<;hHyTX(>e4F0tlI%aSbY@M;RYXMq>{J;}B;Y{?VAzI#C7S0xxT8Z2rY^HO@hqD0a|TG3i7TN1@tG+M}#91fm5p(nw(k2n0N5-6Nt?P1a_T>1>&t z649jE0Lt*(X3X<3u)lNFM_RZj9#i6&H^7AGvC ziv!i(H7JNM3^Z&et3tg}fOeH>;LohJxs}}dc|56%u=vJ11%3dgu*S#Bv!_-fsWSy< zZ#Ieep>3T*(_1l#Apj$7zmi)Qhu9R#9~kQqjecfM`(Pa(wwRJZb|q7F2bc8{;5EG@ z!=Qnqh@la*$)W};X)|CXS`Dxsf9jN<2`xzJXTXJJWTqaL^qxW<_#4FFtO`riPBzl| zWI#nz-0nhHy*$V}(9Y!=?Rsb38MW_;Basfkr>N0)Wbz$wRIzS=kQ=5-XqF$UkVe#j zW=YklNmluZ?CDU}wTwcIB~EVf8w_3(ZhH*5NW3w~ zeoxE?uAl)^;l~?TUY}YaWz{0!@^Vz7847t>7-oTE2-ThBSX`RB;cf)$!lsz9dbKCW z!+^HvBXVg%8tI;~B`_@ACDxFr7;oB(OH+l#8q5L7gtYi=T!p%fsq(?{cC#*~SP6cH z_9j!@UaR^J1Jm;+jc20~BjRf9A^Q$+iA&lO!FPh^t`hiUIE-f#zAMIbYhl-V^)O{*L*#HwDrwspn0 z&ZUz!$HDxP+fF;nM7=aLs{|AJ+-i83Q>&V;-&W2QE?8|P)osuaHMoA82A6FtlNUMp zMNfjfdgXv-^*UBNR?sc8$)-7_U0^0CZN0R>p62Uw11u?#dr+HH1Zfri9A<}_!&Ye@ zz#bhXx8B%q9Vyo}>5<41U(?#uqr5FoBVi2XVwBGI70Xe-XeLVuo3sKurLCr}gUYTR zl-JDzOCN{d9u7a<`2<&YUlL zLlB-J(_tg=av24Z;nG{&o}f<_0N(}uquERffVcDjx?j<2xbPRN;^5lf%|A(H0(64i zus_C5^Bbry2kHB-m}MrCpst5uR(t8Nnykn-Xkj*KJBJv`G%4_>ehF&LlBSTE8!Qib zcyG}}AUzxk9E@yYUPZNra66Jy@-=?pbtnV$>C)eHk_ey#s6p2zC+-kmULQKvb!*8kN4T62T zV7$uvu1quz&JG5Tq8F7DDI&dJ{G`Y}G_jDN&+=92N|A+QTbjHp#Z-*y0>YD`(Uiqp z5=Xom3VDWY`*Zc01Ae|L)pvu|c=0c>+LYU<^Rn|{e|eFLXLX}?QTG{KqJL} z+SgbOYohO^S;aF`KAvCA=`66Z++!zx$m*rvDA~%0@K*RJQa#Z?EI8pPKjMk_uJ~(L zb|#@5?VLj!p-U%cU8S{mHM~eZ*c5Mw+%a@wSnflQiYQLK=x$NXyVIp4_(s2fhRk4>QLq1r;eQ8q& zE;Y5?8&t+6FGC0nWN5Y*#cK6BD~4;OqgCjtK#c%8BUm)hRS?Wrg}?p-G<6BwJ8SJdapYS+|1ex8H!1$GOL7OH5m>+yVfcP2oK2v}H+2<<1- zjSMK^9t9Cv4`Dh#LphJ@AWJEqhoCAzLX7X96BL`wq$N;FTXjwY2a1G6+hK42U0AB0 z!bGo6+{&Ikz(j&eU_W}}?p6%aIT>a;PukB*;?zKgd^baCxgZR!+U&$|;3|uY{B-bF zRQPWaPFYQ&C#hCOZDJMHB$5ERNv$1ifPYb|68}qM5Mx8T(RAyx`yF@g^!x0(=hMPO zJ8z8pk5$5wxMq1RA>rXOMv{1qvCZJ0^xc8*CaqhTpQMI{-(gJQFQUebC zNv@&9$(py8VGzzJE_6_savaKv4RoLDb{ocEvNqa&tvia&7W!3l5v8NlPdxHFD*OGX zt*&N-*7HiI{_FX@dei$25soG)PU^fDs<_v&?0m_?zJ68+h(rmRwiy#T$byD+jPhaRI>G#X^)s7u!^91kGQn@?mNdf!C8D~pW`caSKsKD zBL&Tq0%zKp<0=qTJvTt}3OKe7l+}&LfZCB6u*70OQ5j-sX(HMS(9+N(m=WagqRqcw zdGo6tzUj|G(#f&SnZUcacPT1AL&8bib@ARE7;R^XRAo^{saObvS*j=4uz$c zy5ZbuO=wAMc_7>04nDo3%^vnTM)tgNo#ah$J|@Hww8BE^!~D!kO{R6P=_)Nm3h2RqYfRcha1~~ip5x|14{i=D;-PQ*4)Yo? zh)X*yRrnJ#QHG^t*MC4{#@*i(mcM|;`3CLmGmG?XzacJkVK*>%yx?MVKrHj+{7aLB z4YadT$0Wd_+v2;xlmT_ovHG!;)mIjQyix-@G$dCgpT&I(ST%nfVf(6e{cSi#flI}O zCNAb^1v4n(850KdSkM}H1%$8tAkUs>i{ICx&6b{9-nO{^HS%Lz@_{hosy=F4bF_FE z@)W(R`J1&vhz9^^yFy*vjhr%#mC^oG*#aEd?>uH5o*k;DjxN&Evv4nj)bTwT_|}o} z$}u06Qvfz8!U0Ig=PMo6Y=o=VG%j$Kgv?3hu^~1Yz-tcP>?i{#%J@yTG?SzRm6G)=h11=D#dCW@D?20}NBtxLg@z#dM?f)z9(k;R zOdyw`x+5iYA7Hx0q=&q_q#XVPd1-szaD-|>=q|c@6Thc17$L<&@!T)(817(D*dy#R z$a%00i7R-A8jPv!fzM|LTi7V7FmT!b%B%UL`{{2+o}}+j!KtD2!s}FM4Pq!wE7xE7 zM=4N%Hmih`!fR^;+z8gRPxt{ZsuOSuToar)m4KAi8?tE{me0eZJzGJurfpnB3RG>s zm8Crp4A5@}@E1!XcW8AdN+LYcu44+Ik|)>zL=|9jbMmWHrr0El{InXNW^DwKJ6Nx=un!MpCx3M2Ma&93Ub_gQ9)7ar8so82kwt+!0ra(_IYBe9 z22o0ECLeyuSC;tTsE)VKywO z8zDFp?D{4baEx?iZLd*m^V4(hG`QG7-cOfYqS@jW(R{J8tO} z&ZRyyKkvG!LIheC>VWC1NR`8#0BV+-v&dm~adKz*-$gJ1P$!^Iqdo| zE+XN|veaAFhc;bHJ31zIhGIKA1SFO&&cI|Kk$%JZRyGcWv&;qP<{)l9b>9h#1Ovp9 zZLJl@&N5P=6w&J!o~{^<%w?IqmfPsIji~3P7rZB{B-<^?8+(8M)&>v!2z|H|GVC^M zk0)lNsw{pexGa1}5N4xs*t~0MRE6|IOoEPR|39?&i<5O>9q|GC6fnnKY$;vY-mrc=bTjA%Bxv97nT_nCeSDtg_ z;|(D3oKU&Pk;JZAa%qE{-@;dsr0fz#&CCzmkbKlccG9!{i%|CK;_3*loaOyNRTyO!?QiE@W3(PfwbgOF; z;YN6F-wg%(xB}am^gkh=fGmt!5R8xXf1u z%wWr?`b2DO$+L!=IAbiz%Az{HMd zCLLA}<}#)>+q70lon^4tN+j@~7C2{Pvn@J1hiE*l>*~3aDRPUAzolp*$3DOJifY&dMObSH=#r&HFOnmPGshG+10Zs%%Pt(3US?~q z>oS&FZ{4;}t}{kY{F(Pg&RUE#oTYD<~^9eH@sVO>|O zE7D#4Ks){rb{S(^8YBw%DGM+?s?BW1kuBAO@CKRP1C|-D z{MOyfyQ1lDBB(_T>4yy{-a@a#261BL^r%~de(T$#Ozaz3^ntOZwis*P<+`wb6I}TjCjz*H;3D+mX?};R49__3pgspSN@6MNXa#Z}2Fa}0E zKALR6s}c5$X|wuM!v--|@ty$_lh>88@Z6J~KD9{#SkBA5MbDvsJ`={KiL8uTK2djVZ6C@wa$7w_bP9n1uD{K1EP$u)b?1-I z*0OMIEIYcYj)hjqns&FhF=5C%4$fj& z@JW?wFlwx&ox7p?y_S`JX4$ED8k8>p)7kWobOw#$nxY+NPuD}h?zgs0RnQ|S{}{~V zc^I*bIwE=|4oPu0Y(r8!Emj@N7_|{(MH8IEg@sJ^b%~eWAg4Zo1aMUFU#Rjh$S_ht zY}$ash*8I_yazRw{oAB2U$kU$bH|~U8l=w0OHBET0-9PNo|@itw2lT=X2VK%T)3)C zerGFbhNJzINs1EkD3PvG4zQ-&IV_#mZxn7qb-M3siml-LxoI32*O=HvlZR!PG%OWu zE|4%tl;L3x0saXSN*%YjeL7>PWofb{ZFn~{r-xjCqft3%pp)Rsrx4lmY(WsOM|*AO zbt8iS4I6Brv#tlkQ1Nzy;a4;*fyZl^tx*N7OIT<9@G({&y3VXonnhB>)ymUs9ROem zJy0hG)GwI#q}Gwtu}MWa1qSMr16)!!LDgnYm1~<6i`&pW)>6LU)V7g4rOQfj z59e=}F?48n!p|mutUAFh5b{d7r}1}w;z=cQ^D>S?Qn&(?Gt<81t1h#*aQG(8@tdM~ zto0sn2cqA>2TGpL9QR{JM4KBToK*!Vvq2cv88KC{I>Wd+ls;1;0(Eoc9e8Fry5kv- zR@okl$!0+;<3 zwKCB|d(^>=bYJ&WA`O!j2uAd-2=V=Pnl}HE8J~GTRc!zQkyx;=HJ+%wv{RO}tsOq>~F46Cq*28@2&N_G{yqNznz9M9H%#q)mUCBWyv zg=`8OLJ4|**Y=`NoWL5@R04Qt6O*vcnv<>NW_(6rce=7>I{qEpEg>RH;xW2OmfUNP ztMYp8P;iRKjHpbuH-)~4Nf8CLOYpvygsm1iYLCy~{^m77E!Wz!R(|I)$!P!d{(^{U zj|4FKVnY#=19RXyaMy%^eF$eh6r#N4!S`ybS%tuE)!6%*WXk}h_|lflzulqV#;7$0 z_CPUbSwQe{d58%N>+6p9HRu?w4!S#P)LSW?6q(%b{9F3W<#WE_)I_+ejv~w!m<~65O6M zm6UgVU~oX5dalgu-Q>E0a-t?^y%SeYHh=BEy9$f(dVw1J)nP}z18vygZs zngiGSC^Lr0=)^(Mj7Yp#lMW%S-HkqV_5ITY*m|*JnAhoQ->qFciUsF1`Fn%rC>^XH`0^i(>S1K! zHtl=v^n~ES#-93*!PydwSaC~Y9F0llCsa3Mt8TSGRow>ak`(IDQ^*T>wu>H61%|gv5gxD-)~%Q6ke5qelJ_M`6TZ zhuyAyjR&WdEX_IEb!Q;nU~kBJ{6>@$BI+X8G`4Rm3yDvyyhuRx9T2s$Ld$rDQNeFW31uM1j@zi0*Tm z>A{;6bQOxA#&B>G>rov-;uFX|fqgcaAmVmyy@jyLv&^Sd0fp1_xfS!kv#Z4vx5FGm z^uib;#d@5y#NMl;*y6_Uq$vLgAE#D;d>W#vFfj=e?3xy$g{0wh1Sk}h^Hs?CuA~Ht z0|fR=kz`JF?xuLLWjo$vEXVqvD7}oGE`A@YBs{XUl%1{MY(G=cECy*KdFcg-s$t${ zEG%I@-S_WK!TN0J6ScGfLGkp4^!5chrHiFkW_zZ?H3&VI?oF2eu}=T}4{H@oVZOYN zei^7|Vus8Rc7Cd9qB+7wj%rlDPDG#Bq+8Y#rWldfu;l4p@W?cQfr%mj)xP5hc#65Q zY9E!qZc|~e~!4lHA7kfsuQq(*Y=Ff1Wsf)BOU!->0i0gN15 z4mzv&Dvw*#M&F=fAkHZk)(#8JG-OeS+z#bvSXO^=1xFOuKA!qW3NRRWD4L~2BNfgp z27C=h_q!;HxVg3XKAt=A)rDwNh|JL$(jbNX8y&_fb7~yasjK5|j_MmJveXV8r~A44(dEfLUOrFd$aJ6}k6m%|jXgW$Y(gH&o&pd&aZ=ul+k*mqSiL#dG3@V;J$RJaa% zj)m3wc#fY}Qqcj41M~W^DdJj(k7E(pO9h-Xd-2``SDvC31hdhm;l<+ES&OD-^Ok>7 zyN&*XJNQ@n(4sdudp3~n-Kf<>fpyiGav;$Z*UP;)c#bE4KNr`-0Jror6aAb}7qILl z#^XQ*Zd4;y>q5~)1ZY%L(NQItOP?1%mg>KrhRO8Irs4jRof5M5*>n5Dp%dzNhl|A! zZWZ}PcTd~Ho5V0Dfi~oZj%?NG%B^9h-uEW?)9?Qsekbj&xQqK2e!s8<0FeLxlAv^S zv2}9(7pDBLQW;t&-;q4X`c?W_9r= z3))}JYZlD&y&nb`8Y&Xq^@C*z%P;ryW1^{Y53U&1(dIXy@hz_lU#+z+CAWeTE}Esq z<{1Q%PLx+8L7XE1mZudK%090uNtp!TZ-1_Qsl4zdzZ5f?tE&G8Xi(#%-Cez^PuJOdpO$!{ zdvE~qv1E^LpwG}JYI`ounxy$WZ+DwFteoLNrVWQUj*v5rR`RNFSlx7Ml?sEj?Z6f5 z0J1sqKxs{0tP9<_8QGr%%!hdPNQ5B_H9N~qtTSg2JO5md+R5xOo_)TwN%oW~fwJ5? zy0TnYj9t0WDON@K(WykTV1C!nYrK2;e#;6z)Gm_E(&tDks=88su}-Uqg~(fadtQt# zSP!L}v|KEVE}GL{{71*apSm(rGZ$j~$-c$A&7XW2Q((&@&ykmlrKXJ>uQw}H7k^)y z&<=O3cwuwm;2+2Vp=MnozEqrvmJ1E(#l2EEii}Imx8oNV1+Y17Rz%JiYVilH3Iv{~ zZ1ESMBZ-QQqDBT*=ZBKBCTg3*Rs3QQ+)Fw0=!Mw3VBuX#D{Ev2{{#A&$f;0m&xn(< zEHpnb_&nNexij^&(u6;6ihCCOlZ6S>@x+T-x&^tO_Jr`xegI{ z{{yN})(BWTmCOlS6nJ|`Eduy0Hdq|wGWQ-B;Rv@MI0a4Ui3{ko^G#ikQ)(CWLwIub zIi;WaNqIJ!Rx*@j@16~mU!Xt4BaJrFv6QLjK9Q8=^RZ8TvP7vlY6L}O!t$Y8lrMIz zgc*N5BYKAJ3Pw){75 zep$kju&8@r-T_f@Vwdh?>YQ)WC_M?nP(LN|*1e7(z>Zy=6Hqh>bC{NtL|V+DmBN3K z^LHhq_3jU$Py#3oZ#xoDe0YMd$5;ZTpNulHM{1JHJsr))0z)J&ABvf_Wk__XAFVDXVXo2*ooB-q$hl*PwtCaDrgbhB=47 zE?P)4@?#{psyt%w)FHpY4^So&@wboWqtHkPNz&+e#2hKUR3{RB5@5v$6^Jvw=gFb% z)cHoC?aZ?%5Wk~slZHj1^)ZpqHlqfXi#BAC>hR7f#c}T~$-trtfJN5saTnPqH6+LR zMDR(!;>;?|M%zmQ0fXAoes{_LF$DNz9k29vDu|ZffAR~uu3i{kecqt|Pj0!_Cx7+Z zcQ7!@e@Y>_|F4zN(8|o#!QAP$wf=vsjf{p4rl$WdX^?Y@oNeY1V$jnk#;Q{>6kEoX z?);(@B019bu%)nSkX8)?&g?_kvG3JC=B^%t4zYBgjvkV`o14^WGP12NsLkhT=q-Hh zs<=2?Ptdou%iE{D_4Q&2AlMH29J=Bn672lCn@HEMs;UOGznu-8HDe)1GZn85f!@+X ze0ASPzP!H9&J?n*x;C;2<&hPlv9180pO1Z@u8z<9$FZ*LEF$^r&d=@iEt@MFz00hu zE8qKpqpR1`lee(}-z-6Wy$r(6O@KE*SI?{C%i_!1i+5w)=k4)e=khUI|B{L6GRsz9 zP*;yXQ*Z@E_nSbVXz6! z*CI7LE|`*z=FSo}{Je%jRxQoBk{1)TnHICU;RG7mN;A(#O|Lb&)nmUs6T88@D)mKE zAbPgA1q+#+t|44f0-`g41$C&?7Ax%B3KpyIQMF_$?ax}3T|GpTim7yE++B)*A3P>? zOMMJW>><)?X(e;1P2P5IG&D<_sm>h&Q(t)o6{Wd6t&Uop4%&!qd;_}jZ^zVq6*d6; z<}hVAd2u{-Q#+qBO(gDo5~m%kO{Y7-8jNqL zB_cW*tc6<2u)SQ|n}7M_%-kL%XyKUSAW7>DILx$#%Nn;%)PR2oolq-;3Lv&)N(|={ z#2J<}2x|TLH-VcFCb%Z28LAJkBpDQ12I@K3ZTXAyMb|XA3uFnE_^LhPB4$9?s^NlF zrTl$Z)d5an&*AwqeDgRh*qM#M5+iS2OlyVa9JOC~nx2tm=6!XO#nHSF_^R}4dAK(! zET!nAWKJq5rbCv;Wex?f>bDvWzdG>S%8q+cprF{5Vw5sna`vJ75^ydi*&Cch%wz8u zja2RU^u;{Xb4`?jjm5Y>RaZ1U)O%ym!WNZdC|Y5Y{PmCqF&rTr7UvlHOD;{>rRFlf_=}Hk2A@mG3J2wI^OEn3WFW7AwHi+oEyhSmyyKQJ&k!iXw9; zhhbdd+LN(6l{GZB$nLhCFjFxdg`+2-pn!_#=DkDk<~HC=|+&LaA`KhoY;&o8*v#0#Qk3G)9OIF+%9oox&f;|g4 z#M{VKWpba%8y;iR@zbfw=D_(p$vhx7N7>Zl(yQKzD(q4W4zx*rFEjF?(agyV$JBx@ zt&o96y@y~h`&eg1vi&IRri!^zrW3HbS^m>Oe4oS(ic8ZW<#;G8Yqe?4)G$36G z#0<%zsKbvcNFz}*S+9sZcVc<1&I)fJ#X0d?I>BZ?c`)8Zy|t__2dPS6N^WvL_6C{m z9%+59{dFH{9#+Af>(%Rs0_P$-Z_FAT!kN2dL_?E^lM0(EqoO7s&!u`K zpkkQQGi>@LXQn`kv)6GCcEZ+@eG@uT9>#MSys;@#l#A3C<6m9-2;v#X7`WpY&7SD% zUwO6AaJ3q|t4gdZnXwmt(XGEbh@jJ|XUNQLQq~dJJAF%o1&&SI2%y^+n}q1hjU}Ww zOXOdvxo|!p)^FGYk}yisr(A|vWHrF=dyVjnwZuF8ja^1jO=knIYGOsYGrG`pK;YjwcPy|swmB$Qc^k4kC$7L>5KIlX=#`}zjA&5l? zPJpRHbZ3dkJwTC@u+Ks>Jg6sG$7ONqy2~I%X62D16}XOkG{dOlCR9p?l=(r-LUp*j zzSGX$-3$g>NQ{{S=@7T`*|$r>kjb0w1xj6 z%>LiCg#!Ow1ZM4QFJ8!eL#1y?qzW* zR~-3HCCywuuev$2g(D5Q2>@)KLP&dlHf&jKsg=7mx%bkY>m609XYcJFY&bv8s5w-E-{=kN~{;jtMp2 zU9F)aMU}~aMU!M@crS&z1#AB}OZ;+u_(@1|}I z@9}D$;oA^z5Hh}-?$vO_b0F%mncSgpPV3uy3G$8iNa4Q+ZOQ5*QfvT)E7iwo~it zc2!2*(6_G3H-vNEE!>##31?!3T%Acs>~AqO&D8&HZHmy#k<()A9A7$fA8P~{dgeEA z?Tn1_79Tk9t~<*gF45`AJ~ypY8E>akVk@?8eur{-Kk;Nw@5<>K8Da(DR!wzDzJ*nA zDmKC?L0YTw<4V!$e!=&4MeCV@UVQXK^xj@fTffDu-Bws})|q~+}+i9aEZy)R#%eETf zaP)Ohwa?wO-#RCrwB?rWP4wEIJxzSnZRVU8Ms5_k3rZX{}R1Z0)n8g9hb}ds@ z&p+*QD(OSzj^MY(R^Z^V^sHTvsI~Xf!lJy&p z(~&=^zGEu{Xfk^1gB{nOLzG8qe(r_e=TdolPi#=B z`uuB@K#!NMkaic4a7{a9lI&@bmf0$G)XjqA9Gpvng)>OHcQv!^Oaj~PNGQm4y&m#+ z4@b>%V~J?Jwv;b7aIlq|+0uvsnF}ri7js;h0KgDlgfZ5t>LI@q@c>`d=5mqBR!yu& z^9E~GcJ5R-t9O`#8Q(pP_>8qW8~%n14+rDF1Nse@*32E>!Jz?5s2fN#q)31=>2F`f zk%5#%#*cb7lU-|P3P#biD%S!5(uo&2>-%#>*&p`Q&a3=OitRcS@%O$pm{Ls2jdXl_%TI| z4uAEhQ|$NMSemH|Q&pS@gu&ufHZ6eom`Si;dW1c^bNCCt=!WUVstu22Jn#CvLBQm? z^U7hv`nruV(hRLuH_TCL8_%GhA;z6k+w9$7f9h|=xs}L>d8Ab2MIZ0TTyDqKYrJmm z(j=PQj=5ytj)}SEz-ucc=DxFEVf*?-jIlObQGTiQCI~0t*_&~QsdEf(7wGgFc}H#} z2GB~pt{j{1WCYS`VG9%4p($`A3HAj6vx3h#1&;AwYhB{;5CthJtG*NJU~1TK#2?x5 zUxPnyh^d0&#p3)6b1KHWu+V$IFu$m>zkTtz<&W@ciFC>0BDx20duUhenn0Zr-F-Na zB5wYk_&T4L#vkmOaa3NktQ#~Z>u`FOwx~>Co5VTms8PWVn0V~>;je57Wd2U41uY3< zo1hrgUOSo$6VetPHIB1^Xm4tJ2SK0w^=sMqph!WC0S)Px9`p5Dp)PCKjTcX#smX<99IVse- z$?HRPWvPnW%u@UK5IBe4fZlHcZL0cSA;p zzo^0C)5kCI($%29$y&enHVeTG7mEPg?VLbD?TpT%3f^BwFBZWVbA71ZL9bC?newlJ z99)yG=2B2c?NrCs>M0kT=nC)@CXU+|YUNl=ydyATYQARwNTUWtnU) zH9+uSn?oAKfe2r(8*1WjpWB8cRj&vwX)eR^??mgDNETCHRid%m_aiJO;Y1z$!n>6N zVXc&kn@+p-yspjb*QLqZz!jP;8C?OKcsAvX4L9Ua)m0Js5iL{s#cLYU!z`x)!+EzM>@`Uw#RSi!1fMQRj7*N1HA_^1Vpfup^OlwQdqJw zswqI;h~p(fM$QWv9!A_#9j!6wFGm&>@)Abhp+DQ~4{uhQ*NNV7oDubR_~fLSJKD^M ze>tb$vpMOLO;aQyukb1B-(`pSFc30ZgN2<%XYQq=d248DN=7z9g zAkT)h^=t8rWS-c3GY6bIUY-Y{-D4>r8v+H`)>meLZcFY)I}E%EoYN|=Q0Q1{h-BRl zY{aH?fIGcu_#IQB8Nh`iUKs#$J}W&8*HDD7EtBZu5XNO8(p`h6*0$u3w_boROCBWd znnRH2OuEuf4f1eS#f^Znx$Tvvz@MiH=g`O%2u#=Yh1|L!%Or|ks!7oU6A=n(YdJG` zHHR;?(FZAASaQjw54@|rYuTo!bhWtCoVycA%23~FQf}eq%v-=t)8#5OvQoXcq`JY# zk|Tb_k~EgIMeRFYo_NhcJ4(G&B0=I(1pkG=#8=*OR9@i*NPxXT=JT}u>xuy6i; z+lK9!;2A+gwms&}Fa{MnC0?D>{9W>#lES@|L zV2TjQ891BF{pJe9Kxm>s`g{IIwGzh1FHwc+AT2CMd2`RxIF~_JQN<3-in1a!V>E&P z_k}k~ii*6U17ZUi;|DQblyl`P4~o^ZA_c9L;xrM&?@_QA@FNOcK8gw52Q&mLYcP6; zSyU*d=NPyAUbiVjxqgcwN)d}%_a?GvoGhu4{pg?6Q2Ew~-WVF@ILP$5Z_-6* z7@EjvQzFh) z!^l}G)+Ms5cYb!;_Ye^a4cQ(1p<3!~EsV>-nnzXG6cb=mm1_J#^aRkE@^2l$%pC()ALt zlj^3?}1bV^xH+(8Dz%6qZZiQE|t#0_*EI?g??QSJr}C=x&3XC4vJl+wjSh9g^Jm<##?bd!69{>aFJj?9f`P7}g^ zMWpL{73FawkcYYY%O46)!f8!M9&lx-QNksYu??p~7Bucd^^%VevP}LxzhX#d=Nf{# zXYvhu6{$8F+;ea0(ikCqp;%|TLSQH~5{~wx0Pj~*&`uys!4FO-QFomOAbzDF3{Yd z1O)6NZ==I%i?%e%d7a4IDY7}6H2fnfd!`ejIr>FrbHsM!af=fB7M(#5xmsL+$F`4f zwP)5BatEPdzhuS2It$F0ho9x1xZerNFS?yL0c?(ILX~L0V$R2n7F4*MOzBc_11!Al zs0TP;>GBAy1ovLY7)}Cx5?D8lEcnQu^oL4Co*>Byp0gzNg-UzxUi>=KVEBW%ZgwGB zI9nqE{<@L7s+Q8*)WshY-KuDU7`76V(jLDz*YmA<@^4MIH!`DT{#uE-36|dFsM>Y2 zj$$?S#1RW`WGY++gVAzWBj=oIl^qb`6vP?NqpeY9I1F$@a$VGm+CXoVJF*y&Xn&L!i2xA7R&Sx0%bOF z`(QmSN{mR0P9-DSMPwyVy?)B!lcD_GFS$l^TLhI-kvZ92i&49ymXgEZ11(z}Vgkap z00PC#3^Pbqg4QJ$lZKjQYsoZpNdSaW^iui>f5~W}e^?C#G2h6b%#c3o+y#yCn}l zLC7hszZN3r4*1i&dJh*lnU>i11LFlz8fJw_GPiLhnoghj)ZM_c?6{7M>hZS2-Rp=<0IB^S%)cb(-J9TiFriAT^GSP z>Er!xdD;ZTgajR#Gh-l2s6;Fnffjt~5oP=M7I8-RNP4lbr$?uzFAqJ{R(wk(=R{7+ zAINPUlq^e>?^QH>z(H|Z7(ft7rJu$LoS$6Nxi#@g`@N2o=#yN11Eg8wedKdnn?cgJ zOk+ni^459=cRkCe>cV$~Hav1$qnN!PyktB3sCZPqs>eZ&HMz_=TD3>|QLcpUOYbLrSn+l`QviP<|vLw1$%tnnbp!mjZ-?QzkgZLe1oL~YR#=9+!bB2sD44K-o6&R2NPvt}_KHdvSd8 zR#%QV(n2FfWPlbhZ6(dfmB?Jdsr}X2*c{aX*eti(zbeLxWXBZj077Vhr783#$(~-$ z9-f>QhO#w!v;|p;E>0tofsyB^U9^lOs@~ZE-KvD3zX(bs)gPrnOiZu;Y=IjSU-6N( z1{=pN*ci;;Vw2yPdhi6*g6WK&4PL}D8qDNcHG#J1aySX(G7|A%^w+(rj`eY~F|JaR zx35Ks14$hP!Pm;d!bW9K)$SWfaRhQy0e||K3hmSytVzwNhaHJK@A1zmUku95qMG3{ zK5bZRIh1PA%slRghRa(zEd_biEl(mx@Hk^z32S%7F_7PntjC=41Cpk?oc1zlvT zyR^@h@7j?UwaZt!`o@#VC8c$C5)H=EdhPTGWwVMzPNh#-pJ`-U51+0xp6F$XhE|W< z&$(I_{@@}{wKU3)GBNo0L~v9q>;^p}--rxGCYBeLSYk8xxJ(H5sg?J-9I2u}V()Ag zu3Uofra-Guj=vDz@x>Rsk1pX^ay{-xmZ!g&s&pSY{$Y#dPED_eO;G89J|e&urt%H> zRzpl=34VSa)13y**qoE-$2N84$Q3l|Od1KXdB8?~v2e1Nn>O=_ai(IsV^b^t?|0^V zYm$EgWZTa+Y|{)06wg9uFzCP*kIL{7-P~>zoXzlK9!MRFIFABR0wvsWIXVG`*T*}R z05~#FGUSQ$Xuk1UUwJPMm~Q(}NXEZZ9EMqKtNRW9Y#W6xVOZED2-deEmJ$UtU@2Xb zTHVA$G?StWPl!Gd-)jlqM!2rAm{+;MMCF&aaX%YR?+KR#`1J)Vgpz6Fe<-A@X<|T- zu`d-R7c9>rsiu(!lKcd3XWj>T96eWCkc=0NyTNR2L09c#HOQbr#HBD#CR6p8tqL~y zfDI(yT3M0{p86$*7)%aR@d|P}cA}AJ;n}WwvlJKh6nSmG>Q+A|l)t&Dk{J%!1+>9v zY8;T0$=^mu+{uM_R_=la(7>S3SAad1)N~F(@W%*^m^HL z+fPQ-#Iy4~%`1%gUz4;zB(PPaqGZW0q-}x{W#NvSt_lwx>bh_=4Opz?!s@eX$Zyk0 zk^Wq9Kbf6wzTc9U(MOxj2tKUrG>T|DC~44etGv=)F5=F>WIf)1S;Po(Yn`H>2PyeL zzwcQGBf4Bi!}+gB4F(%O&6b`D4i4!bN&{q_Jmm)c+Ax& zT*$mfQj*%sEPtx^14!^p&-g7NovW{TgW|mj*Ui#ho5~r`QA)_=1uT6i^HTJU5jw$( zgy3@c)TG~E`&LLsa>@n=As8<~+RwgbL|dlv8$B#G+#znfZPXtj`0iBuu3(SKAD7)( zV=3-b1T`%??AAZ|5U8k+;>!Nw-{Z=XTrOZ3`j00CRBjt%F=TyX5=#9u39+qvw+82+ zCok~E0JX`V)D36~{=BWw!W*fc`=O0D0wO4WCNW8S(O|Z2c~?*-Eby>Ue(IA3MybL# zqu$e1^AY^ICRQ#Iq=loq?yNi5s~oxQQHt)SYmytRg=Iw77C ziA_;G0gY01g79SMxxE)IsNhftu8*&As#dJHlXOT6uhdg4Dl$^p;{AH}3&fX?2PTaD z*G5c>=A!n?NEsaT`u|ZB%bqvx?(ZG}M%NCzTHNYJXDfvlZ(Q}+H9GVgx#v&&Iz1RBg zvX<#jrV|rTSY7j?Yju^rVi7BhEFiKE?*C+`2<75QgShB*;stutOuADa0=F>ac zcKiVKl8;QfR~F6Gdsw%XPGFs^*IW0{d7S0z{0Q)Imi>NP{W&!Cd0)Quetl7V_4S~h z-Sv8z?)yBJ{kfm)`*fH6*|+tzU;TAi-1Qs}c&Y!_BN^~j{`KkF^?s7=^D*%R@Oqd4 zcz=def4wO9=3@Cic4bfbJiTlQeqBEx>-)YjeO0f86PNKYTm^J`O{! zIzO6yeLX*IyWSp&x?YE>eIFi>zdpJ2Kf1c+OtQN?zMPNFIzKZR-v*ezb{9;1Zx)dC zy}w>UvV9ASJ)J+RJ72Q1yWT$UY(eK=&l9cyueXn-tuFVbp%bpDd+x7S&8Rustextv z*TZbzj|JDSG@r+MBEAo-?5~rvD~=Gr+haE1ivxc5&b4dopzHne?uZC-4M5_3gX5E-laf1Lq-e-_Hf)?5~5htIp5jYM%#VqPLa|)z`h`cSa&dojYHjkG`)D zE`8sxf3V-jwtOG{&BAlKPw|nv&OOoV@z#sT$5%b{*-+Mv?bkb%@5_ezSK$4OoA=w9 z@7rVg)W0eF(D!-U*!+C_2caOK>+2L$-8#Z$3uv0s_xad(0=!>jceS2keZ9VXy&V3# z4G&eXf7iHu0dVghaeWR++`mt5dDLRPO?7@eENyu`l8^TEiTYQu4Tq=8-NO(%%sjyz;gudF=a}IZGYPVi@rt`cqzABt>e^a?yn^)~9SYN&H zP`R{DN;=dlx->kNd1`B3pB;3yX|Cuh^sI4;vC#Wk&#GS1@x+sR(i*ymzoa+wUR>t* z#V>K8D%z0TwDjtB_F{YSr1PZg@bI9G_fw(s0wgHH0MEjy$*SGG*Q2 zI&neoNnmdxXI`znX|~26%WrFOvo_S&m@zi*7&Rb#q za+w{B!#dxlg0C{G0A%S2B>edy8n9JlQ@<|w07@$9)Csp*XM}kXl(4Dj$~co7bDkr( z@F)=|qGZcINx4)xbs^kx7ICc(p>s?x0dvn8tRB@MHA1fWj53%{A?3`CO zksKTx*Se064xJBvae<+4Wl6xeL^)E6-wbMiK2Aqzpz+a0Zt_!IqOQWmrmWkCw_FS6 z#!ZqO5pAh@TtU>YZ~BHSf5+2&SVwd>nwxgdGuIX&7E_|)==0If8Z2vN18=7@EG#7fO9#J$Zb(a3cck@+Qp}5x^;FaLFZN-rX#Ff=dePtT^O7t6ezV8*{2rP?uNZH8Y%oGq^~;x1xVQw660$&U>qk zB{Kfgav)vqi!q&CYsr-Em=^K|3>LzByUMZTT}PX zO;h(wwmcARWRf50 z&#ZvQ_fuJhPdF?`EgTHT5Vw_{WXc;sAT&6i%G}QKw5gtl!-xd+-ardloXhQP{?gf) z!;tuJ?SXpZp`98*@byBaE7kmKON8^JCoFJ5$n8XD>2{_rS+CNT%OOpxQMp_1P>T5E zSySV1lVamkUZ&{|woSRI1_2Fm@0b}eq0@9!FZR#AQe-9V1Mf5x ziFktxFR9X&2_EoPQR~~7ndr^MDnXXqd>wtY6}*am(N*lhQsWo56}IN4w^yv%!aTnW zlIFRKcLU~ew@(--PQ_ZzmT++tt&cY$pj-3gjIq#K*9x{uHGzDpo~g54J-!@#HcHxS z>R@|szAo-`b4|8Z=G5J^ai=Rq^J0ZR-PcpC+-csRx{+fH4W&(d!`jE3{{)GZh~9(( zeB4<2{R%Rl*R#jfnjS}w&2!5_gJRsNUfb6=ym2d?p$TYgATBd1J-A=`(Ckd3&I5`2 z&qLF?;?HoD;qrc)HA2uq9jHFY1Z*!%MFKr#(@q*(fdrp%E<@i+FtdcXKo$g zOJ^QRdIob)6yet6p(NJ)`LLv_^W-FP!E!OJelHAScE*<9MQ2lZd<$bo8w~$w$03{pfIx{gBxB3KOOMy$U}3 zx}OEGR!$byF8C*x9C;V4Ci9b7^fqOWjhzLT^-s}PAAtcp_d6bzZMsNxnEz@*W!xz@ z8dJsn79}2dl=yvN$zG_aV2oG&s5sRZE^AS!J@I>M_(u>* zT#bC?K2|*PCfD`FKc6!ar?wf#YE$57ng!e`YS3CPY0T>{Yh*&Cxq>K2h6jLZ>e?Rr z4xALmZ@H)mmRNv`VAXuhr0#4jNKpGvphattTsbV?G36i1*jhus!d4rgwgyOT)ngT6 ziQu$kWa$`{BKAI(tf3)nTrI2q_aZ$MG`y$)?ALyZ12l=LJ~IqSTB*q3JjEY3bvX!?lg5+MSR& zhnO~rY`?!S&6Msxfunomp_ZLvp_geeI1C;LyGnfa6!O|zUv*sjE2b!?SMjn4ZiI8f)g$5@SnlOZn?p^NxD#A0Lv0-qZr znQ-)K4ug;6*xKY?9Qbl+c8vmZG|l{MiJ~Wy9>v^%xfU<`?+NS3GTKM4=EO^9P@P^_ zs%S#JW#+4WWwAk2iHmx+UFpFvchc~91k3x>WFiG+^#qEcf)OwiG6$TGRn`|EPk~%- zZM1+jZi(cg57R5y1ee@>!9LLzlXdh zVC?YWwZ^o!u}|%Kbh_fyM>^s(xxVyU)dF2+?%K3Ra?ITP35GdAKfgSyJ#sH#6%m(( z9~Ri2Y{)KW<}ob1@y1ze9A`_vZ_h)?W|YWTm_m^`j~UC|d$#$^SP-2gCc32=ZXsN! z3M*s*ZzBanRD%mw6wwLL@70Op?Td<=+K-JtBDZd?9VQrwai-$49nHHZ?59p@rRl2m z@v7$JFJ>1PKf84i7+pszlfJD7#-7?Iv9$tBu8rp+?i4GNl^Ei3Sue`5Y6_CA_WS|c zZkhBlmS3%cH}G>b=-O)pIHVr5WabX=B`*po`B3+qQ24p3k>GzGcs8MGUPq)a#Z1=F zDjiVy{wP^KqEtHM!}y3fGD~Tx;3{AN{RXu*SQ*2~O9F6L30!wykgm}r<5BC^#FtE~ z(5B=5PE;u^6~&E_mX35tq3w3bBx^Za>{-&yFFUMi2POyqN)jM3r(Oxsj`r#!r_Ix> zMqS9(QjW^_;fcU{gQl!Sv^gW32DOq6HTHJl;S?#Q&{Qs{488Wb?i_7U_&)BEmFd%z z+)%(yoVJN+(`s$BKk(^cqeKsUQ$D3QA#h2DL#_nxJ6|#w$)8=nI+q-q0=i|PEQ305 zSm+qiD)}C_dY58us!kGS2;t6m+IcRG%%jG~>7r(8-pJi4=ICe9@xYdp&>lHqf!ZsU zE9=tnJQNCxvFzp~h(rPDQpB3cqeZ7~Um9Mhaf=s=?%o=b(fT7T)766Mv_#B?T%hA-mE`IWYYh2OEL@#%fetiXPL&js5hnpA z%T2=_{?Hjiwn)RHTk#TB4|tM3IqZ40*~i<_avA40n;S z1?XDNnjeaBcV=XMb9I%40-5o|RUO7rFGisjI9Z6oSsDt;^Kluh`8P`#QnW98Hk9Kv3JOjjdmW2`pyQ zAsjru=JfJjmx+R74?isFK+;p0fRs$^d(`6Zj^=f`)Ae+89`Xg*oUX4LJu=uQNX6}E2j(|+}z z9g`EHY0i`CZ3&vP2QPiZCs=?wsO0}7^qppa` z8?U1G0tURJgk(DPdgK?dcW9+UEj}SL01WuQ>Kkbq)0P;13C z8N{LrecEB~c$_-^cuX_P1arGiJZQDXO~0lH2wXHVIx9-`)J6OY4&Z*u(E+)tIduNc zxRM_jEE(&?{jIcB@9R8g2a2l5hnFB>Iwcpte-}NscA2f~%m1poe{@(lANNw&L~)1RUFFONL^ zv&}};pG3bQa~HTEp(sJ}NTJDX;ys&c7~<3g>?Xu>{|a1)8V{HAy)#tQo(|rr5`y_F za_+xc-+mZs?u3vhE4biJyK(3cS_^jojV*EmEn~i8Cvfq4a)}5b3*ly9@Yr8bg(`c} zx}!ChEd#!-Pa#p49&%BpvcI#Gq9ug(8yCdD)gg&aA;)|81}Cx%`yItdT_3LT8ro?p z8fT++%)6Ai(XMi9Y1c{cHPQPHGsjfV2F`R2{7@;4}^iZ~44dN-1B1*dl zH)%bLpVv||EP7qj8MheyI|Nmu(Ioj*L+VSG>rY?q#o2z=+6GYuB#wAAjzDwLrIjpS zkwvRpPWWzU9&T`fxda1wu-s6(e{y@53VLAbYSTguQ{e_x>wIKNkjg)5<05o(%@vfq zvjs|T_la@6EhoZEVZgXfU2aeD=#iz@Zj~|MbxWmc`W;C_ zISXo7uI4$I-x!hEDcNhe$Ts}#lx8O<&uq_fhvw`ttRsqD~F?+2F5^c%U zq@|EljhV8&J2@fdIYk5qUU6o_QVmICcb=Ry-bU%eXn;A#3DKEa$=OIQ(i}$0B83mO z-}1#p@3Wj4B7-}o{^9W)Pf=mFkoR138yCbmlE2-8{O*rrB3_5!d?@AQgH!y1o{BCB zg-0ckryFfh@kbV$o*<*MflwHfiz3`V-z3qllS`pJU4g;koSx-ZDVeo{P8sb(a%EzE z{3I>;d&I9Oe!?i=t1HuoS>WCeLm{GJ{{)PpOqc|XmL_Q$gIwYXmjroDJ*`kQJTF9k zszZLDxqVgZEK+O4Q?HAl@4=J;)5ze?ji8tmxkXSoPu>z*6RkEN4Xk?C;E*Fv3!blq z$i-v-CsVQkCGxC@jzjvMyha>%M?+c#?8V@r-$lP;-(`FgmE2iw=486XUylM8MJNX) zbW}~GKS*&PLOqX?K{=uYNt~g9xP9X(!(!vQe92M%tp1d6W*^$r9PrE0nbJ9>*8DCo zBa7*4YNZ&h2Zn4`Z)%a=AMJZDU1=30jwK^hSuF-)K$S<496ngLuAc@%ye1IQJmH$a5*caWZR;7-KxcME;k^EWEjN z%jK~fqUPBy6e6=l;z#!|PUj60!!|Qo@;KjTw|pB##1^Cj;eb^wszCj${K3)AT{D<6 z>U8GEVMUq|#Y&mH1CH{~LfH}dOCHTHClyZi zrg>}~d}nJsvf!MCh->6E|Mt8o5i1sqISEq4v6Q zQYW-W0Mnh_khBN3>Iu8~fIU%+B-pU3Un+FzkTtEf&Lj-mm4o&XO1|By22K0GV8Tmt z?je|tgdHqMVY830Fr-%s+R)7|WmH1Zv%FZ*nHK@$ey6YLCt{MxcbeZN|NC2?$BdH} z%oV;oB4e)O&@m1jXa(cSA<62Au*!hub%QL|ptN+3bv!0~KNH z*yIenBQs1G@Z0jOR7imJl*KHr{V53(n60adyAqr69I#yb9 zvRkw~Arb_kR_k%eZ4{F^a>U{GD9ee{abILq>t#6Y=t{Y@-BXBkhVfj74PV4pZQwC3 z(q(yV@FdOMj=votk~0kzvbWws?PHV9lUke4Vk=}t=K4^abcjQ#w|{l;)1^iYf3v@x z^J_ZT&q?Z*Vi(f?9zM=r53gtH^gyu+Sbe`eXR!EQqCl+8gReQFAjcK#7<;0iJjLVJ zjJ_H@E;`GYxT9j_E=gTiPiQswY6)s8n=Jl(iC@adnD+-UTPvaHQz25@${d;C#q@%- z1BR5SX4p$L&*9W*!4dEQb4W0z-=(BXw;#lU7ZkH z(Mj@MVaUw>T7+|zMu04^HUL3ROhht6I2O~x>yM%=gq)8=*aFK?NCd=m?C+=ih)Wj$ zshTjBoR@u?R7i~W0NP2QBnf@+I8M&h>m>xHHyqX>(bGWMmHqyRb{yUfU?>zjM(Ofy zO5e!>)xcU|s_U_Ka5g7LXIMH*# zow8_KTR;`x^-kzocd-I>F9-JlWh2dkEQk=U!No=<-wwEiY>CzhJ(qWMFL}i%3WrvQ>9 zk$0x-QsTp<-nA}ttAr#$*rFIF(Yh>+a;IuRYH&KpPrO;0a>LGwyCQin>!^oqLnMG$ zSC~3d43)Cm&7r+XfELW?ux);MucjFbsWA_7xUWZ7Lr4~#qm8rn1sQ%ALSdVxdYPcu zSLf(ijxe>%=gnYt%{Rf4+}}WurvuffV@WCEAvn_10(r7XhXdZlC?MlN3=Q-2b-WLM z>>&Xt!jfK<2DCZEbYqM3X-U`)A)QHovopr@C2)d`G?x~acOsg7)4VN@jOgIr>{IBzONm$at%NgF@OX*(RPmk?ov4c zHm3}b3@!PkkM9LtUV(||MZguOgO4Hn%tIl9_Ge`25yF=d~^SeZf+ zn`%j7mMU%lZG{Y?K3f+?oi6b&yzsUyh|d5|8b}<2&nBfJ64=H`I>j&T(!$f003);0 zqLA!(sK9Gfs%Wh<|w+%wm@GZ)%J z1s&z>rxiry^)}z2*JYz#vJvr z(jqA;^Nr>PAyvmAus7m-gvDFbnAPhg+L$gkE5K52RokOIwz77WjWL&(Yq4qx0;K3v z=vy0u1S0BBMUFS=bk2F~%ir)S$4A5+ZJnK2X?g^{$e?V8DZ_v`p5U`%k-Is zAyH;=J-QZY?^NkYA>bQ7qY}>=Z9UU@7au1`ny2`0_l!!mP2Vpbzy?^7?|kjL>=S*3 zDVVi85?L#)H<`!9LXVtR2T(>ONGELtEM$LEtjp{k&S;`ugi^{DQf>lg29DYuhAcHV zs|?I#HrGqYTl7bPw$xN(e$yy`G7Gphwz_sGE~eJp3*#YbG6Xk`QwX8N=-Z~$*4a+~2YZtD?Qzv%)9!KK6y6;5D@44t zMCQU8EoD3bD@zuyusStkB+4@QUos{;q3(qEB2ZLz>Uny3t%p4y{n%|Y84jp@>iGb{ z_N>42fJ@Ll5KT9yldmOuzrXpwo^=T|IoAJm=jP$`5nuv!9d(wZY|KEiC|x`^`B~J= z)LgFkudVWXnU2TTNmC5z`4V*t#@ms--j7^ZqT0zdDo)Q|&g)%cuyryKX+q{W;$>1W zRLO+l3S{bQrP=O!nUb{ejS)oA_kUh0+1W0s)$b;6+_Uv6cd#C zI@mB?mnSpF2jVMXgj3*}f`q0So>l@;>wDR!WE5p1EdXdU4`3kxXcpX4PEFl)E3A0w zOfuLbKm#&aC}Y*~DG6IpK4wJ&cbgfVd?0~BlR8w&=^QBo)k(0cM?HwN;zkkHn4V+` zwB&N+_<9NhvHx3GAeAIhV`2HNgp;Yy!bc}&PLjQ`v2=geqr$xo$O%lVG=OC49*#&S z_D0E%$w(J3BCp*-+6GRa^#~ayPv_H5EvdRyQIRl zNNsPxk|zbi&SjI^B_=lmx2?sVNw!tPQ!ek7dkAE^NEA$RP^W`yM4aRt@O&-*_AIt| z1jKp7vvc^UV>AKGB&rM|gW;QymT1Y1`8r=Mr<58V-DI)nW(YP>su`qIU^kkqgl2jG zIlWYlZtV@OhiQGdghbXmwN>F)fp4e;Ny{UH#e@R-mjUDO&h-+Y9~6uvs#&B(?`3=J z_vI3jW|D^0D*%0yRQJB%2zY|z_y%Q3p;oCQpz<(1?q;^*=D;E}11ha$k;a4IblBGv zChv2k$h=?P3k-#20|OdX!G|L)OKF`{UyB}*M?VvI!II6D&fG79h5P%9PYPDL_;8z~gzt`ku z0IU-TTTsEAuSlR7+J=qkbwC9eNlB1gH>xh~IRA+w*0~v|h5@)N$WFvi|6!;S4}~x< zFQ6W)4MQtJirFw;s%brqCh&9)XQ3s=sY6PFInQMDH= z2gT7#kOQwPQ$5|SV&H|j4aPaK>aNi4soQZCEKEs6?l-Q zbsRKKfFw=N^yLxxEUbhPPSo{q!Vhr3-T!EN8vw;{8ptrANfxmt6<@E+Ttvtz7si%)qs(zWfYFz<-LD2X$}iYCbZR-e=&;fPpAAzICFJ>GeOQZ zZx&1jzYWwCzHg4?$Y#D4u!05^8g%Duz`NPbN1l}mBIN!UD?OL)e5716Wo=-J>*C6K*ky+|jrVHPtOOTDJzw%WY;NjONuyV< zQWgo^EuUP_)^vtix#46yrGk5jU!vwEDZP4HKi}F4GCN7#f%n6^6LqQ~N>6zz`n^ z$OgsP`@Q{O>YEfDmlWiiRA~}8ETg?(GtgD0#{@6T2T4Q|kR6CNENE|?G@jc^e`-2*ndLzuSBD52DI1E-*Z{H?E@ z4_}+Yr45qRfb&f_EaUG1lp&FG{N=l^R{;E+$6v__A}LI5O83fbYJGsL0AX){Z}6?c z!6*B!gSab0-N2-uP$f>U?yhVxLyt{s)%Z{qeyh{=>~usB+u9ez~}>F4P^e5>NcQ_ffX4(9iu zW-S4nkYNmtr$FWR$zmAh-%2bL>8H(YCb`88Mb3>B&0Hiy=KR`%>l>aqhkm1SBV03$ z$QD(O9a?2%xHSy7Iw5x)W-0hj@viYeDDwy28muC~E+Gls-aB0Qp+v@WKs^XoIuB<9 zyeG^u?-G}85_!DO>ew#lp#iFx&c*ww@+7F<4&-wWz2)*uvoaFiMNASn z&alvRkOMavKXN!>2@X0i((waU*cveqsNO&zEX-#&b<@^YlNUg)Z4#Dp0ThD^=ymVw zVRqvD+iJ2vH5c&vh?ZUs$K3&Evo@Q>3bg8U)0pS-Zx60;J>I1V;y9C_m6Nfot)3yO zrHV6A1ZHMRQ;leEyfIvAhntG}YqP{UPMD})Z>Fkp6QpV5)wpD8Z}T$BUQhB7WvGsB zk)#~K=LsDdyvNTVB8j9DLHCQKm0~6^3?!$iAUGXdD`ipv=?{u( z5o28{Ws(uf=XP6hX3kS*9{7gHvqRcfowz1v-9eSToM4$CPDyvl3dhcD)6Q&*b{>=Z zwGBEUW8!yUI^AC2+xa~bxEqy{WDrWUQGkA+LbI{WM=XId{!FTYP!&^ry+(Jj?;A<9 z4&=Fab7?>bD3eN36WD-p_S$MhwL0%F0lS#AR-me0+r5KAX@?Yx8{s+9Om?4-(r21&HQS>pY}1W2D99qq z3y7PiBc!#z&*PUvOQW7U`z{(Db##>(zxo4wqa8zx$GzsMetjc}z zK=Tc)$kHe!s|%h{ zNuC*wFd!mYDY(VO%h`nxmcnwn7!Yl(_>~oA_K6c1R0*dmVuPm|2p=*5lE7w8YKT;5C7?^9kn#8Fz2+&TvyGfVXioMojDBq(oFHVbJmp~k~t0(5sn1U^e8 zPIt}t`REz}oS0t?Av4=ERCG+LT&<50zysg1)7hkzAQ?*3#X3~^CK-?WNNXBWPzHE5 zT1$aB9YJEM{fdW|LyPY?)=>FvClK_GZ?+3(0(!&s>@rv7;z;uhv{IdMa}gEyn`^jQ$-NLJK94wpm}!t z>0yz;J_e+UXyZ<$Yj9o`xEpD=5yW}MNXb>2u{QCoD64n4l#UkJCIb=O>(PhGamXhd zRq^voa`~7Q7V3GoZTCmrINcSfY=<_Cle7SY0~9Kmms7l%wY!r@we=8Pb|hsQmSBlA z??&I8f*Z803hn`}nch;-Ugh;vKNvtJrW35}!T)c{0KL?=BNpgdVL0tY& zI-mzAJyYqpxsfEYJOSAv6A?Ce2U^E%w&R(I_*Pq7I}G@k>;;SKe00^m!z|ZQLnW7I z3Ho?U^ktsMV;^sP`EbX=Z8f_gBT%+C=@oxZ^zs1YPxl%E4pLn!{eh`SQ$Z~QatQ!x z__X&0ul0!f(MV7Z2y4eno~jRg-P1O9&QJ!~+Q!c-RvceuNrDXuR$^CL6vT6v)8yC{ zIH&>2qQW^;$9@o2&>09GYfXBg;ur4u;F_f!Nuh+zdv?IKPKT(FQiSRWV#BT*Y8R>y z>iA3zK}{+Bs)Ephju+7`$y!$e1#V2F!|i%>r6As6En>>I1;r1o`|yu)I$P4~^$PkP zWlf?GCWZ_JhT{qCO>G=? z(8rQ)*s?3Kx8ZUjkW`*hIX>XBYtX${V+RPUhD52!Nsk;Q>~yLI(jJTpP(}>;Y>k-H z3hEu$30U9;+bJ!Hsm#A!k3M$4)3;y+)PU2*oIV;(Zx;{vNmW?27Qxx4fCLmkgsj^z z@m~pK@H6ydFmvxB?7$G)vV(<~+k-VC$02ye_(BE>L;Qh8~F9zUdjHB8L6GPs?3!*@&7Jxr-Y=Y&4ylUIU%AQg~W@Jei_ zD`HCF6m-o6i6w0wY|kJ!Ca1fH)#t;VL`wt5>6NIdp+RQ~M2`&OM32=Tm>aU;wD`#W z7Rv9FTOG7Wnc~btI_XF3o`AAiT~?4D$t(NuL|Ep#`zFE5)&ntdyB>Wg7J6kRKZ=Du zn91w)-Cc1$nWxCfixLSet$N~-8|}s3?c5Eo2mA0P=StE&$ldh$bHc&^}S1 z;8>SnBk!7GMG<{X=Hl3+LiKb=ryy3r?&R+P*$jSm9<_S*hQ^~$I4$MRQ*)d_z;>(% zHe*KUo9}-&(4kURS17J|6PYw92ecd5TA0zBC^xyHbG#CXq2_5w^ax2 zlsN_Z(jruggxmnxZS^QIq1u!~e=)oDdBtMuB5*~tYNL{5_EY!K*tqZ`R3kR9kSXok zBQ0WbU{Wkdlt#j35*Cdha*NH6+v8iQ25`+9-4MWeYEq~+vSCl$a))4T3!91_IuI&` z7f{0hH{}&gwI=%=AVs-dkDv=NqY1G-cz`BxX&B?4^7@$9+Rx+aawz{Tj+#M#A4yLT z$!Q#{zhC@yzewDF+L+wj3!p&-6P2hOQz!uWq4UavR$3%i(%Yi33a-V%P&*x3ua^Kxqpuw%QFq6MC)K4n?qy=oFlD-q3a%p;)|>c(K7^2D zZE2Hp|GHxdE|Lsd0JW9^8kG+Md2j zP#;$;TkBoWijv0WZY_0LTA5x6kL3~6y z*E4du;+S^{l1aOX+uj}7g6a3%a)%t$kCx%hYod>g~Igc{?%5+r~zn^DAJa6o>VrlOn1zv^U+75Quxy$ z$SAZ#*Nwoa9g1rN#v!#_!MJW-GNbj<~9#M2W<)~wIw1ldJYyxGl z3!c1L;=|M!J06V7$m4ZYu%~#g{4Xkq5sCl_4Pog>N)0oY4lKd=y*#D|p|XW(Lh1q% z+gwS=%0m!BeS)iJkzQ(c6LC!-CK+CJUgY^i$w`8)Zhs6ZmY0@el;mo;8D>aOh zXrOcSDB!n`O=s4i%?mB)&a&*%@Hw>c0*t5I zMNrZ~Ob+E3#Jhev_rBeGMQIR!LdE_h4%kAQ2-1RTyJ~C zYshyZ-bReK1B*l`-S(+d8s9-?g(h{Z;tONH7X_478u#kqwnvQdC^MB)NNfF(S z-btt&nzd}(QwmQy56Z6Hm(YD&vHiFdHXAn8N{1l@&-!L$1X38|kB;z>evl+A=9>?? zaQyhq)gF6n=~D3&(`sYa+%OjkWm{6X#`>_d@_)EnWOSmpW(5+7fliyTKY|>l6~b=+ zGJ!;s=KwSTT&i&^Z$vTuuw5r}V8HP{41Xe27u|*v4>;*X5)Hd0Mrg%K$?V&SJ~~|i zWOc|P`rMFuog`#RP=s>_fKaKaDWPs@>n>xEcrEzMfkjzB9=gH-G6NFMbJSbi-6>mI zbURZ2!=+k0Pe@>heB6kGwqrMUG^sRw2Bde9!0s2ayVpwOjGu=Szb(^>VFC!E%$Rm2 zdp~g!m-I*gNLHW5<8eYmLS-|)q?fn^0W_fmA!V4e5svPy4k`AYay^fuIPG#0M}MCE#P&HGH~PDx5&5k7X~ zp>8#0XS4U{UF8Zmt%HU&w{{UdymtdJUmK}b(kwa{6i(2me zkQIwLAQ?upsz#k?uo44~aeJ5~B~_`wFel9_n}TZ1JT%;qHWpU=2+NT=K=Lbf30rs> zVC?L{Ft@qcaL1VX_+EQ8h*YC3Z`Y%1(beu2$J5d5C-?ND>WXaAr8;AkT{Y80H}_+U zcD2QQ|0>qPoBCJ^Lx@4Z4Q%cTs_AeClk-(ftXZ}oz@i_0N5O6H_wP|_Wlz3CDPa-Y z;J3w9O8XtjZPw58Rtn{+CsfjWF?WSJMuanmcvV$e_PbpU@*3 zh(9RDz<#R{`;4Blk{Phy?F=`0fGvX@YvVnxVmxsb_@$&Ff>>%$xltj|f8_M?H2whw zs9T7aZ|?a=3;WUEH2?fKvw3&J z)NN(ypwH9NCJ#-N1OOE{s`1|LeK4v1S0B6omHODhyt!I?AZ_W?8QpwoQb*-Yespjq z%}$$!@sP^oGC@s1q7+r%#CY|UfkT&HHXkK(T7``s$yD_P2%FAk)g73FW6W^Nfp2P& zEWKK$8i?0zlo9Zo7;A084?d^60*7Mh`L6z@=U_GxGJ zf*r<|x?PVxboPb{Xv2IR3*a9i;z#~=;HI55ah!P|0aNtnsIFPtB5_N|kT6u+)61P{ z8%HJ;*6Ro}Edf8F|2PcppPbzW6Z($j`+bmFzGok{B3>Xkg=%=iip=8}n%gVw-n{83MV%wYWdO!ss zEt0gDi(+W7NMXSXfbcZZV>dD6EpThV;MzNvUmF)$Zm2A@1}RA6}zJHl2De_5NLL3!!cGQ z%Wuyx0pm1otAj5U^w0qjAD~i(%0pV~Mh$EN`gVU%choPk3O1OuRNoNsrA7zI^s~)Ll_M3^s(az^>l>lo7i%pXXkL|l4PBVecWt0 zhYf8ums(w`v=hm5?BVU4KD=Go8`B+tEPUyj4b@xdFbm&}^vQI$dp6AV3OBcg1Q!;I z?CE({GY(fNJJv5jqS-JF3}nMOh*q5GrrBhM!XRoNG(lb69JCgotfx?&mm$l>0#QEaTayiKYa4_2)L42_S%4;0KH>nJ5Gq2 z$IQaGa#pm*Sny z3sb$h7jlx(aFhfu^zYt8um_{2plCwXy1D|*LMQmk(bcGe5+73tnun)t{jjNI>|b*7 z2PW>+jR%Vd(t5l&!@7XEBGF#sQg8MWAPku~N`_2oW>T7!me-J>LRvs3WMN7qR{D>> zb&BUW1u6>|(K-TsQ9;}W80Cmmx)!3$V6om#SFHY_nh-vKoSu}bjA~x3Ze~nLK$rzE zufbj64i7d82cQRtzyp&7ohM`j`=aL4q&C~@3=uW#Fl9$vXwpW)`Rd?!q-kX_o;4Ug z-K?f8)(vc`LQ={G3N^1_EXZ*I*g79&_u&F)0!VI>#vaYG?#NpZ#PjkKi=a%kiGvtdV8J|_p`MxdBJp3e* z_?4na&#_!(MjCbof7b$g%x-G?vhLEDNwUs4xt}T&y(zjBqP)BWWOUs0H9(d-h%T>Y z9AUa9xjx&FGH@BJ&^+uiFcPiO&`k#CGhJXB9ZIKJ>5jgi4rRYGp}?OEOoa@bwt4+z zD9tCD4g2dNDt+cXAxVZk#pt`Xdf09zH`seXW-~Yx;@k$G78m!@oBAy>Vm=%eNH@ro z1gZ}^6vmw~AB9>3^l6EcBlDBaS5VJEMGU&S+n}vbM7VU84tu*Bzjn9OGyOR@-hh}_ z7?B+-q#T(x)MQ)J7yK{GVsb|9c0IaUF7QX})3~keS$=bD0ojD5d>JJQ2Lq5dIN28D z4tiW~+_As0O4HF*uEii#Xk2IjMtaKJpkRODx$^o(sDcoBkLuqq2~vuG_7RmD$<>Cn zJt0Ui=t${EcCK{zaYa8erd0IMl9BpN$(=h?=WwFa4A7~XQeESTn;K1Nh|eW>q^sLV z(v=gCQ~59%01gdZDbU?qkKh!e$5)DWirBey5=?17>7Ym3PKUYABf8Ga>|F-6Ne>l} z!L9-dc~i>>JdIWk_L^~g(XoSeGC`qI-2I!P3PQDWx<&0sZ$mfxxmJ~-1~1{MFk5|d zBl$*V`uuzAQt}T8qi5`f48aZw* zr|Z3DeFnW3_YQfJ9yuBnyA>cjRfd*=EuRJMXg-CX7NfTKs7pu1e%V1v_f^jn;8j2}zo* z1q_-yAi>t$wg*u}uhgx&#jG(rfO=;=uxhX7_h6hHQds0#!o<T zogEHeflp7G1hSXL3cJHDm4-?4#!X%s93UIznfQg$J%Gn?7;kVUfJOp^Z98};J^*Wt z1R52#9|L5em6sUDg4zAYdN$A-k#g|+77y0{z*49anSV)^yNGq zz_AVHdxNsbMGkt4=65dXM?kh6n-xOL>-^Rq)mvKmcheruplv0(PF295%JKH;dRo3^ z&!m{sk5sko&kPtbb)qTWpLXW;5aa0vkn1Ark;4smUXfP<`xPj+z%%7uo+HXm>9SBE zqDre7C%{Gue&|C%wI*#xfWh7j!URMO?lLMEDZR8_j$&ihtINfL(bg4xq}ZeCjq(R} zvRy_w9O$KLpvxZ^llzg}!wE4Wfbi)By9~Y4hY`px=Q)!Kg;sqLS7-N+jQnGu#Tab* zNUs1pq@=KWz6IFtM^=oSN2j2U3w zN!IQGNYK!-I~}3%Ki8~>#iPlXEL$pdJc{6cB-?UyoUl-XZHS?13pd6(Wm07@J`I^n zCP%In6$;f|Vkro&E*o|02I$Oa&SL&61a=`*^dNtb% zB5`qV=E~oyd*&wE*u$6kW>aRD$AT_$s|AP#6D<~r9A%1LC=$c;i+*4Pj$3`(u|BPi z^$q4NwhRzSuarXJeL1+vU`0EAVgqhaS0y@nMEPTOt~1$rsduuPMrs*XB)bT91{zJ% z`f6Xkj-S|=O-yT2JAt*;o}k0DYzkZb0IRZDChHYzTOlpUl`7+~3!jI{%+C^#>qcMmD9b2&kKYEWz3EW_&HV%Oi@0)uMY9wkT&C;81 zlBy=sCqbOdf(mN{4)$E`b24Xesf*MoM`!s0+2UmmXyZ3sN}r<@=JRj|+CWkV z@e=mNNYNZ?d0*ZeK+%t-p6Z2Nb)EVV@igwH^sfuK097gctwfU_u_gOzX6mQFY_|Qa zp2(})HazT_d#Ft8_rmtG!)@;%u?MkX%AN{0)niZQg@Zg7&=3nseO4S^jfO&r~%4i_xK_KJ> ziy!1@q@nA9yLRgu&Dkp0Au9r|F0s)rz-t;r;VW%+g3pK3O>Hq!=&8}~fRCX5?5hE1 zAg}X-qHFw^bO<9 zd3>qY#lT zfoU9QP1%&4+PoROjQC3hW`q>5Mc?rx_iM*B4+}5W*zJ0xMNP{cu!o7f70t9sXl(5n zGd$*22GMz37Y@jYSDBa8_)K?NW9^f|P33i<JK_&9=?xR|};-?YSaics)CbL_$lF z$GZx&{A~Za17sl)7xkkw!v^tHA9HK;8HxIc1wXG?wNHaS%oH=V>?Ymq__;~7;;B*s zOD>v-oEK3FT&`|XVm2EpnzFHPFx&;pO0x?|0Ymtk;BM(P+k>DINF-`?1aN?WqT9?l zHwEDqM5igb?!dPMIwa>7ZMqwPPhx|&k4G?jLg)k#wUYq{Ax?ejV{YBF*bOeGt0#sD zI;fF3PJ&J%rl~a}k%kokIvT^w9sVe>qTx<->-fO&I-Me=zSzMe{R8e`t&>xAMBWLC zV(V7>r)Z0TdB1v{P@w>JuDgDY28GlF<@X}^(K8l8Sa3>`{jJ4LD%Zvtbh@x_`M#aIGI_Ttc1yp&^57)>(Mo6jPl4)*;$_WgNN!I z=a|%r89$Mp-ajz&&|T_afufeU6Xbcl&zrpC4We^O-C2&*8xV_+C_5uuO?O>fL27S@ zsMC9!KRpo$U@9izhiK_j3>aD9_2Bb>eMg3Tgu1uu6(pqTik|Jang@Lc)C(JNnzo;C zYaMHG4%E|&n+$5iR6VkSSl%N#n!xn4ktfI@yeFP4$drgp8C=*2WX1FXW2U#amj-%v zmtnQon^5NnF};;kG)+=;@ARU$9xu4jN3>5sizmEabwmVy->-9>FuInCW zz>u&^^6user0;i^Yrn>D?spUH*8>u6_8Y2s3BY}-uu@QUL-nT{d^7RAm$wMNyqZXY znIVx?_uUVYA*eUwm$h+(H$Hd|AVH`HlxX(sQb~}2g>BQVB2QRVx!52W+zGduozDd> zbrmu4Do0h~zq)yYn}!Coem76VwzAk-VJ$k3^}I3~u`~&?kX=gAtJuBS^rmdP7$<1Z z65j2*u17FxQicJ!5#<6z!?u0FvqL05x|h^^-mmNR{tO{ov7$R58ISfq6@;aGAZ@Rw zl+hsBM$>vG4FUrIQ}C*O3Jx70QU((hppNi}rT&)QKR?<;!i!H1utn=E>B$PV+0T9K z*Fo=A`qD&n@p;86f+~&!8oVd%3;X+lmZ7a@f(a6k{SA!2yTWZGp`@S*rGtalLZk{2 zxaj~BYU9EzyLlC>p_TV(w%Mq218&DFQN{d++dgQJFCU+!H zn%?KhwCVjmT^B@z?}iUcSYmFaz1_9>2!vTrV_*b#zqT__cz)f!boWtbE+MZ`kBX^H zRYL?|pz1Hj6}JgCBtwCLaFuUp_Ivq5G#VfC4jDL|L8B=Vh6FVc?)Ob9jSS8nd=oY;WJztmEBo7qFoDYER8A)GSv~bTLAh+$cbxi1##XtdJ z5RHI3psXSyfYYsvn~5EF*dz>V?EnZ;=+oQ{!R3>bjWS4N9kEU-Wi8x>hxIK)^P7 zPIOD@s7{@3Na9T#zn*lZ*og>I37qIll$JqYK^?&ZsPcA~;@8CpDuYmhV>l=D+~BUV zD)MZKsbnVOOl7uR&|!votar!Ch?=#)k!DW`v(YA4h8|+tdCTr=o2b5b zq(nM}3W-#sPaI4h^kCV7OJB288Q&S~ONDWy63OGHt*qH}%~?Ah&Lx(#U7}J?<4j}P z-8Jc_qif0XexFe@47+a3#k@ajmI<4k@qqChU@1H<_@A; zB0<$anpBTFgMc8^sCg&A4b@<6irx`9c$kRY$Rz>=(LTOa_E~a9j;?pOEa#(orzIT7 zJLK@VyDt;2%{+04AK4L{)YHhW*R_0)KM^>A&%IM^x(BidX$RxtYbJjjfNX9>+0hZ2 zB4s9hvTqTD&=*kW4FV`097Z&_Pv6GH9*qRG{=oz0)}k{g6onAX_DA;)8UuECtV@a3 zCzmTWJE(nkCMC&u=ufj7p6*N;vR2C0R~Nh;Ky9&(bZ;Sw9<*QKx+nJ_gAFEy=-)t& zHSKl+9{0u=1n(BPP;&br$97XRLgw9_(PHX*!GCUk>wygotoLxbED?cf@15@bC_ z6;<>VL{q-yOw5l?f}pB3_F6eXTqkF5lKFp7CPH@zs&WfJdA?p@xE1+K_}gWUXKfBr zD4J#3%f>}O8Y$#pNqdU1V46vMFdEIdPQqgg#zVO#4xo4oV<}X_p@U!_AVVkuFPyx9gN8=Li^iQtAGYV3&Ox6)ABie?Xq%>1aE# zJFqtewjK&4R{i7##yI1|*di^glo}lbC>gtByClcTIs1~w**yrR?{xE4*yNhjT0ca=c$ zp@DA!dc!&(zUv8wQ=XVoZsm_K!F@4T86^k&Jcbe0onn6c*FF{CO(=~iI;5R%f@e&6 zW|2f@S7m?CBU5^T3iH3dEP1@CynLgX8 zm)3*C_7d2vN1JnOUjM4*#6{QQA6nwFs|Q}@YqQAx%ZoXWcGt+tZRswg8DnK0T_y${ z9;jmAcUxH;l`kjwNJ&f>&u|g95vC|kip-HfPDOR(w%g3A2XEK;3LrhXMO#VyqfY+r zU`gz0zXQ)mO-oJImn(xLb_|lG_8XpPI;|zjFx>kgt!~UYx@7J_TiYyTjd<|phg2Gh zi9pbjmg;V+bF-yw18GEwQ9MvLJ)ncvBu;TgBYAT=y7CmiE>3^w+YiO*>-7%nb ze!>oJFsIl;fUDbsec+^(!)|_F5&88O#d6dhX`bCGCSV!hQHiR#UQ!=O6PC!jA-V>Q zn1WzqIHd^_LC^9@6n}vbG#VP)_0kQQU%ugw!2O34!y)L??T=MYp9taFLwp ze`&!Ac~b;M8Wm-|y_trn0aP}>rQZzVqs{Xz9`btNk1e_S;E0k^d2yC&wJ%*W`I= zsA9}}oC9ZJJD(p*v=2Uibmb{HJ_wS*)#cf<3YnNwA*s>@{#ST|_Pl$yIFpCtm^mLk0!UMrqHGhk2ou-4E!kIuUSV6FBzi@GJE@Xg&fvjaZ zu07&)hD0QTfFj`9Mc6qbc5T~h&2DSG33d2a>hnVXSXtRt?q>7v`oJZ~!SmTgf`r)w zl&rr^NKCq56z`4;Qs^o~+6&q<53;7q*9+~L0m(DvpwYb7bRsaQb($_)_&p7N4Ly)U zUl6P6dG8X)T5<=^zot}y>9lnqMbDlA76=sT8!y*pQlFz2aGx>q)&9Ag^e284M}QGp zlI4T0Persa0HO_bb6Wl)4|oiOyh*^_zwnX$-E(;=VU10&+(1(x%^ESpW0e+c01ViB zx9{V7e@bH#edOLEKz(^S5~7TmMBqJVv#^lx8@H`G9EXRjbD6mDKq1%&+QVbilC%&q zeX=DYA|TxPET*^N4P@Q`Rg!^aKoLUx#OH?Q)M|y2p3%^ruB4?pqa`gglDw2eWf$1T z@U7kat%8+V9XroGgEQR(<+Bknx z%1W*J%Y=59?Iz4++LgBOE?m3N2#(?iDO!9rtzILJ|3PTXu-#HU}g~hk- z18h`HpF&PCo97eWOb{$;&?yo1uKPlT$n`Yw99HXBkhz7Er=<>d%I)4KlX+n=EN6(! z{VTTqz&?`+{O|_Zo3@0VHqyc$QY2#ud&|r>a=yPqLaq0v-Eul4!PZv?nMlIn2i6T6 zM?}<_`==WzyNK{DNkPaDz4e`!O9B755aDOhs17fOvB0jWltpyH%=IiUds~hyMA(%M z?qV9WNFYzpqSO0m4<$MEk3(2CfTp?BRxc8XJlRklbU`va$i$}UO~~FJ2`3P`{@SK+ z&Hhpib%G3=j3stexx6C^_7p&k-%SzI3EF2uFpmP5^4LH&S3Lqt%DCpc^HaQF7(|dY z%f+^BD2MBr`8x8c{FpakkHJ?yJ%IjAlX(v7RboW-V+k92ozfts*PcdyJ`1s{nw^6@ z=?-N-k*JY_eolASHLdL1`I%&+o&JqL%ln{znXM{gEAhYRI_rVl-0vwlA4AH+Kn^L8=XA?lX!*2a^*v~m!E?q^k zfr=51I(JcsL$_p-7RsF7_bYO9apzJKEf?>z;AaF#b?W?yyr_^U>(X+tHl360;AO%& zzkw6&c&7paeYU|&NH*rgmo_WYsQvyzX(#smLQeK6{rPslPFa1aLzp;O5-5IR=YMW4 zMY%Ax$w<-@UhBcGR9ENLp6!n1C$V z0ntSgfx$h@%Y@$8--c)GKuN_s$eX{y`3IlP3#(%3u1W?=POKG4KZS3xk}|$(r-zFj z`=&O+6L@Dj86ok2Z9od~F>Pd_PppllxL$}pyTPem80BM36}gWDPw(6z$czPlzq_Ee zV!9V510L{!_MQZpG>?$8Z`Qp`+UW6**>6gcKeV*Ec{RP_p>YXN6s<|UQRtQJ*TmrOMrMk7hP?m{J9C{DN;QMoWE=FXVBlv>ojSsTXm&nz z_SivJh(WA2X$qu);bYyLWOf|psT2I+BZ|{^L|*v|cZtiZ6~WNVOdKJejt5Z523t#B z@M>?LM}f^OUJGJX=mwp?CV+2;L=+mD;w^DA7cGyA$dCh{YFRZV>yLRE$0E`#3)m_K zj8@EWC(aEBi z7!npcha{XZm*T7yu`oAno_)AXkTn#=fD{Q}Ab)juJiib`WF4-tJ~h->p`fyZoZI6~ zxjxtjkvz-ajoiHuc}Y$|7V>+IqGcA$%ju!JQII=h?P_(dsg`y8FSYq9@Z-Rzk=%H; zdCoGNMSz-MYHsziY~cXI@Z+hbM4+VE^s?>Wd@Qxl?x?=vBm_?S$)MuK+qom`P-9>Ed8w|2xoh z1J??Jn27M9nUZxSWD|G{v!BgAv9r@VHd#HtqO;@8;`vp0!b)RM2c)E!-X+Qa&eApn zY*>YGBW?@Db3FHCM1`4r_mF5hFWjhE(#d&1pBqX>1`t0`gdrP?)H7)tF4gGoI}+SU zkN8%6?+WRWkGV1@EW0PoADCVvm1!pa6AvJ5xDrP3TST5!SIxtP>w?Zh(Y>__Z!$LO zB~e!T*Gf=Peq58-b66l-(a_7h%s)&z(Yo%-rliYVV07$Ra=Ne%;=;thnsN-MpcJQxo8w9`4EHbll8O9wsgpQ~KB{KMS0R?DQZ96Gl_{x|eXJdC zmg$%H%<)Zw|Bzj9=>GS)5a3GO{A_{_1OF~+jvT*RTX3b>zP2?w)Tj8m0i0(~gJ$O` zvV1Kwrk1CX!mA}3(NNF@kv4WyEBg`7T_scxbddxu$|U~DG9jmR`}ORYTB#1$@E zv22U9hOu~Vd)q)JNu2F_6!-I0ag0Es7n(>=CX{g1G+S;vNLCKKD@5iTSWY)D$QVCj z*!ldb)~S37+T1b^jwKrn-9_)DkVi8)kvHW5LyXqAOuQddvTxA_$OL=cKyI3%N!$H- z2ylY-3Nzo4)%CLO;XZGJQk$VNM)t({6sN-wiD}9wA4pn@VLna-09jOfOyVF)y;8j7 z_~Ch7)~3Io+EU2$x?;ft`JFO~AKXHCJq?{f5eb2HN8meYaufiGKJp4D1c-UG0g!Kb zOd=kzW40U91r1aHg<8y#_!#EKFcVe?scA>a&MMH9`A<~ zOD=SqmFGO6J#ELVbo~h?xjyD6FY$t20{&4>OO}i{wSi(`Ks>1@rd=$Yzdc#Uv=B03 zVlZa(fe9V(+8|l65!1%eoMTr?C!uWTd2!{f=MQ1GYr9XCY_>=JW1y8{#mg+;9NpnZ zqiUn0#>IBCs^GTroE8Fz%lGGwxJUZa5F@9P(7e0EQfR*mtdgelf>X+x+v%j1{O&F3 zJh&%|WsY*TQ7i!W$O@D*YKPqpC7^zpsz#+RxnO9h{6gc^ph`vVWl!JyAnXMuqIzmz z1Nq5|Qpd}qfe`syMuS)cJ;_J&6%vRxpYbb;83KO&_ZrolEVZi~tCYx^r| z2y1}G4Dk2U{?!f&Z<&A!k5pc+$ZXtK6?NThr;%`_>W6`#3}uSom%`<{XlW-f9?jB> zS&9vMGg036SUMzs*2SWY>}^@pp3UR?yBA*n7MM@V)%-eXe!FL5xSh#jkr!Qv5*}F@ z$@F=1+$V{;Xl<=hQMOx$>s%PH1rz=Nq|rLIJm0;xSuSCvWGQ0$I-0EdsJzd^tEq7{u>W-+n zu&1qxwPWTAw9&mlGbnY@^N*z`MW$)v1Ih=Z4a~EBG&TdV=jC_<2wMr#<`m7670)#m zPdUPD@5KQv!l*t0GBG%xyi!g!raNis&F{(2558Q;JFuUiMS3ErW_=7$V#U@b0YTMY z0<6Ts1*M4Dl3}i-pT1pPL>7@C(C_F7_H?I)f+|aZ9tcy#xeQm{bWc$Sjov?ApvvEc zDu1G*-9FpzQP18M$WpGDv9_l*U@^Ck(72GEEU(8*0`%trEJGwszQNVlW&%n>AlX{% z55m0&ZaU~S19f5Dg}}?oi&mKb^V$gOrgfE93g3@dgaJGGLd%b(>o0^) zKA&IDRR}F9+g6@r=1ssn(t86@z+#c!m3OBCYsgD%iRx}GZ~ydO#$;!z_{7sBsBPK_ zVZsbOZM^WSz}d^b>5oIa^U{DO36C#ssV;P}99YNvA(k-J+OYc;TrGpo_%xS+Ia&_+ zlaRAI^}HWP%gUsZoEk2XZJXpDQ)`I0BV#s&uWUcJKSY19*}&kUcYrFdCdU74I4N;H zxxVwXNioDur^4;b?Qledv_#q~0x9zit;&wz=4DwC0uSBoFL*8jZ|md$_8vpUVCwBZaqBQ^2}is% zibE}a5stn2ICXt~jmquv(eOxDUw(2bk{Tt>r<4U2d_5Gee#EalJ;^2`Xdad6U5rW@ ztV-}99lZS6lxf0G8Y18-qXnI3yHtF918Z_vG?cF0hiO_O8-V;&cJMOMoz~x^1SNhZ zXqVTq)h+V`8fbj@Y?_dG(?o^8Vu^e9{ak#z{RRH7P_zv2uA>eR003Wf003zK8~_^= zX9H^!14mm6TQfaV3u_Z6dV2#SJtreOM-#pO35sTH;$-A#Vef2VXG`bo?yRB=2>?9M zzOVBi)5RSc01)H{7y#fumuvM++e20apI5abU|B##xASTlEdBW^wl$E>STN$na0Cfw z5fALwORj{fW@E(`w)H%UIdm;1Zl`@h4jcq1mG|omxCGIiHeAP%?>wK&BmGR))sgR6F59qI^5BUqtq;it5{DI+UPv#~gK2~AJ z5FI}6bm;2Pc{eDo4^Ot&p}8o>>*U1poA0cqk2H(y2pp(-+i#Y9e|FVvjs%JjA6!=+j2SMdn5 zB9Y&Q3B|`d92n^=d4)+a5r+6oJeoiw`dpFhavhxb-O6^V2F0WCCBNwo?DpNl#B%@o z=j5{B6qiUDQAI7<5U4`MD4W;cG zKkZtNy5{KJ=p8D%5{WY%%{$P@WvfaHWx@s8 zXa$}IvK6LXoRnm|BdE1j_ zA8NtNp^V0_eW#Dc6gb*@sYBS`+G<3_iaHB6 z%YLQAe0~)lWX*hcYkrG3=WbfqP=iPMeS7)4AWSy!Sngy55(?`LSO*J^a<<{-66>L00tlk z1QCdiAvgxX38LqR&WVXHLL!Pn82!u17ds+wMDz&r76v5BunD3NokDOLf-^*~4_y<( zA%H_f#)QQd1}EmlKpY4|6is3{2tuTcQa2EWCf1h1-p4w?I>EZYy1{zDdcpb#1|;Im1$) zv@EVDSf`2CPZz}e-W&>Pt~Isea4 z%WWNv#G?_1zq@+<=)KXL@*1z16nJnU<;4No8!D6wGs@ zicr);*XLjz*x!WIkCTmmKd;gCd_PVq?R0$~-X=rcdOdzub933Mw~N9}sw z9?f>WUr##KYX5$oXR_`7eeUGzc|S-zZN25=?)lz7pPlu6Kk9rqxb(G)?cx5uJ|BDx zJ$19=^Y|X!^}WAV%WW3Z`+azPm80MNT2=ePycHw&`MwzyIvtnD{eF7;`F>aG@%H?> zo1DB^&E~p(oxR=V>;1l$i{<-v{OLTg`k6h2|Ly*);p6W9x_f#VJdBk~?P=^ii|oBA zV4G;wd4dh)Yv$(d=Ki&Z`+c|N`|JL3ufy?vU+Prq`>Adm$MC%a`*`0(@5Z{`Vvl9|}o}8dUCmaI*6j zzt{KY{C$w`?=QD@cI6`8@9Ug}Tx=-sO($Ef-PYRe&;8@;?O!ZiPW+8FLk`X!d5#VT zA8+@=hNCljzSx+bQF?m(oo?Tq1un;bo2P?^mpSlu+HE+y9S^;{-?N*g)l!LtT459W zu;RyeXeHj>hYRu~J?@|T$J@IT6Bm%zOD* zil4KKF7$MAe11OWQM>zne12*9m7_b8{bS?d{rcFSIDNS)MEbsd_nMsA>+SKCuQ^+A zw)6ef>Sh;7<@0;onR=yB`*VJO+`bVO-5P1`{r>gvGl0yF4I zL#L~>65W4l`Zaz;y@Xxb9S8$tjFWl7B#Hn`#(Q!H1+mN37l4wP3C@9Zj>)!jrR!$z zp|Z=jy-@6VkkxHz1{*8R9)uiq=#1OI=>}7!$@pjAzQ+h}en3ZuBmXF7D$q4L^b9~_ z67|^rnW(4F6^9TMC}~IF?xYLQws#4;3^w~TBC_#Fv>*D zpyl@`C;^7`1coPr`TCUIKKSfH0YWAWc)&182JbjGnIk(ozNGKr?Tqx+B)N zAVfh$;uKEW%U%Rgld8g&tWv7Ts3`amSTfAI8Hd0pBnQi`m_>*TT5qh0 zGssKHOdF`tRKDA)n3zghd)19tgUW|jFv$rC6{b2Bw1(68RfsBALK$BSZbg+qr;alB zFcoh+^1*5(jfQ=TS`=k5U+^z6OsU$9+F8!|Y#w!Y@*~)uuF6))e+Gxe2J)CPEywrOCo4$3uy?YXC~8L4B08K=*=>FgS+ zp)fC_{6`eRf5~WStQ0Duh^Lzv+0f?RPn&}exk9$wcu*ZUD4?Q6(j&Se_E8O+l;FDY z&H?QQaWcsb?Wd-kT#pjS$tzGdCRo^d5flh9LX<)tGk%;%ZQAqR+qcUL)BtOa9F)@6 zVMY8Ov?r~^bqzn?j@hW9qFu5&?h-hSOF$(2aC@Q~LimruX z*tVe^OL;ea4tmW;z^O;7vl?&sze3ko=|6NyAS8&k;d|5>07~v+!!5HDM1p*aT0c6! z!S!Wk$k^g1ZDqKGg}i2ry4-`I)rD5K(%$P;A#a{4|&Iid$E@T~=4HOX)4+o(GMqU z0-Z~oY}-2;J6tb7GAiO>N6WmkB|#%MP?pHnWKM!xp|Tmv6=H*O9Vkt639qTwR2>2V z$6TC_d!?o^(1>!g*Ifnsvb6@u#D>?YQYbK~tWlDErD19r3pQyRzg}+YnoW`7m=Fc} z6~Na$<<7B08^@IHA1tyA*>d;C=Qhd&@fI55pMI158wCr0RehP{&K>i0yv?c0Vyr>1 z#H3BL8_zS`WFR@jy%~^`pX7YhAOInAkPapO=ozWtJZ%qg$CI{7!b~$^u_(kD_~9xog;W&9WVAb2JL= zBQIFzF#ARa-e3-u$&`BHYpW}qNrWm zWG^Ff3rn$o@1P`Vsmskr>NIVpzoQ-kLN_f=J_jYTTHJ64tQ=eqieC+^IEMIXOvt{^q9XRRr^DnuuGE_XBW8c?O~u zYXp{{0Pxhze=EkxZ_%IxhRZ66>ihVn(f>#0yy|Pnf^PnSin2_UpDt(zrP_o=k|1Io znYW}9cT^;Gh=k)X0ztz-QvaRZhH>H3XB8x1MphPED5rcJwq?0*mI^QVYZAyLU}wFrfuGpa)Q zlj}(HOI;SMO{`)XP#|F?qDRBcvY-4WkE@mFL=}JwysWLU`4`LGP@cs%4&g|ls`wO? z2wd<@%hWXn^(lU<6GhFU*g<2(q!UdXg=#ZtkFw4Xh!)65BZVCx5b&IJkBCk+S({0w zvt@EhM3ZU*D8qA`G0(@q{?1h&k(?Zm`r)UM*f(DuU}B7<0%R)2)|IwYImu5YnoxaO zoUn*44pe*Bpdi99(6E`T3iV0>+Eu23KeN{6R&wj-@uV`s;v4T2_yL&08Xqsuo?3~d z&J>)z*(BnJwsj6oZ^a~r0F1Q#N^V^oVpAx8V5~>IQ0T-5$nR-~#dkT5rZxDmCDlAbu z*+}b?0ToSgy9;6U@*wX(JC|#;>z#FH)V?Q^1KOgG$fXHsqylF2kO%)bvFb5^s0EE@@8$-wB?#LL=qF2907f4elj-M>0@A zW~gf=c-tL}=Mo~_rW$gN63}THVA<-~?RTE5{MGr%A0lr}hJ1{aO3?0L_&jO!KT_ki zp*!D{Ag4ujl8h*ngw8A|MpMjVuC++oU7Fg;Iv6?z^PHR8l9L>QUZF_Uz{fTpy0sF3 zDu!CNaDO-m|Ly>z_LoQ_p(!qhm4~_M3&2t(k3*Gt!yR}Pfy_u!X4fn=t#ZH+t9tR; z))nJAmrmLo2lGpAJMAnJ_0rI+5=`iGtKnfzt!lb{TRB&_V6~N0w?RYH;QDPET(+@H zUgYE#Jqhyal>?g9>sakrLAT5%o92{uftjGR_0j@+ny=3du%txpL2XhIq*eHHm>p^k zTcv#fdvuiCdSkzJq+HjeM0w+^0qvUgfWzhQ99dKEJyvKnJgu2(hBU9wwk&Q zD!Y16UN;XceH?y!IQ(?y6I|VWVN@U^43?8QdoLr=iCEq@^`s2EBZDtoZv_wgG2W8d ztv%%oL3oBthmFL`WfVw;OK)*|f<9RQd>8bOW-}=O-qHi;enqe0!e6Y4gKK{`|0I6CmYGO`x*mpE?WMzNvLfH0h1sO-9AYTbq`;s0C8#w^nnGr7 zusr19y+sp&^l&I}FtUkx71bKT?MP0^*Z76kp$yQcOMlai0xH<>Fid1FKKt))7xNHd%U{R*A{@5Z!dE{Yh}&E9sF?Bua5`tNFGGF*$b(zon8l z2=?iM@hb1TGSN6VI~Y8QUQ|w`i1dE(lOp@j#6p5T%U7iuQ|1p zH0Y`OgtoG2-|AUM?Sh&5QDVZx!Hs-f$DK4tPb?d}zZ?uVMWp6a?BeV`%w6|b>%|F> zr4#=NC}Sz&GL|>%prk9B&f;7`KI|-2JY=r_*b9pm8SBWTW@#P{ zWwB+b_<`7?O35)QO~HuRwKY^NUuq&}>PK&={cgVGvXgIxiF{p1#~CbE#a@~W`CtwA zrA;BY)YNuwP#KrJ3?VR(q1j#(tJUkQ7_OC$R-vl`H3I02V9`KVK`>(#{`wEl-2Fkl z5mj?mpZ4PA3JdEfvPD=oyeweFqMj+cwi5T0he=rXD#ejhqAj_c7=qOohLKM#&$QFg ze?n&|po;3CQl1XvJ~9X?D&jLblw!E`KqieN3c{zlo<`j(q{{%Ugxy-#$EHDLuPsY^ z(JdX_3f+r7j!i2BBn#mj2+An>G9=LbNy}hXkv3`Eh$^XIDVcn;#@4T`)G#*6o?W&( z6qa7>~r7VuUfvCD_xvUn5@R>GAG7C8^OjS0S}DvzY~zfn*bk@ zUKBd+9t4UV8QL<4xv8KS8H~X{}EYi3AhPcp$-N4}Sf{W1svCNnA zFHI6Q(9TL7lK_iui|+zc2Gm8z>c>)6Us(k5N)7DLkX)5~7WXY+)%B(L2KX@5We<_JTM*CA`3vgt=^O$vbcBq;=ab-evr0%QytYQbjbKgtgdgytIsvD^HNlBf2}o(ZA)BUQ`8+(@vlTRJ+QwC+ zK-KnJS=s}^0R46Vf3Y-jhgNr@B*G)@I;H?Bd4df7fE1p zFF+vf$ZrXZhr?J{ zW||K}6hr`w9owK{)y=5HWFatOg$$hz@|Vk zRMmxpbDBj(Jz&~dI?L+6`fQ1QfE6!K-Y(!ar{2|kr*CDwodf|c(`In)C9QC=p)QVy z9urD>sAEW2^MkX+JVaD7GICQIEO6Y@J%g@>n%L2ebtW$YwU@>l`N5GFuGE{R$u#}Z z>e-VY%>|XlAZqc%LJtLkAy7LLm9ToIti1zY_AXIilUQ7|itqN3{;lT8>w8blYO@0! zX2X)Y5rRX(u5WSy$4FP!_8P@DKRx$OgNq&H{dCDCnk{Y-%{Tmv_8d9;fqpaW+)JRl zoQju5 zjB|D47F`(2`{57tkZBN1O3HaoFe3P5$-p!*hg<0k_qw%$#5$#kn#8J<;{x>zsQ! zyU?G~X@oZF=TY2k?#3Swm_ME~xJUOBX{|AtB#t4Nh~kBTX1Cw!$h)m~Hxoxl(7>*r z!>%9WA`-4FOTA@%Xw$W{qhn%cD7LdhKw|0Q3`_?7wEF%?45xsul>5AdVT$b5uxs7hyhMRTLniASJX?fxX5E-B) z#p{kFq32b&0=)>yd0{S=AiJeDKJ_L>+B^am7?xd@tURM~*%a-z6@Ctxn~Gb}MdI6Y z1wo$wy6OCq3)G2xY%69^Yziz=e-h zbTZ-(T^%`3SIrX(fyw<$^U$_?X9K0Or+p9u#Y+m7?sv)zr%q$T1s5QZT}Kf>lfLY1Y{*zxtWeCkdoyFnC^%p7GRTZY~nrw1$)#nVj0 zhGf}->ZP=ql+iroxHwph^uQ!7F1Lv+TdQB%jV*k}HB}@AV<~7}Osz=0q_?|xC#_Uc z>Vhag(JK5Y_P~)q;MSt`&fR)*Y)YQ0=JDAu^Fj>lrafyU-E2~}OStrz)G631S_4=tsF|>JW z1+)si#26@0ym+b(aAzphw;D`)cHz4{U@g*aR5f)JL6!FGO*EC&V{_A0b~*KSICq@e z@yCO8{1k0si>uy#f9xa$PkUf(xb!YU3QVKiv&1cR#D!B~$DF)AV^6nxrxeWyX7X&& z_}5`Gxiay-CE!$ZW}a=-#mnFo%0mISG&ZK|tPBEtW$ZB9yf5=@Nwn`zgTgjq@W2Q>$O9N9}9ca~?HJ-=h zWwz$JE@Md<*qEiVPgN8gJ>3ma1QJqsdb+>$(Ba?Lk6YdmU1odT6%H(?wv_4Lk%t!@ z)^)|YBHh&wwBrw9=K=NE1VD06|5KdWeejmxA#56_L85@4vH;Vg+RSDg*-||SZ;;tN zV43mCZ{5wjE1Lc$f?Cv&e%OHGE%Z8U5GPhnkGeJJx4u2f#J-V59~fI|i?QZiu3I~n z&!Cm+Mcv}JX*Mjz!sP!r-az4gg%C+mVjkj z+82IEjZ5<9%P0--;z58*o<1L>njk|ob^Uf{?MLXq@55Y8kiiPG5#r=)*|A&xN{6$M z@QdLP!z)hc$S23c7*j8a=@Ejx-nsK4 zUBCclN-sFpDOz=@_H{qg36(jAn7=WQ$jYeY6Lr_t_Mwa;x79;Lrw|z6`rG`*0(kmf zcmDWnEeqGivZJdy{@uS>+ca0ARI^6Dj?lp|%DGZ10e2Bpq=D5?R9nnzou#!D6NbFw z;4Fp(pH!&^qsCg=xf{CQYgy@MmYs^HLHPnOolXBpXV56FDcW)NbUg&@erwxQ1wDfD zkHJiyhY`!DBcf;GkQ8^rHYCNu6wQHmr2V zg{#WsceavdINDE{q$nYe66q@C0Bg#f!_s;EM&Tw@r~AI9*b2U%o5q20jfq_}d02)? z!&1@a0ttge86M^k;GZy|)Nza3r!$6HmL^-$hId1AddLMh8kK_vItjjf3Xwg}76kEn zwAXfCH!=v&u)zj8>v}*86>m2fenry~c)XU`8dcD`gmu;rA7k~Q>&zOZStK=Ftvt=v z0RV>319f6R{epQT(ERS4-(7^~6Ot$4aGuF% z7qoBJOk_x>F%Ve7FXJdAg)2ZgGwoZx>N0x^hi}px zzbTr>TJHgOAo?AApyc_?aX)56w7DU|Syg~C8-!t<5mObbGmNW4=`$rFP&ZfJfoGPZ zJD%}qB|FPC4^&bJ{&B?&31qhvXBT(e|I!#k5?NEIY5%n3?(!ljpHEjKUug{)h%%aokCUmsL`)APDnTvcmpg=#0W($wZaB+^R9!(E_|!W(;PBM3`0TGY1Gpp zaM^EBD-%7mM;+Wq_jOM-zV6ZU6E9|;--6As!-yyziB7aW;w}_vryVsJsA7@qj@T;D z(|_eyaXejB=yK}T`_|Dc?AWon7r$M7?s8#LgO)BLvn_$_ON>S;&WnmlZXil+ujWzp zste>lwAFbA#!cTE`Bhj8XYAjlHi)whT~;FKx;E+a3CC zj9O!04-|8j1q2_LhnT>yzV3KmgO1_qpu3|+y_M2Qk;(nezopMyjwf}#sb)<^J_M#x z0*Gbn)|6pY?NECTfPHIfqjna}Ws&t!1W8WBw^Tt}K^tV)Ss_~IvM1rQjbuV=3k(-5 z!R8;u&))G2%#I+j%=d8|Zlem2D?6 z3yDXfIdHv?GGmC0P8<}?h{TIE=@8=D-RM(S-#=Y|trt6nc^%I{*GeuwzRiJ`^|pCk zwo!*CAtnF5KEw_3HKl_K<3|IT3`SdQYofDfn(}u=a%{t}uMhSV6GMl}|2DB(s5@b8 zS^EQT_hs~H>lsLHiViTS39@d)m1My4&qarv{|PE)-ab9qqI_CkCV3Jzf4D|$XVu71 z{lEbBR>MK$=s#wVLlt0~1F=jcHh)X>>o=?Xsm8$a#Qp-Tb+ZlD*&>t6o>TWF@Xf8L z61(}KrX9@cVj}v!l{!_9pn-Z5g(09jAqlHEJq2JUIOecyf=ObV?cr!A>yBM;7kpEw z9!4f^)4u0UPY51t?5Y14oGrnK6}KeD(U@d@LUl8?>Q)O>)oq|INudrsg}jhwyXY|) zhK_1)vT7GczCBHWyUj`v(gAcK{Y5t4MszdKDm@gNIaOaGQk=UB{DrRYUCer z6h;ho*zMZacyLWBazUkoeTfiv(0pJ~AK^4YxWN z8AHZ=y95Vg93OyiD=GhoBEUBn`XOeQ(`gGe-q_oiO08KBgd`^wGcA1~>m&BK_i)eFhs5o~#~YGz>v!Uj7&$i37Ar$*!aTy~ zUL<|MJsPc>E1uHl52&7ZoSJp{kmP_g3{Ja-H%_nyNx^})4cSfPro%MIBuJ#cbG6Bi zPHhrTkw|xLOWJ%Pyl?4Ssrt%PGSzA+S>~*gR(3Y`MSbY9N(M9Za-Dxe6j*JK z=su^J9=u6GSD^@M3-}acTv~ry;5e6O%B(u4y4!NE%K@fI?9@Uxl3S zN=l$OKw!@lN#<1NZi*LMw&P94a;*Om)63ZD;`gyi!XsNt+1U!t_A?dDVvshHmtK&l z8s=@r!V>1wegEzhtk0G{QA-;T6i;tRZ(pEOx>$N;wr4tAgV1y7-emb7>-69MuvXC& z=F9u&mw|dFX2=X-=ck$`nj>uFs7CecMD&SGx@A3KiV=woOP=lpk4zI7m?#2J?K_Tu zrODYOiyxS2BaVHO~yBz(7I${vis?b~oUPC+5~=f6dJYZ=?7 zu?$I^O!vPlB(npBa+S5V(b!p7o^o`_E~l94z%rHwX)3WtY9z-F!xAzk_)u#-oVbe@ zz{s)XptFjv^0-B9^bINo;+$e(?Xb{HLl$+&?NE+}W%UIgMXzDEqa5qX9MZpjap3Z@%}pc3Wv*bQbrpLRho}GwrEj@yU)@hgJifA4q@jBdx32o>_Mj+ccz*U~rL0M5 zygXLvq1xb63+x{+qDYC9QyZpHbTJa?PFcNR)olKq;=ARuIx1|Mpwbb0Uw*f4XW#Z^ z-Bo5=(Ee&(vtXX@{V>4LP?6}aA1q5)ez~6?6HS$SaK)&OHopmtZ+Tt#YOQrCxfPsn z(JU=C&mfR=qP!Xj;v506Jgu-$_IXuF$|L}P`*Y<><%KW#rI^uNRo#||{{+YfeiLkJ?PTs4*CAipG<*6d zzOvjKnzCGY?5}d8Q_PC;qf-fxKz_H%HSWFqfOQ2gN;mOV>Axr&%DPfMu`Zj)#i(04 z2c93@FrG>`X}Op`x~Wf1_>PW+KXhfLXD`I~Kz_x$%^$o!rU5IX|DrCJO3fPCUv5?@ zFaEqVp&sto@WAB6!#$7*hM9MZ_)&5sSuZxE7xzhFD>5uM-%eay6u{)P*$}$?P)j&y zRlxT;WlgwfKa!}}ENY}@ad{{?Yof9{T*E7FhkY(*8oLmC6DYh(X=RD(^x6B1r)fL9GPy=CarTQlW_gz()%BfD0@#t0eQ+ zy$&JRzypd<)+nHZQs#s;8muF<79Q*t3lI;n!nFq=80GQ@Bc~2Kacw{CdR5osklICg z7oM8?moh;0q&ycxBN@iLchB0MU!XtCEsZ+bxtyuzF`1m@`@TJye4g7j3C|s3AZqcmKb3N{?eED+-S)sizD@< z29gEnNu!2r%fRe_0L627HdQ3&^h!kb7;6+WmL@q>_l^^sc3*87a zTf{`lKTB8=24xT66Br#YcIh#$&ha{f+?yy2sUnfL;e7-SICgVMMAjt8VO&-cX|aS- ziZCJL>jt6r?GGbU2vQi{b|xbGa)(@xGY6r~{_7mfMG!ScMSK_v$?a4Uq}b8?{!6q4 zD=OlRpOZ{K;<@&gWnfVF0a3MkTtyDa z4WM}6NM7j|>^Y^m7zat9;E=Yo{~hQ*x&WK1^-TZb`wI1+5#~&r zJz6j@000v@0HFB4J2@KKnAvAFvK0Tw`v+{Dr zN&ypHADjc^{W-oVo7=Wzg$_v>eQ&DAnQ+S{q(0$^fOPoycY6{adW5I^@%)~?%}~Ad zadYW>ck#SaT&$2i%^CBw4C;P=8Gdb6|NMA=c@K5->+*cKJ6K$NZT@`w^>FCy_IdB^ zWuHWV7dG~x3I^2E~qwjk?TFu$K ztk8@Ub42KKeUsk(@Vq{M(v6hOAV2MM@<67cC-NLfba|}lkxP8I&=Dnr>;zunfo)* zMtszJ^)DWMS~+Tb<6`oVfYhxRgU8q*60oCrmQH+3`4#?`jmpt0MSgj~f+PQ!UMOT6 zMTC_qL^QS^)$IS#oQ7KqFr?uX+uM9gT+0CfqR!|%pH8~;!V^e_^ckx2QUxoZBXi4?YDP#bhBxoqU`4CgfUxaqO#+r`c!~gT_M}JnxqDrG^7TZtr6=gfJ~% z2|(9$!3Ul!jX0X=h!grW2pfGlU+K+Uf~)I?F_oy-Ow)AGhVY4*UQJrM%I#pZ z1GBbRlx8J0X=;RNy3bH;)W0R%-YWOyN}1zm!Kw_>EV_)QRTn9Ljc-#n6ao5JBY!Zi zhgMJ|6X1h$qgRGm-7}@|+%O_Ctdz%*W#rXBY_N&`L5H93;cPFY`9TPt!dS*F)T_18 z>VVF7WpFvs4CUl?^eq7K54M%|za|Q0re?OlnviN1ytCY)FdYQ@oM{aOOyOnhsL9oq z5k-8RhKUC1eXZ+7UH1DlVtSEZa`|`Yh|_M1J};^+-pqDgvs45wpoUu7`su};A5VdJ zRb^2~-&rJQ=iNflI{tbL7QaZlX3b`_4sO7emd&5k37?>?AFi+vw_Sk=I9ePXKJJP* z*_>pV8e1s2DzCOa0?&(>$9KxuSu7XdK?D^gri}DZ+D0OxKd37K*N5HmI%)akT^;B< zK9k=qxkuWS5#8T4d%bIrwb;k)%1?oM|(2sJA?A}$*TznS8S`BPu(sqRY0m;VE zEXjHkC-3?*-AzVf&*la^7mm?Br&XC1jttM#%Cbv*i%Cnk(>C(@njHZfxkkLpy4y@` z>`@{KkWVoC`p{BAQx9l0kV}K*#t!ez6jebw1tRiG-x(L| zsk(?=+B3Y4j-wK+-+kf9K$~J7VppD|qrca%icm{ySQeJnv0yI)V%e?!4wjq9Zp`F# zGpRL6k&;WL17=!0+#O>5CZwqPkH*9ZC{A^gC6qB-rGa8AxvbZgO zJT`Y%kErG^QoYGO3#lhGz+9s0m;is@!N#v%a{l%`Y8SKd$6*SS9ir~Vbv|g!tLQpZ zVLn2ym2my+GuP8=O1|F5YUk$cF;$$a>}1?|pdAu6Gn^EdkL#vwwqcL@frfaF9mf_B zXi)dm&c4Xcxb4b^)U_fGYkh5Ut@Whh9&~lwg<1>Elk0a9A)5iZF5j(RLv^O-2x+HFUy_ zRMB2K+SHw5o)SIm1etE1?Xe7p1m-snFysvd)VZ{2U^R1dpiSd4?78DLM{G04bv{o` z1PPt9aWEBxxG0Q(Dg4H}=4RKC9*dqK4G>Ua`a^834PuJ1kc9N=u1bpr=EzW~z2P^u*r z)4R#`4D;5YJ8Xh&F1gJwH0S{Gmi6K7V}= z75kSv=9h8!|FE|IZ`NUXqL0G=mvt!gUvJ4AO^pq0Z1mksEzK>QO-=NTEbR;(Js6B_ z44s@T%`A-#|AVEi@AS*i(aDtYzi+|PP51xDbi?ch)R0L;0N}n608suv(6h0*|BbTM+!N%tsG3l0Ubavut0Re2yRQllsXf;LXuSP|Y6Y z1$|88V*wPu;eqyMmHe!37;rVFnlC_%YxL?}n>aJZ?Iu$c(CGNE&CbVYgfmKY9+~#O z%rcI6#4?{QnbN-fbUm5p?Sjt37+q zTYmSpQmW~wRmyi-DVcHlwoo&%iqoF$57KE*p46_bW0ntYHD|erwyrPMk!!v5_+5n` z*~z%I=9%Kr?x@?`Q@GD-lv%U$Y4?fowfrb`pqt1VyZ~{t#~X>=FJ07M>8%PKKHm5{ zP1wVKn(RkA&~I4e>R8IulC_`pu-#TYmutSHCiGg0-PYc0x<>DuUW#Y>y(~%Ii|^%* zJT9jSURD|9Y@aH2&^mm))*cQMFPf?HVG3ntx}DD`+hBF;K3-AJ4pIB-$Zpz+{&5y^7Ub`M)cYk@uQBGGB+opEJ zvY%Lbhb^DA6kl-Ob^`SkHjG?2iiuN|yGS;9d)fX401wY@>-&Y;d|i67cQn1Vk%&9;`OPgUBUd8=gU zK;`y<;|~ihIHY9~{fk33&le5ytI@*s>{}2$>5<_T7B7-QwG@9x`(gvl-k+f+?12p} zPXU3sr9ta9txK0jaIfsL7mYh3xvh(CpsKG8L)0XKpcck+p&|GDq9!W8ce(p}L&rc% z_6(S=a~Dq-$FplnK{PUru5UJXzG@qL5`N&5T%1j~x3DXZJ>+=Q9wmT(jgzY$1>de~ zMfcvKx|;jdV(jZuVzr#5%j<8=_-hxpsx4^QR>!L_MDqDu+;!@3?W>a+c1-Jv*P(B& zHS}wAyZm(L)|(LhU5A;CUzNbGibUj$iS+oOwa6`*cgw?8LGO%TE|gg`j+`LUVMSkP z?t~m6|9yqsFXsD+u|OnK0R;Y#EGK^m(Pw-j3%n75k)eZ8&u$*r>>(F-Z_j1JTPlgr z;o?C2BHwRo#$B7rGD`}hj;*0I0^^(go5t4Dzn)Tux>TlcYQQKx;FnVC8E{Lo5?&GJ zI5f%OU{^M1M#d7oUB{F0wI#ANh^LRA>9ySzob7ztzn{NfSf_=vPWn7i!SRUUjq88j zHQSr&bdZ@g$S$+qrnQ^;1`vJ3$9+t$J-=Y5v!=YayUUnGT^nMEFbMy&a|0<0LBUcENH&Q(m z3!+J4^j8P0&ci;OFnD&X(iyfwaH|>(t}xToc!Ln`b02$o^8wgXIM(odWrGGDSMmAj z>nzoMw)5OY_~M`Sk{H3zd0esZUj_E7bS25$js|i|8gjAznSdP%>Pqr&V-V2}Bx3&# zBcc@|`5T6kid)0TjI!3(W}82FGI-J1*fRJIH225nd--fT%=*U%2IeS>Cpj>Vp%b(I zhbpRZPfK1F1X}n&v7&CuZ%H)?+@*ux{@*n5pecqXk?7$F;2)&|5wPf76DU-N0X5bE z{$B-9YaQ@vc-|SuqZ&Tt`+mmQ{pRz*cv>UEva1}BED}h;xEoI*azYFf2ZV;^Q~~83 zkvzZCkB%vX?d_I_l9P_n#LO$q&v9bit4%_!ZVGu(!3H`Cs5!p?UDXD` zM~2e_tbYkaxD7$AGbI*${#4Xdc}ziY6aXD~cmp#H%e*}QAWQLNS;Z%qYdq%1DoU>6 zo^Oo@AwyI@&>UqAIw)%uQV0ouSTd!&IV!*=?*Hjah*807bjZnF zld&}~=jJ8bZ`r(&p)D7yLDxavz+xTpMLku4X=OC*gRrXxZqR~#K-WPp&asaw((5L4 z%&UZCBNT5)p}T!zIpSvQ65=!*5Vf#vwDF57h8mJuQKN(UEb@iCIOr=-d#HCsOyRld zSnD`hR6`cXu9UG^AbTIyAz}ohOvJ-#iph^D^6_U6ZT}4DWr0f$ zKyIey=2@f~tofV1;QH(OeTE0kD%(0f_py}%yT@9N#eX@?1W?Uh3edAwb}LOxf$}Qh zhPB&pEsRye>DQeKoa8=QvMAyrQS;)bzZUK@fDPW4ZUN^huO4})6)-G%9Y2;Yv8VzD z)7i+KnrW^G^s{(0Uf4;hT0+bv-GV7^$o7|+sJ>fW=J)Axkj$4;H;g&ZKE#VO8TxA*`@O~zBlDul$VNg98(*|q;6BGj8ZZ|rq!?ovN#N>u8yz$s+3X|5mibvj$Tah#= zevg4Khv4SxW?AmrE<%6LLA8lI;K|Jq95n1>`Ml5PWgm(*AqSRe5l)uVTFdTDh;+7SE ztJ0z`%@hOB)hw)&FhpGW33}yie}ikmFmz9mM{a7(qV-%=|BkpH2;1q(Y%RZtX!zoF zsJLYhn)hQ!wne25x6t)Bq`*ZF1=Q+jV4c@0#y91W?25CzEEjQj_kKo_i#2yQh{!x+r zBUDURMIU*vb;npBxCv<^l-y$$`(asGbwl$a2kQZ=zy3GPwDccl?!O1r-H&^&314#q zL)C`E<_|y!E5@Z+7O6Ed)iZ=k#wVBU5h-T+QXr9MNx4Has%`s2>I^k$4RG@z8boi- zL~R>kX!4%UdO$e|KN#gJ60eHYA>yxgU*uoQ0a2b6qj=KdRqpGdmGSlnI7RXwWGCkN zw!CgJVbp__qVE!Irz{z?XF+|NUf4nFfyV=DQt*Ee>!{VoI5=iF%ocDL7z6c^JzW__#2f8~sijjVY54r$O z`X83C#jT1Zp$vw@mZdZHriJ@?p7zu|_{+cthklssCXa=0EDxawgxXk7<&!1KpQx>_ z)8U$0-xvm3ihANY;Ay5t@SuCehsictx=;}HN!|ooPpk7i!MK+pcUVIt177`xuL-VqFesv`w!S+|zo&WLhiw<^ca`fXS}iMZGsPGb6l5hLiJUB^OI!Ssc~$3Uw1 zjb1A#;)3&efA0`iq1pGzj(@{}r4AI~u#^slpXsFd`xJPoZ|9c296T{QK(SwzzJ`Fsw|_A4y*a`~w4dd|-7e*L(! zZ$JP1H|Zwm{rbMV-R;pde8M?>&-MAD89i^GwNu^wa+vM+zUcOu=KEMr$or0&{dsbB z#U3j7`j{>F$qu)B=hi)b(EWCKcSP8JS^c?7Dd_rQuJ7|MDERqQ{qcVHBt38a=iU4^8Q&5bvB%JWB>U^>G!;;{uy*X>+bV<=J)!TKK(Uh@A|&4n_C}`Um+9( zc7L9>SGSIG+6y*K>-)ZMJ_){EWOuioVt&3ne?A|6y@uyc@2_XvJ_~Z~A8~#ROWeOr zZF|;YzD{?&KP+#1Ka}hHwhuj)>vwkFx9oz3+x2uk;U{ocAGzKon)s)>lWo7gY~2Os z=f|1f+oQUDw^ydm)8g5Ex_xxL;KyM1zsYJs^UwY1k6p^{x6$lr7YUbi<~~a+>?V8?7e7TCKuyap?q|>T7f(7* z%8m~Y+PEqTT^H>FBAk8#ybn7sMNO1Zi{(i38?Mu~oore7`jbS6(QGvz^ip=8M)jkj?~C0MWy~G=Z*orshNa{uaqfD_wFH*dS_K~+s`EBDWy&6XYYlwE z0>hCppN@Ifk76LN(KH?Usx~LnIiva5ViZZ8cBh&}9*L)$qEdPOn98fvRH=97R^VPz zGs~`^&+n}Q>iNsdLATUqjz;5fg5(RG$ECFu_nH+~n}UV1!O<#C`OF2VJ2dNqW9tT` z<`hFRg>=pJ?1<2%_@cP>qgXHb)^mqx%kA4^1f_+4Tc!AAQH2Q8j)H`ZUfA?VKBLDf zxLV^%aJH8oat`59-dTI|BLtN3m)`HIoy8Av`6O%{S2t1Y?CjUNPLGaV5B~8%VXtM$ z!1yFN5-SUOHNk!kCuyMZ(Pl2_sV+%ZVRK8?{oO~d1!MCjS&opVR6V{R+T@$Q;p(4> zG+&lc-Oc8v-Sf=#CGe$`=yG*(KX;E5=zq4LTyAHz%#vzlgH-4!jTWMJ?On);!ptFRyNx+2;lp|`UG9@1olI-ljP{rY zVSA%N)$S|k<&r5)GG%E$&c)8ItkZ{=SQx{wa)d&q_<^N~GnY-FWyr($`F4Ez8;&%J z$`}k~q4j20(45Nd|i}kB==gk4)pNak(^|cBA*5DRQs- z**f|%d-TC(dtKH%C280FqT3rd{R{dM8e#hBfY=y3gkYV)Ck3SQvlLbW^9 z@^f2+=hL}&SSwhp9MY0v4Hrq!t2qjxApbn;JA<8X_7^Hg4@=?pW)SBH8+IoMpZwD59iQ*y8gW9hdmh7hY&}-5 zZ7U7(3FmrkKjVnT?R5Gkps|6t%$W4he(6KA3$;2o1kRTtlx@ZDk!Zt}{We?p;Dg4) zpHLxLG){tAVB&V63$Z`%rZz2Z>>s}uMDEiZ2cvgktd-_;7-g;QLYN*<&weCZ5{=6@ zpFK&ZjZ4q4OpI7>I)2IEI1O4$8H?>%-36J0q6QJeBke<{`-=Y$w8qZTa@M z*zED>FT$}u)Y!+1VjJcm2It|=)uUkbC<&>U8N`<>;WUSnoV1SV3wFw%8LVZz4#tMy z7)z}R&$Z~ReaB9aIB8hPjS0p%4~z>iYrrKQbHTmVa2cK~W5o#-cabDK30+F`IW z!>e%uVVr4XUI`l7_MYS;?z(3MHIsX? zwIJF%RDf2k!E)s=yvG#3DdK7k{R>;|fZ7`EYHOZr;LG@@C8Nv7DCBYXabFma@Cmi7 zdW$7GNGLc_D?$YzYMbSO^#EDy+}A3K{L^MBq>%lp?K#p-PokxHK-dw{$HN%&S|QbU z`ttxfiOpw(>f-_syCgMhP-yl|eMb5?<#2s7x^^dY-Z8dKBHRDZ_ht$Y72wz&S(tU# zc-UpycWio3_+2Gl2XegcQQ495{9+06%Q&+X3F0K>M;A-JS|+GENs97+9LY*MGO?30 zOW1B3oGMloXR)f|Q#$+5P{Jv;K|QtuiC z=4hJx+Y`o2B|nO}19PpO_umpXkYu!vUMz{0&mg|Uh ze7}>1!^L0Ory><8D67X;3=@d_J}Gm+@mOVh(eA~c>!XcZEQ4Y2HcDOp3Ovq3y{N~y zf#mQRoLP}U`&mvPYlQ7h9>A2QYiUKzM54f1I{n(>(3^?tr=k@T=x@3$O_I*3W%Tv8=)wo6R6*( z6VKBh9Y4JvmvBU8+gv+>KN{;o$!kBBcTX@tmE20*UF+*z&B0g9CN6$<>&id2fm$Yg zTMvvobx3Ay6*Rpzo{zj!tOP01$LF$Klw;NufNb^x1pm5c(#cqVwhG+9%~PXkuj6Br zc+!wsI>MDaE2QK@-g7|W<^GHU`~ASZ1zGblDt#$tx{g}uh{F3@$@&qw(lP(Lub2~) zl$I)v0w&OZNNbaYA%d(V5N8eFZTA`R8dWj@rG8y}*{lk6CjQT4mC|xi{5VPJXr~nF zZnq3b%f)KXns#BuaZNi26k?LhPi#rG8mb-R-A_i7r&*1%n60H8oq_HJ&vJvRtVOsr zE1U+onhiPrdg19DC8f|*E}#sx{;}Z_V^H`u;hL4{+XQMTU?WP~!mw+#HQFEi@U&B+ z1HLMslAqwaro$qA0YA={4My|lHm=Sk$EShrS;#Bxo!HE@^l6p6kK29Au{TvGiL(T- z=Q|zTmqwP+6BD%2vo)_|9^~_Mb7;7LWhK-{4j7>Js`cuIbOJZI!crWYB{2eFV7e5M zCTOha)csS#8zp|J)1vSpwr8%iPU7CSy_p>c2q($NO->9!MT}3@eu1;Mj%c*;NW*xw zXf`7eyD1msv{fa!Hp~)Bb`%F&CtRQdMVnJ4#c0HVkHLJ?aECX1MxQOx@aSH=jM)p8 ztWR;g@T)}MN_g0vHtuI7YP_C@(=P?B6=8%EGPx0kdSNrWPPCMnu_YW|v^lI8$UF-9 zY$Y{sp)_TlWakyWf!d7BOr7|P5NB?M9Py<;6+bWRu;Y~P&-FG7I4C8Q>)qp$$ijxL z=sN;aazb=m`K#j0_^;fe$U>4Y!ZI`DDaM&9FkLe|M8+4PYB_4q731&BN&V;RDhv5D z6NsuhjiaB9!mO~f5QMWd6qFa@Gg$UDJwc?Kyk%m9w49>r8q=%f8Zh;IVlKQ3)DnxD z!b4}Z1^m-mhcE6In5j4Uv4blxBEXdKYai}zaZe4TzL!S05`LoW9H41jfA0&3hm^a> zr3OVLp2*Lb(XuksUYl9H(4=?~!$|lPaz4)R{diriPx&Q!x85^hsr~-$*0%A7Nv@3B z82)Tg(l5^rb;mpdgaqcr5<77SV(gZrHBsub7&B23x17X>KRBdE32gUd@FS?aomB0) z8sr)_Tst&(mSPhDerQ??Bu`!ufj7a8H3W8Kc6lpzb>kUi@6PW0Whw7#J&4WR`gEpL zxhY!klxXXOT3kso*EAo*E@6k|Us{*(^SbNE#Wu09$@NgWx;y?rH_Wf!Fa0#ccsn^tRe9V+i-kh*9lVT zZhN7*QJ!M0V^k(Gad#&oTYW`%7wzx zfYCCcXaCS^afU3DbqQ6<5l)(f`CYOv| z3itgoOmRJ028#iwqFEr;W|_PuwNwoT`%$XRv;$>TRNiTbUl=f)q77eDe1xUzP2YG^uue==Ky znc#d(V7(^Lgr1~oxqB%JX`^;DH+r|{C5X^RjpmV)+O8YJf*cSP)oGl>(_8#wp$`$- zBl9$EKc3$6zG8C*7N&Qv!CoJOv^xWt@|n0jZWE=lS~Mg z+vI~*Yy8Yh3P1lv6N8JQRBv76*X96LMUEE8S6QV$Efz9K z=uEDCJ|w{ewW*7vB?$}~z48p>Bw4nnm?MC``46`|-9Nj{s^3Ze! ze~I?&s=pJZE@CwyoCj3kfY*4sp6{KZp!9a~Oqbv>= zA+Qzh1R7iA23f~KV>g13pz zZ-gnfdM;=I5Gs{mjrzSRDuz7ZOB=MI)`k3UVG1&`+DM(oYz!R*g%(ADYbzh?RbXlQ zoDD~S5ZsOYU2>YyP8BkD7@JRmoW z_KQ5`T0sX)U29sbVJzIFY+Z;-30CzR8eW2GuDODgcd

p3y5x95PLEexE{smtvx z9vfL^c{;WvChtbhwVm<8|CbYG0VEzB3+uK=n-@0p7DmQd3-NUInPeC~{PMjn!fiaO z`G&(?nv%8>e=ZkN&H_DH2_`X5O{x(H}kujM&f-WZYoQgYC8m2LRb zCCx@gmf4XdUlC0eo&~fEIPCpLWTqf@|0m5yWnzR(+s&O;cH)m&rJm<)epev4S z7|LO3tge%j#@lH9?-~G?cp+Ld8#z14CF;W{S;UB;j$7XNn0@9mLnJV#)Zg4*6Dg`} zR`Oo!?h^voNAkDZ5YPdLrs8$*E{9UizSzajXsKwDkhqi*dAczM6~AS%=pi#v55?`5G9zRJ-T8#P^B}^Ixes*X2GV$O0 z|4@i**gpY~mkAT2(oiSQ{2-Hf!XZXlS5GSxjmQg?pYD_&Z0=aoI*Za8_0sFc?|(2O z$1pOubH^_xL23~Y&Xc!>(nPHdOaoN!8XU6cX~FTf5W0Hqt1yBLD3InvbR5(7la{n=PGJYR&Hk7+K9+Qz^x2J*+HgI?+D7A~P&<4a`l*@i;Bi!9?6z1X2h^8_Z4bBh#d*_XjNf z;Q#Jg{Gs&)&pq*foX!(0 zhGlNF?0LS=X8k&bfF(fNjt!_xK@v*6V}-oX0wP> zN{T!KA*4r1i!%yz+M-ABc1CydJ3@bnIwA9u_r|Igq=~kREyH$ z@jq8HU^vB%?kO;tUG)wNJjy={V?*F8d9=KoQaIV0;kI}5o2&86f^{Ays*%_H)BCDK zq*yTSEKDi%^G_G}()@A9c6aFCg~TynP?OTL z8I_Q9%+EHomPNpLi}ZEkjbhb2@y4M|)X^mHu&_Nl+iD2??GJs=`N>xY1hB_` z=fEbM(}5Ez5@dGjVT9C$t{Tq~arKvE{bj5ir1MhPjgg3jx)q5?nFU(n%ln_Z7pU(bdDiuN7cqdlB%wrz-Io% zy1l7vs`%qMVL2mXA)uYfRtZ_35`oHI=ExK`wy#}Vz_QX2F9q%cP~+{!S&}B=EqkAKK??~kjyV&<;ThnB@4$lRmNcS=Cj!!4a8tN zy}j&2q6*^gLbm^a-95HU6&4G*g}Rx@+Oxo0UBi)y53L}lBnf}I8V-Q4ysQ|DC>O?j z|5;2PT~J1h*b4Z#q7>wS^Z8Zr+aDQBKxrCNat6pJO;sxL5wHf#A%pR`=8~UUJR^rJ z;>24bo}Cw3OM3tKSaw$H`ip~7(p-Z_W~lLtZvA{5 z?zVAdlI)COb7Q=R|C@|{#MRaLaeHulHM7n_TU+SI_JjPfmRl{j3*+}EFjLaW0J=ZO zRlS1}6x)a`!8Tz_F#Mok*6*p|kU$h3>7#($CK(ny2b*VhH1ZVxzoY3hBC#s0Ftmi+ z|HL>{obnm}sN$9ShnW+SgRM%%;w*lvD`fI)U=#^-MK_at(@a5|I`>wO(Pi`)q=Nqr z?OF^@7JvA4&SZB*^GquX?p$;Vo9SO!w9tqBjAjk``QY&48IY^RZ4e{;-qKd>&%3Ix zUCcl??>45eu!n~F-SSY@Xr=AjI%lA4$;#lq0!%1FUjTjX*^fF>0QcawHeoR!o-EF-=v;ZXzi_LO!MQw$On4;^i(8Vu>-3 zdY71MZUNk*PAU{)Ymka)PY532?%t&X$?u#PjH)kQFA1uFd%)Q^;@%Rtf=HYR^lN}V zSUzLNH6#BO5XA<<=peQDApJqwe6>Y&y!Nowgk%zZt|0mPYmT-DuGGQOEdWbg1?7fM zR;0L^kEE*_zrhhf=m)buB+smC4A`YIHyB4Td@MZub79QYH(kBoVaMVMmaLswd$53?Q1RzJf8{sBtbvR+ z1quV)(EEEcv550sOJ!SUA5*!xk9R_8G3T&LBVHV-y8Q<^&r0Epw88s-F)#*P+l;=Q z2@6q@nGzo=L-Wxw`;SgG@Oc8-m=c)`H;f(EP4s`J z0SY)$c!ARtg(RpeLqMKE#*4YW`D6fT{d`{RxqC^EuXF^e$&k!SrHX>2`zv;*kkB|g z0%S_qH@>JVgf{XG{iSOu z44kw*%6323^ZA?VT$7@hK_Kf|D;V&CW7T7A+(1Z@Qw8y8&J*V;kMr^EYjt3|1VGcZ zZh0NU+VHz;BkqS0cM!JW#mvj_)9LtqY6!8CnFD_thUJy&36S_Nu3$duG>$F*yay&B zrBqt+Kl`Uh=_a{tMuHwFjrc3?i?(w#pJ3{gMqX4++DgvU;X#p@4E67d668{pw%_PX z{8TE&&k3e1^7Mj~$1s%bz|Ene*9b!B*g4DLCrmk(Wk$5`C4V<9s2cmuQ~9&NwXip} zxl%Ed1&L7XfuK{l8>q;2p=}Np)`kuE(|8ijzLolN_uZs(6|}+a5nHi9az?2@=MEgN z7cDHU%71?MihsIrSTvl!bl67U!h4_<>u41F0;AHK@`2IHU{u%eup5(&vHa6#q2SHE z4Ejf+sOoy^;ptHs%1`*dV#|t+SmJZ~4CsBV=)Uupnc+v4<19|7Cim>_e(uR~fnn!f z`pVKD?%bmofV<$kBp29B2-Q*P@^L2y!A?r9$p5jz@I#$y1)&s$8~PS z=xF4vjk43oPwP*e=^EF#u%K9q9CGcNDl@5$2JOI_oLU#IUV5g+H=+t*dK9PAj@T;? zLxwpe!g!#t?c!e-0n7-{1fm5vNCH2K5GGh)r%un9euRyp_Rh(b+F z4cZ;$yHdi5JR{)ICh%whTs0nIVB}-gQnUyO23>h>4CW0B<1u==NS084tx|!UYcFLU z7TA69Q&dQxl5N0aaf{3TDB%7OooPcyOMKy3VbTLpvJQXWr8Nvv`I{CHRD_+S2Cj7G zwOVX~Zc0iIuwX?pAO=`qY^cb~ZMx7_xSz=#z4^r&$j3XzLZ#cYte1a$ zXAgyA$jwh)9|I`qgJ~e2QTl`3rn2U`Ent4Bo+-0G*dMXuKTmv$e7t>PJomE^hJE?J zgy6>KTj{?>DPL3SYsaarKSc{3d0#)kf_M4D@r9u~m~haO4BYj3_HGWbL)K<%A$f*X z%GNu`Y4AV~pBOss=To<5_hunu5zw?3^fgJ@+V{j$e(ReuO;{(V;Dl(d9LxeX{p?p^ zxJvSZHffhamvvMu4zw1_*vGr}rvH4TYjTGi@EE9f`d4Di1~x%f22T>WlOCIFF&g#E zqfcL)h(oYxQhQ_u&7o9=U7%{fnN|^G<_X~LRE@T~K&T3}^l1qaTjFi}xugEuz626D zP8v8E1pWd7+q<56K+PdUk0dM8ED*mk^*Vn#K#ezx4_TxI_#~)4b^V2Q57_?)WeUty zD<#djW#YdyYuY>PAz_1{sWgon3jFK5vW!k~%of-IejypoX@s07~%P8P5eQTstUfGbEwayI-Ie|9Qh@srZ9US~NZ2YX*e};{|i| z0W6$XhLxGL5aoWX(!d~5;%SrI9T{hp%L5Ou5xziJ4^qI^Ornq^Rp&-vC0qmf`OigX zTtx#mk^cr%K&!t=J-ij`W3oKVmy(jRU?P#WnSO$#0u%Knj2q zLEC}bBD&=O)hEYOCwVB!`nVQXh?20_(puA#ikuvLH&pr$h%F)n)kdqtiPq$s?TL!X z$XDz3paj#dKA8DPm6M?Tqs_J!DhpT6n-iA~%~7=%EC4Zn34$OyJk~n1VH^Q77lTwsU_Q@k`;K6rga=NPJkp$&-CRH`7Er25l+YJTFJ)!;&XVMJPHrJJ7 zvrC=5|_*-GI^vU{lEp|8Ma#Y*O*4QaNomAIs;Rjp$SI8to>QL;UJ(?|^_l+8GT^l5wr~v9Z$6Tbl z8kh_@Luw!2`$wB^`Huwm4Z<++I@@lIj6;UR$~1DXi3L30JNh!iWlOyw+_jsFp&hHo zUgikOI|- zPC{9dz5(4V5F-LXx$UyaG;heH?je93R0?TKS5F;tUjEjtvq zGnPDnw^4APfnjy3&j9(vs;eLlfliHeTk?JN0ePXz5E>+VkDHdkpVr)Ou#NSFUBA5Z zKWnn3qo}oPQI}0J%rV~f8uaPOrt+X*+jzF&7w!gOFE%(M><2u|Ot*$Hqaog^?tG+N zGi7aHitFOade~)$H;wmd)2sv+MLl2gJ8W+1Qc0s%uTmBX+%2D6(AIQ@TDjq5Jf(tr ziR3=9+)m6Zf|?M$k$epB3@}ESCoPE1cZz@|_#sn%@!n=;a@?ptqN#SB@2(kw=(t2} zcA;>hFSPo*tql{SZbH> zSzUp8({}(+nnBq?eSoaf&Bc7|5(#(`?_pR#sNKL|?ZK()l5~BLt7)16*%-v(P!b2G zP<ZA$mbZEAghtN>wefN$`v!oer|u7kKML*JTVvu`Od|7f82 z`{f-?1o!&Op@moclyx;OC={oI+sppre|n+N%dbW-I6MJW}hR zw{ib=$2TZJBTTuX%QAGP@egmXyd>K?5#pk^%roRDD*j;BE7_sL=y=HE&z z6zQkUZ6>+J4Mon46wO>DL+1S2g6kWeIfs6uawA+bjmQ>NjvZQMWVkg9w>lwr9A+u_ zPw}qtKq&JE-WseTz%C&P-QGJ~_@P9`b3i=^S2_=81H329GVcV2`6-+*)&~4jS&8wz~x)2NYNgbDzSPFJ&$xbR+i0=NUnco}&SRvSH z4ejO!X_7-IX~N;256?{SgVd>$WH~(?|9%hc{%`Nkc}R|&Jgg)W#_tj}VnCYUy?GY0)$B$E zHR{+d=b-_rn9jxfsq!SK-VWq*5548`OtUf)-bG9jIL@%pb&vx$89#D3VF?a8Fw*e@ zR@fRb5UAcjAS}#hH+9q2SCbb&u5A*Qasd>B3+Q$4>tS}{{M%}>Ks6We`-qla4#(XA zXR|h&#R|0QbkmsU@^25Wa6R6o2;w-Cpp}!ctgW6Qs-=oEQ3PgYN>hz!Z@e*FYKNPO z`fIbqI!>6VUvH+WaucL!vR5<&Ny zS=G*0$VNV$mL4n7UBQJw)be97Br8@?SVZVZ9!?D0{Nb(8g`2h!c}UIB_2^oxXpd}89`iBDk77m1 ziF3Mkz5aZ11gd5XlQyFKiI6z1@go2Ga)ODsLb(EHW!#`S-C%+pZL2HC0O+Bc&KENe z(M*-)+rVMT4_6ar7@(99Z--O`Jj@gFDUnqm@PT6OX8g$Wl1Wb%;&j>zy9=I@94cq4 zfth4G6EdG~o$f-ws(6a%>o5@Q6Yg!T^Hwv?O^(c+_6R|yZEod!QxRo_szDFH(i+EE z(nBG~rP}bxOZED@yp>`mFbpK8sUSEVTq|W#0qGBlY7t{yDrJ%p%I9`lac0g_XCC;5 z$g@M*SDm;fXWc=Sy_{f~AWlhl$_mHMY}3wci*_E9`n3%@A!FiqU^?Ai;M@5<61W?c zl4KA{v{8V5phB~;%||SOGX6}eflw7we7#0@vF{s6v<~FCcXMe#2q=?EQWMyKarWA3 zM728aF9Exlv{s<1UfaEcLTQH-iyPrN(&N4+WHP}Py;t*Vhl1T<6@g~H#rnXW0Zy?+ zN2c>zKN%#|T&%h;$kgLV+qjUZPjHI@u4(KOa?a0beh}lq>3ZJA@%q8=>8F=p2y$TZ zDbl-xAY52j6jdvXw;22337$Xv-=No1G?R|K#)Pyp$9KEw1E>Yjr`S}XNN2x4Dv<#rClgQ^XchrL4+*@<8(qt;o_SC94abQRHmGohxfD zXh9nEI~u3HJ?}&I@D|n2q;e@2j%r!Lg}GXfO9IN0f~W9Z8-UjxZo1S}C~2#mm`+5SGGn zx)=~`vzyfx#EmdR-rNb{eTgyI-!+*uFasYGrYP2oB&b%!e)yFYX7-5_7*q+TD`JDE z8VDaU0g}LGPHKo$XeFRa;)68#C{l_kbJ~_5^O#ls*R=M1Y-xeTa%CqQp^J zk=!~59y3ez4V-1A5F{vXQ#K1}Frmi7U;=b^Lj*ocB~EwE`1$A>0i2j$4IwkzGgNd; zs$8v)5x@iAveVh5l^_{P)Wtef`6d~U`$%gVQcwnXHd;%8IUPY_s{M+GmqUy1IMz`4 zZ6^@)$7y}w7S6szuH91-yeWyi-LG!8_VRHlTTS`{`kkz&-|~ifH3brE73r7PuQ} zw-Lm7#z@Iknz1(VtthK^xRj0-*(L)K-RseZ%5lgi8&&c1Omg{{6&C7ww{7=F-8kJ9 zsBDKejFYqggaZ^RnU_<%nYFuYy;ZB$Y_!C{?tCB5IBOP4`Oj3 z))UT#3EP7k?u_g;4&Z4}*$H?w2vc{uJDAdEf$3oTraGVpC_Pi@xVe!evOEFVA`=ld zcn4a?ZMNf?i1=1pTssW-nCu0M>wI+8zQZinQbQ$|X9@avO!Q@*$73IFeED$4!fiFX zAtO+>H|Z6BPxSHtD^?s|W=Vn#3RYrQS`@@{m(%3f6*#B?%A&$KRL6c0R?ry;9&1f{ zq2d?r`QVzR9Z8{t&3kshwoZqrkWz%|31Y*p8)_G-5bF3$4M9yQ{i=e{f{qu_F3DO~ z0tIeNq{HocbfqBPVJ%|Hw*|!yt^4qgaynbm>-7ry9%W6U5GIBU1%~4Z?M-bQa>HGr zH+sN+a?X|3$~(09pGGzZ)ui#;G~KLBl|d& znA?LjBF7MUQsU zn<3X*lp7f9%m!^XJt0XO5XC#=4$1JK)txI!!1~Ej189A_9+|imGQdeSEOu6=fBAll zUtWJbk(Wquyy_>SAsdvsX8S>`NgvWLx$JMSFy#7>tl3aiQ0K0sAqQ$HNjoRF?hiCR z0X0m_w=%e$dc${1)jdq9MCXJ)=95=~ejpW)TJTD2rz>Jg;S_Yu1&JkX9&FDbHzudM zht=o9okU9m$LW=*si8q<3Pg_#;zWDBUB|nOVKA6et_1#@@KAES;$%_&REUkLt zksIyB-tF8CuLuI}(pDExQev5|DWG=4H6Am%lkEFqDW$O$ypHcJsnz7J((SPWgpI!GZ?qXajIMkMe}|Uy}7+sa&T)yNib=%cCzehl%AU@k?03 z>2HL4l~z9|+-!PQuC#ws*x)NJ@W}{sCjG z<{l)2&=+g)aYZoKZNy@5IYC9xZ{@*f%7`W(1JFKEpx{`SU?cCEVnq>sP3Gd*qeAs` zNT(oH!S3Ym0ND(Fb{@5Q_J+ozPdF{*&r@@pLBMvb2sUFz=$r3mqPPv}&W0WcE|{(b%}~BUB?cu#hS3+aoPva$r&{NR&pxWfB&RAaaY% zkK5y0sRnS(8r=}Ud1_LqHnL$)+;WFtZ3~-<9y$;zh8Ix705|0oO|>Tb9Uw)yU5}s( zF{25wK6rp8acLOip7Q#b*V@nH>T)RmEsmN&e;-Lt5XosAtiNCUb-zg5f7+Pb+zX&V z1rwF198)L&`Jwa5gH~E3SJK<8cst^bG}N8ReTf3UU16Vu)&2-oQ3|fb!caRMTCbM? zNu#eFCQ*0Cg(uaeIqqd*&@g4XjtZ_L7uK8jf%cx-Ei! z)ScFVq!(8x*9b$h&~cL8aLVur5>~PS0<%h0(gt&IiGr?G zOW{s4tMJ?1+tWE}H8#u0o4En8Q>k%z;2zwEjoO~RN>CqHEL-bc(2A1A=58%@Sz4K1 z3FT;40?>g@Goj>hzaTqVXd6oHfq8UeAB>}>iQC>9wYyp_ zZkthJRFAK#i#*jktouZbe>c;uuON%sPoZBqEh(NA;>SMwwA}F%4a@%N(WJ?OY~&> z4nit(`LpQY-IXi*Yv2+l6xrg5P3AKBO!Fbr_C-%BxGl(NMU=gm1Ghqc^nMRNO)Pyk zd<`>DtFWe;!f!ky&$KI$7~H}mJYBH}yk2D#m_qWd_)xRQ76!{jXT}tSrnFSo@2~?2 zEQ9+bVRSR8F!qTCDO{jA>?O?TAxfnTVz~!<*P#0|o>qzX78&Sp5*tg~pms8)d+UGj ztb)A*rn9A$PemyakVx;cz8+C@Jmsikb+m-4glqz3unV5NS>nUg7&{(}%gEz(Rj{Xc zuKX`5h!Kha2@PTCNJ+Z9 z>Ox3_3TRtheFLD+$~5Wf!^Q>)UDE3=tAAqKbk}6_S#i;-=6N*WD8{1(Pl%h7XdwbM z_;PcW;r<{;*tRp+=5dDZhY%O=+vswDn1#SO?hAqNWFo?sGX^*Z`vJ(N4OyU?nZCxc z&Oy#Y4nWJRyMy%zzPDtusfWb`-f7Y*9^uTnN-H&tlW3rG^eEuBk4twm7MK}-)iW#_Fq1(MH<5*@3S zZX$v6+KO-z~QIfww$yjKEWosVe7 zOg$-@4GX3b5VS+GC^DH>O~J7}^M&^RI8gu$6I^e5#B0cRBHl)fw*!krDBqY-ELoZ# z^r&D!;L;MN608IF3TctqGq8|`$C{3FZ}WfSYDp2@j^0VA9GbOk+fxcpIuFXO-IvgP zT(SMQ6gC?+)k=pU1<(3sWCT(eN>GGz2Y^ti zsVSjuY3nXyka#Wl%z;H&Kpwin0Wt#;&U4gT-Q6i$T68;7|HGwPJWohqhqRg0fCVM||5|{Ky07zD!#^Z59LqcUU zzNDA91OYUm1R>B2ebR%ETASB0cmN;<{j&w=R(p=}N6E82ShFj*GT0PNVY|ubqqRhO zn-du2ycG>`h%mZIrA?>)*oh{$r^&jv%;Wr4!z-rsW@waRqztwl{Ao!#iqva>EHA*@Hln>Hn7^UtFrws@rm2ZqUxmVNLjbNSk z>at32Z23y~RP;8~ZZsCJ1VrU}bj|xr=uSyWU=coc9K8j{-VNPr}J2Clsg?$>0v9vYvUdzxDSBqNi{g4%lIUpHEv#Lg&Xs{9kj&XaK zBqdd;z%VDxDw~38%{(;Rkv0}q{0PgDIzaL(bqQN|8DQ+}!7#VE*>K00`uJXZHHcKB zEpOMOYthy27RS@k>?im1qw0!m(xp0Mm0dN{L^t@&nHt=4EQ9%&YAQi70xsw{i29QN> zAn@}dJ})+eqjv)^c7mNPdV@ksM!}jFBCNHH4&$ z-DB-JuMgatRQ{J$l8rF&a97g~m6|(s)5xH|QJ>Hw8i+qA$H0E85&MjuvXU9F-|Y-H zd4Mg09Bbn}u3|iK75Js3A%a+HP`Obd(0}Cg@-+Sd2B=$zm*a~1ADGl>{cDc*a6NXw zs1uf3X0_5MT_#ubT<8uW>?L|}V+rn1axT7t0j7iSKv30mno*RX(Kk0u#+t3s!B%gC zuc?p%{X7dWk(k2g0lmq}3II8j9 z?tL(+{#PHn|CRdK!MwRzdmwG;)EV7;X;Me!O@4H6Ce2QphVhWf6Asi zO0cJ3bL@UC?j24lT^}A2QhYPa$QGKRZ4~cNboOay^@1J7mbzV!K6Lhm3TVT89Sh(e zA>v2=cHpL+HF2DIAOTbK=cuk(+ahsG$dE8p+tbUPX&Xl-71rwrGc5r>q5n7x?w_39 z2ISaRIhDqo7V%r)6kgBx1BQ<82Zlcq@jc4+0Q(+6WT{YCDmohD=9;(j6>nh~yW1>K zk29hIhzuAIOX#PpnW1FeP0Px{Lz`_;yJFj$@OnT6AuW=$n2Ta)ut;IS3xM!6(qlI< z{KT3-6|PrWL!Q@{&-N)evZQX~QvAB+GBlFahHqZT10{V7;P5xv$W+!Dth>-YN!fq|VNZr6m%YHnZI;y(6{h?>) zrU5WeE6A;@;)%LbxGJG=Z0D3-8^gDpl%sX5N5)*d_a}ov=o&^2@6lwFLYec{fvNCc)5lf<_dfOL)HaKz@ z#gEmbM}tNHEtYK|z?@wI$sw^=1WSa<2iR^f?E{Bw+)WKmhe}h4oR2=5TCY5#hpF|! zF*@Q<)YLbdp=alC=aOWdihbN{Ifo5xHkVpmtF#lzbL`>moIbo= z*&EXxfGm9Jnhn)k=r9Z4jr7TMw|h3s^$It)h6EQDi|pxnRx=J)DLd9LL893(4Gd(% zIfz!A>89Caxj|2P2f?xmO(3`p^$56W+c|!Cz8I0FZdzSy z7!C6@JX`U=w!0Axj#fO+D!OGiV^aZUkIL(f1pP)fEGST@N1^!E4@UJr-RtC>c?(m$xfgPh(QuRmFZA!;M6d^= zrl4p-)w;R@%|a*m%hA=Sf)XE72%3kdZT+yRWb9vZ@&_jF)Qty=2hw`HIK#StxgybC z<5F+-5+DqjIZB31YGzWJm6q3#p+Z_fCS+ksBv$&5zjcb|I0Y&T7|}WceNjQ&1{mdt zRJskJV!>@a0VTxilp!ujgpc%*4%F`hLTKHaRQEY=NdszOrA1`0K=U@XXS z0oXbpW%uC%XaY!XlExm*vgI+)`Qgm&?UEBbCMvMxG^b5-;nB^gvBS6ay3Cfppq42? zL00)a6vW}$6Hjx#0OywOX*1-DCK;bq&-uPB&piAjlK7RPNYAlcWkwoy27lKAd(3WX z`?BuRm`SqEIk}%I6ul|B6r#Mm1Y~sF^ff@1JBTi?W*lLS_kTP%?tk68{GB6UY z($Gx?=QCYk8XZcfS?P|xpAKceGNHhq3`~U#oVI!WWGKxinhpEwA}W36Jt0YkJ;ms| zwtCoZCO6o7KxQ*I6yn?lpB5MQ(wq7%GGaa)7DzY9lmw~|I~2y9F&~9m1oUZ%lOyw! z&R0;+K}8I@y4#?wP(-+NmJWNn8^3n9)HD4#INpGmR~V5UE2JEmHq>NW(--_N%wlpz z?RGu7S}yQM?9;fd?OA?vYysJXrFNOrDt_;E!)GNx4Y z(2|k*P05`*ROfJ_(+tq5no?ckiJKZtX^77yc%-Y_NYa%PkW=|E82}CqT`ADrT#w)s zqsLc@c8b`!bP`NyKk1-H+fIkM&m+3d%Zkio733VBn@2t18e4)&UHe9^Ik zb}~VsQr!KUq6$K_bGk+CNN+_LmF4J5;IL4Kk*+O%FNq$Aei}J$E~o3gW_<>|7xxZ%lO8!56}uH6 zJXMC4g5@P8(0eFie3n5PeD!L9@(?FIQq{0Tfhq}17|b6=FS#h~e00Tx4HI+|7;Pu! zm&2%#9y{-|6DEv8G+O*=9j;2`K?OT%_>I5@{Cq4jcjRYDMw&ZO}(3pX-gnD@d4C%N> zaDF#KdMW(X4r@8I2Hu|A_}UJ-T>eQnG;z@N4D{tZ9l)^-=6i#($VCo%i{^JO=|@1e z9h((G%3UkeWzVFT(~nfO?avGtF?FIT-k)~n z^$_Ff29WC_>yg6^cwUiL0{ayxx4<*yUY;Y$PU*5xA)-pF87II-3V!HALA54rM}WcJ z48jCN4el~37%9E9UXEg8)~n0Kg3;C$eWci<>W%UTcCuYYIUMMvYM{#>7?b;v+`|bm zB7pGe1-lHr(}xkrFXuUv3WZjE5m#sTkBt0dpv4$$`be(;JEWwrd%gwO??+aQoJXg) zGeA%TWWWfyg5`*)f_B<$L5F}nU$G^3%Y|H|!8zw9e=FWLKy82sACR2w6co{}RMNLa z^xWimRKiHz-{Co!!K-GDE_!P0BT^~=Ua`*>lQSxKVnaBk0x7ZF#TG^8v~8l#>WZS# z6eU3loT4O0idphNCftTTJ0Imc)y0yDn>hv@|LJh;oJrR10Z7o$vO682@jutBhsC4G zm@Hc=bv%mTek9v+beynIgKdbRX$v>TI%QI2Fg^{LOeROJ6%`8AU1BK+t}YvO>jvn| zXwG8(D+G2SRP-hT=XoE?C!k(L&Y1k#=*l$Vx|xQ$VEsQBP~+)Sb8D+~@PA0hnz9K_ zA8%76G>mWo;bJB1&Ph?`04r;w830B*_Nj?o$XvkxF#~T4Sx(-J#px%jh+Cu6o9>`T zx!>h@5WY66d+_8{H$KN`4hd(-o<#IxKWcc+`|3D@?;>$=Z|2J1s(a=p+StRF`DRmQ zm&bxGa;pW11`{n7i5z8$UMLd7^oxFA1ddyM+p#{aj`a=ZEw&60Nw1Vb;e9!{$zVl0 zeqsY|P*){7dPMnScCItod8v1@nnr3FS0uX#b_Nna4rt>yT}q#$73TAB2iibV2jn!H#x-m}RKt=p#)=%uV_)=3U&nRo zM;z#E5a!j>X>iV=X(vAetBCbfYF*c?4d7szV=k`i*5 zA=VJiN}A*(S=71v zrvt!2o(qg0h}dguU+$zW1o#}S0)Vkl3bLS?x2TSSVPd}a%RMjU*ctfmaPe3qS>vKF zgUj&Ctx+`lxK`1lQ^G*QQL0-xNo!S5HFGi3Kp`aKwYtBo6w2KsS>z_+pwr`$jNXra z-WuCUIt5}eC;$CRf54)GH9#z@f|Yk6PZ8$i*IrJm}A zU3H!M5%Dzcru451xd2ru{H;WjAF(C-YG&%Ez-+ett)9rM+crGxntP~B?DxX`H#T=x#k7vIl$& zsLJ5h09j8GqPpHuUaI5L$62UZ48R1vT(LMwj_g5Z42vJ+Xr!U*fxC9=8qL`%*dZ$d zt}e0BF2HLVMByuKc7o4`(@kwLQs}AC?|_e>{_Lv(XCSZhgQ9X=l=0&PDID5J6foZ; zC_xv4nu?qn*;K!uk;P$&ExJwyOKc9zbUd;&%BsNE8!iglE#Qm&PbICf3{te3AZ^df};?TErDqqXieFao!Yz^yo~ru1!ja4 zutneTB=>8_H4h6f*4XWOq(x239k7RqycNx~NoZ{C88bZQRtC{|To(?=iC3AI)c8zy zT4U{#!cFCMnL=p7jLo*q=vND+Kkd09Vt73}i9|w6lE=FWwES%Ux&ve(5f}BNG{Xk* zRUdO}^cjizhy_2dShY`sKFky|wd^L{?fAJ#wc@E#0!uEMh@2Nu3S6#kQerk6Dw?vf zZ!p{i%Sy8gNdZIno8WHgHQR%r5=bO!bp&vLfTG*XIX4C27DT5hy6(WY1Ue+=7Hzs4 zfKOtBw~t3KdqU_05Vey52O&;<>SJ!*wAc+UrmH802|B2eIZlF3BBrS|Bawy`0XiDP z%pLwHv7+HlbnE!Q@j9I%rM}p~B>e;KV6Br=bwu6?iel?l`=@A&fqB1rolv0wcCNdA zjs}I)1m*W4_|Y>KLRfH0lI0dStG++wqtgjZRhpv&VW6DPZ;u1niTU zr}(`dq&S&boUDY&jnFl*jO)=gXpHj6QQ29Z_=AV)9OszSiWxtVp58w&^Uz)DV1c5R zxD(`gz0aGx;|-#7O5ItG)Ef|sk0?7MTupafTtRAYhp5wgn?F4f2w*BE;fHAHQw$hc z;Pv41fPF`Xe1y8U>lGxV>5881x0(li2h_=o zI-0=rvymsrA-pG^EXb6IO&MI+31r3e0%NAPx0eQbc9&tb*qc!22r<2tR5VRebno<{ zxE?W}MElGVW$S1op9~cIxxbGA)V4cYr0excjjrn+XTXrKO!DsL3Z(CMmutVqaPD^# z?AHSlZuT3hc?rOMs<2W}bwl;18+{3aPfQ4<-ts+lYRk_$87~BcBnw`%DE_D?#@+wDF;=j6igPVp1w0<{F z#I~~7T460ZkM+DV8nHA9vXEU$(W}_K+4QDtyBH^E&=TJ5yRJttYEp&)xe?_8M8mdy z!LvgoKf0IHeBQ6?_5KVYTd|@$AQ_MLKNWR{GLJbn$t`DuODG0~)+1 z?F;++ftI1IXMzb5ko^sezq`V1B%!3B38jOB*FvNU5xD696KdnaEW3FXtD%+mX|~y@ za|3UYS};h#jz)vDV9)|62QGhKz0uF_r05h>Hy2O_bvn2Lg`kNB8N>2^ZrUgK8h*+1 z{KScJ#$H2j3Wy!dwJi5;DeK0}?@EhV7kcEb0Va1OPnzE6$+YSHK3x|?gzttAOITuV zrM=y?`3QtrPh(&NcfYnXP? zX!d*gLo^y6^9~s}ok6215rzac5$^X*Dvb=z9()zR$%jj++HsG`eMqB#s}Lc8Q7Izu zl(cr!#@cl=J{xi@!xj-AunePlkalI-#d3k&^{BWV7;rAQ3v|2YS);Q38CQD*oa-|| zzdc`<*(47X$D9v+VGxagI-sl~B7oDajGKuaci1Eg z(e3e?CDZeLnA#P@1Dmoe#_a(^!Ync2!RVjqRD>zgBO7?RWP>=zm3*K%Thx)KVHg9N zX9KKE)g}ne8JLx^fv%=LsDNN4e@Lg=u&D|FI83T2n=$3#G?V^1ivq_Uxh-B{2-~B} zo#fa~SZfRPk|na-PU^at-3>~oWMA}lJ-Svkc0j;3dQNmp>8MVfZb;%y9KW7)rPzrG zQVE>sOO%#DU_l+h1E}(Lm*UsO2r7e6f@3%*^xWXCvMTayim7BK<4k3?UC?2Md8~KG z%7~h^#WVMMy2}b^I66*qNQbA+Ih?FYn!OPccer*g$jvOqfZ=69`s<@f=ges zR2knH>`R4lq!P*Frmd{mbj?{i9?m6}v|XZ7PvcBu+TAtjr=x4h@_wIDGYq?K%*DJv zYnBO{o$-M2I<;Oji0rgKBQ3I-rF9QG6?!B1cUxpNUjz=>hsp3M?VKN0w!0dF507LaprlHZh9_(7hDP6^eF&IFyZ1<^jfRrXnOMvks`xGd+RdZ#5E$UEflxVtYCuFX7gh#%PzoYd3E zuGh7Ek3SJOfzQ2DZMp}t2x$l7;%g><9Drjw29YAfdj&yG!iXOCI;kqaHAcGAih3MZvjy3Ie0v`9q7zFPYxlnTZA;)%8 zG(zUxmur^jJh`~|FYkpUky1j;F{c@x2OkmLlRa1=XntX+^=sM9di@FLUc6M=2LOFn z{J1y7ui1BB6E-2dQ6_YQalAf3^+SW)6z$*|Z4zWXMio``6+~0M zd@vf#xlY1k3&umaCJvx@3u7r%!=Zy?qS0OzPi6}~7+T)C(cU5=m*HeO*8K6k*y7WS zq}1!*u16o4))L@e_aA<4>BG&ErI9XMaZyerD$*dnjA8}URiZwwzn+EQqr++|p~mNC z;i)BRShwqxCFckjc~a^Akzkj78x<*WCx1Ym;^}BRu{*Fg1-2dvC07092F5t!#MmM& ztdtrZ1SlOOchK1TE5AemH{e&h$5MCI5u#{quYsf5!x2FiVggsh3W`EQ;wkbHvGMyK zfu9Qrl7}EP-3$x|* z1gB$yGc^-%)~q%o&&m3+36?crGWXRW<`ogOTz~q7RKRJ(0f@NBP7Dfh*GyAO!7XFc zP|jag#)PtTV3VV)QU*3Ajl81N9k>=v%1I~PTz8c~@}Yrm0eZtaAinDfhEtxHQf}ps zFu{E>R~aP-{5*yc)}3O0{MSAe;7urvDLSN`Z-Qq`dS;PCW>;l^3}W&JhRZhJ_~V}5 zNA@D)JkUWfMoTO9Xb&yzF3tM^vk z+`K%sDAght?aKpo12Ek0xLuD5VTp!))lJ&flbJr-sh8G+#P$-{tVf%3Y+nDW=EOzU z;vZV#va1JP=4-Ra{mY9vk9OC{%5CW`q#0vn9$h8|9UiD+;CEYD9F;F8_((}i7|(DK zw-Kf&PKwNtKu$$<Ff0l>*Mh6>AZJwonqP=6G6}iYzWyD03Oou zaI&@>L{LuR0#V$8hlJD}ObLPW#Y882xJ9?6KX8$p>3?a#3VBlmMH&@ly}g-+sN@&~ z-YKpd!I4_1EZ!YOcB3wkf|JyEs6t3ZblZ7)zXE0NuwE?MSjaqu5F?=!%=`1t?$@H1h0CC98lX5hU5kiIT@B_mmP)3 z1#NDp#(QuYGLd6>?eJa#@~gpbOLHeOMbRf2!a!O*_Oh%op!3iNH8<(e$dhn|>GO6y zqUGX*O&wl6;A@7;&{ZU?UIR8CB@24+^BgbeKmegBpMc$Q0Uv2SL_E69^t#m5ZRm5V zgh@xC~Lw_W=)YP;E5x_0uXI`Kf(RM6|ghva5*QO(X31Q}d*~7(O%N18cDy0G&PVNT zzic>eY}*RseoOo6VC7U)Zcpype%Zq71mgAR>XJ!k$_h?_ZHA7SLX1A%brOfy2$=R3 z^*s|>b^P`%=wT%FDZ^vYd(D#r1#FmJH;M5qAFeL~alnK&R_VXg1lX0jJ%9o7LCu)i z0#bUu!ZipKEAD%CwIzMz_6L2)C)dHy?wRyJ6mkH@EhN?Ay!%oXhyWtmSZFOkqWN6-(45%xi%g2J{EVOZwm(|Kf18O zd`_hn98I470kR(8SNZscPlXbA%t7*Zw~t3>ED+>n8yE$I#dCrmGpsQ<&u_fm(&HyP zh(Q}hN@(2g#-Pcuy2E}sz0J*N$x8;q10Q1soM4ZlPEJeLflY_KsNa&%n{TJKKGN-V zEJDW|LsDt{wAbws$ubbLnr_@twCfd{)7X=xnevTv;xj~D9mJ&-8y1LECf(G#^R)|G z+_J0Zs3@`gl}MYQjRP-WF9QoX0F07SGQz1j3K~DzeFI~yFP0}L&!ByYwIGE?VY^9; z`Cj%a5j*tC^@y*4qMO_tpj%i5=RA18O`Gaz#ZO`rJpV`8@;J_$1M>I@znIh;n8vn1 zhNMku4evBR#_8eSBr9L|T(;-LNjJc-36mr_fCjw{kRl?xpEA7?ounc7lu<)~7q|I7 zJ5e9yqY(bnW3V2cF`{p@r!_{(XLjhIo!w6+hD3HQ4(iPXq(PuUX}f&n;1rdYGI5BV z09t5E+`keeP$tBn3!;?|1j^fH?uOvtBsu~YVA$J4uHn?KE0Rq&O==sgsZeqh8_WQ% zfbOI-<`(;sg*{OQRLH`eJFyTomdRTAbo7xzhzNviHR|1NhdFlWXxS6zP{@2CzG3{K zF92Zfv}m3-?30SE^QJc!uv5b+d*E5Yp*16RS8s7pCEh8)B1U6zV4l6a?JZ9D>F7$c zZ(#00w)w+>sBTs|0gDqQ+{wKhG}kb{9!MGJG+jb0b7kJ>Yefi> zkmf4!ARKj<7)E}E+e@cz=*j}$#dpEb8}*($b-bibgHnoeUGR(%#P23bX;eCV@zzh! zkm*axw(9xM2k0OYuvkEoJ{1WR*-TFjZM)042dG4z|AWf89_K+pz;1;z5^ZQB>uuJ} z6~Tc+nC5W7LvwJJ!J%&hbDY4J#2Fl%Nfdxb=`!x?MmreYCL{+g_l1=UqB~EK*RNDqpXiE?54w}=4?_W`$X>=l$!jn&Bdj1z|7i-l0MG6F$o?6Z3Lqq8EsAr z=4@n3JN6>`y$34xF;I?XQat2B=kRz4CS*eY{k#g?j=j>$g7O~%;PgT>TI3U*?%8`~ zxGx{&IPRvE`deGQK3aQ`3mqU7QfwuB6tHxUh$zv|O7o8qg=+PB$?uo8mEu#pmgvWg z+Zxz`*!EygV3mwj0nY>OREhfnDl;i1iFOrv(G~(-|1Z|oW8PQ4+~TtD=CD|BL15OA z{f&D1Cn!I*I&tp~3@Fcq;@=6fQ4e9(VLA^%=W-1MTD$bSzHbYvM+n!nT0xiw9~$oFo4Vtm zSRgV#B9LHm6V`qX>m5E@iZHb_BZ^3w9ssL14X-Dn_i%8aC>u0LuLNtOcMGA|K_71t zB8P{Td`a%mjZhsCFrE*WYOKgkff?(NHATHn0k=m78riZWq-h&{H%bL>iuxoY>8Rf%*3;*QB~d(pT2x4*~7pHmCr!E6TQ-l>nA8ChX3sp zTW5tnI(c5&qMy!ec5CTcSDfXD94zdE`r>`FCuGmH&}xY_K`S84(AVpz(&7u^%bkLl zZj3SZ83AcVoeC@MfG;nrbYx|CDiNglhFgYpb!x6bpV&kP>pfg5=oKD4J%h=@;rRLN z{5h9&yI$D>_eF8I<~!=(q6>sXGey|c>f*}b{ex!O9cvgPA|dj%DK^%I+XKiM!8dS| zySsdkl~|#wySn&k_4h=|Ygxlx1g51gb&O#SYHo%G#Z@8NklYk{$)D!NRWv8x<08$V ziDR`&b9BOBJPJ)s0Uv`!Fr6ADdX(J`mt%hU5)S6RNZgUX)+OY9XjD24IC?BH48LPl z%`-sec>-JxrZGtG5k1#-TI15~qPycp;Ml5sCO0e8Mm8_^s|7|t9c7Q{4FYaPhwGr5 zGBaI`UU=1JCDptJE;yV|UJYqZwbGv6bcJS#JsaB5X$a;>L?Z)QPcY{NDCd*g$y1In z@%G_LlR$5m%N&z^R%c}FhH4c{buJF8mX|5OHoEv~nd=`kk+j3=vi=Sif$^WLy+g1l z!4{=`Y}>YN+r~Y%ZQJHOwr$(CZQDlQ*WK~|(Ub1U8&pPSO)5t#vU0DzJ_vAS9szbC zhk<_wHAl|htu46HY(Lu?9qLnj-9WChr$Mvx6gmEu8B@#C+%tadY<{6gqvfq0_i>I+ z5O&xsXv3QSvmU>?V3deIXw5u_3Xw+2$F z_(Cq9%v3kAi@n1vP%w5A;6`McxusU#YT^o)tvL2YTEjRzx4ms3lVq;;J&ODJs(3~q zu?tNkC=*J!YML##9V9CUz7-;KPAsRJ7i5edaqN5nHS1JJvE=RkJOnr)d&QaWsOow-_XwZ2LFvsfStEPm ze2UXyh@>=?lMf`V#c&@d0)Q;4J!T0I6;K;4F9-fI=-6DSQlbW0(mm z1oBILepG_yMLqFjI(VV58VC3tn5Q5s+q<|cEJGOB*YOqH&ZdwIibz-+`i%b3YIdY9 z0sV)v90%v5@g_p&??cWXg7vHn4*56BA&>V%iX|7i&B}A$u%5PKHoE>qlUyJ3la~Y` zFTsFlrzJ~9oZ2Aqa3J2)6VonMuHT-lV_FE=aB&zj`k=%Pcx{lZxX5Ya7_PA^<&!Y> z^St=-*7Jw(+qK=NN_N|${xQ%>@#1CHZ_e(BqfzzIQR8B}Sv7E51uhFg#O3>QN8BU* zX^4^2Noc-ZVrjJB1vV+ud7&v4&FyqjO9A(mbY9$(#WF{E+h|sRdt^n*8TG?%hZ0c# zOf{p@ms~J3Q~}}fYEb1O_p+yN0T7M?6EQt?uz~!)i_*u-qd^e)TSkLe1mp*QzlW{9 zbF}wl>(9Uj!FSrGNF2SJ0(1#x>um8($U! zgMj{yM8&#tA|pVmr6sBTA^mH%VqN$_SU}$gXPYp~qi3m3Q7y)`PD59N9j10G#<>H4 znrm-(bRyRU!bKN^o%ADG%4r&6v*Nb+S7B{`MIB)c(3k=KembDqLGdjUQ1Ow<%N3cO z=c=NvyX`azuJq4g5GX^L68NP^`7T=835-Xx3}cp3gWgQE_dS*l3DdfGjFG)9tNOEf zLVx$dt8an%w0zC4ljgU3Hip}o92R-eg&5(Hm62?pH|Kq_n2XldDivkBb%f4^0ecAH z4?sFi$G#+J%$d!)gpGb2NM>F>Whg};>N@)F?&b57KdV$}Uu!xkI>cCG&Ib6VeRZiB z5LKc=9vYu^Ij>O6;o~WqDaO4;UkEqrCM2R>84a*+khu0XxlGhwfZ4o?l8rL`(6Qds zZCXi2{{Bkj+$FV~La_%=TU!&?1cL6Ce|V^T{ifzs7VY zO}+U&`S~H23wa0j6SPQA1l4Shfy!*y+9V*T`b&V7Sh%1Rv0Jh%mGslMtBc5@k_7r4 z9U-3X)KE}miO>V#YB-k>Dx2;pe?VjQj~A%&ccIFk=xDdk_Iv(hZwqFrRLof0(;Bdv z+ed0#$V`^kV(Ty$ESK=rsd%VcUhk%gT#UoM(D% zgmu%p$}5HMM=WyvM-ARO+%$j-A1 zQ{=g`LQHKE+Z+b~LYmKTUW5Y6Y-?^evhrvR1`zFFC5`lkuHvzVNlr5~7-E(UL{s2i zJH(+Q2>@OyW;hnlDt_`em;1o7s1>3>fy2+geVokn9 z-;pY$gJ7X5I!@xK?ZN*z*8*X{{(GV2&)Ve+;giqr-*Xj8OUk~LCzW{&f+0i6pBZOMN1^|rBMQE@r!Wm&Bv+h^J`Rom!F1Lruy=e zONrDdX+EVasNn0Nc=aP;<>^W8FM{S#ncl^yw85$*KhnVq)23__e)13jcNs0{Jo}~6 z;~Q9$!=j-~?LJJ?64?Ocr;3A@iSD%iCM78GGeNt8j;(H)C(uCS!)MckVBLje2-b$-}YlGF03iQg0FE1*I2k!w*#C!qPUr0I{C}m5 z|Jk%XWJBP=K;7h9shF;8Bgoa=jJ@iI{D$3adv_KoL3*55@d(!SE`v z80~nSoK*heE2rrb9Yr*Q*ArgEl=x((%@@ls=a?A~y+p1i;pC*t#?G`mR)qrZ2*)1l zlOy{?wM)J}kN!GTMP{&=lmYnzH9N8#J$2!5VOGMObbx6w-#c{}E&h-&H<+%?KvRTg;6y$2}vzAuP?UY!JQFoTQVK}vUJVYtqwWw?ry$znPaukaT;0~ zBoO;?N`=dPjSHKd!N^Mf-H5r!z<>~4X4>;_s$4v8yyBNcMHHHdxvIdJzJ8tbl``&{vF+4(l08S`N#0cH8&8(qY_$!I zTxQzrWdO8X^D4k}xPh8GQMLZjYi3fD^ExlVRUtyOSwiI3(euTM1!cM9S}c{TD#q^SQ2a2*t9_^^g0~ujY-!)hS$oh$VMm9h)0zmLjdr{Rz@Hq0 zc1+PYmbYjf%M@p7s$A9Dkj%M#=Abx|*{COO|8 zq{1H7P6n&|jb5TjJ;ko@mJmBSXWP}y*soP%(RHFbXk`l-O+^^UQu+_Bi8k~p2I=(i~sC*UiL~)dh7i1>H1B+9AMho zT~4tC_wxyQ;8QUqMDol=i?4jWb6TfA7yu2=2Nb*qlNF@3$JPdX6X>vkrKT5C<%5uzpWL1UwdcH0W>;axd5( zQyXqI&}y(aSi22R8{ZayJ$Q7_!5xhUI|n%K;d$@<4#=I*8!UIQb#L7cf&&OTAf``nkLV6z&KekmP$)E>xCvnc0ymgW2tGe9eynVrfD7ZkKO8|UnxP;V zkvv+$U6JsB11412HompL3-AO%3y-|Hp{L6tTUW>Iim>7;TVJt#aj>YjH0XZQn z?MQ@_oUUDm9%MgUPR`qrC>gmhi{f~Q(rwM&MSQidamtNK{<6A3u9#vYPO`nIAgQW% z{B6HJc@wc>i(h$Hv#hA%b@!uIYDwI;p2B={zC`)b9h+8EwL&klv}k8AN~LSa;)`A5 zT107#FZI6F8IPePE{3n<7x+JXNvMB13IIT4zc2ScC;xkp`Y-6HqltmBjmiHfq4s~U zbpG?WxI+T~g8Tpj0Q}G4|72Vy;u=tPQ2+pFPXPcZ{TKFNYGUB*;%K60Was4k|3+MH z>u4k$jX3!3>h+`d#&9WUyk=71!G)F=2WpSUMG~@>kc-?8-|GAYSJ0qfnHyDt zq8_?F2kXH8CZv9xZ1nxSM%VNEIH|PL^?P`m40G%C_+8D-!PDFMd7q@`^LZS#>wSAP z+x31u=~S=v{XEZP-}Qa&%@7-ant6^?N_+d^ouDvy1ED`My3Md<;8v zv*Y*p9^Li3zgEv}7T5cIczl(o-~C!u|H8Z#C-?cj85KSqm(2Zsdi(i)SMKrl{JNW* zyjjiWzJ8s(-R1B7zL$^V|91T8JhA$jJ%#`6{;c8W>HfNVdKo;7lTYnw>^+O>y(wUy zXw`Xw4dZX-;p^u4wTJtCx8?VB|G3xTe7`SsD)syPV;s-_a$m}RtK0ke{(gNZzgfC) z&pzoTR_gonwtxS<2<|-^N^_n4u)NvJ-`(ZMbtefvD{}vP5{3_jq(u!X|2{a`d5hoc z_jCR}$nX2hqn%y3i1+(CXCWUK#&^@no@=+YcKdVx_j1s(y0A8zdvr@2#;xvGWUM}diWW{=iB6vzESd9soeGbInR{W3uuNv zVd0he(sTcqKEj9jnXHvnW_rSyax~6}J~s2<+hn7}J%RCks^>GsX7Uwwe3ON%I(wm`w2G2@d9vuP*>4GZUHv=^T@5=T6tn-b3Y3 zXnUd9^B}9+(hMd+atfzu74N|O!9zI~4s+5CWxi9r5Q%2cFlbm$p?#w6;o zWtym`&y|1>5-e#);OV3b)V6mCzYIK5AU2+d*jBR5#Z`s01+Bs^3tpp%W-!V`&7c+V zAV}Cm+U!a4%NVtD2uQ|v*Ioj2oPaQyfFMm%(F13`|BRWg{?bwdC$AMz&Q<-Otn5CE2Exh>C zuO=D~7Aa7!wMvIZI|q&TD-_9UQxV>5XaWe#pYSiPw2n-44~i>b2n8{SGMpzvt$v`2 zLN9u~13qBdBDUZ8IK$UsxWNLn(_88MNM56K9Z* zl7%)%qp5tiRVgWzwDzhSu?CeNuV9i35-MD6DtHa2@v9J3zJxNN7~F~~k?s%5+{09Y z@yG|8kqjF4EoxD;#eBiP{4nKeH)>~j=d*d#-N}y-d%7xH<$xI+RvXB(o^f$dE)Q>E z#jN0+qd$BBM~hx+zybRzC}R(;qRMy9P_s<~i*-=08SKwJea%Q6BP=+5&P`|6Pz{B7 z8Rb7>5CKa@Q)8u2kwv`S#K?v=_x{?P{Kyq@<;H`5fP(`oTBJN;D&ij1u}KN88}A&@ zeh?><-Ozq&%E|R8ftk$-~1R-US1dLZH>q5wFQAEX}YKu`Q$HGtr#8Y%Fl)|?S z?O4mZ>2uI)J_1iYQk~WLBL0=V#!CNzOoAXmv<=^*&j3(z7aML_oFEbvTGac|1q`k) zGegG~KWQr?BrO!|v*&bc<*zSvQVqQ-=h=@^xrBWSn46v|iDNI4RRj?GKG^IT8KbD? z*cvY07*UPk+vIe2N<2{bHI|0?$}(dY(H48bOFAE?;pb%UZC@*!cJIP}gjV7RQDv(ysu% z?x}Q+CD}NpbpK$HWyqDgKR&lnCQ7u>5dZX>?B6I_1gPoD{_Wf`U&q^=x-7;T1WQWZ zG`sOEbZMlTgYI@Qi~9h;qx7Ph!bRs+acC$NdY`XBK?g8t4gHz)K$GhSF4v}TwSDIq z%r%2)i?rSOhord!upnJ`dr{TmwU=Zt*{o%{0BeOx^TXapUS~<*rR4EPC?)U{OeO*m zs-yD5A1;YhH_^W+SajsgrqW+Fs4`Sl0|cSo+K_lme{+6ccX+0IZbavstGW5?06xeN zixf7~UOL8fs5%%|G6t9v*feIM_bfG3jSZ4tS8+B7u8G;E&M9Q1>vv39y!Wrhc#a`V zAwGeoGX}lJ%nBElHl+5emgZ9qobh^De+=%g7{I-_fFrL{Hq_7SN5dd1tN*Q#jimzW zRRVEy&}oHH$2OXC5U37rP42JiapeG{Fd5UzEa`u;J69I;VtW+R+saeJv1^v?V4I^+XdiXK zHiy|aI`9T_s6wXPlTcel5xVyCBdVmkE=2yqL;>T8bNZVazu#+|XPK&!z>c+s&XAt? z0mqSog=6-?aAF6&a8=EPs^QT1?1}0#j~G;DSu1w_35XR?{aXL-5*lbGMWe;|C#$6m z^lGI6pQzGWrL`!i1DL3Vb{{L8)+9-3q|W!9b$Br>i4v^{4}J$Rq4^l?O_`#0ag(Er z$Spj@{=I{esHHA9AF0!{nf{J?2ngM@`0qI=k=5dcJ7DGDdU%3@XDrwQJRFV-=%6QD znv;p-`tcG}bx{S-fu)<_%9lZiF6BLGnkLNJDNJED-5*y zQ~8+z`iK=`zl?B6h29vYGTmrs2@kaOpbBl;C1n45cwYcTlq!j8W~)UQ{G3r03RA8l z%`bIXh&HjRX<&h*m8c#K59|KlHw9d+BqypsT;OGGjZI%HcS8kMzj%Zr#j4^{P$F=l zH!V}wSk$M4txgnmi(&_j6_ZXh2^6Z$iA z=;A@OcMS?64TB7u$*NGV6ro*Z8w4_IZEmHuejZOMBQ3u1PJthQDXj7F^6aUVN$O0& zIhsu(e`s6h(DYVJVhO-V+ppx;B_KA1^9ROy#A2RV(mvS6hb^XLkzL8u+`;9%1o=!a z$uMZ(C}L@ZY_h1qO4gD^-G@ zp}ol#x7Vt@!@%^s$>7;&#EQCFd&s>5T;h`UMDm~Dc`G(jK5Wn^HPhf;a&-I!3d{_1 ztpsnogYjHKq}x`jJ-hwRb5*!HKVc&B)?~=XNT~$v4uQ{;L1&U4w+-9* zrUW@Hs*_?wp(J!>MKPLU8FQ^g((cmKR?)%GF_`Dt+?JZ;6!Ho~ss=u``OvME1XMND zvV~*fB=p?@M(r<=K|)hn4lfUP(-(xL`a2F)<_&k?RRl64MVVc*)U?V8L#*b-XIoc{ z>s&f%a~vWdwe7UCOw>z5vq~_b&!diqIkl?k`fcT0;eypxQr!j(QG@HhX>i%bI(d)| zX9&VGbUJ(_K|Z5EDne$9#}o9)0^qx#e>9s}5%88CK=&(V4Hy1mRRUc5yZI-%Ops2f z8}`T8X?_FsAl3e@#5+-ff!R+A0+1})qsZRZd}g(e05)IU+ZS;`bLbA$CE z5AQ982&9Kok&}^K+^eY85N=0mN}ndz`(8uix%GA!-wOZQr+gv0sF35|LAx6eEzn5u zpY}CY!<*=PX;$&fRF3CYb2?zbkz0 z%FZNJVw`hmBX#NIt*f;5u7-EY!>E_P5I?C9UMzJhZ*9)JSaT~gUJ>I~iGIzgrDZ@* z-6ynFO#4>PI%*fp{v0JGP8{4Q)OFm+fb_(%!w1O2@K8i)KE*B0?!(-5kF{Q$09iT- zoPaWxA}(WjvkgkQqUkKoB?fjrzqMpY(T0xY^r_9@$|peP28_M1YLT&xOlp?q;ZPP^ zhDjWVKdP1-qtX1Y+YDpDhW&IlC^bQOd!R^hMz0L|SW)EiMX zXZ2|>UaqjRjUrow=LE&2=Ef6{EVohR+*BXMe=L%y3KwOkN^R%>=*IB=E2MSeQ) z6%+AI#wn{w@+8&Ds7tIw4Gbu1RTg+@3}$R-H=1sJcE97voqnHP_k3EI zXy=P{zntnXqfZq7rM#<-!oEzA%h>YI&xej$sO$ zrGP4`gGzZikpIXaq^L;9=unR3(F2(@jx30n>UtV=uaGGNv=VV^T_2kUmAkeq?M1hA zbSrc(`ZzYN5R@u}b08?A=*y5q4c5`ft2e#h5an!==AzDfp^ASU%g&cN?CWQPfJl<0X`3;jgDhxB$5>xm z5vyf$B1onu?C`P1-p$VyKG9GVWStAlkwM^BGFwd`o4p2%8$D|E55-&`n23*W|APy! ztjkbdX6@w&Sw>U>@kgV_-Z9`54V7&DW!fV)7_1_=-XlJ(zWdJcO=uS1+2{D`kE>tI z%aNkyNr5x%%yAV6s-7F5c?BGM2g>TkV_@yb3|LYzpqMPNj0_R&1!!qlGRz2aMA4@2 zSKj=phhO@$u*~1M=1kyS+`AOjpCOTCp1K6@4ve<5B&xD#Bel5twUrviM!B=gc89{! zOWg>bv?jD<_B@d7ZwH^=(Pj^O9V2@_`A+gCI3E+@NLmr$^kD&(CGwmiRjc@@;Yov; z+lm4WI4`U)!pQGfu9AJ8+xu0^7jxx{(+QK+cwLsHcxWTo1SH^rF@bjivIG<0L(+>v z$K8V<@gqZ925~o46eEK%_$Jf3*L2kuB1QBN-x`zlP+Y}X<>&ahu!EZeiv;M~zQeo* z4C2yGOI3lSOq5}1x%D3q*>U$b#pN%casEL&`^+MJ+i!>qUDyo_UN5*<9T3ZW`GC@7 z5d-b4)Gj<9{zx&bzvqrjyS z!V?#Bv_csa35}_B)SRhi8XssiTYZ^sGD!p>_OE27Yy9 zeDW+uqM{R>7hgdTaU zflMTqrMe>}bRS^8#iWP4yQCcc1bJzD-f)C!LFg{Jdy}}QF&H7mL-E`%?-=f2P~0Qz zGRS$b4UI2&hZ>Bn?Saqd09)87sxWZb|H`ZRr2FY_MxLbaPr<37^up^@Yz<~8O)J-5 z`4?WG2yIpgDUH|G2)GfVX`lE5UQ{RO6tpHZaViNZqc>#JG^~(^M|-w{W=-3;iWH>Q zek(_NAQY(I4iF%oM()t+PLxb|q+Q1xNTooq0f;Kd?&joQsY0Rz!CGWQw)&!j)XcuN~UW7@aG;?X(PrPKvn zIzl56Q+BuV&z;3cXIw7b&RP|0)1qQLQ~NsMs+QW#nWI_7)M~JzIiZ0rTkRl^5e<6? zmeB(#*u`ii6}vWq$P=R1SlEXLvXehL^CE5qo}gU>Q4c@aa*gp=sl=+tlL-3S(VVCm zRD&q3Q&_XAuT0D2CZJb>-L%yFEGo6>`WR(;oLA#nh0%m({@X}^jWhL}oCuo&#ZXlj z0nTL>9sPi5XXz}b|LU_P_5oJBJbAl-+njn=^PRqx^>z{rxJ;YDwU@lY&5pV_B6dtD zz(qMt}p6(fRHO$10Zmjd~B2as2f{{NQdErXEX_{;klUC23 z!e}n23_`qE$k-kIZj1Z(iSfYF3*a=rB8$ z^o=ka3U+;y3phr)iniA%w)yF~cN$#WAm67;F41gpi&(zlXN>2_*$?!aVdq{V-5rn2 z3fEE}n!k75R3QQ_D|O)XRg~)CP9Qbw%~{kihXlE^!tWxO6yAEFnYbbROwv?>3}U>i z8;{t+Sl*97kcVu8P;zq4bD|N!Cu;`2N&X@j9b&qZSO~PXR+)7gdo zlujeGQ9rNJZgV&OfZ+V`oWVW1zi4ZX$s}=;mM^{y%;b7KsLk zCEHpnj-6$s!YN|cFT7o`oSDnAdo8yyZ5z?gOD}j&R>`(oQaAPi0j&)l_>uZ>DP-7f z)*eqRNL5(^P;goJjvy>XtF5E4oPhd#=3a zD#sf@5;z*Tv&o%?-HlaY{}` z0%5Bo=jm#B;-N6PpJ^W2cJJ(~qR@hAQ!Ntp2+suG$xq>+h9d|S<`lJS|Z z445I7(e+8#*vP?dC`~T327{0-tmaWi@-Z1x*U0>F=j%wjuFq~jyfKe)HdUr@X)Zuj zPRUy7;G5a?{X<=0~#E!cv)|%w)^Zd*k$gg<^P`N!XCA zTTs1}Hj}cNhnyD&i%}k!q{ZbnQDtlOOS^G}&$y;a#9*ui&5NlOsh9M27w@E%%F103 zc2Vk-9Ys-PJbRN&<@DIybX8nVy&cXS=XL_{ zU>!fjn%Lv3x8EN-Nx{<|*cvXqi;#lSDEBOJOC53HR5>swug^Hr?cOQHGD4U=TQvUl z+f1%Zyl)9Q)ts4U8+Gw9c!lv&z%7l9={l=`0ACq9%r@`Kep?dl2hd<~>!`6SOJzZ0 zTHjz^l^M@Cm=>tT)AZ7#Ld}0^p2%R+z8o}XmVkP?-qe_>(fZNA6jujX^=6IdF?*S< zxvtAvQU*0^7 zaERd*FMQ;a<6(@cm(2VK!O?Nx+I2LN3{SW|8F5b}IrC^|Eh%E1Gk$lztdpY}kc=@f z;_=aB173}=XH1*bpBg@hxr+A;nDlpD1q;vpuhXYCNg(TanYY+E^v`GF*pz~E=S8}p z0nC(MNSsrQ+EVT7ex?&DOAfI>V-S&*QOhUluC47u8E0;*hp0{=Fu?V<`Hcne^u6x< z@!47yu8n0!S9QXBK(n@Ku4JiZjY1uvgJZOFrF0_hBB*Eso1vJtxYs&sYbhoSdB?$7 zEGs^#at%g}wTyE&bidcK^3N;>6>o#e1z!>55XVQ=~Ps27O#nWQdv8+)WK~@aGIb3+?WM7v==?!w~6G$Lug}{XxFM}*2 z6~v|uIE*-T{K|W9W7)q$>heWPCJ#?MYN&jcO-Ji!P-Ql(Ovi<* z>g0E}vStL@Pnnb$A+Iv&D&+uM%ALc~dHqJ=CRC^UzNYvJzQ3EsfpLwAT?~16hDpOx z(dGgPgJc;V<`5uLxNz#Y#qHA>LoI8QEosBMp*cO|0vwI%K?9v6e?Eoio@Wb!L_OMT zJD(dF1ZenR1D$m}Acm^98;pRGX$d@D%WRDrXkFqu+lPFhPPlJbzYLZhH(Z2o{VP&~JkC#kH#ZL`_7AV-Adcjx@>B0QgvJc&f`PR6*P zeY<8NLpqJ2kPlgqy4d@jRM~D{`wFfGd#Iy|tlmOa%CMcY0m9-obdR-^FF3Vr{GHNe zqqv6)u*(=aG&~VtS2$Li;1LXcrQFl-ou7D8&D^|@6I=Npt+B zXdY|52i$?^ckqEy;5Wzpm=V?HfrwyJ1tI5+GGD!+YLDXr~(;;x# zZ&5E3JG4g~+(`FxPc^>o(esxm=9u4t&9K9WEFOtTvOeM|6m6#+H5sU4mFkY%D$vt^ z(*T~+LI>el<#(JbuPvALJHU48CyVOEEhDI&8if$U3)K`PFRj{e&~l-gd+tL9Y~ z#Ji-_%5r#S-^XgKA7fou4u*=oTnFx&IIs`l%#T8pw>Z8fVH)U6hGU-Q>8P&uKrCDXS%?AsW% z#=ssZ_ACnsK0XgIkzsw^@xBHf!_`4|N1b{rrIR9)=Ure+pQRj6`g~K}nv8r1Otl0M z%hs(a!>Zb$_8b8F*3?G*EQZ@6>!S#goQQv^g0_M-*s!xgtj=Xm(q|jVgw_@qE<}>Y zbEcB=t`7_j$Wzaig`=BXS4dvW1g&@C>dD5}{=2KN7|&IxTcA_@W^8hOQPEr;U zuVi!3dLL!R5E-2WD4G$87hCcn#I?K8r>=fLx*&Tmb}Y*}o`J5Fe11Zk10UOM^SWH4 z4sT*g{(XI@8{}(B2RFu#1~M6pw)oaWXU{a{?~2sehGAbH>?tOO4pqQyQnzq-;@YzI z2j1?>=+o9Sko*)KU~m&;-H0p6fG5*MhkU>ZDrVk3J=vl{T3;r4GPXd3MqFpr$WZ;j z0QOeHLDc9!f{;TMV4DN6Y$dipOU&yxo5HEa!1BcY0<3kj4c6Hrv&)`S_a*Snt(Y>0 z`JtvA%<5ti`o7g4svIGMKTQ;dfbN7OY!dVofSKT!!*YoxNo}@=qn&I!b|GExO<{T% znYc~+o;y9Ec(8G&0b_8s1S3{FQW!^LQu&G1&Dd&NEl^drLAoS`I`k9@!k+D7$7C2f zYQ2Bex;XRgX$stJKBCNK59ytiM-U3CK^bZ=8iMu7rA0#%z>HN0)_^FH>4{OJm?Tgb zF*sniYhM$R@CI z8T0KD9gJ~&03xiU10st6-(2X2SX@r0E!6qqZeuI8W;N|uFB}~6N347Vxlv2>pp*aI zOm$rNv?VzSco_&wO)6zt`a#x5?(yv5o~;i_*h!2xB+&PX18EqXb`5WwU=5Oj18*C0n8;6uYmiBj$b9E&lO3Jf zB%LCW?%bBN`9XN!(zjCem8oW`*HW_1StYORZ0?Un@l@5z!LF1HX6WTQ|As2E*&fk- zPBTAvlY*{75!4tCZel(Dfsp(JvQOlg{YwyeySCm!*yUN~Q>uuJ z0>uded!|G(r#5#}yx6jxU^13t{g0ns#zB{`k5v*8)mqBIUU0Ubsbm(5wDEW81&OL* z-exR3aX#J8cc)-|w)BZw#(j_hwNPJl8bT4FNn!vzB34m(faRfZY zTt%&qDnPfXF5!Gv`2Eyi2R*fx!I zNb+R5|6TDf2T&MyS!)}OorUEoXP4Y^im47PV`;FaGDnog-?(8|LdHZNYORM8cZmWR zdG;K1Hi=bUx9E+&L8Ty^Q!K0k))M=8?!;FYVojm4M~fIW*$_*jO6;{Y^o`N>?4hc~Bnw~^uaPl~ z5VFtLC_Ku86!vd)7^^I)@ldC(j=MQ(Z=}f5J9J#`=k7X%sDdEZd9!WTT;oO6g@^2i`c+%kP-PdTunUaLmKFJXitcR`%?C?r$-!= zGY40!ho=~6Er1p7Qu*!tp%GmUQ^XEJ^WF{8sZ~La;9Q_XQI%ugRmBXY!s^5OdL7ac zIvhC`R_o(A{$9yN2P6(G>&K>uYaKq0MPx4(a55aldlTGwN>&goMw^Bgi(_Xknwrg9 z0mZjX-qL|_N*_(~B zCZ+N6Sha_0gI_(Uf4qnyB}!g>m`2IPNVGd;^@2^a`FD!{mfz~AuxWxyNBn*H-MXD) z+mCHmg?&N$t9i|WWxn^r07FAnvb%n;EOGheett|WRsO*hqdLa?CM==lb>XYE)}`cD zXu?IawAeg@K+1{oY9yFz1iscUBcwN%jj_Nj5Ux zzS8gly{hu`LM)Z9uvhsG`$=xds)`%2mvE>2EykL>p`1sg$2xjB=7Gk63$+*0Q}I3{ zA04%q^1_(s^i1$aM|yVtT7)Onuk@hxhZ}VkU~T*@=4Pebw3+qeex3X}{G$cwWZ#+# zGA|MOnS?*WtXIg7ob9jGa#LpMfCQ!j-D>N@)b(`{WL}3gz6+{a(s8>yuGa-~(sk#l zSk-n(GcA+LbJ=AJh3&}(PH899dnLofwa6D=@nc#$Q%pC{GxDX-g+P7(xU-TB7>{J= zRh;`;Px?i*2~WW+#{obzF@RWbJKmz13?=}vz*G^yPa4pW3lsvgIOD&yHa_6cGonDw zI6xP<^f_}ZU{_c@4B!I>KqBxO#~}dTIEOzVDP_dDYv)DJr@Ahi!~x>B;LO5X+7QL7 z(n1`Scm(6&6LV)_k=`h$6w-M2YPPP&bV`oz_YuYUDw)QFAqc(+m(~nIUG7y`I?bR$C!S0LykoiyBj>KFR^G55p{JI}ITFE3m8M!N7(AV>if=$V;Q zG6G$uK-DCo$P4%xymKejmM-q+vglYf3hHmvRJ{E_bCv-nVU;8f(Bk5{i>o+n+2F+lrafUZz|Hj z(I^5LKbgTpcQS%6XXxD|V=(gkTla82tgsOh-1A6iezzh&*}ew2y>J^wOwP4B)Tz3- zk2K7_Il}iQOsSt2KGu3DXAIg=(79yV#wQv~>AbwpU3}7AXV5tyZm4k6qGPzLCfvN- zBq5e6rwBAfm^t7v!t`I9z0;*wB%%?*3~EjhC(>Wl>A$|okRo{U1X&OTvPk<4ez8dV zON{aaUr2jIk+DdF^n_Hch{2V@O<6?RT#Jf{9EYpYkchkhG4+QWB@QV~sfoVP+)^Ky z3yKSI4&svhBU)1akE#Fp1mH{qmlOm!P@8!`VNvhxJMD+>C)oe#GiNa7PLP5C0ATzl znackKeKfE(vvag?Hn-9Hk3!NJIGURN-};7P)aPxrMPYny)vuuK06k23T28A0E0iE3 zQ5H@ql2Kza$o`R%;KTh+-FbRCqo54!ypV0YPd7$KM_(Q*(fM_Cdq3WvZ(bhv?=R(V zZ(m#G;n`F9d%oT;mY?DIx3#-Hz9$>aX6b8dYju4-PdE9ln zCl`D9uK91^-NHIY!?rKk`McXZpSRET>~8)1ygRtNKJSLfcXxJncfaa;KfkvPem7I` z{64O3`MiIcJ|FMDhi~j(4SwI=?dw15ei{7uc<^lh`NX?=y}Ujzzkdv0U(R0^JEOJ_ z_qTt}=|S$lkDrelHK}xx?e2a9dkC?&%hDwXzv$58ZgVZ@3uU7&(55rckO~>WV6V4r zEGfBUv1B4CV3F{YG$t^7vKLG;7-TFlO%0n5Tq=q$n97YZocTlSxzgEK4U}*VlsLT+ zUyGe>d8**+ncCtyVkr?5J~ETFR@E%S0&?+7o7@xLDK9Irz~Q}@&DZt@bP(v47Z!>@ zl#bYh#RI%TqSQ>Y|18@YF!s}|WLT&wofjrQ(aERVD`C5+*cfvdHEkTE-4%0WO|2jj z%GDb(#G;RL85gY2@GT=%{QVP_WLYc~nFPoKxRtU)QwVw1&TMEgXmIGiAOK+FJdi6p zN--%$+-V}DiNJt~VVoV(YiB*ag!vf272SN3c~F$vhY+P;P-pF)09s z!}X>k>V8ld)qrdqS5isHRX&DEUdwt=k<=_YP6%AM548R-127)=I-ya5en#i6yo7(! z^g(SMs{uqApl?6ci3vj;_;jbpK^?Ro3St7&)3{kK@4l_fWho#cRX;5m^>e6QGekH# zS%sK%rpOYuFyfJ;cYzQx3`;f)NbHG-v%5)~?- zf$dy8GrUqZZxmoETynp}Ts~JEq3j-J>Qb}I7<9cBt>_ z&~qPtGL@uZ+mKY_SEUGQlYpuK+5?k6(EPu@kaspg&|TP{OiUGs1ZyZX^Sk75DBv=s zgbi*d*$u_u{-mB7qzxWs3F0!jOjVG9Sn3CuNNB0f`oKS#4C21-7s0F-5H-S``*UQP zHtRA#jm~D^si7Zq$@$fm`*P?#crGUJ0KOXn1D19=!51*q9YBh_8P zFlS?-XvL}p*I3`T2M+PFs|yUp^<|d zxU`bceJmN*Al&l_`ZkUkH>C--$On$HZ1*r5rs>S~V?&HxvNZ%qh4AM}+k$j01{3c2 z#;qx=_@(_Fw5Ev%b7W3DUC%AgSm6*qR>?GJ08+}~w*G-VU}46ELdYA@$c>4jH2}mQ zvf9?f_@E1w# z1=mrJi)d7@FXV_8G;r4w9z>B9$|BCkd9xvNB+7(&yT@ocJiBBH>zfy;kBumk8 z0e$;4`nXyME$U^BeB)z>gaaAs^wlORN>N+o6nJZrCRvtPlnM#kPFd=TUM|+SJhg`2oP;LJm_F( zWIvVHR3Ye4KxhuGtfx)~Z-D<*BUl9u9Vu7mC14uOa1eTK39bj@rHrdLL0Zhn2uptb z7v_XHX(Eo*F^RDPYU~CI7g@c)9y5OSgv=Ba96UUPPUPxI+LJ(m{mWQ^0$s>Bt__>~ zuW6m9h&i3gstArvxP(y`CfWgvaRZDdrfsyy=^jue&)gnQ_?N9*LuJ+6VCal;I!uE_ zRI+?#ZiJ+%DO5^qGt9J|qgbUjh-`wdEU&uGE!A=prKQMDhRbZXlAx=A?`AIGN+N!@wD;QBGiRhQH2c<1!i2(PwK;Mom9e#}P1y7hUG`&fcHV_D3qB>_-pDaJ`lA58mAj z_XmrJfH2yf1UV(_>J2^97Rc=pOgInG>inmdFVRj5Ke~arAWaC+PKVKJ64!pzT3-mk zp4W)Mp03yO3Kh_g_m8%~p_zf<(gJw^&C%w7QjIqaR@0q|^`{Jm+&yrG)}6!lRU z;*OX*t~(E#5x*OqZ=sGt`b5>F2X>&$eLNDaUU_}is-817-yArp~f_x!Qv{TE*Ok^+U0GNZ>Q6*xve=Rg@oobFowQ{)%9747;3rb zH@(%as?VpwK^`HmN_196PeE0D`|m0zMg?U98HfV1i2 z28O)}hZq;tz`@Vmy1>R|&rlDqgc{H5OQ-)S;rkCM^#3a1lls4JYfMe-O&rZE%#BRU z%sgC8%zTU-JnY?V%|0kkz1G$7GDh)@RJyW+yas*uV2R`lRH>ia&Qiw$#a6n6F?q( z%LikUV?#n*I;uhL$&OslcG*>O_fH+^igPLZt_| zoDmK^hs^>n96~$PToIc4tYnm(i_5z3$=IJ>4Kj#vTC& z$8vSU*(wzB@M4l@3Lt;Mo)lo%JLtA)HVR@~53FpM@S(rD-8L(h_%tt34-W6dlkB@>Rk|Qa z%cNu=ZvOGq%Vb>iaT)c-k+Np-Fn^0Lz7S9{IGyKtE~>5)Ug!$rCShq`$pi){VIL`X zKnPzS<8(mR2z&%axqCzzgK}s6Ea$LtQW*~!v~wiiHx2@_Sh`A_bata7W%plK_bc7$ zg3|W&3(RlLZq7+2T8c@I(_oiP_Yjey;h~z+&SZf0#wNX&ma`{+X*aWi#+!Xq*Bq&J zEq=Qv?1pGXoNVn|^OY%_cKR56K z@%sEVvN7##8FbBt+#E0#*$>2mt9)u9(E)blbq>~8cuHQB2-kZ^YEr02i(%ze{gMtZ zoW0v5dpkX2(kVJ^4DVPxHf@87)4M7Cld;SWn1@RQo=vN|Q7)@$s_-kIo?%Z&FTd<23`^RlHfX#2TSk1kB zyJIPd^{gv#kCl=VWPV!su>7m=Lx)?A{E3S8@+tQwfS^zZ~u_-HQwOm zjqbfUe~)~R(*h^Y#@r^{lRMpVl|yt@vnrB7oJ9)OCadGH;n>iUC5}7F_K$mOc7NNu zQ?h+fHFHyRMvgpaOm4z-L4yDT*}cL2gS}JY@8vYMhPhrARmFZWQHc+uV`h|&YMwgctQSS3%E_jwRV;Js|L#^*(CzB=H=8mu~? zH_GAf$v2e-m-7r+?EMOsWxNc#O^haSP)8-lg&fOLZ?ick;IX#$_<|UhYkk9Jthn9J zdC9$fk0LSZ=6p{OQaA*adnPs!F5O7IZya#ED6Ff7eIc&5Qy=a4s=nT8{c3K~yNZ$1 zM(mKo+Y{1!hXf~!gDsr5o4o8V%(uuERWUQLS1pBw{s&({9>z{$0-|xqzdbK`!l?e( z9aY0Ly*;E&6qKFks2Z&ho2g;UMEr}k^1G6OrZO@rH$$NU*BG_)k~bD(Gq1g;WJp;mU}a&fMIgY@#UXqv|MiL$7#^V`6`z|x5cV?`jm8Sdb>$r zDCtLY@LP2G^-DZ1BL4g13k5;aTC565k@vY?*Rb^G$R@U#+mqxUxFD(z>~6%5NBz7R z0Xdw4!5ax2=X3tV3G8z@kecyS_c_!bqhM}g9kw=b#1D!ra1IgIVACLC-OECh4ca_H zAG0JDw;%R)%`&wX>0xZ=NDp0HC4v|nC*&PTqq;YfftrKcPOs@$Kd8>aTR3(2op&4R z7r5ss{~_3PxWr_#8>S|_e6!D=u8J`DjXx2xmKRmODLft33BM&6D=9dxEkHr#!Dfj^ z1{Cw5$>O(3UzWQ;wis|H43|_==LkiLpyUWu(Y0o&q>B~DSWj2RY+|=lqTxXFWusuA-JqJ-q~+chaAK+ z&7r|b8$N#>B0}fCga^J^)zS%%`X8SOHf}BK>ou#6FwUzZnuU)iPs0ng-H2hIwsg0-IqWGdcwHQk@LIiErwEdHDH zcko!L-|=UW%qIEN^}G=4qOorT|K&1E9(~vX&Jft*#F5ucVg;K%T5EFUIX?nxXLx5mMDv|T2Kambp{7n!Wa&s(c8igQ;ZJA zEV9%TY(9BjrVF@wb)$x&AaoK%W^bD@em=yK;DXD8<3MHqHIGJJ$MH;WGpE1LM&@Rm4P4gu?NKT!4B>Fr?o|&c zkI7ngt$!HPRH5g=uD$tX4pur}0AT|U#1~Um~2M!wm*&(d; zwZk$pgOP4;@rk9^*;3n;kNY=c$SyrT5}?j*sf)BOCs z=2ReKa!!(c(^-zK896w!4rL3fKthb-ZFp{wl3zYkY%-FEzN8UVF^g2;V2W*9td)qD zG%ux>mZAu4ql}r+n%VU-LB3&-S@iE%zXKunHWZ<$h#m!&wy8hMU+l1?M6XFiie6l3 znjE4gFu_8w)ut5Z1%yV&e^1>amoPR-kI7OM`)5{oFRr-LwLT5G>Uj)yNkwx^{r03zMpu{ z*3XeAUTtWn?kZJuAtL+uhZK4+<4(s5ZAbH%#4PfAqb^144D*de$a&fLx~VLrMVwZ> z8jOKzbpW~$*4o*oH)6hYKYuHq=2lVrV+s{nmg=M%oulPx3ihAN5n_t+%;N%;s7qN> zWjKG{Sx85`hmyT3LHYPeui)X^l~kHc>EQiUa8@tGPM5J;qZFoBCE4!urpf$KJy$9~ zIzxl7xgZg}y+VsaI*47aVeR(AtLpLkbLw7m|22WC5Ko|Zi^bjkPm}?Kzy?07SAYCY z?6GZdNvfR?jI^URhhRX0>ZGG<>Gw-MTH6HYZ=eLTGWmj;DWHHh{QInyBI(cV!T%vG%cI5~pQA z_G8<`oU((8gHL95)*G)7SZn^%l41W(^Q`q{2Ng~l6g;6=a8PmOBdb%FnZvj<1H!|V z&n?%v_K@6vLuTePO zyi$7GgmDjKJ^Z+AIH9wKzrlQvWdSmV78?O6#IV?5BfqTF`G)|}jyO0yxUJ;OXjw|c zww;pmN$QKf{^^yd*~S6Y2F@nqafr+pE0#-#q2)2h)0Te+-WKb=BG-mW)SMX)T8W>u z&)fvB>Xt-Z)nC9BJu4gU1XPf+>^f2icT*T30X zVmeo2>GsQ6Q4}F;-a^NXy5qW?0nxEF?^czPj!n{GxZ}(O!b(O3nQ(@K!**=x_tFg` zMhvg9EG7MK;hREeTFEsPut^)I3&cZ-FTXVCV@rR}X>_p4@Z#X|Lx;j4(+Pr{hPW9i zxe-u`J-|w0$cm3GqEMlJdfBjrq*U;4`}~4fABuxuN3t$39uOM{_W9d}6mRmhg)bd) z(YoU+qwMp?N7<_()l;c$Fgzfmm^a93c4#tpQiH<*2X+FnWGGIaWLSI<#`Zji4wF*Z z*My}57_Zaw0I6ydX3{sN{#DL+#7VIka*riPB6`B@aE^V{=0PV1P0V#x6TGE(#Y@pF z8E+{6Tp*E)xG9Fy_q_MSIpVph7QzGcQ@fNr_v!lFXqMly8KgX2&p*_9Z?cmRNM?bXrD%$W4)T~12AT3f*lZMRx zsM2c}h}A;jYKoeiqoQ9u*oDw4ENTy?IgR$eyH2>2JryDoTHATRWdhBq&T*tSOc6p- z#BPKz%Vomn-Pwa`#n%JJSu;+zeeUy)8mGre*rymu=&}N=a}2Q~%RA>C*9LbpjTA_b z%?$rqG1(K>c5!E_m;7ti2GaSkAUpi2d<7B8sQpUH-bd=H_Q~JyT5&tdw<(N)wP^${ z!VXv8bZ?E$OV_b)r%Adon&g^4FS)MfgAbTVA^t>1v^(zLTxe>3DHP>q%*(+QEP2MY zfnXuJ6&pQiJl0uoVWEGb*6NGLD^ zp31IH+!kE4_hhnNfIyHKXEY9@@;=4XYb+pHs4K!|p7~5RD=MSj;_-~A)aQd1gOX$7 z)&l8yRqMZJuPnOqn_M$b!g+G^o#PShheS@@qnzRTyC@mPf=7Xp_>lfFZ%#2Dk`6S@ z0)nLBEpsCG#;@9ZAn!M%zGF z205@wotWYn=Yyk~7GwjDF9kZIUxa2Jy3*JP#aK9+Rc5d0JAze2eh$kGg{w)c>d@t) zG$ywdV>o6y$riq5rF1RiWzs{zBJOXy!KTvl7d9~YRM3q{?;VMVW)bVC7IL}EB<*TI zDB9*<)=^^4Zaci!?)Qim~ky%EB}A~m06PcaFJ zXKWM%)N%v~x!a$akn{a)&PrUAIqHTOHdS9cDc_^mby&!*co^mQw}MZ`!h3 z!=bz5Lg(|9=(;Ib7V@@6SK=}I>SSuqOE;(ew_Fk3dCGA!9jqWUkhWL`d81=w^I*&$ z0dzgO_T&c!-JdN^1^B&{h*Ya`<4J%$&@zP3Q4VndlM~oKmKrO-fQ{^BvZ67G(rXaE z67S{zP;MWT>hBOQ`F9%rktsfm1%|k9Qf5ntAw+`{cI3{+ee^P3NA6=@(Y=!^Ne=-h z(WB3M)QkzuW}OPA3e@2c`=raSBxf%+~@)klYcI_%DT=h_Sb2QAzp=+jVhCpTv0XdAtU% z-amTd%x_eP1w=MZ;>sdv&Xx|mZ?l8b?SU=%?M~O;j`%^0h1u=@NDz;+)$Tq~|MyO) zNFIUAZ+kE>0&H+Fo&U0u-T(02@-(scu=xM?L(e<)8Yq`Fs-7onwu&~%`~BBbgo)fm z8cmS-QYnw5D}(LZkjfA$=(8L@0%D!U_t7u-DP;TPF)Ray4O$rl*C_e|3=TZT**?z> zh2Kwe-fn6>hNeF5DmLG)&Py&o@3nHeUoJ9&p2l)M_ELf#Z*xBSH$V4kJ}*kTpAv+h z8@@)Q2!B?5et31i9p?nTPdp0y-%kh!eEh8Wd{zp|!wGum&Y22)eBKoLyt+p<3VNN$ z`8d1$I4JoVB`loterptP|Nbcae(>|M>%Ao?$nV3c`}KjO`(>ym=>8t{^Ml*yy}NtP zBB$H;)8p{8>m!T#b&%zAXTc)qdI8la;Pd%sPEb*apT|c{*K4Q~TNH(}b6> z|LZ$wv)iY6=$L!zj_315CuYtmd%LFlqCz4 zCnw_0tylNhe)rqO?IB6~Ma}09jj-p7l~KUEu<++&&By!gqp;_~)cvPZ(EIAe*=o?U z!)>ly;1J$ilJM(9&FA&4*XQo%%h;6B`&Gu}#@g0xeYdXP-oD3>WYEU~YR>2W>SfnQ zNloCr8OiJ4OpTY_ls9G)H~rh7!1w;ocW$Gg&o9_-W1B(uU$gL(5m<8Qqkl*8a*G4(ZN??!>I>l+^rUl0nzx<60aYg$LR zorIgGi~`@+ABEq}bGlnka6Vt2Kc5f2Zo^l@|LYt#&%!)=huj}SQg?5Yo4$28uTx#` z_n=Mx`wFAL_J0o*MxEVve|J)b+6@hSk;Vy@A9&uSngu7jlO4XEZ2dXT=T|xA?Lo__ z+b=8NapCkX!zrdg_+zm9X`+V2>T_@EV~3{uZ6s$;)$zD%z4^84(r2&rG4mISo~>Kr zL^tpJCZFACz~STcwr8iQT!3T1-r8ZdqXS6q4zSzhpm%I`u!{Rwx1j&eP^vRP_A%h7 z|6sWDtVQ*(vxO;o)(%uMvHaK^t9r}jvE(-+yt?=*-vugvoJf33K7DpNf7E|eb-lmWBT!T7 zI&T*e;|>zyzu$f-Zl;M|s6d@t^PF<%^qM$l^doYyP_U`d+pt;{h!=3OySn~-rF#_j@ZUdxA3Dwea;@gT-B#Ttm#}_3A(g?yIbu7pVZ@ZahalEY}I92n#?;}D_}RNg?&djAY`ME zZtmh@&?~KltI6yaaq_wT!{W-4ckPm=ec^oh;7B#MV%9v&EvDW6kzJ!oOR5Q_QijfI z&hMX#z~cD!!#F?1)-#tWo6VaeWR>}+jWVM0=py7PS7DMSKRhPXfRQ6L0^KncM28C> z1(#pZ{@J^8!^AYe3;*}!&XW810&)(n%j;-PPR=U>w+Gj*`(R*r)N6S%0GOmeZfnh? zDcsNHCJQh-T+d5+tWPpfTHlcOeh*Oii@koGtUy9vrUfjFF^4uXS&ke}4`d%PSZ`_G zIm=pIgjh_C0Wua~o;KRm$%o%eXI}W`H1=tKuMPmVH*;BW(qL4#LqXu24$QCMugJ7- zR=l5a=J3vY#$nsoko?hHbX}%Gq%3g90TuYB=;KL_ipf2#|B2@p;YMW@X+I)}4q zrR2rQmp>H%FxUfsK2JPYUU1yBVxJb!6VW|0fU7)%1vspDMIZlJH@cq*5NGSq7fL_> z@XK(>i6ZLStjBhPcgbSx1uk~vsDKorjMfVx9<=WO{Lb!n)s@^bIHx5)-?BSIGXk)u znuHAav|qA*Q*lKUEA~|m42sux-2nqO+t{KLKI{fF6h4_VD0M-W3`g|Hn`@2gj$fcb zrIR|8sI+Rt3fL~2jj$iawuI6trJQMs@MzW~@B*CbN z0va1MgAJ{TG!N4BAS|gEis)41eLo1;Id^Sebe`(CwJmw*|goAs&l?9kP+mk zS>Zl!T+_rkhJn^5x$Y2XBXEquPQqwG1wL*j`*sOgz~|TN<-mw%_}y>QR-0Ri>(SLpdSM4ftf{sx`;su&v2K~ep77>^=z5ZK$`a3W+n|AqIV z$vIXW*Ek0`IEQqm6^&p>Lqf~OED5SY)EP>0(>-D;+^%?Lwv+SQAN_~OQf6Cprpss- zIC_lAP5+(BjChQD->eX)7DDPFcN7)C17^eE4TjOgDE^7p(q>1BdGN!X`PD3eB;Mjr zekmsA=C1Su!KzX0Z_d4+@d=d{Dto(?{Dk#th4A+7who?z#}}OW=j;~qli7@p&#M$7|jbx{&Q1Y5?2T z9|{%l{72Lw)bVvD!9}f(06pz?%@yAj2oTXp=?Lfuohtq=o}#hw*SKbOgY_aK6bz!c zEr}8Uqs?aDZh$gw_G=YI)39HRDB`?qd;a5PDD}5xK-3i_z{d>dN+~UH>T@41i6dZ` z_TwBZZc%2&xX2P(Yg+aw^t+%i}XhrzGsS@s`={Bi}D2;PstU$xY7& zs5sH*dB)hyB2pkPl3-A&Kt0=p|}DDr}%@Ryc(UZJPDNjbCS|f23@_t zkd?PO?VA!eH-G%ihG;-Qk=+HgPk03xpG^QB(2-)opXsH_}8j16u;ddI%zat)?O>U*@t_$?9;Swm}kd!>V@e~|g!zh=%X#m8ZxFmmX z6}GrCoBMOCT$Q521k7VUufVA-OtIe$72fjBVwAJ{Y!$jjoTJ0kTP4CH_ob(>aYZbB zR!S{^y5oW(%+rVl54q>vfU12Nk-dvKJ+rd{n^PJKIY`qtupL_+O!o#qd>vI70k5hjRL4Y~ z83?FV;QePHruLQ6hiZ;-7Lx!Bq=NCl1rN|$wp(74P2iv+SWLNyu-UE~ZwZG^nR3J$AG}LIIKAM>M$|`NZ381) z(IIb!c#SFyprMbOvy$$TDDp9-!Z34#sHI~kX6p3lB7Pv|EN(1RKDA;B$$;zTHR?D^K2Sr ziwcUo5{IHh~= z;(d;jc3luRxCAc-NFBQh;O&<5)kf`mX+kU&B+bbMSj6}DzNGL`@D_X2qKg4ZgUncL zs(w1@v1t{Vmn>kLik?8t#hbhzt!nnEzQpV__$Dm2-`(CgG<`GAlk*xSnki0l_U+Je z%{N9)U|T44l!PS1`DK6%fn|L3IPjQ&QaYuibF$N(-+MqXlhVsxA@@<|>IU zPm2XPdy$oCzH*&w zbQT&}cPABK#bm#LcTpLQ#I;&xqkUeLg2ZGfsl$xw4j(AV-nw$>L2YBqViVz}9j#U` zmr%5SDcYEz37@uNMRQj370{={)C`a0&H-ie=T@Z^laaWvyYc5%wZVc{N53 zml?mZMJUc5L{*zsrU{StAk${ifxaxRXtsjcCp;JsBO=#j*sHjJyG<_}W&4gT4*n#8 zgpORj1_ofSBBMK#2%ITJh2f;|cKRxx3H~Ak4nqT$RxVFWECz^eOuR)m_d zlGJT>F2teiH4kS;Zg>5Jk^AT{eR9*)Oul!O z>(dDEy}ON0M%WbHnP@c6WbN_m=yj4hOcF6gfgSL-yzZSLzSFF75j6cVN z!SiNj4`rF&`Y&Fi9)g+z1Ax1RQ$KRXi*nx>WM&AAta8%o?>gfEh^s69E=9q1Pbox% z6F+nCoVaFZpSCR3_alJYuDR{`n6XAzQj1XnC$mAC3u44@>Y1#sA zGxAwzB|b#0ujkqBDLQ&@C*M>l(flPPPoyq1mWj^xPs$V}PyA_bPJLnr(N2JwZCDB(~w;!#J6%gH=8gXB}m;9Q4k4&vI}26&_u^dMW;9#-L%=xSHAUd9a@{ z33eE;)zPt3p?YNMH<_V6$L6;r~=F`OfcG(cc^Am5=0@fb}Nqt}a{y!K0g&rau8?3#`TehDTV1Wl9aB;{pe+H;QXRqc{@^X95OixD7dIIXg&f(f)=Du?L2i2V8J2(O3~IFN)zy>FHU?2uNFL zUZ`a$TBm89k52ue9`tW$5vHZ~5=zm-7Ok)6*sQ^c3u&e(Y+S!SueW4$7{vZ~WJ5;P zjh5#y?ML*K8*L3B8ySu2cEX$!HSrfk!(WN;^$eI`9y<7ntS=^LI<1Aq=PgUk{}9V`P^s2PI`zN48>R z4DGLMfMe)E?-Qx362YWDg>8Na?h6N#`}xd{L0ysvB2+yjiPIMH_+RcyqR?w026X-mb21|PdN9jengJ^k_-~T#p_<^x|Y^Nru;BILlynf@U>KwL;eyiT&LU@OY zH=B@fp(qxT^++BEGVXzRCC`{?n9@)LG*bBnvBs4l^0i0f{PO-Ov66AvjSNK?}EQ6{b}4g2BRwf z>JZV>B~O2&lzPM`LtWKMFB1Qq|5I_QQ*p4RV@3BgT6e_Hu$!p=-jWL2)cDq$sDvE# zuaIcIq8*G5MqOAsSj~>{0cXB0BL80!Pv1Q?mK0-Z)LAio*Nk07Z6Hr)V|pe0`M-nU z^8vU1i-cwxh10yO$qd^_-$GAiC|4CsbRCos6kt2Cp>OFw1(F46yrDthp4pU1iCKMt z^r%4gKD;?@QcxzvN@I3f^INV+vzJ>l{npdrX2PFTCqN?j@{?(^hz?f(h-{M zzs3>(^#`$Belug6AxMg4f$2I^W^=57w(Rp# zR4bO%jOhsdm;ipUX?BH1wq`US{$IKuq`bPsGRVbiY|Zb(Qxs`;`|SM?Pq%-A;f#dO ze2Kp0dlurkTebWdV?M=3^(*oyy1wwv<8>aQ<=rY0p>RSG!1OiC;QJwgYh?=ZJ=^23 zdmTl_6=G<|1FK$Chf=G;;_Tq5{g*oGej3baPo5dWM_=MN%${cDE&WHoK09csW94Z` zKJLI5<ekn-00>x1AnMIWZqC7=g~d?+^QwAwN&$ z?WNZ1O$Bj3={(4fi58W@;ilnb@#OOx4a#GfV?|Svl?F`xsLswP`vv?m3bbiR5kndV z{4uZz2=&4GjLZ`7%jH`kyj9URirKEi$55x93DSFgClVB2glUxv&1hY|;4@8Q<`aUL zo@G?iyCYN7id5lo=S`ftbwd)C44e2+c_30#GS)&^_c2UaHT}sHUjCpSDp67l z@C4}YabT^qp3f^X$U@Vb0o)jv43B^4hPx$61vA!ifr%>0OR$UcV9)iR#^y7IXU0k_ zL5wNOKn=K`U6#B>%He>OrL!hyrUay`%lvr&TLI)!!UtUOC{8Y%Qb84S6E2d?%!#Zd zy?;b{5^f+H(_-;U9p5<0ZjL}K^yr$DpVoOgbJ0j!Y4gebYx?p=cs}~&wRUNq?2c`J zZMI7kLdi7j>FNHkIXJeQRd21QC-QCcUhzoRs}9101@;l#f?^_+F%qr1cTkFY6Xov@ z`=~{D!IU50Vbgv=0nqtm4@2{s<=6>b?4LO?sZxWtMlzS&>kIFU!Q?FF@B9vHCW$h=qA#SxUd@q+(8<`D?`*u=otpBEiCWmOxMf55E30 zIXy9b)60K!F1STa_b)A28zFqgd=J;ScX{y*&C}&Ijun0X+g20Izigye!pu15HL5hf zi-`eib0BZJ)CRrE9VTD8G`Oe4ulniHm@=}Cs!b3vzM(JV<%Z9TX)TFMlpL3>Ax0<= z%9MBdt)2pmcW_gWqy!8|8Shnmrn=K#w9&-uUJ*_yx|EMgJ=4Y*?OI|hYU|85tkj{C z?%F^3tDW43<+SvtgE(cdwpJ>iDpFGY>O@{AAQ*f6sMaCSr=}PWv5XDX^>2-|mbeUd zRzY;b12kSBu9!(iv2R-LlGfpoP7G_)_~C@H=LB~zaBWj0t^{(S)C@qkL&h^Z58+cU z^AmDokd|~;1PS@}&Z7f0=!^`Uwhw5S1k=bn;BNN&&IYiALY4&xYJ}SdopKUbQaOi4 zbEIH*P*{IZe4}W&+@L*Lx!-6;HIF${l74lbW#~aHbFuLX#g$Y;zZQ@eE2$A6@2Vkc zbVU~V#ug04H{%%#exb?>&Q*dGhs5+;6nhD6U>Fj0B-!(=BrKNfZCsoz^ufyf7`-p$ zyNlvtB?#m;)G&4?#uEn|>Ly#F^J}#E-kH6N0Hy&0JPX)TT#vE?u-F$W4e-M4?M}y` z%yli6Z=8Ni=H)%yiloP$AuJC2aitmb@8>?N{9c193VkXnLPKdmcAyF; zz{nPG`g|dw?K-&4p`9j%kzorkO{RAQbNGRjFQkVfmBoC`(s9+y6gdT^gfBxFHbq@T zjt@O6=x?JBV8}Fv$ZENF{KE(J=2O4E(v?(ZZiXIJM~&42 z!RC6;q-eI36uUZGc!rkFevg9CCM1>WA{m)v$4%9EkL^zn2u*XXcgPe1lF^O z;KP(R9M9x@`eo?xWb7_2f=tEIMX(Lq=2HC_K(<97T!1l!?;tqmgF`|ilV0-V^!P`% zSz(ifxCcfXcz4f(s-!tam3J(mbzj7C1*6%#Qe%v$@TambpA!jCwZ( zE{(U5mdXImet&*t$XGC)FX0rrEReVFI)kUM4RM#umK};aS`995;ApjYesNjx6V?y- zci zQ}t7jkZ+G`|Pqqg&$zT|EQXPVda!V>`9$n@# zYo^cR)L46n$7JA2I`@=#Xo-lH!LGd?-!cv#>`gDCcl zOvsSHrwit(eIE<27`u|HOF}&8$#-o$XHo>j?&%_5#Bg4wO>wWgkh`Df3?xcbBZo=0 zhlnF6DUVbl_$EHxhJ~5fZ3@5vVdvhGt)n>2}1#mwZnDm(-#Y8@E+vnT6{*M!?E=L#DH>yg$!9_uv4{qq# z#C5NLu05wW8x5D3zP+%oS;oPs2S@|mH))ZuN=3~L*-|x_4QSTrS7W|R@`E#Pm%);E zRW1p$lgQj7yz*!Id|+($h8pl0XmEQfHRAvurz}Sz58KX&OST?~e&#b`DoMmAUN^5h zw1neQDaR{RH|9>S47c->vRtQ7^3uZZ>~S9hXbFs1U;qn9P=D;&g7XgDdxEh5rQD3?h4tE%BAR_&+fP}=??EOi>BxjQ)@A(`pN2|8~F-s+& z=jv#vf>mO|=_&VS*2v`Itwcm0dx${e0swLP+~v#{JfBe%i>-L2 z5#WCrhZP5uKTS$8P=Bzpu;da~Ad54I{aR)`eR4;}ST+$XcR`EZ;h(yadmgfMkIC>kOENfY(fFAC_~rntcRBzf4<8SrMAO zIF*4x(!`TygMy8~Oyp1tQWXPHi+dU8QNKopwN4*$q2{WEQlDj#5^~=Q zCYrJ75w#{WFjG(_RGM%YVFzdA;vj1n)CAY7Ot*ftqEf=lY8G)eRXtkiB2d> z9b~@^tM{rkOeub%e3mPi&XoNajqhwgK$%qvcEUHLU)YYHip2ZX#@l?A&Tx8;g?dEp zW4+>Ab&UQa9x*V=;ac;xGH>#~u=iWYb&{<$W&F+~su(gnKyRnRwk(ZCXI%kd22yH$ zx3|a0?1@>{ajR*X@ z?*vhgYEzzsr)DtJzGNlL;gTSQ4TKx}hC=S_4@tu>7L5T017npLo=(1=)aOK>@ zp#K>Lyf?fP~7qzuzwSrtoy8>Fir7 zi;f9I^AyBm|27P`H~zZ!aufRHKZ(B4pq~@T~$5wBGj7II=zi z{b|n1mtb7~wOwx=7Q|+X8Qq?fEzz=~Ni99|2kp60f=e0mOX(~45ax8{$ryBA-h zI@NYiuJU1s5)8OAvGUs;)6jm0TLO}Ys(D>R+$#U8CQ90L+If(P97%Mj<o78$kIe5&qWCG8OGeL?e5!rtnF~vRDlx4&^E}3wkZXHCHoQ%er(5DnX zcv}fQCCKLe8Cc*-q5nO47+WKTNY}#rFDFt9ADI`SCEb(Kb@jCf?JevEekFMiGa3B) z2EYDOp2R;l5kWXDK2$5n4w`ZHONosMtR1!2Q%yV9kM zOS)svzc$IT$pLV6(xuiZONY2W*5T;{r9oxR~wDfoKF(%M*;lCc*7{)7at{Qn}iVBWJ@;4=LfJhdURna4alL{qyG(4 zK&-!ETU;5cv7Ulie1pju77mn*o2@`706)xIZz~_}HN0a`hAdOIfi^XBSADRpRyd7v zKN5APD_lcDFj*T(ml>E2wQ@h`qAw}_N2)&|q7zB;5=FCIsNN3ba}T}c@=UWb65d5j5;)GV&~=alHyJ;2IAIA6Ixy1l16J4?F%YQUKp-s4 zXE$}z)>o4kK(1{PmU00UgA3?&@9SZ9;{4lcvOqN#@cW3CUJl3I0cW!|o5c#W>U7hX z=kjk4u5dlxr3m6Ulc1H8v8=70A*!W{Gf@O)W=d0yXm7kRTxy4#iu!A_#5zuxs9$fU zs&W&gY2($nWNL5oGRj_0@)2dIj&6~p9Kq)a9T~jG&mba+q!K~*n_1P)SI9;_ot7Re z(OtoXK-BVMG2`Xm@(DmqAtZ0vHP9F0y$&X@1$%%8icD??5as;Yo z43jpZ{E3h_uJI!O`*MPbw?erBXl2}>Io)7_9c`;C#{lS|o6Z+A57A7O<=env$q!c( zW*DH95pRc71w70X@+py3An<`=?PmPQ^O8wV7UFc;3%d)Rk{l{$tAUwhI}P(~9b7AAQUU1?ifR#KT`Fag5z6OwTXAO2Q)eFdhRCx++E<;pCTHD2 zmA#x`nIKL{cghOK&TP}pY>Rdtllrv{Iw51?cVIf*Uf|pLJrcMZm6Bu-O0-dcexO3L zvCT&;finI~s)0}yQ+&Nfcd_pqNwf~+xp#AEKnN(4N>UTpfN}QPYDBdw<*(Wlr{p-5-HKPr&#kZSFS^)RdGZiY1K5EdGe zl3?Noy2YlDY~7fri1R^60zGz#@|tz4ZChNSa1T0w;OD?Z$aHrIsJZkRmOB8O4{!bC zjs(=Nli<8vk3N`6+VvRDWI`5^Z93UX*m&)utpuFqL=0<%TB&CpIq0P%4=CFLxv;(Q zJGD%9pO4aKnr$`PqbY3DjWj68BFhVio2MhBwZG5fmqSaVo;&+48Xk3Yl^MVK1AL<$ zLyX70=G<59e8u*$5mUq#Ql+fQeeyu_4Xw!1C?%^4o>Am%!ksH?E@(j-^g9}-zCG_l z_V53>`_H8ICX@B3dc9#l_3ng%Fm)a=I80ZL^!z7Q~G( zL*Cp8;eClQ+21vpG%y1n6Q(HEj3lU5#eVpe6=wE{6Btwprz>KEry2+!G69mnW=?8| zRA?ojOX7nx`6yC|2wY!G?e~Bj74`&f)s#L58$^JegMEmKCZfbqT9Mp32Ocv^^$nb5 zr4S@2Z&Nl4X)vM2!(ak*cS8g|OC?Ts&G`A~8UdV`UkxEM+cQ*jOsZV1j}gEF-?G!$ zq?I5UO4P+VRQVN>_`E4f<^v7v^;N$#f z7S=fJoYhz&Csy`<27G8U@KTf7)?B{YZW zV5&vU^##0xFuYSm5WzdzLpGp!cKhjJk-$C%q>5}7TG2P5#8(2hstrtCmU7q^GtI2m=zZ4dADu%N8LEx6{u{7HjIA1O(B(gjK*&-7WHh2eG$8EObnTYsS zTU8sYp{nEdz210BZQO_XV%@i2Bh;P!0%d$4j274}9IzHg?WX2HD!i z&ns3OUuH>y4GLCbS6URrbC=WP*cCXa0m`DnIaJ4f5LVC`2p(%qdZFSM?)l)Fr5#D3 zgw1<)z_w0@sE|^G>Iq`Qt{Z9>su1e zLXHhW%d9Y&mJl4?1l5W_tE3&uYav_jZo>DnJ;IeDby;oxg2&;xf zsmV!?93|{@ss_>?j0;dk4Ek)1n9~aC9oPw2;0D_%Es3ejzg>?$cE8iNU&ymi zHa#Ip8xX}i;||I2pw*o#O2GQbQUhpxyB?Xi6*9m{H7s^krhoZpgxb6=$J^?jM%(pVQoqEG} zOVvG0sYK_5KIW5Gf_@+skXrCcY^N(?O5qfA%>{`iZ60jTAU7tbyNA{1!<|G+1IOu= zsHverX9`4*4B|wO)gG7|vf;G&$o>|}?~_{{v`Cra%tJcqN9>+}vRYkMkRHh^`|(6r z=DYhQ!OPYIF>$*deJB=sWhFm~g+7?c>-F7TaXy)+$jOTm2`sI8;*lHe#oq1Q4X+3S z?$TBlP*P%1YFRI*;;%^3`hC-F;I!s%~>dzDr{C){j$ zR<5*vX76SYGkXB9DCVLk_2T%4yI{~1(t>@9x&sPL{N@5fJ~ZLjZyCXX5~BI|?Rs?W zd)5gZG4l0@T_x9WK3fb+QJw{KyxvxiVMnTn>!cjG64>A?F65i|MFs&t%}#Qk!px$h zC8He^LU?*YeWKkRkQ{Qc#^U#QIl~Wo3ZEa8Q1dF2vVvHb;Qj$)tmYmhgU}ai@Nq>j z*KNdNa5+Im&~N3zhe)2=b(#(a%RYw)i}2mP>TIR4@9vwERsn=0J|%{W@pc&8MvE&e z(qZ~Y>D&PFcOotT*vg0|9|O=nQJ~;hmtZ6Bnqox}eNE=#*rP)AbV#QlR>AJ%?*Q2h zes&(UdiI9Kqfa<3<c#fQdUXLD)95xqV2NmywWqgL2kn$O1^Ut=REvb%0NHKzC^4bhltX_pyY+d+V(TJs zMYL+8l4SN%_tDt6@FP?sHn5N>?b{l#}6C&YT#=0$ne$<`TfTR~!DAx!> zve0po-EhkA2@+Pa0Rpp1Rni7?aEq3B+FjIV3_Fh3qcg)3s*8rpTdAko>_#7p5$Gpq31-P_YS zYBe^?$eXzVu~VsWdEg%0hmG2vzDiIZS1eoWUC@e>#^!DDv|Srq74BRyn+ zRdF1Mn66ezI6k|{~8KrG2BaTc0FVz=O=?2S|pEbzQp<0Iyb(Mv}^yvQ8R>i0R zXptz=mUNy}Hn2>0%&7CxN1{^r(;>(&r?!^Iq{?SLdrAjUsY~=^`wl`XbNRFA;N6ug z`)lA5CKTD?iB0A*`AqX6)AmJAD!47kXhoF0m;<*$e)N71KTRxsH+&5Aq*YB_c2`q#ABw=(jsWA45 z1}R*iIqW6O=pjm_3}U$ld)J`*GoDt7_ZAuGa1t9!+n{zbrF-jt@T`Kp1E#a3l}|+} z5s*mlvc4WsbUfv#V|BEIs)TF;Wv~mLyjkMI)EGM+jLXR5bycvZc&_{}Du@w^00|9Y z=}1ZqGnWo5!T7yArU#+2g=s?S0utL?Nyj}r0W*ZXbNmL02n-w`!0JLsgbHX|U3~+f z&&o9E>chqc30>0bE~|fH+jQ4t^I37xs^)n#;3&qU22Y5alV~9VHTZIKmf`*&N7%MA z*yeGD?uQT;@Z0EefS85AIqnOA@MI#wmoo-92m1lYrVUx3nwh@FvCcuxLk>X8tGk2s z2)?&uvZ;r~1m0=VDjwm?xk@WFjFV`fbMz?Sw~tL{)}YM`E$Gg&?9%W#wDAIrzK6I% zBGctU3o23Mp*;=Pzrjq?Up!Z{S*=A-(m_lQI%VgrIR%o>j1nEImT#5eKm%ePBM8k= zUr6y5B+;N9W@!r{20tS#a8Z)KLCIKXgXG>(`A%z*3c;cqOP!Bs#!Njanhgu45fHRP zvM4f{S53jOJ@bY3|2R>$d&FzVcOu?KjJE@eL@3{wQ7l=SAM~hTK;Y66rV^|J z_X=r|*)y<^hR2$Yb8qv1<7!C}-HzT#s2rNLY}-=`PdX3EuHBc=eO$5qxD+-UHq}ap zAqCI+W@H3X7~_wQ@R5FyBrN8e54v#t_|4TGdu-`a@f6c)W7pg;7Yb!tQn<$Yu(a}j zxLag&qPJ!R5{ZFMo3THF9Hte*ZvQfYM3d(LGyz)fu$RYaNkb0dYWJ*wka|eJ>si`TUZfWZ-V~}_) z_{@PtSwJ4T!T~Y^63%nfTix9$TUvBGQvbuHT0BokV2FI&h=aCcH+M9tG<^o7cagyE z7qYw8O5}{6hZDao(~4mN2%^lGb|!m2aT1sGNB~GypT^^HLPJ7jGrpvkxC8+-p#&k& z41Lmrk6N48GI#(W2K}=I=vI4<@<++DJy^3VxH8xjO<}vq=%ckndYcm%<-8RQafmRw zNu^Dv{@95ox2MUvx6I@GR>Lc%_2cF??^riCx`Ese0Et`Tfj$|8CT>wcm7KmAtMRvN z9>3Lh?jZQ28KdPVvNcLN0+bKZWf-OI4W|tVBb9HAH@R2S+KphH_v*4rZ*2KW_*C>Z z)NV8uuLMNpdUVbEOz2KYN?;K_cH^OLHDzbB_vl^a3OKEUhBdc#5k0(j12JD4saDb~ zIvC^jWfdU_f(t3i5ivLDUa*o`iO$yUEc?E&ZX6|76FLZT#1RLec$@YCbv}w_ZDCGq zH#;%;dWC%&jj^;f@?Oi(5?6~_?){Jzi#Z?}Mzg9$ooKKU1CDWfm?R}tslYHN%_^IM zYRx<}+>tgGR{RLdkvc%~D|HE5co|^q?7=X%x!G{XnELo$do_quqb+aOqifOC?iR<> z(d;Mp^rPyEY|^DVW0hSs(?mD-V~cjR#eM%O*20_mSPDakLBI`c?h2~ua0ip~RZOf| zwjjWwAALu`ZSVK*QEO#SzC$Tt5!>Lm#Z^lCBc^DQ44N*4M}5754ebzA?UTqG9T9^x zZP2XBCELL0X(!;4kqA%#Pu5zYvo`Qvh*3ch)F2hF8@ZDj#0HQ>Zy@mVB0euRgrj!@ zFm{5SEqa4OOGd$(Q8Q%vK}O5KggNlj0E0y8Q3LNl@#*NQL^Xt@jNN1HIj;}gn^gXn zRg#S`@o-nu4wafab<@b8zfqsiBN~W5D96Bls}cK*p0bh|u;1+rH+g_9gB)w)J+5Lr zaTWNbq#=S>YEZdRA<%#1^ztL)6ymnO_T%x6*#K#-tK)css2|VyZ@E? z*ulKHT6-XE>C_qBd}&fgk9K%*Ym+p=}iJP;~ZbXZ3;|#+JHWk3Mwvh6-rId>sqmA0gsL{&wJ|oi%Zs zc_0B(^yjFqS=%CUOURHgRNK?booO3KCKcA}2s14KKcW9P4DO$t-3H{?S2>l&oEGt0 z;1piZ_ydNH?+1oI67fCC_5k}HL1d{=SSmUi(aB0*DM45KHK% zteK%?-A&8N!b6*FQM+Q>oA7!-1tBexw3v%xXs}3O!3%)!G}2=?G2|_9b+bDb5Fh1! zQh_b~L!&avSQW6q3$RC>90fHnKxXvUnb#29G!Nf{D5g`03s!Oj)HP!nMOCtQ`yR+{ zatrUKccvA)qVkeZmemkwc4@;gRwT=B&oBYwG;XVdFBSCA0TCadQijSyTI)s)Yy$dr ze^7YsFloZV0qKxV%w{KLM2L|1TEcEEz)0P|Ny~mboI0wyyZxbO=cWNLP%Fr-tKx~e zQ@AjlL#HnbRjUO84)6lkqid>`b~D9s)hLl#J?aj}w*Y!RPve@!$4`7ZC1F|@ZaYTo zj%;=%rFz>Jfi^gD7sZd&q(_5B046#7I zTj($g-;MOibhmpp%=HR4w}u247K`lZc~&zHS1CKzFF~T&Fbxc3!#Rjnoav_7WVu04 zc?ZF=3QZumBSf(U%Llo2j8R~Ama){8Xw62v5gte_O=1%Pj@+G@=z-56VlnAq+LCb= zb=g0B^7RO~l3DiJfS>@qV`V!|h?>XD!nkr)w97dkUgJ`4_7WfrnK?>^ zOloFQnw6HK7gE_l&XwsUaf9sOi4hP1u(C{UEvN7HVOxz2Z+D}lLeh8WCZ)7=F_A$+v^Mw zHS92DM_g#qM#A~(;CQ5IWig&L7(U&srYzPCY^p+1$_5HGuV5_5aRJymA7%I90%!tA zZj#0x&9db&&-vlZ?(LEjJSHlzWK|xmeJru;@+7nN6 zz5wTz?rAgRizXSLRnPgpEzdmsB$D`*qDarNTxCWYb_Rdf0(;DEYWuS8(wIrI&N;cC zDipmbx)h?kyaZ%)-1Id-mOF?puVx%!x+b|k+mJGF8LZGe>@qMCtY4t zr&;NazMl?dzcQh~pA1Ze44k%k{bVT3Cz=iW>mn+B<~<=vhCRjTyS94RZYDR_dq8G0 zI27XC2A>ue_tKmCEiz(092Q77$dm-C4?7gboiQJUS_JfIiIXGqlg?LA&p|~Dy1Lt- ztx!a`be0Z#yBoiDx70KJIXK>cm{%B)9V?_9nKsm9ThkZ(FU(?cM(uVzx>_#qN9@zM zt?gNUb8G?Ggr$5LB?<=vkT*El7UT|kTyNa5zp+Zw(N(U+AXR8wXaGif%G{t}f8n|E z`bMaN5PFa5-!BPLihuSIl^V&_hP6E*NHFL~=}302bog;aKQg9N^w5%#`c285J5=Xz zqSFk}shUz<aeUFSgLX1Op;Fxao1zLr zwR5^f?MQD!H~YC(m7xYN;i)iNeRCuEN6`#;*~;1q3H7+5t{P-`^W*~4VMlVP-MxRN z+m+?$OW?3jhmo!=elLk0h<+M5ZZ4m&OXa!!DJEN%O`{UKt!98|9h! zh0;BM$8i{Ma3+980)=fmcqcvpYmEdN6}IGUO3;{rv4nbg1Ptl8M{s^OLwYIv)edVp zv@wCz0-#g z$S>zPlM01aeGyk@_m7PHW1z(tZ2CyA06V0luzS7**zZSHjGRZOxHCXd1Z2Pnxq{`0 zsDgIdY(a;BJzudUcguxbq`^7oCVwm5Hb8BF2p^D~?GzNzu2j;uMfBX{c~rtk-QVFk zn8B-NjxKs?>?2Yt0A8`r7Lzk7cw$31r2;9j+{G3}=Cp01&+3Yz(G(>?3Y?-OM~YeU zKqlOVK06=fJJrRKiJLhF9slWY?VL&0?g2>9(6T!nq47W0tcS&;$(Sr#Ds?=H;C>|A za&(-qP=jrVp=k>@#yVwEWiUPsnM@`}t`!vu)m>sK2(B(0b?XM`%xKPH{woA_Ayo7x z1Lt`k%O{{-M9!G}+UUwO;kucIx?uf37*ONsQ*&#pbnt&j$C|PUP9JYmBs7e00pVgL z?9NG1<^U^eqZt53JNBuGUC3O(|1kq^3t3LyjK%3EtB6~p)0^&~N4ekSco4oet9$U| zRX0AzXbuTy$eu*>V?Szm&im>(gYP17ac}0z->Q4&CfeA;m-%K>W|zl;E^?~{hz1ia z7Kt2Xie4xZ!}N=OU<8g^ecQ1D1|Ey?PQ zk4XvKV5T+>ff4VUdk<YXlDVT<&u+XK<;D)F?-1`2yME zWe#ZLH(g4fqZQ`!a0l8zQU~NTo5nS4Kvct$GscP>%41*jOJB!z>PH;tY!K)S0<1ec ziiivZv}OQe|6$f?qz`7iiPg$Gk1cNp(s5vG_hh)gBNz4f%cWr*+60TgA)89Au0}Nje2$G2~N) z8cG93O6fd3e3kn_1@-6oaL1IV0S@sJ_Qpuj9BX-B-Wx#CkENdKg`VsLo?xysw z3%LMQDg3QOlOM4q`)X$Dr@(Bs{jHwJtJ^j_?3#P1Ozii<_Orun?;x=Ux^HNa&E=2Q z93&VhI)Ipk%y)J(ie!;R2)+~=LCR(+M6qE50g#YfLOuKqiv2C!Cv5}mwCqZLyy$K{9kK^}45-TB)&N;g5~8}^ zQC_O!(#Kh-Sq#7gyw&v=>l)43D%c?_0{(jKY+2b@?RUFU8` zsXtV-7()RvcwSK!5L-48#J(mY`3V*g;lnJ*vErO#Eku8B~9B571l%3kV8N7`6O9f_x6tG3#@g($f&FEJPr9bVtB4T(wJBdU>OOnUC3bg!e|GEQYArTk#qcp~|3A8x8DMffw@5vTaQ9;7&#S)8nd z$&Ju8v5f1{HE4|T$Whr@p7?`@>Kx~o)QTBDk)GZ^F!Rt|>R^GQmbeq-dA-k@yyFd` zb4uM=j?^0vi;pNfBV0{)U0gwGZ-=PUdz(K!5eQ%^CgF!@=~E0CS>W~H^MHLvhJ1v& zx9b%or0I&D?YEi-eFxMF8*!SppKxm(YjF*a>9C^a5k1x3`xDdUls#wb+|b=Lj*ql~goMQgrY1qPQM0phWx35@qXX zBcBWu{JFo60o1lTTcqptN{z1T9%sOiuuSsq<_e_mcb99w#&GU;6YSRm5^nYzs(A^( zeX6ihP<2D~ryG1T@x7O~2*13VNP?LmkyZEI50W9MH{+MJafCNMcn=^!s0Wm2_Uuwg zkbs44)2$*;SXH^$AQ;>Ux0;>L1uk_JG4d)$RpP(8d4rpV2DE-RPsFye*jiyNI*;|d zG8(Zo39^t~O3|y>z1j4pY`YjIXwVYg?Ypi=FlthU0l5+70z|{MeZjLsBtN>B)O_Br z>-GK&AzQJcJ0Ka4_CFPbrF$T4ucwsJAlXLKdL|750{~O-s(uO%9UxK$6BVG2@Q9`U zmfk-<+C;*OPY$p}>n!QX3bxtLeeBmk?^gQKM0D|a#VUd-jsqIJC+!RS`+=6Bt!IJ> z5|I53jK90WZ6u+jpb4degV#c&3K6*J026BC!YsRa6|13@_i47-sB;5vkyiamZwiPV z%(X1{ZYk@=%Zbt^p=@Bu|>&=gG9`{XSh6M1=2#4@+2LZl%55wfP8ySx;kN z1b4r-Gf;Sb-M)19QD-h8uThVRsZCWw1Yn@*FUJ+P2{t4{fq`(9Z)o;=`9m}sAM*|w zIGsVGDG`PQH4*OjO)8BH&K`Ufz{!V8soHUm$$dzpfU6K8fKe$T@RYQ6)5h9$Gd>$~ zEW;KNAFvFgd60Hx+Qo8#-Sw!r9T;#fxC?Z<=2@e%{TWw#1f1(LLBBm;m)Rr_6vvzo zfbJPdUs|+q&mbVT?X`7G=#<4k0bvl0fI6V8A|im(t&E$A9e3Cy3(@WInkCcoeVE!6 z!~>hMEXM5tL&7XE;lb#i=~RR%(jyyqxnzSl$CZ4bIa}0`r(qZanr8#7Ow}d`&Ka1M zv4O6pKB$0TC4We#+OVk#060vlD4Q|m;WU%}I*S6w9=R=EVF=r!%bn!dPFQOT^pYjA z+)nDcnB5IZr(|FBc0IaQHFiM2HhNBUOX;Xioo-0tO&q_Tbfwsd2vP~0=u4EAL0~~0 z!2_uBc9-JU#Rw{cP=aGPC-mIluCglfY>KI5CgV(Hwq4L+hIy=a$I6JBwZ${{db-OB zXgE4fb4Z80s0gJ|=^^|tDAl;N4TdvJ#)vjHgO{KxZHrdC&=Z_??a)i=mWqvvU`d4z z+$oV}PYJWpCRm0ZV%mAj?rWQufWJ08v@mb6`>QcvSdW7^#{>8GP>$?|@mQ8Ns?Zp_8JKWml=o1O81@jA6$ zG>Gi9KO-%&nWc3PI~95(_;*`mHD3e{*@wySDdWkInsYi)y!c%x9-*CCTFurrSpD7L zFaz>7>cepI)qk5+$9LPK5@VL7#6RyoXaflz;5uDW1$gbD5e2+g7IDyZ- zQ*F8jvIuDh|U}G`LUS#>F0u1hxLb z1LoGEGbj{=5X|;R_YN8Zc6h8yiPk5VD>gf*eRn1$$$02bvm2i7Oc}CP%GOsGyd6Mo zv5s_aA&MTfU*WnZ_aK7}CWYwVK#n!-b^;#v#uxyd=nbs2E zUiTkkHwCsH3ME$k!OTjH;(@@S|R>p*~bYPRC ztWpLxCXKwJ)E&4MP0C3p-CTE-K=Pr1ZvlG4Iv~F535HXim{M-#k1)Y~F;^KS2mCyS z5!Rhze*D)y72r)MjVU^$oo|9?OnPRKL}piIfDB^t2ZqZw-}vL6-beN#<2=woFh?Ok zbrYE~9*iPdD|9$2tJ840Ua`4fC=J<=i;3pFhmS3(cPE!EPeIdE>D2?!HZOHd)nj7r zW!db+Z5w?x!t;f`ZlK-ML2?S6MyG+gr+=fSXj>fjV9%2?!K?RH-rT%AwJ6mh7wyXf zbptTm@3>u$3So(cebr6c)svY%+o_kq;8_5w{VhC{BvZkw8vG zb>z0&%&G@(*ZB${J-J0&N&KTu{_bE&>}kIP&qz&6P1ctygCuqglBM<=o@hF)CCV_| z`ys7v%sIMb?m=7IEM$#%@aBh98j6WP(2|zwZmV;%rELRgM2S&6P&YlGgV!WZaYiF~ zb2_^66u&M`f9Tr}#p&zy4(sFa@9DgEah+v0%OOo@a++@@g^w7l01EhuJGnKO&@(yF zPxcmRH%^HocK?j#7poxW^%U1<_?Ldd4sI}~*g}A-+k<`Jq?N;NeqIsz^%uo*)E;S` z-6|$v8Q@Wgs<~cLA4n6H$hslA29214U}HF?2@^rk2W$x06aXI5@o=)X97Iq~;sR0J zf`^3E9ZU&<^Tk9bd$>inr9W_yoaujQ!3ud(1VtJZWxc(bhN$Ei1l}pG8^MuUs4U(c zMRubukb;xcc&I{1MRePFf1h*;e*|COT=TZUnMMs#GdDw|52GS(y|P8I3|f|AqSexc zd`Y7jn?-)hPp)mEHp5YX7On5tMFg*Uh#XMc5{Bdk5IGr^W|tj>$OUa~r^b758Zwb% zdF}9C0`jZDZ%cD0GDXoR8NxtXJ@&G!F`)C%2Q@e8(a4iYAG z%FtCLtX=~)A0-QV@berm=s*CWDW8DdaRDD`Jw!aZ&Gfp|)oti=s)R{QT$jvYbtr4X zPG(J!Dd34C!2%F%dq2Vb!4k|RQ@Am4D(>gwr3VGR{zmRBli1+kV-?>jdKU=<1S5XUYmrfo+D4nL>;{-gOd(*9e&Q7WF+7T6O&PE$Cq+ z^(n(+(R1`lj+1>)_=*L8J-U*NWHUt%de5915+AeO zYr1m9#B854JRSuIHz2u*j#rdh5Sx9SNs5XHct2^)-49_+_yJXw15E`GhJZ-W#L!o- zLzMLv7WC{)OS_FqX<3weQ$|kGILIGtcY$k#R5o(E>3W5mcS2A*P`H#OGQZCO^8c!hBAp7935U{sFQc z;8*$hhEIhOc+5facejs6XDkrpWg8d;gvE1$A2X~mIL~jq-qPbIJBUFWMoMVh@5Z3X zvAV;4IlaxzXvs?k!vh~<2Ap7zqE1dr*MUuky{O-k(3@|kwm#DBbu2>1979rR{Iu8Y z5y>(Tvzl()QMBt7o732nrJ3@LbmB8aT^+=w6&n_aR3_cjyYsaRTimj%=cp*L{FO+X zpp64BU@rp;IRK23QZmA+ISLv-*?j|JtuK}*D9@mMiM1ewMq#^2jQL*nDiJ&M%Jqn^ zfTEk+9H3iR2Io9@z)hR#X~j=s6FmP%+44Bfn*;Lr3BQ=s8<@tnK!&7EY7Or+KgQ|d z-Xtqu_*}N<#7Q^6unChSIe-Se4Ui%tyPq<>5}l+W_>@sYfETyM=LWl^2Y&GiLZihK`=xEs!=TOLeA--Y!p)UYn?zCv0Htds% zt@EZg7qC;qDSO~q!J#!Hc2{q4P$k|e!6HUuabTXkyzMPc`040Mvu|MTLALqBfv9d) zF}EWXrPXZ0pOv7$Tmg#{CEUrq95mN3zaB^#=rmnIEOTYv=xap?l91*q@gN*^ml#HV zhTBW0Zs^Ja-^F*q&>QuhJ9WIIPJ>d4a$WF@5ybB%N@-L&eDT&#(2(g%%C_qH&j;ur z60lf6lRgy*6xmEq4Q;#2xd*63p8tc&xgO_1LBMW>GZJlRBkOI}%@x6cLzw1p!9#O! zmcgNK19P0fm&6$yoJkabN9i)|>qa{m-6kXlF876%3!*z?J>S+A=SL@0*n-^6wFV3E z4iL&cF%NDqZrThzVN;ar6+9EbNs8q5+YVmu__-0S@_f4yqi&FbL8sFnbRP%lLg(;!2qt7g|NXoQ+>X7{%YyPB0^syQGg{;mo$lFtWwoE)??mRzue-o@8+;ra6w?!k^PN&`X?wqwmNa| z4h$&Ih2q}{vQZCV)?qpiLF85Ju;Kp}eMM=<@i&I_J4>lPT z6D&P2B1OX#et5ETOSksmJ{Zv6!$%j5)nTHlKsNn2w7Q_8fv%1p-;0|@dWiM{#dJI8 zqf500ZJlAh))*h0%;$0q1X{cFyS{G=sz(Udv|2%!1|J&k=9{|XpjaR>KO&G|aue2m z4(lC0TZ%BXG$V>gnH~VEHw~{RqW5rcpeP$ONUsEIqjw9T*g+p}5+a9(mV8O>(2YHK0VDl(m6Q90(DcQro2$j!3y%W9Ci|Z#SCWim*6kBJ7K00|`+M=J% zY<6quT34Lqh#V~JgZkoqvnOQFwa{vbH9;#N%+S~CsM6vK;>(?am~MP z?SL;Yt8`>#cq$R3`G#ADb#-d4L7&(}2kSjtD(Dp+Jw1cT!r}P&?EE>Gbh}>J0{2C6 zxaK?R;GzqJL^DO$)av5O;r)YV+8t{cBO)R4wJA2%hT8+k8NoMjle@cokCj-Vs=K=Q zY4!I+%4=D}T?D43FLjJz4r*?O2E|n&+K}87ddZ*W##J;Y-{T_9powF(N^^9=U_1&< zO#vT+MKGNjC3=+I4wqwo`4SH1zDV4Wzt$z>eP~oV4LEu%G7P_CRn0R%=6M2K4yG|k z?-4!Mc3R`o?V`KmM&Q`0d?q(5)J8Th_p1d)Kpka|=?wyIMu+R5n=&(9jb3=wW+m0U z1}-?9PhJgaPPNjW-gJd#i9H+I(P;?gNJJw8T2CazY07lHAg2nSO#$vC?NHw~Oq#6ijR zH^BGoWK5$IEp9&mmU};WxhotEt>+>r8E!uVJaY9fMdk)X5~dJ$vL#B8qdI+?mv=_$ z;NzSH1~+8`vlA@P)VDpYBdT@Wk2ljTVvino;N1dclsdWKDDly(kTC*A>RJ*CWhoA= zEZleA5^Hfj)*@&*8guUU6BzzS8=*i}Y?nV#Ts%$ka;Dm{y`=di3FOY6i|(m{#4&6g zGd}t%PWX*s6CmKd0(V+Ql-5oaSz@M^n+_|@Q>^oVhj!U$<`gaw1t`|iVbZqn!(4dHxj%+VDhM*adeZ( zv>|l;<2n8y*_@g_u(=7%@wppdDz>GFv0 z*x&T4H*Vy?$OWrMrPVe4IlYPG$5+S4WTkJ)$!?a9pqrm2*&5j3qIc? zOBVmqHcDpELd;ZJ-I*n7KzO#9JssxLsB%wkj5Pr96HzJ7SL3~LrUihTR%563p9U5c zKtsu`1yiJWsPz3n1URB)MqgJ3!l9MZOXbzag9xGAW5XICC*qa;#cTbZT;NTy=mx_F z-nh&pa(uLbULen~ZXQ)T^SqpnmnA|?vqc;$npVQzy+hwk(p{frq}qUd0)|OyVdq=J z2BN0vxztqluwtM`fv^Iv$Y2Wx5Va%#<+`o%>UBe737ot_-^SOmRuqM#F7MSJ?d9_S2oKmu1&WwEV8eJqZ*+-3IaBekM^=Og7z0&u7qG-)e zuK#md{dk=icj!(UC#32|A>?agO~8CP@svh&Tin+wDYN~gfLnI#tp$g`f>2itJ1r)L5xl2D))K3u6i zLXG3|=N4ud@oB(?J2l%05nre*1Ac=?T-_&3igYVW)LpfsZdG6D+tt0-**igP?Bn zWM>3$J~{XRh}MK$I!2sMNS|a#mgE_^zZ?am7_H_4Wnt;d>{OLxF6Okqp}wJ^h3nFw z;&93>67(RgHuHj0HXB?d1QlS^0jnAv1t^Z(O*E@7&F|OK6GcfNV7j`6dUz`e3aF9` z!iZKJ>xop`c$QcNjp60eDxu#Bsq`)_-RR-ISe@NLoK&jLY;Y}MHE3{(S?Wy7sj(T9 z0f9XMs1YTV?j2d(GXj_pB;3=%gdOUIwBo;K1$8#x2p*iEjZx7u_p=RmTkN2x55W z00I)xnsn0&2AjCGxnpa2w}b#ix-}(=d<(2RYz&l`VqwHHO@bCsJJ{nKE+l~i)TJ=x zN1UbffM`zo8fmXWC=?DKq3`q0T0gby<+EDx~F; zIIq4R`srH)FdLQXTYxpZeh7V`KY+gMhAkyG+@MOE?t;-0`Le9NQ>YNdoSCzZQHhO+g`oT>CQ>to7?xMlQWYsGUt35^;D_U`2YHd0vC()uDm-H*g{@n zOH_AjdHbjLGA27y#V4L7L2c7c2oq-LY2$@o1bI4N;GxxVwXNioDur^4;b?Qlkfv_#q~0x9zit;&wz=4DwC z0uSBoFL*8jZ|md$_8vpUVC;A{=}3aq9Z~8kO7Sqv4URzWn4;BsEH$Pbmv3 z_&^#*ByBL)+Se4*II(T8)lxf0G8Y1{pMhiO6eyRBQ2G-=TXeeE~ z57V?nHURml?BHdhJFUM-2}=A-&@QiIt6Sy?G|>3)*)$>XriluF#TxhQ_qq6X`wRR( z($SXvkobE*002VK0RU9~+vsS!P1|vVqXL4j0K}z3`dZVCiTF6z5J6@-E6Gb zz`mYGF^8_j%}X9UB6rQD!L>MB{9FT%tZ< znZFXrW39mc&@M~oZML$iJ*Bl*`W)I|E1WJFh(V>jRR%8f=gCNj#B9xFFtxqx()X!* zAf{($mWPogNc+#Rc?CEhc{Q)8OLj86o^Vfn!V{|%9ykU@y9__r1v0g98z+6HmPT=d z^-xgO0IX52Sz_13tAy*+Fb`v;!rEIASzzx#qYEo?r)Szn`nhb8yQt<9eI*Sm{Oo++ z7$j=6w>$`P`*}YT$J2ubOZvtr;(K>SB@wkqP894~l^gLZxHA(QLsJh@beYMbVPe3N zYGltq8H&)R#8c=)W|bvpQys--9$Giz=EWV(W{az@q>|#*g?(jP^^zh@Bezuxej`$e zVwZZ7qI#a&t6ECfAizo)dDT$luw++OT>%QMX;F4dp+uVL$dtLVUN-uQ71?D79&kC&p_1Wnb$k(QR@g}mP0JT121m7^`xO>8l%wAC-x1md?}d3^&2fv+O#p z=Wdn@>(&);1CZsK7Xb``>y=LOb%#z={K=~AkKR<7;+)oTey$4P0&Ua+&yMyF)^ljr zC2ix~BRj)QHkFVWTPM=Rsu^VuJ&APn=0#Vh<^*;{<>D&o$DO?GNwW{NzssSF#;<*+ zk45x2I(w-@*x%Y}M8%3a3pV8uW-1HQI(n5#YfpQ zKlwVp5-xe$Hg;4{8~uL0{9SU%Y2n&-izoFAH$HISblh(c2thZM<6mwh-)r*n>uQ9Nge|B2Rno zcR=ohT@bo3x&5bixNmgcl4GIxeenBYcSayEfMR6q!n_3lh|_F>D8!~=oQB}_k!wO%#Bd0qVc{^-VDtT9 z@S?#ChQWxWQ5*)sP{gVj^8*m83!)q{S*Ef|rR|GH7B4J5L-^!C?S=28I1Xds!m>1u z1@XwSSz$**B;-__4&yrA2*Jc$ocH75^0HBm1xd)ITSni@xZ1$84Vsk|b6O?};tIFC zWV9kRtd)P~R0NAQ0c+NIl}S$tMY-93Y zdDK=NOXok<|CC4n$F!b+Ye3mW0RW&q1puJ_-xi6fiGj0=qluo8os;wbnR&Ubqmg(t z;^4ch*N@&C%_Xn#nn{5N7gAmvs68GVLC9J{E__2}n%eMutCI{auR*~wH>wClJ#>8z z)`9&^Nc}k3==*t%uIKl0Qfa5__wY6u>elP=yPBJWr?>O-K1t8(^EhhP`}SzI>-~Dt zsaEUzd7jC>>-*fv*Ykdmc-nf)$KCV2e?B|w_kPs*aB%5o7u&=AeSJRo7<%ev$LH}q zy6bm;t(My?ruX~s_$o)g`?ad}g?TGR?(=;!Ds(z7k^BAh_VfL&)Z^{>bvHSAvzqIF9e-zLfn|xA*h?{rXUDvvlE}ebP&` z)c5CY|NeUs+

_hYHelph}8uF|COm_aja zDwNiMZ=Fa`t3T(txJhy_FjSa2gti?=?ntyNTI|AzSkQ*O&{ZxNF5SEGvFA8yx`O{5 z)BEyuO4k5B(9=6%yn`YsT-ePp=zvS1vu=B{6{VG+US6%5Vgo)Gk|)FU#8Yjz!Kq5^ zf=7c&SG?7Z_7nxR$zb$blOG!0QqCs0B^e5Uas9$4R1ye`io1TnB=F}D+E96k_q{+% z7uwKGvt|fzQW!A5Gj8e0>;@+E86L<>O&%j#zA)x}D31JXDEA z-OQ)Z)@{DGhFNfe7Nt4uK*&jQ({dup9F9@b+am={nsRk;18JL>b=KUTfnF!8W=Z=a z6p{`vfaM4Fc-2tYMD5xF7D%ZIXt2ZYICwX+e& zJAT4~4h4Zir~@!00oY2f)p6c+uvsz^T}aC~Czr!#z!z|Hq?NWk9`G^rtt%Rk)v8{Se(7 zO?jz0B$hsEzKJ{lx)Ea1+N2E?8Ni9XX_46O^2l+OozHt~`M%BT3xKv944FfR@sG;v zkr4y<{b@@l5p_eZIOLaMj$%06sIj`8h@P!$(%Ef+E=QVx9RrnW3pNgD!R^$(3e!TI z7%>rvIma+`s6(`OvFG8RBZ}*!r<^yV+N{x*U{O;UB!*^6kQj=u#;Wy-fl>~MVh`2f z=%G}76X_h|sVOtZgqM-|{dK)Sqath@g)ic2eoC&Tyb}k-Us;QW(=#?<1m${z^J5^4>Z}N<8TJh(Q$w{sf%S?Dn#5UGb!jG+itecRBHi z`7VlR`~GG>DfL@|i}#@w1*OqRcRHN0Gg#?@wxzR42{xVP)VhS&_j8bw8+daYy|}r| zC9FD%f(0|DT$pGZ)p13sOXyMUF)B5uc#8f${57NR-*%#=mD?W`8(xjXQ!sLI3^;$W z!4)z?B`dajl-(orC1j43EN8B^gKK6-k<%gWng+JsZuC23GDJrCbd9_4Xoe39X*KU!E*gacJutZzKW>|k4Yu12y`{gh`951N=CL9 zZ@y~7*=6Y!Z51XT%iHM?v9I6OG3ba`-BGb_t;BFpet0n*Anyqv8*V5f&fc%O+RT7R zJhA+hND4D9e}qs1+b0j8<$ZXK3jL0@1W zB3f`%Q{o%Yft-C1&0z!=k6ZUM;@{2G$dZdp@Z7{oCElYgoe8U)XtT>!AxtV=`fxj} zXOizjD)CGLebrVThl?#nLt?zOcqeRqe91$Z=`M$|uUl#aK1Z5SsEkxf@N+~s^i@_8 z^Bv~~-;JE8aE_&mj-Oxfp=w3GM>3KuxmAw zvnoxX;aJ83YUN0*omVG#Cpa@#+ck{#-CdtAj}+v6_`I>>k3S$AChOP{zSd;&^zm!t|OA_O;-VLv4W+j{_*SDO=regQo*&i2q)O_L+L&0(ijs$J zInQ?LlSW7db?!G54%gQ%tt#mVSDDJpi;&-n_*}l4QQZ!kk>=@Phv*Hn(D$DTv=b$D z&o0Y!C6QbbbwisTLoiP+DL?Cwg>M|z|FrVgqX{6QJVz_p5y`X1p-SEoqF>*L_iX$7vD`{3OD>Z6=?0 zJ_Ss=7PPz?Z_5Psxzoq_Dzt&&j`MyV`9MXGCPWW1X1dwiylZEtF04cARAfG3r6?Sy z*IZlMCx6*bb;?}qI97gv{C|b0U>=TSlnn?74mv1^%Kt!8@qcE-ZiY6lrvF_YdfBPn zK)R|{^*U9vQ?yOo@3WaIfafCEXb8`pMtUMq8DP@}R|Zo-oo)B&8)rXph439V*B6CWy$r|qegD`+f}Cj%XseR zL8|}reeP%f_SZqp*HuaPOQOJQ!*`8Tfv<|MPxtPBr@4L~lg|P^kCOtvpW!uMuL}No z82(S)xzm2nuiN}zH;;(={_m5ypBLAkM50IK3e?!y+7@{ z-=7G&--c`aA0H9FJ~{M1y1VC1bGyC1T#wJYKC|iH2N}Nh7ES$c7ZLS+zh1+0{fkPx zT|aBOUUPH1-#_o|+b_OeCfx;m-anSNyFHtSPdTO^IKSSsV(0C1c5Axdj&l7!7Tv!x z{GJ*Jcs?+4zfRAu*}?_hpK=Ah*kJeW-MhyRyZ>F?9}~1+)qL$y2)MnO>-&BP2z))) ze16em`MSM#|Jwh08=uzyxXHZU+Ss|T z?*@1u9J&sR`F}1V=6)TnUw3_$)c8Fb6TJV;QhVD^{YOvWtaI=0_tF3L!J+T}^&R%V z@ooRd?^$@s^eZ{`)OjFyJK25}{`hKuI3Lcrwg37@;s3g+@fGqg>*4!;?*IOjIsH9l zANqdpn_Hhx-yswRcYmF=*R+mu*b6jI>-&9dJ`4Q2%4C zH}g$(r`UdvZ2cw1*XOzazb6g*Ztra0=f(4fO#9ddfzQG2m&qCe^RI*H&pnFnf1|kv z%66w+o6YZC*PaKh&so0_w5^4>pc->};2{AEfrX zY_(5~kJd4t>lSr}^u#-TrJsFI`j19BFItq3J6q^t=B$@XCRd-E z^op+xPh_6kS~li}-0fN_yNkSQUE;0uzBY1dmUX;w+*6Fl@%>hLYC0fnC( zs`ED3<;tFYYmL0a{KL_4UrzbfPok;bqZvAM)osqEb4K&=CCCyw?asA}+~Uu-#bxq* zaaGsp>5?Bzty25REzElYzJXhXRP$F?gYM}q>`lhM@l!5!o|e{DJZe|mYzi032S=+p zDU%@92pN4q#jf*7nL%&hdAQPtN}Q2tEby%I9OXv*a$-45&ZS2!zsrCi7 zH?y0wQJ_?}gM(q54=k)R^eD z3Cp}}M#piWcIgqsAyek)UqV^7l5%0>%bxQ}(bxdPUnif;u2}C{(a#I0@u*&DK$TuW zd~Mg(QN|9kXeBflD2^O3QMQ`kMt|Pnr)> zJPt0_)s-BQ80RJ7|8hEnGJVmfoA`CPHQ%zM$k@Y*<@+iJ21V+-9)P~vZA>wVpVot! za$oeBq=02JniFdH?TtoNyYHZvOQ*C*m8JbT7CXCg&K}?5p$#J|;ER-i14|RICN#f^%f}w`OAn4Q{@V@-}uT#y+KXVB?!I}^!8?+pIc_0*~U5J zav3`9MjyLVWZn<6^>pR-XoD^Gx-9uhV(3!N?(kOlyzP~#IYq^f0tluBF+q$9I6NxK zGCV@R|ENcSU>Rb!(h^Pk!tq6h6;N0@Sf4dFaIqN?AwL)ZAjCL4-WRT1joAzdkJcY4 zHy=AF;rQP!RlCzIzqWMY;Q)~D)K*>gB$0GgD0^o}G6PhYe&kG9A* z&*WvA@1fh2n`_}v;78B7Qk3NkK}ML9CU8y|;gh<|#`F>b50ql6s2{m!z=^~f-MERB zwoP%Q?i2w($IV4J=^v1~pA=<0wdNVw<*pmb|A3%mlQiWS|qq(*Lf1 z6mWa@y4%uX>9KfkTWOL_xHM?{8%H&5XVNuG85@Ynj7bk2ls&e%QfYAg!2b5Mwyg{t zi8Wj~XtRY2J#0Esg9yu^b`}7DirIxP#H-y;ZCc#gKS35oA5fo!qIIFKmHp{3%30m} zVR}e4`-de`{aBK*cq0Fl20zhl+H+G81LCr#Dj6cqCXk3U<3nu>bXADt_6U36n0|cds zR^$t-rOlcU^(fSZ{@pl{Ai*>`zZ4a9dtc%SXI(!oitQjgA+gd_>0rN-2e)3O5ZcDY z%GM3{^olM2lG$`&Du>pt{Hdv{@T%cC?)oDnnCoHJ%eqY$p&tF4;h1%=++<7vv=AX2 zd=j^~v}P?*R4~S=c~YG2kCL@2(w?-~9zpA+tYF~V*3?xIHXFDze`}l~&C@JU?q|j! zZFb*S3jCZEKeNv|QJ)4y(JJIjQ-=V!WiW2Mu9FB7=kX&U7#<4LP}cQ2c4DQ`|I9;9 zvc?d&3{xx6O6kc3{Alk`k+NzHm8*c}IUx@uPpC5tC~CEn($;KOU-Mc6TgE#p9bG;_ zCQEooAZcv;J)xe{V6j9C4go7-MW7&s(q?&RJwO^i_q~cDso5-r6|r5ny+*t1iT`aG z5OPBB^)$w~QAqcj{yKz8X7wGR{JaE-Uy__PC^Gx0F(ZAFcC@}3TellN?-buAo*NJW z*+TBAA~m*88fo1%9(k1kiACoHx2MG8NQMg;lN&A1Cz>d~j6F-9C`Mdye7WQYFhS8t zR+Nu$A}#C4#!AUHZEX$)pYIBdX}3)i3!c5A(OhD~c>(o0mh+Nz3r(VB5euPRotT9? zCt$`*Fw1cnWw{oG*0EP6Ho6!fV?$ox9A`ETOZ{<`3{lMEB^oaq9P-i>!+@n%dlYso z$I>SE>co>rwPzIkN6S3Go*-^23h8l`sU%BO4 ze|bVEMe>rKeRpOU|teB!4vIq|71vQ?>17yEk8+uQpDJ47$O; zQL2V_sgrz^%LepYaCYCpnH3pSwF-P$BP<`XAjS+`ODifyVg-(}>G!{mz1dhh^!9)j z_fqyAl;SxwYcy|XmEf0G&Tj)a<9SUFf5-cp) zLj#>nh`g49@P-p?gS9!v=^v>E{Wxa%SUbT6N(G0#TvU18t+ye?;Q=psZ-Mcn$G2Lu zzNUWlo3WY7Ge7C*v($#NpVf;r*?H?TUa9f(3#Y#<@dkM1nH>@P1lHiOnRub4I#LZ; z<;=ZCgf`#V>x_Z+v_us0*$e0ioPFn8FZ6|RDWW3VT2WR)^=i<9RFfa#6#eDu9yRjLvlFpv4N0;9Gt)n-3PV8u0P zmH!rYo(ffa9S@7xi<-pJ3AXfAA*}%XfgKz-Pb~&C@R4f^y!LHW`byMv9i_?%nI}-m z`U$DZsQ}VX)R|Edpo*=4Ar&wL*kq=UA}tNZUc+tSQlG1t45gtMod;K zEf)jFiOWViB~kXeWl{mIR{Pd83oA}*+99c7CMkSGmXxdE+HpSpq}2IZHOPy(0Oi;$ zG;cWOTNGsg!Pcx$2KZ_&`1t#!mrIPKLURSbGQ|4lhHIQb(Z30|oNT}5)W*W^)9@{H zyH;DHgTYTPJ0)7FcjYs(Q#`j!7{n^j{)=UU(So^+>kEnTX(^8!q?PtgEG8Pdj4Ga| z?Y`ys+v?M#S$vp_-43oRBg@!{37Xj1+ILb(veEL@qLg zr36+>B6xz}Oi4nm)Uo0-k1tIhWZ+V#MbTq?&sn=`s61I4yF25Lc<<332Id+Ks`@q2L-^25d0~r+z5SxkeOX4YTC@$ z61E@e9A+GNKDm6ZlDdyzhB9}G%L?y6T~>Cs4)8L}g-hX&*iuBL+NB*I5^sYxfw^u$0Tl>@0cmamGpvw=7TL@kNL__F6PW z;JrCXz!a8@&Yi6`9RAnm1L8rT$F%@Lu6fZdX-EQ zx`9{Jm1luUd{Iki=)A6wZyIp)=5dLUep?VfxB@LKl{S9k%hfIBrHR=0)&yJ1N0|FZ z$~2+B_brv1gsa%K7Fifb=x@wmSru-t&7@IeQnH9{BysDK6HXYZ)s+t8+3d^=LR{FVFq@hs907Z1Mj zw2!qOgcdG+T2sorGyoh0>N){{Gg;<_`jf~t@~GmQ)&ZQ?T}LXhiHAvf4QJ!>C)JW& z`gNSLnq)1%QX&SB=3Z>KMZ%0{T9mS}#vZ~EaGxTH?M5Eu?8cNmjB zU<1XNTi32#$t?{SEW?~NV^r#85{nM6g&Gspq0`sQDK2Wh@#R`{&CqZztl);x_e!m( zwD^VHjUUIl)ujiLsjs;NDj!gqQnEV5RG&;Fvl@;ZG^c!s3fs2@872egC#3i&S_{Pb zn-$4fj4-5fDOhE&kXMn4>#;JJ^w^ax{P8x+WVPvK>d;tEl5M6P$g3jq#%rj30)xJ> zf-+rtz4D8gyVTN=Rv+jhpf93u$ne!0AX3a#L{t}|ezT>B5NsqK_G=05e7|vlBR{29 z)~?SCO$YE!XNxfsT~6?=*Z7;!l2tADu0+6X)Q{&z@Ath0;QOdhJ^!S)>&7u71;xa4 z8Ygr2mi$`iLxA$kKFc@&(s?~pZqC3!_wF~^>!Xu&XCYC%61696BG-v&(TTH zM3ucpf7b=8l!&T)R4D?wOKKr3l*omx+vF`X1Nmm!1}bfVHg8VzTgqQW_COs&V%&Bc zoz>?%dbqZD@MOVrx%T<6L=%+eF8064pipR4=jf*?vOOj2L3AwS$$`*t;ugz1Zz&F~jNmDpglUTzor=g7#to!rx(OJ*7gIF6URIa=p7bkI^X&PDEAa4Ywq zUgHF4*NgKs)B2Av#@Eb+EP#YdCR(FFR>#DV1%1;^8tYuic8b!Fkkm)&HD}{!$;koa zg>J39EZ4zhnR7PmL4vqv`Z%x0@!=wOq9}ug=nN>YSWe%Znohi-fuzA{5&hh_YKt1r z;RM1(0k3FkgvB41Vka*)Msy}4o*>9qBe_Cw12|?!vK{JR&oN}N+P&B*8^3wEt+jr~ zZD_e-m!Lv{Q8XD%kzO~Zzvj5B_;W7J4KUX=iqIjjC8Dr}SQ0O<=J*RQ+1#?ZBn!@#H59)`VS2+LA2CfgUh>GA@%i~8aLRp z!_5{2Pw3R=^_Gl{EHgi!SQ3$SBjwr7c;mhNiLsC(8Xb%5wnv>8GV~Eb!d?sWa`T;} zA3k#9Yb(ZSIEWfyeR`$FiXfHUPoVwE3=`Nm4;ayh%FTbC>d z3ts<&z0b4>D&+z z)Hywn%X=bCmDNh#d);G#AM05DZu#J)G;2q>CR`$t!9)ssuO=g?PShoIz!v zEG8{pR#zjwP5Z5&_ibSIF!{w(tU^~8^ zSLu)(!J-7#@F4KOc-pYUxV}JQj5lW>EsD{PI{gpm)!1y=yi#jHH;9qd%nhYdJm8To zm)Vz6xDTyk|Ft`#lE}Gqlp^P^fvA+~lkguNV*|_KA0(^16T?uT-2BDL$2X$^WPB}3 z;>KtoVaP$52pD=AfTy^J%tLq##ojh4MG4p^<<$KWyfY1*+ss+Uy3RjSa~Iyh^G(#BlQ1K4bcvtqfa zOYBCN)6G33qIK$X{8u{GUWR4kkGv3$9f_}@F1Z?YM#jsBfTZg21y>&Ke#G#`70Iu}som%LOA0#@gH2XfB+34@XMM+H7 zQ6{>~Dtx_&Y`iF}vked>3%zJLyt=FbzlIwoPPXVWo` z9Bc*Q_(PP_8*P&X!NY;Wrg=>xm6l2jZjia(^1(ZbS^LbBfI3l6oyg71`s!ky9+uis8R(&Z7y7`Q3W}UFz19*1?6e3fELuXxCxOt`s7_z z8&3(!`UZTP`8VtK=JKhM&)3A|tgMBgc1BwzBz+2aN_&}OQ=Is|c5Qz1VVy>#KCNUl z3uJ@FE>c;lYf+iCakrfVr0Xp7QCCQB5zfK&*S}0p@9?~QMJQp}DGbw}E4viT>_1f* zLeW~zXMZ&kf$H@3vJr|XhrTS*5N=83{tV#VL zh4#JSl%HBWCj&3$z+EDmoflk7{`ib=!`*^6phV*lKfSY)-W~;8>;V{-pVzrLuv18w zYjVpBHGLb#y`KDb-?%nOaY47aHQvVyB&8d1b8~sx9volIuD8(E7W}pSD1QQQuLE;s zfP4luC7BGOjXBcUWEmkhJfoulwHNSfFVK^w=5vioPZ2;RWcrX@n?MzqgNw?aIhPinZ!S>G}NgJ zAB{L&2G2oCI7leB5-?KW(RZH7=7#E(Q6Ad4=o~rIzp`kd5Azkv5~B9#_~sRq2jDV@ z6Z-huRujOps;^x_Pdo2Erm(P&iUMhQBx|(N_H&&hShjR!@IZk_`OCF2b#xO^6DMq9 zONZax8Ji2$LJSixB|b+@7?(GQF7NzTJqZZc;I=kF2?&rh!M*rGd9S}{tBKyFB7{_E zB_ES~wv9H%sl-ai%7J@Cp+hUfsekHsJFzFjd1<(<2x)+(MjE#=Typ*TWL~FK0Q$s9 zt*xJDO)(a1855$@-x>)G5lQsyf|!P{UG>%WSVi}tzjwcP=COLY6 z8{2~MB|jERjispeh&bmKz&z_E!+&fIQWEY9!olA^xOO1=Ul4&(_5t;hAsV>`T#Tb0 zETvWuh_a>p8=(%D&)IOy$Q*)VSX0qENGv`{ev!0XZ&9ACJ#IB4n#5ixNW44D(e%KU zIa<00VT!3B-}1@|m(=hQch%rEI>8J6VhRA~o^^`@y;9}^WiNq?hogHfio5=)s}~q~ zBG&V(Bsh-f--HNJ(382zDRN&bi=+Hff|Jh&5 ze&l3odb87F*bd{h-_|%J@W>RV!YOGAfLfV&;6C3^_Zz}}teYdIqP~wgf*?{GCaUGw z^9dQ$UP$}?OV`rqIcR#6?bOx__?qk8l4F=sQ?2W)puq}HR8O>Vg29Q;6vSdVPFIPdVbhuNg3`#K*VJ;e z8Iz7NzvwfQ^WG>8a=dCSOf2YBztmLSd8fw?FH5Pw zoxY;D!Vaw`bZ*7zXy&htvQf)V>rb8Q8rQlqBU=g|aqgKaGpde;?82CwSr@Hdd8GrJ zPy{hNOEPIj?3IVXBb}2An#Wezjhjid1em~}A602!a%KM*Y2zA|jSSH@2SCd+V9wpL z3OS`U00j7hZrry9^M*x0^qwx_ zB@~BM%2b!SE1AayHb1;H6=H}K8?bnslJW=zoL?d{ZD^=TZ`>=4dZ`pF!;pJ4h9N3D z89^b%SUKuoO6T6IB_?R5By=DaEQkg~AQtEwD)Mrht~8Y%r?Si|B!WMfwJ41j@2H@) z75UA?ez<31W&02Y3MX=_j-BFC{aBQ$t&rf0XxtM=UP{X`foVK&I(YGd`BC?!tx1?n zs$^{VS{0I5Hr*#dVI~jktgCbJUoPnDL>PgVtzZo>Im3^Yq<9h^jT@E3U?8s96#jn5 ziF<;HLc4F-Api8i77k0FSCFzk2BM@7s)=+?5deCZ&XVW8fbs3yuFUpmf6RvWI`Jj^ z`R@z;wV#zB@|$2Ah660H(tnRpzM;_9PEcQejuklexp{;E>k5G7jYM-a`40eAK&ihr zEh5D8!g|5o#x(@7pIQ%kiLFetaZOk{2*>GRa^#~ayPv_H5EvdRyQIRlNNsPxk|zbi z&SjI^B_=lmx2?sVNw!tPQ!ek7dkAE^NEA$RP^W`yM4aRt@O&-*_AIt|1jKp7vvc^U zV>AKGB&rM|gW;QymT1Y1`8r=Mr<58V-DI)nW(YP>su`qIU^kkqgl2jGIlWYlZtV@O zhiQGdghbXmwN>F)fp4e;Ny{UH#e@R-mjUDO&h-+Y9~6uvs#&B(?`3=J_vI3jW|D^0 zD*%0yRQJB%2zY|z_y%Q3p;oCQpz<(1?q;^*=D;E}11ha$k;a4IblBGvChv2k$h=?P z3k-#20|OdX!G|L)OKF`{UyB}*M?VvI!II6D&fG79h5P%9PYPDL_;8z~gzt`ku0IU-TTTsEA zuSlR7+J=qkbwC9eNlB1gH>xh~IRA+w*0~v|h5@)N$WFvi|6!;S4}~xh&9)XQ3s=sY6PFInQMDH=2gT7#kOQwP zQ$5|SV&H|j4aPaK>aNi4soQZCEKEs6?l-QbsRKKfFw=N z^yLxxEUbhPPSo{q!Vhr3-T!EN8vw;{8ptrANfxmt6<@E z+Ttvtz7si%)qs(zWfYFz<-LD2X$}iYCbZR-e=&;fPpAAzICFJ>GeOQZZx&1jzYWwC zzHg4?$Y#D4u!05^8g%Duz`NPbN1l}mBIN!UD?OL)e5716Wo=-J>*C6K*ky+|jrVHPtOOTDJzw%WY;NjONuyVU!vwEDZP4HKi}F4GCN7#f%n6^6LqQ~N>6zz`n^$OgsP`@Q{O z>YEfDmlWiiRA~}8ETg?(GtgD0#{@6T2T4Q|kR6CNENE|?G@jc^e`-2*ndLzuSBD52DI1E-*Z{H?E@4_}+Yr45qR zfb&f_EaUG1lp&FG{N=l^R{;E+$6v__A}LI5O83fbYJGsL0AX){Z}6?c!6*B!gSab0 z-N2-uP$ zf>U?yhVxLyt{s)%Z{qeyh{=>~usB+u9ez~}>F4P^e5>NcQ_ffX4(9iuW-S4nkYNmt zr$FWR$zmAh-%2bL>8H(YCb`88Mb3>B&0Hiy=KR`%>l>aqhkm1SBV03$$QD(O9a?2% zxHSy7Iw5x)W-0hj@viYeDDwy28muC~E+Gls-aB0Qp+v@WKs^XoIuB<9yeG^u?-G}8 z5_!DO>ew#lp#iFx&c*ww@+7F<4&-wWz2)*uvoaFiMNASn&alvRkOMav zKXN!>2@X0i((waU*cveqsNO&zEX-#&b<@^YlNUg)Z4#Dp0ThD^=ymVwVRqvD+iJ2v zH5c&vh?ZUs$K3&Evo@Q>3bg8U)0pS-Zx60;J>I1V;y9C_m6Nfot)3yOrHV6A1ZHMR zQ;leEyfIvAhntG}YqP{UPMD})Z>Fkp6QpV5)wpD8Z}T$BUQhB7WvGsBk)#~K=LsDd zyvNTVB8j9DLHCQKm0~6^3?!$iAUGXdD`ipv=?{u(5o28{Ws(uf z=XP6hX3kS*9{7gHvqRcfowz1v-9eSToM4$CPDyvl3dhcD)6Q&*b{>=ZwGBEUW8!yU zI^AC2+xa~bxEqy{WDrWUQGkA+LbI{WM=XId{!FTYP!&^ry+(Jj?;A<94&=Fab7?>b zD3eN36WD-p_S$MhwL0%F0lS#AR-me0+r5KAX@?Yx8{s+9Om?4-(r21&HQS>pY}1W2D99qq3y7PiBc!#z z&*PUvOQW7U`z{(Db##>(zxo4wqa8zx$GzsMetjc}zK=Tc)$kHe! zs|%h{NuC*wFd!mY zDY(VO%h`nxmcnwn7!Yl(_>~oA z_K6c1R0*dmVuPm|2p=*5lE7w8YKT;5C7?^9kn#8Fz2+&TvyGfVXioMojDBq(oFHVbJmp~k~t0(5sn1U^e8PIt}t`REz} zoS0t?Av4=ERCG+LT&<50zysg1)7hkzAQ?*3#X3~^CK-?WNNXBWPzHE5T1$aB9YJEM z{fdW|LyPY?)=>FvClK_GZ?+3(0(!&s>@rv7;z;uhv{IdMa}gEyn`^jQ$-NLJK94wpm}!t>0yz;J_e+U zXyZ<$Yj9o`xEpD=5yW}MNXb>2u{QCoD64n4l#UkJCIb=O>(PhGamXhdRq^voa`~7Q z7V3GoZTCmrINcSfY=<_Cle7SY0~9Kmms7l%wY!r@we=8Pb|hsQmSBlA??&I8f*Z80 z3hn`}nch;-Ugh;vKNvtJrW35}!T)c{0KL?=BNpgdVL0tY&I-mzAJyYqp zxsfEYJOSAv6A?Ce2U^E%w&R(I_*Pq7I}G@k>;;SKe00^m!z|ZQLnW7I3Ho?U^ktsM zV;^sP`EbX=Z8f_gBT%+C=@oxZ^zs1YPxl%E4pLn!{eh`SQ$Z~QatQ!x__X&0ul0!f z(MV7Z2y4eno~jRg-P1O9&QJ!~+Q!c-RvceuNrDXuR$^CL6vT6v)8yC{IH&>2qQW^; z$9@o2&>09GYfXBg;ur4u;F_f!Nuh+zdv?IKPKT(FQiSRWV#BT*Y8R>y>iA3zK}{+B zs)Ephju+7`$y!$e1#V2F!|i%>r6As6En>>I1;r1o`|yu)I$P4~^$PkPWlf?GCWZ_J zhT{qCO>G=?(8rQ)*s?3K zx8ZUjkW`*hIX>XBYtX${V+RPUhD52!Nsk;Q>~yLI(jJTpP(}>;Y>k-H3hEu$30U9; z+bJ!Hsm#A!k3M$4)3;y+)PU2*oIV;(Zx;{vNmW?27Qxx4fCLmkgsj^z@m~pK@H6yd zFmvxB?7$G)vV(<~+k-VC$02ye_(BE>L;Qh8~F9zUdjHB8L6GPs?3!*@&7Jxr-Y=Y&4ylUIU%AQg~W@Jei_D`HCF6m-o6 zi6w0wY|kJ!Ca1fH)#t;VL`wt5>6NIdp+RQ~M2`&OM32=Tm>aU;wD`#W7Rv9FTOG7W znc~btI_XF3o`AAiT~?4D$t(NuL|Ep#`zFE5)&ntdyB>Wg7J6kRKZ=Dun91w)-Cc1$ znWxCfixLSet$N~-8|}s3?c5Eo2mA0P=StE&$ldh$bHc&^}S1;8>SnBk!7G zMG<{X=Hl3+LiKb=ryy3r?&R+P*$jSm9<_S*hQ^~$I4$MRQ*)d_z;>(%He*KUo9}-& z(4kURS17J|6PYw92ecd5TA0zBC^xyHbG#CXq2_5w^ax2lsN_Z(jrug zgxmnxZS^QIq1u!~e=)oDdBtMuB5*~tYNL{5_EY!K*tqZ`R3kR9kSXokBQ0WbU{Wkd zlt#j35*Cdha*NH6+v8iQ25`+9-4MWeYEq~+vSCl$a))4T3!91_IuI&`7f{0hH{}&g zwI=%=AVs-dkDv=NqY1G-cz`BxX&B?4^7@$9+Rx+aawz{Tj+#M#A4yLT$!Q#{zhC@y zzewDF+L+wj3!p&-6P2hOQz!uWq4UavR$3%i(%Yi3 z3a-V%P&*x3ua^Kxqpuw%QFq6MC)K4n?qy=oFlD-q3a%p;)|>c(K7^2DZE2Hp|GHxdE|Lsd0JW9^8kG+Md2jP#;$;TkBoW zijv0WZY_0LTA5x6kL3~6y*E4du;+S^{ zl1aOX+uj}7g6a3 z%a)%t$kCx%hYod>g~Igc{?%5+r~zn^DAJa6o>VrlOn1zv^U+75Quxy$$SAZ#*Nwoa9g1rN#v!#_!MJW-GNbj<~9#M2W<)~wIw1ldJYyxGl3!c1L;=|M! zJ06V7$m4ZYu%~#g{4Xkq5sCl_4Pog>N)0oY4lKd=y*#D|p|XW(Lh1q%+gwS=% z0m!BeS)iJkzQ(c6LC!-CK+CJUgY^i$w`8)Zhs6ZmY0@el;mo;8D>aOhXrOcSDB!n` zO=s4i%?mB)&a&*%@Hw>c0*t5IMNrZ~Ob+E3#Jhev_rBeGMQIR!LdE_h4%kAQ2-1RTyJ~CYshyZ-bReK z1B*l`-S(+d8s9-?g(h{Z;tONH7X_478u#kqwnvQdC^MB)NNfF(S-btt&nzd}( zQwmQy56Z6Hm(YD&vHiFdHXAn8N{1l@&-!L$1X38|kB;z>evl+A=9>??aQyhq)gF6n z=~D3&(`sYa+%OjkWm{6X#`>_d@_)EnWOSmpW(5+7fliyTKY|>l6~b=+GJ!;s=KwST zT&i&^Z$vTuuw5r}V8HP{41Xe27u|*v4>;*X5)Hd0Mrg%K$?V&SJ~~|iWOc|P`rMFu zog`#RP=s>_fKaKaDWPs@>n>xEcrEzMfkjzB9=gH-G6NFMbJSbi-6>mIbURZ2!=+k0 zPe@>heB6kGwqrMUG^sRw2Bde9!0s2ayVpwOjGu=Szb(^>VFC!E%$Rm2dp~g!m-I*g zNLHW5<8eYmLS-|)q?fn^0W_fmA!V4e5svPy4k`AYay^fuIPG#0M}MCE#P&HGH~PDx5&5k7X~p>8#0XS4U{ zUF8Zmt%HU&w{{UdymtdJUmK}b(kwa{6i(2mekQIwLAQ?up zsz#k?uo44~aeJ5~B~_`wFel9_n}TZ1JT%;qHWpU=2+NT=K=Lbf30rs>VC?L{Ft@qc zaL1VX_+EQ8h*YC3Z`Y%1(beu2$J5d5C-?ND>WXaAr8;AkT{Y80H}_+UcD2QQ|0>qP zoBCJ^Lx@4Z4Q%cTs_AeClk-(ftXZ}oz@i_0N5O6H_wP|_Wlz3CDPa-Y;J3w9O8XtjZPw58Rtn{+CsfjWF?WSJMuanmcvV$e_PbpU@*3h(9RDz<#R{ z`;4Blk{Phy?F=`0fGvX@YvVnxVmxsb_@$&Ff>>%$xltj|f8_M?H2whws9T7aZ|?a=3;WUEH2?fKvw3&J)NN(ypwH9N zCJ#-N1OOE{s`1|LeK4v1S0B6omHODhyt!I?AZ_W?8QpwoQb*-Yespjq%}$$!@sP^o zGC@s1q7+r%#CY|UfkT&HHXkK(T7``s$yD_P2%FAk)g73FW6W^Nfp2P&EWKK$8i?0zlo9Zo7;A084?d^60*7Mh`L6z@=U_GxGJf*r<|x?PVx zboPb{Xv2IR3*a9i;z#~=;HI55ah!P|0aNtnsIFPtB5_N|kT6u+)61P{8%HJ;*6Ro} zEdf8F|2PcppPbzW6Z($j`+bmFzGok{B3>Xkg=%=iip=8}n%gVw-n{83MV%wYWdO!ssEt0gDi(+W7 zNMXSXfbcZZV>dD6EpThV;MzNvUmF)$Zm2A@1}RA6}zJHl2De_5NLL3!!cGQ%Wuyx0pm1o ztAj5U^w0qjAD~i(%0pV~Mh$EN`gVU%choP zk3O1OuRNoNsrA7zI^s~)Ll_M3^s(az^>l>lo7i%pXXkL|l4PBVecWt0hYf8ums(w` zv=hm5?BVU4KD=Go8`B+tEPUyj4b@xdFbm&}^vQI$dp6AV3OBcg1Q!;I?CE({GY(fN zJJv5jqS-JF3}nMOh*q5GrrBhM!XRo zNG(lb69JCgotfx?&mm$l>0#QEaTayiKYa4_2)L42_S%4;0KH>nJ5Gq2$IQaGa#pm< zIXW{_18SmwDxjkpHE;;66!@nvCmAAn_Z}kWK$UDVG{psqbYcZWqyf<+XPkA31ytwT zIevJ)7?GxKT3u@x4f8ZSTk*iQyAchJRy@xuB8H4bm5G3Zp)2sAJgkXiw5}$D4v?Xb zI}KrI`*Sny3sb$h7jlx( zaFhfu^zYt8um_{2plCwXy1D|*LMQmk(bcGe5+73tnun)t{jjNI>|b*72PW>+jR%Vd z(t5l&!@7XEBGF#sQg8MWAPku~N`_2oW>T7!me-J>LRvs3WMN7qR{D>>b&BUW1u6>| z(K-TsQ9;}W80Cmmx)!3$V6om#SFHY_nh-vKoSu}bjA~x3Ze~nLK$rzEufbj64i7d8 z2cQRtzyp&7ohM`j`=aL4q&C~@3=uW#Fl9$vXwpW)`Rd?!q-kX_o;4Ug-K?f8)(vc` zLQ={G3N^1_EXZ*I*g79&_u&F)0!VI>#vaYG?#NpZ#PjkKi=a%kiGvtdV8J|_p`MxdBJp3e*_?4na&#_!( zMjCbof7b$g%x-G?vhLEDNwUs4xt}T&y(zjBqP)BWWOUs0H9(d-h%T>Y9AUa9xjx&F zGH@BJ&^+uiFcPiO&`k#CGhJXB9ZIKJ>5jgi4rRYGp}?OEOoa@bwt4+zD9tCD4g2dN zDt+cXAxVZk#pt`Xdf09zH`seXW-~Yx;@k$G78m!@oBAy>Vm=%eNH@ro1gZ}^6vmw~ zAB9>3^l6EcBlDBaS5VJEMGU&S+n}vbM7VU84tu*Bzjn9OGyOR@-hh}_7?B+-q#T(x z)MQ)J7yK{GVsb|9c0IaUF7QX})3~keS$=bD0ojD5d>JJQ2Lq5dIN28D4tiW~+_As0 zO4HF*uEii#Xk2IjMtaKJpkRODx$^o(sDcoBkLuqq2~vuG_7RmD$<>CnJt0Ui=t${E zcCK{zaYa8erd0IMl9BpN$(=h?=WwFa4A7~XQeESTn;K1Nh|eW>q^sLV(v=gCQ~59% z01gdZDbU?qkKh!e$5)DWirBey5=?17>7Ym3PKUYABf8Ga>|F-6Ne>l}!L9-dc~i>> zJdIWk_L^~g(XoSeGC`qI-2I!P3PQDWx<&0sZ$mfxxmJ~-1~1{MFk5|dBl$*V`uuzAQt}T8qi5`f48aZw*r|Z3DeFnW3 z_YQfJ9yuBnyA>cjRfd*=EuRJMXg-CX7NfTKs7pu1e%V1v_f^jn;8j2}zo*1q_-yAi>t$ zwg*u}uhgx&#jG(rfO=;=uxhX7_h6hHQds0#!o<TogEHeflp7G z1hSXL3cJHDm4-?4#!X%s93UIznfQg$J%Gn?7;kVUfJOp^Z98};J^*Wt1R52#9|L5em6sUDg4zAYdN$A-k#g|+77y0{z*49anSV)^yNGqz_AVHdxNsb zMGkt4=65dXM?kh6n-xOL>-^Rq)mvKmcheruplv0(PF295%JKH;dRo3^&!m{sk5sko z&kPtbb)qTWpLXW;5aa0vkn1Ark;4smUXfP<`xPj+z%%7uo+HXm>9SBEqDre7C%{Gu ze&|C%wI*#xfWh7j!URMO?lLMEDZR8_j$&ihtINfL(bg4xq}ZeCjq(R}vRy_w9O$KL zpvxZ^llzg}!wE4Wfbi)By9~Y4hY`px=Q)!Kg;sqLS7-N+jQnGu#Tab*NUs1pq@=KW zz6IFtM^=oSN2j2U3wN!IQGNYK!- zI~}3%Ki8~>#iPlXEL$pdJc{6cB-?UyoUl-XZHS?13pd6(Wm07@J`I^nCP%In6$;f| zVkro&E*o|02I$Oa&SL&61a=`*^dNtb%B5`qV=E~oy zd*&wE*u$6kW>aRD$AT_$s|AP#6D<~r9A%1LC=$c;i+*4Pj$3`(u|BPi^$q4NwhRzS zuarXJeL1+vU`0EAVgqhaS0y@nMEPTOt~1$rsduuPMrs*XB)bT91{zJ%`f6Xkj-S|= zO-yT2JAt*;o}k0DYzkZb0IRZDChHYzTOlpUl`7+~3!jI{%+C^#>qcMmD9b2&kKYEWz3EW_&HV%Oi@0)uMY9wkT&C;81lBy=sCqbOd zf(mN{4)$E`b24Xesf*MoM`!s0+2UmmXyZ3sN}r<@=JRj|+CWkV@e=mNNYNZ? zd0*ZeK+%t-p6Z2Nb)EVV@igwH^sfuK097gctwfU_u_gOzX6mQFY_|Qap2(})HazT_ zd#Ft8_rmtG!)@;%u?M zkX%AN{0)niZQg@Zg7&=3nseO4S^jfO&r~%4i_xK_KJ>iy!1@q@nA9 zyLRgu&Dkp0Au9r|F0s)rz-t;r;VW%+g3pK3O>Hq!=&8}~fRCX5?5hE1Ag}X-qHFw^bO<9d3>qY#lTfoU9QP1%&4 z+PoROjQC3hW`q>5Mc?rx_iM*B4+}5W*zJ0xMNP{cu!o7f70t9sXl(5nGd$*22GMz3 z7Y@jYSDBa8_)K?NW9^f|P33i<JK_&9=?xR|};-?YSaics)CbL_$lF$GZx&{A~Za z17sl)7xkkw!v^tHA9HK;8HxIc1wXG?wNHaS%oH=V>?Ymq__;~7;;B*sOD>v-oEK3F zT&`|XVm2EpnzFHPFx&;pO0x?|0Ymtk;BM(P+k>DINF-`?1aN?WqT9?lHwEDqM5igb z?!dPMIwa>7ZMqwPPhx|&k4G?jLg)k#wUYq{Ax?ejV{YBF*bOeGt0#sDI;fF3PJ&J% zrl~a}k%kokIvT^w9sVe>qTx<->-fO&I-Me=zSzMe{R8e`t&>xAMBWLCV(V7>r)Z0T zdB1v{P@w>JuDgDY28GlF<@X}^(K8l8Sa3>`{jJ4LD%Zvtbh@x_`M#aIGI_Ttc1yp&^57)>(Mo6jPl4)*;$_WgNN!I=a|%r89$Mp z-ajz&&|T_afufeU6Xbcl&zrpC4We^O-C2&*8xV_+C_5uuO?O>fL27S@sMC9!KRpo$ zU@9izhiK_j3>aD9_2Bb>eMg3Tgu1uu6(pqTik|Jang@Lc)C(JNnzo;CYaMHG4%E|& zn+$5iR6VkSSl%N#n!xn4ktfI@yeFP4$drgp8C=*2WX1FXW2U#amj-%vmtnQon^5Nn zF};;kG)+=;@ARU$9xu4jN3>5sizmEabwmVy->-9>FuInCWz>u&^^6use zr0;i^Yrn>D?spUH*8>u6_8Y2s3BY}-uu@QUL-nT{d^7RAm$wMNyqZXYnIVx?_uUVY zA*eUwm$h+(H$Hd|AVH`HlxX(sQb~}2g>BQVB2QRVx!52W+zGduozDd>brmu4Do0h~ zzq)yYn}!Coem76VwzAk-VJ$k3^}I3~u`~&?kX=gAtJuBS^rmdP7$<1Z65j2*u17Fx zQicJ!5#<6z!?u0FvqL05x|h^^-mmNR{tO{ov7$R58ISfq6@;aGAZ@Rwl+hsBM$>vG z4FUrIQ}C*O3Jx70QU((hppNi}rT&)QKR?<;!i!H1utn=E>B$PV+0T9K*Fo=A`qD&n z@p;86f+~&!8oVd%3;X+lmZ7a@f(a6k{SA!2yTWZGp`@S*rGtalLZk{2xaj~BYU9Ez zyLlC>p_TV(w%Mq218&DFQN{d++dgQJFCU+!Hn%?KhwCVjm zT^B@z?}iUcSYmFaz1_9>2!vTrV_*b#zqT__cz)f!boWtbE+MZ`kBX^HRYL?|pz1Hj z6}JgCBtwCLaFuUp_Ivq5G#VfC4jDL|L8B=Vh6FVc?)Ob9jSS8nd=oY;WJztmEBo7qFoDYER8A)GSv~bTLAh+$cbxi1##XtdJ5RHI3psXSy zfYYsvn~5EF*dz>V?EnZ;=+oQ{!R3>bjWS4N9kEU-Wi8x>hxIK)^P7PIOD@s7{@3 zNa9T#zn*lZ*og>I37qIll$JqYK^?&ZsPcA~;@8CpDuYmhV>l=D+~BUVD)MZKsbnVO zOl7uR&|!votar!Ch?=#)k!DW`v(YA4h8|+tdCTr=o2b5bq(nM}3W-#s zPaI4h^kCV7OJB288Q&S~ONDWy63OGHt*qH}%~?Ah&Lx(#U7}J?<4j}P-8Jc_qif0X zexFe@47+a3#k@ajmI<4k@qqChU@1H<_@A;B0<$anpBTF zgMc8^sCg&A4b@<6irx`9c$kRY$Rz>=(LTOa_E~a9j;?pOEa#(orzIT7JLK@VyDt;2 z%{+04AK4L{)YHhW*R_0)KM^>A&%IM^x(BidX$RxtYbJjjfNX9>+0hZ2B4s9hvTqTD z&=*kW4FV`097Z&_Pv6GH9*qRG{=oz0)}k{g6onAX_DA;)8UuECtV@a3CzmTWJE(nk zCMC&u=ufj7p6*N;vR2C0R~Nh;Ky9&(bZ;Sw9<*QKx+nJ_gAFEy=-)t&HSKl+9{0u= z1n(BPP;&br$97XRLgw9_(PHX*!GCUk>wygotoLxbED?cf@15@bC_6;<>VL{q-y zOw5l?f}pB3_F6eXTqkF5lKFp7CPH@zs&WfJdA?p@xE1+K_}gWUXKfBrD4J#3%f>}O z8Y$#pNqdU1V46vMFdEIdPQqgg#zVO#4xo4oV<}X_p@U!_AVVkuFPyx9gN8=Li^iQtAGYV3&Ox6)ABie?Xq%>1aE#JFqtewjK&4 zR{i7##yI1|*di^glo}lbC>gtByClcTIs1~w**yrR?{xE4*yNhjT0ca=c$p@DA!dc!&( zzUv8wQ=XVoZsm_K!F@4T86^k&Jcbe0onn6c*FF{CO(=~iI;5R%f@e&6W|2f@S7m?< zV)6%u%QoNmCBU5^T3iH3dEP1@CynLgX8m)3*C_7d2v zN1JnOUjM4*#6{QQA6nwFs|Q}@YqQAx%ZoXWcGt+tZRswg8DnK0T_y${9;jmAcUxH; zl`kjwNJ&f>&u|g95vC|kip-HfPDOR(w%g3A2XEK;3LrhXMO#VyqfY+rU`gz0zXQ)m zO-oJImn(xLb_|lG_8XpPI;|zjFx>kgt!~UYx@7J_TiYyTjd<|phg2Ghi9pbjmg;V+ zbF-yw18GEwQ9MvLJ)ncvBu;TgBYAT=y7CmiE>3^w+YiO*>-7%nbe!>oJFsIl; zfUDbsec+^(!)|_F5&88O#d6dhX`bCGCSV!hQHiR#UQ!=O6PC!jA-V>Qn1WzqIHd^_ zLC^9@6n}vbG#VP)_0kQQU%ugw!2O34!y)L??T=MYp9taFLwpe`&!Ac~b;M z8Wm-|y_trn0aP}>rQ zukz;x7@LmG)tHEzeb0;!I(I*+gKw3TavaB(n^Uw!1 zH|f#HlW>LU^L9O=<>G`*9bP@)Ylh0uRV1ul12!Ke3wrSL953iV0HGA zJi5*Fy42Ne=yR%sNlaXq%wcsXYr;-uO_3?!i6g-R5N&%u!TrG%vzh!k^f;_*bgagw zx*cROr5g}6x0RKk!8LUXHNs2~0~54|t_GPjk_||QX(^mYf$fLAlh)F6+ZW1RA3^eG zize+*FwYW<)b!_?racHhtkLfiY?xggmO7FnLaHF&aMJ4P=|f=>7!zA&$y~;J=n^7L z5EManydk^JN9}IEY&dRg+X~};OZ)0zA%zj z*p<3HfC2JB&6wE&QhL6^H3$?d?t6B%C4J=f2YtvV*TK;4ne;#uasb9HB-P@)`%)H& z03zC0Xe~gbc@G#6pvwW@eh-Vmz0jalh_kc}JOa!FwONiM$g62L05BWi@A=8#GN+D{ zeNXs`1%Exdl8j_CMGtzc}KpEEoj1qe4FxrvTflv@y+eVs{)iU@c= zY0cdaVNLh}Rh0ux1rUaSNYKR4SFb~q^%fTN>`Y6$jY?@*lzUS~PSQBYA8dDlYlT!c za=Ynzg`0OmP&-h#lqKZjhg3R!QYa`)v>nXZ^azkeI>AEYJ2s*lrh~xWT^H=RHXZan z7I&d<3kN1Yy0F50PNfzcO`iS%vL4`9`S^xUg%WtoLGpLEk4I-L5aeYW7zKpIbAlf; ztT8yxZ@k{p<0m_aK^sO&Xx#6{pvkei!+trv&CO`ZO9sOOA7cibV2`3sPD|H;O^3ax z-;&UqZ>P3C((QFDLdP6KQfd6O*XG7z(xZro9{>lK^R*psE1@{M%jGelh-#HAG* z7Kl_P-PF7DwF_I^va9E)D6#yNNSmOI1214N0}D9-jFM6^!l^k58b8^617oc(mM19B zpnZw8AcaO@yGe}sUiK;xJM_x+h_8U6o7^0rTUZ9?Jb1uOo9b!BPht~1|3}&KIL?~` z^7skAnA97X#EYfaD_{6rw&%o2H^8t7lO#ES2E7fCA|kt=GQAR= zq#^i}QA2dghDL7+lu zyL{x}6qT1UafqD&T4+n$zY-)+Cd8l%qLmK>%G+k{hTz~NIszA9*xN*|;nc1xl1(>F zY8$MnP;wL-%mA){?xZv37WfLUKId*l0ATL4Xr4CglZvhLrZ*R`Q^P5H;90?;H6wOcZ*fp1-YLN%Mq_bc zp1r*7El&99=t{G1VD3S-`NM&zZdNh3BNe69Y{H+Fpub!JixVZ>$-Nvj*D${xNEzrf zT|z8#W!~s(MF^6R<|^?Z9Ceo%Mt+9dOQ&w=$^zfTcfrsb^`1L*yrfQpQi^h2@Qe|} z?iN$H=pYiXSU{6L6$upCOivAMyUV!;s6?LsgUY!c=RrZh zZiO=vZD=FwZPv{d!GS}V=5WD7b8wcyp>G3ooWPgF862ER6o5zRGVbd}I~d(2BnK|{ zg_R4UJ7YcH))wbSCsf#i+|9KH3-JyR$~`d;ZZK}z3_W2}l#YBo^8EeB2D)SM*6Fw%7180%|w`7e$i;2YG0y zL-;uO#m;X9B}aKDwfu=d!}VyRtdB12ro*P@Y*HorMDH4un*6WL#ieq<%-V^PKF+%_ z2_6G&1fw4rZB7g3Y-CG2_9FYe2P*b4P>yC&Jmf;>@OTI&WJ3S_yb9coz0%8q@*e`= z^g=UQWT9Uv4^Y$bdYuyl`zDACVK^N$gQYV~@_ z@0Yff;#0hq=*NxQ8rXr@_Fzw7m5fyZ&japMiTeU7GbtsBb`^Qi76M)WFV@y$-dDfe z;nT1ma+aLmB4NBr+w_qyHa5`u%PtXTC8pS5^ZJ|t71}Uc-%aaaN>Nb zvsUEd{ET_d(F#HKa5Mu_6o7&J_0h?~Vla_)gvQ47a8sp%$}VzVuQ%n!P(MV<9DfgT z&tlXSIR#nh?+uEUSqLwehwf%!-mJB&)rF>7_Q}80=Ifx3L!TycQL+Hb)oWOM3XvP;zKnu|vDC!%3dipA7V-t5O#Nzi*txy*i%UXfFbz$%;9&M^~ zR6HJ@pozhQxA*HW&NSyP-3_2u5r_8EKbQN3(80pYBJ0bShm!~2L8hCyRv5%Ygbyv0 zY^$MLz~h($?Dk1rUEXmi>IIcuoo^P;ufmg78bdlDr6u$((FSmqwxM7nDukQy+bEtB zd8eZ)EaZDfL^JsjMlF&~&V%~@z&1VwK-zGnjN-S5JZr9+M~gRwT}Pt(>y_SQ?9|JmZ1k^HprZV^ zCUFJeXGvQVO0f+0F9(uI{B~(mI7vRLX2#c{mZ(xLbZ}LvX_WnJoo|*ISNJRm%|n-s zKXB*)_jwTDO5FVHf)0cK4r-2^zuVhzWjTJfwK~*i__~2y=TAds7pby*t+S?$RtIIuNmL=>2|e8Aj} z&-5avV8TeH2yTs}lJP}cK3QpQqL=$e*`Q$TCcsU|H1o@CJT=6XF57YJOSFb@cy9YU zKqkpt9s3mb3)S(AK%$qLNKht}a5XgBZo5cU4!o;G=A2kge_xO>e#Ec~_*Jdb_!P9c zWgZ+$HyeA3-$@~lXLFa`PS|N~M>GPoyv@Gd~ozTGxhSfU2@4`F64q@9{}vvU3RW}nbP$b^f*n9&C%cEW3e zWXDC$7{_ppUn`x4v0voJSF~L`gx{|3Jyo&W9uJIzR*99Yuzqv)L>!N)jg1+X*v+Ye z+sboU2q3Q9UpV3(>(4-po=rjX?h#9&{VuXenl1=VD{Jm#kXrJ)w`TC*o-UO;%GpM< z0^B1jP|m6y^*EG*`e&&cmA&MFp`r2%P1JxY6}y)|ee;8G6q<u<{- zMTZz~%H0ItvacyK1ENZl&qw3cuHX@jIeI)pGsU>K=nwhBx&?`-S55=$8ziQ^LoOYa z0x*|fS-M%SA3ENbwnHny$Tv`hoVRRv1?afl$Fg>|ve{PQ)bi95S$k+;HCUTVG+S0Y&saS57_*}f2ecTY z<`l@p;9}}pImMXnw7IXKx1b>8YBB%Nev%gHiJ*q@TovakLV3$QRUI_u;AD}iU=OO|iH>&X{GeAo zXGb7gxpLOpp4NcX+&)s{QhKVQ0W%pefCsP~ku>EmuEq{CP&xw1_R>Hw?qx{xVV@bO z3)>z9UUq(r!UEH46Rexob$%KA0AjIg80+4Ci?hSjSQ^qO48x}F9n3;?B!%fuo2BuB zMh}lZz<2a3M^3(7m;(2m6=GVm=+*=P5Yj@X^AZ$TR(nf_k(EbVFn~xWD`})RbTzj% zOme!R!7#H-Ae#K2^&=cQk^tc45{478?2;#cbJ-6pi#kCH6gd2X+s8RgzLx8gMIN&* zy}BapB_PT?-&`QdfnCWWItUi3;*%tfx?cQ`3oQ@^?37C_f7Wha2%iEz|K96RT2l7y ze95f8feT3QjYNS<#d_D?T?%ZWFL9-+dv&}6Gy9oSU1{QzPg9__>8FH=v-GqH!mk47 zF9)VS4hhc7gPJ5fez;}2&?RzUoeM`;!c^-c?%Qy63_cSxe+<{BoYkop{5e}! zraEWZ&C7DdEA>xmX*%iKW{N0#{n6TNw;9_=xs;(y|m^Pi1xSm|!dD^8I;%3s| zcIS6FBSTxG>=l8O`G(hIM{)DBtq6gK?+z9{mwj5clqTo< z2Ie3$ZU;{W=y;3tWhW=E=0+rzVo+0G<^JIKC{m8fi$HPfuxJTKzBGwLEqxJ=zxgy*}~42 z&e`2rMHvzRc(CI@=Rd>69U1@-RYx)YzRKDYRAB`fQoJxH8NQG3)SrF zAYE}_Me`8|64EXkqu(E~Bvzyu$=5P$6i`nit1xrg?&5G_o01AEAvEj`Iu$prN~9Ul zv(|A9D=+#KNf}hGEaTo*DQg=!G;Ee#8$)a+qD)`EZ)D%%V zXq+Dx@;9c;KGZT6!q|*o`q!?^X>hdn&xWzTwb6)56?2ws{zV$4EY0ZXRuVbCuDziv zkjNYbgKgW*P8CyBT|KI<)g9}WiEy&^!MMgOogcR5hs-wBwl&c!_MjL3?+~tl`6l3KdiY?@vtn_S0a@Bm) zA>R77n#GR)ihb*GT@Fc2{K@$)(D{{c$=|WDqk@VF`t=QP&!cKUvS^a28k6pN<)m4+ z-yaN$10du8KHEoYhyCl{wU5^hkLMp&k3rMepubU%qfSQyp9)Xyk2+{t&uQ&wG32V> zRnN1|dkx?U5jJpOPs4$-5O6ejzyD?r#6hSHmNwL~ziCfNh+Yf67KYsqd%)<9lM6Z* zde&d$&EwA74WJ8=H*j`G`^M%C-V2>qdNc&T9|nN|6x%PTPi&9S4#5q=vIQUz9#41- zK^x2_2wo61FLX{!!i2>Z4kzl(KopEf98F;`3`wkr(J&Z}Cfc6LHpn{6I?cMwy3Kmb zdd>QT=$8#~tU5%05H2K3;%JzF9Fy&SEKEX9#;Gt7A!W-BEx=Kjgq)WRaWYInF3IUI ziOZ(DGr@XC6IDaiXj)~>L^=7_NRhDOyf{mGI3$z5a)+6d)v%2B23^nZ*4Uq z&n~W)`}^i%=rQcf&5qCGdu-3|{zfgYMNIGa;qg_De(!5d?F;i(jNIq@@0ifpghbx= z)7#JYyHc;W=hxlT)Zew7KR2&)w|jhj-}iEHeBX{gU8hz*b7%0sJ)gCF+&y1+PcK79 zadK(BO?~H4eSZttC);$MV8i%YxOscHf9>IZ-);GP-9PSiIN$HfoXY$@)s5r%Uhd1- zZ*}`V-`{VJ zZ?+q9a`nn{b~^ZYyB{?kpVRZj#r}-Z)8p^<`0XzKas2o3Z0P7J7v4_09cQoep^x`_ zZmX@dFpR({sf9bh@%pBvx{7lu!C^0=@OgkE9MxU5@@NTi(w!YPk`5EvI zUCz3M&t3n7pYNh@G zqYhq?nhyeI(36JFP-!E&|J3wv`iOoBzp^_N2FRQs^Mpwf0hmhgaN&vF|}vzpWW!tT=ZVdfcfqVFRZdLX|EPkaPPUE4=jq9TS24 zqnM>Y*W}PU2#rb9Ys)m*K%XZLAt+GVfxz8G7pQIT5`GnUEKh8_0I{QJn}@3cX$xA7 zT^_tn6U|_hg_=pr??I5Tg|yY1S=Ma#L@2sG6_MNuB->neE%6UQ}d;z z3d)IQ-a>UptZzYxf{Mf?oV=g21fnKYjV)QNROJD$(iK)YvUf(@1BbToI7+e%9*+aX z)~~$48Zk#NrCW5VKAWFLwv zU>F54h%%fzQ?+5RnnEvt9coTC{7veFw;JS~&R>@-PgS6|NTEnk@FS>ngl#Jxflo*d zmP0X{5E-<=SQBT6my(4xNTa!8uT3#2jkNB%2eB5F53g{F3lb_^bvk$*r|GK*Rj!mW zp#fY2xh&^4ktx~`& z4yz61dGCZ6D3^z~kV1Cw?y)*=!10ooDsaGoGRpWvn~2h#Gt^x3;8H!5YbN`1Z+{C? z=O_zKzjO2X4OC-MerCmwC`7=r(e!v3RAezv4>7W#&Aq=iCm(X9Y=!ZVI&g4cWviq| zOl91o8a64xP1BtN+7IGXvK!h@Z3Vd=C6JR>kZx>zgViSg<|-Qp&e^Q4}C6r?ML95N1C%5Z^XZv*Lc}Kib)V8h_>N-^f>@Z-csW& zixWhme5=|3I={iqRaWTu(kE?IgoK5>ea^gYo!rf(PMV=t)dKr*8kdl7A#?Ln6>;ok zvNAtn{|B2rBV!cRJX_=C8zZVQe7mgfZm9+sa>DM$J0s+T7oX&ft=5f%-3bWTe1^e>#M#-ec*Xc4SFsbY@k^`j?Y8eZ5X&e7O zZtB`Ck&@U@1^QLM*L~%#@gy6^)Se$KvP{_u_s8dU%0%&28seV;lY_qs76GdIGAUiV z<{Nlh(^n-}LtshCTV{Vfi(Hzh=AnCB%;G)(@F=~grg72#C_6Nk3BE7XqM!qqw1xgm zd7#M-09R;JxZ1vR59OJ`v`5D zJvX8A&e#6^>;yi{6pa)z(_TKobf`X@P&5XZ=if4BqW3H_REZ6e+fa5k2(FFUq0TL0 zq#JNdUAp(L!FY}#OeH>rrZWb;!ORX9k}{JR>Qe-9bI@snQNuQxci^uHZc84h?seq=q~ypVduc=1o)Bvk4Q&Z1 z@v;^uw^Z#Gr?8+%*W_CU<76jNRu`+rS9X40J~o$Gd61*Sz z<=8XJaj?zRD6)^bWShtA9~*pwIZ`H5>P@JtrU+gC`4Lgn-4G;yVWNQX#5wy-i$CZy z&bLfcPGHAcM`y@L{D9*~#lkWBU^umdUc9d1Le+3+diF&1SwIXbx2zMr_yoiXsCjL8 zcL@zNlcdpNRL^d02fbcx#3!nI5cgr9Hq3r!`4Z9If|#XB}AzOQJ+8#)IEQ zOlUbldsCvQTiW6%Cvpo&+6YgO z_lyO5fQQ3z0Uh#$OLsDn*f?2+swu7nI<#~%T>UZ#(WTt2Gg>`8O@IF8rs-1z^J<=q z-$?KWa7TFtq84icmY@Le)GT-_!6|6fpah1?E{*R0_@>eS2kE@-Z_I{n`GJbIOp>1| z>;R?Of<=-bVjEqsq!V{kBy~uD%1Lb$x5Sa4vRJEU`zmzEh zsgH#KdEVTF=SW0PpE$?bA>C{=>90?(47ct86k+pGSeH+irJxI_skMx$(mZaW@IUuQ zb-Nli*A%x6Xde`gn;;ksY6oyu#5O&wLJ3^sCaeQ!74ip9?1D4VLvv+iu9%gKCds=t zeg^Y#cSlnLb%lXed#X4$Kp(Y29FP_&t<)Q*RH7RTE#-!`9#W=Fzk(ci5AP44h*BX@ z$!fC*gP%95Mq$cxr1_;T578!8F%2w~uoBUu;buKZd6UQ0N^+tK#06f_*4XmJayOJ` z^@~S1R;VsH10@0%eA6;@jYWM**zQ76vnX-UST*TF6Gx%iO5UfeHw2;uGSW!p00;uS zVB066Q%%ukmg#DpnikQd+62n<++oc3F|faL)kh>J2c&-ZX(IM3kO!C?C#eLPjCqA*)8cR)BVuY2?qUv$>Vr{&_sDinRE~I|F_I zrm)7x%eSXiBB?h8=V&pB{Gn}|N7Gw1i6sCd?YNfP5Qo?jDi|E^6^(gjN&jG*7_pd^ zL3SlmbqAOA65utxBEz78qll#uw8^FhD{VJmBw7o!o_Ok#pA9Qa9bmwPWn`fqk@TKM z9`p@na8`vS>L43!dorM+Dd}(_tXUc29qjny8smCr-4%V{i6fB#z^ACue{Av{cwD(* zfRGoiN@!LPrjSn5iDpUFrR4C0PQ1(RTOT4V+hrg>{wEk zx9M&K>%y*>xpuuT$islP& zCj>rU8l6dM!ZvL8n-b)#xL%SGg_6*j6~$r?Rg{Y5)W{qG{pIZ$Nb9znF_1nt1(gmx%w5A;z zq88VG%iyY=b?P#=p!i9USFZxltUK`ygUGD3Qr+Y|K30^qxFU@V7O0q~X{K=&(V9T)y` zO&nbNyX7ajT!2on2lmIw#64do5+-g4qR+A0+FIu=w`tA{iGEFM{ znSY{Mi=-)J)+XyiKHggl5lAnm0w*K8m{)O~A>6Lyw0y09L_NwNeTMWm-2^`Gu~H>t zPMD^dnbm=rjeKhhA9Ehg6)>>+3~oK~bW^jX$62+Qj1SS@9yO*!_x;jd2}Pn*2e;a9 zn^2Pr$B0`hd4mw2ZWynM{%aGB!}G(T;yW%yaqdrZ~1?{Q@#-0 zRLJq~pgoO<7HFjSPX`)n;m!1YG;4Tf$|nnJxm|@emirvU57~Y6o2A>Ck=_a)#j2+o zh=r$|6~{c0-<7_0<>wO0G0wTPk-Btp*40}3*CV?XVbm*Mh@VslFP6Ghw>IZqta(+L zuZVGLM8D?LQqrJj?vvWeru}Q@opp<5>c>fmlZSuh>pSnHL3-oZ;REDgxGADEpW>G0 z4q)zj#@jAWfh?W)PeB>W5Ld9g*@h%t(R7yP69c=R-&!*zX+y_z`&DOg)oh zwaC~;r!>p*aVSeH!^9889#u+DP-zNB#cr&j>iE)1Y+XDo`VU&I%R}b{B>) zR^xB{0L|YYHW*R0WcOTve`8cOkCume1YA9qlGD2?0Gz2 z-JJ{2A_5lGAVT{K^&kUExJN^TH9(jy%u+7kI>=JW=Od^JkPzbs6!{RT_7FcC2?w`L%y3OwOka2 zR&8-&ICPc8MSeQ;6&3bP#wo8&@+8&DtV^oKnnDsFH>tCu4Gbu5Q{sPV3TABVFq&z5 zcE97!n|Ytx@O)aF?BI=cznUH>=b>wAZR2GHD-rcf-LsXrr#wo=x>qTQq7rS*5jeq^L~D>{N>7)&rR`jx3Cr?tU6`uaqtav=Vk} z+Zdk#mA$bn>qED6bSrW%{x~tM6p$=}b08?E=+Bfu4ro)CYgsh!*K`VvX}h}xq3xo|Y2apIzrpl=Q-rfwiihq1+2`b1-PJGV4lF|clQ7A&a*P*jFkTAGOV z60|HV8DqD}Q0l!!P4mNIE61B@1{D_byfCXIMCyyFS6Y6Qlh+iK;x>NHwlu zeYKXcN%s7z!=b3`N;iT#y%{Z;Js)J}+rg)Ati{7#$H<;nu8X`G&c}o}l2%wKV}ze& znLM{x#VS5(WXfRnwz5zI&I>DyF!DQ=tMq{T_I}Os#a!v~Y|>;cUY8{)9@+>t0SS0; zod2DGEWrf$i1f0^aqlol?AXwjLCj4B#mHbBzS*??HAAJ9NC7>>x7MU16jxzR={bHr z?C|fQMFR9~|51J;260)Jr3!yi7Rrc}?8Xm>%!K=!!paxW1mBRIeO9r)?Ki}wF6<@- zj~8654v1xeTtHc}uz_}V+PDN*OnX8%m@=R)I@SP|vij;0kXKqzr-tO3V6A)Drp zBW!<-Zh#Hv7;u@m(B$Pjtzafa0%PK!9xGZSuYmB4KjiuIT*>=-jM?&Y>)ST>zv6zZ zO92o@eDz0NTdo!lL%yPS4S$PvDDfa5ZFiWfyOC3tu`(J{wJpH0{qAG-(fN^T+Sn33 zJuCNOXg%MPfnPltuN=#91qEQUA{>B(e1Xz&?Pi2}ZPOxGY3RIEK09Kw0lenW-(6+k zBpLsy))tb~K=U9;UJIyP#*Ewa4vKRZe#fcX?MRD|9cJOb`JP$>z^*fBy7DuMHqc|` z*5lcUcNk_ZW$83?p$T-bkVm5QJVojwhex*iQobP+p3#-Ukf-PeQgLc2 zz3{pe+JYI%(kt{=|0NeFK$}%TO5wFP0d9t9+9&>i7uO3o1+5ECo=HGT>kZp9kI3ia z(VnlOS<^PHAqA;++{)4(3I^(T00fAolRLDz6D1QKYu7UeQppo+0-_4AyE*w+DN}5b zMSWTg>(b?F?Y|=6Dq|AM=Ypdsuna$(zoNMB)17(8*nx4ixmRw6%)bV}GwF{q-qOa; zn)WZVcyvv7D|Q2yjnat6l;5rXvt}{U8COVkuvQ1#v??3V*1b-;s;2dD=4w_mwHd5x zPHLdb)HujtM8h6|W%fb}baR|-wtM7Uti7;Rcin7(y^2^lFWYtwT?h95miLT78`oUJ z&Gt)R4lI$tzfB7!mhI1ej%VMv0XU=NK;nQn^t!{mFqmE+1+PdOy9sG@z;O zsC~Ov8EQzqz;y}@{_ zQe;)&P6U1JY)RA%szsF2DXLx5SE6Nd7kAxVa*TD9{Uhk#mLA_X|Tw7PxlPE7G`2cH{O-91k_QMVB`-+ zUbNa^nl97Kq}97GKb8k7jX~7viG>~p1Vf;9E-GR5Oj&mazT#b~z%H@0WR=k4BmG;$ zli&ZImfda#I>L@6^;ZZE1-qfy1so$oS=(z2+x+apI~^`=i1*Vak7%x>RkXnHGsbiD z{0I8Yuxme&?v7h}m20^l&ELCzx(I=ml{#?dI!fheH;|h3?|IY+hd8;j{O=N&B;H1m znV2E`Y|?asG-AA~8@K4WK6kcUj8U~+QqbD|N!Cu=6YNx>2r9b$%)Xb7~nR=P4d z5Pq<$&4?{Nx|0(mc&ZjE4Vhd;owm?EVVuZ>sbtvg)*eqRNY&Z=P;lA!jvy>X6R`O=)W+4*Z?clA&+G340K;A7!Cuos zJ0`79+yEkjw4``FQ6%)d3fG{Qp}8+CB@$$})W&Dt#7JAm-~uDEE0R^`R4!YhJ+{Kn zq4U%6tGY;h`>s3}$|svZ;<;gRkE2Q5b>z|pxxYoPBFQ=wxI>6NgSl-1D9bO zHzgC>EsePFaf(hx{9$XO7a6MgVxcg3pXnajcJJ(zGx4}HNGjkH>lYY8uN~o*@u&VKNtp4SD&m^C zq>+h9yxT1*67gBD445I7(G5x1*vP?dD9tXk21AgotmaY2axt0HH^_W(7aK@>uFq~j zJTZ@PHr1wZ=`KLkPRUvs;9EHj1H;`aO%2D`oJgp$bQ-&Uo|aEN8DxJEg(S1an8{Y4 z_b2E9i$w7>ldvIKx1stdZKh;2k2o(6m!do{NlPkhqRQ7BmiOX{o^eeTiNRP4Tb9x) z)2`_4F5gM3l$5$5Do(YEeo8!WBoMf@Xub2c-yEA$rfYb7HqE>cgL-H$I+~Z7Hi%Fy zyN{4MrO^qu6oQgEW0-Z=JXp$^+ila^h}}A6p>4uc*K8X^ew4(q*Z)pgC{IpMEzccQ zDY1F|)SwvJytM&ZghmO0|Wsc>LU-JEk| z*u7JVW`;0(wrc!qwwYR;eBTyusy#Q)G3w@J@CxIhfLk6P*L79~0lqeNm}@zZ`L-lF z2%y3Gqoc~MB$*A3Y5f=Ty4-ly!L(2{o~Dl;6>8y2^Hds}_T{i8s}$7J^>3}2Dy<(4 zOi4|URbTc*KC_qEy6c9FC1p@kw#orjaY)Qe4@5CYXwli(!S+LEK>q-4MQ2R8?M-(C zu$xet|G!Oh9eBGc%fsTTn}SRy=3M`2#(G}*Y4xdWO%}jsfc?biP=XxYYAcN z+=;u36`fp_fMkrpQIC&i8}J&0ePi0}fwb@;%r(4cz@(H7Wh^}R6sJ#Zl0epra&OTK z=%3HT@o9PIuFDJo1DI*OkT|Cp)#bX^gDfXhmRw@~rXV6Kqt;K6rGxxd+C+F+gxHguZ-8BjC0WI35c@kxswet0Z4vx{zRZ@w#OQ0f+Y=)xR zVqP1pZDp7+JYs6i-XlCo)Fu1lchJ7jWUBQ~llIWq*;= zonMZ9#RpA83lL;`_U4 z92(b}*u{{CXPPuF7jG?+Fi4c+VGaW_g$t!kSlm9HGt{v*+mbfE8=BKYF2d2M95&KP z@D)&q?0dE%h&Q0UcJR89L4bx2HPTr(0Ai?kyTR})nwG-jwa(S5g4QQ)uzmO#s}J8~ z)hf*)so`qnYqkvnFoYed69eiOE_hPwNb1<6p_~B&b;$uPtDB%|bEL_&FBs#llNz|4 z&Fy#xT0_kW2-9J`(%EZnC*>n;hekoq+NgswP&~I*Bq?vmj$QO}?XcOlB1eQ3^yL2T zAv~XwJPAkeOvSjMeY<8MLpqJ4kPlmsy4d@jR@?5}_zJ8Ad#IrbuiZjcNwZzB0m9-o z_KdeyEIPGsrcCRyQQX4?*kukM8J_a9%b%!DatnmMQtoT`E=)eDWNlr=Q%DL|f^ucq zw|>=U^%af$O?UjJXc=$22i%1iaPWbW=QGFsm=)3HhKOKO0m^C=hIK|vQ>@7}t_h>h zl88jzT73teU5V*@#-o+&s?a=CNhSD)7&9c0-Bz4i+I9D(F@_|vrcl%VY0cZ?MN+<) zsX@Nh6n5@8SS@bymE6-Ubp@S9Rr;vauQN_eJQH{WEK9-&LoTzz26FSRhsQ2@tIgLO zHc1LcLDXr|(;;v_e2hw(JboRwYe9+U3>0!VOE2dE+(@rh3rp?K`P0Q zj!tPLO6#cMQT3`1;#pQ~V>vpv?`JjEkFhSQ07J#zbQRlmuT8NG zR7xmo&GPLD`!+_cHLwSYJA{bb{7|J_|wg6A`M3?NYo zxeY!#nZ{m4^3@Z4kj|2V+6Ij1vXmQ&%DO}EgUivaDr=|G@R`KQj}!TfIh_=FEU@Fe zlc){!Jc!D^o0N^jBheDH(NCEoapP|EsjDB5A;8{;9m}$TXP|2( zSCG)|z{_^qvLV}~!;_d=aNiK>2Kk!W`3K`i1DOm)TWou>t9OR-cU5wH)3CoE_6!q4 zhbrJUsYj?MaeYPm18?tT>}mTMNN$=AFt{1Ae$Uo@_}zy+4aQ z8Jj;sBd)7@bhu$~5PQ4vFly`{KFFaOu-$=JrV5+CHRko3P5w+{aAopf5!SlJ2J3u@ z*=66U=L-1mt*8=*`H`j_%-T{C`hk@?Rj#0cdNYL~pgSQ6n>alMU=}#$h-{)sQoHTZ zSQp!_T}U^4bC@1R7H;!_=WcH(9&FrMz&M;O!Kf9tB*yW$WIAkcoy{J&cSYV}V_wgE5W|K!lZ4Kx8rCn+yFgi_6)Jg&J?%ZETg+oTfeNrGsO^ zsFjbvAJkGk=;VJlQ=OMS?MY7jUIs#vQ;J!Zevl23``r7u=NrS~cH$F_$$1UC2}q2b zn`lc_VYT5N5%VvSKHwfr)-9D!84HJ0&$~`7x_n4-KpF;T-6NZ)SVN@Xz&nN`ZoTL)uN+|>=Tu&bp* znRl>|v-Jaz>WeO-< zrq6Adho0T7rnsHv7^0WP7^&71q^0&=oh2534Nr>;j`4Bo1juI~s*946F~M$VAzDcq z&qjg5P`O@(obO6Yp*TTc&lO4LRpImV3kHhwUu$O7oHzv zDVoJ1ZKf>0AW=0g*o=oKE@b%m?iOy$l|4~Q8xWMtY)bE3qEot9dS!KFIoyEI|Iz)M z9dM#EaR0+rO;c1L@1tK1>Y0=&GmKr3W}0MwjVM& zLttQ{2takuB_Tm6`)82-I^tPI0|=Q^T%-- z3L&82Eyi2R*fyPYSmJbM;9Vhw11RiId0RV;orUEYXSeK1s;LevV_C4K5=WFqO56x6 zA!DKswbsL_yLcgt9D6Q0oA?@!Tl8lCkYW(d85Y(qE6ogKai`oa)P`t)fam-O$S6ZOLR%ie972C2Zh0$cO?Pu4ZoZVGZ;< zv?s)(14;YXvttg**~4qrqce>3R=`Sk$$}2P(1>n_X<`S#1@A_wwCW&7a4yi{sH*Yr z>Jo-BA+?bMy-ukJ9gbWJtBr|Vf3M`?LlOs;jT2ME^-dqhVzQS?IBAZO{mDQ1idGOT zMq7rLOXKIQnwl-!0m&UU`Va2lUm3$o-ryWLK)QEhR+EL+)#J)RMALs>?#01#Jpls# z{CODUmVRcYpBL%|mc7Dw9IV8RZo+C?ES`)6jgBrpt|D{k_u|J={a4d4nVH=(Jb1EG zLiRp??szzILjCS^u^7g!Cg1GoZGU)^7~vw&hTPPVtv*}5HO$ic-Xeec{XY%yq&<~) z@&63*m$m=^s{chFrK5|jlk-1I<^SDFxst75cgTSfviFYavBB<&`lqKzV)_a?I1DfO`fNYLibbXken4A~i}*ZG=YA#Ym(lb?uT(v*mZ1@0QQ%xTtxO zN=NK{<=wi2W5SQ$f@Pua!vI4=MWUx+s627y<$hsYG)?Zo6{9A`{BKx7 z>+9lITb)bkt>C1KW?6}OCV`|A<@IPV*C>GHS*3-t&#Ovu76JGh)3qO!7rx||VrENq zO?wjl6CfY_x!8bTuRByjxDPQCcg`%X4c!$YwnLGmN#2|a zuA({m9u;tX7({$_B+T<>}Vi9;R<@iXrnmt?^w@Rg+FS zPjMo?nyx&qydyI;*birK-n|RC+-2up@$2JZ3*XET zIt~OWqyjxRb4o^_%i^z|LKJ=hKZkctMpyuzCqAbjbcBzwlk=h^TqWhl&n*fL&qXmr z&l5Bj_srA|AIamSew~7S~#2A=>10_=?olAP5=LzhHTQM?6k$;LSBDRH>YKh zQLc2lD40b@`40giyU|#%2+^v;2&bwBzFyzo%B$ewQ@6o7J*vishJ+OPbbEhK4ljr7 z@aJrM`*?r9x6;D8veL?~qV#-!ZXSkK`h0tOzn<=2-EMum+dDhI?v~1`Ud>+R_H^xX zZEtIDf1O_b;QM~PKW(LT)w$KZ*1gWjnc?$&fBZZQ@yYh}c)T6GpZq*O9_jIVcs<_P zf4e(Gk>j`MiBhdu&?!9P2u}tvfS|bBlZVU3$EFo>{Ade{Ey`^=<#d z*X7gk{__5H{r>#8GxZz3pNFo7e%pBLOviV3%l%CtA*x9klTwvT_W1Qa)wN)Z$~=)? zyegW=rK-t#qEeY@oM3<^0*_fClaaWIRn@w26u&xCY?%07`=X&V<-;~%kdUUq@~1em zIk9+!%tG8DNqZ-$Hg{O*@CV*U()cjSm~P6ZrJW0%&%Ku?r3t_JsbU(pq*-Ap^JAY4 z5uD6G>5RgkNlcg4nsZx#hZI z`v`N$(jYPJwKE{Rno`}kW{wGr{K$XV7XV9ihoKIaBr}?6!j|`|%GT&DMmzFY`%n0q z>I>(P5;<6YvjR%=!dpd&h$V@L`qVsyqM95}9q7=rld@w>Pj7Kt$%!_72-Y81#PR? z8&el&DYWgegF5PZSBq($i7E}Y+|@_qZH=Z^$hntagF>hNxUIvpUJD+ z77#eu;JvMJQaOzi3OXpoc?Sw#frw_z!RnXKb6D1RfuaN@Q)pq?kgkv^wo4J%(~oK^ z&h7BGVwK0WObs`bPna`+7t__7C=Z(q<#I<-kzb-%)I}0z(70!DRR0S<1wXAQF9mm3 zF`nf@H~HUz;m~X>mh~fYZqCQS;x24JSrSNwtdKdEu;CrwB z(XpC5bU(a^stlTzQuRD+2%p>2&8oyr3J6nx_dfUKWMy!qBdWT(s5})kn1$qa(!^X= zm^C=75I@EH;jqUps=PKPxOft{eOlz{f+{<90T1Fx#xqYq!Z6J7$LQK0ZaC%eoCR9e z*pd*anbsn4n;&cyI^_#%22fLlP4NB_9CB`4!2w`>XiM9Fc#_Phm+KrW90O6eKC$?~ z3)1Oc1A9s~yW}gpOG#|-&KAF^Bl2Cne=i-K!p)lJ;Ohs4OvBkLsx?_`yqOCT0SO^L zte+m}W8cGOwq}F|N?oT^+#Doc6jBWrlQYoeDAQNT!4ot8!XPShz7r(Y zVIzH}W;I;;(yRo(nKzLIQl%q)~3!cojgVqW_&58CuY z&T`$0H}X4i?C2+HXhHb>!cx&oa`eCIKt=t%`-*7AnJ_MN&N4W=5JysF`SBG-6KGkHU&7uKyJUM+OFWUG44s)R}M#Bi!z@vH0V zvwvCV-DCYJ(tCVHv(^1jVpVx73#yOLw2<{8RjJjna`}T7Ut8$V0kP|*(I%M}{mim& zezetbhcJc4Ro^a`ZRFfiz(yF^S?KhqO$^F>o@>3{OLPU@Aw4fdHdM95#eKU=qG`4K z(3yK9D#hL$VscWQ6}ifZ=cC>+^3u9Wt024nY`AZ4gpnk{U}KHQzd#&)fCT1F1I=vE z$QoB~fw|<-dMQo>(~aBK_CQ9d_)c9J?&)WWF%TWwe(z9@QhO0AZA`#qs=dUXu81x) z#yZfVs9~y+M3Gn?* z{pCOo6A2UV<^bz@QY=7GdVY>8afPUdcG5r~92g1*I(}Mat2^5n1^MJgl@~YkymO*W z;)`n>cmZKeE21)w&x6k!d8X7zuKvb89oR=91!~kvhE!V1f=_04k-Ws z`Br0lJ2Ojj7svmMf(*NapvUd`{A2cq@k|eU|O1u>gqyaE7b5auonsmBG-Ik%`>M)-*1D^wNd2u)rt9p zFjJR1m#%covs?4u(wXyTi`2=O^SZB3qkM^d=D-QF9lJnLrpwI6_S0CC z2hg!)r+MZA9g@ST`AH%|fnFD}cS7Bl45kLDm0yFIzu*T0N z*PUD4GGRhUxD)5tqgxWTP~=B?2He;a3B^HF_zdopxc#ul+5(UIYKh8oksODp23Ml6 zFm}u?$sR(laDuBrb*pcUs_EP0-a}e;l}lgQvPmEZ4zNzs=Dbw%+5udq_b}$CJ}2E; z5VUzTH+GwqTM!LlFWUladWx+%6_h!CG-tnmdvso(3-rD{om|+CoAfBGVEyI=6S!HL zafcyzxq0K7zvK$65=lOx#bogw@@`!3f33<~d9?3v{&>4!JL{Y+P4D}Akj6pnj|jUG9e^g;G?B|-jdVG#aybad^#e|9-i0|-7w+P zCh&DdW%a24s<$!~y?OCY>7yJdT#=2TTVanqX*Y#o|7 zbS)2!wmz68gY7i+`L92y5!D^-_>wNH(Y)gNRX0=~yJ7 z_z69`jRBo`Hm0aYtvR$A*g+g|bCNI3TBPT#ASy_!xjUrhOKLM!F5BD1-Q*QG#yXsx zIZQ>!>8(9U8VuC(hjm;}BUCzp9i+pZ5W)Mx~j{FVzm_eU}X09#Er&1_O1jg~1nOc}H3yX#!SpT!k)T4l$XrtBpPC%F@c5{_5E ziTkh&fHko_Wd}xvd`io#S)-*#n%rEgEyMo$qIF?V)AY{#(XZ4smP6fwb@BGKYJ;uM zY*|Es)|1!atz3H7Os6fQrKL0!eCLU^a}S4DM#5Qw+R+y7bUThW-z;w7GUuC<;NW?B<8%FGKjd z0UndDV97@3wJ1`o?U6XWzU`i>+VvC=rh4pi1j@kT?;fAL&vm(?zrBL5PW`hd`!J3} z(&FP&)(Y!_mZQ=nwd?HSh)mZQ?`@Xe=_DVDYTMyun!Wflw0d!4D%9Zv%_m3wj3tR@ zt3Uxe@~S=K;Iw8U-Qlb~!}zRbVhiqzk8g4ZC7h76(f$JfBlHwRO+Vb^ct+SoRoJ?e z`WMFXb$jW4=ye+#R6&2gp>+8v2aNBb*!*4P#oz;#+%U1q(X_!j@$V10%uTj%Er>#) z1@a*H0z1(0LC!UKze(3K8$N}M)QzNm*T(mB54&nUDqZ=Nv^iLVHfOc3b7tg067`(S zhX!^muIQO2^9E0Oj3_*x7VHCrbA&sO!1jrCmo3M6@`u0WLqD?r0z0R^?cq0uFA`0e z!w{!gTzn)!2ds|I+)a05=F%+`7hYnb7a!XYmwkLf@;M#a?(us$(^IGpx(>p=JabL$ zq4zYe4MWu(BGCx1XA3a(4~_CvQ@=jEpyryr6LeYRVO+WnfuL^c7a>ckcJ>zX&sO1>TxDY)tc1QDrq zNJEOKLkY7XXKw`>R_xIZ=XFb={6RHxqSUA1%IBz+c~4X#Wa3?VmMTA8In(~AZi3{M zvFU8PS*$9h*-s3v^;ztpG=!rPBC?pdaj1rEPm6349+AU1W+5vwBJ{wbQh>#8V#0T2 zRMSmFQ8o8+m4Bp1#rQSsxAa{qQ4KMWC+d_TIZ8`Y1gXmk zNH?ngQBgZ;-qgZg4<)4RaPt_~{G*9$QPGU(1HD%Nj6{VSht)gt-wIaYh} zv)wag>1BpNPl!H*Q~Km?qOgfkbI)fljO@#E1gOtFLDHCM)wtB+toiOGSv8O0D7}o+ zfWnziU0>1KtAubC_5zMKq(L4=3U1S;ce(U~se#P4V5fCeXL^+_8sP3jQUR^8cGh(o zfrYcn6vu_QqJ%20qM-LFNzM>r|3~yZ&PGLYd!k}<-PqAZ92UKPi2x2;VN2Qmyk5BX$2mHf$%v%#Awr50f zB-@|F#bd2sWxw~NSW}nqQgLf8r)9~^m&UjH$z_~S2cOq#t#}OV+%IFI^+$BG78tqA zNs!txo&)nwOc(P+?>;`=s#k+)i=z-&q-e$zZ(yclx9QyKAbRMCTKDmW zU)Q`}^5evF?Mo^LN3G8IV<&P+#4Tx7pdXAXD5~bU*)p4FpfZ*L6=>a>G(i57-jktD zN`251m~Pu;mupXSKL(+u-V);~qt|a?eGan3&uPo|54@^~n|ehSBKeje7Z=v{EU*AD zVK5P{ZgwbSeI3+W>!yp_ zoD5@KGicqYlmt@euE@Q9$W6{&LHJT^jIdQM+F}Cqvz-J13$OIp;q{0PjQ9r5D|OvG zGl7OMVzMlm)v*t)ogcn}^ww%34lzw-0A2QR{Lc%9cTej9#W&NM_z>QJ?_xjas9^+K zG#{)Dmr-1zpMEi^~_hv^p1`=E&YCW%)zYQ7?AkF7&Cw3D_o$m6>F_B zxZqDEG>xy`>7k#F9#RdwaWmTIeZR;S}_Cgd#4He-KeI9D2f@V8y=#exo zMk%Y4tT1BETSaukF@2E-7}UAl?}BV{p5!hGBzRfV+2Ca|n8AxW zn)l&}BzDo#wxnTli4>b5^SIIIi~B%{rOiLx)-H9k^!`0FOJ%)3CQ)nK8eemRKrSuI zWy<-N^(y)z#fa$Ke?>u%n`k*4Yiac@V$oTb%h*U^G!#-)yS0LUc(^Z-nu3kGIUC~j z)SubKj(W$wK1T$;UUC(_29kbL6Pa}jlJwylOR;ibbSf=8h-F7%`iC$fAZ2(R6g8Vd z`W3UVQ~J34jUPY)n;fZv#Fzc|5F;=xq=fFApu|q8dZkX;Cc$5buq?>EE7kjeC=^Y$ z$KeF<>~jhE3<6Kv`=bOq$+&1|xyO3FOA2dB?4rs`xw&eW&JPz2&9#OL=j5ry8X6nH z5x}X5rxyAU=J=003t=1C1H~NR*#3*Ou+~tcj6y;i#@Bpx?evK2?EMc4DN$?D?Th-zfB>W_B&Li-K%iyiJ3}96#akfiuXSMN@qZ8WTj^*vEZbvvY z$3uxpNFI;d#5Yw9MCFkmr3S&qFSJ-_2aHIjA!}ryRH9DFs{DPLCm;7I;E&6HiLuc< zSPo2~t$NA)i9$H)5vI2fO||uEU!?b>SsWq57=|)&?GrpLw!N)hb;E$pLYm*!!?3{& z@YtMn1?iwuOavA+L?FW^l-1c8fb1pu10A9>w%#6Pn4F~BRL8>g$x&EEzDUo_a@P2` z2bv}rgm9Ucc_Up}(~BOe{=YQy-<=7MryZz=1U$xM=JR4KRCq|{bcID<+}+q75f%C% zi1x+1dy;^EdN9xBY1EH!TSg^qoI;l{C!5*&05|Oz%pJmYWMXARu6#*al zyL)c3t}_hS`%%X9xk>7Mp0QM3GVXh@NVy-BiHu;!%76A5d%~Zp-QRHzh#gYRt#hC; zBOUqOC9zJ@|IuUrGVJU-42J!hZQ1fwV&6^%oOag#iOyy#B8pK`Frv3;X_i99GoXOG zCg2-4Vh2AR)8u49$r?LXhL_%*&P+Z)lX-zn5^dPwmoRV4RZrO)4oK#{CmCA08|9=h z5Vm^x^^qD?Wf>RahGEl{2QDrkH9`&P8yMq-8r$bT)@J%**inVQh;$K2Tn(aQ^$khu zc%ipij>Qn(<3vo|Wx9al`sGrUF>6m}j^$=dgj#5zoLeu!|jGL zB1R1*5JFt-uW^Rs9}@wI@!5Sh?-YQ<=gd-u?{Pgf+u0Na8!%P?u_aOVa(+A*arAa= zC1)_ZMdX1SglB)xf+9~)?{JGr7S0KU3jP>d#l+qf{@fzEkWsV-%wA2yA3AE`oz!eci-O zYbF1i@DvKm07>61+I*3On_A=A;^v!);`&E=T)*x)z^UhvJxFK_37p2GkAPAGl78NJ zA|m4xfxm<%E0`d;^A=k#OBMS4pR*x)tv>k$sJ+~e9}pP}QRAiTgGqqT4W3Og^f?$c zYP#ZUi*O+jzfEz^Lv%3v;A40S>F_zb9DEb zWzRRGHpJ^{V}`^PQM{KoOJ$OnWWBUBC*$$T4Mp_(u%jG$gn+Cvoka!@{Dx3FEYN3A zQycq^r6vRzTIiXF)vSkJ>Gl@#4T0!0-~?2v8h3R1Pp*~e)><^~Ck{O|PN>M|B!U{m z{h%Z8(1x7mOO>dum<*I*e_CugFoS+r^__AI8S8Ds%8V6tF-D%{DlLF5BxFQ6O6T!2 z<503PMG%vy5=`1)&Cod>kH>Zj5#Py)6#ogZVWA%k6TB|1HKR;tjFpG`Xn4+O$IXp@ zr3O3tP#dGNEvIpRHwNn5nf(XZ-{6{*dH)Fh(W`7bM5b#A`8XWz>!62at4A27JF+oc z+k?IEMpL;yNE2vs1!YSTg=33niZsWm>pc(5zlSspBtZi0T7!3k1GlD{^S4J;qDv87y`zc_)TV&f>QX(WG zqtqZ_<0N4;&OGS^r!p(XJSvS~V!sc&$hw#$O^tOK!ScVmd2FmeNf$MdkH6`aj$*j3 zZHR?E()YBZBau1Tcqmv)y=;_CDF4o0e6C8=+uTbO{ky!GOS{|^e9G+azT~T|TK=>y zR#?yY2J6TOje{Oi=}ucsHx~vfL7)Zy^{k$N^~xVa!se%jNV^fw1C=(;s(llKfAw z{=$>T5i8C8Fv6uZzJFP~KyTLS{(Wmi%`(^PKB8%`9)H)}w5i7-`}p;hLkH5bEA?Nr zd*-*`$!HXM)VZ=zE_6A!!)QiVF4RuvE&B9UrJR5IW%gSvrt3mgG^;m?di^|m1g>E0sF|=_;B(wKU>(wODBY&?C+9(I)0R~){;i4RH-RAhJOzcEPe3!{ zuaB}Q!@}&)qCf+7Ks_LOYeRX5kQ`Z+ zrD(WnQV`>Bz@2cuXj^;HyyolTVdY<{`*b^)YxjkEG!_P;jz5Ky;vXgU)m3u!T;m^~}%>5txB1p0DuH3p`yUEm7%P?JDQXZ$8W6AY z&-31#$`TI>{JOt;eA^|8glv>2M#$pRKJZ7A^6H_`%!p9heddZL7$27JpznA#@DzP^m3HtONmrFpa>BSQyah})JT}LOsnAK!2E*#%D7=na!QTkNd4=*e_1Ot z%UG1n7@tclr;xLj4S_HfQ}D<;z>$f%UV3_W35{KP((CDz@M$pYMUpE&HQ&XMCk!4? zjf~SOJU(%KotHiA!;Gf6_a6EMdzYBz_~`}vp}a;c0eS@4-d~T^zKNrGLKeuWjo|Da zfmg^@`W0E zNAkYcf=u%7#@TTqud`lLYli>mEbP5po4p=(oZmOv9&*7{-haoLk2L}>LVvY%*Q40? zBfrFZVlIv)Fj~^NnzD#0+0I}0`$!gHsQo=Q~t+qA=#!&Fjd z+kN`P*-suJU-6Jg_sgP~`Hbqe(eQ1O^!w-@yG$~DU7QGf0CV4OYd=S3KJP1c-v3>e zUVlBP=k~l_W%@l&9oBwbmG(R*3cNIa&qx*cs{HzN>v=!R_5GN7 z67YVQ67cy9tNnUW@XN>ad+f=b@qK#P;s5&gfT-{HHkJE%as7E*`aMfPAot@=-{;}u zN#Nr+?7I7-)z8oC)4u2Jk+A1=q}K1@0rBgTL;s_vXTdbL$MehO1lawVP5(B;@U_2W z>UXn*sPFUj5|-;%T}xP^{N@W zV4t&B+w*#y>-Vwb_LbrL*ht9pftmYt2E1kq6L@>f75HL<-M@3|nKpxrCVeb+mEa{aIS;`(RA?_BTuQ^&s_~p3q6> z&d>K_;Om1!-|y=??DvTszlZNtc+T`KJ@L@FCwx8Kc@h5jYWx8l$+@xrdZ+Mv*;4-s zzMpgVc?0^rJ!Z~)FWHB_@7vb)=i_$>ML|7Z=N+|eV;uGZEi?MQA6riX@0YnfZReO@ zuP8xNDD`AL1>1(>*D+-+#8@67%a5==c7pZr|gT?enw*yw9|cZ4~$%>Uo~3 zB{cs!ocY|R=y@N@Jyfzg>)vX4>%R6lYIUP72?Yd}HI_YYqi3Gr#Aun#l;P>3bXOEh>Npy0|l@E66GAA@c@k60W|ohZ1_7PQ2;8Vp!*= zZd=j#C~RJ>vVySL7TQ(uROb|RSbIqkD1V3q?UdLxZt_2Lq?C2*MA>Z8L%r~e+g0^s z0p%uK7Vs}U%lJyjSqjh6u2jxl33gnB-D<;VoHEPVe45oyN}!f{#4DIO_21>5iVVxi z&Jx`9QtAmTt+k3iJ5?5Ja4M8M`q!IyNBBpgFy~Z+h=xW-XOy`Xj;!BYw zbUK{smbk^AZc57K`Qoat)6*qCnA)Tcl3SVg1$+Xwi>MZ^u7=#wTiKh9BMDM2bsm@3 zSKaGYU2TdMD~85uIOMYzf83#3ADvn^DYm8=k}70sZsbOVEdxptI!@xfw4MX z+u5uu&)lYd4VD@oKt~I^IU5B^O$P)RCU9_Z9cNv#W2f@tf-RS8(KQ~!(vmP*ZP{^+ z43@Oe8FQu3GgSv$Y+P98c`G`O1GQU^Fb3T66W%rx`sa%;S1N0G6ImdN*44GxJk zbl(GfcG{U@5yp@kHdf2_}28oAhvh>`CNw0?+j zxW6r4xfrt<5*=?mP;NbRQo`}SUaIt@TYl{bv!C@v1}_P^pXsdJ&NZa!RoinoW@t4l z_39l<5}iG3Y8-EqZJo=@wA?|rE49?Yp}>y;xl)wm3_(VjlO}Ob8R3(<&BpZ-0uB{p zs;M8iXCa8inq0YmDejo!O5Og}f}AiHxw%}&&yibfpslrmRnafGPB>a={^GR3(%km( zPEcD~!AV;5gq@Pur@1(OARP!**M^kfY{f)gPfb)J_6gpkj7mi}9*=(_0od_K#3S(TCKh zA!ywg>*aZ!MmcNy;HF1ZbDt@eL=*C@z$fX93F%qp$x-Vq$8VIOBVpVj!>Cf~QKhzb zV+7E>D{2MksiE8HxR=#x_wB-?RWev;cRZ9s{FCK}g9& zFSb$*t2vVFq;*PHv{(5;Z!P0>G(HT=P;OOxp+#%$JAQ`9LCr#DOfbQ5WL$(<2PXcQ zH;yR931Ug(4uaB5EAoZi+HOsRdK}_R|7M&>m|z-RP=<=Sb0G1EyP+Q!#da8$kXU7^ zcz967gV&&31a0GNW$TJ}cEwh3$!xkfokMF^@z~s5bk+D2cl{9@#C5;tY2B`i(17vn zT$OdF)ND)vun-{{dK9;~v}P^-tze8>`}liiAWGJ%SbNH1XB4fEvXX&sM?+Ux*lh6D z{IzMCG+(1oX@D7*w8d?6IpA|n{MTGw@`QRr|Kc_~DQ%4o zwRO*Ruoe9Cvayv@WU_?&1d^tv$Vs)FMvG-yh##;bR)h*tDD9R<)`O(+^WUc^lB&&e zXffM$`%AQ&p7`I^K_N#39}i>9e+ucoGhatPlUaR6DL*ek;+G}o42sPl)n}zo(~dW` zV(a(9798W-#dH0`p<2m3l%>WGNF%JfCnBygps?vY;r11I9LVsXVsfM9`9u@tS8(RY z6UBa2o?I^bYMG$uB>$EVcO)(E%*Ia1Hf?JOf>`JdiRrLS6APNVqS07p!+i$zJdyL1 zbqz_PWf2ReU7MVP0}?W0C79(nkFi{fLhINo{W7{3Bx6HfLKi72Ce@@h%6 z0)*)H##BWS?60s~AE-zOp-5iVv+v0ag}Rf5#l>Geq$Ck8s%XUj9l;+BH6?Sz{#avs z+2O^P@1u=dDuZG0K1S8}CUshXa@mM+1HtYyG`lK;s#-}PYlQ7h7RZ>PYiUKr_)CGK zeCF-1Lti%bF1@{0tJ@kr0H>O6s+>isLRm>i;SYtkl8NlGsmo~a{LwN<2SiUNIV}%+ z4O_^4HysLJ&oV45*?l9OO|ZPCg7BszY@@X~=2@OpqkbH-e5{>dBc;Nh{ajRe-R;-m z-y?&b@?HWH#}BV{X8p|rYX8P(tImC;qt8?<1tC$$@)|GF36Qx4>woxkK+#!XfO~CY@@j~?7@9I=VIzT@2WhG`^ zQL4>BpunzsHm!{HR~!Eg>;e_4_69!oFHdR`OGntU7lpJ!h_+Zc9jwnm3TpPqJ#v{G+M=VWL2u9+~1)u00xD+XhQ^PATf5)(60 z?m0-S9bMQ=G;|r&JdZp5EAclqXGwDeFc*8BTvtYxv6GWDv2%5Aq#k4owDYLAAS;R} zkL=J=+H2Npo6?C~WD3g(td_*^gh82-M4GANCFky68s5l&m{>U@%SydV5jXGiS-fYc+!&umGhc3r4xv*XJ+zNqt9aS#RM^0|s?-hvrQ+$qkhyo2>w+1WaP%TQ-7g*>t4 z@G8|yJM09d`wP9TB6bRKrAGIJWYUNcE4t30w7gIqSH7AAGrnuL7}C&`%ZThOdGZOy zDoocb58;WWANA~YXukn><|O_L4b{bb*@;9oUB_XcMTI+q#w@id=+UQ>%9oAT>7-8l=*2|a1^K;gj$@*GXJPQiCrR&E5CW=fCb$R zq*9xBn3R_=HZDI>P1$Aila#e2Yx&huF)eBCr4Cyptazp+DI05?VO#;XX_DB!6pEsa zfh!*kg`oxj`ne_~=@wDNzkNechbmqW0Ug zRg}$ps1(m<3zWn>`*fGC6`e7x&sHz86Tp; z&P`#4$sq8QlmJC@@t6KqWpWlH45?fSb~!B6RmAU&SQ#vOoT^s-c$*cny7Y22XzWMH zcGFJeH4%B^b<}==A)i=5nQpy4`6aA9YUv284-65|XHht0_?k@+DduWostZxyxiZ8b zY$WdX>j`drk$8YnNU7EJ>vKcXLHx7163j&BQv&OC{uZ=k70dlA5eOT#llif`11|yi zekxRtyz~y;IA)~4n3yi(WbVFF^u>OJpB~xg8HWHm&-<#aSs3WPgC=`@43eHKB#IZ} zj)X1bdNEBp(b!_&PUt%>=dJ+HSw@*qPPeHCtv0~yYZ@QlWedH_Z^^!f=V}ztd@F4em9|iu zH>c$_4m{G>60)4N0mcC}DM=}TBG@E3tqIAC?2t``SDWaPds z?wKRkef3Il;y`9@h}YtA~T4Io_?XpOr!oM@sj_J zH@FS|X#O_NMebU3t#GGa=hV_}5a(&3^&4f3ubmHG1PPN&v_^rdiHRc%{N}qi)w_`G z7JsWy)J7XL=Hh6{$+gIfT-$hAu7k=m=WW;n1@X@HabHg2!$fXHQHBgL7*JlY9luHa zUHHR;NkcOt`nhp67PTHD2}DZ*p3&3@OW>Dcr_VM^tIG5)Knd_TG=n&WvQP_el zf32+L_z5rD+_J;={@~&SxK`0hrLVUv)iD%rQM4__q=l&X4Ua7U zXsx@3kaw{{>hC=>ZnS5In=1~Q)M?1?D;*zQVSYNbBqr-Y%D0{M!hg<-v5+Dj8;|I* zM_mvy^cF(GSr7Ge^_ij{IdfoM z8YTVH>`P|&9QQ8^Muq=L>RAxQ(!eE3rD%kW%Ldl0d&m0ot*Eupn^SjPL1d?b4!2h_ zUDN+W8p4HN!+O2I(elQKF^AMtU`zwg_4R z0xejczl5%yhsq482IPqI!a9zb2l5&K&aS46D(K7MWBxRk&x7 z>u(50MO0)>ga8CU2Z5ev*{~epk_7h15a7^w#<0}5p-^I+H)k*{iqV%kJrDG1e6D;! zv8}KN#K>y)AEjcv)&pHGvk#?kKU(L(OHW1>u~XR?Mb2LXQ7M&2;XEE=1IrO`k~QAR zk)NR4{3S{!|Hk~u_*xakjnP0tkpnXkF!Sk6(0tl+F3S+j88$L!Lv><(c!Xz}<(imU zkN`M;sX~ajv(fVjy}I zyA5ydh$TJgL%WVL=9hsWZl>A(i5Syq8iu0~_) zw_;vmBlB-E)bOLQY z<#vO~w^t8Y&I#$`h`i>uyf2Myi;>2D#j6p|dv<`cEu9QbFOm_`y`>Aq@}l;Lv@sJA ze$C`1(b0Yz&Pjs`zBkHRo1%h%n-T<9^T;#ACUwNi_4nRt+s%%gh?hh@_~%rsV$o>x z=$c5%<*h2|*^e`MBf|AYRGo;ft_YhYoN^MRS#Uu;3L2aVkSQ>gp@z(?Qqb$QJAUj9 zp?8w`?vtlL``#&{2OWDt1W%Y*#VfTKEpGn{H3Rx{+}PeClesnTh@g`~U<4~XZ`q^e z)wIId!7P`(qu+d;XAX?>2vME9W_aJ5BGK=n31=Y+LDleXu;qo*&YhmH-ISCuK^W<{ zz1|;WE9wLea3H$#TM`aZZF)lPz93IzV@Y<*>Q@RqIwUO{ZF5PZe=0i;;mROyRYPWd zp)g>j*$?2%#v+fFB(Ye>nCLRA`R!=tSF)-hXqjGYXe>*l02Y}W`pFn1^4*qqso%Ht zMGSy6f4PQ45ex<6|s@`PReC0PC# zD+T|C`okh;;1iQ&N{828Xrn?b)kt2- zP@3WLZ$({?n-rO+Pu^3p@sOZwXe6*%c(v|mshBSPd`Vo%%32KUV6;_4(x-r@w3j(C z#f|Up(B?NE(P={J*GxvUKsIRVCY7bS7L{3_aNRvby3WEFbAj>_;T+m{`OEb92G7e^ zj1roi!Z7o>x<|px4ynQrg4PP0LvJDm)#>YFBN9;%`w+B!mg?!XWvsGT%rDl>M$(>> zy45uto&3}ac1jlar>$cL5t5e`WftMYSQr4t70?D}#fh$hP5hRG7<9h4E`1M|!2~JK zU`)wM^~q3?jD7@Jm&zlB_W8#tKfMGbgDBy^TPB`c5L{3G_zZW&+lDuwMB@=ZyS076TsNrhGmu*m1yX=5Bqt%SbY@kbRpH8UIsrXxFkc`yLW!fZuI{Wa9n zxRv0N8#bo$yQKUvCQs{ZeLZSRuwl!Y5FP*4N~nuSVq_P_G(JLN`(g6D=NG{|8YIKOw}&W+4g}%g@9teX5&bTR zK`HwIddWYUxCWh#qwX!GRuPD^rTm(H9<2b`aLvg61jewYVsw&Ne3GD(v|evho~}P^ zw;-CtUMNVs{h6ofg)MimbPL21Q%1hwl@%_n<^9!Ni{Io3FNn_M55Ya>8V7o%#0AP; z3KtJY_fi~p4XLXa5OFHji(VQONBll1LLB&LZgPg)pUUDOzg)Gl;xy7YaUskZ4;bko zUZwJCvSjJXIe-CC{Qni3x?{p6)(@&UeFiE20w?Xha43!`LO zNg1V3+kx1Iz!mUoV~S_f-!OFkYoQCD0a3t_#0#1sFaCwHIxNLK#Be#^zmNh#(zs9% zf8k!%>nk0FVlphVTCJ=g;r@o*B`7$-1`m-I0m&P)<~?}!VESfCQ(LsjveM3ixh?{U ze{@-jE_(F9zLR&bp8ded)ck6v$*>d3Yrms@MChI=Oodz8?EiCh@}3)bknTH-_fS9o zi;DU#=J*G(>IiWi$G&&)koIEQ_by#eqvxRMRkBmvDCBEta7~V3N=>z{w}J*MI#oH< z#tnk_b*>;5%W>vB?Qt=&^G{u>LmWiYwP9rg!`cwawHf!*h$|S|@N)KbB?hX&x<`?CE4)hC21 zt(gZ!lct&@ePl>DE=wKiw>X(3g)JnliJx-S#09~$MS)(h(l~~a9hmu#*mZ(XS~iYK zxJgs?6`4`(dx?OSMHOS;g>NVW>|eH)c2`RJieO>#gVdktTuqc@x<74>7S~4%_%gT? zfsp0CT>UqhoJH-h2gFv)5F9beKl2AqH%b){=Sl^t|-uxInXUEq_<(kN5784ZvLRT~dl{CIuTPb$K~nG#4iq zb!wh#t8cy1V@Fn`l;O@^P+VY#HxjzG<8(9%*2mbW(| zW5IhcCg;}0YgeA>fMygyOpnq`no)bD5r_z<*V`WAm^c?PWc8&<)`@*3w}9g!cC(?fPAg{~B^!Y}Z6G>P0=Ag&q@anSN{>uFkq z1VgUeHwFua#Q==n?qAC&f7U2do$Ie;9v0br@za!l{YbF^i^nak2v@*G7nyBGLrr?+ zUS-rvrC=F>+NUuLR^H7B3@*XWQ3F#1daae3pqY};fmpC08W4k6U~DSO%Wb*PRJotY zGOv;df-`GU8ZX^aL2Li!HxmPQ%f!z1CJGQvWHv1##Ph;>!QI9+1hJo54|<8MOtW!KSUL#D>0xr@qba+e!JZHp z9xA(}!nR0lZ@`i#1;Wl{liMXGHv_k=#hyvFRl`#*@0EK9WV=WdOma}CgKI>bq*P!x znyiFodH^}SRE}=#4X%f2eYk`~);qOT;a7ogs02yNBZI|+0{WK$xHWl5n{sUx8BFg@;Ow&Ui&A~XXkt!0tM zgWz=7*Ayo2bEL?;U)~E0g=GT+8dkxFBP~m5olJK5jY}v~<^IwoJWhOY3AMq>NoZr3C%{TlW<4Bt&(e(w7TAcCj#Qodej&j zZU#zb4?x-TaF_ak(_z<0lI7hi6JAOCoF;iM?J=+A3JZIjYGz76C*+V(#Kj<#>b6bu zBsZ>g^*TFZ9X^S`4}#v%lt4!)z1)MX3$!KdRn>GGP+&AdN#$Lx1(PRcyTY(AiCN!* z#g6oaem`P6W{m^BAH_BGe93Q{Y(NTt6hYg8+akK<0M#eQQzv;S%KEq#SBR3Z*wR|l zlZu=id^c434~Q)y1l2~X#EI7Ao9&5;$;emh_MimQu0ELgNR^YI{iDsc7Agx@&YKgL z4$V=u7c2+G(Mym6uPakM-K}Eag}DzT2ca}pG;fk{KmvBdVG_fys zY$f|l(nPZr|6UW0bk#=Wx9|0Q#c$u-^2uay>ozN~Wtfr(D%uaKPRFXnPv~#c&$PFri5nxnsxJ$k+PR z&Xz2DlWX(lrs|uWKs}-U4`%h3yBe4bIYVk6-}^_KZ~2b|_YJ}@@H*RWjf_Kv z#L6^suZaab-#hv;!(~goA>6f_i=iE>$6n?UyD1iCsErXY+Se0)Bv&(RY!aftq#2!R zv>Lx35vnzx{0{H+bjfd<@cr&Od5{9tiB3XUlD+}mED$3CLAmX+$>nTh@YGh;*r~E- zdax-*Ww#bX+@z~u;dR>LETX;>Ig8bRk*Z}Bj^E|Ie>7HK&NXiqOa{LV)D^yOj^xN@z80{81{E4~=WM{c+0I9vl?o!{{unDm2?5f82I&#I zFX&1NV248&^?b>1pY4fbDlt@(X)QYxw=QYIgSFchQ3EVB8T+r5ZhFZDdWIUyUdx_*evD{9~D}tI3y^(wj@C-0UnI|oX&UcD{ zCHNsze(~OBW^&x9KccC2o$sz0g6OzJZFZq>qA#@iyR8iqqvS5r3APv-m=P6*xZhLz zLCU}o9|_0?#o7D4{b1^w6djio=Frh67OMH zK&aioVC})F>XLMQkgI8$0ofSD;ZPC>rciw!C2+&TYk=~k>(RCB6__tsVFI?#0{6q= z1_5{)-FVr)5>Bf`roMhGepE_5HsEmY5QP&S9Ud;25Fw3l5kcJpHo8NYw#_J^)N=!; zpo09ZubmHHo5H0HlGT9oO*ky$?*Wt{k#qdzyRTOO{G7*M$q6DUOl?Z{%57?WfUE#v zZ-8&`t-`@4`>unyD?{I!VY6>3F#l+v`1|D@O$7J)%b|r={N)fdo&SD(C+w=&26D!_ z@!;p9kDNka^Pr=er`oFrLS`%H@jO!Npto`VcE>j;K_g7LqRTRLrtuGNu)HMOIubz} z#B?n%J8S7DC^HDyk|KgrckYJsP{ytumEUjT_DG1ylx?s$TAv+$RSoIq={AAi0i2Lw434Kj<@d>A80OzfEEMUd&21*R#SKNyjTFsXBtz!>+JfsFo;inp zqjDo$GmXd=RgN86Wn{QD47WNVcN}IZ_)qb!@jxi^2i_X2BET*o3Ekd1T==0x#&bYD z2v<4}X9K(^%rfs1mu?a~f+O?&hCbZuMo=_EDH&P36(*yD$T8zbs4Uo8%lV4mA)X>h z2(d?~XqVXM7=_&lDLqGlvKYKv+Z@}pV3~00lx)gLJ75{e4i!v3q|j~KSIw)YiMkLA z_DLO=lvoOOX~|A1S%~ibr2WeX=gO{@nf}YA@TU;5cv7Ulie1pju z77mn*o2@`706)xIZz~_}HN0a`hAdOIfi^XBSADRpRyd7vKN5APD_lcDFj*T(ml>E2 zwQ@h`qAw}_N2)&|q7zB;5=FCIsNN3ba}T}c@=UWb z65d5j5;)GV&~=alHyJ;2IAIA6Ixy1l16J4?F%YQUKp-s4XE$}z)>o4kK(1{PmU00U zgA3?&@9SZ9;{4lcvOqN#@cW3CUJl3I0cW!|o5c#W>U7hX=kjk4u5dlxr3m6Ulc1H8 zv8=70A*!W{Gf@O)W=d0yXm7kRTxy4#iu!A_#5zuxs9$fUs&W&gY2($nWNL5oGRj_0 z@)2dIj&6~p9Kq)a9T~jG&mba+q!K~*n_1P)SI9;_ot7Re(OtoXK-BVMG2`Xm@(DmqAtZ0vHP9F0y$&X@1$%%8icD??5as;Yo43jpZ{E3h_uJI!O`*MPb zw?erBXl2}>Io)7_9c`;C#{lS|o6Z+A57A7O<=env$q!c(W*DH95pRc71w70X@+py3 zAn<`=?PmPQ^O8wV7UFc;3%d)Rk{l{$tAUwhI}P(~9b7AA zQUU1?ifR#KT`Fag5z6OwTXAO2Q)eFdhRCx++E<;pCTHD2mA#x`nIKL{cghOK&TP}p zY>Rdtllrv{Iw51?cVIf*Uf|pLJrcMZm6Bu-O0-dcexO3LvCT&;finI~s)0}yQ+&Nf zcd_pqNwf~+xp#AEKnN(4N>UTpfN}QPYDBdw<*(Wlr{p-5-HKPr&#kZSFS^)RdGZiY1K5EdGel3?Noy2YlDY~7fri1R^6 z0zGz#@|tz4ZChNSa1T0w;OD?Z$aHrIsJZkRmOB8O4{!bCjs(=Nli<8vk3N`6+VvRD zWI`5^Z93UX*m&)utpuFqL=0<%TB&CpIq0P%4=CFLxv;(QJGD%9pO4aKnr$`PqbY3D zjWj68BFhVio2MhBwZG5fmqSaVo;&+48Xk3Yl^MVK1AL<$LyX70=G<59e8u*$5mUq# zQl+fQeeyu_4Xw!1C?%^4o>Am%!ksH?E@(j-^g9}-zCG_l_V53>`_H8ICX@B3dc9#l_3ng%Fm)a=I80ZL^!z7Q~G(L*Cp8;eClQ+21vpG%y1n z6Q(HEj3lU5#eVpe6=wE{6Btwprz>KEry2+!G69mnW=?8|RA?ojOX7nx`6yC|2wY!G z?e~Bj74`&f)s#L58$^JegMEmKCZfbqT9Mp32Ocv^^$nb5r4S@2Z&Nl4X)vM2!(ak* zcS8g|OC?Ts&G`A~8UdV`UkxEM+cQ*jOsZV1j}gEF-?G!$q?I5UO4P+VRQVN>_`E4f<^v7v^;N$#f7S=fJoYhz&Csy`<27G8U@KTf7)?B{YZWV5&vU^##0xFuYSm5Wzdz zLpGp!cKhjJk-$C%q>5}7TG2P5#8(2 zhstrtCmU7q^GtI2m=zZ4dADu%N8LEx6{u{7HjIA1O(B(gjK*&-7WHh2eG$8EObnTYsSTU8sYp{n zEdz210BZQO_XV%@i2Bh;P!0%d$4j274}9IzHg?WX2HD!i&ns3OUuH>y4GLCbS6URr zbC=WP*cCXa0m`DnIaJ4f5LVC`2p(%qdZFSM?)l)Fr5#D3gw1<)z_w0@sE|^G>Iq`Q zt{Z9>su1eLXHhW%d9Y&mJl4?1l5W_tE3&uYav_jZo>DnJ;IeDby;oxg2&;xfsmV!?93|{@ss_>?j0;dk z4Ek)1n9~aC9oPw2;0D_%Es3ejzg>?$cE8iNU&ymiHa#Ip8xX}i;||I2pw*o# zO2GQbQUhpxyB?Xi6*9m{H7s^krhoZpgxb6=$J^?jM%(pVQoqEG}OVvG0sYK_5KIW5Gf_@+s zkXrCcY^N(?O5qfA%>{`iZ60jTAU7tbyNA{1!<|G+1IOu=sHverX9`4*4B|wO)gG7| zvf;G&$o>|}?~_{{v`Cra%tJcqN9>+}vRYkMkRHh^`|(6r=DYhQ!OPYIF>$*deJB=s zWhFm~g+7?c>-F7TaXy)+$jOTm2`sI8;*lHe#oq1Q4X+3S?$TBlP*P%1YFRI*;;% z^3`hC-F;I!s%~>dzDr{C){j$R<5*vX76SYGkXB9DCVLk z_2T%4yI{~1(t>@9x&sPL{N@5fJ~ZLjZyCXX5~BI|?Rs?Wd)5gZG4l0@T_x9WK3fb+ zQJw{KyxvxiVMnTn>!cjG64>A?F65i|MFs&t%}#Qk!px$hC8He^LU?*YeWKkRkQ{Qc z#^U#QIl~Wo3ZEa8Q1dF2vVvHb;Qj$)tmYmhgU}ai@Nq>j*KNdNa5+Im&~N3zhe)2= zb(#(a%RYw)i}2mP>TIR4@9vwERsn=0J|%{W@pc&8MvE&e(qZ~Y>D&PFcOotT*vg0| z9|O=nQJ~;hmtZ6Bnqox}eNE=#*rP)AbV#QlR>AJ%?*Q2hes&(UdiI9Kqfa<3<c#fQdUXLD)95xqV2Nmy zwWqgL2kn$O1^Ut=REvb%0NHKzC^4bhltX_pyY+d+V(TJsMYL+8l4SN%_tDt6@FP?s zHn5N>?b{l#}6C&YT#=0$ne$<`TfTR~!DAx!>ve0po-EhkA2@+Pa0Rpp1 zRni7?aEq3B+FjIV3_Fh3qcg)3s*8rpTdAko>_#7p5$Gpq31-P_YSYBe^?$eXzVu~VsWdEg%0 zhmG2vzDiIZS1eoWUC@e>#^!DDv|Srq74BRyn+RdF1Mn66ezI6k|{~ z8KrG2BaTc0FVz=O=?2S|pEbzQp<0Iyb(Mv}^yvQ8R>i0RXptz=mUNy}Hn2>0%&7Cx zN1{^r(;>(&r?!^Iq{?SLdrAjUsY~=^`wl`XbNRFA;N6ug`)lA5CKTD?iB0A*`AqX6 z)AmJAD!47kXhoF0m;<*$e)N71KTRxsH+&5Aq*YB_c2`q#ABw=(jsWA451}R*iIqW6O=pjm_3}U$l zd)J`*GoDt7_ZAuGa1t9!+n{zbrF-jt@T`Kp1E#a3l}|+}5s*mlvc4WsbUfv#V|BEI zs)TF;Wv~mLyjkMI)EGM+jLXR5bycvZc&_{}Du@w^00|9Y=}1ZqGnWo5!T7yArU#+2 zg=s?S0utL?Nyj}r0W*ZXbNmL02n-w`!0JLsgbHX|U3~+f&&o9E>chqc30>0bE~|fH z+jQ4t^I37xs^)n#;3&qU22Y5alV~9VHTZIKmf`*&N7%MA*yeGD?uQT;@Z0EefS85A zIqnOA@MI#wmoo-92m1lYrVUx3nwh@FvCcuxLk>X8tGk2s2)?&uvZ;r~1m0=VDjwm? zxk@WFjFV`fbMz?Sw~tL{)}YM`E$Gg&?9%W#wDAIrzK6I%BGctU3o23Mp*;=Pzrjq? zUp!Z{S*=A-(m_lQI%VgrIR%o>j1nEImT#5eKm%ePBM8k=Ur6y5B+;N9W@!r{20tS# za8Z)KLCIKXgXG>(`A%z*3c;cqOP!Bs#!Njanhgu45fHRPvM4f{S53jOJ@bY3|2R>$d&FzVcOu?KjJE@eL@3{wQ7l=SAM~hTK;Y66rV^|J_X=r|*)y<^hR2$Yb8qv1 z<7!C}-HzT#s2rNLY}-=`PdX3EuHBc=eO$5qxD+-UHq}apAqCI+W@H3X7~_wQ@R5Fy zBrN8e54v#t_|4TGdu-`a@f6c)W7pg;7Yb!tQn<$Yu(a}jxLag&qPJ!R5{ZFMo3THF z9Hte*ZvQfYM3d(LGyz)fu$RYaNkb0dYWJ*wka|eJ>si`TUZfWZ-V~}_)_{@PtSwJ4T!T~Y^63%nf zTix9$TUvBGQvbuHT0BokV2FI&h=aCcH+M9tG<^o7cagyE7qYw8O5}{6hZDao(~4mN z2%^lGb|!m2aT1sGNB~GypT^^HLPJ7jGrpvkxC8+-p#&k&41Lmrk6N48GI#(W2K}=I z=vI4<@<++DJy^3VxH8xjO<}vq=%ckndYcm%<-8RQafmRwNu^Dv{@95ox2MUvx6I@G zR>Lc%_2cF??^riCx`Ese0Et`Tfj$|8CT>wcm7KmAtMRvN9>3Lh?jZQ28KdPVvNcLN z0+bKZWf-OI4W|tVBb9HAH@R2S+KphH_v*4rZ*2KW_*C>Z)NV8uuLMNpdUVbEOz2KY zN?;K_cH^OLHDzbB_vl^a3OKEUhBdc#5k0(j12JD4saDb~IvC^jWfdU_f(t3i5ivLD zUa*o`iO$yUEc?E&ZX6|76FLZT#1RLec$@YCbv}w_ZDCGqH#;%;dWC%&jj^;f@?Oi( z5?6~_?){Jzi#Z?}Mzg9$ooKKU1CDWfm?R}tslYHN%_^IMYRx<}+>tgGR{RLdkvc%~ zD|HE5co|^q?7=X%x!G{XnELo$do_quqb+aOqifOC?iR<>(d;Mp^rPyEY|^DVW0hSs z(?mD-V~cjR#eM%O*20_mSPDakLBI`c?h2~ua0ip~RZOf|wjjWwAALu`ZSVK*QEO#S zzC$Tt5!>Lm#Z^lCBc^DQ44N*4M}5754ebzA?UTqG9T9^xZP2XBCELL0X(!;4kqA%# zPu5zYvo`Qvh*3ch)F2hF8@ZDj#0HQ>Zy@mVB0euRgrj!@Fm{5SEqa4OOGd$(Q8Q%v zK}O5KggNlj0E0y8Q3LNl@#*NQL^Xt@jNN1HIj;}gn^gXnRg#S`@o-nu4wafab<@b8 zzfqsiBN~W5D96Bls}cK*p0bh|u;1+rH+g_9gB)w)J+5LraTWNbq#=S>YEZdRA<%#1 z^ztL)6ymnO_T%x6*#K#-tK)css2|VyZ@E?*ulKHT6-XE>C_qBd}&fg zk9K%*Ym+p=}iJ zP;~ZbXZ3;|#+JHWk3Mwvh6-rId>sqmA0gsL{&wJ|oi%Zsc_0B(^yjFqS=%CUOURHg zRNK?booO3KCKcA}2s14KKcW9P4DO$t-3H{?S2>l&oEGt0;1piZ_ydNH?+1oI67fCC z_5k}HL1d{=SSmUi(aB0*DM45KHK%teK%?-A&8N!b6*FQM+Q> zoA7!-1tBexw3v%xXs}3O!3%)!G}2=?G2|_9b+bDb5Fh1!Qh_b~L!&avSQW6q3$RC> z90fHnKxXvUnb#29G!Nf{D5g`03s!Oj)HP!nMOCtQ`yR+{atrUKccvA)qVkeZmemkw zc4@;gRwT=B&oBYwG;XVdFBSCA0TCadQijSyTI)s)Yy$dre^7YsFloZV0qKxV%w{KL zM2L|1TEcEEz)0P|Ny~mboI0wyyZxbO=cWNLP%Fr-tKx~eQ@AjlL#HnbRjUO84)6lk zqid>`b~D9s)hLl#J?aj}w*Y!RPve@!$4`7ZC1F|@ZaYToj%;=%rFz>Jfi^gD7sZd&q(_5B046#7ITj($g-;MOibhmpp%=HR4 zw}u247K`lZc~&zHS1CKzFF~T&Fbxc3!#Rjnoav_7WVu04c?ZF=3QZumBSf(U%Llo2 zj8R~Ama){8Xw62v5gte_O=1%Pj@+G@=z-56VlnAq+LCb=b=g0B^7RO~l3DiJfS>@q zV`V!|h?>XD!nkr)w97dkUgJ`4_7WfrnK?>^OloFQnw6HK7gE_l&XwsUaf9s zOi4hP1u(C{UEvN7HVOxz2Z+D}lLeh8WCZ)7=F_A$+v^MwHS92DM_g#qM#A~(;CQ5I zWig&L7(U&srYzPCY^p+1$_5HGuV5_5aRJymA7%I90%!tAZj#0x&9db&&-vlZ?(LEj zJSHlzWK|xmeJru;@+7nN6z5wTz?rAgRizXSLRnPgp zEzdmsB$D`*qDarNTxCWYb_Rdf0(;DEYWuS8(wIrI&N;cCDipmbx)h?kyaZ%)-1Id- zmOF?puVx%!x+b|k+mJGF8LZGe>@qMCtY4tr&;NazMl?dzcQh~pA1Ze z44k%k{bVT3Cz=iW>mn+B<~<=vhCRjTyS94RZYDR_dq8G0I27XC2A>ue_tKmCEiz(0 z92Q77$dm-C4?7gboiQJUS_JfIiIXGqlg?LA&p|~Dy1Lt-tx!a`be0Z#yBoiDx70KJ zIXK>cm{%B)9V?_9nKsm9ThkZ(FU(?cM(uVzx>_#qN9@zMt?gNUb8G?Ggr$5LB?<=v zkT*El7UT|kTyNa5zp+Zw(N(U+AXR8wXaGif%G{t}f8n|E`bMaN5PFa5-!BPLihuSI zl^V&_hP6E*NHFL~=}302bog;aKQg9N^w5%#`c285J5=XzqSFk}shUz<aeUFSgLX1Op;Fxao1zLrwR5^f?MQD!H~YC(m7xYN z;i)iNeRCuEN6`#;*~;1q3H7+5t{P-`^W*~4VMlVP-MxRN+m+?$OW?3jhmo!=elLk0 zh<+M5ZZ4m&OXa!!DJEN%O`{UKt!98|9h!h0;BM$8i{Ma3+980)=fm zcqcvpYmEdN6}IGUO3;{rv4nbg1Ptl8M{s^OLwYIv)edVpv@wCz0-#g$S>zPlM01aeGyk@_m7PH zW1z(tZ2CyA06V0luzS7**zZSHjGRZOxHCXd1Z2Pnxq{`0sDgIdY(a;BJzudUcguxb zq`^7oCVwm5Hb8BF2p^D~?GzNzu2j;uMfBX{c~rtk-QVFkn8B-NjxKs?>?2Yt0A8`r z7Lzk7cw$31r2;9j+{G3}=Cp01&+3Yz(G(>?3Y?-OM~YeUKqlOVK06=fJJrRKiJLhF z9slWY?VL&0?g2>9(6T!nq47W0tcS&;$(Sr#Ds?=H;C>|Aa&(-qP=jrVp=k>@#yVwE zWiUPsnM@`}t`!vu)m>sK2(B(0b?XM`%xKPH{woA_Ayo7x1Lt`k%O{{-M9!G}+UUwO z;kucIx?uf37*ONsQ*&#pbnt&j$C|PUP9JYmBs7e00pVgL?9NG1<^U^eqZt53JNBuG zUC3O(|1kq^3t3LyjK%3EtB6~p)0^&~N4ekSco4oet9$U|RX0AzXbuTy$eu*>V?Szm z&im>(gYP17ac}0z->Q4&CfeA;m-%K>W|zl;E^?~{hz1ia7Kt2Xie4xZ!}N=OU<8g^ zecQ1D1|Ey?PQk4XvKV5T+>ff4VUdk<

YXlDVT<&u+XK<;D)F?-1`2yMEWe#ZLH(g4fqZQ`!a0l8z zQU~NTo5nS4Kvct$GscP>%41*jOJB!z>PH;tY!K)S0<1eciiivZv}OQe|6$f?qz`7i ziPg$Gk1cNp(s5vG_hh)gBNz4f%cWr*+60TgA)89Au0}Nje2$G2~N)8cG93O6fd3e3kn_1@-6o zaL1IV0S@sJ_Qpuj9BX-B-Wx#CkENdKg`VsLo?xysw3%LMQDg3QOlOM4q`)X$D zr@(Bs{jHwJtJ^j_?3#P1Ozii<_Orun?;x=Ux^HNa&E=2Q93&VhI)Ipk%y)J(ie!;R z2)+~=LCR(+M6qE50g#YfLOuKqiv2C!Cv5}mwCqZLyy$K{9kK^}45-TB)&N;g5~8}^QC_O!(#Kh-Sq#7gyw&v=>l)43D%c?_0{(jKY+2b@?RUFU8`sXtV-7()RvcwSK!5L-48 z#J(mY`3V*g;lnJ*v zErO#Eku8B~9B571l%3kV8N7`6O9f_x6tG3#@g($f&FEJPr9bVtB4T(w zJBdU>OOnUC3bg!e|GEQYArTk#qcp~|3A8x8DMffw@5vTaQ9;7&#S)8nd$&Ju8v5f1{HE4|T$Whr@ zp7?`@>Kx~o)QTBDk)GZ^F!Rt|>R^GQmbeq-dA-k@yyFd`b4uM=j?^0vi;pNfBV0{) zU0gwGZ-=PUdz(K!5eQ%^CgF!@=~E0CS>W~H^MHLvhJ1v&x9b%or0I&D?YEi-eFxMF z8*!SppKxm(YjF*a>9C^a5k1 zx3`xDdUls#wb+|b=Lj*ql~goMQgrY1qPQM0phWx35@qXXBcBWu{JFo60o1lTTcqpt zN{z1T9%sOiuuSsq<_e_mcb99w#&GU;6YSRm5^nYzs(A^(eX6ihP<2D~ryG1T@x7O~ z2*13VNP?LmkyZEI50W9MH{+MJafCNMcn=^!s0Wm2_Uuwgkbs44)2$*;SXH^$AQ;>U zx0;>L1uk_JG4d)$RpP(8d4rpV2DE-RPsFye*jiyNI*;|dG8(Zo39^t~O3|y>z1j4p zY`YjIXwVYg?Ypi=FlthU0l5+70z|{MeZjLsBtN>B)O_Br>-GK&AzQJcJ0Ka4_CFPb zrF$T4ucwsJAlXLKdL|750{~O-s(uO%9UxK$6BVG2@Q9`Umfk-<+C;*OPY$p}>n!QX z3bxtLeeBmk?^gQKM0D|a#VUd-jsqIJC+!RS`+=6Bt!IJ>5|I53jK90WZ6u+jpb4de zgV#c&3K6*J026BC!YsRa6|13@_i47-sB;5vkyiamZwiPV%(X1{ZYk@=%Zb zt^p=@Bu|>&=gG9`{XSh6M1=2#4@+2LZl%55wfP8ySx;kN1b4r-Gf;Sb-M)19QD-h8 zuThVRsZCWw1Yn@*FUJ+P2{t4{fq`(9Z)o;=`9m}sAM*|wIGsVGDG`PQH4*OjO)8BH z&K`Ufz{!V8soHUm$$dzpfU6K8fKe$T@RYQ6)5h9$Gd>$~EW;KNAFvFgd60Hx+Qo8# z-Sw!r9T;#fxC?Z<=2@e%{TWw#1f1(LLBBm;m)Rr_6vvzofbJPdUs|+q&mbVT?X`7G z=#<4k0bvl0fI6V8A|im(t&E$A9e3Cy3(@WInkCcoeVE!6!~>hMEXM5tL&7XE;lb#i z=~RR%(jyyqxnzSl$CZ4bIa}0`r(qZanr8#7Ow}d`&Ka1Mv4O6pKB$0TC4We#+OVk# z060vlD4Q|m;WU%}I*S6w9=R=EVF=r!%bn!dPFQOT^pYjA+)nDcnB5IZr(|FBc0IaQ zHFiM2HhNBUOX;Xioo-0tO&q_Tbfwsd2vP~0=u4EAL0~~0!2_uBc9-JU#Rw{cP=aGP zC-mIluCglfY>KI5CgV(Hwq4L+hIy=a$I6JBwZ${{db-OBXgE4fb4Z80s0gJ|=^^|t zDAl;N4TdvJ#)vjHgO{KxZHrdC&=Z_??a)i=mWqvvU`d4z+$oV}PYJWpCRm0ZV%mAj z?rWQufWJ08v@mb6`> zQcvSdW7^#{>8GP>$?|@mQ8Ns?Zp_8JKWml=o1O81@jA6$G>Gi9KO-%&nWc3PI~95( z_;*`mHD3e{*@wySDdWkInsYi)y!c%x9-*CCTFurrSpD7LFaz>7>cepI) zqk5+$9LPK5@VL7#6RyoXaflz;5uDW1$gbD5e2+g7IDyZ-Q*F8jvIuDh|U}G`LUS#>F0u1hxLb1LoGEGbj{=5X|;R_YN8Z zc6h8yiPk5VD>gf*eRn1$$$02bvm2i7Oc}CP%GOsGyd6Mov5s_aA&MTfU*WnZ_aK7} zCWYwVK#n!-b^;#v#uxyd=nbs2EUiTkkHwCsH3ME$k!OTjH;(@@S|R>p*~bYPRCtWpLxCXKwJ)E&4MP0C3p z-CTE-K=Pr1ZvlG4Iv~F535HXim{M-#k1)Y~F;^KS2mCyS5!Rhze*D)y72r)MjVU^$ zoo|9?OnPRKL}piIfDB^t2ZqZw-}vL6-beN#<2=woFh?OkbrYE~9*iPdD|9$2tJ840 zUa`4fC=J<=i;3pFhmS3(cPE!EPeIdE>D2?!HZOHd)nj7rW!db+Z5w?x!t;f`ZlK-M zL2?S6MyG+gr+=fSXj>fjV9%2?!K?RH-rT%AwJ6mh7wyXfbptTm@3>u$3So(cebr6c z)svY%+o_kq;8_5w{VhC{BvZkw8vGb>z0&%&G@(*ZB${J-J0& zN&KTu{_bE&>}kIP&qz&6P1ctygCuqglBM<=o@hF)CCV_|`ys7v%sIMb?m=7IEM$#% z@aBh98j6WP(2|zwZmV;%rELRgM2S&6P&YlGgV!WZaYiF~b2_^66u&M`f9Tr}#p&zy z4(sFa@9DgEah+v0%OOo@a++@@g^w7l01EhuJGnKO&@(yFPxcmRH%^HocK?j#7poxW z^%U1<_?Ldd4sI}~*g}A-+k<`Jq?N;NeqIsz^%uo*)E;S`-6|$v8Q@Wgs<~cLA4n6H z$hslA29214U}HF?2@^rk2W$x06aXI5@o=)X97Iq~;sR0Jf`^3E9ZU&<^Tk9bd$>in zr9W_yoaujQ!3ud(1VtJZWxc(bhN$Ei1l}pG8^MuUs4U(cMRubukb;xcc&I{1MRePF zf1h*;e*|COT=TZUnMMs#GdDw|52GS(y|P8I3|f|AqSexcd`Y7jn?-)hPp)mEHp5YX z7On5tMFg*Uh#XMc5{Bdk5IGr^W|tj>$OUa~r^b758Zwb%dF}9C0`jZDZ%cD0GDXoR z8NxtXJ@&G!F`)C%2Q@e8(a4iYAG%FtCLtX=~)A0-QV@berm z=s*CWDW8DdaRDD`Jw!aZ&Gfp|)oti=s)R{QT$jvYbtr4XPG(J!Dd34C!2%F%dq2Vb z!4k|RQ@Am4D(>gwr3 zVGR{zmRBli1+kV-?>jdKU z=<1S5XUYmrfo+D4nL>;{-gOd(*9e&Q7WF+7T6O&PE$Cq+^(n(+(R1`lj+1>)_=*L8J-U*NWHUt%de5915+AeOYr1m9#B854JRSuIHz2u* zj#rdh5Sx9SNs5XHct2^)-49_+_yJXw15E`GhJZ-W#L!o-LzMLv7WC{)OS_FqX<3we zQ$|kGILIGtcY$k#R5o(E>3W5mcS2A*P`H#OGQZCO^8c!hBAp7935U{sFQc;8*$hhEIhOc+5facejs6 zXDkrpWg8d;gvE1$A2X~mIL~jq-qPbIJBUFWMoMVh@5Z3XvAV;4IlaxzXvs?k!vh~< z2Ap7zqE1dr*MUuky{O-k(3@|kwm#DBbu2>1979rR{Iu8Y5y>(Tvzl()QMBt7o732n zrJ3@LbmB8aT^+=w6&n_aR3_cjyYsaRTimj%=cp*L{FO+Xpp64BU@rp;IRK23QZmA+ zISLv-*?j|JtuK}*D9@mMiM1ewMq#^2jQL*nDiJ&M%Jqn^fTEk+9H3iR2Io9@z)hR# zX~j=s6FmP%+44Bfn*;Lr3BQ=s8<@tnK!&7EY7Or+KgQ|d-Xtqu_*}N<#7Q^6unChS zIe-Se4Ui%tyPq<>5}l+W_>@sYfETyM= zLWl^2Y&GiLZihK`=xEs!=TOLeA--Y!p)UYn?zCv0Htds%t@EZg7qC;qDSO~q!J#!H zc2{q4P$k|e!6HUuabTXkyzMPc`040Mvu|MTLALqBfv9d)F}EWXrPXZ0pOv7$Tmg#{ zCEUrq95mN3zaB^#=rmnIEOTYv=xap?l91*q@gN*^ml#HVhTBW0Zs^Ja-^F*q&>Quh zJ9WIIPJ>d4a$WF@5ybB%N@-L&eDT&#(2(g%%C_qH&j;ur60lf6lRgy*6xmEq4Q;#2 zxd*63p8tc&xgO_1LBMW>GZJlRBkOI}%@x6cLzw1p!9#O!mcgNK19P0fm&6$yoJkab zN9i)|>qa{m-6kXlF876%3!*z?J>S+A=SL@0*n-^6wFV3E4iL&cF%NDqZrThzVN;ar z6+9EbNs8q5+YVmu__-0S@_f4yqi&FbL8sFnbRP%lLg(;!2qt7g z|NXoQ+>X7{%YyPB0^syQGg{;mo$lFtWwoE)??mRzue-o@8+;ra6w?!k^PN&`X?wqwmNa|4h$&Ih2q}{vQZCV)?qpi zLF85Ju;Kp}eMM=<@i&I_J4>lPT6D&P2B1OX#et5ETOSksm zJ{Zv6!$%j5)nTHlKsNn2w7Q_8fv%1p-;0|@dWiM{#dJI8qf500ZJlAh))*h0%;$0q z1X{cFyS{G=sz(Udv|2%!1|J&k=9{|XpjaR>KO&G|aue2m4(lC0TZ%BXG$V>gnH~VE zHw~{RqW5rcpeU@pV~i+KyRF-{ZQHhOW3_GD?$x$!+qP}nw%uoaJ0~~U=f^!a*|(B8 zQ&p*C&R-)l^^SKu*envYAXbHL(D`eE_;yG{p`j_>k~eeF3b=?2Iq<2LRbz4in3r*^ zqTOWNV>kTAD7T7-QQlNGbA0$$|ak1D%o5uQ&RD!ofpY}1+@5%*Pz(O*st1-55 zi?kV?tV)R?VX<>aA_;RT&RP)*bJOP8hsy+6Ls1M!kpKn?SBJ;*3&BLz;Tr2xLyZ-R zsyoQJJ>Hb-gMARmvjW}7-3yVI

Bfzt<>QW`Fs(Jajh-a%ZevtaGL5iaNBNa9`{dKnII3i>@tQ z98Bzg2bpf*T44|q5k53ivaN({0*_(#v)d$i^b|OxlJ^ zHS&E&f;;Jv*oyC6AwBXjSK)%?@T6ga=`~WFW)?W{0MdplVU)N<p4LI+ow zl1kaf*70VUeu>W#-!yp9@B@b)aGwhSuFNCAF61!q@1W+$`Mb3RSDNi-TcbmLimw~U zb@nu9cAg@~-!fxrd7691ubs^=6k)Wy)#EWX_4@ zbn}9Y@gt6%FQ8_f%CD%+Bm3Z3veD38^iB$SG?No~Qyw_PXpPIv_dzB17Hxn`u-6Ub zrYV-R-Jgd5CuFZU^Bq}TFXtZa^EN2G87gaJPn=J2It-DRrgHLuq_r64<3s?EMYYE) z0ixV1%}0(Op4Vk<`unLZjZCjA{&yh1Q&#DNM;NcCp))ulA*k*Md?!tw0wB>xLGgqD zF^@J7@-2^9)B|?Rc4NArfeN5di$w|_!`v8V!U}=>QlB4{pm|YG{Fn}2D73}_eh20$ z$jbIE_6o}o2KIG)1-G;5Zw5sKtPOoe|7bNkQkQ`KLs^c4bK-atq4W14=MTYpRtAUs zo8^$l`ys`W3*BbrId5oB+c6tme}YM_kNL?20DEoO{TzTvHL)h)w?o%bZ?NR?2Xr*}Z zGV3>Iclgn$`sk=}vE8g1xUB-0g&^Yc{kbFVk^VHq$mt|B-!8E<+V29Jl#sn#G^LCJahlFCb+VV-zE z9Z>CSsp!0b&6^+u5}u^8thQDTQSZZ0MuN2!=n?qE)XudAne2+(Na#+zcwpw zi^&RW`zz`QYk#g>^xT$b=_^Jk#MDd4ue1$%9Ow_Map;4 z(oSGJnq?TXlp6GAqP*|1bV!)i#iNbvZCTZy&ExyK7hZh}%%|mRew{SG-Lo;=&g8Jj zi!Q_nkF1Pj`@A{tlf+!KwpOVq+pWWOE)3ZJ68-?B({$`hf<~X&tV`JF$AV<$X_xZ~MISz%qM2gcTlD?q zX5EBD)GMO__6-u(-X@ocOa_?Et0>th(+?TzP2HxIWaRI!M9y6@yaaUI>Sb9yUEXLd zcWQp>j;Oh?r>%;$W8n_6(Y-)3D0R^bh@~e*rfK5`$_Jwj%CmhmHUqKe<9q`MTM5?Y z63dd4$TgNoIl^r3#Q`nCs6GKQF*u*RQb{(ZJ8A08@5#^qd%2KzU_U{N^h8k2_86$l zhOJEkf~vm+Sc!!TN)fXq%Th@{eY?7dEGkK$-_h~c)14X$sw@F|AWRMCGF)ZTJ>?H* z^#1VzRsJqi`4b)O_St^VpX_bHER~8GYkOJ)R&)CZjSHE{@_Ni9zyMyrGDOnk8(fWT zW}q|#lC8!5VBCwpO$WVZpe}5?5O`U6(Tej-uZ^&7T330c@coEIuA!{Ed(F-clcT9f zBQOjbws$b|RS^`XKdqL=^BUc}`T*ZiuN>KVcA<(qcUFk0O=6ql06<9d8P1DPV3}>r z?M7A}t-%1I9jv4g-q2M%)-XwFh6Y2-vVmv{+-rw8bR+@5OT`Sw;#tK{{^oKYSQfQH z6ew```L~a=n*7aI#|yk>oqDx}+KWJxxxP6-l>IwWg>(=sR7J;$9JM|8ALm*i4A{vR zTK=qEz7Rh7{Qf;xA+)6ITX|BMH-Ym=?+rwOi$!`@-kpkUAuq8dYP+?3{nL9Hlbxv& z6Hk+%wrMAX2{ZJx@glE+XD|DvKMwKEO9PrDyneW)y3ob)U>);^SRzzw!|q#fwG2Mv z)7%E;XgTCh!p?uF=lwZbRwk9@)p3bz+oYIGts&x$jM){xvi;qdh?ub1!Qi5IfGV#h z#+f#pl)0W<-+9}l8DghX;dbVBI3q$@BJGucRQQKhFLpccOf$KHILx<0=~<#+jMcx9?DKe?1hjS}Zm%7O~M9*S2#;#Z!YkXj38{QPoq!G;|Vfh2=$({^NM*M6dj)6M- z-Z_xfq4RD~+#inYuS0WDj@QYF3B5GQa9~o+~#4u4?rUb;#@u*B& zSt--aG^-6|LqS=C(MGwZiCq(~imp?`yiApnYfnX{fxQEbF09C%o>?E^=QBm_LYhzX zlg>7HIY9y8$W&DCc@Shwi$Ny#SI3QIO3jhP4O-1A9-3h^=~#6dZp5u#&P=QfEjB)ILw6B(wX(&uwJ)=%81 zh_T$@X}!Lkgf)M+9Gj8H=6~8}M&({(LgcQ~LK43laTl2w5UR>tdp^y$OJ)+stA5KS zrOul=OTF?*4?E}T?sd%1&q#?6`ufK~ zgU`<8Tk6JHEJ+#oOMy)+DQF5Qa;A=Rj3+%kT6cR=D#f|5v_oo@yflJ-~9=XfadUz<``xhY+}w!4>6V5|0``*e56>J}1) zsx}NXHO*h-l?HQ5@Mfq*6PQ8EzppfM8J7N<{1G=a^f$+1OB&05(eAH}ybdBi{1?9q zio3l@U5o3xt(W}ea7~@pvZDD8cf0fmx5}hwL{=>p&uQ4Ne4)VTc-|loh=gEfdpKKw zZvDLW_&fmc{ls?|a6!Usa5(5O;G?0)eUSSi_J(aRbfMC8I&FB`__hG-!Px!L`;6{5 zIl!?*o_F5vfL#c>AoL-#drog~-spU!M}zQtp?Ab@j6fm&#P$en5Ii7BcK!q+2!vsX zOu;z$L35&KginYI&O*V60~mAQ-2eHTjTNZDm zI1XdsB62j21@XwSSz$**B;-__4&&RVgb?D+jt7az`B@mpB9!EE?V}-;+#O&!ChdwQ zJJ!jnbboL7$V@H_k}0dj2bW*cvDB&?I@aAQ`K{)&o`;1NeG9is)Xsez$u6Y63vwGw zAL$(L6Hd-|jhhkEQ$9&X@aH{-(m2^ZB7Wfiqn=Oz|G9|l_vQZU=6_FO|3me3G%+x? zG5LRl)BX>Z&VR=LA)NnRy%TT^D7z>C0JNt70ObCM>zkSwIJ-ES=o#5LIse~*mfJcS ziAN(2zPoz;=)KWg3L3AO6nJnU<;8*8_kH&mvn4bQha$>0ha6fAS2N>J28 z*XLjz*x!WIkCTnQpV#Pmejg{5cDjBKZJy&k`-xjA@xJ3sG}^n5;#qjtS-k7m2x zuP2@AwZ5O{ne4m1&z<}|?+1yet+)IUE1v$KBhN1YD`mwtA!Jv`sn=Yx--r*3xq z9^a$8e)revxy|BwzYmYE^7Ol3tLk5vx8meJ-#4Ser{j{j-%oEp-|xyj-kx7~lan{A z+1%H!v$wnaz2EopvHahTKbahq3ahJ&nC*k-aws>=Ugz zPq3l<%{+YFJiqpEzwfsEzV09QI-KwKrB0=OpMQ+w_+Rcz*>81wKi}W459K#Y7w*|7 zy~Ij=f8O@*zZb#1M?+|?vmcf>d-=P&{J8EU!DmJ8e@{a3p^&twLFL~ECp&NPd;NaS z-v{}9e|faCD;M#8U*|03V?+6FI@xpWw$^Tc?jK)oeX(@8@Hg5FIk|ciI6EACyxk8Q zj?U=$V`F|s>FM!zy8U(*xE=q!JRLl|%z?MlZo}E_cnCasS*u-roITO!^pY^?QH6Kd#mLw~h9}TKwGKpQp}IKWhqSzRSl_{G45M z;ir@1^Ybx}+THKt^GnOG9Nn4h9~%$v*T??E>C07N()abd*W}b*Z;!8h&Dnypo$s$! zH@iqGpWoxo)GLkJpY!|U_KmRU)<|>j_pgVaL43YV4(S^uzm>{e-=FhLdA)#U_!Aah znJ+!}kLe?Pn4ig7S!Jdtj44OsjHqKX557&7+m^T5Q9lE|!HZe?+=L~o7S6z3D9DXj zIzLQwT0Tv5{4Gbmu}+=G=_koPGZH?r-;fg+-=}&$Q*0(*VaGRFxT-_DlTqziw{Z~9 z+<+zB_Q)p;<$iVw?|T=kWhdA4_a20a<*bk$a( z`%g{(#*e6%uuHoG5rB+wGEbO9QGm&KPoCgl4*B{5P%<;2IgrjVxpwY!-RwP74u!TC ziaigqx-HGW#!9mXAx9lL<2G=*f2q=B1F~=5V?;JTprgZ)f0Qy6=^7n+2B0yCdTf~{ z>gjVOAcO=<+7Wm<=>oOwUBWH{j}(ZF=OMP0Y;$o{A#Fjcu*-tiXrdU5GEp;V1w07i zH<31b68$np?HmG<@ZGhS039bFOeP>m(^T}pneRWNr>noT)Id4W%$uq1i1jT9QBaY% zM3VNh7eUmetFWc2lq)^pRXal~hIdbiyW!C0A4f=*z~gYB*!ooFS;J@PrF9E0{`9Mf z#(_l$lxwZhq0!DkPDQ=1B2!)g2~M3pb0j4uYaqDr9qgEIFp6>mK9 z!Db|bhJA}#6lF1A@GmV)x!R4|S>E|<9(8x}d35oIdBKvumh^!n};~ zA2Eo4C8MdaQmBX`-fm)KLz{blZBBmV3b}IQ!9T#kffX%M9?=!CkLuW@1lNss4ro7! zlSyu9KQ-m#dXzv;UO~Dse?_bpL4gn>#3&RnDJ0$U+AP7dR5M|AEj~$`xY=aJyjCNTqLOoAohK**)uXmQq8e7 zT)Z)&8pF5A>F$(xpzv!f4fB;{#w?;O_JWsmK1f}1BkRy?poo%qAnE->5oSmXEk`P5 z$atX|z6tlUZ1DO_%m-3lN+gZA;qtdfO0fz{jNT`radZ#43BvZ7EB4WkemGeZ=v?At z+uqUG;d%j*Q4tS2TIQWC2^qP8vPQNha}nGMm(5tN5F3>1KxvvwdQH8i>JSJz=HhhR zD>sdSMwFYq?kd`stu;s`HoQ)iLV-zVjgssu4^zupu*=x^_wrEJY>F1ggecOl0KV?2 zbdDw3IHq*}V3B3WmAgMaw^1fYw9pX$^qcJ8C|U%l>B}Z}?wGIRZBAVlV-12OCT*JC zcow=eQq4hkyO_m(0N_!2QBC2ZbE`NslnTAi*Px&Sn6!rcOnRWn^#hk{Q@Gl`^9<&i z!L&u#?)(GL+yPjSuDiXcYVq1jGMH@EGF^bRLZ$g(?<23X#Pd?}_#>1O_z5Nvfe6)6 z`QZ*t$^Uc-Ve0BgIWQauw zn`ti{V>(nFj4K%f%n587GtqmN8mh(w$*-$88wA%xZ&T+KGSc-srYzq3S7SU!6Q&TK zK+_q6USnp32}>JN`&CQxDF@DYy{tb5_g4(yUR=PD*C`w7=k=pukd@W{R>;Os0re_@ zxH;&w!l+{#%{d5E2e&5mSM|7Z08(;flD)JdY>kUIh=nu<6nj|>C|;gE>?oQ|^hct)d87`}q-7(p?uKe_^74@x(d(O^w^{HO{k4Rf%WET0>_@Pxyf2 zNWsD}`(QY+gI>6*=0eqQXngiW^_fQuDzmH=JO2d43aEaqe|HH9G?SvyV*HcU(gu39 z(tuA?X|2*)6x0Ds)Iz(D6-H~4s5DaN`_4MN7@A0lR)hz?gBag@jP|BXQMnx$}&-5x}Y7DY7-Vol89|&-jYtj zQHj(c9x6MfLBbM8lFDMWj_s?&4Oj-K3ji5SnKl31&Wd`#fVVCN0_1sP1D+!RJ#G99 zYnybV!KAM)ts>029Z;0bM{!LdO^$*tpt{B~x>EC~k;4DXAJy%0$XrvxHlS@lBzBx& zB&ZF*Sqa<~$K4%G9n=*DTK%c~ z%m97F3b9{CxTHdFj8d6yG^B(F+ImohHtiC!|2?cPfFe?rL^ZS3A{2hks0xKC*OBIz zy6mquv8ricfuxnF9t{uce)5|Fu2!NGRUj_#vbM&iFP6KZ0;^vf!jWQC@hK<~xX_!H zscQ`CQ~XvZin>LygT{(UCz=Eb)n?KjWt|}qEs&8$3I{+C;5pkK5uIAHHnVJJ%jA@( zCe;Q|hUYe8o{xe3ovS_~IXNKp!%rizU%mps#285h$W)B2D{ZMtlD}#+;h$*R~DGDdYj)Uz zDw^VU7sBf0LB4@@Zr5nnJL}G)sTI;zErKpDM1I8z~<)Xq1|1a4$JJl7RvLA(FL=gFWmNsrrx?tD{% zoEFtdF``fsIzo(g(6i0AKQHB)=C1Z8fw|X zF>w<5?f|3qm&hQYDJ_SUhq>ts!crxVLzQ{M9e5Ri%t%pY*DN)ya>5X+dGXoS72`UW zPTCy*6_DC?+F2&*rJ-3Rn9%1@$HSaj)pY%~a;|W}YAdO3gNCTV_1`qOY-63g$jL8y z65`V<2Q;hKvD&eMZkbIs%_;2yGeK$Vr3LmhU!NObO^Mut+N2^#s|esUJJcMuO8Wrz z=qS1M#(wKaxvoi%M3(%T)}|iiYk3+8V<;D=bhfWpj`~G2SxVTX72GLpHFX_SarL0Q zZXQ_rIQ;f-`036kxVrnos6a*-EGKjJUPhu5wY+cYNf~%Y24A?|`aA5;cuQut_LMUO z;TbX=HWDwNQ6LpAv&G{H`eXs{UC=+8&8!G`OAny?6}^TFf3Ye7uKnHolT;>1C)5r5 zW9&4)f%P#0$S&?xRBH&gBQ>Q^;~!p!GC-d$^G!F7&v&F;!I&MYX=Y}%Z)T&=(#+4C zi*pGK{AU`sj(DoE$6j|JB)9If6Y?95M5Nraqpnr4Tu(Kr1($! z8mnPV^u08zcxEcc^Q$?X1vZv@9K;V8IkXYFbn@0!T6A#(%9URbrr*hVHbOY?9ji!DPX z4#Xc-OO8=#3P!}Qt)Xi9QxmyTKYBy$ck`u|o%||H6zW1c&S0@B_R?g@2WxmPZ3@Aq zr?z{8%eWO}34wtO&Gw?$tX^luajkT;3SAYc5kO~ziUzt0{xVkKum1qe-5=B&Q8j1v zX)j)`u(FLJTZH8V#ir#I^?9<{HT935=b(In-GZZqDp~A$JYU|O3DP107FHud`wMp? z14_C_L4?*rn9k2o&f_}BQ7Ysis0oq~;|Jsf$0jpt36|1UozuX9B4N>X*xP>>mg=W4 z)9Vwra%2xMli(8AkKVYu6@zq6hMCTj_VbZAHP9j7&5&9yh(N0~J24!%%HbkE9r%ig z_$J|$)g*e7YGu?WR$)yd36h)C+R+9E6tyY~yfg+gHnbZ}w?4by@#Idw&#rquEljlY z#kgNi^_TI|HMX?!v4Rzgd8X{zO5Rf*CSl#H7DrNvwd8VP2vuJgMn1JX(@sY-h0any z71cqdJRQh?WDrtR#AkFU$MEQZOd3ZNgim!njk;IJlmS|axV5g2O@qo^TbA~sTROTG zx)*&Mn^p))6~Z|Xlu`6$NTLUjmcgtdZPK_ARZ_uHGK*a6gMTv|tDVPD+Y|SMu8C;p zdkY51XQ&RV(Upj@oPRRXqNZGbl9{4~66P=iUE;yLC$)|9#C?mY&oz`5@CcI{aOzKT z4<%04ytNF2a7A&WgTj>KP*!Z9`&_r%F#aWLqwUwaqv&j*Uo{t1K1%(>Bfq1v-+$Wb zYDQ>1uXO6ap5LoCz26Y!Y?9`p&U>MXdmYQpmpbh0XM=!9l%#2!F`c(SW?Z^yRVlkkYEU}CX5$y$NX=oD62y%GQrteqY z{Hljv`m?Z1a%^)Z@GkCMit5jhND@z7ymtpi+gT!2S(K4lZ2j6w4P&F+*=4&!Vd7p)G^Bn<|Qt!5Dm#Y29nOY73Df`d{A~llBl?#aZR&xVg}Sn*)n@=-a--yao*7 z(oRcNfy7LdVQIPb9}wAb_cz7mFQ9S$K|A}*B7NI$hzniV4Gdl{xELJ}%Y6BO(j*ZB z?X1)>NwDa)_%1LNKwWgKek_$gD~mv0sX-kYQmazW5`G12nm>-Pebu@FHk_ltr4qsu z7jv{i85Hr12?KhpXbpUVBG>+qXV0_6?`zR!OV2HDTRi`&`7ti}Kp1gVAGNJHTD%N- zO5W81&DtTv1Aw$$p|0*mPMO9kXiQbM07v#ak6DLjhia*#i}duYJPRRp{7(jcb!2?< zEJx)OfK5tp0Fny%%11RD;eTox7r077=A`r35t|I)H3x5YRDcs@{U=+RNm2sMgQWN@ zpmG?~Z`0Z-&Rhf>CvUeREdFjYi`?XSYV`xVPNV56Oe@(ykCs`FWhLBUm^GKC(aeU# z)4@U>iqZ2H{uw?vwB3{T{Yw$jDIcHc=r0P!?(hgNr@B*G)@I_5ws1%eGgR6%w(C;v(nicPY}Ppctax*V;&R|H%Y zOk#x`a1=$Bp@*|q6!$&4Q}1XyFs@ejijBW>uL1B(`Xh|Dv~e@0eM>AJol{*(UBIOy zG!oHecPszASd4VW<1eAZx?WzQ}1fN)3>tTPJ#iKX*0O?l2*9cQ5Q$VjtQka z{$NO23xKo7JVaD7GV)LwEO6e_J%g@>n%L2ebtW$YwU@>l`NNSHuGE{R$u==*_3SB( z=7P##5Vd$>p@#y&5U8JtNm@Nq*4}|HdzUD(OD-;2#drJ2{8sbk^}VNNwb_9Vvtvo$ z2*aUZ*EhL*r!>%9W zCK9PEOTA@#Xw$W{qhsb^D7LdhKw|CU3Q7hN?KhlnW#?2p%UpnN4(8$i<2PZEV1QV% zt+nFVSwOE+Tej7p%0frhTUfE z@x+2ul_dZLmxb>L!eTTIn|DoZTs8G3C#Ckh_TCRL)L9nnH6^@l((=RuAUZ%xiq{=U zLeHmo1$q&Z^TJXrNp?$ZeCkb%w0Q(BI4rj;Re46`vMJVWEAkvNHx;*{i^RX@%6qPI zya6PU6Dt2WlGs&CE@P1MTlgxPlwHE8nfYNGl8>6mL3-AI5z290JigW3fD0e16u9hbr0+aih=Amu(&JIdtPx~MaikI|Prr#+uoH~si7hKS^)Zkn30&|TZ-Rhcz z_$+uj4tE+!6?}Z{97Fh(<5lo++hv*t(oHh%#NQ+lGp<8bLNkXnA|a7)t65btF7uTE z^RH!8eIhnCa`X#*Uw-|v!co7Yb@ilNP0E1*^A zCB{I3(#2DCpgTjUzSUsbvkU+20b7xFqnfFsD5{KSZ=$K39=n^aip#0D!@1+!P5>UP zPU3?5)p}Z7uOJifY&MF|lSH=#r&HJ+7mPGpjG+5j^YV68VSt)kUS?~q z>#~-VL5*3e`&31Lqo=zeia^z`8n*d1e>3@n-yAR$HJVZ?6G)NTjQx;%))SB6iBU`Em;SDmo2P`vQ z1+2SScE!@)L{W?7LA+u6$L}Q!qC)^mHbR_yEjxBATb(hXF*PTB; zTg$?=vFzxoj(-nm);7(REY+-0s3UZ6jB>7&PQYCR6>VTM6w?;>T4!x7#e^a6I5>-8 z#V1v+!Kks8aqfoh_gYr|ndPA3ZBV%YOlQ|W(it?0Yl?Q9JzWn0yWiS2RYi}WWHOk^ z^Dtr^bwu<`9Fpc~*oLHdTC6&jHEJWsiY7RR3k#X->yjwFK~8-F3FNF0xKQI|kY%KT z*t7wM5vPt@c@J(Z`*%oPzG%th;fX^nHAtP0mz)X^12nZjJT<-PXdMlz%!ZZexNudS z{LWU^3`hGZlM*B3RVH1f9AHbib67gB-zeOK>U7`N6koyjchfj9t}(HTCJ)OnX;>=S zTp(ePEW^Vb0%QskP93+neL7>PWo@!0ZFn~{r-xjCqftF*pp)d!rx4xqY(bEyM|*AO zb0dQQ4I6Brv#tlkQ1y0$5l}KMfyZl^tx*H5OIT<7@G<@~be&nFJd31`tCgqOIsm{B zdhmxBP`_Z_lUheg$0il!6d0&e9&qW8392?ns(jnLG42|vf!pcqws)X4)Qq4A9o8$I zz2;V89@17wB=n5UA8-bW=a%wBm36t%^B%5kHrp2D@X-A3oZnr9=M$1Ak#OG0Xcx3^ z*Gyzcr!f@rAq!F$d%u$^+wE&#!PQ_7byShnTgXZowsST>Slov0v6k`$r?!pcDP1;- zd$<6*jG;rr69IOGW3>q$!H`$VJq_Rai6_;}&C56nDUk|Lu1x!uue!|M!r_}V$8U<} zvDSOQ9f*DhA1DQWbKH*^QEeWGa5hz-%mxuyXT(&c>I~!RQ2I>C2-MA$ci@@j=#FPR zTB*)*%>&gGf`3dgLjt)irP;+DcV8M~NFr+rb?u*)++98-mGkLp@okl$!0+;<3 z^)j(Td(^>=bU*i0A`O!j2uAdx_iC=Pnm!b!eF)GTRc!zQkyx;=HJ+%wv{RO}tsOq>~F46Cq*28@2&N)A=KqNznT9M9H%rSpE3CBWyP zg=`8OLP`1n*Y=`NoS+)DR04Qt6O*vcnv<>NW_(5wce=7>I)NSBEny-{;xW2O*4%54 ztMYoDP;iRKjHpcZH^sh)Nl``hOYpvygsm2NYLCy~{^m6yE!Wz!RsrWSspx?8{(^{U zj|4FKVnb1r19RXyaMy%^eF$fM6r#N4!S`ybS;e4kwb=WbWXnM1_|le4-|oxSl8gvX-2i+ZY>aCPcicFq&fh~QOay;qtO?7KB@*yzQ5 zm6UgVU~oX5daf)S-Q>DL@?s`vy%SeYHoo@XU4_MXJ_APpk|mH^;3E^M?3E;6-BJ5# zEXk;?z=$r3IU%U5+w?xT99?R1cB=KCiL3%R5zm-Yi4jMF+s@kw+Ca|(sO&q5SxCH+ z%|YvZlo>;0bP}LwMkHQrNrw>E?na-w`T^;J?7i4AEbDj%x>oY}@of%#Y`4wpa*aB? z2`Tya^&xJMuPGhe7(W`wWH8#|TN9l<)0Dp}Qezv2eSNT}m>4=#0k?_W!rcjL%i14! zyDy_pThBo9Q*?mAO^|gXt|SAVOcx#U0Vk-KdHeKaiwbFdndC{>0^u65omC@4^#cRg zTMY-1qyKn84po3{4#cvR*a9uluitD6ry2vx6Z;FW*3C9pXN$}(drsY#z&E#I${gm0 znszX&i;3v_R)45+gbe;PQ5XWc6Oyn=&{F_rf@2QLC72|(*&dE|vhCRY?SgL#)x*fd zZQA$T=?TGujXez*gR>@)Ch8*CG`4Rm3yDvyyhuRx9S(s$LFurDQNeFW31uM3K$*i0*Tm z`N5kMbQOxA#&B>G>+uhS^AJLB#FadJAEfXPHl_A_|x3b1UY7XIG0UZihLB z*o84hiuE{YiM>}xvBizyNm2e0K2EJ5`7}gTVPX;{*flLg3rWN22v8_0*Q>DeT}cTP zCkX7B63Lv}+)eRf%XYlUSdR5ST6!4=UHm>)NqA&yDF=JO*?y*ySq#!f^3n?uRl~f^ zSXjb*x}Wb(!TN0J6Sa&1LGkp4%=QI3rHiFkW_zZ?H3&Vo?oC#}u}=T}4_g&YVZMTo zei^7|VutJxc7Cd9qB+7wj#^Z|PDG#hq+8Y#rZ|!Ku+-_^-;rqo0}~|xs(r^1@Dy_u zwLYo<-KM(u^Ihc=EX9`L6gN|bKFk7yl!Wh=Ou2&*xP2RL$0;a;fc&>;Z!KfnG}a-> zlj;6<#bgekQ0}tUHX1t%%TvxSx#bj79azTFU`=I?NR8y!VOT=O1RrXxhZA>+0vLJr z9CS8`RbIEKjlMypAe>VytQ}UGX~?1u`5ns9u&n;#3eG6*eY`&-DZpUhp=g$pjZ`?Z z81OY1-S1*366V$t`*`leR~KSUA+kq{7&X}tOQK5bwKepOQTFU1s>LJ=U=*(r(TotX z&(|nC%7Ya4Z*&-|EU9r&r>>5>IcjgD$kID>T<+)YN0%r2c=^1QCp&LB;f-}^Q$Q|h zWz(x63UtglGSzNWtp!_BNuU%xMiqDphJdV13WU%%=B}@UBGge z7>@%LxKWK*tqVmH5ui~~MMsrnE`44CSZe=j8Ya^-n}+*OcFM@!XV2{qhfb*99WE9_ zxK-pE-92p&Z<51Y1lo`rI&xK~E4PN3df%JmPrv`C1D>?I;x6u=1OCDm06^*gCl95g zi>;IMKRf0B-AB2crD%7+f%13v9o1u<-4&I)DBsQmDr&CD0`&aK=vc|liZz^i)VWpj z+AlYa5SOKbjn-BCRRW>{D3reCwtaO!A@TUiT8W14LBhJ~r`v;~nBn=^n~kz2rSbAu zwTEhhUp=UQyoe$tQeJ(SM#;rUv^!<>f=#pecZ&a(-|DEaX@W{e{C)Y|x}9U&k8M|l zeL?%HdCh`lzW2ibLqk=vyMC}NVfp2LeoQP?{=pTaI@Pso3xvIJ?5&sF0AO1|d-!IsA z^arskA9GXOVu`D-!v|8<;5gfsRTNXeg;FzhLolLYuDwQuR?2SpmT4H?0)Du>t{~o( z`pS$rAPMa`ktG^t6jjaMY6tz&rIUqcv0v?c;SAfMP|75CRuxxC_6u1_HX_cx((nSk zs`B(gES0aYSNRV6Np8rhiW{+)aHsq&+M2wfoJXX`I%+xkfyRLgwHMM;@jfFT9krM8 z!kFjuOz=lXdUpO=geTRn^q}>J8+8_7ZTv0zW~JP;nf2p-o%}lNqXp??-w-D)y7N@5YP+PF zmdWM0?6QTz_GAO6v=i#Rl40Un6Cp)_tuf z{i520r(l-j03eDOKrFZ&Z_!K!6M$G?stDjG4QR*(3V~Ui@!zvHKH$(ZqCn0#Ko`06 zIdcqPS7<#9-~$Fg0`MBgApqVuhd&@GW%#*k=S9z_x-Of<0phpd%)(pR5XGy~LM)Yd zIOE|Hb7x_Z-YBOO(s=i3wywu?N{;XM5ykl`nZ|@62)+s9w@yi+$ekiu;@!OPC9)d; z3C)YZqamaJ^Uv=caT|Rk;Z~|(8@+IuA!d-hp4jhJTOmn2u_vV@NuaJ2Qpm0}iU^NHdf@)3l<{8)@jBG~9mb7yA1#_rwl6&$3@HFI(6~y6{mTNC6e-nVC}(0$rv+ z)g+?G3-}qla}vTl@Eq|O1)(E+w4J;cCE*IG0Dewka99qCA$qQmv4nq)pg1JrA%IU% zOoGUb$D}&j=Nv+Rk|2n(SizR}DGBwC;}Nj znZZMM5`r&h$lWAkF!KCc_i#R}un`j6^GHa3w<15;z6Q9xa2rN+&b2z!sk*q2G|av^ z!uKXjsh<}<)_MqMG}=+nxn$bLCmKxYyu8m{T;g44&^aJ(h;Y-QW096W^vTqWk2%bDa7DRz8(td+q4ATA*qddVE z(jHMn4ALMyAyq44aHVil7LhjBqGAHa;i@zwA}>I6{UJw*LrPO>f^QVJ)CcB*;zF#0 zxFr9GmX!Zv>VK91oN3^af*=QKGY=>%>b-rZ{qX$+`|tkpm_HSl)eryx703Vp^8Z6^ zG_W?abF^?ax6%8LI?@?9nwtKR9*T3w}&eRZRk zZ&mO8`r3T{%=G*5^!mOZytUi;`X|v&-#>Qvb~`#eJl#1rIego@d|#gjZoY5(r}%!K zj=$GGPxhYfz8!CO_p5HJx4%D!cDH!H?!F!zK0hDix!&GChcEZ<&(E`buMax(ygq*2 zZ?li7laY&qtDmR0rIV4huX@*?i;w3oH=C`aq4I9Oo?X3NZ%>Y|AMXzb?@#YP$GyhC zv6Y#X?4+sA>AMg3deTk<-Oo(askNM1c8Kw~_yF#i=*R4sMX6c^ z6tunX9vX*FjoH|XSCv&81#LbdLTL|wRMj+vdz((o+_5xztJNH{Nde)fC6<9c`1%pF zi(3^x&Qu_`0OxFrge0W<7~th53TFiTA0z&7B9Hbk#btl6wA?}BrlNMH*JD);29LtSLFPo z>~V5(-j9%#Fd_$5Nfr>t))Ohyk7Q_%BsC*tl_fP4uwr{qbJLewOgD(lD^kRd!dZm) zXnlDWaT*L@3G$k$G3qn_(r=O0F%P38AK}tywk%-7NgozAdFCoT(5?A{hH96QALStaBuvF10MpI#KkEH_=Gq0qN~OwqUL zo?b8~W0oadJR@lAU|lkoKP>V&>33#PEO+FEk=#_+x47Eny`8C4XQDXHcBAg$?%Z_B^DLHv9OM^!X;3|NMJ094h!bXqMt9cif~655zuo*HzRk? zu3b0Sl|d6PlvHR>d?R>{`e!P5(Z zOX2#OY~{lyfBEB#o?D!V+w}q(dYO{|pBnzdq?zG>^p8ge0>^?HRNfTQ)vT(I8Qu2& z5eDX|bp;ygQ7Hi=(?SP7-9!a5{A~BN-UkdhUt<Mx#IFJIUkLR|x!s{gZE z-ne*Mp`(cvr!$e`&$0~gVrbm0NMNaRTYfdxsjeCKP*C)A4~s7f0>-wy?-giL)k^KO zR)v7HKX!rG4AztJk&$255T`2|*KAitJhcJFJ_#ypRZo~-NWe(uSkWGo=re2*Z#@ht zU4>h_BAMRZPZs(zX&v&TCS@Dn(1zW!;tpA#<1+rqKc@Nz?_fNYagwTl=$XQDjM^CN zX4?KQJEGA}Jfvtj^kGJF#VxRiZEjdQg;RuFb6#lEoVfUg36_C9{Cbo=rJ!a4Oka_O zBqNiPj2f>=)QZN&Eg#n8daHFUx!uY5ulTPn<8;ZjKe7GTWIEFCFx$t3CMUnEI;w(| zW*|AFhN;vYM=R>==u&n>-U&_qmO-kbOUs zRy&}In!LX#Xv}!Jq4jg#Gx1ePB8T(Y<>dUK-`N%7uoCNc(33m22a{!cbMPi)QSf$pfr0@%19gX7&1h z5GVCJwgel##05KT-<~Kcp5`VSymc}4wM;oAb_|SohP%s#ZbV~K*sxxK9JS^)+Mscx1^ zbILyz+~%!{&qXJ+wP2Ley9^kmi!|fh`B45g@$CLW;)w+`oyz`MK@%9Curfu^VHlws5&@T<2ZHfx*Qyj;GYzx4#gw92wv$e^8dsg#~e6 zuN0lLR)iu_0hvDD*+)C~pY#p0dKAK<1c_Q)HO-+^EUS;r4J30dofU@CB>t*bo({N9 zN+W?hZ@AZ@Dg9|*P-YI7TTiT1vvGe`Z7(tNLEJvY^)Uzv%dpj^Dbq10lm0p3A=_O%pGmO1o! zVur1G6x&Qw70g^C!jLHFGqhJ-z^K4S{1BZXN0A0@fT|Z(%AFI8rsxETb6(@E=9=_z zA;?$3OJvtw=h-zSlgFJcfgCU#k$@=%sEsP)sWAhgRI^89sXQF0+3HrO<( zF<{>kg1eDHuC#K+E}DZ{SqT#HZ}VSz%wG3*O5$9qW(hORCI1q#YSm$_o}e+s#dO4* zT+d=!qpS?fOY9@QTz0?yJ1ej<$zSomvjPSG=T(!Fv8kb}spB`@&feY5&Dzw?*wvNs zKU-@HYg6O@pimpSIvKmTnlt^^A}Q1S;Q!7BM78KRtwjU@A#DKxQTqRWuBoGgg|(%- z%YTCT2IuPWUr7EZLbtxIYb7^YK<>_-^J&1?#`4A0#>L?Da*xUo4XnG}4ajuXMePn2 zl%4SEYJbh&vP#yG5WIMu>Z{sJEdGddX6@mzb5#Q~V`Bp$EV?(vjNwWr!ZfpF$rcV6 z4M*!PR*}CBisd_?GAEq1s@cuou$V1erIMDCzdMM|SdHiP*PP^z-yW7G9j%z^mnRZt0wS z_>ESlx<^XJV0*06H5Mr|4QC{9#9xzXrI-kPX4%fI+F4r!Mz99Lze=wxc?{*WE?5(D zkHB8KApm&U2hoWn+jgGJk;o@~KiG^N6dI3rEjl^9yjx1c|b21kM!jQII(N6`rLJSw$SWJPLzImViGcJ* zZuD4@=yY$?#dcQWL}s&>N2`gBGY;>ezk7~RMR~kcw=tL%)-^Ne&p=@B3D1=0o_zxM ztHnKw(yv-RLSVlx?PMD3=w=&IaR78gf$p>!372lcLbN=sbixj3;yAuEGB8T{{&Q%E zt>JBhN*?XsP)CNAwLA$7zOy{ySPj=+6*bUK6Q>1fU#yX7&9rai&KAxbwr+f*XY}1V zcG);X+Tr2bvLtSp8_W`T+q4w0%-paV!9D)az!tIO>A|3#$X_oJe^}q ztfG!>n_=JA`7r}AJo2=;dT|p%BRhjQkx{b^#QCcmk$HSVV9JqC-eKk64BKKYsGtH3 zgDEA1y-b7YAx|0c*FQ7eKBcBlp|skl-TfUV4_ZBh17F1de(AFS+Sh+-4O%teyHW3a zy7&XC=lhd<$7zW&%VB?=u9<(_lBvOybH!lzG@_!EL?2L z*Oq5d=jUMlk;d;?i(k=8nV{Zaf|J=?)+`8yWnt+d8VinvJQFv@^sXCHaRQaZdcOr4 zAO4!A$BD(&A1^u?(f2|^RD+K}ueL4PRV*d6v)7F5&vz0fim~|cGZJ2~Akry?y*$ZI z+2zP%s4ESdw?nr<+g-w5kuGq%jRU|r_b4Lzd7YQN!8x4n>O{t;)~iU28Y@X?O>Wi* zbv>-wZ-&skp_n?&4*BOg9=pIU1~2i`exXl^J=3sqLlDdne|@~EqFU6Jz!Z2jg4DJf z9!bIMr9wP7&P}Z_&7$oN2R3ADHx0xMW2hNCaVYE!_g-|*0at`(!3K$c_TF(ddAk?5GythtjWlrvM}2401GfU10>yY!jx#_j!7PoG zscOrsV8Eo8j1CLZD!_qYn>N<`u0TVb%43p-6%Ff?`6Z@F@g8#x0B8)xnr$pJ>)=I9 zd(_S@;QWNq)RUp7)r=&5ti?cIPJO&lDTqcS@vIX+njw_iGlSzkZXPYsk9Ng^d@d*h zcquPZI38B%-3c#;(F9`@OS6ThbtMPAPK(({u%tq`PPR!tkT6?74ok_(T~c!Xetj#= zqg+69nB-{8NutwmuOsNru6v)+PfMrq0tftL?6!>NV5AIZCIj<{MMWnO6d6v(O>1Pe zsr`-pgIv-8e}kPgHWz1fMhEMlMm7;i z4ZIb0;QQtC&R6|Amz$Q7)@`wrF`Dd+xkpKQeCy}{H*6)WE88)vCbH@A!%BqdDzc^re%>otq z9iI#x{7ZWx*hhO&+|Qz$l-1eh1OJ}~Sx<|j5Hj0a*m!OdrvaJ*rX*1G_WEM68PWiab6X0~23!6zi~<(Ei@xskC24O*H;Z5joDr^fO1*;4i-j$FlGHJF%P z!|Kp7sJZG6f}SZ^tV}C(s(z|g@?i*PT1spndZY!Vw)hd@k%##iMzSc5DZZU*NmEf% z2-CS$>bO9sunH>7G!H2ZEr1ENpE&_;NQ!DrGsD2;E=?L5R=%Hvq{L`g8p5j>D(K% zYZCesbbcAmSt8BoVBk}U-5TTtCSB}I*+=Ywk>AVy z!h;Fy?exGBLuT0kkhJ6dlQ(X-0uWbp{2iFzbw~D6&i!ou;b%Cnb>5{@%3%|sXKblw z;#`;1u+^Tw)uaH%hd|R?eKlgX&frzD3q7M8Y&*dS>+RwtiJ#H{C@P1dNiu} z`9A8$jz$%Qg3hC8If>G`9-Kquql|zM)TQg+oD)hL@X^XV;R7&My5uliCDRzxGDFk; zsSR9_%C(K`Bi8#;jvkXlN1ZPm)5FJh(cA2KuHU|lS>mn6ile;6fwhXDw1zB0unmy1 zHq2numqo~zAIPChC#iu=6k6gHZw86Mhes^~;fni=%i(?6vAN>qCIDRJCuQ>e3_1xr18@ddQXK#V zOmS0{_hgP?tgW3ej{WD$kFQ^zI*f@E7G@?vS3WuNamYtcBFuj=; zL7~utm0O^G z>FWs4Qwi4;Rv=a74VViH9jfa_|GfwSbV`KCl-s)}`k#;5H|ML)J9Ue0)~9=5X8_!5~UmsGt0Kam4DV zDOT=U94&1Ry-2!>L`@`cBK{>2jL9y+ajTeYsa-EI6@P9*wMNYKMKun^nK!1jY740J zwB(bzTQVcD)S$@O(kEd-XzCtOBVX9*C^7%$)@-U`HC@spfb_5#!*vo7O4^DH`$y@V zU7$2jgU`r8InrB-?>;c*Ra;e=@;yTB?89qAOFa zkDh_zZjG8#Vf3uFjXW_ye|j-!bx6mEH{Y{YKZ4o?+oUX#0P8SyvoaV^PZ7D)^oG~2elu~5fr{hVy9{EYvhyF106C`WRa z-YRB%LC5|Y`Y{5599sIB@wY3?xpzKF%3CeyycQ~8$rAEjLmIgme_++p2Y;aTEp4Zo zMR5lQws_nGUJ_#I^{X=ZK@_^u)dRgd?vmG(jlzuF?^ z21l+HQM%X*O};+U8+>)1dP?y>IsB%V~<$v@9J!U}2J#ZM!_|WZDKQSPJWTR{< z0sesb6U+HpapqVhj+8t8#AtrzcsHesLsY^Ux;Di8`1OAt^9$$WOGn#*fZ$_+f++ug zZyf)(kc@}1oxAz}`=I}_Tep#7O}+YMy7q7J4%wjJR;m!8n{bmcB5xYSiBwgPT?axr zT;-2!hfn`F$H_y~D?W0W0XZ}a|55#RI)P2{0e}4?_eqwoi({dW^W68l+Ru@h&xgvL z_nXVo>#s-6+}_u#%)oyWxt|BAfzS83pMyJJ2en^UrM>?WgA{z$2P33-GTz?*we)kd*%Kf-E z^nd(#7Wz1fxbFFA4Gi@CbnJb5BJO=1sSSL5ME?5ZGW_W6T`_4#r?KJWR=W_%lF z`r2DE54>GMHuV2`iO3BsF73>u}?`=d-jn;L()$tu0IabwBl; zk=Rx5J}}^8@au!iF!1Xe_WQ(6;Ny1`{$&P~9((IO5Wk-6yoi2$HA0<_b$JVpZ`(&w^6(f4cksQZ22`{0=Cb42pteR{{I9{X*k=i_l@$M3PyFrf3#Q>9^d z??c;O>PV-7z7N7A{@N4whh&SubZ?6N_m^$B#Qyp`4}5>pbnNxb_J3YFf5>!>iVyNd$emym;I(Yo$s~jb@7z< zo7(lpqI!4H=K7_N+Le7u%8^0ImGOz}b4Tmu{2xz;)~eoO-#WK=8-uUSoZ1yVUwrvz zosrALD|$=+X@ZUssAy%MJbnen*?zjgjW&tcgWGmoi#L_acGP`q*4Y z$JTNlhu`um7e!gm{gw|rHZvK)HT`cxeMJSZ=k8w2>5B4-yQus>wTV|>Im3y4PbS_B zUa@TRQ@3sC{1rE^R@p%~>xW^cZS7T+QcA z7UD}$rSv*o>y~&WpKnXb6$Ij{uhY||KUmrU`^l}WdqVy{w~J^OuC9hX(_1;4O@9%k zT|fFGAg;+a8|SHYvBJ8B-``YH#F5M=S$N5;~9LeHGd- zoM)_e?oJSu7yoUS6IR3&BhI)85jXqdG9depov7mLOen+KUwO+r|BCU;*zMvlYs=n6_Qo>Ua#}2qz7YMq z$+liD^lmom$|tvJK#Q%$AK2N#X~{v2R?`UqhJ8M?xQ@3j-MLfwalw(xv*;0zWo=FT zTVvT}jS`-s&<%T~&?i+7S7KaL_TSd;I4<-a1L8PTnjFJR80&Tl9_#|SbAAAw9Wdf$ z>dE4Y{jMGByzmDh?F${K@(YN+{kmt&>F+JW$2os7mM%TPjLT2oO#9p@!k(Q5ELT|P zZ2ErSa#ybMihPveR$=&))&qdg$<4N=ic1>%yforHr%NQ$A8V#rP@h-pH9MM;GrUA$ zplWDXtfA)t=)cp!5|j98JDe&1#h6Kavq{b28}v%qv^Ir`Od!`%cTdjQ z<7+&uaa1K@u`+OIdGg$SOLzt12)NKm$Z*SXy&=KQ&o}W6Zw^=5ej!R7U~c;#-&VN7zg@kq1v*hPaN_V{Ne6PmQtb9}oiIm!v4OtU4qnZ$gQ;z@#;Z`Jwn)F^N~7Kc+L-3AstW&hls^^_50~9t6Rq> z=%U{TKTg6hda%~Z^SVrO*7m^74{7H;lQ6k5-pWilpYW?3gkZMR&$MOa*j;|`cc zRZ%jtt-iv8M9^L2ITL`@(JQuq8o13Ugz8GfgB;F#laphK|0oFZG1KVHg+9 zaS)M^SY@t!uwTVT(4blbYv*QT??G^S#Zhp{YQ8v~L+?=W)Z9~a)%YBD{Sg|%^RVk< z+o6xtfb}i1pLMU&Y)TEZ5+fOYlC-+CWiM7zG{vudQkofzma{3=owC{)#ptK0WD?lX z(pMF=7`n53ZJMUY*D6#QWW}dw@!VYg`8g+f=9qP&F$0RGUBsQH0j1-S!MyphK`u;| zFNlI{g z%ABMfZEVHX??x=R#CJ&M21P=*QhBQa#`h_rYu;8GI1-l=++~37}(ge=7)x zCn~Jq%~2&vkX0UEE(hqCq3IW%NEq+xP}JDL}hy#JYvx^dFvGl|@&tB$6}1^`i`C&d|5E zp=Bmhjo@(NlDy_ERyFJ2H144V^z+2I++8 z?V_UR46 zE9h^({!tnk@=@>=nmBrVt+N}Q+3>J_HF4FS6$Y1LsP@eQUpk? zY1SfiHWb^3<)Af?YtYrL_e5mVojnw)GYdpnq0$hVg zkscIXUX3)92`3>I%Wcy=!N@s7u4vPfSLq6NKWK^})ybk$nW2ryh!I@svV-l(A97!l4^IZFYPaF93ZWCk~>3N+DNS!%sLvg*U}*m4B!{D?3{ccp2`- zqnIbL99gA)>42M{@^E3WRm4dxsnY0`kW3LZV#ClCl9m^)=OIv&U?Fhr8AB1Cav7DK zr9d^oT!rnCArHJZ!?lOJxoc;H0>1 z$?IaY=W!NdqMmt4kCAxf$BFC@6bPeeeBCsi`C61(cHFx^?ybcqg9FiZ7RjD{qeE`P zn(K%hC>#n_?;9qvC_dc01S--#*87lJc?{{zY4X!_5U9~Nh;_J=Wp93blDbD7Remci z0vGf*P)hCM;Zj~AICugnwB?pDk5kr?Y!y~ZC3IwXmpbiHaN=2(0Cu)`fAEDor^#dc zQ>cqJ2Cw|J6b~obC3&j#ZsCkcSJ_8^Nj;~?<$ImQmL>&C^)4~lD5SmJlz?^P!$O{A zB@|+p8tLuMMHO;ldQ=$fJ`no0c zMeVo0lr}>PEP@+5gz@it<#u#>qN3iWk7NCsvIFVVmt11i4;Za)hq@BlPZshyO=m8; zQvqbfo!i0;v!U}73L-S^MKZ&!%H%9&I12d`+;Vv6t0<+7SXmrKysB2gc)JzKy7Y1l zSllP+4)ZS5H8BO#b@TzDVgFcR*&c&_g(aNbA2LxkA6R0b|HKhc5oj|C$zX*V%kbu?o>oa5XA;QzS66{2`6C&Gn!4`~UHS4`AF$g=2fUA~6FLKV4;?QWt)BqS+?Wj4}bHNS}Q&Wg8L&9=PErN#;yRi3|x@Bde+Dh9* zr!Umy&uMv0X;b3-sb@?^&}pZ)_I$^P&=C)jEPO8Cxe%UchSt)<*_I3ngHe5sb($jA zSIQa8&>G33K>yEStLA5N(1^7MT$pgIAVrMu^k0&FhgxWo^d+1Y#Eal6Jg_<+kBj|t zRMh@%-kCDO#cK-gNF7KFW9{7tid01pyjd>}JtBLNZh)yxeyD9c3{DakuP>LVFtRXS z7ABALHFcDV?+Z6(uO)b2%(3a=mQ+&a1ql6)=nfuqdvwez8iAQ94uwrJ2bF>#c^-^!Ft z_3o5^i_=h0G)5b==Hlq7sC1}`Jlgr$u0zT*=j}Lyg$d3K@n4SPBgF2+(T0t&n9yEu zUA{F&y9xgcB@NGr8Ro{-Sk-!uB##IfgP;w;wNM^A|s_t@f|DO>K9)Qgj$F>SmK^itDEImmE*kK@2!d!94q+UaQf9d$>3hVQUH7R8;O1}N9FX6wu7%Kqj z*mzX0Bl?1fv7ZPE-g>x?hyN7g$kDgsatVI(c^xDkPkCDYcTt01A;xDa>uj~wNmveji^~u8U5DnnOj(-s++qkL-gKq`tQ27p;9vxN;IXfJn1DVx? z67ZS*)&+@oWa~20NNXl6*xudT5DVOXhlgHs=E72s$l&yxo;Kgb8bWJ&W({lUDJQ^_)ey&vD+y4Zg@=!;vOs9?SR7sBbiGyAh;h% zy9MBuzM!Y0OF`gMOBU$I8CCt1!=We4>S-bp>C8tF8CqKDoLC&3Hj>H4qz5|2*OnS-E?pYLRld~iuQvxWfqe{@)?g5 zc|$X!SS-3ALSd#`VYszxUFSSTXUx~2mvHdWf)dNb=-!L4lnl8|P^3V?7D^kfJ|qLA zcF*XDqd*6quZ`Hl=RlPy)rbmtUR2K|b6-IV$lcwPQ3ZSX=P2lM$aU~4v4vXxJU@Fn z(ho)W~`S zoP3Rcas&pHSFl9o_+~7KQlM2?(i8(E95px-2|J(B48y-8=dujhl4&DzHe4^(pHFm_ zRlbR(1qF!LMjJ-LqeCQ(ShC5|@-aF?p8jyiItcdfzAXsGQ0T&k5VOFe2-nTB_4fqh zITo^Sv3v3Dl~+EG(+CaE-(q2MMnq@Tz z)oKimF7CQNY2$9^K^%5uS+Tr7N*zX7(=ENEe(N>l1g>_i{~M8uKk`93b|$-qx#Vfm z8=b(IPr2J*3FtFGm3Kw?IHan%EALO^*kY!0T=8ur@S7dvZcnE~F!;p`>DAJWW_?k6 zNYR*yjJRg@lIY^Njo_+9i`W-!t4m!$#7hH$uYKqfW|unR>!Gx}+J3tuFXk(i5B@pT zs$4YMJh~>9a(SmtaSC;+U_!j!h^`ma-5q7OgjY_EG7BzjKuw1?0Wt-qHr$YzRSJ5& zb}xv#A@WW>-*fyN?ASL&@~G!XjN}71t9+#qqr)3?p<%>$h9BEkWHz_v7Zq|`cpk-$ z$Y1tkeKoCkxQvczfKUc` zrye#N0F4DN!?}-OG4|_lNg9WJjD;byTF`-RekH3Kf}Z8Yj?TIS0JO^7FigfGSLm_6 zPyODlFJb{@1oK6%j>(xDIP^@ThTB27@Yn)$&?g&yB=WMdzqr|J3I838_{dY|ZvcRB#6xr963^?w3zrD7xb`tY=)=@Zdgw=E_CJm`o0-NI^#sH zqoyyC>kiO(51ERB4tUbmK36MOu+(g0#oV2@DCdNfiH+K7{T60ebuFnX9Z8^NaPf zQFP}3clySolb<@FuE~-?^mUvdA_{WitYX|)3xnrz1@xg=apJ3B6H3w$Lv9z>rSFll z*dXN@%qdx^{uyf0zn?(X0eKX#{x{qT(@W=+5G7m$%cOG)!t2Q&pOGE}+lWRq7<`ha zcMdW;V_-{tI>r^}^&U=~)KZpOyt03qzsbHYC%-&5ugy~2uXtInFL;hCF7BhFL0ccmnXGm|Zg7Rj zm8}jRDDtU%xi_VbZ6Rynhfi+n33|HX@t|8t;1H(7=ctPk@CP&GpJO(VgYXRR=n|KL z04WkYOD18_u*GfRBZ z^Hw#FPqbs1ncZ$ar49K)W9)kSXgNsdY0dc93^ zvi`W;f@~Ihp(yp{G*8zDU+!$}8H^*Lih9d0Ct6y|Pu5dQ*yMsJjL8xN!8_*>2YRK# z1Ik&75Rbs{QXF>;sc-Nz>O`Usvos`*^nFr{H2BHV>=bn%mCadUxoTy_b)<3PLX4+o+S1-$V8tFRSi3t+M2iKxAq9lI zaiJjo!mF$=KqeZ^?2qhfwW^|&*BfrPuzk;bkeN_~9exPTu}{_9Hh-^Q(h4(@r?QHS~{cjpX2jynHKpSCZawlE#S4^Wyb}& ze;93AGas5ZT{Tzw$gpT!mL{~4B&9U9JtVzZpla2`1<|xsfkCLsIF^b7m?c!~I#D=1 z2UjJ+q&eq`?5OU8)X$bhHPe6vnjlvAHjb7K4;sdbP*JM=RG4(0CK^h87`wy8^${b1 z4Bo_Z$npT5f!j>(q7L|dQX5tXt{7FA{GpSLlEvjUg)eAd;Pa*PlJUZo^A5%~!6Th` zSF`xh03L%mKPa6nR&5pw$QD!%-y(0^n~Y943aKHk+40>mGy_H4LFW&UR` zApK8Oz4t+~vjQmcT&0P%ls>(^FMT=gupB(g-&y*j-3L@7Fn2=tlp?!H;RYIgey$ho z#mPmznt!#`cfRSdBP#$^gwq!^clbXW3EkUqdRhhRV;nyeW(=p#^-b&CSy8P;kGS{D zRhZSrLU-ZJ&TNaMErKBAfOkQJ2$l z%;1_1Tn_&Ez=79)>1vTAQz)Anzf^}Nmd^}GQClbgyX))S0#^zLx{*d<Vv9`*U7#xW}VU)axO}xA-3XK2Lv~M3|~+5 zEnW~q-b0P9|H@3+K_@9H5XeGyGviaN#$sN04H-(4@QAj|>W?j8IF&1Ki`0y`GO9u? zd_Z`*)nXkk5o;o>{o6vtm-(92cQt=HmO%g}$U}xh!Cq5A_iv^j(ej8fep6Iv7fD>3 z`(8XBq5&;{;Y)NN{)uYOJ%3@mf)D;dnFI1Q%E|NYm<6sZTK13n$k@SuRGTOK3Hj@` zx`IJ z#-@iJo|BCSuccEJ*s3=98`h3Y0iDmT!R`oj5(e`a+>ggigS%&l|BVMcWUpuPu%mpF zn-_1y{4zj~bz71=XuW>)8+P2;yc;ufI_IRTH%|k?9CTW!T0jVYFkm4$7nM_P6aX>3 z;!E5K*cE~(a6^PDTmrj4>WpmHF5U@*m96U_pZr8eSIkC9L4deSwp8=8~b z8@?gh75`dWpHG21f?GlJFh&|x;$-cDvBJZ);>InOb8PWt#1*<~PSyd!!=op@;oJl|=&oj0jW$Co&WXT$;K8{Q3vnBQXIL*wi& zwcj)I4&N*5pt)=}>1K0gbUtC_h`|wRCoPsmSqv)k`U-jgg(jPw9a=$O5pK3k6J9Sd zY6VB`JJR)|+jl%~KCXDZhjwN+iyF(T1wH6I(Mv%I73LVRJBQtY?AA!|GE_}VDS<4%E)`4P0o<LDwCo*tG z5n|Z?G0<`^qfCE%%a~a+@L{yFrSJ!}Dm&%=GYYD< zf=A&SmzmO^yS(XG7xNgV-`jOBWZO_&Sk`Q(oGQ;c$+G=~_i#E^a+hbc;nlK>eFbIA z#K}#50nI-t*BZ`JNV;8dwItAXVpA{E9Ti|p$Y<9Lcs8j&(7gY1yHD2 ztAYi)$&jlzyX+RO)4prQUmWZkbs$*3sW3?woPpAv;D>O7z$}?g%@8^5`74O>AE|1< z?Y4!5%VwESLAgivbq#LtNt?L6N4`@5Q`2i_H6nW90pkK}3l3fyDrR`wj_RApcpnNe z>?qXm+FSRB(OD)2VJgN!rkXVxH+^BZ5!Q=w*F6_1y2yA43RYv|DdXcsbRkhG*~aRYhx|pvaLR& z%l`5+pqqburF91A8qUzJ5*l=gl0Pw})8L?O&|T5_T=NBH8k4p$Sj!C9^WZKNs2ARr z*IEynLZx^~PXK*@nv4MFU#Dx7Fmxh9BE~cn(ikUHku7k}4zaYBDVqF6SMbuj;P>j< zY-GdCfnr49Qqa9TH~s!KXcB3H>-%A6ttQBO)`;O4EE%nwz0C8tz0EgD5M#$6$D8Vb ze(JZHkTyE8oebkfM_T4DtGs`2hlKJFtVVduUDD2r^0e;jM6{}D#e>!ge9v8-C zi5lJK6=syKS3-+g&>i3MtDNVxI)8bLaM6g6%e5Cwu)3ApB0JV~QEc#{iQo^pF);Jl zo>J4W!K?zwf>k}Q!ta&-)DR}`IPN`3M~)@g*KzCQ5(cOvo*thH_*L!=t4?daBON{G zSulWR#Tz5UmI@y}kD1^c?a0t$oR&`6Q?(BxN=(OKOzBaG9lfsypX2B7LymoNUuRagKavi_l2{ z6YZx|g%D{0I^Z5nj^vmef2EG`LKueCdSEvPc}DIgWU)Naj7NarD**ZO)A4%7TRJw2 zzls9=&ovr{iEzf>Zf~rHr3*<&j(L9%SO)3VYsFqyyV3Cs_Gol zisD~v$`BS7GO96Fj$JHzHW8V>UTqtPcoovh3>`}BL!!lb? zy``sSA5V0utm5nxi0)a&;!GsQqFg&XxAr?J_P=j4(PH~NgnkTuaQaPpE+9V65r`6touXcN>)Zho3v zTD*_m0|AOydv5FSo$xt8f*+QZKhw?l&{=aGv#&(^D!hK3oHL_bqF>op-)pc<%g0V; zKf#EU*gG8hsiK;PRfr(>&Et(|&x?_c6tCZm$v(bzb|sroF(XivmXmN3>d~32fSw}% zvOzV~|6vY99Y6Nab+(vRwIuo%KH4a@u9@9NW%<8IJE!(axL{i+E4FRhc2;bo!;Wpc zW82P(ZQD*dwrv~T-|lmHZuZUo3G<@rnPXJdcz1wly}3qNr2j6Crr^ha+b#XJi}4^% z`D{hvCo?_tG~(Ye+~4{TLhm3*OEyE5Yf%jh0Esm>&Pp=|vHw-53R1yR_v@ANY`i0o zZ9zWn^mMfrg{4iAQ^Qyfbbj5mk!a<=V*&5ZY|#Q$8E*B4h*?Kq#ta4FZTz=14 z2cRAeUij>eJ$w${UzBJ3gmp1`q+1EYpG-~qaJMk<1$gk`T@i5pwtgRsjq@bJ-N%*1 z#-%_nIU_LtzVY{JSPsi8%E16Wi}qvTX71dN{);;N3#lOIl3J64`9n03D_vt3bKJB0 z0-^$Hp?qDVP{y)fgg)^u(dH)dg|+(JA)NCP6&pV}+2|0aYqTuUZYQ%m@(Cyd5b}t6 z%yzG~*V8X_Lg5eGKO2ykaPx$%=zPXY7XZsaar|=}54l{QvfpEnAeFYv{TPweiNhS&cI;a5x~9y{K9x~+ZVH~QL$rapqLR$4RV z|0w!|R6DUqOI3}KuX=1Zl*eGpYz4%B7Qip>6S@yMHnh+eP8+xJGgPJ(BG<2Nxwp_a7pehrfvf@#ngmljX3r{pC&%k92Ar zB9Ux9UOX!)xh`GD=s(vl3^jeor3)9;zBy+FdnArke0Lk}z(0y@V~;C~lNEvlql|Vn zma;-?G~hiA%i=W(16KpCoWUfd93Pg19Xxr!Ldhml!-@(de;C5~L8}%CtN&Bs<}HvA z>lM;rB~=)PKq4?N#6L2cx@bgIH))$6hL}VC1u56O6qSr+pQuQY~B11gpj(1h%!=489dEDB6|LEHidzFtOND+|Z_#<&42J91~i?0OA z%}-xdt9p9Yv79g~5{U!1w`4wm9lL5;uLsweiZkNEs7iA71*#{6wMShNbx>eaoEzlr z@pWOGG$0IJ5^Z&)RBG#Fh}}V8-HtZmJti}$ncc|!P)l3w*^wHLy+I9uwsW-*G37i# zL!kNaq~P+AiG_XX-ERLTy~DeMRbdyw9h=eu77Bq$pOvBBlhxXtBGdYUYT!X>j-PH6 z#d#;Smm5sbQVV|zUYE72wqSVjq9KASN?nFgb3OQ<;}lr$aytd~xvsJ37nRSgLH$4< zp&i8Lz=T!O)8A#`VpQJXViXf+(p8M#;c#0a1h=u*T*&*@DjRTKoUrPiT>_blmO&Rr zUuFCt7+N9qi;m7D$|gbZ4RQ=LZZ8moT$j=2C6=Yc3aryiz6u+G%Zjaw8&>Nd$a|~L9KP_Z~k>IsVv~@8@Tr-xA z*Rgmjz^96a_%y}?P_^dqV2N!NeF_h8Za?<3ONnF)LuY+G2HKP&DiYN~FX2pv>j-Rc zs!%WA6_9|6bbqRX=Y!*M7MLe>wE<8L3MIq;{Nz&7U1KJbsdlJQl-jTR1Q&k2#j6+i zPhxY$B2?;dM5yp1xEn=#LYHBKLLQ%i0GHost5X6k?*&raFe@bwTbw&J3TYDi-?aqa zv{pbVn<%E_IVp_8nIZpNB>g05!7{5&i#cR#dZ`4sS2NJ|+(Kls1gN6|N63@+pp`rZxq;6~ zH3%#JZsM#CEcO9eE=CWnvR@wou|Exe2_i1CZGipoNuCA>re;-N&s3UMYu(?QGo z#9!>GyA(a57MZNW8FBs#>FfN!CS3!qMB+(?uLp@Ar`=!FgVU=JY|h=p{|}&9*TdeO)Gzhb!YU46~n-=X#6R2u?_aVF z3fljXVMKA4Z6b;0uXuX6UM%+FCR0wTHF$e`2eyG2;6dE#^BO)@tjx=FGN1rln0==Z%rYNeg=x=&%|RZJ9|(t$W+pvG3Wh>lF75~N7{o*}paz*YW*50n?4$5$Dnv_{QhRZ$#!KKZo^v^G(mx8& zZ(v*yy7CuRQx(2$bVx;jNVh%%Ot!e5h+5TiJd9?gl<(HuWS-T$D;~a%0%tYnJyB3%8j;!>_7||gZw~mtcxi)U^#C!umne&b`4g%Y2G<`$7$*w4qdAW9MCMMxd`p* z6qD|?X|QaGC=kC^%rT5n6%#(KS6RM!EiLrgdVhyCxTG}PYn@^xF7_h^InrXUWg$5k zK%$0WfKaxiL!jA(1%ZkG-^FYHi2Q*PNA>;I&ejs24Hx(HL{2Aoqk5&Y_dfA4_THefzTiKFn?xt~-($BIDiCdk{(^n{gjf$Xtb zC~7AhVR;OwvX#q3pY#+~ganS&6O4IsxX`unKtf}s=}Z`zS*In(E8IPKvS8C5d z5w8GXS;0}gfSyh-;F?${)Qv!PPYWA*KL5L-HCh2~l|;Lk?v}5?-Jsw(Q}MJQU8MHD zL^i=v-j+UIGG!_3D+i-mDgSKtg)Bzz`q6y8zmvxBWxJ0~jk$3crFhEzq3}eu^eU-L z-@!CtC7Ifr3DGVV@Em|5qR8ma)1zG1tP7h`T*RA!yJD+hb^snJM;I z_pZ`BjtxC6X*q@&TGpbmcGmy_#~AWOY3#;P$@&@vO{WjyyQZ2sCzC5hH0gx#T?2h_ zb_a%BhjM4;E;?jWaN-IGLRVu-+KkiU<#YUti>(? z=v(@-<3fVq9HG6g-_TL8h=imAmtr!Z@K$;T{t%$KDb9<_;W219gT*W6>R%>XZfj;w zI?fAKr{0|Vv}484%8@xKR%8IKcNf?6gTJKG>untCr_SNG5vVx#Tjwr;nPCHbw+KP^ zX5{~7ozT26U*HpENIIbGtiG|v-@xDE$fDEL-61cqeeGsVl@X(myVi^J#0N8-atvIt zDOMoYXWISt1VzhL&@2Me`R}Id#e2S%2S99JM28sp(uo)Xz3OqH5Yk%zpu*BmLG5Op zT1k~fpaKfN`B^O=RPtObI#G4HW7>fzgb6VP?5yffnFnmj1rU*iH3Av52nC~)YH~kt z@`qK(@viEJl5G;$WlDQ`qZ&ysBV`82XOB zBeoNsVnEBIOK>6I{Q_nZZ$l?N5UNjjPeXgD~PRat)lFa10dQ3rTdOZYn9OQMt+MtterB z18_6<39`@aM4voLGdYz|SC~MR%1XLI2kR?l(?l<(_n9C`9Z}-0A#ipG^xv+_dOJIG zdb)1^wgQGrbp@4Wj3WKEBSYFuc|18QIsKtaJd*})ahkf<GGH=V1!G6fV&Y=v9DOAj9gyIhg^*|S7`(0`ht&b55w zAEhUog?=Sc}x0Uf>(S()5Imm4@gB> zG{-HyMJh*vrATv(<>}O3+Ke;Gd%J9sbF}-Ad)Ij8UUuNbu7{L3@o}5|QaD$Vl`}#U z_BbkZ;4-&temgfff#r89JoEG2(3dMxqnOEYt=M*9M|)-Jway*{s?rUq$BD56S*)8cWALE1gl4I9+OXqn7q_rwkn6&Cs1m7 zAe8TZ2`oh7QWfG(^x@*XN`fo2BzDL2hOZuFJ4fShhGudc>w}Z_5^`F$fcw$}s9&{U zo&rBogm!!&pc?HfHoqwuuVL>7k;glbDTL7WNT|+4&>8E!w1h19hmA_7D%t$6P->^KO=+Yf1kuZ2C%|Gs&f zy*-DQ?aePhb4!cVD2l0&U|76W54*Sitd)PYNxJ__`&=LF>D2WlwZB-#+wrqjlRi-l zaC4%bRmur}NSr!m2P%pdVQTJ;c)g?riD&%eCZTfG4T~e5)Odk{&G~JyLX;U|I^#4Q z^-n3ey@tbE4f_!x?30ikO8@oPFU#X{&h;h*SARaMsMFaZcb1<>WYySO zn$EK(kfz33y@KUn0v9R#j~DT_Aa@7)cwL(>Z7di2L2n<^aPA!I$JUS5+`Xttv`JvAhrW3Sq6vXyi1 zBML>vl-!aTV`9}HV?2luc+a_L%ENZdNY3JUI=ZOdL)9D3*<7FDW0Teljj%lgo6vhU72SYR(H{3vP$|2s+EWJ zFq;G4a&>xR{OXbAiwQEA44##|yY z@m+VwfoVWyXFzs3KrolTs;m1?q2*Z_II9Z7L~Zq9RF`@OYo2&H3xa0#KP7k%_*aWn z=^h-T&MUM@x?!bBj z5n;Ejh@=!DryiA?Ae^mRFIXH=`7+4Hf2gu5j*EEz+;{~0;udH!=)_KrBCMes5@n*6!w)=*N-{T8xN3zcAW*`Wx*VJ4< zDKdL}W~jqOt7V#xpFnEXsM&5oUO6e`-y1WKX= ze1Xz3*F6eK<7|sBz^e~8smwvwz!n$jiBnMTVW6!W4JlqraE^gIJXSI-Dlnvw;l5C? zCd#a(jyeQ6u`odZ$=K>EGaYqzviPbf63bK_+#+G>Dh~3G6gMNSN`-Xc`zngmi^$H^ zajrLldT%F=OY(wKDkRJE-L6CfOmP{=N^GU83ot82^FF<>QW1^KS70akg5i;x<10ETViuoVxPpy^T6S9cT4JG?t|+ycu^gq= zXF|Vw^cgP{Jp%3mKfqrVxDAba5~#<14GLP1tE z6N*3%#&ykVF0F61C^htPx$jkCZO)d@ zg)6*MQ^vT8WLG7oHU$mORd2*N;S9dcPh$Rd0I&kcI3$k|&o-T~&3zoq+}n#Y4yQn*bw-VW%@M`7-ui07AYZvDgq0cxBjPQx%%5^T{`wEcF``HTz3J?{-5OdqS z|CrIv(qtojU6IrKpZ@b@!i7f9yW4!$9jJNMeMK!ecrg#Rp$o8a-WGE{nb=K)39)5( zCQ>1*Py{f?^4LhibRnOuqf#iIQhsZz%mF14ySlbOwJH)FZt2Lr-30v}?aLg0_;_~% zGqrJ(h!{!;v$&Vd^pxY~hlgqHf@QNhDNB=YcX zU>_s<=sRvV_BYBcEojz_l4@G%TY*XWOl^aVejRi^eo9pYqxvLV?yT698|LE z`MXklNG~9IZA@*#!@YDA)#00YJsks(Gf*w(6gw$3VB!cMS z6mVxqq*yv>Z~WXwLy<7tFmUZ@PBVrp5{U)wnlY$j#W;1pt%>U7j)71DHt%t(&f0LY z*|kuJ*#e}4tJ`fbsuK0fwOyk%Y=VJ4mO>3S^`~z}xrAyzj&+Ad7FMY&l!Z#w?JVmc z^icDNkbed#D`!JCWsJdh4>PBQ%RY@M8sZU>5}ahchb;;PjyvNzI#CBo0mAGByl8~M zY~%j%hTD$q<>oVk8bf`&ArUV+q@4nI#C_<#SCDP_TA+9Hx866AyY&Cc@^CuOzaT!J z2oeo6BAxn%G)+h%`^21jGG9oGwh`rEiOpPnuad0j>3(=F5&s6+Q*|k64uGZ}UN3xf z`^@J`GM5rGT5skMaoOC9=ynS{PG3-uE}#>y$gh%yk-Q9(v;kjOwCw{NCx-bHQ%V<2 zDr7}+)p#@mQt$!i;=opFd+ETzUBn?s38Y(gdR)5R7Q=WG)79$f(cpzuEdP@A;>);4 z7>_0cDL%sUQX9}T32w?)()KVM8+qXU1yna;?@|QX1j`v;3VfKBKbp4XTSDx&DQFbe z?o*~7OrWHs!M6`;{Z3jdHdng5gF{pW(rf^>Gx#L3BG_Zo4j2(A^s|x5b2?1FWRUNS z`WM44x2Sy}EJ-R^(MjAu8&DUmp;Q*y>SfghMqUH_IT?35kW_3H+J-braDrucq!)=B;C1p;)UABUF~~+tjQxhZiNhIIgu~%Y${^o_516R zH{_1o+1c@QYOl)olzMZ0IOF5rZdAs2FtbZFR3tP86Lu7#d9)!d`tWm1fDvT8p8r;( zaBchx(kW9LBC2eCs@Q=)U&o}8q(FcPgoBDU6~B+fhp9&$$A)*MXH&M64W2e-H>?LV zzT(oS&CkC0g7r*A1zRLwlPD=b?*GN#Ei`lAMff{4kPmC>Xy zvvZWFX`~~w3)V3;g43Xvs;yEt^E{t=sKVKzDwKho7zkjkdYCkts6wc~hyn@&mAOXpU@k#A6^=P>gl@CIA!|tS^np^`=vDDxgF{`BA`Yk<@;o~V^6 zfdnQ;m_=tJ+c&e0ehQqI_vun&qD{z{ezF|7+J@_3eNINc_V=rjYo%)>p^{<#MyY7D zz=g){?gQ%9AeePRAvv50oBxv0T{6iwTW#slQ3XPKoZuv`pPX!#8ja7Z;okk^e~ZjP zTxIS!I=84dybHc3a5%|jA5-cqcYc*a3;^>HVo4&`ZEb(vmM}rS6V?I(8&%+&Kw0e- z@xjI>eXr9(EGO%D?{`iQKPYvMidaIX@26{3Ilnrz7K+t}M2A(Y^!Q|LHC4D=*_K4f zD34mZ`fD(!b|`g@sSiL)N6E3f2?4wKTeAF6CTz#*Vfb@7 za&Y7a^w%+&1wvczpSuL23kjw9mth9(d7o$xoVR7q^$49{nsnc_5-B8&R9`k0m*oR? zZGUR>C#tu&1+TMxD-^EZhF+V8?e9n%VY(cxl4eg5RtYI_Y5D(|PBWe!9MNV;lCXW4 z_`oR(I*CSO2=K&2eKDbLq?mO3-4ONQ*PfYd*X?1Qzpe^%8eCv83xx&D(gILZU~HZ~ zkV@?sHuHsLqggn5Ks6g7p@qPwj(YXGaU$+cS_JdZ^_1%!WY9hMt$T#j_%n3kO|LZg z(3P`@0*yVyP0O`hK+m~@5K$A1A7aJS{wkP`GI~+huBu_x(c-vHg%08XWUqcBuWbA^ z&;l`rxWGJMi!X7&VDAw;7qO&h&!%=>xo{Vl0Q!4f z4Q@$V{}fW>qM!P31W8A@DTB$nM@b9B3CWJm#oy3P1pD9$2kkN811rl{O5qGPD(at+ zj%JT;CAyPE=lYrLOf42uqD#Bb7Ky9CU~+$u7@RpsSBhw~;EIE@dl{-?qv~;^jr38= zg%&t%50}B1=sqvBSaUf5^Z|&59FJO&c9(hLVgRmo$KO21{&;D5Clq{6lHF$EW+ zCNTwB6Iv;5q~>k*;Pu45%R^a&Vm3(bJy5>cj&q?Pu&l1%_>@R0P7^gmMD<|Jw#Zu9 z-QaNuo@zlacIYf8{lcV*Tdgtc7Q?~W6 zQVk%_oE@2qqYAHI=b&c7O+cUB)ab~%4K*#-j35gx{+_e%aKW_|0z#Lev~h=Y3JfgT zn&IPtI>44Fr|Y^MXouvdJj1r{A_!DA329G|X7v?2heT~n0f!+|^1n27*lAfuVlJzu z35VmaNa9lpRw6H}<_FY~d4)(IZm$-yCODDG`bTV$9t1Twr{@T%$W?AfCw`d#H70>pfI?r<(?B0+ z;}}t~xN z5nB!3loR-IJoIL?!}j-N z%LHeUC279xpd^!3@Em2dI*0NId!dj5D_~NsUa!?;69dESmuktCe`t2Dlj3f}wP)we zK{2~yu|3jDYaaG@d57eLL$?I*D37!?t?L>y@h80Wu;Vv!!Zjrt$SqW__ij9w*>&U6 zSk0sF-gMVVv)ZVIgLk5x03?Rn{0E!GDMh+6mi>0?uU`HllIb-T36SC?yoz9jmj3gH z>dR9XNH?UGe5jEC5Mb)}RRQqGM5Joa7+9eA`^eJ2>ANB#n-W7Y%E%3pSBw{v@NFA& z{?}{3JFU+}3AYFDQTkx|cnJI+nj3_#ub}k8TFYQK^u#|%vHjgc_LPMsAh^P}*<{PbA7KL=iO{=n6!{+}4yC<2tsk(VI?AR-ke>Z*94cM`JAH0%s+P=T^{NTc zm5Q3;kVc?OmT|0hIBrVupdhC-N2^!g4{=U>k|$(5-aKG29a=af7uxNcB7GdO`_vYXrLj$S4q*Zp#$jwUk_N=FjAJ+8;zNvV2ZQGY7aE~- zixdQ2on|4t<7s{%KE36na${VGY)gn5g%xVp-*}m6jX`x~!WDr=RKH}(HybM9!FUy_ zXmi+34meZ(^2Y$ii94ALjbXQnr}K{2m44VFJSS5%Z7W@NW$qx!m7D#1{--9jxI9D$ zxP6q|LUg7HiLQNaPJz=@u|q{)=`c(pRQ?mD_(vjSDA>O3fNNB3$kw(+Lex0 zq;L*}u}E(2kPHyLeT-JmJVvg^&Aj|&dURa0k!%?9HeHg1GF$F;jbQno%^G-nc-Qh_&L!xRM`g7&8*Z~Y0>reqSe-oAB>VA-EcTb%uC~ zT%<=1ToKLINDv4KBweoXrWrt5F|v=|MdS0zjNF-^0zfSY_i4WBxJbpubCBFv zK+7gKr}l4hL<^nAQ8_~b&=M0$ads&0jg1n=LAL%Kgg9L-;-JM0L!Eh*enZBG@bIdU zk6NY4VBi+0@b{#qn)EtXZYIdTRlsY~np)My=^+xeZ;W?yB#S0)`FYSq^F|tiL>+d% zCmPRn;{hOk8^eXX!|qgZ`63(lQ0Ejo-N~>`KNsxmcxSl_qfAm0n_(-*%ZDLB+es1n zL1q-Wgc&7 zkE}+UZN51@CMk+fcWfl<8{KG>E$hdMKijD^WQs5*-bsxN@qNHCW z?Ta;mTGlKQuItYa+bf$?e1|k&lR*HEBpGO z;>}?+AhroWdjvjOO#G1U5xw!@izKKFcc$=5OFkJlDMK+Wd64vQesxWaKjbfUjBt3Z zd5_d}y%+Ub)Ye54)e!FDmk~nq$qQt;X)p&DhSO>dH^toKXCquXml6Bh3h5~pTC4uA z&oGSv>k;tCjtM11c;__*b@@Ri(xll9$~_#x%b|W@^__Q$&Uo*fvnPGqV5=3?8LF;x(sf-^wmpFU51UF zKWULR5@VuHdm|EY0zKdw=6nUx;lo6%ef$6)oF*6wzXU@PqkInK>AggXT5_FacT0=#ilxc`dc=1NMI z@K$BUYRJed`g)CYj;o#Fe^sX=l52+~x`$2GFovhL%@cp7WB3w0)+VOxFPKa4OwEZ6 zsG*67D@^u1^>hf+SZLm5WEF7doY30Q%4n6MXOcvfB;AkOwq~ke9M?YJNrgHk^<=+` zmHyru$zEIcL(Iu2$}6sBFoaNgASRc-669O)*HTTfZhw*0I?V8OncV{QwiwOgyzWmU zH1MONMCX&!FCD5~n@jB>tR&qXjy-GUqw?7L;GHt$CE9C_Mg)J;kXeJ$u1i zvwY787(#YnEngQ4tlkRo-!DlUb_g&;`*^__by+61&flS@Hp1IijjHN2R3nkWNT28; z>w=EU3^p?5Err7X&coQjZeW5DFFrUmwGm3FC>;V+w}T@>mx&bMf}TbtXnZ>)+e<~9 zl5#J8Sr7j61|cQ!vCMmWv%)KmaUpl<>IeptbSoHli6lLWnG=Ikmc1&(WF+0TynhwA z!T*B%xu^fsHkc)aq{hJ`UV>6g?$FyL*@&o{Zc?x9pqWa@o$ZwPp-9(0s_}o7cvYd;8 z719+1?l^;!SPMxAcFjI_Ew9+0yHo7B4R^#EsE$ zG4w1Z^~ZeA(a9B2F@YXEWZl1|ZyfsVcpdDv;Ew|2zb=hVG&CHPZ?^(AhOQtFU(PR+ zWi6-Rm4fWU<)^t;ybuz8iXPgYIvZ`{K&j^y?E*iHum$if_!(JoV8A>uey}d zBJTu=ooA43mE{$KDW;(hIp_i*JfbgvoUqvt;@X~~Fs zY)*g+6*}q~Ou|va#yjWOv%7diBM=EBkdm|7z8eEgHg*jM@b%8lT20DB4?~ccLgyMK z6e%gmuLUVa^j7R8&*wOnw+S!axHF246N;-@f48jNpiQ!)HfK9*tF~NWYgy81Xy-uwQLUt5QpVk~u^)!7^k@RlQ!kPAfkipngdn{i&}V{P1ByA9)}zWM z7=r&vZ#?q;&VhJ*hwIN=CdeFX6Adp>v_ZP#6dAiX*rjCn`L5q~HX_S2&;XZ0$rlLS zisvpfDnq(P#~waEN;X1_bK@SI7aJR z5_Jd5bqvXhJc>31HI*tZm;^Imb%D1f9H-b{$zejO&>9m%c>;~7Tu1 zDpb34gXB`C&PMsYJvU5B?-%EC&5lO5z_yR!pb7^qwswkt(RFr11!MpyIQb6ZK+dczOV6k#kl%nb!)B>V2^PM8Xb z{Qp9xI*HGKzyXIa4s?4fiYxooS**bz@yt`FL2*~7^ua<$2-n8>NJnNAZ&BPi{)=jSs(V#9i%YS<1Gxdk%mN zFF1~53IvR|RfYIF@B8R)M<^<3Z<^NcDAC~;tKymj=IgTIvQs-XsxOH<{M794`q|M^ zmw}nhsAS>a-E45g5cZJ8BzX2p2&Z;t@&d2qU%fE&uWTyu7HWdWVrTbWu+(N^|NZHK z-;VEHoDO~@0qj4Eap)dnZuyce(m z2LG|FuA1&@_irCI-JLf4Z~_{wCjE)?`J|y0Y2$akLq?>X3p?C{Yf?oqUNz=N0Um2! zHyr$jeT-kBfgXK-&#xRZBw>4eE-5>o8KZ8nio%VDiNPquN-jB!8Q|6AD&PLPFN{0C zJ1mV8w_;pWCCdGWx1|eI6jT>avKNyhqWD+a#BRgWD$c^^B~pJ z6umh)foKUhW$Gbqi#-tv*MTIC=;RR*>3*`e^KmLfXsr1t88$T&N#{ycI}j?tf#azcp-1SMN$Mo;v>yOJ-b_}LHjqFe|ug|)SKhr}hO>~1c# zl<>%ed31^7`E)nE=*9V2tGuHX(%g|)R`eJ!W0mWplZC}la=R#hS+=F!e0Mg!#P+j-bt=xXPl{7Y@V4*5LvZ>BMu zYn``=;sP>|&dje}RV*GN98Tv2XF9~Pz2bpeOXa~c+yt_)RJ)m(d(oqne1z+TySv-e z0Sne8V6KGp>CdrL;DagqlTj{u0!?z$N@CJFDQo6hS64HJYR&{s1Tfe+XOxfxT8$gzovbe_@BilHH zLyu~bZ6>ZKMf}%`Y1=UIqG&66mvLTYSQtM*=u3MNzln}w@ zf;H@^j06RQt}^Qx6zy5c)}(qo>%;4TT#Be;#x!Ayzm}!hb+`?dygM^;b$SNf0C(5B zP1Y3&XHv`XWz%0|oS=sSSQvFdQ64epp?@DW7ry_tw~@>9emT?`Fr1MX2J@dk4_jWO zD~YuIwy-%XI2X~+6A_CxUD@vQn&j<<|Nn<*4yFgb{TrTdC_cQOzn z)t-^l;yH*w2)ewmNS;DVpqHU~Hc`nX7W4b(WOzzl?jPksLGzeHG-EK%FSiTTQdGHb zC-5vWnIsT z2?|BE>@q}D^#v6lUCK6_dP_d2;EsRi$J|y1kFePhaR`6XE4{}VW03Cm0z7r4Qg;SH zsK{bYs=xol)HW)4MftxE%Wp*}nmSPw(w>dNretcIexmCwMf$svg5}chbI3rd_sa{@ zkVJuc>@5EK(wE0zF_aD)D(qHN`xF!>?rZ7}jZO|}IELBHRHg+>@mEnjB}D@<1;f3A zI3&H1CLA_ri<;=ciuE|3k-^NUAAE(tPhcn1PEoETJ&)7|JRp$!UU@r zDUE2!5|Z47q7Rvy5It)a$3Jncej34Z0ZOcFzj%ziTi<)G=5aV4oPes9E?wdJ!`B;i zJgz-HZdU3zrv>An!fy>kTY0!}Av!jig&jMaMiAbkkjMUSkz3AUL2O1tcPER=M%1e< zONi)nslr9sA(jj50YjDUxAsx5a~V`%j+SZpYXLMimZM%Hl9Nd!|ZmD!3Ox1X z#%EB8DK<+IGGuEF?e>cADJ~imn3rq@`T@daZH&+KhM|kfE;^SrUK>n6(m@S?`UxWh z7}Qouu#>LZ$4hr{Q@Gq7vAwaiMlI~M5J1{kOuPpl1}dCZW{TDisx-@0yW&r>B9;MU zhom{dpElaGwNm^WjLdZe5n6Xre7j&+1x_Z%r%FAL{LCQ)DSsu)`7FZhS3Q@~RMrnx zwNcl>%~(+$W`k;-Ro`>KRiEg6Jur9#ud5n*JI-Q|%YPh&K(SV+!CXmH?qO%1B6_zf zu;r>X{rny4_keFe$-W^SXX@m@rTt=+G}ycN7Eok0t6cY=tL`7KJY3InC48EaODVEr zTT{gWKfZ@lDR;f?HF~;EyC{Q8W1cXwzhGI6T?evIap(3MGWJFZkU5}2x^UWHtPPyK zy{nh!KrXrRf%Ysa9N3BG{7slGr`mE$0DZCw2wPacQb;WB=;;jG0{6jsAWVR33l8mP z1tUa2h_wC=jY3Qs*c_;;Y_q~Be4;;Nhe?)AWUv~eVAo@s?RT#t0Yrk zCX;@Q*V#`9Rf1c43NSamn7-CXGh;q&=`ZXnEDXC^1RXj}F`++`)^a}ut8)|RQ$k`H zErVC%6G73&Z!2sfQmafS)?!A zgRgvMX4*MF==+hk1I*Q^`fcaLWXxsd6s>cqFkRV*mkJ&v1YUtgm3B*{v%>+%M5Wwb z8Vn`63~M>;w}f)%-h(C11;wc@u)j4UdFow*%25WdNBob)9pDZKT4B%c?0kgNo0mwJI*Jpr)( zg(87{*WpZ5Jli0-oZH|9^p7U;;H8qE*M8lq+~KbYWm+0)$_GE>jfP!5@= zWXZo-n35#kfak9V7Jr?S+?I!QDTRI!l^Y_IDnoZI9N|mQuaA0dBiFO~PtFP$TVdzZ zJd3;iU|0y`Yg?UGSJoyXcj%B~x3GgvJT~J|{mu*YWG81Q;DJVt+XYl#PffCKx~lU( zdwd9W$g?KQW+3m*@A5^5x5YTA0W?HL)|AGGK)JSL5F_^oi#|&bI|ljS{U`A8xO*4m z-bK_Zpb+(``ToH7F%?O8K?6=biRjm6 z8Th4dvWa(p*PgHMapgS`Mj?gTt1o^vD$|sO^oo$8@5j=$&!pAoXQeb$-Q$X%m*eur zYqBEfhp+5giY+9mBcuWqOi&9vS87l1&@IkOCJOZjh%L+1LvUXj&c5b`vqoEVP!unu zohk+nh7~@5q2|Z0mMPhHT`ZJquEdvLUrX>XDXVR zxvQy*wbTD2do#Ovx&5y!?H^kXN8G6XZ`#KYO5kdq7qyD`Mhi7O>yX_E&=gBisIqR7 z-UM$~0?9S4W@>Fb8z9LTf6?-pUfgk{sPytr&g=GnWJC*jX(Y@i0b> z)>0rgf3u6a;Dk+~Rt%9{p>5~tWZ2zIcy^38!gtH3%B5~=@x_4Y4&3&g zqNMV``lpn#kW|+wIdNqj`cRl6%>*BdzYQ^c1F){o%so0g&#_-Ud-T$U1R8LvnpO_5Gt@^b!kZ(kn-v%wS8i zsp#j$tribOuDLdEi?xe9-1+yD)XE&e*younqf3_?i_^teOU^Zc4Q6^!(EdoPlTQB@ zV|KA$8Bc^+73$m<*Jt_`?Ha?iz++O41oDNL)?15GmSWu{Cso@1Cqs^+aW^ONZ zKQyKs)8Fkn5Be6E-IyI}J5ou1JDRs)QOn$#Uk1P`bZ!DoMqO$7Q?xpt{24E#t6Rq< zxywWgbukLPdfMOFuwmR5bxn2-?Tt3r)k6Q;Ig>BQnpbqy7Ry$xAGkR;CvzyPmQ=|; z^yhC)n!js=EQK+fy!4$sm{8&A?xqdle(7qElql=X+m=T*)0`bIuT(^SZLxiescJ27 z7689wJ#bgL_mC&$#52nD@r+ z-cI*l_Ez6OyhcnvXwm;@`5p~r^(T}ft8C%LvRS5|_cASM?|FcR5T1F^U5#wuqIMl+ zj#&RQOF2&ME892H)HI~du1ghD;VDx~XE4T6r|mL#oL2=^eJAT^La2=AON7;Xn@=>G$sy>{3WckLX$}eNr*p{J=xd^|(bQ z5)H=64s>>a+ysB=^SuS)1&ix5=7d97<8;vFB)}t-2cir{?T^^|)rQB=?y~3Q;M)eg ziDU=H5jwi-=z+i+dD{Q52YxH;f!u|~A2hSabF2579FHgvKsXS$I|_vb65A)VOLB{( z*aQ3rNhp{|d<@Ac7(p1bAZ%7base7y9Lo5gQNF|>krT3KxQ{3>Nv3TumG~5r^ALg& zW_{SI1RfCrGAcGat|%laKNj*pII?&$(|#~Ab+o3TC@iV24DKGzKF%@DInFiCJgv(ei;#A7`2(!N1ELU+;^0fT*m0s;M3`JY>OBEBJY7ZnhY?i3J^;(r@~ znW>?RtCOjLvAwg)|2fxkOHV85aKzDX$DkjxH-=kL>m`ed03o!z+Gm94DHa>8AJ!}7cuR_WTW556{dmz`*EeczW@E}WSD!e=g&%B zE`h=J_uC{Rzwg7Seedgo`Hs)aai>PD-^W=N$By4qr$Eo!e$q+njR0@Y*WTI8jQ`tV z=l%YLzkOT}@7LAY;6vDnyS;$t*XWM_-IYdOvxLFV{lkj_Ap&(+J!&5l6t*PTL~z?ai^=dtzo%n9O8_eYHYZ};cz zU_wK$HAbmzd>8^6_mo|C@y1V?jZ>1n-MDKo% z!w6webZEg9-UcT-ZwPz+zt7$V1^j+^b#p2g2!38>EfwOz_^&%T^6WQPZ@%vyUT*ww z^tlPw+l;uldlb1k9DRK}4jK+m83p2EzegDv3Aelbx954B{{1`|Jh;e3wAXFJ+v&LP z<^P)5094B)73qXe?7>SO-C~sb_#DhDlJ$6e>>X|Gd^0D%kGA^1z1GSf?pEP~3B0~PQ zcKec&*6ZW>S)e^rc)I=d+3Ic|MdSN()R}gvRr`H*chtTf9@83S;q&%+|2;^^zriVU zt?a*Cx#RbJmZe}2*o=70$|w72;PF0vNC@{mSu3aV>k(_p$s{xS$lQ~EgY~B6wRY6s zkbm%ehA}U3(Yl4}&khXq`V50VHYPp4HYVYw6aQGJ-ox~xRG&E+zxYq+F`VCHJ--?5 zFFz5dS2={L1N-Aq-5K|BFt5D8Mg8`uM=X_o4l18JSL-Eb+_U$p2>M?b>*l-aP$CaH zsXP$0!Aml;L7+?qvalIytt59J+5wI4(a+%*_WPnhnd1~*a7ki7lL=nD!NHsg^@ZRR z=EAdJon!LtJQ?~qyXc&XZO>G@o)mSP+94*&Gy9>39eU%o2>Ky3>2iTNH*c|`8}G0& z5vbqFSxO9zjy(gg*d#r6zb5J#^CY2!g-Y9zcsm*X=sLKDU;H^#BsH0X+ETX5!&ig0 z1Fym@3tpv*W-`t~&!iXhBudyo+2~2~&m6UP3`{2U&|L&}nt(E$fFe&0XPyqmKCrXf>>D_x~h>4~V;8CEg8b3)pUfHC(lLbeDQ zj|aovr#i&fW=1~zRpyAl3ha#`K%XK{U8tu>sYqG)J*ae;eIp)8 zKtutaQ#qR$6};X=8*h-Gnw36CtEqgaRXHh*y!NsixdvT;pm35K8YWzQDtHyI@v{hB zp_DqI1k#!&kwFt}_I@hCWaORQSQZ2K2E91ia<1^7X_!j2JFSa?%jq2Y&g6TD14EUa zO5k5SHe2Y^o^c6qZciT(rR?DCLrwm`!v$}3(7-)aw6XhEF_l{vn3<-5g*q6wOpd3X zzGjq;5mvlDm!{Jzn1-VK%<^w>sK7Z{*2jcZ~0va!Lbg5NGco{n!vun+0$XPN*3^Oe+$`dL+ePLeO$3f+j1}bs>~?Xky|~ zwIyiOV`1om5~=!@%Hdl^_H51SQ%^PQOcLwzBcJmQWo&>khtCL%d{mxRID zJ>)I~-)EuJ$2j`#Y(u1XftO=<-Qr)tbUhbR$ys*J_#6 zuv`yD+d|5F>LpE&NXRJoD`&|e zYa7tZOIx!cRuUVk#JCLnva8xTmSpRc+Wn0~kttv9@$l3}ohaEtNBZ4wx_7N)8L0kS zE~RtZVvS&9>Y@Z^5F#mg!~EK-$hDDX7Pi~fJnkKcfZCg83Lle4)v*B}{5DsEh6!Za z8u~ryi6P$)TCPjwX7|QBm}d^x7HPNrFNfw9$dY``{aHKbbTA2n|P5`f5GD47IIxQ@o3aJV#5!}Rxg;er!i4$bc+!zv>+bzm^s&2`C#j8~Vp zHOD80r$$Wv*_!K*4$%Ef@kkMK-Nhqp$Ey8tWfP!T!3~pNj9vgEwb&qqHB}eG;F_2% z+T0>$hJL5ig}Z=itfv^_RMKNu1{3fr?Cfw686#T%Y8if&KYzWS*B*lVD+ch-&k-o= zRE&P-_hVpDl-2)K$i>ot^eTh7JLM^d!_)QH8F4e~T&WuL)B=|DuBP!aMm% zi{I-t$+t>VP2j*;#bnAzd`I9+#lbUwXF9froxiN+M%Qv|eDXr~okI>Pv#J$8`vArX ztbVC~a}E7tE={MyteM@?27bBRKuA()quN>=)B#G;LcfO-PH&o|JW}WP#x}eVmPCzF zOn|tJoX~uP@v1^qyRgAoM&cfx>hRV+R@MGcCOT^il@{zdouUpVJwUqd!*^EXVi zRg&U#VLLd@20V%s3H!*L6@#RcGPz>{OipTpq!peNjpa%m`)8>;s4Peq5GsZWTfv#V zHSK^QUtKH|*wgwtB4;9I`uHi%7WsOEXMYv5nuo%0q(yC&*JQYJ=b&XX_rS@SX zRlsQgy8FeDg|?(!VB3Ib+&IxlP#choGOpQvCEA}AUgBDy7LfqR#7+cL0}MAdmh!*S z(PVj-CQlH)9v&DP;BIiR8jt0thL|JP$o;Y+r4m)hT{J{rZ($(7V z!|^auWb=K4ae{VNg%eWs&P_nVb^SrdbEc^x9(1_ce65b^DD>NeN7Q|J_LHU!VvyF-BGa zHWh2oy9O%HjO2MAaB1^Sd)a>5GfcK>k*H6VoiT% zA0M`yl0$W)Q1^h8_ZH$eyP&|LL!gSK6SmE!g(z(^WF}epV>ABPsrWanFtwivAD)?& zc39eH3U$CQn8`&Qo}`^(r1jB|hOVUDmAHCokbj_^$1TR~)}}Lh&kIi~14uwwtMAbC z>(61unjunNxH_?UL6}lHNe6}%O{a?EBPI#=ZnLHkK)+rW!}yQADSO$a=VpcV%k&N` zQ}tL!;8e@G6Ls(M9o{dwm|G=e+bx{eA~M5<25PPf$VobI*~;n7SH7F#<=OEs5+7}* z0<6?Z@a_=Ad|Aw2GUIk(+h5dRC&hKr%xKiaE^KJVQ>+PbQGSbBzY+#6fc zlU%~yVJOw0N4D?!wNk)pMmlx~zqp9~wn5SROJz|ol$XNG!`*)i!PBIS!<6|T?0Xl3 z{gtN9sab4V;esPo_vW{&E5UaGOxhlW2ug1`Z!eMb($TFDP5kE7Ai$nl(RTZ?cByd1 zX)CR6gN3TW57;ogXk(i^&n+l^6y`T52R5(Qv);CbZJ9|i%LQ~nn4-1y(t~(Y+$w>*x7GnGqFyEs%V zMgL%!E+%f!3vB~h&D;i6-8`wUngFE^j}vDo~LI%PCxZmQWbPtnQk6 zQU~5pA?L3)LxuyGZz$|nA9IJGyh5kLM-mh=3#B7uH+j9lA1#5t3j0TMSd@To7=iRZ zV^;AI&sQWNb-$Xwlgoq{guCItO`PY}(VzD-_Fk~dOr^oy4#KT>GvKw^QLiz=ZPT|8 zuvF<%5l;dVHJYW(ptIK5?(+#=V@SYyxRkhHK+0GNT(W`tUOPuB;jO+eHB*$Q=O1LJCcsCUB4cqnS88rX-{;bs830~zVyueLh0M4to$NEzI zrY_o7e*^DsK(@pnCw$z~S_yAr?4?^FFjqaATgmM#w6)shB)!k>Wn3@a%#8F= zdM{Q#)yKu40#y*N_8J-&ym!4nT=f!T{)IWZfi}nd|1Bnr) zY`Nq4ba8tsM2`$yRE-Q9AkvKrEaedm6;=;rHusl$4&PCpS}`9!$ z2tZ$TMuz~7f44=qh^xDI{1gvv1+-#1#pP$URfdTdQ zFS*scD6D$3Gt<7CJU;5 zhIZrW)+di!-n{9znKiG+`H6P^SdWXT{xUv>#+FuoHi!~&uhbnosXOX}WSl#-k|-MS zmOO4O;p%gvsK=Hk`stWoVKY=P#dR>LkNXPmnZ#5T37H)#vAhOglO~ac5mQ}{qaGEq zWx&>=?yYNM)8O(~R)AhiD<}6NkK*?uvkD>UA_PaGGOE5zDa=6fGPo6#4LWy{N*Z`- z7SXHUkY7wk>Syt^4x~L{tD?FEK0-kXnQFu845ebMXCKV;=&4s96lNG<#JMaW7X%1z z$!+7j@n7N^vkibkUJ-IbuHTb9LrIf0uPwu1+|fLk;Be)5)D`QPzE|zG%pnwQ^!+-w zRGlr1D;8oZhiM-Kl(#evdyku4%}A|hmCpTFbG!9sck5zYO)}iH`Oh@*FJn0c(g%J0 z>`+iiQgm&9O&OpI8#1ugR+q(V*`0}!8Hqc5ZE$xA@1pEmpIRj~bpd{CiVTsDFQSd((H2-+)C5=(JK3Ov*wVmXTlk29q22cvV{Z1Q{0?w0FoV{%!XK zb*>M;j>YGV5UU4fRiF?ENES8J%}yJWf{1BL=z>rM*2l!@$5GW>UI6h<3+m94UXgy1 z^e<%B{&s@ztJV*+bMr8E&N5NO_*G>Gbm*}CkbQ7^ zpq@6mz{tqPJ0Dsn@M!2?N5QYadRR^c+@y>EB&ArOa#*t-p;^;7&s`ciE0fQG++>KT zJ$SvX3YsJrFxk>fmioscNSfagCYL$mCcT~N)K$=F@@6yAGGvQI^g7>5ryta98be=k zTG@?P=W!rUj|H`1V!u zj?Qp|oB+*hue@WpgGp(ZxXUp2-Yzu0@C{}#wzdbcfD>YVy|}{Ab?-C3=7Zt8zZrFs zu|E~BhT5B;Q>itW36Ng?d-lJknmNpNfmP;(Ue$+?oVIPJa!pQd}8b&xLZ9c)Y6Id$%-Utqu4C}>PtQ+T z_b9AM^(DgJJ}K<}1v0q2b1Mw^GvQNiPE7QlX+kocj`u-9O%Qc!G@r> z6SaeCkY)6WYF2)$(EoB5G$_SwT5Nt2liqN9h%!6MukotF zY9g@sX(YnMn|ex4ginQGs;Y~CphBVadPI9ju1D<|KXZPGE>qb zUSRYQ<27>n4f|@;xtqvv%PYIgz1W8l;8QnMghbCq`)B$xO6_3#4=vmEY1A;MB&Cbu z&jN%r!CH~Igc0K3q^Sg1XfzH{-)AQd`dX2Eg{d~$h&E13pLUTv6hIg0&Vy!i%lccdk6Vd!| zu$&H?9r-u)9_ErriCVbzv$*wRJS3u(Wob9;_ig%C_6#hXOeOaANGNPw+(9WIV*N&Q ztsGoRr&;r`&B43^n*I}(iH69fTRO{5on_=Asp419d|k0zSxa)eEjKZ3>(Nh(&jgRw z$#$F4*A9V!tqq=pk-rgADRA3tJRez6s1q^H5t@%YmyYLMfrXILUHoG(HTTdvb|(C$+4#{s5E z*zp}|lG?fCk%>wCo6Txc@mViS*dbQY^+~w6sKM@NO|JBYgU~H(7Ey-^F_}|Wr~+|k zYbZNzPwpUmF%NOJRb~k3t{_#;$vPR38#(p;LtSc(^@q4zDCqJGTHF3!R*&5o6xYZi z(pjS{6icwXQJ&c3CFQnJWvlgzJ8?x%_-4wa z5Nw6b3uzT;7mW7jZ{(FKDqT?J$2vvdC7yUvNW41qK6#t3PE9FO)qK9|=HAFb-SlVe zO$&`{B5wqfckcJ*T4Dw4RX*OQj2 z6XP_CGY6F_?B3tiXhycLt-#h{7gz&@%IA;Oe>|7~zpV$;pIilQ_SuVd8`aI6#L#8E zdXvoL4LIEORb5Yf9M7C)w*v{_oj$~yIO40f-X1#1A=B^K8!mi`QG(K`cdhUNPWT9F zoY<3Br<@t~Z`9(MAuL`kTK_~glgkrtn?lYtrxrQJUHnYmVSH2wi(_N@E~;RlmnM!g z&3kfRRwR3YbT~YE>KrQ4*|69)*Vvb3CVw5x3f1H3dKuAS=03HLWpU}B_nWgy!M)tB zYs}T@{psLJs)MY1v&ZvUyvhqf`ti#< zV#@5Ux*|XoG#0Z0JMsx)!n-cnmu0*9LAC>->^)&Vnt;ge7{5!>y7ylbJw?spwaAnR zQ|IA&)SEd>qFSm45e>7t2dpw*1Z}!mcf>PZ#n6izG7cKhd_-P`4dW%s8PPWf1J<@i zSvc3T8H3^gc37)E<@&W_1xz|=-n1t14oIr&M{F;Gk}aeOE(!S0M1xSa-k2Pc-+*#I2h^$j;@Bx zLKt5Do;E#kz`X%_ED^^J&=+w)i%<6J(>NXY{9cGhk+A@*nkZ8|ZS7`z^;={=;N3z) zn8_Nu5$gDJ$*EiMQjfcc_><`X%R64=&^Oo91lu5)zp?b(uEu3BI+*1(A8dy_3>HPWsLeRh9Z_#pNQ!4q&&%9<(;fk%q-hc4M4 zwzD!H@iW-(kHoPlMVHR=3?W0fDT9zW=NR?H+Lyg7XLQzFQo+U`5^Lj@5A+>7yZbV( zyjD*!y&_Pct1pXdOVH^%{kfyl)ogrQtB$Vfgtx$EU9&tXfOd^y9kHWRv`eK-BK`un zSOdF}xUPiv8e1y>8;-JL|1_43kX)q(tHwswr5m>2dr9SchLeV`LG>IsgX8z1-k@=O zQ;gHh$yzAH-R72=8fGN*FT=n2p2lpWPRL$KLo&P#ThLUG3spyQ#%)B|F+^tw;h~d# zU6O!n)U-#iKU@`p=jwb+a?CVP8@7;e614HlZ^4aa{|>23=Pg;hyz%G&!?d{ssi{D5 zU^7eP6SM1%*3qEK9C+D|b2qiguN)Qa2#oJCX>npc74jA80ru2e$HlYy^`Z@!PLDlp ziDkk7cddPs8dLii%J59nhQ;EIc`_!cG6L)&;9ub)Y2%hRkEcwvY)y9L4R1ykjL`E4 zbZYwz3{nCGRARecEl85}7%%Po?i5hq;e!nfHub<*YCi68g34y4hy*P&HR|AXiEHfd zz9yPOS6MYGGbkGPI{DhI13*k+`1z2+_s)FrO!@vhY)C=IdoujEXbjFq&~qb6#7EFtK~O0@u-%yaS=}? zEm{H2o#oK-S(nvYG<==z^hMP?)_MoL4b|`H3!^Atf&czjOqUlbf?W+Ht3edr1vyQ* zI@6>&j4?|p5`AO&4fO9)Ove)ey>w@}_P$yw(Z6UhBO>`t<(Y+T4?j8+Xc8ML4c+gS zyd8cN)wAhp)Jttqm+rmg;zmE|9sN=_@F{ea_nP0eCW(nBLa)GpB&;x0fHf|NyH6b= zZqaK^zV?u5QaBp2UZa5?k?UTIMw$451Nz{4hQCLe$yJX*fMf~h+$MacJyvANNKBH= zA#agbJN>BXKoy&GcjRWF!S5HYWv7!Br7q`ggD*YpqK<9bJIR}sr!H3(4OrP?3cFJ1 zzN8qGlKklClm?Qt_G&(L@46tqMdeo3gHwk-Hk02mHbvzS=(yW%S$Ka*v8==I8?gHA zDmm5ci>DUY@w{65mCyQB7lEIG=5wg+0~xi@86S3B080pZ@5*=n4|@K|QQ7eiB$2rO36SI`F= zbykSix$a8&ZlRdc+kqm4Nb!38t)#x~gFpcBGH_$%?55NgRuDJE=$*KHwDoiN>MANB z@Ete=k}8GXgdCYj9Kw^ytGNMaMji+sYK zN{T!b+H%=S)CGAOK{=uCr=nsD5Aoce7zXYV==hkYg2an#lS61?csSxC*Dmfwn!|%0d$6p0y@TuCSqI6O|FL2QeADBqJ4Y79{qt ze4=Smo87@^C;PU2NEc#Lm;qK6e$$@Uc26h)eB4Rk7=j(qh&8V?*5R0RL1J|?uKH#R zOw~=0K3S0-BbB0vSG)KT1(u$AZ;E;sSAhdvp@;2zl=;j7ql?N2QeibXQw>%_@NY^P zvCsqv6IG&B5NcFLQuL@_l4#6WobX$Y$*OO^W4gR>B*h-xl zZ3nhqQHQ}BSv(M7Lke-b;%@vOsbNe(;+s@7U0w@X~T81ZG!|TU5gXEB) zTSlCw3e(|Q6jEfeUwOI|hbOj4Cn)6GH>GX1QpBMsgR7Ge!n_nSDHq=k*-9)Snx<+*rMsNCTKR!t7nqL?yA zZ4F~%v;#+|S_#=a1l3Dq3^SD6(-j)8${>})D+AUFYg#=Uo@%C$OL}Ok06o_kj+4M@NA_GgVY_&U0YvHDJGB{O_am4~IXdHAzfh~6v zFXoUI<}Jn}a?zf&L+r^RC-vX`OSXd(tn?P(3J>Xmc7f1{F2^ZSN8vf22AQ;~ASXy} z@S&*6v9GEUCV+^>@SZ`3OoSe1uBG+bcy54qa`8TyBkS6c8S-j}uTwF_a|MDdXUXma zPrkA>6sz%u(fPvIX^XaY^JZXjyY26L56I7qp#>jE&KwZ^+fnO@LYt~F)gY27p65GB z$XqX=Kpvj^0bbcB7RFhTE>QUktcQUL{OCrU*7@RzNbu@T(}-yL;O1U!{h*iFBdY_2jEgmT!!*48ArfAAkN= zB0PC_#cljQBK)}>5RmHsZ!AhDS375ye}u~aGm~;DTgiT(6D?%t4c&8%!wsFMxWL{N zCVIBX68!Aa_(<8_nk|BM)TLGX%0Dli7@xI*o!(92MG~q4B#g1;rhR2EG3n^iMwyP` zUec!OyW5khgz4$Yhn>16wejLet%qh^KqIJsyqGFANzEUETQFP{TQO$=Mmm0CM(T`00) zw!KD`UfO>6=GQQyCE{>-U15S7?WH+sU^2#25^D_HD7w0X^)}{(YbPu3LcjXi{3)(u zk+f;vj2gbO+$XBCTx7gMrO`QNRprUKcp86EugWd%qx_I{6%TSR@pk!Zj16T&Ij?As zP4rUCJ)I*rdM~t>(p_c&CVDURxe4#dsnEBc%*@=CC~ulSV88X72Ym);b^JBvdb!-J zneF{NIbZmV8L7#8;DeJsu<`y9c0KA z28l(2`QKWX5P0YbSul4TsEbnOj3pMhE36(4_#O)=5pzFd>oBL1oOcoOJ`BB!6=tB%6Rumj=tw~YOdecA=TLmh1P@- z7@;ZimtJX+=&ce)((Rnc1*$s`8QrtsgAsGU)A!FUX&Yk{@n)J(8>2{>5q6M+f%wm6 zTM=0TsTZ{r*&lsrl+Ya+=)E)`z&swgXfuq%uW2RWSMso18HByGPt3O?o{4P^-X;HD zKKAhS43Wb>V1+c`r{>PdNDNtmRg=h~&yc5xF3Cu9ptGc>RK!k*G4=}H)Wpl=f`qw6 z!Qr`RMwofRCXxZULK4u(2SC0-v5BJBo|78vAG1jP$wFW%;)R<&Cm=wl?ykv5S_Juw zD@wv`mJmu&CS<(5X($7Sqev726o&WR$w+=&p|_LF!Kibu-NOa&BE~2PPa~lP-AV!! zds>hVB5hbPxmOx6CmIsIGH`nqNM9Rp0Do^noV8G{7>vW9GpY3T4-7cKoPzIde9~=a z&>1j(s7TX-Q@EQp!kqjhF^(FSC@fW&1@IBlbPC??$zm)D$p~>KEtjY>`H$LkieECc zD1ib|HdLV;%3gziEXv*@vjWj4$}UM{EXp7wF-I*q!HNttG9OS( z{Q+mGV`@`cqF*$R^gH&v(tMnwgp|ODjC+JCkH{@cJU4MhpkW)WCa+Xb@(a^@+-pSI%!q(tF{gJ`Y$;|A3Clzu{m$T0qg%5f8 zMqhUUfU#uX=q)Zw!jmEFj#&$-g=p8&W6wWTociDVWAN(J>k>%^>*^zTdU!~lry<(= zcXs%_jDAFI+yDT1`uzUwy}o{3?eAAB0{mT_-=jAig#0}}57Su&H8r&YT^|>t7p<6x zag2bC(aw*oFn_(z@gJX`iwlLko8GNF0(nG*ILsS?@9*cq&ff0t$LGo3yj(*0yq@pf ztR1@>JN@h2+#CPL;gg&9^Rti1VgFoy1O04*o^1hN0X==6?jNfkUmxzRO}~%lqrL0r zJcDaS#_L>r1AaYy-W>idfuFS7A3s;$C-H{j$xjx6v@>(;BI%Tj4J7PJJ$_$57H$1u zI!Ap|Y0N&&}_et~}$pQ+>rNY9)`sTQr{yIcWZ7_x2gCl6JId zByypfKglC4DE?L%d5NJEwA2sQ&{3Clcoe1z5rMZs=7eK}_ws6{a=XIafj9`}4l~^cIL5)sY)T4Cc^X~yL|v3|`=lmR zWeewwLsb?5*zGZjC^A42RZAC-GIb2jVk)}>v|VP%ADvPcCx)V@|Z6?%FqKGoZI0%a^@aS;xsT!iJ&BnzuC-ngesbM&eWv>30#n? z1dBm-(bTaA2>!>&#Kmr3}Rf<>2amzo17)-{# z`peqnB5axPz+j~2z+)ilrBPt26lyHW`K`95<)tx@fD*Z^98ca3o%%-~VFcY7#A$gE zv~>@$*(U!{HzM^{kTbV#$QtSvpXPjC z7aaNskudVMSu`E5jD0oX>aueo8AqEe!_N7#pou}LMff>VT^UDRuSSskS16M! zPd2sa=}r8)f=PB^na0MxY3v1OO52LM(0I5Vd5}C3v

&%}|@iQianoZehoc$D{p3 zEK9V=cqGRY;fJtL>l&EsM5h**58<>{QTP>I?i`n0>1A>%S-ez3SWujBT)kBbOTU=F zLn%b?f4+~^#T!`7s1ejPW%|VhyXQ2eFeA4`i=@k0e5GMZY00XZP7tC;=_}~o15x11QGNRd1(?x=_Q3n@=H0ay1FshxVtk$G5?n|eX6I_SE zM7Zvst4b}woW^j3>dz)0R5wsqV){DvBF)sbZV+vt!f7xX0=#6|zyN4&Z*zKR4JA?>-B8s9~&C=mxECSE@LP9TIzhacDWEpN>Z6uD2|h z8Ab>mtkviUn6elfOs{S<*x(MQyQY9=;cpL-h2mZ{+RFN~k*Efz z7o-hgZIS97kTe!J-VTx!VirHR-@K12urIT6$8W&EU3f}FH?;`6sIsUrD9TeqXsXhp zG%oy!!)yc2sQGtWx;G|URNHY*v>^V>BJ90uCa{^@|GN=M@^MDObOSR*&n_7+d5#PW zLxS1i;C#Y6tWR%F3c7z;=*73zjAy$oz><1@nWuED{^Ur{muFX00^d12C$R)T;Ou^4 zim6MhveJkkr5Ne*3Y~S$p2L^!>T}wUmAtd!*n)~si2hmuYix!P>n8cb5U%GKO*H2m z4|5u?)gO0jQdkcGQ?JRruJmgyC*dj_)fUQ07?nmNTV`RKq5I<2J@Ds|ox#V1#R|E!O?V*lhyYY(8k=TXZ(4 z3Po#;dh{-T1+KG1J~I9rN1}VniH>usF!poi*NCI$H7dsj^Qc*&p@>;}c;^_O4Xa0F z?I`Uemqwq{g!AYP3o$M?`k9%f%sV75DEeHaU4}K@&7#IQ(+_RuPb8^=fW3Ji5hWt=s+jlo?(1a8 z&x64KqcyBd_Eq?QYYqASzfWO?#>TErhQ^)-wyriVmc}-Q&dv<~RV~ddjSc@J%rh05Z_N z_rC(U(lbb_Y=if|F&{tHO#Rtz&9(h#@jZQhOc^Abf-^@QLos`n7w|Prj0d6!gp;y2 ztL9^I$AGOf)q0o0xJ9eow@EN#+-WgIP8**bwb}a~k8(k-EhN)9lv%+Mi(28;BU3uG zpJ^oXnw~rebj>WeXmx$m9g6xj-53bywoNMYKBw8XTR35*#`)qTh)KIs$+e%fznj=9 z^wIOy(U8(LaY-ACS)8no?Uk4`QR(s0X{1_9J>zD0F!G+bO-9rDZqY7LdKT4N?HcK> zp0UKXjbvq%=Sm$X*_szDYLHB{hs6<8ZW`G#xpQj_Bln`9&h5-Q@R8rYua;~%X_xey zQA%Z;xi8U5uHmp}4M#lhE0o-~b;|X{t>vgR(b4n6I&o{1o_whCCp(+8*1AwU*&lbG ze~I+{h%sw*Iqy9)zLg)R4ssV>hvO%1_538UbI?O^$ZA*U^7X;rYr!53Z?PZmLc9AV z*YK-CJyqvTAKQJ+Yo+d=w}gHhzc*#V>1@cn^uaf~u(Cm3^4mCC$jlu*lY=-Xho@!Txg zxU-*D+}dxftmmCkixV~vP&@ZRv)#em@w>Ly?in_hcOf91wy>m=RQg!6O0|$7NIl$| zS=Yy$UVhrBkS(fc&$CQ2xvTg*H9ws&`sjIv*>~_spq!}!*k*Lavz=M`M66u20j@ai zyQK^iHjP}_0RT^qEj=lMbsdqa!_=dTpBxb;!=hLj=k!+YYP060U90S|6IV ziR~lkoN+nFIS0PqT~;>bg*?a1d`o+uamC@>t+tluFEu(|g==J4Qp%k}r{5OZu!t)p z23N~C)A z#j!{l~bo$oO_Wt9p+XwY6Ly78CzU znbk_J9#4QZqr*OKO-JyGt*&=TsKnc)nA_ju^?#epvSC_ReT;y+)zPoh?eo!HT5o|4 z_8jLl|CZag9#;*76->zy@;z4BIWRv? zO#~sD^276m<~j#}ioD?yS>TQFkBuCSd-Za|xk!S63?8z(d&39xY+r2LSBAdS!YJFO#8l2!E%e@O&a_>wA!2Mc9EGj$*!>6 zXLg$U{UQ2JO!%H&e|yKyVoCq%^pG)&xiw}7S4;DiYIJ#4aJOeq3lms{I}KYOLO8 ziGX9nDxYO70kNvn(y?vZb~5=d z=3*{p&0N%4b#rR%s(n!V?6aRYN05XabwU5}}TX42D= zmj#agXP{U~FC{=qT@rusAiy6|ivWh|-vlxPJQ2d9bPysAy=y#`+7L*Mb)f(E1yXAr z_-1t88ON*kXVUlMjH&zG=acEQMwE3|B``@eh>B?!NG5th3L6K2f#Fh3&O0J|`Cu3s zRgBo%QDtXlnZJ;Q%=$IITI?oJAaeaR z`~j3MSb=@iob>zIyrK(&SBV&(0TG1poA=C|@>}8pwm+l+6C}y$)lahC+7f`5Z`d@V zL0q4mL8LB_tAJKtfH{#urqEy_5ot3kY;R!kHhqQ#4pj|%71)c6NoUfxRUYPyxdZY1 zwnK;qwXKJ~N#}De$G5{VKMPYdjD0e*>2oxHA_o~)kxOKY3g5RHpmL`um7@i757XiW0Ow%$i&p+5wB1ul^3HF+R<*|yIySN9k@gVdc z4JNvyoM8ubtzrr>F{WkH6}SaQnCYrLz*yVyhx6;<4U?0+Qt^ z_!E{Kst8ru5s`6gX2{5F;{Nv>5qt-;L*#B%aqy0ca0&b2eep4>1dYFQa#v+-&C7ZC zDE3=6Ze;1o#cMEhQPy$T{`sPvs=~H18TEnNRRh-PKtEyXU>0UMMwA%z;ydP4L$eW! z*QGJszHl7zGj@sb8V*QW*w@4Ir0upouZ82$}l;gnn$oSd7da+!r1 z`q)zzRdS-m)TBxg|3`^mmpbm2HwPp6JIJ}p6d3q%CVi5$V%uR@JrU(piepYvkOL4d z|7|w-(z#rXicqElvH^%!41B*`@1*^sGYcy&KV;!e!11FnajmO(_~W=0S&J%Q6l^IJ zKVL7)a^H3VqGYLA>uLdgv7oc@x+BAP*=HvQ+FnaWyP1f`nb^{mnZ39=0^gG}z5m=p zbsVdjmRQrK zL6~Jwz_jgdTM-0o-|GQx>C!%j<{GO5vF;gId*%0B9`G>U;^bN&o>DyiK)TPkv<}HA zS)ZM@(KGw#*jfk?csGj5Arvy<&>A6rSiqrVCeRe6?t-Fjt4zXj&aO&WR%}xvCC`ydQ|z>B?*^zld!3uHsO2%N{iE z$C7G`N*!vU?{7$fkNy`}tE-7~UaJIb%A?4kZV;&w5Ww%Cv1AC`UG0T(d_QEhxn3cL z2$50Z-`-U6e{I;E=nOUyr@VBTGZg#){!D`~rG(4{{@x>cejndsMK(ApvVVk%>8j|X z46*JQ4FdZ`-UucCn8k5eR#x56yuitJz~*lNsg;(FY33dLgYGQ2Z^H{WcrEC7U{x9+9I1|0W0aF~n$v6^f1Xhg#9)xpg!CgDG~s<) zQB<(*rXd%rhzfrCD)7p2O3Xad=ESR~Y7(w)#g)dKFqyPH+2p00;Zu@}JEg z-lm#U6bVZ4s_}@RV)EZ$?p^c?7y?Pwjp%$t%pHyuB~<w60W#a zsU(ciXvng3+TOHqKhM*iwuf*D@bJ4IHoM7V9+LGT43Ss|=c#<6MCA*u)phERmNq2g zKub|iTnECB$zcMRUWp-!^_DJFBm=T{q1Mytd{0oGC5RoiP^rK-zoBa)ubAxEp`Qj| zM}IYtL$s}1%WtQ}xNuulfHQvUR!^cX_C^!f{-C6Yy6D%jP*t#fVF)o0s(vHaic0uk z{NB(V5~@G;eX@b?cyP2qqMVj8Aqdl*RNGHMmj>o8`jA)srvy~%Aa~Ya9oXa8!we^> z)8pFUC6{di#jVESJs3xL(>=<%o?mQgy66yK274i34%soZf`6DrIi%%Lx+>f$TGom& ziA8amnYg-=r9h1m8cSwSM)^I40#ai<(6Mu}%-|obTfUvAi*D#&$`O!)Q}l`aKT|5g zx%kqNHXtDQSfKwKw&(vJ%>OsF$JNNj#q|Hqs9bdF)KjedtbCcQ-Y(oC>-XMB79w;O zZZJaRO`$lJs_?gILnwtS|B-3;=@Vl=et>$(M=sMRhi2w8tk+5>uuk6Rqj%^s&hmAB zB=m8X{eD~hIW+ZoU%vHzeNlY%^`Md6{d$@1_dJ&Uxu5L!beH|vzxB0W{dHN~{Twg! zQvcl|S?H_$>(j0K{UqD>W8z83`(Z-J=QFJO>qXHo7u)Z#JA2Ca>19js>-quN!0&A$ z`}6$j^RW24m5@;O$DM)C!^e}*$6?r2*GIFTpVy~-_uC_J_v=u#-@^m)*C&_3M|by} zX?C~gm&?&v*Jmc<+aS}|?t-b`%>uH4&(}*>wqIefm&<2$*GqPG_uJ>4ef#;>^Msp_ z_uI$PR<}pf&tVLv$Aa5en(t#hG2aJv_SebT6-SuR+hexS z7YF?Aom=qR`t!_1Ddv+t=RL>)4dR$94ME=KA(sUAMN^{(;Mogx}`^a`xB3+Ev$Q zakcM*3GrJ?#?RNiI|?f0^w@fCbOiz=J)oPKK1Rg4+Gz~jm^);ZxafFy1!1_t6N97 z?1h@9417N}o`l{nvb$SPvA)dH-=?}g z9+tMeAIc4U+y6b58+3Nxx9lblwd?D7B8=m&JaT_XHVI62C)s{~*}4ntug^2T_eTx; zZm&$Ar-ifobo=Ofq0hnY=ZR`!^RNA>&t2;7_mS*<6}ywJji$G*E06uwr;I;HI#x~z z6Wu)XTfEkzK1WZ}JFcC^vOab``|C$pcD748_kg`FTb&b=!!?|z+6CQz`jVYKGEY9o z{fEPy=glfdoy`nUv(`(+6Dvn?O&S0-156CQ7BS8MY>I}6rVFFe&QZIhA?^@}czj%AVEcOfAOUIq~iGSpo6cf z*mcn^D9Yt0$oH`GTGT`xwNQ>cx9&P++vzrOLGMNQ+f?4NT4&RIjX#dx-s)y;sIf6) zY~I_kgqG2zK%lrbI@{j9xs=E5ul&+^LB>I|zquzFh@hWmZAEwHJU$^+POZtH`c? zUGSkjsiaFc(q^3z`bALEuA)2ROn%H|j_|^>M4*U@E&n9tQti~0Xv;;^tvZa(DZP}# zr%~gm2zsGgvW%s};9dTyz^Ih+B+gwwsg}snTD#!0Lv79muS~_GZ?%DcNO0(H%$H-H z^`m&Q*GQT!LsgrT>8$ZwY%!{oZo5;>0vU|<4t4+at+2BYOmqO+|)E&C@!LfCNa&w9ig<`ta zT6SdEBCsf~{V3K;q4oUtl;zg#F{1MP^JXbwSyUn7l%o)FqZcj%vd_q|D!%rZGQ91j zhy3q9QQld5bHhZ`z)SCsmCoXaxO_5p&a0a!4i1iMJ*P*Z^7EQNs=f2QK|tfi2em>V6+lGp61IsqPNl9 zw0oYpwg|SE5)Gu!M?Y(@u9XYEozA%Q%x>t@WUKN4wl{H_b5NsIwL^empAF2f;;l-z zZp(~hn z@#&Rrn;k*ewN;1Z1p7Oaz8ARIk*&NWA7QYOANr_y58!ifwyvt+lEywO4tvk)5J~sJ znral($(T}Y_+jO#eZ54rptdZrc-Dynb95pK-^kyP_z3s zdZ}bmi$X=lk87c`E9>;(H5S$=q8zbM891;we&(_vyaaIwoNFg!xZzBrs*J%>5ngKs zC@NfppPQt$*{K-*|1i>}JYo#Zh@<9-Y z2+gOqbg({cs^{S_CPlqB)P|Daa(|n@bTQ#DA~{@ppxJonpg|CPy-@2;wfx!=*+ArFpnXxpAr>+jIxprqWb{fQC47#*?HX zZv-;TnlO%k%#4`OWj3lG7qG7!Rr%wAcN&6JqQRAyOnJ)`A8@Or4LN2mc5|^xm?b}7 zM_+9NuVzqm6?d@I_{D95qqXVd9jCr9&o7Ilb?)lZfOXvC`==eKW-D(`v^a{^&zlg` zqjhq|SZJ$f16%bofnutjsjEXHz8rKmTE=_oV0&-A4tTn`CRZza>S5Nn)19JqvBID3 z=cQimJa1Ut$T5b7(k8KC>ubq>g2YNpZ%PR|ZX)x31)k6A)$3+UkE_q-wPmGAIqqDq z<7X1txRuV(1TZm_kR6rzw_p0u>_V%-1CI9%f3mFz7>+ht-fy!-2svmx{0SAB^}|U> z8&twBY(Dnq-QD)ok8tKKpaGTq#Nze~NoEhIt;)&x- z|K^pTqi^j=J>st!#6)uJhsDKLm@4n@RqzqisTRQ6I9u7e5}aIeq1PAfl?|53b=^@o&eG408+^IC0Py;Q*NCqDzEiSCt3zZa2@T(t{rurl0 ztO|7|EVhO*dTGj;1hzEwR7K4OZp~jCCMj|?^Hus;@hO_z))xalXCzPUGmh1#K+&`c zxKq@jv|ZDf*I(Aig~@USQIL!dgsN$3dw+N0rZ7V0q9#~l3tfc%%-2fl$<_vM?@$F; zwT8%-!}1+d1yIG+8u=Ht+5vPl+tpV+SHYGDPfJFYj!`M&?&HWC8vcx{XVqIQ(nCPO zi&+sX0?^tl53C0$VrRcsQIwxHi=l-aS8XqU-Sj0}ng>K2k$gN%u&)(UeW$(-U=rDV zhG{-8Kw=lAXABF?AT_3Cj#CcTHlk~H!sZ-f+a$C7!=an0JX8UrdlV7YU1JfKY0$U~ zo(Q|je7`9Pprf+?DhP}yzf6QG;`0=bl?w`fZj55W)vq}WJ(6c@lYepK%cb2l4$9Fo_qQjG znM`^VcL(HJz3jgytRu_n9KBkSES*7gd1L=X6YeXsTp7Oq)!6sNiOHp*)5x(Bq9Qz~( zP;U^!st|1_Tu-Cuu$zspptt$@PibhtQ^8AU?C{~W#;mWgU;TP?y5iJV=I?28eJNzs z0$pbA+O%hK?A-hbrX}G3zXI!Ti- z9~XZ_VcT3gOgIwjLd|DCns-k$K$Fx;+gxj&yxak^Nr6Vd|fU@-?N~L2yw6C}mv$VDvo+38D z|DW~-D`O-@Nf6#Dq1)~Y(lwe?JZk-##FAMR+B7hHqDpzG2slPoI?^ePw%aY6tnFg8 zXH7T1?6|5EoE-WqNr2RnW+hA~#=D>5N1j$S>O!`*N^}N>7Xs@Inu<2@=8Q-h#7Z{A z*xQAtbCk4VQ@NlD)Y|8|ON?RR`?zaXrf*YnLjgNU+9sA=tF7_=;HRgZGCkl;<&^S- z&@~+nxe~Phe93Sme|G)qTxx6z;GTuD+}?@HLdTF+$@jR`w-kF*b&@bc1b4pE!E53;0;_Q(kf&{?rwS(l0Dp;TOqW49zlBo0cKCecbBEjo4o z()30JE_PZJKE(FSmext$+qO4z-~r&Jn0P2j!D&bdDcUda_STS$*B^f{T`ibROU7=< z2Rm(6Nv#gC#!?)`!PSWr=t9xuR7o=#a}r{)+%((~44pA#i#9yE7cXJ=f+iVI9nU+I z7+8r6xzok{tV9FqdpJ2LYA=f*o>0gSGuDfk*>$3)Oph+&`J&I_#6aXxDP$|Fdkd$j z@FqDg^AFT!WM=9DFG8Jp6mujN!z+GX*x|;h+@I@j6mU{Ys?@v3B~nBTSuu13rR0R_ zx(ZaqnF(CEMNxz%T|{JNC{T?tS75tlc!-WIK-F^AU?>6a%*p-d>M9EbGUG|AI!&Tq zj3caYvk*nHG!<3mff=m(TAs<|8+>Kr#B^L@YnoFll$x;h{NgTr^R$u+S|b0>Y6}FW zv=3k1FR)W@@?!^=VMPHcW7j@B-4dRf$bGMk@FfBy**O5yxc=VPWL|QfB9|IeQ6Pz* z36o`In7s~*M&YmG1uSEcQ;4}ZqmSb?^*)u?=-qnH_{H}7yIb2v%wM^(Zli=VMTric z9U6{#hKTVj3ng|E;H0=MiK}9?XEA1CqHZ|}58-&^NAc|U6bQp;e4RAyxtf%kHrzWu z?kvT}1O3po=gFSDB7<&18f%E`DD3i<@9M@fC_bFs1wDv5vbAE zh_$&BWv_pHlDb43mVaX{fpdCmD8)9ha7iy=96Wv$T5^k+M@cIQ)(XqT652An3+=Wj zII%1X02^z(fA~UfljPC8Nz?^v{g*zPiU(t@l022VH*iLz%j`qIgsv0h(%tq#bK^Xv zTF0nN6w>ZaO2DeoK|ar-5(=?nmGox&yb3w7(NJQC3FSQ=P=d8}^~#0H(vZnA)JZc+ zwN5s^@Zd_MAzmFeb=92uy!u*OE$6U z2aM*oDqs=qCky$E#&0gV69Ht!t(*L`Ujt{y6hvrR^JE4a<%t>0a1`=MxTWyWmk~;9 z(Xu#cY)J%#!KggLI!Ti2Ddr4hXb$I5pnta8s0v8*AF_0X3lWYMq=*up+$PzxtA-{? zUBGEVJP)kE1FP|LJ>NS+MeXh6ohl)mzoOs{*M`I}(%K25NLF;kn|9~WC9)Oi1ejRm z23yC%;3RPIdU1&gBMakYVDkLFqK;7U`r(1rT(&F(xjuzNQ+minmCF9kQi_%kF< z3s;9EHiaDT=@**FGVFg8D}8;q#%tuDrDT$g+Bxr9=Kf=qTU)12lCO#0Zq5C*n1X_$K3u0c6GKl$rA<}f+RD#%6;zr&Yr`2ROmJ#| z|8f)?CUz^1HfV&!g!Y2#_ziUJB>Xp!FgPV?fv5Y)@zY!QS3e6vHj4t21nD9UJ^Uc99BKm5GbT7P2JwOsIu(P6--8;vI^t{PHb zvfNbtxEE&!SZf=^7?3#P(Kv!F$(B~K{6rURZaLw5pm?}J1?CbA6+m($=)UX3Tr21S zsjE#3HB5yY)UESTDIscp|ArQ!nrp5g6kM!O`g%@G>g_oZW(tGGb?b6_i${l-Sf7q9 zNh!Nga&4!*2%mGJEC8eTq=yM`Q-XbV?tD&B*J`;>Xhu7@x_oLzJs*M2=&!_an>6#tGM2y@^bpqy8nq=!h^Bo_UdMbv)v<%Eq|(9AJ%l zckD241; z6e^-={$v5{0uOtiNgNgNC%wt7@&a&P*%_QKW_I;z6NTX+>cENZw-Lu%xGD$(Zv^R3 z`Su$g?3N5T+w7qInAL>h@R_|=1&Oz0Ytm9ktH#XO-kqHh^PK;N248Vz!%`2);B=jw zG~PxVKx=}y0EOwyY~<~v7HJQoj+WA<6jjF3T{QUiFr##7YTtrWc0+{XoR zj}&gVz+nQBOeN|NTn?q3eQ}Fl&{NT+An>Ur^Ymg2D+1(j=m|5r8i+*Nb5TSF=9?rt zbaN?nrYkU6T+*}rE2Xk_(5Yg4Nv}*TkDp|uEJpl`;wOxQzPdAgnFa3sF%|zd?4N*8 zmWhy}{h&>n#-xyZ!Xrgq(?}~6i_8mCnCes*Z0=aqK8w;G@zU=m?0+z$#4c5O{qLx3)&74fP3im8lg}ohhADZq4roF}9k%rcsX7eqhLE^`Q~%!|2$1=}xO4bt)O5 z&T26f2dF)Y=J1&qS`LAeukeo#!GQ7#7O5OvkN8sxG%HJ*V1R_82Bsrn=Q94n@M+7s zC_y%7T1%e})s6Py6P;$2Z(wOc0phjLhLG@R6GU`$UVMCS?&HJO+ZkRRI++FIgZe5n&a>wfP zkX-DcC(_YxvMZPio(A3FF^t)y+cg&79(`1KCzOu^s;b-4-V}}vW;**NuX+OS>3;6k zR7wQ>Kg^KsO`T|#=hX)k_36lnE5Bai9ql&}oHS_>dm^oMsLP0WX+ZF`4m?9_l83xp zm3Ed}Z?@#cyrgo$KPQ@%3x*qqSHzMoZhumoK%FQU6R*~z>qc~TM%XOim6D@OgA40Z z)8UPQOn|8k)}?0@gI=xN3F5Aaypzv%9X$ow_e_vH=-Lw_dBROAU#drG^ZK8w8#12a zNB0!`npyFV2s+9?i(p6OFL|`QoK!s7o93~1^qZ~m%z|?sBB@c(3h#YWCQ&LFa~7c% z{u$l{wm5g(vDF>6os={p3?~z_(*s4hq(Sr>0Yq@Q>isJn_Y$kJtT(=VfJoakkYI-tG_k$oa`%2LR!Sh33R1p4EjH zE)rsK>S2P=gQ*(J5q0&KVskK2308u3V3RlWiOMi#AZW|CQ6mM^Qx&tg4WuMYV70C) z?MiOObHH-#d%`X-E(q4yXkDP40`2Bs<+gtCgh>)c*sKGS+bAb<>ykv!Z2#)yr%R0OqYTe#cHd*}n z62Fv@F(25@Y^#i7K#fRaFMDK)AKTZiBWOOP+kn!im55=1YS_?4AxC>9F1tGBy1kEb zm4P+l0_`QnJ-GJL!t(fr$S+Wc7Ml5es`7JrhnkfWQjIADqxozGvw;*;x3`yrL`+fQ zL)i8i(A{IpTwyVvTd0?bqB8@y)iW9%|I`k4N|f}cui*p{QIHd7730R5>pzRhqYuuA z5nl!yQ<8=la6Z2(eh-($1}RNrPRdC3NmG;l`v|fM$f1Dsx#m`wTsWhIDB>bmB%PTP zUQPV?40k2iL^Pzq;FCPLwUgNz0bA(NHYz)-b#>sRmNM7mmHpTFU0MBd{Kswm>Q|C8 zmd%aH9$^3l!?3HX^W)aw*h*%dg^rFe=GKG4v9?<+m%TmB-IvD zONdRxBCJ4i2pe?j9|!;{ugpT7}sJj3gF>) zoXO#e?wM8=(z)OiG2OqsU||6F70nj>^WpcaXJD>2k710+M@w6^Ki`UhPBA0>oZG14 z{2n?QwB@0k@p2pF8ds29$@1X7BA?2aOGEO=2C^o8==i3tpqmpO54wc}4q;Mk)=yCa z{y>J@Gt4@25T3y;9pYjTAVr*8(YeZQf8k~$qjPyMg~)Oq4%JK>eUxLdm57xC@33Nr zR+?k~jYCyao2ckj}H>~~HIO4A3_PlRgV8E`g;?P$uY^BuQq9pS06T;kbgy=D@wgN%+mG1m;SbN3&fF7MZMvd6D_XhC+n&v zY;Z&r#$@q_;GJ=e0lie=0p%=4h(%y{DU7*-)YA`$IF{(aEDnkxeIFMi4SY2Jb%NTL z%=TMhv0`b-X{dheT$DQ&IMhwLOzYKP$<~>*2M6*K3V80bt*{Yc4Pdep#eOQU1e37qY*Ng*D*XW(;{IB1}zTN_waQ z!%xrRbM|s6uIV_q!>*Ysik5B#Fi!em2Vxt7kSC~vEt$!9!_;xz#1K9OqKGF=5Hv+q zNQSoj55POfbTQjEmjpsyKbIGK?q1U4D-((K>!0j$rK+No`x|bju<#fMB1B3AB!AS3 z_rS@6>6(+8=jOhft(Hhe&I>sPN9k$`BNttdirvq( ze1WDq*Tg86krt`p};kMpsuYYjlVB#4%4-O?JCwGp&y zBmSo`PcW|0#q{gY)9KiKYAC6)*>8b1EXymk69DNpzED2e6rQcXoCh{BwRBqXv;EUw znI`!yCZZlFP2iRHMcX;LPY7*FBOjU;T_soQ(4c5ch6c2fB&9U9EhPOfKh=t{bD~L$ zJpEvmQ7jcZFmtHrRiaRO4z6;9aZ}DE*I zl5D_Y@r%pC74b2}rrR*k6JB|jne~&Y*@mEZ>5PI^x6=ZHi*U2l!IaOuR*HXNn36Mq zSg;`*l7d)Zt*a`?Z@AD^xSzL>}CNr1bh<7Rr31c=7-s*Roy z(1I@jYRaVq!W(uaQ5RFPe!(^DJMKSw!hzR*>1dK9QYf1ky;KIrmrnIbQJX0MJ8Ns5 z{g(3kI+2E9<;vOpf1MIVOH)5ej>L>eV>6LeZ+w>w4#=H@4@ zje;l}fNG+gQTv15rn2R_&0~K9&Qv%a?2kAIU&g;gKi|KwUi#UIBfh0xLh*t5Rt9fT zD%aEoI&tc2Ptih0-q#OsU|s(3{1F(xe{nL94czs4_HO*+fT+#ZM)r)Tl&k+OugMED zbYkSVpHJJK-J6AiL-eD)psz{V*1iWw4cRwo8ox$K#RcA6IhX}#`q{6_c$Me{^Q&DN zL(WmDILKN&W1ryKo8jw`zR4Y8z+<4^>AA#&9dw+c41p|YCp|XFVkGK?*MOlo0gq_o zSM8A*45xA#Zh@L1S6W4|nI{NOr&_e#1!7g0rB6$+_#$89&mD~b`w|Gi7cQtM zCBK5V18igOFktw)haAF>0@A=B-kqOcR10S#!p^~|&dvF1j69*8*qZVphn0rAtQyjv z`e_@*ACA_lQG0${gJ`aMY2OT_0v$$2sW+oqItO<(H0s1f7!n5nh}GpPW4z@4iXdNX z#VrX1ZJ{4l7*P5$DnUaIVP;~=Au2}{qZ9qJ!mM9rDG)^ie#-So06QUB7VjX{T?w>K zZfM&Kqz2oa_MFGTw}X-~Ll$1Sb4Y&mpEFvL1|DqCq3Z+QGN?S5E?BA$;NZP7tjuJD zsrO@*2L?$JPMhTKD7dOz9(ehT2?Zm1kOMcT6NIH`I@g0L;TtH=e=a!VD;ctj0uJ=5 zAmgdc+WTslBeb>x2cz#pe1(kp%%Z@%hzB_yyhrTQZG_?cF+|n^*2vlpKo+QnRH;2^ zMqgX7G{S*3%d^^Z@qaQ1yt&j~k;56tAQmLc2O<{t(=Q_aj0|g>J?22nRSBlNNGHbU zeB@6wVf}#Dm`q1cMw(D=#HNQHoRN(MuclKK*r+t#4rxWEfX-!CXSWAB2!pxy@5SP# zz}+##{>1|xu-7uV+EBj9&5GA!e(9q}J1H|uq*ge$WPrLpuW&o1fwd<^b9>O2db)~Cd*EGgA*SvzX%AT8>pH6^Vf*by`Fox3U;&WG-R(kp3ZRJ1v%3Nfavc>Jqvy zg$A3A4O(7L0dA&M170^VY8glNJJQvI^S3{*KQ4JaV!;+76hyUvU7#EEDCA%wtrvA8 z2R6U1=hYXNOY6)%YiK1+o3X^!3(5_5Tvm7K^SaP^A{YGQ%1kk$w+=e}*e#LZWvCh! zlKfcyTqqX51Gt@B{MbY$Iqb3Ipt=#~F}v)e_lM28uxbLKF!7v^Yu@x;yTKtm_K%OB!vIljT{*SvDW> z?oURFZgULR+?#f=FQJT>IJwEsq4~$!8M1Y!7o9`3TX%l(7Y2Go>H#SCrPQhgH{?Li@i9flfQdF%WzJWal=feC^b;sLqU+F?qC>Jm4@}>7xlM&$DcDOl2TlF`c9 z$~=wQT76pyVr1`ce_h$vOZ`?A)Iuk=m1b0LPs{vek@Ng^KqwFK^g5OsqVax!!k+o% z>rH)LwhZO?ukB|0C_g4c)bK7RKdpGR99qu} zvVBbl#TqY~2>yUG12doX2{jEH%rcNHP}S`+^iJtt6=C9*{qCc5_(+0n4YzhSq3=(` zlcSUG>aE>D)k%$aq{F8iGX~I%SR;h!V&Q|Q5o4UgEg5=@li~?ms@6e7iODF830(@Y z!}ry|GyE)`FyDMfa$UBlVXU9ggxaRRS~w7zc}d*}%xIq#Z>nyDY3`sm@hZrA7)fC_ zHu-d)bH)ET2@Akz@FJUocTkVBUZEx1q45Wj-+hD%uAfONv5V2-YIR^z`9f- zFKdsz{MjPSA_KtGN|jh7FCXEAY{1eANCnj4bm*=TtTh;>I-Dlhi~{%!afgvGk$L9MvZiI zam#A60=2w@MsTr^68@%F3e$iHjqBLc5q~SJnka?Rt2PC2ayV)D)Ksc-M>z_ixfIjs ziit97;RBL!Q;-@Rg^u%>Xg{UOgh=zy0e5I}BuC` zcmxQ(e336c?XRXhq@y$VD=5(aU7>Lp3#V;&dSKNpoJ&G-oGvpS<${)_Ob))4-cpo& zE6X_{cOKlz)w7~)O30ycPu5Jk#Da6-3ORBc!6NZ8&)$HmFXOSGlxzx<(2tvQxy&tU z#A6Fr*`g&mf?@>0AG=(g8M*GhuUXXzb6{V;g(vAF4Q+s$*K?*1!Zn;a&!rsQ+9Ovv z2y7O@>~hEFB*xcPRA!Nu75-sUhA=aeQH`>&?_klj3QPa$L?_ldzvdRT-6*k&mlrla{W@W#}0wfJ)tyBz&6`9Y>&8a9P5|W~Q)Q4e#f=*po zQ~X$(NC9E}eW!9dB)tjMLwaKR;aI!EBF096=#FJ1##mw`!l})*^V8cAqRb2-o-^+~ zB9^=AhV1LjF)*T8v=z+4bUlY>!!VH0ww!JVage;oamHtr|Mb%61O+57Mv5TJ;)>%0oz4xa@i_+efikot=coi*D&^HQ{@%>B>t z88gZS`lW5?89q&N1`zm zGXh0%DG4{BE}f|g=n3*4D^wG`AErRm(IZ!#-)57l=0wk-!}VgT8ksFr<{Ki3of(Fy zBnkkxQMjQ=o4HAwNLQlx*G5EMQj=XbL*5Pj?X?#Hv=;nC;4c)J24#QW9MSsP2`Tyj z)=#;z03}RS?@md#+ADlO1JZu0o0Fw53{AX@3i`7D@5fau@kZV&CeZej2KAgW{k6^@ zQOhulNc!6VSyV)m;q*8gRebVpbE7TQbl(pr^X%|B9MiSy)T+H;mz+8E7 z&+yqNEuVY;M!6BRhNot!ILvHOS;3beV#mj2B(&pVlKVq+9tuDeJn9GmtLfKDIu<(FD!(HgM z!J=53jg;c>I}vFx0oRDzG?#K)9o<|9WM2R6qaN{LXE&IV)_dF}K9F=|`;Yx-i23UH z?GC*d!4#R*2b{@YqF8aZyel%x?icx$5E%#J2-YFmrRIb@$AuKgG;3h3HJvwnjdHHp zF|+1rwj68uv^H~5>@>q(dvo2BMh7HrHWy zd?Hv@wm4GRnZb$Ci)a@AQk19<`aUOOnmtCK3P znIGg;d!fROd6TxrQ&EC)=hT)B;2{(-F8lZsmMj)|#l5KXVoQy3KV<2%z4va8wa7yr zAP<&quNgZN3G#~G#!~?4?5(S;UOqNqUrdl1j>v}7SupL(id8nI(}DAwl0EFiuuNj& z0kR{AxkFU~rI&A2j5Ei>_3gwcu1656;E%GXSSwWEs2gW9VWYo?squf_+{rnr2d z+L7AW9xvVafTHJ0VTzYz7{Puexs?%!-%ts630jr9sWPK~@SrAyBTQ9ta-qR;ad@J4m6E`>Pbs$97P>J=n5G+DyKWN_K{FGQWMt+rk~>dEy%v& z6CN0niXPoZK&iB<2m`nyEQr-}!xLonL-^~Av=S1G^^j~<8M61r9H;A~64c7-zbQI( z0s}KZAR?T)v4%FrfK&3^{xT+a3Fv5EACKCQFN*p!E)0>iyl3t%_T|fVS|K4IH+aI! zwWmoTtRz+?_!QQ-zY5<9yA0*@Sq@P|p7x_Ga5^v=dxmjDTLTQzPOhN;dq265WYdVj zc(fUE5V`X0I>wP#XZGv??v= zI#wZIgxxcQ$bu?Jwc=OvYBpl^t`z%2Uyuy~yvy_pgDzpFmu)^zV*S>O4~X zgf_D`kqQ9uEK}>beGT*eYiz}e(odzMssd7ID_Q^_b;~6(yC5is+;}r zY$X-r)GFE>*M1Hnwzl~5H)GnuTFj2(y%di=9!RS_sz;=a~ z5b@qXs1plt2i}<5;0FWXgMO)Qu}FJl7}Z!l6MI<10ricnX5Wc@uvenG5cRr^r+QU` zpV^fe3Y!;5HO*lj^~3ISSi9jg#%!QZWE(h~{QDu8m>~PkvOR8Z09xM5^Y>u5@LGf= zWVw9^*P~o=5Y$1CRnCHR?$}h1F_J0^KPw<;L0jIYW86=FmVg2ko=|Yi?iB4`@h`uev7^OEg= zu>+5Y#I*fEZ-Tdv8~$T@3bX)o5u8H^nbRp_M?B>PO!u%bvDBPU|k_Wp~sbBYly=(cs+ zwr$(CZQHhOW4CSFw!3%Rwr!vNpWNK!yxjY6R#KHpJ=JZ&&{%Gn6P7sDefnRneG*|hnH(D{O($SLJG$`kO5&45poNP_@M9D_L(rd?5)YN1GNd6vc%5LU71L``V+Ov_XDLIV%Ch=;{=!5WJcw6DI8@atw3i#?Y+LyX>QAwjGcC@aVS{Ztf#}?{9bAfWq1xJp3 z1l*by*izSlo-W463@r164^_O8+hNd4qk;;X*|b!NkD-OIfT-O;D8~o$o$L4bxp`!) z7JlVB#zs?O;7yu^HkOV&f<7W#tISTF2kGvRNs?WWn>$!7U0QT@S&Bw#^%+*pg)xxf zwKWOOccr9PQyyWcpiP)1w$#LNGC=gp=!)lHQ>l5Z?xhib+GVasmkMrV%N^gsVj#mtCrs}$kA#aaqViPjwBr_{e|pk^+l2g{{NVXzX?)pzV3`%F>Ps^R?Rki2 zo2e5pwg!oLbQ>VL^-NLHg&u0(6q?4+z@|jaM^Qpbn^jhC>wzI@f5bG^FNt$)goWfFwY#2u%$Qi-Iz+eFa7VTgL;5Vn@UpTMJW*z zByJmf@f2Nnj?0Z*n?ou?Y=BJbp>w-64~CX7xbb49#t&Up>&rZ9zv!tV5TgJfVGYkE zq+prT;AjTkdU#5~3mQW$g)V@QHgzP&czFPsLwcOQz#<`Fa0vP@gd{?sEVOlff%9i4 zSzpabtri2iAvU>x9|fa9gGn)#U-y`v3(1^)`e2~1UW1wTXd zwwW|mgpYviST0cE?N4_|QM1M*TjV=GQS7zF%bKs`&p^|j{cXHjxZ`ZT2a4$pZxwOzIPy(69(T+EIL$vrdE>D8!b|iMJS);ib%kg)T)zXclgy zV9HRJ9cbWv41M|KPy~QM*0J{G;tj#{MbtH${{h4QV4Md4^XJ6pe(SnyreTu@SuI0 z4iwcC!0Q|YWCBL|>%64By-lgDYgG1dycwgm4bV zcOf`lD{Ga>1AqYRy9xA6%{%Tve4>e0qlvnmX$?izuxsM1s71sp3mN0M8x__uf}vBg zM7Hc_Et0IQY@)Mm`tVC_h`RLo@PzGFgPpxIa0?zFQu`o~4>N?!PEjBwyBD+SzRd=& zZ`u8Z;H?<5=$O2bRf-Gf5B!2DM#&9q87u^GihT@whx+11tU=D}MWd9x&AZf_$`jj? z9Sd3=h{&;*%gm?DnVh7Q0V1E}uneH(ZM@?`TYnvM)Z?`)zqdsnNQeg^Uxs;nTZ z86K9cL^d?FFNmo`E`bMXS4eFSOoOYd0fx4Yb%(L^k2gJU)(Dj%TKm-^>-psyw6Vqc zEArQ;qKhb-g%U2~6kXOu5%yQ(Xt$OT-(RYg!K@DrDB%dfeps6usLFE(@Z?8TnQF~e zg8o8$U-1Fk+t*t~Yg74NVM;^9&Aqk}RjE(nQi~**Ou7(p-<}}YYXp_+^1Y-)2n7K76|I6MwmZEDML~om0@OO}j%2K1umn+ig1bi|K6^X%-hqnS1H18VjmbrlIf-@@|L^00zF zMek^_!1g!q;?SS-yDYz798I3?Lrb=%=lQdX3uN%Je?S0GIV$gWZhRRke$?^1ep5cy z2D&@6ya{a2moT?|ES1EMW&K?AoMJh-WSYih#jDkUlG` z&L}Y1mYtd$6wb0$>@wQSy7LkMC8SMiPLDRSsFyGtfDX80Sukd2Jfd}QZQ@2lz(4j= z3Z(rOE@GP0K&|hGt{2b20cB+nFne0fSVh=kf!__LC{>PtR&xSbVl~DTRc&L!`Kl@;R0P{WN0-L)USP8=70UE6VDXEK=l4Ju;6XBCzk5x`~v&=~dH7583p11=R zr@3_?kdWOWoRPp#azTCoQ7j*etVR0a8kwAg5Ty%Cyf)DT**KigN8*=@#e>BFXj5$o z`?Ga{CI}leK%_$^!Po2}aTbh<`RWtI0^VR-!Hea{yI`$%N0FBRiChW5I z83Xig?9+rxn>X9^GZcGU7zjFA^wqhi8Z(?aCHD%wVBtCgER4wzXM|ezdFi@IhoHPC z_&_6+EI~W5NCW5;c!z6@qJdj#!%~XHj951!o&>F|5i(%hR!+uQ;6DH>?JvkiCa0fAFGgaiotCaO2J9mXkM@0kYZoGH zoEpz8HBnfMXi8*2Z~^sQ0Z(J3cl+@LP0wFB{G8027619)`KG&#I4p1s+jCn2~Uq!(!FpT2}l}@z6X@iCxIrZwF zf=mRyfb85ql`&R&HFjn)k^tcXjJd2BeB0HfG%K(+l|o9BU{I`e1~lW2fNNa%rmy=z7C;hq zNuzi(jmM0$@B7m?*Gb4cWK{J>EM>CJ`|~s94MS}&oztm(`KgqGf@<%)D8u_3$nws; z0H>)pmdr=dWSDuXv+upMvpioUNpC5I60;3DreaubVE(H>-qUtgpN%&Q>5`3qIXm*G zPo>&D^xg+T6%y0}l$vXHp6PO$@tLJ#Gm>7_17UpEZPMLKLuyCcy zG|%ID^WaSTmW(?A*++ zQ4nc&Nrm(7!tQfxTVnp^Yuq{Ii46bc+Tft_O=?Fi0G$9}7!uR&^YUbS|FK-EIW0zk}bax34!f4}45 zL##Xq!7Kh^mmX3n%Krs11#4nCtc90Qg5gZ!T*9r^<;_u)pQ%*kX)Y=8Lv|vETjkWD za1my%B2!8CFft2E)-qz2)T6j+Q(_?nIYIfI0TTcYR+n;aTPJ=GrD#k&$}MWG!v#sk zlK(tkd{ZvGW0tt{@AQ=}Sc@b)YQjJ*5R|9#6a-JK8r(~!qtARpup5(LfzsA@Rw0y7 zi*u*wwS>Je`)7w*N&y)Ceg%WJuQTzx_&kiq`s4-_6|x( zkx!~1md~?Q-~$3VuhMe8NFWuY48}CR7<~uDwU5h42rRM-GKMYrN!n0(0v`8m6Eazh z5R2BAC2prb?iU5bL)|<)LeRCLx`6r2+VoAxPu?NuovamGMqJM zGuTe<=Gz8br_PTgJB#DoGtAR54{m=0?1^`96Umg`v<3`W;N%AGv7lV^m(bFhRlBp2cv) zar#rvWT;RzUqp5D*Z7z(gWXYZR3ET9t)1(Wk8=PnAK5?cIIO z^nog~I65z@D`F)o0KJA;v}9%~4ukxD8$gMf_whe>hO`GiF6umDp#JUJwK97 zCztGcg<#u=0!wK2Q7%&@QwBcR3>h-W=I5|1tvGLu<kLQX!8S`6t{QbP(2)-q9bhJO2+^zIJA=_9RN_+p;8!x z-Po;`dEK+Zy)jeF3R9h=3&ITyOLpaZRtBv|y&N623nx z8d$JKIaC}`KqiEH*fmCFDct)o)!olo&$hy{pjr7^A;==(k6PgQ5@2)@V+1I!b^{2% zSG*+TmO&{STp;3YmN&c=i51DrLY93}CD{VGVD@wnlr;$4i_VQRlet4h^b&=*`8&}1 zfhnBDA-k?rUJ>faqXYPcfD-r-OV%i>jeyEf67y&j&ZM#aleGSr>xDSZnGM1j3{W*U zPgDd9NV6G$(XZi(Wih;2cjWRUcRZ~<_=2N>^{YvHA2Is#+f<1m?gmJ7A7PeMP5o7n z4hs!u+uLRFmE099Z}AXUgIE+|zBi!~Jz0(VHa z1Ek2+ql49AgW*{fmG5y!AnPe8FE|T>*gydzwR?TUff1-ADHsVy%hjWd8ES5 z9Ywv$!-X-;a!r_ikRF`|4#~JHK{J?W{3DCvg3G`zw@a*|b`VfnkXSb`O0%O#My>*aM%u!l2;HzHk00<(Q|HpXTP$x$Vphzod?0N~jg_`0Wd z;uZWV%^1RfOdfiQXb9SDh{L^PQXzQ`TSKMbMp{qv&6ccoKsrD$ddv@MC@Hhu>EhJ; zxRptx5psVeQlR`>b^k!Nanc4AAtcfUTH;v9uK&r|=*H}hd6Nb;3qh$D-MuGywH{-I zho)ayyY?y;m7T(^50C6dooSY|u(`%$<~dOVoA=Z~hZ~Pn&q!N(lX2HFzD^#LJ$B6y zvKWk+X`MM!hc4m2?jSPq;N^}K5t2?C>w;?jvia=yT_Ys89lurpt`TMpuN=skKuwV#B7&Y;3S+*aDrBYJ!#o z6n@LvvpZ+q49-WAAhB`+~+??iQ2i->#DWmMX>2Cw!A~{8~?Zgg{Hwtcx7iaJm zIs-vyNd^Qbl=Hp#=hfT121{33MIHvpRi4Nx7bHhiTER-37={YSg*7yd`$N%CxG(2& z`PP5Wl|xk0yEZ`b4SZuzldSA2vI7}qbgA}PW)U6e+2@s`Pypao+vOJr7PgNJhywg>~RGnzxD=>=O9jyVn zE|FBCcp;v<+kxAgGshoFJU{47?gj{GZ*b*~aDfebrFP69xO%Z$9e`N&)`M5fk)fGg z9WoI+$J!8cInQbl^DwTsu9^E59Dadwn!Xf;$)A|J!_V+Ir+Z-#SeV+5eB{|Po7H^? zTj(sctr@q3jSwAA!A-0q>#nOKXk{N>zUZCB&x;6QKt;A6ZgH**gQ2Y3tm3!Tm3GNHN1dZ*O*_!8Ijce@)x3n0Y1!Fa=!uwAL+ac!4?0;%XBp)a2 zCeNiA{}UmLsj~)YGV%muT-w%_2Il3KX4tZprEm(t*ps5dBCF(b`&8JD$CxkjIgMm` zX+fL^gYwnU7Z149%B@{|`79M%dx>Y>pHMIJaB~7xeCwWO-8brRw38}uzjKygTz3b zSa7q!91sOKoxgt1{OoP<8Hx%!x&oLhS36K>ek2&?(1V}d2KgJ7-^AnBaHP|yp0GVs z1nzVT&8r>d<>>USRJ10#*HJAXnLCMm=FaIv^Z7TQF1kp_Z5X_?hLK%K&vwf@M2M!m zu>pvCUn{eMhu?Ou%d4U}9buhfd{l+3sxXK_{ssLQY8xaQiEv<`LyEn{i}$-Q3l{z( z941GOU=bx^7ziuk&8sX`EX-d#AJqPdaOsK-$M^|fiK2ii1i}DCN}{_z&2EdM8?MZJ z*iJM>fRvPm&}5Fy!vVDB=9I> z+3*23n3DQh%?`5!1Z~$Xt} zrl&Ln)|T>>2N;n0OO$QYS3m)9$W&5fF&^);B>!+l1C8Nz(CQgNY!*%B9IeS|SVKQ0 zkv4A0U3C7#5=oVr=s*3-Ce>Ko1p3*$O4AhXY;|D*~U z2;vR^mp{0r9juN(Pz5Ul8FP?7ZSB=IQa#v2RhW<+%9yt5&KZVzR=bZ)Mr5{(Fu!_r zPC;34aLGAKgm;%ilqjAPe$i7bx3|ECG0Tibu(1WFYf2JsGSAN$WSMVD4gGNryTX zBTXEZ)>N;%%xH4s{UuFp)-6<$Ka?@v*k(O1E3%$=@XJ$VhUv1K9_i+{PKC_MxevJK zYSCkfxN7Dnrftk9afRnb^&)q~Sr#UV1DkH@i=RUe?@1jSpo#Anez+XqqURoTlU2XA+=SSoS zob6Gz?F4UxumnFmuqN}z39zvfU7ttHBr+xQZL~)WnL}4Ng$1I(!x3X?m){*7!HWfH z`56E{St(?uKnX>dei46#!vc2msFsdgAy1=b<1Y7gCnKHkIFoO}&b^VrY*d?EQAh8_ z6|ilHbFn9k!fV#w?<9XE2!1cnWzYQfxR1%tIEW^wN$4A^w=G7py@G~rbwqBp{`i4By>sHwS2k=$@YG)s=TX)w- zW+UuYBy$ON?B)}E77pG*x!%LFMKa+PLsfl36;bXzmYI&13(Bu#ebM3+)FC&wPkQ%P zAQN(jsC0rB@a)wyY)5^UdD}FNGq;6PqR2Gem>Lxoz@j`dko1mfFvyhjVZfT{kQ+3H z9u@8&gUk1THl|b=DhM_%oYzB@H-qNG(Aw#o-ytGO+c)7_`MU2}J6MbOv!y2vCzV{F0g202vHEt2uOVxDoRNBfbs#U;retj4rk?5fU#Y&CN8ypePeW8u>Py6N~Cq|7*onHAY# zrJ9X+cH*M}GL_YU>2;al1QoGG$NwW#rQdQGFrs~24hG6`RYqBQfYx|X;jg~hXh@@r zL2^;G5*QmJZ*(Lc^a*};G)X;GVmN5y{pW! zvC)m``qi*4wlC}8V`#5k{`R7v6I6~^Szu?Ge^IH0HqNWTY%=pe)vMahRyR)z#WE54 z^%SrxFvHeuyKX#`VPu%^vh2okat7aKMcPX6@C(vfwRo1ZjoxqN$*}0^0lwBjV->jG ziFMQX_f#}zyxZ#dr0qFfF{ZI8o-P>}E?&9Op1ZatP6|Ewn}nnc#;gNk8)7MiT-0=; zfSjo7QS0V(CAj_SI}`zLhp4vXSCQP$je+EqWgqaYgsimeN*Z-Ai5pB($qRO_1y>E} zB*WGxVU681XQ#=Hp!Oz>Nvy}+Nw^ADRHVRMQtE|Wm2*>xEwH6Xq#}<3J8ypQDv6w< z7_p}_SLXvtADx{4*(UtR`QB4!dAGY`jw#y|p{#{draf6`JVrGD)LRsH2W$Eq zGv~bg3z{XnTqLpEZ_EjL6~XB<%BuNWn%^Lt9YfigP(amYuKWhn^C?1aj(lCt)X8YE+|Pftcz`4I-l4rML&K%!PT z%?_@pTM=DCQ1X&TxsZgY%ciG)o(q&ei2kn5W9vYM7_1UAyKpgl5s_U_qi6$+R;p3q z@0BV*bx{4CuXWy8gA$~sI6VusJ8*pnw+dOJg--{^J}tEBpEUc(Oz2`J)?e;qgI;EUq5! zo^{ydoGMAR9`HZm4chDO5r_Uv0HA;}FJRjd5Z{tlgr{@nsdH)B_RP65dm)%+k|oW+^WOfA#z= zvQ4@!ZYoz2u}~$!uEWB`#ksH{5(cui*@Vuh_ZbqAECPz4TQ_0XU-4_ZJ{t}@n@y-g zKk3g4gJTsHJNcVUzxBaOkb~#5izG?&NhrBM+pxF{p%}g$SETS&i1dH1_q@nju3s;- zXNDxtltaez-ZRO-pf>4x?2-30__g#vj{PBQYUh1RAZsa|y#Fqx3eKdj11Wj+4zfa^ zP~UjFwUGKAy@30UldlfU-DEropf~}H(vmD6bbl(LMF0?OXjsq+6nnyBAmmR0?)t$; z5A@9Ct41_6!}0)4gS2SI5sz0{vI8(+@7=yn>@k(aCHu<1MS=SBb|%G`FpI)_&1PdE z;WuqtcRGy>+vG9x;DJK06LdtzssGVN$nwpRjEaKr;J2LFhBuUb15`}`mIXx!3y_!_ zo>Q+A{_~86_H-p9-4!cksrkoSSxjz$V;tYcJ zm#-29g-kQ<^!gJ-f+*L3&>=TlA$DnS|E@0jRRxQOvu9?;VnP~h2pHcOEZZQyP)<&IHBdIZr+hbQF3Y~Gm2ctNl}2a`M_9@7 zt9kVrae@h6&o!=g=~AN>#+q|d#kbPw49>h zD@7tJKNgtYfF2l{6Cfr@iD|Hc6AFue-51!nhCY>?Vm9A5vV|a2%&3aYyuHcR~}OjQcf}9+aE>Y0Q9zZK5j+)<06Eg zMdNzBT*gBC<}y~XNprWe{G4rhvM>=hI=G7&&|<-SA8(B_ z5(Tp1eCWayc#z3WvzxHJJrXV;bc3}`k=p>IwWOc3CUz>IwxX6zpk$+JKv4<`cBf zq)=W(FqQGa9PS1L*3=2DcbBI`p$Lc&8`g_$yKqjoGmCZPQ-yIK!d}C#0(t<0n`Vn# zwyWf*n#WRh^m^qXO7A_*fdW=yH+6eQ1=1bL0U|MDM}yp+?rU1PxAQZpCVPV$!PfVo zz;Zh^#x~-A)^#?6c`5mQI)Ks0q+1g`I4r9SeRvX>XDqrxG|r|wXV#(+NeL%Z_XIeu zX&7RDVVlxrc4w17yCd!cbJ)+h+^*fla=}VbPP%t7h{LyJQkE)QKKCo~a|!3tldTu; zv*2e0Nc9>5$$Y4gDC;uvu(n-O9pL36xxc}a9eAgLg8g=(%t*Et#Fw@!GpGZB!s#at z1H#S@sRIS}z|PtIX~UQ}*^(#$;^#~^mtx!)+hio*bg!W)(1qN_yt^xCr{d%)Ye8Ex zEnv|1a0rvUjPQ00b{jCfhRS~~QF)O2C8LgBHsSPc-#QR$9(=kGi|i7D1j`ojT}VXK z+=>>B_1Q?!*57@$V0P4>BuQFV@0fwCH~`T_lYzlKEy{)8INnD7+Jlmcdy=<&MG6c( zTNG8t(_NJgm7drrk$#HYV*Sbds+$=paq6Gmh)m-9+rVs165+6Y^KP&YZj~Ht8>@{(l)|zsl+?{$h7Q2bH7P$6e zAa9ToqY5|+e4`Ti2}H$ANgC;f>N$@Fr29+1vSPfW-nVtoczaT>;UH+RlK36#^+8U- z-^%593kO3n8+xz{*{BF_xMIi!FLc7#=~f)sUk$0Z*;C}MqdCMr3X9<8jq z0?&qtf=t89KqNYZ;^)!mEZz39$A>YuGbD~4yKGokDZuv4(cGC|7+4#JzZ;z;<`wY* zG~&X2S|r^9W}9Q!yMlqAJ$>o~3!>Ha)YWSbT`3N++N>p*4u+3)bCT70n6E*=#7~r9 z;Do&52X~3frya%6!a^J+k%0$L#SU9XUij+ZkWYclB2fooUE~g3uqKFak3^M zcw7AG5I6I#T$l|kB(u62XP2-@o7u&xlpGcjKZhieG?(h49knnwW07;XOprYs!+;bG zV5o3)cs#!lLSz%ExjsGIRH>-CgPhmvL%BZG50Nq}(1YBw5PeBbK^FFVjiPNH%E#rY zw^5k)*T&8ITuVLs_@8U@Rq)4wZxgx6Y|ETgBb!SvkfW%qL}eU*!esRtEe@q3tdn5(N*tzh1o2>2zh4%Hbd9xre3#Nhth`_&g`noF17 zI?$`AWBUpB#aLoEY`qwH@F#%lD_;XkwJF)P~{46GB-B>-3 zWi!&{ZZJBIY-?Ge3r!Kp^L^JIP}2#JP2@Q9szbC$H9L; zYEGQLTU&5tIsSIFy40umdO=)gPebPCsdD_Sf6c5;^UnBna`=U!jF-21JtjE1K-gik zm4^i`~m$S0^w6iCt(R zL77s*)zECY?;u$_@~seAaAG;%ydY!zh+`KBsM)0PE9&sbJ~)+bH1-s~lR_T-&5gdP z2pVRz!DZ(ApptuwHAE)Z>j84t68p0~kdFW-QXJG!Pp&Lh(IZAf}ET-MluxPanx z1R^UiNdojY-nE)W0YL8h0M7d9zj~qWTzuU&__ftn2nO;vkbg-aHR_TLB7_Yan zD|pEd~cEuUG`6L#EgW2Uf?3ZO`vMG7Co!USg08iD-M zfFG5hWl>-Jm=0bjyw(wZ2j(f*+U_p?3d;xv_H|+fx2riclOhV%mOgV}tcD$_TfpF< zJlD}Bd7_!n<@=EHhhRNBlSBT^YS{DrkYdS|ZnNr~H@vs~n2l~A$u!T`;^ZY!$XhTl z#(Bw#5vML#JOYR}?Zm8`mFu@R`!TWU<^y-Y$j};2v3#@~`?~k7FrlK$enoL!Itq576JJ|%J+!%cdpK! zY{MDY5cp2}G>MZ>bD$o3PSJ%1gabzIb3gP~ErzG&n*F3BYk@&BKMOurXwIx6tnBYJ(cuYA{gB zXmp$#Co%%0T6(gY25E}UMU5EOIt^VdcDUND80QWEYMz79 z(TQ9)2p3&2cJhyC8K+sO?TY(iio)9diaNp?pa}!~{Y+quqvBf@pyDHyw;M7$&sAl8 zPy1;!T$#pUFepR068NP^#V%U<35;ip3}d!ZqyFC*pL;A_5~g+WSYrn}R`q9##DSiL zSHD7w8Tr~@XRU9K91Qm}IV|$x3o*hYYh&4dAI|$fVy@a-t5lR7Hj%m)hU}q)KL8mt zo%@oYv1hjH61E2MAX)hZlwlM>sO#vvyO+;T0jyGG{cRbf=n&&gxf|e{4mD-wKvYQz z`DlDP6}&>RhmWUdW*GOD{h{2fn~;e58*LTNEl)jBwHFSw)$#T$+`+bb7iflMuKI!T^rXl%?fgIm zV6?&cc8@0JAP#(-ZvYW1Av#=Q*>VzjCK9Pfm>qpMpv4$9CqSl#=TlcIDJFC$&3y&E z1qGp(3;72Qle9=r1T}1rLCS2{IwT;d21|fdSh%1Raa*!1RrE8rtBc5@k^}~wouOVH z)KF06Nzj84YB-mXDw`gu8lbWJ#|u;iyHFKRbhO)N`@I@D+k)9Dm49sUW7Iu^qGUYvh70PW#`8#&NIC>!MbZ- z<(I(^AQrp*Lk91)xHwLYr6G;NFl^Y}!OT}jQJDR-S((gh_V5}2e8;?U&5>#*9Kw0PPxzyVD0vU@GamE=)DS~C1u~rm&&>cnn!wXBnnzA*1z)U zQe+Ezi7!>#t>YV**~^^jN|Ts;ngX>;KOs!|OHZ39@+x@tvTyd|nCP-Js71o-k6We( zT_O+GIe&;HLbW#Hu?1Jh;5#wHZD@g(Oa3J6qCq_$z}dPor7W+GOJvtB#bjm!k#J+b+!zKa&QxGq=MT71kQ&yC}&L^-M;3dN(#qAe2j(j)=3_(eGW=Ih-3`86iL%TL2AQ*-&rr9^6+ zJfB(~T=?}+vigy@^7JH^f}nL&u75EmZMZ7Qk96?Dv?<$+|7VzhyPOtup8Znk@eQom zanVSoZXc$3iEI$^Q^nESRBy&$lM^K*G{k83uv(E;j?*C@=XgB{)#o>+5dC# z?e-V=e?rkXFltNNKmY*z&;bBs{toN(e5(u_aXvTPJkPa`WZvpOCUaN-*h zitHuTYL}&ISu>S#&KcPqfX2}?I|_8ps&R`ocuF7{5# zm&ckb7Y_EL$FZm-^%p-Jg8~yb0mnsrO+sPV%S@eVr&?&N?g`-cOB>{yB=wFzDY#D# z@G_QZZ2XkKgZc)WTv?I3yfQx`&+#PgLz>U@Q_nYec)Wp`$yJsf1QBISO2Mc0mZr=V z%dF7FP1>v~9-0v}nb-}QZp7@sU6|MzT6z&$%1yS7k|USYq8kRwQI!9&58+4HHPxJ~ z3>8}iX}ySBm-i`KZLhzY^^yy#hH4HvRooBgW){5u5hM+$MxsS2LqWZqy$vHdU=3ou zhNZeSs)wf#sio$P#5RW9SRfat(=o3rvTXgzm$_W;=)eJ^0;tu zVskbaGn4n%<18^TAk|d3cmK0|Fk!po&ummv-Pm62nM=CYowM;l*W&V;h~RKyxMvGq zV2KIARFNLmXgt)SGGp%YO!7x<%rg7ks_~?6j^2UZuDmVQG~d>OAB3sYy%tOtbiL$F zx@xx@Ic2hL`?(d>tvt15N_?S8q_}ZbDaG);lSv4J|GZ?gka`-I9q`31H2CSJOJ>gcztKY-pB%cY?Cnni&J z7gkXcq%#p8MaWu8E^|pH>LzdJ$!u*)`|U1Nc}j~+^cjrC#Uvd7j0->-XHn-}`=$ zeA;%)&(r(8e?B|w|9;flSb_xn0$DIXus zchkk5XTP;}`*Z*Jdh3U!$A!PqZp6vetH9am=@ZedX%? z&H1tQ^m%<8NS?V|6()UOzk5wd>+|vaD$trOJlpyHYIC=brt^CRj6Z;K2f$@84;4{N!@)LG?lZC53v_BcsnRTB4@yZKa((8zR!cZPyr|`LVwO)3{ zKL4nWq-8?eFyB*y5PsB7KgfzD8EBf9_83TXO>d5O5RKM(=PoFMap zNfrf|O7!9h3E_}$C>Y&0B7onLad3^e z3_4OEHkpUmR`nI19J6-}{DbeIvjpfg z31K=3L7J|j56*o589P(+rL6|aiDuD4bw_MqNr-}q#3k})FJ}=%UAh`us#>|q6JE6| zymDmsl(+{DZT@kTWC=V02a2s4N}xh}l@5(|4jS)QD2mm# zGNQ%E6cCs{@t-?sJ(=hp6j$Id3Suy21W%?~!(cUqejq#4tX#yK^f6yG$U9wt9$B86 zU~iFPk&@6yaOnu!Wguske>QZ^wnXoHCs&JZ6Z3vIAwbH#3(QgRw;-Bk}_Eh;}= z;S?7nRD{}e$Qn-5R}rdwDP>{_xHVN0od(L>!*rs_=m(pz3>x+=YH^I^eBnR4Fy$I| zY8QEzvw76rsgFO#u8AZB^dt%&Fw5EPgM zA!U&SOjarDL&@z>M8%@(N>C`r!%+pqQ}ryBBDRg}Su1+zbJ1%*f=)ftT-5m@|EXN# zW&gM(!H^(2M(_W{+B?RI7IbZb$F^@Z-csWoixWhme5=|3I={iqRaWTu(kE?IgoK5>ea^gYo!rf( zPMV=t)dKr*8kdl7A#?Ln6>;okvNAtn{|B2rBV!cRJX_=C8zZVQe7mgfZm9v7etU}^rf0NKSdWPKuVEfG#`sv3$oU93Su5fZ}?`iCCy#UFmh({bP^Us$B zjod(4quNrq2=0W+XDwHW4Jve?G|eTvreD)^2m~DSa611gHIIWvR+zo+DcF~k?HSOlo*%cOMens4B3O<$E@4S^*kZ<*bC7P&N0%|rLNn8ken;8A)} zP2-~fQFdr76MSE&ML`EJX$$?C@<5Xv0ItxcaJ7Bs9?CO=X^*ts{pUb)4`4yM;r61U z#bYnQV6s)mbP3i5mF|aqfV{zyz(dLHk5ES7Cy-18B3Mu5hd)vpsb->oS-9lLlS8Gy zVo+_Uq6!E?y}c>^nDOTPzTxmp_uPceJ70VI*$I4@DH(*p=b;+&%b5N zMDJN7W#ch;pmGrR@)C}`Udd2De*g`Gti0j3QYMxPs812Z%|WLPMh)9&-hsa+xGj00 zy4RHhkdh;d?4=E1dqS*HG_)n4#LHTs+)}k$oWg=4U6XGajFX*6SzW9eU)lM2`Pf`$ zq=*9Lprmu~=mSfK>$H6vNqsTt$l5HNde{Ap#=17@LsW+jnnj&=l z=SM_QcSDf;g^2>j6X)zVE&ia-INvf&Ie{H(9i1T~@dJ({6${7ggW=Q;dhxo33su9R z>Dd$2X8|#&+_Fye;u8=npysvV-6b^8Op->6Q9Zl09rSv&5ud2aTDh$_s1ul|mG%HD zoYo{sakSp|opod>EQu1W7!Q6IF`?xI?M;cIZfT37oX9Oa)&9MclBl&luK=mbw1xhj zdKd`Zv?S#Ml*npn(;cvCXd^s9-ZK{L0Ui#=1$4+0F5SsQV&h~Ps;0OS=+M&5aP`X| zM3-{6&S>@YH2wLTo2E|@%&U1aej~vjz#Zioh+3=(Sb_q;Q?uZ$1gD@?gAy1nyEMB0 z|%VFxJH7A%qk5!>j3C7rmVBB?_HR8DH6xFwDRmBm^; z+gGU@uryFN05X~qYr%z`74@J2Pkk%|$n)kVJVzpW`ouZb4(Vp2Nq>EMWw><*pa`3f z!n%CAECpRaO|4~2mF961h5xxfs@v7Dxu&>nK>MI@+yudBP& ztB^l-ai82r3ZH40OnBh4>$d5AW#ifLe>gq4UM4L9pS z%9}i{R+1A{ATIEVw#Jq(mb;-mt6x0Au|jpp87L9B;G34IYb@$h!gd#mnnj6&#;Qpd znm7v8R`Nb&y&(`Skda0z2S5~vX~(tP zhB(BQP{H7MuV~COOZo@f#E8YT46-Yksyn!>mjJKn6&VH%97QaRpiMS4SZTWfBhgx* z^~6(`{A^fZ>Hq^SEF%l`h@|&4@}O@pgR?3uQ3u&*+mitmO-Y9fVa>`A?_kFt*BIA( z>#pbnPaKI106s;H{$rExz~jmd1BASARYJ3ZFokrYPBcrZE+vO2bRw?(7IlF#-3Dzm zqd+?ow(@I_?MkcHnLTKRn(@wn>DEg}%D&aVG;97?!W(~y`M?!4U@HB2gDM)*Dy6Ji z1zcW^OEtqFuZqGga15b(k{wIR@;2R#U|rZ1GuN*71$h|ImV896Oh}{LGq(jsq`So$ zlN1w7`*3NhvDiX5A(@euzKyFLP-(g^S-=y(uG-5?u ztvzJl0j_XKdn5Ty@w^q9C?7Uy6kBL;uQ)nWfC96^T&uv_?_oTb5$U$nkaLxQ&e8$P z*Us<0^IheyFHV_=yfqmLFjA{PdqUvzrO}zBCTzoYzbQe^it8mAQ78$WSy7CpS;k%K zkhHrswUu=+bPN`_wss_^I0e1JkZOQWY(8}BBmh+mwQS*-I0=1sfl&uarIFASSHdg8 z-Sh=usZu7O%Dv$Zy^29*B`I@imz&o(VTe_|cx~%TaGlGhY)(S>C3l>5SBUy(Xx0cO z^|{sXFsIivUB9iID_yYKOKaMpA!>2`w+yb@S*I>@3yPlvdG#s)%^Gy9cCDaW=Tb~_ z%eui#P}=)wfj!MP<_B3*qxPY;s0h+4131l&G)Jt`KY%?tOYgj~-#SxoYBQpcCB9~~ zsmFL*pGLzOD#R$A?JHNJf6+{q6SrsucFWpKU5AuiJt%Kl2A4mMzC9d%dI|`x@4qlA zkr9R}$eg`bkmy7#|2Frg4!$FUFWzj2jQBI&k=d<39xGKc=7edQnOPl}*~quH@Gy6>0vN+=SgI=Iz-+k~22I7Zx2$s2_Dbi;U6^k1839G)Kz9mgywCs9Os zzxYd$eQ07KL7x|>(v=|#$F(+lSBa?@)dxl-$Dk>Txg?ExH5T!V*be0BwFLfrRjKa< zuk+$xVJFZ5<~7)1e9Ql1OZh@{Qz6H{gZ4BcTA-2QKOJbSg*Vgp(X8Q_DW5E?<#rX? zSnhKWKV@Eyptj6E?0h<4N*kDA}lHISpbhXOLHim2wo*NXGo?qPW z$!6C)FmaKK@&$GWjuxh9vFGu8b$>2EiwIa$g9z;})PoEt;T{bU)&OC;FiW|B>mW-h zpO2s_KthZkkQ*GA!mK4wMq7PB0|$zPMcZj_|6Nq3pUO+V(p z(lr%sxOXAc>hkQRvYPl#3t=i(maOf(Fi~MxxD=O@pj8k5l#s^l6qRUe9v6mS&81<~Q|mMBObk=l90gQyJyhz`q1;C%Aw^|EW~Wjt zw;srpab#h{bobMkd!=+app~#&+s60|sO*hpSs%Kkqg#=C@yCg2rGR7+oC85QMSrFQ zdH`uT%o@@bjT=!F6)YvQ@QptBH^YhQMLe}Vac|hVuy%pBK#*Lf%7`jmsR+x(zXxd4 z)SFK-Q?xL`TxOsvJh=Dd_6hFzZ&9`R#u=7ZaQojL&(}`2ej@fx?1Vi%te%r(?0RY@2Tt$p0>MN5ZW%PoCazfi@$j^`9e9`z5fK|mx)(6rB*&_Nb9W?*cruZq^OIT0k&6L$JoWA7E@37u*v z2(Zov=1L>*DVnV%kj>oy#*H1f`G;a|3{J+!cc|mSE9o*+lv{f_LY5PiLa1x>+B*iE zp`ns(yv%sS27^@wH+aOSH}u>)z6s9ZJNukmtGoKeyc{cNo)$XO&Yo0*pz65+npeWH zccQFqJ_go}&VnVC0E)^GOG^{cUV@f|CBuv&M-*@Qe&sK$dH7{K3rVNMwPXSB;ohgJ z{0s{xbJr($cVe`kCsCD08>z-MtgqHGHp!k}bvP82UFk+}r#GV|v*&~Cd^`B`jkS2# z>loSd%5{-9!}*vHN74!lWsLB%ER*LJt60THjZ7KL-c=TAzp#kG#2_x~vQ*(u%0d~DlHK?Lk(qFR zQ&{-|n&2C!|;PHZs)d8_AkP9eF7BeP^2lYAEUD`eCBafI!!(G9TS90M*B7n;1BrxnbkNMKAH)MG_!isJ~}^AO&eRHr)T9}46WyTGVrS>F|=sw7Nhe;24e?>X+ z3G&kZyy*zliqKPh|0e#I#$c2b55@DKqI0B^L1CY;+aUMBHZ;EQ9cn1Ht{1+518i}# zxYEGo;48oOlkR7r1$l~oAQh*U(hILkp)Hu9EWJX1^`BOO0<>8bq!eCz6X0furhVcM zcyYaeQ_#BL zv35OkAeB79CLpQ+yPK1Ll`_Q^S=6W1ur6J$*8VF3t}-UEd@eYO0?Y8j`74V1KHZsj zj2#$Pn|tME$oy*nJd^$?;~j1MtZDx;i$~XVw_-PN*(i;8O!@ulKQI;}opFU!2Wxe( zO{=o;Y~Aamt7=*gXRc;7Q=7r6=A;I?OpSvaMl|dZSY|JzKsU$PX1hnu#o7y7b=S=n z*sF+@^RjK{&~;$%Z+Xucv~kTf+-$!D=D-pOOx`63#69^Pfe~?UM1WZ)XOvj!aE|dJ zAe9?s)1Ms3`bxA+Zv1+s*v-o= z&mxjru8&csC;7FW)fmlq=D$q@*f`VA$%(M3Pz=@e5#U^A(a{f>c9zbv`ma9Qq90%- zD^quixGib-wci=r*>9)8fGf0_T>HtZf7nr%Mnz8uB|X$JB&_+t*<&9fs~8!%DGe4m z|I$5!u7#P{(T#VdECF?tB^dd`kr%Btn5N4#Gimki%a7%ON@EbUdSao60l^Tcor_9X zJyX`*gRgj(DzHl|Em_(=cO@Z|Tur)9U>fsU|aN!<#;p_ zc&Ed~4e@@uQ!Y;vXbIln}D9`dhm74Kb>FdPwO;68x8O%?zQyb4+<=t%p3eg z_ZMlaHJKugC76ung@NX<-|o!6Yj8IcM@ZDbZkWez82>{gTveWS$M(>!YiUQv%)wA% zXNQ2q+RYV|0wgkExX{MVsc@dP2;CCQ&8O}+X_07vSh}ON>ey9IDwHaE^TN{|%bB$z zv)_6b)4mz~y!?XqWR+~YEqQAn5YX1>fgh<4mr91+Ztd~Jf>fQ&4+WQv?+C(TGy$7` zLv36={U$4^`n>)=05IHD9_%$Ov}4lx#0?-aNK1;>6GcMLt8fi^8JhdTQX)ZiM{RuO zO^md43@$JtyCPY2PUW&C+G8vH96CQ8zp9JGx9`exp?tClB%T{4_c)r=T}Li$ko#No zDw3R2%BY$3VH;Y2n#4hRK5!YvaZ@s}-O`8)AE)SK#2>acdXb@;FBS@u_nGdYZTHR& zN@Y*`AO?z;93nm7lodgp&W;N%U|MGIt#FCCPLN@BLqdEWJQI&QgQNmJv3`Le^vdxn z@VMhLLj&n15r67$l7t!GsUohKOB$J&#Jk<1A`zeU%77VS8QqYCjg1`ahSKapYcK@a z%4!~UEEkhGeS^#wcd>!A=lbjh#1r!vXH#tom+k^o?UbyQ0lt;fFfiP$($sK_&5496 zOQ*5x=V|%WlR3=x((GwX)`6GdBk~nxD@4qNm^22 z6IH(6u)G&n^o(n&NDRhW*s_#XnRZ2Qcll0QrKHpiQE{qO^i$%2BZ0uJMeCin{pQ%5 zGF`*tvuWmq7}P_1(b2rrv_XVw*?ol6DUD9Jr4W?V8N;l@=D||V+-{rRM(oxp3vCmo zx@OxT@}neY3( zcX!BEtlgw)>L`LL?b(-PDyzrtrmO67=IwCdIKLZ!2kZDL+RPqbv-AGgMGBt&z}9%> zU5pfzPPuQ1Tjq!hr^10bb#u;8y2 z^Hds}_T{i8s}$7J^|sbbmDZ02rlcmwsxNyYpV`Z7-E~98k}{|%TjhYNI3#AK2cj4x zwCL>YVEdsnpnm|jqBEx4_NF@mSWay@E1)wUFDAVEnr&6OdjM!R0K(1#>a!Vuxes4=D2BPRi~|c-Da$gf@s#93 z@3P_v6TjkMh*ubzS`u?XSlI{a^u$58M#%9*EZegFh$CuT5~eSsbim68fj{!}1t2v9 znWAYMcf0F9LWlkz=4yfrR+vo?r(Y|MJ@VH&Tt$Rm3`ZDV@j}NwxgN%tddbX>5FDL{ zuHDC@$?$|5QxSiOBxWD&tR;l4b0_XER&;Vz0+KNXM?F58ZNO^~_Kj(?2hzfaFxT*& z0h3ZTl(F#KQ=C4vNdj3f%DqJ|pnpCS$EW3;yDl>X3}B}9LgJiaRF~^s53-z4S#pW_ zn}Ud}j9NcY_iSw+$~p7eJVbPgfB|m4&2KG$Xa4FgoSd&`otIygoUjk;Z>mTb38O1lpIL@7Igo6Fu-Z51{kECQWn9cVvVjXit^h_F-;%?l5qY z&En>cM=di*TS$XHLoRyRS_=17xkUogg9CpB<8o7?dYw1%1$5T?U=rL))EPRd8x4vm7IwNVFWpm=Vr zNK)RA9lPk|+F`S8MUDt7=*j)vLwG(Vc@mD`nTl~i`*zJjhIAT7As@CNb+Pw5t+w5{ z@fBDL_E19=Ub};=l4iSL1BAtG>=|#ZSafRNOqteYqxcIKV3#?3WO&NYE`Opr$t@82 zO1ZD$yD<5rlC^aePa!E>3Cfja-}+Ub)mJofo9_5c(K6ol7jPG1z`+Mfp3fZjV^&0) z8zO>D1t_ag7}gmvO|d4^xF(D~OCl0=YxNy?b|t3s8IM-7t3vZoC6(ZxshA;w?6%_E z(yqHNjWHyVHHDh?Pix*DFOu@bObzn2rm%C*!D?}nujHO?sVnF-s?tZTew}e*;+eo3 zU|AAI7;>2vHjtZlJv?^NTW!APut`!l3ZhPvo(_S_L91H1=#f3@&}N38dz$f0ub#hn z3CF@VY^EJXWXWhulJzlnkw^#an8{!@t7K2)cA=jBE9a`?*_uMPQ;*)aj%HEkuFYTZ zyS3+T7iKkR>0&b5Qpo0oy7L6}#f;B{m$-wgJV90p(@D=b*(L3LHWS`T*CC;xL?`TGccH zcxV%o@UGg^?UfdMMqzil@)kP&UEFOUB1__Nx+&JY8;|RX2JSF$im1%!EcQ2r{>Uj2 z1+^>i{?^3pRyk^q&)lpdQt@zqKirfl3Kwty#W3Vc*86wFdS;vFF)9@bUSGi3}SXj(=;> zFMj7V?@Vpf&SU;q zWPcPxk`wVQSJGC}1{-!&iq^aAOZe;{nb6t-!-Yt2d(Ku--uHvS0eR}VvT*c}>k7(= znxOSfUO(CR+JAQ!mEidd9s@{}LT-bPPNuO}k$m+;AEdLSptb=cx-8{}qO$JL``~hP ztIFD`G<+tp^5aB4V@@YU9t-R^?<8siJrAO??F^|`7W{1pb%T6O?firBqk&8Y zqb;^Q+0{Ek`MWAPzG>Ls4||4*p+gmLm((NFleoU3{eidlGWN9n3?w&A2N>K8SwHGZ zGU&;4*(n!rii(+kKu@+LpWdHEo{Y^Op%K?rJv!VlIEcO7co;SI&oIcL8nE4gSf&b_ zzcuFdn@#>qV{m2iU=h~3#RltqiP>e}spks#_D)oZ!~96o4rXmB3H`uIohnz*K)sp5 z5YU~FgiV~D0x%04b3``LB&pr@XsnBE*Dj<<|E2%?ug!5 zX%wNb29%)|qcK>YTuLM~0nAvLU>%4OnVuLmib))W5rYGEr|vZYoK~_d_ju2pfq0Xn zG5hfwQBH`cn_$b>zP&s&A+72%5!I8A49G;otsX|kkg>on(ZLwU2Oz>qDj>2L@XdvO zn8oF6#zKuZ?k=`UYfjUi_0qwyVARS-;16o49(3}*m#NN6pY|jtelG(d$tlGwOFzhl z$bIg8-1CiLaXaye#^k()-2@~?&P}wXs<7H{kBIpfNgr^JChL~Ur;LR|s^?v&7F|9h zIUo&#v+j}2Q>-CUaNr$74imYVa1AmE66xwk9X*om*|u(mR?yMSq?WK^nY}3vja|a2LArARnrs|$ouG*gL)=q$_!%{q?sm}BW&iX zMi1yj_KQuqWj|qx5s8gRp6!Q>&JY-wC<0I&IF5p+nk%dJQw8WY*C$-;DV<^|w2q{@ znKJZa79yl3ez#`H9*)8t*!*#vhC&D^c#HAYGPX@;9hNwq8F*Jn;Q$KzQ{L82V`pJ` z#@Q{ql4`00%UBkysl*YbkrFooOURh$L#_32>MmXgBgdYL&L+Ob;}*TyKcpChbB2Yr z%Stl?S==eNOF0&vJy24~8U5z~Pkl5M7z{iN%~GO?3TF-jz80hBT@*#!+*hu7wfT!wo_boS~sXl!g$R)jeW-U~njyYGl#*M12a9c7N zl%m(DatRwa4l<&^hO3zyeOLqi9_*Ey%n(1U9zBqFEpat zVVc-MaKXD#Dy=%m5u6KjII3#=ySjv-Oh|3yK(A9OLWd*Q!fIn8*WW9-_>jbbW#hyY zalO;Wv6$?o5>A?Tbi_w_*cg8k~cU<4v_Bs znAK#Vb@jM%5YhCXm%rlRxt;(4fBrlSa!Wrm)6WZa1Iu1vJPuakMmJ%#Ef!Bkf<{Ld zA6JpN^n3ARss8I}n9R&>86G^@DIt5GKX*JFIiY@cx>yY3R+De`^tL~|NsMq2XhUx5 z$X1`N-Wg`;eQ%LJ{r=Aoc+#HA`}ltl_)A*=0Hyz3AWBCUTPNp#c*_5GAmvK7g54np zO32cSqE9#%(0y`6^==o*~(2HxM6Gb~K)(Gk`=Qhn7zr1upT$V~UT34}GafnKw zF#6WJjd~ z%090u$yo&8Z%o&IR9^U!Uy7M6)iv!&_)ma*@aJL!e!;$DKZxB0m|J2N%Uu1PK9Dj7 zCposPBAEOxl$vRq0+EgL9kt4|l6E6^Oe63X@FNxVg$b_I*Ji{4$!O0>EHN-+sH*l> zyXaRgT`b&71F9E`=hzNKk|ud`D!7U=U&x9wk@5CbhL`BoRcDu?X}m>!O83}Lvcp!@ ze-Qf!cPrjvtjQZIxP^PIqgP@cXdJju`yf3P{$>`SqxMl=8grkW3;gIv%`MyrbEo;0 z9k%`aL7f9wpLmP8U9B)}Vg2~KL4Fhd(Ta3>V9f=Yp9uX-!WUuIC+J7cmSVNioK-d` zjww&K*7h)cb5jhN-)W8Sf~uNy(jkZIb;+D`({(0Vy;Isk%jEK0e$`50d%B5J)&=!m z#V~mz{KZ@HnBKt@)5HCYd?k3v-!L%YtSAk}EfIPh=f2*Xaam)+T{y>a2oOySAR63( zw`3-b2|&z0T@3J(4m9ing}^Mv`0raAA8_~?kw13=pqpIkf;kqjJFEc)@BsrL5qO>B z2mo(_!yk~8GUCFu>$3M#O_xpl5b;}JcJVEJnBrA&F^) zN7rK}HP`q1nBroMOk>gz1mA@5Tc@;0_+9}m>3%`z3fT>Sgyx0+(U8&q`RDhZxSc+V za63()onENi5HrYLPxN=Yy@(`%*ppI%Bv4lpDRfT?@*oYMY!Qc4xCP3dX+}ZtjWq0D z3hp573;q4%&*UyU_ljR14_o+VhR|^!NFf#IxtUWk0$mn=^%SD;3-~#_b27pL@I3K3 z1)(E+jGdepCE+S5KYngeaCk0?A$p#mvABP(fEXm=5r9unY@+b3$CMh|=RCqdvH*yZ zXyLZ^84$p^n@ci+23|h>nu1`v1(-sVF$s5H8q(nL7y=m|nZZL(GJ-E>==~I9F!I7% z&qx8RkP#Bx^Jr*6j{+arfd;s}P&-CU?u{DMnVOi76wHA+!uJ+TnV%Ov)BA(jfKFf>J&Ip7JxObX8a*>Wrr z(I{aiHK(u>>95L6if=NcFrFMiHbkKe(m|tNEYiU;qa48((mqjSEYc7?AypeYBF{>6RYUhbrjruAL;;S|}!|NF9&p^8G))bKl&_DBnsqg~vzqw;X z8_Z4XAQ`P%gW)+r-@oeG@_zolJ>7pDJl=78c)7jaT)f=o^}TK{vHM*-w$-WqdVab4Jb&H4 z|NPy0d|o;GoclaW8#{Y#>-+h*IJkYW%e$?syUn%j^KI|-^!~B`eX?`^O-o~^*LzyM z==2HuU3vS#cH;8IHH6|^Tak;1fq1zDybUi8I}3dWLy2w zT)(c*hCS3wT`|3)>7iC;&0$g9+X=^%-5`qZApN$8BWs!!j!>q~j4>8P0>5D%8zt8k zNQHc5uXwjC;lSii?*C^pi)GVamy1}H4TlYmf)<2fmE>!~xh9njYr_V6G1PyepCF5- zx<{}q+UjOLhK^SP$Q{$VXr{!po4?%z*DMq?KyiymJ~ae}!}-1|>V8x#sS(>a&a{${ z>tY-ey^i(pGOI;qf)Kdq0BB=ilY2biWn9CU?Tqeic`C1Yqg$mlfMx&%z`p$`8)o#m z-vbVlTScHAEYL9oPvZvLoSV)P*+u`56oZV`Xph6~%04_H(YAOvMft{{*+Dl=jg#bH zKK};cXs0M>8iu1Hh0kHuKL3x0y5=imRWi|ALjo9EjS!*yD%qnkvO^5)lcql=Bjq+q z&dDPb5bSP%hfY*lHNodj;1kXYD~{QGL5?2k>ZIDl?3z50GW?f7MIuTlrK;x19%-dM zNF`8Xd>SB1INZ17@faAYH~X8NsXU>uBZYeYfEE!KOunSdNA`4&xfmjt-AAY7`TaCO zOgHGCxyP+e_vYW^cw8cWz8!4QbuM~Ia~ls8yv?~%RyBIIGmnJ6Wu5bG>R5Kg`w zsu1SH0j>PlR^oHDoX~VO;tis3RVcNw7zlo?xJQ_ zPmIR^;c$7@+28^j(T+-~ow6?Eh!?f7m*UTb zuoTO~PuIG$AaW$j1$lbLXgWN)r3>q$?@1e3Kwq-Df8yEQZ9Sosu7 z1Oxz?@ZZz5H36)n0{k-JV3VOwcs!iz`DjTbrgR0A=S;5R-gE1!)2svt&M}>FiO)=v zwuYRQ1rml|8o{U9$Wn3{jAK}!QCz}Hn&QVCGQANA&oX(plTZsGf%&XBIXpIO%e>{$ zU>&v$EOb4YfC-$d(a4=`q|vPB5-!mt64F+dc#1nl{QjoJQ?V@0X^hp-U;8E4k{pq;^3)p_74$V5|rAH1n*$wWM6 zp^(^6FhHPsK(mCzm!}W1&ZY4!PSZNfwZ~@|4;5yQn;TZ36)G(U>nvDW7mN0)k7mQN zt62_lsxW@coypS)3b&)&w0tY$(`tipb&H92n4!4|NOhSEv%ny^MXwXdWY#O!#?vEjZI|ev`rgE6UvY4>a;()|vokDDA*I|ALEjWfSOq{1(X&-kT2}13s7|1gw zH@M>$TKaK_BxrW_!UxNfCdo4M;pxJm7YybP2D#l%SD%ENbXm5va;|8;;%|vD<+)q) zJRL>wMQx@W;Ni?joAIVX{e1)9J?+*MGI_!xTGP1rofq(1bW;+BHQT^G7A|htf}pnX za^M4L^eK=yefGGEiQc!3`t{Lmz%2zerl}M}>U-ATy<;2gez;&bNGW;*p%-p-@HYuE{$I28s{f_VYeov4fdc?|YWZIln*ZNY8`;^KT9~;w{?~r~?a#^ezcw@6 zaiH^`_@AhQH#&N0Rqe2TcV-i(>Z!lGZMim|tv=^3Pbotr(=cYp<4C42vV1-!iSYnr z0WcDFrZqgwZfMZ;CK?|SX!j^}ht>(E^n0x)h-nj3W7Y>h6H(5Hb%i8a$5LxpB2jDH zIwT6mc5_W6o-jer82@!`3M2I-qs;BfJMxx2e5es` zJ?jwnol{7qpL;0LNUmkKV+n`9>@O5Qv~kS!!LDPkGS<@Z#W-_qlAL<1_9MBNveLMc zKRcXoTYQc5`HV4bbH41mFuIqWpbT;o+=SsJZ1eacvbEPivd`*}>-O=+J!r)o3vacX z=tlX=B-6-LshXFN||$d*6msydZ%$1tIe{n)3` zcI%}@ylL++t+;K#NKwZtqYf)#cLMobb(u{@cRcHbg?Gf- zReRYD`$La}p4_&f3tL&42m6kW1m32WQ1x-@$yM@w3_Mu#W)Hvq(T}HG;akY4*7pWV zS{aY8Tx4pkXS-+0_|TRrJiL_Ed0)~4`h-+-L{xh~cO-|_Daf&78`ZaM>%^f8kPKSg z%x&Y_wM*KRU6q!1+O>09&z$J1lk2rp+H<0fZSI0uS{waTt*t6}SL87qd6rG5<*b-X zd$h)&S}n(&ri3dbw%NyQ%0VZgRi*aaIQ8Ri_6zqEziOYk}L_uuHo|^ zb4_UYH6p#6Qx?w;b<(?ulC8XlG+N>_gFB2r@N#wHJl$O@%~VI>qpg_3+nS!dyo;+N zR-Kx+E-ye{d6ggPkA^b4H+>SyzSeXxGq8M`XzwKkoJ%VjNIc$E?%&Pb!|i$VfI7~- zToG*VuIa_G2vj=0d7MScovbOiL9a3i){))u(Z zf^Hi1?kGyF%1tnfL}0{3Is)%xu1YU}iC=e|Nb{na123v4N_j`A9T5zsR}Sv89Fat;3v$ zEy+EzJN3;WPOnO2&oq7wqqXnG<=aRcMMaSs%=KNPOzZg9sz({R0waDA>mr&H%n+h{ zI$-pi4q`>Xuwqoqvy=c?)~jj)EY02LDj&04 z7p%aPsWge9g+LT?#KZgxsDII$BK0s4#3^RL!5lsfH5%NT;@?Rps1-!O3Jxi#87dY4 zNlwA3ZfHv0;A_3d6EYLB;%sCQ@|d<5?gL)6&;_~m^Mi&y!Q@FQkwDjj-h`@*WYphY zm0_#FtZ83)?N?%3y62bR$1`+on*WX@4x07Av6v%-T50>|3)ZWf3sC^G0m$&OYFi_ z1BIYe;)om`12Sk+SRSLL%<*W{a5m{fxM+Y~4)+9pvO1<&;t*Y<&G{;>Gn^s8GQg*0ezH71i$F_D}1(`A=+RKXtrHBGGz z(fw;-4#DtCdRp29u#yxfZNusLPe_$1LY=!Ee9b;GSs}h=8Zxj&zd#-3X1*7?%M*;Z zBm(T_;Ewrnb8deMN{T{;U}!P`Z5SARQ+12NLjPb3kyL5~^LMB`UdE3CVg^*onx+xQeUMJ0v;z@3@k92F%m z?abBh!Jg8cc#z=nB9TMW6C&c*jLi-P2`S*3Avo3e+wAB$kq-h}=w8rPmJ?=_GIC74>xh^T}uQ+Rl0VgD!Ah(9NbW5jN?3DAymP|3&PgGq5pI4utO1)EYfW>uVj zNRHcg{z_3-3D=`&BWz*5)=6wr1O7lXLaZ#ZPRP^hBy}$-h33JQ zZHuG0{bD#`XCLBYHJ=c)vutj^?XDz$h-+#|@AzS8IB^JDNkYV;)$uRk^Wf}w2u~q=+S}D3!B&UOUm2*PbZo8Jm zt6~l5%m&SHo~@dfauBI{@zCB24C+IL980zXaaGlge^c`slzvW~%a)thN`%l@%Uqgj ztn&^rd9>WviYZ$FFQz;IDsD>;RT?XQTizB8Y6GduGQx!{=tl=IvWc#Y%q}gHxh#T> zeIF?Z$~#e_s*|M)kCI|HW=y*kE_{ZZ1%5qj_fSS^EkX**jG24muu_$zZ1t9nf1h{2Ymfy@0L0wV4!&O)*+Zi-k`@7HF=-kQn> zqNti%rJxDA^56}~+5`Y;Lellkl1A@nE+F^c)dfU74M!aG=5$ouL^c28MJT!DjhGFg ziFL+gjJ4AYHK#+zjs`Ypt7BX@$S1ZIlH`*&3)J%PV0Tkk&;}lE4uCm+oibV9Z{UN3 zNXm2X@5=jsG#}3N1RDvH-nuO5^L{6)WP+HGf)*q?9O1ovO&>AA>z$R_y@18_Ru7Vf zSanYZ0T~mwfXTe%vYu8})-|`SurZ%7`|E*fWM-k7x(5uaxu5sn<9`+eg{h83ES;pm zt{YY4nrGBY)y?Ct8eQCWMWvhSiKhv^i^-gtQta8CQf8}4ssovgQo(t%Cu>;?K#=zL zGyzl*@c@#4!v9gSJcYCG@o^V=KZLQS>UVoqEmCU!K-YBuD@$~hO53-$ ziTWH#vV4+K5-gyQ%s-LqL*yF>98sp7*dkce1J(_BMBSwSOc2EoMg#mq+Rj#lIhY}i z`1=J%++UMP(Y;m82OKW=-Mz zN)YN@XC{>?SF9pQ@SdFLX}s@Fg8g?cg6Ll?rT9-} z&0l~m9%T&xfQ<$Sp!~n1sQ*8-a9jSrBH2)>d7rJ@FWDm+^4>|~ z!*%9wHh|?yCpj0Z_P6eYser1Y%CY_NiL;wNLA?D#EIBBRWa=}n(?QL%O+4tMbLuk9 z_-b%r(t)!-)(+=UO#qypD*}*A7gcX&(H9`KkRzn zeSJ$Nd3~P0E@xN0KRI-tBlN$AD<*z_SK#%0e&56L{7TBaTz=|$-}CbNK7StVx~_lU zX59F^Kfl-Z{u=;dK%Kw8dA7&tH_!Aq|N2>u({G;PufO`{pMU+;cmJAx_Ol;5{THAA z>Fe(`hd=xFpFjUK3*z&4zsu#n{OW_|M#!{{qy%fcGb`I z|8IZ&+wSb!fB9|mpi6$o8Pkb;m`2 z{?!kk|K;27zW%4Le)VG?`?#D{`s0`UE+2DT^M8K*!~gv^Uw{Ao*Wdih-+%S1-~9NS zey8VoT=XCM=->H5{r|HD>&Kq)Z~y!!fBD^4|Ah3>czyfpfBW{E&)@v;{a=3j-QNE8 z^LPKGgTMdkyRW&)H*Y_-&tHE1-SKFg|`TzOm^Y=e`Rear7|MK}iIOOLy z{>Mi2{LbHcu^0Q#zWw#r-+c9}Uw*sB_N!mLzx24`m&dzaU3v`68`<_a{YJJu&cBvz z$LTk+?HB+4>wo-3v%jAu~z z`@Zja`eYs!7+_t4>Xy<$s!Zhhq3=bmnD*IV>S0{>G z827Q&T*h8Ty?sjeedxpH$i3{hQs=$5EsfG^*Qd?d_qLBx^EP$Zd(l7J*85Dp*Q)o; zl=qZ5tZmjd2Jhfrb&GShylvcdD|_!_@1@Oo`&94S)K$Ht+}iDWRPJq-mgZij?qTRl z_qw;-(#Cggx2(UVd$4qnYF_``p0C$jdmBynTF32^?ks10*4Q;vecAnNLkm;SoH6Ss&Q?HijR2=*#eCeNWu(*fan&ZKH0iJFT9i zyK8$%9#j28gWslZv)n%QyOyZFT#IbnE(iHOMsBRXoa;@?b<-W~dZ<07CNrbynC?{S zb{^k#LU*r+D|4HvHGQ>L=cWhMzecJtykGoH_oQK|CFzlPUY4;QX=_@f{=8JvY#ORk zdBWPXJjXuAjF10vqHFdz^^v0@G&b+?jWsnj@LhBEa=!I| zl7xx-s+LKTl}#dQXf0Mh!!~nDNn_{Tp_livm0eHW-b{G?eNDI)-SL)9A1bL=lBAAg z1z%y=h|HRPGRa-tx3=Q!o!iy|)|ifxETa?av}>_Ug!0gw?s}iJ!dNq|eUiJjHfJ>_ z60W-h0vGGV)Y=-_NW0&w{p>ZLIn;KL7HMf8j8@ii*(D1kqjVS7qk5NI8AZxMOZeT4 zk2e#(ULTJUD4t*K2x++_ITxZ#>pV#-Y?6oh&Gu&QH1fF?>!T_iDfue_QTYIUZQ6H@ z>5Sc9_0fGpF}z$pn(h93n{A(o9lqbP7IO)4oh-qU*1Vrl*G-9E-J^!~SoBFdaCB>~ ztT#v(>!*kOp1eVueHPEPK3zIk`kjjh=1N31Nw=4W&_%#MaE~b=^SM1m=>{ zyB#g@Hku}_j}}ru+qX$iPVI>7Wm2mYiXKv8!Gg6)rw)BbE^lUe{@!MJonxNMO`2T0 zV*TOHC42PIvMs7_6vel1T{EIlMHWeOcMWPDJiDxyo~5SsKMrN1je;;w8#_}{OFKb| zpjL|j8DSc^EFsT*3_Xd4qbF%_YwEVMIW=M-5%geM21Pe``_%4rV>MwQ=G*ls-SxaE z3_kkpRKJ;N|KCD3>A{MG)j)XZT-Wl^vX@@1Z#Qz9TD4NZUgk(3>G)Y#=H4aU>P}0x z^9j0Awy+Em4aw^{mQqb&0LU_w%p2%14V1oS%3hIyZ97$`Zpml!L%o>FX|~ZCz{$u2qyI&$BPSyjPQ}0n(02e9z_JfYN- zu}kWCX0_qJm|5-FJez-eR;zT>V6U|`%MuW}Mcr+FX*}r;G9N5uB>~K1O8oBwqdI%R zcWo>gUNd{&YFH)AbhBH3V~e%ln=Q6UV_-!~%1XxMsr9#YDOWiI8W%}bZLERFj$zp3 zx@`=F<+bKj28Nz0b3a-EMQ(@GkX7@}-DvS|_6MYlIrQOrYr0(yvKELOES&RFwPPoeXhR^y_C{#! zYc!=$Ph%Ue_E68mi%e>zTg_X2kMm`n1j@p;)6*!!jErws+jt zf&S4(>TjIrR?h(vS)@3aRt8#jnlRk?+ao|Cp*|1}VdX!sD6zqDy&0G;1)&j?YZpK5 zhE=0kmcXA{1G~IaNou);O{)nz=q7+) zB&oce8J3-8xweA@)4qj`yKR_7gVF9QSk^Tg_yKz7O?pgrg_JBaMT9Kb%+U<%A2Jw` zW}bA(Gw(zRTyIh$V(i2vH4)S}%Q9nzmjmvV2BtfDM378_g!r|MFBx92=7#4@GrCRr zayy|Z;$nlKS!aN6ACJzK_C$C|p=$Z${FK}eOIzpqX&~3@1ipM1RT4nLs%hH8s*!~! zmDXNwzng{!^GXXF9oXb3BSNIAvmH>0K8y#=OXW89u5InRdp)6jTN|fK7GV6zDYN@Y z=%Nzsvlj6LHhsm=*h>>ts~${Z8GI#zFq&OyYAG-xPdBO0`?ztzof+URZ(6r^*&UJ^ ztu4)|Gg7KkQe01PCwx+5Oy*^E$b{16)&RQ%zq}?aJ2tL0h4j8H5e|k1|eVl#ClG9Kg;Iz`mkogwZm5M z%FwLpeoBn$m$Rt_uGK4@q1(`Umypxc=@n@m#5_(iaSy^j+6@{pJPAp>mtizbXL`s% z-TOANW0G$yU~V&*YJl=Ok^nvB(|CkaZ_JZ_1+d9>ge?xhcP~etfSsJf}28MeFi* zJ<8k?s!H;+Ns;PtmNU6OW$fmr*T}p)&2O7qFz4Y~zAI;CP)ZOKbnRKJg3F zDHB`U9T_5eDkEuf3#j=|n4<*rHUu1@RgD3G^*P=Iu=>eL?nGtV60LF(JAqMa_7JUd zqX&LOb1+C+R2okgNr1*$-7+qVB&dTL+8uY|`3~?BN0Sk=+eO!At`g9ah|R81*QS!o zBB{x}ND|_|OP5-8YU67w)Yx*`TZET}WR^W4X(-u;TaSFhBr2j=uMyRsT2hXj6nhLZ z@e)#6AKuYHngrRO1k3yFdUTCQPqwQ*QU=@p+;xv&rTolxo}GTSm$albAe!D5Zny9=^b{P3Jh>@5t7!uXr=X#z|yI*M)YmFw>26`3r8HJbl5z^v7DWqT$1gEbU9C;Mb?O{RqbvWky7;RUfrZNCq`1U zc#)MPiN|U!yn~pahFo?%u90Rv?{|$PhL-J#ot#c4%P_1LcAR-a%hd+4Hn0>>lJjRH zfYY+Ar9GXKAg;Y*j`?DVY8jplL7rGoQq+>1GUQo$mm}Rfsze_vH$&T4&)Fqd39~#z zbvx84M%7tC#_O@9JoX|0EETQOIqWT({@VnONz(~)8Q=-XDpQu>bKvwD-(yY%z& zZM79-p-Xql9nq$haC*E~a-v`jlQGq(Zbbtj8Mu|&Ln-TzW*0;`G++*ey zGP&CCbtY+GFKLEzSdCM%OOvJL??JSaRtcHfXD95h6{Xc#Ew%{^bTkBEjPp^s6D%I2 zzqI}5U9PC)NSFDaA5;AZSV>G=rYZh0BhgoJH!JBq4^Q9mqnc#I>*ypN5?RzDU)`&f zoG=O`O_mv{3kU9rP}`167%m#Dw?2?;*Cnu-sa6RY_DH&Ayb;+$PGi~=!av1*jzCMk zUcpY)7~v{PiE8~8Ljm|i3=s{k+yF^3tsyDwM1Ikz4+b?OcyHQ~cRh+6lE?)~*xK*v zVP(V}>CvValXU1FYuY}x6qQtI;_gEPY*y#e$@AlTKMleu6M3MSyIqem5Q2@3yFVImy6Y+Tv* z@-7UzkOY-L>rK1UhLdCxw!SvFl>rD8t?L%)mQ28>Hi9j;h&fIy_-@^uzGgvR%iA=a7d(@AAx&qH6IkFIF1dB>3; z)jxWSDh|~H?M3MLY+@~B$aw$}?#Q1(48$8|=1ja+0{4w2j$6SRmfVUwI%`>qkK9>x z8mAKo0w=X%&Lr=&nfab~Q-3(t(!?^cv_mZpHIouGglQtz*`9ww)@<@N3%~|BH~~G= zlweK(p@}Q-rlxkREJ>N#w$-pNrQIjF)Qy;hRDZ_D)&{q*?*vYDY7oM}>0`5RHVo0F zgUnpAp~0uh$k#IW=xHG(zYw8q(0d*Dpp9rrA<4>#zi$)ML1BDB5E|NsdUk!yF309} zJ-VhFwd=428ab($p)~KOe4`ZpmkGxu+A|(<%S-gh*q36I@L9o?FaFAasDu*@2qb!< zi!-u0L@7~X85=bMwO#Wx2>YbBvwS#R+D}8@sx2G9NSRrH=Sl>1K3HqoqH<~RVk2-- zaawjvw1~b+G$bUQyq-?1jwp(rNp6DX!T2C@PaDpBBWj|782U6VbnVgNU9puw3k0$Q zA8+6d%(yqK9fI*bD-k>7wDoS+UpBgK2Y)bBKam?SeA+%A-%GHXm?5sqW;6VBbPb|( zjE02oHlGP6$4VH!>FKrdj$a2@0Ckc;D`sp>Zb#NB=8a4ai^jb2JCcv&G3#Lu0dtvw zIeW~QR5WT%d)s)-J{jju^4|jpf(~UxvJ7?RNOXP~r}-0^6GsFaCq|-Ok6^5UOc*R3 zoW8UkLA#+4LB!OyM!a01C0o#}6)|<2o`9`Kp5YTFkA5NVWohX5N=InPfTokpVuSy` zoqjMq^nevljI`wG041tHArdMnme3U>M)w*a58GFor9+FNhfdGEPQ|yp+FI+4TFHA} zza7K%W+fJ4VDoq#ry>uyK0%^RYX(^ov4ffbui{|lHKJMzJGdWqdgN0tmtZCj(n4Hv zo8wMck~qDznyd-!#^!+S#B;;$leQ;K>!F>_n`(%9q;^mQa#0fzSq^~?lDi&WdZ@-t zAJs=0$HkK5W6xoyW3r=1$~>OLn2RueDw&`G?#j`r(^0vjzW`!t(C{cxkXrREpE7+Y zLc$chq#1N)w~Wg!>lP1trFEt8?s%a&aK|V);H5w#Zw@>Y zdSjb9g+v%_#=yg0DNn96kPfHiC)oQ^swr*6nH znP+%Mj9jvwf7ra*_{U6;=6Vk2aU$;s?K!ku$jcPcmh#&%e!yN4O>+?a zIw$}eS><|#Y?sG-c2MEix$)mkLwiS|1OCmoQKZPV$k{`d?ebM5=?3XeVjtSG6I_YC zy@_nq8mS`?&;vkTCo3Au0L=na#j=~6rvdNT^FA5dLHr}qzUTLXZ@|qU^;~Zs4tfgE zQSMfaS~`I2R$wmb8;-!FWYO7tiG$~(>xncCJgD|*?#@r?0TqCyIxzUu!aI3rI45$V2RfW)g2NnxTHscIt$%^IGNEkH9F)f>| zK5i@0*PN{cEHU|PD`}=ll4zUTUzHXkPQGdLmyhrL)E1ITKA}PG=OZw(HC$tf#kl}C zpdp^YjBK0+J2x9OSnn%In3p01V4LH&{XEGSu=hKW6U?$IlR7dq`E+eqK;XlN{h(Dm zvPl3nkOwvyAJSY4K&C%2bHFbIT?nqrph9+lM>B9^QiMK%@(=ZP~t}3lQju9XL5M`b^&{Ifq6aiEP}Gm%1g+ zpiLl)aS+&*(31ZnC)}zTyc1EOvB1v_D`As2=CuDH@45kyc1o(Ex(gwyfNXLes+#bX*{bDBxthJS&-w$OUoo(}1?+@8{ts81kUIUHHZPfa(5bCKy!QqMVnw-efr%tO*HmElxuc}FO+ zvmR{I`fIzks?MkW`IFb2oYaE5Fl|u~UndPHZ%gN88<4`gTRvyUT(l6vS(OxM5n!~w zBupvqL`&MscHra?@0=FNbq9MxIK|ufxH0zo4Lm)i5{8|YFVFti;U_XS1ynDE6u0Zq z6|^A%T*1LLAVW_@{XlK_Nx#5tFsx|Qp`~4&5L?kn@?Bxb%>G)0bCpJbEU-2JK~79W zGDA2P)5GhJqAY}*k3`r4%TP!J#B}WMr~HUZ7XYc6FqWK`eVSBAjP?N9NuVSNeegI= z&eiKB1g19})*;c;K-!i4{)l!Q-VIXj@xA72owv=vsHN0(CD3_W@-i z&4Mh55U#<+Mke15xP@$q)(JhAg);}4iUdg!I!fn)p|(ph9C2*~w;kQ&tmvZXM3v@b$~b`)ku{8^Y2&g^_wk6kZ$ z#V8C12zI9eB$DRsdUQ2*6MCAeg}v2tiO|E?)o5P||Bf?+RrPWDdV--^Ibe@L{kXcX zfIHb=Tc<88(|5{G)Z-Hp2WjR^V%WL`+c$$usMy8iPdifob+nhs-49qBhRohvK6i5( zITL6_7#)_7o>f8|Jc2Eu>lLyi06E3qTOX$Yk|dFLrtDJU!=>J}E_ADeBth7s7$(uW zERAxfYC&pnI>=AFS(OfetEB^ z84IZ~4|2G#M^{5g7M-Jwv-SlUei%Ywo2Gi1px9UE=vj_1wan+uV0O(n!IIqHK#->c z)u>}hDdHhG($oTZvPg#m-o+>&<3J1z^YnGR4}a_-0Vu+fUX=#4ImC2hi}Yzp*bX6` zNq@8m<=5*q8)hYNf{iqn7Q7>9{v#D5x9{F4=-Tz(whv^E>`zDgaMCNl3#oBwcY+v2 zQxWkzOhTuxJR-iY9kg-{Kov271Uk`nj{)vdIRQ4O43G>h`K6EV1zlc&iReYZ6{mxb zA^glkA%ga2Wa$yYmosrr)~wgpbH}sG>_Rw@lE=Rx*eQFoX*0c^;|c&(29oT3;7;9% zYe_F`q))^-2;}3?kPg$n)~ep?{?4JH_VYlRkL(K8$a^-KUBl&|`B925D~06X78b;I zx?%5W> zKtRhi4Fs5p1w4(}e8lPXW&33-ucX{F*Sa$o+Cv2$m}NlE;lQ{Qf^h-qdm5=c9o4WmzQg?Y6$|Q=v3%i8-oNQ>Q6MkPom zZ3Qf3e^ac>>>kc&qF#hj$`(>?0%rz}+8%~1H8-ma%w;y$OUPUFM}fA~RAYYAD1b5x zxHY!Ab|@~YghD3cmk60Tv?wKA2yErl+Q(o$nmUu~1*v{H!tt9pq`C{^A!;%NH;q#W zp~UFhrqtHiPX7mclK1U#)nU`_ao-f)9Q7+iytPE;!Wu1QJOL|97O${6HDe^oGWcII zCOe_-g!m#*RCelldU>sfJs}g&^-`MH>Z=YC3?TV`M{oa z2{k#^|8?i);q(z;0(BjAmZWUVK(Z)ZJU97S)XdaeuK2I5@_U(%$Ja?y4C(n2bqmJZ zk-grJTv(#o$u%lY&tJ~#U1P9yG7)J)<~ZVIQZQ7>gy9Ng>T9Lh?s}P$wGu!Vd8eBz z$Zt}|1c`H%qqfS~Hegy>Pa73O(7F^8l=?c@FkY7@Gsg$wD`A9F;F^MjrWu}A z0#WOG*{5U_Wg{&BXfqFBApmF=+*3|X-E}Lhcu&YNsh_vEH5!RTVWD2z8a^(1W3InnKTUa2KBvE5w`K^SL zsn5bkCuUBPy|J-$f7heJy$;9;Osh12Wa=J{NGJA2$&blM7cU~O-9p+7qa5fM5-j6s zvrhK?HHQl>p`(`D$^cPc0ay_0Cx8L+nKPkx)E8yfvQjnmZ|5}~`ON=9{`~(I`NK3H ziA_w_h8vQjYG3<}Qt&5VTai}V_l`g3eDL)O0=|F?Jc$%>WHv1##Ph;>!QI9+1hJo5 z4|<8MOtW!KSUL#D>0xr@qba+e!JZHp9xA(}!nR0lZ@`i#1;Wl{liMXGHv_k=#hyvF zRl`#*@0EK9WV=WdOma}CgKI>bq*P!xnyiFodH^}SRE}=#4X%f2eYk`~);qOT;a7og zs02yNBZI|+0{WK$xH zWl5n{sUx8BFg@;Ow&Ui&A~XXkt!0tMgWz=7*Ayo2bEL?;U)~E0g=GT+8dkxFBP~m5 zolJK5jY}v~<^IwoJWhOY3AMq>NoZr z3C%{TlW<4Bt&(e(w7TAcCj#Qodej&jZU#zb4?x-TaF_ak(_z<0lI7hi6JAOCoF;iM z?J=+A3JZIjYGz76C*+V(#Kj<#>b6buBsZ>g^*TFZ9X^S`4}#v%lt4!)z1)MX3$!Kd zRn>GGP+&AdN#$Lx1(PRcyTY(AiCN!*#g6oaem`P6W{m^BAH_BGe93Q{Y(NTt6hYg8 z+akK<0M#eQQzv;S%KEq#SBR3Z*wR|llZu=id^c434~Q)y1l2~X#EI7Ao9&5;$;emh z_MimQu0ELgNR^YI{iDsc7Agx@&YKgL4$V=u7c2+G(Mym6uPakM-K}Eag}DzT2ca}p zG;fk{KmvBdVG_fysY$f|l(nPZr|6UW0bk#=Wx9|0Q#c$u-^2uay z>ozN~Wtfr(D%u zaKPRFXnPv~#c&$PFri5nxnsxJ$k+PR&Xz2DlWX(lrs|uWKs}-U4`%h3yBe4b zIYVk6-}^_KZ~2b|_YJ}@@H*RWjf_Kv#L6^suZaab-#hv;!(~goA>6f_i=iE>$6n?U zyD1iCsErXY+Se0)Bv&(RY!aftq#2!Rv>Lx35vnzx{0{H+bjfd<@cr&Od5{9tiB3XU zlD+}mED$3CLAmX+$>nTh@YGh;*r~E-dax-*Ww#bX+@z~u;dR>LETX;>Ig8bRk*Z}B zj^E|Ie>7HK&NXiqOa{LV)D^yOj^xN@z80{81{E4~ z=WM{c+0I9vl?o!{{unDm2?5f82I&#IFX&1NV248&^?b>1pY4fbDlt@(X)QYxw=QYIgSFchQ3EVB8T+r5ZhFZDdWIUyUdx_*e zvD{9~D}tI3y^(wj@C-0UnI|oX&UcD{CHNsze(~OBW^&x9KccC2o$sz0g6OzJZFZq> zqA#@iyR8iqqvS5r3APv-m=P6*xZhLzLCU}o9|_0?#o7D4{b1^w6djio=Frh67OMHK&aioVC})F>XLMQkgI8$0ofSD;ZPC>rciw! zC2+&TYk=~k>(RCB6__tsVFI?#0{6q=1_5{)-FVr)5>Bf`roMhGepE_5HsEmY5QP&S z9Ud;25Fw3l5kcJpHo8NYw#_J^)N=!;po09ZubmHHo5H0HlGT9oO*ky$?*Wt{k#qdz zyRTOO{G7*M$q6DUOl?Z{%57?WfUE#vZ-8&`t-`@4`>unyD?{I!VY6>3F#l+v`1|D@ zO$7J)%b|r={N)fdo&SD(C+w=&26D!_@!;p9kDNka^Pr=er`oFrLS`%H@jO!Npto`V zcE>j;K_g7LqRTRLrtuGNu)HMOIubz}#B?n%J8S7DC^HDyk|KgrckYJsP{ytumEUjT z_DG1ylx?s$TAv+$RSoIq={AAi0i2Lw434Kj<@d>A80OzfEEMUd z&21*R#SKNyjTFsXBtz!>+JfsFo;inpqjDo$GmXd=RgN86Wn{QD47WNVcN}IZ_)qb! z@jxi^2i_X2BET*o3Ekd1T==0x#&bYD2v<4}X9K(^%rfs1mu?a~f+O?&hCbZuMo=_E zDH&P36(*yD$T8zbs4Uo8%lV4mA)X>h2(d?~XqVXM7=_&lDLqGlvKYKv+Z@}pV3~00 zlx)gLJ75{e4i!v3q|j~KSIw)YiMkLA_DLO=lvoOOX~|A1S%~ibr z2WeX=gO{@nf}YA@TU;5cv7Ulie1pju77mn*o2@`706)xIZz~_}HN0a`hAdOIfi^XB zSADRpRyd7vKN5APD_lcDFj*T(ml>E2wQ@h`qAw}_N2)&|q7zB;5=FCIsNN3ba}T}c@=UWb65d5j5;)GV&~=alHyJ;2IAIA6Ixy1l16J4? zF%YQUKp-s4XE$}z)>o4kK(1{PmU00UgA3?&@9SZ9;{4lcvOqN#@cW3CUJl3I0cW!| zo5c#W>U7hX=kjk4u5dlxr3m6Ulc1H8v8=70A*!W{Gf@O)W=d0yXm7kRTxy4#iu!A_ z#5zuxs9$fUs&W&gY2($nWNL5oGRj_0@)2dIj&6~p9Kq)a9T~jG&mba+q!K~*n_1P) zSI9;_ot7Re(OtoXK-BVMG2`Xm@(DmqAtZ0vHP9F0y$&X@1$%%8i zcD??5as;Yo43jpZ{E3h_uJI!O`*MPbw?erBXl2}>Io)7_9c`;C#{lS|o6Z+A57A7O z<=env$q!c(W*DH95pRc71w70X@+py3An<`=?PmPQ^O8wV7UFc;3%d)Rk{l{$tAUwh zI}P(~9b7AAQUU1?ifR#KT`Fag5z6OwTXAO2Q)eFdhRCx+ z+E<;pCTHD2mA#x`nIKL{cghOK&TP}pY>Rdtllrv{Iw51?cVIf*Uf|pLJrcMZm6Bu- zO0-dcexO3LvCT&;finI~s)0}yQ+&Nfcd_pqNwf~+xp#AEKnN(4N>UTpfN}QPYDBd< z?=Jznn6y@)s$Sc@gFw<*(Wlr{p-5-HKPr&#kZSFS^)RdG zZiY1K5EdGel3?Noy2YlDY~7fri1R^60zGz#@|tz4ZChNSa1T0w;OD?Z$aHrIsJZkR zmOB8O4{!bCjs(=Nli<8vk3N`6+VvRDWI`5^Z93UX*m&)utpuFqL=0<%TB&CpIq0P% z4=CFLxv;(QJGD%9pO4aKnr$`PqbY3DjWj68BFhVio2MhBwZG5fmqSaVo;&+48Xk3Y zl^MVK1AL<$LyX70=G<59e8u*$5mUq#Ql+fQeeyu_4Xw!1C?%^4o>Am%!ksH?E@(j- z^g9}-zCG_l_V53>`_H8ICX@B3dc9#l_3ng%Fm)a=I80 zZL^!z7Q~G(L*Cp8;eClQ+21vpG%y1n6Q(HEj3lU5#eVpe6=wE{6Btwprz>KEry2+! zG69mnW=?8|RA?ojOX7nx`6yC|2wY!G?e~Bj74`&f)s#L58$^JegMEmKCZfbqT9Mp3 z2Ocv^^$nb5r4S@2Z&Nl4X)vM2!(ak*cS8g|OC?Ts&G`A~8UdV`UkxEM+cQ*jOsZV1 zj}gEF-?G!$q?I5UO4P+VRQVN>_`E4f< z^v7v^;N$#f7S=fJoYhz&Csy`<27G8U@K zTf7)?B{YZWV5&vU^##0xFuYSm5WzdzLpGp!cKhjJk-$C%q>5}7TG2P5#8(2hstrtCmU7q^GtI2m=zZ4dADu%N8LEx6{u{7 zHjIA1O(B(gjK*&-7WHh2eG z$8EObnTYsSTU8sYp{nEdz210BZQO_XV%@i2Bh;P!0%d$4j274}9Iz zHg?WX2HD!i&ns3OUuH>y4GLCbS6URrbC=WP*cCXa0m`DnIaJ4f5LVC`2p(%qdZFSM z?)l)Fr5#D3gw1<)z_w0@sE|^G>Iq`Qt{Z9>su1eLXHhW%d9Y&mJl4?1l5W_tE3&uYav_jZo>DnJ;IeDb zy;oxg2&;xfsmV!?93|{@ss_>?j0;dk4Ek)1n9~aC9oPw2;0D_%Es3ejzg>?$cE8iN zU&ymiHa#Ip8xX}i;||I2pw*o#O2GQbQUhpxyB?Xi6*9m{H7s^krhoZpgxb6=$J^?jM z%(pVQoqEG}OVvG0sYK_5KIW5Gf_@+skXrCcY^N(?O5qfA%>{`iZ60jTAU7tbyNA{1 z!<|G+1IOu=sHverX9`4*4B|wO)gG7|vf;G&$o>|}?~_{{v`Cra%tJcqN9>+}vRYkM zkRHh^`|(6r=DYhQ!OPYIF>$*deJB=sWhFm~g+7?c>-F7TaXy)+$jOTm2`sI8;*lHe z#oq1Q4X+3S?$TBlP*P%1YFRI*;;%^3`hC-F;I!s%~> zdzDr{C){j$R<5*vX76SYGkXB9DCVLk_2T%4yI{~1(t>@9x&sPL{N@5fJ~ZLjZyCXX z5~BI|?Rs?Wd)5gZG4l0@T_x9WK3fb+QJw{KyxvxiVMnTn>!cjG64>A?F65i|MFs&t z%}#Qk!px$hC8He^LU?*YeWKkRkQ{Qc#^U#QIl~Wo3ZEa8Q1dF2vVvHb;Qj$)tmYmh zgU}ai@Nq>j*KNdNa5+Im&~N3zhe)2=b(#(a%RYw)i}2mP>TIR4@9vwERsn=0J|%{W z@pc&8MvE&e(qZ~Y>D&PFcOotT*vg0|9|O=nQJ~;hmtZ6Bnqox}eNE=#*rP)AbV#Ql zR>AJ%?*Q2hes&(UdiI9Kqfa<3<c#fQdUXLD)95xqV2NmywWqgL2kn$O1^Ut=REvb%0NHKzC^4bhltX_p zyY+d+V(TJsMYL+8l4SN%_tDt6@FP?sHn5N>?b{l#}6C&YT#=0$ne$<`T zfTR~!DAx!>ve0po-EhkA2@+Pa0Rpp1Rni7?aEq3B+FjIV3_Fh3qcg)3s*8rpTdAko z>_#7p5$ zGpq31-P_YSYBe^?$eXzVu~VsWdEg%0hmG2vzDiIZS1eoWUC@e>#^!DDv|Srq74BRyn+RdF1Mn66ezI6k|{~8KrG2BaTc0FVz=O=?2S|pEbzQp<0Iyb(Mv} z^yvQ8R>i0RXptz=mUNy}Hn2>0%&7CxN1{^r(;>(&r?!^Iq{?SLdrAjUsY~=^`wl`X zbNRFA;N6ug`)lA5CKTD?iB0A*`AqX6)AmJAD!47kXhoF0m;<*$e)N71KTRxsH+&5< zP^++}n!;~9BhR!ekQm&;BRpNP2)tfp6qrKtuJ};1#})?5MQ6qogr>Aq*YB_c2`q#A zBw=(jsWA451}R*iIqW6O=pjm_3}U$ld)J`*GoDt7_ZAuGa1t9!+n{zbrF-jt@T`Kp z1E#a3l}|+}5s*mlvc4WsbUfv#V|BEIs)TF;Wv~mLyjkMI)EGM+jLXR5bycvZc&_{} zDu@w^00|9Y=}1ZqGnWo5!T7yArU#+2g=s?S0utL?Nyj}r0W*ZXbNmL02n-w`!0JLs zgbHX|U3~+f&&o9E>chqc30>0bE~|fH+jQ4t^I37xs^)n#;3&qU22Y5alV~9VHTZIK zmf`*&N7%MA*yeGD?uQT;@Z0EefS85AIqnOA@MI#wmoo-92m1lYrVUx3nwh@FvCcux zLk>X8tGk2s2)?&uvZ;r~1m0=VDjwm?xk@WFjFV`fbMz?Sw~tL{)}YM`E$Gg&?9%W# zwDAIrzK6I%BGctU3o23Mp*;=Pzrjq?Up!Z{S*=A-(m_lQI%VgrIR%o>j1nEImT#5e zKm%ePBM8k=Ur6y5B+;N9W@!r{20tS#a8Z)KLCIKXgXG>(`A%z*3c;cqOP!Bs#!Nja znhgu45fHRPvM4f{S53jOJ@bY3|2R>$d&FzVcOu?KjJE@eL@3{wQ7l=SAM~hT zK;Y66rV^|J_X=r|*)y<^hR2$Yb8qv1<7!C}-HzT#s2rNLY}-=`PdX3EuHBc=eO$5q zxD+-UHq}apAqCI+W@H3X7~_wQ@R5FyBrN8e54v#t_|4TGdu-`a@f6c)W7pg;7Yb!t zQn<$Yu(a}jxLag&qPJ!R5{ZFMo3THF9Hte*ZvQfYM3d(LGyz)fu$RYaNkb0dYWJ*wka|eJ>si`TU zZfWZ-V~}_)_{@PtSwJ4T!T~Y^63%nfTix9$TUvBGQvbuHT0BokV2FI&h=aCcH+M9t zG<^o7cagyE7qYw8O5}{6hZDao(~4mN2%^lGb|!m2aT1sGNB~GypT^^HLPJ7jGrpvk zxC8+-p#&k&41Lmrk6N48GI#(W2K}=I=vI4<@<++DJy^3VxH8xjO<}vq=%ckndYcm% z<-8RQafmRwNu^Dv{@95ox2MUvx6I@GR>Lc%_2cF??^riCx`Ese0Et`Tfj$|8CT>wc zm7KmAtMRvN9>3Lh?jZQ28KdPVvNcLN0+bKZWf-OI4W|tVBb9HAH@R2S+KphH_v*4r zZ*2KW_*C>Z)NV8uuLMNpdUVbEOz2KYN?;K_cH^OLHDzbB_vl^a3OKEUhBdc#5k0(j z12JD4saDb~IvC^jWfdU_f(t3i5ivLDUa*o`iO$yUEc?E&ZX6|76FLZT#1RLec$@YC zbv}w_ZDCGqH#;%;dWC%&jj^;f@?Oi(5?6~_?){Jzi#Z?}Mzg9$ooKKU1CDWfm?R}t zslYHN%_^IMYRx<}+>tgGR{RLdkvc%~D|HE5co|^q?7=X%x!G{XnELo$do_quqb+aO zqifOC?iR<>(d;Mp^rPyEY|^DVW0hSs(?mD-V~cjR#eM%O*20_mSPDakLBI`c?h2~u za0ip~RZOf|wjjWwAALu`ZSVK*QEO#SzC$Tt5!>Lm#Z^lCBc^DQ44N*4M}5754ebzA z?UTqG9T9^xZP2XBCELL0X(!;4kqA%#Pu5zYvo`Qvh*3ch)F2hF8@ZDj#0HQ>Zy@mV zB0euRgrj!@Fm{5SEqa4OOGd$(Q8Q%vK}O5KggNlj0E0y8Q3LNl@#*NQL^Xt@jNN1H zIj;}gn^gXnRg#S`@o-nu4wafab<@b8zfqsiBN~W5D96Bls}cK*p0bh|u;1+rH+g_9 zgB)w)J+5LraTWNbq#=S>YEZdRA<%#1^ztL)6ymnO_T%x6*#K#-tK)c zss2|VyZ@E?*ulKHT6-XE>C_qBd}&fgk9K%*Ym+p=}iJP;~ZbXZ3;|#+JHWk3Mwvh6-rId>sqmA0gsL z{&wJ|oi%Zsc_0B(^yjFqS=%CUOURHgRNK?booO3KCKcA}2s14KKcW9P4DO$t-3H{? zS2>l&oEGt0;1piZ_ydNH?+1oI67fCC_5k}HL1d{=SSmUi(aB z0*DM45KHK%teK%?-A&8N!b6*FQM+Q>oA7!-1tBexw3v%xXs}3O!3%)!G}2=?G2|_9 zb+bDb5Fh1!Qh_b~L!&avSQW6q3$RC>90fHnKxXvUnb#29G!Nf{D5g`03s!Oj)HP!n zMOCtQ`yR+{atrUKcdVULkSDOS@5i=n+qP}nwr$(CtsVcyv*Vc^+qTWU`#*K-oR?d5 zUrwr$O64heNLRW${ryy+14e zMRhJE;O#&@JOY)xu9>WdhJeqL6s9dG&BdaU-gJf>=m(^>2c)M0`0{vax&hzCmZufK zOiHxl^|c4l-Kw2T`J$DKuf8O*`uf;p17 zTqYSR0Ysl+Pdt#P4>CdAtOX_;HVL`rz;q27X}2X0uMjDt8l9IYkfYPcR~lLUJjl#* zptK^21$Xz8O=-SRx=Vk$OKaK@+PRj30wKuXTkrl zrp)KY8!65h?(2en56@7YDcUa^K}A5lrsli~Q8{DNL!B-ftyA1Qtg4_kC4n?Wr8w+P zQ1xhUm5h|wB;KzCB&R~@b}Vco;4y9{;9_OK^JJEJ?$O|Cr<>dXUVT`}6%IQ3wpj3w zY<#-+1ML80_yi5USz3~aIPvu8pwMF4yJEh&XtUM^ico~4;zV9}W2?`s45Xcj(#w)4 zbW>Gei^R#xc(88?4mv8OD#_xv6-0?=!R^bVJa1Ul-Y!g+lzFE#Fvh3b9kC|J(uyK0 zk>&1gkQoumx2gH%st^=HRj8cEO8f7oYO^atmj_6So27WjSjYvR9ot|eL$xfH`c62u@u;a>a+jEUuXHg$Wq?YOQdF-^~@!3JRkyBL42njY(86V z7P6#U{&05{(xCNHccYc`^njVUZ9TCIW$;EZG+N*pcT;o;Y$mZdVb{&^U9m8s)Gy4* zL!7ee8{y!~RBE3m^c5nQ_p=la6vD}c!{xPmf16RwQ0BmYUXsxGpZtr)?aG}xY_})hn4$blfyuQCUJ7DV;mv)=s9jR_czNd&Z{?!5UE?~SpiA- zOl}n!1@3n}d`MJ@q^D#00(|r{hd4N3`)1OEH~EolAK%xV<@x3$j$GJVL{A!Q0DU4)tIW zE>_w4&MkovX>;uszm{?o<^1f@NH2z9+^=TR^>-zGmz;<5TA$p2fqw2RX}~e_dOQMI zG?sL!+U^ak*i1h@lih1licD^x4`7TYhzh{rc3`Ny?6vTR=*4_R@J=P*#z6<56Y;4> zWP}`(lh655nfPT01V12A@~bR2i3QU@%VA9uN-%a&UHiF>gu$b_p<>xno@5SJ zB@yu6{z9dQ6JgW&vc_+aIT8lvwRwwQan^*E&Z!57%i$#&T-j=eR2FMos_!1DW9AF; zvE*;EX*_u~%EMF9I?@^XJ-I1s*9A5k6;+AsF(xRk~s*nHbS=+^s@=45l8dxm>D=EEOohClHQZ5bCw@QFR~WH^@;YRAt- z7n#2NS|ME4)p_?^B=}QgPu{JdK2S7y|8nl5(`PR&G-?MTnL9nY=X&%}f$> zcMcAK>s^dQ9%nq|PK62A@JH6Pd`*n|GA$axvip!~1mZ0#Yx3;_U%Qpmh|80#>|_;E z0y7(c>IylIstWPguoI39;x}w2_nZpXD;wlKr3j?m;SjP9f+9@HyGBEsZ%|w45P#Y_ z@>Uy?+TPvg&Kj(>NMP`>xgu4e0WxTw#YknP@iGzRDhHu%-WtIWKWViunZ1Z9nMWfR zhRG`-lZ?@R5Te+M5a1;|=3O31iO%JQ!2f&hbe$)?aswEf2xq!el&RcsB*Im%wXDwHf{CC ziDl(kX1rHi>YMUq{+ z$6iQ%9JRi8N9vpcV^#>zmg5CtPo{!4UVlC>xI%A9oShv{CUPr)CWoimN9;GP6O*KC%%ndkd7 zf)~$})F2Mz#)1mhtAFWczB?*-wi3{5Dl?jK2XE z+fSS;Q{QwIqQ^$Z-SKu=cBOCy&tEpo(<~8#95mky0PIt=1w*dk@ylS2+x$&Iamgay zXtSk3LE;VTae@}RdUUc`Y&Jftg?{ss{UbOFdYQHD=-jH>^v3s=$m%4Mb3~@S)b&{j zIv`vC8%G$mW@~GBQ$`Q=hF1>~)T{*4Qk30Ml@MZV()TjO&v?9s{dVhguSKSPSi%@O zbvIS7%=X!-F<+`WBs8pCqsuLAtFFZE%DgBu$M2W^DGdI6`Y+*?Rtb!7kMc4qHIHZOZMcIgG@n>ra1pNW zp8tV-&vsMs)Ck)Jq)z=+FP2K!O#W$OaZx#7*Zy5!FkZXK!FQG8TP1h(I`r~u*#4HN z8M52aDtYEOaRrYIi;Cx8&}rJ!gEhu1Ssbbl4M%v=f?BNE7!)`uNl%3T3qCf(ekW8l ze9_{Kd_V(bYb>AF`Ec<9nuP>#|b^t$y*CZOlcfgisG(hsg` za&HAlTM?zCdq>%z=5S$5yGk2w0IXN9nM*q1O4tG}mhi}`toSk*;BkpFvre-{ObgP$ z&^*sDV2dNRPiyZHG8?%lZ_li9Ry}_klvwn0RSRrM)c6=$;-Z&!e+Wj6w;_ebv`a=M zj2W5}lZUgelLYk69#OPQk0V@Nxm*sdzh2e&1b;Ylcq0Z#5t{91ursw-NR27)MqVJO z0fNlaBGf;1kgOI|YsC@;X7Msm#z4~LK>poFArn^Uv^87-X`=Hq-)hb70HOy1XTbWP zftEholO;*Jk6)8A8l~_@Djg=UT|W)FgPT626e*cL*c#7DemyOJqX)Yu?oAflA`Gob zeD|L0)pm>>5teaf?b@eITz(3_DKfeTeWq2~%I+GQmG49YV%}R16Jb1BGdpwXP0mxt z^g4A&{@6WR#A+yRrhVp26SiFNx{Jibi=Q`IOhh(i3;@&mW%sum)QlJz^SP957~)=? z(AD+{JNhFET(D8AO)*qp8jZE>iemfvtxLM?pjrh)mLVr94^tUNufajZgael*r={7E zX$yQx<`=9qu;^Rvp2Io&W@r(L6q$`Xm{U+t$>uaSC-^>wSS5A$%|Hhj57{Y(eK&59 zqDg2+q9l{A$Qc+?TPiRlv4a1_KfT`8HF$=KTFMA$o~mSSg%AajifVS!zO_NJZ_pc~`czeSu^s3blS_@yN~_pl?|z?rrD7nD20%ap zMEDZe@hi#OJS&EXmXl1%F$Py{pWt!nBDB1EPAQmiQQoX$BIx=tcz_#3*$$wdr7qdX zUuYDyCsqr5T`Hwq`9iW_w+p{7e@-x*bbiQ}(gPUS(dfz(=>ix2O5>PGc=ckpE(p2u zt(TyTD_c9SE^H!xj=eeVa-Q8P?qOVcUAy2dH1Y!PG;1jaTQIp`N08}pPXEFvxFn+! z^~k$-Hn-;xzQk2#TRUM17b!N8ikDPR-cw&s*v2ukYSA}WkRKV!h=yW6(&}6l4oiFQ zdG9gEUoyfkth?h9oviFUrr^3G2htyM2^QBmr@he07T?wxVQDco7tZFl8vnmM*bXUA z&cM^JWXf^k9?Al`@w6y;Y`ryTvymqd(~6FcOb8#3Oyjn-T%}Ve=H7HQHhC4d+ozIF z0_GyI&uJ9%ODobsIJB>>{zTxVc3$1G%V(MRhD!p=futt6hno|avRltg+x}6f8&A}$ zRZtX%7u*_e=mGzlCKV-j(dSBBKi2!+%nqpjo*b!=Y~p0~*KNcU;wsL=%r@sRP601O zBH=2?G^>{@73rj)aQnr25=AZbt~Da8E$EJ%{8?}YcXZ|l8cFqo{%-Hk+z5!)08Yi< z?Ja9M#`HXi&pj+S&1}#uN%}JLwQIfWPZf3mEJ~|6l$~opgCvuUN(68h@-c|maJ&ED zFHthV?#v~>oyN=OA2?#&<`P~a?8FyA^sw@O@u9}bw0WX+iA5g@co0w^)w`Mi7zBJG z6$n)HqJ+Ds@}G=<+^G$*p;$$PCh<$U^9h*t^;!R`RpDEWj|DNed+!mN5Sj!~oF2++ z*w4@4jN*FB5NMR7Z}_-=z>qyzaaj>`d1&aWNEr-r=Q$9n&EXIoz#F}`IQ`YSeZ9&R z)G=9yNop`bBDB?L4ulGlA<(dQe)hKP3{8y_QwhSIrxPT+C>jEL_#wbygYpe0VB+!X zceKl>fv6))6#jG@-Kzuj<>>USOsqDh&rt&~g(sO}&d%vX>-o2UKBidMZ3LpMmWe}o z?{?ceRG7A+sS%iGe>Q!dJYBP#MIrDb?IpYZLI?u7m zsGPPD)>ohIDHtm*9tBsa$et>wa^-X4FGi~6&Nlc6R=LqA4vx^wA{zTxjh;EwJdf)+ z*^7*r_!uMUaD*-DWD7;;U-J}N5dg(3sX0&+;{*Y(1>X)b}9?F?+Y_p$NmfB7{1QaT>!T}tnM|uQp zGoW+x?}P4n+6>sDu3802>07eP-4S`weMsLtb*jC2n4cM4WX^ZXRHorqT#x32J;R5tpZMCkQ3FB*M!=IckZ#p+Q7TRo;{X@XJQ_ zF+0fIURhDwTDk9U&mDmN4gvi^e{943T0mfC&w(B!K-_U0UJ!%_}K>nIv zot(cL*F6nUIf#3dDcnMxdjy1^MMJmHuJ>^4QOx)y(AA&N#Z>!_<)#xALW*kGUv#*I z^(ZYJQ{DrWC`3G=YFuE2y!#A{JJH|e-Zsq>EbS3gsB+9VrbfkuaHx-rq&aHTxd7C)F+X-OEHNKA^M!61uuaVH# zrQzU%QQ?z$&aHjY_KN{??r8$XR6LiFORVf0gG)Dtk%ci#Ks0~D#E6I6`O9n7t)s_=yVP{ayE)SLH6Ts3-8tVVC1w~!BEEqofm{yKgJ zt27N^Wk+>dspBA>o%m>m&R{oUd0i$tK}T-W3;YOI8?gQj9Mw6l00-l|DyJ$tNN>8R z^v6(RG_1wVD7Cas1%iVjzNcgfzY{}#QkrsG&of=@xyz6WemyBR`lL59k-plKGLv3Wcv0>Rw?-!J{I>}sDJA=P9C%?s4 z4+wP*T52H;PHcaTe^147Cwi=oPuia|lwq5i6X;Vw;1g7t?0M>HSn<@8Pcm(N64yISb9bBF2`Jv>t|31JljMZIlp{HxIDbOJ@4^!&Nt_nB9^yu$#J9zPsFMN zf_aPK?PAZGW96P#e8I5hREQ?^_#1bESxa>KjJ9g|mKiXF;J{S5CK6P;8S1}RmOktd zV1WGoj5*@6L||R8O+#UXy{8;q(`BGSD219a-c8&M5uX)eWXe?zjVe3`X$Q5A21KxM z@6^(cC#Ixu0HVAV5*fCHF9#j`I3h;r+bP~rF6fk;cOGax_%AjHCXRz{-rJiUQFVj` zwnN>3JD99fMYn@5?orB+6q35+RV5-N?zZV2Sm*{52xhpe_t-v|Edj5>$|+igSW4p1 z+alf!r;}k)vb+%PCtZr2D;_YV*kE76F{~VfQGdH84%X-sg{QQM5^)59#EqR{k%=EZ z4sP#IIqK_#!;)<3eSK#D@=!bIDCcI4WN+%qg6k&cq&}hWgJmqmwGz`TO5D=Z zX-)+{lwjTbEvj9XK7IyI3aLm9(XP|N#l^X(F$xx{uEm7jsP7pHi98aTutyIu;E&|B zL%$uDgWV>~VSw!Ch0(E^nuFraX2AN;CD_69*+q)9H7<5a)p~4vq8vC9t*hZvKBoslqc^ z>mVvVeM9U}XtX!J9&P0QM=y~66H@&Z-(tW6`MXo% z%vi+{eP;7;PzZl*+jhH*{I)A(~gC5iJ4iX8HCl;Bo`4(#e}kHgMJR@&cufmGc>ePxE$NVs%yF^6!$w>~2wCfxxn>=Eb`}mP zd834JC1k9&ZbyqIn!ASsxO-=2tR`e3hCvBUA@Yn8OB57jSA*pvd#iR+=5ie?+XWV` z-RVTe@I+OtzgkzXktbM?e&sl9DYsst=Ps_LmS;c2yB)BK>Er({N5{p8Qlr>j0z9_C z>Tb2vo>oycf2B+17R7@y8Zm;xas$PssIUxGbHm^WuKRUDYM@P#=ajd>t*cgYLBpK} zY6!Y1V?Dw6ObO>#hEN+H%I9rHV$Yb+d3SqC7Kwrivtz&5c8KKmII~(uJyja_BknW) zDrN*Sx@om4;J8YSsei2C#B5R>ruN;_9xP@j_0V*5RwCb_9wd=4bv7#K?YX8`csoCn z`Q>PIBi#Nz99-p~!Q4UmPrA--s4%^#Uk^AIm3(WW7msa~sh>a!`;1LrgwEA`=gd|d zDkbTJ=AH=8JrheJAYxOt(&20pWOu}Ka1QsmfY-gJOd(Vy#zp@w4*B;jg^aZtkKg@@ z;#|_X>}31J`z+)c5lWMmP^thrG}^kHBD_6dstdA8tl)3xWEa7yu<(FGI4g?173rn@ z$_)BouxQqa)1auUQ^sJiBdBZMK<003ygX^NAjxxK!YH4Hqz`cq%BQw^fzs8`s;&A4hO< zNpH%wuV|s+XRFe>M24%1;ffPG74lE9Tb#6)a20~=-%@fAo_n9S5{1SH2b#>T5eC8w48*E zR+7KteLg6u1lxI>ZxP_AX2TD5p<9#@jaQ6$5QUG_ubTG%VjSUAsiDN&-SH@f4vEs|#xEO})Ch5WbG3FCl>|2= z5bnn2O87**fQ-2Do|ekCLD=UT_pM+N=1rfvz=P=ko&bH0ur-oUtF1c1SrCLcHz&E> zhecXMEP^CSMlPr;0SK4)0=h9wZEU15QrQGRwVd#clqIiDPDNC>Y*LM2wxyo1#cRTZ zjwmFNkr{r{H*@hy_{dBJh?zEZ;|jspmr3m6y$Zk`l8`v-Eo5cZxc;0n;EpjLWHS6o ziMVB(W=4-R!nb9gP6;#bswH`#B66#%2@Xk%^f>@_mDGr+#5okPl(`Hy-I#^B8LRxm zWum;_aZD(&K*mZ}hsW~^VI+3Z+UwK5f7K|f@1Pd;`BASA4?v~Q3iYD)F2r6^Qc*V<>HrOaB{A1@~d#Sfi!0qnt4 zen^%>9P)RD+`03F_hPREHcX6Fd~NCCU~>OE)M5kQ7K@aG_@RxOV&&~9dxY#?)1Bc)=2mOUgH@DQmUr$=&zLm(iNNS0bQ1nmtx2Fx7rp0ZPK?q8>Yozn^-tsEh`Z;X}m$ zd6f@7QG&h}Kv+ylXwwnoPL?7SP^!O@@(B@g5q${sTM?_cH~hH6#!N{I4N$2rn+zeA zl^NWmEfVFWksvxz+oGZ5F$1DVWP>x}4%}0yt;1d76^;oU{OiODKA<%`hbji%o-t=| ztez94N66@*s=(PTb)uEn?fa1XhiE-7hfDFz=C}9zA=Q#Q!)EO{e`H_hF$cq7ig}^G z)yYe;h_7&Poa>SeGhSn;WE2R0=7~iQJI~*~ykmMO`6x*^OUBTYZbUt>yu_Fpvv{8I zE7g-o&hw(A>W=e=sN1#Or&>;jqrq|TTFLTd_HXXq=%X>su`#o9$5{JCGcRGfD$sl7Cr6iEg*fi#XA!9pUnkpUfd*FRjii<&~`^@!|_ zt~P66tcHT5kH#i=aHAqYYhzHS8`i~+pl;orYo)OuV^Bz0h=))-p>TrJ1f8C0xLh# z_&`dB2;P}hJrIysX$(eRqtYCoxpjw$uZ}tv>5)0^Sj5#FnZm5=3lrhr3Pb|FE0WNRuv;KzNRCgHBTwMs+XWf!e~ zVayp$`~#Ft*S#+d9)D)PE@f|&2$ow^OdUZLg1(NqyL!vgExdLW#aeG?klu!;^eAXHLsn^G<|9cZ?wredSY zC}O-nbDLh8S#YoxwQ$Mg64+&{pKbMYd84D+we6`lrs2YgzAn*`jW^WZ-~z+A(%mpP zk&zsgu2T@C7=k{u$l=k<63j_}`wb{+B}|V;B2Pi8&`c`h2)nBv54;Sk{shF__co(MniGhCmY`;${e_J?Dt>%xN z6TLCJl~auNh1^tiGj zuKXg$NM*h|Tjb1EiOmTh5R~~Gw?!C;+|ITxQ(NzjFd*@6cJdfM*g8HtxU?)2x4Mv4R3Yf2ukAnj6d*ZBm1iJ`;5*={p!y`G5@ldiZRelz!7e zN4#QBdJgJ z{Cw#D!5i1wbU5Td@_*Gl0#yK3@jS1W$1$3(<6Hv+Btn$VMI%Yexv!6YeC@+W8aXRZ)Uwf#MXUF}qOKe_#F``P%rtx4K_p?o3UC*cE zumP+ObC{DCPMuA+X_(HecY_CP1dLC~95s_gRSd1C`^>ef3Q~xU_Lk{mPO<5|F+Ym~Yb=cImkYQT0aT>MUNH{>cv2ilB^&zEGnJ=4^MlWf^ zHjC7vscwotMSkVd$#AzZQEe7M^kQtD-Jxx^dj!Oos<^p#s_S81C~T2it6U3EaJ!Xz zF;kT@2{}42!XZ}z$0;XjMPr7hdvTkHQ{zla0qi7G=Vn^7=L@>m%{^(zab6MWeZO5r zb^dO-v|*0V-|1PZR)&@~tFl(?n|X1V>Oe?@ATR&%{5QRz%bEE|?5`=c@pJ2~UUjO^mW)7?Jbufn%%`vag%c0XYD1sm z_`un0n@b~a+1{1ptU^K4PhS=nw_y%w>(10vLF=M(dz>#?pR)YW%$|?rFnbwTy|kjk z)7v}!jr*;KK~kZTzi9tY(+U--bLF!?F{+`QOF* zf2G}vw(T8hVB$mn1_XN*(ln!3|B|mAlLNeR)2%t}4TUBG5p#i_?qPJo-vj~n2s#l7 zf+Cx+=vtbM)|>FO80Zi)5NU(Zhb)@7?Od#fJ&XWN{A&VNKps$$gZoZ&Txj#bheP*! zZ%!aw#Ci~!!)<#TPSnJh4UnsmxC8L}%wBi};Ik2@1EqfaUIINp22h2Ar+4&k9Da~} zu!R+e!-#v4P*@-d10qJGPDq_ld|+&wK*CW;#79u{VH_fm#j$guXQX5-I2=(35`Ii1 zVaTL$RK~xdNma0#hoUefIx{$i*nhK6voEo4u^+Ktu|FXP=0P243^VRWi7Jq}m?Wdd z=Xo88kx^1`D^EnrI&i`YbCsl`7Un@6|E8jp;dY+H=P=lwV85e_t*2?RsI_CEp1d(t zA+A0v%cYBkWe-*ZSlZ~wrmxJLY<_9Sp7bP{;#v-%j`Bpz2Qc0VN zZEj2jhW7XMIYc+^H!&fS1#fMDPILF#G8tsr0D_x}0pY~cG5;Njph&@r)(@B8|E z_%ZU-(^1g-du%uG{#vuJP15l1!{e(W@Ad2K?XF<|_q}4G;J3>U;KcT4_7w4N?`MM`U+>r5)64K-qGD#>ul}>x z{+klc$qxM|_(;JvK7n4ozfK5$-yH-4ygu&rx!>lC-`}qf z6*nsv?m4G?B`O1c-uCam7a{$|BIvI3AC@=!1$%n}dG4ekXT|RSo-u+=t`xxs8e1E?`ZZ!P2jjrKF!ot5lPk^yNJ1RGU z%g0K>f?Z6}r<3FJ^KtLS-S6Y`OPj9(gFpE{_TGN4kAtZ*m#d=W@9TH3>6!h0-e1K! zvn6Lc-(MY`jEGe?AQKU0nJsw_`f(=KK?amSY40-J2N?Qe}^fyM&E7qg6oDNDBP z+#$O#&>OQ1f!LVz0y>z4TP_0Q0R6|AC+Pu8G69Ld5hri~Pt5`rxGVvpE^qP(b%%~8 zV|ufm6JS1t!Ak~Ru}@g4gPc@;_wKgKuDItPbv|-D#bD^M2 zhH|jk>K!EapE^OmKH^@YE*%fVfN~}%eBe^Wfu@pu_`<@t6q`%HDJ(_izyRY4UA)-_ z`FrSGN}VrMd)^dHTRP!pDzgU>N8S1p_6P>yG+FY&`M2*0Vw)eZ@zJP1D!IxGznuGq zV6jR199Sls84IPLM1(84koW)$A$m^kQI{b{N~C7_70kwJd=vhWt zgVGDFK@IUFh!~-2-Bkt*`Z-vFzala0_BBy$Cg#APg315nN}DLe_h5K}f1@FXQb+OS zXfzMiQ5gnv!ptf}y~!R6)PcP-1Q}2iY6$n0DwnE=e1ukvaBLEI0uP_xm8YPVMJcBrIgk~d!UA~&E55|m8wK*L07Ooy%E{rW0JSFE5;E{C+GNny}J zn|qi}HXHrmFqOl=y+tpJvz{;cryr(T?@8;X=yo=bzB~01?!-{%pc?!KkKG>ntZzaR zoX6WwR5>qf=SWK+_-N5r12lMF4Q>3PLtORF4Q94=Xt4>#BZu?3Z=emOdz1}tz^(P{ z8m6VRD5v^I0xEdPbb7oJCZ>$PmlV~+{ys>LTM)HIq1tR%3p6aGrd`H6z9#We6PKLm z`q!N^#t-sTnkU9jLp7x#HHfQks6j%wnB5{c2y%=Bl@fOH1c~O1&%Iw@k1x0}&KxB; zb)fT#)IWqz)?i~;B>-xcAJc|}u^CB;MHpHhMaXQGx+$E}0Zm*Ywy_+IdOQ+cNHW8~ zS|w`R#F4$am$3k|;UnbKJJU^5Ao^e0YrOIw$RrdROwZ&!?hFX6aIxi<%@ry|sa-+yvGg!)n{ZPXKAuq#%ET~uo9J{5zj_qV4o{D7760OQz) zs~wU4C0@S69i1b-FE9lS>4=L>(bDNqs zB4L+8yzYC|)^YHdYRlJMWv8mO7MawR*Xc?a2-&Xtf@d zhr>JHaG@n!XN<$nKP1f^kTvN;C8KQ&(vQYBHK za2g4iNE1yU;YdY{rn%8Y$)XE?K8?|`ah-{}1~3@y)`rw$_M6-Ly7M!`^Dj(+xrUq1 zZqS1qi5O8!y`^Jp=emOl6*HhYp-nRuMxRO(^@LExbu~BRu!i_;+JaJMhC!E%#rvRo ztmk;*4AK)=1~c$$?7S#ZSrgj8dRYP0kUzdJ>yKfBHADCp7YLM1swPH7gBVy8Rn32E z`c=R@o%K85G;vMmoQ3McI?@K~`aHORskw3~UOJGrCL~)VBHDt>eeHy+Y&3eL zsH~~7bOe_mxH(DGv?S{Y)!d$!j;!RDAEx3vSfztbcIT=>UmT9&`#bm=xOOe`ogE6a zOPyjbIOebi#)jVD4%H}B`;r^$s3O*We#BJ_)d?0XT;NV$)Fr7HUE?m{~pldt-diFv0pGOX@vT2k!{{+Sfu77QQcaI3Ml%dmQ z*2-(|1ixBoAtb4_Q|l-T?FJ=jr{BkkqBl=f8Ep!9XCGONOr^#sBS73iPHsEKcvGcn zT-@ZUBJqsMaC+~iCTVXfEJguXv@zb%{szIcC{I5JC$U}J@B*$KUXMyv@=1VrKt#ZE z2Osu9$Z|E8UO!%fsV}PmIk53GS@|*!H=y2WG+jA4$$I|gqw80J@NJz;T2BrF@fcUVQFoOFLxD-;@9a_Pnuy z$d!VbHF1WsO}^1$KG2j^6J^&0EY9Jtyrz_;K*bPT-(VA8t8?^=D(EZ--ShIdm5!7{ zaOaR%;snuXXeW@H3a-ULEn3JbA8{j4yJ!$(3IM^}5W|C=wfc`t99iL&*)xQ{mluX6 zxCb1p=2P{VG3KZ(@}Qh(MUCM&wJO6{LEt%zEUkMNIjld zYetH=j3EhJZ`Zga#w66FjiL_yN*UHezC|dv(f(Fu>*w*LHpcp!;1u)$l**2fpvZ|< zm8{7ElB>-;=7+vx4#RN8Jb?&;yz5GFT?%Sbw0LN|Pa^)AE$f40V#In{9@T?F!wXWu zS6IN}k^+kkfhvJc#6FJ}qN3B7nPfG@ZsG}`^e3_;V~`0So|%nyM8E zq>Ey-(-N*UXx76p4RJK*sJimrsyd0pRY|t!$ET~q;Rxr3W<^>2HmgHl##aAef4f_> z5|SI~Q48652j{bd%&@76TA&JYngv|7dUpF=MosL-j%GT|Htx}gqSvFNr>2jkZ#>Vlxh*rrE#e!AQV)7;|6$N5 z4XkdW>wv(*O&qWTiauB&hk~K99913VX(S9!lRg1c<%e+KTL$(=hC07tsdbebj#R@} zz@e!e->q`W{y1DnX4`dVnWUeNZk1@#h)(pz_1$FvRU7D#}>AIHr=A2vIoK(t+SsV)W>RlZiqc2b`NHghA68hnA`GD zXT&z^1Jt{_;?@uMtvlnoAv+dT`fEmyc1)oCX*7zdT9Vq$sb)FuFNXP2$|k+=PGyIM z$FQ1*H}!Sf(9*}@x3}|8Z!yu;-4|93D$;N@g`3|p3WK=KeQRIF&^s#R!u3}8ND%Wa zh2z>&!EY#^h?%I-WW}5knP|B!J|FNWYoPCv!LfW+W#C&zAcL>?HGIU2RVhfl@3x<` zDq#kZUicp~*ZB?fmxJv6SL`Zt8E}uoDBHbkcpVPZ8;mIXterzFHM$JM)1Va1HW>@( z+zs}JB7(Pg60kmQWo~9pN#C+Y6NDX^X{Cms=q9uw#%#H7h6zG}Bh?z_{74;3OWS=* zd!_a^LDoXNOHfd)8T=;F>0hlj-luhv^8O?@y_zg3UV9aN(kdhw&Ylh5_7UdiF44C% zO2*;-J#fC&16StS2WJPvNAZhlsZ_CkFF~>tA38WFuxG^@43(&2iS4a^wUX+lO(D@~ z@fd27?x~}`Ev5V;4uge;Z6QBjwOYGjYXXFqxXBE_h0Tsw-%9_=rvjjQXi$^h!FyYf ztue?6pZ2v^qgomJ=~f9W)sE*^3jihdHhWy84|)BJ8x>nQF@DM)Wf~{i$R#J-)kpj> z-!%b_RcF#_@ook5F$N5Zc6GXYS0g*sk+jQS$e%PwFE$3XxAteg?1i;CugHn3B!8`F zW#zz6y(ab4ECyE3x*HcPwT@C#CJ%0un!4}g!1@w75rY-s_^4uao)Q;l_u=k($2%@g zKx|xvPQaNfk(Y7&IEH0BF!UGaQbGXFZ|ylU^bz9)0~&wu6_fuDYws8%OcZU2wr%5U z+qP}nw(ah(zqW1Lwr$(CZB5^sym@mc^D=qK{ZTuqN=}_!b&^xN_Fj7}$Q=LCXBI6| z*5L`wlH5NOMV2Ar`(h6&#Yd>r`NLvY)=)KkDG8h@?>!;*J9&~zPJZPk^0mS3r?6P% zyQwl{1J&FYHU;2PlUqH3rCjnd1i(OsX1kHBRxdMRI957Z1+EHI2%yt~h5eoRK@636 zYu`Y#cl&illuenv+6x!UEUY8Q7NOYzF{!zQy`HRgjeTQh*(jf2H{fU?iWWN_PZzhR z0yKz#1yzX9e}%e`0VUicAwudPOy{O4=5QQjDdckzR0W8M@cgp_W0IJ(1WITs)* zk+5jm?d`t`O7xSN==6zN*t7bXh;i`kN3Px7iaS>X0r%5d5g`riO zoapymWpR)n_kBf$eG~tbRwsCpXr?`G=ZD?-cWdSP^^-SKemAIogNW{8RDT<&JZO-Au5Ue^k zjCgE*qM3?f44EN^Dy)S{e%zOPPbVNRk4tY?isse>nJ^B|51Z_K9C0s~E(Np_c57K1 zodT7;vMlLAw{&zXa4&p6GA$R7EP!*sFD36ymq7O?DTP@<+MsqLte}LYU=qI42mhi! zQay{MvM1^eSryjK^A-q@OII0Er7ad=KKo#xK~28;AT>n`A;@L|y1<2dOKctEj{Oo< zo2@U&=N2L{;LxAo8cdj|er+BC;f&-$2Zbs7Ls7nt?sL^fltwFC609F3NU8mu;p(s z=34)FY;2o44!n{seOalsmm_2;VKIcdMz_7A{|Oo@>Du#@M|2=qd0?GKY-(NCt>dfU z44$*k(WSbpU)1xVg646)GtKl-B?zjX8=!eP99uie%KF2qh;Y9A*IPZ3h*3$&a(nurKn7Y;F zYK8{c(~CBTf|3i}Fz(bwv_!UCkgYEVpPrE>4|^RWdtSK?vPL)`6QXb$VWG4ke&$88 z>_Qc**odJCgXx>{d<{4+tPq0muV~KVJ?@*k70YLHrSp?wSwj=$qby+^%RD*%l0;zx?aY)>39zWvxK1!-KwWgKJ}hPRv#ei%-q3o7}(k{b-jw zAdJ|`_nMY$Egt$@Mei#9ChcINen6Ve5Lb62rwn6dG{#C>fJ6K3hs=Z11J#s~1v)ww z?)l(azDEPUT2fv)=EE{_z(z$l015d#rNip=F!kz&dCubCS*ct$#6|;n&4KG}W#9yv zzZ1<(#L55610;DZpt2d#Zc^LGPhI#OCvG;wErPa~gs*cwwfcZvr_glerxb0VM@p?n zGvjYD%$iD4sb_-YXkj4_MCo`6)Q9#DY9kjI+hPd_8FP#UGwgf0*HhCU)Ai zcahnnW3p4R6S!oUT0E-scKH{b#XxIZCe_AL8EDh2Y&>1_GVZFH(#4UjS;^R9u&g<* zfi6?!AcqkNdjOW+4JpvcezM-`k#)B6%v#xTwE^}bV&%MO+dgpluluL8YXsW3>Jo0c zR|2zdffy#|90cN)>;~V6s5{KxES)1ltavcXcpi|_jiP}o3-YjZpg!R3`10I=vFS-fa>Ml@!t^M&+Ora)5!d{u0U!I% zcX(-5rL$KI);QbKR8?TeRu@}12=`iJjWgF6X;5ai5=}|N74dNTS=VJUpTUY z?i39BcHnp^NC?_vcuiG>BLxGo>*pDLc* z-nW#@Ry)ukHY}-YAvhH5x<(gpj5K9!uMuqXlQZvBxR?Rn50@OmnWARVJj0JD&*9T= z=vTvz-FVttZs}#t#a^_(-nEkj2sA8I|E4Y@R1UWPQL$W~MhvlwlR3-(EPzSkt`(Sx z8NyE|OvXtg#=5$3i_VYce)9)-$kYoaCT2gy8{vPjq~n?7Er8J?ra6fQL2GNJDw6@> z1-jY{+2Wx)IYELaYoSt;%9Yh<3+)o*SZk~w6Q~(9L#Vo};yS`cgWKv=WQ?zI8!J=dSqX@EBB<5Apc>cZ<6m^+#^xI_Od(o$_QK@^QY9?1&>&2GQho_ka0 zZYGWpuYp}Ri(NO$MJQZRnsUQ>->PeAN6W-cUu0*8fW*?t8IS}d(q}l=!p5O+nlTUE z6v)k|?l*1`Z-7|5rM2wXQA#3|EPD0K(;3Z?u_UwGd=u5W9{IHRjQePnXuBzSZSU{j zQtyEmt`CH!fZ4Kn|noNTsiqFE2;Xl`ql?9*ijnjH7T@Z z()`E`Akt4mg4-2AOvkHm33?u!{mfh>L3%@FeBw=nv~dV7FeJMqS#e6~vLV`KEBq8Z zI~lvIi^R9<%5$cCv<@Vm9U}KIoX}ZACT)=YQ}7~^m{rW6nelELoQImgPIB6J9>RWA zG`87Pj{_g0=w!qnvNC*@rkX1j43qPb>Y;7-#s*4hPjfE@iklcD-RG1MMwQBj11?}% zV(_JKj=74TW_3kObQ(Ani!+6!0zS5Sh9UIA{vzq$qk4n z>LJFa(iAS$1*p;~Q7a96Bde}&uv4X>?hu;;300O>W82Tu^06z8^cqn}GGl~^bP0NQ zj1I6s6jw6=8$m5R$0=E{8ch2UkV`I`}6_3xlnHOR}7tL8)<3ht4A*yBP z0aCj(I>ClQKtg*IlMbr~b174+ZE6dVTe~c@O^E7>ZJo%sk~sG2^@N4;_!#Bl%t3_` ztJilGilNPG3!qiV1xA0q;`w9MKX>{PeXD`gCl|h(ebz$l231o>5maf4#nDk+XJruJOJj$brahT2OTs;W zYAh}tRW>EbOlVB&Ys|}1<7o%eeAQU$9y(O0xlhewX>6M3{icj!P*2zEYBNWtuKm||4`I_-4Pphn`j)@2OJb z``zjxN8_<v7J7CnRh z{)iu)ly~kpPZKbJnbZr4af(u1ta;hXa6)CyCgN`hAha@S{y^QawY@Lp$Z7Eq(J251 zxcV}`wg8^G)15myUCqR?v25?GihJ{K(l*VJDABBzuO)DBjC8J$ipN<16{%-66x9~< zT4QM`!Gs}e-#?9J!6Q+s#;CTIcJ6}i^IB5+o?)ltsaHM+Ok>kO)EO{}ZH#i9Iavz^ zyW89{RY4D@U^JM{^)O-?aYXb?7?k3!--0B6T&O&fF>1xnjKV*I3k{y=?G!J$MoxJI z`NvVtf3C_yFT+3yv0(!aBSsaw{1(_y`WvJ!oi}H2bH}2V7^KX_Nlf~S0-9PNo|s;@ zw~Pc-WWh?epS!9|d}S$VhM|3zN{SNjD3PpC^s^@4IxL>mtru)Sb-3?oiY?>)b<@~4 zt~RlYA`4A7sb4JIm?x%}D8*;gk5)X$&uq|%Ypu}MKW0S4-j16))$LDgnYk!zhZ##tpXa66gV z^8RNHH7y`ai}gZlueq6!i?kUW0X=P_4o*-0)LfRJye2zx*3G%aYTJw)7LwPM{j-Dc zbWHpx9L6&d<%0I*nt=@IG>SqtXhGs)?{{2jyLII&uoCE@hAO;r16d)>dd3O}i&Nh< z+FUm8)ViKDsmn@!2j_2>K6qew%+Dr&q&m(m5d1>1tKmC0{-~0%aS=-{DO?W9nPK1j zS)0*QFm#>j_(k3{+HwcD4bkV|10~OAj`Kb(qRkBv#;O98Q7;VZjF_TWm2O-WLYE;C zj=Hh@20XnK)&7J_BiT`=xv!Fp|En@)h%dXTIJ2I*F?EUaena93Ouo@CsOxfDwXRVucOl=3NVqUGQ3+t2t7R)1aq=@3PmdRw{a6k2Ns*g99n(!Ee-DyjkX!*Bs zHiZZ+iAHHBSaPmBF3ak;L%_)+(jzn2UKM)7Cqxv~F2H-6<2Re-s60M?`kGb+wOngX zTlk$zC8PY)`trl4JmSIViVQ_e_RWE7!Cm9~_aL14PzZCE2HvWyW)uRtRAcU{lPv!! z#g#N?_;!VS8KYJk*aJnMW&**-<|4+^udO-WRik6LI_PezQEeu7kY{ke@o(xgm*GmC zZKzq3k`02X6a!+}x;3U-RXNn00bt*l+NhmIaam-(7ebN|@-3Frl+y$nc9e_Oy6j5$ zY$2J@*aE`^NpO2kS5Vycg24fK>bWwrcaiA|%88nw^^9LW+W6Xkbruxi`t%CrAQwnr4Gb|vojWIS(|^FKz#%-y3SU64=h%^*v} z<`2_|>8KnYtn2T`-mKq`82MEYa;OAsbs&&o=0KUEvRbn?k(6obDSx7+Nvr?zb7BoV$6$(Zk5VY25SN?heLofi zqClo2LXBV)M`6HVhux}qi36vREXh9Hai=F*XRptE_(GHuBJ9N9Ft%?k4US8xIFCp5 zW+mkxUI_T=LO00lax!J1#v5}JU7tP9`e-o-gx8x*$_AFEHysoRc2V&GUu zTc`-B4)q9|eU|hA_h_(gDt}Cy+oycmc52e)Ly`m1FgWQPT0h1bAOQ#7GGsTAn+nw+ zl^~Y>%F!l0Jh4eQK_c0{DQ@+H@V=pIq3kVH$xy4IV41Z_T;AT;8;RhqtdoUZE*?nN z%W?h*R$#R~r2Uv;y7wjlU4g={HXPW%dQgXu_yDqxXP-&J55HMmYbNOQEcGc-K;blf zYQfz1>})p0X*b6ZJvYWkwjLuXw)bi;vbZ)pF3dZ``%@!8HU&{xkdTN8c0~ixOk965 z3>1RO`6A?eTU-pq0RnrfNIa`LdtJ29ycK6Mnr;27pI*vN8@Gp592U`1!p@d|x|gA7 z7LBx?wD^ofSwCkp8X7;B=I6Vezcy3yNF{B6Uo^EYy>*UG;bQ5P(U#$G1wzNAd!6Zj zq|Ukd7(kS;TbotI*oV2-e!ts2><6W%K};gx#u_xo@}nH+DqxL+gKZSwxe{6rO-T->}E>eiC{TFJS|hUbd?)rOa7*0A}D#cQTYNka13Nvo(*RsH~OFk`YqZc zV!@uIee}s8JH_<=CCkAHMrt!)xw~Xu8((l(r^6(XgW#NZy;MqNfFn33=wL*}=vQSC zeTk6T(4JnqRG1EXwuRN&SoUA9#KL`I2j;aSQ^eJFAICz{=W;k{_M+W!u3SYc2xg-V z!}EpF(`HT0rcM9EHXHqWcks`&!3A${_ADUX+YzhreCx_l(@N7>2e=e^3 zes1X}Cc0UnPGH#!jEDYmoX7^OmifZ*aL~xe!ovzumtHS^EY;tdhRM|QhT-0$of5M5 z=~LVNffMRiyNksjP9@oTS9k0EtHcl|zBc5#j%?-0@{M7J-q!}%*V~4sr`b5{XB#a* z&n}IQ6z!~7!l*`^TQslya#9I!n9EsdT*Y3*AOK;X~>|1`UJIZYH+Mi9U7R+-!?*G+aP6qmz+oWlT?C*>B(J})YX8TjC@jF*0tUU-t9is?<2 zRjmnlkAQser(%76fxaW(h@E+u8)6oVoW1QnkTM2GS+*=9nEWmjnknl7;q|j^)yg!I zc0)IeL+}>xLuIx3ajsOCW<>spXio{uQ7|K@s`ggf=oc;>%-joos%P`3*bW7fCOI=I zIEpf#$ci%IvGx^)=jfFcC+DImyahc3Qg=Jrw80+$X03-#St=b63LLDSjpUE#F+IGXSe&uTj^_Wu{Fm?{{lt zSE29CNXL8DoRGQk&`-pCVP-voeq^jkR!fZ;CH>-<^0X^0_mfvwg^;=J)_5+cstHGJ zayVY+ObJ&VC!&>G#Z5GfE>EQw&E&Sn>wii*px!Fz$FGDxd5a!W+Zdy|xSxKn8X-< z&$aOY2cHo6v&R5B$)wJhq5(TY>RO{y96U006cp->a7ZGJb**ISdQ2r}`+gmgpRJH;j2nXBnJ|3m z6c-5JDxf9Y&Iw&0y8#eWKl48rGW>n|{<$S;rHdfgOc7|M6Dl>t46xS|{n>0SAdVyQ zq>v!~rz?pRydwpnNuPHZ3chk1fuXW_$j<|BElT-EYT@Bfg^mBotzg1!7>RyUUorXXf}!= zdXAv6_}^>+F-XJ%0H1*9c;Rc02{qP_S%khs0T3n8{7vr@Ab?XhmqY{&++4a91;JJe zFog(XV(y+4r2fMZ1X4aygZr*T1YeHe+X;q1bMSH8CG4m_2iZuMLM}yM*D3U8{2r{5nbN_fR=Swm}>OHaTWZ&nnsP|%5Ff{7CRpX?FBH6~>1f6^N?27}e4 zczAeYcst$iZ%0!XzbrFbzJ9)+2Q6~&ZY^?p+dZFeC3-vE-ES9kH#^;44;vBaeBGUG z9v|;=H@3PuTYhiP-XBK;TOS`idSAi6=yB)om6DDcx0#ojml(b3?aq&*rHvIo-k*!B z0Y2QE>l{Ac?=3REOYD5uz8xN4zwYjL?-!4s z&->TU@8^q^kPrA4Kd;t~?)O{gkE?^HrGtl&juJnDe?6U@9&h9z@g38az{fho?(&C8QY#n#Bd5_S{P zm2RD~O)fGC-OXcxX?4$4FKYC+8m05uuAah1|u_%VaR0 zpX?@s5tDLbHJ*PuZ4o|ar<_aa%|Lyh`Vj_nyYgag@!()-wTi9JR|owU8%QI*rwLlQhoWWy5x zxdAs7whQOL9_KOZ>i23L_$>(j){*Yb6k9bl&W+k`#Ik}&gMqCX8(g7snrK^v*gD@y zAoWh@W82WO9Dw%IT(C1yLnN-F`PWk$_vQuc==}0HrYCOUdoe4)&Pj+(x4Ks$s_j(T zNWx)az@m@BEguWOHHu9WHgLH0mFRvKP90a(!x}>t1NphU(cFGmJw=Yox1`w87i??d`g|8hG2dRH+iAi zX$-n{gI4$}zOlz10C)9SUaQ(I>DnEPl;^n%FPT(GFI_iJ`9rG!N+gdO8`KGt%kO-u zM##rny4%s`#}oiX5HHjR1iOtiX8@pLIe%h&Ey5K}@4m32A{b%@W&yHvRvXU0>L8LW z;2>ZDuZnPia6AfcmsKl(+$Uqs_J>p0s&I;we!|(+9=r5L6jf6ddWIZix9vTZko_#BZ}TZbZ1KKQFB_Z7-6>WiYuGtO*40o%TN8#gNT)o4{?)zu6$wH%wt=H8{4LCy zX-b3rh+uv3HFYjh9{i~iw?Iw(-stP5F>7K|4oR=O@k41XTSA+jb~JbaDL}k;{$Gpanr<%`bC#=t}t;o7iF<>bQ}IW(qgk0M^bmf zJh-nt&35W5Wi8{?XU!tCp9SL~5~> z;;yWjU|C|7%^_?`VW}%{aW&g+(>{teRskxw;!twnoITpw@Xi5_YtnE>Qs@E)?E7GGzE2g*@T%< zng-;XKbhx)WD<3!xPgx85?dHKN87ZH-pejx3oejM4u27}Eqp(t&0)yajkJB#QwFGN zxnBJ`lVLS+uGJfb-7%ZW+N#g=7G7cibC|rqsM#v*B^j6@P%D^YOnLg)Ke8J17(v4D z>{}E?G)tLv4#dY^KsyvJ01o}Qjj5%WEd42eZTno?Y%jzaZ|v`W-Pd9Q)jPeVRRcj;4kB=K@P%WRCSTMMT0?(?ZPf-#^_-00cI zF#+n>2`(^LhdGx#^0=XD80EjKq3IK+M4yKhgI)@OGgQq|P*H62_KOd}4=Omvs!yx~ zyu=YPvrD@vvck&cLj6kD+2#jF8Y)c_RAM zBJR(vT4|$W#7m<+O$Qs_cscdzB`2~hK*2-R;1PryxP~vJjPyGJq-bTX2@MEfkLH3} z3=r<e=-+%n|{jWWI{Qu{m z#*{(N&BVgY+}Xrf&&I{t*}}-$z{!dBKdptSg^|I3bUo`i*&8@Inb7^u04Bra@P7|r z{%bf@JKp&o4G#ct*$M!l_}?(Ik)5rng_(=vf4cMQo|EhU^yU8$OkLsVBowgT^Yjck zlr6n)wkTg;)4bTXwX22KrD0OCviDlMUa#muZ>;{U?HlfHN|BwK24TT(de@@4L#pmK zMkKS-yC;N>IK6Uew(psz)(xz{mqa_8ZvyMv=ZQjBKzQ9)Acopr*ta*pA-j7w!s#^K z%MV>BJh8#^Kxwqo3G%@08CFoe7}>LIh0&X5rt1!*dI{h6xw6p_F-V@(0%B978|M5( zqb`uSyS9<}M^3lzqF$)s8+nUlBJp0QIV5l|sI%G`l2ujXfM{LMJ{8T7JV!Wfkt+}< zGM54hi$9KaP+j2OvF4A|iG(WGHDk~%YH$}P@@7Guzhf0!IFaD)fHZh91rEEPz*#$* zPah*&aI)vw5PpAu_eK9OMM*1L@QpfTcOx<+R23q&6S^by_z68_ySM7M5VV^WZv=7^ z$c*SMY`gy|p*_GUwoB@W?$L3pIcP%&M_`jO+G(=K7#&YYPBJ0Uoic3GD2w@=Tl|*h z=2(F_DN#h=FJO@%rDSR!7M` zWGmhoC&BN)2^i~2S4{ul`yC5(4?C*mvq6?t&g+$B9INxZ@7$OW-Zp`Un>;G-M`AKZ z#IBEYvOoPKDQJw5M2yx&@r?pW<%XqXRDX1q-5h%hqE2Kp(tmg z^u*ceE1I?|vxjD*Q$yF9DZb;6&Aq!yPRzX{V*PQGEU8lzl|34ltkz1$rN`Q;zKGjY zf#>+dnVte#qwdD;qMc2{qhNysXF{nf+}xMK3L0*uNc;M<#qE7tc(>?@u3w}cb7CfV zSJ5RmdlvE2u5FQpN+4jA%>r~AfhRBb{_>Dlr{=Z$GmIyC#k>51waoHWkAl3n?R3-> zET6jRS(^dJ(h^b_T&GH_uST}eri^(EEvFU^Xa@6*SXlrxDqYW1rXppgG)c&=Pl8AW zwN7ldtW}@m)q7k3?j=^{I>a-bo;9B5>`Ka}kNnZE(QlHvsfOt-f@iOXMd#_aVx{#_ zoXXe!h^?w?d!*3iavmG7bky_eUABQ{jj%sKb8Wi*Gf;h8)qH8W#Sx-2)r653je;KW z5_&4}ZE)=)0h{+Bz>>pJ%JQ|2-2j94PPk&@ywkDXC$hsO1-*^_#{PKfHM5O#8gcXI z-+Ittg0nVOmD_yW8+&!o!9fRFd2b4+)mGY_yDys>_- z0;Y?%X@eVEy#?h=^J`DBxH*!3_3!VT9v_PSOB%|5CY{-FOPk?uao~4Kf}d~^WUQqhY(f!8Bg<|c!Prd+$ zpiy*_$%kIwjVAOj0G@^-;cKwsw=wo2vcL{-9(6e%b5^vKaij79aZoZ@l)0V>LC6}( z42Z=!ecMmR?xK~s-Sh?Ql%S#<b!kk2eyK(F3^YQ})`dUdD={VJKd)$P~}Hq>7gU zJLAM}r+t}u^qH8#rl3ZGJK_8`$az%)vFQOI1T+J{eSt*3bQTN^Md~efJKaH3K}(N@ z7C{Xu^PxK66LTGq6F;924d$4g$VH(k-KGtwi6}yyAVy2>8V@J4u zWg1t3BQM$bm}y=MYr!e4dnJrk41`_(to3~(t}6KMT)KNg%}Nq>3r;Yrn<{K3ieY1d z_XW--Yt4eSHi|jvg9p9w1M;VLENVZVfOi?WW)`1s2(W?|L>ValI}(&MHLIDhZg!7( zLmqqi+7G}HyQ$DN>-o9gu=C6LiQue^CC(nLESk+718Pr0lYEgljs()j`gDZl7=N6P z$vwF&IBoNMek6zV$krT}NoWpFj9V_uIA3HOK#CFazcCEVwou9uO;U6uUNUJy9o;o= z*4g=xbB$<^seF7zu~Y69i)O=3M^iHF0^&7UrrIScS-xqhUdi`w))cQd)kh^SJiX>e z5`+KyEvx{n_T+s$W;vAxDm0yKk{dlqRV#q|1BvCvkspF|5g`uk6uk_vrV3&^mW13e z-(zsNEf3sAo~|`ywl;BbX(qOOrNv=j&p4vVSTqXBG&ds+=gqRiadm?B^}#cRaUM@t zBX4QZwpa`8U^<<&l_bF1{F=^H#)u*)p*#-s)ht@Jg`fVEv{mCVwQt@Am!O~35eH z`RIc)8w>HaPoM+B{x9PD^@%URP8V>y;&Q0WH5RIg`H(h_WGEFMu~j?^gWh9_ziuMB6!?6sm0l(5nhuMNba=u=m`?`>~V!1=g z0lNN~ma|l`ZtEQ$Hta{D`K=!&qt^D}e&(~u0lV||6LASP>+skLQhBW?qS&D84dw<( z04-Qy;~0$Uruue=%G`?nol;i3jPSyV0_6Wn8j(ujd7y1eqebjZaB8P)8QAHPw zefnvjQUak`iGMOJ6Z7?MWvn;2UVY88)1IwW@k?YfP)El+s`zE6tw0?eb*bbN9yI;i zrHTgGyF=0oc86)ype%|QHjlVhGo5UsOv8x`+m;u~GmND3VznWedn>!~Uus@M)2>VQ zvdT`|fSwjRa56?d@K1m+&`~%wp+Mk;l?FmdUxIpLI!4!cyg z1d{@&e#&Ctj*)sDp=!wgfg8cVDA<)#4&jzimnYS+PoCowmbN8AQ6ondr%ElGg})^` zV_6us3AC>;QoQB9;6CxQG5cN(4wAA?7a3;y$NnP)tQt;3ElKguA%atBxvdkM@`9X? zc2ZehM3%$1Y!^K^C{>`S(mwi|yuZtQiL4Ej1X>NtW=mItp_x=6YTxSP0;J1p;Ie7~ zJ+lJ#-i7|`eH%A8=yKsN^x|*oCE~Ofe7<&J+7r;Ow$(xb&C{AJ42NQ=_QF%jyM$G- zshDN5=|~b2arVc-wlheHErKd|RUCnS11?7kwgkHOg2Rad3S*rvl`*^30J-Q{L*LlLlIPCmB0kD}~N9^uzP6AM1S%C4-Yv zft_V|8)a8e^e|Gef{JEeJK8+m$BJT8rC}s|hr`h@^FqH-+9=MJy&Z z$rr9iJSH>YS`(#Hy+^zQdx6pQMnfCBnSE9!$0Kx%Q97b+*PI$-A}DAZ4VuJ(YWyAo zBS72Bb6JaA0!_f7Wh6dskL_d&EazJwcT`cHdv{un-$&c}M3xj&9>jZ%IkiG=sz^d! zRS|&_vxHw+hZ2m0+dC`^m&3!Cx57Sp&k{UJ{;zf6a<0Tq!@71-QPy+wrs?pe?<;k7 zQJaR$=u0eI=hR-==g0QV?d&DHWAS$zN@^;KI`rfKuJ*o89X|!)9Ay~F7r>h_Y>M;? zS!6qH90|XKh-|sz2w?0C7k!P0LB4{d`u$9#%AZ%)X}dZofdDXhj9!TBDOHop&tC07A4$5b{~=g_kIg#ZtyyoT{InCVcf<4X8D3 znp98Qlp1HkeZ1hZew?}CZid;els}@)!8WH|$5bo4kwhDt+Fi-i0JC2pOyYDn|0l@f z?2AOEkmr|_6d!S4tNNE~T;eTj67l7YyZmqDSJdPP>F`&%;JCOH;fifaIthh6!30TB zlCg=rgXlRw1g$3J-f&VUP?|2cZ+ z;0ynZyDI|FsbS7A`eWf+t|4I&=n%rL#qXqjt%ZBSTf6xpRwe|@>r>ecXf*z%l(XYV z6#O~Or}nc7!vPA43eck`f6G1Zp|DFYVaB&F3n&cw_XP?c#8Jwdl(}IUp!@^Za~M_V zz8m!(<0A*_o2Y67%41tFdP&bg7gso1z4;>L`Ke?4c0l~-5FB;D{YDZ| zB|2Y7rL@ZiDu8UZ9Vou&v-c19f6WZ==V6OSSOWlHqXGVZQTqJ<%1Qh`j6SXg)-ERh z_d3f}msSJuno8B%bj@zj4&i|JRtg`EGk>E2EN3e5saWM->votjs0zv~+i#y3yU8QO zYc3+merY6ApHZDQDxOWEejlA6+ho@J)dVezF$)>eBMt}d_LbH zH9v21etGDA&pkOazAtY(yg#>3@Opk9Q#s$4H{Zu4zoYp0a=sq)e4f5u_`Z%qZo0o( z{QSJW?Rq|*@p|4zYW$v_;D5f^^uBs}7EE$_JbzqHF1o+7Xg`MNe)gA4{O*?E^?ZKb zLUR0yO1xaYYr5ZZa(X_#AM83Ve_p5D_`E;9R(5(knn%vqW**sp-Zi2Z?6UW2dftz7 z{Jxgle$suP8}PWk&~tvyE^b&t_&%O<_c$^uBI0 zZnigfAL@HFy$+9DMnwF+m*8`Ljy7((ze{R-pN#N6S~FGN4^lp9@tm|D{CvL#e!kfB z{C^r-kPcpx^Ew!J($2oVOj|;z# z=Zu-(CHvCz{n*<6e*Sf#Freq>yrZUVjLnX(c}CCoYwLyY^D3vO?Hv8*{q5)N`1dh9 zRlI-iasS50et5$6JtF$}IlbdqhyF3s{q?l6um;j}EB5~q4N7I!$U#(hp+X7^mX45fT*eGWHIvTbcv zv>qi6x^1-1jE*-jUh0;#hjm4}d?a6dP6v)hyDnQ4Pr6!YqUNnuN~YFcnqw3nSY1}V z=J+<2Kcu@?%3r1uUlKVgZI)TVZgzEDBAl8#Z8lw~yl#wcil#h1ly5c`Rk{i{*RMR4 zuWgc(k9CW$4Nj$A+FLf~huv&jDtn5&YMo;(b$>RqYgV+quw`B}N3IgCsm*+rS6Pg? zMXywZ8dI8A-rX(~c)6bT-iw>bqLwP)7dBmIY`Wa0 zuBg3m986>^YP7b^Hn`)s?JVy$Mw*&3Cl};Sb0g{Dt!qB>=fHJZ1O&JB$sw;M_6ytg1_;K+E(^tUdT+iEZ|&u zmhu#nG8LSqUMruw;_kQzy48eGIc1cw_%x}V6oW7Ih?X;S>V3+*6dII~oW;58CfDIw zSZNl1cPcMfW0fm<^shH^kMNE}#{4+uTRjV>c#WlN(^R)Rnamq5#Fik6X?Hl)E^&&! z+!dF}^2Ai#q@{^}F|B(*T^^ZEF17g8)-Uk|yZwXilBh2th)X+JNoue#T+x>^@5 zmJf|pv&m*Hf;}Kx9i3V=Dzu~;5X)s~Y~)0QEXNneb)3X{$+lfO%vkK)pTa6EzHXP{ zlt&f8&N%YnHF;su!267yDq(9*C_vj>d&oG1M|o!-ER5ok#b0}Wt#y?=#T5`Tv)Nq_+c0c`%4-ES#PnL*Jk|DG-r%~k_V0Ds|Fgn@FOL?hJ(vjQRmUjR0k!eNS zx=WV9qbyU4FN`(@)iYQNolN&-9MjoqY2Lrg+E@l!PK}PIE3OuupxKr;>946XX{--ShNvypyx|pa7$2G$A`R4J)2%L-?yP%6j0(& zyioxvyaD*wth+^>MQ-Um&G`s1bZYaaUwwOJ*yM!abnn!oIYByPQTN3!cjhRp$b{)_ z6$C%4KT2@fJ6ly(vWcT#l!Sa{cM4?qpv^S#>Ts&RXGM^(1{cfrR}Kyd)ptL}`|Pwc zL?wJ%4Q0su&}I;8u9#AtQo`BqLz)t!C*^d1Xo5LN+Oq!2&2 zJbB@=#lHe_9KXv680rrZu?sKri)DPKVZO~T0y))JSyqcSDCsQ8Hw&ZHpf zAH5tlmy)6smq2)~Y6JkL0ahC|!Hf?KPgrmPnT5U8d2<5?iy;BxqrN7X2%G!I;Q>EesOu*ab(jqKpB+C}ZLz_9;DVVz=qIZk+$2LR1yy6Xz@l zfk>k(C!xZQ3AV(&ye8;`nb6(UI!?CCVm)<@HMFu`@lD*(O4AR!HHOBvk9VBv(jvDM zoW`ZAPb1oCuWxuqyt0jq9q#ft(g0^-K(EHx1#OXyjx}VpN+R)016_BgT0#Zje6*zZ z%+c<_VtxGi_J(ww)VYUg(_T-i#?=~khM$*eh4Z3*O%uxm5<Px z;G~h{=M8WHr&phw4K=1Nlh=-=I?1GSgO;CBMAJ?NO|yiNzKGPgm6tm`k-NC|xofl$ z)C&{`7_o_)-8Pr~zvo2H?J`eQX8@5j3fWUt!8Bdd={Mgti1-QfcoE-rqJ zFjHwk^AHoQ(D|-{RSGncdvi2_J35slEZc%)Dj>N|N&QLV>J0uCwb@E&sduQZd#(em z;GCC^t(+o~#689lH8zG%s%AHsFH?hnK?_;p$w?r!TO3&p635Q}enk;fte1m}SZ>i8(tKxrjv$hleMZT@uK;3~#pm>kOhMIVB~Me2H@2ed_CgjM z{|5kIK%c*mx<;RXhzGYOc~eQs<0Oe|zRQX3ngl(2aTVMPCZ1EPS+aI zCX+=XtLN?3ep+M_T$NBxh;k&Uyqy`Aon^VUg9OvQg^jyym_>up?kiZ

Q=dgo1g zOm>BoEHg!fEZNM_4C@~<7?Eb4bjdUCLVwfNvj<&X)E>cuApZ`Q-eR+z(4z=lW?N z*Xsnnd>2&`K*FkN+QX`mg(sEPUT?pfh6nRX3mYBSDW_2hB_6HukP< z?YnzDp?zB$r%M)K{K+Y^`$_1c6791V@dP$~#n9MG6IH7oOkx>)C4w-TU1@45Fd|Pk zsn7ekalxG#;4W`kw|Chck{Yco&8ag|s#8*2PjDxEQe;f#Wp&7e(&g3wy9B?yCM-KP zt~G`9zAX|xzt#&2CG@i`Y=ll(PeR;s3$try7wMo$vtN%gPL6IsvnWq+;-3>vUEcb} zP{+i2PJ2Jg=JoopX2P|@R`ANutm=MBjOv%OsRgdpE1jX+(0Z4U)70q|X&uBoPBL*1 z!av##8ZkTxNxYX~G)-rE$U)uvHnC%pZ!F|-IVLk=l{TfAC7sk3kvPKZ-uN!0kCsG3 z-&u=lL9$f|KxzwaNJ$M=3X4!eT!UZ2T#`D&j*`#hk(1{nZMU(=9m$Y&Cp&M-qVyib zD3-Y?zp;LNuQoiVG(|=0@^(GS+!Crv^0P^i>T#Acxj$v_W7CZDP_36yEs~)!?W-5C zq?c^tftpw`A?Xm1oSop9HCW>0fF^7-UP7v z$x7}-W!n<1auGX$QET=Pt#YFWenfLHNLo}HPZvpm##-GnE{i0ngBsc$cjEaD@DfLp z5wqJx*JiE~(2|JFu2I*flFK5g$-PJt;=fCmT6JpUYb(^)a@t#jmxg4PJt1i**@s(? ze8VIvqFJvI)t_2Yj+_*G3^MT&Qd%G0(LtI7*`EZ<`|WykjYvLuwNcAE+htpUUA*I>z`(%kRtN6XJi zc%Bg0yS*7PD=nI>JNDng*O2k9>6FnP3hd#$IqqUQSwY6@v7|lETITwA?H8Hxqmaee2|`Jj%kX_U zzeRdj55oGj3#dIHpP)%)k;B3ImpUUkrgg9pLXseVV=rl~hi_XkSEXd9)fQqw>IBn~ zVg2aaR>D&Hj^eX=lh(WR^YU%A6=b1Hcgh{nrj>AdyjOCfU=5Qo)u?Vo10fl>mD)oo z>yKtbLU*8)^@tiH=aV$_Bn-$BJH05op;#qYY&hX6^JzrPf`1cQ)FStLi-YlnwXhy; zByOVE^RmmFB>i-FJ*t@f+Q1ICIbSSgD502I3?~!YA6ht-OYkcGA$5u7tB6`%lG9z= z5T|UA3YPhYH93DISxnXy=a$)&YfjjwMOsQa)w^vF7?3g5No;Ex#v4C(&LfV&mM5g! z!|r`pPvy(GMmeoq@d{xf*fhtIl)mW?sp7k(XJVpIv2u-_5Fx+4B}v+0<)b;)P!Jz- zu1May)KViKVaJucV+4u4;47!j6l|buFyVHh~WD>T%Hn^1m2o$aB7U`Buz^68X zEw_j{PAvFt-L8R^fX8We3WY$9J|vAm&fp;9Zm$O*nbjD!4eWxf8UnC-cI@Np5fQDaLD5>T*CYdz+F{u)iiNs^1kP5O6L(dPBdVhwMG01@DkVx?6 z+d5%zSx{r25xvxQs(32eI9gk>Mmx4Ff5pjgKXQ^;S8wv2`Wj zrI-*9R>-ng(T^-9CR!$>cDXz=?}MqE(rh+@Kpg5{8~NvuhePs262)N_FB16~ayO(= zy&cBHl*P|OU!RYzXs>z4ks#GSdW=201tZJ=2t6P5_~aEAXbKcC0K(ncBA1urHdtM zx3KR7PIYP!!ocZcvu`#G(WQgTT(Y6Tr^?9JGWY0dAtk>Mp>5E69r&P)Xh|W-%89>k z6VpLqd_fQz+J<^|ea$Y%=5{^0rW>{Eumu`9shFWO@27mD6#kb9$0gb`9&*b|^vT$l zVwCV%!Idxm%7CbZ6AcI?dZLRnvN=R4QDPYzH3GF=^E3$iq_?wtI9=LLL*J?`8^B1J zS%BwC1a&@GYuci6Y4Ktsa8Yquc1*O0zDhJCB%QpTPOOe7ik?Ysg66^aAaYL|&U_@3k@jfdNJLI(WZr5Knx^4%5FjPO08!&v@ zJ|Evpu$q`5uFGaK{B(2;qI8Uggzq+=2`9%&7{2N0wepT%2Uq}gl0YkFY)x)Q)+y$V zOb(02yz)DekK{4yVGjXwnSnWb%$QU(YEFCGc+5T-=T7q90|>KPei^6v z6PXi71REztqFs+*tbt4zEFGM_v>rjbp%6jD)V4;vT%jdf(5w|Pb(@}mtw)~W6DE&- zA@5~r==VxTXvu)4lg(m-|G%AnFg^5u6;F(`y28;dtSdC!}Vq*7GhxYcpaxA54b)-qE2fDSrV~>ngFljVCFTV zS_?b4A9i}=Q!kfbCJ)j=TymS^PFRvSy|kLF3GK$_fbGO{!|s!|Cr#_2oz9zTh zxud@TVrtOvC{d7F^(~(=eJDc06ujg4znZKebEwHDp15E{NmTYipgUt=X~zR2**wh0 z2mv}irc&qd$$%w2w^2r86aa=1f}0K(p)O<;eY&3Qs1Ii^+ndmaT#kJ^LeMfzv@y02 zk{Y-vg&;Xv9!L$Q+%dNO$j>90^yMvL=7-wSFdhFyhYx~L#7(Oez833-zFy%K5cujM4On-2}y zWcibFZ{7@ZSSby_i)&jasvbHh02^85dWCG4$9r~A;n=zH-%Ue%N1+4$&9_mc$hFAX zLzeCGRV3*K=}uxF+Orc}iM_pvY}Fd6BM{I7Kwc*+8p{C90#wDao1CWs@7eP{8QVep zBhtR-_kwT0%^>w$ZyyeN3eZvRR*hOZfb3RaF6tYOz@%i+*?ftE=cDV1Gz~nc_G#|U zPwC^(Id5`*(y)4-3UtRHuufTs!TV7lP^8OrK_@^}h`s{W)9Ae4zlY$KmdQX0N{`{d z1|`>59&ro+UDDl0aY?yeL3n-u_a(`cZZcQyPrqCvw%fVUl*n+ayW|Y01@cve(0&IN z3raTQ3}(rS<<&?SG|4e7o2@=>E7I4TtpqGF`D`m`rb&`$o7-QN79&o+Y4ewl@BP#k zl1e_ILGI@xFtRmVV~NGN05_l^p23W4oCZ5L8#P$(D@mA_A_QQYx}$7VAxX0)r!jZLn3T;Hx!O?TM&6S(2{+_=3DqvuKC*0C?Goye zZq)ir-zqtWMjeT4+>)2NCC;EtAd7Jj*p<+d|05^dsu{c!QK7NG&kZYKlQ-tH{~+(W z0g!e|s-n6JA-BMDXmbLWoncR(-}{Mik(TV1xwq?)tV}}F!w_^_Ac-j8WW78qnU2T> zaq`oEw&moYmcU3fKwk)p%sjITUS@RlCE7pQ{POHAY6z%LAX_;cS;YO=}%J6 zIe@nl+K(LdoApu;$!8IU5PeuJeZTLyQz-=(BXw;#lU7ZkH(Mj@M zVaUw>T7+|zMu04^HUL3ROhht6I2O~x>yM%=gq)8=*aFK?NCd=m?C+=ih)Wj$shTjB zoR@u?R7i~W0NP2QBnf@+I8M&h>m>xHHyqX>(bGWMmHqyRb{yUfU?>zjM(OfyO5e!> z)xcU|s_U_Ka5g7LXIMH*#ow8_K zTR;`x^-kzocd-I>F9-JlWh2dkEQk=U!No=<-wwEiY>CzhJ(qWMFL}i%3WrvQ>9k$0x- zQsTp<-nA}ttAr#$*rFIF(Yh>+a;IuRYH&KpPrO;0a>LGwyCQin>!^oqLnMG$SC~3d z43)Cm&7r+XfELW?ux);MucjFbsWA_7xUWZ7Lr4~#qm8rn1sQ%ALSdVxdYPcuSLf(i zjxe>%=gnYt%{Rf4+}}WurvuffV@WCEAvn_10(r7XhXdZlC?MlN3=Q-2b-WLM>>&Xt z!jfK<2DCZEbYqM3X-U`)A)QHovopr@C2)d`G?x~acOsg7)4VN@jOgIr>{IBzONm$at%NgF@OX*(RPmk?ov4cHm3}b z3@!PkkM9LtUV(||MZguOgO4Hn%tIl9_Ge`25yF=d~^SeZf+n`%j7 zmMU%lZG{Y?K3f+?oi6b&yzsUyh|d5|8b}<2&nBfJ64=H`I>j&T(!$f003);0qLA!( zsK9Gfs%Wh<|w+%wm@GZ)%J1s&z> zrxiry^)}z2*JYz#vJvr(jqA; z^Nr>PAyvmAus7m-gvDFbnAPhg+L$gkE5K52RokOIwz77WjWL&(Yq4qx0;K3v=vy0u z1S0BBMUFS=bk2F~%ir)S$4A5+ZJnK2X?g^{$e?V8DZ_v`p5U`%k-IsAyH;= zJ-QZY?^NkYA>bQ7qY}>=Z9UU@7au1`ny2`0_l!!mP2Vpbzy?^7?|kjL>=S*3DVVi8 z5?L#)H<`!9LXVtR2T(>ONGELtEM$LEtjp{k&S;`ugi^{DQf>lg29DYuhAcHVs|?I# zHrGqYTl7bPw$xN(e$yy`G7Gphwz_sGE~eJp3*#YbG6Xk`QwX8N=-Z~$*4a+~2YZtD?Qzv%)9!KK6y6;5D@44tMCQU8 zEoD3bD@zuyusStkB+4@QUos{;q3(qEB2ZLz>Uny3t%p4y{n%|Y84jp@>iGb{_N>42 zfJ@Ll5KT9yldmOuzrXpwo^=T|IoAJm=jP$`5nuv!9d(wZY|KEiC|x`^`B~J=)LgFk zudVWXnU2TTNmC5z`4V*t#@ms--j7^ZqT0zdDo)Q|&g)%cuyryKX+q{W;$>1WRLO+l z3S{bQrP=O!nUb{ejS)oA_kUh0+1W0s)$b;6+_Uv6cd#CI@mB? zmnSpF2jVMXgj3*}f`q0So>l@;>wDR!WE5p1EdXdU4`3kxXcpX4PEFl)E3A0wOfuLb zKm#&aC}Y*~DG6IpK4wJ&cbgfVd?0~BlR8w&=^QBo)k(0cM?HwN;zkkHn4V+`wB&N+ z_<9NhvHx3GAeAIhV`2HNgp;Yy!bc}&PLjQ`v2=geqr$xo$O%lVG=OC49*#&S_D0E% z$w(J3BCp*-+6GRa^#~ayPv_H5EvdRyQIRlNNsPx zk|zbi&SjI^B_=lmx2?sVNw!tPQ!ek7dkAE^NEA$RP^W`yM4aRt@O&-*_AIt|1jKp7 zvvc^UV>AKGB&rM|gW;QymT1Y1`8r=Mr<58V-DI)nW(YP>su`qIU^kkqgl2jGIlWYl zZtV@OhiQGdghbXmwN>F)fp4e;Ny{UH#e@R-mjUDO&h-+Y9~6uvs#&B(?`3=J_vI3j zW|D^0D*%0yRQJB%2zY|z_y%Q3p;oCQpz<(1?q;^*=D;E}11ha$k;a4IblBGvChv2k z$h=?P3k-#20|OdX!G|L)OKF`{UyB}*M?VvI!II6D&fG79h5P%9PYPDL_;8z~gzt`ku0IU-T zTTsEAuSlR7+J=qkbwC9eNlB1gH>xh~IRA+w*0~v|h5@)N$WFvi|6!;S4}~xh&9)XQ3s=sY6PFInQMDH=2gT7# zkOQwPQ$5|SV&H|j4aPaK>aNi4soQZCEKEs6?l-QbsRKK zfFw=N^yLxxEUbhPPSo{q!Vhr3-T!EN8vw;{8ptrANfxm zt6<@E+Ttvtz7si%)qs(zWfYFz<-LD2X$}iYCbZR-e=&;fPpAAzICFJ>GeOQZZx&1j zzYWwCzHg4?$Y#D4u!05^8g%Duz`NPbN1l}mBIN!UD?OL)e5716Wo=-J>*C6K*ky+|jrVHPtOOTDJzw%WY;NjONuyVU!vwEDZP4HKi}F4GCN7#f%n6^6LqQ~N>6zz`n^$OgsP z`@Q{O>YEfDmlWiiRA~}8ETg?(GtgD0#{@6T2T4Q|kR6CNENE|?G@jc^e`-2*ndLzuSBD52DI1E-*Z{H?E@4_}+Y zr45qRfb&f_EaUG1lp&FG{N=l^R{;E+$6v__A}LI5O83fbYJGsL0AX){Z}6?c!6*B! zgSab0-N z2-uP$f>U?yhVxLyt{s)%Z{qeyh{=>~usB+u9ez~}>F4P^e5>NcQ_ffX4(9iuW-S4n zkYNmtr$FWR$zmAh-%2bL>8H(YCb`88Mb3>B&0Hiy=KR`%>l>aqhkm1SBV03$$QD(O z9a?2%xHSy7Iw5x)W-0hj@viYeDDwy28muC~E+Gls-aB0Qp+v@WKs^XoIuB<9yeG^u z?-G}85_!DO>ew#lp#iFx&c*ww@+7F<4&-wWz2)*uvoaFiMNASn&alvR zkOMavKXN!>2@X0i((waU*cveqsNO&zEX-#&b<@^YlNUg)Z4#Dp0ThD^=ymVwVRqvD z+iJ2vH5c&vh?ZUs$K3&Evo@Q>3bg8U)0pS-Zx60;J>I1V;y9C_m6Nfot)3yOrHV6A z1ZHMRQ;leEyfIvAhntG}YqP{UPMD})Z>Fkp6QpV5)wpD8Z}T$BUQhB7WvGsBk)#~K z=LsDdyvNTVB8j9DLHCQKm0~6^3?!$iAUGXdD`ipv=?{u(5o28{ zWs(uf=XP6hX3kS*9{7gHvqRcfowz1v-9eSToM4$CPDyvl3dhcD)6Q&*b{>=ZwGBEU zW8!yUI^AC2+xa~bxEqy{WDrWUQGkA+LbI{WM=XId{!FTYP!&^ry+(Jj?;A<94&=Fa zb7?>bD3eN36WD-p_S$MhwL0%F0lS#AR-me0+r5KAX@?Yx8{s+9Om?4-(r21&HQS>pY}1W2D99qq3y7Pi zBc!#z&*PUvOQW7U`z{(Db##>(zxo4wqa8zx$GzsMetjc}zK=Tc) z$kHe!s|%h{NuC*w zFd!mYDY(VO%h`nxmcnwn7!Yl( z_>~oA_K6c1R0*dmVuPm|2p=*5lE7w8YKT;5C7?^9kn#8Fz2+&TvyGfVXioMojDBq(oFHVbJmp~k~t0(5sn1U^e8PIt}t z`REz}oS0t?Av4=ERCG+LT&<50zysg1)7hkzAQ?*3#X3~^CK-?WNNXBWPzHE5T1$aB z9YJEM{fdW|LyPY?)=>FvClK_GZ?+3(0(!&s>@rv7;z;uhv{IdMa}gEyn`^jQ$-NLJK94wpm}!t>0yz; zJ_e+UXyZ<$Yj9o`xEpD=5yW}MNXb>2u{QCoD64n4l#UkJCIb=O>(PhGamXhdRq^vo za`~7Q7V3GoZTCmrINcSfY=<_Cle7SY0~9Kmms7l%wY!r@we=8Pb|hsQmSBlA??&I8 zf*Z803hn`}nch;-Ugh;vKNvtJrW35}!T)c{0KL?=BNpgdVL0tY&I-mzA zJyYqpxsfEYJOSAv6A?Ce2U^E%w&R(I_*Pq7I}G@k>;;SKe00^m!z|ZQLnW7I3Ho?U z^ktsMV;^sP`EbX=Z8f_gBT%+C=@oxZ^zs1YPxl%E4pLn!{eh`SQ$Z~QatQ!x__X&0 zul0!f(MV7Z2y4eno~jRg-P1O9&QJ!~+Q!c-RvceuNrDXuR$^CL6vT6v)8yC{IH&>2 zqQW^;$9@o2&>09GYfXBg;ur4u;F_f!Nuh+zdv?IKPKT(FQiSRWV#BT*Y8R>y>iA3z zK}{+Bs)Ephju+7`$y!$e1#V2F!|i%>r6As6En>>I1;r1o`|yu)I$P4~^$PkPWlf?G zCWZ_JhT{qCO>G=?(8rQ) z*s?3Kx8ZUjkW`*hIX>XBYtX${V+RPUhD52!Nsk;Q>~yLI(jJTpP(}>;Y>k-H3hEu$ z30U9;+bJ!Hsm#A!k3M$4)3;y+)PU2*oIV;(Zx;{vNmW?27Qxx4fCLmkgsj^z@m~pK z@H6ydFmvxB?7$G)vV(<~+k-VC$02ye_(BE>L;Qh8~F9zUdjHB8L6GPs?3!*@&7Jxr-Y=Y&4ylUIU%AQg~W@Jei_D`HCF z6m-o6i6w0wY|kJ!Ca1fH)#t;VL`wt5>6NIdp+RQ~M2`&OM32=Tm>aU;wD`#W7Rv9F zTOG7Wnc~btI_XF3o`AAiT~?4D$t(NuL|Ep#`zFE5)&ntdyB>Wg7J6kRKZ=Dun91w) z-Cc1$nWxCfixLSet$N~-8|}s3?c5Eo2mA0P=StE&$ldh$bHc&^}S1;8>Sn zBk!7GMG<{X=Hl3+LiKb=ryy3r?&R+P*$jSm9<_S*hQ^~$I4$MRQ*)d_z;>(%He*KU zo9}-&(4kURS17J|6PYw92ecd5TA0zBC^xyHbG#CXq2_5w^ax2lsN_Z z(jruggxmnxZS^QIq1u!~e=)oDdBtMuB5*~tYNL{5_EY!K*tqZ`R3kR9kSXokBQ0Wb zU{Wkdlt#j35*Cdha*NH6+v8iQ25`+9-4MWeYEq~+vSCl$a))4T3!91_IuI&`7f{0h zH{}&gwI=%=AVs-dkDv=NqY1G-cz`BxX&B?4^7@$9+Rx+aawz{Tj+#M#A4yLT$!Q#{ zzhC@yzewDF+L+wj3!p&-6P2hOQz!uWq4UavR$3%i(%Yi33a-V%P&*x3ua^Kxqpuw%QFq6MC)K4n?qy=oFlD-q3a%p;)|>c(K7^2DZE2Hp|GHxdE|Lsd0JW9^8kG+Md2jP#;$; zTkBoWijv0WZY_0LTA5x6kL3~6y*E4du z;+S^{l1aOX+uj} z7g6a3%a)%t$kCx%hYod>g~Igc{?%5+r~zn^DAJa6o>VrlOn1zv^U+75Quxy$$SAZ#*Nwoa9g1rN#v!#_!MJW-GNbj<~9#M2W<)~wIw1ldJYyxGl3!c1L z;=|M!J06V7$m4ZYu%~#g{4Xkq5sCl_4Pog>N)0oY4lKd=y*#D|p|XW(Lh1q%+gwS< zJv;$3guQe828jp^93a5zLP&%PXj@%<1E9~!H0kQY#s&#p((5j(e`4Eo*JSfqanY*g zc{JcC#-j#Lh?|pWAp$k{a&wm9{vb!#wlmn~afa@P5Et;<=yHIVg}^!P3xV)tBEpw5 z1~>=%0m!BeS)iJkzQ(c6LC!-CK+CJUgY^i$w`8)Zhs6ZmY0@el;mo;8D>aOhXrOcS zDB!n`O=s4i%?mB)&a&*%@Hw>c0*t5IMNrZ~ zOb+E3#Jhev_rBeGMQIR!LdE_h4%kAQ2-1RTyJ~CYshyZ z-bReK1B*l`-S(+d8s9-?g(h{Z;tONH7X_478u#kqwnvQdC^MB)NNfF(S-btt& znzd}(QwmQy56Z6Hm(YD&vHiFdHXAn8N{1l@&-!L$1X38|kB;z>evl+A=9>??aQyhq z)gF6n=~D3&(`sYa+%OjkWm{6X#`>_d@_)EnWOSmpW(5+7fliyTKY|>l6~b=+GJ!;s z=KwSTT&i&^Z$vTuuw5r}V8HP{41Xe27u|*v4>;*X5)Hd0Mrg%K$?V&SJ~~|iWOc|P z`rMFuog`#RP=s>_fKaKaDWPs@>n>xEcrEzMfkjzB9=gH-G6NFMbJSbi-6>mIbURZ2 z!=+k0Pe@>heB6kGwqrMUG^sRw2Bde9!0s2ayVpwOjGu=Szb(^>VFC!E%$Rm2dp~g! zm-I*gNLHW5<8eYmLS-|)q?fn^0W_fmA!V4e5svPy4k`AYay^fuIPG#0M}MCE#P&HGH~PDx5&5k7X~p>8#0 zXS4U{UF8Zmt%HU&w{{UdymtdJUmK}b(kwa{6i(2mekQIwL zAQ?upsz#k?uo44~aeJ5~B~_`wFel9_n}TZ1JT%;qHWpU=2+NT=K=Lbf30rs>VC?L{ zFt@qcaL1VX_+EQ8h*YC3Z`Y%1(beu2$J5d5C-?ND>WXaAr8;AkT{Y80H}_+UcD2QQ z|0>qPoBCJ^Lx@4Z4Q%cTs_AeClk-(ftXZ}oz@i_0N5O6H_wP|_Wlz3CDPa-Y;J3w9 zO8XtjZPw58Rtn{+CsfjWF?WSJMuanmcvV$e_PbpU@*3h(9RD zz<#R{`;4Blk{Phy?F=`0fGvX@YvVnxVmxsb_@$&Ff>>%$xltj|f8_M?H2whws9T7a zZ|?a=3;WUEH2?fKvw3&J)NN(y zpwH9NCJ#-N1OOE{s`1|LeK4v1S0B6omHODhyt!I?AZ_W?8QpwoQb*-Yespjq%}$$! z@sP^oGC@s1q7+r%#CY|UfkT&HHXkK(T7``s$yD_P2%FAk)g73FW6W^Nfp2P&EWKK$ z8i?0zlo9Zo7;A084?d^60*7Mh`L6z@=U_GxGJf*r<| zx?PVxboPb{Xv2IR3*a9i;z#~=;HI55ah!P|0aNtnsIFPtB5_N|kT6u+)61P{8%HJ; z*6Ro}Edf8F|2PcppPbzW6Z($j`+bmFzGok{B3>Xkg=%=iip=8}n%gVw-n{83MV%wYWdO!ssEt0gD zi(+W7NMXSXfbcZZV>dD6EpThV;MzNvUmF)$Zm2A@1}RA6}zJHl2De_5NLL3!!cGQ%Wuyx z0pm1otAj5U^w0qjAD~i(%0pV~Mh$EN`gVU%choPk3O1OuRNoNsrA7zI^s~)Ll_M3^s(az^>l>lo7i%pXXkL|l4PBVecWt0hYf8u zms(w`v=hm5?BVU4KD=Go8`B+tEPUyj4b@xdFbm&}^vQI$dp6AV3OBcg1Q!;I?CE({ zGY(fNJJv5jqS-JF3}nMOh*q5GrrBh zM!XRoNG(lb69JCgotfx?&mm$l>0#QEaTayiKYa4_2)L42_S%4;0KH>nJ5Gq2$IQaG za#pm*Sny3sb$h z7jlx(aFhfu^zYt8um_{2plCwXy1D|*LMQmk(bcGe5+73tnun)t{jjNI>|b*72PW>+ zjR%Vd(t5l&!@7XEBGF#sQg8MWAPku~N`_2oW>T7!me-J>LRvs3WMN7qR{D>>b&BUW z1u6>|(K-TsQ9;}W80Cmmx)!3$V6om#SFHY_nh-vKoSu}bjA~x3Ze~nLK$rzEufbj6 z4i7d82cQRtzyp&7ohM`j`=aL4q&C~@3=uW#Fl9$vXwpW)`Rd?!q-kX_o;4Ug-K?f8 z)(vc`LQ={G3N^1_EXZ*I*g79&_u&F)0!VI>#vaYG?#NpZ#PjkKi=a%kiGvtdV8J|_p`MxdBJp3e*_?4na z&#_!(MjCbof7b$g%x-G?vhLEDNwUs4xt}T&y(zjBqP)BWWOUs0H9(d-h%T>Y9AUa9 zxjx&FGH@BJ&^+uiFcPiO&`k#CGhJXB9ZIKJ>5jgi4rRYGp}?OEOoa@bwt4+zD9tCD z4g2dNDt+cXAxVZk#pt`Xdf09zH`seXW-~Yx;@k$G78m!@oBAy>Vm=%eNH@ro1gZ}^ z6vmw~AB9>3^l6EcBlDBaS5VJEMGU&S+n}vbM7VU84tu*Bzjn9OGyOR@-hh}_7?B+- zq#T(x)MQ)J7yK{GVsb|9c0IaUF7QX})3~keS$=bD0ojD5d>JJQ2Lq5dIN28D4tiW~ z+_As0O4HF*uEii#Xk2IjMtaKJpkRODx$^o(sDcoBkLuqq2~vuG_7RmD$<>CnJt0Ui z=t${EcCK{zaYa8erd0IMl9BpN$(=h?=WwFa4A7~XQeESTn;K1Nh|eW>q^sLV(v=gC zQ~59%01gdZDbU?qkKh!e$5)DWirBey5=?17>7Ym3PKUYABf8Ga>|F-6Ne>l}!L9-d zc~i>>JdIWk_L^~g(XoSeGC`qI-2I!P3PQDWx<&0sZ$mfxxmJ~-1~1{MFk5|dBl$*V`uuzAQt}T8qi5`f48aZw*r|Z3D zeFnW3_YQfJ9yuBnyA>cjRfd*=EuRJMXg-CX7NfTKs7pu1e%V1v_f^jn;8j2}zo*1q_-y zAi>t$wg*u}uhgx&#jG(rfO=;=uxhX7_h6hHQds0#!o<TogEHe zflp7G1hSXL3cJHDm4-?4#!X%s93UIznfQg$J%Gn?7;kVUfJOp^Z98};J^*Wt1R52# z9|L5em6sUDg4zAYdN$A-k#g|+77y0{z*49anSV)^yNGqz_AVH zdxNsbMGkt4=65dXM?kh6n-xOL>-^Rq)mvKmcheruplv0(PF295%JKH;dRo3^&!m{s zk5sko&kPtbb)qTWpLXW;5aa0vkn1Ark;4smUXfP<`xPj+z%%7uo+HXm>9SBEqDre7 zC%{Gue&|C%wI*#xfWh7j!URMO?lLMEDZR8_j$&ihtINfL(bg4xq}ZeCjq(R}vRy_w z9O$KLpvxZ^llzg}!wE4Wfbi)By9~Y4hY`px=Q)!Kg;sqLS7-N+jQnGu#Tab*NUs1p zq@=KWz6IFtM^=oSN2j2U3wN!IQG zNYK!-I~}3%Ki8~>#iPlXEL$pdJc{6cB-?UyoUl-XZHS?13pd6(Wm07@J`I^nCP%In z6$;f|Vkro&E*o|02I$Oa&SL&61a=`*^dNtb%B5`qV z=E~oyd*&wE*u$6kW>aRD$AT_$s|AP#6D<~r9A%1LC=$c;i+*4Pj$3`(u|BPi^$q4N zwhRzSuarXJeL1+vU`0EAVgqhaS0y@nMEPTOt~1$rsduuPMrs*XB)bT91{zJ%`f6Xk zj-S|=O-yT2JAt*;o}k0DYzkZb0IRZDChHYzTOlpUl`7+~3!jI{%+px5DkGo!qF6g$e|euSHN@QniS8%Nma1*I4$@(v;cS1uD!G=shUG08=4up+LJU}F?Cw4wYYhZn z$KjPipRoFygyfV#xY=q=g@nKz(&GRra{c6BwbW>MUJd!~BmIYe4)`i#$KJ6;x#6AX zJ)YS?D(jd;bGh@Y9C$!54>|@vV%^$W|F(n{|Cb58H z?WwzezC_`q^o_FNC$37|^(T`Q7|-$?ppWp-IUVAJ+fn>SIt#l~}; zr8 zt}Uo!#ml=8@YGSSd^e8E)=2?v8nT{zy#rTp4|wYuE;jxQmT=Q64mfn>$S*@~3w+aZ zE#=p9ZpVw;1nvVFu34c8GXT=7)5swicP(fJ6ODgtQBrsn z*zIEzPDl+@N7uMOH(-q=azJhC7Bm;WBx}p4bY8h|7Z6|YdtD7^PEh|8T;!ya z@^Az~iL)tz!mvj|A&4HF6_tavp_Kse!5UVuM~fv`S-w&Vsk>28{|tLHdvq((og^^V zPiJFnwwN4M+J(4?R|No`qk*q`W+z_BuhN1c49Mi6r-+82&4xJAMT2X(eZ(!%BjlbPpK4Q#owWV&3mV0>tUr5kThLZL=L(l zlun(Uk`Ws=RaR4@J;OHWv{W;+B%ttn_P*T(>sD|9k_3sBD~LltK+)C=Cp+i?nn*ci z*X=+%2p7p2nr#<$fV@#~d%QSHrLo;k%Q`GwSv7eWBv(Zur(BR6 zQCTG`abg%MAQ#r~4DK&QW6^<}%hmh916M9lY2W%F$q(?YL2a_KtH>^7l+l&iSGh%W zpl82Vu0kPzTV1zb99ZZw$jKYg`vNnXkcNX)(g_+zbszsp@e-u0YF06bVL{HEeLV2S z38^y5@%+&&`Ma<)b;)Z74dvN$A&RNEC6efRS z-Y!4G)4cAbL10l@2lBCJ?_74z5p0pO)Q)D{GB!eVJOwwgj;y<`j-Zu&c*T-;Ha{;S zgaH-VLAb?*G7N_1{>%PTfUkI%Pe@n$6)H)|MO5BRdltAa_%bxMV^&+fgEg+TBi!;* zbT*9DhzjpN9c-7FD{J7nIgxyVu!lU4W+Ej*7E@;((q!}*$hfS%Jsr%;E#0tnJzL=n zg0VMMg+*4$SV;izPD|Zf*&J~sMkxE?k zu)oVQI6Dli#gAQnq^)UP%aE2U{-uWrtC0n=DM43ip?bY{)JY|7kLu|>5 z3>F9&K>5DP4+;*KKnV;PwIJ?3qVzXym!E%AWGGr5u0iyQ=3)}6ZDY>&dQI?7?Q>DY z<-v25Du^l$7^{c;2KwtOFs-oGJO~mg;RiORzkA4*q_Cs_vNSk&O{fG4vEu>&+3IMB zrrQ&>x-j+4vTdWn8Q4Bii$P*APAs^^U>=A9oX%grcVX_X*}; zbNJEEZj<~S%Wv}OdnD3nOi$PzDgt+=mFCTk@@j16PAXax-Rrmxkj$M#K5O@EvgP83 zPZwPz_{{;??^@m<0>}2@aDZSFo58F${zi@%BxYDi-Dso)7B4WVm$Yrepl1 zuS9V`H3DIPA|=s%N{idl*rqEpAGQC%&L#wO|raCQ^T*bSiY5DnwO;CcRqN(kj5F}6piNifHF zs!su@_HtsmAqH@kIdF~43K_&7%#75=z`8QNiU0#re~I$VhDs;^4w)*7Y{ruVmgHZq zXrOVtPFg)fh^^x3+~ajQ4QuG6A1<13}zD;EG4L^ux7L2&!O(AY%^l=k5KvCaOo9s7e#E zBN@{+-Fd?>&l>mfsfeuBQRX+Vu4yO>4lX%oiSV8Zh*HH1!f$$tm5x@}FlL#t2sXCh z^a3i|Xtkbs+Fg?_Yx7VP^d_i zVtE`Hyk~=rAZh((70f-%Yw1wO5~Rtavf7#rmsw42yg#IAExJWY@<%epn>(x*<;B*M zkAC@z%rM<{GowBH)@hL0xeoykT&;R65!WsJ#I#MBrLOSYs9wbH?%F1-`XF!{;gk22 zN0SLz&RmK;@46Itg}Itqv#V?xKRf$JH#zufZQWQw@bM;u1tn6+VdT~~VPboQz7QE= zy?Q~ppq*H(c*0ABylP{t6^7XFsE=u+C+jY0$D2Y z+`$0^%N3sF1F$Owwo$u?oE{kwI}@ZppbLC`s;`<(VsUlu9W-afUge}X6Wqu59o_WE zb+bH<5%?R3Im=5Eb!)9Y@%)G!fpfjewq4*&5SHLaht_2NH~}_xq8kf{SwyB}zD@Rs zA@k@8XRtsNcsOD#9rAl)qj<3(t-pi7r>jNG6eytxGq2)ra9F@@9yQXDtK{j_Y}^&T z?qsBs9_RAS*m<`ym`!R^tLo@IxB|9~aW3|RQFtx-2VLZE1i`Rmq33%7ajZ9PKzMIs z7$A3OoeEB0gyU|?EJCL{>DFoIJe?iy^mm~oiAq8g=5LHgl0-k+(h8?IMGVfcaaptygN)%b9o6}>W z0$7yC29n-UjRskgJ`7m1opM9Q&||`#WN-x@(8iQ1!-c`dMGJbU@@CL{7+Sks3%f)_ z=?5lUtKSd3>xWA*CCgWPb>jG$t4RBLZ}@(87sInjrHORfj*4<5REq?CF-D-vszrQt z{duAL#lu$Ag=1%C2bH8&8tqppq-PKqo=O+Li3aJu*r=3_i#9(9I_=AmzqE%&f=`t2J!IbCaKqkZG(2Om8a$r>KanI{u%bDg%}yfDs)N zaxhSiYck5xgS5s=3V-y~#zLB043dj$l)%`?Vta~~aXZjtrzFXDblg(~9t#8Q0lkK~ z2>Pxd!<6$flE7=ff=^%I&|Zq3?svQS7bL=k~8_XD$`ekM_!TE zYs9miZS?+DosNjE9pY;pHdTY`omw|f{7gr4#=EUeOxa%0m0%j1;^~rs;o?;o?YnDh z;-u1(ze`BUV9Yrnwj-8N$VJU03do7N9=C1HRDs*CeLxZLc8Y3CeizIA-Wo_=TlNFb zNytjeuBKB5leobomA+!P%S95q{f#+AuO>KqL0L0@Pxl*wvtuY< z7YeA}3ijPENgcNH(?k4tK_7Km#eN%hmq1RN=pyU_i_Hi!Fy<(Q zL>8O}w}IF|0l-^)aA<175mAsk1XA1%3J+Pvm4OU=8Wkb;?htJ+<#$NTx$w6f{8t+U z5ye6^?d{DBt2o90*`=(*9!%7#pxMP0bt|Sz2ufb|s1TA6b=mUt&v$|H2hrctd1@QX z6oXY_W*07jFDA0H-M0>#npY*M(F6V`yg_^2KjF}y3jh>S<_GLJ0^(cp zitu#JK6fpz*q%FAh5R#~N}n-wrLZ27Gm}lE+>az41VXU&<{$hSL~YLe=6vR;v398$ zEAPTJVJyW)U~NxLEf{2Vg<>6&K>$W>39kdoWJ!XR2)CrnkqEpA?@eA^blyUr(#02i zG>;~`QDB%uidlX>%PQlA;ICP@L$=A##ZBW%A{MG5*mGFCyu1)LM8ZJUHk;HL^FBu+ zl0`rfbn7AP{v&>4*KfmNXR`%$g*VVkdvQ<+m|(1#xvY<29fcK_QH#-<@)_fdu~YbLOE=_;60lR3~H01#~%4WgI`Avfu#rv-*Rd6k9`jK8a4$VaXe4M#Tb7qAI$U;X1fOgQavE>RiductnH1jdm<&arK7k8u- z6&o$$A02bK`>7RLd%LOntb(HPJ5?gPAQqV3fF2l{6Cfr@iD{^k6AFue!xz}NmOhP~ zVy?h9vXvlI%&=QB=0oq53X%JH>LsE+peTDAr$Ac+?2N}_KsNi*a#Y?3ndh(g#v{j{ zY~aUt$o`CF?2NHCfv^%;E7&_0{;`XLT@q^jcb&Gg5lQxe2FPR*&H%7p*aRY??tG?R zq}&q1_Y_6p0QB|`K5j+)lVXJ5CF2IXJjNpXmU33HDRZ~;g4`W>vM>=hI=IVO&=SD{ zAFs_b5(To60_dVtc#x?rv)i!!eG)Dpbc6LRk-CHBI_e}@ zc3CUzno0#H6zmy*x`5jf=2NtW_yEM!M2a#zzRDx#&+U=)^#>R`Kbl{I)Ks0q}!9dI4o-n{df|X z=PbHHG|r~G=hmVSNeQP^4+J=_=@?>uVO!GWcIQ(-d!z1y^VlzW+^#((a=}VbPP+Fo zh$DAoQkE)QJ`bz%^9dKyQ*Dt{Ju(sXPo!}KBd4GeaI`Pf~ z1qbXxnUQQQh_7r{XHf?Og)>ea28Ep+(gq9dft_;((nm0HawJg##4ng`uf(`9cF0J; z>E1$9p^LbUdG}V)&cw-8)`PZZTfv|o;1H&G8R6|1>^5O|4V6=_Pi|VRqG@B}v-W?wNtCH~`T_lYzlK zEh>cHIo?PA*n^Uady=<)M+ywTSQOX9(_NPhmz~-uk$#EXVWnh!*UyfYIt|QhMkew7 z>1Kq)1GWVz#>cdkgFdw}k>-9S`sxLzdSz6IGgIO@7CO84fFLsw`uo!ZwH?>HI34te z548U*#H@9UoO`?BZQ4PPf5LHFmino!-OH!tod}IffTCnW>VwkY5+6aaFemjCj~Ht8 z;x&1f-j;f(+>>@b9=nCL9=QH$Aa9TwqY5|&e5(@q1w_S6NgC;f>bZahr2ChCb=7!R zy?^_#>F%^a!$HtsHSs6b>yw;plhM@v^hQD9vh{$6ypm{-Ir(5MUdS+R60m~F0M-zo-v&diw;EQnV3b9bLTbd@;7 zT8oxo1{glp?P+$`QGo^l6F*UcffMqoAKVo#pLP^OD+_UyL?#|UH9KrQdC{ALLjeUg zi$pz$b+J2i;kqEcJrYrPc$$yo?R=~PE+Ru7e7aT5gj^uzRRXJMuN+{zI5^fu6G@3B zwlBLBsD0EYi4<=_Ja);JiNO<<;9bd=L)`3#a#0Sjkj&azoL#~aZB{p{QgT>C{5+CK z(tMhWcGTkhtVQn83PH|D3aK59P-207UAX zKreFdV)PX`1zFhN8x(ExP(ChCz0IQhKQ?aG7h38$C;wcVuY*4ieVfTm=33{iB3T8g z31;Tkt|}G}p%15XgEH)57+)~~EyZ%7sBZ%3>8o8#Og*R&OFqK1!(3gh>IC!GMZm9k zb*Ro!@pyTICx;H+Kd!$y(_OmtHh|tl9Xn3BFZYX}LqwQG*OxC3rw)FC%{FnZF^Gu> zA6qHeR>QV{Col)u9g@4ded1F!3ah%i-Ys9=M5e4Yhjl^9O6gr=4B@Qo!oWsV2{#kA zQM@MePsdbQ$oGzjW(y*XTcw;`h75S1WMu*IgGCu~u}HmAcHq*D{XUT3PWvRb6Z=<5 zk9{pvxL`TFXqaI7ja6rv1x`JIbl}PuCGHS;*W9#@7H^8WkHq%Zt9;1Vsh7pr=-;YA z#RPCo<1b)=?8L&a3bL7)bz}8BR?J9Odcf#7a^&@3E#+Dl^LAZA%_4cRHy11+P9;Q$ zfwklr&Oj;7lD8(6;}{-Z52TU>?9-=ll6}?8O|HYNP^Dez;HuNoDF@iP-mNmP@L3XD zhA*3b;m`vg@*%*Lc?8&n9Ebj0)SNi~Zg0bt=la{#=~AEJ>jiP0KM$KkS{GG5v4^O)r524RQIQ63STd|@AWyEc(2My9pn2T1eg_w1y`fvr6wqQJD{ z2j+2lp%*;`6G19NaBm`&N+{;?%}#e0yWBs@0R>|>1#U*BnO|<_ttGB<-HvBpqBV-g zbKlQH zC%U##&Lh(IeOP)cT-MluxRBy(6e2l8<@6Itdnv-#nE)V%YM)sGM7dv@j~qX;pvT7S z@0X4=GQFO7=uly|tkNfsFkW9%cSuxHaKkb9ZiYMsK(eob;wb@Q0c{ZEdjYekC+vjX z=4??D6+p2zixfVFg$c}*H3IpS0Y551>yp0s2_3vpc%38sF3fYVwcUOEHI@+!?Azoj zZg)#)7DW`SEq&JDcr80pkAT5rMV_Ne@?;C4%g+(#FTqAm7Ki-1)rjZE5yi4A-B$Gl zZ+KtF2^-yDl4-uL#p!FJkhfr9jPtS;BTjv=cmxn{`l(qDE7#w?oD*6I*$8nMbNb+< zE_fY~ocO3&lUS~aYvt2$_KSjq%Jz%Lh`aT@=W2Gl2KOFSx`e*{1{icH1y)xm}eQp%N3b&1iu?WZyQ-4OSfAVzpWgE}IhQW6`W=NcTS_1V5<{IqqRJosea^liSg=CvW zaj3J@hIV^}_hc7!^G!=O1AKtOB&`h3w1>fp$}T#WRbCs63d92&fa)gv8umVBo6j2zWEhd%*xmOb=Lav$i;9!m%}12xfCNjwl@Zo$& z5p&hvUZbMyw29QcG-MAY`~}FQ={k@EjXk&Bkgzp~2gxodqzt17Lft^$+q-&s4q%lk zA85}cMTeMZ&f5gva;Pmg2ck++C_v-WspJ)kJ$gDrGsAeW90=uR-GW5aub=_;3l`Ve zA(x3x1(++SD%-3u2%G3n-=URcEH({1fvZuuzNBw2XWxzdI(JppoW5~NP-@UP{X;3RN3-K(*TVf$&(o{lsI!?0<053^7cMPc^a zZe_Bd*~@DH@DuaKky~INuE=w5jhNmdwlxU=gtU<5vIGT|-O<`l~euP6u5(vCp%5Wl{Q~DfWA@_-8SuaF^0*7CC_cW))-+Fzr z$ZOuMUtg@V1Vowdmj^^SxGPmm2f;#Ba+1tZ--rKsp$)=-oqDMqz}n*n;akWb(03h1 zOUk}oAeDU^w1D){L=?1CqJQnvt;iPk8egWiSI;*%yPq}Poh~u;JPm4>aY~rO#lYvBk>glc`%V;ioX!FO_&+t30nkNjEKMT2@F zfU|9NT3KElm&mR|ipk6dBH`GCUGY0Nz@3SR37Z`ZE_N5F`g&@TY13Jm>)Gvtw?mpC zel{I$cYc>MDy%KqK?z8Oe`HN=47VW1nh<#8{$SB-33x|053v6PDh^}sg2=OoOraoH zd1}6Ya1Ju_Zs=r?j<482ZffdkZd7t97B%fno*TzkiE>Oq6pBZeMO!55wOImc>6>uk z-PgJ2>w8>&kDrEDruOQKONrDtc_FPLxaj+_bnP>7_4!#Y6+!E`LjQ7H+Hg&hAL;Ow zX-l>RKV^h~yMh*Uf&EJ9=^d=aamh%g{s5+BnQRF1OU2RKRBzT`ixQOhg`iVG*G{j( z3uvhM@vCJ@@?8rR{+c!6#s6#R{q8UD|Ky@Y7i*ICfB*mlq5}ZP|1S{JO-!AQoh%*x zgKwvE@o@Qn;?gy??2gzFeBadHf#m>|+%IZnu?!Y!*w;a}#Dg0zL?TGax~|85KI2HN zNi+XZe%UCXo<~<<=5#zH;KVm271cUvR-;~PhBEhther`}j5e5?~y zFM4OztlIeoRz)fEqb)A9q6^JBtpf7h@SBQIHpGxblnp&=@mOxw;~^1q4cB1xNs1`K z7T}?nKQ9^HB$i^FZc>sfU;X5?d}E@C{^0dRlrSYdo9pn!G0Zz<2gWRut4TOJ>#?yj z?M>96z&pXQ$NA>TK2zSPhu$#BINvTUZ&`_)6W$Q+Ai-57LbO#zka>NHreIeqTAUa1(~cH%m) zUTr~vqicXZg8j2|YBWsBRjj>KDqmHM-QBVDah_M_NM8hREf(3zp`Ek-u!q8)4okN^ z2|O3=WE+4#B^K?3qIsfV$tI2|-pow7roAbJbLZSqaV)!8U)1Xm~!LkbRh(VC{Xj4&d7$ z?|nWG0K6fihKaEt(_trr&ITS0z8U~Ei0r}BhRIUct-zb1SA(wx;B`VAu<+r=eFYKl zIOy@hxV0eb;nEPD4m=%vI{@~O@qI@RG#>0c;P}Uv{f~Pf4?-WX{NeWf zt$W;eI-jgKc>VxC}J&)1FTI5b!2sBb!ByD^s?&xYJACEcu?dcDq_s4Q6Xuf4r;K7AemImoe#zzsdmXV9xQkkVUz1-=hf-7iJ zu*{DuK~ay~T!3|9{}56?O*i{}-Jt9Hf1Xy`>-j&vPlvnrd;VR^&%@K-{r#Ax=kt9U zx9@*{GT-xgJMC7l_xrlYX5aIB>E`eII7~ikzvJiW`#HFnoAduT?tVPH^0$xgradP!3 zaCSNR`gj~Q9iP+l$H)DS)6?Vc_WJKGay$JyJR3f`%7eGp>A>0RdhF->ncFI_l};|! zj+i=tl{mRaEA#O=T2vtE^Y}VA+1dMLO!*vd_y72KIH}kF7e?oBJ%0Y*-{2CvsY`vq#qmiZ>j11KAzu& zT60C`yFcIU?)K4CzJE`;)2}t_e=iW}hVo%t`pf{)U~x_&qoBnPD^e2|K;Z!qptv zpN{Lyxle+4gDdEawv4XQtW$@HEe5znkda3h8=h5PTIohg;Hh62Ik&<#EEQuLdQlT|0-oG(ltBw z4MAfP_1Q5^HPYuxKnMwzbt3R|(*@}`xJFzB9V-x;CHrTM+dBrP;CtvS13FDXm`*{EW~k_cGe3OA&encwtATQ&S+r8! z6B}3(qM#yiiKOi3E`g{^*I-N4C|7&Jt9FN1jqaTh_rjqqJdKeogD2oXu??s!utv_& zOY0S1Y7DB0CV)i=RBEr$q0!DmF8 zXrhHP%ty&W8?4z@Y+p*9n;j??^Ij4EG7nOF*LO_fBa zfinL%lV~#b$!08rhJA-x5@Weg^v^s@xz?T9Mc(Cn0d;TsGt_~u#!fl#4-Ts>DBjPsU(R5q%?q0+S%5ERul98f8N$xgCnASaf|U3gtvNs(^T! zo~2U6j*&fUWiNdmdfjKxnP<9-I$z{J&1<6kAH^gX5=6)7Bjy|cC4Z^uj>Q=wNuf=B z5M99V<|;dEV(E*vI#SY7!6A2EuU`J@|_XY1inK~Z@0`7gN?ZV7FvAH1ylQR<2tS(j!LMU=!7N&g?2 zFiT=&C0a2{#v9e>U3idXlh=1@A&BxyB4x}Sm%lSwid9%*{2>{Qqj$tz5O%;qae#jO z)7geV_X;Q1?w-aT*Bg+Gig?t?s^ENC$k-i}HM%{Oi{MVU;*ZrTv0KM!@?mS|~Q zm?Hfu;M=}R_e8R-Q(EsY7Fm{DrN`4t2W65(8x8UApy|P_qGh0(foy8`uEhr4*34BY z)-YId%9i=9SFvj|)jV{st9kq<03M|`)eJ5=w~Av^xzNW#9SS;tX?xi3v?rR}AaJD) zg`3?6&v3puOh=U6?mr&QJ%A>51yug+T6TMftk!oD9{Dz8)VMtx<4s~8JBi*1=+R{Tn zEyhbMVH)u%G@S|P4Q5V+u(T1if2}m1a?l^|*Nvx;!KxwL%S$-&24y3IfbP}?vA?cFzVRG^Ns?wA?+!HHGOUzfRr5BWUuWA+mqr=VqvX;rQSAz6;^6J z5)_sc8Cv|yV4UnkDjMQ7_$n?h%f}Y7E05E$?aYz^r+f1i!LN45vHk5lbsT%ioA8OMZB*Jzg1dl;+Gr23B4|yMmBt$UK3GSW!jmb{O7P%!5ffWa(B73P z>X)`SDu~=8(i}dzD2dt{@(Yo=&06X2sYig&%}P@*K#8oEHa!5Vhc_Y;6};lW9^v6| zTtSDu;4+*|B{xo%p=wL2fDWzPjaI)6L-iy1}WPcvSAcxd{Sz`R?g5;hV806b7$ zfT+crfh8#bytE46OK}R@G%11Ma>`-`K7VKo{y{pg2byxATYsTqtdbRGi#kE6wqTJY ziP**#tmq`1lt>*Dp>orjB&={GsVvtT*uKl$fn|Vt0Fcp?Sqm@ht*M6$c^l#&KwdUC z;W?7fGbYcmc1SmyOa~e=sv>MU0Y%w-71tFq$t3Ows8={X{BM!<4msROcP%6`nhn4X_+YGDFW?VrIenboeQbengsAjiYhQrSr z*Pt-vJJI~5t_am3Ry7MMlC&1pr{Q5eNPSnp)lPP%3c>|m(b3%U!}2gvVD(QxI99AF zJp&~I7kbw=bBjZLPTcNBQMW90)Lb>~Mw39H+Dh4{Y%l_%1v1u5;{XT-ykOfWqEk!N zVV3P~o1PKXqS^$?^4ei6@HKR}cQZgFCkLc{{B0)oFH``SnjonHnTfM=qb*lS2~dqC z)R>hZETKyP)!8#FiZTi|Y9*^dy;g*FlWh{nuD89D+Wvhyt&Xz%!8-$f1g5aT$18B4 zRwijM1LtTpjryf+pGVVQHH{+xBkjDF-;jXV5-uE?=o5>5VafPpn;f;AkwtbRQ}Y0q z^A_YYyCTD&fuo3{5wgvp1}p0@WF%S(vYCADR`?TMls3qK3(LqtJu2lhgFNIH!r-C? zOVmj=*8XfrMN``8N?5xx%s14@?H22HZ_^!f;DsZZ3Ba$UIdE+H6Leg)VTh0)p+;z4 z7_OK>)P-h6)vfIKj84S0->M;4uGgr8W*lU1%2sjhxm{)bHoFJSP&?5TIMa6NMA^Uk zK(iKrC9?5A%nz=p2~!oo7hKtxUL|eaCg}QlT&5Kcc~u-?iDLxSo8nYjp1G;qtr@+d&SX} z3KWzb?p6)naS!9Qj7Yboj-00qbd~{Fv37peR0Y}=ze+8YXAAcM{% zJ!u!d`$GwGR?;BFh(bx|!ir)%!!qGkkEGM1rK6&Yp=-FnwY4KP%_-y^j#LYLV*9CA zFA1n>q-_Vs#7XG43yeBgCWC~gv=UJn;cg%ZOO-kaRpA47=v@NxM~X7HZn>(KJ&(T}I&Z*L*N_5C+S6*9tbC7FxQ3KE^D)k8~P+Rz6w_~OlW=x6}r z9hv?5bKVGqSJ-UCSfYGZkyND2HjfwRvn9Y!(cpM4vm)RfJ%HYK>^d&|<(dSz&QI%a zN`)YuP%rGSiSxoH>g!?V!5e0UsT8Q&QH1q=Cae}4@-13~ZN}~qh6+s@{8>PfdaINf zWcDWOV*%cKED=Z_ry?gKySR5ry%F55)Qm!1Kx6~T5Phc158Wg_-?4HPV{W*Xxw-X$ zxvfH5D?f8S&J{4Q#w>0F@l11zmFHQFxU4VHZLd00lE;2opQI8|nxlK&k8POgg;V4m zm4acYZx4)j<-oP6=HdC_@Nw*tN-{;X&uf4**{2p367+eY8eKWENPJt1PqnzJaYIn# zzw(lbxNGv5cT+L%sNG<`erwS0ceTb|$T}bX6?P&WV1A=L#*f0k-YGwb9xCL7576Ew zL`yVM{O1GBwTKq_ewsBrbCr{YwY=^kTdREz;>VnR`pvTKtSBGF&l0s$O~j&8&dOun zsGll7`-*c(l~|WN+9*9bd7B#T{p-=)%5dtHZ^SPugjXxQ>O0$WZ`S!Ia|bZ@y%X)1 zr$AQD0;iyi<%la-K5WBMZfLqo^GQM7FYj$xQnX`*HaOD#r^8+VdS+&X7#-_E( z3veh)t->V^#h+BmPEcuz#>8)Ipz8V4leyAA`@v*ngi@~L5cKSmqxD{jxfq{(7_hZrAp4D}R+GS=X4`~uBC z95xzLwdM@yEM2X#vW+8KM&t#@XB3nSc(K{H3{GC;p?rhgfun^hS?+nhT-~1w(jo#D z*CIj(2=^ibN_xaVgf~K%E&QQez;%?PR471D6C@$V56laRPi58?ET^rxpn(HL!lLbR zaQG=MH%McqHz023$Q@!P!6k4Qzx8l01?irSFk2uUv_Lu$1s0X%MQSy0WRwXD^4ChEtK{1dei5^H=0kej*MdMCXO$AHIEOKK2{=;yhc9B5s zK-?F;E}~QDBN!~7r8=rcS0>7G@x@4sns)O=W`-6{n8yrsg$MVM(lN=C@FS)^-&9`2 zBTQ<@X)w(_l004a-Zl!t6~m1V3R8(gS+$Aod(&yl7)sVbJE(n6(cMPBW+AG4oc@JJ zeoy6a@VwpAiqL*h?L2t1u-|C*uqn#fBF#lz@Jf~NHj!H>bu=)@1_6;QNz?Jiln%0} zDHCI3eO0WU&6yyDp0LZ;279kCU-(p0QIK^$C{G4~U&(wek!0M|J-^KUj zTEoph_Vrj%>$J#)_RmQT2&%q2phXoNdl$;u=2KAp*dMUuQa~|TVi_4C+Dp*#@D!La z2^=)!z}36rP4epDv7!^JJ=u7-O~g#`V=Y#%8(m zt4_z_@+-Yao{Sc>6!rp;ogYWv{_$2%2VG+aKKX9)7C2v1;wV}X;mlD1mSyt15>@Mj z=+SAzKX+9{nsDA&;e=5?aa?5wJa-RkR<9PymuFL^YYBQR$qCTLu!%^(LlXiY1Z0V( zz(=H)#ZG&N!Q#h8b`0X~swl>W6Ywo&4R4vMZA6Obp?-Cyong3&bILCX^WlfLhn9)Z zcLPTSO&G-G-Bzjs$=N8Q(sCQWAhMGl?}{tmK$HB#_72%426jIXmwK?97`)zaak?N@ zh4O*rDI$hCIq4IUV6h#EJzy$;dgxe#SSlK;OF-V~!CjhCYf>)~{zYtBzfQ0NwR(ZJ zoa4ad62eoL^Rz-)6p4&UL;9>}O?-kPHvy36FLR|I>#^p`FKzGJJpaV~IM+fTjD(uc z`u03+UWNiCpIU)doiO4dK-!*gHxFayY!ekUrW!kdV~5?RoTKw2we;~NdU{r##jpnc zXG8x6GCp~h<4Ov^79}_UNrgh?m4d7DITg;oDsm;AGi=>9$sq zv>=OMDLzZ6JjTqsj82MkR{^K#yX`2;&>d!x+X64`L14F8G(Ck`C0pq63Y&?XqRLeqJOR^ zM$ltVb&yHqvQ+n^gdRi8cbN2$_g9poUm&j?FPlzKZ3w+3_wN!9G=^iOcqm>6m0hD< z42t`NJ%)LYc3}xcA5g<_^?mS#9AJx^B~^y52j2yCUv$5Nt;o~#gK0Q*l-_vVitQl` zq|qLn$EiS2i4)m zw0qt^tw&*sq5vjN9Z+%WV~9MV`pv}ycp$rl<9}Yot-%v@N+25Hr`v8Yo~o5t6?u|C z-@00pw1VpprFDzz)(n(sncM~R%dlIPTVF(_w%neg%}xsHylOC7@GSl|6JX=ayrd+- zra>{(G(>`Pna9LDV%l4|$Qiu(Zi{__m99+RE#kJO-`D+QZs)w8h5)Y6W^wJOta7uX zE{%zu5K4J!U`W~sfV0OvMpZL1@=zKsaz4<#fUbp`+S5&Rr!E0?mM0nqz>ycPHkxI~ zwlHb;?JJDugUVnKwRvHohXcV7sGo~TTE9@%--ECClqs@HE-hIn_WH{Ft>rBk_(;#` zum>Gw$CADkhC{(_Y;gt0$W+ns9>=ygyYR_?iy!9ua?K~2D{T`iH2RA58aw}memCmg zPolf$ky+(h9zYB5X_zTSpk<{Fn!S!zJ=zVTX1zU+9_5fAcTxDe1SW;IQEV=51pg;_ zCQ$}4!OfjVY;mICS0LC^wn-=@CGRE4nBa>w3*WSG35*Ug(^)JOT1PuWg&YV!#Lafp z4jNTj+V{f_OiL(j^dj+uj@)ZQKeiM59-I2A~A&}gBZom25Vdl9-dgoj_l zf66k+5V34Wd)2ACf>bz7?BZsAuzDL$rC@y z04|LTyTiuwnFXmPM*s>g2j2;V#ds37;D*|yX69W^O6_I+V-R4ZyCTGUMtH}x?U@Ha zbcmJ|ulK)dJM*xbwl;vDB9*B`X;6wvMT4P)qJdOODwR^wEX@-QLZShYib_N?BAPW4 z5t@)B4Me1rh$ut8eeStiXC24Y*=v2@AHI8^`#jF?S?k@u{jPVtYwbg#vMcp_S$aYe zKQBqs67QdH+qG*hS4$_W>aF#S+L>>pd|EWhTvrc<+^>mzyoGDk^Bv0F8(%$T)=Ui5 z`*=IL^zu9%tHhrvU)IE&PG4x2@YOXWi91?R>`g^asA6B*-PZgZL5`yaUN$PBO}D$_ zjnA$PnSAD3oS(Vd@KP2jcj8p-*H*-DPF}RaiIpX6{my7mO<9wJe zdc|yxXKgv1T~S$Tl$+ftu#Ag)y^Lx59{=s1%HqY}O;guSs9G%EIO+LaS*8>XA+u-! zcKOzc*Cbu4bv_>|$d6Af?YzsOb5F7#Jv*8*C}?jmcY=I!enaeq*tfE7J;P#| zhK8kV=U;D58At}$Zej(y}k4$SFz4a zkrw^n=;DaQ77Bh#&Mm&`8do@XXYu+;E}_OvuGwow3^fIwysLKHc<-*%qxzSbh6({A zmu6VI3>7jtg}&vzk!;ZO>C!%5xs0t&H{%9b?!+J3#|(w$p2 z`gHB`==Qwd9waoy^P5KA(#T6~!yik;SmOp1a^CJrU!&7joks@Dp-y6kv zw_!<*lLr?27nov)ZK@yN8*Wwg%6hZyv`y(sxqwh*iK&n7+}Yy2as0SmTaWtu4&5Ky z=X3-u;9p^3ywp(p)FeLVcYM9)Z0kJilZ_)6T$AOV_~5(QYaIdU&oA;5(pmg>yvuSh zmfo{qa@wU}r)#I~o?RT^@MK4`?sm!G+*3v!QmF?bYRlMC$A_eJb#=536bD_e5IkQT zan7}`G<=+%$)kjz;Aw+8!#nbd$p>q3p)?lV4Pv%a%OTh#y+RothK>GH1pv^)I)qBG;amtc7_O>|OS}VUe=;PfS3Ey7MP-*eg2CuQN zwrQ4s_T$~0GPC$}JnMC#=1m?O0+lmvyoz%1iZ~=!pAp~TxMSV+4E{UEW0H4eq~5jL zle(Yn%j5-FqTAG_tRI*k7j@InqkGcGx2QWc8@x+;;#I9C*H|7n>J?%9=<=721TXF- ziE~wQgXcKe6nx`;=IT0dZrPbazcm&q<4Ao!w!L#4SNmbhgI8~!oDy`|UR-+V*lixO;LS(Bq+tzo`4pjo~!gKzS@;umj@$j=rt%;L>**6}Wz zR1wf|hbeyP)=rC?Hj#M|p7mYLArn5dw%HraJSe%ys_v|xjeM2oH2>(z zbxJvH>=K_EF22&Wxhi}rLb!WsSV;BtQq7Ea(_=r4-?!|7N{_L!obEy?wiXxG$!q6F zJ|5nmdk#xd8+!^8l$0X5Gpu4C9Mh@^(qOW8oYrOkuDGx&IP>%ro#LJyM%6z~8=8gl zjGWWf5K%T1Ym&U7aN@nkqwegdDJ>IAd^^n6KAydIr|ApZEIYS|d0{8*avr6&JQS7F zIw!;=Q0u8eAcY+21zdT z#iqGTTazF7&$rOFaEYDKHIBJNkLl4SJ8pBuSiP$cYz3c)S?%nqZ`-xcd19UFY8n17 zGVW%r(Pz0@Lk>-!b z56_Ps3DeoV?#!51=_uyozUTEt*S5ahs!jX-Ot@D!^|NQ{D0C|@O%co~yHjxfq1V-? z$7{AINPL(Y6SNL; zw>%ymSJxO(JSZfsU2@*+h0zIN>=R?w!s}ZN>Kod9cQ3GIpW`fHVm?xE=GjWFjor1E zruUky_AcvqoSM5^``MQC9V|86hF`O`Ubc;j>QWtI%82F-ou1(&z`S$U6%K)vp{%oJ zm3Gl#Gp1SOT3QJEbQGAJ(|GC5ee-Gj9^Y8ozH-aGnrVs;TBn?J<2{&mJ0jY-Qz>Ol zk#v>ajf?WyWd~c6Ew_GI_SmzlNx#&q%<_kYSxRxc%LmQ=romF5#U_(l8MS!&~8+ zsc7VuTGOB~%fGO~pu1w@Bc{RNho>cGiD=0N?I=nOofVvA94pK*$<8jUB-^^2Myn`XVn?!~roS6FvM-RNNRUNvLR*~Xj0 zmz?VLgUgJMe#kn$eV^g6jDm#SWuZT8xwEX?nUB0V#mo_Tc3PBNbF=4%teLz!Jhrr( z%x^tWB9WjptkSx5$$6o5-7O~0;`1se7^O4uyY9?8>2%5CayLn!-`>UKO~eYvQ(sfr z=gnF5=z{bG>HXFv7c{Q;Jl6_r9XIuWmXd#6rsVtU6Q(lzTkcq*ST=8qnx2Lo z&$WBKpImmk|0qpK6AHZ1NzzJZZ)LrGFLr6B==ZY29dS#Ja~F=A=F^ZE!Y$t>8z`t) zYP{afDEnKqyvnSDgM2m72Rl{UyxXG8nFnuhFKv%L#igv3AKZLh@?@pBj3x_@jc9;E z%uBXDUz=}Rwg$zkF1;pjWJ$A-)fOkcq+?e-Rx0%8H?PmNP>wp0^dUQBC;OKZ#VdG6 zOs9)aHecI%ucW+I^5-K)E`}@UKaJF(fq5>v(HtZS_hf+YGj!9 z=doY8y+ibd|Dv8^y`b0Jd}ljk#Tz!nT~C-7BcKv)dbH%??aJ&MHw0R9UL2~zJ|E<9 zk?E?(T-{6om4b*bKNL1}ncir;*YR+QbG{4zn})?c&%Mgtj(gXyVW_z6rJ390riSR5 z9Zs9164k6Webx14Rf$5o+ksM!yiiNt1i`!x|Mv0_ zp(#hZg6>Rp6~67Hq|MuTM>{F%Qoew3Yr({e{lQyAQ!Hd9HmLg-X}l8WwJ^SR+_-dE zlKX;WUze|k9O_@ndK=#6Ouob-m&Kd2f9t$;YeJ4qu-z#9gjsUB>|E|ci!^5}Q zB)2FJ+gX96aHn-a2UD4bd}J$^_;l4enyft9e)o3r304sIvYJ-IBd)TuNK394!_@=p1(@pbkPRyC?_G4TNmp$ca$PCR2^9#TK zSv!!`FV{K$!l(ELFQf+Bz4EuL;?iR_wdyLp_4GCWO)=JSZPto*dbMGu;##6QKhBto zcXqi%cX5fe_orXo!?vqmwovN&Iim!V%aZcfI1 zS8(l=`BuAlV3!z6(?sDc>zgh7A2+dSePecyQmj8Ne6asXbAd>y|GB^n{Ta*c2MhUL z_?H&g3l?wV)#$P1J>h&;EZsezIL+~$_3PB6&e^jrtIn%syOfZ0u(q=zZh+@6s9y+K$=XNp!|Oo_9dK>4{MB~F176-4dt=@U+6W`i7Yj0_H zJwI@}R@lnUfF#x7d7JgbwvEQurGmEPT{+hM%QsE zsU0pWJZmO$1|pbLfh5g;y82Id1P-R=U3NguTU-g&F(J z3>6QV9zS|(ipavK!1{TW-99mH0&6JGcCB2gS@paeS-fS#8Z$JvIcs(Z`OfX_(Z~zY?QG!9 zI?eWIjlt5(S+co@-IsJE<*bXn80^WqoTc(m=A9oG)8sPLO>T8q7OxApP)u}mYQCGeHy|eUg{a4p=2!O9 zo)iaqriy>QFjYq}?fJbGXAPX#me{md_cYvjQ($J6-x?HC2AU-nbxmIG4ZhVd}ku zEQb%Lc4mtETn|v;H^%lf>}u;;tUErr8BX8zX0T}Br5E>)Vjss!!He^rmX%)}7}C16 zT-cobsm1z>U61>%6D)tU%=`3{wjo}u?85sUB0)ATyxpd~u}APa;ijaB!D`WY&q?x37%KrJk-b39h)C zDskeFp2@8R20k`x%1$)(D46B{tXb8+%BeFY@1B&!+Tq4w=OV?nJqph@E`4bJJ^zX0 zk_XqmTJf41X_aN)JQvmY`NM-d8nJoGS#{flmKIC9)Ne=3!?8=mDhVKPFt*>;txMJEhk@oXL5zg~+&MU1hcRt)0F|fd6IrlYofBg?9 zlV);XlkBlo>UyI(VzI9NLEmbn*gY993P)CO*OQ*y9g2AO_`H3-{MQf7^ZLTR7I3}p za9+-SHfqwK=&EpsYifJuDI9ld%uC3)p~<&Frm1kCrmruR{cN%GY#(mp=vPI0f&o2? zqx(v_G%mKK=Swg089ev4K*II)(^(lM6NfY9?)9zyzB27&T+yP4GNr-kZ`FEKvMcU- z8|X|>(hBK4>ieWTzUPviQgXfG3)11aB#r$=LJbZ&e5AQ5HL0YLIOa;9iJXhqF2w%b zd^S_%;53!QyQI>2>$(>oVJZ#Hp3F4BONtuzMDZm_=&s^kCNatI?j0pP<=;%UC}_Tz z_CvMq;ZR(q#215yN2S(=FMRoFaY;(5Wz{lmuDfMTr?>dko=DvNqf?^0N!;|F_4wI# z3x8Oor>uUj&lCOrf%@C&J4vDoKC67RUbuH~!x$KDZ3%6K?SY<2!b_eHh(YC~e7@7GQ3i2Xiu_|=Mg?Msy! z_gqs}2zwf@-nnmlvJ}f32d@}TnFN)K)zemgW_`oq9mDxx-2J(4Bt$$pBHZ)>Bt;&J zsmxAH*&miT!+Pc!HCxTSiK=Var@bTv1|Nx9{m!r2MB&?g&Wae-@rD}7t-HFINpE)g z#BiDlot16USG(#sLI04gsM57qt{a_IoZ_p*tp>_sICn1#d0)M7|MUk#Ww(;1sM~N& z9lRZqRHna5yu*~$UHvL=L}H)G#4eMyf$JuBY~%dVGC5;Uz-<2JkYy1(ox$B&aZkVT zOwM?q7x+Fh`h7`oHU~oIOXlIvy?56aLQie9plmTsLJ!qzb3)&s5Jl zHOG8;gMO6a%cgbg)09aO*)J8-Jx=7sM(sYlLi;P=AB$6QlKUz6ExOqByZ*#FE8&bx~ z)878S4h{Llsk&J#oqFKQFn4=RouOvTYONCg<*M^k5}31c6_n1m@M`s5_p4Yi;p4Ne z6p8k(H3{i1145T&{Cs_VJNqwN{`B_w_&T<5=FVs9_4fSy`k~=R+sBuEje4s_Jb%1? zZMo{%mTLju-e#ISvutZMzcyS|lF&4hzANkQeGVtQZ$F?Sp*qM469AD~x zrtICbrOmu7YC-p+Jyq9YyGxdQJf~||S9auN!_{41hb|NN-&L+p?QJ-!g*RfP5J>~paRWtT9T(SAQ_QgECl~T(bO{6Wl zX4&#jYd)sEfA4x-n@IcG6hHrmA*>4)e3Xz5FUx;ymh(u$^GweD&PXMVd!-)Cfzwu1 z8Qz`XaLI4tR`2!wZB8@V92UrJJ{}(u^gtsasNvlOt9aj)l5UQ929plF)7eqOoLi?_ zZBTIgwSQh>p2^{$cte4$5-g$`4@Fm`2RM9GTRh*dKwK+L-yr;Kp3}+9-ebk0b<+<_ za_p@OIx)SLvrfw9lfhz9Qbk_w`dq)m!L6#xx*jZ>9~Ai|$*o)_%;~i08yPJ=2eF|i zWs6KtrJ0`4i8al5pYgnR@km#cR@bq~@wm-nKGJ>u&m|DD6f?@f9t$OcoRU z-d{+H3dr<-^iG*uoqyK^#UtmB-;NcUTvPJ-K;Go;_w(fW?bBE*ihb{^`47}bI{MvA z;!U}jsIN6Y$^Ifh(s<$HTtuH3AMA_BxFI>AN&{T6i8^@4QlBuz>eeIOMyl|Kj?_g#!bTEO|X=cWq49p8ZnM?wXcK$yuHA z`!tMnm0!=ksTy=jZe7W=SQO1P&wC?4Kg zCz011xyEAC_++!72GW$|-u)JC9gF(bNt&8-M8q92v4}jORrV>kG~>>RWa+n)?yCuD z>aK0cf111dj;n=y*s#z^KC2RYxv+M5H+74z^TTiRIYxI(6S=GYbdJmRdCPR4 zCQIxwSsWIhZ0)2ek$F6KDBxc07rs6Dryn2V706e#znq*lm96xZSzPWz2gwSts>{+3 zbKc*dW4JP>KTBOT{P8ZOd$tu*w>+tPc~5*W?!fyfy#l5cOC-(8a{021T+X}8>z~`O zxx-R8g`*ux3G*HCdpR<~mb;j)_$pa{I3bmww)w>47^!0^m6<%}*tR9qK4yEB)66{F zC-x}6qLedvD$mA4KR+CJC;PU%Y185Z9S$YlI_sRDUeXD447N|1`!$Vw_iTZH#aSgg zuN3Z;w&kzl*D4t(-^wizu<(}AEDa%<)b>;F%r-B6>+{?myP-;?H^Ab{E%Vgb-*e-n z<}(Yu={?+JW}`F|HFK){GX=j0ee8?PD;^HbNH|NB99&^naE~|cgSvAFYwaxuk(TY5Y)e`}oe~+o=Oymm|b1YZR=P94%H*I^yM^Y<0qkHOga) z{kORMe*W9j-7K6NGB2+#85nYzTgG|k(dW?S^J)S5j+tubR!GDai0B+NEHj?avgpM0r)9yd8rQj56ebH-6PhCGDj&aFsbOPl^G>EW;l#7<)aFa)gT%zAZD4+X_2_V#EAPJh zAr-t`c2Qp%e@}Xo|x&uU9q`0vb&tcU$-P}AcL#v zO5njEY4v4HOEb;mBVP*UwyoaMw5+7dNQwE|FWgFJxjC8b>){ zIG5&x;dB!3zh@0ONk3J||L9`pZRKod<>~6^>R@T_=xpaDYqXJtG|rRWp~2C=_(Dve z-oJm-KmVry{KdNT|6==vmM%Wd-i|iTR$g8*|NQA_?`UI1KJjg7Yv<}^NBQmRMIFB% zSS0^8mN$~vNaWv^cHg2x=jt)Ma|L|aZUOWC7 zi80WqBnfC9awd@kc$i390F5>e-60;sNZ^MkcUU?5*x|`7HZfzsNq*j7qL0-f8n7Q< z1K?~5CW`ah;r#zXp&xuIzhZ=@`r1)6T}xi)@jdc8(6)yzOlNLo{CQR-O!)#;@qdN5^R-l70z^WD3mILv%1` z)MEzV)zu>#D?GXgxJdoI(9ku^MPb+_A;th-pAr+Qxg;Pb5t=un{;xPA%SlNM85@nQ zT>tc59rXjXt-y$@LWhM#0pKS*r0+Bg|Y`SL_~s9 z{S|m*!R7nY<+Ivh=V|3&XE`c5Ye!crPd~UMYW0~+IxarkE3LUlq5>?3>;lV{fXM!R+HD;L3USO%!27qk$4+LZgM7Hi%Jds6a&CEOuj8PavGUOn~{5TYzAsa4=%pD)n_?Su%k{`i`YxVA$&~L;#0KSfgn?Sy5`3# z%EijY)6J5;t{6#6`=?^#lS96zhT2yV2q@6#3XFC(P59BlV~T*SxXWvj$Tzed zz>+ULV8Dd);lT*V>h~Lu@OlJUR(8ekP&BThd-oAoN}x`+z^Lj*z=O{ooT|Xgd0{8~ zIN@BxOtrmx?(JeM!IH$X2QcFGyW!aoiE72KHz8S6aRYS0uu9CYH6wjVF;cY*{%B&1bc&WgYn zh_@#+1R5!UseeTTG!`CA1hhn*9f2>lCtsnt2#AR`L&EnsDH+l%5(_<5G7?8LYGN=; z0wd}SJPtl}aB?yN^4iyu(4&N75z`ZOwgd({i!3x6B0((!1ZAj5m&@JC#?s5i4ZC0% z#e!`Pn@avHtP0^?O(Nm7Q%Y`o8|LTHND0hI9P}Z^0}mboIy%W(+F6WcNqev|61aWt zTMj`)Kt#0lBlTbLLe7x#ZbHz|D2Tzr5g0iODwwQwfFBGA zK38zs0oOfc$tZ}3dFN_hW8-dYHFf}7je#-UlmSD9NIZ*v1rb?1t(WK`VJS5I=ZmqL zG#KT7Ky}|Iah_eyX zR;ZCvybiW0wh&uyfltAZ02)3-B3u0HZATVaqbOatKfZJs^p<}-TXoQ65-Bv4_DF=9 z7@;3VqbZGl$AFS}s4XIdg2xL1D^VvwY5NaKZsM)0l#xMh8bCCZ6-GMQkyc zcs#J@4GipAVNh6z1T?z)K+a$kkI-crd-}&%QD?*Q)UF0T5=jO7XeYSSn2!bojgY|f zedc*2V}yql0Wndp!@yn8MLZMfiHXtqBOoZ+IuLFJilF=jkZ{q6iowDV7-3AakRajH z2q!G?h@8$Eq-ey%)hnVjQ59Q*eZ(3Q!3V%i8wnmFfsGa%$pTX`q{~L#$3edu&EQ(> z%DvLBM>t5N-mA1)FMuRKqbzXiw8jQ*RPZ1t;411iAaH~E`F%Jj0+OQb0m4@T6iJCZ zh6AFp6N3#vU~-TI$%4-qoR+|s%d4MoJ21#hy9S!~{jbxo zjIIB|l#|9!m_;JBVAp(r6_$B5#D5))s=#C=`wej(JbDQD=|G~##3HN&wHxbN#7|OC z%3>8plmGD2`}_0|kP>bE2`{u$GN*55#9?Sm#9;9WjGCRBiF4qy1g9S`i!0m@&qK^P ztJo`p3bEB&1GahtBe_1DI1?h#fF074qUi!z+1Oy$nA!OKp?iqI{D^wb9~_U}Fh7q* zHDC+e{bY#oz@wOeYZQ62(hL7}4cNKT!7+uNYsNN11Y|>-0gYzX|Lmr5L(tIJhQYiC zjGR~)5PYKGbOSDgo~(oz861e1Z|?Rkk6erSM(Vc?=@04

6&BUiVS1LzifDX-4Tc zLQ*H9_71qfP}GH|K%*b9mFJi_JQp5I1S~{d+Xkci{9Sl31Tw-i4oc?pt_~gw&PiZY zdDg@8;Ijv(CUB)wJcN#mn4ze9iol3}`U9Tr|01f~2~+94ck!+7zjQ@1wiKh@iUUJR zniZbn{~@ZwBJf;zs1pzs(=(RON` zYNi1s5uBO8JTnd!2A?!IErE}ITL9z;;ZVe!^eWavsuXh)2e!NeL;UMrNH9b~0*luw z2kBD%rD8!mW5VCHl-g_##IK{#5Ll>SPa{4Wc-#;$5OqNbf8Hqr#K*-+&p>0E9s=^A zttWxSYyFwTVQ9R=U^xklnpy}Be3syp0~W8Rs1hF+oQIfgmMki&S%%rB_qXD;ml|BKs;Qkw~@Jmw3Q;*yKsX ze;tj6z|3dkOyWFv+z>F(`pt*hjIi3!6YS&&{=8E%AKU%Jfe6Tlwk8B-KFh<2!_at# z!CDX)H3bkH_$nhQMum)|FiJUQlD3Aq27k zx9!{1AZXxh14fRSIs^zlQE<8e)2;+(1VF@mLw(&V7|wYf5KxGO10L-t-AR`S-wpeZ zvL$2AVU<~QPgfYbc;FH3(T+vFa0F6vHDQ-wf2 z;L#5ANpL`L{sFU#R!A0n#^4kL?#2nr!;KHlM9e}#9q;d9_Y;unuzC*|-Ac>hxc)C9 za#W`aMPB&*-?rqz5PAkl@c$6e@-1+qf`p0cfF{?!pC+wA||N5-vm{l291VDP~Z)Z&)3oA!nf!D+%7cs>?t!QP%pyBL~$b4v9K+g z_J&6z9Vmc5i0Q_E`;v^Lk|dyc$eBbE;9;W4P+o6^Lc$}PR!VLY2Uap z-;p5EkQ7mnuzV$fBEf($IDDJ>Al&;`mp~=K!0SyzQ=?8k33ZW{KLP0fc-(L zY;>8N-5e~P-Mqa1&>%!*pFjROrW+F5KfliRf#u3~>~m!0*tL$}W0ec>>uA&i79tkU zAwC*-AT#J4^7MS-2w9F7tik>u^`pdr=t<{~vqgy&+GQHp9~2lv90txfVAKplaNx59 zryDS1T~3ze$`4k!Zlq=-?WKKiFRGzKYAHxYre)`$WFjhHq9U(2|hfna>_I42;d#ywmr zhl4qCGc;zi}yo;^ap_MI))d93Z2W6c|~@Q2^l+2q!A= zqdo2F2u2DABPOe`>|@(6Vu{luEO7$E{--7aI7Gr)3EaTA%z!Q#RbkY}+1t^^*~-gH z=ASW+aO$0ne-Ed7)8Mz0Gg+Nkx z0|G@-3+iE!;0y&uQx-H0K5uY(0uAtK2zHEcDq?P;{>m#D=UgMOWQfExS{EQ+_aQKz zpWge}YKNVtm4lt-sG$DdfHa0w>PqctmJe9D;yiZR44k-FBphf21#aE)aH1F?Jgf*v zN>D_n#tciQUSKECz?hN?M1h7tRQLjvA}a2ED6rsU1x8l_5+ZyK;e@pb$kbnDq8KZj zjF_~VOWOO1a5`dAD{Rshx{j?9N3c~Q_&kt&Q4t~%8<>$92GPYE zYlVctUWk&6n7lr1yv2J1lh+JvYYdG21_cy!h{UA;)VM@d=%W4G zolJlI$XEvE`tkA)1#S|_;sl9=*G4FfEHQ+C8;zjwo@feSeIPm}I1Mq$9A{hk${G8d ztti&)1jg^T&qRq3i3PkZpyxYXoL`%Ks1|tC=TyKhk*z=A2+&9d+{Z4NDM&BX81ty$ z!9zeb)VD$68)=kSuHO#_g+Mr?WdgErCLM$Wg3}HdMTa0+@EL=X4|w{cJ{N9$a3*33 zqP`sxjPAEr;kXcq2t1^Fse&%lUm7kMT#@NK3Z3u{Q}Xfx+P*O18xTK_MnT}4EBZbJ z5FR-M{6pPy3BK7=m4)a@acnb0Kt8lhm*64YXKD~MH0JrssW{qp0#M85>JT9KM8RnX ze7sLPBLE`i9ET72~;VhPhfI=jk)j$nrp8{PX+7sq~UU4z#9_of~@GDcKrD#ad zXb5bAv(P}phQ}2FBT-)x0JaYL<)VQjASc>v2w(nCcGGTMMFU3TDFzcGFv@g`&@kaM z38ySGU?(m91zI*@_L8$*aZDRamlk5BXz(d0>q5haNMv};lhPp`_nt1??|n^wRvBZp z>D5;%7M;V!XfIZq2FGdU2Sg}nR0i&LY?vTS@4d+QIN?tK0hduXIDxxFE?$V}2*`}K zV;o<=P-JH0i-?TIW(;%y`GYj*mMl~`yV zg$zW1h=jHj$jo@M=+fZ_kWp-}QQtHGZU}@D|8X?Z0uPbSxJh^{@Zce!De4;r@FFv% z>OEIZgq}plGB*UoM4M%e9*rUwyoZ($W}#6LgNYRwF<$b7G4LsZlMmR=pR)xThnQ}t zZxjH7_^u6MBt(J%5BZvGr_W<&W997p?{&EJEd#OU!(XbD=co=A0gY_%CO`_5s6XVm z;PFgAIn*Df1xp9Zx*(|#hzC668_)|$1g9S`lD@;j;FAU?A#i1`$}@-VYhuh#7!E~D zMeo+$U$qun$;n`=6fnfM2ta}%5)ybrrK~7js?o+^$`#%8oy6GUje6%FZ0I`&4}nHX z;I{3ARnTLD2NMA;WiJt2O8y@0?>AHmUWDc%AST+PIG&17mTFen&`dOHVz4w0MpP#} z4nB2oasnF*=eIzQ5{^YoPrV{a6IC%i{eDBG_H$@7M1lhQl4f?(<@&X!fU2| z!YmT0rJnXe*a%1hG-?80y#f>F(tFE|c~tNyBH$(JZ8~s2O4bVw3W1dHB_l;jF1~O; za7Mxl^OOv?50V9+F*qH8FSv!taN~nB5p&Th_R63_%tZ}Yj{q3mx*2d>h(rV~-6F2g zh5FU?@@Ek-xOAiLO#;Vdd^RKk8Wn+gZNN21C_IV?xQMz(30%5GE)b z-jQ6e9{EfhhQ>P#uFJrv@&7`c1D_>0<$zgPv9K7u*Tk5fA)JSpZKyw;1V-{hG2%>! zM6(zkD{W=h48J4tfPc zg-AT3Z!PlmBi?W6B8?WOe_cO<GAR09>xLgCHC_oL41)niEDS<`nYffmGi0Np)uI&+JtZ4oI zx1x26I~*4x5sd~DIfF{_qYK4I=P`rTYwFu4!MBNsKPnb9asn@`NL7!2;(e4f`m^aoW?*&Z0QtAI7(6<(U71~78{_A>bE@ zXvPa?Bj&N~-E(gjV@pfP-wpv!NJYbkNMzudIsHp?;mBL4s6MyiAJ0}DG#UHSQ7G*t zdN+vwI2v7n+uOH`36BLHI|K}s{JgF<8<9_en*h31i?>1g9Q6-=xgYR%2)!Vzz0u+`4-JyO{DZc0?Bp;+>lb zBOwwDSWNG;qRWFH{6uD%*Gok9RZb$2YO(L8;O|q)B-vQQ5ui~GSWLg{3de*8GXdXF zpV0-2={H;8pa@8Zb~%R&kdg_VehvpjV;%ChI$!Aytw1Ys5$-!NDK2Q!^O z;u!c8!Knt!bV{!g9u*vim}Snh7O}-(nU2SAnNC9`VI)L?84VnAP3Y~t|Kj;uMVmp@ zP=AyJtZUd;Bfvl-9k5eo#xn$9crX#rPnyxAt>h!Q`+oc4uC&eqy6@3{Po4jBkLX_$ zRRSWS&3^E7jFSC$=_8<`Q4)hG5ExlQD1h(@gp(6kXH*S9Fj6=eF-^US^^hvX%0C=f z`3DSp>%9oz5D5#s0PbQqU9#W5#PjFaXRJw_`ZM9+49-7@2nCI}z|Cm(Ohj;ah!Kz% z_4ky(_!8%prYA4PXOV!wXfr8%89))3rywFS8i_HOSAmiC4HXnVnQ$TlFM!iri)h4f zIASuR{(Lz2D7fe#0z@P<@Tg0s5nVcWD;rBM8#hloOC+njY4*k4m$7VA1pE2`-knTY z#>_T``F%9%0@L=5wlG9^6cO;%N!HTNVl3I(gC$$w!p8mq3=#oJ(PmkrJGtbeE-8;; zaA?fLV0r~ck4O^?2|ibFI>LLy{;iynWazzG#`rwpM8q7lYeebtH!PtN#}Xt^iiB$C{37sDCe zg#<$+q|uKEl50_~AJV0w{YU|le2Dr31>pX~)&_VAG+F|Kspthf7amUp%tZZR0`Sp$ zpeIZ3h0D;iA&?WkSwN8!pAkG1oS(p`I*ot_pFKE5fjv^McEgVo&PC8tR39P$Bc5eH zJR2fW;X6N+j}WvTrVGYM^#Y=D4lZuZBT%uRkrvpk%aMeN4i7Q{>Y^?~fql->Q|0KX zi@-z@5EyM83eQ3*0`s4a3XMi%3>KomXd6ER6%{_8a54jrxELs-rX!{_>d$bo7kn_ab&!CdQ5yKhyl9UE4Uc~UKBG>>z$~Mx0SOxc znbFp(@ahLeW?aokxM)nqVBrdkuy`~`_%y<443tDC^Fn%WB}Sh@9F3U6G9O1}tinoJ z>e@e8+0<vIU zB}_&ae!H%vJ{FD(k%(3TH*K;c(S;h_F8%XjkHH!ibqfMs>!3VgfzS+SWCY$OS8y42 zWboi7pd;#51bi(;8LXOFi|OfzfeAw(B>ZFPl;z!&Ij~4@N&=(lC^QW|Z*WoqJKh@C z!HyA5MNCW7tqEY9f7}2|hDc0cdxD=CT`Vgb8y`QO%aKWLMF@EC*@IIRSPW+hfFCEEiB((!P4?BD(eSxXW- literal 0 HcmV?d00001 diff --git a/metalearning/metalearning_files/accuracy_binary.classification_dense/algorithm_runs.arff b/metalearning/metalearning_files/accuracy_binary.classification_dense/algorithm_runs.arff new file mode 100755 index 0000000..bf3c4bb --- /dev/null +++ b/metalearning/metalearning_files/accuracy_binary.classification_dense/algorithm_runs.arff @@ -0,0 +1,152 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE accuracy NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2120,1.0,1,0.07967939651107969,ok +75193,1.0,2,0.038371068099909755,ok +2117,1.0,3,0.16709064962461995,ok +75156,1.0,4,0.20291026677445434,ok +75129,1.0,5,0.10097087378640779,ok +75243,1.0,6,0.0,ok +75110,1.0,7,0.11622380643767549,ok +75239,1.0,8,0.0,ok +75223,1.0,9,0.10304601425793913,ok +75221,1.0,10,0.39791873141724476,ok +258,1.0,11,0.009708737864077666,ok +75121,1.0,12,0.0,ok +253,1.0,13,0.44855967078189296,ok +261,1.0,14,0.23333333333333328,ok +75240,1.0,15,0.022020725388601003,ok +75120,1.0,16,0.03929273084479368,ok +75124,1.0,17,0.09121395036887991,ok +75176,1.0,18,0.01541623843782114,ok +75103,1.0,19,0.005894736842105286,ok +75207,1.0,20,0.161849710982659,ok +75095,1.0,21,0.016917293233082664,ok +273,1.0,22,0.04413702239789197,ok +75174,1.0,23,0.11705240755520085,ok +75153,1.0,24,0.08028116907140215,ok +75093,1.0,25,0.17483296213808464,ok +75119,1.0,26,0.035363457760314354,ok +75201,1.0,27,0.0808678500986193,ok +75215,1.0,28,0.027412280701754388,ok +75172,1.0,29,0.10303030303030303,ok +75169,1.0,30,0.03420132141469101,ok +75202,1.0,31,0.20329670329670335,ok +75233,1.0,32,0.060673325934147204,ok +75231,1.0,33,0.19924098671726753,ok +75196,1.0,34,0.015665796344647487,ok +248,1.0,35,0.22878787878787876,ok +75191,1.0,36,0.1289905886694962,ok +75217,1.0,37,0.0,ok +260,1.0,38,0.02657807308970095,ok +75115,1.0,39,0.017681728880157177,ok +75123,1.0,40,0.32728592162554426,ok +75108,1.0,41,0.0,ok +75101,1.0,42,0.2740140932130053,ok +75192,1.0,43,0.48305752561071713,ok +75232,1.0,44,0.12356321839080464,ok +75173,1.0,45,0.1165605095541401,ok +75197,1.0,46,0.15517241379310343,ok +266,1.0,47,0.019685039370078705,ok +75148,1.0,48,0.13560975609756099,ok +75150,1.0,49,0.2848664688427299,ok +75100,1.0,50,0.00379609544468551,ok +75178,1.0,51,0.7840550682597786,ok +75236,1.0,52,0.0323809523809524,ok +75179,1.0,53,0.17943026267110618,ok +75213,1.0,54,0.05249343832021003,ok +2123,1.0,55,0.05882352941176472,ok +75227,1.0,56,0.10151430173864273,ok +75184,1.0,57,0.10772320613474529,ok +75142,1.0,58,0.0715825466438712,ok +236,1.0,59,0.038787878787878816,ok +2122,1.0,60,0.10952689565780949,ok +75188,1.0,61,0.24319066147859925,ok +75166,1.0,62,0.0995190529041805,ok +75181,1.0,63,0.0,ok +75133,1.0,64,0.005123278898495065,ok +75134,1.0,65,0.08723783614874181,ok +75198,1.0,66,0.12143310082435,ok +262,1.0,67,0.0027570995312931057,ok +75234,1.0,68,0.024160524160524166,ok +75139,1.0,69,0.010707070707070665,ok +252,1.0,70,0.15000000000000002,ok +75117,1.0,71,0.05500982318271119,ok +75113,1.0,72,0.006526315789473713,ok +75098,1.0,73,0.027575757575757587,ok +246,1.0,74,0.010606060606060619,ok +75203,1.0,75,0.09555345316934716,ok +75237,1.0,76,0.00039570658356824495,ok +75195,1.0,77,0.0015609901137292326,ok +75171,1.0,78,0.1620421753607103,ok +75128,1.0,79,0.02218114602587795,ok +75096,1.0,80,0.005164788382626018,ok +75250,1.0,81,0.34347287891393896,ok +75146,1.0,82,0.11329072074057744,ok +75116,1.0,83,0.00982318271119842,ok +75157,1.0,84,0.4192200557103064,ok +75187,1.0,85,0.01678951678951679,ok +2350,1.0,86,0.3744580607974338,ok +242,1.0,87,0.010606060606060619,ok +244,1.0,88,0.1106060606060606,ok +75125,1.0,89,0.03339882121807469,ok +75185,1.0,90,0.12816966343937297,ok +75163,1.0,91,0.060374149659863985,ok +75177,1.0,92,0.019292604501607746,ok +75189,1.0,93,0.019401337253296624,ok +75244,1.0,94,0.06339958875942431,ok +75219,1.0,95,0.03479668217681575,ok +75222,1.0,96,0.04524886877828049,ok +75159,1.0,97,0.06849315068493156,ok +75175,1.0,98,0.09954485391278811,ok +75109,1.0,99,0.30417434008594224,ok +254,1.0,100,0.0,ok +75105,1.0,101,0.018121212121212094,ok +75106,1.0,102,0.07212121212121214,ok +75212,1.0,103,0.2517482517482518,ok +75099,1.0,104,0.12374100719424463,ok +75248,1.0,105,0.10040485829959511,ok +233,1.0,106,0.002846299810246644,ok +75235,1.0,107,0.0011111111111110628,ok +75226,1.0,108,0.0030422878004259246,ok +75132,1.0,109,0.051244509516837455,ok +75127,1.0,110,0.3330355738331199,ok +251,1.0,111,0.0,ok +75161,1.0,112,0.06437225897569321,ok +75143,1.0,113,0.010471204188481686,ok +75114,1.0,114,0.023575638506876273,ok +75182,1.0,115,0.1081404628890662,ok +75112,1.0,116,0.12061822817080947,ok +75210,1.0,117,0.0,ok +75205,1.0,118,0.18110236220472442,ok +75090,1.0,119,0.06118881118881114,ok +275,1.0,120,0.03612167300380231,ok +288,1.0,121,0.12969696969696964,ok +75092,1.0,122,0.0935550935550935,ok +3043,1.0,123,0.020096463022508004,ok +75249,1.0,124,0.003215434083601254,ok +75126,1.0,125,0.0491159135559921,ok +75225,1.0,126,0.04904306220095689,ok +75141,1.0,127,0.0540140584535701,ok +75107,1.0,128,0.053212121212121266,ok +75097,1.0,129,0.05835568297419769,ok +80001,1.0,1,0.06944444444444442,ok +80003,1.0,1,0.09230769230769231,ok +80006,1.0,1,0.09375,ok +80008,1.0,1,0.11111111111111116,ok +80009,1.0,1,0.10526315789473684,ok +80010,1.0,1,0.13793103448275867,ok +80011,1.0,1,0.037735849056603765,ok +80012,1.0,1,0.045454545454545414,ok +80013,1.0,1,0.0,ok +80014,1.0,1,0.045454545454545414,ok +80015,1.0,1,0.09523809523809523,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/accuracy_binary.classification_dense/configurations.csv b/metalearning/metalearning_files/accuracy_binary.classification_dense/configurations.csv new file mode 100755 index 0000000..e710c68 --- /dev/null +++ b/metalearning/metalearning_files/accuracy_binary.classification_dense/configurations.csv @@ -0,0 +1,141 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:fast_ica:algorithm,preprocessor:fast_ica:fun,preprocessor:fast_ica:n_components,preprocessor:fast_ica:whiten,preprocessor:feature_agglomeration:affinity,preprocessor:feature_agglomeration:linkage,preprocessor:feature_agglomeration:n_clusters,preprocessor:feature_agglomeration:pooling_func,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:pca:keep_variance,preprocessor:pca:whiten,preprocessor:polynomial:degree,preprocessor:polynomial:include_bias,preprocessor:polynomial:interaction_only,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,rescaling:__choice__,rescaling:quantile_transformer:n_quantiles,rescaling:quantile_transformer:output_distribution,rescaling:robust_scaler:q_max,rescaling:robust_scaler:q_min +weighting,one_hot_encoding,0.00011717632475982632,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.045388141846341344,deviance,10.0,0.29161769341843435,None,0.0,20.0,2.0,0.0,278.0,0.7912571599269661,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7249853037185638,None,0.0,1.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,median,extra_trees_preproc_for_classification,False,gini,None,0.9424908623661876,None,0.0,7.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.03474109838999682,deviance,4.0,0.5687034678818491,None,0.0,18.0,12.0,0.0,408.0,0.5150113945430513,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.6328939517938,f_classif,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.251097788175676,deviance,6.0,0.35679099363539235,None,0.0,13.0,11.0,0.0,157.0,0.4791732272983235,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,adaboost,SAMME,0.28738775989203896,10.0,423.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.8031499675923353,0.13579938270386765 +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.14159526341015916,deviance,7.0,0.8010488230155749,None,0.0,3.0,20.0,0.0,401.0,0.8073562440607731,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.002385546176068135,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.7729472304882845,,,0.0004789329856033374,rbf,-1.0,True,6.58869648864534e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,177.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5368752992317617,None,0.0,16.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.8382117756438685,mutual_info,,,,quantile_transformer,11480.0,normal,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9455638720565652,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,most_frequent,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8255464552647293,0.19162485555463185 +weighting,one_hot_encoding,0.18137532678800647,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9094110110427254,None,0.0,7.0,12.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,195.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.6682079659377479,None,0.0,4.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,mean,extra_trees_preproc_for_classification,True,entropy,None,0.5552350997943013,None,0.0,8.0,5.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.02345017287074443,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.053517066400173056,deviance,10.0,0.542144980834302,None,0.0,20.0,13.0,0.0,233.0,0.7398539900055563,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0614425536709615,fwe,f_classif,robust_scaler,,,0.9523118062307264,0.13434811490315818 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.040936424602789435,deviance,7.0,0.5495014745530306,None,0.0,20.0,18.0,0.0,141.0,0.6905343807995293,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7974565919616314,None,0.0,12.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,median,extra_trees_preproc_for_classification,True,entropy,None,0.9772091846790169,None,0.0,10.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2538107344750156,False,True,hinge,1.5099542326343014e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,8532.0,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.1958974686405233,deviance,5.0,0.33885235607979314,None,0.0,6.0,4.0,0.0,125.0,0.9448890820738562,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2538107344750156,False,True,hinge,1.5099542326343014e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,8532.0,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,0.0007038280350320558,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4138778052607317,0.7995003430482459,5.0,5.430044692638861,poly,-1.0,True,0.02455501006004393,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,31,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.010000000000000005,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.0518326156691958,deviance,6.0,0.8807456180216267,None,0.0,7.0,19.0,0.0,366.0,0.7314831276137047,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,,False,adaboost,SAMME,0.4391375941344922,3.0,386.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7439738358430176,0.20581080574615795 +none,one_hot_encoding,0.00011294596229850895,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7515.1213255144885,0.9576762936062476,3.0,0.019002536385919932,poly,-1.0,False,0.010632086351533369,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,51.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,97282.0,normal,, +none,one_hot_encoding,0.00012586572428922356,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5240592829918601,None,0.0,10.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1751.4736515133566,0.62404114475118,3.0,1.6087076997410432,poly,-1.0,False,3.5353792826856045e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1198.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.2422926485206341,,1.0,None,0.0,15.0,9.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.02102683283349326,deviance,10.0,0.2797288369369436,None,0.0,14.0,9.0,0.0,480.0,0.5778972273820631,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58.88123233170863,mutual_info,,,,robust_scaler,,,0.75,0.25 +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828776,None,0.0,2.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2155613360930585,deviance,4.0,0.3198803116198433,None,0.0,8.0,13.0,0.0,275.0,0.28870176110739404,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,mean,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.7062102387181676,None,0.0,1.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,most_frequent,fast_ica,,,,,,,,,,,parallel,exp,100.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7065776353150109,0.23782974987118105 +none,one_hot_encoding,0.16334152321884812,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00011572473434870852,True,True,squared_hinge,0.00019678754114665057,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.045742431094098604,fpr,chi2,none,,,, +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.3795924768593385,deviance,2.0,0.33708497069988536,None,0.0,15.0,13.0,0.0,451.0,0.7716323242090217,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2573946506994812,fwe,f_classif,quantile_transformer,1000.0,uniform,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.609975998293528,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,most_frequent,fast_ica,,,,,,,,,,,parallel,logcosh,2000.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8430415644014919,0.2863750565331575 +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.39536192447534535,None,0.0,19.0,3.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5.0,None,11.0,11.0,1.0,12.0,,,,,,robust_scaler,,,0.8928631650245873,0.1581877760687084 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.3126027672745337,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,870.2240970463429,0.5325949351918051,3.0,0.010682839357128344,poly,-1.0,False,2.485160860440657e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4608103694360143,fdr,f_classif,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.9348409326933208,rbf,-1.0,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1754.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,198.72528686512538,False,True,1.0,squared_hinge,ovr,l2,0.026260652523566803,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,robust_scaler,,,0.913511520078368,0.2742229325455444 +none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9260795160807372,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.03644212536682547,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.05965671477918361,deviance,8.0,0.4858133247974158,None,0.0,14.0,7.0,0.0,480.0,0.5726186552917335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.15318294164619112 +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18887.81504976871,,,0.23283562663398755,rbf,-1.0,True,2.3839685780861318e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3937607155568376,fwe,chi2,robust_scaler,,,0.941018778984854,0.2144110585080491 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3428971645958825,,,0.2229870623330047,rbf,-1.0,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8138233157708883,None,0.0,2.0,9.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54.27504292568562,f_classif,,,,minmax,,,, +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00016781524591321165,True,True,squared_hinge,1.5119200923218881e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.556190405302504,False,True,1.0,squared_hinge,ovr,l2,0.0007318628304090552,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,most_frequent,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,3.5602014542183973,948.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +weighting,one_hot_encoding,0.00034835629696198427,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8245132980938538,0.08947420373097192 +weighting,one_hot_encoding,0.00016967940959070708,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9439080311935252,None,0.0,2.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,170.0,None,,0.014191958374153584,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,,False,adaboost,SAMME.R,0.8220362681234727,5.0,183.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.363262678765884,fdr,chi2,robust_scaler,,,0.8826612080363588,0.2189605310171097 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,normalize,,,, +none,one_hot_encoding,,False,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6396026761675004,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06544340428506021,fwe,f_classif,none,,,, +weighting,one_hot_encoding,0.004980497345831963,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1255.9137433589426,,,0.08351549479967445,rbf,-1.0,True,0.00017919875199222518,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1.0,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.3837398524575939,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.018356703878357986,deviance,3.0,0.9690352514774068,None,0.0,12.0,3.0,0.0,234.0,0.3870344708308441,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,most_frequent,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6864970915330799,False,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5670424455696162,None,0.0,8.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,standardize,,,, +weighting,one_hot_encoding,0.00031737236118003484,True,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,244.0,None,,2.3065111488706024e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,85,mean,fast_ica,,,,,,,,,,,deflation,exp,1862.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7851234479882973,0.2237528085136715 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,86,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,156.0,auto,,0.0001987333852871589,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1.0,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,no_encoding,,,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.96834823420249e-05,False,True,hinge,0.00016639250831671168,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,89,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.628430584359224,mutual_info,,,,quantile_transformer,6634.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,90,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,91,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,93,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2636201374253461,deviance,7.0,0.8344964130784466,None,0.0,9.0,2.0,0.0,298.0,0.7517549950523315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,95,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,96,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,97,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.07463196642416367,deviance,7.0,0.8603242247379981,None,0.0,2.0,6.0,0.0,500.0,0.8447665577491962,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,98,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.0433556140045585,deviance,10.0,0.33000096635982235,None,0.0,15.0,13.0,0.0,388.0,0.8291104221904706,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,99,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,robust_scaler,,,0.7496393440951183,0.2853682991120835 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,100,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,102,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0020580843703898177,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.945774573434192,None,0.0,19.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,103,median,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,15209.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,104,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,105,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,106,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.005332972692819521,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5565918060287016,None,0.0,5.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,107,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,173.0,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9527068489270144,0.04135311355893583 +weighting,one_hot_encoding,0.001279467383882126,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,108,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0903354518102121,fwe,f_classif,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,109,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.002615346832354839,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7884268823432835,None,0.0,20.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,110,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +weighting,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56.086963007482865,,,0.013609964993119377,rbf,-1.0,True,0.00196831255706268,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,111,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.15374716583918388 +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.2472984547885781,deviance,3.0,0.6564306719064884,None,0.0,15.0,14.0,0.0,220.0,0.8082564085714649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,112,median,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,332.0,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,113,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,115,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,116,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,117,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,118,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.20202014999292287,False,True,1.0,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,no_encoding,,,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6390376923528961,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,119,median,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,164.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,62508.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,120,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.27041927277584e-06,True,0.00010000000000000009,0.033157325660763994,True,0.0008114527992546482,invscaling,modified_huber,elasticnet,0.13714427818877545,0.055179642772545036,,,,,,,,,,,,,,,,,,121,median,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,122,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,123,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.19998727075532635,deviance,10.0,0.9377656718112952,None,0.0,7.0,13.0,0.0,214.0,0.6062346326014357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,124,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,125,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,126,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.5765793990908161,None,0.0,11.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,127,median,fast_ica,,,,,,,,,,,deflation,exp,10.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,129,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.04651467280022463,True,adaboost,SAMME,0.6506122230393502,6.0,143.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,extra_trees_preproc_for_classification,False,entropy,None,0.348720215832657,None,0.0,17.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,26025.0,normal,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3386310102795006,None,0.0,6.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,True,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.0,None,,5.861221938384061e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.22985730375167915,fpr,f_classif,quantile_transformer,40589.0,uniform,, +none,no_encoding,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00012437731009611307,True,,0.08709904468181932,True,,invscaling,squared_hinge,l1,0.6231564675290102,0.008071279833697563,,,,,,,,,,,,,,,,,,134,median,fast_ica,,,,,,,,,,,parallel,logcosh,814.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.714869937384006,None,0.0,8.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, diff --git a/metalearning/metalearning_files/accuracy_binary.classification_dense/description.txt b/metalearning/metalearning_files/accuracy_binary.classification_dense/description.txt new file mode 100755 index 0000000..29d109c --- /dev/null +++ b/metalearning/metalearning_files/accuracy_binary.classification_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: accuracy +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/accuracy_binary.classification_dense/feature_costs.arff b/metalearning/metalearning_files/accuracy_binary.classification_dense/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/accuracy_binary.classification_dense/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/accuracy_binary.classification_dense/feature_runstatus.arff b/metalearning/metalearning_files/accuracy_binary.classification_dense/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/accuracy_binary.classification_dense/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/accuracy_binary.classification_dense/feature_values.arff b/metalearning/metalearning_files/accuracy_binary.classification_dense/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/accuracy_binary.classification_dense/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/accuracy_binary.classification_dense/readme.txt b/metalearning/metalearning_files/accuracy_binary.classification_dense/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/accuracy_binary.classification_sparse/algorithm_runs.arff b/metalearning/metalearning_files/accuracy_binary.classification_sparse/algorithm_runs.arff new file mode 100755 index 0000000..4226576 --- /dev/null +++ b/metalearning/metalearning_files/accuracy_binary.classification_sparse/algorithm_runs.arff @@ -0,0 +1,152 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE accuracy NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2120,1.0,1,0.0895803866100896,ok +75193,1.0,2,0.038371068099909755,ok +2117,1.0,3,0.16709064962461995,ok +75156,1.0,4,0.21988682295877127,ok +75129,1.0,5,0.10097087378640779,ok +75243,1.0,6,0.015434985968194592,ok +75110,1.0,7,0.2743573125945129,ok +75239,1.0,8,0.0,ok +75223,1.0,9,0.30989414560380213,ok +75221,1.0,10,0.39791873141724476,ok +258,1.0,11,0.01833872707659112,ok +75121,1.0,12,0.003929273084479323,ok +253,1.0,13,0.44855967078189296,ok +261,1.0,14,0.23333333333333328,ok +75240,1.0,15,0.022020725388601003,ok +75120,1.0,16,0.03929273084479368,ok +75124,1.0,17,0.09121395036887991,ok +75176,1.0,18,0.016590808985464722,ok +75103,1.0,19,0.008000000000000007,ok +75207,1.0,20,0.161849710982659,ok +75095,1.0,21,0.016917293233082664,ok +273,1.0,22,0.044795783926218746,ok +75174,1.0,23,0.11705240755520085,ok +75153,1.0,24,0.12134665186829452,ok +75093,1.0,25,0.17483296213808464,ok +75119,1.0,26,0.035363457760314354,ok +75201,1.0,27,0.0808678500986193,ok +75215,1.0,28,0.028234649122807043,ok +75172,1.0,29,0.09090909090909094,ok +75169,1.0,30,0.03420132141469101,ok +75202,1.0,31,0.20329670329670335,ok +75233,1.0,32,0.06511283758786535,ok +75231,1.0,33,0.19924098671726753,ok +75196,1.0,34,0.023498694516971286,ok +248,1.0,35,0.26515151515151514,ok +75191,1.0,36,0.12370055975887306,ok +75217,1.0,37,0.0,ok +260,1.0,38,0.02657807308970095,ok +75115,1.0,39,0.017681728880157177,ok +75123,1.0,40,0.32728592162554426,ok +75108,1.0,41,0.0018373909049149706,ok +75101,1.0,42,0.28272963283471386,ok +75192,1.0,43,0.48305752561071713,ok +75232,1.0,44,0.14655172413793105,ok +75173,1.0,45,0.11751592356687901,ok +75197,1.0,46,0.13300492610837433,ok +266,1.0,47,0.03280839895013121,ok +75148,1.0,48,0.19024390243902434,ok +75150,1.0,49,0.3204747774480712,ok +75100,1.0,50,0.00379609544468551,ok +75178,1.0,51,0.8079287243594171,ok +75236,1.0,52,0.0323809523809524,ok +75179,1.0,53,0.17943026267110618,ok +75213,1.0,54,0.05249343832021003,ok +2123,1.0,55,0.05882352941176472,ok +75227,1.0,56,0.10151430173864273,ok +75184,1.0,57,0.13967500456454263,ok +75142,1.0,58,0.0813201516390396,ok +236,1.0,59,0.042424242424242475,ok +2122,1.0,60,0.2743573125945129,ok +75188,1.0,61,0.24319066147859925,ok +75166,1.0,62,0.0995190529041805,ok +75181,1.0,63,0.0,ok +75133,1.0,64,0.005123278898495065,ok +75134,1.0,65,0.10170395014980793,ok +75198,1.0,66,0.12143310082435,ok +262,1.0,67,0.0027570995312931057,ok +75234,1.0,68,0.05978705978705978,ok +75139,1.0,69,0.012121212121212088,ok +252,1.0,70,0.1636363636363637,ok +75117,1.0,71,0.06679764243614927,ok +75113,1.0,72,0.006526315789473713,ok +75098,1.0,73,0.027575757575757587,ok +246,1.0,74,0.024242424242424288,ok +75203,1.0,75,0.09555345316934716,ok +75237,1.0,76,0.00039570658356824495,ok +75195,1.0,77,0.0015609901137292326,ok +75171,1.0,78,0.1672216056233814,ok +75128,1.0,79,0.02218114602587795,ok +75096,1.0,80,0.3974640209074891,ok +75250,1.0,81,0.34347287891393896,ok +75146,1.0,82,0.12210711924178974,ok +75116,1.0,83,0.00982318271119842,ok +75157,1.0,84,0.4192200557103064,ok +75187,1.0,85,0.027846027846027854,ok +2350,1.0,86,0.3744580607974338,ok +242,1.0,87,0.01666666666666672,ok +244,1.0,88,0.1106060606060606,ok +75125,1.0,89,0.035363457760314354,ok +75185,1.0,90,0.12816966343937297,ok +75163,1.0,91,0.060374149659863985,ok +75177,1.0,92,0.019292604501607746,ok +75189,1.0,93,0.019401337253296624,ok +75244,1.0,94,0.06339958875942431,ok +75219,1.0,95,0.08375480477442854,ok +75222,1.0,96,0.04524886877828049,ok +75159,1.0,97,0.06849315068493156,ok +75175,1.0,98,0.11701659080898541,ok +75109,1.0,99,0.34315531000613875,ok +254,1.0,100,0.0,ok +75105,1.0,101,0.018121212121212094,ok +75106,1.0,102,0.07212121212121214,ok +75212,1.0,103,0.27738927738927743,ok +75099,1.0,104,0.12374100719424463,ok +75248,1.0,105,0.10040485829959511,ok +233,1.0,106,0.015180265654648917,ok +75235,1.0,107,0.0016666666666667052,ok +75226,1.0,108,0.004259202920596339,ok +75132,1.0,109,0.051244509516837455,ok +75127,1.0,110,0.3345075170228544,ok +251,1.0,111,0.02631578947368418,ok +75161,1.0,112,0.08280680889021041,ok +75143,1.0,113,0.010471204188481686,ok +75114,1.0,114,0.023575638506876273,ok +75182,1.0,115,0.1081404628890662,ok +75112,1.0,116,0.12061822817080947,ok +75210,1.0,117,0.0,ok +75205,1.0,118,0.18110236220472442,ok +75090,1.0,119,0.10139860139860135,ok +275,1.0,120,0.03612167300380231,ok +288,1.0,121,0.14363636363636367,ok +75092,1.0,122,0.0935550935550935,ok +3043,1.0,123,0.020096463022508004,ok +75249,1.0,124,0.004823151125401881,ok +75126,1.0,125,0.0491159135559921,ok +75225,1.0,126,0.04904306220095689,ok +75141,1.0,127,0.05808361080281166,ok +75107,1.0,128,0.053212121212121266,ok +75097,1.0,129,0.05835568297419769,ok +80001,1.0,1,0.06944444444444442,ok +80003,1.0,1,0.12307692307692308,ok +80006,1.0,1,0.1875,ok +80008,1.0,1,0.2222222222222222,ok +80009,1.0,1,0.1578947368421053,ok +80010,1.0,1,0.13793103448275867,ok +80011,1.0,1,0.037735849056603765,ok +80012,1.0,1,0.045454545454545414,ok +80013,1.0,1,0.0,ok +80014,1.0,1,0.045454545454545414,ok +80015,1.0,1,0.09523809523809523,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/accuracy_binary.classification_sparse/configurations.csv b/metalearning/metalearning_files/accuracy_binary.classification_sparse/configurations.csv new file mode 100755 index 0000000..2e88382 --- /dev/null +++ b/metalearning/metalearning_files/accuracy_binary.classification_sparse/configurations.csv @@ -0,0 +1,141 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,preprocessor:truncatedSVD:target_dim,rescaling:__choice__ +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7249853037185638,None,0.0,1.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,median,extra_trees_preproc_for_classification,False,gini,None,0.9424908623661876,None,0.0,7.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.6682079659377479,None,0.0,4.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,mean,extra_trees_preproc_for_classification,True,entropy,None,0.5552350997943013,None,0.0,8.0,5.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7974565919616314,None,0.0,12.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,median,extra_trees_preproc_for_classification,True,entropy,None,0.9772091846790169,None,0.0,10.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2538107344750156,False,True,hinge,1.5099542326343014e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,8532.0,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.034465366914659866,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.12057775675278172,deviance,10.0,0.8011153303489733,None,0.0,2.0,16.0,0.0,370.0,0.6078295352200873,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,mean,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0007038280350320558,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4138778052607317,0.7995003430482459,5.0,5.430044692638861,poly,-1.0,True,0.02455501006004393,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,31,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.0010015637584068037,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.037611630308856295,deviance,5.0,0.8840126779516314,None,0.0,10.0,2.0,0.0,444.0,0.7599997167603434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,most_frequent,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1751.4736515133566,0.62404114475118,3.0,1.6087076997410432,poly,-1.0,False,3.5353792826856045e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,mean,kernel_pca,,,,,,,,,,,,,,cosine,1198.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.8657388713119849,,1.0,None,0.0,19.0,13.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828776,None,0.0,2.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.027324741616523342,deviance,10.0,0.8623781459430139,None,0.0,10.0,20.0,0.0,329.0,0.8595750155424215,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,mean,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1100.6211008501205,0.5921425829232616,2.0,0.0337546254878617,poly,-1.0,True,0.09641299736884308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.9348409326933208,rbf,-1.0,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,mean,kernel_pca,,,,,,,,,,,,,,cosine,1754.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3428971645958825,,,0.2229870623330047,rbf,-1.0,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00016781524591321165,True,True,squared_hinge,1.5119200923218881e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.556190405302504,False,True,1.0,squared_hinge,ovr,l2,0.0007318628304090552,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,most_frequent,kitchen_sinks,,,,,,,,,,,,,,,,3.5602014542183973,948.0,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4047.618729304337,,,2.0237366768707754,rbf,-1.0,True,0.04369127828878843,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,one_hot_encoding,0.010000000000000005,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10.0,1.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,,none +weighting,one_hot_encoding,0.0008685602750053468,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9896334290292654,None,0.0,11.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,141.76310800864283,False,True,1.0,squared_hinge,ovr,l1,0.004317884655117431,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,,normalize +none,one_hot_encoding,0.003443779683191071,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.004980497345831963,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1255.9137433589426,,,0.08351549479967445,rbf,-1.0,True,0.00017919875199222518,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1.0,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.3451627750042988,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.3163640203509378,None,0.0,17.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,median,extra_trees_preproc_for_classification,False,gini,None,0.8916956785028156,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,85,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,86,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0019686649916896212,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.04641055832142541,True,True,hinge,8.540468968077405e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87,mean,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,911.0,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1.0,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,89,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,90,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,91,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,93,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,95,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,96,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,97,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,98,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,99,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,100,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,102,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,103,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,104,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,105,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,106,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.006372860318416312,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6467376360604045,None,0.0,1.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,107,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,108,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,109,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3823734947460288,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,110,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,adaboost,SAMME,0.11042308042695524,5.0,117.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,111,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,112,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,113,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,115,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,116,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,117,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,118,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.20202014999292287,False,True,1.0,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,119,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,120,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,121,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,122,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,123,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,124,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,125,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,126,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,127,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,129,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.04329010470998136,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7260331062271381,None,0.0,7.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,134,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.714869937384006,None,0.0,8.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/metalearning/metalearning_files/accuracy_binary.classification_sparse/description.txt b/metalearning/metalearning_files/accuracy_binary.classification_sparse/description.txt new file mode 100755 index 0000000..29d109c --- /dev/null +++ b/metalearning/metalearning_files/accuracy_binary.classification_sparse/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: accuracy +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/accuracy_binary.classification_sparse/feature_costs.arff b/metalearning/metalearning_files/accuracy_binary.classification_sparse/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/accuracy_binary.classification_sparse/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/accuracy_binary.classification_sparse/feature_runstatus.arff b/metalearning/metalearning_files/accuracy_binary.classification_sparse/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/accuracy_binary.classification_sparse/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/accuracy_binary.classification_sparse/feature_values.arff b/metalearning/metalearning_files/accuracy_binary.classification_sparse/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/accuracy_binary.classification_sparse/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/accuracy_binary.classification_sparse/readme.txt b/metalearning/metalearning_files/accuracy_binary.classification_sparse/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/accuracy_multiclass.classification_dense/algorithm_runs.arff b/metalearning/metalearning_files/accuracy_multiclass.classification_dense/algorithm_runs.arff new file mode 100755 index 0000000..bf3c4bb --- /dev/null +++ b/metalearning/metalearning_files/accuracy_multiclass.classification_dense/algorithm_runs.arff @@ -0,0 +1,152 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE accuracy NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2120,1.0,1,0.07967939651107969,ok +75193,1.0,2,0.038371068099909755,ok +2117,1.0,3,0.16709064962461995,ok +75156,1.0,4,0.20291026677445434,ok +75129,1.0,5,0.10097087378640779,ok +75243,1.0,6,0.0,ok +75110,1.0,7,0.11622380643767549,ok +75239,1.0,8,0.0,ok +75223,1.0,9,0.10304601425793913,ok +75221,1.0,10,0.39791873141724476,ok +258,1.0,11,0.009708737864077666,ok +75121,1.0,12,0.0,ok +253,1.0,13,0.44855967078189296,ok +261,1.0,14,0.23333333333333328,ok +75240,1.0,15,0.022020725388601003,ok +75120,1.0,16,0.03929273084479368,ok +75124,1.0,17,0.09121395036887991,ok +75176,1.0,18,0.01541623843782114,ok +75103,1.0,19,0.005894736842105286,ok +75207,1.0,20,0.161849710982659,ok +75095,1.0,21,0.016917293233082664,ok +273,1.0,22,0.04413702239789197,ok +75174,1.0,23,0.11705240755520085,ok +75153,1.0,24,0.08028116907140215,ok +75093,1.0,25,0.17483296213808464,ok +75119,1.0,26,0.035363457760314354,ok +75201,1.0,27,0.0808678500986193,ok +75215,1.0,28,0.027412280701754388,ok +75172,1.0,29,0.10303030303030303,ok +75169,1.0,30,0.03420132141469101,ok +75202,1.0,31,0.20329670329670335,ok +75233,1.0,32,0.060673325934147204,ok +75231,1.0,33,0.19924098671726753,ok +75196,1.0,34,0.015665796344647487,ok +248,1.0,35,0.22878787878787876,ok +75191,1.0,36,0.1289905886694962,ok +75217,1.0,37,0.0,ok +260,1.0,38,0.02657807308970095,ok +75115,1.0,39,0.017681728880157177,ok +75123,1.0,40,0.32728592162554426,ok +75108,1.0,41,0.0,ok +75101,1.0,42,0.2740140932130053,ok +75192,1.0,43,0.48305752561071713,ok +75232,1.0,44,0.12356321839080464,ok +75173,1.0,45,0.1165605095541401,ok +75197,1.0,46,0.15517241379310343,ok +266,1.0,47,0.019685039370078705,ok +75148,1.0,48,0.13560975609756099,ok +75150,1.0,49,0.2848664688427299,ok +75100,1.0,50,0.00379609544468551,ok +75178,1.0,51,0.7840550682597786,ok +75236,1.0,52,0.0323809523809524,ok +75179,1.0,53,0.17943026267110618,ok +75213,1.0,54,0.05249343832021003,ok +2123,1.0,55,0.05882352941176472,ok +75227,1.0,56,0.10151430173864273,ok +75184,1.0,57,0.10772320613474529,ok +75142,1.0,58,0.0715825466438712,ok +236,1.0,59,0.038787878787878816,ok +2122,1.0,60,0.10952689565780949,ok +75188,1.0,61,0.24319066147859925,ok +75166,1.0,62,0.0995190529041805,ok +75181,1.0,63,0.0,ok +75133,1.0,64,0.005123278898495065,ok +75134,1.0,65,0.08723783614874181,ok +75198,1.0,66,0.12143310082435,ok +262,1.0,67,0.0027570995312931057,ok +75234,1.0,68,0.024160524160524166,ok +75139,1.0,69,0.010707070707070665,ok +252,1.0,70,0.15000000000000002,ok +75117,1.0,71,0.05500982318271119,ok +75113,1.0,72,0.006526315789473713,ok +75098,1.0,73,0.027575757575757587,ok +246,1.0,74,0.010606060606060619,ok +75203,1.0,75,0.09555345316934716,ok +75237,1.0,76,0.00039570658356824495,ok +75195,1.0,77,0.0015609901137292326,ok +75171,1.0,78,0.1620421753607103,ok +75128,1.0,79,0.02218114602587795,ok +75096,1.0,80,0.005164788382626018,ok +75250,1.0,81,0.34347287891393896,ok +75146,1.0,82,0.11329072074057744,ok +75116,1.0,83,0.00982318271119842,ok +75157,1.0,84,0.4192200557103064,ok +75187,1.0,85,0.01678951678951679,ok +2350,1.0,86,0.3744580607974338,ok +242,1.0,87,0.010606060606060619,ok +244,1.0,88,0.1106060606060606,ok +75125,1.0,89,0.03339882121807469,ok +75185,1.0,90,0.12816966343937297,ok +75163,1.0,91,0.060374149659863985,ok +75177,1.0,92,0.019292604501607746,ok +75189,1.0,93,0.019401337253296624,ok +75244,1.0,94,0.06339958875942431,ok +75219,1.0,95,0.03479668217681575,ok +75222,1.0,96,0.04524886877828049,ok +75159,1.0,97,0.06849315068493156,ok +75175,1.0,98,0.09954485391278811,ok +75109,1.0,99,0.30417434008594224,ok +254,1.0,100,0.0,ok +75105,1.0,101,0.018121212121212094,ok +75106,1.0,102,0.07212121212121214,ok +75212,1.0,103,0.2517482517482518,ok +75099,1.0,104,0.12374100719424463,ok +75248,1.0,105,0.10040485829959511,ok +233,1.0,106,0.002846299810246644,ok +75235,1.0,107,0.0011111111111110628,ok +75226,1.0,108,0.0030422878004259246,ok +75132,1.0,109,0.051244509516837455,ok +75127,1.0,110,0.3330355738331199,ok +251,1.0,111,0.0,ok +75161,1.0,112,0.06437225897569321,ok +75143,1.0,113,0.010471204188481686,ok +75114,1.0,114,0.023575638506876273,ok +75182,1.0,115,0.1081404628890662,ok +75112,1.0,116,0.12061822817080947,ok +75210,1.0,117,0.0,ok +75205,1.0,118,0.18110236220472442,ok +75090,1.0,119,0.06118881118881114,ok +275,1.0,120,0.03612167300380231,ok +288,1.0,121,0.12969696969696964,ok +75092,1.0,122,0.0935550935550935,ok +3043,1.0,123,0.020096463022508004,ok +75249,1.0,124,0.003215434083601254,ok +75126,1.0,125,0.0491159135559921,ok +75225,1.0,126,0.04904306220095689,ok +75141,1.0,127,0.0540140584535701,ok +75107,1.0,128,0.053212121212121266,ok +75097,1.0,129,0.05835568297419769,ok +80001,1.0,1,0.06944444444444442,ok +80003,1.0,1,0.09230769230769231,ok +80006,1.0,1,0.09375,ok +80008,1.0,1,0.11111111111111116,ok +80009,1.0,1,0.10526315789473684,ok +80010,1.0,1,0.13793103448275867,ok +80011,1.0,1,0.037735849056603765,ok +80012,1.0,1,0.045454545454545414,ok +80013,1.0,1,0.0,ok +80014,1.0,1,0.045454545454545414,ok +80015,1.0,1,0.09523809523809523,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/accuracy_multiclass.classification_dense/configurations.csv b/metalearning/metalearning_files/accuracy_multiclass.classification_dense/configurations.csv new file mode 100755 index 0000000..e710c68 --- /dev/null +++ b/metalearning/metalearning_files/accuracy_multiclass.classification_dense/configurations.csv @@ -0,0 +1,141 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:fast_ica:algorithm,preprocessor:fast_ica:fun,preprocessor:fast_ica:n_components,preprocessor:fast_ica:whiten,preprocessor:feature_agglomeration:affinity,preprocessor:feature_agglomeration:linkage,preprocessor:feature_agglomeration:n_clusters,preprocessor:feature_agglomeration:pooling_func,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:pca:keep_variance,preprocessor:pca:whiten,preprocessor:polynomial:degree,preprocessor:polynomial:include_bias,preprocessor:polynomial:interaction_only,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,rescaling:__choice__,rescaling:quantile_transformer:n_quantiles,rescaling:quantile_transformer:output_distribution,rescaling:robust_scaler:q_max,rescaling:robust_scaler:q_min +weighting,one_hot_encoding,0.00011717632475982632,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.045388141846341344,deviance,10.0,0.29161769341843435,None,0.0,20.0,2.0,0.0,278.0,0.7912571599269661,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7249853037185638,None,0.0,1.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,median,extra_trees_preproc_for_classification,False,gini,None,0.9424908623661876,None,0.0,7.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.03474109838999682,deviance,4.0,0.5687034678818491,None,0.0,18.0,12.0,0.0,408.0,0.5150113945430513,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.6328939517938,f_classif,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.251097788175676,deviance,6.0,0.35679099363539235,None,0.0,13.0,11.0,0.0,157.0,0.4791732272983235,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,adaboost,SAMME,0.28738775989203896,10.0,423.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.8031499675923353,0.13579938270386765 +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.14159526341015916,deviance,7.0,0.8010488230155749,None,0.0,3.0,20.0,0.0,401.0,0.8073562440607731,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.002385546176068135,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.7729472304882845,,,0.0004789329856033374,rbf,-1.0,True,6.58869648864534e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,177.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5368752992317617,None,0.0,16.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.8382117756438685,mutual_info,,,,quantile_transformer,11480.0,normal,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9455638720565652,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,most_frequent,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8255464552647293,0.19162485555463185 +weighting,one_hot_encoding,0.18137532678800647,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9094110110427254,None,0.0,7.0,12.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,195.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.6682079659377479,None,0.0,4.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,mean,extra_trees_preproc_for_classification,True,entropy,None,0.5552350997943013,None,0.0,8.0,5.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.02345017287074443,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.053517066400173056,deviance,10.0,0.542144980834302,None,0.0,20.0,13.0,0.0,233.0,0.7398539900055563,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0614425536709615,fwe,f_classif,robust_scaler,,,0.9523118062307264,0.13434811490315818 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.040936424602789435,deviance,7.0,0.5495014745530306,None,0.0,20.0,18.0,0.0,141.0,0.6905343807995293,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7974565919616314,None,0.0,12.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,median,extra_trees_preproc_for_classification,True,entropy,None,0.9772091846790169,None,0.0,10.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2538107344750156,False,True,hinge,1.5099542326343014e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,8532.0,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.1958974686405233,deviance,5.0,0.33885235607979314,None,0.0,6.0,4.0,0.0,125.0,0.9448890820738562,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2538107344750156,False,True,hinge,1.5099542326343014e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,8532.0,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,0.0007038280350320558,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4138778052607317,0.7995003430482459,5.0,5.430044692638861,poly,-1.0,True,0.02455501006004393,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,31,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.010000000000000005,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.0518326156691958,deviance,6.0,0.8807456180216267,None,0.0,7.0,19.0,0.0,366.0,0.7314831276137047,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,,False,adaboost,SAMME,0.4391375941344922,3.0,386.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7439738358430176,0.20581080574615795 +none,one_hot_encoding,0.00011294596229850895,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7515.1213255144885,0.9576762936062476,3.0,0.019002536385919932,poly,-1.0,False,0.010632086351533369,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,51.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,97282.0,normal,, +none,one_hot_encoding,0.00012586572428922356,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5240592829918601,None,0.0,10.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1751.4736515133566,0.62404114475118,3.0,1.6087076997410432,poly,-1.0,False,3.5353792826856045e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1198.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.2422926485206341,,1.0,None,0.0,15.0,9.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.02102683283349326,deviance,10.0,0.2797288369369436,None,0.0,14.0,9.0,0.0,480.0,0.5778972273820631,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58.88123233170863,mutual_info,,,,robust_scaler,,,0.75,0.25 +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828776,None,0.0,2.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2155613360930585,deviance,4.0,0.3198803116198433,None,0.0,8.0,13.0,0.0,275.0,0.28870176110739404,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,mean,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.7062102387181676,None,0.0,1.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,most_frequent,fast_ica,,,,,,,,,,,parallel,exp,100.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7065776353150109,0.23782974987118105 +none,one_hot_encoding,0.16334152321884812,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00011572473434870852,True,True,squared_hinge,0.00019678754114665057,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.045742431094098604,fpr,chi2,none,,,, +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.3795924768593385,deviance,2.0,0.33708497069988536,None,0.0,15.0,13.0,0.0,451.0,0.7716323242090217,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2573946506994812,fwe,f_classif,quantile_transformer,1000.0,uniform,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.609975998293528,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,most_frequent,fast_ica,,,,,,,,,,,parallel,logcosh,2000.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8430415644014919,0.2863750565331575 +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.39536192447534535,None,0.0,19.0,3.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5.0,None,11.0,11.0,1.0,12.0,,,,,,robust_scaler,,,0.8928631650245873,0.1581877760687084 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.3126027672745337,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,870.2240970463429,0.5325949351918051,3.0,0.010682839357128344,poly,-1.0,False,2.485160860440657e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4608103694360143,fdr,f_classif,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.9348409326933208,rbf,-1.0,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1754.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,198.72528686512538,False,True,1.0,squared_hinge,ovr,l2,0.026260652523566803,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,robust_scaler,,,0.913511520078368,0.2742229325455444 +none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9260795160807372,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.03644212536682547,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.05965671477918361,deviance,8.0,0.4858133247974158,None,0.0,14.0,7.0,0.0,480.0,0.5726186552917335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.15318294164619112 +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18887.81504976871,,,0.23283562663398755,rbf,-1.0,True,2.3839685780861318e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3937607155568376,fwe,chi2,robust_scaler,,,0.941018778984854,0.2144110585080491 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3428971645958825,,,0.2229870623330047,rbf,-1.0,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8138233157708883,None,0.0,2.0,9.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54.27504292568562,f_classif,,,,minmax,,,, +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00016781524591321165,True,True,squared_hinge,1.5119200923218881e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.556190405302504,False,True,1.0,squared_hinge,ovr,l2,0.0007318628304090552,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,most_frequent,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,3.5602014542183973,948.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +weighting,one_hot_encoding,0.00034835629696198427,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8245132980938538,0.08947420373097192 +weighting,one_hot_encoding,0.00016967940959070708,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9439080311935252,None,0.0,2.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,170.0,None,,0.014191958374153584,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,,False,adaboost,SAMME.R,0.8220362681234727,5.0,183.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.363262678765884,fdr,chi2,robust_scaler,,,0.8826612080363588,0.2189605310171097 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,normalize,,,, +none,one_hot_encoding,,False,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6396026761675004,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06544340428506021,fwe,f_classif,none,,,, +weighting,one_hot_encoding,0.004980497345831963,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1255.9137433589426,,,0.08351549479967445,rbf,-1.0,True,0.00017919875199222518,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1.0,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.3837398524575939,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.018356703878357986,deviance,3.0,0.9690352514774068,None,0.0,12.0,3.0,0.0,234.0,0.3870344708308441,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,most_frequent,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6864970915330799,False,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5670424455696162,None,0.0,8.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,standardize,,,, +weighting,one_hot_encoding,0.00031737236118003484,True,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,244.0,None,,2.3065111488706024e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,85,mean,fast_ica,,,,,,,,,,,deflation,exp,1862.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7851234479882973,0.2237528085136715 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,86,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,156.0,auto,,0.0001987333852871589,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1.0,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,no_encoding,,,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.96834823420249e-05,False,True,hinge,0.00016639250831671168,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,89,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.628430584359224,mutual_info,,,,quantile_transformer,6634.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,90,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,91,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,93,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2636201374253461,deviance,7.0,0.8344964130784466,None,0.0,9.0,2.0,0.0,298.0,0.7517549950523315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,95,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,96,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,97,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.07463196642416367,deviance,7.0,0.8603242247379981,None,0.0,2.0,6.0,0.0,500.0,0.8447665577491962,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,98,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.0433556140045585,deviance,10.0,0.33000096635982235,None,0.0,15.0,13.0,0.0,388.0,0.8291104221904706,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,99,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,robust_scaler,,,0.7496393440951183,0.2853682991120835 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,100,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,102,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0020580843703898177,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.945774573434192,None,0.0,19.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,103,median,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,15209.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,104,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,105,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,106,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.005332972692819521,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5565918060287016,None,0.0,5.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,107,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,173.0,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9527068489270144,0.04135311355893583 +weighting,one_hot_encoding,0.001279467383882126,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,108,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0903354518102121,fwe,f_classif,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,109,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.002615346832354839,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7884268823432835,None,0.0,20.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,110,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +weighting,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56.086963007482865,,,0.013609964993119377,rbf,-1.0,True,0.00196831255706268,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,111,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.15374716583918388 +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.2472984547885781,deviance,3.0,0.6564306719064884,None,0.0,15.0,14.0,0.0,220.0,0.8082564085714649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,112,median,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,332.0,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,113,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,115,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,116,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,117,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,118,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.20202014999292287,False,True,1.0,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,no_encoding,,,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6390376923528961,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,119,median,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,164.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,62508.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,120,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.27041927277584e-06,True,0.00010000000000000009,0.033157325660763994,True,0.0008114527992546482,invscaling,modified_huber,elasticnet,0.13714427818877545,0.055179642772545036,,,,,,,,,,,,,,,,,,121,median,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,122,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,123,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.19998727075532635,deviance,10.0,0.9377656718112952,None,0.0,7.0,13.0,0.0,214.0,0.6062346326014357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,124,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,125,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,126,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.5765793990908161,None,0.0,11.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,127,median,fast_ica,,,,,,,,,,,deflation,exp,10.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,129,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.04651467280022463,True,adaboost,SAMME,0.6506122230393502,6.0,143.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,extra_trees_preproc_for_classification,False,entropy,None,0.348720215832657,None,0.0,17.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,26025.0,normal,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3386310102795006,None,0.0,6.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,True,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.0,None,,5.861221938384061e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.22985730375167915,fpr,f_classif,quantile_transformer,40589.0,uniform,, +none,no_encoding,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00012437731009611307,True,,0.08709904468181932,True,,invscaling,squared_hinge,l1,0.6231564675290102,0.008071279833697563,,,,,,,,,,,,,,,,,,134,median,fast_ica,,,,,,,,,,,parallel,logcosh,814.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.714869937384006,None,0.0,8.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, diff --git a/metalearning/metalearning_files/accuracy_multiclass.classification_dense/description.txt b/metalearning/metalearning_files/accuracy_multiclass.classification_dense/description.txt new file mode 100755 index 0000000..29d109c --- /dev/null +++ b/metalearning/metalearning_files/accuracy_multiclass.classification_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: accuracy +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/accuracy_multiclass.classification_dense/feature_costs.arff b/metalearning/metalearning_files/accuracy_multiclass.classification_dense/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/accuracy_multiclass.classification_dense/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/accuracy_multiclass.classification_dense/feature_runstatus.arff b/metalearning/metalearning_files/accuracy_multiclass.classification_dense/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/accuracy_multiclass.classification_dense/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/accuracy_multiclass.classification_dense/feature_values.arff b/metalearning/metalearning_files/accuracy_multiclass.classification_dense/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/accuracy_multiclass.classification_dense/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/accuracy_multiclass.classification_dense/readme.txt b/metalearning/metalearning_files/accuracy_multiclass.classification_dense/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/accuracy_multiclass.classification_sparse/algorithm_runs.arff b/metalearning/metalearning_files/accuracy_multiclass.classification_sparse/algorithm_runs.arff new file mode 100755 index 0000000..4226576 --- /dev/null +++ b/metalearning/metalearning_files/accuracy_multiclass.classification_sparse/algorithm_runs.arff @@ -0,0 +1,152 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE accuracy NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2120,1.0,1,0.0895803866100896,ok +75193,1.0,2,0.038371068099909755,ok +2117,1.0,3,0.16709064962461995,ok +75156,1.0,4,0.21988682295877127,ok +75129,1.0,5,0.10097087378640779,ok +75243,1.0,6,0.015434985968194592,ok +75110,1.0,7,0.2743573125945129,ok +75239,1.0,8,0.0,ok +75223,1.0,9,0.30989414560380213,ok +75221,1.0,10,0.39791873141724476,ok +258,1.0,11,0.01833872707659112,ok +75121,1.0,12,0.003929273084479323,ok +253,1.0,13,0.44855967078189296,ok +261,1.0,14,0.23333333333333328,ok +75240,1.0,15,0.022020725388601003,ok +75120,1.0,16,0.03929273084479368,ok +75124,1.0,17,0.09121395036887991,ok +75176,1.0,18,0.016590808985464722,ok +75103,1.0,19,0.008000000000000007,ok +75207,1.0,20,0.161849710982659,ok +75095,1.0,21,0.016917293233082664,ok +273,1.0,22,0.044795783926218746,ok +75174,1.0,23,0.11705240755520085,ok +75153,1.0,24,0.12134665186829452,ok +75093,1.0,25,0.17483296213808464,ok +75119,1.0,26,0.035363457760314354,ok +75201,1.0,27,0.0808678500986193,ok +75215,1.0,28,0.028234649122807043,ok +75172,1.0,29,0.09090909090909094,ok +75169,1.0,30,0.03420132141469101,ok +75202,1.0,31,0.20329670329670335,ok +75233,1.0,32,0.06511283758786535,ok +75231,1.0,33,0.19924098671726753,ok +75196,1.0,34,0.023498694516971286,ok +248,1.0,35,0.26515151515151514,ok +75191,1.0,36,0.12370055975887306,ok +75217,1.0,37,0.0,ok +260,1.0,38,0.02657807308970095,ok +75115,1.0,39,0.017681728880157177,ok +75123,1.0,40,0.32728592162554426,ok +75108,1.0,41,0.0018373909049149706,ok +75101,1.0,42,0.28272963283471386,ok +75192,1.0,43,0.48305752561071713,ok +75232,1.0,44,0.14655172413793105,ok +75173,1.0,45,0.11751592356687901,ok +75197,1.0,46,0.13300492610837433,ok +266,1.0,47,0.03280839895013121,ok +75148,1.0,48,0.19024390243902434,ok +75150,1.0,49,0.3204747774480712,ok +75100,1.0,50,0.00379609544468551,ok +75178,1.0,51,0.8079287243594171,ok +75236,1.0,52,0.0323809523809524,ok +75179,1.0,53,0.17943026267110618,ok +75213,1.0,54,0.05249343832021003,ok +2123,1.0,55,0.05882352941176472,ok +75227,1.0,56,0.10151430173864273,ok +75184,1.0,57,0.13967500456454263,ok +75142,1.0,58,0.0813201516390396,ok +236,1.0,59,0.042424242424242475,ok +2122,1.0,60,0.2743573125945129,ok +75188,1.0,61,0.24319066147859925,ok +75166,1.0,62,0.0995190529041805,ok +75181,1.0,63,0.0,ok +75133,1.0,64,0.005123278898495065,ok +75134,1.0,65,0.10170395014980793,ok +75198,1.0,66,0.12143310082435,ok +262,1.0,67,0.0027570995312931057,ok +75234,1.0,68,0.05978705978705978,ok +75139,1.0,69,0.012121212121212088,ok +252,1.0,70,0.1636363636363637,ok +75117,1.0,71,0.06679764243614927,ok +75113,1.0,72,0.006526315789473713,ok +75098,1.0,73,0.027575757575757587,ok +246,1.0,74,0.024242424242424288,ok +75203,1.0,75,0.09555345316934716,ok +75237,1.0,76,0.00039570658356824495,ok +75195,1.0,77,0.0015609901137292326,ok +75171,1.0,78,0.1672216056233814,ok +75128,1.0,79,0.02218114602587795,ok +75096,1.0,80,0.3974640209074891,ok +75250,1.0,81,0.34347287891393896,ok +75146,1.0,82,0.12210711924178974,ok +75116,1.0,83,0.00982318271119842,ok +75157,1.0,84,0.4192200557103064,ok +75187,1.0,85,0.027846027846027854,ok +2350,1.0,86,0.3744580607974338,ok +242,1.0,87,0.01666666666666672,ok +244,1.0,88,0.1106060606060606,ok +75125,1.0,89,0.035363457760314354,ok +75185,1.0,90,0.12816966343937297,ok +75163,1.0,91,0.060374149659863985,ok +75177,1.0,92,0.019292604501607746,ok +75189,1.0,93,0.019401337253296624,ok +75244,1.0,94,0.06339958875942431,ok +75219,1.0,95,0.08375480477442854,ok +75222,1.0,96,0.04524886877828049,ok +75159,1.0,97,0.06849315068493156,ok +75175,1.0,98,0.11701659080898541,ok +75109,1.0,99,0.34315531000613875,ok +254,1.0,100,0.0,ok +75105,1.0,101,0.018121212121212094,ok +75106,1.0,102,0.07212121212121214,ok +75212,1.0,103,0.27738927738927743,ok +75099,1.0,104,0.12374100719424463,ok +75248,1.0,105,0.10040485829959511,ok +233,1.0,106,0.015180265654648917,ok +75235,1.0,107,0.0016666666666667052,ok +75226,1.0,108,0.004259202920596339,ok +75132,1.0,109,0.051244509516837455,ok +75127,1.0,110,0.3345075170228544,ok +251,1.0,111,0.02631578947368418,ok +75161,1.0,112,0.08280680889021041,ok +75143,1.0,113,0.010471204188481686,ok +75114,1.0,114,0.023575638506876273,ok +75182,1.0,115,0.1081404628890662,ok +75112,1.0,116,0.12061822817080947,ok +75210,1.0,117,0.0,ok +75205,1.0,118,0.18110236220472442,ok +75090,1.0,119,0.10139860139860135,ok +275,1.0,120,0.03612167300380231,ok +288,1.0,121,0.14363636363636367,ok +75092,1.0,122,0.0935550935550935,ok +3043,1.0,123,0.020096463022508004,ok +75249,1.0,124,0.004823151125401881,ok +75126,1.0,125,0.0491159135559921,ok +75225,1.0,126,0.04904306220095689,ok +75141,1.0,127,0.05808361080281166,ok +75107,1.0,128,0.053212121212121266,ok +75097,1.0,129,0.05835568297419769,ok +80001,1.0,1,0.06944444444444442,ok +80003,1.0,1,0.12307692307692308,ok +80006,1.0,1,0.1875,ok +80008,1.0,1,0.2222222222222222,ok +80009,1.0,1,0.1578947368421053,ok +80010,1.0,1,0.13793103448275867,ok +80011,1.0,1,0.037735849056603765,ok +80012,1.0,1,0.045454545454545414,ok +80013,1.0,1,0.0,ok +80014,1.0,1,0.045454545454545414,ok +80015,1.0,1,0.09523809523809523,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/accuracy_multiclass.classification_sparse/configurations.csv b/metalearning/metalearning_files/accuracy_multiclass.classification_sparse/configurations.csv new file mode 100755 index 0000000..2e88382 --- /dev/null +++ b/metalearning/metalearning_files/accuracy_multiclass.classification_sparse/configurations.csv @@ -0,0 +1,141 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,preprocessor:truncatedSVD:target_dim,rescaling:__choice__ +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7249853037185638,None,0.0,1.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,median,extra_trees_preproc_for_classification,False,gini,None,0.9424908623661876,None,0.0,7.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.6682079659377479,None,0.0,4.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,mean,extra_trees_preproc_for_classification,True,entropy,None,0.5552350997943013,None,0.0,8.0,5.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7974565919616314,None,0.0,12.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,median,extra_trees_preproc_for_classification,True,entropy,None,0.9772091846790169,None,0.0,10.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2538107344750156,False,True,hinge,1.5099542326343014e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,8532.0,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.034465366914659866,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.12057775675278172,deviance,10.0,0.8011153303489733,None,0.0,2.0,16.0,0.0,370.0,0.6078295352200873,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,mean,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0007038280350320558,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4138778052607317,0.7995003430482459,5.0,5.430044692638861,poly,-1.0,True,0.02455501006004393,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,31,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.0010015637584068037,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.037611630308856295,deviance,5.0,0.8840126779516314,None,0.0,10.0,2.0,0.0,444.0,0.7599997167603434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,most_frequent,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1751.4736515133566,0.62404114475118,3.0,1.6087076997410432,poly,-1.0,False,3.5353792826856045e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,mean,kernel_pca,,,,,,,,,,,,,,cosine,1198.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.8657388713119849,,1.0,None,0.0,19.0,13.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828776,None,0.0,2.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.027324741616523342,deviance,10.0,0.8623781459430139,None,0.0,10.0,20.0,0.0,329.0,0.8595750155424215,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,mean,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1100.6211008501205,0.5921425829232616,2.0,0.0337546254878617,poly,-1.0,True,0.09641299736884308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.9348409326933208,rbf,-1.0,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,mean,kernel_pca,,,,,,,,,,,,,,cosine,1754.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3428971645958825,,,0.2229870623330047,rbf,-1.0,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00016781524591321165,True,True,squared_hinge,1.5119200923218881e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.556190405302504,False,True,1.0,squared_hinge,ovr,l2,0.0007318628304090552,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,most_frequent,kitchen_sinks,,,,,,,,,,,,,,,,3.5602014542183973,948.0,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4047.618729304337,,,2.0237366768707754,rbf,-1.0,True,0.04369127828878843,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,one_hot_encoding,0.010000000000000005,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10.0,1.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,,none +weighting,one_hot_encoding,0.0008685602750053468,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9896334290292654,None,0.0,11.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,141.76310800864283,False,True,1.0,squared_hinge,ovr,l1,0.004317884655117431,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,,normalize +none,one_hot_encoding,0.003443779683191071,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.004980497345831963,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1255.9137433589426,,,0.08351549479967445,rbf,-1.0,True,0.00017919875199222518,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1.0,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.3451627750042988,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.3163640203509378,None,0.0,17.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,median,extra_trees_preproc_for_classification,False,gini,None,0.8916956785028156,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,85,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,86,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0019686649916896212,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.04641055832142541,True,True,hinge,8.540468968077405e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87,mean,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,911.0,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1.0,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,89,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,90,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,91,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,93,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,95,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,96,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,97,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,98,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,99,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,100,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,102,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,103,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,104,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,105,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,106,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.006372860318416312,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6467376360604045,None,0.0,1.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,107,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,108,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,109,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3823734947460288,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,110,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,adaboost,SAMME,0.11042308042695524,5.0,117.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,111,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,112,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,113,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,115,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,116,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,117,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,118,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.20202014999292287,False,True,1.0,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,119,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,120,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,121,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,122,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,123,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,124,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,125,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,126,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,127,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,129,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.04329010470998136,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7260331062271381,None,0.0,7.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,134,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.714869937384006,None,0.0,8.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/metalearning/metalearning_files/accuracy_multiclass.classification_sparse/description.txt b/metalearning/metalearning_files/accuracy_multiclass.classification_sparse/description.txt new file mode 100755 index 0000000..29d109c --- /dev/null +++ b/metalearning/metalearning_files/accuracy_multiclass.classification_sparse/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: accuracy +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/accuracy_multiclass.classification_sparse/feature_costs.arff b/metalearning/metalearning_files/accuracy_multiclass.classification_sparse/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/accuracy_multiclass.classification_sparse/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/accuracy_multiclass.classification_sparse/feature_runstatus.arff b/metalearning/metalearning_files/accuracy_multiclass.classification_sparse/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/accuracy_multiclass.classification_sparse/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/accuracy_multiclass.classification_sparse/feature_values.arff b/metalearning/metalearning_files/accuracy_multiclass.classification_sparse/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/accuracy_multiclass.classification_sparse/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/accuracy_multiclass.classification_sparse/readme.txt b/metalearning/metalearning_files/accuracy_multiclass.classification_sparse/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/average_precision_binary.classification_dense/algorithm_runs.arff b/metalearning/metalearning_files/average_precision_binary.classification_dense/algorithm_runs.arff new file mode 100755 index 0000000..2f2b16d --- /dev/null +++ b/metalearning/metalearning_files/average_precision_binary.classification_dense/algorithm_runs.arff @@ -0,0 +1,107 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE average_precision NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2117,1.0,1,0.029763412078921747,ok +75156,1.0,2,0.12484133162374556,ok +75129,1.0,3,0.5509676520037908,ok +75239,1.0,4,0.0,ok +75121,1.0,5,0.0,ok +261,1.0,6,0.34827785855784543,ok +75240,1.0,7,0.021625766203890007,ok +75120,1.0,8,0.0038345464653961603,ok +75124,1.0,9,0.4693606817760053,ok +75176,1.0,10,0.0006455579447136595,ok +75103,1.0,11,0.02951776592788602,ok +75095,1.0,12,0.1310482747296341,ok +273,1.0,13,0.01651975450582066,ok +75174,1.0,14,0.1099369477474399,ok +75153,1.0,15,0.01940792987005946,ok +75093,1.0,16,0.5610262019139592,ok +75119,1.0,17,0.0034686647480737243,ok +75215,1.0,18,0.0026711031737276514,ok +75233,1.0,19,0.0065369152681066245,ok +75196,1.0,20,0.0015409149616926188,ok +75191,1.0,21,0.05865691848533505,ok +75115,1.0,22,0.003608078852377905,ok +75108,1.0,23,0.0,ok +75101,1.0,24,0.17798285005870063,ok +75192,1.0,25,0.47828880962934817,ok +75232,1.0,26,0.10901653634695707,ok +75173,1.0,27,0.044001429223304744,ok +75148,1.0,28,0.061688256785514706,ok +75150,1.0,29,0.21389156888631333,ok +75100,1.0,30,0.9109304029111561,ok +75179,1.0,31,0.22710194032341147,ok +75213,1.0,32,0.0703757952423949,ok +75227,1.0,33,0.0968144517136571,ok +75184,1.0,34,0.08001188318555863,ok +75142,1.0,35,0.014452793889804472,ok +75166,1.0,36,0.03149795085598606,ok +75133,1.0,37,0.5399517233307587,ok +75234,1.0,38,0.0025570511535965013,ok +75139,1.0,39,0.0009693043859110295,ok +75117,1.0,40,0.004429642740899409,ok +75113,1.0,41,0.007698621473944622,ok +75237,1.0,42,1.4490458017935026e-06,ok +75195,1.0,43,4.804147355819133e-06,ok +75171,1.0,44,0.08679723674798512,ok +75128,1.0,45,0.002269455858836311,ok +75146,1.0,46,0.028501461903155723,ok +75116,1.0,47,0.0005316412120749403,ok +75157,1.0,48,0.4966163639201907,ok +75187,1.0,49,0.0012858105310531442,ok +2350,1.0,50,0.5701843684762365,ok +75125,1.0,51,0.015610498243288973,ok +75185,1.0,52,0.056497863657582026,ok +75163,1.0,53,0.03294512114604309,ok +75177,1.0,54,0.04431163294742679,ok +75189,1.0,55,0.001498468597945446,ok +75244,1.0,56,0.6396249192249502,ok +75219,1.0,57,0.006236957400466925,ok +75222,1.0,58,0.19520737730362725,ok +75159,1.0,59,0.5554253128675686,ok +75175,1.0,60,0.043724929956395875,ok +254,1.0,61,0.0,ok +75105,1.0,62,0.9105548999195258,ok +75106,1.0,63,0.8139488561016881,ok +75212,1.0,64,0.1643427548092009,ok +75099,1.0,65,0.4954491042072686,ok +75248,1.0,66,0.6903924386995031,ok +233,1.0,67,0.0001259012142367233,ok +75226,1.0,68,3.573571383674867e-05,ok +75132,1.0,69,0.8959629024463337,ok +75127,1.0,70,0.31400548553273844,ok +75161,1.0,71,0.012747168482758253,ok +75143,1.0,72,0.004237743497681468,ok +75114,1.0,73,0.004221920504468679,ok +75182,1.0,74,0.09645923800280876,ok +75112,1.0,75,0.0893404199685851,ok +75210,1.0,76,0.0,ok +75092,1.0,77,0.39396362482609915,ok +3043,1.0,78,0.04431163294742679,ok +75249,1.0,79,0.004924430916126599,ok +75126,1.0,80,0.004174477450688219,ok +75225,1.0,81,0.49003061622169397,ok +75141,1.0,82,0.012239223874392802,ok +75107,1.0,83,0.45187587856064537,ok +75097,1.0,84,0.014888060125247127,ok +80001,1.0,1,0.13441751596644147,ok +80003,1.0,1,0.056462920980274944,ok +80006,1.0,1,0.030561698363555934,ok +80008,1.0,1,0.050000000000000044,ok +80009,1.0,1,0.04595959595959609,ok +80010,1.0,1,0.201106301106301,ok +80011,1.0,1,0.009153318077803174,ok +80012,1.0,1,0.004761904761904967,ok +80013,1.0,1,0.0,ok +80014,1.0,1,0.04117647058823526,ok +80015,1.0,1,0.047371031746031744,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/average_precision_binary.classification_dense/configurations.csv b/metalearning/metalearning_files/average_precision_binary.classification_dense/configurations.csv new file mode 100755 index 0000000..22176e0 --- /dev/null +++ b/metalearning/metalearning_files/average_precision_binary.classification_dense/configurations.csv @@ -0,0 +1,96 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:fast_ica:algorithm,preprocessor:fast_ica:fun,preprocessor:fast_ica:n_components,preprocessor:fast_ica:whiten,preprocessor:feature_agglomeration:affinity,preprocessor:feature_agglomeration:linkage,preprocessor:feature_agglomeration:n_clusters,preprocessor:feature_agglomeration:pooling_func,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:pca:keep_variance,preprocessor:pca:whiten,preprocessor:polynomial:degree,preprocessor:polynomial:include_bias,preprocessor:polynomial:interaction_only,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,rescaling:__choice__,rescaling:quantile_transformer:n_quantiles,rescaling:quantile_transformer:output_distribution,rescaling:robust_scaler:q_max,rescaling:robust_scaler:q_min +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.12713527337147906,deviance,4.0,0.6041596127474019,None,0.0,14.0,17.0,0.0,83.0,0.8426859880999615,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.018505477121829747,deviance,7.0,0.4568365303752941,None,0.0,10.0,2.0,0.0,484.0,0.5253264455070624,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,quantile_transformer,79618.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0020558425106452084,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5570247081444077,None,0.0,15.0,12.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1439.888157185443,False,True,1.0,squared_hinge,ovr,l2,0.03129899277866517,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42.97633372190379,f_classif,,,,quantile_transformer,38835.0,uniform,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.4909422458748719,None,0.0,11.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,mean,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7146659106968425,True,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5591770675079091,None,0.0,6.0,10.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,5.495094435971385,False,True,1.0,squared_hinge,ovr,l1,0.003918906913173503,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9455638720565652,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,most_frequent,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8255464552647293,0.19162485555463185 +weighting,one_hot_encoding,0.2525494306850917,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.2443616095326089,None,0.0,10.0,5.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,extra_trees_preproc_for_classification,False,gini,None,0.2684660070865902,None,0.0,8.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.889025323699763,0.1496018430807889 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.02345017287074443,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.053517066400173056,deviance,10.0,0.542144980834302,None,0.0,20.0,13.0,0.0,233.0,0.7398539900055563,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0614425536709615,fwe,f_classif,robust_scaler,,,0.9523118062307264,0.13434811490315818 +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.8149627329153046,None,0.0,15.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.040936424602789435,deviance,7.0,0.5495014745530306,None,0.0,20.0,18.0,0.0,141.0,0.6905343807995293,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +weighting,one_hot_encoding,0.18137532678800647,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9094110110427254,None,0.0,7.0,12.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,mean,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,195.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.1958974686405233,deviance,5.0,0.33885235607979314,None,0.0,6.0,4.0,0.0,125.0,0.9448890820738562,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,0.010000000000000005,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.0518326156691958,deviance,6.0,0.8807456180216267,None,0.0,7.0,19.0,0.0,366.0,0.7314831276137047,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.7464505951074157,None,0.0,6.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,most_frequent,fast_ica,,,,,,,,,,,parallel,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9504673483378582,0.13375455137243772 +none,one_hot_encoding,0.00012586572428922356,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5240592829918601,None,0.0,10.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +weighting,one_hot_encoding,0.004090774134315939,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7983157215145903,None,0.0,2.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.2422926485206341,,1.0,None,0.0,15.0,9.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.022939738050158573,deviance,10.0,0.4185394344134278,None,0.0,2.0,10.0,0.0,309.0,0.5979695608086252,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.25383213391991144 +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828776,None,0.0,2.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9412423746065944,None,0.0,9.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.7702464686370823,0.17046298103332982 +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.014398770417266825,deviance,5.0,0.3847309634051567,None,0.0,13.0,4.0,0.0,369.0,0.7446964555890218,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7509814655573623,0.05673098788555319 +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.609975998293528,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,most_frequent,fast_ica,,,,,,,,,,,parallel,logcosh,2000.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8430415644014919,0.2863750565331575 +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.39536192447534535,None,0.0,19.0,3.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5.0,None,11.0,11.0,1.0,12.0,,,,,,robust_scaler,,,0.8928631650245873,0.1581877760687084 +weighting,one_hot_encoding,0.3451627750042988,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.3163640203509378,None,0.0,17.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,median,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,2.0,None,10.0,19.0,1.0,47.0,,,,,,quantile_transformer,21674.0,uniform,, +weighting,no_encoding,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.1708463626384465e-06,True,,0.09722688351233316,True,,constant,squared_hinge,l2,,0.00953454743007943,,,,,,,,,,,,,,,,,,31,mean,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,272.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,198.72528686512538,False,True,1.0,squared_hinge,ovr,l2,0.026260652523566803,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,robust_scaler,,,0.913511520078368,0.2742229325455444 +none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9260795160807372,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3428971645958825,,,0.2229870623330047,rbf,-1.0,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.00034835629696198427,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8245132980938538,0.08947420373097192 +weighting,one_hot_encoding,0.00016967940959070708,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9439080311935252,None,0.0,2.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.03528169333197684,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3416063836589199,None,0.0,9.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,median,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1.0,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9529424105127215,None,0.0,10.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,27669.16213464114,False,True,1.0,squared_hinge,ovr,l1,0.004408124070202508,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8563847395036035,0.008522828523482722 +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.4932996544760619,None,0.0,2.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,mean,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,340.0,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.12713527337147906,deviance,4.0,0.6041596127474019,None,0.0,14.0,17.0,0.0,83.0,0.8426859880999615,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.4421938468644326,deviance,5.0,0.5709932933214351,None,0.0,15.0,8.0,0.0,155.0,0.4040373361127008,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.03741851720151596,fwe,f_classif,robust_scaler,,,0.95547292996163,0.03028628950622218 +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.00031737236118003484,True,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,244.0,None,,2.3065111488706024e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,mean,fast_ica,,,,,,,,,,,deflation,exp,1862.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7851234479882973,0.2237528085136715 +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.35533396539961937,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4165632766388807,fpr,chi2,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.9342950927678112,None,0.0,20.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2636201374253461,deviance,7.0,0.8344964130784466,None,0.0,9.0,2.0,0.0,298.0,0.7517549950523315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,normalize,,,, +weighting,one_hot_encoding,0.06906109437088473,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6358117195033023,None,0.0,20.0,3.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.36477607756252617,fdr,chi2,quantile_transformer,38156.0,uniform,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.6490762204984749,None,0.0,18.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,8.0,None,8.0,9.0,1.0,47.0,,,,,,quantile_transformer,53985.0,uniform,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.07463196642416367,deviance,7.0,0.8603242247379981,None,0.0,2.0,6.0,0.0,500.0,0.8447665577491962,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3721522140614508,0.3541746628756037,3.0,0.0009148519644429074,poly,-1.0,False,2.916672898330067e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,37866.0,normal,, +weighting,one_hot_encoding,0.010000000000000005,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.02674155532989549,deviance,3.0,0.14973922320166708,None,0.0,7.0,18.0,0.0,309.0,0.35532673462283193,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.010000000000000005,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.44875674701568935,None,0.0,8.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,median,fast_ica,,,,,,,,,,,parallel,exp,1536.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,0.00013442810992750476,True,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1.0,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,median,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,8.0,None,13.0,16.0,1.0,28.0,,,,,,quantile_transformer,41502.0,uniform,, +weighting,one_hot_encoding,0.002615346832354839,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7884268823432835,None,0.0,20.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.2472984547885781,deviance,3.0,0.6564306719064884,None,0.0,15.0,14.0,0.0,220.0,0.8082564085714649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,median,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,332.0,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.03905145156995541,deviance,5.0,0.2281306656230014,None,0.0,14.0,13.0,0.0,493.0,0.8793075442604774,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,25382.0,normal,, +weighting,one_hot_encoding,0.4273021248657385,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.11426689271753405,deviance,8.0,0.8516012179439271,None,0.0,12.0,15.0,0.0,66.0,0.6029647610646524,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0733000338152003,False,True,1.0,squared_hinge,ovr,l2,0.033752542733220474,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.6840756728731969,,0.00980445380551526,sigmoid,161.0,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2806.9858667073186,False,True,1.0,squared_hinge,ovr,l2,0.03738539536055984,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0006525467941596606,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6372778346125954,None,0.0,20.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.04216014834704712,False,True,1.0,squared_hinge,ovr,l1,3.668632753533501e-05,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.0006290513932903491,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8680626684393846,None,0.0,9.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,54639.0,normal,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.2604623554399743,None,0.0,18.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.282079367058228,fpr,chi2,none,,,, +none,one_hot_encoding,0.04651467280022463,True,adaboost,SAMME,0.6506122230393502,6.0,143.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,extra_trees_preproc_for_classification,False,entropy,None,0.348720215832657,None,0.0,17.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,26025.0,normal,, +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6386579574499105,None,0.0,6.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,134,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.8015164166877907,None,0.0,1.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76.35224288088253,f_classif,,,,quantile_transformer,1766.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, diff --git a/metalearning/metalearning_files/average_precision_binary.classification_dense/description.txt b/metalearning/metalearning_files/average_precision_binary.classification_dense/description.txt new file mode 100755 index 0000000..ce45ffd --- /dev/null +++ b/metalearning/metalearning_files/average_precision_binary.classification_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: average_precision +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/average_precision_binary.classification_dense/feature_costs.arff b/metalearning/metalearning_files/average_precision_binary.classification_dense/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/average_precision_binary.classification_dense/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/average_precision_binary.classification_dense/feature_runstatus.arff b/metalearning/metalearning_files/average_precision_binary.classification_dense/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/average_precision_binary.classification_dense/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/average_precision_binary.classification_dense/feature_values.arff b/metalearning/metalearning_files/average_precision_binary.classification_dense/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/average_precision_binary.classification_dense/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/average_precision_binary.classification_dense/readme.txt b/metalearning/metalearning_files/average_precision_binary.classification_dense/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/average_precision_binary.classification_sparse/algorithm_runs.arff b/metalearning/metalearning_files/average_precision_binary.classification_sparse/algorithm_runs.arff new file mode 100755 index 0000000..f4b7850 --- /dev/null +++ b/metalearning/metalearning_files/average_precision_binary.classification_sparse/algorithm_runs.arff @@ -0,0 +1,107 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE average_precision NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2117,1.0,1,0.03166250826446193,ok +75156,1.0,2,0.13439057860758963,ok +75129,1.0,3,0.5509676520037908,ok +75239,1.0,4,0.0,ok +75121,1.0,5,0.0,ok +261,1.0,6,0.36086761510484555,ok +75240,1.0,7,0.023316837310866867,ok +75120,1.0,8,0.0038855659671372944,ok +75124,1.0,9,0.485855833319911,ok +75176,1.0,10,0.0008796308521696883,ok +75103,1.0,11,0.036187095783448475,ok +75095,1.0,12,0.1310482747296341,ok +273,1.0,13,0.017244171224201965,ok +75174,1.0,14,0.11637685128708752,ok +75153,1.0,15,0.06326124581994363,ok +75093,1.0,16,0.5698502392122984,ok +75119,1.0,17,0.0034686647480737243,ok +75215,1.0,18,0.0061038072779725505,ok +75233,1.0,19,0.00919090874507289,ok +75196,1.0,20,0.005872213783543856,ok +75191,1.0,21,0.05500676424181439,ok +75115,1.0,22,0.003608078852377905,ok +75108,1.0,23,0.0035320622799644985,ok +75101,1.0,24,0.19681102936009776,ok +75192,1.0,25,0.47828880962934817,ok +75232,1.0,26,0.13918767539587396,ok +75173,1.0,27,0.053164427875162845,ok +75148,1.0,28,0.10309564790954906,ok +75150,1.0,29,0.26588055700524404,ok +75100,1.0,30,0.9764966664876966,ok +75179,1.0,31,0.23536018800237657,ok +75213,1.0,32,0.0703757952423949,ok +75227,1.0,33,0.0968144517136571,ok +75184,1.0,34,0.1308482523346186,ok +75142,1.0,35,0.024613375164497797,ok +75166,1.0,36,0.03149795085598606,ok +75133,1.0,37,0.5399517233307587,ok +75234,1.0,38,0.011648333934904054,ok +75139,1.0,39,0.0015604292424358235,ok +75117,1.0,40,0.005675853351771343,ok +75113,1.0,41,0.007698621473944622,ok +75237,1.0,42,1.4490458017935026e-06,ok +75195,1.0,43,4.804147355819133e-06,ok +75171,1.0,44,0.0922448627903868,ok +75128,1.0,45,0.005292787171687796,ok +75146,1.0,46,0.03178239318269793,ok +75116,1.0,47,0.0014054217648633571,ok +75157,1.0,48,0.4966163639201907,ok +75187,1.0,49,0.0031635902143212213,ok +2350,1.0,50,0.5701843684762365,ok +75125,1.0,51,0.015610498243288973,ok +75185,1.0,52,0.06113377205677295,ok +75163,1.0,53,0.03294512114604309,ok +75177,1.0,54,0.04431163294742679,ok +75189,1.0,55,0.001498468597945446,ok +75244,1.0,56,0.6396249192249502,ok +75219,1.0,57,0.025723272040794498,ok +75222,1.0,58,0.2018483977432518,ok +75159,1.0,59,0.6193915130571525,ok +75175,1.0,60,0.059455273798825536,ok +254,1.0,61,0.0,ok +75105,1.0,62,0.9296680538699866,ok +75106,1.0,63,0.8323180256155066,ok +75212,1.0,64,0.1643427548092009,ok +75099,1.0,65,0.4976095614297399,ok +75248,1.0,66,0.6903924386995031,ok +233,1.0,67,0.0006509460140258216,ok +75226,1.0,68,3.573571383674867e-05,ok +75132,1.0,69,0.9043187590977146,ok +75127,1.0,70,0.3158801471006941,ok +75161,1.0,71,0.020190119991083666,ok +75143,1.0,72,0.004237743497681468,ok +75114,1.0,73,0.004221920504468679,ok +75182,1.0,74,0.10492768394444252,ok +75112,1.0,75,0.09040697502820083,ok +75210,1.0,76,0.0,ok +75092,1.0,77,0.39396362482609915,ok +3043,1.0,78,0.04431163294742679,ok +75249,1.0,79,0.004924430916126599,ok +75126,1.0,80,0.004174477450688219,ok +75225,1.0,81,0.49003061622169397,ok +75141,1.0,82,0.015310983755628782,ok +75107,1.0,83,0.45958293633137015,ok +75097,1.0,84,0.023515885742064757,ok +80001,1.0,1,0.13441751596644147,ok +80003,1.0,1,0.06871304221565566,ok +80006,1.0,1,0.06216153127917834,ok +80008,1.0,1,0.050000000000000044,ok +80009,1.0,1,0.04595959595959609,ok +80010,1.0,1,0.201106301106301,ok +80011,1.0,1,0.009153318077803396,ok +80012,1.0,1,0.004761904761904967,ok +80013,1.0,1,0.0,ok +80014,1.0,1,0.04117647058823526,ok +80015,1.0,1,0.047371031746031744,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/average_precision_binary.classification_sparse/configurations.csv b/metalearning/metalearning_files/average_precision_binary.classification_sparse/configurations.csv new file mode 100755 index 0000000..132f736 --- /dev/null +++ b/metalearning/metalearning_files/average_precision_binary.classification_sparse/configurations.csv @@ -0,0 +1,96 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,preprocessor:truncatedSVD:target_dim,rescaling:__choice__ +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,15.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.2380793644102286,None,0.0,1.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.15248352254459802,fwe,chi2,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.0010015637584068037,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.037611630308856295,deviance,5.0,0.8840126779516314,None,0.0,10.0,2.0,0.0,444.0,0.7599997167603434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,most_frequent,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.004090774134315939,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7983157215145903,None,0.0,2.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828776,None,0.0,2.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.002173124111626734,None,0.0,14.0,4.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,6.0,None,13.0,2.0,1.0,23.0,,,,,,,normalize +weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.05109091151009953,False,True,1.0,squared_hinge,ovr,l2,0.002478527812626596,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,31,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3428971645958825,,,0.2229870623330047,rbf,-1.0,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4047.618729304337,,,2.0237366768707754,rbf,-1.0,True,0.04369127828878843,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9896334290292654,None,0.0,11.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1.0,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.3451627750042988,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.3163640203509378,None,0.0,17.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,median,extra_trees_preproc_for_classification,False,gini,None,0.8916956785028156,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.002630811782675973,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9828367182452932,None,0.0,18.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.35533396539961937,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4165632766388807,fpr,chi2,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3823734947460288,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0733000338152003,False,True,1.0,squared_hinge,ovr,l2,0.033752542733220474,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,-0.6840756728731969,,0.00980445380551526,sigmoid,161.0,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.00214097329599271,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7996802015738327,None,0.0,7.0,12.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.1052247187777527,False,True,1.0,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.2604623554399743,None,0.0,18.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.282079367058228,fpr,chi2,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,134,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/metalearning/metalearning_files/average_precision_binary.classification_sparse/description.txt b/metalearning/metalearning_files/average_precision_binary.classification_sparse/description.txt new file mode 100755 index 0000000..ce45ffd --- /dev/null +++ b/metalearning/metalearning_files/average_precision_binary.classification_sparse/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: average_precision +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/average_precision_binary.classification_sparse/feature_costs.arff b/metalearning/metalearning_files/average_precision_binary.classification_sparse/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/average_precision_binary.classification_sparse/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/average_precision_binary.classification_sparse/feature_runstatus.arff b/metalearning/metalearning_files/average_precision_binary.classification_sparse/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/average_precision_binary.classification_sparse/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/average_precision_binary.classification_sparse/feature_values.arff b/metalearning/metalearning_files/average_precision_binary.classification_sparse/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/average_precision_binary.classification_sparse/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/average_precision_binary.classification_sparse/readme.txt b/metalearning/metalearning_files/average_precision_binary.classification_sparse/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/average_precision_multiclass.classification_dense/algorithm_runs.arff b/metalearning/metalearning_files/average_precision_multiclass.classification_dense/algorithm_runs.arff new file mode 100755 index 0000000..2f2b16d --- /dev/null +++ b/metalearning/metalearning_files/average_precision_multiclass.classification_dense/algorithm_runs.arff @@ -0,0 +1,107 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE average_precision NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2117,1.0,1,0.029763412078921747,ok +75156,1.0,2,0.12484133162374556,ok +75129,1.0,3,0.5509676520037908,ok +75239,1.0,4,0.0,ok +75121,1.0,5,0.0,ok +261,1.0,6,0.34827785855784543,ok +75240,1.0,7,0.021625766203890007,ok +75120,1.0,8,0.0038345464653961603,ok +75124,1.0,9,0.4693606817760053,ok +75176,1.0,10,0.0006455579447136595,ok +75103,1.0,11,0.02951776592788602,ok +75095,1.0,12,0.1310482747296341,ok +273,1.0,13,0.01651975450582066,ok +75174,1.0,14,0.1099369477474399,ok +75153,1.0,15,0.01940792987005946,ok +75093,1.0,16,0.5610262019139592,ok +75119,1.0,17,0.0034686647480737243,ok +75215,1.0,18,0.0026711031737276514,ok +75233,1.0,19,0.0065369152681066245,ok +75196,1.0,20,0.0015409149616926188,ok +75191,1.0,21,0.05865691848533505,ok +75115,1.0,22,0.003608078852377905,ok +75108,1.0,23,0.0,ok +75101,1.0,24,0.17798285005870063,ok +75192,1.0,25,0.47828880962934817,ok +75232,1.0,26,0.10901653634695707,ok +75173,1.0,27,0.044001429223304744,ok +75148,1.0,28,0.061688256785514706,ok +75150,1.0,29,0.21389156888631333,ok +75100,1.0,30,0.9109304029111561,ok +75179,1.0,31,0.22710194032341147,ok +75213,1.0,32,0.0703757952423949,ok +75227,1.0,33,0.0968144517136571,ok +75184,1.0,34,0.08001188318555863,ok +75142,1.0,35,0.014452793889804472,ok +75166,1.0,36,0.03149795085598606,ok +75133,1.0,37,0.5399517233307587,ok +75234,1.0,38,0.0025570511535965013,ok +75139,1.0,39,0.0009693043859110295,ok +75117,1.0,40,0.004429642740899409,ok +75113,1.0,41,0.007698621473944622,ok +75237,1.0,42,1.4490458017935026e-06,ok +75195,1.0,43,4.804147355819133e-06,ok +75171,1.0,44,0.08679723674798512,ok +75128,1.0,45,0.002269455858836311,ok +75146,1.0,46,0.028501461903155723,ok +75116,1.0,47,0.0005316412120749403,ok +75157,1.0,48,0.4966163639201907,ok +75187,1.0,49,0.0012858105310531442,ok +2350,1.0,50,0.5701843684762365,ok +75125,1.0,51,0.015610498243288973,ok +75185,1.0,52,0.056497863657582026,ok +75163,1.0,53,0.03294512114604309,ok +75177,1.0,54,0.04431163294742679,ok +75189,1.0,55,0.001498468597945446,ok +75244,1.0,56,0.6396249192249502,ok +75219,1.0,57,0.006236957400466925,ok +75222,1.0,58,0.19520737730362725,ok +75159,1.0,59,0.5554253128675686,ok +75175,1.0,60,0.043724929956395875,ok +254,1.0,61,0.0,ok +75105,1.0,62,0.9105548999195258,ok +75106,1.0,63,0.8139488561016881,ok +75212,1.0,64,0.1643427548092009,ok +75099,1.0,65,0.4954491042072686,ok +75248,1.0,66,0.6903924386995031,ok +233,1.0,67,0.0001259012142367233,ok +75226,1.0,68,3.573571383674867e-05,ok +75132,1.0,69,0.8959629024463337,ok +75127,1.0,70,0.31400548553273844,ok +75161,1.0,71,0.012747168482758253,ok +75143,1.0,72,0.004237743497681468,ok +75114,1.0,73,0.004221920504468679,ok +75182,1.0,74,0.09645923800280876,ok +75112,1.0,75,0.0893404199685851,ok +75210,1.0,76,0.0,ok +75092,1.0,77,0.39396362482609915,ok +3043,1.0,78,0.04431163294742679,ok +75249,1.0,79,0.004924430916126599,ok +75126,1.0,80,0.004174477450688219,ok +75225,1.0,81,0.49003061622169397,ok +75141,1.0,82,0.012239223874392802,ok +75107,1.0,83,0.45187587856064537,ok +75097,1.0,84,0.014888060125247127,ok +80001,1.0,1,0.13441751596644147,ok +80003,1.0,1,0.056462920980274944,ok +80006,1.0,1,0.030561698363555934,ok +80008,1.0,1,0.050000000000000044,ok +80009,1.0,1,0.04595959595959609,ok +80010,1.0,1,0.201106301106301,ok +80011,1.0,1,0.009153318077803174,ok +80012,1.0,1,0.004761904761904967,ok +80013,1.0,1,0.0,ok +80014,1.0,1,0.04117647058823526,ok +80015,1.0,1,0.047371031746031744,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/average_precision_multiclass.classification_dense/configurations.csv b/metalearning/metalearning_files/average_precision_multiclass.classification_dense/configurations.csv new file mode 100755 index 0000000..22176e0 --- /dev/null +++ b/metalearning/metalearning_files/average_precision_multiclass.classification_dense/configurations.csv @@ -0,0 +1,96 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:fast_ica:algorithm,preprocessor:fast_ica:fun,preprocessor:fast_ica:n_components,preprocessor:fast_ica:whiten,preprocessor:feature_agglomeration:affinity,preprocessor:feature_agglomeration:linkage,preprocessor:feature_agglomeration:n_clusters,preprocessor:feature_agglomeration:pooling_func,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:pca:keep_variance,preprocessor:pca:whiten,preprocessor:polynomial:degree,preprocessor:polynomial:include_bias,preprocessor:polynomial:interaction_only,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,rescaling:__choice__,rescaling:quantile_transformer:n_quantiles,rescaling:quantile_transformer:output_distribution,rescaling:robust_scaler:q_max,rescaling:robust_scaler:q_min +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.12713527337147906,deviance,4.0,0.6041596127474019,None,0.0,14.0,17.0,0.0,83.0,0.8426859880999615,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.018505477121829747,deviance,7.0,0.4568365303752941,None,0.0,10.0,2.0,0.0,484.0,0.5253264455070624,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,quantile_transformer,79618.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0020558425106452084,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5570247081444077,None,0.0,15.0,12.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1439.888157185443,False,True,1.0,squared_hinge,ovr,l2,0.03129899277866517,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42.97633372190379,f_classif,,,,quantile_transformer,38835.0,uniform,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.4909422458748719,None,0.0,11.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,mean,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7146659106968425,True,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5591770675079091,None,0.0,6.0,10.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,5.495094435971385,False,True,1.0,squared_hinge,ovr,l1,0.003918906913173503,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9455638720565652,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,most_frequent,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8255464552647293,0.19162485555463185 +weighting,one_hot_encoding,0.2525494306850917,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.2443616095326089,None,0.0,10.0,5.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,extra_trees_preproc_for_classification,False,gini,None,0.2684660070865902,None,0.0,8.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.889025323699763,0.1496018430807889 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.02345017287074443,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.053517066400173056,deviance,10.0,0.542144980834302,None,0.0,20.0,13.0,0.0,233.0,0.7398539900055563,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0614425536709615,fwe,f_classif,robust_scaler,,,0.9523118062307264,0.13434811490315818 +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.8149627329153046,None,0.0,15.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.040936424602789435,deviance,7.0,0.5495014745530306,None,0.0,20.0,18.0,0.0,141.0,0.6905343807995293,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +weighting,one_hot_encoding,0.18137532678800647,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9094110110427254,None,0.0,7.0,12.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,mean,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,195.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.1958974686405233,deviance,5.0,0.33885235607979314,None,0.0,6.0,4.0,0.0,125.0,0.9448890820738562,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,0.010000000000000005,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.0518326156691958,deviance,6.0,0.8807456180216267,None,0.0,7.0,19.0,0.0,366.0,0.7314831276137047,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.7464505951074157,None,0.0,6.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,most_frequent,fast_ica,,,,,,,,,,,parallel,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9504673483378582,0.13375455137243772 +none,one_hot_encoding,0.00012586572428922356,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5240592829918601,None,0.0,10.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +weighting,one_hot_encoding,0.004090774134315939,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7983157215145903,None,0.0,2.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.2422926485206341,,1.0,None,0.0,15.0,9.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.022939738050158573,deviance,10.0,0.4185394344134278,None,0.0,2.0,10.0,0.0,309.0,0.5979695608086252,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.25383213391991144 +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828776,None,0.0,2.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9412423746065944,None,0.0,9.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.7702464686370823,0.17046298103332982 +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.014398770417266825,deviance,5.0,0.3847309634051567,None,0.0,13.0,4.0,0.0,369.0,0.7446964555890218,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7509814655573623,0.05673098788555319 +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.609975998293528,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,most_frequent,fast_ica,,,,,,,,,,,parallel,logcosh,2000.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8430415644014919,0.2863750565331575 +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.39536192447534535,None,0.0,19.0,3.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5.0,None,11.0,11.0,1.0,12.0,,,,,,robust_scaler,,,0.8928631650245873,0.1581877760687084 +weighting,one_hot_encoding,0.3451627750042988,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.3163640203509378,None,0.0,17.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,median,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,2.0,None,10.0,19.0,1.0,47.0,,,,,,quantile_transformer,21674.0,uniform,, +weighting,no_encoding,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.1708463626384465e-06,True,,0.09722688351233316,True,,constant,squared_hinge,l2,,0.00953454743007943,,,,,,,,,,,,,,,,,,31,mean,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,272.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,198.72528686512538,False,True,1.0,squared_hinge,ovr,l2,0.026260652523566803,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,robust_scaler,,,0.913511520078368,0.2742229325455444 +none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9260795160807372,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3428971645958825,,,0.2229870623330047,rbf,-1.0,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.00034835629696198427,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8245132980938538,0.08947420373097192 +weighting,one_hot_encoding,0.00016967940959070708,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9439080311935252,None,0.0,2.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.03528169333197684,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3416063836589199,None,0.0,9.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,median,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1.0,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9529424105127215,None,0.0,10.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,27669.16213464114,False,True,1.0,squared_hinge,ovr,l1,0.004408124070202508,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8563847395036035,0.008522828523482722 +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.4932996544760619,None,0.0,2.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,mean,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,340.0,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.12713527337147906,deviance,4.0,0.6041596127474019,None,0.0,14.0,17.0,0.0,83.0,0.8426859880999615,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.4421938468644326,deviance,5.0,0.5709932933214351,None,0.0,15.0,8.0,0.0,155.0,0.4040373361127008,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.03741851720151596,fwe,f_classif,robust_scaler,,,0.95547292996163,0.03028628950622218 +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.00031737236118003484,True,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,244.0,None,,2.3065111488706024e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,mean,fast_ica,,,,,,,,,,,deflation,exp,1862.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7851234479882973,0.2237528085136715 +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.35533396539961937,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4165632766388807,fpr,chi2,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.9342950927678112,None,0.0,20.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2636201374253461,deviance,7.0,0.8344964130784466,None,0.0,9.0,2.0,0.0,298.0,0.7517549950523315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,normalize,,,, +weighting,one_hot_encoding,0.06906109437088473,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6358117195033023,None,0.0,20.0,3.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.36477607756252617,fdr,chi2,quantile_transformer,38156.0,uniform,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.6490762204984749,None,0.0,18.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,8.0,None,8.0,9.0,1.0,47.0,,,,,,quantile_transformer,53985.0,uniform,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.07463196642416367,deviance,7.0,0.8603242247379981,None,0.0,2.0,6.0,0.0,500.0,0.8447665577491962,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3721522140614508,0.3541746628756037,3.0,0.0009148519644429074,poly,-1.0,False,2.916672898330067e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,37866.0,normal,, +weighting,one_hot_encoding,0.010000000000000005,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.02674155532989549,deviance,3.0,0.14973922320166708,None,0.0,7.0,18.0,0.0,309.0,0.35532673462283193,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.010000000000000005,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.44875674701568935,None,0.0,8.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,median,fast_ica,,,,,,,,,,,parallel,exp,1536.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,0.00013442810992750476,True,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1.0,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,median,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,8.0,None,13.0,16.0,1.0,28.0,,,,,,quantile_transformer,41502.0,uniform,, +weighting,one_hot_encoding,0.002615346832354839,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7884268823432835,None,0.0,20.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.2472984547885781,deviance,3.0,0.6564306719064884,None,0.0,15.0,14.0,0.0,220.0,0.8082564085714649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,median,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,332.0,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.03905145156995541,deviance,5.0,0.2281306656230014,None,0.0,14.0,13.0,0.0,493.0,0.8793075442604774,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,25382.0,normal,, +weighting,one_hot_encoding,0.4273021248657385,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.11426689271753405,deviance,8.0,0.8516012179439271,None,0.0,12.0,15.0,0.0,66.0,0.6029647610646524,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0733000338152003,False,True,1.0,squared_hinge,ovr,l2,0.033752542733220474,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.6840756728731969,,0.00980445380551526,sigmoid,161.0,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2806.9858667073186,False,True,1.0,squared_hinge,ovr,l2,0.03738539536055984,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0006525467941596606,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6372778346125954,None,0.0,20.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.04216014834704712,False,True,1.0,squared_hinge,ovr,l1,3.668632753533501e-05,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.0006290513932903491,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8680626684393846,None,0.0,9.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,54639.0,normal,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.2604623554399743,None,0.0,18.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.282079367058228,fpr,chi2,none,,,, +none,one_hot_encoding,0.04651467280022463,True,adaboost,SAMME,0.6506122230393502,6.0,143.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,extra_trees_preproc_for_classification,False,entropy,None,0.348720215832657,None,0.0,17.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,26025.0,normal,, +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6386579574499105,None,0.0,6.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,134,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.8015164166877907,None,0.0,1.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76.35224288088253,f_classif,,,,quantile_transformer,1766.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, diff --git a/metalearning/metalearning_files/average_precision_multiclass.classification_dense/description.txt b/metalearning/metalearning_files/average_precision_multiclass.classification_dense/description.txt new file mode 100755 index 0000000..ce45ffd --- /dev/null +++ b/metalearning/metalearning_files/average_precision_multiclass.classification_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: average_precision +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/average_precision_multiclass.classification_dense/feature_costs.arff b/metalearning/metalearning_files/average_precision_multiclass.classification_dense/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/average_precision_multiclass.classification_dense/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/average_precision_multiclass.classification_dense/feature_runstatus.arff b/metalearning/metalearning_files/average_precision_multiclass.classification_dense/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/average_precision_multiclass.classification_dense/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/average_precision_multiclass.classification_dense/feature_values.arff b/metalearning/metalearning_files/average_precision_multiclass.classification_dense/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/average_precision_multiclass.classification_dense/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/average_precision_multiclass.classification_dense/readme.txt b/metalearning/metalearning_files/average_precision_multiclass.classification_dense/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/average_precision_multiclass.classification_sparse/algorithm_runs.arff b/metalearning/metalearning_files/average_precision_multiclass.classification_sparse/algorithm_runs.arff new file mode 100755 index 0000000..f4b7850 --- /dev/null +++ b/metalearning/metalearning_files/average_precision_multiclass.classification_sparse/algorithm_runs.arff @@ -0,0 +1,107 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE average_precision NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2117,1.0,1,0.03166250826446193,ok +75156,1.0,2,0.13439057860758963,ok +75129,1.0,3,0.5509676520037908,ok +75239,1.0,4,0.0,ok +75121,1.0,5,0.0,ok +261,1.0,6,0.36086761510484555,ok +75240,1.0,7,0.023316837310866867,ok +75120,1.0,8,0.0038855659671372944,ok +75124,1.0,9,0.485855833319911,ok +75176,1.0,10,0.0008796308521696883,ok +75103,1.0,11,0.036187095783448475,ok +75095,1.0,12,0.1310482747296341,ok +273,1.0,13,0.017244171224201965,ok +75174,1.0,14,0.11637685128708752,ok +75153,1.0,15,0.06326124581994363,ok +75093,1.0,16,0.5698502392122984,ok +75119,1.0,17,0.0034686647480737243,ok +75215,1.0,18,0.0061038072779725505,ok +75233,1.0,19,0.00919090874507289,ok +75196,1.0,20,0.005872213783543856,ok +75191,1.0,21,0.05500676424181439,ok +75115,1.0,22,0.003608078852377905,ok +75108,1.0,23,0.0035320622799644985,ok +75101,1.0,24,0.19681102936009776,ok +75192,1.0,25,0.47828880962934817,ok +75232,1.0,26,0.13918767539587396,ok +75173,1.0,27,0.053164427875162845,ok +75148,1.0,28,0.10309564790954906,ok +75150,1.0,29,0.26588055700524404,ok +75100,1.0,30,0.9764966664876966,ok +75179,1.0,31,0.23536018800237657,ok +75213,1.0,32,0.0703757952423949,ok +75227,1.0,33,0.0968144517136571,ok +75184,1.0,34,0.1308482523346186,ok +75142,1.0,35,0.024613375164497797,ok +75166,1.0,36,0.03149795085598606,ok +75133,1.0,37,0.5399517233307587,ok +75234,1.0,38,0.011648333934904054,ok +75139,1.0,39,0.0015604292424358235,ok +75117,1.0,40,0.005675853351771343,ok +75113,1.0,41,0.007698621473944622,ok +75237,1.0,42,1.4490458017935026e-06,ok +75195,1.0,43,4.804147355819133e-06,ok +75171,1.0,44,0.0922448627903868,ok +75128,1.0,45,0.005292787171687796,ok +75146,1.0,46,0.03178239318269793,ok +75116,1.0,47,0.0014054217648633571,ok +75157,1.0,48,0.4966163639201907,ok +75187,1.0,49,0.0031635902143212213,ok +2350,1.0,50,0.5701843684762365,ok +75125,1.0,51,0.015610498243288973,ok +75185,1.0,52,0.06113377205677295,ok +75163,1.0,53,0.03294512114604309,ok +75177,1.0,54,0.04431163294742679,ok +75189,1.0,55,0.001498468597945446,ok +75244,1.0,56,0.6396249192249502,ok +75219,1.0,57,0.025723272040794498,ok +75222,1.0,58,0.2018483977432518,ok +75159,1.0,59,0.6193915130571525,ok +75175,1.0,60,0.059455273798825536,ok +254,1.0,61,0.0,ok +75105,1.0,62,0.9296680538699866,ok +75106,1.0,63,0.8323180256155066,ok +75212,1.0,64,0.1643427548092009,ok +75099,1.0,65,0.4976095614297399,ok +75248,1.0,66,0.6903924386995031,ok +233,1.0,67,0.0006509460140258216,ok +75226,1.0,68,3.573571383674867e-05,ok +75132,1.0,69,0.9043187590977146,ok +75127,1.0,70,0.3158801471006941,ok +75161,1.0,71,0.020190119991083666,ok +75143,1.0,72,0.004237743497681468,ok +75114,1.0,73,0.004221920504468679,ok +75182,1.0,74,0.10492768394444252,ok +75112,1.0,75,0.09040697502820083,ok +75210,1.0,76,0.0,ok +75092,1.0,77,0.39396362482609915,ok +3043,1.0,78,0.04431163294742679,ok +75249,1.0,79,0.004924430916126599,ok +75126,1.0,80,0.004174477450688219,ok +75225,1.0,81,0.49003061622169397,ok +75141,1.0,82,0.015310983755628782,ok +75107,1.0,83,0.45958293633137015,ok +75097,1.0,84,0.023515885742064757,ok +80001,1.0,1,0.13441751596644147,ok +80003,1.0,1,0.06871304221565566,ok +80006,1.0,1,0.06216153127917834,ok +80008,1.0,1,0.050000000000000044,ok +80009,1.0,1,0.04595959595959609,ok +80010,1.0,1,0.201106301106301,ok +80011,1.0,1,0.009153318077803396,ok +80012,1.0,1,0.004761904761904967,ok +80013,1.0,1,0.0,ok +80014,1.0,1,0.04117647058823526,ok +80015,1.0,1,0.047371031746031744,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/average_precision_multiclass.classification_sparse/configurations.csv b/metalearning/metalearning_files/average_precision_multiclass.classification_sparse/configurations.csv new file mode 100755 index 0000000..132f736 --- /dev/null +++ b/metalearning/metalearning_files/average_precision_multiclass.classification_sparse/configurations.csv @@ -0,0 +1,96 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,preprocessor:truncatedSVD:target_dim,rescaling:__choice__ +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,15.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.2380793644102286,None,0.0,1.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.15248352254459802,fwe,chi2,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.0010015637584068037,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.037611630308856295,deviance,5.0,0.8840126779516314,None,0.0,10.0,2.0,0.0,444.0,0.7599997167603434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,most_frequent,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.004090774134315939,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7983157215145903,None,0.0,2.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828776,None,0.0,2.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.002173124111626734,None,0.0,14.0,4.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,6.0,None,13.0,2.0,1.0,23.0,,,,,,,normalize +weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.05109091151009953,False,True,1.0,squared_hinge,ovr,l2,0.002478527812626596,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,31,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3428971645958825,,,0.2229870623330047,rbf,-1.0,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4047.618729304337,,,2.0237366768707754,rbf,-1.0,True,0.04369127828878843,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9896334290292654,None,0.0,11.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1.0,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.3451627750042988,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.3163640203509378,None,0.0,17.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,median,extra_trees_preproc_for_classification,False,gini,None,0.8916956785028156,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.002630811782675973,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9828367182452932,None,0.0,18.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.35533396539961937,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4165632766388807,fpr,chi2,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3823734947460288,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0733000338152003,False,True,1.0,squared_hinge,ovr,l2,0.033752542733220474,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,-0.6840756728731969,,0.00980445380551526,sigmoid,161.0,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.00214097329599271,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7996802015738327,None,0.0,7.0,12.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.1052247187777527,False,True,1.0,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.2604623554399743,None,0.0,18.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.282079367058228,fpr,chi2,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,134,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/metalearning/metalearning_files/average_precision_multiclass.classification_sparse/description.txt b/metalearning/metalearning_files/average_precision_multiclass.classification_sparse/description.txt new file mode 100755 index 0000000..ce45ffd --- /dev/null +++ b/metalearning/metalearning_files/average_precision_multiclass.classification_sparse/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: average_precision +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/average_precision_multiclass.classification_sparse/feature_costs.arff b/metalearning/metalearning_files/average_precision_multiclass.classification_sparse/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/average_precision_multiclass.classification_sparse/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/average_precision_multiclass.classification_sparse/feature_runstatus.arff b/metalearning/metalearning_files/average_precision_multiclass.classification_sparse/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/average_precision_multiclass.classification_sparse/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/average_precision_multiclass.classification_sparse/feature_values.arff b/metalearning/metalearning_files/average_precision_multiclass.classification_sparse/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/average_precision_multiclass.classification_sparse/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/average_precision_multiclass.classification_sparse/readme.txt b/metalearning/metalearning_files/average_precision_multiclass.classification_sparse/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/balanced_accuracy_binary.classification_dense/algorithm_runs.arff b/metalearning/metalearning_files/balanced_accuracy_binary.classification_dense/algorithm_runs.arff new file mode 100755 index 0000000..7486507 --- /dev/null +++ b/metalearning/metalearning_files/balanced_accuracy_binary.classification_dense/algorithm_runs.arff @@ -0,0 +1,152 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE balanced_accuracy NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2120,1.0,1,0.10170900792413518,ok +75193,1.0,2,0.05570147909402934,ok +2117,1.0,3,0.17058181059794586,ok +75156,1.0,4,0.2059468405268512,ok +75129,1.0,5,0.17411530154510713,ok +75243,1.0,6,0.0,ok +75110,1.0,7,0.11182111215625956,ok +75239,1.0,8,0.0,ok +75223,1.0,9,0.12451335740985625,ok +75221,1.0,10,0.4873924872950214,ok +258,1.0,11,0.009581814407771616,ok +75121,1.0,12,0.0,ok +253,1.0,13,0.44373242034274996,ok +261,1.0,14,0.2943722943722944,ok +75240,1.0,15,0.017683772538141462,ok +75120,1.0,16,0.20465235173824126,ok +75124,1.0,17,0.17233122467134532,ok +75176,1.0,18,0.015737392757574353,ok +75103,1.0,19,0.0031496062992126816,ok +75207,1.0,20,0.1890011398955863,ok +75095,1.0,21,0.04425501079002059,ok +273,1.0,22,0.046593731735826927,ok +75174,1.0,23,0.12864050014146122,ok +75153,1.0,24,0.0802757619738752,ok +75093,1.0,25,0.32381202028228084,ok +75119,1.0,26,0.08442148760330581,ok +75201,1.0,27,0.0997376911529182,ok +75215,1.0,28,0.028514586427562882,ok +75172,1.0,29,0.13559824204240767,ok +75169,1.0,30,0.0340573619843737,ok +75202,1.0,31,0.42493459653140053,ok +75233,1.0,32,0.06969987414076873,ok +75231,1.0,33,0.15778968253968262,ok +75196,1.0,34,0.01378294036061023,ok +248,1.0,35,0.23327202689622628,ok +75191,1.0,36,0.1286172567369568,ok +75217,1.0,37,0.0,ok +260,1.0,38,0.049350759010592826,ok +75115,1.0,39,0.08750260579528879,ok +75123,1.0,40,0.32871263757742397,ok +75108,1.0,41,0.0,ok +75101,1.0,42,0.2753956012356946,ok +75192,1.0,43,0.48263188489323006,ok +75232,1.0,44,0.13627022861546367,ok +75173,1.0,45,0.11644480519480527,ok +75197,1.0,46,0.2211380803516111,ok +266,1.0,47,0.018660135427440383,ok +75148,1.0,48,0.1358001187955955,ok +75150,1.0,49,0.2876075003524602,ok +75100,1.0,50,0.2540244186950774,ok +75178,1.0,51,0.784206721465269,ok +75236,1.0,52,0.032106570444582316,ok +75179,1.0,53,0.20149749000255257,ok +75213,1.0,54,0.06273157272368945,ok +2123,1.0,55,0.17781746031746037,ok +75227,1.0,56,0.11446626012925343,ok +75184,1.0,57,0.12532912571721433,ok +75142,1.0,58,0.07161311525180047,ok +236,1.0,59,0.03889089023011372,ok +2122,1.0,60,0.11253171534644457,ok +75188,1.0,61,0.4727873168498168,ok +75166,1.0,62,0.09969265347610456,ok +75181,1.0,63,0.0,ok +75133,1.0,64,0.15654041218375303,ok +75134,1.0,65,0.1481357660616962,ok +75198,1.0,66,0.12654657786302836,ok +262,1.0,67,0.0027336509054473046,ok +75234,1.0,68,0.024155509645569007,ok +75139,1.0,69,0.012643391866273612,ok +252,1.0,70,0.1510545149907132,ok +75117,1.0,71,0.11955388784657073,ok +75113,1.0,72,0.005274971941638618,ok +75098,1.0,73,0.028062456815564074,ok +246,1.0,74,0.010490317336848243,ok +75203,1.0,75,0.11023617811471986,ok +75237,1.0,76,0.0002948416740189419,ok +75195,1.0,77,0.0015167841344365662,ok +75171,1.0,78,0.16208341454974717,ok +75128,1.0,79,0.059041798537596835,ok +75096,1.0,80,0.3341299192137569,ok +75250,1.0,81,0.3478893206205086,ok +75146,1.0,82,0.11384157742648315,ok +75116,1.0,83,0.020634603301536547,ok +75157,1.0,84,0.44266509433962264,ok +75187,1.0,85,0.016824909805437382,ok +2350,1.0,86,0.4423678825304047,ok +242,1.0,87,0.011186441482494147,ok +244,1.0,88,0.10808472308576977,ok +75125,1.0,89,0.06985388361958478,ok +75185,1.0,90,0.12909185691725056,ok +75163,1.0,91,0.06128065774804903,ok +75177,1.0,92,0.023309264934301632,ok +75189,1.0,93,0.02093625722530723,ok +75244,1.0,94,0.18044671695057302,ok +75219,1.0,95,0.03536781923790411,ok +75222,1.0,96,0.11136904761904765,ok +75159,1.0,97,0.18577787197965234,ok +75175,1.0,98,0.1037395977530613,ok +75109,1.0,99,0.3603409856614681,ok +254,1.0,100,0.0,ok +75105,1.0,101,0.29488259839445896,ok +75106,1.0,102,0.327921279550357,ok +75212,1.0,103,0.2512390226936788,ok +75099,1.0,104,0.2277781422198898,ok +75248,1.0,105,0.18643306379155433,ok +233,1.0,106,0.002793457808655364,ok +75235,1.0,107,0.0007102272727272929,ok +75226,1.0,108,0.008218517371262557,ok +75132,1.0,109,0.3636469111367553,ok +75127,1.0,110,0.33766899823884766,ok +251,1.0,111,0.0,ok +75161,1.0,112,0.0643528394880094,ok +75143,1.0,113,0.017181695373184702,ok +75114,1.0,114,0.034791524265208484,ok +75182,1.0,115,0.12771381137622617,ok +75112,1.0,116,0.13777827445022783,ok +75210,1.0,117,0.0,ok +75205,1.0,118,0.1916783952293486,ok +75090,1.0,119,0.061027366747011924,ok +275,1.0,120,0.040221214056733956,ok +288,1.0,121,0.1294895850789981,ok +75092,1.0,122,0.09411421911421913,ok +3043,1.0,123,0.03952394945636206,ok +75249,1.0,124,0.012821882679773466,ok +75126,1.0,125,0.06940535469437825,ok +75225,1.0,126,0.10485406091370564,ok +75141,1.0,127,0.05180158915235733,ok +75107,1.0,128,0.24811618052967832,ok +75097,1.0,129,0.21628382400897928,ok +80001,1.0,1,0.1502463054187192,ok +80003,1.0,1,0.09095218032441543,ok +80006,1.0,1,0.09607843137254901,ok +80008,1.0,1,0.125,ok +80009,1.0,1,0.10555555555555562,ok +80010,1.0,1,0.2222222222222222,ok +80011,1.0,1,0.05263157894736836,ok +80012,1.0,1,0.0625,ok +80013,1.0,1,0.0,ok +80014,1.0,1,0.050000000000000044,ok +80015,1.0,1,0.10096153846153844,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/balanced_accuracy_binary.classification_dense/configurations.csv b/metalearning/metalearning_files/balanced_accuracy_binary.classification_dense/configurations.csv new file mode 100755 index 0000000..2686140 --- /dev/null +++ b/metalearning/metalearning_files/balanced_accuracy_binary.classification_dense/configurations.csv @@ -0,0 +1,141 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:fast_ica:algorithm,preprocessor:fast_ica:fun,preprocessor:fast_ica:n_components,preprocessor:fast_ica:whiten,preprocessor:feature_agglomeration:affinity,preprocessor:feature_agglomeration:linkage,preprocessor:feature_agglomeration:n_clusters,preprocessor:feature_agglomeration:pooling_func,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:pca:keep_variance,preprocessor:pca:whiten,preprocessor:polynomial:degree,preprocessor:polynomial:include_bias,preprocessor:polynomial:interaction_only,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,rescaling:__choice__,rescaling:quantile_transformer:n_quantiles,rescaling:quantile_transformer:output_distribution,rescaling:robust_scaler:q_max,rescaling:robust_scaler:q_min +weighting,one_hot_encoding,0.00011717632475982632,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.045388141846341344,deviance,10.0,0.29161769341843435,None,0.0,20.0,2.0,0.0,278.0,0.7912571599269661,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7249853037185638,None,0.0,1.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,median,extra_trees_preproc_for_classification,False,gini,None,0.9424908623661876,None,0.0,7.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.12713527337147906,deviance,4.0,0.6041596127474019,None,0.0,14.0,17.0,0.0,83.0,0.8426859880999615,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.03474109838999682,deviance,4.0,0.5687034678818491,None,0.0,18.0,12.0,0.0,408.0,0.5150113945430513,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.6328939517938,f_classif,,,,standardize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9265375980300852,None,0.0,13.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.251097788175676,deviance,6.0,0.35679099363539235,None,0.0,13.0,11.0,0.0,157.0,0.4791732272983235,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,adaboost,SAMME,0.28738775989203896,10.0,423.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.8031499675923353,0.13579938270386765 +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.14159526341015916,deviance,7.0,0.8010488230155749,None,0.0,3.0,20.0,0.0,401.0,0.8073562440607731,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3469031665162168,None,0.0,4.0,3.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,mean,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,83.0,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.002385546176068135,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.7729472304882845,,,0.0004789329856033374,rbf,-1.0,True,6.58869648864534e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,177.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5368752992317617,None,0.0,16.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.8382117756438685,mutual_info,,,,quantile_transformer,11480.0,normal,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.8384447520019118,None,0.0,13.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.36637567531287824,fdr,f_classif,normalize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3759438793746945,None,0.0,7.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3689420905780977,fwe,f_classif,quantile_transformer,25061.0,uniform,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.6291601746046639,None,0.0,11.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,median,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7146659106968425,True,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.12713527337147906,deviance,4.0,0.6041596127474019,None,0.0,14.0,17.0,0.0,83.0,0.8426859880999615,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9455638720565652,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,most_frequent,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8255464552647293,0.19162485555463185 +weighting,one_hot_encoding,0.18137532678800647,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9094110110427254,None,0.0,7.0,12.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,195.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.6682079659377479,None,0.0,4.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,mean,extra_trees_preproc_for_classification,True,entropy,None,0.5552350997943013,None,0.0,8.0,5.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0005560197158932037,True,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00010000000000000026,False,,0.01,True,,invscaling,log,l2,0.25,0.00010000000000000009,,,,,,,,,,,,,,,,,,21,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1392.583799745855,False,True,1.0,squared_hinge,ovr,l1,0.0034975921213842307,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,49486.0,normal,, +none,one_hot_encoding,0.02345017287074443,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.053517066400173056,deviance,10.0,0.542144980834302,None,0.0,20.0,13.0,0.0,233.0,0.7398539900055563,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0614425536709615,fwe,f_classif,robust_scaler,,,0.9523118062307264,0.13434811490315818 +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.8149627329153046,None,0.0,15.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.040936424602789435,deviance,7.0,0.5495014745530306,None,0.0,20.0,18.0,0.0,141.0,0.6905343807995293,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +weighting,one_hot_encoding,0.010000000000000005,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9641046312686136,None,0.0,18.0,12.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8622423450611333,0.2960428898664952 +weighting,one_hot_encoding,0.00034835629696198427,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8245132980938538,0.08947420373097192 +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2398150931290834,,,0.4015139801872962,rbf,-1.0,False,2.402997750662158e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88.40698357592571,chi2,,,,normalize,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.1958974686405233,deviance,5.0,0.33885235607979314,None,0.0,6.0,4.0,0.0,125.0,0.9448890820738562,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2538107344750156,False,True,hinge,1.5099542326343014e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,8532.0,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,0.0007038280350320558,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4138778052607317,0.7995003430482459,5.0,5.430044692638861,poly,-1.0,True,0.02455501006004393,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,31,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.04749094903835669,deviance,3.0,0.6184047395714717,None,0.0,17.0,8.0,0.0,428.0,0.7515561640094087,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,,False,adaboost,SAMME,0.4391375941344922,3.0,386.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7439738358430176,0.20581080574615795 +none,one_hot_encoding,0.00011294596229850895,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7515.1213255144885,0.9576762936062476,3.0,0.019002536385919932,poly,-1.0,False,0.010632086351533369,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,51.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,97282.0,normal,, +none,one_hot_encoding,0.00012586572428922356,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5240592829918601,None,0.0,10.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.15944469021885255,None,0.0,18.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,14.0,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,6315.0,uniform,, +weighting,one_hot_encoding,0.03192699980429505,True,adaboost,SAMME.R,0.10000000000000002,1.0,50.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,quantile_transformer,8407.0,normal,, +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1751.4736515133566,0.62404114475118,3.0,1.6087076997410432,poly,-1.0,False,3.5353792826856045e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1198.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.2422926485206341,,1.0,None,0.0,15.0,9.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.02102683283349326,deviance,10.0,0.2797288369369436,None,0.0,14.0,9.0,0.0,480.0,0.5778972273820631,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58.88123233170863,mutual_info,,,,robust_scaler,,,0.75,0.25 +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828776,None,0.0,2.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2155613360930585,deviance,4.0,0.3198803116198433,None,0.0,8.0,13.0,0.0,275.0,0.28870176110739404,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,mean,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.7062102387181676,None,0.0,1.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,most_frequent,fast_ica,,,,,,,,,,,parallel,exp,100.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7065776353150109,0.23782974987118105 +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.3233601927284725,None,0.0,3.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2991312911384725,fdr,chi2,normalize,,,, +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.3795924768593385,deviance,2.0,0.33708497069988536,None,0.0,15.0,13.0,0.0,451.0,0.7716323242090217,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2573946506994812,fwe,f_classif,quantile_transformer,1000.0,uniform,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.609975998293528,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,most_frequent,fast_ica,,,,,,,,,,,parallel,logcosh,2000.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8430415644014919,0.2863750565331575 +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.39536192447534535,None,0.0,19.0,3.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5.0,None,11.0,11.0,1.0,12.0,,,,,,robust_scaler,,,0.8928631650245873,0.1581877760687084 +weighting,one_hot_encoding,0.0010446150978844174,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.2100039691650936,None,0.0,19.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,10.0,None,3.0,18.0,1.0,42.0,,,,,,quantile_transformer,44341.0,uniform,, +weighting,one_hot_encoding,0.3126027672745337,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1100.6211008501205,0.5921425829232616,2.0,0.0337546254878617,poly,-1.0,True,0.09641299736884308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2507440474920336e-05,True,,0.049622652766554566,True,0.009105043727227265,constant,squared_hinge,elasticnet,,0.00010112719671669047,,,,,,,,,,,,,,,,,,53,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61.69949680034141,chi2,,,,none,,,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.9348409326933208,rbf,-1.0,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1754.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.010488491664540746,True,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6793271069375356,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0903156116813716,fdr,f_classif,robust_scaler,,,0.976245783323518,0.2189244634478133 +weighting,one_hot_encoding,0.010000000000000005,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.011886849251540706,deviance,10.0,0.716738790505292,None,0.0,5.0,6.0,0.0,472.0,0.9128424273302038,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,198.72528686512538,False,True,1.0,squared_hinge,ovr,l2,0.026260652523566803,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,robust_scaler,,,0.913511520078368,0.2742229325455444 +none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9260795160807372,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.03644212536682547,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.05965671477918361,deviance,8.0,0.4858133247974158,None,0.0,14.0,7.0,0.0,480.0,0.5726186552917335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.15318294164619112 +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18887.81504976871,,,0.23283562663398755,rbf,-1.0,True,2.3839685780861318e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3937607155568376,fwe,chi2,robust_scaler,,,0.941018778984854,0.2144110585080491 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3428971645958825,,,0.2229870623330047,rbf,-1.0,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.006909187206474195,True,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00024264014379190562,True,,0.04987297125937914,True,,optimal,hinge,l2,,4.0861541221464815e-05,,,,,,,,,,,,,,,,,,64,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.05153104953418389,False,True,1.0,squared_hinge,ovr,l1,0.0001939386474290285,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.003566024581260295,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7974297391104296,None,0.0,5.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,median,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,f_classif,,,,standardize,,,, +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00016781524591321165,True,True,squared_hinge,1.5119200923218881e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.349459944355116,,,0.00024028983491736645,rbf,-1.0,True,1.139421622432356e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3636266268105085,fwe,chi2,none,,,, +weighting,one_hot_encoding,0.00034835629696198427,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8245132980938538,0.08947420373097192 +weighting,one_hot_encoding,0.00016967940959070708,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9439080311935252,None,0.0,2.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,170.0,None,,0.014191958374153584,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9940718718674404,None,0.0,15.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.05347123056931161,deviance,3.0,0.2250677489759125,None,0.0,16.0,4.0,0.0,309.0,0.7245595517718859,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19466188848884064,fdr,chi2,standardize,,,, +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,normalize,,,, +none,one_hot_encoding,,False,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6396026761675004,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06544340428506021,fwe,f_classif,none,,,, +weighting,one_hot_encoding,0.005326467497783115,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.058055900067082,,,0.1626688094236879,rbf,-1.0,True,0.048381159429370636,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.9188519169916218,None,0.0,1.0,5.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,median,fast_ica,,,,,,,,,,,parallel,cube,306.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1.0,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.3837398524575939,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.018356703878357986,deviance,3.0,0.9690352514774068,None,0.0,12.0,3.0,0.0,234.0,0.3870344708308441,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.4932996544760619,None,0.0,2.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,340.0,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,most_frequent,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6864970915330799,False,,,,,,,,,,,,,,,,normalize,,,, +weighting,one_hot_encoding,0.0001486770773839718,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5256280540657592,None,0.0,8.0,12.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5670424455696162,None,0.0,8.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.00010817282861262362,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.799803680241154,None,0.0,13.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,85,mean,fast_ica,,,,,,,,,,,deflation,exp,1793.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9071932815811076,0.03563842252368924 +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.35533396539961937,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,86,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4165632766388807,fpr,chi2,none,,,, +weighting,one_hot_encoding,,False,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,156.0,auto,,0.0001987333852871589,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1.0,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,no_encoding,,,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.96834823420249e-05,False,True,hinge,0.00016639250831671168,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,89,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.628430584359224,mutual_info,,,,quantile_transformer,6634.0,normal,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8522973640879423,None,0.0,13.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,90,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,91,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,decision_tree,,,,,,,entropy,1.94608233492308,,1.0,None,0.0,15.0,11.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2613520842796237,fdr,chi2,quantile_transformer,1000.0,uniform,, +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,93,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0387325491437111,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.32034732923549136,None,0.0,20.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94,median,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,quantile_transformer,1000.0,uniform,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2636201374253461,deviance,7.0,0.8344964130784466,None,0.0,9.0,2.0,0.0,298.0,0.7517549950523315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,95,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,normalize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,96,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.2263596964804377,True,adaboost,SAMME,0.15143691959318842,2.0,233.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,97,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.07951518163998639,fwe,f_classif,minmax,,,, +weighting,one_hot_encoding,0.010000000000000005,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.14904790197542306,deviance,6.0,0.7561346995577642,None,0.0,5.0,14.0,0.0,340.0,0.6548040792383665,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,98,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.0433556140045585,deviance,10.0,0.33000096635982235,None,0.0,15.0,13.0,0.0,388.0,0.8291104221904706,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,99,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,robust_scaler,,,0.7496393440951183,0.2853682991120835 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,100,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3721522140614508,0.3541746628756037,3.0,0.0009148519644429074,poly,-1.0,False,2.916672898330067e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,37866.0,normal,, +weighting,one_hot_encoding,0.010000000000000005,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.04952755495565772,deviance,3.0,0.7249041896998006,None,0.0,6.0,17.0,0.0,174.0,0.3718608680080454,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,102,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.0020580843703898177,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.945774573434192,None,0.0,19.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,103,median,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,15209.0,normal,, +weighting,one_hot_encoding,0.010000000000000005,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.44875674701568935,None,0.0,8.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,104,most_frequent,fast_ica,,,,,,,,,,,deflation,cube,1367.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.10324969243867224,True,decision_tree,,,,,,,gini,0.7467478023293801,,1.0,None,0.0,12.0,8.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,105,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84.22876326806853,mutual_info,,,,minmax,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,106,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.005332972692819521,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5565918060287016,None,0.0,5.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,107,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,173.0,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9527068489270144,0.04135311355893583 +weighting,one_hot_encoding,0.001279467383882126,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,108,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0903354518102121,fwe,f_classif,standardize,,,, +weighting,one_hot_encoding,0.00013442810992750476,True,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1.0,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,109,median,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,8.0,None,13.0,16.0,1.0,28.0,,,,,,quantile_transformer,41502.0,uniform,, +weighting,one_hot_encoding,0.002615346832354839,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7884268823432835,None,0.0,20.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,110,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +weighting,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56.086963007482865,,,0.013609964993119377,rbf,-1.0,True,0.00196831255706268,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,111,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.15374716583918388 +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.2472984547885781,deviance,3.0,0.6564306719064884,None,0.0,15.0,14.0,0.0,220.0,0.8082564085714649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,112,median,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,332.0,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,113,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.03905145156995541,deviance,5.0,0.2281306656230014,None,0.0,14.0,13.0,0.0,493.0,0.8793075442604774,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,115,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,25382.0,normal,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.12713527337147906,deviance,4.0,0.6041596127474019,None,0.0,14.0,17.0,0.0,83.0,0.8426859880999615,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,116,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,117,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,118,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.20202014999292287,False,True,1.0,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,no_encoding,,,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6390376923528961,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,119,median,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,164.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,62508.0,normal,, +weighting,one_hot_encoding,0.1885493528549979,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.603604171705261,False,True,squared_hinge,4.4620804452839e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,120,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,quantile_transformer,57665.0,uniform,, +weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.27041927277584e-06,True,0.00010000000000000009,0.033157325660763994,True,0.0008114527992546482,invscaling,modified_huber,elasticnet,0.13714427818877545,0.055179642772545036,,,,,,,,,,,,,,,,,,121,median,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.7879059827470586,None,0.0,3.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,122,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54.1024239468849,f_classif,,,,quantile_transformer,89842.0,normal,, +weighting,one_hot_encoding,0.010000000000000005,True,decision_tree,,,,,,,entropy,1.5841974853345435,,1.0,None,0.0,8.0,14.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,123,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,25.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.04994122399949775,deviance,9.0,0.8621747362759826,None,0.0,1.0,13.0,0.0,84.0,0.6449569386396325,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,124,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44.533266302815896,chi2,,,,minmax,,,, +weighting,one_hot_encoding,0.000738320402221022,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,125,median,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.854322697199371,False,True,1.0,squared_hinge,ovr,l1,0.00013359426815085846,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,30.0,normal,, +weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0733000338152003,False,True,1.0,squared_hinge,ovr,l2,0.033752542733220474,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,126,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.6840756728731969,,0.00980445380551526,sigmoid,161.0,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.5765793990908161,None,0.0,11.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,127,median,fast_ica,,,,,,,,,,,deflation,exp,10.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.1166829447028879,deviance,4.0,0.8451196958788597,None,0.0,1.0,8.0,0.0,58.0,0.8652814520124915,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.03406306803297533,False,True,1.0,squared_hinge,ovr,l1,0.00019257971888767865,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.0006290513932903491,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8680626684393846,None,0.0,9.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,129,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,54639.0,normal,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.2604623554399743,None,0.0,18.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.282079367058228,fpr,chi2,none,,,, +none,one_hot_encoding,0.04651467280022463,True,adaboost,SAMME,0.6506122230393502,6.0,143.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,extra_trees_preproc_for_classification,False,entropy,None,0.348720215832657,None,0.0,17.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,26025.0,normal,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3386310102795006,None,0.0,6.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,True,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.0,None,,5.861221938384061e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.22985730375167915,fpr,f_classif,quantile_transformer,40589.0,uniform,, +none,no_encoding,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00012437731009611307,True,,0.08709904468181932,True,,invscaling,squared_hinge,l1,0.6231564675290102,0.008071279833697563,,,,,,,,,,,,,,,,,,134,median,fast_ica,,,,,,,,,,,parallel,logcosh,814.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.714869937384006,None,0.0,8.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, diff --git a/metalearning/metalearning_files/balanced_accuracy_binary.classification_dense/description.txt b/metalearning/metalearning_files/balanced_accuracy_binary.classification_dense/description.txt new file mode 100755 index 0000000..192068b --- /dev/null +++ b/metalearning/metalearning_files/balanced_accuracy_binary.classification_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: balanced_accuracy +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/balanced_accuracy_binary.classification_dense/feature_costs.arff b/metalearning/metalearning_files/balanced_accuracy_binary.classification_dense/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/balanced_accuracy_binary.classification_dense/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/balanced_accuracy_binary.classification_dense/feature_runstatus.arff b/metalearning/metalearning_files/balanced_accuracy_binary.classification_dense/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/balanced_accuracy_binary.classification_dense/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/balanced_accuracy_binary.classification_dense/feature_values.arff b/metalearning/metalearning_files/balanced_accuracy_binary.classification_dense/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/balanced_accuracy_binary.classification_dense/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/balanced_accuracy_binary.classification_dense/readme.txt b/metalearning/metalearning_files/balanced_accuracy_binary.classification_dense/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/balanced_accuracy_binary.classification_sparse/algorithm_runs.arff b/metalearning/metalearning_files/balanced_accuracy_binary.classification_sparse/algorithm_runs.arff new file mode 100755 index 0000000..1a66002 --- /dev/null +++ b/metalearning/metalearning_files/balanced_accuracy_binary.classification_sparse/algorithm_runs.arff @@ -0,0 +1,152 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE balanced_accuracy NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2120,1.0,1,0.12055882293523357,ok +75193,1.0,2,0.05570147909402934,ok +2117,1.0,3,0.17548618736408272,ok +75156,1.0,4,0.22313433229150748,ok +75129,1.0,5,0.21139308855291583,ok +75243,1.0,6,0.02741169837497348,ok +75110,1.0,7,0.292400218490373,ok +75239,1.0,8,0.0,ok +75223,1.0,9,0.3513704849874423,ok +75221,1.0,10,0.5127596762099687,ok +258,1.0,11,0.01797504123780258,ok +75121,1.0,12,0.045454545454545414,ok +253,1.0,13,0.4487779181115149,ok +261,1.0,14,0.31385281385281383,ok +75240,1.0,15,0.017683772538141462,ok +75120,1.0,16,0.2546523517382413,ok +75124,1.0,17,0.2107615894039736,ok +75176,1.0,18,0.016640035468590053,ok +75103,1.0,19,0.01660181821534601,ok +75207,1.0,20,0.1890011398955863,ok +75095,1.0,21,0.04590735712181182,ok +273,1.0,22,0.049601841028638294,ok +75174,1.0,23,0.1520296780824476,ok +75153,1.0,24,0.12110304789550075,ok +75093,1.0,25,0.3286900890421619,ok +75119,1.0,26,0.1288429752066116,ok +75201,1.0,27,0.0997376911529182,ok +75215,1.0,28,0.029887049159286416,ok +75172,1.0,29,0.10886921886815126,ok +75169,1.0,30,0.0340573619843737,ok +75202,1.0,31,0.42493459653140053,ok +75233,1.0,32,0.08355891180172326,ok +75231,1.0,33,0.15778968253968262,ok +75196,1.0,34,0.028346047156726728,ok +248,1.0,35,0.2701991669030601,ok +75191,1.0,36,0.12329305343461994,ok +75217,1.0,37,0.0,ok +260,1.0,38,0.07198862873636425,ok +75115,1.0,39,0.09070773400041698,ok +75123,1.0,40,0.32871263757742397,ok +75108,1.0,41,0.00232727928992138,ok +75101,1.0,42,0.28377248501244257,ok +75192,1.0,43,0.48263188489323006,ok +75232,1.0,44,0.1759384976698103,ok +75173,1.0,45,0.11733360389610392,ok +75197,1.0,46,0.2040950900493621,ok +266,1.0,47,0.031108344044042946,ok +75148,1.0,48,0.19038707564842594,ok +75150,1.0,49,0.32022768927111245,ok +75100,1.0,50,0.27498250252741274,ok +75178,1.0,51,0.8079895490982436,ok +75236,1.0,52,0.032106570444582316,ok +75179,1.0,53,0.20149749000255257,ok +75213,1.0,54,0.06273157272368945,ok +2123,1.0,55,0.256388888888889,ok +75227,1.0,56,0.12397340187955186,ok +75184,1.0,57,0.18267180532449523,ok +75142,1.0,58,0.08134030645745782,ok +236,1.0,59,0.04240504096821618,ok +2122,1.0,60,0.292400218490373,ok +75188,1.0,61,0.4727873168498168,ok +75166,1.0,62,0.09969265347610456,ok +75181,1.0,63,0.0,ok +75133,1.0,64,0.31850429480226317,ok +75134,1.0,65,0.14906265793135665,ok +75198,1.0,66,0.12654657786302836,ok +262,1.0,67,0.0027336509054473046,ok +75234,1.0,68,0.05981527192723568,ok +75139,1.0,69,0.013389237134515009,ok +252,1.0,70,0.1626862498888315,ok +75117,1.0,71,0.12062226391494679,ok +75113,1.0,72,0.010561357454012876,ok +75098,1.0,73,0.028062456815564074,ok +246,1.0,74,0.02464555428242643,ok +75203,1.0,75,0.11023617811471986,ok +75237,1.0,76,0.00040449626783733983,ok +75195,1.0,77,0.0015167841344365662,ok +75171,1.0,78,0.16706480960317482,ok +75128,1.0,79,0.07016624831750884,ok +75096,1.0,80,0.6475478379912987,ok +75250,1.0,81,0.391127054727214,ok +75146,1.0,82,0.1222599514053454,ok +75116,1.0,83,0.020634603301536547,ok +75157,1.0,84,0.44266509433962264,ok +75187,1.0,85,0.027932128591676264,ok +2350,1.0,86,0.4423678825304047,ok +242,1.0,87,0.017081000552456538,ok +244,1.0,88,0.10808472308576977,ok +75125,1.0,89,0.07105870289669314,ok +75185,1.0,90,0.1293167333629761,ok +75163,1.0,91,0.06128065774804903,ok +75177,1.0,92,0.03815960706939259,ok +75189,1.0,93,0.02093625722530723,ok +75244,1.0,94,0.18253811312937274,ok +75219,1.0,95,0.08686090936376534,ok +75222,1.0,96,0.11136904761904765,ok +75159,1.0,97,0.25561678677405686,ok +75175,1.0,98,0.12715986367288612,ok +75109,1.0,99,0.40368927333473925,ok +254,1.0,100,0.0,ok +75105,1.0,101,0.42618761507557956,ok +75106,1.0,102,0.4177538160920802,ok +75212,1.0,103,0.2773454482218938,ok +75099,1.0,104,0.29830589084229864,ok +75248,1.0,105,0.1981334600032404,ok +233,1.0,106,0.01525546388768273,ok +75235,1.0,107,0.001061350868232891,ok +75226,1.0,108,0.009036055111661834,ok +75132,1.0,109,0.4536697094634172,ok +75127,1.0,110,0.33895073146733024,ok +251,1.0,111,0.020161426212381595,ok +75161,1.0,112,0.08277383139196026,ok +75143,1.0,113,0.017181695373184702,ok +75114,1.0,114,0.034791524265208484,ok +75182,1.0,115,0.1360109923276095,ok +75112,1.0,116,0.1477333735943931,ok +75210,1.0,117,0.0,ok +75205,1.0,118,0.1916783952293486,ok +75090,1.0,119,0.10262125025576962,ok +275,1.0,120,0.041190711460148854,ok +288,1.0,121,0.14344937049478246,ok +75092,1.0,122,0.10984848484848486,ok +3043,1.0,123,0.04425716804500235,ok +75249,1.0,124,0.013258182854293588,ok +75126,1.0,125,0.12520747636573581,ok +75225,1.0,126,0.10485406091370564,ok +75141,1.0,127,0.05863227105456237,ok +75107,1.0,128,0.28251633008867305,ok +75097,1.0,129,0.47931992380750144,ok +80001,1.0,1,0.1502463054187192,ok +80003,1.0,1,0.12212976616810622,ok +80006,1.0,1,0.196078431372549,ok +80008,1.0,1,0.19999999999999996,ok +80009,1.0,1,0.15555555555555556,ok +80010,1.0,1,0.2222222222222222,ok +80011,1.0,1,0.05263157894736836,ok +80012,1.0,1,0.0625,ok +80013,1.0,1,0.0,ok +80014,1.0,1,0.050000000000000044,ok +80015,1.0,1,0.10096153846153844,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/balanced_accuracy_binary.classification_sparse/configurations.csv b/metalearning/metalearning_files/balanced_accuracy_binary.classification_sparse/configurations.csv new file mode 100755 index 0000000..b7334ed --- /dev/null +++ b/metalearning/metalearning_files/balanced_accuracy_binary.classification_sparse/configurations.csv @@ -0,0 +1,141 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,preprocessor:truncatedSVD:target_dim,rescaling:__choice__ +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7249853037185638,None,0.0,1.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,median,extra_trees_preproc_for_classification,False,gini,None,0.9424908623661876,None,0.0,7.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.585711203872775,None,0.0,5.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,median,extra_trees_preproc_for_classification,True,entropy,None,0.29512530534048065,None,0.0,7.0,10.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,bernoulli_nb,,,,,0.11565661797517847,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,median,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,9.0,None,13.0,18.0,1.0,29.0,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.039533063907190934,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1878805519245509,fdr,chi2,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,15.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7238850981243719,None,0.0,4.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,5.282738216059151e-05,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.6682079659377479,None,0.0,4.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,mean,extra_trees_preproc_for_classification,True,entropy,None,0.5552350997943013,None,0.0,8.0,5.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0009580347867777607,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,0.10000000000000006,rbf,-1.0,True,0.0010000000000000002,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.35040453084365497,False,True,1.0,squared_hinge,ovr,l1,0.006810889378452772,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.010000000000000005,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9727149851116396,None,0.0,18.0,13.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,,none +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.7735071203475309,,1.0,None,0.0,4.0,4.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,mean,extra_trees_preproc_for_classification,True,entropy,None,0.8171332500590935,None,0.0,15.0,13.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2398150931290834,,,0.4015139801872962,rbf,-1.0,False,2.402997750662158e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88.40698357592571,chi2,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.034465366914659866,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.12057775675278172,deviance,10.0,0.8011153303489733,None,0.0,2.0,16.0,0.0,370.0,0.6078295352200873,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,mean,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0007038280350320558,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4138778052607317,0.7995003430482459,5.0,5.430044692638861,poly,-1.0,True,0.02455501006004393,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,31,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.0010015637584068037,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.037611630308856295,deviance,5.0,0.8840126779516314,None,0.0,10.0,2.0,0.0,444.0,0.7599997167603434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,most_frequent,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.004230062585802822,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.4946412825074172,None,0.0,9.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.004090774134315939,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7983157215145903,None,0.0,2.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,normalize +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1751.4736515133566,0.62404114475118,3.0,1.6087076997410432,poly,-1.0,False,3.5353792826856045e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,mean,kernel_pca,,,,,,,,,,,,,,cosine,1198.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.8657388713119849,,1.0,None,0.0,19.0,13.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828776,None,0.0,2.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.027324741616523342,deviance,10.0,0.8623781459430139,None,0.0,10.0,20.0,0.0,329.0,0.8595750155424215,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,mean,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.002173124111626734,None,0.0,14.0,4.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,6.0,None,13.0,2.0,1.0,23.0,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1100.6211008501205,0.5921425829232616,2.0,0.0337546254878617,poly,-1.0,True,0.09641299736884308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2507440474920336e-05,True,,0.049622652766554566,True,0.009105043727227265,constant,squared_hinge,elasticnet,,0.00010112719671669047,,,,,,,,,,,,,,,,,,53,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61.69949680034141,chi2,,,,,none +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.9348409326933208,rbf,-1.0,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,mean,kernel_pca,,,,,,,,,,,,,,cosine,1754.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,one_hot_encoding,0.039533063907190934,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.3668390168981173,None,0.0,9.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1878805519245509,fdr,chi2,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3428971645958825,,,0.2229870623330047,rbf,-1.0,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7866272157881596,None,0.0,6.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00016781524591321165,True,True,squared_hinge,1.5119200923218881e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.349459944355116,,,0.00024028983491736645,rbf,-1.0,True,1.139421622432356e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3636266268105085,fwe,chi2,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4047.618729304337,,,2.0237366768707754,rbf,-1.0,True,0.04369127828878843,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,one_hot_encoding,0.010000000000000005,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10.0,1.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,,none +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9015027772227234,None,0.0,19.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,one_hot_encoding,0.039533063907190934,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1878805519245509,fdr,chi2,,standardize +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,,normalize +none,one_hot_encoding,0.003443779683191071,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.005326467497783115,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.058055900067082,,,0.1626688094236879,rbf,-1.0,True,0.048381159429370636,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1.0,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.3451627750042988,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.3163640203509378,None,0.0,17.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,median,extra_trees_preproc_for_classification,False,gini,None,0.8916956785028156,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.002630811782675973,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9828367182452932,None,0.0,18.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,85,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.35533396539961937,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,86,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4165632766388807,fpr,chi2,,none +weighting,one_hot_encoding,0.0019686649916896212,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.04641055832142541,True,True,hinge,8.540468968077405e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87,mean,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,911.0,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1.0,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,89,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,90,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,91,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,decision_tree,,,,,,,gini,1.7984076825537865,,1.0,None,0.0,14.0,2.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2897525995758022,fwe,chi2,,none +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,93,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.9376772805635799,None,0.0,18.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94,median,extra_trees_preproc_for_classification,True,entropy,None,0.8613889689810683,None,0.0,10.0,4.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,95,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,96,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,97,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,98,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.00022308163276069302,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,99,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,100,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.039533063907190934,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1878805519245509,fdr,chi2,,standardize +weighting,one_hot_encoding,0.039533063907190934,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,102,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1878805519245509,fdr,chi2,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,103,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,bernoulli_nb,,,,,0.014801515930977628,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,104,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,105,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,106,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.006372860318416312,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6467376360604045,None,0.0,1.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,107,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,,False,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.37163783119625016,False,True,1.0,squared_hinge,ovr,l2,1.2049944334095187e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,108,median,kitchen_sinks,,,,,,,,,,,,,,,,0.33181838105513195,7347.0,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,109,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3823734947460288,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,110,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,adaboost,SAMME,0.11042308042695524,5.0,117.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,111,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,112,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,113,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.3997572853391576,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.21008613984919333,None,0.0,11.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,115,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,116,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,117,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,118,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.20202014999292287,False,True,1.0,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,119,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.00012696985090051145,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.163979712833404,False,True,hinge,0.02438592885755657,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,120,mean,kernel_pca,,,,,,,,,,,,,0.03910740617243662,rbf,1851.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,121,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8804227616935514,None,0.0,10.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,122,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.0898983119899223,False,True,1.0,squared_hinge,ovr,l1,0.016048982920294167,,,,,,,,,,,,,,,,,,,none +weighting,no_encoding,,,decision_tree,,,,,,,entropy,1.301605991416264,,1.0,None,0.0,8.0,3.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,123,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,124,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,125,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0733000338152003,False,True,1.0,squared_hinge,ovr,l2,0.033752542733220474,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,126,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,-0.6840756728731969,,0.00980445380551526,sigmoid,161.0,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,127,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.00214097329599271,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7996802015738327,None,0.0,7.0,12.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.1052247187777527,False,True,1.0,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,129,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.2604623554399743,None,0.0,18.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.282079367058228,fpr,chi2,,none +none,one_hot_encoding,0.04329010470998136,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7260331062271381,None,0.0,7.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.010000000000000005,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15.0,1.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,134,median,extra_trees_preproc_for_classification,False,entropy,None,0.8213051377774989,None,0.0,3.0,13.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.714869937384006,None,0.0,8.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/metalearning/metalearning_files/balanced_accuracy_binary.classification_sparse/description.txt b/metalearning/metalearning_files/balanced_accuracy_binary.classification_sparse/description.txt new file mode 100755 index 0000000..192068b --- /dev/null +++ b/metalearning/metalearning_files/balanced_accuracy_binary.classification_sparse/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: balanced_accuracy +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/balanced_accuracy_binary.classification_sparse/feature_costs.arff b/metalearning/metalearning_files/balanced_accuracy_binary.classification_sparse/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/balanced_accuracy_binary.classification_sparse/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/balanced_accuracy_binary.classification_sparse/feature_runstatus.arff b/metalearning/metalearning_files/balanced_accuracy_binary.classification_sparse/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/balanced_accuracy_binary.classification_sparse/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/balanced_accuracy_binary.classification_sparse/feature_values.arff b/metalearning/metalearning_files/balanced_accuracy_binary.classification_sparse/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/balanced_accuracy_binary.classification_sparse/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/balanced_accuracy_binary.classification_sparse/readme.txt b/metalearning/metalearning_files/balanced_accuracy_binary.classification_sparse/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/balanced_accuracy_multiclass.classification_dense/algorithm_runs.arff b/metalearning/metalearning_files/balanced_accuracy_multiclass.classification_dense/algorithm_runs.arff new file mode 100755 index 0000000..7486507 --- /dev/null +++ b/metalearning/metalearning_files/balanced_accuracy_multiclass.classification_dense/algorithm_runs.arff @@ -0,0 +1,152 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE balanced_accuracy NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2120,1.0,1,0.10170900792413518,ok +75193,1.0,2,0.05570147909402934,ok +2117,1.0,3,0.17058181059794586,ok +75156,1.0,4,0.2059468405268512,ok +75129,1.0,5,0.17411530154510713,ok +75243,1.0,6,0.0,ok +75110,1.0,7,0.11182111215625956,ok +75239,1.0,8,0.0,ok +75223,1.0,9,0.12451335740985625,ok +75221,1.0,10,0.4873924872950214,ok +258,1.0,11,0.009581814407771616,ok +75121,1.0,12,0.0,ok +253,1.0,13,0.44373242034274996,ok +261,1.0,14,0.2943722943722944,ok +75240,1.0,15,0.017683772538141462,ok +75120,1.0,16,0.20465235173824126,ok +75124,1.0,17,0.17233122467134532,ok +75176,1.0,18,0.015737392757574353,ok +75103,1.0,19,0.0031496062992126816,ok +75207,1.0,20,0.1890011398955863,ok +75095,1.0,21,0.04425501079002059,ok +273,1.0,22,0.046593731735826927,ok +75174,1.0,23,0.12864050014146122,ok +75153,1.0,24,0.0802757619738752,ok +75093,1.0,25,0.32381202028228084,ok +75119,1.0,26,0.08442148760330581,ok +75201,1.0,27,0.0997376911529182,ok +75215,1.0,28,0.028514586427562882,ok +75172,1.0,29,0.13559824204240767,ok +75169,1.0,30,0.0340573619843737,ok +75202,1.0,31,0.42493459653140053,ok +75233,1.0,32,0.06969987414076873,ok +75231,1.0,33,0.15778968253968262,ok +75196,1.0,34,0.01378294036061023,ok +248,1.0,35,0.23327202689622628,ok +75191,1.0,36,0.1286172567369568,ok +75217,1.0,37,0.0,ok +260,1.0,38,0.049350759010592826,ok +75115,1.0,39,0.08750260579528879,ok +75123,1.0,40,0.32871263757742397,ok +75108,1.0,41,0.0,ok +75101,1.0,42,0.2753956012356946,ok +75192,1.0,43,0.48263188489323006,ok +75232,1.0,44,0.13627022861546367,ok +75173,1.0,45,0.11644480519480527,ok +75197,1.0,46,0.2211380803516111,ok +266,1.0,47,0.018660135427440383,ok +75148,1.0,48,0.1358001187955955,ok +75150,1.0,49,0.2876075003524602,ok +75100,1.0,50,0.2540244186950774,ok +75178,1.0,51,0.784206721465269,ok +75236,1.0,52,0.032106570444582316,ok +75179,1.0,53,0.20149749000255257,ok +75213,1.0,54,0.06273157272368945,ok +2123,1.0,55,0.17781746031746037,ok +75227,1.0,56,0.11446626012925343,ok +75184,1.0,57,0.12532912571721433,ok +75142,1.0,58,0.07161311525180047,ok +236,1.0,59,0.03889089023011372,ok +2122,1.0,60,0.11253171534644457,ok +75188,1.0,61,0.4727873168498168,ok +75166,1.0,62,0.09969265347610456,ok +75181,1.0,63,0.0,ok +75133,1.0,64,0.15654041218375303,ok +75134,1.0,65,0.1481357660616962,ok +75198,1.0,66,0.12654657786302836,ok +262,1.0,67,0.0027336509054473046,ok +75234,1.0,68,0.024155509645569007,ok +75139,1.0,69,0.012643391866273612,ok +252,1.0,70,0.1510545149907132,ok +75117,1.0,71,0.11955388784657073,ok +75113,1.0,72,0.005274971941638618,ok +75098,1.0,73,0.028062456815564074,ok +246,1.0,74,0.010490317336848243,ok +75203,1.0,75,0.11023617811471986,ok +75237,1.0,76,0.0002948416740189419,ok +75195,1.0,77,0.0015167841344365662,ok +75171,1.0,78,0.16208341454974717,ok +75128,1.0,79,0.059041798537596835,ok +75096,1.0,80,0.3341299192137569,ok +75250,1.0,81,0.3478893206205086,ok +75146,1.0,82,0.11384157742648315,ok +75116,1.0,83,0.020634603301536547,ok +75157,1.0,84,0.44266509433962264,ok +75187,1.0,85,0.016824909805437382,ok +2350,1.0,86,0.4423678825304047,ok +242,1.0,87,0.011186441482494147,ok +244,1.0,88,0.10808472308576977,ok +75125,1.0,89,0.06985388361958478,ok +75185,1.0,90,0.12909185691725056,ok +75163,1.0,91,0.06128065774804903,ok +75177,1.0,92,0.023309264934301632,ok +75189,1.0,93,0.02093625722530723,ok +75244,1.0,94,0.18044671695057302,ok +75219,1.0,95,0.03536781923790411,ok +75222,1.0,96,0.11136904761904765,ok +75159,1.0,97,0.18577787197965234,ok +75175,1.0,98,0.1037395977530613,ok +75109,1.0,99,0.3603409856614681,ok +254,1.0,100,0.0,ok +75105,1.0,101,0.29488259839445896,ok +75106,1.0,102,0.327921279550357,ok +75212,1.0,103,0.2512390226936788,ok +75099,1.0,104,0.2277781422198898,ok +75248,1.0,105,0.18643306379155433,ok +233,1.0,106,0.002793457808655364,ok +75235,1.0,107,0.0007102272727272929,ok +75226,1.0,108,0.008218517371262557,ok +75132,1.0,109,0.3636469111367553,ok +75127,1.0,110,0.33766899823884766,ok +251,1.0,111,0.0,ok +75161,1.0,112,0.0643528394880094,ok +75143,1.0,113,0.017181695373184702,ok +75114,1.0,114,0.034791524265208484,ok +75182,1.0,115,0.12771381137622617,ok +75112,1.0,116,0.13777827445022783,ok +75210,1.0,117,0.0,ok +75205,1.0,118,0.1916783952293486,ok +75090,1.0,119,0.061027366747011924,ok +275,1.0,120,0.040221214056733956,ok +288,1.0,121,0.1294895850789981,ok +75092,1.0,122,0.09411421911421913,ok +3043,1.0,123,0.03952394945636206,ok +75249,1.0,124,0.012821882679773466,ok +75126,1.0,125,0.06940535469437825,ok +75225,1.0,126,0.10485406091370564,ok +75141,1.0,127,0.05180158915235733,ok +75107,1.0,128,0.24811618052967832,ok +75097,1.0,129,0.21628382400897928,ok +80001,1.0,1,0.1502463054187192,ok +80003,1.0,1,0.09095218032441543,ok +80006,1.0,1,0.09607843137254901,ok +80008,1.0,1,0.125,ok +80009,1.0,1,0.10555555555555562,ok +80010,1.0,1,0.2222222222222222,ok +80011,1.0,1,0.05263157894736836,ok +80012,1.0,1,0.0625,ok +80013,1.0,1,0.0,ok +80014,1.0,1,0.050000000000000044,ok +80015,1.0,1,0.10096153846153844,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/balanced_accuracy_multiclass.classification_dense/configurations.csv b/metalearning/metalearning_files/balanced_accuracy_multiclass.classification_dense/configurations.csv new file mode 100755 index 0000000..2686140 --- /dev/null +++ b/metalearning/metalearning_files/balanced_accuracy_multiclass.classification_dense/configurations.csv @@ -0,0 +1,141 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:fast_ica:algorithm,preprocessor:fast_ica:fun,preprocessor:fast_ica:n_components,preprocessor:fast_ica:whiten,preprocessor:feature_agglomeration:affinity,preprocessor:feature_agglomeration:linkage,preprocessor:feature_agglomeration:n_clusters,preprocessor:feature_agglomeration:pooling_func,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:pca:keep_variance,preprocessor:pca:whiten,preprocessor:polynomial:degree,preprocessor:polynomial:include_bias,preprocessor:polynomial:interaction_only,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,rescaling:__choice__,rescaling:quantile_transformer:n_quantiles,rescaling:quantile_transformer:output_distribution,rescaling:robust_scaler:q_max,rescaling:robust_scaler:q_min +weighting,one_hot_encoding,0.00011717632475982632,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.045388141846341344,deviance,10.0,0.29161769341843435,None,0.0,20.0,2.0,0.0,278.0,0.7912571599269661,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7249853037185638,None,0.0,1.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,median,extra_trees_preproc_for_classification,False,gini,None,0.9424908623661876,None,0.0,7.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.12713527337147906,deviance,4.0,0.6041596127474019,None,0.0,14.0,17.0,0.0,83.0,0.8426859880999615,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.03474109838999682,deviance,4.0,0.5687034678818491,None,0.0,18.0,12.0,0.0,408.0,0.5150113945430513,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.6328939517938,f_classif,,,,standardize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9265375980300852,None,0.0,13.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.251097788175676,deviance,6.0,0.35679099363539235,None,0.0,13.0,11.0,0.0,157.0,0.4791732272983235,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,adaboost,SAMME,0.28738775989203896,10.0,423.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.8031499675923353,0.13579938270386765 +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.14159526341015916,deviance,7.0,0.8010488230155749,None,0.0,3.0,20.0,0.0,401.0,0.8073562440607731,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3469031665162168,None,0.0,4.0,3.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,mean,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,83.0,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.002385546176068135,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.7729472304882845,,,0.0004789329856033374,rbf,-1.0,True,6.58869648864534e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,177.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5368752992317617,None,0.0,16.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.8382117756438685,mutual_info,,,,quantile_transformer,11480.0,normal,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.8384447520019118,None,0.0,13.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.36637567531287824,fdr,f_classif,normalize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3759438793746945,None,0.0,7.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3689420905780977,fwe,f_classif,quantile_transformer,25061.0,uniform,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.6291601746046639,None,0.0,11.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,median,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7146659106968425,True,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.12713527337147906,deviance,4.0,0.6041596127474019,None,0.0,14.0,17.0,0.0,83.0,0.8426859880999615,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9455638720565652,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,most_frequent,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8255464552647293,0.19162485555463185 +weighting,one_hot_encoding,0.18137532678800647,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9094110110427254,None,0.0,7.0,12.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,195.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.6682079659377479,None,0.0,4.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,mean,extra_trees_preproc_for_classification,True,entropy,None,0.5552350997943013,None,0.0,8.0,5.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0005560197158932037,True,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00010000000000000026,False,,0.01,True,,invscaling,log,l2,0.25,0.00010000000000000009,,,,,,,,,,,,,,,,,,21,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1392.583799745855,False,True,1.0,squared_hinge,ovr,l1,0.0034975921213842307,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,49486.0,normal,, +none,one_hot_encoding,0.02345017287074443,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.053517066400173056,deviance,10.0,0.542144980834302,None,0.0,20.0,13.0,0.0,233.0,0.7398539900055563,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0614425536709615,fwe,f_classif,robust_scaler,,,0.9523118062307264,0.13434811490315818 +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.8149627329153046,None,0.0,15.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.040936424602789435,deviance,7.0,0.5495014745530306,None,0.0,20.0,18.0,0.0,141.0,0.6905343807995293,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +weighting,one_hot_encoding,0.010000000000000005,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9641046312686136,None,0.0,18.0,12.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8622423450611333,0.2960428898664952 +weighting,one_hot_encoding,0.00034835629696198427,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8245132980938538,0.08947420373097192 +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2398150931290834,,,0.4015139801872962,rbf,-1.0,False,2.402997750662158e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88.40698357592571,chi2,,,,normalize,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.1958974686405233,deviance,5.0,0.33885235607979314,None,0.0,6.0,4.0,0.0,125.0,0.9448890820738562,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2538107344750156,False,True,hinge,1.5099542326343014e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,8532.0,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,0.0007038280350320558,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4138778052607317,0.7995003430482459,5.0,5.430044692638861,poly,-1.0,True,0.02455501006004393,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,31,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.04749094903835669,deviance,3.0,0.6184047395714717,None,0.0,17.0,8.0,0.0,428.0,0.7515561640094087,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,,False,adaboost,SAMME,0.4391375941344922,3.0,386.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7439738358430176,0.20581080574615795 +none,one_hot_encoding,0.00011294596229850895,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7515.1213255144885,0.9576762936062476,3.0,0.019002536385919932,poly,-1.0,False,0.010632086351533369,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,51.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,97282.0,normal,, +none,one_hot_encoding,0.00012586572428922356,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5240592829918601,None,0.0,10.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.15944469021885255,None,0.0,18.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,14.0,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,6315.0,uniform,, +weighting,one_hot_encoding,0.03192699980429505,True,adaboost,SAMME.R,0.10000000000000002,1.0,50.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,quantile_transformer,8407.0,normal,, +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1751.4736515133566,0.62404114475118,3.0,1.6087076997410432,poly,-1.0,False,3.5353792826856045e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1198.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.2422926485206341,,1.0,None,0.0,15.0,9.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.02102683283349326,deviance,10.0,0.2797288369369436,None,0.0,14.0,9.0,0.0,480.0,0.5778972273820631,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58.88123233170863,mutual_info,,,,robust_scaler,,,0.75,0.25 +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828776,None,0.0,2.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2155613360930585,deviance,4.0,0.3198803116198433,None,0.0,8.0,13.0,0.0,275.0,0.28870176110739404,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,mean,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.7062102387181676,None,0.0,1.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,most_frequent,fast_ica,,,,,,,,,,,parallel,exp,100.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7065776353150109,0.23782974987118105 +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.3233601927284725,None,0.0,3.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2991312911384725,fdr,chi2,normalize,,,, +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.3795924768593385,deviance,2.0,0.33708497069988536,None,0.0,15.0,13.0,0.0,451.0,0.7716323242090217,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2573946506994812,fwe,f_classif,quantile_transformer,1000.0,uniform,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.609975998293528,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,most_frequent,fast_ica,,,,,,,,,,,parallel,logcosh,2000.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8430415644014919,0.2863750565331575 +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.39536192447534535,None,0.0,19.0,3.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5.0,None,11.0,11.0,1.0,12.0,,,,,,robust_scaler,,,0.8928631650245873,0.1581877760687084 +weighting,one_hot_encoding,0.0010446150978844174,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.2100039691650936,None,0.0,19.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,10.0,None,3.0,18.0,1.0,42.0,,,,,,quantile_transformer,44341.0,uniform,, +weighting,one_hot_encoding,0.3126027672745337,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1100.6211008501205,0.5921425829232616,2.0,0.0337546254878617,poly,-1.0,True,0.09641299736884308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2507440474920336e-05,True,,0.049622652766554566,True,0.009105043727227265,constant,squared_hinge,elasticnet,,0.00010112719671669047,,,,,,,,,,,,,,,,,,53,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61.69949680034141,chi2,,,,none,,,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.9348409326933208,rbf,-1.0,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1754.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.010488491664540746,True,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6793271069375356,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0903156116813716,fdr,f_classif,robust_scaler,,,0.976245783323518,0.2189244634478133 +weighting,one_hot_encoding,0.010000000000000005,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.011886849251540706,deviance,10.0,0.716738790505292,None,0.0,5.0,6.0,0.0,472.0,0.9128424273302038,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,198.72528686512538,False,True,1.0,squared_hinge,ovr,l2,0.026260652523566803,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,robust_scaler,,,0.913511520078368,0.2742229325455444 +none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9260795160807372,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.03644212536682547,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.05965671477918361,deviance,8.0,0.4858133247974158,None,0.0,14.0,7.0,0.0,480.0,0.5726186552917335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.15318294164619112 +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18887.81504976871,,,0.23283562663398755,rbf,-1.0,True,2.3839685780861318e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3937607155568376,fwe,chi2,robust_scaler,,,0.941018778984854,0.2144110585080491 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3428971645958825,,,0.2229870623330047,rbf,-1.0,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.006909187206474195,True,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00024264014379190562,True,,0.04987297125937914,True,,optimal,hinge,l2,,4.0861541221464815e-05,,,,,,,,,,,,,,,,,,64,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.05153104953418389,False,True,1.0,squared_hinge,ovr,l1,0.0001939386474290285,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.003566024581260295,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7974297391104296,None,0.0,5.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,median,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,f_classif,,,,standardize,,,, +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00016781524591321165,True,True,squared_hinge,1.5119200923218881e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.349459944355116,,,0.00024028983491736645,rbf,-1.0,True,1.139421622432356e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3636266268105085,fwe,chi2,none,,,, +weighting,one_hot_encoding,0.00034835629696198427,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8245132980938538,0.08947420373097192 +weighting,one_hot_encoding,0.00016967940959070708,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9439080311935252,None,0.0,2.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,170.0,None,,0.014191958374153584,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9940718718674404,None,0.0,15.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.05347123056931161,deviance,3.0,0.2250677489759125,None,0.0,16.0,4.0,0.0,309.0,0.7245595517718859,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19466188848884064,fdr,chi2,standardize,,,, +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,normalize,,,, +none,one_hot_encoding,,False,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6396026761675004,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06544340428506021,fwe,f_classif,none,,,, +weighting,one_hot_encoding,0.005326467497783115,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.058055900067082,,,0.1626688094236879,rbf,-1.0,True,0.048381159429370636,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.9188519169916218,None,0.0,1.0,5.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,median,fast_ica,,,,,,,,,,,parallel,cube,306.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1.0,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.3837398524575939,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.018356703878357986,deviance,3.0,0.9690352514774068,None,0.0,12.0,3.0,0.0,234.0,0.3870344708308441,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.4932996544760619,None,0.0,2.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,340.0,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,most_frequent,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6864970915330799,False,,,,,,,,,,,,,,,,normalize,,,, +weighting,one_hot_encoding,0.0001486770773839718,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5256280540657592,None,0.0,8.0,12.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5670424455696162,None,0.0,8.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.00010817282861262362,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.799803680241154,None,0.0,13.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,85,mean,fast_ica,,,,,,,,,,,deflation,exp,1793.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9071932815811076,0.03563842252368924 +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.35533396539961937,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,86,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4165632766388807,fpr,chi2,none,,,, +weighting,one_hot_encoding,,False,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,156.0,auto,,0.0001987333852871589,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1.0,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,no_encoding,,,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.96834823420249e-05,False,True,hinge,0.00016639250831671168,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,89,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.628430584359224,mutual_info,,,,quantile_transformer,6634.0,normal,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8522973640879423,None,0.0,13.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,90,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,91,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,decision_tree,,,,,,,entropy,1.94608233492308,,1.0,None,0.0,15.0,11.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2613520842796237,fdr,chi2,quantile_transformer,1000.0,uniform,, +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,93,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0387325491437111,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.32034732923549136,None,0.0,20.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94,median,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,quantile_transformer,1000.0,uniform,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2636201374253461,deviance,7.0,0.8344964130784466,None,0.0,9.0,2.0,0.0,298.0,0.7517549950523315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,95,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,normalize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,96,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.2263596964804377,True,adaboost,SAMME,0.15143691959318842,2.0,233.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,97,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.07951518163998639,fwe,f_classif,minmax,,,, +weighting,one_hot_encoding,0.010000000000000005,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.14904790197542306,deviance,6.0,0.7561346995577642,None,0.0,5.0,14.0,0.0,340.0,0.6548040792383665,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,98,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.0433556140045585,deviance,10.0,0.33000096635982235,None,0.0,15.0,13.0,0.0,388.0,0.8291104221904706,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,99,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,robust_scaler,,,0.7496393440951183,0.2853682991120835 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,100,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3721522140614508,0.3541746628756037,3.0,0.0009148519644429074,poly,-1.0,False,2.916672898330067e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,37866.0,normal,, +weighting,one_hot_encoding,0.010000000000000005,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.04952755495565772,deviance,3.0,0.7249041896998006,None,0.0,6.0,17.0,0.0,174.0,0.3718608680080454,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,102,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.0020580843703898177,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.945774573434192,None,0.0,19.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,103,median,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,15209.0,normal,, +weighting,one_hot_encoding,0.010000000000000005,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.44875674701568935,None,0.0,8.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,104,most_frequent,fast_ica,,,,,,,,,,,deflation,cube,1367.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.10324969243867224,True,decision_tree,,,,,,,gini,0.7467478023293801,,1.0,None,0.0,12.0,8.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,105,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84.22876326806853,mutual_info,,,,minmax,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,106,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.005332972692819521,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5565918060287016,None,0.0,5.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,107,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,173.0,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9527068489270144,0.04135311355893583 +weighting,one_hot_encoding,0.001279467383882126,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,108,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0903354518102121,fwe,f_classif,standardize,,,, +weighting,one_hot_encoding,0.00013442810992750476,True,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1.0,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,109,median,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,8.0,None,13.0,16.0,1.0,28.0,,,,,,quantile_transformer,41502.0,uniform,, +weighting,one_hot_encoding,0.002615346832354839,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7884268823432835,None,0.0,20.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,110,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +weighting,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56.086963007482865,,,0.013609964993119377,rbf,-1.0,True,0.00196831255706268,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,111,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.15374716583918388 +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.2472984547885781,deviance,3.0,0.6564306719064884,None,0.0,15.0,14.0,0.0,220.0,0.8082564085714649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,112,median,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,332.0,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,113,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.03905145156995541,deviance,5.0,0.2281306656230014,None,0.0,14.0,13.0,0.0,493.0,0.8793075442604774,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,115,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,25382.0,normal,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.12713527337147906,deviance,4.0,0.6041596127474019,None,0.0,14.0,17.0,0.0,83.0,0.8426859880999615,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,116,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,117,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,118,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.20202014999292287,False,True,1.0,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,no_encoding,,,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6390376923528961,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,119,median,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,164.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,62508.0,normal,, +weighting,one_hot_encoding,0.1885493528549979,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.603604171705261,False,True,squared_hinge,4.4620804452839e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,120,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,quantile_transformer,57665.0,uniform,, +weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.27041927277584e-06,True,0.00010000000000000009,0.033157325660763994,True,0.0008114527992546482,invscaling,modified_huber,elasticnet,0.13714427818877545,0.055179642772545036,,,,,,,,,,,,,,,,,,121,median,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.7879059827470586,None,0.0,3.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,122,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54.1024239468849,f_classif,,,,quantile_transformer,89842.0,normal,, +weighting,one_hot_encoding,0.010000000000000005,True,decision_tree,,,,,,,entropy,1.5841974853345435,,1.0,None,0.0,8.0,14.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,123,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,25.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.04994122399949775,deviance,9.0,0.8621747362759826,None,0.0,1.0,13.0,0.0,84.0,0.6449569386396325,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,124,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44.533266302815896,chi2,,,,minmax,,,, +weighting,one_hot_encoding,0.000738320402221022,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,125,median,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.854322697199371,False,True,1.0,squared_hinge,ovr,l1,0.00013359426815085846,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,30.0,normal,, +weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0733000338152003,False,True,1.0,squared_hinge,ovr,l2,0.033752542733220474,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,126,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.6840756728731969,,0.00980445380551526,sigmoid,161.0,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.5765793990908161,None,0.0,11.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,127,median,fast_ica,,,,,,,,,,,deflation,exp,10.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.1166829447028879,deviance,4.0,0.8451196958788597,None,0.0,1.0,8.0,0.0,58.0,0.8652814520124915,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.03406306803297533,False,True,1.0,squared_hinge,ovr,l1,0.00019257971888767865,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.0006290513932903491,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8680626684393846,None,0.0,9.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,129,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,54639.0,normal,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.2604623554399743,None,0.0,18.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.282079367058228,fpr,chi2,none,,,, +none,one_hot_encoding,0.04651467280022463,True,adaboost,SAMME,0.6506122230393502,6.0,143.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,extra_trees_preproc_for_classification,False,entropy,None,0.348720215832657,None,0.0,17.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,26025.0,normal,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3386310102795006,None,0.0,6.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,True,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.0,None,,5.861221938384061e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.22985730375167915,fpr,f_classif,quantile_transformer,40589.0,uniform,, +none,no_encoding,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00012437731009611307,True,,0.08709904468181932,True,,invscaling,squared_hinge,l1,0.6231564675290102,0.008071279833697563,,,,,,,,,,,,,,,,,,134,median,fast_ica,,,,,,,,,,,parallel,logcosh,814.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.714869937384006,None,0.0,8.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, diff --git a/metalearning/metalearning_files/balanced_accuracy_multiclass.classification_dense/description.txt b/metalearning/metalearning_files/balanced_accuracy_multiclass.classification_dense/description.txt new file mode 100755 index 0000000..192068b --- /dev/null +++ b/metalearning/metalearning_files/balanced_accuracy_multiclass.classification_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: balanced_accuracy +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/balanced_accuracy_multiclass.classification_dense/feature_costs.arff b/metalearning/metalearning_files/balanced_accuracy_multiclass.classification_dense/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/balanced_accuracy_multiclass.classification_dense/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/balanced_accuracy_multiclass.classification_dense/feature_runstatus.arff b/metalearning/metalearning_files/balanced_accuracy_multiclass.classification_dense/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/balanced_accuracy_multiclass.classification_dense/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/balanced_accuracy_multiclass.classification_dense/feature_values.arff b/metalearning/metalearning_files/balanced_accuracy_multiclass.classification_dense/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/balanced_accuracy_multiclass.classification_dense/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/balanced_accuracy_multiclass.classification_dense/readme.txt b/metalearning/metalearning_files/balanced_accuracy_multiclass.classification_dense/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/balanced_accuracy_multiclass.classification_sparse/algorithm_runs.arff b/metalearning/metalearning_files/balanced_accuracy_multiclass.classification_sparse/algorithm_runs.arff new file mode 100755 index 0000000..1a66002 --- /dev/null +++ b/metalearning/metalearning_files/balanced_accuracy_multiclass.classification_sparse/algorithm_runs.arff @@ -0,0 +1,152 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE balanced_accuracy NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2120,1.0,1,0.12055882293523357,ok +75193,1.0,2,0.05570147909402934,ok +2117,1.0,3,0.17548618736408272,ok +75156,1.0,4,0.22313433229150748,ok +75129,1.0,5,0.21139308855291583,ok +75243,1.0,6,0.02741169837497348,ok +75110,1.0,7,0.292400218490373,ok +75239,1.0,8,0.0,ok +75223,1.0,9,0.3513704849874423,ok +75221,1.0,10,0.5127596762099687,ok +258,1.0,11,0.01797504123780258,ok +75121,1.0,12,0.045454545454545414,ok +253,1.0,13,0.4487779181115149,ok +261,1.0,14,0.31385281385281383,ok +75240,1.0,15,0.017683772538141462,ok +75120,1.0,16,0.2546523517382413,ok +75124,1.0,17,0.2107615894039736,ok +75176,1.0,18,0.016640035468590053,ok +75103,1.0,19,0.01660181821534601,ok +75207,1.0,20,0.1890011398955863,ok +75095,1.0,21,0.04590735712181182,ok +273,1.0,22,0.049601841028638294,ok +75174,1.0,23,0.1520296780824476,ok +75153,1.0,24,0.12110304789550075,ok +75093,1.0,25,0.3286900890421619,ok +75119,1.0,26,0.1288429752066116,ok +75201,1.0,27,0.0997376911529182,ok +75215,1.0,28,0.029887049159286416,ok +75172,1.0,29,0.10886921886815126,ok +75169,1.0,30,0.0340573619843737,ok +75202,1.0,31,0.42493459653140053,ok +75233,1.0,32,0.08355891180172326,ok +75231,1.0,33,0.15778968253968262,ok +75196,1.0,34,0.028346047156726728,ok +248,1.0,35,0.2701991669030601,ok +75191,1.0,36,0.12329305343461994,ok +75217,1.0,37,0.0,ok +260,1.0,38,0.07198862873636425,ok +75115,1.0,39,0.09070773400041698,ok +75123,1.0,40,0.32871263757742397,ok +75108,1.0,41,0.00232727928992138,ok +75101,1.0,42,0.28377248501244257,ok +75192,1.0,43,0.48263188489323006,ok +75232,1.0,44,0.1759384976698103,ok +75173,1.0,45,0.11733360389610392,ok +75197,1.0,46,0.2040950900493621,ok +266,1.0,47,0.031108344044042946,ok +75148,1.0,48,0.19038707564842594,ok +75150,1.0,49,0.32022768927111245,ok +75100,1.0,50,0.27498250252741274,ok +75178,1.0,51,0.8079895490982436,ok +75236,1.0,52,0.032106570444582316,ok +75179,1.0,53,0.20149749000255257,ok +75213,1.0,54,0.06273157272368945,ok +2123,1.0,55,0.256388888888889,ok +75227,1.0,56,0.12397340187955186,ok +75184,1.0,57,0.18267180532449523,ok +75142,1.0,58,0.08134030645745782,ok +236,1.0,59,0.04240504096821618,ok +2122,1.0,60,0.292400218490373,ok +75188,1.0,61,0.4727873168498168,ok +75166,1.0,62,0.09969265347610456,ok +75181,1.0,63,0.0,ok +75133,1.0,64,0.31850429480226317,ok +75134,1.0,65,0.14906265793135665,ok +75198,1.0,66,0.12654657786302836,ok +262,1.0,67,0.0027336509054473046,ok +75234,1.0,68,0.05981527192723568,ok +75139,1.0,69,0.013389237134515009,ok +252,1.0,70,0.1626862498888315,ok +75117,1.0,71,0.12062226391494679,ok +75113,1.0,72,0.010561357454012876,ok +75098,1.0,73,0.028062456815564074,ok +246,1.0,74,0.02464555428242643,ok +75203,1.0,75,0.11023617811471986,ok +75237,1.0,76,0.00040449626783733983,ok +75195,1.0,77,0.0015167841344365662,ok +75171,1.0,78,0.16706480960317482,ok +75128,1.0,79,0.07016624831750884,ok +75096,1.0,80,0.6475478379912987,ok +75250,1.0,81,0.391127054727214,ok +75146,1.0,82,0.1222599514053454,ok +75116,1.0,83,0.020634603301536547,ok +75157,1.0,84,0.44266509433962264,ok +75187,1.0,85,0.027932128591676264,ok +2350,1.0,86,0.4423678825304047,ok +242,1.0,87,0.017081000552456538,ok +244,1.0,88,0.10808472308576977,ok +75125,1.0,89,0.07105870289669314,ok +75185,1.0,90,0.1293167333629761,ok +75163,1.0,91,0.06128065774804903,ok +75177,1.0,92,0.03815960706939259,ok +75189,1.0,93,0.02093625722530723,ok +75244,1.0,94,0.18253811312937274,ok +75219,1.0,95,0.08686090936376534,ok +75222,1.0,96,0.11136904761904765,ok +75159,1.0,97,0.25561678677405686,ok +75175,1.0,98,0.12715986367288612,ok +75109,1.0,99,0.40368927333473925,ok +254,1.0,100,0.0,ok +75105,1.0,101,0.42618761507557956,ok +75106,1.0,102,0.4177538160920802,ok +75212,1.0,103,0.2773454482218938,ok +75099,1.0,104,0.29830589084229864,ok +75248,1.0,105,0.1981334600032404,ok +233,1.0,106,0.01525546388768273,ok +75235,1.0,107,0.001061350868232891,ok +75226,1.0,108,0.009036055111661834,ok +75132,1.0,109,0.4536697094634172,ok +75127,1.0,110,0.33895073146733024,ok +251,1.0,111,0.020161426212381595,ok +75161,1.0,112,0.08277383139196026,ok +75143,1.0,113,0.017181695373184702,ok +75114,1.0,114,0.034791524265208484,ok +75182,1.0,115,0.1360109923276095,ok +75112,1.0,116,0.1477333735943931,ok +75210,1.0,117,0.0,ok +75205,1.0,118,0.1916783952293486,ok +75090,1.0,119,0.10262125025576962,ok +275,1.0,120,0.041190711460148854,ok +288,1.0,121,0.14344937049478246,ok +75092,1.0,122,0.10984848484848486,ok +3043,1.0,123,0.04425716804500235,ok +75249,1.0,124,0.013258182854293588,ok +75126,1.0,125,0.12520747636573581,ok +75225,1.0,126,0.10485406091370564,ok +75141,1.0,127,0.05863227105456237,ok +75107,1.0,128,0.28251633008867305,ok +75097,1.0,129,0.47931992380750144,ok +80001,1.0,1,0.1502463054187192,ok +80003,1.0,1,0.12212976616810622,ok +80006,1.0,1,0.196078431372549,ok +80008,1.0,1,0.19999999999999996,ok +80009,1.0,1,0.15555555555555556,ok +80010,1.0,1,0.2222222222222222,ok +80011,1.0,1,0.05263157894736836,ok +80012,1.0,1,0.0625,ok +80013,1.0,1,0.0,ok +80014,1.0,1,0.050000000000000044,ok +80015,1.0,1,0.10096153846153844,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/balanced_accuracy_multiclass.classification_sparse/configurations.csv b/metalearning/metalearning_files/balanced_accuracy_multiclass.classification_sparse/configurations.csv new file mode 100755 index 0000000..b7334ed --- /dev/null +++ b/metalearning/metalearning_files/balanced_accuracy_multiclass.classification_sparse/configurations.csv @@ -0,0 +1,141 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,preprocessor:truncatedSVD:target_dim,rescaling:__choice__ +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7249853037185638,None,0.0,1.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,median,extra_trees_preproc_for_classification,False,gini,None,0.9424908623661876,None,0.0,7.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.585711203872775,None,0.0,5.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,median,extra_trees_preproc_for_classification,True,entropy,None,0.29512530534048065,None,0.0,7.0,10.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,bernoulli_nb,,,,,0.11565661797517847,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,median,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,9.0,None,13.0,18.0,1.0,29.0,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.039533063907190934,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1878805519245509,fdr,chi2,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,15.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7238850981243719,None,0.0,4.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,5.282738216059151e-05,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.6682079659377479,None,0.0,4.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,mean,extra_trees_preproc_for_classification,True,entropy,None,0.5552350997943013,None,0.0,8.0,5.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0009580347867777607,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,0.10000000000000006,rbf,-1.0,True,0.0010000000000000002,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.35040453084365497,False,True,1.0,squared_hinge,ovr,l1,0.006810889378452772,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.010000000000000005,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9727149851116396,None,0.0,18.0,13.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,,none +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.7735071203475309,,1.0,None,0.0,4.0,4.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,mean,extra_trees_preproc_for_classification,True,entropy,None,0.8171332500590935,None,0.0,15.0,13.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2398150931290834,,,0.4015139801872962,rbf,-1.0,False,2.402997750662158e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88.40698357592571,chi2,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.034465366914659866,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.12057775675278172,deviance,10.0,0.8011153303489733,None,0.0,2.0,16.0,0.0,370.0,0.6078295352200873,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,mean,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0007038280350320558,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4138778052607317,0.7995003430482459,5.0,5.430044692638861,poly,-1.0,True,0.02455501006004393,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,31,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.0010015637584068037,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.037611630308856295,deviance,5.0,0.8840126779516314,None,0.0,10.0,2.0,0.0,444.0,0.7599997167603434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,most_frequent,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.004230062585802822,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.4946412825074172,None,0.0,9.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.004090774134315939,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7983157215145903,None,0.0,2.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,normalize +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1751.4736515133566,0.62404114475118,3.0,1.6087076997410432,poly,-1.0,False,3.5353792826856045e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,mean,kernel_pca,,,,,,,,,,,,,,cosine,1198.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.8657388713119849,,1.0,None,0.0,19.0,13.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828776,None,0.0,2.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.027324741616523342,deviance,10.0,0.8623781459430139,None,0.0,10.0,20.0,0.0,329.0,0.8595750155424215,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,mean,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.002173124111626734,None,0.0,14.0,4.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,6.0,None,13.0,2.0,1.0,23.0,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1100.6211008501205,0.5921425829232616,2.0,0.0337546254878617,poly,-1.0,True,0.09641299736884308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2507440474920336e-05,True,,0.049622652766554566,True,0.009105043727227265,constant,squared_hinge,elasticnet,,0.00010112719671669047,,,,,,,,,,,,,,,,,,53,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61.69949680034141,chi2,,,,,none +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.9348409326933208,rbf,-1.0,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,mean,kernel_pca,,,,,,,,,,,,,,cosine,1754.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,one_hot_encoding,0.039533063907190934,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.3668390168981173,None,0.0,9.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1878805519245509,fdr,chi2,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3428971645958825,,,0.2229870623330047,rbf,-1.0,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7866272157881596,None,0.0,6.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00016781524591321165,True,True,squared_hinge,1.5119200923218881e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.349459944355116,,,0.00024028983491736645,rbf,-1.0,True,1.139421622432356e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3636266268105085,fwe,chi2,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4047.618729304337,,,2.0237366768707754,rbf,-1.0,True,0.04369127828878843,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,one_hot_encoding,0.010000000000000005,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10.0,1.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,,none +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9015027772227234,None,0.0,19.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,one_hot_encoding,0.039533063907190934,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1878805519245509,fdr,chi2,,standardize +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,,normalize +none,one_hot_encoding,0.003443779683191071,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.005326467497783115,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.058055900067082,,,0.1626688094236879,rbf,-1.0,True,0.048381159429370636,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1.0,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.3451627750042988,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.3163640203509378,None,0.0,17.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,median,extra_trees_preproc_for_classification,False,gini,None,0.8916956785028156,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.002630811782675973,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9828367182452932,None,0.0,18.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,85,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.35533396539961937,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,86,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4165632766388807,fpr,chi2,,none +weighting,one_hot_encoding,0.0019686649916896212,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.04641055832142541,True,True,hinge,8.540468968077405e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87,mean,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,911.0,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1.0,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,89,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,90,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,91,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,decision_tree,,,,,,,gini,1.7984076825537865,,1.0,None,0.0,14.0,2.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2897525995758022,fwe,chi2,,none +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,93,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.9376772805635799,None,0.0,18.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94,median,extra_trees_preproc_for_classification,True,entropy,None,0.8613889689810683,None,0.0,10.0,4.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,95,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,96,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,97,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,98,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.00022308163276069302,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,99,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,100,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.039533063907190934,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1878805519245509,fdr,chi2,,standardize +weighting,one_hot_encoding,0.039533063907190934,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,102,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1878805519245509,fdr,chi2,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,103,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,bernoulli_nb,,,,,0.014801515930977628,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,104,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,105,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,106,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.006372860318416312,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6467376360604045,None,0.0,1.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,107,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,,False,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.37163783119625016,False,True,1.0,squared_hinge,ovr,l2,1.2049944334095187e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,108,median,kitchen_sinks,,,,,,,,,,,,,,,,0.33181838105513195,7347.0,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,109,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3823734947460288,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,110,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,adaboost,SAMME,0.11042308042695524,5.0,117.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,111,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,112,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,113,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.3997572853391576,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.21008613984919333,None,0.0,11.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,115,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,116,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,117,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,118,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.20202014999292287,False,True,1.0,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,119,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.00012696985090051145,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.163979712833404,False,True,hinge,0.02438592885755657,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,120,mean,kernel_pca,,,,,,,,,,,,,0.03910740617243662,rbf,1851.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,121,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8804227616935514,None,0.0,10.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,122,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.0898983119899223,False,True,1.0,squared_hinge,ovr,l1,0.016048982920294167,,,,,,,,,,,,,,,,,,,none +weighting,no_encoding,,,decision_tree,,,,,,,entropy,1.301605991416264,,1.0,None,0.0,8.0,3.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,123,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,124,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,125,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0733000338152003,False,True,1.0,squared_hinge,ovr,l2,0.033752542733220474,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,126,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,-0.6840756728731969,,0.00980445380551526,sigmoid,161.0,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,127,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.00214097329599271,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7996802015738327,None,0.0,7.0,12.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.1052247187777527,False,True,1.0,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,129,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.2604623554399743,None,0.0,18.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.282079367058228,fpr,chi2,,none +none,one_hot_encoding,0.04329010470998136,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7260331062271381,None,0.0,7.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.010000000000000005,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15.0,1.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,134,median,extra_trees_preproc_for_classification,False,entropy,None,0.8213051377774989,None,0.0,3.0,13.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.714869937384006,None,0.0,8.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/metalearning/metalearning_files/balanced_accuracy_multiclass.classification_sparse/description.txt b/metalearning/metalearning_files/balanced_accuracy_multiclass.classification_sparse/description.txt new file mode 100755 index 0000000..192068b --- /dev/null +++ b/metalearning/metalearning_files/balanced_accuracy_multiclass.classification_sparse/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: balanced_accuracy +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/balanced_accuracy_multiclass.classification_sparse/feature_costs.arff b/metalearning/metalearning_files/balanced_accuracy_multiclass.classification_sparse/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/balanced_accuracy_multiclass.classification_sparse/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/balanced_accuracy_multiclass.classification_sparse/feature_runstatus.arff b/metalearning/metalearning_files/balanced_accuracy_multiclass.classification_sparse/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/balanced_accuracy_multiclass.classification_sparse/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/balanced_accuracy_multiclass.classification_sparse/feature_values.arff b/metalearning/metalearning_files/balanced_accuracy_multiclass.classification_sparse/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/balanced_accuracy_multiclass.classification_sparse/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/balanced_accuracy_multiclass.classification_sparse/readme.txt b/metalearning/metalearning_files/balanced_accuracy_multiclass.classification_sparse/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/f1_binary.classification_dense/algorithm_runs.arff b/metalearning/metalearning_files/f1_binary.classification_dense/algorithm_runs.arff new file mode 100755 index 0000000..0378150 --- /dev/null +++ b/metalearning/metalearning_files/f1_binary.classification_dense/algorithm_runs.arff @@ -0,0 +1,107 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE f1 NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2117,1.0,1,0.10832227183138254,ok +75156,1.0,2,0.18361375274323333,ok +75129,1.0,3,0.5268817204301075,ok +75239,1.0,4,0.0,ok +75121,1.0,5,0.0,ok +261,1.0,6,0.41121495327102797,ok +75240,1.0,7,0.028349082823791005,ok +75120,1.0,8,0.02004008016032066,ok +75124,1.0,9,0.46118721461187207,ok +75176,1.0,10,0.013738060970822863,ok +75103,1.0,11,0.043887147335423204,ok +75095,1.0,12,0.18120805369127513,ok +273,1.0,13,0.05682782018659882,ok +75174,1.0,14,0.19505315686699942,ok +75153,1.0,15,0.07888040712468192,ok +75093,1.0,16,0.558858501783591,ok +75119,1.0,17,0.018711018711018657,ok +75215,1.0,18,0.02418964683115621,ok +75233,1.0,19,0.043570669500531345,ok +75196,1.0,20,0.02857142857142858,ok +75191,1.0,21,0.13488132758731586,ok +75115,1.0,22,0.009544008483563182,ok +75108,1.0,23,0.0,ok +75101,1.0,24,0.25764268278507496,ok +75192,1.0,25,0.37826350941105036,ok +75232,1.0,26,0.17991631799163188,ok +75173,1.0,27,0.11531190926275992,ok +75148,1.0,28,0.1317535545023697,ok +75150,1.0,29,0.25396825396825395,ok +75100,1.0,30,0.922077922077922,ok +75179,1.0,31,0.29820749592612716,ok +75213,1.0,32,0.1123595505617977,ok +75227,1.0,33,0.17411121239744765,ok +75184,1.0,34,0.17141196978500872,ok +75142,1.0,35,0.07081986482515423,ok +75166,1.0,36,0.10216483099126472,ok +75133,1.0,37,0.4999999999999999,ok +75234,1.0,38,0.024032586558044855,ok +75139,1.0,39,0.016242721422004336,ok +75117,1.0,40,0.02947368421052632,ok +75113,1.0,41,0.05429071803852892,ok +75237,1.0,42,0.00024933381122316245,ok +75195,1.0,43,0.0013143894348125462,ok +75171,1.0,44,0.16404494382022472,ok +75128,1.0,45,0.012725344644750725,ok +75146,1.0,46,0.0980392156862745,ok +75116,1.0,47,0.005847953216374324,ok +75157,1.0,48,0.4766917293233083,ok +75187,1.0,49,0.017190775681341752,ok +2350,1.0,50,0.5196824930177864,ok +75125,1.0,51,0.020310633213859064,ok +75185,1.0,52,0.13409961685823746,ok +75163,1.0,53,0.0650779101741521,ok +75177,1.0,54,0.13636363636363635,ok +75189,1.0,55,0.014574596962072528,ok +75244,1.0,56,0.6633663366336633,ok +75219,1.0,57,0.03865168539325847,ok +75222,1.0,58,0.2777777777777778,ok +75159,1.0,59,0.5319148936170213,ok +75175,1.0,60,0.12208258527827653,ok +254,1.0,61,0.0,ok +75105,1.0,62,0.8763197586726998,ok +75106,1.0,63,0.7574316290130796,ok +75212,1.0,64,0.26086956521739135,ok +75099,1.0,65,0.4918566775244301,ok +75248,1.0,66,0.6080218778486782,ok +233,1.0,67,0.003058103975535076,ok +75226,1.0,68,0.001797914419273683,ok +75132,1.0,69,0.8638414084026237,ok +75127,1.0,70,0.37607613005551144,ok +75161,1.0,71,0.06386430678466082,ok +75143,1.0,72,0.006923837784371889,ok +75114,1.0,73,0.015037593984962405,ok +75182,1.0,74,0.1936604429005645,ok +75112,1.0,75,0.18030544791429215,ok +75210,1.0,76,0.0,ok +75092,1.0,77,0.37579617834394896,ok +3043,1.0,78,0.14444444444444438,ok +75249,1.0,79,0.020618556701030855,ok +75126,1.0,80,0.027870680044593144,ok +75225,1.0,81,0.525179856115108,ok +75141,1.0,82,0.0652368185880251,ok +75107,1.0,83,0.4416498993963782,ok +75097,1.0,84,0.030135154496394367,ok +80001,1.0,1,0.19999999999999996,ok +80003,1.0,1,0.08999999999999997,ok +80006,1.0,1,0.10344827586206895,ok +80008,1.0,1,0.1428571428571429,ok +80009,1.0,1,0.09999999999999998,ok +80010,1.0,1,0.2857142857142857,ok +80011,1.0,1,0.05555555555555558,ok +80012,1.0,1,0.03448275862068961,ok +80013,1.0,1,0.0,ok +80014,1.0,1,0.05263157894736836,ok +80015,1.0,1,0.125,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/f1_binary.classification_dense/configurations.csv b/metalearning/metalearning_files/f1_binary.classification_dense/configurations.csv new file mode 100755 index 0000000..0cf6ebe --- /dev/null +++ b/metalearning/metalearning_files/f1_binary.classification_dense/configurations.csv @@ -0,0 +1,96 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:fast_ica:algorithm,preprocessor:fast_ica:fun,preprocessor:fast_ica:n_components,preprocessor:fast_ica:whiten,preprocessor:feature_agglomeration:affinity,preprocessor:feature_agglomeration:linkage,preprocessor:feature_agglomeration:n_clusters,preprocessor:feature_agglomeration:pooling_func,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:pca:keep_variance,preprocessor:pca:whiten,preprocessor:polynomial:degree,preprocessor:polynomial:include_bias,preprocessor:polynomial:interaction_only,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,rescaling:__choice__,rescaling:quantile_transformer:n_quantiles,rescaling:quantile_transformer:output_distribution,rescaling:robust_scaler:q_max,rescaling:robust_scaler:q_min +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.03474109838999682,deviance,4.0,0.5687034678818491,None,0.0,18.0,12.0,0.0,408.0,0.5150113945430513,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.6328939517938,f_classif,,,,standardize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9265375980300852,None,0.0,13.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5368752992317617,None,0.0,16.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.8382117756438685,mutual_info,,,,quantile_transformer,11480.0,normal,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3759438793746945,None,0.0,7.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3689420905780977,fwe,f_classif,quantile_transformer,25061.0,uniform,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.12713527337147906,deviance,4.0,0.6041596127474019,None,0.0,14.0,17.0,0.0,83.0,0.8426859880999615,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9455638720565652,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,most_frequent,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8255464552647293,0.19162485555463185 +weighting,one_hot_encoding,0.18137532678800647,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9094110110427254,None,0.0,7.0,12.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,195.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.02345017287074443,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.053517066400173056,deviance,10.0,0.542144980834302,None,0.0,20.0,13.0,0.0,233.0,0.7398539900055563,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0614425536709615,fwe,f_classif,robust_scaler,,,0.9523118062307264,0.13434811490315818 +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.8149627329153046,None,0.0,15.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.040936424602789435,deviance,7.0,0.5495014745530306,None,0.0,20.0,18.0,0.0,141.0,0.6905343807995293,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.9727149851116396,None,0.0,10.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,25.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7974565919616314,None,0.0,12.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,median,extra_trees_preproc_for_classification,True,entropy,None,0.9772091846790169,None,0.0,10.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.1958974686405233,deviance,5.0,0.33885235607979314,None,0.0,6.0,4.0,0.0,125.0,0.9448890820738562,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,0.010000000000000005,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.0518326156691958,deviance,6.0,0.8807456180216267,None,0.0,7.0,19.0,0.0,366.0,0.7314831276137047,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,adaboost,SAMME,0.4391375941344922,3.0,386.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7439738358430176,0.20581080574615795 +none,one_hot_encoding,0.00012586572428922356,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5240592829918601,None,0.0,10.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.2422926485206341,,1.0,None,0.0,15.0,9.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.02102683283349326,deviance,10.0,0.2797288369369436,None,0.0,14.0,9.0,0.0,480.0,0.5778972273820631,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58.88123233170863,mutual_info,,,,robust_scaler,,,0.75,0.25 +weighting,no_encoding,,,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,0.3170009238992427,rbf,1955.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8333938697866604,0.10426506601169797 +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2155613360930585,deviance,4.0,0.3198803116198433,None,0.0,8.0,13.0,0.0,275.0,0.28870176110739404,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,mean,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.7062102387181676,None,0.0,1.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,most_frequent,fast_ica,,,,,,,,,,,parallel,exp,100.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7065776353150109,0.23782974987118105 +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.609975998293528,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,most_frequent,fast_ica,,,,,,,,,,,parallel,logcosh,2000.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8430415644014919,0.2863750565331575 +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.39536192447534535,None,0.0,19.0,3.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5.0,None,11.0,11.0,1.0,12.0,,,,,,robust_scaler,,,0.8928631650245873,0.1581877760687084 +weighting,one_hot_encoding,0.060155186012325876,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.3163640203509378,None,0.0,16.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,median,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,2.0,None,11.0,20.0,1.0,47.0,,,,,,quantile_transformer,21065.0,uniform,, +weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2507440474920336e-05,True,,0.049622652766554566,True,0.009105043727227265,constant,squared_hinge,elasticnet,,0.00010112719671669047,,,,,,,,,,,,,,,,,,31,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61.69949680034141,chi2,,,,none,,,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.9348409326933208,rbf,-1.0,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1754.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +weighting,one_hot_encoding,0.010000000000000005,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.011886849251540706,deviance,10.0,0.716738790505292,None,0.0,5.0,6.0,0.0,472.0,0.9128424273302038,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,198.72528686512538,False,True,1.0,squared_hinge,ovr,l2,0.026260652523566803,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,robust_scaler,,,0.913511520078368,0.2742229325455444 +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.8770766409674923,None,0.0,13.0,13.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,84023.0,normal,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3428971645958825,,,0.2229870623330047,rbf,-1.0,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.00034835629696198427,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8245132980938538,0.08947420373097192 +weighting,one_hot_encoding,0.00016967940959070708,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9439080311935252,None,0.0,2.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,adaboost,SAMME.R,0.8220362681234727,5.0,183.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.363262678765884,fdr,chi2,robust_scaler,,,0.8826612080363588,0.2189605310171097 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1.0,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.3837398524575939,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.018356703878357986,deviance,3.0,0.9690352514774068,None,0.0,12.0,3.0,0.0,234.0,0.3870344708308441,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.0037042565030130366,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.9610802347688104,None,0.0,17.0,9.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,172.0,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.00010817282861262362,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.799803680241154,None,0.0,13.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,mean,fast_ica,,,,,,,,,,,deflation,exp,1793.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9071932815811076,0.03563842252368924 +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.35533396539961937,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4165632766388807,fpr,chi2,none,,,, +weighting,no_encoding,,,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.96834823420249e-05,False,True,hinge,0.00016639250831671168,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.628430584359224,mutual_info,,,,quantile_transformer,6634.0,normal,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8522973640879423,None,0.0,13.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,8.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.9376772805635799,None,0.0,18.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,median,extra_trees_preproc_for_classification,True,entropy,None,0.8613889689810683,None,0.0,10.0,4.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2636201374253461,deviance,7.0,0.8344964130784466,None,0.0,9.0,2.0,0.0,298.0,0.7517549950523315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.010000000000000005,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.14904790197542306,deviance,6.0,0.7561346995577642,None,0.0,5.0,14.0,0.0,340.0,0.6548040792383665,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0009243833519832893,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9829888014816668,None,0.0,18.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,minmax,,,, +weighting,one_hot_encoding,0.0020580843703898177,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.945774573434192,None,0.0,19.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,median,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,15209.0,normal,, +weighting,one_hot_encoding,0.010000000000000005,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.44875674701568935,None,0.0,8.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,most_frequent,fast_ica,,,,,,,,,,,deflation,cube,1367.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0506220137415751,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.4433458237042269,None,0.0,11.0,10.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.8740590455827745,0.19483217552098045 +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.001279467383882126,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0903354518102121,fwe,f_classif,standardize,,,, +weighting,one_hot_encoding,0.00013442810992750476,True,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1.0,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,median,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,8.0,None,13.0,16.0,1.0,28.0,,,,,,quantile_transformer,41502.0,uniform,, +weighting,one_hot_encoding,0.002615346832354839,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7884268823432835,None,0.0,20.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.2472984547885781,deviance,3.0,0.6564306719064884,None,0.0,15.0,14.0,0.0,220.0,0.8082564085714649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,median,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,332.0,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.03905145156995541,deviance,5.0,0.2281306656230014,None,0.0,14.0,13.0,0.0,493.0,0.8793075442604774,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,25382.0,normal,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.12713527337147906,deviance,4.0,0.6041596127474019,None,0.0,14.0,17.0,0.0,83.0,0.8426859880999615,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.7879059827470586,None,0.0,3.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54.1024239468849,f_classif,,,,quantile_transformer,89842.0,normal,, +weighting,one_hot_encoding,0.010000000000000005,True,decision_tree,,,,,,,entropy,1.5841974853345435,,1.0,None,0.0,8.0,14.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,25.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.19998727075532635,deviance,10.0,0.9377656718112952,None,0.0,7.0,13.0,0.0,214.0,0.6062346326014357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.003777272888571546,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4583291745177553,None,0.0,14.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.27741957612588863,fdr,f_classif,none,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.5765793990908161,None,0.0,11.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,median,fast_ica,,,,,,,,,,,deflation,exp,10.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,adaboost,SAMME,0.7603752531440509,7.0,413.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,28.04113884899283,False,True,1.0,squared_hinge,ovr,l1,0.0001084726092097629,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,26106.0,uniform,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3386310102795006,None,0.0,6.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,True,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.0,None,,5.861221938384061e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.22985730375167915,fpr,f_classif,quantile_transformer,40589.0,uniform,, +none,no_encoding,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00012437731009611307,True,,0.08709904468181932,True,,invscaling,squared_hinge,l1,0.6231564675290102,0.008071279833697563,,,,,,,,,,,,,,,,,,134,median,fast_ica,,,,,,,,,,,parallel,logcosh,814.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.714869937384006,None,0.0,8.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, diff --git a/metalearning/metalearning_files/f1_binary.classification_dense/description.txt b/metalearning/metalearning_files/f1_binary.classification_dense/description.txt new file mode 100755 index 0000000..8045f6f --- /dev/null +++ b/metalearning/metalearning_files/f1_binary.classification_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: f1 +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/f1_binary.classification_dense/feature_costs.arff b/metalearning/metalearning_files/f1_binary.classification_dense/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/f1_binary.classification_dense/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/f1_binary.classification_dense/feature_runstatus.arff b/metalearning/metalearning_files/f1_binary.classification_dense/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/f1_binary.classification_dense/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/f1_binary.classification_dense/feature_values.arff b/metalearning/metalearning_files/f1_binary.classification_dense/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/f1_binary.classification_dense/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/f1_binary.classification_dense/readme.txt b/metalearning/metalearning_files/f1_binary.classification_dense/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/f1_binary.classification_sparse/algorithm_runs.arff b/metalearning/metalearning_files/f1_binary.classification_sparse/algorithm_runs.arff new file mode 100755 index 0000000..3bfc9ea --- /dev/null +++ b/metalearning/metalearning_files/f1_binary.classification_sparse/algorithm_runs.arff @@ -0,0 +1,107 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE f1 NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2117,1.0,1,0.10832227183138254,ok +75156,1.0,2,0.19883040935672514,ok +75129,1.0,3,0.5438596491228069,ok +75239,1.0,4,0.0,ok +75121,1.0,5,0.002049180327868827,ok +261,1.0,6,0.4320987654320987,ok +75240,1.0,7,0.028349082823791005,ok +75120,1.0,8,0.02004008016032066,ok +75124,1.0,9,0.5238095238095238,ok +75176,1.0,10,0.014815786023338218,ok +75103,1.0,11,0.06161137440758291,ok +75095,1.0,12,0.18120805369127513,ok +273,1.0,13,0.05841924398625431,ok +75174,1.0,14,0.20862968231389278,ok +75153,1.0,15,0.1207658321060383,ok +75093,1.0,16,0.5637305699481865,ok +75119,1.0,17,0.018711018711018657,ok +75215,1.0,18,0.024825259098578023,ok +75233,1.0,19,0.04636459430979978,ok +75196,1.0,20,0.04347826086956519,ok +75191,1.0,21,0.1299683319330447,ok +75115,1.0,22,0.009544008483563182,ok +75108,1.0,23,0.0060422960725075026,ok +75101,1.0,24,0.2677202224173252,ok +75192,1.0,25,0.5012264922322158,ok +75232,1.0,26,0.22666666666666668,ok +75173,1.0,27,0.11666139740752446,ok +75148,1.0,28,0.18589132507149664,ok +75150,1.0,29,0.3176470588235294,ok +75100,1.0,30,0.9658119658119658,ok +75179,1.0,31,0.29820749592612716,ok +75213,1.0,32,0.1123595505617977,ok +75227,1.0,33,0.17420596727622717,ok +75184,1.0,34,0.24985439720442626,ok +75142,1.0,35,0.08059525563577419,ok +75166,1.0,36,0.10216483099126472,ok +75133,1.0,37,0.4999999999999999,ok +75234,1.0,38,0.06196943972835334,ok +75139,1.0,39,0.01832620647525962,ok +75117,1.0,40,0.03703703703703709,ok +75113,1.0,41,0.05429071803852892,ok +75237,1.0,42,0.00024933381122316245,ok +75195,1.0,43,0.0013143894348125462,ok +75171,1.0,44,0.16568914956011738,ok +75128,1.0,45,0.012725344644750725,ok +75146,1.0,46,0.10476190476190483,ok +75116,1.0,47,0.005847953216374324,ok +75157,1.0,48,0.4766917293233083,ok +75187,1.0,49,0.02857142857142858,ok +2350,1.0,50,0.5129083465619517,ok +75125,1.0,51,0.021531100478468845,ok +75185,1.0,52,0.13694581280788165,ok +75163,1.0,53,0.0650779101741521,ok +75177,1.0,54,0.14457831325301207,ok +75189,1.0,55,0.014574596962072528,ok +75244,1.0,56,0.6633663366336633,ok +75219,1.0,57,0.09526000920386557,ok +75222,1.0,58,0.2777777777777778,ok +75159,1.0,59,0.5319148936170213,ok +75175,1.0,60,0.14866629360193984,ok +254,1.0,61,0.0,ok +75105,1.0,62,0.8763197586726998,ok +75106,1.0,63,0.7932833387675252,ok +75212,1.0,64,0.2686230248306998,ok +75099,1.0,65,0.5443037974683544,ok +75248,1.0,66,0.6210350584307178,ok +233,1.0,67,0.01632653061224487,ok +75226,1.0,68,0.0025179856115107313,ok +75132,1.0,69,0.8934986682967296,ok +75127,1.0,70,0.37693004057912305,ok +75161,1.0,71,0.08227474150664704,ok +75143,1.0,72,0.006923837784371889,ok +75114,1.0,73,0.015037593984962405,ok +75182,1.0,74,0.19805115712545673,ok +75112,1.0,75,0.1851308388359012,ok +75210,1.0,76,0.0,ok +75092,1.0,77,0.38157894736842113,ok +3043,1.0,78,0.14970059880239517,ok +75249,1.0,79,0.030927835051546393,ok +75126,1.0,80,0.027870680044593144,ok +75225,1.0,81,0.569377990430622,ok +75141,1.0,82,0.07126645483431693,ok +75107,1.0,83,0.4416498993963782,ok +75097,1.0,84,0.030135154496394367,ok +80001,1.0,1,0.19999999999999996,ok +80003,1.0,1,0.12244897959183676,ok +80006,1.0,1,0.23076923076923084,ok +80008,1.0,1,0.19999999999999996,ok +80009,1.0,1,0.1428571428571428,ok +80010,1.0,1,0.2857142857142857,ok +80011,1.0,1,0.05555555555555558,ok +80012,1.0,1,0.03448275862068961,ok +80013,1.0,1,0.0,ok +80014,1.0,1,0.05263157894736836,ok +80015,1.0,1,0.125,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/f1_binary.classification_sparse/configurations.csv b/metalearning/metalearning_files/f1_binary.classification_sparse/configurations.csv new file mode 100755 index 0000000..9c1e0f0 --- /dev/null +++ b/metalearning/metalearning_files/f1_binary.classification_sparse/configurations.csv @@ -0,0 +1,96 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,preprocessor:truncatedSVD:target_dim,rescaling:__choice__ +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.585711203872775,None,0.0,5.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,median,extra_trees_preproc_for_classification,True,entropy,None,0.29512530534048065,None,0.0,7.0,10.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.039533063907190934,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1878805519245509,fdr,chi2,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7238850981243719,None,0.0,4.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,5.282738216059151e-05,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.010000000000000005,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9727149851116396,None,0.0,18.0,13.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,,none +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7974565919616314,None,0.0,12.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,median,extra_trees_preproc_for_classification,True,entropy,None,0.9772091846790169,None,0.0,10.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.0010015637584068037,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.037611630308856295,deviance,5.0,0.8840126779516314,None,0.0,10.0,2.0,0.0,444.0,0.7599997167603434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,most_frequent,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.8657388713119849,,1.0,None,0.0,19.0,13.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828776,None,0.0,2.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.002173124111626734,None,0.0,14.0,4.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,6.0,None,13.0,2.0,1.0,23.0,,,,,,,normalize +weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2507440474920336e-05,True,,0.049622652766554566,True,0.009105043727227265,constant,squared_hinge,elasticnet,,0.00010112719671669047,,,,,,,,,,,,,,,,,,31,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61.69949680034141,chi2,,,,,none +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.9348409326933208,rbf,-1.0,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,mean,kernel_pca,,,,,,,,,,,,,,cosine,1754.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3428971645958825,,,0.2229870623330047,rbf,-1.0,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4047.618729304337,,,2.0237366768707754,rbf,-1.0,True,0.04369127828878843,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,one_hot_encoding,0.0008685602750053468,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9896334290292654,None,0.0,11.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,141.76310800864283,False,True,1.0,squared_hinge,ovr,l1,0.004317884655117431,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1.0,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.3451627750042988,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.3163640203509378,None,0.0,17.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,median,extra_trees_preproc_for_classification,False,gini,None,0.8916956785028156,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.00011600321198702641,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,most_frequent,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.9376772805635799,None,0.0,18.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,median,extra_trees_preproc_for_classification,True,entropy,None,0.8613889689810683,None,0.0,10.0,4.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.039533063907190934,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1878805519245509,fdr,chi2,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9091193924897338,None,0.0,2.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3823734947460288,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8804227616935514,None,0.0,10.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.0898983119899223,False,True,1.0,squared_hinge,ovr,l1,0.016048982920294167,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0733000338152003,False,True,1.0,squared_hinge,ovr,l2,0.033752542733220474,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,-0.6840756728731969,,0.00980445380551526,sigmoid,161.0,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.04329010470998136,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7260331062271381,None,0.0,7.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,134,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.714869937384006,None,0.0,8.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/metalearning/metalearning_files/f1_binary.classification_sparse/description.txt b/metalearning/metalearning_files/f1_binary.classification_sparse/description.txt new file mode 100755 index 0000000..8045f6f --- /dev/null +++ b/metalearning/metalearning_files/f1_binary.classification_sparse/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: f1 +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/f1_binary.classification_sparse/feature_costs.arff b/metalearning/metalearning_files/f1_binary.classification_sparse/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/f1_binary.classification_sparse/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/f1_binary.classification_sparse/feature_runstatus.arff b/metalearning/metalearning_files/f1_binary.classification_sparse/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/f1_binary.classification_sparse/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/f1_binary.classification_sparse/feature_values.arff b/metalearning/metalearning_files/f1_binary.classification_sparse/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/f1_binary.classification_sparse/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/f1_binary.classification_sparse/readme.txt b/metalearning/metalearning_files/f1_binary.classification_sparse/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/f1_macro_binary.classification_dense/algorithm_runs.arff b/metalearning/metalearning_files/f1_macro_binary.classification_dense/algorithm_runs.arff new file mode 100755 index 0000000..35c2b72 --- /dev/null +++ b/metalearning/metalearning_files/f1_macro_binary.classification_dense/algorithm_runs.arff @@ -0,0 +1,152 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE f1_macro NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2120,1.0,1,0.09863699061974485,ok +75193,1.0,2,0.07053913770243558,ok +2117,1.0,3,0.22016388715121737,ok +75156,1.0,4,0.20517634339962032,ok +75129,1.0,5,0.3214977322529684,ok +75243,1.0,6,0.0,ok +75110,1.0,7,0.10200025693919046,ok +75239,1.0,8,0.0,ok +75223,1.0,9,0.11233542228045845,ok +75221,1.0,10,0.5040889236894779,ok +258,1.0,11,0.009601295255084352,ok +75121,1.0,12,0.0,ok +253,1.0,13,0.46022536370557854,ok +261,1.0,14,0.3042621851556935,ok +75240,1.0,15,0.023175600360006987,ok +75120,1.0,16,0.3066432991949175,ok +75124,1.0,17,0.2702948651675712,ok +75176,1.0,18,0.015649763049385368,ok +75103,1.0,19,0.02352335249867532,ok +75207,1.0,20,0.19167470561705713,ok +75095,1.0,21,0.09504043828172026,ok +273,1.0,22,0.04645375931247009,ok +75174,1.0,23,0.14063581407174652,ok +75153,1.0,24,0.08030649358117337,ok +75093,1.0,25,0.36485273326184275,ok +75119,1.0,26,0.17006979506979514,ok +75201,1.0,27,0.1016688555093509,ok +75215,1.0,28,0.02790760013917082,ok +75172,1.0,29,0.1163016848530457,ok +75169,1.0,30,0.03400551137758434,ok +75202,1.0,31,0.3975374941597515,ok +75233,1.0,32,0.0717244334104361,ok +75231,1.0,33,0.1934670319360723,ok +75196,1.0,34,0.019681397738951723,ok +248,1.0,35,0.2374592328133599,ok +75191,1.0,36,0.12923709215459378,ok +75217,1.0,37,0.0,ok +260,1.0,38,0.16748288897263175,ok +75115,1.0,39,0.06322370509405584,ok +75123,1.0,40,0.324936422863935,ok +75108,1.0,41,0.0,ok +75101,1.0,42,0.2751249717555737,ok +75192,1.0,43,0.48369309402485317,ok +75232,1.0,44,0.13700411085577213,ok +75173,1.0,45,0.11657417742597098,ok +75197,1.0,46,0.21609039185648782,ok +266,1.0,47,0.01884828724596055,ok +75148,1.0,48,0.13572602348234064,ok +75150,1.0,49,0.28914628914628915,ok +75100,1.0,50,0.4708700327642449,ok +75178,1.0,51,0.7943792108909513,ok +75236,1.0,52,0.03264671942247066,ok +75179,1.0,53,0.2207044139691714,ok +75213,1.0,54,0.07330306295213163,ok +2123,1.0,55,0.3110222521987227,ok +75227,1.0,56,0.12291620089177402,ok +75184,1.0,57,0.12497648542498574,ok +75142,1.0,58,0.07159074909415064,ok +236,1.0,59,0.03882544890043427,ok +2122,1.0,60,0.11015119716205723,ok +75188,1.0,61,0.4478290859210454,ok +75166,1.0,62,0.0995858413881675,ok +75181,1.0,63,0.0,ok +75133,1.0,64,0.25128741551335687,ok +75134,1.0,65,0.15207046263817536,ok +75198,1.0,66,0.12304795088323683,ok +262,1.0,67,0.0027254798407814196,ok +75234,1.0,68,0.02416120888215134,ok +75139,1.0,69,0.012114128527786816,ok +252,1.0,70,0.15483880334271605,ok +75117,1.0,71,0.181655960028551,ok +75113,1.0,72,0.028881275695263886,ok +75098,1.0,73,0.027741174038947825,ok +246,1.0,74,0.01080523476682127,ok +75203,1.0,75,0.10817708318810615,ok +75237,1.0,76,0.0006037948926751469,ok +75195,1.0,77,0.0016179413547929844,ok +75171,1.0,78,0.16206633155923522,ok +75128,1.0,79,0.04952813994827465,ok +75096,1.0,80,0.32558394257629053,ok +75250,1.0,81,0.3922262829556027,ok +75146,1.0,82,0.11598105087172117,ok +75116,1.0,83,0.018261399921070565,ok +75157,1.0,84,0.44392303716489667,ok +75187,1.0,85,0.016798669153195833,ok +2350,1.0,86,0.4528595252099479,ok +242,1.0,87,0.010714131119591963,ok +244,1.0,88,0.10946429095173504,ok +75125,1.0,89,0.05711664257378035,ok +75185,1.0,90,0.1286982096968351,ok +75163,1.0,91,0.06069121519809906,ok +75177,1.0,92,0.07337212960050321,ok +75189,1.0,93,0.021791341807692488,ok +75244,1.0,94,0.40109883346395137,ok +75219,1.0,95,0.035146298914436436,ok +75222,1.0,96,0.15120415982484947,ok +75159,1.0,97,0.2842590573502384,ok +75175,1.0,98,0.10319521702947754,ok +75109,1.0,99,0.3436276698888191,ok +254,1.0,100,0.0,ok +75105,1.0,101,0.4471433966694359,ok +75106,1.0,102,0.4340205549128604,ok +75212,1.0,103,0.2520564042303173,ok +75099,1.0,104,0.3070544961367747,ok +75248,1.0,105,0.3907920994759915,ok +233,1.0,106,0.002860019157243987,ok +75235,1.0,107,0.0007062499127162836,ok +75226,1.0,108,0.005839668672087406,ok +75132,1.0,109,0.4725130619506115,ok +75127,1.0,110,0.3374555588649363,ok +251,1.0,111,0.0,ok +75161,1.0,112,0.0643763314232011,ok +75143,1.0,113,0.014198115211204287,ok +75114,1.0,114,0.034791524265208484,ok +75182,1.0,115,0.1362133930353292,ok +75112,1.0,116,0.13728951252914046,ok +75210,1.0,117,0.0,ok +75205,1.0,118,0.18921026998354384,ok +75090,1.0,119,0.061536201945949776,ok +275,1.0,120,0.04161216611241303,ok +288,1.0,121,0.13019553868760525,ok +75092,1.0,122,0.2245440519048938,ok +3043,1.0,123,0.07785480454457916,ok +75249,1.0,124,0.01118111793203247,ok +75126,1.0,125,0.1172411251462635,ok +75225,1.0,126,0.2863994518670778,ok +75141,1.0,127,0.05566133858694178,ok +75107,1.0,128,0.23498076035212945,ok +75097,1.0,129,0.2826865204707536,ok +80001,1.0,1,0.12100840336134455,ok +80003,1.0,1,0.0923101199242583,ok +80006,1.0,1,0.0945812807881774,ok +80008,1.0,1,0.11688311688311692,ok +80009,1.0,1,0.10555555555555562,ok +80010,1.0,1,0.18831168831168832,ok +80011,1.0,1,0.042063492063492136,ok +80012,1.0,1,0.05057471264367819,ok +80013,1.0,1,0.0,ok +80014,1.0,1,0.04631578947368409,ok +80015,1.0,1,0.10096153846153844,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/f1_macro_binary.classification_dense/configurations.csv b/metalearning/metalearning_files/f1_macro_binary.classification_dense/configurations.csv new file mode 100755 index 0000000..5953eb1 --- /dev/null +++ b/metalearning/metalearning_files/f1_macro_binary.classification_dense/configurations.csv @@ -0,0 +1,141 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:fast_ica:algorithm,preprocessor:fast_ica:fun,preprocessor:fast_ica:n_components,preprocessor:fast_ica:whiten,preprocessor:feature_agglomeration:affinity,preprocessor:feature_agglomeration:linkage,preprocessor:feature_agglomeration:n_clusters,preprocessor:feature_agglomeration:pooling_func,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:pca:keep_variance,preprocessor:pca:whiten,preprocessor:polynomial:degree,preprocessor:polynomial:include_bias,preprocessor:polynomial:interaction_only,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,rescaling:__choice__,rescaling:quantile_transformer:n_quantiles,rescaling:quantile_transformer:output_distribution,rescaling:robust_scaler:q_max,rescaling:robust_scaler:q_min +weighting,one_hot_encoding,0.00011717632475982632,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.045388141846341344,deviance,10.0,0.29161769341843435,None,0.0,20.0,2.0,0.0,278.0,0.7912571599269661,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7249853037185638,None,0.0,1.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,median,extra_trees_preproc_for_classification,False,gini,None,0.9424908623661876,None,0.0,7.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.12713527337147906,deviance,4.0,0.6041596127474019,None,0.0,14.0,17.0,0.0,83.0,0.8426859880999615,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.03474109838999682,deviance,4.0,0.5687034678818491,None,0.0,18.0,12.0,0.0,408.0,0.5150113945430513,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.6328939517938,f_classif,,,,standardize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9265375980300852,None,0.0,13.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.251097788175676,deviance,6.0,0.35679099363539235,None,0.0,13.0,11.0,0.0,157.0,0.4791732272983235,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,adaboost,SAMME,0.28738775989203896,10.0,423.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.8031499675923353,0.13579938270386765 +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.14159526341015916,deviance,7.0,0.8010488230155749,None,0.0,3.0,20.0,0.0,401.0,0.8073562440607731,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.002385546176068135,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.7729472304882845,,,0.0004789329856033374,rbf,-1.0,True,6.58869648864534e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,177.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5368752992317617,None,0.0,16.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.8382117756438685,mutual_info,,,,quantile_transformer,11480.0,normal,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3759438793746945,None,0.0,7.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3689420905780977,fwe,f_classif,quantile_transformer,25061.0,uniform,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.4909422458748719,None,0.0,11.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,mean,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7146659106968425,True,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.12713527337147906,deviance,4.0,0.6041596127474019,None,0.0,14.0,17.0,0.0,83.0,0.8426859880999615,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9455638720565652,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,most_frequent,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8255464552647293,0.19162485555463185 +weighting,one_hot_encoding,0.18137532678800647,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9094110110427254,None,0.0,7.0,12.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,195.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.6682079659377479,None,0.0,4.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,mean,extra_trees_preproc_for_classification,True,entropy,None,0.5552350997943013,None,0.0,8.0,5.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.02345017287074443,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.053517066400173056,deviance,10.0,0.542144980834302,None,0.0,20.0,13.0,0.0,233.0,0.7398539900055563,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0614425536709615,fwe,f_classif,robust_scaler,,,0.9523118062307264,0.13434811490315818 +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.8149627329153046,None,0.0,15.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.040936424602789435,deviance,7.0,0.5495014745530306,None,0.0,20.0,18.0,0.0,141.0,0.6905343807995293,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.9727149851116396,None,0.0,10.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,25.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7974565919616314,None,0.0,12.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,median,extra_trees_preproc_for_classification,True,entropy,None,0.9772091846790169,None,0.0,10.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2398150931290834,,,0.4015139801872962,rbf,-1.0,False,2.402997750662158e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88.40698357592571,chi2,,,,normalize,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.1958974686405233,deviance,5.0,0.33885235607979314,None,0.0,6.0,4.0,0.0,125.0,0.9448890820738562,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0007038280350320558,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4138778052607317,0.7995003430482459,5.0,5.430044692638861,poly,-1.0,True,0.02455501006004393,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,31,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.010000000000000005,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.0518326156691958,deviance,6.0,0.8807456180216267,None,0.0,7.0,19.0,0.0,366.0,0.7314831276137047,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,,False,adaboost,SAMME,0.4391375941344922,3.0,386.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7439738358430176,0.20581080574615795 +none,one_hot_encoding,0.00011294596229850895,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7515.1213255144885,0.9576762936062476,3.0,0.019002536385919932,poly,-1.0,False,0.010632086351533369,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,51.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,97282.0,normal,, +none,one_hot_encoding,0.00012586572428922356,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5240592829918601,None,0.0,10.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.03192699980429505,True,adaboost,SAMME.R,0.10000000000000002,1.0,50.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,quantile_transformer,8407.0,normal,, +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1751.4736515133566,0.62404114475118,3.0,1.6087076997410432,poly,-1.0,False,3.5353792826856045e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1198.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.2422926485206341,,1.0,None,0.0,15.0,9.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.02102683283349326,deviance,10.0,0.2797288369369436,None,0.0,14.0,9.0,0.0,480.0,0.5778972273820631,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58.88123233170863,mutual_info,,,,robust_scaler,,,0.75,0.25 +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828776,None,0.0,2.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2155613360930585,deviance,4.0,0.3198803116198433,None,0.0,8.0,13.0,0.0,275.0,0.28870176110739404,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,mean,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.7062102387181676,None,0.0,1.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,most_frequent,fast_ica,,,,,,,,,,,parallel,exp,100.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7065776353150109,0.23782974987118105 +none,one_hot_encoding,0.16334152321884812,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00011572473434870852,True,True,squared_hinge,0.00019678754114665057,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.045742431094098604,fpr,chi2,none,,,, +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.3795924768593385,deviance,2.0,0.33708497069988536,None,0.0,15.0,13.0,0.0,451.0,0.7716323242090217,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2573946506994812,fwe,f_classif,quantile_transformer,1000.0,uniform,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.609975998293528,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,most_frequent,fast_ica,,,,,,,,,,,parallel,logcosh,2000.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8430415644014919,0.2863750565331575 +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.39536192447534535,None,0.0,19.0,3.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5.0,None,11.0,11.0,1.0,12.0,,,,,,robust_scaler,,,0.8928631650245873,0.1581877760687084 +weighting,one_hot_encoding,0.060155186012325876,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.3163640203509378,None,0.0,16.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,median,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,2.0,None,11.0,20.0,1.0,47.0,,,,,,quantile_transformer,21065.0,uniform,, +weighting,one_hot_encoding,0.3126027672745337,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1100.6211008501205,0.5921425829232616,2.0,0.0337546254878617,poly,-1.0,True,0.09641299736884308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.9348409326933208,rbf,-1.0,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1754.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,198.72528686512538,False,True,1.0,squared_hinge,ovr,l2,0.026260652523566803,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,robust_scaler,,,0.913511520078368,0.2742229325455444 +none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9260795160807372,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.03644212536682547,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.05965671477918361,deviance,8.0,0.4858133247974158,None,0.0,14.0,7.0,0.0,480.0,0.5726186552917335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.15318294164619112 +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18887.81504976871,,,0.23283562663398755,rbf,-1.0,True,2.3839685780861318e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3937607155568376,fwe,chi2,robust_scaler,,,0.941018778984854,0.2144110585080491 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3428971645958825,,,0.2229870623330047,rbf,-1.0,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8138233157708883,None,0.0,2.0,9.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54.27504292568562,f_classif,,,,minmax,,,, +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00016781524591321165,True,True,squared_hinge,1.5119200923218881e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.349459944355116,,,0.00024028983491736645,rbf,-1.0,True,1.139421622432356e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3636266268105085,fwe,chi2,none,,,, +weighting,one_hot_encoding,0.00034835629696198427,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8245132980938538,0.08947420373097192 +weighting,one_hot_encoding,0.00016967940959070708,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9439080311935252,None,0.0,2.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35.0,None,,0.005389483044810356,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,most_frequent,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,9.441661069727422e-05,1896.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.03528169333197684,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3416063836589199,None,0.0,9.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,median,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,normalize,,,, +none,one_hot_encoding,,False,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6396026761675004,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06544340428506021,fwe,f_classif,none,,,, +weighting,one_hot_encoding,0.004980497345831963,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1255.9137433589426,,,0.08351549479967445,rbf,-1.0,True,0.00017919875199222518,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1.0,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.3837398524575939,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.018356703878357986,deviance,3.0,0.9690352514774068,None,0.0,12.0,3.0,0.0,234.0,0.3870344708308441,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,mean,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6524770456279243,False,,,,,,,,,,,,,,,,robust_scaler,,,0.7602889314749132,0.25 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5670424455696162,None,0.0,8.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.00010817282861262362,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.799803680241154,None,0.0,13.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,85,mean,fast_ica,,,,,,,,,,,deflation,exp,1793.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9071932815811076,0.03563842252368924 +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.35533396539961937,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,86,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4165632766388807,fpr,chi2,none,,,, +weighting,one_hot_encoding,,False,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,156.0,auto,,0.0001987333852871589,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1.0,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,no_encoding,,,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.96834823420249e-05,False,True,hinge,0.00016639250831671168,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,89,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.628430584359224,mutual_info,,,,quantile_transformer,6634.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,90,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,91,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,8.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,93,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.9376772805635799,None,0.0,18.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94,median,extra_trees_preproc_for_classification,True,entropy,None,0.8613889689810683,None,0.0,10.0,4.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2636201374253461,deviance,7.0,0.8344964130784466,None,0.0,9.0,2.0,0.0,298.0,0.7517549950523315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,95,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,96,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,97,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.07463196642416367,deviance,7.0,0.8603242247379981,None,0.0,2.0,6.0,0.0,500.0,0.8447665577491962,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,98,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.0433556140045585,deviance,10.0,0.33000096635982235,None,0.0,15.0,13.0,0.0,388.0,0.8291104221904706,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,99,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,robust_scaler,,,0.7496393440951183,0.2853682991120835 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,100,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0009243833519832893,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9829888014816668,None,0.0,18.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,102,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,minmax,,,, +weighting,one_hot_encoding,0.0020580843703898177,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.945774573434192,None,0.0,19.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,103,median,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,15209.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,104,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0506220137415751,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.4433458237042269,None,0.0,11.0,10.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,105,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.8740590455827745,0.19483217552098045 +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,106,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.005332972692819521,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5565918060287016,None,0.0,5.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,107,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,173.0,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9527068489270144,0.04135311355893583 +weighting,one_hot_encoding,0.001279467383882126,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,108,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0903354518102121,fwe,f_classif,standardize,,,, +weighting,one_hot_encoding,,False,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4569434193011598,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,109,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,0.002615346832354839,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7884268823432835,None,0.0,20.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,110,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +weighting,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56.086963007482865,,,0.013609964993119377,rbf,-1.0,True,0.00196831255706268,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,111,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.15374716583918388 +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.2472984547885781,deviance,3.0,0.6564306719064884,None,0.0,15.0,14.0,0.0,220.0,0.8082564085714649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,112,median,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,332.0,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,113,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,115,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,116,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,117,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,118,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.20202014999292287,False,True,1.0,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,no_encoding,,,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6390376923528961,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,119,median,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,164.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,62508.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,120,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.27041927277584e-06,True,0.00010000000000000009,0.033157325660763994,True,0.0008114527992546482,invscaling,modified_huber,elasticnet,0.13714427818877545,0.055179642772545036,,,,,,,,,,,,,,,,,,121,median,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.7879059827470586,None,0.0,3.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,122,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54.1024239468849,f_classif,,,,quantile_transformer,89842.0,normal,, +weighting,one_hot_encoding,0.010000000000000005,True,decision_tree,,,,,,,entropy,1.5841974853345435,,1.0,None,0.0,8.0,14.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,123,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,25.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.19998727075532635,deviance,10.0,0.9377656718112952,None,0.0,7.0,13.0,0.0,214.0,0.6062346326014357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,124,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,125,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.003777272888571546,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4583291745177553,None,0.0,14.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,126,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.27741957612588863,fdr,f_classif,none,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.5765793990908161,None,0.0,11.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,127,median,fast_ica,,,,,,,,,,,deflation,exp,10.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7945458151995424,None,0.0,1.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,129,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,38400.0,uniform,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.04651467280022463,True,adaboost,SAMME,0.6506122230393502,6.0,143.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,extra_trees_preproc_for_classification,False,entropy,None,0.348720215832657,None,0.0,17.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,26025.0,normal,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3386310102795006,None,0.0,6.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,True,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.0,None,,5.861221938384061e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.22985730375167915,fpr,f_classif,quantile_transformer,40589.0,uniform,, +none,no_encoding,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00012437731009611307,True,,0.08709904468181932,True,,invscaling,squared_hinge,l1,0.6231564675290102,0.008071279833697563,,,,,,,,,,,,,,,,,,134,median,fast_ica,,,,,,,,,,,parallel,logcosh,814.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.714869937384006,None,0.0,8.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, diff --git a/metalearning/metalearning_files/f1_macro_binary.classification_dense/description.txt b/metalearning/metalearning_files/f1_macro_binary.classification_dense/description.txt new file mode 100755 index 0000000..8f5411e --- /dev/null +++ b/metalearning/metalearning_files/f1_macro_binary.classification_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: f1_macro +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/f1_macro_binary.classification_dense/feature_costs.arff b/metalearning/metalearning_files/f1_macro_binary.classification_dense/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/f1_macro_binary.classification_dense/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/f1_macro_binary.classification_dense/feature_runstatus.arff b/metalearning/metalearning_files/f1_macro_binary.classification_dense/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/f1_macro_binary.classification_dense/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/f1_macro_binary.classification_dense/feature_values.arff b/metalearning/metalearning_files/f1_macro_binary.classification_dense/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/f1_macro_binary.classification_dense/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/f1_macro_binary.classification_dense/readme.txt b/metalearning/metalearning_files/f1_macro_binary.classification_dense/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/f1_macro_binary.classification_sparse/algorithm_runs.arff b/metalearning/metalearning_files/f1_macro_binary.classification_sparse/algorithm_runs.arff new file mode 100755 index 0000000..b26f000 --- /dev/null +++ b/metalearning/metalearning_files/f1_macro_binary.classification_sparse/algorithm_runs.arff @@ -0,0 +1,152 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE f1_macro NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2120,1.0,1,0.11408447810374378,ok +75193,1.0,2,0.07053913770243558,ok +2117,1.0,3,0.22679879319205365,ok +75156,1.0,4,0.22238084663134627,ok +75129,1.0,5,0.3260625370177481,ok +75243,1.0,6,0.034148845248020865,ok +75110,1.0,7,0.27745880194558303,ok +75239,1.0,8,0.0,ok +75223,1.0,9,0.34253647958285205,ok +75221,1.0,10,0.5040889236894779,ok +258,1.0,11,0.01806999455734748,ok +75121,1.0,12,0.02483411397345825,ok +253,1.0,13,0.46022536370557854,ok +261,1.0,14,0.32176368540350253,ok +75240,1.0,15,0.023175600360006987,ok +75120,1.0,16,0.3187511182680265,ok +75124,1.0,17,0.30830132152112777,ok +75176,1.0,18,0.016832413445363903,ok +75103,1.0,19,0.033004852648699456,ok +75207,1.0,20,0.19167470561705713,ok +75095,1.0,21,0.09504043828172026,ok +273,1.0,22,0.047372015155520364,ok +75174,1.0,23,0.14498779364354286,ok +75153,1.0,24,0.12134945880394854,ok +75093,1.0,25,0.382260536424438,ok +75119,1.0,26,0.17006979506979514,ok +75201,1.0,27,0.1016688555093509,ok +75215,1.0,28,0.028777421414557525,ok +75172,1.0,29,0.09039624333028018,ok +75169,1.0,30,0.03400551137758434,ok +75202,1.0,31,0.3975374941597515,ok +75233,1.0,32,0.07784068224806762,ok +75231,1.0,33,0.1934670319360723,ok +75196,1.0,34,0.029789219880220874,ok +248,1.0,35,0.27213781889621136,ok +75191,1.0,36,0.12398891920870292,ok +75217,1.0,37,0.0,ok +260,1.0,38,0.16748288897263175,ok +75115,1.0,39,0.06477200424178153,ok +75123,1.0,40,0.324936422863935,ok +75108,1.0,41,0.0035628598455712535,ok +75101,1.0,42,0.28362109608195185,ok +75192,1.0,43,0.48369309402485317,ok +75232,1.0,44,0.16747346072186842,ok +75173,1.0,45,0.11752222902137532,ok +75197,1.0,46,0.19706797497393058,ok +266,1.0,47,0.03145621309838542,ok +75148,1.0,48,0.1903482599383457,ok +75150,1.0,49,0.32050017611835147,ok +75100,1.0,50,0.5009508285791905,ok +75178,1.0,51,0.8084431154999884,ok +75236,1.0,52,0.03264671942247066,ok +75179,1.0,53,0.2207044139691714,ok +75213,1.0,54,0.07330306295213163,ok +2123,1.0,55,0.3110222521987227,ok +75227,1.0,56,0.12291620089177402,ok +75184,1.0,57,0.17621306838361817,ok +75142,1.0,58,0.0813267307281782,ok +236,1.0,59,0.04214387679881482,ok +2122,1.0,60,0.27745880194558303,ok +75188,1.0,61,0.4478290859210454,ok +75166,1.0,62,0.0995858413881675,ok +75181,1.0,63,0.0,ok +75133,1.0,64,0.25128741551335687,ok +75134,1.0,65,0.17624410373174515,ok +75198,1.0,66,0.12304795088323683,ok +262,1.0,67,0.0027254798407814196,ok +75234,1.0,68,0.059861302142657724,ok +75139,1.0,69,0.013690721710313158,ok +252,1.0,70,0.16633663122289843,ok +75117,1.0,71,0.18851851851851853,ok +75113,1.0,72,0.028881275695263886,ok +75098,1.0,73,0.027741174038947825,ok +246,1.0,74,0.024495160481337708,ok +75203,1.0,75,0.10817708318810615,ok +75237,1.0,76,0.0006037948926751469,ok +75195,1.0,77,0.0016179413547929844,ok +75171,1.0,78,0.16723591159857998,ok +75128,1.0,79,0.04952813994827465,ok +75096,1.0,80,0.6993134761188856,ok +75250,1.0,81,0.3922262829556027,ok +75146,1.0,82,0.12512843908084614,ok +75116,1.0,83,0.018261399921070565,ok +75157,1.0,84,0.44392303716489667,ok +75187,1.0,85,0.02786398904609766,ok +2350,1.0,86,0.4528595252099479,ok +242,1.0,87,0.0165453764866339,ok +244,1.0,88,0.10946429095173504,ok +75125,1.0,89,0.06021609968978381,ok +75185,1.0,90,0.1286982096968351,ok +75163,1.0,91,0.06069121519809906,ok +75177,1.0,92,0.07745711528283672,ok +75189,1.0,93,0.021791341807692488,ok +75244,1.0,94,0.40109883346395137,ok +75219,1.0,95,0.08499462554056092,ok +75222,1.0,96,0.15120415982484947,ok +75159,1.0,97,0.2842590573502384,ok +75175,1.0,98,0.1225718588213065,ok +75109,1.0,99,0.3964626746304919,ok +254,1.0,100,0.0,ok +75105,1.0,101,0.4471433966694359,ok +75106,1.0,102,0.43728243375753073,ok +75212,1.0,103,0.27739530637860854,ok +75099,1.0,104,0.3070544961367747,ok +75248,1.0,105,0.4099296083174433,ok +233,1.0,106,0.01525546388768273,ok +75235,1.0,107,0.0010593648299854763,ok +75226,1.0,108,0.008162345862954501,ok +75132,1.0,109,0.473419477760615,ok +75127,1.0,110,0.33879906227331147,ok +251,1.0,111,0.0759050854617499,ok +75161,1.0,112,0.0828102721449141,ok +75143,1.0,113,0.014198115211204287,ok +75114,1.0,114,0.034791524265208484,ok +75182,1.0,115,0.1362133930353292,ok +75112,1.0,116,0.13728951252914046,ok +75210,1.0,117,0.0,ok +75205,1.0,118,0.18921026998354384,ok +75090,1.0,119,0.10304792476768632,ok +275,1.0,120,0.04161216611241303,ok +288,1.0,121,0.1443862359478315,ok +75092,1.0,122,0.22609454740800428,ok +3043,1.0,123,0.08023590905221001,ok +75249,1.0,124,0.016771676898048704,ok +75126,1.0,125,0.1172411251462635,ok +75225,1.0,126,0.3105943894863552,ok +75141,1.0,127,0.060141500910758205,ok +75107,1.0,128,0.23498076035212945,ok +75097,1.0,129,0.47431066312883763,ok +80001,1.0,1,0.12100840336134455,ok +80003,1.0,1,0.12308015989901122,ok +80006,1.0,1,0.19433198380566807,ok +80008,1.0,1,0.2250000000000001,ok +80009,1.0,1,0.1578947368421052,ok +80010,1.0,1,0.18831168831168832,ok +80011,1.0,1,0.042063492063492136,ok +80012,1.0,1,0.05057471264367819,ok +80013,1.0,1,0.0,ok +80014,1.0,1,0.04631578947368409,ok +80015,1.0,1,0.10096153846153844,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/f1_macro_binary.classification_sparse/configurations.csv b/metalearning/metalearning_files/f1_macro_binary.classification_sparse/configurations.csv new file mode 100755 index 0000000..80cd140 --- /dev/null +++ b/metalearning/metalearning_files/f1_macro_binary.classification_sparse/configurations.csv @@ -0,0 +1,141 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,preprocessor:truncatedSVD:target_dim,rescaling:__choice__ +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7249853037185638,None,0.0,1.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,median,extra_trees_preproc_for_classification,False,gini,None,0.9424908623661876,None,0.0,7.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.585711203872775,None,0.0,5.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,median,extra_trees_preproc_for_classification,True,entropy,None,0.29512530534048065,None,0.0,7.0,10.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.003005033889617757,True,adaboost,SAMME.R,0.09171378528184512,5.0,97.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,33.361778338113126,False,True,1.0,squared_hinge,ovr,l1,0.0018992130480287718,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.6682079659377479,None,0.0,4.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,mean,extra_trees_preproc_for_classification,True,entropy,None,0.5552350997943013,None,0.0,8.0,5.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7974565919616314,None,0.0,12.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,median,extra_trees_preproc_for_classification,True,entropy,None,0.9772091846790169,None,0.0,10.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2398150931290834,,,0.4015139801872962,rbf,-1.0,False,2.402997750662158e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88.40698357592571,chi2,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.034465366914659866,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.12057775675278172,deviance,10.0,0.8011153303489733,None,0.0,2.0,16.0,0.0,370.0,0.6078295352200873,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,mean,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0007038280350320558,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4138778052607317,0.7995003430482459,5.0,5.430044692638861,poly,-1.0,True,0.02455501006004393,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,31,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.0010015637584068037,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.037611630308856295,deviance,5.0,0.8840126779516314,None,0.0,10.0,2.0,0.0,444.0,0.7599997167603434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,most_frequent,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1751.4736515133566,0.62404114475118,3.0,1.6087076997410432,poly,-1.0,False,3.5353792826856045e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,mean,kernel_pca,,,,,,,,,,,,,,cosine,1198.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.8657388713119849,,1.0,None,0.0,19.0,13.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828776,None,0.0,2.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.027324741616523342,deviance,10.0,0.8623781459430139,None,0.0,10.0,20.0,0.0,329.0,0.8595750155424215,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,mean,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1100.6211008501205,0.5921425829232616,2.0,0.0337546254878617,poly,-1.0,True,0.09641299736884308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.9348409326933208,rbf,-1.0,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,mean,kernel_pca,,,,,,,,,,,,,,cosine,1754.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3428971645958825,,,0.2229870623330047,rbf,-1.0,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7322410244259964,None,0.0,6.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,median,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00016781524591321165,True,True,squared_hinge,1.5119200923218881e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.349459944355116,,,0.00024028983491736645,rbf,-1.0,True,1.139421622432356e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3636266268105085,fwe,chi2,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4047.618729304337,,,2.0237366768707754,rbf,-1.0,True,0.04369127828878843,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,one_hot_encoding,0.010000000000000005,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10.0,1.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,,none +weighting,one_hot_encoding,0.0008685602750053468,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9896334290292654,None,0.0,11.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,141.76310800864283,False,True,1.0,squared_hinge,ovr,l1,0.004317884655117431,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,,normalize +none,one_hot_encoding,0.003443779683191071,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.004980497345831963,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1255.9137433589426,,,0.08351549479967445,rbf,-1.0,True,0.00017919875199222518,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1.0,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.3451627750042988,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.3163640203509378,None,0.0,17.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,median,extra_trees_preproc_for_classification,False,gini,None,0.8916956785028156,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,85,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.35533396539961937,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,86,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4165632766388807,fpr,chi2,,none +weighting,one_hot_encoding,0.0019686649916896212,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.04641055832142541,True,True,hinge,8.540468968077405e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87,mean,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,911.0,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1.0,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,89,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,90,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,91,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,93,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.9376772805635799,None,0.0,18.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94,median,extra_trees_preproc_for_classification,True,entropy,None,0.8613889689810683,None,0.0,10.0,4.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,95,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,96,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,97,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,98,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,99,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,100,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.039533063907190934,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,102,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1878805519245509,fdr,chi2,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,103,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,104,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,105,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,106,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.006372860318416312,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6467376360604045,None,0.0,1.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,107,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,108,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,109,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3823734947460288,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,110,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,adaboost,SAMME,0.11042308042695524,5.0,117.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,111,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,112,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,113,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,115,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,116,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,117,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,118,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.20202014999292287,False,True,1.0,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,119,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,120,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,121,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9930358173100328,None,0.0,6.0,3.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,122,most_frequent,extra_trees_preproc_for_classification,False,entropy,None,0.4655848826291386,None,0.0,13.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,123,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,124,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,125,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.039533063907190934,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,126,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1878805519245509,fdr,chi2,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,127,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,129,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.04329010470998136,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7260331062271381,None,0.0,7.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.010000000000000005,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15.0,1.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,134,median,extra_trees_preproc_for_classification,False,entropy,None,0.8213051377774989,None,0.0,3.0,13.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.714869937384006,None,0.0,8.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/metalearning/metalearning_files/f1_macro_binary.classification_sparse/description.txt b/metalearning/metalearning_files/f1_macro_binary.classification_sparse/description.txt new file mode 100755 index 0000000..8f5411e --- /dev/null +++ b/metalearning/metalearning_files/f1_macro_binary.classification_sparse/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: f1_macro +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/f1_macro_binary.classification_sparse/feature_costs.arff b/metalearning/metalearning_files/f1_macro_binary.classification_sparse/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/f1_macro_binary.classification_sparse/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/f1_macro_binary.classification_sparse/feature_runstatus.arff b/metalearning/metalearning_files/f1_macro_binary.classification_sparse/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/f1_macro_binary.classification_sparse/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/f1_macro_binary.classification_sparse/feature_values.arff b/metalearning/metalearning_files/f1_macro_binary.classification_sparse/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/f1_macro_binary.classification_sparse/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/f1_macro_binary.classification_sparse/readme.txt b/metalearning/metalearning_files/f1_macro_binary.classification_sparse/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/f1_macro_multiclass.classification_dense/algorithm_runs.arff b/metalearning/metalearning_files/f1_macro_multiclass.classification_dense/algorithm_runs.arff new file mode 100755 index 0000000..35c2b72 --- /dev/null +++ b/metalearning/metalearning_files/f1_macro_multiclass.classification_dense/algorithm_runs.arff @@ -0,0 +1,152 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE f1_macro NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2120,1.0,1,0.09863699061974485,ok +75193,1.0,2,0.07053913770243558,ok +2117,1.0,3,0.22016388715121737,ok +75156,1.0,4,0.20517634339962032,ok +75129,1.0,5,0.3214977322529684,ok +75243,1.0,6,0.0,ok +75110,1.0,7,0.10200025693919046,ok +75239,1.0,8,0.0,ok +75223,1.0,9,0.11233542228045845,ok +75221,1.0,10,0.5040889236894779,ok +258,1.0,11,0.009601295255084352,ok +75121,1.0,12,0.0,ok +253,1.0,13,0.46022536370557854,ok +261,1.0,14,0.3042621851556935,ok +75240,1.0,15,0.023175600360006987,ok +75120,1.0,16,0.3066432991949175,ok +75124,1.0,17,0.2702948651675712,ok +75176,1.0,18,0.015649763049385368,ok +75103,1.0,19,0.02352335249867532,ok +75207,1.0,20,0.19167470561705713,ok +75095,1.0,21,0.09504043828172026,ok +273,1.0,22,0.04645375931247009,ok +75174,1.0,23,0.14063581407174652,ok +75153,1.0,24,0.08030649358117337,ok +75093,1.0,25,0.36485273326184275,ok +75119,1.0,26,0.17006979506979514,ok +75201,1.0,27,0.1016688555093509,ok +75215,1.0,28,0.02790760013917082,ok +75172,1.0,29,0.1163016848530457,ok +75169,1.0,30,0.03400551137758434,ok +75202,1.0,31,0.3975374941597515,ok +75233,1.0,32,0.0717244334104361,ok +75231,1.0,33,0.1934670319360723,ok +75196,1.0,34,0.019681397738951723,ok +248,1.0,35,0.2374592328133599,ok +75191,1.0,36,0.12923709215459378,ok +75217,1.0,37,0.0,ok +260,1.0,38,0.16748288897263175,ok +75115,1.0,39,0.06322370509405584,ok +75123,1.0,40,0.324936422863935,ok +75108,1.0,41,0.0,ok +75101,1.0,42,0.2751249717555737,ok +75192,1.0,43,0.48369309402485317,ok +75232,1.0,44,0.13700411085577213,ok +75173,1.0,45,0.11657417742597098,ok +75197,1.0,46,0.21609039185648782,ok +266,1.0,47,0.01884828724596055,ok +75148,1.0,48,0.13572602348234064,ok +75150,1.0,49,0.28914628914628915,ok +75100,1.0,50,0.4708700327642449,ok +75178,1.0,51,0.7943792108909513,ok +75236,1.0,52,0.03264671942247066,ok +75179,1.0,53,0.2207044139691714,ok +75213,1.0,54,0.07330306295213163,ok +2123,1.0,55,0.3110222521987227,ok +75227,1.0,56,0.12291620089177402,ok +75184,1.0,57,0.12497648542498574,ok +75142,1.0,58,0.07159074909415064,ok +236,1.0,59,0.03882544890043427,ok +2122,1.0,60,0.11015119716205723,ok +75188,1.0,61,0.4478290859210454,ok +75166,1.0,62,0.0995858413881675,ok +75181,1.0,63,0.0,ok +75133,1.0,64,0.25128741551335687,ok +75134,1.0,65,0.15207046263817536,ok +75198,1.0,66,0.12304795088323683,ok +262,1.0,67,0.0027254798407814196,ok +75234,1.0,68,0.02416120888215134,ok +75139,1.0,69,0.012114128527786816,ok +252,1.0,70,0.15483880334271605,ok +75117,1.0,71,0.181655960028551,ok +75113,1.0,72,0.028881275695263886,ok +75098,1.0,73,0.027741174038947825,ok +246,1.0,74,0.01080523476682127,ok +75203,1.0,75,0.10817708318810615,ok +75237,1.0,76,0.0006037948926751469,ok +75195,1.0,77,0.0016179413547929844,ok +75171,1.0,78,0.16206633155923522,ok +75128,1.0,79,0.04952813994827465,ok +75096,1.0,80,0.32558394257629053,ok +75250,1.0,81,0.3922262829556027,ok +75146,1.0,82,0.11598105087172117,ok +75116,1.0,83,0.018261399921070565,ok +75157,1.0,84,0.44392303716489667,ok +75187,1.0,85,0.016798669153195833,ok +2350,1.0,86,0.4528595252099479,ok +242,1.0,87,0.010714131119591963,ok +244,1.0,88,0.10946429095173504,ok +75125,1.0,89,0.05711664257378035,ok +75185,1.0,90,0.1286982096968351,ok +75163,1.0,91,0.06069121519809906,ok +75177,1.0,92,0.07337212960050321,ok +75189,1.0,93,0.021791341807692488,ok +75244,1.0,94,0.40109883346395137,ok +75219,1.0,95,0.035146298914436436,ok +75222,1.0,96,0.15120415982484947,ok +75159,1.0,97,0.2842590573502384,ok +75175,1.0,98,0.10319521702947754,ok +75109,1.0,99,0.3436276698888191,ok +254,1.0,100,0.0,ok +75105,1.0,101,0.4471433966694359,ok +75106,1.0,102,0.4340205549128604,ok +75212,1.0,103,0.2520564042303173,ok +75099,1.0,104,0.3070544961367747,ok +75248,1.0,105,0.3907920994759915,ok +233,1.0,106,0.002860019157243987,ok +75235,1.0,107,0.0007062499127162836,ok +75226,1.0,108,0.005839668672087406,ok +75132,1.0,109,0.4725130619506115,ok +75127,1.0,110,0.3374555588649363,ok +251,1.0,111,0.0,ok +75161,1.0,112,0.0643763314232011,ok +75143,1.0,113,0.014198115211204287,ok +75114,1.0,114,0.034791524265208484,ok +75182,1.0,115,0.1362133930353292,ok +75112,1.0,116,0.13728951252914046,ok +75210,1.0,117,0.0,ok +75205,1.0,118,0.18921026998354384,ok +75090,1.0,119,0.061536201945949776,ok +275,1.0,120,0.04161216611241303,ok +288,1.0,121,0.13019553868760525,ok +75092,1.0,122,0.2245440519048938,ok +3043,1.0,123,0.07785480454457916,ok +75249,1.0,124,0.01118111793203247,ok +75126,1.0,125,0.1172411251462635,ok +75225,1.0,126,0.2863994518670778,ok +75141,1.0,127,0.05566133858694178,ok +75107,1.0,128,0.23498076035212945,ok +75097,1.0,129,0.2826865204707536,ok +80001,1.0,1,0.12100840336134455,ok +80003,1.0,1,0.0923101199242583,ok +80006,1.0,1,0.0945812807881774,ok +80008,1.0,1,0.11688311688311692,ok +80009,1.0,1,0.10555555555555562,ok +80010,1.0,1,0.18831168831168832,ok +80011,1.0,1,0.042063492063492136,ok +80012,1.0,1,0.05057471264367819,ok +80013,1.0,1,0.0,ok +80014,1.0,1,0.04631578947368409,ok +80015,1.0,1,0.10096153846153844,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/f1_macro_multiclass.classification_dense/configurations.csv b/metalearning/metalearning_files/f1_macro_multiclass.classification_dense/configurations.csv new file mode 100755 index 0000000..5953eb1 --- /dev/null +++ b/metalearning/metalearning_files/f1_macro_multiclass.classification_dense/configurations.csv @@ -0,0 +1,141 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:fast_ica:algorithm,preprocessor:fast_ica:fun,preprocessor:fast_ica:n_components,preprocessor:fast_ica:whiten,preprocessor:feature_agglomeration:affinity,preprocessor:feature_agglomeration:linkage,preprocessor:feature_agglomeration:n_clusters,preprocessor:feature_agglomeration:pooling_func,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:pca:keep_variance,preprocessor:pca:whiten,preprocessor:polynomial:degree,preprocessor:polynomial:include_bias,preprocessor:polynomial:interaction_only,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,rescaling:__choice__,rescaling:quantile_transformer:n_quantiles,rescaling:quantile_transformer:output_distribution,rescaling:robust_scaler:q_max,rescaling:robust_scaler:q_min +weighting,one_hot_encoding,0.00011717632475982632,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.045388141846341344,deviance,10.0,0.29161769341843435,None,0.0,20.0,2.0,0.0,278.0,0.7912571599269661,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7249853037185638,None,0.0,1.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,median,extra_trees_preproc_for_classification,False,gini,None,0.9424908623661876,None,0.0,7.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.12713527337147906,deviance,4.0,0.6041596127474019,None,0.0,14.0,17.0,0.0,83.0,0.8426859880999615,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.03474109838999682,deviance,4.0,0.5687034678818491,None,0.0,18.0,12.0,0.0,408.0,0.5150113945430513,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.6328939517938,f_classif,,,,standardize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9265375980300852,None,0.0,13.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.251097788175676,deviance,6.0,0.35679099363539235,None,0.0,13.0,11.0,0.0,157.0,0.4791732272983235,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,adaboost,SAMME,0.28738775989203896,10.0,423.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.8031499675923353,0.13579938270386765 +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.14159526341015916,deviance,7.0,0.8010488230155749,None,0.0,3.0,20.0,0.0,401.0,0.8073562440607731,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.002385546176068135,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.7729472304882845,,,0.0004789329856033374,rbf,-1.0,True,6.58869648864534e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,177.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5368752992317617,None,0.0,16.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.8382117756438685,mutual_info,,,,quantile_transformer,11480.0,normal,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3759438793746945,None,0.0,7.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3689420905780977,fwe,f_classif,quantile_transformer,25061.0,uniform,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.4909422458748719,None,0.0,11.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,mean,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7146659106968425,True,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.12713527337147906,deviance,4.0,0.6041596127474019,None,0.0,14.0,17.0,0.0,83.0,0.8426859880999615,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9455638720565652,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,most_frequent,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8255464552647293,0.19162485555463185 +weighting,one_hot_encoding,0.18137532678800647,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9094110110427254,None,0.0,7.0,12.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,195.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.6682079659377479,None,0.0,4.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,mean,extra_trees_preproc_for_classification,True,entropy,None,0.5552350997943013,None,0.0,8.0,5.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.02345017287074443,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.053517066400173056,deviance,10.0,0.542144980834302,None,0.0,20.0,13.0,0.0,233.0,0.7398539900055563,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0614425536709615,fwe,f_classif,robust_scaler,,,0.9523118062307264,0.13434811490315818 +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.8149627329153046,None,0.0,15.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.040936424602789435,deviance,7.0,0.5495014745530306,None,0.0,20.0,18.0,0.0,141.0,0.6905343807995293,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.9727149851116396,None,0.0,10.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,25.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7974565919616314,None,0.0,12.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,median,extra_trees_preproc_for_classification,True,entropy,None,0.9772091846790169,None,0.0,10.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2398150931290834,,,0.4015139801872962,rbf,-1.0,False,2.402997750662158e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88.40698357592571,chi2,,,,normalize,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.1958974686405233,deviance,5.0,0.33885235607979314,None,0.0,6.0,4.0,0.0,125.0,0.9448890820738562,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0007038280350320558,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4138778052607317,0.7995003430482459,5.0,5.430044692638861,poly,-1.0,True,0.02455501006004393,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,31,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.010000000000000005,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.0518326156691958,deviance,6.0,0.8807456180216267,None,0.0,7.0,19.0,0.0,366.0,0.7314831276137047,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,,False,adaboost,SAMME,0.4391375941344922,3.0,386.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7439738358430176,0.20581080574615795 +none,one_hot_encoding,0.00011294596229850895,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7515.1213255144885,0.9576762936062476,3.0,0.019002536385919932,poly,-1.0,False,0.010632086351533369,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,51.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,97282.0,normal,, +none,one_hot_encoding,0.00012586572428922356,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5240592829918601,None,0.0,10.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.03192699980429505,True,adaboost,SAMME.R,0.10000000000000002,1.0,50.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,quantile_transformer,8407.0,normal,, +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1751.4736515133566,0.62404114475118,3.0,1.6087076997410432,poly,-1.0,False,3.5353792826856045e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1198.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.2422926485206341,,1.0,None,0.0,15.0,9.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.02102683283349326,deviance,10.0,0.2797288369369436,None,0.0,14.0,9.0,0.0,480.0,0.5778972273820631,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58.88123233170863,mutual_info,,,,robust_scaler,,,0.75,0.25 +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828776,None,0.0,2.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2155613360930585,deviance,4.0,0.3198803116198433,None,0.0,8.0,13.0,0.0,275.0,0.28870176110739404,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,mean,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.7062102387181676,None,0.0,1.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,most_frequent,fast_ica,,,,,,,,,,,parallel,exp,100.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7065776353150109,0.23782974987118105 +none,one_hot_encoding,0.16334152321884812,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00011572473434870852,True,True,squared_hinge,0.00019678754114665057,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.045742431094098604,fpr,chi2,none,,,, +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.3795924768593385,deviance,2.0,0.33708497069988536,None,0.0,15.0,13.0,0.0,451.0,0.7716323242090217,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2573946506994812,fwe,f_classif,quantile_transformer,1000.0,uniform,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.609975998293528,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,most_frequent,fast_ica,,,,,,,,,,,parallel,logcosh,2000.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8430415644014919,0.2863750565331575 +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.39536192447534535,None,0.0,19.0,3.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5.0,None,11.0,11.0,1.0,12.0,,,,,,robust_scaler,,,0.8928631650245873,0.1581877760687084 +weighting,one_hot_encoding,0.060155186012325876,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.3163640203509378,None,0.0,16.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,median,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,2.0,None,11.0,20.0,1.0,47.0,,,,,,quantile_transformer,21065.0,uniform,, +weighting,one_hot_encoding,0.3126027672745337,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1100.6211008501205,0.5921425829232616,2.0,0.0337546254878617,poly,-1.0,True,0.09641299736884308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.9348409326933208,rbf,-1.0,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1754.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,198.72528686512538,False,True,1.0,squared_hinge,ovr,l2,0.026260652523566803,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,robust_scaler,,,0.913511520078368,0.2742229325455444 +none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9260795160807372,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.03644212536682547,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.05965671477918361,deviance,8.0,0.4858133247974158,None,0.0,14.0,7.0,0.0,480.0,0.5726186552917335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.15318294164619112 +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18887.81504976871,,,0.23283562663398755,rbf,-1.0,True,2.3839685780861318e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3937607155568376,fwe,chi2,robust_scaler,,,0.941018778984854,0.2144110585080491 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3428971645958825,,,0.2229870623330047,rbf,-1.0,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8138233157708883,None,0.0,2.0,9.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54.27504292568562,f_classif,,,,minmax,,,, +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00016781524591321165,True,True,squared_hinge,1.5119200923218881e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.349459944355116,,,0.00024028983491736645,rbf,-1.0,True,1.139421622432356e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3636266268105085,fwe,chi2,none,,,, +weighting,one_hot_encoding,0.00034835629696198427,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8245132980938538,0.08947420373097192 +weighting,one_hot_encoding,0.00016967940959070708,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9439080311935252,None,0.0,2.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35.0,None,,0.005389483044810356,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,most_frequent,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,9.441661069727422e-05,1896.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.03528169333197684,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3416063836589199,None,0.0,9.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,median,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,normalize,,,, +none,one_hot_encoding,,False,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6396026761675004,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06544340428506021,fwe,f_classif,none,,,, +weighting,one_hot_encoding,0.004980497345831963,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1255.9137433589426,,,0.08351549479967445,rbf,-1.0,True,0.00017919875199222518,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1.0,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.3837398524575939,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.018356703878357986,deviance,3.0,0.9690352514774068,None,0.0,12.0,3.0,0.0,234.0,0.3870344708308441,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,mean,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6524770456279243,False,,,,,,,,,,,,,,,,robust_scaler,,,0.7602889314749132,0.25 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5670424455696162,None,0.0,8.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.00010817282861262362,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.799803680241154,None,0.0,13.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,85,mean,fast_ica,,,,,,,,,,,deflation,exp,1793.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9071932815811076,0.03563842252368924 +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.35533396539961937,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,86,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4165632766388807,fpr,chi2,none,,,, +weighting,one_hot_encoding,,False,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,156.0,auto,,0.0001987333852871589,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1.0,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,no_encoding,,,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.96834823420249e-05,False,True,hinge,0.00016639250831671168,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,89,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.628430584359224,mutual_info,,,,quantile_transformer,6634.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,90,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,91,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,8.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,93,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.9376772805635799,None,0.0,18.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94,median,extra_trees_preproc_for_classification,True,entropy,None,0.8613889689810683,None,0.0,10.0,4.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2636201374253461,deviance,7.0,0.8344964130784466,None,0.0,9.0,2.0,0.0,298.0,0.7517549950523315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,95,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,96,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,97,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.07463196642416367,deviance,7.0,0.8603242247379981,None,0.0,2.0,6.0,0.0,500.0,0.8447665577491962,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,98,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.0433556140045585,deviance,10.0,0.33000096635982235,None,0.0,15.0,13.0,0.0,388.0,0.8291104221904706,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,99,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,robust_scaler,,,0.7496393440951183,0.2853682991120835 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,100,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0009243833519832893,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9829888014816668,None,0.0,18.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,102,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,minmax,,,, +weighting,one_hot_encoding,0.0020580843703898177,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.945774573434192,None,0.0,19.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,103,median,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,15209.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,104,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0506220137415751,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.4433458237042269,None,0.0,11.0,10.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,105,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.8740590455827745,0.19483217552098045 +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,106,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.005332972692819521,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5565918060287016,None,0.0,5.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,107,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,173.0,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9527068489270144,0.04135311355893583 +weighting,one_hot_encoding,0.001279467383882126,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,108,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0903354518102121,fwe,f_classif,standardize,,,, +weighting,one_hot_encoding,,False,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4569434193011598,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,109,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,0.002615346832354839,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7884268823432835,None,0.0,20.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,110,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +weighting,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56.086963007482865,,,0.013609964993119377,rbf,-1.0,True,0.00196831255706268,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,111,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.15374716583918388 +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.2472984547885781,deviance,3.0,0.6564306719064884,None,0.0,15.0,14.0,0.0,220.0,0.8082564085714649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,112,median,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,332.0,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,113,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,115,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,116,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,117,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,118,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.20202014999292287,False,True,1.0,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,no_encoding,,,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6390376923528961,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,119,median,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,164.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,62508.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,120,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.27041927277584e-06,True,0.00010000000000000009,0.033157325660763994,True,0.0008114527992546482,invscaling,modified_huber,elasticnet,0.13714427818877545,0.055179642772545036,,,,,,,,,,,,,,,,,,121,median,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.7879059827470586,None,0.0,3.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,122,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54.1024239468849,f_classif,,,,quantile_transformer,89842.0,normal,, +weighting,one_hot_encoding,0.010000000000000005,True,decision_tree,,,,,,,entropy,1.5841974853345435,,1.0,None,0.0,8.0,14.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,123,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,25.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.19998727075532635,deviance,10.0,0.9377656718112952,None,0.0,7.0,13.0,0.0,214.0,0.6062346326014357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,124,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,125,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.003777272888571546,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4583291745177553,None,0.0,14.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,126,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.27741957612588863,fdr,f_classif,none,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.5765793990908161,None,0.0,11.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,127,median,fast_ica,,,,,,,,,,,deflation,exp,10.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7945458151995424,None,0.0,1.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,129,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,38400.0,uniform,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.04651467280022463,True,adaboost,SAMME,0.6506122230393502,6.0,143.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,extra_trees_preproc_for_classification,False,entropy,None,0.348720215832657,None,0.0,17.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,26025.0,normal,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3386310102795006,None,0.0,6.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,True,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.0,None,,5.861221938384061e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.22985730375167915,fpr,f_classif,quantile_transformer,40589.0,uniform,, +none,no_encoding,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00012437731009611307,True,,0.08709904468181932,True,,invscaling,squared_hinge,l1,0.6231564675290102,0.008071279833697563,,,,,,,,,,,,,,,,,,134,median,fast_ica,,,,,,,,,,,parallel,logcosh,814.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.714869937384006,None,0.0,8.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, diff --git a/metalearning/metalearning_files/f1_macro_multiclass.classification_dense/description.txt b/metalearning/metalearning_files/f1_macro_multiclass.classification_dense/description.txt new file mode 100755 index 0000000..8f5411e --- /dev/null +++ b/metalearning/metalearning_files/f1_macro_multiclass.classification_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: f1_macro +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/f1_macro_multiclass.classification_dense/feature_costs.arff b/metalearning/metalearning_files/f1_macro_multiclass.classification_dense/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/f1_macro_multiclass.classification_dense/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/f1_macro_multiclass.classification_dense/feature_runstatus.arff b/metalearning/metalearning_files/f1_macro_multiclass.classification_dense/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/f1_macro_multiclass.classification_dense/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/f1_macro_multiclass.classification_dense/feature_values.arff b/metalearning/metalearning_files/f1_macro_multiclass.classification_dense/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/f1_macro_multiclass.classification_dense/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/f1_macro_multiclass.classification_dense/readme.txt b/metalearning/metalearning_files/f1_macro_multiclass.classification_dense/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/f1_macro_multiclass.classification_sparse/algorithm_runs.arff b/metalearning/metalearning_files/f1_macro_multiclass.classification_sparse/algorithm_runs.arff new file mode 100755 index 0000000..b26f000 --- /dev/null +++ b/metalearning/metalearning_files/f1_macro_multiclass.classification_sparse/algorithm_runs.arff @@ -0,0 +1,152 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE f1_macro NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2120,1.0,1,0.11408447810374378,ok +75193,1.0,2,0.07053913770243558,ok +2117,1.0,3,0.22679879319205365,ok +75156,1.0,4,0.22238084663134627,ok +75129,1.0,5,0.3260625370177481,ok +75243,1.0,6,0.034148845248020865,ok +75110,1.0,7,0.27745880194558303,ok +75239,1.0,8,0.0,ok +75223,1.0,9,0.34253647958285205,ok +75221,1.0,10,0.5040889236894779,ok +258,1.0,11,0.01806999455734748,ok +75121,1.0,12,0.02483411397345825,ok +253,1.0,13,0.46022536370557854,ok +261,1.0,14,0.32176368540350253,ok +75240,1.0,15,0.023175600360006987,ok +75120,1.0,16,0.3187511182680265,ok +75124,1.0,17,0.30830132152112777,ok +75176,1.0,18,0.016832413445363903,ok +75103,1.0,19,0.033004852648699456,ok +75207,1.0,20,0.19167470561705713,ok +75095,1.0,21,0.09504043828172026,ok +273,1.0,22,0.047372015155520364,ok +75174,1.0,23,0.14498779364354286,ok +75153,1.0,24,0.12134945880394854,ok +75093,1.0,25,0.382260536424438,ok +75119,1.0,26,0.17006979506979514,ok +75201,1.0,27,0.1016688555093509,ok +75215,1.0,28,0.028777421414557525,ok +75172,1.0,29,0.09039624333028018,ok +75169,1.0,30,0.03400551137758434,ok +75202,1.0,31,0.3975374941597515,ok +75233,1.0,32,0.07784068224806762,ok +75231,1.0,33,0.1934670319360723,ok +75196,1.0,34,0.029789219880220874,ok +248,1.0,35,0.27213781889621136,ok +75191,1.0,36,0.12398891920870292,ok +75217,1.0,37,0.0,ok +260,1.0,38,0.16748288897263175,ok +75115,1.0,39,0.06477200424178153,ok +75123,1.0,40,0.324936422863935,ok +75108,1.0,41,0.0035628598455712535,ok +75101,1.0,42,0.28362109608195185,ok +75192,1.0,43,0.48369309402485317,ok +75232,1.0,44,0.16747346072186842,ok +75173,1.0,45,0.11752222902137532,ok +75197,1.0,46,0.19706797497393058,ok +266,1.0,47,0.03145621309838542,ok +75148,1.0,48,0.1903482599383457,ok +75150,1.0,49,0.32050017611835147,ok +75100,1.0,50,0.5009508285791905,ok +75178,1.0,51,0.8084431154999884,ok +75236,1.0,52,0.03264671942247066,ok +75179,1.0,53,0.2207044139691714,ok +75213,1.0,54,0.07330306295213163,ok +2123,1.0,55,0.3110222521987227,ok +75227,1.0,56,0.12291620089177402,ok +75184,1.0,57,0.17621306838361817,ok +75142,1.0,58,0.0813267307281782,ok +236,1.0,59,0.04214387679881482,ok +2122,1.0,60,0.27745880194558303,ok +75188,1.0,61,0.4478290859210454,ok +75166,1.0,62,0.0995858413881675,ok +75181,1.0,63,0.0,ok +75133,1.0,64,0.25128741551335687,ok +75134,1.0,65,0.17624410373174515,ok +75198,1.0,66,0.12304795088323683,ok +262,1.0,67,0.0027254798407814196,ok +75234,1.0,68,0.059861302142657724,ok +75139,1.0,69,0.013690721710313158,ok +252,1.0,70,0.16633663122289843,ok +75117,1.0,71,0.18851851851851853,ok +75113,1.0,72,0.028881275695263886,ok +75098,1.0,73,0.027741174038947825,ok +246,1.0,74,0.024495160481337708,ok +75203,1.0,75,0.10817708318810615,ok +75237,1.0,76,0.0006037948926751469,ok +75195,1.0,77,0.0016179413547929844,ok +75171,1.0,78,0.16723591159857998,ok +75128,1.0,79,0.04952813994827465,ok +75096,1.0,80,0.6993134761188856,ok +75250,1.0,81,0.3922262829556027,ok +75146,1.0,82,0.12512843908084614,ok +75116,1.0,83,0.018261399921070565,ok +75157,1.0,84,0.44392303716489667,ok +75187,1.0,85,0.02786398904609766,ok +2350,1.0,86,0.4528595252099479,ok +242,1.0,87,0.0165453764866339,ok +244,1.0,88,0.10946429095173504,ok +75125,1.0,89,0.06021609968978381,ok +75185,1.0,90,0.1286982096968351,ok +75163,1.0,91,0.06069121519809906,ok +75177,1.0,92,0.07745711528283672,ok +75189,1.0,93,0.021791341807692488,ok +75244,1.0,94,0.40109883346395137,ok +75219,1.0,95,0.08499462554056092,ok +75222,1.0,96,0.15120415982484947,ok +75159,1.0,97,0.2842590573502384,ok +75175,1.0,98,0.1225718588213065,ok +75109,1.0,99,0.3964626746304919,ok +254,1.0,100,0.0,ok +75105,1.0,101,0.4471433966694359,ok +75106,1.0,102,0.43728243375753073,ok +75212,1.0,103,0.27739530637860854,ok +75099,1.0,104,0.3070544961367747,ok +75248,1.0,105,0.4099296083174433,ok +233,1.0,106,0.01525546388768273,ok +75235,1.0,107,0.0010593648299854763,ok +75226,1.0,108,0.008162345862954501,ok +75132,1.0,109,0.473419477760615,ok +75127,1.0,110,0.33879906227331147,ok +251,1.0,111,0.0759050854617499,ok +75161,1.0,112,0.0828102721449141,ok +75143,1.0,113,0.014198115211204287,ok +75114,1.0,114,0.034791524265208484,ok +75182,1.0,115,0.1362133930353292,ok +75112,1.0,116,0.13728951252914046,ok +75210,1.0,117,0.0,ok +75205,1.0,118,0.18921026998354384,ok +75090,1.0,119,0.10304792476768632,ok +275,1.0,120,0.04161216611241303,ok +288,1.0,121,0.1443862359478315,ok +75092,1.0,122,0.22609454740800428,ok +3043,1.0,123,0.08023590905221001,ok +75249,1.0,124,0.016771676898048704,ok +75126,1.0,125,0.1172411251462635,ok +75225,1.0,126,0.3105943894863552,ok +75141,1.0,127,0.060141500910758205,ok +75107,1.0,128,0.23498076035212945,ok +75097,1.0,129,0.47431066312883763,ok +80001,1.0,1,0.12100840336134455,ok +80003,1.0,1,0.12308015989901122,ok +80006,1.0,1,0.19433198380566807,ok +80008,1.0,1,0.2250000000000001,ok +80009,1.0,1,0.1578947368421052,ok +80010,1.0,1,0.18831168831168832,ok +80011,1.0,1,0.042063492063492136,ok +80012,1.0,1,0.05057471264367819,ok +80013,1.0,1,0.0,ok +80014,1.0,1,0.04631578947368409,ok +80015,1.0,1,0.10096153846153844,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/f1_macro_multiclass.classification_sparse/configurations.csv b/metalearning/metalearning_files/f1_macro_multiclass.classification_sparse/configurations.csv new file mode 100755 index 0000000..80cd140 --- /dev/null +++ b/metalearning/metalearning_files/f1_macro_multiclass.classification_sparse/configurations.csv @@ -0,0 +1,141 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,preprocessor:truncatedSVD:target_dim,rescaling:__choice__ +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7249853037185638,None,0.0,1.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,median,extra_trees_preproc_for_classification,False,gini,None,0.9424908623661876,None,0.0,7.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.585711203872775,None,0.0,5.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,median,extra_trees_preproc_for_classification,True,entropy,None,0.29512530534048065,None,0.0,7.0,10.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.003005033889617757,True,adaboost,SAMME.R,0.09171378528184512,5.0,97.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,33.361778338113126,False,True,1.0,squared_hinge,ovr,l1,0.0018992130480287718,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.6682079659377479,None,0.0,4.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,mean,extra_trees_preproc_for_classification,True,entropy,None,0.5552350997943013,None,0.0,8.0,5.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7974565919616314,None,0.0,12.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,median,extra_trees_preproc_for_classification,True,entropy,None,0.9772091846790169,None,0.0,10.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2398150931290834,,,0.4015139801872962,rbf,-1.0,False,2.402997750662158e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88.40698357592571,chi2,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.034465366914659866,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.12057775675278172,deviance,10.0,0.8011153303489733,None,0.0,2.0,16.0,0.0,370.0,0.6078295352200873,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,mean,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0007038280350320558,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4138778052607317,0.7995003430482459,5.0,5.430044692638861,poly,-1.0,True,0.02455501006004393,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,31,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.0010015637584068037,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.037611630308856295,deviance,5.0,0.8840126779516314,None,0.0,10.0,2.0,0.0,444.0,0.7599997167603434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,most_frequent,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1751.4736515133566,0.62404114475118,3.0,1.6087076997410432,poly,-1.0,False,3.5353792826856045e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,mean,kernel_pca,,,,,,,,,,,,,,cosine,1198.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.8657388713119849,,1.0,None,0.0,19.0,13.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828776,None,0.0,2.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.027324741616523342,deviance,10.0,0.8623781459430139,None,0.0,10.0,20.0,0.0,329.0,0.8595750155424215,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,mean,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1100.6211008501205,0.5921425829232616,2.0,0.0337546254878617,poly,-1.0,True,0.09641299736884308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.9348409326933208,rbf,-1.0,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,mean,kernel_pca,,,,,,,,,,,,,,cosine,1754.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3428971645958825,,,0.2229870623330047,rbf,-1.0,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7322410244259964,None,0.0,6.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,median,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00016781524591321165,True,True,squared_hinge,1.5119200923218881e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.349459944355116,,,0.00024028983491736645,rbf,-1.0,True,1.139421622432356e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3636266268105085,fwe,chi2,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4047.618729304337,,,2.0237366768707754,rbf,-1.0,True,0.04369127828878843,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,one_hot_encoding,0.010000000000000005,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10.0,1.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,,none +weighting,one_hot_encoding,0.0008685602750053468,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9896334290292654,None,0.0,11.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,141.76310800864283,False,True,1.0,squared_hinge,ovr,l1,0.004317884655117431,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,,normalize +none,one_hot_encoding,0.003443779683191071,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.004980497345831963,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1255.9137433589426,,,0.08351549479967445,rbf,-1.0,True,0.00017919875199222518,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1.0,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.3451627750042988,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.3163640203509378,None,0.0,17.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,median,extra_trees_preproc_for_classification,False,gini,None,0.8916956785028156,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,85,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.35533396539961937,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,86,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4165632766388807,fpr,chi2,,none +weighting,one_hot_encoding,0.0019686649916896212,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.04641055832142541,True,True,hinge,8.540468968077405e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87,mean,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,911.0,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1.0,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,89,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,90,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,91,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,93,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.9376772805635799,None,0.0,18.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94,median,extra_trees_preproc_for_classification,True,entropy,None,0.8613889689810683,None,0.0,10.0,4.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,95,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,96,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,97,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,98,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,99,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,100,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.039533063907190934,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,102,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1878805519245509,fdr,chi2,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,103,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,104,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,105,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,106,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.006372860318416312,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6467376360604045,None,0.0,1.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,107,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,108,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,109,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3823734947460288,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,110,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,adaboost,SAMME,0.11042308042695524,5.0,117.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,111,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,112,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,113,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,115,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,116,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,117,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,118,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.20202014999292287,False,True,1.0,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,119,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,120,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,121,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9930358173100328,None,0.0,6.0,3.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,122,most_frequent,extra_trees_preproc_for_classification,False,entropy,None,0.4655848826291386,None,0.0,13.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,123,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,124,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,125,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.039533063907190934,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,126,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1878805519245509,fdr,chi2,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,127,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,129,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.04329010470998136,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7260331062271381,None,0.0,7.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.010000000000000005,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15.0,1.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,134,median,extra_trees_preproc_for_classification,False,entropy,None,0.8213051377774989,None,0.0,3.0,13.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.714869937384006,None,0.0,8.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/metalearning/metalearning_files/f1_macro_multiclass.classification_sparse/description.txt b/metalearning/metalearning_files/f1_macro_multiclass.classification_sparse/description.txt new file mode 100755 index 0000000..8f5411e --- /dev/null +++ b/metalearning/metalearning_files/f1_macro_multiclass.classification_sparse/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: f1_macro +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/f1_macro_multiclass.classification_sparse/feature_costs.arff b/metalearning/metalearning_files/f1_macro_multiclass.classification_sparse/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/f1_macro_multiclass.classification_sparse/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/f1_macro_multiclass.classification_sparse/feature_runstatus.arff b/metalearning/metalearning_files/f1_macro_multiclass.classification_sparse/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/f1_macro_multiclass.classification_sparse/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/f1_macro_multiclass.classification_sparse/feature_values.arff b/metalearning/metalearning_files/f1_macro_multiclass.classification_sparse/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/f1_macro_multiclass.classification_sparse/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/f1_macro_multiclass.classification_sparse/readme.txt b/metalearning/metalearning_files/f1_macro_multiclass.classification_sparse/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/f1_micro_binary.classification_dense/algorithm_runs.arff b/metalearning/metalearning_files/f1_micro_binary.classification_dense/algorithm_runs.arff new file mode 100755 index 0000000..635502d --- /dev/null +++ b/metalearning/metalearning_files/f1_micro_binary.classification_dense/algorithm_runs.arff @@ -0,0 +1,152 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE f1_micro NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2120,1.0,1,0.07967939651107969,ok +75193,1.0,2,0.038371068099909755,ok +2117,1.0,3,0.16709064962461995,ok +75156,1.0,4,0.20291026677445445,ok +75129,1.0,5,0.10097087378640779,ok +75243,1.0,6,0.0,ok +75110,1.0,7,0.11622380643767549,ok +75239,1.0,8,0.0,ok +75223,1.0,9,0.10304601425793913,ok +75221,1.0,10,0.39791873141724476,ok +258,1.0,11,0.009708737864077666,ok +75121,1.0,12,0.0,ok +253,1.0,13,0.44855967078189296,ok +261,1.0,14,0.23333333333333328,ok +75240,1.0,15,0.022020725388601003,ok +75120,1.0,16,0.03929273084479368,ok +75124,1.0,17,0.09121395036888003,ok +75176,1.0,18,0.01541623843782114,ok +75103,1.0,19,0.005894736842105286,ok +75207,1.0,20,0.16184971098265888,ok +75095,1.0,21,0.016917293233082664,ok +273,1.0,22,0.04413702239789197,ok +75174,1.0,23,0.11705240755520085,ok +75153,1.0,24,0.08028116907140215,ok +75093,1.0,25,0.17483296213808464,ok +75119,1.0,26,0.03536345776031424,ok +75201,1.0,27,0.0808678500986193,ok +75215,1.0,28,0.027412280701754388,ok +75172,1.0,29,0.10303030303030303,ok +75169,1.0,30,0.03420132141469101,ok +75202,1.0,31,0.20329670329670335,ok +75233,1.0,32,0.060673325934147204,ok +75231,1.0,33,0.19924098671726764,ok +75196,1.0,34,0.015665796344647487,ok +248,1.0,35,0.22878787878787876,ok +75191,1.0,36,0.1289905886694962,ok +75217,1.0,37,0.0,ok +260,1.0,38,0.02657807308970095,ok +75115,1.0,39,0.017681728880157177,ok +75123,1.0,40,0.32728592162554426,ok +75108,1.0,41,0.0,ok +75101,1.0,42,0.2740140932130053,ok +75192,1.0,43,0.48305752561071713,ok +75232,1.0,44,0.12356321839080464,ok +75173,1.0,45,0.1165605095541401,ok +75197,1.0,46,0.15517241379310331,ok +266,1.0,47,0.019685039370078594,ok +75148,1.0,48,0.13560975609756099,ok +75150,1.0,49,0.2848664688427299,ok +75100,1.0,50,0.00379609544468551,ok +75178,1.0,51,0.7840550682597786,ok +75236,1.0,52,0.0323809523809524,ok +75179,1.0,53,0.17943026267110618,ok +75213,1.0,54,0.05249343832021003,ok +2123,1.0,55,0.05882352941176472,ok +75227,1.0,56,0.10151430173864273,ok +75184,1.0,57,0.10772320613474529,ok +75142,1.0,58,0.0715825466438712,ok +236,1.0,59,0.038787878787878816,ok +2122,1.0,60,0.10952689565780949,ok +75188,1.0,61,0.24319066147859925,ok +75166,1.0,62,0.0995190529041805,ok +75181,1.0,63,0.0,ok +75133,1.0,64,0.005123278898495065,ok +75134,1.0,65,0.08723783614874181,ok +75198,1.0,66,0.12143310082435,ok +262,1.0,67,0.0027570995312931057,ok +75234,1.0,68,0.024160524160524166,ok +75139,1.0,69,0.010707070707070665,ok +252,1.0,70,0.15000000000000002,ok +75117,1.0,71,0.05500982318271119,ok +75113,1.0,72,0.006526315789473713,ok +75098,1.0,73,0.027575757575757587,ok +246,1.0,74,0.010606060606060619,ok +75203,1.0,75,0.09555345316934716,ok +75237,1.0,76,0.00039570658356824495,ok +75195,1.0,77,0.0015609901137292326,ok +75171,1.0,78,0.1620421753607103,ok +75128,1.0,79,0.02218114602587795,ok +75096,1.0,80,0.005164788382626018,ok +75250,1.0,81,0.34347287891393896,ok +75146,1.0,82,0.11329072074057744,ok +75116,1.0,83,0.00982318271119842,ok +75157,1.0,84,0.4192200557103064,ok +75187,1.0,85,0.01678951678951679,ok +2350,1.0,86,0.3744580607974338,ok +242,1.0,87,0.010606060606060619,ok +244,1.0,88,0.1106060606060606,ok +75125,1.0,89,0.03339882121807469,ok +75185,1.0,90,0.12816966343937297,ok +75163,1.0,91,0.060374149659863985,ok +75177,1.0,92,0.019292604501607746,ok +75189,1.0,93,0.019401337253296624,ok +75244,1.0,94,0.06339958875942431,ok +75219,1.0,95,0.03479668217681575,ok +75222,1.0,96,0.04524886877828049,ok +75159,1.0,97,0.06849315068493156,ok +75175,1.0,98,0.099544853912788,ok +75109,1.0,99,0.30417434008594224,ok +254,1.0,100,0.0,ok +75105,1.0,101,0.018121212121212094,ok +75106,1.0,102,0.07212121212121225,ok +75212,1.0,103,0.2517482517482518,ok +75099,1.0,104,0.12374100719424463,ok +75248,1.0,105,0.10040485829959511,ok +233,1.0,106,0.002846299810246644,ok +75235,1.0,107,0.0011111111111110628,ok +75226,1.0,108,0.0030422878004259246,ok +75132,1.0,109,0.051244509516837455,ok +75127,1.0,110,0.3330355738331199,ok +251,1.0,111,0.0,ok +75161,1.0,112,0.06437225897569321,ok +75143,1.0,113,0.010471204188481686,ok +75114,1.0,114,0.023575638506876273,ok +75182,1.0,115,0.1081404628890662,ok +75112,1.0,116,0.12061822817080947,ok +75210,1.0,117,0.0,ok +75205,1.0,118,0.18110236220472442,ok +75090,1.0,119,0.06118881118881114,ok +275,1.0,120,0.03612167300380231,ok +288,1.0,121,0.12969696969696964,ok +75092,1.0,122,0.0935550935550935,ok +3043,1.0,123,0.020096463022508004,ok +75249,1.0,124,0.003215434083601254,ok +75126,1.0,125,0.04911591355599221,ok +75225,1.0,126,0.04904306220095689,ok +75141,1.0,127,0.0540140584535701,ok +75107,1.0,128,0.053212121212121266,ok +75097,1.0,129,0.05835568297419769,ok +80001,1.0,1,0.06944444444444442,ok +80003,1.0,1,0.09230769230769242,ok +80006,1.0,1,0.09375,ok +80008,1.0,1,0.11111111111111116,ok +80009,1.0,1,0.10526315789473684,ok +80010,1.0,1,0.13793103448275867,ok +80011,1.0,1,0.037735849056603765,ok +80012,1.0,1,0.045454545454545414,ok +80013,1.0,1,0.0,ok +80014,1.0,1,0.045454545454545414,ok +80015,1.0,1,0.09523809523809523,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/f1_micro_binary.classification_dense/configurations.csv b/metalearning/metalearning_files/f1_micro_binary.classification_dense/configurations.csv new file mode 100755 index 0000000..e710c68 --- /dev/null +++ b/metalearning/metalearning_files/f1_micro_binary.classification_dense/configurations.csv @@ -0,0 +1,141 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:fast_ica:algorithm,preprocessor:fast_ica:fun,preprocessor:fast_ica:n_components,preprocessor:fast_ica:whiten,preprocessor:feature_agglomeration:affinity,preprocessor:feature_agglomeration:linkage,preprocessor:feature_agglomeration:n_clusters,preprocessor:feature_agglomeration:pooling_func,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:pca:keep_variance,preprocessor:pca:whiten,preprocessor:polynomial:degree,preprocessor:polynomial:include_bias,preprocessor:polynomial:interaction_only,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,rescaling:__choice__,rescaling:quantile_transformer:n_quantiles,rescaling:quantile_transformer:output_distribution,rescaling:robust_scaler:q_max,rescaling:robust_scaler:q_min +weighting,one_hot_encoding,0.00011717632475982632,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.045388141846341344,deviance,10.0,0.29161769341843435,None,0.0,20.0,2.0,0.0,278.0,0.7912571599269661,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7249853037185638,None,0.0,1.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,median,extra_trees_preproc_for_classification,False,gini,None,0.9424908623661876,None,0.0,7.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.03474109838999682,deviance,4.0,0.5687034678818491,None,0.0,18.0,12.0,0.0,408.0,0.5150113945430513,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.6328939517938,f_classif,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.251097788175676,deviance,6.0,0.35679099363539235,None,0.0,13.0,11.0,0.0,157.0,0.4791732272983235,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,adaboost,SAMME,0.28738775989203896,10.0,423.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.8031499675923353,0.13579938270386765 +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.14159526341015916,deviance,7.0,0.8010488230155749,None,0.0,3.0,20.0,0.0,401.0,0.8073562440607731,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.002385546176068135,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.7729472304882845,,,0.0004789329856033374,rbf,-1.0,True,6.58869648864534e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,177.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5368752992317617,None,0.0,16.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.8382117756438685,mutual_info,,,,quantile_transformer,11480.0,normal,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9455638720565652,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,most_frequent,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8255464552647293,0.19162485555463185 +weighting,one_hot_encoding,0.18137532678800647,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9094110110427254,None,0.0,7.0,12.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,195.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.6682079659377479,None,0.0,4.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,mean,extra_trees_preproc_for_classification,True,entropy,None,0.5552350997943013,None,0.0,8.0,5.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.02345017287074443,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.053517066400173056,deviance,10.0,0.542144980834302,None,0.0,20.0,13.0,0.0,233.0,0.7398539900055563,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0614425536709615,fwe,f_classif,robust_scaler,,,0.9523118062307264,0.13434811490315818 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.040936424602789435,deviance,7.0,0.5495014745530306,None,0.0,20.0,18.0,0.0,141.0,0.6905343807995293,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7974565919616314,None,0.0,12.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,median,extra_trees_preproc_for_classification,True,entropy,None,0.9772091846790169,None,0.0,10.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2538107344750156,False,True,hinge,1.5099542326343014e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,8532.0,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.1958974686405233,deviance,5.0,0.33885235607979314,None,0.0,6.0,4.0,0.0,125.0,0.9448890820738562,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2538107344750156,False,True,hinge,1.5099542326343014e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,8532.0,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,0.0007038280350320558,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4138778052607317,0.7995003430482459,5.0,5.430044692638861,poly,-1.0,True,0.02455501006004393,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,31,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.010000000000000005,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.0518326156691958,deviance,6.0,0.8807456180216267,None,0.0,7.0,19.0,0.0,366.0,0.7314831276137047,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,,False,adaboost,SAMME,0.4391375941344922,3.0,386.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7439738358430176,0.20581080574615795 +none,one_hot_encoding,0.00011294596229850895,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7515.1213255144885,0.9576762936062476,3.0,0.019002536385919932,poly,-1.0,False,0.010632086351533369,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,51.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,97282.0,normal,, +none,one_hot_encoding,0.00012586572428922356,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5240592829918601,None,0.0,10.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1751.4736515133566,0.62404114475118,3.0,1.6087076997410432,poly,-1.0,False,3.5353792826856045e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1198.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.2422926485206341,,1.0,None,0.0,15.0,9.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.02102683283349326,deviance,10.0,0.2797288369369436,None,0.0,14.0,9.0,0.0,480.0,0.5778972273820631,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58.88123233170863,mutual_info,,,,robust_scaler,,,0.75,0.25 +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828776,None,0.0,2.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2155613360930585,deviance,4.0,0.3198803116198433,None,0.0,8.0,13.0,0.0,275.0,0.28870176110739404,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,mean,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.7062102387181676,None,0.0,1.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,most_frequent,fast_ica,,,,,,,,,,,parallel,exp,100.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7065776353150109,0.23782974987118105 +none,one_hot_encoding,0.16334152321884812,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00011572473434870852,True,True,squared_hinge,0.00019678754114665057,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.045742431094098604,fpr,chi2,none,,,, +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.3795924768593385,deviance,2.0,0.33708497069988536,None,0.0,15.0,13.0,0.0,451.0,0.7716323242090217,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2573946506994812,fwe,f_classif,quantile_transformer,1000.0,uniform,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.609975998293528,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,most_frequent,fast_ica,,,,,,,,,,,parallel,logcosh,2000.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8430415644014919,0.2863750565331575 +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.39536192447534535,None,0.0,19.0,3.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5.0,None,11.0,11.0,1.0,12.0,,,,,,robust_scaler,,,0.8928631650245873,0.1581877760687084 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.3126027672745337,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,870.2240970463429,0.5325949351918051,3.0,0.010682839357128344,poly,-1.0,False,2.485160860440657e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4608103694360143,fdr,f_classif,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.9348409326933208,rbf,-1.0,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1754.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,198.72528686512538,False,True,1.0,squared_hinge,ovr,l2,0.026260652523566803,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,robust_scaler,,,0.913511520078368,0.2742229325455444 +none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9260795160807372,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.03644212536682547,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.05965671477918361,deviance,8.0,0.4858133247974158,None,0.0,14.0,7.0,0.0,480.0,0.5726186552917335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.15318294164619112 +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18887.81504976871,,,0.23283562663398755,rbf,-1.0,True,2.3839685780861318e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3937607155568376,fwe,chi2,robust_scaler,,,0.941018778984854,0.2144110585080491 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3428971645958825,,,0.2229870623330047,rbf,-1.0,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8138233157708883,None,0.0,2.0,9.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54.27504292568562,f_classif,,,,minmax,,,, +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00016781524591321165,True,True,squared_hinge,1.5119200923218881e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.556190405302504,False,True,1.0,squared_hinge,ovr,l2,0.0007318628304090552,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,most_frequent,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,3.5602014542183973,948.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +weighting,one_hot_encoding,0.00034835629696198427,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8245132980938538,0.08947420373097192 +weighting,one_hot_encoding,0.00016967940959070708,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9439080311935252,None,0.0,2.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,170.0,None,,0.014191958374153584,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,,False,adaboost,SAMME.R,0.8220362681234727,5.0,183.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.363262678765884,fdr,chi2,robust_scaler,,,0.8826612080363588,0.2189605310171097 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,normalize,,,, +none,one_hot_encoding,,False,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6396026761675004,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06544340428506021,fwe,f_classif,none,,,, +weighting,one_hot_encoding,0.004980497345831963,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1255.9137433589426,,,0.08351549479967445,rbf,-1.0,True,0.00017919875199222518,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1.0,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.3837398524575939,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.018356703878357986,deviance,3.0,0.9690352514774068,None,0.0,12.0,3.0,0.0,234.0,0.3870344708308441,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,most_frequent,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6864970915330799,False,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5670424455696162,None,0.0,8.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,standardize,,,, +weighting,one_hot_encoding,0.00031737236118003484,True,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,244.0,None,,2.3065111488706024e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,85,mean,fast_ica,,,,,,,,,,,deflation,exp,1862.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7851234479882973,0.2237528085136715 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,86,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,156.0,auto,,0.0001987333852871589,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1.0,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,no_encoding,,,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.96834823420249e-05,False,True,hinge,0.00016639250831671168,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,89,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.628430584359224,mutual_info,,,,quantile_transformer,6634.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,90,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,91,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,93,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2636201374253461,deviance,7.0,0.8344964130784466,None,0.0,9.0,2.0,0.0,298.0,0.7517549950523315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,95,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,96,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,97,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.07463196642416367,deviance,7.0,0.8603242247379981,None,0.0,2.0,6.0,0.0,500.0,0.8447665577491962,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,98,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.0433556140045585,deviance,10.0,0.33000096635982235,None,0.0,15.0,13.0,0.0,388.0,0.8291104221904706,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,99,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,robust_scaler,,,0.7496393440951183,0.2853682991120835 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,100,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,102,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0020580843703898177,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.945774573434192,None,0.0,19.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,103,median,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,15209.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,104,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,105,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,106,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.005332972692819521,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5565918060287016,None,0.0,5.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,107,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,173.0,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9527068489270144,0.04135311355893583 +weighting,one_hot_encoding,0.001279467383882126,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,108,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0903354518102121,fwe,f_classif,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,109,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.002615346832354839,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7884268823432835,None,0.0,20.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,110,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +weighting,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56.086963007482865,,,0.013609964993119377,rbf,-1.0,True,0.00196831255706268,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,111,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.15374716583918388 +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.2472984547885781,deviance,3.0,0.6564306719064884,None,0.0,15.0,14.0,0.0,220.0,0.8082564085714649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,112,median,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,332.0,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,113,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,115,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,116,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,117,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,118,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.20202014999292287,False,True,1.0,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,no_encoding,,,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6390376923528961,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,119,median,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,164.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,62508.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,120,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.27041927277584e-06,True,0.00010000000000000009,0.033157325660763994,True,0.0008114527992546482,invscaling,modified_huber,elasticnet,0.13714427818877545,0.055179642772545036,,,,,,,,,,,,,,,,,,121,median,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,122,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,123,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.19998727075532635,deviance,10.0,0.9377656718112952,None,0.0,7.0,13.0,0.0,214.0,0.6062346326014357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,124,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,125,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,126,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.5765793990908161,None,0.0,11.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,127,median,fast_ica,,,,,,,,,,,deflation,exp,10.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,129,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.04651467280022463,True,adaboost,SAMME,0.6506122230393502,6.0,143.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,extra_trees_preproc_for_classification,False,entropy,None,0.348720215832657,None,0.0,17.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,26025.0,normal,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3386310102795006,None,0.0,6.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,True,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.0,None,,5.861221938384061e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.22985730375167915,fpr,f_classif,quantile_transformer,40589.0,uniform,, +none,no_encoding,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00012437731009611307,True,,0.08709904468181932,True,,invscaling,squared_hinge,l1,0.6231564675290102,0.008071279833697563,,,,,,,,,,,,,,,,,,134,median,fast_ica,,,,,,,,,,,parallel,logcosh,814.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.714869937384006,None,0.0,8.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, diff --git a/metalearning/metalearning_files/f1_micro_binary.classification_dense/description.txt b/metalearning/metalearning_files/f1_micro_binary.classification_dense/description.txt new file mode 100755 index 0000000..563e881 --- /dev/null +++ b/metalearning/metalearning_files/f1_micro_binary.classification_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: f1_micro +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/f1_micro_binary.classification_dense/feature_costs.arff b/metalearning/metalearning_files/f1_micro_binary.classification_dense/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/f1_micro_binary.classification_dense/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/f1_micro_binary.classification_dense/feature_runstatus.arff b/metalearning/metalearning_files/f1_micro_binary.classification_dense/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/f1_micro_binary.classification_dense/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/f1_micro_binary.classification_dense/feature_values.arff b/metalearning/metalearning_files/f1_micro_binary.classification_dense/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/f1_micro_binary.classification_dense/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/f1_micro_binary.classification_dense/readme.txt b/metalearning/metalearning_files/f1_micro_binary.classification_dense/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/f1_micro_binary.classification_sparse/algorithm_runs.arff b/metalearning/metalearning_files/f1_micro_binary.classification_sparse/algorithm_runs.arff new file mode 100755 index 0000000..0b76f96 --- /dev/null +++ b/metalearning/metalearning_files/f1_micro_binary.classification_sparse/algorithm_runs.arff @@ -0,0 +1,152 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE f1_micro NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2120,1.0,1,0.0895803866100896,ok +75193,1.0,2,0.038371068099909755,ok +2117,1.0,3,0.16709064962461995,ok +75156,1.0,4,0.21988682295877138,ok +75129,1.0,5,0.10097087378640779,ok +75243,1.0,6,0.015434985968194592,ok +75110,1.0,7,0.2743573125945129,ok +75239,1.0,8,0.0,ok +75223,1.0,9,0.30989414560380213,ok +75221,1.0,10,0.39791873141724476,ok +258,1.0,11,0.01833872707659112,ok +75121,1.0,12,0.003929273084479323,ok +253,1.0,13,0.44855967078189296,ok +261,1.0,14,0.23333333333333328,ok +75240,1.0,15,0.022020725388601003,ok +75120,1.0,16,0.03929273084479368,ok +75124,1.0,17,0.09121395036888003,ok +75176,1.0,18,0.016590808985464722,ok +75103,1.0,19,0.008000000000000007,ok +75207,1.0,20,0.16184971098265888,ok +75095,1.0,21,0.016917293233082664,ok +273,1.0,22,0.044795783926218746,ok +75174,1.0,23,0.11705240755520085,ok +75153,1.0,24,0.12134665186829452,ok +75093,1.0,25,0.17483296213808464,ok +75119,1.0,26,0.03536345776031424,ok +75201,1.0,27,0.0808678500986193,ok +75215,1.0,28,0.028234649122807043,ok +75172,1.0,29,0.09090909090909094,ok +75169,1.0,30,0.03420132141469101,ok +75202,1.0,31,0.20329670329670335,ok +75233,1.0,32,0.06511283758786535,ok +75231,1.0,33,0.19924098671726764,ok +75196,1.0,34,0.023498694516971286,ok +248,1.0,35,0.26515151515151525,ok +75191,1.0,36,0.12370055975887306,ok +75217,1.0,37,0.0,ok +260,1.0,38,0.02657807308970095,ok +75115,1.0,39,0.017681728880157177,ok +75123,1.0,40,0.32728592162554426,ok +75108,1.0,41,0.0018373909049149706,ok +75101,1.0,42,0.28272963283471386,ok +75192,1.0,43,0.48305752561071713,ok +75232,1.0,44,0.14655172413793105,ok +75173,1.0,45,0.11751592356687901,ok +75197,1.0,46,0.13300492610837433,ok +266,1.0,47,0.03280839895013121,ok +75148,1.0,48,0.19024390243902445,ok +75150,1.0,49,0.3204747774480712,ok +75100,1.0,50,0.00379609544468551,ok +75178,1.0,51,0.8079287243594171,ok +75236,1.0,52,0.0323809523809524,ok +75179,1.0,53,0.17943026267110618,ok +75213,1.0,54,0.05249343832021003,ok +2123,1.0,55,0.05882352941176472,ok +75227,1.0,56,0.10151430173864273,ok +75184,1.0,57,0.13967500456454263,ok +75142,1.0,58,0.0813201516390396,ok +236,1.0,59,0.042424242424242475,ok +2122,1.0,60,0.2743573125945129,ok +75188,1.0,61,0.24319066147859925,ok +75166,1.0,62,0.0995190529041805,ok +75181,1.0,63,0.0,ok +75133,1.0,64,0.005123278898495065,ok +75134,1.0,65,0.10170395014980782,ok +75198,1.0,66,0.12143310082435,ok +262,1.0,67,0.0027570995312931057,ok +75234,1.0,68,0.05978705978705978,ok +75139,1.0,69,0.012121212121212088,ok +252,1.0,70,0.1636363636363637,ok +75117,1.0,71,0.06679764243614927,ok +75113,1.0,72,0.006526315789473713,ok +75098,1.0,73,0.027575757575757587,ok +246,1.0,74,0.024242424242424288,ok +75203,1.0,75,0.09555345316934716,ok +75237,1.0,76,0.00039570658356824495,ok +75195,1.0,77,0.0015609901137292326,ok +75171,1.0,78,0.1672216056233814,ok +75128,1.0,79,0.02218114602587795,ok +75096,1.0,80,0.3974640209074891,ok +75250,1.0,81,0.34347287891393896,ok +75146,1.0,82,0.12210711924178974,ok +75116,1.0,83,0.00982318271119842,ok +75157,1.0,84,0.4192200557103064,ok +75187,1.0,85,0.027846027846027854,ok +2350,1.0,86,0.3744580607974338,ok +242,1.0,87,0.01666666666666672,ok +244,1.0,88,0.1106060606060606,ok +75125,1.0,89,0.03536345776031424,ok +75185,1.0,90,0.12816966343937297,ok +75163,1.0,91,0.060374149659863985,ok +75177,1.0,92,0.019292604501607746,ok +75189,1.0,93,0.019401337253296624,ok +75244,1.0,94,0.06339958875942431,ok +75219,1.0,95,0.08375480477442854,ok +75222,1.0,96,0.04524886877828049,ok +75159,1.0,97,0.06849315068493156,ok +75175,1.0,98,0.1170165908089853,ok +75109,1.0,99,0.34315531000613875,ok +254,1.0,100,0.0,ok +75105,1.0,101,0.018121212121212094,ok +75106,1.0,102,0.07212121212121225,ok +75212,1.0,103,0.27738927738927743,ok +75099,1.0,104,0.12374100719424463,ok +75248,1.0,105,0.10040485829959511,ok +233,1.0,106,0.015180265654648917,ok +75235,1.0,107,0.0016666666666667052,ok +75226,1.0,108,0.004259202920596339,ok +75132,1.0,109,0.051244509516837455,ok +75127,1.0,110,0.3345075170228544,ok +251,1.0,111,0.02631578947368418,ok +75161,1.0,112,0.08280680889021041,ok +75143,1.0,113,0.010471204188481686,ok +75114,1.0,114,0.023575638506876273,ok +75182,1.0,115,0.1081404628890662,ok +75112,1.0,116,0.12061822817080947,ok +75210,1.0,117,0.0,ok +75205,1.0,118,0.18110236220472442,ok +75090,1.0,119,0.10139860139860135,ok +275,1.0,120,0.03612167300380231,ok +288,1.0,121,0.14363636363636367,ok +75092,1.0,122,0.0935550935550935,ok +3043,1.0,123,0.020096463022508004,ok +75249,1.0,124,0.004823151125401881,ok +75126,1.0,125,0.04911591355599221,ok +75225,1.0,126,0.04904306220095689,ok +75141,1.0,127,0.05808361080281166,ok +75107,1.0,128,0.053212121212121266,ok +75097,1.0,129,0.05835568297419769,ok +80001,1.0,1,0.06944444444444442,ok +80003,1.0,1,0.12307692307692308,ok +80006,1.0,1,0.1875,ok +80008,1.0,1,0.2222222222222222,ok +80009,1.0,1,0.1578947368421053,ok +80010,1.0,1,0.13793103448275867,ok +80011,1.0,1,0.037735849056603765,ok +80012,1.0,1,0.045454545454545414,ok +80013,1.0,1,0.0,ok +80014,1.0,1,0.045454545454545414,ok +80015,1.0,1,0.09523809523809523,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/f1_micro_binary.classification_sparse/configurations.csv b/metalearning/metalearning_files/f1_micro_binary.classification_sparse/configurations.csv new file mode 100755 index 0000000..2e88382 --- /dev/null +++ b/metalearning/metalearning_files/f1_micro_binary.classification_sparse/configurations.csv @@ -0,0 +1,141 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,preprocessor:truncatedSVD:target_dim,rescaling:__choice__ +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7249853037185638,None,0.0,1.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,median,extra_trees_preproc_for_classification,False,gini,None,0.9424908623661876,None,0.0,7.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.6682079659377479,None,0.0,4.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,mean,extra_trees_preproc_for_classification,True,entropy,None,0.5552350997943013,None,0.0,8.0,5.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7974565919616314,None,0.0,12.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,median,extra_trees_preproc_for_classification,True,entropy,None,0.9772091846790169,None,0.0,10.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2538107344750156,False,True,hinge,1.5099542326343014e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,8532.0,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.034465366914659866,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.12057775675278172,deviance,10.0,0.8011153303489733,None,0.0,2.0,16.0,0.0,370.0,0.6078295352200873,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,mean,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0007038280350320558,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4138778052607317,0.7995003430482459,5.0,5.430044692638861,poly,-1.0,True,0.02455501006004393,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,31,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.0010015637584068037,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.037611630308856295,deviance,5.0,0.8840126779516314,None,0.0,10.0,2.0,0.0,444.0,0.7599997167603434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,most_frequent,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1751.4736515133566,0.62404114475118,3.0,1.6087076997410432,poly,-1.0,False,3.5353792826856045e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,mean,kernel_pca,,,,,,,,,,,,,,cosine,1198.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.8657388713119849,,1.0,None,0.0,19.0,13.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828776,None,0.0,2.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.027324741616523342,deviance,10.0,0.8623781459430139,None,0.0,10.0,20.0,0.0,329.0,0.8595750155424215,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,mean,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1100.6211008501205,0.5921425829232616,2.0,0.0337546254878617,poly,-1.0,True,0.09641299736884308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.9348409326933208,rbf,-1.0,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,mean,kernel_pca,,,,,,,,,,,,,,cosine,1754.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3428971645958825,,,0.2229870623330047,rbf,-1.0,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00016781524591321165,True,True,squared_hinge,1.5119200923218881e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.556190405302504,False,True,1.0,squared_hinge,ovr,l2,0.0007318628304090552,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,most_frequent,kitchen_sinks,,,,,,,,,,,,,,,,3.5602014542183973,948.0,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4047.618729304337,,,2.0237366768707754,rbf,-1.0,True,0.04369127828878843,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,one_hot_encoding,0.010000000000000005,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10.0,1.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,,none +weighting,one_hot_encoding,0.0008685602750053468,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9896334290292654,None,0.0,11.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,141.76310800864283,False,True,1.0,squared_hinge,ovr,l1,0.004317884655117431,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,,normalize +none,one_hot_encoding,0.003443779683191071,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.004980497345831963,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1255.9137433589426,,,0.08351549479967445,rbf,-1.0,True,0.00017919875199222518,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1.0,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.3451627750042988,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.3163640203509378,None,0.0,17.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,median,extra_trees_preproc_for_classification,False,gini,None,0.8916956785028156,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,85,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,86,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0019686649916896212,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.04641055832142541,True,True,hinge,8.540468968077405e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87,mean,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,911.0,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1.0,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,89,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,90,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,91,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,93,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,95,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,96,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,97,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,98,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,99,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,100,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,102,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,103,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,104,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,105,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,106,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.006372860318416312,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6467376360604045,None,0.0,1.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,107,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,108,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,109,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3823734947460288,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,110,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,adaboost,SAMME,0.11042308042695524,5.0,117.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,111,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,112,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,113,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,115,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,116,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,117,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,118,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.20202014999292287,False,True,1.0,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,119,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,120,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,121,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,122,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,123,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,124,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,125,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,126,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,127,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,129,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.04329010470998136,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7260331062271381,None,0.0,7.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,134,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.714869937384006,None,0.0,8.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/metalearning/metalearning_files/f1_micro_binary.classification_sparse/description.txt b/metalearning/metalearning_files/f1_micro_binary.classification_sparse/description.txt new file mode 100755 index 0000000..563e881 --- /dev/null +++ b/metalearning/metalearning_files/f1_micro_binary.classification_sparse/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: f1_micro +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/f1_micro_binary.classification_sparse/feature_costs.arff b/metalearning/metalearning_files/f1_micro_binary.classification_sparse/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/f1_micro_binary.classification_sparse/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/f1_micro_binary.classification_sparse/feature_runstatus.arff b/metalearning/metalearning_files/f1_micro_binary.classification_sparse/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/f1_micro_binary.classification_sparse/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/f1_micro_binary.classification_sparse/feature_values.arff b/metalearning/metalearning_files/f1_micro_binary.classification_sparse/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/f1_micro_binary.classification_sparse/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/f1_micro_binary.classification_sparse/readme.txt b/metalearning/metalearning_files/f1_micro_binary.classification_sparse/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/f1_micro_multiclass.classification_dense/algorithm_runs.arff b/metalearning/metalearning_files/f1_micro_multiclass.classification_dense/algorithm_runs.arff new file mode 100755 index 0000000..635502d --- /dev/null +++ b/metalearning/metalearning_files/f1_micro_multiclass.classification_dense/algorithm_runs.arff @@ -0,0 +1,152 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE f1_micro NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2120,1.0,1,0.07967939651107969,ok +75193,1.0,2,0.038371068099909755,ok +2117,1.0,3,0.16709064962461995,ok +75156,1.0,4,0.20291026677445445,ok +75129,1.0,5,0.10097087378640779,ok +75243,1.0,6,0.0,ok +75110,1.0,7,0.11622380643767549,ok +75239,1.0,8,0.0,ok +75223,1.0,9,0.10304601425793913,ok +75221,1.0,10,0.39791873141724476,ok +258,1.0,11,0.009708737864077666,ok +75121,1.0,12,0.0,ok +253,1.0,13,0.44855967078189296,ok +261,1.0,14,0.23333333333333328,ok +75240,1.0,15,0.022020725388601003,ok +75120,1.0,16,0.03929273084479368,ok +75124,1.0,17,0.09121395036888003,ok +75176,1.0,18,0.01541623843782114,ok +75103,1.0,19,0.005894736842105286,ok +75207,1.0,20,0.16184971098265888,ok +75095,1.0,21,0.016917293233082664,ok +273,1.0,22,0.04413702239789197,ok +75174,1.0,23,0.11705240755520085,ok +75153,1.0,24,0.08028116907140215,ok +75093,1.0,25,0.17483296213808464,ok +75119,1.0,26,0.03536345776031424,ok +75201,1.0,27,0.0808678500986193,ok +75215,1.0,28,0.027412280701754388,ok +75172,1.0,29,0.10303030303030303,ok +75169,1.0,30,0.03420132141469101,ok +75202,1.0,31,0.20329670329670335,ok +75233,1.0,32,0.060673325934147204,ok +75231,1.0,33,0.19924098671726764,ok +75196,1.0,34,0.015665796344647487,ok +248,1.0,35,0.22878787878787876,ok +75191,1.0,36,0.1289905886694962,ok +75217,1.0,37,0.0,ok +260,1.0,38,0.02657807308970095,ok +75115,1.0,39,0.017681728880157177,ok +75123,1.0,40,0.32728592162554426,ok +75108,1.0,41,0.0,ok +75101,1.0,42,0.2740140932130053,ok +75192,1.0,43,0.48305752561071713,ok +75232,1.0,44,0.12356321839080464,ok +75173,1.0,45,0.1165605095541401,ok +75197,1.0,46,0.15517241379310331,ok +266,1.0,47,0.019685039370078594,ok +75148,1.0,48,0.13560975609756099,ok +75150,1.0,49,0.2848664688427299,ok +75100,1.0,50,0.00379609544468551,ok +75178,1.0,51,0.7840550682597786,ok +75236,1.0,52,0.0323809523809524,ok +75179,1.0,53,0.17943026267110618,ok +75213,1.0,54,0.05249343832021003,ok +2123,1.0,55,0.05882352941176472,ok +75227,1.0,56,0.10151430173864273,ok +75184,1.0,57,0.10772320613474529,ok +75142,1.0,58,0.0715825466438712,ok +236,1.0,59,0.038787878787878816,ok +2122,1.0,60,0.10952689565780949,ok +75188,1.0,61,0.24319066147859925,ok +75166,1.0,62,0.0995190529041805,ok +75181,1.0,63,0.0,ok +75133,1.0,64,0.005123278898495065,ok +75134,1.0,65,0.08723783614874181,ok +75198,1.0,66,0.12143310082435,ok +262,1.0,67,0.0027570995312931057,ok +75234,1.0,68,0.024160524160524166,ok +75139,1.0,69,0.010707070707070665,ok +252,1.0,70,0.15000000000000002,ok +75117,1.0,71,0.05500982318271119,ok +75113,1.0,72,0.006526315789473713,ok +75098,1.0,73,0.027575757575757587,ok +246,1.0,74,0.010606060606060619,ok +75203,1.0,75,0.09555345316934716,ok +75237,1.0,76,0.00039570658356824495,ok +75195,1.0,77,0.0015609901137292326,ok +75171,1.0,78,0.1620421753607103,ok +75128,1.0,79,0.02218114602587795,ok +75096,1.0,80,0.005164788382626018,ok +75250,1.0,81,0.34347287891393896,ok +75146,1.0,82,0.11329072074057744,ok +75116,1.0,83,0.00982318271119842,ok +75157,1.0,84,0.4192200557103064,ok +75187,1.0,85,0.01678951678951679,ok +2350,1.0,86,0.3744580607974338,ok +242,1.0,87,0.010606060606060619,ok +244,1.0,88,0.1106060606060606,ok +75125,1.0,89,0.03339882121807469,ok +75185,1.0,90,0.12816966343937297,ok +75163,1.0,91,0.060374149659863985,ok +75177,1.0,92,0.019292604501607746,ok +75189,1.0,93,0.019401337253296624,ok +75244,1.0,94,0.06339958875942431,ok +75219,1.0,95,0.03479668217681575,ok +75222,1.0,96,0.04524886877828049,ok +75159,1.0,97,0.06849315068493156,ok +75175,1.0,98,0.099544853912788,ok +75109,1.0,99,0.30417434008594224,ok +254,1.0,100,0.0,ok +75105,1.0,101,0.018121212121212094,ok +75106,1.0,102,0.07212121212121225,ok +75212,1.0,103,0.2517482517482518,ok +75099,1.0,104,0.12374100719424463,ok +75248,1.0,105,0.10040485829959511,ok +233,1.0,106,0.002846299810246644,ok +75235,1.0,107,0.0011111111111110628,ok +75226,1.0,108,0.0030422878004259246,ok +75132,1.0,109,0.051244509516837455,ok +75127,1.0,110,0.3330355738331199,ok +251,1.0,111,0.0,ok +75161,1.0,112,0.06437225897569321,ok +75143,1.0,113,0.010471204188481686,ok +75114,1.0,114,0.023575638506876273,ok +75182,1.0,115,0.1081404628890662,ok +75112,1.0,116,0.12061822817080947,ok +75210,1.0,117,0.0,ok +75205,1.0,118,0.18110236220472442,ok +75090,1.0,119,0.06118881118881114,ok +275,1.0,120,0.03612167300380231,ok +288,1.0,121,0.12969696969696964,ok +75092,1.0,122,0.0935550935550935,ok +3043,1.0,123,0.020096463022508004,ok +75249,1.0,124,0.003215434083601254,ok +75126,1.0,125,0.04911591355599221,ok +75225,1.0,126,0.04904306220095689,ok +75141,1.0,127,0.0540140584535701,ok +75107,1.0,128,0.053212121212121266,ok +75097,1.0,129,0.05835568297419769,ok +80001,1.0,1,0.06944444444444442,ok +80003,1.0,1,0.09230769230769242,ok +80006,1.0,1,0.09375,ok +80008,1.0,1,0.11111111111111116,ok +80009,1.0,1,0.10526315789473684,ok +80010,1.0,1,0.13793103448275867,ok +80011,1.0,1,0.037735849056603765,ok +80012,1.0,1,0.045454545454545414,ok +80013,1.0,1,0.0,ok +80014,1.0,1,0.045454545454545414,ok +80015,1.0,1,0.09523809523809523,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/f1_micro_multiclass.classification_dense/configurations.csv b/metalearning/metalearning_files/f1_micro_multiclass.classification_dense/configurations.csv new file mode 100755 index 0000000..e710c68 --- /dev/null +++ b/metalearning/metalearning_files/f1_micro_multiclass.classification_dense/configurations.csv @@ -0,0 +1,141 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:fast_ica:algorithm,preprocessor:fast_ica:fun,preprocessor:fast_ica:n_components,preprocessor:fast_ica:whiten,preprocessor:feature_agglomeration:affinity,preprocessor:feature_agglomeration:linkage,preprocessor:feature_agglomeration:n_clusters,preprocessor:feature_agglomeration:pooling_func,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:pca:keep_variance,preprocessor:pca:whiten,preprocessor:polynomial:degree,preprocessor:polynomial:include_bias,preprocessor:polynomial:interaction_only,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,rescaling:__choice__,rescaling:quantile_transformer:n_quantiles,rescaling:quantile_transformer:output_distribution,rescaling:robust_scaler:q_max,rescaling:robust_scaler:q_min +weighting,one_hot_encoding,0.00011717632475982632,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.045388141846341344,deviance,10.0,0.29161769341843435,None,0.0,20.0,2.0,0.0,278.0,0.7912571599269661,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7249853037185638,None,0.0,1.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,median,extra_trees_preproc_for_classification,False,gini,None,0.9424908623661876,None,0.0,7.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.03474109838999682,deviance,4.0,0.5687034678818491,None,0.0,18.0,12.0,0.0,408.0,0.5150113945430513,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.6328939517938,f_classif,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.251097788175676,deviance,6.0,0.35679099363539235,None,0.0,13.0,11.0,0.0,157.0,0.4791732272983235,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,adaboost,SAMME,0.28738775989203896,10.0,423.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.8031499675923353,0.13579938270386765 +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.14159526341015916,deviance,7.0,0.8010488230155749,None,0.0,3.0,20.0,0.0,401.0,0.8073562440607731,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.002385546176068135,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.7729472304882845,,,0.0004789329856033374,rbf,-1.0,True,6.58869648864534e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,177.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5368752992317617,None,0.0,16.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.8382117756438685,mutual_info,,,,quantile_transformer,11480.0,normal,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9455638720565652,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,most_frequent,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8255464552647293,0.19162485555463185 +weighting,one_hot_encoding,0.18137532678800647,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9094110110427254,None,0.0,7.0,12.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,195.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.6682079659377479,None,0.0,4.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,mean,extra_trees_preproc_for_classification,True,entropy,None,0.5552350997943013,None,0.0,8.0,5.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.02345017287074443,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.053517066400173056,deviance,10.0,0.542144980834302,None,0.0,20.0,13.0,0.0,233.0,0.7398539900055563,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0614425536709615,fwe,f_classif,robust_scaler,,,0.9523118062307264,0.13434811490315818 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.040936424602789435,deviance,7.0,0.5495014745530306,None,0.0,20.0,18.0,0.0,141.0,0.6905343807995293,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7974565919616314,None,0.0,12.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,median,extra_trees_preproc_for_classification,True,entropy,None,0.9772091846790169,None,0.0,10.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2538107344750156,False,True,hinge,1.5099542326343014e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,8532.0,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.1958974686405233,deviance,5.0,0.33885235607979314,None,0.0,6.0,4.0,0.0,125.0,0.9448890820738562,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2538107344750156,False,True,hinge,1.5099542326343014e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,8532.0,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,0.0007038280350320558,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4138778052607317,0.7995003430482459,5.0,5.430044692638861,poly,-1.0,True,0.02455501006004393,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,31,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.010000000000000005,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.0518326156691958,deviance,6.0,0.8807456180216267,None,0.0,7.0,19.0,0.0,366.0,0.7314831276137047,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,,False,adaboost,SAMME,0.4391375941344922,3.0,386.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7439738358430176,0.20581080574615795 +none,one_hot_encoding,0.00011294596229850895,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7515.1213255144885,0.9576762936062476,3.0,0.019002536385919932,poly,-1.0,False,0.010632086351533369,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,51.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,97282.0,normal,, +none,one_hot_encoding,0.00012586572428922356,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5240592829918601,None,0.0,10.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1751.4736515133566,0.62404114475118,3.0,1.6087076997410432,poly,-1.0,False,3.5353792826856045e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1198.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.2422926485206341,,1.0,None,0.0,15.0,9.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.02102683283349326,deviance,10.0,0.2797288369369436,None,0.0,14.0,9.0,0.0,480.0,0.5778972273820631,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58.88123233170863,mutual_info,,,,robust_scaler,,,0.75,0.25 +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828776,None,0.0,2.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2155613360930585,deviance,4.0,0.3198803116198433,None,0.0,8.0,13.0,0.0,275.0,0.28870176110739404,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,mean,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.7062102387181676,None,0.0,1.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,most_frequent,fast_ica,,,,,,,,,,,parallel,exp,100.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7065776353150109,0.23782974987118105 +none,one_hot_encoding,0.16334152321884812,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00011572473434870852,True,True,squared_hinge,0.00019678754114665057,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.045742431094098604,fpr,chi2,none,,,, +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.3795924768593385,deviance,2.0,0.33708497069988536,None,0.0,15.0,13.0,0.0,451.0,0.7716323242090217,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2573946506994812,fwe,f_classif,quantile_transformer,1000.0,uniform,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.609975998293528,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,most_frequent,fast_ica,,,,,,,,,,,parallel,logcosh,2000.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8430415644014919,0.2863750565331575 +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.39536192447534535,None,0.0,19.0,3.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5.0,None,11.0,11.0,1.0,12.0,,,,,,robust_scaler,,,0.8928631650245873,0.1581877760687084 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.3126027672745337,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,870.2240970463429,0.5325949351918051,3.0,0.010682839357128344,poly,-1.0,False,2.485160860440657e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4608103694360143,fdr,f_classif,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.9348409326933208,rbf,-1.0,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1754.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,198.72528686512538,False,True,1.0,squared_hinge,ovr,l2,0.026260652523566803,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,robust_scaler,,,0.913511520078368,0.2742229325455444 +none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9260795160807372,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.03644212536682547,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.05965671477918361,deviance,8.0,0.4858133247974158,None,0.0,14.0,7.0,0.0,480.0,0.5726186552917335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.15318294164619112 +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18887.81504976871,,,0.23283562663398755,rbf,-1.0,True,2.3839685780861318e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3937607155568376,fwe,chi2,robust_scaler,,,0.941018778984854,0.2144110585080491 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3428971645958825,,,0.2229870623330047,rbf,-1.0,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8138233157708883,None,0.0,2.0,9.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54.27504292568562,f_classif,,,,minmax,,,, +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00016781524591321165,True,True,squared_hinge,1.5119200923218881e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.556190405302504,False,True,1.0,squared_hinge,ovr,l2,0.0007318628304090552,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,most_frequent,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,3.5602014542183973,948.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +weighting,one_hot_encoding,0.00034835629696198427,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8245132980938538,0.08947420373097192 +weighting,one_hot_encoding,0.00016967940959070708,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9439080311935252,None,0.0,2.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,170.0,None,,0.014191958374153584,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,,False,adaboost,SAMME.R,0.8220362681234727,5.0,183.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.363262678765884,fdr,chi2,robust_scaler,,,0.8826612080363588,0.2189605310171097 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,normalize,,,, +none,one_hot_encoding,,False,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6396026761675004,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06544340428506021,fwe,f_classif,none,,,, +weighting,one_hot_encoding,0.004980497345831963,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1255.9137433589426,,,0.08351549479967445,rbf,-1.0,True,0.00017919875199222518,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1.0,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.3837398524575939,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.018356703878357986,deviance,3.0,0.9690352514774068,None,0.0,12.0,3.0,0.0,234.0,0.3870344708308441,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,most_frequent,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6864970915330799,False,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5670424455696162,None,0.0,8.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,standardize,,,, +weighting,one_hot_encoding,0.00031737236118003484,True,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,244.0,None,,2.3065111488706024e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,85,mean,fast_ica,,,,,,,,,,,deflation,exp,1862.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7851234479882973,0.2237528085136715 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,86,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,156.0,auto,,0.0001987333852871589,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1.0,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,no_encoding,,,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.96834823420249e-05,False,True,hinge,0.00016639250831671168,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,89,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.628430584359224,mutual_info,,,,quantile_transformer,6634.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,90,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,91,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,93,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2636201374253461,deviance,7.0,0.8344964130784466,None,0.0,9.0,2.0,0.0,298.0,0.7517549950523315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,95,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,96,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,97,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.07463196642416367,deviance,7.0,0.8603242247379981,None,0.0,2.0,6.0,0.0,500.0,0.8447665577491962,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,98,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.0433556140045585,deviance,10.0,0.33000096635982235,None,0.0,15.0,13.0,0.0,388.0,0.8291104221904706,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,99,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,robust_scaler,,,0.7496393440951183,0.2853682991120835 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,100,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,102,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0020580843703898177,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.945774573434192,None,0.0,19.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,103,median,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,15209.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,104,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,105,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,106,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.005332972692819521,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5565918060287016,None,0.0,5.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,107,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,173.0,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9527068489270144,0.04135311355893583 +weighting,one_hot_encoding,0.001279467383882126,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,108,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0903354518102121,fwe,f_classif,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,109,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.002615346832354839,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7884268823432835,None,0.0,20.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,110,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +weighting,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56.086963007482865,,,0.013609964993119377,rbf,-1.0,True,0.00196831255706268,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,111,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.15374716583918388 +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.2472984547885781,deviance,3.0,0.6564306719064884,None,0.0,15.0,14.0,0.0,220.0,0.8082564085714649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,112,median,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,332.0,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,113,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,115,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,116,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,117,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,118,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.20202014999292287,False,True,1.0,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,no_encoding,,,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6390376923528961,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,119,median,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,164.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,62508.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,120,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.27041927277584e-06,True,0.00010000000000000009,0.033157325660763994,True,0.0008114527992546482,invscaling,modified_huber,elasticnet,0.13714427818877545,0.055179642772545036,,,,,,,,,,,,,,,,,,121,median,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,122,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,123,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.19998727075532635,deviance,10.0,0.9377656718112952,None,0.0,7.0,13.0,0.0,214.0,0.6062346326014357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,124,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,125,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,126,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.5765793990908161,None,0.0,11.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,127,median,fast_ica,,,,,,,,,,,deflation,exp,10.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,129,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.04651467280022463,True,adaboost,SAMME,0.6506122230393502,6.0,143.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,extra_trees_preproc_for_classification,False,entropy,None,0.348720215832657,None,0.0,17.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,26025.0,normal,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3386310102795006,None,0.0,6.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,True,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.0,None,,5.861221938384061e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.22985730375167915,fpr,f_classif,quantile_transformer,40589.0,uniform,, +none,no_encoding,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00012437731009611307,True,,0.08709904468181932,True,,invscaling,squared_hinge,l1,0.6231564675290102,0.008071279833697563,,,,,,,,,,,,,,,,,,134,median,fast_ica,,,,,,,,,,,parallel,logcosh,814.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.714869937384006,None,0.0,8.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, diff --git a/metalearning/metalearning_files/f1_micro_multiclass.classification_dense/description.txt b/metalearning/metalearning_files/f1_micro_multiclass.classification_dense/description.txt new file mode 100755 index 0000000..563e881 --- /dev/null +++ b/metalearning/metalearning_files/f1_micro_multiclass.classification_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: f1_micro +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/f1_micro_multiclass.classification_dense/feature_costs.arff b/metalearning/metalearning_files/f1_micro_multiclass.classification_dense/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/f1_micro_multiclass.classification_dense/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/f1_micro_multiclass.classification_dense/feature_runstatus.arff b/metalearning/metalearning_files/f1_micro_multiclass.classification_dense/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/f1_micro_multiclass.classification_dense/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/f1_micro_multiclass.classification_dense/feature_values.arff b/metalearning/metalearning_files/f1_micro_multiclass.classification_dense/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/f1_micro_multiclass.classification_dense/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/f1_micro_multiclass.classification_dense/readme.txt b/metalearning/metalearning_files/f1_micro_multiclass.classification_dense/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/f1_micro_multiclass.classification_sparse/algorithm_runs.arff b/metalearning/metalearning_files/f1_micro_multiclass.classification_sparse/algorithm_runs.arff new file mode 100755 index 0000000..0b76f96 --- /dev/null +++ b/metalearning/metalearning_files/f1_micro_multiclass.classification_sparse/algorithm_runs.arff @@ -0,0 +1,152 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE f1_micro NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2120,1.0,1,0.0895803866100896,ok +75193,1.0,2,0.038371068099909755,ok +2117,1.0,3,0.16709064962461995,ok +75156,1.0,4,0.21988682295877138,ok +75129,1.0,5,0.10097087378640779,ok +75243,1.0,6,0.015434985968194592,ok +75110,1.0,7,0.2743573125945129,ok +75239,1.0,8,0.0,ok +75223,1.0,9,0.30989414560380213,ok +75221,1.0,10,0.39791873141724476,ok +258,1.0,11,0.01833872707659112,ok +75121,1.0,12,0.003929273084479323,ok +253,1.0,13,0.44855967078189296,ok +261,1.0,14,0.23333333333333328,ok +75240,1.0,15,0.022020725388601003,ok +75120,1.0,16,0.03929273084479368,ok +75124,1.0,17,0.09121395036888003,ok +75176,1.0,18,0.016590808985464722,ok +75103,1.0,19,0.008000000000000007,ok +75207,1.0,20,0.16184971098265888,ok +75095,1.0,21,0.016917293233082664,ok +273,1.0,22,0.044795783926218746,ok +75174,1.0,23,0.11705240755520085,ok +75153,1.0,24,0.12134665186829452,ok +75093,1.0,25,0.17483296213808464,ok +75119,1.0,26,0.03536345776031424,ok +75201,1.0,27,0.0808678500986193,ok +75215,1.0,28,0.028234649122807043,ok +75172,1.0,29,0.09090909090909094,ok +75169,1.0,30,0.03420132141469101,ok +75202,1.0,31,0.20329670329670335,ok +75233,1.0,32,0.06511283758786535,ok +75231,1.0,33,0.19924098671726764,ok +75196,1.0,34,0.023498694516971286,ok +248,1.0,35,0.26515151515151525,ok +75191,1.0,36,0.12370055975887306,ok +75217,1.0,37,0.0,ok +260,1.0,38,0.02657807308970095,ok +75115,1.0,39,0.017681728880157177,ok +75123,1.0,40,0.32728592162554426,ok +75108,1.0,41,0.0018373909049149706,ok +75101,1.0,42,0.28272963283471386,ok +75192,1.0,43,0.48305752561071713,ok +75232,1.0,44,0.14655172413793105,ok +75173,1.0,45,0.11751592356687901,ok +75197,1.0,46,0.13300492610837433,ok +266,1.0,47,0.03280839895013121,ok +75148,1.0,48,0.19024390243902445,ok +75150,1.0,49,0.3204747774480712,ok +75100,1.0,50,0.00379609544468551,ok +75178,1.0,51,0.8079287243594171,ok +75236,1.0,52,0.0323809523809524,ok +75179,1.0,53,0.17943026267110618,ok +75213,1.0,54,0.05249343832021003,ok +2123,1.0,55,0.05882352941176472,ok +75227,1.0,56,0.10151430173864273,ok +75184,1.0,57,0.13967500456454263,ok +75142,1.0,58,0.0813201516390396,ok +236,1.0,59,0.042424242424242475,ok +2122,1.0,60,0.2743573125945129,ok +75188,1.0,61,0.24319066147859925,ok +75166,1.0,62,0.0995190529041805,ok +75181,1.0,63,0.0,ok +75133,1.0,64,0.005123278898495065,ok +75134,1.0,65,0.10170395014980782,ok +75198,1.0,66,0.12143310082435,ok +262,1.0,67,0.0027570995312931057,ok +75234,1.0,68,0.05978705978705978,ok +75139,1.0,69,0.012121212121212088,ok +252,1.0,70,0.1636363636363637,ok +75117,1.0,71,0.06679764243614927,ok +75113,1.0,72,0.006526315789473713,ok +75098,1.0,73,0.027575757575757587,ok +246,1.0,74,0.024242424242424288,ok +75203,1.0,75,0.09555345316934716,ok +75237,1.0,76,0.00039570658356824495,ok +75195,1.0,77,0.0015609901137292326,ok +75171,1.0,78,0.1672216056233814,ok +75128,1.0,79,0.02218114602587795,ok +75096,1.0,80,0.3974640209074891,ok +75250,1.0,81,0.34347287891393896,ok +75146,1.0,82,0.12210711924178974,ok +75116,1.0,83,0.00982318271119842,ok +75157,1.0,84,0.4192200557103064,ok +75187,1.0,85,0.027846027846027854,ok +2350,1.0,86,0.3744580607974338,ok +242,1.0,87,0.01666666666666672,ok +244,1.0,88,0.1106060606060606,ok +75125,1.0,89,0.03536345776031424,ok +75185,1.0,90,0.12816966343937297,ok +75163,1.0,91,0.060374149659863985,ok +75177,1.0,92,0.019292604501607746,ok +75189,1.0,93,0.019401337253296624,ok +75244,1.0,94,0.06339958875942431,ok +75219,1.0,95,0.08375480477442854,ok +75222,1.0,96,0.04524886877828049,ok +75159,1.0,97,0.06849315068493156,ok +75175,1.0,98,0.1170165908089853,ok +75109,1.0,99,0.34315531000613875,ok +254,1.0,100,0.0,ok +75105,1.0,101,0.018121212121212094,ok +75106,1.0,102,0.07212121212121225,ok +75212,1.0,103,0.27738927738927743,ok +75099,1.0,104,0.12374100719424463,ok +75248,1.0,105,0.10040485829959511,ok +233,1.0,106,0.015180265654648917,ok +75235,1.0,107,0.0016666666666667052,ok +75226,1.0,108,0.004259202920596339,ok +75132,1.0,109,0.051244509516837455,ok +75127,1.0,110,0.3345075170228544,ok +251,1.0,111,0.02631578947368418,ok +75161,1.0,112,0.08280680889021041,ok +75143,1.0,113,0.010471204188481686,ok +75114,1.0,114,0.023575638506876273,ok +75182,1.0,115,0.1081404628890662,ok +75112,1.0,116,0.12061822817080947,ok +75210,1.0,117,0.0,ok +75205,1.0,118,0.18110236220472442,ok +75090,1.0,119,0.10139860139860135,ok +275,1.0,120,0.03612167300380231,ok +288,1.0,121,0.14363636363636367,ok +75092,1.0,122,0.0935550935550935,ok +3043,1.0,123,0.020096463022508004,ok +75249,1.0,124,0.004823151125401881,ok +75126,1.0,125,0.04911591355599221,ok +75225,1.0,126,0.04904306220095689,ok +75141,1.0,127,0.05808361080281166,ok +75107,1.0,128,0.053212121212121266,ok +75097,1.0,129,0.05835568297419769,ok +80001,1.0,1,0.06944444444444442,ok +80003,1.0,1,0.12307692307692308,ok +80006,1.0,1,0.1875,ok +80008,1.0,1,0.2222222222222222,ok +80009,1.0,1,0.1578947368421053,ok +80010,1.0,1,0.13793103448275867,ok +80011,1.0,1,0.037735849056603765,ok +80012,1.0,1,0.045454545454545414,ok +80013,1.0,1,0.0,ok +80014,1.0,1,0.045454545454545414,ok +80015,1.0,1,0.09523809523809523,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/f1_micro_multiclass.classification_sparse/configurations.csv b/metalearning/metalearning_files/f1_micro_multiclass.classification_sparse/configurations.csv new file mode 100755 index 0000000..2e88382 --- /dev/null +++ b/metalearning/metalearning_files/f1_micro_multiclass.classification_sparse/configurations.csv @@ -0,0 +1,141 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,preprocessor:truncatedSVD:target_dim,rescaling:__choice__ +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7249853037185638,None,0.0,1.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,median,extra_trees_preproc_for_classification,False,gini,None,0.9424908623661876,None,0.0,7.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.6682079659377479,None,0.0,4.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,mean,extra_trees_preproc_for_classification,True,entropy,None,0.5552350997943013,None,0.0,8.0,5.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7974565919616314,None,0.0,12.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,median,extra_trees_preproc_for_classification,True,entropy,None,0.9772091846790169,None,0.0,10.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2538107344750156,False,True,hinge,1.5099542326343014e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,8532.0,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.034465366914659866,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.12057775675278172,deviance,10.0,0.8011153303489733,None,0.0,2.0,16.0,0.0,370.0,0.6078295352200873,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,mean,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0007038280350320558,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4138778052607317,0.7995003430482459,5.0,5.430044692638861,poly,-1.0,True,0.02455501006004393,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,31,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.0010015637584068037,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.037611630308856295,deviance,5.0,0.8840126779516314,None,0.0,10.0,2.0,0.0,444.0,0.7599997167603434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,most_frequent,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1751.4736515133566,0.62404114475118,3.0,1.6087076997410432,poly,-1.0,False,3.5353792826856045e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,mean,kernel_pca,,,,,,,,,,,,,,cosine,1198.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.8657388713119849,,1.0,None,0.0,19.0,13.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828776,None,0.0,2.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.027324741616523342,deviance,10.0,0.8623781459430139,None,0.0,10.0,20.0,0.0,329.0,0.8595750155424215,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,mean,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1100.6211008501205,0.5921425829232616,2.0,0.0337546254878617,poly,-1.0,True,0.09641299736884308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.9348409326933208,rbf,-1.0,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,mean,kernel_pca,,,,,,,,,,,,,,cosine,1754.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3428971645958825,,,0.2229870623330047,rbf,-1.0,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00016781524591321165,True,True,squared_hinge,1.5119200923218881e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.556190405302504,False,True,1.0,squared_hinge,ovr,l2,0.0007318628304090552,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,most_frequent,kitchen_sinks,,,,,,,,,,,,,,,,3.5602014542183973,948.0,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4047.618729304337,,,2.0237366768707754,rbf,-1.0,True,0.04369127828878843,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,one_hot_encoding,0.010000000000000005,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10.0,1.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,,none +weighting,one_hot_encoding,0.0008685602750053468,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9896334290292654,None,0.0,11.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,141.76310800864283,False,True,1.0,squared_hinge,ovr,l1,0.004317884655117431,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,,normalize +none,one_hot_encoding,0.003443779683191071,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.004980497345831963,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1255.9137433589426,,,0.08351549479967445,rbf,-1.0,True,0.00017919875199222518,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1.0,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.3451627750042988,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.3163640203509378,None,0.0,17.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,median,extra_trees_preproc_for_classification,False,gini,None,0.8916956785028156,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,85,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,86,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0019686649916896212,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.04641055832142541,True,True,hinge,8.540468968077405e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87,mean,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,911.0,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1.0,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,89,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,90,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,91,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,93,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,95,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,96,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,97,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,98,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,99,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,100,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,102,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,103,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,104,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,105,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,106,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.006372860318416312,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6467376360604045,None,0.0,1.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,107,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,108,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,109,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3823734947460288,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,110,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,adaboost,SAMME,0.11042308042695524,5.0,117.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,111,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,112,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,113,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,115,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,116,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,117,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,118,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.20202014999292287,False,True,1.0,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,119,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,120,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,121,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,122,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,123,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,124,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,125,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,126,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,127,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,129,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.04329010470998136,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7260331062271381,None,0.0,7.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,134,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.714869937384006,None,0.0,8.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/metalearning/metalearning_files/f1_micro_multiclass.classification_sparse/description.txt b/metalearning/metalearning_files/f1_micro_multiclass.classification_sparse/description.txt new file mode 100755 index 0000000..563e881 --- /dev/null +++ b/metalearning/metalearning_files/f1_micro_multiclass.classification_sparse/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: f1_micro +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/f1_micro_multiclass.classification_sparse/feature_costs.arff b/metalearning/metalearning_files/f1_micro_multiclass.classification_sparse/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/f1_micro_multiclass.classification_sparse/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/f1_micro_multiclass.classification_sparse/feature_runstatus.arff b/metalearning/metalearning_files/f1_micro_multiclass.classification_sparse/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/f1_micro_multiclass.classification_sparse/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/f1_micro_multiclass.classification_sparse/feature_values.arff b/metalearning/metalearning_files/f1_micro_multiclass.classification_sparse/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/f1_micro_multiclass.classification_sparse/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/f1_micro_multiclass.classification_sparse/readme.txt b/metalearning/metalearning_files/f1_micro_multiclass.classification_sparse/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/f1_multiclass.classification_dense/algorithm_runs.arff b/metalearning/metalearning_files/f1_multiclass.classification_dense/algorithm_runs.arff new file mode 100755 index 0000000..0378150 --- /dev/null +++ b/metalearning/metalearning_files/f1_multiclass.classification_dense/algorithm_runs.arff @@ -0,0 +1,107 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE f1 NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2117,1.0,1,0.10832227183138254,ok +75156,1.0,2,0.18361375274323333,ok +75129,1.0,3,0.5268817204301075,ok +75239,1.0,4,0.0,ok +75121,1.0,5,0.0,ok +261,1.0,6,0.41121495327102797,ok +75240,1.0,7,0.028349082823791005,ok +75120,1.0,8,0.02004008016032066,ok +75124,1.0,9,0.46118721461187207,ok +75176,1.0,10,0.013738060970822863,ok +75103,1.0,11,0.043887147335423204,ok +75095,1.0,12,0.18120805369127513,ok +273,1.0,13,0.05682782018659882,ok +75174,1.0,14,0.19505315686699942,ok +75153,1.0,15,0.07888040712468192,ok +75093,1.0,16,0.558858501783591,ok +75119,1.0,17,0.018711018711018657,ok +75215,1.0,18,0.02418964683115621,ok +75233,1.0,19,0.043570669500531345,ok +75196,1.0,20,0.02857142857142858,ok +75191,1.0,21,0.13488132758731586,ok +75115,1.0,22,0.009544008483563182,ok +75108,1.0,23,0.0,ok +75101,1.0,24,0.25764268278507496,ok +75192,1.0,25,0.37826350941105036,ok +75232,1.0,26,0.17991631799163188,ok +75173,1.0,27,0.11531190926275992,ok +75148,1.0,28,0.1317535545023697,ok +75150,1.0,29,0.25396825396825395,ok +75100,1.0,30,0.922077922077922,ok +75179,1.0,31,0.29820749592612716,ok +75213,1.0,32,0.1123595505617977,ok +75227,1.0,33,0.17411121239744765,ok +75184,1.0,34,0.17141196978500872,ok +75142,1.0,35,0.07081986482515423,ok +75166,1.0,36,0.10216483099126472,ok +75133,1.0,37,0.4999999999999999,ok +75234,1.0,38,0.024032586558044855,ok +75139,1.0,39,0.016242721422004336,ok +75117,1.0,40,0.02947368421052632,ok +75113,1.0,41,0.05429071803852892,ok +75237,1.0,42,0.00024933381122316245,ok +75195,1.0,43,0.0013143894348125462,ok +75171,1.0,44,0.16404494382022472,ok +75128,1.0,45,0.012725344644750725,ok +75146,1.0,46,0.0980392156862745,ok +75116,1.0,47,0.005847953216374324,ok +75157,1.0,48,0.4766917293233083,ok +75187,1.0,49,0.017190775681341752,ok +2350,1.0,50,0.5196824930177864,ok +75125,1.0,51,0.020310633213859064,ok +75185,1.0,52,0.13409961685823746,ok +75163,1.0,53,0.0650779101741521,ok +75177,1.0,54,0.13636363636363635,ok +75189,1.0,55,0.014574596962072528,ok +75244,1.0,56,0.6633663366336633,ok +75219,1.0,57,0.03865168539325847,ok +75222,1.0,58,0.2777777777777778,ok +75159,1.0,59,0.5319148936170213,ok +75175,1.0,60,0.12208258527827653,ok +254,1.0,61,0.0,ok +75105,1.0,62,0.8763197586726998,ok +75106,1.0,63,0.7574316290130796,ok +75212,1.0,64,0.26086956521739135,ok +75099,1.0,65,0.4918566775244301,ok +75248,1.0,66,0.6080218778486782,ok +233,1.0,67,0.003058103975535076,ok +75226,1.0,68,0.001797914419273683,ok +75132,1.0,69,0.8638414084026237,ok +75127,1.0,70,0.37607613005551144,ok +75161,1.0,71,0.06386430678466082,ok +75143,1.0,72,0.006923837784371889,ok +75114,1.0,73,0.015037593984962405,ok +75182,1.0,74,0.1936604429005645,ok +75112,1.0,75,0.18030544791429215,ok +75210,1.0,76,0.0,ok +75092,1.0,77,0.37579617834394896,ok +3043,1.0,78,0.14444444444444438,ok +75249,1.0,79,0.020618556701030855,ok +75126,1.0,80,0.027870680044593144,ok +75225,1.0,81,0.525179856115108,ok +75141,1.0,82,0.0652368185880251,ok +75107,1.0,83,0.4416498993963782,ok +75097,1.0,84,0.030135154496394367,ok +80001,1.0,1,0.19999999999999996,ok +80003,1.0,1,0.08999999999999997,ok +80006,1.0,1,0.10344827586206895,ok +80008,1.0,1,0.1428571428571429,ok +80009,1.0,1,0.09999999999999998,ok +80010,1.0,1,0.2857142857142857,ok +80011,1.0,1,0.05555555555555558,ok +80012,1.0,1,0.03448275862068961,ok +80013,1.0,1,0.0,ok +80014,1.0,1,0.05263157894736836,ok +80015,1.0,1,0.125,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/f1_multiclass.classification_dense/configurations.csv b/metalearning/metalearning_files/f1_multiclass.classification_dense/configurations.csv new file mode 100755 index 0000000..0cf6ebe --- /dev/null +++ b/metalearning/metalearning_files/f1_multiclass.classification_dense/configurations.csv @@ -0,0 +1,96 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:fast_ica:algorithm,preprocessor:fast_ica:fun,preprocessor:fast_ica:n_components,preprocessor:fast_ica:whiten,preprocessor:feature_agglomeration:affinity,preprocessor:feature_agglomeration:linkage,preprocessor:feature_agglomeration:n_clusters,preprocessor:feature_agglomeration:pooling_func,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:pca:keep_variance,preprocessor:pca:whiten,preprocessor:polynomial:degree,preprocessor:polynomial:include_bias,preprocessor:polynomial:interaction_only,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,rescaling:__choice__,rescaling:quantile_transformer:n_quantiles,rescaling:quantile_transformer:output_distribution,rescaling:robust_scaler:q_max,rescaling:robust_scaler:q_min +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.03474109838999682,deviance,4.0,0.5687034678818491,None,0.0,18.0,12.0,0.0,408.0,0.5150113945430513,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.6328939517938,f_classif,,,,standardize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9265375980300852,None,0.0,13.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5368752992317617,None,0.0,16.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.8382117756438685,mutual_info,,,,quantile_transformer,11480.0,normal,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3759438793746945,None,0.0,7.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3689420905780977,fwe,f_classif,quantile_transformer,25061.0,uniform,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.12713527337147906,deviance,4.0,0.6041596127474019,None,0.0,14.0,17.0,0.0,83.0,0.8426859880999615,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9455638720565652,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,most_frequent,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8255464552647293,0.19162485555463185 +weighting,one_hot_encoding,0.18137532678800647,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9094110110427254,None,0.0,7.0,12.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,195.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.02345017287074443,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.053517066400173056,deviance,10.0,0.542144980834302,None,0.0,20.0,13.0,0.0,233.0,0.7398539900055563,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0614425536709615,fwe,f_classif,robust_scaler,,,0.9523118062307264,0.13434811490315818 +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.8149627329153046,None,0.0,15.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.040936424602789435,deviance,7.0,0.5495014745530306,None,0.0,20.0,18.0,0.0,141.0,0.6905343807995293,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.9727149851116396,None,0.0,10.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,25.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7974565919616314,None,0.0,12.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,median,extra_trees_preproc_for_classification,True,entropy,None,0.9772091846790169,None,0.0,10.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.1958974686405233,deviance,5.0,0.33885235607979314,None,0.0,6.0,4.0,0.0,125.0,0.9448890820738562,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,0.010000000000000005,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.0518326156691958,deviance,6.0,0.8807456180216267,None,0.0,7.0,19.0,0.0,366.0,0.7314831276137047,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,adaboost,SAMME,0.4391375941344922,3.0,386.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7439738358430176,0.20581080574615795 +none,one_hot_encoding,0.00012586572428922356,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5240592829918601,None,0.0,10.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.2422926485206341,,1.0,None,0.0,15.0,9.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.02102683283349326,deviance,10.0,0.2797288369369436,None,0.0,14.0,9.0,0.0,480.0,0.5778972273820631,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58.88123233170863,mutual_info,,,,robust_scaler,,,0.75,0.25 +weighting,no_encoding,,,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,0.3170009238992427,rbf,1955.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8333938697866604,0.10426506601169797 +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2155613360930585,deviance,4.0,0.3198803116198433,None,0.0,8.0,13.0,0.0,275.0,0.28870176110739404,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,mean,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.7062102387181676,None,0.0,1.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,most_frequent,fast_ica,,,,,,,,,,,parallel,exp,100.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7065776353150109,0.23782974987118105 +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.609975998293528,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,most_frequent,fast_ica,,,,,,,,,,,parallel,logcosh,2000.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8430415644014919,0.2863750565331575 +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.39536192447534535,None,0.0,19.0,3.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5.0,None,11.0,11.0,1.0,12.0,,,,,,robust_scaler,,,0.8928631650245873,0.1581877760687084 +weighting,one_hot_encoding,0.060155186012325876,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.3163640203509378,None,0.0,16.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,median,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,2.0,None,11.0,20.0,1.0,47.0,,,,,,quantile_transformer,21065.0,uniform,, +weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2507440474920336e-05,True,,0.049622652766554566,True,0.009105043727227265,constant,squared_hinge,elasticnet,,0.00010112719671669047,,,,,,,,,,,,,,,,,,31,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61.69949680034141,chi2,,,,none,,,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.9348409326933208,rbf,-1.0,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1754.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +weighting,one_hot_encoding,0.010000000000000005,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.011886849251540706,deviance,10.0,0.716738790505292,None,0.0,5.0,6.0,0.0,472.0,0.9128424273302038,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,198.72528686512538,False,True,1.0,squared_hinge,ovr,l2,0.026260652523566803,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,robust_scaler,,,0.913511520078368,0.2742229325455444 +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.8770766409674923,None,0.0,13.0,13.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,84023.0,normal,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3428971645958825,,,0.2229870623330047,rbf,-1.0,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.00034835629696198427,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8245132980938538,0.08947420373097192 +weighting,one_hot_encoding,0.00016967940959070708,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9439080311935252,None,0.0,2.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,adaboost,SAMME.R,0.8220362681234727,5.0,183.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.363262678765884,fdr,chi2,robust_scaler,,,0.8826612080363588,0.2189605310171097 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1.0,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.3837398524575939,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.018356703878357986,deviance,3.0,0.9690352514774068,None,0.0,12.0,3.0,0.0,234.0,0.3870344708308441,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.0037042565030130366,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.9610802347688104,None,0.0,17.0,9.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,172.0,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.00010817282861262362,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.799803680241154,None,0.0,13.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,mean,fast_ica,,,,,,,,,,,deflation,exp,1793.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9071932815811076,0.03563842252368924 +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.35533396539961937,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4165632766388807,fpr,chi2,none,,,, +weighting,no_encoding,,,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.96834823420249e-05,False,True,hinge,0.00016639250831671168,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.628430584359224,mutual_info,,,,quantile_transformer,6634.0,normal,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8522973640879423,None,0.0,13.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,8.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.9376772805635799,None,0.0,18.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,median,extra_trees_preproc_for_classification,True,entropy,None,0.8613889689810683,None,0.0,10.0,4.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2636201374253461,deviance,7.0,0.8344964130784466,None,0.0,9.0,2.0,0.0,298.0,0.7517549950523315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.010000000000000005,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.14904790197542306,deviance,6.0,0.7561346995577642,None,0.0,5.0,14.0,0.0,340.0,0.6548040792383665,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0009243833519832893,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9829888014816668,None,0.0,18.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,minmax,,,, +weighting,one_hot_encoding,0.0020580843703898177,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.945774573434192,None,0.0,19.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,median,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,15209.0,normal,, +weighting,one_hot_encoding,0.010000000000000005,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.44875674701568935,None,0.0,8.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,most_frequent,fast_ica,,,,,,,,,,,deflation,cube,1367.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0506220137415751,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.4433458237042269,None,0.0,11.0,10.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.8740590455827745,0.19483217552098045 +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.001279467383882126,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0903354518102121,fwe,f_classif,standardize,,,, +weighting,one_hot_encoding,0.00013442810992750476,True,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1.0,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,median,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,8.0,None,13.0,16.0,1.0,28.0,,,,,,quantile_transformer,41502.0,uniform,, +weighting,one_hot_encoding,0.002615346832354839,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7884268823432835,None,0.0,20.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.2472984547885781,deviance,3.0,0.6564306719064884,None,0.0,15.0,14.0,0.0,220.0,0.8082564085714649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,median,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,332.0,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.03905145156995541,deviance,5.0,0.2281306656230014,None,0.0,14.0,13.0,0.0,493.0,0.8793075442604774,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,25382.0,normal,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.12713527337147906,deviance,4.0,0.6041596127474019,None,0.0,14.0,17.0,0.0,83.0,0.8426859880999615,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.7879059827470586,None,0.0,3.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54.1024239468849,f_classif,,,,quantile_transformer,89842.0,normal,, +weighting,one_hot_encoding,0.010000000000000005,True,decision_tree,,,,,,,entropy,1.5841974853345435,,1.0,None,0.0,8.0,14.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,25.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.19998727075532635,deviance,10.0,0.9377656718112952,None,0.0,7.0,13.0,0.0,214.0,0.6062346326014357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.003777272888571546,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.4583291745177553,None,0.0,14.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.27741957612588863,fdr,f_classif,none,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.5765793990908161,None,0.0,11.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,median,fast_ica,,,,,,,,,,,deflation,exp,10.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,adaboost,SAMME,0.7603752531440509,7.0,413.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,28.04113884899283,False,True,1.0,squared_hinge,ovr,l1,0.0001084726092097629,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,26106.0,uniform,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3386310102795006,None,0.0,6.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,True,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.0,None,,5.861221938384061e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.22985730375167915,fpr,f_classif,quantile_transformer,40589.0,uniform,, +none,no_encoding,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00012437731009611307,True,,0.08709904468181932,True,,invscaling,squared_hinge,l1,0.6231564675290102,0.008071279833697563,,,,,,,,,,,,,,,,,,134,median,fast_ica,,,,,,,,,,,parallel,logcosh,814.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.714869937384006,None,0.0,8.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, diff --git a/metalearning/metalearning_files/f1_multiclass.classification_dense/description.txt b/metalearning/metalearning_files/f1_multiclass.classification_dense/description.txt new file mode 100755 index 0000000..8045f6f --- /dev/null +++ b/metalearning/metalearning_files/f1_multiclass.classification_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: f1 +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/f1_multiclass.classification_dense/feature_costs.arff b/metalearning/metalearning_files/f1_multiclass.classification_dense/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/f1_multiclass.classification_dense/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/f1_multiclass.classification_dense/feature_runstatus.arff b/metalearning/metalearning_files/f1_multiclass.classification_dense/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/f1_multiclass.classification_dense/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/f1_multiclass.classification_dense/feature_values.arff b/metalearning/metalearning_files/f1_multiclass.classification_dense/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/f1_multiclass.classification_dense/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/f1_multiclass.classification_dense/readme.txt b/metalearning/metalearning_files/f1_multiclass.classification_dense/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/f1_multiclass.classification_sparse/algorithm_runs.arff b/metalearning/metalearning_files/f1_multiclass.classification_sparse/algorithm_runs.arff new file mode 100755 index 0000000..3bfc9ea --- /dev/null +++ b/metalearning/metalearning_files/f1_multiclass.classification_sparse/algorithm_runs.arff @@ -0,0 +1,107 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE f1 NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2117,1.0,1,0.10832227183138254,ok +75156,1.0,2,0.19883040935672514,ok +75129,1.0,3,0.5438596491228069,ok +75239,1.0,4,0.0,ok +75121,1.0,5,0.002049180327868827,ok +261,1.0,6,0.4320987654320987,ok +75240,1.0,7,0.028349082823791005,ok +75120,1.0,8,0.02004008016032066,ok +75124,1.0,9,0.5238095238095238,ok +75176,1.0,10,0.014815786023338218,ok +75103,1.0,11,0.06161137440758291,ok +75095,1.0,12,0.18120805369127513,ok +273,1.0,13,0.05841924398625431,ok +75174,1.0,14,0.20862968231389278,ok +75153,1.0,15,0.1207658321060383,ok +75093,1.0,16,0.5637305699481865,ok +75119,1.0,17,0.018711018711018657,ok +75215,1.0,18,0.024825259098578023,ok +75233,1.0,19,0.04636459430979978,ok +75196,1.0,20,0.04347826086956519,ok +75191,1.0,21,0.1299683319330447,ok +75115,1.0,22,0.009544008483563182,ok +75108,1.0,23,0.0060422960725075026,ok +75101,1.0,24,0.2677202224173252,ok +75192,1.0,25,0.5012264922322158,ok +75232,1.0,26,0.22666666666666668,ok +75173,1.0,27,0.11666139740752446,ok +75148,1.0,28,0.18589132507149664,ok +75150,1.0,29,0.3176470588235294,ok +75100,1.0,30,0.9658119658119658,ok +75179,1.0,31,0.29820749592612716,ok +75213,1.0,32,0.1123595505617977,ok +75227,1.0,33,0.17420596727622717,ok +75184,1.0,34,0.24985439720442626,ok +75142,1.0,35,0.08059525563577419,ok +75166,1.0,36,0.10216483099126472,ok +75133,1.0,37,0.4999999999999999,ok +75234,1.0,38,0.06196943972835334,ok +75139,1.0,39,0.01832620647525962,ok +75117,1.0,40,0.03703703703703709,ok +75113,1.0,41,0.05429071803852892,ok +75237,1.0,42,0.00024933381122316245,ok +75195,1.0,43,0.0013143894348125462,ok +75171,1.0,44,0.16568914956011738,ok +75128,1.0,45,0.012725344644750725,ok +75146,1.0,46,0.10476190476190483,ok +75116,1.0,47,0.005847953216374324,ok +75157,1.0,48,0.4766917293233083,ok +75187,1.0,49,0.02857142857142858,ok +2350,1.0,50,0.5129083465619517,ok +75125,1.0,51,0.021531100478468845,ok +75185,1.0,52,0.13694581280788165,ok +75163,1.0,53,0.0650779101741521,ok +75177,1.0,54,0.14457831325301207,ok +75189,1.0,55,0.014574596962072528,ok +75244,1.0,56,0.6633663366336633,ok +75219,1.0,57,0.09526000920386557,ok +75222,1.0,58,0.2777777777777778,ok +75159,1.0,59,0.5319148936170213,ok +75175,1.0,60,0.14866629360193984,ok +254,1.0,61,0.0,ok +75105,1.0,62,0.8763197586726998,ok +75106,1.0,63,0.7932833387675252,ok +75212,1.0,64,0.2686230248306998,ok +75099,1.0,65,0.5443037974683544,ok +75248,1.0,66,0.6210350584307178,ok +233,1.0,67,0.01632653061224487,ok +75226,1.0,68,0.0025179856115107313,ok +75132,1.0,69,0.8934986682967296,ok +75127,1.0,70,0.37693004057912305,ok +75161,1.0,71,0.08227474150664704,ok +75143,1.0,72,0.006923837784371889,ok +75114,1.0,73,0.015037593984962405,ok +75182,1.0,74,0.19805115712545673,ok +75112,1.0,75,0.1851308388359012,ok +75210,1.0,76,0.0,ok +75092,1.0,77,0.38157894736842113,ok +3043,1.0,78,0.14970059880239517,ok +75249,1.0,79,0.030927835051546393,ok +75126,1.0,80,0.027870680044593144,ok +75225,1.0,81,0.569377990430622,ok +75141,1.0,82,0.07126645483431693,ok +75107,1.0,83,0.4416498993963782,ok +75097,1.0,84,0.030135154496394367,ok +80001,1.0,1,0.19999999999999996,ok +80003,1.0,1,0.12244897959183676,ok +80006,1.0,1,0.23076923076923084,ok +80008,1.0,1,0.19999999999999996,ok +80009,1.0,1,0.1428571428571428,ok +80010,1.0,1,0.2857142857142857,ok +80011,1.0,1,0.05555555555555558,ok +80012,1.0,1,0.03448275862068961,ok +80013,1.0,1,0.0,ok +80014,1.0,1,0.05263157894736836,ok +80015,1.0,1,0.125,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/f1_multiclass.classification_sparse/configurations.csv b/metalearning/metalearning_files/f1_multiclass.classification_sparse/configurations.csv new file mode 100755 index 0000000..9c1e0f0 --- /dev/null +++ b/metalearning/metalearning_files/f1_multiclass.classification_sparse/configurations.csv @@ -0,0 +1,96 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,preprocessor:truncatedSVD:target_dim,rescaling:__choice__ +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.585711203872775,None,0.0,5.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,median,extra_trees_preproc_for_classification,True,entropy,None,0.29512530534048065,None,0.0,7.0,10.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.039533063907190934,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1878805519245509,fdr,chi2,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7238850981243719,None,0.0,4.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,5.282738216059151e-05,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.010000000000000005,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9727149851116396,None,0.0,18.0,13.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,,none +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7974565919616314,None,0.0,12.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,median,extra_trees_preproc_for_classification,True,entropy,None,0.9772091846790169,None,0.0,10.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.0010015637584068037,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.037611630308856295,deviance,5.0,0.8840126779516314,None,0.0,10.0,2.0,0.0,444.0,0.7599997167603434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,most_frequent,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.8657388713119849,,1.0,None,0.0,19.0,13.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828776,None,0.0,2.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.002173124111626734,None,0.0,14.0,4.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,6.0,None,13.0,2.0,1.0,23.0,,,,,,,normalize +weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2507440474920336e-05,True,,0.049622652766554566,True,0.009105043727227265,constant,squared_hinge,elasticnet,,0.00010112719671669047,,,,,,,,,,,,,,,,,,31,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61.69949680034141,chi2,,,,,none +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.9348409326933208,rbf,-1.0,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,mean,kernel_pca,,,,,,,,,,,,,,cosine,1754.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3428971645958825,,,0.2229870623330047,rbf,-1.0,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4047.618729304337,,,2.0237366768707754,rbf,-1.0,True,0.04369127828878843,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,one_hot_encoding,0.0008685602750053468,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9896334290292654,None,0.0,11.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,141.76310800864283,False,True,1.0,squared_hinge,ovr,l1,0.004317884655117431,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1.0,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.3451627750042988,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.3163640203509378,None,0.0,17.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,median,extra_trees_preproc_for_classification,False,gini,None,0.8916956785028156,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.00011600321198702641,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,most_frequent,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.9376772805635799,None,0.0,18.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,median,extra_trees_preproc_for_classification,True,entropy,None,0.8613889689810683,None,0.0,10.0,4.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.039533063907190934,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1878805519245509,fdr,chi2,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9091193924897338,None,0.0,2.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3823734947460288,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8804227616935514,None,0.0,10.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.0898983119899223,False,True,1.0,squared_hinge,ovr,l1,0.016048982920294167,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0733000338152003,False,True,1.0,squared_hinge,ovr,l2,0.033752542733220474,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,-0.6840756728731969,,0.00980445380551526,sigmoid,161.0,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.04329010470998136,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7260331062271381,None,0.0,7.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,134,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.714869937384006,None,0.0,8.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/metalearning/metalearning_files/f1_multiclass.classification_sparse/description.txt b/metalearning/metalearning_files/f1_multiclass.classification_sparse/description.txt new file mode 100755 index 0000000..8045f6f --- /dev/null +++ b/metalearning/metalearning_files/f1_multiclass.classification_sparse/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: f1 +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/f1_multiclass.classification_sparse/feature_costs.arff b/metalearning/metalearning_files/f1_multiclass.classification_sparse/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/f1_multiclass.classification_sparse/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/f1_multiclass.classification_sparse/feature_runstatus.arff b/metalearning/metalearning_files/f1_multiclass.classification_sparse/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/f1_multiclass.classification_sparse/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/f1_multiclass.classification_sparse/feature_values.arff b/metalearning/metalearning_files/f1_multiclass.classification_sparse/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/f1_multiclass.classification_sparse/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/f1_multiclass.classification_sparse/readme.txt b/metalearning/metalearning_files/f1_multiclass.classification_sparse/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/f1_weighted_binary.classification_dense/algorithm_runs.arff b/metalearning/metalearning_files/f1_weighted_binary.classification_dense/algorithm_runs.arff new file mode 100755 index 0000000..dd63558 --- /dev/null +++ b/metalearning/metalearning_files/f1_weighted_binary.classification_dense/algorithm_runs.arff @@ -0,0 +1,152 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE f1_weighted NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2120,1.0,1,0.08041237676446022,ok +75193,1.0,2,0.03825452279163666,ok +2117,1.0,3,0.17066951869137215,ok +75156,1.0,4,0.20324146258890163,ok +75129,1.0,5,0.11985367176495254,ok +75243,1.0,6,0.0,ok +75110,1.0,7,0.11648340299707849,ok +75239,1.0,8,0.0,ok +75223,1.0,9,0.10326169932682816,ok +75221,1.0,10,0.4104088119418554,ok +258,1.0,11,0.009704531781493553,ok +75121,1.0,12,0.0,ok +253,1.0,13,0.4419219872587776,ok +261,1.0,14,0.254290815615859,ok +75240,1.0,15,0.02190680155196323,ok +75120,1.0,16,0.04981860127911408,ok +75124,1.0,17,0.10580512322180602,ok +75176,1.0,18,0.015417641830919315,ok +75103,1.0,19,0.005774697630436032,ok +75207,1.0,20,0.16288734167144403,ok +75095,1.0,21,0.017835118710502584,ok +273,1.0,22,0.044143856430088446,ok +75174,1.0,23,0.1178207749003396,ok +75153,1.0,24,0.08027853110163441,ok +75093,1.0,25,0.20540618737303462,ok +75119,1.0,26,0.033579267862175466,ok +75201,1.0,27,0.08086588964872377,ok +75215,1.0,28,0.02743062586610312,ok +75172,1.0,29,0.10345512305951998,ok +75169,1.0,30,0.034129556530982486,ok +75202,1.0,31,0.2396259361510158,ok +75233,1.0,32,0.06075665190946333,ok +75231,1.0,33,0.20981683120608396,ok +75196,1.0,34,0.015572950069634772,ok +248,1.0,35,0.23258814022143115,ok +75191,1.0,36,0.1292791019034626,ok +75217,1.0,37,0.0,ok +260,1.0,38,0.026998407680968617,ok +75115,1.0,39,0.01820883508633664,ok +75123,1.0,40,0.3220199977182707,ok +75108,1.0,41,0.0,ok +75101,1.0,42,0.2742885709851963,ok +75192,1.0,43,0.4838727111853699,ok +75232,1.0,44,0.12343990745075895,ok +75173,1.0,45,0.11655005765215176,ok +75197,1.0,46,0.15990558565323876,ok +266,1.0,47,0.019626935449329252,ok +75148,1.0,48,0.1356911432669068,ok +75150,1.0,49,0.2882068163967273,ok +75100,1.0,50,0.005690533730990155,ok +75178,1.0,51,0.7943166037466941,ok +75236,1.0,52,0.03235056748186682,ok +75179,1.0,53,0.18556100311237933,ok +75213,1.0,54,0.0518783755232074,ok +2123,1.0,55,0.06539918823655844,ok +75227,1.0,56,0.10165813170606963,ok +75184,1.0,57,0.10777407575427433,ok +75142,1.0,58,0.07158590737002735,ok +236,1.0,59,0.03875927505711274,ok +2122,1.0,60,0.10951556356133851,ok +75188,1.0,61,0.26465348624082274,ok +75166,1.0,62,0.09953908944937662,ok +75181,1.0,63,0.0,ok +75133,1.0,64,0.006078946850412992,ok +75134,1.0,65,0.08730010916367836,ok +75198,1.0,66,0.12258268813365325,ok +262,1.0,67,0.002756677062924262,ok +75234,1.0,68,0.024161314223939967,ok +75139,1.0,69,0.010712909121264436,ok +252,1.0,70,0.15518767652207754,ok +75117,1.0,71,0.05441384136226013,ok +75113,1.0,72,0.0066279535588466,ok +75098,1.0,73,0.02762928168631018,ok +246,1.0,74,0.010601765442530664,ok +75203,1.0,75,0.09622991262956981,ok +75237,1.0,76,0.00039562768586520747,ok +75195,1.0,77,0.0015608321665472324,ok +75171,1.0,78,0.16204803140883772,ok +75128,1.0,79,0.022725364810403215,ok +75096,1.0,80,0.005377674169948499,ok +75250,1.0,81,0.34358492714587807,ok +75146,1.0,82,0.11301039690636294,ok +75116,1.0,83,0.009847570622209645,ok +75157,1.0,84,0.4401806517094792,ok +75187,1.0,85,0.01678999849286833,ok +2350,1.0,86,0.4348957135748658,ok +242,1.0,87,0.010606337746570604,ok +244,1.0,88,0.11120952401278628,ok +75125,1.0,89,0.0339049942348123,ok +75185,1.0,90,0.12834457802817323,ok +75163,1.0,91,0.0604226420363001,ok +75177,1.0,92,0.018684969388137018,ok +75189,1.0,93,0.01938414877240202,ok +75244,1.0,94,0.08431968291195124,ok +75219,1.0,95,0.03480519211769084,ok +75222,1.0,96,0.04868525659600709,ok +75159,1.0,97,0.0745997329490441,ok +75175,1.0,98,0.09972153034778797,ok +75109,1.0,99,0.31166181636334855,ok +254,1.0,100,0.0,ok +75105,1.0,101,0.027098972971820845,ok +75106,1.0,102,0.10352576469506813,ok +75212,1.0,103,0.2521591217243391,ok +75099,1.0,104,0.14012657045167765,ok +75248,1.0,105,0.133683555717799,ok +233,1.0,106,0.0028461118739864233,ok +75235,1.0,107,0.0011111221947569527,ok +75226,1.0,108,0.0030496655082195012,ok +75132,1.0,109,0.06641886401898822,ok +75127,1.0,110,0.33328270764840817,ok +251,1.0,111,0.0,ok +75161,1.0,112,0.06437096492695238,ok +75143,1.0,113,0.010503848664593085,ok +75114,1.0,114,0.023575638506876273,ok +75182,1.0,115,0.11028726475492456,ok +75112,1.0,116,0.12272977173151256,ok +75210,1.0,117,0.0,ok +75205,1.0,118,0.181570513151477,ok +75090,1.0,119,0.06088527309316605,ok +275,1.0,120,0.036131286609341284,ok +288,1.0,121,0.1302581858230315,ok +75092,1.0,122,0.10267439074852436,ok +3043,1.0,123,0.0199289436742357,ok +75249,1.0,124,0.003230606814741299,ok +75126,1.0,125,0.04964265488272113,ok +75225,1.0,126,0.0638584813611206,ok +75141,1.0,127,0.05380859125413884,ok +75107,1.0,128,0.059474822395280236,ok +75097,1.0,129,0.06669445026792797,ok +80001,1.0,1,0.07273576097105527,ok +80003,1.0,1,0.09228340080971653,ok +80006,1.0,1,0.0940270935960591,ok +80008,1.0,1,0.11399711399711399,ok +80009,1.0,1,0.10526315789473684,ok +80010,1.0,1,0.15136587550380654,ok +80011,1.0,1,0.03824498352800254,ok +80012,1.0,1,0.04618599791013589,ok +80013,1.0,1,0.0,ok +80014,1.0,1,0.045741626794258305,ok +80015,1.0,1,0.09523809523809523,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/f1_weighted_binary.classification_dense/configurations.csv b/metalearning/metalearning_files/f1_weighted_binary.classification_dense/configurations.csv new file mode 100755 index 0000000..a796521 --- /dev/null +++ b/metalearning/metalearning_files/f1_weighted_binary.classification_dense/configurations.csv @@ -0,0 +1,141 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:fast_ica:algorithm,preprocessor:fast_ica:fun,preprocessor:fast_ica:n_components,preprocessor:fast_ica:whiten,preprocessor:feature_agglomeration:affinity,preprocessor:feature_agglomeration:linkage,preprocessor:feature_agglomeration:n_clusters,preprocessor:feature_agglomeration:pooling_func,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:pca:keep_variance,preprocessor:pca:whiten,preprocessor:polynomial:degree,preprocessor:polynomial:include_bias,preprocessor:polynomial:interaction_only,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,rescaling:__choice__,rescaling:quantile_transformer:n_quantiles,rescaling:quantile_transformer:output_distribution,rescaling:robust_scaler:q_max,rescaling:robust_scaler:q_min +weighting,one_hot_encoding,0.00011717632475982632,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.045388141846341344,deviance,10.0,0.29161769341843435,None,0.0,20.0,2.0,0.0,278.0,0.7912571599269661,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7249853037185638,None,0.0,1.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,median,extra_trees_preproc_for_classification,False,gini,None,0.9424908623661876,None,0.0,7.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.03474109838999682,deviance,4.0,0.5687034678818491,None,0.0,18.0,12.0,0.0,408.0,0.5150113945430513,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.6328939517938,f_classif,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.251097788175676,deviance,6.0,0.35679099363539235,None,0.0,13.0,11.0,0.0,157.0,0.4791732272983235,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,adaboost,SAMME,0.28738775989203896,10.0,423.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.8031499675923353,0.13579938270386765 +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.14159526341015916,deviance,7.0,0.8010488230155749,None,0.0,3.0,20.0,0.0,401.0,0.8073562440607731,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.002385546176068135,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.7729472304882845,,,0.0004789329856033374,rbf,-1.0,True,6.58869648864534e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,177.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5368752992317617,None,0.0,16.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.8382117756438685,mutual_info,,,,quantile_transformer,11480.0,normal,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.039533063907190934,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1878805519245509,fdr,chi2,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9455638720565652,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,most_frequent,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8255464552647293,0.19162485555463185 +weighting,one_hot_encoding,0.18137532678800647,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9094110110427254,None,0.0,7.0,12.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,195.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.6682079659377479,None,0.0,4.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,mean,extra_trees_preproc_for_classification,True,entropy,None,0.5552350997943013,None,0.0,8.0,5.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.02345017287074443,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.053517066400173056,deviance,10.0,0.542144980834302,None,0.0,20.0,13.0,0.0,233.0,0.7398539900055563,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0614425536709615,fwe,f_classif,robust_scaler,,,0.9523118062307264,0.13434811490315818 +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.8149627329153046,None,0.0,15.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.040936424602789435,deviance,7.0,0.5495014745530306,None,0.0,20.0,18.0,0.0,141.0,0.6905343807995293,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7974565919616314,None,0.0,12.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,median,extra_trees_preproc_for_classification,True,entropy,None,0.9772091846790169,None,0.0,10.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2398150931290834,,,0.4015139801872962,rbf,-1.0,False,2.402997750662158e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88.40698357592571,chi2,,,,normalize,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.1958974686405233,deviance,5.0,0.33885235607979314,None,0.0,6.0,4.0,0.0,125.0,0.9448890820738562,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2538107344750156,False,True,hinge,1.5099542326343014e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,8532.0,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,0.0007038280350320558,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4138778052607317,0.7995003430482459,5.0,5.430044692638861,poly,-1.0,True,0.02455501006004393,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,31,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.010000000000000005,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.0518326156691958,deviance,6.0,0.8807456180216267,None,0.0,7.0,19.0,0.0,366.0,0.7314831276137047,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,,False,adaboost,SAMME,0.4391375941344922,3.0,386.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7439738358430176,0.20581080574615795 +none,one_hot_encoding,0.00011294596229850895,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7515.1213255144885,0.9576762936062476,3.0,0.019002536385919932,poly,-1.0,False,0.010632086351533369,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,51.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,97282.0,normal,, +none,one_hot_encoding,0.00012586572428922356,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5240592829918601,None,0.0,10.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.03192699980429505,True,adaboost,SAMME.R,0.10000000000000002,1.0,50.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,quantile_transformer,8407.0,normal,, +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1751.4736515133566,0.62404114475118,3.0,1.6087076997410432,poly,-1.0,False,3.5353792826856045e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1198.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.2422926485206341,,1.0,None,0.0,15.0,9.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.02102683283349326,deviance,10.0,0.2797288369369436,None,0.0,14.0,9.0,0.0,480.0,0.5778972273820631,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58.88123233170863,mutual_info,,,,robust_scaler,,,0.75,0.25 +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828776,None,0.0,2.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2155613360930585,deviance,4.0,0.3198803116198433,None,0.0,8.0,13.0,0.0,275.0,0.28870176110739404,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,mean,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.7062102387181676,None,0.0,1.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,most_frequent,fast_ica,,,,,,,,,,,parallel,exp,100.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7065776353150109,0.23782974987118105 +none,one_hot_encoding,0.16334152321884812,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00011572473434870852,True,True,squared_hinge,0.00019678754114665057,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.045742431094098604,fpr,chi2,none,,,, +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.3795924768593385,deviance,2.0,0.33708497069988536,None,0.0,15.0,13.0,0.0,451.0,0.7716323242090217,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2573946506994812,fwe,f_classif,quantile_transformer,1000.0,uniform,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.609975998293528,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,most_frequent,fast_ica,,,,,,,,,,,parallel,logcosh,2000.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8430415644014919,0.2863750565331575 +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.39536192447534535,None,0.0,19.0,3.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5.0,None,11.0,11.0,1.0,12.0,,,,,,robust_scaler,,,0.8928631650245873,0.1581877760687084 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.3126027672745337,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,870.2240970463429,0.5325949351918051,3.0,0.010682839357128344,poly,-1.0,False,2.485160860440657e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4608103694360143,fdr,f_classif,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.9348409326933208,rbf,-1.0,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1754.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,198.72528686512538,False,True,1.0,squared_hinge,ovr,l2,0.026260652523566803,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,robust_scaler,,,0.913511520078368,0.2742229325455444 +none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9260795160807372,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.03644212536682547,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.05965671477918361,deviance,8.0,0.4858133247974158,None,0.0,14.0,7.0,0.0,480.0,0.5726186552917335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.15318294164619112 +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18887.81504976871,,,0.23283562663398755,rbf,-1.0,True,2.3839685780861318e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3937607155568376,fwe,chi2,robust_scaler,,,0.941018778984854,0.2144110585080491 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3428971645958825,,,0.2229870623330047,rbf,-1.0,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8138233157708883,None,0.0,2.0,9.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54.27504292568562,f_classif,,,,minmax,,,, +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00016781524591321165,True,True,squared_hinge,1.5119200923218881e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.556190405302504,False,True,1.0,squared_hinge,ovr,l2,0.0007318628304090552,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,most_frequent,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,3.5602014542183973,948.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +weighting,one_hot_encoding,0.00034835629696198427,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8245132980938538,0.08947420373097192 +weighting,one_hot_encoding,0.00016967940959070708,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9439080311935252,None,0.0,2.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35.0,None,,0.005389483044810356,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,most_frequent,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,9.441661069727422e-05,1896.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.03528169333197684,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3416063836589199,None,0.0,9.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,median,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,normalize,,,, +none,one_hot_encoding,,False,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6396026761675004,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06544340428506021,fwe,f_classif,none,,,, +weighting,one_hot_encoding,0.004980497345831963,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1255.9137433589426,,,0.08351549479967445,rbf,-1.0,True,0.00017919875199222518,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1.0,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.3837398524575939,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.018356703878357986,deviance,3.0,0.9690352514774068,None,0.0,12.0,3.0,0.0,234.0,0.3870344708308441,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,most_frequent,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6864970915330799,False,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5670424455696162,None,0.0,8.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.00010817282861262362,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.799803680241154,None,0.0,13.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,85,mean,fast_ica,,,,,,,,,,,deflation,exp,1793.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9071932815811076,0.03563842252368924 +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.35533396539961937,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,86,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4165632766388807,fpr,chi2,none,,,, +weighting,one_hot_encoding,,False,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,156.0,auto,,0.0001987333852871589,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1.0,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,no_encoding,,,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.96834823420249e-05,False,True,hinge,0.00016639250831671168,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,89,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.628430584359224,mutual_info,,,,quantile_transformer,6634.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,90,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,91,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,8.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,93,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2636201374253461,deviance,7.0,0.8344964130784466,None,0.0,9.0,2.0,0.0,298.0,0.7517549950523315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,95,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,96,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,97,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.07463196642416367,deviance,7.0,0.8603242247379981,None,0.0,2.0,6.0,0.0,500.0,0.8447665577491962,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,98,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.0433556140045585,deviance,10.0,0.33000096635982235,None,0.0,15.0,13.0,0.0,388.0,0.8291104221904706,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,99,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,robust_scaler,,,0.7496393440951183,0.2853682991120835 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,100,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,102,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0020580843703898177,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.945774573434192,None,0.0,19.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,103,median,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,15209.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,104,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,105,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,106,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.005332972692819521,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5565918060287016,None,0.0,5.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,107,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,173.0,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9527068489270144,0.04135311355893583 +weighting,one_hot_encoding,0.001279467383882126,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,108,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0903354518102121,fwe,f_classif,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,109,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.002615346832354839,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7884268823432835,None,0.0,20.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,110,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +weighting,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56.086963007482865,,,0.013609964993119377,rbf,-1.0,True,0.00196831255706268,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,111,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.15374716583918388 +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.2472984547885781,deviance,3.0,0.6564306719064884,None,0.0,15.0,14.0,0.0,220.0,0.8082564085714649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,112,median,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,332.0,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,113,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,115,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,116,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,117,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,118,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.20202014999292287,False,True,1.0,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,no_encoding,,,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6390376923528961,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,119,median,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,164.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,62508.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,120,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.27041927277584e-06,True,0.00010000000000000009,0.033157325660763994,True,0.0008114527992546482,invscaling,modified_huber,elasticnet,0.13714427818877545,0.055179642772545036,,,,,,,,,,,,,,,,,,121,median,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9930358173100328,None,0.0,6.0,3.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,122,most_frequent,extra_trees_preproc_for_classification,False,entropy,None,0.4655848826291386,None,0.0,13.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,123,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.19998727075532635,deviance,10.0,0.9377656718112952,None,0.0,7.0,13.0,0.0,214.0,0.6062346326014357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,124,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,125,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,126,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.5765793990908161,None,0.0,11.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,127,median,fast_ica,,,,,,,,,,,deflation,exp,10.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7945458151995424,None,0.0,1.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,129,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,38400.0,uniform,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,adaboost,SAMME,0.7603752531440509,7.0,413.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,28.04113884899283,False,True,1.0,squared_hinge,ovr,l1,0.0001084726092097629,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,26106.0,uniform,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3386310102795006,None,0.0,6.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,True,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.0,None,,5.861221938384061e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.22985730375167915,fpr,f_classif,quantile_transformer,40589.0,uniform,, +none,no_encoding,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00012437731009611307,True,,0.08709904468181932,True,,invscaling,squared_hinge,l1,0.6231564675290102,0.008071279833697563,,,,,,,,,,,,,,,,,,134,median,fast_ica,,,,,,,,,,,parallel,logcosh,814.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.714869937384006,None,0.0,8.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, diff --git a/metalearning/metalearning_files/f1_weighted_binary.classification_dense/description.txt b/metalearning/metalearning_files/f1_weighted_binary.classification_dense/description.txt new file mode 100755 index 0000000..f4d0383 --- /dev/null +++ b/metalearning/metalearning_files/f1_weighted_binary.classification_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: f1_weighted +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/f1_weighted_binary.classification_dense/feature_costs.arff b/metalearning/metalearning_files/f1_weighted_binary.classification_dense/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/f1_weighted_binary.classification_dense/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/f1_weighted_binary.classification_dense/feature_runstatus.arff b/metalearning/metalearning_files/f1_weighted_binary.classification_dense/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/f1_weighted_binary.classification_dense/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/f1_weighted_binary.classification_dense/feature_values.arff b/metalearning/metalearning_files/f1_weighted_binary.classification_dense/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/f1_weighted_binary.classification_dense/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/f1_weighted_binary.classification_dense/readme.txt b/metalearning/metalearning_files/f1_weighted_binary.classification_dense/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/f1_weighted_binary.classification_sparse/algorithm_runs.arff b/metalearning/metalearning_files/f1_weighted_binary.classification_sparse/algorithm_runs.arff new file mode 100755 index 0000000..9fd78de --- /dev/null +++ b/metalearning/metalearning_files/f1_weighted_binary.classification_sparse/algorithm_runs.arff @@ -0,0 +1,152 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE f1_weighted NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2120,1.0,1,0.09184560117484108,ok +75193,1.0,2,0.03825452279163666,ok +2117,1.0,3,0.17066951869137215,ok +75156,1.0,4,0.22026758993168338,ok +75129,1.0,5,0.11985367176495254,ok +75243,1.0,6,0.015617243640308476,ok +75110,1.0,7,0.27972402767070514,ok +75239,1.0,8,0.0,ok +75223,1.0,9,0.3106766376998267,ok +75221,1.0,10,0.4104088119418554,ok +258,1.0,11,0.018350207054443124,ok +75121,1.0,12,0.004018801310984532,ok +253,1.0,13,0.4419219872587776,ok +261,1.0,14,0.254290815615859,ok +75240,1.0,15,0.02190680155196323,ok +75120,1.0,16,0.04981860127911408,ok +75124,1.0,17,0.10580512322180602,ok +75176,1.0,18,0.016587552062598432,ok +75103,1.0,19,0.008072010526220086,ok +75207,1.0,20,0.16288734167144403,ok +75095,1.0,21,0.017835118710502584,ok +273,1.0,22,0.044912223755791625,ok +75174,1.0,23,0.11830526729492052,ok +75153,1.0,24,0.1213380151432053,ok +75093,1.0,25,0.20540618737303462,ok +75119,1.0,26,0.033579267862175466,ok +75201,1.0,27,0.08086588964872377,ok +75215,1.0,28,0.028270400591125844,ok +75172,1.0,29,0.09090829801598754,ok +75169,1.0,30,0.034129556530982486,ok +75202,1.0,31,0.2396259361510158,ok +75233,1.0,32,0.06557863245191664,ok +75231,1.0,33,0.20981683120608396,ok +75196,1.0,34,0.023462952895589195,ok +248,1.0,35,0.2668282951427886,ok +75191,1.0,36,0.1240334236646079,ok +75217,1.0,37,0.0,ok +260,1.0,38,0.026998407680968617,ok +75115,1.0,39,0.01844124945050607,ok +75123,1.0,40,0.3220199977182707,ok +75108,1.0,41,0.001835113058128779,ok +75101,1.0,42,0.2828603545677709,ok +75192,1.0,43,0.4838727111853699,ok +75232,1.0,44,0.1487629645898919,ok +75173,1.0,45,0.11750578000964573,ok +75197,1.0,46,0.1395061958359226,ok +266,1.0,47,0.03271906775864197,ok +75148,1.0,48,0.19030912587610016,ok +75150,1.0,49,0.3204239801075106,ok +75100,1.0,50,0.005690533730990155,ok +75178,1.0,51,0.8083869128005314,ok +75236,1.0,52,0.03236382574407237,ok +75179,1.0,53,0.18556100311237933,ok +75213,1.0,54,0.0518783755232074,ok +2123,1.0,55,0.06539918823655844,ok +75227,1.0,56,0.10165813170606963,ok +75184,1.0,57,0.1464874342661746,ok +75142,1.0,58,0.08132210905398984,ok +236,1.0,59,0.04230806465282844,ok +2122,1.0,60,0.27972402767070514,ok +75188,1.0,61,0.26465348624082274,ok +75166,1.0,62,0.09953908944937662,ok +75181,1.0,63,0.0,ok +75133,1.0,64,0.006078946850412992,ok +75134,1.0,65,0.10065294069585673,ok +75198,1.0,66,0.12258268813365325,ok +262,1.0,67,0.002756677062924262,ok +75234,1.0,68,0.059859575576248436,ok +75139,1.0,69,0.012117466274937372,ok +252,1.0,70,0.16672301815374768,ok +75117,1.0,71,0.06144073346430923,ok +75113,1.0,72,0.0066279535588466,ok +75098,1.0,73,0.02762928168631018,ok +246,1.0,74,0.024171481047592258,ok +75203,1.0,75,0.09622991262956981,ok +75237,1.0,76,0.00039562768586520747,ok +75195,1.0,77,0.0015608321665472324,ok +75171,1.0,78,0.16725021757377856,ok +75128,1.0,79,0.022725364810403215,ok +75096,1.0,80,0.42588242341225635,ok +75250,1.0,81,0.34358492714587807,ok +75146,1.0,82,0.1218585567585766,ok +75116,1.0,83,0.009847570622209645,ok +75157,1.0,84,0.4401806517094792,ok +75187,1.0,85,0.027848345420230403,ok +2350,1.0,86,0.4348957135748658,ok +242,1.0,87,0.016624785509831264,ok +244,1.0,88,0.11120952401278628,ok +75125,1.0,89,0.035819469538836746,ok +75185,1.0,90,0.12834457802817323,ok +75163,1.0,91,0.0604226420363001,ok +75177,1.0,92,0.019184692607764897,ok +75189,1.0,93,0.01938414877240202,ok +75244,1.0,94,0.08431968291195124,ok +75219,1.0,95,0.08399570797186784,ok +75222,1.0,96,0.04868525659600709,ok +75159,1.0,97,0.0745997329490441,ok +75175,1.0,98,0.11790160247163806,ok +75109,1.0,99,0.3572958030354616,ok +254,1.0,100,0.0,ok +75105,1.0,101,0.027098972971820845,ok +75106,1.0,102,0.10352576469506813,ok +75212,1.0,103,0.27738023390528044,ok +75099,1.0,104,0.14012657045167765,ok +75248,1.0,105,0.133683555717799,ok +233,1.0,106,0.015180265654648917,ok +75235,1.0,107,0.0016666741480566571,ok +75226,1.0,108,0.004266071627929913,ok +75132,1.0,109,0.06641886401898822,ok +75127,1.0,110,0.33467911028125896,ok +251,1.0,111,0.025512056468401045,ok +75161,1.0,112,0.08280465928384262,ok +75143,1.0,113,0.010503848664593085,ok +75114,1.0,114,0.023575638506876273,ok +75182,1.0,115,0.11028726475492456,ok +75112,1.0,116,0.12272977173151256,ok +75210,1.0,117,0.0,ok +75205,1.0,118,0.181570513151477,ok +75090,1.0,119,0.10146999872014384,ok +275,1.0,120,0.036131286609341284,ok +288,1.0,121,0.14444376884583165,ok +75092,1.0,122,0.10267439074852436,ok +3043,1.0,123,0.0199289436742357,ok +75249,1.0,124,0.004845910222111671,ok +75126,1.0,125,0.04964265488272113,ok +75225,1.0,126,0.0638584813611206,ok +75141,1.0,127,0.05798894785784603,ok +75107,1.0,128,0.059474822395280236,ok +75097,1.0,129,0.08115392650430098,ok +80001,1.0,1,0.07273576097105527,ok +80003,1.0,1,0.1230575021443947,ok +80006,1.0,1,0.19205465587044546,ok +80008,1.0,1,0.22777777777777786,ok +80009,1.0,1,0.1578947368421053,ok +80010,1.0,1,0.15136587550380654,ok +80011,1.0,1,0.03824498352800254,ok +80012,1.0,1,0.04618599791013589,ok +80013,1.0,1,0.0,ok +80014,1.0,1,0.045741626794258305,ok +80015,1.0,1,0.09523809523809523,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/f1_weighted_binary.classification_sparse/configurations.csv b/metalearning/metalearning_files/f1_weighted_binary.classification_sparse/configurations.csv new file mode 100755 index 0000000..538f341 --- /dev/null +++ b/metalearning/metalearning_files/f1_weighted_binary.classification_sparse/configurations.csv @@ -0,0 +1,141 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,preprocessor:truncatedSVD:target_dim,rescaling:__choice__ +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7249853037185638,None,0.0,1.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,median,extra_trees_preproc_for_classification,False,gini,None,0.9424908623661876,None,0.0,7.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.039533063907190934,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1878805519245509,fdr,chi2,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.6682079659377479,None,0.0,4.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,mean,extra_trees_preproc_for_classification,True,entropy,None,0.5552350997943013,None,0.0,8.0,5.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7974565919616314,None,0.0,12.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,median,extra_trees_preproc_for_classification,True,entropy,None,0.9772091846790169,None,0.0,10.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2398150931290834,,,0.4015139801872962,rbf,-1.0,False,2.402997750662158e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88.40698357592571,chi2,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.034465366914659866,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.12057775675278172,deviance,10.0,0.8011153303489733,None,0.0,2.0,16.0,0.0,370.0,0.6078295352200873,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,mean,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0007038280350320558,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4138778052607317,0.7995003430482459,5.0,5.430044692638861,poly,-1.0,True,0.02455501006004393,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,31,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.0010015637584068037,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.037611630308856295,deviance,5.0,0.8840126779516314,None,0.0,10.0,2.0,0.0,444.0,0.7599997167603434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,most_frequent,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1751.4736515133566,0.62404114475118,3.0,1.6087076997410432,poly,-1.0,False,3.5353792826856045e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,mean,kernel_pca,,,,,,,,,,,,,,cosine,1198.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.8657388713119849,,1.0,None,0.0,19.0,13.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828776,None,0.0,2.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.027324741616523342,deviance,10.0,0.8623781459430139,None,0.0,10.0,20.0,0.0,329.0,0.8595750155424215,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,mean,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1100.6211008501205,0.5921425829232616,2.0,0.0337546254878617,poly,-1.0,True,0.09641299736884308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.9348409326933208,rbf,-1.0,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,mean,kernel_pca,,,,,,,,,,,,,,cosine,1754.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3428971645958825,,,0.2229870623330047,rbf,-1.0,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7322410244259964,None,0.0,6.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,median,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00016781524591321165,True,True,squared_hinge,1.5119200923218881e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.556190405302504,False,True,1.0,squared_hinge,ovr,l2,0.0007318628304090552,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,most_frequent,kitchen_sinks,,,,,,,,,,,,,,,,3.5602014542183973,948.0,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4047.618729304337,,,2.0237366768707754,rbf,-1.0,True,0.04369127828878843,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,one_hot_encoding,0.010000000000000005,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10.0,1.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,,none +weighting,one_hot_encoding,0.0008685602750053468,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9896334290292654,None,0.0,11.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,141.76310800864283,False,True,1.0,squared_hinge,ovr,l1,0.004317884655117431,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,,normalize +none,one_hot_encoding,0.003443779683191071,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.004980497345831963,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1255.9137433589426,,,0.08351549479967445,rbf,-1.0,True,0.00017919875199222518,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1.0,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.3451627750042988,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.3163640203509378,None,0.0,17.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,median,extra_trees_preproc_for_classification,False,gini,None,0.8916956785028156,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,85,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.35533396539961937,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,86,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4165632766388807,fpr,chi2,,none +weighting,one_hot_encoding,0.0019686649916896212,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.04641055832142541,True,True,hinge,8.540468968077405e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87,mean,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,911.0,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1.0,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,89,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,90,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,91,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,93,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,95,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,96,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,97,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,98,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,99,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,100,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,102,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,103,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,104,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,105,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,106,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.006372860318416312,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6467376360604045,None,0.0,1.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,107,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,108,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,109,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3823734947460288,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,110,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,adaboost,SAMME,0.11042308042695524,5.0,117.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,111,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,112,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,113,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,115,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,116,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,117,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,118,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.20202014999292287,False,True,1.0,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,119,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,120,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,121,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9930358173100328,None,0.0,6.0,3.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,122,most_frequent,extra_trees_preproc_for_classification,False,entropy,None,0.4655848826291386,None,0.0,13.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,123,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,124,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,125,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,126,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,127,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,129,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.04329010470998136,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7260331062271381,None,0.0,7.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.010000000000000005,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15.0,1.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,134,median,extra_trees_preproc_for_classification,False,entropy,None,0.8213051377774989,None,0.0,3.0,13.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.714869937384006,None,0.0,8.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/metalearning/metalearning_files/f1_weighted_binary.classification_sparse/description.txt b/metalearning/metalearning_files/f1_weighted_binary.classification_sparse/description.txt new file mode 100755 index 0000000..f4d0383 --- /dev/null +++ b/metalearning/metalearning_files/f1_weighted_binary.classification_sparse/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: f1_weighted +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/f1_weighted_binary.classification_sparse/feature_costs.arff b/metalearning/metalearning_files/f1_weighted_binary.classification_sparse/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/f1_weighted_binary.classification_sparse/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/f1_weighted_binary.classification_sparse/feature_runstatus.arff b/metalearning/metalearning_files/f1_weighted_binary.classification_sparse/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/f1_weighted_binary.classification_sparse/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/f1_weighted_binary.classification_sparse/feature_values.arff b/metalearning/metalearning_files/f1_weighted_binary.classification_sparse/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/f1_weighted_binary.classification_sparse/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/f1_weighted_binary.classification_sparse/readme.txt b/metalearning/metalearning_files/f1_weighted_binary.classification_sparse/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/f1_weighted_multiclass.classification_dense/algorithm_runs.arff b/metalearning/metalearning_files/f1_weighted_multiclass.classification_dense/algorithm_runs.arff new file mode 100755 index 0000000..dd63558 --- /dev/null +++ b/metalearning/metalearning_files/f1_weighted_multiclass.classification_dense/algorithm_runs.arff @@ -0,0 +1,152 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE f1_weighted NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2120,1.0,1,0.08041237676446022,ok +75193,1.0,2,0.03825452279163666,ok +2117,1.0,3,0.17066951869137215,ok +75156,1.0,4,0.20324146258890163,ok +75129,1.0,5,0.11985367176495254,ok +75243,1.0,6,0.0,ok +75110,1.0,7,0.11648340299707849,ok +75239,1.0,8,0.0,ok +75223,1.0,9,0.10326169932682816,ok +75221,1.0,10,0.4104088119418554,ok +258,1.0,11,0.009704531781493553,ok +75121,1.0,12,0.0,ok +253,1.0,13,0.4419219872587776,ok +261,1.0,14,0.254290815615859,ok +75240,1.0,15,0.02190680155196323,ok +75120,1.0,16,0.04981860127911408,ok +75124,1.0,17,0.10580512322180602,ok +75176,1.0,18,0.015417641830919315,ok +75103,1.0,19,0.005774697630436032,ok +75207,1.0,20,0.16288734167144403,ok +75095,1.0,21,0.017835118710502584,ok +273,1.0,22,0.044143856430088446,ok +75174,1.0,23,0.1178207749003396,ok +75153,1.0,24,0.08027853110163441,ok +75093,1.0,25,0.20540618737303462,ok +75119,1.0,26,0.033579267862175466,ok +75201,1.0,27,0.08086588964872377,ok +75215,1.0,28,0.02743062586610312,ok +75172,1.0,29,0.10345512305951998,ok +75169,1.0,30,0.034129556530982486,ok +75202,1.0,31,0.2396259361510158,ok +75233,1.0,32,0.06075665190946333,ok +75231,1.0,33,0.20981683120608396,ok +75196,1.0,34,0.015572950069634772,ok +248,1.0,35,0.23258814022143115,ok +75191,1.0,36,0.1292791019034626,ok +75217,1.0,37,0.0,ok +260,1.0,38,0.026998407680968617,ok +75115,1.0,39,0.01820883508633664,ok +75123,1.0,40,0.3220199977182707,ok +75108,1.0,41,0.0,ok +75101,1.0,42,0.2742885709851963,ok +75192,1.0,43,0.4838727111853699,ok +75232,1.0,44,0.12343990745075895,ok +75173,1.0,45,0.11655005765215176,ok +75197,1.0,46,0.15990558565323876,ok +266,1.0,47,0.019626935449329252,ok +75148,1.0,48,0.1356911432669068,ok +75150,1.0,49,0.2882068163967273,ok +75100,1.0,50,0.005690533730990155,ok +75178,1.0,51,0.7943166037466941,ok +75236,1.0,52,0.03235056748186682,ok +75179,1.0,53,0.18556100311237933,ok +75213,1.0,54,0.0518783755232074,ok +2123,1.0,55,0.06539918823655844,ok +75227,1.0,56,0.10165813170606963,ok +75184,1.0,57,0.10777407575427433,ok +75142,1.0,58,0.07158590737002735,ok +236,1.0,59,0.03875927505711274,ok +2122,1.0,60,0.10951556356133851,ok +75188,1.0,61,0.26465348624082274,ok +75166,1.0,62,0.09953908944937662,ok +75181,1.0,63,0.0,ok +75133,1.0,64,0.006078946850412992,ok +75134,1.0,65,0.08730010916367836,ok +75198,1.0,66,0.12258268813365325,ok +262,1.0,67,0.002756677062924262,ok +75234,1.0,68,0.024161314223939967,ok +75139,1.0,69,0.010712909121264436,ok +252,1.0,70,0.15518767652207754,ok +75117,1.0,71,0.05441384136226013,ok +75113,1.0,72,0.0066279535588466,ok +75098,1.0,73,0.02762928168631018,ok +246,1.0,74,0.010601765442530664,ok +75203,1.0,75,0.09622991262956981,ok +75237,1.0,76,0.00039562768586520747,ok +75195,1.0,77,0.0015608321665472324,ok +75171,1.0,78,0.16204803140883772,ok +75128,1.0,79,0.022725364810403215,ok +75096,1.0,80,0.005377674169948499,ok +75250,1.0,81,0.34358492714587807,ok +75146,1.0,82,0.11301039690636294,ok +75116,1.0,83,0.009847570622209645,ok +75157,1.0,84,0.4401806517094792,ok +75187,1.0,85,0.01678999849286833,ok +2350,1.0,86,0.4348957135748658,ok +242,1.0,87,0.010606337746570604,ok +244,1.0,88,0.11120952401278628,ok +75125,1.0,89,0.0339049942348123,ok +75185,1.0,90,0.12834457802817323,ok +75163,1.0,91,0.0604226420363001,ok +75177,1.0,92,0.018684969388137018,ok +75189,1.0,93,0.01938414877240202,ok +75244,1.0,94,0.08431968291195124,ok +75219,1.0,95,0.03480519211769084,ok +75222,1.0,96,0.04868525659600709,ok +75159,1.0,97,0.0745997329490441,ok +75175,1.0,98,0.09972153034778797,ok +75109,1.0,99,0.31166181636334855,ok +254,1.0,100,0.0,ok +75105,1.0,101,0.027098972971820845,ok +75106,1.0,102,0.10352576469506813,ok +75212,1.0,103,0.2521591217243391,ok +75099,1.0,104,0.14012657045167765,ok +75248,1.0,105,0.133683555717799,ok +233,1.0,106,0.0028461118739864233,ok +75235,1.0,107,0.0011111221947569527,ok +75226,1.0,108,0.0030496655082195012,ok +75132,1.0,109,0.06641886401898822,ok +75127,1.0,110,0.33328270764840817,ok +251,1.0,111,0.0,ok +75161,1.0,112,0.06437096492695238,ok +75143,1.0,113,0.010503848664593085,ok +75114,1.0,114,0.023575638506876273,ok +75182,1.0,115,0.11028726475492456,ok +75112,1.0,116,0.12272977173151256,ok +75210,1.0,117,0.0,ok +75205,1.0,118,0.181570513151477,ok +75090,1.0,119,0.06088527309316605,ok +275,1.0,120,0.036131286609341284,ok +288,1.0,121,0.1302581858230315,ok +75092,1.0,122,0.10267439074852436,ok +3043,1.0,123,0.0199289436742357,ok +75249,1.0,124,0.003230606814741299,ok +75126,1.0,125,0.04964265488272113,ok +75225,1.0,126,0.0638584813611206,ok +75141,1.0,127,0.05380859125413884,ok +75107,1.0,128,0.059474822395280236,ok +75097,1.0,129,0.06669445026792797,ok +80001,1.0,1,0.07273576097105527,ok +80003,1.0,1,0.09228340080971653,ok +80006,1.0,1,0.0940270935960591,ok +80008,1.0,1,0.11399711399711399,ok +80009,1.0,1,0.10526315789473684,ok +80010,1.0,1,0.15136587550380654,ok +80011,1.0,1,0.03824498352800254,ok +80012,1.0,1,0.04618599791013589,ok +80013,1.0,1,0.0,ok +80014,1.0,1,0.045741626794258305,ok +80015,1.0,1,0.09523809523809523,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/f1_weighted_multiclass.classification_dense/configurations.csv b/metalearning/metalearning_files/f1_weighted_multiclass.classification_dense/configurations.csv new file mode 100755 index 0000000..a796521 --- /dev/null +++ b/metalearning/metalearning_files/f1_weighted_multiclass.classification_dense/configurations.csv @@ -0,0 +1,141 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:fast_ica:algorithm,preprocessor:fast_ica:fun,preprocessor:fast_ica:n_components,preprocessor:fast_ica:whiten,preprocessor:feature_agglomeration:affinity,preprocessor:feature_agglomeration:linkage,preprocessor:feature_agglomeration:n_clusters,preprocessor:feature_agglomeration:pooling_func,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:pca:keep_variance,preprocessor:pca:whiten,preprocessor:polynomial:degree,preprocessor:polynomial:include_bias,preprocessor:polynomial:interaction_only,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,rescaling:__choice__,rescaling:quantile_transformer:n_quantiles,rescaling:quantile_transformer:output_distribution,rescaling:robust_scaler:q_max,rescaling:robust_scaler:q_min +weighting,one_hot_encoding,0.00011717632475982632,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.045388141846341344,deviance,10.0,0.29161769341843435,None,0.0,20.0,2.0,0.0,278.0,0.7912571599269661,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7249853037185638,None,0.0,1.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,median,extra_trees_preproc_for_classification,False,gini,None,0.9424908623661876,None,0.0,7.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.03474109838999682,deviance,4.0,0.5687034678818491,None,0.0,18.0,12.0,0.0,408.0,0.5150113945430513,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.6328939517938,f_classif,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.251097788175676,deviance,6.0,0.35679099363539235,None,0.0,13.0,11.0,0.0,157.0,0.4791732272983235,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,adaboost,SAMME,0.28738775989203896,10.0,423.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.8031499675923353,0.13579938270386765 +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.14159526341015916,deviance,7.0,0.8010488230155749,None,0.0,3.0,20.0,0.0,401.0,0.8073562440607731,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.002385546176068135,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.7729472304882845,,,0.0004789329856033374,rbf,-1.0,True,6.58869648864534e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,177.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5368752992317617,None,0.0,16.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.8382117756438685,mutual_info,,,,quantile_transformer,11480.0,normal,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.039533063907190934,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1878805519245509,fdr,chi2,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9455638720565652,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,most_frequent,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8255464552647293,0.19162485555463185 +weighting,one_hot_encoding,0.18137532678800647,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9094110110427254,None,0.0,7.0,12.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,195.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.6682079659377479,None,0.0,4.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,mean,extra_trees_preproc_for_classification,True,entropy,None,0.5552350997943013,None,0.0,8.0,5.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.02345017287074443,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.053517066400173056,deviance,10.0,0.542144980834302,None,0.0,20.0,13.0,0.0,233.0,0.7398539900055563,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0614425536709615,fwe,f_classif,robust_scaler,,,0.9523118062307264,0.13434811490315818 +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.8149627329153046,None,0.0,15.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.040936424602789435,deviance,7.0,0.5495014745530306,None,0.0,20.0,18.0,0.0,141.0,0.6905343807995293,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7974565919616314,None,0.0,12.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,median,extra_trees_preproc_for_classification,True,entropy,None,0.9772091846790169,None,0.0,10.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2398150931290834,,,0.4015139801872962,rbf,-1.0,False,2.402997750662158e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88.40698357592571,chi2,,,,normalize,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.1958974686405233,deviance,5.0,0.33885235607979314,None,0.0,6.0,4.0,0.0,125.0,0.9448890820738562,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2538107344750156,False,True,hinge,1.5099542326343014e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,8532.0,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,0.0007038280350320558,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4138778052607317,0.7995003430482459,5.0,5.430044692638861,poly,-1.0,True,0.02455501006004393,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,31,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.010000000000000005,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.0518326156691958,deviance,6.0,0.8807456180216267,None,0.0,7.0,19.0,0.0,366.0,0.7314831276137047,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,,False,adaboost,SAMME,0.4391375941344922,3.0,386.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7439738358430176,0.20581080574615795 +none,one_hot_encoding,0.00011294596229850895,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7515.1213255144885,0.9576762936062476,3.0,0.019002536385919932,poly,-1.0,False,0.010632086351533369,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,51.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,97282.0,normal,, +none,one_hot_encoding,0.00012586572428922356,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5240592829918601,None,0.0,10.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.03192699980429505,True,adaboost,SAMME.R,0.10000000000000002,1.0,50.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,quantile_transformer,8407.0,normal,, +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1751.4736515133566,0.62404114475118,3.0,1.6087076997410432,poly,-1.0,False,3.5353792826856045e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1198.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.2422926485206341,,1.0,None,0.0,15.0,9.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.02102683283349326,deviance,10.0,0.2797288369369436,None,0.0,14.0,9.0,0.0,480.0,0.5778972273820631,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58.88123233170863,mutual_info,,,,robust_scaler,,,0.75,0.25 +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828776,None,0.0,2.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2155613360930585,deviance,4.0,0.3198803116198433,None,0.0,8.0,13.0,0.0,275.0,0.28870176110739404,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,mean,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.7062102387181676,None,0.0,1.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,most_frequent,fast_ica,,,,,,,,,,,parallel,exp,100.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7065776353150109,0.23782974987118105 +none,one_hot_encoding,0.16334152321884812,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00011572473434870852,True,True,squared_hinge,0.00019678754114665057,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.045742431094098604,fpr,chi2,none,,,, +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.3795924768593385,deviance,2.0,0.33708497069988536,None,0.0,15.0,13.0,0.0,451.0,0.7716323242090217,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2573946506994812,fwe,f_classif,quantile_transformer,1000.0,uniform,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.609975998293528,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,most_frequent,fast_ica,,,,,,,,,,,parallel,logcosh,2000.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8430415644014919,0.2863750565331575 +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.39536192447534535,None,0.0,19.0,3.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5.0,None,11.0,11.0,1.0,12.0,,,,,,robust_scaler,,,0.8928631650245873,0.1581877760687084 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.3126027672745337,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,870.2240970463429,0.5325949351918051,3.0,0.010682839357128344,poly,-1.0,False,2.485160860440657e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4608103694360143,fdr,f_classif,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.9348409326933208,rbf,-1.0,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1754.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,198.72528686512538,False,True,1.0,squared_hinge,ovr,l2,0.026260652523566803,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,robust_scaler,,,0.913511520078368,0.2742229325455444 +none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9260795160807372,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.03644212536682547,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.05965671477918361,deviance,8.0,0.4858133247974158,None,0.0,14.0,7.0,0.0,480.0,0.5726186552917335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.15318294164619112 +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18887.81504976871,,,0.23283562663398755,rbf,-1.0,True,2.3839685780861318e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3937607155568376,fwe,chi2,robust_scaler,,,0.941018778984854,0.2144110585080491 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3428971645958825,,,0.2229870623330047,rbf,-1.0,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8138233157708883,None,0.0,2.0,9.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54.27504292568562,f_classif,,,,minmax,,,, +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00016781524591321165,True,True,squared_hinge,1.5119200923218881e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.556190405302504,False,True,1.0,squared_hinge,ovr,l2,0.0007318628304090552,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,most_frequent,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,3.5602014542183973,948.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +weighting,one_hot_encoding,0.00034835629696198427,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8245132980938538,0.08947420373097192 +weighting,one_hot_encoding,0.00016967940959070708,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9439080311935252,None,0.0,2.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35.0,None,,0.005389483044810356,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,most_frequent,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,9.441661069727422e-05,1896.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.03528169333197684,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3416063836589199,None,0.0,9.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,median,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,normalize,,,, +none,one_hot_encoding,,False,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6396026761675004,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06544340428506021,fwe,f_classif,none,,,, +weighting,one_hot_encoding,0.004980497345831963,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1255.9137433589426,,,0.08351549479967445,rbf,-1.0,True,0.00017919875199222518,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1.0,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.3837398524575939,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.018356703878357986,deviance,3.0,0.9690352514774068,None,0.0,12.0,3.0,0.0,234.0,0.3870344708308441,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,most_frequent,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6864970915330799,False,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5670424455696162,None,0.0,8.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.00010817282861262362,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.799803680241154,None,0.0,13.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,85,mean,fast_ica,,,,,,,,,,,deflation,exp,1793.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9071932815811076,0.03563842252368924 +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.35533396539961937,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,86,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4165632766388807,fpr,chi2,none,,,, +weighting,one_hot_encoding,,False,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,156.0,auto,,0.0001987333852871589,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1.0,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,no_encoding,,,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.96834823420249e-05,False,True,hinge,0.00016639250831671168,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,89,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.628430584359224,mutual_info,,,,quantile_transformer,6634.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,90,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,91,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,8.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,93,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2636201374253461,deviance,7.0,0.8344964130784466,None,0.0,9.0,2.0,0.0,298.0,0.7517549950523315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,95,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,96,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,97,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.07463196642416367,deviance,7.0,0.8603242247379981,None,0.0,2.0,6.0,0.0,500.0,0.8447665577491962,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,98,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.0433556140045585,deviance,10.0,0.33000096635982235,None,0.0,15.0,13.0,0.0,388.0,0.8291104221904706,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,99,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,robust_scaler,,,0.7496393440951183,0.2853682991120835 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,100,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,102,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0020580843703898177,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.945774573434192,None,0.0,19.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,103,median,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,15209.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,104,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,105,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,106,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.005332972692819521,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5565918060287016,None,0.0,5.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,107,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,173.0,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9527068489270144,0.04135311355893583 +weighting,one_hot_encoding,0.001279467383882126,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,108,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0903354518102121,fwe,f_classif,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,109,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.002615346832354839,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7884268823432835,None,0.0,20.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,110,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +weighting,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56.086963007482865,,,0.013609964993119377,rbf,-1.0,True,0.00196831255706268,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,111,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.15374716583918388 +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.2472984547885781,deviance,3.0,0.6564306719064884,None,0.0,15.0,14.0,0.0,220.0,0.8082564085714649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,112,median,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,332.0,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,113,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,115,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,116,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,117,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,118,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.20202014999292287,False,True,1.0,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,no_encoding,,,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6390376923528961,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,119,median,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,164.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,62508.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,120,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.27041927277584e-06,True,0.00010000000000000009,0.033157325660763994,True,0.0008114527992546482,invscaling,modified_huber,elasticnet,0.13714427818877545,0.055179642772545036,,,,,,,,,,,,,,,,,,121,median,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9930358173100328,None,0.0,6.0,3.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,122,most_frequent,extra_trees_preproc_for_classification,False,entropy,None,0.4655848826291386,None,0.0,13.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,123,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.19998727075532635,deviance,10.0,0.9377656718112952,None,0.0,7.0,13.0,0.0,214.0,0.6062346326014357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,124,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,125,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,126,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.5765793990908161,None,0.0,11.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,127,median,fast_ica,,,,,,,,,,,deflation,exp,10.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7945458151995424,None,0.0,1.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,129,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,38400.0,uniform,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,adaboost,SAMME,0.7603752531440509,7.0,413.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,28.04113884899283,False,True,1.0,squared_hinge,ovr,l1,0.0001084726092097629,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,26106.0,uniform,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3386310102795006,None,0.0,6.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,True,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.0,None,,5.861221938384061e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.22985730375167915,fpr,f_classif,quantile_transformer,40589.0,uniform,, +none,no_encoding,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00012437731009611307,True,,0.08709904468181932,True,,invscaling,squared_hinge,l1,0.6231564675290102,0.008071279833697563,,,,,,,,,,,,,,,,,,134,median,fast_ica,,,,,,,,,,,parallel,logcosh,814.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.714869937384006,None,0.0,8.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, diff --git a/metalearning/metalearning_files/f1_weighted_multiclass.classification_dense/description.txt b/metalearning/metalearning_files/f1_weighted_multiclass.classification_dense/description.txt new file mode 100755 index 0000000..f4d0383 --- /dev/null +++ b/metalearning/metalearning_files/f1_weighted_multiclass.classification_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: f1_weighted +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/f1_weighted_multiclass.classification_dense/feature_costs.arff b/metalearning/metalearning_files/f1_weighted_multiclass.classification_dense/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/f1_weighted_multiclass.classification_dense/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/f1_weighted_multiclass.classification_dense/feature_runstatus.arff b/metalearning/metalearning_files/f1_weighted_multiclass.classification_dense/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/f1_weighted_multiclass.classification_dense/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/f1_weighted_multiclass.classification_dense/feature_values.arff b/metalearning/metalearning_files/f1_weighted_multiclass.classification_dense/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/f1_weighted_multiclass.classification_dense/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/f1_weighted_multiclass.classification_dense/readme.txt b/metalearning/metalearning_files/f1_weighted_multiclass.classification_dense/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/f1_weighted_multiclass.classification_sparse/algorithm_runs.arff b/metalearning/metalearning_files/f1_weighted_multiclass.classification_sparse/algorithm_runs.arff new file mode 100755 index 0000000..9fd78de --- /dev/null +++ b/metalearning/metalearning_files/f1_weighted_multiclass.classification_sparse/algorithm_runs.arff @@ -0,0 +1,152 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE f1_weighted NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2120,1.0,1,0.09184560117484108,ok +75193,1.0,2,0.03825452279163666,ok +2117,1.0,3,0.17066951869137215,ok +75156,1.0,4,0.22026758993168338,ok +75129,1.0,5,0.11985367176495254,ok +75243,1.0,6,0.015617243640308476,ok +75110,1.0,7,0.27972402767070514,ok +75239,1.0,8,0.0,ok +75223,1.0,9,0.3106766376998267,ok +75221,1.0,10,0.4104088119418554,ok +258,1.0,11,0.018350207054443124,ok +75121,1.0,12,0.004018801310984532,ok +253,1.0,13,0.4419219872587776,ok +261,1.0,14,0.254290815615859,ok +75240,1.0,15,0.02190680155196323,ok +75120,1.0,16,0.04981860127911408,ok +75124,1.0,17,0.10580512322180602,ok +75176,1.0,18,0.016587552062598432,ok +75103,1.0,19,0.008072010526220086,ok +75207,1.0,20,0.16288734167144403,ok +75095,1.0,21,0.017835118710502584,ok +273,1.0,22,0.044912223755791625,ok +75174,1.0,23,0.11830526729492052,ok +75153,1.0,24,0.1213380151432053,ok +75093,1.0,25,0.20540618737303462,ok +75119,1.0,26,0.033579267862175466,ok +75201,1.0,27,0.08086588964872377,ok +75215,1.0,28,0.028270400591125844,ok +75172,1.0,29,0.09090829801598754,ok +75169,1.0,30,0.034129556530982486,ok +75202,1.0,31,0.2396259361510158,ok +75233,1.0,32,0.06557863245191664,ok +75231,1.0,33,0.20981683120608396,ok +75196,1.0,34,0.023462952895589195,ok +248,1.0,35,0.2668282951427886,ok +75191,1.0,36,0.1240334236646079,ok +75217,1.0,37,0.0,ok +260,1.0,38,0.026998407680968617,ok +75115,1.0,39,0.01844124945050607,ok +75123,1.0,40,0.3220199977182707,ok +75108,1.0,41,0.001835113058128779,ok +75101,1.0,42,0.2828603545677709,ok +75192,1.0,43,0.4838727111853699,ok +75232,1.0,44,0.1487629645898919,ok +75173,1.0,45,0.11750578000964573,ok +75197,1.0,46,0.1395061958359226,ok +266,1.0,47,0.03271906775864197,ok +75148,1.0,48,0.19030912587610016,ok +75150,1.0,49,0.3204239801075106,ok +75100,1.0,50,0.005690533730990155,ok +75178,1.0,51,0.8083869128005314,ok +75236,1.0,52,0.03236382574407237,ok +75179,1.0,53,0.18556100311237933,ok +75213,1.0,54,0.0518783755232074,ok +2123,1.0,55,0.06539918823655844,ok +75227,1.0,56,0.10165813170606963,ok +75184,1.0,57,0.1464874342661746,ok +75142,1.0,58,0.08132210905398984,ok +236,1.0,59,0.04230806465282844,ok +2122,1.0,60,0.27972402767070514,ok +75188,1.0,61,0.26465348624082274,ok +75166,1.0,62,0.09953908944937662,ok +75181,1.0,63,0.0,ok +75133,1.0,64,0.006078946850412992,ok +75134,1.0,65,0.10065294069585673,ok +75198,1.0,66,0.12258268813365325,ok +262,1.0,67,0.002756677062924262,ok +75234,1.0,68,0.059859575576248436,ok +75139,1.0,69,0.012117466274937372,ok +252,1.0,70,0.16672301815374768,ok +75117,1.0,71,0.06144073346430923,ok +75113,1.0,72,0.0066279535588466,ok +75098,1.0,73,0.02762928168631018,ok +246,1.0,74,0.024171481047592258,ok +75203,1.0,75,0.09622991262956981,ok +75237,1.0,76,0.00039562768586520747,ok +75195,1.0,77,0.0015608321665472324,ok +75171,1.0,78,0.16725021757377856,ok +75128,1.0,79,0.022725364810403215,ok +75096,1.0,80,0.42588242341225635,ok +75250,1.0,81,0.34358492714587807,ok +75146,1.0,82,0.1218585567585766,ok +75116,1.0,83,0.009847570622209645,ok +75157,1.0,84,0.4401806517094792,ok +75187,1.0,85,0.027848345420230403,ok +2350,1.0,86,0.4348957135748658,ok +242,1.0,87,0.016624785509831264,ok +244,1.0,88,0.11120952401278628,ok +75125,1.0,89,0.035819469538836746,ok +75185,1.0,90,0.12834457802817323,ok +75163,1.0,91,0.0604226420363001,ok +75177,1.0,92,0.019184692607764897,ok +75189,1.0,93,0.01938414877240202,ok +75244,1.0,94,0.08431968291195124,ok +75219,1.0,95,0.08399570797186784,ok +75222,1.0,96,0.04868525659600709,ok +75159,1.0,97,0.0745997329490441,ok +75175,1.0,98,0.11790160247163806,ok +75109,1.0,99,0.3572958030354616,ok +254,1.0,100,0.0,ok +75105,1.0,101,0.027098972971820845,ok +75106,1.0,102,0.10352576469506813,ok +75212,1.0,103,0.27738023390528044,ok +75099,1.0,104,0.14012657045167765,ok +75248,1.0,105,0.133683555717799,ok +233,1.0,106,0.015180265654648917,ok +75235,1.0,107,0.0016666741480566571,ok +75226,1.0,108,0.004266071627929913,ok +75132,1.0,109,0.06641886401898822,ok +75127,1.0,110,0.33467911028125896,ok +251,1.0,111,0.025512056468401045,ok +75161,1.0,112,0.08280465928384262,ok +75143,1.0,113,0.010503848664593085,ok +75114,1.0,114,0.023575638506876273,ok +75182,1.0,115,0.11028726475492456,ok +75112,1.0,116,0.12272977173151256,ok +75210,1.0,117,0.0,ok +75205,1.0,118,0.181570513151477,ok +75090,1.0,119,0.10146999872014384,ok +275,1.0,120,0.036131286609341284,ok +288,1.0,121,0.14444376884583165,ok +75092,1.0,122,0.10267439074852436,ok +3043,1.0,123,0.0199289436742357,ok +75249,1.0,124,0.004845910222111671,ok +75126,1.0,125,0.04964265488272113,ok +75225,1.0,126,0.0638584813611206,ok +75141,1.0,127,0.05798894785784603,ok +75107,1.0,128,0.059474822395280236,ok +75097,1.0,129,0.08115392650430098,ok +80001,1.0,1,0.07273576097105527,ok +80003,1.0,1,0.1230575021443947,ok +80006,1.0,1,0.19205465587044546,ok +80008,1.0,1,0.22777777777777786,ok +80009,1.0,1,0.1578947368421053,ok +80010,1.0,1,0.15136587550380654,ok +80011,1.0,1,0.03824498352800254,ok +80012,1.0,1,0.04618599791013589,ok +80013,1.0,1,0.0,ok +80014,1.0,1,0.045741626794258305,ok +80015,1.0,1,0.09523809523809523,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/f1_weighted_multiclass.classification_sparse/configurations.csv b/metalearning/metalearning_files/f1_weighted_multiclass.classification_sparse/configurations.csv new file mode 100755 index 0000000..538f341 --- /dev/null +++ b/metalearning/metalearning_files/f1_weighted_multiclass.classification_sparse/configurations.csv @@ -0,0 +1,141 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,preprocessor:truncatedSVD:target_dim,rescaling:__choice__ +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7249853037185638,None,0.0,1.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,median,extra_trees_preproc_for_classification,False,gini,None,0.9424908623661876,None,0.0,7.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.039533063907190934,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1878805519245509,fdr,chi2,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.6682079659377479,None,0.0,4.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,mean,extra_trees_preproc_for_classification,True,entropy,None,0.5552350997943013,None,0.0,8.0,5.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7974565919616314,None,0.0,12.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,median,extra_trees_preproc_for_classification,True,entropy,None,0.9772091846790169,None,0.0,10.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2398150931290834,,,0.4015139801872962,rbf,-1.0,False,2.402997750662158e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88.40698357592571,chi2,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.034465366914659866,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.12057775675278172,deviance,10.0,0.8011153303489733,None,0.0,2.0,16.0,0.0,370.0,0.6078295352200873,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,mean,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0007038280350320558,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4138778052607317,0.7995003430482459,5.0,5.430044692638861,poly,-1.0,True,0.02455501006004393,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,31,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.0010015637584068037,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.037611630308856295,deviance,5.0,0.8840126779516314,None,0.0,10.0,2.0,0.0,444.0,0.7599997167603434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,most_frequent,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1751.4736515133566,0.62404114475118,3.0,1.6087076997410432,poly,-1.0,False,3.5353792826856045e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,mean,kernel_pca,,,,,,,,,,,,,,cosine,1198.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.8657388713119849,,1.0,None,0.0,19.0,13.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828776,None,0.0,2.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.027324741616523342,deviance,10.0,0.8623781459430139,None,0.0,10.0,20.0,0.0,329.0,0.8595750155424215,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,mean,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1100.6211008501205,0.5921425829232616,2.0,0.0337546254878617,poly,-1.0,True,0.09641299736884308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.9348409326933208,rbf,-1.0,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,mean,kernel_pca,,,,,,,,,,,,,,cosine,1754.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3428971645958825,,,0.2229870623330047,rbf,-1.0,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7322410244259964,None,0.0,6.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,median,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00016781524591321165,True,True,squared_hinge,1.5119200923218881e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.556190405302504,False,True,1.0,squared_hinge,ovr,l2,0.0007318628304090552,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,most_frequent,kitchen_sinks,,,,,,,,,,,,,,,,3.5602014542183973,948.0,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4047.618729304337,,,2.0237366768707754,rbf,-1.0,True,0.04369127828878843,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,one_hot_encoding,0.010000000000000005,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10.0,1.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,,none +weighting,one_hot_encoding,0.0008685602750053468,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9896334290292654,None,0.0,11.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,141.76310800864283,False,True,1.0,squared_hinge,ovr,l1,0.004317884655117431,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,,normalize +none,one_hot_encoding,0.003443779683191071,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.004980497345831963,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1255.9137433589426,,,0.08351549479967445,rbf,-1.0,True,0.00017919875199222518,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1.0,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.3451627750042988,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.3163640203509378,None,0.0,17.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,median,extra_trees_preproc_for_classification,False,gini,None,0.8916956785028156,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,85,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.35533396539961937,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,86,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4165632766388807,fpr,chi2,,none +weighting,one_hot_encoding,0.0019686649916896212,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.04641055832142541,True,True,hinge,8.540468968077405e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87,mean,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,911.0,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1.0,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,89,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,90,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,91,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,93,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,95,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,96,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,97,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,98,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,99,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,100,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,102,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,103,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,104,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,105,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,106,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.006372860318416312,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6467376360604045,None,0.0,1.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,107,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,108,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,109,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3823734947460288,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,110,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,adaboost,SAMME,0.11042308042695524,5.0,117.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,111,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,112,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,113,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,115,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,116,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,117,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,118,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.20202014999292287,False,True,1.0,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,119,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,120,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,121,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9930358173100328,None,0.0,6.0,3.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,122,most_frequent,extra_trees_preproc_for_classification,False,entropy,None,0.4655848826291386,None,0.0,13.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,123,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,124,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,125,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,126,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,127,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,129,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.04329010470998136,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7260331062271381,None,0.0,7.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.010000000000000005,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15.0,1.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,134,median,extra_trees_preproc_for_classification,False,entropy,None,0.8213051377774989,None,0.0,3.0,13.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.714869937384006,None,0.0,8.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/metalearning/metalearning_files/f1_weighted_multiclass.classification_sparse/description.txt b/metalearning/metalearning_files/f1_weighted_multiclass.classification_sparse/description.txt new file mode 100755 index 0000000..f4d0383 --- /dev/null +++ b/metalearning/metalearning_files/f1_weighted_multiclass.classification_sparse/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: f1_weighted +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/f1_weighted_multiclass.classification_sparse/feature_costs.arff b/metalearning/metalearning_files/f1_weighted_multiclass.classification_sparse/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/f1_weighted_multiclass.classification_sparse/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/f1_weighted_multiclass.classification_sparse/feature_runstatus.arff b/metalearning/metalearning_files/f1_weighted_multiclass.classification_sparse/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/f1_weighted_multiclass.classification_sparse/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/f1_weighted_multiclass.classification_sparse/feature_values.arff b/metalearning/metalearning_files/f1_weighted_multiclass.classification_sparse/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/f1_weighted_multiclass.classification_sparse/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/f1_weighted_multiclass.classification_sparse/readme.txt b/metalearning/metalearning_files/f1_weighted_multiclass.classification_sparse/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/log_loss_binary.classification_dense/algorithm_runs.arff b/metalearning/metalearning_files/log_loss_binary.classification_dense/algorithm_runs.arff new file mode 100755 index 0000000..ff68d28 --- /dev/null +++ b/metalearning/metalearning_files/log_loss_binary.classification_dense/algorithm_runs.arff @@ -0,0 +1,152 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE log_loss NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2120,1.0,1,1.2551394289251652,ok +75193,1.0,2,1.1410633614512347,ok +2117,1.0,3,1.3704205093126214,ok +75156,1.0,4,1.4664015513645272,ok +75129,1.0,5,1.291330559897119,ok +75243,1.0,6,1.0012093831882118,ok +75110,1.0,7,1.924062570651559,ok +75239,1.0,8,1.0,ok +75223,1.0,9,1.3756283913988565,ok +75221,1.0,10,2.1910654733015056,ok +258,1.0,11,1.1398615508692935,ok +75121,1.0,12,1.0179349097490955,ok +253,1.0,13,1.9521382064539403,ok +261,1.0,14,1.4926572676240042,ok +75240,1.0,15,1.1171331721317688,ok +75120,1.0,16,1.1360518251763463,ok +75124,1.0,17,1.2487240937668993,ok +75176,1.0,18,1.0376591522371303,ok +75103,1.0,19,1.0246605532500268,ok +75207,1.0,20,1.636092600811709,ok +75095,1.0,21,1.0949287146988107,ok +273,1.0,22,1.12439876671779,ok +75174,1.0,23,1.2784990570399395,ok +75153,1.0,24,1.1943098297225512,ok +75093,1.0,25,1.4959972708806597,ok +75119,1.0,26,1.1151144994882052,ok +75201,1.0,27,1.3305012457513827,ok +75215,1.0,28,1.07091282347108,ok +75172,1.0,29,1.6474251960037334,ok +75169,1.0,30,1.4657877944511197,ok +75202,1.0,31,1.9917696737957225,ok +75233,1.0,32,1.1495053896802812,ok +75231,1.0,33,2.3986113472316815,ok +75196,1.0,34,1.0449907693400367,ok +248,1.0,35,1.7371616329941335,ok +75191,1.0,36,1.3341796163477095,ok +75217,1.0,37,1.0748503900926225,ok +260,1.0,38,1.150516019374749,ok +75115,1.0,39,1.0959244285247467,ok +75123,1.0,40,1.72626162180506,ok +75108,1.0,41,1.0,ok +75101,1.0,42,1.5317830705802749,ok +75192,1.0,43,1.692598064928378,ok +75232,1.0,44,1.3347015455418705,ok +75173,1.0,45,1.287930436247951,ok +75197,1.0,46,1.6545973611935814,ok +266,1.0,47,1.0777676811736654,ok +75148,1.0,48,1.3315515829350164,ok +75150,1.0,49,1.5465209868125815,ok +75100,1.0,50,1.0971794317495644,ok +75178,1.0,51,3.244487724784548,ok +75236,1.0,52,1.430747892175402,ok +75179,1.0,53,1.4164286468645246,ok +75213,1.0,54,1.1653454279541735,ok +2123,1.0,55,1.1686923701391971,ok +75227,1.0,56,1.2571423236490658,ok +75184,1.0,57,1.2934586358622462,ok +75142,1.0,58,1.1544608098235858,ok +236,1.0,59,1.1218356229614856,ok +2122,1.0,60,1.5720493920174783,ok +75188,1.0,61,2.161721554407234,ok +75166,1.0,62,1.269054495322524,ok +75181,1.0,63,1.0006041403110455,ok +75133,1.0,64,1.0500469796075564,ok +75134,1.0,65,1.3592378096201074,ok +75198,1.0,66,1.633723206380605,ok +262,1.0,67,1.0776299565115677,ok +75234,1.0,68,1.0607077502193207,ok +75139,1.0,69,1.0393005883776283,ok +252,1.0,70,1.3945071000605822,ok +75117,1.0,71,1.1717459972259865,ok +75113,1.0,72,1.0262388515639744,ok +75098,1.0,73,1.2051704445975586,ok +246,1.0,74,1.0939800377144957,ok +75203,1.0,75,1.4041315047670357,ok +75237,1.0,76,1.0021029915838504,ok +75195,1.0,77,1.008340271895055,ok +75171,1.0,78,1.3728507847125155,ok +75128,1.0,79,1.1141668511228324,ok +75096,1.0,80,1.0893852871349736,ok +75250,1.0,81,2.0437916881356095,ok +75146,1.0,82,1.2590783166501973,ok +75116,1.0,83,1.071510164283055,ok +75157,1.0,84,1.6881145547691494,ok +75187,1.0,85,1.0926551650755993,ok +2350,1.0,86,1.661593411811027,ok +242,1.0,87,1.214510607149339,ok +244,1.0,88,1.565132095876404,ok +75125,1.0,89,1.2121728694062746,ok +75185,1.0,90,1.299276450313559,ok +75163,1.0,91,1.2253502234101268,ok +75177,1.0,92,1.0453937912856202,ok +75189,1.0,93,1.074536159542699,ok +75244,1.0,94,1.1931588850472432,ok +75219,1.0,95,1.1044120327961304,ok +75222,1.0,96,1.129116138388567,ok +75159,1.0,97,1.2790765765429257,ok +75175,1.0,98,1.245159060909976,ok +75109,1.0,99,1.9119672936973653,ok +254,1.0,100,1.0001431169182953,ok +75105,1.0,101,1.1301667903861539,ok +75106,1.0,102,1.260929408309586,ok +75212,1.0,103,1.5192546397013404,ok +75099,1.0,104,1.3587954056813556,ok +75248,1.0,105,1.2386717446902502,ok +233,1.0,106,1.0189136516327566,ok +75235,1.0,107,1.0117083651456051,ok +75226,1.0,108,1.016164829848488,ok +75132,1.0,109,1.3915851942598134,ok +75127,1.0,110,1.6076160853607535,ok +251,1.0,111,1.0923346413046187,ok +75161,1.0,112,1.1507461810861586,ok +75143,1.0,113,1.1229171501716395,ok +75114,1.0,114,1.1209920174481114,ok +75182,1.0,115,1.2764619749562676,ok +75112,1.0,116,1.2989304603286411,ok +75210,1.0,117,1.0011578587287928,ok +75205,1.0,118,1.6871177696401585,ok +75090,1.0,119,1.6080069481085828,ok +275,1.0,120,1.1857485517615118,ok +288,1.0,121,1.3149173992382588,ok +75092,1.0,122,1.185341128502467,ok +3043,1.0,123,1.0453937912856202,ok +75249,1.0,124,1.0139396715643851,ok +75126,1.0,125,1.1500583445243464,ok +75225,1.0,126,1.140532912878079,ok +75141,1.0,127,1.1628562384548615,ok +75107,1.0,128,1.2005767699749859,ok +75097,1.0,129,1.2623476555767466,ok +80001,1.0,1,1.2578486306592822,ok +80003,1.0,1,1.3761997032528503,ok +80006,1.0,1,1.2323743620781897,ok +80008,1.0,1,1.2369807212348354,ok +80009,1.0,1,1.3438370084684146,ok +80010,1.0,1,1.474967037295473,ok +80011,1.0,1,1.110989374963057,ok +80012,1.0,1,1.2487788296897304,ok +80013,1.0,1,1.0,ok +80014,1.0,1,1.4425953301516445,ok +80015,1.0,1,1.3526643729280858,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/log_loss_binary.classification_dense/configurations.csv b/metalearning/metalearning_files/log_loss_binary.classification_dense/configurations.csv new file mode 100755 index 0000000..a7a1397 --- /dev/null +++ b/metalearning/metalearning_files/log_loss_binary.classification_dense/configurations.csv @@ -0,0 +1,141 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:fast_ica:algorithm,preprocessor:fast_ica:fun,preprocessor:fast_ica:n_components,preprocessor:fast_ica:whiten,preprocessor:feature_agglomeration:affinity,preprocessor:feature_agglomeration:linkage,preprocessor:feature_agglomeration:n_clusters,preprocessor:feature_agglomeration:pooling_func,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:pca:keep_variance,preprocessor:pca:whiten,preprocessor:polynomial:degree,preprocessor:polynomial:include_bias,preprocessor:polynomial:interaction_only,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,rescaling:__choice__,rescaling:quantile_transformer:n_quantiles,rescaling:quantile_transformer:output_distribution,rescaling:robust_scaler:q_max,rescaling:robust_scaler:q_min +weighting,one_hot_encoding,0.0003571493489893277,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.7207211219619362,None,0.0,1.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,median,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,25.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7136841657452827,0.2443375775688061 +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7249853037185638,None,0.0,1.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,median,extra_trees_preproc_for_classification,False,gini,None,0.9424908623661876,None,0.0,7.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.12713527337147906,deviance,4.0,0.6041596127474019,None,0.0,14.0,17.0,0.0,83.0,0.8426859880999615,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.013255438916868357,deviance,9.0,0.3592430187012816,None,0.0,10.0,7.0,0.0,411.0,0.691160823398122,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,median,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1.0,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.3766459280999309,None,0.0,4.0,5.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,False,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.251097788175676,deviance,6.0,0.35679099363539235,None,0.0,13.0,11.0,0.0,157.0,0.4791732272983235,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.10000000000000002,deviance,6.0,1.0,None,0.0,7.0,2.0,0.0,278.0,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3469031665162168,None,0.0,4.0,3.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,mean,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,83.0,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,,False,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,2.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09818715003413464,fwe,f_classif,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9874546479752576,None,0.0,20.0,3.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.039533063907190934,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1878805519245509,fdr,chi2,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9455638720565652,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,most_frequent,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8255464552647293,0.19162485555463185 +weighting,one_hot_encoding,0.18137532678800647,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9094110110427254,None,0.0,7.0,12.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,195.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.9835753358328776,None,0.0,4.0,4.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.02345017287074443,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.053517066400173056,deviance,10.0,0.542144980834302,None,0.0,20.0,13.0,0.0,233.0,0.7398539900055563,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0614425536709615,fwe,f_classif,robust_scaler,,,0.9523118062307264,0.13434811490315818 +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.8149627329153046,None,0.0,15.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.040936424602789435,deviance,7.0,0.5495014745530306,None,0.0,20.0,18.0,0.0,141.0,0.6905343807995293,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2538107344750156,False,True,hinge,1.5099542326343014e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,8532.0,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.1958974686405233,deviance,5.0,0.33885235607979314,None,0.0,6.0,4.0,0.0,125.0,0.9448890820738562,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0006939450481567022,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.05694967271793989,0.9005883757146016,5.0,6.300159702718475,poly,-1.0,False,0.024026910265824642,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,31,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.04749094903835669,deviance,3.0,0.6184047395714717,None,0.0,17.0,8.0,0.0,428.0,0.7515561640094087,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.6409514454358402,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4107013198375944,fdr,f_classif,normalize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9562349611127512,None,0.0,4.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,median,fast_ica,,,,,,,,,,,parallel,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.941692818067464,0.0440072111980354 +weighting,one_hot_encoding,0.0009664614609258606,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3714759002919618,None,0.0,18.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,fast_ica,,,,,,,,,,,deflation,exp,917.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.00012586572428922356,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5240592829918601,None,0.0,10.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.004090774134315939,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7983157215145903,None,0.0,2.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.9397100247778196,None,0.0,3.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,mean,fast_ica,,,,,,,,,,,parallel,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7272465857046146,0.24493108573961345 +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.2422926485206341,,1.0,None,0.0,15.0,9.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.022939738050158573,deviance,10.0,0.4185394344134278,None,0.0,2.0,10.0,0.0,309.0,0.5979695608086252,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.25383213391991144 +none,one_hot_encoding,0.31138539716704705,True,adaboost,SAMME.R,0.3026113597945332,1.0,50.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.05578036113726603,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.04310920427249598,deviance,9.0,0.7762532463369333,None,0.0,19.0,7.0,0.0,89.0,0.9651993549499902,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,median,fast_ica,,,,,,,,,,,parallel,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.00353232434213139,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.9407432771644124,None,0.0,1.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,median,fast_ica,,,,,,,,,,,parallel,cube,100.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7446318916516063,0.23782974987118105 +weighting,one_hot_encoding,0.010000000000000005,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.756120720182025,True,True,squared_hinge,0.020536827217281145,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,4188.0,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.3795924768593385,deviance,2.0,0.33708497069988536,None,0.0,15.0,13.0,0.0,451.0,0.7716323242090217,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2573946506994812,fwe,f_classif,quantile_transformer,1000.0,uniform,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.609975998293528,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,most_frequent,fast_ica,,,,,,,,,,,parallel,logcosh,2000.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8430415644014919,0.2863750565331575 +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.39536192447534535,None,0.0,19.0,3.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5.0,None,11.0,11.0,1.0,12.0,,,,,,robust_scaler,,,0.8928631650245873,0.1581877760687084 +weighting,one_hot_encoding,0.0009372940901663342,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.026081477437122958,deviance,2.0,0.5887618067763795,None,0.0,11.0,6.0,0.0,179.0,0.5309995755533717,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6897958091880166,None,0.0,2.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,870.2240970463429,0.1223321395075887,2.0,0.0035847433873211405,poly,-1.0,False,2.485160860440657e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4612682306567311,fpr,f_classif,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.010000000000000005,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.011886849251540706,deviance,10.0,0.716738790505292,None,0.0,5.0,6.0,0.0,472.0,0.9128424273302038,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8363534397818381,None,0.0,4.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,False,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9260795160807372,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.03644212536682547,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.05965671477918361,deviance,8.0,0.4858133247974158,None,0.0,14.0,7.0,0.0,480.0,0.5726186552917335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.15318294164619112 +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18887.81504976871,,,0.23283562663398755,rbf,-1.0,True,2.3839685780861318e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3937607155568376,fwe,chi2,robust_scaler,,,0.941018778984854,0.2144110585080491 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7856.587651350424,,,0.0017305319997054556,rbf,-1.0,False,1.7622421003766454e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8138233157708883,None,0.0,2.0,9.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54.27504292568562,f_classif,,,,minmax,,,, +none,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9742178951431336,None,0.0,2.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11400034542737113,fdr,chi2,none,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.8490054877538417,None,0.0,2.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.0001449312804440222,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,mean,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4047.618729304337,,,2.0237366768707754,rbf,-1.0,True,0.04369127828878843,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +weighting,one_hot_encoding,,False,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26.0,1.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,median,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.07589752815117898,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.7727592543547,None,0.0,10.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.03739654507085451,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8447775967498866,None,0.0,1.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,2.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09818715003413464,fwe,f_classif,none,,,, +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2538107344750156,False,True,hinge,1.5099542326343014e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,8532.0,,,,,,,,,,,,,,,,,,none,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.9188519169916218,None,0.0,1.0,5.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,median,fast_ica,,,,,,,,,,,parallel,cube,306.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.904983674005564,None,0.0,18.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,mean,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,275.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.4932996544760619,None,0.0,2.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,340.0,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.002353704397784248,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,most_frequent,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7766828206753251,True,,,,,,,,,,,,,,,,normalize,,,, +weighting,one_hot_encoding,0.0001486770773839718,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5256280540657592,None,0.0,8.0,12.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.0037042565030130366,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.9610802347688104,None,0.0,17.0,9.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,172.0,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.001479024648966236,True,decision_tree,,,,,,,gini,0.0029321615410515807,,1.0,None,0.0,17.0,19.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,robust_scaler,,,0.8019276898414001,0.2172955112207602 +none,one_hot_encoding,0.00010817282861262362,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.799803680241154,None,0.0,13.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,85,mean,fast_ica,,,,,,,,,,,deflation,exp,1793.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9071932815811076,0.03563842252368924 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,86,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0019686649916896212,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.04641055832142541,True,True,hinge,8.540468968077405e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87,mean,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,911.0,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,89,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.9342950927678112,None,0.0,20.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,90,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.1340879632520081,deviance,5.0,0.4391917863054552,None,0.0,18.0,5.0,0.0,227.0,0.8290252815444993,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,91,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,93,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2636201374253461,deviance,7.0,0.8344964130784466,None,0.0,9.0,2.0,0.0,298.0,0.7517549950523315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,95,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,96,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,97,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.07463196642416367,deviance,7.0,0.8603242247379981,None,0.0,2.0,6.0,0.0,500.0,0.8447665577491962,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,98,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.03425735525476273,deviance,7.0,0.5825782146709433,None,0.0,17.0,6.0,0.0,313.0,0.5043438213864502,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,99,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3805181287718124,fdr,f_classif,robust_scaler,,,0.8478326986019474,0.2878840415105679 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,100,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,102,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,103,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,104,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,105,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,106,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.006372860318416312,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6467376360604045,None,0.0,1.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,107,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,108,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,109,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.002615346832354839,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7884268823432835,None,0.0,20.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,110,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,3.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,111,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.010000000000000005,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.2020480296503026,deviance,3.0,0.4640458524354476,None,0.0,8.0,19.0,0.0,486.0,0.6791755979205191,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,112,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,113,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.03905145156995541,deviance,5.0,0.2281306656230014,None,0.0,14.0,13.0,0.0,493.0,0.8793075442604774,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,115,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,25382.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,116,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,117,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6886293142639995,None,0.0,2.0,13.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,118,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16.670108971732134,chi2,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,119,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.1885493528549979,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.603604171705261,False,True,squared_hinge,4.4620804452839e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,120,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,quantile_transformer,57665.0,uniform,, +weighting,one_hot_encoding,0.010000000000000005,True,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.825805258222736e-06,True,0.00010000000000000009,0.038198103889192085,True,0.14999999999999974,invscaling,modified_huber,elasticnet,0.13714427818877545,0.04372308852525775,,,,,,,,,,,,,,,,,,121,most_frequent,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,122,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,123,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.19998727075532635,deviance,10.0,0.9377656718112952,None,0.0,7.0,13.0,0.0,214.0,0.6062346326014357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,124,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,125,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,126,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,127,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7794633670276021,None,0.0,9.0,10.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,129,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,75840.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.09467364358164987,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,minmax,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.06597655581840225,deviance,1.0,0.21809176698911087,None,0.0,8.0,9.0,0.0,313.0,0.28171885744131103,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,mean,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,364.0,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11611.973346148121,-0.605048235954617,4.0,0.002797249824906932,poly,-1.0,True,4.813307514117123e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,192.0,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,134,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.8015164166877907,None,0.0,1.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76.35224288088253,f_classif,,,,quantile_transformer,1766.0,normal,, +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.714869937384006,None,0.0,8.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,no_encoding,,,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,146.10741262807508,False,True,1.0,squared_hinge,ovr,l1,0.0014510814556456804,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, diff --git a/metalearning/metalearning_files/log_loss_binary.classification_dense/description.txt b/metalearning/metalearning_files/log_loss_binary.classification_dense/description.txt new file mode 100755 index 0000000..b9d73dd --- /dev/null +++ b/metalearning/metalearning_files/log_loss_binary.classification_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: log_loss +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/log_loss_binary.classification_dense/feature_costs.arff b/metalearning/metalearning_files/log_loss_binary.classification_dense/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/log_loss_binary.classification_dense/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/log_loss_binary.classification_dense/feature_runstatus.arff b/metalearning/metalearning_files/log_loss_binary.classification_dense/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/log_loss_binary.classification_dense/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/log_loss_binary.classification_dense/feature_values.arff b/metalearning/metalearning_files/log_loss_binary.classification_dense/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/log_loss_binary.classification_dense/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/log_loss_binary.classification_dense/readme.txt b/metalearning/metalearning_files/log_loss_binary.classification_dense/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/log_loss_binary.classification_sparse/algorithm_runs.arff b/metalearning/metalearning_files/log_loss_binary.classification_sparse/algorithm_runs.arff new file mode 100755 index 0000000..447a84a --- /dev/null +++ b/metalearning/metalearning_files/log_loss_binary.classification_sparse/algorithm_runs.arff @@ -0,0 +1,152 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE log_loss NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2120,1.0,1,1.2583635793704644,ok +75193,1.0,2,1.1410633614512347,ok +2117,1.0,3,1.3820774928236736,ok +75156,1.0,4,1.4774967666350984,ok +75129,1.0,5,1.3542807631429683,ok +75243,1.0,6,1.0901073282963525,ok +75110,1.0,7,1.924062570651559,ok +75239,1.0,8,1.0,ok +75223,1.0,9,1.9304054956273577,ok +75221,1.0,10,2.353499541816701,ok +258,1.0,11,1.2424737193190696,ok +75121,1.0,12,1.0179349097490955,ok +253,1.0,13,1.968280717454575,ok +261,1.0,14,1.4926572676240042,ok +75240,1.0,15,1.1171331721317688,ok +75120,1.0,16,1.1360518251763463,ok +75124,1.0,17,1.2487240937668993,ok +75176,1.0,18,1.0446988068144,ok +75103,1.0,19,1.0336848984749292,ok +75207,1.0,20,1.636092600811709,ok +75095,1.0,21,1.0949287146988107,ok +273,1.0,22,1.1728608792360746,ok +75174,1.0,23,1.3032329125393278,ok +75153,1.0,24,1.4328165179710455,ok +75093,1.0,25,1.4959972708806597,ok +75119,1.0,26,1.1151144994882052,ok +75201,1.0,27,1.3305012457513827,ok +75215,1.0,28,1.1176155647649066,ok +75172,1.0,29,1.3399190978496223,ok +75169,1.0,30,1.4657877944511197,ok +75202,1.0,31,1.9917696737957225,ok +75233,1.0,32,1.1716852859241451,ok +75231,1.0,33,2.4874718596830085,ok +75196,1.0,34,1.082893435164858,ok +248,1.0,35,1.7675684539996315,ok +75191,1.0,36,1.3161370219331083,ok +75217,1.0,37,1.0748503900926225,ok +260,1.0,38,1.150516019374749,ok +75115,1.0,39,1.0959244285247467,ok +75123,1.0,40,1.7558293205791347,ok +75108,1.0,41,1.024079629964656,ok +75101,1.0,42,1.5565488854166876,ok +75192,1.0,43,1.6929081941421245,ok +75232,1.0,44,1.346087302156221,ok +75173,1.0,45,1.3470342160339,ok +75197,1.0,46,1.4935039451402414,ok +266,1.0,47,1.1133877600120317,ok +75148,1.0,48,1.4112509081730755,ok +75150,1.0,49,2.725239945213567,ok +75100,1.0,50,1.100013284735915,ok +75178,1.0,51,3.280875609398758,ok +75236,1.0,52,1.434242183140346,ok +75179,1.0,53,1.4164286468645246,ok +75213,1.0,54,1.1653454279541735,ok +2123,1.0,55,1.1686923701391971,ok +75227,1.0,56,1.261530669255624,ok +75184,1.0,57,1.3436404080272144,ok +75142,1.0,58,1.2573438931271694,ok +236,1.0,59,1.3150329535598444,ok +2122,1.0,60,1.924062570651559,ok +75188,1.0,61,2.161721554407234,ok +75166,1.0,62,1.2698634017529333,ok +75181,1.0,63,1.0006041403110455,ok +75133,1.0,64,1.0500469796075564,ok +75134,1.0,65,1.3792294243561976,ok +75198,1.0,66,1.633723206380605,ok +262,1.0,67,1.0963726642181846,ok +75234,1.0,68,1.1971081929925118,ok +75139,1.0,69,1.0393005883776283,ok +252,1.0,70,1.523688363362894,ok +75117,1.0,71,1.1717459972259865,ok +75113,1.0,72,1.028613238953054,ok +75098,1.0,73,1.2051704445975586,ok +246,1.0,74,1.4619441995735873,ok +75203,1.0,75,1.4041315047670357,ok +75237,1.0,76,1.0022620398299384,ok +75195,1.0,77,1.008340271895055,ok +75171,1.0,78,1.3974217139522136,ok +75128,1.0,79,1.153567915879919,ok +75096,1.0,80,2.1664982309882617,ok +75250,1.0,81,2.3620252351230144,ok +75146,1.0,82,1.2720480574902622,ok +75116,1.0,83,1.071510164283055,ok +75157,1.0,84,1.690071982842634,ok +75187,1.0,85,1.205425535787864,ok +2350,1.0,86,1.661593411811027,ok +242,1.0,87,1.214510607149339,ok +244,1.0,88,1.571382321823727,ok +75125,1.0,89,1.2121728694062746,ok +75185,1.0,90,1.3123110517268635,ok +75163,1.0,91,1.2870776892598514,ok +75177,1.0,92,1.0453937912856202,ok +75189,1.0,93,1.074536159542699,ok +75244,1.0,94,1.1931588850472432,ok +75219,1.0,95,1.2790140735018254,ok +75222,1.0,96,1.129116138388567,ok +75159,1.0,97,1.2790765765429257,ok +75175,1.0,98,1.2889719434980056,ok +75109,1.0,99,1.9903553108889964,ok +254,1.0,100,1.0001431169182953,ok +75105,1.0,101,1.1301667903861539,ok +75106,1.0,102,1.260929408309586,ok +75212,1.0,103,1.5192546397013404,ok +75099,1.0,104,1.3587954056813556,ok +75248,1.0,105,1.2386717446902502,ok +233,1.0,106,1.0627328896562815,ok +75235,1.0,107,1.0117083651456051,ok +75226,1.0,108,1.016164829848488,ok +75132,1.0,109,1.3915851942598134,ok +75127,1.0,110,1.6088418423708373,ok +251,1.0,111,1.206137674978297,ok +75161,1.0,112,1.2175895657297868,ok +75143,1.0,113,1.1229171501716395,ok +75114,1.0,114,1.1209920174481114,ok +75182,1.0,115,1.2998157333038562,ok +75112,1.0,116,1.2989304603286411,ok +75210,1.0,117,1.0011578587287928,ok +75205,1.0,118,1.6871177696401585,ok +75090,1.0,119,1.6080069481085828,ok +275,1.0,120,1.204977636190774,ok +288,1.0,121,1.4016246726205854,ok +75092,1.0,122,1.185341128502467,ok +3043,1.0,123,1.0453937912856202,ok +75249,1.0,124,1.0382484670045675,ok +75126,1.0,125,1.1500583445243464,ok +75225,1.0,126,1.140532912878079,ok +75141,1.0,127,1.1628562384548615,ok +75107,1.0,128,1.2005767699749859,ok +75097,1.0,129,1.3263513429685516,ok +80001,1.0,1,1.2578486306592822,ok +80003,1.0,1,1.3793307082464872,ok +80006,1.0,1,1.3467568102059886,ok +80008,1.0,1,1.440346831248866,ok +80009,1.0,1,1.3438370084684146,ok +80010,1.0,1,1.474967037295473,ok +80011,1.0,1,1.1632782811263822,ok +80012,1.0,1,1.2487788296897304,ok +80013,1.0,1,1.1161077248398215,ok +80014,1.0,1,1.4425953301516445,ok +80015,1.0,1,1.3526643729280858,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/log_loss_binary.classification_sparse/configurations.csv b/metalearning/metalearning_files/log_loss_binary.classification_sparse/configurations.csv new file mode 100755 index 0000000..a08c9ca --- /dev/null +++ b/metalearning/metalearning_files/log_loss_binary.classification_sparse/configurations.csv @@ -0,0 +1,141 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,preprocessor:truncatedSVD:target_dim,rescaling:__choice__ +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7249853037185638,None,0.0,1.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,median,extra_trees_preproc_for_classification,False,gini,None,0.9424908623661876,None,0.0,7.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.002655175930910743,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3997691365353215,None,0.0,20.0,10.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,median,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.039533063907190934,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1878805519245509,fdr,chi2,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.9835753358328776,None,0.0,4.0,4.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.2380793644102286,None,0.0,1.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.15248352254459802,fwe,chi2,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2538107344750156,False,True,hinge,1.5099542326343014e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,8532.0,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.034465366914659866,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.12057775675278172,deviance,10.0,0.8011153303489733,None,0.0,2.0,16.0,0.0,370.0,0.6078295352200873,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,mean,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0006939450481567022,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.05694967271793989,0.9005883757146016,5.0,6.300159702718475,poly,-1.0,False,0.024026910265824642,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,31,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.039533063907190934,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1878805519245509,fdr,chi2,,standardize +none,one_hot_encoding,0.0010015637584068037,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.037611630308856295,deviance,5.0,0.8840126779516314,None,0.0,10.0,2.0,0.0,444.0,0.7599997167603434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,most_frequent,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.004090774134315939,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7983157215145903,None,0.0,2.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,normalize +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.2864228295610195,None,0.0,9.0,10.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,median,kernel_pca,,,,,,,,,,,,,,cosine,239.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.8657388713119849,,1.0,None,0.0,19.0,13.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828776,None,0.0,2.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.027324741616523342,deviance,10.0,0.8623781459430139,None,0.0,10.0,20.0,0.0,329.0,0.8595750155424215,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,mean,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1100.6211008501205,0.5921425829232616,2.0,0.0337546254878617,poly,-1.0,True,0.09641299736884308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3428971645958825,,,0.2229870623330047,rbf,-1.0,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9742178951431336,None,0.0,2.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11400034542737113,fdr,chi2,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4047.618729304337,,,2.0237366768707754,rbf,-1.0,True,0.04369127828878843,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,one_hot_encoding,0.010000000000000005,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10.0,1.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.03739654507085451,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8447775967498866,None,0.0,1.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.003443779683191071,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2538107344750156,False,True,hinge,1.5099542326343014e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,8532.0,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.002630811782675973,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9828367182452932,None,0.0,18.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,85,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,86,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0019686649916896212,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.04641055832142541,True,True,hinge,8.540468968077405e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87,mean,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,911.0,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1.0,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,89,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,90,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,91,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,93,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,95,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,96,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,97,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,98,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,99,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,100,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,102,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,103,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,104,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,105,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,106,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.006372860318416312,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6467376360604045,None,0.0,1.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,107,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,108,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,109,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3823734947460288,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,110,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,111,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,112,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,113,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,115,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,116,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,117,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6886293142639995,None,0.0,2.0,13.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,118,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16.670108971732134,chi2,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,119,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.00012696985090051145,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.163979712833404,False,True,hinge,0.02438592885755657,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,120,mean,kernel_pca,,,,,,,,,,,,,0.03910740617243662,rbf,1851.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,121,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,122,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,123,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,124,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,125,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,126,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,127,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,129,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.04329010470998136,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7260331062271381,None,0.0,7.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,134,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.714869937384006,None,0.0,8.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/metalearning/metalearning_files/log_loss_binary.classification_sparse/description.txt b/metalearning/metalearning_files/log_loss_binary.classification_sparse/description.txt new file mode 100755 index 0000000..b9d73dd --- /dev/null +++ b/metalearning/metalearning_files/log_loss_binary.classification_sparse/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: log_loss +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/log_loss_binary.classification_sparse/feature_costs.arff b/metalearning/metalearning_files/log_loss_binary.classification_sparse/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/log_loss_binary.classification_sparse/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/log_loss_binary.classification_sparse/feature_runstatus.arff b/metalearning/metalearning_files/log_loss_binary.classification_sparse/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/log_loss_binary.classification_sparse/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/log_loss_binary.classification_sparse/feature_values.arff b/metalearning/metalearning_files/log_loss_binary.classification_sparse/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/log_loss_binary.classification_sparse/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/log_loss_binary.classification_sparse/readme.txt b/metalearning/metalearning_files/log_loss_binary.classification_sparse/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/log_loss_multiclass.classification_dense/algorithm_runs.arff b/metalearning/metalearning_files/log_loss_multiclass.classification_dense/algorithm_runs.arff new file mode 100755 index 0000000..ff68d28 --- /dev/null +++ b/metalearning/metalearning_files/log_loss_multiclass.classification_dense/algorithm_runs.arff @@ -0,0 +1,152 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE log_loss NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2120,1.0,1,1.2551394289251652,ok +75193,1.0,2,1.1410633614512347,ok +2117,1.0,3,1.3704205093126214,ok +75156,1.0,4,1.4664015513645272,ok +75129,1.0,5,1.291330559897119,ok +75243,1.0,6,1.0012093831882118,ok +75110,1.0,7,1.924062570651559,ok +75239,1.0,8,1.0,ok +75223,1.0,9,1.3756283913988565,ok +75221,1.0,10,2.1910654733015056,ok +258,1.0,11,1.1398615508692935,ok +75121,1.0,12,1.0179349097490955,ok +253,1.0,13,1.9521382064539403,ok +261,1.0,14,1.4926572676240042,ok +75240,1.0,15,1.1171331721317688,ok +75120,1.0,16,1.1360518251763463,ok +75124,1.0,17,1.2487240937668993,ok +75176,1.0,18,1.0376591522371303,ok +75103,1.0,19,1.0246605532500268,ok +75207,1.0,20,1.636092600811709,ok +75095,1.0,21,1.0949287146988107,ok +273,1.0,22,1.12439876671779,ok +75174,1.0,23,1.2784990570399395,ok +75153,1.0,24,1.1943098297225512,ok +75093,1.0,25,1.4959972708806597,ok +75119,1.0,26,1.1151144994882052,ok +75201,1.0,27,1.3305012457513827,ok +75215,1.0,28,1.07091282347108,ok +75172,1.0,29,1.6474251960037334,ok +75169,1.0,30,1.4657877944511197,ok +75202,1.0,31,1.9917696737957225,ok +75233,1.0,32,1.1495053896802812,ok +75231,1.0,33,2.3986113472316815,ok +75196,1.0,34,1.0449907693400367,ok +248,1.0,35,1.7371616329941335,ok +75191,1.0,36,1.3341796163477095,ok +75217,1.0,37,1.0748503900926225,ok +260,1.0,38,1.150516019374749,ok +75115,1.0,39,1.0959244285247467,ok +75123,1.0,40,1.72626162180506,ok +75108,1.0,41,1.0,ok +75101,1.0,42,1.5317830705802749,ok +75192,1.0,43,1.692598064928378,ok +75232,1.0,44,1.3347015455418705,ok +75173,1.0,45,1.287930436247951,ok +75197,1.0,46,1.6545973611935814,ok +266,1.0,47,1.0777676811736654,ok +75148,1.0,48,1.3315515829350164,ok +75150,1.0,49,1.5465209868125815,ok +75100,1.0,50,1.0971794317495644,ok +75178,1.0,51,3.244487724784548,ok +75236,1.0,52,1.430747892175402,ok +75179,1.0,53,1.4164286468645246,ok +75213,1.0,54,1.1653454279541735,ok +2123,1.0,55,1.1686923701391971,ok +75227,1.0,56,1.2571423236490658,ok +75184,1.0,57,1.2934586358622462,ok +75142,1.0,58,1.1544608098235858,ok +236,1.0,59,1.1218356229614856,ok +2122,1.0,60,1.5720493920174783,ok +75188,1.0,61,2.161721554407234,ok +75166,1.0,62,1.269054495322524,ok +75181,1.0,63,1.0006041403110455,ok +75133,1.0,64,1.0500469796075564,ok +75134,1.0,65,1.3592378096201074,ok +75198,1.0,66,1.633723206380605,ok +262,1.0,67,1.0776299565115677,ok +75234,1.0,68,1.0607077502193207,ok +75139,1.0,69,1.0393005883776283,ok +252,1.0,70,1.3945071000605822,ok +75117,1.0,71,1.1717459972259865,ok +75113,1.0,72,1.0262388515639744,ok +75098,1.0,73,1.2051704445975586,ok +246,1.0,74,1.0939800377144957,ok +75203,1.0,75,1.4041315047670357,ok +75237,1.0,76,1.0021029915838504,ok +75195,1.0,77,1.008340271895055,ok +75171,1.0,78,1.3728507847125155,ok +75128,1.0,79,1.1141668511228324,ok +75096,1.0,80,1.0893852871349736,ok +75250,1.0,81,2.0437916881356095,ok +75146,1.0,82,1.2590783166501973,ok +75116,1.0,83,1.071510164283055,ok +75157,1.0,84,1.6881145547691494,ok +75187,1.0,85,1.0926551650755993,ok +2350,1.0,86,1.661593411811027,ok +242,1.0,87,1.214510607149339,ok +244,1.0,88,1.565132095876404,ok +75125,1.0,89,1.2121728694062746,ok +75185,1.0,90,1.299276450313559,ok +75163,1.0,91,1.2253502234101268,ok +75177,1.0,92,1.0453937912856202,ok +75189,1.0,93,1.074536159542699,ok +75244,1.0,94,1.1931588850472432,ok +75219,1.0,95,1.1044120327961304,ok +75222,1.0,96,1.129116138388567,ok +75159,1.0,97,1.2790765765429257,ok +75175,1.0,98,1.245159060909976,ok +75109,1.0,99,1.9119672936973653,ok +254,1.0,100,1.0001431169182953,ok +75105,1.0,101,1.1301667903861539,ok +75106,1.0,102,1.260929408309586,ok +75212,1.0,103,1.5192546397013404,ok +75099,1.0,104,1.3587954056813556,ok +75248,1.0,105,1.2386717446902502,ok +233,1.0,106,1.0189136516327566,ok +75235,1.0,107,1.0117083651456051,ok +75226,1.0,108,1.016164829848488,ok +75132,1.0,109,1.3915851942598134,ok +75127,1.0,110,1.6076160853607535,ok +251,1.0,111,1.0923346413046187,ok +75161,1.0,112,1.1507461810861586,ok +75143,1.0,113,1.1229171501716395,ok +75114,1.0,114,1.1209920174481114,ok +75182,1.0,115,1.2764619749562676,ok +75112,1.0,116,1.2989304603286411,ok +75210,1.0,117,1.0011578587287928,ok +75205,1.0,118,1.6871177696401585,ok +75090,1.0,119,1.6080069481085828,ok +275,1.0,120,1.1857485517615118,ok +288,1.0,121,1.3149173992382588,ok +75092,1.0,122,1.185341128502467,ok +3043,1.0,123,1.0453937912856202,ok +75249,1.0,124,1.0139396715643851,ok +75126,1.0,125,1.1500583445243464,ok +75225,1.0,126,1.140532912878079,ok +75141,1.0,127,1.1628562384548615,ok +75107,1.0,128,1.2005767699749859,ok +75097,1.0,129,1.2623476555767466,ok +80001,1.0,1,1.2578486306592822,ok +80003,1.0,1,1.3761997032528503,ok +80006,1.0,1,1.2323743620781897,ok +80008,1.0,1,1.2369807212348354,ok +80009,1.0,1,1.3438370084684146,ok +80010,1.0,1,1.474967037295473,ok +80011,1.0,1,1.110989374963057,ok +80012,1.0,1,1.2487788296897304,ok +80013,1.0,1,1.0,ok +80014,1.0,1,1.4425953301516445,ok +80015,1.0,1,1.3526643729280858,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/log_loss_multiclass.classification_dense/configurations.csv b/metalearning/metalearning_files/log_loss_multiclass.classification_dense/configurations.csv new file mode 100755 index 0000000..a7a1397 --- /dev/null +++ b/metalearning/metalearning_files/log_loss_multiclass.classification_dense/configurations.csv @@ -0,0 +1,141 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:fast_ica:algorithm,preprocessor:fast_ica:fun,preprocessor:fast_ica:n_components,preprocessor:fast_ica:whiten,preprocessor:feature_agglomeration:affinity,preprocessor:feature_agglomeration:linkage,preprocessor:feature_agglomeration:n_clusters,preprocessor:feature_agglomeration:pooling_func,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:pca:keep_variance,preprocessor:pca:whiten,preprocessor:polynomial:degree,preprocessor:polynomial:include_bias,preprocessor:polynomial:interaction_only,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,rescaling:__choice__,rescaling:quantile_transformer:n_quantiles,rescaling:quantile_transformer:output_distribution,rescaling:robust_scaler:q_max,rescaling:robust_scaler:q_min +weighting,one_hot_encoding,0.0003571493489893277,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.7207211219619362,None,0.0,1.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,median,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,25.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7136841657452827,0.2443375775688061 +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7249853037185638,None,0.0,1.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,median,extra_trees_preproc_for_classification,False,gini,None,0.9424908623661876,None,0.0,7.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.12713527337147906,deviance,4.0,0.6041596127474019,None,0.0,14.0,17.0,0.0,83.0,0.8426859880999615,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.013255438916868357,deviance,9.0,0.3592430187012816,None,0.0,10.0,7.0,0.0,411.0,0.691160823398122,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,median,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1.0,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.3766459280999309,None,0.0,4.0,5.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,False,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.251097788175676,deviance,6.0,0.35679099363539235,None,0.0,13.0,11.0,0.0,157.0,0.4791732272983235,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.10000000000000002,deviance,6.0,1.0,None,0.0,7.0,2.0,0.0,278.0,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3469031665162168,None,0.0,4.0,3.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,mean,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,83.0,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,,False,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,2.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09818715003413464,fwe,f_classif,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9874546479752576,None,0.0,20.0,3.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.039533063907190934,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1878805519245509,fdr,chi2,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9455638720565652,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,most_frequent,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8255464552647293,0.19162485555463185 +weighting,one_hot_encoding,0.18137532678800647,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9094110110427254,None,0.0,7.0,12.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,195.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.9835753358328776,None,0.0,4.0,4.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.02345017287074443,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.053517066400173056,deviance,10.0,0.542144980834302,None,0.0,20.0,13.0,0.0,233.0,0.7398539900055563,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0614425536709615,fwe,f_classif,robust_scaler,,,0.9523118062307264,0.13434811490315818 +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.8149627329153046,None,0.0,15.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.040936424602789435,deviance,7.0,0.5495014745530306,None,0.0,20.0,18.0,0.0,141.0,0.6905343807995293,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2538107344750156,False,True,hinge,1.5099542326343014e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,8532.0,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.1958974686405233,deviance,5.0,0.33885235607979314,None,0.0,6.0,4.0,0.0,125.0,0.9448890820738562,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0006939450481567022,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.05694967271793989,0.9005883757146016,5.0,6.300159702718475,poly,-1.0,False,0.024026910265824642,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,31,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.04749094903835669,deviance,3.0,0.6184047395714717,None,0.0,17.0,8.0,0.0,428.0,0.7515561640094087,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.6409514454358402,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4107013198375944,fdr,f_classif,normalize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9562349611127512,None,0.0,4.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,median,fast_ica,,,,,,,,,,,parallel,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.941692818067464,0.0440072111980354 +weighting,one_hot_encoding,0.0009664614609258606,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3714759002919618,None,0.0,18.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,fast_ica,,,,,,,,,,,deflation,exp,917.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.00012586572428922356,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5240592829918601,None,0.0,10.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.004090774134315939,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7983157215145903,None,0.0,2.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.9397100247778196,None,0.0,3.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,mean,fast_ica,,,,,,,,,,,parallel,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7272465857046146,0.24493108573961345 +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.2422926485206341,,1.0,None,0.0,15.0,9.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.022939738050158573,deviance,10.0,0.4185394344134278,None,0.0,2.0,10.0,0.0,309.0,0.5979695608086252,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.25383213391991144 +none,one_hot_encoding,0.31138539716704705,True,adaboost,SAMME.R,0.3026113597945332,1.0,50.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.05578036113726603,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.04310920427249598,deviance,9.0,0.7762532463369333,None,0.0,19.0,7.0,0.0,89.0,0.9651993549499902,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,median,fast_ica,,,,,,,,,,,parallel,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.00353232434213139,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.9407432771644124,None,0.0,1.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,median,fast_ica,,,,,,,,,,,parallel,cube,100.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7446318916516063,0.23782974987118105 +weighting,one_hot_encoding,0.010000000000000005,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.756120720182025,True,True,squared_hinge,0.020536827217281145,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,4188.0,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.3795924768593385,deviance,2.0,0.33708497069988536,None,0.0,15.0,13.0,0.0,451.0,0.7716323242090217,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2573946506994812,fwe,f_classif,quantile_transformer,1000.0,uniform,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.609975998293528,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,most_frequent,fast_ica,,,,,,,,,,,parallel,logcosh,2000.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8430415644014919,0.2863750565331575 +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.39536192447534535,None,0.0,19.0,3.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5.0,None,11.0,11.0,1.0,12.0,,,,,,robust_scaler,,,0.8928631650245873,0.1581877760687084 +weighting,one_hot_encoding,0.0009372940901663342,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.026081477437122958,deviance,2.0,0.5887618067763795,None,0.0,11.0,6.0,0.0,179.0,0.5309995755533717,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6897958091880166,None,0.0,2.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,870.2240970463429,0.1223321395075887,2.0,0.0035847433873211405,poly,-1.0,False,2.485160860440657e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4612682306567311,fpr,f_classif,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.010000000000000005,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.011886849251540706,deviance,10.0,0.716738790505292,None,0.0,5.0,6.0,0.0,472.0,0.9128424273302038,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8363534397818381,None,0.0,4.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,False,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9260795160807372,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.03644212536682547,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.05965671477918361,deviance,8.0,0.4858133247974158,None,0.0,14.0,7.0,0.0,480.0,0.5726186552917335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.15318294164619112 +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18887.81504976871,,,0.23283562663398755,rbf,-1.0,True,2.3839685780861318e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3937607155568376,fwe,chi2,robust_scaler,,,0.941018778984854,0.2144110585080491 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7856.587651350424,,,0.0017305319997054556,rbf,-1.0,False,1.7622421003766454e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8138233157708883,None,0.0,2.0,9.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54.27504292568562,f_classif,,,,minmax,,,, +none,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9742178951431336,None,0.0,2.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11400034542737113,fdr,chi2,none,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.8490054877538417,None,0.0,2.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.0001449312804440222,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,mean,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4047.618729304337,,,2.0237366768707754,rbf,-1.0,True,0.04369127828878843,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +weighting,one_hot_encoding,,False,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26.0,1.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,median,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.07589752815117898,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.7727592543547,None,0.0,10.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.03739654507085451,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8447775967498866,None,0.0,1.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,2.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09818715003413464,fwe,f_classif,none,,,, +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2538107344750156,False,True,hinge,1.5099542326343014e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,8532.0,,,,,,,,,,,,,,,,,,none,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.9188519169916218,None,0.0,1.0,5.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,median,fast_ica,,,,,,,,,,,parallel,cube,306.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.904983674005564,None,0.0,18.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,mean,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,275.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.4932996544760619,None,0.0,2.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,340.0,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.002353704397784248,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,most_frequent,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7766828206753251,True,,,,,,,,,,,,,,,,normalize,,,, +weighting,one_hot_encoding,0.0001486770773839718,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5256280540657592,None,0.0,8.0,12.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.0037042565030130366,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.9610802347688104,None,0.0,17.0,9.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,172.0,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.001479024648966236,True,decision_tree,,,,,,,gini,0.0029321615410515807,,1.0,None,0.0,17.0,19.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,robust_scaler,,,0.8019276898414001,0.2172955112207602 +none,one_hot_encoding,0.00010817282861262362,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.799803680241154,None,0.0,13.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,85,mean,fast_ica,,,,,,,,,,,deflation,exp,1793.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9071932815811076,0.03563842252368924 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,86,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0019686649916896212,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.04641055832142541,True,True,hinge,8.540468968077405e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87,mean,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,911.0,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,89,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.9342950927678112,None,0.0,20.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,90,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.1340879632520081,deviance,5.0,0.4391917863054552,None,0.0,18.0,5.0,0.0,227.0,0.8290252815444993,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,91,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,93,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2636201374253461,deviance,7.0,0.8344964130784466,None,0.0,9.0,2.0,0.0,298.0,0.7517549950523315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,95,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,96,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,97,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.07463196642416367,deviance,7.0,0.8603242247379981,None,0.0,2.0,6.0,0.0,500.0,0.8447665577491962,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,98,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.03425735525476273,deviance,7.0,0.5825782146709433,None,0.0,17.0,6.0,0.0,313.0,0.5043438213864502,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,99,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3805181287718124,fdr,f_classif,robust_scaler,,,0.8478326986019474,0.2878840415105679 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,100,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,102,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,103,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,104,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,105,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,106,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.006372860318416312,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6467376360604045,None,0.0,1.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,107,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,108,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,109,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.002615346832354839,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7884268823432835,None,0.0,20.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,110,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,3.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,111,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.010000000000000005,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.2020480296503026,deviance,3.0,0.4640458524354476,None,0.0,8.0,19.0,0.0,486.0,0.6791755979205191,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,112,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,113,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.03905145156995541,deviance,5.0,0.2281306656230014,None,0.0,14.0,13.0,0.0,493.0,0.8793075442604774,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,115,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,25382.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,116,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,117,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6886293142639995,None,0.0,2.0,13.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,118,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16.670108971732134,chi2,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,119,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.1885493528549979,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.603604171705261,False,True,squared_hinge,4.4620804452839e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,120,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,quantile_transformer,57665.0,uniform,, +weighting,one_hot_encoding,0.010000000000000005,True,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.825805258222736e-06,True,0.00010000000000000009,0.038198103889192085,True,0.14999999999999974,invscaling,modified_huber,elasticnet,0.13714427818877545,0.04372308852525775,,,,,,,,,,,,,,,,,,121,most_frequent,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,122,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,123,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.19998727075532635,deviance,10.0,0.9377656718112952,None,0.0,7.0,13.0,0.0,214.0,0.6062346326014357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,124,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,125,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,126,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,127,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7794633670276021,None,0.0,9.0,10.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,129,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,75840.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.09467364358164987,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,minmax,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.06597655581840225,deviance,1.0,0.21809176698911087,None,0.0,8.0,9.0,0.0,313.0,0.28171885744131103,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,mean,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,364.0,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11611.973346148121,-0.605048235954617,4.0,0.002797249824906932,poly,-1.0,True,4.813307514117123e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,192.0,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,134,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.8015164166877907,None,0.0,1.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76.35224288088253,f_classif,,,,quantile_transformer,1766.0,normal,, +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.714869937384006,None,0.0,8.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,no_encoding,,,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,146.10741262807508,False,True,1.0,squared_hinge,ovr,l1,0.0014510814556456804,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, diff --git a/metalearning/metalearning_files/log_loss_multiclass.classification_dense/description.txt b/metalearning/metalearning_files/log_loss_multiclass.classification_dense/description.txt new file mode 100755 index 0000000..b9d73dd --- /dev/null +++ b/metalearning/metalearning_files/log_loss_multiclass.classification_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: log_loss +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/log_loss_multiclass.classification_dense/feature_costs.arff b/metalearning/metalearning_files/log_loss_multiclass.classification_dense/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/log_loss_multiclass.classification_dense/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/log_loss_multiclass.classification_dense/feature_runstatus.arff b/metalearning/metalearning_files/log_loss_multiclass.classification_dense/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/log_loss_multiclass.classification_dense/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/log_loss_multiclass.classification_dense/feature_values.arff b/metalearning/metalearning_files/log_loss_multiclass.classification_dense/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/log_loss_multiclass.classification_dense/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/log_loss_multiclass.classification_dense/readme.txt b/metalearning/metalearning_files/log_loss_multiclass.classification_dense/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/log_loss_multiclass.classification_sparse/algorithm_runs.arff b/metalearning/metalearning_files/log_loss_multiclass.classification_sparse/algorithm_runs.arff new file mode 100755 index 0000000..447a84a --- /dev/null +++ b/metalearning/metalearning_files/log_loss_multiclass.classification_sparse/algorithm_runs.arff @@ -0,0 +1,152 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE log_loss NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2120,1.0,1,1.2583635793704644,ok +75193,1.0,2,1.1410633614512347,ok +2117,1.0,3,1.3820774928236736,ok +75156,1.0,4,1.4774967666350984,ok +75129,1.0,5,1.3542807631429683,ok +75243,1.0,6,1.0901073282963525,ok +75110,1.0,7,1.924062570651559,ok +75239,1.0,8,1.0,ok +75223,1.0,9,1.9304054956273577,ok +75221,1.0,10,2.353499541816701,ok +258,1.0,11,1.2424737193190696,ok +75121,1.0,12,1.0179349097490955,ok +253,1.0,13,1.968280717454575,ok +261,1.0,14,1.4926572676240042,ok +75240,1.0,15,1.1171331721317688,ok +75120,1.0,16,1.1360518251763463,ok +75124,1.0,17,1.2487240937668993,ok +75176,1.0,18,1.0446988068144,ok +75103,1.0,19,1.0336848984749292,ok +75207,1.0,20,1.636092600811709,ok +75095,1.0,21,1.0949287146988107,ok +273,1.0,22,1.1728608792360746,ok +75174,1.0,23,1.3032329125393278,ok +75153,1.0,24,1.4328165179710455,ok +75093,1.0,25,1.4959972708806597,ok +75119,1.0,26,1.1151144994882052,ok +75201,1.0,27,1.3305012457513827,ok +75215,1.0,28,1.1176155647649066,ok +75172,1.0,29,1.3399190978496223,ok +75169,1.0,30,1.4657877944511197,ok +75202,1.0,31,1.9917696737957225,ok +75233,1.0,32,1.1716852859241451,ok +75231,1.0,33,2.4874718596830085,ok +75196,1.0,34,1.082893435164858,ok +248,1.0,35,1.7675684539996315,ok +75191,1.0,36,1.3161370219331083,ok +75217,1.0,37,1.0748503900926225,ok +260,1.0,38,1.150516019374749,ok +75115,1.0,39,1.0959244285247467,ok +75123,1.0,40,1.7558293205791347,ok +75108,1.0,41,1.024079629964656,ok +75101,1.0,42,1.5565488854166876,ok +75192,1.0,43,1.6929081941421245,ok +75232,1.0,44,1.346087302156221,ok +75173,1.0,45,1.3470342160339,ok +75197,1.0,46,1.4935039451402414,ok +266,1.0,47,1.1133877600120317,ok +75148,1.0,48,1.4112509081730755,ok +75150,1.0,49,2.725239945213567,ok +75100,1.0,50,1.100013284735915,ok +75178,1.0,51,3.280875609398758,ok +75236,1.0,52,1.434242183140346,ok +75179,1.0,53,1.4164286468645246,ok +75213,1.0,54,1.1653454279541735,ok +2123,1.0,55,1.1686923701391971,ok +75227,1.0,56,1.261530669255624,ok +75184,1.0,57,1.3436404080272144,ok +75142,1.0,58,1.2573438931271694,ok +236,1.0,59,1.3150329535598444,ok +2122,1.0,60,1.924062570651559,ok +75188,1.0,61,2.161721554407234,ok +75166,1.0,62,1.2698634017529333,ok +75181,1.0,63,1.0006041403110455,ok +75133,1.0,64,1.0500469796075564,ok +75134,1.0,65,1.3792294243561976,ok +75198,1.0,66,1.633723206380605,ok +262,1.0,67,1.0963726642181846,ok +75234,1.0,68,1.1971081929925118,ok +75139,1.0,69,1.0393005883776283,ok +252,1.0,70,1.523688363362894,ok +75117,1.0,71,1.1717459972259865,ok +75113,1.0,72,1.028613238953054,ok +75098,1.0,73,1.2051704445975586,ok +246,1.0,74,1.4619441995735873,ok +75203,1.0,75,1.4041315047670357,ok +75237,1.0,76,1.0022620398299384,ok +75195,1.0,77,1.008340271895055,ok +75171,1.0,78,1.3974217139522136,ok +75128,1.0,79,1.153567915879919,ok +75096,1.0,80,2.1664982309882617,ok +75250,1.0,81,2.3620252351230144,ok +75146,1.0,82,1.2720480574902622,ok +75116,1.0,83,1.071510164283055,ok +75157,1.0,84,1.690071982842634,ok +75187,1.0,85,1.205425535787864,ok +2350,1.0,86,1.661593411811027,ok +242,1.0,87,1.214510607149339,ok +244,1.0,88,1.571382321823727,ok +75125,1.0,89,1.2121728694062746,ok +75185,1.0,90,1.3123110517268635,ok +75163,1.0,91,1.2870776892598514,ok +75177,1.0,92,1.0453937912856202,ok +75189,1.0,93,1.074536159542699,ok +75244,1.0,94,1.1931588850472432,ok +75219,1.0,95,1.2790140735018254,ok +75222,1.0,96,1.129116138388567,ok +75159,1.0,97,1.2790765765429257,ok +75175,1.0,98,1.2889719434980056,ok +75109,1.0,99,1.9903553108889964,ok +254,1.0,100,1.0001431169182953,ok +75105,1.0,101,1.1301667903861539,ok +75106,1.0,102,1.260929408309586,ok +75212,1.0,103,1.5192546397013404,ok +75099,1.0,104,1.3587954056813556,ok +75248,1.0,105,1.2386717446902502,ok +233,1.0,106,1.0627328896562815,ok +75235,1.0,107,1.0117083651456051,ok +75226,1.0,108,1.016164829848488,ok +75132,1.0,109,1.3915851942598134,ok +75127,1.0,110,1.6088418423708373,ok +251,1.0,111,1.206137674978297,ok +75161,1.0,112,1.2175895657297868,ok +75143,1.0,113,1.1229171501716395,ok +75114,1.0,114,1.1209920174481114,ok +75182,1.0,115,1.2998157333038562,ok +75112,1.0,116,1.2989304603286411,ok +75210,1.0,117,1.0011578587287928,ok +75205,1.0,118,1.6871177696401585,ok +75090,1.0,119,1.6080069481085828,ok +275,1.0,120,1.204977636190774,ok +288,1.0,121,1.4016246726205854,ok +75092,1.0,122,1.185341128502467,ok +3043,1.0,123,1.0453937912856202,ok +75249,1.0,124,1.0382484670045675,ok +75126,1.0,125,1.1500583445243464,ok +75225,1.0,126,1.140532912878079,ok +75141,1.0,127,1.1628562384548615,ok +75107,1.0,128,1.2005767699749859,ok +75097,1.0,129,1.3263513429685516,ok +80001,1.0,1,1.2578486306592822,ok +80003,1.0,1,1.3793307082464872,ok +80006,1.0,1,1.3467568102059886,ok +80008,1.0,1,1.440346831248866,ok +80009,1.0,1,1.3438370084684146,ok +80010,1.0,1,1.474967037295473,ok +80011,1.0,1,1.1632782811263822,ok +80012,1.0,1,1.2487788296897304,ok +80013,1.0,1,1.1161077248398215,ok +80014,1.0,1,1.4425953301516445,ok +80015,1.0,1,1.3526643729280858,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/log_loss_multiclass.classification_sparse/configurations.csv b/metalearning/metalearning_files/log_loss_multiclass.classification_sparse/configurations.csv new file mode 100755 index 0000000..a08c9ca --- /dev/null +++ b/metalearning/metalearning_files/log_loss_multiclass.classification_sparse/configurations.csv @@ -0,0 +1,141 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,preprocessor:truncatedSVD:target_dim,rescaling:__choice__ +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7249853037185638,None,0.0,1.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,median,extra_trees_preproc_for_classification,False,gini,None,0.9424908623661876,None,0.0,7.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.002655175930910743,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3997691365353215,None,0.0,20.0,10.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,median,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.039533063907190934,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1878805519245509,fdr,chi2,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.9835753358328776,None,0.0,4.0,4.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.2380793644102286,None,0.0,1.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.15248352254459802,fwe,chi2,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2538107344750156,False,True,hinge,1.5099542326343014e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,8532.0,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.034465366914659866,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.12057775675278172,deviance,10.0,0.8011153303489733,None,0.0,2.0,16.0,0.0,370.0,0.6078295352200873,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,mean,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0006939450481567022,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.05694967271793989,0.9005883757146016,5.0,6.300159702718475,poly,-1.0,False,0.024026910265824642,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,31,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.039533063907190934,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1878805519245509,fdr,chi2,,standardize +none,one_hot_encoding,0.0010015637584068037,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.037611630308856295,deviance,5.0,0.8840126779516314,None,0.0,10.0,2.0,0.0,444.0,0.7599997167603434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,most_frequent,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.004090774134315939,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7983157215145903,None,0.0,2.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,normalize +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.2864228295610195,None,0.0,9.0,10.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,median,kernel_pca,,,,,,,,,,,,,,cosine,239.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.8657388713119849,,1.0,None,0.0,19.0,13.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828776,None,0.0,2.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.027324741616523342,deviance,10.0,0.8623781459430139,None,0.0,10.0,20.0,0.0,329.0,0.8595750155424215,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,mean,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1100.6211008501205,0.5921425829232616,2.0,0.0337546254878617,poly,-1.0,True,0.09641299736884308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3428971645958825,,,0.2229870623330047,rbf,-1.0,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9742178951431336,None,0.0,2.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11400034542737113,fdr,chi2,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4047.618729304337,,,2.0237366768707754,rbf,-1.0,True,0.04369127828878843,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,one_hot_encoding,0.010000000000000005,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10.0,1.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.03739654507085451,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8447775967498866,None,0.0,1.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.003443779683191071,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2538107344750156,False,True,hinge,1.5099542326343014e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,8532.0,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.002630811782675973,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9828367182452932,None,0.0,18.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,85,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,86,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0019686649916896212,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.04641055832142541,True,True,hinge,8.540468968077405e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87,mean,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,911.0,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1.0,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,89,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,90,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,91,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,93,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,95,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,96,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,97,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,98,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,99,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,100,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,102,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,103,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,104,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,105,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,106,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.006372860318416312,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6467376360604045,None,0.0,1.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,107,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,108,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,109,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3823734947460288,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,110,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,111,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,112,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,113,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,115,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,116,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,117,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6886293142639995,None,0.0,2.0,13.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,118,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16.670108971732134,chi2,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,119,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.00012696985090051145,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.163979712833404,False,True,hinge,0.02438592885755657,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,120,mean,kernel_pca,,,,,,,,,,,,,0.03910740617243662,rbf,1851.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,121,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,122,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,123,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,124,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,125,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,126,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,127,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,129,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.04329010470998136,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7260331062271381,None,0.0,7.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,134,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.714869937384006,None,0.0,8.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/metalearning/metalearning_files/log_loss_multiclass.classification_sparse/description.txt b/metalearning/metalearning_files/log_loss_multiclass.classification_sparse/description.txt new file mode 100755 index 0000000..b9d73dd --- /dev/null +++ b/metalearning/metalearning_files/log_loss_multiclass.classification_sparse/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: log_loss +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/log_loss_multiclass.classification_sparse/feature_costs.arff b/metalearning/metalearning_files/log_loss_multiclass.classification_sparse/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/log_loss_multiclass.classification_sparse/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/log_loss_multiclass.classification_sparse/feature_runstatus.arff b/metalearning/metalearning_files/log_loss_multiclass.classification_sparse/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/log_loss_multiclass.classification_sparse/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/log_loss_multiclass.classification_sparse/feature_values.arff b/metalearning/metalearning_files/log_loss_multiclass.classification_sparse/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/log_loss_multiclass.classification_sparse/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/log_loss_multiclass.classification_sparse/readme.txt b/metalearning/metalearning_files/log_loss_multiclass.classification_sparse/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/pac_score_binary.classification_dense/algorithm_runs.arff b/metalearning/metalearning_files/pac_score_binary.classification_dense/algorithm_runs.arff new file mode 100755 index 0000000..5d81f69 --- /dev/null +++ b/metalearning/metalearning_files/pac_score_binary.classification_dense/algorithm_runs.arff @@ -0,0 +1,152 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE pac_score NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2120,1.0,1,0.2743853631304761,ok +75193,1.0,2,0.18773586269855957,ok +2117,1.0,3,0.7276341645801123,ok +75156,1.0,4,0.7485129622976275,ok +75129,1.0,5,0.8951327973739391,ok +75243,1.0,6,0.0017355204722208084,ok +75110,1.0,7,0.6494236072893236,ok +75239,1.0,8,1.2847215913325272e-07,ok +75223,1.0,9,0.34315333213656096,ok +75221,1.0,10,0.8713658958696701,ok +258,1.0,11,0.08967729832006155,ok +75121,1.0,12,0.10898189896173682,ok +253,1.0,13,0.9405063128381406,ok +261,1.0,14,0.8509813469369898,ok +75240,1.0,15,0.18503800543257864,ok +75120,1.0,16,0.8330719116393059,ok +75124,1.0,17,0.7225519699734184,ok +75176,1.0,18,0.07447018078248535,ok +75103,1.0,19,0.11484063174902903,ok +75207,1.0,20,0.512703350784506,ok +75095,1.0,21,0.3826346375656122,ok +273,1.0,22,0.24002034412158613,ok +75174,1.0,23,0.5371373828619556,ok +75153,1.0,24,0.35326241856112506,ok +75093,1.0,25,0.9628107264544831,ok +75119,1.0,26,0.6111894441869999,ok +75201,1.0,27,0.34562309149167814,ok +75215,1.0,28,0.1380577511686255,ok +75172,1.0,29,0.5369909406966044,ok +75169,1.0,30,0.38558731111829425,ok +75202,1.0,31,0.6400358659099892,ok +75233,1.0,32,0.30222613423916256,ok +75231,1.0,33,0.7354855537529592,ok +75196,1.0,34,0.09968508248905783,ok +248,1.0,35,0.5797493841168782,ok +75191,1.0,36,0.5681653801872639,ok +75217,1.0,37,0.08292779796644634,ok +260,1.0,38,0.3048842266227839,ok +75115,1.0,39,0.3744058283993963,ok +75123,1.0,40,0.7746836702148528,ok +75108,1.0,41,1.720076782119051e-07,ok +75101,1.0,42,0.825833278165562,ok +75192,1.0,43,0.9995031817554063,ok +75232,1.0,44,0.600189335152944,ok +75173,1.0,45,0.500463882487415,ok +75197,1.0,46,0.5247273457370218,ok +266,1.0,47,0.08733990219138965,ok +75148,1.0,48,0.5644035030655359,ok +75150,1.0,49,0.8423794461399563,ok +75100,1.0,50,2.1077741320034065,ok +75178,1.0,51,0.9931721362070052,ok +75236,1.0,52,0.38899523852079376,ok +75179,1.0,53,0.7467592769313054,ok +75213,1.0,54,0.36831228224411516,ok +2123,1.0,55,0.4911594340439359,ok +75227,1.0,56,0.4907471014469743,ok +75184,1.0,57,0.5435186107536409,ok +75142,1.0,58,0.28625163174361845,ok +236,1.0,59,0.1193048437141877,ok +2122,1.0,60,0.477382368857346,ok +75188,1.0,61,0.7198985509814408,ok +75166,1.0,62,0.4617170505940329,ok +75181,1.0,63,0.0006376437651373079,ok +75133,1.0,64,0.8025255324642442,ok +75134,1.0,65,0.33853427176089845,ok +75198,1.0,66,0.4760842957492827,ok +262,1.0,67,0.08300650300905998,ok +75234,1.0,68,0.11780359825928022,ok +75139,1.0,69,0.08204142242730916,ok +252,1.0,70,0.362378517423401,ok +75117,1.0,71,0.6459554639317393,ok +75113,1.0,72,0.1247323894163972,ok +75098,1.0,73,0.19335338413888703,ok +246,1.0,74,0.07296824907135557,ok +75203,1.0,75,0.40824976165284543,ok +75237,1.0,76,0.00473320913858577,ok +75195,1.0,77,0.016915136387811613,ok +75171,1.0,78,0.6224906256630065,ok +75128,1.0,79,0.3010211970166028,ok +75096,1.0,80,0.07706155900075085,ok +75250,1.0,81,0.6863830041382419,ok +75146,1.0,82,0.4631017809005502,ok +75116,1.0,83,0.19335635798818618,ok +75157,1.0,84,1.001521844111647,ok +75187,1.0,85,0.17702775896599832,ok +2350,1.0,86,1.004941489591669,ok +242,1.0,87,0.21461584650210785,ok +244,1.0,88,0.47990814371842194,ok +75125,1.0,89,0.4266337330685913,ok +75185,1.0,90,0.5177674402365288,ok +75163,1.0,91,0.40405749890463416,ok +75177,1.0,92,0.2057574737406812,ok +75189,1.0,93,0.13986525576970965,ok +75244,1.0,94,0.7623438686518899,ok +75219,1.0,95,0.19923894529395347,ok +75222,1.0,96,0.449478470055402,ok +75159,1.0,97,0.8728632794113166,ok +75175,1.0,98,0.4420151411698223,ok +75109,1.0,99,0.7651055338204128,ok +254,1.0,100,0.00028661895253534464,ok +75105,1.0,101,1.1081843905042723,ok +75106,1.0,102,0.9814668174102382,ok +75212,1.0,103,0.8101273413585854,ok +75099,1.0,104,0.8286653301684626,ok +75248,1.0,105,0.7704270429994281,ok +233,1.0,106,0.03756468326315454,ok +75235,1.0,107,0.016673004572354544,ok +75226,1.0,108,0.04579254100810792,ok +75132,1.0,109,1.4311848426146723,ok +75127,1.0,110,0.91607797296413,ok +251,1.0,111,0.1550373295593579,ok +75161,1.0,112,0.2798834295600173,ok +75143,1.0,113,0.15668557978453235,ok +75114,1.0,114,0.2802509783950846,ok +75182,1.0,115,0.5337267434507844,ok +75112,1.0,116,0.5386542656770761,ok +75210,1.0,117,0.002334397641262753,ok +75205,1.0,118,0.5462330752281889,ok +75090,1.0,119,0.4971859018554544,ok +275,1.0,120,0.26514461127103583,ok +288,1.0,121,0.40523053236813267,ok +75092,1.0,122,0.5833034312489822,ok +3043,1.0,123,0.2057574737406812,ok +75249,1.0,124,0.057435800451474894,ok +75126,1.0,125,0.450045064721568,ok +75225,1.0,126,0.6644303804683288,ok +75141,1.0,127,0.30639986019837717,ok +75107,1.0,128,0.7600575081398804,ok +75097,1.0,129,1.0735035892725673,ok +80001,1.0,1,0.5843376070679424,ok +80003,1.0,1,0.6274740431047592,ok +80006,1.0,1,0.41551421465175087,ok +80008,1.0,1,0.4246216838343858,ok +80009,1.0,1,0.582719062436241,ok +80010,1.0,1,0.8188826728383342,ok +80011,1.0,1,0.21918612862823272,ok +80012,1.0,1,0.4580779237047369,ok +80013,1.0,1,1.3152505429214756e-07,ok +80014,1.0,1,0.71824401797839,ok +80015,1.0,1,0.6121458691351616,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/pac_score_binary.classification_dense/configurations.csv b/metalearning/metalearning_files/pac_score_binary.classification_dense/configurations.csv new file mode 100755 index 0000000..a391d9f --- /dev/null +++ b/metalearning/metalearning_files/pac_score_binary.classification_dense/configurations.csv @@ -0,0 +1,141 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:fast_ica:algorithm,preprocessor:fast_ica:fun,preprocessor:fast_ica:n_components,preprocessor:fast_ica:whiten,preprocessor:feature_agglomeration:affinity,preprocessor:feature_agglomeration:linkage,preprocessor:feature_agglomeration:n_clusters,preprocessor:feature_agglomeration:pooling_func,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:pca:keep_variance,preprocessor:pca:whiten,preprocessor:polynomial:degree,preprocessor:polynomial:include_bias,preprocessor:polynomial:interaction_only,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,rescaling:__choice__,rescaling:quantile_transformer:n_quantiles,rescaling:quantile_transformer:output_distribution,rescaling:robust_scaler:q_max,rescaling:robust_scaler:q_min +weighting,one_hot_encoding,0.0003571493489893277,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.7207211219619362,None,0.0,1.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,median,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,25.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7136841657452827,0.2443375775688061 +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7249853037185638,None,0.0,1.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,median,extra_trees_preproc_for_classification,False,gini,None,0.9424908623661876,None,0.0,7.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.12713527337147906,deviance,4.0,0.6041596127474019,None,0.0,14.0,17.0,0.0,83.0,0.8426859880999615,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.013255438916868357,deviance,9.0,0.3592430187012816,None,0.0,10.0,7.0,0.0,411.0,0.691160823398122,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,median,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1.0,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.251097788175676,deviance,6.0,0.35679099363539235,None,0.0,13.0,11.0,0.0,157.0,0.4791732272983235,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.10000000000000002,deviance,6.0,1.0,None,0.0,7.0,2.0,0.0,278.0,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3469031665162168,None,0.0,4.0,3.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,mean,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,83.0,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,,False,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,2.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09818715003413464,fwe,f_classif,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9874546479752576,None,0.0,20.0,3.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.039533063907190934,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1878805519245509,fdr,chi2,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9455638720565652,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,most_frequent,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8255464552647293,0.19162485555463185 +weighting,one_hot_encoding,0.18137532678800647,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9094110110427254,None,0.0,7.0,12.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,195.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9742178951431336,None,0.0,2.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11400034542737113,fdr,chi2,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.02345017287074443,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.053517066400173056,deviance,10.0,0.542144980834302,None,0.0,20.0,13.0,0.0,233.0,0.7398539900055563,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0614425536709615,fwe,f_classif,robust_scaler,,,0.9523118062307264,0.13434811490315818 +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.8149627329153046,None,0.0,15.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.040936424602789435,deviance,7.0,0.5495014745530306,None,0.0,20.0,18.0,0.0,141.0,0.6905343807995293,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2538107344750156,False,True,hinge,1.5099542326343014e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,8532.0,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.1958974686405233,deviance,5.0,0.33885235607979314,None,0.0,6.0,4.0,0.0,125.0,0.9448890820738562,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0006939450481567022,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.05694967271793989,0.9005883757146016,5.0,6.300159702718475,poly,-1.0,False,0.024026910265824642,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,31,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.04749094903835669,deviance,3.0,0.6184047395714717,None,0.0,17.0,8.0,0.0,428.0,0.7515561640094087,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.6409514454358402,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4107013198375944,fdr,f_classif,normalize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9562349611127512,None,0.0,4.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,median,fast_ica,,,,,,,,,,,parallel,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.941692818067464,0.0440072111980354 +weighting,one_hot_encoding,0.0009664614609258606,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3714759002919618,None,0.0,18.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,fast_ica,,,,,,,,,,,deflation,exp,917.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.00012586572428922356,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5240592829918601,None,0.0,10.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.004090774134315939,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7983157215145903,None,0.0,2.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.9397100247778196,None,0.0,3.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,mean,fast_ica,,,,,,,,,,,parallel,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7272465857046146,0.24493108573961345 +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.2422926485206341,,1.0,None,0.0,15.0,9.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.022939738050158573,deviance,10.0,0.4185394344134278,None,0.0,2.0,10.0,0.0,309.0,0.5979695608086252,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.25383213391991144 +none,one_hot_encoding,0.31138539716704705,True,adaboost,SAMME.R,0.3026113597945332,1.0,50.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.05578036113726603,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.04310920427249598,deviance,9.0,0.7762532463369333,None,0.0,19.0,7.0,0.0,89.0,0.9651993549499902,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,median,fast_ica,,,,,,,,,,,parallel,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.00353232434213139,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.9407432771644124,None,0.0,1.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,median,fast_ica,,,,,,,,,,,parallel,cube,100.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7446318916516063,0.23782974987118105 +weighting,one_hot_encoding,0.010000000000000005,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.756120720182025,True,True,squared_hinge,0.020536827217281145,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,4188.0,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.3795924768593385,deviance,2.0,0.33708497069988536,None,0.0,15.0,13.0,0.0,451.0,0.7716323242090217,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2573946506994812,fwe,f_classif,quantile_transformer,1000.0,uniform,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.609975998293528,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,most_frequent,fast_ica,,,,,,,,,,,parallel,logcosh,2000.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8430415644014919,0.2863750565331575 +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.39536192447534535,None,0.0,19.0,3.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5.0,None,11.0,11.0,1.0,12.0,,,,,,robust_scaler,,,0.8928631650245873,0.1581877760687084 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6897958091880166,None,0.0,2.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,870.2240970463429,0.1223321395075887,2.0,0.0035847433873211405,poly,-1.0,False,2.485160860440657e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4612682306567311,fpr,f_classif,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8363534397818381,None,0.0,4.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,False,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9260795160807372,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.03644212536682547,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.05965671477918361,deviance,8.0,0.4858133247974158,None,0.0,14.0,7.0,0.0,480.0,0.5726186552917335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.15318294164619112 +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18887.81504976871,,,0.23283562663398755,rbf,-1.0,True,2.3839685780861318e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3937607155568376,fwe,chi2,robust_scaler,,,0.941018778984854,0.2144110585080491 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7856.587651350424,,,0.0017305319997054556,rbf,-1.0,False,1.7622421003766454e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8138233157708883,None,0.0,2.0,9.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54.27504292568562,f_classif,,,,minmax,,,, +none,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9742178951431336,None,0.0,2.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11400034542737113,fdr,chi2,none,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.8490054877538417,None,0.0,2.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.0001449312804440222,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,mean,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4047.618729304337,,,2.0237366768707754,rbf,-1.0,True,0.04369127828878843,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +weighting,one_hot_encoding,,False,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26.0,1.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,median,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.07589752815117898,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.7727592543547,None,0.0,10.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,minmax,,,, +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,normalize,,,, +weighting,one_hot_encoding,,False,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,2.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09818715003413464,fwe,f_classif,none,,,, +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2538107344750156,False,True,hinge,1.5099542326343014e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,8532.0,,,,,,,,,,,,,,,,,,none,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.9188519169916218,None,0.0,1.0,5.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,median,fast_ica,,,,,,,,,,,parallel,cube,306.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.904983674005564,None,0.0,18.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,mean,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,275.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.002353704397784248,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,most_frequent,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7766828206753251,True,,,,,,,,,,,,,,,,normalize,,,, +weighting,one_hot_encoding,0.0001486770773839718,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5256280540657592,None,0.0,8.0,12.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.0037042565030130366,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.9610802347688104,None,0.0,17.0,9.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,172.0,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.001479024648966236,True,decision_tree,,,,,,,gini,0.0029321615410515807,,1.0,None,0.0,17.0,19.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,robust_scaler,,,0.8019276898414001,0.2172955112207602 +none,one_hot_encoding,0.00010817282861262362,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.799803680241154,None,0.0,13.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,85,mean,fast_ica,,,,,,,,,,,deflation,exp,1793.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9071932815811076,0.03563842252368924 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,86,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0019686649916896212,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.04641055832142541,True,True,hinge,8.540468968077405e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87,mean,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,911.0,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,89,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.9342950927678112,None,0.0,20.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,90,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6856119355718316,None,0.0,2.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,91,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,robust_scaler,,,0.9070067796375252,0.2232396978725172 +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,93,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2636201374253461,deviance,7.0,0.8344964130784466,None,0.0,9.0,2.0,0.0,298.0,0.7517549950523315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,95,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,96,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,97,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.07463196642416367,deviance,7.0,0.8603242247379981,None,0.0,2.0,6.0,0.0,500.0,0.8447665577491962,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,98,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.03425735525476273,deviance,7.0,0.5825782146709433,None,0.0,17.0,6.0,0.0,313.0,0.5043438213864502,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,99,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3805181287718124,fdr,f_classif,robust_scaler,,,0.8478326986019474,0.2878840415105679 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,100,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,102,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,103,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,104,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,105,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,106,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.006372860318416312,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6467376360604045,None,0.0,1.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,107,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,108,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,109,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.002615346832354839,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7884268823432835,None,0.0,20.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,110,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,3.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,111,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.010000000000000005,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.2020480296503026,deviance,3.0,0.4640458524354476,None,0.0,8.0,19.0,0.0,486.0,0.6791755979205191,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,112,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,113,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.03905145156995541,deviance,5.0,0.2281306656230014,None,0.0,14.0,13.0,0.0,493.0,0.8793075442604774,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,115,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,25382.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,116,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,117,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6886293142639995,None,0.0,2.0,13.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,118,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16.670108971732134,chi2,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,119,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.1885493528549979,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.603604171705261,False,True,squared_hinge,4.4620804452839e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,120,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,quantile_transformer,57665.0,uniform,, +weighting,one_hot_encoding,0.010000000000000005,True,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.825805258222736e-06,True,0.00010000000000000009,0.038198103889192085,True,0.14999999999999974,invscaling,modified_huber,elasticnet,0.13714427818877545,0.04372308852525775,,,,,,,,,,,,,,,,,,121,most_frequent,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,122,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,123,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.19998727075532635,deviance,10.0,0.9377656718112952,None,0.0,7.0,13.0,0.0,214.0,0.6062346326014357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,124,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,125,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,126,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,127,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7945458151995424,None,0.0,1.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,129,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,38400.0,uniform,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.09467364358164987,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,minmax,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.06597655581840225,deviance,1.0,0.21809176698911087,None,0.0,8.0,9.0,0.0,313.0,0.28171885744131103,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,mean,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,364.0,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11611.973346148121,-0.605048235954617,4.0,0.002797249824906932,poly,-1.0,True,4.813307514117123e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,192.0,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,134,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.8015164166877907,None,0.0,1.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76.35224288088253,f_classif,,,,quantile_transformer,1766.0,normal,, +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.714869937384006,None,0.0,8.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,no_encoding,,,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,146.10741262807508,False,True,1.0,squared_hinge,ovr,l1,0.0014510814556456804,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, diff --git a/metalearning/metalearning_files/pac_score_binary.classification_dense/description.txt b/metalearning/metalearning_files/pac_score_binary.classification_dense/description.txt new file mode 100755 index 0000000..bf4ad29 --- /dev/null +++ b/metalearning/metalearning_files/pac_score_binary.classification_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: pac_score +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/pac_score_binary.classification_dense/feature_costs.arff b/metalearning/metalearning_files/pac_score_binary.classification_dense/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/pac_score_binary.classification_dense/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/pac_score_binary.classification_dense/feature_runstatus.arff b/metalearning/metalearning_files/pac_score_binary.classification_dense/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/pac_score_binary.classification_dense/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/pac_score_binary.classification_dense/feature_values.arff b/metalearning/metalearning_files/pac_score_binary.classification_dense/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/pac_score_binary.classification_dense/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/pac_score_binary.classification_dense/readme.txt b/metalearning/metalearning_files/pac_score_binary.classification_dense/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/pac_score_binary.classification_sparse/algorithm_runs.arff b/metalearning/metalearning_files/pac_score_binary.classification_sparse/algorithm_runs.arff new file mode 100755 index 0000000..c4a9f7b --- /dev/null +++ b/metalearning/metalearning_files/pac_score_binary.classification_sparse/algorithm_runs.arff @@ -0,0 +1,152 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE pac_score NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2120,1.0,1,0.2774242815722795,ok +75193,1.0,2,0.18773586269855957,ok +2117,1.0,3,0.7464428970157602,ok +75156,1.0,4,0.7624112408226631,ok +75129,1.0,5,0.8951327973739391,ok +75243,1.0,6,0.12372825986996794,ok +75110,1.0,7,0.6494236072893236,ok +75239,1.0,8,1.2847215913325272e-07,ok +75223,1.0,9,0.6430785592014303,ok +75221,1.0,10,0.9084994799735421,ok +258,1.0,11,0.23926430919167896,ok +75121,1.0,12,0.10898189896173682,ok +253,1.0,13,0.9499707620161727,ok +261,1.0,14,0.8509813469369898,ok +75240,1.0,15,0.18503800543257864,ok +75120,1.0,16,0.8330719116393059,ok +75124,1.0,17,0.7225519699734184,ok +75176,1.0,18,0.0880823902130643,ok +75103,1.0,19,0.13894941494974622,ok +75207,1.0,20,0.512703350784506,ok +75095,1.0,21,0.3826346375656122,ok +273,1.0,22,0.30833306681040906,ok +75174,1.0,23,0.5476780386259217,ok +75153,1.0,24,0.7027761139689175,ok +75093,1.0,25,0.9628107264544831,ok +75119,1.0,26,0.6111894441869999,ok +75201,1.0,27,0.34562309149167814,ok +75215,1.0,28,0.18823441926129736,ok +75172,1.0,29,0.32468128222501347,ok +75169,1.0,30,0.38558731111829425,ok +75202,1.0,31,0.6400358659099892,ok +75233,1.0,32,0.3311557656129698,ok +75231,1.0,33,0.7585346030386128,ok +75196,1.0,34,0.18025357075812087,ok +248,1.0,35,0.5956788353588593,ok +75191,1.0,36,0.5420958962639859,ok +75217,1.0,37,0.08292779796644634,ok +260,1.0,38,0.3048842266227839,ok +75115,1.0,39,0.3744058283993963,ok +75123,1.0,40,0.7958300175288696,ok +75108,1.0,41,0.046291764870867835,ok +75101,1.0,42,0.8539762389159519,ok +75192,1.0,43,0.999813449764645,ok +75232,1.0,44,0.6172823118182033,ok +75173,1.0,45,0.5469308013179239,ok +75197,1.0,46,0.4255032170547436,ok +266,1.0,47,0.1251319492676456,ok +75148,1.0,48,0.6743847933750642,ok +75150,1.0,49,1.3665865813098508,ok +75100,1.0,50,2.1077741320034065,ok +75178,1.0,51,0.9961403296604606,ok +75236,1.0,52,0.3915154301736812,ok +75179,1.0,53,0.7467592769313054,ok +75213,1.0,54,0.36831228224411516,ok +2123,1.0,55,0.4911594340439359,ok +75227,1.0,56,0.4907471014469743,ok +75184,1.0,57,0.6224907982424815,ok +75142,1.0,58,0.39600184469544164,ok +236,1.0,59,0.2770989699485852,ok +2122,1.0,60,0.6494236072893236,ok +75188,1.0,61,0.7198985509814408,ok +75166,1.0,62,0.473110192838222,ok +75181,1.0,63,0.0006376437651373079,ok +75133,1.0,64,0.8025255324642442,ok +75134,1.0,65,0.3627321546195248,ok +75198,1.0,66,0.4760842957492827,ok +262,1.0,67,0.09729812480206579,ok +75234,1.0,68,0.3577965524849742,ok +75139,1.0,69,0.08204142242730916,ok +252,1.0,70,0.38075896608545157,ok +75117,1.0,71,0.6459554639317393,ok +75113,1.0,72,0.13585892428818858,ok +75098,1.0,73,0.19335338413888703,ok +246,1.0,74,0.4112399378093273,ok +75203,1.0,75,0.40824976165284543,ok +75237,1.0,76,0.005663565532001202,ok +75195,1.0,77,0.016915136387811613,ok +75171,1.0,78,0.655926950940406,ok +75128,1.0,79,0.3010211970166028,ok +75096,1.0,80,1.0998207957312425,ok +75250,1.0,81,0.727952824947856,ok +75146,1.0,82,0.48328039617007423,ok +75116,1.0,83,0.19335635798818618,ok +75157,1.0,84,1.0035001961647172,ok +75187,1.0,85,0.37148939171077866,ok +2350,1.0,86,1.004941489591669,ok +242,1.0,87,0.21461584650210785,ok +244,1.0,88,0.4838442452614642,ok +75125,1.0,89,0.4266337330685913,ok +75185,1.0,90,0.5369862315317429,ok +75163,1.0,91,0.40701491802746437,ok +75177,1.0,92,0.2057574737406812,ok +75189,1.0,93,0.13986525576970965,ok +75244,1.0,94,0.7623438686518899,ok +75219,1.0,95,0.4892671302117101,ok +75222,1.0,96,0.449478470055402,ok +75159,1.0,97,0.8728632794113166,ok +75175,1.0,98,0.5023436052789889,ok +75109,1.0,99,0.7962496911016407,ok +254,1.0,100,0.00028661895253534464,ok +75105,1.0,101,1.1081843905042723,ok +75106,1.0,102,0.9814668174102382,ok +75212,1.0,103,0.8101273413585854,ok +75099,1.0,104,0.8286653301684626,ok +75248,1.0,105,0.7704270429994281,ok +233,1.0,106,0.12191250223411498,ok +75235,1.0,107,0.016673004572354544,ok +75226,1.0,108,0.04579254100810792,ok +75132,1.0,109,1.4311848426146723,ok +75127,1.0,110,0.9174202398418094,ok +251,1.0,111,0.3274396558856145,ok +75161,1.0,112,0.39111039323171937,ok +75143,1.0,113,0.15668557978453235,ok +75114,1.0,114,0.2802509783950846,ok +75182,1.0,115,0.5383179666177805,ok +75112,1.0,116,0.5386542656770761,ok +75210,1.0,117,0.002334397641262753,ok +75205,1.0,118,0.5462330752281889,ok +75090,1.0,119,0.4971859018554544,ok +275,1.0,120,0.2898839549144505,ok +288,1.0,121,0.49615738237926754,ok +75092,1.0,122,0.5833034312489822,ok +3043,1.0,123,0.2057574737406812,ok +75249,1.0,124,0.12849223093402007,ok +75126,1.0,125,0.450045064721568,ok +75225,1.0,126,0.6644303804683288,ok +75141,1.0,127,0.30639986019837717,ok +75107,1.0,128,0.7600575081398804,ok +75097,1.0,129,1.1150409801891992,ok +80001,1.0,1,0.5843376070679424,ok +80003,1.0,1,0.6317687474485061,ok +80006,1.0,1,0.5871942591256936,ok +80008,1.0,1,0.7168215144226284,ok +80009,1.0,1,0.582719062436241,ok +80010,1.0,1,0.8188826728383342,ok +80011,1.0,1,0.31431498271905167,ok +80012,1.0,1,0.4580779237047369,ok +80013,1.0,1,0.24189168220063972,ok +80014,1.0,1,0.71824401797839,ok +80015,1.0,1,0.6121458691351616,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/pac_score_binary.classification_sparse/configurations.csv b/metalearning/metalearning_files/pac_score_binary.classification_sparse/configurations.csv new file mode 100755 index 0000000..1cf6722 --- /dev/null +++ b/metalearning/metalearning_files/pac_score_binary.classification_sparse/configurations.csv @@ -0,0 +1,141 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,preprocessor:truncatedSVD:target_dim,rescaling:__choice__ +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7249853037185638,None,0.0,1.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,median,extra_trees_preproc_for_classification,False,gini,None,0.9424908623661876,None,0.0,7.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.002655175930910743,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3997691365353215,None,0.0,20.0,10.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,median,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.039533063907190934,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1878805519245509,fdr,chi2,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9742178951431336,None,0.0,2.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11400034542737113,fdr,chi2,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2538107344750156,False,True,hinge,1.5099542326343014e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,8532.0,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.034465366914659866,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.12057775675278172,deviance,10.0,0.8011153303489733,None,0.0,2.0,16.0,0.0,370.0,0.6078295352200873,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,mean,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0006939450481567022,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.05694967271793989,0.9005883757146016,5.0,6.300159702718475,poly,-1.0,False,0.024026910265824642,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,31,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.039533063907190934,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1878805519245509,fdr,chi2,,standardize +none,one_hot_encoding,0.0010015637584068037,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.037611630308856295,deviance,5.0,0.8840126779516314,None,0.0,10.0,2.0,0.0,444.0,0.7599997167603434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,most_frequent,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.004090774134315939,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7983157215145903,None,0.0,2.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,normalize +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.2864228295610195,None,0.0,9.0,10.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,median,kernel_pca,,,,,,,,,,,,,,cosine,239.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.8657388713119849,,1.0,None,0.0,19.0,13.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828776,None,0.0,2.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.027324741616523342,deviance,10.0,0.8623781459430139,None,0.0,10.0,20.0,0.0,329.0,0.8595750155424215,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,mean,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1100.6211008501205,0.5921425829232616,2.0,0.0337546254878617,poly,-1.0,True,0.09641299736884308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3428971645958825,,,0.2229870623330047,rbf,-1.0,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9742178951431336,None,0.0,2.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11400034542737113,fdr,chi2,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4047.618729304337,,,2.0237366768707754,rbf,-1.0,True,0.04369127828878843,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,one_hot_encoding,0.010000000000000005,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10.0,1.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,,normalize +none,one_hot_encoding,0.003443779683191071,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2538107344750156,False,True,hinge,1.5099542326343014e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,8532.0,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.002630811782675973,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9828367182452932,None,0.0,18.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,85,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,86,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0019686649916896212,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.04641055832142541,True,True,hinge,8.540468968077405e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87,mean,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,911.0,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1.0,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,89,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,90,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,91,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,93,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,95,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,96,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,97,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,98,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,99,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,100,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,102,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,103,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,104,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,105,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,106,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.006372860318416312,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6467376360604045,None,0.0,1.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,107,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,108,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,109,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3823734947460288,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,110,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,111,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,112,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,113,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,115,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,116,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,117,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6886293142639995,None,0.0,2.0,13.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,118,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16.670108971732134,chi2,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,119,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.00012696985090051145,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.163979712833404,False,True,hinge,0.02438592885755657,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,120,mean,kernel_pca,,,,,,,,,,,,,0.03910740617243662,rbf,1851.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,121,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,122,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,123,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,124,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,125,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,126,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,127,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,129,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.04329010470998136,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7260331062271381,None,0.0,7.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,134,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.714869937384006,None,0.0,8.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/metalearning/metalearning_files/pac_score_binary.classification_sparse/description.txt b/metalearning/metalearning_files/pac_score_binary.classification_sparse/description.txt new file mode 100755 index 0000000..bf4ad29 --- /dev/null +++ b/metalearning/metalearning_files/pac_score_binary.classification_sparse/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: pac_score +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/pac_score_binary.classification_sparse/feature_costs.arff b/metalearning/metalearning_files/pac_score_binary.classification_sparse/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/pac_score_binary.classification_sparse/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/pac_score_binary.classification_sparse/feature_runstatus.arff b/metalearning/metalearning_files/pac_score_binary.classification_sparse/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/pac_score_binary.classification_sparse/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/pac_score_binary.classification_sparse/feature_values.arff b/metalearning/metalearning_files/pac_score_binary.classification_sparse/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/pac_score_binary.classification_sparse/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/pac_score_binary.classification_sparse/readme.txt b/metalearning/metalearning_files/pac_score_binary.classification_sparse/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/pac_score_multiclass.classification_dense/algorithm_runs.arff b/metalearning/metalearning_files/pac_score_multiclass.classification_dense/algorithm_runs.arff new file mode 100755 index 0000000..5d81f69 --- /dev/null +++ b/metalearning/metalearning_files/pac_score_multiclass.classification_dense/algorithm_runs.arff @@ -0,0 +1,152 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE pac_score NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2120,1.0,1,0.2743853631304761,ok +75193,1.0,2,0.18773586269855957,ok +2117,1.0,3,0.7276341645801123,ok +75156,1.0,4,0.7485129622976275,ok +75129,1.0,5,0.8951327973739391,ok +75243,1.0,6,0.0017355204722208084,ok +75110,1.0,7,0.6494236072893236,ok +75239,1.0,8,1.2847215913325272e-07,ok +75223,1.0,9,0.34315333213656096,ok +75221,1.0,10,0.8713658958696701,ok +258,1.0,11,0.08967729832006155,ok +75121,1.0,12,0.10898189896173682,ok +253,1.0,13,0.9405063128381406,ok +261,1.0,14,0.8509813469369898,ok +75240,1.0,15,0.18503800543257864,ok +75120,1.0,16,0.8330719116393059,ok +75124,1.0,17,0.7225519699734184,ok +75176,1.0,18,0.07447018078248535,ok +75103,1.0,19,0.11484063174902903,ok +75207,1.0,20,0.512703350784506,ok +75095,1.0,21,0.3826346375656122,ok +273,1.0,22,0.24002034412158613,ok +75174,1.0,23,0.5371373828619556,ok +75153,1.0,24,0.35326241856112506,ok +75093,1.0,25,0.9628107264544831,ok +75119,1.0,26,0.6111894441869999,ok +75201,1.0,27,0.34562309149167814,ok +75215,1.0,28,0.1380577511686255,ok +75172,1.0,29,0.5369909406966044,ok +75169,1.0,30,0.38558731111829425,ok +75202,1.0,31,0.6400358659099892,ok +75233,1.0,32,0.30222613423916256,ok +75231,1.0,33,0.7354855537529592,ok +75196,1.0,34,0.09968508248905783,ok +248,1.0,35,0.5797493841168782,ok +75191,1.0,36,0.5681653801872639,ok +75217,1.0,37,0.08292779796644634,ok +260,1.0,38,0.3048842266227839,ok +75115,1.0,39,0.3744058283993963,ok +75123,1.0,40,0.7746836702148528,ok +75108,1.0,41,1.720076782119051e-07,ok +75101,1.0,42,0.825833278165562,ok +75192,1.0,43,0.9995031817554063,ok +75232,1.0,44,0.600189335152944,ok +75173,1.0,45,0.500463882487415,ok +75197,1.0,46,0.5247273457370218,ok +266,1.0,47,0.08733990219138965,ok +75148,1.0,48,0.5644035030655359,ok +75150,1.0,49,0.8423794461399563,ok +75100,1.0,50,2.1077741320034065,ok +75178,1.0,51,0.9931721362070052,ok +75236,1.0,52,0.38899523852079376,ok +75179,1.0,53,0.7467592769313054,ok +75213,1.0,54,0.36831228224411516,ok +2123,1.0,55,0.4911594340439359,ok +75227,1.0,56,0.4907471014469743,ok +75184,1.0,57,0.5435186107536409,ok +75142,1.0,58,0.28625163174361845,ok +236,1.0,59,0.1193048437141877,ok +2122,1.0,60,0.477382368857346,ok +75188,1.0,61,0.7198985509814408,ok +75166,1.0,62,0.4617170505940329,ok +75181,1.0,63,0.0006376437651373079,ok +75133,1.0,64,0.8025255324642442,ok +75134,1.0,65,0.33853427176089845,ok +75198,1.0,66,0.4760842957492827,ok +262,1.0,67,0.08300650300905998,ok +75234,1.0,68,0.11780359825928022,ok +75139,1.0,69,0.08204142242730916,ok +252,1.0,70,0.362378517423401,ok +75117,1.0,71,0.6459554639317393,ok +75113,1.0,72,0.1247323894163972,ok +75098,1.0,73,0.19335338413888703,ok +246,1.0,74,0.07296824907135557,ok +75203,1.0,75,0.40824976165284543,ok +75237,1.0,76,0.00473320913858577,ok +75195,1.0,77,0.016915136387811613,ok +75171,1.0,78,0.6224906256630065,ok +75128,1.0,79,0.3010211970166028,ok +75096,1.0,80,0.07706155900075085,ok +75250,1.0,81,0.6863830041382419,ok +75146,1.0,82,0.4631017809005502,ok +75116,1.0,83,0.19335635798818618,ok +75157,1.0,84,1.001521844111647,ok +75187,1.0,85,0.17702775896599832,ok +2350,1.0,86,1.004941489591669,ok +242,1.0,87,0.21461584650210785,ok +244,1.0,88,0.47990814371842194,ok +75125,1.0,89,0.4266337330685913,ok +75185,1.0,90,0.5177674402365288,ok +75163,1.0,91,0.40405749890463416,ok +75177,1.0,92,0.2057574737406812,ok +75189,1.0,93,0.13986525576970965,ok +75244,1.0,94,0.7623438686518899,ok +75219,1.0,95,0.19923894529395347,ok +75222,1.0,96,0.449478470055402,ok +75159,1.0,97,0.8728632794113166,ok +75175,1.0,98,0.4420151411698223,ok +75109,1.0,99,0.7651055338204128,ok +254,1.0,100,0.00028661895253534464,ok +75105,1.0,101,1.1081843905042723,ok +75106,1.0,102,0.9814668174102382,ok +75212,1.0,103,0.8101273413585854,ok +75099,1.0,104,0.8286653301684626,ok +75248,1.0,105,0.7704270429994281,ok +233,1.0,106,0.03756468326315454,ok +75235,1.0,107,0.016673004572354544,ok +75226,1.0,108,0.04579254100810792,ok +75132,1.0,109,1.4311848426146723,ok +75127,1.0,110,0.91607797296413,ok +251,1.0,111,0.1550373295593579,ok +75161,1.0,112,0.2798834295600173,ok +75143,1.0,113,0.15668557978453235,ok +75114,1.0,114,0.2802509783950846,ok +75182,1.0,115,0.5337267434507844,ok +75112,1.0,116,0.5386542656770761,ok +75210,1.0,117,0.002334397641262753,ok +75205,1.0,118,0.5462330752281889,ok +75090,1.0,119,0.4971859018554544,ok +275,1.0,120,0.26514461127103583,ok +288,1.0,121,0.40523053236813267,ok +75092,1.0,122,0.5833034312489822,ok +3043,1.0,123,0.2057574737406812,ok +75249,1.0,124,0.057435800451474894,ok +75126,1.0,125,0.450045064721568,ok +75225,1.0,126,0.6644303804683288,ok +75141,1.0,127,0.30639986019837717,ok +75107,1.0,128,0.7600575081398804,ok +75097,1.0,129,1.0735035892725673,ok +80001,1.0,1,0.5843376070679424,ok +80003,1.0,1,0.6274740431047592,ok +80006,1.0,1,0.41551421465175087,ok +80008,1.0,1,0.4246216838343858,ok +80009,1.0,1,0.582719062436241,ok +80010,1.0,1,0.8188826728383342,ok +80011,1.0,1,0.21918612862823272,ok +80012,1.0,1,0.4580779237047369,ok +80013,1.0,1,1.3152505429214756e-07,ok +80014,1.0,1,0.71824401797839,ok +80015,1.0,1,0.6121458691351616,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/pac_score_multiclass.classification_dense/configurations.csv b/metalearning/metalearning_files/pac_score_multiclass.classification_dense/configurations.csv new file mode 100755 index 0000000..a391d9f --- /dev/null +++ b/metalearning/metalearning_files/pac_score_multiclass.classification_dense/configurations.csv @@ -0,0 +1,141 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:fast_ica:algorithm,preprocessor:fast_ica:fun,preprocessor:fast_ica:n_components,preprocessor:fast_ica:whiten,preprocessor:feature_agglomeration:affinity,preprocessor:feature_agglomeration:linkage,preprocessor:feature_agglomeration:n_clusters,preprocessor:feature_agglomeration:pooling_func,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:pca:keep_variance,preprocessor:pca:whiten,preprocessor:polynomial:degree,preprocessor:polynomial:include_bias,preprocessor:polynomial:interaction_only,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,rescaling:__choice__,rescaling:quantile_transformer:n_quantiles,rescaling:quantile_transformer:output_distribution,rescaling:robust_scaler:q_max,rescaling:robust_scaler:q_min +weighting,one_hot_encoding,0.0003571493489893277,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.7207211219619362,None,0.0,1.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,median,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,25.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7136841657452827,0.2443375775688061 +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7249853037185638,None,0.0,1.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,median,extra_trees_preproc_for_classification,False,gini,None,0.9424908623661876,None,0.0,7.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.12713527337147906,deviance,4.0,0.6041596127474019,None,0.0,14.0,17.0,0.0,83.0,0.8426859880999615,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.013255438916868357,deviance,9.0,0.3592430187012816,None,0.0,10.0,7.0,0.0,411.0,0.691160823398122,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,median,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1.0,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.251097788175676,deviance,6.0,0.35679099363539235,None,0.0,13.0,11.0,0.0,157.0,0.4791732272983235,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.10000000000000002,deviance,6.0,1.0,None,0.0,7.0,2.0,0.0,278.0,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3469031665162168,None,0.0,4.0,3.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,mean,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,83.0,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,,False,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,2.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09818715003413464,fwe,f_classif,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9874546479752576,None,0.0,20.0,3.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.039533063907190934,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1878805519245509,fdr,chi2,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9455638720565652,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,most_frequent,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8255464552647293,0.19162485555463185 +weighting,one_hot_encoding,0.18137532678800647,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9094110110427254,None,0.0,7.0,12.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,195.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9742178951431336,None,0.0,2.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11400034542737113,fdr,chi2,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.02345017287074443,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.053517066400173056,deviance,10.0,0.542144980834302,None,0.0,20.0,13.0,0.0,233.0,0.7398539900055563,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0614425536709615,fwe,f_classif,robust_scaler,,,0.9523118062307264,0.13434811490315818 +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.8149627329153046,None,0.0,15.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.040936424602789435,deviance,7.0,0.5495014745530306,None,0.0,20.0,18.0,0.0,141.0,0.6905343807995293,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2538107344750156,False,True,hinge,1.5099542326343014e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,8532.0,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.1958974686405233,deviance,5.0,0.33885235607979314,None,0.0,6.0,4.0,0.0,125.0,0.9448890820738562,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0006939450481567022,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.05694967271793989,0.9005883757146016,5.0,6.300159702718475,poly,-1.0,False,0.024026910265824642,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,31,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.04749094903835669,deviance,3.0,0.6184047395714717,None,0.0,17.0,8.0,0.0,428.0,0.7515561640094087,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.6409514454358402,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4107013198375944,fdr,f_classif,normalize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9562349611127512,None,0.0,4.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,median,fast_ica,,,,,,,,,,,parallel,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.941692818067464,0.0440072111980354 +weighting,one_hot_encoding,0.0009664614609258606,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3714759002919618,None,0.0,18.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,fast_ica,,,,,,,,,,,deflation,exp,917.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.00012586572428922356,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5240592829918601,None,0.0,10.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.004090774134315939,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7983157215145903,None,0.0,2.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.9397100247778196,None,0.0,3.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,mean,fast_ica,,,,,,,,,,,parallel,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7272465857046146,0.24493108573961345 +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.2422926485206341,,1.0,None,0.0,15.0,9.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.022939738050158573,deviance,10.0,0.4185394344134278,None,0.0,2.0,10.0,0.0,309.0,0.5979695608086252,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.25383213391991144 +none,one_hot_encoding,0.31138539716704705,True,adaboost,SAMME.R,0.3026113597945332,1.0,50.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.05578036113726603,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.04310920427249598,deviance,9.0,0.7762532463369333,None,0.0,19.0,7.0,0.0,89.0,0.9651993549499902,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,median,fast_ica,,,,,,,,,,,parallel,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.00353232434213139,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.9407432771644124,None,0.0,1.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,median,fast_ica,,,,,,,,,,,parallel,cube,100.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7446318916516063,0.23782974987118105 +weighting,one_hot_encoding,0.010000000000000005,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.756120720182025,True,True,squared_hinge,0.020536827217281145,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,4188.0,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.3795924768593385,deviance,2.0,0.33708497069988536,None,0.0,15.0,13.0,0.0,451.0,0.7716323242090217,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2573946506994812,fwe,f_classif,quantile_transformer,1000.0,uniform,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.609975998293528,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,most_frequent,fast_ica,,,,,,,,,,,parallel,logcosh,2000.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8430415644014919,0.2863750565331575 +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.39536192447534535,None,0.0,19.0,3.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5.0,None,11.0,11.0,1.0,12.0,,,,,,robust_scaler,,,0.8928631650245873,0.1581877760687084 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6897958091880166,None,0.0,2.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,870.2240970463429,0.1223321395075887,2.0,0.0035847433873211405,poly,-1.0,False,2.485160860440657e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4612682306567311,fpr,f_classif,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8363534397818381,None,0.0,4.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,False,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9260795160807372,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.03644212536682547,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.05965671477918361,deviance,8.0,0.4858133247974158,None,0.0,14.0,7.0,0.0,480.0,0.5726186552917335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.15318294164619112 +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18887.81504976871,,,0.23283562663398755,rbf,-1.0,True,2.3839685780861318e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3937607155568376,fwe,chi2,robust_scaler,,,0.941018778984854,0.2144110585080491 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7856.587651350424,,,0.0017305319997054556,rbf,-1.0,False,1.7622421003766454e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8138233157708883,None,0.0,2.0,9.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54.27504292568562,f_classif,,,,minmax,,,, +none,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9742178951431336,None,0.0,2.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11400034542737113,fdr,chi2,none,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.8490054877538417,None,0.0,2.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.0001449312804440222,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,mean,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4047.618729304337,,,2.0237366768707754,rbf,-1.0,True,0.04369127828878843,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +weighting,one_hot_encoding,,False,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26.0,1.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,median,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.07589752815117898,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.7727592543547,None,0.0,10.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,minmax,,,, +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,normalize,,,, +weighting,one_hot_encoding,,False,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0,2.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09818715003413464,fwe,f_classif,none,,,, +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2538107344750156,False,True,hinge,1.5099542326343014e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,8532.0,,,,,,,,,,,,,,,,,,none,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.9188519169916218,None,0.0,1.0,5.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,median,fast_ica,,,,,,,,,,,parallel,cube,306.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.904983674005564,None,0.0,18.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,mean,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,275.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.002353704397784248,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,most_frequent,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7766828206753251,True,,,,,,,,,,,,,,,,normalize,,,, +weighting,one_hot_encoding,0.0001486770773839718,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5256280540657592,None,0.0,8.0,12.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.0037042565030130366,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.9610802347688104,None,0.0,17.0,9.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,172.0,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.001479024648966236,True,decision_tree,,,,,,,gini,0.0029321615410515807,,1.0,None,0.0,17.0,19.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,robust_scaler,,,0.8019276898414001,0.2172955112207602 +none,one_hot_encoding,0.00010817282861262362,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.799803680241154,None,0.0,13.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,85,mean,fast_ica,,,,,,,,,,,deflation,exp,1793.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9071932815811076,0.03563842252368924 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,86,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0019686649916896212,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.04641055832142541,True,True,hinge,8.540468968077405e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87,mean,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,911.0,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,89,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.9342950927678112,None,0.0,20.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,90,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6856119355718316,None,0.0,2.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,91,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,robust_scaler,,,0.9070067796375252,0.2232396978725172 +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,93,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2636201374253461,deviance,7.0,0.8344964130784466,None,0.0,9.0,2.0,0.0,298.0,0.7517549950523315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,95,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,96,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,97,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.07463196642416367,deviance,7.0,0.8603242247379981,None,0.0,2.0,6.0,0.0,500.0,0.8447665577491962,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,98,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.03425735525476273,deviance,7.0,0.5825782146709433,None,0.0,17.0,6.0,0.0,313.0,0.5043438213864502,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,99,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3805181287718124,fdr,f_classif,robust_scaler,,,0.8478326986019474,0.2878840415105679 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,100,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,102,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,103,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,104,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,105,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,106,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.006372860318416312,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6467376360604045,None,0.0,1.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,107,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,108,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,109,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.002615346832354839,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7884268823432835,None,0.0,20.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,110,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,3.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,111,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.010000000000000005,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.2020480296503026,deviance,3.0,0.4640458524354476,None,0.0,8.0,19.0,0.0,486.0,0.6791755979205191,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,112,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,113,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.03905145156995541,deviance,5.0,0.2281306656230014,None,0.0,14.0,13.0,0.0,493.0,0.8793075442604774,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,115,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,25382.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,116,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,117,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6886293142639995,None,0.0,2.0,13.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,118,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16.670108971732134,chi2,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,119,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.1885493528549979,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.603604171705261,False,True,squared_hinge,4.4620804452839e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,120,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,quantile_transformer,57665.0,uniform,, +weighting,one_hot_encoding,0.010000000000000005,True,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.825805258222736e-06,True,0.00010000000000000009,0.038198103889192085,True,0.14999999999999974,invscaling,modified_huber,elasticnet,0.13714427818877545,0.04372308852525775,,,,,,,,,,,,,,,,,,121,most_frequent,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,122,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,123,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.19998727075532635,deviance,10.0,0.9377656718112952,None,0.0,7.0,13.0,0.0,214.0,0.6062346326014357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,124,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,125,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,126,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,127,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7945458151995424,None,0.0,1.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,129,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,38400.0,uniform,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.09467364358164987,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,minmax,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.06597655581840225,deviance,1.0,0.21809176698911087,None,0.0,8.0,9.0,0.0,313.0,0.28171885744131103,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,mean,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,364.0,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11611.973346148121,-0.605048235954617,4.0,0.002797249824906932,poly,-1.0,True,4.813307514117123e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,192.0,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,134,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.8015164166877907,None,0.0,1.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76.35224288088253,f_classif,,,,quantile_transformer,1766.0,normal,, +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.714869937384006,None,0.0,8.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,no_encoding,,,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,146.10741262807508,False,True,1.0,squared_hinge,ovr,l1,0.0014510814556456804,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, diff --git a/metalearning/metalearning_files/pac_score_multiclass.classification_dense/description.txt b/metalearning/metalearning_files/pac_score_multiclass.classification_dense/description.txt new file mode 100755 index 0000000..bf4ad29 --- /dev/null +++ b/metalearning/metalearning_files/pac_score_multiclass.classification_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: pac_score +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/pac_score_multiclass.classification_dense/feature_costs.arff b/metalearning/metalearning_files/pac_score_multiclass.classification_dense/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/pac_score_multiclass.classification_dense/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/pac_score_multiclass.classification_dense/feature_runstatus.arff b/metalearning/metalearning_files/pac_score_multiclass.classification_dense/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/pac_score_multiclass.classification_dense/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/pac_score_multiclass.classification_dense/feature_values.arff b/metalearning/metalearning_files/pac_score_multiclass.classification_dense/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/pac_score_multiclass.classification_dense/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/pac_score_multiclass.classification_dense/readme.txt b/metalearning/metalearning_files/pac_score_multiclass.classification_dense/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/pac_score_multiclass.classification_sparse/algorithm_runs.arff b/metalearning/metalearning_files/pac_score_multiclass.classification_sparse/algorithm_runs.arff new file mode 100755 index 0000000..c4a9f7b --- /dev/null +++ b/metalearning/metalearning_files/pac_score_multiclass.classification_sparse/algorithm_runs.arff @@ -0,0 +1,152 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE pac_score NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2120,1.0,1,0.2774242815722795,ok +75193,1.0,2,0.18773586269855957,ok +2117,1.0,3,0.7464428970157602,ok +75156,1.0,4,0.7624112408226631,ok +75129,1.0,5,0.8951327973739391,ok +75243,1.0,6,0.12372825986996794,ok +75110,1.0,7,0.6494236072893236,ok +75239,1.0,8,1.2847215913325272e-07,ok +75223,1.0,9,0.6430785592014303,ok +75221,1.0,10,0.9084994799735421,ok +258,1.0,11,0.23926430919167896,ok +75121,1.0,12,0.10898189896173682,ok +253,1.0,13,0.9499707620161727,ok +261,1.0,14,0.8509813469369898,ok +75240,1.0,15,0.18503800543257864,ok +75120,1.0,16,0.8330719116393059,ok +75124,1.0,17,0.7225519699734184,ok +75176,1.0,18,0.0880823902130643,ok +75103,1.0,19,0.13894941494974622,ok +75207,1.0,20,0.512703350784506,ok +75095,1.0,21,0.3826346375656122,ok +273,1.0,22,0.30833306681040906,ok +75174,1.0,23,0.5476780386259217,ok +75153,1.0,24,0.7027761139689175,ok +75093,1.0,25,0.9628107264544831,ok +75119,1.0,26,0.6111894441869999,ok +75201,1.0,27,0.34562309149167814,ok +75215,1.0,28,0.18823441926129736,ok +75172,1.0,29,0.32468128222501347,ok +75169,1.0,30,0.38558731111829425,ok +75202,1.0,31,0.6400358659099892,ok +75233,1.0,32,0.3311557656129698,ok +75231,1.0,33,0.7585346030386128,ok +75196,1.0,34,0.18025357075812087,ok +248,1.0,35,0.5956788353588593,ok +75191,1.0,36,0.5420958962639859,ok +75217,1.0,37,0.08292779796644634,ok +260,1.0,38,0.3048842266227839,ok +75115,1.0,39,0.3744058283993963,ok +75123,1.0,40,0.7958300175288696,ok +75108,1.0,41,0.046291764870867835,ok +75101,1.0,42,0.8539762389159519,ok +75192,1.0,43,0.999813449764645,ok +75232,1.0,44,0.6172823118182033,ok +75173,1.0,45,0.5469308013179239,ok +75197,1.0,46,0.4255032170547436,ok +266,1.0,47,0.1251319492676456,ok +75148,1.0,48,0.6743847933750642,ok +75150,1.0,49,1.3665865813098508,ok +75100,1.0,50,2.1077741320034065,ok +75178,1.0,51,0.9961403296604606,ok +75236,1.0,52,0.3915154301736812,ok +75179,1.0,53,0.7467592769313054,ok +75213,1.0,54,0.36831228224411516,ok +2123,1.0,55,0.4911594340439359,ok +75227,1.0,56,0.4907471014469743,ok +75184,1.0,57,0.6224907982424815,ok +75142,1.0,58,0.39600184469544164,ok +236,1.0,59,0.2770989699485852,ok +2122,1.0,60,0.6494236072893236,ok +75188,1.0,61,0.7198985509814408,ok +75166,1.0,62,0.473110192838222,ok +75181,1.0,63,0.0006376437651373079,ok +75133,1.0,64,0.8025255324642442,ok +75134,1.0,65,0.3627321546195248,ok +75198,1.0,66,0.4760842957492827,ok +262,1.0,67,0.09729812480206579,ok +75234,1.0,68,0.3577965524849742,ok +75139,1.0,69,0.08204142242730916,ok +252,1.0,70,0.38075896608545157,ok +75117,1.0,71,0.6459554639317393,ok +75113,1.0,72,0.13585892428818858,ok +75098,1.0,73,0.19335338413888703,ok +246,1.0,74,0.4112399378093273,ok +75203,1.0,75,0.40824976165284543,ok +75237,1.0,76,0.005663565532001202,ok +75195,1.0,77,0.016915136387811613,ok +75171,1.0,78,0.655926950940406,ok +75128,1.0,79,0.3010211970166028,ok +75096,1.0,80,1.0998207957312425,ok +75250,1.0,81,0.727952824947856,ok +75146,1.0,82,0.48328039617007423,ok +75116,1.0,83,0.19335635798818618,ok +75157,1.0,84,1.0035001961647172,ok +75187,1.0,85,0.37148939171077866,ok +2350,1.0,86,1.004941489591669,ok +242,1.0,87,0.21461584650210785,ok +244,1.0,88,0.4838442452614642,ok +75125,1.0,89,0.4266337330685913,ok +75185,1.0,90,0.5369862315317429,ok +75163,1.0,91,0.40701491802746437,ok +75177,1.0,92,0.2057574737406812,ok +75189,1.0,93,0.13986525576970965,ok +75244,1.0,94,0.7623438686518899,ok +75219,1.0,95,0.4892671302117101,ok +75222,1.0,96,0.449478470055402,ok +75159,1.0,97,0.8728632794113166,ok +75175,1.0,98,0.5023436052789889,ok +75109,1.0,99,0.7962496911016407,ok +254,1.0,100,0.00028661895253534464,ok +75105,1.0,101,1.1081843905042723,ok +75106,1.0,102,0.9814668174102382,ok +75212,1.0,103,0.8101273413585854,ok +75099,1.0,104,0.8286653301684626,ok +75248,1.0,105,0.7704270429994281,ok +233,1.0,106,0.12191250223411498,ok +75235,1.0,107,0.016673004572354544,ok +75226,1.0,108,0.04579254100810792,ok +75132,1.0,109,1.4311848426146723,ok +75127,1.0,110,0.9174202398418094,ok +251,1.0,111,0.3274396558856145,ok +75161,1.0,112,0.39111039323171937,ok +75143,1.0,113,0.15668557978453235,ok +75114,1.0,114,0.2802509783950846,ok +75182,1.0,115,0.5383179666177805,ok +75112,1.0,116,0.5386542656770761,ok +75210,1.0,117,0.002334397641262753,ok +75205,1.0,118,0.5462330752281889,ok +75090,1.0,119,0.4971859018554544,ok +275,1.0,120,0.2898839549144505,ok +288,1.0,121,0.49615738237926754,ok +75092,1.0,122,0.5833034312489822,ok +3043,1.0,123,0.2057574737406812,ok +75249,1.0,124,0.12849223093402007,ok +75126,1.0,125,0.450045064721568,ok +75225,1.0,126,0.6644303804683288,ok +75141,1.0,127,0.30639986019837717,ok +75107,1.0,128,0.7600575081398804,ok +75097,1.0,129,1.1150409801891992,ok +80001,1.0,1,0.5843376070679424,ok +80003,1.0,1,0.6317687474485061,ok +80006,1.0,1,0.5871942591256936,ok +80008,1.0,1,0.7168215144226284,ok +80009,1.0,1,0.582719062436241,ok +80010,1.0,1,0.8188826728383342,ok +80011,1.0,1,0.31431498271905167,ok +80012,1.0,1,0.4580779237047369,ok +80013,1.0,1,0.24189168220063972,ok +80014,1.0,1,0.71824401797839,ok +80015,1.0,1,0.6121458691351616,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/pac_score_multiclass.classification_sparse/configurations.csv b/metalearning/metalearning_files/pac_score_multiclass.classification_sparse/configurations.csv new file mode 100755 index 0000000..1cf6722 --- /dev/null +++ b/metalearning/metalearning_files/pac_score_multiclass.classification_sparse/configurations.csv @@ -0,0 +1,141 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,preprocessor:truncatedSVD:target_dim,rescaling:__choice__ +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7249853037185638,None,0.0,1.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,median,extra_trees_preproc_for_classification,False,gini,None,0.9424908623661876,None,0.0,7.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.002655175930910743,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3997691365353215,None,0.0,20.0,10.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,median,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.039533063907190934,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1878805519245509,fdr,chi2,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9742178951431336,None,0.0,2.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11400034542737113,fdr,chi2,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2538107344750156,False,True,hinge,1.5099542326343014e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,8532.0,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.034465366914659866,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.12057775675278172,deviance,10.0,0.8011153303489733,None,0.0,2.0,16.0,0.0,370.0,0.6078295352200873,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,mean,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0006939450481567022,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.05694967271793989,0.9005883757146016,5.0,6.300159702718475,poly,-1.0,False,0.024026910265824642,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,31,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.039533063907190934,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1878805519245509,fdr,chi2,,standardize +none,one_hot_encoding,0.0010015637584068037,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.037611630308856295,deviance,5.0,0.8840126779516314,None,0.0,10.0,2.0,0.0,444.0,0.7599997167603434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,most_frequent,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.004090774134315939,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7983157215145903,None,0.0,2.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,normalize +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.2864228295610195,None,0.0,9.0,10.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,median,kernel_pca,,,,,,,,,,,,,,cosine,239.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.8657388713119849,,1.0,None,0.0,19.0,13.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828776,None,0.0,2.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.027324741616523342,deviance,10.0,0.8623781459430139,None,0.0,10.0,20.0,0.0,329.0,0.8595750155424215,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,mean,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1100.6211008501205,0.5921425829232616,2.0,0.0337546254878617,poly,-1.0,True,0.09641299736884308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3428971645958825,,,0.2229870623330047,rbf,-1.0,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9742178951431336,None,0.0,2.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11400034542737113,fdr,chi2,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4047.618729304337,,,2.0237366768707754,rbf,-1.0,True,0.04369127828878843,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,one_hot_encoding,0.010000000000000005,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10.0,1.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,,normalize +none,one_hot_encoding,0.003443779683191071,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2538107344750156,False,True,hinge,1.5099542326343014e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,8532.0,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.002630811782675973,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9828367182452932,None,0.0,18.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,85,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,86,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0019686649916896212,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.04641055832142541,True,True,hinge,8.540468968077405e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87,mean,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,911.0,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1.0,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,89,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,90,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,91,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,93,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,95,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,96,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,97,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,98,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,99,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,100,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,102,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,103,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,104,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,105,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,106,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.006372860318416312,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6467376360604045,None,0.0,1.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,107,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,108,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,109,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3823734947460288,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,110,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,111,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,112,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,113,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,115,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,116,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,117,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.6886293142639995,None,0.0,2.0,13.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,118,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16.670108971732134,chi2,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,119,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.00012696985090051145,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.163979712833404,False,True,hinge,0.02438592885755657,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,120,mean,kernel_pca,,,,,,,,,,,,,0.03910740617243662,rbf,1851.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,121,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,122,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,123,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,124,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,125,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,126,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,127,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,129,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.04329010470998136,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7260331062271381,None,0.0,7.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,134,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.714869937384006,None,0.0,8.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/metalearning/metalearning_files/pac_score_multiclass.classification_sparse/description.txt b/metalearning/metalearning_files/pac_score_multiclass.classification_sparse/description.txt new file mode 100755 index 0000000..bf4ad29 --- /dev/null +++ b/metalearning/metalearning_files/pac_score_multiclass.classification_sparse/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: pac_score +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/pac_score_multiclass.classification_sparse/feature_costs.arff b/metalearning/metalearning_files/pac_score_multiclass.classification_sparse/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/pac_score_multiclass.classification_sparse/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/pac_score_multiclass.classification_sparse/feature_runstatus.arff b/metalearning/metalearning_files/pac_score_multiclass.classification_sparse/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/pac_score_multiclass.classification_sparse/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/pac_score_multiclass.classification_sparse/feature_values.arff b/metalearning/metalearning_files/pac_score_multiclass.classification_sparse/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/pac_score_multiclass.classification_sparse/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/pac_score_multiclass.classification_sparse/readme.txt b/metalearning/metalearning_files/pac_score_multiclass.classification_sparse/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/precision_binary.classification_dense/algorithm_runs.arff b/metalearning/metalearning_files/precision_binary.classification_dense/algorithm_runs.arff new file mode 100755 index 0000000..e89753c --- /dev/null +++ b/metalearning/metalearning_files/precision_binary.classification_dense/algorithm_runs.arff @@ -0,0 +1,107 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE precision NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2117,1.0,1,0.050738007380073835,ok +75156,1.0,2,0.19480519480519476,ok +75129,1.0,3,0.5,ok +75239,1.0,4,0.0,ok +75121,1.0,5,0.0,ok +261,1.0,6,0.31034482758620685,ok +75240,1.0,7,0.054171180931744334,ok +75120,1.0,8,0.01498929336188437,ok +75124,1.0,9,0.3972602739726028,ok +75176,1.0,10,0.01210207840042099,ok +75103,1.0,11,0.03157894736842104,ok +75095,1.0,12,0.0757575757575758,ok +273,1.0,13,0.04529616724738672,ok +75174,1.0,14,0.17985257985257985,ok +75153,1.0,15,0.07720320466132558,ok +75093,1.0,16,0.42805755395683454,ok +75119,1.0,17,0.004514672686230292,ok +75215,1.0,18,0.028420038535645453,ok +75233,1.0,19,0.0368509212730318,ok +75196,1.0,20,0.039215686274509776,ok +75191,1.0,21,0.08602881217722202,ok +75115,1.0,22,0.014799154334038,ok +75108,1.0,23,0.0,ok +75101,1.0,24,0.2550022091775548,ok +75192,1.0,25,0.47594501718213056,ok +75232,1.0,26,0.17924528301886788,ok +75173,1.0,27,0.10620601407549579,ok +75148,1.0,28,0.14869888475836435,ok +75150,1.0,29,0.3006134969325154,ok +75100,1.0,30,0.9571428571428572,ok +75179,1.0,31,0.25308641975308643,ok +75213,1.0,32,0.11392405063291144,ok +75227,1.0,33,0.17021276595744683,ok +75184,1.0,34,0.11914217633042101,ok +75142,1.0,35,0.07483889865260696,ok +75166,1.0,36,0.09494640122511488,ok +75133,1.0,37,0.19999999999999996,ok +75234,1.0,38,0.027288732394366244,ok +75139,1.0,39,0.014127764127764175,ok +75117,1.0,40,0.007874015748031482,ok +75113,1.0,41,0.021739130434782594,ok +75237,1.0,42,1.5589193571030613e-05,ok +75195,1.0,43,0.0008766437069505084,ok +75171,1.0,44,0.1615326821938392,ok +75128,1.0,45,0.016042780748663055,ok +75146,1.0,46,0.08476562499999996,ok +75116,1.0,47,0.007009345794392496,ok +75157,1.0,48,0.4478527607361963,ok +75187,1.0,49,0.011904761904761862,ok +2350,1.0,50,0.5576171875,ok +75125,1.0,51,0.028436018957345932,ok +75185,1.0,52,0.11693548387096775,ok +75163,1.0,53,0.05380333951762528,ok +75177,1.0,54,0.07692307692307687,ok +75189,1.0,55,0.012808304860060904,ok +75244,1.0,56,0.36111111111111116,ok +75219,1.0,57,0.031680440771349905,ok +75222,1.0,58,0.1333333333333333,ok +75159,1.0,59,0.42105263157894735,ok +75175,1.0,60,0.10357815442561202,ok +254,1.0,61,0.0,ok +75105,1.0,62,0.8873626373626373,ok +75106,1.0,63,0.5,ok +75212,1.0,64,0.2233502538071066,ok +75099,1.0,65,0.34545454545454546,ok +75248,1.0,66,0.5476190476190477,ok +233,1.0,67,0.004073319755600768,ok +75226,1.0,68,0.0022164758034725063,ok +75132,1.0,69,0.7962721342031687,ok +75127,1.0,70,0.371534833787048,ok +75161,1.0,71,0.06151116368475529,ok +75143,1.0,72,0.00893743793445878,ok +75114,1.0,73,0.015037593984962405,ok +75182,1.0,74,0.1436004162330905,ok +75112,1.0,75,0.12591815320041977,ok +75210,1.0,76,0.0,ok +75092,1.0,77,0.37037037037037035,ok +3043,1.0,78,0.07692307692307687,ok +75249,1.0,79,0.01041666666666663,ok +75126,1.0,80,0.011737089201877882,ok +75225,1.0,81,0.18181818181818177,ok +75141,1.0,82,0.08086253369272234,ok +75107,1.0,83,0.11111111111111116,ok +75097,1.0,84,0.019743560649041147,ok +80001,1.0,1,0.09090909090909094,ok +80003,1.0,1,0.053763440860215006,ok +80006,1.0,1,0.0,ok +80008,1.0,1,0.0,ok +80009,1.0,1,0.09999999999999998,ok +80010,1.0,1,0.0,ok +80011,1.0,1,0.0,ok +80012,1.0,1,0.06666666666666665,ok +80013,1.0,1,0.0,ok +80014,1.0,1,0.0,ok +80015,1.0,1,0.125,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/precision_binary.classification_dense/configurations.csv b/metalearning/metalearning_files/precision_binary.classification_dense/configurations.csv new file mode 100755 index 0000000..88f6dab --- /dev/null +++ b/metalearning/metalearning_files/precision_binary.classification_dense/configurations.csv @@ -0,0 +1,96 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:fast_ica:algorithm,preprocessor:fast_ica:fun,preprocessor:fast_ica:n_components,preprocessor:fast_ica:whiten,preprocessor:feature_agglomeration:affinity,preprocessor:feature_agglomeration:linkage,preprocessor:feature_agglomeration:n_clusters,preprocessor:feature_agglomeration:pooling_func,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:pca:keep_variance,preprocessor:pca:whiten,preprocessor:polynomial:degree,preprocessor:polynomial:include_bias,preprocessor:polynomial:interaction_only,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,rescaling:__choice__,rescaling:quantile_transformer:n_quantiles,rescaling:quantile_transformer:output_distribution,rescaling:robust_scaler:q_max,rescaling:robust_scaler:q_min +weighting,no_encoding,,,decision_tree,,,,,,,gini,1.8218848145904327,,1.0,None,0.0,3.0,12.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2930349562854553,fwe,f_classif,none,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.03474109838999682,deviance,4.0,0.5687034678818491,None,0.0,18.0,12.0,0.0,408.0,0.5150113945430513,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.6328939517938,f_classif,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5368752992317617,None,0.0,16.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.8382117756438685,mutual_info,,,,quantile_transformer,11480.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.6291601746046639,None,0.0,11.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,median,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7146659106968425,True,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.010000000000000005,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9954201822467588,None,0.0,4.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.12964080341374426 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.040936424602789435,deviance,7.0,0.5495014745530306,None,0.0,20.0,18.0,0.0,141.0,0.6905343807995293,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.00034835629696198427,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8245132980938538,0.08947420373097192 +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.1958974686405233,deviance,5.0,0.33885235607979314,None,0.0,6.0,4.0,0.0,125.0,0.9448890820738562,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,0.010000000000000005,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.8495948473718509,None,0.0,19.0,12.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,median,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,173.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7360755084850574,0.13979965846112485 +none,one_hot_encoding,0.00353232434213139,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.8709229440057928,None,0.0,3.0,13.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,mean,fast_ica,,,,,,,,,,,parallel,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7446318916516063,0.23782974987118105 +none,one_hot_encoding,0.00012586572428922356,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5240592829918601,None,0.0,10.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +weighting,one_hot_encoding,0.03192699980429505,True,adaboost,SAMME.R,0.10000000000000002,1.0,50.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,quantile_transformer,8407.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.031898383885633236,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9284140023853044,None,0.0,19.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,20571.0,uniform,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828776,None,0.0,2.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.609975998293528,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,most_frequent,fast_ica,,,,,,,,,,,parallel,logcosh,2000.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8430415644014919,0.2863750565331575 +weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,751.7276898356944,False,True,1.0,squared_hinge,ovr,l2,2.0336913984983305e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,0.060155186012325876,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.3163640203509378,None,0.0,16.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,median,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,2.0,None,11.0,20.0,1.0,47.0,,,,,,quantile_transformer,21065.0,uniform,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,31,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9260795160807372,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3428971645958825,,,0.2229870623330047,rbf,-1.0,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.00016967940959070708,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9439080311935252,None,0.0,2.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,multinomial_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.159004432545846,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66.89874989654642,chi2,,,,robust_scaler,,,0.859256110723552,0.2759054267310719 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.9188519169916218,None,0.0,1.0,5.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,median,fast_ica,,,,,,,,,,,parallel,cube,306.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1.0,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.3837398524575939,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.018356703878357986,deviance,3.0,0.9690352514774068,None,0.0,12.0,3.0,0.0,234.0,0.3870344708308441,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.4932996544760619,None,0.0,2.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,mean,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,340.0,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.12713527337147906,deviance,4.0,0.6041596127474019,None,0.0,14.0,17.0,0.0,83.0,0.8426859880999615,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,standardize,,,, +none,one_hot_encoding,0.061267514817403314,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00041820552318733856,True,True,hinge,0.0007969587715456845,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,median,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.9447144786798995,False,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.96834823420249e-05,False,True,hinge,0.00016639250831671168,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.628430584359224,mutual_info,,,,quantile_transformer,6634.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.25185682718144353,deviance,7.0,0.9252293870435426,None,0.0,5.0,8.0,0.0,309.0,0.9923577831919878,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.0018037858350872134,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.1075752886620514,deviance,7.0,0.9041944194819348,None,0.0,6.0,20.0,0.0,454.0,0.6635283542555824,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0020580843703898177,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.945774573434192,None,0.0,19.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,median,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,15209.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.002615346832354839,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7884268823432835,None,0.0,20.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.2472984547885781,deviance,3.0,0.6564306719064884,None,0.0,15.0,14.0,0.0,220.0,0.8082564085714649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,median,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,332.0,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +none,one_hot_encoding,,False,decision_tree,,,,,,,gini,1.9629324531036465,,1.0,None,0.0,13.0,20.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.19998727075532635,deviance,10.0,0.9377656718112952,None,0.0,7.0,13.0,0.0,214.0,0.6062346326014357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.000738320402221022,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,median,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.854322697199371,False,True,1.0,squared_hinge,ovr,l1,0.00013359426815085846,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,30.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0006290513932903491,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8680626684393846,None,0.0,9.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,54639.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.04651467280022463,True,adaboost,SAMME,0.6506122230393502,6.0,143.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,extra_trees_preproc_for_classification,False,entropy,None,0.348720215832657,None,0.0,17.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,26025.0,normal,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3386310102795006,None,0.0,6.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,True,,,,,,,,,,,,,normalize,,,, +weighting,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.0,None,,5.861221938384061e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.22985730375167915,fpr,f_classif,quantile_transformer,40589.0,uniform,, +none,no_encoding,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00012437731009611307,1.0,,0.08709904468181932,1.0,,invscaling,squared_hinge,l1,0.6231564675290102,0.008071279833697563,,,,,,,,,,,,,,,,,,134,median,fast_ica,,,,,,,,,,,parallel,logcosh,814.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.714869937384006,None,0.0,8.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, diff --git a/metalearning/metalearning_files/precision_binary.classification_dense/description.txt b/metalearning/metalearning_files/precision_binary.classification_dense/description.txt new file mode 100755 index 0000000..3cecb38 --- /dev/null +++ b/metalearning/metalearning_files/precision_binary.classification_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: precision +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/precision_binary.classification_dense/feature_costs.arff b/metalearning/metalearning_files/precision_binary.classification_dense/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/precision_binary.classification_dense/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/precision_binary.classification_dense/feature_runstatus.arff b/metalearning/metalearning_files/precision_binary.classification_dense/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/precision_binary.classification_dense/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/precision_binary.classification_dense/feature_values.arff b/metalearning/metalearning_files/precision_binary.classification_dense/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/precision_binary.classification_dense/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/precision_binary.classification_dense/readme.txt b/metalearning/metalearning_files/precision_binary.classification_dense/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/precision_binary.classification_sparse/algorithm_runs.arff b/metalearning/metalearning_files/precision_binary.classification_sparse/algorithm_runs.arff new file mode 100755 index 0000000..75c94a3 --- /dev/null +++ b/metalearning/metalearning_files/precision_binary.classification_sparse/algorithm_runs.arff @@ -0,0 +1,107 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE precision NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2117,1.0,1,0.05489353389333851,ok +75156,1.0,2,0.21037463976945248,ok +75129,1.0,3,0.5,ok +75239,1.0,4,0.0,ok +75121,1.0,5,0.004089979550102263,ok +261,1.0,6,0.31034482758620685,ok +75240,1.0,7,0.054171180931744334,ok +75120,1.0,8,0.019189765458422214,ok +75124,1.0,9,0.3972602739726028,ok +75176,1.0,10,0.013392857142857095,ok +75103,1.0,11,0.03157894736842104,ok +75095,1.0,12,0.0757575757575758,ok +273,1.0,13,0.04529616724738672,ok +75174,1.0,14,0.17985257985257985,ok +75153,1.0,15,0.1076233183856502,ok +75093,1.0,16,0.42805755395683454,ok +75119,1.0,17,0.0050000000000000044,ok +75215,1.0,18,0.03252032520325199,ok +75233,1.0,19,0.056308654848800876,ok +75196,1.0,20,0.04807692307692313,ok +75191,1.0,21,0.07591982427237787,ok +75115,1.0,22,0.014893617021276562,ok +75108,1.0,23,0.0,ok +75101,1.0,24,0.2733766988035776,ok +75192,1.0,25,0.47594501718213056,ok +75232,1.0,26,0.17924528301886788,ok +75173,1.0,27,0.10620601407549579,ok +75148,1.0,28,0.19736842105263153,ok +75150,1.0,29,0.3053892215568862,ok +75100,1.0,30,0.9823788546255506,ok +75179,1.0,31,0.25308641975308643,ok +75213,1.0,32,0.11392405063291144,ok +75227,1.0,33,0.17021276595744683,ok +75184,1.0,34,0.11914217633042101,ok +75142,1.0,35,0.08302718589272595,ok +75166,1.0,36,0.09494640122511488,ok +75133,1.0,37,0.19999999999999996,ok +75234,1.0,38,0.027288732394366244,ok +75139,1.0,39,0.014879107253564783,ok +75117,1.0,40,0.015837104072398245,ok +75113,1.0,41,0.021739130434782594,ok +75237,1.0,42,0.00010909884355225774,ok +75195,1.0,43,0.0008766437069505084,ok +75171,1.0,44,0.1705137751303053,ok +75128,1.0,45,0.02103049421661407,ok +75146,1.0,46,0.08847817538340541,ok +75116,1.0,47,0.007009345794392496,ok +75157,1.0,48,0.4478527607361963,ok +75187,1.0,49,0.025295109612141653,ok +2350,1.0,50,0.5576171875,ok +75125,1.0,51,0.028503562945368155,ok +75185,1.0,52,0.11693548387096775,ok +75163,1.0,53,0.05380333951762528,ok +75177,1.0,54,0.07692307692307687,ok +75189,1.0,55,0.012808304860060904,ok +75244,1.0,56,0.36111111111111116,ok +75219,1.0,57,0.07044917257683214,ok +75222,1.0,58,0.1333333333333333,ok +75159,1.0,59,0.42105263157894735,ok +75175,1.0,60,0.1103313840155945,ok +254,1.0,61,0.0,ok +75105,1.0,62,0.8873626373626373,ok +75106,1.0,63,0.5,ok +75212,1.0,64,0.2710280373831776,ok +75099,1.0,65,0.34545454545454546,ok +75248,1.0,66,0.5476190476190477,ok +233,1.0,67,0.01632653061224487,ok +75226,1.0,68,0.0022164758034725063,ok +75132,1.0,69,0.7962721342031687,ok +75127,1.0,70,0.37375446991002914,ok +75161,1.0,71,0.07860002966038859,ok +75143,1.0,72,0.009861932938856066,ok +75114,1.0,73,0.015037593984962405,ok +75182,1.0,74,0.1436004162330905,ok +75112,1.0,75,0.12591815320041977,ok +75210,1.0,76,0.0,ok +75092,1.0,77,0.37037037037037035,ok +3043,1.0,78,0.07692307692307687,ok +75249,1.0,79,0.02083333333333337,ok +75126,1.0,80,0.03111111111111109,ok +75225,1.0,81,0.18181818181818177,ok +75141,1.0,82,0.08086253369272234,ok +75107,1.0,83,0.11111111111111116,ok +75097,1.0,84,0.0551781892621197,ok +80001,1.0,1,0.09090909090909094,ok +80003,1.0,1,0.09473684210526312,ok +80006,1.0,1,0.09090909090909094,ok +80008,1.0,1,0.33333333333333337,ok +80009,1.0,1,0.11111111111111116,ok +80010,1.0,1,0.0,ok +80011,1.0,1,0.0,ok +80012,1.0,1,0.06666666666666665,ok +80013,1.0,1,0.0,ok +80014,1.0,1,0.0,ok +80015,1.0,1,0.125,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/precision_binary.classification_sparse/configurations.csv b/metalearning/metalearning_files/precision_binary.classification_sparse/configurations.csv new file mode 100755 index 0000000..f5e619e --- /dev/null +++ b/metalearning/metalearning_files/precision_binary.classification_sparse/configurations.csv @@ -0,0 +1,96 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,preprocessor:truncatedSVD:target_dim,rescaling:__choice__ +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,15.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.7735071203475309,,1.0,None,0.0,4.0,4.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,mean,extra_trees_preproc_for_classification,True,entropy,None,0.8171332500590935,None,0.0,15.0,13.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.0010015637584068037,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.037611630308856295,deviance,5.0,0.8840126779516314,None,0.0,10.0,2.0,0.0,444.0,0.7599997167603434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,most_frequent,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.004090774134315939,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7983157215145903,None,0.0,2.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828776,None,0.0,2.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.002173124111626734,None,0.0,14.0,4.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,6.0,None,13.0,2.0,1.0,23.0,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,31,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3428971645958825,,,0.2229870623330047,rbf,-1.0,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9015027772227234,None,0.0,19.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1.0,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.002630811782675973,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9828367182452932,None,0.0,18.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3823734947460288,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.04329010470998136,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7260331062271381,None,0.0,7.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.010000000000000005,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15.0,1.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,134,median,extra_trees_preproc_for_classification,False,entropy,None,0.8213051377774989,None,0.0,3.0,13.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.714869937384006,None,0.0,8.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/metalearning/metalearning_files/precision_binary.classification_sparse/description.txt b/metalearning/metalearning_files/precision_binary.classification_sparse/description.txt new file mode 100755 index 0000000..3cecb38 --- /dev/null +++ b/metalearning/metalearning_files/precision_binary.classification_sparse/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: precision +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/precision_binary.classification_sparse/feature_costs.arff b/metalearning/metalearning_files/precision_binary.classification_sparse/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/precision_binary.classification_sparse/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/precision_binary.classification_sparse/feature_runstatus.arff b/metalearning/metalearning_files/precision_binary.classification_sparse/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/precision_binary.classification_sparse/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/precision_binary.classification_sparse/feature_values.arff b/metalearning/metalearning_files/precision_binary.classification_sparse/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/precision_binary.classification_sparse/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/precision_binary.classification_sparse/readme.txt b/metalearning/metalearning_files/precision_binary.classification_sparse/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/precision_macro_binary.classification_dense/algorithm_runs.arff b/metalearning/metalearning_files/precision_macro_binary.classification_dense/algorithm_runs.arff new file mode 100755 index 0000000..140ec9b --- /dev/null +++ b/metalearning/metalearning_files/precision_macro_binary.classification_dense/algorithm_runs.arff @@ -0,0 +1,152 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE precision_macro NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2120,1.0,1,0.09451778417172896,ok +75193,1.0,2,0.054740279347473164,ok +2117,1.0,3,0.22411538859220193,ok +75156,1.0,4,0.20402024446142097,ok +75129,1.0,5,0.29158215010141986,ok +75243,1.0,6,0.0,ok +75110,1.0,7,0.0892798166572355,ok +75239,1.0,8,0.0,ok +75223,1.0,9,0.09818756159624242,ok +75221,1.0,10,0.48940373988484964,ok +258,1.0,11,0.009572743794146077,ok +75121,1.0,12,0.0,ok +253,1.0,13,0.44323194448419845,ok +261,1.0,14,0.26362829614604455,ok +75240,1.0,15,0.027444528010739377,ok +75120,1.0,16,0.3228132754342432,ok +75124,1.0,17,0.23635933303707712,ok +75176,1.0,18,0.015561024201105722,ok +75103,1.0,19,0.01903695408734607,ok +75207,1.0,20,0.1555821732958531,ok +75095,1.0,21,0.04506833036244795,ok +273,1.0,22,0.04489384633555771,ok +75174,1.0,23,0.1367984402089819,ok +75153,1.0,24,0.08033092563893351,ok +75093,1.0,25,0.29082419037612395,ok +75119,1.0,26,0.19982453772438924,ok +75201,1.0,27,0.0915977034341795,ok +75215,1.0,28,0.02725073173601611,ok +75172,1.0,29,0.06684715747215753,ok +75169,1.0,30,0.03367846529227081,ok +75202,1.0,31,0.2893937069656084,ok +75233,1.0,32,0.07053519393690022,ok +75231,1.0,33,0.179623015873016,ok +75196,1.0,34,0.025176080184206917,ok +248,1.0,35,0.23119855794963673,ok +75191,1.0,36,0.12527083939572425,ok +75217,1.0,37,0.0,ok +260,1.0,38,0.1270383423999858,ok +75115,1.0,39,0.012499999999999956,ok +75123,1.0,40,0.3160372511936105,ok +75108,1.0,41,0.0,ok +75101,1.0,42,0.2744936331378274,ok +75192,1.0,43,0.48251399330722244,ok +75232,1.0,44,0.13771929824561402,ok +75173,1.0,45,0.11658236249657183,ok +75197,1.0,46,0.16766341120339778,ok +266,1.0,47,0.018856695063872664,ok +75148,1.0,48,0.13492439104447995,ok +75150,1.0,49,0.277309682187731,ok +75100,1.0,50,0.4796988242873248,ok +75178,1.0,51,0.7570443605083729,ok +75236,1.0,52,0.03264430918305139,ok +75179,1.0,53,0.20464539965756512,ok +75213,1.0,54,0.08276290055664215,ok +2123,1.0,55,0.22052520748172932,ok +75227,1.0,56,0.12183624079862865,ok +75184,1.0,57,0.11415122947001066,ok +75142,1.0,58,0.07153265687347332,ok +236,1.0,59,0.038368905473278314,ok +2122,1.0,60,0.10477484887271771,ok +75188,1.0,61,0.32791115106607094,ok +75166,1.0,62,0.09937012258821953,ok +75181,1.0,63,0.0,ok +75133,1.0,64,0.1022486347574687,ok +75134,1.0,65,0.13011914936317492,ok +75198,1.0,66,0.11620007374442909,ok +262,1.0,67,0.0027122228739830945,ok +75234,1.0,68,0.024093261641772612,ok +75139,1.0,69,0.011579234261353433,ok +252,1.0,70,0.15240439490984203,ok +75117,1.0,71,0.1514138619947749,ok +75113,1.0,72,0.01366348564653741,ok +75098,1.0,73,0.027209476301432778,ok +246,1.0,74,0.011039063766381196,ok +75203,1.0,75,0.09977711773939923,ok +75237,1.0,76,0.0008027835891410984,ok +75195,1.0,77,0.0017184974204100811,ok +75171,1.0,78,0.1620345626712636,ok +75128,1.0,79,0.02578242268082609,ok +75096,1.0,80,0.3125936622043263,ok +75250,1.0,81,0.3905530096187665,ok +75146,1.0,82,0.11764573682920343,ok +75116,1.0,83,0.01585035190954187,ok +75157,1.0,84,0.4293317857735035,ok +75187,1.0,85,0.016722662844829594,ok +2350,1.0,86,0.4464958582936187,ok +242,1.0,87,0.010133609776173058,ok +244,1.0,88,0.1092657446261539,ok +75125,1.0,89,0.04170201934498552,ok +75185,1.0,90,0.12728677337133776,ok +75163,1.0,91,0.059868702725845546,ok +75177,1.0,92,0.047791479089188904,ok +75189,1.0,93,0.022630958077993757,ok +75244,1.0,94,0.2103959441745702,ok +75219,1.0,95,0.03491306888380219,ok +75222,1.0,96,0.08608414239482198,ok +75159,1.0,97,0.23509278977791292,ok +75175,1.0,98,0.10170422727528006,ok +75109,1.0,99,0.30930314701275097,ok +254,1.0,100,0.0,ok +75105,1.0,101,0.45167586503729296,ok +75106,1.0,102,0.2860346708691963,ok +75212,1.0,103,0.24960616138631186,ok +75099,1.0,104,0.22507102272727275,ok +75248,1.0,105,0.3201439554404958,ok +233,1.0,106,0.0029247593449406306,ok +75235,1.0,107,0.0007002801120448154,ok +75226,1.0,108,0.003424853179772036,ok +75132,1.0,109,0.4196666826890417,ok +75127,1.0,110,0.3371864218357312,ok +251,1.0,111,0.0,ok +75161,1.0,112,0.06438786883789338,ok +75143,1.0,113,0.011122916933824278,ok +75114,1.0,114,0.034791524265208484,ok +75182,1.0,115,0.1197809086169026,ok +75112,1.0,116,0.12211239467801316,ok +75210,1.0,117,0.0,ok +75205,1.0,118,0.18552205279651734,ok +75090,1.0,119,0.060938650203326894,ok +275,1.0,120,0.04125451982594841,ok +288,1.0,121,0.12853100078031787,ok +75092,1.0,122,0.22373144069179318,ok +3043,1.0,123,0.047791479089188904,ok +75249,1.0,124,0.00651495354239251,ok +75126,1.0,125,0.09069716775599135,ok +75225,1.0,126,0.1145454545454545,ok +75141,1.0,127,0.05826556510929248,ok +75107,1.0,128,0.08717270509114028,ok +75097,1.0,129,0.3096485466651212,ok +80001,1.0,1,0.07824143070044709,ok +80003,1.0,1,0.09060721062618593,ok +80006,1.0,1,0.09126984126984128,ok +80008,1.0,1,0.08333333333333326,ok +80009,1.0,1,0.10555555555555562,ok +80010,1.0,1,0.08333333333333326,ok +80011,1.0,1,0.02777777777777779,ok +80012,1.0,1,0.033333333333333326,ok +80013,1.0,1,0.0,ok +80014,1.0,1,0.038461538461538436,ok +80015,1.0,1,0.10096153846153844,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/precision_macro_binary.classification_dense/configurations.csv b/metalearning/metalearning_files/precision_macro_binary.classification_dense/configurations.csv new file mode 100755 index 0000000..bc5674b --- /dev/null +++ b/metalearning/metalearning_files/precision_macro_binary.classification_dense/configurations.csv @@ -0,0 +1,141 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:fast_ica:algorithm,preprocessor:fast_ica:fun,preprocessor:fast_ica:n_components,preprocessor:fast_ica:whiten,preprocessor:feature_agglomeration:affinity,preprocessor:feature_agglomeration:linkage,preprocessor:feature_agglomeration:n_clusters,preprocessor:feature_agglomeration:pooling_func,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:pca:keep_variance,preprocessor:pca:whiten,preprocessor:polynomial:degree,preprocessor:polynomial:include_bias,preprocessor:polynomial:interaction_only,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,rescaling:__choice__,rescaling:quantile_transformer:n_quantiles,rescaling:quantile_transformer:output_distribution,rescaling:robust_scaler:q_max,rescaling:robust_scaler:q_min +weighting,one_hot_encoding,0.00011717632475982632,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.045388141846341344,deviance,10.0,0.29161769341843435,None,0.0,20.0,2.0,0.0,278.0,0.7912571599269661,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.00025860501899244284,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5930474355681373,None,0.0,2.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9233683283234232,None,0.0,8.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.03474109838999682,deviance,4.0,0.5687034678818491,None,0.0,18.0,12.0,0.0,408.0,0.5150113945430513,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.6328939517938,f_classif,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.251097788175676,deviance,6.0,0.35679099363539235,None,0.0,13.0,11.0,0.0,157.0,0.4791732272983235,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,adaboost,SAMME,0.28738775989203896,10.0,423.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.8031499675923353,0.13579938270386765 +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.14159526341015916,deviance,7.0,0.8010488230155749,None,0.0,3.0,20.0,0.0,401.0,0.8073562440607731,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.002385546176068135,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.7729472304882845,,,0.0004789329856033374,rbf,-1.0,True,6.58869648864534e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,177.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5368752992317617,None,0.0,16.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.8382117756438685,mutual_info,,,,quantile_transformer,11480.0,normal,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.8384447520019118,None,0.0,13.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.36637567531287824,fdr,f_classif,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.039533063907190934,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1878805519245509,fdr,chi2,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9455638720565652,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,most_frequent,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8255464552647293,0.19162485555463185 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.040936424602789435,deviance,7.0,0.5495014745530306,None,0.0,20.0,18.0,0.0,141.0,0.6905343807995293,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7974565919616314,None,0.0,12.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,median,extra_trees_preproc_for_classification,True,entropy,None,0.9772091846790169,None,0.0,10.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2538107344750156,False,True,hinge,1.5099542326343014e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,8532.0,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.1958974686405233,deviance,5.0,0.33885235607979314,None,0.0,6.0,4.0,0.0,125.0,0.9448890820738562,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0007038280350320558,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4138778052607317,0.7995003430482459,5.0,5.430044692638861,poly,-1.0,True,0.02455501006004393,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,31,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.010000000000000005,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.0518326156691958,deviance,6.0,0.8807456180216267,None,0.0,7.0,19.0,0.0,366.0,0.7314831276137047,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,,False,adaboost,SAMME,0.4391375941344922,3.0,386.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7439738358430176,0.20581080574615795 +none,one_hot_encoding,0.00011294596229850895,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7515.1213255144885,0.9576762936062476,3.0,0.019002536385919932,poly,-1.0,False,0.010632086351533369,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,51.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,97282.0,normal,, +none,one_hot_encoding,0.00012586572428922356,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5240592829918601,None,0.0,10.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1751.4736515133566,0.62404114475118,3.0,1.6087076997410432,poly,-1.0,False,3.5353792826856045e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1198.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.2422926485206341,,1.0,None,0.0,15.0,9.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.02102683283349326,deviance,10.0,0.2797288369369436,None,0.0,14.0,9.0,0.0,480.0,0.5778972273820631,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58.88123233170863,mutual_info,,,,robust_scaler,,,0.75,0.25 +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828776,None,0.0,2.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2155613360930585,deviance,4.0,0.3198803116198433,None,0.0,8.0,13.0,0.0,275.0,0.28870176110739404,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,mean,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.7062102387181676,None,0.0,1.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,most_frequent,fast_ica,,,,,,,,,,,parallel,exp,100.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7065776353150109,0.23782974987118105 +weighting,one_hot_encoding,0.010000000000000005,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.756120720182025,True,True,squared_hinge,0.020536827217281145,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,4188.0,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.3795924768593385,deviance,2.0,0.33708497069988536,None,0.0,15.0,13.0,0.0,451.0,0.7716323242090217,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2573946506994812,fwe,f_classif,quantile_transformer,1000.0,uniform,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.609975998293528,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,most_frequent,fast_ica,,,,,,,,,,,parallel,logcosh,2000.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8430415644014919,0.2863750565331575 +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.39536192447534535,None,0.0,19.0,3.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5.0,None,11.0,11.0,1.0,12.0,,,,,,robust_scaler,,,0.8928631650245873,0.1581877760687084 +weighting,one_hot_encoding,0.060155186012325876,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.3163640203509378,None,0.0,16.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,median,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,2.0,None,11.0,20.0,1.0,47.0,,,,,,quantile_transformer,21065.0,uniform,, +weighting,one_hot_encoding,0.3126027672745337,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,870.2240970463429,0.5325949351918051,3.0,0.010682839357128344,poly,-1.0,False,2.485160860440657e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4608103694360143,fdr,f_classif,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.9348409326933208,rbf,-1.0,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1754.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8363534397818381,None,0.0,4.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,False,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9260795160807372,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.03644212536682547,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.05965671477918361,deviance,8.0,0.4858133247974158,None,0.0,14.0,7.0,0.0,480.0,0.5726186552917335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.15318294164619112 +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18887.81504976871,,,0.23283562663398755,rbf,-1.0,True,2.3839685780861318e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3937607155568376,fwe,chi2,robust_scaler,,,0.941018778984854,0.2144110585080491 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3428971645958825,,,0.2229870623330047,rbf,-1.0,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00016781524591321165,True,True,squared_hinge,1.5119200923218881e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.349459944355116,,,0.00024028983491736645,rbf,-1.0,True,1.139421622432356e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3636266268105085,fwe,chi2,none,,,, +weighting,one_hot_encoding,0.00034835629696198427,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8245132980938538,0.08947420373097192 +weighting,one_hot_encoding,0.00016967940959070708,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9439080311935252,None,0.0,2.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35.0,None,,0.005389483044810356,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,most_frequent,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,9.441661069727422e-05,1896.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,,False,adaboost,SAMME.R,0.8220362681234727,5.0,183.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.363262678765884,fdr,chi2,robust_scaler,,,0.8826612080363588,0.2189605310171097 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,normalize,,,, +none,one_hot_encoding,,False,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6396026761675004,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06544340428506021,fwe,f_classif,none,,,, +weighting,one_hot_encoding,0.004980497345831963,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1255.9137433589426,,,0.08351549479967445,rbf,-1.0,True,0.00017919875199222518,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1.0,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.3837398524575939,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.018356703878357986,deviance,3.0,0.9690352514774068,None,0.0,12.0,3.0,0.0,234.0,0.3870344708308441,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,mean,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6524770456279243,False,,,,,,,,,,,,,,,,robust_scaler,,,0.7602889314749132,0.25 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5670424455696162,None,0.0,8.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,standardize,,,, +weighting,one_hot_encoding,0.00031737236118003484,True,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,244.0,None,,2.3065111488706024e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,85,mean,fast_ica,,,,,,,,,,,deflation,exp,1862.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7851234479882973,0.2237528085136715 +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.35533396539961937,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,86,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4165632766388807,fpr,chi2,none,,,, +weighting,one_hot_encoding,,False,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,156.0,auto,,0.0001987333852871589,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1.0,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,89,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,90,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,91,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,93,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2636201374253461,deviance,7.0,0.8344964130784466,None,0.0,9.0,2.0,0.0,298.0,0.7517549950523315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,95,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,96,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,97,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.07463196642416367,deviance,7.0,0.8603242247379981,None,0.0,2.0,6.0,0.0,500.0,0.8447665577491962,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,98,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.0433556140045585,deviance,10.0,0.33000096635982235,None,0.0,15.0,13.0,0.0,388.0,0.8291104221904706,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,99,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,robust_scaler,,,0.7496393440951183,0.2853682991120835 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,100,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,102,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0020580843703898177,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.945774573434192,None,0.0,19.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,103,median,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,15209.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,104,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,105,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,106,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.005332972692819521,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5565918060287016,None,0.0,5.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,107,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,173.0,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9527068489270144,0.04135311355893583 +weighting,one_hot_encoding,0.001279467383882126,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,108,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0903354518102121,fwe,f_classif,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,109,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.002615346832354839,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7884268823432835,None,0.0,20.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,110,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +weighting,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56.086963007482865,,,0.013609964993119377,rbf,-1.0,True,0.00196831255706268,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,111,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.15374716583918388 +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.2472984547885781,deviance,3.0,0.6564306719064884,None,0.0,15.0,14.0,0.0,220.0,0.8082564085714649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,112,median,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,332.0,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,113,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,115,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,116,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,117,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,118,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.20202014999292287,False,True,1.0,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,no_encoding,,,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6390376923528961,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,119,median,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,164.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,62508.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,120,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.27041927277584e-06,True,0.00010000000000000009,0.033157325660763994,True,0.0008114527992546482,invscaling,modified_huber,elasticnet,0.13714427818877545,0.055179642772545036,,,,,,,,,,,,,,,,,,121,median,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,122,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,123,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.19998727075532635,deviance,10.0,0.9377656718112952,None,0.0,7.0,13.0,0.0,214.0,0.6062346326014357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,124,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,minmax,,,, +none,no_encoding,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.745231770090803e-07,False,6.503623129182133e-05,0.034765176880257306,True,,constant,modified_huber,l2,,7.878097869194373e-05,,,,,,,,,,,,,,,,,,125,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,751.0,uniform,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,126,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2806.9858667073186,False,True,1.0,squared_hinge,ovr,l2,0.03738539536055984,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,127,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7945458151995424,None,0.0,1.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,129,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,38400.0,uniform,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.04651467280022463,True,adaboost,SAMME,0.6506122230393502,6.0,143.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,extra_trees_preproc_for_classification,False,entropy,None,0.348720215832657,None,0.0,17.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,26025.0,normal,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3386310102795006,None,0.0,6.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,True,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.0,None,,5.861221938384061e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.22985730375167915,fpr,f_classif,quantile_transformer,40589.0,uniform,, +none,no_encoding,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00012437731009611307,True,,0.08709904468181932,True,,invscaling,squared_hinge,l1,0.6231564675290102,0.008071279833697563,,,,,,,,,,,,,,,,,,134,median,fast_ica,,,,,,,,,,,parallel,logcosh,814.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.714869937384006,None,0.0,8.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, diff --git a/metalearning/metalearning_files/precision_macro_binary.classification_dense/description.txt b/metalearning/metalearning_files/precision_macro_binary.classification_dense/description.txt new file mode 100755 index 0000000..e790e8e --- /dev/null +++ b/metalearning/metalearning_files/precision_macro_binary.classification_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: precision_macro +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/precision_macro_binary.classification_dense/feature_costs.arff b/metalearning/metalearning_files/precision_macro_binary.classification_dense/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/precision_macro_binary.classification_dense/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/precision_macro_binary.classification_dense/feature_runstatus.arff b/metalearning/metalearning_files/precision_macro_binary.classification_dense/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/precision_macro_binary.classification_dense/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/precision_macro_binary.classification_dense/feature_values.arff b/metalearning/metalearning_files/precision_macro_binary.classification_dense/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/precision_macro_binary.classification_dense/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/precision_macro_binary.classification_dense/readme.txt b/metalearning/metalearning_files/precision_macro_binary.classification_dense/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/precision_macro_binary.classification_sparse/algorithm_runs.arff b/metalearning/metalearning_files/precision_macro_binary.classification_sparse/algorithm_runs.arff new file mode 100755 index 0000000..b5133ba --- /dev/null +++ b/metalearning/metalearning_files/precision_macro_binary.classification_sparse/algorithm_runs.arff @@ -0,0 +1,152 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE precision_macro NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2120,1.0,1,0.10395992580342572,ok +75193,1.0,2,0.054740279347473164,ok +2117,1.0,3,0.22411538859220193,ok +75156,1.0,4,0.22120941933224003,ok +75129,1.0,5,0.29158215010141986,ok +75243,1.0,6,0.011893570087039729,ok +75110,1.0,7,0.2511371338718129,ok +75239,1.0,8,0.0,ok +75223,1.0,9,0.3291815803990831,ok +75221,1.0,10,0.48940373988484964,ok +258,1.0,11,0.01807873351917144,ok +75121,1.0,12,0.002044989775051187,ok +253,1.0,13,0.45569838348413194,ok +261,1.0,14,0.26362829614604455,ok +75240,1.0,15,0.027444528010739377,ok +75120,1.0,16,0.3228132754342432,ok +75124,1.0,17,0.23635933303707712,ok +75176,1.0,18,0.01701943889443891,ok +75103,1.0,19,0.01903695408734607,ok +75207,1.0,20,0.1555821732958531,ok +75095,1.0,21,0.04506833036244795,ok +273,1.0,22,0.04489384633555771,ok +75174,1.0,23,0.1367984402089819,ok +75153,1.0,24,0.12121092659209243,ok +75093,1.0,25,0.29082419037612395,ok +75119,1.0,26,0.19982453772438924,ok +75201,1.0,27,0.0915977034341795,ok +75215,1.0,28,0.027499725864310598,ok +75172,1.0,29,0.059523415799145996,ok +75169,1.0,30,0.03367846529227081,ok +75202,1.0,31,0.2893937069656084,ok +75233,1.0,32,0.07146642933522851,ok +75231,1.0,33,0.179623015873016,ok +75196,1.0,34,0.031206920319823528,ok +248,1.0,35,0.27164730984032814,ok +75191,1.0,36,0.11920364332976963,ok +75217,1.0,37,0.0,ok +260,1.0,38,0.1270383423999858,ok +75115,1.0,39,0.012499999999999956,ok +75123,1.0,40,0.3160372511936105,ok +75108,1.0,41,0.0047890749991836845,ok +75101,1.0,42,0.2833721913888413,ok +75192,1.0,43,0.48251399330722244,ok +75232,1.0,44,0.15573834398877273,ok +75173,1.0,45,0.1174657210516985,ok +75197,1.0,46,0.15823239753975438,ok +266,1.0,47,0.031453999162528556,ok +75148,1.0,48,0.18996210099284716,ok +75150,1.0,49,0.3203416696019725,ok +75100,1.0,50,0.492117071097562,ok +75178,1.0,51,0.8083745568289286,ok +75236,1.0,52,0.0326744774282266,ok +75179,1.0,53,0.20464539965756512,ok +75213,1.0,54,0.08276290055664215,ok +2123,1.0,55,0.22052520748172932,ok +75227,1.0,56,0.12183624079862865,ok +75184,1.0,57,0.13247293738284927,ok +75142,1.0,58,0.0812999948717541,ok +236,1.0,59,0.041431968403706776,ok +2122,1.0,60,0.2511371338718129,ok +75188,1.0,61,0.32791115106607094,ok +75166,1.0,62,0.09937012258821953,ok +75181,1.0,63,0.0,ok +75133,1.0,64,0.1022486347574687,ok +75134,1.0,65,0.13011914936317492,ok +75198,1.0,66,0.11620007374442909,ok +262,1.0,67,0.0027122228739830945,ok +75234,1.0,68,0.057671931281409794,ok +75139,1.0,69,0.013990379840231548,ok +252,1.0,70,0.16936450516183632,ok +75117,1.0,71,0.18610421836228297,ok +75113,1.0,72,0.01366348564653741,ok +75098,1.0,73,0.027209476301432778,ok +246,1.0,74,0.024005965864471235,ok +75203,1.0,75,0.09977711773939923,ok +75237,1.0,76,0.0008027835891410984,ok +75195,1.0,77,0.0017184974204100811,ok +75171,1.0,78,0.16683679004309793,ok +75128,1.0,79,0.02578242268082609,ok +75096,1.0,80,0.6999878740963849,ok +75250,1.0,81,0.3905530096187665,ok +75146,1.0,82,0.12598182647369827,ok +75116,1.0,83,0.01585035190954187,ok +75157,1.0,84,0.4293317857735035,ok +75187,1.0,85,0.027774943341102754,ok +2350,1.0,86,0.4464958582936187,ok +242,1.0,87,0.015703212456911197,ok +244,1.0,88,0.1092657446261539,ok +75125,1.0,89,0.04170201934498552,ok +75185,1.0,90,0.12728677337133776,ok +75163,1.0,91,0.059868702725845546,ok +75177,1.0,92,0.047791479089188904,ok +75189,1.0,93,0.022630958077993757,ok +75244,1.0,94,0.2103959441745702,ok +75219,1.0,95,0.08207748586408792,ok +75222,1.0,96,0.08608414239482198,ok +75159,1.0,97,0.23509278977791292,ok +75175,1.0,98,0.11569324735400544,ok +75109,1.0,99,0.3183847901925966,ok +254,1.0,100,0.0,ok +75105,1.0,101,0.45167586503729296,ok +75106,1.0,102,0.2860346708691963,ok +75212,1.0,103,0.2770609006495488,ok +75099,1.0,104,0.22507102272727275,ok +75248,1.0,105,0.3201439554404958,ok +233,1.0,106,0.01525546388768273,ok +75235,1.0,107,0.0010568810487612268,ok +75226,1.0,108,0.006568036386672471,ok +75132,1.0,109,0.4196666826890417,ok +75127,1.0,110,0.33861992971938615,ok +251,1.0,111,0.11490189610398005,ok +75161,1.0,112,0.08281715342930007,ok +75143,1.0,113,0.011122916933824278,ok +75114,1.0,114,0.034791524265208484,ok +75182,1.0,115,0.1197809086169026,ok +75112,1.0,116,0.12211239467801316,ok +75210,1.0,117,0.0,ok +75205,1.0,118,0.18552205279651734,ok +75090,1.0,119,0.0999602441856563,ok +275,1.0,120,0.04125451982594841,ok +288,1.0,121,0.14238043603502393,ok +75092,1.0,122,0.22373144069179318,ok +3043,1.0,123,0.047791479089188904,ok +75249,1.0,124,0.01215882694541226,ok +75126,1.0,125,0.09926583052921378,ok +75225,1.0,126,0.1145454545454545,ok +75141,1.0,127,0.06150044923629827,ok +75107,1.0,128,0.08717270509114028,ok +75097,1.0,129,0.3154678825098477,ok +80001,1.0,1,0.07824143070044709,ok +80003,1.0,1,0.12236842105263157,ok +80006,1.0,1,0.16450216450216448,ok +80008,1.0,1,0.16666666666666674,ok +80009,1.0,1,0.15340909090909083,ok +80010,1.0,1,0.08333333333333326,ok +80011,1.0,1,0.02777777777777779,ok +80012,1.0,1,0.033333333333333326,ok +80013,1.0,1,0.0,ok +80014,1.0,1,0.038461538461538436,ok +80015,1.0,1,0.10096153846153844,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/precision_macro_binary.classification_sparse/configurations.csv b/metalearning/metalearning_files/precision_macro_binary.classification_sparse/configurations.csv new file mode 100755 index 0000000..58aa467 --- /dev/null +++ b/metalearning/metalearning_files/precision_macro_binary.classification_sparse/configurations.csv @@ -0,0 +1,141 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,preprocessor:truncatedSVD:target_dim,rescaling:__choice__ +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.00025860501899244284,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5930474355681373,None,0.0,2.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9233683283234232,None,0.0,8.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.002655175930910743,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3997691365353215,None,0.0,20.0,10.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,median,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.039533063907190934,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1878805519245509,fdr,chi2,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7974565919616314,None,0.0,12.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,median,extra_trees_preproc_for_classification,True,entropy,None,0.9772091846790169,None,0.0,10.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2538107344750156,False,True,hinge,1.5099542326343014e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,8532.0,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.034465366914659866,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.12057775675278172,deviance,10.0,0.8011153303489733,None,0.0,2.0,16.0,0.0,370.0,0.6078295352200873,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,mean,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0007038280350320558,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4138778052607317,0.7995003430482459,5.0,5.430044692638861,poly,-1.0,True,0.02455501006004393,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,31,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.0010015637584068037,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.037611630308856295,deviance,5.0,0.8840126779516314,None,0.0,10.0,2.0,0.0,444.0,0.7599997167603434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,most_frequent,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1751.4736515133566,0.62404114475118,3.0,1.6087076997410432,poly,-1.0,False,3.5353792826856045e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,mean,kernel_pca,,,,,,,,,,,,,,cosine,1198.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.8657388713119849,,1.0,None,0.0,19.0,13.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828776,None,0.0,2.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.027324741616523342,deviance,10.0,0.8623781459430139,None,0.0,10.0,20.0,0.0,329.0,0.8595750155424215,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,mean,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.002173124111626734,None,0.0,14.0,4.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,6.0,None,13.0,2.0,1.0,23.0,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1100.6211008501205,0.5921425829232616,2.0,0.0337546254878617,poly,-1.0,True,0.09641299736884308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.9348409326933208,rbf,-1.0,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,mean,kernel_pca,,,,,,,,,,,,,,cosine,1754.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3428971645958825,,,0.2229870623330047,rbf,-1.0,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00016781524591321165,True,True,squared_hinge,1.5119200923218881e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.349459944355116,,,0.00024028983491736645,rbf,-1.0,True,1.139421622432356e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3636266268105085,fwe,chi2,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4047.618729304337,,,2.0237366768707754,rbf,-1.0,True,0.04369127828878843,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,one_hot_encoding,0.010000000000000005,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10.0,1.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,,normalize +none,one_hot_encoding,0.003443779683191071,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.004980497345831963,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1255.9137433589426,,,0.08351549479967445,rbf,-1.0,True,0.00017919875199222518,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1.0,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.3451627750042988,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.3163640203509378,None,0.0,17.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,median,extra_trees_preproc_for_classification,False,gini,None,0.8916956785028156,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,85,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.35533396539961937,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,86,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4165632766388807,fpr,chi2,,none +weighting,one_hot_encoding,0.0019686649916896212,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.04641055832142541,True,True,hinge,8.540468968077405e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87,mean,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,911.0,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1.0,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,89,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,90,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,91,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,93,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,95,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,96,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,97,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,98,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,99,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,100,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,102,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9091193924897338,None,0.0,2.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,103,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,104,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,105,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,106,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.006372860318416312,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6467376360604045,None,0.0,1.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,107,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,108,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,109,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3823734947460288,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,110,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,111,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,112,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,113,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,115,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,116,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,117,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,118,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.20202014999292287,False,True,1.0,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,119,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,120,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,121,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,122,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,123,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,124,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,125,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,126,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,127,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,129,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.04329010470998136,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7260331062271381,None,0.0,7.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,134,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.714869937384006,None,0.0,8.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/metalearning/metalearning_files/precision_macro_binary.classification_sparse/description.txt b/metalearning/metalearning_files/precision_macro_binary.classification_sparse/description.txt new file mode 100755 index 0000000..e790e8e --- /dev/null +++ b/metalearning/metalearning_files/precision_macro_binary.classification_sparse/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: precision_macro +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/precision_macro_binary.classification_sparse/feature_costs.arff b/metalearning/metalearning_files/precision_macro_binary.classification_sparse/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/precision_macro_binary.classification_sparse/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/precision_macro_binary.classification_sparse/feature_runstatus.arff b/metalearning/metalearning_files/precision_macro_binary.classification_sparse/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/precision_macro_binary.classification_sparse/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/precision_macro_binary.classification_sparse/feature_values.arff b/metalearning/metalearning_files/precision_macro_binary.classification_sparse/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/precision_macro_binary.classification_sparse/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/precision_macro_binary.classification_sparse/readme.txt b/metalearning/metalearning_files/precision_macro_binary.classification_sparse/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/precision_macro_multiclass.classification_dense/algorithm_runs.arff b/metalearning/metalearning_files/precision_macro_multiclass.classification_dense/algorithm_runs.arff new file mode 100755 index 0000000..140ec9b --- /dev/null +++ b/metalearning/metalearning_files/precision_macro_multiclass.classification_dense/algorithm_runs.arff @@ -0,0 +1,152 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE precision_macro NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2120,1.0,1,0.09451778417172896,ok +75193,1.0,2,0.054740279347473164,ok +2117,1.0,3,0.22411538859220193,ok +75156,1.0,4,0.20402024446142097,ok +75129,1.0,5,0.29158215010141986,ok +75243,1.0,6,0.0,ok +75110,1.0,7,0.0892798166572355,ok +75239,1.0,8,0.0,ok +75223,1.0,9,0.09818756159624242,ok +75221,1.0,10,0.48940373988484964,ok +258,1.0,11,0.009572743794146077,ok +75121,1.0,12,0.0,ok +253,1.0,13,0.44323194448419845,ok +261,1.0,14,0.26362829614604455,ok +75240,1.0,15,0.027444528010739377,ok +75120,1.0,16,0.3228132754342432,ok +75124,1.0,17,0.23635933303707712,ok +75176,1.0,18,0.015561024201105722,ok +75103,1.0,19,0.01903695408734607,ok +75207,1.0,20,0.1555821732958531,ok +75095,1.0,21,0.04506833036244795,ok +273,1.0,22,0.04489384633555771,ok +75174,1.0,23,0.1367984402089819,ok +75153,1.0,24,0.08033092563893351,ok +75093,1.0,25,0.29082419037612395,ok +75119,1.0,26,0.19982453772438924,ok +75201,1.0,27,0.0915977034341795,ok +75215,1.0,28,0.02725073173601611,ok +75172,1.0,29,0.06684715747215753,ok +75169,1.0,30,0.03367846529227081,ok +75202,1.0,31,0.2893937069656084,ok +75233,1.0,32,0.07053519393690022,ok +75231,1.0,33,0.179623015873016,ok +75196,1.0,34,0.025176080184206917,ok +248,1.0,35,0.23119855794963673,ok +75191,1.0,36,0.12527083939572425,ok +75217,1.0,37,0.0,ok +260,1.0,38,0.1270383423999858,ok +75115,1.0,39,0.012499999999999956,ok +75123,1.0,40,0.3160372511936105,ok +75108,1.0,41,0.0,ok +75101,1.0,42,0.2744936331378274,ok +75192,1.0,43,0.48251399330722244,ok +75232,1.0,44,0.13771929824561402,ok +75173,1.0,45,0.11658236249657183,ok +75197,1.0,46,0.16766341120339778,ok +266,1.0,47,0.018856695063872664,ok +75148,1.0,48,0.13492439104447995,ok +75150,1.0,49,0.277309682187731,ok +75100,1.0,50,0.4796988242873248,ok +75178,1.0,51,0.7570443605083729,ok +75236,1.0,52,0.03264430918305139,ok +75179,1.0,53,0.20464539965756512,ok +75213,1.0,54,0.08276290055664215,ok +2123,1.0,55,0.22052520748172932,ok +75227,1.0,56,0.12183624079862865,ok +75184,1.0,57,0.11415122947001066,ok +75142,1.0,58,0.07153265687347332,ok +236,1.0,59,0.038368905473278314,ok +2122,1.0,60,0.10477484887271771,ok +75188,1.0,61,0.32791115106607094,ok +75166,1.0,62,0.09937012258821953,ok +75181,1.0,63,0.0,ok +75133,1.0,64,0.1022486347574687,ok +75134,1.0,65,0.13011914936317492,ok +75198,1.0,66,0.11620007374442909,ok +262,1.0,67,0.0027122228739830945,ok +75234,1.0,68,0.024093261641772612,ok +75139,1.0,69,0.011579234261353433,ok +252,1.0,70,0.15240439490984203,ok +75117,1.0,71,0.1514138619947749,ok +75113,1.0,72,0.01366348564653741,ok +75098,1.0,73,0.027209476301432778,ok +246,1.0,74,0.011039063766381196,ok +75203,1.0,75,0.09977711773939923,ok +75237,1.0,76,0.0008027835891410984,ok +75195,1.0,77,0.0017184974204100811,ok +75171,1.0,78,0.1620345626712636,ok +75128,1.0,79,0.02578242268082609,ok +75096,1.0,80,0.3125936622043263,ok +75250,1.0,81,0.3905530096187665,ok +75146,1.0,82,0.11764573682920343,ok +75116,1.0,83,0.01585035190954187,ok +75157,1.0,84,0.4293317857735035,ok +75187,1.0,85,0.016722662844829594,ok +2350,1.0,86,0.4464958582936187,ok +242,1.0,87,0.010133609776173058,ok +244,1.0,88,0.1092657446261539,ok +75125,1.0,89,0.04170201934498552,ok +75185,1.0,90,0.12728677337133776,ok +75163,1.0,91,0.059868702725845546,ok +75177,1.0,92,0.047791479089188904,ok +75189,1.0,93,0.022630958077993757,ok +75244,1.0,94,0.2103959441745702,ok +75219,1.0,95,0.03491306888380219,ok +75222,1.0,96,0.08608414239482198,ok +75159,1.0,97,0.23509278977791292,ok +75175,1.0,98,0.10170422727528006,ok +75109,1.0,99,0.30930314701275097,ok +254,1.0,100,0.0,ok +75105,1.0,101,0.45167586503729296,ok +75106,1.0,102,0.2860346708691963,ok +75212,1.0,103,0.24960616138631186,ok +75099,1.0,104,0.22507102272727275,ok +75248,1.0,105,0.3201439554404958,ok +233,1.0,106,0.0029247593449406306,ok +75235,1.0,107,0.0007002801120448154,ok +75226,1.0,108,0.003424853179772036,ok +75132,1.0,109,0.4196666826890417,ok +75127,1.0,110,0.3371864218357312,ok +251,1.0,111,0.0,ok +75161,1.0,112,0.06438786883789338,ok +75143,1.0,113,0.011122916933824278,ok +75114,1.0,114,0.034791524265208484,ok +75182,1.0,115,0.1197809086169026,ok +75112,1.0,116,0.12211239467801316,ok +75210,1.0,117,0.0,ok +75205,1.0,118,0.18552205279651734,ok +75090,1.0,119,0.060938650203326894,ok +275,1.0,120,0.04125451982594841,ok +288,1.0,121,0.12853100078031787,ok +75092,1.0,122,0.22373144069179318,ok +3043,1.0,123,0.047791479089188904,ok +75249,1.0,124,0.00651495354239251,ok +75126,1.0,125,0.09069716775599135,ok +75225,1.0,126,0.1145454545454545,ok +75141,1.0,127,0.05826556510929248,ok +75107,1.0,128,0.08717270509114028,ok +75097,1.0,129,0.3096485466651212,ok +80001,1.0,1,0.07824143070044709,ok +80003,1.0,1,0.09060721062618593,ok +80006,1.0,1,0.09126984126984128,ok +80008,1.0,1,0.08333333333333326,ok +80009,1.0,1,0.10555555555555562,ok +80010,1.0,1,0.08333333333333326,ok +80011,1.0,1,0.02777777777777779,ok +80012,1.0,1,0.033333333333333326,ok +80013,1.0,1,0.0,ok +80014,1.0,1,0.038461538461538436,ok +80015,1.0,1,0.10096153846153844,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/precision_macro_multiclass.classification_dense/configurations.csv b/metalearning/metalearning_files/precision_macro_multiclass.classification_dense/configurations.csv new file mode 100755 index 0000000..bc5674b --- /dev/null +++ b/metalearning/metalearning_files/precision_macro_multiclass.classification_dense/configurations.csv @@ -0,0 +1,141 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:fast_ica:algorithm,preprocessor:fast_ica:fun,preprocessor:fast_ica:n_components,preprocessor:fast_ica:whiten,preprocessor:feature_agglomeration:affinity,preprocessor:feature_agglomeration:linkage,preprocessor:feature_agglomeration:n_clusters,preprocessor:feature_agglomeration:pooling_func,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:pca:keep_variance,preprocessor:pca:whiten,preprocessor:polynomial:degree,preprocessor:polynomial:include_bias,preprocessor:polynomial:interaction_only,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,rescaling:__choice__,rescaling:quantile_transformer:n_quantiles,rescaling:quantile_transformer:output_distribution,rescaling:robust_scaler:q_max,rescaling:robust_scaler:q_min +weighting,one_hot_encoding,0.00011717632475982632,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.045388141846341344,deviance,10.0,0.29161769341843435,None,0.0,20.0,2.0,0.0,278.0,0.7912571599269661,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.00025860501899244284,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5930474355681373,None,0.0,2.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9233683283234232,None,0.0,8.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.03474109838999682,deviance,4.0,0.5687034678818491,None,0.0,18.0,12.0,0.0,408.0,0.5150113945430513,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.6328939517938,f_classif,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.251097788175676,deviance,6.0,0.35679099363539235,None,0.0,13.0,11.0,0.0,157.0,0.4791732272983235,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,adaboost,SAMME,0.28738775989203896,10.0,423.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.8031499675923353,0.13579938270386765 +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.14159526341015916,deviance,7.0,0.8010488230155749,None,0.0,3.0,20.0,0.0,401.0,0.8073562440607731,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.002385546176068135,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.7729472304882845,,,0.0004789329856033374,rbf,-1.0,True,6.58869648864534e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,177.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5368752992317617,None,0.0,16.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.8382117756438685,mutual_info,,,,quantile_transformer,11480.0,normal,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.8384447520019118,None,0.0,13.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.36637567531287824,fdr,f_classif,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.039533063907190934,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1878805519245509,fdr,chi2,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9455638720565652,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,most_frequent,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8255464552647293,0.19162485555463185 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.040936424602789435,deviance,7.0,0.5495014745530306,None,0.0,20.0,18.0,0.0,141.0,0.6905343807995293,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7974565919616314,None,0.0,12.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,median,extra_trees_preproc_for_classification,True,entropy,None,0.9772091846790169,None,0.0,10.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2538107344750156,False,True,hinge,1.5099542326343014e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,8532.0,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.1958974686405233,deviance,5.0,0.33885235607979314,None,0.0,6.0,4.0,0.0,125.0,0.9448890820738562,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0007038280350320558,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4138778052607317,0.7995003430482459,5.0,5.430044692638861,poly,-1.0,True,0.02455501006004393,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,31,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.010000000000000005,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.0518326156691958,deviance,6.0,0.8807456180216267,None,0.0,7.0,19.0,0.0,366.0,0.7314831276137047,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,,False,adaboost,SAMME,0.4391375941344922,3.0,386.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7439738358430176,0.20581080574615795 +none,one_hot_encoding,0.00011294596229850895,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7515.1213255144885,0.9576762936062476,3.0,0.019002536385919932,poly,-1.0,False,0.010632086351533369,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,51.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,97282.0,normal,, +none,one_hot_encoding,0.00012586572428922356,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5240592829918601,None,0.0,10.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1751.4736515133566,0.62404114475118,3.0,1.6087076997410432,poly,-1.0,False,3.5353792826856045e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1198.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.2422926485206341,,1.0,None,0.0,15.0,9.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.02102683283349326,deviance,10.0,0.2797288369369436,None,0.0,14.0,9.0,0.0,480.0,0.5778972273820631,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58.88123233170863,mutual_info,,,,robust_scaler,,,0.75,0.25 +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828776,None,0.0,2.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2155613360930585,deviance,4.0,0.3198803116198433,None,0.0,8.0,13.0,0.0,275.0,0.28870176110739404,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,mean,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.7062102387181676,None,0.0,1.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,most_frequent,fast_ica,,,,,,,,,,,parallel,exp,100.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7065776353150109,0.23782974987118105 +weighting,one_hot_encoding,0.010000000000000005,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.756120720182025,True,True,squared_hinge,0.020536827217281145,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,4188.0,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.3795924768593385,deviance,2.0,0.33708497069988536,None,0.0,15.0,13.0,0.0,451.0,0.7716323242090217,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2573946506994812,fwe,f_classif,quantile_transformer,1000.0,uniform,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.609975998293528,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,most_frequent,fast_ica,,,,,,,,,,,parallel,logcosh,2000.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8430415644014919,0.2863750565331575 +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.39536192447534535,None,0.0,19.0,3.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5.0,None,11.0,11.0,1.0,12.0,,,,,,robust_scaler,,,0.8928631650245873,0.1581877760687084 +weighting,one_hot_encoding,0.060155186012325876,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.3163640203509378,None,0.0,16.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,median,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,2.0,None,11.0,20.0,1.0,47.0,,,,,,quantile_transformer,21065.0,uniform,, +weighting,one_hot_encoding,0.3126027672745337,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,870.2240970463429,0.5325949351918051,3.0,0.010682839357128344,poly,-1.0,False,2.485160860440657e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4608103694360143,fdr,f_classif,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.9348409326933208,rbf,-1.0,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1754.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8363534397818381,None,0.0,4.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,False,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9260795160807372,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.03644212536682547,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.05965671477918361,deviance,8.0,0.4858133247974158,None,0.0,14.0,7.0,0.0,480.0,0.5726186552917335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.15318294164619112 +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18887.81504976871,,,0.23283562663398755,rbf,-1.0,True,2.3839685780861318e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3937607155568376,fwe,chi2,robust_scaler,,,0.941018778984854,0.2144110585080491 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3428971645958825,,,0.2229870623330047,rbf,-1.0,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00016781524591321165,True,True,squared_hinge,1.5119200923218881e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.349459944355116,,,0.00024028983491736645,rbf,-1.0,True,1.139421622432356e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3636266268105085,fwe,chi2,none,,,, +weighting,one_hot_encoding,0.00034835629696198427,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8245132980938538,0.08947420373097192 +weighting,one_hot_encoding,0.00016967940959070708,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9439080311935252,None,0.0,2.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35.0,None,,0.005389483044810356,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,most_frequent,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,9.441661069727422e-05,1896.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,,False,adaboost,SAMME.R,0.8220362681234727,5.0,183.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.363262678765884,fdr,chi2,robust_scaler,,,0.8826612080363588,0.2189605310171097 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,normalize,,,, +none,one_hot_encoding,,False,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6396026761675004,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06544340428506021,fwe,f_classif,none,,,, +weighting,one_hot_encoding,0.004980497345831963,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1255.9137433589426,,,0.08351549479967445,rbf,-1.0,True,0.00017919875199222518,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1.0,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.3837398524575939,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.018356703878357986,deviance,3.0,0.9690352514774068,None,0.0,12.0,3.0,0.0,234.0,0.3870344708308441,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,mean,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6524770456279243,False,,,,,,,,,,,,,,,,robust_scaler,,,0.7602889314749132,0.25 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5670424455696162,None,0.0,8.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,standardize,,,, +weighting,one_hot_encoding,0.00031737236118003484,True,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,244.0,None,,2.3065111488706024e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,85,mean,fast_ica,,,,,,,,,,,deflation,exp,1862.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7851234479882973,0.2237528085136715 +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.35533396539961937,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,86,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4165632766388807,fpr,chi2,none,,,, +weighting,one_hot_encoding,,False,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,156.0,auto,,0.0001987333852871589,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1.0,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,89,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,90,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,91,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,93,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2636201374253461,deviance,7.0,0.8344964130784466,None,0.0,9.0,2.0,0.0,298.0,0.7517549950523315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,95,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,96,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,97,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.07463196642416367,deviance,7.0,0.8603242247379981,None,0.0,2.0,6.0,0.0,500.0,0.8447665577491962,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,98,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.0433556140045585,deviance,10.0,0.33000096635982235,None,0.0,15.0,13.0,0.0,388.0,0.8291104221904706,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,99,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,robust_scaler,,,0.7496393440951183,0.2853682991120835 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,100,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,102,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0020580843703898177,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.945774573434192,None,0.0,19.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,103,median,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,15209.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,104,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,105,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,106,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.005332972692819521,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5565918060287016,None,0.0,5.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,107,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,173.0,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9527068489270144,0.04135311355893583 +weighting,one_hot_encoding,0.001279467383882126,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,108,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0903354518102121,fwe,f_classif,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,109,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.002615346832354839,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7884268823432835,None,0.0,20.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,110,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +weighting,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56.086963007482865,,,0.013609964993119377,rbf,-1.0,True,0.00196831255706268,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,111,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.15374716583918388 +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.2472984547885781,deviance,3.0,0.6564306719064884,None,0.0,15.0,14.0,0.0,220.0,0.8082564085714649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,112,median,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,332.0,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,113,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,115,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,116,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,117,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,118,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.20202014999292287,False,True,1.0,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,no_encoding,,,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6390376923528961,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,119,median,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,164.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,62508.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,120,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.27041927277584e-06,True,0.00010000000000000009,0.033157325660763994,True,0.0008114527992546482,invscaling,modified_huber,elasticnet,0.13714427818877545,0.055179642772545036,,,,,,,,,,,,,,,,,,121,median,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,122,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,123,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.19998727075532635,deviance,10.0,0.9377656718112952,None,0.0,7.0,13.0,0.0,214.0,0.6062346326014357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,124,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,minmax,,,, +none,no_encoding,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.745231770090803e-07,False,6.503623129182133e-05,0.034765176880257306,True,,constant,modified_huber,l2,,7.878097869194373e-05,,,,,,,,,,,,,,,,,,125,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,751.0,uniform,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,126,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2806.9858667073186,False,True,1.0,squared_hinge,ovr,l2,0.03738539536055984,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,127,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7945458151995424,None,0.0,1.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,129,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,38400.0,uniform,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.04651467280022463,True,adaboost,SAMME,0.6506122230393502,6.0,143.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,extra_trees_preproc_for_classification,False,entropy,None,0.348720215832657,None,0.0,17.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,26025.0,normal,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3386310102795006,None,0.0,6.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,True,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.0,None,,5.861221938384061e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.22985730375167915,fpr,f_classif,quantile_transformer,40589.0,uniform,, +none,no_encoding,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00012437731009611307,True,,0.08709904468181932,True,,invscaling,squared_hinge,l1,0.6231564675290102,0.008071279833697563,,,,,,,,,,,,,,,,,,134,median,fast_ica,,,,,,,,,,,parallel,logcosh,814.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.714869937384006,None,0.0,8.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, diff --git a/metalearning/metalearning_files/precision_macro_multiclass.classification_dense/description.txt b/metalearning/metalearning_files/precision_macro_multiclass.classification_dense/description.txt new file mode 100755 index 0000000..e790e8e --- /dev/null +++ b/metalearning/metalearning_files/precision_macro_multiclass.classification_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: precision_macro +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/precision_macro_multiclass.classification_dense/feature_costs.arff b/metalearning/metalearning_files/precision_macro_multiclass.classification_dense/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/precision_macro_multiclass.classification_dense/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/precision_macro_multiclass.classification_dense/feature_runstatus.arff b/metalearning/metalearning_files/precision_macro_multiclass.classification_dense/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/precision_macro_multiclass.classification_dense/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/precision_macro_multiclass.classification_dense/feature_values.arff b/metalearning/metalearning_files/precision_macro_multiclass.classification_dense/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/precision_macro_multiclass.classification_dense/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/precision_macro_multiclass.classification_dense/readme.txt b/metalearning/metalearning_files/precision_macro_multiclass.classification_dense/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/precision_macro_multiclass.classification_sparse/algorithm_runs.arff b/metalearning/metalearning_files/precision_macro_multiclass.classification_sparse/algorithm_runs.arff new file mode 100755 index 0000000..b5133ba --- /dev/null +++ b/metalearning/metalearning_files/precision_macro_multiclass.classification_sparse/algorithm_runs.arff @@ -0,0 +1,152 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE precision_macro NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2120,1.0,1,0.10395992580342572,ok +75193,1.0,2,0.054740279347473164,ok +2117,1.0,3,0.22411538859220193,ok +75156,1.0,4,0.22120941933224003,ok +75129,1.0,5,0.29158215010141986,ok +75243,1.0,6,0.011893570087039729,ok +75110,1.0,7,0.2511371338718129,ok +75239,1.0,8,0.0,ok +75223,1.0,9,0.3291815803990831,ok +75221,1.0,10,0.48940373988484964,ok +258,1.0,11,0.01807873351917144,ok +75121,1.0,12,0.002044989775051187,ok +253,1.0,13,0.45569838348413194,ok +261,1.0,14,0.26362829614604455,ok +75240,1.0,15,0.027444528010739377,ok +75120,1.0,16,0.3228132754342432,ok +75124,1.0,17,0.23635933303707712,ok +75176,1.0,18,0.01701943889443891,ok +75103,1.0,19,0.01903695408734607,ok +75207,1.0,20,0.1555821732958531,ok +75095,1.0,21,0.04506833036244795,ok +273,1.0,22,0.04489384633555771,ok +75174,1.0,23,0.1367984402089819,ok +75153,1.0,24,0.12121092659209243,ok +75093,1.0,25,0.29082419037612395,ok +75119,1.0,26,0.19982453772438924,ok +75201,1.0,27,0.0915977034341795,ok +75215,1.0,28,0.027499725864310598,ok +75172,1.0,29,0.059523415799145996,ok +75169,1.0,30,0.03367846529227081,ok +75202,1.0,31,0.2893937069656084,ok +75233,1.0,32,0.07146642933522851,ok +75231,1.0,33,0.179623015873016,ok +75196,1.0,34,0.031206920319823528,ok +248,1.0,35,0.27164730984032814,ok +75191,1.0,36,0.11920364332976963,ok +75217,1.0,37,0.0,ok +260,1.0,38,0.1270383423999858,ok +75115,1.0,39,0.012499999999999956,ok +75123,1.0,40,0.3160372511936105,ok +75108,1.0,41,0.0047890749991836845,ok +75101,1.0,42,0.2833721913888413,ok +75192,1.0,43,0.48251399330722244,ok +75232,1.0,44,0.15573834398877273,ok +75173,1.0,45,0.1174657210516985,ok +75197,1.0,46,0.15823239753975438,ok +266,1.0,47,0.031453999162528556,ok +75148,1.0,48,0.18996210099284716,ok +75150,1.0,49,0.3203416696019725,ok +75100,1.0,50,0.492117071097562,ok +75178,1.0,51,0.8083745568289286,ok +75236,1.0,52,0.0326744774282266,ok +75179,1.0,53,0.20464539965756512,ok +75213,1.0,54,0.08276290055664215,ok +2123,1.0,55,0.22052520748172932,ok +75227,1.0,56,0.12183624079862865,ok +75184,1.0,57,0.13247293738284927,ok +75142,1.0,58,0.0812999948717541,ok +236,1.0,59,0.041431968403706776,ok +2122,1.0,60,0.2511371338718129,ok +75188,1.0,61,0.32791115106607094,ok +75166,1.0,62,0.09937012258821953,ok +75181,1.0,63,0.0,ok +75133,1.0,64,0.1022486347574687,ok +75134,1.0,65,0.13011914936317492,ok +75198,1.0,66,0.11620007374442909,ok +262,1.0,67,0.0027122228739830945,ok +75234,1.0,68,0.057671931281409794,ok +75139,1.0,69,0.013990379840231548,ok +252,1.0,70,0.16936450516183632,ok +75117,1.0,71,0.18610421836228297,ok +75113,1.0,72,0.01366348564653741,ok +75098,1.0,73,0.027209476301432778,ok +246,1.0,74,0.024005965864471235,ok +75203,1.0,75,0.09977711773939923,ok +75237,1.0,76,0.0008027835891410984,ok +75195,1.0,77,0.0017184974204100811,ok +75171,1.0,78,0.16683679004309793,ok +75128,1.0,79,0.02578242268082609,ok +75096,1.0,80,0.6999878740963849,ok +75250,1.0,81,0.3905530096187665,ok +75146,1.0,82,0.12598182647369827,ok +75116,1.0,83,0.01585035190954187,ok +75157,1.0,84,0.4293317857735035,ok +75187,1.0,85,0.027774943341102754,ok +2350,1.0,86,0.4464958582936187,ok +242,1.0,87,0.015703212456911197,ok +244,1.0,88,0.1092657446261539,ok +75125,1.0,89,0.04170201934498552,ok +75185,1.0,90,0.12728677337133776,ok +75163,1.0,91,0.059868702725845546,ok +75177,1.0,92,0.047791479089188904,ok +75189,1.0,93,0.022630958077993757,ok +75244,1.0,94,0.2103959441745702,ok +75219,1.0,95,0.08207748586408792,ok +75222,1.0,96,0.08608414239482198,ok +75159,1.0,97,0.23509278977791292,ok +75175,1.0,98,0.11569324735400544,ok +75109,1.0,99,0.3183847901925966,ok +254,1.0,100,0.0,ok +75105,1.0,101,0.45167586503729296,ok +75106,1.0,102,0.2860346708691963,ok +75212,1.0,103,0.2770609006495488,ok +75099,1.0,104,0.22507102272727275,ok +75248,1.0,105,0.3201439554404958,ok +233,1.0,106,0.01525546388768273,ok +75235,1.0,107,0.0010568810487612268,ok +75226,1.0,108,0.006568036386672471,ok +75132,1.0,109,0.4196666826890417,ok +75127,1.0,110,0.33861992971938615,ok +251,1.0,111,0.11490189610398005,ok +75161,1.0,112,0.08281715342930007,ok +75143,1.0,113,0.011122916933824278,ok +75114,1.0,114,0.034791524265208484,ok +75182,1.0,115,0.1197809086169026,ok +75112,1.0,116,0.12211239467801316,ok +75210,1.0,117,0.0,ok +75205,1.0,118,0.18552205279651734,ok +75090,1.0,119,0.0999602441856563,ok +275,1.0,120,0.04125451982594841,ok +288,1.0,121,0.14238043603502393,ok +75092,1.0,122,0.22373144069179318,ok +3043,1.0,123,0.047791479089188904,ok +75249,1.0,124,0.01215882694541226,ok +75126,1.0,125,0.09926583052921378,ok +75225,1.0,126,0.1145454545454545,ok +75141,1.0,127,0.06150044923629827,ok +75107,1.0,128,0.08717270509114028,ok +75097,1.0,129,0.3154678825098477,ok +80001,1.0,1,0.07824143070044709,ok +80003,1.0,1,0.12236842105263157,ok +80006,1.0,1,0.16450216450216448,ok +80008,1.0,1,0.16666666666666674,ok +80009,1.0,1,0.15340909090909083,ok +80010,1.0,1,0.08333333333333326,ok +80011,1.0,1,0.02777777777777779,ok +80012,1.0,1,0.033333333333333326,ok +80013,1.0,1,0.0,ok +80014,1.0,1,0.038461538461538436,ok +80015,1.0,1,0.10096153846153844,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/precision_macro_multiclass.classification_sparse/configurations.csv b/metalearning/metalearning_files/precision_macro_multiclass.classification_sparse/configurations.csv new file mode 100755 index 0000000..58aa467 --- /dev/null +++ b/metalearning/metalearning_files/precision_macro_multiclass.classification_sparse/configurations.csv @@ -0,0 +1,141 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,preprocessor:truncatedSVD:target_dim,rescaling:__choice__ +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.00025860501899244284,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5930474355681373,None,0.0,2.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9233683283234232,None,0.0,8.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.002655175930910743,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3997691365353215,None,0.0,20.0,10.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,median,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.039533063907190934,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1878805519245509,fdr,chi2,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7974565919616314,None,0.0,12.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,median,extra_trees_preproc_for_classification,True,entropy,None,0.9772091846790169,None,0.0,10.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2538107344750156,False,True,hinge,1.5099542326343014e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,8532.0,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.034465366914659866,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.12057775675278172,deviance,10.0,0.8011153303489733,None,0.0,2.0,16.0,0.0,370.0,0.6078295352200873,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,mean,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0007038280350320558,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4138778052607317,0.7995003430482459,5.0,5.430044692638861,poly,-1.0,True,0.02455501006004393,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,31,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.0010015637584068037,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.037611630308856295,deviance,5.0,0.8840126779516314,None,0.0,10.0,2.0,0.0,444.0,0.7599997167603434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,most_frequent,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1751.4736515133566,0.62404114475118,3.0,1.6087076997410432,poly,-1.0,False,3.5353792826856045e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,mean,kernel_pca,,,,,,,,,,,,,,cosine,1198.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.8657388713119849,,1.0,None,0.0,19.0,13.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828776,None,0.0,2.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.027324741616523342,deviance,10.0,0.8623781459430139,None,0.0,10.0,20.0,0.0,329.0,0.8595750155424215,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,mean,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.002173124111626734,None,0.0,14.0,4.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,6.0,None,13.0,2.0,1.0,23.0,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1100.6211008501205,0.5921425829232616,2.0,0.0337546254878617,poly,-1.0,True,0.09641299736884308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.9348409326933208,rbf,-1.0,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,mean,kernel_pca,,,,,,,,,,,,,,cosine,1754.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3428971645958825,,,0.2229870623330047,rbf,-1.0,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00016781524591321165,True,True,squared_hinge,1.5119200923218881e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.349459944355116,,,0.00024028983491736645,rbf,-1.0,True,1.139421622432356e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3636266268105085,fwe,chi2,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4047.618729304337,,,2.0237366768707754,rbf,-1.0,True,0.04369127828878843,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,one_hot_encoding,0.010000000000000005,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10.0,1.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,,normalize +none,one_hot_encoding,0.003443779683191071,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.004980497345831963,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1255.9137433589426,,,0.08351549479967445,rbf,-1.0,True,0.00017919875199222518,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1.0,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.3451627750042988,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.3163640203509378,None,0.0,17.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,median,extra_trees_preproc_for_classification,False,gini,None,0.8916956785028156,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,85,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.35533396539961937,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,86,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4165632766388807,fpr,chi2,,none +weighting,one_hot_encoding,0.0019686649916896212,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.04641055832142541,True,True,hinge,8.540468968077405e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87,mean,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,911.0,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1.0,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,89,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,90,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,91,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,93,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,95,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,96,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,97,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,98,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,99,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,100,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,102,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9091193924897338,None,0.0,2.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,103,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,104,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,105,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,106,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.006372860318416312,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6467376360604045,None,0.0,1.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,107,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,108,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,109,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3823734947460288,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,110,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,111,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,112,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,113,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,115,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,116,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,117,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,118,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.20202014999292287,False,True,1.0,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,119,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,120,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,121,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,122,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,123,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,124,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,125,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,126,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,127,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,129,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.04329010470998136,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7260331062271381,None,0.0,7.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,134,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.714869937384006,None,0.0,8.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/metalearning/metalearning_files/precision_macro_multiclass.classification_sparse/description.txt b/metalearning/metalearning_files/precision_macro_multiclass.classification_sparse/description.txt new file mode 100755 index 0000000..e790e8e --- /dev/null +++ b/metalearning/metalearning_files/precision_macro_multiclass.classification_sparse/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: precision_macro +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/precision_macro_multiclass.classification_sparse/feature_costs.arff b/metalearning/metalearning_files/precision_macro_multiclass.classification_sparse/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/precision_macro_multiclass.classification_sparse/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/precision_macro_multiclass.classification_sparse/feature_runstatus.arff b/metalearning/metalearning_files/precision_macro_multiclass.classification_sparse/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/precision_macro_multiclass.classification_sparse/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/precision_macro_multiclass.classification_sparse/feature_values.arff b/metalearning/metalearning_files/precision_macro_multiclass.classification_sparse/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/precision_macro_multiclass.classification_sparse/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/precision_macro_multiclass.classification_sparse/readme.txt b/metalearning/metalearning_files/precision_macro_multiclass.classification_sparse/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/precision_micro_binary.classification_dense/algorithm_runs.arff b/metalearning/metalearning_files/precision_micro_binary.classification_dense/algorithm_runs.arff new file mode 100755 index 0000000..0d9bac6 --- /dev/null +++ b/metalearning/metalearning_files/precision_micro_binary.classification_dense/algorithm_runs.arff @@ -0,0 +1,152 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE precision_micro NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2120,1.0,1,0.07967939651107969,ok +75193,1.0,2,0.038371068099909755,ok +2117,1.0,3,0.16709064962461995,ok +75156,1.0,4,0.20291026677445434,ok +75129,1.0,5,0.10097087378640779,ok +75243,1.0,6,0.0,ok +75110,1.0,7,0.11622380643767549,ok +75239,1.0,8,0.0,ok +75223,1.0,9,0.10304601425793913,ok +75221,1.0,10,0.39791873141724476,ok +258,1.0,11,0.009708737864077666,ok +75121,1.0,12,0.0,ok +253,1.0,13,0.44855967078189296,ok +261,1.0,14,0.23333333333333328,ok +75240,1.0,15,0.022020725388601003,ok +75120,1.0,16,0.03929273084479368,ok +75124,1.0,17,0.09121395036887991,ok +75176,1.0,18,0.01541623843782114,ok +75103,1.0,19,0.005894736842105286,ok +75207,1.0,20,0.161849710982659,ok +75095,1.0,21,0.016917293233082664,ok +273,1.0,22,0.04413702239789197,ok +75174,1.0,23,0.11705240755520085,ok +75153,1.0,24,0.08028116907140215,ok +75093,1.0,25,0.17483296213808464,ok +75119,1.0,26,0.035363457760314354,ok +75201,1.0,27,0.0808678500986193,ok +75215,1.0,28,0.027412280701754388,ok +75172,1.0,29,0.10303030303030303,ok +75169,1.0,30,0.03420132141469101,ok +75202,1.0,31,0.20329670329670335,ok +75233,1.0,32,0.060673325934147204,ok +75231,1.0,33,0.19924098671726753,ok +75196,1.0,34,0.015665796344647487,ok +248,1.0,35,0.22878787878787876,ok +75191,1.0,36,0.1289905886694962,ok +75217,1.0,37,0.0,ok +260,1.0,38,0.02657807308970095,ok +75115,1.0,39,0.017681728880157177,ok +75123,1.0,40,0.32728592162554426,ok +75108,1.0,41,0.0,ok +75101,1.0,42,0.2740140932130053,ok +75192,1.0,43,0.48305752561071713,ok +75232,1.0,44,0.12356321839080464,ok +75173,1.0,45,0.1165605095541401,ok +75197,1.0,46,0.15517241379310343,ok +266,1.0,47,0.019685039370078705,ok +75148,1.0,48,0.13560975609756099,ok +75150,1.0,49,0.2848664688427299,ok +75100,1.0,50,0.00379609544468551,ok +75178,1.0,51,0.7840550682597786,ok +75236,1.0,52,0.0323809523809524,ok +75179,1.0,53,0.17943026267110618,ok +75213,1.0,54,0.05249343832021003,ok +2123,1.0,55,0.05882352941176472,ok +75227,1.0,56,0.10151430173864273,ok +75184,1.0,57,0.10772320613474529,ok +75142,1.0,58,0.0715825466438712,ok +236,1.0,59,0.038787878787878816,ok +2122,1.0,60,0.10952689565780949,ok +75188,1.0,61,0.24319066147859925,ok +75166,1.0,62,0.0995190529041805,ok +75181,1.0,63,0.0,ok +75133,1.0,64,0.005123278898495065,ok +75134,1.0,65,0.08723783614874181,ok +75198,1.0,66,0.12143310082435,ok +262,1.0,67,0.0027570995312931057,ok +75234,1.0,68,0.024160524160524166,ok +75139,1.0,69,0.010707070707070665,ok +252,1.0,70,0.15000000000000002,ok +75117,1.0,71,0.05500982318271119,ok +75113,1.0,72,0.006526315789473713,ok +75098,1.0,73,0.027575757575757587,ok +246,1.0,74,0.010606060606060619,ok +75203,1.0,75,0.09555345316934716,ok +75237,1.0,76,0.00039570658356824495,ok +75195,1.0,77,0.0015609901137292326,ok +75171,1.0,78,0.1620421753607103,ok +75128,1.0,79,0.02218114602587795,ok +75096,1.0,80,0.005164788382626018,ok +75250,1.0,81,0.34347287891393896,ok +75146,1.0,82,0.11329072074057744,ok +75116,1.0,83,0.00982318271119842,ok +75157,1.0,84,0.4192200557103064,ok +75187,1.0,85,0.01678951678951679,ok +2350,1.0,86,0.3744580607974338,ok +242,1.0,87,0.010606060606060619,ok +244,1.0,88,0.1106060606060606,ok +75125,1.0,89,0.03339882121807469,ok +75185,1.0,90,0.12816966343937297,ok +75163,1.0,91,0.060374149659863985,ok +75177,1.0,92,0.019292604501607746,ok +75189,1.0,93,0.019401337253296624,ok +75244,1.0,94,0.06339958875942431,ok +75219,1.0,95,0.03479668217681575,ok +75222,1.0,96,0.04524886877828049,ok +75159,1.0,97,0.06849315068493156,ok +75175,1.0,98,0.09954485391278811,ok +75109,1.0,99,0.30417434008594224,ok +254,1.0,100,0.0,ok +75105,1.0,101,0.018121212121212094,ok +75106,1.0,102,0.07212121212121214,ok +75212,1.0,103,0.2517482517482518,ok +75099,1.0,104,0.12374100719424463,ok +75248,1.0,105,0.10040485829959511,ok +233,1.0,106,0.002846299810246644,ok +75235,1.0,107,0.0011111111111110628,ok +75226,1.0,108,0.0030422878004259246,ok +75132,1.0,109,0.051244509516837455,ok +75127,1.0,110,0.3330355738331199,ok +251,1.0,111,0.0,ok +75161,1.0,112,0.06437225897569321,ok +75143,1.0,113,0.010471204188481686,ok +75114,1.0,114,0.023575638506876273,ok +75182,1.0,115,0.1081404628890662,ok +75112,1.0,116,0.12061822817080947,ok +75210,1.0,117,0.0,ok +75205,1.0,118,0.18110236220472442,ok +75090,1.0,119,0.06118881118881114,ok +275,1.0,120,0.03612167300380231,ok +288,1.0,121,0.12969696969696964,ok +75092,1.0,122,0.0935550935550935,ok +3043,1.0,123,0.020096463022508004,ok +75249,1.0,124,0.003215434083601254,ok +75126,1.0,125,0.0491159135559921,ok +75225,1.0,126,0.04904306220095689,ok +75141,1.0,127,0.0540140584535701,ok +75107,1.0,128,0.053212121212121266,ok +75097,1.0,129,0.05835568297419769,ok +80001,1.0,1,0.06944444444444442,ok +80003,1.0,1,0.09230769230769231,ok +80006,1.0,1,0.09375,ok +80008,1.0,1,0.11111111111111116,ok +80009,1.0,1,0.10526315789473684,ok +80010,1.0,1,0.13793103448275867,ok +80011,1.0,1,0.037735849056603765,ok +80012,1.0,1,0.045454545454545414,ok +80013,1.0,1,0.0,ok +80014,1.0,1,0.045454545454545414,ok +80015,1.0,1,0.09523809523809523,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/precision_micro_binary.classification_dense/configurations.csv b/metalearning/metalearning_files/precision_micro_binary.classification_dense/configurations.csv new file mode 100755 index 0000000..e710c68 --- /dev/null +++ b/metalearning/metalearning_files/precision_micro_binary.classification_dense/configurations.csv @@ -0,0 +1,141 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:fast_ica:algorithm,preprocessor:fast_ica:fun,preprocessor:fast_ica:n_components,preprocessor:fast_ica:whiten,preprocessor:feature_agglomeration:affinity,preprocessor:feature_agglomeration:linkage,preprocessor:feature_agglomeration:n_clusters,preprocessor:feature_agglomeration:pooling_func,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:pca:keep_variance,preprocessor:pca:whiten,preprocessor:polynomial:degree,preprocessor:polynomial:include_bias,preprocessor:polynomial:interaction_only,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,rescaling:__choice__,rescaling:quantile_transformer:n_quantiles,rescaling:quantile_transformer:output_distribution,rescaling:robust_scaler:q_max,rescaling:robust_scaler:q_min +weighting,one_hot_encoding,0.00011717632475982632,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.045388141846341344,deviance,10.0,0.29161769341843435,None,0.0,20.0,2.0,0.0,278.0,0.7912571599269661,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7249853037185638,None,0.0,1.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,median,extra_trees_preproc_for_classification,False,gini,None,0.9424908623661876,None,0.0,7.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.03474109838999682,deviance,4.0,0.5687034678818491,None,0.0,18.0,12.0,0.0,408.0,0.5150113945430513,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.6328939517938,f_classif,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.251097788175676,deviance,6.0,0.35679099363539235,None,0.0,13.0,11.0,0.0,157.0,0.4791732272983235,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,adaboost,SAMME,0.28738775989203896,10.0,423.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.8031499675923353,0.13579938270386765 +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.14159526341015916,deviance,7.0,0.8010488230155749,None,0.0,3.0,20.0,0.0,401.0,0.8073562440607731,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.002385546176068135,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.7729472304882845,,,0.0004789329856033374,rbf,-1.0,True,6.58869648864534e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,177.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5368752992317617,None,0.0,16.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.8382117756438685,mutual_info,,,,quantile_transformer,11480.0,normal,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9455638720565652,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,most_frequent,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8255464552647293,0.19162485555463185 +weighting,one_hot_encoding,0.18137532678800647,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9094110110427254,None,0.0,7.0,12.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,195.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.6682079659377479,None,0.0,4.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,mean,extra_trees_preproc_for_classification,True,entropy,None,0.5552350997943013,None,0.0,8.0,5.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.02345017287074443,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.053517066400173056,deviance,10.0,0.542144980834302,None,0.0,20.0,13.0,0.0,233.0,0.7398539900055563,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0614425536709615,fwe,f_classif,robust_scaler,,,0.9523118062307264,0.13434811490315818 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.040936424602789435,deviance,7.0,0.5495014745530306,None,0.0,20.0,18.0,0.0,141.0,0.6905343807995293,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7974565919616314,None,0.0,12.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,median,extra_trees_preproc_for_classification,True,entropy,None,0.9772091846790169,None,0.0,10.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2538107344750156,False,True,hinge,1.5099542326343014e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,8532.0,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.1958974686405233,deviance,5.0,0.33885235607979314,None,0.0,6.0,4.0,0.0,125.0,0.9448890820738562,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2538107344750156,False,True,hinge,1.5099542326343014e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,8532.0,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,0.0007038280350320558,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4138778052607317,0.7995003430482459,5.0,5.430044692638861,poly,-1.0,True,0.02455501006004393,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,31,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.010000000000000005,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.0518326156691958,deviance,6.0,0.8807456180216267,None,0.0,7.0,19.0,0.0,366.0,0.7314831276137047,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,,False,adaboost,SAMME,0.4391375941344922,3.0,386.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7439738358430176,0.20581080574615795 +none,one_hot_encoding,0.00011294596229850895,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7515.1213255144885,0.9576762936062476,3.0,0.019002536385919932,poly,-1.0,False,0.010632086351533369,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,51.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,97282.0,normal,, +none,one_hot_encoding,0.00012586572428922356,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5240592829918601,None,0.0,10.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1751.4736515133566,0.62404114475118,3.0,1.6087076997410432,poly,-1.0,False,3.5353792826856045e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1198.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.2422926485206341,,1.0,None,0.0,15.0,9.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.02102683283349326,deviance,10.0,0.2797288369369436,None,0.0,14.0,9.0,0.0,480.0,0.5778972273820631,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58.88123233170863,mutual_info,,,,robust_scaler,,,0.75,0.25 +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828776,None,0.0,2.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2155613360930585,deviance,4.0,0.3198803116198433,None,0.0,8.0,13.0,0.0,275.0,0.28870176110739404,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,mean,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.7062102387181676,None,0.0,1.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,most_frequent,fast_ica,,,,,,,,,,,parallel,exp,100.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7065776353150109,0.23782974987118105 +none,one_hot_encoding,0.16334152321884812,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00011572473434870852,True,True,squared_hinge,0.00019678754114665057,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.045742431094098604,fpr,chi2,none,,,, +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.3795924768593385,deviance,2.0,0.33708497069988536,None,0.0,15.0,13.0,0.0,451.0,0.7716323242090217,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2573946506994812,fwe,f_classif,quantile_transformer,1000.0,uniform,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.609975998293528,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,most_frequent,fast_ica,,,,,,,,,,,parallel,logcosh,2000.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8430415644014919,0.2863750565331575 +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.39536192447534535,None,0.0,19.0,3.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5.0,None,11.0,11.0,1.0,12.0,,,,,,robust_scaler,,,0.8928631650245873,0.1581877760687084 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.3126027672745337,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,870.2240970463429,0.5325949351918051,3.0,0.010682839357128344,poly,-1.0,False,2.485160860440657e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4608103694360143,fdr,f_classif,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.9348409326933208,rbf,-1.0,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1754.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,198.72528686512538,False,True,1.0,squared_hinge,ovr,l2,0.026260652523566803,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,robust_scaler,,,0.913511520078368,0.2742229325455444 +none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9260795160807372,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.03644212536682547,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.05965671477918361,deviance,8.0,0.4858133247974158,None,0.0,14.0,7.0,0.0,480.0,0.5726186552917335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.15318294164619112 +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18887.81504976871,,,0.23283562663398755,rbf,-1.0,True,2.3839685780861318e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3937607155568376,fwe,chi2,robust_scaler,,,0.941018778984854,0.2144110585080491 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3428971645958825,,,0.2229870623330047,rbf,-1.0,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8138233157708883,None,0.0,2.0,9.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54.27504292568562,f_classif,,,,minmax,,,, +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00016781524591321165,True,True,squared_hinge,1.5119200923218881e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.556190405302504,False,True,1.0,squared_hinge,ovr,l2,0.0007318628304090552,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,most_frequent,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,3.5602014542183973,948.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +weighting,one_hot_encoding,0.00034835629696198427,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8245132980938538,0.08947420373097192 +weighting,one_hot_encoding,0.00016967940959070708,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9439080311935252,None,0.0,2.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,170.0,None,,0.014191958374153584,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,,False,adaboost,SAMME.R,0.8220362681234727,5.0,183.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.363262678765884,fdr,chi2,robust_scaler,,,0.8826612080363588,0.2189605310171097 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,normalize,,,, +none,one_hot_encoding,,False,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6396026761675004,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06544340428506021,fwe,f_classif,none,,,, +weighting,one_hot_encoding,0.004980497345831963,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1255.9137433589426,,,0.08351549479967445,rbf,-1.0,True,0.00017919875199222518,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1.0,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.3837398524575939,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.018356703878357986,deviance,3.0,0.9690352514774068,None,0.0,12.0,3.0,0.0,234.0,0.3870344708308441,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,most_frequent,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6864970915330799,False,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5670424455696162,None,0.0,8.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,standardize,,,, +weighting,one_hot_encoding,0.00031737236118003484,True,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,244.0,None,,2.3065111488706024e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,85,mean,fast_ica,,,,,,,,,,,deflation,exp,1862.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7851234479882973,0.2237528085136715 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,86,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,156.0,auto,,0.0001987333852871589,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1.0,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,no_encoding,,,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.96834823420249e-05,False,True,hinge,0.00016639250831671168,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,89,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.628430584359224,mutual_info,,,,quantile_transformer,6634.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,90,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,91,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,93,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2636201374253461,deviance,7.0,0.8344964130784466,None,0.0,9.0,2.0,0.0,298.0,0.7517549950523315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,95,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,96,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,97,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.07463196642416367,deviance,7.0,0.8603242247379981,None,0.0,2.0,6.0,0.0,500.0,0.8447665577491962,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,98,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.0433556140045585,deviance,10.0,0.33000096635982235,None,0.0,15.0,13.0,0.0,388.0,0.8291104221904706,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,99,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,robust_scaler,,,0.7496393440951183,0.2853682991120835 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,100,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,102,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0020580843703898177,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.945774573434192,None,0.0,19.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,103,median,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,15209.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,104,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,105,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,106,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.005332972692819521,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5565918060287016,None,0.0,5.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,107,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,173.0,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9527068489270144,0.04135311355893583 +weighting,one_hot_encoding,0.001279467383882126,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,108,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0903354518102121,fwe,f_classif,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,109,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.002615346832354839,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7884268823432835,None,0.0,20.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,110,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +weighting,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56.086963007482865,,,0.013609964993119377,rbf,-1.0,True,0.00196831255706268,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,111,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.15374716583918388 +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.2472984547885781,deviance,3.0,0.6564306719064884,None,0.0,15.0,14.0,0.0,220.0,0.8082564085714649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,112,median,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,332.0,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,113,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,115,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,116,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,117,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,118,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.20202014999292287,False,True,1.0,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,no_encoding,,,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6390376923528961,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,119,median,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,164.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,62508.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,120,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.27041927277584e-06,True,0.00010000000000000009,0.033157325660763994,True,0.0008114527992546482,invscaling,modified_huber,elasticnet,0.13714427818877545,0.055179642772545036,,,,,,,,,,,,,,,,,,121,median,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,122,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,123,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.19998727075532635,deviance,10.0,0.9377656718112952,None,0.0,7.0,13.0,0.0,214.0,0.6062346326014357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,124,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,125,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,126,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.5765793990908161,None,0.0,11.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,127,median,fast_ica,,,,,,,,,,,deflation,exp,10.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,129,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.04651467280022463,True,adaboost,SAMME,0.6506122230393502,6.0,143.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,extra_trees_preproc_for_classification,False,entropy,None,0.348720215832657,None,0.0,17.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,26025.0,normal,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3386310102795006,None,0.0,6.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,True,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.0,None,,5.861221938384061e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.22985730375167915,fpr,f_classif,quantile_transformer,40589.0,uniform,, +none,no_encoding,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00012437731009611307,True,,0.08709904468181932,True,,invscaling,squared_hinge,l1,0.6231564675290102,0.008071279833697563,,,,,,,,,,,,,,,,,,134,median,fast_ica,,,,,,,,,,,parallel,logcosh,814.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.714869937384006,None,0.0,8.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, diff --git a/metalearning/metalearning_files/precision_micro_binary.classification_dense/description.txt b/metalearning/metalearning_files/precision_micro_binary.classification_dense/description.txt new file mode 100755 index 0000000..377b59c --- /dev/null +++ b/metalearning/metalearning_files/precision_micro_binary.classification_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: precision_micro +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/precision_micro_binary.classification_dense/feature_costs.arff b/metalearning/metalearning_files/precision_micro_binary.classification_dense/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/precision_micro_binary.classification_dense/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/precision_micro_binary.classification_dense/feature_runstatus.arff b/metalearning/metalearning_files/precision_micro_binary.classification_dense/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/precision_micro_binary.classification_dense/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/precision_micro_binary.classification_dense/feature_values.arff b/metalearning/metalearning_files/precision_micro_binary.classification_dense/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/precision_micro_binary.classification_dense/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/precision_micro_binary.classification_dense/readme.txt b/metalearning/metalearning_files/precision_micro_binary.classification_dense/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/precision_micro_binary.classification_sparse/algorithm_runs.arff b/metalearning/metalearning_files/precision_micro_binary.classification_sparse/algorithm_runs.arff new file mode 100755 index 0000000..01fa17b --- /dev/null +++ b/metalearning/metalearning_files/precision_micro_binary.classification_sparse/algorithm_runs.arff @@ -0,0 +1,152 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE precision_micro NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2120,1.0,1,0.0895803866100896,ok +75193,1.0,2,0.038371068099909755,ok +2117,1.0,3,0.16709064962461995,ok +75156,1.0,4,0.21988682295877127,ok +75129,1.0,5,0.10097087378640779,ok +75243,1.0,6,0.015434985968194592,ok +75110,1.0,7,0.2743573125945129,ok +75239,1.0,8,0.0,ok +75223,1.0,9,0.30989414560380213,ok +75221,1.0,10,0.39791873141724476,ok +258,1.0,11,0.01833872707659112,ok +75121,1.0,12,0.003929273084479323,ok +253,1.0,13,0.44855967078189296,ok +261,1.0,14,0.23333333333333328,ok +75240,1.0,15,0.022020725388601003,ok +75120,1.0,16,0.03929273084479368,ok +75124,1.0,17,0.09121395036887991,ok +75176,1.0,18,0.016590808985464722,ok +75103,1.0,19,0.008000000000000007,ok +75207,1.0,20,0.161849710982659,ok +75095,1.0,21,0.016917293233082664,ok +273,1.0,22,0.044795783926218746,ok +75174,1.0,23,0.11705240755520085,ok +75153,1.0,24,0.12134665186829452,ok +75093,1.0,25,0.17483296213808464,ok +75119,1.0,26,0.035363457760314354,ok +75201,1.0,27,0.0808678500986193,ok +75215,1.0,28,0.028234649122807043,ok +75172,1.0,29,0.09090909090909094,ok +75169,1.0,30,0.03420132141469101,ok +75202,1.0,31,0.20329670329670335,ok +75233,1.0,32,0.06511283758786535,ok +75231,1.0,33,0.19924098671726753,ok +75196,1.0,34,0.023498694516971286,ok +248,1.0,35,0.26515151515151514,ok +75191,1.0,36,0.12370055975887306,ok +75217,1.0,37,0.0,ok +260,1.0,38,0.02657807308970095,ok +75115,1.0,39,0.017681728880157177,ok +75123,1.0,40,0.32728592162554426,ok +75108,1.0,41,0.0018373909049149706,ok +75101,1.0,42,0.28272963283471386,ok +75192,1.0,43,0.48305752561071713,ok +75232,1.0,44,0.14655172413793105,ok +75173,1.0,45,0.11751592356687901,ok +75197,1.0,46,0.13300492610837433,ok +266,1.0,47,0.03280839895013121,ok +75148,1.0,48,0.19024390243902434,ok +75150,1.0,49,0.3204747774480712,ok +75100,1.0,50,0.00379609544468551,ok +75178,1.0,51,0.8079287243594171,ok +75236,1.0,52,0.0323809523809524,ok +75179,1.0,53,0.17943026267110618,ok +75213,1.0,54,0.05249343832021003,ok +2123,1.0,55,0.05882352941176472,ok +75227,1.0,56,0.10151430173864273,ok +75184,1.0,57,0.13967500456454263,ok +75142,1.0,58,0.0813201516390396,ok +236,1.0,59,0.042424242424242475,ok +2122,1.0,60,0.2743573125945129,ok +75188,1.0,61,0.24319066147859925,ok +75166,1.0,62,0.0995190529041805,ok +75181,1.0,63,0.0,ok +75133,1.0,64,0.005123278898495065,ok +75134,1.0,65,0.10170395014980793,ok +75198,1.0,66,0.12143310082435,ok +262,1.0,67,0.0027570995312931057,ok +75234,1.0,68,0.05978705978705978,ok +75139,1.0,69,0.012121212121212088,ok +252,1.0,70,0.1636363636363637,ok +75117,1.0,71,0.06679764243614927,ok +75113,1.0,72,0.006526315789473713,ok +75098,1.0,73,0.027575757575757587,ok +246,1.0,74,0.024242424242424288,ok +75203,1.0,75,0.09555345316934716,ok +75237,1.0,76,0.00039570658356824495,ok +75195,1.0,77,0.0015609901137292326,ok +75171,1.0,78,0.1672216056233814,ok +75128,1.0,79,0.02218114602587795,ok +75096,1.0,80,0.3974640209074891,ok +75250,1.0,81,0.34347287891393896,ok +75146,1.0,82,0.12210711924178974,ok +75116,1.0,83,0.00982318271119842,ok +75157,1.0,84,0.4192200557103064,ok +75187,1.0,85,0.027846027846027854,ok +2350,1.0,86,0.3744580607974338,ok +242,1.0,87,0.01666666666666672,ok +244,1.0,88,0.1106060606060606,ok +75125,1.0,89,0.035363457760314354,ok +75185,1.0,90,0.12816966343937297,ok +75163,1.0,91,0.060374149659863985,ok +75177,1.0,92,0.019292604501607746,ok +75189,1.0,93,0.019401337253296624,ok +75244,1.0,94,0.06339958875942431,ok +75219,1.0,95,0.08375480477442854,ok +75222,1.0,96,0.04524886877828049,ok +75159,1.0,97,0.06849315068493156,ok +75175,1.0,98,0.11701659080898541,ok +75109,1.0,99,0.34315531000613875,ok +254,1.0,100,0.0,ok +75105,1.0,101,0.018121212121212094,ok +75106,1.0,102,0.07212121212121214,ok +75212,1.0,103,0.27738927738927743,ok +75099,1.0,104,0.12374100719424463,ok +75248,1.0,105,0.10040485829959511,ok +233,1.0,106,0.015180265654648917,ok +75235,1.0,107,0.0016666666666667052,ok +75226,1.0,108,0.004259202920596339,ok +75132,1.0,109,0.051244509516837455,ok +75127,1.0,110,0.3345075170228544,ok +251,1.0,111,0.02631578947368418,ok +75161,1.0,112,0.08280680889021041,ok +75143,1.0,113,0.010471204188481686,ok +75114,1.0,114,0.023575638506876273,ok +75182,1.0,115,0.1081404628890662,ok +75112,1.0,116,0.12061822817080947,ok +75210,1.0,117,0.0,ok +75205,1.0,118,0.18110236220472442,ok +75090,1.0,119,0.10139860139860135,ok +275,1.0,120,0.03612167300380231,ok +288,1.0,121,0.14363636363636367,ok +75092,1.0,122,0.0935550935550935,ok +3043,1.0,123,0.020096463022508004,ok +75249,1.0,124,0.004823151125401881,ok +75126,1.0,125,0.0491159135559921,ok +75225,1.0,126,0.04904306220095689,ok +75141,1.0,127,0.05808361080281166,ok +75107,1.0,128,0.053212121212121266,ok +75097,1.0,129,0.05835568297419769,ok +80001,1.0,1,0.06944444444444442,ok +80003,1.0,1,0.12307692307692308,ok +80006,1.0,1,0.1875,ok +80008,1.0,1,0.2222222222222222,ok +80009,1.0,1,0.1578947368421053,ok +80010,1.0,1,0.13793103448275867,ok +80011,1.0,1,0.037735849056603765,ok +80012,1.0,1,0.045454545454545414,ok +80013,1.0,1,0.0,ok +80014,1.0,1,0.045454545454545414,ok +80015,1.0,1,0.09523809523809523,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/precision_micro_binary.classification_sparse/configurations.csv b/metalearning/metalearning_files/precision_micro_binary.classification_sparse/configurations.csv new file mode 100755 index 0000000..2e88382 --- /dev/null +++ b/metalearning/metalearning_files/precision_micro_binary.classification_sparse/configurations.csv @@ -0,0 +1,141 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,preprocessor:truncatedSVD:target_dim,rescaling:__choice__ +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7249853037185638,None,0.0,1.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,median,extra_trees_preproc_for_classification,False,gini,None,0.9424908623661876,None,0.0,7.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.6682079659377479,None,0.0,4.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,mean,extra_trees_preproc_for_classification,True,entropy,None,0.5552350997943013,None,0.0,8.0,5.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7974565919616314,None,0.0,12.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,median,extra_trees_preproc_for_classification,True,entropy,None,0.9772091846790169,None,0.0,10.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2538107344750156,False,True,hinge,1.5099542326343014e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,8532.0,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.034465366914659866,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.12057775675278172,deviance,10.0,0.8011153303489733,None,0.0,2.0,16.0,0.0,370.0,0.6078295352200873,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,mean,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0007038280350320558,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4138778052607317,0.7995003430482459,5.0,5.430044692638861,poly,-1.0,True,0.02455501006004393,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,31,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.0010015637584068037,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.037611630308856295,deviance,5.0,0.8840126779516314,None,0.0,10.0,2.0,0.0,444.0,0.7599997167603434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,most_frequent,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1751.4736515133566,0.62404114475118,3.0,1.6087076997410432,poly,-1.0,False,3.5353792826856045e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,mean,kernel_pca,,,,,,,,,,,,,,cosine,1198.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.8657388713119849,,1.0,None,0.0,19.0,13.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828776,None,0.0,2.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.027324741616523342,deviance,10.0,0.8623781459430139,None,0.0,10.0,20.0,0.0,329.0,0.8595750155424215,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,mean,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1100.6211008501205,0.5921425829232616,2.0,0.0337546254878617,poly,-1.0,True,0.09641299736884308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.9348409326933208,rbf,-1.0,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,mean,kernel_pca,,,,,,,,,,,,,,cosine,1754.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3428971645958825,,,0.2229870623330047,rbf,-1.0,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00016781524591321165,True,True,squared_hinge,1.5119200923218881e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.556190405302504,False,True,1.0,squared_hinge,ovr,l2,0.0007318628304090552,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,most_frequent,kitchen_sinks,,,,,,,,,,,,,,,,3.5602014542183973,948.0,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4047.618729304337,,,2.0237366768707754,rbf,-1.0,True,0.04369127828878843,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,one_hot_encoding,0.010000000000000005,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10.0,1.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,,none +weighting,one_hot_encoding,0.0008685602750053468,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9896334290292654,None,0.0,11.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,141.76310800864283,False,True,1.0,squared_hinge,ovr,l1,0.004317884655117431,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,,normalize +none,one_hot_encoding,0.003443779683191071,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.004980497345831963,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1255.9137433589426,,,0.08351549479967445,rbf,-1.0,True,0.00017919875199222518,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1.0,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.3451627750042988,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.3163640203509378,None,0.0,17.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,median,extra_trees_preproc_for_classification,False,gini,None,0.8916956785028156,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,85,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,86,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0019686649916896212,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.04641055832142541,True,True,hinge,8.540468968077405e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87,mean,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,911.0,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1.0,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,89,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,90,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,91,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,93,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,95,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,96,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,97,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,98,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,99,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,100,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,102,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,103,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,104,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,105,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,106,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.006372860318416312,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6467376360604045,None,0.0,1.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,107,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,108,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,109,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3823734947460288,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,110,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,adaboost,SAMME,0.11042308042695524,5.0,117.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,111,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,112,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,113,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,115,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,116,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,117,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,118,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.20202014999292287,False,True,1.0,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,119,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,120,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,121,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,122,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,123,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,124,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,125,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,126,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,127,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,129,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.04329010470998136,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7260331062271381,None,0.0,7.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,134,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.714869937384006,None,0.0,8.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/metalearning/metalearning_files/precision_micro_binary.classification_sparse/description.txt b/metalearning/metalearning_files/precision_micro_binary.classification_sparse/description.txt new file mode 100755 index 0000000..377b59c --- /dev/null +++ b/metalearning/metalearning_files/precision_micro_binary.classification_sparse/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: precision_micro +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/precision_micro_binary.classification_sparse/feature_costs.arff b/metalearning/metalearning_files/precision_micro_binary.classification_sparse/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/precision_micro_binary.classification_sparse/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/precision_micro_binary.classification_sparse/feature_runstatus.arff b/metalearning/metalearning_files/precision_micro_binary.classification_sparse/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/precision_micro_binary.classification_sparse/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/precision_micro_binary.classification_sparse/feature_values.arff b/metalearning/metalearning_files/precision_micro_binary.classification_sparse/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/precision_micro_binary.classification_sparse/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/precision_micro_binary.classification_sparse/readme.txt b/metalearning/metalearning_files/precision_micro_binary.classification_sparse/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/precision_micro_multiclass.classification_dense/algorithm_runs.arff b/metalearning/metalearning_files/precision_micro_multiclass.classification_dense/algorithm_runs.arff new file mode 100755 index 0000000..0d9bac6 --- /dev/null +++ b/metalearning/metalearning_files/precision_micro_multiclass.classification_dense/algorithm_runs.arff @@ -0,0 +1,152 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE precision_micro NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2120,1.0,1,0.07967939651107969,ok +75193,1.0,2,0.038371068099909755,ok +2117,1.0,3,0.16709064962461995,ok +75156,1.0,4,0.20291026677445434,ok +75129,1.0,5,0.10097087378640779,ok +75243,1.0,6,0.0,ok +75110,1.0,7,0.11622380643767549,ok +75239,1.0,8,0.0,ok +75223,1.0,9,0.10304601425793913,ok +75221,1.0,10,0.39791873141724476,ok +258,1.0,11,0.009708737864077666,ok +75121,1.0,12,0.0,ok +253,1.0,13,0.44855967078189296,ok +261,1.0,14,0.23333333333333328,ok +75240,1.0,15,0.022020725388601003,ok +75120,1.0,16,0.03929273084479368,ok +75124,1.0,17,0.09121395036887991,ok +75176,1.0,18,0.01541623843782114,ok +75103,1.0,19,0.005894736842105286,ok +75207,1.0,20,0.161849710982659,ok +75095,1.0,21,0.016917293233082664,ok +273,1.0,22,0.04413702239789197,ok +75174,1.0,23,0.11705240755520085,ok +75153,1.0,24,0.08028116907140215,ok +75093,1.0,25,0.17483296213808464,ok +75119,1.0,26,0.035363457760314354,ok +75201,1.0,27,0.0808678500986193,ok +75215,1.0,28,0.027412280701754388,ok +75172,1.0,29,0.10303030303030303,ok +75169,1.0,30,0.03420132141469101,ok +75202,1.0,31,0.20329670329670335,ok +75233,1.0,32,0.060673325934147204,ok +75231,1.0,33,0.19924098671726753,ok +75196,1.0,34,0.015665796344647487,ok +248,1.0,35,0.22878787878787876,ok +75191,1.0,36,0.1289905886694962,ok +75217,1.0,37,0.0,ok +260,1.0,38,0.02657807308970095,ok +75115,1.0,39,0.017681728880157177,ok +75123,1.0,40,0.32728592162554426,ok +75108,1.0,41,0.0,ok +75101,1.0,42,0.2740140932130053,ok +75192,1.0,43,0.48305752561071713,ok +75232,1.0,44,0.12356321839080464,ok +75173,1.0,45,0.1165605095541401,ok +75197,1.0,46,0.15517241379310343,ok +266,1.0,47,0.019685039370078705,ok +75148,1.0,48,0.13560975609756099,ok +75150,1.0,49,0.2848664688427299,ok +75100,1.0,50,0.00379609544468551,ok +75178,1.0,51,0.7840550682597786,ok +75236,1.0,52,0.0323809523809524,ok +75179,1.0,53,0.17943026267110618,ok +75213,1.0,54,0.05249343832021003,ok +2123,1.0,55,0.05882352941176472,ok +75227,1.0,56,0.10151430173864273,ok +75184,1.0,57,0.10772320613474529,ok +75142,1.0,58,0.0715825466438712,ok +236,1.0,59,0.038787878787878816,ok +2122,1.0,60,0.10952689565780949,ok +75188,1.0,61,0.24319066147859925,ok +75166,1.0,62,0.0995190529041805,ok +75181,1.0,63,0.0,ok +75133,1.0,64,0.005123278898495065,ok +75134,1.0,65,0.08723783614874181,ok +75198,1.0,66,0.12143310082435,ok +262,1.0,67,0.0027570995312931057,ok +75234,1.0,68,0.024160524160524166,ok +75139,1.0,69,0.010707070707070665,ok +252,1.0,70,0.15000000000000002,ok +75117,1.0,71,0.05500982318271119,ok +75113,1.0,72,0.006526315789473713,ok +75098,1.0,73,0.027575757575757587,ok +246,1.0,74,0.010606060606060619,ok +75203,1.0,75,0.09555345316934716,ok +75237,1.0,76,0.00039570658356824495,ok +75195,1.0,77,0.0015609901137292326,ok +75171,1.0,78,0.1620421753607103,ok +75128,1.0,79,0.02218114602587795,ok +75096,1.0,80,0.005164788382626018,ok +75250,1.0,81,0.34347287891393896,ok +75146,1.0,82,0.11329072074057744,ok +75116,1.0,83,0.00982318271119842,ok +75157,1.0,84,0.4192200557103064,ok +75187,1.0,85,0.01678951678951679,ok +2350,1.0,86,0.3744580607974338,ok +242,1.0,87,0.010606060606060619,ok +244,1.0,88,0.1106060606060606,ok +75125,1.0,89,0.03339882121807469,ok +75185,1.0,90,0.12816966343937297,ok +75163,1.0,91,0.060374149659863985,ok +75177,1.0,92,0.019292604501607746,ok +75189,1.0,93,0.019401337253296624,ok +75244,1.0,94,0.06339958875942431,ok +75219,1.0,95,0.03479668217681575,ok +75222,1.0,96,0.04524886877828049,ok +75159,1.0,97,0.06849315068493156,ok +75175,1.0,98,0.09954485391278811,ok +75109,1.0,99,0.30417434008594224,ok +254,1.0,100,0.0,ok +75105,1.0,101,0.018121212121212094,ok +75106,1.0,102,0.07212121212121214,ok +75212,1.0,103,0.2517482517482518,ok +75099,1.0,104,0.12374100719424463,ok +75248,1.0,105,0.10040485829959511,ok +233,1.0,106,0.002846299810246644,ok +75235,1.0,107,0.0011111111111110628,ok +75226,1.0,108,0.0030422878004259246,ok +75132,1.0,109,0.051244509516837455,ok +75127,1.0,110,0.3330355738331199,ok +251,1.0,111,0.0,ok +75161,1.0,112,0.06437225897569321,ok +75143,1.0,113,0.010471204188481686,ok +75114,1.0,114,0.023575638506876273,ok +75182,1.0,115,0.1081404628890662,ok +75112,1.0,116,0.12061822817080947,ok +75210,1.0,117,0.0,ok +75205,1.0,118,0.18110236220472442,ok +75090,1.0,119,0.06118881118881114,ok +275,1.0,120,0.03612167300380231,ok +288,1.0,121,0.12969696969696964,ok +75092,1.0,122,0.0935550935550935,ok +3043,1.0,123,0.020096463022508004,ok +75249,1.0,124,0.003215434083601254,ok +75126,1.0,125,0.0491159135559921,ok +75225,1.0,126,0.04904306220095689,ok +75141,1.0,127,0.0540140584535701,ok +75107,1.0,128,0.053212121212121266,ok +75097,1.0,129,0.05835568297419769,ok +80001,1.0,1,0.06944444444444442,ok +80003,1.0,1,0.09230769230769231,ok +80006,1.0,1,0.09375,ok +80008,1.0,1,0.11111111111111116,ok +80009,1.0,1,0.10526315789473684,ok +80010,1.0,1,0.13793103448275867,ok +80011,1.0,1,0.037735849056603765,ok +80012,1.0,1,0.045454545454545414,ok +80013,1.0,1,0.0,ok +80014,1.0,1,0.045454545454545414,ok +80015,1.0,1,0.09523809523809523,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/precision_micro_multiclass.classification_dense/configurations.csv b/metalearning/metalearning_files/precision_micro_multiclass.classification_dense/configurations.csv new file mode 100755 index 0000000..e710c68 --- /dev/null +++ b/metalearning/metalearning_files/precision_micro_multiclass.classification_dense/configurations.csv @@ -0,0 +1,141 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:fast_ica:algorithm,preprocessor:fast_ica:fun,preprocessor:fast_ica:n_components,preprocessor:fast_ica:whiten,preprocessor:feature_agglomeration:affinity,preprocessor:feature_agglomeration:linkage,preprocessor:feature_agglomeration:n_clusters,preprocessor:feature_agglomeration:pooling_func,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:pca:keep_variance,preprocessor:pca:whiten,preprocessor:polynomial:degree,preprocessor:polynomial:include_bias,preprocessor:polynomial:interaction_only,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,rescaling:__choice__,rescaling:quantile_transformer:n_quantiles,rescaling:quantile_transformer:output_distribution,rescaling:robust_scaler:q_max,rescaling:robust_scaler:q_min +weighting,one_hot_encoding,0.00011717632475982632,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.045388141846341344,deviance,10.0,0.29161769341843435,None,0.0,20.0,2.0,0.0,278.0,0.7912571599269661,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7249853037185638,None,0.0,1.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,median,extra_trees_preproc_for_classification,False,gini,None,0.9424908623661876,None,0.0,7.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.03474109838999682,deviance,4.0,0.5687034678818491,None,0.0,18.0,12.0,0.0,408.0,0.5150113945430513,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.6328939517938,f_classif,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.251097788175676,deviance,6.0,0.35679099363539235,None,0.0,13.0,11.0,0.0,157.0,0.4791732272983235,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,adaboost,SAMME,0.28738775989203896,10.0,423.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.8031499675923353,0.13579938270386765 +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.14159526341015916,deviance,7.0,0.8010488230155749,None,0.0,3.0,20.0,0.0,401.0,0.8073562440607731,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.002385546176068135,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.7729472304882845,,,0.0004789329856033374,rbf,-1.0,True,6.58869648864534e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,177.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5368752992317617,None,0.0,16.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.8382117756438685,mutual_info,,,,quantile_transformer,11480.0,normal,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9455638720565652,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,most_frequent,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8255464552647293,0.19162485555463185 +weighting,one_hot_encoding,0.18137532678800647,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9094110110427254,None,0.0,7.0,12.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,195.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.6682079659377479,None,0.0,4.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,mean,extra_trees_preproc_for_classification,True,entropy,None,0.5552350997943013,None,0.0,8.0,5.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.02345017287074443,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.053517066400173056,deviance,10.0,0.542144980834302,None,0.0,20.0,13.0,0.0,233.0,0.7398539900055563,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0614425536709615,fwe,f_classif,robust_scaler,,,0.9523118062307264,0.13434811490315818 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.040936424602789435,deviance,7.0,0.5495014745530306,None,0.0,20.0,18.0,0.0,141.0,0.6905343807995293,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7974565919616314,None,0.0,12.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,median,extra_trees_preproc_for_classification,True,entropy,None,0.9772091846790169,None,0.0,10.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2538107344750156,False,True,hinge,1.5099542326343014e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,8532.0,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.1958974686405233,deviance,5.0,0.33885235607979314,None,0.0,6.0,4.0,0.0,125.0,0.9448890820738562,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2538107344750156,False,True,hinge,1.5099542326343014e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,8532.0,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,0.0007038280350320558,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4138778052607317,0.7995003430482459,5.0,5.430044692638861,poly,-1.0,True,0.02455501006004393,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,31,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.010000000000000005,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.0518326156691958,deviance,6.0,0.8807456180216267,None,0.0,7.0,19.0,0.0,366.0,0.7314831276137047,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,,False,adaboost,SAMME,0.4391375941344922,3.0,386.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7439738358430176,0.20581080574615795 +none,one_hot_encoding,0.00011294596229850895,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7515.1213255144885,0.9576762936062476,3.0,0.019002536385919932,poly,-1.0,False,0.010632086351533369,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,51.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,97282.0,normal,, +none,one_hot_encoding,0.00012586572428922356,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5240592829918601,None,0.0,10.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1751.4736515133566,0.62404114475118,3.0,1.6087076997410432,poly,-1.0,False,3.5353792826856045e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1198.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.2422926485206341,,1.0,None,0.0,15.0,9.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.02102683283349326,deviance,10.0,0.2797288369369436,None,0.0,14.0,9.0,0.0,480.0,0.5778972273820631,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58.88123233170863,mutual_info,,,,robust_scaler,,,0.75,0.25 +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828776,None,0.0,2.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2155613360930585,deviance,4.0,0.3198803116198433,None,0.0,8.0,13.0,0.0,275.0,0.28870176110739404,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,mean,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.7062102387181676,None,0.0,1.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,most_frequent,fast_ica,,,,,,,,,,,parallel,exp,100.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7065776353150109,0.23782974987118105 +none,one_hot_encoding,0.16334152321884812,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00011572473434870852,True,True,squared_hinge,0.00019678754114665057,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.045742431094098604,fpr,chi2,none,,,, +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.3795924768593385,deviance,2.0,0.33708497069988536,None,0.0,15.0,13.0,0.0,451.0,0.7716323242090217,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2573946506994812,fwe,f_classif,quantile_transformer,1000.0,uniform,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.609975998293528,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,most_frequent,fast_ica,,,,,,,,,,,parallel,logcosh,2000.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8430415644014919,0.2863750565331575 +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.39536192447534535,None,0.0,19.0,3.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5.0,None,11.0,11.0,1.0,12.0,,,,,,robust_scaler,,,0.8928631650245873,0.1581877760687084 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.3126027672745337,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,870.2240970463429,0.5325949351918051,3.0,0.010682839357128344,poly,-1.0,False,2.485160860440657e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4608103694360143,fdr,f_classif,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.9348409326933208,rbf,-1.0,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1754.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,198.72528686512538,False,True,1.0,squared_hinge,ovr,l2,0.026260652523566803,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,robust_scaler,,,0.913511520078368,0.2742229325455444 +none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9260795160807372,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.03644212536682547,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.05965671477918361,deviance,8.0,0.4858133247974158,None,0.0,14.0,7.0,0.0,480.0,0.5726186552917335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.15318294164619112 +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18887.81504976871,,,0.23283562663398755,rbf,-1.0,True,2.3839685780861318e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3937607155568376,fwe,chi2,robust_scaler,,,0.941018778984854,0.2144110585080491 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3428971645958825,,,0.2229870623330047,rbf,-1.0,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8138233157708883,None,0.0,2.0,9.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54.27504292568562,f_classif,,,,minmax,,,, +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00016781524591321165,True,True,squared_hinge,1.5119200923218881e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.556190405302504,False,True,1.0,squared_hinge,ovr,l2,0.0007318628304090552,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,most_frequent,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,3.5602014542183973,948.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +weighting,one_hot_encoding,0.00034835629696198427,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8245132980938538,0.08947420373097192 +weighting,one_hot_encoding,0.00016967940959070708,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9439080311935252,None,0.0,2.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,170.0,None,,0.014191958374153584,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,,False,adaboost,SAMME.R,0.8220362681234727,5.0,183.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.363262678765884,fdr,chi2,robust_scaler,,,0.8826612080363588,0.2189605310171097 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,normalize,,,, +none,one_hot_encoding,,False,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6396026761675004,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06544340428506021,fwe,f_classif,none,,,, +weighting,one_hot_encoding,0.004980497345831963,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1255.9137433589426,,,0.08351549479967445,rbf,-1.0,True,0.00017919875199222518,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1.0,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.3837398524575939,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.018356703878357986,deviance,3.0,0.9690352514774068,None,0.0,12.0,3.0,0.0,234.0,0.3870344708308441,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,most_frequent,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6864970915330799,False,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5670424455696162,None,0.0,8.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,standardize,,,, +weighting,one_hot_encoding,0.00031737236118003484,True,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,244.0,None,,2.3065111488706024e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,85,mean,fast_ica,,,,,,,,,,,deflation,exp,1862.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7851234479882973,0.2237528085136715 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,86,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,156.0,auto,,0.0001987333852871589,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1.0,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,no_encoding,,,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.96834823420249e-05,False,True,hinge,0.00016639250831671168,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,89,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.628430584359224,mutual_info,,,,quantile_transformer,6634.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,90,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,91,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,93,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2636201374253461,deviance,7.0,0.8344964130784466,None,0.0,9.0,2.0,0.0,298.0,0.7517549950523315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,95,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,96,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,97,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.07463196642416367,deviance,7.0,0.8603242247379981,None,0.0,2.0,6.0,0.0,500.0,0.8447665577491962,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,98,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.0433556140045585,deviance,10.0,0.33000096635982235,None,0.0,15.0,13.0,0.0,388.0,0.8291104221904706,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,99,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,robust_scaler,,,0.7496393440951183,0.2853682991120835 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,100,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,102,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0020580843703898177,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.945774573434192,None,0.0,19.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,103,median,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,15209.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,104,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,105,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,106,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.005332972692819521,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5565918060287016,None,0.0,5.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,107,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,173.0,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9527068489270144,0.04135311355893583 +weighting,one_hot_encoding,0.001279467383882126,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,108,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0903354518102121,fwe,f_classif,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,109,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.002615346832354839,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7884268823432835,None,0.0,20.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,110,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +weighting,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56.086963007482865,,,0.013609964993119377,rbf,-1.0,True,0.00196831255706268,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,111,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.15374716583918388 +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.2472984547885781,deviance,3.0,0.6564306719064884,None,0.0,15.0,14.0,0.0,220.0,0.8082564085714649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,112,median,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,332.0,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,113,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,115,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,116,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,117,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,118,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.20202014999292287,False,True,1.0,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,no_encoding,,,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6390376923528961,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,119,median,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,164.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,62508.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,120,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.27041927277584e-06,True,0.00010000000000000009,0.033157325660763994,True,0.0008114527992546482,invscaling,modified_huber,elasticnet,0.13714427818877545,0.055179642772545036,,,,,,,,,,,,,,,,,,121,median,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,122,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,123,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.19998727075532635,deviance,10.0,0.9377656718112952,None,0.0,7.0,13.0,0.0,214.0,0.6062346326014357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,124,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,125,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,126,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.5765793990908161,None,0.0,11.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,127,median,fast_ica,,,,,,,,,,,deflation,exp,10.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,129,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.04651467280022463,True,adaboost,SAMME,0.6506122230393502,6.0,143.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,extra_trees_preproc_for_classification,False,entropy,None,0.348720215832657,None,0.0,17.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,26025.0,normal,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3386310102795006,None,0.0,6.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,True,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.0,None,,5.861221938384061e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.22985730375167915,fpr,f_classif,quantile_transformer,40589.0,uniform,, +none,no_encoding,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00012437731009611307,True,,0.08709904468181932,True,,invscaling,squared_hinge,l1,0.6231564675290102,0.008071279833697563,,,,,,,,,,,,,,,,,,134,median,fast_ica,,,,,,,,,,,parallel,logcosh,814.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.714869937384006,None,0.0,8.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, diff --git a/metalearning/metalearning_files/precision_micro_multiclass.classification_dense/description.txt b/metalearning/metalearning_files/precision_micro_multiclass.classification_dense/description.txt new file mode 100755 index 0000000..377b59c --- /dev/null +++ b/metalearning/metalearning_files/precision_micro_multiclass.classification_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: precision_micro +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/precision_micro_multiclass.classification_dense/feature_costs.arff b/metalearning/metalearning_files/precision_micro_multiclass.classification_dense/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/precision_micro_multiclass.classification_dense/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/precision_micro_multiclass.classification_dense/feature_runstatus.arff b/metalearning/metalearning_files/precision_micro_multiclass.classification_dense/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/precision_micro_multiclass.classification_dense/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/precision_micro_multiclass.classification_dense/feature_values.arff b/metalearning/metalearning_files/precision_micro_multiclass.classification_dense/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/precision_micro_multiclass.classification_dense/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/precision_micro_multiclass.classification_dense/readme.txt b/metalearning/metalearning_files/precision_micro_multiclass.classification_dense/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/precision_micro_multiclass.classification_sparse/algorithm_runs.arff b/metalearning/metalearning_files/precision_micro_multiclass.classification_sparse/algorithm_runs.arff new file mode 100755 index 0000000..01fa17b --- /dev/null +++ b/metalearning/metalearning_files/precision_micro_multiclass.classification_sparse/algorithm_runs.arff @@ -0,0 +1,152 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE precision_micro NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2120,1.0,1,0.0895803866100896,ok +75193,1.0,2,0.038371068099909755,ok +2117,1.0,3,0.16709064962461995,ok +75156,1.0,4,0.21988682295877127,ok +75129,1.0,5,0.10097087378640779,ok +75243,1.0,6,0.015434985968194592,ok +75110,1.0,7,0.2743573125945129,ok +75239,1.0,8,0.0,ok +75223,1.0,9,0.30989414560380213,ok +75221,1.0,10,0.39791873141724476,ok +258,1.0,11,0.01833872707659112,ok +75121,1.0,12,0.003929273084479323,ok +253,1.0,13,0.44855967078189296,ok +261,1.0,14,0.23333333333333328,ok +75240,1.0,15,0.022020725388601003,ok +75120,1.0,16,0.03929273084479368,ok +75124,1.0,17,0.09121395036887991,ok +75176,1.0,18,0.016590808985464722,ok +75103,1.0,19,0.008000000000000007,ok +75207,1.0,20,0.161849710982659,ok +75095,1.0,21,0.016917293233082664,ok +273,1.0,22,0.044795783926218746,ok +75174,1.0,23,0.11705240755520085,ok +75153,1.0,24,0.12134665186829452,ok +75093,1.0,25,0.17483296213808464,ok +75119,1.0,26,0.035363457760314354,ok +75201,1.0,27,0.0808678500986193,ok +75215,1.0,28,0.028234649122807043,ok +75172,1.0,29,0.09090909090909094,ok +75169,1.0,30,0.03420132141469101,ok +75202,1.0,31,0.20329670329670335,ok +75233,1.0,32,0.06511283758786535,ok +75231,1.0,33,0.19924098671726753,ok +75196,1.0,34,0.023498694516971286,ok +248,1.0,35,0.26515151515151514,ok +75191,1.0,36,0.12370055975887306,ok +75217,1.0,37,0.0,ok +260,1.0,38,0.02657807308970095,ok +75115,1.0,39,0.017681728880157177,ok +75123,1.0,40,0.32728592162554426,ok +75108,1.0,41,0.0018373909049149706,ok +75101,1.0,42,0.28272963283471386,ok +75192,1.0,43,0.48305752561071713,ok +75232,1.0,44,0.14655172413793105,ok +75173,1.0,45,0.11751592356687901,ok +75197,1.0,46,0.13300492610837433,ok +266,1.0,47,0.03280839895013121,ok +75148,1.0,48,0.19024390243902434,ok +75150,1.0,49,0.3204747774480712,ok +75100,1.0,50,0.00379609544468551,ok +75178,1.0,51,0.8079287243594171,ok +75236,1.0,52,0.0323809523809524,ok +75179,1.0,53,0.17943026267110618,ok +75213,1.0,54,0.05249343832021003,ok +2123,1.0,55,0.05882352941176472,ok +75227,1.0,56,0.10151430173864273,ok +75184,1.0,57,0.13967500456454263,ok +75142,1.0,58,0.0813201516390396,ok +236,1.0,59,0.042424242424242475,ok +2122,1.0,60,0.2743573125945129,ok +75188,1.0,61,0.24319066147859925,ok +75166,1.0,62,0.0995190529041805,ok +75181,1.0,63,0.0,ok +75133,1.0,64,0.005123278898495065,ok +75134,1.0,65,0.10170395014980793,ok +75198,1.0,66,0.12143310082435,ok +262,1.0,67,0.0027570995312931057,ok +75234,1.0,68,0.05978705978705978,ok +75139,1.0,69,0.012121212121212088,ok +252,1.0,70,0.1636363636363637,ok +75117,1.0,71,0.06679764243614927,ok +75113,1.0,72,0.006526315789473713,ok +75098,1.0,73,0.027575757575757587,ok +246,1.0,74,0.024242424242424288,ok +75203,1.0,75,0.09555345316934716,ok +75237,1.0,76,0.00039570658356824495,ok +75195,1.0,77,0.0015609901137292326,ok +75171,1.0,78,0.1672216056233814,ok +75128,1.0,79,0.02218114602587795,ok +75096,1.0,80,0.3974640209074891,ok +75250,1.0,81,0.34347287891393896,ok +75146,1.0,82,0.12210711924178974,ok +75116,1.0,83,0.00982318271119842,ok +75157,1.0,84,0.4192200557103064,ok +75187,1.0,85,0.027846027846027854,ok +2350,1.0,86,0.3744580607974338,ok +242,1.0,87,0.01666666666666672,ok +244,1.0,88,0.1106060606060606,ok +75125,1.0,89,0.035363457760314354,ok +75185,1.0,90,0.12816966343937297,ok +75163,1.0,91,0.060374149659863985,ok +75177,1.0,92,0.019292604501607746,ok +75189,1.0,93,0.019401337253296624,ok +75244,1.0,94,0.06339958875942431,ok +75219,1.0,95,0.08375480477442854,ok +75222,1.0,96,0.04524886877828049,ok +75159,1.0,97,0.06849315068493156,ok +75175,1.0,98,0.11701659080898541,ok +75109,1.0,99,0.34315531000613875,ok +254,1.0,100,0.0,ok +75105,1.0,101,0.018121212121212094,ok +75106,1.0,102,0.07212121212121214,ok +75212,1.0,103,0.27738927738927743,ok +75099,1.0,104,0.12374100719424463,ok +75248,1.0,105,0.10040485829959511,ok +233,1.0,106,0.015180265654648917,ok +75235,1.0,107,0.0016666666666667052,ok +75226,1.0,108,0.004259202920596339,ok +75132,1.0,109,0.051244509516837455,ok +75127,1.0,110,0.3345075170228544,ok +251,1.0,111,0.02631578947368418,ok +75161,1.0,112,0.08280680889021041,ok +75143,1.0,113,0.010471204188481686,ok +75114,1.0,114,0.023575638506876273,ok +75182,1.0,115,0.1081404628890662,ok +75112,1.0,116,0.12061822817080947,ok +75210,1.0,117,0.0,ok +75205,1.0,118,0.18110236220472442,ok +75090,1.0,119,0.10139860139860135,ok +275,1.0,120,0.03612167300380231,ok +288,1.0,121,0.14363636363636367,ok +75092,1.0,122,0.0935550935550935,ok +3043,1.0,123,0.020096463022508004,ok +75249,1.0,124,0.004823151125401881,ok +75126,1.0,125,0.0491159135559921,ok +75225,1.0,126,0.04904306220095689,ok +75141,1.0,127,0.05808361080281166,ok +75107,1.0,128,0.053212121212121266,ok +75097,1.0,129,0.05835568297419769,ok +80001,1.0,1,0.06944444444444442,ok +80003,1.0,1,0.12307692307692308,ok +80006,1.0,1,0.1875,ok +80008,1.0,1,0.2222222222222222,ok +80009,1.0,1,0.1578947368421053,ok +80010,1.0,1,0.13793103448275867,ok +80011,1.0,1,0.037735849056603765,ok +80012,1.0,1,0.045454545454545414,ok +80013,1.0,1,0.0,ok +80014,1.0,1,0.045454545454545414,ok +80015,1.0,1,0.09523809523809523,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/precision_micro_multiclass.classification_sparse/configurations.csv b/metalearning/metalearning_files/precision_micro_multiclass.classification_sparse/configurations.csv new file mode 100755 index 0000000..2e88382 --- /dev/null +++ b/metalearning/metalearning_files/precision_micro_multiclass.classification_sparse/configurations.csv @@ -0,0 +1,141 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,preprocessor:truncatedSVD:target_dim,rescaling:__choice__ +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7249853037185638,None,0.0,1.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,median,extra_trees_preproc_for_classification,False,gini,None,0.9424908623661876,None,0.0,7.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.6682079659377479,None,0.0,4.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,mean,extra_trees_preproc_for_classification,True,entropy,None,0.5552350997943013,None,0.0,8.0,5.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7974565919616314,None,0.0,12.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,median,extra_trees_preproc_for_classification,True,entropy,None,0.9772091846790169,None,0.0,10.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2538107344750156,False,True,hinge,1.5099542326343014e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,8532.0,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.034465366914659866,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.12057775675278172,deviance,10.0,0.8011153303489733,None,0.0,2.0,16.0,0.0,370.0,0.6078295352200873,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,mean,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0007038280350320558,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4138778052607317,0.7995003430482459,5.0,5.430044692638861,poly,-1.0,True,0.02455501006004393,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,31,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.0010015637584068037,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.037611630308856295,deviance,5.0,0.8840126779516314,None,0.0,10.0,2.0,0.0,444.0,0.7599997167603434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,most_frequent,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1751.4736515133566,0.62404114475118,3.0,1.6087076997410432,poly,-1.0,False,3.5353792826856045e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,mean,kernel_pca,,,,,,,,,,,,,,cosine,1198.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.8657388713119849,,1.0,None,0.0,19.0,13.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828776,None,0.0,2.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.027324741616523342,deviance,10.0,0.8623781459430139,None,0.0,10.0,20.0,0.0,329.0,0.8595750155424215,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,mean,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1100.6211008501205,0.5921425829232616,2.0,0.0337546254878617,poly,-1.0,True,0.09641299736884308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.9348409326933208,rbf,-1.0,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,mean,kernel_pca,,,,,,,,,,,,,,cosine,1754.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3428971645958825,,,0.2229870623330047,rbf,-1.0,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00016781524591321165,True,True,squared_hinge,1.5119200923218881e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.556190405302504,False,True,1.0,squared_hinge,ovr,l2,0.0007318628304090552,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,most_frequent,kitchen_sinks,,,,,,,,,,,,,,,,3.5602014542183973,948.0,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4047.618729304337,,,2.0237366768707754,rbf,-1.0,True,0.04369127828878843,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,one_hot_encoding,0.010000000000000005,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10.0,1.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,,none +weighting,one_hot_encoding,0.0008685602750053468,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9896334290292654,None,0.0,11.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,141.76310800864283,False,True,1.0,squared_hinge,ovr,l1,0.004317884655117431,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,,normalize +none,one_hot_encoding,0.003443779683191071,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.004980497345831963,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1255.9137433589426,,,0.08351549479967445,rbf,-1.0,True,0.00017919875199222518,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1.0,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.3451627750042988,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.3163640203509378,None,0.0,17.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,median,extra_trees_preproc_for_classification,False,gini,None,0.8916956785028156,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,85,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,86,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0019686649916896212,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.04641055832142541,True,True,hinge,8.540468968077405e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87,mean,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,911.0,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1.0,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,89,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,90,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,91,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,93,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,95,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,96,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,97,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,98,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,99,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,100,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,102,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,103,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,104,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,105,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,106,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.006372860318416312,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6467376360604045,None,0.0,1.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,107,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,108,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,109,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3823734947460288,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,110,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,adaboost,SAMME,0.11042308042695524,5.0,117.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,111,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,112,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,113,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,115,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,116,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,117,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,118,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.20202014999292287,False,True,1.0,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,119,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,120,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,121,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,122,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,123,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,124,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,125,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,126,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,127,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,129,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.04329010470998136,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7260331062271381,None,0.0,7.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,134,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.714869937384006,None,0.0,8.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/metalearning/metalearning_files/precision_micro_multiclass.classification_sparse/description.txt b/metalearning/metalearning_files/precision_micro_multiclass.classification_sparse/description.txt new file mode 100755 index 0000000..377b59c --- /dev/null +++ b/metalearning/metalearning_files/precision_micro_multiclass.classification_sparse/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: precision_micro +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/precision_micro_multiclass.classification_sparse/feature_costs.arff b/metalearning/metalearning_files/precision_micro_multiclass.classification_sparse/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/precision_micro_multiclass.classification_sparse/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/precision_micro_multiclass.classification_sparse/feature_runstatus.arff b/metalearning/metalearning_files/precision_micro_multiclass.classification_sparse/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/precision_micro_multiclass.classification_sparse/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/precision_micro_multiclass.classification_sparse/feature_values.arff b/metalearning/metalearning_files/precision_micro_multiclass.classification_sparse/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/precision_micro_multiclass.classification_sparse/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/precision_micro_multiclass.classification_sparse/readme.txt b/metalearning/metalearning_files/precision_micro_multiclass.classification_sparse/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/precision_multiclass.classification_dense/algorithm_runs.arff b/metalearning/metalearning_files/precision_multiclass.classification_dense/algorithm_runs.arff new file mode 100755 index 0000000..e89753c --- /dev/null +++ b/metalearning/metalearning_files/precision_multiclass.classification_dense/algorithm_runs.arff @@ -0,0 +1,107 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE precision NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2117,1.0,1,0.050738007380073835,ok +75156,1.0,2,0.19480519480519476,ok +75129,1.0,3,0.5,ok +75239,1.0,4,0.0,ok +75121,1.0,5,0.0,ok +261,1.0,6,0.31034482758620685,ok +75240,1.0,7,0.054171180931744334,ok +75120,1.0,8,0.01498929336188437,ok +75124,1.0,9,0.3972602739726028,ok +75176,1.0,10,0.01210207840042099,ok +75103,1.0,11,0.03157894736842104,ok +75095,1.0,12,0.0757575757575758,ok +273,1.0,13,0.04529616724738672,ok +75174,1.0,14,0.17985257985257985,ok +75153,1.0,15,0.07720320466132558,ok +75093,1.0,16,0.42805755395683454,ok +75119,1.0,17,0.004514672686230292,ok +75215,1.0,18,0.028420038535645453,ok +75233,1.0,19,0.0368509212730318,ok +75196,1.0,20,0.039215686274509776,ok +75191,1.0,21,0.08602881217722202,ok +75115,1.0,22,0.014799154334038,ok +75108,1.0,23,0.0,ok +75101,1.0,24,0.2550022091775548,ok +75192,1.0,25,0.47594501718213056,ok +75232,1.0,26,0.17924528301886788,ok +75173,1.0,27,0.10620601407549579,ok +75148,1.0,28,0.14869888475836435,ok +75150,1.0,29,0.3006134969325154,ok +75100,1.0,30,0.9571428571428572,ok +75179,1.0,31,0.25308641975308643,ok +75213,1.0,32,0.11392405063291144,ok +75227,1.0,33,0.17021276595744683,ok +75184,1.0,34,0.11914217633042101,ok +75142,1.0,35,0.07483889865260696,ok +75166,1.0,36,0.09494640122511488,ok +75133,1.0,37,0.19999999999999996,ok +75234,1.0,38,0.027288732394366244,ok +75139,1.0,39,0.014127764127764175,ok +75117,1.0,40,0.007874015748031482,ok +75113,1.0,41,0.021739130434782594,ok +75237,1.0,42,1.5589193571030613e-05,ok +75195,1.0,43,0.0008766437069505084,ok +75171,1.0,44,0.1615326821938392,ok +75128,1.0,45,0.016042780748663055,ok +75146,1.0,46,0.08476562499999996,ok +75116,1.0,47,0.007009345794392496,ok +75157,1.0,48,0.4478527607361963,ok +75187,1.0,49,0.011904761904761862,ok +2350,1.0,50,0.5576171875,ok +75125,1.0,51,0.028436018957345932,ok +75185,1.0,52,0.11693548387096775,ok +75163,1.0,53,0.05380333951762528,ok +75177,1.0,54,0.07692307692307687,ok +75189,1.0,55,0.012808304860060904,ok +75244,1.0,56,0.36111111111111116,ok +75219,1.0,57,0.031680440771349905,ok +75222,1.0,58,0.1333333333333333,ok +75159,1.0,59,0.42105263157894735,ok +75175,1.0,60,0.10357815442561202,ok +254,1.0,61,0.0,ok +75105,1.0,62,0.8873626373626373,ok +75106,1.0,63,0.5,ok +75212,1.0,64,0.2233502538071066,ok +75099,1.0,65,0.34545454545454546,ok +75248,1.0,66,0.5476190476190477,ok +233,1.0,67,0.004073319755600768,ok +75226,1.0,68,0.0022164758034725063,ok +75132,1.0,69,0.7962721342031687,ok +75127,1.0,70,0.371534833787048,ok +75161,1.0,71,0.06151116368475529,ok +75143,1.0,72,0.00893743793445878,ok +75114,1.0,73,0.015037593984962405,ok +75182,1.0,74,0.1436004162330905,ok +75112,1.0,75,0.12591815320041977,ok +75210,1.0,76,0.0,ok +75092,1.0,77,0.37037037037037035,ok +3043,1.0,78,0.07692307692307687,ok +75249,1.0,79,0.01041666666666663,ok +75126,1.0,80,0.011737089201877882,ok +75225,1.0,81,0.18181818181818177,ok +75141,1.0,82,0.08086253369272234,ok +75107,1.0,83,0.11111111111111116,ok +75097,1.0,84,0.019743560649041147,ok +80001,1.0,1,0.09090909090909094,ok +80003,1.0,1,0.053763440860215006,ok +80006,1.0,1,0.0,ok +80008,1.0,1,0.0,ok +80009,1.0,1,0.09999999999999998,ok +80010,1.0,1,0.0,ok +80011,1.0,1,0.0,ok +80012,1.0,1,0.06666666666666665,ok +80013,1.0,1,0.0,ok +80014,1.0,1,0.0,ok +80015,1.0,1,0.125,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/precision_multiclass.classification_dense/configurations.csv b/metalearning/metalearning_files/precision_multiclass.classification_dense/configurations.csv new file mode 100755 index 0000000..88f6dab --- /dev/null +++ b/metalearning/metalearning_files/precision_multiclass.classification_dense/configurations.csv @@ -0,0 +1,96 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:fast_ica:algorithm,preprocessor:fast_ica:fun,preprocessor:fast_ica:n_components,preprocessor:fast_ica:whiten,preprocessor:feature_agglomeration:affinity,preprocessor:feature_agglomeration:linkage,preprocessor:feature_agglomeration:n_clusters,preprocessor:feature_agglomeration:pooling_func,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:pca:keep_variance,preprocessor:pca:whiten,preprocessor:polynomial:degree,preprocessor:polynomial:include_bias,preprocessor:polynomial:interaction_only,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,rescaling:__choice__,rescaling:quantile_transformer:n_quantiles,rescaling:quantile_transformer:output_distribution,rescaling:robust_scaler:q_max,rescaling:robust_scaler:q_min +weighting,no_encoding,,,decision_tree,,,,,,,gini,1.8218848145904327,,1.0,None,0.0,3.0,12.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2930349562854553,fwe,f_classif,none,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.03474109838999682,deviance,4.0,0.5687034678818491,None,0.0,18.0,12.0,0.0,408.0,0.5150113945430513,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.6328939517938,f_classif,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5368752992317617,None,0.0,16.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.8382117756438685,mutual_info,,,,quantile_transformer,11480.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.6291601746046639,None,0.0,11.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,median,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7146659106968425,True,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.010000000000000005,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9954201822467588,None,0.0,4.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.12964080341374426 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.040936424602789435,deviance,7.0,0.5495014745530306,None,0.0,20.0,18.0,0.0,141.0,0.6905343807995293,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.00034835629696198427,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8245132980938538,0.08947420373097192 +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.1958974686405233,deviance,5.0,0.33885235607979314,None,0.0,6.0,4.0,0.0,125.0,0.9448890820738562,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,0.010000000000000005,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.8495948473718509,None,0.0,19.0,12.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,median,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,173.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7360755084850574,0.13979965846112485 +none,one_hot_encoding,0.00353232434213139,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.8709229440057928,None,0.0,3.0,13.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,mean,fast_ica,,,,,,,,,,,parallel,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7446318916516063,0.23782974987118105 +none,one_hot_encoding,0.00012586572428922356,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5240592829918601,None,0.0,10.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +weighting,one_hot_encoding,0.03192699980429505,True,adaboost,SAMME.R,0.10000000000000002,1.0,50.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,quantile_transformer,8407.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.031898383885633236,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9284140023853044,None,0.0,19.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,20571.0,uniform,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828776,None,0.0,2.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.609975998293528,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,most_frequent,fast_ica,,,,,,,,,,,parallel,logcosh,2000.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8430415644014919,0.2863750565331575 +weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,751.7276898356944,False,True,1.0,squared_hinge,ovr,l2,2.0336913984983305e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,0.060155186012325876,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.3163640203509378,None,0.0,16.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,median,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,2.0,None,11.0,20.0,1.0,47.0,,,,,,quantile_transformer,21065.0,uniform,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,31,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9260795160807372,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3428971645958825,,,0.2229870623330047,rbf,-1.0,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.00016967940959070708,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9439080311935252,None,0.0,2.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,multinomial_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.159004432545846,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66.89874989654642,chi2,,,,robust_scaler,,,0.859256110723552,0.2759054267310719 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.9188519169916218,None,0.0,1.0,5.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,median,fast_ica,,,,,,,,,,,parallel,cube,306.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1.0,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.3837398524575939,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.018356703878357986,deviance,3.0,0.9690352514774068,None,0.0,12.0,3.0,0.0,234.0,0.3870344708308441,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.4932996544760619,None,0.0,2.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,mean,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,340.0,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.12713527337147906,deviance,4.0,0.6041596127474019,None,0.0,14.0,17.0,0.0,83.0,0.8426859880999615,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,standardize,,,, +none,one_hot_encoding,0.061267514817403314,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00041820552318733856,True,True,hinge,0.0007969587715456845,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,median,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.9447144786798995,False,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.96834823420249e-05,False,True,hinge,0.00016639250831671168,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.628430584359224,mutual_info,,,,quantile_transformer,6634.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.25185682718144353,deviance,7.0,0.9252293870435426,None,0.0,5.0,8.0,0.0,309.0,0.9923577831919878,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.0018037858350872134,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.1075752886620514,deviance,7.0,0.9041944194819348,None,0.0,6.0,20.0,0.0,454.0,0.6635283542555824,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0020580843703898177,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.945774573434192,None,0.0,19.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,median,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,15209.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.002615346832354839,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7884268823432835,None,0.0,20.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.2472984547885781,deviance,3.0,0.6564306719064884,None,0.0,15.0,14.0,0.0,220.0,0.8082564085714649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,median,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,332.0,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +none,one_hot_encoding,,False,decision_tree,,,,,,,gini,1.9629324531036465,,1.0,None,0.0,13.0,20.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.19998727075532635,deviance,10.0,0.9377656718112952,None,0.0,7.0,13.0,0.0,214.0,0.6062346326014357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.000738320402221022,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,median,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.854322697199371,False,True,1.0,squared_hinge,ovr,l1,0.00013359426815085846,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,30.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0006290513932903491,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8680626684393846,None,0.0,9.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,54639.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.04651467280022463,True,adaboost,SAMME,0.6506122230393502,6.0,143.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,extra_trees_preproc_for_classification,False,entropy,None,0.348720215832657,None,0.0,17.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,26025.0,normal,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3386310102795006,None,0.0,6.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,True,,,,,,,,,,,,,normalize,,,, +weighting,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.0,None,,5.861221938384061e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.22985730375167915,fpr,f_classif,quantile_transformer,40589.0,uniform,, +none,no_encoding,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00012437731009611307,1.0,,0.08709904468181932,1.0,,invscaling,squared_hinge,l1,0.6231564675290102,0.008071279833697563,,,,,,,,,,,,,,,,,,134,median,fast_ica,,,,,,,,,,,parallel,logcosh,814.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.714869937384006,None,0.0,8.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, diff --git a/metalearning/metalearning_files/precision_multiclass.classification_dense/description.txt b/metalearning/metalearning_files/precision_multiclass.classification_dense/description.txt new file mode 100755 index 0000000..3cecb38 --- /dev/null +++ b/metalearning/metalearning_files/precision_multiclass.classification_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: precision +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/precision_multiclass.classification_dense/feature_costs.arff b/metalearning/metalearning_files/precision_multiclass.classification_dense/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/precision_multiclass.classification_dense/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/precision_multiclass.classification_dense/feature_runstatus.arff b/metalearning/metalearning_files/precision_multiclass.classification_dense/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/precision_multiclass.classification_dense/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/precision_multiclass.classification_dense/feature_values.arff b/metalearning/metalearning_files/precision_multiclass.classification_dense/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/precision_multiclass.classification_dense/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/precision_multiclass.classification_dense/readme.txt b/metalearning/metalearning_files/precision_multiclass.classification_dense/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/precision_multiclass.classification_sparse/algorithm_runs.arff b/metalearning/metalearning_files/precision_multiclass.classification_sparse/algorithm_runs.arff new file mode 100755 index 0000000..75c94a3 --- /dev/null +++ b/metalearning/metalearning_files/precision_multiclass.classification_sparse/algorithm_runs.arff @@ -0,0 +1,107 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE precision NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2117,1.0,1,0.05489353389333851,ok +75156,1.0,2,0.21037463976945248,ok +75129,1.0,3,0.5,ok +75239,1.0,4,0.0,ok +75121,1.0,5,0.004089979550102263,ok +261,1.0,6,0.31034482758620685,ok +75240,1.0,7,0.054171180931744334,ok +75120,1.0,8,0.019189765458422214,ok +75124,1.0,9,0.3972602739726028,ok +75176,1.0,10,0.013392857142857095,ok +75103,1.0,11,0.03157894736842104,ok +75095,1.0,12,0.0757575757575758,ok +273,1.0,13,0.04529616724738672,ok +75174,1.0,14,0.17985257985257985,ok +75153,1.0,15,0.1076233183856502,ok +75093,1.0,16,0.42805755395683454,ok +75119,1.0,17,0.0050000000000000044,ok +75215,1.0,18,0.03252032520325199,ok +75233,1.0,19,0.056308654848800876,ok +75196,1.0,20,0.04807692307692313,ok +75191,1.0,21,0.07591982427237787,ok +75115,1.0,22,0.014893617021276562,ok +75108,1.0,23,0.0,ok +75101,1.0,24,0.2733766988035776,ok +75192,1.0,25,0.47594501718213056,ok +75232,1.0,26,0.17924528301886788,ok +75173,1.0,27,0.10620601407549579,ok +75148,1.0,28,0.19736842105263153,ok +75150,1.0,29,0.3053892215568862,ok +75100,1.0,30,0.9823788546255506,ok +75179,1.0,31,0.25308641975308643,ok +75213,1.0,32,0.11392405063291144,ok +75227,1.0,33,0.17021276595744683,ok +75184,1.0,34,0.11914217633042101,ok +75142,1.0,35,0.08302718589272595,ok +75166,1.0,36,0.09494640122511488,ok +75133,1.0,37,0.19999999999999996,ok +75234,1.0,38,0.027288732394366244,ok +75139,1.0,39,0.014879107253564783,ok +75117,1.0,40,0.015837104072398245,ok +75113,1.0,41,0.021739130434782594,ok +75237,1.0,42,0.00010909884355225774,ok +75195,1.0,43,0.0008766437069505084,ok +75171,1.0,44,0.1705137751303053,ok +75128,1.0,45,0.02103049421661407,ok +75146,1.0,46,0.08847817538340541,ok +75116,1.0,47,0.007009345794392496,ok +75157,1.0,48,0.4478527607361963,ok +75187,1.0,49,0.025295109612141653,ok +2350,1.0,50,0.5576171875,ok +75125,1.0,51,0.028503562945368155,ok +75185,1.0,52,0.11693548387096775,ok +75163,1.0,53,0.05380333951762528,ok +75177,1.0,54,0.07692307692307687,ok +75189,1.0,55,0.012808304860060904,ok +75244,1.0,56,0.36111111111111116,ok +75219,1.0,57,0.07044917257683214,ok +75222,1.0,58,0.1333333333333333,ok +75159,1.0,59,0.42105263157894735,ok +75175,1.0,60,0.1103313840155945,ok +254,1.0,61,0.0,ok +75105,1.0,62,0.8873626373626373,ok +75106,1.0,63,0.5,ok +75212,1.0,64,0.2710280373831776,ok +75099,1.0,65,0.34545454545454546,ok +75248,1.0,66,0.5476190476190477,ok +233,1.0,67,0.01632653061224487,ok +75226,1.0,68,0.0022164758034725063,ok +75132,1.0,69,0.7962721342031687,ok +75127,1.0,70,0.37375446991002914,ok +75161,1.0,71,0.07860002966038859,ok +75143,1.0,72,0.009861932938856066,ok +75114,1.0,73,0.015037593984962405,ok +75182,1.0,74,0.1436004162330905,ok +75112,1.0,75,0.12591815320041977,ok +75210,1.0,76,0.0,ok +75092,1.0,77,0.37037037037037035,ok +3043,1.0,78,0.07692307692307687,ok +75249,1.0,79,0.02083333333333337,ok +75126,1.0,80,0.03111111111111109,ok +75225,1.0,81,0.18181818181818177,ok +75141,1.0,82,0.08086253369272234,ok +75107,1.0,83,0.11111111111111116,ok +75097,1.0,84,0.0551781892621197,ok +80001,1.0,1,0.09090909090909094,ok +80003,1.0,1,0.09473684210526312,ok +80006,1.0,1,0.09090909090909094,ok +80008,1.0,1,0.33333333333333337,ok +80009,1.0,1,0.11111111111111116,ok +80010,1.0,1,0.0,ok +80011,1.0,1,0.0,ok +80012,1.0,1,0.06666666666666665,ok +80013,1.0,1,0.0,ok +80014,1.0,1,0.0,ok +80015,1.0,1,0.125,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/precision_multiclass.classification_sparse/configurations.csv b/metalearning/metalearning_files/precision_multiclass.classification_sparse/configurations.csv new file mode 100755 index 0000000..f5e619e --- /dev/null +++ b/metalearning/metalearning_files/precision_multiclass.classification_sparse/configurations.csv @@ -0,0 +1,96 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,preprocessor:truncatedSVD:target_dim,rescaling:__choice__ +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,15.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.7735071203475309,,1.0,None,0.0,4.0,4.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,mean,extra_trees_preproc_for_classification,True,entropy,None,0.8171332500590935,None,0.0,15.0,13.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.0010015637584068037,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.037611630308856295,deviance,5.0,0.8840126779516314,None,0.0,10.0,2.0,0.0,444.0,0.7599997167603434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,most_frequent,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.004090774134315939,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7983157215145903,None,0.0,2.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828776,None,0.0,2.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.002173124111626734,None,0.0,14.0,4.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,6.0,None,13.0,2.0,1.0,23.0,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,31,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3428971645958825,,,0.2229870623330047,rbf,-1.0,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9015027772227234,None,0.0,19.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1.0,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.002630811782675973,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9828367182452932,None,0.0,18.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3823734947460288,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.04329010470998136,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7260331062271381,None,0.0,7.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.010000000000000005,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15.0,1.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,134,median,extra_trees_preproc_for_classification,False,entropy,None,0.8213051377774989,None,0.0,3.0,13.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.714869937384006,None,0.0,8.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/metalearning/metalearning_files/precision_multiclass.classification_sparse/description.txt b/metalearning/metalearning_files/precision_multiclass.classification_sparse/description.txt new file mode 100755 index 0000000..3cecb38 --- /dev/null +++ b/metalearning/metalearning_files/precision_multiclass.classification_sparse/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: precision +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/precision_multiclass.classification_sparse/feature_costs.arff b/metalearning/metalearning_files/precision_multiclass.classification_sparse/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/precision_multiclass.classification_sparse/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/precision_multiclass.classification_sparse/feature_runstatus.arff b/metalearning/metalearning_files/precision_multiclass.classification_sparse/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/precision_multiclass.classification_sparse/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/precision_multiclass.classification_sparse/feature_values.arff b/metalearning/metalearning_files/precision_multiclass.classification_sparse/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/precision_multiclass.classification_sparse/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/precision_multiclass.classification_sparse/readme.txt b/metalearning/metalearning_files/precision_multiclass.classification_sparse/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/precision_weighted_binary.classification_dense/algorithm_runs.arff b/metalearning/metalearning_files/precision_weighted_binary.classification_dense/algorithm_runs.arff new file mode 100755 index 0000000..e7e621f --- /dev/null +++ b/metalearning/metalearning_files/precision_weighted_binary.classification_dense/algorithm_runs.arff @@ -0,0 +1,152 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE precision_weighted NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2120,1.0,1,0.08022477157951025,ok +75193,1.0,2,0.03802311063996522,ok +2117,1.0,3,0.14265765376577388,ok +75156,1.0,4,0.20319334833220415,ok +75129,1.0,5,0.08669351050724516,ok +75243,1.0,6,0.0,ok +75110,1.0,7,0.11585531099100344,ok +75239,1.0,8,0.0,ok +75223,1.0,9,0.10308991908426168,ok +75221,1.0,10,0.4027914897274013,ok +258,1.0,11,0.009652166726836442,ok +75121,1.0,12,0.0,ok +253,1.0,13,0.3971756304204712,ok +261,1.0,14,0.2449416835699797,ok +75240,1.0,15,0.02080660971852688,ok +75120,1.0,16,0.04153101819938165,ok +75124,1.0,17,0.08426824349929629,ok +75176,1.0,18,0.015417968255781567,ok +75103,1.0,19,0.005399083293820106,ok +75207,1.0,20,0.15680923713512063,ok +75095,1.0,21,0.01757107415002146,ok +273,1.0,22,0.04414982125934308,ok +75174,1.0,23,0.11405408911206805,ok +75153,1.0,24,0.08026959777662745,ok +75093,1.0,25,0.2046330800648536,ok +75119,1.0,26,0.03094839521094317,ok +75201,1.0,27,0.07984962449794286,ok +75215,1.0,28,0.027400741489915892,ok +75172,1.0,29,0.090985809925204,ok +75169,1.0,30,0.033784207000311706,ok +75202,1.0,31,0.2320892056839039,ok +75233,1.0,32,0.06082093106795827,ok +75231,1.0,33,0.1651825246227523,ok +75196,1.0,34,0.015215605393662401,ok +248,1.0,35,0.226396668333682,ok +75191,1.0,36,0.12497876304132682,ok +75217,1.0,37,0.0,ok +260,1.0,38,0.027127490200162252,ok +75115,1.0,39,0.017854592566009853,ok +75123,1.0,40,0.31171476267136955,ok +75108,1.0,41,0.0,ok +75101,1.0,42,0.27420411371363684,ok +75192,1.0,43,0.4824466988315518,ok +75232,1.0,44,0.1233010687638636,ok +75173,1.0,45,0.1164184654283339,ok +75197,1.0,46,0.15862792301177187,ok +266,1.0,47,0.019389161056556747,ok +75148,1.0,48,0.1350453378185531,ok +75150,1.0,49,0.2782413408164295,ok +75100,1.0,50,0.0054590998300326765,ok +75178,1.0,51,0.7570393510813636,ok +75236,1.0,52,0.03185030716965764,ok +75179,1.0,53,0.17908750793261574,ok +75213,1.0,54,0.05064961320936123,ok +2123,1.0,55,0.06664228507628078,ok +75227,1.0,56,0.10178562268737412,ok +75184,1.0,57,0.10782255468946933,ok +75142,1.0,58,0.0715535466788122,ok +236,1.0,59,0.03833279025178438,ok +2122,1.0,60,0.1090803225748227,ok +75188,1.0,61,0.21503227998943397,ok +75166,1.0,62,0.09945031583527553,ok +75181,1.0,63,0.0,ok +75133,1.0,64,0.0058744901587642895,ok +75134,1.0,65,0.08702477561737332,ok +75198,1.0,66,0.12025000319215506,ok +262,1.0,67,0.002751972512609613,ok +75234,1.0,68,0.024088457176147382,ok +75139,1.0,69,0.010714278670329258,ok +252,1.0,70,0.15278932029242331,ok +75117,1.0,71,0.05184242948952755,ok +75113,1.0,72,0.006479553075845801,ok +75098,1.0,73,0.027471753088248563,ok +246,1.0,74,0.01051167211381765,ok +75203,1.0,75,0.09549480420909973,ok +75237,1.0,76,0.00039539777599506554,ok +75195,1.0,77,0.001560114030179971,ok +75171,1.0,78,0.1620392045550726,ok +75128,1.0,79,0.02232168365143683,ok +75096,1.0,80,0.005585999087350801,ok +75250,1.0,81,0.31437017866239836,ok +75146,1.0,82,0.11227759906567902,ok +75116,1.0,83,0.009857921438566342,ok +75157,1.0,84,0.42721657693375314,ok +75187,1.0,85,0.016775752742081207,ok +2350,1.0,86,0.4107400261716929,ok +242,1.0,87,0.010497196266900377,ok +244,1.0,88,0.11022260983726562,ok +75125,1.0,89,0.033798127147098844,ok +75185,1.0,90,0.12773060459472851,ok +75163,1.0,91,0.06024005149369582,ok +75177,1.0,92,0.017495768774297926,ok +75189,1.0,93,0.01935454704780004,ok +75244,1.0,94,0.059028736950657135,ok +75219,1.0,95,0.03480221342625667,ok +75222,1.0,96,0.04781443570706845,ok +75159,1.0,97,0.07331254920484964,ok +75175,1.0,98,0.09974423015772416,ok +75109,1.0,99,0.3066035817140491,ok +254,1.0,100,0.0,ok +75105,1.0,101,0.02382020105699334,ok +75106,1.0,102,0.09647572803854332,ok +75212,1.0,103,0.2493001484774634,ok +75099,1.0,104,0.13310959982764126,ok +75248,1.0,105,0.07634255384916955,ok +233,1.0,106,0.0028441203787273883,ok +75235,1.0,107,0.0011079987550576265,ok +75226,1.0,108,0.0030443004196948342,ok +75132,1.0,109,0.06647783581906219,ok +75127,1.0,110,0.33347516619713424,ok +251,1.0,111,0.0,ok +75161,1.0,112,0.06435771828213677,ok +75143,1.0,113,0.010482521920672894,ok +75114,1.0,114,0.023575638506876273,ok +75182,1.0,115,0.10979433133488292,ok +75112,1.0,116,0.12095417307543233,ok +75210,1.0,117,0.0,ok +75205,1.0,118,0.18099003287994075,ok +75090,1.0,119,0.05949999802669681,ok +275,1.0,120,0.03613820618166086,ok +288,1.0,121,0.12845154789859747,ok +75092,1.0,122,0.06477381636956114,ok +3043,1.0,123,0.018200350964109324,ok +75249,1.0,124,0.0032279797849654734,ok +75126,1.0,125,0.04846397960751947,ok +75225,1.0,126,0.04555750377260681,ok +75141,1.0,127,0.05271470493960073,ok +75107,1.0,128,0.05958493591294611,ok +75097,1.0,129,0.0596624483932221,ok +80001,1.0,1,0.07050008279516484,ok +80003,1.0,1,0.08928461376279206,ok +80006,1.0,1,0.0925099206349207,ok +80008,1.0,1,0.09259259259259245,ok +80009,1.0,1,0.10526315789473684,ok +80010,1.0,1,0.11494252873563215,ok +80011,1.0,1,0.03563941299790363,ok +80012,1.0,1,0.042424242424242475,ok +80013,1.0,1,0.0,ok +80014,1.0,1,0.04195804195804198,ok +80015,1.0,1,0.09523809523809523,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/precision_weighted_binary.classification_dense/configurations.csv b/metalearning/metalearning_files/precision_weighted_binary.classification_dense/configurations.csv new file mode 100755 index 0000000..97ae5c1 --- /dev/null +++ b/metalearning/metalearning_files/precision_weighted_binary.classification_dense/configurations.csv @@ -0,0 +1,141 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:fast_ica:algorithm,preprocessor:fast_ica:fun,preprocessor:fast_ica:n_components,preprocessor:fast_ica:whiten,preprocessor:feature_agglomeration:affinity,preprocessor:feature_agglomeration:linkage,preprocessor:feature_agglomeration:n_clusters,preprocessor:feature_agglomeration:pooling_func,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:pca:keep_variance,preprocessor:pca:whiten,preprocessor:polynomial:degree,preprocessor:polynomial:include_bias,preprocessor:polynomial:interaction_only,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,rescaling:__choice__,rescaling:quantile_transformer:n_quantiles,rescaling:quantile_transformer:output_distribution,rescaling:robust_scaler:q_max,rescaling:robust_scaler:q_min +weighting,one_hot_encoding,0.00011717632475982632,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.045388141846341344,deviance,10.0,0.29161769341843435,None,0.0,20.0,2.0,0.0,278.0,0.7912571599269661,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7249853037185638,None,0.0,1.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,median,extra_trees_preproc_for_classification,False,gini,None,0.9424908623661876,None,0.0,7.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.12713527337147906,deviance,4.0,0.6041596127474019,None,0.0,14.0,17.0,0.0,83.0,0.8426859880999615,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.03474109838999682,deviance,4.0,0.5687034678818491,None,0.0,18.0,12.0,0.0,408.0,0.5150113945430513,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.6328939517938,f_classif,,,,standardize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9265375980300852,None,0.0,13.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.251097788175676,deviance,6.0,0.35679099363539235,None,0.0,13.0,11.0,0.0,157.0,0.4791732272983235,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,adaboost,SAMME,0.28738775989203896,10.0,423.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.8031499675923353,0.13579938270386765 +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.14159526341015916,deviance,7.0,0.8010488230155749,None,0.0,3.0,20.0,0.0,401.0,0.8073562440607731,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3469031665162168,None,0.0,4.0,3.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,mean,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,83.0,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.002385546176068135,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.7729472304882845,,,0.0004789329856033374,rbf,-1.0,True,6.58869648864534e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,177.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5368752992317617,None,0.0,16.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.8382117756438685,mutual_info,,,,quantile_transformer,11480.0,normal,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.8384447520019118,None,0.0,13.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.36637567531287824,fdr,f_classif,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.6291601746046639,None,0.0,11.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,median,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7146659106968425,True,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.12713527337147906,deviance,4.0,0.6041596127474019,None,0.0,14.0,17.0,0.0,83.0,0.8426859880999615,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9455638720565652,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,most_frequent,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8255464552647293,0.19162485555463185 +weighting,one_hot_encoding,0.18137532678800647,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9094110110427254,None,0.0,7.0,12.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,195.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.6682079659377479,None,0.0,4.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,mean,extra_trees_preproc_for_classification,True,entropy,None,0.5552350997943013,None,0.0,8.0,5.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.02345017287074443,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.053517066400173056,deviance,10.0,0.542144980834302,None,0.0,20.0,13.0,0.0,233.0,0.7398539900055563,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0614425536709615,fwe,f_classif,robust_scaler,,,0.9523118062307264,0.13434811490315818 +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.8149627329153046,None,0.0,15.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.040936424602789435,deviance,7.0,0.5495014745530306,None,0.0,20.0,18.0,0.0,141.0,0.6905343807995293,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7974565919616314,None,0.0,12.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,median,extra_trees_preproc_for_classification,True,entropy,None,0.9772091846790169,None,0.0,10.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2398150931290834,,,0.4015139801872962,rbf,-1.0,False,2.402997750662158e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88.40698357592571,chi2,,,,normalize,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.1958974686405233,deviance,5.0,0.33885235607979314,None,0.0,6.0,4.0,0.0,125.0,0.9448890820738562,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0007038280350320558,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4138778052607317,0.7995003430482459,5.0,5.430044692638861,poly,-1.0,True,0.02455501006004393,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,31,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.010000000000000005,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.0518326156691958,deviance,6.0,0.8807456180216267,None,0.0,7.0,19.0,0.0,366.0,0.7314831276137047,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,,False,adaboost,SAMME,0.4391375941344922,3.0,386.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7439738358430176,0.20581080574615795 +none,one_hot_encoding,0.00011294596229850895,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7515.1213255144885,0.9576762936062476,3.0,0.019002536385919932,poly,-1.0,False,0.010632086351533369,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,51.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,97282.0,normal,, +none,one_hot_encoding,0.00012586572428922356,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5240592829918601,None,0.0,10.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1751.4736515133566,0.62404114475118,3.0,1.6087076997410432,poly,-1.0,False,3.5353792826856045e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1198.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.2422926485206341,,1.0,None,0.0,15.0,9.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.02102683283349326,deviance,10.0,0.2797288369369436,None,0.0,14.0,9.0,0.0,480.0,0.5778972273820631,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58.88123233170863,mutual_info,,,,robust_scaler,,,0.75,0.25 +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828776,None,0.0,2.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2155613360930585,deviance,4.0,0.3198803116198433,None,0.0,8.0,13.0,0.0,275.0,0.28870176110739404,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,mean,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.7062102387181676,None,0.0,1.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,most_frequent,fast_ica,,,,,,,,,,,parallel,exp,100.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7065776353150109,0.23782974987118105 +none,one_hot_encoding,0.16334152321884812,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00011572473434870852,True,True,squared_hinge,0.00019678754114665057,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.045742431094098604,fpr,chi2,none,,,, +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.3795924768593385,deviance,2.0,0.33708497069988536,None,0.0,15.0,13.0,0.0,451.0,0.7716323242090217,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2573946506994812,fwe,f_classif,quantile_transformer,1000.0,uniform,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.609975998293528,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,most_frequent,fast_ica,,,,,,,,,,,parallel,logcosh,2000.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8430415644014919,0.2863750565331575 +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.39536192447534535,None,0.0,19.0,3.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5.0,None,11.0,11.0,1.0,12.0,,,,,,robust_scaler,,,0.8928631650245873,0.1581877760687084 +weighting,one_hot_encoding,0.0010446150978844174,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.2100039691650936,None,0.0,19.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,10.0,None,3.0,18.0,1.0,42.0,,,,,,quantile_transformer,44341.0,uniform,, +weighting,one_hot_encoding,0.3126027672745337,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1100.6211008501205,0.5921425829232616,2.0,0.0337546254878617,poly,-1.0,True,0.09641299736884308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2507440474920336e-05,True,,0.049622652766554566,True,0.009105043727227265,constant,squared_hinge,elasticnet,,0.00010112719671669047,,,,,,,,,,,,,,,,,,53,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61.69949680034141,chi2,,,,none,,,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.9348409326933208,rbf,-1.0,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1754.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,198.72528686512538,False,True,1.0,squared_hinge,ovr,l2,0.026260652523566803,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,robust_scaler,,,0.913511520078368,0.2742229325455444 +none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9260795160807372,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.03644212536682547,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.05965671477918361,deviance,8.0,0.4858133247974158,None,0.0,14.0,7.0,0.0,480.0,0.5726186552917335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.15318294164619112 +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18887.81504976871,,,0.23283562663398755,rbf,-1.0,True,2.3839685780861318e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3937607155568376,fwe,chi2,robust_scaler,,,0.941018778984854,0.2144110585080491 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3428971645958825,,,0.2229870623330047,rbf,-1.0,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8138233157708883,None,0.0,2.0,9.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54.27504292568562,f_classif,,,,minmax,,,, +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00016781524591321165,True,True,squared_hinge,1.5119200923218881e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.349459944355116,,,0.00024028983491736645,rbf,-1.0,True,1.139421622432356e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3636266268105085,fwe,chi2,none,,,, +weighting,one_hot_encoding,0.00034835629696198427,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8245132980938538,0.08947420373097192 +weighting,one_hot_encoding,0.00016967940959070708,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9439080311935252,None,0.0,2.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35.0,None,,0.005389483044810356,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,most_frequent,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,9.441661069727422e-05,1896.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.0008685602750053468,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9896334290292654,None,0.0,11.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,141.76310800864283,False,True,1.0,squared_hinge,ovr,l1,0.004317884655117431,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.7696635139179984,None,0.0,7.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,mean,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,261.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,normalize,,,, +none,one_hot_encoding,,False,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6396026761675004,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06544340428506021,fwe,f_classif,none,,,, +weighting,one_hot_encoding,0.004980497345831963,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1255.9137433589426,,,0.08351549479967445,rbf,-1.0,True,0.00017919875199222518,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1.0,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.3837398524575939,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.018356703878357986,deviance,3.0,0.9690352514774068,None,0.0,12.0,3.0,0.0,234.0,0.3870344708308441,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,most_frequent,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6864970915330799,False,,,,,,,,,,,,,,,,normalize,,,, +weighting,one_hot_encoding,0.0001486770773839718,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5256280540657592,None,0.0,8.0,12.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5670424455696162,None,0.0,8.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,standardize,,,, +weighting,one_hot_encoding,0.00031737236118003484,True,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,244.0,None,,2.3065111488706024e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,85,mean,fast_ica,,,,,,,,,,,deflation,exp,1862.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7851234479882973,0.2237528085136715 +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.35533396539961937,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,86,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4165632766388807,fpr,chi2,none,,,, +weighting,one_hot_encoding,,False,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,156.0,auto,,0.0001987333852871589,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1.0,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,no_encoding,,,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.96834823420249e-05,False,True,hinge,0.00016639250831671168,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,89,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.628430584359224,mutual_info,,,,quantile_transformer,6634.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,90,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,91,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,8.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,93,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0387325491437111,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.32034732923549136,None,0.0,20.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94,median,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,quantile_transformer,1000.0,uniform,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2636201374253461,deviance,7.0,0.8344964130784466,None,0.0,9.0,2.0,0.0,298.0,0.7517549950523315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,95,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,96,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.2263596964804377,True,adaboost,SAMME,0.15143691959318842,2.0,233.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,97,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.07951518163998639,fwe,f_classif,minmax,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.07463196642416367,deviance,7.0,0.8603242247379981,None,0.0,2.0,6.0,0.0,500.0,0.8447665577491962,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,98,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.0433556140045585,deviance,10.0,0.33000096635982235,None,0.0,15.0,13.0,0.0,388.0,0.8291104221904706,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,99,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,robust_scaler,,,0.7496393440951183,0.2853682991120835 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,100,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.6960837129194184,,,0.0004821713821548537,rbf,-1.0,False,0.040691702958042086,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,most_frequent,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6520469084729728,False,,,,,,,,,,,,,,,,quantile_transformer,97329.0,normal,, +weighting,one_hot_encoding,0.010000000000000005,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.04952755495565772,deviance,3.0,0.7249041896998006,None,0.0,6.0,17.0,0.0,174.0,0.3718608680080454,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,102,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.0020580843703898177,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.945774573434192,None,0.0,19.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,103,median,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,15209.0,normal,, +weighting,no_encoding,,,bernoulli_nb,,,,,0.023108348799909733,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,104,median,extra_trees_preproc_for_classification,True,gini,None,0.43664414575861454,None,0.0,1.0,4.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8569382605464568,0.07491671292996571 +weighting,one_hot_encoding,0.10324969243867224,True,decision_tree,,,,,,,gini,0.7467478023293801,,1.0,None,0.0,12.0,8.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,105,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84.22876326806853,mutual_info,,,,minmax,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,106,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.005332972692819521,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5565918060287016,None,0.0,5.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,107,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,173.0,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9527068489270144,0.04135311355893583 +weighting,one_hot_encoding,0.001279467383882126,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,108,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0903354518102121,fwe,f_classif,standardize,,,, +weighting,one_hot_encoding,0.00013442810992750476,True,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1.0,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,109,median,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,8.0,None,13.0,16.0,1.0,28.0,,,,,,quantile_transformer,41502.0,uniform,, +weighting,one_hot_encoding,0.002615346832354839,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7884268823432835,None,0.0,20.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,110,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +weighting,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56.086963007482865,,,0.013609964993119377,rbf,-1.0,True,0.00196831255706268,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,111,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.15374716583918388 +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.2472984547885781,deviance,3.0,0.6564306719064884,None,0.0,15.0,14.0,0.0,220.0,0.8082564085714649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,112,median,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,332.0,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,113,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,115,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,116,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,117,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,118,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.20202014999292287,False,True,1.0,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,no_encoding,,,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6390376923528961,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,119,median,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,164.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,62508.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,120,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.27041927277584e-06,True,0.00010000000000000009,0.033157325660763994,True,0.0008114527992546482,invscaling,modified_huber,elasticnet,0.13714427818877545,0.055179642772545036,,,,,,,,,,,,,,,,,,121,median,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.7879059827470586,None,0.0,3.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,122,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54.1024239468849,f_classif,,,,quantile_transformer,89842.0,normal,, +weighting,one_hot_encoding,0.010000000000000005,True,decision_tree,,,,,,,entropy,1.5841974853345435,,1.0,None,0.0,8.0,14.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,123,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,25.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.19998727075532635,deviance,10.0,0.9377656718112952,None,0.0,7.0,13.0,0.0,214.0,0.6062346326014357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,124,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.000738320402221022,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,125,median,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.854322697199371,False,True,1.0,squared_hinge,ovr,l1,0.00013359426815085846,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,30.0,normal,, +weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0733000338152003,False,True,1.0,squared_hinge,ovr,l2,0.033752542733220474,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,126,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.6840756728731969,,0.00980445380551526,sigmoid,161.0,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.5765793990908161,None,0.0,11.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,127,median,fast_ica,,,,,,,,,,,deflation,exp,10.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7945458151995424,None,0.0,1.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,129,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,38400.0,uniform,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.04651467280022463,True,adaboost,SAMME,0.6506122230393502,6.0,143.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,extra_trees_preproc_for_classification,False,entropy,None,0.348720215832657,None,0.0,17.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,26025.0,normal,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3386310102795006,None,0.0,6.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,True,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.0,None,,5.861221938384061e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.22985730375167915,fpr,f_classif,quantile_transformer,40589.0,uniform,, +none,no_encoding,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00012437731009611307,True,,0.08709904468181932,True,,invscaling,squared_hinge,l1,0.6231564675290102,0.008071279833697563,,,,,,,,,,,,,,,,,,134,median,fast_ica,,,,,,,,,,,parallel,logcosh,814.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.714869937384006,None,0.0,8.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, diff --git a/metalearning/metalearning_files/precision_weighted_binary.classification_dense/description.txt b/metalearning/metalearning_files/precision_weighted_binary.classification_dense/description.txt new file mode 100755 index 0000000..29decd3 --- /dev/null +++ b/metalearning/metalearning_files/precision_weighted_binary.classification_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: precision_weighted +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/precision_weighted_binary.classification_dense/feature_costs.arff b/metalearning/metalearning_files/precision_weighted_binary.classification_dense/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/precision_weighted_binary.classification_dense/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/precision_weighted_binary.classification_dense/feature_runstatus.arff b/metalearning/metalearning_files/precision_weighted_binary.classification_dense/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/precision_weighted_binary.classification_dense/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/precision_weighted_binary.classification_dense/feature_values.arff b/metalearning/metalearning_files/precision_weighted_binary.classification_dense/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/precision_weighted_binary.classification_dense/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/precision_weighted_binary.classification_dense/readme.txt b/metalearning/metalearning_files/precision_weighted_binary.classification_dense/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/precision_weighted_binary.classification_sparse/algorithm_runs.arff b/metalearning/metalearning_files/precision_weighted_binary.classification_sparse/algorithm_runs.arff new file mode 100755 index 0000000..93bcafb --- /dev/null +++ b/metalearning/metalearning_files/precision_weighted_binary.classification_sparse/algorithm_runs.arff @@ -0,0 +1,152 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE precision_weighted NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2120,1.0,1,0.0914588058443484,ok +75193,1.0,2,0.03802311063996522,ok +2117,1.0,3,0.14600179503393484,ok +75156,1.0,4,0.2202371796139948,ok +75129,1.0,5,0.09739316144227628,ok +75243,1.0,6,0.015326980830657644,ok +75110,1.0,7,0.2791055112350753,ok +75239,1.0,8,0.0,ok +75223,1.0,9,0.30989160860389275,ok +75221,1.0,10,0.41604629436800145,ok +258,1.0,11,0.018275918726794016,ok +75121,1.0,12,0.0039132024379171515,ok +253,1.0,13,0.4118043355781812,ok +261,1.0,14,0.2449416835699797,ok +75240,1.0,15,0.02080660971852688,ok +75120,1.0,16,0.04692297703176518,ok +75124,1.0,17,0.09668072562393082,ok +75176,1.0,18,0.016579094876151057,ok +75103,1.0,19,0.007761648345969929,ok +75207,1.0,20,0.15680923713512063,ok +75095,1.0,21,0.01757107415002146,ok +273,1.0,22,0.04480426499945889,ok +75174,1.0,23,0.11874754260900566,ok +75153,1.0,24,0.12094450290177006,ok +75093,1.0,25,0.2046330800648536,ok +75119,1.0,26,0.03094839521094317,ok +75201,1.0,27,0.07984962449794286,ok +75215,1.0,28,0.028143815911082615,ok +75172,1.0,29,0.0785068594257502,ok +75169,1.0,30,0.033784207000311706,ok +75202,1.0,31,0.2320892056839039,ok +75233,1.0,32,0.06556145836437832,ok +75231,1.0,33,0.1651825246227523,ok +75196,1.0,34,0.023410600507795865,ok +248,1.0,35,0.26626032168990044,ok +75191,1.0,36,0.11888148413028976,ok +75217,1.0,37,0.0,ok +260,1.0,38,0.027127490200162252,ok +75115,1.0,39,0.017854592566009853,ok +75123,1.0,40,0.31171476267136955,ok +75108,1.0,41,0.0018295873778977345,ok +75101,1.0,42,0.28289397954182716,ok +75192,1.0,43,0.4824466988315518,ok +75232,1.0,44,0.1483079896976507,ok +75173,1.0,45,0.11725056741521045,ok +75197,1.0,46,0.1365155356818566,ok +266,1.0,47,0.03227496507340599,ok +75148,1.0,48,0.19002713209581124,ok +75150,1.0,49,0.31994234606367644,ok +75100,1.0,50,0.005577448615881275,ok +75178,1.0,51,0.8083205011237397,ok +75236,1.0,52,0.03185030716965764,ok +75179,1.0,53,0.17908750793261574,ok +75213,1.0,54,0.05064961320936123,ok +2123,1.0,55,0.06664228507628078,ok +75227,1.0,56,0.10178562268737412,ok +75184,1.0,57,0.1374114281944938,ok +75142,1.0,58,0.08131090777123995,ok +236,1.0,59,0.04173036030013244,ok +2122,1.0,60,0.2791055112350753,ok +75188,1.0,61,0.21503227998943397,ok +75166,1.0,62,0.09945031583527553,ok +75181,1.0,63,0.0,ok +75133,1.0,64,0.0058744901587642895,ok +75134,1.0,65,0.0967448909673646,ok +75198,1.0,66,0.12025000319215506,ok +262,1.0,67,0.002751972512609613,ok +75234,1.0,68,0.05769681514618219,ok +75139,1.0,69,0.012112268734996712,ok +252,1.0,70,0.16921296956991572,ok +75117,1.0,71,0.05184242948952755,ok +75113,1.0,72,0.006590920947779688,ok +75098,1.0,73,0.027471753088248563,ok +246,1.0,74,0.023766136391797477,ok +75203,1.0,75,0.09549480420909973,ok +75237,1.0,76,0.00039539777599506554,ok +75195,1.0,77,0.001560114030179971,ok +75171,1.0,78,0.16670851818300347,ok +75128,1.0,79,0.02232168365143683,ok +75096,1.0,80,0.37762113409098086,ok +75250,1.0,81,0.34154961033958486,ok +75146,1.0,82,0.12092830470068494,ok +75116,1.0,83,0.009857921438566342,ok +75157,1.0,84,0.42721657693375314,ok +75187,1.0,85,0.027829779959187895,ok +2350,1.0,86,0.4107400261716929,ok +242,1.0,87,0.016283152149369595,ok +244,1.0,88,0.11022260983726562,ok +75125,1.0,89,0.035831177861333385,ok +75185,1.0,90,0.12773060459472851,ok +75163,1.0,91,0.06024005149369582,ok +75177,1.0,92,0.019059036742538282,ok +75189,1.0,93,0.01935454704780004,ok +75244,1.0,94,0.06135430801666708,ok +75219,1.0,95,0.08320902919630935,ok +75222,1.0,96,0.04781443570706845,ok +75159,1.0,97,0.0776637730203249,ok +75175,1.0,98,0.11665288784872319,ok +75109,1.0,99,0.33304613280657847,ok +254,1.0,100,0.0,ok +75105,1.0,101,0.031779437551376244,ok +75106,1.0,102,0.10293221951604781,ok +75212,1.0,103,0.27713228689731584,ok +75099,1.0,104,0.14036952256376722,ok +75248,1.0,105,0.08421507663826677,ok +233,1.0,106,0.015180265654648917,ok +75235,1.0,107,0.0016658985700102047,ok +75226,1.0,108,0.004267314764131003,ok +75132,1.0,109,0.07681019978505155,ok +75127,1.0,110,0.3348237349905919,ok +251,1.0,111,0.022689055973266536,ok +75161,1.0,112,0.08277295403500784,ok +75143,1.0,113,0.010482521920672894,ok +75114,1.0,114,0.023575638506876273,ok +75182,1.0,115,0.10979433133488292,ok +75112,1.0,116,0.12095417307543233,ok +75210,1.0,117,0.0,ok +75205,1.0,118,0.18099003287994075,ok +75090,1.0,119,0.09805748376790224,ok +275,1.0,120,0.03613820618166086,ok +288,1.0,121,0.14230766359347347,ok +75092,1.0,122,0.06900191530112798,ok +3043,1.0,123,0.01972215046168735,ok +75249,1.0,124,0.0048510434289000814,ok +75126,1.0,125,0.05003141198539285,ok +75225,1.0,126,0.04555750377260681,ok +75141,1.0,127,0.05775410437107076,ok +75107,1.0,128,0.05958493591294611,ok +75097,1.0,129,0.08507551646212685,ok +80001,1.0,1,0.07050008279516484,ok +80003,1.0,1,0.1213765182186235,ok +80006,1.0,1,0.1691017316017316,ok +80008,1.0,1,0.14814814814814825,ok +80009,1.0,1,0.15321637426900592,ok +80010,1.0,1,0.11494252873563215,ok +80011,1.0,1,0.03563941299790363,ok +80012,1.0,1,0.042424242424242475,ok +80013,1.0,1,0.0,ok +80014,1.0,1,0.04195804195804198,ok +80015,1.0,1,0.09523809523809523,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/precision_weighted_binary.classification_sparse/configurations.csv b/metalearning/metalearning_files/precision_weighted_binary.classification_sparse/configurations.csv new file mode 100755 index 0000000..4d2e097 --- /dev/null +++ b/metalearning/metalearning_files/precision_weighted_binary.classification_sparse/configurations.csv @@ -0,0 +1,141 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,preprocessor:truncatedSVD:target_dim,rescaling:__choice__ +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7249853037185638,None,0.0,1.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,median,extra_trees_preproc_for_classification,False,gini,None,0.9424908623661876,None,0.0,7.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.585711203872775,None,0.0,5.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,median,extra_trees_preproc_for_classification,True,entropy,None,0.29512530534048065,None,0.0,7.0,10.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.002655175930910743,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3997691365353215,None,0.0,20.0,10.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,median,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,15.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7238850981243719,None,0.0,4.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,5.282738216059151e-05,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.6682079659377479,None,0.0,4.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,mean,extra_trees_preproc_for_classification,True,entropy,None,0.5552350997943013,None,0.0,8.0,5.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7974565919616314,None,0.0,12.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,median,extra_trees_preproc_for_classification,True,entropy,None,0.9772091846790169,None,0.0,10.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2398150931290834,,,0.4015139801872962,rbf,-1.0,False,2.402997750662158e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88.40698357592571,chi2,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.034465366914659866,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.12057775675278172,deviance,10.0,0.8011153303489733,None,0.0,2.0,16.0,0.0,370.0,0.6078295352200873,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,mean,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0007038280350320558,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4138778052607317,0.7995003430482459,5.0,5.430044692638861,poly,-1.0,True,0.02455501006004393,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,31,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.0010015637584068037,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.037611630308856295,deviance,5.0,0.8840126779516314,None,0.0,10.0,2.0,0.0,444.0,0.7599997167603434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,most_frequent,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1751.4736515133566,0.62404114475118,3.0,1.6087076997410432,poly,-1.0,False,3.5353792826856045e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,mean,kernel_pca,,,,,,,,,,,,,,cosine,1198.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.8657388713119849,,1.0,None,0.0,19.0,13.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828776,None,0.0,2.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.027324741616523342,deviance,10.0,0.8623781459430139,None,0.0,10.0,20.0,0.0,329.0,0.8595750155424215,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,mean,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.002173124111626734,None,0.0,14.0,4.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,6.0,None,13.0,2.0,1.0,23.0,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1100.6211008501205,0.5921425829232616,2.0,0.0337546254878617,poly,-1.0,True,0.09641299736884308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2507440474920336e-05,True,,0.049622652766554566,True,0.009105043727227265,constant,squared_hinge,elasticnet,,0.00010112719671669047,,,,,,,,,,,,,,,,,,53,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61.69949680034141,chi2,,,,,none +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.9348409326933208,rbf,-1.0,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,mean,kernel_pca,,,,,,,,,,,,,,cosine,1754.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3428971645958825,,,0.2229870623330047,rbf,-1.0,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7322410244259964,None,0.0,6.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,median,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00016781524591321165,True,True,squared_hinge,1.5119200923218881e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.349459944355116,,,0.00024028983491736645,rbf,-1.0,True,1.139421622432356e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3636266268105085,fwe,chi2,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4047.618729304337,,,2.0237366768707754,rbf,-1.0,True,0.04369127828878843,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,one_hot_encoding,0.010000000000000005,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10.0,1.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,,none +weighting,one_hot_encoding,0.0008685602750053468,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9896334290292654,None,0.0,11.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,141.76310800864283,False,True,1.0,squared_hinge,ovr,l1,0.004317884655117431,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,,normalize +none,one_hot_encoding,0.003443779683191071,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.004980497345831963,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1255.9137433589426,,,0.08351549479967445,rbf,-1.0,True,0.00017919875199222518,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1.0,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.3451627750042988,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.3163640203509378,None,0.0,17.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,median,extra_trees_preproc_for_classification,False,gini,None,0.8916956785028156,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.002630811782675973,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9828367182452932,None,0.0,18.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,85,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.35533396539961937,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,86,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4165632766388807,fpr,chi2,,none +weighting,one_hot_encoding,0.0019686649916896212,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.04641055832142541,True,True,hinge,8.540468968077405e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87,mean,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,911.0,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1.0,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,89,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,90,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,91,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,93,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.20875514426569566,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.53183372054125,None,0.0,18.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,13818.683783129034,False,True,1.0,squared_hinge,ovr,l1,1.009528987119941e-05,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,95,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,96,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,97,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,98,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,99,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,100,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,102,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9091193924897338,None,0.0,2.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,103,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,104,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.039533063907190934,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,105,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1878805519245509,fdr,chi2,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,106,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.006372860318416312,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6467376360604045,None,0.0,1.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,107,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,108,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,109,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3823734947460288,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,110,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,adaboost,SAMME,0.11042308042695524,5.0,117.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,111,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,112,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,113,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,115,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,116,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,117,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,118,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.20202014999292287,False,True,1.0,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,119,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,120,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,121,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8804227616935514,None,0.0,10.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,122,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.0898983119899223,False,True,1.0,squared_hinge,ovr,l1,0.016048982920294167,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,123,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,124,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,125,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0733000338152003,False,True,1.0,squared_hinge,ovr,l2,0.033752542733220474,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,126,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,-0.6840756728731969,,0.00980445380551526,sigmoid,161.0,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,127,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,129,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.04329010470998136,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7260331062271381,None,0.0,7.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.010000000000000005,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15.0,1.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,134,median,extra_trees_preproc_for_classification,False,entropy,None,0.8213051377774989,None,0.0,3.0,13.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.714869937384006,None,0.0,8.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/metalearning/metalearning_files/precision_weighted_binary.classification_sparse/description.txt b/metalearning/metalearning_files/precision_weighted_binary.classification_sparse/description.txt new file mode 100755 index 0000000..29decd3 --- /dev/null +++ b/metalearning/metalearning_files/precision_weighted_binary.classification_sparse/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: precision_weighted +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/precision_weighted_binary.classification_sparse/feature_costs.arff b/metalearning/metalearning_files/precision_weighted_binary.classification_sparse/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/precision_weighted_binary.classification_sparse/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/precision_weighted_binary.classification_sparse/feature_runstatus.arff b/metalearning/metalearning_files/precision_weighted_binary.classification_sparse/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/precision_weighted_binary.classification_sparse/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/precision_weighted_binary.classification_sparse/feature_values.arff b/metalearning/metalearning_files/precision_weighted_binary.classification_sparse/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/precision_weighted_binary.classification_sparse/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/precision_weighted_binary.classification_sparse/readme.txt b/metalearning/metalearning_files/precision_weighted_binary.classification_sparse/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/precision_weighted_multiclass.classification_dense/algorithm_runs.arff b/metalearning/metalearning_files/precision_weighted_multiclass.classification_dense/algorithm_runs.arff new file mode 100755 index 0000000..e7e621f --- /dev/null +++ b/metalearning/metalearning_files/precision_weighted_multiclass.classification_dense/algorithm_runs.arff @@ -0,0 +1,152 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE precision_weighted NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2120,1.0,1,0.08022477157951025,ok +75193,1.0,2,0.03802311063996522,ok +2117,1.0,3,0.14265765376577388,ok +75156,1.0,4,0.20319334833220415,ok +75129,1.0,5,0.08669351050724516,ok +75243,1.0,6,0.0,ok +75110,1.0,7,0.11585531099100344,ok +75239,1.0,8,0.0,ok +75223,1.0,9,0.10308991908426168,ok +75221,1.0,10,0.4027914897274013,ok +258,1.0,11,0.009652166726836442,ok +75121,1.0,12,0.0,ok +253,1.0,13,0.3971756304204712,ok +261,1.0,14,0.2449416835699797,ok +75240,1.0,15,0.02080660971852688,ok +75120,1.0,16,0.04153101819938165,ok +75124,1.0,17,0.08426824349929629,ok +75176,1.0,18,0.015417968255781567,ok +75103,1.0,19,0.005399083293820106,ok +75207,1.0,20,0.15680923713512063,ok +75095,1.0,21,0.01757107415002146,ok +273,1.0,22,0.04414982125934308,ok +75174,1.0,23,0.11405408911206805,ok +75153,1.0,24,0.08026959777662745,ok +75093,1.0,25,0.2046330800648536,ok +75119,1.0,26,0.03094839521094317,ok +75201,1.0,27,0.07984962449794286,ok +75215,1.0,28,0.027400741489915892,ok +75172,1.0,29,0.090985809925204,ok +75169,1.0,30,0.033784207000311706,ok +75202,1.0,31,0.2320892056839039,ok +75233,1.0,32,0.06082093106795827,ok +75231,1.0,33,0.1651825246227523,ok +75196,1.0,34,0.015215605393662401,ok +248,1.0,35,0.226396668333682,ok +75191,1.0,36,0.12497876304132682,ok +75217,1.0,37,0.0,ok +260,1.0,38,0.027127490200162252,ok +75115,1.0,39,0.017854592566009853,ok +75123,1.0,40,0.31171476267136955,ok +75108,1.0,41,0.0,ok +75101,1.0,42,0.27420411371363684,ok +75192,1.0,43,0.4824466988315518,ok +75232,1.0,44,0.1233010687638636,ok +75173,1.0,45,0.1164184654283339,ok +75197,1.0,46,0.15862792301177187,ok +266,1.0,47,0.019389161056556747,ok +75148,1.0,48,0.1350453378185531,ok +75150,1.0,49,0.2782413408164295,ok +75100,1.0,50,0.0054590998300326765,ok +75178,1.0,51,0.7570393510813636,ok +75236,1.0,52,0.03185030716965764,ok +75179,1.0,53,0.17908750793261574,ok +75213,1.0,54,0.05064961320936123,ok +2123,1.0,55,0.06664228507628078,ok +75227,1.0,56,0.10178562268737412,ok +75184,1.0,57,0.10782255468946933,ok +75142,1.0,58,0.0715535466788122,ok +236,1.0,59,0.03833279025178438,ok +2122,1.0,60,0.1090803225748227,ok +75188,1.0,61,0.21503227998943397,ok +75166,1.0,62,0.09945031583527553,ok +75181,1.0,63,0.0,ok +75133,1.0,64,0.0058744901587642895,ok +75134,1.0,65,0.08702477561737332,ok +75198,1.0,66,0.12025000319215506,ok +262,1.0,67,0.002751972512609613,ok +75234,1.0,68,0.024088457176147382,ok +75139,1.0,69,0.010714278670329258,ok +252,1.0,70,0.15278932029242331,ok +75117,1.0,71,0.05184242948952755,ok +75113,1.0,72,0.006479553075845801,ok +75098,1.0,73,0.027471753088248563,ok +246,1.0,74,0.01051167211381765,ok +75203,1.0,75,0.09549480420909973,ok +75237,1.0,76,0.00039539777599506554,ok +75195,1.0,77,0.001560114030179971,ok +75171,1.0,78,0.1620392045550726,ok +75128,1.0,79,0.02232168365143683,ok +75096,1.0,80,0.005585999087350801,ok +75250,1.0,81,0.31437017866239836,ok +75146,1.0,82,0.11227759906567902,ok +75116,1.0,83,0.009857921438566342,ok +75157,1.0,84,0.42721657693375314,ok +75187,1.0,85,0.016775752742081207,ok +2350,1.0,86,0.4107400261716929,ok +242,1.0,87,0.010497196266900377,ok +244,1.0,88,0.11022260983726562,ok +75125,1.0,89,0.033798127147098844,ok +75185,1.0,90,0.12773060459472851,ok +75163,1.0,91,0.06024005149369582,ok +75177,1.0,92,0.017495768774297926,ok +75189,1.0,93,0.01935454704780004,ok +75244,1.0,94,0.059028736950657135,ok +75219,1.0,95,0.03480221342625667,ok +75222,1.0,96,0.04781443570706845,ok +75159,1.0,97,0.07331254920484964,ok +75175,1.0,98,0.09974423015772416,ok +75109,1.0,99,0.3066035817140491,ok +254,1.0,100,0.0,ok +75105,1.0,101,0.02382020105699334,ok +75106,1.0,102,0.09647572803854332,ok +75212,1.0,103,0.2493001484774634,ok +75099,1.0,104,0.13310959982764126,ok +75248,1.0,105,0.07634255384916955,ok +233,1.0,106,0.0028441203787273883,ok +75235,1.0,107,0.0011079987550576265,ok +75226,1.0,108,0.0030443004196948342,ok +75132,1.0,109,0.06647783581906219,ok +75127,1.0,110,0.33347516619713424,ok +251,1.0,111,0.0,ok +75161,1.0,112,0.06435771828213677,ok +75143,1.0,113,0.010482521920672894,ok +75114,1.0,114,0.023575638506876273,ok +75182,1.0,115,0.10979433133488292,ok +75112,1.0,116,0.12095417307543233,ok +75210,1.0,117,0.0,ok +75205,1.0,118,0.18099003287994075,ok +75090,1.0,119,0.05949999802669681,ok +275,1.0,120,0.03613820618166086,ok +288,1.0,121,0.12845154789859747,ok +75092,1.0,122,0.06477381636956114,ok +3043,1.0,123,0.018200350964109324,ok +75249,1.0,124,0.0032279797849654734,ok +75126,1.0,125,0.04846397960751947,ok +75225,1.0,126,0.04555750377260681,ok +75141,1.0,127,0.05271470493960073,ok +75107,1.0,128,0.05958493591294611,ok +75097,1.0,129,0.0596624483932221,ok +80001,1.0,1,0.07050008279516484,ok +80003,1.0,1,0.08928461376279206,ok +80006,1.0,1,0.0925099206349207,ok +80008,1.0,1,0.09259259259259245,ok +80009,1.0,1,0.10526315789473684,ok +80010,1.0,1,0.11494252873563215,ok +80011,1.0,1,0.03563941299790363,ok +80012,1.0,1,0.042424242424242475,ok +80013,1.0,1,0.0,ok +80014,1.0,1,0.04195804195804198,ok +80015,1.0,1,0.09523809523809523,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/precision_weighted_multiclass.classification_dense/configurations.csv b/metalearning/metalearning_files/precision_weighted_multiclass.classification_dense/configurations.csv new file mode 100755 index 0000000..97ae5c1 --- /dev/null +++ b/metalearning/metalearning_files/precision_weighted_multiclass.classification_dense/configurations.csv @@ -0,0 +1,141 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:fast_ica:algorithm,preprocessor:fast_ica:fun,preprocessor:fast_ica:n_components,preprocessor:fast_ica:whiten,preprocessor:feature_agglomeration:affinity,preprocessor:feature_agglomeration:linkage,preprocessor:feature_agglomeration:n_clusters,preprocessor:feature_agglomeration:pooling_func,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:pca:keep_variance,preprocessor:pca:whiten,preprocessor:polynomial:degree,preprocessor:polynomial:include_bias,preprocessor:polynomial:interaction_only,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,rescaling:__choice__,rescaling:quantile_transformer:n_quantiles,rescaling:quantile_transformer:output_distribution,rescaling:robust_scaler:q_max,rescaling:robust_scaler:q_min +weighting,one_hot_encoding,0.00011717632475982632,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.045388141846341344,deviance,10.0,0.29161769341843435,None,0.0,20.0,2.0,0.0,278.0,0.7912571599269661,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7249853037185638,None,0.0,1.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,median,extra_trees_preproc_for_classification,False,gini,None,0.9424908623661876,None,0.0,7.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.12713527337147906,deviance,4.0,0.6041596127474019,None,0.0,14.0,17.0,0.0,83.0,0.8426859880999615,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.03474109838999682,deviance,4.0,0.5687034678818491,None,0.0,18.0,12.0,0.0,408.0,0.5150113945430513,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.6328939517938,f_classif,,,,standardize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9265375980300852,None,0.0,13.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.251097788175676,deviance,6.0,0.35679099363539235,None,0.0,13.0,11.0,0.0,157.0,0.4791732272983235,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,adaboost,SAMME,0.28738775989203896,10.0,423.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.8031499675923353,0.13579938270386765 +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.14159526341015916,deviance,7.0,0.8010488230155749,None,0.0,3.0,20.0,0.0,401.0,0.8073562440607731,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3469031665162168,None,0.0,4.0,3.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,mean,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,83.0,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.002385546176068135,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.7729472304882845,,,0.0004789329856033374,rbf,-1.0,True,6.58869648864534e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,177.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5368752992317617,None,0.0,16.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.8382117756438685,mutual_info,,,,quantile_transformer,11480.0,normal,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.8384447520019118,None,0.0,13.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.36637567531287824,fdr,f_classif,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.6291601746046639,None,0.0,11.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,median,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7146659106968425,True,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.12713527337147906,deviance,4.0,0.6041596127474019,None,0.0,14.0,17.0,0.0,83.0,0.8426859880999615,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9455638720565652,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,most_frequent,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8255464552647293,0.19162485555463185 +weighting,one_hot_encoding,0.18137532678800647,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9094110110427254,None,0.0,7.0,12.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,195.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.6682079659377479,None,0.0,4.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,mean,extra_trees_preproc_for_classification,True,entropy,None,0.5552350997943013,None,0.0,8.0,5.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.02345017287074443,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.053517066400173056,deviance,10.0,0.542144980834302,None,0.0,20.0,13.0,0.0,233.0,0.7398539900055563,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0614425536709615,fwe,f_classif,robust_scaler,,,0.9523118062307264,0.13434811490315818 +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.8149627329153046,None,0.0,15.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.040936424602789435,deviance,7.0,0.5495014745530306,None,0.0,20.0,18.0,0.0,141.0,0.6905343807995293,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7974565919616314,None,0.0,12.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,median,extra_trees_preproc_for_classification,True,entropy,None,0.9772091846790169,None,0.0,10.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2398150931290834,,,0.4015139801872962,rbf,-1.0,False,2.402997750662158e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88.40698357592571,chi2,,,,normalize,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.1958974686405233,deviance,5.0,0.33885235607979314,None,0.0,6.0,4.0,0.0,125.0,0.9448890820738562,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0007038280350320558,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4138778052607317,0.7995003430482459,5.0,5.430044692638861,poly,-1.0,True,0.02455501006004393,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,31,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.010000000000000005,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.0518326156691958,deviance,6.0,0.8807456180216267,None,0.0,7.0,19.0,0.0,366.0,0.7314831276137047,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,,False,adaboost,SAMME,0.4391375941344922,3.0,386.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7439738358430176,0.20581080574615795 +none,one_hot_encoding,0.00011294596229850895,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7515.1213255144885,0.9576762936062476,3.0,0.019002536385919932,poly,-1.0,False,0.010632086351533369,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,51.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,97282.0,normal,, +none,one_hot_encoding,0.00012586572428922356,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5240592829918601,None,0.0,10.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1751.4736515133566,0.62404114475118,3.0,1.6087076997410432,poly,-1.0,False,3.5353792826856045e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1198.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.2422926485206341,,1.0,None,0.0,15.0,9.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.02102683283349326,deviance,10.0,0.2797288369369436,None,0.0,14.0,9.0,0.0,480.0,0.5778972273820631,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58.88123233170863,mutual_info,,,,robust_scaler,,,0.75,0.25 +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828776,None,0.0,2.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2155613360930585,deviance,4.0,0.3198803116198433,None,0.0,8.0,13.0,0.0,275.0,0.28870176110739404,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,mean,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.7062102387181676,None,0.0,1.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,most_frequent,fast_ica,,,,,,,,,,,parallel,exp,100.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7065776353150109,0.23782974987118105 +none,one_hot_encoding,0.16334152321884812,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00011572473434870852,True,True,squared_hinge,0.00019678754114665057,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.045742431094098604,fpr,chi2,none,,,, +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.3795924768593385,deviance,2.0,0.33708497069988536,None,0.0,15.0,13.0,0.0,451.0,0.7716323242090217,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2573946506994812,fwe,f_classif,quantile_transformer,1000.0,uniform,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.609975998293528,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,most_frequent,fast_ica,,,,,,,,,,,parallel,logcosh,2000.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8430415644014919,0.2863750565331575 +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.39536192447534535,None,0.0,19.0,3.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5.0,None,11.0,11.0,1.0,12.0,,,,,,robust_scaler,,,0.8928631650245873,0.1581877760687084 +weighting,one_hot_encoding,0.0010446150978844174,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.2100039691650936,None,0.0,19.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,10.0,None,3.0,18.0,1.0,42.0,,,,,,quantile_transformer,44341.0,uniform,, +weighting,one_hot_encoding,0.3126027672745337,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1100.6211008501205,0.5921425829232616,2.0,0.0337546254878617,poly,-1.0,True,0.09641299736884308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2507440474920336e-05,True,,0.049622652766554566,True,0.009105043727227265,constant,squared_hinge,elasticnet,,0.00010112719671669047,,,,,,,,,,,,,,,,,,53,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61.69949680034141,chi2,,,,none,,,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.9348409326933208,rbf,-1.0,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1754.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,198.72528686512538,False,True,1.0,squared_hinge,ovr,l2,0.026260652523566803,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,robust_scaler,,,0.913511520078368,0.2742229325455444 +none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9260795160807372,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.03644212536682547,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.05965671477918361,deviance,8.0,0.4858133247974158,None,0.0,14.0,7.0,0.0,480.0,0.5726186552917335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.15318294164619112 +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18887.81504976871,,,0.23283562663398755,rbf,-1.0,True,2.3839685780861318e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3937607155568376,fwe,chi2,robust_scaler,,,0.941018778984854,0.2144110585080491 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3428971645958825,,,0.2229870623330047,rbf,-1.0,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8138233157708883,None,0.0,2.0,9.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54.27504292568562,f_classif,,,,minmax,,,, +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00016781524591321165,True,True,squared_hinge,1.5119200923218881e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.349459944355116,,,0.00024028983491736645,rbf,-1.0,True,1.139421622432356e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3636266268105085,fwe,chi2,none,,,, +weighting,one_hot_encoding,0.00034835629696198427,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8245132980938538,0.08947420373097192 +weighting,one_hot_encoding,0.00016967940959070708,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9439080311935252,None,0.0,2.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35.0,None,,0.005389483044810356,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,most_frequent,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,9.441661069727422e-05,1896.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.0008685602750053468,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9896334290292654,None,0.0,11.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,141.76310800864283,False,True,1.0,squared_hinge,ovr,l1,0.004317884655117431,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.7696635139179984,None,0.0,7.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,mean,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,261.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,normalize,,,, +none,one_hot_encoding,,False,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6396026761675004,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06544340428506021,fwe,f_classif,none,,,, +weighting,one_hot_encoding,0.004980497345831963,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1255.9137433589426,,,0.08351549479967445,rbf,-1.0,True,0.00017919875199222518,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1.0,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.3837398524575939,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.018356703878357986,deviance,3.0,0.9690352514774068,None,0.0,12.0,3.0,0.0,234.0,0.3870344708308441,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,most_frequent,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6864970915330799,False,,,,,,,,,,,,,,,,normalize,,,, +weighting,one_hot_encoding,0.0001486770773839718,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5256280540657592,None,0.0,8.0,12.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5670424455696162,None,0.0,8.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,standardize,,,, +weighting,one_hot_encoding,0.00031737236118003484,True,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,244.0,None,,2.3065111488706024e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,85,mean,fast_ica,,,,,,,,,,,deflation,exp,1862.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7851234479882973,0.2237528085136715 +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.35533396539961937,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,86,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4165632766388807,fpr,chi2,none,,,, +weighting,one_hot_encoding,,False,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,156.0,auto,,0.0001987333852871589,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1.0,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,no_encoding,,,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.96834823420249e-05,False,True,hinge,0.00016639250831671168,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,89,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.628430584359224,mutual_info,,,,quantile_transformer,6634.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,90,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,91,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,8.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,93,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0387325491437111,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.32034732923549136,None,0.0,20.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94,median,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,quantile_transformer,1000.0,uniform,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2636201374253461,deviance,7.0,0.8344964130784466,None,0.0,9.0,2.0,0.0,298.0,0.7517549950523315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,95,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,96,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.2263596964804377,True,adaboost,SAMME,0.15143691959318842,2.0,233.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,97,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.07951518163998639,fwe,f_classif,minmax,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.07463196642416367,deviance,7.0,0.8603242247379981,None,0.0,2.0,6.0,0.0,500.0,0.8447665577491962,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,98,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.0433556140045585,deviance,10.0,0.33000096635982235,None,0.0,15.0,13.0,0.0,388.0,0.8291104221904706,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,99,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,robust_scaler,,,0.7496393440951183,0.2853682991120835 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,100,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.6960837129194184,,,0.0004821713821548537,rbf,-1.0,False,0.040691702958042086,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,most_frequent,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6520469084729728,False,,,,,,,,,,,,,,,,quantile_transformer,97329.0,normal,, +weighting,one_hot_encoding,0.010000000000000005,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.04952755495565772,deviance,3.0,0.7249041896998006,None,0.0,6.0,17.0,0.0,174.0,0.3718608680080454,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,102,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.0020580843703898177,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.945774573434192,None,0.0,19.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,103,median,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,15209.0,normal,, +weighting,no_encoding,,,bernoulli_nb,,,,,0.023108348799909733,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,104,median,extra_trees_preproc_for_classification,True,gini,None,0.43664414575861454,None,0.0,1.0,4.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8569382605464568,0.07491671292996571 +weighting,one_hot_encoding,0.10324969243867224,True,decision_tree,,,,,,,gini,0.7467478023293801,,1.0,None,0.0,12.0,8.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,105,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84.22876326806853,mutual_info,,,,minmax,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,106,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.005332972692819521,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5565918060287016,None,0.0,5.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,107,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,173.0,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9527068489270144,0.04135311355893583 +weighting,one_hot_encoding,0.001279467383882126,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,108,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0903354518102121,fwe,f_classif,standardize,,,, +weighting,one_hot_encoding,0.00013442810992750476,True,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1.0,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,109,median,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,8.0,None,13.0,16.0,1.0,28.0,,,,,,quantile_transformer,41502.0,uniform,, +weighting,one_hot_encoding,0.002615346832354839,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7884268823432835,None,0.0,20.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,110,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +weighting,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56.086963007482865,,,0.013609964993119377,rbf,-1.0,True,0.00196831255706268,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,111,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.15374716583918388 +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.2472984547885781,deviance,3.0,0.6564306719064884,None,0.0,15.0,14.0,0.0,220.0,0.8082564085714649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,112,median,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,332.0,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,113,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,115,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,116,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,117,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,118,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.20202014999292287,False,True,1.0,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,no_encoding,,,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6390376923528961,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,119,median,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,164.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,62508.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,120,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.27041927277584e-06,True,0.00010000000000000009,0.033157325660763994,True,0.0008114527992546482,invscaling,modified_huber,elasticnet,0.13714427818877545,0.055179642772545036,,,,,,,,,,,,,,,,,,121,median,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.7879059827470586,None,0.0,3.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,122,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54.1024239468849,f_classif,,,,quantile_transformer,89842.0,normal,, +weighting,one_hot_encoding,0.010000000000000005,True,decision_tree,,,,,,,entropy,1.5841974853345435,,1.0,None,0.0,8.0,14.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,123,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,25.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.19998727075532635,deviance,10.0,0.9377656718112952,None,0.0,7.0,13.0,0.0,214.0,0.6062346326014357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,124,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.000738320402221022,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,125,median,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.854322697199371,False,True,1.0,squared_hinge,ovr,l1,0.00013359426815085846,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,30.0,normal,, +weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0733000338152003,False,True,1.0,squared_hinge,ovr,l2,0.033752542733220474,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,126,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.6840756728731969,,0.00980445380551526,sigmoid,161.0,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.5765793990908161,None,0.0,11.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,127,median,fast_ica,,,,,,,,,,,deflation,exp,10.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7945458151995424,None,0.0,1.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,129,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,38400.0,uniform,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.04651467280022463,True,adaboost,SAMME,0.6506122230393502,6.0,143.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,extra_trees_preproc_for_classification,False,entropy,None,0.348720215832657,None,0.0,17.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,26025.0,normal,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3386310102795006,None,0.0,6.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,True,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.0,None,,5.861221938384061e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.22985730375167915,fpr,f_classif,quantile_transformer,40589.0,uniform,, +none,no_encoding,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00012437731009611307,True,,0.08709904468181932,True,,invscaling,squared_hinge,l1,0.6231564675290102,0.008071279833697563,,,,,,,,,,,,,,,,,,134,median,fast_ica,,,,,,,,,,,parallel,logcosh,814.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.714869937384006,None,0.0,8.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, diff --git a/metalearning/metalearning_files/precision_weighted_multiclass.classification_dense/description.txt b/metalearning/metalearning_files/precision_weighted_multiclass.classification_dense/description.txt new file mode 100755 index 0000000..29decd3 --- /dev/null +++ b/metalearning/metalearning_files/precision_weighted_multiclass.classification_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: precision_weighted +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/precision_weighted_multiclass.classification_dense/feature_costs.arff b/metalearning/metalearning_files/precision_weighted_multiclass.classification_dense/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/precision_weighted_multiclass.classification_dense/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/precision_weighted_multiclass.classification_dense/feature_runstatus.arff b/metalearning/metalearning_files/precision_weighted_multiclass.classification_dense/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/precision_weighted_multiclass.classification_dense/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/precision_weighted_multiclass.classification_dense/feature_values.arff b/metalearning/metalearning_files/precision_weighted_multiclass.classification_dense/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/precision_weighted_multiclass.classification_dense/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/precision_weighted_multiclass.classification_dense/readme.txt b/metalearning/metalearning_files/precision_weighted_multiclass.classification_dense/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/precision_weighted_multiclass.classification_sparse/algorithm_runs.arff b/metalearning/metalearning_files/precision_weighted_multiclass.classification_sparse/algorithm_runs.arff new file mode 100755 index 0000000..93bcafb --- /dev/null +++ b/metalearning/metalearning_files/precision_weighted_multiclass.classification_sparse/algorithm_runs.arff @@ -0,0 +1,152 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE precision_weighted NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2120,1.0,1,0.0914588058443484,ok +75193,1.0,2,0.03802311063996522,ok +2117,1.0,3,0.14600179503393484,ok +75156,1.0,4,0.2202371796139948,ok +75129,1.0,5,0.09739316144227628,ok +75243,1.0,6,0.015326980830657644,ok +75110,1.0,7,0.2791055112350753,ok +75239,1.0,8,0.0,ok +75223,1.0,9,0.30989160860389275,ok +75221,1.0,10,0.41604629436800145,ok +258,1.0,11,0.018275918726794016,ok +75121,1.0,12,0.0039132024379171515,ok +253,1.0,13,0.4118043355781812,ok +261,1.0,14,0.2449416835699797,ok +75240,1.0,15,0.02080660971852688,ok +75120,1.0,16,0.04692297703176518,ok +75124,1.0,17,0.09668072562393082,ok +75176,1.0,18,0.016579094876151057,ok +75103,1.0,19,0.007761648345969929,ok +75207,1.0,20,0.15680923713512063,ok +75095,1.0,21,0.01757107415002146,ok +273,1.0,22,0.04480426499945889,ok +75174,1.0,23,0.11874754260900566,ok +75153,1.0,24,0.12094450290177006,ok +75093,1.0,25,0.2046330800648536,ok +75119,1.0,26,0.03094839521094317,ok +75201,1.0,27,0.07984962449794286,ok +75215,1.0,28,0.028143815911082615,ok +75172,1.0,29,0.0785068594257502,ok +75169,1.0,30,0.033784207000311706,ok +75202,1.0,31,0.2320892056839039,ok +75233,1.0,32,0.06556145836437832,ok +75231,1.0,33,0.1651825246227523,ok +75196,1.0,34,0.023410600507795865,ok +248,1.0,35,0.26626032168990044,ok +75191,1.0,36,0.11888148413028976,ok +75217,1.0,37,0.0,ok +260,1.0,38,0.027127490200162252,ok +75115,1.0,39,0.017854592566009853,ok +75123,1.0,40,0.31171476267136955,ok +75108,1.0,41,0.0018295873778977345,ok +75101,1.0,42,0.28289397954182716,ok +75192,1.0,43,0.4824466988315518,ok +75232,1.0,44,0.1483079896976507,ok +75173,1.0,45,0.11725056741521045,ok +75197,1.0,46,0.1365155356818566,ok +266,1.0,47,0.03227496507340599,ok +75148,1.0,48,0.19002713209581124,ok +75150,1.0,49,0.31994234606367644,ok +75100,1.0,50,0.005577448615881275,ok +75178,1.0,51,0.8083205011237397,ok +75236,1.0,52,0.03185030716965764,ok +75179,1.0,53,0.17908750793261574,ok +75213,1.0,54,0.05064961320936123,ok +2123,1.0,55,0.06664228507628078,ok +75227,1.0,56,0.10178562268737412,ok +75184,1.0,57,0.1374114281944938,ok +75142,1.0,58,0.08131090777123995,ok +236,1.0,59,0.04173036030013244,ok +2122,1.0,60,0.2791055112350753,ok +75188,1.0,61,0.21503227998943397,ok +75166,1.0,62,0.09945031583527553,ok +75181,1.0,63,0.0,ok +75133,1.0,64,0.0058744901587642895,ok +75134,1.0,65,0.0967448909673646,ok +75198,1.0,66,0.12025000319215506,ok +262,1.0,67,0.002751972512609613,ok +75234,1.0,68,0.05769681514618219,ok +75139,1.0,69,0.012112268734996712,ok +252,1.0,70,0.16921296956991572,ok +75117,1.0,71,0.05184242948952755,ok +75113,1.0,72,0.006590920947779688,ok +75098,1.0,73,0.027471753088248563,ok +246,1.0,74,0.023766136391797477,ok +75203,1.0,75,0.09549480420909973,ok +75237,1.0,76,0.00039539777599506554,ok +75195,1.0,77,0.001560114030179971,ok +75171,1.0,78,0.16670851818300347,ok +75128,1.0,79,0.02232168365143683,ok +75096,1.0,80,0.37762113409098086,ok +75250,1.0,81,0.34154961033958486,ok +75146,1.0,82,0.12092830470068494,ok +75116,1.0,83,0.009857921438566342,ok +75157,1.0,84,0.42721657693375314,ok +75187,1.0,85,0.027829779959187895,ok +2350,1.0,86,0.4107400261716929,ok +242,1.0,87,0.016283152149369595,ok +244,1.0,88,0.11022260983726562,ok +75125,1.0,89,0.035831177861333385,ok +75185,1.0,90,0.12773060459472851,ok +75163,1.0,91,0.06024005149369582,ok +75177,1.0,92,0.019059036742538282,ok +75189,1.0,93,0.01935454704780004,ok +75244,1.0,94,0.06135430801666708,ok +75219,1.0,95,0.08320902919630935,ok +75222,1.0,96,0.04781443570706845,ok +75159,1.0,97,0.0776637730203249,ok +75175,1.0,98,0.11665288784872319,ok +75109,1.0,99,0.33304613280657847,ok +254,1.0,100,0.0,ok +75105,1.0,101,0.031779437551376244,ok +75106,1.0,102,0.10293221951604781,ok +75212,1.0,103,0.27713228689731584,ok +75099,1.0,104,0.14036952256376722,ok +75248,1.0,105,0.08421507663826677,ok +233,1.0,106,0.015180265654648917,ok +75235,1.0,107,0.0016658985700102047,ok +75226,1.0,108,0.004267314764131003,ok +75132,1.0,109,0.07681019978505155,ok +75127,1.0,110,0.3348237349905919,ok +251,1.0,111,0.022689055973266536,ok +75161,1.0,112,0.08277295403500784,ok +75143,1.0,113,0.010482521920672894,ok +75114,1.0,114,0.023575638506876273,ok +75182,1.0,115,0.10979433133488292,ok +75112,1.0,116,0.12095417307543233,ok +75210,1.0,117,0.0,ok +75205,1.0,118,0.18099003287994075,ok +75090,1.0,119,0.09805748376790224,ok +275,1.0,120,0.03613820618166086,ok +288,1.0,121,0.14230766359347347,ok +75092,1.0,122,0.06900191530112798,ok +3043,1.0,123,0.01972215046168735,ok +75249,1.0,124,0.0048510434289000814,ok +75126,1.0,125,0.05003141198539285,ok +75225,1.0,126,0.04555750377260681,ok +75141,1.0,127,0.05775410437107076,ok +75107,1.0,128,0.05958493591294611,ok +75097,1.0,129,0.08507551646212685,ok +80001,1.0,1,0.07050008279516484,ok +80003,1.0,1,0.1213765182186235,ok +80006,1.0,1,0.1691017316017316,ok +80008,1.0,1,0.14814814814814825,ok +80009,1.0,1,0.15321637426900592,ok +80010,1.0,1,0.11494252873563215,ok +80011,1.0,1,0.03563941299790363,ok +80012,1.0,1,0.042424242424242475,ok +80013,1.0,1,0.0,ok +80014,1.0,1,0.04195804195804198,ok +80015,1.0,1,0.09523809523809523,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/precision_weighted_multiclass.classification_sparse/configurations.csv b/metalearning/metalearning_files/precision_weighted_multiclass.classification_sparse/configurations.csv new file mode 100755 index 0000000..4d2e097 --- /dev/null +++ b/metalearning/metalearning_files/precision_weighted_multiclass.classification_sparse/configurations.csv @@ -0,0 +1,141 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,preprocessor:truncatedSVD:target_dim,rescaling:__choice__ +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7249853037185638,None,0.0,1.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,median,extra_trees_preproc_for_classification,False,gini,None,0.9424908623661876,None,0.0,7.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.585711203872775,None,0.0,5.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,median,extra_trees_preproc_for_classification,True,entropy,None,0.29512530534048065,None,0.0,7.0,10.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.002655175930910743,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3997691365353215,None,0.0,20.0,10.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,median,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,15.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7238850981243719,None,0.0,4.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,5.282738216059151e-05,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.6682079659377479,None,0.0,4.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,mean,extra_trees_preproc_for_classification,True,entropy,None,0.5552350997943013,None,0.0,8.0,5.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7974565919616314,None,0.0,12.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,median,extra_trees_preproc_for_classification,True,entropy,None,0.9772091846790169,None,0.0,10.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2398150931290834,,,0.4015139801872962,rbf,-1.0,False,2.402997750662158e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88.40698357592571,chi2,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.034465366914659866,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.12057775675278172,deviance,10.0,0.8011153303489733,None,0.0,2.0,16.0,0.0,370.0,0.6078295352200873,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,mean,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0007038280350320558,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4138778052607317,0.7995003430482459,5.0,5.430044692638861,poly,-1.0,True,0.02455501006004393,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,31,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.0010015637584068037,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.037611630308856295,deviance,5.0,0.8840126779516314,None,0.0,10.0,2.0,0.0,444.0,0.7599997167603434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,most_frequent,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1751.4736515133566,0.62404114475118,3.0,1.6087076997410432,poly,-1.0,False,3.5353792826856045e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,mean,kernel_pca,,,,,,,,,,,,,,cosine,1198.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.8657388713119849,,1.0,None,0.0,19.0,13.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828776,None,0.0,2.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.027324741616523342,deviance,10.0,0.8623781459430139,None,0.0,10.0,20.0,0.0,329.0,0.8595750155424215,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,mean,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.002173124111626734,None,0.0,14.0,4.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,6.0,None,13.0,2.0,1.0,23.0,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1100.6211008501205,0.5921425829232616,2.0,0.0337546254878617,poly,-1.0,True,0.09641299736884308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2507440474920336e-05,True,,0.049622652766554566,True,0.009105043727227265,constant,squared_hinge,elasticnet,,0.00010112719671669047,,,,,,,,,,,,,,,,,,53,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61.69949680034141,chi2,,,,,none +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.9348409326933208,rbf,-1.0,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,mean,kernel_pca,,,,,,,,,,,,,,cosine,1754.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3428971645958825,,,0.2229870623330047,rbf,-1.0,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7322410244259964,None,0.0,6.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,median,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00016781524591321165,True,True,squared_hinge,1.5119200923218881e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.349459944355116,,,0.00024028983491736645,rbf,-1.0,True,1.139421622432356e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3636266268105085,fwe,chi2,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4047.618729304337,,,2.0237366768707754,rbf,-1.0,True,0.04369127828878843,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,one_hot_encoding,0.010000000000000005,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10.0,1.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,,none +weighting,one_hot_encoding,0.0008685602750053468,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9896334290292654,None,0.0,11.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,141.76310800864283,False,True,1.0,squared_hinge,ovr,l1,0.004317884655117431,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,,normalize +none,one_hot_encoding,0.003443779683191071,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.004980497345831963,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1255.9137433589426,,,0.08351549479967445,rbf,-1.0,True,0.00017919875199222518,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1.0,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.3451627750042988,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.3163640203509378,None,0.0,17.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,median,extra_trees_preproc_for_classification,False,gini,None,0.8916956785028156,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.002630811782675973,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9828367182452932,None,0.0,18.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,85,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.35533396539961937,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,86,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4165632766388807,fpr,chi2,,none +weighting,one_hot_encoding,0.0019686649916896212,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.04641055832142541,True,True,hinge,8.540468968077405e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87,mean,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,911.0,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1.0,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,89,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,90,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,91,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,93,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.20875514426569566,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.53183372054125,None,0.0,18.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,13818.683783129034,False,True,1.0,squared_hinge,ovr,l1,1.009528987119941e-05,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,95,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,96,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,97,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,98,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,99,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,100,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,102,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9091193924897338,None,0.0,2.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,103,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,104,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.039533063907190934,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,105,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1878805519245509,fdr,chi2,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,106,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.006372860318416312,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6467376360604045,None,0.0,1.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,107,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,108,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,109,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3823734947460288,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,110,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,adaboost,SAMME,0.11042308042695524,5.0,117.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,111,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,112,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,113,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,115,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,116,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,117,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,118,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.20202014999292287,False,True,1.0,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,119,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,120,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,121,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8804227616935514,None,0.0,10.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,122,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.0898983119899223,False,True,1.0,squared_hinge,ovr,l1,0.016048982920294167,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,123,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,124,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,125,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0733000338152003,False,True,1.0,squared_hinge,ovr,l2,0.033752542733220474,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,126,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,-0.6840756728731969,,0.00980445380551526,sigmoid,161.0,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,127,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,129,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.04329010470998136,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7260331062271381,None,0.0,7.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.010000000000000005,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15.0,1.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,134,median,extra_trees_preproc_for_classification,False,entropy,None,0.8213051377774989,None,0.0,3.0,13.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.714869937384006,None,0.0,8.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/metalearning/metalearning_files/precision_weighted_multiclass.classification_sparse/description.txt b/metalearning/metalearning_files/precision_weighted_multiclass.classification_sparse/description.txt new file mode 100755 index 0000000..29decd3 --- /dev/null +++ b/metalearning/metalearning_files/precision_weighted_multiclass.classification_sparse/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: precision_weighted +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/precision_weighted_multiclass.classification_sparse/feature_costs.arff b/metalearning/metalearning_files/precision_weighted_multiclass.classification_sparse/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/precision_weighted_multiclass.classification_sparse/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/precision_weighted_multiclass.classification_sparse/feature_runstatus.arff b/metalearning/metalearning_files/precision_weighted_multiclass.classification_sparse/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/precision_weighted_multiclass.classification_sparse/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/precision_weighted_multiclass.classification_sparse/feature_values.arff b/metalearning/metalearning_files/precision_weighted_multiclass.classification_sparse/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/precision_weighted_multiclass.classification_sparse/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/precision_weighted_multiclass.classification_sparse/readme.txt b/metalearning/metalearning_files/precision_weighted_multiclass.classification_sparse/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/recall_binary.classification_dense/algorithm_runs.arff b/metalearning/metalearning_files/recall_binary.classification_dense/algorithm_runs.arff new file mode 100755 index 0000000..fef218a --- /dev/null +++ b/metalearning/metalearning_files/recall_binary.classification_dense/algorithm_runs.arff @@ -0,0 +1,107 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE recall NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2117,1.0,1,0.0919220055710307,ok +75156,1.0,2,0.16765578635014833,ok +75129,1.0,3,0.15384615384615385,ok +75239,1.0,4,0.0,ok +75121,1.0,5,0.0,ok +261,1.0,6,0.2727272727272727,ok +75240,1.0,7,0.0,ok +75120,1.0,8,0.0,ok +75124,1.0,9,0.21192052980132448,ok +75176,1.0,10,0.01309243257397219,ok +75103,1.0,11,0.0,ok +75095,1.0,12,0.048192771084337394,ok +273,1.0,13,0.057627118644067776,ok +75174,1.0,14,0.1323866239120477,ok +75153,1.0,15,0.08055152394775034,ok +75093,1.0,16,0.35179640718562877,ok +75119,1.0,17,0.0020661157024793875,ok +75215,1.0,18,0.017006802721088454,ok +75233,1.0,19,0.03620873269435565,ok +75196,1.0,20,0.0,ok +75191,1.0,21,0.17877640737574796,ok +75115,1.0,22,0.0,ok +75108,1.0,23,0.0,ok +75101,1.0,24,0.24622463426144403,ok +75192,1.0,25,0.20124804992199685,ok +75232,1.0,26,0.16806722689075626,ok +75173,1.0,27,0.11812500000000004,ok +75148,1.0,28,0.10058027079303677,ok +75150,1.0,29,0.161849710982659,ok +75100,1.0,30,0.4285714285714286,ok +75179,1.0,31,0.19751552795031058,ok +75213,1.0,32,0.08139534883720934,ok +75227,1.0,33,0.13218390804597702,ok +75184,1.0,34,0.1571925754060325,ok +75142,1.0,35,0.06574087752991575,ok +75166,1.0,36,0.107008289374529,ok +75133,1.0,37,0.13636363636363635,ok +75234,1.0,38,0.0180327868852459,ok +75139,1.0,39,0.01712538226299698,ok +75117,1.0,40,0.008547008547008517,ok +75113,1.0,41,0.0,ok +75237,1.0,42,0.00038952944842629567,ok +75195,1.0,43,0.0017517517517517955,ok +75171,1.0,44,0.1501120238984317,ok +75128,1.0,45,0.004278074866310155,ok +75146,1.0,46,0.09547169811320755,ok +75116,1.0,47,0.004683840749414525,ok +75157,1.0,48,0.4528301886792453,ok +75187,1.0,49,0.018425460636515956,ok +2350,1.0,50,0.4400191938579654,ok +75125,1.0,51,0.009638554216867434,ok +75185,1.0,52,0.1290944123314065,ok +75163,1.0,53,0.07427536231884058,ok +75177,1.0,54,0.012195121951219523,ok +75189,1.0,55,0.01633457982177078,ok +75244,1.0,56,0.04102564102564099,ok +75219,1.0,57,0.04123711340206182,ok +75222,1.0,58,0.09523809523809523,ok +75159,1.0,59,0.2142857142857143,ok +75175,1.0,60,0.12517882689556514,ok +254,1.0,61,0.0,ok +75105,1.0,62,0.18394648829431437,ok +75106,1.0,63,0.3378151260504202,ok +75212,1.0,64,0.2534562211981567,ok +75099,1.0,65,0.11650485436893199,ok +75248,1.0,66,0.0,ok +233,1.0,67,0.0020408163265306367,ok +75226,1.0,68,0.0007199424046075986,ok +75132,1.0,69,0.29160263942872644,ok +75127,1.0,70,0.3800735683152352,ok +75161,1.0,71,0.06620567897601881,ok +75143,1.0,72,0.003968253968253954,ok +75114,1.0,73,0.01253132832080206,ok +75182,1.0,74,0.14933577645442053,ok +75112,1.0,75,0.1763628034814475,ok +75210,1.0,76,0.0,ok +75092,1.0,77,0.038461538461538436,ok +3043,1.0,78,0.04878048780487809,ok +75249,1.0,79,0.020408163265306145,ok +75126,1.0,80,0.01342281879194629,ok +75225,1.0,81,0.0625,ok +75141,1.0,82,0.04036697247706422,ok +75107,1.0,83,0.3247588424437299,ok +75097,1.0,84,0.003728414442700112,ok +80001,1.0,1,0.2142857142857143,ok +80003,1.0,1,0.09900990099009899,ok +80006,1.0,1,0.1333333333333333,ok +80008,1.0,1,0.0,ok +80009,1.0,1,0.09999999999999998,ok +80010,1.0,1,0.2222222222222222,ok +80011,1.0,1,0.10526315789473684,ok +80012,1.0,1,0.0,ok +80013,1.0,1,0.0,ok +80014,1.0,1,0.09999999999999998,ok +80015,1.0,1,0.125,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/recall_binary.classification_dense/configurations.csv b/metalearning/metalearning_files/recall_binary.classification_dense/configurations.csv new file mode 100755 index 0000000..2ab23db --- /dev/null +++ b/metalearning/metalearning_files/recall_binary.classification_dense/configurations.csv @@ -0,0 +1,96 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:fast_ica:algorithm,preprocessor:fast_ica:fun,preprocessor:fast_ica:n_components,preprocessor:fast_ica:whiten,preprocessor:feature_agglomeration:affinity,preprocessor:feature_agglomeration:linkage,preprocessor:feature_agglomeration:n_clusters,preprocessor:feature_agglomeration:pooling_func,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:pca:keep_variance,preprocessor:pca:whiten,preprocessor:polynomial:degree,preprocessor:polynomial:include_bias,preprocessor:polynomial:interaction_only,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,rescaling:__choice__,rescaling:quantile_transformer:n_quantiles,rescaling:quantile_transformer:output_distribution,rescaling:robust_scaler:q_max,rescaling:robust_scaler:q_min +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.013255438916868357,deviance,9.0,0.3592430187012816,None,0.0,10.0,7.0,0.0,411.0,0.691160823398122,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,median,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1.0,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.0007295694672502942,True,multinomial_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.011064778564152866,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,median,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,7.0,None,11.0,18.0,1.0,35.0,,,,,,quantile_transformer,36528.0,normal,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0020558425106452084,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5570247081444077,None,0.0,15.0,12.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5263936397022638,None,0.0,6.0,12.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,7.795044474549774,False,True,1.0,squared_hinge,ovr,l1,0.0006963037026795237,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9455638720565652,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,most_frequent,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8255464552647293,0.19162485555463185 +weighting,one_hot_encoding,0.18137532678800647,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9094110110427254,None,0.0,7.0,12.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,195.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.0009580347867777607,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,0.10000000000000006,rbf,-1.0,True,0.0010000000000000002,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.35040453084365497,False,True,1.0,squared_hinge,ovr,l1,0.006810889378452772,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.02345017287074443,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.053517066400173056,deviance,10.0,0.542144980834302,None,0.0,20.0,13.0,0.0,233.0,0.7398539900055563,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0614425536709615,fwe,f_classif,robust_scaler,,,0.9523118062307264,0.13434811490315818 +weighting,one_hot_encoding,0.010000000000000005,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.10000000000000002,deviance,3.0,1.0,None,0.0,1.0,2.0,0.0,100.0,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7070968825037495,0.2946729770960392 +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.040936424602789435,deviance,7.0,0.5495014745530306,None,0.0,20.0,18.0,0.0,141.0,0.6905343807995293,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +weighting,one_hot_encoding,0.010000000000000005,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9641046312686136,None,0.0,18.0,12.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8622423450611333,0.2960428898664952 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.010000000000000005,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.8700514193862113,None,0.0,6.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,most_frequent,fast_ica,,,,,,,,,,,parallel,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.939200003837416,0.13375455137243772 +none,one_hot_encoding,0.00012586572428922356,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5240592829918601,None,0.0,10.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.2422926485206341,,1.0,None,0.0,15.0,9.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.022939738050158573,deviance,10.0,0.4185394344134278,None,0.0,2.0,10.0,0.0,309.0,0.5979695608086252,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.25383213391991144 +weighting,no_encoding,,,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,0.3170009238992427,rbf,1955.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8333938697866604,0.10426506601169797 +weighting,one_hot_encoding,0.004739913305088818,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mae,0.2770776012493639,deviance,7.0,0.3198803116198433,None,0.0,3.0,13.0,0.0,298.0,0.2768658913317291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.00353232434213139,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.9407432771644124,None,0.0,1.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,median,fast_ica,,,,,,,,,,,parallel,cube,100.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7446318916516063,0.23782974987118105 +none,one_hot_encoding,0.0010670931302549814,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.8709229440057928,None,0.0,3.0,13.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,median,fast_ica,,,,,,,,,,,deflation,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8372068127698562,0.23782974987118105 +none,one_hot_encoding,0.0007295694672502942,True,multinomial_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.011064778564152866,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,median,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,7.0,None,11.0,18.0,1.0,35.0,,,,,,quantile_transformer,36528.0,normal,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.002173124111626734,None,0.0,14.0,4.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,6.0,None,13.0,2.0,1.0,23.0,,,,,,normalize,,,, +weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2507440474920336e-05,True,,0.049622652766554566,True,0.009105043727227265,constant,squared_hinge,elasticnet,,0.00010112719671669047,,,,,,,,,,,,,,,,,,31,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61.69949680034141,chi2,,,,none,,,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.9348409326933208,rbf,-1.0,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1754.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.4386768819247876e-05,True,,0.02855319677129068,True,1.5957742829949438e-07,constant,perceptron,elasticnet,,3.431368200869824e-05,,,,,,,,,,,,,,,,,,34,median,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8992576790440515,0.20839368148068493 +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.8770766409674923,None,0.0,13.0,13.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,84023.0,normal,, +weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7856.587651350424,,,0.0017305319997054556,rbf,-1.0,False,1.7622421003766454e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.006909187206474195,True,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00024264014379190562,True,,0.04987297125937914,True,,optimal,hinge,l2,,4.0861541221464815e-05,,,,,,,,,,,,,,,,,,37,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.05153104953418389,False,True,1.0,squared_hinge,ovr,l1,0.0001939386474290285,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.00034835629696198427,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8245132980938538,0.08947420373097192 +none,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4047.618729304337,,,2.0237366768707754,rbf,-1.0,True,0.04369127828878843,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,bernoulli_nb,,,,,0.02802461704612387,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,median,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,6.0,None,12.0,20.0,1.0,75.0,,,,,,quantile_transformer,10068.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1.0,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.3451627750042988,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.3163640203509378,None,0.0,17.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,median,extra_trees_preproc_for_classification,False,gini,None,0.8916956785028156,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.00010817282861262362,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.799803680241154,None,0.0,13.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,mean,fast_ica,,,,,,,,,,,deflation,exp,1793.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9071932815811076,0.03563842252368924 +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.35533396539961937,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4165632766388807,fpr,chi2,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8522973640879423,None,0.0,13.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6856119355718316,None,0.0,2.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,robust_scaler,,,0.9070067796375252,0.2232396978725172 +weighting,no_encoding,,,decision_tree,,,,,,,entropy,1.94608233492308,,1.0,None,0.0,15.0,11.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2613520842796237,fdr,chi2,quantile_transformer,1000.0,uniform,, +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0387325491437111,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.32034732923549136,None,0.0,20.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,median,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,quantile_transformer,1000.0,uniform,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2636201374253461,deviance,7.0,0.8344964130784466,None,0.0,9.0,2.0,0.0,298.0,0.7517549950523315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,normalize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.2263596964804377,True,adaboost,SAMME,0.10845020299646906,2.0,248.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.22655400374385,fwe,f_classif,minmax,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.04064013793978874,deviance,7.0,0.3397533378274365,None,0.0,10.0,4.0,0.0,280.0,0.8476773466327144,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,most_frequent,extra_trees_preproc_for_classification,True,gini,None,0.9072459027692912,None,0.0,6.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8834597195173531,0.09983935639458053 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.6960837129194184,,,0.0004821713821548537,rbf,-1.0,False,0.040691702958042086,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,most_frequent,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6520469084729728,False,,,,,,,,,,,,,,,,quantile_transformer,97329.0,normal,, +weighting,one_hot_encoding,0.010000000000000005,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.04952755495565772,deviance,3.0,0.7249041896998006,None,0.0,6.0,17.0,0.0,174.0,0.3718608680080454,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9091193924897338,None,0.0,2.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +weighting,no_encoding,,,bernoulli_nb,,,,,0.023108348799909733,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,median,extra_trees_preproc_for_classification,True,gini,None,0.43664414575861454,None,0.0,1.0,4.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8569382605464568,0.07491671292996571 +weighting,one_hot_encoding,0.10324969243867224,True,decision_tree,,,,,,,gini,0.7467478023293801,,1.0,None,0.0,12.0,8.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84.22876326806853,mutual_info,,,,minmax,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.001279467383882126,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0903354518102121,fwe,f_classif,standardize,,,, +none,one_hot_encoding,0.0007295694672502942,True,multinomial_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.011064778564152866,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,median,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,7.0,None,11.0,18.0,1.0,35.0,,,,,,quantile_transformer,36528.0,normal,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3823734947460288,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.2472984547885781,deviance,3.0,0.6564306719064884,None,0.0,15.0,14.0,0.0,220.0,0.8082564085714649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,median,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,332.0,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.03905145156995541,deviance,5.0,0.2281306656230014,None,0.0,14.0,13.0,0.0,493.0,0.8793075442604774,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,25382.0,normal,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.12713527337147906,deviance,4.0,0.6041596127474019,None,0.0,14.0,17.0,0.0,83.0,0.8426859880999615,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.7529073739756954,None,0.0,11.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,25.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,93335.0,normal,, +weighting,one_hot_encoding,0.4941395236323653,True,decision_tree,,,,,,,gini,1.7020201228673206,,1.0,None,0.0,20.0,8.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,True,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0002525813164610038,True,,0.0601553136224701,True,0.057326377025764375,optimal,squared_hinge,elasticnet,,0.004555880871360689,,,,,,,,,,,,,,,,,,81,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,2.0,None,18.0,4.0,1.0,92.0,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.5765793990908161,None,0.0,11.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,median,fast_ica,,,,,,,,,,,deflation,exp,10.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.1166829447028879,deviance,4.0,0.8451196958788597,None,0.0,1.0,8.0,0.0,58.0,0.8652814520124915,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.03406306803297533,False,True,1.0,squared_hinge,ovr,l1,0.00019257971888767865,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.2604623554399743,None,0.0,18.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.282079367058228,fpr,chi2,none,,,, +none,no_encoding,,,adaboost,SAMME,0.7603752531440509,7.0,413.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,28.04113884899283,False,True,1.0,squared_hinge,ovr,l1,0.0001084726092097629,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,26106.0,uniform,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3386310102795006,None,0.0,6.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,True,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,134,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.0019726875156463528,True,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3603.9877392982157,0.0,1.0,1.0,squared_hinge,ovr,l2,0.0004637091297122247,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,52.66421770620247,False,True,1.0,squared_hinge,ovr,l1,0.004627354576003774,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, diff --git a/metalearning/metalearning_files/recall_binary.classification_dense/description.txt b/metalearning/metalearning_files/recall_binary.classification_dense/description.txt new file mode 100755 index 0000000..6c51f24 --- /dev/null +++ b/metalearning/metalearning_files/recall_binary.classification_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: recall +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/recall_binary.classification_dense/feature_costs.arff b/metalearning/metalearning_files/recall_binary.classification_dense/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/recall_binary.classification_dense/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/recall_binary.classification_dense/feature_runstatus.arff b/metalearning/metalearning_files/recall_binary.classification_dense/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/recall_binary.classification_dense/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/recall_binary.classification_dense/feature_values.arff b/metalearning/metalearning_files/recall_binary.classification_dense/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/recall_binary.classification_dense/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/recall_binary.classification_dense/readme.txt b/metalearning/metalearning_files/recall_binary.classification_dense/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/recall_binary.classification_sparse/algorithm_runs.arff b/metalearning/metalearning_files/recall_binary.classification_sparse/algorithm_runs.arff new file mode 100755 index 0000000..06aec09 --- /dev/null +++ b/metalearning/metalearning_files/recall_binary.classification_sparse/algorithm_runs.arff @@ -0,0 +1,107 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE recall NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2117,1.0,1,0.0919220055710307,ok +75156,1.0,2,0.18694362017804156,ok +75129,1.0,3,0.1923076923076923,ok +75239,1.0,4,0.0,ok +75121,1.0,5,0.0,ok +261,1.0,6,0.303030303030303,ok +75240,1.0,7,0.0,ok +75120,1.0,8,0.0,ok +75124,1.0,9,0.2715231788079471,ok +75176,1.0,10,0.01518722178580778,ok +75103,1.0,11,0.02622950819672132,ok +75095,1.0,12,0.048192771084337394,ok +273,1.0,13,0.06440677966101693,ok +75174,1.0,14,0.23545579477782863,ok +75153,1.0,15,0.13352685050798263,ok +75093,1.0,16,0.3697604790419161,ok +75119,1.0,17,0.0020661157024793875,ok +75215,1.0,18,0.017006802721088454,ok +75233,1.0,19,0.03620873269435565,ok +75196,1.0,20,0.03883495145631066,ok +75191,1.0,21,0.17712785443888146,ok +75115,1.0,22,0.0,ok +75108,1.0,23,0.00303030303030305,ok +75101,1.0,24,0.26197498820198206,ok +75192,1.0,25,0.5117004680187207,ok +75232,1.0,26,0.26890756302521013,ok +75173,1.0,27,0.12687499999999996,ok +75148,1.0,28,0.17408123791102514,ok +75150,1.0,29,0.32947976878612717,ok +75100,1.0,30,0.4285714285714286,ok +75179,1.0,31,0.19751552795031058,ok +75213,1.0,32,0.08139534883720934,ok +75227,1.0,33,0.13218390804597702,ok +75184,1.0,34,0.2529002320185615,ok +75142,1.0,35,0.07815039149061898,ok +75166,1.0,36,0.10926902788244164,ok +75133,1.0,37,0.6363636363636364,ok +75234,1.0,38,0.09426229508196726,ok +75139,1.0,39,0.01712538226299698,ok +75117,1.0,40,0.008547008547008517,ok +75113,1.0,41,0.003389830508474523,ok +75237,1.0,42,0.00038952944842629567,ok +75195,1.0,43,0.0017517517517517955,ok +75171,1.0,44,0.1501120238984317,ok +75128,1.0,45,0.004278074866310155,ok +75146,1.0,46,0.09547169811320755,ok +75116,1.0,47,0.004683840749414525,ok +75157,1.0,48,0.4528301886792453,ok +75187,1.0,49,0.031825795644891075,ok +2350,1.0,50,0.4005346860433233,ok +75125,1.0,51,0.009638554216867434,ok +75185,1.0,52,0.1560693641618497,ok +75163,1.0,53,0.07608695652173914,ok +75177,1.0,54,0.04878048780487809,ok +75189,1.0,55,0.01633457982177078,ok +75244,1.0,56,0.06666666666666665,ok +75219,1.0,57,0.11878081577767818,ok +75222,1.0,58,0.09523809523809523,ok +75159,1.0,59,0.2857142857142857,ok +75175,1.0,60,0.18383404864091557,ok +254,1.0,61,0.0,ok +75105,1.0,62,0.8060200668896321,ok +75106,1.0,63,0.7336134453781513,ok +75212,1.0,64,0.2534562211981567,ok +75099,1.0,65,0.22330097087378642,ok +75248,1.0,66,0.05737704918032782,ok +233,1.0,67,0.01632653061224487,ok +75226,1.0,68,0.0017998560115191076,ok +75132,1.0,69,0.7243966374401156,ok +75127,1.0,70,0.3800735683152352,ok +75161,1.0,71,0.08592025893776667,ok +75143,1.0,72,0.003968253968253954,ok +75114,1.0,73,0.01253132832080206,ok +75182,1.0,74,0.1694915254237288,ok +75112,1.0,75,0.23683005038937244,ok +75210,1.0,76,0.0,ok +75092,1.0,77,0.09615384615384615,ok +3043,1.0,78,0.060975609756097615,ok +75249,1.0,79,0.020408163265306145,ok +75126,1.0,80,0.01342281879194629,ok +75225,1.0,81,0.0625,ok +75141,1.0,82,0.046788990825688104,ok +75107,1.0,83,0.5522508038585209,ok +75097,1.0,84,0.003728414442700112,ok +80001,1.0,1,0.2142857142857143,ok +80003,1.0,1,0.14851485148514854,ok +80006,1.0,1,0.33333333333333337,ok +80008,1.0,1,0.0,ok +80009,1.0,1,0.09999999999999998,ok +80010,1.0,1,0.2222222222222222,ok +80011,1.0,1,0.10526315789473684,ok +80012,1.0,1,0.0,ok +80013,1.0,1,0.0,ok +80014,1.0,1,0.09999999999999998,ok +80015,1.0,1,0.125,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/recall_binary.classification_sparse/configurations.csv b/metalearning/metalearning_files/recall_binary.classification_sparse/configurations.csv new file mode 100755 index 0000000..2d40863 --- /dev/null +++ b/metalearning/metalearning_files/recall_binary.classification_sparse/configurations.csv @@ -0,0 +1,96 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,preprocessor:truncatedSVD:target_dim,rescaling:__choice__ +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.039533063907190934,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1878805519245509,fdr,chi2,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7238850981243719,None,0.0,4.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,5.282738216059151e-05,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0009580347867777607,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,0.10000000000000006,rbf,-1.0,True,0.0010000000000000002,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.35040453084365497,False,True,1.0,squared_hinge,ovr,l1,0.006810889378452772,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.2380793644102286,None,0.0,1.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.15248352254459802,fwe,chi2,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.010000000000000005,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9727149851116396,None,0.0,18.0,13.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.0011292655810309046,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.05492831427533807,deviance,5.0,0.3620138827518105,None,0.0,2.0,14.0,0.0,380.0,0.6590220190321725,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,median,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.8657388713119849,,1.0,None,0.0,19.0,13.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.002173124111626734,None,0.0,14.0,4.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,6.0,None,13.0,2.0,1.0,23.0,,,,,,,normalize +weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2507440474920336e-05,True,,0.049622652766554566,True,0.009105043727227265,constant,squared_hinge,elasticnet,,0.00010112719671669047,,,,,,,,,,,,,,,,,,31,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61.69949680034141,chi2,,,,,none +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.9348409326933208,rbf,-1.0,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,mean,kernel_pca,,,,,,,,,,,,,,cosine,1754.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3428971645958825,,,0.2229870623330047,rbf,-1.0,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4047.618729304337,,,2.0237366768707754,rbf,-1.0,True,0.04369127828878843,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.039533063907190934,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1878805519245509,fdr,chi2,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1.0,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.3451627750042988,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.3163640203509378,None,0.0,17.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,median,extra_trees_preproc_for_classification,False,gini,None,0.8916956785028156,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.00011600321198702641,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,most_frequent,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,decision_tree,,,,,,,gini,1.7984076825537865,,1.0,None,0.0,14.0,2.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2897525995758022,fwe,chi2,,none +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.20875514426569566,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.53183372054125,None,0.0,18.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,13818.683783129034,False,True,1.0,squared_hinge,ovr,l1,1.009528987119941e-05,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.039533063907190934,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1878805519245509,fdr,chi2,,standardize +weighting,one_hot_encoding,0.039533063907190934,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1878805519245509,fdr,chi2,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9091193924897338,None,0.0,2.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,one_hot_encoding,,False,bernoulli_nb,,,,,0.014801515930977628,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.039533063907190934,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1878805519245509,fdr,chi2,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3823734947460288,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.3997572853391576,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.21008613984919333,None,0.0,11.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8804227616935514,None,0.0,10.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.0898983119899223,False,True,1.0,squared_hinge,ovr,l1,0.016048982920294167,,,,,,,,,,,,,,,,,,,none +weighting,no_encoding,,,decision_tree,,,,,,,entropy,1.301605991416264,,1.0,None,0.0,8.0,3.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0002525813164610038,True,,0.0601553136224701,True,0.057326377025764375,optimal,squared_hinge,elasticnet,,0.004555880871360689,,,,,,,,,,,,,,,,,,81,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,2.0,None,18.0,4.0,1.0,92.0,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.00214097329599271,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7996802015738327,None,0.0,7.0,12.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.1052247187777527,False,True,1.0,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.2604623554399743,None,0.0,18.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.282079367058228,fpr,chi2,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,134,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.0019726875156463528,True,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3603.9877392982157,0.0,1.0,1.0,squared_hinge,ovr,l2,0.0004637091297122247,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,52.66421770620247,False,True,1.0,squared_hinge,ovr,l1,0.004627354576003774,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/metalearning/metalearning_files/recall_binary.classification_sparse/description.txt b/metalearning/metalearning_files/recall_binary.classification_sparse/description.txt new file mode 100755 index 0000000..6c51f24 --- /dev/null +++ b/metalearning/metalearning_files/recall_binary.classification_sparse/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: recall +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/recall_binary.classification_sparse/feature_costs.arff b/metalearning/metalearning_files/recall_binary.classification_sparse/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/recall_binary.classification_sparse/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/recall_binary.classification_sparse/feature_runstatus.arff b/metalearning/metalearning_files/recall_binary.classification_sparse/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/recall_binary.classification_sparse/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/recall_binary.classification_sparse/feature_values.arff b/metalearning/metalearning_files/recall_binary.classification_sparse/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/recall_binary.classification_sparse/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/recall_binary.classification_sparse/readme.txt b/metalearning/metalearning_files/recall_binary.classification_sparse/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/recall_macro_binary.classification_dense/algorithm_runs.arff b/metalearning/metalearning_files/recall_macro_binary.classification_dense/algorithm_runs.arff new file mode 100755 index 0000000..2a7d5e0 --- /dev/null +++ b/metalearning/metalearning_files/recall_macro_binary.classification_dense/algorithm_runs.arff @@ -0,0 +1,152 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE recall_macro NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2120,1.0,1,0.10170900792413518,ok +75193,1.0,2,0.05570147909402934,ok +2117,1.0,3,0.17058181059794586,ok +75156,1.0,4,0.2059468405268512,ok +75129,1.0,5,0.17411530154510713,ok +75243,1.0,6,0.0,ok +75110,1.0,7,0.11182111215625956,ok +75239,1.0,8,0.0,ok +75223,1.0,9,0.12451335740985625,ok +75221,1.0,10,0.4873924872950214,ok +258,1.0,11,0.009581814407771616,ok +75121,1.0,12,0.0,ok +253,1.0,13,0.44373242034274996,ok +261,1.0,14,0.2943722943722944,ok +75240,1.0,15,0.017683772538141462,ok +75120,1.0,16,0.20465235173824126,ok +75124,1.0,17,0.17233122467134532,ok +75176,1.0,18,0.015737392757574353,ok +75103,1.0,19,0.0031496062992126816,ok +75207,1.0,20,0.1890011398955863,ok +75095,1.0,21,0.04425501079002059,ok +273,1.0,22,0.046593731735826927,ok +75174,1.0,23,0.12864050014146122,ok +75153,1.0,24,0.0802757619738752,ok +75093,1.0,25,0.32381202028228084,ok +75119,1.0,26,0.08442148760330581,ok +75201,1.0,27,0.0997376911529182,ok +75215,1.0,28,0.028514586427562882,ok +75172,1.0,29,0.13559824204240767,ok +75169,1.0,30,0.0340573619843737,ok +75202,1.0,31,0.42493459653140053,ok +75233,1.0,32,0.06969987414076873,ok +75231,1.0,33,0.15778968253968262,ok +75196,1.0,34,0.01378294036061023,ok +248,1.0,35,0.23327202689622628,ok +75191,1.0,36,0.1286172567369568,ok +75217,1.0,37,0.0,ok +260,1.0,38,0.049350759010592826,ok +75115,1.0,39,0.08750260579528879,ok +75123,1.0,40,0.32871263757742397,ok +75108,1.0,41,0.0,ok +75101,1.0,42,0.2753956012356946,ok +75192,1.0,43,0.48263188489323006,ok +75232,1.0,44,0.13627022861546367,ok +75173,1.0,45,0.11644480519480527,ok +75197,1.0,46,0.2211380803516111,ok +266,1.0,47,0.018660135427440383,ok +75148,1.0,48,0.1358001187955955,ok +75150,1.0,49,0.2876075003524602,ok +75100,1.0,50,0.2540244186950774,ok +75178,1.0,51,0.784206721465269,ok +75236,1.0,52,0.032106570444582316,ok +75179,1.0,53,0.20149749000255257,ok +75213,1.0,54,0.06273157272368945,ok +2123,1.0,55,0.17781746031746037,ok +75227,1.0,56,0.11446626012925343,ok +75184,1.0,57,0.12532912571721433,ok +75142,1.0,58,0.07161311525180047,ok +236,1.0,59,0.03889089023011372,ok +2122,1.0,60,0.11253171534644457,ok +75188,1.0,61,0.4727873168498169,ok +75166,1.0,62,0.09969265347610456,ok +75181,1.0,63,0.0,ok +75133,1.0,64,0.15654041218375303,ok +75134,1.0,65,0.1481357660616962,ok +75198,1.0,66,0.12654657786302836,ok +262,1.0,67,0.0027336509054473046,ok +75234,1.0,68,0.024155509645569007,ok +75139,1.0,69,0.012643391866273612,ok +252,1.0,70,0.1510545149907132,ok +75117,1.0,71,0.11955388784657073,ok +75113,1.0,72,0.005274971941638618,ok +75098,1.0,73,0.028062456815564074,ok +246,1.0,74,0.010490317336848243,ok +75203,1.0,75,0.11023617811471986,ok +75237,1.0,76,0.0002948416740189419,ok +75195,1.0,77,0.0015167841344365662,ok +75171,1.0,78,0.16208341454974717,ok +75128,1.0,79,0.059041798537596835,ok +75096,1.0,80,0.334129919213757,ok +75250,1.0,81,0.3478893206205086,ok +75146,1.0,82,0.11384157742648315,ok +75116,1.0,83,0.020634603301536547,ok +75157,1.0,84,0.44266509433962264,ok +75187,1.0,85,0.016824909805437382,ok +2350,1.0,86,0.4423678825304047,ok +242,1.0,87,0.011186441482494147,ok +244,1.0,88,0.10808472308576977,ok +75125,1.0,89,0.06985388361958478,ok +75185,1.0,90,0.12909185691725056,ok +75163,1.0,91,0.06128065774804903,ok +75177,1.0,92,0.023309264934301632,ok +75189,1.0,93,0.02093625722530723,ok +75244,1.0,94,0.18044671695057302,ok +75219,1.0,95,0.03536781923790411,ok +75222,1.0,96,0.11136904761904765,ok +75159,1.0,97,0.18577787197965234,ok +75175,1.0,98,0.1037395977530613,ok +75109,1.0,99,0.3603409856614681,ok +254,1.0,100,0.0,ok +75105,1.0,101,0.29488259839445896,ok +75106,1.0,102,0.327921279550357,ok +75212,1.0,103,0.2512390226936788,ok +75099,1.0,104,0.2277781422198898,ok +75248,1.0,105,0.18643306379155433,ok +233,1.0,106,0.002793457808655364,ok +75235,1.0,107,0.0007102272727272929,ok +75226,1.0,108,0.008218517371262557,ok +75132,1.0,109,0.3636469111367553,ok +75127,1.0,110,0.33766899823884766,ok +251,1.0,111,0.0,ok +75161,1.0,112,0.0643528394880094,ok +75143,1.0,113,0.017181695373184702,ok +75114,1.0,114,0.034791524265208484,ok +75182,1.0,115,0.12771381137622617,ok +75112,1.0,116,0.13777827445022783,ok +75210,1.0,117,0.0,ok +75205,1.0,118,0.1916783952293486,ok +75090,1.0,119,0.061027366747011924,ok +275,1.0,120,0.040221214056733956,ok +288,1.0,121,0.1294895850789981,ok +75092,1.0,122,0.09411421911421913,ok +3043,1.0,123,0.03952394945636206,ok +75249,1.0,124,0.012821882679773466,ok +75126,1.0,125,0.06940535469437825,ok +75225,1.0,126,0.10485406091370564,ok +75141,1.0,127,0.05180158915235733,ok +75107,1.0,128,0.24811618052967832,ok +75097,1.0,129,0.21628382400897928,ok +80001,1.0,1,0.1502463054187192,ok +80003,1.0,1,0.09095218032441543,ok +80006,1.0,1,0.09607843137254901,ok +80008,1.0,1,0.125,ok +80009,1.0,1,0.10555555555555562,ok +80010,1.0,1,0.2222222222222222,ok +80011,1.0,1,0.05263157894736836,ok +80012,1.0,1,0.0625,ok +80013,1.0,1,0.0,ok +80014,1.0,1,0.050000000000000044,ok +80015,1.0,1,0.10096153846153844,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/recall_macro_binary.classification_dense/configurations.csv b/metalearning/metalearning_files/recall_macro_binary.classification_dense/configurations.csv new file mode 100755 index 0000000..2686140 --- /dev/null +++ b/metalearning/metalearning_files/recall_macro_binary.classification_dense/configurations.csv @@ -0,0 +1,141 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:fast_ica:algorithm,preprocessor:fast_ica:fun,preprocessor:fast_ica:n_components,preprocessor:fast_ica:whiten,preprocessor:feature_agglomeration:affinity,preprocessor:feature_agglomeration:linkage,preprocessor:feature_agglomeration:n_clusters,preprocessor:feature_agglomeration:pooling_func,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:pca:keep_variance,preprocessor:pca:whiten,preprocessor:polynomial:degree,preprocessor:polynomial:include_bias,preprocessor:polynomial:interaction_only,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,rescaling:__choice__,rescaling:quantile_transformer:n_quantiles,rescaling:quantile_transformer:output_distribution,rescaling:robust_scaler:q_max,rescaling:robust_scaler:q_min +weighting,one_hot_encoding,0.00011717632475982632,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.045388141846341344,deviance,10.0,0.29161769341843435,None,0.0,20.0,2.0,0.0,278.0,0.7912571599269661,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7249853037185638,None,0.0,1.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,median,extra_trees_preproc_for_classification,False,gini,None,0.9424908623661876,None,0.0,7.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.12713527337147906,deviance,4.0,0.6041596127474019,None,0.0,14.0,17.0,0.0,83.0,0.8426859880999615,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.03474109838999682,deviance,4.0,0.5687034678818491,None,0.0,18.0,12.0,0.0,408.0,0.5150113945430513,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.6328939517938,f_classif,,,,standardize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9265375980300852,None,0.0,13.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.251097788175676,deviance,6.0,0.35679099363539235,None,0.0,13.0,11.0,0.0,157.0,0.4791732272983235,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,adaboost,SAMME,0.28738775989203896,10.0,423.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.8031499675923353,0.13579938270386765 +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.14159526341015916,deviance,7.0,0.8010488230155749,None,0.0,3.0,20.0,0.0,401.0,0.8073562440607731,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3469031665162168,None,0.0,4.0,3.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,mean,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,83.0,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.002385546176068135,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.7729472304882845,,,0.0004789329856033374,rbf,-1.0,True,6.58869648864534e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,177.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5368752992317617,None,0.0,16.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.8382117756438685,mutual_info,,,,quantile_transformer,11480.0,normal,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.8384447520019118,None,0.0,13.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.36637567531287824,fdr,f_classif,normalize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3759438793746945,None,0.0,7.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3689420905780977,fwe,f_classif,quantile_transformer,25061.0,uniform,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.6291601746046639,None,0.0,11.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,median,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7146659106968425,True,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.12713527337147906,deviance,4.0,0.6041596127474019,None,0.0,14.0,17.0,0.0,83.0,0.8426859880999615,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9455638720565652,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,most_frequent,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8255464552647293,0.19162485555463185 +weighting,one_hot_encoding,0.18137532678800647,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9094110110427254,None,0.0,7.0,12.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,195.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.6682079659377479,None,0.0,4.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,mean,extra_trees_preproc_for_classification,True,entropy,None,0.5552350997943013,None,0.0,8.0,5.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0005560197158932037,True,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00010000000000000026,False,,0.01,True,,invscaling,log,l2,0.25,0.00010000000000000009,,,,,,,,,,,,,,,,,,21,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1392.583799745855,False,True,1.0,squared_hinge,ovr,l1,0.0034975921213842307,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,49486.0,normal,, +none,one_hot_encoding,0.02345017287074443,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.053517066400173056,deviance,10.0,0.542144980834302,None,0.0,20.0,13.0,0.0,233.0,0.7398539900055563,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0614425536709615,fwe,f_classif,robust_scaler,,,0.9523118062307264,0.13434811490315818 +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.8149627329153046,None,0.0,15.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.040936424602789435,deviance,7.0,0.5495014745530306,None,0.0,20.0,18.0,0.0,141.0,0.6905343807995293,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +weighting,one_hot_encoding,0.010000000000000005,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9641046312686136,None,0.0,18.0,12.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8622423450611333,0.2960428898664952 +weighting,one_hot_encoding,0.00034835629696198427,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8245132980938538,0.08947420373097192 +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2398150931290834,,,0.4015139801872962,rbf,-1.0,False,2.402997750662158e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88.40698357592571,chi2,,,,normalize,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.1958974686405233,deviance,5.0,0.33885235607979314,None,0.0,6.0,4.0,0.0,125.0,0.9448890820738562,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2538107344750156,False,True,hinge,1.5099542326343014e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,8532.0,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,0.0007038280350320558,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4138778052607317,0.7995003430482459,5.0,5.430044692638861,poly,-1.0,True,0.02455501006004393,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,31,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.04749094903835669,deviance,3.0,0.6184047395714717,None,0.0,17.0,8.0,0.0,428.0,0.7515561640094087,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,,False,adaboost,SAMME,0.4391375941344922,3.0,386.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7439738358430176,0.20581080574615795 +none,one_hot_encoding,0.00011294596229850895,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7515.1213255144885,0.9576762936062476,3.0,0.019002536385919932,poly,-1.0,False,0.010632086351533369,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,51.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,97282.0,normal,, +none,one_hot_encoding,0.00012586572428922356,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5240592829918601,None,0.0,10.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.15944469021885255,None,0.0,18.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,14.0,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,6315.0,uniform,, +weighting,one_hot_encoding,0.03192699980429505,True,adaboost,SAMME.R,0.10000000000000002,1.0,50.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,quantile_transformer,8407.0,normal,, +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1751.4736515133566,0.62404114475118,3.0,1.6087076997410432,poly,-1.0,False,3.5353792826856045e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1198.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.2422926485206341,,1.0,None,0.0,15.0,9.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.02102683283349326,deviance,10.0,0.2797288369369436,None,0.0,14.0,9.0,0.0,480.0,0.5778972273820631,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58.88123233170863,mutual_info,,,,robust_scaler,,,0.75,0.25 +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828776,None,0.0,2.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2155613360930585,deviance,4.0,0.3198803116198433,None,0.0,8.0,13.0,0.0,275.0,0.28870176110739404,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,mean,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.7062102387181676,None,0.0,1.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,most_frequent,fast_ica,,,,,,,,,,,parallel,exp,100.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7065776353150109,0.23782974987118105 +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.3233601927284725,None,0.0,3.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2991312911384725,fdr,chi2,normalize,,,, +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.3795924768593385,deviance,2.0,0.33708497069988536,None,0.0,15.0,13.0,0.0,451.0,0.7716323242090217,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2573946506994812,fwe,f_classif,quantile_transformer,1000.0,uniform,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.609975998293528,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,most_frequent,fast_ica,,,,,,,,,,,parallel,logcosh,2000.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8430415644014919,0.2863750565331575 +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.39536192447534535,None,0.0,19.0,3.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5.0,None,11.0,11.0,1.0,12.0,,,,,,robust_scaler,,,0.8928631650245873,0.1581877760687084 +weighting,one_hot_encoding,0.0010446150978844174,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.2100039691650936,None,0.0,19.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,10.0,None,3.0,18.0,1.0,42.0,,,,,,quantile_transformer,44341.0,uniform,, +weighting,one_hot_encoding,0.3126027672745337,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1100.6211008501205,0.5921425829232616,2.0,0.0337546254878617,poly,-1.0,True,0.09641299736884308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2507440474920336e-05,True,,0.049622652766554566,True,0.009105043727227265,constant,squared_hinge,elasticnet,,0.00010112719671669047,,,,,,,,,,,,,,,,,,53,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61.69949680034141,chi2,,,,none,,,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.9348409326933208,rbf,-1.0,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1754.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.010488491664540746,True,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6793271069375356,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0903156116813716,fdr,f_classif,robust_scaler,,,0.976245783323518,0.2189244634478133 +weighting,one_hot_encoding,0.010000000000000005,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.011886849251540706,deviance,10.0,0.716738790505292,None,0.0,5.0,6.0,0.0,472.0,0.9128424273302038,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,198.72528686512538,False,True,1.0,squared_hinge,ovr,l2,0.026260652523566803,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,robust_scaler,,,0.913511520078368,0.2742229325455444 +none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9260795160807372,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.03644212536682547,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.05965671477918361,deviance,8.0,0.4858133247974158,None,0.0,14.0,7.0,0.0,480.0,0.5726186552917335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.15318294164619112 +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18887.81504976871,,,0.23283562663398755,rbf,-1.0,True,2.3839685780861318e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3937607155568376,fwe,chi2,robust_scaler,,,0.941018778984854,0.2144110585080491 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3428971645958825,,,0.2229870623330047,rbf,-1.0,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.006909187206474195,True,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00024264014379190562,True,,0.04987297125937914,True,,optimal,hinge,l2,,4.0861541221464815e-05,,,,,,,,,,,,,,,,,,64,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.05153104953418389,False,True,1.0,squared_hinge,ovr,l1,0.0001939386474290285,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.003566024581260295,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7974297391104296,None,0.0,5.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,median,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,f_classif,,,,standardize,,,, +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00016781524591321165,True,True,squared_hinge,1.5119200923218881e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.349459944355116,,,0.00024028983491736645,rbf,-1.0,True,1.139421622432356e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3636266268105085,fwe,chi2,none,,,, +weighting,one_hot_encoding,0.00034835629696198427,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8245132980938538,0.08947420373097192 +weighting,one_hot_encoding,0.00016967940959070708,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9439080311935252,None,0.0,2.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,170.0,None,,0.014191958374153584,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9940718718674404,None,0.0,15.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.05347123056931161,deviance,3.0,0.2250677489759125,None,0.0,16.0,4.0,0.0,309.0,0.7245595517718859,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19466188848884064,fdr,chi2,standardize,,,, +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,normalize,,,, +none,one_hot_encoding,,False,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6396026761675004,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06544340428506021,fwe,f_classif,none,,,, +weighting,one_hot_encoding,0.005326467497783115,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.058055900067082,,,0.1626688094236879,rbf,-1.0,True,0.048381159429370636,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.9188519169916218,None,0.0,1.0,5.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,median,fast_ica,,,,,,,,,,,parallel,cube,306.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1.0,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.3837398524575939,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.018356703878357986,deviance,3.0,0.9690352514774068,None,0.0,12.0,3.0,0.0,234.0,0.3870344708308441,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.4932996544760619,None,0.0,2.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,340.0,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,most_frequent,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6864970915330799,False,,,,,,,,,,,,,,,,normalize,,,, +weighting,one_hot_encoding,0.0001486770773839718,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5256280540657592,None,0.0,8.0,12.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5670424455696162,None,0.0,8.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.00010817282861262362,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.799803680241154,None,0.0,13.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,85,mean,fast_ica,,,,,,,,,,,deflation,exp,1793.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9071932815811076,0.03563842252368924 +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.35533396539961937,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,86,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4165632766388807,fpr,chi2,none,,,, +weighting,one_hot_encoding,,False,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,156.0,auto,,0.0001987333852871589,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1.0,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,no_encoding,,,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.96834823420249e-05,False,True,hinge,0.00016639250831671168,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,89,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.628430584359224,mutual_info,,,,quantile_transformer,6634.0,normal,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8522973640879423,None,0.0,13.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,90,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,91,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,decision_tree,,,,,,,entropy,1.94608233492308,,1.0,None,0.0,15.0,11.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2613520842796237,fdr,chi2,quantile_transformer,1000.0,uniform,, +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,93,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0387325491437111,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.32034732923549136,None,0.0,20.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94,median,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,quantile_transformer,1000.0,uniform,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2636201374253461,deviance,7.0,0.8344964130784466,None,0.0,9.0,2.0,0.0,298.0,0.7517549950523315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,95,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,normalize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,96,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.2263596964804377,True,adaboost,SAMME,0.15143691959318842,2.0,233.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,97,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.07951518163998639,fwe,f_classif,minmax,,,, +weighting,one_hot_encoding,0.010000000000000005,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.14904790197542306,deviance,6.0,0.7561346995577642,None,0.0,5.0,14.0,0.0,340.0,0.6548040792383665,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,98,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.0433556140045585,deviance,10.0,0.33000096635982235,None,0.0,15.0,13.0,0.0,388.0,0.8291104221904706,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,99,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,robust_scaler,,,0.7496393440951183,0.2853682991120835 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,100,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3721522140614508,0.3541746628756037,3.0,0.0009148519644429074,poly,-1.0,False,2.916672898330067e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,37866.0,normal,, +weighting,one_hot_encoding,0.010000000000000005,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.04952755495565772,deviance,3.0,0.7249041896998006,None,0.0,6.0,17.0,0.0,174.0,0.3718608680080454,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,102,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.0020580843703898177,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.945774573434192,None,0.0,19.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,103,median,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,15209.0,normal,, +weighting,one_hot_encoding,0.010000000000000005,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.44875674701568935,None,0.0,8.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,104,most_frequent,fast_ica,,,,,,,,,,,deflation,cube,1367.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.10324969243867224,True,decision_tree,,,,,,,gini,0.7467478023293801,,1.0,None,0.0,12.0,8.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,105,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84.22876326806853,mutual_info,,,,minmax,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,106,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.005332972692819521,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5565918060287016,None,0.0,5.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,107,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,173.0,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9527068489270144,0.04135311355893583 +weighting,one_hot_encoding,0.001279467383882126,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,108,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0903354518102121,fwe,f_classif,standardize,,,, +weighting,one_hot_encoding,0.00013442810992750476,True,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1.0,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,109,median,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,8.0,None,13.0,16.0,1.0,28.0,,,,,,quantile_transformer,41502.0,uniform,, +weighting,one_hot_encoding,0.002615346832354839,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7884268823432835,None,0.0,20.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,110,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +weighting,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56.086963007482865,,,0.013609964993119377,rbf,-1.0,True,0.00196831255706268,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,111,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.15374716583918388 +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.2472984547885781,deviance,3.0,0.6564306719064884,None,0.0,15.0,14.0,0.0,220.0,0.8082564085714649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,112,median,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,332.0,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,113,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.03905145156995541,deviance,5.0,0.2281306656230014,None,0.0,14.0,13.0,0.0,493.0,0.8793075442604774,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,115,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,25382.0,normal,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.12713527337147906,deviance,4.0,0.6041596127474019,None,0.0,14.0,17.0,0.0,83.0,0.8426859880999615,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,116,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,117,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,118,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.20202014999292287,False,True,1.0,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,no_encoding,,,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6390376923528961,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,119,median,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,164.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,62508.0,normal,, +weighting,one_hot_encoding,0.1885493528549979,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.603604171705261,False,True,squared_hinge,4.4620804452839e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,120,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,quantile_transformer,57665.0,uniform,, +weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.27041927277584e-06,True,0.00010000000000000009,0.033157325660763994,True,0.0008114527992546482,invscaling,modified_huber,elasticnet,0.13714427818877545,0.055179642772545036,,,,,,,,,,,,,,,,,,121,median,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.7879059827470586,None,0.0,3.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,122,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54.1024239468849,f_classif,,,,quantile_transformer,89842.0,normal,, +weighting,one_hot_encoding,0.010000000000000005,True,decision_tree,,,,,,,entropy,1.5841974853345435,,1.0,None,0.0,8.0,14.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,123,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,25.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.04994122399949775,deviance,9.0,0.8621747362759826,None,0.0,1.0,13.0,0.0,84.0,0.6449569386396325,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,124,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44.533266302815896,chi2,,,,minmax,,,, +weighting,one_hot_encoding,0.000738320402221022,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,125,median,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.854322697199371,False,True,1.0,squared_hinge,ovr,l1,0.00013359426815085846,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,30.0,normal,, +weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0733000338152003,False,True,1.0,squared_hinge,ovr,l2,0.033752542733220474,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,126,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.6840756728731969,,0.00980445380551526,sigmoid,161.0,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.5765793990908161,None,0.0,11.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,127,median,fast_ica,,,,,,,,,,,deflation,exp,10.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.1166829447028879,deviance,4.0,0.8451196958788597,None,0.0,1.0,8.0,0.0,58.0,0.8652814520124915,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.03406306803297533,False,True,1.0,squared_hinge,ovr,l1,0.00019257971888767865,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.0006290513932903491,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8680626684393846,None,0.0,9.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,129,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,54639.0,normal,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.2604623554399743,None,0.0,18.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.282079367058228,fpr,chi2,none,,,, +none,one_hot_encoding,0.04651467280022463,True,adaboost,SAMME,0.6506122230393502,6.0,143.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,extra_trees_preproc_for_classification,False,entropy,None,0.348720215832657,None,0.0,17.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,26025.0,normal,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3386310102795006,None,0.0,6.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,True,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.0,None,,5.861221938384061e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.22985730375167915,fpr,f_classif,quantile_transformer,40589.0,uniform,, +none,no_encoding,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00012437731009611307,True,,0.08709904468181932,True,,invscaling,squared_hinge,l1,0.6231564675290102,0.008071279833697563,,,,,,,,,,,,,,,,,,134,median,fast_ica,,,,,,,,,,,parallel,logcosh,814.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.714869937384006,None,0.0,8.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, diff --git a/metalearning/metalearning_files/recall_macro_binary.classification_dense/description.txt b/metalearning/metalearning_files/recall_macro_binary.classification_dense/description.txt new file mode 100755 index 0000000..5cc465b --- /dev/null +++ b/metalearning/metalearning_files/recall_macro_binary.classification_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: recall_macro +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/recall_macro_binary.classification_dense/feature_costs.arff b/metalearning/metalearning_files/recall_macro_binary.classification_dense/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/recall_macro_binary.classification_dense/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/recall_macro_binary.classification_dense/feature_runstatus.arff b/metalearning/metalearning_files/recall_macro_binary.classification_dense/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/recall_macro_binary.classification_dense/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/recall_macro_binary.classification_dense/feature_values.arff b/metalearning/metalearning_files/recall_macro_binary.classification_dense/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/recall_macro_binary.classification_dense/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/recall_macro_binary.classification_dense/readme.txt b/metalearning/metalearning_files/recall_macro_binary.classification_dense/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/recall_macro_binary.classification_sparse/algorithm_runs.arff b/metalearning/metalearning_files/recall_macro_binary.classification_sparse/algorithm_runs.arff new file mode 100755 index 0000000..f40e13b --- /dev/null +++ b/metalearning/metalearning_files/recall_macro_binary.classification_sparse/algorithm_runs.arff @@ -0,0 +1,152 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE recall_macro NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2120,1.0,1,0.12055882293523357,ok +75193,1.0,2,0.05570147909402934,ok +2117,1.0,3,0.17548618736408272,ok +75156,1.0,4,0.22313433229150748,ok +75129,1.0,5,0.21139308855291583,ok +75243,1.0,6,0.02741169837497348,ok +75110,1.0,7,0.292400218490373,ok +75239,1.0,8,0.0,ok +75223,1.0,9,0.3513704849874423,ok +75221,1.0,10,0.5127596762099687,ok +258,1.0,11,0.01797504123780258,ok +75121,1.0,12,0.045454545454545414,ok +253,1.0,13,0.4487779181115149,ok +261,1.0,14,0.31385281385281383,ok +75240,1.0,15,0.017683772538141462,ok +75120,1.0,16,0.2546523517382413,ok +75124,1.0,17,0.2107615894039736,ok +75176,1.0,18,0.016640035468590053,ok +75103,1.0,19,0.01660181821534601,ok +75207,1.0,20,0.1890011398955863,ok +75095,1.0,21,0.04590735712181182,ok +273,1.0,22,0.049601841028638294,ok +75174,1.0,23,0.1520296780824476,ok +75153,1.0,24,0.12110304789550075,ok +75093,1.0,25,0.3286900890421619,ok +75119,1.0,26,0.1288429752066116,ok +75201,1.0,27,0.0997376911529182,ok +75215,1.0,28,0.029887049159286416,ok +75172,1.0,29,0.10886921886815126,ok +75169,1.0,30,0.0340573619843737,ok +75202,1.0,31,0.42493459653140053,ok +75233,1.0,32,0.08355891180172326,ok +75231,1.0,33,0.15778968253968262,ok +75196,1.0,34,0.028346047156726728,ok +248,1.0,35,0.2701991669030601,ok +75191,1.0,36,0.12329305343461994,ok +75217,1.0,37,0.0,ok +260,1.0,38,0.07198862873636425,ok +75115,1.0,39,0.09070773400041698,ok +75123,1.0,40,0.32871263757742397,ok +75108,1.0,41,0.00232727928992138,ok +75101,1.0,42,0.28377248501244257,ok +75192,1.0,43,0.48263188489323006,ok +75232,1.0,44,0.1759384976698103,ok +75173,1.0,45,0.11733360389610392,ok +75197,1.0,46,0.2040950900493621,ok +266,1.0,47,0.031108344044042946,ok +75148,1.0,48,0.19038707564842594,ok +75150,1.0,49,0.32022768927111245,ok +75100,1.0,50,0.27498250252741274,ok +75178,1.0,51,0.8079895490982436,ok +75236,1.0,52,0.032106570444582316,ok +75179,1.0,53,0.20149749000255257,ok +75213,1.0,54,0.06273157272368945,ok +2123,1.0,55,0.256388888888889,ok +75227,1.0,56,0.12397340187955186,ok +75184,1.0,57,0.18267180532449523,ok +75142,1.0,58,0.08134030645745782,ok +236,1.0,59,0.04240504096821618,ok +2122,1.0,60,0.292400218490373,ok +75188,1.0,61,0.4727873168498169,ok +75166,1.0,62,0.09969265347610456,ok +75181,1.0,63,0.0,ok +75133,1.0,64,0.31850429480226317,ok +75134,1.0,65,0.14906265793135665,ok +75198,1.0,66,0.12654657786302836,ok +262,1.0,67,0.0027336509054473046,ok +75234,1.0,68,0.05981527192723568,ok +75139,1.0,69,0.013389237134515009,ok +252,1.0,70,0.1626862498888315,ok +75117,1.0,71,0.12062226391494679,ok +75113,1.0,72,0.010561357454012876,ok +75098,1.0,73,0.028062456815564074,ok +246,1.0,74,0.02464555428242643,ok +75203,1.0,75,0.11023617811471986,ok +75237,1.0,76,0.00040449626783733983,ok +75195,1.0,77,0.0015167841344365662,ok +75171,1.0,78,0.16706480960317482,ok +75128,1.0,79,0.07016624831750884,ok +75096,1.0,80,0.6475478379912987,ok +75250,1.0,81,0.391127054727214,ok +75146,1.0,82,0.1222599514053454,ok +75116,1.0,83,0.020634603301536547,ok +75157,1.0,84,0.44266509433962264,ok +75187,1.0,85,0.027932128591676264,ok +2350,1.0,86,0.4423678825304047,ok +242,1.0,87,0.017081000552456538,ok +244,1.0,88,0.10808472308576977,ok +75125,1.0,89,0.07105870289669314,ok +75185,1.0,90,0.1293167333629761,ok +75163,1.0,91,0.06128065774804903,ok +75177,1.0,92,0.03815960706939259,ok +75189,1.0,93,0.02093625722530723,ok +75244,1.0,94,0.18253811312937274,ok +75219,1.0,95,0.08686090936376534,ok +75222,1.0,96,0.11136904761904765,ok +75159,1.0,97,0.25561678677405686,ok +75175,1.0,98,0.12715986367288612,ok +75109,1.0,99,0.40368927333473925,ok +254,1.0,100,0.0,ok +75105,1.0,101,0.42618761507557956,ok +75106,1.0,102,0.4177538160920802,ok +75212,1.0,103,0.2773454482218938,ok +75099,1.0,104,0.29830589084229864,ok +75248,1.0,105,0.1981334600032404,ok +233,1.0,106,0.01525546388768273,ok +75235,1.0,107,0.001061350868232891,ok +75226,1.0,108,0.009036055111661834,ok +75132,1.0,109,0.4536697094634172,ok +75127,1.0,110,0.33895073146733024,ok +251,1.0,111,0.020161426212381595,ok +75161,1.0,112,0.08277383139196026,ok +75143,1.0,113,0.017181695373184702,ok +75114,1.0,114,0.034791524265208484,ok +75182,1.0,115,0.1360109923276095,ok +75112,1.0,116,0.1477333735943931,ok +75210,1.0,117,0.0,ok +75205,1.0,118,0.1916783952293486,ok +75090,1.0,119,0.10262125025576962,ok +275,1.0,120,0.041190711460148854,ok +288,1.0,121,0.14344937049478246,ok +75092,1.0,122,0.10984848484848486,ok +3043,1.0,123,0.04425716804500235,ok +75249,1.0,124,0.013258182854293588,ok +75126,1.0,125,0.12520747636573581,ok +75225,1.0,126,0.10485406091370564,ok +75141,1.0,127,0.05863227105456237,ok +75107,1.0,128,0.28251633008867305,ok +75097,1.0,129,0.47931992380750144,ok +80001,1.0,1,0.1502463054187192,ok +80003,1.0,1,0.12212976616810622,ok +80006,1.0,1,0.196078431372549,ok +80008,1.0,1,0.19999999999999996,ok +80009,1.0,1,0.15555555555555556,ok +80010,1.0,1,0.2222222222222222,ok +80011,1.0,1,0.05263157894736836,ok +80012,1.0,1,0.0625,ok +80013,1.0,1,0.0,ok +80014,1.0,1,0.050000000000000044,ok +80015,1.0,1,0.10096153846153844,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/recall_macro_binary.classification_sparse/configurations.csv b/metalearning/metalearning_files/recall_macro_binary.classification_sparse/configurations.csv new file mode 100755 index 0000000..b7334ed --- /dev/null +++ b/metalearning/metalearning_files/recall_macro_binary.classification_sparse/configurations.csv @@ -0,0 +1,141 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,preprocessor:truncatedSVD:target_dim,rescaling:__choice__ +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7249853037185638,None,0.0,1.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,median,extra_trees_preproc_for_classification,False,gini,None,0.9424908623661876,None,0.0,7.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.585711203872775,None,0.0,5.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,median,extra_trees_preproc_for_classification,True,entropy,None,0.29512530534048065,None,0.0,7.0,10.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,bernoulli_nb,,,,,0.11565661797517847,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,median,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,9.0,None,13.0,18.0,1.0,29.0,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.039533063907190934,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1878805519245509,fdr,chi2,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,15.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7238850981243719,None,0.0,4.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,5.282738216059151e-05,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.6682079659377479,None,0.0,4.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,mean,extra_trees_preproc_for_classification,True,entropy,None,0.5552350997943013,None,0.0,8.0,5.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0009580347867777607,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,0.10000000000000006,rbf,-1.0,True,0.0010000000000000002,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.35040453084365497,False,True,1.0,squared_hinge,ovr,l1,0.006810889378452772,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.010000000000000005,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9727149851116396,None,0.0,18.0,13.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,,none +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.7735071203475309,,1.0,None,0.0,4.0,4.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,mean,extra_trees_preproc_for_classification,True,entropy,None,0.8171332500590935,None,0.0,15.0,13.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2398150931290834,,,0.4015139801872962,rbf,-1.0,False,2.402997750662158e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88.40698357592571,chi2,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.034465366914659866,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.12057775675278172,deviance,10.0,0.8011153303489733,None,0.0,2.0,16.0,0.0,370.0,0.6078295352200873,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,mean,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0007038280350320558,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4138778052607317,0.7995003430482459,5.0,5.430044692638861,poly,-1.0,True,0.02455501006004393,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,31,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.0010015637584068037,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.037611630308856295,deviance,5.0,0.8840126779516314,None,0.0,10.0,2.0,0.0,444.0,0.7599997167603434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,most_frequent,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.004230062585802822,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.4946412825074172,None,0.0,9.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.004090774134315939,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7983157215145903,None,0.0,2.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,normalize +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1751.4736515133566,0.62404114475118,3.0,1.6087076997410432,poly,-1.0,False,3.5353792826856045e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,mean,kernel_pca,,,,,,,,,,,,,,cosine,1198.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.8657388713119849,,1.0,None,0.0,19.0,13.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828776,None,0.0,2.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.027324741616523342,deviance,10.0,0.8623781459430139,None,0.0,10.0,20.0,0.0,329.0,0.8595750155424215,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,mean,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.002173124111626734,None,0.0,14.0,4.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,6.0,None,13.0,2.0,1.0,23.0,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1100.6211008501205,0.5921425829232616,2.0,0.0337546254878617,poly,-1.0,True,0.09641299736884308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2507440474920336e-05,True,,0.049622652766554566,True,0.009105043727227265,constant,squared_hinge,elasticnet,,0.00010112719671669047,,,,,,,,,,,,,,,,,,53,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61.69949680034141,chi2,,,,,none +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.9348409326933208,rbf,-1.0,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,mean,kernel_pca,,,,,,,,,,,,,,cosine,1754.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,one_hot_encoding,0.039533063907190934,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.3668390168981173,None,0.0,9.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1878805519245509,fdr,chi2,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3428971645958825,,,0.2229870623330047,rbf,-1.0,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7866272157881596,None,0.0,6.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00016781524591321165,True,True,squared_hinge,1.5119200923218881e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.349459944355116,,,0.00024028983491736645,rbf,-1.0,True,1.139421622432356e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3636266268105085,fwe,chi2,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4047.618729304337,,,2.0237366768707754,rbf,-1.0,True,0.04369127828878843,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,one_hot_encoding,0.010000000000000005,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10.0,1.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,,none +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9015027772227234,None,0.0,19.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,one_hot_encoding,0.039533063907190934,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1878805519245509,fdr,chi2,,standardize +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,,normalize +none,one_hot_encoding,0.003443779683191071,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.005326467497783115,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.058055900067082,,,0.1626688094236879,rbf,-1.0,True,0.048381159429370636,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1.0,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.3451627750042988,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.3163640203509378,None,0.0,17.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,median,extra_trees_preproc_for_classification,False,gini,None,0.8916956785028156,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.002630811782675973,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9828367182452932,None,0.0,18.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,85,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.35533396539961937,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,86,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4165632766388807,fpr,chi2,,none +weighting,one_hot_encoding,0.0019686649916896212,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.04641055832142541,True,True,hinge,8.540468968077405e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87,mean,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,911.0,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1.0,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,89,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,90,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,91,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,decision_tree,,,,,,,gini,1.7984076825537865,,1.0,None,0.0,14.0,2.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2897525995758022,fwe,chi2,,none +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,93,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.9376772805635799,None,0.0,18.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94,median,extra_trees_preproc_for_classification,True,entropy,None,0.8613889689810683,None,0.0,10.0,4.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,95,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,96,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,97,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,98,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.00022308163276069302,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,99,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,100,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.039533063907190934,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1878805519245509,fdr,chi2,,standardize +weighting,one_hot_encoding,0.039533063907190934,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,102,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1878805519245509,fdr,chi2,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,103,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,bernoulli_nb,,,,,0.014801515930977628,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,104,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,105,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,106,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.006372860318416312,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6467376360604045,None,0.0,1.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,107,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,,False,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.37163783119625016,False,True,1.0,squared_hinge,ovr,l2,1.2049944334095187e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,108,median,kitchen_sinks,,,,,,,,,,,,,,,,0.33181838105513195,7347.0,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,109,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3823734947460288,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,110,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,adaboost,SAMME,0.11042308042695524,5.0,117.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,111,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,112,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,113,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.3997572853391576,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.21008613984919333,None,0.0,11.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,115,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,116,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,117,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,118,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.20202014999292287,False,True,1.0,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,119,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.00012696985090051145,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.163979712833404,False,True,hinge,0.02438592885755657,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,120,mean,kernel_pca,,,,,,,,,,,,,0.03910740617243662,rbf,1851.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,121,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8804227616935514,None,0.0,10.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,122,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.0898983119899223,False,True,1.0,squared_hinge,ovr,l1,0.016048982920294167,,,,,,,,,,,,,,,,,,,none +weighting,no_encoding,,,decision_tree,,,,,,,entropy,1.301605991416264,,1.0,None,0.0,8.0,3.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,123,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,124,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,125,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0733000338152003,False,True,1.0,squared_hinge,ovr,l2,0.033752542733220474,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,126,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,-0.6840756728731969,,0.00980445380551526,sigmoid,161.0,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,127,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.00214097329599271,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7996802015738327,None,0.0,7.0,12.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.1052247187777527,False,True,1.0,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,129,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.2604623554399743,None,0.0,18.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.282079367058228,fpr,chi2,,none +none,one_hot_encoding,0.04329010470998136,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7260331062271381,None,0.0,7.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.010000000000000005,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15.0,1.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,134,median,extra_trees_preproc_for_classification,False,entropy,None,0.8213051377774989,None,0.0,3.0,13.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.714869937384006,None,0.0,8.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/metalearning/metalearning_files/recall_macro_binary.classification_sparse/description.txt b/metalearning/metalearning_files/recall_macro_binary.classification_sparse/description.txt new file mode 100755 index 0000000..5cc465b --- /dev/null +++ b/metalearning/metalearning_files/recall_macro_binary.classification_sparse/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: recall_macro +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/recall_macro_binary.classification_sparse/feature_costs.arff b/metalearning/metalearning_files/recall_macro_binary.classification_sparse/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/recall_macro_binary.classification_sparse/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/recall_macro_binary.classification_sparse/feature_runstatus.arff b/metalearning/metalearning_files/recall_macro_binary.classification_sparse/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/recall_macro_binary.classification_sparse/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/recall_macro_binary.classification_sparse/feature_values.arff b/metalearning/metalearning_files/recall_macro_binary.classification_sparse/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/recall_macro_binary.classification_sparse/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/recall_macro_binary.classification_sparse/readme.txt b/metalearning/metalearning_files/recall_macro_binary.classification_sparse/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/recall_macro_multiclass.classification_dense/algorithm_runs.arff b/metalearning/metalearning_files/recall_macro_multiclass.classification_dense/algorithm_runs.arff new file mode 100755 index 0000000..2a7d5e0 --- /dev/null +++ b/metalearning/metalearning_files/recall_macro_multiclass.classification_dense/algorithm_runs.arff @@ -0,0 +1,152 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE recall_macro NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2120,1.0,1,0.10170900792413518,ok +75193,1.0,2,0.05570147909402934,ok +2117,1.0,3,0.17058181059794586,ok +75156,1.0,4,0.2059468405268512,ok +75129,1.0,5,0.17411530154510713,ok +75243,1.0,6,0.0,ok +75110,1.0,7,0.11182111215625956,ok +75239,1.0,8,0.0,ok +75223,1.0,9,0.12451335740985625,ok +75221,1.0,10,0.4873924872950214,ok +258,1.0,11,0.009581814407771616,ok +75121,1.0,12,0.0,ok +253,1.0,13,0.44373242034274996,ok +261,1.0,14,0.2943722943722944,ok +75240,1.0,15,0.017683772538141462,ok +75120,1.0,16,0.20465235173824126,ok +75124,1.0,17,0.17233122467134532,ok +75176,1.0,18,0.015737392757574353,ok +75103,1.0,19,0.0031496062992126816,ok +75207,1.0,20,0.1890011398955863,ok +75095,1.0,21,0.04425501079002059,ok +273,1.0,22,0.046593731735826927,ok +75174,1.0,23,0.12864050014146122,ok +75153,1.0,24,0.0802757619738752,ok +75093,1.0,25,0.32381202028228084,ok +75119,1.0,26,0.08442148760330581,ok +75201,1.0,27,0.0997376911529182,ok +75215,1.0,28,0.028514586427562882,ok +75172,1.0,29,0.13559824204240767,ok +75169,1.0,30,0.0340573619843737,ok +75202,1.0,31,0.42493459653140053,ok +75233,1.0,32,0.06969987414076873,ok +75231,1.0,33,0.15778968253968262,ok +75196,1.0,34,0.01378294036061023,ok +248,1.0,35,0.23327202689622628,ok +75191,1.0,36,0.1286172567369568,ok +75217,1.0,37,0.0,ok +260,1.0,38,0.049350759010592826,ok +75115,1.0,39,0.08750260579528879,ok +75123,1.0,40,0.32871263757742397,ok +75108,1.0,41,0.0,ok +75101,1.0,42,0.2753956012356946,ok +75192,1.0,43,0.48263188489323006,ok +75232,1.0,44,0.13627022861546367,ok +75173,1.0,45,0.11644480519480527,ok +75197,1.0,46,0.2211380803516111,ok +266,1.0,47,0.018660135427440383,ok +75148,1.0,48,0.1358001187955955,ok +75150,1.0,49,0.2876075003524602,ok +75100,1.0,50,0.2540244186950774,ok +75178,1.0,51,0.784206721465269,ok +75236,1.0,52,0.032106570444582316,ok +75179,1.0,53,0.20149749000255257,ok +75213,1.0,54,0.06273157272368945,ok +2123,1.0,55,0.17781746031746037,ok +75227,1.0,56,0.11446626012925343,ok +75184,1.0,57,0.12532912571721433,ok +75142,1.0,58,0.07161311525180047,ok +236,1.0,59,0.03889089023011372,ok +2122,1.0,60,0.11253171534644457,ok +75188,1.0,61,0.4727873168498169,ok +75166,1.0,62,0.09969265347610456,ok +75181,1.0,63,0.0,ok +75133,1.0,64,0.15654041218375303,ok +75134,1.0,65,0.1481357660616962,ok +75198,1.0,66,0.12654657786302836,ok +262,1.0,67,0.0027336509054473046,ok +75234,1.0,68,0.024155509645569007,ok +75139,1.0,69,0.012643391866273612,ok +252,1.0,70,0.1510545149907132,ok +75117,1.0,71,0.11955388784657073,ok +75113,1.0,72,0.005274971941638618,ok +75098,1.0,73,0.028062456815564074,ok +246,1.0,74,0.010490317336848243,ok +75203,1.0,75,0.11023617811471986,ok +75237,1.0,76,0.0002948416740189419,ok +75195,1.0,77,0.0015167841344365662,ok +75171,1.0,78,0.16208341454974717,ok +75128,1.0,79,0.059041798537596835,ok +75096,1.0,80,0.334129919213757,ok +75250,1.0,81,0.3478893206205086,ok +75146,1.0,82,0.11384157742648315,ok +75116,1.0,83,0.020634603301536547,ok +75157,1.0,84,0.44266509433962264,ok +75187,1.0,85,0.016824909805437382,ok +2350,1.0,86,0.4423678825304047,ok +242,1.0,87,0.011186441482494147,ok +244,1.0,88,0.10808472308576977,ok +75125,1.0,89,0.06985388361958478,ok +75185,1.0,90,0.12909185691725056,ok +75163,1.0,91,0.06128065774804903,ok +75177,1.0,92,0.023309264934301632,ok +75189,1.0,93,0.02093625722530723,ok +75244,1.0,94,0.18044671695057302,ok +75219,1.0,95,0.03536781923790411,ok +75222,1.0,96,0.11136904761904765,ok +75159,1.0,97,0.18577787197965234,ok +75175,1.0,98,0.1037395977530613,ok +75109,1.0,99,0.3603409856614681,ok +254,1.0,100,0.0,ok +75105,1.0,101,0.29488259839445896,ok +75106,1.0,102,0.327921279550357,ok +75212,1.0,103,0.2512390226936788,ok +75099,1.0,104,0.2277781422198898,ok +75248,1.0,105,0.18643306379155433,ok +233,1.0,106,0.002793457808655364,ok +75235,1.0,107,0.0007102272727272929,ok +75226,1.0,108,0.008218517371262557,ok +75132,1.0,109,0.3636469111367553,ok +75127,1.0,110,0.33766899823884766,ok +251,1.0,111,0.0,ok +75161,1.0,112,0.0643528394880094,ok +75143,1.0,113,0.017181695373184702,ok +75114,1.0,114,0.034791524265208484,ok +75182,1.0,115,0.12771381137622617,ok +75112,1.0,116,0.13777827445022783,ok +75210,1.0,117,0.0,ok +75205,1.0,118,0.1916783952293486,ok +75090,1.0,119,0.061027366747011924,ok +275,1.0,120,0.040221214056733956,ok +288,1.0,121,0.1294895850789981,ok +75092,1.0,122,0.09411421911421913,ok +3043,1.0,123,0.03952394945636206,ok +75249,1.0,124,0.012821882679773466,ok +75126,1.0,125,0.06940535469437825,ok +75225,1.0,126,0.10485406091370564,ok +75141,1.0,127,0.05180158915235733,ok +75107,1.0,128,0.24811618052967832,ok +75097,1.0,129,0.21628382400897928,ok +80001,1.0,1,0.1502463054187192,ok +80003,1.0,1,0.09095218032441543,ok +80006,1.0,1,0.09607843137254901,ok +80008,1.0,1,0.125,ok +80009,1.0,1,0.10555555555555562,ok +80010,1.0,1,0.2222222222222222,ok +80011,1.0,1,0.05263157894736836,ok +80012,1.0,1,0.0625,ok +80013,1.0,1,0.0,ok +80014,1.0,1,0.050000000000000044,ok +80015,1.0,1,0.10096153846153844,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/recall_macro_multiclass.classification_dense/configurations.csv b/metalearning/metalearning_files/recall_macro_multiclass.classification_dense/configurations.csv new file mode 100755 index 0000000..2686140 --- /dev/null +++ b/metalearning/metalearning_files/recall_macro_multiclass.classification_dense/configurations.csv @@ -0,0 +1,141 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:fast_ica:algorithm,preprocessor:fast_ica:fun,preprocessor:fast_ica:n_components,preprocessor:fast_ica:whiten,preprocessor:feature_agglomeration:affinity,preprocessor:feature_agglomeration:linkage,preprocessor:feature_agglomeration:n_clusters,preprocessor:feature_agglomeration:pooling_func,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:pca:keep_variance,preprocessor:pca:whiten,preprocessor:polynomial:degree,preprocessor:polynomial:include_bias,preprocessor:polynomial:interaction_only,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,rescaling:__choice__,rescaling:quantile_transformer:n_quantiles,rescaling:quantile_transformer:output_distribution,rescaling:robust_scaler:q_max,rescaling:robust_scaler:q_min +weighting,one_hot_encoding,0.00011717632475982632,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.045388141846341344,deviance,10.0,0.29161769341843435,None,0.0,20.0,2.0,0.0,278.0,0.7912571599269661,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7249853037185638,None,0.0,1.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,median,extra_trees_preproc_for_classification,False,gini,None,0.9424908623661876,None,0.0,7.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.12713527337147906,deviance,4.0,0.6041596127474019,None,0.0,14.0,17.0,0.0,83.0,0.8426859880999615,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.03474109838999682,deviance,4.0,0.5687034678818491,None,0.0,18.0,12.0,0.0,408.0,0.5150113945430513,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.6328939517938,f_classif,,,,standardize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9265375980300852,None,0.0,13.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.251097788175676,deviance,6.0,0.35679099363539235,None,0.0,13.0,11.0,0.0,157.0,0.4791732272983235,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,adaboost,SAMME,0.28738775989203896,10.0,423.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.8031499675923353,0.13579938270386765 +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.14159526341015916,deviance,7.0,0.8010488230155749,None,0.0,3.0,20.0,0.0,401.0,0.8073562440607731,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3469031665162168,None,0.0,4.0,3.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,mean,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,83.0,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.002385546176068135,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.7729472304882845,,,0.0004789329856033374,rbf,-1.0,True,6.58869648864534e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,177.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5368752992317617,None,0.0,16.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.8382117756438685,mutual_info,,,,quantile_transformer,11480.0,normal,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.8384447520019118,None,0.0,13.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.36637567531287824,fdr,f_classif,normalize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3759438793746945,None,0.0,7.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3689420905780977,fwe,f_classif,quantile_transformer,25061.0,uniform,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.6291601746046639,None,0.0,11.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,median,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7146659106968425,True,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.12713527337147906,deviance,4.0,0.6041596127474019,None,0.0,14.0,17.0,0.0,83.0,0.8426859880999615,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9455638720565652,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,most_frequent,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8255464552647293,0.19162485555463185 +weighting,one_hot_encoding,0.18137532678800647,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9094110110427254,None,0.0,7.0,12.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,195.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.6682079659377479,None,0.0,4.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,mean,extra_trees_preproc_for_classification,True,entropy,None,0.5552350997943013,None,0.0,8.0,5.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0005560197158932037,True,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00010000000000000026,False,,0.01,True,,invscaling,log,l2,0.25,0.00010000000000000009,,,,,,,,,,,,,,,,,,21,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1392.583799745855,False,True,1.0,squared_hinge,ovr,l1,0.0034975921213842307,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,49486.0,normal,, +none,one_hot_encoding,0.02345017287074443,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.053517066400173056,deviance,10.0,0.542144980834302,None,0.0,20.0,13.0,0.0,233.0,0.7398539900055563,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0614425536709615,fwe,f_classif,robust_scaler,,,0.9523118062307264,0.13434811490315818 +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.8149627329153046,None,0.0,15.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.040936424602789435,deviance,7.0,0.5495014745530306,None,0.0,20.0,18.0,0.0,141.0,0.6905343807995293,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +weighting,one_hot_encoding,0.010000000000000005,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9641046312686136,None,0.0,18.0,12.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8622423450611333,0.2960428898664952 +weighting,one_hot_encoding,0.00034835629696198427,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8245132980938538,0.08947420373097192 +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2398150931290834,,,0.4015139801872962,rbf,-1.0,False,2.402997750662158e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88.40698357592571,chi2,,,,normalize,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.1958974686405233,deviance,5.0,0.33885235607979314,None,0.0,6.0,4.0,0.0,125.0,0.9448890820738562,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2538107344750156,False,True,hinge,1.5099542326343014e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,8532.0,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,0.0007038280350320558,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4138778052607317,0.7995003430482459,5.0,5.430044692638861,poly,-1.0,True,0.02455501006004393,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,31,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.04749094903835669,deviance,3.0,0.6184047395714717,None,0.0,17.0,8.0,0.0,428.0,0.7515561640094087,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,,False,adaboost,SAMME,0.4391375941344922,3.0,386.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7439738358430176,0.20581080574615795 +none,one_hot_encoding,0.00011294596229850895,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7515.1213255144885,0.9576762936062476,3.0,0.019002536385919932,poly,-1.0,False,0.010632086351533369,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,51.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,97282.0,normal,, +none,one_hot_encoding,0.00012586572428922356,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5240592829918601,None,0.0,10.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.15944469021885255,None,0.0,18.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,14.0,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,6315.0,uniform,, +weighting,one_hot_encoding,0.03192699980429505,True,adaboost,SAMME.R,0.10000000000000002,1.0,50.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,quantile_transformer,8407.0,normal,, +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1751.4736515133566,0.62404114475118,3.0,1.6087076997410432,poly,-1.0,False,3.5353792826856045e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1198.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.2422926485206341,,1.0,None,0.0,15.0,9.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.02102683283349326,deviance,10.0,0.2797288369369436,None,0.0,14.0,9.0,0.0,480.0,0.5778972273820631,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58.88123233170863,mutual_info,,,,robust_scaler,,,0.75,0.25 +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828776,None,0.0,2.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2155613360930585,deviance,4.0,0.3198803116198433,None,0.0,8.0,13.0,0.0,275.0,0.28870176110739404,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,mean,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.7062102387181676,None,0.0,1.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,most_frequent,fast_ica,,,,,,,,,,,parallel,exp,100.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7065776353150109,0.23782974987118105 +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.3233601927284725,None,0.0,3.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2991312911384725,fdr,chi2,normalize,,,, +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.3795924768593385,deviance,2.0,0.33708497069988536,None,0.0,15.0,13.0,0.0,451.0,0.7716323242090217,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2573946506994812,fwe,f_classif,quantile_transformer,1000.0,uniform,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.609975998293528,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,most_frequent,fast_ica,,,,,,,,,,,parallel,logcosh,2000.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8430415644014919,0.2863750565331575 +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.39536192447534535,None,0.0,19.0,3.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5.0,None,11.0,11.0,1.0,12.0,,,,,,robust_scaler,,,0.8928631650245873,0.1581877760687084 +weighting,one_hot_encoding,0.0010446150978844174,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.2100039691650936,None,0.0,19.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,10.0,None,3.0,18.0,1.0,42.0,,,,,,quantile_transformer,44341.0,uniform,, +weighting,one_hot_encoding,0.3126027672745337,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1100.6211008501205,0.5921425829232616,2.0,0.0337546254878617,poly,-1.0,True,0.09641299736884308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2507440474920336e-05,True,,0.049622652766554566,True,0.009105043727227265,constant,squared_hinge,elasticnet,,0.00010112719671669047,,,,,,,,,,,,,,,,,,53,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61.69949680034141,chi2,,,,none,,,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.9348409326933208,rbf,-1.0,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1754.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.010488491664540746,True,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6793271069375356,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0903156116813716,fdr,f_classif,robust_scaler,,,0.976245783323518,0.2189244634478133 +weighting,one_hot_encoding,0.010000000000000005,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.011886849251540706,deviance,10.0,0.716738790505292,None,0.0,5.0,6.0,0.0,472.0,0.9128424273302038,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,198.72528686512538,False,True,1.0,squared_hinge,ovr,l2,0.026260652523566803,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,robust_scaler,,,0.913511520078368,0.2742229325455444 +none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9260795160807372,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.03644212536682547,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.05965671477918361,deviance,8.0,0.4858133247974158,None,0.0,14.0,7.0,0.0,480.0,0.5726186552917335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.15318294164619112 +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18887.81504976871,,,0.23283562663398755,rbf,-1.0,True,2.3839685780861318e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3937607155568376,fwe,chi2,robust_scaler,,,0.941018778984854,0.2144110585080491 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3428971645958825,,,0.2229870623330047,rbf,-1.0,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.006909187206474195,True,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00024264014379190562,True,,0.04987297125937914,True,,optimal,hinge,l2,,4.0861541221464815e-05,,,,,,,,,,,,,,,,,,64,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.05153104953418389,False,True,1.0,squared_hinge,ovr,l1,0.0001939386474290285,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.003566024581260295,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7974297391104296,None,0.0,5.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,median,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,f_classif,,,,standardize,,,, +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00016781524591321165,True,True,squared_hinge,1.5119200923218881e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.349459944355116,,,0.00024028983491736645,rbf,-1.0,True,1.139421622432356e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3636266268105085,fwe,chi2,none,,,, +weighting,one_hot_encoding,0.00034835629696198427,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8245132980938538,0.08947420373097192 +weighting,one_hot_encoding,0.00016967940959070708,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9439080311935252,None,0.0,2.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,170.0,None,,0.014191958374153584,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9940718718674404,None,0.0,15.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.05347123056931161,deviance,3.0,0.2250677489759125,None,0.0,16.0,4.0,0.0,309.0,0.7245595517718859,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19466188848884064,fdr,chi2,standardize,,,, +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,normalize,,,, +none,one_hot_encoding,,False,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6396026761675004,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06544340428506021,fwe,f_classif,none,,,, +weighting,one_hot_encoding,0.005326467497783115,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.058055900067082,,,0.1626688094236879,rbf,-1.0,True,0.048381159429370636,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.9188519169916218,None,0.0,1.0,5.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,median,fast_ica,,,,,,,,,,,parallel,cube,306.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1.0,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.3837398524575939,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.018356703878357986,deviance,3.0,0.9690352514774068,None,0.0,12.0,3.0,0.0,234.0,0.3870344708308441,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.4932996544760619,None,0.0,2.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,340.0,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,most_frequent,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6864970915330799,False,,,,,,,,,,,,,,,,normalize,,,, +weighting,one_hot_encoding,0.0001486770773839718,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5256280540657592,None,0.0,8.0,12.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5670424455696162,None,0.0,8.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.00010817282861262362,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.799803680241154,None,0.0,13.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,85,mean,fast_ica,,,,,,,,,,,deflation,exp,1793.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9071932815811076,0.03563842252368924 +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.35533396539961937,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,86,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4165632766388807,fpr,chi2,none,,,, +weighting,one_hot_encoding,,False,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,156.0,auto,,0.0001987333852871589,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1.0,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,no_encoding,,,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.96834823420249e-05,False,True,hinge,0.00016639250831671168,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,89,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.628430584359224,mutual_info,,,,quantile_transformer,6634.0,normal,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8522973640879423,None,0.0,13.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,90,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,91,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,decision_tree,,,,,,,entropy,1.94608233492308,,1.0,None,0.0,15.0,11.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2613520842796237,fdr,chi2,quantile_transformer,1000.0,uniform,, +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,93,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0387325491437111,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.32034732923549136,None,0.0,20.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94,median,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,quantile_transformer,1000.0,uniform,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2636201374253461,deviance,7.0,0.8344964130784466,None,0.0,9.0,2.0,0.0,298.0,0.7517549950523315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,95,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,normalize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,96,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.2263596964804377,True,adaboost,SAMME,0.15143691959318842,2.0,233.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,97,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.07951518163998639,fwe,f_classif,minmax,,,, +weighting,one_hot_encoding,0.010000000000000005,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.14904790197542306,deviance,6.0,0.7561346995577642,None,0.0,5.0,14.0,0.0,340.0,0.6548040792383665,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,98,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.0433556140045585,deviance,10.0,0.33000096635982235,None,0.0,15.0,13.0,0.0,388.0,0.8291104221904706,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,99,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,robust_scaler,,,0.7496393440951183,0.2853682991120835 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,100,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3721522140614508,0.3541746628756037,3.0,0.0009148519644429074,poly,-1.0,False,2.916672898330067e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,37866.0,normal,, +weighting,one_hot_encoding,0.010000000000000005,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.04952755495565772,deviance,3.0,0.7249041896998006,None,0.0,6.0,17.0,0.0,174.0,0.3718608680080454,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,102,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.0020580843703898177,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.945774573434192,None,0.0,19.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,103,median,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,15209.0,normal,, +weighting,one_hot_encoding,0.010000000000000005,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.44875674701568935,None,0.0,8.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,104,most_frequent,fast_ica,,,,,,,,,,,deflation,cube,1367.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.10324969243867224,True,decision_tree,,,,,,,gini,0.7467478023293801,,1.0,None,0.0,12.0,8.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,105,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84.22876326806853,mutual_info,,,,minmax,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,106,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.005332972692819521,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5565918060287016,None,0.0,5.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,107,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,173.0,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9527068489270144,0.04135311355893583 +weighting,one_hot_encoding,0.001279467383882126,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,108,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0903354518102121,fwe,f_classif,standardize,,,, +weighting,one_hot_encoding,0.00013442810992750476,True,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1.0,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,109,median,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,8.0,None,13.0,16.0,1.0,28.0,,,,,,quantile_transformer,41502.0,uniform,, +weighting,one_hot_encoding,0.002615346832354839,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7884268823432835,None,0.0,20.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,110,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +weighting,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56.086963007482865,,,0.013609964993119377,rbf,-1.0,True,0.00196831255706268,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,111,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.15374716583918388 +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.2472984547885781,deviance,3.0,0.6564306719064884,None,0.0,15.0,14.0,0.0,220.0,0.8082564085714649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,112,median,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,332.0,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,113,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.03905145156995541,deviance,5.0,0.2281306656230014,None,0.0,14.0,13.0,0.0,493.0,0.8793075442604774,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,115,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,25382.0,normal,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.12713527337147906,deviance,4.0,0.6041596127474019,None,0.0,14.0,17.0,0.0,83.0,0.8426859880999615,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,116,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,117,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,118,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.20202014999292287,False,True,1.0,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,no_encoding,,,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6390376923528961,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,119,median,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,164.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,62508.0,normal,, +weighting,one_hot_encoding,0.1885493528549979,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.603604171705261,False,True,squared_hinge,4.4620804452839e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,120,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,quantile_transformer,57665.0,uniform,, +weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.27041927277584e-06,True,0.00010000000000000009,0.033157325660763994,True,0.0008114527992546482,invscaling,modified_huber,elasticnet,0.13714427818877545,0.055179642772545036,,,,,,,,,,,,,,,,,,121,median,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.7879059827470586,None,0.0,3.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,122,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54.1024239468849,f_classif,,,,quantile_transformer,89842.0,normal,, +weighting,one_hot_encoding,0.010000000000000005,True,decision_tree,,,,,,,entropy,1.5841974853345435,,1.0,None,0.0,8.0,14.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,123,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,25.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.04994122399949775,deviance,9.0,0.8621747362759826,None,0.0,1.0,13.0,0.0,84.0,0.6449569386396325,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,124,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44.533266302815896,chi2,,,,minmax,,,, +weighting,one_hot_encoding,0.000738320402221022,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,125,median,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.854322697199371,False,True,1.0,squared_hinge,ovr,l1,0.00013359426815085846,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,30.0,normal,, +weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0733000338152003,False,True,1.0,squared_hinge,ovr,l2,0.033752542733220474,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,126,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.6840756728731969,,0.00980445380551526,sigmoid,161.0,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.5765793990908161,None,0.0,11.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,127,median,fast_ica,,,,,,,,,,,deflation,exp,10.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.1166829447028879,deviance,4.0,0.8451196958788597,None,0.0,1.0,8.0,0.0,58.0,0.8652814520124915,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.03406306803297533,False,True,1.0,squared_hinge,ovr,l1,0.00019257971888767865,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.0006290513932903491,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8680626684393846,None,0.0,9.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,129,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,54639.0,normal,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.2604623554399743,None,0.0,18.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.282079367058228,fpr,chi2,none,,,, +none,one_hot_encoding,0.04651467280022463,True,adaboost,SAMME,0.6506122230393502,6.0,143.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,extra_trees_preproc_for_classification,False,entropy,None,0.348720215832657,None,0.0,17.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,26025.0,normal,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3386310102795006,None,0.0,6.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,True,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.0,None,,5.861221938384061e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.22985730375167915,fpr,f_classif,quantile_transformer,40589.0,uniform,, +none,no_encoding,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00012437731009611307,True,,0.08709904468181932,True,,invscaling,squared_hinge,l1,0.6231564675290102,0.008071279833697563,,,,,,,,,,,,,,,,,,134,median,fast_ica,,,,,,,,,,,parallel,logcosh,814.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.714869937384006,None,0.0,8.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, diff --git a/metalearning/metalearning_files/recall_macro_multiclass.classification_dense/description.txt b/metalearning/metalearning_files/recall_macro_multiclass.classification_dense/description.txt new file mode 100755 index 0000000..5cc465b --- /dev/null +++ b/metalearning/metalearning_files/recall_macro_multiclass.classification_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: recall_macro +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/recall_macro_multiclass.classification_dense/feature_costs.arff b/metalearning/metalearning_files/recall_macro_multiclass.classification_dense/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/recall_macro_multiclass.classification_dense/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/recall_macro_multiclass.classification_dense/feature_runstatus.arff b/metalearning/metalearning_files/recall_macro_multiclass.classification_dense/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/recall_macro_multiclass.classification_dense/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/recall_macro_multiclass.classification_dense/feature_values.arff b/metalearning/metalearning_files/recall_macro_multiclass.classification_dense/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/recall_macro_multiclass.classification_dense/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/recall_macro_multiclass.classification_dense/readme.txt b/metalearning/metalearning_files/recall_macro_multiclass.classification_dense/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/recall_macro_multiclass.classification_sparse/algorithm_runs.arff b/metalearning/metalearning_files/recall_macro_multiclass.classification_sparse/algorithm_runs.arff new file mode 100755 index 0000000..f40e13b --- /dev/null +++ b/metalearning/metalearning_files/recall_macro_multiclass.classification_sparse/algorithm_runs.arff @@ -0,0 +1,152 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE recall_macro NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2120,1.0,1,0.12055882293523357,ok +75193,1.0,2,0.05570147909402934,ok +2117,1.0,3,0.17548618736408272,ok +75156,1.0,4,0.22313433229150748,ok +75129,1.0,5,0.21139308855291583,ok +75243,1.0,6,0.02741169837497348,ok +75110,1.0,7,0.292400218490373,ok +75239,1.0,8,0.0,ok +75223,1.0,9,0.3513704849874423,ok +75221,1.0,10,0.5127596762099687,ok +258,1.0,11,0.01797504123780258,ok +75121,1.0,12,0.045454545454545414,ok +253,1.0,13,0.4487779181115149,ok +261,1.0,14,0.31385281385281383,ok +75240,1.0,15,0.017683772538141462,ok +75120,1.0,16,0.2546523517382413,ok +75124,1.0,17,0.2107615894039736,ok +75176,1.0,18,0.016640035468590053,ok +75103,1.0,19,0.01660181821534601,ok +75207,1.0,20,0.1890011398955863,ok +75095,1.0,21,0.04590735712181182,ok +273,1.0,22,0.049601841028638294,ok +75174,1.0,23,0.1520296780824476,ok +75153,1.0,24,0.12110304789550075,ok +75093,1.0,25,0.3286900890421619,ok +75119,1.0,26,0.1288429752066116,ok +75201,1.0,27,0.0997376911529182,ok +75215,1.0,28,0.029887049159286416,ok +75172,1.0,29,0.10886921886815126,ok +75169,1.0,30,0.0340573619843737,ok +75202,1.0,31,0.42493459653140053,ok +75233,1.0,32,0.08355891180172326,ok +75231,1.0,33,0.15778968253968262,ok +75196,1.0,34,0.028346047156726728,ok +248,1.0,35,0.2701991669030601,ok +75191,1.0,36,0.12329305343461994,ok +75217,1.0,37,0.0,ok +260,1.0,38,0.07198862873636425,ok +75115,1.0,39,0.09070773400041698,ok +75123,1.0,40,0.32871263757742397,ok +75108,1.0,41,0.00232727928992138,ok +75101,1.0,42,0.28377248501244257,ok +75192,1.0,43,0.48263188489323006,ok +75232,1.0,44,0.1759384976698103,ok +75173,1.0,45,0.11733360389610392,ok +75197,1.0,46,0.2040950900493621,ok +266,1.0,47,0.031108344044042946,ok +75148,1.0,48,0.19038707564842594,ok +75150,1.0,49,0.32022768927111245,ok +75100,1.0,50,0.27498250252741274,ok +75178,1.0,51,0.8079895490982436,ok +75236,1.0,52,0.032106570444582316,ok +75179,1.0,53,0.20149749000255257,ok +75213,1.0,54,0.06273157272368945,ok +2123,1.0,55,0.256388888888889,ok +75227,1.0,56,0.12397340187955186,ok +75184,1.0,57,0.18267180532449523,ok +75142,1.0,58,0.08134030645745782,ok +236,1.0,59,0.04240504096821618,ok +2122,1.0,60,0.292400218490373,ok +75188,1.0,61,0.4727873168498169,ok +75166,1.0,62,0.09969265347610456,ok +75181,1.0,63,0.0,ok +75133,1.0,64,0.31850429480226317,ok +75134,1.0,65,0.14906265793135665,ok +75198,1.0,66,0.12654657786302836,ok +262,1.0,67,0.0027336509054473046,ok +75234,1.0,68,0.05981527192723568,ok +75139,1.0,69,0.013389237134515009,ok +252,1.0,70,0.1626862498888315,ok +75117,1.0,71,0.12062226391494679,ok +75113,1.0,72,0.010561357454012876,ok +75098,1.0,73,0.028062456815564074,ok +246,1.0,74,0.02464555428242643,ok +75203,1.0,75,0.11023617811471986,ok +75237,1.0,76,0.00040449626783733983,ok +75195,1.0,77,0.0015167841344365662,ok +75171,1.0,78,0.16706480960317482,ok +75128,1.0,79,0.07016624831750884,ok +75096,1.0,80,0.6475478379912987,ok +75250,1.0,81,0.391127054727214,ok +75146,1.0,82,0.1222599514053454,ok +75116,1.0,83,0.020634603301536547,ok +75157,1.0,84,0.44266509433962264,ok +75187,1.0,85,0.027932128591676264,ok +2350,1.0,86,0.4423678825304047,ok +242,1.0,87,0.017081000552456538,ok +244,1.0,88,0.10808472308576977,ok +75125,1.0,89,0.07105870289669314,ok +75185,1.0,90,0.1293167333629761,ok +75163,1.0,91,0.06128065774804903,ok +75177,1.0,92,0.03815960706939259,ok +75189,1.0,93,0.02093625722530723,ok +75244,1.0,94,0.18253811312937274,ok +75219,1.0,95,0.08686090936376534,ok +75222,1.0,96,0.11136904761904765,ok +75159,1.0,97,0.25561678677405686,ok +75175,1.0,98,0.12715986367288612,ok +75109,1.0,99,0.40368927333473925,ok +254,1.0,100,0.0,ok +75105,1.0,101,0.42618761507557956,ok +75106,1.0,102,0.4177538160920802,ok +75212,1.0,103,0.2773454482218938,ok +75099,1.0,104,0.29830589084229864,ok +75248,1.0,105,0.1981334600032404,ok +233,1.0,106,0.01525546388768273,ok +75235,1.0,107,0.001061350868232891,ok +75226,1.0,108,0.009036055111661834,ok +75132,1.0,109,0.4536697094634172,ok +75127,1.0,110,0.33895073146733024,ok +251,1.0,111,0.020161426212381595,ok +75161,1.0,112,0.08277383139196026,ok +75143,1.0,113,0.017181695373184702,ok +75114,1.0,114,0.034791524265208484,ok +75182,1.0,115,0.1360109923276095,ok +75112,1.0,116,0.1477333735943931,ok +75210,1.0,117,0.0,ok +75205,1.0,118,0.1916783952293486,ok +75090,1.0,119,0.10262125025576962,ok +275,1.0,120,0.041190711460148854,ok +288,1.0,121,0.14344937049478246,ok +75092,1.0,122,0.10984848484848486,ok +3043,1.0,123,0.04425716804500235,ok +75249,1.0,124,0.013258182854293588,ok +75126,1.0,125,0.12520747636573581,ok +75225,1.0,126,0.10485406091370564,ok +75141,1.0,127,0.05863227105456237,ok +75107,1.0,128,0.28251633008867305,ok +75097,1.0,129,0.47931992380750144,ok +80001,1.0,1,0.1502463054187192,ok +80003,1.0,1,0.12212976616810622,ok +80006,1.0,1,0.196078431372549,ok +80008,1.0,1,0.19999999999999996,ok +80009,1.0,1,0.15555555555555556,ok +80010,1.0,1,0.2222222222222222,ok +80011,1.0,1,0.05263157894736836,ok +80012,1.0,1,0.0625,ok +80013,1.0,1,0.0,ok +80014,1.0,1,0.050000000000000044,ok +80015,1.0,1,0.10096153846153844,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/recall_macro_multiclass.classification_sparse/configurations.csv b/metalearning/metalearning_files/recall_macro_multiclass.classification_sparse/configurations.csv new file mode 100755 index 0000000..b7334ed --- /dev/null +++ b/metalearning/metalearning_files/recall_macro_multiclass.classification_sparse/configurations.csv @@ -0,0 +1,141 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,preprocessor:truncatedSVD:target_dim,rescaling:__choice__ +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7249853037185638,None,0.0,1.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,median,extra_trees_preproc_for_classification,False,gini,None,0.9424908623661876,None,0.0,7.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.585711203872775,None,0.0,5.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,median,extra_trees_preproc_for_classification,True,entropy,None,0.29512530534048065,None,0.0,7.0,10.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,bernoulli_nb,,,,,0.11565661797517847,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,median,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,9.0,None,13.0,18.0,1.0,29.0,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.039533063907190934,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1878805519245509,fdr,chi2,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,15.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7238850981243719,None,0.0,4.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,5.282738216059151e-05,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.6682079659377479,None,0.0,4.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,mean,extra_trees_preproc_for_classification,True,entropy,None,0.5552350997943013,None,0.0,8.0,5.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0009580347867777607,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,0.10000000000000006,rbf,-1.0,True,0.0010000000000000002,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.35040453084365497,False,True,1.0,squared_hinge,ovr,l1,0.006810889378452772,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.010000000000000005,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9727149851116396,None,0.0,18.0,13.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,,none +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.7735071203475309,,1.0,None,0.0,4.0,4.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,mean,extra_trees_preproc_for_classification,True,entropy,None,0.8171332500590935,None,0.0,15.0,13.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2398150931290834,,,0.4015139801872962,rbf,-1.0,False,2.402997750662158e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88.40698357592571,chi2,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.034465366914659866,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.12057775675278172,deviance,10.0,0.8011153303489733,None,0.0,2.0,16.0,0.0,370.0,0.6078295352200873,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,mean,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0007038280350320558,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4138778052607317,0.7995003430482459,5.0,5.430044692638861,poly,-1.0,True,0.02455501006004393,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,31,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.0010015637584068037,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.037611630308856295,deviance,5.0,0.8840126779516314,None,0.0,10.0,2.0,0.0,444.0,0.7599997167603434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,most_frequent,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.004230062585802822,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.4946412825074172,None,0.0,9.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.004090774134315939,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7983157215145903,None,0.0,2.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,normalize +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1751.4736515133566,0.62404114475118,3.0,1.6087076997410432,poly,-1.0,False,3.5353792826856045e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,mean,kernel_pca,,,,,,,,,,,,,,cosine,1198.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.8657388713119849,,1.0,None,0.0,19.0,13.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828776,None,0.0,2.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.027324741616523342,deviance,10.0,0.8623781459430139,None,0.0,10.0,20.0,0.0,329.0,0.8595750155424215,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,mean,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.002173124111626734,None,0.0,14.0,4.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,6.0,None,13.0,2.0,1.0,23.0,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1100.6211008501205,0.5921425829232616,2.0,0.0337546254878617,poly,-1.0,True,0.09641299736884308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2507440474920336e-05,True,,0.049622652766554566,True,0.009105043727227265,constant,squared_hinge,elasticnet,,0.00010112719671669047,,,,,,,,,,,,,,,,,,53,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61.69949680034141,chi2,,,,,none +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.9348409326933208,rbf,-1.0,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,mean,kernel_pca,,,,,,,,,,,,,,cosine,1754.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,one_hot_encoding,0.039533063907190934,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.3668390168981173,None,0.0,9.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1878805519245509,fdr,chi2,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3428971645958825,,,0.2229870623330047,rbf,-1.0,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7866272157881596,None,0.0,6.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00016781524591321165,True,True,squared_hinge,1.5119200923218881e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.349459944355116,,,0.00024028983491736645,rbf,-1.0,True,1.139421622432356e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3636266268105085,fwe,chi2,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4047.618729304337,,,2.0237366768707754,rbf,-1.0,True,0.04369127828878843,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,one_hot_encoding,0.010000000000000005,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10.0,1.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,,none +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9015027772227234,None,0.0,19.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,one_hot_encoding,0.039533063907190934,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1878805519245509,fdr,chi2,,standardize +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,,normalize +none,one_hot_encoding,0.003443779683191071,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.005326467497783115,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.058055900067082,,,0.1626688094236879,rbf,-1.0,True,0.048381159429370636,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1.0,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.3451627750042988,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.3163640203509378,None,0.0,17.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,median,extra_trees_preproc_for_classification,False,gini,None,0.8916956785028156,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.002630811782675973,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9828367182452932,None,0.0,18.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,85,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.35533396539961937,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,86,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4165632766388807,fpr,chi2,,none +weighting,one_hot_encoding,0.0019686649916896212,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.04641055832142541,True,True,hinge,8.540468968077405e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87,mean,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,911.0,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1.0,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,89,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,90,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,91,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,decision_tree,,,,,,,gini,1.7984076825537865,,1.0,None,0.0,14.0,2.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2897525995758022,fwe,chi2,,none +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,93,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.9376772805635799,None,0.0,18.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94,median,extra_trees_preproc_for_classification,True,entropy,None,0.8613889689810683,None,0.0,10.0,4.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,95,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,96,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,97,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,98,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.00022308163276069302,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,99,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,100,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.039533063907190934,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1878805519245509,fdr,chi2,,standardize +weighting,one_hot_encoding,0.039533063907190934,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,102,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1878805519245509,fdr,chi2,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,103,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,bernoulli_nb,,,,,0.014801515930977628,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,104,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,105,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,106,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.006372860318416312,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6467376360604045,None,0.0,1.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,107,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,,False,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.37163783119625016,False,True,1.0,squared_hinge,ovr,l2,1.2049944334095187e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,108,median,kitchen_sinks,,,,,,,,,,,,,,,,0.33181838105513195,7347.0,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,109,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3823734947460288,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,110,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,adaboost,SAMME,0.11042308042695524,5.0,117.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,111,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,112,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,113,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.3997572853391576,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.21008613984919333,None,0.0,11.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,115,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,116,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,117,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,118,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.20202014999292287,False,True,1.0,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,119,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.00012696985090051145,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.163979712833404,False,True,hinge,0.02438592885755657,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,120,mean,kernel_pca,,,,,,,,,,,,,0.03910740617243662,rbf,1851.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,121,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8804227616935514,None,0.0,10.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,122,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.0898983119899223,False,True,1.0,squared_hinge,ovr,l1,0.016048982920294167,,,,,,,,,,,,,,,,,,,none +weighting,no_encoding,,,decision_tree,,,,,,,entropy,1.301605991416264,,1.0,None,0.0,8.0,3.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,123,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,124,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,125,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0733000338152003,False,True,1.0,squared_hinge,ovr,l2,0.033752542733220474,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,126,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,-0.6840756728731969,,0.00980445380551526,sigmoid,161.0,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,127,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.00214097329599271,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7996802015738327,None,0.0,7.0,12.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.1052247187777527,False,True,1.0,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,129,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.2604623554399743,None,0.0,18.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.282079367058228,fpr,chi2,,none +none,one_hot_encoding,0.04329010470998136,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7260331062271381,None,0.0,7.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.010000000000000005,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15.0,1.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,134,median,extra_trees_preproc_for_classification,False,entropy,None,0.8213051377774989,None,0.0,3.0,13.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.714869937384006,None,0.0,8.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/metalearning/metalearning_files/recall_macro_multiclass.classification_sparse/description.txt b/metalearning/metalearning_files/recall_macro_multiclass.classification_sparse/description.txt new file mode 100755 index 0000000..5cc465b --- /dev/null +++ b/metalearning/metalearning_files/recall_macro_multiclass.classification_sparse/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: recall_macro +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/recall_macro_multiclass.classification_sparse/feature_costs.arff b/metalearning/metalearning_files/recall_macro_multiclass.classification_sparse/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/recall_macro_multiclass.classification_sparse/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/recall_macro_multiclass.classification_sparse/feature_runstatus.arff b/metalearning/metalearning_files/recall_macro_multiclass.classification_sparse/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/recall_macro_multiclass.classification_sparse/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/recall_macro_multiclass.classification_sparse/feature_values.arff b/metalearning/metalearning_files/recall_macro_multiclass.classification_sparse/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/recall_macro_multiclass.classification_sparse/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/recall_macro_multiclass.classification_sparse/readme.txt b/metalearning/metalearning_files/recall_macro_multiclass.classification_sparse/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/recall_micro_binary.classification_dense/algorithm_runs.arff b/metalearning/metalearning_files/recall_micro_binary.classification_dense/algorithm_runs.arff new file mode 100755 index 0000000..f8a432d --- /dev/null +++ b/metalearning/metalearning_files/recall_micro_binary.classification_dense/algorithm_runs.arff @@ -0,0 +1,152 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE recall_micro NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2120,1.0,1,0.07967939651107969,ok +75193,1.0,2,0.038371068099909755,ok +2117,1.0,3,0.16709064962461995,ok +75156,1.0,4,0.20291026677445434,ok +75129,1.0,5,0.10097087378640779,ok +75243,1.0,6,0.0,ok +75110,1.0,7,0.11622380643767549,ok +75239,1.0,8,0.0,ok +75223,1.0,9,0.10304601425793913,ok +75221,1.0,10,0.39791873141724476,ok +258,1.0,11,0.009708737864077666,ok +75121,1.0,12,0.0,ok +253,1.0,13,0.44855967078189296,ok +261,1.0,14,0.23333333333333328,ok +75240,1.0,15,0.022020725388601003,ok +75120,1.0,16,0.03929273084479368,ok +75124,1.0,17,0.09121395036887991,ok +75176,1.0,18,0.01541623843782114,ok +75103,1.0,19,0.005894736842105286,ok +75207,1.0,20,0.161849710982659,ok +75095,1.0,21,0.016917293233082664,ok +273,1.0,22,0.04413702239789197,ok +75174,1.0,23,0.11705240755520085,ok +75153,1.0,24,0.08028116907140215,ok +75093,1.0,25,0.17483296213808464,ok +75119,1.0,26,0.035363457760314354,ok +75201,1.0,27,0.0808678500986193,ok +75215,1.0,28,0.027412280701754388,ok +75172,1.0,29,0.10303030303030303,ok +75169,1.0,30,0.03420132141469101,ok +75202,1.0,31,0.20329670329670335,ok +75233,1.0,32,0.060673325934147204,ok +75231,1.0,33,0.19924098671726753,ok +75196,1.0,34,0.015665796344647487,ok +248,1.0,35,0.22878787878787876,ok +75191,1.0,36,0.1289905886694962,ok +75217,1.0,37,0.0,ok +260,1.0,38,0.02657807308970095,ok +75115,1.0,39,0.017681728880157177,ok +75123,1.0,40,0.32728592162554426,ok +75108,1.0,41,0.0,ok +75101,1.0,42,0.2740140932130053,ok +75192,1.0,43,0.48305752561071713,ok +75232,1.0,44,0.12356321839080464,ok +75173,1.0,45,0.1165605095541401,ok +75197,1.0,46,0.15517241379310343,ok +266,1.0,47,0.019685039370078705,ok +75148,1.0,48,0.13560975609756099,ok +75150,1.0,49,0.2848664688427299,ok +75100,1.0,50,0.00379609544468551,ok +75178,1.0,51,0.7840550682597786,ok +75236,1.0,52,0.0323809523809524,ok +75179,1.0,53,0.17943026267110618,ok +75213,1.0,54,0.05249343832021003,ok +2123,1.0,55,0.05882352941176472,ok +75227,1.0,56,0.10151430173864273,ok +75184,1.0,57,0.10772320613474529,ok +75142,1.0,58,0.0715825466438712,ok +236,1.0,59,0.038787878787878816,ok +2122,1.0,60,0.10952689565780949,ok +75188,1.0,61,0.24319066147859925,ok +75166,1.0,62,0.0995190529041805,ok +75181,1.0,63,0.0,ok +75133,1.0,64,0.005123278898495065,ok +75134,1.0,65,0.08723783614874181,ok +75198,1.0,66,0.12143310082435,ok +262,1.0,67,0.0027570995312931057,ok +75234,1.0,68,0.024160524160524166,ok +75139,1.0,69,0.010707070707070665,ok +252,1.0,70,0.15000000000000002,ok +75117,1.0,71,0.05500982318271119,ok +75113,1.0,72,0.006526315789473713,ok +75098,1.0,73,0.027575757575757587,ok +246,1.0,74,0.010606060606060619,ok +75203,1.0,75,0.09555345316934716,ok +75237,1.0,76,0.00039570658356824495,ok +75195,1.0,77,0.0015609901137292326,ok +75171,1.0,78,0.1620421753607103,ok +75128,1.0,79,0.02218114602587795,ok +75096,1.0,80,0.005164788382626018,ok +75250,1.0,81,0.34347287891393896,ok +75146,1.0,82,0.11329072074057744,ok +75116,1.0,83,0.00982318271119842,ok +75157,1.0,84,0.4192200557103064,ok +75187,1.0,85,0.01678951678951679,ok +2350,1.0,86,0.3744580607974338,ok +242,1.0,87,0.010606060606060619,ok +244,1.0,88,0.1106060606060606,ok +75125,1.0,89,0.03339882121807469,ok +75185,1.0,90,0.12816966343937297,ok +75163,1.0,91,0.060374149659863985,ok +75177,1.0,92,0.019292604501607746,ok +75189,1.0,93,0.019401337253296624,ok +75244,1.0,94,0.06339958875942431,ok +75219,1.0,95,0.03479668217681575,ok +75222,1.0,96,0.04524886877828049,ok +75159,1.0,97,0.06849315068493156,ok +75175,1.0,98,0.09954485391278811,ok +75109,1.0,99,0.30417434008594224,ok +254,1.0,100,0.0,ok +75105,1.0,101,0.018121212121212094,ok +75106,1.0,102,0.07212121212121214,ok +75212,1.0,103,0.2517482517482518,ok +75099,1.0,104,0.12374100719424463,ok +75248,1.0,105,0.10040485829959511,ok +233,1.0,106,0.002846299810246644,ok +75235,1.0,107,0.0011111111111110628,ok +75226,1.0,108,0.0030422878004259246,ok +75132,1.0,109,0.051244509516837455,ok +75127,1.0,110,0.3330355738331199,ok +251,1.0,111,0.0,ok +75161,1.0,112,0.06437225897569321,ok +75143,1.0,113,0.010471204188481686,ok +75114,1.0,114,0.023575638506876273,ok +75182,1.0,115,0.1081404628890662,ok +75112,1.0,116,0.12061822817080947,ok +75210,1.0,117,0.0,ok +75205,1.0,118,0.18110236220472442,ok +75090,1.0,119,0.06118881118881114,ok +275,1.0,120,0.03612167300380231,ok +288,1.0,121,0.12969696969696964,ok +75092,1.0,122,0.0935550935550935,ok +3043,1.0,123,0.020096463022508004,ok +75249,1.0,124,0.003215434083601254,ok +75126,1.0,125,0.0491159135559921,ok +75225,1.0,126,0.04904306220095689,ok +75141,1.0,127,0.0540140584535701,ok +75107,1.0,128,0.053212121212121266,ok +75097,1.0,129,0.05835568297419769,ok +80001,1.0,1,0.06944444444444442,ok +80003,1.0,1,0.09230769230769231,ok +80006,1.0,1,0.09375,ok +80008,1.0,1,0.11111111111111116,ok +80009,1.0,1,0.10526315789473684,ok +80010,1.0,1,0.13793103448275867,ok +80011,1.0,1,0.037735849056603765,ok +80012,1.0,1,0.045454545454545414,ok +80013,1.0,1,0.0,ok +80014,1.0,1,0.045454545454545414,ok +80015,1.0,1,0.09523809523809523,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/recall_micro_binary.classification_dense/configurations.csv b/metalearning/metalearning_files/recall_micro_binary.classification_dense/configurations.csv new file mode 100755 index 0000000..e710c68 --- /dev/null +++ b/metalearning/metalearning_files/recall_micro_binary.classification_dense/configurations.csv @@ -0,0 +1,141 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:fast_ica:algorithm,preprocessor:fast_ica:fun,preprocessor:fast_ica:n_components,preprocessor:fast_ica:whiten,preprocessor:feature_agglomeration:affinity,preprocessor:feature_agglomeration:linkage,preprocessor:feature_agglomeration:n_clusters,preprocessor:feature_agglomeration:pooling_func,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:pca:keep_variance,preprocessor:pca:whiten,preprocessor:polynomial:degree,preprocessor:polynomial:include_bias,preprocessor:polynomial:interaction_only,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,rescaling:__choice__,rescaling:quantile_transformer:n_quantiles,rescaling:quantile_transformer:output_distribution,rescaling:robust_scaler:q_max,rescaling:robust_scaler:q_min +weighting,one_hot_encoding,0.00011717632475982632,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.045388141846341344,deviance,10.0,0.29161769341843435,None,0.0,20.0,2.0,0.0,278.0,0.7912571599269661,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7249853037185638,None,0.0,1.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,median,extra_trees_preproc_for_classification,False,gini,None,0.9424908623661876,None,0.0,7.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.03474109838999682,deviance,4.0,0.5687034678818491,None,0.0,18.0,12.0,0.0,408.0,0.5150113945430513,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.6328939517938,f_classif,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.251097788175676,deviance,6.0,0.35679099363539235,None,0.0,13.0,11.0,0.0,157.0,0.4791732272983235,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,adaboost,SAMME,0.28738775989203896,10.0,423.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.8031499675923353,0.13579938270386765 +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.14159526341015916,deviance,7.0,0.8010488230155749,None,0.0,3.0,20.0,0.0,401.0,0.8073562440607731,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.002385546176068135,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.7729472304882845,,,0.0004789329856033374,rbf,-1.0,True,6.58869648864534e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,177.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5368752992317617,None,0.0,16.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.8382117756438685,mutual_info,,,,quantile_transformer,11480.0,normal,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9455638720565652,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,most_frequent,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8255464552647293,0.19162485555463185 +weighting,one_hot_encoding,0.18137532678800647,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9094110110427254,None,0.0,7.0,12.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,195.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.6682079659377479,None,0.0,4.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,mean,extra_trees_preproc_for_classification,True,entropy,None,0.5552350997943013,None,0.0,8.0,5.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.02345017287074443,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.053517066400173056,deviance,10.0,0.542144980834302,None,0.0,20.0,13.0,0.0,233.0,0.7398539900055563,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0614425536709615,fwe,f_classif,robust_scaler,,,0.9523118062307264,0.13434811490315818 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.040936424602789435,deviance,7.0,0.5495014745530306,None,0.0,20.0,18.0,0.0,141.0,0.6905343807995293,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7974565919616314,None,0.0,12.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,median,extra_trees_preproc_for_classification,True,entropy,None,0.9772091846790169,None,0.0,10.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2538107344750156,False,True,hinge,1.5099542326343014e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,8532.0,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.1958974686405233,deviance,5.0,0.33885235607979314,None,0.0,6.0,4.0,0.0,125.0,0.9448890820738562,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2538107344750156,False,True,hinge,1.5099542326343014e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,8532.0,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,0.0007038280350320558,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4138778052607317,0.7995003430482459,5.0,5.430044692638861,poly,-1.0,True,0.02455501006004393,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,31,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.010000000000000005,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.0518326156691958,deviance,6.0,0.8807456180216267,None,0.0,7.0,19.0,0.0,366.0,0.7314831276137047,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,,False,adaboost,SAMME,0.4391375941344922,3.0,386.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7439738358430176,0.20581080574615795 +none,one_hot_encoding,0.00011294596229850895,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7515.1213255144885,0.9576762936062476,3.0,0.019002536385919932,poly,-1.0,False,0.010632086351533369,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,51.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,97282.0,normal,, +none,one_hot_encoding,0.00012586572428922356,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5240592829918601,None,0.0,10.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1751.4736515133566,0.62404114475118,3.0,1.6087076997410432,poly,-1.0,False,3.5353792826856045e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1198.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.2422926485206341,,1.0,None,0.0,15.0,9.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.02102683283349326,deviance,10.0,0.2797288369369436,None,0.0,14.0,9.0,0.0,480.0,0.5778972273820631,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58.88123233170863,mutual_info,,,,robust_scaler,,,0.75,0.25 +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828776,None,0.0,2.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2155613360930585,deviance,4.0,0.3198803116198433,None,0.0,8.0,13.0,0.0,275.0,0.28870176110739404,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,mean,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.7062102387181676,None,0.0,1.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,most_frequent,fast_ica,,,,,,,,,,,parallel,exp,100.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7065776353150109,0.23782974987118105 +none,one_hot_encoding,0.16334152321884812,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00011572473434870852,True,True,squared_hinge,0.00019678754114665057,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.045742431094098604,fpr,chi2,none,,,, +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.3795924768593385,deviance,2.0,0.33708497069988536,None,0.0,15.0,13.0,0.0,451.0,0.7716323242090217,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2573946506994812,fwe,f_classif,quantile_transformer,1000.0,uniform,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.609975998293528,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,most_frequent,fast_ica,,,,,,,,,,,parallel,logcosh,2000.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8430415644014919,0.2863750565331575 +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.39536192447534535,None,0.0,19.0,3.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5.0,None,11.0,11.0,1.0,12.0,,,,,,robust_scaler,,,0.8928631650245873,0.1581877760687084 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.3126027672745337,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,870.2240970463429,0.5325949351918051,3.0,0.010682839357128344,poly,-1.0,False,2.485160860440657e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4608103694360143,fdr,f_classif,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.9348409326933208,rbf,-1.0,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1754.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,198.72528686512538,False,True,1.0,squared_hinge,ovr,l2,0.026260652523566803,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,robust_scaler,,,0.913511520078368,0.2742229325455444 +none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9260795160807372,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.03644212536682547,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.05965671477918361,deviance,8.0,0.4858133247974158,None,0.0,14.0,7.0,0.0,480.0,0.5726186552917335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.15318294164619112 +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18887.81504976871,,,0.23283562663398755,rbf,-1.0,True,2.3839685780861318e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3937607155568376,fwe,chi2,robust_scaler,,,0.941018778984854,0.2144110585080491 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3428971645958825,,,0.2229870623330047,rbf,-1.0,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8138233157708883,None,0.0,2.0,9.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54.27504292568562,f_classif,,,,minmax,,,, +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00016781524591321165,True,True,squared_hinge,1.5119200923218881e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.556190405302504,False,True,1.0,squared_hinge,ovr,l2,0.0007318628304090552,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,most_frequent,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,3.5602014542183973,948.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +weighting,one_hot_encoding,0.00034835629696198427,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8245132980938538,0.08947420373097192 +weighting,one_hot_encoding,0.00016967940959070708,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9439080311935252,None,0.0,2.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,170.0,None,,0.014191958374153584,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,,False,adaboost,SAMME.R,0.8220362681234727,5.0,183.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.363262678765884,fdr,chi2,robust_scaler,,,0.8826612080363588,0.2189605310171097 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,normalize,,,, +none,one_hot_encoding,,False,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6396026761675004,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06544340428506021,fwe,f_classif,none,,,, +weighting,one_hot_encoding,0.004980497345831963,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1255.9137433589426,,,0.08351549479967445,rbf,-1.0,True,0.00017919875199222518,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1.0,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.3837398524575939,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.018356703878357986,deviance,3.0,0.9690352514774068,None,0.0,12.0,3.0,0.0,234.0,0.3870344708308441,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,most_frequent,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6864970915330799,False,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5670424455696162,None,0.0,8.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,standardize,,,, +weighting,one_hot_encoding,0.00031737236118003484,True,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,244.0,None,,2.3065111488706024e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,85,mean,fast_ica,,,,,,,,,,,deflation,exp,1862.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7851234479882973,0.2237528085136715 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,86,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,156.0,auto,,0.0001987333852871589,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1.0,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,no_encoding,,,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.96834823420249e-05,False,True,hinge,0.00016639250831671168,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,89,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.628430584359224,mutual_info,,,,quantile_transformer,6634.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,90,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,91,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,93,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2636201374253461,deviance,7.0,0.8344964130784466,None,0.0,9.0,2.0,0.0,298.0,0.7517549950523315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,95,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,96,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,97,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.07463196642416367,deviance,7.0,0.8603242247379981,None,0.0,2.0,6.0,0.0,500.0,0.8447665577491962,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,98,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.0433556140045585,deviance,10.0,0.33000096635982235,None,0.0,15.0,13.0,0.0,388.0,0.8291104221904706,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,99,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,robust_scaler,,,0.7496393440951183,0.2853682991120835 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,100,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,102,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0020580843703898177,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.945774573434192,None,0.0,19.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,103,median,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,15209.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,104,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,105,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,106,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.005332972692819521,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5565918060287016,None,0.0,5.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,107,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,173.0,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9527068489270144,0.04135311355893583 +weighting,one_hot_encoding,0.001279467383882126,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,108,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0903354518102121,fwe,f_classif,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,109,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.002615346832354839,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7884268823432835,None,0.0,20.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,110,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +weighting,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56.086963007482865,,,0.013609964993119377,rbf,-1.0,True,0.00196831255706268,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,111,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.15374716583918388 +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.2472984547885781,deviance,3.0,0.6564306719064884,None,0.0,15.0,14.0,0.0,220.0,0.8082564085714649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,112,median,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,332.0,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,113,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,115,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,116,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,117,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,118,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.20202014999292287,False,True,1.0,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,no_encoding,,,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6390376923528961,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,119,median,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,164.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,62508.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,120,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.27041927277584e-06,True,0.00010000000000000009,0.033157325660763994,True,0.0008114527992546482,invscaling,modified_huber,elasticnet,0.13714427818877545,0.055179642772545036,,,,,,,,,,,,,,,,,,121,median,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,122,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,123,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.19998727075532635,deviance,10.0,0.9377656718112952,None,0.0,7.0,13.0,0.0,214.0,0.6062346326014357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,124,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,125,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,126,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.5765793990908161,None,0.0,11.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,127,median,fast_ica,,,,,,,,,,,deflation,exp,10.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,129,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.04651467280022463,True,adaboost,SAMME,0.6506122230393502,6.0,143.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,extra_trees_preproc_for_classification,False,entropy,None,0.348720215832657,None,0.0,17.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,26025.0,normal,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3386310102795006,None,0.0,6.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,True,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.0,None,,5.861221938384061e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.22985730375167915,fpr,f_classif,quantile_transformer,40589.0,uniform,, +none,no_encoding,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00012437731009611307,True,,0.08709904468181932,True,,invscaling,squared_hinge,l1,0.6231564675290102,0.008071279833697563,,,,,,,,,,,,,,,,,,134,median,fast_ica,,,,,,,,,,,parallel,logcosh,814.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.714869937384006,None,0.0,8.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, diff --git a/metalearning/metalearning_files/recall_micro_binary.classification_dense/description.txt b/metalearning/metalearning_files/recall_micro_binary.classification_dense/description.txt new file mode 100755 index 0000000..57b4a3b --- /dev/null +++ b/metalearning/metalearning_files/recall_micro_binary.classification_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: recall_micro +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/recall_micro_binary.classification_dense/feature_costs.arff b/metalearning/metalearning_files/recall_micro_binary.classification_dense/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/recall_micro_binary.classification_dense/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/recall_micro_binary.classification_dense/feature_runstatus.arff b/metalearning/metalearning_files/recall_micro_binary.classification_dense/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/recall_micro_binary.classification_dense/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/recall_micro_binary.classification_dense/feature_values.arff b/metalearning/metalearning_files/recall_micro_binary.classification_dense/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/recall_micro_binary.classification_dense/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/recall_micro_binary.classification_dense/readme.txt b/metalearning/metalearning_files/recall_micro_binary.classification_dense/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/recall_micro_binary.classification_sparse/algorithm_runs.arff b/metalearning/metalearning_files/recall_micro_binary.classification_sparse/algorithm_runs.arff new file mode 100755 index 0000000..646aac6 --- /dev/null +++ b/metalearning/metalearning_files/recall_micro_binary.classification_sparse/algorithm_runs.arff @@ -0,0 +1,152 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE recall_micro NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2120,1.0,1,0.0895803866100896,ok +75193,1.0,2,0.038371068099909755,ok +2117,1.0,3,0.16709064962461995,ok +75156,1.0,4,0.21988682295877127,ok +75129,1.0,5,0.10097087378640779,ok +75243,1.0,6,0.015434985968194592,ok +75110,1.0,7,0.2743573125945129,ok +75239,1.0,8,0.0,ok +75223,1.0,9,0.30989414560380213,ok +75221,1.0,10,0.39791873141724476,ok +258,1.0,11,0.01833872707659112,ok +75121,1.0,12,0.003929273084479323,ok +253,1.0,13,0.44855967078189296,ok +261,1.0,14,0.23333333333333328,ok +75240,1.0,15,0.022020725388601003,ok +75120,1.0,16,0.03929273084479368,ok +75124,1.0,17,0.09121395036887991,ok +75176,1.0,18,0.016590808985464722,ok +75103,1.0,19,0.008000000000000007,ok +75207,1.0,20,0.161849710982659,ok +75095,1.0,21,0.016917293233082664,ok +273,1.0,22,0.044795783926218746,ok +75174,1.0,23,0.11705240755520085,ok +75153,1.0,24,0.12134665186829452,ok +75093,1.0,25,0.17483296213808464,ok +75119,1.0,26,0.035363457760314354,ok +75201,1.0,27,0.0808678500986193,ok +75215,1.0,28,0.028234649122807043,ok +75172,1.0,29,0.09090909090909094,ok +75169,1.0,30,0.03420132141469101,ok +75202,1.0,31,0.20329670329670335,ok +75233,1.0,32,0.06511283758786535,ok +75231,1.0,33,0.19924098671726753,ok +75196,1.0,34,0.023498694516971286,ok +248,1.0,35,0.26515151515151514,ok +75191,1.0,36,0.12370055975887306,ok +75217,1.0,37,0.0,ok +260,1.0,38,0.02657807308970095,ok +75115,1.0,39,0.017681728880157177,ok +75123,1.0,40,0.32728592162554426,ok +75108,1.0,41,0.0018373909049149706,ok +75101,1.0,42,0.28272963283471386,ok +75192,1.0,43,0.48305752561071713,ok +75232,1.0,44,0.14655172413793105,ok +75173,1.0,45,0.11751592356687901,ok +75197,1.0,46,0.13300492610837433,ok +266,1.0,47,0.03280839895013121,ok +75148,1.0,48,0.19024390243902434,ok +75150,1.0,49,0.3204747774480712,ok +75100,1.0,50,0.00379609544468551,ok +75178,1.0,51,0.8079287243594171,ok +75236,1.0,52,0.0323809523809524,ok +75179,1.0,53,0.17943026267110618,ok +75213,1.0,54,0.05249343832021003,ok +2123,1.0,55,0.05882352941176472,ok +75227,1.0,56,0.10151430173864273,ok +75184,1.0,57,0.13967500456454263,ok +75142,1.0,58,0.0813201516390396,ok +236,1.0,59,0.042424242424242475,ok +2122,1.0,60,0.2743573125945129,ok +75188,1.0,61,0.24319066147859925,ok +75166,1.0,62,0.0995190529041805,ok +75181,1.0,63,0.0,ok +75133,1.0,64,0.005123278898495065,ok +75134,1.0,65,0.10170395014980793,ok +75198,1.0,66,0.12143310082435,ok +262,1.0,67,0.0027570995312931057,ok +75234,1.0,68,0.05978705978705978,ok +75139,1.0,69,0.012121212121212088,ok +252,1.0,70,0.1636363636363637,ok +75117,1.0,71,0.06679764243614927,ok +75113,1.0,72,0.006526315789473713,ok +75098,1.0,73,0.027575757575757587,ok +246,1.0,74,0.024242424242424288,ok +75203,1.0,75,0.09555345316934716,ok +75237,1.0,76,0.00039570658356824495,ok +75195,1.0,77,0.0015609901137292326,ok +75171,1.0,78,0.1672216056233814,ok +75128,1.0,79,0.02218114602587795,ok +75096,1.0,80,0.3974640209074891,ok +75250,1.0,81,0.34347287891393896,ok +75146,1.0,82,0.12210711924178974,ok +75116,1.0,83,0.00982318271119842,ok +75157,1.0,84,0.4192200557103064,ok +75187,1.0,85,0.027846027846027854,ok +2350,1.0,86,0.3744580607974338,ok +242,1.0,87,0.01666666666666672,ok +244,1.0,88,0.1106060606060606,ok +75125,1.0,89,0.035363457760314354,ok +75185,1.0,90,0.12816966343937297,ok +75163,1.0,91,0.060374149659863985,ok +75177,1.0,92,0.019292604501607746,ok +75189,1.0,93,0.019401337253296624,ok +75244,1.0,94,0.06339958875942431,ok +75219,1.0,95,0.08375480477442854,ok +75222,1.0,96,0.04524886877828049,ok +75159,1.0,97,0.06849315068493156,ok +75175,1.0,98,0.11701659080898541,ok +75109,1.0,99,0.34315531000613875,ok +254,1.0,100,0.0,ok +75105,1.0,101,0.018121212121212094,ok +75106,1.0,102,0.07212121212121214,ok +75212,1.0,103,0.27738927738927743,ok +75099,1.0,104,0.12374100719424463,ok +75248,1.0,105,0.10040485829959511,ok +233,1.0,106,0.015180265654648917,ok +75235,1.0,107,0.0016666666666667052,ok +75226,1.0,108,0.004259202920596339,ok +75132,1.0,109,0.051244509516837455,ok +75127,1.0,110,0.3345075170228544,ok +251,1.0,111,0.02631578947368418,ok +75161,1.0,112,0.08280680889021041,ok +75143,1.0,113,0.010471204188481686,ok +75114,1.0,114,0.023575638506876273,ok +75182,1.0,115,0.1081404628890662,ok +75112,1.0,116,0.12061822817080947,ok +75210,1.0,117,0.0,ok +75205,1.0,118,0.18110236220472442,ok +75090,1.0,119,0.10139860139860135,ok +275,1.0,120,0.03612167300380231,ok +288,1.0,121,0.14363636363636367,ok +75092,1.0,122,0.0935550935550935,ok +3043,1.0,123,0.020096463022508004,ok +75249,1.0,124,0.004823151125401881,ok +75126,1.0,125,0.0491159135559921,ok +75225,1.0,126,0.04904306220095689,ok +75141,1.0,127,0.05808361080281166,ok +75107,1.0,128,0.053212121212121266,ok +75097,1.0,129,0.05835568297419769,ok +80001,1.0,1,0.06944444444444442,ok +80003,1.0,1,0.12307692307692308,ok +80006,1.0,1,0.1875,ok +80008,1.0,1,0.2222222222222222,ok +80009,1.0,1,0.1578947368421053,ok +80010,1.0,1,0.13793103448275867,ok +80011,1.0,1,0.037735849056603765,ok +80012,1.0,1,0.045454545454545414,ok +80013,1.0,1,0.0,ok +80014,1.0,1,0.045454545454545414,ok +80015,1.0,1,0.09523809523809523,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/recall_micro_binary.classification_sparse/configurations.csv b/metalearning/metalearning_files/recall_micro_binary.classification_sparse/configurations.csv new file mode 100755 index 0000000..2e88382 --- /dev/null +++ b/metalearning/metalearning_files/recall_micro_binary.classification_sparse/configurations.csv @@ -0,0 +1,141 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,preprocessor:truncatedSVD:target_dim,rescaling:__choice__ +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7249853037185638,None,0.0,1.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,median,extra_trees_preproc_for_classification,False,gini,None,0.9424908623661876,None,0.0,7.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.6682079659377479,None,0.0,4.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,mean,extra_trees_preproc_for_classification,True,entropy,None,0.5552350997943013,None,0.0,8.0,5.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7974565919616314,None,0.0,12.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,median,extra_trees_preproc_for_classification,True,entropy,None,0.9772091846790169,None,0.0,10.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2538107344750156,False,True,hinge,1.5099542326343014e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,8532.0,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.034465366914659866,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.12057775675278172,deviance,10.0,0.8011153303489733,None,0.0,2.0,16.0,0.0,370.0,0.6078295352200873,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,mean,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0007038280350320558,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4138778052607317,0.7995003430482459,5.0,5.430044692638861,poly,-1.0,True,0.02455501006004393,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,31,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.0010015637584068037,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.037611630308856295,deviance,5.0,0.8840126779516314,None,0.0,10.0,2.0,0.0,444.0,0.7599997167603434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,most_frequent,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1751.4736515133566,0.62404114475118,3.0,1.6087076997410432,poly,-1.0,False,3.5353792826856045e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,mean,kernel_pca,,,,,,,,,,,,,,cosine,1198.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.8657388713119849,,1.0,None,0.0,19.0,13.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828776,None,0.0,2.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.027324741616523342,deviance,10.0,0.8623781459430139,None,0.0,10.0,20.0,0.0,329.0,0.8595750155424215,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,mean,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1100.6211008501205,0.5921425829232616,2.0,0.0337546254878617,poly,-1.0,True,0.09641299736884308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.9348409326933208,rbf,-1.0,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,mean,kernel_pca,,,,,,,,,,,,,,cosine,1754.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3428971645958825,,,0.2229870623330047,rbf,-1.0,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00016781524591321165,True,True,squared_hinge,1.5119200923218881e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.556190405302504,False,True,1.0,squared_hinge,ovr,l2,0.0007318628304090552,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,most_frequent,kitchen_sinks,,,,,,,,,,,,,,,,3.5602014542183973,948.0,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4047.618729304337,,,2.0237366768707754,rbf,-1.0,True,0.04369127828878843,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,one_hot_encoding,0.010000000000000005,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10.0,1.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,,none +weighting,one_hot_encoding,0.0008685602750053468,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9896334290292654,None,0.0,11.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,141.76310800864283,False,True,1.0,squared_hinge,ovr,l1,0.004317884655117431,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,,normalize +none,one_hot_encoding,0.003443779683191071,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.004980497345831963,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1255.9137433589426,,,0.08351549479967445,rbf,-1.0,True,0.00017919875199222518,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1.0,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.3451627750042988,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.3163640203509378,None,0.0,17.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,median,extra_trees_preproc_for_classification,False,gini,None,0.8916956785028156,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,85,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,86,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0019686649916896212,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.04641055832142541,True,True,hinge,8.540468968077405e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87,mean,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,911.0,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1.0,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,89,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,90,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,91,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,93,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,95,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,96,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,97,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,98,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,99,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,100,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,102,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,103,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,104,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,105,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,106,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.006372860318416312,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6467376360604045,None,0.0,1.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,107,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,108,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,109,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3823734947460288,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,110,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,adaboost,SAMME,0.11042308042695524,5.0,117.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,111,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,112,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,113,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,115,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,116,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,117,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,118,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.20202014999292287,False,True,1.0,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,119,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,120,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,121,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,122,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,123,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,124,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,125,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,126,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,127,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,129,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.04329010470998136,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7260331062271381,None,0.0,7.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,134,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.714869937384006,None,0.0,8.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/metalearning/metalearning_files/recall_micro_binary.classification_sparse/description.txt b/metalearning/metalearning_files/recall_micro_binary.classification_sparse/description.txt new file mode 100755 index 0000000..57b4a3b --- /dev/null +++ b/metalearning/metalearning_files/recall_micro_binary.classification_sparse/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: recall_micro +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/recall_micro_binary.classification_sparse/feature_costs.arff b/metalearning/metalearning_files/recall_micro_binary.classification_sparse/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/recall_micro_binary.classification_sparse/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/recall_micro_binary.classification_sparse/feature_runstatus.arff b/metalearning/metalearning_files/recall_micro_binary.classification_sparse/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/recall_micro_binary.classification_sparse/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/recall_micro_binary.classification_sparse/feature_values.arff b/metalearning/metalearning_files/recall_micro_binary.classification_sparse/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/recall_micro_binary.classification_sparse/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/recall_micro_binary.classification_sparse/readme.txt b/metalearning/metalearning_files/recall_micro_binary.classification_sparse/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/recall_micro_multiclass.classification_dense/algorithm_runs.arff b/metalearning/metalearning_files/recall_micro_multiclass.classification_dense/algorithm_runs.arff new file mode 100755 index 0000000..f8a432d --- /dev/null +++ b/metalearning/metalearning_files/recall_micro_multiclass.classification_dense/algorithm_runs.arff @@ -0,0 +1,152 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE recall_micro NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2120,1.0,1,0.07967939651107969,ok +75193,1.0,2,0.038371068099909755,ok +2117,1.0,3,0.16709064962461995,ok +75156,1.0,4,0.20291026677445434,ok +75129,1.0,5,0.10097087378640779,ok +75243,1.0,6,0.0,ok +75110,1.0,7,0.11622380643767549,ok +75239,1.0,8,0.0,ok +75223,1.0,9,0.10304601425793913,ok +75221,1.0,10,0.39791873141724476,ok +258,1.0,11,0.009708737864077666,ok +75121,1.0,12,0.0,ok +253,1.0,13,0.44855967078189296,ok +261,1.0,14,0.23333333333333328,ok +75240,1.0,15,0.022020725388601003,ok +75120,1.0,16,0.03929273084479368,ok +75124,1.0,17,0.09121395036887991,ok +75176,1.0,18,0.01541623843782114,ok +75103,1.0,19,0.005894736842105286,ok +75207,1.0,20,0.161849710982659,ok +75095,1.0,21,0.016917293233082664,ok +273,1.0,22,0.04413702239789197,ok +75174,1.0,23,0.11705240755520085,ok +75153,1.0,24,0.08028116907140215,ok +75093,1.0,25,0.17483296213808464,ok +75119,1.0,26,0.035363457760314354,ok +75201,1.0,27,0.0808678500986193,ok +75215,1.0,28,0.027412280701754388,ok +75172,1.0,29,0.10303030303030303,ok +75169,1.0,30,0.03420132141469101,ok +75202,1.0,31,0.20329670329670335,ok +75233,1.0,32,0.060673325934147204,ok +75231,1.0,33,0.19924098671726753,ok +75196,1.0,34,0.015665796344647487,ok +248,1.0,35,0.22878787878787876,ok +75191,1.0,36,0.1289905886694962,ok +75217,1.0,37,0.0,ok +260,1.0,38,0.02657807308970095,ok +75115,1.0,39,0.017681728880157177,ok +75123,1.0,40,0.32728592162554426,ok +75108,1.0,41,0.0,ok +75101,1.0,42,0.2740140932130053,ok +75192,1.0,43,0.48305752561071713,ok +75232,1.0,44,0.12356321839080464,ok +75173,1.0,45,0.1165605095541401,ok +75197,1.0,46,0.15517241379310343,ok +266,1.0,47,0.019685039370078705,ok +75148,1.0,48,0.13560975609756099,ok +75150,1.0,49,0.2848664688427299,ok +75100,1.0,50,0.00379609544468551,ok +75178,1.0,51,0.7840550682597786,ok +75236,1.0,52,0.0323809523809524,ok +75179,1.0,53,0.17943026267110618,ok +75213,1.0,54,0.05249343832021003,ok +2123,1.0,55,0.05882352941176472,ok +75227,1.0,56,0.10151430173864273,ok +75184,1.0,57,0.10772320613474529,ok +75142,1.0,58,0.0715825466438712,ok +236,1.0,59,0.038787878787878816,ok +2122,1.0,60,0.10952689565780949,ok +75188,1.0,61,0.24319066147859925,ok +75166,1.0,62,0.0995190529041805,ok +75181,1.0,63,0.0,ok +75133,1.0,64,0.005123278898495065,ok +75134,1.0,65,0.08723783614874181,ok +75198,1.0,66,0.12143310082435,ok +262,1.0,67,0.0027570995312931057,ok +75234,1.0,68,0.024160524160524166,ok +75139,1.0,69,0.010707070707070665,ok +252,1.0,70,0.15000000000000002,ok +75117,1.0,71,0.05500982318271119,ok +75113,1.0,72,0.006526315789473713,ok +75098,1.0,73,0.027575757575757587,ok +246,1.0,74,0.010606060606060619,ok +75203,1.0,75,0.09555345316934716,ok +75237,1.0,76,0.00039570658356824495,ok +75195,1.0,77,0.0015609901137292326,ok +75171,1.0,78,0.1620421753607103,ok +75128,1.0,79,0.02218114602587795,ok +75096,1.0,80,0.005164788382626018,ok +75250,1.0,81,0.34347287891393896,ok +75146,1.0,82,0.11329072074057744,ok +75116,1.0,83,0.00982318271119842,ok +75157,1.0,84,0.4192200557103064,ok +75187,1.0,85,0.01678951678951679,ok +2350,1.0,86,0.3744580607974338,ok +242,1.0,87,0.010606060606060619,ok +244,1.0,88,0.1106060606060606,ok +75125,1.0,89,0.03339882121807469,ok +75185,1.0,90,0.12816966343937297,ok +75163,1.0,91,0.060374149659863985,ok +75177,1.0,92,0.019292604501607746,ok +75189,1.0,93,0.019401337253296624,ok +75244,1.0,94,0.06339958875942431,ok +75219,1.0,95,0.03479668217681575,ok +75222,1.0,96,0.04524886877828049,ok +75159,1.0,97,0.06849315068493156,ok +75175,1.0,98,0.09954485391278811,ok +75109,1.0,99,0.30417434008594224,ok +254,1.0,100,0.0,ok +75105,1.0,101,0.018121212121212094,ok +75106,1.0,102,0.07212121212121214,ok +75212,1.0,103,0.2517482517482518,ok +75099,1.0,104,0.12374100719424463,ok +75248,1.0,105,0.10040485829959511,ok +233,1.0,106,0.002846299810246644,ok +75235,1.0,107,0.0011111111111110628,ok +75226,1.0,108,0.0030422878004259246,ok +75132,1.0,109,0.051244509516837455,ok +75127,1.0,110,0.3330355738331199,ok +251,1.0,111,0.0,ok +75161,1.0,112,0.06437225897569321,ok +75143,1.0,113,0.010471204188481686,ok +75114,1.0,114,0.023575638506876273,ok +75182,1.0,115,0.1081404628890662,ok +75112,1.0,116,0.12061822817080947,ok +75210,1.0,117,0.0,ok +75205,1.0,118,0.18110236220472442,ok +75090,1.0,119,0.06118881118881114,ok +275,1.0,120,0.03612167300380231,ok +288,1.0,121,0.12969696969696964,ok +75092,1.0,122,0.0935550935550935,ok +3043,1.0,123,0.020096463022508004,ok +75249,1.0,124,0.003215434083601254,ok +75126,1.0,125,0.0491159135559921,ok +75225,1.0,126,0.04904306220095689,ok +75141,1.0,127,0.0540140584535701,ok +75107,1.0,128,0.053212121212121266,ok +75097,1.0,129,0.05835568297419769,ok +80001,1.0,1,0.06944444444444442,ok +80003,1.0,1,0.09230769230769231,ok +80006,1.0,1,0.09375,ok +80008,1.0,1,0.11111111111111116,ok +80009,1.0,1,0.10526315789473684,ok +80010,1.0,1,0.13793103448275867,ok +80011,1.0,1,0.037735849056603765,ok +80012,1.0,1,0.045454545454545414,ok +80013,1.0,1,0.0,ok +80014,1.0,1,0.045454545454545414,ok +80015,1.0,1,0.09523809523809523,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/recall_micro_multiclass.classification_dense/configurations.csv b/metalearning/metalearning_files/recall_micro_multiclass.classification_dense/configurations.csv new file mode 100755 index 0000000..e710c68 --- /dev/null +++ b/metalearning/metalearning_files/recall_micro_multiclass.classification_dense/configurations.csv @@ -0,0 +1,141 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:fast_ica:algorithm,preprocessor:fast_ica:fun,preprocessor:fast_ica:n_components,preprocessor:fast_ica:whiten,preprocessor:feature_agglomeration:affinity,preprocessor:feature_agglomeration:linkage,preprocessor:feature_agglomeration:n_clusters,preprocessor:feature_agglomeration:pooling_func,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:pca:keep_variance,preprocessor:pca:whiten,preprocessor:polynomial:degree,preprocessor:polynomial:include_bias,preprocessor:polynomial:interaction_only,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,rescaling:__choice__,rescaling:quantile_transformer:n_quantiles,rescaling:quantile_transformer:output_distribution,rescaling:robust_scaler:q_max,rescaling:robust_scaler:q_min +weighting,one_hot_encoding,0.00011717632475982632,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.045388141846341344,deviance,10.0,0.29161769341843435,None,0.0,20.0,2.0,0.0,278.0,0.7912571599269661,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7249853037185638,None,0.0,1.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,median,extra_trees_preproc_for_classification,False,gini,None,0.9424908623661876,None,0.0,7.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.03474109838999682,deviance,4.0,0.5687034678818491,None,0.0,18.0,12.0,0.0,408.0,0.5150113945430513,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.6328939517938,f_classif,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.251097788175676,deviance,6.0,0.35679099363539235,None,0.0,13.0,11.0,0.0,157.0,0.4791732272983235,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,adaboost,SAMME,0.28738775989203896,10.0,423.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.8031499675923353,0.13579938270386765 +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.14159526341015916,deviance,7.0,0.8010488230155749,None,0.0,3.0,20.0,0.0,401.0,0.8073562440607731,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.002385546176068135,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.7729472304882845,,,0.0004789329856033374,rbf,-1.0,True,6.58869648864534e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,177.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5368752992317617,None,0.0,16.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.8382117756438685,mutual_info,,,,quantile_transformer,11480.0,normal,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9455638720565652,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,most_frequent,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8255464552647293,0.19162485555463185 +weighting,one_hot_encoding,0.18137532678800647,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9094110110427254,None,0.0,7.0,12.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,195.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.6682079659377479,None,0.0,4.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,mean,extra_trees_preproc_for_classification,True,entropy,None,0.5552350997943013,None,0.0,8.0,5.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.02345017287074443,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.053517066400173056,deviance,10.0,0.542144980834302,None,0.0,20.0,13.0,0.0,233.0,0.7398539900055563,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0614425536709615,fwe,f_classif,robust_scaler,,,0.9523118062307264,0.13434811490315818 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.040936424602789435,deviance,7.0,0.5495014745530306,None,0.0,20.0,18.0,0.0,141.0,0.6905343807995293,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7974565919616314,None,0.0,12.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,median,extra_trees_preproc_for_classification,True,entropy,None,0.9772091846790169,None,0.0,10.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2538107344750156,False,True,hinge,1.5099542326343014e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,8532.0,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.1958974686405233,deviance,5.0,0.33885235607979314,None,0.0,6.0,4.0,0.0,125.0,0.9448890820738562,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2538107344750156,False,True,hinge,1.5099542326343014e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,8532.0,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,0.0007038280350320558,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4138778052607317,0.7995003430482459,5.0,5.430044692638861,poly,-1.0,True,0.02455501006004393,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,31,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.010000000000000005,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.0518326156691958,deviance,6.0,0.8807456180216267,None,0.0,7.0,19.0,0.0,366.0,0.7314831276137047,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,,False,adaboost,SAMME,0.4391375941344922,3.0,386.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7439738358430176,0.20581080574615795 +none,one_hot_encoding,0.00011294596229850895,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7515.1213255144885,0.9576762936062476,3.0,0.019002536385919932,poly,-1.0,False,0.010632086351533369,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,51.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,97282.0,normal,, +none,one_hot_encoding,0.00012586572428922356,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5240592829918601,None,0.0,10.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1751.4736515133566,0.62404114475118,3.0,1.6087076997410432,poly,-1.0,False,3.5353792826856045e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1198.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.2422926485206341,,1.0,None,0.0,15.0,9.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.02102683283349326,deviance,10.0,0.2797288369369436,None,0.0,14.0,9.0,0.0,480.0,0.5778972273820631,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58.88123233170863,mutual_info,,,,robust_scaler,,,0.75,0.25 +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828776,None,0.0,2.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2155613360930585,deviance,4.0,0.3198803116198433,None,0.0,8.0,13.0,0.0,275.0,0.28870176110739404,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,mean,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.7062102387181676,None,0.0,1.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,most_frequent,fast_ica,,,,,,,,,,,parallel,exp,100.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7065776353150109,0.23782974987118105 +none,one_hot_encoding,0.16334152321884812,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00011572473434870852,True,True,squared_hinge,0.00019678754114665057,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.045742431094098604,fpr,chi2,none,,,, +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.3795924768593385,deviance,2.0,0.33708497069988536,None,0.0,15.0,13.0,0.0,451.0,0.7716323242090217,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2573946506994812,fwe,f_classif,quantile_transformer,1000.0,uniform,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.609975998293528,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,most_frequent,fast_ica,,,,,,,,,,,parallel,logcosh,2000.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8430415644014919,0.2863750565331575 +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.39536192447534535,None,0.0,19.0,3.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5.0,None,11.0,11.0,1.0,12.0,,,,,,robust_scaler,,,0.8928631650245873,0.1581877760687084 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.3126027672745337,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,870.2240970463429,0.5325949351918051,3.0,0.010682839357128344,poly,-1.0,False,2.485160860440657e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4608103694360143,fdr,f_classif,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.9348409326933208,rbf,-1.0,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1754.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,198.72528686512538,False,True,1.0,squared_hinge,ovr,l2,0.026260652523566803,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,robust_scaler,,,0.913511520078368,0.2742229325455444 +none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9260795160807372,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.03644212536682547,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.05965671477918361,deviance,8.0,0.4858133247974158,None,0.0,14.0,7.0,0.0,480.0,0.5726186552917335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.15318294164619112 +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18887.81504976871,,,0.23283562663398755,rbf,-1.0,True,2.3839685780861318e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3937607155568376,fwe,chi2,robust_scaler,,,0.941018778984854,0.2144110585080491 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3428971645958825,,,0.2229870623330047,rbf,-1.0,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8138233157708883,None,0.0,2.0,9.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54.27504292568562,f_classif,,,,minmax,,,, +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00016781524591321165,True,True,squared_hinge,1.5119200923218881e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.556190405302504,False,True,1.0,squared_hinge,ovr,l2,0.0007318628304090552,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,most_frequent,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,3.5602014542183973,948.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +weighting,one_hot_encoding,0.00034835629696198427,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8245132980938538,0.08947420373097192 +weighting,one_hot_encoding,0.00016967940959070708,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9439080311935252,None,0.0,2.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,170.0,None,,0.014191958374153584,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,,False,adaboost,SAMME.R,0.8220362681234727,5.0,183.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.363262678765884,fdr,chi2,robust_scaler,,,0.8826612080363588,0.2189605310171097 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,normalize,,,, +none,one_hot_encoding,,False,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6396026761675004,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06544340428506021,fwe,f_classif,none,,,, +weighting,one_hot_encoding,0.004980497345831963,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1255.9137433589426,,,0.08351549479967445,rbf,-1.0,True,0.00017919875199222518,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1.0,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.3837398524575939,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.018356703878357986,deviance,3.0,0.9690352514774068,None,0.0,12.0,3.0,0.0,234.0,0.3870344708308441,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,most_frequent,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6864970915330799,False,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5670424455696162,None,0.0,8.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,standardize,,,, +weighting,one_hot_encoding,0.00031737236118003484,True,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,244.0,None,,2.3065111488706024e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,85,mean,fast_ica,,,,,,,,,,,deflation,exp,1862.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7851234479882973,0.2237528085136715 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,86,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,156.0,auto,,0.0001987333852871589,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1.0,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,no_encoding,,,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.96834823420249e-05,False,True,hinge,0.00016639250831671168,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,89,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.628430584359224,mutual_info,,,,quantile_transformer,6634.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,90,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,91,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,93,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2636201374253461,deviance,7.0,0.8344964130784466,None,0.0,9.0,2.0,0.0,298.0,0.7517549950523315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,95,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,96,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,97,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.07463196642416367,deviance,7.0,0.8603242247379981,None,0.0,2.0,6.0,0.0,500.0,0.8447665577491962,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,98,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.0433556140045585,deviance,10.0,0.33000096635982235,None,0.0,15.0,13.0,0.0,388.0,0.8291104221904706,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,99,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,robust_scaler,,,0.7496393440951183,0.2853682991120835 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,100,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,102,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0020580843703898177,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.945774573434192,None,0.0,19.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,103,median,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,15209.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,104,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,105,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,106,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.005332972692819521,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5565918060287016,None,0.0,5.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,107,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,173.0,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9527068489270144,0.04135311355893583 +weighting,one_hot_encoding,0.001279467383882126,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,108,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0903354518102121,fwe,f_classif,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,109,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.002615346832354839,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7884268823432835,None,0.0,20.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,110,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +weighting,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56.086963007482865,,,0.013609964993119377,rbf,-1.0,True,0.00196831255706268,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,111,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.15374716583918388 +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.2472984547885781,deviance,3.0,0.6564306719064884,None,0.0,15.0,14.0,0.0,220.0,0.8082564085714649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,112,median,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,332.0,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,113,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,115,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,116,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,117,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,118,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.20202014999292287,False,True,1.0,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,no_encoding,,,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6390376923528961,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,119,median,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,164.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,62508.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,120,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.27041927277584e-06,True,0.00010000000000000009,0.033157325660763994,True,0.0008114527992546482,invscaling,modified_huber,elasticnet,0.13714427818877545,0.055179642772545036,,,,,,,,,,,,,,,,,,121,median,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,122,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,123,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.19998727075532635,deviance,10.0,0.9377656718112952,None,0.0,7.0,13.0,0.0,214.0,0.6062346326014357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,124,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,125,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,126,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.5765793990908161,None,0.0,11.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,127,median,fast_ica,,,,,,,,,,,deflation,exp,10.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,129,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.04651467280022463,True,adaboost,SAMME,0.6506122230393502,6.0,143.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,extra_trees_preproc_for_classification,False,entropy,None,0.348720215832657,None,0.0,17.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,26025.0,normal,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3386310102795006,None,0.0,6.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,True,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.0,None,,5.861221938384061e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.22985730375167915,fpr,f_classif,quantile_transformer,40589.0,uniform,, +none,no_encoding,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00012437731009611307,True,,0.08709904468181932,True,,invscaling,squared_hinge,l1,0.6231564675290102,0.008071279833697563,,,,,,,,,,,,,,,,,,134,median,fast_ica,,,,,,,,,,,parallel,logcosh,814.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.714869937384006,None,0.0,8.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, diff --git a/metalearning/metalearning_files/recall_micro_multiclass.classification_dense/description.txt b/metalearning/metalearning_files/recall_micro_multiclass.classification_dense/description.txt new file mode 100755 index 0000000..57b4a3b --- /dev/null +++ b/metalearning/metalearning_files/recall_micro_multiclass.classification_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: recall_micro +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/recall_micro_multiclass.classification_dense/feature_costs.arff b/metalearning/metalearning_files/recall_micro_multiclass.classification_dense/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/recall_micro_multiclass.classification_dense/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/recall_micro_multiclass.classification_dense/feature_runstatus.arff b/metalearning/metalearning_files/recall_micro_multiclass.classification_dense/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/recall_micro_multiclass.classification_dense/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/recall_micro_multiclass.classification_dense/feature_values.arff b/metalearning/metalearning_files/recall_micro_multiclass.classification_dense/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/recall_micro_multiclass.classification_dense/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/recall_micro_multiclass.classification_dense/readme.txt b/metalearning/metalearning_files/recall_micro_multiclass.classification_dense/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/recall_micro_multiclass.classification_sparse/algorithm_runs.arff b/metalearning/metalearning_files/recall_micro_multiclass.classification_sparse/algorithm_runs.arff new file mode 100755 index 0000000..646aac6 --- /dev/null +++ b/metalearning/metalearning_files/recall_micro_multiclass.classification_sparse/algorithm_runs.arff @@ -0,0 +1,152 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE recall_micro NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2120,1.0,1,0.0895803866100896,ok +75193,1.0,2,0.038371068099909755,ok +2117,1.0,3,0.16709064962461995,ok +75156,1.0,4,0.21988682295877127,ok +75129,1.0,5,0.10097087378640779,ok +75243,1.0,6,0.015434985968194592,ok +75110,1.0,7,0.2743573125945129,ok +75239,1.0,8,0.0,ok +75223,1.0,9,0.30989414560380213,ok +75221,1.0,10,0.39791873141724476,ok +258,1.0,11,0.01833872707659112,ok +75121,1.0,12,0.003929273084479323,ok +253,1.0,13,0.44855967078189296,ok +261,1.0,14,0.23333333333333328,ok +75240,1.0,15,0.022020725388601003,ok +75120,1.0,16,0.03929273084479368,ok +75124,1.0,17,0.09121395036887991,ok +75176,1.0,18,0.016590808985464722,ok +75103,1.0,19,0.008000000000000007,ok +75207,1.0,20,0.161849710982659,ok +75095,1.0,21,0.016917293233082664,ok +273,1.0,22,0.044795783926218746,ok +75174,1.0,23,0.11705240755520085,ok +75153,1.0,24,0.12134665186829452,ok +75093,1.0,25,0.17483296213808464,ok +75119,1.0,26,0.035363457760314354,ok +75201,1.0,27,0.0808678500986193,ok +75215,1.0,28,0.028234649122807043,ok +75172,1.0,29,0.09090909090909094,ok +75169,1.0,30,0.03420132141469101,ok +75202,1.0,31,0.20329670329670335,ok +75233,1.0,32,0.06511283758786535,ok +75231,1.0,33,0.19924098671726753,ok +75196,1.0,34,0.023498694516971286,ok +248,1.0,35,0.26515151515151514,ok +75191,1.0,36,0.12370055975887306,ok +75217,1.0,37,0.0,ok +260,1.0,38,0.02657807308970095,ok +75115,1.0,39,0.017681728880157177,ok +75123,1.0,40,0.32728592162554426,ok +75108,1.0,41,0.0018373909049149706,ok +75101,1.0,42,0.28272963283471386,ok +75192,1.0,43,0.48305752561071713,ok +75232,1.0,44,0.14655172413793105,ok +75173,1.0,45,0.11751592356687901,ok +75197,1.0,46,0.13300492610837433,ok +266,1.0,47,0.03280839895013121,ok +75148,1.0,48,0.19024390243902434,ok +75150,1.0,49,0.3204747774480712,ok +75100,1.0,50,0.00379609544468551,ok +75178,1.0,51,0.8079287243594171,ok +75236,1.0,52,0.0323809523809524,ok +75179,1.0,53,0.17943026267110618,ok +75213,1.0,54,0.05249343832021003,ok +2123,1.0,55,0.05882352941176472,ok +75227,1.0,56,0.10151430173864273,ok +75184,1.0,57,0.13967500456454263,ok +75142,1.0,58,0.0813201516390396,ok +236,1.0,59,0.042424242424242475,ok +2122,1.0,60,0.2743573125945129,ok +75188,1.0,61,0.24319066147859925,ok +75166,1.0,62,0.0995190529041805,ok +75181,1.0,63,0.0,ok +75133,1.0,64,0.005123278898495065,ok +75134,1.0,65,0.10170395014980793,ok +75198,1.0,66,0.12143310082435,ok +262,1.0,67,0.0027570995312931057,ok +75234,1.0,68,0.05978705978705978,ok +75139,1.0,69,0.012121212121212088,ok +252,1.0,70,0.1636363636363637,ok +75117,1.0,71,0.06679764243614927,ok +75113,1.0,72,0.006526315789473713,ok +75098,1.0,73,0.027575757575757587,ok +246,1.0,74,0.024242424242424288,ok +75203,1.0,75,0.09555345316934716,ok +75237,1.0,76,0.00039570658356824495,ok +75195,1.0,77,0.0015609901137292326,ok +75171,1.0,78,0.1672216056233814,ok +75128,1.0,79,0.02218114602587795,ok +75096,1.0,80,0.3974640209074891,ok +75250,1.0,81,0.34347287891393896,ok +75146,1.0,82,0.12210711924178974,ok +75116,1.0,83,0.00982318271119842,ok +75157,1.0,84,0.4192200557103064,ok +75187,1.0,85,0.027846027846027854,ok +2350,1.0,86,0.3744580607974338,ok +242,1.0,87,0.01666666666666672,ok +244,1.0,88,0.1106060606060606,ok +75125,1.0,89,0.035363457760314354,ok +75185,1.0,90,0.12816966343937297,ok +75163,1.0,91,0.060374149659863985,ok +75177,1.0,92,0.019292604501607746,ok +75189,1.0,93,0.019401337253296624,ok +75244,1.0,94,0.06339958875942431,ok +75219,1.0,95,0.08375480477442854,ok +75222,1.0,96,0.04524886877828049,ok +75159,1.0,97,0.06849315068493156,ok +75175,1.0,98,0.11701659080898541,ok +75109,1.0,99,0.34315531000613875,ok +254,1.0,100,0.0,ok +75105,1.0,101,0.018121212121212094,ok +75106,1.0,102,0.07212121212121214,ok +75212,1.0,103,0.27738927738927743,ok +75099,1.0,104,0.12374100719424463,ok +75248,1.0,105,0.10040485829959511,ok +233,1.0,106,0.015180265654648917,ok +75235,1.0,107,0.0016666666666667052,ok +75226,1.0,108,0.004259202920596339,ok +75132,1.0,109,0.051244509516837455,ok +75127,1.0,110,0.3345075170228544,ok +251,1.0,111,0.02631578947368418,ok +75161,1.0,112,0.08280680889021041,ok +75143,1.0,113,0.010471204188481686,ok +75114,1.0,114,0.023575638506876273,ok +75182,1.0,115,0.1081404628890662,ok +75112,1.0,116,0.12061822817080947,ok +75210,1.0,117,0.0,ok +75205,1.0,118,0.18110236220472442,ok +75090,1.0,119,0.10139860139860135,ok +275,1.0,120,0.03612167300380231,ok +288,1.0,121,0.14363636363636367,ok +75092,1.0,122,0.0935550935550935,ok +3043,1.0,123,0.020096463022508004,ok +75249,1.0,124,0.004823151125401881,ok +75126,1.0,125,0.0491159135559921,ok +75225,1.0,126,0.04904306220095689,ok +75141,1.0,127,0.05808361080281166,ok +75107,1.0,128,0.053212121212121266,ok +75097,1.0,129,0.05835568297419769,ok +80001,1.0,1,0.06944444444444442,ok +80003,1.0,1,0.12307692307692308,ok +80006,1.0,1,0.1875,ok +80008,1.0,1,0.2222222222222222,ok +80009,1.0,1,0.1578947368421053,ok +80010,1.0,1,0.13793103448275867,ok +80011,1.0,1,0.037735849056603765,ok +80012,1.0,1,0.045454545454545414,ok +80013,1.0,1,0.0,ok +80014,1.0,1,0.045454545454545414,ok +80015,1.0,1,0.09523809523809523,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/recall_micro_multiclass.classification_sparse/configurations.csv b/metalearning/metalearning_files/recall_micro_multiclass.classification_sparse/configurations.csv new file mode 100755 index 0000000..2e88382 --- /dev/null +++ b/metalearning/metalearning_files/recall_micro_multiclass.classification_sparse/configurations.csv @@ -0,0 +1,141 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,preprocessor:truncatedSVD:target_dim,rescaling:__choice__ +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7249853037185638,None,0.0,1.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,median,extra_trees_preproc_for_classification,False,gini,None,0.9424908623661876,None,0.0,7.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.6682079659377479,None,0.0,4.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,mean,extra_trees_preproc_for_classification,True,entropy,None,0.5552350997943013,None,0.0,8.0,5.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7974565919616314,None,0.0,12.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,median,extra_trees_preproc_for_classification,True,entropy,None,0.9772091846790169,None,0.0,10.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2538107344750156,False,True,hinge,1.5099542326343014e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,8532.0,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.034465366914659866,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.12057775675278172,deviance,10.0,0.8011153303489733,None,0.0,2.0,16.0,0.0,370.0,0.6078295352200873,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,mean,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0007038280350320558,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4138778052607317,0.7995003430482459,5.0,5.430044692638861,poly,-1.0,True,0.02455501006004393,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,31,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.0010015637584068037,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.037611630308856295,deviance,5.0,0.8840126779516314,None,0.0,10.0,2.0,0.0,444.0,0.7599997167603434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,most_frequent,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1751.4736515133566,0.62404114475118,3.0,1.6087076997410432,poly,-1.0,False,3.5353792826856045e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,mean,kernel_pca,,,,,,,,,,,,,,cosine,1198.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.8657388713119849,,1.0,None,0.0,19.0,13.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828776,None,0.0,2.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.027324741616523342,deviance,10.0,0.8623781459430139,None,0.0,10.0,20.0,0.0,329.0,0.8595750155424215,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,mean,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1100.6211008501205,0.5921425829232616,2.0,0.0337546254878617,poly,-1.0,True,0.09641299736884308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.9348409326933208,rbf,-1.0,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,mean,kernel_pca,,,,,,,,,,,,,,cosine,1754.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3428971645958825,,,0.2229870623330047,rbf,-1.0,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00016781524591321165,True,True,squared_hinge,1.5119200923218881e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.556190405302504,False,True,1.0,squared_hinge,ovr,l2,0.0007318628304090552,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,most_frequent,kitchen_sinks,,,,,,,,,,,,,,,,3.5602014542183973,948.0,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4047.618729304337,,,2.0237366768707754,rbf,-1.0,True,0.04369127828878843,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,one_hot_encoding,0.010000000000000005,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10.0,1.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,,none +weighting,one_hot_encoding,0.0008685602750053468,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9896334290292654,None,0.0,11.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,141.76310800864283,False,True,1.0,squared_hinge,ovr,l1,0.004317884655117431,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,,normalize +none,one_hot_encoding,0.003443779683191071,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.004980497345831963,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1255.9137433589426,,,0.08351549479967445,rbf,-1.0,True,0.00017919875199222518,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1.0,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.3451627750042988,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.3163640203509378,None,0.0,17.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,median,extra_trees_preproc_for_classification,False,gini,None,0.8916956785028156,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,85,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,86,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0019686649916896212,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.04641055832142541,True,True,hinge,8.540468968077405e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87,mean,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,911.0,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1.0,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,89,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,90,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,91,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,93,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,95,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,96,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,97,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,98,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,99,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,100,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,102,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,103,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,104,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,105,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,106,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.006372860318416312,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6467376360604045,None,0.0,1.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,107,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,108,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,109,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3823734947460288,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,110,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,adaboost,SAMME,0.11042308042695524,5.0,117.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,111,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,112,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,113,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,115,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,116,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,117,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,118,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.20202014999292287,False,True,1.0,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,119,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,120,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,121,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,122,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,123,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,124,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,125,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,126,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,127,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,129,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.04329010470998136,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7260331062271381,None,0.0,7.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,134,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.714869937384006,None,0.0,8.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/metalearning/metalearning_files/recall_micro_multiclass.classification_sparse/description.txt b/metalearning/metalearning_files/recall_micro_multiclass.classification_sparse/description.txt new file mode 100755 index 0000000..57b4a3b --- /dev/null +++ b/metalearning/metalearning_files/recall_micro_multiclass.classification_sparse/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: recall_micro +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/recall_micro_multiclass.classification_sparse/feature_costs.arff b/metalearning/metalearning_files/recall_micro_multiclass.classification_sparse/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/recall_micro_multiclass.classification_sparse/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/recall_micro_multiclass.classification_sparse/feature_runstatus.arff b/metalearning/metalearning_files/recall_micro_multiclass.classification_sparse/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/recall_micro_multiclass.classification_sparse/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/recall_micro_multiclass.classification_sparse/feature_values.arff b/metalearning/metalearning_files/recall_micro_multiclass.classification_sparse/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/recall_micro_multiclass.classification_sparse/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/recall_micro_multiclass.classification_sparse/readme.txt b/metalearning/metalearning_files/recall_micro_multiclass.classification_sparse/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/recall_multiclass.classification_dense/algorithm_runs.arff b/metalearning/metalearning_files/recall_multiclass.classification_dense/algorithm_runs.arff new file mode 100755 index 0000000..fef218a --- /dev/null +++ b/metalearning/metalearning_files/recall_multiclass.classification_dense/algorithm_runs.arff @@ -0,0 +1,107 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE recall NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2117,1.0,1,0.0919220055710307,ok +75156,1.0,2,0.16765578635014833,ok +75129,1.0,3,0.15384615384615385,ok +75239,1.0,4,0.0,ok +75121,1.0,5,0.0,ok +261,1.0,6,0.2727272727272727,ok +75240,1.0,7,0.0,ok +75120,1.0,8,0.0,ok +75124,1.0,9,0.21192052980132448,ok +75176,1.0,10,0.01309243257397219,ok +75103,1.0,11,0.0,ok +75095,1.0,12,0.048192771084337394,ok +273,1.0,13,0.057627118644067776,ok +75174,1.0,14,0.1323866239120477,ok +75153,1.0,15,0.08055152394775034,ok +75093,1.0,16,0.35179640718562877,ok +75119,1.0,17,0.0020661157024793875,ok +75215,1.0,18,0.017006802721088454,ok +75233,1.0,19,0.03620873269435565,ok +75196,1.0,20,0.0,ok +75191,1.0,21,0.17877640737574796,ok +75115,1.0,22,0.0,ok +75108,1.0,23,0.0,ok +75101,1.0,24,0.24622463426144403,ok +75192,1.0,25,0.20124804992199685,ok +75232,1.0,26,0.16806722689075626,ok +75173,1.0,27,0.11812500000000004,ok +75148,1.0,28,0.10058027079303677,ok +75150,1.0,29,0.161849710982659,ok +75100,1.0,30,0.4285714285714286,ok +75179,1.0,31,0.19751552795031058,ok +75213,1.0,32,0.08139534883720934,ok +75227,1.0,33,0.13218390804597702,ok +75184,1.0,34,0.1571925754060325,ok +75142,1.0,35,0.06574087752991575,ok +75166,1.0,36,0.107008289374529,ok +75133,1.0,37,0.13636363636363635,ok +75234,1.0,38,0.0180327868852459,ok +75139,1.0,39,0.01712538226299698,ok +75117,1.0,40,0.008547008547008517,ok +75113,1.0,41,0.0,ok +75237,1.0,42,0.00038952944842629567,ok +75195,1.0,43,0.0017517517517517955,ok +75171,1.0,44,0.1501120238984317,ok +75128,1.0,45,0.004278074866310155,ok +75146,1.0,46,0.09547169811320755,ok +75116,1.0,47,0.004683840749414525,ok +75157,1.0,48,0.4528301886792453,ok +75187,1.0,49,0.018425460636515956,ok +2350,1.0,50,0.4400191938579654,ok +75125,1.0,51,0.009638554216867434,ok +75185,1.0,52,0.1290944123314065,ok +75163,1.0,53,0.07427536231884058,ok +75177,1.0,54,0.012195121951219523,ok +75189,1.0,55,0.01633457982177078,ok +75244,1.0,56,0.04102564102564099,ok +75219,1.0,57,0.04123711340206182,ok +75222,1.0,58,0.09523809523809523,ok +75159,1.0,59,0.2142857142857143,ok +75175,1.0,60,0.12517882689556514,ok +254,1.0,61,0.0,ok +75105,1.0,62,0.18394648829431437,ok +75106,1.0,63,0.3378151260504202,ok +75212,1.0,64,0.2534562211981567,ok +75099,1.0,65,0.11650485436893199,ok +75248,1.0,66,0.0,ok +233,1.0,67,0.0020408163265306367,ok +75226,1.0,68,0.0007199424046075986,ok +75132,1.0,69,0.29160263942872644,ok +75127,1.0,70,0.3800735683152352,ok +75161,1.0,71,0.06620567897601881,ok +75143,1.0,72,0.003968253968253954,ok +75114,1.0,73,0.01253132832080206,ok +75182,1.0,74,0.14933577645442053,ok +75112,1.0,75,0.1763628034814475,ok +75210,1.0,76,0.0,ok +75092,1.0,77,0.038461538461538436,ok +3043,1.0,78,0.04878048780487809,ok +75249,1.0,79,0.020408163265306145,ok +75126,1.0,80,0.01342281879194629,ok +75225,1.0,81,0.0625,ok +75141,1.0,82,0.04036697247706422,ok +75107,1.0,83,0.3247588424437299,ok +75097,1.0,84,0.003728414442700112,ok +80001,1.0,1,0.2142857142857143,ok +80003,1.0,1,0.09900990099009899,ok +80006,1.0,1,0.1333333333333333,ok +80008,1.0,1,0.0,ok +80009,1.0,1,0.09999999999999998,ok +80010,1.0,1,0.2222222222222222,ok +80011,1.0,1,0.10526315789473684,ok +80012,1.0,1,0.0,ok +80013,1.0,1,0.0,ok +80014,1.0,1,0.09999999999999998,ok +80015,1.0,1,0.125,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/recall_multiclass.classification_dense/configurations.csv b/metalearning/metalearning_files/recall_multiclass.classification_dense/configurations.csv new file mode 100755 index 0000000..2ab23db --- /dev/null +++ b/metalearning/metalearning_files/recall_multiclass.classification_dense/configurations.csv @@ -0,0 +1,96 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:fast_ica:algorithm,preprocessor:fast_ica:fun,preprocessor:fast_ica:n_components,preprocessor:fast_ica:whiten,preprocessor:feature_agglomeration:affinity,preprocessor:feature_agglomeration:linkage,preprocessor:feature_agglomeration:n_clusters,preprocessor:feature_agglomeration:pooling_func,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:pca:keep_variance,preprocessor:pca:whiten,preprocessor:polynomial:degree,preprocessor:polynomial:include_bias,preprocessor:polynomial:interaction_only,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,rescaling:__choice__,rescaling:quantile_transformer:n_quantiles,rescaling:quantile_transformer:output_distribution,rescaling:robust_scaler:q_max,rescaling:robust_scaler:q_min +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.013255438916868357,deviance,9.0,0.3592430187012816,None,0.0,10.0,7.0,0.0,411.0,0.691160823398122,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,median,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1.0,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.0007295694672502942,True,multinomial_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.011064778564152866,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,median,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,7.0,None,11.0,18.0,1.0,35.0,,,,,,quantile_transformer,36528.0,normal,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0020558425106452084,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5570247081444077,None,0.0,15.0,12.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5263936397022638,None,0.0,6.0,12.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,7.795044474549774,False,True,1.0,squared_hinge,ovr,l1,0.0006963037026795237,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9455638720565652,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,most_frequent,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8255464552647293,0.19162485555463185 +weighting,one_hot_encoding,0.18137532678800647,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9094110110427254,None,0.0,7.0,12.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,195.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.0009580347867777607,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,0.10000000000000006,rbf,-1.0,True,0.0010000000000000002,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.35040453084365497,False,True,1.0,squared_hinge,ovr,l1,0.006810889378452772,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.02345017287074443,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.053517066400173056,deviance,10.0,0.542144980834302,None,0.0,20.0,13.0,0.0,233.0,0.7398539900055563,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0614425536709615,fwe,f_classif,robust_scaler,,,0.9523118062307264,0.13434811490315818 +weighting,one_hot_encoding,0.010000000000000005,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.10000000000000002,deviance,3.0,1.0,None,0.0,1.0,2.0,0.0,100.0,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7070968825037495,0.2946729770960392 +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.040936424602789435,deviance,7.0,0.5495014745530306,None,0.0,20.0,18.0,0.0,141.0,0.6905343807995293,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +weighting,one_hot_encoding,0.010000000000000005,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9641046312686136,None,0.0,18.0,12.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8622423450611333,0.2960428898664952 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.010000000000000005,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.8700514193862113,None,0.0,6.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,most_frequent,fast_ica,,,,,,,,,,,parallel,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.939200003837416,0.13375455137243772 +none,one_hot_encoding,0.00012586572428922356,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5240592829918601,None,0.0,10.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.2422926485206341,,1.0,None,0.0,15.0,9.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.022939738050158573,deviance,10.0,0.4185394344134278,None,0.0,2.0,10.0,0.0,309.0,0.5979695608086252,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.25383213391991144 +weighting,no_encoding,,,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,0.3170009238992427,rbf,1955.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8333938697866604,0.10426506601169797 +weighting,one_hot_encoding,0.004739913305088818,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mae,0.2770776012493639,deviance,7.0,0.3198803116198433,None,0.0,3.0,13.0,0.0,298.0,0.2768658913317291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.00353232434213139,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.9407432771644124,None,0.0,1.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,median,fast_ica,,,,,,,,,,,parallel,cube,100.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7446318916516063,0.23782974987118105 +none,one_hot_encoding,0.0010670931302549814,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.8709229440057928,None,0.0,3.0,13.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,median,fast_ica,,,,,,,,,,,deflation,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8372068127698562,0.23782974987118105 +none,one_hot_encoding,0.0007295694672502942,True,multinomial_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.011064778564152866,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,median,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,7.0,None,11.0,18.0,1.0,35.0,,,,,,quantile_transformer,36528.0,normal,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.002173124111626734,None,0.0,14.0,4.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,6.0,None,13.0,2.0,1.0,23.0,,,,,,normalize,,,, +weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2507440474920336e-05,True,,0.049622652766554566,True,0.009105043727227265,constant,squared_hinge,elasticnet,,0.00010112719671669047,,,,,,,,,,,,,,,,,,31,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61.69949680034141,chi2,,,,none,,,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.9348409326933208,rbf,-1.0,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1754.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.4386768819247876e-05,True,,0.02855319677129068,True,1.5957742829949438e-07,constant,perceptron,elasticnet,,3.431368200869824e-05,,,,,,,,,,,,,,,,,,34,median,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8992576790440515,0.20839368148068493 +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.8770766409674923,None,0.0,13.0,13.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,84023.0,normal,, +weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7856.587651350424,,,0.0017305319997054556,rbf,-1.0,False,1.7622421003766454e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.006909187206474195,True,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00024264014379190562,True,,0.04987297125937914,True,,optimal,hinge,l2,,4.0861541221464815e-05,,,,,,,,,,,,,,,,,,37,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.05153104953418389,False,True,1.0,squared_hinge,ovr,l1,0.0001939386474290285,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.00034835629696198427,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8245132980938538,0.08947420373097192 +none,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4047.618729304337,,,2.0237366768707754,rbf,-1.0,True,0.04369127828878843,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,bernoulli_nb,,,,,0.02802461704612387,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,median,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,6.0,None,12.0,20.0,1.0,75.0,,,,,,quantile_transformer,10068.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1.0,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.3451627750042988,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.3163640203509378,None,0.0,17.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,median,extra_trees_preproc_for_classification,False,gini,None,0.8916956785028156,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.00010817282861262362,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.799803680241154,None,0.0,13.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,mean,fast_ica,,,,,,,,,,,deflation,exp,1793.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9071932815811076,0.03563842252368924 +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.35533396539961937,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4165632766388807,fpr,chi2,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8522973640879423,None,0.0,13.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6856119355718316,None,0.0,2.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,robust_scaler,,,0.9070067796375252,0.2232396978725172 +weighting,no_encoding,,,decision_tree,,,,,,,entropy,1.94608233492308,,1.0,None,0.0,15.0,11.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2613520842796237,fdr,chi2,quantile_transformer,1000.0,uniform,, +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0387325491437111,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.32034732923549136,None,0.0,20.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,median,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,quantile_transformer,1000.0,uniform,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2636201374253461,deviance,7.0,0.8344964130784466,None,0.0,9.0,2.0,0.0,298.0,0.7517549950523315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,normalize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.2263596964804377,True,adaboost,SAMME,0.10845020299646906,2.0,248.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.22655400374385,fwe,f_classif,minmax,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.04064013793978874,deviance,7.0,0.3397533378274365,None,0.0,10.0,4.0,0.0,280.0,0.8476773466327144,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,most_frequent,extra_trees_preproc_for_classification,True,gini,None,0.9072459027692912,None,0.0,6.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8834597195173531,0.09983935639458053 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.6960837129194184,,,0.0004821713821548537,rbf,-1.0,False,0.040691702958042086,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,most_frequent,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6520469084729728,False,,,,,,,,,,,,,,,,quantile_transformer,97329.0,normal,, +weighting,one_hot_encoding,0.010000000000000005,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.04952755495565772,deviance,3.0,0.7249041896998006,None,0.0,6.0,17.0,0.0,174.0,0.3718608680080454,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9091193924897338,None,0.0,2.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +weighting,no_encoding,,,bernoulli_nb,,,,,0.023108348799909733,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,median,extra_trees_preproc_for_classification,True,gini,None,0.43664414575861454,None,0.0,1.0,4.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8569382605464568,0.07491671292996571 +weighting,one_hot_encoding,0.10324969243867224,True,decision_tree,,,,,,,gini,0.7467478023293801,,1.0,None,0.0,12.0,8.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84.22876326806853,mutual_info,,,,minmax,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.001279467383882126,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0903354518102121,fwe,f_classif,standardize,,,, +none,one_hot_encoding,0.0007295694672502942,True,multinomial_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.011064778564152866,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,median,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,7.0,None,11.0,18.0,1.0,35.0,,,,,,quantile_transformer,36528.0,normal,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3823734947460288,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.2472984547885781,deviance,3.0,0.6564306719064884,None,0.0,15.0,14.0,0.0,220.0,0.8082564085714649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,median,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,332.0,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.03905145156995541,deviance,5.0,0.2281306656230014,None,0.0,14.0,13.0,0.0,493.0,0.8793075442604774,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,25382.0,normal,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.12713527337147906,deviance,4.0,0.6041596127474019,None,0.0,14.0,17.0,0.0,83.0,0.8426859880999615,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.7529073739756954,None,0.0,11.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,25.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,93335.0,normal,, +weighting,one_hot_encoding,0.4941395236323653,True,decision_tree,,,,,,,gini,1.7020201228673206,,1.0,None,0.0,20.0,8.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,True,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0002525813164610038,True,,0.0601553136224701,True,0.057326377025764375,optimal,squared_hinge,elasticnet,,0.004555880871360689,,,,,,,,,,,,,,,,,,81,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,2.0,None,18.0,4.0,1.0,92.0,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.5765793990908161,None,0.0,11.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,median,fast_ica,,,,,,,,,,,deflation,exp,10.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.1166829447028879,deviance,4.0,0.8451196958788597,None,0.0,1.0,8.0,0.0,58.0,0.8652814520124915,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.03406306803297533,False,True,1.0,squared_hinge,ovr,l1,0.00019257971888767865,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.2604623554399743,None,0.0,18.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.282079367058228,fpr,chi2,none,,,, +none,no_encoding,,,adaboost,SAMME,0.7603752531440509,7.0,413.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,28.04113884899283,False,True,1.0,squared_hinge,ovr,l1,0.0001084726092097629,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,26106.0,uniform,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3386310102795006,None,0.0,6.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,True,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,134,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.0019726875156463528,True,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3603.9877392982157,0.0,1.0,1.0,squared_hinge,ovr,l2,0.0004637091297122247,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,52.66421770620247,False,True,1.0,squared_hinge,ovr,l1,0.004627354576003774,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, diff --git a/metalearning/metalearning_files/recall_multiclass.classification_dense/description.txt b/metalearning/metalearning_files/recall_multiclass.classification_dense/description.txt new file mode 100755 index 0000000..6c51f24 --- /dev/null +++ b/metalearning/metalearning_files/recall_multiclass.classification_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: recall +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/recall_multiclass.classification_dense/feature_costs.arff b/metalearning/metalearning_files/recall_multiclass.classification_dense/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/recall_multiclass.classification_dense/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/recall_multiclass.classification_dense/feature_runstatus.arff b/metalearning/metalearning_files/recall_multiclass.classification_dense/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/recall_multiclass.classification_dense/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/recall_multiclass.classification_dense/feature_values.arff b/metalearning/metalearning_files/recall_multiclass.classification_dense/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/recall_multiclass.classification_dense/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/recall_multiclass.classification_dense/readme.txt b/metalearning/metalearning_files/recall_multiclass.classification_dense/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/recall_multiclass.classification_sparse/algorithm_runs.arff b/metalearning/metalearning_files/recall_multiclass.classification_sparse/algorithm_runs.arff new file mode 100755 index 0000000..06aec09 --- /dev/null +++ b/metalearning/metalearning_files/recall_multiclass.classification_sparse/algorithm_runs.arff @@ -0,0 +1,107 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE recall NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2117,1.0,1,0.0919220055710307,ok +75156,1.0,2,0.18694362017804156,ok +75129,1.0,3,0.1923076923076923,ok +75239,1.0,4,0.0,ok +75121,1.0,5,0.0,ok +261,1.0,6,0.303030303030303,ok +75240,1.0,7,0.0,ok +75120,1.0,8,0.0,ok +75124,1.0,9,0.2715231788079471,ok +75176,1.0,10,0.01518722178580778,ok +75103,1.0,11,0.02622950819672132,ok +75095,1.0,12,0.048192771084337394,ok +273,1.0,13,0.06440677966101693,ok +75174,1.0,14,0.23545579477782863,ok +75153,1.0,15,0.13352685050798263,ok +75093,1.0,16,0.3697604790419161,ok +75119,1.0,17,0.0020661157024793875,ok +75215,1.0,18,0.017006802721088454,ok +75233,1.0,19,0.03620873269435565,ok +75196,1.0,20,0.03883495145631066,ok +75191,1.0,21,0.17712785443888146,ok +75115,1.0,22,0.0,ok +75108,1.0,23,0.00303030303030305,ok +75101,1.0,24,0.26197498820198206,ok +75192,1.0,25,0.5117004680187207,ok +75232,1.0,26,0.26890756302521013,ok +75173,1.0,27,0.12687499999999996,ok +75148,1.0,28,0.17408123791102514,ok +75150,1.0,29,0.32947976878612717,ok +75100,1.0,30,0.4285714285714286,ok +75179,1.0,31,0.19751552795031058,ok +75213,1.0,32,0.08139534883720934,ok +75227,1.0,33,0.13218390804597702,ok +75184,1.0,34,0.2529002320185615,ok +75142,1.0,35,0.07815039149061898,ok +75166,1.0,36,0.10926902788244164,ok +75133,1.0,37,0.6363636363636364,ok +75234,1.0,38,0.09426229508196726,ok +75139,1.0,39,0.01712538226299698,ok +75117,1.0,40,0.008547008547008517,ok +75113,1.0,41,0.003389830508474523,ok +75237,1.0,42,0.00038952944842629567,ok +75195,1.0,43,0.0017517517517517955,ok +75171,1.0,44,0.1501120238984317,ok +75128,1.0,45,0.004278074866310155,ok +75146,1.0,46,0.09547169811320755,ok +75116,1.0,47,0.004683840749414525,ok +75157,1.0,48,0.4528301886792453,ok +75187,1.0,49,0.031825795644891075,ok +2350,1.0,50,0.4005346860433233,ok +75125,1.0,51,0.009638554216867434,ok +75185,1.0,52,0.1560693641618497,ok +75163,1.0,53,0.07608695652173914,ok +75177,1.0,54,0.04878048780487809,ok +75189,1.0,55,0.01633457982177078,ok +75244,1.0,56,0.06666666666666665,ok +75219,1.0,57,0.11878081577767818,ok +75222,1.0,58,0.09523809523809523,ok +75159,1.0,59,0.2857142857142857,ok +75175,1.0,60,0.18383404864091557,ok +254,1.0,61,0.0,ok +75105,1.0,62,0.8060200668896321,ok +75106,1.0,63,0.7336134453781513,ok +75212,1.0,64,0.2534562211981567,ok +75099,1.0,65,0.22330097087378642,ok +75248,1.0,66,0.05737704918032782,ok +233,1.0,67,0.01632653061224487,ok +75226,1.0,68,0.0017998560115191076,ok +75132,1.0,69,0.7243966374401156,ok +75127,1.0,70,0.3800735683152352,ok +75161,1.0,71,0.08592025893776667,ok +75143,1.0,72,0.003968253968253954,ok +75114,1.0,73,0.01253132832080206,ok +75182,1.0,74,0.1694915254237288,ok +75112,1.0,75,0.23683005038937244,ok +75210,1.0,76,0.0,ok +75092,1.0,77,0.09615384615384615,ok +3043,1.0,78,0.060975609756097615,ok +75249,1.0,79,0.020408163265306145,ok +75126,1.0,80,0.01342281879194629,ok +75225,1.0,81,0.0625,ok +75141,1.0,82,0.046788990825688104,ok +75107,1.0,83,0.5522508038585209,ok +75097,1.0,84,0.003728414442700112,ok +80001,1.0,1,0.2142857142857143,ok +80003,1.0,1,0.14851485148514854,ok +80006,1.0,1,0.33333333333333337,ok +80008,1.0,1,0.0,ok +80009,1.0,1,0.09999999999999998,ok +80010,1.0,1,0.2222222222222222,ok +80011,1.0,1,0.10526315789473684,ok +80012,1.0,1,0.0,ok +80013,1.0,1,0.0,ok +80014,1.0,1,0.09999999999999998,ok +80015,1.0,1,0.125,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/recall_multiclass.classification_sparse/configurations.csv b/metalearning/metalearning_files/recall_multiclass.classification_sparse/configurations.csv new file mode 100755 index 0000000..2d40863 --- /dev/null +++ b/metalearning/metalearning_files/recall_multiclass.classification_sparse/configurations.csv @@ -0,0 +1,96 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,preprocessor:truncatedSVD:target_dim,rescaling:__choice__ +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.039533063907190934,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1878805519245509,fdr,chi2,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7238850981243719,None,0.0,4.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,5.282738216059151e-05,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0009580347867777607,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,0.10000000000000006,rbf,-1.0,True,0.0010000000000000002,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.35040453084365497,False,True,1.0,squared_hinge,ovr,l1,0.006810889378452772,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.2380793644102286,None,0.0,1.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.15248352254459802,fwe,chi2,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.010000000000000005,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9727149851116396,None,0.0,18.0,13.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.0011292655810309046,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.05492831427533807,deviance,5.0,0.3620138827518105,None,0.0,2.0,14.0,0.0,380.0,0.6590220190321725,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,median,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.8657388713119849,,1.0,None,0.0,19.0,13.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.002173124111626734,None,0.0,14.0,4.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,6.0,None,13.0,2.0,1.0,23.0,,,,,,,normalize +weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2507440474920336e-05,True,,0.049622652766554566,True,0.009105043727227265,constant,squared_hinge,elasticnet,,0.00010112719671669047,,,,,,,,,,,,,,,,,,31,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61.69949680034141,chi2,,,,,none +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.9348409326933208,rbf,-1.0,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,mean,kernel_pca,,,,,,,,,,,,,,cosine,1754.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3428971645958825,,,0.2229870623330047,rbf,-1.0,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4047.618729304337,,,2.0237366768707754,rbf,-1.0,True,0.04369127828878843,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.039533063907190934,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1878805519245509,fdr,chi2,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1.0,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.3451627750042988,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.3163640203509378,None,0.0,17.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,median,extra_trees_preproc_for_classification,False,gini,None,0.8916956785028156,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.00011600321198702641,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,most_frequent,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,decision_tree,,,,,,,gini,1.7984076825537865,,1.0,None,0.0,14.0,2.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2897525995758022,fwe,chi2,,none +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.20875514426569566,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.53183372054125,None,0.0,18.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,13818.683783129034,False,True,1.0,squared_hinge,ovr,l1,1.009528987119941e-05,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.039533063907190934,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1878805519245509,fdr,chi2,,standardize +weighting,one_hot_encoding,0.039533063907190934,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1878805519245509,fdr,chi2,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9091193924897338,None,0.0,2.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,one_hot_encoding,,False,bernoulli_nb,,,,,0.014801515930977628,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.039533063907190934,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1878805519245509,fdr,chi2,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3823734947460288,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.3997572853391576,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.21008613984919333,None,0.0,11.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.8804227616935514,None,0.0,10.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.0898983119899223,False,True,1.0,squared_hinge,ovr,l1,0.016048982920294167,,,,,,,,,,,,,,,,,,,none +weighting,no_encoding,,,decision_tree,,,,,,,entropy,1.301605991416264,,1.0,None,0.0,8.0,3.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0002525813164610038,True,,0.0601553136224701,True,0.057326377025764375,optimal,squared_hinge,elasticnet,,0.004555880871360689,,,,,,,,,,,,,,,,,,81,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,2.0,None,18.0,4.0,1.0,92.0,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.00214097329599271,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7996802015738327,None,0.0,7.0,12.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.1052247187777527,False,True,1.0,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.2604623554399743,None,0.0,18.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.282079367058228,fpr,chi2,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,134,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.0019726875156463528,True,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3603.9877392982157,0.0,1.0,1.0,squared_hinge,ovr,l2,0.0004637091297122247,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,52.66421770620247,False,True,1.0,squared_hinge,ovr,l1,0.004627354576003774,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/metalearning/metalearning_files/recall_multiclass.classification_sparse/description.txt b/metalearning/metalearning_files/recall_multiclass.classification_sparse/description.txt new file mode 100755 index 0000000..6c51f24 --- /dev/null +++ b/metalearning/metalearning_files/recall_multiclass.classification_sparse/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: recall +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/recall_multiclass.classification_sparse/feature_costs.arff b/metalearning/metalearning_files/recall_multiclass.classification_sparse/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/recall_multiclass.classification_sparse/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/recall_multiclass.classification_sparse/feature_runstatus.arff b/metalearning/metalearning_files/recall_multiclass.classification_sparse/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/recall_multiclass.classification_sparse/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/recall_multiclass.classification_sparse/feature_values.arff b/metalearning/metalearning_files/recall_multiclass.classification_sparse/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/recall_multiclass.classification_sparse/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/recall_multiclass.classification_sparse/readme.txt b/metalearning/metalearning_files/recall_multiclass.classification_sparse/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/recall_weighted_binary.classification_dense/algorithm_runs.arff b/metalearning/metalearning_files/recall_weighted_binary.classification_dense/algorithm_runs.arff new file mode 100755 index 0000000..41354b5 --- /dev/null +++ b/metalearning/metalearning_files/recall_weighted_binary.classification_dense/algorithm_runs.arff @@ -0,0 +1,152 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE recall_weighted NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2120,1.0,1,0.07967939651107969,ok +75193,1.0,2,0.038371068099909755,ok +2117,1.0,3,0.16709064962461995,ok +75156,1.0,4,0.20291026677445434,ok +75129,1.0,5,0.10097087378640779,ok +75243,1.0,6,0.0,ok +75110,1.0,7,0.11622380643767549,ok +75239,1.0,8,0.0,ok +75223,1.0,9,0.10304601425793913,ok +75221,1.0,10,0.39791873141724476,ok +258,1.0,11,0.009708737864077666,ok +75121,1.0,12,0.0,ok +253,1.0,13,0.44855967078189296,ok +261,1.0,14,0.23333333333333328,ok +75240,1.0,15,0.022020725388601003,ok +75120,1.0,16,0.03929273084479368,ok +75124,1.0,17,0.09121395036887991,ok +75176,1.0,18,0.01541623843782114,ok +75103,1.0,19,0.005894736842105286,ok +75207,1.0,20,0.161849710982659,ok +75095,1.0,21,0.016917293233082664,ok +273,1.0,22,0.04413702239789197,ok +75174,1.0,23,0.11705240755520085,ok +75153,1.0,24,0.08028116907140215,ok +75093,1.0,25,0.17483296213808464,ok +75119,1.0,26,0.035363457760314354,ok +75201,1.0,27,0.0808678500986193,ok +75215,1.0,28,0.027412280701754388,ok +75172,1.0,29,0.10303030303030303,ok +75169,1.0,30,0.03420132141469101,ok +75202,1.0,31,0.20329670329670335,ok +75233,1.0,32,0.060673325934147204,ok +75231,1.0,33,0.19924098671726753,ok +75196,1.0,34,0.015665796344647487,ok +248,1.0,35,0.22878787878787876,ok +75191,1.0,36,0.1289905886694962,ok +75217,1.0,37,0.0,ok +260,1.0,38,0.02657807308970095,ok +75115,1.0,39,0.017681728880157177,ok +75123,1.0,40,0.32728592162554426,ok +75108,1.0,41,0.0,ok +75101,1.0,42,0.2740140932130053,ok +75192,1.0,43,0.48305752561071713,ok +75232,1.0,44,0.12356321839080464,ok +75173,1.0,45,0.1165605095541401,ok +75197,1.0,46,0.15517241379310343,ok +266,1.0,47,0.019685039370078705,ok +75148,1.0,48,0.13560975609756099,ok +75150,1.0,49,0.2848664688427299,ok +75100,1.0,50,0.00379609544468551,ok +75178,1.0,51,0.7840550682597786,ok +75236,1.0,52,0.0323809523809524,ok +75179,1.0,53,0.17943026267110618,ok +75213,1.0,54,0.05249343832021003,ok +2123,1.0,55,0.05882352941176472,ok +75227,1.0,56,0.10151430173864273,ok +75184,1.0,57,0.10772320613474529,ok +75142,1.0,58,0.0715825466438712,ok +236,1.0,59,0.038787878787878816,ok +2122,1.0,60,0.10952689565780949,ok +75188,1.0,61,0.24319066147859925,ok +75166,1.0,62,0.0995190529041805,ok +75181,1.0,63,0.0,ok +75133,1.0,64,0.005123278898495065,ok +75134,1.0,65,0.08723783614874181,ok +75198,1.0,66,0.12143310082435,ok +262,1.0,67,0.0027570995312931057,ok +75234,1.0,68,0.024160524160524166,ok +75139,1.0,69,0.010707070707070665,ok +252,1.0,70,0.15000000000000002,ok +75117,1.0,71,0.05500982318271119,ok +75113,1.0,72,0.006526315789473713,ok +75098,1.0,73,0.027575757575757587,ok +246,1.0,74,0.010606060606060619,ok +75203,1.0,75,0.09555345316934716,ok +75237,1.0,76,0.00039570658356824495,ok +75195,1.0,77,0.0015609901137292326,ok +75171,1.0,78,0.1620421753607103,ok +75128,1.0,79,0.02218114602587795,ok +75096,1.0,80,0.005164788382626018,ok +75250,1.0,81,0.34347287891393896,ok +75146,1.0,82,0.11329072074057744,ok +75116,1.0,83,0.00982318271119842,ok +75157,1.0,84,0.4192200557103064,ok +75187,1.0,85,0.01678951678951679,ok +2350,1.0,86,0.3744580607974338,ok +242,1.0,87,0.010606060606060619,ok +244,1.0,88,0.1106060606060606,ok +75125,1.0,89,0.03339882121807469,ok +75185,1.0,90,0.12816966343937297,ok +75163,1.0,91,0.060374149659863985,ok +75177,1.0,92,0.019292604501607746,ok +75189,1.0,93,0.019401337253296624,ok +75244,1.0,94,0.06339958875942431,ok +75219,1.0,95,0.03479668217681575,ok +75222,1.0,96,0.04524886877828049,ok +75159,1.0,97,0.06849315068493156,ok +75175,1.0,98,0.09954485391278811,ok +75109,1.0,99,0.30417434008594224,ok +254,1.0,100,0.0,ok +75105,1.0,101,0.018121212121212094,ok +75106,1.0,102,0.07212121212121214,ok +75212,1.0,103,0.2517482517482518,ok +75099,1.0,104,0.12374100719424463,ok +75248,1.0,105,0.10040485829959511,ok +233,1.0,106,0.002846299810246644,ok +75235,1.0,107,0.0011111111111110628,ok +75226,1.0,108,0.0030422878004259246,ok +75132,1.0,109,0.051244509516837455,ok +75127,1.0,110,0.3330355738331199,ok +251,1.0,111,0.0,ok +75161,1.0,112,0.06437225897569321,ok +75143,1.0,113,0.010471204188481686,ok +75114,1.0,114,0.023575638506876273,ok +75182,1.0,115,0.1081404628890662,ok +75112,1.0,116,0.12061822817080947,ok +75210,1.0,117,0.0,ok +75205,1.0,118,0.18110236220472442,ok +75090,1.0,119,0.06118881118881114,ok +275,1.0,120,0.03612167300380231,ok +288,1.0,121,0.12969696969696964,ok +75092,1.0,122,0.0935550935550935,ok +3043,1.0,123,0.020096463022508004,ok +75249,1.0,124,0.003215434083601254,ok +75126,1.0,125,0.0491159135559921,ok +75225,1.0,126,0.04904306220095689,ok +75141,1.0,127,0.0540140584535701,ok +75107,1.0,128,0.053212121212121266,ok +75097,1.0,129,0.05835568297419769,ok +80001,1.0,1,0.06944444444444442,ok +80003,1.0,1,0.09230769230769231,ok +80006,1.0,1,0.09375,ok +80008,1.0,1,0.11111111111111116,ok +80009,1.0,1,0.10526315789473684,ok +80010,1.0,1,0.13793103448275867,ok +80011,1.0,1,0.037735849056603765,ok +80012,1.0,1,0.045454545454545414,ok +80013,1.0,1,0.0,ok +80014,1.0,1,0.045454545454545414,ok +80015,1.0,1,0.09523809523809523,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/recall_weighted_binary.classification_dense/configurations.csv b/metalearning/metalearning_files/recall_weighted_binary.classification_dense/configurations.csv new file mode 100755 index 0000000..e710c68 --- /dev/null +++ b/metalearning/metalearning_files/recall_weighted_binary.classification_dense/configurations.csv @@ -0,0 +1,141 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:fast_ica:algorithm,preprocessor:fast_ica:fun,preprocessor:fast_ica:n_components,preprocessor:fast_ica:whiten,preprocessor:feature_agglomeration:affinity,preprocessor:feature_agglomeration:linkage,preprocessor:feature_agglomeration:n_clusters,preprocessor:feature_agglomeration:pooling_func,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:pca:keep_variance,preprocessor:pca:whiten,preprocessor:polynomial:degree,preprocessor:polynomial:include_bias,preprocessor:polynomial:interaction_only,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,rescaling:__choice__,rescaling:quantile_transformer:n_quantiles,rescaling:quantile_transformer:output_distribution,rescaling:robust_scaler:q_max,rescaling:robust_scaler:q_min +weighting,one_hot_encoding,0.00011717632475982632,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.045388141846341344,deviance,10.0,0.29161769341843435,None,0.0,20.0,2.0,0.0,278.0,0.7912571599269661,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7249853037185638,None,0.0,1.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,median,extra_trees_preproc_for_classification,False,gini,None,0.9424908623661876,None,0.0,7.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.03474109838999682,deviance,4.0,0.5687034678818491,None,0.0,18.0,12.0,0.0,408.0,0.5150113945430513,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.6328939517938,f_classif,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.251097788175676,deviance,6.0,0.35679099363539235,None,0.0,13.0,11.0,0.0,157.0,0.4791732272983235,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,adaboost,SAMME,0.28738775989203896,10.0,423.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.8031499675923353,0.13579938270386765 +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.14159526341015916,deviance,7.0,0.8010488230155749,None,0.0,3.0,20.0,0.0,401.0,0.8073562440607731,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.002385546176068135,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.7729472304882845,,,0.0004789329856033374,rbf,-1.0,True,6.58869648864534e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,177.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5368752992317617,None,0.0,16.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.8382117756438685,mutual_info,,,,quantile_transformer,11480.0,normal,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9455638720565652,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,most_frequent,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8255464552647293,0.19162485555463185 +weighting,one_hot_encoding,0.18137532678800647,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9094110110427254,None,0.0,7.0,12.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,195.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.6682079659377479,None,0.0,4.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,mean,extra_trees_preproc_for_classification,True,entropy,None,0.5552350997943013,None,0.0,8.0,5.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.02345017287074443,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.053517066400173056,deviance,10.0,0.542144980834302,None,0.0,20.0,13.0,0.0,233.0,0.7398539900055563,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0614425536709615,fwe,f_classif,robust_scaler,,,0.9523118062307264,0.13434811490315818 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.040936424602789435,deviance,7.0,0.5495014745530306,None,0.0,20.0,18.0,0.0,141.0,0.6905343807995293,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7974565919616314,None,0.0,12.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,median,extra_trees_preproc_for_classification,True,entropy,None,0.9772091846790169,None,0.0,10.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2538107344750156,False,True,hinge,1.5099542326343014e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,8532.0,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.1958974686405233,deviance,5.0,0.33885235607979314,None,0.0,6.0,4.0,0.0,125.0,0.9448890820738562,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2538107344750156,False,True,hinge,1.5099542326343014e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,8532.0,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,0.0007038280350320558,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4138778052607317,0.7995003430482459,5.0,5.430044692638861,poly,-1.0,True,0.02455501006004393,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,31,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.010000000000000005,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.0518326156691958,deviance,6.0,0.8807456180216267,None,0.0,7.0,19.0,0.0,366.0,0.7314831276137047,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,,False,adaboost,SAMME,0.4391375941344922,3.0,386.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7439738358430176,0.20581080574615795 +none,one_hot_encoding,0.00011294596229850895,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7515.1213255144885,0.9576762936062476,3.0,0.019002536385919932,poly,-1.0,False,0.010632086351533369,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,51.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,97282.0,normal,, +none,one_hot_encoding,0.00012586572428922356,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5240592829918601,None,0.0,10.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1751.4736515133566,0.62404114475118,3.0,1.6087076997410432,poly,-1.0,False,3.5353792826856045e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1198.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.2422926485206341,,1.0,None,0.0,15.0,9.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.02102683283349326,deviance,10.0,0.2797288369369436,None,0.0,14.0,9.0,0.0,480.0,0.5778972273820631,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58.88123233170863,mutual_info,,,,robust_scaler,,,0.75,0.25 +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828776,None,0.0,2.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2155613360930585,deviance,4.0,0.3198803116198433,None,0.0,8.0,13.0,0.0,275.0,0.28870176110739404,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,mean,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.7062102387181676,None,0.0,1.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,most_frequent,fast_ica,,,,,,,,,,,parallel,exp,100.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7065776353150109,0.23782974987118105 +none,one_hot_encoding,0.16334152321884812,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00011572473434870852,True,True,squared_hinge,0.00019678754114665057,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.045742431094098604,fpr,chi2,none,,,, +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.3795924768593385,deviance,2.0,0.33708497069988536,None,0.0,15.0,13.0,0.0,451.0,0.7716323242090217,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2573946506994812,fwe,f_classif,quantile_transformer,1000.0,uniform,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.609975998293528,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,most_frequent,fast_ica,,,,,,,,,,,parallel,logcosh,2000.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8430415644014919,0.2863750565331575 +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.39536192447534535,None,0.0,19.0,3.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5.0,None,11.0,11.0,1.0,12.0,,,,,,robust_scaler,,,0.8928631650245873,0.1581877760687084 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.3126027672745337,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,870.2240970463429,0.5325949351918051,3.0,0.010682839357128344,poly,-1.0,False,2.485160860440657e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4608103694360143,fdr,f_classif,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.9348409326933208,rbf,-1.0,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1754.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,198.72528686512538,False,True,1.0,squared_hinge,ovr,l2,0.026260652523566803,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,robust_scaler,,,0.913511520078368,0.2742229325455444 +none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9260795160807372,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.03644212536682547,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.05965671477918361,deviance,8.0,0.4858133247974158,None,0.0,14.0,7.0,0.0,480.0,0.5726186552917335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.15318294164619112 +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18887.81504976871,,,0.23283562663398755,rbf,-1.0,True,2.3839685780861318e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3937607155568376,fwe,chi2,robust_scaler,,,0.941018778984854,0.2144110585080491 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3428971645958825,,,0.2229870623330047,rbf,-1.0,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8138233157708883,None,0.0,2.0,9.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54.27504292568562,f_classif,,,,minmax,,,, +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00016781524591321165,True,True,squared_hinge,1.5119200923218881e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.556190405302504,False,True,1.0,squared_hinge,ovr,l2,0.0007318628304090552,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,most_frequent,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,3.5602014542183973,948.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +weighting,one_hot_encoding,0.00034835629696198427,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8245132980938538,0.08947420373097192 +weighting,one_hot_encoding,0.00016967940959070708,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9439080311935252,None,0.0,2.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,170.0,None,,0.014191958374153584,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,,False,adaboost,SAMME.R,0.8220362681234727,5.0,183.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.363262678765884,fdr,chi2,robust_scaler,,,0.8826612080363588,0.2189605310171097 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,normalize,,,, +none,one_hot_encoding,,False,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6396026761675004,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06544340428506021,fwe,f_classif,none,,,, +weighting,one_hot_encoding,0.004980497345831963,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1255.9137433589426,,,0.08351549479967445,rbf,-1.0,True,0.00017919875199222518,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1.0,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.3837398524575939,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.018356703878357986,deviance,3.0,0.9690352514774068,None,0.0,12.0,3.0,0.0,234.0,0.3870344708308441,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,most_frequent,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6864970915330799,False,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5670424455696162,None,0.0,8.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,standardize,,,, +weighting,one_hot_encoding,0.00031737236118003484,True,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,244.0,None,,2.3065111488706024e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,85,mean,fast_ica,,,,,,,,,,,deflation,exp,1862.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7851234479882973,0.2237528085136715 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,86,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,156.0,auto,,0.0001987333852871589,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1.0,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,no_encoding,,,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.96834823420249e-05,False,True,hinge,0.00016639250831671168,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,89,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.628430584359224,mutual_info,,,,quantile_transformer,6634.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,90,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,91,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,93,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2636201374253461,deviance,7.0,0.8344964130784466,None,0.0,9.0,2.0,0.0,298.0,0.7517549950523315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,95,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,96,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,97,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.07463196642416367,deviance,7.0,0.8603242247379981,None,0.0,2.0,6.0,0.0,500.0,0.8447665577491962,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,98,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.0433556140045585,deviance,10.0,0.33000096635982235,None,0.0,15.0,13.0,0.0,388.0,0.8291104221904706,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,99,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,robust_scaler,,,0.7496393440951183,0.2853682991120835 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,100,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,102,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0020580843703898177,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.945774573434192,None,0.0,19.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,103,median,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,15209.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,104,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,105,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,106,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.005332972692819521,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5565918060287016,None,0.0,5.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,107,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,173.0,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9527068489270144,0.04135311355893583 +weighting,one_hot_encoding,0.001279467383882126,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,108,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0903354518102121,fwe,f_classif,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,109,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.002615346832354839,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7884268823432835,None,0.0,20.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,110,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +weighting,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56.086963007482865,,,0.013609964993119377,rbf,-1.0,True,0.00196831255706268,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,111,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.15374716583918388 +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.2472984547885781,deviance,3.0,0.6564306719064884,None,0.0,15.0,14.0,0.0,220.0,0.8082564085714649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,112,median,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,332.0,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,113,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,115,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,116,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,117,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,118,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.20202014999292287,False,True,1.0,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,no_encoding,,,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6390376923528961,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,119,median,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,164.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,62508.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,120,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.27041927277584e-06,True,0.00010000000000000009,0.033157325660763994,True,0.0008114527992546482,invscaling,modified_huber,elasticnet,0.13714427818877545,0.055179642772545036,,,,,,,,,,,,,,,,,,121,median,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,122,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,123,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.19998727075532635,deviance,10.0,0.9377656718112952,None,0.0,7.0,13.0,0.0,214.0,0.6062346326014357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,124,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,125,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,126,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.5765793990908161,None,0.0,11.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,127,median,fast_ica,,,,,,,,,,,deflation,exp,10.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,129,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.04651467280022463,True,adaboost,SAMME,0.6506122230393502,6.0,143.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,extra_trees_preproc_for_classification,False,entropy,None,0.348720215832657,None,0.0,17.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,26025.0,normal,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3386310102795006,None,0.0,6.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,True,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.0,None,,5.861221938384061e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.22985730375167915,fpr,f_classif,quantile_transformer,40589.0,uniform,, +none,no_encoding,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00012437731009611307,True,,0.08709904468181932,True,,invscaling,squared_hinge,l1,0.6231564675290102,0.008071279833697563,,,,,,,,,,,,,,,,,,134,median,fast_ica,,,,,,,,,,,parallel,logcosh,814.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.714869937384006,None,0.0,8.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, diff --git a/metalearning/metalearning_files/recall_weighted_binary.classification_dense/description.txt b/metalearning/metalearning_files/recall_weighted_binary.classification_dense/description.txt new file mode 100755 index 0000000..7d775c3 --- /dev/null +++ b/metalearning/metalearning_files/recall_weighted_binary.classification_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: recall_weighted +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/recall_weighted_binary.classification_dense/feature_costs.arff b/metalearning/metalearning_files/recall_weighted_binary.classification_dense/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/recall_weighted_binary.classification_dense/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/recall_weighted_binary.classification_dense/feature_runstatus.arff b/metalearning/metalearning_files/recall_weighted_binary.classification_dense/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/recall_weighted_binary.classification_dense/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/recall_weighted_binary.classification_dense/feature_values.arff b/metalearning/metalearning_files/recall_weighted_binary.classification_dense/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/recall_weighted_binary.classification_dense/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/recall_weighted_binary.classification_dense/readme.txt b/metalearning/metalearning_files/recall_weighted_binary.classification_dense/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/recall_weighted_binary.classification_sparse/algorithm_runs.arff b/metalearning/metalearning_files/recall_weighted_binary.classification_sparse/algorithm_runs.arff new file mode 100755 index 0000000..e50138e --- /dev/null +++ b/metalearning/metalearning_files/recall_weighted_binary.classification_sparse/algorithm_runs.arff @@ -0,0 +1,152 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE recall_weighted NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2120,1.0,1,0.0895803866100896,ok +75193,1.0,2,0.038371068099909755,ok +2117,1.0,3,0.16709064962461995,ok +75156,1.0,4,0.21988682295877127,ok +75129,1.0,5,0.10097087378640779,ok +75243,1.0,6,0.015434985968194592,ok +75110,1.0,7,0.2743573125945129,ok +75239,1.0,8,0.0,ok +75223,1.0,9,0.30989414560380213,ok +75221,1.0,10,0.39791873141724476,ok +258,1.0,11,0.01833872707659112,ok +75121,1.0,12,0.003929273084479323,ok +253,1.0,13,0.44855967078189296,ok +261,1.0,14,0.23333333333333328,ok +75240,1.0,15,0.022020725388601003,ok +75120,1.0,16,0.03929273084479368,ok +75124,1.0,17,0.09121395036887991,ok +75176,1.0,18,0.016590808985464722,ok +75103,1.0,19,0.008000000000000007,ok +75207,1.0,20,0.161849710982659,ok +75095,1.0,21,0.016917293233082664,ok +273,1.0,22,0.044795783926218746,ok +75174,1.0,23,0.11705240755520085,ok +75153,1.0,24,0.12134665186829452,ok +75093,1.0,25,0.17483296213808464,ok +75119,1.0,26,0.035363457760314354,ok +75201,1.0,27,0.0808678500986193,ok +75215,1.0,28,0.028234649122807043,ok +75172,1.0,29,0.09090909090909094,ok +75169,1.0,30,0.03420132141469101,ok +75202,1.0,31,0.20329670329670335,ok +75233,1.0,32,0.06511283758786535,ok +75231,1.0,33,0.19924098671726753,ok +75196,1.0,34,0.023498694516971286,ok +248,1.0,35,0.26515151515151514,ok +75191,1.0,36,0.12370055975887306,ok +75217,1.0,37,0.0,ok +260,1.0,38,0.02657807308970095,ok +75115,1.0,39,0.017681728880157177,ok +75123,1.0,40,0.32728592162554426,ok +75108,1.0,41,0.0018373909049149706,ok +75101,1.0,42,0.28272963283471386,ok +75192,1.0,43,0.48305752561071713,ok +75232,1.0,44,0.14655172413793105,ok +75173,1.0,45,0.11751592356687901,ok +75197,1.0,46,0.13300492610837433,ok +266,1.0,47,0.03280839895013121,ok +75148,1.0,48,0.19024390243902434,ok +75150,1.0,49,0.3204747774480712,ok +75100,1.0,50,0.00379609544468551,ok +75178,1.0,51,0.8079287243594171,ok +75236,1.0,52,0.0323809523809524,ok +75179,1.0,53,0.17943026267110618,ok +75213,1.0,54,0.05249343832021003,ok +2123,1.0,55,0.05882352941176472,ok +75227,1.0,56,0.10151430173864273,ok +75184,1.0,57,0.13967500456454263,ok +75142,1.0,58,0.0813201516390396,ok +236,1.0,59,0.042424242424242475,ok +2122,1.0,60,0.2743573125945129,ok +75188,1.0,61,0.24319066147859925,ok +75166,1.0,62,0.0995190529041805,ok +75181,1.0,63,0.0,ok +75133,1.0,64,0.005123278898495065,ok +75134,1.0,65,0.10170395014980793,ok +75198,1.0,66,0.12143310082435,ok +262,1.0,67,0.0027570995312931057,ok +75234,1.0,68,0.05978705978705978,ok +75139,1.0,69,0.012121212121212088,ok +252,1.0,70,0.1636363636363637,ok +75117,1.0,71,0.06679764243614927,ok +75113,1.0,72,0.006526315789473713,ok +75098,1.0,73,0.027575757575757587,ok +246,1.0,74,0.024242424242424288,ok +75203,1.0,75,0.09555345316934716,ok +75237,1.0,76,0.00039570658356824495,ok +75195,1.0,77,0.0015609901137292326,ok +75171,1.0,78,0.1672216056233814,ok +75128,1.0,79,0.02218114602587795,ok +75096,1.0,80,0.3974640209074891,ok +75250,1.0,81,0.34347287891393896,ok +75146,1.0,82,0.12210711924178974,ok +75116,1.0,83,0.00982318271119842,ok +75157,1.0,84,0.4192200557103064,ok +75187,1.0,85,0.027846027846027854,ok +2350,1.0,86,0.3744580607974338,ok +242,1.0,87,0.01666666666666672,ok +244,1.0,88,0.1106060606060606,ok +75125,1.0,89,0.035363457760314354,ok +75185,1.0,90,0.12816966343937297,ok +75163,1.0,91,0.060374149659863985,ok +75177,1.0,92,0.019292604501607746,ok +75189,1.0,93,0.019401337253296624,ok +75244,1.0,94,0.06339958875942431,ok +75219,1.0,95,0.08375480477442854,ok +75222,1.0,96,0.04524886877828049,ok +75159,1.0,97,0.06849315068493156,ok +75175,1.0,98,0.11701659080898541,ok +75109,1.0,99,0.34315531000613875,ok +254,1.0,100,0.0,ok +75105,1.0,101,0.018121212121212094,ok +75106,1.0,102,0.07212121212121214,ok +75212,1.0,103,0.27738927738927743,ok +75099,1.0,104,0.12374100719424463,ok +75248,1.0,105,0.10040485829959511,ok +233,1.0,106,0.015180265654648917,ok +75235,1.0,107,0.0016666666666667052,ok +75226,1.0,108,0.004259202920596339,ok +75132,1.0,109,0.051244509516837455,ok +75127,1.0,110,0.3345075170228544,ok +251,1.0,111,0.02631578947368418,ok +75161,1.0,112,0.08280680889021041,ok +75143,1.0,113,0.010471204188481686,ok +75114,1.0,114,0.023575638506876273,ok +75182,1.0,115,0.1081404628890662,ok +75112,1.0,116,0.12061822817080947,ok +75210,1.0,117,0.0,ok +75205,1.0,118,0.18110236220472442,ok +75090,1.0,119,0.10139860139860135,ok +275,1.0,120,0.03612167300380231,ok +288,1.0,121,0.14363636363636367,ok +75092,1.0,122,0.0935550935550935,ok +3043,1.0,123,0.020096463022508004,ok +75249,1.0,124,0.004823151125401881,ok +75126,1.0,125,0.0491159135559921,ok +75225,1.0,126,0.04904306220095689,ok +75141,1.0,127,0.05808361080281166,ok +75107,1.0,128,0.053212121212121266,ok +75097,1.0,129,0.05835568297419769,ok +80001,1.0,1,0.06944444444444442,ok +80003,1.0,1,0.12307692307692308,ok +80006,1.0,1,0.1875,ok +80008,1.0,1,0.2222222222222222,ok +80009,1.0,1,0.1578947368421053,ok +80010,1.0,1,0.13793103448275867,ok +80011,1.0,1,0.037735849056603765,ok +80012,1.0,1,0.045454545454545414,ok +80013,1.0,1,0.0,ok +80014,1.0,1,0.045454545454545414,ok +80015,1.0,1,0.09523809523809523,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/recall_weighted_binary.classification_sparse/configurations.csv b/metalearning/metalearning_files/recall_weighted_binary.classification_sparse/configurations.csv new file mode 100755 index 0000000..2e88382 --- /dev/null +++ b/metalearning/metalearning_files/recall_weighted_binary.classification_sparse/configurations.csv @@ -0,0 +1,141 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,preprocessor:truncatedSVD:target_dim,rescaling:__choice__ +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7249853037185638,None,0.0,1.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,median,extra_trees_preproc_for_classification,False,gini,None,0.9424908623661876,None,0.0,7.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.6682079659377479,None,0.0,4.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,mean,extra_trees_preproc_for_classification,True,entropy,None,0.5552350997943013,None,0.0,8.0,5.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7974565919616314,None,0.0,12.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,median,extra_trees_preproc_for_classification,True,entropy,None,0.9772091846790169,None,0.0,10.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2538107344750156,False,True,hinge,1.5099542326343014e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,8532.0,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.034465366914659866,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.12057775675278172,deviance,10.0,0.8011153303489733,None,0.0,2.0,16.0,0.0,370.0,0.6078295352200873,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,mean,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0007038280350320558,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4138778052607317,0.7995003430482459,5.0,5.430044692638861,poly,-1.0,True,0.02455501006004393,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,31,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.0010015637584068037,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.037611630308856295,deviance,5.0,0.8840126779516314,None,0.0,10.0,2.0,0.0,444.0,0.7599997167603434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,most_frequent,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1751.4736515133566,0.62404114475118,3.0,1.6087076997410432,poly,-1.0,False,3.5353792826856045e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,mean,kernel_pca,,,,,,,,,,,,,,cosine,1198.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.8657388713119849,,1.0,None,0.0,19.0,13.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828776,None,0.0,2.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.027324741616523342,deviance,10.0,0.8623781459430139,None,0.0,10.0,20.0,0.0,329.0,0.8595750155424215,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,mean,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1100.6211008501205,0.5921425829232616,2.0,0.0337546254878617,poly,-1.0,True,0.09641299736884308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.9348409326933208,rbf,-1.0,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,mean,kernel_pca,,,,,,,,,,,,,,cosine,1754.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3428971645958825,,,0.2229870623330047,rbf,-1.0,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00016781524591321165,True,True,squared_hinge,1.5119200923218881e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.556190405302504,False,True,1.0,squared_hinge,ovr,l2,0.0007318628304090552,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,most_frequent,kitchen_sinks,,,,,,,,,,,,,,,,3.5602014542183973,948.0,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4047.618729304337,,,2.0237366768707754,rbf,-1.0,True,0.04369127828878843,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,one_hot_encoding,0.010000000000000005,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10.0,1.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,,none +weighting,one_hot_encoding,0.0008685602750053468,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9896334290292654,None,0.0,11.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,141.76310800864283,False,True,1.0,squared_hinge,ovr,l1,0.004317884655117431,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,,normalize +none,one_hot_encoding,0.003443779683191071,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.004980497345831963,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1255.9137433589426,,,0.08351549479967445,rbf,-1.0,True,0.00017919875199222518,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1.0,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.3451627750042988,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.3163640203509378,None,0.0,17.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,median,extra_trees_preproc_for_classification,False,gini,None,0.8916956785028156,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,85,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,86,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0019686649916896212,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.04641055832142541,True,True,hinge,8.540468968077405e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87,mean,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,911.0,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1.0,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,89,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,90,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,91,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,93,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,95,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,96,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,97,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,98,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,99,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,100,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,102,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,103,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,104,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,105,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,106,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.006372860318416312,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6467376360604045,None,0.0,1.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,107,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,108,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,109,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3823734947460288,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,110,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,adaboost,SAMME,0.11042308042695524,5.0,117.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,111,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,112,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,113,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,115,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,116,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,117,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,118,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.20202014999292287,False,True,1.0,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,119,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,120,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,121,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,122,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,123,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,124,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,125,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,126,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,127,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,129,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.04329010470998136,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7260331062271381,None,0.0,7.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,134,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.714869937384006,None,0.0,8.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/metalearning/metalearning_files/recall_weighted_binary.classification_sparse/description.txt b/metalearning/metalearning_files/recall_weighted_binary.classification_sparse/description.txt new file mode 100755 index 0000000..7d775c3 --- /dev/null +++ b/metalearning/metalearning_files/recall_weighted_binary.classification_sparse/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: recall_weighted +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/recall_weighted_binary.classification_sparse/feature_costs.arff b/metalearning/metalearning_files/recall_weighted_binary.classification_sparse/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/recall_weighted_binary.classification_sparse/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/recall_weighted_binary.classification_sparse/feature_runstatus.arff b/metalearning/metalearning_files/recall_weighted_binary.classification_sparse/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/recall_weighted_binary.classification_sparse/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/recall_weighted_binary.classification_sparse/feature_values.arff b/metalearning/metalearning_files/recall_weighted_binary.classification_sparse/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/recall_weighted_binary.classification_sparse/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/recall_weighted_binary.classification_sparse/readme.txt b/metalearning/metalearning_files/recall_weighted_binary.classification_sparse/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/recall_weighted_multiclass.classification_dense/algorithm_runs.arff b/metalearning/metalearning_files/recall_weighted_multiclass.classification_dense/algorithm_runs.arff new file mode 100755 index 0000000..41354b5 --- /dev/null +++ b/metalearning/metalearning_files/recall_weighted_multiclass.classification_dense/algorithm_runs.arff @@ -0,0 +1,152 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE recall_weighted NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2120,1.0,1,0.07967939651107969,ok +75193,1.0,2,0.038371068099909755,ok +2117,1.0,3,0.16709064962461995,ok +75156,1.0,4,0.20291026677445434,ok +75129,1.0,5,0.10097087378640779,ok +75243,1.0,6,0.0,ok +75110,1.0,7,0.11622380643767549,ok +75239,1.0,8,0.0,ok +75223,1.0,9,0.10304601425793913,ok +75221,1.0,10,0.39791873141724476,ok +258,1.0,11,0.009708737864077666,ok +75121,1.0,12,0.0,ok +253,1.0,13,0.44855967078189296,ok +261,1.0,14,0.23333333333333328,ok +75240,1.0,15,0.022020725388601003,ok +75120,1.0,16,0.03929273084479368,ok +75124,1.0,17,0.09121395036887991,ok +75176,1.0,18,0.01541623843782114,ok +75103,1.0,19,0.005894736842105286,ok +75207,1.0,20,0.161849710982659,ok +75095,1.0,21,0.016917293233082664,ok +273,1.0,22,0.04413702239789197,ok +75174,1.0,23,0.11705240755520085,ok +75153,1.0,24,0.08028116907140215,ok +75093,1.0,25,0.17483296213808464,ok +75119,1.0,26,0.035363457760314354,ok +75201,1.0,27,0.0808678500986193,ok +75215,1.0,28,0.027412280701754388,ok +75172,1.0,29,0.10303030303030303,ok +75169,1.0,30,0.03420132141469101,ok +75202,1.0,31,0.20329670329670335,ok +75233,1.0,32,0.060673325934147204,ok +75231,1.0,33,0.19924098671726753,ok +75196,1.0,34,0.015665796344647487,ok +248,1.0,35,0.22878787878787876,ok +75191,1.0,36,0.1289905886694962,ok +75217,1.0,37,0.0,ok +260,1.0,38,0.02657807308970095,ok +75115,1.0,39,0.017681728880157177,ok +75123,1.0,40,0.32728592162554426,ok +75108,1.0,41,0.0,ok +75101,1.0,42,0.2740140932130053,ok +75192,1.0,43,0.48305752561071713,ok +75232,1.0,44,0.12356321839080464,ok +75173,1.0,45,0.1165605095541401,ok +75197,1.0,46,0.15517241379310343,ok +266,1.0,47,0.019685039370078705,ok +75148,1.0,48,0.13560975609756099,ok +75150,1.0,49,0.2848664688427299,ok +75100,1.0,50,0.00379609544468551,ok +75178,1.0,51,0.7840550682597786,ok +75236,1.0,52,0.0323809523809524,ok +75179,1.0,53,0.17943026267110618,ok +75213,1.0,54,0.05249343832021003,ok +2123,1.0,55,0.05882352941176472,ok +75227,1.0,56,0.10151430173864273,ok +75184,1.0,57,0.10772320613474529,ok +75142,1.0,58,0.0715825466438712,ok +236,1.0,59,0.038787878787878816,ok +2122,1.0,60,0.10952689565780949,ok +75188,1.0,61,0.24319066147859925,ok +75166,1.0,62,0.0995190529041805,ok +75181,1.0,63,0.0,ok +75133,1.0,64,0.005123278898495065,ok +75134,1.0,65,0.08723783614874181,ok +75198,1.0,66,0.12143310082435,ok +262,1.0,67,0.0027570995312931057,ok +75234,1.0,68,0.024160524160524166,ok +75139,1.0,69,0.010707070707070665,ok +252,1.0,70,0.15000000000000002,ok +75117,1.0,71,0.05500982318271119,ok +75113,1.0,72,0.006526315789473713,ok +75098,1.0,73,0.027575757575757587,ok +246,1.0,74,0.010606060606060619,ok +75203,1.0,75,0.09555345316934716,ok +75237,1.0,76,0.00039570658356824495,ok +75195,1.0,77,0.0015609901137292326,ok +75171,1.0,78,0.1620421753607103,ok +75128,1.0,79,0.02218114602587795,ok +75096,1.0,80,0.005164788382626018,ok +75250,1.0,81,0.34347287891393896,ok +75146,1.0,82,0.11329072074057744,ok +75116,1.0,83,0.00982318271119842,ok +75157,1.0,84,0.4192200557103064,ok +75187,1.0,85,0.01678951678951679,ok +2350,1.0,86,0.3744580607974338,ok +242,1.0,87,0.010606060606060619,ok +244,1.0,88,0.1106060606060606,ok +75125,1.0,89,0.03339882121807469,ok +75185,1.0,90,0.12816966343937297,ok +75163,1.0,91,0.060374149659863985,ok +75177,1.0,92,0.019292604501607746,ok +75189,1.0,93,0.019401337253296624,ok +75244,1.0,94,0.06339958875942431,ok +75219,1.0,95,0.03479668217681575,ok +75222,1.0,96,0.04524886877828049,ok +75159,1.0,97,0.06849315068493156,ok +75175,1.0,98,0.09954485391278811,ok +75109,1.0,99,0.30417434008594224,ok +254,1.0,100,0.0,ok +75105,1.0,101,0.018121212121212094,ok +75106,1.0,102,0.07212121212121214,ok +75212,1.0,103,0.2517482517482518,ok +75099,1.0,104,0.12374100719424463,ok +75248,1.0,105,0.10040485829959511,ok +233,1.0,106,0.002846299810246644,ok +75235,1.0,107,0.0011111111111110628,ok +75226,1.0,108,0.0030422878004259246,ok +75132,1.0,109,0.051244509516837455,ok +75127,1.0,110,0.3330355738331199,ok +251,1.0,111,0.0,ok +75161,1.0,112,0.06437225897569321,ok +75143,1.0,113,0.010471204188481686,ok +75114,1.0,114,0.023575638506876273,ok +75182,1.0,115,0.1081404628890662,ok +75112,1.0,116,0.12061822817080947,ok +75210,1.0,117,0.0,ok +75205,1.0,118,0.18110236220472442,ok +75090,1.0,119,0.06118881118881114,ok +275,1.0,120,0.03612167300380231,ok +288,1.0,121,0.12969696969696964,ok +75092,1.0,122,0.0935550935550935,ok +3043,1.0,123,0.020096463022508004,ok +75249,1.0,124,0.003215434083601254,ok +75126,1.0,125,0.0491159135559921,ok +75225,1.0,126,0.04904306220095689,ok +75141,1.0,127,0.0540140584535701,ok +75107,1.0,128,0.053212121212121266,ok +75097,1.0,129,0.05835568297419769,ok +80001,1.0,1,0.06944444444444442,ok +80003,1.0,1,0.09230769230769231,ok +80006,1.0,1,0.09375,ok +80008,1.0,1,0.11111111111111116,ok +80009,1.0,1,0.10526315789473684,ok +80010,1.0,1,0.13793103448275867,ok +80011,1.0,1,0.037735849056603765,ok +80012,1.0,1,0.045454545454545414,ok +80013,1.0,1,0.0,ok +80014,1.0,1,0.045454545454545414,ok +80015,1.0,1,0.09523809523809523,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/recall_weighted_multiclass.classification_dense/configurations.csv b/metalearning/metalearning_files/recall_weighted_multiclass.classification_dense/configurations.csv new file mode 100755 index 0000000..e710c68 --- /dev/null +++ b/metalearning/metalearning_files/recall_weighted_multiclass.classification_dense/configurations.csv @@ -0,0 +1,141 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:fast_ica:algorithm,preprocessor:fast_ica:fun,preprocessor:fast_ica:n_components,preprocessor:fast_ica:whiten,preprocessor:feature_agglomeration:affinity,preprocessor:feature_agglomeration:linkage,preprocessor:feature_agglomeration:n_clusters,preprocessor:feature_agglomeration:pooling_func,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:pca:keep_variance,preprocessor:pca:whiten,preprocessor:polynomial:degree,preprocessor:polynomial:include_bias,preprocessor:polynomial:interaction_only,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,rescaling:__choice__,rescaling:quantile_transformer:n_quantiles,rescaling:quantile_transformer:output_distribution,rescaling:robust_scaler:q_max,rescaling:robust_scaler:q_min +weighting,one_hot_encoding,0.00011717632475982632,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.045388141846341344,deviance,10.0,0.29161769341843435,None,0.0,20.0,2.0,0.0,278.0,0.7912571599269661,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7249853037185638,None,0.0,1.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,median,extra_trees_preproc_for_classification,False,gini,None,0.9424908623661876,None,0.0,7.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.03474109838999682,deviance,4.0,0.5687034678818491,None,0.0,18.0,12.0,0.0,408.0,0.5150113945430513,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.6328939517938,f_classif,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.251097788175676,deviance,6.0,0.35679099363539235,None,0.0,13.0,11.0,0.0,157.0,0.4791732272983235,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,adaboost,SAMME,0.28738775989203896,10.0,423.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.8031499675923353,0.13579938270386765 +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.14159526341015916,deviance,7.0,0.8010488230155749,None,0.0,3.0,20.0,0.0,401.0,0.8073562440607731,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.002385546176068135,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.7729472304882845,,,0.0004789329856033374,rbf,-1.0,True,6.58869648864534e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,177.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5368752992317617,None,0.0,16.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.8382117756438685,mutual_info,,,,quantile_transformer,11480.0,normal,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9455638720565652,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,most_frequent,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8255464552647293,0.19162485555463185 +weighting,one_hot_encoding,0.18137532678800647,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9094110110427254,None,0.0,7.0,12.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,195.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.6682079659377479,None,0.0,4.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,mean,extra_trees_preproc_for_classification,True,entropy,None,0.5552350997943013,None,0.0,8.0,5.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.02345017287074443,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.053517066400173056,deviance,10.0,0.542144980834302,None,0.0,20.0,13.0,0.0,233.0,0.7398539900055563,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0614425536709615,fwe,f_classif,robust_scaler,,,0.9523118062307264,0.13434811490315818 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.040936424602789435,deviance,7.0,0.5495014745530306,None,0.0,20.0,18.0,0.0,141.0,0.6905343807995293,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7974565919616314,None,0.0,12.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,median,extra_trees_preproc_for_classification,True,entropy,None,0.9772091846790169,None,0.0,10.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2538107344750156,False,True,hinge,1.5099542326343014e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,8532.0,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.1958974686405233,deviance,5.0,0.33885235607979314,None,0.0,6.0,4.0,0.0,125.0,0.9448890820738562,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2538107344750156,False,True,hinge,1.5099542326343014e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,8532.0,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,0.0007038280350320558,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4138778052607317,0.7995003430482459,5.0,5.430044692638861,poly,-1.0,True,0.02455501006004393,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,31,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.010000000000000005,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.0518326156691958,deviance,6.0,0.8807456180216267,None,0.0,7.0,19.0,0.0,366.0,0.7314831276137047,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,,False,adaboost,SAMME,0.4391375941344922,3.0,386.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7439738358430176,0.20581080574615795 +none,one_hot_encoding,0.00011294596229850895,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7515.1213255144885,0.9576762936062476,3.0,0.019002536385919932,poly,-1.0,False,0.010632086351533369,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,51.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,97282.0,normal,, +none,one_hot_encoding,0.00012586572428922356,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5240592829918601,None,0.0,10.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1751.4736515133566,0.62404114475118,3.0,1.6087076997410432,poly,-1.0,False,3.5353792826856045e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1198.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.2422926485206341,,1.0,None,0.0,15.0,9.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.02102683283349326,deviance,10.0,0.2797288369369436,None,0.0,14.0,9.0,0.0,480.0,0.5778972273820631,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58.88123233170863,mutual_info,,,,robust_scaler,,,0.75,0.25 +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828776,None,0.0,2.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2155613360930585,deviance,4.0,0.3198803116198433,None,0.0,8.0,13.0,0.0,275.0,0.28870176110739404,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,mean,fast_ica,,,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.7062102387181676,None,0.0,1.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,most_frequent,fast_ica,,,,,,,,,,,parallel,exp,100.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7065776353150109,0.23782974987118105 +none,one_hot_encoding,0.16334152321884812,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00011572473434870852,True,True,squared_hinge,0.00019678754114665057,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.045742431094098604,fpr,chi2,none,,,, +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.3795924768593385,deviance,2.0,0.33708497069988536,None,0.0,15.0,13.0,0.0,451.0,0.7716323242090217,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2573946506994812,fwe,f_classif,quantile_transformer,1000.0,uniform,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.609975998293528,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,most_frequent,fast_ica,,,,,,,,,,,parallel,logcosh,2000.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8430415644014919,0.2863750565331575 +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.39536192447534535,None,0.0,19.0,3.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5.0,None,11.0,11.0,1.0,12.0,,,,,,robust_scaler,,,0.8928631650245873,0.1581877760687084 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.3126027672745337,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,870.2240970463429,0.5325949351918051,3.0,0.010682839357128344,poly,-1.0,False,2.485160860440657e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4608103694360143,fdr,f_classif,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.9348409326933208,rbf,-1.0,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1754.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,198.72528686512538,False,True,1.0,squared_hinge,ovr,l2,0.026260652523566803,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,robust_scaler,,,0.913511520078368,0.2742229325455444 +none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9260795160807372,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.03644212536682547,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.05965671477918361,deviance,8.0,0.4858133247974158,None,0.0,14.0,7.0,0.0,480.0,0.5726186552917335,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.15318294164619112 +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18887.81504976871,,,0.23283562663398755,rbf,-1.0,True,2.3839685780861318e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3937607155568376,fwe,chi2,robust_scaler,,,0.941018778984854,0.2144110585080491 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3428971645958825,,,0.2229870623330047,rbf,-1.0,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.8138233157708883,None,0.0,2.0,9.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54.27504292568562,f_classif,,,,minmax,,,, +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00016781524591321165,True,True,squared_hinge,1.5119200923218881e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.556190405302504,False,True,1.0,squared_hinge,ovr,l2,0.0007318628304090552,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,most_frequent,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,,,3.5602014542183973,948.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +weighting,one_hot_encoding,0.00034835629696198427,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8245132980938538,0.08947420373097192 +weighting,one_hot_encoding,0.00016967940959070708,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9439080311935252,None,0.0,2.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,170.0,None,,0.014191958374153584,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,,False,adaboost,SAMME.R,0.8220362681234727,5.0,183.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.363262678765884,fdr,chi2,robust_scaler,,,0.8826612080363588,0.2189605310171097 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,normalize,,,, +none,one_hot_encoding,,False,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6396026761675004,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.06544340428506021,fwe,f_classif,none,,,, +weighting,one_hot_encoding,0.004980497345831963,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1255.9137433589426,,,0.08351549479967445,rbf,-1.0,True,0.00017919875199222518,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1.0,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.3837398524575939,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.018356703878357986,deviance,3.0,0.9690352514774068,None,0.0,12.0,3.0,0.0,234.0,0.3870344708308441,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,most_frequent,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6864970915330799,False,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5670424455696162,None,0.0,8.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,standardize,,,, +weighting,one_hot_encoding,0.00031737236118003484,True,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,244.0,None,,2.3065111488706024e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,85,mean,fast_ica,,,,,,,,,,,deflation,exp,1862.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7851234479882973,0.2237528085136715 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,86,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,156.0,auto,,0.0001987333852871589,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87,mean,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1.0,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,no_encoding,,,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.96834823420249e-05,False,True,hinge,0.00016639250831671168,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,89,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.628430584359224,mutual_info,,,,quantile_transformer,6634.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,90,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,91,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,93,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2636201374253461,deviance,7.0,0.8344964130784466,None,0.0,9.0,2.0,0.0,298.0,0.7517549950523315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,95,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,96,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,97,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.07463196642416367,deviance,7.0,0.8603242247379981,None,0.0,2.0,6.0,0.0,500.0,0.8447665577491962,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,98,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.0433556140045585,deviance,10.0,0.33000096635982235,None,0.0,15.0,13.0,0.0,388.0,0.8291104221904706,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,99,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,robust_scaler,,,0.7496393440951183,0.2853682991120835 +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,100,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,102,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0020580843703898177,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.945774573434192,None,0.0,19.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,103,median,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,15209.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,104,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,105,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,106,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.005332972692819521,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5565918060287016,None,0.0,5.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,107,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,cosine,complete,173.0,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9527068489270144,0.04135311355893583 +weighting,one_hot_encoding,0.001279467383882126,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,108,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0903354518102121,fwe,f_classif,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,109,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.002615346832354839,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7884268823432835,None,0.0,20.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,110,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +weighting,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56.086963007482865,,,0.013609964993119377,rbf,-1.0,True,0.00196831255706268,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,111,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.15374716583918388 +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.2472984547885781,deviance,3.0,0.6564306719064884,None,0.0,15.0,14.0,0.0,220.0,0.8082564085714649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,112,median,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,332.0,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,113,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,115,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,116,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,117,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,118,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.20202014999292287,False,True,1.0,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,,,,,none,,,, +none,no_encoding,,,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6390376923528961,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,119,median,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,164.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,62508.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,120,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.27041927277584e-06,True,0.00010000000000000009,0.033157325660763994,True,0.0008114527992546482,invscaling,modified_huber,elasticnet,0.13714427818877545,0.055179642772545036,,,,,,,,,,,,,,,,,,121,median,fast_ica,,,,,,,,,,,parallel,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,122,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,123,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.19998727075532635,deviance,10.0,0.9377656718112952,None,0.0,7.0,13.0,0.0,214.0,0.6062346326014357,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,124,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,125,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,126,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.5765793990908161,None,0.0,11.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,127,median,fast_ica,,,,,,,,,,,deflation,exp,10.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,129,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.04651467280022463,True,adaboost,SAMME,0.6506122230393502,6.0,143.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,extra_trees_preproc_for_classification,False,entropy,None,0.348720215832657,None,0.0,17.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,26025.0,normal,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3386310102795006,None,0.0,6.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,True,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.0,None,,5.861221938384061e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.22985730375167915,fpr,f_classif,quantile_transformer,40589.0,uniform,, +none,no_encoding,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00012437731009611307,True,,0.08709904468181932,True,,invscaling,squared_hinge,l1,0.6231564675290102,0.008071279833697563,,,,,,,,,,,,,,,,,,134,median,fast_ica,,,,,,,,,,,parallel,logcosh,814.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.714869937384006,None,0.0,8.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, diff --git a/metalearning/metalearning_files/recall_weighted_multiclass.classification_dense/description.txt b/metalearning/metalearning_files/recall_weighted_multiclass.classification_dense/description.txt new file mode 100755 index 0000000..7d775c3 --- /dev/null +++ b/metalearning/metalearning_files/recall_weighted_multiclass.classification_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: recall_weighted +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/recall_weighted_multiclass.classification_dense/feature_costs.arff b/metalearning/metalearning_files/recall_weighted_multiclass.classification_dense/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/recall_weighted_multiclass.classification_dense/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/recall_weighted_multiclass.classification_dense/feature_runstatus.arff b/metalearning/metalearning_files/recall_weighted_multiclass.classification_dense/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/recall_weighted_multiclass.classification_dense/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/recall_weighted_multiclass.classification_dense/feature_values.arff b/metalearning/metalearning_files/recall_weighted_multiclass.classification_dense/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/recall_weighted_multiclass.classification_dense/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/recall_weighted_multiclass.classification_dense/readme.txt b/metalearning/metalearning_files/recall_weighted_multiclass.classification_dense/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/recall_weighted_multiclass.classification_sparse/algorithm_runs.arff b/metalearning/metalearning_files/recall_weighted_multiclass.classification_sparse/algorithm_runs.arff new file mode 100755 index 0000000..e50138e --- /dev/null +++ b/metalearning/metalearning_files/recall_weighted_multiclass.classification_sparse/algorithm_runs.arff @@ -0,0 +1,152 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE recall_weighted NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2120,1.0,1,0.0895803866100896,ok +75193,1.0,2,0.038371068099909755,ok +2117,1.0,3,0.16709064962461995,ok +75156,1.0,4,0.21988682295877127,ok +75129,1.0,5,0.10097087378640779,ok +75243,1.0,6,0.015434985968194592,ok +75110,1.0,7,0.2743573125945129,ok +75239,1.0,8,0.0,ok +75223,1.0,9,0.30989414560380213,ok +75221,1.0,10,0.39791873141724476,ok +258,1.0,11,0.01833872707659112,ok +75121,1.0,12,0.003929273084479323,ok +253,1.0,13,0.44855967078189296,ok +261,1.0,14,0.23333333333333328,ok +75240,1.0,15,0.022020725388601003,ok +75120,1.0,16,0.03929273084479368,ok +75124,1.0,17,0.09121395036887991,ok +75176,1.0,18,0.016590808985464722,ok +75103,1.0,19,0.008000000000000007,ok +75207,1.0,20,0.161849710982659,ok +75095,1.0,21,0.016917293233082664,ok +273,1.0,22,0.044795783926218746,ok +75174,1.0,23,0.11705240755520085,ok +75153,1.0,24,0.12134665186829452,ok +75093,1.0,25,0.17483296213808464,ok +75119,1.0,26,0.035363457760314354,ok +75201,1.0,27,0.0808678500986193,ok +75215,1.0,28,0.028234649122807043,ok +75172,1.0,29,0.09090909090909094,ok +75169,1.0,30,0.03420132141469101,ok +75202,1.0,31,0.20329670329670335,ok +75233,1.0,32,0.06511283758786535,ok +75231,1.0,33,0.19924098671726753,ok +75196,1.0,34,0.023498694516971286,ok +248,1.0,35,0.26515151515151514,ok +75191,1.0,36,0.12370055975887306,ok +75217,1.0,37,0.0,ok +260,1.0,38,0.02657807308970095,ok +75115,1.0,39,0.017681728880157177,ok +75123,1.0,40,0.32728592162554426,ok +75108,1.0,41,0.0018373909049149706,ok +75101,1.0,42,0.28272963283471386,ok +75192,1.0,43,0.48305752561071713,ok +75232,1.0,44,0.14655172413793105,ok +75173,1.0,45,0.11751592356687901,ok +75197,1.0,46,0.13300492610837433,ok +266,1.0,47,0.03280839895013121,ok +75148,1.0,48,0.19024390243902434,ok +75150,1.0,49,0.3204747774480712,ok +75100,1.0,50,0.00379609544468551,ok +75178,1.0,51,0.8079287243594171,ok +75236,1.0,52,0.0323809523809524,ok +75179,1.0,53,0.17943026267110618,ok +75213,1.0,54,0.05249343832021003,ok +2123,1.0,55,0.05882352941176472,ok +75227,1.0,56,0.10151430173864273,ok +75184,1.0,57,0.13967500456454263,ok +75142,1.0,58,0.0813201516390396,ok +236,1.0,59,0.042424242424242475,ok +2122,1.0,60,0.2743573125945129,ok +75188,1.0,61,0.24319066147859925,ok +75166,1.0,62,0.0995190529041805,ok +75181,1.0,63,0.0,ok +75133,1.0,64,0.005123278898495065,ok +75134,1.0,65,0.10170395014980793,ok +75198,1.0,66,0.12143310082435,ok +262,1.0,67,0.0027570995312931057,ok +75234,1.0,68,0.05978705978705978,ok +75139,1.0,69,0.012121212121212088,ok +252,1.0,70,0.1636363636363637,ok +75117,1.0,71,0.06679764243614927,ok +75113,1.0,72,0.006526315789473713,ok +75098,1.0,73,0.027575757575757587,ok +246,1.0,74,0.024242424242424288,ok +75203,1.0,75,0.09555345316934716,ok +75237,1.0,76,0.00039570658356824495,ok +75195,1.0,77,0.0015609901137292326,ok +75171,1.0,78,0.1672216056233814,ok +75128,1.0,79,0.02218114602587795,ok +75096,1.0,80,0.3974640209074891,ok +75250,1.0,81,0.34347287891393896,ok +75146,1.0,82,0.12210711924178974,ok +75116,1.0,83,0.00982318271119842,ok +75157,1.0,84,0.4192200557103064,ok +75187,1.0,85,0.027846027846027854,ok +2350,1.0,86,0.3744580607974338,ok +242,1.0,87,0.01666666666666672,ok +244,1.0,88,0.1106060606060606,ok +75125,1.0,89,0.035363457760314354,ok +75185,1.0,90,0.12816966343937297,ok +75163,1.0,91,0.060374149659863985,ok +75177,1.0,92,0.019292604501607746,ok +75189,1.0,93,0.019401337253296624,ok +75244,1.0,94,0.06339958875942431,ok +75219,1.0,95,0.08375480477442854,ok +75222,1.0,96,0.04524886877828049,ok +75159,1.0,97,0.06849315068493156,ok +75175,1.0,98,0.11701659080898541,ok +75109,1.0,99,0.34315531000613875,ok +254,1.0,100,0.0,ok +75105,1.0,101,0.018121212121212094,ok +75106,1.0,102,0.07212121212121214,ok +75212,1.0,103,0.27738927738927743,ok +75099,1.0,104,0.12374100719424463,ok +75248,1.0,105,0.10040485829959511,ok +233,1.0,106,0.015180265654648917,ok +75235,1.0,107,0.0016666666666667052,ok +75226,1.0,108,0.004259202920596339,ok +75132,1.0,109,0.051244509516837455,ok +75127,1.0,110,0.3345075170228544,ok +251,1.0,111,0.02631578947368418,ok +75161,1.0,112,0.08280680889021041,ok +75143,1.0,113,0.010471204188481686,ok +75114,1.0,114,0.023575638506876273,ok +75182,1.0,115,0.1081404628890662,ok +75112,1.0,116,0.12061822817080947,ok +75210,1.0,117,0.0,ok +75205,1.0,118,0.18110236220472442,ok +75090,1.0,119,0.10139860139860135,ok +275,1.0,120,0.03612167300380231,ok +288,1.0,121,0.14363636363636367,ok +75092,1.0,122,0.0935550935550935,ok +3043,1.0,123,0.020096463022508004,ok +75249,1.0,124,0.004823151125401881,ok +75126,1.0,125,0.0491159135559921,ok +75225,1.0,126,0.04904306220095689,ok +75141,1.0,127,0.05808361080281166,ok +75107,1.0,128,0.053212121212121266,ok +75097,1.0,129,0.05835568297419769,ok +80001,1.0,1,0.06944444444444442,ok +80003,1.0,1,0.12307692307692308,ok +80006,1.0,1,0.1875,ok +80008,1.0,1,0.2222222222222222,ok +80009,1.0,1,0.1578947368421053,ok +80010,1.0,1,0.13793103448275867,ok +80011,1.0,1,0.037735849056603765,ok +80012,1.0,1,0.045454545454545414,ok +80013,1.0,1,0.0,ok +80014,1.0,1,0.045454545454545414,ok +80015,1.0,1,0.09523809523809523,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/recall_weighted_multiclass.classification_sparse/configurations.csv b/metalearning/metalearning_files/recall_weighted_multiclass.classification_sparse/configurations.csv new file mode 100755 index 0000000..2e88382 --- /dev/null +++ b/metalearning/metalearning_files/recall_weighted_multiclass.classification_sparse/configurations.csv @@ -0,0 +1,141 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,preprocessor:truncatedSVD:target_dim,rescaling:__choice__ +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7249853037185638,None,0.0,1.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,median,extra_trees_preproc_for_classification,False,gini,None,0.9424908623661876,None,0.0,7.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.6682079659377479,None,0.0,4.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,mean,extra_trees_preproc_for_classification,True,entropy,None,0.5552350997943013,None,0.0,8.0,5.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.7974565919616314,None,0.0,12.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,median,extra_trees_preproc_for_classification,True,entropy,None,0.9772091846790169,None,0.0,10.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.2538107344750156,False,True,hinge,1.5099542326343014e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,8532.0,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.034465366914659866,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.12057775675278172,deviance,10.0,0.8011153303489733,None,0.0,2.0,16.0,0.0,370.0,0.6078295352200873,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,mean,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0007038280350320558,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4138778052607317,0.7995003430482459,5.0,5.430044692638861,poly,-1.0,True,0.02455501006004393,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,31,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.0010015637584068037,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.037611630308856295,deviance,5.0,0.8840126779516314,None,0.0,10.0,2.0,0.0,444.0,0.7599997167603434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,most_frequent,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1751.4736515133566,0.62404114475118,3.0,1.6087076997410432,poly,-1.0,False,3.5353792826856045e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,mean,kernel_pca,,,,,,,,,,,,,,cosine,1198.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.8657388713119849,,1.0,None,0.0,19.0,13.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828776,None,0.0,2.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.027324741616523342,deviance,10.0,0.8623781459430139,None,0.0,10.0,20.0,0.0,329.0,0.8595750155424215,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,mean,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1100.6211008501205,0.5921425829232616,2.0,0.0337546254878617,poly,-1.0,True,0.09641299736884308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.9348409326933208,rbf,-1.0,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,mean,kernel_pca,,,,,,,,,,,,,,cosine,1754.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3428971645958825,,,0.2229870623330047,rbf,-1.0,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00016781524591321165,True,True,squared_hinge,1.5119200923218881e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.556190405302504,False,True,1.0,squared_hinge,ovr,l2,0.0007318628304090552,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,most_frequent,kitchen_sinks,,,,,,,,,,,,,,,,3.5602014542183973,948.0,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4047.618729304337,,,2.0237366768707754,rbf,-1.0,True,0.04369127828878843,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,one_hot_encoding,0.010000000000000005,True,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10.0,1.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,,none +weighting,one_hot_encoding,0.0008685602750053468,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9896334290292654,None,0.0,11.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,141.76310800864283,False,True,1.0,squared_hinge,ovr,l1,0.004317884655117431,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,,normalize +none,one_hot_encoding,0.003443779683191071,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.004980497345831963,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1255.9137433589426,,,0.08351549479967445,rbf,-1.0,True,0.00017919875199222518,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1.0,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.3451627750042988,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.3163640203509378,None,0.0,17.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,median,extra_trees_preproc_for_classification,False,gini,None,0.8916956785028156,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,85,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,86,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0019686649916896212,True,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.04641055832142541,True,True,hinge,8.540468968077405e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87,mean,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,cosine,911.0,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.010000000000000005,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.736566293163854,,,3.690774279954552,rbf,-1.0,True,0.03907331735692288,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,88,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,89,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,90,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,91,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,93,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,94,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,95,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,96,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,97,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,98,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,99,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,100,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,101,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,102,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,103,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,104,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,105,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,106,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.006372860318416312,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.6467376360604045,None,0.0,1.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,107,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,108,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,109,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3823734947460288,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,110,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,adaboost,SAMME,0.11042308042695524,5.0,117.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,111,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,112,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,113,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,114,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,115,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,116,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,117,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,118,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.20202014999292287,False,True,1.0,squared_hinge,ovr,l1,0.026650505297677905,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,119,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,120,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,121,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,122,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,123,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,124,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,125,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,126,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,127,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,128,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,129,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.04329010470998136,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7260331062271381,None,0.0,7.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,134,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.714869937384006,None,0.0,8.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/metalearning/metalearning_files/recall_weighted_multiclass.classification_sparse/description.txt b/metalearning/metalearning_files/recall_weighted_multiclass.classification_sparse/description.txt new file mode 100755 index 0000000..7d775c3 --- /dev/null +++ b/metalearning/metalearning_files/recall_weighted_multiclass.classification_sparse/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: recall_weighted +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/recall_weighted_multiclass.classification_sparse/feature_costs.arff b/metalearning/metalearning_files/recall_weighted_multiclass.classification_sparse/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/recall_weighted_multiclass.classification_sparse/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/recall_weighted_multiclass.classification_sparse/feature_runstatus.arff b/metalearning/metalearning_files/recall_weighted_multiclass.classification_sparse/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/recall_weighted_multiclass.classification_sparse/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/recall_weighted_multiclass.classification_sparse/feature_values.arff b/metalearning/metalearning_files/recall_weighted_multiclass.classification_sparse/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/recall_weighted_multiclass.classification_sparse/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/recall_weighted_multiclass.classification_sparse/readme.txt b/metalearning/metalearning_files/recall_weighted_multiclass.classification_sparse/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/roc_auc_binary.classification_dense/algorithm_runs.arff b/metalearning/metalearning_files/roc_auc_binary.classification_dense/algorithm_runs.arff new file mode 100755 index 0000000..89e1b67 --- /dev/null +++ b/metalearning/metalearning_files/roc_auc_binary.classification_dense/algorithm_runs.arff @@ -0,0 +1,107 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE roc_auc NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2117,1.0,1,0.08616284256544937,ok +75156,1.0,2,0.13865419989353356,ok +75129,1.0,3,0.13959960126266813,ok +75239,1.0,4,0.0,ok +75121,1.0,5,0.0,ok +261,1.0,6,0.21050330141239226,ok +75240,1.0,7,0.00889068386457914,ok +75120,1.0,8,0.08476482617586922,ok +75124,1.0,9,0.0923544529010576,ok +75176,1.0,10,0.00081713932448646,ok +75103,1.0,11,0.0010223312249904026,ok +75095,1.0,12,0.009571664052110518,ok +273,1.0,13,0.01180961426066629,ok +75174,1.0,14,0.0531511925885505,ok +75153,1.0,15,0.020717474053180585,ok +75093,1.0,16,0.25832619985746696,ok +75119,1.0,17,0.04962809917355371,ok +75215,1.0,18,0.0033248070117535278,ok +75233,1.0,19,0.01453125504243713,ok +75196,1.0,20,0.0005894590846047265,ok +75191,1.0,21,0.07894154495071293,ok +75115,1.0,22,0.034135918282259814,ok +75108,1.0,23,0.0,ok +75101,1.0,24,0.19244568752880575,ok +75192,1.0,25,0.4910967139322515,ok +75232,1.0,26,0.07526329309016189,ok +75173,1.0,27,0.04872240259740257,ok +75148,1.0,28,0.06187080217487306,ok +75150,1.0,29,0.20069082193712107,ok +75100,1.0,30,0.17174741426238438,ok +75179,1.0,31,0.11353631478705917,ok +75213,1.0,32,0.016200236499802956,ok +75227,1.0,33,0.04187745540393961,ok +75184,1.0,34,0.05408480640081892,ok +75142,1.0,35,0.015133610214270066,ok +75166,1.0,36,0.03115196894551442,ok +75133,1.0,37,0.03838204684705815,ok +75234,1.0,38,0.0024355396957419506,ok +75139,1.0,39,0.0005057172245516162,ok +75117,1.0,40,0.046539503856577014,ok +75113,1.0,41,0.0005025775647244934,ok +75237,1.0,42,4.725332067301302e-06,ok +75195,1.0,43,7.034135398931163e-06,ok +75171,1.0,44,0.08440502497815372,ok +75128,1.0,45,0.012714176579722736,ok +75146,1.0,46,0.040944896061433145,ok +75116,1.0,47,0.002713200434112162,ok +75157,1.0,48,0.4334669811320755,ok +75187,1.0,49,0.0012636634024824067,ok +2350,1.0,50,0.41812392128985587,ok +75125,1.0,51,0.04066905921558572,ok +75185,1.0,52,0.05301802929867516,ok +75163,1.0,53,0.025278851960237825,ok +75177,1.0,54,0.0033793711431089335,ok +75189,1.0,55,0.003182247087857304,ok +75244,1.0,56,0.10568095143930611,ok +75219,1.0,57,0.005379910856541437,ok +75222,1.0,58,0.03830357142857144,ok +75159,1.0,59,0.1071428571428571,ok +75175,1.0,60,0.03417611353704009,ok +254,1.0,61,0.0,ok +75105,1.0,62,0.20474684765938922,ok +75106,1.0,63,0.2676929726822146,ok +75212,1.0,64,0.1797887140248674,ok +75099,1.0,65,0.15456081081081097,ok +75248,1.0,66,0.14541815798388635,ok +233,1.0,67,0.00010493559125790419,ok +75226,1.0,68,0.0001951906715831342,ok +75132,1.0,69,0.308963011548568,ok +75127,1.0,70,0.2742794807110678,ok +75161,1.0,71,0.013489738650252936,ok +75143,1.0,72,0.006682105466299992,ok +75114,1.0,73,0.014331282752335306,ok +75182,1.0,74,0.05289115303093994,ok +75112,1.0,75,0.06029259702749379,ok +75210,1.0,76,0.0,ok +75092,1.0,77,0.04926483772637613,ok +3043,1.0,78,0.0033793711431089335,ok +75249,1.0,79,0.0004941767282828913,ok +75126,1.0,80,0.026899761853215076,ok +75225,1.0,81,0.07447652284263961,ok +75141,1.0,82,0.008768207852483001,ok +75107,1.0,83,0.13364221677792265,ok +75097,1.0,84,0.15388423113578398,ok +80001,1.0,1,0.06773399014778314,ok +80003,1.0,1,0.04213187276174435,ok +80006,1.0,1,0.027450980392156876,ok +80008,1.0,1,0.04999999999999993,ok +80009,1.0,1,0.05555555555555558,ok +80010,1.0,1,0.16666666666666663,ok +80011,1.0,1,0.006191950464396245,ok +80012,1.0,1,0.008928571428571397,ok +80013,1.0,1,0.0,ok +80014,1.0,1,0.05833333333333335,ok +80015,1.0,1,0.028846153846153855,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/roc_auc_binary.classification_dense/configurations.csv b/metalearning/metalearning_files/roc_auc_binary.classification_dense/configurations.csv new file mode 100755 index 0000000..984b09d --- /dev/null +++ b/metalearning/metalearning_files/roc_auc_binary.classification_dense/configurations.csv @@ -0,0 +1,96 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:fast_ica:algorithm,preprocessor:fast_ica:fun,preprocessor:fast_ica:n_components,preprocessor:fast_ica:whiten,preprocessor:feature_agglomeration:affinity,preprocessor:feature_agglomeration:linkage,preprocessor:feature_agglomeration:n_clusters,preprocessor:feature_agglomeration:pooling_func,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:pca:keep_variance,preprocessor:pca:whiten,preprocessor:polynomial:degree,preprocessor:polynomial:include_bias,preprocessor:polynomial:interaction_only,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,rescaling:__choice__,rescaling:quantile_transformer:n_quantiles,rescaling:quantile_transformer:output_distribution,rescaling:robust_scaler:q_max,rescaling:robust_scaler:q_min +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.12713527337147906,deviance,4.0,0.6041596127474019,None,0.0,14.0,17.0,0.0,83.0,0.8426859880999615,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.018505477121829747,deviance,7.0,0.4568365303752941,None,0.0,10.0,2.0,0.0,484.0,0.5253264455070624,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,quantile_transformer,79618.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.4909422458748719,None,0.0,11.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,mean,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7146659106968425,True,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9455638720565652,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,most_frequent,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8255464552647293,0.19162485555463185 +weighting,one_hot_encoding,0.18137532678800647,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9094110110427254,None,0.0,7.0,12.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,195.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.0009580347867777607,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,0.10000000000000006,rbf,-1.0,True,0.0010000000000000002,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.35040453084365497,False,True,1.0,squared_hinge,ovr,l1,0.006810889378452772,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.02345017287074443,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.053517066400173056,deviance,10.0,0.542144980834302,None,0.0,20.0,13.0,0.0,233.0,0.7398539900055563,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0614425536709615,fwe,f_classif,robust_scaler,,,0.9523118062307264,0.13434811490315818 +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.8149627329153046,None,0.0,15.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.040936424602789435,deviance,7.0,0.5495014745530306,None,0.0,20.0,18.0,0.0,141.0,0.6905343807995293,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.9727149851116396,None,0.0,10.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,25.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,multinomial_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.8868217696423089,None,0.0,20.0,13.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,9957.0,uniform,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.1958974686405233,deviance,5.0,0.33885235607979314,None,0.0,6.0,4.0,0.0,125.0,0.9448890820738562,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,0.010000000000000005,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.0518326156691958,deviance,6.0,0.8807456180216267,None,0.0,7.0,19.0,0.0,366.0,0.7314831276137047,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.7464505951074157,None,0.0,6.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,most_frequent,fast_ica,,,,,,,,,,,parallel,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9504673483378582,0.13375455137243772 +none,one_hot_encoding,0.00012586572428922356,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5240592829918601,None,0.0,10.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.2422926485206341,,1.0,None,0.0,15.0,9.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.022939738050158573,deviance,10.0,0.4185394344134278,None,0.0,2.0,10.0,0.0,309.0,0.5979695608086252,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.25383213391991144 +weighting,no_encoding,,,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,0.3170009238992427,rbf,1955.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8333938697866604,0.10426506601169797 +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9412423746065944,None,0.0,9.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.7702464686370823,0.17046298103332982 +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.014398770417266825,deviance,5.0,0.3847309634051567,None,0.0,13.0,4.0,0.0,369.0,0.7446964555890218,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7509814655573623,0.05673098788555319 +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.609975998293528,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,most_frequent,fast_ica,,,,,,,,,,,parallel,logcosh,2000.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8430415644014919,0.2863750565331575 +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.39536192447534535,None,0.0,19.0,3.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5.0,None,11.0,11.0,1.0,12.0,,,,,,robust_scaler,,,0.8928631650245873,0.1581877760687084 +weighting,one_hot_encoding,0.3451627750042988,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.3163640203509378,None,0.0,17.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,7.0,None,6.0,20.0,1.0,47.0,,,,,,quantile_transformer,21674.0,uniform,, +weighting,no_encoding,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.1708463626384465e-06,True,,0.09722688351233316,True,,constant,squared_hinge,l2,,0.00953454743007943,,,,,,,,,,,,,,,,,,31,mean,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,272.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.9348409326933208,rbf,-1.0,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1754.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,198.72528686512538,False,True,1.0,squared_hinge,ovr,l2,0.026260652523566803,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,robust_scaler,,,0.913511520078368,0.2742229325455444 +none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9260795160807372,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3428971645958825,,,0.2229870623330047,rbf,-1.0,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.8954806456480866,None,0.0,18.0,13.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,fast_ica,,,,,,,,,,,deflation,cube,45.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.00034835629696198427,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8245132980938538,0.08947420373097192 +weighting,one_hot_encoding,0.00016967940959070708,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9439080311935252,None,0.0,2.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.03528169333197684,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3416063836589199,None,0.0,9.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,median,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1.0,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.904983674005564,None,0.0,18.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,mean,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,275.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.4932996544760619,None,0.0,2.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,mean,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,340.0,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.12713527337147906,deviance,4.0,0.6041596127474019,None,0.0,14.0,17.0,0.0,83.0,0.8426859880999615,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.4421938468644326,deviance,5.0,0.5709932933214351,None,0.0,15.0,8.0,0.0,155.0,0.4040373361127008,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.03741851720151596,fwe,f_classif,robust_scaler,,,0.95547292996163,0.03028628950622218 +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.00031737236118003484,True,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,244.0,None,,2.3065111488706024e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,mean,fast_ica,,,,,,,,,,,deflation,exp,1862.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7851234479882973,0.2237528085136715 +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.35533396539961937,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4165632766388807,fpr,chi2,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.9342950927678112,None,0.0,20.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.5855957814188109,None,0.0,17.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,109.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2636201374253461,deviance,7.0,0.8344964130784466,None,0.0,9.0,2.0,0.0,298.0,0.7517549950523315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.2263596964804377,True,adaboost,SAMME,0.15143691959318842,2.0,233.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.07951518163998639,fwe,f_classif,minmax,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.07463196642416367,deviance,7.0,0.8603242247379981,None,0.0,2.0,6.0,0.0,500.0,0.8447665577491962,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3721522140614508,0.3541746628756037,3.0,0.0009148519644429074,poly,-1.0,False,2.916672898330067e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,37866.0,normal,, +weighting,one_hot_encoding,0.010000000000000005,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.02674155532989549,deviance,3.0,0.14973922320166708,None,0.0,7.0,18.0,0.0,309.0,0.35532673462283193,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.0020580843703898177,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.945774573434192,None,0.0,19.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,median,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,15209.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,0.00013442810992750476,True,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1.0,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,median,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,8.0,None,13.0,16.0,1.0,28.0,,,,,,quantile_transformer,41502.0,uniform,, +weighting,one_hot_encoding,0.002615346832354839,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7884268823432835,None,0.0,20.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.2472984547885781,deviance,3.0,0.6564306719064884,None,0.0,15.0,14.0,0.0,220.0,0.8082564085714649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,median,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,332.0,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.03905145156995541,deviance,5.0,0.2281306656230014,None,0.0,14.0,13.0,0.0,493.0,0.8793075442604774,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,25382.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0733000338152003,False,True,1.0,squared_hinge,ovr,l2,0.033752542733220474,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.6840756728731969,,0.00980445380551526,sigmoid,161.0,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2806.9858667073186,False,True,1.0,squared_hinge,ovr,l2,0.03738539536055984,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0006079518254197428,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5357045097570147,None,0.0,19.0,13.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.06121586732873325,False,True,1.0,squared_hinge,ovr,l1,0.00014224609210090503,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7794633670276021,None,0.0,9.0,10.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,75840.0,normal,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.2604623554399743,None,0.0,18.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.282079367058228,fpr,chi2,none,,,, +none,no_encoding,,,adaboost,SAMME,0.7603752531440509,7.0,413.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,28.04113884899283,False,True,1.0,squared_hinge,ovr,l1,0.0001084726092097629,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,26106.0,uniform,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3386310102795006,None,0.0,6.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,True,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,134,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, diff --git a/metalearning/metalearning_files/roc_auc_binary.classification_dense/description.txt b/metalearning/metalearning_files/roc_auc_binary.classification_dense/description.txt new file mode 100755 index 0000000..5bafa00 --- /dev/null +++ b/metalearning/metalearning_files/roc_auc_binary.classification_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: roc_auc +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/roc_auc_binary.classification_dense/feature_costs.arff b/metalearning/metalearning_files/roc_auc_binary.classification_dense/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/roc_auc_binary.classification_dense/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/roc_auc_binary.classification_dense/feature_runstatus.arff b/metalearning/metalearning_files/roc_auc_binary.classification_dense/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/roc_auc_binary.classification_dense/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/roc_auc_binary.classification_dense/feature_values.arff b/metalearning/metalearning_files/roc_auc_binary.classification_dense/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/roc_auc_binary.classification_dense/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/roc_auc_binary.classification_dense/readme.txt b/metalearning/metalearning_files/roc_auc_binary.classification_dense/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/roc_auc_binary.classification_sparse/algorithm_runs.arff b/metalearning/metalearning_files/roc_auc_binary.classification_sparse/algorithm_runs.arff new file mode 100755 index 0000000..9f82f3e --- /dev/null +++ b/metalearning/metalearning_files/roc_auc_binary.classification_sparse/algorithm_runs.arff @@ -0,0 +1,107 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE roc_auc NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2117,1.0,1,0.09220377468810481,ok +75156,1.0,2,0.1434003931882507,ok +75129,1.0,3,0.13959960126266813,ok +75239,1.0,4,0.0,ok +75121,1.0,5,0.0,ok +261,1.0,6,0.21050330141239226,ok +75240,1.0,7,0.00889068386457914,ok +75120,1.0,8,0.0849693251533743,ok +75124,1.0,9,0.0923544529010576,ok +75176,1.0,10,0.0011557397364430066,ok +75103,1.0,11,0.0016231167825334625,ok +75095,1.0,12,0.009571664052110518,ok +273,1.0,13,0.013420514319111732,ok +75174,1.0,14,0.05567512614515935,ok +75153,1.0,15,0.05940137470219342,ok +75093,1.0,16,0.2607596025459341,ok +75119,1.0,17,0.057107438016528955,ok +75215,1.0,18,0.005544553850291178,ok +75233,1.0,19,0.018620389195469045,ok +75196,1.0,20,0.002063106796116543,ok +75191,1.0,21,0.07504394620024568,ok +75115,1.0,22,0.034135918282259814,ok +75108,1.0,23,0.0006587258617578584,ok +75101,1.0,24,0.20634796935003008,ok +75192,1.0,25,0.49300207677096886,ok +75232,1.0,26,0.0853730138343547,ok +75173,1.0,27,0.05552313311688317,ok +75148,1.0,28,0.10632015412967,ok +75150,1.0,29,0.2536127167630058,ok +75100,1.0,30,0.17563574150400507,ok +75179,1.0,31,0.11570990058184827,ok +75213,1.0,32,0.016200236499802956,ok +75227,1.0,33,0.04187745540393961,ok +75184,1.0,34,0.08839169963333282,ok +75142,1.0,35,0.02305385448270303,ok +75166,1.0,36,0.03115196894551442,ok +75133,1.0,37,0.08732520301369051,ok +75234,1.0,38,0.011145394542673825,ok +75139,1.0,39,0.0008095903616680555,ok +75117,1.0,40,0.05649364185949546,ok +75113,1.0,41,0.0005025775647244934,ok +75237,1.0,42,4.725332067301302e-06,ok +75195,1.0,43,7.034135398931163e-06,ok +75171,1.0,44,0.08919150063841585,ok +75128,1.0,45,0.018025391974971883,ok +75146,1.0,46,0.04520522742498323,ok +75116,1.0,47,0.005597760895641679,ok +75157,1.0,48,0.4334669811320755,ok +75187,1.0,49,0.0029007886655499915,ok +2350,1.0,50,0.41812392128985587,ok +75125,1.0,51,0.04066905921558572,ok +75185,1.0,52,0.05586348296135013,ok +75163,1.0,53,0.025278851960237825,ok +75177,1.0,54,0.0033793711431089335,ok +75189,1.0,55,0.003182247087857304,ok +75244,1.0,56,0.11933764607286468,ok +75219,1.0,57,0.02234428983391712,ok +75222,1.0,58,0.03830357142857144,ok +75159,1.0,59,0.13204747774480718,ok +75175,1.0,60,0.04566909318952361,ok +254,1.0,61,0.0,ok +75105,1.0,62,0.21099207510003404,ok +75106,1.0,63,0.3000205555769009,ok +75212,1.0,64,0.1820493870098251,ok +75099,1.0,65,0.15456081081081097,ok +75248,1.0,66,0.14541815798388635,ok +233,1.0,67,0.0005536257056014682,ok +75226,1.0,68,0.0001951906715831342,ok +75132,1.0,69,0.3460031068425532,ok +75127,1.0,70,0.27651568685596795,ok +75161,1.0,71,0.020809752128342796,ok +75143,1.0,72,0.006682105466299992,ok +75114,1.0,73,0.014331282752335306,ok +75182,1.0,74,0.056353538740398834,ok +75112,1.0,75,0.06029259702749379,ok +75210,1.0,76,0.0,ok +75092,1.0,77,0.04926483772637613,ok +3043,1.0,78,0.0033793711431089335,ok +75249,1.0,79,0.0004941767282828913,ok +75126,1.0,80,0.026899761853215076,ok +75225,1.0,81,0.07447652284263961,ok +75141,1.0,82,0.01085048658548371,ok +75107,1.0,83,0.14705684295631083,ok +75097,1.0,84,0.243280464359657,ok +80001,1.0,1,0.06773399014778314,ok +80003,1.0,1,0.06256583105119029,ok +80006,1.0,1,0.05882352941176472,ok +80008,1.0,1,0.04999999999999993,ok +80009,1.0,1,0.05555555555555558,ok +80010,1.0,1,0.16666666666666663,ok +80011,1.0,1,0.006191950464396245,ok +80012,1.0,1,0.008928571428571397,ok +80013,1.0,1,0.0,ok +80014,1.0,1,0.05833333333333335,ok +80015,1.0,1,0.028846153846153855,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/roc_auc_binary.classification_sparse/configurations.csv b/metalearning/metalearning_files/roc_auc_binary.classification_sparse/configurations.csv new file mode 100755 index 0000000..d172fb3 --- /dev/null +++ b/metalearning/metalearning_files/roc_auc_binary.classification_sparse/configurations.csv @@ -0,0 +1,96 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,preprocessor:truncatedSVD:target_dim,rescaling:__choice__ +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,15.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0009580347867777607,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,0.10000000000000006,rbf,-1.0,True,0.0010000000000000002,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.35040453084365497,False,True,1.0,squared_hinge,ovr,l1,0.006810889378452772,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.2380793644102286,None,0.0,1.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.15248352254459802,fwe,chi2,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.010000000000000005,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9727149851116396,None,0.0,18.0,13.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,,none +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.0010015637584068037,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.037611630308856295,deviance,5.0,0.8840126779516314,None,0.0,10.0,2.0,0.0,444.0,0.7599997167603434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,most_frequent,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828776,None,0.0,2.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.002173124111626734,None,0.0,14.0,4.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,6.0,None,13.0,2.0,1.0,23.0,,,,,,,normalize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,31,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.9348409326933208,rbf,-1.0,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,mean,kernel_pca,,,,,,,,,,,,,,cosine,1754.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3428971645958825,,,0.2229870623330047,rbf,-1.0,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4047.618729304337,,,2.0237366768707754,rbf,-1.0,True,0.04369127828878843,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9896334290292654,None,0.0,11.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1.0,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.3451627750042988,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.3163640203509378,None,0.0,17.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,median,extra_trees_preproc_for_classification,False,gini,None,0.8916956785028156,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.002630811782675973,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9828367182452932,None,0.0,18.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.35533396539961937,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4165632766388807,fpr,chi2,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.9376772805635799,None,0.0,18.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,median,extra_trees_preproc_for_classification,True,entropy,None,0.8613889689810683,None,0.0,10.0,4.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.039533063907190934,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1878805519245509,fdr,chi2,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3823734947460288,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0733000338152003,False,True,1.0,squared_hinge,ovr,l2,0.033752542733220474,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,-0.6840756728731969,,0.00980445380551526,sigmoid,161.0,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.00214097329599271,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7996802015738327,None,0.0,7.0,12.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.1052247187777527,False,True,1.0,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.2604623554399743,None,0.0,18.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.282079367058228,fpr,chi2,,none +none,one_hot_encoding,0.04329010470998136,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7260331062271381,None,0.0,7.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,134,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/metalearning/metalearning_files/roc_auc_binary.classification_sparse/description.txt b/metalearning/metalearning_files/roc_auc_binary.classification_sparse/description.txt new file mode 100755 index 0000000..5bafa00 --- /dev/null +++ b/metalearning/metalearning_files/roc_auc_binary.classification_sparse/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: roc_auc +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/roc_auc_binary.classification_sparse/feature_costs.arff b/metalearning/metalearning_files/roc_auc_binary.classification_sparse/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/roc_auc_binary.classification_sparse/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/roc_auc_binary.classification_sparse/feature_runstatus.arff b/metalearning/metalearning_files/roc_auc_binary.classification_sparse/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/roc_auc_binary.classification_sparse/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/roc_auc_binary.classification_sparse/feature_values.arff b/metalearning/metalearning_files/roc_auc_binary.classification_sparse/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/roc_auc_binary.classification_sparse/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/roc_auc_binary.classification_sparse/readme.txt b/metalearning/metalearning_files/roc_auc_binary.classification_sparse/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/roc_auc_multiclass.classification_dense/algorithm_runs.arff b/metalearning/metalearning_files/roc_auc_multiclass.classification_dense/algorithm_runs.arff new file mode 100755 index 0000000..89e1b67 --- /dev/null +++ b/metalearning/metalearning_files/roc_auc_multiclass.classification_dense/algorithm_runs.arff @@ -0,0 +1,107 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE roc_auc NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2117,1.0,1,0.08616284256544937,ok +75156,1.0,2,0.13865419989353356,ok +75129,1.0,3,0.13959960126266813,ok +75239,1.0,4,0.0,ok +75121,1.0,5,0.0,ok +261,1.0,6,0.21050330141239226,ok +75240,1.0,7,0.00889068386457914,ok +75120,1.0,8,0.08476482617586922,ok +75124,1.0,9,0.0923544529010576,ok +75176,1.0,10,0.00081713932448646,ok +75103,1.0,11,0.0010223312249904026,ok +75095,1.0,12,0.009571664052110518,ok +273,1.0,13,0.01180961426066629,ok +75174,1.0,14,0.0531511925885505,ok +75153,1.0,15,0.020717474053180585,ok +75093,1.0,16,0.25832619985746696,ok +75119,1.0,17,0.04962809917355371,ok +75215,1.0,18,0.0033248070117535278,ok +75233,1.0,19,0.01453125504243713,ok +75196,1.0,20,0.0005894590846047265,ok +75191,1.0,21,0.07894154495071293,ok +75115,1.0,22,0.034135918282259814,ok +75108,1.0,23,0.0,ok +75101,1.0,24,0.19244568752880575,ok +75192,1.0,25,0.4910967139322515,ok +75232,1.0,26,0.07526329309016189,ok +75173,1.0,27,0.04872240259740257,ok +75148,1.0,28,0.06187080217487306,ok +75150,1.0,29,0.20069082193712107,ok +75100,1.0,30,0.17174741426238438,ok +75179,1.0,31,0.11353631478705917,ok +75213,1.0,32,0.016200236499802956,ok +75227,1.0,33,0.04187745540393961,ok +75184,1.0,34,0.05408480640081892,ok +75142,1.0,35,0.015133610214270066,ok +75166,1.0,36,0.03115196894551442,ok +75133,1.0,37,0.03838204684705815,ok +75234,1.0,38,0.0024355396957419506,ok +75139,1.0,39,0.0005057172245516162,ok +75117,1.0,40,0.046539503856577014,ok +75113,1.0,41,0.0005025775647244934,ok +75237,1.0,42,4.725332067301302e-06,ok +75195,1.0,43,7.034135398931163e-06,ok +75171,1.0,44,0.08440502497815372,ok +75128,1.0,45,0.012714176579722736,ok +75146,1.0,46,0.040944896061433145,ok +75116,1.0,47,0.002713200434112162,ok +75157,1.0,48,0.4334669811320755,ok +75187,1.0,49,0.0012636634024824067,ok +2350,1.0,50,0.41812392128985587,ok +75125,1.0,51,0.04066905921558572,ok +75185,1.0,52,0.05301802929867516,ok +75163,1.0,53,0.025278851960237825,ok +75177,1.0,54,0.0033793711431089335,ok +75189,1.0,55,0.003182247087857304,ok +75244,1.0,56,0.10568095143930611,ok +75219,1.0,57,0.005379910856541437,ok +75222,1.0,58,0.03830357142857144,ok +75159,1.0,59,0.1071428571428571,ok +75175,1.0,60,0.03417611353704009,ok +254,1.0,61,0.0,ok +75105,1.0,62,0.20474684765938922,ok +75106,1.0,63,0.2676929726822146,ok +75212,1.0,64,0.1797887140248674,ok +75099,1.0,65,0.15456081081081097,ok +75248,1.0,66,0.14541815798388635,ok +233,1.0,67,0.00010493559125790419,ok +75226,1.0,68,0.0001951906715831342,ok +75132,1.0,69,0.308963011548568,ok +75127,1.0,70,0.2742794807110678,ok +75161,1.0,71,0.013489738650252936,ok +75143,1.0,72,0.006682105466299992,ok +75114,1.0,73,0.014331282752335306,ok +75182,1.0,74,0.05289115303093994,ok +75112,1.0,75,0.06029259702749379,ok +75210,1.0,76,0.0,ok +75092,1.0,77,0.04926483772637613,ok +3043,1.0,78,0.0033793711431089335,ok +75249,1.0,79,0.0004941767282828913,ok +75126,1.0,80,0.026899761853215076,ok +75225,1.0,81,0.07447652284263961,ok +75141,1.0,82,0.008768207852483001,ok +75107,1.0,83,0.13364221677792265,ok +75097,1.0,84,0.15388423113578398,ok +80001,1.0,1,0.06773399014778314,ok +80003,1.0,1,0.04213187276174435,ok +80006,1.0,1,0.027450980392156876,ok +80008,1.0,1,0.04999999999999993,ok +80009,1.0,1,0.05555555555555558,ok +80010,1.0,1,0.16666666666666663,ok +80011,1.0,1,0.006191950464396245,ok +80012,1.0,1,0.008928571428571397,ok +80013,1.0,1,0.0,ok +80014,1.0,1,0.05833333333333335,ok +80015,1.0,1,0.028846153846153855,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/roc_auc_multiclass.classification_dense/configurations.csv b/metalearning/metalearning_files/roc_auc_multiclass.classification_dense/configurations.csv new file mode 100755 index 0000000..984b09d --- /dev/null +++ b/metalearning/metalearning_files/roc_auc_multiclass.classification_dense/configurations.csv @@ -0,0 +1,96 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:fast_ica:algorithm,preprocessor:fast_ica:fun,preprocessor:fast_ica:n_components,preprocessor:fast_ica:whiten,preprocessor:feature_agglomeration:affinity,preprocessor:feature_agglomeration:linkage,preprocessor:feature_agglomeration:n_clusters,preprocessor:feature_agglomeration:pooling_func,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:pca:keep_variance,preprocessor:pca:whiten,preprocessor:polynomial:degree,preprocessor:polynomial:include_bias,preprocessor:polynomial:interaction_only,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,rescaling:__choice__,rescaling:quantile_transformer:n_quantiles,rescaling:quantile_transformer:output_distribution,rescaling:robust_scaler:q_max,rescaling:robust_scaler:q_min +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.12713527337147906,deviance,4.0,0.6041596127474019,None,0.0,14.0,17.0,0.0,83.0,0.8426859880999615,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.018505477121829747,deviance,7.0,0.4568365303752941,None,0.0,10.0,2.0,0.0,484.0,0.5253264455070624,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,quantile_transformer,79618.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.4909422458748719,None,0.0,11.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,mean,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7146659106968425,True,,,,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9455638720565652,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,most_frequent,fast_ica,,,,,,,,,,,deflation,cube,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8255464552647293,0.19162485555463185 +weighting,one_hot_encoding,0.18137532678800647,True,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9094110110427254,None,0.0,7.0,12.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,feature_agglomeration,,,,,,,,,,,,,,,manhattan,complete,195.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.0009580347867777607,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,0.10000000000000006,rbf,-1.0,True,0.0010000000000000002,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.35040453084365497,False,True,1.0,squared_hinge,ovr,l1,0.006810889378452772,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.02345017287074443,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.053517066400173056,deviance,10.0,0.542144980834302,None,0.0,20.0,13.0,0.0,233.0,0.7398539900055563,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0614425536709615,fwe,f_classif,robust_scaler,,,0.9523118062307264,0.13434811490315818 +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.8149627329153046,None,0.0,15.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.040936424602789435,deviance,7.0,0.5495014745530306,None,0.0,20.0,18.0,0.0,141.0,0.6905343807995293,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.75,0.25 +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.9727149851116396,None,0.0,10.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,euclidean,ward,25.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,multinomial_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.8868217696423089,None,0.0,20.0,13.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,9957.0,uniform,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.1958974686405233,deviance,5.0,0.33885235607979314,None,0.0,6.0,4.0,0.0,125.0,0.9448890820738562,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,0.010000000000000005,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.0518326156691958,deviance,6.0,0.8807456180216267,None,0.0,7.0,19.0,0.0,366.0,0.7314831276137047,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.7464505951074157,None,0.0,6.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,most_frequent,fast_ica,,,,,,,,,,,parallel,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.9504673483378582,0.13375455137243772 +none,one_hot_encoding,0.00012586572428922356,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5240592829918601,None,0.0,10.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,decision_tree,,,,,,,entropy,0.2422926485206341,,1.0,None,0.0,15.0,9.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.022939738050158573,deviance,10.0,0.4185394344134278,None,0.0,2.0,10.0,0.0,309.0,0.5979695608086252,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.75,0.25383213391991144 +weighting,no_encoding,,,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,0.3170009238992427,rbf,1955.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8333938697866604,0.10426506601169797 +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9412423746065944,None,0.0,9.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,False,,,,,,,,,,,,,robust_scaler,,,0.7702464686370823,0.17046298103332982 +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.014398770417266825,deviance,5.0,0.3847309634051567,None,0.0,13.0,4.0,0.0,369.0,0.7446964555890218,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7509814655573623,0.05673098788555319 +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.609975998293528,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,most_frequent,fast_ica,,,,,,,,,,,parallel,logcosh,2000.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8430415644014919,0.2863750565331575 +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.39536192447534535,None,0.0,19.0,3.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,5.0,None,11.0,11.0,1.0,12.0,,,,,,robust_scaler,,,0.8928631650245873,0.1581877760687084 +weighting,one_hot_encoding,0.3451627750042988,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.3163640203509378,None,0.0,17.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,7.0,None,6.0,20.0,1.0,47.0,,,,,,quantile_transformer,21674.0,uniform,, +weighting,no_encoding,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.1708463626384465e-06,True,,0.09722688351233316,True,,constant,squared_hinge,l2,,0.00953454743007943,,,,,,,,,,,,,,,,,,31,mean,feature_agglomeration,,,,,,,,,,,,,,,euclidean,average,272.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.9348409326933208,rbf,-1.0,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,mean,kernel_pca,,,,,,,,,,,,,,,,,,,,,,cosine,1754.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,198.72528686512538,False,True,1.0,squared_hinge,ovr,l2,0.026260652523566803,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,robust_scaler,,,0.913511520078368,0.2742229325455444 +none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9260795160807372,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3428971645958825,,,0.2229870623330047,rbf,-1.0,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.8954806456480866,None,0.0,18.0,13.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,fast_ica,,,,,,,,,,,deflation,cube,45.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.00034835629696198427,True,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.8245132980938538,0.08947420373097192 +weighting,one_hot_encoding,0.00016967940959070708,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9439080311935252,None,0.0,2.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.03528169333197684,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3416063836589199,None,0.0,9.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,median,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1.0,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.904983674005564,None,0.0,18.0,11.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,mean,feature_agglomeration,,,,,,,,,,,,,,,cosine,average,275.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.4932996544760619,None,0.0,2.0,20.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,mean,feature_agglomeration,,,,,,,,,,,,,,,manhattan,average,340.0,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.12713527337147906,deviance,4.0,0.6041596127474019,None,0.0,14.0,17.0,0.0,83.0,0.8426859880999615,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +none,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.4421938468644326,deviance,5.0,0.5709932933214351,None,0.0,15.0,8.0,0.0,155.0,0.4040373361127008,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.03741851720151596,fwe,f_classif,robust_scaler,,,0.95547292996163,0.03028628950622218 +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.00031737236118003484,True,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,244.0,None,,2.3065111488706024e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,mean,fast_ica,,,,,,,,,,,deflation,exp,1862.0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,robust_scaler,,,0.7851234479882973,0.2237528085136715 +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.35533396539961937,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4165632766388807,fpr,chi2,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.9342950927678112,None,0.0,20.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,False,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.5855957814188109,None,0.0,17.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,most_frequent,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,109.0,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.2636201374253461,deviance,7.0,0.8344964130784466,None,0.0,9.0,2.0,0.0,298.0,0.7517549950523315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,normalize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.2263596964804377,True,adaboost,SAMME,0.15143691959318842,2.0,233.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.07951518163998639,fwe,f_classif,minmax,,,, +weighting,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.07463196642416367,deviance,7.0,0.8603242247379981,None,0.0,2.0,6.0,0.0,500.0,0.8447665577491962,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,True,False,,,,,,,,,,,,,none,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3721522140614508,0.3541746628756037,3.0,0.0009148519644429074,poly,-1.0,False,2.916672898330067e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,37866.0,normal,, +weighting,one_hot_encoding,0.010000000000000005,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.02674155532989549,deviance,3.0,0.14973922320166708,None,0.0,7.0,18.0,0.0,309.0,0.35532673462283193,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,one_hot_encoding,0.0020580843703898177,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.945774573434192,None,0.0,19.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,median,fast_ica,,,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,15209.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,mean,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,False,True,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none,,,, +weighting,one_hot_encoding,0.00013442810992750476,True,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1.0,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,median,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,8.0,None,13.0,16.0,1.0,28.0,,,,,,quantile_transformer,41502.0,uniform,, +weighting,one_hot_encoding,0.002615346832354839,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.7884268823432835,None,0.0,20.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +none,one_hot_encoding,,False,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.2472984547885781,deviance,3.0,0.6564306719064884,None,0.0,15.0,14.0,0.0,220.0,0.8082564085714649,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,median,feature_agglomeration,,,,,,,,,,,,,,,euclidean,complete,332.0,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,1000.0,uniform,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,friedman_mse,0.03905145156995541,deviance,5.0,0.2281306656230014,None,0.0,14.0,13.0,0.0,493.0,0.8793075442604774,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,25382.0,normal,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0733000338152003,False,True,1.0,squared_hinge,ovr,l2,0.033752542733220474,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.6840756728731969,,0.00980445380551526,sigmoid,161.0,,,,,,,,,,,,,,,,,,standardize,,,, +weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2806.9858667073186,False,True,1.0,squared_hinge,ovr,l2,0.03738539536055984,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,median,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,False,True,,,,,,,,,,,,,standardize,,,, +weighting,one_hot_encoding,0.0006079518254197428,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5357045097570147,None,0.0,19.0,13.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,0.06121586732873325,False,True,1.0,squared_hinge,ovr,l1,0.00014224609210090503,,,,,,,,,,,,,,,,,,,,,,,minmax,,,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7794633670276021,None,0.0,9.0,10.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,median,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,75840.0,normal,, +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.2604623554399743,None,0.0,18.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.282079367058228,fpr,chi2,none,,,, +none,no_encoding,,,adaboost,SAMME,0.7603752531440509,7.0,413.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,,,28.04113884899283,False,True,1.0,squared_hinge,ovr,l1,0.0001084726092097629,,,,,,,,,,,,,,,,,,,,,,,quantile_transformer,26106.0,uniform,, +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.3386310102795006,None,0.0,6.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,most_frequent,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,True,True,,,,,,,,,,,,,minmax,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,134,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize,,,, diff --git a/metalearning/metalearning_files/roc_auc_multiclass.classification_dense/description.txt b/metalearning/metalearning_files/roc_auc_multiclass.classification_dense/description.txt new file mode 100755 index 0000000..5bafa00 --- /dev/null +++ b/metalearning/metalearning_files/roc_auc_multiclass.classification_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: roc_auc +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/roc_auc_multiclass.classification_dense/feature_costs.arff b/metalearning/metalearning_files/roc_auc_multiclass.classification_dense/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/roc_auc_multiclass.classification_dense/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/roc_auc_multiclass.classification_dense/feature_runstatus.arff b/metalearning/metalearning_files/roc_auc_multiclass.classification_dense/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/roc_auc_multiclass.classification_dense/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/roc_auc_multiclass.classification_dense/feature_values.arff b/metalearning/metalearning_files/roc_auc_multiclass.classification_dense/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/roc_auc_multiclass.classification_dense/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/roc_auc_multiclass.classification_dense/readme.txt b/metalearning/metalearning_files/roc_auc_multiclass.classification_dense/readme.txt new file mode 100755 index 0000000..e69de29 diff --git a/metalearning/metalearning_files/roc_auc_multiclass.classification_sparse/algorithm_runs.arff b/metalearning/metalearning_files/roc_auc_multiclass.classification_sparse/algorithm_runs.arff new file mode 100755 index 0000000..9f82f3e --- /dev/null +++ b/metalearning/metalearning_files/roc_auc_multiclass.classification_sparse/algorithm_runs.arff @@ -0,0 +1,107 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE roc_auc NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2117,1.0,1,0.09220377468810481,ok +75156,1.0,2,0.1434003931882507,ok +75129,1.0,3,0.13959960126266813,ok +75239,1.0,4,0.0,ok +75121,1.0,5,0.0,ok +261,1.0,6,0.21050330141239226,ok +75240,1.0,7,0.00889068386457914,ok +75120,1.0,8,0.0849693251533743,ok +75124,1.0,9,0.0923544529010576,ok +75176,1.0,10,0.0011557397364430066,ok +75103,1.0,11,0.0016231167825334625,ok +75095,1.0,12,0.009571664052110518,ok +273,1.0,13,0.013420514319111732,ok +75174,1.0,14,0.05567512614515935,ok +75153,1.0,15,0.05940137470219342,ok +75093,1.0,16,0.2607596025459341,ok +75119,1.0,17,0.057107438016528955,ok +75215,1.0,18,0.005544553850291178,ok +75233,1.0,19,0.018620389195469045,ok +75196,1.0,20,0.002063106796116543,ok +75191,1.0,21,0.07504394620024568,ok +75115,1.0,22,0.034135918282259814,ok +75108,1.0,23,0.0006587258617578584,ok +75101,1.0,24,0.20634796935003008,ok +75192,1.0,25,0.49300207677096886,ok +75232,1.0,26,0.0853730138343547,ok +75173,1.0,27,0.05552313311688317,ok +75148,1.0,28,0.10632015412967,ok +75150,1.0,29,0.2536127167630058,ok +75100,1.0,30,0.17563574150400507,ok +75179,1.0,31,0.11570990058184827,ok +75213,1.0,32,0.016200236499802956,ok +75227,1.0,33,0.04187745540393961,ok +75184,1.0,34,0.08839169963333282,ok +75142,1.0,35,0.02305385448270303,ok +75166,1.0,36,0.03115196894551442,ok +75133,1.0,37,0.08732520301369051,ok +75234,1.0,38,0.011145394542673825,ok +75139,1.0,39,0.0008095903616680555,ok +75117,1.0,40,0.05649364185949546,ok +75113,1.0,41,0.0005025775647244934,ok +75237,1.0,42,4.725332067301302e-06,ok +75195,1.0,43,7.034135398931163e-06,ok +75171,1.0,44,0.08919150063841585,ok +75128,1.0,45,0.018025391974971883,ok +75146,1.0,46,0.04520522742498323,ok +75116,1.0,47,0.005597760895641679,ok +75157,1.0,48,0.4334669811320755,ok +75187,1.0,49,0.0029007886655499915,ok +2350,1.0,50,0.41812392128985587,ok +75125,1.0,51,0.04066905921558572,ok +75185,1.0,52,0.05586348296135013,ok +75163,1.0,53,0.025278851960237825,ok +75177,1.0,54,0.0033793711431089335,ok +75189,1.0,55,0.003182247087857304,ok +75244,1.0,56,0.11933764607286468,ok +75219,1.0,57,0.02234428983391712,ok +75222,1.0,58,0.03830357142857144,ok +75159,1.0,59,0.13204747774480718,ok +75175,1.0,60,0.04566909318952361,ok +254,1.0,61,0.0,ok +75105,1.0,62,0.21099207510003404,ok +75106,1.0,63,0.3000205555769009,ok +75212,1.0,64,0.1820493870098251,ok +75099,1.0,65,0.15456081081081097,ok +75248,1.0,66,0.14541815798388635,ok +233,1.0,67,0.0005536257056014682,ok +75226,1.0,68,0.0001951906715831342,ok +75132,1.0,69,0.3460031068425532,ok +75127,1.0,70,0.27651568685596795,ok +75161,1.0,71,0.020809752128342796,ok +75143,1.0,72,0.006682105466299992,ok +75114,1.0,73,0.014331282752335306,ok +75182,1.0,74,0.056353538740398834,ok +75112,1.0,75,0.06029259702749379,ok +75210,1.0,76,0.0,ok +75092,1.0,77,0.04926483772637613,ok +3043,1.0,78,0.0033793711431089335,ok +75249,1.0,79,0.0004941767282828913,ok +75126,1.0,80,0.026899761853215076,ok +75225,1.0,81,0.07447652284263961,ok +75141,1.0,82,0.01085048658548371,ok +75107,1.0,83,0.14705684295631083,ok +75097,1.0,84,0.243280464359657,ok +80001,1.0,1,0.06773399014778314,ok +80003,1.0,1,0.06256583105119029,ok +80006,1.0,1,0.05882352941176472,ok +80008,1.0,1,0.04999999999999993,ok +80009,1.0,1,0.05555555555555558,ok +80010,1.0,1,0.16666666666666663,ok +80011,1.0,1,0.006191950464396245,ok +80012,1.0,1,0.008928571428571397,ok +80013,1.0,1,0.0,ok +80014,1.0,1,0.05833333333333335,ok +80015,1.0,1,0.028846153846153855,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/roc_auc_multiclass.classification_sparse/configurations.csv b/metalearning/metalearning_files/roc_auc_multiclass.classification_sparse/configurations.csv new file mode 100755 index 0000000..d172fb3 --- /dev/null +++ b/metalearning/metalearning_files/roc_auc_multiclass.classification_sparse/configurations.csv @@ -0,0 +1,96 @@ +balancing:strategy,categorical_encoding:__choice__,categorical_encoding:one_hot_encoding:minimum_fraction,categorical_encoding:one_hot_encoding:use_minimum_fraction,classifier:__choice__,classifier:adaboost:algorithm,classifier:adaboost:learning_rate,classifier:adaboost:max_depth,classifier:adaboost:n_estimators,classifier:bernoulli_nb:alpha,classifier:bernoulli_nb:fit_prior,classifier:decision_tree:criterion,classifier:decision_tree:max_depth,classifier:decision_tree:max_depth_factor,classifier:decision_tree:max_features,classifier:decision_tree:max_leaf_nodes,classifier:decision_tree:min_impurity_decrease,classifier:decision_tree:min_samples_leaf,classifier:decision_tree:min_samples_split,classifier:decision_tree:min_weight_fraction_leaf,classifier:extra_trees:bootstrap,classifier:extra_trees:criterion,classifier:extra_trees:max_depth,classifier:extra_trees:max_features,classifier:extra_trees:max_leaf_nodes,classifier:extra_trees:min_impurity_decrease,classifier:extra_trees:min_samples_leaf,classifier:extra_trees:min_samples_split,classifier:extra_trees:min_weight_fraction_leaf,classifier:extra_trees:n_estimators,classifier:gradient_boosting:criterion,classifier:gradient_boosting:learning_rate,classifier:gradient_boosting:loss,classifier:gradient_boosting:max_depth,classifier:gradient_boosting:max_features,classifier:gradient_boosting:max_leaf_nodes,classifier:gradient_boosting:min_impurity_decrease,classifier:gradient_boosting:min_samples_leaf,classifier:gradient_boosting:min_samples_split,classifier:gradient_boosting:min_weight_fraction_leaf,classifier:gradient_boosting:n_estimators,classifier:gradient_boosting:subsample,classifier:k_nearest_neighbors:n_neighbors,classifier:k_nearest_neighbors:p,classifier:k_nearest_neighbors:weights,classifier:lda:n_components,classifier:lda:shrinkage,classifier:lda:shrinkage_factor,classifier:lda:tol,classifier:liblinear_svc:C,classifier:liblinear_svc:dual,classifier:liblinear_svc:fit_intercept,classifier:liblinear_svc:intercept_scaling,classifier:liblinear_svc:loss,classifier:liblinear_svc:multi_class,classifier:liblinear_svc:penalty,classifier:liblinear_svc:tol,classifier:libsvm_svc:C,classifier:libsvm_svc:coef0,classifier:libsvm_svc:degree,classifier:libsvm_svc:gamma,classifier:libsvm_svc:kernel,classifier:libsvm_svc:max_iter,classifier:libsvm_svc:shrinking,classifier:libsvm_svc:tol,classifier:multinomial_nb:alpha,classifier:multinomial_nb:fit_prior,classifier:passive_aggressive:C,classifier:passive_aggressive:average,classifier:passive_aggressive:fit_intercept,classifier:passive_aggressive:loss,classifier:passive_aggressive:tol,classifier:qda:reg_param,classifier:random_forest:bootstrap,classifier:random_forest:criterion,classifier:random_forest:max_depth,classifier:random_forest:max_features,classifier:random_forest:max_leaf_nodes,classifier:random_forest:min_impurity_decrease,classifier:random_forest:min_samples_leaf,classifier:random_forest:min_samples_split,classifier:random_forest:min_weight_fraction_leaf,classifier:random_forest:n_estimators,classifier:sgd:alpha,classifier:sgd:average,classifier:sgd:epsilon,classifier:sgd:eta0,classifier:sgd:fit_intercept,classifier:sgd:l1_ratio,classifier:sgd:learning_rate,classifier:sgd:loss,classifier:sgd:penalty,classifier:sgd:power_t,classifier:sgd:tol,classifier:xgradient_boosting:base_score,classifier:xgradient_boosting:booster,classifier:xgradient_boosting:colsample_bylevel,classifier:xgradient_boosting:colsample_bytree,classifier:xgradient_boosting:gamma,classifier:xgradient_boosting:learning_rate,classifier:xgradient_boosting:max_delta_step,classifier:xgradient_boosting:max_depth,classifier:xgradient_boosting:min_child_weight,classifier:xgradient_boosting:n_estimators,classifier:xgradient_boosting:normalize_type,classifier:xgradient_boosting:rate_drop,classifier:xgradient_boosting:reg_alpha,classifier:xgradient_boosting:reg_lambda,classifier:xgradient_boosting:sample_type,classifier:xgradient_boosting:scale_pos_weight,classifier:xgradient_boosting:subsample,idx,imputation:strategy,preprocessor:__choice__,preprocessor:extra_trees_preproc_for_classification:bootstrap,preprocessor:extra_trees_preproc_for_classification:criterion,preprocessor:extra_trees_preproc_for_classification:max_depth,preprocessor:extra_trees_preproc_for_classification:max_features,preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes,preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease,preprocessor:extra_trees_preproc_for_classification:min_samples_leaf,preprocessor:extra_trees_preproc_for_classification:min_samples_split,preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf,preprocessor:extra_trees_preproc_for_classification:n_estimators,preprocessor:kernel_pca:coef0,preprocessor:kernel_pca:degree,preprocessor:kernel_pca:gamma,preprocessor:kernel_pca:kernel,preprocessor:kernel_pca:n_components,preprocessor:kitchen_sinks:gamma,preprocessor:kitchen_sinks:n_components,preprocessor:liblinear_svc_preprocessor:C,preprocessor:liblinear_svc_preprocessor:dual,preprocessor:liblinear_svc_preprocessor:fit_intercept,preprocessor:liblinear_svc_preprocessor:intercept_scaling,preprocessor:liblinear_svc_preprocessor:loss,preprocessor:liblinear_svc_preprocessor:multi_class,preprocessor:liblinear_svc_preprocessor:penalty,preprocessor:liblinear_svc_preprocessor:tol,preprocessor:nystroem_sampler:coef0,preprocessor:nystroem_sampler:degree,preprocessor:nystroem_sampler:gamma,preprocessor:nystroem_sampler:kernel,preprocessor:nystroem_sampler:n_components,preprocessor:random_trees_embedding:bootstrap,preprocessor:random_trees_embedding:max_depth,preprocessor:random_trees_embedding:max_leaf_nodes,preprocessor:random_trees_embedding:min_samples_leaf,preprocessor:random_trees_embedding:min_samples_split,preprocessor:random_trees_embedding:min_weight_fraction_leaf,preprocessor:random_trees_embedding:n_estimators,preprocessor:select_percentile_classification:percentile,preprocessor:select_percentile_classification:score_func,preprocessor:select_rates:alpha,preprocessor:select_rates:mode,preprocessor:select_rates:score_func,preprocessor:truncatedSVD:target_dim,rescaling:__choice__ +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,15.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.0009580347867777607,True,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,0.10000000000000006,rbf,-1.0,True,0.0010000000000000002,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.35040453084365497,False,True,1.0,squared_hinge,ovr,l1,0.006810889378452772,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.2380793644102286,None,0.0,1.0,17.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.15248352254459802,fwe,chi2,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.010000000000000005,True,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9727149851116396,None,0.0,18.0,13.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16,mean,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,,none +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.0010015637584068037,True,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,mse,0.037611630308856295,deviance,5.0,0.8840126779516314,None,0.0,10.0,2.0,0.0,444.0,0.7599997167603434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21,most_frequent,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,22,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,24,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,False,entropy,None,0.9541039630394388,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,25,most_frequent,extra_trees_preproc_for_classification,True,entropy,None,0.9082628722828776,None,0.0,2.0,18.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.002173124111626734,None,0.0,14.0,4.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30,most_frequent,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,6.0,None,13.0,2.0,1.0,23.0,,,,,,,normalize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,31,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82.27108214899228,,,0.9348409326933208,rbf,-1.0,False,0.00090919103756734,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32,mean,kernel_pca,,,,,,,,,,,,,,cosine,1754.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,33,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,,False,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3428971645958825,,,0.2229870623330047,rbf,-1.0,False,2.006345264381097e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,no_encoding,,,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4047.618729304337,,,2.0237366768707754,rbf,-1.0,True,0.04369127828878843,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,39,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,False,gini,None,0.9896334290292654,None,0.0,11.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40,most_frequent,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,41,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,42,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59.0,1.0,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,43,most_frequent,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,8074.423891892491,False,True,1.0,squared_hinge,ovr,l1,0.003592235404478327,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.3451627750042988,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.3163640203509378,None,0.0,17.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44,median,extra_trees_preproc_for_classification,False,gini,None,0.8916956785028156,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,45,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.002630811782675973,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.9828367182452932,None,0.0,18.0,16.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,46,most_frequent,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,47,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,49,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.35533396539961937,None,0.0,17.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50,median,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4165632766388807,fpr,chi2,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.41094614430753584,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5686453602598863,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,55,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.9376772805635799,None,0.0,18.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,56,median,extra_trees_preproc_for_classification,True,entropy,None,0.8613889689810683,None,0.0,10.0,4.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,57,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,59,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,61,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.039533063907190934,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.4044792917812593,None,0.0,9.0,6.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1878805519245509,fdr,chi2,,standardize +weighting,one_hot_encoding,0.0018568208330940047,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.7983157215145903,None,0.0,4.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,63,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.4971515945303584,False,True,1.0,squared_hinge,ovr,l1,0.00010268311046018636,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,64,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,65,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,66,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,67,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,adaboost,SAMME.R,1.6308355175471712,6.0,467.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.3823734947460288,None,0.0,16.0,14.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,70,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,72,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.001532792329695102,True,extra_trees,,,,,,,,,,,,,,,,True,entropy,None,0.712362002844248,None,0.0,16.0,15.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73,mean,extra_trees_preproc_for_classification,False,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,75,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,79,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,no_encoding,,,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0733000338152003,False,True,1.0,squared_hinge,ovr,l2,0.033752542733220474,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81,median,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,-0.6840756728731969,,0.00980445380551526,sigmoid,161.0,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,extra_trees,,,,,,,,,,,,,,,,True,gini,None,0.6025857717358056,None,0.0,16.0,19.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,82,most_frequent,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,0.00214097329599271,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7996802015738327,None,0.0,7.0,12.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83,mean,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,0.1052247187777527,False,True,1.0,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,84,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +weighting,one_hot_encoding,,False,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,0.2604623554399743,None,0.0,18.0,8.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,130,most_frequent,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.282079367058228,fpr,chi2,,none +none,one_hot_encoding,0.04329010470998136,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7260331062271381,None,0.0,7.0,7.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,131,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,132,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,134,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,135,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,136,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,137,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,138,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,139,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +none,one_hot_encoding,0.01,True,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,0.5,None,0.0,1.0,2.0,0.0,100.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,140,mean,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/metalearning/metalearning_files/roc_auc_multiclass.classification_sparse/description.txt b/metalearning/metalearning_files/roc_auc_multiclass.classification_sparse/description.txt new file mode 100755 index 0000000..5bafa00 --- /dev/null +++ b/metalearning/metalearning_files/roc_auc_multiclass.classification_sparse/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step NumberOfClasses: NumberOfClasses +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step LogDatasetRatio: LogDatasetRatio +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassProbabilityMean: ClassProbabilityMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step SymbolsMin: SymbolsMin +feature_step SymbolsMax: SymbolsMax +feature_step SymbolsMean: SymbolsMean +feature_step SymbolsSTD: SymbolsSTD +feature_step SymbolsSum: SymbolsSum +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step KurtosisMean: KurtosisMean +feature_step KurtosisSTD: KurtosisSTD +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step SkewnessMin: SkewnessMin +feature_step SkewnessMax: SkewnessMax +feature_step SkewnessMean: SkewnessMean +feature_step SkewnessSTD: SkewnessSTD +feature_step ClassEntropy: ClassEntropy +feature_step LandmarkLDA: LandmarkLDA +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step Landmark1NN: Landmark1NN +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, MissingValues, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassOccurences, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, NumSymbols, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, Kurtosisses, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, Skewnesses, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCA, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,130,131,132,133,134,135,136,137,138,139,140 +algorithms_stochastic: +performance_measures: roc_auc +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/metalearning/metalearning_files/roc_auc_multiclass.classification_sparse/feature_costs.arff b/metalearning/metalearning_files/roc_auc_multiclass.classification_sparse/feature_costs.arff new file mode 100755 index 0000000..eaca495 --- /dev/null +++ b/metalearning/metalearning_files/roc_auc_multiclass.classification_sparse/feature_costs.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC + +@DATA +233,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00081,0.00061,1e-05,0.00013,1e-05,9e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00018,3e-05,1e-05,3e-05,0.00011,0.02825,0.0,1e-05,0.01473,0.01347,4e-05,0.00025,3e-05,4e-05,5e-05,0.00012,0.00028,4e-05,4e-05,6e-05,0.00015,0.00057,0.09369,0.01843,0.04234,0.01474,0.01495,0.48165,0.00128,3e-05,0.00047,0.00078 +236,1.0,0.0,0.0,0.00033,0.0,0.0,0.00372,0.00317,1e-05,0.00032,1e-05,0.00024,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00234,0.10384,0.10603,0.48002,0.0668,0.04076,1.21997,0.00163,2e-05,0.00059,0.00102 +242,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00116,0.00063,1e-05,0.00028,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00036,0.11706,0.03995,0.91453,0.29276,0.01943,0.50029,0.00216,3e-05,0.00053,0.00159 +244,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.00091,0.0006,1e-05,0.00018,1e-05,0.00015,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00014,3e-05,1e-05,3e-05,7e-05,0.00036,0.0,0.0,9e-05,0.00021,5e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00056,0.04681,0.02547,0.62989,0.50899,0.02187,0.21005,0.00139,4e-05,0.00037,0.00097 +246,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00068,0.00046,1e-05,0.00014,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00038,0.05705,0.02387,0.51183,0.42842,0.02209,0.18038,0.00109,4e-05,0.00034,0.00072 +248,1.0,0.0,0.0,4e-05,0.0,0.0,0.00042,0.00036,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00037,0.02503,0.01831,0.04078,0.02486,0.0214,0.02067,0.00091,2e-05,0.00026,0.00063 +251,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00035,0.0003,0.0,5e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.0001,0.00027,3e-05,5e-05,5e-05,0.00014,0.00033,0.03688,0.0133,0.01249,0.00695,0.01316,0.04503,0.001,2e-05,0.0003,0.00069 +252,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00058,0.00042,1e-05,0.00011,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03802,0.02319,0.46378,0.31944,0.0275,0.11199,0.00105,2e-05,0.00033,0.0007 +253,1.0,0.0,0.0,3e-05,0.0,0.0,0.00032,0.00026,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00025,3e-05,8e-05,5e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.0003,0.05051,0.0118,0.02704,0.00692,0.01355,0.04076,0.00103,1e-05,0.00031,0.00071 +254,1.0,0.0,0.0,8e-05,0.0,0.0,0.0018,0.00148,1e-05,0.00021,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00108,0.26667,0.07906,0.13586,0.05442,0.03361,4.0096,0.00405,4e-05,0.00176,0.00226 +258,1.0,0.0,0.0,9e-05,0.0,0.0,0.00157,0.00108,1e-05,0.00025,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00074,0.07268,0.04172,0.32853,0.04696,0.02231,1.1031,0.0021,4e-05,0.00052,0.00154 +260,1.0,0.0,0.0,5e-05,0.0,0.0,0.0011,0.00095,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00072,0.02985,0.01849,0.2278,0.04064,0.02034,0.07096,0.00106,2e-05,0.00032,0.00072 +261,1.0,0.0,0.0,3e-05,0.0,0.0,0.00027,0.00021,1e-05,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00024,0.04884,0.01151,0.06166,0.00896,0.02781,0.10239,0.00227,3e-05,0.00059,0.00165 +262,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00236,0.00201,1e-05,0.00024,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00136,0.14191,0.0412,0.32063,0.05453,0.02615,0.24783,0.00127,2e-05,0.00045,0.0008 +266,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00055,0.00042,1e-05,0.0001,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.0004,0.02958,0.01722,0.08584,0.04752,0.01788,0.0505,0.00101,3e-05,0.00031,0.00068 +273,1.0,0.0,0.0,5e-05,0.0,0.0,0.00129,0.00092,1e-05,0.00022,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00011,0.00062,0.05059,0.01977,0.3672,0.04405,0.0162,0.53449,0.00171,4e-05,0.00045,0.00122 +275,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00096,0.00068,1e-05,0.00017,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,5e-05,5e-05,0.00013,0.00048,0.38409,0.07374,0.22338,0.05415,0.02988,1.9556,0.00417,7e-05,0.00186,0.00223 +288,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00123,0.00093,1e-05,0.00018,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00066,0.04337,0.02046,0.80912,0.10454,0.01948,0.64918,0.00171,3e-05,0.00045,0.00122 +2117,1.0,0.0,0.0,0.00035,1e-05,0.0,0.00911,0.00781,1e-05,0.00089,1e-05,0.00042,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00023,3e-05,4e-05,6e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00558,1.82582,0.52123,3.33655,0.54438,0.14958,132.65252,0.02141,5e-05,0.01012,0.01124 +2119,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00028,0.0,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,5e-05,0.00011,0.00021,2e-05,4e-05,4e-05,0.0001,0.0003,0.02172,0.01729,0.03202,0.0097,0.01496,0.03239,0.00095,2e-05,0.00026,0.00066 +2120,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00186,0.00134,1e-05,0.00035,1e-05,0.0002,0.0,2e-05,1e-05,3e-05,7e-05,2e-05,0.0,4e-05,1e-05,0.00021,7e-05,1e-05,3e-05,0.0001,0.00033,0.0,0.0,9e-05,0.0002,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00086,0.05479,0.02945,0.47963,0.06678,0.02217,0.38195,0.00141,2e-05,0.00048,0.00091 +2122,1.0,1e-05,0.0,0.00037,1e-05,0.0,0.00533,0.00482,0.0,0.00041,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,4e-05,0.00012,0.00323,0.31419,0.19372,0.55157,0.09075,0.06219,16.2308,0.00532,2e-05,0.00233,0.00297 +2123,1.0,0.0,0.0,3e-05,0.0,0.0,0.00034,0.00025,0.0,6e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00026,0.0,1e-05,6e-05,0.00014,5e-05,0.00021,3e-05,3e-05,5e-05,0.0001,0.00028,4e-05,5e-05,5e-05,0.00013,0.00028,0.03804,0.01119,0.05745,0.0197,0.01322,0.03231,0.00097,1e-05,0.00029,0.00067 +2350,1.0,1e-05,0.0,0.00099,2e-05,0.0,0.271,0.25208,3e-05,0.01582,4e-05,0.00318,1e-05,2e-05,2e-05,6e-05,7e-05,6e-05,1e-05,9e-05,1e-05,9e-05,1e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00015,6e-05,0.00031,5e-05,5e-05,7e-05,0.00014,0.00032,5e-05,6e-05,7e-05,0.00014,0.01427,0.0,1e-05,0.0,0.0,1e-05,1.47708,3e-05,0.0,0.0,2e-05 +3043,1.0,0.0,0.0,4e-05,0.0,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.06062,0.01965,0.04292,0.01649,0.02116,0.30143,0.00146,2e-05,0.00049,0.00095 +75090,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00532,0.00229,2e-05,0.00142,3e-05,0.00168,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00028,4e-05,5e-05,6e-05,0.00013,0.00031,4e-05,6e-05,7e-05,0.00014,0.00051,1.08999,0.16883,2.15334,0.65437,0.0353,5.61534,0.0059,0.00012,0.00271,0.00307 +75092,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00048,0.00033,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00038,3e-05,4e-05,0.00021,0.00011,0.00029,0.02585,0.01048,0.06074,0.02105,0.01229,0.03935,0.00098,2e-05,0.0003,0.00066 +75093,1.0,0.0,0.0,9e-05,0.0,0.0,0.00229,0.00189,1e-05,0.00025,1e-05,0.00016,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00131,0.05059,0.02513,0.60774,0.07634,0.02254,0.62882,0.00139,2e-05,0.00047,0.00089 +75095,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00093,0.00083,1e-05,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00021,3e-05,4e-05,4e-05,0.0001,0.0007,0.02125,0.01471,0.06342,0.03386,0.0197,0.02895,0.00097,2e-05,0.0003,0.00065 +75096,1.0,1e-05,0.0,0.01011,1e-05,0.0,0.18201,0.16013,1e-05,0.01586,1e-05,0.00605,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,0.0001,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00014,0.11311,3.07528,2.25679,30.14783,3.00325,1.01729,149.003,0.03284,2e-05,0.01601,0.01681 +75097,1.0,0.0,0.0,0.0002,1e-05,0.0,0.00608,0.00539,1e-05,0.00051,1e-05,0.00019,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00134,0.00029,0.0003,0.00033,0.00042,0.00136,0.0003,0.0003,0.00033,0.00042,0.00369,0.0,1e-05,0.0,0.0,0.0,4.58546,3e-05,1e-05,0.0,2e-05 +75098,1.0,1e-05,0.0,0.0009,1e-05,0.0,0.09898,0.04366,3e-05,0.0232,4e-05,0.03218,0.0,2e-05,2e-05,5e-05,6e-05,5e-05,0.0,7e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.0003,5e-05,5e-05,6e-05,0.00014,0.00031,4e-05,6e-05,7e-05,0.00015,0.00771,19.97777,4.69948,102.62242,7.47821,0.56479,1998.07769,0.50335,0.00015,0.25532,0.24788 +75099,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00049,0.00038,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00037,0.02386,0.01093,0.06539,0.01629,0.01305,0.03097,0.00096,2e-05,0.00029,0.00065 +75100,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00138,0.00104,1e-05,0.00023,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0007,0.04097,0.0175,0.09462,0.02888,0.01887,0.29424,0.00159,2e-05,0.00042,0.00115 +75101,1.0,1e-05,0.0,0.00081,1e-05,0.0,0.02118,0.01718,1e-05,0.00233,1e-05,0.00169,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00024,3e-05,4e-05,5e-05,0.00013,0.00026,3e-05,4e-05,5e-05,0.00014,0.01092,0.45625,0.20272,20.61065,1.94752,0.10365,331.44902,0.00696,2e-05,0.0032,0.00375 +75103,1.0,1e-05,0.0,0.00011,1e-05,0.0,0.00759,0.00419,1e-05,0.00152,1e-05,0.00191,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00013,0.00164,0.64992,0.14466,2.17795,0.29301,0.04613,24.62193,0.00676,9e-05,0.00312,0.00355 +75105,1.0,1e-05,0.0,0.00029,1e-05,0.0,0.02748,0.01502,1e-05,0.00567,2e-05,0.00683,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00514,0.00117,0.00118,0.00128,0.00151,0.00542,0.00123,0.00121,0.00129,0.00169,0.0055,0.0,1e-05,0.0,0.0,1e-05,31.32266,2e-05,1e-05,0.0,1e-05 +75106,1.0,1e-05,0.0,0.00036,1e-05,0.0,0.03052,0.01659,1e-05,0.00627,2e-05,0.0077,0.0,1e-05,1e-05,3e-05,5e-05,3e-05,0.0,5e-05,1e-05,0.00014,2e-05,1e-05,2e-05,9e-05,0.00026,0.0,0.0,6e-05,0.00015,4e-05,0.00516,0.00115,0.00116,0.00125,0.00161,0.00517,0.00121,0.0012,0.00128,0.00149,0.00631,0.0,1e-05,0.0,0.0,1e-05,343.40503,2e-05,0.0,0.0,1e-05 +75107,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.02733,0.01507,1e-05,0.00533,2e-05,0.00695,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00513,0.00118,0.00119,0.00128,0.00148,0.00521,0.0012,0.00121,0.00129,0.00151,0.00557,0.0,1e-05,0.0,0.0,1e-05,31.48695,2e-05,1e-05,0.0,1e-05 +75108,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00305,0.00178,1e-05,0.00065,1e-05,0.00065,0.0,1e-05,1e-05,3e-05,4e-05,2e-05,0.0,4e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,4e-05,4e-05,5e-05,0.00012,0.00085,0.59471,0.16507,3.59308,0.35472,0.06794,5.29875,0.00733,6e-05,0.00341,0.00386 +75109,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00294,0.00233,1e-05,0.00038,1e-05,0.00025,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00016,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.0015,0.07519,0.03426,1.9421,0.62954,0.04013,1.66446,0.00204,3e-05,0.00052,0.00149 +75110,1.0,0.0,0.0,0.00037,1e-05,0.0,0.00529,0.00476,1e-05,0.00042,1e-05,0.00012,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00013,0.00319,0.31943,0.19846,0.55267,0.08994,0.06807,18.16641,0.00602,3e-05,0.0028,0.00319 +75112,1.0,0.0,0.0,0.00016,1e-05,0.0,0.00339,0.00295,1e-05,0.00033,1e-05,0.00013,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00019,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00222,0.05195,0.03419,1.45146,0.21562,0.05163,0.3898,0.00158,2e-05,0.00055,0.00101 +75113,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00491,0.00314,1e-05,0.00088,1e-05,0.00092,0.0,1e-05,1e-05,2e-05,4e-05,2e-05,0.0,3e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00167,0.2276,0.09207,0.91529,0.13788,0.03224,11.06743,0.0043,6e-05,0.00159,0.00265 +75114,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03119,0.01045,0.00028,0.01129,0.00029,0.01003,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00057,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00027,0.00036,0.00114,0.00025,0.00025,0.00027,0.00037,0.00028,6.13414,0.76105,76.53345,18.48784,0.13575,15.059,0.03042,0.00028,0.01514,0.015 +75115,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03039,0.01028,0.00028,0.01071,0.00029,0.00997,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00013,5e-05,0.00113,0.00024,0.00025,0.00027,0.00037,0.00117,0.00025,0.00026,0.00029,0.00038,0.00028,5.8842,0.7228,65.39984,18.31213,0.12438,15.1475,0.07228,0.00028,0.03914,0.03286 +75116,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03126,0.01054,0.00028,0.01136,0.00033,0.00997,0.0,0.0002,0.0002,0.0004,0.00042,0.00057,0.0,0.00059,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00157,0.00033,0.00035,0.0004,0.00049,0.00132,0.0003,0.00031,0.00033,0.00038,0.0003,9.82942,0.80371,41.21346,19.43119,0.12201,16.04505,0.03032,0.00028,0.01471,0.01533 +75117,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03203,0.01095,0.00038,0.01144,0.00035,0.01037,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00068,1e-05,0.00012,2e-05,0.0,2e-05,7e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00123,0.00025,0.00026,0.0003,0.00043,0.00119,0.00025,0.00026,0.00031,0.00038,0.00028,5.95227,0.75322,79.4673,18.27546,0.12355,15.12122,0.03044,0.00028,0.01466,0.01551 +75119,1.0,1e-05,0.0,4e-05,1e-05,1e-05,0.0303,0.01023,0.00028,0.01067,0.00028,0.00997,0.0,0.00019,0.0002,0.00039,0.0004,0.00055,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00115,0.00024,0.00025,0.0003,0.00036,0.00116,0.00025,0.00026,0.00028,0.00037,0.00028,9.50884,0.75253,65.13059,18.25334,0.12427,15.17844,0.03081,0.00028,0.01501,0.01553 +75120,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03041,0.01018,0.00028,0.01077,0.00029,0.01004,0.0,0.00019,0.00019,0.0004,0.0004,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00116,0.00024,0.00025,0.00029,0.00038,0.00116,0.00025,0.00026,0.00029,0.00038,0.00029,5.85726,0.722,73.05187,17.90233,0.12523,15.00912,0.03087,0.00028,0.01497,0.01562 +75121,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03081,0.01021,0.00029,0.01094,0.00029,0.01024,0.0,0.00019,0.00019,0.00039,0.0004,0.00056,0.0,0.0006,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00118,0.00024,0.00026,0.0003,0.00038,0.0013,0.00027,0.00029,0.00031,0.00042,0.00028,5.83538,0.75281,23.2313,17.71045,0.12738,15.19359,0.03152,0.00032,0.01546,0.01573 +75123,1.0,0.0,0.0,6e-05,0.0,0.0,0.00089,0.00076,1e-05,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00058,0.02736,0.0158,0.11241,0.02419,0.01814,0.04044,0.00106,1e-05,0.00033,0.00073 +75124,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00094,0.00077,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,4e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00065,0.06709,0.02422,0.17123,0.02265,0.01661,0.50247,0.00157,3e-05,0.00053,0.00102 +75125,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.03097,0.01042,0.00028,0.01125,0.0003,0.00987,0.0,0.00019,0.00019,0.00039,0.00041,0.00056,0.0,0.00058,1e-05,9e-05,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00121,0.00024,0.00025,0.00031,0.00041,0.00129,0.00027,0.00029,0.00031,0.00042,0.00028,5.87248,0.76219,60.066,18.37728,0.13125,15.08823,0.0444,0.00029,0.02421,0.01991 +75126,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.03088,0.01041,0.00028,0.01125,0.00029,0.0098,0.0,0.0002,0.0002,0.00041,0.00041,0.00061,0.0,0.00057,1e-05,9e-05,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00113,0.00024,0.00025,0.00028,0.00036,0.00127,0.00027,0.00028,0.00031,0.00041,0.00028,5.84923,0.73663,81.34966,18.44881,0.13081,15.16159,0.03004,0.00028,0.01458,0.01517 +75127,1.0,1e-05,0.0,0.00475,1e-05,0.0,0.09306,0.08346,1e-05,0.00728,1e-05,0.00234,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,2e-05,1e-05,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00778,0.00194,0.00194,0.00194,0.00194,0.00778,0.00194,0.00194,0.00194,0.00194,0.05914,0.00194,0.00194,0.00194,0.00194,0.00194,0.00194,0.00583,0.00194,0.00194,0.00194 +75128,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00933,0.00358,4e-05,0.00276,5e-05,0.00309,0.0,3e-05,3e-05,8e-05,9e-05,9e-05,0.0,0.0001,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.00036,6e-05,7e-05,8e-05,0.00015,0.00036,6e-05,7e-05,8e-05,0.00015,0.00048,7.70031,0.21354,5.03949,0.28976,0.04577,4.76279,0.0105,0.00015,0.00507,0.00529 +75129,1.0,0.0,0.0,3e-05,0.0,0.0,0.00043,0.0003,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00031,0.02666,0.01062,0.0913,0.02307,0.01271,0.03915,0.00099,2e-05,0.0003,0.00067 +75132,1.0,1e-05,0.0,0.01351,1e-05,0.0,0.2849,0.25274,1e-05,0.02391,1e-05,0.00828,1e-05,1e-05,1e-05,3e-05,5e-05,2e-05,1e-05,8e-05,6e-05,0.00021,3e-05,1e-05,3e-05,0.00014,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00025,3e-05,3e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.17499,3.51764,1.96262,127.49455,8.34314,1.16282,17365.73566,0.04868,2e-05,0.02276,0.0259 +75133,1.0,1e-05,0.0,9e-05,1e-05,0.0,0.00337,0.00258,1e-05,0.00047,1e-05,0.00035,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00174,0.06054,0.02676,0.18271,0.04498,0.01792,0.47713,0.00217,2e-05,0.00057,0.00158 +75134,1.0,1e-05,0.0,0.002,1e-05,0.0,0.02986,0.02693,1e-05,0.00224,1e-05,0.00071,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.01772,0.82344,0.47814,10.81836,3.48295,0.22581,12.46802,0.01211,2e-05,0.00554,0.00655 +75139,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00377,0.0028,1e-05,0.00049,1e-05,0.00051,0.0,1e-05,1e-05,3e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,4e-05,5e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00173,0.10415,0.04628,0.26532,0.05385,0.02594,5.36192,0.00226,3e-05,0.00085,0.00139 +75141,1.0,0.0,0.0,8e-05,0.0,0.0,0.00157,0.00138,1e-05,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,5e-05,0.00017,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00099,0.03042,0.01754,0.23747,0.07637,0.02683,0.14061,0.00123,2e-05,0.00038,0.00082 +75142,1.0,1e-05,0.0,0.00034,1e-05,0.0,0.00772,0.00679,1e-05,0.00069,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00465,0.10071,0.06717,0.22633,0.05855,0.04769,1.99611,0.00226,2e-05,0.00081,0.00143 +75143,1.0,0.0,0.0,4e-05,0.0,0.0,0.00078,0.00068,0.0,9e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,1e-05,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00062,0.02124,0.01266,0.01024,0.00778,0.01876,0.12154,0.00108,2e-05,0.00029,0.00077 +75146,1.0,0.0,0.0,0.00015,1e-05,0.0,0.00327,0.00258,1e-05,0.00038,1e-05,0.00033,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,3e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.0016,0.08356,0.03843,0.66155,0.07516,0.02561,4.77749,0.00491,5e-05,0.00192,0.00294 +75148,1.0,0.0,0.0,4e-05,0.0,0.0,0.00067,0.00058,1e-05,8e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00045,3e-05,4e-05,5e-05,0.00034,0.00026,3e-05,5e-05,6e-05,0.00013,0.00051,0.02647,0.01263,0.08615,0.02798,0.01741,0.01983,0.00094,2e-05,0.00029,0.00063 +75150,1.0,0.0,0.0,2e-05,0.0,0.0,0.00025,0.00021,0.0,4e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,5e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00025,0.01685,0.00987,0.00764,0.00488,0.01277,0.01171,0.00089,1e-05,0.00025,0.00062 +75153,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00175,0.00139,1e-05,0.00021,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.001,0.05357,0.02328,1.28269,0.31109,0.02642,1.52018,0.00148,4e-05,0.00055,0.0009 +75154,1.0,0.0,0.0,6e-05,1e-05,0.0,0.00052,0.00035,1e-05,0.00012,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,3e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00035,0.08449,0.12678,0.42272,0.12376,0.03073,0.1432,0.00137,4e-05,0.00044,0.00089 +75156,1.0,0.0,0.0,6e-05,1e-05,0.0,0.01174,0.00458,5e-05,0.00333,5e-05,0.00394,0.0,5e-05,5e-05,0.00011,0.0001,0.00014,0.0,0.00011,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00037,6e-05,7e-05,8e-05,0.00016,0.00037,6e-05,7e-05,8e-05,0.00015,0.00052,10.65282,0.26691,5.16858,0.69053,0.05346,13.78396,0.03759,0.00028,0.01756,0.01976 +75157,1.0,0.0,0.0,3e-05,0.0,0.0,0.00045,0.00039,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00017,2e-05,3e-05,4e-05,8e-05,0.00022,2e-05,3e-05,5e-05,0.00012,0.00038,0.01743,0.0102,0.0512,0.01071,0.02153,0.01326,0.00089,2e-05,0.00027,0.0006 +75159,1.0,0.0,0.0,2e-05,0.0,0.0,0.0003,0.00021,1e-05,7e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00025,0.02158,0.0091,0.04355,0.01397,0.01251,0.01637,0.00091,2e-05,0.00027,0.00062 +75161,1.0,1e-05,0.0,0.00032,1e-05,0.0,0.00782,0.00693,1e-05,0.00065,1e-05,0.00025,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00465,0.09553,0.06384,1.79222,0.16999,0.05192,1.85248,0.00205,2e-05,0.00079,0.00124 +75163,1.0,0.0,0.0,7e-05,0.0,0.0,0.00126,0.00112,1e-05,0.00012,1e-05,4e-05,0.0,1e-05,1e-05,4e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,4e-05,0.00012,0.00089,0.06346,0.01688,0.08778,0.01678,0.0179,0.04639,0.00124,2e-05,0.00036,0.00086 +75166,1.0,0.0,0.0,7e-05,0.0,0.0,0.00155,0.00136,0.0,0.00015,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.0002,3e-05,3e-05,5e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.001,0.04545,0.01772,0.3316,0.08664,0.02728,0.11557,0.00108,2e-05,0.00033,0.00073 +75168,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01425,0.0036,8e-05,0.01056,9e-05,0.00025,1e-05,7e-05,6e-05,0.00013,0.00015,0.00022,1e-05,0.00021,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00049,9e-05,9e-05,0.00011,0.00019,0.00057,0.00011,0.00015,0.00011,0.0002,0.00027,1e-05,2e-05,0.0,0.0,1e-05,0.09191,0.00147,0.00018,0.00045,0.00085 +75169,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00983,0.00447,2e-05,0.00252,3e-05,0.00288,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,6e-05,1e-05,0.0001,3e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00026,4e-05,4e-05,6e-05,0.00012,0.00027,4e-05,5e-05,6e-05,0.00012,0.00096,1.65201,0.58729,25.54291,19.17657,0.09439,25.08148,0.0193,0.00011,0.00934,0.00985 +75171,1.0,0.0,0.0,9e-05,0.0,0.0,0.00164,0.00143,1e-05,0.00017,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00106,0.02909,0.01763,0.36561,0.09468,0.02636,0.11326,0.00118,2e-05,0.00035,0.00082 +75172,1.0,1e-05,0.0,4e-05,2e-05,1e-05,0.0127,0.00237,9e-05,0.01038,9e-05,0.00014,0.0,6e-05,7e-05,0.00013,0.00015,0.00018,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,6e-05,0.00012,3e-05,0.0005,0.0001,0.0001,0.00011,0.00019,0.0005,9e-05,0.0001,0.00011,0.0002,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03276,0.00126,0.00021,0.00034,0.00071 +75173,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00182,0.0016,1e-05,0.00018,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00013,3e-05,0.0,2e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00131,0.03937,0.02152,0.13365,0.01952,0.01899,0.18973,0.00106,2e-05,0.00035,0.00069 +75174,1.0,1e-05,0.0,0.0002,1e-05,0.0,0.00462,0.00391,1e-05,0.00049,1e-05,0.00023,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00259,0.07029,0.04021,2.03595,0.31151,0.04623,2.65139,0.00198,2e-05,0.00074,0.00123 +75175,1.0,0.0,0.0,0.00017,1e-05,0.0,0.0039,0.00344,1e-05,0.00035,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00255,0.04984,0.03237,0.71353,0.09694,0.03667,0.21911,0.00149,2e-05,0.00049,0.00098 +75176,1.0,1e-05,0.0,0.00016,1e-05,0.0,0.00356,0.00315,1e-05,0.00032,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00237,0.05086,0.03401,0.32299,0.09965,0.03562,0.23948,0.00164,2e-05,0.00048,0.00114 +75177,1.0,0.0,0.0,4e-05,1e-05,0.0,0.0009,0.00071,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.0002,3e-05,3e-05,4e-05,9e-05,0.00024,3e-05,4e-05,5e-05,0.00012,0.00053,0.09721,0.0225,0.04649,0.01752,0.01617,0.31834,0.00146,2e-05,0.00053,0.00091 +75178,1.0,1e-05,0.0,0.00327,1e-05,0.0,0.04857,0.04165,1e-05,0.00474,1e-05,0.00221,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00025,3e-05,4e-05,5e-05,0.00014,0.00027,3e-05,4e-05,5e-05,0.00015,0.02849,1.00543,0.69328,69.30981,9.05038,1.00177,91.28626,0.01209,2e-05,0.00525,0.00682 +75179,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00158,1e-05,0.00025,1e-05,0.00017,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,3e-05,0.00021,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00102,0.06522,0.02257,1.32888,0.27197,0.0266,1.42489,0.0014,3e-05,0.00047,0.0009 +75181,1.0,0.0,0.0,0.00067,1e-05,0.0,0.00796,0.00736,1e-05,0.00052,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00023,3e-05,3e-05,4e-05,0.00012,0.00024,3e-05,4e-05,5e-05,0.00013,0.00514,0.4181,0.29926,1.51988,0.69376,0.37729,35.98075,0.00718,3e-05,0.00317,0.00398 +75182,1.0,0.0,0.0,0.00017,1e-05,0.0,0.00435,0.00388,1e-05,0.00037,1e-05,0.00012,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00266,0.05333,0.03647,0.96343,0.14938,0.03874,0.98254,0.00145,2e-05,0.00049,0.00094 +75184,1.0,0.0,0.0,0.00014,1e-05,0.0,0.00333,0.00282,1e-05,0.00034,1e-05,0.00019,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00205,0.06258,0.03357,0.54712,0.05845,0.02705,3.08086,0.0015,2e-05,0.00055,0.00093 +75185,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00143,0.00121,1e-05,0.00017,1e-05,7e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00087,0.03044,0.01741,0.28565,0.04133,0.02503,0.20613,0.00115,2e-05,0.00037,0.00076 +75187,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00157,0.00127,1e-05,0.0002,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,6e-05,0.00016,4e-05,0.00025,6e-05,4e-05,5e-05,0.00011,0.00025,3e-05,5e-05,5e-05,0.00012,0.00105,0.05794,0.022,0.86986,0.18563,0.0282,0.86541,0.00433,4e-05,0.00263,0.00167 +75188,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.03302,0.00374,0.00022,0.02936,0.00022,0.00036,1e-05,0.00015,0.00015,0.00031,0.00037,0.00043,0.0,0.00049,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00097,0.0002,0.0002,0.00023,0.00034,0.00098,0.0002,0.00021,0.00024,0.00033,0.00028,0.0,1e-05,0.0,0.0,0.0,0.11304,3e-05,1e-05,0.0,2e-05 +75189,1.0,1e-05,0.0,0.00409,2e-05,0.0,1.01983,1.00504,1e-05,0.0094,1e-05,0.00542,1e-05,1e-05,1e-05,2e-05,4e-05,2e-05,1e-05,4e-05,1e-05,0.00011,2e-05,1e-05,2e-05,7e-05,0.00028,0.0,0.0,6e-05,0.00016,6e-05,0.00024,3e-05,3e-05,5e-05,0.00013,0.00024,3e-05,4e-05,5e-05,0.00012,0.05365,0.0,1e-05,0.0,0.0,1e-05,56.48399,0.0203,3e-05,0.01008,0.0102 +75191,1.0,1e-05,0.0,0.0008,1e-05,0.0,0.25649,0.21152,1e-05,0.03196,1e-05,0.01303,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00015,2e-05,1e-05,2e-05,0.00011,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00029,3e-05,8e-05,7e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.01083,0.0,1e-05,0.0,0.0,1e-05,23.2173,0.03199,5e-05,0.01557,0.01637 +75192,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00071,0.00062,1e-05,8e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,7e-05,0.00024,0.0,0.0,5e-05,0.00014,5e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00062,0.02544,0.01282,0.15451,0.02924,0.02503,0.02459,0.00099,2e-05,0.0003,0.00067 +75193,1.0,1e-05,0.0,0.00641,1e-05,0.0,0.14721,0.10924,2e-05,0.01933,3e-05,0.01868,0.0,1e-05,1e-05,2e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,3e-05,0.00011,0.00024,0.0,0.0,5e-05,0.00014,3e-05,0.00026,3e-05,4e-05,5e-05,0.00014,0.00028,4e-05,4e-05,5e-05,0.00015,0.0643,1.15076,6.06862,61.638,7.35486,1.58947,21046.409,2e-05,0.0,0.0,1e-05 +75195,1.0,0.0,0.0,0.00033,1e-05,0.0,0.00781,0.00688,1e-05,0.00068,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00011,0.00479,0.15705,0.07999,0.79074,0.34664,0.04523,1.13814,0.00341,1e-05,0.00141,0.00199 +75196,1.0,0.0,0.0,3e-05,1e-05,0.0,0.0003,0.00024,1e-05,6e-05,1e-05,2e-05,0.0,1e-05,2e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00024,0.0,0.0,6e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00026,0.04331,0.01238,0.03121,0.01556,0.01897,0.05364,0.00132,3e-05,0.00033,0.00096 +75197,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.01473,0.00553,6e-05,0.00889,6e-05,0.00043,0.0,4e-05,4e-05,9e-05,0.00012,0.00011,0.0,0.00014,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0004,7e-05,8e-05,9e-05,0.00016,0.00041,7e-05,8e-05,9e-05,0.00016,0.00036,0.0,1e-05,0.0,0.0,0.0,0.24362,0.00251,0.00024,0.00081,0.00145 +75198,1.0,1e-05,0.0,0.00023,1e-05,1e-05,0.12697,0.0225,0.0007,0.10301,0.00076,0.00292,1e-05,0.00046,0.00046,0.00103,0.00095,0.00155,0.0,0.00153,1e-05,0.00011,2e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,5e-05,0.00015,6e-05,0.00245,0.00055,0.00056,0.0006,0.00074,0.00264,0.00057,0.0006,0.00073,0.00075,0.00114,0.0,1e-05,0.0,0.0,0.0,3.16624,3e-05,1e-05,0.0,2e-05 +75201,1.0,1e-05,0.0,6e-05,1e-05,0.0,0.05043,0.00717,0.00032,0.04337,0.00033,0.00053,0.0,0.00022,0.00022,0.00044,0.00046,0.00065,0.0,0.0008,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00127,0.00027,0.00028,0.00031,0.00041,0.00169,0.00036,0.00033,0.00044,0.00055,0.00043,0.0,1e-05,0.0,0.0,0.0,0.23499,2e-05,0.0,0.0,2e-05 +75202,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.01619,0.00362,0.0001,0.01258,0.00011,0.0002,0.0,7e-05,7e-05,0.00016,0.00017,0.00021,0.0,0.00024,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00024,0.0,0.0,5e-05,0.00014,4e-05,0.00055,0.00011,0.00012,0.00013,0.0002,0.00056,0.00011,0.00011,0.00013,0.0002,0.00034,0.0,1e-05,0.0,0.0,0.0,0.06574,0.00155,0.00026,0.00048,0.0008 +75203,1.0,1e-05,0.0,7e-05,1e-05,1e-05,0.0536,0.00758,0.00038,0.04608,0.00035,0.00067,0.0,0.00026,0.00027,0.00059,0.00049,0.00071,1e-05,0.00071,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00026,0.0,0.0,5e-05,0.00015,5e-05,0.00136,0.00029,0.0003,0.00033,0.00044,0.0014,0.0003,0.00031,0.00034,0.00045,0.00051,0.0,2e-05,0.0,0.0,1e-05,0.25568,3e-05,1e-05,0.0,2e-05 +75205,1.0,1e-05,0.0,0.00017,1e-05,1e-05,0.06704,0.02466,0.0003,0.04195,0.0003,0.00103,0.0,0.0002,0.0002,0.00041,0.00043,0.0006,0.0,0.00074,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00029,0.0,0.0,6e-05,0.00017,6e-05,0.00128,0.00026,0.00027,0.00036,0.00039,0.00122,0.00026,0.00027,0.0003,0.00039,0.0013,0.0,1e-05,0.0,0.0,1e-05,1.68332,3e-05,1e-05,0.0,2e-05 +75207,1.0,2e-05,1e-05,4e-05,1e-05,0.0,0.01331,0.0024,0.00011,0.01099,0.00011,0.00014,0.0,9e-05,7e-05,0.00013,0.00016,0.00017,0.0,0.00019,1e-05,9e-05,1e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0005,9e-05,0.0001,0.00012,0.00019,0.00093,0.00017,0.00019,0.00023,0.00034,0.0002,0.0,1e-05,0.0,0.0,0.0,0.03597,0.0014,0.00023,0.00041,0.00076 +75210,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00172,0.00155,1e-05,0.00015,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,6e-05,0.00014,3e-05,0.00018,3e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00114,0.03343,0.01955,0.03255,0.03687,0.02648,0.04889,0.00116,1e-05,0.00036,0.00079 +75212,1.0,0.0,0.0,3e-05,0.0,0.0,0.00038,0.00027,1e-05,8e-05,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00013,4e-05,0.0002,4e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00028,0.05096,0.01499,0.1384,0.02992,0.01338,0.07654,0.0015,3e-05,0.00036,0.00111 +75213,1.0,0.0,0.0,3e-05,0.0,0.0,0.00026,0.00021,0.0,5e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00025,0.0,0.0,7e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00026,0.03318,0.01089,0.0136,0.012,0.02711,0.03959,0.00113,2e-05,0.00031,0.0008 +75215,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00256,0.00194,1e-05,0.00041,1e-05,0.00022,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00027,0.0,0.0,5e-05,0.00018,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00024,3e-05,4e-05,5e-05,0.00012,0.00134,0.17429,0.09645,0.19633,0.05078,0.03226,4.09574,0.00335,3e-05,0.00143,0.00189 +75217,1.0,0.0,0.0,4e-05,0.0,0.0,0.00059,0.00044,1e-05,0.00011,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,3e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00036,0.03439,0.02207,0.04266,0.03057,0.01501,0.10583,0.00103,3e-05,0.00031,0.00069 +75219,1.0,0.0,0.0,0.00013,1e-05,0.0,0.00277,0.00235,1e-05,0.0003,1e-05,0.00014,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00014,2e-05,0.0,2e-05,9e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,9e-05,0.00021,3e-05,4e-05,4e-05,0.00011,0.00187,0.05022,0.02959,0.72356,0.06692,0.02456,0.41697,0.00174,2e-05,0.00048,0.00124 +75221,1.0,0.0,0.0,9e-05,1e-05,0.0,0.00163,0.00118,1e-05,0.00026,1e-05,0.0002,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00012,3e-05,0.0,2e-05,7e-05,0.00023,0.0,0.0,5e-05,0.00014,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00011,0.00083,0.06276,0.03277,1.02559,0.24027,0.02286,0.35379,0.00146,2e-05,0.00052,0.00092 +75222,1.0,0.0,0.0,3e-05,0.0,0.0,0.00033,0.00025,0.0,6e-05,1e-05,3e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,5e-05,0.00015,4e-05,0.00021,3e-05,4e-05,4e-05,0.0001,0.00021,3e-05,4e-05,5e-05,0.0001,0.00028,0.03831,0.01005,0.05464,0.0175,0.0246,0.03186,0.00096,2e-05,0.00029,0.00065 +75223,1.0,0.0,0.0,0.00038,1e-05,0.0,0.00513,0.00465,1e-05,0.00039,1e-05,0.00012,1e-05,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.0002,0.0,0.0,5e-05,0.00012,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00023,3e-05,4e-05,5e-05,0.00012,0.00309,0.21322,0.16358,0.39811,0.06981,0.05143,8.48921,0.00372,2e-05,0.00166,0.00204 +75225,1.0,0.0,0.0,3e-05,0.0,0.0,0.00078,0.00051,1e-05,0.00016,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,2e-05,1e-05,0.00013,2e-05,0.0,2e-05,8e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00011,0.00042,0.04791,0.01526,0.37891,0.06739,0.01949,0.27347,0.00139,3e-05,0.00058,0.00078 +75226,1.0,0.0,0.0,8e-05,1e-05,0.0,0.00198,0.00169,1e-05,0.0002,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00117,0.04157,0.02222,0.53223,0.14999,0.03472,0.23899,0.00135,2e-05,0.0004,0.00092 +75227,1.0,0.0,0.0,6e-05,0.0,0.0,0.00107,0.00094,0.0,0.0001,1e-05,4e-05,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.00021,3e-05,4e-05,5e-05,0.0001,0.00069,0.02304,0.01374,0.12706,0.03786,0.02125,0.02917,0.00096,2e-05,0.00029,0.00064 +75230,1.0,1e-05,0.0,8e-05,1e-05,0.0,0.00079,0.00054,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,3e-05,6e-05,2e-05,0.0,4e-05,1e-05,0.00017,3e-05,1e-05,4e-05,8e-05,0.00032,0.0,0.0,8e-05,0.00019,5e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00046,0.08534,0.12653,1.14077,1.21951,0.04992,0.07754,0.00121,2e-05,0.00032,0.00087 +75231,1.0,0.0,0.0,5e-05,1e-05,0.0,0.00053,0.00036,1e-05,0.00012,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00022,3e-05,4e-05,5e-05,0.00011,0.00034,0.08702,0.12713,0.64656,0.23738,0.03325,0.12794,0.00131,3e-05,0.00059,0.00069 +75232,1.0,0.0,0.0,3e-05,0.0,0.0,0.00035,0.00022,1e-05,9e-05,1e-05,5e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00015,3e-05,0.0,5e-05,7e-05,0.00025,0.0,0.0,6e-05,0.00015,4e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00026,0.02246,0.00984,0.06298,0.01837,0.01356,0.03332,0.00098,3e-05,0.0003,0.00065 +75233,1.0,0.0,0.0,0.0001,1e-05,0.0,0.00181,0.00149,1e-05,0.00022,1e-05,0.00012,0.0,1e-05,1e-05,3e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,2e-05,0.0,3e-05,0.00011,0.00537,0.0,1e-05,0.00377,0.00155,4e-05,0.00021,3e-05,3e-05,4e-05,0.00011,0.00023,3e-05,4e-05,5e-05,0.00012,0.00115,0.08971,0.02436,0.63479,0.09518,0.02026,0.67388,0.00154,2e-05,0.00047,0.00106 +75234,1.0,0.0,0.0,7e-05,1e-05,0.0,0.00153,0.00127,1e-05,0.00017,1e-05,0.00011,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,5e-05,9e-05,0.00022,3e-05,3e-05,4e-05,0.00011,0.00098,0.03728,0.01888,1.57713,0.13432,0.02337,0.81449,0.00134,2e-05,0.00052,0.00079 +75235,1.0,0.0,0.0,7e-05,0.0,0.0,0.0012,0.00096,1e-05,0.00016,1e-05,0.0001,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,5e-05,0.00011,0.00079,0.0375,0.02001,0.27494,0.11239,0.01945,0.19908,0.00111,3e-05,0.00035,0.00073 +75236,1.0,1e-05,0.0,5e-05,1e-05,0.0,0.00108,0.00055,1e-05,0.00029,1e-05,0.00027,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,4e-05,1e-05,0.00011,3e-05,1e-05,2e-05,6e-05,0.00027,0.0,0.0,6e-05,0.00017,4e-05,0.00032,5e-05,6e-05,7e-05,0.00015,0.0003,4e-05,5e-05,6e-05,0.00015,0.00034,0.20601,0.04855,0.20338,0.03329,0.01897,0.45015,0.00721,9e-05,0.00144,0.00567 +75237,1.0,1e-05,0.0,0.00185,1e-05,0.0,0.03991,0.03692,1e-05,0.00255,1e-05,0.00046,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.00011,2e-05,0.0,2e-05,6e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00024,3e-05,3e-05,6e-05,0.00012,0.00024,3e-05,4e-05,4e-05,0.00013,0.0267,0.34459,0.28378,1.30168,0.33029,0.22363,1.887,0.00501,1e-05,0.00232,0.00268 +75239,1.0,0.0,0.0,3e-05,1e-05,0.0,0.00051,0.00038,1e-05,9e-05,1e-05,6e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00024,0.0,0.0,6e-05,0.00015,4e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,5e-05,0.00012,0.00036,0.02719,0.01162,0.06598,0.03562,0.013,0.0591,0.00101,2e-05,0.00031,0.00068 +75240,1.0,0.0,0.0,7e-05,0.0,0.0,0.0019,0.00139,1e-05,0.00027,1e-05,0.00026,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00013,3e-05,0.00027,4e-05,5e-05,6e-05,0.00012,0.00029,5e-05,5e-05,6e-05,0.00013,0.00087,2.64938,0.33281,1.01985,0.33037,0.08694,12.71828,0.01625,0.00019,0.00778,0.00828 +75243,1.0,1e-05,0.0,0.00015,1e-05,0.0,0.00253,0.00224,1e-05,0.00023,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,5e-05,1e-05,0.0,3e-05,1e-05,0.00016,3e-05,1e-05,3e-05,9e-05,0.0003,0.0,0.0,6e-05,0.00019,4e-05,0.0002,3e-05,3e-05,4e-05,0.0001,0.00024,3e-05,4e-05,5e-05,0.00012,0.00176,0.11149,0.05386,0.1013,0.03991,0.03821,1.70533,0.00496,2e-05,0.00363,0.00132 +75244,1.0,1e-05,0.0,0.00011,1e-05,1e-05,0.00359,0.00257,1e-05,0.00058,1e-05,0.00047,0.0,1e-05,1e-05,4e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00014,3e-05,1e-05,3e-05,8e-05,0.00031,0.0,0.0,8e-05,0.00019,4e-05,0.00025,3e-05,4e-05,6e-05,0.00012,0.00026,3e-05,5e-05,5e-05,0.00013,0.00168,1.12934,0.19193,0.63915,0.1768,0.0754,11.78166,0.01944,7e-05,0.01214,0.00723 +75248,1.0,1e-05,0.0,6e-05,0.0,0.0,0.00215,0.00156,1e-05,0.00033,1e-05,0.00027,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,3e-05,1e-05,0.0001,2e-05,0.0,2e-05,6e-05,0.00023,0.0,0.0,5e-05,0.00014,3e-05,0.00025,4e-05,4e-05,5e-05,0.00012,0.00026,3e-05,4e-05,5e-05,0.00013,0.00096,0.72252,0.1529,0.51182,0.1571,0.05094,7.60731,0.00766,7e-05,0.00367,0.00393 +75249,1.0,0.0,0.0,4e-05,1e-05,0.0,0.00087,0.00068,1e-05,0.00013,1e-05,8e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00022,0.0,0.0,5e-05,0.00014,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00023,3e-05,4e-05,4e-05,0.00012,0.00054,0.05954,0.01909,0.02737,0.01675,0.01515,0.29733,0.00144,2e-05,0.00047,0.00095 +75250,1.0,1e-05,0.0,0.00214,1e-05,0.0,0.02543,0.02316,1e-05,0.00188,1e-05,0.0004,0.0,1e-05,1e-05,2e-05,3e-05,1e-05,0.0,2e-05,1e-05,0.0001,2e-05,1e-05,2e-05,5e-05,0.00021,0.0,0.0,5e-05,0.00013,3e-05,0.00018,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,4e-05,4e-05,0.00012,0.01743,0.40906,0.3955,4.33854,0.83155,0.70032,1.02353,0.0039,1e-05,0.00158,0.0023 +80001,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.05751,0.01541,0.00276,0.03091,0.00224,0.0162,0.0,0.00108,0.00107,0.00213,0.00294,0.00472,1e-05,0.0051,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00218,0.0,0.0,0.0009,0.00118,0.0001,0.00521,0.00116,0.00117,0.00131,0.00157,0.00508,0.00116,0.00118,0.00124,0.0015,0.00024,41.32416,2.46917,5.70892,3.16955,0.74034,4.44229,0.12885,0.00014,0.06419,0.06452 +80003,1.0,1e-05,0.0,4e-05,1e-05,0.0,0.00717,0.002,0.00017,0.00341,0.00017,0.0021,0.0,0.0001,0.0001,0.0002,0.00022,0.00032,0.0,0.00033,1e-05,0.00012,2e-05,1e-05,3e-05,7e-05,0.00197,0.0,0.0,0.00087,0.00105,4e-05,0.00064,0.00013,0.00013,0.00015,0.00023,0.00066,0.00013,0.00013,0.00015,0.00025,0.00031,9.07768,0.31945,2.95983,1.90525,0.08014,2.49229,0.03126,0.00036,0.0076,0.02329 +80006,1.0,1e-05,0.0,3e-05,1e-05,1e-05,0.00018,9e-05,1e-05,0.0001,1e-05,2e-05,0.0,1e-05,1e-05,4e-05,6e-05,3e-05,0.0,3e-05,1e-05,0.00014,2e-05,1e-05,3e-05,8e-05,0.00191,0.0,0.0,0.00085,0.00102,3e-05,0.00031,4e-05,5e-05,7e-05,0.00015,0.00035,4e-05,5e-05,7e-05,0.00019,0.00014,0.30801,0.01135,0.01796,0.03186,0.02231,0.01983,0.00109,4e-05,0.00037,0.00068 +80008,1.0,0.0,0.0,2e-05,1e-05,0.0,6e-05,3e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.00011,1e-05,1e-05,3e-05,6e-05,0.00087,0.0,0.0,0.00029,0.00054,3e-05,0.00019,3e-05,3e-05,4e-05,9e-05,0.00022,3e-05,3e-05,5e-05,0.00011,8e-05,0.00998,0.00754,0.00422,0.00443,0.00753,0.00838,0.00064,1e-05,0.00025,0.00039 +80009,1.0,0.0,0.0,2e-05,0.0,0.0,6e-05,4e-05,1e-05,3e-05,1e-05,1e-05,0.0,1e-05,1e-05,2e-05,4e-05,1e-05,0.0,2e-05,1e-05,0.0001,1e-05,1e-05,2e-05,6e-05,0.00166,0.0,0.0,0.0006,0.00096,9e-05,0.00018,2e-05,3e-05,4e-05,8e-05,0.0002,3e-05,3e-05,4e-05,0.0001,9e-05,0.01113,0.00707,0.00448,0.00439,0.00704,0.00756,0.00062,1e-05,0.00024,0.00038 +80010,1.0,0.0,0.0,2e-05,1e-05,0.0,0.00029,9e-05,2e-05,0.00019,2e-05,5e-05,0.0,2e-05,2e-05,4e-05,6e-05,4e-05,0.0,5e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00081,0.0,0.0,0.00029,0.00047,3e-05,0.00028,4e-05,5e-05,6e-05,0.00014,0.00029,4e-05,5e-05,6e-05,0.00014,0.0001,0.16442,0.01036,0.07177,0.06707,0.01113,0.01843,0.00119,5e-05,0.00044,0.00071 +80011,1.0,1e-05,0.0,2e-05,1e-05,0.0,0.00024,0.00011,1e-05,0.00011,1e-05,5e-05,0.0,1e-05,1e-05,3e-05,5e-05,2e-05,0.0,3e-05,1e-05,0.00012,1e-05,1e-05,3e-05,7e-05,0.00223,0.0,0.0,0.00091,0.00128,3e-05,0.00024,3e-05,4e-05,5e-05,0.00011,0.00039,5e-05,6e-05,8e-05,0.0002,0.00015,0.12153,0.00926,0.03022,0.03987,0.01226,0.02669,0.00122,6e-05,0.00041,0.00074 +80012,1.0,1e-05,1e-05,5e-05,2e-05,1e-05,0.12944,0.00948,0.00319,0.1228,0.01508,0.01544,1e-05,0.00278,0.00282,0.01608,0.01045,0.01441,1e-05,0.00969,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00712,0.0,0.0,0.00114,0.00588,0.0001,0.04937,0.01071,0.01504,0.00299,0.02064,0.04368,0.01964,0.00291,0.00302,0.01811,0.00022,12.97783,1.48448,14.6192,13.97161,0.21822,0.84397,0.06431,6e-05,0.02827,0.03598 +80013,1.0,1e-05,1e-05,6e-05,3e-05,2e-05,0.1199,0.03695,0.01935,0.102,0.0027,0.00302,1e-05,0.00225,0.00228,0.01796,0.02116,0.00532,1e-05,0.01363,2e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01429,0.0,0.0,0.00164,0.01255,0.0001,0.03417,0.00241,0.02652,0.0025,0.00274,0.0226,0.00241,0.00243,0.01501,0.00275,0.00022,13.60523,1.36616,7.1372,14.36814,0.29548,2.06563,0.07615,6e-05,0.03263,0.04346 +80014,1.0,1e-05,0.0,5e-05,2e-05,1e-05,0.14445,0.02412,0.00325,0.1205,0.01401,0.0171,1e-05,0.01586,0.0027,0.01885,0.01476,0.02184,1e-05,0.01489,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.01423,0.0,0.0,0.00045,0.0137,7e-05,0.05236,0.01712,0.0134,0.01858,0.00326,0.03909,0.00295,0.00304,0.01819,0.01491,0.00022,13.87276,1.58319,16.86404,20.31262,0.33334,1.18626,0.0891,6e-05,0.03898,0.05005 +80015,1.0,1e-05,0.0,5e-05,1e-05,1e-05,0.1144,0.0252,0.00321,0.09278,0.00325,0.0029,1e-05,0.02377,0.00271,0.00544,0.02689,0.00966,0.0,0.01107,1e-05,0.00016,2e-05,1e-05,4e-05,0.0001,0.00488,0.0,1e-05,0.00116,0.00362,8e-05,0.03073,0.00291,0.02156,0.003,0.00327,0.03713,0.01299,0.0029,0.01796,0.00327,0.00021,11.11203,1.42789,14.52747,18.30546,0.31599,0.9872,0.08237,4e-05,0.03526,0.04707 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/roc_auc_multiclass.classification_sparse/feature_runstatus.arff b/metalearning/metalearning_files/roc_auc_multiclass.classification_sparse/feature_runstatus.arff new file mode 100755 index 0000000..65f2169 --- /dev/null +++ b/metalearning/metalearning_files/roc_auc_multiclass.classification_sparse/feature_runstatus.arff @@ -0,0 +1,205 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} + +@DATA +233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +242,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +246,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +251,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +252,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +253,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +254,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +258,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +260,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +261,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +262,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +266,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +273,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +275,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +288,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2122,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +2350,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +3043,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75090,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75092,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75093,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75095,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75096,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75097,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75098,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75099,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75100,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75101,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75103,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75105,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75106,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75107,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,other,other,other +75108,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75109,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75110,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75112,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75113,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75114,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75115,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75116,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75117,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75119,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75120,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75121,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75123,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75124,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75125,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75126,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75127,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,ok,other,other,other,other,ok,other,other,other,other,other,other,other,other,other,other +75128,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75129,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75132,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75133,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75134,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75139,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75141,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75142,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75143,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75146,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75148,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75150,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75153,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75154,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75156,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75157,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75159,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75161,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75163,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75166,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75168,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75169,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75171,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75172,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75173,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75174,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75175,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75176,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75177,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75178,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75179,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75181,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75182,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75184,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75185,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75187,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75188,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75189,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75191,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,other,ok,ok,ok,ok +75192,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75193,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,ok,ok,ok,ok,ok,other,other,other +75195,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75196,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75197,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75198,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75201,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75202,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75203,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75205,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,other,other,other +75207,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,other,other,other,other,ok,ok,ok,ok,ok +75210,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75212,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75213,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75215,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75217,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75219,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75221,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75222,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75223,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75225,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75226,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75227,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75230,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75231,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75232,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75233,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75234,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75235,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75236,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75237,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75239,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75240,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75243,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75244,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75248,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75249,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +75250,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80001,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80003,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80006,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80008,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80009,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80010,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80011,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80012,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80013,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80014,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +80015,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/roc_auc_multiclass.classification_sparse/feature_values.arff b/metalearning/metalearning_files/roc_auc_multiclass.classification_sparse/feature_values.arff new file mode 100755 index 0000000..d049873 --- /dev/null +++ b/metalearning/metalearning_files/roc_auc_multiclass.classification_sparse/feature_values.arff @@ -0,0 +1,199 @@ +@RELATION auto-sklearn_FEATURE_VALUES + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC + +@DATA +233,1.0,0.999272896213,0.515873015873,0.5,0.484126984127,0.015873015873,0.0168067226891,59.5,2137.00036782,77.7042379374,-1.97698872639,348.650173646,0.886959734208,0.663342506681,0.994872812912,0.945845779597,0.59848852005,0.539667963588,-4.08597631255,4.08597631255,3.58351893846,7.66949525101,0.0,2.0,36.0,0.0,2142.0,0.0,0.0,36.0,0.424657534247,-0.922846794128,0.464067935944,0.0,0.0,0.0,0.0,0.0,46.249332428,0.0578633099794,-46.249332428,8.92753591886,0.0,0.0,0.0,0.0,0.0 +236,1.0,4.69928394786,0.0411940298507,0.0384615384615,0.034552238806,0.0015347192176,0.00119402985075,837.5,2.06066122975,0.687462200601,-0.421328164701,0.663300539275,0.942402560778,0.072386936046,0.863654536917,0.703244107403,0.639147159314,0.0593247580013,-6.7304212637,6.7304212637,2.77258872224,9.50300998594,0.0,26.0,16.0,0.0,13400.0,0.0,0.0,16.0,0.75,-0.380815505981,0.028198171407,0.0,0.0,0.0,0.0,0.0,1.16014313698,0.291224405635,-0.314185470343,0.451257987881,0.0,0.0,0.0,0.0,0.0 +242,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.161194029851,6.2037037037,1.47705389163,-0.320585833198,-1.39555700143,0.539893815413,0.953887008358,0.207594098406,0.867289582736,0.976069326716,0.917911239761,0.190276512695,-1.82514648526,1.82514648526,5.37527840768,7.20042489294,0.0,10.0,216.0,0.0,1340.0,0.0,0.0,216.0,0.138888888889,-0.952774763107,-0.0304282046854,0.0,0.0,0.0,0.0,0.0,0.813711464405,0.0165488742145,-1.08211100101,0.421930399115,0.0,0.0,0.0,0.0,0.0 +244,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0567164179104,17.6315789474,1.97631406784,0.0994548453866,-1.1362007318,0.615125670382,0.767297846807,0.207538026238,0.740749596472,0.802805378089,0.761592579829,0.141000519745,-2.86969155266,2.86969155266,4.33073334029,7.20042489294,0.0,10.0,76.0,0.0,1340.0,0.0,0.0,76.0,0.763157894737,-0.611301183701,-0.395152509212,0.0,0.0,0.0,0.0,0.0,1.03990912437,0.541582835054,-0.134199678898,0.248376006794,0.0,0.0,0.0,0.0,0.0 +246,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0477611940299,20.9375,0.610815512161,-0.0478360678691,-0.952713977644,0.261066868665,0.948478315483,0.202998487694,0.770797434335,0.947025363291,0.92524324353,0.134323024269,-3.04154180959,3.04154180959,4.15888308336,7.20042489294,0.0,10.0,64.0,0.0,1340.0,0.0,0.0,64.0,0.78125,-0.244627714157,0.302690923214,0.0,0.0,0.0,0.0,0.0,0.398750066757,-0.0110553486202,-0.344300329685,0.164522777123,0.0,0.0,0.0,0.0,0.0 +248,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0044776119403,223.333333333,0.365449105742,-0.503090653659,-1.07466833274,0.467629276764,0.650739538109,0.211250205448,0.657726923585,0.732062061863,0.635907337577,0.206713633223,-5.40866542372,5.40866542372,1.79175946923,7.20042489294,0.0,10.0,6.0,0.0,1340.0,0.0,0.0,6.0,0.5,-1.17463731766,-0.0688808187842,0.0,0.0,0.0,0.0,0.0,1.01843345165,0.519759883483,-0.0460014343262,0.334873639502,0.0,0.0,0.0,0.0,0.0 +251,1.0,1.19220287695,0.718480138169,0.25,0.0405872193437,0.277625793268,0.00518134715026,193.0,-0.532056222247,-1.02203946743,-1.55607253639,0.417832789889,0.626877172475,0.718552554677,0.974102556215,0.895628902765,0.803091964277,0.718552554677,-5.2626901889,5.2626901889,1.79175946923,7.05444965813,0.0,4.0,6.0,0.0,1158.0,0.0,0.0,6.0,0.714285714286,-0.499476452566,-0.030827689521,0.0,0.0,0.0,0.0,0.0,1.21158729679,0.963197188624,0.666278818223,0.224079692061,0.0,0.0,0.0,0.0,0.0 +252,1.0,3.32041944491,0.10671641791,0.1,0.0917910447761,0.00455162706925,0.0350746268657,28.5106382979,4.77061871216,0.714096208571,-0.983288772922,1.39439355822,0.791067880086,0.199236678108,0.641993124315,0.811257788913,0.726474938004,0.194709767152,-3.35027729123,3.35027729123,3.85014760171,7.20042489294,0.0,10.0,47.0,0.0,1340.0,0.0,0.0,47.0,0.340425531915,-0.0262751579285,0.252534598112,0.0,0.0,0.0,0.0,0.0,1.79809057713,0.778331822646,-0.00112716155127,0.482265625274,0.0,0.0,0.0,0.0,0.0 +253,1.0,1.54449146176,0.422492401216,0.333333333333,0.233029381966,0.077748377204,0.00911854103343,109.666666667,49.8519091847,4.00313231091,-1.98363636364,11.0280934027,0.44573283859,0.422479901051,0.441764584622,0.521882086168,0.442723149866,0.422479901051,-4.6974454621,4.6974454621,2.19722457734,6.89467003943,0.0,3.0,9.0,0.0,987.0,0.0,0.0,9.0,0.625,-0.747097235648,0.179397301146,0.0,0.0,0.0,0.0,0.0,7.20082697922,1.2527294309,-3.2278304349,2.07322318766,0.0,0.0,0.0,0.0,0.0 +254,1.0,0.999279825932,0.515797207935,0.5,0.484202792065,0.0157972079353,0.00404114621602,247.454545455,1809.66721804,92.7560122576,-3.0,256.090764418,1.0,0.880781668818,0.999816513761,0.999448528169,0.951318739012,0.770934653526,-5.51122691041,5.51122691041,3.09104245336,8.60226936377,0.0,2.0,22.0,1.0,5444.0,1694.0,1694.0,22.0,0.504273504274,-1.54639201537,0.353637410569,0.0454545454545,0.311168258633,0.0141440117561,0.0,0.0,42.5636842629,5.67229650145,-6.06325335433,7.91135965974,0.0,0.0,0.0,0.0,0.0 +258,1.0,3.32147428461,0.103558151885,0.1,0.0947955390335,0.00250278739627,0.0169941582581,58.84375,3761.0001372,156.137968285,-3.0,525.521849351,0.973442729294,0.199397608087,0.882623141628,0.948741429125,0.678836762777,0.104088459128,-4.07488562586,4.07488562586,4.15888308336,8.23376870922,0.0,10.0,64.0,0.0,3766.0,0.0,0.0,64.0,0.640625,0.494631052017,0.27237829566,0.0,0.0,0.0,0.0,0.0,61.3432998657,5.08894696726,-1.31375944614,10.9604333768,0.0,0.0,0.0,0.0,0.0 +260,1.0,0.643088088352,0.89555494955,0.2,0.00572675211344,0.348332460381,0.00272702481593,366.7,3324.62310889,454.689992553,0.726310315867,967.058538342,0.963180389904,0.9334707026,0.959381929724,0.946013137915,0.86206179203,0.89556694077,-5.90454407508,5.90454407508,2.30258509299,8.20712916807,0.0,5.0,10.0,0.0,3667.0,0.0,0.0,10.0,0.7,109.797424316,8.22018623352,0.0,0.0,0.0,0.0,0.0,56.4270515442,12.7446427584,-0.828330397606,15.7223587647,0.0,0.0,0.0,0.0,0.0 +261,1.0,0.881290899231,0.7,0.5,0.3,0.2,0.0298507462687,33.5,162.506006006,10.3543050861,-1.99568258046,25.0535258514,0.664117913108,0.700008646607,0.683457046319,0.747791789715,0.655251150664,0.700008646607,-3.51154543883,3.51154543883,2.99573227355,6.50727771239,0.0,2.0,20.0,0.0,670.0,0.0,0.0,20.0,0.672131147541,0.0188030356073,0.563845846839,0.0,0.0,0.0,0.0,0.0,12.8259894747,2.16182777621,-4.77594005825,2.74575976875,0.0,0.0,0.0,0.0,0.0 +262,1.0,3.32067304561,0.105906313646,0.1,0.0930074677529,0.00416606343707,0.00217243720299,460.3125,2.77193020897,-0.521490314257,-1.6819919882,1.04595534688,0.992118074724,0.203526670177,0.956951206597,0.877643454944,0.860006786323,0.196193975536,-6.13190560666,6.13190560666,2.77258872224,8.9044943289,0.0,10.0,16.0,0.0,7365.0,0.0,0.0,16.0,0.625,-0.864243030548,0.634598791599,0.0,0.0,0.0,0.0,0.0,0.971705973148,-0.0332035149913,-1.45298993587,0.648936092127,0.0,0.0,0.0,0.0,0.0 +266,1.0,2.80621328586,0.153100775194,0.142857142857,0.137596899225,0.00570974835668,0.0122739018088,81.4736842105,357.489860067,37.9013191208,-3.0,92.6731707323,0.955311366342,0.292639974908,0.956608963978,0.910150868675,0.752557539821,0.292639974908,-4.40028007498,4.40028007498,2.94443897917,7.34471905415,0.0,7.0,19.0,0.0,1548.0,0.0,0.0,19.0,0.526315789474,-0.498939752579,0.820329546928,0.0,0.0,0.0,0.0,0.0,17.6498298645,3.20952717823,-0.853714942932,4.84805496192,0.0,0.0,0.0,0.0,0.0 +273,1.0,0.968982336215,0.60330846578,0.5,0.39669153422,0.10330846578,0.0184884852416,54.0877192982,1761.55940072,239.971301849,3.90830966677,329.415941566,0.903002984071,0.796627159249,0.906574412642,0.879664186946,0.807319379649,0.700633589711,-3.99060716005,3.99060716005,4.04305126783,8.03365842789,0.0,2.0,57.0,0.0,3083.0,0.0,0.0,57.0,0.842105263158,83.9751281738,7.99759721756,0.0,0.0,0.0,0.0,0.0,38.266456604,11.1916515179,1.49012053013,7.6081346188,0.0,0.0,0.0,0.0,0.0 +275,1.0,1.48437812864,0.514967259121,0.333333333333,0.241347053321,0.128438129204,0.0280636108513,35.6333333333,2133.00046795,303.840440346,-1.9999964997,725.045652658,0.716592563341,0.623532437268,0.935942977004,0.942965424948,0.739967835744,0.514971004636,-3.57328152936,3.57328152936,4.09434456222,7.66762609158,0.0,3.0,60.0,0.0,2138.0,0.0,0.0,60.0,0.582456140351,2121.78246537,46.0266231844,0.0,0.0,0.0,0.0,0.0,46.2060652723,7.8669832507,-0.304691537091,15.6189312976,0.0,0.0,0.0,0.0,0.0 +288,1.0,1.58480205464,0.340298507463,0.333333333333,0.328955223881,0.00497910288684,0.0119402985075,83.75,0.156339921079,-0.199790197878,-0.649121004238,0.252314536062,0.720584460078,0.568649816572,0.743889422647,0.854929315144,0.79553415025,0.524469648124,-4.42783617071,4.42783617071,3.68887945411,8.11671562482,0.0,3.0,40.0,0.0,3350.0,0.0,0.0,40.0,0.85,-1.04597449303,0.0169709250331,0.0,0.0,0.0,0.0,0.0,0.273815393448,0.0343443818158,-0.253659516573,0.12118453768,0.0,0.0,0.0,0.0,0.0 +2117,1.0,0.791054085769,0.762383498854,0.5,0.237616501146,0.262383498854,0.000427807486631,2337.5,4085.62524452,421.341801104,-1.97361727441,770.381750291,0.796057266587,0.762383516884,0.800946915335,0.853444157698,0.710311363988,0.762383516884,-7.75683726116,7.75683726116,2.63905732962,10.3958945908,0.0,2.0,14.0,3.0,32725.0,2361.0,4259.0,14.0,0.79674796748,-1.20623522238,-0.00808692092476,0.214285714286,0.0721466768526,0.00929608206919,0.0,0.0,63.9345387449,13.7332001662,-4.31025699997,15.3190672948,0.0,0.0,0.0,0.0,0.0 +2119,1.0,2.49405265377,0.31256281407,0.1,0.00502512562814,0.110695431015,0.00804020100503,124.375,96.1730638637,23.5318656471,0.289336462313,35.4615158565,0.521682455581,0.422481490666,0.522437180395,0.596531317428,0.131363101407,0.327459345063,-4.82330119548,4.82330119548,2.07944154168,6.90274273716,0.0,10.0,8.0,0.0,995.0,0.0,0.0,8.0,0.875,0.84527349472,0.549412727356,0.0,0.0,0.0,0.0,0.0,9.81375312805,2.68379187025,-1.62825334072,3.92795101422,0.0,0.0,0.0,0.0,0.0 +2120,1.0,2.48441452237,0.236481782316,0.166666666667,0.101647714087,0.0616159037983,0.00835460663727,119.694444444,1.30405437463,-0.133565705603,-0.93160611535,0.849137044524,0.903221385431,0.436291519826,0.847731352998,0.835202610448,0.797373699925,0.425861324032,-4.78494219916,4.78494219916,3.58351893846,8.36846113762,0.0,6.0,36.0,0.0,4309.0,0.0,0.0,36.0,0.166666666667,-1.01136720181,0.0822907611728,0.0,0.0,0.0,0.0,0.0,0.914371311665,0.0395210185541,-0.693340539932,0.560722941136,0.0,0.0,0.0,0.0,0.0 +2122,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +2123,1.0,0.535774508781,0.907572383073,0.333333333333,0.0400890868597,0.406079119466,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.921033713478,0.907697050141,0.915476776657,0.935406846587,0.719308001611,0.907697050141,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,3.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +2350,1.0,0.942941489802,0.639688456601,0.5,0.360311543399,0.139688456601,0.0123555840822,80.9350649351,-2.0,-2.35464535465,-3.0,0.478405713881,?,?,?,?,?,?,-4.3936471657,4.3936471657,6.90875477932,11.302401945,0.0,2.0,1001.0,0.0,81016.0,0.0,0.0,1001.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.00799200799201,-1.0,0.803299927277,0.0,0.0,0.0,0.0,0.0 +3043,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75090,1.0,3.31608450333,0.111876075731,0.1,0.0873493975904,0.00900165535102,0.33734939759,2.96428571429,2319.00060676,246.285684806,-3.0,595.44692594,0.867027749596,0.203961958403,0.715178631066,0.790408449703,0.585557083657,0.186334413071,-1.08663609762,1.08663609762,6.66440902035,7.75104511797,0.0,10.0,784.0,0.0,2324.0,0.0,0.0,784.0,0.297193877551,1.32184314728,1.17731952667,0.0,0.0,0.0,0.0,0.0,48.1767654419,7.98432078413,-0.221062287688,13.3394336269,0.0,0.0,0.0,0.0,0.0 +75092,1.0,0.554596216538,0.871033776868,0.5,0.128966223132,0.371033776868,0.0378710337769,26.4054054054,307.228420462,73.1986436727,-1.49002473105,83.8268551286,0.854647144706,0.871057620174,0.865902980999,0.894610740266,0.832187007887,0.871057620174,-3.2735687394,3.2735687394,3.61091791264,6.88448665204,0.0,2.0,37.0,0.0,977.0,0.0,0.0,37.0,0.405405405405,13.672712326,2.99844360352,0.0,0.0,0.0,0.0,0.0,14.9804191589,5.60367639141,-0.434899806976,3.80746374708,0.0,0.0,0.0,0.0,0.0 +75093,1.0,0.716242743687,0.802824626354,0.5,0.197175373646,0.302824626354,0.00287947346771,347.285714286,1099.30408654,249.456392561,3.40061397691,295.020874524,0.759504877604,0.802824889469,0.745925364261,0.809132840307,0.799534209613,0.802824889469,-5.85014782526,5.85014782526,3.04452243772,8.89467026298,0.0,2.0,21.0,5.0,7293.0,4.0,20.0,21.0,0.380952380952,119.084976196,7.93084049225,0.238095238095,0.000548471136706,0.000130588365882,0.0,0.0,25.6689071655,10.3935895591,1.92362296581,6.47938017543,0.0,0.0,0.0,0.0,0.0 +75095,1.0,0.306806412139,0.945112550108,0.5,0.0548874498921,0.445112550108,0.00154178230034,648.6,142.871655849,56.7327632926,0.231013261817,60.8411561207,0.967007479735,0.945114709852,0.975638070793,0.942650316507,0.886523757861,0.945114709852,-6.47481619387,6.47481619387,1.60943791243,8.08425410631,0.0,2.0,5.0,0.0,3243.0,0.0,0.0,5.0,0.8,95.4308853149,6.82952308655,0.0,0.0,0.0,0.0,0.0,8.73403549194,3.78358723521,-0.6337890625,3.91974861085,0.0,0.0,0.0,0.0,0.0 +75096,1.0,1.41635357334,0.501146693809,0.1,8.73671473316e-06,0.18237697845,1.45611912219e-05,68675.7,-1.21375398964,-1.28759469151,-1.36177660052,0.0728474278899,0.499939481769,0.501146694877,0.619945768953,0.501146694877,0.501146694877,0.501146694877,-11.1371507038,11.1371507038,2.30258509299,13.4397357968,0.0,10.0,10.0,0.0,686757.0,0.0,0.0,10.0,1.0,-0.276484966278,-0.000731337349862,0.0,0.0,0.0,0.0,0.0,0.00427993573248,-7.83437888458e-05,-0.00362038123421,0.00220376678795,0.0,0.0,0.0,0.0,0.0 +75097,1.0,0.319921228295,0.941883767535,0.5,0.0581162324649,0.441883767535,0.000409910730552,2439.55555556,-2.0,-2.83677103428,-3.0,0.3695744451,0.923984589915,?,?,?,?,?,-7.79957115233,7.79957115233,2.19722457734,9.99679572967,0.0,2.0,9.0,0.0,21956.0,0.0,0.0,9.0,?,?,?,0.0,0.0,0.0,0.0,0.0,1.0,0.0924469505861,-1.0,0.393296995983,0.0,0.0,0.0,0.0,0.0 +75098,1.0,3.3197854104,0.112686567164,0.1,0.0905756929638,0.00548370842142,0.0167164179104,59.8214285714,46895.0163688,3003.69805268,-3.0,9183.90996924,0.941727870507,0.202045614109,0.864074048704,0.862536698528,0.523646163666,0.1843069957,-4.09136393408,4.09136393408,6.66440902035,10.7557729544,0.0,10.0,784.0,0.0,46900.0,0.0,0.0,784.0,0.414540816327,1.3625254631,1.1639302969,0.0,0.0,0.0,0.0,0.0,216.557189941,23.7267943443,-0.238432213664,48.2843670982,0.0,0.0,0.0,0.0,0.0 +75099,1.0,0.628799576069,0.842291371994,0.5,0.157708628006,0.342291371994,0.0148514851485,67.3333333333,110.319590087,34.5482385607,1.28655859812,32.1934841871,0.823192600028,0.842301859563,0.801945831613,0.848644952091,0.818937350732,0.842301859563,-4.20965540873,4.20965540873,3.04452243772,7.25417784646,0.0,2.0,21.0,0.0,1414.0,0.0,0.0,21.0,0.333333333333,20.9032917023,3.62549352646,0.0,0.0,0.0,0.0,0.0,8.83521938324,4.31837968599,1.12928807735,2.07608237366,0.0,0.0,0.0,0.0,0.0 +75100,1.0,0.0397772338043,0.995727636849,0.5,0.00427236315087,0.495727636849,0.00961281708945,104.027777778,2117.59484987,597.0928022,-1.03454018425,587.571395641,0.992256676846,0.995729764448,0.993326195562,0.991185007622,0.964751442512,0.995729764448,-4.6446579575,4.6446579575,3.58351893846,8.22817689595,0.0,2.0,36.0,0.0,3745.0,0.0,0.0,36.0,0.277777777778,650.664245605,19.8559150696,0.0,0.0,0.0,0.0,0.0,41.3153152466,16.6491614655,-2.66185569763,12.239529498,0.0,0.0,0.0,0.0,0.0 +75101,1.0,0.997248526884,0.530870399123,0.5,0.469129600877,0.0308703991232,0.000426218528328,2346.21428571,62.4506316794,8.00258712521,-1.86122943453,15.030167406,0.589322875015,0.607544001849,0.625825770484,0.639921656568,0.598243625764,0.530870397584,-7.76055836593,7.76055836593,3.33220451018,11.0927628761,0.0,2.0,28.0,0.0,65694.0,0.0,0.0,28.0,0.821428571429,12.9773368835,2.51856350899,0.0,0.0,0.0,0.0,0.0,5.95728397369,1.36706479669,-0.0112664336339,1.64295848128,0.0,0.0,0.0,0.0,0.0 +75103,1.0,0.328390811786,0.939761534474,0.5,0.0602384655262,0.439761534474,0.0223950233281,44.6527777778,9640.00329122,412.223146975,-3.0,1205.01701718,0.980092979861,0.939761666399,0.989008770406,0.983099344563,0.965679590056,0.939761666399,-3.79891651766,3.79891651766,5.37527840768,9.17419492534,0.0,2.0,216.0,0.0,9645.0,0.0,0.0,216.0,0.768518518519,2.51708889008,1.40736579895,0.0,0.0,0.0,0.0,0.0,98.1937103271,11.9622064832,-1.18286156654,16.4548361994,0.0,0.0,0.0,0.0,0.0 +75105,1.0,0.127986833522,0.982358208955,0.5,0.0176417910448,0.482358208955,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75106,1.0,0.381002983392,0.925910447761,0.5,0.0740895522388,0.425910447761,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,0.86668688323,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75107,1.0,0.376199211223,0.927223880597,0.5,0.072776119403,0.427223880597,0.00686567164179,145.652173913,32642.0000306,14.4628516113,-3.0,534.366222155,?,?,?,?,?,?,-4.98122140889,4.98122140889,5.43807930892,10.4193007178,0.0,2.0,230.0,211.0,33500.0,33500.0,5376290.0,230.0,?,?,?,0.917391304348,1.0,0.697766385464,0.0,0.0,180.676506582,0.147690612529,-1.79455613167,3.83922786346,0.0,0.0,0.0,0.0,0.0 +75108,1.0,0.623177503715,0.84460529292,0.5,0.15539470708,0.34460529292,0.0377742592174,26.4730538922,4416.00022624,384.89251373,-1.62978069235,899.603242031,0.999095531018,0.857951823434,0.990951217554,0.999322288387,0.99841730638,0.844606434343,-3.27612738141,3.27612738141,5.11799381242,8.39412119383,0.0,2.0,167.0,0.0,4421.0,0.0,0.0,167.0,0.455223880597,-1.41229313333,0.269744588772,0.0,0.0,0.0,0.0,0.0,66.4680391334,10.4393080553,-1.47496001766,16.6350415521,0.0,0.0,0.0,0.0,0.0 +75109,1.0,2.19118482218,0.295086923658,0.2,0.0988662131519,0.0827522119911,0.00483749055178,206.71875,174.465091115,31.6397194895,6.98006630894,33.0414581553,0.600750706561,0.448075217834,0.49557132911,0.454583515563,0.422105022661,0.358902480816,-5.33135917375,5.33135917375,3.4657359028,8.79709507655,0.0,5.0,32.0,0.0,6615.0,0.0,0.0,32.0,0.625,7.46919441223,2.15719389915,0.0,0.0,0.0,0.0,0.0,4.34733104706,0.529852224194,-1.75926208496,1.72648564391,0.0,0.0,0.0,0.0,0.0 +75110,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,11.2988068803,3.02829031564,-1.96147480373,2.51946462021,0.454177689164,0.179755953381,0.746197559818,0.35886704289,0.125214802991,0.158633473601,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.8,-1.20203325215,0.00126834174541,0.0,0.0,0.0,0.0,0.0,3.64675292284,2.14172190614,0.19627836425,0.664317388316,0.0,0.0,0.0,0.0,0.0 +75112,1.0,0.93715479628,0.646500313873,0.5,0.353499686127,0.146500313873,0.000784682988073,1274.4,16.8916431645,4.25878063156,-0.530386545382,5.36692192066,0.811283197187,0.716730348094,0.804537854443,0.784133934872,0.726221099401,0.716730348094,-7.1502307586,7.1502307586,2.30258509299,9.45281585159,0.0,2.0,10.0,0.0,12744.0,0.0,0.0,10.0,0.7,0.244805812836,0.586186289787,0.0,0.0,0.0,0.0,0.0,3.38979673386,0.659515073895,-1.14245140553,1.25746030529,0.0,0.0,0.0,0.0,0.0 +75113,1.0,0.332486609263,0.938724727838,0.5,0.0612752721617,0.438724727838,0.0111975116641,89.3055555556,9640.00426535,556.312160596,-3.0,1718.93074719,0.990046973221,0.938724859263,0.986315219917,0.983517505892,0.960809871538,0.938724859263,-4.49206369822,4.49206369822,4.68213122712,9.17419492534,0.0,2.0,108.0,0.0,9645.0,0.0,0.0,108.0,0.768518518519,2.4252038002,1.42401814461,0.0,0.0,0.0,0.0,0.0,98.1937179565,13.0089539592,-1.18243312836,19.7154156441,0.0,0.0,0.0,0.0,0.0 +75114,1.0,0.770739752552,0.774131274131,0.5,0.225868725869,0.274131274131,10.555019305,0.0947416552355,1028.52771355,44.0091247603,-1.14680430432,107.259314261,0.920926242043,0.923746399232,0.919835075927,0.965261833636,0.960435115047,0.774151285608,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0556012802926,-0.560082435608,0.28782582283,0.0,0.0,0.0,0.0,0.0,32.0826797485,3.85942410464,-0.121237434447,4.14985285733,0.0,0.0,0.0,0.0,0.0 +75115,1.0,0.4093488423,0.917953667954,0.5,0.0820463320463,0.417953667954,10.555019305,0.0947416552355,1027.28918157,43.3685717369,-0.964897222717,102.652664519,0.956589850279,0.974887798286,0.957542231232,0.973953732352,0.953686919876,0.917978235357,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.572831869125,0.361229658127,0.0,0.0,0.0,0.0,0.0,32.0549507141,3.85518592769,-0.0889379307628,4.03804150888,0.0,0.0,0.0,0.0,0.0 +75116,1.0,0.66183919185,0.828185328185,0.5,0.171814671815,0.328185328185,10.555019305,0.0947416552355,1028.62304869,42.6605440961,-0.819603790954,100.488743508,0.971059577677,0.97007918552,0.97007918552,0.981636500754,0.981636500754,0.829166666667,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0559670781893,-0.486678600311,0.397382259369,0.0,0.0,0.0,0.0,0.0,32.084903717,3.84717712187,-0.0746779441833,3.98064955932,0.0,0.0,0.0,0.0,0.0 +75117,1.0,0.402587193355,0.919884169884,0.5,0.0801158301158,0.419884169884,10.555019305,0.0947416552355,1028.8966008,40.1923217385,-0.969790689895,93.9983863034,0.911197411003,0.919916782247,0.901599630143,0.931437817846,0.882182154415,0.919916782247,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0558756287151,-0.701246500015,0.293553590775,0.0,0.0,0.0,0.0,0.0,32.0912742615,3.75534706928,-0.0179622154683,3.82802927737,0.0,0.0,0.0,0.0,0.0 +75119,1.0,0.287216455064,0.949806949807,0.5,0.0501930501931,0.449806949807,10.555019305,0.0947416552355,1029.62132967,40.8427164739,-1.08313437284,94.245425477,0.928628240691,0.949829830364,0.932437764501,0.948877627227,0.902451989758,0.949829830364,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.530739307404,0.319510936737,0.0,0.0,0.0,0.0,0.0,32.108291626,3.77395516924,0.0206038150936,3.87459207365,0.0,0.0,0.0,0.0,0.0 +75120,1.0,0.240341507722,0.960424710425,0.5,0.0395752895753,0.460424710425,10.555019305,0.0947416552355,1029.8629087,41.0415691128,-0.704793734748,95.6273735794,0.944061310857,0.960436004125,0.945966072762,0.9604640101,0.940196664177,0.960436004125,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.574395895004,0.328372776508,0.0,0.0,0.0,0.0,0.0,32.1138839722,3.77815465102,-0.0262571237981,3.87937297388,0.0,0.0,0.0,0.0,0.0 +75121,1.0,0.266379433469,0.954633204633,0.5,0.0453667953668,0.454633204633,10.555019305,0.0947416552355,1029.63988022,43.7730425239,-0.835580270567,104.4127933,0.996125657134,0.993231706424,0.987443805005,0.995164118672,0.996125657134,0.954657594927,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.602682113647,0.3265863657,0.0,0.0,0.0,0.0,0.0,32.1087188721,3.86035677573,-0.197968944907,4.09343551315,0.0,0.0,0.0,0.0,0.0 +75123,1.0,1.58381731695,0.349410503751,0.333333333333,0.316898892462,0.0132752137157,0.00285816362987,349.875,2.56860282793,-0.109414547053,-1.70547855097,1.16813638928,0.568407102122,0.57628893284,0.554135167193,0.634541155045,0.572712283131,0.555217156973,-5.85757594784,5.85757594784,2.07944154168,7.93701748952,0.0,3.0,8.0,0.0,2799.0,0.0,0.0,8.0,0.4,-0.58695078317,-0.0466525045772,0.0,0.0,0.0,0.0,0.0,0.812069328966,0.33219387749,-0.639258108728,0.516697673611,0.0,0.0,0.0,0.0,0.0 +75124,1.0,0.535402028451,0.877887788779,0.5,0.122112211221,0.377887788779,0.00528052805281,189.375,211.433213338,23.2730747947,-1.99988846098,37.8720723516,0.858745874587,0.877887788779,0.860726072607,0.893729372937,0.8300330033,0.877887788779,-5.24372917626,5.24372917626,2.77258872224,8.0163178985,0.0,2.0,16.0,0.0,3030.0,0.0,0.0,16.0,0.705882352941,0.235066439588,0.931811266085,0.0,0.0,0.0,0.0,0.0,14.6093536249,3.24265471222,-8.02174598889,3.58166228525,0.0,0.0,0.0,0.0,0.0 +75125,1.0,0.691595434977,0.814671814672,0.5,0.185328185328,0.314671814672,10.555019305,0.0947416552355,1028.87875412,41.8855693627,-0.993046045303,103.100883577,0.959455439383,0.955655250898,0.955590614887,0.971059426011,0.968109641168,0.814686688716,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0562414266118,-0.600806951523,-0.305236846209,0.0,0.0,0.0,0.0,0.0,32.0909500122,3.79861278387,-0.0161020699888,4.02351120791,0.0,0.0,0.0,0.0,0.0 +75126,1.0,0.560922169438,0.868725868726,0.5,0.131274131274,0.368725868726,10.555019305,0.0947416552355,1027.24781167,43.7795101218,-0.970656612389,104.050080981,0.889012322629,0.911202389843,0.9160660941,0.91997759522,0.831067961165,0.869716206124,2.35660151029,-2.35660151029,9.29972393311,6.94312242282,0.0,2.0,10935.0,0.0,1036.0,0.0,0.0,10935.0,0.0560585276635,-0.605868339539,0.321388810873,0.0,0.0,0.0,0.0,0.0,32.053981781,3.87234596142,-0.0496538355947,4.08159091027,0.0,0.0,0.0,0.0,0.0 +75127,1.0,0.991311042762,0.554820732345,0.5,0.445179267655,0.0548207323451,1.93698168445e-05,51626.7142857,?,?,?,?,?,?,?,?,?,?,-10.8517945362,10.8517945362,1.94591014906,12.7977046853,0.0,2.0,7.0,0.0,361387.0,0.0,0.0,7.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 +75128,1.0,0.589483471374,0.85798816568,0.5,0.14201183432,0.35798816568,0.709148839326,1.41014120668,2191.9998428,212.748336672,-1.12449462278,132.926458192,0.955375336455,0.906217690448,0.964034606275,0.960825843509,0.78517237396,0.857991741001,-0.343689845954,0.343689845954,7.35115822643,7.69484807238,0.0,2.0,1558.0,0.0,2197.0,0.0,0.0,1558.0,0.183568677792,169.337173462,12.4814958572,0.0,0.0,0.0,0.0,0.0,46.8401489258,14.0381068456,-1.26891887188,4.20269623131,0.0,0.0,0.0,0.0,0.0 +75129,1.0,0.478600692373,0.896946564885,0.5,0.103053435115,0.396946564885,0.0353053435115,28.3243243243,874.694492983,200.7487406,-1.46700556696,226.000284171,0.876016483516,0.89695970696,0.855961538462,0.887380952381,0.390631868132,0.89695970696,-3.34372095224,3.34372095224,3.61091791264,6.95463886488,0.0,2.0,37.0,0.0,1048.0,0.0,0.0,37.0,0.324324324324,246.701797485,12.6564016342,0.0,0.0,0.0,0.0,0.0,28.6533489227,9.79013447504,-0.560551762581,7.87151073942,0.0,0.0,0.0,0.0,0.0 +75132,1.0,0.26273400876,0.955460002274,0.5,0.0445399977259,0.455460002274,8.97682184599e-06,111398.0,552221.168774,61369.3765334,-1.07768338943,173542.316217,0.919708310803,0.955460002311,0.915451303537,0.955463992027,0.94138833485,0.955460002311,-11.620864653,11.620864653,2.19722457734,13.8180892303,0.0,2.0,9.0,0.0,1002582.0,0.0,0.0,9.0,0.888888888889,24.3675231934,4.25315666199,0.0,0.0,0.0,0.0,0.0,699.80456543,79.6538730057,-0.878350317478,219.269689246,0.0,0.0,0.0,0.0,0.0 +75133,1.0,0.0619679303475,0.992747911083,0.5,0.00725208891692,0.492747911083,0.0059908560618,166.921052632,3172.1966846,738.874649273,-1.96869012413,876.157558572,0.992747696815,0.992748941128,0.99416825345,0.977455190517,0.938980497016,0.992748941128,-5.11752096191,5.11752096191,3.63758615973,8.75510712163,0.0,2.0,38.0,0.0,6343.0,0.0,0.0,38.0,0.368421052632,749.784851074,21.0152416229,0.0,0.0,0.0,0.0,0.0,55.1996040344,17.977442621,-2.01598954201,15.5103493565,0.0,0.0,0.0,0.0,0.0 +75134,1.0,2.7114014308,0.330617344306,0.0909090909091,0.00841956598495,0.0980342234739,6.33730773061e-05,15779.5714286,1.20109530542,-0.475890136744,-1.20190109236,0.806907016177,0.743846143613,0.33061735079,0.826140541158,0.391482749362,0.406827833347,0.33061735079,-9.66647143488,9.66647143488,1.94591014906,11.6123815839,0.0,11.0,7.0,0.0,110457.0,0.0,0.0,7.0,0.642857142857,-1.26443797263,-0.0257557160505,0.0,0.0,0.0,0.0,0.0,1.78916050298,0.874254965312,-0.00565886701934,0.688401964641,0.0,0.0,0.0,0.0,0.0 +75139,1.0,0.923767464483,0.661094527363,0.5,0.338905472637,0.161094527363,0.00477611940299,209.375,144.542816466,17.5135930246,-3.0,33.1751970968,0.958608714603,0.661094655039,0.975420985809,0.874424000289,0.660995155309,0.661094655039,-5.34412690258,5.34412690258,3.87120101091,9.21532791349,0.0,2.0,48.0,0.0,10050.0,0.0,0.0,48.0,0.395833333333,0.490350008011,0.831942021847,0.0,0.0,0.0,0.0,0.0,11.5513057709,2.5916065642,0.0,3.22016513085,0.0,0.0,0.0,0.0,0.0 +75141,1.0,0.973185555396,0.596101293496,0.5,0.403898706504,0.0961012934961,0.0014574603753,686.125,5.99462232855,0.527315457537,-1.3756378438,2.91308429416,0.871027761158,0.79595355856,0.910188942192,0.905091402236,0.901262609341,0.596101533463,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,0.779330253601,0.171156719327,0.0,0.0,0.0,0.0,0.0,2.05943989754,0.455381662177,-0.365326762199,0.902382745064,0.0,0.0,0.0,0.0,0.0 +75142,1.0,0.999999836609,0.500237964488,0.5,0.499762035512,0.000237964488376,0.000366099212887,2731.5,-1.48943004399,-1.55168673081,-1.99991071627,0.149569644773,0.878198549965,0.801171338163,0.905289625384,0.832948269335,0.806369799896,0.506278906749,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.337645769119,0.0136278076097,0.0,0.0,0.0,0.0,0.0,0.0122651197016,-0.00150358646351,-0.0146135129035,0.00747492765935,0.0,0.0,0.0,0.0,0.0 +75143,1.0,0.789110569531,0.763535911602,0.5,0.236464088398,0.263535911602,0.00257826887661,387.857142857,73.5553116067,16.6863808817,-1.98725671908,27.1298798146,0.983428670219,0.994471717867,0.992995703107,0.951009105033,0.956152153595,0.76353788748,-5.96063708331,5.96063708331,1.94591014906,7.90654723237,0.0,2.0,7.0,0.0,2715.0,0.0,0.0,7.0,1.0,0.0561573505402,0.515937030315,0.0,0.0,0.0,0.0,0.0,7.90272378922,2.75709193093,-0.141120478511,3.01508030366,0.0,0.0,0.0,0.0,0.0 +75146,1.0,0.984891570166,0.572234885488,0.5,0.427765114512,0.0722348854879,0.00434169108868,230.325,5698.53981279,740.144320479,-0.633732230533,1633.62273998,0.798653916563,0.754695274199,0.830022851054,0.874632960237,0.65591928534,0.583307445469,-5.43949135499,5.43949135499,3.68887945411,9.12837080911,0.0,2.0,40.0,0.0,9213.0,0.0,0.0,40.0,0.45,6.15325164795,2.21310305595,0.0,0.0,0.0,0.0,0.0,2.30728960037,-3.77885209978,-72.7904891968,15.3024178752,0.0,0.0,0.0,0.0,0.0 +75148,1.0,0.99995739826,0.503842459174,0.5,0.496157540826,0.00384245917387,0.0028818443804,347.0,11.5456267273,2.4406018334,0.248121740224,4.07567166605,0.793452190449,0.742060312241,0.764164395228,0.850171946983,0.744478093734,0.540373347095,-5.84932477995,5.84932477995,1.79175946923,7.64108424917,0.0,2.0,6.0,0.0,2082.0,0.0,0.0,6.0,0.5,0.486580371857,0.317952305079,0.0,0.0,0.0,0.0,0.0,0.590785861015,-0.00363333026568,-1.27251684666,0.717950537222,0.0,0.0,0.0,0.0,0.0 +75150,1.0,0.999986244545,0.502183406114,0.5,0.497816593886,0.00218340611354,0.00291120815138,343.5,5.67373237695,0.494207587888,-1.81884301914,2.66205025698,0.64512605042,0.745309950067,0.700332480818,0.697329192547,0.66367494824,0.5965771526,-5.83918711166,5.83918711166,0.69314718056,6.53233429222,0.0,2.0,2.0,0.0,687.0,0.0,0.0,2.0,0.8,-1.44170543852,-0.0749284063937,0.0,0.0,0.0,0.0,0.0,2.77015024447,1.25507060202,0.425625399694,0.804463861156,0.0,0.0,0.0,0.0,0.0 +75153,1.0,0.999672234909,0.510657678994,0.5,0.489342321006,0.0106576789944,0.00582984150118,171.53125,-1.16699381662,-1.19661253612,-1.22487842124,0.0158148626753,0.55419120244,0.649115151757,0.834935793256,0.6429223889,0.643469171795,0.510657753797,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.96875,-0.182676553726,0.0290163010359,0.0,0.0,0.0,0.0,0.0,0.0420262813568,0.000922275965422,-0.0278525575995,0.0159495672935,0.0,0.0,0.0,0.0,0.0 +75154,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,190.476150593,11.2867896573,-0.238348222137,26.5029341231,0.744839106611,0.0250062270593,0.463135386644,0.794345757925,0.703872483487,0.0232118742817,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.578125,-1.01557791233,0.30808493495,0.0,0.0,0.0,0.0,0.0,12.8931465149,2.25735188276,0.681575715542,1.85753277849,0.0,0.0,0.0,0.0,0.0 +75156,1.0,0.995151186559,0.540970564837,0.5,0.459029435163,0.0409705648369,0.706443914081,1.41554054054,2508.99903485,143.619684293,-3.0,288.575391552,0.735486624929,0.740613735534,0.731472522608,0.667466641371,0.627679757162,0.546942389173,-0.347511465598,0.347511465598,7.48211892355,7.82963038915,0.0,2.0,1776.0,0.0,2514.0,0.0,0.0,1776.0,0.255067567568,20.6923522949,3.49043321609,0.0,0.0,0.0,0.0,0.0,50.1098670959,7.92739508786,-24.5036964417,7.86022067913,0.0,0.0,0.0,0.0,0.0 +75157,1.0,0.99153545016,0.554109589041,0.5,0.445890410959,0.0541095890411,0.00205479452055,486.666666667,11.2866220489,3.22309464904,-0.840579748154,5.70183443517,0.515059399291,0.547968984476,0.510982104698,0.543849948104,0.546584950466,0.548653915983,-6.18757942603,6.18757942603,1.09861228867,7.2861917147,0.0,2.0,3.0,0.0,1460.0,0.0,0.0,3.0,1.0,1.32400083542,0.952465176582,0.0,0.0,0.0,0.0,0.0,3.32110714912,0.845561850816,-0.846618890762,1.78936303517,0.0,0.0,0.0,0.0,0.0 +75159,1.0,0.350281319373,0.934139784946,0.5,0.0658602150538,0.434139784946,0.0282258064516,35.4285714286,309.028492938,108.702300686,16.4959310322,75.9332972758,0.909923978773,0.930050598544,0.913960014809,0.927329877823,0.889687769962,0.931420461557,-3.56751859711,3.56751859711,3.04452243772,6.61204103483,0.0,2.0,21.0,0.0,744.0,0.0,0.0,21.0,0.333333333333,93.5186386108,7.78525733948,0.0,0.0,0.0,0.0,0.0,16.5121841431,8.19720264844,2.52108240128,3.4214187603,0.0,0.0,0.0,0.0,0.0 +75161,1.0,0.999997075391,0.501006772835,0.5,0.498993227165,0.00100677283544,0.000366099212887,2731.5,-1.18625748664,-1.19626132348,-1.2053625393,0.00596154548438,0.818231781621,0.701518651693,0.866301085686,0.83580497332,0.863665157862,0.497454997204,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,1.0,-0.473239421844,0.0101596461609,0.0,0.0,0.0,0.0,0.0,0.0146745359525,0.00239459545119,-0.011635562405,0.00701100616799,0.0,0.0,0.0,0.0,0.0 +75163,1.0,0.997284549235,0.530667783127,0.5,0.469332216873,0.0306677831275,0.001046682018,955.4,1.50519823693,0.391040463194,-1.13856394028,0.926875191593,0.917098526342,0.936355506852,0.908938624056,0.942007558421,0.933421377202,0.584050112991,-6.86213010096,6.86213010096,1.60943791243,8.47156801339,0.0,2.0,5.0,0.0,4777.0,0.0,0.0,5.0,0.8,-0.124227285385,-0.00294997822493,0.0,0.0,0.0,0.0,0.0,0.686752855778,0.127960930299,-0.147077143192,0.308497470403,0.0,0.0,0.0,0.0,0.0 +75166,1.0,0.999783913828,0.508653670978,0.5,0.491346329022,0.00865367097832,0.0014574603753,686.125,-1.17489011886,-1.19093529891,-1.21424821449,0.0117122245197,0.799235238589,0.687557598239,0.755329876484,0.726914862397,0.73383953451,0.561662630856,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.23499751091,0.0230095051229,0.0,0.0,0.0,0.0,0.0,0.00871477741748,-0.00708532680437,-0.040518630296,0.014164872761,0.0,0.0,0.0,0.0,0.0 +75168,1.0,2.59712691666,0.411706349206,0.0769230769231,0.00595238095238,0.113236138067,2.8630952381,0.349272349272,43.0212560649,0.701904936944,-3.0,5.37261656712,0.614012164872,?,?,?,?,?,1.0519032907,-1.0519032907,7.96762673933,6.91572344863,0.0,13.0,2886.0,0.0,1008.0,0.0,0.0,2886.0,0.160083160083,5.85413135311,2.23428149572,0.0,0.0,0.0,0.0,0.0,6.70978736877,1.05501261796,-1.15470099449,1.25782820195,0.0,0.0,0.0,0.0,0.0 +75169,1.0,4.69897300701,0.041539050536,0.0384615384615,0.0344563552833,0.00172439022371,0.118108728943,8.46677471637,129.646300604,3.38897308257,-1.47726605092,14.2596774214,0.853325265486,0.0824995675167,0.807323631953,0.946229835373,0.809282239263,0.0713934258025,-2.13614964705,2.13614964705,6.42486902391,8.56101867096,0.0,26.0,617.0,0.0,5224.0,0.0,0.0,617.0,0.32414910859,-1.22328591347,0.0413333065808,0.0,0.0,0.0,0.0,0.0,10.6125946045,0.431966574042,-2.33922529221,1.77163707484,0.0,0.0,0.0,0.0,0.0 +75171,1.0,0.999997103032,0.501002004008,0.5,0.498997995992,0.00100200400802,0.0014574603753,686.125,-1.18295283724,-1.19689460079,-1.21210894799,0.00826382462926,0.735103638999,0.703404331698,0.758062103626,0.824920891335,0.823099065321,0.498450068472,-6.53105982687,6.53105982687,2.07944154168,8.61050136855,0.0,2.0,8.0,0.0,5489.0,0.0,0.0,8.0,1.0,-0.378799915314,8.85702465894e-05,0.0,0.0,0.0,0.0,0.0,0.0457000285387,-0.00645520881517,-0.0311335846782,0.0216041864198,0.0,0.0,0.0,0.0,0.0 +75172,1.0,3.14129034899,0.199108469539,0.1,0.0460624071322,0.0521121818718,4.72808320951,0.211502199874,49.0188222004,0.0967270560285,-3.0,4.34904081324,0.241419618218,?,?,?,?,?,1.55351987925,-1.55351987925,8.0652652089,6.51174532964,0.0,10.0,3182.0,0.0,673.0,0.0,0.0,3182.0,0.172218730358,0.539391293216,0.668863793282,0.0,0.0,0.0,0.0,0.0,7.14274930954,0.922077206096,-2.04124140739,1.10334568712,0.0,0.0,0.0,0.0,0.0 +75173,1.0,0.999999130824,0.50054884742,0.5,0.49945115258,0.000548847420417,0.000940881292144,1062.83333333,3.92134972866,0.568410603177,-0.523851292139,1.53996880863,0.832369930462,0.834412709102,0.82249750758,0.880831361335,0.876909419666,0.738439626471,-6.96869357709,6.96869357709,1.79175946923,8.76045304632,0.0,2.0,6.0,0.0,6377.0,0.0,0.0,6.0,1.0,-0.196735858917,-0.0717093348503,0.0,0.0,0.0,0.0,0.0,0.163519650698,-0.00610541505739,-0.212556123734,0.126398669815,0.0,0.0,0.0,0.0,0.0 +75174,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.00104808070221,954.125,2186.67197796,154.935576502,-1.09535062281,524.911521997,0.830931380987,0.711774707229,0.838138524626,0.818159868618,0.806499761648,0.711774707229,-6.86079469012,6.86079469012,2.77258872224,9.63338341236,0.0,2.0,16.0,0.0,15266.0,0.0,0.0,16.0,0.75,1.12573719025,0.341136485338,0.0,0.0,0.0,0.0,0.0,39.2902641296,4.03321982361,-5.82556533813,9.55366014502,0.0,0.0,0.0,0.0,0.0 +75175,1.0,0.973326874399,0.595849302191,0.5,0.404150697809,0.095849302191,0.000578494468147,1728.625,77.2659551658,17.839653864,-1.31628506071,24.5820785141,0.824065534247,0.76440810629,0.842361401052,0.841709595428,0.748066086749,0.630776547554,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.5,20.768245697,3.26646232605,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.13454865338,-0.304556399584,1.80271025766,0.0,0.0,0.0,0.0,0.0 +75176,1.0,0.985086308038,0.571769469954,0.5,0.428230530046,0.0717694699544,0.000578494468147,1728.625,77.2659551658,18.0460945837,-1.09436188208,24.4267840051,0.963772093744,0.966592155227,0.975487801386,0.964279389941,0.953286904377,0.592959230706,-7.45508157378,7.45508157378,2.07944154168,9.53452311546,0.0,2.0,8.0,0.0,13829.0,0.0,0.0,8.0,0.625,20.9508361816,3.28987717628,0.0,0.0,0.0,0.0,0.0,4.89002847672,2.29565700935,0.061472479254,1.62651012274,0.0,0.0,0.0,0.0,0.0 +75177,1.0,0.323222158666,0.941060126582,0.5,0.0589398734177,0.441060126582,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.952142418468,0.964407980725,0.982602396737,0.952145567926,0.265011101838,0.941062626963,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75178,1.0,3.3219155997,0.100900318627,0.1,0.0993865587191,0.000416343015728,7.9373178669e-05,12598.7142857,1.65195404963,-0.0731041803903,-1.99983729837,0.752438969953,0.22817497811,0.102085222807,0.12559102087,0.107488281999,0.105209048962,0.101495640455,-9.44135004692,9.44135004692,2.63905732962,12.0804073765,0.0,10.0,14.0,0.0,176382.0,0.0,0.0,14.0,0.857142857143,-0.4373960495,0.609312295914,0.0,0.0,0.0,0.0,0.0,0.202129408717,-0.153095683474,-0.744593560696,0.25959740889,0.0,0.0,0.0,0.0,0.0 +75179,1.0,0.900682614537,0.683366733467,0.5,0.316633266533,0.183366733467,0.00582984150118,171.53125,9.64460892711,0.924001105869,-1.31178397834,3.3256970083,0.684101596315,0.738571287846,0.732197707601,0.812536945378,0.798686946043,0.683549176097,-5.14476546575,5.14476546575,3.4657359028,8.61050136855,0.0,2.0,32.0,0.0,5489.0,0.0,0.0,32.0,0.9375,-0.600250482559,0.348770290613,0.0,0.0,0.0,0.0,0.0,2.29501199722,0.62242937076,-0.0240411274135,0.891027766745,0.0,0.0,0.0,0.0,0.0 +75181,1.0,4.244951696,0.0651365977701,0.05,0.0293082089066,0.0158162824321,9.78026993545e-05,10224.6666667,29.1503265967,24.8596869707,-1.20191383436,7.20376960773,1.0,0.127860831397,1.0,0.370997312038,1.0,0.0648104668632,-9.23255838054,9.23255838054,1.09861228867,10.3311706692,0.0,20.0,3.0,0.0,30674.0,0.0,0.0,3.0,0.939393939394,9.23367573525,3.18103094592,0.0,0.0,0.0,0.0,0.0,5.58124776342,5.00922883948,-0.00892275315885,1.31095327809,0.0,0.0,0.0,0.0,0.0 +75182,1.0,0.879780315773,0.701231494825,0.5,0.298768505175,0.201231494825,0.000524040351107,1908.25,1889.65081051,266.485367868,-1.09535062281,617.335510405,0.825299593434,0.78900947478,0.842525999735,0.836567201344,0.800996633761,0.711971985702,-7.55394187068,7.55394187068,2.07944154168,9.63338341236,0.0,2.0,8.0,0.0,15266.0,0.0,0.0,8.0,1.0,1.65167379379,0.669536530972,0.0,0.0,0.0,0.0,0.0,36.4079322815,6.75766504556,-0.3013715446,11.9894688474,0.0,0.0,0.0,0.0,0.0 +75184,1.0,0.88878526467,0.693760115087,0.5,0.306239884913,0.193760115087,0.00161841395432,617.888888889,7558.09049376,842.576212682,-0.584103408743,2374.29632855,0.771261330985,0.776928986699,0.797611809698,0.850297886932,0.749236136552,0.693580247513,-6.42630864985,6.42630864985,2.8903717579,9.31668040775,0.0,2.0,18.0,0.0,11122.0,0.0,0.0,18.0,0.555555555556,1.85490083694,1.20166671276,0.0,0.0,0.0,0.0,0.0,84.8768463135,8.9122309128,-1.49226009846,26.866750682,0.0,0.0,0.0,0.0,0.0 +75185,1.0,0.995823988064,0.538024971623,0.5,0.461975028377,0.0380249716232,0.00317820658343,314.642857143,1.31791800707,0.00568390652317,-1.20949737919,0.705243416996,0.805454030097,0.810439600082,0.798858998145,0.849709338281,0.803635848279,0.727811276026,-5.75143820876,5.75143820876,2.63905732962,8.39049553837,0.0,2.0,14.0,0.0,4405.0,0.0,0.0,14.0,0.571428571429,0.25635099411,0.64928650856,0.0,0.0,0.0,0.0,0.0,0.95220798254,0.496664506649,-0.0109428865835,0.280485475476,0.0,0.0,0.0,0.0,0.0 +75187,1.0,0.999932388471,0.504840661557,0.5,0.495159338443,0.00484066155708,0.0040338846309,247.9,0.042078451056,-0.0600492674348,-0.157618736429,0.0503797346958,0.94008500416,0.674859940025,0.853761470747,0.976603065772,0.976199431027,0.683538257076,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,-1.27478218079,0.0179459266365,0.0,0.0,0.0,0.0,0.0,0.0706005617976,0.0187106904224,-0.0386667735875,0.0280147199494,0.0,0.0,0.0,0.0,0.0 +75188,1.0,3.70404858071,0.221797323136,0.05,0.00286806883365,0.051511009521,8.0879541109,0.123640661939,518.224636301,0.679620877273,-3.0,10.3994282714,0.266354233859,?,?,?,?,?,2.09037580798,-2.09037580798,9.0431044526,6.95272864462,0.0,20.0,8460.0,0.0,1046.0,0.0,0.0,8460.0,?,?,?,0.0,0.0,0.0,0.0,0.0,21.9384994507,0.960107399617,-2.26778626442,1.32381540263,0.0,0.0,0.0,0.0,0.0 +75189,1.0,0.918350813104,0.666611677802,0.5,0.333388322198,0.166611677802,2.44394954466e-05,40917.375,4.3198237665,2.0116642582,-1.16464894247,1.61041965481,?,?,?,?,?,?,-10.6193100684,10.6193100684,2.07944154168,12.6987516101,0.0,2.0,8.0,0.0,327339.0,0.0,0.0,8.0,0.75,-0.901625085259,0.333707477992,0.0,0.0,0.0,0.0,0.0,1.64916145802,0.414881534874,-1.08753228188,1.07142127556,0.0,0.0,0.0,0.0,0.0 +75191,1.0,0.999990305975,0.501832944527,0.5,0.498167055473,0.00183294452692,0.00151483018754,660.14,119.722562144,34.0594826129,-1.43771536847,34.9340216831,?,?,?,?,?,?,-6.49245193374,6.49245193374,4.60517018599,11.0976221197,0.0,2.0,100.0,0.0,66014.0,0.0,0.0,100.0,0.82,2.0960166187,1.27974051897,0.0,0.0,0.0,0.0,0.0,7.91752958298,3.37988784466,-1.43087458611,2.18862324718,0.0,0.0,0.0,0.0,0.0 +75192,1.0,0.999981671353,0.502520356727,0.5,0.497479643273,0.00252035672741,0.00193873594416,515.8,0.185483623895,-0.100620725118,-0.354667256037,0.176864988209,0.471855945297,0.500167469718,0.493994399664,0.492012760074,0.495941444761,0.499817448691,-6.24571909345,6.24571909345,1.60943791243,7.85515700588,0.0,2.0,5.0,0.0,2579.0,0.0,0.0,5.0,0.8,-0.024133682251,0.171741262078,0.0,0.0,0.0,0.0,0.0,0.150236770511,0.0379167355597,-0.162291049957,0.113618584339,0.0,0.0,0.0,0.0,0.0 +75193,1.0,1.73860145411,0.487639970304,0.142857142857,0.0046881542544,0.182839214474,0.000138717988897,7208.87037037,194634.500005,4437.07618348,-1.9581676254,27474.4043981,0.929975627381,0.487639972007,0.930376411952,?,0.085709692443,0.487639972007,-8.8830675426,8.8830675426,3.98898404656,12.8720515892,0.0,7.0,54.0,0.0,389279.0,0.0,0.0,54.0,?,?,?,0.0,0.0,0.0,0.0,0.0,441.176268633,0.0284469472131,-441.176268633,66.6247421222,0.0,0.0,0.0,0.0,0.0 +75195,1.0,0.97222102693,0.597803404723,0.5,0.402196595277,0.0978034047227,0.000366099212887,2731.5,3.48169129779,-0.759243371121,-1.82272628165,1.21338972151,0.942778213775,0.794178862649,0.996229063939,0.952516815257,0.701189697155,0.652901643753,-7.91260618784,7.91260618784,2.30258509299,10.2151912808,0.0,2.0,10.0,0.0,27315.0,0.0,0.0,10.0,0.642857142857,-0.768453709816,0.999457705961,0.0,0.0,0.0,0.0,0.0,2.34130119758,0.252784150697,-1.15036656145,0.888950737787,0.0,0.0,0.0,0.0,0.0 +75196,1.0,0.898760171096,0.685089974293,0.5,0.314910025707,0.185089974293,0.0192802056555,51.8666666667,773.001287001,96.9765697255,-1.9923459929,161.82748455,0.874062856974,0.951242849977,0.961483874775,0.933209195867,0.828970396692,0.685116571193,-3.94867632308,3.94867632308,2.7080502011,6.65672652418,0.0,2.0,15.0,6.0,778.0,58.0,178.0,15.0,0.75,-0.0368389757163,0.581515442286,0.4,0.0745501285347,0.0152527849186,0.0,0.0,27.8388449294,7.76424541354,-0.0874871824774,6.16236516628,0.0,0.0,0.0,0.0,0.0 +75197,1.0,3.57947430574,0.198061780739,0.0588235294118,0.0157480314961,0.0548005994223,1.21138703816,0.8255,593.367769926,25.419272882,-3.0,39.4442948397,0.583920493096,?,?,?,?,?,0.191766015622,-0.191766015622,7.60090245954,7.40913644392,0.0,17.0,2000.0,0.0,1651.0,0.0,0.0,2000.0,0.301,251.562215049,12.1729655231,0.0,0.0,0.0,0.0,0.0,22.5965328217,3.93261161113,-2.84605407715,2.51983135483,0.0,0.0,0.0,0.0,0.0 +75198,1.0,5.26928703325,0.0733916302311,0.0227272727273,0.0107745159275,0.0129398719617,4.18988132417,0.238670244484,1690.96010049,11.4775322914,-3.0,52.7817622501,0.321626981642,?,?,?,?,?,1.43267240995,-1.43267240995,10.1973504841,8.76467807412,0.0,44.0,26832.0,0.0,6404.0,0.0,0.0,26832.0,?,?,?,0.0,0.0,0.0,0.0,0.0,38.0540924072,1.84704968494,-2.84604978561,2.82136579834,0.0,0.0,0.0,0.0,0.0 +75201,1.0,2.42956243885,0.29403202329,0.166666666667,0.083454633673,0.078799922294,6.03202328967,0.165781853282,570.460480721,2.35047836408,-3.0,11.0217345809,0.292600038871,?,?,?,?,?,1.79708249172,-1.79708249172,9.42802907261,7.63094658089,0.0,6.0,12432.0,0.0,2061.0,0.0,0.0,12432.0,?,?,?,0.0,0.0,0.0,0.0,0.0,22.212184906,1.28411574775,-2.47487211227,1.52034970136,0.0,0.0,0.0,0.0,0.0 +75202,1.0,3.79966619891,0.221422142214,0.04,0.00720072007201,0.0534402350967,3.38253825383,0.295635976583,48.4813749308,0.596095539589,-3.0,5.07378275456,0.446360035155,?,?,?,?,?,1.21862639033,-1.21862639033,8.23164217997,7.01301578964,0.0,25.0,3758.0,0.0,1111.0,0.0,0.0,3758.0,0.186801490154,7.09512768464,2.35727657658,0.0,0.0,0.0,0.0,0.0,6.43665647507,1.04944557725,-1.5,1.19620288892,0.0,0.0,0.0,0.0,0.0 +75203,1.0,2.44779062711,0.289706567303,0.166666666667,0.0833721471821,0.0735642348157,6.14578481602,0.16271314892,1381.00080559,2.71684373995,-3.0,18.2883411194,0.345091758175,?,?,?,?,?,1.81576645106,-1.81576645106,9.48759324894,7.67182679788,0.0,6.0,13195.0,0.0,2147.0,0.0,0.0,13195.0,?,?,?,0.0,0.0,0.0,0.0,0.0,37.1887245178,1.34354848962,-2.26778674126,1.58169385983,0.0,0.0,0.0,0.0,0.0 +75205,1.0,3.2746430558,0.146409947854,0.1,0.0607033025806,0.0254909787237,1.53295895173,0.652333187963,305.00312999,1.99517146406,-3.0,8.45309938542,0.219777095689,?,?,?,?,?,0.4271998231,-0.4271998231,9.34705419529,8.91985437219,0.0,10.0,11465.0,0.0,7479.0,0.0,0.0,11465.0,?,?,?,0.0,0.0,0.0,0.0,0.0,17.5215053558,1.21636063648,-2.04124045372,1.37700096167,0.0,0.0,0.0,0.0,0.0 +75207,1.0,3.20365726573,0.15625,0.1,0.0525568181818,0.0400970945161,4.59943181818,0.217418159358,46.6531799432,0.0298941620374,-3.0,4.19897958761,0.0699645691667,?,?,?,?,?,1.52593277808,-1.52593277808,8.08271113424,6.55677835616,0.0,10.0,3238.0,0.0,704.0,0.0,0.0,3238.0,0.180049413218,1.65808672412,0.741908024023,0.0,0.0,0.0,0.0,0.0,6.39033460617,0.901922258651,-1.50000011921,1.064552093,0.0,0.0,0.0,0.0,0.0 +75210,1.0,0.994742619201,0.542659758204,0.5,0.457340241796,0.0426597582038,0.000690846286701,1447.5,-0.0129636195981,-0.67701351287,-1.25410828572,0.445711216771,0.996718477047,0.885660639793,0.999827288428,0.965627968641,0.697238931138,0.542659809723,-7.27759320945,7.27759320945,1.38629436112,8.66388757057,0.0,2.0,4.0,0.0,5790.0,0.0,0.0,4.0,0.8,-0.510763008954,1.20318411398,0.0,0.0,0.0,0.0,0.0,1.21822683081,0.154686741166,-1.21822683081,0.801664691271,0.0,0.0,0.0,0.0,0.0 +75212,1.0,0.99548905735,0.539518900344,0.5,0.460481099656,0.0395189003436,0.0378006872852,26.4545454545,868.001146789,71.6019201164,-1.66405529954,120.241956956,0.658676311742,0.689595000763,0.667717028871,0.710167981637,0.632239530133,0.539525583225,-3.27542799437,3.27542799437,3.49650756147,6.77193555584,0.0,2.0,33.0,30.0,873.0,764.0,5310.0,33.0,0.734939759036,2.31450013616,1.23927443779,0.909090909091,0.875143184422,0.184317400812,0.0,0.0,29.4957818474,6.38547640449,-0.612929837683,5.50711698267,0.0,0.0,0.0,0.0,0.0 +75213,1.0,0.758987706482,0.78064516129,0.5,0.21935483871,0.28064516129,0.00645161290323,155.0,34.6157894578,11.2857269889,-1.99850099933,5.85077591835,0.722560772561,0.940626040626,0.929020979021,0.930236430236,0.670979020979,0.780636030636,-5.04342511692,5.04342511692,1.60943791243,6.65286302935,0.0,2.0,5.0,0.0,775.0,0.0,0.0,5.0,0.846153846154,0.526767815547,0.667822674298,0.0,0.0,0.0,0.0,0.0,5.17006316366,3.40987359418,-0.0645497224368,1.18532519636,0.0,0.0,0.0,0.0,0.0 +75215,1.0,0.991757835291,0.553395436749,0.5,0.446604563251,0.053395436749,0.00405022276225,246.9,87.5984185205,2.78784913136,-1.99189892893,11.1877514783,0.960440768627,0.888484112684,0.957334664599,0.930472495335,0.711349233753,0.56203489084,-5.50898339635,5.50898339635,3.40119738166,8.91018077801,0.0,2.0,30.0,0.0,7407.0,0.0,0.0,30.0,0.441176470588,2.0755682928,1.66159913824,0.0,0.0,0.0,0.0,0.0,9.46564411546,0.439148095977,-4.56733486463,2.14359466345,0.0,0.0,0.0,0.0,0.0 +75217,1.0,2.89605149901,0.270175438596,0.1,0.0266666666667,0.0791908914679,0.0245614035088,40.7142857143,232.504243564,15.3818154125,-1.35641541452,40.9068874386,0.995748606301,0.446523170662,1.0,1.0,0.999275362319,0.273883906326,-3.70657903121,3.70657903121,3.55534806149,7.2619270927,0.0,10.0,35.0,0.0,1425.0,0.0,0.0,35.0,0.628571428571,0.163221597672,0.476672917604,0.0,0.0,0.0,0.0,0.0,15.3135328293,2.33423098896,-0.991580963135,3.12757931642,0.0,0.0,0.0,0.0,0.0 +75219,1.0,0.992045865952,0.552455913121,0.5,0.447544086879,0.0524559131215,0.00139483909535,716.928571429,10031.6886977,5866.99320339,201.768678863,4141.89171581,0.809897529666,0.595098905282,0.82056115849,0.64341731925,0.455621459836,0.595098905282,-6.5749762142,6.5749762142,2.63905732962,9.21403354381,0.0,2.0,14.0,0.0,10037.0,0.0,0.0,14.0,0.357142857143,8331.09960938,86.4098434448,0.0,0.0,0.0,0.0,0.0,100.167526245,54.7130042144,-16.8841705322,47.0231680355,0.0,0.0,0.0,0.0,0.0 +75221,1.0,2.29298935592,0.421707317073,0.166666666667,0.0790243902439,0.117960111383,0.0124390243902,80.3921568627,1990.36835836,178.359384278,-0.981849410355,412.801649192,0.560679653852,0.421714980799,0.531194658486,0.472706945915,0.177076892313,0.435627895533,-4.38691661997,4.38691661997,3.93182563272,8.31874225269,0.0,6.0,51.0,0.0,4100.0,0.0,0.0,51.0,0.43137254902,8.02380180359,2.42221260071,0.0,0.0,0.0,0.0,0.0,44.3444480896,6.89482387272,-1.83650052547,9.67336221779,0.0,0.0,0.0,0.0,0.0 +75222,1.0,0.444521394392,0.907572383073,0.5,0.0924276169265,0.407572383073,0.0178173719376,56.125,174.605599104,9.85032855882,-1.70860828473,36.0036292761,0.932079406237,0.915175810457,0.932091890631,0.951007120221,0.793928880109,0.903126174699,-4.02758134606,4.02758134606,2.77258872224,6.8001700683,0.0,2.0,16.0,1.0,898.0,14.0,14.0,16.0,0.5,0.591816422058,0.70192337534,0.0625,0.0155902004454,0.00097438752784,0.0,0.0,13.289303936,1.68017860287,-1.7583331361,2.71805554929,0.0,0.0,0.0,0.0,0.0 +75223,1.0,3.49785169588,0.159857431642,0.0555555555556,0.000904351526758,0.0522198323752,0.000319182891797,3133.0,9.86244587815,2.55959508735,-1.92774289656,2.77391378718,0.552604314168,0.190341380333,0.772464228332,0.3020056044,0.0562346476725,0.159858506001,-8.04974629095,8.04974629095,1.79175946923,9.84150576018,0.0,18.0,6.0,0.0,18798.0,0.0,0.0,6.0,0.826086956522,-1.40620509749,0.306621905977,0.0,0.0,0.0,0.0,0.0,3.44419016289,1.90248857705,-0.00786865852141,0.910855598954,0.0,0.0,0.0,0.0,0.0 +75225,1.0,0.350662815628,0.934040047114,0.5,0.0659599528857,0.434040047114,0.0424028268551,23.5833333333,39.8474709964,0.805693724529,-0.950819715135,4.69796328695,0.922244144414,0.934047056381,0.894606713442,0.92816462202,0.690837928445,0.934047056381,-3.16054024786,3.16054024786,4.27666611902,7.43720636687,0.0,2.0,72.0,0.0,1698.0,0.0,0.0,72.0,0.277777777778,-0.547496080399,0.475032389164,0.0,0.0,0.0,0.0,0.0,5.52675676346,0.0450488749064,-1.27711522579,0.928227127853,0.0,0.0,0.0,0.0,0.0 +75226,1.0,0.647464825551,0.834432124663,0.5,0.165567875337,0.334432124663,0.00209769253821,476.714285714,3.62062995206,-0.0499390431997,-0.873383380035,1.04788456836,0.996551943859,0.834432634656,0.969281958929,0.952055164763,0.960143936097,0.834432634656,-6.1669173297,6.1669173297,2.63905732962,8.80597465931,0.0,2.0,14.0,0.0,6674.0,0.0,0.0,14.0,0.714285714286,-1.12681794167,0.0651805922389,0.0,0.0,0.0,0.0,0.0,1.68170011044,0.119449785511,-0.358011335135,0.496538588217,0.0,0.0,0.0,0.0,0.0 +75227,1.0,0.87363197491,0.706158519746,0.5,0.293841480254,0.206158519746,0.00138083402375,724.2,1.86204020746,0.377113953238,-0.854905995671,1.15325451703,0.887299850081,0.757246636558,0.856643597608,0.756697955371,0.764410705642,0.757246636558,-6.58506759733,6.58506759733,1.60943791243,8.19450550977,0.0,2.0,5.0,0.0,3621.0,0.0,0.0,5.0,1.0,-1.244176507,0.0595014877617,0.0,0.0,0.0,0.0,0.0,1.50542223454,0.802885743976,0.232223287225,0.494553106446,0.0,0.0,0.0,0.0,0.0 +75230,1.0,6.61834612758,0.0149253731343,0.01,0.00559701492537,0.00186604473881,0.0597014925373,16.75,8.50001740455,3.73536356533,0.019822354359,2.36158481279,0.606778562996,0.0252479059419,0.406463163404,0.487056849578,0.511491953213,0.0268454481846,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.046875,2.76372051239,1.22544920444,0.0,0.0,0.0,0.0,0.0,2.32372617722,1.41730287578,0.357127130032,0.653359190168,0.0,0.0,0.0,0.0,0.0 +75231,1.0,6.62042066889,0.0130597014925,0.01,0.00559701492537,0.00177032480064,0.0597014925373,16.75,115.789142731,15.7543331244,0.900311470032,18.0618138204,0.792627477379,0.0197953422294,0.484586037111,0.776133713792,0.642826853768,0.0248244411253,-2.81839825827,2.81839825827,4.15888308336,6.97728134163,0.0,100.0,64.0,0.0,1072.0,0.0,0.0,64.0,0.625,-1.11506950855,0.0145113645121,0.0,0.0,0.0,0.0,0.0,8.88522911072,3.13926960342,0.92069041729,1.47979608448,0.0,0.0,0.0,0.0,0.0 +75232,1.0,0.920170202724,0.664780763791,0.5,0.335219236209,0.164780763791,0.0579915134371,17.243902439,585.451078479,41.4803042206,-0.25291273102,95.1756888915,0.831670020121,0.701710261569,0.790583501006,0.859919517103,0.662032193159,0.70430583501,-2.84745859919,2.84745859919,3.7135720667,6.5610306659,0.0,2.0,41.0,0.0,707.0,0.0,0.0,41.0,0.536585365854,3.04518032074,0.868757605553,0.0,0.0,0.0,0.0,0.0,23.2383441925,3.83264719977,-1.50036358833,4.50838803832,0.0,0.0,0.0,0.0,0.0 +75233,1.0,0.882468001072,0.699034432501,0.5,0.300965567499,0.199034432501,0.00382583348515,261.380952381,619.96201548,76.9042228765,1.10505046363,139.012605143,0.912552221993,0.858454448759,0.906539626249,0.91637670833,0.894329627494,0.699034672439,-5.56597893083,5.56597893083,3.04452243772,8.61050136855,0.0,2.0,21.0,0.0,5489.0,0.0,0.0,21.0,0.619047619048,4.19317579269,1.6563218832,0.0,0.0,0.0,0.0,0.0,21.7760543823,5.34575112945,-0.772116839886,4.83291927802,0.0,0.0,0.0,0.0,0.0 +75234,1.0,0.999839301781,0.507462686567,0.5,0.492537313433,0.00746268656716,0.0040338846309,247.9,1.93095751487,1.53323016663,1.32535228091,0.136478817904,0.737800970043,0.6002481892,0.867283915287,0.763008326105,0.981844633293,0.587335584048,-5.51302543904,5.51302543904,2.99573227355,8.5087577126,0.0,2.0,20.0,0.0,4958.0,0.0,0.0,20.0,0.95,0.530919790268,0.705406665802,0.0,0.0,0.0,0.0,0.0,-0.107623174787,-0.233150891215,-0.336832880974,0.0525672542936,0.0,0.0,0.0,0.0,0.0 +75235,1.0,1.70679194497,0.410557986871,0.25,0.0557986870897,0.1492903086,0.00656455142232,152.333333333,13.7695655438,2.34675810677,-1.61452068061,4.52461618329,0.859151932634,0.698606172433,0.993420824489,0.653989101115,0.517219346319,0.407821793855,-5.02607110223,5.02607110223,3.17805383035,8.20412493257,0.0,4.0,24.0,0.0,3656.0,0.0,0.0,24.0,0.875,-0.411980628967,0.666000425816,0.0,0.0,0.0,0.0,0.0,3.77921462059,1.51595425121,0.00435591023415,1.06579779006,0.0,0.0,0.0,0.0,0.0 +75236,1.0,3.32087519342,0.104868913858,0.1,0.0936329588015,0.00381029774333,0.239700374532,4.171875,22.4225195157,-0.922546708095,-1.99977553401,1.93528583028,0.88688123144,0.205972648482,0.717476567073,0.84378519442,0.741674711279,0.155329932501,-1.42836557504,1.42836557504,5.54517744448,6.97354301952,0.0,10.0,256.0,0.0,1068.0,0.0,0.0,256.0,0.60546875,-0.290847301483,0.482162296772,0.0,0.0,0.0,0.0,0.0,4.94191360474,0.818654326074,-0.619498193264,0.638167906159,0.0,0.0,0.0,0.0,0.0 +75237,1.0,0.737873614799,0.791880089409,0.5,0.208119910591,0.291880089409,1.8271625992e-05,54729.6666667,-0.597554662787,-0.792603328267,-0.914023680425,0.139293469991,0.999445761086,0.882330748887,0.999226500683,0.932029587589,0.923929140388,0.791880089593,-10.9101611936,10.9101611936,1.09861228867,12.0087734823,0.0,2.0,3.0,0.0,164189.0,0.0,0.0,3.0,0.666666666667,-0.593678712845,0.57039141655,0.0,0.0,0.0,0.0,0.0,-0.0398797653615,-0.334802189221,-0.622391700745,0.2378660231,0.0,0.0,0.0,0.0,0.0 +75239,1.0,0.944354703027,0.637970791699,0.5,0.362029208301,0.137970791699,0.0253651037663,39.4242424242,30.9630483611,5.22995826123,-1.88300416727,9.15929154021,0.985413975338,0.637974163241,1.0,0.783247210804,0.997698179683,0.637974163241,-3.67438091705,3.67438091705,3.49650756147,7.17088847851,0.0,2.0,33.0,0.0,1301.0,0.0,0.0,33.0,0.515151515152,1.50215339661,1.46063673496,0.0,0.0,0.0,0.0,0.0,5.59168434143,1.51518104643,-0.876339256763,1.68213781281,0.0,0.0,0.0,0.0,0.0 +75240,1.0,0.947332198223,0.634275994046,0.5,0.365724005954,0.134275994046,0.0127578141612,78.3833333333,4698.00021268,1114.36603354,-3.0,1521.17609133,0.82968288386,0.975760943217,0.963850566924,0.967467136468,0.798848534128,0.635125807472,-4.36161131972,4.36161131972,4.09434456222,8.45595588195,0.0,2.0,60.0,17.0,4703.0,4703.0,29447.0,60.0,0.748484848485,-1.13285129651,0.697302044724,0.283333333333,1.0,0.104355376001,0.0,0.0,68.5565475551,25.6338461836,-22.7938033881,21.4254260854,0.0,0.0,0.0,0.0,0.0 +75243,1.0,1.71188447845,0.33529140751,0.25,0.0246486984566,0.130455006899,0.000921446671274,1085.25,0.378982912001,-0.965621997307,-1.99993122464,0.713200266361,0.826181712343,0.665514989436,0.993204349759,0.419156358679,0.842887460851,0.332184576411,-6.98956565418,6.98956565418,2.07944154168,9.06900719586,0.0,4.0,8.0,0.0,8682.0,0.0,0.0,8.0,0.666666666667,-1.98085364061,-0.00805534429775,0.0,0.0,0.0,0.0,0.0,1.54239518671,0.934255222582,-0.00829309133559,0.401926836341,0.0,0.0,0.0,0.0,0.0 +75244,1.0,0.339018265116,0.937057036787,0.5,0.062942963213,0.437057036787,0.00944988187648,105.821428571,5921.00016878,514.684922359,-3.0,1253.72175613,0.899583674584,0.937058080808,0.904655223405,0.92794044044,0.615933547184,0.937058080808,-4.6617530374,4.6617530374,4.02535169074,8.68710472813,0.0,2.0,56.0,14.0,5926.0,5926.0,23694.0,56.0,0.569343065693,-0.880109002846,0.859650224839,0.25,1.0,0.0713984378767,0.0,0.0,76.9610301957,12.8724033019,-8.65948472275,18.7329578122,0.0,0.0,0.0,0.0,0.0 +75248,1.0,0.497112837426,0.890927218345,0.5,0.109072781655,0.390927218345,0.0109670987039,91.1818181818,5010.00019944,484.191000973,-3.0,1061.39024029,0.845461820582,0.890928329795,0.844850657251,0.871587308252,0.66160372005,0.890928329795,-4.51285551516,4.51285551516,4.00733318523,8.5201887004,0.0,2.0,55.0,12.0,5015.0,4938.0,21515.0,55.0,0.561264822134,-0.885863894081,0.860388808565,0.218181818182,0.984646061815,0.0780023565667,0.0,0.0,70.7954814903,12.3220485222,-50.0449837542,18.2849293169,0.0,0.0,0.0,0.0,0.0 +75249,1.0,0.389167549215,0.923655063291,0.5,0.0763449367089,0.423655063291,0.0114715189873,87.1724137931,416.335712398,46.5714128364,-3.0,84.6977796294,0.920492967035,0.980605623408,0.994063266795,0.941457503089,0.21398045353,0.923659777212,-4.46788792528,4.46788792528,3.36729582999,7.83518375527,0.0,2.0,29.0,7.0,2528.0,2528.0,4109.0,29.0,0.461538461538,6.33263086428,2.6652556066,0.241379310345,1.0,0.0560481230904,0.0,0.0,20.4532567675,0.888485533872,-20.4532567675,6.74669104588,0.0,0.0,0.0,0.0,0.0 +75250,1.0,3.99235462684,0.146342438508,0.0454545454545,0.00579692762836,0.0394925931116,3.997881123e-05,25013.25,2.14760841842,1.47195705723,0.304518250114,0.697335121798,0.559853379482,0.147301613523,0.577384584198,0.276144142584,0.351064696137,0.147301613523,-10.1271609634,10.1271609634,1.38629436112,11.5134553246,0.0,22.0,4.0,0.0,100053.0,0.0,0.0,4.0,1.0,2.16378736496,0.858991086483,0.0,0.0,0.0,0.0,0.0,1.21358597279,0.399668060243,-0.389679968357,0.761602951052,0.0,0.0,0.0,0.0,0.0 +80001,1.0,0.7066477892488323,0.8075117370892019,0.5,0.19248826291079812,0.3075117370892019,271.0610328638498,0.003689206041291395,208.0047169811321,26.221308400322222,-3.0,61.220986816703956,0.8124600037643515,0.8358554488989272,0.8919348767174856,0.4044419348767175,0.8267457180500658,0.8076980990024467,5.602344009042684,-5.602344009042684,10.963636174752109,5.3612921657094255,0.0,2.0,57736.0,0.0,213.0,0.0,0.0,57736.0,0.003048358043508383,0.020836440906033538,0.31656668011814415,0.0,0.0,0.0,0.0,0.0,14.491539496586693,2.6787248103228203,-14.491539496586693,4.540006540148839,0.0,0.0,0.0,0.0,0.0 +80003,1.0,0.9993146180819128,0.5154109589041096,0.5,0.4845890410958904,0.015410958904109595,8.085616438356164,0.12367640830156713,5.299649547037767,-0.17044028739431372,-1.424392902183749,0.9150474218228104,0.7552201441652056,0.9846853691798169,0.9829612312487823,0.575739333722969,0.9350311708552503,0.5154305474381454,2.090086734817231,-2.090086734817231,8.459987717645458,6.369900982828227,0.0,2.0,4722.0,0.0,584.0,0.0,0.0,4722.0,0.09360440491317239,0.9842980706561235,-0.04434394219205728,0.0,0.0,0.0,0.0,0.0,2.3371512234770586,0.6432471841109485,-0.4374262489822357,0.42583948284429135,0.0,0.0,0.0,0.0,0.0 +80006,1.0,0.99498482818597,0.5416666666666666,0.5,0.4583333333333333,0.04166666666666666,0.7083333333333334,1.411764705882353,40.888097812747674,6.4034750814039745,-0.9708246403268017,10.898817571753607,0.7923232323232323,0.6685858585858585,0.6583838383838383,0.6213131313131313,0.7812121212121211,0.5815151515151515,-0.3448404862917295,0.3448404862917296,4.219507705176107,4.564348191467836,0.0,2.0,68.0,6.0,96.0,4.0,8.0,68.0,0.3382352941176471,1.905257339745705,-0.17664000845662303,0.08823529411764706,0.041666666666666664,0.0012254901960784314,0.0,0.0,5.506539125300938,0.13199194505162468,-5.505710897469262,1.8086571144718122,0.0,0.0,0.0,0.0,0.0 +80008,1.0,0.9988455359952018,0.52,0.5,0.48,0.020000000000000018,0.28,3.5714285714285716,3.6211793945955195,1.411494225237541,-1.2897197658975563,1.6832358989563667,0.875,0.8916666666666666,0.875,0.85,0.6666666666666666,0.6083333333333333,-1.2729656758128873,1.2729656758128876,1.9459101490553132,3.2188758248682006,0.0,2.0,7.0,0.0,25.0,0.0,0.0,7.0,0.7142857142857143,2.235468662993581,0.5037891974160894,0.0,0.0,0.0,0.0,0.0,1.704478799787861,0.5230756100198196,-0.20042053808144492,0.578178589023633,0.0,0.0,0.0,0.0,0.0 +80009,1.0,1.0,0.5,0.5,0.5,0.0,0.14285714285714285,7.0,3.7933724408651894,-0.11327354504479836,-1.3603087364138704,1.617430664397527,0.9,0.8,0.8333333333333333,0.8583333333333334,0.7916666666666667,0.7999999999999999,-1.9459101490553135,1.9459101490553132,2.0794415416798357,4.02535169073515,0.0,2.0,8.0,0.0,56.0,0.0,0.0,8.0,0.625,-0.20621857439601365,0.7128871277214843,0.0,0.0,0.0,0.0,0.0,0.2841127817425812,-0.5677948811244409,-2.0746305620049244,0.7665036876795649,0.0,0.0,0.0,0.0,0.0 +80010,1.0,0.8976844934141645,0.686046511627907,0.5,0.313953488372093,0.18604651162790697,5.244186046511628,0.19068736141906872,31.933284554531497,1.7929905104026447,-1.216698175878973,3.5246438521665695,0.6880952380952381,0.7371031746031745,0.6982142857142857,0.7228174603174604,0.7353174603174603,0.6880952380952381,1.6571200432491706,-1.6571200432491706,6.111467339502679,4.454347296253507,0.0,2.0,451.0,0.0,86.0,0.0,0.0,451.0,0.11751662971175167,1.8594900172153563,0.7505317685497828,0.0,0.0,0.0,0.0,0.0,2.9617813953883685,-0.23482163882632273,-4.634102377516366,0.9407605899357461,0.0,0.0,0.0,0.0,0.0 +80011,1.0,0.9433118205484854,0.6392405063291139,0.5,0.36075949367088606,0.13924050632911392,0.810126582278481,1.234375,141.72777142686098,14.985220329415535,-0.8394869060700341,24.55073111551511,0.8857352941176471,0.9620833333333334,0.9628676470588236,0.7092401960784314,0.8798529411764706,0.7781862745098038,-0.21056476910734964,0.21056476910734964,4.852030263919617,5.062595033026967,0.0,2.0,128.0,0.0,158.0,0.0,0.0,128.0,0.4296875,0.3157146945300875,-0.27208806547401904,0.0,0.0,0.0,0.0,0.0,11.772598172686084,2.5154609116350817,-0.37500268086278415,2.3402027407895707,0.0,0.0,0.0,0.0,0.0 +80012,1.0,0.9327076126690367,0.6515151515151515,0.5,0.3484848484848485,0.1515151515151515,828.4090909090909,0.001207133058984911,56.31937903017474,2.1359470577662143,-1.7349293439082636,4.323829310606569,0.9166666666666666,0.8541666666666667,0.8458333333333334,0.8833333333333334,0.8791666666666667,0.7,6.719507103518543,-6.719507103518543,10.909161845544968,4.189654742026425,0.0,2.0,54675.0,0.0,66.0,0.0,0.0,54675.0,0.0010242341106538638,8.506686334694916,2.33314779919167,0.0,0.0,0.0,0.0,0.0,7.5004455212486985,0.23288000879531923,-7.203044537579985,1.0825691502334627,0.0,0.0,0.0,0.0,0.0 +80013,1.0,0.8865408928220898,0.6956521739130435,0.5,0.30434782608695654,0.19565217391304346,653.6376811594203,0.0015298995587680983,56.00663048419816,2.374372679082332,-1.810541449236015,4.318958909029313,0.9238095238095239,1.0,1.0,0.9547619047619049,0.9589285714285714,0.7684523809523809,6.482553193596696,-6.482553193596696,10.716659698193956,4.23410650459726,0.0,2.0,45101.0,0.0,69.0,0.0,0.0,45101.0,0.0011529677834194363,0.798671418920256,1.1739835175195437,0.0,0.0,0.0,0.0,0.0,7.373183093031708,0.3541859439104096,-6.843056045812127,1.156974011333122,0.0,0.0,0.0,0.0,0.0 +80014,1.0,0.9893755831922303,0.5606060606060606,0.5,0.4393939393939394,0.06060606060606058,828.4242424242424,0.0012071109810520154,41.64861358938362,0.3075532030743809,-1.8539464762189937,1.1958230421057898,0.7928571428571429,0.5685714285714286,0.63,0.690952380952381,0.6942857142857142,0.6228571428571428,6.719525393246118,-6.719525393246118,10.909180135272544,4.189654742026425,0.0,2.0,54676.0,1.0,66.0,66.0,66.0,54676.0,0.0010242341106538638,-0.7176370818401119,0.5665277552848095,1.8289560318969934e-05,1.0,1.8289560318969934e-05,0.0,0.0,5.9981006322644435,0.14297745849996615,-5.261479260728645,0.4968181865745509,0.0,0.0,0.0,0.0,0.0 +80015,1.0,0.9669852958320848,0.6065573770491803,0.5,0.39344262295081966,0.10655737704918034,896.311475409836,0.0011156835848193873,30.26409321660175,-0.2993458205516883,-1.8422238037585195,1.5029848334703013,0.8133333333333335,0.6595238095238094,0.739047619047619,0.8266666666666668,0.6628571428571428,0.779047619047619,6.798287981371657,-6.798287981371657,10.909161845544968,4.110873864173311,0.0,2.0,54675.0,0.0,61.0,0.0,0.0,54675.0,0.0006035665294924555,-1.1234713432828434,0.8825357321528141,0.0,0.0,0.0,0.0,0.0,4.772398109332369,0.025489234530848934,-4.03491988192813,0.820617929433286,0.0,0.0,0.0,0.0,0.0 +% +% +% \ No newline at end of file diff --git a/metalearning/metalearning_files/roc_auc_multiclass.classification_sparse/readme.txt b/metalearning/metalearning_files/roc_auc_multiclass.classification_sparse/readme.txt new file mode 100755 index 0000000..e69de29 From ce48b40f0f7af2b8506778831acec0217e8d33ed Mon Sep 17 00:00:00 2001 From: Tony Liu Date: Fri, 13 Dec 2019 13:36:26 -0600 Subject: [PATCH 2/2] issue 19: add end user functionality of selecting final model --- asklb/widget.py | 129 +++++++++++++++++++++++++++++++++------ config/widget_config.ini | 2 +- 2 files changed, 113 insertions(+), 18 deletions(-) diff --git a/asklb/widget.py b/asklb/widget.py index 092a4f6..8b2df2c 100644 --- a/asklb/widget.py +++ b/asklb/widget.py @@ -2,13 +2,14 @@ IPyWidget implementation of ASKLB. """ # built-in modules +import configparser +import copy from io import BytesIO import os import sys import time import threading import warnings -import configparser # widget modules import ipywidgets as widgets @@ -41,7 +42,7 @@ def thresholdout(train_acc, test_acc, threshold=0.01, noise=0.03): threshold_hat = threshold + np.random.laplace(0, 2*noise) if np.abs(train_acc - test_acc) > (threshold_hat + np.random.laplace(0, 4*noise)): - return test_acc + np.random.laplace(0, noise) + return np.clip(test_acc + np.random.laplace(0, noise), 0, 1) else: return train_acc @@ -83,6 +84,7 @@ def __init__(self, **kwargs): # We make the assumption that the first column are the labels. # TODO can add "checksum" for the y's for the data to be in the same order self.data = [] + self.models = [] # We make the assumption that the data are uploaded in the same order. self.train_idxs = [] self.test_idxs = [] @@ -93,7 +95,7 @@ def __init__(self, **kwargs): description='Username:' ) - self.password_text_widget = widgets.Text( + self.password_text_widget = widgets.Password( placeholder='Password', description='Password:' ) @@ -102,14 +104,14 @@ def __init__(self, **kwargs): description="Sign In", layout = widgets.Layout(width='auto'), button_style='primary', - disabled=False) # init with fit button disabled + disabled=False) self.sign_in_widget.on_click(self.on_sign_in_widget_click) self.register_widget = widgets.Button( description="Register", layout = widgets.Layout(width='auto'), button_style='primary', - disabled=False) # init with fit button disabled + disabled=False) self.register_widget.on_click(self.on_register_widget_click) self.auth_label_widget = widgets.Label(value="Please sign in or register") @@ -159,10 +161,10 @@ def assemble_widget(self, **kwargs): runtime_slider = widgets.HBox([widgets.Label('Run time (min):'), self.runtime_widget]) budget_slider = widgets.HBox([widgets.Label('Query budget:'), self.budget_widget]) - models_accordian = widgets.Accordion(children=[self.metrics_output_widget, + self.models_accordian = widgets.Accordion(children=[self.metrics_output_widget, self.model_output_widget]) - models_accordian.set_title(0, 'Performance Metrics') - models_accordian.set_title(1, 'Models and Weights Data') + self.models_accordian.set_title(0, 'Performance Metrics') + self.models_accordian.set_title(1, 'Models and Weights Data') main_layout = widgets.Layout( display='flex', @@ -174,13 +176,17 @@ def assemble_widget(self, **kwargs): auth_widget_items = [self.user_text_widget, self.password_text_widget, self.sign_in_widget, self.register_widget] self.auth_widget = widgets.VBox([widgets.HBox(auth_widget_items), self.auth_label_widget]) + self.tab_nest = widgets.Tab() + self.tab_nest.children = [self.models_accordian] + self.tab_nest.set_title(0, "Model Run Info") + automl_widget_items = [runtime_slider, budget_slider, self.upload_widget, self.fit_button_widget, self.progress_widget, self.event_output_widget, - models_accordian] + self.tab_nest] self.automl_widget = widgets.VBox(automl_widget_items) self.automl_widget.layout.visibility = 'hidden' @@ -263,9 +269,9 @@ def on_sign_in_widget_click(self, button): self.password_text_widget.disabled = True self.register_widget.disabled = True else: - self.auth_label_widget.value = "Incorrect password!" + self.auth_label_widget.value = "Incorrect password." else: - self.auth_label_widget.value = "No user found!" + self.auth_label_widget.value = "No user found. First time users must register." else: # Upon sign out, enable auth widgets and hide automl widgets self.auth_label_widget.value = "Signed out!" @@ -302,7 +308,7 @@ def on_register_widget_click(self, button): if existing_user is None: hashpass = bcrypt.hashpw(password.encode('utf-8'), bcrypt.gensalt()) users.insert({'username' : username, 'password' : hashpass}) - self.auth_label_widget.value = "Registered successfully!" + self.auth_label_widget.value = "Registered successfully! You may now sign in." else: self.auth_label_widget.value = "That username exists!" @@ -352,8 +358,13 @@ def fit_automl(self, run_time): automl (AutoSklearnClassifier): fitted auto-sklearn model. """ - automl = autosklearn.classification.AutoSklearnClassifier( - time_left_for_this_task = run_time) + automl_args = {} + + automl_args['time_left_for_this_task'] = run_time + # TODO functionality to laod this from Mongo + automl_args['metadata_directory'] = "../metalearning/metalearning_files/" + + automl = autosklearn.classification.AutoSklearnClassifier(**automl_args) thread = threading.Thread(target=self.update_progress, args=(self.progress_widget,)) thread.start() @@ -375,6 +386,9 @@ def fit_automl(self, run_time): with HiddenPrints(): automl.fit(X_train, y_train) + # Automl has finished fitting: + self.models.append(copy.deepcopy(automl)) + with self.event_output_widget: print("FITTING COMPLETED WITH FITTING TIME PARAMETER AS ", int(run_time/60), " MINUTES") @@ -387,7 +401,7 @@ def fit_automl(self, run_time): thresholdout_score = thresholdout(train_accuracy_score, test_accuracy_score) - output_str = "train acc: {}, test acc: {}\n".format(train_accuracy_score, thresholdout_score) + output_str = "Run {}: train acc: {}, test acc: {}\n".format(self.queries, train_accuracy_score, thresholdout_score) print(output_str) with self.model_output_widget: @@ -395,12 +409,93 @@ def fit_automl(self, run_time): print(automl.get_models_with_weights()) self.upload_widget.disabled = False - #self.fit_button_widget.disabled = False + + if self.queries == self.budget_widget.value: + self.on_budget_completion() return automl + def update_progress(self, progress): """Updates progress widget""" for i in range(int(progress.max/5)): time.sleep(5) - progress.value = progress.value+5 \ No newline at end of file + progress.value = progress.value+5 + + + def on_budget_completion(self): + """Defines widget behavior when the query budget is exhausted. + + Side Effects: + - creates "Final Model Selection" tab + - final model selection dropdown + - final model selection button + - final model results output + - disables upload_widget + - disables fit_button_widget + + """ + self.upload_widget.disabled = True + self.fit_button_widget.disabled = True + + with self.event_output_widget: + print("QUERY LIMIT MET.") + print("SELECT FINAL MODEL.") + + self.final_model_dropdown = widgets.Dropdown( + options = [("Model {}".format(i), i) for i in range(1, self.queries+1)], + disabled=False + ) + + self.final_model_button = widgets.Button( + description="Confirm Final Model Choice", + layout = widgets.Layout(width='auto'), + button_style='primary', + disabled=False) + self.final_model_button.on_click(self.on_final_model_button_clicked) + + self.final_output_widget = widgets.Output(layout={'border': '1px solid black'}) + + final_model_tab = widgets.VBox([self.final_model_dropdown, + self.final_model_button, + self.final_output_widget]) + + # TODO some way of appending to current tab children? Re-setting children here is ugly + self.tab_nest.children = [self.models_accordian, final_model_tab] + self.tab_nest.set_title(0, "Model Run Info") + self.tab_nest.set_title(1, "Select Final Model") + + + def on_final_model_button_clicked(self, button): + """Displays final model information when a model is selected. + + Side Effects: + - disables final_model_button + - disables final_model_dropdown + + """ + self.final_model_button.disabled = True + self.final_model_dropdown.disabled = True + + with self.final_output_widget: + print("Chosen model true test performance:") + # for off by 1 query indexing + model_idx = self.final_model_dropdown.value - 1 + sel_model = self.models[model_idx] + sel_data = self.data[model_idx] + + y = sel_data[:, 0] + X = sel_data[:, 1:] + + X_train = X[self.train_idxs] + y_train = y[self.train_idxs] + + X_test = X[self.test_idxs] + y_test = y[self.test_idxs] + + y_test_hat = sel_model.predict(X_test) + y_test_prob = sel_model.predict_proba(X_test)[:,1] + test_accuracy_score = metrics.accuracy_score(y_test, y_test_hat) + test_auc_score = metrics.roc_auc_score(y_test, y_test_prob) + output_str = "Accuracy: {}\nAUC: {}".format(test_accuracy_score, test_auc_score) + print(output_str) diff --git a/config/widget_config.ini b/config/widget_config.ini index 330b4d5..6c46e33 100644 --- a/config/widget_config.ini +++ b/config/widget_config.ini @@ -1,5 +1,5 @@ [DEFAULT] -mongo_uri = mongodb://127.0.0.1:27017/ +mongo_uri = mongodb://asklbUser:super_sekrit_password@127.0.0.1:27018/ max_time = 60 max_budget = 10 train_size = 0.75 \ No newline at end of file

H<~sXfd9#h#7Qff;=lp$; z&-a&GJG*ib@Aq}iLM}Fx_okCQ*KTX=_UHcb_0|_lmkWQR&5)C;N1n68!N=SEu;J*8 zo-a1$XOx~Ef2Z4TXW@_Izn`arhnG3^82cW5Qv z-iHhFBt7n*`^Ve6Ka5Eqqpg1L@At>GdjG;`AFRdC{r!3B9QCuNaOS;yEXB{+MHhNH zIX*uh^QhhZK0d#+{L0at$^NnN@P2*lPn^D76(W6Kzk5wi?e+Hf%GaDNINSODYIU=V zr1JSa?o7SXsQo#=KW^U$i*AiH_kRC+_!-3K-Q-1YrA&y>>(Xof#w;gSB* zbN`q=!iV{ptd&t>dcv4;G|q@RHuK=!WVvm5s~z<-;2pe}rO!=RvTETB+=YVNn5FZ> zM5pD|M91H9EHMf^%8bzcOVRqF;3_Ny;ht9YSobF$$G?{?x+xHmZ%@644aO5AwOa;0|hn@jwOrjoJrips` zTyY3Nfs%Fv?oPTuZF`rn%fKUfV&i#;ZAIH$Top)L&?@Y*;5C{k2BS>W3|f8fFyi(?Il3R2?&!32+}lVJ#gmx&*x=dRXQ}&Qg*ptu5tP!NMC!?-h4>j$bR^a9wS zW@W?Pq>g#3K;G&6b;)v71$qh<3Ka!Ef=Y(jHscWZgydj36tf7CLF9;V`rM?Tn$ zq|vZ%QH!E1<_rD}6jQ2pqjr{aKAT6~o&5M~PgiBD6flFsY6E%JGcE?o<>4))kQKah zq|O^~wCJS@9I&s9GWO6aqIBmBHQO|>SO?{r!T#LS*NoIL!h+N1+;nyg)lit1QT`(e z5wK)5HC750QN+_tjBIFg@2}0thg>0BZakjvSQI&tXOUAJZqT zzc#qE6JnMJ-I9pD9zmW-08$2t-*}a>?k~A5iil`rZ7~YvSSTvLSc^a?9x$6s^R70=IdG@1JE+O9n=BB4g;+TshWq!oI4>o&7#z?9;wuXy0 zMpR??Hd)=B5)TwUjiq7Uvdox8w8dWVlFkRo%Rk6EG#e-)Bpyh5|7e97;zP@k3K`N~ zsD^Js{VW?iJ`?kSl$YX3BW}2S?U9nKLgJ(MiD(?%Lv8}FedY>%^rIh6)&x42IN7#$ zGt9Q~Qdy%U`%1&qG8XL8HvYZb)HRzT#W5iY^ecd`d&-?- zi8hWY-9K1l8M5W>kI!wC3F0j@#6SHe`!@;}0jl~k$(=jq>v)?}m&I6vV2MeaW;dRN zE{#-k(A_R(u^#|EbIoAd zB5Zg5F>3ArEJ)YgUR1Pr>?IgXHfxzKz*?cw{IK_t*ID9uD7pO+N(uY~l88VA>!|$j zhf5;VO!O}b79DxAsq~i(sti?B0YRv@HpCy(-<;pq9iHi)8_{{^YHmI|fDbZ6BZSPf zmyR(Vst(2#jREHPH;tL-JxdK$VuIw>m7NWOYofQQa|#*h`W;gi@BOPWo}&pJf;K=Kg4E6K+(J;u$>VGR_VyJ+66+zq_ zbXsB5u#M&%_^X3kllrTATsZ(KIWoy!S`oI!#TrCIngfcxtOd#}RlCF~EGW`6`If*q z*@=|Z#j5a?ou8ME%w?7zCZk)KCHzlz=gNX!Y>%RQTe)jEcFnRKY;!aU?ISPP<}mw4 z2i{-~mC2NP;%loYLe_qML=<(`1<7BSC}2EsPJdJ5_Ir)U`f>hZjQ=Dbb4X;CB$?n~%}nlqhN!H#y3P+`>}q z-#aLYTIzE1kvdJA>F=nAfY42glg~kktQI%i0V@aB!{X&VW56EZ;c#3)2R-4^oJ=Iv zkC&jTizfpMMD=}q)9C*LcV6{1WI;FoKt)+5%1;-xgHmn6B1sUjjm%rpi90Hi zI>bX|r!%u)&jH$`GY5P!kOryxw0~s&qzj*O@hqD0a|TG3i7TN1@tG+M}#91fm5p(n#R|2m(B3+asb=P1a_X>1>&t649jE z0Lt*(X3X<3u)lNFM_RZj9(i6&H^7AGvCiv!i( zH7JNM3^Hsct3tg}fOeH>;LohJxs}}dc|56%u=vJ11%3dgu*S#Bv!_-fsWS!VXf}!X zp>3T*(_1l#Apj$7zmi)Qhu9R#9~kQqjecfH`(PU%wwRJZb|q7F2bc8{;5EG@!=Qnq zh@la*$)W};X)|CXS`D-wf9jN<2`xzJXTXJJWT76G^qxW<@C{~gR)r;MCmU&fGN7U< zZg(N9ULNEfX#e9H?Rsb38MW_;Basfkr>N0)Wbz$&RIzS=kQ=5-XqF$UkVe#jW=Ykl zNmluZ?CDXlKG!cIB~EVf8w_3(ZhH))6q(a^XnXyK+yn>W?M7eoxE? zuAl)^;m;dXUY}YaWz{0!@^Vz7847t>7-oTE2-ThBSX`RB;cf)$!mgOHdbKCW!+^Hv zBXVg%8tI;~B`_@ACDxFr7;oB(OH+l#_Lmcq8ENs`xC(U{Q{{v8?Pgs}u@d|Y?M`hiR{(<&znv8orZZCx?0bLphb z@n3$)ZKs`OqFx%BRe}k9ZZ$m2sZ~waZ!6~t7p%6D>NaSI8eIQPgUdG7$%~x)q9;LK zy>dXadL64BE9jQlWYe6|E-(|6wq9CbPxJM;0oIhrJ*Z78g0zYNPP0SJVXL$cV2_TH zTW{>Qj+E<~^hjihuW4=SQQnrPkuZjGF-m9qish(ZG?S%-O#e`T{*1R|c56>LLlB-J z(_tg=av24Z;nG{&o}f<_0N(}uquI;~fVcDjx?j<2xbPRN;^5lf%|A(H0(64ius_C5 z^Bbry2kHB-m}MrCpst5uR(t8Nnrz57Xkj*KJBJv`G%4_>{t0T$lBSTE8>|m`cyG}} zAU&K4oQ&*ZUPZNra66Jy@-_b9btnV$>C)eHhJ{(Uiqp5=Xom z3VDWY`*Zc01Ao3M)pvu}c=0c>+LYU<^TDqd?C81kmKG#yBiQK&`9y0_BB?+ zn&^9JR`JY~kLOo&Ity$p_c(|jvU=$^O13g0ycIr*R8KSz3r;x8k9Z=!D}3$B&Loth zopWd-bm`=*tF-p6hIh(Csh7VHKdBI2EOjeyZO*({b1O4m5o1@0e$A<+q(M*JC$yDK z`&Q37Y8TAZj}j9m4sPV@I_{)FdScn(1LR=1DIztWVi#xkVeY!eS}#t3ES>mIKp9IB zm$AIr1|?n5bQb3l0z04IS~4VQL&kFYRA+GI;vsVb#$H&p$k;|EHB0kwD2pva#Sg?D zRZ5OgX$nTfuC1YJ`BD?PQa^e_?RWDfm!147OyuiAI?iCRD)!Q3$OmheF7l zTw!G!MYagb35re2E9&!Pvuo-fKhHt=0=orA3stn(^?1I#I}@Nq1T3sZg!UKeMh28{ zkAeuThcKO=p`6EckfoH*Lr@hUA;u5L364!>))FYCtvaWH14Y83?Xb81E-ckgVW!t7 zZso`xU?#yOuphl~cPj?zoD4IaC++7YacZDLzMCPnTo8sSK!1DQ0ACikSv6AASk2g%aA}1AT5JgMcSlsBdVl=rDPVq)(8J)I95H6qqZmR30)J`&i57w zlFLvTR;4QuVLAU~q(x1+{v~r{43;n9Oh|*E&Cm#77mHqzHR#!7Z z>v^S9|MmP{z3Kgi2xpTN7j@nXRov@XcE03cUq2fJM4|*u+l&bvWI;nZ#`@ZdXf2x) zK@vS-hmSS(Zho%NiH3pz>s(-tGy@{HQ=uxYG2MS%vK z7gi`?#CHr=$v*e({i@}Qxzfeygvn~0E=yt@v=MAP67awn|2qL$yb16j=|!RA?m>{) zk)bVvn41cUk-->zlWE;+x=IU?0{UOy8k6=AT!mSs=eW7hgPQ}3c<9@{!@LFz;?hn_ z75>Cblwm2^^&b$KarZZcXK}v*Hq9SL*uHAr02|Ix;8Jm+iHkW} z!3>Ic#)JVqR9=X^6lX5{j+3`r5f*>9nT2oiJhl3PU8m7><);;GphwHB$FdUcFwB}u(`aTx;^|-^ z4@K#D3e|@X4sG|OeE(8}bjk(j!s%|n;<>${l^qg~qkfWrLPHS!o0MV*J@QxsnLsW> zbw^6*KEQm7Ne_8<0 z5?AmJH5gOd1E0?Uwy;rDVc@d=l~?mg_tW2uJW1c5f>T53h1aRj8q83dR<6JDZ<>Ju zv{@yj6kb~+;KpA~`-C6xqB;Smpf$mXQwd0Ey&;>XVfj2f+OrijYud(Dq#)JyTUpuz z!9e|XfB>;Ha)(xTq9np2?Ko`mZ68UR}}X>x>N6HJ20+R_lk|bbFTsLO!^~?x3qCHrhQ8+9-UKNie12^BQ)aC zWp^w8fs)Wc7|DzYkYCxE_oG$&{V)gVgg z6xOWjE73B!@#~dfH!U?ki%4#|K1P}z=hb*tVKm{H|27g}<4ip#CBUXYF;vxsgL9ch zMLl5JSvt$=zxr&6et;D(Pu?!zHmBaze5Y?^y`2ODF4Jak?Io@JVMkpY5j`f9^iao; zu;vG6k9mlwWMt%~G+5xgr+Wrn4K=Z&8|zG71ZpphH}Z!gFI=fNO_OP2((2ihAI$}o z#vp3(#6k}Rf+0{l6P2)frmVdKU-m9hV3$~2w2JTck^Zgb$?JPh&1$m)9cIUpx)Fjy z!LDy|0mn#J*7h33Ha|W0PJ@dbk6ElRLNt}w8MvQZH;}%^Q z%lqLE@{nl|OiIdmPB0?)WX-@g$zKGcLrixP{R^$Fm8MJ%gdgl`Gi-~G?&Jgso}z_H zLnc>Vt1Yxgm}{-EaYCqO&;p_AvWDjf8v|~uSD87%N{Vx30(zqB!PhzWbatUXrPBy) z)X$^1+uV&mATWPCXK;`1FVb3LGD#dmFcHNI1I=N-)sc5w?`|fJkf4EGKZjjE_J>Hg zvMlwM?V(NA(vFUqgQ3{Y4gra^iz_G@NTlCzzLlL*;Vg3jx;dDePu*|ABEbN$WLs;+ zv9pX+C`I)8g{LcqGjmyHujMwnZ6oS==>_k}D#>tSc2@9+W6F)7-{neTwqvsS+eqs%4Jiu+gA8FWNs>MMHh*0&z0v~`FI0J zJSSA{aU`*;mR#B(=eO`xBq_UuQ8V+yHY6W4k%RQC|00y*x_Er6xd9hGR?*3bKXi5E zJY6+UECeR^GtEQW?wuW!%AWQ?3=}WvuXMjtW;k^kJ1)3@X{o`t!Ug6ULAup73GrF* zbR6z9k_!0v+Bt^ME61zAWWLLQy=;L~KabEvQ~f zn@JhXL(YqX#YhiK(&BQP$g;KirQO)VXIxW7VldW%=Ec;C)JuB1i+9pWC8aKi@)NDX zpJERj2?TB}TJPMgH^-*rscIgd4Kpvqpl;gp_NK+gbs|*Du0x~_X>`I(g`mWaXl5NY z50*0KHrupTVz&-iXq!;gRoi-zA0=_@wVO!`<%w~srP;$uB{r|0Y7|48w^l%_&`XSg z0>z7`>OgmfQhlqzv}YH-+XJ>D?M78oM-fzM&)!5+Sv__)U1gV3Z-;Zoxt#z!SjSJ% zCib}M?f1t{Qt-3~wuVdZBBY=+$~{ZmQb$}k6%NeF>obmYyLU>_jK9pDEgJs}IFlp8O-ZiTAvCU}zPhcVwr7_u;LpJIMR+lnxvx*f>Q; zGR&5y?_0P^S(ajrB_|blmllni_!R|1yu#4bkeCa?%05u1B@DPVK#nC~*_QT&A5!Cz zFnt-N0bV=^{E?^62dO5=5KUdb-C6q)I`IE6R}*Bg!fb>%`C4}DmcP>BDkS`3IK=RZ z6FTzA@i4~JOJaV6;OIDT?K&Dsf+t*`48JFmn0d6bmJqhi8NWMU*2z%`NWvHx@%U)6 z0k1~bGp5bzPYoNyT*Z3^OiW%^#=>(?cKXyN31mGl^AWnRpXr3kl0(ej7(`@c)bfeCYis*Z#+lpdA)-?V3~>Ezeq#YVeXl!ze72T_ zYh&5bRUQ8x(5!8mD^aRhBVR}8;27mxDV2b`2rAORW+L6xS5(ID5Jt0(QT(ZK{GELCIt=ljmW? zI_ik%nK&fH-LMTw@w8ZVEMwG0kQGgE4i^?O+1DjrdV`$$1QN(u!GEF3!yv;*1+i%Z z4kJb#xAGp`SoW_-UA}0^1Z7ds?3I!?znJOnf%UH z(hNuYDU%c>OxpP=Luiq%#gz9wP*A!d9_jl7cFs?DNizW}tFlksS+FT%E zkSN2$90Ftt6G|PoxP3ZfsAX-kC2e>&G^dALfTK}4XrPne%cl_8^K3y7uSa`r=XE24 z01X>#ptG(A#8B~egW*>+ErG{tnXOR;txH&E`|vSVAG*%0QJO_k!_~^uY#jh#2t80I z2GlQ@_oUX5)UioLIRysllmlE+H$m0rNR?}wH^yBfHE=tf-S!T&hMEx&ro(!rv)9~8 z%tP7=iG-f9Q3q$Bcy1|AR9=@IJ@4V#X0vTU4iC-m&iUO%cs?O{5)S8?jCMi$cFjbF zbQ(h;AF?2IvG+TvvfaM+6<7`SP(u}7y@jllW;9&0IIaBAB~p3-HbxQ7d{ z%NRN|JmF`TKUSUK76^Hz+|%%#pLkNq+`NpVkQA-}<;t{g`Krt8EgZf{bNr@g9&5b^ z+=1wK@PU%&Gspdy5z*#`2xn6P%4`sZbw*58tj;j54yDhOh(O(3c?X_Zj_!EIqm}F| z*E~>3A^3+KGbE7RQk-4fardP$h9t74P}BZt$=&5eQa+!qM!wP%cJAI^DQfhU+|?~{ z1)V}w`l!*bHBLx46?g+IO~eRAF15l2a`Uc($1Z%U$NcVG3HNNiA^A|7XnBRiUu)~Nb9*Iu0KH@GEX{Q}E8K`2F?2gzf(9?hATyZ>I zRp@f+*8A4cEbQ2^xfj1(eeQB$R)dx`RPBD$a|FN^T%ZZLj80^{NZvSyF6e zIXtuPV>Q-~wk|9OL&e^4&BU1@#;^)|Xu#;Vt>jR#E1Ftl!|`nGS3K`mUIKg$TF9or zA(WsGaBVLN#R;lWO(lSbHZck7tU1|QZpLR6cBdS7}LzLh#vj-Y{h6NMq5J0S_1I6VbmCOGD>Y=TK*o9*FfC)qFvp;^PfTx%E5oNQ|5tXp5DhHDMm%b1#xU;2w?E z%@t4S^9NMVJ5J5Id`NOY8V09b!y6}9gQVcV+lCw_a?@cNWD+FO-?`dkN2fN4r%0qb zw>>`=gQERrRv4D>HHL$mSdZ!u5}!c!2^_P@1QEAu>n(&`o@G9z3MgEr&#jmTo?R`bxE#CUfa+x&bn*LGCE<~+r5x-9XZx9oW-&+`$xAOtR1NbsV_^yN z>3+UD1?#h=Pt?)|1jW-E(%Tp4lrENDneCYl*C6zNbZ@c(j&=I)f7q&M3iIWC^vgg! z6EkFnu=7(*6U`Aea#W-Gbt3x2Cf%~0FvW<(h9yt;{*Fu&7?>ymQ0+U8fTx%%tM*X^ z=r+~GpYJN2U@5c=r?{Ci^kEhtq$GT|WXc|l!0p@oah!re2*`hn_SQ1CO=BIBIGOH$ zS4idn3jI^o+D2n%VR_2gCA*wrssqbd8my_r5vh?JI}A(6nBYUL^>E@YUH~J_x9`07HmDMaRI5u+v>Vo5}iy|#wFG0L7jM5UNy0gU1`BAO9G=J^_h zTWOHO{*4Y}l_fO}>eSV7H%Il26j^GAj?4Yr{pj*!A1|M$@?_^NC%myPZ3@UGt!#QV zM4pa0N4naLs3v?*5a_qaRn4wfiZFpa=Ln>T{BgevO zeLTnCE2-#!#DQh~*c5TC!^g3R?4<%unxlAc;!mEU6$FdXrs2in*jbCFX7g4+QoD`* zgFE+?%k->M1ghHm~s%&)Ss7oaqt{ZfPg=L9tOCjpPA|Bgt~xbFEJhm zDsZD3v04|3CL%ziqKb|x$z1xp__0*~-D#Li&ukj*KiMfEd!IeGKO8!tes{Q74B=Lh zZ*=#xJ-kT_a}j7mZs^EXovz#(X6k)!l0W_a&mwu!?uxs(eMR z^FLwb|5;JFoTXrQz=862_Z`(^o!u4nPf@;|2~^ZvlLhGcmC>=HofT_1^{8{J=CxmL z8X+!A1skoa*sC~11yCq`%WeDWenR5$m9-)b-GjJw)latvMKQzkvo{-MO-keCu}Tls z2A^6`|9BBaN~E0HFpZ*%kw|yS>IIu-^Y0YjEuYm9LO=x_}>%v!StxL(R;Dn22X|Z_* zfus}V)krYc2!Q2jg@v-ut4dNP0r(r!l^>NCzT}r;MsrnlTO$4wARqjhSifJe@8}O= zS3c&Zn8gxTUxyE*jKOiXEvpD7zYC>i>V`l>!(4lfGOeWD@Ga9YyaoJld0j!gEA^Eb zaX=EG$_v_@>VIM6>C;Qf1ka-Ev&m?@| zX1#)bH-}VvPU3webOmo)P(T#sRv>rOugS z0J}o#VE`X601|-LI1T~u#yR`}Nh!n6T{|y&KGk&D#19a^1!flB(uOEr6>u#KIX5 zpO`xfi}Xf0C6UIvSF?3Jrc-i!zmF)+SIIOc3_fkF z3crA#!8<1*%mdF6pHUDx!bjW5c~KIskn-c_6b6Uopcta(3L1<1=Lm>FA|3+x1jQr> z-*`-_v3<@V^d|{`D2Wzqd7lCSoVmFqA!y*`(XT29wpoBFL>iND_ogBZ9E~E7@sSxk zbSEMBa)#VZG6o~hzjY7i!wMN8!999kqJCXjXOegy$K?>u^5oAFW$RO=E_{AXYFEPpyd?D=- zMZ_Qt(i2j(A_i9qHDwWLb1f<)a2&2mK_c=1MAsj3lsKd`r6%}B{gM2@Tu@kubr6%_ z8_|;Ve@y+4BLHU_xFjLSf!fRi3X6Jg-)TR5Kf(S-&H0n^Fvb7`000UK0D#7STb4Ai zHnVfIa5lHm`;RE3<09N(^#Vr zqR~i&baA1G{CeRhFgH5J)u>V%3}+MS^>|BMd{(yd>HHI7AAj$^IeLD4uFAo?jh>EX zPUiZ49bare_xkpdB3cZgysV?!xyzoP6Tz{XW0n zOm3h5IR8GmyZ`Q=wyNFwx__@84&uwrzLv)3{vIUu;^IH8?b^k(w$jgzZuTA%-SXl>6Bkc zS|SNJVWKUB(x6R`ZSrereFr26c|6YW#$1@a{d_SS)N*d+~hbk7gtBzhXiuyMf zDTr%Uo*2MGDO~n-{!PUeVv^baBF;m%uoCG+1Ga zgv*LBn zu&JRdoej$I1WJ3vMxuVCzU)j|YB}ayXJJm<>TfHxwVat^n1Rw{HCzMi`KL4rM|-$H zmgwzVVg|+*rNb#LU#A9e{Wf~U+s!m}zBwO5Q6+lpYz zoP0V0g_W?GN|+I0zXSlbmF-ZZeH3YKe-J{UP2X?|);P8?MXeJVtT3aDYR!UF9)N?{ zWZkK#G*%yDqW(E?DbB!%&qf&a{n(j0a6=6%M>>;^sx;zFmJ@9Hmp-GbB@P3l4TRt^ zAVAe5tH`KbMLS^zCBnb)%ehPI2b)tl6l3vK0$Zw73mFr1BM0FKiMnU0@|E#e5vj(c zL~SyVBS68NsKP48xwojvyDBJ0co|gS1+nEv(b1pUQLUIfl}}8Fl(-!?Tp2tRT1R1Y zuknw<_?qDaHnv7X)nRZj7oKiA{SL^W|L z|DjU8YnM9AnMxvK%$HL_?v|=H6tkO|r%sX~UMrD0^S&UyPR6??5om=WmMM6Vv|X#5 zLdtgLnVy9zN2N%e??iRJ8cbnf{S+|v4ghsh1~|YsaC$5k(!lQ}Y&t_ZxTfW!^OX(B z+T%dm%PY}6&S*Q69FgJMGwZ zqDkbC!8w(0HanJf7bcCY97en_B*sw;_ml3-16itsE0MVuKtm*-5p**ub0%J6|+h-Xj?F66G=C|KR86wH5ly*L{BB>C9^H)GwZf_RSp$v{@^`H;Mar(J9x_`5{CmXZ4j zJjrX~Q~y1JyX!&R3F8(lN#6gFpG`Y@pS(9tg7f{c=cu}Q2_E9D&bZ^DctZDUYn{b@ zz1a4~hN&%2XnOl11yX|faCMup%FOEQBLIaVWE=pbq>P5B3RQA2urLSrRxbU%d-^|Q z$hzV8%jQpACSF5pZXxDx^E$2R;xUQVESnQASNhZrd!z9T{km;8CtYgf_y=ffolkBHo{tU=+2*!k zBel|0J&8JNWq;cm)S0%~#rZr)DS72mnPb{TgbRb!^~En?lY`R}s^d+hbDTjcZY zVr$hizhLph{h+DFyZL>C_8qRw3!iE4cjNcm7e}`}kyg|&mXr~gfDQR4LHd<}LI17m z!be9Lo(^tm=r9SME%}D&<@v*1V|Lzn}~W6Hca?-5eP$_-;w%a?|nJ zZC$s6hu@ZrY);H-3C4TNP8C`oJ0*tvpBxY%t)FW#w)7r@p8Hj*PNdON zz6Ko`Rb=IA!KA$ENU^{owr4(qug)u*hU}XX)T8Finp13mcG%j9ru6Crl4$;9lJdq( z5=x)#BFW1OtZi1FZ4$f1`RgRu?=6zSbmd>)C`eYKo7Hc=|S*v;J7iF#dwH z%<5uHz85jCiED|liganv>1+Qq;Nbq))X%_G@Mqbvt(N<%_JcHToTV?RNO@)N-w$+a z{nfm*X4z;xA@KC%@ry;-y{2=8Rurz%5aMbXT zZ{h1Zv1Xy>_j6F{c)Uaeb+`bRZr&@FA)6Ym(kRzV^kxL$X6V!a8bw=lE9U5djdj<-q47Y~15Bccl+BLY@mgqK0 z;mb>}=E7R&{X{KLwrr&OHcy)0_IVanBf|F9rhD=bo_QL(P@q_~bH&H6ci;A{$JX3Z zG2k)yLNdu$n~i(ieC!d9So)g&uIO6JHw(9PrhpfsiHtvK}2C~n0^+R&iCO`kE!$NhYq;R0T){UW?MhWkAPko((s zt|Mc=PTB2cXTyn(6qV~26L3JOz#*oKR^skRFE{h@`&F_LFrb4?c z8`sl0nESw`@h>i8uLA4jgRmMXCph%8V3;u7-f}N~y@Xf_&+kHG%dgKNWt`DMlQidH zkt!c3vub5D0XPycvm_GLcy81#{0I9RwrK^ zLK{yPcFq#3$hq&21B353#N%}Lao*-9VHmpK89HY4Y6g$q_YO^=UZfTz3QkXn65!x_ zJI0rxB=AF_QZnF1y%L2{!M{DAyN-zYj&T%?Y0rypr6Y1fXRnoHS}g_f2a#E5#^N}H zOB?VmsJgON8fR8qI=mk2;^eOJHGZ-0i`z1x1H)$y$MrmW^P3?`!q>Tm zh0FagCyPRx*tLnV&giGKb!Fij z)GL928{k2oD6y;$G!}vL!OnSxyc4l25)mpf<)qNl0h99-kcE$g)Kb2CB|Ro#(a5q^ z!r|ha70xYT(eS6=(zTc!YlU$#Xrd-MuZPl%-^Lzg z(V1^E`(*zWhp!ZQNkk@Y&E3mRRz%tHc8^=c+K~z#hdgoC_9RW>2+Dkr=c;Nt&k~{y zx)jHD>em)>aDsVjisxk21}yi1mWv`tw`=%;u^;rPer2d+0>c0^E!8e}C!sD8SA5A& z#%{Y1ZaQEp$#PRjGOkO{2DzjnST1XD5y5dlL$@j%{Ig%pv*oFl)-H(NOInQaAwrX%Pw z>;)0&h)^nB?Zgw_Ag|ILwzs_=nm3;W*CHfR?jTP1UEs;m2V*m$GK=hr@G!%@@zU|>HAYlwB%mM+lgnzdKsZbMmmQcB%Hxtp zQrg^G-8OrCC~SQbY*<>+SyxXB%7}OdIcC7IWR5s3P@o!ClRS=D=9-%EN%~JSQnP}i_P8>|+ap&z)zt$MP}x|G zK@1Jvk=PXKEVfPym0;A?qO~Pu z`d|paC7im2O{S7jzkCk<84~N+*0R{U)oOjhY0(a0p@IUIf#2x+&2Q+MbtN&KQ));T zmC*vDp)L(9^o%?1f@o(xvgX8WalY`SC)y>FF&1L$FhAkc|1_@>Kdf_5m`nfy=kVhB zBmq(kv1u95U<|(Wkw`#3WVI_c%gM5YC=bfdZvT;z{OJ=;1C5?tA-&&j?P0%$J~^k! zYsSo|JX>r-ZWI(kVZ0JIDxo80Ld(@{FS;x3kyR^ZWs|5BgI;)KOmAyPH8dYe&u#x8 zyMX>htaOWCE2AdZ{A-&=t}BvNA&*7~5+0AJ>PZ}iAs=r@!cTvkE`&1bvp4T-poTya ze(^8K%@!@Ovr&p78qS}hw4wNzt1d{zzHjZMp#4G_*-7ixT*kfM2Vl@tBbkJyv#NsC zR#P4?#$-vVk$4C7UUS;sy*{-x;K9GjgF>@G!hJpO6Zr351Gi(;n^Fh)=Zt`TN%+Bf z4=gA$Is(tyNLgVf48iTA^UB!^&>Zm1nhd;}kG@eyhot=Om;ua9V3p$8=%t z|Hg-_=|1Vp47YP1Y^|DiN~4#Un8JOy^tRWisnGNX8ZxfO_a*foM;T8c6<&`-MUtk_jXe>1vu@>(rb@ zQR%QNta{wu;+DyIPrRY!*f<^Md=||B!(SCyMV_GYewyj4cDYyO=FMy!AthyH*s=_r zc^>}u@!{0919xg0^PiG7XfJd(xvsy*n{-lB7@ZUGEc=LdnnoL<`Zr zIvVehD6pkqPrV(7#OsUM6TmCm)EM`lwTF)p>OKOApyxm>IZY^K+#BOOa?A%VO!xKM zH~;gO(ARNWnMg8?GfhhTLRs{_zhbZ}c02Tc)BLG&JHkC%O;$hyx3JX$Dr1U5&Rl}L z4JFk{#idYpk#JxR!Ogv497P-VX!iq1Kj3w>5$Q&mGwtewu4>I!okex~d|Vu{dJQc% zVK*yIJ4NKm=PrCSSTuL2kADjk5ZnmY(KC+BBX3H~3xK`E7HEn0~{DE#pj7@!Bs^}(2nfo->1nTHA(Nv&U>1l>)Lb%_h7qJB{mKN$<;dB67mNGQG zs3K;dJx9VwVOIQY1gMIy8=UZRv~H?!L@b|`UNk_x#Kf~8&eT{QF)U6-kq=^9$Ew;u z(sq1QKOwIzGpGOJVlC(=@tsW;i=)wy!TG&w~`u00v>zOM~7SY*c!Em0|*J`n$`K;561@fKy12 z79zy2x6EM}@6Hb;*1)8aWH=OMJc!rUAy^oUTlucBDa!)M7cv%6jic6CQr_;4wVaLea2JmE12Nc0<9CiYwY{M5}z65Gl zIVWnB!LLW-F3Nz>^kp`<=s-tMCc9BovFx`}ZtL(E6E#)_(N}u6Ny~MjMkZ-VzV>Rn zzq^tC_JO({-#I{VosRm=*)nAhs$d;z=FW@~(oJ%$r{;rc-$O8K#*-}UT6 zpGc9uEt<*hI}VTV>;q3#mu4{K;2#IyG4(KcArph>;_F6c!RAB%rV>hvcLs8G60RRA<>vT%zu3%-UAa(aKkw`OKmf&Tq=6boRmEeKMwm6hy-ey z6f0fCG)&m5Sc12c{S_VXv=B+1U3yX;4QpEzmT@$#?@)WYV^0M2+x+78{gZ{!|nCBt#~80B@iB7HO{R=5&Z_MV3Z= zm41c69H1n;{Am|_f zvth{Y6$eWTy5>=ce5+cY^O0%yY0;YBN9{Mgo|RJr0#}kMe7J}Y=xa;SH7;n_jt;{N zTqk70#&hA{;-1*4TZ4zURKJYQyr#y*&4zgIt;EbTO>2J%>okUe30{;1OdJMZRJkAb z*5P-%(36S2{w;KkJF^e!oIB3rHX#j~V^?7PW^N4WpY~fUuU2)6`(ZgmH9@`mt#sDR5(ym@)Gkkvc6t-{6 zY*A~9R)xda+Tk1m#0(W>u&=`XD~$OAsh937)boK33D=fZOtY&8!8!UFWC3;-SB7$nEL&#OW@N&gJbLe*{D< z-5cnCU{)-A9v1^D_s23F&c1(}G4k|P9^l_}91{5Xkgwz5{CN~EebebSUKofCQf;4E zdmKP=Mzht8o-LoLOE7U#-l}<@-oJ}g^;>iXndOp=Seg%ja}AVqnHEjybQDDH+*ODN z9r;~22v=Iu3DSDB@V4Lti0;c=t))hkDCQu0A`7aQHY{VkFm6J6=bHER3FiA1%M3p) zcY_9@)~^>#CD1O`zz&qUdaTRc_adj3@C)F@%Yl zs9|5{kn|b5KKK7d*d)6o)h)=TnWQ2QPT-JK=o<}(R(;FrxhrDKcDCG9sXoj-W*_7a zsnn-+-Ka=NtNBIo{wCQC5BoB2O?b;`iN8nZD8u%_BtUhrv~-)}BCKPFdfi242}vc5 zy=+0M#AuC^FU|(l>u1|4hi9(Gi2SJ!bJqv1up;!AJO;yaXvp_VHTan*W{H>7tET8N zQ#g{h>Y|45;?B#`YtdT#+V1A5wvdwY@9We*hk&f7`HlUghrufT;3eWgIi6|q$fO7r zciqd$j);zugGk{I@i$^=?DwuZ_@MJfQrM=kXcV7~3b31)BT2vb$`x@@q3C;6#-Ch+ zc6TFua3gkmxUx_@c_clIDgHfl$xIRl=6Me${tl zR%WsW8u#caMM|~L%6HS^1J{>wpXO3dBtS&c)@8U|NjDz&wkg6TEi;H}XpWHAU+P?Y zD;XCXke^tIg#I%+oP}|YJ>F2lPRMB&+Q~#F-@lD^9du4atglXs zzCc8;VbR9>Zoa>trp>sGu)^{M2blo2T2D6*+;ro`jXMT#xP6DGK+~Vbr{>wX64wg(6N~){l8=;-o5h zHI(y0lPyk9Bz)oMq{!+>&Khn&y6v&zClH&Xuv!V~eQ|^ZMBkMWd?T;QNNFsGhBe&J1!tKNKBJnvz{_oj3A2!-gu;^aPV1q1rvRGx=?)UrKr;P7G{b>=IBm4q=lSZbAp1 z#4W2JcXlN?L+~-(Dj?5D8K{%dq&i#yAt{lM`r4asi%eOQzyVn#t_ZuC)?R$7k_FCHqbS@WtcG)>#T9cfB}Hsc(WlFZ4DQ5Q zPa_cEFzw_|9f`Bsj1v!Kb{TI~tb8?I=Esct5qfVP?IJ)LWUvWdr8qx-%^)CXJbm>5 z{eRDpP#&)2UmGAGTnu0!_5Y5%^?#5`HzONYv;S-uy6VztAX`(bdY!J>E!rU&@YzZc z#CH*DG=k?zB|DX<46td3D}$+^$+G+MjkTXVLcZoDmF|~CG4~zSYoq1gB<=UrJ9eF9 z{=PgB{JhBd_*e5aGV}FNvGZ|zRdVzFsFBn2cAeq>GLiFjnBxC@pYt`a^L<$JeO=P? zk|6ln@G~Ms@Vnys%f090EXVJ2>RHg|aZ1qlE4=3WRlz?G)BmX_XU6aObw}X)_7Ty* z|9vXw>+&Zp;R~Ezj5aaj$l9~VA5~6|c_iK2Le^H6I>sL+pYfetj``5jF$L06S zl)IqM`{&9|k7x798RyIc*Y}%N%z}OPUQN&2agP7zlKXeM-%|r2?w!fZ!N+=BO`9ANc zX&d9T7i^v}@cZ0)7W}x%>1jL1{C<1=em(xV3@Vo5Wpv;`$VC=AZ6Kw*9%Y^;ekRUl;x# zPa5_;-dVoSOBW9r_Aw2DUqd}FQ#FJZ--k0_`;yS)c4ysN&F|eeo`-GEnUM%O zR?dl2J=}{sJl5mBC(pBcZe7MQzIMKcn`BEy^cdE%edz)+;4bYtPNG%J&?utKM^h8_Vyq-7DqKQ;E-s+?BS=91u6V z`mTSSn>%ecU1_~yF z6!wt$0<{QN-#Eeuy-z3J_1`e8^HR30XnhqnuUFZC*li2!DtW823p=d6r3h3$MT2*W z?HV=(9y^jtyLJECY%)N;3W(cP_GDhjO}H-LUwM`C7gMknoTXl?p1Tq3xQe*fgwr}_ zl(G9ZX`B>8F7=3)Gj|$%$UPStm64ytd*~z878!yC_AWP_WIM*)mh(F&Im&x;oG?_#aBwy)1Ew8V7)ULYO6fTwzja75XXDve9qgo%GS~n`Sq#BVa zWN2;V{0(0Q6vua*#CglNT{_NK?)*E2S6Y18F2gU6E`pzN5+rQ$#->N~9XnOQ)t*p- zwY~O~bBv7k$v#*ZC7=Xc`+Tl-l|055kg##w+(omqv)}4DKRIyz{pwzg$GK7Hj{F}CiKyjNi6`z;vIoykGaTu1Cgi-3tPHW__WCbpmD+OLDy4b(QMPy#KqGCBwyY&fUk*Tr` zuAnU2$ha}{WiR-oXl(%DuTxLv*KGgVFfIyc@Tp&Eft6l?d~Mg=qtBwY3?AovMVUKw z1=6p+yfbWbBJjI+>M@+59kb~A0Lz^@N-J^^23rMTPnr)>ybdnb)s>u*m=`7CAK9J4 z8NL`ZO#*s6nr~Tu$vMJ`<@+lKheYeU9{|2P?aa{$U)Dnza^DOYWZEm{w5K%iJDZKF zc0Wb0lum1rDNFlvE_HQhpFh6EK^sL>z!xb22A3xWb69r*Nj9O)EQu^7ri z8!b`_@>hQ@&6F$9A_gmhv z(S};=^;q+jeql&CyTe-%@O4zCWET}b3L=;lL{%kTs(%@`5wAmxw#e&1%B*; zJ6T!I2xycgaT52G2|lsgd|W?1@K7nbisq4L7L52;qZ<#2(vBIf)IUXS@CggiyQ_8l zY`MjHx*8i;RfFQ2_@kAkZ!Q}wt!-bQc=e@4J{bh9OE=#}jMHAf$PR$2t(-l<@;J%> zPhxPd*4YI^k*%H$bhTO{*-Qgtcc(@|1@L@~w9m}Z?!jU`;Cy>Swoc~U)4XY~Cspfe zjW5IBTfM?%(Xgh8eF6ok{nwVQpC#WJ0t+FX89DHziS)+}XaSFRpSvv`wmz%(j+G|) zg*H6ag7V0Lkn=18qqNNl!R2w48K;YV6{e)oV8Es3->Bs8LVZzkH&{#8OyAS zF16{b{l?D_IcZqQO$a79k4y?NYeB@Ha>o&+xPUBaJ%CV}=tRG(}h7~V}1 z2;s=?0)C4U4LynfzOrU3QdBU(t$9+M z8Tcz}RircZduJ4_kE()^e@9bKMZ|pYpT%3_G+CZzf${(gE?Kkt=5pZIocOtY=BfG& zFp5?oSE@RMwp%*W=IaKj5J{c@5`xi@U=3AWpJNwxDg$^Pa-ub+;8mDffmU*Fjy7mV zr;3zSTc}(GH18=zAVqwgQ9x0fos^Ddhx)qLI>-wCdFj~7DKdHdLp*6?W8|cIcEj&w zIxq-WQ7b|PDU^20BkMu3xcQ$|6iLlyIjo5Nru{X_U0=MlWl-1&!PnCS^Hw3vZ|3_5 zDv8Z^l!QuOm4gWOPoHJil0i{0hz-MZzzVij%8lKW$SK-6Tc%KTc$2omtq)S!Qj` z!C(vBq0t?-slS5fu4y%w*>PWhy-wu3WZgm&=~#b-(5+3*!CertV8xqfyNt2kh(YVx zE0Y*s4wADYFLF(=n1rQ(UME2m^LmNJ$p(kKG(|IF>(?HKoyf7a%e^}B=27n(2j^;8 z1lSYCPA5N!c}V41y&irfZX(L)oV-~Qtz3Y0`(Ub}2=$j+t`C&Qhf*dj>)ZEaghAd* z!{Xwv9#WBt6qYyOD@F)JK~BjWaXeMqUUhi$=lSa3mdIcjevDB!yi1+tqg*v$+<|fU z4$ZF0psH06$Qon&kOwiP>seY+Gm$88md(7kI`(B@?=skHx45t218}P7r^;BB%T<(h z6&xsiluc!i&0I%A=8u+vI>355Dd>1Ps@X#yy6I8)dzN8g$sZc%Z9?R=6htmG0 zFwb(O8Vq7t^|s%J6-NfW7i z+!UjHQN zVgNIT@d+nnwk>s|_+xRdl)U!i`40qxRLO1BJ#~IQH5_~;Y`=b8{Bz?U-$W^w{?{NC zf9{yf+9qgrYqAh^uUM6$L=VVgxvIddEljaF2ol`&$fA?6{%#YvgI%CT)!D$uCh?*n zwRD0leN{*;0DIs7!^=~P1`d4W-Uh3E8PtlGIkk zQNWZ67}nlmVfaf{8jQ1!@4o+vaEl_5fZVY0YsI`8Wft&fs#a?yCk`iW`%uj4dwHB@u>oY(`ldn~Syp*G@9FvLW4aahaqO480Jtv$F zww41n@qXpy5-q9FTp^$gvGKL(8f#eeG3l0_<=33jSja|{zKvnmW@~&n^yOuzL?`vG zd`^Cb@0J0BSOq+Axnek0Fu!?oDKRl4<&llF+R=s0OiQ0$#rw3=zY=#>eU>;!0CTz5 z$$f2X88bOa8#7n?PUcCzKsS$y3$&ty^27lxrL$(ewke&!O|GyU&t^#sPZ*pbNu-rB zUVQHHt?7ddSnm2=^cdGWUsf;vVB68cjw1yt!N^Tc3`#|ePu6jTbFhJ6y!k}Kc(Y_a zD;~Ec7vj8KEwMhr5=V9t4^uB(s0%@xTP?|G%z=-=eAjr7H*!IrBhvWfQL=*B2b^p` zak}VGYG5Ti;z1j)R)qr4_jGnp&|VdWKO>VHWoQsKx9dVpogH7s@k5=*iUrH3kk3(4 z_Yq20=1F#0 zoSGY^>&9OlZ_a3&;w;&BzsIMyG&q^Sw z?lOsaHIA^t&W0Dx)>Kel1Z1)tYI&uQZt<3j5z=ysZfMS|k!wOX@QJzdE>epxX$cQs z)D`m2Xdl0MTw$i&6~qm#LW@YHPTcx(_x$qGMC^ZSf-U7I%E^^7iy!EFOW`5qE_SU& z76B0Xn=o2dh1=^eYZRH5EMXW6pMx#L8-1Q`sP`+s#q2kDB`kM5-2by}LO0EmaUaK@ zD^7Co>eO(`H-t}MUMjWw1xk$FnzSxTeGzLeD&n4-`1l8h^dy1pfedaGg|~~UBTtiD z(}ru0=H60lGRPlQdy(YX`)}}FXj3hL9hqJJ>V5r0CfTQp2Y-3$=Xx(f3%3EC8C71Y zHXJ4D2B9`rlFTj57qM%^am5eC9k8IcfmC7>2b267&d%*mrX{vS$3=QYP24)m>uhfQ0M^M<)_<5pNU3w^) z@|r`a@(HE+6Odg@{l!c=r{Ty+d&ZBbuya?CZaR2zN=AU9wMb&HRgsj*1Vbj5j9mr` zc^#p+5hH`efK%Bb5NESOUYk~?4vqaJ*>2W}ye2AdvX0s>IOH26B-5?mC%=TXMe-V377?B2m5)cf@ZY*ZtC>7mF$K>x91NcIgWAnq`s+<8q&R z)NTXJzNPZ>Uo|thDoXa%NBw*pz^KU4N^#b(>;9Q@Cp$7+G0_M7sj}A?=)Po=5>=J| zTZ(|;l2Ql@C3 zu45rA!4##ro1--e_~!@S1;$yjY;Ori5Pi!ZZh5*FyRGWLq<|4iH<(bN7=a(V>hvzr zfn5zGQQ8t#GyG*xB@Rffm)qsR1u}A97tc&7{^AW8*B@ULGaBP1gf%QPKq?P59TtI z#E3zY!Z?_E1koA91TX)vB<9h8lQ_xS;|(4o2Q5XD9OSM=w{j1fbuMk4dU4)nI{#6o zxSIKpMWArW1Zxz?>gZVVpdadeW1TDcZc!={lKN=9=3Fct1%)<6p<5dt>rHT3#=H$j zkPzOv0q*NbT)5~zF_a-A3`Ue!Y^R_6f-d~w!Nj2%QG=Y=>fbe zzfNCljOk5BJ%Ny~M{|VX264@gWjobDo}@GN>HIdD4UF@ z$!;3cUbEd*{JECr2U+SGMd=aP6HwSgEJ;?@vi(JtZT@k<_Cj!T0rM{;8p;FZMbQ3W z5!@>2q|(-#mueY{wkX>cqfjK#!2Gvl2K>$UKa#rWhyQYV{>CevJo|hM>Yjk$L6+Wv%BHqgFD; z#$^X-(z|DadM|8g@Zr*1R}kGPr^oG;Ow$TDkp^=W(6nC9ce1=QCUsD9)OM3?{L?MX z_Tyaa%$Bc=p^D6wvI{!ydm(aCz@7FXwaO2~dSjz^xtiP8t4k7s1*-=owBLoFY~`%P z54sbeMdm$he6(9J;Appl^k-5PjK^j2Sr;JOk*Q5jC9a+@XZ>(-fzNk|3Jba6$bqID zk;dviJ8SwEV*sfMQ%4Q|+ zz2Pw_fPEtWZwC}A2*K=EJ)G;Yq>CSR$t!9asstD=rFgzxtYKxKEEXMpW_Kfja7P}J z@Ze&zc&Bb2xz21QIndRxTw8ZbRfZ~KH)IF5+GF1OJ@^BU=HwX0 zhW8%$B_xQg0>b(7)(~1Kb;0RCHT#Cg?D^WTysd<8UWY1-DTWk?^CG%V83*#30Isga z^h)Tf;p2d-LFa+%gl0;)i@dDq468q0g>H&qPD-fAS_pv%fDQtEuhL;T!X*jpks-jL z$&68nNqvFDIA8W)>R%>5nzUTt>+!j=1*Nuv9w1|@*;^{5IPFLJ92Q?Hk$$w!gV&z) zN@C~IG0N;#Loq4UCy`uU6GO`pP|`KN$q^`E9)V)zliRTXa{d-2aT7G4Fyx>N1k5}J zQ#9Z9?5k2l3&xF%*)ZK0UtW<}7P&^|W+VVkD|ILlxAu=ks(6#R`EzuJH0|MtWdP*m zzBK^KK=9HFA3fi#5ZlF~C2E4<0t3;z$hGM1+9QwKVT6i%w@8T89)S75fZc{9Glqwz#BP)&&B8+>O1D1S zf3m#Iin~#FbOLQY`QHY!U#~v0oHNqr5k>XCvc6RIEhbv~74HT- zpV?cC`J-%U;)6M~VB-Rp%QU(q0Ngagu>-;!{YYSS0?@B?}#A4{}j z(YRLV(Isu(Xq!tMb*Sh#gewLArxrTv2Z;eI&2a!{J{EbjB#Ff~#!R14C16K8zmi!6 zM#ubWLu*+q1^At@VUUDDD&K8+pYqeKFJb_s1@eTiPe_^S*>z1KhT4EQbBVM2qHHoD zcsX&|HE(F8Qd3C43^NW|KKaJ5YM*)IQ77wZ5_nkHUR`W8g?0zRKXcXi>ZO3#;~+V( z3FmcTg^C54oqHL<^q{IIaz)$%Bv>6xltUCD9a!ZIeWNqY=<(VMY*dM*8YoJb-3L<> zr!d;q75Bxr6WF0S551t57?uR;ZM3dX&H;9dZ?Zcy+~MN*5jLBEly>szTsfk@j!3IX zvw&|>%FS}@PE@75y51RhTB8KcN)c(d+k zE}t&>dQDi#%v=oWV6s(0GN6Q~vX?nA!;S0j&=Ig0(QQQP*GfYBjcnN1O(siyBPO#x z;kJ8-bd!lO<_hU8$~Cm{+RFU&4$sG5gc6pO%sBJ4x<|>v0j|mziq>*5hu%mGtlQVe zP9&=E>r=?~MXIOQmZ|dhVqTG67Lv}K)IU9=(aA6E5a%TE0J>TZAYpk~F&0rSjD>-V z*nGN>%viBikO@UeutAs0o05+|GMGSR=}gI)DZc5dl2K1U>r%O7(7v}^^3zKf>Mj++3b^h9=gs>VNC#2%+yh%Aabx*MYb)LOuhVkxm8C{Xwek z8xp73L1+!NiCBi_PYGp(Op64QLgtY^3Ce4hVZn2>d1Xf>PYu`|%a|33Q(=XsCFFh) z<4|!fVEm(sR~8UqK}Zg~CK-pb1YTdnK>-B7*K%R{@CoFirjR+oMoz%LK3V*h?*97pc8R(QS z&@H%+D=Z$MqCi?6%Nnn?gKuyK%a*PV9V+lDf4erOjBO!m;)YFb>k7C#<8Y(?{)L5~ z9G9&og2xv`pLc;?PYT36w4*~<0t6t7cQ3wF-XAF1Zenn$2q6<*&BvmcYp08LDzOr_ za^M+N=+sJg8kmmkAn|0pC=It2B@58hNaaz6ORC?P%IlH}z?eL(we|C?DaM8^V@7mp zt&z|WmBh#@h;DcS$M(Y#G0G_RO3Pin;_L%#?g+(|fG(AqNKx+- zbImV;c-BjXgKiH|5giD@!QVf)b|U&;5(8891N4(18o38uO#VJtO06OgXG!@tLLIGK zu;ZGOI|N0urC@ZD{{A9GCvCaeraE1J+-^oRjk#2icz2km?S(CKv~&-``lW(=$0sXN zQo~2mU4!4~1TTcn900~M=N1cmt;`L~Q34kSNB>$Bdjqbg9~g1^s~5c_IF|ThQj|F8 z$-?vuxj%)~QGU5{WyN`BA9v|d+UqC%7sYf~X0=L1LBiu5yGuxDf*l?# zH3FP3dd+9>?9uGqjJBq5lXaz?6?0t_9RKL51YPXtkz*(KU_I-Ri@E8|PK$9TjL&{Y z6rQS{>fi>0k(?|1is3wSnfAP#*tyk^>JSIga;sn2 zz_2!gbZf%>GUg7!HoBU98+ksTct{H)Rx)?wZ^y8_Q9Y9)-o+IxK$*d@8Hqhsf+fSWYqSdkgkd5{QfUQ{*lTc8SHfo)}PZg-<% zC=U^#I7op?<8GuP*MqV-T3jD7)R24i^t|?FyF#;bFaN0Y z$9oURh9Iu^uE~WqlS1`WdVHL(T8op5y45c=RsX!xVn$Y^RN&5DQCwk%H{!dtV|6w2 z*T>jtzM08J=Dn4TpWw4?UQBVZBE zNd?X0YaAxcq*{W^AkdGhv@kicxyCwp#$}_!49x-1@{CyXcWgpWWz{adx}p%1(?fPA z1#XnEBCqgxv#5p=1Ve5-cZLf_MF5Q6Zjxmbhc&7cm%3}2$3=EO z{8SYZh-4d(INXx*KMJ_$qOL5xN-fJbM zXlA7JK)+cL4T*t%V{EF(%Wb*RR(hPtvaFH{fwE{(nJoRIhSpIOF#iSmvllVThbT}a zfk$=x438RgNvgI&g8xtBzBux7YPKm%61Mi zmLabod1D+%$pBar>4Gu<_+J`pp8F!^56@Sb{n7q}9shOmTjcBG8{>6=jWFUT*)R+j zP+(>79<6*!X`mCYzVRF*c;a*W2m{g`0LvGF=4i@6PcnGl@71?8%nnwUqmAelQ6<~p zD5uE-HF9R;bXY*$k<*urghfEpQP|%sX=~pLpak!qHcQwbr{Dx_sT#_bYE~OiVYo^1 zhBEDtM3Z$=ED5$2%RI!p^`ZZMqHFd58}uA(aDFK@VFR8dD~BTq-phzf{yi4`%40xZ zl88gFWmgPPKODU4vbussapPP#+G(&23m0kz3W}rN7fwnqW0@uE_8Kii zrE>H&PtGUr;{e^%H!3xH+e-@OPX=yakl?|`C!&S56=CP#T<`93GeMfzL1;~WmCHiK zRbB%gK>56j6aYhQ)ug?+t3j~PvvOz-REY|uqu7^OBbkf69u|G(Dg=%tB^9U3SYA!nK2tHb)d%yLU)=3s^8(kpvuV(W2^0y=PK*GF~y)9KpbP zXIhy{3sD}%DGd%0C7w6S-IH-vyFT*p8siH@^dbgr&n5~XV?hA1fU6T1a6SD9|0{< zj;K(2QjNd0VrcvU)UM8JFC;`};`wl@z9IghCjna$uNaJ2KFqj^h#VW$xOmD1Tc{RD zeU(f~$o(vsYQ~^})tJseO+lDaYQm(09Ga7f1FfM|;oqt<-VJR-B!kRjQ)jaWI0%8b z4IIQ_r^4LR$3@`)j@arL-E7F;W#`2j(7*LjV_cTR4_j^?eTM9JHvf&8I$Utj)|sUO zpbk6CRm{PJKIt(K91F|HH}V1K9&yF4cx>_kWO6ePN64>ql_ALTi;oWdhz`A5-@m1~b$wbc;FLU|R>9e1saUAq;$+Aj;&Ur?33p;R9;kbWfyq$N6 z4ExtuuxI#wrdys3yXYvGHc#x&JN&#Fv|)#q1n)`LkIf zz)DjzE+zXjM_wtEd`NLQyZW;VPqW)&$U^kMFQRwb#~hBDcVpBBL7?NfoY?estW8T~ zC&7>$eqqsyLIckSK%60WFrG@o7>0{t{_lg9a}{Ai^MgpXq~}FzVNK=>Xi;*=2{dvo zp8?Sf^b(S9Qnn?Gxqx`P{Mx5bm(|SHF!Ex5+-ub= zoHP2_YM~Mg^B2FtKVBQVb7iRljT&VP=uP?@-PvWAFzvQIQ@)}g?}$Tzx=r~>+Msl# zu6Q4~TR0~1G)nqE)9#T0C{pN8idX!t2;hZ(AtC|q;}U52c)YQ-a9 zjFXU3VyJOsPxYe~<_&3y?gPedm;vS~MW`saMlHWpqp*T7iO-84?6vd)y%yYo6w`&* zgna`l7p8`ME>WO8P;QJgg-W-24X^sj&H*mIaTS*7z-w58dx|KK#frX!q^wmjClQRFMcio5;j{8Xj+Id&axkU~)j@Cg`Fk%*%m zkcGEEIod^2o2RJq7M(#$a)UmqYO)XwGWv_)0ZRe*a$Ix=H^7O+@y;Jd9W@$2ADJTt zW6&hjvbNICU?yhVxLyt{s)%Z{qey zh{=>~usB+u9ez~}>F4P^e5>NcQ_ffX4(9iuW-S4nkYNmtr$FWR$zmAh-%2bL>8H(Y zCb`88Mb3>B&0Hiy=KR`%>l>aqhkm1SBV03$$QD(O9a?2%xHSy7Iw5x)W-0hj@viYe zDDwy28muC~E+Gls-aB0Qp+v@WKs^XoIuB<9yeG^u?-G}85_!DO>ew#l zp#iFx&c*ww@+7F<4&-wWz2)*uvoaFiMNASn&alvRkOMavKXN!>2@X0i((waU*cveq zsNO&zEX-#&b<@^YlNUg)Z4#Dp0ThD^=ymVwVRqvD+iJ2vH5c&vh?ZUs$K3&Evo@Q> z3bg8U)0pS-Zx60;J>I1V;y9C_m6Nfot)3yOrHV6A1ZHMRQ;leEyfIvAhntG}YqP{U zPMD})Z>Fkp6QpV5)wpD8Z}T$BUQhB7WvGsBk)#~K=LsDdyvNTVB8j9DLHCQKm0~6^3?!$iAUGXdD`ipv=?{u(5o28{Ws(uf=XP6hX3kS*9{7gHvqRcf zowz1v-9eSToM4$CPDyvl3dhcD)6Q&*b{>=ZwGBEUW8!yUI^AC2+xa~bxEqy{WDrWU zQGkA+LbI{WM=XId{!FTYP!&^ry+(Jj?;A<94&=Fab7?>bD3eN36WD-p_S$MhwL0%F z0lS#AR-me0+r5KAX@?Yx8{s+9Om?4-(r21&HQS>pY}1W2D99qq3y7PiBc!#z&*PUvOQW7U`z{(Db##>( zzxo4wqa8zx$GzsMetjc}zK=Tc)$kHe!s|%h{NuC*wFd!mYDY(VO%h`nxmcnwn7!Yl< zo7EP?jW9#r+zH`*i80yVHJLOp10NHnDAtT5s8+>(_>~oA_K6c1R0*dmVuPm|2p=*5 zlE7w8YKT;5C7?^9kn#8Fz2+&Tvy zGfVXioMojDBq(oFHVbJmp~k~t0(5sn1U^e8PIt}t`REz}oS0t?Av4=ERCG+LT&<50 zzysg1)7hkzAQ?*3#X3~^CK-?WNNXBWPzHE5T1$aB9YJEM{fdW|LyPY?)=>FvClK_< zX?@`1{AU){IU|8h0-JAGkkN|(`V+c2qy=<*Tv1l5%gt}69GQp>GZ?+3(0(!&s>@rv z7;z;uhv{IdMa}gEyn`^jQ$-NLJK94wpm}!t>0yz;J_e+UXyZ<$Yj9o`xEpD=5yW}M zNXb>2u{QCoD64n4l#UkJCIb=O>(PhGamXhdRq^voa`~7Q7V3GoZTCmrINcSfY=<_C zle7SY0~9Kmms7l%wY!r@we=8Pb|hsQmSBlA??&I8f*Z803hn`}nch;-Ugh;vKNvtJ zrW35}!T)c{0KL?=BNpgdVL0tY&I-mzAJyYqpxsfEYJOSAv6A?Ce2U^E% zw&R(I_*Pq7I}G@k>;;SKe00^m!z|ZQLnW7I3Ho?U^ktsMV;^sP`EbX=Z8f_gBT%+C z=@oxZ^zs1YPxl%E4pLn!{eh`SQ$Z~QatQ!x__X&0ul0!f(MV7Z2y4eno~jRg-P1O9 z&QJ!~+Q!c-RvceuNrDXuR$^CL6vT6v)8yC{IH&>2qQW^;$9@o2&>09GYfXBg;ur4u z;F_f!Nuh+zdv?IKPKT(FQiSRWV#BT*Y8R>y>iA3zK}{+Bs)Ephju+7`$y!$e1#V2F z!|i%>r6As6En>>I1;r1o`|yu)I$P4~^$PkPWlf?GCWZ_JhT{qCO>G=?(8rQ)*s?3Kx8ZUjkW`*hIX>XBYtX${ zV+RPUhD52!Nsk;Q>~yLI(jJTpP(}>;Y>k-H3hEu$30U9;+bJ!Hsm#A!k3M$4)3;y+ z)PU2*oIV;(Zx;{vNmW?27Qxx4fCLmkgsj^z@m~pK@H6ydFmvxB?7$G)vV(<~+k-VC z$02ye_(BE>L;Qh8~F9zUdjHB8L6 zGPs?3!*@&7Jxr-Y=Y&4ylUIU%AQg~W@Jei_D`HCF6m-o6i6w0wY|kJ!Ca1fH)#t;V zL`wt5>6NIdp+RQ~M2`&OM32=Tm>aU;wD`#W7Rv9FTOG7Wnc~btI_XF3o`AAiT~?4D z$t(NuL|Ep#`zFE5)&ntdyB>Wg7J6kRKZ=Dun91w)-Cc1$nWxCfixLSet$N~-8|}s3 z?c5Eo2mA0P=StE&$ldh$bHc&^}S1;8>SnBk!7GMG<{X=Hl3+LiKb=ryy3r z?&R+P*$jSm9<_S*hQ^~$I4$MRQ*)d_z;>(%He*KUo9}-&(4kURS17J|6PYw92ecd5TA0zBC^xyHbG#CXq2_5w^ax2lsN_Z(jruggxmnxZS^QIq1u!~e=)oD zdBtMuB5*~tYNL{5_EY!K*tqZ`R3kR9kSXokBQ0WbU{Wkdlt#j35*Cdha*NH6+v8iQ z25`+9-4MWeYEq~+vSCl$a))4T3!91_IuI&`7f{0hH{}&gwI=%=AVs-dkDv=NqY1G- zcz`BxX&B?4^7@$9+Rx+aawz{Tj+#M#A4yLT$!Q#{zhC@yzewDF+L+wj3!p&-6P2hO zQz!uWq4UavR$3%i(%Yi33a-V%P&*x3ua^Kxqpuw% zQFq6MC)K4n?qy=oFlD-q3a%p;)|>c(K7^2DZE2Hp|GHxdE|Lsd0JW9^8kG+Md2jP#;$;TkBoWijv0WZY_0LTA5x6kL3~6y*E4du;+S^{l1aOX+uj}7g6a3%a)%t$kCx%hYod>g~Igc z{?%5+r~zn^DAJa6o>VrlOn1zv^U+75Quxy$$SAZ#*Nwoa9g1rN# zv!#_!MJW-GNbj<~9#M2W<)~wIw1ldJYyxGl3!c1L;=|M!J06V7$m4ZYu%~#g{4Xkq z5sCl_4Pog>N)0oY4lKd=y*#D|p|XW(Lh1q%+gwS=%0m!BeS)iJkzQ(c6LC!-C zK+CJUgY^i$w`8)Zhs6ZmY0@el;mo;8D>aOhXrOcSDB!n`O=s4i%?mB)&a&*%@Hw>c z0*t5IMNrZ~Ob+E z3#Jhev_rBeGMQIR!LdE_h4%kAQ2-1RTyJ~CYshyZ-bReK1B*l`-S(+d8s9-?g z(h{Z;tONH7X_478u#kqwnvQdC^MB)NNfF(S-btt&nzd}(QwmQy56Z6Hm(YD&vHiFd zHXAn8N{1l@&-!L$1X38|kB;z>evl+A=9>??aQyhq)gF6n=~D3&(`sYa+%OjkWm{6X z#`>_d@_)EnWOSmpW(5+7fliyTKY|>l6~b=+GJ!;s=KwSTT&i&^Z$vTuuw5r}V8HP{ z41Xe27u|*v4>;*X5)Hd0Mrg%K$?V&SJ~~|iWOc|P`rMFuog`#RP=s>_fKaKaDWPs@ z>n>xEcrEzMfkjzB9=gH-G6NFMbJSbi-6>mIbURZ2!=+k0Pe@>heB6kGwqrMUG^sRw z2Bde9!0s2ayVpwOjGu=Szb(^>VFC!E%$Rm2dp~g!m-I*gNLHW5<8eYmLS-|)q?fn^ z0W_fmA!V4e5svPy4k z`AYay^fuIPG#0M}MCE#P&HGH~PDx5&5k7X~p>8#0XS4U{UF8Zmt%HU&w{{UdymtdJ zUmK}b(kwa{6i(2mekQIwLAQ?upsz#k?uo44~aeJ5~B~_`w zFel9_n}TZ1JT%;qHWpU=2+NT=K=Lbf30rs>VC?L{Ft@qcaL1VX_+EQ8h*YC3Z`Y%1 z(beu2$J5d5C-?ND>WXaAr8;AkT{Y80H}_+UcD2QQ|0>qPoBCJ^Lx@4Z4Q%cTs_AeC zlk-(ftXZ}oz@i_0N5O6H_wP|_Wlz3CDPa-Y;J3w9O8XtjZPw z58Rtn{+CsfjWF?WSJMuanmcvV$e_PbpU@*3h(9RDz<#R{`;4Blk{Phy?F=`0fGvX@ zYvVnxVmxsb_@$&Ff>>%$xltj|f8_M?H2whws9T7aZ|?a=3;WUEH2?fKvw3&J)NN(ypwH9NCJ#-N1OOE{s`1|LeK4v1 zS0B6omHODhyt!I?AZ_W?8QpwoQb*-Yespjq%}$$!@sP^oGC@s1q7+r%#CY|UfkT&H zHXkK(T7``s$yD_P2%FAk)g73FW6W^Nfp2P&EWKK$8i z?0zlo9Zo7;A084?d^60*7Mh`L6z@=U_GxGJf*r<|x?PVxboPb{Xv2IR3*a9i;z#~= z;HI55ah!P|0aNtnsIFPtB5_N|kT6u+)61P{8%HJ;*6Ro}Edf8F|2PcppPbzW6Z($j`+bmFzGok{B z3>Xkg=%=iip=8}n%gVw-n{83MV%wYWdO!ssEt0gDi(+W7NMXSXfbcZZV>dD6EpThV;MzN zvUmF)$Zm2A@1}RA6}zJHl2De_5NLL3!!cGQ%Wuyx0pm1otAj5U^w0qjAD~i(%0pV~ zMh$EN`gVU%choPk3O1OuRNoNsrA7zI^s~) zLl_M3^s(az^>l>lo7i%pXXkL|l4PBVecWt0hYf8ums(w`v=hm5?BVU4KD=Go8`B+t zEPUyj4b@xdFbm&}^vQI$dp6AV3OBcg1Q!;I?CE({GY(fNJJv5jqS-JF3}nMOh*q5G zrrBhM!XRoNG(lb69JCgotfx?&mm$l z>0#QEaTayiKYa4_2)L42_S%4;0KH>nJ5Gq2$IQaGa#pm*Sny3sb$h7jlx(aFhfu^zYt8um_{2plCwX zy1D|*LMQmk(bcGe5+73tnun)t{jjNI>|b*72PW>+jR%Vd(t5l&!@7XEBGF#sQg8MW zAPku~N`_2oW>T7!me-J>LRvs3WMN7qR{D>>b&BUW1u6>|(K-TsQ9;}W80Cmmx)!3$ zV6om#SFHY_nh-vKoSu}bjA~x3Ze~nLK$rzEufbj64i7d82cQRtzyp&7ohM`j`=aL4 zq&C~@3=uW#Fl9$vXwpW)`Rd?!q-kX_o;4Ug-K?f8)(vc`LQ={G3N^1_EXZ*I*g79& z_u&F)0!VI>#vaYG? z#NpZ#PjkKi=a%kiGvtdV8J|_p`MxdBJp3e*_?4na&#_!(MjCbof7b$g%x-G?vhLED zNwUs4xt}T&y(zjBqP)BWWOUs0H9(d-h%T>Y9AUa9xjx&FGH@BJ&^+uiFcPiO&`k#C zGhJXB9ZIKJ>5jgi4rRYGp}?OEOoa@bwt4+zD9tCD4g2dNDt+cXAxVZk#pt`Xdf09z zH`seXW-~Yx;@k$G78m!@oBAy>Vm=%eNH@ro1gZ}^6vmw~AB9>3^l6EcBlDBaS5VJE zMGU&S+n}vbM7VU84tu*Bzjn9OGyOR@-hh}_7?B+-q#T(x)MQ)J7yK{GVsb|9c0IaU zF7QX})3~keS$=bD0ojD5d>JJQ2Lq5dIN28D4tiW~+_As0O4HF*uEii#Xk2IjMtaKJ zpkRODx$^o(sDcoBkLuqq2~vuG_7RmD$<>CnJt0Ui=t${EcCK{zaYa8erd0IMl9BpN z$(=h?=WwFa4A7~XQeESTn;K1Nh|eW>q^sLV(v=gCQ~59%01gdZDbU?qkKh!e$5)DW zirBey5=?17>7Ym3PKUYABf8Ga>|F-6Ne>l}!L9-dc~i>>JdIWk_L^~g(XoSeGC`qI z-2I!P3PQDWx<&0sZ$mfxxmJ~-1~1{MFk5|dBl$*V`uuzAQt}T8qi5`f48aZw*r|Z3DeFnW3_YQfJ9yuBnyA>cjRfd*= zEuR zJMXg-CX7NfTKs7pu1e%V1v_f^jn;8j2}zo*1q_-yAi>t$wg*u}uhgx&#jG(rfO=;= zuxhX7_h6hHQds0#!o<TogEHeflp7G1hSXL3cJHDm4-?4#!X%s z93UIznfQg$J%Gn?7;kVUfJOp^Z98};J^*Wt1R52#9|L5em6sU zDg4zAYdN$A-k#g|+77y0{z*49anSV)^yNGqz_AVHdxNsbMGkt4=65dXM?kh6n-xOL z>-^Rq)mvKmcheruplv0(PF295%JKH;dRo3^&!m{sk5sko&kPtbb)qTWpLXW;5aa0v zkn1Ark;4smUXfP<`xPj+z%%7uo+HXm>9SBEqDre7C%{Gue&|C%wI*#xfWh7j!URMO z?lLMEDZR8_j$&ihtINfL(bg4xq}ZeCjq(R}vRy_w9O$KLpvxZ^llzg}!wE4Wfbi)B zy9~Y4hY`px=Q)!Kg;sqLS7-N+jQnGu#Tab*NUs1pq@=KWz6IFtM^=oSN2j2U3wN!IQGNYK!-I~}3%Ki8~>#iPlXEL$pd zJc{6cB-?UyoUl-XZHS?13pd6(Wm07@J`I^nCP%In6$;f|Vkro&E*o|02I$Oa&SL&6 z1a=`*^dNtb%B5`qV=E~oyd*&wE*u$6kW>aRD$AT_$ zs|AP#6D<~r9A%1LC=$c;i+*4Pj$3`(u|BPi^$q4NwhRzSuarXJeL1+vU`0EAVgqha zS0y@nMEPTOt~1$rsduuPMrs*XB)bT91{zJ%`f6Xkj-S|=O-yT2JAt*;o}k0DYzkZb z0IRZDChHYzTOlpUl`7+~3!jI{%+C^#>qcMmD z9b2&kKYEWz3EW_&HV%Oi@0)uMY9wkT&C;81lBy=sCqbOdf(mN{4)$E`b24Xesf*Mo zM`!s0+2UmmXyZ3sN}r<@=JRj|+CWkV@e=mNNYNZ?d0*ZeK+%t-p6Z2Nb)EVV z@igwH^sfuK097gctwfU_u_gOzX6mQFY_|Qap2(})HazT_d#Ft8_rmtG!)@;%u?MkX%AN{0)niZQg@Zg7&=3 znseO4S^jfO&r~%4i_xK_KJ>iy!1@q@nA9yLRgu&Dkp0Au9r|F0s)r zz-t;r;VW%+g3pK3O>Hq!=&8}~fRCX5?5hE1Ag}X-qHFw^bO<9d3>qY#lTfoU9QP1%&4+PoROjQC3hW`q>5Mc?rx z_iM*B4+}5W*zJ0xMNP{cu!o7f70t9sXl(5nGd$*22GMz37Y@jYSDBa8_)K?NW9^f| zP33i<JK_&9=?xR|};-?YSaics)CbL_$lF$GZx&{A~Za17sl)7xkkw!v^tHA9HK; z8HxIc1wXG?wNHaS%oH=V>?Ymq__;~7;;B*sOD>v-oEK3FT&`|XVm2EpnzFHPFx&;p zO0x?|0Ymtk;BM(P+k>DINF-`?1aN?WqT9?lHwEDqM5igb?!dPMIwa>7ZMqwPPhx|& zk4G?jLg)k#wUYq{Ax?ejV{YBF*bOeGt0#sDI;fF3PJ&J%rl~a}k%kokIvT^w9sVe> zqTx<->-fO&I-Me=zSzMe{R8e`t&>xAMBWLCV(V7>r)Z0TdB1v{P@w>JuDgDY28GlF z<@X}^(K8l8Sa3>`{jJ4LD%Zvtbh@x_`M#a zIGI_Ttc1yp&^57)>(Mo6jPl4)*;$_WgNN!I=a|%r89$Mp-ajz&&|T_afufeU6Xbcl z&zrpC4We^O-C2&*8xV_+C_5uuO?O>fL27S@sMC9!KRpo$U@9izhiK_j3>aD9_2Bb> zeMg3Tgu1uu6(pqTik|Jang@Lc)C(JNnzo;CYaMHG4%E|&n+$5iR6VkSSl%N#n!xn4 zktfI@yeFP4$drgp8C=*2WX1FXW2U#amj-%vmtnQon^5NnF};;kG)+=;@ARU$9xu4jN3>5sizmEabwmVy->-9>FuInCWz>u&^^6user0;i^Yrn>D?spUH*8>u6 z_8Y2s3BY}-uu@QUL-nT{d^7RAm$wMNyqZXYnIVx?_uUVYA*eUwm$h+(H$Hd|AVH`H zlxX(sQb~}2g>BQVB2QRVx!52W+zGduozDd>brmu4Do0h~zq)yYn}!Coem76VwzAk- zVJ$k3^}I3~u`~&?kX=gAtJuBS^rmdP7$<1Z65j2*u17FxQicJ!5#<6z!?u0FvqL05 zx|h^^-mmNR{tO{ov7$R58ISfq6@;aGAZ@Rwl+hsBM$>vG4FUrIQ}C*O3Jx70QU((h zppNi}rT&)QKR?<;!i!H1utn=E>B$PV+0T9K*Fo=A`qD&n@p;86f+~&!8oVd%3;X+l zmZ7a@f(a6k{SA!2yTWZGp`@S*rGtalLZk{2xaj~BYU9EzyLlC>p_TV(w%Mq218&DFQN{d++dgQJFCU+!Hn%?KhwCVjmT^B@z?}iUcSYmFaz1_9> z2!vTrV_*b#zqT__cz)f!boWtbE+MZ`kBX^HRYL?|pz1Hj6}JgCBtwCLaFuUp_Ivq5 zG#VfC4jDL|L8B=Vh6FVc?)Ob9jSS8nd=oY;WJztmE zBo7qFoDYER8A)GSv~bTLAh+$cbxi1##XtdJ5RHI3psXSyfYYsvn~5EF*dz>V?EnZ;=+oQ{!R3>bjWS4N9kEU-Wi8x>hxIK)^P7PIOD@s7{@3Na9T#zn*lZ*og>I37qIl zl$JqYK^?&ZsPcA~;@8CpDuYmhV>l=D+~BUVD)MZKsbnVOOl7uR&|!votar!Ch?=#< zGxvJB%L-^XI!<#)hrFl=rBUf2{4XfgxU~(2Gfc*aHa3Hopek*PR=m&?oObQdOX`-2 zjf!ANg$~>)k!DW`v(YA4h8|+tdCTr=o2b5bq(nM}3W-#sPaI4h^kCV7OJB288Q&S~ zONDWy63OGHt*qH}%~?Ah&Lx(#U7}J?<4j}P-8Jc_qif0XexFe@47+a3#k@ajmI<4k z@qqChU@1H<_@A;B0<$anpBTFgMc8^sCg&A4b@<6irx`9 zc$kRY$Rz>=(LTOa_E~a9j;?pOEa#(orzIT7JLK@VyDt;2%{+04AK4L{)YHhW*R_0) zKM^>A&%IM^x(BidX$RxtYbJjjfNX9>+0hZ2B4s9hvTqTD&=*kW4FV`097Z&_Pv6GH z9*qRG{=oz0)}k{g6onAX_DA;)8UuECtV@a3CzmTWJE(nkCMC&u=ufj7p6*N;vR2C0 zR~Nh;Ky9&(bZ;Sw9<*QKx+nJ_gAFEy=-)t&HSKl+9{0u=1n(BPP;&br$97XRLgw9< zYnJFdxw!Z*?}a3hQbNozrx~6H9}(S?Jy;=VeqpHfYuU|u{R!w^yj0o;0DV{dxHrVF z*>_(PHX*!GCUk>wygotoLxbED?cf@15@bC_6;<>VL{q-yOw5l?f}pB3_F6eXTqkF5 zlKFp7CPH@zs&WfJdA?p@xE1+K_}gWUXKfBrD4J#3%f>}O8Y$#pNqdU1V46vMFdEId zPQqgg#zVO#4xo4oV<}X_p@U!_AVVkuFPy zx9gN8=Li^iQtAGYV3&Ox6)ABie?Xq%>1aE#JFqtewjK&4R{i7##yI1|*di^glo}lb zC> zgtByClcTIs1~w**yrR?{xE4*yNhjT0ca=c$p@DA!dc!&(zUv8wQ=XVoZsm_K!F@4T z86^k&Jcbe0onn6c*FF{CO(=~iI;5R%f@e&6W|2f@S7m?CBU5^T3iH3dEP1@CynLgX8m)3*C_7d2vN1JnOUjM4*#6{QQA6nwF zs|Q}@YqQAx%ZoXWcGt+tZRswg8DnK0T_y${9;jmAcUxH;l`kjwNJ&f>&u|g95vC|k zip-HfPDOR(w%g3A2XEK;3LrhXMO#VyqfY+rU`gz0zXQ)mO-oJImn(xLb_|lG_8XpP zI;|zjFx>kgt!~UYx@7J_TiYyTjd<|phg2Ghi9pbjmg;V+bF-yw18GEwQ9MvLJ)ncv zBu;TgBYAT=y7CmiE>3^w+YiO*>-7%nbe!>oJFsIl;fUDbsec+^(!)|_F5&88O z#d6dhX`bCGCSV!hQHiR#UQ!=O6PC!jA-V>Qn1WzqIHd^_LC^9@6n}vbG#V zP)_0kQQU%ugw!2O34!y)L??T=MYp9taFLwpe`&!Ac~b;M8Wm-|y_trn0aP}>rQukz;x7@LmG)tHEzeb0;!I(I*+gKw3TavaB(n^Uw!1H|f#HlW>LU^L9O=<>G`* z9bP@)Ylh0uRV1ul12!Ke3wrSL953iV0HGAJi5*Fy42Ne=yR%sNlaXq z%wcsXYr;-uO_3?!i6g-R5N&%u!TrG%vzh!k^f;_*bgagwx*cROr5g}6x0RKk!8LUX zHNs2~0~54|t_GPjk_||QX(^mYf$fLAlh)F6+ZW1RA3^eGize+*FwYW<)b!_?racHh ztkLfiY?xggmO7FnLaHF&aMJ4P=|f=>7!zA&$y~;J=n^7L5EManydk^JN9}IEY&dRg z+X~};OZ)0zA%zj*p<3HfC2JB&6wE&QhL6^ zH3$?d?t6B%C4J=f2YtvV*TK;4ne;#uasb9HB-P@)`%)H&03zC0Xe~gbc@G#6pvwW@ zeh-Vmz0jalh_kc}JOa!FwONiM$g62L05BWi@A=8#GN+D{eNXs`1%Exdl8j_CMGtz< zoEj1zv)yaDa>c}KpEEoj1qe4FxrvTflv@y+eVs{)iU@c=Y0cdaVNLh}Rh0ux1rUaS zNYKR4SFb~q^%fTN>`Y6$jY?@*lzUS~PSQBYA8dDlYlT!ca=Ynzg`0OmP&-h#lqKZj zhg3R!QYa`)v>nXZ^azkeI>AEYJ2s*lrh~xWT^H=RHXZan7I&d<3kN1Yy0F50PNfzc zO`iS%vL4`9`S^xUg%WtoLGpLEk4I-L5aeYW7zKpIbAlf;tT8yxZ@k{p<0m_aK^sO& zXx#6{pvkei!+trv&CO`ZO9sOOA7cibV2`3sPD|H;O^3ax-;&UqZ>P3C((QFDLdP6K zQfd6O*XG7z(xZro9{>lK^R*psE1@{M%jGelh-#HAG*7Kl_P-PF7DwF_I^va9E) zD6#yNNSmOI1214N0}D9-jFM6^!l^k58b8^617oc(mM19BpnZw8AcaO@yGe}sUiK;x zJM_x+h_8U6o7^0rTUZ9?Jb1uOo9b!BPht~1|3}&KIL?~`^7skAnA97X#EYfaD_{6rw&%o2H^8t7lO#ES2E7fCA|kt=GQAR=q#^i}QA2dghDL7+luyL{x}6qT1UafqD&T4+n$ zzY-)+Cd8l%qLmK>%G+k{hTz~NIszA9*xN*|;nc1xl1(>FY8$MnP;wL-%mA){?xZv3 z7WfLUKId*l0ATL4 zXr4CglZvhLrZ*R`Q^P5H;90?;H6wOcZ*fp1-YLN%Mq_bcp1r*7El&99=t{G1VD3S- z`NM&zZdNh3BNe69Y{H+Fpub!JixVZ>$-Nvj*D${xNEzrfT|z8#W!~s(MF^6R<|^?Z z9Ceo%Mt+9dOQ&w=$^zfTcfrsb^`1L*yrfQpQi^h2@Qe|}?iN$H=pYiXSU{6L6$upCOivAMyUV!;s6?LsgUY!c=RrZhZiO=vZD=FwZPv{d!GS}V z=5WD7b8wcyp>G3ooWPgF862ER6o5zRGVbd}I~d(2BnK|{g_R4UJ7YcH))wbSCsf#i z+|9KH3-JyR$~`d;ZZK}z3_W2}l#YBo^8EeB2D)SM*6Fw%7180%|w`7e$i;2YG0yL-;uO#m;X9B}aKDwfu=d z!}VyRtdB12ro*P@Y*HorMDH4un*6WL#ieq<%-V^PKF+%_2_6G&1fw4rZB7g3Y-CG2 z_9FYe2P*b4P>yC&Jmf;>@OTI&WJ3S_yb9coz0%8q@*e`=^g=UQWT9Uv4^Y$bdYuyl`zDACVK^N$gQYV~@_@0Yff;#0hq=*NxQ8rXr@ z_Fzw7m5fyZ&japMiTeU7GbtsBb`^Qi76M)WFV@y$-dDfe;y?`M3T^!bX$tj(9LL_zLugJ#+tYZxOUA@a2;Hr9sQ1IQV{H*k}?yL^w8 zSfQ%By7+1J_e9ETS;Jifrll`+jA0IHZiWWMRUz7t+!T7rpXSC@G$-HVBF&(QW3@_i zbi!af3QbJ`AA?0Oof;*2l-&-OV}AJ(4(7f{+>yW5CFFf*R5}efdMq*wzhhO+GeG8f z0$dKJF-Y$bJ=bXS*s6RcH!IXeHZS+91x7#}Wsm6%0&Yf!>!6!5GhK~d zc-3Yl)w~8SIGj&j4QWob(w^RQg=UF88`{xn2mM|cw8QGM{tg#`@t+6>Q#?R7LUFXTr=5Rpu3anVA6|kw==8&9>6T7Ew$5g1=bz|5T%A8cieuH>zIe$vy9754 zoKwU>$@MqD_v~a$qZ2J|KLD0{KY6(;91X4KA}ASdKLb2+^)E%{21F935O=aAN|2*E zeVdneM(W_>oCO9qWdpMlEYQ@qJ*^|Eb=;3P(=B3;9(Ul~0%epsx!@@A(X5a$0!Hdu z5(;H04y`QQcis|faX!`}XgL~l?)DQH{zn_3Kvry*KT%vfP4aT4+OoZ*`6UVD&Yp|z zse;5YY#lQ``YKNNjbRfY;JpHOT1J%J!JrThLU2*e{fn)!PI!rZ_`^%vhfQN}BcUkg z#R!v_ROb2;ThoYrb07emD&R9n1XI6DJ|`cEpuB8m|NC4^6E9vzg~Fk`O;Y+iLms}e zyM&68gRtZW-k47)0F!-CQRW~Kpe=$4_@FaIc@4;J+RdV}DgdKfG)f;BXk-k^YY->v zU_TWgwbNfm>6SW4fn~`9lNxJnkb45Z7-U^$E`OKyg*>` zsGf0jlgYFpbp7Kw{vg?$nmAARYQ%Z@<0#a1F59c=J%_%y{I54H|d~| zskiC!i0|0n^s6^+2e+9V9f}| z=gSK|-y=&F|I#)}X3;{-R9fAcC2Bx;wwXO0=F_NhPi~Ag0Pz!1Db82py>X@mfSXoh zr}m!)78O84$*l!bq*MkC; z#*2=2ITH{GR+y7kSS6Kb)@-BtA)+t94sMapJYQ;8D6K|1*(@%u8;4f!MmgR96`yco z)>jbN0c0=_@y&s)aZ&i20a5cRdv+5yJnX8l zy}0L#4yRb-g9R|CQU~iqsoxfu<_vkYOfj5NvR}@OeDNAxBr@4Yjbm_bHCOd$k-@#v z_kN;i%}=iXb6Wj)ofvoMP8uhq>P8{tYhz8od^z!yMs-`<*D5KwY>8d!VK;>#{s5UQ zy5UI$jp%LJNNr$`1e>6tDTXM571K0+AbzgyxFC`N!xIY4+ z1}%f2Zu4Yk1aLk%_yCC3gj_mCoK8reWJs3e8M(h41*90Q<^pA5>C5a?m1HjFw7;Rg zp`nHA(xBpS$}JM~Agwm@f>SmdTqFb)VAKJt8XW~Fj@(T&t1r#(*V7Y4Ng!alx`ldp zD+&szk_*C!RvhbzRNHu#SggHctY|^EEx2vlwr$(S*|u$)XWO=I+qP}nMxXD#&P%^e z=e_=MS5lQ)D=Vq_qcSp6bBsAaqxX*&sPcEA%Ae?Hx6k%_{$y_pW~o%nSliPYu$tRP zXk5rlme*q@0S52_mLZZR-{5L&GXtd|kZdjX2jgCZG#&Jsfx58mLf~cPMJvuTy*9$S zXH~a7y>evd*@Y?c z+*u)}Hi>PH0{|h-XE-lHfn~New;Ne`v<3r+cCeC0ctcn5Si>Zx85#^R%Lbw;aIYQW z(2)cHFBLN!i)R%-`J2mqU|G}(QJ}!#=iffgYVtQ<9WU^jb?Vg?YA*s&=KAIUQTFdh z71BYlP!$~~a@6+Vf1GQ9FkmNNX!)~t`9k>Q^ZWN)h0>C;Z{OvRGgLTXwVu?_#4ZCl_)iU^uPjefXqveo42|NFxp7-Z$S(#LpSH~r?ZIfa$wT6g0 zGG+PR~OurpC?x*7`)R&u> zxSSo9T#QCdd6nnJ@lm22Q4odV(P7aNiFj$0fLi<_9DDO|>iYZ|mEYy3;gzYr{Nz$1 zHA7?n0ymE=b{cwyRH(GI&x5_>@a07B3K0F?fFtkH~3oQxbT?42#_Z0VfcomEvJ0e}bE_jUd= zT->1n06~6$0RaAMy8g3id&q|1^Q!&^EC;CMc3v%ur9WTAz6P=>9#nrW5>8UmaW(S& z5kq1{nx1qe%SHk9G`s>ctL-iUC%z%6sMb!il3AhBx!n<^j7e(~TR(hbPLW(b@yash zZ56M2-aWNy*~-_qB1)MXX@0I5nQz)|>7VP0-%x}aA8)$op^w0G2c6N z813sG14brSMsXZOlqTT}AKG7^I$IF6Oc*0^vm{mQK$c)`*=M{Rv-NytZmDnWLn);= z*3d6TTwabQ9HK@!($aVazpkE+?r3eGz$OUmN!v2N&)Fh-{nfM{S5Q3|eL$xEdcY@l z;P}fplSe%qDM%Rt=;`HV5zYapALTh9)rCd~7Z@l8C;!z&a z)?Lb-Pk7j=(0H$7es)Ged(hWEwh2;EVn861heZ^cfVrx`n7)3U_?0s5nz8NFd6GRx z<4N9B=^ICu-E6fDja+8h>}3G7T=Ocxbhv?lk+++!Brtbv{^#r*U|IE ziUno5BsHXWY-hO3qM9*d?L^7$fLM7&m&(Lsd3vp8NnKk!#+-h65+vA|I{Q?sR0wY~ zb`?;oHm|_Z(MKQ7{?R@$94h4^)>nWpKNtWF&j%E|2a^?~wa3;5 zd=u!k$L9`!H(*#dHWFwu=&0Xb$Mc7;3P24av;U-Sycl*f;CkRi|FaHwjSvScY_NV$ zK?FPodNk;85OOcr9#b1`HPC9XI9R(4PaEGBfIWD0&%qsy2RjEi_ThQ&{SL^T&>Jjw zuyt?q4)=}DJ0k|3-yfeyAA$o2Iv~1FaF6H?Va^&Dgit6nj<^Y70|GagP6$3fHg2qJ zoq!ADzCRp6EQ+BZ7?C_m!e9uBSQBF(YXd?ZS)Ey3S=~uJQN2-pQT)q+C{ByDH<%cX zGk)x^s2q#qK|FGNR@#vWDLGxc4n4?zxSX7~BT*7^VHU;l5T)Ciy^Hv2U*nV;mHcIO zgIqDiMyzCeQ9)u=@A%t(ebOdk#TLKvu4Y+L#p~`zt<;jZZ#{+i=6s3rr8_pQsA`2? zL}}5^V5Cadki{3f#mrt1})$No+J<$^U>-D1iSwME3h~|F!tPC)xiVD0MV3 zFt#!IpD1eo2TSKawSAP z+x31u=~S=v{XEZP-}Qa&%@7-ant6^?N_+d^ouDvy1KF`My3Md<;8v zv*Y*p9^Li3zgEv}7T5cIczl(o-~C!u|H8Z#C-?cj85KSqm(2Zsdi(i)SMKrl{JNW* zyjjiWzJ8s(-R1B7zL$^X|91T8JhA$jJ%#`6{;c8W>HfNVdKo;7l~3(y>^+O@y(wUy zXw`Xw4dZX-;p^u4wTJtCx8?VB|G3xTe7`SsD)syPV;sl-a$m}RtK0ke{(gNZzgfC) z&pzoTR_gonwtxS<2<|-^N^_n4u)NvJ-`(ZMbtefvD{}vP5{3_jq(u!X|2{a`d5hoc z_jCR}$nX2hqn%y3i1+(CXCWUO#&^@no@=+YcKdVx_jO4+AN%onM@QM9~p1}A%)$^HRGx-WTzRAK>9on6YYR|fj zgLvi!Ea|pKK4B>Lvr~BAyI3tdVV{3g{iS6>+c4WxgAjhyO5p~h4qld?3j$`)lYvfG zZ6&(@)bwxsh4GZUHv=^T@5=T6tn-b3Y3 zXnUd9^B}9+(hMd+atfzu74N|O!9zI~4o+5CWx{)_yhl&MJ9=+H9&jY-sF z%QR6>pDO_&Bv{gpz|%<=sBP~Oei?YAKx{k@v8`mAi>nH03tEL;7Q99i#bA_)nn5ez zK@h)*wAqvBmoaMR5RioLuDt~4I00cY0YRFkq6f}={~0}9{iUS_%86#)Om#=BZ$XHH zio_+7w3odIqAp#9Emfsl=>f0W8CEg8drI65hc^E>Lb3!NhXcjdr!vp_ca~mSxA5Xm zznW+qScE{i)+!wu?Hn}TuTTW5O+|RKp$Q-`fBe6`rFCSYdr({fLnw$rl;J!XYV`wE z6nX*dP_uI3Z_>wnRUq$l{<>tjYJxq5iiJu-A3-IlqgZn!*0u)7OmDF~Wk==iGF54b@PX zmr?#B1`)7iG&NQV6;Z_7O^j@4bMLRs$&XwiS8hD`2RJyeqD9Ihx+3;b9h;Qky7A5d z?FVr($qntNrkq@l63EFbNH-=##Cj1F2r)v8LIE>=oJf7z^WNL9%L~*1YmOY0($8T< z;-8mKT7PYDX(z-iFS;cWeLaE#lOUull7R6lWnBolEsCgEWNk4DFz%}3y=N2;?r-`{`rud&j9jwV5nAlioSQD*=sxr+_AEKU#!3N7mW=mG}U zmzkksi=VWWe`N}e57SR@a!Am+Hq%OIUb!awFL`gi5^!~XNW=ISz zM=EB>c%d4;3HP&X@cK;52U1>2B#pS?^0!Ayu?kC!-Y24QbPu@+!uFXf_R)`iI9U_u zT;gQg-qG0MdI6GA5f3|B=AA7G8M%S7Mz$t%5!?!w%~-Aw8q$@=(`oiWbL&D$=h2 zzV4}XjwRYSrgZ;ck!8r0yFWg+Q6@;V&=CLho9y2xS_G)+%O-d3n6Kk)PF)sb4T2>m zZJOP97P>T2%|Um&n8kho;8A)}P2r+*t2i{23cb(Qpr8Yow1)mndZ5Ym1D9)4xZ1w+ z4Cb1_v_;tN{PU){1F#@ncY9IQ;du~MMo2$9`>;OK< z5Q`8t(_T8pbf`KQS26~e6WBCnqW3H{RE-IeUsrK92(F3Vrp_s3r0aJ~S-khJ#(0h< zOd&pjrZWb;#>@&AmNumJtCr?d4xI6NS$_=fuNc6+xPT+CQ#RDk>qo;NE35ylkd2`N z>Qw@9bI@spQO7o#a}cNwZcXa1>T%@&q~ypXduc`38W(R63vCW4_Occ%vsCMnps=7w z)8tauQD`4| z!8V84H#+bJbErb5+!J40MG?C8^CPOHyDmij!bAb%iF5j!8n@qToM)M;63>pchR%?l z@Bzn>f`w!D!Ej;+y>L~{g{tAu`0R=5GmjWlW?3tC{t1W`Q2ko}?h+bkCPkyg_$RBS z4fJZI0iUSSTBWrpr~{a&g?1k+oYo{!X{65goppFIERhne2oHV-F~0d2?M<1Yc5#!V zjL0oK#s0m6lBlIFHy^3fw3+^ndI$*Jv^e=3l*npv!yT}4a6LR;!7~Qz0Ui#=1$598 zF3rhAa{YJ-s=BBG=)ls=aOKM&M3-`>)@bGAB<=Z|ho)Bv%&Tc4Zav-~z#Zioh+4c6 zSds$3Q#1dq7$?6)gAy1nt0b!L(ge8t7mBnfu+gFJjunbTa05X~~YyP>N74?7tZ(R%o$n(YqJVye0 z+V~mPHt9x#Nnc%BMYwf4peUP<;+jI590grKb&X|orRGs1h5wm9s@vs|xu%3|K-+*w z>^Q+lP#b`=61M3}MWiZ;YG$iN82p@36$(?X zBh4>$S%@~Vs%c<>q?M>14G-&n@|yy#R-zMCATIE-w#KF}mb;+>t6v<#kz!TxDJT)R z(3_U2YYggB{8lH5x<#>r#)?TNngj~fX3`#Iogok{kda0T2S59+7)b@lRE(`FZK+C+z>fg_*E|lzs+WSVk7=VJYt^&~csPaMf~0DdKnz9WY z4m3-uPGyHDbRw?3=0Ad^y7k&4CZv(>8C!zGGF{>giAwRNy|^@0SZpDjkjzMn-^Nv_%b2PktZz5#;!2g^ zXJ~J-#qG6f?=UbuZ!&l`8Zn}-)*f>20GGI=JrVpTc;1SQln)y;O3gI5mmD3*K!KTI zu9e_zcQBqyh;*Ck$T`YDr)hv?t7o_0d9Dgq=O;`=-kJ>g7%7#Y-68OKGU!awOHHesFvMzJe71GP zxXz`MHpd|XQrk{D%S632G^+#?`aJ4*m{Y5ouHRP96)sq9CDm=v5H+~|n+BI{tdkcx z`9)7ce0t@8X7xH&J66ywv&p78rCneqC~dv8z@Fyoa|5g?k$X^^R0L@i0i0%sn!{FU zAHW_RCAZ$#ZyhPuHR+Mal3&x>)T4YYPb1+B<>Hji_7%%fzi1{)37fQnJEg6ru7fJB z9+cP314|!=-yRM>-T4GpcV8G4$OwbwWX|5pNOYo>_f0)11MkS-3)fpA!~Tr7WOi#$ zIYSVhq0`|b@$wl3Qh#N(csxO$EC9X>`bV>w6#;MQ0d&8j*Kpx4Rwcl-zng!O$^_|z zx?z8eo#r=CUk=juUop!}q(EH{!>#txVKv#1Z_vVR(sm9pRA^G*PyG|ro25)4GdEZt z^6=iGi9mWd6*(E%#l4DZ4dHgArW9)Y|JI=l(5K6M(~aZv9Vu5ZW`}8-nOW_d*(kI$ z^E2n-Tml3CnZ~Uno@#8e^f;{&m-QjK=~ib-aNjHGkyIi|ad4~owh1*kcl>)xrC<=^ z(*@&I-gjl9ad37pcoe;;l1LHh{o*f8_MwS|1bvpTMpud~65G<`T`8_=R2TU7-#wFx zxJ%-QS3@E1ux)>?UUT5jSLL7G;59z{OYC?$z}$K}jBka1Ix1g?E-K`>chK$zL<=-h z{HJ}5)$k_zUYb=rGnM1{)tt@(8_PWo;)kqW`i+vUj0kVVk0P}b4a9;I&hjJPi0=wt zyRtJ$m1yT2+6Y}bdFv{zy{qA!@-XVc2-D9m6CqR}? z0w6nj8*vSKR|Q$2lYl& z%~^fgif+WQF0Xf03$;?`UrLz+>w z6YYF4?w3>jWxRBaEvLFKM3OMB5R z9o-7ui$0D`D+Hwq;T#CcDEcxa(E~`!U{;YfY21h^sbDFYMXvS1zZs6z&f}=REO2*N<>-CKN)FJQ?5VBOwqy!bC`iH@!;N*+Qxa}zQxq%8cGXzgh>rJ z^(VQ95+`fkT82TmqPWpPVajnRD>l%5uG?)GL&(}_`?c;UI$P*h%|(@uQa|y?@2Kqe zpSHT15n9hHo%*lm_v%gWH$*v`q`9c`UZ~<;$FlRK4*UAqARrPYY1(E?=pYLk(lOT8 zR>W%AoCuQW2|IkOv3K)xg-X3@v3CqOMMEW9f0_1(2?nbOuJ?#btM9&Zd=r|*clJ5H`s3;s z{c@zJc~am^J9AtGf~w~RXkG!w-hr~Z@fcV;G6R-a3@9c`EF(iidjVP+mIO0`{I_V+ z_bYFH)x$6SSy(1HwmB1c7xykj^=C*ViKi~!y91-`ERm`#%1AA?er=_Ou~F{qvfZJu z^iuaPPg)aN5_=xV_P2vi?`X4!y^fJRpL{2I6P%9;aRjZ1aQd(S%My7`k*ZZ(@Y=a#oEo_~e@7?*q?jJT?g+SVK` zUWPm+?`nZ&?NH(YK-#V_S9c?)Ok))^rYc*2Bm14lti!WIwbao?dU{r#h0r?wCj-Aa zGCp~hqjCzsCM7rkNrimnqneGse`*>RxJp9jr1RJjn+)JJ2XA&%fD>i?CtI3HQUc9` zr1&hLav0NZ)7mM{Tm&2^Z?_^WLbjPjZt^^}`hi`i(R3B2m299#%dE$;67Dd}noHAY zW<%rYU?C61=y?nO3?CfY?n(QGP=t2M2k64-ZouNXy`hyI5|5*Pl7K=(5dG_rVhBC* zSOb|rE=zSsO6Weoe2YmBd3Q-U{0Z{X_PpT;)q>DnboVB4Ph&7biihI4U*0j?!JxQD z*kzFOU>h1&@D4Q?Q`-Zd&jGfuQB+~zvj3G=^GWy9-;6v--=Bh0L+ORrsn{CKP?}b* zzw)n(fg-e7C8RW7TO;5`h^BqQ4|q|Xpi|JA(8Q@Eq>SE>P1CSK9vB}85{pOYRF_g0 zaOnt*M0DBR%0HSGBb{-%bUSNRuuY4K@l5UOgsWO=H)oD!6;rFhispm{x@@(BJVq4k zAy`Haq+l1v=|-DJ_W9}yTUF=vCfKW}mGhEq$KX|9&u>}xD710)72HgpBxe622~6$< z2*e%vErAhn&))#E49-aLlA&zl1wbk{%0}*N$fL5shM@P8s|y2~s`i?=4Vl2JoDd10&JYA=cEMK6exzO zy1(FDW>HZOn0A)Va{8}6TVfwz#mkeo3%JdxcQxPXTUl=>!GO!O8C-iwE8Og;iz8yk zgi;=VFeI%7z}aISA}Sdfc_P^#Ro0znE z_7p~QL1i$AT0F7P!+>B2)X&5ut)3}s@4%P6OBC597Z_CUv zv7~Q=;ZU&an_R##(p9v*MzPIL&%M*&Vh8y?U2=(Li(AC<4L_qjN6vnr-wZqV66o%D zWLCJA`q2En>!u13Xj!QPr>`Pa4|f8oS#QoFhdCt3ofUo;!KCok3(dq0;b#)3;$;xy zT-|uY7RK^^1cE$d8-$XQa-I{62tHXe@J;d;!RQdvoy0<*wYAbz$bs;KU2TSK@zI@} zAi-0#P-)2I%WJiT_Xu;XH8xHN)eTx8)Lho^9ARU?ZS^WMCs;{wu1r8rbUpYx=bp|k z^rv(hp^f@^m3EuE@dpIwkLL{T(fvhRYfL7IV+baq_+X$p?6*4dZtLC6BoGobu&Li>L@LWtZ`mH&bS>@Zm^m1V?d%YcSi87_l7U3~4d+|gITg<`7oeMidHDbMO;{ut zAeL-vtvGg;kqW1XUBB>l#c*aW%kH(@Mz?K5JukiBJy|8$Zb{wP2L!Y>c;H9q!=;d6 zw^@5Uu^?4t2|&SR;X8t`7>&c`T~ixZO})uUsXec~_X7-dmIZrF32&RUJn;aC4$zX~ zbw`rW^C@0|UWDenuoO#@-BKH$dJ`jU9)SxE%PmV)o>94MignwHJcrIr#jWTf@$b3v zo~s;h07>M8$v=)HcGZ&0807pGzKSMgmoRE(e%OZQqb72Yp7mdZaarj=@%pr|PNaWjUR+Ws) zd}Y84v5cxu#KuMrc0*}$p*0wUY+*HzJd%&jn7T&hk3C;U+I4+)1LBQ-jJ2sUg-de* zs&Y!wN(bM}uJ0e}Qf;h1!sbLmm7~+x@$P{!SK@^tC9Aze3hTa>e2P_oB(@eyM zWZi=5rL>up)jZ_9I9QDIz$7g$w}~uUt6$oUEqul`RU!ssEofd$tw_D3x4U>JtyEU- zf+#=HD*P$-z>!4Y(W3Rv-FkCuN}j6b_1Q4X1Pv+*Axo z?1*O8Ve?=qV{WreYbAE;kb|}fQ(LvI7yVI|z+StVv{0ECr&^jltW;+6`l&`Sw0Ua< zvc&ZL`XDHRT8cch3;lDj#E7ERMGj$Y2mGSING?mk1chgmIIrVlpcbwY^ zz=L)C6l-FStKNQp>?8$Gdtht0^e#dQN~7Gf#4UBig;V9goV-5cNVj{Z6w3%<_H5Dk zC%lv{nd1y?#WJ{+9ZLj=Vjhv=g>c&31d?V&Yc(O zf(9^CdLgk+(P~Szult!!s4O|e0*ygLRz@wKsJphd4`rOWtsbH}g}?yU-{v!D!xTid3p=n<4m z1~YjcMy#Wbh@Oc<(mV~@kQ7ggRmZYMZ3J1-1m|$!p_6@G5~VlDsZSt*oD~8WYP<}x zj8qVtHsCPg)Nw2C!Hs4AlGNpkmP{U=IMh;u)cJVHsQ@uRQwzjX)0>Xg(V)s~SecFs zSJlbyY-P>AXg_6AVuZZPq^pzzY$ueuB#(##cGi#J*k<@Xu@-$lq z02sm!{tyG|7tDK7>qzO?q@tVx19i#+F8wh<)#gZ*Z<{y9T_ZJcJDuJ34zz}v5fq`r zdZn}1+)B(t+6s+?p0W7@&Oq_pQl6-?E;oAK!?n$3+k*TzEWbPFcNgLLgyc!&FYjcu z3);79CNiYc7z+821*wa@-$|A2_O-9zYOseos>tdsWTgz-IU67>ZbSE2OZkFR+eY$~ zE*r%?T!3B1(4pap0K3Am+60ea=qu%(hVT5ulWOMXWgLZ+NChZYrhUs-U1o3L@J*WI zH%0SU>pkEOM8AU%lmfpw?#GO%HV?#KHdUa^1`$|i#8jo~4CCrB`b^0P)XkN5;F;y< zj%Petsm^lE1Jx9Qe|BSr1aezSvx__KzBI;=MAj7Q+CMG1yL?D0=hM~5SDGTu-TNy= zjlNR5x+SilQ>e-xHTt#22??iyZ-AwV7-7hzR@gvp-gWTUg>N-^nnNau;V6hYje0r+ zF8eL&Wnzc+sDm5ne(tHp*FAdv62%Kc~Mcx4MeH!)x2t6 zbwRvKO06u1XZC%p#`@9Lh2>zV*gLM7I5Wf;R^bl~82z@D9IAFjQ;Tdko~`{#=lv>6 zfX_h-*%Ua0lJo(t?L}cYK{aZr1n|%%CgGhmCtJ(S_>3a%bY;zS0z0@{!bFzDV|0_O zxz`?7<@G#a;1rP=QJL&-ihU82qKfL5;C(F#TP^a`9-qJc&1*thuC-^a0?uVp(E;iG z1rgI831IZahN311=D>B}t_cJC5YGH4M0v}D@6}ebib36KvG+B}mVwIgr7fAh-C^Ix zs5J)mKrv@oK=5&ShzShq>yGy|=oqdJx;yIBTPd9snLO_TTly^Jc+%&a>egiBLtv^U zfLOL}O&M0z4z=e1*te!O>Sxj17Fi!fkmN-COBJ*gw84g*6=HQRdy+oeNG7zlz;Gdw zJf1U^ly`k#a6q1Vt}Gng^q5B zNW7BGLF;{#8AD`r5};^CBwlPuhY;89MxVO+0qKJ5z1T4<>v#sbR`U7rZ4P{Fx6SKv zjXJyuDf##Hp>B|`DIMGxKN`qnFxui<6P-QNl)o!dV;hEjeXysP7&=q|w~5`t-3e>U z+8=nkFQZRe&p`51bb!H4kaZ)jBmGw|t44@UDtH``#HEi$|8IdxwG-`t8R zbC@4$+QF6>NWvyTPXU+-jyWusV3OEodpO$3wqqC41>Y2= zhmncfwC}mo6N(2Ldm1nXXG<_*#Uq7rG$xgwP~D8Jw$%bvbsMBhQm8{up&;zpE_O_Y zp`+HDtk%VuZ%ym*8NG;{)*5N;)8-2=L8?eu%~8blO6lFZMR3QfpSzp7p}PF@MC$N01w}L=QUY z-_2CVg-=_elYp0ju+*edrllWbeZ(Hm9`4!tkc6GYctcWd{Z2d*Bj*O%Vr5uOxX0hQ z7bzcbk4Ed}il_AX1FGj8r)FJ#BzYhWgVV0zjT5XvQgGmHLk<)9>2M7)NfMdwTy3(W zQ=7z7B+{MRk~TjG?_2s-s=hMSO!Zny);X)Bm7UG~(MX=EdO6sYlEDnUT<70VMK;?b zy3c9m2X9i)RVacQ!@*6g$3GB~pFs8r9J9#;5w~mWErea3Wj>{fC|st`t(XU%T`i`# z9p)Hf7sePV*5jll_Ff&u7B_|`MfpegIJJW0(-2jKiAk7X*R&8VBn_t{Kw+p{ufooE zB_&XtAh2gjBy(zWH^qxB+wmr2IoAJ-)XO;N;`gyi{zkTza$N;wr4tAgV1y9-ed(F>-69MuvO6% z<}3KuFs73YbMD&SIx@A3KiW7+sOP%h8j7$?4m?!~I?K_Tu zr;Zat)Xv>vS$xfEhbq2qj-&o zW`vM^zDD6u9;C2;qr+HbNsWU#b#>g$QF|jrmfoS`azA%Jx;)v(%jc~;*?G(P+gO)2 z1>}-eHoY3EK*yXTQ|(68TCgRR1WM6kRI!K+91Hn3--fG+2YpBb{SNI3v2b6?KIZg@ zgL3BJiuLdmBdrCn!d)uAoj>$%m%|jXgV4NpgLGo?w+=XH_2fx0&U0*9l5I0m0QD1z3)x(r{DiKIi9q;;x6tVIsU>H06^ov zmrCjAV(aAmk5&19rc*9wDcT)ypoHwcqk62fyP|Rz<=dG+Ma?x?fSz9&9V^*cvHqnV zb#B$X_RCEp#AT^qqjeR3m4K)K3ZrkiZC~9_NIbr>R-&PMkg%@$>Gq%~W_W(~W}~c0 zX}mmE?V;M>R}bnRFQQ0^lvf|7QF1X7?M_*}VAE{=o#Macw>m0pnxN7Ve_wvLZs*wc zW7}0>U(o()UbA4C@BJ{q&`_1^t{*H*Sbn*m9}`QJe{jX9jyAsui*I>d_-d_nDY+Gz zaM3I+HqRiCa-zH%3FaCBusp4>Q1N+HP0Azye`C7xqw>O+`clehuBvWJ#D4Aq0~&>5R7P;Yp+qEm9iVYWg3RJfFCZe zD~NZczA_^YNJ4u~WQm3uMOCx6+Cjf`>15$q>{mNqIKy@*lrqVkRmD}3{X$lfjfk_a zG`v8ssyw|AOXVxpmE?r?S=GIywAu- zNA0D&Fy=Wu6a3MUo}IrI;Ysx?J!t*mMx6y%8-I(wSt&PdX8pKdC%+E=XhAyJx8{P( zOMreR;s0yaE96JcmTa}$lvz3;fvG^Z+WIhceO&~Z*I|wCf~uBy+%Avnb-|o?-FYfj zwO!Io%jEK0cG*H%P{Leo<}0Q!vYM01!nCAQs$?w`eAV2|z3`RRr*p1~lXXg}^M%`0rgCA8_ayQ6Og= zpo?7koH+)tE36&{@BsrL0eFq$5CCtS!yk~8^6$B8=S9z_x-Of<0phpd%)(pR5XGy~ zLM)Z|U&g~H=FY+*y-`jnr19?6Y+aA(lpNphBZ~7?GK~pC5PTEHZ=I4tkvm1S#JhRn zOJp|y5}Fr*M?*&c=bzs@;x_t7!mU)nHhSSQL(Cw1J+a@dwnCD4Voyp*l0aQ4q|jYy z$o*7+(ghq+k!C1+rfEf?H`1^>X}JB=FZB0g?ui|Co@KvYUbgU!bm5~wkOC^uGc%_o z1iDOts!2qV7w|K9=Ol!A;5p(m3PMNtXghf?O2QRV0sNf8;P4z2L-brBV+sEpL2*dL zLja$km;{j-VC4C??%{k`VIw5C=aJC-Zbg2weGPDX;WmuuoNINcQ+074 zX_$R;gzrt5Qa>+zto2aNXtblCbIG)gPc)d)d3m3^xWv28pmRXnP~oOU$8c9oxOurr zLM&BI5on4qbHHPS>13R})1??Bq7lLjYEBU+(qGl-WZxu65j=T-&EG z{r#_L^8U%g=&fImr_1-CJiEG`?(f6F@M`Mns(NjYhtv0i{p;Pi^XJ`*yVL9DK01&0 z_xI0c>EY<%`Hl1Io4xz@Nonf8Kezq7yna6q7q`#1!~A?6udlzqr}0NScXzX6dcQX> z<#SheN@sK7{P=o$IkIl}aPawa>EQ5V@UQ;Su+L9k4o5#bTU&c~@af^~a&7s$J2`lA zc5rilgb;RhboF}5iWfxRp?>MlsIeMj#~Y<7FJC?G-+qSUnAT-3m<)~;%c!S0E#Tr! zl%+9E2N)=Q=8ASqYpYW^N!8+Q$zaKnFKAS}@C#Wjwg!sinB$wF3|toag}n+H2U0h- zQhU7rwnm6EtY{UlPDE^bJQ33F8tVO-wdsdroBK5-Ej)@y2VtbQFeNz>T%PsZ!RI~%ML}$cKI0K-C=BcTQ9W~LQL1PR|G9# zscWDjB0SjQiGpDm`EX&N1WMatwaM6|0kIg+%Mp6h9L*xN4mGRA2o}ZiHwB%D*fRT| z7{~|>J+lU`Y|@md0K>9h7#9&sF0cudSGG!(Knq|rHONch1AH^~7ht$(K{vEV4r*#1 z00tls<(kAfuNPO1PBfVVg7FRkFTy4_$_8boR0Rzni(V*0lXM#x`NNsPBLTk7&=x)BB?QRsu3mYD>xtx!{#teFgks(|@-x~~G8)FoHP zU~!v}%&E-4t|reGI74LtUoR|D^$5!9D4X=3j>ZhQLm7WBS7t=2Pzh`V4+DF_3CIkD zb&p(%0XJg?-*zT@g%=b~;A#{Lr!jS9PFJ zcgxgg1p+f8jLE$P>YTBCc|90�r)Bt#jX@ui7@uxJm7#ZBkP8Nn+EqpOIdqMVt z0-$)!%p{|lr)qB~@$fG#1fSHyi;2wIdZ{Y8wOdtUL3p7|DuQz*7~Mcc)LMigiz^cX zACNem6i_${t+MomlJMf4f98=31WkzhA&>0&U>3t;VgOyl+(43Nfu)And?Exp=8GW` zQB8ZQLA~sy1PN6#s4l7WHl)~_#Lzd&G*Jp`JOBoYnZF^EnliWw)(wn_AR4L!S(uU7 zu9&y!Ob}|8SivRHH()oS(}j#dMKcks(!%8#?L}KYpEhJ;(U-c&3}9-~I>FPbb5zLF zV}50*ln%&3@Azeq4CIqL#2Dnw(JsJc>uiX7I;Kf(EB8di-E<4|S_3LZhqS%X{6|4; z)aNeijUNo%*BU)90kFAP0Ra@+Qt--GSB=?)tPFJQ#^YJ7A#PSIWSuaZ>k-^Mr#e4 z>Z+}vR<3I_ z%Wb%(_JpWj@GmwkN=%(Ef&iKfQ5o383Tv@lI1ZWFY8$1sfaT6PQrRTY#C~agod@Rw z6m!gvqNrXv;yLt_zq3+G>G&(Eg^fcHs^d+gt;z`zT^wGJni^}!!tcR!i*iXHtjRfm zApQiq#9KaX!ywF~fNL-o0}4BfKc!bk7Dc~yvsM9M;+U)b&a|MJ?J13LW@R)r2yY^( zy#+in@_<>gj>p+y<>8J*&N{>{s#Pg}v&;*!5CbPuZR#NhD!MufR|A_g z0b7TqlYthQn%BUknG)mDo!z#7Z&fw6Pmn1i$ z>$o(lQt*X3t{RG9mV&0PufODGka@ES_L6pGy(WjPV)2d%ATwTzW_sm zFv=v@vVDxs10n@k6veWvi}o8a01S4msqdbh%P4IozptydRJ-P%6u$85ChELBT=D1+ zzYh2Re#8DJo%tUczRDyY`TwVePvC!>&e#|jIoj#jxL7+|7+D)QInn*sY+-6)Wbhy5 zv!1but&<7;|4dvmO^*KevORD*rU_XB008x+|C12=|KH9=cDAM#W-gBZ70tisoLv8B zEdL)u>@A(~ghL6(?n^C>rnS_b@sXmewHp9LJf?sm5Wl^~Hsf4evA$vMD6@ zhr@)@e36sakv_g-ud6-}zEbR|T^vz7*Pb&8KjocI-ZQ(8S4*BTo2}<;KGYG9$EnWZ zt!>^nh2m}+o?Lr$klhwZhPglS){Te9%$`qi#;4Lf^7O;!TO{t2a$yG@o5$n(u(9@E zGT^a2r>RHo6Oqb^hM6`G%(^V?Sq)dIu(|}?E<2sFNIp}52}iivU2gmZ-m?pn&q>+0 z$^m}2y3!$kDbD0ze6mwAy9dCZKN&!uk3=tTaE zjUSCe@;7fE>f92^7c0lU-~*&L^j6e`1^9Xa`=EI(`C_s_fhYu0WjB9EvqfaNs-g|P7+i3DaI znm1mVjpfLf`EM7u@kD#~w6iqah3(^02EknPQ~XZ~;)`2$(U%#p7@J1;KZyZB_~*X4 za5$f+J?z+?NJ?rAoWNm|=QmCbd^;Y)eXYXvvjKL740%pGP#`f+=i(_v`0l%R_0u(D z_fO)k7h{&emI~oSO*;4Can8Em>=3_{pAs?Wls5ktYv&Z5X{@&4w$rKIPHo$^ZTG8f z+t$=y?Wt`|ZQHhOPuty-|KLB`C;K=nSx=JpUCEQ&xi6v`6|WU^5T~D(rW=#ZQu*?K z%1a-1iX$gGv5CV|QCrU}!4wT$R_<}w=Ds(Ix6=b z@~DRSXdP$V{sO%4;ktmjNfLdbO1SooAFt`KN?l+!J)qa={2fA2q5OvlJMir&^Ddvm zoA3A{lPcHwNp9gN=i?CMNbk=sj+rj;8T*l5*`ZQSr{t;4ksjGu_>oR!(Q_4SM+wL+ zq4k0D-tcx!cpwQ~2m!_G2P(=MUw0BV7*D%VZuS_3-1heUk0VF8%W4>W@%TL~ca1x$ zO^Rq`f~X(|-yrv`##5Q3Ox#;}vTqVEVD}`izsMPj8(?^v*WVBK#nx@Z&k{kv=^VRN zD8}fMryh~ei16XdY8ufzgwjGZxzg}fx3a?WV+TuSBYI&dN2Q>o?ja->ppWa-w>2~kdud8U`{0mpIz(AVS*AjYIB;|DXL3q-@z|H z+&=K|uF?CP9C@ICIcLytIXxO?D0PWtz1wu%Hk99Z$|Jz5$9F3a|ISUMWaCkaME>{7 zA0BhQC$1k9i0^66&ExD5y;3TOskn@l`#E+J=%QpsHN+35zIkTrK)SqqwVo-bZoXI+J33 z9eO)-vumEt{9=_`6uy{ek+##$;N_pipcwhkDAp({z>XQm<8$lNWOWFS?wRl<>%PNF z4qLZ8osq0gdEIC-=Ts8o!h!DmW=xIlEbhOJ17@>k^rpUVVD`vL0OIgS*V)&=XdW`K z!DVe1XLM`+FY#e0u)3kG{~TIb2ZjM7MTV{td_C!LV#7ou^q5sv2m$^iNiF&vLOU{G z{meECjvxzVNT?3?9^d@b@|uJ7pdJXzBIE|XzYQjldq9+E0B^n}CBbkhni{^w2}^8j zNS<}q(JM0Nu2<1*RP<(DZCG5W0gx(g&3ezd%v1O7K=jdhTK`qk;&otdw$T^G23LMR zXKlyla?{yGj)%vN$tR;*@gn!5z2&z>HX{J2WDaC15epu#XK!SAw40d0d-_a-shEjZ zl>u5b)xoik!N0$+82W~r{E!sPX=Td5>5Z|LX~v~n$w~&|O+vU?G@P5I z+DB$66SQ9dlLKn%=L&{6#ep}lr;X}pOicR*E@Ne_$wS9C$Y*A|1xcZmzyWTtYU}aHs%#O=um-pXO;dkkl}_B_eXUNa0Q7pC+}5UZt77q( zaHc+vL1O^eH@^!>-H5o=76f`83?=TTT2jpySP%6Q$YoIQ@EYX{DI2j?I{!t<4bdsD z7m`f(COPxc+>i!qyGoz<6*m9ot|wY~WXO05n)gxLvTnX`rKMO11y-pu+TP?^nClKk zg-*Lx+Uk1Q&)76P>qa85FSpqI0)26pAf-nSk-J^%1Cf10!Vvix3T9SBf>Qj@)pXd? z=nxfj7Z{zswj?j()+^h;7e9-vZn1c)S!32g5ND1o=U1F6!{V}|rx4UAGh#b#2CQhn zN0$VgwAg!R-ER3UAuVRcRniaaz#Pa*!mAhu0x<18WWAnl5gItiO)P?sgRlD8`|OoL z22IFDj^i%-cq=w+tf5rSnPcz9f)Bz8TxABTdkB!3b7t?;ueT*`UZH|y7?7x>qb!xM zhYVN);sfy4VCgw`1o~8X9KtE*RP?y!;lzU0qvcZLrGHR@M6*OuipW=ct1lwZv9(NK z_K4b%UHH{(lqK&-dqQ$TzuLTj-~Jj4$u)T$m8RbCBmjrmGWl%+hd3LHoh!7{GY3QY z6S>N?x4i|~V_U-Ggrbkb%z}NFLikEm>JW)j`AVGUs*C5q5?e5Qm{CxmCswe%TkY?%P z8mGB71nZQ(Eo4H=5XD|foT;LuJOyyaaEB?LG>wrl5lFa1VQAMl&fnggabrLH3pIFY zMc;0k303gn4O8GXj8?QQ<;wu2l;5&+azU{w;u|8Y_=xWD2I-}oDN5#g9334*xN8WxyMMro61xsO~P1Y>rgFyUT|)GwsnZd%Kd zIJbpUH8$`udLuVKxa*+2!rx5}a7w6Pzp6Hg$;eK4OPWC-%R+6W?L@dyPXdq&(9&rL zOf+k5SyaY6Zf1H5eiB)sr9nM6Y_LEK!)fI4J>$Q*I%AOe3m2O1jAteMW$uGS9W~#?Q=UHkDou5? z=BEn9fj{W{B_BPeYp5RLCax_iivR|v{o;Ei4wCG}TlTzxwRExdab|{j>W_{P{~5}A z-E+^OHGeNq}stIJn*0~ zWsrnd9So$Ex!hKv>C@gwog$Dli1IY`@9{~zU)=zcekXx~DK6e2QEjHK#Cn8Ya$5in z;0HanTq}%lQeG$s643cKNZB!Eqh}3#0e4`Y5rhQi+_RulRZ&U`TtVIvSCRPvi|V0> z-MP=cOES3g)g}}d<}zSmZN7@TL0v}>;^!AbbWj)bnZNE&CY$3?>n7h_MITFx&WAk= zrRHuZiXt%OQ;pydGJ$Il#hY%cA}6)rNK*+sAE-UcEJBdohW@)+IC!$;Ey;gHh1O5-85z?I(N3x@P!FQEnRjmDoKOM%+mlVs`D)sp zM&Fvi70b7v3?!u={wxg0W+!$O$hf%$O=!F(C#EHu{xo50%%0G4b!bfv(qly88q=~9 zwxzou(Qb=tm-TpR6Ncsj6yXN@L62HZd1)b<1JYSu19a{itYjfI`r9NGZRZ_Vj;!zd zntT$Ky|x~rv=AaU#+FLxRq!S`q+);pQgu&8uq*?28fOOW+?goQ5d>w8h=5rQb=KPI ztm2+A+LajDUK}-R&PUA|6(trY5HsVT($;3s8>j>lFz-(QWzWu%rRpp(CF-x zPGKKgyd-*=(IqGF59JU)gIkmXT<-Z%7#uxFB>pq1z)$QqWq=GlIZzIs+bf!2mJ}Ba}5pBt4O9K zk}a5{`yf-q?e%Q-u(7cZX9sQH5=+V6;oE;dcVQ1LMX&&=sEXW*hABB ze=zJDR=I5xhR*h3_Av_qMJs?VotPJqDMXj>9Z(ueUa^+@9N7%c3}cq{Lc&_*mln&T z!^^bohx>s8gcpp6xWBt+=EOJQXZ4m@aegB!SI>#9W62C+D# zghL{$lFl-I^9iqMbyU3zpzBf)h%yRXw-z3zj$NQ|nhlm@`%ZgcT$zSeMH==OEhK%W?1we#2PT#$h81$VMyGHaL#5ZjveA zyM-An7=&{UTd#GiaSIq)Lm{G`Mbz}M?1nX(|67s_!4n~8Iw(AAeXFKZ=j#kl=BMR( zF2HSJRllVm*I@gGuAR`Y!4AjygT28oq{?=}8@r@Mb9XK zt-=AKos{GUYwf@OVPGsF0sO3SeM>BDr{qy!EaIBuZ}J4n+y6?Sn?ko;z zyox9q4`?;a=xuak_G|S4S|PX4pOR*%_N(+bqw_(wb>;A)T=*F7^If;Q_}W=+@tF#ZF{B zt7n_3VhPFSSmbf){uYP(v{n?pyz)#b4zziLVaq&9yYd6EmlBFNab2)e@(L@BQ#LSeGau@3A1^f?irr&=Dl2VwkH@} z!9#&R0J}efNB*~^?WEh%kT^bC5wtWalJst8P(0d$Xf{7dV{Rpt3dawYFT)^y)*K~7;X?- z1cI7R)}OPANE6Y>oJWgrAx)7e1TQ3s-OZ2R@bp;;GZNEQ?%JN%?mF_$1Lmqnp!uoT zkZ(honyZwpdnNXiW(Bo8fNY^n23&l=fkCu2x7O4>1h;yDhIdVVsP`u*Mxqjr5CLOyov&vURAhE6p$w>a;+X!pOSMk}9|r7ZlmyMxsK#Jamo z?(mwc`?<(>jo&(Xd4IvHuLP43uj@GPea3(|9&wF>&r~k|^!ekgcX5KAKYpTkKDSvh zJ$8jmfxo4A!(JVk^9w`jCW?IJ=PGUNl{XfhIGx6pao;_WL!<{#esZ1cg4_0r)Kr_P zKbMl6;Z}R)^>@=RX*K2p2YrCB!=k#U=C{QF!rv3DAs

WHi1uCfmGP;OoFGLx$i6EQ+0Jm zrMvCG&EaeCqt}b86d9{&SjAw6h=gp>IKe#+)W~({tT)V80W@8YpErgJ6Y^J@5wk_V zxl{mzeU>3t^dmh{JrkJAU>K+7<-iK~MtZlvKU1p6T-GL-s&D@=2Q)g}ou6kLMZn1$ zRKO(F`q08%_KPsOE66wl`RY;cw<=~72k+lrjtN6S!2Tm!i;xt|2cKQT9J?%p!5*~@k^jPCC;2p0?Bti4;PiFW!J9Tb!^nW zkd2=Z0-B4sD>^|dO%FLYAYUdX2HIQ5W^NbBMBxKeBORasfgBMey)e3<)gYGkii!Dh zBQMtzFKeTd=BFm9xWqk4WbYMh7p~6f+H>g}p6ey5%%|L$Msac+sm>dqN=+@_N-(?m z0-YqmJ0yC{$61V59y+*hfOXTwN$?_w=~d(e2Sq8=-8k&8ScZwOQ1BPDneagC^CLnj z9MyYPfKv9rs9hd!IPA=-nkzKPBnFp1oHIF z%~?;$L5={3%pmiO6N?oU<<^50BKs=$Qx;qTQp>WR;@ywf#0>~W%P{aTqtq#PS9+e?;PiHyYtAdFn|{)zbAj;yCSxW5 z91lobiYn`H6%Q<~&}IO@td1#zih3Rx5Zy)`E@{#u6ZdKOMvKDtGW{CW5LBGIgAdfx zhCJu@9#qJ^vKdn{MdSZZYV(QvcP`-R17;v=1utv7T{xmt!4~13U1iA(l> z3sSmm%C=A{J6Z3ZE-Tb6KVe(39fY1Wa%LL7hPvR zT$m0V&)aW!~|Lm9oNl(kXk^?zJbT1dK-n{L1QoQJv~Ms3s4K^hFZeHXx{L5!L}bu0oDJ2%n(W$PZ4P57SS;(zc}UF7Ib*2E3E-AD zm^q4rpC^MJBz4JpcP+_>y-P_B#rPhc4p+=)Ca}MTc`ikzz7euB*9Hmqh=@Ebz=Gt+ z?6?IlV4{|GjlqvLC=+w?wvA|b|Ivj~_vGJ$QsS5tB2uwT6D>UQYF$#B$aqseQhsKy zO7SgzkDHeB(=azy<~lgL6`dmZyN3ma5a0k_LWJX>2zO?0F30yq`rQXb`^KW2V5!P~ zB6|Md4NYk-`rn^k*qwyFrJ0Z?BCvxOQ8wKZwEVkGKZ{N#qEqg>^7Jn~y*@!*zhpQ9 zVsuq|3V-xQ_rxgb#d+E1M3ivbSKq1o%=Yvnwcd=2iTG{Yjo^(pVSaJ1>!IgLKF@ z>0iSp%9}lRMF7|BdFknQf~%H-UT@VE$$})py*tb8J_c$NvkH+W8M~sb1tMM(2Jsq@Zv&o5@RU@B+AGA)I2q$YlkR67fp6&5fUFMea+#ofGCh)r#`~ zqVntO364q23^_do!hL9cOR?8f)RYnb-JG=2Cd)N+f!4x5aW*;=SAT5rDug7Fw>Z06F&0+(?=|4k-kR}SohRD zUb-#rIhH)wsP?DiqFa&VWO}cGkQ64cNW4S%V>p4zC$)GOEE`;EDL z2^xc=5J-lfkWUvKK0@6&DK(Cx5P;wHl}X|ZQXI7$*{hjCHmQU+AUD%X^6KC?4+4_Q z0?>j+zp&CFP)Annv6INP%wU>G=yk9QW|79*bwK^NSewKGCV8cc3Tr`wSVzC(wTEiw zBDh9s&4cT9_lAc3R|>C4SkpdJNJXDt;mNgptGTcAlLF@CcR}o3RmdodJpr5G7p>w) zya^idK_8fxu4LNo5D*zr)LG^CpV+!4Mepc9WvFmu@DAv+p>|=DI=0- zWW5X09{fwFo#R8|4X!CX!u!-3K~HOV4s{HI15?h>L>(7uudwk`Wr2%(>QpO<`=4W; zzr>q)IowJgwxd3u$J8qxjN3Jr0+Iclr<{yKDHeqR)@N_YqJAR5ac(QNEcgwfQc++6 znP-;09K8Sa=bbV@D?~}bTQP;EbR+45=OxC>naA@^-l&~La$N$GsyZ&8qV6~LUuw7< zPlhHTYNX0mIsWkUMW0M)O-z`VIn8TAIV$toh@h-KUb+&T7|%hEpU=Pv?vu%3{<}krw1|QTnpn>FPu%bkUC42re zE9!5Jra-CSMzDrSV>l?}Hgb^D?z*Qd4>2>Cyk61$@%1K6%=J*P^zqmPPaZU67|pCy zO>K&Ft2O(QFOp)WK}5&od43~XE$TW+-c5SOdb~)@dr6)>5R5`+(~~pBUT|KLlHjeVWxv4|Dg$Dt6+ zm8wwJ;#K>YS!eJ*ZSpL6s?A2f={L|8vv* z6QNgnc;XRQN8{Y6FL*Gs@7=V=Di z7veh3=MXhcJbiL-4C58h8e9Sh>VzEy_8O+S`}Jis2^nJJ?(T43Z#r1m$`rWaC{6t9 zX!ULH3~h+`!_y_&qJ7w^7erb47KIo4qNj@ojw6$kUw$n-1$JKNEQAEJCw{;$?Yi+Fx15y z_hnef+|ITxGdrJ-Fc67u4vH9mxLSUD__QojlTkK>5KLvhjbnU9@?gM98S|-BUfD~K zwc;19O@k;kIwDcg{qwx8P}|Mvl7Ll@QA3ITG8j!^U;!A-(4K4wBQ!g0>1ir=LqE~i zr5-plUiy_@5JztybU=|%Q2$K?0|nO(P&W52WD)hVnKWd%)ab^)M};%uEwNm4zd>+l z?jUETCsTU*Wd_1A>x?AjHxom$_`Ar(+o9!Omt^;qVO?^8Uj!9~aAiu6-HXS#;>IqXRU<;CDJ z868JUh6t{kNK)8Nx^HLVWO-TxjW-t8BR9}O*`C_W6uY;sd1zjf<)&gMnAmG-Wxl3Y zm!Sb89XOLw0mY&bInTXa7N-#yF@nuJI~#94tW!1ag|i7dJ7_8j!{wnlEPr9=)@&&~ z-!@*^cJo~x;X7W0=sw5!`K9pCsM2WPsM+Hz%OPjZv}dfUJGnQCHv6=r^RU?E$~oZ(}9P%_P-2b$0 zT1#$6;*C4d5MBW0JGXwFK*&=DG%@3}3E$r}&|^+O)@mwAFJi@_2k?-T8MH>(-1X)m z4x_V?F3+#*3jRXQRy;OYG;h#bm$_b3s7DObTxqEBuWi+0SGS#5;kjJ*lur!?HId+W ze6!;#pH7u&_^(oyg52r;GQm3KF~73qG2eP=Q6*w5I3&3pMm;|QlqJ{}m8JBilM*Af zU{|1J0Cp{6vu-=bOI+zIhe}2`|Q09D3JI^rn?{mC-Fw6ldYn|ttRn(3TDO0*7eG@tDM{!G4}KR z@P-_7LAm{H@5ei!LY%jX|FN1&M{|N?6C*1x=b1&Y$;ugE=Fl|>3>r?9=g)9Cz72)h zb5%Izc*iw#lQUj%VX?1mWLMPQE}kNIp_T7of$zcCYvGaE1&p3Ww_Eu%S@DkHOgRIXnut1{nrJsHcfo4Qf&xY^oIk_dmnb7ZNytpHgK-cv;Jax z=E(#+ggQWsL74rK`}F(VchFv#JuPR0h9ZTXxMAe{INrX6Jwuk5zPJ zT=LOs5ataFUXWa%M6m5rU^XcpV7_N9%MFo~GV+}IT9zl7Bs`yt>Xt}c0Zw+#7N7o; z?+63*9~Y_JzU+T?{@+I6zn{<1JG=s%nPIhQ8j z>r;180Rd@E0Rc(;_op^7)^~DtFxEA+b#(ealO?yc)f10K?0t51`!Rc?x#ZPfGpPs= zLduH+w8mp2h}lXgg>Ps~QX8IcwUZ&`)u~wLMipUbhpx{dI&i;s@3{@o@a9G`aE~?^}HV> zp0?id@$`J}pU=+vz8`fy99;U^#`f@hU!M;?hMv0G^0|MH?)u(etK~L}>Hd9qe3fI| z{aRJ~!oC%w^!~mX6*?W4$o+nL`}uxX>hbdUx|^K5Svmy6~5cKGQ$ zvHY1mMf}_SS;NQE{dM>BGI$s(m)g_VdluPyQ@}CNs{I5X%Gb=p+s*UW4&m>+4WEzO z$GtY^`+ccnsqg0>qd2~o`%;cuo!-y)_v=Hs&C-Q?j!93^QlFo<{rm4lNUzZly6fzR z<;`Bc?k-=hI|;~H;rqWQp@c9fnzZ0@?}L+_w}idEKj-g*d_I49w6ZG~3I4v$najn7 z^4@fE9-6^~iH}*n4}q9X1@DG4jR6{ERX(67F>S?ksRS z{CjvhczBtEXsgwRx7+d1%lkdMSz0ZXSg08`u@5hPe1}ou<#o6qPuAo1xqrO9`@@{{ zG1}_;{(gU4tNSmE_Q6`h+`m6houj^1R8G8?kEMh;yO=^xC&%aKWA3%P-^b^d7GF6! zGuc1Z?q08t{fX0;t3u@O>vylosl8tAU-=rd1!p_oU#+gTku=_ak2_PZ)N6mv?~mIz z!lGLv&Ai^f9)1Q1c{c%4H;TS1mAgJa=b3W4{>_LdtiPqdblpCtj|ky@CTnGsem!AL zIT&R`9hhlg>%rfRCELpa22JFH>Z_G0IVq?Iq;5kYCldt zN%WbL@rwQpIf3(es^>Mq{pBO%@Fs&$b!dAssx|964(5^TzogS1`Glp^&q3vN?`*m3 zhd+py zM$ieSNt5x!#U|;o`883`m@5t?C{WUl#M8+Tpk?PA zb{TLaPiiy|wXJBAi?0H0173w&7PLke#blU?o09Jw3odIrY2Q|D_Nyf>5iz< z8Co&CdrI1kfHD6#Lbe1MhX=#nr##OVKFcVjQ+V;GUsWUyB7(nMbCm&ueh!x4uV4h5 zbwyaSfiW;BU;Mu+p>-4@doW!7Lukl>)L}dss`Ud^RJ#5gFtf5@Z&Js+RbcN7emWGn zsscTQ3WbV-AAu#q?3-~&d_r>Y0L3g~RPcHu4ZJ~KYF7F{^``ROR>j0r^4hCz4bI>1#&m7-7Zhb80%fhG{6w%P9X5h4NoAoEj^Ii75KrO^Rw@ zeeb8m$%k4YTW&P?2Q(<4qD9g@x+3;b4VRqgy7A5);|F;%$ra z*lH0R1UW*KN*+6YoJ4KfL#%V?C1m zF9B#76n>*s>bhV`8#EEo$l78w>akFCez6oCbH%W216#K8ZpIwUnvZ}}_f#h}-td2? zO=G41_#uJNU|I(6QD;DCxr+_Atd39#@-1rpnEd+Jmzg1Bi=XtB;S%QZcG+_}wQ|=N z+NlPfmGc}&sa!%n1uRWZm83BjNy_}leIM+0%*>HAbL> zlSW+e`Pw5T*@VPL?-MZq-9xSd@O@?qeT<_Yj#fn4mw4GWcXYP+p1>3|q{9vtd1p(4 zhOXdjk*&#GM7Kg^GZrhP`sLa%8fFrnQ?IGoL;?=EcpdjjO=I8@<)*K@3U*~{4U&ls zuT!Nk5K>vAWcy0Pv@+%#($;>xJhU~NBE>Nw3XChjuY1a!V~N%dDcwIf6dAJRZjaAx z)CuA(bfiE1#``x4=KiXBGRd7gX6po-QHo2EA&h0cvMbFkgcrm-JD1k|21 zQ}~$N%JvPVg75P+XqZ68tsy^??ijNDpygUrE;jExgSn<~Z4ov*|41-*K<4D@t}iN@ zzwIQLj5lk4T|l(Lr1|3Rqpq{Y|EA{gLn+ ztY2lIq6!Q~yR{+ynEvMUzHa}_@Z5;WJ6Ch_*#UZxAsQiMsUx zi_xRhKqV$nZe7_)Kd2^pn>MGAnW5hyW%1sx8tXZlIEC~Cmca=88apdYNXmfLw_1u< zDPYF)W&JUzzhVIY;sSxPPRT$ouO9=8qOAUJg-i?$NUtK8tG#wBoEom-oIQVaP-{|u zRgVh*m>Q5t@zRR4H7?d58q(}v>}e%XW}(_8PGwG&rop!a!O20Q{70;cP}%8u>BvlG z`C&4;l|{nuWOuGC@WtjRy0?|52C!?IZEusKUT7D2!9IuGH#+bJcc@IE)DvG@MHRC4 z^CP0Dvo1*a@{0=21Ml>2YTSOWQJzJray$pl8YWYE!UqB%1qaXcgXzQ;cHyd;3tip5 z@!132dmcHk%%WEG{1X_*zxuWQ-8m${RFY1U`A=3$8~D{q10hMJm2zuQU3a(weK#+wpV?cye&jKno8#qPa>nxv&JHy@?bq?z%K zb_fL1q&WE;oWyc*!wtA{a6K$u-XjL$0TBVu8GO(KA%?(2h+yxF+?Wz1sA9KVKxnEkSq(XO$T8Uva zq=X07YEYRz?Gn2GJ*>~4DpG|^C9~B$6mib53hh^}1KnTRvS2Mz6_bDh2}==OIv%$D zY23P%0}zf;>A~C9*mbNIz zx;WIPQ2xMJk7)EWYuX3<_^|nu45|x-svD%NrvR_XB?T580#yv1pmi24L`j=IGs$Xz z)%a7V{7h&;NVQuWlaneuNjt?z>ythWU2(fJarN>b??5}ZOSH?K zRcF+`2cASa5TBxY-;wcmz){7zK2mO&DzRyPs6rY^2ZjYrr;_~>CJEPG^B;jyoq8<{ z!vI@j_OdJYtqRN6>0MZ+>ah<0sg?@|>fV)mx>Y|M;q`k`K1c<1xC%es!1DUk3MtDL z0q2*a5{*#k%fc{oJOh~SB!}YC+zmHFcxMj9jMb|>!QV_6i{2ua#^jN18CwFw(p_Q= ziHh+iz4&xhIPAfk&@3p6-$qsF%h)O(Y;QN~Vv3cJXBclX#qG7K?{IKEZ_)(T>MlzAZ>cou=pNK$9lEH$li!jYbY7ASZeSo@ml-zpZzICKr*Q7_HN_CPv*y8FVa zKt&oXr*QIGMqv=KxNqu78F)v9T)5r}9`<9trLbLl${B+42$>EWiI>YLkPMgJ;_(1~ zG6(uD=pW5yQ2@SW1k(A6Uc*PcSQUrV`fmP7DidH3?1uj_a-82le>q6sf5k2{mIQY> z471!zhu2_7y}<~xPTM)eQl?8mJoQUZYnC*D&fH*o$Rl`*CIRc=RN!Rh5c4dmH9**r zoRY8c3$H^PU`&_(W*8^rJyNP*&JNWuHMQI~wU%#b=3~jly95ROGmT$II@Q=@;eJ{r zCgV+V)2;R^!ELXkM?#S##oo2%+d9Pf+#&pyMqWSIy9>^%~us;zI)m1@IJTw9t5QtGur44xDH=mr%sFwyv!U?!uuXriZgarTSLL7G zpfz5?OWb${;M{s!tZ(^$t|cF+E*jLhcku28WOEF1!l!-p)vzYUUbq0ut;BhMU(qt$HYj`fL3n8VZwtIuhxaDPtK|u^m_oCP> zUuVVeEwwcZT@+}Mz-I)D2D%D@nX3rbe?aE$59$qRnzQ<}7B5%W*hf*#!*T*+)AEY? zJlJiU`p3_6(7qsUAu&P~&3E0OFYnF-=#ha7tC3;-gt}3ICETK*LhGSS=4Ytq@$F@) z<@1nK1;|JV{d0n1lUXzcO6jZ4=@7tCaOgYi?7j<2^-@?E^+;O**#j(O_(XQ2H*T)Q zV4ag;CiCR|ykw3I45)WA zl*YBT^a1`wtxEhajX}%}?S|8>&u(`-xzq2n>mE-F6Yab)ZkJR2Wxp93TUvS9Ac{pj zQg&@5?x_!xaPC!#BWXlia=EYst1k>9pIV;jr=x#`&Qie?)xo4Z9msuT5K~peXLKmV z@aTd~8buU@Pjx+wx>ZP*0b2^Ywyuv&gUeo9l=fm;IJg$N6@46=R0v2GBG?m^QT1g= zVEU7n!L6cf(z%jU(!f)*2w&?#els1bp2yMJk@keH32Wth2?WYzs0^zzl!&mNe=^gf zr(Azhm|%nw=dgfW5+J-MwT<({eT%BiHIx?c2$Ab^>P>PFB~I47wG4xCMR8+-!D~W9Q(1 zih)kC{xa_R^NT+@FqA*=;VET^~c3G`sGMLR9c#Z{sO!-Gzo45HN0rk=PPf1)!jG!Sx7oLwmB1Y7ym9r zkNX0TPa(GgI=C-0h9l;YPlsMu$hO1Aqq;&j4zHBN^$F%H%cJ{|>hV2uBrh$7w?^pO0b&|&u=Q0&OShDpp-1(?W$mF%xZAN_PDROlI8e zO=0;9WSno%)-JP1&*mHILI-{W>$fLDj5e4>zMOw)lCZv3R_d4pM08tx7lbmf4kk`N zj`E+CMG()_zz%iERmo>@-vV}x9|!opY8`)T&QZ`(aiNKeIeNhis(9vv0bMqX23`T- zYd`3-=h@=-wP@3&=a#oEo`1*cG0yoQSaDS!wXHdtznStBy{h?}wL(Y-fa$wJUEB;E zGmVrnepT539og+XW*wd#s-})EGBUF9EQHkYJ?Z<_QSi#K9+gu8Hz^_jNyz6b9o1}v z|EXzQ;3^53lgi^jZqi587`)k022GUln`~(&O9?Owl;kys$ze{vO>3t*bLMxLyxod0 z58h@GzRB~@><4w3#?X3l| zm@1@G&R+*XX9J$V^$nx!kaQgVlMEadisWBC5(C(g#~SDaN*S6va$>gumRoE_=(|hm z;ZLxaw&x88m=>h&qPsWodpi9Qaso7u{qm0C4km>?;x7H12b++%f_Ip~nA#r1d;rA4 zMp1>n^Zr*}%_qZ8e>3VNV}A->4Yemhr$TEGQ)ybc-paq40Sd6DmC#ZIZH>Si!5Vf6 zKafRr0*--ef)l3_(9*g?)=k6mc?9%lD;QSvjjJeus_nP3^ap|gdhI~|Vri82t!^Yq z#7A0nECDp~L>s{90vxW6ewE5pn-r0smP0xWIhuQ~NchUwr1CkCXbP-D4`;7vZhH); zUeUG?T&->u8^Lq0{)oTyMwoBu<7Q0ymRQ|8r@9ooKubsH#G}jZR{n{im>G=9rP|r5 zf~;GVjb>_JCtOrhyE$_-s(!WVuV_rDW6D(9%V9;qA3|jGKnrvMPB+@zv(Hyw*sD6P zHz8g{ES;8YItH%-dj6JmkHQ*NUm?u&NnrOclELL(fI;0+-Vzy-_JsSJW^hJ|l?-JY zEdbNFQa5sELm!n5HUz$(TwUnXRkhc=-Kz}Nr`-LXX+8{55CJlBXoHDa8$sp?)@>~8 zBLLgUADwv-vxJP-DuSvy!b<57jxL}2!}kq8%W>NzO^J_UxUsxBOo%QPzL0o&HXNmlRGdrR~K zqIh}ob^*UR^{(bSeJktjBnWt!K7(s7X@#2ueQ`wem{`*N50-=#KO{%YLqsJrGY_@? z0_Q!$Gx%z#u`R<`XYwLQduhC(9|C3JO1(*%Ow%vTo;~@|TySYDk`@mf%uohh>zkY*vC@^bJV$ZO zPS3s45Ml>;Kb><)W{X=y^9??uJx0!cVBZWn_YxTHc%)ajmijRKyy~V3k?7fI1E#Mc zRStIoXxVPgB8LIuluq)07a=4G)(cI=3=n4$r{blN<6K;ML>I>Le)t33Wf}yNl5(CC z42eG3G6;?H7aFVYM{Vlqo?7gIuhKZ3rIjGbh-{@ve-)PjuY*I_I9wF7&3f8(|Ime=F`bcM}c>%pcF` z-(&iTwAL6;lEx5CMDfDG0_?Ur@^0(hOvRBB)N$+QaO=moNrWrQQg7KG+H@>z8CU>J z#kRIcC~RF^fyp2u{RZ=`9GnVgnG3MZK|Fkad?(Bk^pQ)pHCG%u%gBXNM6X|dcg1jK zF3aq-+(x%;L_IIP5Ik8X*=$MP*!lanHn)0Ke3`zW%0uxWDz=mu^NuU z=UvkpRZYFgN~%7uz4rqRb(RHrP6=%rw>GUAV2HB2nAdhk8d?M;3LK=IvVnau8y3itLBM?z~z3Xxog?J zbAZ#>(LacR6C?#o_d8~W)24CYLkgIb>VGR-V6PFSTV9iqo&`DnINP&gH$;tX{JMNX4m%*b*VJgAK`MM zpvy9-@A!IHJawm2+#m}{W{$E@EW_@NGXfWi5@;miLbGkb^io?-%4i&NUK}h&x?__U zms>}ct<^8>#uh%~n<$b(uoW~frdFh0GTL6elUFJ!bwQP%XcqnyyW>e9@o3U} zG$l_}|MuQ6^+XQrray0QT5Mb=LAU5SMCp*mB;HgAOzene(PnpNEn{i3Noyr_?U04F z4pm*XsTcWC631P;nKW0P7^hj9J*-q>_x!0wGq8SZ1-1;m#2P42ym+b(aAPXfvm8u& zcILZ1U@y{YR5fuBL6`REO*E0!<#5$ec0Tp8KX;hh@h5)K1RQJ5%(4x;c$qvy ze^VhWjg9FzDT9Gt8QIS^@5_8!knH=@;c#oKawth=!D3t8U|*FP&Dfh1sK(LtGNQxG ze`%aZG^ z{uHNnAG{^F3!B8LlPM6UEWq`sHggz7wp0%y>SuNjSY*8NTXnPUil)DbpcgfyA2y(Q z3B3;M$BC6QqHhiQt#6OA05-B117k~Vu-3fFb!x}*nKV;9Xrl_ zkV5GxvK3GM@zTV5Ufi)X3o$#g(;@o^*8kYcd;cySI8Ly3jE-cQEluAycagFv#TiRZ zD)1^T8aMVW3W9otqpKk^6NHz2piN5{aBYAdOTe)y?F&Do#V7mqWtawh@gTr0&zKKZ zO_U*;x_-N}_9JxQ_hF_c$YhD#2zBzc?9eTLrOj1H{Ka&LT%fh#|=;*4BfA??JGRc)F z)u@rLBer*la;lU{z+VIxX<#=H)e`euXKO9RhNJ8_IE!H;Bv-1zsW1z2Tvqy- z1RS9yE+=iguViT@Qh{-`X}&!Hl5(r9YGBZpb$3fb5YtB*oLP4Ndj5 zSamF8*hZ8UO>~YB7Bbn_C0=@in)(D5z*)h6q57LihM5Lx(;5;^j5coNJ*ctl-$&~5 zMN1|RPaJxwe(HR@#FW1%u!%YHsmV=8>u6wQHoSDlg^SAMceav7IL1$zq$u%kCGu73 z0rr$T`=#^xjlxZsPPcsxu@yo;SM>v<8e`jN%CHRMhNYs-1u`ayG6L)&;9p@vspICi zPiIWEY)v-g4ethKjL-`RbSei83=(|#R3dvGElA?^7_aTTt`tzM7eHS;uD2Y{GD5B`t>>lMs<&}vI+Tc@I( zf`W9)0WbYAM%MzQ%C*fK;jfYFyPnQ&dj(j*%m@fG;Jh-}X>29tp=^aj!p>O#fn=h3 zZYfVxUY8v`@8Q~Jw`oBQ56$n+`MZnsd_wjl9R7PU+8N{9B@-3eaSV-e$ei5S&iACs zX8YPlU^U2H4PAKk7P?ZJ{hS>b9>1Y`tfhRxv27!HN{5~59>L!>W9ZP}gr7tHSapI& zAmo*LPu*vJ;z=cQ^D>S~Qn&(~E7Pv!t1h#*aQG(8;hU;?to0sv2ddxR8%Cbb4F6+B zM2iP1oLvPZvq2c%2{~1V?slrd8x0)2Dk9du?ny5pIEUb3@X<3J^a=pWq6fJk;r zadvUX&4^-4q7se6B=sL@AqSEs}UdxA3hdPh-e9F$@h^yHQu0$a%j-txWXL4t;PV-PbME=(m}}*s)`M zFMhlF+~v%o1}j}eVN(L#ml%ywoEH_9+(44rUj19uvo7%Wl42|C;h9|@n~`3$Rbe>< zI_{23Cf*DwmSxyO16IFHB|yctXljui&!e?p@w{Jo3HUj1A)5-1Sc1{trM)N=FR(^6 zl?V~m*f^}S=45NRnUGo7jiIcWfqw^oONhjRbc|t=E%(~}s=S^j6p|`3BPx^QO`$Jh zQba-R60)x)VXH-s*8THufAgB4rc3QvE5B2jWVC;Je?i2wdjbSwv4M#3ff;BWq)WoU zK9mz58cE*r;Cr>@tU_S7YV3VYvPFPWd}&LjPj~3I5qgcj9Z1Ys76@Wo9&!TH`ntn? z4JMY0z0Qsr?N&-BRVL3n|CSzWIf2yqrkWK6zkj#@k z=@9DL&G1u4&p%y&qZc=Zb)7(8$5JjozRjMO{kC~swo&_cLQ4L9eTXacYf1+<){i`M%yAYc~b+Iz>oAy0+dO`@`V^96Z5NwD> zEO{icj>aVO6RMkWRkvDTs%`^y$O^R?spN$`+C`5ku(VZslU2Jo^X=#g+^jz$O=k}o zos>q93aY`GYOorD^eCl7LgFEel!?|rs8Jb7(IbC}qcLLv;J0gE<00uKOLLBP-Iz!> z01a7>-^g-8Bwa+CMs{swA@Qk|7YXPdd=wzY>aKNgG6u}~wh8t|c-}zamQwx^MZj;) zj6r_<(Yys@`2m722}c5D~+4*4UN-U8g{CAzRl|8}N2F1*_k9r-==g(N2xGcA0f z>m&Af_VCZvhs15g#~YGz>v!T&m^n8v7Ar$*!ra5>UL?IC-5af%E1uHl4``lu9Gi9c zP~`G<{_% znQFDvY;%@LD?6L}qmevS^|J6QC4(8dxlVsW6xeN!7(S<29=yoGS7C^13`)|JIt^|FO0BKtj5Vp>^wV)&2J1& zit>*L@oEJqr=h9}6O*tZuIZs#$Qn*ZKtj>EUWJ_QN=jfj!Qjsn$>vn&Zi*LMw&RV* za;*Mg%gX=^@%uO>;gPMS0FHvQ{Y*vE7?h3Vr56;MhI#9;u!Q+^U!R?V_1V%VT4{Zv z;^__P?F&q5XA95F_DuV0Fh*{jn=Joh?f&~8_A0um^U8NHog_hwI zR}-c_>;j~egzuJ2*@F>;eQR!qDHtUG{I_T?O(UB$wjqg=>Hc?xWB^DgcUfy2ovpdW zDQB1La*ByIJacJ~h7urBJvnw5o|rkon^yDT#7(>aPL3l7lU;oEw`QVzLDYs@I5UW+<8GYcw9EK`Og92CP-q)Hs+^ z7l+*()i-ifsT~F`w{y3n%aeVA{NI%)J8wDRjdf{LAkJxJ)2ku!3@ka))vh$H1zVCy z;8Z<^6^po_vC!f9)?7_Im_zEAcNkB|h5M3rF{eiW>Y0Npw!>4bv=-nBH_7~VzL4-P z`zca;!FjI+snn`K2S_gPp~%Xy@2X;^QX#eBeccYJaBV=2x#jwJj-O{z(E*t~>-w++tLlMPG1%fo7cyEF`Ptg*J)o|0`VsY%OMMI-`%Ri~zTJOOP@+*C4(F+og4We^5 zYB^D0RW+s@NHWF!axV^<;{oK)&HXUIBmK<6I49HvDtn3bI8cEf)rix&P&5$%9u-w| zR7v67=gE(w`tMA`czR~jVE@Th3DxWDx&7hL5&gTv*?b7UigKg7r|scQVwj6a3wlFa zw(4}{)*w^&dz13%@Bb=AC-1Jfi~A=jTVt50xh8Y)^DDz+MO#a@aN1F)R*h@l+%#f*)(Uod7qM4ys0xr! z#+KXm)%}FT<0~sgI)(>vtE!)FcdBBh=Vvc=>Y9|s%VU)unhid+!2abC%kNh0fNfv)U1g31t*_=abJqFZ4}C0k6^ZWp z!Lo$qm;3oK(Nwty7p&@NvzyTPme+-^)>`M1TfqrujnZPX3?fNK>Z_3;t`Q)M(+YEC z?^l(iOd`m)Ust{~o`jNLiW$vS)oqD{Pr!VLXJY-nK|Z5D$X)r^n_}imTzwth&@%eR z**0t<*!<4a8mSus5e;+gHOlmow!^o-h7rvXhs)~<;$3L3OiBHdFrE`xqv1x;RqZTy zFfW}uS$P)wRnHgBaP12vjdN#J@D*jgP!(k&;_NC7E-zRognO)_mZKl&?77f;p*k@8O!0sW+b3^~Ifv4}DM8*32)4?QFE=Zph&QA(Y&!~l1N*24imU;!n7t^p2# z2*v?^z~t27=PsQWJ)dei?BWN=-vToWZ)rnRuZjz?G-Bb*hfge>g+;ofoRTQx-K*I; z?$aqbKHo=F=c^Rz69!;}#?0T^C56Iw3K)rZ^Fo)Xu0UjTFZ_=N%zn>5fA2`!7$b?d zQU%%=g~|-D1MPH0|8BJvlEssHP)m>n=t!c3>`FoJrvjBO;E@YA!`S_rRuFt6551E@ z*iZe!d_U%%*x}$=_U--69=4G#bQAzqKm&ee>X?MYkjY;)i7fmAd4}kegftI2M|ws@ z?0^_;E9Xf~yh6@Tm{S-OmV;)1nJZ`{?w2DV2910OHAlPOOp%7_A#?zaMGH^7CM8QX)|InR;#w{M2@y$M(9>q&^S9>N)oaTIti zk+$)P0arRN=Y1EKc-I+t4vZfn)U@aj=AwZxFFQ$$qrxc+OBHGce2g@mjJJ2X6oW!C zLYzU%DeOr8S7kccCka}ZK#nL2sz3&1zri;KWq*lTj_3Dg}-F8z{Q|5Kv;D(v+Iu6U8n0fxV!x5Nj_c!8f8Q>GzoWA5#F&G;m2m zQG&FY`4<-T-oDd+_)3^*k*FTb)TxeHDs!ZlZk}4nrP00n<){=%nGcyTh5U&pq#Q^_1-EZ9TibA zkvc1WdZWC*y^XD6+U{iQGnbXtXhx;Etk2e=5v(sW;Za**Yt%#krorBZ+CceF#f1iG)hu3Rod%JtS zJ^$J{zB#-GQvRLb^ElWaujS_XZS%A4yKRHO;oAAVsNdPXx&6HR`Z>7#+-lp(kz4z> zZQGh7m#~Jg*NWqFFm-#dH|(=@adHuLqTS}PvT1triKcW!SlRJ#DmudIRaeepT!cFa z&!E~q!APq~aU4}}g&0m8*=AUo4z;-m1L}Yu3dx)|=~HZ&PcrFFSW3Ft+)^1|2nPyW zFfF0E!uX3hCV2^Yc{Xpvpj?5pCPj(nF{iI7Mk0*2ISE~jd>J|(fjKs}jhwp&Q5YV@ z;X$dPNeju0#DpoClFHsMYr06?@q{M9!rX|H3BbyfJrlC0K~tp%JL@>x5u=nft(;** zs&5$)VVAQn+_(Ndge+Mij%&<<1h{0Mu@A6EY!mW!6g-JGWdc2b!#tiSFT-xI(`0GiN^oQPH_&$(Uc5`K2D{GZc|eu8+hu zNr3FQ>M#PY3WFA8dZ`Q%+*_9jx2zbzp4IJHHMQ3l}vIZIMh35Npa;0oIW z=h2v#QLL&yFo%R_$-y!a5kVfyh=?1qFt4=#vIyncj2 zgk1ool`=sCOP*o#NYpogYKMlHF{p=^Pi6H|pTkHqY;NwNQz$emW~n9`9)%yuo<~kR z32Sr85ZMd7%AOdHcGE$}p%GwVx)Tca$}-9rAtoLZ8pl3=?31A#M+yp0I5K2N$eI;XnzszP;Qfb5qYhG8 zIrTnQ!c<(7I~yxx4&G?|tb;2Et$7*CHDW|`UeAMSNQ28vc+O9Hj=X#;sS%#EFd{2fGkp-iNbAnW_PnIy-|vfD7{$Bhxml-tC%eR~RC=W2)s(_ zfLkntv-s!V$SbJ$R3C1n!oKgER3Dzl@lh{D_jirZIphH7z-q!CKh6#5#~?f<*+)`$ z7p8vMlmxdCzSVhI<Yxe*qWd z=|T9m4olzflDcSkadwyW7#DbK<{O+OK}8zE+=L*JBoUD;jLKSPC6Q?LZpixY(y9f( zgrL%pv4Q3wlMnskT2OvxH=XpG274&1yGGGF1!AfS8i%>M^i@l_4B#e1#5tQmN$L5- zz724}IAe8dJtMu0MlWfuW@$WLjBjwLDpSqW5;>zo;<1dn@*_=OzoKBv zNF4_QHf`X8?V*KXkwQz&Tpiz$j6&A(kJ*~?h*z{ea0yOT=j=gNNhrBi0GeF1DQ7B$ z1$%P2oTL9&ioOB`<@*AO^y4!G#ytXR7%l4Z39Bdm@#WOg^>wNJ$|aq(Y~v5p`9<@& zlbMs_BkEJPac%Fw4ZG_1Dlf!o3LXo9!)R;|da-_wS4Tia{XJ;Sr9+~&nm zllz9vJMePafIHK$#a15}7pB$ESN{xr3*2V?+dIbDJ4U}VZ^yfVEP(jnm#)wg^@#Pe zWirNYLGnF2K&!&qcB0CaTHB~RTj5Xdu&(bb2YKe1uoy1$0*EvzgvfKFGo3y>GRD!h zDgK5?FWat>3{K0mPf0U%L6!R}OO&xf@3E}FRqbf8I*FX7f$E+NPjn^KEUDZkM_Bg= z2dZV&%BfpM>*nv2qaN?NAypfZ^in@tEhfFW8!!;d&(%!mLiDQE??y%jG0!KH3jZ3e zwl2w|J^YH$K;ycn+q|!2_2A302#>MKh=?LEA~lUlr_goYNEWCk^(=(zFugA3KEZdvcyZ2*{*cMOXM6bEU547Ym0^hW zknH5Vvo4PB)2!?oV1THh>fIb8MZ{t&7C@&Ze5$vH)qNC1;3-={F{!ndMi8$+)cPY~82w^00;~ zr*P}v^g4IlT+K{vpETv;c4PHbwqMYQx){fiM|g)eLX^_;BNnJNlO7pG2uL(CJ`gL^ zY%3)ZgZtjv>CP%6d!>^*`t7dYP-an}`vc2=9A!-Y=dL?LET9tM0o=67_n(D#-SlZRl51bk(C zcN@gk(bb&2mv&Lz)ItMeaZfj+Q54PzSBm%GKA1 zi7x_ovs8g=E=Mncz~7}CX?n>R=68%|_<5NAP-Ac}diuVsUrm2QuWqNI zAvyVJJb7uO-nyBrF}33upZ;2lq@;Mg|gAxtD7CNapU zM7#pS>G*CY#z&3XHxg4W?)lmix|ICpVUqa>?uz7xnoEAOTuJGC@V9ek?Hh0%2Xs`mCBHC%uAE)7$b#Q0KiM z|61Z87N~ca7LWHquzO2M-oX6^?4cXzU97oW7Xu;0fmT{h;7XUaO0x=w?;hQv6k&rB z^ieF>ir8wAbwZC*uW5vLU2~0o2W|Y!1zn)GLtVUgzGm>s^~~#k?YwgnLTyVTIe-0^}x z$p8_JH=_!K(cf(*nvBDHWcUgm9i*EYw+V98uNX%XKm|=H@|G<2mU_i}2TtqmBj8rq>9c@e81BCK*(c zMC2^)XA8M~vKK#sqZf2k-Jv7u6q<%;oc;aM1{UrQBQY~`wJeaCvrajW&E_h6n&l++ zL@YwldQW~3{Hwq!#@~KA86?d%8=6>{HXJNJsfBaXpN0749?dul3J{@JRQw+B7A&O1 zyb2!^HuKg>>Vf#R8R|*cL;z88Pr{UC#3m9oa02e)9VAH;gr+Be60rZKz*;D1NV5M_ zS)>@H1EA{$#_B=N!{u|DmxP6p6(KDM-y2O^i`W|&{4O2}Nqf_perwuv;9kFGsoi_T z!s@gKA5)q<7&+J51zx<@4mRgF^=K_2Yd#!0Nl14OgRi7#aPKRRGh&RAFRot*GUs`J zr#QB=$j9rVOpHJ|Pg_4SeE<^NyMwpE(1X##aDEBzq9vB7&kG<1VG(NCIS~)HI-dZS zi>Td%fXzunYr4Il5nBVx-TN#s%Ak79JX)J_7i-QK_ z2^xq1MGVgS{t+iBk+S+%e#v7q)_gD8{;PDNqh88D#EPm9D2r@jgqyZP+Y0KK?x?+ct8qra-6= z5gh3>m`N1L_-4Uu=(reFKcG1uKSm%xhrUKXf3)^#4E-Q1F5%CVVEd8>kXtEaa%ET~ z?5ufBV+jh@K^%r8ZB!7~4&|rgKFgt;VUgobCn@i$x6{yR#97QxAx%N900yGBd%Oi; zib#Mh;SZ_IE$%WTaG^}1MM1qXmcge&U)|`_IW>WR&7(!Ow^1-)G=pg-9wBA?2k?yf ziRYL1s43vxk?%eGOoksmYTTI1m`e5U=bSk_)HK(3c=mUgW=VQ3uL@NTd+(K(L8VnZ z{GzlV6B(g9uISD~AyMwI{}^ZOR(zMHaTqSPqa@IcQbF(u(0dJy8IY?KxPTsZ6f%25 z4EHL_7(P=@F0rrly$Re_d=}r_ zG5*`_ML+S=JPWGAGG}R2b{N3+zX>7&z-zJI_@nLcZQ%Ma?d+bydLTy-e=^`JBp3i$ z3Bc|*2}ap(m`HFU3uM{Bxy}ZX5+5mNqcvs~a1CLWGa$u`HRr>R0j{9Q_}pol|rsLAQou z+cqc3#J2Orn%K7Oi8JvR+qUgwV%wS6Hc!sQxj0w<#edUlb+1*ucGvEz>e{>a^CktI zbL@~38(@UPig25&T93w5z}XlM-nZCYA6MO;5uu}suQ7^94<`AFds@vBg3m+xnxI)$ zF=58C8=#OE;lP4bgNrC_2Qa?;IY7Ln&e@~1ls&5NjJbXvd;Wd>KJ|St*2DA1GMvDT z`MxOqeXhF&>#t=v0f>oV>Tpcn`}+TJGod_usYn|T5PU395QYD8FY$j!1~)?+SJVF- zJiF-9ZlGBGRrNAevs1K9HsG_FB1q^W)M$vvlS*+cSs7r{j!*_y@gvLb(>Kp?xI=k+qf z|9L#;b3euZ=`QDUVEb#o=IgSg=Q%;}rQy3pir`np*Qa~W`$>-9$K;cs&%>mk?`K%e z*NcLG9=88uPtLU8)62HN*YyLkzW>`~&gc2n=V8frD?!1Wk2`(ehmR-0kHfI5?vECK zfA3HGp0`Khp4Z_T|AzkDi`+)0`f!FV~~9?$4~BZ$pemIYCb>io&?<%rysuT{Xf<(&)5B5Z0~Yq{DyJolLX%;Yrbyo+`sm|UdN~PKdv*b zwl;R|>U*@j_YYi$#r;1Qk#oKd)~~uhOKSWcjEUb`Gk?AArM&+ncGkJ`_xl+5`ry>} z|N1ujeSF*h;oA$(8Ga>4o;vr$ugBXjA|GE3P-ny0H}+re)c!A<>R-Y4vmU;0XZ~-G z8Pne``_T7$+uZtm{5GL5sORgnqo!??(_XN7THo(u^GWdiBB!VA6#MJ-Lb^OL^JdHe|?_$zdx$m_jqUdJ}sWzXV}Ly2!0OrJWtjTn}6+3f9_KE zypQJWE8CrPZ#KVmUwQ7gJ!Squ(zbF=ob2IV*ygbw^F4Z+`RCSUB;#x6yT5UiZD+fz zeGk~{w$(l{K3vCns$10gt0&RrEB)kqJa9PDb>5D=6DyWvXbePw)AH0k-KdbPgrtE+Hh?ZQj- z(l$ByP_OvX@L1-ly=7zWue)7KWlxcJtxKGh-q%KU&9aU+zTA`6@I}HUy}9qw3cCrP z#KkYs#+2seSC6w7`->-?CuOIH2W@;6h3<wNKi_EtCR!%a<@;|o4crL;d?3;9dxVsh;5TgtfYqU4s(3p1bk z%G)PI+IDl{ykJc;+vORgib zwAL#8>{Ok%!7Eqx>|bl-8x|Ojiv4oRw|*2$@g7arVW@6*Hk~t?k1Iiy)ah`pUF4B? zx+yM`=Z~$rN=uXaU~U8KCAF~Z3i|%uDx{siyc}{*YvE`z{zH^}q4T)3w&GE{;$~C0 zP(CzT%_*O?0Ck6MeQ<2usML~bNTHCSxtLI{mTx9XASAw^_^ptb_6YZ0|H$Or|4ZQUESnVo#h%X>xV60S21mS=Cj)>Il&AV6U4_jpS&t82xmK*r zn`AlSA7$#m!Wa`seZ$r8iF7}fQQgg!=H2tG^(C;S)EFRr0s2{^b)9VR?M&vSS59NU z25Yr1u%ns7oSho2x&s0X`)qJw4R1}VW4q$xoIQto!7UEU(vmnzZOLhs5}u;K1$()` zD@6xad`v{nqCKsUwhn@#&pmn-f9Uy9D?o6#NrK-}JFRJHpydbxB;lR{bApL4ORJNxwEH4fG=q5`o<2{^bk zapt-yv+Ur8Jb;OvfQMa0)pnUY;p{2+*AS`Zz` zq=3(>qAbHJ{3ll}5(L)}uZ^B`+82R8BD8?o(!u()xq+MAh!pkSKnqHo)8lR7($$#V zkmPXvfoAielLkTH^+L5L&GKtogyW>|Pw=9U$BEAJ?QDIDUX?wkQ@U1@aslpO!FOVyK-|a0vh7z8F#X>oFT{vOX39nF%x29x7nCp{O^6G=&BzNJTnla z;*D-RWJ=qn_<&nQEy!_m(VL4k!fd&Pdiok0cvb!4tN4TErY|lV9L+6XpLn&!1wI)h z&2u;3My%srzds#7Ra-fGqNOpk0iML5Ud@xUpGCI1Hn7#d5-Fw|7`r>w6DmOGVx)bh z4|etz>Vc#}t+r=Dg_|9Vn2FIM?7{JqsGTow#!n%KwDP};>eZT&3yPLNoL=}jp? zCyb@vufPj2dW~y|=A2C?{MRwEc}Eo3=9;ngPZJ;xc2>fA`BCT3l(>xxw+i z$sV?qzei#WSN7X&5kd}{4u3&~X8&*&)B+W^3tNc$bvLzXaby1oT^P0h<2VGP8*8mB zx6>$lbr;<9fOhsX*^*>jzUAymI(=MvhGk;Jdei9}XW&E}yU#eHoN`d1?b8r(=Ft_o zeCDO3XD|;z8EHEaL2Aplzr|)xz;N*g_fw61yg06L9(-sX;aoi$PLGWmjdLCt7h>0fNj&C`Ap^KT zEa^Nz(3&Hg2?}x=FRGKR7?^W^= z)TcAp zGzydlSnw&D-8Ytgf6hvr+GifCO@pFo7ILMkL20?AGi|)AlM9jM37{Yu9thUZ)b%-b z;imqC%tKAI#umH?{Z*iu+?%5X-qEQ7uxbmDtAOP_rut14UuPIl)Mf|J*62`M^I8L2 zCOj=2T|P#ojK7a3Z*2TCp_biXu|y961utqvtN=i3w>+>Oq==jQUPV!U*(`+?v0t^n zM7irpw6+WiJ0bac8e?B8r1?#M9l#{9`Hs+hUVy|cNzED*nL(=0NFS#ju5ZTF{R^9S ziffn12?&R7q4HD#jO|fGSa**{T&6?gGI$~ED)Bl}5brc+^eUwdP4^3T1g69sFKsq3LI;rS+IjY%1?zD# zU;&*ehHP@?UL(SrZya^TKzsUqM{X)sqa==^RLZOctaz^ev#sZ!g|W$EqT8C0R>Jka zV1=yU?W6$T+15x!5uG6YejOmsKn!qtKR)4z!nUPugm5&@m73RnEdQQpkS4i}wx`a| zr-p;CgiT!h?ADEcYy+)a`nCZOf9jab+9qgvZ9E@!r&yJu!~o1=xv0RdEljc53l!Y( z$fB3A{%RArfuE;E*Ip;YCG+}0Zs`PH`l66p0CCR&L6G+=8ua%A_ZCF$>!|dlnCUuN zl@luOZzbzTlq#nJXg@J$CMhjdJOyk(z+bIRmY%{$ zXfwd@$!ewLV&FJg*=Uy(+HQ|bik7R@o;BUViqo2Qa7w62GC!#$&1#r-tj_?&k9^G< z)WsYv<(NziZv>VbG-WN~ty$r8h}9g3@wW>vmuM-4<_ZC2sP)ec*I0w1_X)S`EWhTI z#zHod^ersAHd~|pp-(S6C3?V{@+svBp<4zVauw*n`Le-i!Q95xx#ajXz#|)FrK1a% znT{d7iuZB5e>v`^`Xq6d2=4q}C-EP2b!!;b-dtEs&6Gc>_Hd*s|pRM=jrUAptT~5ctRmJ^0PtM z%&rSPb!Kb{&kubLCl(@~NSGfGb>96coFKtt&l6e6khr3!VWiH z`TksQvyg*YLb<^sK8Ye?*ovVuC^a`!$Bn-_-i-gsJ(?mk`641KQ=V#^sS?{Q(^F)8 z5vq=(7DEwuXHFh4Utd+kpOrvT-DMo}ViaM8n~f-(t)Za20L*0B*Yrvu-{dV9Bc|gN zUDudirPP3J;1hG@U7(d%)D-@ER#(VBt#$b7aex4L^?8)%ulR zV|E+75|%pd?{00IFirAg+{XxKi<2C@I@O)>4G)Hp zNpM%`+`t)ztyqP)K{aC;@AR2L;?qiYUZR)lyp>3(Dlg zhQmpn#+3JXKyj9~wJTRDO9MvBP-l&3m3o18wP|H)u(*#>?WUcmtD^G8 zYv}!gL%uOWGTnN8@{2hCen>}HePD@#K8qosB35sJ09dL>Y0t&{W=oNw*vUQY*W%sz z{}2F2AOS0DSEq)igM=ru#n=fh$3)g^0?imns+PN#q7XJ}M{}chd)|VG{j}(wxoI7` zu`DQo(a~MTNj!Zem<#<#FrHbb>H9ziulvf)893O!y+(U|Eb^XA6zUh!j`&T~I&n<~ zv6v#iPS`tcm#*JlGfXm}T<(((T5Z6Y*HnJ~i{_uMic)>`QQsd2a4K?i0L~hAo$y(A ziUWgXV?AJamA(2v_cB*6bZ{Er4SxQ^xW2M@`ieKB{g!VXyB%w38j``386SU@Tj@Bel7>uejtdnHf-V%;LhL&({dHQF& z&FbGt0mGJVa3Ml50u<3gQ#&Mkb~Vr>X^S|`i06Tocwn_&Zs&VvsHlBiJkzCw3s)3e z;aZRwhMNDvC{h&M@Mb*Nb%<<*y8y;kdBN6kFgS^vJl>olLdZgRnV8&;SJV;8-akCi zTFO@hAvdOxXvz*bsnXcqnaj`;BL0= zP`eh~$~}IpacODSOYk<+`;Rci)yxGifP_gUSffE#N5@hIely-0>s%>!ic(Qf)JEzx zW@G88sI;gG-P-tAuY$@l=4?0ug$Pdd@n4SO!bESy(1r}L7|~vEoxW*(U4(xJ6Njcn z^>bpYEowZ6<4G0;y`p{~ErMT&A3xg|F_?^af}mcGl-DEUHan+dilI^bI&$To+$WqrR%7Db4fW{tdNw&P2?Ju%q zbISqW3&qU^%0HiIAPNlMv+l4m>PP57J}Z2=%19gFC(N1qoq^btnE zTMPAa^PT)TeE2OQT8!UxRtt&8U6z{1c2(k2fbp3EJ50I8O5`*yb3eM0Vw8BT*_Xul zITlbFjE)FH;guiBTF)&;t7L?W&kok4d&dUzR@lXrWtS~4dE)F zVZD~`WO-vm?x5tTxoc5dBdm>ep%}U;T-D5%k_elPB8yqGO$yB@^!Szte#SgdS1w9R25(1xE zB40Pwpz^mY4n1LJcO#K-M;?ms;6k%Rr%oQF_Dm%vvuj3nK$T?nKXj^CKhi5x%i||$ zNsG~d;)F?~ps$`RKPLYB08E9b#{Clz%5q^+v>&v|Gnf<-Pk5xr>+0!6qLKMw^3z@N zLoJq68SbS+j`Y}59UV73iNu5hask2)R!~m*~BDuWA2A0F%#Ql z*P{WH{4Gin#uy->sDT+s*m*xqFnrsyFG`Wk8P_vrLUm$%c|~Se_zi zT0~Na#T(4cA0yM`Y4-;#17Od0tpPClg6Cd@nE7snxGv@`QR6?)u#mlrT#IflJ@U96 zhH1EWiiF7Rk@(QPj5Byb#Bj`wmc7pR*{t8j5OD(xV* zb4K|%psKzt>q}+dWTLZQ_HH2XnHk_}OQS^4`@;n3(cFb*d0uls(U5_RxN7o};AFpr z;H*K5*c)lBOruc8A?Pgm})LSwS{ByEJsc@ueWK}fz;`SHC z3Dk+a5%F3Bx=ut_SA@+XUKu&c47iXUH67kK$RwERP<=*b3Fy`8odE8-@H_cj_t8_J zeeWd6gN{8hk{8^J(xqCo7Ei#rn!(Rg{FvTCli5|Dh@hi_vj{dszS2j_%PEDEy%}zM zC;z!xuWUG%VUk*T&G5cAB@)HLaTj4~pX!;VI^@mkZL^6Z4iz2y2&Is>zd~mGpt0bkIrb3D zM*kcvO5w1LGBadU3E0uiEoW9i&@;c-&{-A(fEF3+`bk*i^4*qqDc`&G1uUSnK%VgB z5jj&myN+?hP#XwmE@^gOluafSFDE{`#uXhPHH8eqAY-rPgKre4_K7zheWLzH0uKw@ zi;Jy>(9S^ENA54adH@J}95e?m@th94P_ZDhb1x%=E==`!u83QJB&&n5auli+mXc+tkhA?3;TWHMp4`@Q7GEhVGCzRgqC*lvvt!c5N0$~e0_k`=AJBZT zpPSq(#U`W=6FI@x0Iz4}@<6#Jxb}W~&R_*osz9R6O`tidAjcW%9DkyqJk1@@g1#0z zAv*Un>7S~NrzA~%1Ch=At93_n`Bcg0OTuzy=0acxldTepJ~bkZz08p*eq4Wtwt)Gt zP9sXcW)g-4szGBng)Hrrn9SO^+s;19RVLP`E3~&L*UW;54Dd1UXgAViuNqvR@ZQ3;!`Ww zIY}abzLo<-SYB3)MU)F`e&8%NpFTJB3qDLFI6H(gaK z>JelOkV^sUd(9<3wRlDeQOrrOL^?Y!w3hVo8SX}~g=j#7!7FicYbU)u3bxp*WmtYz z=jOmcEorX7BlEZEyKL^|_>cR>l}WM-md%av9^r2ah7mV6m&fg)@ztz)3vF#7%@R#-Y>?q@L$73TuRa8-h`fCzJ9O3+oQIJ`y3`XVN;Mn;h!Hw-h$ zcg|Y`Te1QykXPEWmAlyUS+QcOwK#F+x;&bKQfugOZ zpDq=_6v8X{I8?Lk^wCZwR>D>eJR=I7n(0mhQ-3eQ!)!$<0yNZ9d6W^7>enap zx&Q%K6UVi-ex5bOxbS7n$WE;_lIo&TSXl+p4UdqxemEkA8O2^{xhv{N$C@!rO%q3x z25ys_eZY-vp|}$8#ZqGc?JgLj=LB*!Fgx!R&RUVGSTMmC8#SCD*j zn4{~3FLShX55y5yLA~LV6)CCVBkQgqY;-~t!ekDB;F)!c1-(?}2IVM0h(lm_DT=*< z)YbbPaV*}8SrQaW`aU5_8u(~#a)R2Q!s;l$RJpwDJlrsTF2WTD9PS}qq4jRGWbMk{ zg9G^m1w8lNk>8B41~A$bDhzVN?C;IQA6spN9kJXPfogCWxHSN1^mtRZb{M1DJj-wN!e^v;9+)bhF$xBT+Au2Jp(~qWv7*H-t8|i5E?iu8K2l zct|8RQyp4Sf>Mgw7Lwk?U!`*VoM_4-UoTjB3`^M#%p59ajVP3!owEXA!jxlKW<>j5 z@^|xssUh8_m!1U=gal6qq#bMjA?87@LEIwP6GPbe@DW$TC0f{+kT0 z!glyQQY#h+&S({wyusu3;)SJE`7da1;M0ZUqT&3d<2J??!2_LGXOq}rKOTcAA1Iv+ zR!uDryD`NW%dr{U@H3gBs@tijmuFQNKk>(kEh{cksqg6vNZ(^+&s~7b3_pq- zXGua0rB_eSOK-L-EIaqocb5L}-#w}Um@A=ca-q$HP(6(az&yozf5qsrfhzRGTg66SR4&!EWO+jWb*auZQxE$GBBW(hsvXQ?(n*(6w z8FA)r*n}R-s$F_@M4=|8hU|_C+^FG2UJwcB5_q&g+%z6yVdZ1jQniSQhTM2=4CW1s zfLOiVWJ_oct28Mtb(b;^3+#S`sVZbp$u?kd_$B4x3iz0!Gwm4YiLX2>OnNEQti#Z| zbcVqyJL!SJ#kkpOU`l7+t0g8FrsNDD7Ocnyq#zbp8!Ga0o33<~9w)LaE964pESfaN zi?_6}+KK{Z;^6KXxLH0VzeN&wRL4#TXu%f&wH1>5;f=czs7tBYCUA}WPW#VZaNu=c z+8QKD6iUX1FIB+_Wz+qV)MoO)uDUuG|K)=IE~F7y*$TD*lT)G?De6aw(b!QbY(}!0 zO_4Q(0)k^4H2OWu2KmPi_AvOLc?HSqV<1ZUpc*J=)ZfC&X{>qf3)tWE8fEqe`y+P3 zmx(Wt&-X8^mjO27h;NR6C_b>jO8+fd`I=f^J6>)5DMs+f=lTH-tUCanF9O5SgoA-> z@UGvhZ}TrZL|u*+vR6cvY=fhm1`o{eiJ{Ye0c}T4Up5L3(T|S8{$?p#`(7Y5WdD?D z!a5}tCwNQMP&T0X*MQ2;t0Zq2lMX2iStrGkAZxMAeS&KrhObBZW)Fx#&%p-g=Tc)f z&6O7|ULf3EsxfvKh}B`1 zzOBJxOT10L{;B`AFNFY%lLrk2gT1DJ?p;qkpyd){L{XG$7K&e)dY?ZXpaIQ*p^J1N zz6q*N-8(QIf&0%;rhq)PGV~qGf1Q4nF57`Q&{ZV4M0z03+AE7#kT?K9oDOIC&r7ba2=b*i+|p3cR{9b7LB%h_QZ(ccCPv0wq6$P&I*~uC zOnT*({LwVvr<{)ju#*zy2@aAyRY2>M#`dj1YOviIuLT@@J1A*0WTDkNhm_ZVdBbHX z;K3#xx*p&ylgg9vg1P1Z4&FP{%1l~_dOuESaEK)Fv{~+sg0tH7frrFSM zXVgB!MhGqdLwNo7I$8Sx$RhQy3biNA*lR16dN{ClWln28;ZG)k52xxYaySDS#G*vS zV8qgX#zn-R(Gm5t$6Sc{YJt=jsicJ5kAlf&tRL{|QyJ(fNRvuU*!0jtvodkuHFPTc zn^i_TA#KPM(0OcXZ1zA0AuzXry*S)dxI2cpC_LZ+TOFgD4dt8coLB?qmmYeI%c8`7 z%hiL=kp1???Wl>v8OOI0R4Nc=zr#$$3_|FG0Sn2ou#9p&ABgD@SL{l_CLcf{H+_GA z`a)kBj4HqI;Lwlk(7X9%;f)V6l^)-hGPrRbIH|6WEv?WBWT`IO{DanhQy-xaJykr<Z~?4X!*T`xLH<>cs<0ZOowFrpZlFUwmBisMPp%aA#oeO|ELG55XmWDG36UF}D7cJ)^!uZEG z5Xh2&7o&wWnJ=J4$sy;rp=-wUC(k2_F6gid&=B-D(`;*b)+gyWnkLG{amr#a`99-n*(0miJEuqW> zq+8{eK7~50rnUxu&UVMVR!qY^W zZU0R8iUPeO_66!TqQ}k9f2BJee3!#IL*Ky zOu^XCP_;zkqA%z&V4e9@JPg4&0SypCkE3|3A2By;NK14dFml5VFiR;yN5eO4u~3b| z3B)EnExxzc)cx%>?+&7vF1#x28&EkvIplMW2J3-#WvC%ky4h=R(N}f~bn%U=uuKPC z#Topkhz4D(=u1rLFfd>pa9emb+jNeZ%BX1w);taNH1H1!)B|tRW3?MizCx_H+mGH) zRhpk;$KeVk1f7VG=w~VlX|w~X@FqA%yJ%|jBu(CeGk8gE;Cod~7P5Xue-R>ZG2l*) zi+=A4G?6sk`Td}yMjhlmb69^AmW)=`R{Cko*6Q0z5JUR_`|GOyKI*sXpjJB3?R3Ki zds?P1i`?h814227r`Pek5cT&16t=7{KOgGz@)anjzwI|WM+LE&A_jN41?eSg70@DP zbVoOQ%4fMPj$dxWoHWAZvTga}EH1@2$o93J6ze=_!uW$O3{1S%C)6~oFe^Z^Ko$4P z&^yJy)r3jg_PdW#;iHMRwOm>`gnqvePmWIbeJXZ`RHoG5kq)17%@{y4;|vjEN`wxc zMvZU|x25SZPD&6L{v?In+~U=F&J+9VEW{6^&Vy_Y-bp>da)p*+hsGC3 ze)kb7uyH1#$ReVIblo*v!B}4-vIH7G%M|{cN zX{;DZuhRURgWXx(x3)@+E80mA&9#J1M^uDK6CaR*n~K!rBzT<9Nc$;SE=Zb>4!A>; zB{?F;U#|UmE(F73+5ayad0O^5XrU~@gqxq>%MbbD)BbAOQz|BtuaW}&?-d%mkx=?h zmnT;J;<*GQ`{~Nhqdd^^)TyDjvRjJMZ`mMc-b$$9cL6n;zByi-vN5pYe-;jO1IR!?vh_r!Om~P~9ZyE#=+E&mF zgB<5&OkuMk&&nnptoIs@BCDgD;p8Tw+J~t^@=c)M(#HQHx&Cc*VfH?92Lvc!?YgYN zcfe-@34WMY{7y6BMQ6#e&$<-pE%*3ye8z-wfqrRQb*IKUB^NW1^#~(eY-@Mmqk?K0 zQZ9_#JBK%{IVVawT(ovQD)aE#(V1jK#e_gnQbxi>s6%I}40?k6#|qV0_lGGEb?nGZ z+tF-F#hmClbfiIaO+BlX%6wBesVmbUjYJ;cJ_a{DWivlz6X`~j@Y;mPLu$P1Zospt zx3m7jkJgHx1T;aBZd3~J%N1#;o0O#g&GIQ*{#y}K#ivWcz3vJh(1^6(=I(4M1VfV` zt&F}B;P|*^CDz1q#R%Gw+NhRWuD9M5Bw`tc5sCkgdv33r%7mp4t4saPIxKM05c9WL z_t0x^HmH1^xMcC2wjeIWqE2NyywfceHbT$TiV4CywSw?7gsY~Z!kA*+%tT(Da+@+sAzXWoE;nh77jUD;Ss*+`-QJZ z?LtUaeg?Ymy-*J(PU`0M&^I*f6GB$nDX}sGLqjNzJy~@NW7xgp1iTb-wq!*$Ps+SU z@JHNLoYh7615?GZT`1cr3Kkv^Xs`?2K2#iMvzb~FekUvi#_tw!o91(A6m zhF~3{Rc21eeOyF=OtTKwR@-&M+a&v4zBp%|Zp*%2Kx;D}%|!Mxu|l?_g3f*v1(+FhOz=a zTWQ~>Ka0-#r-d-yJuhBKH~*FB<2BaebfFV1c8rx~e@L5FO^1%C9&C&*-TLQE*UrI2 zR6Dt+{WAIa`(V1uG;D+f?H2;6o3D`_(d&wfgZY4^?dfJGr(|L@0wEw9H-?FXOpCU4 z=sW5cf|A_t)Q*E<+mNw{H5kn#y0roO&o_d3b(=knjR~9>y_jYxilS6)$nQA`)9f(< z9Y+iUWEhRk}2=+fB~3`yC{T!45hsLI)Ta?3^0(MBSBu#+qnxNU3_$u)GEd&?Mn#K z!Vy_B^49v z)U(C*Nph2G6SLGNj3X+c5hNHK<40PGN@rSAM}kz-1B$L2g(+ULK?K{C#CB#Neq$Bf zC1`cpmh!CL!Go$Gju2HbdgXcFm;D$>*Fp<9*0GkM&>N-KrEX1cH-QcKTJMNO{N3k* zKmiI@U;(m`Bgqo_R4D9v5dLMW1x87#?u+li?;8R$mfogm!>~fUTXIVsHIogl% zz?r}p?Af2ATIyhscCv*7-|^%kk}X39qp=pqA>^vJ>sTiq?YXlDxK|=mxqK9=Pz1=( zJ=hC5TLPy6-8@dOUO%U)q@^+5#^)Rf4(P?gn{~F$O4%f_o%bsIcNz;}39ASO;Ist# z?nJ+DYafkzuf@8KOKU7$ela+&nMe$x$ap2cA$IRDA~UK0)vA-l4ByhsFwkzLp02*- zpA%`EYPK@Bz)rpOOqaSbG)zUPSlK8bmVolyr5tLGC<7a_gCIF@RhMnE#t8fG2N{jr4{LJGdT-3+ zd_{KNi?UuH6q33g#7;I$>hbqkYOUdrGpgaF(|hXI|`2@(wHDZC=t6dgb=Q}Rn?!gL-Jnw@QAIEqd& zh?F2Q1wlO4Z7zd!SvwIQ=WGJXmV=)ZfR8f7yN5J-ox7MtC*Auxstj)7+d-7pipkOo zV5fIeEB1f!i_+Kg=Z{a3a_l^%&L_oBY&Uxot^^RzF}9uC*D~$D##OE={!%=uE+mDv zq6P3$w_YN%34pT8PPDMJW_b;`%PjKCmWdq&3mhh>2l|m<#%Wzk+k8(>(*H1ACLtpz zZHa8ANjRxWI>Hu@2C~Ftz=uzgO3U(u&Edpi5q8C57l~5w>;E-Ak9=Z{T_tD}HcF-K zPKffHNS^^GrqGdp$=`O;u{nNkY=2@yygJ$u;HbreV| z85xa4gQc(X+KtD*7}~nnK&*iQyPQ+QR#Y}htESC$8(=45ZBICVGomf3!|aT?VkleO zub!t22u1Ns0;4Rr44X9Wc zvV}z)P~XUC^q<%Vdnc(0Qg7IJsZ}@nn_ZcquzG{k&>ZGdKkPn-br{TG%mw;Jwu8gT zy&r;!3b6fKvB&NEjh6rN{5=>hyq91JnQtG$bt#t}1hf%kl(HdRI=9qf4JFIN&k6}z z(N=coe(q;Hi$j45P0Bmv^iSj$P!s~vhxXQTzN=D5j2BseeEdDv}`+QrVC)WMwRziWq!GKq+@eBgKIHD&0sN z)mQFtccnn&!9}WsM6K^~{|aOk!OxAT$?GxnZ-D|A!@;zQbEa^wTO|yKvBYu4l|Sc9 zp0b)cO|>U_8XFDFQGnf2&>!ozxWU7(D z8z8H;4>ZhqYl))%%pDJ&Lq=z04&seg1wfNWm8aR2)@yQAa*o0AL zsNM)k#M^XZbK#i_VCw`t0;n7Nu>lTG$1c|cVj_nS`_MY>y;k7yD@AFS);xYU_H8qIl+scFu#=G zs)za&c?q>@Qa}I&HH1`3?!zI}i$&a*rbaVNrtL9s)#TVq8P9lQ9Fg{1#P zkkHBgY5#3!2=rUXS_&w{R;O8p5v`wb1a3NvUBjZO)q{dHNdKCHa(0MJ_E^(hFh}5x zSuS85KrfFB9oH@^Sv!~He{8zG!t9%uALz17un-l|h(roE+isi%0>Oxt(RJaA=QZ(v zwPS)~;C-Du_6*AWlV`2CUf*1w=eA8chIOXR(B)h4q0Ag??~q##T9DIw?>X~AsiriK|D>e*cL5+)> z|HTL{Zd6^qss)Fo4|=08bYU%HdW?Xg)yd^Pr<^z@l`cXsYJ>J(&V6IeR*QJT!aa6C z!>=KJ&k>4D13gZYWAp5T`|rle((!CQ@?YJ18}FZ453CwgEG(h0pHP+#OiJ z;U$V1AK7#LRa@dw{m%F!9Q6+fETZ9wlr$o13KD(aa~EGRbY6XkrN}85+PV&4n4b@n zHMq<99V!9_fqe7QbT&VFX4Y*n~;6yI-$6r z-#|x-5jAtu)4KoGO$kqlA_MY+26zAo@4aCQZj4m0*$Ud{48} z5-VrElsgH}a6DCiK6lOCcmo#I9o8yBHh&-j|3hg%j~{Wlr%!fXpHIC(vr1fXPFNP1 z$MmR*8xlAjg+^GBY@hT`2;7K>9A;8UU+M;nVm6o1%o3gyN|2o1K?&F+JgQ+4w7;eF zB5xfJ*O!kklND?6SC*eP9TC156{S(Mo`xk&U9PW=|1RY5i%SUv0bR%5d(e8sooq`Y zss%h!jAlDkiC#0E_`cLYXlD+s7_Acg3C=Qpm9;i5bT~c6+5XOd&kBIL8M6f_B}i|w zSyzEm;6Wa%-b|VC)v5bv#o)l_SdmjXBb~h%^GB^NwX;F*jR1l_nJV^XC^|CNi8vZ!2c?9?ko4&JdV~OvE~ts?2OY2gUO#rb))hYbVN(J zmjaPe%lW(KC?YCi%GzC+(h32}mIpyPgWx+C6swi7%;Et;0`<`Vd93Cgb1OO8z^~Cj z*UGYjA!pbzdR)*X=9PhtbI^&2=orq_4k(f<`CN&hYAG3QZ<*M8R~w)!e%L$YxYFR{ zZ2!H13=*Ne`&$4vn95E`C@HfWx8kP7hNx%J{gULl5Vzofx}IH{2P_eJ&J?HU60rmk zhCInWinC39el6M{>*=&!+TP|x`dRg!W5JFMs|G^sz{_RwP4-wGAZ>su;I@}%$8Kug z@N}$u0LN7o6w2P>hRWaR4$iEPU#2!k&x5n~Xo^V+8wgK3hRV!&tXB)vjF?)wq<`zK zwIiTZN9TcY8bpQ8^~iVxbrtwyW@{*K!^zA1;uYeH7hTuLeycY=gQgy-b@>s88Ocq8 zE74Fck!4Yf1|H++ok~IZtE`uadh$m`pw%QF+eSPGmfAbacs!5LEuAZ@wg;}k`FS5x zOWUf$aO&%`o;Q2AY5{}&a)I^i;w9Gb{OmdP(|y6|ADg)%9^)ik_IWYRC*w%BrXk<2 zA4~liuNrWo;ljO$HkWYaCl1gFcfVvSHJeF#^ND>W`)seCu9U1z6}m-f4N=#3T84f} zzmXSP01?uE221*O1tMOcs9q$D;1i+-vDwa4j!~}n9$QlH0OMg4K@^s>@)|g<^}>~e zQ5HxrYw+6=@CKpc#O+D0@5lrgIf4t^!I|6wvlzUBL}VoT(v?_EUWFybdxN>J=RtzR zPL;r~glBjFJW){TB}GN%lo@JQp_mi zgx@Dl9K)=iQN=iT)o-jlVBX+40~JdQI6hS{E)1azKmq$ZavJbK{n+u z%LIJL2B-;PM1PtCMkXv&37-_zxs-sn1Nm?dQ1ZNHvK|@&K2KDbwxBc@i%fje83ND` zNNo>DPxF+qe-~MvmIE^>(T>;E97J`gb};3MRxrY<*Zj)_??C?D#p-l-))A*= zsw5r$3xu(DAa4Q1!|ahUMPD9rV;xKYuf$cno!h42XFvQ8aG{q%P)m0cJ5hK7P_r#C zHEqdKvV7oa5@O2hvD(RQjwKnf)`Wo26K~++G_M{M3aST`3kn2UKG+X1n)PFet=IrU zGmEnbvTSjg_a1aXrVm~7}Ie)5)B4^F{-!UCelg;v?{kH75BjD{h zGIaE7T2LokuJ*5U1$y+?aToCe>3q+#;g@yKm|%C~o+ez|eK@9{VL02v!7wpmuFkzQ zSP?X-c~=;Pi`SXp;mn4)BQdoiN~B;m2*X;Fb8MYMNCe6>+#E%g*3 z2#G}ryzs_WpPA`MI};_B#gXWys=yWrlb3N|-x3^jR7#bSMQF$mq)o?u&TYC zn9j-bj;UabPq#Z_jgTefg;pZVU0onEB9w1a^UIaND1@p|*%1}?-;GsfSBB0HkQ6sd zagZ^P3*J3k!(D-$B=ZJ`Dh@9wL~vQ$GU0MI7Ao0k>8puFB0548X2vpP8XpP$ZqcV) z;54vU^E@Da%EDU^SjUlST^PmFMol{knzcVg*+>F`x%sKpu{H*E01G*3p-gb5hC%&( z1Vj!P$U?}-0niliJW4aL&*GECQjX0_IAr!KCUZ$Ws|AUn_lrHxQfqVOe0D5>?b>p> z6?nU95tT^@81@Dux^XAqH6B9q*L@Hx5Gg?ND8WqgG4t&E{`AdtGAbVhP2&+;g}lrD z{7hxjaQjQwbb5bbI<>H{#ydaS@BIx_MVCIH)ASo_)}t5->;m=K_dfbrzAv)mx3prZ z*(N=62^@EbfK?EmX@JdV^UXr0bn_qX&H@^=Ug|Ei;+`HbGuN#rcA<3MNQMRrJmW5k zPJzut7Dw#bIle0vCX~8`IeCavR(&HJe3=UE^Z3331oM8DqJaW9xiGlgHm`3psu{{` z_|Hoc8o!f&x=c9#$XR!r_ZonLbInJ{f|U#HU<;xE1M_tu=YyWbL;w#%igP>_tQwIQ zZ8VRWFiZ#R$vP^9^fBd+rqZmiI9ylPW?`L@Schvmf=@SYzkB-<>o+&oPGF`cRuVpK zDP9)Gl9}!z<`?6%iL=D@zt##JhC9it#g4jbZGRU#;PuclUu<$1NYF%1u62wf1aCcu zt)~7anZ(xVfrq75&4g92|M;$XRmF(wa@Fg*4Pe z)psUrAOv`Q>bY&bggvy9G0kZA=(SE)6gg|b^Fql@g^12s(yl+#SNagG(unAZgY_UV zUaC`2d~q5GFWFAM^GzY{EW$-9Ti-dwFe0rkUEIH5RCg(OuBw9r0zp^vq^6(K1 zgA^6B!J0EhVY-JI(!!-5N97H$@rZDbv);lM_yfnBupAsI0wqAg?0G#Y1%Pbh{*gP| zj_hUTGJ~2zy}iKT&O0O>{W(RwslS#HY`I$?cJephHW0e>exy0soaUb4o{stO2b$nd zd_tPX#Sy$?PCOXSC577XbI?VmFTYj@mvwdCJr)W66xx$_DX0$=PTs$qd+YR>&z5E` zCaASu&%$9byA@OK6u6%}BORVYBwmtSCJn=T8YF3ge4}eNQTMhO#G9C|)J%;8&#$2Sm9CXs#63W|H|tAq z+5R3Mqk^4M24UA4gUOyKmSDM+EX4Hj#Twh3SyJGakqg7*7L!RvYd;84Y=sN(5+3s|k0eLsa6{n#J$Jm$m0r04 zmYSPu=EX0%7hJ847%7>^q|>EwP0w3S!f{kC-ZCLY?5uiD=WOq=vxp= z>F`|D#kR)vE5!M zZH1lKk#owLNZpdfoSQ<)&fQ~Aq&|)spSvSHOac6+#zt}7e8za z`{E1MGUerLRd|D1m-7pUTkK$TzkzC+wJ9#qZFPz1C(>vR8(|9~CZ?B1lETc+5TmBx z56#Y*MpbZ6f}X24%UsR#yc@uaW{Rs32XbOSh3izqB$0*WLV16?#i}hu_#CEt_`4X` zS6LUes9q}tTP3Dy1f4Gd$CNNfg5&EpfeCsiNJDQKm2)5jA>ZcsAli`Gkj*UQI3!n7 zEKmyPP6xx-f+4)<-MFw=I#$IjQAt|9gRCEzBUl}B>dO?Apr1TCLT(7DKpwH>jI!Ga zsSPKyjz;56ni@XI8jiVLNaCH@A)Ud2)bjF0N5X-%SOA&)HD0kUMYQONTAt)hpm%^= za5A!eHS6dn#e9C7Dl^940E_7-&XK8Wyb9K1qvLLWyDYs@xPs>|9p-70h(->aZ|d&e zr)Uj=T*Kp+!5p{wn~dU|Nxac&OM`;M8`|RtEq3+jXtUU4d{zVf<}3S0a2E73bKAkG zMYZvb?=6AVQ6~F{Ona&GvjTKLI3G5aFmlb-*6^m39_$UT4kWNi38uL)tGzNl*x01+ zWs0Bicn$mQ*7078O#85yF=Xm)s!o~hvqNLPM0H4LSh-r4TiRA#iQR>HQHYrAu(hkd z7Hx8yO#6spzi{y|Id&((e+Oq%ng`s3`A9VkM+Q_v=A4h;H|!7H@_7~vk} zWmIY&&(_Ou2Ulo5p)CI*Oy4c<1Nolqru?Y^wi8I5`m0VXg|LbI)5hYWV!*ELyDopc zW|M>OD%+=0?&@{u<=3$NEm0F>mxEQ(%yGgB9vK!D&p**=+QXeS+AK*Nst*lEc+!Ge ztjQP@I5AOAg#QaZCf$A~L^b%OXFA(uYgqfoMQ&D&9VlkLsDMF|7km2g~qf?MkS0Hk{z9kv#yf}^v)h$xJ!>CTvf4L2Cctd+3*B^ICFR- z)}1Ug+s|NUYO##u!5(Q@QG*HGs(&a$@-A5r4THv@f zTn=fX^EBUL%?<$31A#MOeb7Klo9)Szq}|7_P9BX^_#>4D6VRrg3f<04A6$ZzL?2|0 zXC=R$nzzw|-4pvJ3vLmL)+oMvPxfj%#*PTfxUzQbT`Dd=h2Izv)q_6MB5h@Njm^q; zq5(1QrH6?y9;KO;vGgYAp<{ZTG9-WOmL*~}6g$&4bEXMfCV1URV&ciq8zm+pn>^MH z)AD8aw+qya7#Z`qgl!n&UY*dz_6a-cBNAM&L910UL|__?we^Z(`}(a@y7iz+1w@u1 zJ2Dqj8Ah)jpkl&-OOxH)na!(w-p6M)vF{(>ZbraixjyAc} z_^hys3G(Xq&QmG^apgc%-`+A@5`GL3?rQ%@}YDG26iyI z@<6)4g}>4`W)fb#*sTpju6XMuDCNr1&aDldh?`??ioKj?w~BojS6YdH$IfO5Ek=fRcU&2L-Nuc5-)sy$o*AupJh^Soj z$r0p7hBBg|*pIL}SB1mU-h1AA4D^!>_YLi8zeFc1J&(@6ZqJ7FgIt2eb;@olaJ0p@ zbwXHLjLCts`K`wPuMW0D%9B0t^ec&SoVbTFpKd%gQXX4x4cctv3B!iAW?|37Km3a-|}j7#L>1SVy9$rQW$lgtZ0To}D)f&ftd5 z{6HhAe$e0L6_OJU(c;gk_`9unO~;s?C*iq=1*eG(x;ar_X1->vcm1i{t{aQeY7S-R zy1QPI$wnm{xD)vpL~OXtZ}68WnP6AOlJ8E#S2F0$+=eMeAmLu@EU5us81lJ0y0rfq%J?`l=} zR^wwq%=O-DgeI6K9u%jC@*4K@Gbp{N&N3JpCGi_Rw!eGGo~)>}5V|ZRWL2aT2D#%L z2-W6rh_2fUy{0Jb)w*rH(goBZNry>lFkT|0#b^$M3X&nfuy=m;w)6~5jT2J|!i}c` zB&;wB0(-hl2;c6 zGb+4b97Au1<{%Rd3UW+yuzK-%7iGgCd_=(J$`dZ3Mh*vKN4|NLr-_66L*R=(FcBeJ zwc(U7;U`rRSc60ys7y_Am)hdKIJ)7+DuC-uH;jQy*a%nRmFRB1G2$Gw{ao3BkLAeF z0qfd*qFvJbjjibemV3!6+|O^Au}+4FHkOMRc!Mo%sMF#&OGMOu-RdTHmO3H?Y>X5a z$WvHLLK3)G7n>7>@3BS(JAZwjO}_Z(m$`xd8#JexZuA;hbcl|5fAFl}TrHG(ffU!X z(=3E*EX}u&TX!j`!WauQ+Y+=^ZkYn=4|Zl+Q&4@mKxLp2d4N>KMq?EW5SLsvRSxs< zK3mEUPYlQyK_|U|G1O+sRNm2=f|f1pQ!;t;mcm6><~F=cg_+^gzid*2-Cbya!&|{s z_PkOd4P$u-8BVc#l@rGA{Eh7300T_ z8p**Cl2J%wAEVJThnnktJtupS9vv5LBprsZMV(}!2>olGLMxoWWW#If#x|$2&!bd~ zCk!S^twa$|j^N2?Jy_Q8mm2mi_LXdyQz^>CVR>Ecy6cQKFTo%3^cMYM6~#k2(~WKR z^NJGNi3k4zWmdRu!1PFuplv#IPTqasJx{9vTjW)XASr!wR+$?jFS84}pZbfovqXHoM+e<$iFXA#-Z<~k zT}L+~YW)nKQzYR!a_-XNc>QXtZ-O8)SI}&)s(lw^Gn6&t;ejnh0A8RSKzw~3Ih(|s z!mrr@Idl$F=@cG>iU3c7twV8lbc7%dto3IQ^kk)&l?p8kY5GO-6#)m--LqCUYK1a` zmV>v_&x3+|!t+e=7jFKI9CowDGc)MyHbVC-IoODx1j3c7|>G8DD3|8{=&lS(1v#G;;-ZJ|rRXb_%~1v6a7Z z<+JVjjQ2P6nL&@*1|*Q5=2r*j?}l{`LsSmp9%TyG5T_mi;b+m1Ewt-B9D5WqehGB- zCv-8@zGJ!R1cl(jYW5c$Zecx2ONZq503`|$52$KqSRt=I1LF?#ce%Gs^LR^p1Qn`m z^Np!daUmS)BO_^_=q96VXx<{=vBwZxe4L zCwLnH47tYl5yU9h;qNsP`r1?+d@w3}GLN~nPuhMlV9q_w?lBdQW#nQj`-Y&BjbUVA z3=evdLIU8S-h#gG2{&E2}CzAwLu`0^xP$JrY+9 zo)oK5o9E5sLs$!+MzFt*pTR0jgIU>89am~ONM|QLnxNC!jaXiniB8awTlE4y!qf(= ze*;H$j4QywIIYU5$_~<-E-L*o)EEtIb~Q>Vsa1jCpor@!Uc&Fdke`&M+}86*6M85L zasc)o<{|34gbr8D&r|^%Com2AF>7LzLw@mvBd_F{_=K!#6o?7ki5??}Fo9P-TxGf5H)xuIsU5X6$`(7JNb)GRu788meoQo*k$ z#zdX;MkLTzc~EB1D+({FoiBsVaWJ~cEt{I$n6F>`w#W5jA9@Vy0~BsA3OmE(NmK-N zRRoljTj}Dx8qKD#4%WPC>}>V$wNfpUU|vswx`8rn-FE0Fz!*n``z_0FET?1%Y*uBg zg#3O%S*w-Iaj`S_TYd6deD#1(=b*U;(%{7Q*ZB8T40nS2>iDGnIYTM7sX2i@1q41p zrOBR$t~Op8BjuZvv>eu~BXT=(Ih8{6bdr#QxZ6?N=5#fr!|FQ>5r3z+uJl)l!q1J7 z^p$l#=&Y2yto%v_Z3vk=Tyoh9ZoU;y9r+~F)+cctV4Azj>_%93lg=#8^X?=<4JSHE zXf8SZ0#M`ftIQtMS}aPLPl=PiFl3cX!AXMD%Y~=w0j-}wA>eEiape5+8R7Eq?)JRL z!zs_4V~SYb$~oJCA}j%`76|4onzxfZbB>jJUhxIPno}W))ctSl31$t^=`-4@C++JUMA?6lzSdv zJ@`)>1QW+WH}CDu3a>oE0^6ak#~n=4sifP%7k4jVNDNL{@~jk*5_jG73Mg=e2>>(P z)q89k%#wgtVdWGpMJyo!^frq(!Re%%6fZA?`AU~y=ZO1HDc0K;a||m7V$|KPiGwwG zN8%}MqJ$qoAaP@-TV&vejf2|*Dn@-AaafW}y{_*JKptua9b{c7S;Xflv4@0o_+Of9 znF}B1AlW(3=f_hV!_FVw`HPPaLgn!d`1fqXC+F13Y7IdD72aUI?;i0O&xC-As0#wO zoq!3g`NjCUW}dp1mhI16szd)7Pi0ISyHVK=DOkuSQSU{O4uT-r`v?wx52F9d`r>}# zsP!r3%kvu7jv}_YJW_q0!#>xVMt~9lb#MjZ>};&fR1^386Uyjnb1X zAM|{xU_=6uY-m~03zd2yVj&ey0q^=F#tin(6{<)6YJukinFedsjwci0@pa9 zokx&um>P$3_qlgij)#a6uTn=U`O#s78jqg}6_s2I{`C42RH8V~pvWOFM=@@B$iS{X z=2a&L{*wTPN09?8B!ysCvptoqN!`xpSyQv#%e+qVi=Ur6e8Csp;$pd zb~Q*oqPKE4c`nDHqD^4o+Koy* zOdtPu89FXTq#DKcQukvktnOBG&1of7(^r~QPGKAw)8unxLtt^v7G9yQ7Q`u^=YV|9h4qM{2`b-T z$@K@WKRKWeZ_xdjOSqY%Z9?It@;30dY=Wcb`#WT`hHrXpr@y5+2O6PM$hZR`df^jE zNV*GHdQtL9iQiI{MFTP0-vxM;36D#Veils|3G$hX9a<{bB_=K1&kFOl6)D2S+!+uq zX245@3q`EEeUJ9g(lS__BJ+TBtYo+PQOJ}ieiy+d?ttSD?_M_59RSTA+e`V=)Ai=C5c2rh1#)S zYy%>=-OsGnQBRe|eTn;wzls=vjBZ-2@;RZIWZemhpBz`vLaoRr9S zs0T?TOr4DKdwZ_w72eLzWPUjq-3Yh64+m8OG??2-|5?}B4Hcvn_Ui%1pptJ*^y0Ct zGW8QkVV|+-i_p24@0{6+LnSAk(A*Q@xn*EU_=j)GRshZ>L3T$x2Ip{}^LgERN) zqMh~cVv&E}Qpi}V@%Y}aD9$CG%TBgkyw5_O5ur3{38e_2L!+(BDZ<-#Pjy08iskEOlFVXo>2c)Bq zUN#X7Zr?hQ>mGc&kxKxH!NL`bgsx;_8tx^FriL7380+u8TW~v?Pts&dS{&X`#6M))-l@MavE5M%EnaT3LkbL$+(!4M$#apQG9f_RYc|uW`iTwTUf!T`h zU6=}fAOzWa5@FRjLe0Bb_c8BaBs}K2DNp;*)$J9~@kxTkCqh%PBlksXbWMn)nxB<< zOh68^dG?;T&1g$IQ0+-S8;je-SqoZwF;X;2i&Y1n1-(&=`UIh2r6!MZNB5e?0MY-; zxUyopquIZ8(0qH+sO2bZw3754=lwxRCD_K}bc+B-H5+!Y3*D@YXuM*~gD8BYe$}}D z7vl)0QVk{g?v6(>WJt{R=uBLCE;B||e+7{P8x56?pNT|#7|q|a*+sVfWlsQWZf95$ zGj7?qxLSzgo2#X(usEnbo^UrNN5VVu1!TmP_q0T|6~aExxNilEFn9XY86Hfh`>DIn z0k&EaYPCg2I1_>p=jJ4*>#$IZh((Yj(a0Hf#UJ4kUqCmSsg;d1S}KbGsD=~1fwK73 z(Xo&Umrbex%(lb>wrEY5&;f-cA|l;a`erUp2_Km$A2GwGc3dF{`!bPTyjKCZT@n&! zy_u}k8rP3g2HYX~gG`1$F#)%9)6D3RM)hO4eA(X@}N_&0!_pfSY^&QlLK40qf z;Q^?$S)pFk-i4S;N-B!*zt?EGmSF-sUIrV*1%K?^ZO?TybC3VIHeZE&9QggBG@EUm zvx#CCrX`x5TfMAYIDkEv$_vf}#4^8N16xbv!O&g@GBVbxR3z+0+Xc ztcgKh^6Sx@p%d`)hfECZzrA05ac8)88?1x8iaT|j@Luc{!-k5nimxqQ98B(ihgfXj z+hUQD5I?k1bF74Kf{tSkayq7Tcl#!!X%$s>cfDCZzlu%TY7gs!m6tKP#Tp~n0Ky?g z)QL9|x6r&N3Qk7V*(i4pNoES8Oj~7KT!)PKVC3b22}8u0@^Hw#Q@0T^O#R!jFmxVbjtom^Vp34^G%RLYbT)Bz{@YV{g3;8>)VHQ#RxEu4< zP$yDiq@X&AOsC*frzx8gs_{(sFZ(hnLJk>Icqx7wmS$JsHt4c$34@ga*MpXHUbH=jjT9 zZGS9mP7BTi_3{KoqD_~#`aCDNy1_W%b5(x}PdsxDyk42fm7vlC1cB0h1idE@Q&`Rhol-L?`q z7wJtB2t4+-LCjNmI`^pV=W7$0K_o78P+-id5$fo+Ja$lQodi}$thjMpZeCEaek5^= zgf#3j1eNvp!?_@GgEi!(+g+Uo`J(2+>p9xOyc z5OGxg^Bq&yq~ICl_ckoM86j`#NLoa7Is%oFsdn;#qPrOB=RyROOS8u+1*Y0BD?mvY zRoG)^@%K|t7M0OJGHj@*TVCaZPn4jqxjQsEIi&Fjawk)f3Mj=-N%@2bxsW~>`mK;v z+zWmjurX8IOaoM+%O*pJWn~68X^TX8X(WhF)VgRWdCY()5>fAjxC8eTVhgxSxWX}k zgMXb^!S8Mf%chEkw`a^A9IN9*=@Bw|sLXeAO_^vRcKtr&{vle=&E`^kv-$1yen_?C z#;{p)&L7d&am>Lmm~39)XLa(DB;q3+6zj5N!;IGuA{hz7pK)T*!_M=!FZY-pNxa5Oj$UL#qy%>K>Y8+A0M zIW}fi<}j-P2~gs(7DisaKX=AIGMf2^o0@_Z*d>+4_`AR%V=*r>t){b`MQ$VH*_OqR zf3jHVtO$r@2f9a9rv9UO*y~gd9+;zHTJcfL8#C=3;}1VRDrw{tJ=lLJc09Sm1E9T zX*T>5>wAx*PsXw?8E5JUVAp)MN*e54c=a!~no+F(>!S1RnTO?Zrhr3PdLco4WNRuv z;LClVD&eNPwMs+XX&0q`Vaypu`~#Fl*R?MV9(QKHE@f|&0G3l&L>*2QjJ}S!yLhxjtMpXD}MuW)3L6?5`-pMsSrazuZmwJ?(p#x!vgExdLWFKeG?klu#yhc zKSWY*n^G<&4QRHox_qP3D15v>W1C)@S#YohwP4BQ64-gGpKbMYd856`rS+*dy8gnE zzBa*ujW@*J-~z+A!p$%!fsq`Qu0s%{2!cMO5b$Vb3Fauk{RR}d5~{}|k*gq8U?!D* zgx%SX2VRO*cLHK=d_HxhmS)Cq($ZhlS5y>sxlnlEI7yH4L{!J|7_7>Ht49WgZnOkk zgM$xF6~86VR>L@RySj)fE=^?A)fMLLNecs0nG8D=seyMHrMBsrt_2>qf4o3bv_ zojlqXwwBKhE;ZA{_&2M3FoqKE+94hTSrF(_8Pl<3ZrM|ymBI&(b%O{M8UkU_?c=PD zVC&WK0>5RqVMB@DA_#SXe?AEH;EqfQ0~8xg>2V5ILm%PCxh@zJZrX)zAbXELlwXlx zVBb|ZJvrxAp-j$A@I1hhQ-1TD)GpSQ~4_|(1V491(y>7A#Mkx=4x_+Wy3|4=gIw@ zzeAQOVI~7%XKsf(I=n5$Q3XUz@b{|1D1KqCEivftyZr_4MbK^ieBl0Ln0Tz+a}uv& z3Z=pj)ycX3!CC06+o9t@27wYIg~`dw*%9f*IP~;aMP58V73xtXaTq>*HeIpkmtRsa zi(ka!Z+8&yMlE5a&?!VJSya-Df8)-A;n)0Wvd@aD^E`fX-GOpm4+8%vc{{@ zf+zOk%nfFYHmSi$pNTq^ z^Z^Ey-XKH29zI(prQdYW5wF-2p94M@-){ec{%w8r_0`3DY{;BJx*A6tP!etZov1Ve)gGv%hDDCo8 z6qISEnJ6{mv}S@^i<@zI%L}-_lhTz?C(qQYNITfP)-eZ{W$2l6!q_aS3r5$wdM-!p zjA^DJ$kckg{s7XI9dT#rgxVTObZN2)ENCN?sj|vqfoIn0=-5wy@TeUNm->@<*~aKX zb3FGEWGrb&Xrc)BXM8oD``o7Cv~+1qZnD<*OXP7fyR;$88^_q_jRO?BNl|TF$t9n& zU9PlN?I~$yf3$5jMcSb3)69MPAV1Oug#&*n_FmJfO@p=R*I0eQ`Auw$Wk=cHtZ|jc zJ3M>2T&3zY(p9GH92~kFQ~H9c$+tm-ncMehOl|2$Gn-+G?VGLFGcy}gIzMsrG&%M) zGcopUlW``V5iNI&@1n2KP%~3#*k8;x`?zg&FtbVa-lr98%du^UoUyutglD4ZmNtKx zX^mOVW5OmX!M!~K%$vA4j9R@#qWiQ;&!(WMuZzXyTuzT&k;*fXv;50%UPeG?-Ea_N zD15fN6m;4F&Td34#s2!}re(dwMJl}If28}{OtazOplGY3+6_0E)sy7Bza`1hGPHdZ zJ;YLCZ^iQBZpBjK<5}ns&|+KU3oqE{YdOZi;e+**34g!K&+C1Dg*wq7}VX_&)kGWbU>Fu$3kABGYq%qfc{c3{!}W zCF`tS;=UzbXOztEu2?kbX3R#Xd-ut!LTg=I+lka{9kAo zg|^jY>kKgnJLd>DZXfdDpsex%QY^6YNyo%(D^suUYCyjAZ=;MK|10@z$aC>6JHeax z?XUM&+ON=SV`mA?2K?_g#JzyNiSXkGv$|rK(=BSp-5#k>BouQx5dsvjlQ16=AsG;~ zFi9eo8T4vUjk#V2fgT|Mh%*#-Fm9h=pZgAquWljabQpFo?2g2fxrf{rQXuGdXcfcn z4fqSmA51X3Uu=(v2ucr(0OVRJkVp)PI1-r-nFk751hXh&PO|bF9$5m)G*ng$fh2fb z{D}Ag89dUJ3%M9;{-4=&CXqX`Po$gJhHUXRnE`$Y!a|J~kK8rw{2!ES`uQ7_U2F=# zlCg)JT_@EAO)ad1`DdQQovVC}YfH@wFF%Y-zNVY>YJf(IfOt&UQreQ+(WPCA-ki+6 z;gO4hAVy4ZpPBeed-}$LUh_`G{i!tyZ+s``hrsv$;MJ2j?koIHUcJPB7}=c7jm_-M z|8G!p)WXsK@8RYN3j_@I0}2H6f2RK(S(EXNse7n^fb^#Sb8@|fxv{I8v$>(EgNy6` zA2zwIubpx<>g2y`IEdLF$D^eEnnOi^5MEUltT&MmP0U_SDRx6+kLtZ`}_JFq6_z%nD%k%m;dKAreVOxNsWU+z{A^Agh#*E-_?SA0>ho3_bEmJzsE6$ z{-KP*RQj;yMq1S_lgOE-_AeXC$>Mcr-*-hKkEhgdcW?TUWN}76f^pM^`FJ` z-xPCBw(CE^M+mm^3H0*)bwv344iNPB{J7WWe!s79sR;PgGD{SExv${7HR%6*f4@Fd z+^kr*=bZA9sPO-J+rR%_g!CN?r@PL3Sl;Xx?ClBQxs!&R6}$g?5|_&z+< zeM{IM@N@n?Ea?B2PcN@#k>Ky^oV8*?guqQVXMw}k+U?K%#Im- zw)kx4`>WlE*Tc^+p};1W?2SsmO3kkS&v}lb zVNfgL2^+uMm!ap!%n>2n&s2lFD$5hrw6j@u?6IYnz$V*m+grm}fU&^v#Vlh%@{(;E zcknI@^u{bh05&GQfDR_%mb1WkxBlbIlk|WknSjLK@Dn)yrzQalTo!*(=QnwT+Cztv zF}+!j2{7-1pe2LOm?tdNK~5^)dpFx<7u@rY+9-M!j19{@4Jgq^-E>|E+R$a$xe!n$ zLpj(i^>&i`Po2PDAF(fymktMFK-m)%-f$`6KvPNHe4(LSicQ7f6qX`$VBO;ioxE8F zd3)$wN*ym$dtMZcTRLH8DzgXSM_u|8_6P=HG@0^2dAINJVw)eZaZ#v0DmlswznuDp zV6jR104$SDj0I9qBEsdJNPOK4!FrBvk(a?oN~C7E6p9L zku@R#R=n5Jwku1u|JjTfSNcSQBb?kQ<60>=E~DA^KZA|4FKfZ9BJ)GVW{ zLCJ;IpoVxNM6^(q?kWQY{TwX8Uy*2b`|8M66LVlt!K8o2p^X&cdoVmfztNCGs3ZBZ zHJXNMsSJZSVP+K~-(-&kYQf$a0u3k%G=%#~luJ}ZK0?YzI5rcJ1Vt6$xm0qAQNf$c zbnu1+sM+X4v|Fln+f`CB$Q!PDk?YX~35ut9pkX34rbE~8etngoE0$9yl|kClBr|BC z%{@#fnT>vMn95<`-lCVrTF)2%bMjKH^PqKAbUm9#-<|phb7ZIms0RJPW4DJs>zj}S z=kf9tRn86FInojcI$HG601et#LmPi+7gxP=g_&&`T5N=I&*psY8)!x88fC*9aBVre zhG{M-%&z*8fC^eNogS}%i7w^uB}Fx{zYo;o7DTO9s4^SY0u2qWZjVff7Uqxw14dUVxVh|rDX153qf*dVDrG%X{L83Y1eeWC4;{$GtGe-$d z9pJPg^$(_#IoJ?d(G4}rk7+}~*o36SA`C5$B4oBo-55p*KoggUX(&UZ9*;m5l1w+S zR*Bp;abU0NWz5H{{|G+y%5c>bi27H68n5_=6bXR_(=&OGJp)23SZuy!bAd`$YSSFV z6f(ZP%n2V~{G_jmlD1ZI%$qZ4P`tj-&oJ?+nddyp;1TsNW^H+@A&tLCRTDxU_~3A4 zW{#nm<7mEkV@5Ya>`*Y+DfdDX)Lt49sLY99#8~WyEbo4hx#UIFr`tdiC-Xuv{D%q5 zmioOMqns`0gKqLBI>@%c?>9LgOnoVpI_iNh*cl_kE-E#4pMt^F``bepe!xn3fN|`@ z#g0h-5-$&MN9Ta=1580fI^t|oc(x>B>H*Fk)1JmdbSql<$7Y4pxJn;J$4c5~`ZYtJ zNZ7dmuj^j5WgI-Z%JOwr*|Bo1Sth0Vb-DruLN<4dY+rSRR^FOZ&OWf8kG6hOyevLk znQ;a9bx*B(JjLEQz4r%)B3q%#^YOWZI$5fXj`U~HeE&w-I!MDvKCOGlYMo$n`mzjX z7$PNg)AGi<#O)W&9Bi+fWx@v#0ksd!G(IM;np1Oy$oqUf8YYl=d-%_k7ly(hXq6t7 zJK&vfxWE#wBO0*t4-Rt&WKF*A@uIHF?N2;yy(oIM`N^XTx+7P0SrdFwITJG_2&A%?)1#?{0mcHuKwn; z3-lmcB3jf^Z|NA@srFz(#SCapXw!^^(YwM#Jw8NnUCq@vv_5W|Hot_KVbD2!@jkE) z>p6}%o%95j!3_KwJ2z5P)`T{oPF6rQ_>a%a`eW!|^$`BW1p;NGs)4t9l<3CZcY+4Ey-F!HP`2*BP;pkhpD)BR_VZ#-MPw;7r;?me>-12*RExr6Chu^ z#4+ZAV-9;@Z0HT{P>n*hFR7uHDtztdM_k2VU4-(5g$m9a@APj*;(otbp-qNb5+}|Y zCR0}O2Le|*4xZ%)(}@G@!c`p)y0+7=XK!@BdE}5vn+A#VPhgy&y4R+6xA0&~89H5N zt=zT_@T-+(LXsLgwf54GE>My-`hA>8dh-;O(MJDw_L0Si6l#o80>mBUq}F4MH&v>J z#Z9hC5|7Aq$M-I3lD5WzB9v~6R>nKp-yoP4WohT&B({qip1?K3>yb%H-tiC*hzNLY z;KSYsnJ(ti>&Htlb*0rH2R0riD__Q82GlzZrYk2Wna|&Rbp0w2J}r}p>q&t?o@mb? zw35F-rKy0tb&B4~@QT{BsX-BP%VP&VzUhqq5i_p_nsZ@We_Qj}(jJHcr-;ZdYX zI7a7f7^Iw4$eog4^3t27Z1ALMtXCU3zREp7TTj`bdG*e1)c?>dtCmu(vboL zbqt9mOc0HRbO5=k;94Bipark;5jOy}i3UO@cO#e^Vz{%jR{fEQB`dfxdxr4y^u*8v zcZY-3e5yJ##vHXp9+VR;uQnW~R%I9qFXw}`8&;#wyo4Tnj~oc1icu$1&uO=gK%6tJ zMPn&&ru$1<8Ky_7ZV_B8Z7Xg_$H%^(_NIido8m$fj1Ri3r@iTq<7uMA9*~H1q+DBe z3Qhtk@}_Iy9*_Q%wAGEKXnYu^aXaYhHVyd0a1r!2&&ap?rppmA> zD&O5UH7%|~vjLLrz0F+cXY6?AZiGxp2~7L&^NTc~NC{|ioU9sbIv(IoU!j&7s2)eG zH6uk_%8&@Iw`*J+Z4zSAN>Ph`r3~vX-z=2VV1FyK_49aA6K(xXa0>bWN@Yh#Q0Pdj zO4eur$<=Be{X^eAhhey49!~^8-g%|CE(NtIS~N7?ClU9|mifUkF=9O}kLpgL;R&hW zBP?KXNr6R&Kow6XVxLP3QQl$9OtKnmH}TZ1^e3V?eUJ$so|%nyM8mYtJ?N;W*3&JZoDgKy6wW5x_{-K zZZ!}`Z2g{85K>tit~yX4q^c>STGqBr*zM)0TqgqhvLw{q1I5 zQl$p+4C76{tg}Jm9S*MVO^(1`J6_z~&P(AP=n|j2FIw<~z*qSf^}`08N-G`yC0AD( zNN`SsdktjA9h~e=mgp}W%6`3Vb&uMSfYR(cJ1Zx~{s943qG z1R!GPn;Pu2v{8l`jhfh%9nEx_ZQQ*9MXyIkPfZ_7-*}#9b6aMLTf`>5FjSQwsJ+hB~i)sb!TLj#R@( z0MJ;5?^-cse;g(xv+c68OwvzBw@Nf=#HUGsJ-w>q{%z}8?S|7)Ue^H&RgWLIX?)qi zK6Q~_RQe<$U|0og*`#l~V+-3hn`V(;(F0+Q*3nN7>TR_?H^iPEvj?+DLzGz^#BF(~ zGh&L}l*-!hbM+PG-&Cwc0OCFr7Yz((H+ z2{n`2popXxOjQZD#37&h0{%h!-W-Fbps$Y#&FzpC0ituf1V+%DItT1e#eYI3f0zzh z^!QiE&U#cUObVihU9IKtMy781WkL(p!`bER_Ix|*9WJuFtZt^Y;*E?*U!}J~^&>6R z{3GtN1OCX*a({=?6Didg*KCGJeMSY_N}ZjH!L70|y2TIFcUqJuYyFCAyAvPwoQjNR z)VO8RZ%aBES;%A0FFanmG&2gG9jC-j!l)+l?$lR~#pDpaKFPb(Ho)-2C(| z!cFH$^Vt!YwTsXZa!=x3t@sdwK7UB!(iW~pFg1}U^{qS1aXU|X(IueVOtCh!?F13G zd?!tgs=u1=+^zsxW@58Dq?A`tjszUc*kUJ|!{&Kfg1|;kr@&o_4h3@RS7BdA{-2+f zM5|w5Gq-znCbUgiJ-YMfOY9uO=vLv`!EtH1g+1OJ4voE|r`bO~pst}Y!<4PIy&lhR zPJ|gyK?|x-;eU&CqJv6#M#F^F!I;lZ(aaJ!$Ij!GRG!i! zL89R@v^hF{7L*vKure8vHFN##VkXFzl(y)qN8bW_E zAF7|m(>aoLg{_F{=J^T-D`cn*sxuagv7NsEWWY$de5W+W3?s>A1v@81dQEB><%|Cm z*O;j<$>$THFyb~G=N(8KuYLgzLhwZMVnHI5;nS3_VfkIQ+Wq`P*}~APb3@$@WLmZq zQ#nX|C#1Teb=-Z}=x9P|KCN)+y`0^tGrwIE<8GAUq04=ujej2bns zGG)dHn_r)fy}Gg_Uc=!+oWw-Z=4XqyotGnWq@^UxJ`EIz(flK@#opSi5m*2fvPl(ZN|Q>)9-Iq(=0gL3t0(knIusyw95D?Y8R^Tzq* z*EEr<-{FO(dqB+7fs*!7zAMAjVI>5Hfd{B%ITB~vkL9)dpqim6sKg>raXB(sSyF~G z$da%mgdy~Z!gc?T+}UNXfb>TZ+2pvUOz>@jn-sOL0nsGB+63P=?3R;6+R|tf^|-o~ zrRtvz@+ar5P6Z|B`Vo9-jhIQCxe%M5PJZ3PO~0<@s7jKDc2dk)N?V#k+jhx69T~mMUk*V`j_o`fQ2u@Fs`}XyAP# zLa)S>31;B?6lVp_+k3$h2gdfy5*}(lOpHd58_jE<)75~aN?3pVtIb+N36!Q)9^+@i z_OAA<65y|U_H*m8$x7O-)r1l=e+RSf5M#UBd%fd z`yj>YL0IQ01ePR;8tG=Gjz~epv?O#useN7G;zr^RmEhfv0pnY$8tyvJ93Mu!76}nSPztN`2xc`v|S-kA4z`=kvvi+q6yu#7 z5*~*1pGAo={Lp3ptMqYHqEt+{kVBBe(jU@vFB9eVnN`1;3ic zhp=6#IfS_+t;}%gpN)VLyhR193}H(H=-MA`N5B_!VXd%B@XD{TV<}i!g8{q7LB(7` zhLa^sTZV>Zv|#ntYk7vfUqObgAb}ETR8GyFq)8+Py0xr9w2H)QpculO9xlHtRH@e~ zquy->^ck~tcAilPRB^}@v!Q<|u?^gvJpb_AVLbMYae(4!_AFofGxHpX%wjn7^O_-k z%DiWR&8vN)L%9RIWQbleru1g%A5ZEhqiLB;D|=;#9Z=PDs^)pjT|KpvJ6pSwrP*jn zdrS*UuF6RPI~s8xDx(WlxP$9>t;Or_>GBgtW&7ni)U%k4>w!LLUbXGZju zt<^8LYW;O7H~dpg`=Lr=Ag0bOaIq^xsC<768VY&{A-3{{r=BEipc8ZpVd{{_ftT3# z70T>Nd;rMjwkCjfa5buoUP1M;p$Y?whmb)rUgJX3qnPx%`+b!8VQ#f|C3YjB<#z)y z9{$8*5&$s;j=8cn0-DDnI{FUB!P-^c@Y!!e{0*vTar}CYpegmH`ZIka>*Xi}bde#0 zXD4ZimlI=tNc@mQ+Dj8#%2o)PGxjdB;^$938lySxTgFGobA9!_*ZqV52s=7YIc$UYfzG+Bt{Q~c9Ii%A?K=C}|_ZaV? zlP~xep=5OEHi2@V`cDkU+izv1Itspw~tCOffE=ej^#qW72X~NY4 z3khT7slGAc7ygrSS~KG&?!0?^ppx^HM$}@BssQPYeys+MnD*Kw-rKX#8_y1gNn>Cb_)Cp zGsq)-FTwVihm$kI3B3k*lU{!1?WRtmKH=HJ8KYaQ-(t5nfW!iqh0;j=L6pYX?SFF6JT!2NX2+4xZp-FtJ|a*=9~|rIXA# z_@)p(LCt_MD}WJd@utp_b9*U;NQ(I76Msi6cjltp4)8jrWi9$~;fe6UCdqz7`pPjd zu({rgDAEuqg%YpD*6V={tujjp4k?Ss8G_AZ6fyUb&a`skMP6F{apkoaWT3q?#AiZe z(+v2)2O`$TKtb3UMb0FkbOCu5n*GF9Bt>~mXL{^QhPHkHEj%c{C|z+v>$Wc5X)pR1 zIx`W!q>m=JeP|v=A{WI;?^zhv zWzpzHQ#}E4oU)6FP}uU&X}Wr@L?}YedzzQ7!z(8wts}#o1SDb7AK6})%m}(PP6B9Q z^Ae*^r8Arr;&hu!aKN{T_jQU}L?3Pgn3NaZIm*|3Vr>kh&?vEZ| z{4w`&c9rHxX>MSZE=fA+(CdHedIvhx8tM-4xY01=8MU?oysaNP(seNcpGJBwk+dUq$9%NuZd z=&QON`#POE&uj$}B09f|H*&^TZNA>OQ$VNPanzst7NP~G(d<|glsFS0sd3?qU!HKK zJG|0}XZ&IH25S9NzKk!8y>1A*RG(P>HR%vw_6g&sMp_sd(RWpa0KYJGnr_;a`?MzA z4W!5A)l=tGk_c-m{qEQa)UzpA!SX9%E2 zD5?s!>CPI>W%aRGabK0QrU`DyQro31{1Y?T2~!9WT5x>4yK&bR*waf;))rH0f7uZM zuAs4y8Q7Lf7!%%c!LcOU(F?W}2;<-d_udFXe#`V#l-jxX0`L+wkJlnsB1)M<=u&Uu zG>rmQ^&=Z)cJ^6kJPX-&vTci}zldQJ)~D~+|L_%g9yE%VC}YCd=>NUCIn2tnmcsR&-;%;nvw*@7m}dm~YI4h8i1K%I zX@EYDde{*Fu6;>Q#6BGXIm?Gh8tB=bFs~w09z+#!hIs1g_14Ol$lmWaOO0R5HaHD1 zM<0vMor)KFJOv~l%=_3r@gfI)*I*f`yO}N+Y}sT&4Z)-~Cct-$ZF~E>Qtq5)FEPCW zaFEMS%PTAJ$y@!|!;_UP0z2!rj;e&$z$RVu9H|oRYQ zgwHB_a|sRtRomW4EISc}N;P)1t*mP&e6P=<%GWd(Eq}f08E87E;elSiNql3B^Yrm* zDAeu7rnwqcBn^wvRIZl^`>->rcjAByU;QR5^}~GSp`1wzaaIiRDN=aocu$99$rXC) z14IyaxzL$9KeOCVT9|b^Xaotm_@&p7hSGmOsf%a8Og_GNj1r^N*#xPHKygrWE7W82 ztG4Fh;EKP9vTbMXYU7`ORkR~8ze=UWN%&PLmTCGpQf{0UPV3eR*5TScceN#!h<ltn9K(W<)JrIPH&5MxqHiMePSp;7QtcAC)4)Q`ZjMAcRK;nOajO%8h?dPG=WXZH6t z%Ht9FgJ=Z*c#Iq7r+X$ktjov`ssSqsH^+dZO8d=Af8pg2FAWUQ`4Y7N~lV9vGxn(FCf z75as?sB7o$QelI?^tOJnJLCk0%3HNzjVS=HXk5j92hdovS@y zmKgp6RjSfJ z1K+#3SNXJ8bpiA-carT@`uZyxezA$L&sEp`L&KISg$Z;_xjdSQ8AYcQiKwr2@m3+)c*+l6rz{6U(#*#BB(Gp!WdsUCM*oopSX zl28K7^zRJ&G{vYkas-P#$pS-;&qW0=udX`ZR%2niJLzv}&~2o&Q)lwM3T+s&l@ZFE zu4~v*QVl?<6@%j1do*U)R5{h0g5X`7+i9G{@LFZP6~a=H3NDm0lrw}Fx0j38y6s5$ zZK9bm*n=bek>c~7s-U^)fkFcFHgIR->ZH>Dr66vG**$jgVCV1n*-=nL=+}1uB2^5# z0X;O9%2`4F(HXs)#+HoH436qHpB;+9zRBcAz}2BH@1R!qp2#kQANhzgkr;U(yy?0L z&;@(!!{FRX%tGUrY6@QMp~)DaWR!%&G$HrlNZNc&cP11ZXNsp`<_w*nh<6!I2 z23{w2igW^27IohUx1WX|HXgwgCKy3O8ewaP+{ydASGO3dA zgd()!+AD_!>iYWdHtP4HhX3{QajFDuaUzqez!L(-Jb!X19&7b2j_uAN+BVtYp3Jkl z?YMNFgI`^Xt8iKFYdat;&nIH-+Gx^d|1#2Sq&5ciBq8UJWTFPmgvJ?^2bd+c*zXUw zb8I>M=|FA_Gr-OyXx#PQ>Ix-9j5`h-L9!n_G++gDt}0y-J^Zna%s{RL{k9MGCJ-UTsy+;r+^0EH0Cl>m<-pV zlp>e?%+aMhIJQeXMx)reE^Y~c@x5khrtK+J%haf$VV|){TH0FQ9ggCwtdmDvD(=rP z$Z`D+RpPKeV0@osz4N7jT!tgAHtt`?z1M`1dIxg^a7`x@M_#Y20!cc&OZ`fee(;z- zHskDhcL2=^+AOif&rGpXY)2`I9evu0tgeiY3iA$#@N0yrCSfWI5|ePCE*W5e*k5Qrzr=WNY}u2po!U41C0sdS0tJ5V9bh1XoMU!i?n`c-wZq`1Tt(i`j5KO%KS6P9F zdcC(_9F_D1d5V69|A?iD8FB-7d8y`!mMCl4>e0P=kv$UQ9$61K5~LD?(#Jc0h9-%P z%#=ZBcb$izQ!G{0duRjo8*39zw^fdCm4JgO9_GwFIQb|kfKOni{N51Kt{t!Q1RP3W z-b;+Hj;Vbb`+(HZWbdm|G8b4FZ)tN2y@QqYF?WajVv4yQ;?I&0Z56I4t>m~tM3SEX zKRTVeBTvbE1O?7)EDp(Kevjz2o_^(E{9|0)Eq3}z*upl2Et=u*tlpw>?r7d!Ld~HR za46_7OlzqITKs8j8EylVZ>f$OO1y+c6Z*+R)3*Dm)T( z+Iq>3XsAt_0CP(#om>u8WMs{jt@5C4&fkzuf~4*;DWAs!kAscKv*T&x!y3@Sy1{%v zE!dTIj6FW!qM6#eVBbH+P6L9LdrIfE3Wi2>I8Bf_{hIZymr1P*c82DG9EhqI`K&Br zE)mff+%;&EiO}Q9wz63r&Hn9^RJcd(#I|~9j=Iw3=UhnnRE{LeRkSn4o2zUC!)CH> zd^SIF0@T)S+6YW)wKKf)g#Jh$nD>R|`U|FiGi)=KZ(BK{8caID`*bS_o$U=0$jf`z z$0z&9$}}U=0WN=zecxA35Z!>=JXbgt2^k$-cu+y<*5f0DtNyR1VKzCnZoK>8pn~pu z^4NN}?}G8!=4LfOP)W7c+0}CQA~ncEtP8uQCtrEIbZwkz@VQR)@cnOJo9QL?={MmlR@LJ_`$KWl@b1;L8o@um# zJiRbERCcgokDwcNZPvaF$Vnq1U@PZfaF=+NgeeCLV**~cF7E;o4=-$$=^5`NZ7aVz zy{LtADT+9gGn8N|{qG#5i5JVPMX$K_V4e$Q%2nZ(d9EEfT^K19+V${9_S zRV|4`51@j`Clb8@A^yW(s2zDY>k?KAJUwlGuyRI+f9=`DaD?1wv{ToFBkN~ct5q4K z9R{yi29d3h2g_>n6Wr-8EXV?rFdq}yVi1Ng)E#ZMu+H7u+4$yr)lcV6@SF;y&2pyI z2$bbM(3RyP;~guE&#)>hj?ctX1q!-VZtx!D2W%>NQM*aD%3fk@sp`x4M7wOG7h~?| zop><1VZD`ZGxD%7x@pc#`HoM7zw~6LXD>zhQUgl%n!k84ra@LlUt+G7%FLVC-)>i_ zF2mn|Xh*xYJg~U{_(yWV2#fAt0aP5xHj9m!C4G`Oij2$6cN3SFg|NA8wnT0i>WPP~ z3Isl9tcjQH$KsWn#Z3$>ZjYtsKx+G=HT;rxxYr8iu}jepfuj4gR+gAfzDM-)UuQyf zy`!$mvQT_dp%-zUD_!YlRc3to(_DKX(PSXvA+3b-7P2@XWI_{#AYW-<18#6AtP(%} zUF#Bo4m_d?Wsib%P|2LK#)5W))ggf1VS@m`SGe{;2uHbogHq5$oVvH4b-ioob4c!? zehN>`y`&9LKP%70(Mm-8+<#zgFDNt^=9Weq?Ogt=?=_i{?f-c|eY#AkHD(M!WcKq@ zued<;MhP?VW>(}J-2;T2{z>TG_~-A(ukRbO7N#hYja1!B+ke&4hlXrBuf-RMA-!);n_co zv2uQyO8(9kmViaw2k{Gz1&CgGjcahc&!F@s2}7ue=WqBPgMplQxFw-z5#};2EB$J* zf>MeyCFko-Me92lMxhj>G`j0dLhS z(t;h6eW?L=tRdkigRpCf^0|&s65vCGyBf+JgLx2qDwVeOj)_n*tKfGNpLo+Adz|e#4nlnu~LikP;lyk^X(3`X5t(PSx{B!%%^>SOyjpc3;0Ty!pRF{qOwp z8gO}BLMRZBZ$uCfh5ygJ(a6@q!P&~y($3&Nc1K1dXLIxamwLxfUB)4c6QSc+bbrnANb`7Bv)piFol?b`o$cnY4sQe6m&>=y04kvg3nPM;_~g;($>AD zxAoEV8XZQoo$dc#``*R7fp7oxoEc{z(7_$>ynfoO(HwA1WRGNU?f-Q2^>%l5`esqX z&-ciStPqzO@U#ug?4R7U|8`xmzjF83|Ni`P^m_b!znpwr%xP}FJbK$byFGVr5cJ*T z|A^WN5Nzl25pG%ocG0f4G1N$-U~nWJ=|_2PUUhdeSFJ&XfRyzq!$E6!%o(E#|3uk9 z#mAkME)uWuIt)Rf5`E)8ui_y*^%i9yV&luzZe&iS#i&7R@`_FZt^1O9ikr*ojL=rq z8m1iJ9iY%=CV@6b*N4FgAIZ`ZOUmC7&stO$mf%#D~@hMf>YB`C{z=+?`@N2 zN5;*vhCyPj`^D~f5@}%!HCdIGuU#0VVyf0*UU!y@1B&H%EU7pua;&Fnp&x7s0fCvU z2Tx*qulD4%y3^(z+{0&G+DKiWA4^kfnkl`nogoIE=}oU)$YmKH!xjQg4$ipdJNTDm zHW&A_R@}{*6jAW3z%vMHwY0b`!MmdP=uBdvqq<>}4DDQ6Ih?3XheXMkXB}-rq}f~@ z46zYn9DP0%KN&H@+#>Ky{dwX{rM}9u#90HtyZk2s$CV22fH}F`GR6`5qiP^gkT8;xQO|fTSePV*P5Ueidf2d< zZVdccO{=^{C7e&v-8K1FBsHXKsoSFU5bKYaE%ZM_s--yZ*2g?C(PW4FCnwZ9c;?Gd z%}HseZiBy7D`4k-x(q9^t*NMCEBM>4q@Ghy_y-WB3 z8&{G-cXo@e;Xo348w^k4G!X2U-RJ;SXgwA|%=HP&jnq!hanGhL7#Z!`>rK3sfK5pXuqb3LP{8P>&tn@DbWNti=; zbS}{%X^OTgWkP+9#KBWB5Hz>D<693{a-W4=kQHg@o9i|hwRlLDg5LxfyykoQL!7&4 zYbTp1x{zpPaHNwo&!4}Hoej~HQgB*qs%?Qe1t};dVMJSSREIW6u6>N*is2fG*9CR& zhS)NZ=cc?1fLmOA#T)N0&H?t)c;f?lkplI=FmeE6w49lnunJ-U`Kfh@Y=X`_WK$%X zGhrCK5qUW+cMY!-xx5uKo;7TkSS0vu#Pdv5iPB4NONWSOfBJ`&$2a0^XQVg1p-9pw zLrxpDS@(guoIBEf$-fLx+bEXo*x^lE9dmCT;bNSsd|lzEOBt|`Zi0xKuXdS()atd6 z@!-UfA&#uOY^I&5UEJ4lHG(p@)D^5zJUDP41X*vtZAd)j^NWdOV1S%# z65kOf(^EXJGxSD05Ldli;_q0zQl#_r^?E(eOw5HpaOU_Q5BlNJs7gLwZei2K2%|*aZyV&xsBn_i-wC+b&v;ZjehVXp<% zf7*d0=_=Jr#tzUtDZ-Jgks8OqRSlWhG`Y(C`e=m%-X*!>iNMNS>TW?@VJ218<+};s zbJ(SnEg;tmpc&(%pL}ycUG1*ti|CUxoTHAivqKTR)hXMutoPb)6ApT%O(9_p3R+z zc}44mkljl6$Q|Ee89l3Sx_5nxn9axApKz8=yiU9+T5d z8$S?h*|d{Y>h#Aqt6L{OG@BEYtM_M8U|H~KeTBQzPrx!Tbd#a`;w1`nk;;IWf;>fQ zN!uuLYG@H-AJ}26i|Z6yvt&?o|bYQLv5HNMa(=9Y-eVAs9)0?GK~5H`y;>W{3fsptv$fvDHrtFx0_ zvMo~BP7>4~qJ6zOK;nvOwoc%g(`ca4aV+)b)55U@7bey8Xa=<6m)_!tAW%~D6*cg` z)fjxg`6Y7x6YxWo3{3bj@vmeM7$5!3y29FGl>4Q7H%(%0`4jr3EUx{Sf}n5?*5L5c zX!!m;H7nggS=1zMWyHS{oqcm&$(6@V=MX1aMdi|wXF2lt=J7#Y7wcf8gB4H?WV^*s zsALNk!u@GE47QJbqWNvrJhedJgV{?$oj_He#CgVjuwQsC{f&tDqg4yKof%nYgbsP0 z3!FGrtID-~l_O#vwoxjJqg`RIe%X(%G8^0bf-K$X#i5^VIs2$h`EO(r@*rGtymloy&};)>$sU<$N&$ zc-p%C4=0I7U033&S)cj{n{)!==ZNwsw(;hj%`c0fI~PtiPTo)AsfIi&t@%Y2SmaI0 zjjlnFkjJ1{?EJ??$`!LgF8RMCs%Qavngy!bf2F5;-x)dE`J#H{EvgJ#A3H#2g4_Bs zVs=)%Ts==$^%boAI1W{`bHwbd&lADZ-hx6M(jMSS*3;gKQzTxOQt`7U7n->~XOdEz z9=aZbz-{5(l{EW*8Q38r_$@-)oK{rz=GC-ekTpgCSiCuV6net`G$djMev2_o;5U~0 zN@OS(PG&}4aM&lHTtFw!BG^Fo@gs6f(rCMp3u(i1y(L-AGabGqGV(e3vb#85INP$WQ1u)U#x_&E<>v zgeqgkLl^EbZz5(uzgZ>Faj$NzH~PaDyeJTpg;=G-WZiRBv)!kxTl(~)^SRwIWqFzODff1i7V2yvVGu2ZYAFK!UO$orB;aFsbIsjZW%5VnrRDyoxo-b-A z)j^4h{j2hod%8mFB7gFGI~-Y_bRcG+IV&8nh%DNZZTMj`B!MY{6hIpUxre)!*-a`? z3lc{kh5gCa$CYX}@G{I111|HMt;cq(5DhI-=n*Z8lbh-mPeT?QI&w{n{;qj(uLjt5 zR*V*egxCsXVle}ymiW#7`#n;r*N9uZ=C>j?O;MR@G&+r5zuP@g(iuL^rr1dD4ry7L zAwpp&^(+UxW-Qj7y)GN7f>Ym*!w^R(=@P>-jerbXMXiR{6<}hgGQx1sHEx|N15Aww ze85`XluV-uGDF0GEM4r+(3_GnhOsYUhfMFDy`F}M@+Qs8yAmaeSLGs;Ue;k~&%EiF?O|{e{LT1WB?U-CCs{4 z$v!rgDDbP|AYz1G(gl-~re3mfq6uqC;rX-Fe}~ar$R5ui3MWWQbhx@6fl#B~lezxD zXq$|jsWLub(xz=gZcM07$@BTYWLb2Iv?EMk@&L-S<=_KJ50`=z=jJ^ke7cLzjugbf zA8&;}6Vcv88CZLRQ6?lzTt2i1kQV>$Az1iLJ!X7*gn;}~l1)Pliil5QHy=KGIAP%2 zQ5qg>Lq0?3D9T9TG7f>mjo-hUnXjED^n5`Osf)G9rqM@>B{vC|`rRL{tYpWiD$MoL33dMq}x zND{e-nIyErDV$T0lCVOqTI@7giW;_^wqG_Az5v12;a2bc_(y`%dDbno2%<&z9`>2`{0& zujOu=%G28IYJ?Nhaa*=Z)`~wV>xOA8x9RFCCX@(|p?ul-q&1ZS8!>Y5N; zrM+wK3!S0;Bvl?Jdmfc>Kr&9vt@-plL#$2X9DOdH7HdYE1j_kD-Gyit17T7 zQ2Qu0%Ntr@J|{nAgC8z4J)QM-H#goCd}{-{8AGz+a&RaG*_g~Fg%$X3l;uFyfBY0t zrtahJQSWUp(bF{f#YvwviDtgGK_2F);R1^k{}AhIfB=Clo&h_7Ee6ndJ!^-JR}6Bp z-sFTJ4w6ry`r^0$fnG>1`V@`YYX?I@DW;fZELKJy&MKRMt40t;7+_$dyu0`Ii71N^ zEk~`07a1Y$y72`C)G5b@P?K_8$auB);`fCaG*DbMc&W<0b8(XD28+~3eddY!gy~4b z(fo#h^%M5bs=?rc7O`ln_GA!|AZkB9_P&fDtE72=@Jyc*2WIHzoOc4l85eRa44hYP z*}siK%Ilh(Wb@9V;l#A1h0iX&`6JWNE91DrAMnTAmmRA38f`jwKLozoDWbN&p3ZB& zHNWtn=&6-|tk83VBSc6w&B>0eQ=;RFO++*Vq|E#8`ET>3yRKdP8*n??KhpXbhFMV> zwTPD4%d&kID_KC-Lc2k6Tk9vUpLU{QDBDOVl%UALr0a?c(Zbd<1#ulfU7}Zkz6MF(AaB_< za&jPChoX1Pe-@BS(X@#Q#PyR>x}mHK-F0|#dKP)IXh~!wD zP6@-a7X}sx-dqusgfx4gCpP|D1Sp|`G*b4Dwp2+IuwD4Nsh4v7X~_Aj*b8>W--$&i z(S}yny=1(Rb&?>yFg;>W@wopa$<<81`2t`4^+J=}Ube}Amx!;IhD;@s^S)ZTY zk=w-2?~7x>&-0v*yPB`z>92>1?T?$wlI!nBjhybctIU9x@tm)N)PU#voUi`v?}M7} ztCH@QM8VgFe6$0|G0-m~arv0B^w*|g$ z9+3?K-Y0XuF0Q|hO8$)!6wLX&H}HG>d=~sX3cK$5Yz_$U`Euxfed_?~K z;xzc|?w&Wx>GuA1J3jCF`pft}$n?FpXcln0h-~2Z{Th}NP*mdM_EppMnv>K0{&nxr ze)0V>=^^O*{<*x}?FAe@<(z)t`hL@jop;FIt?7O{$_e;f^!U#3e`+A&{lv=oK0Uu? z4-A{+5+VOyI3Paeayd`KP*5?EZb(`b(_u zuk(P9Ck=;gpTB<3i{}rS4zUe_UxVE*lQl#Z-v`rQdsN*Yqd5o4_NQH&!1u0euY=a- ztVkpsYnPwKo8^+p)n{Ow@;!&!iqEXz`qH~>*K+yuWYTjIcctAD z2h{bBzFU+Fu)}V{jn3!Vs|GFeL=0WaAWP#TlLB=CFMxJ_{!)+=DDqTV{XX9 zzPYly$fwpd-dg{ABfDl<*9TATS$p_0@rvHUZ)t_ylu!IpO{6guxcugM{_1e~toy9& z{P?JYr=rkx*)H&lGeCg%ardnlNENeKfjqzAK5f_OF?mVvL*Qg4XH}!KWwFkez~^9n zyFT31lr_HK>s(61=vK&IQWu-!;Lu#gZ67VSd{LP7+-vc`Z9Sb4RNebN&{LQXd+z4R zoUS0Ju#3tUs718$#t};7b29#}|AuLkm%3$5=cll7wZaC%ZdYJm$y=3O*lyz^MX2&A z61-h(->@O@*q&0_r5k0t!3g~-AZ}mTopmlZ?lw0QcSOvzesns%jn=1#cn_RFIt zjLs#qjNPwEhAu;On~T|;@qBy`KzlzkMw4aCX-0QluO;GrL`5$+7)-(!iDm|(P~clzY9?J=r)HZHjPTn zX-4DLMp(O@8@b~$zwtRDI3T2Z45g*`;DH`e9Bt3Fz@?zWt4&;0P_2@2eab6shle0QhaU{ftTcvKh>j`)14}*IqWKJE29~ z-e^>{{|9=xbV`d{Svr7ov9l}t?C~ug)+nL^u}BFpurzV*wkfm>aRiueCt$ec$e^r> z!&DYpZVl6qLAmQ?(*GieX4$y1E+I_c9U|C{*ff{>5G=;(H6z#nY;|}9=1&xSc`y$ zIC{>VqAX_wGQyHHfp@};nABxHrk@aapcGR@`^Yl`K_b@Z&O@rSZH6axr>G4%ZXt4e zxkivJw@^=CV+*fpP<)+mxZL#3Ws9w~<>#BAzPP|AgQRug?$?NU(&Hc54p6m|b0AzA zL+j^B3hvQ5J!dSk)3b%GR!btEZeZ%_&`7KRor{(Boj%;zU#JJ1ZLQ1J$((taH|=(( zXE5Yd}afKHf5e_VqX@c8t4*wN$Yv-)gX zYf?%|q=yd59-G~0G`PWW|9Pm`RR)g48m%0(*&+NnY&ued z3eBc<5!411vkzN{SG%9uw7hkAf-a0cpgsBXqYHDbEVsirdvy=o?2u;mE5(X)W44gg{5%o_-vE?dwt>I)B?GuK=-HKO68yTO&u_1V- zGV7uXZF(F3u~TGDT2=}Z!g0<+lR~UoF!86{F=Qz&5Gy)Q5VR(Gk#C&lHXCB}qd%^U z?v31-pxrReC}`w~xh>jrUA><3{9iIrwb2m6)0`1LA$BR_hnE13AVHT6_}nGf7qyfsdd=V=xw_p{)U13fmD0>5U(&m6K& z)Tcqwv)SJU-MoATP8Rw9bG;_rAT;4AZu)loKVkh zuw0^tfPxpXCQ^_>YqL7E86c0J`xiw~)NGeRi`cK*UZXwq#ao&Ogq@N6yiBle6w>{t zzYk%O+5ASRzb--Imn3Hmi_9T4W~5Kjj@CD0>vqHDo#WfYbAE?IH&c44NR922N7!_Y zM_gq<<1lz5>?!d&QQ$+zV z)(Q-UnD6=%({7h07Cd`Jr@6$A_X6sDEaxrj{wIl^RV;*lbz&CboQMTG!93e_l=WH^ zR@Xt9)c9h6f*o~%Yn;U-G!^_R8LF7qTQpuaIOL@%h6zW%_9*mNjF^?0+7*7|{5;m*C+k9vT>IL*%s-er-6z zH`rKUo#sk47{sy2$Jz@uP%Aj@<)F*!ZM_XC4i9+C`v{I7J-*eN_cisa-;B*vp7~2h zpQSdGK~^u){momS@lK7OUpU3EA{gM4XK_OA6I?^Y{mBO_)sbq%CTHP2BE0#|QD*{h zpg(Zprerlv<|s;|_`86az}0uY^}<*fmm(^%trcZ0T(1TzWDRdGDTSyG7p3@1H`t(0 z7r@gW3z$AgNIWLDYpxq17>#$M;&m9ye;^#7PHCm-uJiY;;ovJ_6B9eXbLSu1Kr5HN zYmiDfb4p=t6*RjsnUB6#tV&g40OYY;R$$c@rrPcY3GR6QrI)e!ZWXwNpQl0BStr0D z^`<4Wa)vK`RY)s@ksq`k?)7)4$hjJrnQvG!P+ORIRY+j8v0|=k2R$49wjFXm)c1oh{ zb<3n`yIJqs&@HSuujz!OhMK1ElUPx&hUvul_LI}*Yt^7G=4dO&X8rI%V7Wz8)+XAT z70!TI&4Czyzw~yEkyHRy2q;6Xe{HzM85Vs^xM%h>@5t7!uXr=X#z|yI*M)YmFw>26`3r8HJbl5z^v7DWqT$1gEbU9C;Mb?O{RqbvWky7;RUfrZNCq`1U zc#)MPiN|U!yn~pahFo?%u90Rv?{|$PhL-J#ot#c4%P_1LcAR-a%hd+4Hn0>>lJjRH zfYY+Ar9GXKAg;Y*j`?DVY8jplL7rGoQq+>1GUQo$mm}Rfsze_vH$&T4&)Fqd39~#z zbvx84M%7tC#_O@9JoX|0EETQOIqWT({@VnONz(~)8Q=-XDpQu>bKvwD-(yY%z& zZM79-p-Xql9nq$haC*E~a-v`jlQGq(Zbbtj8Mu|&Ln-TzW*0;`G++*ey zGP&CCbtY+GFKLEzSdCM%OOvJL??JSaRtcHfXD95h6{Xc#Ew%{^bTkBEjPp^s6D%I2 zzqI}5U9PC)NSFDaA5;AZSV>G=rYZh0BhgoJH!JBq4^Q9mqnc#I>*ypN5?RzDU)`&f zoG=O`O_mv{3kU9rP}`167%m#Dw?2?;*Cnu-sa6RY_DH&Ayb;+$PGi~=!av1*jzCMk zUcpY)7~v{PiE8~8Ljm|i3=s{k+yF^3tsyDwM1Ikz4+b?OcyHQ~cRh+6lE?)~*xK*v zVP(V}>CvValXU1FYuY}x6qQtI;_gEPY*y#e$@AlTKMleu6M3MSyIqem5Q2@3yFVImy6Y+Tv* z@-7UzkOY-L>rK1UhLdCxw!SvFl>rD8t?L%)mQ28>Hi9j;h&fIy_-@^uzGgvR%iA=a7d(@AAx&qH6IkFIF1dB>3; z)jxWSDh|~H?M3MLY+@~B$aw$}?#Q1(48$8|=1ja+0{4w2j$6SRmfVUwI%`>qkK9>x z8mAKo0w=X%&Lr=&nfab~Q-3(t(!?^cv_mZpHIouGglQtz*`9ww)@<@N3%~|BH~~G= zlweK(p@}Q-rlxkREJ>N#w$-pNrQIjF)Qy;hRDZ_D)&{q*?*vYDY7oM}>0`5RHVo0F zgUnpAp~0uh$k#IW=xHG(zYw8q(0d*Dpp9rrA<4>#zi$)ML1BDB5E|NsdUk!yF309} zJ-VhFwd=428ab($p)~KOe4`ZpmkGxu+A|(<%S-gh*q36I@L9o?FaFAasDu*@2qb!< zi!-u0L@7~X85=bMwO#Wx2>YbBvwS#R+D}8@sx2G9NSRrH=Sl>1K3HqoqH<~RVk2-- zaawjvw1~b+G$bUQyq-?1jwp(rNp6DX!T2C@PaDpBBWj|782U6VbnVgNU9puw3k0$Q zA8+6d%(yqK9fI*bD-k>7wDoS+UpBgK2Y)bBKam?SeA+%A-%GHXm?5sqW;6VBbPb|( zjE02oHlGP6$4VH!>FKrdj$a2@0Ckc;D`sp>Zb#NB=8a4ai^jb2JCcv&G3#Lu0dtvw zIeW~QR5WT%d)s)-J{jju^4|jpf(~UxvJ7?RNOXP~r}-0^6GsFaCq|-Ok6^5UOc*R3 zoW8UkLA#+4LB!OyM!a01C0o#}6)|<2o`9`Kp5YTFkA5NVWohX5N=InPfTokpVuSy` zoqjMq^nevljI`wG041tHArdMnme3U>M)w*a58GFor9+FNhfdGEPQ|yp+FI+4TFHA} zza7K%W+fJ4VDoq#ry>uyK0%^RYX(^ov4ffbui{|lHKJMzJGdWqdgN0tmtZCj(n4Hv zo8wMck~qDznyd-!#^!+S#B;;$leQ;K>!F>_n`(%9q;^mQa#0fzSq^~?lDi&WdZ@-t zAJs=0$HkK5W6xoyW3r=1$~>OLn2RueDw&`G?#j`r(^0vjzW`!t(C{cxkXrREpE7+Y zLc$chq#1N)w~Wg!>lP1trFEt8?s%a&aK|V);H5w#Zw@>Y zdSjb9g+v%_#=yg0DNn96kPfHiC)oQ^swr*6nH znP+%Mj9jvwf7ra*_{U6;=6Vk2aU$;s?K!ku$jcPcmh#&%e!yN4O>+?a zIw$}eS><|#Y?sG-c2MEix$)mkLwiS|1OCmoQKZPV$k{`d?ebM5=?3XeVjtSG6I_YC zy@_nq8mS`?&;vkTCo3Au0L=na#j=~6rvdNT^FA5dLHr}qzUTLXZ@|qU^;~Zs4tfgE zQSMfaS~`I2R$wmb8;-!FWYO7tiG$~(>xncCJgD|*?#@r?0TqCyIxzUu!aI3rI45$V2RfW)g2NnxTHscIt$%^IGNEkH9F)f>| zK5i@0*PN{cEHU|PD`}=ll4zUTUzHXkPQGdLmyhrL)E1ITKA}PG=OZw(HC$tf#kl}C zpdp^YjBK0+J2x9OSnn%In3p01V4LH&{XEGSu=hKW6U?$IlR7dq`E+eqK;XlN{h(Dm zvPl3nkOwvyAJSY4K&C%2bHFbIT?nqrph9+lM>B9^QiMK%@(=ZP~t}3lQju9XL5M`b^&{Ifq6aiEP}Gm%1g+ zpiLl)aS+&*(31ZnC)}zTyc1EOvB1v_D`As2=CuDH@45kyc1o(Ex(gwyfNXLes+#bX*{bDBxthJS&-w$OUoo(}1?+@8{ts81kUIUHHZPfa(5bCKy!QqMVnw-efr%tO*HmElxuc}FO+ zvmR{I`fIzks?MkW`IFb2oYaE5Fl|u~UndPHZ%gN88<4`gTRvyUT(l6vS(OxM5n!~w zBupvqL`&MscHra?@0=FNbq9MxIK|ufxH0zo4Lm)i5{8|YFVFti;U_XS1ynDE6u0Zq z6|^A%T*1LLAVW_@{XlK_Nx#5tFsx|Qp`~4&5L?kn@?Bxb%>G)0bCpJbEU-2JK~79W zGDA2P)5GhJqAY}*k3`r4%TP!J#B}WMr~HUZ7XYc6FqWK`eVSBAjP?N9NuVSNeegI= z&eiKB1g19})*;c;K-!i4{)l!Q-VIXj@xA72owv=vsHN0(CD3_W@-i z&4Mh55U#<+Mke15xP@$q)(JhAg);}4iUdg!I!fn)p|(ph9C2*~w;kQ&tmvZXM3v@b$~b`)ku{8^Y2&g^_wk6kZ$ z#V8C12zI9eB$DRsdUQ2*6MCAeg}v2tiO|E?)o5P||Bf?+RrPWDdV--^Ibe@L{kXcX zfIHb=Tc<88(|5{G)Z-Hp2WjR^V%WL`+c$$usMy8iPdifob+nhs-49qBhRohvK6i5( zITL6_7#)_7o>f8|Jc2Eu>lLyi06E3qTOX$Yk|dFLrtDJU!=>J}E_ADeBth7s7$(uW zERAxfYC&pnI>=AFS(OfetEB^ z84IZ~4|2G#M^{5g7M-Jwv-SlUei%Ywo2Gi1px9UE=vj_1wan+uV0O(n!IIqHK#->c z)u>}hDdHhG($oTZvPg#m-o+>&<3J1z^YnGR4}a_-0Vu+fUX=#4ImC2hi}Yzp*bX6` zNq@8m<=5*q8)hYNf{iqn7Q7>9{v#D5x9{F4=-Tz(whv^E>`zDgaMCNl3#oBwcY+v2 zQxWkzOhTuxJR-iY9kg-{Kov271Uk`nj{)vdIRQ4O43G>h`K6EV1zlc&iReYZ6{mxb zA^glkA%ga2Wa$yYmosrr)~wgpbH}sG>_Rw@lE=Rx*eQFoX*0c^;|c&(29oT3;7;9% zYe_F`q))^-2;}3?kPg$n)~ep?{?4JH_VYlRkL(K8$a^-KUBl&|`B925D~06X78b;I zx?%5W> zKtRhi4Fs5p1w4(}e8lPXW&33-ucX{F*Sa$o+Cv2$m}NlE;lQ{Qf^h-qdm5=c9o4WmzQg?Y6$|Q=v3%i8-oNQ>Q6MkPom zZ3Qf3e^ac>>>kc&qF#hj$`(>?0%rz}+8%~1H8-ma%w;y$OUPUFM}fA~RAYYAD1b5x zxHY!Ab|@~YghD3cmk60Tv?wKA2yErl+Q(o$nmUu~1*v{H!tt9pq`C{^A!;%NH;q#W zp~UFhrqtHiPX7mclK1U#)nU`_ao-f)9Q7+iytPE;!Wu1QJOL|97O${6HDe^oGWcII zCOe_-g!m#*RCelldU>sfJs}g&^-`MH>Z=YC3?TV`M{oa z2{k#^|8?i);q(z;0(BjAmZWUVK(Z)ZJU97S)XdaeuK2I5@_U(%$Ja?y4C(n2bqmJZ zk-grJTv(#o$u%lY&tJ~#U1P9yG7)J)<~ZVIQZQ7>gy9Ng>T9Lh?s}P$wGu!Vd8eBz z$Zt}|1c`H%qqfS~Hegy>Pa73O(7F^8l=?c@FkY7@Gsg$wD`A9F;F^MjrWu}A z0#WOG*{5U_Wg{&BXfqFBApmF=+*3|X-E}Lhcu&YNsh_vEH5!RTVWD2z8a^(1W3InnKTUa2KBvE5w`K^SL zsn5bkCuUBPy|J-$f7heJy$;9;Osh12Wa=J{NGJA2$&blM7cU~O-9p+7qa5fM5-j6s zvrhK?HHQl>p`(`D$^cPc0ay_0Cx8L+nKPkx)E8yfvQjnmZ|5}~`ON=9{`~(I`NK3H ziA_w_h8vQjYG3<}Qt&5VTai}V_l`g3eDL)O0=|F?Jc$%>WHv1##Ph;>!QI9+1hJo5 z4|<8MOtW!KSUL#D>0xr@qba+e!JZHp9xA(}!nR0lZ@`i#1;Wl{liMXGHv_k=#hyvF zRl`#*@0EK9WV=WdOma}CgKI>bq*P!xnyiFodH^}SRE}=#4X%f2eYk`~);qOT;a7og zs02yNBZI|+0{WK$xH zWl5n{sUx8BFg@;Ow&Ui&A~XXkt!0tMgWz=7*Ayo2bEL?;U)~E0g=GT+8dkxFBP~m5 zolJK5jY}v~<^IwoJWhOY3AMq>NoZr z3C%{TlW<4Bt&(e(w7TAcCj#Qodej&jZU#zb4?x-TaF_ak(_z<0lI7hi6JAOCoF;iM z?J=+A3JZIjYGz76C*+V(#Kj<#>b6buBsZ>g^*TFZ9X^S`4}#v%lt4!)z1)MX3$!Kd zRn>GGP+&AdN#$Lx1(PRcyTY(AiCN!*#g6oaem`P6W{m^BAH_BGe93Q{Y(NTt6hYg8 z+akK<0M#eQQzv;S%KEq#SBR3Z*wR|llZu=id^c434~Q)y1l2~X#EI7Ao9&5;$;emh z_MimQu0ELgNR^YI{iDsc7Agx@&YKgL4$V=u7c2+G(Mym6uPakM-K}Eag}DzT2ca}p zG;fk{KmvBdVG_fysY$f|l(nPZr|6UW0bk#=Wx9|0Q#c$u-^2uay z>ozN~Wtfr(D%u zaKPRFXnPv~#c&$PFri5nxnsxJ$k+PR&Xz2DlWX(lrs|uWKs}-U4`%h3yBe4b zIYVk6-}^_KZ~2b|_YJ}@@H*RWjf_Kv#L6^suZaab-#hv;!(~goA>6f_i=iE>$6n?U zyD1iCsErXY+Se0)Bv&(RY!aftq#2!Rv>Lx35vnzx{0{H+bjfd<@cr&Od5{9tiB3XU zlD+}mED$3CLAmX+$>nTh@YGh;*r~E-dax-*Ww#bX+@z~u;dR>LETX;>Ig8bRk*Z}B zj^E|Ie>7HK&NXiqOa{LV)D^yOj^xN@z80{81{E4~ z=WM{c+0I9vl?o!{{unDm2?5f82I&#IFX&1NV248&^?b>1pY4fbDlt@(X)QYxw=QYIgSFchQ3EVB8T+r5ZhFZDdWIUyUdx_*e zvD{9~D}tI3y^(wj@C-0UnI|oX&UcD{CHNsze(~OBW^&x9KccC2o$sz0g6OzJZFZq> zqA#@iyR8iqqvS5r3APv-m=P6*xZhLzLCU}o9|_0?#o7D4{b1^w6djio=Frh67OMHK&aioVC})F>XLMQkgI8$0ofSD;ZPC>rciw! zC2+&TYk=~k>(RCB6__tsVFI?#0{6q=1_5{)-FVr)5>Bf`roMhGepE_5HsEmY5QP&S z9Ud;25Fw3l5kcJpHo8NYw#_J^)N=!;po09ZubmHHo5H0HlGT9oO*ky$?*Wt{k#qdz zyRTOO{G7*M$q6DUOl?Z{%57?WfUE#vZ-8&`t-`@4`>unyD?{I!VY6>3F#l+v`1|D@ zO$7J)%b|r={N)fdo&SD(C+w=&26D!_@!;p9kDNka^Pr=er`oFrLS`%H@jO!Npto`V zcE>j;K_g7LqRTRLrtuGNu)HMOIubz}#B?n%J8S7DC^HDyk|KgrckYJsP{ytumEUjT z_DG1ylx?s$TAv+$RSoIq={AAi0i2Lw434Kj<@d>A80OzfEEMUd z&21*R#SKNyjTFsXBtz!>+JfsFo;inpqjDo$GmXd=RgN86Wn{QD47WNVcN}IZ_)qb! z@jxi^2i_X2BET*o3Ekd1T==0x#&bYD2v<4}X9K(^%rfs1mu?a~f+O?&hCbZuMo=_E zDH&P36(*yD$T8zbs4Uo8%lV4mA)X>h2(d?~XqVXM7=_&lDLqGlvKYKv+Z@}pV3~00 zlx)gLJ75{e4i!v3q|j~KSIw)YiMkLA_DLO=lvoOOX~|A1S%~ibr z2WeX=gO{@nf}YA@TU;5cv7Ulie1pju77mn*o2@`706)xIZz~_}HN0a`hAdOIfi^XB zSADRpRyd7vKN5APD_lcDFj*T(ml>E2wQ@h`qAw}_N2)&|q7zB;5=FCIsNN3ba}T}c@=UWb65d5j5;)GV&~=alHyJ;2IAIA6Ixy1l16J4? zF%YQUKp-s4XE$}z)>o4kK(1{PmU00UgA3?&@9SZ9;{4lcvOqN#@cW3CUJl3I0cW!| zo5c#W>U7hX=kjk4u5dlxr3m6Ulc1H8v8=70A*!W{Gf@O)W=d0yXm7kRTxy4#iu!A_ z#5zuxs9$fUs&W&gY2($nWNL5oGRj_0@)2dIj&6~p9Kq)a9T~jG&mba+q!K~*n_1P) zSI9;_ot7Re(OtoXK-BVMG2`Xm@(DmqAtZ0vHP9F0y$&X@1$%%8i zcD??5as;Yo43jpZ{E3h_uJI!O`*MPbw?erBXl2}>Io)7_9c`;C#{lS|o6Z+A57A7O z<=env$q!c(W*DH95pRc71w70X@+py3An<`=?PmPQ^O8wV7UFc;3%d)Rk{l{$tAUwh zI}P(~9b7AAQUU1?ifR#KT`Fag5z6OwTXAO2Q)eFdhRCx+ z+E<;pCTHD2mA#x`nIKL{cghOK&TP}pY>Rdtllrv{Iw51?cVIf*Uf|pLJrcMZm6Bu- zO0-dcexO3LvCT&;finI~s)0}yQ+&Nfcd_pqNwf~+xp#AEKnN(4N>UTpfN}QPYDBd< z?=Jznn6y@)s$Sc@gFw<*(Wlr{p-5-HKPr&#kZSFS^)RdG zZiY1K5EdGel3?Noy2YlDY~7fri1R^60zGz#@|tz4ZChNSa1T0w;OD?Z$aHrIsJZkR zmOB8O4{!bCjs(=Nli<8vk3N`6+VvRDWI`5^Z93UX*m&)utpuFqL=0<%TB&CpIq0P% z4=CFLxv;(QJGD%9pO4aKnr$`PqbY3DjWj68BFhVio2MhBwZG5fmqSaVo;&+48Xk3Y zl^MVK1AL<$LyX70=G<59e8u*$5mUq#Ql+fQeeyu_4Xw!1C?%^4o>Am%!ksH?E@(j- z^g9}-zCG_l_V53>`_H8ICX@B3dc9#l_3ng%Fm)a=I80 zZL^!z7Q~G(L*Cp8;eClQ+21vpG%y1n6Q(HEj3lU5#eVpe6=wE{6Btwprz>KEry2+! zG69mnW=?8|RA?ojOX7nx`6yC|2wY!G?e~Bj74`&f)s#L58$^JegMEmKCZfbqT9Mp3 z2Ocv^^$nb5r4S@2Z&Nl4X)vM2!(ak*cS8g|OC?Ts&G`A~8UdV`UkxEM+cQ*jOsZV1 zj}gEF-?G!$q?I5UO4P+VRQVN>_`E4f< z^v7v^;N$#f7S=fJoYhz&Csy`<27G8U@K zTf7)?B{YZWV5&vU^##0xFuYSm5WzdzLpGp!cKhjJk-$C%q>5}7TG2P5#8(2hstrtCmU7q^GtI2m=zZ4dADu%N8LEx6{u{7 zHjIA1O(B(gjK*&-7WHh2eG z$8EObnTYsSTU8sYp{nEdz210BZQO_XV%@i2Bh;P!0%d$4j274}9Iz zHg?WX2HD!i&ns3OUuH>y4GLCbS6URrbC=WP*cCXa0m`DnIaJ4f5LVC`2p(%qdZFSM z?)l)Fr5#D3gw1<)z_w0@sE|^G>Iq`Qt{Z9>su1eLXHhW%d9Y&mJl4?1l5W_tE3&uYav_jZo>DnJ;IeDb zy;oxg2&;xfsmV!?93|{@ss_>?j0;dk4Ek)1n9~aC9oPw2;0D_%Es3ejzg>?$cE8iN zU&ymiHa#Ip8xX}i;||I2pw*o#O2GQbQUhpxyB?Xi6*9m{H7s^krhoZpgxb6=$J^?jM z%(pVQoqEG}OVvG0sYK_5KIW5Gf_@+skXrCcY^N(?O5qfA%>{`iZ60jTAU7tbyNA{1 z!<|G+1IOu=sHverX9`4*4B|wO)gG7|vf;G&$o>|}?~_{{v`Cra%tJcqN9>+}vRYkM zkRHh^`|(6r=DYhQ!OPYIF>$*deJB=sWhFm~g+7?c>-F7TaXy)+$jOTm2`sI8;*lHe z#oq1Q4X+3S?$TBlP*P%1YFRI*;;%^3`hC-F;I!s%~> zdzDr{C){j$R<5*vX76SYGkXB9DCVLk_2T%4yI{~1(t>@9x&sPL{N@5fJ~ZLjZyCXX z5~BI|?Rs?Wd)5gZG4l0@T_x9WK3fb+QJw{KyxvxiVMnTn>!cjG64>A?F65i|MFs&t z%}#Qk!px$hC8He^LU?*YeWKkRkQ{Qc#^U#QIl~Wo3ZEa8Q1dF2vVvHb;Qj$)tmYmh zgU}ai@Nq>j*KNdNa5+Im&~N3zhe)2=b(#(a%RYw)i}2mP>TIR4@9vwERsn=0J|%{W z@pc&8MvE&e(qZ~Y>D&PFcOotT*vg0|9|O=nQJ~;hmtZ6Bnqox}eNE=#*rP)AbV#Ql zR>AJ%?*Q2hes&(UdiI9Kqfa<3<c#fQdUXLD)95xqV2NmywWqgL2kn$O1^Ut=REvb%0NHKzC^4bhltX_p zyY+d+V(TJsMYL+8l4SN%_tDt6@FP?sHn5N>?b{l#}6C&YT#=0$ne$<`T zfTR~!DAx!>ve0po-EhkA2@+Pa0Rpp1Rni7?aEq3B+FjIV3_Fh3qcg)3s*8rpTdAko z>_#7p5$ zGpq31-P_YSYBe^?$eXzVu~VsWdEg%0hmG2vzDiIZS1eoWUC@e>#^!DDv|Srq74BRyn+RdF1Mn66ezI6k|{~8KrG2BaTc0FVz=O=?2S|pEbzQp<0Iyb(Mv} z^yvQ8R>i0RXptz=mUNy}Hn2>0%&7CxN1{^r(;>(&r?!^Iq{?SLdrAjUsY~=^`wl`X zbNRFA;N6ug`)lA5CKTD?iB0A*`AqX6)AmJAD!47kXhoF0m;<*$e)N71KTRxsH+&5< zP^++}n!;~9BhR!ekQm&;BRpNP2)tfp6qrKtuJ};1#})?5MQ6qogr>Aq*YB_c2`q#A zBw=(jsWA451}R*iIqW6O=pjm_3}U$ld)J`*GoDt7_ZAuGa1t9!+n{zbrF-jt@T`Kp z1E#a3l}|+}5s*mlvc4WsbUfv#V|BEIs)TF;Wv~mLyjkMI)EGM+jLXR5bycvZc&_{} zDu@w^00|9Y=}1ZqGnWo5!T7yArU#+2g=s?S0utL?Nyj}r0W*ZXbNmL02n-w`!0JLs zgbHX|U3~+f&&o9E>chqc30>0bE~|fH+jQ4t^I37xs^)n#;3&qU22Y5alV~9VHTZIK zmf`*&N7%MA*yeGD?uQT;@Z0EefS85AIqnOA@MI#wmoo-92m1lYrVUx3nwh@FvCcux zLk>X8tGk2s2)?&uvZ;r~1m0=VDjwm?xk@WFjFV`fbMz?Sw~tL{)}YM`E$Gg&?9%W# zwDAIrzK6I%BGctU3o23Mp*;=Pzrjq?Up!Z{S*=A-(m_lQI%VgrIR%o>j1nEImT#5e zKm%ePBM8k=Ur6y5B+;N9W@!r{20tS#a8Z)KLCIKXgXG>(`A%z*3c;cqOP!Bs#!Nja znhgu45fHRPvM4f{S53jOJ@bY3|2R>$d&FzVcOu?KjJE@eL@3{wQ7l=SAM~hT zK;Y66rV^|J_X=r|*)y<^hR2$Yb8qv1<7!C}-HzT#s2rNLY}-=`PdX3EuHBc=eO$5q zxD+-UHq}apAqCI+W@H3X7~_wQ@R5FyBrN8e54v#t_|4TGdu-`a@f6c)W7pg;7Yb!t zQn<$Yu(a}jxLag&qPJ!R5{ZFMo3THF9Hte*ZvQfYM3d(LGyz)fu$RYaNkb0dYWJ*wka|eJ>si`TU zZfWZ-V~}_)_{@PtSwJ4T!T~Y^63%nfTix9$TUvBGQvbuHT0BokV2FI&h=aCcH+M9t zG<^o7cagyE7qYw8O5}{6hZDao(~4mN2%^lGb|!m2aT1sGNB~GypT^^HLPJ7jGrpvk zxC8+-p#&k&41Lmrk6N48GI#(W2K}=I=vI4<@<++DJy^3VxH8xjO<}vq=%ckndYcm% z<-8RQafmRwNu^Dv{@95ox2MUvx6I@GR>Lc%_2cF??^riCx`Ese0Et`Tfj$|8CT>wc zm7KmAtMRvN9>3Lh?jZQ28KdPVvNcLN0+bKZWf-OI4W|tVBb9HAH@R2S+KphH_v*4r zZ*2KW_*C>Z)NV8uuLMNpdUVbEOz2KYN?;K_cH^OLHDzbB_vl^a3OKEUhBdc#5k0(j z12JD4saDb~IvC^jWfdU_f(t3i5ivLDUa*o`iO$yUEc?E&ZX6|76FLZT#1RLec$@YC zbv}w_ZDCGqH#;%;dWC%&jj^;f@?Oi(5?6~_?){Jzi#Z?}Mzg9$ooKKU1CDWfm?R}t zslYHN%_^IMYRx<}+>tgGR{RLdkvc%~D|HE5co|^q?7=X%x!G{XnELo$do_quqb+aO zqifOC?iR<>(d;Mp^rPyEY|^DVW0hSs(?mD-V~cjR#eM%O*20_mSPDakLBI`c?h2~u za0ip~RZOf|wjjWwAALu`ZSVK*QEO#SzC$Tt5!>Lm#Z^lCBc^DQ44N*4M}5754ebzA z?UTqG9T9^xZP2XBCELL0X(!;4kqA%#Pu5zYvo`Qvh*3ch)F2hF8@ZDj#0HQ>Zy@mV zB0euRgrj!@Fm{5SEqa4OOGd$(Q8Q%vK}O5KggNlj0E0y8Q3LNl@#*NQL^Xt@jNN1H zIj;}gn^gXnRg#S`@o-nu4wafab<@b8zfqsiBN~W5D96Bls}cK*p0bh|u;1+rH+g_9 zgB)w)J+5LraTWNbq#=S>YEZdRA<%#1^ztL)6ymnO_T%x6*#K#-tK)c zss2|VyZ@E?*ulKHT6-XE>C_qBd}&fgk9K%*Ym+p=}iJP;~ZbXZ3;|#+JHWk3Mwvh6-rId>sqmA0gsL z{&wJ|oi%Zsc_0B(^yjFqS=%CUOURHgRNK?booO3KCKcA}2s14KKcW9P4DO$t-3H{? zS2>l&oEGt0;1piZ_ydNH?+1oI67fCC_5k}HL1d{=SSmUi(aB z0*DM45KHK%teK%?-A&8N!b6*FQM+Q>oA7!-1tBexw3v%xXs}3O!3%)!G}2=?G2|_9 zb+bDb5Fh1!Qh_b~L!&avSQW6q3$RC>90fHnKxXvUnb#29G!Nf{D5g`03s!Oj)HP!n zMOCtQ`yR+{atrUKccvA)qVkeZmemkwc4@;gRwT=B&oBYwG;XVdFBSCA0TCadQijSy zTI)s)Yy$dre^7YsFloZV0qKxV%w{KLM2L|1TEcEEz)0P|Ny~mboI0wyyZxbO=cWNL zP%Fr-tKx~eQ@AjlL#HnbRjUO84)6lkqid>`b~D9s)hLl#J?aj}w*Y!RPve@!$4`7Z zC1F|@ZaYToj%;=%rFz>Jfi^gD7sZd& zq(_5B046#7ITj($g-;MOibhmpp%=HR4w}u247K`lZc~&zHS1CKzFF~T&Fbxc3!#Rjn zoav_7WVu04c?ZF=3QZumBSf(U%Llo2j8R~Ama){8Xw62v5gte_O=1%Pj@+G@=z-56 zVlnAq+LCb=b=g0B^7RO~l3DiJfS>@qV`V!|h?>XD!nkr)w97dkUgJ`4 z_7WfrnK?>^OloFQnw6HK7gE_l&XwsUaf9sOi4hP1u(C{UEvN7HVOxz2Z+D}lLeh8WCZ)7 z=F_A$+v^MwHS92DM_g#qM#A~(;CQ5IWig&L7(U&srYzPCY^p+1$_5HGuV5_5aRJym zA7%I90%!tAZj#0x&9db&&-vlZ?(LEjJSHlzWK|xme zJru;@+7nN6z5wTz?rAgRizXSLRnPgpEzdmsB$D`*qDarNTxCWYb_Rdf0(;DEYWuS8 z(wIrI&N;cCDipmbx)h?kyaZ%)-1Id-mOF?puVx%!x+b|k+mJGF8LZGe>@qMCtY4tr&;NazMl?dzcQh~pA1Ze44k%k{bVT3Cz=iW>mn+B<~<=vhCRjTyS94R zZYDR_dq8G0I27XC2A>ue_tKmCEiz(092Q77$dm-C4?7gboiQJUS_JfIiIXGqlg?LA z&p|~Dy1Lt-tx!a`be0Z#yBoiDx70KJIXK>cm{%B)9V?_9nKsm9ThkZ(FU(?cM(uVz zx>_#qN9@zMt?gNUb8G?Ggr$5LB?<=vkT*El7UT|kTyNa5zp+Zw(N(U+AXR8wXaGif z%G{t}f8n|E`bMaN5PFa5-!BPLihuSIl^V&_hP6E*NHFL~=}302bog;aKQg9N^w5%# z`c285J5=XzqSFk}shUz<aeUFSgLX1O zp;Fxao1zLrwR5^f?MQD!H~YC(m7xYN;i)iNeRCuEN6`#;*~;1q3H7+5t{P-`^W*~4 zVMlVP-MxRN+m+?$OW?3jhmo!=elLk0h<+M5ZZ4m&OXa!!DJEN%O`{ zUKt!98|9h!h0;BM$8i{Ma3+980)=fmcqcvpYmEdN6}IGUO3;{rv4nbg1Ptl8M{s^O zLwYIv)edVpv@wCz0-#g$S>zPlM01aeGyk@_m7PHW1z(tZ2CyA06V0luzS7**zZSHjGRZOxHCXd z1Z2Pnxq{`0sDgIdY(a;BJzudUcguxbq`^7oCVwm5Hb8BF2p^D~?GzNzu2j;uMfBX{ zc~rtk-QVFkn8B-NjxKs?>?2Yt0A8`r7Lzk7cw$31r2;9j+{G3}=Cp01&+3Yz(G(>? z3Y?-OM~YeUKqlOVK06=fJJrRKiJLhF9slWY?VL&0?g2>9(6T!nq47W0tcS&;$(Sr# zDs?=H;C>|Aa&(-qP=jrVp=k>@#yVwEWiUPsnM@`}t`!vu)m>sK2(B(0b?XM`%xKPH z{woA_Ayo7x1Lt`k%O{{-M9!G}+UUwO;kucIx?uf37*ONsQ*&#pbnt&j$C|PUP9JYm zBs7e00pVgL?9NG1<^U^eqZt53JNBuGUC3O(|1kq^3t3LyjK%3EtB6~p)0^&~N4ekS zco4oet9$U|RX0AzXbuTy$eu*>V?Szm&im>(gYP17ac}0z->Q4&CfeA;m-%K>W|zl; zE^?~{hz1ia7Kt2Xie4xZ!}N=OU<8g^ecQ1D1|Ey?PQk4XvKV5T+>ff4VUdk<YXlDVT<&u+XK<;D z)F?-1`2yMEWe#ZLH(g4fqZQ`!a0l8zQU~NTo5nS4Kvct$GscP>%41*jOJB!z>PH;t zY!K)S0<1eciiivZv}OQe|6$f?qz`7iiPg$Gk1cNp(s5vG_hh)gBNz4f%cWr*+60Tg zA)89Au0} zNje2$G2~N)8cG93O6fd3e3kn_1@-6oaL1IV0S@sJ_Qpuj9BX-B-Wx#CkENdKg z`VsLo?xysw3%LMQDg3QOlOM4q`)X$Dr@(Bs{jHwJtJ^j_?3#P1Ozii<_Orun?;x=U zx^HNa&E=2Q93&VhI)Ipk%y)J(ie!;R2)+~=LCR(+M6qE50g#YfLOuKqiv2C!Cv5}mwCqZLyy$K{9kK^}45-TB z)&N;g5~8}^QC_O!(#Kh-Sq#7gyw&v=>l)43D%c?_0{(jKY+ z2b@?RUFU8`sXtV-7()RvcwSK!5L-48oSjpQC&07s$F^;Ic5K_Wt>4(TZQHhO?ab`h zw(Z%o|C5`WoR@o((@9r4{nGtZ>8k4ben$Ezq(kx@wuZ|fjkTW^TCCUrAi5xM`YaFX zXsL5O84|Sn_*F?`5%Rw!Q(^qu^irVPx#)w6krL?xt?(@6)>Cpfda!$9-ekbdL(m$; zcJIkvZN}LUVHsD}uDwdc&9M}9P#Ini}ny zw!o*Qn_;DZMc%Ua0OxF*!38LiWY(@=4gmp0n=@P-;QJV&<O?rWEEG@9q1_IOZCrk%jiJQey?1`LLj%gZofE) z&?T_rSCY2{77Src2kE3^49@C4f#c#uXgRg4VlbnEoH_e=(Dh?*KNpCSo$lISHHn74 zf@7#X(VF1vl1ZgX7ZQ29o%nsZ^8%ry3q#(NZot6yhF9)L7r5|O>c>n%s~5X90m$WV zy#ytknOZqDA(OH5Y>hFO3v8A#4--o3T6u55;TL$P8B0;v0*QG$0!)wddKZR)MQI(V zN1nZN**%BwMb6UOT5(IbNYU|BJfymE?s~dH*7o5Qi{9A+e8^CSG!*;cmgg#PSXz6} zdyfIW5@9|eUG10XWF_ZOdDrb(kiL*hu(*y{ZTSv1_%@CRON-IjaMmNLeE;fTJEYuM z15eF~loP}~lzDU$DG_qmx@*v;qfa0vW$o?h5MFNSMy+evil_2ZPemOB z%mt#KGbm=4mZbS`XkVTE@xV)MJUS(p&(g7Vmjo6A2@SFjHzzP9x9;gS{bLR{?xu$yeh)+< zp$fZiH6pAn==QAKIdBG7bmj*d3AKa%F3;fXFo+gE4uz4n zrZsINdhYn=o?kePtk6vfdeRHkYrX4FWwzZ|l$P@-JJ;QH5=_?0VZfcp#~`A^ZN7ue zB4h$x=}SI4^_R~-aKt)|MLa~<@h<}CA*KKFL-pk;3q3+G z5UA({ad#1=KWRGx0vn=3(FzC+VwZI1lQ3=TbG}!rLbvK4e?(pGJx6JRXyQO|dMK}9 zKR*N03TrKbpivUO;bZ!{hwR7-OA4S%gM(LvOJI;Y&Vf*^4~OWwJ<+QRQ(vvx)+?Mr z?Gv?`BnRWfgIf&eL8u@Z{0({+=59;Q&{R1v6(L-?J3vAUA|bGcAN&9tly5kGlaF5` zkxpa!BK9y5_%p3^uK?=Hv6)-xXe~^yqdH&;cQS>nozuyd^KX7VOwo|rFhm(mV?b%| zcI!J-h?athA((rA8;hcc-*%tNtC9r+alKM}RHdAn2$*5P1>-n+J2X3)NMN8ts=eil z_qzxy4&fsLHfOF-F*R}+7#s4OZ8o9-XGaoa3oJ1zb8yiRDIEpjj56 zHL}YTP`|M=QX2#7%J?e+49Wc^%QqS-VSqSgtEjS>kM~)Vf4HMT#tAy<^^Kr5i>Gss z*5oy9V4sr6o3`XHx-z!mrOQnXp8iFX>TGU;1MFV%E;8p8^0@%X13A3@JeSh7NGN6K zoHLn}G$BJF{6Wx)2eMal|?vL+kbZ0F_0Hj@v2`ARHs-GG_V9s!#) z=_c6(l>VEtjy6-#fiGr zR-bqQWX{03UKP78$R;Q&$io903V*x+TY%X50&*6K8HI0?J#xrArs63)2o(XIIBSQ( z?${_nELiK$An3_T5epSsDALS}#47?0sGCQPOymk>IxRa-g|9mW`J~60LNjjOjVyMP z`qYXBW)HrgU1OY!J#iF4i@|;ulhZ;Ek>uJ((`mEPC^qGE->INi`uf|sg z$4LFUy8$XYagP#(OR!@PztFQt@D|$j9*!M~DW5pH+7r5{O5d^UOuT$hK^5DJHkXht zrG zVde_Tp8hMLAK-j=HmNj`LB~-^o{Z)XQD2NP_>x)?e_elG=w9)#HErS8srh~- zxwU5dWeWKz6qcv*`A?!@x-Tv|wIij!VBW#GB~K$y0|$5;0SvkN_YuSx=i$g234Kip z4n7zaKAHRc+9z$lC@{yKM)$a~`!aHorCohs@y0N+5QZ^`#t2N5u*d=R14`rlCt+Y2 z)=c52x=a#QV!B*v(jd{n{K~2_ckmBIG=EsFS&#Tty$8i=9TW0!FpjITDl&ugCX0%{ z4b;a%np_N%i))l2*ePOrik9#@FyyABD7SUpQw1Li1MPvmhPjFQE}_Fz@-mbG$MH;q zzRVifs> zB!|57LQE3Gg085UzX)PTv}s*9X{r|;y7U`64XEJP6QUzedc))CE8Qv6=@o<)RnM0} z=h+!uWtUBiZ_L)OM(l8X*@hlN`v3*oi$YE?x#H!4UFH78WtKX4uZDBUEQ8gr>N{IK zyscErB$(IJpst`yTels0@i0b_VZO_98_UU={F{~OE5Rc#C~Gwm+0M55f2&SL#8wXo zwGW!AA@xsenkT-eqq*YURwt(H&KXLuP0R@NC?N0&DvbBsb+qtO87bc+rDU<@9FW_Q z%c$g|W)cPE#axftHfO3J?N{Gni1<3ibfmtD<$rDrrLL^{LFXjpWaL)TX@kk!;F3yT zaPus=YssgWwmyk#0W(}(rZ+-5n{=kJ9(O0dj!^y^_>nfS*3{ z`!nXK(-MJI-Zl+|A@-h9WL2lWGNB}D+C&#|7es7Ekf8}@DKx6kJftnuIvNncpL>U< zHat;9`2!H8t)TFbC45=vz{gQhO79M__EG_d#GG?~tHFP{K`=2KbhF;x%&>|hEU+Ew zI^4lT?Fzabd@;9ThJ>KxC65YWNimmAPyc)u7=JK>UERmF!Ax;@Wfl&R62xK>KyQ;+ zBb;`canbUhP#>vc>})Z=X@xqwBKBdW0F2t(H8HSyuLwNFO_Z=B2qZ4-H1l-)&3)r1J18R6VK~AeUOLhL3&JWHtJr{|axgUU!dpjAw#Ch1B^0+m675R(ztoU9(SJOUrg=&Q&4* zgs0MHj9jT~hUCrV5~=qhNe4lY?7RgAzX#EqGrzc=IBRTNYR1aD@J*RZago^CQ&S5D z*<4}RhGdaIQCq_6ATn8#;3dPYsBg3N%mYQ>RGR9kTXG2!mrzEAG`DvwL{Rd|a6_vhmA2AK@m3L+|HC;!XzLzm6ROJY)P3$1n(Z#E$n#HD zp8V)AT$S5bnTkrb1%GD!2`WL1dr5=F1)v5H)%fGzPye9&$TO^ z@Hn1`vej41>NWD@FQn!yz?M?WC3^PaT5@UTL#)dIi>My{NGUolMuaNG_EPs_E3D2| zQ}t;DRpVExWOhL;D5D`GC@dFHOp@}ip-L_o9D#LTP?K85G)k(u0^i71qEK<8ZmF1e z{TCW!o~Nnji28t{>@BPI0{q!=lHElNcMo7yzL3ESHv;|9SrT-y|_+h3}qO_Jk~v&OBdRCGeaodK!~ zxG7;d!T3xHqk?`aJdvXQ!J*gGnc?@$kth?_VX=Jobm)62h|pGh~{ z8{P=Dy$=Ug0Mwb=N&iXL*$(BW7WC@^N28K&P4?okt}^u#NMfI{>Iu_1o9&$0h(RSK zoY348;kl+`iTj0Z%9I1nra*Q_-3RA!pYwQJdrIVkm7|>W?qZNfZYiX#RJncbR}|(G z&Sj?BF5c%L&xlYOGzF9S(V@}SWfkD?}zy?N(;d z2LnYiP8%F&;lgRf8AV)^I&aLkU=uMhNi+6@tE-Ku3(%> zP^zv4ZOyhqz}_PuP4O`!0+;|BaC}B8DVONHr~^__M=zTQ`nPYL$h8kX-N?m&gdm~v zMM4)cQFXWCMH2&dGK}?ipDnl@jVCFxw$(cp5Nl3gOtEB8NKeZOkvGn_(ckvqwL=Oojh`_rPq$_5PU-dLRVZdlF{RK0?jCS@$;U zU?e=|yeUil(9!AT*Y-|?#V0~jwk7vLYjBB=pjw!devC&BwSM-RyiIRQJy7XMI~$MP z#90emdoffnOpQ?ko&&v6jr;_mVWB3EbVK)CzyQ(v%eb;)vZK+zbQ9EunXO!glM#4#EmF)q;}P?{}h6wPA$UmC=IBgJ zYCa=cMQ;U>9UBdmj*p2%Y#7bYqsdvO{bi3IYkp@~0yB2msHjSi{hPC;tDq>bE{Xh3B+brWL|2*Qjp=i!f*A)CnF;yZfoT&mOi)0&2BITPOp95a;G3yX&w( zljxTKNrIsh>WUx2B|g7S6jLiJX_RCp0Z=sud_85+tAj%U6)vk}J(x|gJ8a>a5TQK^ zNqBggkJQb4tRg-#QyyZvb5{BaP5)$)`iy?7K=)4ydr~>S`PyVUa$on@u@6EFyj$MKozX%|$2b&-|=q?%^^~ z&PWUsN;Hs>;??2t!k-Wl+eoeTnUUryCAA&Y{5~J*_2B`i)H%Uk)ZRbQmy}c#VSlgD zbSy&oxjpqait>Nky4jp-YvdgNQ*FKq{y6Y$rZk;vowtr;6QU)WnP0uE_;UbzFr6Ee z0f=FK!3MSx&xN7A4q#-gb}=>cpg}Hq57P;Ab+xV&%3l+OyyVlRIYTGl;|rb~+JAe$ z`r=A=>DFHdc@=Z)IN`b2D}oIXWf5Cjx;U8H{|+|az_-C7B_V!jrDk6V+XS7!9^`OH z?(X)9Pt`1}>h5~8dVUq1ve6pW11l?Kbd51Wum*%djH(fDBy6F1P3E7Bsj*V-9+J!! zM4GfpJG%@S^1{f;0TTv`G3DZrd!=k6q?`D?qad90Np2rxb9&MJg6lU? zn`IF^@dVLDC}WnqMdn*|(?0xjUDSOjzPDE8L%~72B+kzGS`98Ph;J5u4i5ql55FwP z{>7pftM9RFPQKg&!N8fLpbu{)-})zS$0gJ}k`H%d!3yd`Qj`=_TY>2moa!`rb5bRa z>HcM3I$6*@eHt&>SKY$&D$E*P#+3n~IxU@gfW7O@I`fi{HL+#*qUi?#Gw?ni3Q~nv zkVDvU=-)%liRZY6 zBxg4m2Yimoh|uIS$H42AscbPSJwN~`&0D~;lNJxY_LPJQ8z2D6>-5Yhb^;-aQikN- zL@u3B%xBm1wUQ#9doFWqYsy2|?IF>GyYZZKJ$Lr0?6X%x1Wp zi34dN)#)fya)#>32a3*Ogs(FZP!7!=izJvzzYISmVPrv%t@+{Lx<)xtjI#KJQfy6NbqHuVfBjOI+Q?L!-F8&I~7!Lk*as|J;B{YjF3f_(}YjC`l z1EojM@S!5l(It7Zh1li$kn4wNJtvD(;mvx)^Zk%&$(3QV`kXJkuj81VVKB)o-`Dcw zB~jR0C@{u($(k9jK3F0GgfIQXyoZhZZ(q(aJ(OI81e^t9a8ehdE?7={)U0VN_r#UT zNjS%OK|*Ew`9s9*+U`>|2jFOM0=!zHbeZj&t2gp!Tw{FPwA6l19TK3(Z6$=fe1GnQ ze`GidHFi1;%fCx1gYowdyR`X&@QkYVb|$&CphsIKAO6W=g_8mxh7IT*Rf+ny#$m5x z8F)apx=HydGU;{|~gdr>>QY#tIaaZlbg{z1ObWV@(?$~Oh zI>u@+Na|R0oEsM^60~|ovbrXDs>Osb%|IL2LPMK zvt{C7@1Iw{BFkBYy1&la-yXSG?q~8ilqDD9#78zJasxhG_bKA8I$NtW)Sb4GdKX3< zp~OExnRH$IQsA*?cI%RMhVfw81%=dMR6*$Ln7g}|&rbnt(&Yo~ndF#I6U})Wked#* zsrx@l~_f`X;JZziL$OaX3pnky;y4#ep(WyXl1yyAm6^3CG z{ps8EQp^H_)u{PP#+SfOTm7u7r^_4dmCmhCy-{@+4)itg_N+X?cKR0>M&+&sf$@yw zsB|3yAcYY0!3BUvQwuN$ey%s5h?NjsZt)y>$$V4Ev?J`!emw9JtlAS0Go$nAE7ep} zhLe{5!oI@7(91ss2M$y8C{IMS?2kbz?6|sQVCaTRz|}bT;8by2a;(*iv$v~@sA5t? zhFx8uULLeCFcnF#LlNqDmyxQQ9%-83vHQn=XbN{>DxVnWx6k(bG;_Cwa#X8++d9x2 zu~|AqXvwhTva>wjA_ZfV;BqLJ{N?#40WP zdToYx*SRVvM;t^haSLbL-D`DmoE}d{8G~cm0NlYX)I?F4|Fm11E@<`g83KLBymICi z*oQ0e-q|3hw}@{}0)e0`WVtNDKxB8ccAD6DwubK zS}ZZR^66G$4||C(Q{S!UADrFGn(j`QoO+rD2V|TOC;eumPZWI>I(ykS|8Y!oSsKzN zoaHgH#K@z35^>R_T?pW6TbWi-(7-1FbV&a) zw}nbLGUZVE$_;S;Me+-m0|Fs-2c-IHYVy~HvkLc<+dE%}3{(7UI>OHU4p&rITeO2R zh^oNIs{9y!L5>YE=*ZpvAFoBwZM{6;{$rRptle`G&ms!Nf?$=Y`ToH<=*-)p<3R@g zVng|ZFIyd zwuEQ@&&9Xfzo7pUht{hXG_MK<1hk3)1SI~y$Dx^;Ih#0HIs6BX&EVqU^8ch#Yi+csNIz^8u)tvzCF2G<6m3*XF?rN2ct(cEO{Mqhg&>d?;Sskvdi zM-!jgE&~bJ38Y9bIt{~(81G+6^J;E25@3?>2-R)!&s8dmTzZG$`gX?iHFJ_0@SK!X z_vRHV6Lu>1_`C@}PGy$V>k7m%HTYx)#;i%um{xMlX4_D3)GR#(&hC%3DI`zomwsBh zT^$faTBESzFU8zzSh}gRI5&^i<)7WeL{oN{{bU~De%|5P%H}9mtp_tPWo_c%Wt-6F zSDqN#ho7_?lf&r$;mzJ~kZR{?=i|=az>+agltW*HXUA2NYsdOkBhQ?cE9Ott&s@2t zIW%Y+!~K?KS3aa-n!WRG)yy2zhR6|PLw$3GKi9eAdqi$a;p@nR?J8&wV8p{1hsaTn zmt4RMNAu+(G1GO4z`p6pfgZwWCSjRdnyu?V{l|9lTQIHHS(Un6k*j+NQCh06Hm+&W zU{$>sTjDxj1;{1`0`H5m-M`pyl+iRv)&H$1!H8;qb&+75e3M^Vf0JiDKckvH5FC`; z1gnxC4#*H>i^Ng}=q5)=%>nY(49IPUuU714d5WmLBwEyVRg$MRv|mTSsw&+SWD7RD z5UDuoYTr^UW{dxNSC)G0n2}x-pe{W06+E0@9IV<(b|}$K^*8RG)nQlB<>{OR*>xNg z^`yp_$=~#BNpCgNiknTA(DSdk*u2~Ri0v;K*>$odEYskkKw`diPG-M%rns1ByIKKc zlz5~6Y;9dAPnfmzkM%1~UJgRDb$D|!cB+1S^z6H)Zavb`mgBq;+tblP=9wYnkvs2s zvZKao@3PF6bS`xM@o|2*ghJTGyL#Vz2L$AE^5(NXjX4)QOx>EO;J1LPx zM9P-f$U7e8+x$bby1g3VF0hFzarC$NhY|nzr}QZIx1T`W-xB_LUORhhnAV`be!(93 z42=bz-x(L`7ZoJB@o` z59=4no_W3PpFBSZ|KrnOfc`Ux9roq_d+~pa(Er}2Ihh%m+L`@dcLvbJ(fiMI^?(He z2Kxa80{ULo%+YVx zU=XuEmRnKlHJge6A*`}ANOv+mikPj8QuKz#JiY1pRxcG&QHzRoeq0%bcI5gTq6_z% znD%kH+3)ij)4>1Zq}pEJ|KV*q+`ZrP?`nP?fx*tt`!plJ@8h_A|J$R*uFva9w?@6+ z=Xo~AuHSRFK;Qd8@@f060B_&-{`uUT|NBw*!@;G$eS9D9_x1VkWB94Ny@2QU_^$u` zwMKrcgu&m3$5#c$-LF-RFYH?hO5g9BagozWsr>J!x1aBKl|CP@ue<5#o7G&N>({y4 zU4j1Zdxdy`Z>OK`6Pus8Q^dc$pLGJfy(l9Hp~Cqb4+`Sm;3#^ z?caYdLi&t{(Ou_0EN}J;^!E63-$_BviQfM`2`7X>(V+!bcpskbz9sDU|2cmj7V!Ja ztD9TBNbvV{-bx`pod2eqBj0{&?e^#X@%7dZN1vN;qr;esyHAm;%hA`zVK>E!tQ ze8RJS_xt$#()ue;|99?>ou|+1<6!da<*Eqz`}*B$YI?tq=U1WjT+!Li_gA~SeKd{l z-{bD|E3NvU^ZVn@jfmLxXiJ~>uZN#uLjFxonHy#QmFiu;pYv=5gTPkA6IMRiF9VN{ z*&{-@pXquzm0wR-Gft*iF~=63{F|(|ZEyAC{zm-67jumHNlP|uTtT}q&>M3M{@9rG z{Mwj=TTc8F-FlC+Pf`OGWc=cP!%pD*o*Mbhaew)VIK9as)EwHMjO)(1Pl9>n2QKM% zMn7Sx402HU+`HN=JL8^z)I`$%!q~9bQ->0H)Jfxkpbc4;nGXhKGLVJMRBI=>|I`j> z{)l;rxU@eI1BIg)o^ouVv2i3(AAff~- zbygWL=;vVx{t8F2*;Pff8k+%w3MBp$3T>be+k@c_96>`4rjFpvQg0lpp)v^MfSHqz zc#}EiuK{~!2+*grU&U;BvNrzB~O9>cCI~Pzn5v$7Tn8);B2u&h6t-4bxOykX8934i&g$ zGBZ&Q6IH_3ONwf2cORh3C4gEbUuin52^tbq)h6v3TNVGPflE$w-F)YW@q;{_;*RlC zS4n9=4dUz_tRELDYP$#yf*d7IrHGw4Nun|9b?@Wf;|*?vGfxRl?eDlE`466xF<2i` z-VHU!hiOg1*odU~O9)yHMbLDWx*?PjfF>p$U0;euJrRyBD3PXbr5v$sY|mEN%b15* z_YrjJneL*&ANjA~G*SK!ArcG?rfd8ja|VQ#zu0uk>I{{n*rqXvDQI+knH@H<_(@+K zDP^VTkUOtmuW)^#mu~D`y})sl&Mo3s#M1IqO&WKRqAG|y@WJlD%p6TK&)#(L#*A)? z*dedKQ|5^#ptUs0Uy&WRh_ToYS=Rj^eaVBWN4J3{M&^lP@DB@^B{{Mjt&}C}jc)uV zGRV5Y=R36!M13ilGUkpi&>1bwCL%e0pNzrTJK`<`KVYdez&QTlY)hnfiI)qwqqE2N z2Bx4P9d)uUI9n1naR+CMZcpVVx)rJTZM{NjRH+A}Z7JnF^O~+lB;=Hj*LAPbG65b{ zY4N(NtNBT2pwtu5!6{v0~m)gB!xlXV-b6JWr43V6&X>sFK?AlB- z58LZ%5&r>1Key5+{Jv0!h6!ZW9`-Zsi6K7-TB%Fr26*Qk&bNT;hyv{V zgTdSZS&^^1zo_Z(IY=>?ZPx#~fM|!w@WJKsxekm2L_|v+K_zAd~vl@(Qnw!jhu7YB)| zrbG>)s>}1zk)_=7!*pyri&VhL?tDe?3*acWzn!;^bJrr*5s;@<>=1pyK94;xKJ*56 zs7j&Imsnpz6}I;CBc`msE=>9Iiwe#Q@APka!hXMLfpxlSA_vYICR1k82Lfjr4xYsa z(}_LopQ~DKbS=l`XD@W$1?1og>w59?PhgzD+SkT+*RUW9X*wNd&78Il@T-+3LXv7* z)%KF$E>My-`hA=Tdb4EZu?D|)w$a7#WNM5O0>mBU#MWbsHx;V-#ZAr%68DHShxaaO zlD3BYLX>XvR>nKp5fDuC($sTs5}U;h58&$I^@v18uQ-SYLt{coZoT_OS(P21zGna>qoN z+_WZ1Ydk3$tJMbfuQGQ~S&$weR16ii!gG5Y+94yphBzp&=Zy_S&Lqr?$upd7@{K05 zfrgB#2-{9zF?L_2HN^~hDu%$?I_ubK?W1O@fU^K}_sbDWZAn02$B=0JB+*!K2at<0 zuK7VVTF@#laXnC*NC0F~H-eb~h8r78~^bg#Cek%v|ss7bbo0pLUl>i z%!7)gY{U%cc-i(--xTq6lAURS@IjY#wKn~5Jd73D{1cFllxj*(!AT&6-*n8~;?SQG zx4O|ZtV$iVR?NCFB++O#Q}(DEj6vu@OtjKCfr5e0+4o2o)Khg?+<*F$GYO%zcvy#Lm3<==6yGBJ( z#=*v|6gB8qO0aHnO@i6=cDK@7KaVHXQC8msr=Sm@RJMc!1rD?-WDVw!oULY2KlJVM z7zQh5aYPX0omUF$l2Dr>g+mj4;<3-H86WJEqgFF=sBRSM9+2|hLj2~J6j*c!RB?2| zb~&^VWgSM$B&$KTlTY1>zr%~t2AS~TnOSK^rF~{lhx|gAT-4!7Iw{85pNweeN;_SN zYnO-lhdO!OV%_d+yJPmf@T4+<1eCP~j?BJ;j;hv;kn$tci7g7ll`=@WFsy01RUDr% zNx1h~HHFIc8+9>Eg6z%ME3Q1Zs%&0ocVU@oC%OV>+Af@^`&aJiRs(QE*Y8OMAeFS> zssi|fD;v|RWNg}mTwjjLw8NnT{)DqR5AXvDH4<-fq?6r+J>}nlF7e6xq6AI|e3Y80A2#TeTj}sGIlEFpg0jQisv$e>;JlWQ z88$Ug^He}iGk_~r&u+g9+!U|QPkxd3XfqXJrB#FXh9VZoV*Zku1cdK=Q-htBG)Oa} zQ4_ncp_$CEPPo;h==Nyqs_J3s87**cZc9&d344d5)Pf${edyOq0jnA701$q05&P|c zq7RnIqF^X5M^r|*8w$bGq)x(A_#hm3mw^42rp~QfYFXuiBUSh22Q-x8yOdAc9ft}^ zZ#(ZSll0TktrATc@@f!Z&#Y>@ecQNHx#Dz`)po!_)!_$h8eMjY zH0s&x*ub{UrJCoJ_duATb@bDNdReZ|53!|1@4;-+5M@*aa#fO>Y7-TL6Z zb){X`Wk#b)ea-69j`O!YjYTk3N>IBvR4vE+#V}h++N2lSDQ`D-8&-Amq`qz)TKYKr z_H_K|EhM_S`@*V1MH;T8aPe72VGy&vZ|O@LdPjx)bG;Qh8o+!@VZZj2Hv;7qHXAXP zsE}189VxrT>jnN~1@v7sIG)R*1boW~r2iGWhL3o$Dha9k-TISKA;ciu3;$#4ys&}( za*(00m?`_sFiFUNq*BG48?J3(VY6>xr`Xmiz><%52@0w? zi{C&x)7)b1d0HbO=Sy7DH9SHF?atshDpR zFqm)98uas3t+^Yr#!q;Oo5%p1-)N8Zt@y8T$q%ZB1~uUwytfJ23WJ>RX|@5ue-?3dXJOzA*Y{lqiicH%17y=MEyhyx#)ze@`x|$yUNeL;!H|4)+LWV zN}oZ&wnk^~YILVEoObyO`I83e#ah4m*6z%kEx$VJ6*+#DIj4b%6$CR$B`M~N~ zSN$Ig&7EEhORqVu)cT>#6Sf&UaDyAr})LWeYm^ciS~;V5Nl__6L98oeC(rZHz94QPF~XItc0Hdj@6Lqik%5bAkzoTwdQpL;JYt~2 z8==e>ep4^tJIYfl79gn$k&zMx=7q$kvginv)7PBSA%LUc(04gFd>5A+rm-*@lD2c^ z4zZBo6FH3Ec(|8>bx%i_FOU!NlQ}mrpx*r^xBepvtKRC&bl@hBkNR}rCobxjf>%+O z>_x7VRi9jgGmRodX;yDf9~4;9t|Iu-9KzhxX)@dX>~Y7NKl?tn?)CI%s*^v?<8o%O zf{&rOt(~6@qEy@~Z5JSQPkoqzbFWq!O(Wix&y6Kqdtn^?)b>n28~ZDKjtZuv0VeI~ zK;a{cn5rrjd+ydTrr$-UU4eo9S5nJb~7Mv@d*3RJYJaC|Ds& zZB(71OpNvXlbIep?fR3#93z}Kj|JqC0O38QW0E)FTU=wlsl13+gxrYBaGGZ%dAjbc zZ4`_w1l%j)vQ0I=SyNz+xQcUG2{gZ(5j>cjCX{)Cdsr|g# zdGLB+uhIN|LyW6MhMTtFg(l&3BDYZbaA1%f3MyHOuH&~E19VYSCf54eig-P{Gf@g7 zahI?kybXyyU05Aw$ut!)ChGBxYb$lk&GKiLosPxjm->;s87&wo z90g$8-;Tchx&55WI21 ziKD*bxXbo=Z|_&FUo2HFPN&RP6ZBb=6JSl?6H!2iCIsJ!C=$&;56Lfzopuj`C60^% zOcL&DXeLGzh%M#~ubFCXBubc}esyM@VfadOD$fb?;RiPdR*A5;1BV4oSfu6M)@p*u z*=VCO^6Nifa+4l!O3Pm$lLEu`4%sD!fN!V^efSM5K5v9LJuvG+g~0L@Q6t@)^a&}5 z*p9>=2vuNxOq@X+Rn3(}5byNhE-mR*>1Rp*B6jT`C-{L{{XjdeanN!}k*SM$df_ap zMCPO+12&8%ej(B80O+&lxzhKwSc|3Swznfq!?Q#6^zlVTMmF9*VGROLM*a;H{0gi`l~lkj z$_PMGiiIjibsLeIbAQ3cPd%LEUCC^c827?O?|%Y$tM(?yxLc%QNWa!V($ap%29w z`HD414-Nr)GJc^{VciOW`Uv_P@C5E}7!`-4ljxsh;IL36|0GF_VaFcpppz)&Xzs{~ zJ%(6ru^FN7E~!U9!CpF^H=JPFka|n*-X!nojK;_b(7g65yGFa1l=g^wjPf1;VF^X= zFvD^6eTape5PvpGs*GIszY6L;8GZ&^QKuOP)9~u3y$QON+C!MiGb#;N{>cU?!CF*9 z%Mf%l18;e)Thquc{pK1L@QTXDDY z?_U%%gK4EqCtFR3U7M=u@A}s%H}&*ht~~9UU+qRK+EZGXan5U*l3E=z!};j5s&zZJdXu%@+F2)_rUum=~(;PNlPpzbJdiA+fQ zA_Fb5xS}P>MsiL60MocrH}m8|A5{!D1;3wMT^P~Tbk@Dys|`1%-SPcyJq%M412T2$ zfQefhL*@-NXf7Tg0NW`X|NSCi1DU8>0@a8(-FA)jSgp*a#G3^E+SQt*9bAVjqgPzF zYN$g0%U#f*47X*e^;t}M)9o?Z{J5abs|Kru!18Z15iZ`$b4n6?8VplSLnI`(MNG^C zw!O8Byy2_wmiPxm>GJgLAN=$5a4C{EbhIO6&?=s#WC??VrfrJEGb(- zNRGINsA^_rUTUL1T=xvm;H%+g_6!rX z+PrWu!$IJPG|t4OY@Vs>?;w|b%9J>y78h+2dwpg9*76k$yr<`M*n^L9;K`LtYOp!rS7^$-~=BB2{5S6o?;`%yD|em(f1VSo_{*KFr3kAhBX=F zQ{HXuB^(l3IG#7U#|#i_uQQt_jU$?h;fI6eblB=DxNY>XkVHz-!fl+#ZJgjC5v{IB zzh!^u(6_c{VBuscwYNt?Ve8=zP6ZJgG+t=u;8Hrv{sY?@!YiQZKV_9IyK8MZEB&_J82<*A>ovR*ifJo+r zD?E-R_taC$8s+^heici}Eo0Ws{s4p(q9=2bpABAwb6%HDZnZYyBgQK`n+S%lj-6+! z7f6J`<$q>)>e|0^fYUh8KS+QRq=d>2I%h}HW^mv`3YnK1eJfpHuMuV1T$7QWh0G@4 z&!VV7POhC}iM(>Y3O#PS&eB1gYd;Z#@p4HBV@RO)HtW;WI}G{ zHV%&Ts5Lhp;c}s%%QI;0_KvkaT6#tZZ z;z=R#>d^b-Z@oFSq|Vgx`EFQvBM0}=pLez_Hm{SQTlXBIbje~8Zz=^Rcg3>kv3s&s zuyg=2+DYBJBr zwcGEH-Q&v z0-@|ZVLn@c$nF_`O4EA}-jX~;%@ef9lnB%Q!1bxOa+pT9)ea*XW%mwQXT1vA_OkAZ zXTFJ{mo#M_Hlg{5yp9?rNK`VSZw&{mZ;!KZZsafq$Cm@J)_f}U>n94Cbkec}jG;pHD_Gm?hfo1iC>Z~)~4k%zSSWWT;l zGJr20gm@Gg3&CoMvc%KZZ+F&yL=FNzEH#9gY_R`}wXX`QBna077G2zJvBiC1ad&rj zcXvPd;(Bm*cRM&N?(V+0yL-5)N>a(gO)AODRL#Ro|1(|He^-Cj0DJJZ;MA#brpr}8 z^7d;N$2(4V50LF?f~%kS`xcg?ZO5%+Z!i&=WMwS;`lsaNt-Xz;h)wp$)$xLEwrW5k zPVb=SOQS7J70N#o`plk`us+-+!h7(9q!krBLXRZpR~@oI*5gtiv19m;SK#otf=l~J znxG-Vxc;A5=V-P0n#b)7XH1rCQh|n`pVr2pSIjLtyPHzZoMul^-GXmm=kFF5mft3> z^=9^umNN-#t=c-O;-3SWbj)%jOSGyLYDpZOqFgGZfdq3Vmjj9E3C~WxCoSO zJ4Z3BMC8iVIMp^XE}if_-V4efQyetB^(rUeY3v4jx_!oRjnPh1hbtk_*XtW*s@M_K z%!ZS>p2n<0PN-f9{n9-38*o&2bCvtD#x2B|(Zt6{VIgCGJ0waj&{OUp13AkDPSkjR z$uiNvuGzvMh||U`J_k3Her-}0PCywvJaL#MhAA`glH&nl;AWPnhh`UT%|k&IS%@-i zCvK`^?^()P;aDG~Qeq^$%H&Jby==)>j`PQLs|9P{+da0m#21PD-8FYis!i>qDZ|oD z>*ovCX32g@mJ;IjgENN-r;J!$-W~m_VQsV{uYWeOV1%1RqEp?eXOQI2qZ0k+1wxUi z!+LDxbEklX3hS$9u&D#bQT1_05KuBJMkWMJRjWbO0$12x045s!=NZ+?Q)uc0+PPZI zy{~o3Ft)VS5|_-xc^R2wo;db$DRI*;FAi>O~M;P*aqu(oL#D88aj!FxM8JzfCSg zx7`!cOSPA2?WiUbf8~1_5zDPBP0ek3_|loc{j{M{*ZBbDZ1JI~98XlCpJ|D>bZ##e zHuy?y=@q*{jbkdmR2$To0D*^sPv9j9IHBkz*7y+aKDEgB1y9wvTK%R8VHl{o4f?vo zuG=8>Qn6hJ%)Zq$KaUiX^DcdVi6V}fb;NXgoQR^q=meWRo&wQU`XSTaN>-`Pi1mDZ zgGbIqr^6-14(Cq&cU`T5woTh>iOZ$?4%gr6@G^xIcExai6Qa?Ia-*V>>VKxRR`IHN z*9P&bw19y$DFH8F^`DJX-+#NTww_%TU}V;y!=kJDpU!J%qjI6lYr!>hSR>9|K_ z9{fINHjC;9i6moyTWewHkDzL`6k=p}Q`4~a>Vx%#CL$&g4~Ehv27yh2b>W{@q{9qj ztU2eNXJvIfp)gdD=}{T%Pl|sd#zYm>PhtLofa@T6TF=+do~C6XZMT}EW&xK{spx>T zp8SXjParg7k&&qBj>WfH7&l<=HmnOj#?Rb^zUM0IDaD{pwb<+GB&$H>_!3ZtZ)fPc z31+pS14PVGCIoU^E-LWX%8JuASITbW-XG$%~m{b&sCi+4?%XcN7#60($qrB#YtJVFpK2*el51 zI-|BzS&}fDzoEL$Wrtw0ZZHA}I6Bni>{aVt6Ica)MBL+!Cq(QCZn$g!bs+A0G1)g0 zGSPS?n}SyUQm6M*Fi1dQ8IyUlCGNtWdlRHR@#kV-}v0XN; z$TjHl0+aKu>q6Y&9+TU+aXvKBDG+qT*GJpCCa6CbrG{6H{{BTg#KqC23Ajw?6z&8r zFX+4wZaoa$t=~h)k28P=H^S8px{>vIF`u-_2OMDH=58}m%qgV)&7e%g7YNskZLb{c zuj}o_U$5VZ9Qw-cajXPyaU_+kz!v~TKfbdm9BTG1jBd{&+BDhX9nJlA{pZ|y`t9OU zOqs)CSIZt@X)XbK+ggJrTgXtOk;(|%gM^Gtf{_Y50|s|M4rrRtVz)cg&bDd)rvte$ zR39gUpmE!4vnzxUG4?QE7|D)!(3(dIXKz?44_MWNueJ{QUU?a$M^>QANTnd`)hf16 zfupO|out;mndd;4?_v8AX+E{f=%PG`l3xY&s~V?1*nm=6G$bC{M1^=6f*PHX6f=@p z0)q*M1979~F&>6qsw8`F%i|a6Do1_h?K`Tx@Xrq7H4}%H(vbL+iW4BF7e57rsiu1^ zf~*l!o;}dfD&coiFw>rb2s4%YpU8(J61x8X(AxS*#DA z_;oVXb^>Tga2D`36qXuO%CPc-tBd%@^N-+YrC-8cVx&GXr*1PIjfry=Ypx=+I?OYC z`auc+vqN*g>D;8pk0uYHX?WN%uzG;kM-KCC!-&IFej-ehLXu48Jx7OP z@6a~k5RH8Evbe<$*5{J3ndWb)YKD3ZHS4r>;^OAo_E02GWt|-2VsT%(evZp$h$5Ta z9>eRz?;9U-sHN}3)kb}5c()p`lCKaBK#r*-;)u)T6%a{>S1F)G5rfO@z8QDNs{>?4 z&}M-nc4C5)Y%@Y$?BLy2WO-q9P?)zz^rJ?QassxpAR!SK`kWpXL{@(|2oZ|O^(gFe zRb2d?6B6-AiELVJ`l4tKv=MJQoNe=!Twcn-5WkIA93I(R!oi+@w4I@39)q@;H2;7` zQ$J%n90r_8^Yh)zUzsYoqm?lvE}B@C**L+bcD3@(Xw7guhh*f|yT}aK*X_CfV5_7n z$Ws6qltOtWq|5f>=cSk>SfH$Ct3~zbM*I~YbI-iP75^zdAa(ff&)@{Hp{WuW&9>7Z zOtOWF+FzOgy~f)3<1OU_JVnqzvb))@zqt7*$-s9|hTP5|(zY$P)A)CkfV`(@A8ix6 zRMvjUgNdGJ#Uu`hQ0~&^7CL)Nt3%EXxrJmiT|}mmU@c{iNX?|!0YnleAb?i;=Dfy8=FmHiPt@9^>3e2(2qkryiHcR3An;G`AzDfu*{yKa?U93Z94Y0o)~bJSW@D?AG$ehWve}rqf2ixaCsbi?42HL6Xx+&9Be*ihd0!w zjzhSnmQE~%C@}oamZ@^5Y0h7lN`#{7GA^IP{}u}uo@dL|$b;RliG782hgz^L0bH=)Rh?5EeFZYnjYvm6K?{FL^brhQMsh3Wv403|ug6fZ~7=EuT`c)#VKCrFd zCLONJk!@+cGLr4@omjX-=E$5*cqm7b;VAky%AKoZ4a;J@W^^()d<4?c zYFZCSY_&DG@ql?t>!0(1;mCr}yBe|{&9|u>Rtfq!&i!yL0h8?o7QoGY)5{}s|C@1I zxZ|7LDb8(gIYCqdUh{0>XarPLRN-C)h3j8$0X(%YOv7|ya?NP_&R!Ya=jgumX4e_> zz0K9KpP-U*wX>_`=1FpZi&zJ4RadU^aPiV8L;rn^^6vA0WuudKmS4qvvC&WLz`$hx z-|3T1u6E8YUrfsXJ%w^1Q_+5h1LM!uGp6SXyBj8VVV=F|_o(ScOQ_>B<9#K2Yu0eu zA(v*YbHAKa5(1WTHhMSlM+w++h)_n*W$V&5Fk%19Mv0E$M#84@qtlbB=-2(d4;yuL za>MDqY8TBazj{#5NFh~nq`dk7osz4uXlL@$37b~a=Q#f*zx7^0<0y@;`18WEO)JNS zAKR7+`>f7e)3PPYO!tc+j;5+)XI)<@aN*&4W>_pm{>BZbD%#>AG#>Og`_^3JT6`%q z>Z(;zWRXrR?%s z&5w7ZJu@c_NW{8NV2MT;!c=px-o!q2ZD-+`>rp$NJ;HY^kTT7gQYBE5eM480jfiuo zFgn4mtT;RoOW`Z%R=&c&lk2yxyQI?1uAFyiU)< z#_Xm(G2uBp68zAWo|-uq;Ysl;*=hdZ#+(9M9(jtsSS&MZVtu(@p*#&kNyl+iZwjG1U_GTjdG7Pktwyw;zgC zZWK4sGrQiGo`R_C4px7Zw10oD_%(Vi^2S$mo7&18-N|#0ekyb#P}ei!q9g;&BN=iQ z>#^LGc2Z@^lRw3=0~SRJCKlXEIA<<{3q~q1UI_M)3eoTS9p$$;)7QNY5qSSSszCM# zSO=x_@$VS$j?g*;@EaU3;J0OtT`hNQ?_LHtxbv-tT9n^Qh$=RpWeyT^M z*;pF!aHie6-|Yp3`a_&jXd|6VS$dum$=SZ|dsN3u6q=((kVK|T@4CeWB3Fu730E`1 zr|9lrWONS#w?<6<_aC2Eq%Dk*B##Q;SvJjc}8UZ+ud5(OcZ#q!sE4k5sf++7nLjTXK@-moK*hDBWFh@i% zFsc9hWzo>a+}_F3#llwqKNdy?Lnkw{|4gX!M@`mVn;oU&RsCo-1MdubWS`y+zYrP? z#v!vIuT6-)9|WPzhxvX<;51@Rji|8V3tj5DcX4(`x24zl>fj6b5^pwna-RA5et30n z@bGSC`gweQsXAUBW7*a98TfOuI(fUhy4s%h$L`w~F0P+zJv%lw&pvbjgFE}LyY@Mt zEl}u&VY8o(uJ7~H#g`mp*Pi{+?>xsXv^|ul%d@?;`*TKHp7DIRZ|wHu@_p(4q}F@+ zLLcdLq3WVy$H2cEhWQf893cL-4FSOFt(E&RO#oU+Xq~ zQ(>P*C{f8!iRScusR78bAA>@??94hLq?1_*Ku0Mf$VXIysyW!lg`E-5;5O^(2T)It zkJQE0<+|MbW@9?>cj}O&d%gzV7e$a$M-`w}DwnBw5KEc#&u{MZ>xqC#31AZ8{8(km z!WsF=8t*(IszLrsMcDw4)#OR-d#bZCy;jws${<@b-l$okbQ$Mw$7-&=aR1hL(sYD3 zQjOrB*_0H~06gu=bTaS!;7T2ekdwbI5YF^zl`4s4PT6VBi2>Yy$buy}Fwwvkr15xe zhbtOJ|4?zmp;5X^v(a+NS@S5*1!E^s#3tS>!@B%&;hH(ARzuew9p&&e%0HP6GrOXs z3da3A$;L!V9AChkFr;Pvpu)g1RZ{0}&gM+7wvne-D>n9wmO4l$Cj<&{$)GTU2}6>& zSo0_f#E=lcxK`@V6~=2oK#9@fyQRV>@s?l6rJPYptHWHQbAi>utD>0(KRFus?XkTQ zkAY*`n9i~4>N#03dNe#~W&27&9rr6$o^R|2iM9EHrnQoAbezY6rYW5F^yVtfnHF4C zj#YrwpeEtV!lV=MyCJp2L!u{EZ=m4$gx(KLE7OX^mD979A4W5S%*ua~iMbNTqQ11A zVRHCmn)1F_x-k}Q9u--Q7bfN{@rx)N(n{FGisj^)hEa=6($glM0EQ$S47^ZS%oPDZ zt-sOn!t0=1mU(VRAlYyGx`o6;y*^nw$cd`3F?-pB5i}r3jLpJ?Be&65HYJzL#4O8( zIZk4Lr@4C9ln5R~@17oqjW80AtkP}ZVbDuIjdc=IRh}1W8aO*#eI0GOH+9jfQpe)m z2SZjFsB7Q=XLgiCEdv9>PM6c2OBIaRH83cjun^M|{Icq`R+AqI4xnX&f?t3tjjc$8 zW6K~R4r`S2b;6)`&EE#%Sk=Jd$-(Vl(Zm_I;eVFDNhuj9}gMSKahg)?KT`A8CHaYQ*wErfC!t;N$nI4)R=luyd? zjT6)%kr2mwHTrdWNgYtqP52InO=DIQ8P?6TV5ev52S3=Z_Yx1t??$ zNypo)j#9-95y;9yPWT&YA{KK-;4N1;rm}F@!EX`nNP+t2^ z*Q>xYa%SKE10^!uUl1p!N4{*u?pxmGZp4fb3Lowu7it@^!#9Z`KQW_?DcJ3ZCoskZ48~A8UE|l(#+D>@IN%0`pyo9PR^!`|Ggb# znC|`OLMPRe@h%c{FfigSFfir+v?cu?4sC32XJ%>c>hxdh`Kr&^?Y|e(+q(U~7IUsE zov_Oh(=}!9$PL@2*&M#Iv36!?Zd~)dsA^m#QQwxkqWxj%66QHRS!4yM{DnQ6iK?1O z#)$+HUx7o8q_`wAP(1IPubH*WHLt8q%>?_=Ej^QEk*^rlXWnd(rFAggN+y$CzmAC} zcQ7{RU>`jH%<&sfSaRiBIT#Zi8{%WrRPu94wqtv?&aRTaf9gn=n@ibuYIskcd_rG) z^t`*ElU{=NA>U3Gqdqv!8DY`3-70X$mISRF+`6>u*vrA`)!!REGiB_L>_Dk%be2TB z>)+beYaNc=QxC9Y?BTPptW-CgtwACVFDALANaiot5KHRy3^;EYk1o+|_*OPdc+y;5 zvTD+mm>=JW0;kl8Ec)eD4C*ImW!ib$C?i%KfeSMkLQXZR*X|0 z!5!Fp8TLmT^pT(C;l!)tC6F^nWoZ4WIz{0PEh4VrJ*qnt4{S3FsoLacy{p9}w;;I^ zPtY>O!0=8y(Y|9=r6Yp4L`nwyR+O7|Cf%Z^>-2qj@^Whur;u>zSlsoHfHiwauN_R-Bkt6!Pc>u^_ z>?*O>+>H*C+J9Z!uXLgIOWW5eFu66pIVYNEE+#rogIO`!gGY#lgKSDWlK|Qn81`J6 z&7QPSZDsk5gFKbi?I^TNBV9o6D>4d)0|!GqqhWpbBh22Ih>yi9_>Gc%6U{Ds^7t2k z(DR9OtKOg_SGm4(UH2tU&lY`4qn_pgr|fTA{RRU2z8J8TPt8P{z^=T`ff`dc(TfuP zdRI|3Qk7^SjJ&D{@!-PQyDgHp(=&R_qSMCUj>Ti6Hpn>bo6@L^6=unK*hI;Eu68-J zxh2=~mg?$h-Pt+U8w!FA8O^yV*IDVd);Ey^z0D5U(|S#vN_aH=Nwo|gwTsxD8#c@Z z>u8}tcJqELZvDk-_LbWmGf|9ZEs=YSloUUc)53?9h{6v|c4@{V*&`W;b2O4qr9~{% z@)0M{>D=D_A>C`d?#mnXJ1Bqe=N_vmcAlk)Ww0B2y4f0w;F?-h7_Bg)7>s3B$6>>< zo*82td$?7UOKWyt+q-?TjbAlGQ*=g-%+i?jgwcX3J{qD+gUbhVr%21?G?uD~b{2WX zelbA_ABhUzyK}#k<(RS6^>Ihd;)+#grhq)7^rX}A3!H%b1G|PTS!k@h8{_-@(h|>m z*=UXDi}HLm;Kc&08qgDN+j4@b*x-1cA%(SH!MK8(VZDXcB)rs7$#NmhxZDFWVU>KW ztv$XV#9`amv>YpL_jXuzY2PDF48J+weGP5n6a`#4`? z)9p50GfAHkuT5_^^bI8b0QtW~mtVic$WRku<7xGwG`~uxsGgb*^8Kt(MJ#!Bi~VYH2O z1@tCnYk5i*crPXj=0yRhmVXGGH5=I@!WiBL2J7Ye9$xD5i{GHeB$zVZ1k3y4bz_;| z)~iDFqlf|#6H|I=$pf(sJToZt_&=*bZBvEi-pQIaLXd$IHNi83^?@sd zB=Eszy>ZTBP93rk&VT~_lQunDY(oO)zi8TCS=HhRk2)Wp36{>y%o{apcF+!MBWi_@ zCr`r*R^9MHpH{djquvN|76?_j4K&Ph=cen36uV}0oi3x&^}UchIqwwX_q?^A2PBFf zcQxIgw>h7DAB;X*G!Pu4cn5PCM2|kKB+q2mLcC`{`mkP_ z7ZliZ;~mbmFQ#_SYvmfP?(SVEcg%wlP@U*H(^Aut_OF1w3$}7K$YrvktY)Nvh)#b$ zGibd56dEhIVbalom_^2#f~_aF>olVt|4Q@FU9a znrIgWwGDB{C6lR(Roz=5UQb7=8?!k=#Sj&Y;1$|w>4Us{!=Qv1kLviae{tEpS3iZS z?QBRN_@}M`H2*2@%ES%M=rN0rj{xLO0kj41z6VeYQW?w;_KR322jIXc+l0 zwqOM%CAv?-llI_1QRWadf$`MEPQXY8+_^(xP-P$dQ29p*grGJeR0B>uJvrl zRmr2ZPC8n^^$m8c$abb5%7+HOOxeu?457ATEqrreWSP{37oG}xul6*A-hrxcV^-tb z1y3=E)G@QXP9d& zMB3fb%UN+DE#$Q7Rd;NuR#Q?7!a^h4=tjtkIy6$@G`EVv2c56TtW+~y{~RSxjkoVy z`Ukoo$2<;Do}%bX)L;u*^CTMHu5+pARA!~1a`OA&NgdI4m7YXKxC?zSOXSxYUA%wvS8i_Dh;U1EV zw!_B$i_`j(0VPYG(5h2(K1pTK%O|}OIokkGt?OVo9tY2Gv1+z_7+4;2JZ;v}|F&57 z6%931qU=C-&`S8Eapuf>RktkYr1AnT=T_Nx$D@dlW!;hTLmJJP+Vo0Jqb!l8Qh?rA z5iH1pv`Q^o#6f+$Oyhaou2}8tH{83%S+3-fuyD@F7hgQRKbobLNBK-;x#XJEO~BD*YZX6E&CY_Q5@HjyfmIF&B^_JD!?4Gh2|p_7 zWF>-W4-VV0#NSId_36;u$Fk&gFoU=FP}GxaDqxZ}PZtP>5?>-zX<|zw=Ttj>NpNE0 za6<*cB2x1%*$=VPk+FYADs}}ci6JRIHVsFH40X3;3P`Eo-u8@u*cgg~U`DVgFz6TR z_xEgRLx?wg`iCbTaM8NsC86LM<*DG_km@GiHV_QRDCYDtpB)1T~nBuv1m)T?9xHu zfv_VPTB!c@jgyf69eFqy|4wO6!;aK)mi1@?8 z+7&hvXhMFDExu{=Js?Hsh7Y}5B6!|~*{@c3!*~2w#_6`_ecn;y^f(do6m1E07QiA$ z4=b#^bKY)!U^i1=mI%>Uuf?3+hOoAaJyWG*5TpU5_GCn~jjDWI`XQklk(9lU&{gf3 zzv;f}e3Wll7z1P32wa32uDP%;tYx2D8w3ZLvZ!88MiVAOi+`+og z1bQhHWT(r^!Qm}=#<7H8B)AnCJ!w4F+<=#2(Cu{RLc`_m>hj)c|`dml2i96t+(+mNP@QD zS|Bexq;t%fQ;dtC2}QZ^T~zOuA(4GEqBbAMiHYD_4}m}%^HADvz4n!lTpstrm@aS2 zDI}e@DCdJ=yg#id`k+%g3tLbBvj)gchtj5jpW6rvJ$M*F_bKm2-Km8w1iJsIHeuB!g5)Jo{ZMmxQe^X z&1W$?k5bk#tri$9smJ=+{t`Pe!ZyeUM>ZzGkI#E#zg=K*GT9Z#%=J(r_0x(0f);kBRRc z2?%BpYAO}7xkx1KszS(P=V^HW4wWO%;b$CPn=glMQE+bI->#Mnq^O^edt_mO=x$m> zwNzEC5Pl~AB&SgBX$isD5t_K8{yCEv5I-vzlOQ0(v?`)y65h6YA$0}co#7;}hv_mp zKU(d}f<3WK;#eEe%H{vrdzM{{4Ev?yPEW__(|>`i6r`;#?N^cbnnJ;;z&4H&G1{O_ znnjN9aT;)9up5)XbldRWhM2C1j%*Y{7_Pd zHgLTez``gtpJYQi`3=`V-w&v6x5VdSb7uIHE3`q)?>%ht+hfm$O}g^h*zV}?uaiR$ zd&WEr`^cc2Du_O#=G__=tsO^d&#&akE#9(#w{_|g*V$KlBO6ZYIgOTbIaG%!yRCGv zf10dN{*ImK+Uv1-)>dy;t{BV$%?PQ)jT%Vl+JK|)0~-`A6<_=J1F}Gw zu=iX!vn=+v0g*8&BHjV)vbW)2ZUTCzJ(}4A{e`_Ssgf@GYr(fA=_7jK2-%#Fv9}dL zQJM;?4PjBw#CZ03+y?iaDD82EH*)v_0!w>g1%WgNGh5EL*@5YH-{$;w`)dz7JU_a^ z?Djz-_~UHlyHDi*89)-q!f$KQa=h-3u`)SVGP0h#9)W==L*4x#2$>rz0dQSJtMTXzgSkA{@ir?dH z&PU(Y=U&a{MM?Kl0{?Tv*AprHpB0}U?%i+4Ilk``kNiIO6a0XW(3;O@dA~etzlZLe zDc{HEExymIdt`mT*NL2uv&)ZzlCP)u`E%ZH^#S+qkNockp_g6n&3=B~ANJj^4@BKB zLp6T)_sE|g?E3HB-E*cn-Cm!rho@a1Sq!fOjGsFTrheB8$ohcK=g=I#q7rY{kD9LM zoSg30k6Zipv(KjqcYdGO_oc0FPtedY`_vuB=Zj{{oPG9oP4~+|j^F!&`)9iELjw`_ zJ9f_J@#!UNDF5q24*w@B{LZa=_t<{-+r{l6QTs*B=ME*m+l#qA;GLiU^RedR{q~XH zZDH#E)86lW?c!|B@7eY?SK4<7cP@$lb)x3;`quq(_w!|JO8@;T<8pKT-)()jmiOMi z>yW75#{zQB=l@4Ye6YfGl;%WldW1Cg`Nt)K6E-{(8KzTf9p*>7W8e)nIq z@RZ?Oa_FgZNAz;E^(^%M*#Lbylznag`9|sYyrKRXd^h6(cs=!deaM*lnzDC&-`9=J zkB6@k3WK^oPugo*N7(K8K~ws^?;DT&Z|6DPttZ%@FVCOP2VbAzrRwwbj+?zqDcWr=P zyDmNVS|2kbkhHCw6DPVk=eM}5M*)YA)7x&HM$!N~z~1^{ww>*g_MPNzm#y}(@xdC- zW8H$zpq^MKK@M=Ib&0dm`&`eiS<>;wmwnV4I#0NuGY2d#vzqXTovR8rrht}SJWikO z&mVOjm7MPHwegkZyUyGBgxLM~xbL@Lib0go3l+$7>uyuFo$eFoblwDxrm~hb+MDKU zJn=mCR@ZAoO--3&^FB_c)C{hLyd`xpIrjF=Wt?_VvP)-$nU6i@cbryJ>4DWful;`u z^WjciJ$|Lh%gS$~@&srSEx)jZ5P2Vsz3RPSS?8r}TG0aJ*Dsb?z*uby>?*mdvJ2a- zy(I~i--Uy=itQTK`R?13OS^O;ZPpoJp83S=D!VgJWyf6S2+qAqd5bB27aXTvsGPVF zZn+A%*M!nKXOyu5n$!=AVHUc@%9%R!-((*P4a>-n<303}>j*8awF*BvROW2(%9T8O zR~vbT_=cilKb`WeA4F2TN78lZtJ|DSXN~6KN>Igh+MR0`xWpc>i_7GAW2-LH(j?xQ zzAO@xnwfX_0RfwZ)N>aX1MX?fY)!@ygvsYR4~whI9<|GEHih%$10&V!a#{1xx9HaU zN7jvs&8dcD@)??IIgz1@z~cD!!#Hob)-%T`%dMLuM8)~1%`$@W=pw`^Cw`(PZ(Mq0 zz{rs@zSfu`yzPajtYbv9PxkKIFd-%I!smUZv*bR$;3o^)<#jYGE9;f6^Mg~@y+1HG z{Ixt;5|||W)5?NgjlYl0SxVCQa3e3}u|7#xeq&R{;~gN|g0*p-EK5XFrVcEOF@e%I zTnQUb_hlZ@-Dn2woMo*oLM*1n0O<B zNC@oH{`pnBRf+bkiuW_t9L{;SI4ny`qA0aRrxkK|vH}K^TdPM1L3A>y0XQUqvsKPHK`VN%^rabarK*+`q)Z8HQIN7AXSz7spRsHw2a-4}f#+1oYQz z=@eD5SV{tG&64tR=V522N);Fpgi1y|flX2+NC0sLdo$0EO*79d0wR@_K`wl7uzL#^A z?ljBKEg`n!o`~QD0gq#yrJI@h6ul~YcBgc$CZ)f62NJ}`PnsGBo8%iOa?+q%xHctF zEdmOHH2~HaMD_0H1iZg?S!nB+WB7KqJ=CU*Cv!po*=m zJ>lXgS|3+p&|l5tQ-&g2T^qP+)kLzX2F9)q^@IxW*%&FGsr`Su^Yy@!%{7@i=@U=0 zrtR)j&GQwW3_owR3YU3YU&3zI?81D+7jO443!XY!UwK zHyx-#hh)<@^J{^N+J(-?soqX*SX|pbz!XO9(H#B3=)zhp%k41AUfKC(x=%gxk!(pk zCf9uWD3v}YHO)LeY`x+1W$xod6uZYbtdz1}q3zQUe(KQ~xpeBKsAn(-NgioC9!_G* zv$x4&|AYQK0{26Wb*wn9aqio|9KxA;^mjck{YQpmhjYBHt-S_l&lJcwDGTeB1? z$Q$F=JSa@{MaozeX-`;e4P*3BRWR~yY3M2onf2e8zcfygB!O%DtY`}JZWQN z#JE~^gT*2pBs9FR6_LCoTASs*bw63$>{k~>QMFkNDPp~BdyaC~6KiSi7j!}bcp770 z$*1{FeeT01u>gjtKF-177A0m3ip-$Yr=^Zk57stf>b67YoZ{NVa{R+!nkhV$B}aG3 z!mYc;!Y|TcaOu4eb`-fC$$!8^=S0cziX_M_;muGai2kfNJYV$HGC|WxQjiOCA}j02 z!cERHZ3P8E&UO8XZnsSp4Vt;2)mUW3e**V9l=YHv`;$obTQrz%WqbzVl!zH8-YnZ? z`7zz}!l~;}b0JQIQH2mNyV6g!4tgOi1suJyhGCw|n#E0krW;q_GU%MyMNJC6Dsa z&Kt0N*@eyT1t54%yv7LC4Kj&HCrAiyn=)dl4R<{H}rCCRk2W zUTED3zQNiY`#4v!K|hvRF2+uvflA(CCkI_lck^XXVW{6r&YOSi;Qpo7thcF8?P_$o z^2Apv>LjJ1461s8HY;y!+AAe)ZvGh4lAxbQj@c2pmwy!zkBJ9PvLnTiMb_MFSa9Q& zt44^NFD_Hd#b?OEc0+uwE5TzzW_@LK0E!d!&Mp zPLO`D4v?!a1~|1BpKwTK+gvwHFcRlV$!$NHe@EC)mE20*UFYjl!^Ts>A}V@% zj#e&p(;yjt;+XuqmEZKrcrNNzp(;g@9+<~`UV&X(m}0XV$p6nHi%#15vz6}}evTSl zdyN41rxy*Wr4xMVvwUg+HO~~4p5vdCi(>1gzCsghLMe7HYDyIS% zUlHeD5?U&F^4OC8gIXKR43T7|L3pbK?mN#&S7_o1s10kPOJ>z*)4;HaYQ?2u;MmWy zkxmJ;oo?wAEmx~uYufo`r&aCXln|3-UJ^^Hl~Ci*K;qIXbE{x1)mc1+Q+(UtU=M+xLbCXFDRw4kcBvX6U(mE)@X0w z!^=*QPV!ahg#4JmE#o_K6?os-lEFy9?E2-I_}G-BM>fiGdnYavEq!_w_rq52Qrva* zapDZ&_p|K|&I=>UnDKGin3>vFGEee3x>kl-HmkVaoVsRU?!Ool2;;TcCr_zU-Vg= zSjc<|xg13`AAxiwu4I>Gp8mSbtSlYid58I8SkZgG+9XUd3aW)9L3nLN^G}GPoc2|=sLDq3Tm!CwN5qwT zo?2`{Q*iLKu8?<1>)^%X96Rm0AZ}n8PDnC!>3IlPhT&-`De! z!bQqi>{^Q|1SIw|X0)sdwby1+FES}vz%mj%ft-ssd_P)K>s5M**=g`fSZu$$y|Hb= zG|7{8A0?P6PIB<-PR+U0 zPJKF4s=QP!1WNQZA}x+2=_{HK64&s9iZ2IG;GFInN{LO}_vGhLR!%=MO_@c^!{n7j zYq{kTQ7tL1g?3vMoH(WhNgHdtL41DqNz#~}WXi&|z6*ec{Qg+07-yBv^>;&(WtJge zV%ITp*-m?rxlz7Col|rc3Q2b-x#X(hegWsA0t%5+wZvxoyb>vq;ZRbCG5H-HP?WiK z_0pBX(ty!2#91R+xn4S5ux=e9|$8!#d7CD7}7@VaCYQ&*P9=)mm1wOH?92(;YSIKj_x#0;_4~E zobN?~^~^d+-viQn-BoT(e~0VYZM4_NBJIvZp?oH3kKaJ86V;>_i7E2!fV<^%=?w6i z{v{p4;XZM%)e4+`N#*4|2Qj!RNc7Z4eQgfDE6dVKve&Tcgw42jA^6?A80a z&R8UcRpcT|k+57+3gKae&urZ$u9+DrHd5Eo=?b)YvOzD&EedP_I)*=gwA<*cJl-%M zw8cRt37pEd&xItIpn|058? zM;0wG`Hy(lt_FrUZ2<>_cotZR2T|+gcD8$pirUl3HC0M5e@Vs>rUivzsJR_VmLl(l zH|@cyLuf15DQRq#7i=8|i<8LC<;^Z6fGmKQiOK1BNg1x>P2-8yT)xZ?wLXPJRd&Em zk;d}IRECxq-fvtO_q`rTcnUee%P%B}Y1scTPU7ldjmywMQ^7a~wR7IB+=FJ7Lrc3} zj2lGfH~cHEW;S>pEL0-F8V#m8I+i@}%ki|a&XxRMQ7Q_G+Hk$bOe`G*g%(AjTPx4+ z%b>E1SsS)MfgdOO_|J!Np~5#JXaj~=jA+lePG8=&odkpZi33x@`Z=-H7B!wj@x%-K zUQski3*XK~kDhFd=uL(_!B8)Ta|99k@y!loI@BN@qse2md+?IiBY3#1H6vozHC^#a z&|x7cn~WyOE*sOHv)z^bI2LF7nd=&b>5*6y&{%^le=eFQ~#*=4Gt|rH`iW5%DGyh^!`0IZm?%Vm?;Vx z*Qw9zDH$DJVtzcbBq8rc$+MmICV0w?wvZ$l84d5YN1qck^bthCTMhAY157Xs9ekx~ z7vndb)`7w$81*j=Mn{At^U9C> zUC${(t!RXc&kE6`d&>g*TG-s+!=bw>FT7PwkN;O9P1FBS3euHN!+JH}$@1EW)Irfv z%T1;+tV@c8j4Z1oTdp#ODk59bF7Tk|iP%XVf6|B4Dn9_{g@xYbd}c?tE=d3$vi=*9 z{XfL<7WPVlz-vBQRPMdTd%Golwl+H$zh5f+@%X=dR{4mwq-)btNvg-pe!sc6Am+P7 zg#=%+<-k!6N#S%IA2;2^=)-7$xdH`f&1_`t#22X#qGgaG2Rm-Kfw6l`r-sPj&S?Rh z-s7n%ELL*fYaZi#xQB8#Ti;*Tzqj$p3&3L#Ub%2#qxDy4Jrdz+P`89*h} zi_x+B+?`%Y;#@jHncZR_BB}Bql*?^wU^(=SbcJVp2o{`+uUP5uYQ&$Mw^>ol7y~Q> zH82ASJCDHx1JIUzUW#naxRx;;q7wt)7Mf<3ZDazW0P$L={}6L(5lSEyuQP$(ho?x> z?)Lu+09-($zcRxD`181c2wy+!dL0#_cBC0}XSa;YF6$N#d!==y@$Pt`IdI1)IpC#0 zBX15o6MAEtI)y|SXJXfS?Bq0S`^pU%J}bK!0It?l1XHaPHM}^m#+b@<>wq09{dv;La*tzlFO+$M}p#%QSw^5|XwaD2+mhJLYBEJoTmZr+4DXb+d=#z(!S^Sf^WdhAoW~t z9}ap7&{6JIjaoW@>{ehd>Kl&0q-4?Ae2IhSqw9$@4Lqp!Y3|NX>EqBjZ*qUquzH>f zbjKjDPFaY-`%xfJq|0?M=)B**hv1f$$v_H9kKw=uCD&ITaSQ-m(%naK zNx5D@czyu)CCQX-GFR?Tzg#1>+quz{$Z)H>PY)kqjL z$uTXPtv+rm($}1=1S~Q6Y%6J|Ns?%r+h3IyBTl|)^OukB{nQqcN| ziN(19H=rS&!HjI220J$!HCXQ}Ntl-+1Yn!vxBWcH7_j#{krT|aDw8@gH2HLGSU}*z zhy9>cJhDjuHIN5386VPI3qYnnF>}B#1YHQO%b-GbfJZZMV^V}r2XHe@V1138Wi1@K zqij?mNwX%WF?Yn6l+6~o+EC&~-jg*6H{^N=)h^XOvTRxH66%s})cQ={DmjNn9f@q* zl9#$A&Y(>oi*XRxmC%y^BPZOd8N3rwp|QZv4J%=jH|DheAn&>XkakL{qPhzqx4?5~ za{`y0VNajm`-yRpmh6_fx9gFtOhVJc5OiE1i74P?y*w+Kj>rXZ^3#B}<>a82z(_Pe zUkHoLJhKa4W_0x>+CSR-^6V{Y2&hjWTR9wA$xlr;hI5hWPg2i0fVUIckIX~TE|uX_ zYwyMsj{`r&FoSf8xyD)805ML(^C~r&WWgC#fyIVeI$6T}!!daCR zXc1tvz9dX3??g-5%XZ-85bvB8$#n;NLpa6T`M5Fm`wcujr4ojnmM_o#*x@HKHU(5K zg%r2z(G|2I0bIesH6TMzMg2f+_({LOZ7{58)S;zaoe*2mN%CD`$jts)gmaZffGn^! z06|VnL^4A-7SqG)kD@GuoR37<0?SZH1jKag@2C8TOBVpCnlP4}mwlR4NR0LX+DV`! z34QQ5PR`ZqB?P879M&Px(?Hsl{r-q{9NrCJC=@$J>GEz$-^l{iz*=Fd>#=rlHYZ1C zSUO9_wQLyK`RIyw-0x(TbQWy!W8)oxBrV8xc69dN#mm{9vS?ddKo#HhPUu>9u>y53 z2loMGBh7*=h!C#9#YQII4!DJEiPi}{mxVJ2nTiBS5jslef}yrcG8}Pi`85+In1JBP zo6tg#Q!@!GA369$HdAw>FNjnesep-RA0+|VNRS%U1+t|udbBS>gLV{VNBmis6VB{> zSC3sUdBrFU2MBhj0wj{=?Rs=Ib`yG0d zZGL&LrWp&VF%NRMuSZuyNEV%=jkERz8GaZ-VVkCUnV{HL=jd6EFtyC*&0u!TH^GwJ z-$0P31J$TwNh#tXIMUPtd9p}{1K!0bAmcy`4fFJMybpiuApt1Dl3tYtv^m6dV~g}@ zN!Sh{ok@SR2<6x7H5+CnaDt6AmlnJuX#OJ=Be(C~DCpYt-nI{9j_glI`*6}LzzeBy zX?KDcMN<*+JWN8TuRJ2YuN|~<4L}t!fCM_xc8>w>QaJ%Orwot`E%~L7?*(06fr;ov zz!j&1k0JcbLm`6pXJqLS!k05~PS&i~*K^0S%j`lpkdnv0A=oKriEBwOY@|=bISAzA(2x$(zSgSV>;BH6q4x7Ynvd)X*2sG{nO(!>p!rdXFDr%Q z;1(9dcDiB(>p?&#BxNDyQV2XOF@5X#?nGE}#o;tqnL-kqYDr?2DsBL6g$$xTTNg&1 zF7YqC@U|_8&j3#vNF0ODCZ!@0*v3gZ#V_sB!qb)jBeT<@knDJX^qGbsQD$*Hx)y2gROv|};2S@q z63-fKJ=1v?A16qfr}%I8j7qjm-!C4(23V5seC@jI6Mcm#n6*0+Su3qKna9OKkDONr zP(~$4Cv62RWPekv%j_P`Xrf+(Qpy%mZUScpj@llEEHyW)49sOV*GtG-^hbfV)Kp`B z(jkNPIl}RqIHbA@;~{D? z1UHRS2%*I2+osgk*-rlldy@CHSM7*^`=E52+Wjp~ZOBS!NIyGY? z$};$0GA28r?u7UvP*is6d3t%Rhdm$t*ljZz4yb+V`2fN8tiSVsOVB+KO*f~LuO)iF zzxlwPbqO^&*8g?q=Hc`aU;=d=b(W-T%s{dzT|77WS=7wbT(0=9t@3-Bj>p$YQw-_( z5_Jp4+mXHAk6c)y+Q~I4PS0P?>s@28butlYLgqN)Wl}Iy$%NqwWa?|B+3tFolC=^* z7kQ_fE68tB#{`LUm7})G**0UgBUwK&0txk0D+-@XpkiAbVyVc+F1COPPA?eJ@iaj8 zsjhUuTtf!T&c$x$qjo0?Lir6GE0a1}0CrgQj|onX+L&4&9>sP#@nF+orIHrFz9QBV zaM>uAbg}MB_0%*cn;D&aAb~=YI#kN(94Q0UNwBL&J&3g8MiJJSo@5HNnDH#@|iQCchnbU*RoPI^>61j9r?`vLjL^! z7Wu<8ABjy&)`lCBqiSFKjZ*L@Ut5t@+xLz?=X~(>3Ie`>3p|Mwabz|vBE<8;dcobs zH3YGrS`T`OtxU6VO;|bz$LV2m6wpGJZF7K6l2xPlR6ijkZr-N%moa7wvd@cX>EVg(A#CgQCbNHxZGy%*csth87 z;hT__XvvNFI$tfPlo}r0WU=RF2sTlw8KhKTH=3-3W_kcQy;P2F?G3JnX??haMAkdC zRpD2GZ>R)G%OiuugaZ1P0psw_^%9^T6pSRQS)@hpWqa)RorC->kGfC?#UwOZ8RS02;9*W_jZtP==ZP{Ew9NT3zpavMm$bUxf+qsv(0bGu8*T4n(3C(&D81Z+ ztqZgz>{Zot8&F_0LP_Oat_71PX1l_$Fo{{;g2j&Xg?>L`J7$doz8}Rk^?b>1n`}S| zfD}R7f!iXw-L}o)2=?4`AC(Mp#7uGwiYT2SI(Ogmk!NQwHGW0#nDTU1FtJnJ>9Kh;Dxyl zBnP21Ry1#ta6kff!{d+{mN>|< zq`MlJ3^_w;AK&{&n{WA#1osWXFz`CtZjFpXhQ!J=a<7R6Jl{L|GQ(v{y&>GSn~R|x ztH)mE5xXfCW~hx3FxuA>ek4~jY-|#uz@!oQ^oACYaI(d)+ z)rn3*S(3g1-7F9z0ztX$vdQIaWbo8h*4U}CXL_(HMrF4aL)@gRVBvMz;w+-R6FH03 zfRUqwAGe>F^cU^r~FAcb9H|+LC!UA7EA`e4b&CBZ;s^1X1*4% zf(8{DbmwfqyV=f1o|OtB$Jb<@RaG!x;b*j$*`NXQLAP#{}jdfe{ef0r(q00~&BzupWmcgIa+;6ar^@Ux( zyz@V6vZbS_wQNzBO)|_e-u4>w>B*+@pkUj0w&54<24OEYI3w%_Jj_hDhB2cd-m30= zq+By)ZD5M);>vp1WrsJ7_iEFu1Q$g;U-CO_Zt7A=qgSs|775%fpIp$^bcR~F;bc6e zf_sVNKC#?R%qxPL5WSIn4DbvvMwurqh|YJ4fF<}LQ-1N@W@d8Ss6V2qcAf988G`7z zL~VAVaH21?`n#RfqK(-08pAi*+G4PtkccKeC!elcoOenSU{-Vz+mmcsp^t+eUPhZngQ7u#Nkj9 z2c}ScA0=?Z!)t)@r0dbO>=l?VSz!XU&jR12(!tn6}L*q11B&r=WuTt*@OAUz@_E4U*M>^G!G`U2SR2m=kYvJ z>!7!B|8~bWC_y7kxuVN5bf)nSZ?L>1+d2|K8^m-iFgt7MCnz%r*pecGQ+Mu$^H9dF z9hKj2;`T_0$&_udI9i__epLqbyCLn#?qycH&+gUB)CN2n~=TFd#0-yxnNNeHn=sA!ki=NN_E2`N2CfwCC9T-zMm zv|yQV>XdBCNjqQ}$PN`uKBUlX+gHu2rir=`3-(DJmy}ovc4^5@Dp`o`{->GW90FJ& z*l7*z<_Bq8DT9}@4T7G^VOv}os2L?lrt)P=+j1 zwt+S^b60(^tyVaVaz7Gvrz>1TLNHkyNtYRz4z+SW=%Ozv{zs}mA)*sW^Ab!T=OY`} zurBOC2r_BH;hqoAOz?x$sgq87P8gs zMg=wM*e>Ux0jikJ#rvu9B&gmF9kG7{cJOcFTGu+Vjo12-8zayVfL4mvQ> z@dH-a8Zi*4-asHM%x5=s)7Dp$7eKCU5|(lS6oU)sb?@t8cH;coYO+8z7x4RtmR=6W z-2rE_Hk-u?wCZ%znCJ3u53X=M-lYiQIFq22ld-I=o*}BGiZf9JW@buLjc9MYFTxMXT?^D@d_Px29EsE%%tq#VKL2^|@{$Il=liKG%i z_nTSO&R57rKAn~xE74uSg+SEuV=?39-||MTbsE|Uj(|cXlZQ&#BmF3&OVaX3y6J{8olo4--R0TZD6Y?pMRUq(zV(n)9$n%m(PZr{I+6%i2o{}6Y zXRCpkWIGcwpKqP+LcpqcisMxz@7n4 zu|-Fw^IJa|B-LE3x-ZDo<4D`Mkf={^ivq4`>=Sa%&uM-TJSzhl9FKJ2fD?kkZj$Ur-<`GNCG`}iSn9tt8H6cp>Pj6fZ*rAM96e^ z2&lRA8J0T$n-6dO@du@O_m7E-0G%6;-c^9`-Y(kLaX3!YKrY{H!@ zYc6O(8uU9Fr@lS!L-z0%)z74IDHo1vS;B?6TCclYLtY7vT`SxE@x9>X%#6Atd2iPv z?EJ)ID=yf;UbjSL-0o*9lXAR<~RxW&cG*@Y06 z!g9J85N)%Y)fU8!Fhkzl3E_Q-G1=cWnKUp19}}i1){G>mR>gk!l@(_8i4z!938yP! zgQpq@A2I=wz-CTrh*W4LpiAO|H2Ek}hzML?Ozro892NEiZq<}N2OC6yor8UdiYB7O zQCgAQItLyzOZ5$$Wu*`#C~s3X3u!Q+#=~F&baz7pK1(G|cg^_u=o$f>m|qPcGuty% zbWEyTt&b7F1K+aK*`$>q8A{Z}I#l^48ISu&YZ_8e26#4FOMy8ZL1L=?iiej&i|;tr zQ2A{q5cJ1sec#> zelixS%Uiq{aV0c|>0qiw&GiMmgD|{PMG(O|+Cw&=d3O8hVUfT-2BeB;<4&b(a9$R; z8)>%@#CgU@$yJ)MHu0?}t9Q7RjuzP_0}Up8hc=9pv;c$y6e^jQQ@okAyOT(@^$=ZlBxM?wV2L#EM&F!*8?>zo?g6fu-cr$C z<@HoQ7(gbb6RhmP|8L3wz0|iO7U)`GK>H@^PHX;9e-cr*x8ce*>6(r1C`VEd*zpa&>DQ|Y+5ktDJ_0ofuG z5jJ=STE}g+@#NwD$$C^@#e>NKg(4YsX8T zst8X_PzKrB#?LEO9A9Qhf(;5*Vpm!e#B-O^$`@7pf5I_)HB!O)34Vg3yAF7tt=s zT2}%EZcL=Z?Rs>jAl_juV#>D##Sg9f@Q-pjThi^N*xWOn_ zz-ZfCbhwWzqNW2oWK)h#Sl|ZRDJ_Yq%)eca zK6by;w_pX-fYZjDJ{nGM7Z3PJRamta!P%#P1QbAotlKd0UkPOJGxTFHbMGSTz!2NA zgN2yegEb<@A$Z34NZPaJH;l9NVB1dfO7FbYwy9yo6+jE=Ik7aYqNz>r9zU;m@G&`v zPKzZOM>vJey_}3gTrCqJas>ho_lT>OWfMswOQ%&+%w^LR2Q@Uv$BZjbgCQJ?mkdRZ zcG8<6*ISeu80*XiZ8kk2NgEKwJL3+?@SxS5D@wrn$x;JoeY+l+xD_(MNi{5XR;GXX zevDsUe?5_xNO8RCC!!%6l)7g7L99t1(l5E}Z?G`r`jD*IP*qUpuB0IcYAH!OC%Enp zG(G_}Ow6}3xSe{#cT3ehOsPcYgg)kzSAu>Z6_8r+N^GYqVoKo@bj<~cC2byT&mcD@ zr@M#M=fj;uO9RL0m8hwqL1zj?j|}2OkJTQS8?xcF_{jbi%I}j~9kfW9;><%j=|}9I zfU;U$R*)XaEBo<8SmwL?Cc(?r12J*C9(^bldSxX)iiJLy$?NsqU2#5{r^v~R5(zA= zdg75A?Zw{h+zqb?0`AgQ7f@1SnXW0IcEdFuGrE)P`(i1ju@$_I?=7j-#NB?uXH5K90F0g1b)ng+#%E2I*)6a5|6jg!NyN^5Na@@F@^>OG0NBciCLaUPK2f0HSeIZU@0wyo5q(YO;@G1? z^>j$5AXdTdxbP!XBQ~&*Dec=MEn;$DQY=W6M#5zh7L6cs zi_MSQ<6Ef)aLpRs5WsnAQm8hvVNcw0hhS|Bn~EMf5GsZjP{RN>vnn8abNly^TX&kJ-U;K5yNZfzgnB3e8 zpg{!_m8cw3C;<7P^U8x(S|nG}+pKsy;*K=boymQP0>52hpM=%^2vt!EuEoMoI~`iD zmjFqluN@{)cgKY%)ulP^Wn$1UWx9?Et|J%LoA`o0gpg$9+h}wsq#@x#4ih5bTgJLA zf_~JU)_|lJS18vAL$c6ulHG91@Cg!DvH=3KN>$PZb8w56c-mdmXAC=z*P}DT6RL}b z%Uh|Z+U!R?_Os`=_lE&t4Ep=I1 znO+IyXjcNzflf1_@og`y1s z6}*BJ<>Jx0+3_DIXjO3>h?uTcN;p2d$>ZJ#XPZ79PXVw_3^Shq{=g*x!TZw-d{%W^ zAuD>^&^BYdPC0s>pC&bEeyHAKV#`Jcvst2SX>mM3d_+3eGjh7(n0E=1NxO;L-Ws*L zS}tyzQDRoGnQFA6j0uOXt&nr@rX}plc!m`Pvl*prDMa$E3<=-}OzEBkBU5+)Sc;)zY>GWksNA=CCnPb#=A$Y@2By_f^HLVomq4?j&T zeK&j!Gf=CrrkcWUJR{GvE07r6!XrFgu?W0gWfYh~@~-$$v&R+&%SC6#6ojUGpR84i3TZLpgHU%%;+IXr3_-Z2Yc6``!k+aiT4&6=x`DnOWUA!GNpU# zfAFk=y#uDRrIk-bDG`uJ@3OugQFJ`zsAF}sgsOyW0%foZp1fJ&!_*i%9*oP#<8@WA zr+BXXFDi%;iU0`>Vd+Rp4KtSxEW!A_Jf;VsvW00v>H-qmTuH}0JOML=y>t8qi3kiF zAi(NENQ4S#TU~tvpwG%Q>FUGA1_@o#>n^K*V%v1rWb;{Z(W>ToG~g)4qXtiio0DiE z0yX$@bC%)$AV=7?GuY;FhVF+D7x3HYa)6kHz&Y*$79`Q29cF0@AqGDqEO1ehzd^}ZXoKY5QTa}5kqW`08%v#! zXvR!EDVhxnrV$XdL$W9`nO9B0u|4yJ_Ww9h01OjcZ+paR$af;%MvS)ui$o~jm{BZQ znjiG2U_ju~5~dQY1NRDPk=Zk_kcP*aj&pDGf8%ON5#5g7NvIr}wQSo{3Qsx@%C6m) z(0yF7{kRl18#dKSham;e`etMVQW)cpj_{FwkR&YTn-98h{P@k)9(!!*Qt=eiYGc>j zFc%7CTT-~j`mnU}f4Ey@bfULr1rmvYPMfhmf*ht5!fyXEfkcz%05kzys&OlCL^1ua zT_m;mBxFiZgmVXg zP^qaYp>ApGE@O~*E%?lVMOi=|y21f60}{@2)LY%%DO*}}J5v9{rCK~sNMMM3+=zp= zV>fp+sWg2Cq<4|P?iaGV*GlA!pNA8_Ez^o&0tlkan06+6KXDS5^hf|mR-eY>aY92v zWi!5{m$(E0G@%3`&^iC zM0%SO80EYb4RMGtx=E!?r~cT9Cby@_y0^^Z{8qy&ruF0IHt$$BH@bn`4*-c<;(?hU662qTqmj5oPg)7p(- zo%iapN^flWO88XtHq>r37Owv>(EIJtD_GJ|z34#kL$`LU)=w7grS&7cp?kxMhux=bBR}(r2a>Nk_ zpm>}10d+o#W^G|kY&SbG`Fe$Y8jZ2EHS%7|&=OaRTJHUj6^l6_8Ah|JMxAJ|5(AEL zdzd68RjI%*C(SCGf@;k?G~AIk7FPTS%aJ-j@+)--TX-2@?CilXx4GGH$C&!~UVAl& zRHH3#*Q0CE)$SI@)6wiF_w=Lcifq!QI%AbxHPb{l_hXB8wZ(n^D%QfA`dA7>h(W*& zZ0-uG>2L>=^Hof&S+*d+q91)n!ENvN?@?=IPrgGbVG-Nlx5ZUT`y-}kk_?(Ighze7 zf(`8uRPB?<8yyjYG;PqV$|c*t=xHb5l932d08iFhp|dvdUWida5Y!+QuN%3O8pH;W zMQQMvlK=JA5szfz} zq>SBT?K!Ux+?!PXmsOIDF!69#(+-uIJ9X2@pubU{&?6d%KPbn*eyb7tjGnTR8L;2& z3^#dzErT3u<2|loJaHBHrKBN(SZYwYQ6bQOj*O~0Y9PtI1KKe zoZSZG*jG7~#+(-MTi_I4&-ep|j_(JCKN9gh%Ju;J9zkTOP*^HD8sp}gxAPTmVHvyI zEKrX#q5_Bv7!XV7r>vQwWZg~6%ECjNZBe^o+nexuKm{Q!lC+qMVrZ~PVZjT4@HEn6 zH!L9?okOQD3stKH0uJy3*Q0BymUc76an&f1T0QCx$F~4_K2PJC z#m7&4IwfIR7j8R7?2c@8+h6;?4gLY3c}{_}yRkJOhK~TUYXK=Mqtud3faj49llSFR z=HHuVBq3PIK0x$4fYZ!7TnG{qcL;M51{9tI{s9ze^P_7-zz(u9azYVHqNRG<7lAf7 zau>yq)ucy*MgT3AZ6Uy%T>{A=u~-C4gvtllZZPcwhiu$U4NiwjQ;D39KAKvuJfnxH z^}#VZ;!xH@7!2_AvEvB!bcE`g*m9v~=WyqeWSxqA+-y0A4Q)1;T3xHO6UlSz;q9D0 zyj|HF(;a{;eCe7E)m!K=3*U|O$#l1SHq7-3H@Aia7Z!``>3LQ&4p%8V)-OS#*)R$7IoP_eDd`OxRP1++JK+{y<=rNPKcVv%)+>GRROj0{et5nZk*021 zU27N(^E5nL@xZpb5e<%3JkKj4hKxm(iGYKlEAXK_tchf_t|o&HkfDz|4Pj{en3fRU z$)oF{h!tZ~0cMZN>x~5cMm8)cP^d?t_|^|b^*`O~T^Qks>P*N~w?T0kabVM-)c`j5YLisv{5Dhn9VIs$!BLEHuy z<%m?e7NX2xvEEKstp1^z5I%sMo|LMLYF@2wW=u&ym<2Ge!Cm1F4>k%1pa+P+1Cs@v zCu9WsqUO`2HrwkA5jE^EWk+0S(niAh>fm^!X=O2^PV&8V@%xAwZsmcO8u zDM3M2`8^cG;o1{VbG`uQmhNdYSM*8+RY zZfg6o?$Ve^vd%fVpDGl+DY_J*yu1WtblmhcK$bg*F0W=BVY(){KHHEoa2c%7JnS+s z60OqEO$O&PU0@m=N~c-rj=rA`Wxq0^z@H3Eg$$gwdHrN4%_o`-`|BbqedawONrpYe z=)1Oh*ls2_*n2=`GdL9D+yfbL3Qi^}}5tSOr)rPe_AxJRjNa;v+u5|cuML#m8 zRP@l2k@`)^ojX+LaH7);(5aeIUE_(H8ck`4&n0-ItJ_G@l@pLt`7jv(4h>x?(A`{* z;1r|BSBiFu*tv8POld#qphw$Ihq=!qy3Wk(T?VyD4;7HXt^x{qQ_BcEjaCl!nsI#5 zv4eIpL7`II{hOi+LbY?cMeRs$LpS@mR+XU!FX5>$TYYmQ`A5+Vc-hL@3JLYNqOKZb zc=O}}(_u$)sNKDPrrVX}=u6%C@u2E7;e4tbLvIT{ta z6(BrShL(cmB_+^%C}Mn;K^lDZYJu_)Cp}Wtutb3>2}~HwA4V^^DD8Z7#e@wLbQ2hD zC+3&KsE{5z@3Rvoj6yV8{AnGoO5{NWJ8Jli)^S$}Nt&$%44OM2!PeZi2T???)UCS3 ztT8-*dS^YbYOm(^V4NIMSmav5#L?CzW=}n(Q*39h16vTC9S&cCPfwZzvX{mRyTdM( zhDr0rOoc?1mU zxJPh)H$!?U{M8O?IkX1ep4<4^4!T_aNjEfc(De-TRJHBT3>YzWqAA{= zcINdEmuus!wq;|kyiry6)3mBGv!{MBg#(cvQQzSN~;+sz(xvw=tDuZCT&N6 z!QKqQ1Vjz)GAbA;y|iAAVq?~;%f*7x))jrE*rV!=@&|UZT}C+^=%s3)%O4n%`;pwk z2{9so@aYA+487Bb5y&s+Ig<*7R(%myXZMec{9~ZS7;O4TuK+uwq_BIw1=#OLR*alS zr?@jfPy}Sa2)Tmgh^T^g+H66GfIVNaC3nk(T%^G{=O%wE-ZnsOfCwLuob418(XLd| zw?*{aoTPk%tir{`E+j4ZAuuy|-h@oilJHTAuY+3D&w#VpNGlJ)y0v8Watlx%MIgX=`hAp8|H!as@tV> zWu1Dj1)@MivvNeQ-wznKum-?#C42R)Q5ZHmUn)0UTvvqp|n%&P=aWXlvaW1 z)B%g5F^B~nTd@Q`dXGs7++e0Q4uKKxn|lvxBx)qh(wlFRswUDWL7dEj3Tp%o_FV3B zGG}n9i_|DbXZZrz;$;qK<2PMOpQ9D#^Kb{+KvDJc@`61hi%VV*g>*X`~NkyouGyJC7}I2hwq1YxiWhzatm*_{*hX z9ohtozag7St+4EZTr?~>xA^O%>`v?rJ*2}ru||p!fYGtLyWu9afd|+eJxUkIYX6cF za+o37wQDL85Icmta0x{0^Kfd@v0><|3HW?V&Ogrs>zLhdakNvg_&oTJGjK|pDYinQbq%8#a9IXO?u}})KpqaO*j)GxgzV^#KFXh-7`0sG>SR`5F zqA!EX@XM`HH2b(#(W6tsK*Le0TRBN-6UD$CgGsdK?U{a`EbXSrvVP}686SO(Hv`eU)~!)(T}B` z>V;i(o%#{+H14MKuM4>VRVnZibLw*9T1$gA5nJnWi#s7&nl!uGSn zZSNql2fA-)k_?|05NxZ1WvaTYS5vk#hDjdK~icxJ<)*k zxUW<1#~n7gC<|qT*puwt4xsP>?sbPo$Mgn}?Y&0Jle!S>zJsq+K zd<>|{;MM?HPZFZK-ceqvqJ%rzyJbz_$cCB-U*6g>sI@xXp4b)zj~cep#XNS zyMB%ah13M)_agYwGZsQva7vQp7C5WEKjov-2~Aa+qXc1~oX>BM1KH&Tesl(;-n*?b ztdU@SK*kfii&zEONRp*d>PMd6xgEZp&_9JE(ZYKtb^!rzVC;7g>KhI0R^>23*XrM_ zfDxzoy&j}EnOU5ygvpK2HL;BA(KTp{^2kxyS)TZVhw2>XnAD0HKarl^KQQyqUFu+g zqL#Q5mFyokg!bh?&b=l?{}ALzs7Lx zcN6T_0}^ib8>)E;zs5j%6wQ+x3?W;wqB|fNkM=(mgr$2RZLg=4(IDAI(|RTi0s{b3@Tz_a z4jmv;1``#aj_`=3{+8Z9KiWjXi%$-)Me8i-$qKgF&wcFILGM=j(nNIedBrM%Dvkph zyeI7o`}={Gp{-|v2@;U~4UE6L!fhm>q@W3QXIHR)k7es{bh7U_v zVs53q-L?4$gjr8xU<7x+wlh$8e%-!w_fcmqA+J%7im6RiLj+);>MzF?w+S{RLxF*C zm2YVFd-+2&8Xxlx891FmqbU)F1T_)v_f0B|49*^W6~M`dOR3s%kI8*VqkyXrA%IaS zBJh;7cGJe%bu&I2axB9Z5g)J&qj`{aW!lAZf!+0}xE&a9F1QPHyXIM=vi%uXdjy>8 zGeN&SUzgb=4;06o4}k6&NncvDaL*thx9zocOz4!wKmlP8jet6!tRf54w{yK{S#~!&YUSSB^ zqsyJ-*iKk$3-ppDvfNJUx|rP!N~dIB^maYERyB4&z&3hLbW7=|PMvN@;!PaCo^++y zi3m~&oajrGmO)@a9l-;r@^+Wv*To1bgHVEFI4AVn;I6VN@@$H!WG3TGWwu?=VTO6E zcgMAJQRyN4FDTWxwGDdHiMU-Ds78aywDSz zcJ0th>XwR)ieO2F4%{h`W={#T(I!}i9%9;g%kFENsJ?fkL^_2EiBzLc984bcVA+C8 zU$ayh-x=&ng>j@3$>XN2tl4zUSvwxiC6=^ZqEb)eOk>*JHR-3LYsvC{pHVXmyKc|L9b_7>rNe(2AR6N#(>*oyS4x(EkLDfK-RF6A@fFRVUc_+XP z)nILk-Vr%?n26oTB?1M}KE74u6MXB=c9V3B^<~*nCueVx`F~I*LU#zNatlCtzFuLt75Pl~+hvYtZ4Oc>nq}I{#zjCHDdb>Ddy27O znn`>x8qK**!ea}@L%Ajnpm+;oDOAIugJYu6UKLMf3qBZH-n!A=A|jXJWIERT@x9pM z(~P9l>))h2W#ZYAd0} z=d7Jmj3_{}t;e=)+uCE>wr$(CZQHhO+qSu9=IsCE<|gO9+?UfyS33PvPb=MBUF$PF zs35geZ@Wq+J%hmTP`vm_&`OwnxxvC!P6E}&?02vGpd5J+tpDpFABkn$0!;o%EE zpcG@e;7e615hEc@CM9uz;BbC*O_3|;hb)RWw8o@c^t#TSY%OBzqLFkEWARHLy6NNv zq|7LgnHAZ7wVI81ZsM~6GL==I>1~DJ6cw>W+wU_(x!+&~u23p!W(gR4F%I(e@;c zVZet`4U-i7FLx;7YL21r-!)#;eHn@vO0MD@yFfE(PtycpQ#BKR7{f>K&?&aP_oLih zu~(ua?s)+QF+zS9Y#-Lar;uLT{GBBMN2napvVhJqzoJreEu1&~xg_R+syEf$ z?QZTCiWMUCn<-!yV216xcAYpVgNRU{6`9SIqzvAzinP_B;a8;fYOyRQYu(?<(_xXd zLwwD{#wu{#Q>&)&@2M!xIM=oDNt+A0VoW0wJRLGHT)c9_eK#!)oD_QUcX0`6j5&M6 zHpEg2*~sYxepwNh^%8FYb3G`qQo9fT%C_7eRQ&Z=Ueb27gsNES4a1E7u{|SIVNmVgfixinRa9$aTwJA zQ16j{I#|=^nK>8aUePSrWh01Pf1^*)s|e0sP}a=e(|iZvY#GYd1^uhGf_(OiQ-*AP zbrC;a&_^7X@hoz8sL1p&_Z1>4J9HKC#gS9TI|)0%V$uWkjW|jmkp>FEggym%qffTj_!-AJ_r6B{JMuf?|+C|z*`0Nw1FZ?V9{=E%?h+v_b^z>wemLFq) z>{8ZZ4SC9Xr>w#t}KRlOB7*diTF;*)!G!Y4Jr7e)!eO%fYf=0|2FS&a04A>jM8Pyg_^3KjF}y^8*x6=K1e90ODKl2y=JNJa;ay*ql36 z2LCgjN}D!tp|Bd1HI+%A+>an0079_w;v4uLKyAwS%lXVvZRK1&TGokc%vgerz}l9Q zl0U%e0>wHgjR1_?999dK!IB6o9%ezAEgo=@Xck3wBhN606uta> zmRZUJ!B@R-hisj$gPY2gNGw=MuxGz`d3hmZfP{gpWjdif>UEApB!hq=;Mz^tH7j;w z+h@&TYrO?^1#w*-v29f@X z_QHd#>GJoL_S}Hvg>uMf!D}W77}Pplmp$Tv2EUdb$e}-&P4%L08Du@BljmPks=!S8 z29Tm>?;tA#3iYj*YYVB*@hiB`IQiPZ{B6cFKZ+y3C@sm#VfU9JS~vjFrn)&Tf3XKV z215Q6;GQph)IiUCzDjsgGb}gIG)Rj^Eb(}i1v>x(_Ws?+#6DA5Y?6=MdnBkIPiJDZ zF|!D~=Ug@x5`NQ;Rj1>~uyr0YHy$ViJ3&W8tXi@bLY7aCL}Vm{JD5XTIcoD7v!J|KHh1jJ*{d+p-*BxxQ&%9`E1$NNjWPF|V zgWj?wXgD&tKY)#R`)VKWp>iQe?$ZK~ZvXC*W;6)LCfFFD!6QzA_{`Oe9CLQvyNgHU zf5HW@MJ3I*??&?{8aju3IeTVi%qOJ4hJf*nz_Rt@3T0)b)&gY0ddl|_=dPrBW-YBJm1I1|I3F?#>);NTpkkwiE0gUkcRjU0 zYi&1Hot0BG{7n(h%8vo2*QW=D<^+gNRAd^g;Do~B+wcK4s-aIMr$&*`@|pa&H({^A z-vW97{o7{qT(;|^$eO28cJz9sAxf`(je!DIVplag2YJ$6$^jx#BM1H5p6(l3+4qZc z$tFAfTY=V(p@4E*RmL{rf7W%@gLx_WecFIg$fVm7Jvc0D41IXwnCC1yf;3JhyXRIS z5Q*`pR1X9=E@>E|zM)%EWwz&&Kzk!@1M}D~xqn=`i)DipBOP__qY;Pi$RsV4xx61% z<>uorq$XQ0Kjy&C36Sd5`IC52AyGD@!#{COh!X1O)nRLzt0l%!#jT zR%cKL0))~}?FWRM>{AB{?0}uJ`_qOoak3>){KYPqZm&fDVC;~QfYZH&q(B$`G2+=< zMLQECS6&a?o@oJtet<)me%*r8y?gIKta=oB zhL=e@J^l&DZE4DnbR#loEAQygN5<%{RUU0Q3(p;CA1 z`B=;r)_TDDtG=9mO0){#9Pq7j#1{}1GbL$+E2_r=8j#K}{pzaGu3F#rVdLFty}G@C z{%XQ^jOQmg1z#(d!yOz9#azhY9%Q2eyuqpg7rel+%60w0FWNCyxiV7Z{XLgl(4er@ z@wte^e0r3U&MG_`CJHhQ4+D|N5Q?vRqmxwI>pm~W{O*t#dd!MJVI@D?H%D`4eqlgu zEdE|pmZ)d=E6|AZpR*#V7BHI}gWgpP{Osv7M_3TeuIH{^JLpO=h_z--fpjo@tlQJ9 z&ZB&F0wzA9czs9YRbRL(Twbk6h87m$Nbw9jfGT#_I`YCdd;5F}Y!>l45UV0L=z?_t zd^;qfu&`8biQD-Yd0a$>T=+E0>T%fs%&T}-ksevVHZgFljYg7U3v3^DNl?4UPZCL< z_&Dt1Eo1#BDuKJ=FZh>y@VI#-;l%k=XRXM^`5E(^ zqZNYe;b;b=C;$Wb>!Xu}#b6@q2#t;D;igIjm0je#UT?~cp?-*zIsP8xp2esuatgB0 z-y0Myvk+b`58cheyjg2ks|!uF?2~`4&DTMnhdxc@#&a$6mJzH1)CAM>YggrqhtP*p zIf3c6(TuN{fEJ=TP}Dd6^z>EE#wPAmh{YeFTA?m3mbC(T>%!nyJla&}sCYa)K@)=q z?;qEHIn$iGbT@$BL>$^r|6J}DLI(>oi>xnS9!?&72bpf-T44|q5k9t1vaN=00gq!2 zu-hkfb$Q37s25arb-r7?ya`WQX$s_a+J=ISs1R<(Z=-llT;a1MG!I=i{=lIJJmf)u zD{=F)3pxz`JE%Ew{%&u>mF4)^*6L87;p+x+oj(tmU8KtLwa%Jap5>kMY3J|>MjEYb z_qtDTbb+wLW-AQ~OuVr7zg-(k7a`Ny@&TlJ@p*JmWrKpTn*cW<)66fo@zfAkx@^a>FVPyt;koVa0GT9nb?j3- zEL6uc0*PK~B0-r@!qw1hyX_)bIq~R&*nzmRs;?+TH`YFep1Q4#~2_J?Dqha`PS|N~M>GPoyv@Gd~ozTGxhSfU2@4`F>S=rvlU1J%-z`jka;&wHMWKu-J+R$eX zjMcCsb@S^#mghP+CrvaHI)5K={t#?rXL88BTMm1C98oO0&}~&+@PzfYpRmylB%0*; zn4i8T2zm(wL^~~8GUC()iG>64q@9{}vvU3RW}nbP$b^f*n9&C%cEW3eWXDC$7{_pp zUn`x4v0voJSF~L`hTpC4Jy)^Y9uJIzR*99Yuzqv)L>!N)jg1+X*v+Ye+sboU2q3OJ zTsYz$>(4-po=rjX?h#9&{VuXenl1=VD{Jm#kXrJ)w`TC*o-UO;%GpM<0z4oqP|m6y z^*EG*`e&&cmA&SHp`r2%{iCWY6}y)|fAfQI6q<u<{-MTZz~%H0It zvacyK1ENZl&qw3cuHX@jIeI!nGsSqY=nwhBx&?`-S55=$8ziQ^LoOYa0x*|fS-M%S zA3ENbwnHny$Tv`hoVRRv1?afl$Fg>|ve{PQ)biXDS$k8YBB%Nev%gHnV^R4DNu@TovakLV3$QRUI_u;AD}iU=OO|nT~en{GeAoXGb7gxpLOp zp4NcX+&)s{QhKVQ0W%pefCsP~ku>ENS7V16C>?=hdubpT_cEmUu+I$Cg>4T4FFQX* zVS(wb3D!;PI=>8l0I}FLjCF6n#o1wMEDdQChGEn89%i9BlEU<-&C+;5qlZTy;5+(_ zBPZW3Oo98}3Nfu&bZY_t2x%eHc?k+EtG%Ve$jYNF7(k?xl{C^Dx|-V>COO^EV3=7Z z5KaEi`VkHtNdWM23B!q4cFD89x$GyFMV%l83LJjH-P4>VU(5B$B9B>@UR{y)5)fsc zZ!Qq!z^-Hw9Rv$i@ktU#T`&IUg%$_{cFLueKWn!ygiisVfA4iDEh+nUzGT*I-~!S| zBT?W|vEH?JmjYYpYh0=7ULEhi%zoxnSDN_b^AxCU`YB=JEIn<4@SDK->w)QyLxS`2 zpe6~AA8wg0bcq~T=fV+|FxC2q`!-x1gU`gw9|Lo=T=HijXLafYf6ms`DJ3~ITq4_c zNhVWki1=e;c7?w={%%Y}OxWyTa51|;Ro9afOq)(hT+gl_Jnd2paWiRfyYsu8k)f?o z_KHBte8X$9qqzCmR)oOA_XmrfOTarixqy8qP_Y<$7epR~Wb*kzN|W<_19OlWcY`Mb zbi76SvXhfnb0ZQM=;!-3vN?J%Q4=VioShDt+u=@Ngn}VQuT&{OHCS|ZD!H0DC%CsfZjGsJA@TZ&> zbbG7+1QsP|` z75ATW>HY2(_rBhyFmZ|0?`2gbSiFKl}Iz9XRYHJR$lcf zk}{}VS;oDsQr0$dXxJ>fHip`MAM>naP!v9|aJli#x%G_Lry36i6-c#fsZ3YZ^(=Em+v~8*fEz!JS#y z8CrS~QYcJT42vR`RihdNt5B4-M4rR`X3S+m4(uS+)J#d8m}1>98cgQ6-Gu zF)MKE=={Zs0cEzNO|*MtZ?MXuk}+%Th<8vVqw1qAj*i|m>1ydrQ&U9gpmBa$$lsVU z`&7$V2xBvT?O(exr@_(QKO4sW)LRm@qk`R8ervNWTkTS?^nw)T#$Kq7M#47P1I zJ5@|kb@imWR(GshCc??u4{uT=!Z}XEbk33$G+h+3k(}kFF%f9{TwI;_e!PZ=)604O z-S)5pz}MixTXDiyQJ3w(tZt6QxHe-@Au*)=8`7}~H3f^}TYSXWjBRHpeV=NDRoAh0 zpQ#0u6dC1U5K>@2sE+1xQ?6>z8*QLnbDuj>QH+&;D7KJuveL61$yM`Nhj{1PY8E^G zEB39&bvYz8@h9iIK<8J&C4a}pjtVL!=+`&EJ&&pZ$)ZW7YD~K8jgw~Get$414uFsY z_-r4o9ro70Yag#29?w6l9)qT_L4TtjN1cuaJ{6wYA9c{Qp3~aVV#rm$tDa|__Zq+z zB5dHmo`wTuA>e56VgKD8h=WiYEN!S|f770l5WN7?M~KqhT-{O|(6gZIE@Cb((dVb({5=^_uk=(Jvd~ zSapd0AY4e6#L+MTIVRivSeS&Ij8kDELdupMT7aW42{|tt;$)bDT$0ma5|>SPXM**f zCaQ+2(X`5%iE{GRNRhDOyf{mGI3$z5a)+6>BK5C~s#Zd(qqy-WVGelog;5gy#V^2rR1^x}KZD5rK=!{E|0hWO zZ>XrFiGi_=$^R8ldvz?G|IGhKK>w$dC*m4Vc2fWVXiozGDE)6FVQOOF?BZymXJqH( z{QpN>?&xSF9gjNr?&%Gn_r-9@YrJJq;K7Aflmu!|#6=RamXZtKQkkYTzTD}gfXi!8 zu*{DsLQxOjT!3|Ae-lzaO*Q#`-Jt9FeV$g?>H0msPldVldHk;B<>Kk>{(MZ)^ZGoE z+4a3YneBPMop!0!`F>qwvG4i5bn*3m944K$-SKhveji-S&G~&CcRe0n`Ps$wa(~}k z3_XRNx!Lh~e2?w00}wTS8cK0dw4(eM3TQ~Qf~Cr0k`eLE&}HX)Js{rvv({h`$B z?fLh9YU*|^=g-aC+}$2u-}i%D9N)L&PuHo{&)gaOZ_igPA9v5+`{&o8qd2*=-lo3u zsJ`1m_Q^J#XV@^l7H-}i?q7Sj-w#_pU-!=k9nO!3GN&@XFLmR1zSoB`_B-9auaA$L zBe|`z#Rv8&FVQmJpZ9}@?xG>(^F7`aT?e)8#ho`qYUo2fN{LOYlPOe^g z&Q1p(Z}+3d<8ykxxY(aDdV2ib9>3kiKaT%4&xVe!a^dZ?+i~_fANzQ}=eEjfq>_rX z!Y2=4#ZT_hO1-_07UfBL-MuxA%Q=ab@{8S9dn&$Hv3^?P(xs=4wrd^kd`xEhVkb+v9J6 z=3L?V?)Tp|H@he*pWl zS~&yvpddHr==?CzX?Zo#@wXj$$Gdc%W}YSb%}97fe?w1Ue4iV5O|hALg&f~y;Hr=8 zPRF$8+$KOg^8%K2JEER3lm^%-ydPYwR-CXeKC2^Wnb0=P_EjN-p0rZ`fKdmpNX-WU zGw4Y}XQ;FhJ$z~UH+@FGhF{qo3Ik+Lka@x+i2zI`cyb2^bI3Imf|8jD&VzJ~%Xa+9 z(9PLL<&bZGrP%i%tKZfPF;<*A3_b4DnXrM=4WUYx3COwoh!x)YgpP?o{!z?QplfpI z9fZat>a}H>Y@p8*hY%Df?Lgq}q6^fvcL~1=JeDUmUVzw9w9Ug+fwTp!#x4(Dr-^1T z%0kVg<@X>+*h1RsP4deevvUYY#&_3V26UW+FqwoPO;^?fXMXsKnW_1!r3%W4X5K<| zPpof2h=Pj5C7isUvjn0hRgEoKtyJX!uhJD(IkI;~+yjTU@H9%Y3?7dI#n!L9z#1_} zFQr>_sXm}85)T&1U!k=|hekUOjrS`U$!b#>-ePD12+Wu8&#ttdOk^L5D_|G}F^Dpp zJ5#k`u$n?IfE{X1HvC=cgtr>xgU(-jP$0xeQ3`p?gb5= z0d#(Yo2#tQ@ue@?st5@SdHbAs-8#9OOPw@Buc`(1<1{WI-$Lf*=PKgZ%VcGK#Qsk< zdq&16s(H4?%XdapWB7Ji-Q7|T6h4jR5#I8w*d?^3KJe16N6D){$T~EeC?X^tNP7R^ zgqh;QD^UuW(q5>B??MACn>;>~3xSkZ;>n|KxO^Q^lB`1FV-HDa96iHs0y460jc?OsP+(HoV%LG3bYEjSuOxi+!raaJO2Y@TI zDO_zoxQFu0VA>;XcmE-2?g1=FH{4!Tw0P_#7)-Y6m@dKEpwj)Y50E!l5_l-N{SnFt z`~;GTKm_Zl{P0IgBh^gwFAJ9(d2*=qR}88RRa5~%sJA!8pEBN^KQ%qIUN@eC2Py|~FE8Q9>y-@k^9Rr{$jTdj zD`jG-fcg|c+#Gb;VAQaU<{kKJg4>b@s(W2I04X`L$X?qJwkO0IMMGNxO1!KE$}Ls9 z#VIT((lz;(!8qB8l-0$m@s*ulmXFP4RvxEf+L$H$Pxt1_gI;ZqWBS^-YdQAJavW@P zHHz$`F4^WW`^N^~VUCo^lzJ2DswqO(e||(1bvFdbUzsRiJaNu`)8Y^MjPotiloQyo z*3lU<5Xx=R%8A^T4))}pyo~FNibJO%Gf_XJh#&0C}1GuBS z08xuI0ZUK-cxo2Bm*5n%YES~hWtT?xe}2>G|KoIC_cvxkxBNgwTPDfR6n218ZNVZ* z5V4IeSkj3*Dv~-RK;@)1id*7HP+6?iv;8e~1C|Er20%trVlB9^v!Wg};Hi&=0D0Nm zgy%>^PoFr)+9BO+H0iHTuMD^D02E>KQCOExm!+T!sHwG#snR@dqVPZWM|HazHrEul z4QL+}j+-DD4QdB)R>U?vtU?J~<0h;FXch7YPwav-(L-}(Wv-Z&j3&vuHhux~ad$^k z19gRgR(q~EH$We?LL875Dy`HTr&OXF3oYe_wjNTZO}~O1_z3S0pomf-QORnv2!o$D zszzbTbENsDE)UTrRxu4Ml&})fqv2*fNO_mX)k<=r3d99o(bm}V#d0^4XZ4FmI98}G zIRhmE7kt+;b&W-RPT1~3QL`v<&{#F;LK8=!+DhK1tTzOr1v1h|ZF@GLqABTcA*@*$;vMYx;~L|7Z`~Dr;E5xV0l=rI(SL069e7;1VStbqu1aWD z5T=k$)QM(E)urU{j84S0-=Z#1rrV&6W)x^=!d8Cmv0Z8PHnRuKP&3{cFx`6TNZGgg zK(pqLCA{%K%m=QZ0aNME8&uJdRw-rGD&X>ZT&fucc~ul{fnx~Olk8Yhmbd9{1na`C zn7MYnFUZ4yw&WvnWkMR|p1CbBBHb<4n539s+J{S1jl~wi3CWDK^le;?x`L_l$@+e~ zA*NUbevbAoQ_@kV`T+ye`!0=VqY*3OYV9HW0dR#&+8fDtis!A+MEST$qu4@&d&SY2 z0u-1P=2`{beh=fhj7YbohMcPebe0ZSzIJ~1o$o4teR0Y}H2NuTB76`u;CQB{ITL1(~z=3KE@&fi@5_~OlW$cR7V z9hu$wbM7#NXXs4$Xo6g3p=5;gHn%6}vjxC+;lNl9vjX59J%H}tm~~wE%QbOu?eCVK z3lqj0PJ3|LJzB_%r`RwH8TJ z$gEA)$9%l^7$T5fP6bXzb}_HwIzzZ!$!Ymo|A=~&LHZ2oZ@LM5-eaXo#+)!sGc&6L zGaLEV7Cz=YoGV~p^%>lH;_0SlOOLZ^F&Q7C+a5KhMECvDUI|5_R0p@(Z<|n)3&)5% zDtUttpKchhivDX8jl=W9q2rh(EZGb6nfK8saPH4qC=IV+BN zBEKtr?aI$3lw+K8X(M&%rvBL+*!EjSVX+Fm-%^kqp_l&n) zo&s4q@t=Y+mLaZSd9w{kx}xbU%_jzSy}Y+(O45do=k}}4;>smJ<^_zuvTBjBjZSHn z<>OG6ScZuoian{6o}khcj*8t_L)G!6C2^&F_J!H+6-cf)`Bj?8*N1kV!(vtLr^}EJ z)pB3i6oE@k@AL(i|B;s=1O_rR+mB|mdYco&wbIcla#f&40G$;q9_%g*VXVgA_yL-K zIBYPYYRT@`Ub<$K#K@i zRD%fZFVuq!DB&Ir5!L`wYyoP|icw)Y`_&3RWWOnYw2y@j!W$jP;;W5=AB2n#YA9SaWF@_1yYG zI}^hcHb((fTo0A{d?@#sNk~zdklCpe%dH18WgJ-;G2Q(<=3XgX4rnFp*0wP|11fuC zS=NVc>F8GEUi^7tS}7n|1m{3dPSKw!fgV6w4zq@|MdL^ZrdJ#`; zPuv@}F05VPEf6G^sWPHUS1Q7C@x@4sntJm^W{MU@n9B@wg$MVM+&;k_|1GLE-&j`2 zEktU-sXz5+IBBZ(y>$eHEBX&QC`<(oW#uNi&rOF7V+dJ0?SR%jMOQ2Rnz@M5aoQIi z`8}2W!Si-^3qspPmD9k@!hVD4!=?ylvlJI~{wr1d+jvfa1Wo&_2_0l% zV+O{?`l@Ihn-f7YJz=MhHTGUXp3td=f&lA$V6HR*pQ71X0@>URVBFYon|~2_Lm7ihZWbXO|?@o;N^CYVBXd~6QhV|82#wOYG zs}6^vvMb#P?(}A~WcGZJoo@%9zOfb$dmSTtUb!yvW;h=c;z(Lyp^OoJmSytXVil|S zsF5jy*}KX@4LC2XFv7_1Sgz6o?z@LI%U5%y%d<(7wRl~Yq~Rw4!T5Z_vpj!;~MIi;8Q`LM&=LyH9H zyZ)p6MhxPzE=v{uq%4#XDcOx55Sa=0cZHR|KofjJcJ^7t`nKN?m%6Z<7(8BZu{t1@ z1#$sp$-)NO*=gevU@`3p-C)Xqy69K~Sjy_FOF&*}L7f_sYmzVGeuZqBKaQ~dHM#*d zoMXUc;zE;`^R$AQ6bX!pgLoI1_FRky}-2dwPu`UHb81dDg zb#1v?JPi4Y-ZlI!+M&dQfVACVuI@%oS;op}Ox3mk$M(BV*+=I`s%c|O^z^LUi=p*= z&jx<=WV~`L#}yQS&5CdU67mH~$F-Xg>a|UaT&1D&Qu*wN%?9wAL$|xiz)3RxQ>`r| zse$G}lDrmBxr`Zi=^Yg3F8q#DciWK`Av?^%xA~r01Hi5`Xu9$w4h}IKUP+iz^LW4*urXe$o95v>;E>52WJMQhMQaDYOMM zl%-ebul_SHP=Ge8f|SB*Zvxy5(X>ze0WYo>a0*%%oII0&l-3)zX&#Z!$D=)8MYE=D zT0;s_?YNVrJroSo?*Ir8ODA_|b0W6;Ox^`!w#bw};{gUV1t z>OIeF%TcI;2!OF;J5=oYC?a==UQ{LN*zBvA6?lSnF+>CWRO=1KQ77nlul9Yn!XY(lN-NYDR%R6%ZrHQmg`fL=}CUAXEjDMp80PR0XEL`OL8J? zDilL?eFQj{S#K}I0 zrBTrnLP-yG3<+y~aQ4{8$SOugZc2ki&Ih^|(6ulVJG$|%lqH~!vIHZ4IP#*^2Gewz zW+tuPefhCGP-zUJR!=PSFd!HLwR2Gks~5_;d+-(SQU!L2r6sF`9v|u78lL?AkF@M| zJJ1n!EU8-|I27!LW*2ab3}tPvF>Ld*3-5HexFOyzmpr1ml2*|I!><_6(eoeZcf+pz zM7n!!=~b@fel&mY`spGBT2|`7nd>N(quoGi*4y)_5e{*3XZhbHFiE_PA~P{V_}QfC z1Zl*0S2u3a#qs+^ zaH(Y2?baU8EJ)Sa{7`V&_>LefMia34H`K<})9 zeEY6E7s@A_K;pS!a!;d4-F4*B2D!gQZz9P#rHqk7A&B$sy7MPFWGu>Fl`R0;XjK-wKzQ z>jW8AHzdU8!87r=Ge|1n6YCckLT?;z0#7?GGc=HH67i@0CP|p_ohss*xulVaNxa)F zDiZNoZw#0rmeCDK*x1OyZYa$zv<5?vt*quz$8s^5(>KU`aTgm%d#*2TKs+%|aW>Va zaOo~U)lSJ;8Q@zv4Fkj7DoqW?*qlhHvUD1|ex8=kJsD)Th(eNCW6WeL(EAhgfJLHs znn~D@tlLn1lr~c`nn#?Mhf7f&n4~2YHc{p44a<9RMK8Ffio{^7g)K{Im1$S>c9$Qd zRZ2?T5EZ9dML#7TI1&ilTD0DI+wYFeDbqDPKAUD6H7HxMhyGa4H;_Q#a=v8Fn9(qM0Gg zo~;`H`fa9GCqK3YoNCX_bBwxq8N9-HDBzaI$90{RL4dD~9p+jNWWFtl4gzSf{^+Q( zD@kTUV_M&0UY8rsI+zx!#?$oCqe3nG)jXBPrhPqZ$tnf)biJ)LQ>FEzfhnm8vg*s8 z$Y=I4TX)@%v7`)Y%2qj`Dh`R6>47K)2`xH1JJ^2g4Co)gt>}y?x4r3(0G3l*&I;(v z$BPN?zGhpM?j8Wz4S=xofck0%AbFtwDM{-&d{6WcHjURHQNT}Kgy~gnVKzh z$m$ui%zWdw?qS&z&3G3YDDoCdK)o_7ptI0-5&Da*coHy*vzI6iYv3lSof~b zts5_3&`R^7ZuQ?X8xdn=4tSbqq;S7R2%{y-Q9S(zZWHf)^}x_7LhsDU0Pn}!PnC1Lt&ln!|LDDX#~z5t|#AX7AL<8F8TN9fT1(_BrE!3whp;`Hx|V~_l` z4p$N3Uxp(LuXv$jpIi@POuc00CkT$tL)Y%((PVhSjj4zSB8k~2J8KDH>)eU^ixr(* zm4IZ7!BLOTW*hJtgneV$?18lKA;H4P`7m_Y|iuZIVFNi*j$#3+SJ(#PMl) z=dQ~P0Rxz6y^uJk7}e#vw}UJvRF+&~{-z)zE2Gvg)ID3<$8yfRHV+Y#eX&06_-LI=la=PId0+$B(vMm9rHZ85J6*0wTC z81l};^H^4VQl(mqT5DJu5Gc7p5}f(y9t(5e1z@v>Xwv}ce&&Px7E zRUQTzMkiE@<;HL6_ht!qJ)+}!Bc+@h3w1otT=>SncQwzj1)7#Frv7o9P zSn1A7SCy&n93{;Nw4ZWGQ9>Ri(lyFKw$yuv<%@>RqAjQ{_XAC_ReXOpjYH#F6T2Al z@Jy4&<>IYH5(bHKJj`K0rf{LO35&bubA~$BW?Rz64?}Z$$VE6BmBU6l3BCdfk$ulr z1n~y6w+>!6G6>M{p+-9E20#oIZ#NizMblDvywHvW`_{kpS$#z# zx9N`G6fNUz4}iN60}eh=@_gpFpR*#`+z=6LDnMC{!m!SWX^J(O#x-H|SrU<`TdNmKdpItyhzFyGd0N9n!?UK z2dl+RzLI;orLLgUs7jx;`gO*MiDv@ufMrP-VaR1x*g$UH_3+q5@3r}w!zM}LD2O^u zdO8Fy2d!%5qDS_qLz@|X?rFw1y?XxQB^(Reu$gukktL%sN!G{QMIs%vVwW?_Z@X#hE;a#<-+bb>jjKc18w;RYb?0sT&gGIZ z0T}~@kux5NVDu%1A|{9C!1ds+iGv3a&U`3D`71*oHCA&9K|QK*549nN~Fpbr%5GccwOK=P`dQvOkL<$%*)uD`_ifgAKbXMeAMmC46>}OlWO^ z;X)+1J!h*Z@B6{vfIRhFSvY#gbp_=_P0;!#ub*vv?Z3N=O7MIJj{zh~A-BOtC)3!g zNdERjAEdLSptb=cx-8{}qO$JL``~hPtIFD`G<+qo^5aClU`{7R9t-R^?<8siy$qtV z?F^|`7CbbBx0mR9edt>0g{`h0}O74tRHnH8T4ej?34>QMa9fNpeI|BPw&qnPsZkt(1`1* z9vyBN9K_ykJd7IqM-XzT25fgAmZ`$#Z;g5TW|KeD7+je=ScJ81vB5fDVs_bg>bU~G zy%Sa9FhA0?gIQZjLO-xlr^*#HP;aI%1av1PVH2mP0L%i%9Fa{lNouz}8tY=)wF~Kn zZw}ML$ii(t@Z9YU#eGL?Al~F?%zpYtloKNACfG8zZ!Zr`NUOR`MD^q&12WNY ztA~*>WGt{tbTG#80f?}Y3WzKQe0QNAW^p;2u~6fUyNj*Tn$xsry>xIa7`5^d_=8%i z2c7)yWvcVir#;Du-^)Nqa!N7F(hssBa-Vx2_k3em+)jL=F*&bcHvx%}a}#Z;Dy%l# zBVztl(g)n5$-1TTIb-3F>SfodMVAjr4oJh`tb1hh6l;hS9C*i&!$fW-T!T!4MEW~V zo9y_^Cg}``boZ{b-4DY1j=qhmzg#6tt&Wm)-YR)@ck5s*io3c&7Iw9CC{r)b`8QO7 z&Gwk?YlivJn-p{nilEkTXbbB}9YW#@$Uc!{E`=cSZhfPbu-miTr%VBb%k-rU^U$-q z)fBhW97FWd7$ensg0$4$tFy%7*6_5r;20mLPJny{qPi$4858V=7NV7;@oW?*43+Cm z$oams6p9lB_FR!aUsLcceik3uI!mw+JK;BW>b3S5}ne;(krVY%i#uu{*Uf$cEE|wz{3w) zHBC{$f3fz~L6t-An zGqqK}e^N=MPVy!vb#mnMypNKvLFsp|#0>cXyu4JiL<^MF9QEj4-N+uvardk{97$5i zL7AiNkfBLpLsJzn+8w7M=oE`z>OHgpdJTUQPPSAJah00}Q{2s%dvNkmQh@JGnF_l@ zNISNCP7`n_0eMd`KH4UBX&eL6hm*a}%E{c{!uU#?Tj=dAt&Vs)6c$s=bP-uff&r@B zQCi7ygNP(7KwmoTn?nz&d;~?V94t<$WdZl-)t-KpAp9d-+)WPpN!Y?R#Z8*w@T}gV za-L|u9YW2a6bLBjFib1ydRqKxY~*U}&Swd9DGM8^9YPPX^HYh2Q2G6N?CNZo1#uOw znrf!{Xa}xPwIcF4DC)<^7#0}$dk{LmYCpBZ6C?IAdulw~k(<+2j`|bD57|vdUXK%x z{j26W?6ZN+*{?l^EG_!m?ff?#bo3B$Le%+pm9AQsnyot#sm5-ePG@PnmhZO-qo=6SiVi=$gd#M3BHGG zDd-$8umC>3n?8QIdp4#S(GCcOGwj>Ga)Ri3+~&E$vB>Yy(S`dJl&(GALb&Q*YZ|7L zQ)@;$clN43e2(v1Z}yxq-rHO)2M8*uRy(^|Zl0tEd5LvkS9KLCkCrZtGWFlrsO~=h zM<$-4v-~Rli-|wA0|V3euLLQbT1jWC7A zt@jHW#%OgVpBJBPTDdp;Ik$ds&FQ=~u2{0qcE1>6YpF?h*7lbI7ay)?M*pIFcRX}yVL z-c&LgE2~-(iSED!k&h*N{e%66KTtdJaMmO(7kGQxd|~Ac53=nz#BqdNX#lCK!jW|| zt<}F6Wb6koSqG6Vkq68E<|nw(otu*dBw^ksvd16{W2ie=Z(^OfwzKok_o|=F9pgC` z$e89%s}ZQkzx_~=kBoPyFgnGmtT;NANc~yRt$Kxbr!Zh$$%ooavRU>NV?$L}#xK@o z6TKL7L+{9o(GBaRe4UYph0#rOYQleXEc~G>J3R{$<4^T3*=_#d!yKpRy%_+K(hEH;Nk>SY7W+&zh+14p;F@+Tornn8!e3Z$FD}(^^?$I{EK^ zoQa$Y)%K3MsK`O_ONXAvd8~A$pH`Xj=TCF*f<=>oNd&hN&YR2OfRPDJ6oP%EeH(Cv zLt&F-`8w7i0w1_X70MX}>!6Z7VT%Rt2&+W^zrh9rLacD_ff0^!{|2X^i8yg2N zfHQnGU35PXGN1PQvAJ^+3S*{F75mv5kLY7k}#yIME<(Z(KoPTch@8oEy6!c z%gQ1xmQc!3Cgl9xsc3!s!zh%3l!iB*Nhp3ip;zN9!9Ql7ItTL*MUByr?uSD2I+X<} zceJ1#L|d?9azGkzM;em8vIsjCDDP_sCH~$-xc@?VVlelEPNdUTUojC%W)*#};uEjh zgHFH+LPZq7T^aclgaqoM+>oNq(dYbbUb3t6rXC7$$m+& zVuXssSupwXXghWOv1mIBEQ-W$XxpTbv1t8FB(%+_!4;wnS)@9=^U6T(y=7TgQ~|J< z+CA=K$CQRtpkFkf%nQz(@?4ywq_p6Ww#@I_)c@Q9e5#ID28QZei$y>|VfW=T!;9Z5 z)PGN!AEY|ccEEsvA)|wV{razHAPsHI?VT)LENu1v(?l{FI+>aMuY^OcX|nd~%ZP)I zAL#Wb3Wyg$kBw1txMcwY;P{>lrR6B_Oa1W&YO|M{L`08Ftx}Q&3Cz3evV??$z?bX( zHT@~3&G&~7mwV9jb&bB@)`pINfKJXA6K+qmsr$CKJdRAX_4#o)8Fz$*MYQ>TcfAWb zdDuC-UF^S~JOFhobl)Dn=IVdmpB#=)-hysHd;KTx!}?o(9*@n-{+(^Fd)Frq&-3rO zem5_t7Z*1lr!Obh(?4JDwn{O@5fAAWw0AI1B?sT*XU+#;f<&PE4l7j##nsTYliCx?mah!w~(l!mbeP7=a`J^Ia|3t+kLc0#Zzrm zv(>mO`3{+Lc+z_wkf1miHYc{}<(Q&sv84Ccstd$-)`MCJS)eQd+et2I5BETP(!+*^ z8+E2P5JM(`XGqiYFiShd6iQ%9i8#Qqi8XE+qG-g@S)UgLQ6VQ?`$b_yp~FZk!{xW7 z=YkgbvMs5dk>(*gZ#wZMMvHeEf}%AiyED~MnbV35T~X7tH0lCeftXbNzIup$|It1= zEu2G+4*-W5EBM?|4lpTS*WWz%yWlt6&-r{>vb0k$rcNm*T6~sqAgE4KExJlaN9rKB zoYYtnLyOu-4ZO$-m^W3(zWP9EgT6n8(GtC;;xuioI-9QEM!;5*RFv?5WbeL`GnJ3h zU#u*tZ-O^BErrhA8X>H+lr@u>p}9`;JR<5y7Q(H{tOSuiwbIoX2)pgm3vr-`+MSTr_*1x zEyX}#C|(vgMY?oSyf~tE#b`&#jHNg-MnZUQc=d)^~3p}jhkHZ>Mh6NZnl;%iZ$g78(MgVNHttA&yAV*j-+; zX;|0NM3TskE}?lDs%HaoP9*S7^X^PHGm@?}2js-Vj^zxV5$MyS{w&95u;q^9MwY>6RKsJWa?*DX>IGNrtCR|h$L7{cpvSL>{y2<*9Stvd7)eECB zw(*ZDvEuDfLdY%_nB_PPm~_cBH7Oq6E#*WyOz*;TjKf)7)tMd>u?=P`i#_-8UKyvr z4g}iNbsh8D{_yj%tm@J9M!G2vh%?5G`U3sEWm-^E-j@s}lQFT?h9q9lJ19dT&miQavXnI5ADtU7p zmpD`8o=5CJtFV|V%U^6Jn(?9Xwssd6d2#~tT?*5)tOge)_K>c|n~IcZ$5bi>0`Z%A zdP{!^G^NB;Oy|W|2AGvE5I`exG14USa11~h$cXG=9d{cj#zmM;8s1YjB6th-bsM@Y zT25jS2MSiNW7zPGu~hC=|B_BO(4owte0XQTGcFf~%pHt;z{GGRL6lXKc$<8jadUa|k}kIPJq9%$*C>4cT|sk~U^9)R zj=_Og>}W>KJ8wxmww)AZZ3pI;BX`#X%y$F_+Ynh!GPY=Dvty49PWhnmrh(Z4t$txN z{ktu^TkP#1{idPoF+v#-+0kzD113ytcL`);(qNQpd7k zMilY})7XoOy3wat1?B+!%mYqV&aM1nrIcW1SD|i`dmrPvKK&({!2;32NZIlyDzpx}=(f$=4PYNY_2n-^izsU+)c)$o`BP z2Sxe%jXW;<-uTIIrFL*f^MkymfFEV;cRo{Fx91C9v1`pIoW69Cw+E>%lFcnX7X^~; zT3)={jIf=SNk+LJ2{!e62W(z<@g_&IU5ZSDC+p-M;|gKBZEFXkJBV=(Z*tIaT}P?= z9%E6eiAI^WH*9+Btyy*Fsfc>Se6E}A@@T#j;J|$Xoep=we4pvL@%yCgOVt4X%h9}* z&7yH+=`KONYc#=YuuRpIoch=OBNzX5(<6sx$E2e@#_n~hcY$%ipG^9!3+)~c(E9@M zx&FQ9dQxw3qL8yRmS|#Bg|aBdPN|eryO@h)Q0@_7D|}+h#&)R=aMoeJ4)>DmZqFU0 z1I-c+B|X=v@D_GpEFJwErjHv2M~xD(N}?4FgCAzah7@r!rd}61rDGBWGgd#e_NbnG zeCcwFr5~)FdV+V+p0Jwz`qdmpYo_C*;^JV&u&i{=o8=v&L-knIM6cTxCUDZxjk6hnLx}A<# z1zRbFlQ!sHhsV3r2Q(!5H=p3vBm)Tnx`V1hY)w=`X!Y?97b_#=FEkIg`Y-^30w~F5G zq>$L0+kP(jaQqN_z!wt zG#{6YLBZRj`EwjNKD}4qUa+j#RI)p3op5qmHUPH4xY2D(gsT37cNcY3!+f-sJ8o|Q zQRHAvP~9Ynu~0Qk`^t~sbXc`EAe#};>vZ-CC8SsmVZsS~In2Dt=kgXfI?trZb$*av zILvuJ06WzCvx8@*OM1$As8@ENoYN_FVsofRaT<20Q(5#_1>aEuc1>)(@4P#_RTCCK z#t=+I_5A(=ZB?K<3HKXcyHRfT7?u3i)-A-LBLb)zj!+_g7sp-WhGv5*T7@Vo(7`v* zeY4?2HYpSTT7lxr0~oM#oY!CEjKd2sJjv_tNBHFEw)x2xPQ>jTyICmC?31S+p3s2w z?#pf(-ZX^POftDr|5CfM!UnO8BfB2GFqETOP*QsroD0y$_v@oT|JTJzS&}wzZ(-!L zOcn_v5l2ZGB#>_iPCLlWM=3a`nnTE`_31D{jUTl!&FU1@CB5h1_glg~;NYgg`-~EG zpnx@J&~Q0D8g3|ciEXXhbj>#8r}31>Z?7KT%{;;zH_?*yducL-$frMi<^m5q5LC#o zY0gdKoZ-FFs>rGM%$0jNc9N7i*ntt@>D3D4g>KbGe|Pr0a2Ap#riVjdgyA$Je~E) zDz_+XG0!4xyPe6)KZ{8z;;uowK}?VnJC4ui+NaU#01?wO;Zx3io1YTCc6mA@S)KZ_ z!DP;BH_6<6l0*YU^G#_bOaiIo4y0|s#H3yWZ?%jdxqw}zaQPb?TZ*I2U7sY{4emiGvC*X3`*+oe} zz=8n=5A{?(fVbn^Xc{ef|8`=F(3OK zBaso{?IQldgEt${l;zWn32BY<^E$MZy79|}DcMxM2H_QY=N+_q5B?GTF##*)spJ62{8Avb*EjwS27LQ+ z4nt8lB5kz=gPs3|7WZ8(spj)r5A6~dC@^SvmHLSt8r{89XI>XbvX2kRRV2b0SF<$lc#>Urbx9=d0Z3{`sRq4ALV}MTa^3|gUVVV*>rD`Ge6xG zd62fN%&}i#QzUO4$;v%b#zWw|kJ_en)43}>)j|lcN}bvED%ZkXcQ7hs+O^VF*UNs! zrv6bk0*Q0E*#-mr$z7715i?l+dbJNs?ga%)^n1uRvm!FI;=8WKgPsP5sKA?m==9Yk z1zERVx&GbwSyXk4#cRzPvksy-b5wc1;#65Smu)@8zy{e7+i^2sMLi*=6yUho-aG4h z({Bl7F*B}`abO#6Urq{9)i?lvZSNuH^>B?;&qZls5p)!E(a+guuL3q`LNRg_2kPUm zShul;Q9Wagy%`JI4qJ~dtjf*LHEEs9!Hq1s!05s87Lc>=pf%#PyRuV%e0 zd0WO4nj7}n<_Yo|V=Oq=N2BPsbJXBM4D_T-W$n_6Rs86%ElR4TJ#rdd-T2|O&lV_ch7SZf zNRa(Z6k8ig&RSECA*-hbazxxA)ya!wz4BNZxZ>AI`#p=H_QRZ-56;Ml$~yR%aS;^g%dRn$T3DE zH$Q~Sz`Vl9Mh67tAKx&lHb^Nbj`>TPz@W=QY-H?2d4C-LMlC>3rz0}athr`W9rL)F z=`Hw9VuhX#lMA6}agz&`77EL%DAQ_yqpjln~E0wxp zvy;xT-IITzrs7{{Ddqb|#!9)9Kw^I-0D)2_hwUdCxU_4_P!>gIZi={L z6&jw|D+giXVB1N^X}mzp$bZf`)9Q|U<&^#aG1tLS0P-Y8XKyPvDX8#nl;$!e6A&bbCB2p@p>%`p2mG27$`-$!BS*qxDak5In^FP7Ho`m2oA0M)*eCv&Nbw&b{I`9{)q?i6BJ}!- zYNmaDat?4N4Hsr?Ls*ukHTxW0MWyN+O1)Wgtr%*0WTMp$Hemt#4Jm_U{OaGpT3O3& z6&pY7jnt_E$OEZQQd^FWPoJK>m|4S#sfew;>x$ei6rHP zfS~}LBf-j!s2e=p}8Q@EdUrP&Q? z%7}nSk-RDOR#a&HfS8dn%@pnQs~P4_%r^7JO@bTdcmLL8qjSERwx`h-mbha15}1La z4B^klglcwdM}>-?ThNHkZ*pu}qUlc;y2|PaD_@J==pZvjBB3!YH(^`40~YPJxC*Kx zP@6C`7o-X^*bBVZYRpRu)*O(@@)}@pUuUNXuF>Bjt7tpxxNu~D-P071tn9V*5Tl0@ zy)w2`#;ig#$srd943MjPGJnf5aHn%-($1ZU0v|zA(Fp%NtD(;Rw>qo1XN-O&My?l6 z&6@jOb4FE}%?ZrRIIy&}3H%Bsf$W?2z_kjeZBQv3o&)xnu@iW7c1ov+k1atGqwMIC z6Xd%}u%AIM*4S3r#cCY|cJ-(aj(kn=HsP=f!z0`)d<_c60&7vP)lL!Sg*ID&l~|3@ zG#Y$U`1K#LwB1$e!Im3-Y-Gue=Yhrtx(uM@glSv0P)jhRZN;m z-12r(`YzAmB-xajvNM!F=T(Qv$5%D0zk(lgG$-?5L@dok+o>BrHSyHB5LCb6?e5mhn5Eev8KW=FiUcB6Y6SW7ujwObUdit7no2l(^XaE zH3@{}spT|{Lqh16bz>7Yeh&+@ltxc3Z{!lbBn$5#^29hHR#m)nKhrSoYhqnVty+hy- z11HRg|3i}vn~YgDV8{-+W>H0r@ry0d^N(~fIb;sz177DUIFhy6Lm%EY`kp0@vc1J~oz(gjM;@X*KE`&dRny<-i1!?HOR6Og_kkK6o=eo8Y7wT= zU$FqV5O7HJ_z4AVrR_}U@!q??1_}y@&kY$VVnK}Z3i%rmCrJeY^3low;dNS?7h66z zj+qVcABMfWvUDL9lfi@l?L`6r)ABnWuN!LoJiuhc)ttb%@O2sY$?J?96xOSI)fL+l zpT~%&BQ|xGmz9$#s&kBBCTXFN*#9!h(}4`K*~z=aReywnhdEtHW`LC2Nj!o%LGjk4 z!LbcuiOTED!>W#qxz%m{_>jgbfn@KKG~hL7FF7njwf!L7mqt{AoUpYp2@1(rgQ^5Y zz08mXPLOnGsgsGO$)lK51Pk3kY9Qq1E%~P+B2+j+aeB` z?ZfV46$Xk`09`t<&m&SuK!_bM8cSZWmU~>;OwLSWmUY4+TIC>%<4a8Z!Wp;cZNJkb)v#dp!`T$%-FA~zgEn8PL>Te*MLrNGl>M!zH<}YW& zOM0DO-UYC=DM%z41+JS5cT-0$FnCP{OLBcDBHi2!L9qy9C9*~%x0 zm~AR?f18~LlslxRhp`AgzST84q;3zGW`*V>1Nv+&i2B>MK;jkL;6#W;BorM#luA%1 ze;EhmvhfSwYQh!5g0_mS^tg0MHQ5bR{mS>ILf|3p>pdMYOr7s#yxDk z+O5VbXlMO$70Yh}nIrhf2 za#YIR$%WztU+WWZlnORNprE|4jg z7;jiuODF(8Ykc1lOWP?0G&qa6rueHo!SePN=^0M5r7vSR?@b$uok`c+zbK}RiRv?+ zElAd7(_W`u;Q@y2s1saGsNyYz;oA@d)*GF5E;NDaM;Eg8BEDg@zkP{#_ zcy!Hu%9b|{{h_#UH|%&)qeJBAujjk(2;p+c0lv{VA1+))K4QbGd{*IZ;RES?nsishqnqLj_FsER+MrE`K<2w_#>;I<#O1+^6csH>P)mYxQ=E*B^ zgX~2jFr~FkNLNuB+%Z9w8`ES`C^IhrYDm{51zQ0ZySd_VwtsPaB=cj$zU+TE8aRySqiIn|iYkckSq_j|j&X*3q=a6Nq&HF^JRn_V$m6jCXE;=;tpU2a8-cC< z+-DLd$w9yn5!zt-c0Bn*v94(Gb0bnA8_q&k`5=UJ@5=k0qp0bMpRd^7pyw%F1H=GN z?}YIVs-!RxH^abv9>vbut&LW+R-!ruwQ8z$#9U~;4A)~%wVisWDz$Sy4H{jERy+C= zv>%NIqhGRskm%-e4xvq{5CEL(Cn2%2U_eydj>QlV$IeNPAx^ALPFu$0} zUux-0Mh=ZBQ#?Gxe4N+O#0{b6y8-F-Uu&iUd>l>Ti)~ZabNSE*sqDNo2mDCp)$FJpT&;A7}pTQndi5bB|HLugh4W9#e2 zwKDDa<0&Aug~#jyW)MqMDqi&tt^&ik1ze*m^Yirq{B3s^wbp7cSoca(LAn-&t&dh< zA`gIRgq*ZGX+uK+aN=xSB((z_I{xDn@ZMa$ZS(pBpf3kO=P+Qs(U{#cV*$TDZJ8vJ zZs-+<{4(rOEQc#KcDG}((=|;7yG`)r2$OF|K;^#$>-+SOc50u6X~9m+*vO>ZV^})0 z!P+~x^N5e(#kDe1&Kpr}*62&{KT??_hh|Dpn2ImPs`ZM2(hiB@chzB-AvApx>0INf zDKkgJposkb+Fqbh5w4BmCu#LhYM!OMV+SRStPKbr;Bqu^tSxK5&`&pX)Z|&2vRiYS z4a~mmzbSOM->4p!m(8cKethWiTCbUq)hg|%O9xw0CXpZ6{aMt~ELqr}3h(|aBs#`L zCe9*u8y;2EBIU5if9)J0d8gwe4pSKL9dJsw)62ej!IwJHc)nEJbHat??W#JPN$RZbTDIQu+jx<%V3idWID~Q1%lf1bC8!GcySv&zXIhFR~<&de>10E zm}nc-aYd_5==s%SRBBH35dAgzYsT2WR%1)MP9Y+Gvia+} z`n_eJ-1NoZsf+)VX|1gZj_Vu6)njB`6-yNXi)vaC_`ftG($4ECS-D<<`Konim!)U) ze{lIY-cAR|ef_qMfrq5(j!LzwC5D3v!;9$v1y2CQaD5SJ_FmP+Mg~;ku_Z<#Io!Cy zAyNr^p8}LS2_IVrjXoe!Ab(98ib$Cp>(ZIn*u!?g6&vMWE;~=f7&=Xe9L^r*qjb>#HT8a&a0zTh5Iw9u%gB;UJK(wPLt zs?9tu7hA0Q#CU6oPWZa`lDjg~9WE7Lx72V#t~8?%S?QFZ$M7)Ni>xHp8}4<1D-o%B z66-h2n-2*49a%Luef#rVaHd{VAus|P5fUrI1ryfF6MQwaq6c=z=V_D3>RHjB-o0ia z*ywJQ;x(P?fb3lwuWGOQALF#oLSNszt?#e&k1yztihIH@>iY zhO6>~mhx{uwuE5sa)P6!3~Yv)I+yht)}?lq25j4bH)Rj9p1E}Jt2L9es*T`bIL3l% zPRFr*${ITQr5YY9LwVX(wtFn0tte0M8c8OFAkVJHT z&o4KxXSqz>J+(;`K`X9iF0BfwMpEA&1~6JM9V^-15DeKMw<`b?Q1bPMZSU=>ihQ`# zA;;$;Fy9w%$N2l>W7r2ZMa)!M3d< zd(XcVdKeY7YNW1m&vtwkL6fm#6X{eneW+tk3s#(bm9(xSQtFLbpWd<@+BG{zd1V{} zP=2;tGe1-b4x5pt>0yWHb+eGy?~3#jCAE((%M2wEJd(9T z8y-V&4=yR+Yf(k69M=A{{;We6L`HjzR<liE8@lVspK3HEgp<-GF=VA8dq`Pq0& zHlWX)G0s=9?Hm3$|Hq*ZOgMQ$^dNJlo4w7ec6RE*8mvx5<^xWO;!%3drL}$Xr~Oo? z?D@81<^PqH`HM*fg8};wTp^T)ClzG_28M?T4yO5E1y}svNs-+QZCp+N_oynUQ@fu0 zpIX)PRLyqL7HPlFMyeozi%^3hB5xY`v1H|Mn>K_pxC+{AyAR(u`-ub83qCTLJ~=cq z-x1wbI)QbvK40BK*9n%-^CQ9cv)s3vnvdb>kGqPkw<}P|<>$RdZuiSYrvKA;?#F(r z|Kn}$NB`F6e$D4aN%vEt;B);~iB!SQiq8-C?zfX%zxT;UL7)3cLEn$Cn$KrN|2!=J zhwj{IzsKjTpPyIv$ol@Tler(~mmh~EU!?>EbKh_Eeed5N1>XWTQ?v2s68&MrB^1YaL=1wT39cW>Rh#}B&SE^d#A+AnH8cc}#3Ud;7<-vtFfA8S6| zZyyES7N_q&?fu`^F3#8dpKWjd$odWA%qI)JPS$*0-@1S9eZGuO>%U)RUT&^$-_~^l zy!Q`WhsFIr7Ljv557sWbK1yo*?v06FTe8$%_EO&%iJWzA{r%qiKi|3Z{Xf6LejDHN zzyGR*r%b<+BTt<>qL<^XXOZ{MdZ@GEoNN2fH!A<<4UNy>yIBw4*E9duhs^1(Dtp)W zd)?Ulc=!sTFsS?Uw7sTvl*?YQXMCPCS(;vH3-EX70`zm%P zT^miWU6-Ewt&dreNZMA;Nt4|?3tPO_W4=d^GdpgbMzX$kzWeJ(Id-UAEdM z#)oUzkF|?BLwXXOzA}%#$Nh&Ro#)LeN1e?KF>}_-C6oUio8nY%IbB!0X9d@mUgf%$ z%O59`9+P+~ZI?KqF1Phuqnw*MY}Z}syf2L}izYo^e_gIEsC5>uuY$aOUD&3i9O@Nc z7#_<$wl%NM4Y}JjS9TY9|8p41*Fc=*@kXRya)fB|vJT4XI7b zFCJ&l_Mk_dM-`|0du=>b#V%0$PZ2KvpM3W_FU3t%F^d(*^XqQYww>;iAbM{CM^kyr z8tqN5RbY z-q(Si!hF~>R}bcNMR~;?RQ>=hqLmlUP$KW+@mIYUOzXVVO)ENI#r2C7b}$ax0=r7S zs+_`hYj0^n)pyaLtzx_S^`H0cDWzRHQ8w$0(9b_5>?*sn&g92k=LtYwr2@s2Yy~H2 z7r##32)A5C+-t(b{6ilG;~CCXVk^xxzk3k}OCP7*xyQfdh;tpSA}9lz#n zaLZLZ`&JwHhkp)7$9+2GTR(`UdXHx4FjTiWo6Z@{$Csc=>a;umUF4N`ye=+N5QwX~ zOi!13XK9t*OKxV}74!|*EToygxEOR#Z{}1~(@}yQ44C;dYoX#@R#zz}@sgHHZx{4c{avtx#@-3Jf*D3Nuv}GE= z!dR2<`iB3)Co=q4M|C%vn|9B$*OtC5rNsj23x1q6Sl7x0-^^rPc;zS0<)As_GI&zhlhfy7WJghOh88RIRSpb_)^*(heYe_JViG^B2Q%eA z88gWN%Vu=Pw1`{l4ZrNZf?h72(jr%p@#k9X?8-U4e~E`RjHo~?QU(qzO`N%I2rWY% z0_WQa7_K=pD68TyRfN`>r4wOEFG**o9cNuj7U)L3;W2$KHd1oL=#2egrNtL%u z@uY8*0N=;WMXy1t1Ud2xb@Vkh@W1qnFB1-y8$Y>iu(dXQeG=3c7x-n7w9egp8!(T1 z{36?dzij30375vu`gxOrdbCc?7>jImZD6a_lE|m)nYubO5-Y&xVr6`$54QIf>VT)4 zYjU-+r=DhwJKbqopnv?C{@&^pE(-=VjU3}>C~e{!wtklUCrGSB^rjTx6UH)cmk+Wy8-ja!)vP142&;<95hL;Gd-&8{>WJP^2FxwUO814d#E zSN7X%5kd|c57nSTb7-9f0pQ|xVGHqUw^JJy*Y*$4h0*)8$06uln5$)fI*fAu?LwFy z(9C|MSQ3vbG@m`nWQ@zquuhCvZ#aD=6m}wt+h-b4Nj<30_Nk9J^XQCPKJ!x6Gnj{@ zh_aoCAhG4&-(l8l7MIsF=Fm9EKuoZ#UpQW zUtbFNn3XuS&pKA021nB>OnJL=OoCFKR`kD2>);d0;(29zXZ>7e!ICSqd%UxNLilcGr_=X&w-E zLh|)A#=25W_nZDafJtWe9ijdJfyFOL%^DP$eb<8UC`wnrXe-8CL@kpYdv;DxZO%;!jf4;_;mtso$lsIZJXOPMH6T5$wg@&lNl=_D&D zggcR!b!6kDWSh1&1wqbtg~YVmrilm5UeIYSao|0HdmYJp$+?9j(X)vM)Bl^8MK~j3 z#ZEBGaT#U16ob{VS0ObzAE4ksUEm&PH4aUMxJZU7=JOJZmkSDhYK&pR(ffNCdL+-* zCjacjmq)W}6!b^S{I@+(+*Hbgn1^(p)${&a(mJxN_R)(a@$wmDmk*X2nowW4*pp_~zp4@DqzhOVU*4Kt}CSK0Jyi(_v#&NibxpxON& z0T8!}VX}-(rCe1-N6~@GN5w?$(A0G#cm1>Mb;A*JB~F9mPG@x%LfljqFY)~R>E~^utHYwc2d%a z>Tpp?B053(eL6ti{#fAjenR3Axovap2*GH)D;1ypSpFU10Ch?$O?R!IPYox33A?!X z*^QgP*g9Id%uT&?!l`2lTdSbymGOM^tx{F0G6OJ=6;y%sw=mUaFHmsXBb#2<`m^=t zHT*oy5A8Jq98xb@GD|1;(r3lA0?0c~Nc=pt81R65o=wQVFQYOSVy0_oRZggU0m{}7 zC{<1c(0*dh%u;|~xQbZPzlQ)Dtc+3Qr9rr>1n#@fNLOf*iKz8!;>%{$Xfwd@$!g{0 zV&FJw*=VN}+HSXOD!|oh&zf#w#c5SLI5pHHMS#ST`d^rKoKHVFZN63w>S8WHB{mD) z8-ev2O$9)-IV+q2`7al8{1xQo5+kM9RPj>uZNe=l+pj6Lq3~-;d=t~I z)z)Z#@WabanO^!;<&@%tz%3IFxeC1heA!^MU~c{LTylI`+9L;LrM(k}g^nSkitk~o zZ#n+D`Xp(V5bk`ZgXhA?GInBuE_U|sE4e4dJpJ4cJg{YDvOMjlD!eH!EBphsS=re-Kv1X)kK!NkrSM8MkR48f%H6r%MjQ3X>XQK!!oE${q98E=)1z;BIzLr-i*#=*^7!e(p=$hvAKMGCQ zdVVoiz6BbIMJ?f>v)V#|X~5x&2k8F*UO=J07n$*+kj2;uLP?m*@O?SIMS55d!uquf zs68N`ph;zs!@>HOIwLuzb+8dak|2L$FKMlZZ(A`}rDUho7Ggo_1k;gW{pj0P!czK< z;VuX++F|e-m2NBKLcXgYkv6upVwCZlc)pvdf$# z{d9Oes+j%Szz(-LUo2%Pp_p0>CllKrS~!(U@GAZxb&2Myh+18e(_PyTr)-c4midP@ zIe#QsOx6|Wmf4hRPS~eKT1qBaXqAC#2iM?tNHK<;%H7 zIjvmr3SlAGG{=&ZzUdFC;=841Vxmy7a*dr3A-}yPN!nrMqdC@45Fc``NZz~DQX?N> z$CbQeCG;ASMKZbC?{y|=U@vKgb6Aa2vP+YtT&5}hF(c7eaW^aJJr7Uc@uQk##OvrJ z9uis9B46FBm7FjNBu$nXsS5}0iBQ{)OBgO1thYXpY}X~QnW_mRis1F7;BY1Dxk#{|c z9FoWdN!Z%&>S1NX9qG}g7n5}89&6e@wiK0AY2xlf1Z-C4(#iAVdp`}rDHD01nY&$& zG7y4|jlE-)I>DzE)WQx3d7bK-`3JJmEv~OP)Bfp#wC;1_}k=+roMOiIG zj-`IQ3G+F0y@7hrFiVCwcg^)$xsuTOm_0r1wk~y0NWrp z@-P3R8EVi4i}__<@iH$a-I&}HETdaIp0@gyxKK8M zTwx*{yKG$9_wp_bx{w5wK6T2ur#6Buw}?4TEckBS zu7Q<+$7yy7g+PuzB#l7M;2`5}uLmHR)fl!7?1HQs0L?<6>g zS_KqgS>cBymrr&Y(0Dam9&JIrNn>iEglmup7?d|UdpANr4?r566*+P5Cy7#fDtQvO zs@OjT+|LduspdB(nKb+{sS=cl#ABq83bGDE&kvJ&e}~GP-CAF zz0`K9cq-aBT3fP4JGLx;$S{zv(uC9ihe(ra5(lo0k0pZjRynk>btT}Xm=F+F$g)_` zk1Qr8S|+4+xjZxPgQ=U+Y&L>G9O_>i`R9;_L-Irt#bFjN68RZ&H>6R$9md3z#m_@u zpO3C+uX)FjAk{y5j4BS*1MNlV_-tY=WXO2{5$?#JK@7wjX68)1Rs#2pC5~Ic8kXFO zJUVMxijUk`bsDD=2m&XyV$LM*w3+#ycT;~j*3!f>v9v=i4mFb!HH2v**V&$bLe^~Z zHVeQ8IXD45)0ALN0HKL1@TR7AtSm{H+P2lOFQwflxzvrAg;amW$JPe7uI~iN9|X(?MZ;K@b|+ zhI)2=%`V60c0IbL8@21O1sXZ2n4vW9r+lLn{+9{ICE7C{a?4Bf$=H`-l<-->l`sCv zfT)BM4G1K9qKh-KIYcQ@Vi_AX0<~T9Gzj~ox3hdWUD{7W->NMez(|={fagjCbv{^Y z+M;r4@nR!zQE^&!Otgr;N;D)SoxGk-td1y(o=I+k=E3+Na!(u1d?RY2ff)KUEp+YC z;$5+oKnn!210QeT4a~SVtQ~^!J}VJB3(aC&x+{zUk?;@{V5zSO9gBKr3c!O>RflDdvq#4vWUT@;j1` zT9zny+CJ@kMTPmHwW=>R3FK_L<xIRImPHP5P60w7t0I%X;<~5>P3p=wq09{dv;La*tzlFO+$M}p#%QSw^5|XwaD2+mhJLYBEJoTmZr+4DXb+d=#z(!S^Sf^Wdh zAoW~t9}ap7&{6JIjaoW@>{ehd>Kl&0q-4?Ae2IhSqw9$@4Lqp!Y3|NX>EqBjZ*qUq zuzH>fbjKjDPFaY-`%xfJq|0?M=)B**hv1f$$v_H9kKw=uCD&ITaSQ-m z(%naKNx5D@czyu)CCQX-GFR?Tzg#1>+quz{$Z)H>PY z)kqjL$uTXPtv+rm($}1=1S~Q6Y%6J|Ns?%r+h3IyBTl|)^OukB{nQqcN|iN(19H=rS&!HjI220J$!HCXQ}Ntl-+1Yn!vxBWcH7_j#{krT|aDw8@gH2HLG zSU}*zhy9>cJhDjuHIN5386VPI3qYnnF>}B#1YHQO%b-GbfJZZMV^V}r2XHe@V1138 zWi1@Kqij?mNwX%WF?Yn6l+6~o+EC&~-jg*6H{^N=)h^XOvTRxH66%s})cQ={DmjNn z9f@q*l9#$A&Y(>oi*XRxmC%y^BPZOd8N3rwp|QZv4J%=jH|DheAn&>XkakL{qPhzq zx4?5~a{`y0VNajm`-yRpmh6_fx9gFtOhVJc5OiE1i74P?y*w+Kj>rXZ^3#B}<>a82 zz(_PeUkHoLJhKa4W_0x>+CSR-^6V{Y2&hjWTR9wA$xlr;hI5hWPg2i0fVUIckIX~T zE|uX_YwyMsj{`r&FoSf8xyD)805ML(^C~r&WWgC#fyIVeI$6T}! z!daCRXc1tvz9dX3??g-5%XZ-85bvB8$#n;NLpa6T`M5Fm`wcujr4ojnmM_o#*x@HK zHU(5Kg%r2z(G|2I0bIesH6TMzMg2f+_({LOZ7{58)S;zaoe*2mN%CD`$jts)gmaZf zfGn^!06|VnL^4A-7SqG)kD@GuoR37<0?SZH1jKag@2C8TOBVpCnlP4}mwlR4NR0LX z+DV`!34QQ5PR`ZqB?P879M&Px(?Hsl{r-q{9NrCJC=@$J>GEz$-^l{iz*=Fd>#=rl zHYZ1CSUO9_wQLyK`RIyw-0x(TbQWy!W8)oxBrV8xc69dN#mm{9vS?ddKo#HhPUu>9 zu>y532loMGBh7*=h!C#9#YQII4!DJEiPi}{mxVJ2nTiBS5jslef}yrcG8}Pi`85+I zn1JBPo6tg#Q!@!GA369$HdAw>FNjnesep-RA0+|VNRS%U1+t|udbBS>gLV{VNBmis z6VB{>SC3sUdBrFU2MBhj0wj{=?Rs=Ib`yG0dZGL&LrWp&VF%NRMuSZuyNEV%=jkERz8GaZ-VVkCUnV{HL=jd6EFtyC*&0u!T zH^GwJ-$0P31J$TwNh#tXIMUPtd9p}{1K!0bAmcy`4fFJMybpiuApt1Dl3tYtv^m6d zV~g}@N!Sh{ok@SR2<6x7H5+CnaDt6AmlnJuX#OJ=Be(C~DCpYt-nI{9j_glI`*6}L zzzeByX?KDcMN<*+JWN8TuRJ2YuN|~<4L}t!fCM_xc8>w>QaJ%Orwot`E%~L7?*(06 zfr;ovz!j&1k0JcbLm`6pXJqLS!k05~PS&i~*K^0S%j`lpkdnv0A=oKriEBwOY@|=bISAzA(2x$(zSgSV>;BH6q4x7Ynvd)X*2sG{nO(!>p!rdX zFDr%Q;1(9dcDiB(>p?&#BxNDyQV2XOF@5X#?nGE}#o;tqnL-kqYDr?2DsBL6g$$xT zTNg&1F7YqC@U|_8&j3#vNF0ODCZ!@0*v3gZ#V_sB!qb)jBeT<@knDJX^qGbsQD$*Hx)y2gROv|} z;2S@q63-fKJ=1v?A16qfr}%I8j7qjm-!C4(23V5seC@jI6Mcm#n6*0+Su3qKna9OK zkDONrP(~$4Cv62RWPekv%j_P`Xrf+(Qpy%mZUScpj@llEEHyW)49sOV*GtG-^hbfV z)Kp`B(jkNPIl}RqIHbA@ z;~{D?1UHRS2%*I2+osgk*-rlldy@CHSM7*^`=E52+Wjp~ZOBS!N zIyGY?$};$0GA28r?u7UvP*is6d3t%Rhdm$t*ljZz4yb+V`2fN8tiSVsOVB+KO*f~L zuO)iFzxlwPbqO^&*8g?q=Hc`aU;=d=b(W-T%s{dzT|77WS=7wbT(0=9t@3-Bj>p$Y zQw-_(5_Jp4+mXHAk6c)y+Q~I4PS0P?>s@28butlYLgqN)Wl}Iy$%NqwWa?|B+3tFo zlC=^*7kQ_fE68tB#{`LUm7})G**0UgBUwK&0txk0D+-@XpkiAbVyVc+F1COPPA?eJ z@iaj8sjhUuTtf!T&c$x$qjo0?Lir6GE0a1}0CrgQj|onX+L&4&9>sP#@nF+orIHrF zz9QBVaM>uAbg}MB_0%*cn;D&aAb~=YI#kN(94Q0UNwBL&J&3g8MiJJSo@5HNnDH#@|iQCchnbU*RoPI^>61j9r?`v zLjL^!7Wu<8ABjy&)`lCBqiSFKjZ*L@Ut5t@+xLz?=X~(>3Ie`>3p|Mwabz|vBE<8; zdcobsH3YGrS`T`OtxU6VO;|bz$LV2m6wpGJZF7K6l2xPlR6ijkZr-N%moa7wvd@cX>EVg(A#CgQCbNHxZGy%*c zsth87;hT__XvvNFI$tfPlo}r0WU=RF2sTlw8KhKTH=3-3W_kcQy;P2F?G3JnX??ha zMAkdCRpD2GZ>R)G%OiuugaZ1P0psw_^%9^T6pSRQS)@hpWqa)RorC->kGfC?#UwOZ8RS02;9*W_jZtP==ZP{Ew9NT3zpavMm$bUxf+qsv(0bGu8*T4n(3C(& zD81Z+tqZgz>{Zot8&F_0LP_Oat_71PX1l_$Fo{{;g2j&Xg?>L`J7$doz8}Rk^?b>1 zn`}S|fD}R7f!iXw-L}o)2=?4`AC(Mp#7uGwiYT2SI(Ogmk!NQwHGW0#nDTU1FtJnJ>9Kh z;DxylBnP21Ry1#ta6kff!{d+{mN>|GS zn~R|xtH)mE5xXfCW~hx3FxuA>ek4~jY-|#uz@!oQ^oACYa zI(d)+)rn3*S(3g1-7F9z0ztX$vdQIaWbo8h*4U}CXL_(HMrF4aL)@gRVBvMz;w+-R z6FH03fRUqwAGe>F^cU^r~FAcb9H|+LC!UA7EA`e4b&CBZ;s^1 zX1*4%f(8{DbmwfqyV=f1o|OtB$Jb<@RaG!x;b*j$*`NXQLAP#{}jdfe{ef0r(q00~&BzupWmcgIa+;6ar z^@Ux(yz@V6vZbS_wQNzBO)|_e-u4>w>B*+@pkUj0w&54<24OEYI3w%_Jj_hDhB2cd z-m30=q+By)ZD5M);>vp1WrsJ7_iEFu1Q$g;U-CO_Zt7A=qgSs|775%fpIp$^bcR~F z;bc6ef_sVNKC#?R%qxPL5WSIn4DbvvMwurqh|YJ4fF<}LQ-1N@W@d8Ss6V2qcAf98 z8G`7zL~VAVaH21?`n#RfqK(-08pAi*+G4PtkccKeC!elcoOenSU{-Vz+mmcsp^t+eUPhZngQ7u z#Nkj92c}ScA0=?Z!)t)@r0dbO>=l?VSz!XU&jR12(!tn6}L*q11B&r=WuTt*@OAUz@_E4U*M>^G!G` zU2SR2m z=kYvJ>!7!B|8~bWC_y7kxuVN5bf)nSZ?L>1+d2|K8^m-iFgt7MCnz%r*pecGQ+Mu$ z^H9dF9hKj2;`T_0$&_udI9i__epLqbyCLn#?qycH&+gUB)CN2n~=TFd#0-yxnNNeHn=sA!ki=NN_E2`N2CfwCC9 zT-zMmv|yQV>XdBCNjqQ}$PN`uKBUlX+gHu2rir=`3-(DJmy}ovc4^5@Dp`o`{->GW z90FJ&*l7*z<_Bq8DT9}@4T7G^VOv}os2L?lrt) zP=+j1wt+S^b60(^tyVaVaz7Gvrz>1TLNHkyNtYRz4z+SW=%Ozv{zs}mA)*sW^Ab!T z=OY`}urBOC2r_BH;hqoAOz?x$sgq8 z7P8gsMg=wM*e>Ux0jikJ#rvu9B&gmF9kG7{cJOcFTGu+Vjo12-8zayVfL z4mvQ>@dH-a8Zi*4-asHM%x5=s)7Dp$7eKCU5|(lS6oU)sb?@t8cH;coYO+8z7x4Rt zmR=6W-2rE_Hk-u?wCZ%znCJ3u53X=M-lYiQIFq22ld-I=o*}BGiZf9JW@buLjc9MY zFTxMXT?^D@d_Px29EsE%%tq#VKL2^|@{$Il=l ziKG%i_nTSO&R57rKAn~xE74uSg+SEuV=?39-||MTbsE|Uj(|cXlZQ&#BmF3&OVaX3y6J{8olo4--R0TZD6Y?pMRUq(zV(n)9$n%m(PZr{I+6%i2 zo{}6YXRCpkWIGcwpKqP+LcpqcisMx zz@7n4u|-Fw^IJa|B-LE3x-ZDo<4D`Mkf={^ivq4`>=Sa%&uM-TJSzhl9FKJ2fD?kkZj$Ur-<`GNCG`}iSn9tt8H6cp>Pj6fZ*rA zM96e^2&lRA8J0T$n-6dO@du@O_m7E-0G%6;-c^9`-Y(kLaX3!YKr zY{H!@Yc6O(8uU9Fr@lS!L-z0%)z74IDHo1vS;B?6TCclYLtY7vT`SxE@x9>X%#6At zd2iPv?EJ)ID=yf;UbjSL-0o*9lXAR<~RxW&cG z*@Y06!g9J85N)%Y)fU8!Fhkzl3E_Q-G1=cWnKUp19}}i1){G>mR>gk!l@(_8i4z!9 z38yP!gQpq@A2I=wz-CTrh*W4LpiAO|H2Ek}hzML?Ozro892NEiZq<}N2OC6yor8Ud ziYB7OQCgAQItLyzOZ5$$Wu*`#C~s3X3u!Q+#=~F&baz7pK1(G|cg^_u=o$f>m|qPc zGuty%bWEyTt&b7F1K+aK*`$>q8A{Z}I#l^48ISu&YZ_8e26#4FOMy8ZL1L=?iiej& zi|;trQ2A{q5cJ1sec#>elixS%Uiq{aV0c|>0qiw&GiMmgD|{PMG(O|+Cw&=d3O8hVUfT-2BeB;<4&b( za9$R;8)>%@#CgU@$yJ)MHu0?}t9Q7RjuzP_0}Up8hc=9pv;c$y6e^jQQ@okAyOT(@^$=ZlBxM?wV2L#EM&F!*8?>zo?g6fu z-cr$C<@HoQ7(gbb6RhmP|8L3wz0|iO7U)`GK>H@^PHX;9e-cr*x8ce*>6(r1C`VEd*zpa&>DQ|Y+5ktDJ_ z0ofuG5jJ=STE}g+@#NwD$$C^@#e>NKg(4 zYsX8Tst8X_PzKrB#?LEO9A9Qhf(;5*Vpm!e#B-O^$`@7pf5I_)HB!O)34Vg3yAF z7tt=sT2}%EZcL=Z?Rs>jAl_juV#>D##Sg9f@Q-pjThi^N* zxWOn_z-ZfCbhwWzqNW2oWK)h#Sl|ZRDJ_Yq z%)ecaK6by;w_pX-fYZjDJ{nGM7Z3PJRamta!P%#P1QbAotlKd0UkPOJGxTFHbMGST zz!2NAgN2yegEb<@A$Z34NZPaJH;l9NVB1dfO7FbYwy9yo6+jE=Ik7aYqNz>r9zU;m z@G&`vPKzZOM>vJey_}3gTrCqJas>ho_lT>OWfMswOQ%&+%w^LR2Q@Uv$BZjbgCQJ? zmkdRZcG8<6*ISeu80*XiZ8kk2NgEKwJL3+?@SxS5D@wrn$x;JoeY+l+xD_(MNi{5X zR;GXXevDsUe?5_xNO8RCC!!%6l)7g7L99t1(l5E}Z?G`r`jD*IP*qUpuB0IcYAH!O zC%EnpG(G_}Ow6}3xSe{#cT3ehOsPcYgg)kzSAu>Z6_8r+N^GYqVoKo@bj<~cC2byT z&mcD@r@M#M=fj;uO9RL0m8hwqL1zj?j|}2OkJTQS8?xcF_{jbi%I}j~9kfW9;><%j z=|}9IfU;U$R*)XaEBo<8SmwL?Cc(?r12J*C9(^bldSxX)iiJLy$?NsqU2#5{r^v~R z5(zA=dg75A?Zw{h+zqb?0`AgQ7f@1SnXW0IcEdFuGrE)P`(i1ju@$_I?=7j-#NB?uXH5K90F0g1b)ng+#%E2I*)6a5|6jg!NyN^5Na@@F@^>OG0NBciCLaUPK2f0HSeIZU@0wyo5q(YO z;@G1?^>j$5AXdTdxbP!XBQ~&*Dec=MEn;$DQY=W6M#5zh z7L6csi_MSQ<6Ef)aLpRs5WsnAQm8hvVNcw0hhS|Bn~EMf5GsZjP{RN>vnn8abNly^TX&kJ-U;K5yNZfzg znB3e8pg{!_m8cw3C;<7P^U8x(S|nG}+pKsy;*K=boymQP0>52hpM=%^2vt!EuEoMo zI~`iDmjFqluN@{)cgKY%)ulP^Wn$1UWx9?Et|J%LoA`o0gpg$9+h}wsq#@x#4ih5b zTgJLAf_~JU)_|lJS18vAL$c6ulHG91@Cg!DvH=3KN>$PZb8w56c-mdmXAC=z*P}DT z6RL}b%Uh|Z+U!R?_Os`=_lE&t4 zEp=I1nO+IyXjcNzflf1_@o zg`y1s6}*BJ<>Jx0+3_DIXjO3>h?uTcN;p2d$>ZJ#XPZ79PXVw_3^Shq{=g*x!TZw- zd{%W^AuD>^&^BYdPC0s>pC&bEeyHAKV#`Jcvst2SX>mM3d_+3eGjh7(n0E=1NxO;L z-Ws*LS}tyzQDRoGnQFA6j0uOXt&nr@rX}plc!m`Pvl*prDMa$E3<=-}OzEBkBU5+)Sc;)zY>GWksNA=CCnPb#=A$Y@2By_f^HLVomq z4?j&TeK&j!Gf=CrrkcWUJR{GvE07r6!XrFgu?W0gWfYh~@~-$$v&R+&%SC6#6ojU< zRM+pY0|_jH`y^p>GpR84i3TZLpgHU%%;+IXr3_-Z2Yc6``!k+aiT4&6=x`DnOWUA! zGNpU#fAFk=y#uDRrIk-bDG`uJ@3OugQFJ`zsAF}sgsOyW0%foZp1fJ&!_*i%9*oP# z<8@WAr+BXXFDi%;iU0`>Vd+Rp4KtSxEW!A_Jf;VsvW00v>H-qmTuH}0JOML=y>t8q zi3kiFAi(NENQ4S#TU~tvpwG%Q>FUGA1_@o#>n^K*V%v1rWb;{Z(W>ToG~g)4qXtii zo0DiE0yX$@bC%)$AV=7?GuY;FhVF+D7x3HYa)6kHz&Y*$79`Q29cF0@AqGDqEO1ehzd^}ZXoKY5QTa}5kqW`0 z8%v#!XvR!EDVhxnrV$XdL$W9`nO9B0u|4yJ_Ww9h01OjcZ+paR$af;%MvS)ui$o~j zm{BZQnjiG2U_ju~5~dQY1NRDPk=Zk_kcP*aj&pDGf8%ON5#5g7NvIr}wQSo{3Qsx@ z%C6m)(0yF7{kRl18#dKSham;e`etMVQW)cpj_{FwkR&YTn-98h{P@k)9(!!*Qt=ei zYGc>jFc%7CTT-~j`mnU}f4Ey@bfULr1rmvYPMfhmf*ht5!fyXEfkcz%05kzys&OlC zL^1uaT_m;mBxFiZ zgmVXgP^qaYp>ApGE@O~*E%?lVMOi=|y21f60}{@2)LY%%DO*}}J5v9{rCK~sNMMM3 z+=zp=V>fp+sWg2Cq<4|P?iaGV*GlA!pNA8_Ez^o&0tlkan06+6KXDS5^hf|mR-eY> zaY92vWi!5{m$(E0G@%3`&^iCM0%SO80EYb4RMGtx=E!?r~cT9Cby@_y0^^Z{8qy&ruF0IHt$$BH@bn`4*-c< z;(?hU662qTqmj5oPg z)7p(-o%iapN^flWO88XtHq>r37Owv>(EIJtD_GJ|z34#kL$`LU)=w7grS&7cp?kxMhux=bBR}(r2 za>Nk_pm>}10d+o#W^G|kY&SbG`Fe$Y8jZ2EHS%7|&=OaRTJHUj6^l6_8Ah|JMxAJ| z5(AELdzd68RjI%*C(SCGf@;k?G~AIk7FPTS%aJ-j@+)--TX-2@?CilXx4GGH$C&!~ zUVAl&RHH3#*Q0CE)$SI@)6wiF_w=Lcifq!QI%AbxHPb{l_hXB8wZ(n^D%QfA`dA7> zh(W*&Z0-uG>2L>=^Hof&S+*d+q91)n!ENvN?@?=IPrgGbVG-Nlx5ZUT`y-}kk_?(I zghze7f(`8uRPB?<8yyjYG;PqV$|c*t=xHb5l932d08iFhp|dvdUWida5Y!+QuN%3O z8pH;WMQQMvlK=JA5 zszfz}q>SBT?K!Ux+?!PXmsOIDF!69#(+-uIJ9X2@pubU{&?6d%KPbn*eyb7tjGnTR z8L;2&3^#dzErT3u<2|loJaHBHrKBN(SZYwYQ6bQOj*O~0Y9Pt zI1KKeoZSZG*jG7~#+(-MTi_I4&-ep|j_(JCKN9gh%Ju;J9zkTOP*^HD8sp}gxAPTm zVHvyIEKrX#q5_Bv7!XV7r>vQwWZg~6%ECjNZBe^o+nexuKm{Q!lC+qMVrZ~PVZjT4 z@HEn6H!L9?okOQD3stKH0uJy3*Q0BymUc76an&f1T0QCx$F~4_ zK2PJC#m7&4IwfIR7j8R7?2c@8+h6;?4gLY3c}{_}yRkJOhK~TUYXK=Mqtud3faj49 zllSFR=HHuVBq3PIK0x$4fYZ!7TnG{qcL;M51{9tI{s9ze^P_7-zz(u9azYVHqNRG< z7lAf7au>yq)ucy*MgT3AZ6Uy%T>{A=u~-C4gvtllZZPcwhiu$U4NiwjQ;D39KAKvu zJfnxH^}#VZ;!xH@7!2_AvEvB!bcE`g*m9v~=WyqeWSxqA+-y0A4Q)1;T3xHO6UlSz z;q9D0yj|HF(;a{;eCe7E)m!K=3*U|O$#l1SHq7-3H@Aia7Z!``>3LQ&4p%8V)-OS# z*)R$7IoP_eDd`OxRP1++JK+{y<=rNPKcVv%)+>GRROj0{et5nZ zk*021U27N(^E5nL@xZpb5e<%3JkKj4hKxm(iGYKlEAXK_tchf_t|o&HkfDz|4Pj{e zn3fRU$)oF{h!tZ~0cMZN>x~5cMm8)cP^d?t_|^|b^*`O~T^Qks>P*N~w?T0kabVM-)c`j5YLisv{5Dhn9VIs$!B zLEHuy<%m?e7NX2xvEEKstp1^z5I%sMo|LMLYF@2wW=u&ym<2Ge!Cm1F4>k%1pa+P+ z1Cs@vCu9WsqUO`2HrwkA5jE^EWk+0S(niAh>fm^!X=O2^PV&8V@%xAwZs zmcO8uDM3M2`8^cG;o1{VbG`uQmhNdYSM z*8+RYZfg6o?$Ve^vd%fVpDGl+DY_J*yu1WtblmhcK$bg*F0W=BVY(){KHHEoa2c%7 zJnS+s60OqEO$O&PU0@m=N~c-rj=rA`Wxq0^z@H3Eg$$gwdHrN4%_o`-`|BbqedawO zNrpYe=)1Oh*ls2_*n2=`GdL9D+yfbL3Qi^}}5tSOr)rPe_AxJRjNa;v+u5|cu zML#m8RP@l2k@`)^ojX+LaH7);(5aeIUE_(H8ck`4&n0-ItJ_G@l@pLt`7jv(4h>x? z(A`{*;1r|BSBiFu*tv8POld#qphw$Ihq=!qy3Wk(T?VyD4;7HXt^x{qQ_BcEjaCl! znsI#5v4eIpL7`II{hOi+LbY?cMeRs$LpS@mR+XU!FX5>$TYYmQ`A5+Vc-hL@3JLYN zqOKZbc=O}}(_u$)sNKDPrrVX}=u6%C@u2E7;e4tbLv zIT{ta6(BrShL(cmB_+^%C}Mn;K^lDZYJu_)Cp}Wtutb3>2}~HwA4V^^DD8Z7#e@wL zbQ2hDC+3&KsE{5z@3Rvoj6yV8{AnGoO5{NWJ8Jli)^S$}Nt&$%44OM2!PeZi2T??? z)UCS3tT8-*dS^YbYOm(^V4NIMSmav5#L?CzW=}n(Q*39h16vTC9S&cCPfwZzvX{mR zyTdM(hDr0rOo zc?1mUxJPh)H$!?U{M8O?IkX1ep4<4^4!T_aNjEfc(De-TRJHBT3>YzW zqAA{=cINdEmuus!wq;|kyiry6)3mBGv!{MBg#(cvQQzSN~;+sz(xvw=tDuZ zCT&N6!QKqQ1Vjz)GAbA;y|iAAVq?~;%f*7x))jrE*rV!=@&|UZT}C+^=%s3)%O4n% z`;pwk2{9so@aYA+487Bb5y&s+Ig<*7R(%myXZMec{9~ZS7;O4TuK+uwq_BIw1=#OL zR*alSr?@jfPy}Sa2)Tmgh^T^g+H66GfIVNaC3nk(T%^G{=O%wE-ZnsOfCwLuob418 z(XLd|w?*{aoTPk%tir{`E+j4ZAuuy|-h@oilJHTAuY+3D&w#VpNGlJ)y0v8Watlx%MIgX=`hAp8|H!a zs@tV>Wu1Dj1)@MivvNeQ-wznKum-?#C42R)Q5ZHmUn)0UTvvqp|n%&P=aWX zlvaW1)B%g5F^B~nTd@Q`dXGs7++e0Q4uKKxn|lvxBx)qh(wlFRswUDWL7dEj3Tp%o z_FV3BGG}n9i_|DbXZZrz;$;qK<2PMOpQ9D#^Kb{+KvDJc@`61hi%VV*g>*X`~NkyouGyJC7}I2hwq1YxiWhzatm* z_{*hX9ohtozag7St+4EZTr?~>xA^O%>`v?rJ*2}ru||p!fYGtLyWu9afd|+eJxUkI zYX6cFa+o37wQDL85Icmta0x{0^Kfd@v0><|3HW?V&Ogrs>zLhdakNvg_&oTJGjK|p zDYinQbq%8#a9IXO?u}})KpqaO*j)GxgzV^#KFXh-7`0sG> zSR`5FqA!EX@XM`HH2b(#(W6tsK*Le0TRBN-6UD$CgGsd zK?U{a`EbXSrvVP}686SO(Hv`eU)~!) z(T}B`>V;i(o%#{+H14MKuM4>VRVnZibLw*9T1$gA5nJnWi#s7&nl z!uGSnZSNql2fA-)k_?|05NxZ1WvaTYS5vk#hDjdK~icx zJ<)*kxUW<1#~n7gC<|qT*puwt4xsP>?sbPo$Mgn}?Y&0Jle!S>z zJsq+Kd<>|{;MM?HPZFZK-ceqvqJ%rzyJbz_$cC zB-U*6g>sI@xXp4b)zj~ce zp#XNSyMB%ah13M)_agYwGZsQva7wJ5L$D}7lcle1+qP}nwr$(CZQHiZcWvADef|E9 ziJ6$$%%&HaQBkW}ov5s=JYUiY8b@^>|4H!@q^xRIF^FM7&b)m*@Wu(Kp9@&YZg(wH zO`?IXz!-8*vWWB{uA}+6jZ;+T~tN z0Al%jFJ1{pre;n}$YksSYh%pSBCAErn_$AI+#&Q%Ue`4M)Kf}|4?xjIs zQCbJ`v1jjmcFz%Pk+al}X52D1LUcR@H?fYayRMF)m3?@{l6N*gFCv5i71=?!#f35q zhUWgu{!@Ujc$iN}SNjzzNy$Z2-c5TJxG(rJG`3?_TfT!euC*iF@=|m*jMaz=@4r6S zE-_cuz;kmV`2=APc^=I~N`x$?&N`&Y=rfRUS$lgrn3r3+Ve5Lf!Wjf(Z>kE5tdh&! zb5RE#V}Z!m43g=U1#vzM%6DgfJm7L0w|2?Zi&Sjg6`uJ(LW9iX?I~2roqM`<|CqzA zJM#4!Fp}LXc9kdOfNxcUl7g%7OF6a=^FwcXJ4Am^mPAk{VWR5W4tx?}1^ZEYt78Z| zp9egFUvS zaJU3YV92NiarY6WziGSt{F@>}(eiK&qE|E*lTd9N^S;+>f_G}4e?(j!JV&X5sN#UJ zddP2}zrF&~3Tw@SAdwP&U}O5bhipj-O9~)MgM-(EN}v!sE&!0Nj)rKuJyEL*Q{ODx zHY%Kf?Gv>aBnIQef?Et0fGEJ}{PlYm=kH3+QB>H`6~J7%I)Fk8BEc|+AN}k$$=|X3 zCZE1XBAv$cgzcdsaA#U+-s~u^#%AuMqBYUIj_Ux)+)3oKcF!hTE`IoQ(M3Y;!r-Mf zjOR9KQ_8 z6;J0LughszLq8{xHf_sYc4h3qN|l@FKmV&H)mYsG2H3phT%<25{Imn;4_v@Od9&Ms3O~{U9Oxtu948uHY z+{dRPvRX%(-@Lk}p)5GKAhUBH0v@{tPt#87__6mI=GQ@iIf^tDSu~^9? z+Y-uu!9ov;Ts-0l*lQYG+(6WgCE-ZoRCrdyuFhai;fl101Xl#IRN}dV0|=HYJjn-O zR}5^Ub`d!}G9q>+NP$2X`TA5}HJ!xb>fAeM&WgRtNpU8)kMBFW>5=PZc^o6~HxP4{ zmnQ1gT7BaA5jg_qdzEdwz?&c}!H*8D$^3BwZ0tlg77??EOv!wk>=8p2&=t;Lfhh2B z#8^7y_r^x?VnJGe2Z2vli1I(X5!_73aVIN zwKxTJ$j$AOKKvEPgxn#joS+3f`}7PuP(Nhew@l;AZQ+zCvP?Ip$3z9ND31*!y`vfp zvLt;Nux2~uhK!-dggeRL3Ot~VDOH9GgN=(8^-$%_p!qPgcDojLiHOnD=XOl`3>9idcMuUTOl|_MQqja{|r?bup9x5=$Md$fpT1vQI;O0 zHC|Gf(^ne{X>u`0F0N4mVdIS%fX4`u| z%G(uxB|73+5M&T16mUh(WWtLf(xP_ZpsHSS=+bNK)Te;mNQjO+?G2Bot#l_(r?-#!F0;_ac{7+#W*)45 zQ`_C{;c2BB&vv%a`&)H7 zBD!{nuXWf|4X$@;-8}I#9nBf5w8O9qCES7Eg8uC0laN>BbSAt{3~ z?||5jSVkciHIpbHC+d3Kwlz})Zol>cMZnuBsxA3lEcbhBAbD-s4?Hg+D=oX4P903* z29s3!ik)Y{RZBX>u>D0?Yd6E$WpXR1y+vaZ>v4Y?u7VX6DX@@~c4=4b++1o4Y$+0{ z$fLl{TM)cPBIhVZ?CH$a^@!3>C+B~@1wVRm^#XTwbboizv|snz6vlxhVgNX2~uWN$mDF<`lh};Oqru&HO#xZwStgp?qB^pn5CVcfTZc z*v?N6@#6)3)M**dGH-{9%m8y=F|w*tPYGWFIc=hgunR0UBgnv*qZATZZ~@!~Vgm&L z@6UrnQyY$mg4`jH;&xDY$TF@BWZ=`N2)TEMXnQHYLt@T_zvbY+w?Pn5EL79p-psIy zV+@d8$~x@9M6C*%U0hMOV!DK&Y^7bt%a{XLzhw!us>SS4n5;S%^_ zBD>xu(MA}pG^3)GKcPO7#hBTme$(=Gwnc2iiUDY~ck7}c^Bc_j*8&)r0oZ&g4v@i{zL?0$RMUO*M=KPYd9z>=%m@$qu0x zPj0+LCx;=jxO%+%)?rf%sw6df!2cR=&|ddXIP~WN0ELwK0XvR>_?EmPJYBQTUCS%B z=gw6j|BR>7XAE5_tcT>xWD_a(BZ&ur5Ny5q2Y&`pn=`*TpE+u*U24Y4yKqeyOR*7H z+f!2u23cL9SchZ~fRS6m>%cNul3*pmEh%#(0&l{5lUM&ZZ=p}=;tM{SN0Z$sFw7&x zEI*%RmGMIG*DT&4+hpkCrg0?^3sn*9IsCc2ybv}-!a&wGo75TeK1U*wML-dB>mlr( z6Th+Rx8bm}*@8Oqlm5CiI8jltlfT{a+Zeh6Iea<4Op-L8f|3ig4U5YZis9RJMG9Yo z$aqD2;YHSR{eGoAHzawX95!C`o=pY@waL(9k9?rPucHTY90*}kyXapASx@ca{Wp{< zIGeEnq~z5%#0r5zee3PkO6q(33hp~WzBagUoB1q&;sh{8OR{p<^QD9q0YJ2=VL>ZU z;t7v|P%sU+=La7>*t<}m8qwSW%L6n6(yAFpJW*}Q4#0rDfA=xD&r}|l>?{8s1?tb+ zl@w#bEDG;6pM!;j-@Iep0!0o}!JA?VBqZ6$RnJZ#laIZz%f? zsG15a3yKgHAh9sApk6PW@`8r;d@Up09V=z2nc}T1CijP90^i0x&^lCw&8hpsD>U0( zNP%0SBZc(%C|rfhSBZi`rUiFq;~64BlxtAvh?}hlyDWHMPZ#~VlMVNo56!*M9vYmC zzpG)$N3IkNM>g*lunBKp{o_4MJ`~AgM)1-7UoB})gK%t$jR6`m>Kug6T*JsQZ{M@~ z=ZKs;LJ(U_%3}L&tYEUSYuJyocXrldQW|U+7~dEy#~{8)PEL9)P&T}`VlQbS+rGSw z@6U}ZjnFubu#)9>%i0a%Bojh&mff~u%N1(&(t2`f=3}hOA+v}s?no&rHd=%V+0JtJ zQ!BLgc2o6P1x4d`szi1{EHJ$RJuoyUKunSn(@-TR6c+!6FR*bfeHuB%e1UIdD?zB3 zVYg(=hu$j{BKPyuOGJG@QT8@Yfwl(N8IQ++Z1$z)sJsy}&tLJ4M~=B{;Kz5!{)}bp zjIlO>uo77-*gF>fv5SLU5^DW-owl=%Q43kf|-R+pzt85-uQggY_+ux`X99>LgiqSu5=}T%fZG!0Q?#$7P+mnam5HHT?nVUGv`MWGm*+&G2#63H*2^8ca89>#iw)#6g$W*T1~r!sc*2IXN&?|sd|LRMlob$dqz(p}0yA~9n}gS_6J8(O*d zi*u=FdxKlSwvXY!3OhB%cH)25bv8r!sRjKyfYHdL+mpRGENcw?coLZBEV@E8&ZfKP z)}jze38z#K1URng7-D{5Thirr=Tks?qwa$X*e`k9u017k!Aen1y7w`NBX?v{mMUC6 z53BME2^Z2+ZI>VO;O7KL4H^Q;e5jBp8#3~+w%ya6;1wcye}ktw@y-MV2kb(bk!&r9 zuWVOmQ3nHsGfo`_g`FMJ1`F+hopT1#M=)`6BvAsyFPLtx#JDkb$VkBH-a=EMi@1$> z_g2x)#K~3GgSKZ|!Jr@D5Ti81%_WNifiKOuFHnYPHmJ(zeMh^QnJ45XGcq&24*%RllbPk86ok2Z9$6h zF>U3bPi;)3xnGICdcmn)85QEplz5JX&h9-R$V`O({`5d?$Myc14tm4~+J6>e);dPc zz1{FO?V!g$;kYeJ{nXa(<SR6fe#jvPKfbEB)rK_MQ zur3aNFFISyE8-Ps)P?)3Sh^L=HrKFk6$3wK=FABeM63I`yU!lFN*rRXMN2RP3?J+E zG`s7lK!bpZpD4k=33=5I?h2PrJBp!|g*Zwg6Az%89k!ml=*_{QfC8IEq8`M$*d4lX zT@c?Mi6}fg%}4TfAyxqwks%L0-Ku6nE)er7fmO6u4zOJu9BZSAq{I^2mt6|fKI)T1 ziZ>x1yJXA6;E77`uH?%hZuUdDCW60ZAljAd(Tg zMeflGLC#1F15z}Ap~Cgi$>N_7BAZCfjhT_=Dn->@{<$_^2Y(*=Hj|sow=P&kvI^SAV+%JL-5n&cxU%ot?I`|1T+r+iTASNPwY^7vd4ch{q zz#L?ENbc_TiBHuitm^K1w|sdMnX=X#)&(gmrFV@ngtM{>0~=K(+)UU;@tVv(9aCi? z-#a3jEr>L3m2!3&GWbXLkp;vL7G=oABK1nyflD{``#^#_?UUF}>|Z54_O(#qg5~g{ zVS?#5R-I)QIQ0b5fh%K_xI^S!bJIHdb5qoPB(}d^{EBVn({s14hS@Bd-T*DcAZZZ`UQ%ERq*{bI}swR6>LpSWBMa43y$5 zd23QRj^W|;Kq^_lK7AS|*;mcnRoayft~xE9a)7Pt-7516pCz$n__FC24n6Q8 z9|ByNM}S?(ap>Pg&585x_BLF3uD@NKF7+9{UJ%#$^RW3vnjC-IoSD^G{yD!+F27Ke z@yd3e$0SEL2s>dlRWtLNS+bcDlRR<^E9)C>Xmba5FN^!g4!rEpe6Wc0BtMtx-Il`~D7)X$n{8 zKE=agO#&m3*rgT{lqn@#EzP$3E|Rq)-zt#>CzkW=D>BBfICi0cnoT;tq7IMjqf^;t zQ*X%!Ddh27Ui58c&!FOa*ISjx^|0Ro(mgW}vzbZw)YN2Krj zu=G~Atg!=eA;sA!L~@48=_iu*QiQKF0YDDbKC=Xfa=$bmIeuh8kB!;iFCA%QdOh*b zp~7xirB5DVyuPOHkf@~KhGX#E40#HGWM2ivQv$>S+91gH0%lQ9*a^GM*`g*YfMRVH zDSQkI6PPJ$1oA5bepG_iC4KP|I(VV*I!E|jnCD2t?CQj@V<@{HoCzi z(|lix)7L~HZ^6JA=VdEKocdt#2q50{Q?njcuD^XbC$tc<5#liB^ubA8@H!wl@lmrT zv0M|^%BSJ%7X=BG?H7*`ck6r4)$Df1gA<_D;-xFBKb*ah$K&eb<0hr{^J?IB3S5?g zh${~lPPoSgvk+rv)6jf-#L{Sg|FB7!Eeg%3XzgT@S_ycxW%A;lE>$?m+r_W~JRmDl z&Z!^uI+lS3WUCpMzvhFXp$Z63)`BXRcvL+92!L=DnTqMFgAEm?E=ixPj0Zy$ZW|9{ z5s)9I{)}4xwv@$%?9tJBayXah2d2KK%5D#nss-G}`21jTm0XXTZeY|uPHipdU z5!xGDYg9v93kFIZi;i>SL`HyA%Scw!AWb!2wJH82ETSKPvrC-k(YI2ks1@VdprNb7 z4p+Mq2We+(XMah4E~aVa!o#(w~d*dBD;oVcHOn zHFmILRe!Na9PIt`=2v7fD_{55S?k9m7sLHr4vW0xQjGA}+E{kLhw~vt%vF1Pjf%3< zCQ|p(kUf;}7a)_S>p&7T_S|+u!qy-jB)g!HGK?Y!bpw5G@9O0_fK{q|pgofm9b%$6 zZxejWp|;!{h$=~;0F6(ll2<79=;;j24CBFaAe5VR3ldSkf(FR~hbYZUnd!<#%oM;tUcd@O($rgA%^hZ-3MV@@*6Wi$yyo5d^~E|% zK$Q7@c|eqdyHdq;5G+(BC&?W3efXaj+8_+rsh8RTtUZ1ZzJ>e&eb-^Mr0m-TQrWjb zi%1_$L_td>`qw_)ifm!8@nvdz^?ZY~`&rZ7=@L`V)1Y=4r-Vs!^t6d0Z-VEq2WG#H zi7v}SS|q&wxaE4#rSf21i$_=@RO_Q2+i>*^zLT@uh8AdfBo6gEy&u$;Q9nuW(v*~cV3%i_A zVQtY4ND4FCx83k(48-|0qU%kGE`!S_x57+4Na$^D{M7Rz9L8``K8S#C+X#IK8vt%I~>%D6VgJo|lm&Scm(>q6(a!yq3?*H8&MrU!<=w>6z6U z4;%xdQ&s@%5}DehgOfg6TeGyuW+XUg2-Y~?Jh^A;P10>L+=Ypap`{Ngw!(DDC^>LhExK>00!4UB@;Ur5yT*#MrGa9r0Ie5s>+&9FtIf?% zN?Gap(xJMec3Fw0na8>fzZt1asYfG4Nu$91O+77Q7+|%6yk;bNRH`SZp$LW6tR%Os zSTe(GY{o)<$2PijDst>ccv|nRM)E38yY3Z;lZ(ebs&FZ|xKEi|%-PAK%~%V}D+qOE z?(N=Y+$PF|<%*w8wfNG;UEW;+%xoXnt^D3qopX0a;9e7SVSy&bks~ z_WD-xS9NNKrTc6r%U!7ZJhwsnrI+Q>rhOIM5M-t9RS-k)X0@Aq!?7C`f4XMpvp-$7 zG_QS9fU8QRNC&mZtE=;q^#a;$S;u7W*xqQ1O*L%J&Y5(nW=_RZUoumpb;-@SHHlqG zrL;!oX}4f!+Wb>JbS0e8?f}3%hT7U6<6xcHB7}h*n8m(48=GmX&SEKQ;H`OBeoYKQf$q|*t`|l zw7eg#pd)th=D%%y*!$#c^X9GJ<}azl_F%}e#A98YGOU&uSN#mDT83SLL-8&=X>Lb1 zv5>n?vPZ0MSbEOV1`3Xg^fe7AavIY`l{YI?G3kuY)u_A894jioDxVZ?=ALay){T?d zx7H)w`8SEgkAD^arpJ9Alpg2K{TAx}m2@rGv9+gy+8p%n7wC~s&4|>oUpj4Mxb=kt zr{j5tKp+x=nH%8j0KN_K-skfGzzY)JXT$*ux543{!-0>3Cl5d#irgP@z|?_D*XeTL z?cm!6u!CR^!X7rh=imXy6Mf$QxCine?19jO$sats$9<>sk(vm{AAmm)zc&Vf0TkaS za6t5cpx6Ttj35v~AU1*E6ap`dUJyPfF1ZMSAP!+93X33iLgfkz5bnoT zZF#iVrW|dPsuLkw>{prZ)EjLc>M6Q;l9Ig-K2_MY<#{~s-d`Ujc> z03dWYko)h&|4Q=zQ(Ed|YG`6>`oCq=P6JE#Kk@&_=>IM1Nw|iTJrn={Ix_$O>i<&* zF*7xEadk4)H@0_n`TqkicXTzAkH;MS_VfqQ`(wEjG~co*@ZiEKOM`SK!%g9A; zsm#)wUhZ^L!4)(qSQf^Wpr}V~F2K65e+a3crknk~ZqW7pKToUe_52^-r^DU*J^!xd z=i%w^{(el;^Z7oF+xNdeneX|$op!6&`+Z$xv+w!6bo2Lp944Q&-|_SG{Ty7(&-;HI zcRwCp`P;|$@%-Fe3_pdRx!d!5{*3SWKisJ2w~FikeSCV8r{DWtQ~$=i6DRlmxg8fi zo0QD|d4B)>`B3ij@%p}>p1xhn<-U2FzuV*Q|9Oy)=l^l~?LM{soj-&B+xu0=&(r&T z|NJ_96fd9N*W7;|-G5ueKGm-K3>(hh%EQ;o^Vb3H?}r_~pU3BeF6YNXxpTSymxf6K z|La3J`<-6@*T=`rk^ENqp9l77Z?ST}-}i%upCxdg@i3a3+{cxze*WGbf3AB;@OhDk zzo+5&P)OR;pzradP!3aCSNR`gj~Q9iP+l$H)DS)6?Vc_WJMs;dc7Bc{Y4>l?QLH(}A+$pd{yuk)``b{s@LfHXD-KnZI8C_`S^bP9meO| z;*h>o@?Wjq^ZUKXme&t#g+FECmHF29_?$h)hxwhZmsMtZ#+Y$3$%;8K_vG7Rxodl` zANM!p8@`;U&re#mZsQEvgM!?gr}M`|r{&W^$KQ71o9NbknthfWFel*?`x|x&C+zet3s-Ywe>$!+?>-6Ql^?jQ*BSkcp*+Y=;q%~Xz2c00@mUi|%Y?RRzOM!$ z{G^@64MrWZBE1j{%%Cp=ovGSR^zfw>(EJ(m8gXTRC<2f*N#+HUEDA84=*1Hf!Xe*S z1WINuv;fjQA=k;Bsh7Kt%AwHlO0n-r*08M=YN9lM70d5ht?s z2^|}W{Hv6$NZ0JxHw2AI)Mv*u)kvQ&0U;z<)``H=O&6r&;2LoibgV#ZvIw!GWS5Vt z3TX#ggIy7_P7}jmoQ;}AE8t0xxP`RUm+YT4ZtobFg72ZT4CpikVLAmtnxUc(&iwEd zJ6rp$tp>`8X3qjrUh5iq*C%qSeS05STylpLuBm zndm+gSKtT=VlZU{PnKHaPz{BCAUo8&T*SNd311D!2VH<3S-zTJU$J7blF(;x*(lpq z0s_CVJS>M&4k0pVqlp&IFdrogZLnrba*oB@}X^Bbt9;)1NoUonWlW#gHNa;T^h-dtW=@fUg-?~b2eM%r_?_j;SP!XFYP$0x8F$x9D#7QFc zS+55l{~m8pL#zdIP)dKtRf&HDWpI~He%B!xEhL39Dbo2%@wiKQ>v>PSgT1&7=Py?XhZOWkxM@9IVN<8&@zzar+A z=W61(%M=v>#DPyX2S&zdss*;D%Xdap6Zj4}z1=cT6n@R+QND`oxFxiue(jJ=!YqlAm1xB*8E;ghci};nOj2zDOO>L@rPtI zj@}V>LD&Hc#R2;9PiGqf-7B12yL%dYTyH=!D&kQmtAg`oA!Bz?*68+BE`mGZiaD!Q zV#7*ZC@l*~@0quBT>?R;e4MTa<(3K1s7mvU5gF8t(mJ*tYNU^lr8gHuVU9`ss-p?SM&H!06a=>su^5#ZWYI-a-omKIuvvO z)Aq36X-_n{LEuUq3OBnCp5c6Rn2spB-G6|ZdjLz)4fj`7ZC(dS2Ggy2rc1DPs0@GX z1LO^sL|#gs0EBV^f58+Y5TOPtfBez1D0NeV%c3PG-drk!6~h`MRW(2m>g`R5r_6Vk zj}6Bcx|e2jzJbP}?vA?cFzVRG3yuP{A?+!HHGOUzfRr5BWUuWA+mqr= zVqvX;rQSAz6;^6J5)_sc8Cv|yV4UnkDjMQ7_$n?h%f}Y7E05E$?aYz^r+W((!LN45 zvHk5lbsT%vz z1=hYbez=AOnM=`VGiv0tb%0*4HsKRh+o-gc1a|=wwb346MbMfiD~&byeXx!$g(p*@ zmEgheA||$;puHcxe^Bm*Nz*X;K2i<&?z?eE!fF{G)bW4>aXKxBf!K zSS2gW7IlJBZNVZ*60wafTG2^3DUmuRLgl74Nm$`XQdzDwuzi=g1IqyQ03f3&vld?1 zTT>4i@;1alfV^yO!gC~{XH1@B?T~IZnGQ5$R7Kcy0*bQvDy}PJ$WhP**4A0YR%;zM zQv{p`pt@g;SZGPu1$GRH#7`281$O|rC}Eo&R-**1@etMnvZ7@_GFQ$? z#gOD*o4kPedU&9zgSx>$t3Ows8={X{BM!<4msROcP%6`nhn4X_+YGDFW?VrIenboe zQbengsAjiYhQluy*Pt-vJJI~5t_am3Ry7MMlC&1pr{Q5eNPSnp)lPP%3c>|m(b3%U z!}2gvVD(QxI99AFJp&~I7kbw=bBjZLPTcNBQMW90)Lb>~Mw39H+Dh4{Y%l_%1v1u5 z;{XT-ykOfWqEk!NVV3P~o1PKXqS^$?^4ei6@HKR}cQZgFCkLc{{B0)oFH``SnjonH znTfM=qb*lS2~dqC)R>hZETKyP)!8#FiZTi|Y9*^dy;g*FlWh{nuD89D+Wvhyt&Xz% z!8-$f1g5aT$18B4RwijM1LtTpjryf+UqI7eHH{+xBkjDF-;jXV5-uE?=o5>5VafPp zn;f;AkwtbRQ}Y0q^A_YYyCTD&fuo3{5wgvp1}p0@WF%S(vYCADR+tMfN*iRrg=J)+ z9+mQ$K_2o8VQ^7{CF&#_YkxMRqABfkC9GW;<{Rqdc8hhpx9N^K@WPSI1mIWF95^=p z2|BLYFht0YP$M)i3|Gt`>O!-k>Q;7qMknIhZ`BYi*K5>4GY+yhWvjUM+^({Io85zE zsGaBvoN2psqU>LNpjivR64`hl<_A~QgsBSP3$AQTuadTI6Lft&F4GE!yef{c#4&>E zO>rtM&)@VghIM6E%38bL7vg0=Tk;jXG9``n$l4YhmFW?0N>)lV>&K<3!D0*Lgk(lq z`Z1|NUBOiSWPQKg5Lc=OKSz6)E$ysV`+$MzdzZnp)r=E$v+>?Tg|+#q&{Y zrhMF_QEH{Zz2fLf1q#XzcdG{PxQFptMx@(PN6u3QI?Dj8SUbP_DR5J`zBpwf^3h@_ z#7L_K?G1%5kU?jXp0o?!{hfVMQ^XVVQ8NN7Cuh(oxaH&^27-+S-wt z<`nV{N2&!rvHjGmmjqNb(zb(R;w1Fj1x6h#lR-jLT8XHPa5oTyrAnQIs_=n3^ezFJ zlcLP6TW(q7gdtY*=Cf-k#dRs4wmk_IklJzHT_NhHp;;rCGT>3i!<<>ua{IA%sdB~Y zD68#&hN#00*fPB8V4c3qD=c{y;?u7LG;h?k-nE8qn@=^%EAIg_Md|3L1@^MoSQuhW zi{6LYq9Vws3gk3D(i*kS_yqRsD!cQ+e(y@VsmqK;mi(U8p&sXJdmf8ms1&Dkaj05} z`HN<{oU}zNxLe+C<~FS2=1FpbTokRj?8}jId25QD{MAmEKxqINGeiho5u_E*%IKVXmC82SrPD#9zgFq zb{!Y~a!mqU=cn~Ir9zNSs2BFv#CdTO_4P3G;0?3FR0`DXD8hO_6IP22`4%n0He>e) zLxm;{{wyF#y;aH#GJBKtu>kKqmI$PeQ<0OAUEI5*-Ux12YDS?hAhH2vh(1&1hi(#| z?^wBtF*jVx+}!%W+*YBjm7h5u=L#5DV-~l8c&53<%JZy7T-KN9wpX1g$z#8)Pg02} z&C$K?$2QFL!YT5OO2IJHw+F_%a^Tuj^YHv|_&9b+C7B}H=QTi@>{AO13HrQHjjkM7 zB)+Z1r&?UqxFIMqB^FIZ+%!I^9L~Zy%X)1r$AQD0;iyi<%la-K5WBMZfLqo3rRuUFYj$xQnX`*5xblgR z`GFI!tlDI3W7AsY1vr$YR^bwd;!mn&C#W<_hpmRbc zLp?>Ij5YWhzd#EQhmFQmtvLfaOINF`Y~#q55qZJ!83iQ+UTpR)gOe9|DBoar;Ar7W zmV2HrSNG?Fw1|MkwTRFG!oA3Vk{&S-;f)Yxi*uBVxQ=p^3Izyif+WQFfq5bEsm$7f z<+L>yG;p9uShQUZ4nM`^25HRn2E^?gxkJn(xC9R4w;t}LAl=guW{aeQd?e0IbjbH} zq*i}Kpw(KP84lg#aFL%6{lrB4QgABjlD$Z^v+9#;u%?j&$xZ9+X@dew+LZ-fn?o3z zI*n)BUp(%4@@GHhH@u$zOm*_bd0fp5R`Aj_x3%-Jf|ZJSrR~{CK2RQ|U_GdoMpKEk z<#S;O)m|D!KexTm&c-r@&r?8^G(e?2AIg7b5mHnoW_2mY@#uq0n?w~w&h$KwdsN9( z09uQAq6d;zz^ox{(YO;;Q^8U) zi`*E1|1g}WT_jLD5ch?zi|7>k2nNe%sgA1Am5H)kd@<6ZrrmsznW2Rf<}m|Z;lX{R zbWHLj{D`S9G?f?e2$LFe8ccJKBv049w~c~u#c-p8!c^i=R&ApD-gMeBhLUyA4rINWNOMsayiz5+P2?6z9Ssb! zK|mx+(saz3(m@tAWnyfsuZq>PITNJN6L$I9VDA;?3!iE#3bHN)<;fuME19n)lFi=$ z#*ZJj2ZUj63{52@bZX$jE9)^-R@ituK~@lzL1<|9IXDHLp`ns(yv};Yg@9FsG+=o=usqCFrpvCqNs+ zCL#e3O$dAtkR_S|ACX=bJMA3?iys@=F^IdXq8J-az_*w+yk)Ak5hS|&o@4IC9TVGx&hTd4{pXQPZt%WeFE$WD5^E3SM4P4W-hJ7kv_*!@6U z>cMVe@Os0=>4I1l$_JLGh#2bRq)$kK#dak2fT;lLp<@kVsc5V&0ePnfcWFwkNxew; z7qMymI>8Rq>IK?zjsurV2v1!u&9e9W@d=9D1VEm@%$I(w$C@v{w7qZh z{Ok6|xfTLpB-DJ?x94f|G88EJ)C#ofgb@z`()NVAc^Erqo2Z~M)z|?XJM2E?9GxGj zrH?Ps)3fsY32WegHuP^G62zucmg=6A&|`@C4wD}8{)%$+3*@!qWzz|&4WYN>{$1jM z#&C=j55?=CvTL-9L2;k3$1v~FE-azw18O*~z7M{T1MJUcNtL1N!FNI37v1k*EAlk` zU>Z&xr8i!;VtWWfc}Atd>OU(3MQHPCNNK!|X28u*Er+CE@R9~W=iqgrsWVAP8T}F4 zmQjTQJlgYBG#lFHHKbs*&O15UL!lspPJlr1406YI526&pW1R-(ASwldO+ZvZc6aB1 zY88qtvgj}C5k0y*?fo|dTop`Wg*z}`fyU6$>-hOdMA{#Nvk zLz~oI!_5syVh%2mz~o8yKyP#tbeyXT#2JqlA41u${yfQnlmL*xn7Z!R9d1KBMcpL-Rz22a!}foO!E zZo9#Fs#aoEuOEX3a&$x)-A4EGf<{wau?7q!){q_eG!$~a(jw4J1MC1s=;W% zv-sOgfQ>Wrl9B|Q2E|a*5DCs@9uxD3X>a8sXYl5`E%pgkx-xzD2e&o-zV0V;JLmm0 z1aO5mi)%k+m75)PX-w>dP|8ySL()b7oIUO_s+y6Jhtlv5=L6jf=vug`J>5ii>Jm_A zd7^Ou9C`6-qgjS*3zK%=zQTAus0;>Cn->;(I1mhh`ni~-^$TVFJ@|@GnIgO7(vo#z zudmGCTHb;*LyCi86=@Ztgr{erAJK;|Qi=_+X$p z9Jad(?ixMJB@mJ{u^Sh#8z;DlM5-&&@7NwY^sMaZm^m0q?d=heSbMmFQ-MSWjTYP4 zITg>d|3J5f@bGK+Pgy1zB9`rFuR3*CkP4@X-MsSl#BpY?$nLk@#dd7Qyez-sJzJ;P zZA;xc1O~P@dE!SIz@?F4ci4D7vmn*v2tdK*;5&h^7*E0$+)$g;%)HA^C@0~UWVnpvXn}a-BFvI`4A&*9fJ#w%B@IMpHsPR ziS^ovyo4>xB&_No@$b9wUZ|XG0!ieB%Rh}J_tcZi80P&geiKc}Eo0Qm{eLeG~`9O9_=3bk2^X z&S1v{7c?t3{87BbTqnr1z9At#51CECokdawpIpDd5Psu$6MWinouz?vmrOVfFiplx z=u(x?$|H?RO6J>cRh3N0eq+E4wTfv>#>PetaYt!!r8OLeY-6>EK9-Nonz=#dkH6SJ z+H-qx2jY!=inpyXgUfIQs&P)y&II4eZ5$lwQEhHK#^yvqm7~+#_4l%R?#(2-MHH6G z9%m+7f!?2_2P_uD(@MsMWZj19r?j1x)jHz5JY0(Q#3U`Pw2iJXJbx+)@lq?uupBW%FdIVD7NXXeV~>l7qGlS6j1d6#Z3}z+S(dwp5v# zq*|Uos#a$6{;fqZvVCs{v<|<*7%Eb_e69`hU??}R9?p1i<-a>*E756IGjkF}mGSCN zHj~q5ch^&KJ@awAa9Y?6#DjJE5^G^ksNMN^>Lvxxcw}q3@+md0k;L=V(@>mO#@_j|#Q;t#vAcP5XM-nq3C!<#t|Y6cCh`}6*w@6TiF#` zVRzFL2`sO^oE_LzfEOFlbIrCY(=!OP8wg?V3H8+iK=MHUTbkZ`_@3k`VwRvuqKKdN z2c}Q0mE9z|t#%mRFuQliD(g+ark7<;Ec0CywWKNYs0qbK_-)iMLA;V4b$d8qV`rS1 zV>5?7IKJEtW8J4xuYRJCK|9@>x-DSKd{ms3Iq+$+iNfO=A)J;hSLyU0#!aH{)e}Rz z7`-bu6MO(~L&H(Qm$!WAB+1q}Hkx6+JoCWPP1>p)Ya%tJ$fvwy($v2s1mX>brjEoy z2v+WqIwNVwy$Nz63Cpg0Ao7SBmxSrtI0NwVQIK1Kz7V9AAWJNL<8F8TSNJgC(?VT{ z!5Xs};`DpPsaN4zm#diYo8btfqECrr$V>cBd}G+EMKx*t4^VT(9tQzrCK@(cL`Lq ziOon%N8EdZwY?k@hP>?6*|-id=UeD#~qRQL0h2XZE@L|HLJ zCkWx86TO{MC6}nF_Yl9h%Y{zW`I+RHX`t5aAmJowj<)0yVW5ZBu4%QILkwkjhFSeW;rbjI6QGm;dk~Z*Tr_pe>gxWOsfNALp1l6W z*pd-?4uMW>x1IqYm`5f4&$}5(st)6+O~8W!>PPrMJ%eo>D3+S92b_?yc`+hE^K`ZP zk6Pd=$E%;I=HNwUwaPS#2ENW8?UsHJrm$U2Qc%PES#MfBX+67Cv?Fk^4h7H!O*3>| zu2hBASyTKKawCtU=}q5XwlIH%#Tam&7#y`X691rVghs*swbO)TqIzg9OH^HzA35pf z+2pWqMvVx|>&p4sMtV3TyBCY#pNMh8_;Aleg?1T5qa3s%cXJFlthC>}@E2YV@zOvS zTfTy>kmWex0ENe|?;33`n{#PhOPy9mGd>jJR6I~0=MxTnqW-7lKRbS} zmbrcwPbDo@{(~pevH86=v!`I_GR^ses%f<426PLm&&dx)QP2|q^{==tA5;W~8dzq% z7`!WTs&Z9^X;m0wCLj`hedz`K?_x~*0|C8sN167nS_;v(=a?~({D$)M{FbLbohdYl zEtQ7uXLIhh0E+6#R2AyEwwPv?LKL47J1t z7tF)A77@4Lx%!Xxpjl!#8nRx4fgX|DPP0a-#GWJiz*>5MXR7H%w?Uv(5!dVne1-#7 zWYKU;qU}Ckfp{DJh*^InyL4CNM!tdJ6Zewy(XvvfOP9fio_0a|mfeli)$&898>gg<*KX)#|B4h_Gg6;T_e7 z8;ebZ%wnDlrA-V%TlgEIB-W&(3={0R7hdOOb$nrvR8bkxnVio`y^#~*N*ZU7z0JUl zW(8WWx39jY6%ifxn&TEB*HY=2p!B}{$SE)2PsSo+akE`Z@LEWBVE+!3s~{T5pT&Wf zDw}Dg;4byJo9bliUn&VD&6)mPVIQXG)kcnBvBz0pi1B}rflRBb&NtPVSnf{xTN<<* zDIHXqd@n*9hHPa7GAHXAwiJ|uKh=ssaqK-BGi<7yYED3KugvWKfky0 zcl_uqC?fFd-vqO)%@`r&hRs>?g5)x9OM z3*kjRU{5AS?h9|aZUS||9{SNaw-U2Z_yJA9tG(12gA@!>KQK(ld^nQ!pe{U3-t-NF z(uFyDaAVn435@h@6!H>Uodh_pnpWi-^!R}(c{g>T9?(xI?Yvl@TBsCox{@2?9oor zbnHJnj1=>VX}y`0Nw`80T5%nf!-IAG{kR+TyHO+GenL)_psh}%auv8j%`s0O9EwL; z{fpx}bMUrJb~wlLtZx5Yy3W8auOw8sEcdh>;FjkTF?Vbvr2G#jamK|P7dIHVY< zKrOINoo!ohjborikuTd7$dyK9s!$|p6KbWeq z>O%}EWyC`hewwNht$~DKAk*;yoXWe}uD~OVG z60Ms$ww8t_q*j~)(Y*yJz|6EfYT@LJne!ZgPNsN%AQ3h)L6L=^&u)x^Y;H$WRvH3v zSFsg3)7p;gr%uj!!!~}xyy(RSuu0#QsrFO9)n! z9HhqTlXB~}5>S}A*D&TQ!m7i)B4!??{UE&>Y@5pO(`R>S9=2SX^aW8Az_g5xI)~N{ zaR$gC!8eV$%oL`=wI~2&vLCs+6#GYZiAN~pTUW)c0Z_hIj4d?1rD~ZPHPq}gHc3lc z>pLS+e3f?eXNA%`^Ua$HW}`W_-)`!qTnq_2 zIK>fBEhSu>`Nun%$`-LGYsm|bC^Yr6cBA3I+4KPat^C#Ll6zWNBch_IHQCKmOlmi4 zpUk#Qrwa&1Uj56gpaZ?Wn@^5Px`I4KKf}@=-iaATv1xdaYOLL%s@X{o!diCseCvE&Kyh*sbzkT=(XMfwX;S}(nkYO0sfCd`8X)MHQtgaUcG$~74sT~4O((_Y^#!$Kad93Pe zs0DFl&YEh*hG<95P_-hmxt~-|kul6rat{}1d@2J}j?WBO%WSFfFh}mr+d1mb5>wJ@(S?vV?2q#a|A_PMD4?w+&n9bu(4gO+^hqk*C39k1JupXJY4>w3qtOvJlVmQOjf zo4zImuLN!O3mV61^dw&vUu@gBHUl`eRXOK$-5`zJWTW+>Qhs`qhsMuJ(=m*3o*XbfRf#oPhRwCkd?9L zn9HRy^CtG!n^nq-@YiOP!yQ{5=s!T%2QtA3iyn~xN{(ck#m3B%ekp84hUJ#q$%~6Z z=s)eYgl_2Si3e>8_&%qsi5DG55|x|9P4p~o52a_#RQ88!cqJV$FBMGV7h>-MMR#d! zEHPbt52$A%r$TjoW3I}wKluQm=W(7Z-RY-QW_U=jqGzZcAY^opLU+c@fe)Ww*QBkCQN$am!mW&=rN-F7 zjs_B68?6Oo38dcC0J2~D(kP+ZGSEAzASH8n6Djy>Fx zkhBQ?FfJ>Jv|9aCiZUhR>q$lF-ycDu5Tr1=?Mg!O=MKG|U=Bf@eeN2{gBLYHL3kJr z&FfMUq}b7dbQEpHipjaqfH~5T^pkMl6|00XkrRldK5QgO^p6Q3u}qX9)0dxsz-c*nA$d!X{T9vb z^>zHh6XDjUuhYxr@{`3f<8<8D`{T>!FyC>z{mCFZHYm(fUob@`ZvbhKv&=2 z^J!pYqQ$}kaXY7m(B8i0@{{W=266k#^Yvo>VtM_jf`JB+1Ksk;p}5`>f!3C<;a3lVR>61JC|P|pxy74(CGbb_jXqlCBF4dt7@8f3gpsi6*`5?TtPsQUd#rsLBVe;eV!h@oR`e1Esi!O~GP` zN))S8VF8PFa6Jh!30XFfH*4bp7M`brAW0mNznF0Zn;NBJ3ZZPUal}tg%Z{21HFI%e zv$CkPgp;9{#l|x#`VzUIh&BfetJqso{(uxq;|^Fr5W=hUYjcU|+8xFktXm<{;Zok6 z*T$@)5s++TMF*oDvn38Mm<1u_*gN!#1u` zY#7RhW_j3sB#s(}1yNj^Y?2E# zX0pT|4ya*Gu^^YdeCUh!AT^PbT1UTZsqtYjI2P--?xeJcEt^tF&y>DTs(4qqQOGYp z6;P#+s>&h9kAp>_=Fcd}LDH$N5hbnOG_x&IuyA;kNw%N5BFI-6|M?YNbusSzufjN^ z2`ex;Dn6yrDq*9Ly0C3l&iX*)9$nQ#%3n!EY$W8!Pf_Ps(*PIisRdh7CS~D7AtV+f zNi z59_8_;Hi?RQ}TpOvV@6N7l(C1g2mgO>PaX~OF}}CS-hGE{?cI!yeK-(S-8mA617syW!-{2os?E0Q-UAk0C&yF3?@6(Nz%s2Un# z^3>2^mQq_u6SG-iHsEZ+0+jFj!(RVT<#jN@C6d4$(jt%N)tZqcJP2ahZ#=+AW6&qx z5*l_~v5JxSOSCNU5o7n&}3KKUcq}B(2hV_gH~lJ0T|+N5>Oc6I^p`I zTLb{Daxy$$&6v=6RwX&5n_=2I;NLXI&SmYsg>HbpV8=h$G|_Edk|35{TotxaYQM3&tRfM0_0&VxO3MY~r}54!ugUyT{^Dw5j>VPIw$J)%`3P zfXNOduxecU-Pk{z5?L8EXWE~V02p8;?-_Q>QJK6Vs$5FqoCJo@JxCI7e3rP1UiyqR{FM;I|( zwj%oMy3;4ovQBmE#Iu=`-e4LkEw9FsWaY^H)#M0GXJw`7*sWGc0|7y>7&`)DeeNeH z8hbewfl}H?F%v1q`ucT74s(-Hg+E?@>%5f@t^tq+zc<6U)uI;9re@R=p3*^NwRm_? z_LJCViQmtL*pJy(7gg-dO!83ctc>W2y3ezY9bV}dB^)K-=mN#YEw)eCRsq$FvAqAf zxObwPmgGhb{^XZ)>%kQUG!XN8Ek9*J52bffJpYdQYss+2!TK0c)ku|KftbhdyuOc8 zXDObS&)oB(op*Y<$$!NBxR@thh9_U+=M6shwoXT0g-NO+Fn^L@BpRcma;0D5CZ1{5 z$cOrT~l_Ms70iF|xL9}mM6DZaJrcyOb0qyiww zZ1m53dtNENI@2_Ba@hwD>BF(hKW*)#;%c39RhH-Tbq%8i`PjhC)!W=UP>Z>s4B*dx zGIHUJvhrD)la>5=d)pIGD#z=T;b=8Ksqtt}@x^tw{hv$B|0E7pB>5@)kHkTq|9$r4 zXk=pGV&d@4zcaS7H*)r3FtIgqaj`PDGBNrOnzez8qmi?V8RLJ?k21~n|IZvjWb^iu z8u)MGVlxPc?Eh1rCJy%IRu*o~|1}U71``+e{~pEvj_|Fg<5Ize;-9;{>vZBjw!UkP6z(_8R1G)_Kh~#!p+Jm6g8gf29>HBf&WF+Ete|nHYRwXG~f{V`nP* zrbb5kf*7=K3K>Hcj`(S&$r8=%Q0fj=oh-ut>=jT^JBx;PdMV_0r|eNXO?S$}=S}ym zIyN9aRF;i$Mi}eHv_H7;;0vy~-UAP1T(cz~Lh&(@N0cdQG#P zTC}n@@eHB$g(6EXEw~M2HP2ZRa}U8@x*z~J+51t6L|e8VOyP*fy)>*w_VNu!JLVl6 zUS2OwSugJRm?c_YEH`I5v&V{8yN8s=K4Uq8edieJA1$JAf2q069p$A``^fhh;&cY- zt6jh@Sr8*U!fPlu81~5X@wHiMw6KOKsDC+|ttMY3Ipx|p!t-j*zfbaK&mWq*$mL|t z^Z!QJazQ;2Ftg-QaCxFwobvxR)3J!j{pb7Cc{Rnn9!C@e{adED$MMXf6-KxI#V^*) zC)P;t&yw4c5>$(3Q6{}-ZsB%HkD1dgsV{plJVK)NdX(!X51L?&$$9}%fC2vp$G5U; zKPshm95Q7soBE&90b6*P_~5V~UWg6u%icW9?n6gUa@R_6X4JRzsnZ zO=yVb$K?*_ehn;#mj-$UNuOu?`q*lo)}P6ved}t7kTMp>0l#i74%t^iwN^y*wbI0> zLE09orCT!X8o08BGKZ`h-sl*7HjkXwPZ74cc{eQx>t_eD_}?}x_$@NmE#OuU-q}yj z%)M%Bq#kQm4{zG}2@iHi#5PTWrOk1dSG>=;F7OF&kIYOY(Jz?8PN)$}0@CS@H=7H} z1mEmWu+33zDWop&3O;6=F%YWN+P9{yuHWl8t`0XY;$EU+6I^PaqO*f76bliyKCHDd zjX+G+V~sEA_%R7B&ra#}C6oW=7!fL~VcKNa^>%zrLkx{PZmgVN2UE#R!%t*XZvnCX z>4aq-9pjm>=aIHs`ZYl}TM5W3LqefT3Sur%p}WhGhyC-*Ot(v^?v*d8GHi2uhx!Al z7R-(-?02{LnGfmXH?<0>;{Ux->v%jL2G#WmBHeaeB+s(nTcd5_8@FJr_uyF8A36!E zZqu~cY+GIW#rJA+t8fR!G|m=u6^xF4u7YIL9toSRaqm zk{39;<87_*_|#e@kzqpx5w-F4D!z`pW!v>2iq}uZ4$}j^+4hG{u=9Zn+_Xs4DbXh? z7A^?9S;DW6Hx*>_nqsJY&jyg1HiJV+sNGbEdxzPnWyTqlouPpGY^}!rxFIxE{YQ5B z-JzcIu36x+;0)LRVNlO4XZ;tyxMv-+QN7e0sg9vf0O-MwXvRsB-khFu^hte?>XmR~ zCvfCEgzlw$OfaOuj{Kjrl3bP`d&12yxpAXxtzX?#P`kf;7INF9moiEs!}JRO;ReE&ZnYg;pfR8j{wT9QgWI)LNW5 z6~bk*RpOqA$^6HVq>StZIY;#CTge~tc@+Ce_J*7!T6MQtysqrp_i4SfbSh79KoCQh zMHD*&c_ z8A>*rBHE=8lTk^X{yNMF?+KQPCZ-zJ5;Nfa;%WP8 z;)Unt%+aWS_@*n=3T@rPSzi|&+a7M%?^Ty ztZ%=^bCWpqQRLAjfFjZT$cMY0o^QlEdJ3mbzO&n#2N`8$M^k)i%_6GKr07?Jy!5?}@;DONQy z^qp_hq#&W?`iO{%4Tq#4Jd4oIjm`$uHbZG$^3pMYLW58qifbh9A+5@)&Jk7k$guk6 z@Kh_9)jL}#w3_WvcI}^<@7Cx}J%KyMAwi&XOE6C2X@>j#pNec&ATLnqqNhsUqW26` zAHD!tLS|k5!isSDz!{6H`C~nmQ^bVnqIa2n7tmtu4M+qDW-_$=TjQi!NJz)xUd_NW zNtpZ}j%SdrwU8$;mw^%S$}ax~&ZSmo5}KKZ#|?lsL=nHyKqw7?l*DUt1qN{_=3R!A ze-pmnKUDBr*5d@?S{b9KFKmuDE9kAGdNyZ-}?P%}#jSD6p#03?18!Ec? z&`#2+kJT^q6zjFtt7J;)_e97kYwD>O=LIEnl}EHH<$?*=8GUP^Gyo1Y4}Ct$c@9CQ z8V{}Nr<m)8qRq}*wu)R%b?8;(D z; z@TK&5sihxr0cgvevS=<6Y4oa@A!*O5{g=eDts{Hzbw1>yM?_H(XY)sNu(6$V*1H~S zH!ourI4iMYNN;gqEy74G!AlUV{lqNw(`a?2VRB{rvPgfERKX?+EO3f80!3j%BbIY;!99#(GBp0&{oJ&cxXgLToe!LJOsIk&(GRGj+##RVRN^v;CusK|QLI!5J#=yF@l>Ry?|KbIG1?b9u-pUXZHE7TCU?p%SDDZdb8Gh{o5^=N-TzDA-^`)FNRF-xrK+AwL*S(uh z>}MeM33Y-l*X{0ou#Xm)IxlNDOa<&bAK6qz^W}!y649+P#L}>-{PLB}ZDf45l=A7_ zihmp$g|{-;o{fLBIuC6O8-@oB!<3N2difO_3i8|AXdxtlk;5Je43#lDgX#9TfjIi| zhhV}VAXN~R`eZjn>;^~840R-mtP~9~izFEHV0+u}jYDOhlz+^KpINs{aW0c^V!(ju zmd&-f8cxgSWJASgJdMuI6kDP!(QSH*sL?qs+iS?j2n1qC$!EsD&fiWwa}knWs)1)U zKm8Xie%z@`AvWRmuUL5F_P4yHZC5cXY-2$ejT^&CKrFsK$J8G9D|rX0s@kNEQwrX~ zc*;mIak;~G7DA}FzmF}q6`1?h6f)I2a4w6`#-3~N_L^Mds&Utmiw$e{yCOT5Bb7XQ z)DXRaB9*uPG@T+p`ZZCfG4kN?`ct1%cL`+Gfs8bWd_yeUFBz%zMc+>)?65}eQ`8T# zUnQXIiud2>Ym~nMx}<*;_w%mir_N*_=2zi!Cwv1UF;4%WMig7f)#F2-7SzD<_wGs8 zsW=un&Lnf9l+Ew5)-!Mc07O0-sc`t9>P~R0=>w;_E-D0A+|8x8Z>|F9UvaGNoI+y0 z2M^FAdbr$u`_YUKogTGgeS#mXSvr~uLDIF|=Vvdm2 zL8gbV|GCUBl!q%7WeWm=ivbEE_y4qL{BLS9?nbt5X8-rv{zaEg1KFxt)yvIby zdWUY4%wOk6f*)tO@3%FdBh#Pv72EIE7bRC;4;r~Wua}tt&l96p>ce$Sf+h6-N zUza64&xwLB4c{$N1-~l3K0SKgPjdY~rk(_SAEpHTKErChUK9fIFasWYa;N>DUbY3k zt{)H$0^X)_KhLi|4@id^`z$9EM$Wf3ySy_E08*9&f*he0(+hJR8ZmarkRFM?eAN1UG{68G92 zug|l9_eTwf9-nN#r^U1TOo!M8!Ox+d=cyV(i?99Z&t1x%_p#i4W&4xv&E~i6E3f^w zr>sZ>9c!1QsUGfyZ62F(zoVy_9rrF{89#f!{f(m>d%I=Z1-~c(^S$^5_hHD z635T0fBJ4wF3p{G8*a2dS0-0QQ(kYXSL+LEU4^xbUye_W^z_FI$IX&d(0`GT|vS6(?n34M+y-t=EFZ1PgKtZDreHZE7#K-ldH>??Vzatb?ad;kP0 zAELqA#r6#w0uLQ2rQNzwwi^tPF9H(wl|5N!auaUz_!r)#{KXWk1t)2js;BM*+ioHr zHDRi)$J~3bH?-WCCHMx9WJ$tJQ7bg#bxsRaaC98=~5reZGgSx z7M5K>zo4x`>iNsdA&>MHjwX{xf|Lv0$ECFu&)OAt+rowNp|NUC`Rs+Ccc?Z8$2N^h zEonw%3Yl8#xlv(Dz~Y3Cqj(?rwsWUxtL@ukc%_Bstup-bm?HRTXFR zg9~doYf>HC6(8s9x!eox@fcQCgwg6t&a33GWCgC6%LU%4y4d35A~MgL(Q%xp-TH)a z$W%E77f@DhWZanfvS)k%T3cY)%haR!CEINq##sRkKJ^PNsL~6FpWT{A%t`d7!NaVd zD08Q-K*q(VPo`aN1b+8+J%$UkQ#M^6aH%s_X<079V6!0fQS%*#7zUCAkhc~%nk zp3^Cu>4!1hB%sHm`I;R?&JkKH-(NX6BwF8n5A@q^XO2nyv>D2j`(nr>(_S{GJ*I)* z-e^>{{|BDP0v{6I_e324xaB1?)ZBu9&;t)9Bflq(KkwH-v zhoLOA-U3jNzX(4!Q?5XZBv3Z)`_&{}g5W2~;9&0cxn=H^ZIUw~m!aEX{ITMn z9r3gu9DhV;0i~6r&1rK3H@h)0^1Y$# zuWb>IlfKB1MIp}<-R0Za`c(ZY2Ttbf>!!7d7Q+b)@JLq=h=2|!u z_^~tY6lFOhkWrSTN!(*5_@r+0as7m#eWjQxng^a42x9R@cODX@Z8KcJt)lji2@BDi zi#7ZlxrKVV8e3RZgW{`%gXN|#E?X?EEkEA`^~D7~83e6!cfUrA<6i&B4xp-?oCCqq zILZJ|QgE-<$r(eDot`apwOSI{bOU2|r$%B0=v=I{@ASdHy@h(<>DIbzoy@71dDBi$ zn%2cCUuJ-hdWGwPVNDbJ1PW5S_@t}eLDQky z&(Iti7eQ@Mar>}^c(uE~o0c~YkC27Y`!vVD(Yi6#%Kmg3=dA96n;lTkex_IvO~|*L zJxOOwNYAiLj@oQGe=DUOZ`jw+`fROt9NM4WkcMJ=CsE9o1~Ly$+=O-2yg@$GN1 zIpEP>L}GucvriPqH_n3(&BL8*#K7oN5>hcUh%Z;cYK#j95-gtm3HwsXfjxn$43U@=?x zn?q+`{@B!Ac-in2cl8kx%zeM(ZPTuYP>=Dg#GiGi++;!tv=k*8dX%uduwg4wR4~D< zc~qPph?2D~(wVZ{9!2Y;s$k^b*3?rGF(16Ocy0VkmZw>uJivlW*6gvd6!bYOaq5tD ztUe8jqE*P1rv6jgJ%efEWt~)rBu@Yd!RSD+hN`a5sS7)e;YS{Fk`1QdMW|YVR!VQK zHh4#;3c$MUw_F7@?=eLXMM9lXU{RYrKu5Dfea(9fY#INwbZq$;nLOb>fwZwPa#B5~ z!E%WX;wP-AHK76krQPblW{@m??)xi>q-MJmTEu?U{u1q>FVWgEDC~^j=VgLf|ik86gW+f_aYX80(c7 zw623PiShX$IXm(K*9416Xe#(+^3P&kZ?SmU;E?C07)EUU+QZNzIo5W$7iZo)>RsdD zKUx-n4uo-kQy#@U0eRLh`|n8`h%!1yuU16MXAs@Km})3O{pD6`1LX<7DU+A<9eOfD zA@8JNaq(C7sYpc%%Ny_&BLt!$r(_N|9;@vxI(+!^{B&?jWH1ch$EX|L0LS?#7Y!IU z5FCC(Gb=KvY83>s#@N2(znC)gtgNY-NEA5Brr%nf`m(YAF*s=Az2$ZM_aFjtqLs`v^`PKD^eN_cslwUysjJp888i zpQbjH{it4~&CXk&@lK7OUpPUx!XM<5XK_O87hHqKVdjGdbfy}y$ys=h3U9t~)R_Ps z==PnsDOin@If~NAvllQDxcbkwo*4?`Qp7~JwW6$r>(!uztYPh?0PyNCQHmnE!3O=h zK%RkE;Pie%;t`o$OWi2`SiBo0ufur$J;5MVN*i@goxg7l2VV)BxcJ$vJOB6wO1boH z10dnlDTTF7(Cpe|KKf3vDpiRdn8$KafmvIaYPRLLRtaDJqH9{o>~lO&;$1tMD6RC^re{DI!cu@GH;NQ%_CBka{;8kmkJ1mJ-;kh~Wu?Go^^M zQpbx=J-;-4k%3EHmPHToy>n&t68Cl;E$lb|SV=~1a$;~QVtlfW3!J@m1mlfI8pf+d z^BIZwO}P-4t!l}&5teweqXd|G;X>V?w129l7>zmbF_>=}@9;*>=yOFHA3aN!G5bJM z3@DBl97_$Xg-1MT6V$3ufcjo8jtbfy6&loxH@}-|3GB)2^H=WbC$h*sTs`^A(>~UE5n8wn=*+0{ z(zM|yQP&BzxsqkBX+DYFA`UCQe98=PTWL65(rv3)6&h3f;Cewqpg6KxXQRk}AYM#L*@BfzBY6U4IJ zjv@==e8oEFm~15Co-T60n$bZ4_mUzKp>ws=R>y)eDWTCwa;FLTJq}QurETrXjl#;1 z(JItMGe)IeCb8(?O1Lpm9Xfr@g7UoP+k{Gsz8MzfXP-N~(_t;agFg}vIQj#yvUYW9WHyL@GFyz9 z=z2_GvnJ4tmaJ;EdnpQGt9~>$cDLsv2;WbQ>h&kRLobd6=~qllmq{{DUkUm`KLV6j z_G!jGkly>ga&ra-x^J)1!2pA_Cku)4g}5VO6S+=Yi(V|Y$iEZ%j@z{>$a{uKCX~x# z>Os2=IP;pu&wtU(;HD_mS0DYI9Kfi^(E>PY*mc8aJ;)9WmreA6;Z+VA1KsCr08v%> zs8R$B*VIB-DA98}_o*8eMvBd}4OF@U9lo6A*OXR8jv!ql61)yu-PNaC2DtWkh-9HN zxsLhJL{pUJZjRPuP$;yjGmMiI+1?V4U-T{E-12nK_M6o~$$=wQ?l8ZFVg<-zg#P{` z+Ow~LBuZbzYKA}mRfz*u>+OENcZQ7I*TpkkiobA0#ucvp1IKBKTut8iA_pkdq>v?VY&{ zB`IRiq%a<)9zk>(G0{69G?{rc@F-sD`f#1c$WcqtBp11B!M)s*W{pc*r(S}$nJ!?I zDZXYdWC0{hD$xc7vN|S?{MWZUWn-Ni`M;txBqa6Gdd=B5ItmJHibD4`KGv(?vdlSK zj$cA}rv|t$NAY2zw_+$mMi`7JFWAoCLZe;y!-Gjf)1n5san+VJULy%ai-O+KGzg2} z7vjgyw#M|Pqh27$m!rADaD%w!hq9gOU{5jRu{wP?DI1Y|JT_X9aT{80I3=i1V3bY9 zf61;I(_eBtR06n`<_1~n8b#?5*b`CML##-aS91bHmTYf1V0(XZbAj^DCmG6vX|#&5N~+MVpju3o&WGRRe}cmVUO>UO~vaStIrLo|rT^aKOzL1yAbM=k=A0 zk1n%39a|BT_aNoj&G_Iy|B0~#5RZ*V^f;i-3mf?gBjK!tdb|5gF^n93+cFp9Hl5Y} zz~L@S%lig2_!gjjrb3U9udxz1PsrSlt)vvsN3qs(i%}~X zW8<=eHR;{4LA@2WH28Aqttp6Zm(%0+N~LQB9!W#E3254^@`zug9h`h{R7UJvJXDCO#pUGjpOjw%U(ODU1B7iU-*B#T9dpVi$+Al#9M zBs{p#EYYc(N3JtdiO%ennG;wgnX`jR5$8{QWoC8!BrRz<7Fe7(WgPs~lkLyMe;$rjJU3mQ6w6bA0|KDB|p^Cxu$&mnF{v+*9Oug!Op9Xjr%C?OE5{mpQ69F@i z!4%D}J?Ekn(SmV3b0$2$d={_D-(a;k0qrvp{xRlB`%BniBg_mUIhG$!OjB zoPd?iwdWDp_(N}mBPWt8s0;2!-O&lOxs=;=X8&G&WH}e4j{}P8+p@ki_Dv>Qhh?7z zJl~lCuC{b?IQ>YbAD+!!C|2h+2V@PIi14eXFNw|$TW~I#)bPDgHae8$1UytAxLOC^ zzim@TeB2dxR@!d1uM5AE&UGI>{c`A?B6`quAVlznnNhk_kJ07{ zJXbelIK_?aEi|27^^FKVDmaT^gXb%Kw7UGOaI!bU?cf|RSL>Yv<2piAE3Xya_ohUo zSUBM-Oev%m-VL@if84p<6ZS79WlRW0I&P=;C;74lffF2v-rT076QE6B*wY{6iF_={ zo<-wQp+}drdA)5mY1FZzV;`>c$F17$8GlF&SZR(uIPMr$rwZ4D>lorSnzC0pjs%O_Vi5O}F;rv6K(-&=< z^^=zqmtFIU7Lb-o0%4fB*Yd$PhE@B-n}9l5Pm{>Q!uH~7rz!MrAnfCh8ecsCggqXT z1DkM87gnfPklCe|5ke2Ddg70Wd!Qt%qlt2eBBUd$oS|P#mKi->dx5PgF`$8>gxO;- zEolm)ZB21kVk?mynseV9dXZsKpx##N0_7BFzwj!%O~V}~fgfSJ0ZeTt|NBRdD9Qlvmd~4Nq&7$a&cKMRe5;8t*P$p5G2j+|uh}=>nFTZKRO9{TAVr zkaC{V)^e6mDJwERfaI!66hZaRw2O~6J$Cem)9rj<^TGa~lwK(|Ap@wWNxp_}oP+BF z`I_L``|UZsHB_krkq$SW)|i4EXQ)fUiGuPpcVG+ZTHK`Q97FPss;!qKReb}2?fk1v zM|1h#lFygK<*clQUmZ+#N=OEj@Kg>mM`r&8Qb4W08<4`gTRvyUT(l6vS(OxM5n!~w zBupvqL`&MscHra?@0=FNbq9MxIK|ufxH0zo4Lm)i5{8|YFVFti;U_XS1ynDE6u0Zq z6|^A%T*1LLAVW_@{XlK_Nx#5tFsx|Qp`~4&5L?kn@?Bxb%>G)0bCpJbEU-2JK~79W zGDA2P)5GhJqAY}*k3`r4%TP!J#B}WMr~HUZ7XYc6FqWK`eVSBAjP?N9NuVSNeegI= z&eiKB1g19})*;c;K-!i4{)l!Q-VIXj@xA72owv=vsHN0(CD3_W@-i z&4Mh55U#<+Mke15xP@$q)(JhAg);}4iUdg!I!fn)p|(ph9C2*~w;kQ&tmvZXM3v@b$~b`)ku{8^Y2&g^_wk6kZ$ z#V8C12zI9eB$DRsdUQ2*6MCAeg}v2tiO|E?)o5P||Bf?+RrPWDdV--^Ibe@L{kXcX zfIHb=Tc<88(|5{G)Z-Hp2WjR^V%WL`+c$$usMy8iPdifob+nhs-49qBhRohvK6i5( zITL6_7#)_7o>f8|Jc2Eu>lLyi06E3qTOX$Yk|dFLrtDJU!=>J}E_ADeBth7s7$(uW zERAxfYC&pnI>=AFS(OfetEB^ z84IZ~4|2G#M^{5g7M-Jwv-SlUei%Ywo2Gi1px9UE=vj_1wan+uV0O(n!IIqHK#->c z)u>}hDdHhG($oTZvPg#m-o+>&<3J1z^YnGR4}a_-0Vu+fUX=#4ImC2hi}Yzp*bX6` zNq@8m<=5*q8)hYNf{iqn7Q7>9{v#D5x9{F4=-Tz(whv^E>`zDgaMCNl3#oBwcY+v2 zQxWkzOhTuxJR-iY9kg-{Kov271Uk`nj{)vdIRQ4O43G>h`K6EV1zlc&iReYZ6{mxb zA^glkA%ga2Wa$yYmosrr)~wgpbH}sG>_Rw@lE=Rx*eQFoX*0c^;|c&(29oT3;7;9% zYe_F`q))^-2;}3?kPg$n)~ep?{?4JH_VYlRkL(K8$a^-KUBl&|`B925D~06X78b;I zx?%5W> zKtRhi4Fs5p1w4(}e8lPXW&33-ucX{F*Sa$o+Cv2$m}NlE;lQ{Qf^h-qdm5=c9o4WmzQg?Y6$|Q=v3%i8-oNQ>Q6MkPom zZ3Qf3e^ac>>>kc&qF#hj$`(>?0%rz}+8%~1H8-ma%w;y$OUPUFM}fA~RAYYAD1b5x zxHY!Ab|@~YghD3cmk60Tv?wKA2yErl+Q(o$nmUu~1*v{H!tt9pq`C{^A!;%NH;q#W zp~UFhrqtHiPX7mclK1U#)nU`_ao-f)9Q7+iytPE;!Wu1QJOL|97O${6HDe^oGWcII zCOe_-g!m#*RCelldU>sfJs}g&^-`MH>Z=YC3?TV`M{oa z2{k#^|8?i);q(z;0(BjAmZWUVK(Z)ZJU97S)XdaeuK2I5@_U(%$Ja?y4C(n2bqmJZ zk-grJTv(#o$u%lY&tJ~#U1P9yG7)J)<~ZVIQZQ7>gy9Ng>T9Lh?s}P$wGu!Vd8eBz z$Zt}|1c`H%qqfS~Hegy>Pa73O(7F^8l=?c@FkY7@Gsg$wD`A9F;F^MjrWu}A z0#WOG*{5U_Wg{&BXfqFBApmF=+*3|X-E}Lhcu&YNsh_vEH5!RTVWD2z8a^(1W3InnKTUa2KBvE5w`K^SL zsn5bkCuUBPy|J-$f7heJy$;9;Osh12Wa=J{NGJA2$&blM7cU~O-9p+7qa5fM5-j6s zvrhK?HHQl>p`(`D$^cPc0ay_0Cx8L+nKPkx)E8yfvQjnmZ|5}~`ON=9{`~(I`NK3H ziA_w_h8vQjYG3<}Qt&5VTai}V_l`g3eDL)O0=|F?Jc$%>WHv1##Ph;>!QI9+1hJo5 z4|<8MOtW!KSUL#D>0xr@qba+e!JZHp9xA(}!nR0lZ@`i#1;Wl{liMXGHv_k=#hyvF zRl`#*@0EK9WV=WdOma}CgKI>bq*P!xnyiFodH^}SRE}=#4X%f2eYk`~);qOT;a7og zs02yNBZI|+0{WK$xH zWl5n{sUx8BFg@;Ow&Ui&A~XXkt!0tMgWz=7*Ayo2bEL?;U)~E0g=GT+8dkxFBP~m5 zolJK5jY}v~<^IwoJWhOY3AMq>NoZr z3C%{TlW<4Bt&(e(w7TAcCj#Qodej&jZU#zb4?x-TaF_ak(_z<0lI7hi6JAOCoF;iM z?J=+A3JZIjYGz76C*+V(#Kj<#>b6buBsZ>g^*TFZ9X^S`4}#v%lt4!)z1)MX3$!Kd zRn>GGP+&AdN#$Lx1(PRcyTY(AiCN!*#g6oaem`P6W{m^BAH_BGe93Q{Y(NTt6hYg8 z+akK<0M#eQQzv;S%KEq#SBR3Z*wR|llZu=id^c434~Q)y1l2~X#EI7Ao9&5;$;emh z_MimQu0ELgNR^YI{iDsc7Agx@&YKgL4$V=u7c2+G(Mym6uPakM-K}Eag}DzT2ca}p zG;fk{KmvBdVG_fysY$f|l(nPZr|6UW0bk#=Wx9|0Q#c$u-^2uay z>ozN~Wtfr(D%u zaKPRFXnPv~#c&$PFri5nxnsxJ$k+PR&Xz2DlWX(lrs|uWKs}-U4`%h3yBe4b zIYVk6-}^_KZ~2b|_YJ}@@H*RWjf_Kv#L6^suZaab-#hv;!(~goA>6f_i=iE>$6n?U zyD1iCsErXY+Se0)Bv&(RY!aftq#2!Rv>Lx35vnzx{0{H+bjfd<@cr&Od5{9tiB3XU zlD+}mED$3CLAmX+$>nTh@YGh;*r~E-dax-*Ww#bX+@z~u;dR>LETX;>Ig8bRk*Z}B zj^E|Ie>7HK&NXiqOa{LV)D^yOj^xN@z80{81{E4~ z=WM{c+0I9vl?o!{{unDm2?5f82I&#IFX&1NV248&^?b>1pY4fbDlt@(X)QYxw=QYIgSFchQ3EVB8T+r5ZhFZDdWIUyUdx_*e zvD{9~D}tI3y^(wj@C-0UnI|oX&UcD{CHNsze(~OBW^&x9KccC2o$sz0g6OzJZFZq> zqA#@iyR8iqqvS5r3APv-m=P6*xZhLzLCU}o9|_0?#o7D4{b1^w6djio=Frh67OMHK&aioVC})F>XLMQkgI8$0ofSD;ZPC>rciw! zC2+&TYk=~k>(RCB6__tsVFI?#0{6q=1_5{)-FVr)5>Bf`roMhGepE_5HsEmY5QP&S z9Ud;25Fw3l5kcJpHo8NYw#_J^)N=!;po09ZubmHHo5H0HlGT9oO*ky$?*Wt{k#qdz zyRTOO{G7*M$q6DUOl?Z{%57?WfUE#vZ-8&`t-`@4`>unyD?{I!VY6>3F#l+v`1|D@ zO$7J)%b|r={N)fdo&SD(C+w=&26D!_@!;p9kDNka^Pr=er`oFrLS`%H@jO!Npto`V zcE>j;K_g7LqRTRLrtuGNu)HMOIubz}#B?n%J8S7DC^HDyk|KgrckYJsP{ytumEUjT z_DG1ylx?s$TAv+$RSoIq={AAi0i2Lw434Kj<@d>A80OzfEEMUd z&21*R#SKNyjTFsXBtz!>+JfsFo;inpqjDo$GmXd=RgN86Wn{QD47WNVcN}IZ_)qb! z@jxi^2i_X2BET*o3Ekd1T==0x#&bYD2v<4}X9K(^%rfs1mu?a~f+O?&hCbZuMo=_E zDH&P36(*yD$T8zbs4Uo8%lV4mA)X>h2(d?~XqVXM7=_&lDLqGlvKYKv+Z@}pV3~00 zlx)gLJ75{e4i!v3q|j~KSIw)YiMkLA_DLO=lvoOOX~|A1S%~ibr z2WeX=gO{@nf}YA@TU;5cv7Ulie1pju77mn*o2@`706)xIZz~_}HN0a`hAdOIfi^XB zSADRpRyd7vKN5APD_lcDFj*T(ml>E2wQ@h`qAw}_N2)&|q7zB;5=FCIsNN3ba}T}c@=UWb65d5j5;)GV&~=alHyJ;2IAIA6Ixy1l16J4? zF%YQUKp-s4XE$}z)>o4kK(1{PmU00UgA3?&@9SZ9;{4lcvOqN#@cW3CUJl3I0cW!| zo5c#W>U7hX=kjk4u5dlxr3m6Ulc1H8v8=70A*!W{Gf@O)W=d0yXm7kRTxy4#iu!A_ z#5zuxs9$fUs&W&gY2($nWNL5oGRj_0@)2dIj&6~p9Kq)a9T~jG&mba+q!K~*n_1P) zSI9;_ot7Re(OtoXK-BVMG2`Xm@(DmqAtZ0vHP9F0y$&X@1$%%8i zcD??5as;Yo43jpZ{E3h_uJI!O`*MPbw?erBXl2}>Io)7_9c`;C#{lS|o6Z+A57A7O z<=env$q!c(W*DH95pRc71w70X@+py3An<`=?PmPQ^O8wV7UFc;3%d)Rk{l{$tAUwh zI}P(~9b7AAQUU1?ifR#KT`Fag5z6OwTXAO2Q)eFdhRCx+ z+E<;pCTHD2mA#x`nIKL{cghOK&TP}pY>Rdtllrv{Iw51?cVIf*Uf|pLJrcMZm6Bu- zO0-dcexO3LvCT&;finI~s)0}yQ+&Nfcd_pqNwf~+xp#AEKnN(4N>UTpfN}QPYDBd< z?=Jznn6y@)s$Sc@gFw<*(Wlr{p-5-HKPr&#kZSFS^)RdG zZiY1K5EdGel3?Noy2YlDY~7fri1R^60zGz#@|tz4ZChNSa1T0w;OD?Z$aHrIsJZkR zmOB8O4{!bCjs(=Nli<8vk3N`6+VvRDWI`5^Z93UX*m&)utpuFqL=0<%TB&CpIq0P% z4=CFLxv;(QJGD%9pO4aKnr$`PqbY3DjWj68BFhVio2MhBwZG5fmqSaVo;&+48Xk3Y zl^MVK1AL<$LyX70=G<59e8u*$5mUq#Ql+fQeeyu_4Xw!1C?%^4o>Am%!ksH?E@(j- z^g9}-zCG_l_V53>`_H8ICX@B3dc9#l_3ng%Fm)a=I80 zZL^!z7Q~G(L*Cp8;eClQ+21vpG%y1n6Q(HEj3lU5#eVpe6=wE{6Btwprz>KEry2+! zG69mnW=?8|RA?ojOX7nx`6yC|2wY!G?e~Bj74`&f)s#L58$^JegMEmKCZfbqT9Mp3 z2Ocv^^$nb5r4S@2Z&Nl4X)vM2!(ak*cS8g|OC?Ts&G`A~8UdV`UkxEM+cQ*jOsZV1 zj}gEF-?G!$q?I5UO4P+VRQVN>_`E4f< z^v7v^;N$#f7S=fJoYhz&Csy`<27G8U@K zTf7)?B{YZWV5&vU^##0xFuYSm5WzdzLpGp!cKhjJk-$C%q>5}7TG2P5#8(2hstrtCmU7q^GtI2m=zZ4dADu%N8LEx6{u{7 zHjIA1O(B(gjK*&-7WHh2eG z$8EObnTYsSTU8sYp{nEdz210BZQO_XV%@i2Bh;P!0%d$4j274}9Iz zHg?WX2HD!i&ns3OUuH>y4GLCbS6URrbC=WP*cCXa0m`DnIaJ4f5LVC`2p(%qdZFSM z?)l)Fr5#D3gw1<)z_w0@sE|^G>Iq`Qt{Z9>su1eLXHhW%d9Y&mJl4?1l5W_tE3&uYav_jZo>DnJ;IeDb zy;oxg2&;xfsmV!?93|{@ss_>?j0;dk4Ek)1n9~aC9oPw2;0D_%Es3ejzg>?$cE8iN zU&ymiHa#Ip8xX}i;||I2pw*o#O2GQbQUhpxyB?Xi6*9m{H7s^krhoZpgxb6=$J^?jM z%(pVQoqEG}OVvG0sYK_5KIW5Gf_@+skXrCcY^N(?O5qfA%>{`iZ60jTAU7tbyNA{1 z!<|G+1IOu=sHverX9`4*4B|wO)gG7|vf;G&$o>|}?~_{{v`Cra%tJcqN9>+}vRYkM zkRHh^`|(6r=DYhQ!OPYIF>$*deJB=sWhFm~g+7?c>-F7TaXy)+$jOTm2`sI8;*lHe z#oq1Q4X+3S?$TBlP*P%1YFRI*;;%^3`hC-F;I!s%~> zdzDr{C){j$R<5*vX76SYGkXB9DCVLk_2T%4yI{~1(t>@9x&sPL{N@5fJ~ZLjZyCXX z5~BI|?Rs?Wd)5gZG4l0@T_x9WK3fb+QJw{KyxvxiVMnTn>!cjG64>A?F65i|MFs&t z%}#Qk!px$hC8He^LU?*YeWKkRkQ{Qc#^U#QIl~Wo3ZEa8Q1dF2vVvHb;Qj$)tmYmh zgU}ai@Nq>j*KNdNa5+Im&~N3zhe)2=b(#(a%RYw)i}2mP>TIR4@9vwERsn=0J|%{W z@pc&8MvE&e(qZ~Y>D&PFcOotT*vg0|9|O=nQJ~;hmtZ6Bnqox}eNE=#*rP)AbV#Ql zR>AJ%?*Q2hes&(UdiI9Kqfa<3<c#fQdUXLD)95xqV2NmywWqgL2kn$O1^Ut=REvb%0NHKzC^4bhltX_p zyY+d+V(TJsMYL+8l4SN%_tDt6@FP?sHn5N>?b{l#}6C&YT#=0$ne$<`T zfTR~!DAx!>ve0po-EhkA2@+Pa0Rpp1Rni7?aEq3B+FjIV3_Fh3qcg)3s*8rpTdAko z>_#7p5$ zGpq31-P_YSYBe^?$eXzVu~VsWdEg%0hmG2vzDiIZS1eoWUC@e>#^!DDv|Srq74BRyn+RdF1Mn66ezI6k|{~8KrG2BaTc0FVz=O=?2S|pEbzQp<0Iyb(Mv} z^yvQ8R>i0RXptz=mUNy}Hn2>0%&7CxN1{^r(;>(&r?!^Iq{?SLdrAjUsY~=^`wl`X zbNRFA;N6ug`)lA5CKTD?iB0A*`AqX6)AmJAD!47kXhoF0m;<*$e)N71KTRxsH+&5< zP^++}n!;~9BhR!ekQm&;BRpNP2)tfp6qrKtuJ};1#})?5MQ6qogr>Aq*YB_c2`q#A zBw=(jsWA451}R*iIqW6O=pjm_3}U$ld)J`*GoDt7_ZAuGa1t9!+n{zbrF-jt@T`Kp z1E#a3l}|+}5s*mlvc4WsbUfv#V|BEIs)TF;Wv~mLyjkMI)EGM+jLXR5bycvZc&_{} zDu@w^00|9Y=}1ZqGnWo5!T7yArU#+2g=s?S0utL?Nyj}r0W*ZXbNmL02n-w`!0JLs zgbHX|U3~+f&&o9E>chqc30>0bE~|fH+jQ4t^I37xs^)n#;3&qU22Y5alV~9VHTZIK zmf`*&N7%MA*yeGD?uQT;@Z0EefS85AIqnOA@MI#wmoo-92m1lYrVUx3nwh@FvCcux zLk>X8tGk2s2)?&uvZ;r~1m0=VDjwm?xk@WFjFV`fbMz?Sw~tL{)}YM`E$Gg&?9%W# zwDAIrzK6I%BGctU3o23Mp*;=Pzrjq?Up!Z{S*=A-(m_lQI%VgrIR%o>j1nEImT#5e zKm%ePBM8k=Ur6y5B+;N9W@!r{20tS#a8Z)KLCIKXgXG>(`A%z*3c;cqOP!Bs#!Nja znhgu45fHRPvM4f{S53jOJ@bY3|2R>$d&FzVcOu?KjJE@eL@3{wQ7l=SAM~hT zK;Y66rV^|J_X=r|*)y<^hR2$Yb8qv1<7!C}-HzT#s2rNLY}-=`PdX3EuHBc=eO$5q zxD+-UHq}apAqCI+W@H3X7~_wQ@R5FyBrN8e54v#t_|4TGdu-`a@f6c)W7pg;7Yb!t zQn<$Yu(a}jxLag&qPJ!R5{ZFMo3THF9Hte*ZvQfYM3d(LGyz)fu$RYaNkb0dYWJ*wka|eJ>si`TU zZfWZ-V~}_)_{@PtSwJ4T!T~Y^63%nfTix9$TUvBGQvbuHT0BokV2FI&h=aCcH+M9t zG<^o7cagyE7qYw8O5}{6hZDao(~4mN2%^lGb|!m2aT1sGNB~GypT^^HLPJ7jGrpvk zxC8+-p#&k&41Lmrk6N48GI#(W2K}=I=vI4<@<++DJy^3VxH8xjO<}vq=%ckndYcm% z<-8RQafmRwNu^Dv{@95ox2MUvx6I@GR>Lc%_2cF??^riCx`Ese0Et`Tfj$|8CT>wc zm7KmAtMRvN9>3Lh?jZQ28KdPVvNcLN0+bKZWf-OI4W|tVBb9HAH@R2S+KphH_v*4r zZ*2KW_*C>Z)NV8uuLMNpdUVbEOz2KYN?;K_cH^OLHDzbB_vl^a3OKEUhBdc#5k0(j z12JD4saDb~IvC^jWfdU_f(t3i5ivLDUa*o`iO$yUEc?E&ZX6|76FLZT#1RLec$@YC zbv}w_ZDCGqH#;%;dWC%&jj^;f@?Oi(5?6~_?){Jzi#Z?}Mzg9$ooKKU1CDWfm?R}t zslYHN%_^IMYRx<}+>tgGR{RLdkvc%~D|HE5co|^q?7=X%x!G{XnELo$do_quqb+aO zqifOC?iR<>(d;Mp^rPyEY|^DVW0hSs(?mD-V~cjR#eM%O*20_mSPDakLBI`c?h2~u za0ip~RZOf|wjjWwAALu`ZSVK*QEO#SzC$Tt5!>Lm#Z^lCBc^DQ44N*4M}5754ebzA z?UTqG9T9^xZP2XBCELL0X(!;4kqA%#Pu5zYvo`Qvh*3ch)F2hF8@ZDj#0HQ>Zy@mV zB0euRgrj!@Fm{5SEqa4OOGd$(Q8Q%vK}O5KggNlj0E0y8Q3LNl@#*NQL^Xt@jNN1H zIj;}gn^gXnRg#S`@o-nu4wafab<@b8zfqsiBN~W5D96Bls}cK*p0bh|u;1+rH+g_9 zgB)w)J+5LraTWNbq#=S>YEZdRA<%#1^ztL)6ymnO_T%x6*#K#-tK)c zss2|VyZ@E?*ulKHT6-XE>C_qBd}&fgk9K%*Ym+p=}iJP;~ZbXZ3;|#+JHWk3Mwvh6-rId>sqmA0gsL z{&wJ|oi%Zsc_0B(^yjFqS=%CUOURHgRNK?booO3KCKcA}2s14KKcW9P4DO$t-3H{? zS2>l&oEGt0;1piZ_ydNH?+1oI67fCC_5k}HL1d{=SSmUi(aB z0*DM45KHK%teK%?-A&8N!b6*FQM+Q>oA7!-1tBexw3v%xXs}3O!3%)!G}2=?G2|_9 zb+bDb5Fh1!Qh_b~L!&avSQW6q3$RC>90fHnKxXvUnb#29G!Nf{D5g`03s!Oj)HP!n zMOCtQ`yR+{atrUKccvA)qVkeZmemkwc4@;gRwT=B&oBYwG;XVdFBSCA0TCadQijSy zTI)s)Yy$dre^7YsFloZV0qKxV%w{KLM2L|1TEcEEz)0P|Ny~mboI0wyyZxbO=cWNL zP%Fr-tKx~eQ@AjlL#HnbRjUO84)6lkqid>`b~D9s)hLl#J?aj}w*Y!RPve@!$4`7Z zC1F|@ZaYToj%;=%rFz>Jfi^gD7sZd& zq(_5B046#7ITj($g-;MOibhmpp%=HR4w}u247K`lZc~&zHS1CKzFF~T&Fbxc3!#Rjn zoav_7WVu04c?ZF=3QZumBSf(U%Llo2j8R~Ama){8Xw62v5gte_O=1%Pj@+G@=z-56 zVlnAq+LCb=b=g0B^7RO~l3DiJfS>@qV`V!|h?>XD!nkr)w97dkUgJ`4 z_7WfrnK?>^OloFQnw6HK7gE_l&XwsUaf9sOi4hP1u(C{UEvN7HVOxz2Z+D}lLeh8WCZ)7 z=F_A$+v^MwHS92DM_g#qM#A~(;CQ5IWig&L7(U&srYzPCY^p+1$_5HGuV5_5aRJym zA7%I90%!tAZj#0x&9db&&-vlZ?(LEjJSHlzWK|xme zJru;@+7nN6z5wTz?rAgRizXSLRnPgpEzdmsB$D`*qDarNTxCWYb_Rdf0(;DEYWuS8 z(wIrI&N;cCDipmbx)h?kyaZ%)-1Id-mOF?puVx%!x+b|k+mJGF8LZGe>@qMCtY4tr&;NazMl?dzcQh~pA1Ze44k%k{bVT3Cz=iW>mn+B<~<=vhCRjTyS94R zZYDR_dq8G0I27XC2A>ue_tKmCEiz(092Q77$dm-C4?7gboiQJUS_JfIiIXGqlg?LA z&p|~Dy1Lt-tx!a`be0Z#yBoiDx70KJIXK>cm{%B)9V?_9nKsm9ThkZ(FU(?cM(uVz zx>_#qN9@zMt?gNUb8G?Ggr$5LB?<=vkT*El7UT|kTyNa5zp+Zw(N(U+AXR8wXaGif z%G{t}f8n|E`bMaN5PFa5-!BPLihuSIl^V&_hP6E*NHFL~=}302bog;aKQg9N^w5%# z`c285J5=XzqSFk}shUz<aeUFSgLX1O zp;Fxao1zLrwR5^f?MQD!H~YC(m7xYN;i)iNeRCuEN6`#;*~;1q3H7+5t{P-`^W*~4 zVMlVP-MxRN+m+?$OW?3jhmo!=elLk0h<+M5ZZ4m&OXa!!DJEN%O`{ zUKt!98|9h!h0;BM$8i{Ma3+980)=fmcqcvpYmEdN6}IGUO3;{rv4nbg1Ptl8M{s^O zLwYIv)edVpv@wCz0-#g$S>zPlM01aeGyk@_m7PHW1z(tZ2CyA06V0luzS7**zZSHjGRZOxHCXd z1Z2Pnxq{`0sDgIdY(a;BJzudUcguxbq`^7oCVwm5Hb8BF2p^D~?GzNzu2j;uMfBX{ zc~rtk-QVFkn8B-NjxKs?>?2Yt0A8`r7Lzk7cw$31r2;9j+{G3}=Cp01&+3Yz(G(>? z3Y?-OM~YeUKqlOVK06=fJJrRKiJLhF9slWY?VL&0?g2>9(6T!nq47W0tcS&;$(Sr# zDs?=H;C>|Aa&(-qP=jrVp=k>@#yVwEWiUPsnM@`}t`!vu)m>sK2(B(0b?XM`%xKPH z{woA_Ayo7x1Lt`k%O{{-M9!G}+UUwO;kucIx?uf37*ONsQ*&#pbnt&j$C|PUP9JYm zBs7e00pVgL?9NG1<^U^eqZt53JNBuGUC3O(|1kq^3t3LyjK%3EtB6~p)0^&~N4ekS zco4oet9$U|RX0AzXbuTy$eu*>V?Szm&im>(gYP17ac}0z->Q4&CfeA;m-%K>W|zl; zE^?~{hz1ia7Kt2Xie4xZ!}N=OU<8g^ecQ1D1|Ey?PQk4XvKV5T+>ff4VUdk<YXlDVT<&u+XK<;D z)F?-1`2yMEWe#ZLH(g4fqZQ`!a0l8zQU~NTo5nS4Kvct$GscP>%41*jOJB!z>PH;t zY!K)S0<1eciiivZv}OQe|6$f?qz`7iiPg$Gk1cNp(s5vG_hh)gBNz4f%cWr*+60Tg zA)89Au0} zNje2$G2~N)8cG93O6fd3e3kn_1@-6oaL1IV0S@sJ_Qpuj9BX-B-Wx#CkENdKg z`VsLo?xysw3%LMQDg3QOlOM4q`)X$Dr@(Bs{jHwJtJ^j_?3#P1Ozii<_Orun?;x=U zx^HNa&E=2Q93&VhI)Ipk%y)J(ie!;R2)+~=LCR(+M6qE50g#YfLOuKqiv2C!Cv5}mwCqZLyy$K{9kK^}45-TB z)&N;g5~8}^QC_O!(#Kh-Sq#7gyw&v=>l)43D%c?_0{(jKY+ z2b@?RUFU8`sXtV-7()RvcwSK!5L-48#J(mY`3V*g;lnJ*vErO#Eku8B~9B571l%3kV8N7`6O9f_x6tG3# z@g($f&FEJPr9bVtB4T(wJBdU>OOnUC3bg!e|GEQYArTk#qcp~|3A8x8DMffw@5vTaQ z9;7&#S)8nd$&Ju8v5f1{HE4|T$Whr@p7?`@>Kx~o)QTBDk)GZ^F!Rt|>R^GQmbeq- zdA-k@yyFd`b4uM=j?^0vi;pNfBV0{)U0gwGZ-=PUdz(K!5eQ%^CgF!@=~E0CS>W~H z^MHLvhJ1v&x9b%or0I&D?YEi-eFxMF8*!SppKxm(YjF*a>9C^a5k1x3`xDdUls#wb+|b=Lj*ql~goMQgrY1qPQM0 zphWx35@qXXBcBWu{JFo60o1lTTcqptN{z1T9%sOiuuSsq<_e_mcb99w#&GU;6YSRm z5^nYzs(A^(eX6ihP<2D~ryG1T@x7O~2*13VNP?LmkyZEI50W9MH{+MJafCNMcn=^! zs0Wm2_Uuwgkbs44)2$*;SXH^$AQ;>Ux0;>L1uk_JG4d)$RpP(8d4rpV2DE-RPsFye z*jiyNI*;|dG8(Zo39^t~O3|y>z1j4pY`YjIXwVYg?Ypi=FlthU0l5+70z|{MeZjLs zBtN>B)O_Br>-GK&AzQJcJ0Ka4_CFPbrF$T4ucwsJAlXLKdL|750{~O-s(uO%9UxK$ z6BVG2@Q9`Umfk-<+C;*OPY$p}>n!QX3bxtLeeBmk?^gQKM0D|a#VUd-jsqIJC+!RS z`+=6Bt!IJ>5|I53jK90WZ6u+jpb4degV#c&3K6*J026BC!YsRa6|13@_i47-sB;5v zkyiamZwiPV%(X1{ZYk@=%Zbt^p=@Bu|>&=gG9`{XSh6M1=2#4@+2LZl%55 zwfP8ySx;kN1b4r-Gf;Sb-M)19QD-h8uThVRsZCWw1Yn@*FUJ+P2{t4{fq`(9Z)o;= z`9m}sAM*|wIGsVGDG`PQH4*OjO)8BH&K`Ufz{!V8soHUm$$dzpfU6K8fKe$T@RYQ6 z)5h9$Gd>$~EW;KNAFvFgd60Hx+Qo8#-Sw!r9T;#fxC?Z<=2@e%{TWw#1f1(LLBBm; zm)Rr_6vvzofbJPdUs|+q&mbVT?X`7G=#<4k0bvl0fI6V8A|im(t&E$A9e3Cy3(@WI znkCcoeVE!6!~>hMEXM5tL&7XE;lb#i=~RR%(jyyqxnzSl$CZ4bIa}0`r(qZanr8#7 zOw}d`&Ka1Mv4O6pKB$0TC4We#+OVk#060vlD4Q|m;WU%}I*S6w9=R=EVF=r!%bn!d zPFQOT^pYjA+)nDcnB5IZr(|FBc0IaQHFiM2HhNBUOX;Xioo-0tO&q_Tbfwsd2vP~0 z=u4EAL0~~0!2_uBc9-JU#Rw{cP=aGPC-mIluCglfY>KI5CgV(Hwq4L+hIy=a$I6JB zwZ${{db-OBXgE4fb4Z80s0gJ|=^^|tDAl;N4TdvJ#)vjHgO{KxZHrdC&=Z_??a)i= zmWqvvU`d4z+$oV}PYJWpCRm0ZV%mAj?rWQufWJ08v@mb6`>QcvSdW7^#{>8GP>$?|@mQ8Ns?Zp_8JKWml= zo1O81@jA6$G>Gi9KO-%&nWc3PI~95(_;*`mHD3e{*@wySDdWkInsYi)y!c%x9-*CC zTFurrSpD7LFaz>7>cepI)qk5+$9LPK5@VL7#6RyoXaflz;5uDW1$gbD5 ze2+g7IDyZ-Q*F8jvIuDh|U}G`LUS z#>F0u1hxLb1LoGEGbj{=5X|;R_YN8Zc6h8yiPk5VD>gf*eRn1$$$02bvm2i7Oc}CP z%GOsGyd6Mov5s_aA&MTfU*WnZ_aK7}CWYwVK#n!-b^;#v#uxyd=nbs2EUiTkkHwCsH3ME$k!OTjH;(@@S| zR>p*~bYPRCtWpLxCXKwJ)E&4MP0C3p-CTE-K=Pr1ZvlG4Iv~F535HXim{M-#k1)Y~ zF;^KS2mCyS5!Rhze*D)y72r)MjVU^$oo|9?OnPRKL}piIfDB^t2ZqZw-}vL6-beN# z<2=woFh?OkbrYE~9*iPdD|9$2tJ840Ua`4fC=J<=i;3pFhmS3(cPE!EPeIdE>D2?! zHZOHd)nj7rW!db+Z5w?x!t;f`ZlK-ML2?S6MyG+gr+=fSXj>fjV9%2?!K?RH-rT%A zwJ6mh7wyXfbptTm@3>u$3So(cebr6c)svY%+o_kq z;8_5w{Vh zC{BvZkw8vGb>z0&%&G@(*ZB${J-J0&N&KTu{_bE&>}kIP&qz&6P1ctygCuqglBM<= zo@hF)CCV_|`ys7v%sIMb?m=7IEM$#%@aBh98j6WP(2|zwZmV;%rELRgM2S&6P&YlG zgV!WZaYiF~b2_^66u&M`f9Tr}#p&zy4(sFa@9DgEah+v0%OOo@a++@@g^w7l01Ehu zJGnKO&@(yFPxcmRH%^HocK?j#7poxW^%U1<_?Ldd4sI}~*g}A-+k<`Jq?N;NeqIsz z^%uo*)E;S`-6|$v8Q@Wgs<~cLA4n6H$hslA29214U}HF?2@^rk2W$x06aXI5@o=)X z97Iq~;sR0Jf`^3E9ZU&<^Tk9bd$>inr9W_yoaujQ!3ud(1VtJZWxc(bhN$Ei1l}pG z8^MuUs4U(cMRubukb{dzqlPi1Vh0Z>}?xFNof*_G3$bx+tt76Pr*Gd?Ar-cY{bh zjAn{q!SX_|w?rXkrpVt(xf+`S)v*p1P;s;(^TBxF;E-Z z(h)CvtUvKa9@lreKo6Duc2Z8{e?;cVF$efHd0rZ-81o+Iz**SN=f@K5gU=sbc?ymX zf@E-YdG@SACgxN~s&s+>HQt~-?;dgJ&-elIDRcd{9RTqyc!aq-rk^^NmTk_QDuVuj zr&6a3TqvvtWKCt_DfhyO`+*Q_y!iUR`%xRyzc`-QtE`->MoK$yjTwuv5m;K2lk@sn zT%cG6q!ECTn?h^A(wP%r#X~J9v&8+cL%S1K7MwQGCv@-y9?T-iuH_kKk)oEKPBTh) zAo!~0Z;`FjbZ}ER6Nm*X2zKokE-uc643IF8wM@sgN4(CEh-45@1YElaJ7>hMZF{ZR zZLK$<4*yDhUg#eyE8EK5Z2nyzxCA+PKD$VeFq?pq^|uL$Nf(Ub-Elz*U4=+{L3`#w z)^z!Lp*=God8QmRn)jMc1O~NE(`5_4r@^nG2Xg2OVpTowT>@E4?%?@1lqxWtwhpA| z*)zZbfkJ)b<=RZ@bMyl4Ge*AJKX;S<#E;?#FhWbRe9-l&h!zGww4rWJ%U|RHkAaXk z3Ap9(_u?dzNXbPlRBZhda(t-_u0ekQEeSGgvX-uMz+*<^wFHc88lrfVC zyyt8d77~8rwpEAY@Q`&b6E_|x1RFtnc#K+-7DA>^wnRh(ggc+b^ftVK%p0IeGO!FN zLWrOE+|Zm_tx(c48rsv9v{Yxbq=iP3my)RL0{a-gwVS_HurjM-=ecKarkkKVmwa0i z>Cs`RGN+Fs1%-4I?$r7dM4Sj`zu+Mk>pwMOVBf9|`c*qC?h`MXTfQAMI2m6@-GH}j zF&d6c&JSP%-k#d~TZmjRlKYgvgWJEeq!|sukqI^iXwa}z06tR{Bm1mf*UrKrIainf zwy31}*6m2%czwsR3!35tIP$~mC0_||=ZjjHKW$SG#?e8QUvf<+BFC8FMS zU#Jkdo+h5dYW)f_w{Y^b)WJ@<-TP!RFD!=T43W8i#nvC#XEK2w-XMF^max-CTKGeX zWGrECnfXS}_jgFB_1?5wPKPAe`syGPNjUt#x?$sph&prsbR%UK5xylU2>GG6zVmV^ z;2#$v{45&P;pH$E*fo{1h)$Tfp5bC4(9q3kCTHFD6;>F&Cwm3=!ulWerp zzY%D8AM`J?Rb^}?{)eu!9>`73>(vH~L?+!D@5W(XW$48d$2?=!5u|Z4**UWkfk=ou zp}Hr)aY@Ax{Ts3=Rcd=S0kk{p)<1{+oWtePRU{jz7~!aM7lk-5VEeC7UIoS?gCY`W_Bpf`<{_mce!dhQ~lD=@OM2xlbbE=w%a5_x7zFvHHQg6S2@XEcczmO5)ar0q!1s|Mi%etmjFf4n$#Pm&N()WVt!WgF%~h{^4W9zHnk=BK&dO`Y&3ck zYt4V{MPE)oIZ6d^7WhUv{1b?ZiIOzj71d)N4M^vger3gIN3D13pyBqUPTgKWeXe%92fBP@t!=Tm2o9dv~l#A=hKKpGf6*3C&~$6=m2 z!5==NIDJRtmA`P8xV%~s49(2M5#s510F`X8wd4h__V#%c*v#U!AXbHL(D`cu_;yG{ zp`j_>5;t?v^0kTAD7T7*)lAv}GA0(1Iak1D% zo5uQ&R06j}pY}1+?@9$(z=G1Nt1-55i?kV?EQ*OCVX<>a!U=OJ&RP)*bJOP8hsy+6 zLs1M!kpKqrSBJ;*3qeHI;Tr2xLyZ**Dm%!zJ>Hb-gMARmv;5u2-3yVI

6hP)b=2Dp>PHKIvC>F^mh!Mw=HxLGaw4slDNS$LOsT0h_14f}MW?E# zIyk}L#ODfPu^eZv)1H?TJGUBA9pWHbfQFR~bQ>c`Kr_ylF;@td(beqx$jkY}V|o~o zlDQLKJG#}4>X{VLE{;GU$_%z0--0Iw0fl5n>8ry_v~-Ky4g-EKgeLID=c@e@)i;zX zt(gZ!i>8_*ePl>DE=vPaQJhSY!tN)nslQ6q#3lZ;Wr2Q(@;JJ(J(vYV>^goJEgMHA z+@u-%ip;3aqeNiKqN<7C0%ZU*>>sw4b^s-PMTju@K`K-_R}&?f9+d6T;`)doUj}#L z#m{m-uKv4B&Z2hM10ri?aE=%isQkgxjgrOXHTiEyZ{W+7(~{A`wbKsTHtrLRXlJwN zaX&Vl880Y}40>%HH=7CRIP`QI)5@!lh{A($JUTS}4bq+kQ39xun6*5c%% zZp~|L^}Tm`?8u6g3f$QniW}_kMnczioUUfU`WPFv{EWf$g`P>B8#A(%@GA6ku+O=0Yuo*=V!?QG#X4FA>1U%9uxv*t?joqY$L`#4P4Ejlx1}0ZF&sYc7 zxO{Y&z9j%!o&j_Ij#covyvDUxR|H~mddU8y5I_Md{05IplgOSlgO-n5PtzvA z9|CaS87>$V1JQfCiI-6v*C#t>=7TNsp(o~2cQf$HEaY`#96mWit%(kPUCcSg7 zGU}&Nu#7}CXolwjqkgDG8jua%mjnUTq@4ZMo4@d7Q~I zuaXG%IO>_1Jy~et4BSEwBkk) z)|j4T3bf>MGSRj=oQDb5Gt%Q@Q&%#G1W=@j5v9WZ2*Q3I{4#){it2BUQ z>K=|rC-z3kkI6_EFCwqqLfQP3E{QnmD!!#d>O-$B?8+5g!p>!r+a)G91GlZko=LVj&AJ@u7_!TxP(O3JGE8eSAlP+1WC&ygT;gb`j-LY@XqxTpdS>BB&u1Y zMek*M?Dypol4g>I)hhsflT`P<;0Snv_<6lOElX*gOm_K=ODI$2{?a8pPJD0)wZX_q$nd}; z1b-w2Utg1WJUl{K7~P3(aC5SEbnnX}l)E2mCGDUxC^)LG1%M~_;1GZcDQdM^)ZkYh z)4$i`W&o@c2wPCWoUcfr8QO-8>2*K_7)eQxTsNvN?l}L6Bi6YYsD=T!EXYp8Q2$}5 z5)XwiFE5}Ts|`adLWLyVb@5K<=rb2UP=3$CV4OIF|Xwc3wxYuW=cROJ-i zj`W3oKVmy(jRU?P#WnSO$#0u%Knj2qLEC}bBD&=O)hEYOCwVB!`nVQXh?20_(puA# zikuvLH&pr$h%F)n)kdqtiPq$s?TL!X$XDz3paj#dKA8DPm6M?Tqs_J!DhpT6n-iA~ z%~7=%EC4Zn34$OyJk~n1VH^Q77lTwsU_Q@ zk`;K6rga=NPJkp$&-CRH`7Er25l+YJTFJ)!;&XVMJPHrJJ7vrC=5|_*-GI^vU{lEp|8Ma#Y*O*4QaNomAIs;Rjp$SI8to z>QL;UJ(?|^_l+8GT^l5wr~v9Z$6Tbl8kh_@Luw!2`$wB^`Huwm4Z<++I@@lIj6;UR z$~1DXi3L30JNh!iWlOyw+_jsFp&hHoUgikOI|-PC{9dz5(4V5F-LXx$UyaG;heH?je93R0?TKS5F;tUjEjtvqGnPDnw^4APfnjy3&j9(vs;eLlfliHeTk?JN z0ePXz5E>+VkDHdkpVr)Ou#NSFUBA5ZKWnn3qo}oPQI}0J%rV~f8uaPOrt+X*+jzF& z7w!gOFE%(M><2u|Ot*$Hqaog^?tG+NGi7aHitFOade~)$H;wmd)2sv+MLl2gJ8W+1 zQc0s%uTmBX+%2D6(AIQ@TDjq5Jf(triR3=9+)m6Zf|?M$k$epB3@}ESCoPE1cZz@| z_#sn%@!n=;a@?ptqN#SB@2(kw=(t2}cA;>hFSPo*tql{SZbH>SzUp8({}(+nnBq?eSoaf&Bc7|5(#(`?_pR# zsNKL|?ZK()l5~BLt7)16*%-v(P!b2GP<ZA$mbZEAghtN>we zfN$`v!oer|u7kKML*JTVvu`Od|7f82`{f-?1o!&Op@moclyx z;OC={oI+sppre|n+N%dbW-I6MJW}hRw{ib=$2TZJBTTuX%QAGP@egmXyd>K?5#pk^%roRDD*j;BE7_sL=y=HE&z6zQkUZ6>+J4Mon46wO>DL+1S2g6kWeIfs6u zawA+bjmQ>NjvZQMWVkg9w>lwr9A+u_Pw}qtKq&JE-WseTz%C&P-QGJ~_@P9`b3i=^ zS2_=81H329GVcV2`6-+*)&~4jS&8wz~x)2NY zNgbDzSPFJ&$xbR+i0=NUnco}&SRvSH4ejO!X_7-IX~N;256?{SgVd>$WH~(?|9%hc{%`Nk zc}R|&Jgg)W#_tj}VnCYUy?GY0)$B$EHR{+d=b-_rn9jxfsq!SK-VWq*5548`OtUf) z-bG9jIL@%pb&vx$89#D3VF?a8Fw*e@R@fRb5UAcjAS}#hH+9q2SCbb&u5A*Qasd>B z3+Q$4>tS}{{M%}>Ks6We`-qla4#(XAXR|h&#R|0QbkmsU@^25Wa6R6o2;w-Cpp}!c ztgW6Qs-=oEQ3PgYN>hz!Z@e*FYKNPO`fIbqI!>6VUvH+WaucL!vR5<&NyS=G*0$VNV$mL4n7UBQJw)be97Br8@?SVZVZ9!?D0{Nb(8 zg`2h!c}UIB_2^oxXpd}89`iBDk77m1iF3Mkz5aZ11gd5XlQyFKiI6z1@go2Ga)ODs zLb(EHW!#`S-C%+pZL2HC0O+Bc&KENe(M*-)+rVMT4_6ar7@(99Z--O`Jj@gFDUnqm z@PT6OX8g$Wl1Wb%;&j>zy9=I@94cq4fth4G6EdG~o$f-ws(6a%>o5@Q6Yg!T^Hwv? zO^(c+_6R|yZEod!QxRo_szDFH(i+EE(nBG~rP}bxOZED@yp>`mFbpK8sUSEVTq|W# z0qGBlY7t{yDrJ%p%I9`lac0g_XCC;5$g@M*SDm;fXWc=Sy_{f~AWlhl$_mHMY}3wc zi*_E9`n3%@A!FiqU^?Ai;M@5<61W?cl4KA{v{8V5phB~;%||SOGX6}eflw7we7#0@ zvF{s6v<~FCcXMe#2q=?EQWMyKarWA3M728aF9Exlv{s<1UfaEcLTQH-iyPrN(&N4+ zWHP}Py;t*Vhl1T<6@g~H#rnXW0Zy?+N2c>zKN%#|T&%h;$kgLV+qjUZPjHI@u4(KO za?a0beh}lq>3ZJA@%q8=>8F=p2y$TZDbl-xAY52j6jdvXw z;22337$Xv-=No1G?R|K#)Pyp z$9KEw1E>Yjr`S}XNN2x4Dv<#rClgQ^Xch zrL4+*@<8(qt;o_SC94abQRHmGohxfDXh9nEI~u3HJ?}&I@D|n2q;e@2j%r!Lg}GX< zyIezF365PW+y3#r;N{GWx+8gS*CXuw#F})^35iB2{|q5^ePV7N`q*@EXTxc2>fO9I zN0f~W9Z8-UjxZo1S}C~2#mm`+5SGGnx)=~`vzyfx#EmdR-rNb{eTgyI-!+*uFasYG zrYP2oB&b%!e)yFYX7-5_7*q+TD`JDE8VDaU0g}LGPHKo$XeFRa;)68#C{l z_kbJ~_5^O#ls*R=M1Y-xeTa%CqQp^Jk=!~59y3ez4V-1A5F{vXQ#K1}Frmi7U;=b^ zLj*ocB~EwE`1$A>0i2j$4IwkzGgNd;s$8v)5x@iAveVh5l^_{P)Wtef`6d~U`$%gV zQcwnXHd;%8IUPY_s{M+GmqUy1IMz`4Z6^@)$7y}w7S6szuH91-yeWyi-LG!8_VR zHlTTS`{`kkz&-|~ifH3brE73r7PuQ}w-Lm7#z@Iknz1(VtthK^xRj0-*(L)K-RseZ z%5lgi8&&c1Omg{{6&C7ww{7=F-8kJ9sBDKejFYqggaZ^RnU_<%nYFuYy;ZB$Y_!C{?tCB5IBOP4`Oj3))UT#3EP7k?u_g;4&Z4}*$H?w2vc{uJDAdE zf$3oTraGVpC_Pi@xVe!evOEFVA`=ldcn4a?ZMNf?i1=1pTssW-nCu0M>wI+8zQZin zQbQ$|X9@avO!Q@*$73IFeED$4!fiFXAtO+>H|Z6BPxSHtD^?s|W=Vn#3RYrQS`@@{ zm(%3f6*#B?%A&$KRL6c0R?ry;9&1f{q2d?r`QVzR9Z8{t&3kshwoZqrkWz%|31Y*p z8)_G-5bF3$4M9yQ{i=e{f{qu_F3DO~0tIeNq{HocbfqBPVJ%|Hw*|!yt^4qgaynbm z>-7ry9%W6U5GIBU1%~4Z?M-bQa>HGrH+sN+a?X|3$~(09pGGzZ)ui#;G~KLBl|d&nA?LjBF7MUQsUn<3X*lp7f9%m!^XJt0XO5XC#=4$1JK)txI! z!1~Ej189A_9+|imGQdeSEOu6=fBAllUtWJbk(Wquyy_>SAsdvsX8S>`NgvWLx$JMS zFy#7>tl3aiQ0K0sAqQ$HNjoRF?hiCR0X0m_w=%e$dc${1)jdq9MCXJ)=95=~ejpW) zTJTD2rz>Jg;S_Yu1&JkX9&FDbHzudMht=o9okU9m$LW=*si8q<3Pg_#;zWDBU zB|nOVKA6et_1#@@KAES;$%_&REUkLtksIyB-tF8CuLuI}(pDExQev5|DWG=4H6Am% zlkEFqDW$O$ypHcJsnz7J((SPWgpI!GZ?qXajIMkMe}| zUy}7+sa&T)yNib=%cCzehl%AU@k?03>2HL4l~z9|+-!PQuC#ws*x)NJ@W}{sCjG<{l)2&=+g)aYZoKZNy@5IYC9xZ{@*f%7`W( z1JFKEpx{`SU?cCEVnq>sP3Gd*qeAs`NT(oH!S3Ym0ND(Fb{@5Q_J+ozPdF{*&r@@p zLBMvb2sUFz=$r3mqPPv}&W0WcE|{(b%}~BUB?c zu#hS3+aoPva$r&{NR&pxWfB&RAaaY%kK5y0sRnS(8r=}Ud1_LqHnL$)+;WFtZ3~-< z9y$;zh8Ix705|0oO|>Tb9Uw)yU5}s(F{25wK6rp8acLOip7Q#b*V@nH>T)RmEsmN& ze;-Lt5XosAtiNCUb-zg5f7+Pb+zX&V1rwF198)L&`Jwa5gH~E3SJK<8cst^bG}N8R zeTf3UU16Vu)&2-oQ3|fb!caRMTCbM?Nu#eFCQ*0Cg(uaeIqqd*&@g4XjtZ_L7uK8j zf%cx-Ei!)ScFVq!(8x*9b$h&~cL8aLVur5>~PS0<%h0 z(gt&IiGr?GOW{s4tMJ?1+tWE}H8#u0o4En8Q>k%z;2zwE zjoO~RN>CqHEL-bc(2A1A=58%@Sz4K13FT;40?>g@Goj>hzaTqVXd6oHfq8UeAB>}>iQC>9wYyp_ZkthJRFAK#i#*jktouZbe>c;uuON%sPoZB zqEh(NA;>SMwwA}F%4a@%N(WJ?OY~&>4nit(`LpQY-IXi*Yv2+l6xrg5P3AKBO!Fbr z_C-%BxGl(NMU=gm1Ghqc^nMRNO)Pykd<`>DtFWe;!f!ky&$KI$7~H}mJYBH}yk2D# zm_qWd_)xRQ76!{jXT}tSrnFSo@2~?2EQ9+bVRSR8F!qTCDO{jA>?O?TAxfnTVz~!< z*P#0|o>qzX78&Sp5*tg~pms8)d+UGjtb)A*rn9A$PemyakVx;cz8+C@Jmsikb+m-4 zglqz3unV5NS>nUg7&{(}%gEz(Rj{XcuKX`5h!Kha2@PTCNJ+Z9>Ox3_3TRtheFLD+$~5Wf!^Q>)UDE3=tAAqK zbk}6_S#i;-=6N*WD8{1(Pl%h7XdwbM_;PcW;r<{;*tRp+=5dDZhY%O=+vswDn1#SO z?hAqNWFo?sGX^*Z`vJ(N4OyU?nZCxc&Oy#Y4nWJRyMy%zzPDtusfWb`-f7Y*9^uTn zN-H&tlW3rG^eEuBk4twm7MK}-)iW#_Fq1(MH<5*@3SZX$v6+KO-z~ zQIfww$yjKEWosVe7Og$-@4GX3b5VS+GC^DH>O~J7}^M&^RI8gu$ z6I^e5#B0cRBHl)fw*!krDBqY-ELoZ#^r&D!;L;MN608IF3TctqGq8|`$C{3FZ}WfS zYDp2@j^0VA9GbOk+fxcpIuFXO-IvgPT(SMQ6gC?+)k=pU1<(3sWCT(eN>GGz2Y^tisVSjuY3nXyka#Wl%z;H&Kpwin0Wt#;&U4gT z-Q6i$T68;7|HGwPJWohqh zqRg0fCVM||5|{Ky07zD!#^Z59LqcUUzNDA91OYUm1R>B2ebR%ETASB0cmN;<{j&w= zR(p=}N6E82ShFj*GT0PNVY|ubqqRhOn-du2ycG>`h%mZIrA?>)*oh{$r^&jv%;Wr4 z!z-rsW@waRqztwl{Ao!#iqva>EHA*@H zln>Hn7^UtFrws@rm2ZqUxmVNLjbNSk>at32Z23y~RP;8~ZZsCJ1VrU}bj|xr=uSyW zU=coc9K8j{-VNPr}J2Clsg?$>0v9vYvUdzxD zSBqNi{g4%lIUpHEv#Lg&Xs{9kj&XaKBqdd;z%VDxDw~38%{(;Rkv0}q{0PgDIzaL( zbqQN|8DQ+}!7#VE*>K00`uJXZHHcKBEpOMOYthy27RS@k>?im1qw0!m(xp0Mm0dN{ zL^t@&nHt=4EQ9%&YAQi70xsw{i29QN>An@}dJ})+eqjv)^c7mNPdV@ksM!}jFBCNHH4&$-DB-JuMgatRQ{J$l8rF&a97g~m6|(s)5xH| zQJ>Hw8i+qA$H0E85&MjuvXU9F-|Y-Hd4Mg09Bbn}u3|iK75Js3A%a+HP`Obd(0}Cg z@-+Sd2B=$zm*a~1ADGl>{cDc*a6NXws1uf3X0_5MT_#ubT<8uW>?L|}V+rn1axT7t z0j7iSKv30mno*RX(Kk0u#+t3s!B%gCuc?p%{X7dWk(k2g0lmq}3II8j9?tL(+{#PHn|CRdK!MwRzdmwG;)EV7;X;Me! zO@4H6Ce2QphVhWf6AsiO0cJ3bL@UC?j24lT^}A2QhYPa$QGKRZ4~cN zboOay^@1J7mbzV!K6Lhm3TVT89Sh(eA>v2=cHpL+HF2DIAOTbK=cuk(+ahsG$dE8p z+tbUPX&Xl-71rwrGc5r>q5n7x?w_392ISaRIhDqo7V%r)6kgBx1BQ<82Zlcq@jc4+ z0Q(+6WT{YCDmohD=9;(j6>nh~yW1>Kk29hIhzuAIOX#PpnW1FeP0Px{Lz`_;yJFj$ z@OnT6AuW=$n2Ta)ut;IS3xM!6(qlI<{KT3-6|PrWL!Q@{&-N)evZQ zX~QvAB+GBlFahHqZT10{V7;P5xv$W+!Dt zh>-YN!fq|VNZr6m%YHnZI;y(6{h?>)rU5WeE6A;@;)%LbxGJG=Z0D3-8^gDpl%sX5N5)*d_a}ov=o&^2@ z6lwFLYec{fvNCc)5lf<_dfOL)HaKz@#gEmbM}tNHEtYK|z?@wI$sw^=1WSa<2iR^f z?E{Bw+)WKmhe}h4oR2=5TCY5#hpF|!F*@Q<)YLbdp=alC=aOWd zihbN{Ifo5xHkVpmtF#lzbL`>moIbo=*&EXxfGm9Jnhn)k=r9Z4jr7TMw|h3s^$It) zh6EQDi|pxnRx=J)DLd9LL893(4Gd(%Ifz!A>89Caxj|2P2f?xmO(3`p^$56W+c|!Cz8I0FZdzSy7!C6@JX`U=w!0Axj#fO+D!OGiV^aZUkIL(f1pP)fEGST@N1^!E4@UJr z-RtC>c?(m$xfgPh(QuRmFZA!;M6d^=rl4p-)w;R@%|a*m%hA=Sf)XE72%3kdZT+yR zWb9vZ@&_jF)Qty=2hw`HIK#StxgybC<5F+-5+DqjIZB31YGzWJm6q3#p+Z_fCS+ks zBv$&5zjcb|I0Y&T7|}WceNjQ&1{mdtRJskJV!>@a0VTxilp!ujgpc%*4% zF`hLTKHaRQEY=NdszOrA1`0K=U@XXS0oXbpW%uC%XaY!XlExm*vgI+)`Qgm&?UEBb zCMvMxG^b5-;nB^gvBS6ay3Cfppq42?L00)a6vW}$6Hjx#0OywOX*1-DCK;bq&-uPB z&piAjlK7RPNYAlcWkwoy27lKAd(3WX`?BuRm`SqEIk}%I6ul|B6r#Mm1Y~sF^ff@1 zJBTi?W*lLS_kTP%?tk68{GB6UY($Gx?=QCYk8XZcfS?P|xpAKceGNHhq3`~U# zoVI!WWGKxinhpEwA}W36Jt0YkJ;ms|wtCoZCO6o7KxQ*I6yn?lpB5MQ(wq7%GGaa) z7DzY9lmw~|I~2y9F&~9m1oUZ%lOyw!&R0;+K}8I@y4#?wP(-+NmJWNn8^3n9)HD4# zINpGmR~V5UE2JEmHq>NW(--_N%wlpz?RGu7S}yQM?9;fd?OA?vYysJXrFNOrDt_;E!)GNx4Y(2|k*P05`*ROfJ_(+tq5no?ckiJKZtX^77y zc%-Y_NYa%PkW=|E82}CqT`ADrT#w)sqsLc@c8b`!bP`NyKk1-H+fIkM&m+3d%Zkio733VBn@2t18e4)&UHe9^Ikb}~VsQr!KUq6$K_bGk+CNN+_LmF4J5;IL4Kk*+O%FNq$A zei}J$E~o3gW_<>|7xxZ%lO8!56}uH6JXMC4g5@P8(0eFie3n5PeD!L9@(?FIQq{0T zfhq}17|b6=FS#h~e00Tx4HI+|7;Pu!m&2%#9y{-|6DEv8G+O*=9j;2`K?OT%_>I5@{ zCq4jcjRYDMw&ZO}(3pX-gnD@d4C%N>aDF#KdMW(X4r@8I2Hu|A_}UJ-T>eQnG;z@N z4D{tZ9l)^-=6i#($VCo%i{^JO=|@1e9h((G%3UkeWzVFT(~nfO?avGtF?FIT-k)~n^$_Ff29WC_>yg6^cwUiL0{ayxx4<*yUY;Y$ zPU*5xA)-pF87II-3V!HALA54rM}WcJ48jCN4el~37%9E9UXEg8)~n0Kg3;C$eWci< z>W%UTcCuYYIUMMvYM{#>7?b;v+`|bmB7pGe1-lHr(}xkrFXuUv3WZjE5m#sTkBt0d zpv4$$`be(;JEWwrd%gwO??+aQoJXg)GeA%TWWWfyg5`*)f_B<$L5F}nU$G^3%Y|H| z!8zw9e=FWLKy82sACR2w6co{}RMNLa^xWimRKiHz-{Co!!K-GDE_!P0BT^~=Ua`*> zlQSxKVnaBk0x7ZF#TG^8v~8l#>WZS#6eU3loT4O0idphNCftTTJ0Imc)y0yDn>hv@ z|LJh;oJrR10Z7o$vO682@jutBhsC4Gm@Hc=bv%mTek9v+beynIgKdbRX$v>TI%QI2 zFg^{LOeROJ6%`8AU1BK+t}YvO>jvn|XwG8(D+G2SRP-hT=XoE?C!k(L&Y1k#=*l$V zx|xQ$VEsQBP~+)Sb8D+~@PA0hnz9K_A8%76G>mWo;bJB1&Ph?`04r;w830B*_Nj?o z$XvkxF#~T4Sx(-J#px%jh+Cu6o9>`Tx!>h@5WY66d+_8{H$KN`4hd(-o<#IxKWcc+ z`|3D@?;>$=Z|2J1s(a=p+StRF`DRmQm&bxGa;pW11`{n7i5z8$UMLd7^oxFA1ddyM z+p#{aj`a=ZEw&60Nw1Vb;e9!{$zVl0eqsY|P*){7dPMnScCItod8v1@nnr3FS0uX# zb_Nna4rt>yT}q#$73TAB2iibV z2jn!H#x-m}RKt=p#)=%uV_)=3U&nRoM;z#E5a!j>X> ziV=X(vAetBCbfYF*c?4d7szV=k`i*5A=VJiN}A*(S=71vrvt!2o(qg0h}dguU+$zW1o#}S0)Vkl3bLS? zx2TSSVPd}a%RMjU*ctfmaPe3qS>vKFgUj&Ctx+`lxK`1lQ^G*QQL0-xNo!S5HFGi3 zKp`aKwYtBo6w2KsS>z_+pwr`$jNXra-WuCUIt5}eC; z$CRf54)GH9#z@f|Yk6PZ8$i*IrJm}AU3H!M5%Dzcru451xd2ru{H;WjAF(C-YG&%E zz-+ett)9rM+crGxntP~B?DxX`H#T=x#k7vIl$&sLJ5h09j8GqPpHuUaI5L$62UZ48R1vT(LMw zj_g5Z42vJ+Xr!U*fxC9=8qL`%*dZ$dt}e0BF2HLVMByuKc7o4`(@kwLQs}AC?|_e> z{_Lv(XCSZhgQ9X=l=0&PDID5J6foZ;C_xv4nu?qn*;K! zuk;P$&ExJwyOKc9zbUd;&%BsNE8!iglE#Qm&PbICf3{te3AZ^d zf};?TErDqqXieFao!Yz^yo~ru1!ja4utneTB=>8_H4h6f*4XWOq(x239k7RqycNx~ zNoZ{C88bZQRtC{|To(?=iC3AI)c8zyT4U{#!cFCMnL=p7jLo*q=vND+Kkd09Vt73} zi9|w6lE=FWwES%Ux&ve(5f}BNG{Xk*RUdO}^cjizhy_2dShY`sKFky|wd^L{?fAJ# zwc@E#0!uEMh@2Nu3S6#kQerk6Dw?vfZ!p{i%Sy8gNdZIno8WHgHQR%r5=bO!bp&vL zfTG*XIX4C27DT5hy6(WY1Ue+=7Hzs4fKOtBw~t3KdqU_05Vey52O&;<>SJ!*wAc+U zrmH802|B2eIZlF3BBrS|Bawy`0XiDP%pLwHv7+HlbnE!Q@j9I%rM}p~B>e;KV6Br= zbwu6?iel?l`=@A&fqB1rolv0wcCNdAjs}I)1m*W4_|Y>KLRfH0lI0dStG++wqtgjZ zRhpv&VW6DPZ;u1niTUr}(`dq&S&boUDY&jnFl*jO)=gXpHj6QQ29Z z_=AV)9OszSiWxtVp58w&^Uz)DV1c5RxD(`gz0aGx;|-#7O5ItG)Ef|sk0?7MTupaf zTtRAYhp5wgn?F4f2w*BE;fHAHQw$hc;Pv41fPF`Xe1y8U>lGxV>5881x0(li2h_=oI-0=rvymsrA-pG^EXb6IO&MI+31r3e0%NAP zx0eQbc9&tb*qc!22r<2tR5VRebno<{xE?W}MElGVW$S1op9~cIxxbGA)V4cYr0exc zjjrn+XTXrKO!DsL3Z(CMmutVqaPD^#?AHSlZuT3hc?rOMs<2W}bwl;18+{3aPfQ4<-ts+lYRk_$87~BcB znw`%DE_D?#@+wDF;=j6igPVp1w0<{F#I~~7T460ZkM+DV8nHA9vXEU$(W}_K+4QDt zyBH^E&=TJ5yRJttYEp&)xe?_8M8mdy!LvgoKf0IHeBQ6?_5KVYTd|@$AQ_MLKNWR{GLJbn$t`DuODG0~)+1?F;++ftI1IXMzb5ko^sezq`V1B%!3B38jOB z*FvNU5xD696KdnaEW3FXtD%+mX|~y@a|3UYS};h#jz)vDV9)|62QGhKz0uF_r05h> zHy2O_bvn2Lg`kNB8N>2^ZrUgK8h*+1{KScJ#$H2j3Wy!dwJi5;DeK0}?@EhV7kcEb z0Va1OPnzE6$+YSHK3x|?gzttAOITuVrM=y?`3QtrPh(&NcfYnXP?X!d*gLo^y6^9~s}ok6215rzac5$^X*Dvb=z z9()zR$%jj++HsG`eMqB#s}Lc8Q7Izul(cr!#@cl=J{xi@!xj-AunePlkalI-#d3k& z^{BWV7;rAQ3v|2YS);Q38CQD*oa-||zdc`<*(47X$D9v+VGxagI-sl~B7oDajGKuaci1Eg(e3e?CDZeLnA#P@1Dmoe#_a(^!Ync2!RVjq zRD>zgBO7?RWP>=zm3*K%Thx)KVHg9NX9KKE)g}ne8JLx^fv%=LsDNN4e@Lg=u&D|F zI83T2n=$3#G?V^1ivq_Uxh-B{2-~B}o#fa~SZfRPk|na-PU^at-3>~oWMA}lJ-Svk zc0j;3dQNmp>8MVfZb;%y9KW7)rPzrGQVE>sOO%#DU_l+h1E}(Lm*UsO2r7e6f@3%* z^xWXCvMTayim7BK<4k3?UC?2Md8~KG%7~h^#WVMMy2}b^I66*qNQbA+Ih?F zYn!OPccer*g$jvOqfZ=69`s<@f=gesR2knH>`R4lq!P*Frmd{mbj?{i9?m6}v|XZ7 zPvcBu+TAtjr=x4h@_wIDGYq?K%*DJvYnBO{o$-M2I<;Oji0rgKBQ3I-rF9QG6?!B1 zcUxpNUjz=>hsp3M?VKN0w!0dF507LaprlHZ zh9_(7hDP6^eF&IFyZ1<^jfRrXnOMvks`xGd+R zdZ#5E$UEflxVtYCuFX7gh#%PzoYd3EuGh7Ek3SJOfzQ2DZMp}t2x$l7;%g><9Drjw29YAfdj&yG!iXOCI;kqaHAcGAi zh3MZvjy3Ie0v`9q7zFPYxlnTZA;)%8G(zUxmur^jJh`~|FYkpUky1j;F{c@x2OkmL zlRa1=XntX+^=sM9di@FLUc6M=2LOFn{J1y7ui1BB6E-2dQ6_YQalAf3^+SW)6z$*| zZ4zWXMio``6+~0Md@vf#xlY1k3&umaCJvx@3u7r%!=Zy?qS0Oz zPi6}~7+T)C(cU5=m*HeO*8K6k*y7WSq}1!*u16o4))L@e_aA<4>BG&ErI9XMaZyer zD$*dnjA8}URiZwwzn+EQqr++|p~mNC;i)BRShwqxCFckjc~a^Akzkj78x<*WCx1Ym z;^}BRu{*Fg1-2dvC07092F5t!#MmM&tdtrZ1SlOOchK1TE5AemH{e&h$5MCI5u#{q zuYsf5!x2FiVggsh3W`EQ;wkbHvGMyKfu9Qrl7}EP-3$x|*1gB$yGc^-%)~q%o&&m3+36?crGWXRW<`ogO zTz~q7RKRJ(0f@NBP7Dfh*GyAO!7XFcP|jag#)PtTV3VV)QU*3Ajl81N9k>=v%1I~P zTz8c~@}Yrm0eZtaAinDfhEtxHQf}psFu{E>R~aP-{5*yc)}3O0{MSAe;7urvDLSN` zZ-Qq`dS;PCW>;l^3}W&JhRZhJ_~V}5NA@D)JkUWfMoTO9Xb&yzF3tM^vk+`K%sDAght?aKpo12Ek0xLuD5VTp!))lJ&f zlbJr-sh8G+#P$-{tVf%3Y+nDW=EOzU;vZV#va1JP=4-Ra{mY9vk9OC{%5CW`q#0vn z9$h8|9UiD+;CEYD9F;F8_((}i7|(DKw-Kf&PKwNtKu$$<Ff0l z>*Mh6>AZJwonqP=6G6}iYzWyD03OouaI&@>L{LuR0#V$8hlJD}ObLPW#Y882xJ9?6 zKX8$p>3?a#3VBlmMH&@ly}g-+sN@&~-YKpd!I4_1EZ!YOcB3wkf|JyEs6t3ZblZ7< zpL7a;1Yh4=^R~g6Mh#LkH$$Wkqatp-vPH2BT9#s>)zXE0NuwE?MSjaqu5F?=!%=`1 zt?$@H1h0CC98lX5hU5kiIT@B_mmP)31#NDp#(QuYGLd6>?eJa#@~gpbOLHeOMbRf2 z!a!O*_Oh%op!3iNH8<(e$dhn|>GO6yqUGX*O&wl6;A@7;&{ZU?UIR8CB@24+^Bgbe zKmegBpMc$Q0Uv2SL_E69^t#m5ZRm5Vgh@xC~Lw_W=)YP;E5x_0uXI`Kf(RM z6|ghva z5*QO(X31Q}d*~7(O%N18cDy0G&PVNTzic>eY}*RseoOo6VC7U)Zcpype%Zq71mgAR z>XJ!k$_h?_ZHA7SLX1A%brOfy2$=R3^*s|>b^P`%=wT%FDZ^vYd(D#r1#FmJH;M5q zAFeL~alnK&R_VXg1lX0jJ%9o7LCu)i0#bUu!ZipKEAD%CwIzMz_6L2)C)dHy?wRyJ z6mkH@EhN?Ay!%oXhyWtmSZFOkqWN6-(45%xi%g2J{EVOZwm(|Kf18Od`_hn98I470kR(8SNZscPlXbA%t7*Zw~t3> zED+>n8yE$I#dCrmGpsQ<&u_fm(&HyPh(Q}hN@(2g#-Pcuy2E}sz0J*N$x8;q10Q1s zoM4ZlPEJeLflY_KsNa&%n{TJKKGN-VEJDW|LsDt{wAbws$ubbLnr_@twCfd{)7X=x znevTv;xj~D9mJ&-8y1LECf(G#^R)|G+_J0Zs3@`gl}MYQjRP-WF9QoX0F07SGQz1j z3K~DzeFI~yFP0}L&!ByYwIGE?VY^9;`Cj%a5j*tC^@y*4qMO_tpj%i5=RA18O`Gaz z#ZO`rJpV`8@;J_$1M>I@znIh;n8vn1hNMku4evBR#_8eSBr9L|T(;-LNjJc-36mr_ zfCjw{kRl?xpEA7?ounc7lu<)~7q|I7J5e9yqY(bnW3V2cF`{p@r!_{(XLjhIo!w6+ zhD3HQ4(iPXq(PuUX}f&n;1rdYGI5BV09t5E+`keeP$tBn3!;?|1j^fH?uOvtBsu~Y zVA$J4uHn?KE0Rq&O==sgsZeqh8_WQ%fbOI-<`(;sg*{OQRLH`eJFyTomdRTAbo7xz zhzNviHR|1NhdFlWXxS6zP{@2CzG3{KF92Zfv}m3-?30SE^QJc!uv5b+d*E5Yp*16R zS8s7pCEh8)B1U6zV4l6a?JZ9D>F7$cZ(#00w)w+>sBTs|0gDqQ z+{wKhG}kb{9!MGJG+jb0b7kJ>Yefi>kmf4!ARKj<7)E}E+e@cz=*j}$#dpEb8}*($ zb-bibgHnoeUGR(%#P23bX;eCV@zzh!km*axw(9xM2k0OYuvkEoJ{1WR*-TFjZM)04 z2dG4z|AWf89_K+pz;1;z5^ZQB>uuJ}6~Tc+nC5W7LvwJJ!J%&hbDY4J#2Fl%Nfdxb z=`!x?MmreYCL{+g_l1=UqB~EK*RNDqpXiE?54w}=4?_W`$X>=l$!jn z&Bdj1z|7i-l0MG6F$o?6Z3Lqq8EsAr=4@n3JN6>`y$34xF;I?XQat2B=kRz4CS*eY z{k#g?j=j>$g7O~%;PgT>TI3U*?%8`~xGx{&IPRvE`deGQK3aQ`3mqU7QfwuB6tHxU zh$zv|O7o8qg=+PB$?uo8mEu#pmgvWg+Zxz`*!EygV3mwj0nY>OREhfnDl;i1iFOrv z(G~(-|1Z|oW8PQ4+~TtD=CD|BL15OA{f&D1Cn!I*I&tp~3@Fcq;@=6fQ4e9(VLA^% zV&G>_pE<&U zXm&k!_1ZyKib1S3YYL=;;bYyNW_2Frs}nHs5yk5}BCqz!>+@-q#^d{oae){KfzYscDm|0|f`SNh` z;5*246W0oZn27MPg_3PGbPISKbAa7GsjJI7E=9edva9pm;^j?v(n@1U2c)!w-X+=q z&eAp%Y(#}{GkzPzb0Y6_RE33n?}%t7KfAJ-)A0v5Ve^0$hokpIy*l@ZUwvk@I(Z8?G$J&$d>F`V3z;kn8+; z$m}9jmalcz)bcFvoKHK4PcYJGWxLmXf};zB9X4BOSYYCXz5ngnSh@(A)|L+-)r-%g zgBl06=8T8})0PjI+wp~7ace+S4UnX6-;;$fjWo)Jj&QWFWvgc7cXX4`ES$;yFumB^eE%jxzN z8RJI`yMSNSI*m_3n_K45v2?Ssr}%>u@_05k>b4?qn9&-SnfH@Q_C3Y`nP9&M$W2o; zd1oLW0Z!0fVfH(!ra{&{!smTRYAZ~}$ey@>;%o#WDP8&W6G>|++{cLkAe(BRSsX;E zPl}fuKO(=|+VuBJTMC(8S1e?(pi4&alUoR{x3McYGBK$B7<@NfjshUbM_%ES05P97 z5b`~rS;PZ&+;(%Ou#pO&NQ*@hAH&=jX3`3Q{7Rn>m7rxwPwa#aUNEfI0e%VhY%xBY~TZXnSl&&T}qH9^ozARyXl*^&{bE=VjKh$rpTw40Udw>SHQ z7D6Um491K;D6tb>8zeg}a>h7@Yy4X2G>rWsKfa>v;xYVgeeb!7-S&839JETTWQFyc zvnS$sOl@q;xWsNw72H;y%R&Hg<>A5+_gH@hV)SeZns<*_3hj51P11Bha9UY&Cxg_I z-@P@12lsTT+)>Upnib#yS%Gp^?Wo706x2UU)u`+>4-5^JUudERRH@j#{P~+7grm?z zR8I|Tupnhg>SSds2%=!yXb6jd{4nKv#Oga&dtavE9Bc@Dw|$z#(YrZ7mtd~m7Ek5R zQ+IZ38mXX6lL!uVrt08skIYXU(E&yttz2WhxY&QrOT@ZHCk4PD(X^73L+fs`B z`oXFi!aATa1N_5GK#haKdlsO=6P1@MGCTKmWqnWkSrlBE`cV)lL%AaOm2kx#TKXxB zM~gILwqm2+Y_#_SmJSKihFFY|y)CQSi+RF8&*GbJq4|tl?XQ#Ow|fqT+qoNz_C*~*o(*7mdptmgKS8kf>j6%CllfB`&!<%pyyx40TR%s}Y~ zB-={^!MK+p&4+zvpe}5C5O~@7F$xPzZ%wdnTG#ny@B@g&u3@Zu`z_86Q)6jJqc9Ac zw)Zd#)sYmYKW&!A3mQE<`T*b2ZyY)Kc3}$K_g08$&7xZq06<6!na)d4U|H=g9Y$6j zZNUH{ovfsh-q6+D)-cKGh6cmTGJ$CFf7Xw1=tu&9mrEE<#Ij4C{mo@Ru`KEYDNx|> z3+|rgH2GStPZoL1y7cObw3mP=^L%rGCHk7bfHV+z&aO>u!O1BN8GpJ>KJ?`X8stM zqveu63puM(FZgq|u1+b*so@gYwo5XZT0_Ji8?!6?&GC0*B4Wa32ZM{*1**E9oM75? zQsR1c{orYrVu+hbgWH|o<%|q%jj~q+Qsx_8lO4s)&$c229=<eX!&jy)nP&XtVS+#9w4e*@SBg*XV9gFohSGHhFwM(kgOFd!4qhg@Gx}SU zpu{f(9r8N1y5*iggH4ZL&65)EnyB#Ctnn{?UrXQ8b_i?L!O`$j&+Jh~P$x6=V3CyoWJ zNK=Jzk2rgbW*xeW>iF>>EJjvMTqu-AxusMPjkD=Vsrsa4!D;20PkGW=kd$kfK|ob*{*8YPX^!$H}Duts@iN!^og60XxD zJd9Ne>+i*6fqjEbF09C1p6Q<%7qZ3fqMFb2RWz*da|`|Bkf<@<@*v0^7XwTj&yN}` z8JlB>A3d2>MARa=(Xi{)Zp3fk&P;3!&Amu5Q)s~#i z^%PtAXg!Eqm-jeZt**Y3N{ZK)_EqgQ%ZfBj+*YmlO-QAR-RjAT>iKSOYN_Ev0ITKX z)x%LElHJ+$g($S9#W}4-66vO+)8=wJHc=&$5u@KiQ@U@}5?8rewXZ;&T- zAS9ROkwxWc5q&0Nttv2Pu5TrNR3>*=y3Teo-2}VNbLzETdRQ*4+g8C1KvrsB1uz6| zR=dbI9J)~Pr>b{8`_g1ea@!{OxhjPVwNVQ_J3BsEFQ8qQwT<_V?F_frR6=KMok*9e zXO%tlBr?=nmRy}$64@1%ORA-xcJp_p%s$mZR>ByK-}=vRv}p17(mPhT5XT&O$NFcoHD?}aun6k?gAXt;(> zDV~fC*#yNARx*pxcK<+xq9I?J(={ia2%R4O4SJ$J+s!tVG+^kSVr!hWPq3$+u ztSAF3e^R)bd9)^4HB4mPS`ByQ-6Rk{{wn;Y#eN=?9Oumb6zKd)xa9BH*ik`k4*2!) zcgv-uM`+tEoi;Gs`oMwHalb<#5DCJ}_H(ua-v)Z^^ST4z1&Zx6;(&!&<8aX7z{kRq z`y&rV><`;xYQv>zciQu`^KJv!La+y74;kHaaD(HCJnw(p1Gy7+L+HZf4V>NMzSDV2 zj)&p*!yky<8-c(8itQ8FBf3LS>;VXb69^&@8$)mkf)_;13!M`eUj#!C2Qw0dh7&s? zbcAyY^A-Xi&aerh5SxZ`8iF@OtqWZf!y$x)N5IU0EewRiiv}|o1|yP2a~KFi5vyV> z3_`3eigL(go60Jcbu1lQyt4QVMeN; zr*pEsXaB(eM@OLm{__ypAISc%m;Zl~`fuo{qltmBjmiI(P+N5@o&SvgLqh+jm?z>I zPTHNM>Gq=3t7P_WF8DMC>X-&}xoVt*4-KTS3He%+w!`F);N z+3ET{zE6d@^?CfR<>lh(?f!gB(ewH|joJ0RKbh@$znyle)%kv1WU=r0zI5^RejFy9 zwcYV?_kJH-%+2|I9Ctk)UisO@^>TmTTns&how?cZd3=xU`90jI<+X_E{XRau$+SjXeroD=E$7e8+uYqAU*GqGTpZuG<4@PA z)z91+{BO@!EgyH!-}~p+p`$ptwBDw^^QgYtLiWivooCoEz7}rY9`0XzxZe+3K416G z2OZ9jhcc%!zb|#;c)r(%GWI*&zORpunYEZe4p{cGr{64>*i;p2b-(PO+oT??f-?w=SxwtUi z+b;GzyY2P6pNFTnJ6|kaF8s}QLr$(}pSQ=~0?oO?^WE>iZEki^R6f5aU1`@Ebw3vmCmoyN zF>O)i-XDJ-e}?dRw>YG375!GL_I!UXvgGsvTHsGvc%=X8xqr?avlvvV<-)8kg<(lcAflkIEt6{z|d$K~}%58DgwBcNluysWV{% zryD|*E)$S*_Yo_+^$8slf&8PGr9ju@&^ri?Nz`l0G}%C(Ck`PfP}+gO-9;CuZSN9( z6?iO9Y`g%mqiCCls{&~YT8&*EyiOC%V3dWLNz3m+kg$cc)tls(IcDb&kc{uHy$t9$ z31Kn`L7J|t2hRNP6*E)wS4$O?6V1GZ>YiBNf)E82iAy+nKW7O0kb%)HjwAN6JnrT9^OI<*}=QV>bwEROJ1tL0SC$`@U6jEl8cCEI9qn&F42zjYavH6+fa70n0|y<7H5h#XLR4$c8o# z{@R>;$d$4c#zX4B!GV>nk{&UYaZhU4qy#rj_YP=3h*QaKXg{?T-hSO)paxj;b-`s_5OX}}mPGUo2=Yt< zkTOX8#%q-IA>_6wBBD`sB`B2RVW|9Ksk#=5;X8(QtQ9@Yo%Okwy zS+PrKOMT#_U5}Die~@)(Hc>=KJdpJM;R!RvhgYH$GNrvx4c~`#K#_z&^UUA-2`C!%@z9T$3C5`33RS-a%}Hu>~OsR$*71&94+(Dmj#X7 zKv|>OQn(22gvw_vSBVWObf7fNCA_BJ(sT#}9P@BGAC#KMK_e^7-u4vi%hwwvlN#Tq z%b>udvd2gclt!p!EZC)O{QJ16Yqvy7VnY?^R{`JlmAl51Y#dX2ez3?gWh>mDUfL-W z#an5Je+EnrZWSy7RP|+2x^~Ss@V2I}O0b5&l9IQ~Zas@!nyBWXdtA)oJ^}D3y{M*f z(f=qrG?oc|EYzZ)1DLdh{!DqG$qoQlXj8b_esB-vnZdM2+V1{?(%b`BkZ!oWs%Y`p zOE8#h)iGUywLzu(VILrGuq5zMa{D8c5%>ut6M+cUQ~BYKlt!wV=wB8tIr8LC>8}`6 z8>*-Rf>3X7ia%w%JAZ6AywJTgq4Uny-hOogA7+Y13YlpypI|yvA5JJ51I+Vp88gv) zmKmzV2FYzGI~xSo#_Uk%7BSKdIHoQ=_}5^(#1N(upF-0agWh0fhYLv=Qv20N@hSz* zdcAHu1rJmX;$B|Dk=H93>gNxjVUU$K{8q}uQUUcTg19;8w85xh8_hfL*95mE4^;QM zasX0tWRbnLA#6{GHHwC|1eADL3zS=`c8gP3P^4?}ErW5g6Dg~URpTo=zbqe{%d9+3 z#k4U?_@D00mj}Jt9>?^xao2L}ndLax=4uq#M_sbbWA={?zQY_TlPUEk)Kyc2uK)aq zDC%wqlD{%hz$spYXX*_0Pxf-crU>zXw{$uhRZIE?*IIz z(f>#4yzXzzhHm+RindIWpDFABrP_i;k|1ImU9hARcT^;GNPx;oZ4|e}k)X0zt7rRL z>IN(g)D3`)ro>usVP{1>Xuwk+3jy-7xe3pah@L)ij%FIFdEbj;H-#kdRT=LxW-Lb2hb|y z51!ZsXQGGZ%F0|ZD;Z6acWwLv=Hu>;rUvQ?1FiO4ac+P`kb)cg`#Fr;-Il=(uF3D zLba8=Pg!pWL{lQUFgZ?A2{IjP>q=Xuob0a>L#RF@PFPGA530RqP#9?#WY|Jhje4yB?JCpA zpH*jbC%OIebXpZ@@r`!|{0K~8jgOaaPpw2!Zwk)QViNg7+cuA;w`vkg07lwzEw>>K zu_aV6INmE7^TLw;$u==!F)f4aN~Y=#F6$-0YkEb7K?6q-OCxBLO$}DsZoo*i7HB>3 z+$BF7R+u`#fD6mWLOmksJ&io*8_eLW3QN>MHrn=VKt)s1;X+umGQ>OB@y9jB_1?NG z`oI%MA_IU=QKSFZD>2FI<(-tRPGwov0JdlB!F|;TfHXYrjQZpiH+x8_g)t z&V;S}+GD%Y>TPBZnxSUAGhn*)(vh-n^?_#1A4_=SftU|mK?A1JpEszYA+1u%s#U<{ z^|(|s4DzZd+yciCswdg8q%3dK-3Zo&T`_a*dS8%-0d2`grf>BHT@1 z0G29c0;=2_?$E0kWLA&gkN&UX?KOFkA`NAU{ar3 z4G(jAP1E(;%DK`7tG%?Q9U7t**MH04s-1P}GPj`kS&&z+0?@2M$7y)uB)>_~IOD*Y4KqqFqR8~eR8^`3R&Xsj5hTc zZ|n1DI75XPrL%qIO7t(9$#UWrt-x+so2l!Nva1K>P0Qf&=h3%^!%t5E!S(%Lj7nsL zp$al*?-e9E5zB|>-qgVlWbnnC?T`_F#yc{*_2=AS2+z=&@X-Xh%tFZs>1}RL&}R#P z@4|tx9A*W;J9+@!zcK5$@Rw`i;M(6UKgs0+bb>vwKgLcAo2ajc83%8eZ+SV++41*&vq$ii{0&E8dFDn|8z5y>%V%3>}_qh5_gJR`OPd3r5@KYy##_k!1X z@vpEG=m7H?>@dFN|20tgLUdCh$A5tKG$LA{k>WoeXsm@d)A!M=;h8C)EUe{r71~(t za}Ymf_t9^bZf8b%D|{BKo@yW#o^n{f(P zulz;)qC$AJ)UCR+Irn1CtIB*sj9Vl6HK&%620e41)K)g_Upw!tTQpNYPD-3Syp^x- zyq5;)jbn!okb~i-h|+wHTbet7x$hZoyF3N5bmBh+Wh_Hn!SZGsl5|DWS(;A_?0R`` z&6K1K9nbAooyC<)fXoXRe`VDoV;h~)EX&8CEU^p|KNNdXDLp}@DI68Mv4*PSOH1NP z`|Jy|-z$(@aq_D)k*^Q!Jcq@q+)tMwAFAcPvMB<%0)OwnS`i`jU#7}nFN=>e(cuWtpucN zD%^B|bbyz{sgVx(ewNg7Q5aga#fjn2RTdZd`OsHX*f$xcyf(>`R4cPCsTykvNr2p> z&W<)Pptwzm|FtQYv9ZHwrtQW3o;z>mV{XIqd2zCXH`e`XdZ3(#uBo++mldo;)H8L@ zR^oy3C>iTPr6h_av)X^X~mShm9A8T<>HHx7B%(e zi_8=)j4+oO=n4<+Be{KoJN{c#ZN9OrkXwk~0dy1}B`ZaSArQ@_OJo0-g`-A80?iPf$iz=sqn}z)b(}ztF&Soht z>ik!#__y(#0?DKP0X7JTBng`KSraBQRIl?E#JTS3u_*J881T8DRC`Xzb-?3b!2i$iLYnHF( zN|$GoCTsD!EJ^XuMz9G;z=Pxb9|U9xCcsCemqm_yhe2Y;hPDi1ZYn572IKI}ruA8c=pnwfCLN);3Uf*?@$+Gaw}%!9(0Bbu`HdLFWnGpk{7G3TBT}*(KOiy_?(YgK ze}N|WhV1OKiuG;3Aue@cH!*m;;9_+^EDPiU%94c*w6oL3CBS0Z6S~2a0d>)_2C$UX zSC@di(tdAQJSdJ?w0Gk!z03_rKl#XjRBh+h~ z7P(48=cV%55t|L*HHU6@m4TCF{HI!5NKymMgCuz^pmG^A?$SFb&RzH&r|z~REkbsf zg>UmcwFZD)XV7%zXB2Iq$I7k8vlH(z%v#FQY34!`=wKm_MCo~o)JG1FZ1<&nLnuPK z zxE%b=ul=I?8E8SCq8~`bsipM7>r!Y7W++Rq&|m!*U!VYORs|`A*WLuU8KP;Q_yb;C zFW?lkE;xB60V%CFY|}gzs%y%HQlY)4O})#BOX(JzxvOg#YksdA=SZJ9cT2mdTA@uN1p^x#dMf za?AB8%Jd|^*0UO;8PEK;i2xgC`XxCLHWiAYx;_G&%Pczj5!24nSyuneXIu0WtYl^C zZV|U7?Y{OqV>|o(G#GG&Hj`^VdG!xF>e8s_38AEiI);QbKRA2rV`LR0BR8eNBIg6$ z3+P&yi5=Z|SIQDlM_GcAKOA|{YJ+LIOf!>K@4oz49;h@1QL85wdKeH4f!evKgw+dW z-97k*@bsH zT-*@vmrEYeTuG~Ff#Fw-=jizl^t)l#ej?pHxAZF4azC2Ccl~q`0xc_b;LLTD%F%8h zHS6tp)Ch+-xwHK55|||3MvTuysI0x=;C<(4}Xw{Orv0Oa_&o_5y2O0 zCca6*5*Qs~hLdOrw6<2dGC2@_u&d38Ek3%F6C`-57Ag&yTt%I>&^}?FwZ`Ttp_)M} zgsRIro+E55xUF7Q)+8$_&b0~XsjdfK*ZlMOrT(-|6SUC)kK$fS5B{LQ!pXeB1G>LR zTdm0yaV)`PG%pM^hy8YE{#}E+nK(kC26n?dcEk7|BH^m?v^%!Pc3n$5I%W=r5<5Et zB-U=OpcEjH0mFqhc20%!tVQUSU~WEjze$Tk1H{rDtyRaaa#EpG(VJJE?pV&O6`B3k zyO{RP=$GYJyl1Oq+il5P`+$J9Mi2Z*eYjLI>~?F9XBMRDYgjh`N!6G2j{$(;uJT~7X`vmH)@N=2kwIEgyq+i$dR~QV(96)=SC$e9vO8+yGjC#~ ztz&S35!n^Vs&gurEzurZ;g`_)>G)M$B))xDo(tuZO(5~yFuAAEr0zO$X@lI~qBoJ` zoKi;3tWVp}0@Nf9(({4KFpis&iS3p~T=+OeCnNr_wb6?V)qJr~n7pra4{f^-c2FvN z+D9=^yyOt+0jI18>U4HoZ~@aYgKvdP%yohcs~ZyH^Wd3y+!-Vl@QL*c452rUH-V=e zml+yJH;MRDf0HE4_)ZmZ&0NyR#3bJB78Qy3tTzVC5XBy4QtU^kRz7g~cM$W~VK zsAIX9%;_6szPO7Gq&?RcHz1yvr#PExQ@C^&plYXNtqkz3oQ8qnZk48nV{A?&R9QNW zT|ZCD=bj9*TSOtrtTAS?73lp5dcY!4Jk2C*NY-trK1!P@8OuZY^5xyzO_#=9K9g z9-mD!FT|i8+KZ0nrKSxcRLkxoq)usc!YzfMq|O*-9X1b^a^`m1^fqF*PFZN1Fx55N z29X~naqRWmDGTMv3999}qbemfub&zeL!0+DK&!ATjKM<1%jcRvcZM>3tD*E47rwhg zwqorjRZ~Y1RB6w?BvV;Eb~jyRmosmN3&;8006bX7FVSZ9_?n%Mr!G?P^hdVFEAL{Y zpmfT8OWZO?TsRdD%&D7mjtsjGO3};^X3tiQe;qedtCJtw0#3E(<~c^)ybNApJQQ%t z*_9--p)sv*F|W&wXB|unRpV*;=ux2-{%W2| zW7EDKwq%updb-}$nyJ$I(ZH0{1X=ZEPvkRunXS8S$XHSaHD#+DP!)&7%=ADMgM=2H zogHjHb_Vnh;8t|Tl-u5PM*z#IEoTLE=HtbLcVDxuN_P(c?FK;Dc|d(N1CTt>|CFTl z9KI)d2%E-hkSO4%F2eMxwy+yVwbl&58)WqiT4uiSTlcW+iDtZupcXe~95teN3%!jP z#EVtXqizrRZ|saQb8KeQ2gQ}yVyt^t=+=!FFleQDQMdYUnT?3CG6y_OG*Y-2SdEU(A1Kc3&P4iQl}>lx-~+MCt}%_^+z00x& z@n|wU;l@4Z^-LZT3J~_z>nA-V0z- z%7!u)o_mVZmo`Zt>qWV@=mqr8SK|1zymQxOhJXRgv|dP@Q;h0z-P=Ky6DmtCF@IAK zk(E*F7wVp^?PED-UYm!AP7yG`&A0ij1@O#+?!w9WdN!_&WoLIy!bd=hwrQS3nP#ng zJ)whRv~!hIBJL8XNF$q}sJ58b25VaxCJcG!;dv}8KB-bIMy<89a}V@@*NW2590wIo zqw*zS2D|>T&X7@jbByEM*+wYX!}gA;3VI|Zlfi7hhY{d3dHt<8txVA_;>;IUeRPAXB(d+Jwd3^EpEu zYqKqB()BjmlvoodjP2g~+~VD}s0f+FJ*&8yN&>_)sIAbps%VinkjKzoKa= zJYMTuttx1J;s)ENkFomjO;)Ya9FiKYR=#H2AOJ(yp*k_3e&K>AwT`5YO&ZD>Fi@8q z;Ig_2sy0WOT>FAC?mDS~+u7WXcc3-Ytbi~b)*GF@=5|s((spPR^sJ3KI0MB?Yekas zhV0lyFV_y6Z7Xs_SV2$j?;gU-Dao^N1kY5A3);797BZyMI12f&1*wa@-)Xh&&W*3Y zTCj&2s_@z!WR*1A1sfnNZe!1QYsI2d`)10tE*r%IT!3BX@R8vuKfC;i>Lj;7=o{s} zhVR1Uvr5+1RXl~Ha3v^LmVN8r`mDa9k=t~~Z;F=jwgH` zZWm@XXz5}y+fvB>q!^@<{OIVEMxwNi8Xi@z`XHWV#Wt3sbNhZ)WBnNGq6#on>|NI^ zoLOQFtMJE0i~-v!4i&rN=_NKC&$a=@ivi_jz?Y!K910vl3Hkunj^Z$!pjy>50(fW> zlkl$E)9sZOd`4k+y7Cq}{$1Q{AtFoSak?qiyc>_}iU#g5aEhqR=q&bkh5pDX5e2m? z@c!1s?N&KzkFVc>mUTfb*ShmIe&=$@n1GCd!pIqqL@@diLlKihbKrV#*TlgC2xmSN zqWqPij~c5vg`ghQxQE&l%Rr@sveqo$p0ICY)LH|3pxE^oB%wey%i7TKT0 zkmN*s%ayd1w84g5m7?`7`w~7oNG7zlz;GcF+@7;ll=uB$a6q1Vt}GlqaTE zR2sgLSov`xUofYWB98@joOcqnfnEkt*>{t&k$5Cpf;RdoGl$9O#6i)FNW9pRk05T` zjlOjC12P2I`>g+4+8ub=?piivn{;>*Qwts%Lfs(WQak@({AeJP!Dx$Z zPj>arQ2wq;j&B+$Hr0^(3yZXn*4Ey^cL^zW~Wi(*XuIL)MSFk_>t> zU3SU^oT6gpAJCI6$*1>cktbvGM`*-#RgVrg3=U#%Hy%cf{bL9@R0Fm<5X)3y^S8#l zeY44*X$-DR9xTFIx7c8vFEP99JM~-v-`QvhaxV~)rsnk2Q`9*uRe?b?NO!#9WNVPxSpA9(KehT_4-odt};*%FLeaZ6$x zk4qLL*0f-&Znr{J-v#NC6zR}Y$P0OPh@OyP=&1IksCIJ}*wYlc+k8fu%^lG@D~%!) z)_^k9Vl)QplS_$&CV&|$6RZPKBGVJ2Mlp$_Fk*1P?$o^{fYVBrvB{~@6_y9y$Nd-g}1HQY^53{(O z%~+`M#@)qMY0YWcvtBwl7K~c?2>d}U)q_s{_h+i}(x*MiiQmgWNODRs%hC_BA#$I4 zANPD?Slmv0qA@wIVK)JZk#iGmsVb~C+#_QCRniCCqsh9Z@;PJSkm_aEsYRC$Ne)QE z;H-OO^Au}{6dZWRki$f7CR~F|f<*c|Pn+!c%qHm!iFEg_wA~NF`;NYis=r(%ORbKQ zb>1p@b$9DvEQ-6jK^AtkbSP6V&-pi0fz9@q?rVnm(VG->4T_-FaA*tbNgYDs3&=i^ zV=jdt@@{>jm9X2h+^0+dh0FA%4fD{myVVr8(;P$e(ikJvdV;jn-mA03;@0rAxZoHc zr%r%;2BNwsDH#*&h8Cigr15MNC=8YBP00Dav=oXH1om8!WL|atwq&VwC&6Sq*ZLnp zy_|zC;Q*^NBC4&7gT3(lAWP9K7HKnO`4x$(alvLhJaHkz&v&KGa%|r|#l~Fmmj<=xpL^JZ{mO{X>dDIA>T`yR0-b zkj0&HyOd+$*#jk&oY8*{@YF|Bfx*DT&@3gIsBq>m;A=5@K15N(&8@``@Z5>7FGZU} zWsa9HYI7i#MHJcVYU!Jz?b$6mk+Yuu>X3b!SbK`DBT zDwnW<;~*mnY`B`a(T6qA@6nzSiw-31W6zE`C}$6^S&z;z(pv#5-6ab;_(CJP9j1vL z1Q)y;rP8W{9KpFjhoh>-zpG0a%7oNL4)i*uB6K)%Evz;sa{axMiw{X0ST;^f5!X9? z9E-_bE8(O$O7KnhRMwAmf^v(of5M5`Af&+krV27r;EifZZ-L4PjCCYZVh-uD*y^Y8!ZiYM)&TX1EetGGHxGa@yw60=r;t-WUVf3wc z9cu@PNhjCViZpbO;?~taJsuP#3@y;PfgYC!`N#T2Pga%v+qiY`VX zJ*jJ#Y?>{<(|mV)R>wunlTzWx@eY_m}e45I#FJa26K%9Se{i{DEqvr zBxezTzcXF?QF-A@{#DFusjg{H!hZ(jgFhD=@C)`G`$6n3z}ynESmx^Q^nsKyILWbP z6~W|pq0~&<6o_n`@2FL#m9!hVV;X_CfFG%-FHCTyzBVHcNJe`}Vu^tnLshl6+C{%| z>0;qt8c@AhJjZq@k~GPiQ^8e~`HQS56B%z`Wq65RU3GRTn#Nnyr*x0~EIVvf{Rgp+ zaJS+;#+tmbf?K%PI(jAMk;Z`wwGYx$;UTjC9kq}0(wO_~T;NAXYHs01m^;m{?6B?U z59%Di`ow$8?P`T-3+v~@2Ki0+XDia_fi)Loej@Y>315U+pP(N(TZ+|6b5_})IHo+^ zTHE9F%}p_6ey26Q3#w|;NrxP+*Clh(P1l)d^-gIEEtAVj`Bf{0?dc{?Sr^nt6~p9> z@L%4Nr}Pe{m>%vI0*GNbf94uCGlY%HRqW&z576h(-xB zsX2w6NPkskQhbvkh4JJFvLOm(kPaICVv!D(8RZE6BJC4J#v%>T6H>Jy23H9+XA^02 zEh!{&9IZ(~BJu#lG#qi1I;1wICHhAHk^ID5R9K925R>2=)spmoO8d_z0B0MyBq7Lw z+RX!siu>+9Xg__w!2YMxJiH;nvKj&aU>O+zK>5ERkOtOfc8(U#<~Dl&kw`iNM^n@P zTkDWbx|E%^7+lEP59;Q$EHcWKP8S8Us3Cy^fS4|HBvCZzR3f2Nls?0+4=?X!C2%om zTY&8@)#Lqr{PG+A?vJ;_$L-pi*&6*^-0$DlkDu4ii-XI&4-}`rOm+zOW@12>KySKJ8Gj_SPJ>Hx=9$vprpHF;)zh8%shccr8XQv=jdS^YeVH?>4aqQ*mN&%}$0VytyCqeUXqcu8&k2cJD3uhoj8@pZ za27i=l%<*WR(P!}JLbqZX&#xPOv98DR1=#wNnj~$6RW%ySCZT%w|@)dBBZ?&rcF9( z(A3Tj!ROY>9AAS{c~d%ylG!XZmGHO84*N;0FL6NSPsgiDXz*OJZ1d+!nrSXImoS7n zuj}6^J@j(3^k|_}YfO^0ov2vjI#YR4yts|jX{8?@blDLSSV5zzUo=Mdjd9{P;rWNk zu}@oqgp(A=HfF;7R%~GU5~LEct-v2PCwa}>FNXn=*&+iOvG`h=#bb>T3@eE-x$2 zTJ+WPuw%2cDKrF9AlHN^Gph!YI3NgihV^S$+fwrZN+q#}%s}vAl?F9Agtcu>WA(Ny z;AybQpRVg;wovehwzH#yP|jJBMwd+j5OQtp289FYF#VZ*k29X6n{1)D`WhD;4;%ZF zb0rrf*8{v{n+~(l+@gE)-F4;Jq)C9-)0smGR)FdEkF&+-8nH2*q^zu{e5iske8P5j z@(Kxts)a`(tftKhnM`sE#Pw5)1AS z+}+*X;o=(HT`%tLBOm_v+r=b@o2{thE9c zzqLgFY_nTUlvX*E$R@kgV5La>2E>SHjs?5w6ZpP#4_5ngQu{dAj+PJ}i)*Q2`%Xri z)T%j^{9NVhq?&iN8dSHG-Aa>;W}Lv}U-2nT)(M+Mv_;a#-#Z*2AVb7IdZ}?vy-Xn=ECj-OXi_kYM?`uYMX%*P4(}Y#y&6 zM!0;`iX{FW?>tiMe3{m~lR%8at_-~*_^r?I@aTsk+yIieh8(7jYV|xqn4rhg&8qZG z3K(mU?;hXfWM$}2XADhE2}N2s2rHTGq=~t#2wO-FQ6cKLgWq1e7>c^skWxvIz_gzy z3mPq`QXWLHoHrg28*7#s? z4jEU@u#n$^;f6Oz!AUvQ`omMyiZSx6o<=Tkai;A4U=whiUb(3r@e>>GE0-EnxH-Z@ zkK5;FnsQW5Ih0Wn(MdL`+0SJd?-%06#3wOGXl>NCI^7HVUil-F2{qf7bX4-61#}>j z*J4&%NdbnroP?AHc}{tP^@;)D)lNniYZ()||J2FO7-m_v4+Xa@aC6yvZs8i?E;$Jg zw@md~mZeDLm)1mWRXQ%Sd^RUw6|j{~LXokIR^!Ig3=HX&4er@oe~1%5Qg{RACT_S|6b2D@)v)EW?G(WSThRw#uu!pcbF|fF(L$u_$SKCco65r{9`lMJ#F}P zlKnjnpR#T3*PaQFLza5z(n0v_KqBj=^{}Ra-zh(;$)b8CLcj1TF8IQC{DbCW#|j|ejW&L$q2e7{c|C4oW|Ued?A z&c76=i(HyapCeo7_xMCn(&a0o&;NG$BwE$0kDYq9a5Eatz^3KbT9K_DyT6zoW9qK1 zHlMiFsc0f23zy(VL2b-KgJW`5;1H>#jg+vEV{L5w&B$SGHm>w17-(Ct_Q5v<(Gm7# zn6z2e5!lv_dLmH+#nwuG56OR!+Aa(F*^>IP+Ua3PyqZfNX`h#qT+{Y@)^j4M1XCkY z1CB4zZQWw~MeUR@%$X|&{+9Gj^w5*tC?K5%JGUKPW5I#2|E?FLEb628O-dHrv3@Qa z)dFo!P}Ggpi55xu{4N^$sdbm*`2{RIFFW{WR+{}sEKW-J(&hLHG@)+@d3SU>^D9kL zl|coQL?h8yomHy?QaAA|bH+Zj7b72SF?Q%B*os@dr2J(2ok)7W82jTXrsN-*5L76~ zHwp-_%uwUo%1?$iyGAMj!mP%-7CQ?n@im!dnUgC%1gP(h-TrCorh5px>lcTe_skO@&f!fgC&Cb=@)Xvz&h3UT?tu3rgjsGjP+R(+( z*xALL`9E`{O!I^PGdIw!alQxa3r2j<|CZ?e|8QSZpo4|ArJM7A1M>yW#r;2#{I7`K zdS7%h9`v3W;Eno8l*AU5g$J-PB0HuV9Y>vOPGB?8w&QUH2ybd9!nPXIeQ9GZp`lr* zRI5&Rr>5gcSYk{>`Fzw|F;Vv;Gft!ElXjc}enI3=1?_yEB(g;ky_|$46}uh2MbghZ zp3)0+*|=McSjb0vo4)jz4F6kWp{@edDv{jnl$8_i)W-C)!XHU(OR{?^y0{#Eg1{T@ zUOpz0;SFoZMiUrQ?3Gu+H#X2gQ|Z#Dlk8Qmv_LVs!Vys0-Z#| z!4g3cIE_Dj%4ol#^m9F|JCYwF2d$JXx0rL8<51JphFDmhW&;;*a4fb4p{-OD?ydE> z*e9DjEsDJ0kXFmXI5g`SkInC`RHpux7Za{z^!6?J`{7LSUdY7{28&_*Wp?cj|B*L# zbxnKX0PmQlT${~-od2tA)E(9D)=9gWG4V~VO=TG(L~iNOE#Z-f@|+Q$4+ye3qgm`B?Z;!cOM@G^*ub6(&*6aAA% zC=UK1oWZ<)J&?N|(_w%CfzjNM#CkA?l6Rw%X{J%1Gv9w0eFZr zIoj%J@3sc2gHg{^uAdw3_@IXdvfWZ(16~3+KL-k=8OuYh(OdC4RA(Kc&txUyT^ksg z=83*Fa-&})E9^a~RIXrn+S>z;`QsAgW>PNEpuNy~EWz_JtLr%TlkMu8b92;4%bEZI zKFGhyT?6#EQs|`@VfAm)#;k*7n9qcI_@tdNhdT^XZE*&%rqoOCt$5sM@v={~H)e1i z?+1o2m6bhw+2@H50wf*X{9p$3d7OH8W%5dWuKOYO&^iFnob1xq&+u+ zz^XSDftGEP!hBov8eH3yy25P1BuU4+=C_9t2O9_P7g0SdwT1Z_+a^keHjc^V8_YWW zSzPhOdYx~b1sSE~7-6z|K>Euho9@!vg+G3p75dr|yQROjjoY8zqVr(vgWU1^>+eox zPsR*)R9_Fqf#rT|&APT8;JeGv(}})bqsX3FRf^>Hq0pOB3kG)Q6ImjAOZLu4ag9W@ z@A^mQ=-T?WrRg?C?Yi_IOUJdl#kv|X8DIo&hrB`h>g6}Mdn8f)!Z((8=Z3&VG!124 z+l4jb&P%x?Y{?j3ri<0BFuC2tY8qZK5ZTZxtR9zy;VK068|#;GUgj|*Um};QmjB%C zEAbFDphj(k>A6sMHXjc6f(E_{u>Dvx?zVyJ>=pNVTg3Fb7)Y_1<0xTfNRz~85R$fV}im; zk{jyYcWD~P`8XV`JqnQdG|>A?2PylFs-Wk}SpSEY(-CC`?Nn+7v@QGuj@Bu2d(NT@q z$uS;!Pf;2|Ft5d?^nDRa$@gJP+h{@#3zV*Xx|6VH2)-kseas+yqf4;NwexS4Jy(OO z!~Iws-&*bsYE(C^5RD}SaCoIAAs@UJGqr*&xumWh4^;$8e-=hYw3nF|@&|~xriPpd zT?^Pa#&Oq9%k<9J*?bf#TzdX7c{m0Jyxlyd2K0Lh!rvsgFz`TD4ww~dlidvoPL9yv zFv{RIa|$#Aj4lLjRO-m5vvaE3-odVX9E={yVJA(7ulisLGxv{MO3hP9<)O^@k_Iy$byVtT>b$3PvmUmUN|P zC86aQWoq8Oxo|hE{G9zfI1lC&qF;;UscaWSszkPa)F?%4x$!zyVG*wGi352DbH&^) z?TajQDF}BbQ;nQX3nyr_2lvONPqa_)6grzWqCSKkV-p5#6@#cvaJ(kHLw}SdouKmV zFWLuZHHW_?naXH>d$;=mWrsU+G$@aTGU^n4vQN7`uX`a_aw@d3|2KfTZ~K4Pc?Pz z-*ofdfc9@^9aeKgj+Ilru0lH|U8omtb`;Ad1U$ruL0%5eAE%dfnHHDb%2Gv&%6+t% za&V#g!~t-mJ(#29N%KeE%I*HT?-fgqC*PBT1jc!8c9nw?m=|j!4c9ocqPJq&@|8!- z`151O+{HdW<=uTXho#fV$)E_vqDTbE&UYLsdVi!TGLMXT*c`ezsvq&#P}>>D+HBB= zr+yxDPQSY=Iu*P|ExINHj9pO`YR5d}C{_{3)nBw;LZ3a|J2IVfQO9vjg@ zR}l#z3$IL|4I+%G0mG*7Sy$erHbOZ*UpZP1uIyX~uzMPBFuEd#ZsI%!cNfwMC%8QZ z^n2Pd{99kKpu<=Yr^g1PRVX|gHHk}zmArRJ?k5y_LaY57JHnq>vXFvQH<5lSwoUMR z3|uF87|^)6W{n9ciFmAWiZ{S|9JFFwt6-<(iee*JCItkC1U^9;aDH$cos0shb0#D_ z_S#q8_-+XDFNb`Vft8*^9x7gg7j=y|%Htt5TAKivkXFhv3U%cpcJPBh-pOES5~O>V`?n|Ll9VN%NVJX}hup4-|H$PdQ)0Gq zTht>LTzB!r(~)}MW>+zEzdsu`b@fuB4PiJta>Q?V-4bvOIGFeG)1D?M-e1U;@i@-M zIGyjwmXY@0@4r_eUSj333Z}P!cT9~iNh29J6q z_t_-!vVm6f8QwQjf2S8)fI$ej@|!qO5rtt^|?H`AIqZWe{QabNnygv!QA4OdEytf7f_M z`QNc~KTprEIU|H$A9IC2IT7~mJbEV%d*80^j)^<3YCrd=h238)4gKDQg+HHaKi==2 zgx!~B9zKBq?;BSa8v)PucX@LDBe)C6!mm@cpEq|NpZlLL6ElYIe>1PQH+SzEdUbpb z4&6p113s2eaz76@u6sU8YyBTgiC^2YG+y>o-Y6xeWt8zhJ*j>;yb~ zMd4qjf9bK8-aYZl${>;Po+c<|}0HhW@Wx z+aHf#5Q;*2KhHXA+sC+p!Ywm~{_k5)!f%(kz3pe%pD)j!&qrU6;jQ8Ob&cC+VV;9y z?vD|v`?u*G?|SUlnV$EDl^x%QN<;t7-;b4s-M#m1d#NLx2KwHJllW_oJnvF1g44Yz z_Fr$d;S&4v<2>N)Q4`qflkNAkbbg-+jBOPD81DTyRZDF7c`);_N8S52mV2P;aN4ug z^4fFlbK&Kcs?VJ8#`3Ft&q~G9 zRMJxtZn>BdOr9HPdX!)iB}Ake#@(zW&%=|8sbf%T6IN?f@bFhd(bREz~hfl_(3F?lbn?9#fYLK7>x@3f8r{ z+m;&w2?9Wyn~jm?=B$ZDU*|GfCbuHN()!q3Ah5NZ*CASA<)SF-so(OR*JdUoxTgPg zsIRC1?%d6jC0$8D=?|JfkT&t^3s*R?&&kBA!3&mce(JUjy`R$N)hY)Vr+uMA6@PV3 zQKzjBfJp6KJY=WDp>b2_p);kdNAH*2CKJrFkd#AJZ`QfOgxdn)rFWTN2^D+cY1)uo> z%sXp4U_ZH)ZBN)QXuF7Z;p%GGBfXWY+4Lt-%B9}p^7^W0-Kx7?(PG8$SPi#g_9FBh zhV9{rZIeoCnlXh^ruIheuZU$(NkZpwypLl0h0~1n&g}`3%HqH6a>9z3Vx$>oVd7>V zTt*bXu@g0Xoe32L`ztR6r=KytIr|HvMAV=w-}kld(uagXG7hfmn;1?`&cFIDkIp?0 zfuPW+*NPMXC|QBb#)|QW@F16qEWq@5D?jzAAz5E(Yg^v)-A|znYwIROftapb6I2vy z_RY|EEpjr$pKVNktF>kCB70*QVmU1q#88NF-eg-ZA9_2Rb>*GgG@!*^;|J<&;j-kU zMz86Fg1|l>T3p9lm+9Q8e81q#je3sa&r2iTa=Jt_{jg@5h4lHfUb25ta)p;D4pa>di#PP#gZy?nSYr}DY=<)yKAAEp zbXF|rPw0?#Hk;HPzCf>(O>0xA$_8*Rb@$|)J-o!j8Anwj6{~=TmM72MwnSE-jz9~Y zgp4;_8C2DASgIl$tpFv(%g76J)k@5tM5-qJ!OgOz$bQmHKnt&rZ40k#)0_!~482a1 z_dnB=KKFACj1@r4;Z~qNdx4517Qn>=$%aUvvnn;Gxa2_?*}O0&m_-SnUrkkxU-W0* zk6&Q8#(3=vq%(eqf>Ggx)Ygu+XDyAqoF=4b_eMI+a8xJ&F4_!2f zLNAxJZV9#?E>9{6UVNF|%x`N&ju%<%!Y z$~xaBEX8jw*9mhJ78@99?GV%rORf_RSDHV0>~OTV{d^OCEG-JiA!}c_`!!*m^!fkn z1gYCA0Ew2z(FggGLi)5%&zXwt_3hwlG?FN08ku{#G!rYq=VQMzwZq;0#RkyX_J(}D z+?kg}^Pk={?aMWR%mAMsm9C3MwauIp=%^i%TlW6e0;kAq#0=(?;FG4ZZ`Y88d_Mgi z_6)cN>^?g-T9lKnjk*D*znXV48Cw9RMv`*lvcC_?A6ngLHF+WNzLZYvtAa*jjaLsk z>=DBbn~yZ0!*l3dgmu6r9U>OvHSVUjtZsmhFh$V^Ulk!{57v5lUYAME+8(6&A?@5p ziZ#iEV(a;nY{rD_EZgL$?UwVGyn{1w+yV2bYU*L7u5V-1xo7vUm2+;aqxF0__CraX*79fWg5HB=i;0>sWX;_&gSE>=TN0ME1P8f^+R6aA= z%K03Q|3+Xgw<*5RVX*ZdKSklDW2ZDFn&3V(EyAvYkb2A;M*;AFS<`!hp*J&#f8w@w z*pgrzg}E}lnkEt_m`4|sVPNd+OF!ao7{>kLJcvk0tTIWu8? zWs3b*DcyhO^AI+f!*7)4;}R@>S!T|t*y5Y!tn5kJ(Z*J6{hx>h=lBk(+`vefRw^$w z!1z8zlx@#M)KvxyE~7W%o(jJcB>_xKZnUDHM55vf-W*k;Bw6M0<+8ty8Mc^w16^dG!g_tMA|Z@A zdD#Hin;8ysCyRhjxOzZCE>=|0NT?ho6b&;acgXcvV}IG{Bbe`}i(e{-W%M>i+xQAN zDL}t$#JYjv@*AFAmBY}eB$7A5^`#7E$a=>S5rXil z8K=tGRV&m~^^_c`eO1lmkIdahL+1~d!8)OOyQmoWxoSAW?t2)~1$&nf5Gd~(8SO$9 zwUxv+oe>&sEwNAY0F8!mY>KfCB8@akj(fQniu&6xzm-RZycKMRDD2Y>t> zpRGFcmyJG4Z7ly*vqYbrzcK5b8o#i3`rVpvNI;Rz31vWd9SM(B01nWVYRsWv={+jC z^~zOm3IZ}5IPp@kny5Z}@MWh2_40ViUE;2;A)`T_q- zSxhg)a6k{lHy8_=IY>x6rm%0VA0-@%ccbP9ju+e$4bh~u)ArW;`_^&^lyXQ)p5M9) zj&Gt@$lf*r63(1b*xQB8|C%mD-zit8sxX4`*)A)w>xxqC_Jf6YJ+m3)Y(LwDZV(n| zF?2Twaml>t$gQ0b%AS?d3Zd?~pa}9cV!(qQc(rZ_+Tq4=#N}*0Npj)wN2SXUP`6q1P*Ib zB;t@v84~T(@scyoPc2_G&~mp`@k4yye0hV^y?tjZCmsMnnwgi96q1INkfQSvZ+`>X zWb=`Z`FhD>Rw{l=A=G8NMtXgOEuP{y0lq=BNDrDmuSSO1go_Z1^`_~LVC0-JSFGvL zvvdW!A3Vj7>SWQe%+N-3#FIWjqZ%D#;N{||q_ZlDbV{Kx%G4-o;n0ndHaotI=Z`Uu z69-j5rI@So!&f9jl`qA0RbZ$-D?3{cbQ$i-tCT0X99gAt>42M{dVgWCRm4RtrP}D3 zkW3LZV#C-Kl9m^)=Pp>2U?F(z5knE4av7DKrARfwQibiFA189$Qy`9_ z^LNv9=4(-E+420LyR(*<3=Y81StNV%`4w^#)?7#AK;clZde<iHB6(^o`31DZ7_Zwf>W12j+KZUwzWAMsPOX+Z;U5d9_?*`tObd_TS zl+<&IQoh$&Y-v)UT<;u{jY`_vO$k^xJ}l&2Rz@Xuu94a9TvR0|HXcdtGNru714**A zuV1@SSsO81hr4LSs5QtX79U=VHYNUmOJBF7zNr0jv(aX3fkSlVfHIE0Q)$OwAS&u@ zdOy~$DLar!eaiUxSrr4_>%xM`VFwUetl+a zK16srSAw1BdO~EoF4TgVtZuz`B@Six<9L4TZr?{3X@C~PD=)oMKaLGGI3}jsG?}lz z^!wrfGOSnjS;hf~(fht?YZe}^f4>Q6h(+F;g-ZQQ+L^G0Rxhc|C=pxi-vxKa>)IXU zJ388Vh@Y7qjg_=Uau)D0Um)mGXj21B8)Ku*g`N}DoQke)FaL8qPG+S4r) zVn;kwvdFnY=R$a*8G1_(S6ebTEN1mN)@h1-Uny5GV{0U@BEvt2t(u_Zz!7VA_%M-J zA&MB0>0Oe2hgukt^d+1Yq>JDxJcv4P_lx~=G_?M1zL_$@#cK+lNS$w(#@c@(C{mT& z@n$_a^@!|6y8)&)`JuM)usBKFd_LS_A}AtwS>Jh`uBoF`edxTAmfv6iDJMGcu2#ltrsi_f4WdIy9jvyKKH$IJXZ+Tb&G)K)glMeAO4ukfT>=h4w^ zkm7G)2pDCFubmHF1dEVKv_*%hiHV~O{?fc_s&}K@Elxv4{W030H5bP~MWsVk-?o&*iHC*C~0^`+%Pw;#;Vq9B!Of} z*gKjIc?t4T^5mbL38UGl7Z}>rXs#&Y5WdBceAf?%rx?mu-G023&7T5%w%R}AHnrXG zN-OCgr~`ecrj0-@#JS>- zNxg>r{?hT$6}G1nYf{Qy)O`C{AHsimF;)Q5vGJ&0AjX2Iv9Bm9-g>yVyWbSk$kCU- zYYBeydEGZW-tx43j_Xq2Ld=g;xDm>Ab|U8qx%;uzRFkB?+WpDQALD^#p%_T86y62D z*c*5yXjM#b@i`%y_3t=fUyE8BeR=fPmBe={81egL(zOGRWue@Jv~1T4oULz6$Q@Oj zbll~eB70;xC@8YKaulm#X@2Ga9DWuNpE1%gq@nPsr3&=pjH-gU)3Hj{J z_Gb~i5B#na-E?pYMp+?BicUwHGW(rE>Ish&WkWNgSo~K(gyKxM;&5x%y3Tow&X|uu zFX7;W1tpe=(VZt@DH%$ekZ6ITEwnazeMkma?ViyQXMqj^e;cv8_kkL7su2~+yqKPI z=Dwm9h^MGkjvmzVhgpxd4BeErcI=Gk-IXKvkC^9HgXU$sFTRRyX?0D z@sc#|$S~-@bjG;Uw4qRXTp(vC?H7wbU3wn))%aZbf=YW~FPMqV>|Yv{c%28vTsA)% zu>s7k{pa3{DpHrSG3uN)BME@|qgWolsgd;vB>9@aJsBKYp=UHiahE7E};k8*LZ~uMUw6QpqN3%lqgI zdHVe!+aSchyS6}BL*WZ=!tVv{MYyh(tlNIla0}k8QaU>ie`c7Q1nq_q;wQ5YxE}pvIY2&Wvft+?^S+RU{r4FNP z>6V_-(RvLz0jpi>|3>8FkGzqOoye|XFL|5vMkg@mQ*Jj{{re2i6kJf>523QZ#0wAg!4_CprVS5nZ%sk@|kw>QYw_ z@zH?cYae=t*`<#7xGVozZNJ%35ciSJhy0joRVf;69$gbpxxCe&IE6k{G$CGZ#L$cC z?vAos!Ye07orM%Jpr*&00GonPA8yFZDh0n@yA#6Q5Pc(`?>T-72KG&nJm>+5k-gz( zRjz)-=S+e z`hz`DjwLy;XXEl>w9h4tI#zZbAeMc*)d-vQhrvRS<=RKI82fp+B!j~-#>$vk zE#yEyzmioA#lZS(M{ivM09j>j7$#$pEB095rG8cGi&!98p?uM+V{(=TPCe78;dU_Y zJkp&0XuB+Eer|kDt!sKfS}GZoQRaT@yTBMu-4lNT#$*FsA|D&av#Y(9$nIdod!B|s z0|1OO9)=5-cwP@dq(qq2rH>g(AGT&9Ps}|~n%&VJk)Wf{PMs9c zNL9+}F_e}xh1I^UyeGAt$O*@N;0?FLv?SDEr+tZj269+@k>8=?jgTUYvfBiuc2G{| zDUkefLS0Rs1$~lJZ&u)TVW{NS_st;D8z*ufHGPs?cYw#c%T^S0Adt59xmdYCq-Gl{ zXAgz?3#58(5J_aesj8A2y8h#$V=&y;Se!|{WU4j zh+tsh`aroZy#983!Ds_prbMF4OQ1cbq`)2Sl5nb|I>Q^-im@IyDL&7X{72o+OPZ#k zk;rc0#kRAhV!HI>IdLT`YcaT!#a;!~kQ#{wD0gg*A3xBkD`YvM*MvHtos4OPX4KR} zAy0cPA-6u^zI%XrorN{#2IC{nGraNK#`^e*Bp_If9-f`TJoB;ohnkJ+n>uqCX6yOf z_a;(sz5aeq5^*KTcMY{F!r*ya0Yhk3oWv@`gt83Okn6>D>06{6HduKEOG;L%UxvC&^ds0hAddph?=O$y z^wK#cR0%i1GU?oc$a?boN2EK!Hj)tyCco6_t%K~&7{pSaj&a3#y}Kh9wX~%cpWN@} zFB;+V$xn~XYqJzrEV~=ieZn9L#!+{7*Ti5QCpsSW5N^ycPvGX{ zQ^5?8s5Sk=QdB$0ZDDp%%W#6JVeByJKcN6~QqNy#FM))LaUgBh?AY1EVa7DZ$rd;_;TgH59XWH!+KaxMNyKziFpoOkenF#_2PA z4bvdPz`B=0P=JoU>P${|4DXDJujsD-BKoo1&?v1#eEEP80#Z>lhuxI8{8rCWvjynO8lyyZcV9UTPRxi;gj2XLLM%7 zyckxJID{$jIT~UF0>O;==ieL1!FY#vbcsvBKokicB^RoDgT>p;OsL_*4;-8#bo$y8(e%lP7id{$8~u zxCrH}D9&xQ(wgEjSlNX!jgQ}O{c*&MGfTYF^Hw#FPqbrMnkSE^jNGTV`$3yKBJrh= zOJ$}2+C5U9`9%n?2AK%R?O_^{eGx>YyL-1Tlz-9Fx$@;@~3yN9ng_88E<2-#ILb;Q*M=*}0 z8rqG3yjW?i09j8hVUshG$amI2D84!OIPfb~UU05b#CSx;=i<2QZ~6v7Q74jp-%CT{ zNZ%&KNrN9P%}&te%f$8>)Fk+##n3Zt046DP4q*0Shy_Br*izX{H_Tms zTNoo}z?AT02tsD4ipkJde*^f2nJ?!D7E-{-8y5=VFFeco{AGWkoBftstyWW#_I$}?M1o3-`X&&w<~wxyVE$@OUt6@vzS6;ty)OQZ@bI$qyTsuG*G}I4diDbkYx9eP zHuFxn0B}e1kk~U*j26GNIS_Vr@}BQ}Ki&T~!9)E#87xH#t3O0_nvR)u7qEr|O`wQ7G8b;GP`Anwn}`ZvzKWbfSKuiys0-cBUj5%YEuP?R7D+^H&ql zDFvqO-mtQPWor!M-i-fY!W)Wfd^!6v@^m(FpB_%CV&Np%fn|NIehMJn#TPC_pTV;i zT=2psrk2Sl{Re!CmTghkVJ7N>)&gDoUUpny_=VA?HS?ou(^qq+j|_{&Wog1FOHs;D z+kay)3s9??xFDLgDliCD9mi62fUty)T_*}>;N-4EoHXZJksH;$mkw%KR5$fspb2C{ zXya_@aHnCa2oX<0<20ohXZvT! zM#Z0h75a}%S>652%iF6uLXh}<)t(&}xyinm9>26AQVIXN_-qFzM$!x4DvLoXHTww69=&m>+HOX0XbEo4 z4+xcWpS4mmOmlKZFe`QxBT_IctW7mVg)KMwD$i4SwpDTwNH%R6)1_NlI9+8S3rR?i zOx$c=k|41}KK1ca0$Ru=KwYJ@U}V#t6xwoHjv0K@f%CyXZ+OW1PhBmNWC|5i#$0GKZnvW{y1MrxW@Oko6?Bned>v@oa zIO@ySARHf5Xk+*qqxzTHP&eVn##5~DvG3mpc!-`r1c4|_Co?WavZ1>H@BXddoKW?- zIw;;z)$)x_3R--yBd5mB2Zgkqx&1k)I7D=vMFTA|_P{<6^|yg(^TZ8GDsITu>fszf zi^iZD({-{BtXZcFro6LqX^5>v)&ap^U&hZzh89n#A+MoEmw#oZ9N?1_6^LXZe=_4! ztj1!V`3xCLlkkYP%<7LVV7XK(aEsK9xHGCkExf^ayVYYIE|F>?to_r;QN24AJFrNF{3Few2LIK&3!JO4$(mtpztMnFuz3gr=DF{&)|c9 z(B^>rALZnEw=9BJ7A^ZnePkSvbk*hwze9FiS648pu3d^oy9~Dx5W~zNA@P3nN02kh zS!c_;KgYI&gj{-*j_K`ycP<%5qO!O2G5YxuligIvtY4C8po*+-^ zB(|lz%wwbBsi^%HNd2^n8VFBo)2y?&t4XxbyK-OwR)qnptK6SeE0c%29v*Y*Ch`pj z0EpM)u3)<2`HUi8ZpSSP2XA8-RUA_OG%iC&31eYq&LgTs5~mmYxyE8pVJ#R#19`^% zNB}n_Rgvf@-CGT^O>OGf4yK0KoAqAA!FPa`wLlSByK_u^30yE2bq^qKgUKj z&mZ%k7HWjjo@J5~^WF=mTCnI4G^aB$Qjw=rnz0#RhUetsA#3T?1h=Y9cEj3HC}8qA zesBOmjv^55gZuHgY4CT9@zHpoLymf8cRR{g`FV-P@1F)3v93!}2d&o+zQe$s&D$|E z$8#?Fdh;|8>_Ml6ngx`|J0ljdQ&BnPMgi!%XMBkp0f%BBg~H7JA=)!TRVbR`;)CM= zieul_rDb#&!raXN*;C(-uqsN^g&TSPGb1I2P4ah_`>*|bqE z2V5SE9|E?xYob}OveZUm>LKzGLr(eP{M`$Z>9@gkbl|IrFpU>W zt&{~ zaba1rol2?#+a&AuBi{Y#Sjlak(S~QsAM7h=V`eTM@(UP&N%_`r)8wV=C_G?VfNR0QOGEn}-nOImDmvbWN(?s&J-qhX{cd!YiAk7>d621Yjn2bR z*lol;2WsmF zc`tZ?DQAeTiTVXrEldsjUZBHyqF)i8C~|5pMhNc;w!B)z}Ikw{wSlvlqmZV zQ#uX~+6LYhozFF2d{1N6Hil@Kfp{AH0}bwpx8=FkgRWR9QPShj;IA$#$hGTujT(kQ zL`cMxhDsXah$gxP$<-mA)-pwtzvu#4niu?5U7L+!m^o041X>EbQ{Z9PzXnetO>lWT z?5x!Ud&?Rz9D^gHmA99D8n?IkLJ4LJ3kF$Pd$eJ4EHk{`B{yzNlD*cK+RQvwK_^mnCL&msglkx?Tw*W!R4;Ll?y#a%E)Uw>_n%VTWA>kp-)HT!r5$|E?iS-U04C z%0!MO+1K&ttiB?+uG*W`@0jzz)brgT_H?bfC0EemnS(U$6u*qx)6b7 zvmW@9gEAxkH)OFq(TrD+;L{)F@&kB1<0TWDB~V3y@%tK`(?ld=x7!P=Vd+BZ8|T?7 z({Vm{McVZ6Yxylj*_Vxy3rhFlt$ZUJ`nIGzI?r_7tXn)JH?FWVk1-rFAItm=5fX+;OK!J?Wz9rvks5pS6lZYE5QG!A>vLoGy|)dU1`$r|zps%g2FN2@;FgVC znZxi+XRZrr$G1R~8b`tHV%R;N#JuFh`l{+2(u(4r>?%+e7P4wFR=_S+J)4NkpD(tJ zLwt&9Wro0#3TKQW>;0uRGaKL?+qi+!Et+{*tV2Q(LH{uvA#T^m3<+6O5xVG8ruNdb z=BSiVb_x;woPK|p$l|Rj>;B=+tyGO0-a1@tpcrLhgzVZupA}dJ>_)jbG8+oNg@;Q? z*spICo#TFtOBD1!RJ0^cWQY_IH{bqL&xU2Tpn1tm%|4vyR9VH@DH7eWj>VZsjzzh2 zxOac}Izv@hASQAZyhX+H)ZCDLzB&g-wTiVvSeb9;@opIf6WUkOkAR)zXHH|Yqs+-C zA8zy+kD+K{Sm5L(p#dY*zX?oY+|nj!ko*lYxwLp2y#oQ1u=ZTn5jqiaKm>G_l|kud z{1|Mx!0aorz6#HuC+94vml#*})ptMGrxju+vmaqaOY9vEebvy+!zx5k`sVRQwCBZ1 zM~c_~j>$c|bao}1P_ZCVl$Mk55bDvJtAd}R{Io$c)u%HDp^YEA>pEFXt638L3mDUN_=TX;v+TP^DyGuGT7aC7DR8uPX?Kx z$~LJ4`sax?)=x<@1hIX{R|F}4SM%+b@~FSY2Q(ocw0pQ%i@?$(%Bo_l20A^i+ekF? zT{DAsrZxS@t1#H;4iU4C!2E^(hj)IzhsuntAFEsQ&Nd==%lLbcMbGd{Uk zovtu0#gblC2lMFSzRX5je3_EtFNhlJ3k>J!8Eb#k{r*$$?cX;q{eMqOQw?FAjc#d| z!tlnD5}w^G^nD=Qd2!G2Ii{`u^+(5e5aR6O0I+b#(Tk4p&EGElJnI(2vI{aXMDIm< zzvHBB{T==iQTu?Bmvv66%EZ(XiRVmF-^LvA=sbli`!-j)s-7=n*(*#Je;sdg8Tr6c zdEyYxafXV82Lc)G!E_9l#M^D9l}6r)%0LLZN8M()Rod(7#nHwtO(7GgMPM#&}+A~Q?Li<*09_EW{>3Ul~hned3* zT$6EoUm$%83B{vFx{o$BFMWp}>(SJPuvCky#{F(Z?vScS=V>S_5pq-x>;|&wZJDe@ z@$UKYN_zya#UKA-EzJ};(_+WkXb*(7>(qAYi5tMh>eFw0+;s0APDXc-djVI-FW!bS zTxZ~-q-Z}8N!|TS9Ee_4T^%h4t?kdYy11p2Vh{-dIk>SbBxE|YZNp!yeqpF71I`^d zsP>JSOISlOEaKanaDV)wSl4zq(>Yimi7`rOmZK@kehmBnOa5;07=?kO4p&NV5>ko> zOUwoi>a$R^iBz|u%#+&-;drJ|jfB-Rl)romB>4Ua<*<|_1cNUTm>uF5`ID+(NJS@M zlMjZ7UG4!X%e)wsf(EF@g<#E+|9j8~!qr0@N=S}U(PQf0^9de!R+=FaDf39jtoFrWG}6>i+Cj4ht3GPEa`j$9Bg;eR3R8`?ABqGe;-wr%T; zZQHipv2Ev#ZCf2D9ox38?(^O09h}Me345^W*;Tbx6~MImCnhpeEcT9PRr$k#8s&N1 zCSd>Q+X`!ympoVjEZ0dpX(1Nm6TO?a6w=*aUq!QKde*6eAUg_?9jC8!K9CKodRng+ z$Ayw3^1`@UV)g~HH|NCb!;=?vVH^>v za`f8kp?^+OAbranri zZiV9C#$9tF?b|4Cz@X{ z+K;lM-Zk~~`4Qf;t|4Qu{M}djM;O*r#V9kaU_ZU(pDhZ;&4``+Kb*&h9XNYP$(7r= zFED>8JwJjRE4|@?A*mYB{{&Y_s{t6nU133N-&@}x8(yM57o?SuVC=@_3aXL)cb2$3 z|0qLkY>v&*YZ4e)%7h@oX_{*3V*GZ?TsmIIf&H z*v}~=lqm|E_46ETSB$JoQVqL=H65wPx5chTxqO#L1Srt{RENxmBw#NvP3mfaK|0Ep zj{K)5myzt6Fq%$vK#n5UecdOz^64#Jy}*4EStt~vP(>g>MjXN3C^!(fju;ei`wj-W z{z+e*;%|K~l;nb5DSg=D*r}CI7vKM`!~dqS21wdQGp5W*q94u-2Xqe6XboC#>AQC( z(ifM5^IM80Ac{`c3YuUKjw7<73Q=viTF(ot&X1=!t~b*+cO1BqCaD*w@(S%Y+s*fB znL)$UM2J^S$|Mp{Ub|OAEfU4SD@!7L+Q(-t!Cexr!)otCZP7ADSIrFkhf1CUOC83L z%Rx(Hc|v%KPT0urG8)=#SVJ2XK@b=sVYOQ<4@R)&_w3JREN*vX*Mlfq&7t90TcMm} zwq4j>`qL!K^aa4PtEdQAAxa3hQ9=p zGo@X6FR6=3@zc63zW}u|#EZ;b*G`Qr$Dc{H8_F8WXZ59|(6+QP{M4Pd$Q(kTobodr zY@PYOBVKYVg7Q`3XJJC8sahd{B$!FM_pmTexb|t!7uC@8Dmbf44^aROmahe+a7?&R*5uX8j)99%cE!YGEE+6Bo$p~X{>|v_ z%{F2K4A||WCbqJwX?8s=&~t>7h`l@Y`pcBItO>I>;f}Ft<+y%{GAIJYCmoEk=C<;Ac{zXH$`8%Z@?!pspFeM0>pr#*b(6y}$% zDNMa>@2gqg`pfdp5{2Clq=DwNnEK`LJ+jAm9%C^iAhsJEPT~6$OiYO5VBHCKC>X8y zKk;6%8ofAxt80Z5lg#^X45v_Jb=VPE&rOOKlF(&fNbCYF2wnL=ifV zZFlb?!LsJ?+tisCATjTBG88FYu;r111T`%b2@`w2(|>7S)^vB9o2m&KMj=EQ5&PhY zvn?4*PrqpLDSfdG^5{jz1hPIAMMVrgFQfw8*UmI1zsoU|Kn+khJzOsleQ}qrAkiGU zJ-!3kKnV0CYV&=K7%NfYVLX{rbISt^de*{lnMt1K-u>d9FH+UiqN(>m&*q?kISX=J z4UMtNBA}8-DRdnrYhF6%0vJbIr?pjdXh7ej@j`wslBvgrZG&t$ywI=~ZKjF+V(oeH z88y8kbCzheElDwdReM`l@4P2hC+BKRqP^K7XEb#+R%MV*lN`1{!-^+yjh#*0I?}6x za-PN8K^gS1RjuS|Hc{GkjWl+E&y6ACL!pbnRvrQ2KEYIIqxvEwk?1l=1j4hHz%~ha z2T{4$;2ae^erBz0&K+I4()?pg$N~H!mQg~Pv>~V+F#7fwkly~anYFzQ4+KD>r&kiF z(SUpuJxzsb>QZPeZq<78A0}`t=T8PigAEv%6o#$*h0#!fs~;U!<|ou`$OMroX&|Ih z@fr`OUMb_fwJ@D$vFJ{Kt0%`^&3z9M3QICS-m-S5DJ4A+MM5V#&>QP!3<+G$Ukxh7 z)}mR15o?}zN!f9px`##6Yz75uk=0m)a&wN&@ZL07wnE@fSSw*4L9dRDnAWSR*u0h! zd~LhG!yH;t9O<)3wH6cAjzx;H+-qG(NdY5PMK^#iU(&(X=*9%c!25UcI`CWWK#{%X zersoIiPx5sYkDHL3$#hC%Ef1&Xge}~v!GvAnOKWK@5*34CS~l@kF?Hz#$*@9cwkNq~H=l|Bh)dzf6npb4he&;lb%^s)Y3xn&#+YFff-3?r<(RekNQ5ge8w^ozp8 zoxO_XH5!UmAIN)6Ide`bTZUlT1?{&6{9-TAjDEwyJ$FXKm)in*vX%K={kpFW=>sos zDN9$9phZoRzH9C$QgIhJsWkIw4XX~b2eW8|FX+`d99hQUCyJY%IP%bFtnjY?W~Pfm zjRAp0G`WzLMP$!JVivQ{tiiSZTAQ`+Al@5cm z)-wnI2hL4#TwIQfLBSd<~ob;~&T zOCq)2&c1%?5^)=ef_=Yr?i!R8KFE6uAAE04_Mg`Y#RL5XI#G_O1I)?pA8Yyp_#=iS zGF{UX`U2D6VcuL7ISR3Bvq(pDFw-SV&l#6$4Rm{^+3!eHuv!Jq!ZV)#X}(^(=WTre zi|dc<6eU|a5rwByIW7`JTpJjYUosNVYSF2aP+kPcBlB6D)$u~6%*CM*)?_%PABcdP z5|P8qsu)Q>U{Ndp2`z2lNuh+v8Jty81^|=V)}hC{Dj$k=$#6r(_;UG))?@O5v^j|I z<)|pFV$C#c*;)!iO@dG1um5;dKoHRNo&0{=O?Z**O2&17$BNVJWvMXemx5T604xyJPd)8!+;|e3u2N;)POu%4@XRm3FQ;f7H;s)(fL z_2bq&bl4LOta#m$yqDsZoKQD&%JPAwAun3sl-(j$Ai|KRJH>H#X)SHW8|S}WHp@EM zf5^V8KXWZRa$q$;h@ben&wk0DE6T_kqY8N*6*+QRShc*J8=S!L)P+WHc6g!+_IiP{ z8sS%IE;8`p9KBj#lEQ|-(@vqXa-SPErszcfS-oZW>Th%;pwvR=gK_9LiB)H~$1kP7j9h-Y=YJ&*Ar2ng9-{6wZ_5MG4c44+hqte#I3ur{Fg3-5_xL1TlsZ z*c}Phn+mvKzL%AfhG^R=cd3xh|2lqtpDaO#)`|bUb9xRq= z#6?W$rW+nlG^zdq36uNBa)mG})NICiI@rlJnW0a&96vSdEG?|2h}g6AB2A)Cwz0z4 z?K+V0mQaz-fS3qP)t`N40xPG;0pm{D{nf_mC4_J+qx(OmEbi_ArCB8f$PYiT;)249K_bM*>_oe@;H=pPTlZDHOH)bY9& zZ~9ms)`Q+YhT+^hctpY-bc8V4gW%LF$QKd7yh>S{jt}qXG^gVU)5!$4XNy5Ok-Zo! z3IPX2?!2NlyU2V;VR~vE7sJyQQBk(KIoof(Ttn*n9@aw~%p?kr&p+R93K>P*8uz4!66CXrsW^)xql`Agu-NFv$;&H_uO}|(*9SLwLcInX_)COA z4E`56h%0*MEbx2r&y(&Qeq1vzaNHdc5ZG9;*B3sT>_}R){Hx4>;tdu=1oIKzD9yZ! z@(uGYfRYdNP&1qYNhi5@6XY~>mq(n6v1dlpa=P`bWG^a_G^2tkI%vXne)f6re{F-T z7bcr2E?6EL!v2phFr6tnubV+YkUldDe#NMq@tNUHSIyRGUOsj;aNFWvbifikPG^`p zjQ0v=YFsj(HzKk#k#u`Dj#0=McT-65QqTnot2~crNcFQV-azkuoaAyxT?0ED#3v2` zy@$c}9#q5xO@TRjvWPf|^yr|_BKrFxf!b*E)_ST?q@d*@EGbs?#eP z2|_lnY=pe6rD{%k##&O5sICZ=xrr===4ax7N6Z-yBpp1?0v}j_3Sb)w=OjwK8>@K6 zxM^2WtM+eE4vJ7vUO`%QoULJ0hRNb#X?HodQo!t<8t4-)W(9ffD=b(r?#A7716$0RCNjpj z)BK0GtB?+(kG315xVIO=+_&8MD8j-)V&| z)4kO7QfJ+bPN17T=tfwXKQ1L4G&j!{plCQqgcGEzU?9zfp{V0iswZ1ior0>)= z(D>Iu*W;&TWeAFI;^iJQtZK}!SJZU8$(4vUK~ia!bLk6d&wBTF7YSjua`or=l;lr^ z$$WnGGw0$Zg!!r*RfD7GTs#Gvs5wgS(wZ&l#dP!}wGS3t5F|tc+W8&*#C?pCajh87 znDs7qG@0i*4{D3bml>N~3r%9jrXg46!707tM{o`)C*^M04gCT+awodW6i=n1UfL z0SW#|_IvoEV9>Y=j*~N0kR({R1HU)55Qts;e@&+C#}2adS;0+VzCI8L7oAehf!t!g zwBIX8cD$`ny9HbCn@HXIe`UBiT;^X8o=*e_2b&O2{X&~3B#?Y#PraBfq(s^Yb1_9{ zuD(}^SM+p0yq1Xm039g16*UHdQxC5fzPkMubER2JiRx`Pa|k%B9>uh~g`TG`C`T7i zNmpc7$s>r~hRIrBUzju{qT7tP9~#j-WH)Ps_6f#+g?HYx|HprKuaVF+== zTQ)iz+P+r91XHusn(5Jyg;mUe()E(d_(y2ZW&=rHg7Y$4;50E#>R0mi2rMgE(ESBu z4?^EkBa<- z5T+~SB&ssRbJHFW86;@bMCmmhu3tLDdq(w(ewRzcAqa*zDendYW1(Jsu|wi{``AZ) zSbAsgfH!NX#xjA)*Y=uRl@7$PX$~uuozB}-jHe8YwrP75OX9TEp?K~xrg#B^5&)N1 zOd%Dc^C&{K9U;U|e8Rsnni8GM3q|av}7`iL66Qu zAZ9<1V$0P%uTeSFA*#p*o_mbXLe+vr2r*lpN~#_c6K=)8loqC*3E$%i>U_rfkLh1} z2v1V=XDaYBAL40hprQDmtnbb9DUO5g?1*mTA7LEjqYE`RyBhcVU((|_&9I8ZPHGe! zan7Izrj*xzWK>Q;=6I+y-U6k%_D;M} z`nhWS?vFLNg~qLsV67$!#GlQCZN2||UGjw9k-4}yolfml`<+s4u8(AX{I?sGIUd5~ zS_2sch0cf-O<)mYNP{-=92;m1k)Ri_)g)Ax@Pc^C*p7fA(~u^55Ww3xX)M7XXbSGA ztVPM^EB;~TS`MieX^;cJqNK@D1H>ghS4Y72&5ClHjyp0NFwg65h$H}Ett?^ypJ(1m6Hs_c^XSwUE0(=lq z03SyjwQgr;bX&>@@lH?&7SyB+*9^?=s7wekG3|ey7GyqI$9=zZe$b}SIVxrjoxY#0 zQ{njP)LbZ08x|Q+sn+9_vC~lIbYoo-A*DEK>mI1ZnA)MxIi@-QE*+)B?Is59;%&+B zL7K82tA*pqf=kL?2nhP8e*qw0&i*sJ(keia9?)LLq!$S6e2jMSMHUjv3NFJ9Jn}zL zA2@EypBv!2Ks0E->%>!un<&3*EiWqu?c0Cq3MOi{xCE|q{3_+I--cgXMjY-)nxMO# ztdnO?5?2W*aA^4c6P;$fJlSK+lOo?@SE0a*}&d&M?u17Xn%ytsr^+D z9VN8l?p+nb>Z8SRok|^qL5M#6CLWoDYk(y}Eb*~*Y0*`1kLMNM>;~OBDLq&{Q_}*| zpdFt00lkA~$Xw)-f&;7SdDX&QP$KZ}bq%N$NyAfUv8#UC!x026!KO3@%N_*{06R1% zCJ%2zHwol}GXl8BhzF>uSSf=w*r;rHMm(B5x)tw95t$ocvNy9_OpPh)MqMPT27%7g zCN?;8l&TU|Z^aPX!zxuUIi{SbJZu#>{dz}LQ!aeEoaQDcmTkys z*%o*iP_g&iea8#VtxzDEG=;4PgmX|(@zxA4H{=19cm-|u?O+E4AH^A#LpOeql4)p1 zq7;jt=s5&RTPi3tsbavTnd46DIwDhf4Rr)8Uu80{Vu&JHc@-ylas(zQAKu6e;a{4j z;sZtZtM|bNzI?K>{`Db>AMjh_x>Pj}@m<&$(<{xd3hUTlp8?-|26OQTWzv&fXEnn zZ>%=thIC4q%B57nUKe41{=9HF`NFUtwI?X3lkv3|$|XMHwdM&6VC{0RHVC!iy^pAr zCtD}4Hf%C}p0g?LYJt-_?r}n8L#N<9H1d+*ENeLiS2(#~SD58#-r&+WxHzK|{n)2( zF1PmxvDi&^M<-z!A0;-ChM!zt!OK7&VCxiFx#X8CEQktYOhxFfTOejvi}qm zAQj;s*4=T1Nl|(cQ*hIf104Xp43F=U(_ZLoM`-7Qw7e9Xi(va(UGP62Y?qubXYjct znRm%4yqA}vY*SAQMWeDoR2th}Qm6Ux^!)1+-ZSNRNvwJ%+rLqXO3?zy;=h!rUQ zHG^hxWldg)fbrcmkO;cm&aYQ`^&%Tze???9nA9l$czX(0dgqmCH!$XW>xF*329DB1R$H zow@A4+i>;r7lBl-shFPxH}O>%Gpy`CeW;-#ZGmJ%a>I93U%QR0f>ViK->V=mx&4RELVxhU@b;4?}WLYDxJ*Gqi^|MeA| zQB-FY0*jXPgBUl^GweW7R0@PG3k_WpErmnvyZ}MBJsM`{@xiPqN`JFy->7s0cS_b} zkseBr3~e=@2cv;z`eoF&Fn3paj-k$ttqkSC*9jI5jE2G;c?@*iq<+T>oP7HJ9ql@1 zDCPtgML5&O@a9N+H8yi68>@rudt47nCvwJq#dhsJ5DN}=PIt0?_4^Rx zz$1P_!sW>al+dC^KyaeozA4bfBm5!q#~hrDl&jiwNt_IjE(xkZAq`TYCA&{+^;{a; z^k5glcV!sCLM3iQDDg@1wAdVV4c>XF>?Fi-X6l4@?>W^eY5u|0@`K2`Vh0Qe8f9)! zAY+W@AqU;!${6XkI?s`iblkLg$e*W;ih!D+#0T*KYstuhmg?eiqY1s%Dc~1w9&#v` zo&vHqF@J;SHZzRffQk(>upSJZ7hb4`(JqqXdv}?K@{FhZ19|n9lPgScz;mp?Yvos{ zVE*7{r8foFmkU(}8B_j}uGnm>f&<}^ucpamJvrb={mT~%Hcr&VXlMenRWhA_ysoHi z2mhQx*}ScI*`2k6C|hA}^!y*2)a3LO8RYU+beFrRRLpmjK2#tWC~z-ZkA_i&%{!M% z%K#Vy2#3HcA3ZY<*G6IJLX|_zc&MMZ_v@SK9_?eQ%&Cs#E!qv{O(J}1y~d}aa@t1O z-+a5L;jDT16y2mFdn;keR4z!rnQ2x!+Ylqz<;SA9xI#05bPlnaz4PdKo;UMyml-kf zF~&0CNZYi@mP)WK3sl+>M5dcQ)3$n>?R)r3su+=dK_m)dxh;XU~}^y zf*$zV3^}5%TZPFPo3qP2kohrv$=|*7%sGu9kTxPG?`e-FlXBenRC?bHXo!mPb#!M} zxwLWTcJK_ttSKGcJVE5;5nyQsY0 zSy4L^lwgnxLj4-AIB*%;@#A0xnU+8>o59OB3~LZT^YE zs64@QeQFNf(9JM5&_{=MRKEy<>>VXG7Ep7@ET{sSolwK(v6asd!DxsGBsn^j_Qpnu z;vw4p4uPMp7PHe}grm&7O1&ZBfqQz_%0;hIXVP==R|a@dQBHcFE4APk+{)uNYfi0d zVfPY>I5Z`=JCVi^wHh6CQ@@dfB2tB)?};RE-gtr$y^Y~O++lSoyMB?5d#Z7Wp6+DY zWtGZq?BhA0nF~r{YCL0#tM#AA&m<~_0INA)b$J2$)K*R@ zAHS5TM7?0DUExK1`VCDwF+b$rw=5E@9FSCLax6Bd$0S7XXpfC${9>Anb7cHk@MgOd zht1%}#JZ@EfZp(CwCW>8p=QMkhL}p0@IpAcyWI=BWMr8K=6tK)4}I&0OL3*kS9|qR z#M!H8`-X4CfsPj=vngfCOnNRViWGEi!wkjomyLiGa$;rk^cw)90~XO;(5l(yO( zS80@IFgQM{7k`tDGXwB3X=a`FOEVHU#9 ziSE2w%SApn`Pl@U!D-C)wnB1>iQ1oZ(^(B?O2ROf^u0-Vd5yqeq)quG-3A0y4N zux5AT)RJ0NC@!k_-r{A#PAr8f8R{K1)6wTzS4ZB5G#3K%Vcp&EsG!o(?{rtX|BEZqk>(jSF$64 zc>s$PsfY)9HXBhKnJ&FM4_(cYbGKnrmk|x(MpA6_X=j)3LmXo@*0R4i`+NxMmha22@alM3tudUV1tN>CDvc(lYWmbIzz8sO2<@ zF*C^`iV_~j?OQX|&`xU~a3q3V5_&Sl|lzyUlL_dRq+U@!t2Rk?MFc(IWFH8JCVVZY^aF;5OpXDgw&fg22!< z3Pl%5avwLo?njIPCdFUpTgamqS1(9cNB4IZyf?=k#c zoLTeiybDUNST@{>(d3^0;!d$^NX}j`)~wz$1Ba0uSt`~=gKD-y1NKYPM;rqUQ9oX= zM_reRYzlVhsEl#EQkT6eMWrR&w|ssTy2Jf~Fxu09Y9Gp$L{w$x7Ar+AA#?0&mS{rI z%`h!qSq%4=DZ$N^2%J``cPQo>Q3=ATyIYrlXz-09P~Jj|IEF&u#m%tHBn+Q`ba1Q~ z^K-^yOE&Ylxihg5sgO*&A5A_4hT`BSJoGb!*^>Ru`^-~o=UzKj(M@R1T859p*^!-lncTL z*DlR9KM`irpQ=L!6^niL?mR3#tH3uphd31WW8d&2%_tH ze7`cDn^3&aj+ia@&8C7w+GiPZM?WwS*E55;42E%OUJNWltfzMi{&$oLn9bS%Q}yj1 z=7hnZzxDHMqYOBHg$|gYUK^Uf&3+caa0MA-q*yuZ{Zhq>0wLSfwq_J5^+Co#0ZxPN z1tP}|_00n{qFP!J1;Az?+H?}gCu(fCL0Itj?>;8?*(wrJ1C-ulAb$yVr^K1FOCbBs z<>8?bx9r$;yN>?0FJu=Wf`s8F>55L!O4CEh4ak>?iGlGJwwc{QHc@y7)kp_dfJBK1 zlAixPuhk%y_JW1=d@V266EAC{ljf%;skq29L2U08Y!|N1<=S)M8=mVWs?4w4nMQeh z6sgV^ph`m{-%2>M@eGqB!8as&#Lx9#?^@{Ko&olC7Z>5P5SCYw6FfAPaChUdzhW5{ zfkMGw&}O22t&jHzrEoOw8Nj30e_Yav0p-{N9|tmQ)GdUVy_S_{&Z&2I@rar~3V<&u zYrTCp2Api_9tq^_o1L|ul!F=pCpLr1Gfpg4RFqo_R*3AY+)J6yb*g9=TDbSUAsY@WJ77maokpEy~500T2C#@evEfNWEVFe{9T5Lj}@g(wX@vw)CRA&-CT24 zNz?S5E}aXE2WK{B28ZVbiAzys8?NGo!xP>J05_{+&Y-550|rF5k%UW{^vJ}07{1b> z@;^_#L^T8z=WY`K^|YbR1iS|oaxZO0l}ynE{z+{-^8CpKe|(1>$XdqF8fzDcC{?gU zyyFlayExdTpf`HgZ$JAj!#&stn@YhO1l5O_L`K$A$kvCJUrPF(rXm)E-SHvBuR?rM zg7SCCtdXdIwb-e(f>UzJ%JUqUzoSGIA@0e9bU6!I3IK}Q^!Oj|W29%YxkTlI=vvEd z51>&fQ~d_Q7pEgbOl?`-M(poX@PT0)uWyOhA1v3?rzmhM*y7h#DZ66e&w$hi-IlVS zVtu8A3#ve=PYmbtH=%H5OzM8PKPQVu!Gzg!UhX(X@_L?IZ=jzkPxzDen|v2BgBaho zS{HC#r^eJhm2+b^s*TY4?duE`aguv#Ik_lP?$QpCNt(GB7xeYsFe<)ZoXfU28Q%ih zKSqKp9W_}y$p4G3vmY)@2M*|i#-dYhPxcXTtg#FbN#mY#7>F{sS?r$MNx-Beozgv! z5O`$bNCrl1$yGR>Pl4@?dJWCvzZCF$^p+}ys>Zk)+{dB*zN3=0QRnl2SXG)&x{#Y{ zzxgW2+cVoRigL;F})ioNr^kN$Cjq?GcZZu^cF z8F{fTsZC_ME*~jBwO6J562HSs%lU4Y9W8SmoY{;{5&F}^3QGj;08v7W>!1jKYHu#b z|4R1N2Tk|Ns+?e{DsU`%cJB>CWiI;frx$KJp>J_I_ZU6@cEiu2lbQI0 z=e9ikQ%|o?NY^hJo{$7X)t=HHqtQJvie_O>_9+oH-1fzH@-DMI{ZOqp<9s}R3vWGm z{nc2>I6Y1SbPoJhJ^Blbj-8e=+7r`f0SnCFAM@&}*{;^W_F?ngX`{9?z<4$JC*Jpy znnt*t&*csYfo3lJa1XXw1=(cPgbx{Tta06V@DJ-4uTmW?=Kh{fDRfxe?)Y3nWxZYc8(17%pFq49n=9!X z^$IrX&VN=S*9PT~Z_>YtL!38r=86cR+wb%7mya1<0AUwG)cLxK~M>5`BuG9a7ME8_g7@HuwSDvXD+OpA@o!Ns0KSTjs`3 zbb!0kFXx2W54GYva8dcSwFJkcCB~c{PSwq^Dv-=+I+5U-Zq*OKuxdVOtLlQm=FN2P zG28%qlW^%g<-go7h7S{GmsnrEJe)fC3ANlLw8J4MBYkY6G4lY z*Dk8=>3+9)c@v+q(-|>5RGHD07y5DWHuZTI4TSqRN{~}=rKNP}1s|kp3i@FT| z_fd1@{kOf1RFVJ7vEG3GjMy-Q@BDeh>LNo?xc!f%?OEZuuztRz~l?};M=vidkG5QDU>)`If_>^rEF3O zUqEiAm*nOCQ63}|w*`0$I>Y>OhhQCfwa0cM_Y$LNB9Yhr4wyw6U)Mg(!$NHmE12Y^ zE*hK#Em9rBw%0D2or};anKdt-+wChl&R;3~A`wmdOkov$0fk4`^3CSH(ho}5<39zl zw^bp(S?vkgg+A#N-{VctN%s4|ymTegc7}i`NTSXve|}=?niRdG1KvmEwjveGoXLx5 z&PHKUv(!&N(e#$00^CSI^62*2r6JS?$IaQ|W;&1(Jq}r79BXrgDLWME zD`R0ylC~uysS_q-(a3ri!_#A`fL1jM(=Rl=Uu@En>LL^Be^et zNmU&ek5PB)d(So8j>kh2kTp_eE1W;PebL9`TI1v9WlnRN(2mM{HUQL>hYMH2W8+zv zv9oD-p*?artbdDKvX%>?GwQlK*_5^--tF0fgr`fDu1bz^oFEVADztyJj{026A%k)? z%_?3Cp|CJT#3t(?)k?i9pMOLkc#16~jkKVKi_(|mPFBW4VT!iRM({|e57U1}?S2aM z_Z6DXp+=y0J7*|d{ab?#N#+_Ii8T11dh-%8DMb}pBnaqpG>3Ql#P$>x4GJwvH-r4a zB4lh$&h$p0ipwv$mepSyO@UIujbIHE#_-T6Z4@9U-F1(b9%5#&dA*{0V{1*CSZkqR z>0_}8p1kNNu$oz^n%b1af*_4tvRcaprjAee>D zrpKp>y%2m%q4=qPB`SC=!yQ(=meQ5i4_38M)s(acb3s*}==?maDRGW?d#Q8tq8BnloNX45uJ928hSSJtl zExrX7ThA)h|8vv*@y^HbI#gnc8r>z4; z{Y6FLSBtTCOygzH8azTsnuKiyjvD6KyR{{B2^kXO?(T43 zZ+bYm$`ttFC{2Q^X!R}c3~k8xgOf$NqCL2(XC}s-^Miiv{2f4^di5WBXGRlFYv&l9 zOZn-lCfqd8U_sDIRLb;QLY*CUuq+gc?WLhG!prd1!vQNu53W5JqC8-{$^zS43!<0a zHLwDC2({ERl5=mr&D~{sJQHmUfo0S29$}$2hQ{)5hpqX7PM@GL$WPoGPd?BoQbpk2 z4mGn?a%&O<3~eFDeF+XKx3jIw%+9AH3`C-vlQPC1zE;2y$L*4>M!3_#5E-^}zD4OS1d&ur7t*FTx5# z_%bD^?u8>fak}+U?`@<8mVn7weiLh~0_tZmcWwHGAl~-XX*DG+LNdorSvE_1n51KK zZk6x+ATKsDHhgXO1)u{ba0xhj7i2!gRLa0mwW;}mp*h&>yWx`|CZQ5z#i^;Q zxlx&=c+8A9C4PbcRoXFS2{-`*4n6Uh*A{8GrEk)S_W-xvukUfCJz)kx`MRqwK2=Jy z)P;=7(BkjMvbE3T)#qo$bQIm=N~6nhIg>RRVYI_nwk?HL;q%-* zX6aX?A8y^~T0qJ%_0GFsZI#r8VCrALl%sdWHq#MhX+Pb3g6PSOda`xFY>y_nHQ5Fg zwiC-%S!c5$vTApB9wb6})s9C<|4F)PXLh4Ing0wnkuoASRYLePv6jGlVcT$4x;!pF zRqOvP`ZSeO+K}ywXJY)$1&-UKq&}hSR>0jMUs|jFoIHCl);^aiV_5cO?m2T<5M_(T zMYtUIpk>{z$==j5USD`|8y9QUS@th`LiOpMz(GDwxq6*^jU^`+k0IBLv9N0DeF$mx z?jr_UN9M`gc7$r@cKhwz+}4aCKms#eo^#z?oO8!?f`xBX+Y{@j=zA>8+zb}Jh1K=| zzr7A_F4@8Ntb$`Dt{s^>PH%|#Tr9)N_8%*~3EM?%_+%xduh%b&CLS*1Heb=0e(lop zX;|8uVhMS-vlDle@+{Qs!1CMIQSdnrJmgp^znyMH-3~`r50cj6Km$y(vcBRXRsQln zG6U^qIS2?a^wrTFMq8{J$@0EGQj{2(I(|xC;;C_W;`#A+;;9JQj2lUs;uYh+w32WX092>oz-6QoMJC=y%%uKs@ggaC3#?u?b~mN>bkd}=A+FR zC7)G+&w)(Y`yC-%6{-BFjNFizv)me^R8kYEI_uZ?AIY~_WsCc3HZ6u(^Ra2aA#Kx> zKht&Z5j!k&vQ+X9L*)k3RzGv_HArUD^})58lSw??gUlYg&vR|SD#w+weny|Bqq6?4 z6He^uedqXJMPxP5)XV#~2fcs7)H^Z}8)}Y&t+aX929bTtw0R#Hn(h&@$>&nDt&e?2 zFwj3G=Srs*Ta2WfzfX<-?5%o%fB!!Cs7?M?+vR#VYiw!#dzmA$ixg4q3AwbSG13N;MB;EFKm8200$uJQv>Dzx^? zz{YPc)2Q!mK)nlWr%ssoC-q~*cljeX$)EodsQ*tU0O+&1yNqEA`R@nnLCC-q@bt;5 zp%m_Nhu(R=Pc9M#!Y1Ow^<5I`ZO0D%RNCE=LEt_9av=yww76FY)%hv5&!A21#8 z-opsgEryX&?VFkQ{exn3J2uBQv?~{4f;E=0_1Z zH04agVELsGv+jRbd#l*Wf+jlG%*@Qp9<#^H%*^aDGxOMEW_!#`$IQ&k%*@Qp$G-DN zN|Z$RCb~)<_LlaRT3t`orRufno0&5qbz^j+eB<4qz;0Ce#D2?KK^`v1W7bXpRuwn1 zr&hk0d+``=0)y>)5(A2@j^?y&C+|t)h zIvjTL-!bgN?up@5(tgRLAwmi*EB>uF78gm%Q9>|8%4O7g|Z1hJAKK1&)63 z>I|wKA4p37Fwx-uafNLd@P1t3U=VQsIuYj4>!Vtbuj}*n@p<4NPBFE+q31NJ=emGvyhZ;J zF-)+DPoRtM+Y#ye&0f&o^Zibr=j{&Q0toofGK&{{z5{UG81#I+y}CqG}$gZjf$ zX2H1FuMuWu;_a@0?fIY1|9+kf99-leJLt6%?6lwa2mog`09F4m_PXKYdx%m;w^${< zz6bM46y2U5dq-P4U#!XRBP{`MZ+AyEhX2B78>k`9{r-CF7zwbYaTU0D01)TwV2eH; zADx|zde!UzkIpWvKXVMGv%l=Td|w{=lBO`|Y;en7s{|}p?D&73Whxp5H6b6f^UHl2 zdcIE`5+i&~)X1y;c*L1>Hp_@Uvh)(zV83a8tr-b078p36Va`olv}xw~y#oikKEo7% zi_IvYgH62YEHK)k|1k9^-D^o9An_e~jNt!RCt!j9!(Y_-RUWDGz~OjAZ^mN`!aFx; z(V#8r5l6L;i^liP&1T63|Lnaog7F8|y5+70jOc@I%1gIrwz-7P7k! zoxq0o=;!bYhkY@Sj4>*2gd}m0i3D%HkPvRgx&lZlOOaWKj!}iSpXmnKyO`Wct@?#e)0QIiQH@sW=q9Bmrxzn9Qh$(k_YQo6WuMjkrgQhCK|6; z@^1D5gr;mIzD%WRg%`4VM_BpL&Ix%J64u1ZG=@Q3 za5D=YPfa6`a+#Z z_Q3k8E~7T21#|HUHi-QrX1f3hh8ihBqlB9utuH%kpk8{o7o^^d5N z)>jh(=zy8w$F?S8u0v7!Aq*>zCSD#DSx% zi#Z3o`u+EbSE{R~K*YaWs8PT_u1GK}gr3P;^eG5N?n3~;jpEh0eyWL2#T?gRDzB)20bAo^1$pdwvYHTT?>na>D{B1(LVNde!ua#-?SG}$-^Fmf^AVU9HLSqcS%^>U4tINh`qm*dznYxU2IA8F9@>j zZy6j2eL$(`$cLP*^G+8HL!RnS4prClPkeC1}4> zZ5)M+EVF#sQFbg{t(Qrvf0+cpLCI!~Q0%D=(aT$L$=L<=@X=Rqh!@9(Dl;#GzU-=X zj3(JRr*wVcQDrEUc|JU~(k4nZGmwAvneSaITLozt$^Y%x{je!kf-V*vX;f1Bp2VSN}<8J@PH;`+I z&>CsK{f`iH3t~mN=JBkq%kL=7V!l!H;~c65E-e6m4}Fb2fuEKy5CuRIAe>AFAyP{h zKs;0uscCL>Ua;WIpG{}9WL#;Yt^o=`zqu~;kpAlWw&wK2^wfYYFk5~7(GI?!ArUER zskeB9>r}ZvreX#%E3{$ugV`Hkq8=NpxTfZ698w*#MW0j1%GBqavTzqzh4U0cnnHdI z&twL9g_{*FDr-U?P$es%`g_{vdF>&jue_h|{2Yn8R@KBPuMZ1{sbV7Fb4;>cBD5)}*vD45)LNrcipGj2O-FDM ziie9#O-r(pSk3im@$i@Y()~nC3!8M{@y={%@U#74Oiv46HTRBXwv&C1cA;a`Ip-{H z?@0eE!hsrwtXf&kt&o?? z^~7WqwrVX!!R_E=&5V0^;f&@@!006#z@=$G zymj(liwW|ZwQ0eTvPzH9|FD^tz4ck}OyKQq~00bXF_1oS!8g;BsJ{Am~`C9QkJsHuU|*{I#(#5Krst$lQt8X=A5& zTa@ed=DoFP<>9t%pyHf<%BxCg3N%bXRn^up6*`9vG=Zmqm>w5{zjUPRgIfE=;>JjZ zgIhsdRq!qLD=>bq@R8PlG>Zm8Cw3s28)CV0u$4{AL{sEmnms}Jd3s`LLb@ZsYd)5p z8eC23-ETthB|Ys8e>_hUC60i2ltbmp;uAclmmu%o@8Jna!_<5OXtzYn6MNb_=h%?dq&&Jim4+v@f{(C(#RU}( z0}@RvgNR)gJyc1nF)P{1Z`-lQ4yEa^f|NcMLPS<}`XL$LN%VgI5EfSrM6x!j;g&~Z zI)>slH`1!50fGLupYAd4x3(S8d)@@n=^%nC+P#P7z~6`EYsM(K;Toit`C-awWbIhi zbRDWrkJx0qyG>fc0E0R`EYsf(=A5OMUYq4MFH<}4ELEfJL6gnr&a^$tcML0mcw%dJ zNcBfO+mpmuJU6$b5BJ@^MlsAiMq`=gDFJkR7uR+Xm7? zoD|i{uwu}Xx^iHcPO^`>*P!Wj>gcKIKgrQY|AKASd)JTJ>o9Nmj z{oo<>-v-C*E0IIPQdtTw3->S*Mx^^Y23P8fwC_^{F)c%zUA@@2!h=Ar;Ui#QTTJK* zn6NwgBP6rsvb{vs!@#gYGH%4DNrXGOqT>#YlYb~j2g@>so4BRljXyuqV&&e-( z6cI2i1GTKvx7oIVZ=U&Ukpt+2GRJ7`VFdU7wKm(&krK5Fw?RjeRvyG-d7v|7llBho z)n0Pri~ri5a#fukg)aR$rAI#^(EK*`p(6#b25zL>bdD7+15v2Y(ybN8aX zYU*EnKLC0;eRbuNT;6`-l%t~zlu^0*E}=1rTi-Qyr}V#}L(gAr{uv5ny`gegeasnz z@eZ8|A5KurD3FPe+vM|xe6#`q7W9o|vnhk#FoPI;#;g(|pRY(k>j9g-l1qh|M7j{a z%v|QyF`xI-_g-*I&1E3n55jGB(-Czz(XX+>?b5anaMT!5kWT^=HJfBCU^CY_?(>LV zW5^)7d6aorxg>pxYD|!}WhRxX10!lN`kB+^fJ|e=0*9*Qtl42YmX z1Q+1oT2q9z+{rnSE#l4G#cB;AsR zed-JOhwS@u4V!*{eO74ggsch>U*IP&f#%jZ-~g5WxtaW7I_c2k-ypl{QLV5ji68g0 zSHc^adl*)TEY*(YR&qKD?5uaW$?vmznAb}-Ga`MJ--|SkwNVR>dCCs?BZ1}q4yC8k zYB8=kjFARRinf)yyO%@TWnuJ7pQs;nD9_dg6*qRLJ{-9f884`DD`ell=w;;~Pdvx< z)GT^ePTOndEwv7l636$im1^5>b9r$@s?JTK9-E&Sr(%AD&CtLV z)xxDb?km1$kkXVVWVEZs@)<%*m_-&uOm;qwc$UkRg4&3Aw5*LzK`LBX1A4HnojnRY zi{6hc%7tYLk(@|MX?ioHv4bc}5mwMP7(B=-=n!ey#IB5>fhO&e#CF1O7AFPa+DOVp<7Fc1VIc#7TL`ZMRtz&%gKncy+dO!i6D5Wuv(ZtWe zq>1X+<{=2)=%3h-2xSDc^2@J(u>mQGgZ_^?~RyANL zE4B4;hAky4fzi_Lc61Ip!NR0kd!F)&4S^~Tsq>0YtLwUTeifM^cJ({D)N&7qc|KIu zIWBNzoIa|Az%=v#{Z)>{)sC^U{_wkIcp56H7*s-@TuzRR@f;EmmW(ir9#OR6|Cu+p z;uVnoBr5ket|=3IhwwH<{cBJxnXfj%w;iYTG>NV>+EgR1ZgshuwL#(ZqRpugaA6R^ zm)3}t%#{bR1$6T38ENuz)Hih$Q0$;?MDjByk7N`RO&=0sU!=|{Qn!hZ8k#Vkz9}!z zM)JW6BaH;c@|NuJ-Q2BMKmSraKN&Y)i8o+RiibBvOh5zg9~F8dp-M0ZKcGA>bl%wy zmOM1EXOZ+!$1pV>MQ*gHeMwhuCR4`#<6mvw7D}i*qxuv-8@7MFZO)xPD+U3Q#fWZffv~4fGsSPKF_w!@&99q$|E}9(-SWY3;w#^4T5@#xS@KkT ztAv{LLdp9<89T$=JxyIQ&D5}dRN8|aI&ME?9h@F$q>d~wGjs6Ght>){8VA%;2`I83 zmeGJVsvv<#E9I*mRp+G&fPC{QeazBVYxW z!!%4Ak1+s08DV%Mx@HeK^F?yiyA#gn5``J=^nS!Y}uTfWLk5hoh)04k#! zR=AV96U|pR4;3mL%6y5CFYQfFVb zvigP9D@Lk}KRkpCOYj>Po1VmFHryYgEROQ3y(@7ViGF=Ikl+(cJ|!n2rogdO)=1^3fX4^V~5#L9T?EJ1~uQ{9OQR10N zT}%cl#j+Yb(OuG9TkZ8@QcdG#7!9{oB4@-{XnVto%yAA%f=hGAV*@Y2j@if4bE8T9 z26)pxewCf3F5-USxuaR*JM2L5mTL0}@>r7bXaNLxZpY2`yqh{tODU8@ZTz}f{JPPf zWMUPisW+VWtp?T(Ol;gN#SRWAXdIoq!GFQT`%LCqxOkLLGw0!(Lihx=0>-TpjZsUs zbeEkwN-0HCB(9$MJ7ak=m*jVwZ(>^4qn{R^i5_i|?Kfqv9fN{e>b-~~jgV5P@LO%Y z9@)_u-}*oXJ4!=*CPlZ*n;-c=#QPa3iMpaF zm<5zCA@C$2Obl36bMeTug<+ zR)){gHS#1w5pqA$y!0I2xFG2q8Sf<_iIV@w^|@q5(5G<`LJL~}jDgDMxT_@THdhqn zry*1Egi~nh&||A-IHE7yFTxL7Zc_}f9@6p0f#ylL@$KqTIysb)iAe&RP3qF|nJ+B3 zf2^bHlJN1-Lp(4V-58ArV4FF9MI9=}WK3S63&x$Tq3yUod4Tc9JjB^mS|FvlfmOOB z>!w3*WY_f#cB(hj9pdw#VJa|bZwGi=KX#>4U89Q1WR9>=Ey3@OF@qLL5a}f0!*XoG z_0ZZ)$m<;NobNA0dErtPm)S*?uGTH?#1%dfTBwjiaTGKyq?V^%Fgu*TQC6s`cEXe$ z>lS_$dl5*Z@aZ!8=5D?^H~yWh;`dv(^g#{oVmxbWTxeJ$!?f-^Kx>!7Cf!gDPHK-~ z)93VJFJ)`BPirCfXjg!@3)5J!uM_`LmBL@Wp0H9IAER5GIjB(O^!cj7FtK}W0ksLc z!09hgIe)DB?a2Z#vKdHwaudAS=Pc4|(6De8$CUH#NwQEdFs`9ZK=T+z<^L(6>QU!HI~QbW4Y?S zCT~p}+>oWdM_2SGW~vLO2qLub7Pl&!U^^ zL*E>@VL2qp!4~u|R!`%3i4w+0m9298kE15l{p^LKTZrABoetegw5H{x@kXLSSAH7^*3Li6AQ6)2Ai&d(^{@CgRxxdLs_# z2`PSjnx=uC-wXd#V$O%CBFT_QUAx&{{Sw^|eE+2>!eWEl0CW7g=$GSVhHIG2_5j1(zVF_7IGl`3x;7DUM z-e?D1g|chLnAMjWK7hMI^aPspcTEkC$n&quhaSamjWHzSFADBD#_V=Ycxh-De`i0;iSHNG_R^U^226IQJ zt67A0*6p2D32#A7dKS6T0G(>3T2d$HXx9qaM8X9~@p?`Z2|Y=lHI5blE&_G?{%I@+ zF{NrXPPMI^YZrW<&ywob3^yHrz1lfwI+xL*{(xzGV~q36$yzAX-R72sI(8)O598@P zFH?>YXH@T`L0P`~Em)ezg~}s&(^itK7?Lxj@X(3gPAR}Odg>#@Z=Q0Ya}9nLc~&}@ z4LfKAN&5KZw~&U?e?{ujd2=QoUpywjICU;TdNN1?)WQn&#NxWWWhA&F8&R(P++BSF zn60W4f%R1?BSFfqO1VPY&zW-Tw0KsxUbq3*;kl8qtTwS{>|hUGweJPgZh3wleAzyjrgv2Gm2Cl)=Qg! z2Neut_&`0AZ5=3%x~~UpPAO+Rc5v744NjPZk|p{KL|_M zz7{#CQNf%yy}pdTT`I;2I9P`w=%SW6rXF{yV(Xk4;VPxE$H~l=?{8bUX<;!YycZ@% zoz0{?w9U{c_-Q*WXcn5M=CUNUHHDG0Zr&|U`)2fru>7u^?;Vtyu}H!Prg zCOWLkC87C+rVSU$VbJ@I0>-yhG15TPd zq#%ck!2^?HAugpOjd4EV&==ZWZU4FPNA=8&i+CCtv2sY>OvmQW+RUE9q3bkfAWhR~ z%N^)8OrMh=am2j8l0&8|aBgoBFIPlmUjo~k6oXcr7aje#o-DPkieJO0Hkf}= zrG@?A)UlVt%qYgTunYFxi@k`Tv_u1y0*KqxX;nnmz2>w<$hA}^CMdnHAacqp z5sJCkMBIG;7kDkSdt(0{jH@69S>DpXTb0d>a&VVM++Fow>))ygfaXm9t}vh(X0@>+ zSnO#Q7;=0bY9hCKaHK!deBRR) zw70!bNMPQE?(E!M)CMAo66RPvUAGU96|(pwk%z)tu3L$EU{C#+T-!-mX#CPm z!E3#=8G}?zQjl1t6h55E2QXKjrXL1ILFvL=J@~QgYedEdHj4QPtxf`*H%)5_4f_0v zDfxGGp&qa=DeXUTzO>P)5cDKB$2+>GXup?bM%PVxdl64?arEheZj!o0yAoHI^xlbf zo<|-xpTHC+nLtAtVQYuoDf+#CoVP0m9b@9=?J-j=D5dpgQYYgJMQF!$R1Oc;_4ng% z*6&A+`~&(pRf4uUk;_-$3pK~Q06CRTwELIF_vR68o9ysT7uei(UAiv7uWuw&xqlt# zI3TPnBw_E_Xwl_}7;81sn1FhcQgBK!(|~3|;|?h#nkTi|AB=QxZae(xL~aZ-#K|OV z-1FY<4kbd2I|&*^vL_j~;gi8R9F@sWtZKs7*ldQYya_g-DAZ@BQ4;lTlQ^Qn(bwqt ztI^4m@5oT#Y4;vwIdj15syd8PPzA|SjZ+_DL@g^GngC^{MzRV$wI!)U7z&;L{$_e>m=DQb8IaQO-QXcPsH>VqyjV7_NYaWH(||p zNOUqI@B@jkkqwG00)2I39%OernX=Lph`Wic(4En7n&B!)U2W9n6*h>-rm?7iQ=oQQ$SoU8OSiqb^Q)i=CnU#`j}$7 z_oakffg`Cl8Q8#k(1MZv0CP;_p7~1>d9%9KOxo#P>IYE9;I(*a!QJ=nY_=e5|AixQ zZibU$J4RXJ=+jV zfq1GyF{?3qUA)k|m0&)aWBU(hUdqjsu!mO?5!C|V<|;Vd%T%$9MO**7_>4wZKW8@@ zo;a5t;J;n4HUoI1mop|Qo?4gNI>)AUv-Zhs%XGSeVE$=vofUMX-*@-LS;sLj4-1xF+ya!8L|}8K!u~MQp50I9NjQ|C{MQ&?T{HVMjzQ_; zslGSmzuaJ9KTBI$862#vPk1^NmQpPA5m^BtI;z}J+JEDQ5J_1R{pfY?k3FRd5EQv` zusNkx_&uW6dk0j42~P0vwmBH4V2j!nw`oVhv-*n5d7^*r5orylfI~rtVOdKz&=Jhw zAXnpby-8q5{j!zXBl0A_JeOz;l|Nj-sm_L36j$M@sb+47cH|0GFQ%A>qIrpoVTF-@ zy29X79iVZ1Wx`ouPmPB=ad+Oy(Rigqm)&OK^*r-Dyg1$?%IB{*-hR!AXsAt_1anI( zomvT1Vq(jYtMZ_0DcF=rhNS5>EnmO~kAscKx8rT(!yeSezQuY(E!>lFj6FHzrk&ou z$bebf05}EU@mrbnCMP*>aioQtTQ%aP=`i+9I==Be1gu$yj}oG*-?HtXm#Z3ZQ` z*%{q?LVu^7Q1(Nn4%L zaB-yGO}8$n8QeEkM3WMws5!)-;$|w|m9lcqsnhg5DR?7jb6D6oPNy&Vw)AG(#=RB5 zxueE4ulL!sYQ;X+^KOiztuEbFH&B|m^n5orDv_#q?~YRy^Xob+q4{P0v!%wZ^aQ+%gF8S~s3bwU;SPjnUe$au#J zlXL9Kij#ARRDr@C)m!{Wg+ZIjpQt^g+hwmYw$$}yd}7_U(MvJ+3{Je5J+R)&cNzKE zm_4-TW_%~7!e9EbGjmsBe5nC|{g$tvm@^=&W3MsS%Vic#9Pf8))K}r}&1lDaw!E-; ziSSPpf)SQIA_3H#e{Gf;GXeclxJpbbE%%dGS4FUS?Y6{jm>Nk(ZHk0G=WIz=9VZf% zTP015Kir;5FPdrWkJkwR9dK_IEaO*Vp8~}XX>C7Zy7->ZFGS9T>iWi9Rpg-fq(d*` zJXgEZ&#TP&3TC+XL88e)BtqJV7A)m(LCA$Bi$K28zy{smP}n3{|9$HbgAP8S3gwJ} zbW+QnvBiRRhSec}-s6BIg0FHPfDnyw2ZB=4Mx42KoOgd{8gNSOqXLDe=U>wXX+qYCOF3*Q_3Q*lwr5BNPDUO#Mnna$G5romI8YCIm6e}MGt>N6wpDQTDl~o zFl7o=PN0fCL!TnMCZo)O&yt_gkUAsBI4Jtik}gvU5$68qO=xm{%%b!q3qz<%6m0sQfPtKPxFw@#6Xh|lD2udOK`BR>QSkMoqV*q+ zpil`?8Q*s$qxkcL-cGQFpwGQ_4do+>nxY{+4Tt7;DGO5VX+t}Tw&KL(Txr6cXiECY zBJBM_0d61w0(^+^)(k+p7+)~lr@e@GCJKZqb8 z%KyjN(b(3~!P(07m!08%@*|V6vxUX~%Ea?WL*4fZbi%CWG0ux>-+sw$3sO_9I*x$^T*cAjNuI@ zmm>1Y$<4NZXJ_j>5cqU-f}A@&ZIElIuW#^q|8_oQ{c+pbeb~G-ZUwY`PiMv{W^PJ4kIxn6)D#w|VFUcQ|h+uNT`E!s8qx%N3dm6{#E)TsqQnD3T{iiZZdLmThE z%Z&;(i85uCldT6OjoW_wE|g73V}WKemnM2!<-byXkhHnOfEV1+xbP)5--;;*6kRDN zs#!mkXV4tkZnCU_84IyAqtUK4X(?zNwXb$oSzNXGHNzcK~?%h2ufbA%2vl(oKhU(Zp2YwVL7uk3GFE zy!20l{`#c6)rpy8Tc&-@m=o(sEP%+IOW}$4El!N@sF7Gw`gCxeJC=n&t zS)HDNw=AEA&@DEpBuN&EZeg^%VN(0SxljiHGi=|aIl+yIQ6NQ2i#dzGv6Npk-GDKu z&o+|nX(ED?{<~kM$!siVt?L!xC)jwlg|!aneKKv~JExT#rI2sY1Ur_)Vgc5r1tw|z zJLxb%{BWgFK#*;=43&fq+geavsgeXKJT)DSi8ecCh}jLMb-zTBloZ`LH_HzZ+d-9V z6O~6P`rA@h8a|IVD)9-C^~fJ7q9vev67;Jk5cwwBb53mZ#C986Q|smAt1Mr8+Q;E( z_!VNmz=Fu4T_|;m;s?aie2#;i%_zY1W4dHcQ#<^X6%P-GvvlkitSE2}uTgfHf=$G& zbKI=B;hP*ydDP2H5@|$TckSx=%to@0yuBHh@a|mSOu|&MjCJ}gyr?7w23wJ&y~K5c zXgTgE8;tYO1oHn7Rw3hX#Z1>i$IL@UFSd#B_G>}4$rE{1E@EaGMRN#;_rpb!4&yG} zqVEOfCn+&0YG4-Aq95g3!wyJE4eLsjjhTBJ(a5v^?pNGp3*Ijo(@xse<=uf9h8HHS zjXLgTYYT!MxP?eXJ=M<8q1vk*2I1(3oS+d)%(H*|E=T}9a~+VQQ$t`5u}Tu3O%JK>V3-!E2aM?q3v zoJy{9MSeZn=xE4S782OVhR~f^UaTduv%*81vB_H+$6uIaGw$YD?;c6Wqz5Z9sL&bB zF4xlQda%1-gJsO1+5uG5jL7G3dD$INIe|_`$9_!XT)E(w4PQB36&;a{SzMM0{d3|c zB0}lzE0YJa*6;0v2C{(TlXkM;tr`%R;a7T@bC*_$3Rdl=~nAqEgH95||CJzd%*82nu)8CHP&^HGANKByx~om=v^ z)$}z1HDO%PGDeW$6xu*>sxXSM&#Ew^6ioL9N{VeWrZ{WXfS*vV1~>A1HP2JBF~rpt zypa>m!J+~*%ASc5`1}mgErrO4qkZpr5-FY~^G^(cn6cCCBzfupzbgwDP+CEIj8`$E zf~Pqu$(4Q^jM5@*8$v^?9PUu?tE)nbc*{PyQ6nTOkfyY?J^Gm%#qGU@{X#c<#^oq5nF;K3`Rm?{3oLT!DJ z$CjOVt|jh%FCp82m-O>W_x_5UUo82fLa=K8#_&G}0o;FaIsM0M^xxZ^ME>6o0%Lb` zXJboqLq}(GQ!AH$CQU;VD|=&SFD6r4V;2`I3oBFO|5!H-T^x;_UCf#PTjtL+Km1=C zooc7DA@BZ$QWpIGS)%{{+}qT_-onb#&H29s2KTnpQE|b({?#%MfKm= z>37-__)J-+aL5d^rK@3x3^8B6osCbn%reY{qEse>l}ctPV_@#EHbb0i#q;w8!PgPf zRvWac^R3RLv!1-?55LN-mpPz;mq$&FU_=~vZIUxZ552yxXW6q^yCgoeN;tmY^DQfA zm7Xv$X>zmccUod7zj&nNP4Pu2$eUVbU{F8 zSu)!8!#ZK%XV`<6y3-0s`8Qr|s{O~46I1+-tXA@va(5%lf^So(bmsub{J|9-$rG=XYFcqs_%J?u z)R$HTSkKW*^qM#aicQi3o`R?J$@<8|MGW~>os=eDK=g>M0ls@}%2R|u_QbJ;3u8{^ zLRSdVwkrHt^}g=(!DgYjTSKnSx>MB|yj~4<<{Ux2A2Pg_(l4+tkrnt~kQtZVxhDGA zCaZF#D+(!Mv*LQk^x*&^ZP!H?Au^rvQ0UV0hW zhqSVjXq^!NN8BT?L)s6dx|LT6lbEG<0>wLvwvoimzsM!4<5e^d7`HL-PzdAMLPq92 zUFj@3{k|JDeoj2&gSWM2O8f*2!DZhdM>Uf_^u*N{MGe%|C@4YN=5>mH zPCjdA3Fpo1KdzZ#rS=fHwD~;+-X!M|HGKFs=DHl_j#L7CU3x-im{%suDl@MJ>d4kc zEYtzo0sQMD@*$x!oiY~iY_{a?Ij1ub_+eLG6XY++pmJJc@C8)0RW zKpg(2WltB@CJX#Nzz<@3{*vXjzM!&CER;tgjGNs9nIwm*LT9VZ@R4Em4Ao<%vPe6cufwffz zi}V9X!SV1UGRmL>ewoe&G0{47pI{+^$wtryGSp+PwcT%Xoua;;JomsH2r@k<+PWe= zQYQBq<-k9{+1t4<1{K8+QVW)#}~Br>m(RPc!qZ4r^$7($Qj1>7dQENCH^*pVdSwDSn0R1TDRG3i7y zQr6AQT4Ru*-=OF{>(_MF%q-oEFj#&)$<6kboFCuAI>E(5NzZvUt{+OsfqqW ztc?Y+C5>fd2je14nMQ*KH&+#g*Ol_}lC*M#P=zjVUt2 z8Ah^?KkuN;-nmP9AP^+Yow@U9V~w-h))epw_@%9fTlUfHDLK8`9Ofvl`nTd`i;q3b z4{vF?&`RPjUU#Qy3%@1#XMJ$9yK$(ha3o9t#U<~aYGjb4(#h1}-b?7=xKChM%AYLr zI`SXEcd?^WNF~%hM{{N_Hot^#A%;oC1<7iWb8H>Ont@$Rr|!c0Gp{kR z8ahgjba<$UyB8=x9n`G07{Sz46kyKyd(JXLOYl~wD^hoCP#sTLA-Ow%0BMAoT+Sxz z;wmd~@fkhSMdFy0b8?RR1Vx{8EC(lF#a2+jzriC>4W&X|~hUxlI6D7<$xzU2NELp|q(bHCoac9%z5_=9O$HAEaG1Ag_H ztbheOHA7eyIlz6qI=WY}Q;eLG3)M9IM8ui#Yzv|YR9K0$jtpdo<8tcdUZq151f7x- z;=pD2a)kYMocl{w|IK9J@e(&MQ0dw=#jm2W+F0r+OQjqVpaYK}?1rKWpiXpdjEYS) zkN?5Y3X)6;p$;b`ds|=LE%R>}3#zZAuRz9i_>ANN;az62JvT%@%e(KK3<9IBrk+WdeT4Y+XJebq_4v8Y4}X~9ASiyxrE?vhU#eLZ?IJD zLF!4XuL?#`;HK>DERnGS)|u4g3eitAw()DJO(4h{+t=~$)??|rf!ybaFn%)kZdY|q z#ZW6M(!+!}J^h50PxIKCeW63&e-z73ZRarjZ)cezs^nk!JRluW&Sop`a2$z_u_^=E__85MOZ1?;>DyRL8+Uu zi4ruz`+m?sWZEl;DFG%r<HlB5&2I4y*?{j>svx1OP@^#-ZyLp^WM!aj zJ3<*;1zooNmtUO2o7IolpBFm7riQ z@WIgU3HTxiJPy0*2DStQ_U}p&w9C@cX6a3;6IN=)N@b^z9G;+_=8n2>5>hVnCh0`EB!{OMb^0 z)0IE_&9DFQ^LPK_|N6_%U;Xf#@7~{g^#fB*g0-~7wpfAy>1{P>%Gr{{TG^dI`@-}yrQ|FZ_` z$DZ+T|NJL^`Q2Clg!IvPef#Tw`}Uj9-~90XUw-@D-v0LUcmJe=zyIpHuer%LZ$Gxr zUw-}F_dopYpTD{47ytVC|M}+g_dj}7eBD?7^7%hFIcm7 zz3jJA=e@TrjnZq^r_I^-wvSTtHg(v0(LdYP`%J#qs`t&5_mnxTZPqph@8Di_i*vTT zZQOM$d+%fKrOkQ!RPWo=RlTI#+USFtiPpuuyl}W zUjN*luh(3A8%_6G$L*8uEN6Yz*fnH4M(tHYw3oKm`grU4Z|(P7_fm58^;;WLPrmKD zK0-HtK0Ec{`lhxgjn%6U)7*B?ZM}ER?JRp8ab-rKmb(;e-4s6D18Go$I4?o{e_9^Z9Bcdv&lbDOC(eYIET zrU%u(MyfHqU;It?q+zKg>5+I|ma!gbYg(lKyj0U{8mdxx!rHVv$3Dl5kNGt7iXN8jT=v%5*m$tmc5I~7s>R8!H%^_@2J;hZ<>+g)QMZ_3Bu|@J+n@?yXTO5PPy!H&uQQC=nYZS_}-}wR;&Y4}Hn#QyW^#Q44MEc};PO zjeMTt)5sntlJ0!{*}bRrk)t9sHt+F`H8nNxU32zwzV(2Tgo*pAmPwM8O(JS&Eml9n zHgifzW9QwWm-n)jT~FQKOnCi$O}G}_@s>>=DydhJq>f|-Ut!sZ%$j~O$z9#Iw&Lua z+tvcsn2wSxqZ8}2Yq3m(^3a^_dY`nySTnADlDoDxXEi4huDb*R7wg2-+8WwOyWgw* z>@}Y`)OL^-X=xvfR@QRaB?}~@bQjm7dY4=oMan`;_}z?;Hxs^IACD0zo?q<9$or@(lhNe!!ti;7bvcB|^ec!qbA2vHDVzV^k7;BMK^f+)b4d-HDMs;+w~~j^}HwyKKkucznN+O-$FL&!HR^{ zKzQj~*YeS_mtL)JH*%Un^2jU2F-iRg@&pvoF59SCgv&(vC`e&*k9b)UuN5-rG#EOX_)Mwc)>*S?$?8n}2&& zt8~<0ueCMH5)isY-EDqpJn0THA1q}h0nB4c{O<#!I(xx)Z7dmHGkf1^SS8GKvs-^- zi?!dIEw)KxU`0#HO2*`=^|y5?S2+V37fDrZtbxdmVc6ulZ48CwwdPd@hMp>OKUx7r zZim#6RrAi>Xz_3M2c(STD(Hn2z82c4puA$9+2R9bv+A3*Xw#wUSQJ%C5vs$i6Lz~)|aW0N0|WsIVz z6BFO#NA?@rUyqA5&jAuyq&S#X z23mHSFx>guBS0ddJ`fIJkCV?iAv0;Fvxo$~YSi4r*dfEcl9qOpb*XR)?k@S%xw6u!MR-UtH)+_`D3qxuj zPe~HGY2|CgB#_eWdQ=cqY}SUQHSfCljCWo}xVOYXat?iYV;AsGnSRgzkW7Pw__d8M8D6mFhUZN)x=s0VJE1A!VuPSrXMk@XkIt6% zM0iP|YWd{+l-v(XTj%;|AlK^zzI+!|5-NZSA{zJ)wPD8>dSaVEoA`v-?Ttq7v=17V!i&eZ|n&OA}SA z9!z2xd?kV~nq6sXDKH{WH>uD2xN*Uq8Q?B&TDN!E9g-TYEzPMjQmRu@Tu*Q(d{Sgg z=4Exrgwo~K0J{Xgye2F=Hm)^=^u8?;J-^lq3nlckEo_8NSx-XTatpI-XBX+9NwZ&% zGER#m#!$z^dQN*k%jWg^ux7%w!&dOh(5&iyN{s54v#ABH)hnH$ z+t7NKkki!Z6=@yBJWeuk55hm%4H_{#2}!({VKhx=ddNZD`!=y-l5Z^JaXBV4VwE)!Y-q>q+FL*H48YC*D92|#KKZb(TDRtk$yLR^Di!d#L%!;X^A+*Iz%G?sFO7gQwk?L`lGr2!y z@MF`A^H8mqQ7w|8GVQAuu%wr4i?p-qAst1lgYi%lqwm zbd5+)wyQo;2HXDJb&p`B{LFTqoqo2Lw4^j3n%)*}xou+P#r}G3Q7lROQtBn?9d?@v z53K>i?AKt)qte{(>_^MbNqC+R*t@+MF)J;atvmML!q<@TuIZG~9t!N?ygBY-X^zay zD~{2y`y_cK(=5^x8vxf)681I;Nn6=#+1yH#IVDflku+%`4IhJ>NFZ64%BJdg|5$tx zlGeRwrS*`&(y6jW^liJhH5y3^M=v=gApYP4e2CJ$S$)%W^TO`n9$1B8h#Vf7>@*^CQhGGR(wF3QaYA& z*gV9soSmIqlI@0cIZvQP)`+ZC?QR*7QuORz-J~`rMpCnQk(DHg$7(ISgP5R(Ty{OK zk!C&bca0>5mhFk1oK7aoFsv7LoOwda)dsOPuoO^|^JgP~)3U9lJ)M&vuDxT9`C^G` z8J-P6o>)&()RLSs#?Lg&sygC zcOn9J~eIlo1ESP#PbwF{^{AfKQ~Ws$?d`jLFxq4kzxJl+g8F-`i|nWdXv_>^z-sZ>oFx5pcs;6^{o23|w>e)dWhkMTS_~%>+aFpu zl}qp{{vmaV=BtQWU6RvX+YqO0kP4Rhhc!8WBw0+>73Y@Olxt4dr$t&yI@P;v5Ezg# z)k$n?8pazxcg`b@!Imea+r#dCSWo54xkfpyT=5EFA=os>l9ayb52@n2rDtNIP_c52 zoe&|vy(LN7VdbMa)=&^1a;`|;yVO!6A7RIpykjNw8j?jax!Uh_CTUYC(|Eqe5< z-OvgN!Q-@VUlt?3oDe7ZA-j>?5wS&CEkur`e!L0uIdr{&deAUShB$Z4^;)@-(E6A? zJ?*wGbx=sbvW#v?TMAQ6D@ix6HC%|%cO>Ot*JECih^=o|!@le^Nkddli>MA3beE(H z2t({`cFplJFDBiX+!HLLTRfh&`j)s*Hi2AWA{@JHT-o>XE)2Sm1eHMRO}o>ElVlRM zzBag(0SFYW>lW#jOu(l$f-Sd*IZiD2Zr!edm4L@-b_#_+jy@!fK+fPG<8H49Aeq$| zwhioptQrEadUovN>k$#by*kgR9n|k6IEY#W6k%E6ha{Izb{f!lHC!HTLA^<1YN3Q{ zkO&x*H#&PaLO>5d8k`k5aqlOIQhO?S61S?@KLy;+4k)SSHzt`h{4uE#l!?S+q>u`- z4nxlmlX`!Kl`+VG=a5M7=G!`9a9L1epAo&(cB*(P+BjNUvPL_$EPu!_kgw8&)BuM_ zlWP(Ou8of+g7sE8w6S$1;H8)l5LU>tSkaFxCMH@Yq;|PHGw*|`o6>AHfoda$e%$B#2aSjOuSYC_l+fvTfrKZ+=@IpYgvkq+*x%RrxOSQC$(bEB=5AD`JQ)E ze>m3C#4@q8LoE(9lM*$AX(HFzo_|8tZ1Oe>zy>)u0X@@{U`_y`i7W7?rgp3>NtxQV z)vzz6-6y%!jhKa0f5ykw2Dh;91Wt8o5W>LeW3z8I4AG^7%v`de!Kcc|*E09$X(1)Q z5TR|*dmZ?ojc7?B$;yepZxho&VSGUl8rp_>c74q*$L4lDx~3bo>#zkHIjNYTH1DT; zqZIy^3CAVcGahowOZ3UumtvIgS;3Vr{>p%;gcA)2BzmHYGqO2EDN$k>8#My8UGp>u z`=qzCd^la&Peb3TEgQf{nOT76N(6O2SZms%a%u5mBXCi1T6Rpdh`vfRBqW`@o=&We zD2kp*Zi42)_#kpm8_s+qYNCM{`ZO(c?a|_0v6Vmz1hNAkZ{Q8gxHqgFg7H2p5j*6x z^={W+Ho9&He=t-(ksC04+CCrOOR$=lA+F12GyHUP4We|6hJ^1np9v?&N*KQB>9z8X zUk6wKb&^0UW^7GvN7gCkjZ6-U#=P=7l8@vu>tPQ8bD4oTd(4&ta!yvZF`J zJf6gui!gpFnV6n(m$?WhlD zFWZ~YhFp$)J3`PhO|&t#5Rw|WDTN?8S{_IZrra^M{m9QFne^o{!vgs8xPS;>KkRxP z6{2>e8FXj2jLROmc*Lv*aG;90H z4H!NvyBPqk)>H&jtrRu9IIzZ;%5>|1HEbfBjyfu*Zpbp3XLv`9T(X{j*u2{K$4rps zdJgAtBJT+8Ika5J%M{X<^4l?fz+Mqea}fRICa&_S!I(AOFe`7@d$SyT(8D;nnI#Nf ziZBIuw!IQ*?62h{vzreM*<|^Xa&O)Yb66=2z>8~JC#oJgC;%H-<$8r|m&bc{P~q6Q z@!w5Ddq<%I{>`^hq{y|%*+Z7?@>L}12I)>>AKJ4MT#3ECiEPyxsUr~313+FUD;moH z%>q=#vYVWz0q@!KJ{j9V{3Fu7=l6ndz|A1_TyGx^dJ51{?pBRjI)Lm}U@qz#j=-d3 z(b;^7gXg2`i8Kv7sP<{@&QIy%&^d2%f6}meo(gowAh1qZh{5|&AW)>sbU`OTRfxU< z*3;;`-@k|8mX^st3QCXRzy>APR~~T;0A14EM{!BHUO{+%0QV)ylx{Lt?oYp5BevVQ z(Uiz=tGnb3s0H#>h0uNn77I!?;|ylWisjWv7&OT-Et{=AZY$E)oUH^bG5KsOX{Je% zXq($#l@=pTzG?H9kMI4|7LrOnp+WBFBQUZxTw{sFxd1nyA)djEY@7x=Hybrr?<+}| zmm&mUo8!0rJjoca_dAgj%(5zzIx;l*bZuBb;KPUgpjAAwNdPsF2R0cW(p(Ebrav)r zz%K+{2(HVZLUw>hGjL;4gir@?GfiN9jhtmI9J-@yR3S;TCZ{oX#F&)L7P;C`;zr(+ zH3>K5dI{Ao)jqOpS?v<)l5W)cOy4RwhejQVY}}HUx+Tt_O(2VL5ZIN_lK&$o+^QM8 z6H%eDz|RdUVUsuJwErOQx&e@ON~)r|3n90_b7*q{mz`lxpWpk5agmnnmbtgS_ z)PlP(ZBY^dp!ND~kLr+EhKyCO* zzrbxUtZ3AsrCps6ThU4KU17+~{#t}{l}3Opur>fePE15HLpT=G!|RWtEQFkoMA!n$ zP)G#CbnNe^{D?~z0I8ZVmYkP;np8-P_5j*Rpd<->@HkG+)$1h$rZ*hcA<@%7+LitO zh;|&_4PYn~J4Wg9Zc5+D0@c7;VXEt~c5pT)M`u_%OUAWq7}@#gig(=aWR`RmZ1H2` z9f2e*$aZ#g_TR* ziDw@r0oh298r21|r7(K5FG7QM6lO>KS(p>f?0i>`T`zgXC=3S(cBcX)lIHDtbTxJp zdYY<*z14Gx(8Ji(XkQBdjx&T+^>O-of}vVDV2?rlxVo=^JK0}br!Fkhcgj!H;}a4G zY358~*t!MVH-k*5*u~^eJ5v93w3o@<4_F(9%-&o+cXJv!6KF*k9hQ)uRYDv*f-Rxz z6|y4$ImO>wAEyA4B$0Qf>{8;xrQWqJbgP6ULD-@gCegYqjdG`IL27V1$WOdknsUR= zin}6tFYBm>Z9^o0SXY=jQVf-{+s&c9Nq`p2=&)^md9S7!3#l;=a=5QYS3^h^ouiGj z_5~S!7(!v2rh1v6*jMN1S&lHZ%;(KucFi}zlHA`wkf#IHsAEYf;vqQF)B<_3NQVR7 z#V8=-KnxA@^mV)sf9xRvD8iCnl?Jpq#B^he^l3@h4k4XMf3yhY*XuPKW+iZfjWm}Q zyd!A-BNZdJ@7^fr+V$SH4`hz)Pe=Q3(ks9Vsc~s{f*3_p5%D}sLZ`1hBEGL3v~mqV z6)}JWI?;BI0q#;c0XC-$kPI#PrH}6gU0#8S=taO4r-P3n{LDikg7#-*=@G)0GjUGV ztk>6b$Fs}qLO76;$G;)iDSNbOGrgYU3IJ6GlI(rpPTh%XNiS@qPsBM06#Kqn++A?8vDJS;JN z>-p|PSaQYTG+3EJ5}RsCVwNgy0BwZ~qCQ&}Mx8G4FTC)!Er`zmPZ~%ZgU=?VA`;lf zNjk+Z?b5>2mH;EO)1r{?WYw)<@Gk-pyS$|^*AxL`fgb---bMI-&f@yc$-2g8>h8^ z2-wW@I_u+^e#RX2vC<+bD)Wuz1|e0)A+R^%e1yeY)R@)lCEA!SH!HwWZdKc(J+`uT zm5ni%mus2%I{?91QqS4q1@09kgj*V`6rVFz}#8~$QB zgc)J&(E7*s>dW+*h9OaAaXq>gY423&Ng?1HKcf=Q8f`t(c^4liNSdekZ}*H!woTtJ z9>4}zlJ9)&y6h8ug(;Y|I}%wdtv8v+#X^sqR|il=B}gZ21uSHLQ>@GE9?oc@UW8K0 z7E*2kX9kYi9)>J6H>(WHWj5DK$XoPBfwt6CV}8>pfHDiXHMY8TC@!jmLMG#v2$?yw zC?#D8Y~|G2$6!60I+N=KseU=a@tZiLx(nkWYBB^jjZ+Ar#OT|m)YjQf{|9@L_w8}j zVbkt$-xS^)^(#cYwM6E^8ZBi!0V_)uudq5bVG=|M3&z`#z21*pSfbj=H7ZWeU(V}Y zW3Y8H5oto^IO1heFjUEe;Rclg8x=#)x)c+X`a0M!UY92`#|PpoVT4oQnu3I;8J<=GQR{ozr(_gmBP{@EGY?=P z0B9E6Q%+6Ybt|lR=}a=%BR~T(Stw)G@+k>hP(Efw19zJloqQmHLX$dF%IO>_1Jy~e zt4BSEwBkk))|j4T3bf>MGSRj=oQDb5Gt%Q@Q&%#G1W=@j5v9WZ2*Q3I{ z4#){it2BUQ>K=|rC-z3kkI6_EFCwqqLfQP3E{QnmD!!#d>O-$B?8+5g!p>!r+a)G91GlZko=LVj&AJ@u7_!TxP(O3JGE8eSAlP+1WC&ygT;gb`j-LY@XqxT zpdS>BB&u1YMek*M?Dypol4g>I)hhsflT`P<;0Snv_<6lOElX*gOm_K=ODI$2{?a8pPJD0) zwZX_q$nd};1b-w2Utg1WJUl{K7~P3(aC5SEbnnX}l)E2mCGDUxC^)LG1%M~_;1GZc zDQdM^)ZkYh)4$i`W&o@c2wPCWoUcfr8QO-8>2*K_7)eQxTsNvN?l}L6Bi6YYsD=T! zEXYp8Q2$}55)XwiFE5}Ts|`adLWLyVb@5K<=rb2UP=3$CV4OIF|Xwc3wxYuW=cROJ-ij`W3oKVmy(jRU?P#WnSO$#0u%Knj2qLEC}bBD&=O)hEYOCwVB!`nVQX zh?20_(puA#ikuvLH&pr$h%F)n)kdqtiPq$s?TL!X$XDz3paj#dKA8DPm6M?Tqs_J! zDhpT6n-iA~%~7=%EC4Zn34$OyJk~n1VH^Q z77lTwsU_Q@k`;K6rga=NPJkp$&-CRH`7Er25l+YJTFJ)!;&XVMJPHrJJ7vrC=5|_*-GI^vU{lEp|8Ma#Y*O*4QaNomAIs z;Rjp$SI8to>QL;UJ(?|^_l+8GT^l5wr~v9Z$6Tbl8kh_@Luw!2`$wB^`Huwm4Z<++ zI@@lIj6;UR$~1DXi3L30JNh!iWlOyw+_jsFp&hHoUgikOI|-PC{9dz5(4V5F-LXx$UyaG;heH?je93R0?TKS5F;tUjEjtvqGnPDnw^4APfnjy3&j9(vs;eLl zfliHeTk?JN0ePXz5E>+VkDHdkpVr)Ou#NSFUBA5ZKWnn3qo}oPQI}0J%rV~f8uaPO zrt+X*+jzF&7w!gOFE%(M><2u|Ot*$Hqaog^?tG+NGi7aHitFOade~)$H;wmd)2sv+ zMLl2gJ8W+1Qc0s%uTmBX+%2D6(AIQ@TDjq5Jf(triR3=9+)m6Zf|?M$k$epB3@}ES zCoPE1cZz@|_#sn%@!n=;a@?ptqN#SB@2(kw=(t2}cA;>hFSPo*tql{SZbH>SzUp8({}(+nnBq?eSoaf&Bc7| z5(#(`?_pR#sNKL|?ZK()l5~BLt7)16*%-v(P!b2GP<ZA$mb zZEAghtN>wefN$`v!oer|u7kKML*JTVvu`Od|7f82`{f-?1o!&Op@moclyx;OC={oI+sppre|n+N%dbW-I6MJW}hRw{ib=$2TZJBTTuX%QAGP@egmX zyd>K?5#pk^%roRDD*j;BE7_sL=y=HE&z6zQkUZ6>+J4Mon46wO>DL+1S2 zg6kWeIfs6uawA+bjmQ>NjvZQMWVkg9w>lwr9A+u_Pw}qtKq&JE-WseTz%C&P-QGJ~ z_@P9`b3i=^S2_=81H329GVcV2`6-+*)&~4jS z&8wz~x)2NYNgbDzSPFJ&$xbR+i0=NUnco}&SRvSH4ejO!X_7-IX~N;256?{SgVd>$WH~(? z|9%hc{%`Nkc}R|&Jgg)W#_tj}VnCYUy?GY0)$B$EHR{+d=b-_rn9jxfsq!SK-VWq* z5548`OtUf)-bG9jIL@%pb&vx$89#D3VF?a8Fw*e@R@fRb5UAcjAS}#hH+9q2SCbb& zu5A*Qasd>B3+Q$4>tS}{{M%}>Ks6We`-qla4#(XAXR|h&#R|0QbkmsU@^25Wa6R6o z2;w-Cpp}!ctgW6Qs-=oEQ3PgYN>hz!Z@e*FYKNPO`fIbqI!>6VUvH+WaucL!vR5<&NyS=G*0$VNV$mL4n7UBQJw)be97 zBr8@?SVZVZ z9!?D0{Nb(8g`2h!c}UIB_2^oxXpd}89`iBDk77m1iF3Mkz5aZ11gd5XlQyFKiI6z1 z@go2Ga)ODsLb(EHW!#`S-C%+pZL2HC0O+Bc&KENe(M*-)+rVMT4_6ar7@(99Z--O` zJj@gFDUnqm@PT6OX8g$Wl1Wb%;&j>zy9=I@94cq4fth4G6EdG~o$f-ws(6a%>o5@Q z6Yg!T^Hwv?O^(c+_6R|yZEod!QxRo_szDFH(i+EE(nBG~rP}bxOZED@yp>`mFbpK8 zsUSEVTq|W#0qGBlY7t{yDrJ%p%I9`lac0g_XCC;5$g@M*SDm;fXWc=Sy_{f~AWlhl z$_mHMY}3wci*_E9`n3%@A!FiqU^?Ai;M@5<61W?cl4KA{v{8V5phB~;%||SOGX6}e zflw7we7#0@vF{s6v<~FCcXMe#2q=?EQWMyKarWA3M728aF9Exlv{s<1UfaEcLTQH- ziyPrN(&N4+WHP}Py;t*Vhl1T<6@g~H#rnXW0Zy?+N2c>zKN%#|T&%h;$kgLV+qjUZ zPjHI@u4(KOa?a0beh}lq>3ZJA@%q8=>8F=p2y$TZDbl-xA zY52j6jdvXw;22337$Xv-=No1G?R|K#)Pyp$9KEw1E>Yjr`S}XNN2x4Dv< z#rClgQ^XchrL4+*@<8(qt;o_SC94abQRHmGohxfDXh9nEI~u3HJ?}&I@D|n2q;e@2 zj%r!Lg}GXfO9IN0f~W9Z8-UjxZo1S}C~2#mm`+5SGGnx)=~`vzyfx#EmdR-rNb{eTgyI z-!+*uFasYGrYP2oB&b%!e)yFYX7-5_7*q+TD`JDE8VDaU0g}LGPHKo$XeFRa;)68# zC{l_kbJ~_5^O#ls*R=M1Y-xeTa%CqQp^Jk=!~59y3ez4V-1A5F{vXQ#K1} zFrmi7U;=b^Lj*ocB~EwE`1$A>0i2j$4IwkzGgNd;s$8v)5x@iAveVh5l^_{P)Wtef z`6d~U`$%gVQcwnXHd;%8IUPY_s{M+GmqUy1IMz`4Z6^@)$7y}w7S6szuH91-yeW zyi-LG!8_VRHlTTS`{`kkz&-|~ifH3brE73r7PuQ}w-Lm7#z@Iknz1(VtthK^xRj0- z*(L)K-RseZ%5lgi8&&c1Omg{{6&C7ww{7=F-8kJ9sBDKejFYqggaZ^RnU_<%nYFu< zNVWA4U3Mg88kS&*H19^=oPry)tqSe|u9@Ca(O%{CR6iI%CZ-dt?7{zU$^gC8w<8wl zT46x@C3@~%ufV<>Yy;ZB$Y_!C{?tCB5IBOP4`Oj3))UT#3EP7k?u_g;4&Z4}*$H?w z2vc{uJDAdEf$3oTraGVpC_Pi@xVe!evOEFVA`=ldcn4a?ZMNf?i1=1pTssW-nCu0M z>wI+8zQZinQbQ$|X9@avO!Q@*$73IFeED$4!fiFXAtO+>H|Z6BPxSHtD^?s|W=Vn# z3RYrQS`@@{m(%3f6*#B?%A&$KRL6c0R?ry;9&1f{q2d?r`QVzR9Z8{t&3kshwoZqr zkWz%|31Y*p8)_G-5bF3$4M9yQ{i=e{f{qu_F3DO~0tIeNq{HocbfqBPVJ%|Hw*|!y zt^4qgaynbm>-7ry9%W6U5GIBU1%~4Z?M-bQa>HGrH+sN+a?X|3$~(09pGGzZ)ui#; zG~KLBl|d&nA?LjBF7MUQsUn<3X*lp7f9%m!^XJt0XO5XC#= z4$1JK)txI!!1~Ej189A_9+|imGQdeSEOu6=fBAllUtWJbk(Wquyy_>SAsdvsX8S>` zNgvWLx$JMSFy#7>tl3aiQ0K0sAqQ$HNjoRF?hiCR0X0m_w=%e$dc${1)jdq9MCXJ) z=95=~ejpW)TJTD2rz>Jg;S_Yu1&JkX9&FDbHzudMht=o9okU9m$LW=*si8q<3Pg_# z;zWDBUB|nOVKA6et_1#@@KAES;$%_&REUkLtksIyB-tF8CuLuI}(pDExQev5| zDWG=4H6Am%lkEFqDW$O$ypHcJsnz7J((SPWgpI!GZ?q zXajIMkMe}|Uy}7+sa&T)yNib=%cCzehl%AU@k?03>2HL4l~z9|+-!PQuC#ws*x)NJ@W}{sCjG<{l)2&=+g)aYZoKZNy@5IYC9x zZ{@*f%7`W(1JFKEpx{`SU?cCEVnq>sP3Gd*qeAs`NT(oH!S3Ym0ND(Fb{@5Q_J+oz zPdF{*&r@@pLBMvb2sUFz=$r3mqPPv}&W0WcE|{ z(b%}~BUB?cu#hS3+aoPva$r&{NR&pxWfB&RAaaY%kK5y0sRnS(8r=}Ud1_LqHnL$) z+;WFtZ3~-<9y$;zh8Ix705|0oO|>Tb9Uw)yU5}s(F{25wK6rp8acLOip7Q#b*V@nH z>T)RmEsmN&e;-Lt5XosAtiNCUb-zg5f7+Pb+zX&V1rwF198)L&`Jwa5gH~E3SJK<8 zcst^bG}N8ReTf3UU16Vu)&2-oQ3|fb!caRMTCbM?Nu#eFCQ*0Cg(uaeIqqd*&@g4X zjtZ_L7uK8jf%cx-Ei!)ScFVq!(8x*9b$h&~cL8aLVur z5>~PS0<%h0(gt&IiGr?GOW{s4tMJ?1+tWE}H8#u0o4En8 zQ>k%z;2zwEjoO~RN>CqHEL-bc(2A1A=58%@Sz4K13FT;40?>g@Goj>hzaTqVXd6oH zfq8UeAB>}>iQC>9wYyp_ZkthJRFAK#i#*jktouZbe>c; zuuON%sPoZBqEh(NA;>SMwwA}F%4a@%N(WJ?OY~&>4nit(`LpQY-IXi*Yv2+l6xrg5 zP3AKBO!Fbr_C-%BxGl(NMU=gm1Ghqc^nMRNO)Pykd<`>DtFWe;!f!ky&$KI$7~H}m zJYBH}yk2D#m_qWd_)xRQ76!{jXT}tSrnFSo@2~?2EQ9+bVRSR8F!qTCDO{jA>?O?T zAxfnTVz~!<*P#0|o>qzX78&Sp5*tg~pms8)d+UGjtb)A*rn9A$PemyakVx;cz8+C@ zJmsikb+m-4glqz3unV5NS>nUg7&{(}%gEz(Rj{XcuKX`5h!Kha2@PTCNJ+Z9>Ox3_3TRtheFLD+$~5Wf!^Q>) zUDE3=tAAqKbk}6_S#i;-=6N*WD8{1(Pl%h7XdwbM_;PcW;r<{;*tRp+=5dDZhY%O= z+vswDn1#SO?hAqNWFo?sGX^*Z`vJ(N4OyU?nZCxc&Oy#Y4nWJRyMy%zzPDtusfWb` z-f7Y*9^uTnN-H&tlW3rG^eEuBk4twm7MK}-)iW#_Fq1(MH<5*@3SZ zX$v6+KO-z~QIfww$yjKEWosVe7Og$-@4GX3b5VS+GC^DH>O~J7} z^M&^RI8gu$6I^e5#B0cRBHl)fw*!krDBqY-ELoZ#^r&D!;L;MN608IF3TctqGq8|` z$C{3FZ}WfSYDp2@j^0VA9GbOk+fxcpIuFXO-IvgPT(SMQ6gC?+)k=pU1<(3sWCT(e zN>GGz2Y^tisVSjuY3nXyka#Wl%z;H&Kpwin z0Wt#;&U4gT-Q6i$T68;7|HGwPJWohqhqRg0fCVM||5|{Ky07zD!#^Z59LqcUUzNDA91OYUm1R>B2ebR%ETASB0 zcmN;<{j&w=R(p=}N6E82ShFj*GT0PNVY|ubqqRhOn-du2ycG>`h%mZIrA?>)*oh{$ zr^&jv%;Wr4!z-rsW@waRqztwl{Ao!#i zqva>EHA*@Hln>Hn7^UtFrws@rm2ZqUxmVNLjbNSk>at32Z23y~RP;8~ZZsCJ1VrU} zbj|xr=uSyWU=coc9K8j{-VNPr}J2Clsg?$>0 zv9vYvUdzxDSBqNi{g4%lIUpHEv#Lg&Xs{9kj&XaKBqdd;z%VDxDw~38%{(;Rkv0}q z{0PgDIzaL(bqQN|8DQ+}!7#VE*>K00`uJXZHHcKBEpOMOYthy27RS@k>?im1qw0!m z(xp0Mm0dN{L^t@&nHt=4EQ9%&YAQi70xsw{i29QN>An@}dJ})+eqjv)^c7mNPdV@ks zM!}jFBCNHH4&$-DB-JuMgatRQ{J$l8rF&a97g~ zm6|(s)5xH|QJ>Hw8i+qA$H0E85&MjuvXU9F-|Y-Hd4Mg09Bbn}u3|iK75Js3A%a+H zP`Obd(0}Cg@-+Sd2B=$zm*a~1ADGl>{cDc*a6NXws1uf3X0_5MT_#ubT<8uW>?L|} zV+rn1axT7t0j7iSKv30mno*RX(Kk0u#+t3s!B%gCuc?p%{X7dWk(k2g0lmq}3II8j9?tL(+{#PHn|CRdK!MwRzdmwG; z)EV7;X;Me!O@4H6Ce2QphVhWf6AsiO0cJ3bL@UC?j24lT^}A2QhYPa z$QGKRZ4~cNboOay^@1J7mbzV!K6Lhm3TVT89Sh(eA>v2=cHpL+HF2DIAOTbK=cuk( z+ahsG$dE8p+tbUPX&Xl-71rwrGc5r>q5n7x?w_392ISaRIhDqo7V%r)6kgBx1BQ<8 z2Zlcq@jc4+0Q(+6WT{YCDmohD=9;(j6>nh~yW1>Kk29hIhzuAIOX#PpnW1FeP0Px{ zLz`_;yJFj$@OnT6AuW=$n2Ta)ut;IS3xM!6(qlI<{KT3-6|PrWL!Q z@{&-N)evZQX~QvAB+GBlFahHqZT10{V7;P5xv$W+!Dth>-YN!fq|VNZr6m%YHnZI;y(6{h?>)rU5WeE6A;@;)$%CLv*G=+vj83 zwr$%sI<}K1wr#V+CpJ4aI!-#aZJYh|H;bA1HZz-dao4#|omx~a>N<7l|Lat5XUP*U zXNK3T{^tqag8jRTRT&=a!_G@I$-08)NTY2aK0?X|*~6pCeteWBy4V1Ismla=_YI?u zKE!WOptn+Rb5|oLNkk$@lN~56eeq(7V$ex4a_Y;G#_>*$6$R;!aUqdM{-A{^etj4; zbWa#pG$@RGh;L8~n}=dMiGHLeHWx8e`Jz&v4eVep9vAH4#HAAHP$>|`32ag zIPTWw%Qf``$K4hIfsGY=dFHLfj-*Y?zsxLJw8nynU_QhfrImYDwr1WTs_YFt&;+MQ z(oQbX2ssJe;U1@I?2*y9m~K5S)s0Fd!>DMA4w|r;_wzLPA2yEl%w#>q8Owc5*zf)c zraeXHc|8aS(qm@9uN0L%IyKntqSZXf%g3$`Zd(*c2Q0?pbb_hHcr9n9#wGK9AtE~w zNw;U?7>0~-Gli5W0iC0;%5{&1)HvDT4e;v4NiGBE8rb0=K5z)=-SxM2p&}+|3Cz%w zMZ`&_M+b!#(%%*e)I^&%*HMKcB^4&}Bbr!$WM!aij~8DQMPZt$gIXp|T*O0sNpUgI zs8mQ7zAht6Jqd4J9Oin%tM_zZyQIuHr9m)1-fT-WKo^$*t;Lo)JHe*Ks9z`NmMTKf zh}B`TBg-AW8Y;~%ja=@bsje5}p<|)veY$yuI)ggM<_r(i9G}rh5PtH?MJU)>s%58V ztRxkR>55R9o5)dUy(jj$$DHs$(!t}*@qziPif%&T97Sn#ViippH*PCw*L)XdqX-4% z=BHJ~*&5aYEEQx$vLIRN2MqR*kh$QXfzVO?;Hi*#)aDQ$MaK&zTpJe%sGOH97Sj6G z^HPIv=eu4dHWsY;oH#;THDwITi1t-tY7GW6q$fe8d(ndtlaJGJup3qUok1 z)|t1xsq3p0bO9>5hC_~WMc2K#>59gow&%{Nj6Prnt*EHhn;^#Ty>)bD*Itm5jB6YA z!&oZZ0?nDXUd9=LPl}Y+^dgy=Mtut@JP)YA6)@i^fbB=q^?a6W(;wcB0y>Ny+D?q3 z?rsQkx6MaRkqrJQrg}>PlTNA*p^YRqC)}D@flD?PwA%SuMW_>Y17kcwxpJMegx&%q zi$1o({sIJraD?1e?=N$jY3gjmj|(z7|KopgnFzt*)2>#ZRR9(Ds;`J8I}gVGCR70y z_RD`>xL+T%Gl^IbJ6Fsbo3~RWXz<2IxskI^+_0kcrB#oY-hi@#!wG7(L%M&ubd({u@=`($txL0 zaqf=}t@J`D=DkW5J%3m7H|aS9ueFJFIM}E5qB=ZtuZKgh1ruqPimjf&vW@hkQ~BKn zmB{2e#sKDMqNo5oUI0`1MUSOFR1fwGl6NW*FCJzWCW(M%L`KLFCFQIyjj3OTD4ySw zZSXw`wV>KkgG4YLtOCvyu@rL$&6S_qa2O)G8#;~y^>OA&6b*3+Nbrw-zJ|{W28}x7 zI66@UNr8ns@Ox1Uf!M|WLsGULI>^mt1viHIctaqZwM#n%aEtrUel8)|@is$k=Wo2O zBX#P3%W`u#&pshM9tjZkHzFSUhBl2!BKgD~dorC#i?kBvV2Vv$d@d6&>FK_CE)e|z zI#6~hY4!sr?w-$lbbBpkO0pIbHCnD_5O7%Ci)gnCJdU4G4$h#GF32vDhY-CCleNJ< zFlpNQ*^dnK$tRS~np8-OE zTK=foRxgQhpJu>e9Q${fdJz7Sk_O*i$kiKZt+-t2@^*F+6$tZwn2wO6sEQDeb$ijs zAVH%>O3%q~{gMIR6RJS^Z7vaqAQxjK#cHp$1$LvM{inXR2Y-mHOY%LFDL z+e>maIuOIg8LU)xIxkako>DN{#?4_Y$>U~+qM7rUqB#soQMlY93h5Y~dl9P52qAvr zBmSk~l;|8@D8j#IPFK0I%h#YXv$IY7goSs)D>aeB#p78FdUWmrF}nd28!m3S4Jx4y zQH3_}+#`GzY8EU)h(8smr0Xy-;g$?cX<_P^@ZB$k1RRHxvGa{}EnrM&zjt9lGV zZ&!>9BE|n)8Er78-~X2t+!0-Fd3oG0{#%-PxTcw(o@_n2yZbtv=gUu{(8Xb6)_HDnYNIwMvzfkliV z4cgFSY=ALDf}a0Iqfl+a6XG#rD*}paeVW*wKX3cEu_S+hDL6nyo088*;?2yXj(yF$ z(z7XB$`)6fq6@|Y3Quw2-PWi#pbAAj31j)zG2Rrm$K4N+rZPDNQT*Y!NiJm^BKLP_Gd}*e6jIcGI|w z3n>`&CdU`qmco`|dOpW7rHX2vS~Pbm1kMfu=~@5UmCeejGIo(h+UgB#ZQlaP`heR& zuAmt6_`wN!T|^D~kRxY=(_TblD206_hG4?X=uzHi)cstV;M5-F6alP;Umzw50ixLw z#PqMpvP}uHWq0(_1b-rpl`9H!l;1^k2j?T^M4X*;PiR?~t*@qN5iya^3;QgZc@NvXZt9Euq*CmV) zuLQMVL5(VKO~9XR6$v3GroGRTg3L#&xUV-(cRwg}4vLsVCvPWfRXIM|wdRV|2StWd ztMqtf?KD+5U0D}INGT3lI{RucCblSa4ypEl3kNB2+lc|&cpI{OkfyAM>fv~D;8JpD z0)l?&AEJ;?C;t*&Y2~0ucWBQeGIIoW-bUN_BL9H!`RCyV?s@O1cO2JckM-~!AeywF zwGyesjg%j@mgnXD_N`yF`D4`^TmqNbz7-0WFN4p&h8%848lgKKt&^vZ5|;@maA^4c z1)ZimJ=kN+lOxrt_3JT5zJ+#-W-;5G+bwJipWeKlf9Ycd}>T-C+a*=6$o_h4`PE8fOMs>Ml+5$ zD4Um|Di(?!7wYd`D!H%%r_G@(q0GK`)f+7387DoC^ca}8$9${6=NR-ka znRK|oR)aLyHeSY%Vw7aYU>gE!#kI7&^={nmxL0{d%P@=v$(=ij7rRkTWO(M~)hnM8 zNySORhREn{%;{!XYx^r)c7bCps5x(aY^1Sh?Vp*8uL_>JW>=|$ibw80#jFS8rdy{^ zwc$&JuR6#~y#)EACB)=YM!VpeKkff^f}4|~Vm}pg3_;y#61&LZXi*Lo zn!;djxun{kI*#(?& zZiE8SWGHOiA)JDOiZ-TrxgqzkB+6+!ulw5|_$W@W96IrXlubk15~W#u#ZDnmT2euw zNtOK1%>Y}?tB6cx)zlHNd=<&ON+C*QWtE)d$q|^Ke0W1sgx@rcMSDta7q9(ye0gN0 zy{iKhU*OlqwW;dv65FsbrWaZt<<_ym-hDoKDuo~(bzK1oP~nRZM=xZrbL?1RKb+)J zj<9&DdxejR7hn}Nvx^~2fVne{iQsETkO6K`CEH!KY&FTo{vspj-LXF)*JM&kRnMjK zcRC1r^Jay^$>#=rsXahJ9gQzNQO@xZFSU+XL|4vtYJyP9Uwep3cz){S)`X46&vG`# zUCeP>$K8*quIc2zhDM$foMbJ=;0h<_Zws?L%o>~<2Nz|uqaS+r%;a<*AQrjGZRsQ| z;-kbS((se(D|#B}i`qIyRxJ4D2n(XZ7}HVhMOvS!BjD)lKJ7jP`AbLmg>|-FU{aKv z#pGYLWkdTzFT&$HXSWtO*%8_~BP}k(<{;Sq))4%c2iqp+%kF>tl}tTG+D)C$FqRgj zh^xN}Yd-u4W>(hLmI>wKk!jMhnxk?8!`hRs!J(+;cJo-&PQ(h7_?SYoxUeQKK*0Fy z=t~4$Y~|N0xpQ8D=xW7JzE4lH^wCfvjy7olBTmeS|JmXh+!}j}EHK?h$ zi$9g)`?25kWVXTdb!W?j{3K1*eAz-yA+6v($ZT;A;}-HlCK0WGPP2Z#RFh2#3U^qj zB~$*P*|AE3vkBXloi_u?DUC zUSknI32x%EFlJckzxYsndDwR9r?aq6cE`%-t9Iu=D3jX6GIHR!EDg+iS=?gKg zuWQhOqOb%ATN)a=B31&2+I|LtZhJ7u(B+L;U6}r2)4EpS3htP!%OW$7AQjqdJPSqx z%@kRVd{FWcC#a`tsdMSEFInU~^C9(=tSjMF|JMEABlq z_g98t=t5OO%y_6DH+Sor=^2_ zBD*VKN>$HDKbdKk+FKAK*cC>ixVSb@iE4-;Ygdb$(G8n zzvifZL=c&-drw~5;Uln-|qzQuV~UTimhA5fsmj?e{|8txXh%YeJGYcb@Ax@;CE zXKeae>W<8h=|leNsb|h<1c9^`IetrfFrJj{%BR}>YCuC&n5UyVz0CFFYisx5DwkNR zwF@r49C2}iaEw%>M<%)?lC6=*9~wlmSm90GkGN#)5VMWS>y;I?HAV>rIVaSs z`K;q2l~Cu|&TvxfQ%*@R#(#L*-o=bwH!a{CMZAWZySOk`x6`5pj2UM^y% z!3am0dX|1c!UOm4s*#Ugrp~12;;-=cq@o=6I#vFKpMR}@+oUzItc~4GDB{qV;O0mg zL)2`v*Gc_C5{gI_e!3%)z4mj$!=}h=L`1Kuv=pt8pL1wvz_~Q{hFr{Iu~iTDwSKP zbGMM_lX&PR#?>yK1Dd&@6sG1Qri6O$k-}7>QV6h$^I4ZyRG-?)G36~#g-Xm5rpg6g z#Jktfq#g53;dR3z!O8(ijV9Y-eR4!n1dsO6Sk^bD(KuVyj|FeKLut?qenh;33JK^1 zZ$_&zR2XVjG-rsZYzZ%fqr2TXw@pTtxo6I|{CU^2y1x)tvUssmCr$iw8Ex0_g*X6k zHZ+}5n#`oIC5gSS4nC6qwOM%@&pFQTkY&S z**Mc5ACuOZI#48k|IC`dk-vc(vXuyqQtRswYJ}(D_bM4zYLT@=eQ@#m5UMDaDVX+exEL|m_Kh+2zosmppVs+H)CvnI+xocRx9_+Lj)5anhe?40OM%Qal&Gvn`# zuo;}jY%fbB$C#)s`hoA^8vQoEL8IEoln~&YR}|Fc2N=y3RQ?!gjf6G18K)N4s6la2 z#djAi61HP0PRLSk>3gP&+!qErg8B^ck@Q}`MyTg!sR51>SqA)BwQ(t-KLsLCm-9>m zK39Y=cNJ;kY57X>97C)Wd@PeCEVV6y5KQl(BPO|aUJvrOrJu!XHwY*s$aCWH@gK|XqL#ZuO`9W!C5wM z+6@xnOrj(FmlW5RQhy38_`q4naCI^{b4m0?v#RY8z@9jZ(~1vWSwT`(f+G zR28)2${QSsV27lh>}Rpk_qDO?rA;6BjEthZ;&LW^D1`??O6fCxzBOMh6B(llhqN6g# z-BpdQ>M&FzmO;-L>m==jiq8r$HsdLUMHiigwuf2603n*cb82cOkWf+D2UFb)i40pL zRDccs$6ulLZI^5-6?RI_JqxrM_!k?5ki^5Z=;`?xQE`X^u}xctKai|j!LUsz=~2v- z6q35=RUsxL>9*k=Sl|X12w}9N|Ij+{QwmXyom;#FxtI*l(?CtHl0 zBN;HMT<1{4HKZDZReQ542~qD8MWC{Q7I6rL!i$?>nMoKv2I&AOAMtg|llPIQ#?Gx~q`Z^RoV64mg|jU^9XP=04#zpDfC7%*99akT zlOqLDCensBS0?x>vL|(U-gN_e!hl%x-YS;rN`++xEpGAgB)d!yM!06~2Hif(fG~qE zgyPvmpwFHMV7~!(5Fr0?ZhWMn z0Z_i)2v{4ufY^UJJx`Ifnt)RZc8Ey$DHbQR?T!|?0+aQO^(2U{>;C!7cxpoNL_1_Q z=R2JW4r!ld$Q^yhKwQTR=G-5~rFGV~2(g;pDfq7`RdhOQ4NT3acaReXgZ|prqlMD{ z@EO{FjCy5Y_WI|e2!;#D2qVSPe)oqORul-?`VVVHkrHoY92DRr=uQA~>_E>fP&4XR zGok?46hwv4IgA#h?ds9fX3A|)mHm0-olo{F86*&N66R-ySTcLuRh0&z8)&*qgY)NwYH zU)g|7)#eM#oQ2iY(x3P7Zu{&K286#$G4ZjYG^n-~yB=EL^){QTPbz2{KhtG$fbrnW z#?0XGydZHYYHWj*yl{BJYyRM7wagjRG&4Z|=oXT2DU&YQxHrRRI#mA0iKnRgprV{j z0-)Xxs1pINe#M+~n_*>Bbb-IpYxg{Va=`CjVf(Tc@v}x+MIuTRZ4qxcgh$Tywkhb1 zUiDi~e#>(AH^8P+@CHHkASRKKbrrDnpyid2zNV>)2Vu9p3Gu5E9~Gl~FPJqD<+Bz! zHkWfsO;~xH0`s<%sUjpin2^q=AxlJoVm4iVhr1Z*nQYEcc_6yh@|%5V6e?7|f$&A? z$Pg17me&!xyA*t2*v6|H5_Nlvb@VBU+={mNHI*tZ82D2lbwSr9?8jIiDdB>uP#R-{ zdHjtioEhV~Z*GstVo@+*_MGQifJk1CQ|mSK6O}PP(q5C#LS_)->t^eGuFKSz+J`c3 z><0BATHjrrfkIAl4{b+h70PYe0Wv8wXXE^y?kh&6*RxZ(UyjDtqOEU3!4&{4);98g z(slNO1?j*(eb88R%FXc}0*)1yJ|Y?1Qw{?$23L#iQ#(nRl%!+2I}!r-OdP3zhzhBvWIU5Z=zq@7S*`zc1iPrPC8R%0Iw1yud zsX~~r7;6g3hz?zo9ncjL`F}$vI*3k0Mf(Ba>}U?w*AV3rpvOS zvSWKS$`6SfytM4k`sv|Pm;R~s=oFzpU97M~;0_SQ#JCPh@W=M%^8C+aA3e}?&#Wp5 zmTCfrVkfs=FjVGZf4{onHWPa0CqwRu!FC_T*mV!l^RCx?E!vrhk9e-j(%<#;dW3X+ zli>+TFx2cR{V*Eb5~FD5X5=0cQNwMYe8z7wThsT|yE9Hl<2UeDgIAx8m5tNmG(l&; zuQj4S!06a%DWg3wz2~sN4E{1NFPm*^_igSs-5fXka1u3MPX3Dbd8eikZsl{nK|-LJ z3E$s=ZBj)xSvKKA7CqFwY}osYb%z63UB)5KojP$rgwXAJ?CN!duabsYY1S3Zf+EJdKF;Yp0RA9h6DCVCc0pebK)N6l z(u-kf;UJHZ`AGy)&5c-3UG(DQ1f;>|kg12TEB1shToomDL?erg%vi4V?m1rF;TfZIGURe zBeRdzS)2O(t4dXK8@-^{k9KXSA0~Z9qzAocKK6o|hAQIk6^5QwxDcPW;d)WQAA1kG zGhOZ6qkpQ+m!a?b{=cZrXIf@$qd7(CNv393E-L2t;rA!=Lb3pHtk1ZhHd1+T^jAU5 z%++q@7G89yC2tXW5$^7`b)p5U63`cd`gEt5M1q2$1h72IeN}1i`Opt5=5m3XLr0YqW7(U|#$0M2?)H?@c)4*u67CBe9L1O_pMMY5J zP)U|NJW8LmEu>7dfHyRx<6fD~nGGFlLSea;5vSVn>zTvwUp1d`??bz@vTf#vRbPcBu&F>{h2)nCiX z{4&B8Q{J5ksX8N*wx6r>)%NEFF-LOq(0S805_a%i0SvUdfC#sk^WeXSnhWpW%}u28 zyg)#m0sRTFVF=&pUArJYlgIv!%^muW_C(2yVn&_1~i7Pu%@4 zm*xt^=!^hikPKg8?+$tb#M%=w8eD)dxPZ$Ov*a<91X>x2XA`AdQZb)@PNt{S`R+k3 zBowy=_%C#Z*~K=&TJkFQ%|z}6M$<$h&)qFBi!{EDU7EYOnj}^*sdHU4I15^&T82%} zZ8SS)p=B~_UOd<9XLOuzY5YPFE&EJiReb@)dzZ5Hrk;{FO4!3c`LWlPA-`Ge3E73- z>6BjMP0&epd%!$(rP8(rfG9{}PO5*tVrv_fyrTVIhvYXR70sN;3u#VNUwx#0WGy$FNBPzExA)rt&7wQgM)jFsWN-Ts7t=tBMS;Kd@pH16uV`?b>!K|yL4Bxn z6qsP+u428&Fao$ zH8bGP$uVuKyvgbm&%Ie;-Hp(ifIScaLba1V+>T3?D>I0px2RgUFWmQ8@`$;TP`HzE8 zNR|pU=nIL;9jvTl1n(9F)?BqFqd#$ecX$RAY-`f-W=;T3?I-Kxfu8x7fFkQ@<+{JF zx?f&-IG(3Uc+@55Qly7=W{UlOymx6*?s}UmbhI7z(FW%x+~K6(AU_#8_hcdCPaW1| z9E=koa)5=j5i}u~YuGzG7f+8toO0#;Z9gfoVa9&tuS0J*)s|a<(WR&Wv4r$11;yeI z9!{_?>lMZkV||%hjIyl41J&pSi>4Q# zE}MNED<@0qZI!Mqk3BJU=T3|@iH;onp$>-USSID}M!|{9l;{lY!eE6^jG;ilgSi!i zlMwGKNYrweKA%*sl1zcQOvWK@M;`%X2~O=Xn1#vNDPohTkK$2C={Cu17U>c;m!MfR*>#oJ1|7Kz0TV?q_+lx`k#uh9 zW01AdJDyFw7!3vzapBFwjIMCY<R;|WdtK7_a;y-=Q?B#?X8I{WTC|9(yvREE8;n^OlzlJ zXkdCbd$KE2X5w)Y5|DLFn(~L4F++Qw+HP%TDOhfqhiqZ%nFxQS0{P*qN zyw3vomO(yf-w|8_&dwQ`cM+8eFjReFwr^kt_UFyu(EyWBv9Z#`#Kp|8>_R+d#)~pP zfxjB9effB}b|M9lLq8MuW{(y>>6*Y1zcQRN+B20?||iw{0EO0(3tjEc~r&->Dq z_vGcrN2PQW-NOo_^HF(|67^_z*F-m7V$N;Dg0LE2>-w4(cz-LSC#6Z5rLdB|zu{2T z6het@c*+xdHK#TJ$Mozo7riN|gNdwA;+I2YT&ZDCof_M3fWV7g4fSgCQJ5B!VOK^9 z1&_`BIk%hew2Cs0>#}ukp;StsE6j3vdLe!pJ%IbR&Pr`?^^}|To+bM;J<-V8P$%_~ zQ(c)H0b$3Ig#0-g4%hb2!*ND5Cq1UWxodml)q{D8_LU z3YV9S7gnA82m82yvp}k*7H_WvEGi`$&C6svoM}bGjN!(N84Zh1)V`s^MQ%*kOz1?FGU)iUWRCs{{VIlHy{7Sa3*k2lNU)L~v8EB??oSHD^US zD#0#a%fR#+#9zJuyKPj(IsT&B_uy=e<>gLlDiy^R-+xP&eMQ!s9fr#V>{`EXO40n< zl%*C`sf&)vfctO9=PTwitE%+b{$_o1{_GmK0$o#>dRA-)M?J<2jV>ogT7E+S985~| zvRC7|x}#s;C*hirjpi#NTGi$<1O~gAe2(uYy0a~SrP5wT@5Y1D-i{+q?8#lH_`pK4 zYG~@kU4W~hl~~orWW-RX@AfyPfheqJ!55ig)vo}TS2i@?88ua!Ab5a)tgX#;ceKWA}TT3E|ax!a|_d*f?Q?7p8eY*wo zBJGATL~w-N30B_ix^0mp4ge7jBOXZHV@BnHfdRt~juhd?Ab~-ZfHD17K8_?7GAnsX z`iN>9C?1A7h_i>592!O9g6bXRCjm-Es1!yc)huyILKS83o5++f`8ma!J6e0RBJ|NV zA4&Z~uKAaQuY7aYZ_kKmeD_sCbUmh+PB@!^3j4kV6@Ljxpx6ZEVUI4?X>bWsp<-hzmh^HPf$7-kCgOHeJzU_$0<+Sal#s@tIks`bD8IX zApeK_PU^I$^na7z75*be&c(vS+`;01+c&@uJcIxK+`ZsIKq0=tK|uc3@IOs$3ZV&Y zHw_4g{?vbp0dHwx;^yvRVPxj$>h}L-*Ha*=Kq(iId&~VI-s=?9!zB+^I#Q!3tf0+Cg@NtE06!?Bz?PwTy z|2i4z+2{Rtr68ZkX#4wZl3B?AVbrnj^}%Y#@8!5lyFTFKEQfn1;HgWv_iaD*r0qsn zp!aLh>TKvC^2F0o*!ydAC-Ck{yP!qd=x$ zz;Sc+=KJp9evw%>|E zNa}CIF+#v&qmU&&TY$LBt0Gd(f#dP0{*321gik^6qG3nuBaZq2H;vz&yWNs2{@Hs? zG$R|`_PO;51?{{Y6lh2^fnB z7|JXSBWU)!kND}@PdzP2UM%Ytx?6H%8&V8RG(L&6-Mj?|ZTT8}xf=CqZ)DA`$g1I; z6Y?G;tht8~ibd!o0ywUIjXBQf8D@FI;`1K^T9QdnF(Q?ED@<67v+zWJ#bP)es-jv< zEkMDAlmF?7Hc(0K!tn+F#y}0FjS|S#Y8(5e|ZWx8~C;xTa!YcICGeOz@)F)L1!OOo?C*Il8ICU64Mn zFnX0zrTNeg@UW1oRyptZs>BCvd`gn5U$@R!->8#mo><>?mDEPGV6MKQh6&*k_6v|; zs4-GBD!9qxWZKg{cYcB0zK|w(v(%8ZfzHb^{}?-21NC9$T`)6(*tTTMjVLN?qOgi+ zBIYZ!4dK)P3`wcj`ce$qu}Dl2=?p^~wWuvqN6yL~=6vkB_mC6sOgC+z=zn>svGRYU zkx*C&ebcwNQxJ@Tg{B)0SC|x)R_y_75tFNnoQSc7560?fSsN9nyjjC~<*RdpOjFyh(uBP)>R!no`b|u5@GH(oFoyB3HikyT6 ztc5=4vaWl%3x0G1hII@{3U4%{fAqj?ncqvXs@V#@n5M7d103su{u6T{v==gIBc6o9 z9kFtp;xeOmsaQNczdc0}`>j>`nMdDU?MVzS2=V~84331ppj33^!!EYK(?u~ePe{(# zwsby{8}W)iw#(!ul?HIS*0R1+FPR1;qAmplopPg#*-DjO4^Qp1DKf1L z0R5_YeXAU7o~VZP^oDfR@Xko?!V|};d|Vz65m0HXnpCX2(kG! zoSVwU-sb8sut6-^BEBcRv6KeDEA?qS0B-_A1y%^{F@WuV%$Qpc8_G4$XH7jpCs`JY zje53os5ZE)K>R)QHI8IKT7e*xa*{yNG%^UW2D(7v;j$QQ3*+;m1sB0QI^!ji8dFUz zPzd_Xb(x2suWoN^&QDBFzp#a7>#jdK!S}PJV#KZV7mskAYxc+0%t2;FHq6f*QP^NU%T23#^0?t*G@p5jR}$dBQf%ptFEbECxNP3Z$`<%QHk{`fwx zJ%kNZ4HBN8BT+Y~n;HWLuyCj<8vj-)CeVTPsX=%;8?+&4#>+KX1$YIV!d*wAF@3NJ$Oa+7KNkgg%taC=%jv{qcYpNwx~ zmkm1JnXL$Y1{}uswF%Vm>{#VF1M+o>onp_qW^wyR2VW5mG^o^jlk00}B38e@CDjbq z#HgRyXb^k|PX1;l?e&=hZ8J5Jx$#!9S$?LxBk^S55m>#m96Q3#U)J(r>Nx*;^1<|< zLk+F4t(Q9c0L2TgeQA7ij|j1nW6)#$k=xo1dAZy~Ojd2L(N+@L2~O6^xQ7?TXpyQm z(h%^*IlK^=N{dxOguIQK+;W8Vs!mhCu)$M7<{6dY^wvpB*4j`|h}LDxfM%hf`5?Pw9MwxkMd-`3M~`O_rakaoM? zZ29;&>*-5?p-&CUw|OFIEjb9p3*!llUiuffEDeZ{ZsBVwL1C*7EjUtcSzQ177lZLX zkmhB7Q!aeVH(Z=;s>*ax2PEAFBAP52*T|eLlZ=ZRrE@Y|UPhCQErBeZ%}N8;XPGCs z0$4W)I+i+T;hCcy{h*0pLjnxM)A~9xPYQO{_$l5N<$9Ave?wMPlzj)NB$vPHs!Em; z4O4J!oo#%z?%^+*pwl2s&x_yIx-x*^_CblnagveHb`Uo;e9Qf6jF1%p(t40q@gV4w zE+h*hEDuih%0F^(6a|;&Pf-3|URc_Y9tiN-kCmq;*dunR0}A40RYqg9>P({%WdiW_ zLmG@(7qA0wQT@R*v6>W`Ic+wP$g^fO7;FVD41eh>!u82DEklZA?IevD1UUE7UsVY8 zQeEjn2*H>1bv6RNPQdTMY3zxKfKK%46b+WpJS`S6-;8auSVqeh2_#UI9hb^$GB6wB zg@a?gQt?k5S?^rq!!}cj=pIyBUeHRuqC%DzR5%PsGzko14!QJDW$h-cWGf-|oA z(~X3^D2}orFoo+$b15w^SobnRbmvyfUb);A6J)_!@Rz)>pp5nU=h_Y{bW1m-swG?Y z5i-=^afS23vZF10nb%-0;cC8fzFx0Mt5rjvV!bMscGPRVAt3a=DiAs7BuIMLdn>(x zTo6+B#t0u1`KkV*y9Q2!WK=`;gd+nLu-W9t0g>BZv=ApH4RWj)w4`pF7-mx(V;=Qr`rW$v z8U{E9CUblnTXK`UV!n}Rwctk%?}qiVpqi$703K0c|4O zsTEz1FFUs?cf9tp+ID!DI>Mk0lZ$rF$@Bcel1DKiqe@V#MgzNTJNVX_bj$qmZYT?k z_C7{%AM3T*LC%cWUAPT8lB}v=UaJG$VY{q%aPQ8t8$bNl&Wx+NpRwq&pVRvEqe88Z zBT+1s(zI?)RZDSyu`Cu-HW)>>%iAnHhBQ39X|Gxa7vB%Qyq&*$3P~<+KXIziQHCn1 z-29f%m?UlQntL+_-_W7wuQtPngII5<99JLnf5Z4hOh=6*D`ywUMJsFy_&`3|fP57V zjOMYcg5EHL7=FgD5+a|k$Uy6VwS1>lh%$-wAby*>&aGoU@BiF;!L6{6gY-CvvfKTM zsLO?ZjTPmPwS9o2!H|J`5|pCdB4-Jkv(9-BBzlb}gXrZ|Q}R^_eqVkqCeSn zk2YJ1*KS#_tQuK{vuE9xLxjbdOY{w$ib=SCH-c|v|D}b_{^|bEVf=zdDow24bC5jM zyDlCY{Ar;UQ#rasVr#QswX~*LLr8R5JeG#Ed+La9Q?cMMV4%RLCFJ|F`o~V#su1x7 zeliniL8Bwim&(8XrU003I`pJB$et!t8!SrV$32~usAlFqh7}?!jib4h{H`Jg+g%>= z``kX}^|HQQlxOzM^c31~}gBW3obA2iF3Fd2$Qq_6DZ>V-2?`7+=8 zBAs>$<(6Cnt1MI+B05hI@v3&S6sd>m1TGwkq2;Hx`ob#sRTN3V!Az}o_gb{B=S))23KgU#OUH=5D4ZQHhO+t$2)l3@nPWHy!5 zs_v?8;e6**om0bsB4N^W*xP>>mg=W4(diSlvS$x45#!+7kKVYu6@zq6hMCTj^z#xs zHPHUKn<24W5QbK5cA`IUmBsnBtT|TYe(}ppr}=e z|D`dAp`qPqy7k%pjyreyeRkdRX^4~BZGjvB0i%-DTZ4QWYRdIAbhIp zY1F+!x(v`t*sXPaY#LPd+Oo74&C=1W(7ov6*t9}GvJlPzzl^*uLjo;;qzq;iX_MNG zu#ys%f=T#VAN-sCSoJ)P%ATkvbWK=0-&-J1E<H5pGM@$e{ zMNqv*Tv~nio#UI}ES|H^@s+x(U-Zk7g62tqGtJC#6$px+8=!dw99swS>c->W+L0Ns z#9}~E86s(ELYfQE($FNBkw4)@o4#Lp^Q#_y>CZya$+69uz`HniDJnlh!b#k9@!lQi zZD)y;Wl=_|vGr>!H4KfiXP4~`g{7Ce;oNCWs7Y*jAlu&#KE0#O9`-s$_PlbPWKD2B zCPWc5!b0i8{LD*aIYlZ~agoE51~a!61sZT(n4ts_-!Yse``owptClb3N*AXSCaZC} z%!zT(MzHZnzyo9a@A#zgCcuXz7ln?y2Z3TohPL!#ZYsz|24nC|rgg9BDlLQxXu-ZU zChZ|O3bRVjadV*uHwPB+(6@buc@5}9rJa^4{E3;!!&0*AKOi#W?r#dqUqIu0gLd|r zMf$ei5Er_z8|XY;(i6Jnm>-Pebu@FHXNhCrQ$*p7jra%8RYQ{2?Kg8s13XV!q@(gXV0_6?`zR! zOV2HDTipLX;A33!fzacsK5AQYw0P+A6uqnYo3%rT1^{WgLS5aBoHC7-Q5maj0gmi< z9>PUIzn2*ZI0h<)z03_t|m5ypQ!qsaU7dT5o=A`o2 z5St9(H3x5Ylz|gv{3lzQiBtZX2TJlClFQO}0N)51a?iqi2Est+F=+U`mD29t+$$_41c>2AQ{ zy1k*69TJVBd=i5~LlFLRE-{22d8~m82$u#X?xyqgla+P zF1mXYzo#}BA;CrV+%NAK?x0uLBj_^7d9V$MD|m+*jH&H`&u0f)*eI$naM}OLtNEn; z>2LlsN!Oo(T|?o8+o{kRL|>X#uD|lnT|fcatP)ZRx2+LyBUsZu;Rn2^PQWQ}O>p8= z0#aIU$fjvnJ`b1XYz5Vtrg0T1P__M5mgYe4uYNl~fLI!tL#sPs62Xyn9n)V*dHfAP z6ah9jC;v)i@=emnPpcta+8nLDR|Fho3?lg)aAXDMp@*|qWcNMVQ}1XyFwR!@ijCm8 z*8q4%{Sk&+nz$L$z9nXl&Z#cNF5uD;YVqi@yOn?PDF#~Oa;bKfsvw&dW#gIJ*9lkE z)NYO(%__!LgB8sQ4K$f*2RZa8*h8?49!P;M_S20vkL>f+7uKrI>rJp%5i92<+m6Aj zzdgTY-J{UP)mLyceG(Y`i^MRw7a$OKWViT6L_OgFW*HojVkJY_#tVRyZWN7N*^ozN zgAIZ2Cs!8+)K%>@Z}%#L^(l8eGtGw~3L*f;j%`peYa@uoc@RKdq=#Q0(EDGESpsyXx37Ua5h*CO*HLLncG>mTidL>v*OU=(BlAErNk*3Fa zHJ(-IO}OU2jrdsDQ_o2WuqjaVRdwOuoMur`4;Xfq&a(QiK3k$6V8zRmw+lGUsdqKs z>04QECqaPAG#Q+GNh@4zD2pSa#{`ld>gW>I{NQXc4-u6N4BQk33mo^f&!DTJCU&%A zoym(p?WOTX{%~Z4EA^&nGEIzHJ$v$_xuDYMge{(!XrVwb_-bdO5?0R?wRhml-X#ib z5{rvg@!dYsztuc>eebDRZFZo;Y?x9vLU71f^-V6|=;_MZUZYs%r{~^jaIu5DpDwwC zv&Ai<`G%j-o+D>J&~JvFdkM66+|nzYOMR&R-gQ%j2sA8If2Xe^RStLlQnB2eMGmuz zlR3-(E`mwot{0k#8N$ybPQ^U|*Slp6Gh;b;C4q6)A1@?fnJ2IZ<3O|R;O~tL~BJu6H@|-IlZvctsgvvdRBzD!3NgL$+7QTukWtT8$W`5X) z3D(>#cQ;wA-4 z_d8{VQ>C%tfD4$G8hk5UV65S%TU`?qodr$D;Y=f`fRC@8qYJ&VzY08VyG&C;}XW{TORgWeS((0#xOcq?Hc7nO)yM)TPo`e}u(>gd$6;vE%1y`P7|G zdV?q=nK{Ztx(vNHP6t>himRE3148B~Ty7ItwpPEi8(a8{ zW2#65#!}F{m|Br~NoRNQPEx6))CEy~qE+})?13$Tz^z5&oxAns*pxg~&EvCS=7kv8 zO>^GfwAi>#h+^4wh}0pCMzE<6nAj1`q{HgLT*lO9o7PI?)*%aR6RNsuTQBmXB#yOq zGijkbF;2NOdswN&>h)8NY-sb=3TPF2i9S%Ec=1&I*PXsp-)b=J*@f@+fVD`wQPtE@ z1V!4jH_=p9kIhY2+2z#R;oNa%BCcl1&v{SgK<@6JmX+mpc+TrONRnA|D|~%jYacv(41KU z>gjq@W2Q>uM-5Y49ca~?HJ-=hWwz$JE@Md%*qEiVPgxWkJ>3ma1QJqsdb+>$&=Jtr zk5k?eU1odT6%H(?wv-vrk%t={)^)|YBHh&wv=abf=K=NE1VDUG_fwqOeejmxA#56_ zL9Bq6vH;Vg+RSDg*-||SZ;;tNV43mCZ{5wjE1Lc$f>P9we%OHQE%Z8U5GPhnhq5*3 zzrH=n#J-V57Z_V=i@xSvu3I~nPp_5gMb+ZJX*Mjz!W8g0-azhtg%CTo6|FfhsLwz^wstECJKDv@iUS3Wu2S%P0--;z58*o-QAx8b3ocb^Uf{?MLXq z|HE8OklqTT5#r=)*|A&xN{6$M;EVnc-78M$$S23c7(*|K=@EjxrDS{9CtWk*+a{ChyNwrQ?Jsb-CQ9f5;mlyjw2 z0?s0+NCT^(sJ58bI!kLQ1`Jup!C4Fo9*I&7dX2TTb2oIq*Rs;jEITDngYpGnI-CBH z&Y)3TQ?%pk>3Rs*{noar3R(mOqrps`hY`!DBcf;GkQ8^rHYEAeV%4#XQ5$|%H2yhU zSjc2wmw4&TpVTLizZ@0(7pgq;G7OXun>OGuVpMS}??H`a{|>3k7cH6G+;J$S2C4J$ z5>o-9fTk9Rr=~X@t)qdJ*|5?b7p^Lk-`Pr<;ix}llA;7WN+hck1FR``4om0t8-<%t zo$mXZVk>z5ZW;&1H70h^WMLU54NFCv3&iviWw;nafQ(^6spA&6PiOSCEKRl~4ey5L zbdU>h)G7xJv=V&z z3G1vMKE~=p*O@g+vq)+_dBVw-M;n}SPk+}LlIuR zg{+ijJ!b`k#cAjsYbjrFYTHPj(q$#ThYPUF7&|#x9KK0&{3dT6YrO~Df#`Sefs*Gl$N88M(dLE-XH@~pY!HTZ zMod+#&M>YHrOT9vK-pY*2cB7u?s&$fk?btjJWxr&{}%&hh%dXPIJ>yx?n`Y9NoY;3 zrv1~ByUUBDd_G@e5k2%zsTCHG zn|B>NR^eMsp5~BAVi+=_PNSXeXi z1A4!0CA*4U(bOUz ztU_S7YV3VYvgKc;_|le4-|oLd!U%JEFk!}Jj4Y0^>xSl8Z>lQ2i+Yts;!hx z@=We`{w;mxa$KqNO*Ly$vLP^)5B1keqzNHG9 z3Ys9p&I-{wmpuueZ6p&KTVS|g32x7sN{YKaFgPGjJy&M-ZZcg#IZ+eT-ifOx8(;hH zuEJtmpMfI)i4w>y@R5mBwo2l!?x_7V=46ysU__V2oDdY2Z8{$u_AXUfJC*v+L>7MR zh-Zwc#E2t-ZRhO-ZJ_4?6tO@=D_tnii{yrT5(WRBVsSsq(g{nccV{T{eW}< zwqC3l=5<^HT`Rf#_%;V#*4yTF*+w0ngp~aI`VcqB*OU$}^dF5sq%hiITN9l<(-gle zl4Bc&eSNT}80b2b0k?_WLfr{#%i15fyDy_pThBmpQ?!6VO^|gXuEYbLj29hp0VgOJ zdHZyvi}GoGnPf>={NWn0omC@4^#cP~TMY-1qyHj)9I61@9EfBpvG`k}U%y%9Pc;UX zC-xU$t($Ey&lZ_n_MEyefp2a_mDtS>HSJ(l7ZcI;t<))V1P#=i$PEGA35Z$6>Bs>y z!7+wq6HF4@Y!62}S$FJ$yWpEb_0ThMn)W?+dO~nvV^0Id;B4_nthgo7kH#eP6RMlB zRJU57s%`^yi3@e;$mNAR+eME_(REaNlU2Jo^6jY$+-*K0&1MhjoRvlp3aUZrYtS2l z^vR?|LgK-UmGRerDE`n9p+qu@BQv10!*18U#)H#HmgXGoy3-SFus38qej~~W5q9Bk z8r!#(g~X>;UL>G+@{t0WXt>qE$QUx@+a)*{WBUMvTS)~(6al`u&3{ zA8$y?t>1}9V&K?7U91eP3G)b_dy(`3_h__ku6RnHKcIZxacb7(Ly`m1FgWcR-Z;S= zBmoEBHe@%En-0?;l^~Y>&ebM8I<-kWMIza`Eot+E@V=#MrR*zH$yBSQV41T@TG`p$ zAC2U$s+WabDH+Vr%XR(@QDC(_qWzp^dhjLzU4_E0F&x~)d{l>!_yn>~V4qFKkGNf1 zZz1UNEb}Q}oN^=`cqZy)Z^ku^uNWvG?jIwzx4oDat>>!>$z|n}(<= zOiaQ6yQYC?A#OMw0SZOod=+xOD=C5E0D(PIB%V{9yD465*^W0E%d!3!a9+kv8^4cP z5+2!F%Fb4Bwx6kJ7K5~ry!3)Z*)VT27M3ud?&rHxus&P*L?vy2Up&1by?ud3;bQ5P z*`Dce4MNAIdy^G#tkZx0!&*gMm@n_6Uk2)#m?1NSm7i*wXpXRvqZ-w(6VWF&>6Z0` zAx0=REP1*YJTi@MV4?^>x$igvo?@=7+D93n+f)~SzN>VCsn9Z<;$}+Uhf#o#lJMP< zDSI#iw{OGcI0c0ekpC9#tz~SR#xf*vGTr~KkjxGg%2n3dMr~(ddCJixyPRUG1Ithv zq^ZOnsgWEz3`@X};6tVLaN;gr03*kigT^Yp%HtNb(Ko0Vh<%EQxx+#|4O!G7w?i=+ zmepTe!4bu^kE=eC0t^NoifSp*NQpg*4qt=b{Vs|uZf-5UkLyl!bs^dmB6GBeUXu;6 zB%;VxTSM0vWzQC(QcS!6M*bQR%>W_ue2vVlG)Qj$MvK16oEisp>gu?gqxwejM{0+b z)BW82=<;MAH=n2SWalj>ys<8A3dkj`Y!d)`Ioi8N3%VCPhL2%x?K`ON> z&=H&ybSScN?7OO%zEntUcwetWDqM#>$HHoTJjdTFspx>%fqDJd6mhM?$FYd?r2*Zb?JjW9tfQ##4fLr>RiEd7)3t09N{c)fIC#n&%b)je?0yHYB=%|v^rO%5WQ}y4U zhRO8Irs4jRozfrgv*-4QLnoB)4i}3doGP-7?w+=XH;G|Rd~L`L9oeeWm0QD1z3)x3 zr{Disv`*4paToVbw0>a=0HFT=MbG(Pl$4GxwocCfM3w({HRW=ag53c-a`5guipM&e zD+*UpzMTnF)LfGV==qh=v7((7OE}f2bF1dHUv3%!4s!)7jjPzJI79_dC|%2K`|5r| z;_;QWA~o%UxOLS}w+DGK{qwUoD@9F86yGhM)lp&71f`DH`|`VWJNvdD>#j1}g7#PQng#QG?}q`phKfXY{a{(b^2`1F zm}sipgDZM<1oAh{BU_)LA)!~l^Ib$66$jzb2Q8-imJWU4%($lCo}hA zzv}tI8J0t#q)G0q3XY=8*B?chh&cO7!wa;k%F_$cRNlf~r8}%A*&(YcF2r7fo$|M6 zYqExNZs8v5sO9JfY6nh~UPw=c`;2@vlwOJpWA4*4fgc^I+4*Z>?o_|hgVrA|lv#ka z@we!km2%T&mXG^&vg@#q7NnDXYfi|#1n6gCzHqZ%K|eCqWUJ+-%+djI40+ns)`zL< z>mta!4r@FY6xGDzb~zlc3#P>D&QsB0J!7q{(vMD z;peWM7d@Y9x~$>{h~EM;3vX#d!rNf5sAm{eo^oI~hO5&%&WE!grt1p+v8 zb4fzbz|EsuRS;~m08@xGCg$!gR=rxgNq1jd~P#E|IqJi3(FX zFXwX?mw4A1cn*jYBGk0#80M-8H!nL$fT_YE3{4(t4tR_(os7MAx)g&%I6{y?#UboO z@~bkP?3)BBj4Owq1yLY_wBO(tgS5ZIAcy~jv_}{bgEUA-K-r2IR4LSyMX1fWsF1*Z zxGDvS$O8~vf5=|qkkXWz;2Xsy`GK*Zun_AYCc!tNCF%c|`X5UG&NOgJLXZKqnFkaW z_1?bIeE5EX{f}Dnb&V4QZZH4FS|=vU}JjdWru4AjXMRSxp>-&v?v{PUP1sbaH!^li6Yq>>*4F zE>y_3t>?|z*XjM(ve|>@X19Bb&zdc?bn{UTuUA`d=j;CcR^ZNRxc3I8a{rUc>HT3%NrH$+6MXs{aE|;!`&X4Q+`egs}r;h8#{VUhb z_Gbr|b+@&3HuQS?=9ml+uJ-5S{@LVt_Q$uI^9Hx|bM?^kxy|S5*KY&Qj%{`pzO!>W z7k=~k;-=2yy4mPfiF z!ZMNKcp+k)n%g1oZ*M|%37DsPoC}V`VIlx%pe~|{0Ap}*uc(W#!;fMOae@^ol4Ltd zHl)bNI>l7GJxvJXU0y)ydIe&clO}ppLRmib{(9vS*oei-g0z4!5%;QfLSmstJ6JG~ zW2ADua({jlf0CGnfHNQ(fP8f|k}y5j$YJ}60!=H3g0cdCb@c0oyt;k0YWN(D5ejRW zoxdQ?QuzolKIG|g%$No4d=t%~?`fiM5?W6n#ww1v#wyg0>VtcWAl!>AvnQ>}bU#0qmOT9AAA zxTvOVTYFfQd@_Bd;$jSOp^qbDC556w&3u4RzF zk`oZ1+4?nRIvkECvM$3_RyO~F9TVnZ?J?tIo0Q1^uB9Uy%ccjHwKqn+516+Z>+`WD z#^c9zmO%CzxSGyUrEUNu%Oj-TF?`kZk11niS%P6o8k=F@n2&+V)pM;cRK?4tGKWZS zq0G&s5T6s5xQ6+&+_yhi# zE)pd^72rl;NfS1Lv0d-cn!utBTzl9_a7TY$7uzN)!)!>g9fHLraz|n`q++dR2qMKy zFJv`G)T0I)NUZ_cXM_lS>sK1N0C<#g0sh09kS+MP_CN%oUYuXdW2QY4aYa>Mysn42 z(+U_V%7|B83rOLrCHd0{pVzrsmR%U$!XW?>?O<4tf_y=i4HKNoHl)YEUMpK;6TrnF z^~$mG2sX%QmY?LKL}xv!`Ct`?d< zbgs@H?qKiT2%iz4V^Ntl8ihMg0%+YTg?~IkF># zYz2dW3^^#7e+i^OC-6T91c*UW6e~!}^rli&lBApQJGNH|cZiS85;|BinrwM-yunQ+ z1#zk-%;`dmvV|KI*Np)r(N-k;!@>OFO;*-?_toGLeSleR={MNZ<=p&p za7(ZOAE3==j0rW_W-yQhA(==o3yxM%x=S@WY~oS8$QEb8G-Lzps}q?>3+frm2kHaUCz|&+z6`$s_G!?4c`OxiE7`M#ZLBT~2{;Jru z7Ssiq;C7-!x^Cfe(ApMVCx(+;m+%sNxwa0bF%<7eYIk5d+KY1pj)iXA+buyv*ue~eN zK{Vh10C3O$Z!a7F59=G**_v9Ixj6pkn*P=3q*}mK!N8#46j|7} z_v`6Eyxon^g4EY@ji?ua<-*3}48@>LKCvhKt&Ahf~)|_FbYYX1?TJd;t z@yNPUjvEz2jCick0A#PN&zu$OHdtjN3DfRJflLh_h zo?+b_R_Qo4ZPC7!bq~X93wY<9g?o2-5Ng?}eh~Wl@J$Ncw)E(d{V?nC_Tuv$lfRsN zyp4Mm_kH`vo*Lipx*!sl7D>is#gPg`jLT#&M_MVjqHhu37}}xfM7GNec&TqT8}GcV z8>ZXEG`2OkACq}(kzQ&KLM~kIkFX>zrjSa>=w>kT1Ve6U{YmF7w&JW+Y9(cn@wKwt zG<(k78$2&=%cNYtZGy5L+!>cHoE%?F@H)C)L97bNk zYlbuG8+tw6c@u<2b(s}2IPBcY;laD(J~Y-k?b;2}Bhl|aa!-t~c5k=)pz#uN8r{3H zp3I)(Gx-XuAtUbsrSP|f0v9r2r0^GTfA`d`QKx0ra?C2%22~57ou+o8DZM(8IGR73 zxV&+bn8IhfMDp?iY@3E>oAh>JemjZgdyBNYIP=oX`TTfrvw$Y^ey|1qe)RpOPJ8*3 z$k%&H__0xz;4qMC9M!Ac#%IT5_OL&M{J{)z`48XDPx`G{^&~6tv)3kdw?1n=Q-8Bo z(3g~s7IE3hrV2x8V=7+dmzMA4gUW}I&^XDM)&#A<;`joHO?`&ysC9_h<};x!bvH6j zk;_kyHjQL@x4;fb1;xU(FYM)u-}}{@k=y0^+r3%j_sfb={e>k>I9!KHi?7BOAvIak zC{084j9KS(K3*v;f@S*~uR*c%5leGB`)#LB&!*k!+~Q_NqxNSP(45&Zpek152ZbFlgP)}9Mjg{VA z2H;R&71FE(o*$15XUr0UZ^qHAo4EPom!Kv3RaMKSx89b;%SXqSKJMpSjpS)QIK_>F zi(%$M*C6q;ZKsJg-yqZM5a~&EPi7ua8Go?ANFEo*UjbF0VgS5 ziQ_xRK$LEPf3a%QQflG&xWx}!wyM$NOL=t1c5N znM;R7L9iW;D>j*n4vwwS97mPjaClj27bk5XFji@$lkr?~Q8U7VGO1*Yid;#&>aX`K zCI5_w}(X^kf+boH$H+ zeF8uuJ!60YnZg#D3n!;{|Ct>ZuSx$%4=Ff(hGr3km^^=TdWsiMbHdEa?6mJh>< zdc^&XtL|}=Lieo3GErqO^kX3%6|tp!1rI0ut|bgB-pyJG1;{{4?-t=rQQw}x0^E3y z=Q+>~Xo;k8-CiB5V*fn#Ca$dAy-L^IeZW!B2B}e!jgyP%XSxckMP6OF=c<~I<%4Fs zYL3YpQ2h~Cgi}{OU!F}*dJw2`!ssD;*mRCY>D}u%er$<)N(cL zBl(*lnE%1wFOF6RrZ>AgF4l=6Dy5X0`ep*kN|&teGJ$DWOe+pyryM-#tl9*;s*`Jg zdG#F$Rmm8qJeqJ8<&u1?TShVv3TTx|jR93*Sx$&n%*T?LvVaKd zBbi%_a!lz+MvRpfP^BrMZb__(;>PjWkYC%(dA3Hk8YEVf{ROEMuRXvwr9RMlL%%VD8RpRn8L8cp2D?+d# zalQ}C#y6uj1-j&9Zj_Sm1ayz8*|~N;HlF$`qKhGehtz;4NtS`-UjPFFuVFjK8u>MT zC3U@SV|j#>3>Q9X8l4d+Ao%6@&#}vcn+GZ+7t78d?4SnfrTVEK+N&l9#b={u52)rC zop;KN%w+lni|q$Q?oliT&;<(=Ewuq*aU}qx7BEsPs0vE#FGycS3Ba}F-_a4vHD-8Z zH_n`fW$Sj2ZAKAF(}2v?oZAj@G~NAY6aXxf%6_J;s1;1@z#n+U54n)Kgc;}#OlZs} z6?vrD!6{JisW(V#$-jX2aUM=un*;@igGa+Wxw~OTomYWDrju<0;x!nGUh!}Jc6=?%MDean1KQcxMN6z*^ZYUc9bP%W;iOG+U z1t>n>Ott>sEwpgz;CJ>x!F)G)u}1C+i7$86>|F+3Pq}9UEkUb?tkm*g^qBI+HAi=Z zRo#O=Do^g-5gmt07T_vFo18W&U;S$a0{J5Nl_>s|j=c7~orq}6ocaWSei$J~1FXlN zLJF6EV_Eg5`v5~NL-m8|k2=#IFjF6G@?A+y)q7eVSb8PX`6J0_`SI610z!b+D)DQ> zI&L%;UuFmckWnGv*0}7~A-2#hDr2C2-ScGkexxr4`&B@pNdT?IEBZujkZ_y;d$Gay zw1DljT*9kR)l~B2`vhnD)G5!t0P9tbb5L=fa9_;=$p$6p508w0dX&A3aJxK1Qn8{! zn>68J0Hq|*K(+LR32!kZG|i>aEE}w)=(WdO1aO;Pa36uLq<7R={4v7wzsM~%T1>yg z@VqTVFnul6t6l_RJ}-MBA6^m-`nLk z!%hgQS4Rmdj#tv}fbjyz`jf#*c5GfHx+_i(*Z#>Yk)!&MViaEr#ki7}kb|-fZVq5E zYP1{%W*=x6%$8rEyu>6(cEV4tqUok6=YdIXOuz=862mC+4O!(ZQnajNhQ(xdeBOaQ zl;^dp36X< z16Exf_C@{*hZ-4Z^N@NoB?JoTv5Uh{1a{b&us+hMpDQZG&3dTFknCZlnsB$uT9^>> z2mIlk{UPt0^XG!SQ>%)DRXdKkuxv203e+1>j?H5HNG2RO`b{+SqaT*tuPj;1MQQZs z$gmV!v5y1VSQ^RdLe-R{9oYZKRDa-Z{`&`r6@>P26;euANhk!HN_^79cmoE)_-r9X z`=zVod;*ApBJegqr-KmXc~7glSHt6kLlg8YF90}cd565eIWLj@_}Tvr!-&tp+yjp7 zOT_AO-$&R$O&%FDtT_8cA?CUpT)zJ|qe25z$G}iQ2h0aj42z7MpX209{JG)7&HGUM2Jo8fVO0uu(60u-7D6z1yP~ zNlvL1_H^&<#HYAQluK;T2!j1zY&-b_A4kHvqPRf217`rDKFIm&ElKEYYFOo@CtV>m zl-5OC!54fxNw$n`7PQ9=+8c>Tv`D^-uyTC(6%R9XKXt3U^4IRPmP3+O}Th7^>E3R@|D%BcAKRy<0zHpX4-ZWEgdtpo|lKmR0hvbg5VqnLx%Gf)D%F2-uYsZd2lQzp6O`LTs-x z#ah~1kIBPE8I4ZzNeq*U&W15OF(9|Vjhco8dFw>o{Q70b8C2zc|j?qu}X zI}vZTF7A;kWUkkgjuK=RWm$b%{6gK zAP|9*N`8aCKUBhc8S+1uIDL*Z+}7mz?wP##*WlH9t>5uS2(Y6U6U@xJ8{yghDd zt*A!J;=SHJbU`aCADu#3JhaVD0)`t$!`E~`nHz(^__j+U9%zSH^$$#wwDpeq1Q4AHTXCjUqRb`d z#L>gAG5l@J#DG^#P4=ch`U6Tf^-nvU6sy-4dwZti?nDuP(7`PGkU($s(Jx>Tmy)=% zi0!h_05aP40oQ3i=eZ4|x=#Z3$T-KnjJSQH4U)nvktY6nrQ3rV-Nn)4bn{MQJ;11S z_#-|@Wm0}By~sur#kfjpZvZJ*$!qg;)B&9Ot`;Ns3vrF$g(8M_~Kf^2c;MJYq8%U9a|5OF^JvsDaQ5 zz^cdh-rL>DA7m^5rS=_=^=Aikndu|49Dk*2z|=A}kv0fe`=e7-h@_%Qla9BF?oNQ@ zKbj`OoJqfTc!y78|DI?g;?o2A-`D4hEr#Z3Ji z&+yDKjnEz|0Uf82&k~dqnT&^3la5{9y_|npMGxB2nzFgvhK?NO$^T*BdDH9H%eil~ zjF*YIGh+Q2p+Up%uSM9mi{G|Xvw=cPI+sIjeD`%Rg`-F^k=dS2vSSPo12C?1OQ|4} zu;`lN(zTc!Y)jN+AgMZUpXhRsU9kXqwleaPPiFR=g}D%a+yySM{>e(9P3M7~RkQdH z-@=YI^)YDv=L+4oruDC519~OIav}8* zfMBDZ@9$rIA;LKCEh?v6$rT1IS^kQD!F z%Dg=$5vxarbL6WyVN?gknC~s>n^vaTAmU?=|3t+b2W~-gx%Y;hf5f`u9kBNUw15*ZsEcEy(Q` zy(ip>WWBrFa_=Wm$+(>eUyO6|VB4!ePQ3zepNxfsoMPN&gTsM*DX!#udSB+WC12_{ zq-}C|Gj>etHogq}PLwi4xc_knH6mT!5sVXEY>acg+u|nCQNXzEQm}3-V_|~d)?OpB zl_&+++#;HUcAa>(+5m}5-SdjJN9d)}X46R%T$bWq7xkDy=4Yn`d3t}lQ8Sbq zY8m(WdoYPjU+d! z{;}H8K6vLu$Lb{4owzlHEt~c`5IcF;ud=ao^j&24Z4(~j(<{MX-p_`Pzz6C6+jnM$ zm;B&7>%A(k46lth4KhDzukgTvzJm=b+#5%?EkvIP22K$vxi<&3fo#NXxMY_=cyEN} zcP^f}^R5}lHh-d=lk?4X{^!{yS9^@GeT}d~fkw;D=xFzK)juawT`R49uQG^2sAH=V z*jvZ_lWbB|XILXwCy+;Z_fn#zk4t0Kfl?DyBpAgw{67bqSMHaZ8p2DKDPyj|hP5(# zjTfR4dJ@^(b3i z{#;`AJc7lWxU&Ns59Y%4_khqmwz5H^L)p_}7uVQdOcOCTcfGfA5)MCW0KgD{rS%)4 z1=c-7%ZGwPim`ht-`gY2yhumT-)AaecIOHSY!(q>RB02415UcOSrPGN4~ClpDLigyF1G_Z8k%FXp&JRuaTe3B7r3V0op)Mai~6@Q&rZv#FH(& z*F)@};y*_-DNU0&34<74v7yt)G)j|V03(aDe?e9Msu!lnQ7I~o6RS(XxGR|z!f7AF zIdE{%AR=aMlq&IUJ-6#7YHBLre49msX=>QC?KlgZBm3L4nP+h1?==lI7YR?tuMo!r z32xk~o58M~9)S^_b+lMWTr9aiWg)0#-c+UCf77EhqR+*{cmCE8; z*}C3Pg%*;lE)!X_LSn&2M?bF)WlKxfs!E+gdBXzxUPVAlbz-se`uv=3zT)Zi-o!fF zh%Hj&8-(na(ZOX7$$d*g(`MN^MM-bWbH(}M1vw)P?r$2@+-S-oFY3JlP;irO%#x|u zd|edP5?x3{6|pbXRUd~0;Kie`wk=$=47xCke53kq(=6GTl$If_e{1 zZQ-1lP~!{KWpxo&kVKk2M6+#<_V!?Rw>63Qa@HSWbf3P52b+sGI5X9;SWoN7#6?9b zKvB2AbwCAt+ zzJlG=7Fd9iE_8UCVK*T7rwpZ6RobROlm!ALrAz~s$I!eK)ng>45W^7s5AM;J0j730 z;7XQ>30tlljoli=c1^ z2hq@mTEYFXh!7M5s03%xDH4ysb9gfWLheY~aA*^j4eGVfB`#G$_BWiM;Ou98a;t_L zKNSD#q9qD1-QPrzJl5o!($`4N#S37`t}&RN_CDgn9A8JPU<%mcM{4_s-iIGG7Ge7h+FU0SWq1T{Kd)-xV!>sWfX+q`^UrrBDl7+i7p< zJ!`B-p335i8U^448OBlkFg9>3AuI{CBcH?()=f@u=1;el*TsUajvn3WEi>23bK?ni z^w38N@eMzmtSn-?C2ko<`_zh;`L{oykualAGKmVJ-8GEEAhbqbl;EXS${Sw6NazJ9FE77bt7S%R5j32(kgx*>p_SxMR#*Rq z*fKPWXJQMBdxhwzMb_ILt2yZfFct>;^tU@%@@1;|nm6@rNp&k*EiJnW=kFPjN}llH zy~9+XRtQaT2KhcOZ=xj5gslh!W(?$OSVp9uBm*Td*#E;X`_2hQjv5qN4~;TR_XeYj<@L8<%~ zk+2=NDG~3!%kS~==MNn|A4Y`l;{1eiiGZ;1*l?o_9CrLhP19>#FVb{j8Cdk=vh9WN zp1MMc89f<_PErfcLiQ6uU@Ar{CSgIOy|AoTRwU%$IlI*Dy45`dwVblSODtwD6eL&7 z{W>5}P$9x1Ms((gA^)u;oituHt8j}wA8M<6x&r@=Tp|d+P$)j|UZDSOCWEdZIhJ-| z6GMa%!T=8;H9iQ+GeU`D_-WSAeR}SjFH$Q=zpm1NhLEJ(jD-~%^*w0b@N6Km(xo=MQH;n=p#iEZie0`@){dOuKJ+?bW}T@ zyclT!&w!KeOR&HU7-3xFNWWCpj#D<(Zkm+stqG-T_W#``CxfU2yW=MZ&PcX9kR~&c zP-f*;s2HrISl~|f*`?=tv+Mfr5&4LPCeB!KBAU)tX0u@)A|*%YfcA`I*k;o zKdWD+YIlma$p(BkQ-ug!g`12JdDAG4rK$pL+Y!p)Dt~0#fBMBaOdOzI@{!B*%b}V3 zjp((}32cz}`{^CJO|X2O9|?V&<-Xt6ehyE6-dAqFUtg47eLZO8_PkzZ20V}Fe(t9R zJl*Af4s3tz*M41=_BT^7{<0{d!Rh$j1(N z?8%+>e|p&#{JMTXHVAl|%>6vS`aCTC9wj7{`*COB_wex~^l=z|)&0>L5a9Fa(DU|4 z-19nI8}RUe{PoFY@X^yVZ?so*PhH>f`oV z+x?Q8+w=B$=g@Kf^*rez%suer$kn^v$=k+i*;A7F_E5rY> zk(loTJNN74?202?=_s*kd{GjLk^6rSZd>E@_aqu#qKUo)_)jxK3V8(vi9rd&f{zE>veqE;Nv>;YHMTXuAxWU zXaB%$SR&wa5jpqkVEwB5v$WR#!G!qjPu9=Zz0`L`Vi(=J0RNAHuMaMRfUobc-^aHD z9=@~ioatYBPfNtezFZ0*k7M#0q>6*4n02Eeou>M_nF@+yU^!Q&+}w0vBlT^^ye;h&--ZZzKZ=x z_h!pm_m$Uv+f&wWBpqv)q{$whg>7D&F~6gynSbtG#^M{rIV{qEpaM$oNg;VvqI}jZ*tws z6;G2%Pf0vgc1xU4S3CM{Q7$c=b{lSVK367J#gkrdYFFzEKf8)H)-Jr&F6~lM4)see zjgDoX+FLi~{(9KAR`nG7)Vao6>wj(J)Gq7#;LAU04__o+(p&f~t#Fv~OJ4jGYf5cd ze)T+iakzNWeNu6Lc+kODRqVd#5ESJK5afIK_gd0I9kW=8Jip;SZP(>7c|q?(=wv2u zRjadQvCf~s?_hniKHS`#HNN2MTt>_2RwPhbADipo&|1!8A1%LpUX=CJXK~MCJ)IF$ z)Au&mTT}ph=H|(qt|+hg50(Fy7V*j}XBe^1@%WqmE0#@u>XtQ~pW?>l3OfjgU7>vy zUv*AVhm8+_NcBT3c)P^DaYOK-Bc-fcH_CQ{5&A_?(!Q!E>r8&!ZJzMLyG)>jimmV@ z?NaU3ooL%l)T1_>&Ly*)!>?K6s04bkN3w#Y)8JkHsmQ3D@+84iKc$|?%0|2Bvr}!} z7Oz6ZtADMDe^_uhI_}H4z~)gr)n_zAm!YQJ#ca-aKE4!HO1HzMZjo2=>87MyK_IUB zDm`8LgQX3ym)y#_E9CcUtB7{~@^Z){y_K`sRt zx%(jy7!vtbkpciF%ad7KGN=m;aJt9pxTHQ8$XfE_KI z797-QH60LO*k^+aYj|tY9ov;3=N!2_3-0k)R#wE(>Pyb6l<*XVuGq_k-l@8{5@VvW z&zsS4TA z)uyJ3OB(yEH2giMQzX+5Yr0ubk5}_GJBpGstVE%|YH&!bq5B@_x82SXllW;flqvtk zm`S0%Y)*Il195w!NzMK{=;g90EeaKx0ItQZ?wr$y*LYZ?$V$XwW#Hh_#F^Ws@G`_9 zaK3|(;f6DVsyYr!MR>gxpr~*Wac-tkiSe6A#keo1S*8@pPm0mO-0O48+$-B8XIwr* zx5N13-xQ_K{cHn6g#*S=tAid}fwBY^z{La6nux!nDmACL5>v>$>%WLP1!m7~pROCt}5G3j?nL>o$i%kypF(#?d!h~#knfoAie zlLkTX^+K&D-Rf&wl=GzTcgUi!=ZWs}?QBD;ezgOabB1=aO0WK*G|9=cmgeCW<>sk^ zY|9;NyGlzP0vh7z8BdCeyb;I4*BWXjuS z_<&m_ZOCy8v73uE!W{X92KriCcr}BPtAvB)<}Yqr9IY)s-vsr=1%6p1t#fz3CamLL z|KA-zH9L6+qNOpk0p6tGUagZe#$r1?TiBYPNfgtKOx>LtiIt#pu`<5X2RnNU4Zzc_ zb-8-kQ!n%8e?4hh7pwf40Y2)Lt_y~>%^c%sDD4uPcK%lUCrGTs^k$Tx6DBh6SKx)b zK7Af`^tk$LKHJusloPIvIsqn8&D)s_EdUck3E453zx(A6t!}g$Jm7fW6j{5fUn8+b zEBozs2%!hfhd-gha(=i7X@g4GhcCqcyqnszym5GhE{fj&aU6=#jkQ*u*J+%yx(jZ0 zKs)=HVns5p(0cYHlQAwc!#Xiyv+4Ye&U7Y@+h-b4Nj<34@okJe^X!URKJ!-AH=Ku{ zjIx`EB(>w;-(q(lV7T~=`>D<`UJ~Cl4?Z-HaIO&pr%z2x!@?-BTn(=^oa~}~%uw{N z@`cex*5_dCFFaGZb@912y^a6a2{P9YHcAtsajpZCBJ4Ua$;Z4gWB@ma6`dytS~I=a z7jA324GH>Ts4L@}Ng{EAS#&`eI{NmW)Fb}7L0lBaet1G+m6`JXUKJlfgK811t*f=2 zJHg2%N5KWF*}_x~y?w=Fb9d2Y<5S$#M@TTw{XcJ;c0Hs9tZz57tUHxv6KbHP7|GD1 zq~(PTd$E$D34ZOP()2)-oOQ9zq~-PqMjuTjlfbs7o~o$%;H|}L(-cL%W}(UeD?UYw z$HvmH&soV+hpc1uX;3t+BJMPGC~fx)=8czia$&N3K@=pT1EE@)`aY*F+%!hWeAFZx zY@v&=pM_c}y}8=p9i6HG>$Xt&N?5*Qs$WzI^+tikZT0{i%?|Z7?=`Sx!qc+RN$;;OY{&>@M6})iU72Bs{@-siuk$jT@>Y~?NV4V$5s1Fw1>XrpVmPU zXCyx_6YOinbpPqE1DIrXzY&_x3y}CF=~=^Kb4ZODnd7v>_08D&f8q1a@$Hhiff3NH zR9>opu|0}No9^+*%M55-25*F2Wj-fL0_d3BXaxcBM1^I%S*k<{vdW{2C4X&GG~Hw+ zg$QSg^3H7Blx(xMmSBka?$DSHyEKX5*-JXjB@X;&Q12soZ#nnSBziW95c<`LS%foU zR-6R$9M@5{D{)v|2Ng2o^Fc}u)CKNwR+F$)@XKVV5ZMkup@c4 zcKH`)zI@tUILkhy~;kPe8RPAYmn&Ki!;`)&p_fu1FJc*^@m z2HOw?Ek)4{XZS`N3+$6TK%+q%t3s^3a3hVP<8Cgxg5K8aU!~zeZv`Kr@xzDLI`jVK z0rl&#nW|HNndsBh#&XD-MY`<#^%?Kf`1yqsOe?}ceg#%1xq|WtWgR zyLA^B+d!+3xord_oI0hjwF#MBo6JYwDOIN`GXV2hFDkL?ic)R&f`oQFv*~4RzS;zD z;OA-4b=C=S$-IA%TRFp*y(p#?Lfmsg5aj=i0sZyBvjtK2Ix2H1ZnlnA?TpIzOWEcT zrP{d=+F#s-Sz22SPZ1js_*Z+Al`)E4p{&wN*8Y8XPQYokcwf?!`7H3%eKH;8|?cb8xRK!k_v4v&d zW@o%V^yzJ{Ob>WdIi)-ybkBrCt_B@AUp5>qoZGlMml~f2c;=w2badge&@p6G^F40& zFUQ~1oFvT>!JYr>F3b#L6()#9ywtFI;%FT8#0MJl!{9U z>{g_R#KD=;BwDFsC8wTWn!c#Or7p|jhxp#P@&?I!yN*^4JOI2D6AvXRI1MQwMaKo+ z-a3-;#^VpBt3~q}$@oqA5SOhQskLF&c#5M0xCW6TT`0P|8fhkDPC_h}o2EO0;WLI@ z(WXbw(q-&E&=doz;|0eu18b3CPr8Jk)o4I{FBeBe?G+Kk6AJke#zqly`!4jfnXx52 zfAl$=IEVr&g}*}&MVKp(VxGiOMAgp=d)x$-`*Z!xB2H>a zl}68mWQxdPYlhC?w7f7~cY&G&bAc<57>clzi^%LO1*&o8Ds1;GFVXQusCv#i3?<;5 z1$p3nLv^u0b|Ohlmr3l4aileF4x&horlQIMFpG6x%R7~PldnRYn2t+qU2}SsQWLh3 zU)+svfmU)+OXTlaeUZSl_Tj7N1$O#PVf@evtSBID{MwJFN5WeZx&O5pzD$55HxFQz zFwpm!%1h2u;#P+$3M2_IVX~?YchF(cC^ju!#4;8+g_ut;`Z!)!?^k(^-EH(vTXDc95P?U2l*oQh zfiQx`*G1EjuSuzC%l+@iot5}RPyo920@;&KRPaq`a~+XAg?+)wUBh@5#fPh>KtyU~7Z-7hlL@iafS2g}P{c;L=Z1@nF17lBZht2F{3d zg?$*9)O~_nzS~i3VO*e8?;MkjLfX?s30N~aDCAjELLqjpk>2W9P$4Ha8cyysp}fZf zO0c%AUAa+N88TUgxoF0yHpnIxA6$tvC91=wuUSx^*M1xMYB99HBDk_c7)9SHx1rM$ z741R?MtV=kEaLq8Aroo+fh7j|ERKMRShE2FV67&lJs0<%EklOlAop}wOYji*O#mE$1gxxG zof?@95}wSKU?;jB6WOc@wqPWyS?ykmLD;Gv&5hpe`3NEQ)1rIjrFZDXv7!XU#B`Y? z^Y)cuF7zY8cx9hv>;oCR@2fUv;9&dqnj8$U$a}I-s9#7s5;jrmCA1jCV~hPeVefcc zyMB4kFw2H38xt;su4J#AXX4(cieW4D2PRnb`A0^ITx<+IK9k#lw zPq&N+?eP%F!e{aw^I?gmXf55Gf099AFsje6PEzE0OF4rWS|fNA=%4L3YknmM4qLgy zg$l?*N@ss(DMw3+95gA4higC*n?_Fb4hTzT83{a!m%ct+=QVQFQZmU!?OJfJ@cgmH zt*z4_$=5<3Fv1*PI~TG55-y!+g9cp_6Gs{J?I+Mw??$;(oQ8s;KGL8$8%Iw?rA<}j z-p0>%6G@w2TlgXxGD2)zWJeCr#jeE3}viNA70AFZ+>1It>1AQ zT5foy=rCZ^&Bjv{S54_JIUcG3+)HzVto2P|3`iV_XdEF{WXr2L0isK`x18|3P(0kA z0`p0R3LyEBbl<=U_bPfo`dZ6k9aHfpb=yKrTBusU-{B>w*19VQ1vhJy{@xRlMh8xW z+2Y^{-G=T9~)H-z4Mk zp}RnP34Zfg9V8x4d0IaERjF?w#%C(*Fy$H>k@L9h{pd=naniL`Uoz9@SYTNQIwA~( zcR>_e1CKbZvN0|`2UxS-9XrfhQEQ_wx89nf*mea2ey?=8R^X8ggqxtI&02x8)r~Q^ zqq38>yIfO5w+uT4MRsS7LRBoy?;L=A&|%**iL)a9lrOn;!7rRwb_Umr*m!K9FU!Xrgq*T^Uqiz*0LnC?;-YVBOpK8w*F z_0jJk9C$FN#4oB>k1Yk0^}pbgLWhuGbFUzI7NiiT?xWj868y%=@$~PgGk@I z?5{lWq7?4%5OCjQ+Nji|p-^g!KW8v4irN21dLHQI*lhW{a$8{!h_Us|HH~t-_5(vM zs~?SMKSt-?OHW1>sY}@?buCS%mVi!Tvvi&wL)oVH%#DVqtOz zBz|;nlT6-FaU2WdW$*KScAK{`L>xi74qT9$B{c}uY7CA}?z+EeW3Fd`9JXXxvAjP@ z?MGPCEj*>7bsKU5Ryx<7hvniAy^)Tb$gW^6c$#!a#xdqnZr54-d-YM}T~Iy_sA_J@ z`_ecznduyseHsaTX9l?2(kT)2e=|dRwsfIco!1^vG-e_ruA06iIy-D3xMouJ@F-=Pc-~U1Be@gQ*$M6Lk-iVskW62~mP}WRo}ai^(!$AZRbN zRU-v7QkAlJ45lSbVzsR)?MiMXa=>!!d&4d=E($i-YF(h60__)G<+gwDgi8`e+HL?- z+bO5=fM4X)8x^>n=*s!^z0-(vMu}X9O#H#=xcEkVsnhi|I}=~ zq-Yu%iEQUzZ8};irb<6w5|^{G7J@pM?UYdrs1a!#WRJ}7S6MFP7wcuC=*$9c^^8U)KD9$!k|hJ_>o`F~ z6y(HN#kjHN2hQRO=tHvN#8<$^m82mCUC*yd-y>wPLCQ0jQ?gS1GSsA_A3@dtc@(gI z*W3zIi)WM&C0qncq_gwFYsnv<5$*(Ah=w#6e3B=(_A=X}V2i!lMipoE?v9+)QWl!L zvVWVu5lt`0zdbgtOjBI3Y;R2V2!By9jJUhIK5h?gWh#Za*j-YkSm#xiLXM zftry|2GK{L)btHWQf(vs3AK$}f)z*&WrI%t4FN#ql{pH^Z;@psaI$^jK&MO#+!@WB z5sg=6gQX+pc^2nXbtzoDBfyjbgc}b5Lqd} zp_*-{k8v)w7O{5Z9Z~Gm%5WZ-`rSe1#dKB{ZYM?&sHu_0tAdc+us)gJ1qj5NIIgqv z_o^+yg)e78cK%Zm0Zx1w=$tQE`LJaIH>=swBS2i({e zjxPmYEHeSn?virPEr5A7NQZ-O4bhP72_qoh-Me)n2b`0F()0uMlcAb;23<{}?yUeT zNTk_-fF_uOawjW~ zARGx*)Ej;|(b8IevhG^KCTB!pOqM_h-dXoJ&`T8_P|i|>cm#%*;wyvB#IFO%Ez;nMH zh0RDC0F!Nz;vf&q{@zSH(tP()#n##9RDS;Bop46nIo#5S4`;gGz(L-N@^2=((ES}O ztU>p7W5_cRVQLC9(nA#(etH(avzJS8P3NJ1?3(GKXqnakl-qp zvyk>ZOV`pEx#)UT?0>Eo3bZu1C&#d)rrOk7!-5qZs~zj$2SbpZDoVt1ow!bUosVx{ zYXCYVLA2Z(me;XtjG*0{@js1uLU4^PW?qM%PRH-l!$_6Qodnvktgh5f0HizkLWOA4 zcya&z$@R2_H%TX&5U)M5*>tVbXb;XejkyY!4RJh7AQWcoWYc%l&!!Z!)=y+Tr&|tyv+s zVpL)B2anfF7M4~OzMy@8PZv&$M)Q|W+ZbB}4|L+4&EkjscnoIzpmefWwROB4CKO|= z&jzeieEF9l&tyt!?x$YfUe)0O#2+hmY`92eey1-WeUDW=cY(4q0x0rarHQqa-aS1p zy*X~M96U?kD*fTVdsIU(H$u0RBHIb!1{ytnt{1I^i3Q!7=i2I9pY+(_Wq>Ne$qSkr z{NMG2uB|v-&4RU2jvors22*ExCUtJCs8*tf+`DEf%xa?{|KLndZHiYfz0-lsXu{ZD zrI~ai4l2VCkuJ%FEn};kCN1PzLM&jg4{CI9xpH~NIt0e$BYzoN0$~-HaOQ5YF`l-}x!_d2QMj@&@89^Z> zxH;-z%4a^SrKT8WPN}ZxKU|rCbHU1(KUoZf@2&s z`aP>gg~tz$aCpZ2!j$zf5M={UO_Ve0K+xNCwtSBT>~B)C3de)P5eMPR#Fyyj`xn;B z06THyw`XJ+KCsZ*;4Mbwn%Y1oL4ExxR_Mt0`T-8CI}n~f62r-qlYwmTuHUKOy2ve9b@q zY5a01g8+<^2M>jSy{3ZhT~9rr z`_E8jfPD3G^1NGSflKq2y~AEIcJLq7W(j|TcU)JNF{rLwibgsOw%`#$&A`F&)ceB8 z8Dy=p<=kIlWNB0nKIbU;6?`3GoBKupBiFs;5CIgB1_p_q{QRO?IGd67jxG%zu2JrE>{KPCHGe(`BEEhSs3Ua`Voaer7xp0G~`fbCZ;^1N<=X_(ci1g`W03JF*M+( zT#p2>ladvQj#53e8P;nh3G)Yrgyqh)E}!6qHLKHx2j z%8TiOrSMgES0tH$ktm(Q3`FtQgpXlo`(Sc;}=Be)vA ziSqpCqAR|VA-gExK(7Wek=neYzm7RlYddHt_CC~K$e7PO2E3bih~vR`)FIPW7%mV) zWc}AVS^ELVBK5E;wHM9U>mMwQ2w>gHoc4U;?<@jeF11(W2nI5UMajy+$ff7j>aW#hqX=~M+atBrR;+mI=s z^V!we9e|F)VD1BZ@wjPlcMS2-c)$bpdM0;U$~U<=@kY!qee_t@Malivs|Vj9hwY8q zQB%h=PP%%tG$71=hqxpR$I3H2d(p_F+w4Bs&t+wkin4i6ocoehfk4R26D8NMY__&&1-(VM+15>XJ;bOL9J%jER}Ze=`MmzPjhDnqDucFRhd}Sa{dbN}9D|iER{B8vb)z+odn)M(2rI3{0pn z!-(BJ=n7!BLV}l}YFbPQVEKKaSo#j&c5w?}6Pe<0z>c8J{{G4IBz3xdML zb3L-{?O2_X%1MSJ+yBI&6N3ev3xqmB?O-~Vfinyj!~WkFE%zeQ>F$?F4d9t3bg20v#Fu3KjwLet>6l&C}V8L!M9wO85xsDqaUQk>2QLj3Gwjc{>YK<|FA6d22-MK(Ti1u-X(k3?D#m`MniU#1 zePNd&+sx0BVF;!PXn;6+JjG+fh=qA$dXmS0u{(C4d1^5_8op7hrCKyj5H{&)$-RS? z-Y@TY4-lmckyR1Dz^eJlA>VT}SWmPoBTeD5&0fQczVcI`t6zMjRR-uP&fq^KH0Tl~ zKVnM9fdQMq+oH4C=5x$6CM_edmT9o3fqzh-o_L#{tKDb{mEtAc{`CH8G6I}Cj#nt5 z=tP7>jA+z z_k)gF4UqS&VS`avGFmx1nWr&3>+dK*j2r?TuB-d|sNZUW|ImqTXBag)&@z8n<~_e1 z5XwV5y^iOHYP=tyuxEey`%<4*tUx*cZNJ$$DvZk#HN49!%qU%}gcdcYJG$XlIm>Hx z`f?xUq7fmNYbzLMbuGC;cBt#5Sm#9(!5?&GVCJ(qp{8MjSpkv-sd`+7-6{R8Axz$O z*nO0a7)`RPUKLp{BPs0W7N72OzW84kVF4HoUStdKPU;EPE3{O5H2xs+yN@uzjWbCl zb}?F9txim;!r&2>J*hJK747kt-`k`)WB{0YsWQvdl_Q+cO;}n1sb38^ox1A;>rIB~ zj;BesV*oxw+z}*9Il-bCyLFAp%96--6!=E~VQP{loV(Si!Vj&8%g@sEZ2~Y&(iJPj zi$iED9opd3M#P|;F(Vya+=}{~AT8hEQCuvfr0C3QVHyyj2^|MI;!BQB6QwYE)s|nJ z94;Dub=B(JG0sA0Zl!d(Vxr7i_<&U0G^A!{q2mH3+E1wpA<_bLz#W<#$q_mJavkHj zFbu0z|GymMX}Rm*h4MsG9szi#hpTBX>DVm(Dhl+!S7;o@!WlbVURVu_=aP^d zrz?y{`JfePQ$ug%w-jaH7S%4uT?e;vjjU)}5^`wVQ*|?L@!(vzLeAVqut>bjb2s4X zD|jp@Wn01|^b;0bZu3hTiP*w5c4#TipcujM$8J|=M((@s>(&jz9N5=y5h?me!<(QM zjhvZ7a80MK^Jz!74#+i*0$ar}yWEL+$%*w<)j6aU#lP8kAcLxHVzfJJ`OJtZ~CzgN+RorA!Q$S>5lo1j&F|FBd~%Mdma2 za489mf~4pe^J7?~pi@`Y6hD?GQb5>v|5rT|n%RQtB|SOwaI9Tr8E30NbjLCpXDl%q z>C*1r_37&jQDKgd$XW0n8P8pFL-zIN92D6q+6HE6wvorPX&6LkS4lSva-5$zh0TUM zE0=t*-fJ|9tbuNhlb3|*5UvKvKY@NroA8t5`j_#A`TNKn5TJ;)>$(Qt0iOdT_+e4` zE8Ub2oi*1X`%<*G!t?j>88gZS`lVg++ zkw&pKjqE>E7Mmi;U0H_dBnkkJF}UF=+xaQmD0ia7*JeasQj=W|L*7mOo%I(1v_JUC zKvNW%Cgni?JkiGbNh$hYteaXwtO-TD~9xhhGFf@rWD(EYLPLFHW z;?2BQOrRZUP3n0S`s-c6qE_J;QTYFO=JvX&Oj-M|x-{-=!h<%AFn^hM554y0fGX5W zNR`~_2;ovJ>Q=QgjV$a*um6cJQ*?|1tFbyqcb=NI@<-Y0JMrH7d-K|NeNvig0OM?U zOS>3~H=302;%2Vr1Ln?ydxpI-5zwq;{TL{fA$V3;p z7w*BtN!z?0`Ucd0LdeNDCsk!)XbQ)3q^NCS4108(fR{nemaeGfOI!2^{fNJcx4wvY zV6Hs24`V+?!NLOq4R@j2hf3mYH`7WZ?nI=)1l%KUGu$febae9_k$D4mj(WvMTs>gQ z+U{{v_&_p|9X|GBAr@*AcRKar1k+^J9&n~iMX?g>cvodsJTD5XA+ip{5o|)W%Pk0b zj*BUfY1YBo>bh?Dn&sSc^Rm7X>I3Y*l9+{#^EC}OUa9xd#3kN#bgU}_}&=t zh}>M0aeH3DeG3W2qKCTv*~OY zEk*F|`S41+1+K&%udx=Vi=1h(W39FNL))}#J9NeLVPo~^)<18$b`B<@+sVBgmdVfG z2Qyr!VIw7JzYs{>{fzC2URPWlEe5RYPB%Nbq>^F~2mv{`vCJf7+O&U$zN>zrC@KBU z?KmiQjhTyBgE7ovTN|+d{32Oaw>i?;nZb$COK6s&DazD`{GXFC%^xGtanxW->5PL* z@nDEqL4m#I3f2*7mXvw2yTR-)G%67=x(0F=Pk{uOj}Z2YNrKS$;(^)0ei6T^3I>(6 z6E=9EiP&Tx5VOpRQ7C8}R5{_TnDhS*7=pQah(QR-QYv^%KEE8}fo7!{Ad<4Jv#Ght zRp?g7q4t7rH_WdVC6nOFEDZ6gy-?xCy-C~PsVG5ta%#)|;vp0Z~KWyc%W)mK+py>figp_Cv!0+9`;t7yib z6{})grxVACl0E#yutH+;0kSigxl>gFrH^k-j5E*6{q4jkp;r*9=(pvbLa~jbKGq+6 zt5(!OufNixKQrn%Z>ni3J^m!cVy#kvqi&wfhL1UoP~-o+xs!8#NyEfC^=h?ylG^0n z#4NK7=ZsEl0to@f_>rEf+L_+mktp5#fTHJ4VTPAt7|DJmxt$e+-&75E30jlBr8293 z@SrAyBTQ9-UUlC0;W_ObGV09Nb+LPZepy9~GSve1)l=@?EN9q+*unS3ak$@( zql1)OsfFtVGeGhF7Gz)X2@ecOMUU>|SGlyR2m`n?EQs}U(-UOvL&WQ>v=S1G&9H1v z1+wqvJg57V64dJ2-)TB^0t0hE5F(tqv4%FrpiAof{t6~{8R%F+Kabk5KZ^PcE)0>4 zym$UC_T|e?Mlm5EKV;I!y|+anyewWNy8`9(Sq@P|p7x_6XeKBYdzNuj zTLTQzUan~1yPjN3vSq|zJk|<1gk1f09p}ueGk5j?_ex|YUw}dth5#A12YVrJN8mi5 zm(S(h>+d|3yfntw^qecn3B6c!v(CO*C6_F|^InbrPGcz|X&uP`oR&o2o#^-b(?_Gx zYq_rL`X`RApah)HTr?I@bi7Ky2)lO}kp)$dYSr0thJR^h7-+xJK-bXv&zUq{Ek}h% zaHqj$rc1*F8m2N#ynGZ8M?iV*S^+gj6b-K=iSTM0lQs``O1J{6xdXLM%Me*U(eu43 zx%bSs8bU4v&i~C4#8YsBvk8p&B7C* z5JibA#T`1?l@%?f%YaVm3<-wx6j7OCh7KT?E&ZuFVK$Em&A~o098ISbOiB=yiXai^ zK9@mW-1gUQ+p;GlO`F9~?@ zk2cT`5J*UscIrH(E+EBEYBzrqsR9tsF}0mL)G_bB##gN>{Zu-tDI$forUmd(|G7kF z7X;;yn`mYIlkGj=A-gCbS1x`OB6ygn5#&#T8LxdUWBa{0$^64`orH{}v?H>eCgGwg z?Fe5y8psxx1s^_1E-TL$v49hgL)aCMTO>-uZ}`{rJo1S(c9p0@*d(34J0Zq~c;WTS>(@y@oc=eSm|Atv&Jl&6u{h9ckSt z*?-~?;*+c{M7?3_tzOd)9@L7T-WVwBa(4$;-6x2bGRnCEQ?c7q2Gm@%^I4dIfgSN6m$GD&Q zECB^7JgMNE+dolINLfgTF6!wm>Q}Hy7*F6_|9AJvlVdq&q zN!1|{Meq!^&9##R^RnHbu_KR&#Eip1U$}J6x?2Vk)VN?cOw{#e_o;1h{nce=qB>{@ zg&<{E)QvmNhGY~S-JH>@^vMRutp^z+$jVp*6*2gXpmN@hW~w3iRfe$ys-OJf?nT3}~rg|eJk!aJ4 z%Y$brgsm6!45V_h!r9BYeN9_lo8CKhrukxs%@7G71}LJ8S`k$B7`(gnNN)tJrEP4$ z=ZQd~CzlbZ(ty0=-;D*UYg1^|q zO$7lI))G=FdklwCFBbD$ni!2*FVcDayk0KFnA zY+R?jbnRS9;IaAo3bSusVW7(@(NauQGYTofe7k8D2m~WmLDz#Xnb*Sq*^UX0f%kRt z*fS{mPl2uSdVO=@nKg0aXHBvb^gQ$S$L1^Miy1nqm9hxb)? z(;=nv-k9Mu=~UqH&dH_Xu=Fq6GBF;Pgl^O-u(fjH7x5M6U1oD`-%ay?1p+!E*SGMR zJG2??GnI|i>(Z^83Zh}dYpUa2u1hG-$34R^Ascav?dZs3<-nL0v6WAt##3@RMrQN1 z!;##QL;LTKKNr=9%0Mj>Xy#H}bJRHN@mQihAhEChXIpq0yI z?@iy3M1Q!vG#>A4Ceyvy?4VI$tnNn2AF{s4-I2~eN+|yL!!Tkgk<^t2-YV*U?~g1b zPw&U&i^!%_Syz(L@4!z!-9#6Uv(Zn^uU8M#sc(jnCVE@*tkgJ+1vM^V@fRbcq)Bc0 zstz2MKKPBo$d#>}`7siTRyU95oO0rrRHhigxDDE8Iq!`vM?LZh3-{O&4PSOW&z-f{ z=Pcm5DyR#*q_H?zQGymVUizw`n@HJ}|Depottq4;#1_n~4nC(-Yjx@D90=lk)&!EkS2icZnbSrq2IL&sN zGQCy?@qL-0@Xj1s30f8S6P#7TDqCHA*lMr(6H=2ZIxzb5SOo6mt62+!R6uXD=@HI>A4lg8ncQx!v*od2*4+$M!_!24mjZ zWO;)b8_rBe<VcZNjgY=>#CvB`n93-H zxIhOgm6y}z+gqJ6n#6lDJWu?R&=MwU?+0ZCN1Jk4(AnIi)zNnS?g|(v*5+4~HjMDy zhzM>m;qqWFXZM3D@<`~t#BS(Vmf4Ta4M*=`nE!1A22(JR=1%1!?5)&jqkv{3-5=H!40)rD z(c{8qaqmoYoP$nGM5hSmc0jRw>E}u$Rcq;Jd+WsByZQiK$-~|u=anWGSNpFGWROUm z-Cu&ZAyoFt!pT|PxRp1pwnROP9+xD~MYx3r)D0XmykJSlb7nZjmx!f^FyzS&(OhjB z^J_7N*-xhpG7h#cGS6!FoD25sShW!12i~reZ*s>902xD6LHE6Udk!;;#;0Sw132#L z;4qF>cT|B+4{#O({Brd9-P*dTb?F;o_=WBodyR^-&uCH-4>ojn1i20AZ{ z^B^j8o@eGGsGHz#b2}piTP{A97w=F%yqNkXj$8fl88nS3?aPmN%qSibT*<}?$!yCy zH1Jp_pEL@}pXI&G)RR9lgRCd{**6k6vDDvT#uIpjZ|U4%bv$tm&(HgqTiaG0htpo4 z^?f)Z)Cw6KmJ4lW7ca4f=V#BUpY97!f7{L#^O_{# zixBBWw7rC@IB|qdy!$CvrPV^xTR`k5)n|A0bfs)#rr0e;YlOPK(>nB1=8e3>5{Qt& z7$W7@6@++!qIQuuf=`GV%x*VRHAcDKdu&C$15AKX0#RJj&Tr(r){jsYL0KTdti^9j z#2bW)mvA7tz9SQ4;tVPD0B3d&%4YBm7L}Fk%TQ)BeHD=$?+xL(o(BmQKUD_5623V( zt`O%4DT>}Sx!|cq-pEw@O#KOrVZL$Hm`6&^;B%WF$?H+hogv4I5TY6G^{+&Ii_U2P z;?`|nAaHpFG6WOY>=#CgctTHdc%kXwZDFog#DP`W&8KZ)#T|uv}9*?mOs0= zK!GTq0tST6Re85_?aNg0tx4GRFZF$Gpu0oco5=2L347beN=5QW-p|E>Vn!h=>@H#Q zkVRNts1Qwkci7`8HBdC&fP;|SSvxeAU_$K)94hON`7(Y+u*tN;RG__eB3+MmDQ#r554%T5{8#eZZLc(k-7(vf2w3&;q`X#Zlf+SWJb_7Vb8#xi3RjW>edPj* zl^$5I;2#&-vy@I@>7$8qFHvzUB;VPw_ZZv#Pmtc8PUd; z^-@LyumQJh3nrY*hx87vO}rRLghzfVf%IRZMJ$uQ(ChnQ>m_sWz}cCE%pX^OuOe-+ zA?^lKm8wR-YB+%{v72Cvt2erT5R3zyU~fv@OVPUu?~|0$mXR#otcI91$foRPnSc-2 z09C<^=#O*2$b|VSVUwcT7ZUKcAn$JeN*-5CR)d4UX9)_^=9Fe)5ecu_g8=$|sqKF0 zDL=k!p32UyuL6scQeY+}+VPsIzmc7)?MykMWsI=uRX>s7Ey&NeSefR=I^wuYm8i{s zjxg2+TySmAH(vb=@@h=!5?T&i7OZZ0>4cCkl%PYP12SrY%}Z zk`FjZL`;4;Qaj$wvLGY=H6bAM$Q!UY&8rKAg6ad7VKJZsJA=QSNpl1y`=e79`5!{O~X(zW%f zno%d5FZZvq1-kXtaToCe=zPvH;g@wznP7KgA17Seyg8RDsRoCof{bz9cy4sFcbj3tm?cC7uMgFAlRkVO4uNFrAX- z9a6v;A8&TV>LH6t^DRY|J3B#UL?~aU=9kNZPzY6_GQ-R4zUnJXFAbdTAt|nxVj-g; z7reT;hB^Z}NapnqRP3Koh~R#6%Y?~Uo2z7|rmZFvh-eE@m>S8DX}rhxxkjCEfz!ZZ z&GUfxDhqExU>!xMbz&4w8#e4HXjXq0WFiR!Wap+-##rmu0LIU@o5fC|G zAoC$3`azSyb0|&0J_?T)i#awg;E>rbnam`0Ef*vP-_G|uiml9;bJ?*3wyR6&R^V+b zMN}psVA$&n>Bb#_*LVocUiLvOL8JglqXaXJN6fQt`_tD~NvM1jH1&sUW%ADZ^E2fQ zLv7EU(`kMAY1G2P8gKk)!}}Ykiq5@2Cu!GKtcTGQ*m>%+Z@u)he4k`Vuc?Jnvkkgt z5;$%U{;MF~(*Wy_#_NR)>Bc|Y9eFfpJ=C3Oh27m?rY>8L>_Tb05e#+act)KR9RiyP zEDqS!b9|R9Oei%AbMg=;ta^qx_%dZ$XK}rG2xfgO1^sz&a-ne9t)5?|R5O&B@E;c> zG``0_d6_W&k<+eLuQdP#=bE>WIV%_1-z|td49u5>tao}AV*xx2DbDc}uu4Q;w9y=9 z!cc9nN2|zW(ud?fno6_6;&7dvoB1_LV(l(z2;N<|eQs?_tY6$*I{_J*Sc&+w#dyCt zmP~aPF+Ulnjh!T}ep)NE8Ez#n7u##DwEUcHf!9Mye6Y!3AVCv2xz;fb5xjKmw;K8y zWESVuYe$IGEp;t{B)lfK@(um>JMP~l%7c)+;?H*(p;e;%pOI5>CYHlm_=%;MPNmMI z+-hB39mNG%O4T0ck`mwLCvtezP8`9-YeW)hmT3*3$biMiNB$;cx>O$}W1$eIa@< zUl2T#3Al04yU>aF)Wgz(j>yU9ys3rU@k&JE*RF zTt-6RQC(26>?n`ZhsqNOcyF3eDPly}v_GxzYh@0F!FjD;V^XvFcN2;0m0=z8v>#ggKUktPHRDK<54-U_-kee&=m#ErW)Pm|D<=`V21S%?K zf;Fa(!gLKWq=rd9jLPd{;}PK={eBHy;13vc#Ikpw2#^2?wd3`m6acb`1;asaJ+zaV z%Lr@;@$v+NJ8PG8@Z%KqqW)Y)u;Ffo*vZ{|-9YHn{g&orbDVpEdpzR9?{9!V_6}|w z7f0}lK6Ym~lN4&j&q5cOzW7`rT-MQkb6+I*lW#}fsi5AUKY90j=B3?hHd~yr7_ZiH zH4BHu>{>{@ljnB)gmiEQk#IqBkvIhJp`WM;@_|m(*3WvRpG!KaaMq|qTqsk8P1!F2 z<98}5+(Ka|88EN|HwY#Mf6YRJMcvb^A7^Z`QZ+RaG{1uGTfA0u9(xbz)~F}JjdxmN zoj*m0mHd&oH3ZE}5^#G4-UZjQ7>+#7c*30w6RP2htZDHQAM#D_TU=c4 z-3z{UBdHOSEm_viDx?Hv+7HzcbQDn@#ihBjZTw$LX2xOM2MHYl~dyU(35P-Py+;AMSDszL*#-!O}j%u3^7EXq{^Lfx=6 zf+2p~Y*#pY9#uGxMlKAKT}UPwrFAbvu@xr3OL)Y)JdzZd#SMY~@66#UTYBXhSZZ#r zkr%(^42<=tL*kT-j_l?Of|41tz1JT+PVFO9KDt#=7!Kn&70s@sDBNERnN}md4=rP*Q5G+o|ce6pDChiITn7$Pb zNxCjYWZ##&eZo+j*UuOfprlF8<0bZH;#SML?pfj9m?>e6tx47eNSx zmLV@^qrw~5vYeYg)NBi*^95AZs6}ysZlgm?KaomvP!F3IJ~6#Kk{D`wiWoTse_(pX zG^&Do9QahZS>j@r<5dS*DqN!)Dv2y47s5O28l$!p?tPHv?&qv;S7BAq zta_ysWSNkn5qP!)99_g50gkWJ04C@aFAcqASjvGAhtlM0dhk~1@e$BYn0tqNNp&I zbuiYq?(s6Dgq9y*&N9DpV5j{F`{{QqYUU!X-R^@eoggL=k4JlXZa7){$E3rE>FA5Qp9kg`zRijOA zlW84N?B_2YB*pB+`|aRtO7nmlGassk;>dtX$ei)<`=ovdgFl`86ueT(fD!Ico=2tT z@oYQ|c5sE}<4bbSL-ky9-jVOvu1g>5U^{@+sXuGPk_j8gKdjBq%ld6wziM*Ft2R0K zE;GH$_E};1$hjTyx@~S%#?a|NX% zzcF=$Rjm2>=K~(v>r`*X@HslDV9i3-k}tOr@^68!UBe~D9w8F0dL)4dFB}EsDD6P6 znlEMix=#W8_>GW0aOIPGD?nO`D21In%KDWD3u9X4T5$bfJ-Q8C(s7r<=5W!3hnB?! z7lB=F7dSKPG;74PAhiq)^9=nqIAZ&>c5Xql;fwNi%qpi9^EUzU`QMjSz!pSx55a{_ zx+!-DVAOaUQfN%OWK_bK!I@FnIP2O8KyU0}`MdNu!WCu9CD3~7<#mtn2QvrPVqHl> zvwaMKJ1V{N&l*uHx0kZ$=~p#mbykQtGU zsSKlA3s5oUz@^D-Y_MnA0-us;f|Ujqea+efoUv~P=c7oGS-XNc1OyarPIGgD?_-FS zQFmVVw}J7HonY8?;sz)h2e-vbGI@)ff+4je149xk_@0{pwwu@B8A_`t!=QP}6S)mR9u+M@%d2M=ff?mz&)Ua> zt{;K>xj+=}bk(p_CmQ++jiPo({{ml^N-9x4m(1Pm!0*kO6AUGtAM~bl0|vG?ymUu8 z$A!PtIARiBJ>RVkKrVajAt>hht(9FJG7&q+-Vk#!&u$rWKd!v4mHQeTevWsNu@r?Z zn3%gG$n-F$cWxM1nAVPZ=-D%y)qMb8=q$6X6}N-_WR(uwfd)1Z+=?l38i|d%#n&)7HZ{vut zv>2TQXFaUO|5FFsA?3;Je{4#m94GFk%%vMoiIB(EU4u3qc?2;jZEH)1@N!ExYFW!t zI)P&DNmXN$S8=&{ENmxW&KLWbMlrjvB+Y|E`|Rk82VQFB)hWJsl8LRoAh769sF%CH zK87j2aZk7D8+EvLN4;DHMFBkHR(eAB`&QPgD7lJ0mEroZ-u0xnLG^WKN(KEUPE>!{ zMoc0u=R8PnaSY)U@IWLIE{9C9e7;nXP6!CKTdW~b{H5NpMufEm-Ikd%3(nw*&U{ZJ zss6XG(=#|L45HbOQ*pSpaZTHZo+tjPn+2zV4Z1NwPiDSqt!MqQ)V2$Y(sB-E=c=n# zlF3>n47daN2t;hC)pwvtluWQQeaUC1?&9eij##Imke3KM{#g(`q~s?*R9BWVPqZ$v z=tTh!0t%#hTj>XbfKQ|Xfr_3VcNwCE>e53Kc zAm(!CIYJXe691mVil4icIl z34uLy?+4hRe8usbc=#NSbQ;wcwTFqopKhUh0Z?CzPT$BxYhijF)&f(wlPPBIoJ=&I zeF^AciiO;SAlt=Mpk zpYW9`3aCON4p62hxlL(yTO8eRWfj16q8q|MCai}m@=S0u+Zb^Q+tRS47kRYHqdT%m?a`=yJ~TjJ53o80yaX54dBVI zCLsw}tcl5r#CKmKgPp&+%OqcX@Xgpj9|p~8q#L~g79FHx-XA#4J5vjxULeKw=r9fD z8cX%b=hj(DEHlCa&9nfmmRqKP`h%U3+7MVg)NtMNXw9l6O z%@YkWM$kd8Zv?ejG?jC>rtr%K_A!aPaZBO6Gh-WGrp(mf@h6(pV0RPh=kQW+kv%I{ z$N@s{vE<@WaFz=1E{7^nJ|q5Qq*`uofe&Mq8;#)L z2u{zZv5VH|o^H(4>aFi+w2@>R602aZp-Qz3wuj#Y^yqJgr%;P(|@T&SYbo{j99W zX5!v2Pni|23ot#>EohSlot1MJaL3c4&lYjnEJ#Y<_`Advkr&;I^wnL*l-&RfVLg1} zmik~KA=8;hx%*X*il87zOM7O8WXLY@c8* zT0C!X0MSypCuKkUvY}nn4l=h#M#T0wISBZ?K(G3Io>$$;ca^t zBWmpopJN2!I&${X;&|<9i%+~DGFRYikE&fKWFwRn20sGCQ%Y~%`MIxPop zxvx6~`Gm)*ViRuewH$V%#^j17W;ec&T|=CUJ#iF4v%!8R3D^p{7Uv`ZEj&*N(=j>H-9Ax5qGFcCs-lRUVWo>^f$TJO|v))I|LP~OtX!t zQE?$0>O(_m@2CdDOlcn`oS6=VK@-?f(GCiPd=FR?YPF$)V3WdmeRM^0SOF~UozD3k z5|Z?NQ=XO2yPmbbi!sGZ7rV8Rguho%_Vix}{Qze}Gf5?h3_6a=3S=}3M7=S_;7jU- z0=0d)p?gI`*0cqqC+7PVg%(ps+ku&b|{3(|vK#sU0c(g>wI%S@JgU)^mcl z62Opad>ulJavcn>kqsbo2yPW)j58j_R;d%|SXl@!kNP#%{>+vP^W0j@+W_{~oH=Z#4`Y z(LSyK2jjRZrz$%@Z?dTL$3SB=q|wDNxu{wNf`cNqyKo8rhc-DWO}VY>o+@--5NHqV zHN->IdjTD$nwy~lIErT)@MYG-CWrjw3qxMXG4}gh6+qvUr;4TKDbBGEvXJvKPZTrP zGz)+;zK0B(;@EvX$lZ~ACOP1n6K0Ym7IH=X%|Z}EqD||*82Dz|K6d~LRVIc$gP%RcxJ+6&0vUKDnM$&n}v>@4#y zDz((Xdoi3%W*w+{(b(DQ=4+u^Cc(U#0(Avt+PZ1ii-$3a4D(%<-&jumEwEXUz7jnA zjIvfOndNM&|F80RSbX&_q4wX#DoFifo2K!vsc7zax7G1UyEBGjY!fpAJqieXf^y?M zcO5OfR7T2IDQP*ZSqJ1cA%rRya(a96qs>N%%-yi~j zsccOoplUPNcds~g2;irW{Pu)7; zRRWDFJO^nDwT=cvuyE(l*or5nr0^F+c`GP9WC>pmI`CmcjMBSZysbpgAu;>R-)i6| zHwY$O!Iyb3F)Xfa|D381G@ya7%-&A4!RA=F2@2s=yMZ%VP&u8?C$IRK;PW=$Nd&MN{> zX%i*v5CVxCJIy>DKXe@24p27g?SR9QXySQws}FKtHDE96OvxfXPl-J!q|N`_Sk0XO zFbB!bc{V?u>=1hP;KpBg^fyEvU!Q-^CTwy}ovd0P^r!F!>vj8p$9O6PR6v~RcJ}V?32UZRAR2GpJxLpGdtINjd<6Walk7@HK$m^!tIc6Xw8A_T${}NKFl(c)jVjK6nB4_v!RJN!nr(Mj_BH zEbg~RjKGd7O86>N#xuqfKdQFt=QI7O5!n;F=aT52c%PgAP!OvXBhidXL-I}{r2wYOUfx$ogKr0+Q8>cHIf??)jtC!kS!vgN9u zqM$qkt!B0njpSc})l)&`!I8oOq~?a_H0wlDo-i;TFXd#rVr8tfQoL0q6c)I~32og2 zZ9>&JoVw1uLbKdOlz5feQ^*ew!qs?uRj8=sn(?RCAE6S&c?LudcsUAjON0A&^)N3x zIPf0@Fx(65VIe65JL?C16iP7g)J_&oaMv(BwX6pc~&{z zW9dQ3DJFhRQ5FrrYhd|O&z@q8QGHElfoOo7^v~ zbGw~duA`nPjr$Py8hsWp0vTR6Tjp|HCP&pglyYL$s}50n?`aJbu#>uJ+B+(d?@$kr zNSHVp=Js@7(JQ>3oys)X8(s^yz6}MI12mZ1NPn#BYzOmF^ZRsxqfyDXCVKGLR+;(; zq_9uf^hD^K&2~<0#G#TBj%n_Q@LbceB>ciQWy=7klOVez?gMkUPr1CV-Ng#QDp5{) zw=u}WHxx2fYCJx7D~fXoXR?#6=Wnx+r$i|Azl4$n(4o=R?}zy>{e#b2LeSijvWR>ogLB!3hY6hv-{JBvGKB{(E=pTSgtQ5c(JxA z$RHVBLQ`Q2c}@6tS1?W_Db?13wq{x&VDAu+Ci$5W0Zf1mIDRA5lnZn|)PCux!{*_5lh&2~5rg$Uf6B(xK90TNUyTu^XI}%+I=+krJo=>5a%F zfj?c$&;+1%U`2%3b_%e^wx+VY&mHm9z|HS4^eZj*S*c!83~WLu1izjb#!_Jw7nBy@rlq>Y{`Al>RsX^sOD#79^#Qh zt)ILmZqi#*|EhMUosPwB;;aR(JsT<-rpBlP&w^g7MSg(Luu_vpx}kf{V}R)WV_aD= z+0pFV`rCMOT>r~K*l;EBE7t3sl1i|Z$MFUMj%qga?=EzsGNRFn5f7s9q55V0{y&UE zoN_gksM}i}#o$3Po5NFa>A8$(RlOBN4s0}3I({Y+@gX!nk49(Nw&y(otht>bNzB+~ zqryrdjxVm}&iul_+Bm}9=qw4Zh-Z)y7v7U1*%k=99HZV9EW+&R6DN2u?XJhJUVGR| zNvPFkZQ%?ELY(X4tj>e{Uqmc|BngI2s4IR57x)4?QA{mtq)}4834p3N;p-?1UmP6r zsc_k(>cDJ@++hpWgbD3YNW#O@e59}EVwLcbnQ{@+t*gfs08j zimh;cIc31@qu$A6_!HuBi#JUTA83Scia#9UX5Lf_vq44VR#)Qy35)cZUF<5!VG;3j zC}K%-X)ZcZ3v)A;IS0!`*~2kRDA7PhN|y&m^9vy)wvk%v)5A@b%IZ6)dA&Z=>qGre zsk1^os67kO7nD>KVgIhsbSy#zcs%tt3iJNhy4jp*Yi1w)xHew~zyI}ZqBNatnX`^$ z7p5heo?E>rU-%3AcPb|+0}#Xfj16ohkpn|}6~M??S|ppoVO+h zdBLwsbBa#D&mTN7xc~Zg`N^H`(xtx+@*?ioe$0EmR|p#-#wxzHbpC@v{|YwWz_-C7 zB_Y0Vq2^c#+XNlQ9^iCH?&|W1PyJO;+12@K_4Fb(X`?ly2Uc3b=o({$U=0X^7*QwQ zNZ3O2n#emIRcE8zJs_FMk2GnKadsIr_?ZD(pIt*juagq2Q!llHg!` zsREY}!Z(XQg9iafgkR)mv9RjJ>U%7klP`BeFmPon>cd+pv@GQAxP+QV^5bsITR|O5 ziIIY8D>9vcQ=KGlPN>E)-97KiBn#Q6PvIr|YFLh-b;uLWl{P|IH;{La_g;18`52)3nJt5Q&;yHaBvN%gq5N!Qp zZhew>DyW+yC=z9|yw&S5!PN!E37@SxEIjeV+5d8BDp!O`4-f=O^A_~%pv8l)IU%9K z1_*-kIXy9oA47w z#}l~kZG)Jl@O11^-OX1gFoQ^(YooxJQ6tpQZMpBD*fH9h)yBRKT;y_wJbut2#oS}C7j-s;|;pLajRooMP z9I!D{*hm9Zq{Ajdh-GOCH)(@Jd0{AsPSmn!AbG@qC=y=lh`0my7;FQ$jlaY(hJ$~Z zSi$dV4$Y*Bg12ML92l$NMCleXyf4pnbV;6QCU*Hc;Ql6B&(7phe6=3-d^@08a%I@8 zI^z%TZ9n2*7)Ub9^R+yFP89JL4vcYLvS!As3zm!k;ZHv{?`G%u*PDGr4<#QV31`6= zoYaY^3zi)pHDemfGk&Rh9L{-`pHR_ub{}!Gw)((9~#a;jh;-w3ha`~V*Fd+kTIVZnO4)@{!MNzOlTUM?KGAGFfIyx1p*uS+om@n| zNgR(hQ)6(aM|4+yK`+m&cq70EBuv`c=tO4-qOkO=V@d6~-Z)<}upXpt+z=KLsf7&a zsI%t&+*Q;BI=fqBcXYKu17kH9By}`8&W#%t30fl~S>qRZs>O<3@se2M9gS!T9i4p&N{cAs9FLTfB_hJk)N*eKA#OZGS}*X${zv z3Gr?wu*OmOH49kzfyUbnm6PwXvc9MNBpRXY*Fi8iQ@IM{g;>QdM#eFmXNw$jwo0SH zpBSGz96d6Yb;(#02LQX~lV##S&%%peq2-KX?LTMjFOM87_frKN%Hneg;zJt~`FJHmTy>lbZP~vZ(-*lb((%`YDcI#4hhVfuo`32NrR6*$Ln7g|dPmclY zGG+a3zsWJ7#+!0CAU7Rq$}B)=l9ci>1avF-MPd&gPB6@|?yUMldD%Cikqye}K>dOx zb+;+yqEmrp^D9d?$_>ND`_i}RrI`f>s!;Qmj4yzlw))ssPnI{@Dx6y$d!lO39q6m$ z?b&#P?exzvjLKXM0^=FUQR&(RK?)$~gYy9orWRlh0^F}a5i23OJQCRoQhBCQX@}Sy zeR$x-ST)BWW=3aImujh|49CrV1-%6Yp%)AJe;p?2Q67nEI39vjIdFB!z|aksfU9uu z!Kvc5UgK+Rvw~zGk!>vwgy5ft zHvjFj0C(lsg(Aq#k5!&$d1-=o*SX9uLmWUZb_-|U-D`1ioEl3<8HHoo0NldOS4UBq zf45ni&TIAX8v=dByl~~@+lMRj-P$0hH%n|z0D+*)XSyuHKxDPIbePz9wuJzRce0cJ z(1ok{Y~fNejE#m_<%2Mkc-Ic_7{~%amr9t9B(qB%11uHZajfb@sL&7y3vM1}wFO%) zj~4hXx(w=ybQeLW^Zar_s0VgriWs2SXo`=Lx$1fe-_LZwm~d0ibpqJC{h)jc1Os|6 z!|2I5xAJAOu7l=L-Wo}Q7K;roeY%u6!k*(xHFoO+24?m$r@GRmCLgE30U5`{Nq-pW z6UAPHPoMYAza0}@mIk%S`2F$A^kGXBAv)&|aKvcVMm)9<>X>{dW_XP(F>)y%MO}W; z&IfR}u1u*aYT}as+GSYGZJ`nlO*xf6a{}C1NLX+=ArNABK&mb$Cs;O|Re2uW-uT;P znc`>C5q9QwxTC^aqa9R0)C7lD6-M#%vu%h$hi~^6ycR*X^>Tsxj$q=jcF#yW3n`TH zgH9OgEMLjo3!7P3f zkH7jlcYl12Deel=@ypd*eDJ7{nPHr;Hpa@JLAoc$>I-04Ovx$?H zgNv0tfWgJXMO_UV2z0PxU++J|)dLm?80;Gq2j&#&TS zsfopc2lrMXfoYp?ro{pcBAHnajUB0P+U6x5DbDSVmk+junr{LValP%7=Iqc}xl3}l znjRdr=B66)s)DTBztLu;qQA`u>Yj$2*_a$f#Hk6<2gC}!nc(7jd@#(a5gf&w>e6Up zSrf%nK`AcW4tpgfRY6TIXqBTx(%?C8q~%+%Xqsi1TXV$y$9pHc6HH)(8=_qQj0rCQGOqe@6_-^%Z_8 zM?tVI{Mno*b78eJZ!WujJ`0wK7#$KF(tpuSUg77|#Rqd*64X3fv%0Z7U5csTKFgcQ z2n!4}6k~t5`dQM%QZ#)RP0FedU-554!(;D;X2#x#h)PmgAwwca>`*AN!-E=uEF&?e zOt*(cO-x_s9<|A}E?(wtvfqT5&+``OJq5F!RqI{oX9%%ZZNs9LyY)aOVXxM{s+rHM z{Ntx}ZrPM1Ju63vv`vosd^(CcE1@L8{7C)3LEq&WV z4K?X?-&l{ACVoe;Z^83_3(7YJGCOCNFIVr1m7p^KZ&l@T{LdHI;SaUQP^oh}ZGn=F zo>`-TFi=baA26r^Y%a*Qeme)qE%29q-&-KQkP*YgSn#Q^qkbnN&w8I&>ar^?E+gs0I2wZ zgIhWe4n9bNqlH7-dnv_Hhe-qAVINyBnL1I5Zu7ve(^ox{B;N@ zkq|gyNlW5pWF9EJ5CRcG;zZ>pVHc*q!N|lhSVm&dqzYJxgAwQw%}j&r&B(PBwdS>@ zwa2v-b>?-Y@vjGBL~Yi7(2{sgB#Efe*_MaHq?9Dw%oC9^3c5~R`cQ+&IoYp=VpNn8 z+{%-Ys@L`V=W(@xIn!-4iWfDFG9^?S@zPzN^^%&qrk}5BlQU4iuHBb9F=#5z>D%9{ zP@XCb=wLQHn{SqAcEm6XX_gsBoK@@(#A)>ZvVCV&w~|ns=KXnf=0U_-9Fy2x{0ICW zHH88C&qM66ukhcC|1&B5uc@h%nUSfT+5Z(%z%LxV|BU}fNdKps5Ffr-7d%l{vGxvi&_d^qapw`(wf*%!;Br1g?TMSu`iQ4*v(5g$d& zUP>u;O=F(k_;jO}3aO+;#Wpvl0z*4|bq3Li`$bIqFxBMuafNB%|9)I$ukU~VIu-8T z=lO3nFPFez=lgAnQNZ_M%)amS!D83v<+w|;&hO(ai*wiSsY|f;?Qil)+l?S!@7Mm> z?5zLWVb}fN3xE6gUcRrZv!RFZ6L)(-&#$pv|GO*AycS7=fA2bf^l{uIB zfBZ5{5PZHX=W$i_UE~k zhMX0<`*$2p2!o*I0IczDVv7$5gN#>hyx)8oIh!0Yt0c`|fxk&9@r z+m5%}dEY1SHM?0>Bb!{L6EV3DFLiW_QR?G!u%JZN>+!LFw7vVyobo=_=KuC~cT{Ka zbBvCmI>Nkv-;Z5m{-t+_^(v$`hA~eDH;T}ARe>v%Y7Pnyw4mG!hKKG$*ZzFVof`lX2u*@cnWN? z-L$^ejrki144uz1<|QrJv~maS!a#4#GWcU-(hF!~5^gyOjCbih%sfi>TaXDz{0lpV z^LuO%Fvn%_6Lor(N2orqKOWPab)Nw9$_rf5?}&cHQXSx=^0{-hS$4)fd#{e9XTjL8 z*wcU#eb7ncg`f>tmYoX*WipV1{jJ_ca`&Mf(DWYj9C2a)R}3g~g2D?fSsZ98(Tgu6 zgiEoZ5S+q7WDcxrT%m*aw|>qZI+s%WGu56aMg5j`sHw{A->}0@y$L%6{ZN_=`M{i; zw>YuQci7lS)Nhq6Wrill-a%Mwl3oDIWCLTK6qJZ?X$KNt7ekP)gKNY^(4i8k={(f7 z3Lp<(9U1^$jaweFMi;|ml7*g0FXTy-xQVjao9v%CX73o7Lg=Bp1ne{kWi|;#o}p#{ z$$Iw@J5%$iqXEv1Vc9}+OKNCEjE0WFBbKt4vk0asTa7DIty<-YsNNM`IkI~~+Jk^G z|1e6n1et&b!_luc&mK9;D63y|{%b%(JOLs~s6uCz0fT-Hmf)XA6uVtzM2oQ*FsNYS zkAZ1Dh4>x}Pv9^baxirSU#3RGU^SINAScYMLd2`=kw7)r8$*CTMV^LmZ;^75ipYC# z=?KSW0+OJpB0QH$HZdxAgQ+&&kN`CseXv$@#crEQayoh4We;*Kx*$Q}6c02^gvNBp z8eY?95xQb2bz%vm4NVfmFSNP)=|t1fcMcOd4BQ*^;ux#>!XHO5)f#tN7e$xTdGy_> z_fQ9hYJh6sA3SzD=+oW_NpK!dA5rD(ke$O{0)dB%-Ws5R`)X+8_if^;w=OWV&4Y{e zFm9QgPrdyuD4nBhc>ONTr&lnIMfsT(-x5%POD5CfWiV02{5_nf+|~OJYy^4A2e~viLRP%9WlOLM#_eDQ<6DpJ}?$suP>zfSS$Trb_&yK0{EFr7!# zuaLF*v5GYAJVi|ix&NKRftfj)W{#ur{FNEq6tP`Ff2Y(FO;BrTM4&t?ZV_X#53;oD zUgm-qRgZ21O`OaV#oz~5m?<^99Ic!w=Z$XsDmuWn!S6dcA4Gj2l``s%FW3<+!!9Z{ zc9)F7)idla4Bv05+|M}n?rck>cY&7!xTUkl_Xeh*AsunD&OcoeF>wcHk8VrlA-WMQ z|6{#EYE+>IqireeJ^hldMD(QPG36 ze!sfBtvfz3JT+ko%++3hbb|iPl!y|w&|NyhcC7w8p<)U&C$wqG!su0ItR5GvxUS}6 z6jB?zO`BW9%rM}Tws;p%gY^_koJM*K%U}wAg`FKCDr-#ZUn47^8uZ8edHo?|pmGrZ z{2YO@Ue(wze*goEqP*c>rFNS_LryQ5wkoF=ZxoTE@pNL$K4b*~#2Ff~^e#d90d z)`VoEL|98;iMOqAxwS^O6qOZKhPL1m1UDy%+Aqm!LN%ACr9(^k<@>4FHdg6?exoMR5Ve{Ap-?m&$~wKuV@nksDV`&(Q^e_e#~nS~0@ z3-9D#dcuC6X})#3S|TUT8Ya{4q;~|aG#osOccx=|*oDg)9&|0orYA3S-+AQVa_c&Y zvkzdLz?zqaH`lNr3mG~c=3m*Z?ckRyjf5mswrXv~!JVKat@Qgi5%gxsDx>v&Z|ozB z;mOn(#RP~u$cZgS7_X{Sb&H!^(_L+cTVN?vgg_lO91uHZvn2pP_1((6Y{Fg3-MAb+jhjaNR6LiMS4>P%LSk29XW z_~`mnAiSF=6V?+0fIQHiKxieKK&7dGytE5mOYjO>wWvW6vP)z7-@oV#f5@Ge{f*hM zE#ELP*2zjUg&p8DoA4;oBpjpj)(lckD&&rdFga;-4`Hnf9A{Pl5AU{4zxh+IjS855^C+vFRKX8rXUl@Ycbz~UUf%4}=G!V%|8s?k{Toap}1mWS$+s+$KDO52DV(DAYFr@kuT>m)nV1mS}& z>uPQK;dmG;vHK?=9V%CsoPd)+ioEKWyTzeDCT?}1Xai(7bpQu zj+0e_O~(P;=*!en0@P!Pf6Yh{7c(S)>+TvAMi~biw@_50Un;}8$u|mR)!E(1Y<)i* zS4CNU5uAYDgHqWN668D3s*=^4Lvpp4MSat^&0!dcnb@dUr=DtAyCE9iP&Y+LX@@}F_WwY*-kumDg6mAOdDXr zhi7J^9g*>wMjiAEVRF%cC+VOVZF@ALp)2WdC9YW>5*X~@b&GYowe5=8_rjC@4J4?d z)qiOA6?9m+ZiJK5U!j-(urYB)1~V8h)Ke;*YZoaOus=F!z9SwjHCS0bF0$k zWo8$asb;)0aJu!}iMns)j&3yoM{NC$R1i{G3$8LiAh@C-y;9buRoM0Uuv9x7`l2Yp z3eOm(C&j6xEN{cZ1m2ZXC3E$1PlTTdW6@Xq!i+rHBXdi5M6O%1F?j7ClD!j5J#%{J~s<`?M}|75cBy%l8;(@NTL4gBg6~o`Wp@-RB(v?jvrN)QN4H8eX~?HZfIYpc?e=Bk zQt67*URu)*3ss9BuxWJB&OUXXTTuKcB4AJfY|)@+vtt9>I-6>qThhSuIk59(#P zJ~zmo7QF|vNkf!T8OUvMpgm%f@ebH+UsX~(4 z#i4RJ<{yUHQqm^9@J?Bqx!aJMnE%iz-c!I!7wcTWM)xG75vZ z^<8ss+Ta^1>57e`KlD8?pMoqO1UtDNDutCsq=rac22>WL{YnrF>z+%iS0=ywr$(C?PQXP zZ95b5i*4JsjW7C=lmFtd4(e3>eb*Pe_v+qNyLYYiyz8A^!+6?D-v#27nMy&s?f^)8R#Ihq8ji&K%4iCx^gsKy9+OKL)) zIv~9EM;~Ll42WTrkncdboGCj@%iP?0*W6YC(8T{c_vbkzq{bwEE$Ku-Yk?kM|0V?T{5d z!gIWM2JqZEdu))xm!QfIu7l=#+zWJPJ(?vZIpM>u=JMZ0#%{W00&|ta+2x$}0$Zyc z4$`}xG?VXFkt$$&( zi|=S}G^kHjdKK5UC*G{N6&XOZ*kzJW3tDLz=wpvD9Tl^l<&(CWIdhGJ#DuZED}~y& z8yTpsSay^^c|;znNUew1`RQH6o6eEuvm*#AXMrPVrc$&;TpzZ6DK|{r`I&^E_Qz*H zh7^71NKTL16ux{sd~V>#6RS1_+t9dHY2HujVyiHTJ@I?hl0yu-f+6usn{PGzsfk>v zuiaq|+xb$9&i>`53bmnaCrG&EJ880%{nb3@wuP|L6Pw*3W!ws~#E=k1<~vbr*1&0T zd~03pLN`TPROl(8qP~uTKTMT`s~-?Ew|jNQG)-AOI`ijCtZc*IE&t{O$EM{K^?0$_ zH};O6=KOevxrW6IQ?lIld_2E75u`^0FRVgC2oUc44ld~t1s7HaXEr-UJ&W%sN3D>D zswPNAN*I_E5}W*6TdfxZ%m2e3@SLdYBt)<%{t+pXe>)WoQ62^RdDdi+QDN z+ezM1?F#r9AA(zh)3qmB(kaDaY{W zLyen66ogN7JPdo3%anmzi?}zhj!Z(!U0RiPV_7-57kU)E9-5U4N);kI5|vT)WJqEK zl9wSaqp#DslT^?kQU4aXG=K&DI#fH2qjezd3R@A;$@dWqmd{WfRAVR+WjTFgqQ^+N ze4{YK3?t6@4RKC@{F2l%$`c0?Q=h3XE#MI*H{>)J=N?ELuYLv$LUBcLV?iVS`$=8C zhUI(NYRmM8qJ_R!`-Z9=z_@H7s(g_8MnHK(1Bh1OO&K*nKEU7FQ`w)UR_xdt6_5{N@65#^R>a-&d(J-(o_^= zoe9d3LFHF6Uyi4kz66gQK4=aI#aZnei;HX3z(-Nm`}MEP#@h+LjHCokL$k}lDexE* zgJSh*(laImraYw1GcK*J^Tz2}XqwQ)_wYi)%|H6-KvC=ibZJ-;;?Vc-qIJLbyxC<>|MW*;ndI1}Ovr8gn-tZL z0g)u0+IXKf?3R;6nzATkwb;6qrD~=Ixs&r&$HLNcy>OniM$9DkJg7~Oqi^?clc$5O zu>+rcJ7puXuPJE+y@+u7pa9DPWloW*bzJ1&xZ%`wd4VRfH*Oel1Sp29WS8gqcG>F5 zLiy}?%ycL)eqKE`X=%Hasz74qk3ng<)ek7yQIBWE z#dnBN{(gIh%pwCj5Zsv_(i%3eH*$ppA$WM3_Hc%G4wm1i!rmjA)fzm|eep5@>l6?mf(GMJ=7zVbo!TDV4a z!yH#h=!|q8J6fY5idO&CmI`E|Y`{36i7X|^B3O#g@>>p5`gK|>)rqTs)A;p9gyo;j z-y&CeUfR8oZj+dL3X@8<2*YJIBUuSI*yc^8X>`+}@eD}t`(li|g&Ko<`*u6het)P! z+vNlGkoDG(2;84B%l1h}G2Y0a5#UI^TBR5v4Bc15Cs4}L+>jG{^!>iZVT8XqryhKR zdTM!GbNU8A?JT-^mbj%e93m(9;kEm(ZLsZ^;tp|#Va}ahXk5X|xBi%#E|h!@n7Os0 zazoeM_q^&ihL7H+@8gWUDL<>Jy$RYCn?rt;ru{Qm`f6vOh+tj;FHO+W0KWD|%OT+d zwy0LnIe0~A>{t?BMt{JzaZn+Tfc|6&(}uob89i97^;(X8PbkQs6)aFZjnc8%gCvRg zK&SS15RC%S8aRd^ySsBhg$mU=MdX|HfF47R_6`sgUj>I$AqVz{BFn(t3Gj!<4#Tle zv^@-0vq$;bpBZ2v3bVlw(=~nElv&RLi)Z^phf)V*=@6YnblJ_)7q7*{VDeA8m9;X& z7NBA>RRbJzQ%mjS%+ac3ZZ=%f8q>s*t#XvdjzZdp$>@R??BFp-B4Z=8IyK}2byPaMv9jVWSh)^ z)3{SNaA(6Gl=asKzZ_kh8PZj@RzKgW_SdD{@J==DhboGKnK-q4i&+^$^TSEEVm7FI7CDAO~$3+R{NH7+zg zib}1!-A9@o=2d%DVmA_4d^Ql_{hWAAN3A+DTgCX2+Nx5<4W8^3=eVv=M-1kGYGeU}EB-Hk{+U zWq5>M4l}i97->(QhiEO0Hx59iEL^HHOOtJ6*6!L-7|w;3!6pHC;bMhBAQGvch)G&M zQrFzTF8Y)xvP;g-TgP|$%6wMw=JmX!X0_Nu53=J*UkM}sz^iL?g~d)+(eWO}vp7EW zNkfk9=X-O_C7CV;h~*o-MSBgMd>}jce9ps25v&%PiyNU#B~HZ4pvAel^N7uj%9-PB3gCpddJV|a@d zAlh7QI!+oxG#15&h``~n(Ux~z=V2~^nxKhSH-lFkmcGse3WzX=N<5#i0 zJt{hD2Ul=1glMnPY%@Ej;z{NlLQ@D2zlQ&qWr87E$)@&_Q+pY?aEjRF6K_WhXXc{p z4&XYvWi9G);fdhEI>~NB>dGN7u({roFv0*ig#xd|#`A#%y)sMS8*&z*6BLW_C{o@f ztx4s?vz(OLrYYco2TZh&o}8dFl8lj0@dElRH0Oz>Sd!wJ*5ufS z6n*^wR&Y>mQL5sE#&un+(@x|ubY>!MNe`WW$Bp+?~Gn_V!9UoTEtke*sc!sk=lx}@VMtTx58HYcKt_nN4a*8bs!MjVw9R-*s;>5M7N@(SfM{AH~K!-w$cna%@v~3IY~Pmc0IeUcc4SHq3!^W6CFd2 zL37LB%j%&so#F~jSSoY)H^m~t&L|^zp%{TyA|5>J#;)BEIZJUcZe zPgL>xu9Z;tdd|@b?l?J$zTz$D+VXFMgP`i^JFRe-C~#4OzPeyhhQ6~ zwrp1?`k^d=w{kUZsWLW7voO71q0Hv}QT4;f_PH6{I_w;~uR!VSp(@DZSE+$@f7+uf z|MebQkxqk}nUg4nj8}J}nVdemyPk^cv5(`a)67;N0g}_3SR;E})#l56J2`CH9b5gm zPZ4@>8ugA9eyI~avMLA8_~i*ly8R2aSjL~+Y=4yx-pDD{iZ@R@A`_S*p76; zLJNF9;n3WT%w{PxxeMs~~i zQJmVj_nhDC%e6xFBi*Z=Sl z1`Zm=iT`87*ys;f-5mbSv6jUc99wFKz2fsvuVy6wmv*W*Eg)dsd{CVAci{bKJ(b4= zY8X95w$jm;m77G@lP9)zAy!*zv@ z0-w^NQB(h-5I7(rT{W475R%*-ZCXN~dp-O}0h;#jhwxs&tA)DIFKe6zxTE()r%r_nU9Ljn_h0+i-f_YQzB!&IIQmJy@8LMw z_S`xShLTW-SI5I|NhGK4?QJAQY;s0#P8W4^R0ET+`-VJU8*O2$P5v7no|gHDog#d^C5%icxU`?83mPI$=>Lgzj#gW!0q$lxW3c3q z3N!?hSQ`W0Ft+XN?#eiGn>|H!3n9TSK^9k*kdwE1vxg@uS@^bAZ5>tdFM&-uX1S83 zTGa}*#Ewo;E)~)V`18=B^=w9BI^y1|tj(o3h?H%6Co!yq;?zA{l;;P(N5FHtD!Kr8=GdTSP|6BhEsW-#;n6mXkLi}(meH>@Kg`; zm4~v%Eks$-M5oAqL&tkMBucNor#?Uhah405sqy}jWuk#ww}nL%r;S^B327+%I;1Y1 z0Wx`b;xI}LQ)lBPCj!O5%`DN5&92&-hl4A!k!0G=+*HRw*~(hsm>*?QV#K`48|G5q=JtPIa%IL6Se8N_58yfGSak32fzar+|b0+h5OMQwNT%>f??m zpk!8pLI9YqR)elhSY>*X!u}O@ZG6|8+`K zhX*d4O%)=uUIfVnEmf&1!=x&VF;g-EV}0oba%wTU?U8_9s{NnVo@xrwm+zPnk=%yT z^!%2GADsz2i4B#y&IcfOn-5*(bh7IEg_ej*=k8KbgP+v4UWps@1cvf!wLy(ZLc+1& zGk9qtcG&k)Ydi>dpIQ{W!sqHdtpU@-zdz7)8}xOFTz3KLWn%jd82xMM{vN3&mtFb+ z62%;|8%P=U*b&7;(TO$(JcXjI^uwlom8?>o5gP^i20+dwr{iVC4(CpNkgir?+m`LE z#P#xHhwE>31eqcVyAt@G#Ax*5yr`(;dXm)EDqc13+F;%VrDm4>6NesF6N6}*!hbLr zcw26nKc`5st^eNDWB1xsaH!fBP0X|X^lI)^I_*_i0Dlag%clBCEXf$?)>;(yGq_qU zl?VmF)bwwA_0h&+6Csm`2SZsCgTNO4hA@d0=?KF(Ywo4z#lJe9Fj%U{jHpcZXT_d~ zaZyF}bJ!k0!UjN|*7NPNw`oO4+pXrLS-_=CDmpN|w;*EDGXaLN*htiL&jPX*)-9oL z7tV$M2T9&y|4Wtiv|@0lTI_9gvQ?0Bd?_H)uQLo}f>CYg01yD)FCb}uT9VGG}Ngl7M3hJ947-R@9eK!`4PD(u? zc`;MW?y-vpTR#U-M`1C6Z{Go!WC{ER?9f;$dj;8hXVh*QOEN|?B%14dPACTJCZjJt zM~9l6y=vWCBCEj9h)0}>#E1jIO_$9C9f-$14EC+WEOcJUrr^~c>Wl#j1_@|PV=`~H zq=_!dV#w(F)(_R67wy1qWVjrzUF;V(}i$4c-PM^f1e zJOMy75X7c%tl76XwmXMp(`1W#GXL9k$GP(y^6FYlnZsgV%N}ugJ`rozT7xD>$WWt^ z$_U(pn2b$=kqSH$7H3c{!8EbOZhyF)ZOi^o2TEg@K6WO4~Y`-vK`To zHIEeb!H86TLRAx<+6LfT<#n(gS)nc?m4dKWtJon0wys)tvRVgcz5`u>hwW>m`Sd=c zi}Da^K^63`YV7(D14?Pp(0CXV6`~ag>hFxC7?I2pKbWvNkTz?8@v!t#r8x)N9=}M} zIO?2OIrNle6AUrX?n_3 zGu3OTS!b-1mbTV+ha-6^>*SD@O8PVOb6q||71`_#7~Up--}#V3FMlJdHtJu;z1M(~ ze1mXE;FwM(inv}`1rT?5mHCz`{@^luY{uF1>HwJGw^?9|ota>#*o=~wIC!@eTV5F* z73Cig{;UzCoP?_^OiaRoxuk~!kkua#L4;v&0fk*|N=m+QLLr?fkSt|7{wB<(`}=JbtWK9c(8?GR z6;H0oY@T6JyIOf?wq`nBLNRjdU1bFx>h|7#uvO9(<}3IbltFtXX2=fU<)@k@TA;4w zs73YaM)Zh}yJtP%h?9s9N*(X~8JZ+AG*tql*>xI%O|ei>>!AtMYpjhw-Bv!rRRj#C zxSRdz!6`sZNdN&d<@SbPhCUZc9ahEl>(AisB z9dmZbEvA_1A~BVQXeo0Je%^F{23goTa7E`bN> z#hF-RsO-T!c6Bz~f~XREO*LaflmmOHYBAXy3>7dUnh8$!@$v_cazB;BGXwTAOKRM= zV>hSm9JOci@6uZgTpp($2j@q-1o^xbM_bQ1;SIHE6A-RxWs}RH3JkwmgKyL24J|3CJ-;6WD9guS8*!O+q_)!hG&2vR#5ztXl zMF$lWu07rYxN2WD4b#b~b)($}d*$ywCy%Xn`_34kHdo65{7TBT&aRfbXURb>A|3cO zUAfBRrE8;1eb74P!{`4>$&+`M-^6`U@@IBmMCE@vP3h!n=j`$YR{p<<%Ec^2`#p{y zf3{yRJXhJ>Fu05I?M=T$%`{paxZ9v^+!sH;;N&JR_)Xx8}EgL_Ads8S;3)d%U6 zT#ZFLQyIX3;-wpG~Ybl#g*ELmo|Uk$M}RV6#? z`pXg)pKfPI#8Tz&+_0;nEv~}i0l>NU<{HWiTet|2h1<8n(C zU!ZDICK2p2^MyZ+H=)$KQbto{RZAk_12{j*iFmJnh~MxBT1P(4y13;6S5KQSysY72 zwjHY|j({t*R_dByMEy)_wF#`?@~31=wq9(^EbWuP zQD9hZzMHtbEP~H#vmtcFP)j^)mB;r!`<-~%ek@kGS<*z$?D|-C4xq9-TKier{_UmW z*Vv`VJ74jAS}SvOC(q;ebD=YVy53P2B^ekV$#<4fp@9Ua=AP3AsDMgyu{7f0O#2VN+Y5{IhdHItM?06Z z^*kq2a{NFCRHw@nnqx*#gr-a&-I79)8%4~-n_1!W@9tn^bWZ~JMoa;ZAD=g*EsT-G z8>xaVjKXC`IKdA3VxJo=g=F!hUeuCgL3&c?q1)2%yQyHMb3e&Nn!Y(OPbvyMlZV|% zBk!iZW4#=5k8QE@Ec$ozvi)647d{ArDxiTrF?UWvWyln$97hv*f;~ZTNkW~4oFP4- zB6dQFwwL#&CSD>JAj~NY`J3~@2rE~}L?R$ZP#hj@AIvv6CPC!Nb6lP6Z3eYBNf1g| ztYE|E7y|6X-8BhSlOT_ASy8CP5=Jr7gp8*<6}|6Z7?pyb!tky$3Du7?^k$qXg6q#|ts& zBtyg*w45T&T@sH!n*O^uU3EeS8N4)BI1{B@J!N?VT)LENu1vW1nO& zbTTvhzv+o=HDv8|hmkhl)Gg+-aVO%kb{(*qS(PBeA*t@P6tcrKZo&{xaddxxu6fDA z)N2HYv@st%t`SkbF3|pDX0HBr7wBebb^i<-Py0V zK~HCku{HMg_FEs9pv%mPu$t-Pw;Dn@ca&~@P^f;_+v(kW>3sA2%kKNl^W)j(oBmf* zBGB7mDKPib!^exm|NV|q-p`xsi)I=McK_t#eZRf+c?C^x-8^i@^85RDy-x3!o>$!W zr_S#K?;jUa=To17=ld^LAM4H2D1=v^k1sC1kGrQIFZ+*+`!D;ar`;p_gf;g1-34-& zZJz<%%a>sQ-1xGmd^WE_N6_bt zWeIEc`CM6I1e^HuY?oXV?BJ+t-=|s$;VVUC94HCqWn>Y;beno26k0g(^qOGg7zZlq z!7&wJ7<^rS>-iGINe@{V#5`aTVg9ZUw=#(`JM`*V4s&4*iOxq5-Nb?j#W4vS1mj*a z>eE~hNbx;SVIda(Fxe*u2U_YC)UmC}OR=w4?bh?WEWEUx&&YCZungh{HtRxgRh{az$;4?#t0*hWiULZoXUtJCPSUh zn*V;8#+yNwLF%02vg0^g#B&7{t>ZF-QMQfNRgnQS{u-ZgxXns_pMW-vkfm>k^COmR zzu3dxb6IY?3dtR*Iy2S$=7xV3_Dmz?tQM)R4~&Sx!xJ^|dsMoxe&$;&Q?I$G)Zm8ixQ)LFj+0r-jf7}nt1{A2 z!E*K`7$o|RDP8$Rp%O~Z2zeR<*X*Q^#3bO`??Rrq>h8(CQKEEr|U14(po zB?Ehn$lrsSYy4c$FK;Ws8<0FA4paKuta!`qNc^)o{prOyJ!SU-7w|nJGEF7`IS_>ID(EO_y$T zi}71poh%sG2z(&zUav0V5s82GH2gnrm7U=gP<%J)}D7uQs`sJQ4EDYTH3 zmy3a*;9a%wCJ;eb#%A2T#ubJ_I}4X_=H(ItZ{JEcqQNLqgSJ`on?^j1h4xJf31Ss? z8Ip_t2yr6nJN`f&J9Y_m3G2fTfNFWZ&2UJnfqwD~oP$d(q6?j-ca3FWCD&WNOh94k zHnsZ+7Q!HOYL6{8_=Wh&3$oO+UVhY64AX0G3t|O&}ssNBe0Zum!y_ zt4G3?bI8JkvY(>ks+ev9mnaG?)N^MeuCjlt=CxS8qL>z|>69gqiKG&f=kXUm_vKVO z?dGi!dM}o7iE7hpwKJUq)#(iqidwHM-YL^>cec8IV#>#V>%R1QF3+HH%k73~AJ82! zv5YCEg?&%wxip_jM?_QATguLn6}W%zzG#@&T>WhAOFrMEsaZAIrI}x;4%IKY-kI=l zl%V4)p>@%RmDRlSmF3wvgl|@1_BQLsZfno4pGX2KJ!PxPoQh9KjN~00aJ&aDuskwh z95%?udnq~4`dif;PbcXlsuy-!lPh$!t!#vbPA9^qsSttie=3CZ^NaAnYP_m$08sZxh!ehc=G~PbZj{rpv1kmWa$tg_t`G_O z7>PAD<8YTEem$Iq@tAal^t3)F{Ka2)m8CTT=?@I%s)(V~kwO^-EFwn0ex(^1;i#_; zbE19STGWEC7tZNHB*dQsL>M3bI7Hvo;W|TFRNJ2C_F1tcmun98n6MKc?|fB9T1L== z8X3C0NA#=jra!^{yP&Wl$yfe=6ch^m?~hbQhBk(F#-=9vhQ`LOPKL&w`bL&^hEARg z#x{n|&X#7D#)ki)!`63pFm!S@W&Cevr%cm>|GR`x^=zVx`0FS~)CC5n@}DxdvAvy{ zrTJHo{}GTggR!&Qe~adSrwrGXPTc2+>6*58 z=Tu2QK6hlu&8HqZHGop4p1-dHJs+;rhA|OUZ7jk_C%4 zM3Q>FgU;K=W6N}#ew7WAo-{YtteSMC=BIa}3DfF?76bAs2K7^OvTiv(Uq&+xHp(4c z_=je!&Q2ov8&Wxb3gj8)ZyFuVWkVTfBK^0Rmz)JXBNqg+zjlfno2p+1yzR)poZ31i({i!;V7BG|~kCT9r{a9y}W68T;G+ zILhpefdnjB#cP!8pKNyFlgGRA4ZEDou<8p=c9rYD)OBCx^lZ_$H0o_0bjpF;9xxC% z^!ou<`P@vXnb4KrIap)rCVExMU+*faMye7m^drA2LOi5s?qQqw{rrMnv-rF*q+{vS zs0}Jk`>rf1bCp?g0WL|hfU8{&eSX=syrsH&Mt5%B^^O96Q$}-s+I3F4t@T|bQE#h5 z_PkzGrxF2Oe@ZRWN9`(h?~V;)(K=dah~0bulUskOntkEe%B3}@zYS!cV&h-U&=j4SE3-TJ%g!gqMc1%aae+1%15li2XY>evK%+Ix;^cvSz5K~%o31il%8@r zeT5g0e`43LB@2s{cVh%CEHCqb{*BdmzA7(N`@UL$Rr~ga+qRrxC^k4=W=dfmRxqyO zWLj^dHwiCyRI*%2Gp_UkOjsp>wY8^L1lVkwTbAP`?cNS6F6{@TN#S>w2fUE{fhg>= zu}N?l`YL@BlBY}jTFRJL!rFWF(RObto2?daCWgK1=(%kKwz-@=fq(}@I4LYl{`~!v zRUdw?C8qF-*@1&@fo+1zIr(-<(Xz2w zss{7~5mc4G<#p8*kWkoZi)7ixDI8Wje$W9pZQMjteYZ}Z4FY)*mtzOXUWqavCkblV zw{r_=r#9?fhuos2^U^-f{XZ_(*>t;2*G)2}#cMO#4gG?MJ^%sl(dDt!YLu(n12^)+>$mx5!aL!2cwE%1Sx7+Fji8>3ZrjkD12{Xww9-4LGWUtU|te{ZrMTPtl7*N75?FEV6aiH z@8P8`zXS>OCE#?;p9jPvpC#{pcn6$fN1$W z)Ve<@c|9kFqF)^RXMyGAO@Y*G(O-3M5=2(e}JDALw$$Wmk(Q z0(Cw<6D^&anKx_J?O+_%N7ageXU`*xR^14}pH?`jW8R2z7Kl}O4K&Phm!=y?6#Hg$ zoi1b2^?gu0xgd&(N8Z}cBVt95hnnus``k~y4@RGDng{qFX}@vj5RIp}RJ7d@>Y_34 zc!%;BL{C1fBrjywL%nAK`fy&FR}@%u6CKXAucme{>*X4)?(SWv56nXn(4F6PW~62$ z?caP4uGq@epjOC=vzw6zBRT{8&0zEfQE9B;M@Yv8W0n|e3b&u#Zqtqas#=%~+e@~I zb@W>I2#)}k$8h1itzQ5+AwbmMHPJ2%YMbJY%O=xTYr6MByq=C!cV_bhilHh$LRM*K zq>u6o41*J6JgVdW?!@Kv-H-@X+u4vl@l7Xp?Vq^1I$+Ya6x*;!TZcWx5@LhPfMY@B zw3tMrY+|`(w3*O6<{+`t&H1hBc=gB^7X`97X!NT1md9kTI@Ldot0~fOVAkG6n1Gcn z6hc_SgVAq*X*e-!yjYUD>nG>Mfx%3{S%bs+LU!=07tu=U8^egVH+#m?XznQQ%ft=M z>M@H?jQYx*`_gjo$JMKAX%|)^h2-{Xx7&-bY64z<*PQcYPR)xlZ#zgc0gwW+>X3J! z3PprS-$&*L$+%^*gr@#-(3Cc!$Ym4B9!)dt3bo>M66L4%Qjr#;ZvA7RvtV$#PLydF zWDsl_@3;NQz6iH&Z`slOs!`_o=QHgb@Is~j$eplMUFH5Z~+YXb?Sb;?=V_B<|3p6 zBg>R7g7EZTP_?Ha%pP=w8?zecK6t7@q>lM{*Y7irx%zpMq?;{`w0-%CE(9b`pTHtl z2JD%5zTIdJ!aa+K z^SmkwpYMFdW@VZg`j@ErYP|iI(m%foax7pc$WxU4N|Rs>doUFb1qBg<E>pI=g5Jt^fBMOrRoNXCn6G&77@hw_6r;hc{6{ zZr-Pc%XD71<%8?+(;W3b!yGzI(&V}LzuC!So}q|?3l3#L-i~mqqq;Ql?7bY2%l#Jr zz}eaw>%RUp#c`X$Wj8tTAl-YTGSnB|n}XwoPg)u8DkdQbr--7XQ#s3Qu4woyxra?F zat&54-^HMk0!=rcwj4=HD8Rp@68P&Y2VpzmK>O|K#`B)-Qe#;9&yXk-^i{>YV30ZB zMi2p+Dbl4@3F0S*wc*^PVM5%n2a(~Bxd5cPbMNG;TGk8Vj%TJk=Bk^^%}IDs zp#i=o9!FiBTGW~I)Jh)?3b^bokGfbJi}?mz<6AhE2d9|!?$5X<;vQ~nCaj=2y+|-m zBq>P=J<~1UR03Gc;L!+k74BilXge&d9_&^U2GneM0;^8Zg=Cc_FQ1G`lpF)!YF!7z zi8us?t2MKg$y7{%O2y*p;YV{Ducd*LCsA3^61Y^vQzT7d`eIAVQd|IsX~JSkxMmq)Hi z-IOr4Vy)uOso7b$TuNkuUifF(5@&bvkI(4c9U&ru9kq>$w0jQdQK3T50kUVZmaIJ zZf`(ve8Zzvv9x2GXaw#wEAeL~ovcI%?a^^NrZ}izieLP!U2P0&g4^=&-rUEv3 z>wJ-5IO#P)l_s_<))mpausT$@1iUXEok?s(ZW`<%+?M9K?A~apN*_aKT3B@Sa@? z?)<^a)@;mL*EB{}Ec)_2yL51P5Zq{{7Fs}k;}ldtM?UtCopYM=zsrb_zq2ZHRxib8 z35wrnq&}hDJld_{FF40OG`K3-aE_GC1F0cRQ&EzKjiZ!kGzx`kp|I5i4KGp9Zl0_I zsbrTl2Gi}w`aw>Uj{lyEkbYWNyTWB9n2=v$iEkNw3rrQd|ZOq={NB! z^L*FyG5@4-W`dA;nzocW+t(sj5A$z%=YrkF;C`0AEFqGyUW+-s4MA-edzMP+5I`e= z+LIBbNcCO!&e(!@9rJFws56~m zp2^FK(|Q5;fUy`t7#f20X$R|K6X3N-kex0+7n`^A1=|vW5&vFj?5y!ra}#Q-=Z$_1qo%xGW5)iS)M^us!Z-D&n$QvvkF05K=Hw|;cnqKyrek& z2^dAtG<~lDk7$vW0Fz193-O$wgi5pP3xa%~Cu$6GuAy@?q}xrc&w-7C;94Y!TE2+G z)EI~bi26q)w+<++w+Rv?MqhL-louY>IpxeP!9mo7rd<3cs&~(j#J&|#Tadtsf#_Ec zfk+$kR5oC}{^fyQ9{0+aA#ckmB%Qw`=kvq(a7Iz|NvC!WuAZKxCV`y}wM_#ruMrMr z=s1GzQ{Iix*{Ufpf_v7ZDzI))bZHyOsx5u+@ zOZcRPQ~I$h9G6Ad*;tK@tGLVjLN>GW7-b#PTA|UhdaS?gFR?QtEQ11Y6r;kNgwt!D z&gfUZ*{7~_rk_HLEC9v1TbhmlMS-vp>ERG%aV1UaJmkidwi0x^EPJV7m4J3RS0>kd@T>(W97(8gjt8T=Ih~m zRP1|%_nTz{De7mGURk(=?`~RzwNzEC5dJ1RlGCVEoe8Ms1G z3f5Ma_OD2KOQqmcV4FaV7;Dfb%_hh5I1fBiq|nW3?0+fVWJ$dVbm!PXdhl!zSX6V2 z$G}AqT8GDBSxx%_in%VEpGxb{2XD6mSs29@l5I$*AaM-z{S(yfmib(4E(}Sy!Wz{4 zL4T(pfxVkH8OrP9`(q=&&W=6o8S{VGM+WCsLG&9n@7J(s?Kx6=e!0tT^ZpBb-=Hpa zoqMx4vf-qj*Jvr1Lvxt6+s*(h4DzEYkwDt&7~MV^_xZMbJGSxc4F=txBTOzCxs!xg zt9a*zhdIzZ{H>!L;tD3$uYWu(mWLJ-$=z^GbqcvxH-0VN-RG&?#y`!+HeR%41|CQs zAIu0t&^INq!$%va$_hLBVCgb;9j__Z?FLo z@V|EZax=7XHT{2AUN1Yf8^~5wt6rvRc8a%2`h7N2`SD!@8V!-S(#TH4D+6rW5dVFv zpvkuT@Qt;fI7GYRCY9=wK{xXq(QT#W-5~Ar)je{ZVEVi`<_DeUzTee+3{QVNRBXTB zT$WycKC0(-zg}hfKab~r9HjaK?{h!;w?7YRKCeoG?Hl82{T-F8?Ph!rr}m_xNG=`_=t1Vf$6h z=N=`$+pD?0FNmN26Ik;Bx(D*RElxjv+WUjnuP)a8Uu^I5r2U3*=9Br~CTl)#@7+K5 zKVQeE^+7k8*IOGq_x0Uc-Uo-S!=nBlizvCDhwIl}AEh;ZkH&;=Em^9s`>F2?gw8tm z{(hkTPY}Dl|L0fO@8jG4k6)|soatA3?5XoW_RR0Wl znDy{|JNJKk%AEdMGLXLC+ve8C(^m*Z!QG!{?KQ2V?DqVCX?;J?CXoOAGPk?+4D0jt z<@4p}>ovSoeZHP?_rlL{aLoQOEcWm|we3}h^)~%gjbGmOd92X)Yae>5(C_SiXxU30 zZr9WGLY%-`ec}L#0eGjnQ*6J!Z2cwH=T|D`{Yl-v+dJDAxOo1MX&=+T|1sG8JXu3% z{&_I{u}9hcKAL-=WOv%N33%(e_B?0>W*dnPRUjZ% z>7LDX#e0^2ed$f6Yq=aanFLJYth8NXgSp<(bB%Ncbl7gV(t2MTUl&h$zNuWVFQ|4F zZLD2-sa)Bnq#Ws$Tp6B71KXN6=7!wunk&1Dy=z_Ktn@xNa%z@!yzyj#TEmx#S9IpS zODn7JDO6KS)-0Q?vrbuK&Z zI*)(pRmNLF@w@Oe?Mmg$jbPhV$h{_v);aSZt8bI~aS7~Vw^%t-hyJ@Pu*mQq`Dwg| zUP>K-rL|VkM~BM14Q{!TXWv>Q&oJL`RP3iyf%TI}s`qGy4t;f-v+11Cd|WA-xK6us z?IM>L@V4Zi9B*vZb$Yr4h^bX_Ke?HCkKZ?NtB893>T1wEy_v1aID#PMQs-%DZN;N@ z#m%N@p?q+(nq4k?0p|X@_2G$iqhfQKA(?!p=6Y^q*na_JK%2kTlBSWn=a73&x$JSz zY2Whb4N=ti-l-j`jin9Gaz7undl@YceaYxk8(Paz3vKRsO>v5ie4gXe$Q~z>?tJ~( zy{Gk&qarjm@9~W_H8t>EbM|t+^?;IuiTkRSNs^UKB5G(YRzJfwb4p2L=iQ-~_p+5; zPu<>3c>R4%xE9^D-yS6rGH763Ty95Fk>%`RB8rn#^->d!XHJ>@uc90fnX&;PM z)^gb;3nZg-7uTbDms}Y|%0f%{-HeYn6TV&_j}a)IU+oBKxgNNh@rUhxyI+ zX6`idxfbiADjg~LD*;jY0DW!Rca7c^=zMks! z`j81>iK`8zQIf>g%e=mwzD}kVj&Uq zU|I%6H+cKh?sa1|VIbz)^(fu-yeJGl`t4M|nQ8yuLN@8aiiFibcSy<-YCEe;yOSba~x>B~V3=$2=>p7NEO<@4YGL+04=r9eGzGli^ zk%8nVeu}%2RBcUf9@fed!#b3Mz**_*E~RZ2GgvXbiF+f1=b>Un0h;lG$!?b$q=e|uJ|bktz4wKdBU5V}R( zZGLGy=?*dw{SV$=!F!%7TT$xykef&;sa&nDbfCxX&}o^x9j&xn#)SiGR(+p-E!tzk#Zl8v~aMVABL8i*3w_A#FV5-ZXC{pEn zUMC+YF)l^dtqWPlm2+C?Eu`VA`7Ucqd*#Z;3oof^rR-WPYktb<4>u|^C1W1TH|1lb z7q9|G_MplSNjLy$D|i4BwJbyb8MnG?AQR?=bT0d2lOK^~jH0L$6W`-U_8Z$@kBc?r zhmn)4Wl`kgtUDaCQ=|!ObZTvO9O>>gqU|ze)XttPZmRaRzNGH6_mA)Wgo8Qs;d*Pj zT@JDqh#V}O^HQ~ACz5DGAjS4ZXzgn>rBF{}8?W|I&%=vMYNcDvXy3>b!6>UQJ>OVe zQ$l9M@@M+A&Lp8&qf)~%8zi=O+}45q(MIZToak210TNlHIG9!jT6UT+-1*xhKq8?& z5DsDGKd&gU!EwDAm@WmO5tM5eKkbH9qgj@~pIQUEyi-YPxrI%u2|MT}fhLi$VSuH% zZb@5MyH?tI+5*=d>Zr-r=n*B6^pPX9w2I7Dp0jAyECdD%LuwySNfNqgl(2aTVMPCZ1E zPS+aICX+=XtLN?3ep+M_T$NBxh;k&Uyqy`Aon^VUg9OvQg^jyym_>up?kiZ

x@CnSVJzOe|D%v@*zqz>}|ZWcv#`HWK+)DO-`Q z*|Bd38k4Bcj%liqK3@VtNU*FEfv1};NXNl7;yUO=f!JgbVpqv7A6FI94zvclB4mRm zhQT-+HH%ijlOS;$X}d4kKWp6HF)#(+LuUohX$r!03W794MIW5`@jG_5_K&t2C?}dl zE7b$Bfh8ddDiW7S%0ccjh`MwQwp5LBwI{r4cX-w4{yA|k9NOaZ7|9BF0uB`0fXX6k z7e_BtIJ?E*C3U!f>g+p36GBU3mip5GoU%_RgY}*M4{KE3E97;Ka z$e@iTS~$aelq|Htnk|+4?Mlh%r1dwwh;^v^ctz7(kWdk7Ga(x|&3}qf<;y4&OTn$F zlIS#07M^AjO~$_1jAhWU?@>!)EEkLZ<%22Lx>LKzyId@y?oWS(I?&bFDF@Esu-Zai z^i7I`a(Vg)E9QjkooMg{o-BK-0S6wcpiDfqiz+|3K+U%dEjK{9WwF2Z4YVS4jj`Yi zxU^i{LNyf^WL5r(K?JTC&rFmeQ7 zW0MlxHa|F`{UT1MxTF2nRg&vd0y%pJ>&1nN*ertrAx4Q&C}1W|5~|eA=oo#*TmYctFE`z@I71{U zw5boG3mD#BXNOHJf74b+N?Iy7J?mvui$U2`Mr(rlrKl6WHN z|AP-^NsO#UD`v@fqZ)k(53+3W`c5qdQC>@=jJf0TcScLG3QLSXCZlomj<^fL4p=A- z(2svP+Ysnp5dRLE9^NTh2C5mzrgraHY~pRt zT$f@EgC(bIo8NgAyEaoTK=-F&;G%P@I5w3FeJ<9apaYn;hy6}_qR9;c zSL#r>*?sa1=bOWHMA_~AL(V(^SdwnKzo}~TI!H2@Zr3wifwelo))=X(0fJEPY)L$4ez<&YI=<4qHly<` z)ZKk|0Uu?FMG2ehtej#x)*MYLnE)&ZY@0CAdzBlh#s$l7s<;@2)Wz;n=M^*34LYSQ zKL*rdyv7oy5uZWRnSkD6=0pff8&Uh$O7kfP&3V6VK8Fle4dGs0!I3v88yOS~qG6C# zH2$rUjiUnUR|0W&)NO}R$2MMY6sQepPZ_M~bK?M{YYi;*wh^qb zQtOeRu%yV);$H#dWG7P55U;^kad}-iv5;MTnvQK}mJB%CU#JLvvpb3HZ|AAw*f-C0 zw9C^hc8I=WTfiI`ANqhfRv}aFORTS<2;2Dm6;;yP6e53PqJZ(jIscoUaM*8BV3n?t z$d0vv&XAe(1;>$wg=7B3aAps^bW_WPs_EGL>V@jNh!|X9RWEk=4Tu$3``-BJ8Wv*42fv4y*m{cg zp-fS~yvntBOJWWBuQ0a!h}8Ih>q6$kbN z4~OFlI_w3P;cP0odAb5sTT%sdWaVzO_Qx<(k8-cxctMH=~r?5?v5*RM0EN0;AhsNL^hx2BjDF?ds7b?aoSz)%Q6O?Kj z7D(&-LM-qC*bZ~%BxH=Lx+SOscp-yhMShok8?wCU_9LY=oekIL;6Vsodld4Tag!~9K zLi56K#SEe@G%KoZWycqEBCdm04Z(7~MjbTcAbV4`iW|?JD(m;zeQ1W-iLStzwks#f z{a79g+ssO&=%Et66Y3nvY*SC{0t#HWe;s{F|BdFdKr_%ELEe~T@ zS9YbW^_v4BUIw&fU(st*(rAyY9l=qV9`UARr9`uST$&mzwopz;W~AjGlN!`jOw}*e zkGoBArE2gCv=7^7?{2f89ZCfI8iqnPq|NkYh2R4DE>1%AH`UopzLtBYVeK+7_SvXx@~pjJY}Hs48V%@i~FAfH-($aGbSP*Ervpjv}(}aQ1}8F zbSCLZyYRgqN|5uC1}R1qN8rfLk{2O9{YpUdMqTSYYv{K5RI|MD9xzjsj(%ETFN@8EA=b3$1E_5(f{dy_ zPV-}}QR|E^V9&0ydmrqNuC&{_%xGlEKeIa2<9ux|V-XCM;*>59RjV<7(M(s8wrK_T z%G=G{hE?1=DQ{bcR=$pZJRN^~3khx>{$NxgBMeuPx%jLi(TQ3;w)CY9eIkP|-R^{r z1~A@}*>Ak$jX-#X%|?tR%4ZcxMat~(c!9oH0{j#Wj^{Ef0^ZXD=>3V^z=gkBmjKuK zY5h&95Tq08h5a>gUfe=`JIXwK$E+}w0(Co%us+Cy)nY@wLyNG@*gM8hp-F>34@gpP zl`@0O-eP?!!25_L0_o#agtQK$=uY(N>J&y@M0o5bfkQLbXl4c9U^ zw>~ttRcLGFXU@mD1_suc#cd#-X>PIdJg*U#^(DINRcA`_I4J9rR3b`qbg%od4KuxT zioB;%FbwtWf$^>!xG~i{x;Pp>iCtDnrik`=3y>!J(!xT5z9>|qD@PWIZ)@?X7FRWH z2#QRJMN<)XO&;@ZD&`%v8_d^l4f_34t+5}n!H0j1ok$0m-)N8Vqwvojyk$srAH@kQ=@%wGrCt9PQCgE@tX?a%}THO-uA+qHNQIR9Wj2L z=&uE}v<&FE$CQqW*}(cmSN)Q?#z}J0)X|+nL)U{0NMAfVe4soG4@I=rOZ@WuA$G`0t7Wd5@P(oypZ@*W^KW8+L}umI8Y=k z+Aar&pW0*CQC5BE}#?&%1#Mbbe&66Yp5zTm{Ub^PCc0N|HQZcWzeLKlV%HtHQN7d43DzUbFE)1dCE2HR_ zwpZHOSf=oK3aF9>sI-?O`L8TOimJq{F6B5LeUNFBsG`W3o|kctDwzsEYZ3SM&52o1 zxm&C9esn7*_hOHduT!%sL8)RmM}i89fh9do91 zkVQ?I7@HewV)bm!1S#}{UA{Kh`-S~3KrKr{WD*MWm4l?vw`<%9DV!8TRk0gjUD*pyUAPNd`*d?XhnoGM+I0`$n#26 ztrMb0rw!-stBN$?ys^RwqkiJJ$_{z%AJ?tkER?U#r%cxq^jMM;pp9V@k${IL1U?DK z5>0`RNw12X_K$+aPmJss#NAaP$Psa24m3UlSI>kM52v z6QS=1jtiPFh|9aJR0Wc=QAVZZHh)26Cp|tCSN{M_@(;&G2bQOZ80zGtPe_8rb|m(IsQ~JsV+~@dXsj&*d8Y?=X-ch2y-N5Ov1$D}!4A~w z1=@0s1D8t(PhBn03T06wGA0e_v!XTe35whXKwiAgmws-`*a4h4>^)gD>O&TiPnAGITxsQ&9I!_dD2%JWW5ChEqrBjn}Q% z9>P$bQE9ODFPuOT+PoT48n2@na4S^HA?X*qq(RU*ctdFFToO`7f5f(BRG|Qm_F@gq zhPHVfDOj!ZUXJ!iD9E4_AW%Gm+_Bw*D24Dur-3<$N`YVt5LJ-f-8rCIg<_j5`rCR$ zk1kL9;2i;11(R4I4;)32W#sAN9mV5-?%XHV9*nEqqiQR3;XM$Z$zY7}o;G35Y+!}O zvwNmTsRy`xj7B21;$iKd`ihaxq*A(*wI;;2O~quc{(Z_#ExngBPpgKh-Ed87N)uhS z)=?fK2KE>%s}EAJhvR&!!!!4C{f(`r`*s`bUDVoT#jb1kCaCXkMejJYN$m~X+<+wJ z;4%qJ{uKzs1Nl9HF>zmHpm`Q&w0PM_uE`Q0l{;lKcP`{f#c)&b=h@AbAx%wZ-N&Qq zaAVp7?_BF~n4&0viBktu+{PFpPpE!#@cH8(z*7S$EpUj<{kFyZKRoX1BgOoLH zcGTrDu~R}RPYn!78v$_kxTmOUMn)b=!zIo~x>wNka8rA_iSE>8pw99{;{Z7F;%?APnZLEX1p}Yy zIUV+(qwHAHcfxQe*o`f&;24=II^N^h7U!2f8F2B#eBZA5MDwL>VueQEv0h^rztA5> z-3Li@4?Hq!Tq^@;0X_{g#R#;l)Iqa1(W=LLLDZ~w7tx~}667ulf0x0e@HUIh#f{+S zl4lZS5EIlZ*+zS+np>3zxy@5Hp>{LZNlEGgQcd@I%~e zNA2*@ot+`U)3i}($mA>Qb%YNH^KCS@&Ir{F+aT0jH}ITb}7)tH!5s+AWxPnuGLx+_S9)5LDyczfbFvsYyg+U{dJwqjma-tbOG+nYS` zqYU8E$gn$XJYQIlYH|di;BxStKv;|?VGC}lO=@O7)krgs+cXW~vp4hr#53XL#z^f3ky8InX|dgW{!x$_zSZM^b07n5MPAMCg9E@se(^#Tw(~nbG!>a@4C*?K)OpNoCTOBV&CS<=eV1`=7G$vzXBZs)7w7Ak54nwxFT1217$7apkBJ;;zZX)fwy}ASO#y-c} z)|kO%xB}HUr)Xz_Z|61+j`XNDH=bZ~BB9FBY3})ZS-tdTlHDN+OJ$EUldVD@Owt1u zi{WV{V?(m;K=o7FPRnW?b6y=SM|)zDmR8zES8Oz{?8g_s;+iQDgRvI1E~i(eU(?%P zeUeryEB8QDo@p2VmU`kyBJgO_`sD9?IJKnC)bjdnnR_D!_tIW=wk$Vq5}{i493yqf zpc8H@1}AsLGV8K=vQ#j4*k!a6yLZV!+lH&H+ck>*DobE*+)Z1mOifa)%pX@Pvw8p4 zq8QnJv;$g)Ut9f1*skol| zI9@s}>;>Y%I(>_^uqV{+em-}Tf@eIjHC_9ZAO&Yo9$4X)JK@5qa$rv1UT|dEe^QEN zg))1!Y5w!mOs`FS?g%>9U0CEA_wX@zhx1avtxQblxu}2u-lt(qM7x zs_BP*e+myAU3~tU*J)|lLjh*d?**Ejp(^yTa)P-1lkLPu=j-eZUG>9r2j2V?>+iR@)R*k&?HgBPg{cNQ)^{6iEgVM zhBwUa9kR-L7qIDN*%!HB1n%q(|Ku4%plsXXe<-p%0EPx5L=* zsnn~VC}hx1_oi+O*ft*(XJrn2o@}Du@Hild!o)r8gg%f zoJhj5D<6nFrp6^<`eU2{c=aU6tw3K0QcI8}mcDtvxA7}{6!2xCF2rDs*$i>^XVs}! z;YOFMnD7t7F@|@7@QH7prwOKh3iC4rN7s>C&&gN{JmKbaaJ%#40$ymPGDO8xs`wlgYA9qu%1_#YPUUtyNb~ZQ^HOtBzT|WVu$ILIa_rQ;bWsbQ10|sAvq+;Z&i`a`X^UeD-!LuLQ0H~SDAF3a)>SM!ExoXajSS6s@vmGOMDGKz+Lmmq|Ven zmOLWMv}vVedx?ZWvH}lt1du60IDOLc{^f$9p0&k}wCU5xf*x`Sjz;yUiB6KgkV5pp zs|`V-5$(N`&z%ecG-9}k&ZZF%L)FI}MnK7|3?8p-zD^CaA!(EC%hyC>lw+5 zNF?uctSj1&TQ)MJ^8^a{h$X43ga28L-R`ZQ;ChIsI;zO}J!G{E+a((yEN)ZpL|f&O zbH`Tdj2;`sBV3?;*2uBZnE<=OsoE5eVAwn5fu`T$)Qf8N_H_bjQSGGgjpN8!I z;?cVdryq*eiS|dpJ%~X^Unm8B3*4_cQ5_zLNH$fV>?RRd7sPa>+ANdWaQbY?DAet> zPvE)L*sfPRTB+_zts~Vmf`1e-BLcY{rTOJO4?h|cNFo~wb)Da~{Cz$omCM;$2+auR4Qzlcc0`!4JUlWQ=g+a%*fLcb^7$?Bb8Q0<96#$-HK+4+#UAHg{U2Se;;ud0M~VCO*B)19b!eFqGP^R!f#g`E(t?T9Iy64 zrOQE;6~NcvrCbUeLP`2Sx6YDqoZvdObOLy2Q`3maBzy~te9-}55<9~X;DS>Yw&@#q@6Z-YR~V#gRL7v+HUn1?E)?p zQn7)VgGEuZo=ITzrADHrM;5>h;BHAnhY&9OC`1LT!=JU*^NPW}YVnVCsa8SCiREqC ze!by8Ca86W4nT1iIY96U1&B!uo10FLb?6vwj(U6Q)H`Y26xlqV0y_pQm3Y#Z+v+xC z)szndU~oWQ`fe;7z2tgA@?xfF{ZltDwtfyjJ;kMXzC$Mfl4X!P;A2zi z?A0WHdSebVSW;2jfe~Go^TJSBcj{T1TlUW6DqFym)lA}%pcU^Xqbbwxm zP}%pAbC7r?TZ1Y$=p;bVj7hxNQjQ^RJ&eEg3<5I++555MST^wt^{nL!6FVIF z*zQ|5<(hSQlhO(w8^hco-_yFdF@80X$zXKEcc!}gW-0%!Nlk1S4Gh4ZV`Au11>Pt3 z3il>$tm=H>?Z1t`?7RZW&(Hyev_LkDxseQcFtkf&wj6rx^@ZWV#-9gHz}XRuS@TF?oJ>d+Ce^lLtL?Nw)!YZ`kreCFQz!^~ zb&8#mVd$#$r>gaE7CO)rdDwnMo6jH9yC{z#6xD(<)L}G*7?4Ygh9!cTs1R%bQ6ke5 zqee4HpfF-^!0y(+CxX*TmFJ!8doU1haWv&T{~*c>6ZH^mn>ch-ge9g|UnQY>@sk0W zYPvVT$Qm&g+9x@h;P?VWT1y8;l>mOY(vPsXp3hpU^TprCRcp^{Ij~+iIu(vt`wDWS zmgz&M{QH^ey7KKvb{6n96qcG+%C_={Y>YbKIl#Tx9Fee>m~2YPZ`?~nV&vRHTdoeT zi|~wGc$4x4_iVOlt$N8^JfeEtb8gk+N0JB9G(7Ja-8#b>CItuHHR3RppN-HYlO&P( z$=4w}Ik!zdMaMS=-w_9FOLyX_SLqD;v(z&v*G7 zrpRV@Liat({NzIlx(-E9XEeNx^{fFQ`3>Zd#4(>r5Ou$?*+$soRpDE%h{9#|+KzeT z)zfB%+hu_vc4dN*W;01z=HT5`YI$dLR#JF^k5ey5J_}J(oScFQc1sJF>8!v^ih?LM>xRP&&IMvwMY3>1ySj-I?up3qsGWcb5}*syq1j z%T_~ET&Un{PyyEq62qcWBG)Gy{bYSojg^ zqitfB!8#&&HaqyKn92bZ&Rx;oL1S-eb3QNeC`3t7@7zehP9kuz9Y#TmnWh^H}@1`Gxs zj%Fp!KT1v76 zM)4jM%LpO+dW*uNJWS#6L5H!;%pQIuczy@l#XEP%f-K zdZ^ze9jVKaXKB4TnHS)lQgTG%$g+8AhPct?>r_JaRs|=+QF<`NU7%zQ!D75^bhSKj z(Wa%0K00sbd*WZ4ItBNs^TVcdGE$fjmOC75W2`|VKzJkJXtkemByh)3p? znSMdI2UzYJ<9VnGH>Me@eW_$B3N$9B3|~)q@8oODNK!<<&=Nlw6HPd(+mh*tA;z&hX#!Tb~rSOi}5If3AMo zbaL$av+b*}FX{Yg-LPa??Ef;v&{UP|Z5*yhT77$5oDfTwe{#d9jkUN7Pi%W%`qN(T zT6Qlq<*HR)YLP`C4 zAO1po&_Bd){1>sO5OZ7Ha)oQ4%NJ7C@HE$sRTNXel~OBxOE9Wwp|ehfR?2?#o@o@` z5`MI@p(xRf`o^3%Fa_;3nI#rx997N1dJp~DwVQ=!c~I?g=>praSjseiUKLkK_7Ad> zY*d0nwb2!NP4)SeSUO*Ezw!h2i`)ZX{S7{_Z4&U zZTGoY&2CvMEtBhO#dRBn-PsmSc{kK&HN(`c$REDa=ZsFK*j}DjV*tEKjsQSX%E(K%?yJ6Ubv-tTBg7xUxuuVc z5sG)ErFbgwNXFwA=I-JW{c%nyq{-g(Ts_a(v^>9`6N<}qGR-L?5PVa{AKkKIkq1Sz z8C^Q`*!^Rh*3WeT4JffP}J zUYI+lAkbwC)J!9ayn$c9yQCm20xu9>P!Kx7$J)z#QxdL`3gG7zheYI|7@_A2nMefW z35r7^9s~FW$0do}c}}adeJ>ykrU-&4ixutooC5(|xVxqxXyO&nuPX|5Sb`}=n~?DI zry~uWj3bcolNmnsrXcunhCNI(h9EC~^o|z73L7KAy^e(y_A2s|9cqF*2zOw_=H048 zovVxcO2ZslApC5@l>2+*V{L|U#-g1BUrJ_deWSsYFUtEqBqTp{2VVl>h6%SUJ4Lu@ z!7a*76Jn`yia=9@TL7LS%%m9?{P=tNICAdz_V{^n=;870__yaj!Fc=7rBx;S`p z_jdSygWmUb_4NBJT2rIHP=EDP>Zf#!>9m5aSNGRPhriL2uZfr~t%QV{(+Ev#Ro6_^ zvem9xkb@*~n`@Z9uxjJG(8j5FW=e)jQroO7jb}Ew-LDLn(lQ(Blp4GeC6&eKox|vB zyXl2}BipOQ8D49-w1@}qSjJEC>=Wr}&f5*l;?r(LOXHQijbZzhvQ)y$2Ex{KQ14ds z%&&dnqO(V7VS3Nu;u)E$G|EozVtwzc9A4>W26|;Go3@@p#e0k`7MtahLx`Bl9a6z$ zrbniTB1%xEW``#Lh->!APz3>^|Al|JV~aM>d`L$__oa2J!De4>R)ZOy!WBpcG!eXK z_8}S43J06MMyh&Mhh~FdIV?@8#89bz0p+K)3?|k9jb(%eYK;ROL<0x~E;jTc1ZQKE z^r~k808l2WuFJ&pR+;70G*cKHQ1ehUIWny&W<;lu5iC$Ozb#Rfa~m4hPjv>dz{gVXa`IP!vEr&QI``+kTlbl`%*V3q#MyT7}qAv~OBlE16XmFd=0Jva(BE z@pTWGbqK4>6vp*d26o|Eq-FhnaH-T1vDRd|AJO2z=fPYkj)DC3Kvc`BH8D5|OeE|@ za}ZNN_A3HeE`!w9f71g*p6%t%GdTwNGX z6dKA^tNC85(98&f$!Rj$0+B;yGBBEwEdr720VVAU1`Cuc%CtyVSCQ6Xj9YMz>A8V; zA9a;17t<;sHSU5f*{JMBhSmYN9(Z0uur+3i{bieAQI;J`2^yC$J~X0N0ryavuY zY}N&x*m5UVAanu67q|(<4|}J#3lh) zH~}yLY9VulODFfy*T`D?0z-lxhfoE0O*)46*k%SdHSq-;jHFp>ZCgY7`aL%z@>lDw|vcvhPCNA40-n zZ*>4a*g0AV{-zB2q+muE6+kp)2sj851-8Agc)jlclo(O@3p%xlQh(c$P>&}Sa!6s& z8xJyhBjR<8OIsSw}RPWFWTMa5X95Q>%Z& zrED;&2a8|=&NZ&~A$r755_{;LekC4W&gECt(l4J=Gg_<{mXt!M7&g%hR07>yD9W5R zHLxZ60}c&^gMco3FHBi4uC3LmJ_0NiHl!Q@x$|L`Sd0q{1#=g1g-@OW5u539j1p|! zDubq>nf6u#csVHrh*UD6qEqd*CD>X-QEb&{Ba~cl0-dI%d;!U5+9p563yJFgmrWVs;>9Y%L~25p{7 zIi}3NnJOhCvM`7K86-n_RF1I(S_?EQ@L2}?Vm^-9sy+2V$iTarqW!kOs}Yf%Uo-&m z)LV4L>&BBOqc08W&uf5eF-w>L0*78+BzpSMI}o*z66WeK_{67N#M)yQfbHlbZckTL z@|5e12;UjOXMky1?_#DUXxtIg5U{ql7#IR{gC?z$_~Tq)EMjUBVCVC((}0N~e=ZN= zhJ&IsWh1E?dt*56#6Q5S~nWOBnM5`Tz$_^D|=xR2>|naw9v=6lv&b7{oGt_r+q6lEB@o(GuHj^x#>9& zbsru4_Z$0vI`XPgeC7XFM;`Bg-o2PH>f0I`JK5{oy4tu{8rv8;JJbF5)zZw;*ziBR z%lgg^hEC3=^#7w@$u>Rtug!Syk#ae-2mk=3T>t>`|F72?+uNC0n!7sv_gend=Ir)A z3i;2B)VkW~#S^f8^Y)KlDN?Sv?K!efZJrZnC#k~QF6`#E4@U1dZhpQh7{ohtyM=X~fTjOj{S><&@w`_i~#Whbjp)ExV)F4(Vpw%3gXAY%@%kgEb<#j}%Q$t~M# z_nSFBX?>!#uzBcKTkn@tPZj<&MeQPkZr?}S^S86p@((eScVQCCO|YPqRNUH-N;i~Z zrk(80X|+dNU4I$3MhEwF=2{|?9$LL`&Ml;jzI|^|M+`rTV)ZtqwVA$%?Uj^1((2R? zr?u@{-2KCZj$l?lbJEU!AKFPhE$Cev(Y2H4j)&O$FchrafdXN_Z@*JO>{s-E)~h;t zZVlfnsx7O+t5+j=dR$)}8dkdgxGVkActNunqNydQqmfOg1n6AVEb-5(Y4#)wq>f%U zXVdWc@18t;Ut#T&^6#41KQG?q!2aDa={?D=HgEYnz1?oA%>EoKBkCMpG{dg1I!?sj z3$5|1QJvH|hT4h2Ess66efHebP!@HcIre%QzQk8VW3kg-Ug6JPUV@G#`;<{`qa zEz2HNC^S0FZ$3wPn_LOP+02f1DFu*QqgjPldiQ)NarW z0j06ArVTLEe%{k=YehR~sO>}|R>?ftJ>n(uMB>}=vR5*+=vM$`22U-^=XO3j6jndG z!r6VTF>_dao$&#?pYp--a^XD(0PlL@7Es_!@JWi7E_OA<&6GdJ`e$9WX7EHQ9z0$i z1XJz?Ld~$Zw_5xn)uMy0IC|cp-J$J!gK^Kcw&6al0ls!f8aK@4ylx7%;_Tcdv@KR` zDF~R=6l{vNoU8k6GM1i4c0R%E@gsr0kF?X;M+@lf^_3NNEbF|*>yCj#9DiE>`{#C> zZf37Yw;^VoIg9n(%rA)GH!0zF_UiKsGn+Z%hx;F7@61J{roh}#%uxj@Mr~Yt@n!i( z(#Z+Sjc7Zp4!6Y7XX6VM3#%9-UCas=tJLu6PBT1DTWUU^0 zzR1c-LlwK3;-$A{jvW5%jC43t&_s=a?4=iEg~iksHfRNh1i}&%50osYz{ET3xvKcj*42kfAX^2u=oUH( zDrPdwB1Uxp(72F1JZN#V{@<@@djI^t(~VjS0b3Gw0}xSbPYX*|-Vnpm1=dJc#pw~w zF1RGYEC3KXu4~c^Yfb{_X->jlhCK6Y^0e4Xp`Aics7gU;K`aR&*Di7u;8N=_75?c@ z2J***w;DVru$82zVUpUUv{tk&wNleN>$FcqT^)q2B&8=5nXOw;CJsznWfSo~0g7Z#F6^ySsJImAL2UbHty>_9su! z77Y~>Uj9w+F#?A)V#z<^@Ppjn;a9xfMq3WmicWGEiyPx1wj$9J2biSZM(cg$?vUsh zXBQlV0fB{)B3Zl#_bdiYY64JJu*f(e!6?Vv;FhGsmRtzw%K<3IC!FN|nz3}HQL~5* z+9cQUy_@CoL-LX^N=phtDUU?9)mtROVg>O9?;e3HOAmz)yi8{b%Ri2`>UKpr07T?@ z=O|JShs;V31vYwC)8tr#S1L#gJ4Bul?RvZI%U`*a0Rip9WHKr(y7=U*zC&UPy93m< zv^BvkZALnTAbi?`Y)7zA)b5Ose!2AKHsmGu`?=w_`oZdWF4?g7ESI<`IwW@k7p0Itbc!7V=4r zhV&4*p^3`Y}$V2Otq1XYKDVAczTkuf+Axr?8J|Qt)yR$`VO{NppWb)eHxpL?pVmJ z$uM?QkQ-uz?>^@pmpm09{kW7^J046FDg*Zxy-ac{R)gl|GEns=vtz zNiEXSkcZykq{VOH@>13Ef@#Xo2c?maYiK9s7}!cW1+|h^LawEi;3)|+!n`RsqwI8q z$`IDFgmv3MWINB*F3LEH%%}%qp}NLUq9joVf@Q3#*FaJ5*}g6ku%-&9m*joHPJO|N zPILl{fr6%{35^slsPGr2W*5h}e>QjeKE)#K^OrAyl~mJ%lo;fJq`K4nE#BhJkOr8h z>?zv7i(y!MZAJlJwDZkhIZ?Wx5Au>GC_@9`R{Qt5!2Uq|ZN zMThfxNE|6~z0T8pPc#Fkcg}?!ym3v=#hNX3a!6kHcc9JOW|l@hJ?NcMt8JXBJ(XT~_|8Jz7(_5}z)bF!hE8DGV zW!pVjE4!6#+rRu|+qPXR+dXMz+uZGgeX>v9gZB@Z^BVU(rp6f8_9#u+n(x zlo>dN=;`~4voOslK~#ovqou+!Fz`&6A@fix%@%z{0*8y(wDBoBx39WhtxnuhUI9t> z&Xh+FeefqO%>$JmQB4xLDfOK*yUS_A2|cE;7=uk|h_Qcz>a%8jj_?qvEa_z+yhI81 z*YEyh#{g{-$f2K3sR|>vb;FPazk-D$atAP;STiJiwS!abm)D#XL$?MEzdM*yVMMe5 z=w(Ga2tUsXx+h6X=U{ofb|zm`tBo?rwq<;<|5#qC|GfR-6!^3jlk9)7LMmAu3t4)S zI}sQ;7RWIg?IwhI z!brJJum2BnJMKjwg){_Xhf}4m%Sowk>~(M}GoyWQ{5O+g$Od zOqWfLf29&v=iL#&9=Yveqr0;Ns}<#$-~<8WP*a78;GQV~N9W15A!#h^qM+FRv!kcJ zg{9lguk%@5@ctb^l*ZNKCoNr<4u?zIXnlc6=+$C@B}l^q7xp2R-HbrL^22LRdDM)tPZM!&CTm>>wW4W@e zLV%j;Teg6@v2o>qZ03=lTJZOFsyHWmwPO?)P%X`Yj3fQH#rs@xcB_%?if|sPk^4Z*RnK6l;D~3ME3(7CeV*vv>{K!D0kNjCj z#68Dk22VBm1nVd5Y$|$({4tJ$Jobsa-RO-#G+lPxf$U0=f7hlnUkhp<{1sdWnX*n- z1_}eTaE3cR_ojw#5l!pJx6CU=bKK5ffpyDH8fb-`hjVQzz&Zboj%2~JY7s98TxP0P zSqQ-&u}M&3{*_L3)n&FhsIW1M@cT4|RQ)#Enjy zcjZJZxWX!ln6Q8|fZyNAxAR4qmsWmQO&32Doph@SS%|E7djKhnWY7O!0vFCFkdCqe z10%o&2UGgrW-b07N65{{#?|zHuJ>GY>oiiX{jPqQuH7l#CLi?KOcf$>5pFU<=1rqK zmZ}P{=|Cz+sQj62_vssFKY4(D$wwhGActY*JF3@CFR(!|;H!7&I?3{NekAm9mivBN z`#Cc6d0)Bxetl7T_4S~U+xvQ%>Hj>D`?;U$|8$r8Ik^3`U;A}g+WVX+^wRh}B30hrMl zdz6q+?#G>h@59HF(8po;RnJGOzrXjVeec^NN$=}Ot^dOV>enZi!AEcJf@yBA*O%+j zS@IJsXZXIC8ILT`_`LSGz+yLaxr z69>KTmv=`botL#=yEH;>ujU56A3{Q3Pqm*PcTYlYOEV8&_WmEB%X5(bi|t*WtltRU zLbA}?RPEQzo%`3`_oIKt;Nv>;YHMTXuAx`kd;h?7M8f}b2{rfY0Cd&!Sz7D&U_$cN zmi7B}FZKNwiL>sVzu(8;*9Vt@|JS$K?-Sep58qyR&h#ri^3=U2c|G2K5&igTggG0@ zxv~Fxr}2N;)c6XypY!m2JM({g%$)gl*@uDO+ve8iXT7rDLdr#N4)FJCW*-=E?2+vj_Z+ZQ43{Uff=5y|`a>20rioVS^tkB60QpNC2V zzs|ppl?L6t_iekWBc1wsUPzM!YmeL?k}U$$y(zZef41QQ=j-##|NT+JzSle3_i5?u zKGQz7QRs8H_j#(8#QbZ2=5v>(_kApPU&ZdEXS3z4=gM=x{VD4|6dfz)q^Vw>#cf{e zao?k-*?(@`#=?J_6K)sDVvlygg$?S?D8 z_m#<2@s#JA+7)Q=cX!do`h}O;rEN;ep?=Aw(Xs4PN9)G?Uw6CKs@`JnI+u7W{jZIj z+7(@I0{JKHk&DDj26Nx#RgORWk{7?lno?U@S{lpH!S49&`v)6?-l^1x301 z1^FKSy_U4l#4J^!E^N5X*mk>5T`+hPIhx8_*6M7TgZLBp?X7M=BhAfO6N^4hWpuw> ziv&vRV{`58Tg!RuqUBf4i?W{j&F^`vW-@|m`rn57iVEP*Ts@f673CHGq4Ni7k*vOQ zhLLz5PrT{BVq52@ZduX$DsEh^vV(Eh7TQ(uRp%6ST6+VCRX@anw@d6AHv}I#Q_6aD zqii;Q!M+Ge+Ew*toykwQE)ZRKl?jwkvlX7CU8@CmB23b zN>;FR8NACs6&aONog{eZr_>W$T5A`5cBw7c;8&=44y-rvj|h%L$9*{!SU-xVdXHu3 zGS+lBo6Z|A#FwH=>2^BTE%8b|-ISCo2*g!irKd}Ou(SjAl3Q7Kg?t0Iis%+DFNfXJ zTREFe{v%Gg(0yE9U-hV4b+aj2tQa1v;Zn$6gt^1CJ~*~+Qf^H%qEyV(0_8@9F9S;w zI*;PL723}oXDqjGkCBxZpSQ}1Dq@O}XPkscn!WKDQGLgbRSC2wlo4$&J>?z$i}A_X zTNov#0bcritaX<@Bovafb6(xVaBy&3>p4F<^*jUsLn7ZQQUJhYd2%ZYMs=YQh6qp5o?~oX3Z+d>i)WO^Q6p&vFf5QS2XR1EaNw$qYZ%F}=;!mfiDg&@#kw zS}c&E5c90bx?V2ib~fwME4OJtldZ-V*xABq&Ow7w(+LHEb2hZNj=wJ5xn22j&XLQr z=oXJ{X-N{TzU;I{g-BWGg0oWSm8y#;F)k|mycr$Gh1sJ|5{FKkV{idy*-puWQy_Q7 z51_XJhQCZbnq9Krwqu_a{v@J%p$Auf0rRz8caJ%V-ZXfa^A%(1(iP0O`1H=S&5b1L z*>1pghIhg7O;dT_&owYs*kcX1+Uv0uC`(`i zoZXSFi1|CKQgez+9)wU#3uA(q6$$uMRb=@@{>xL30>d-HZ)YHz@kJ7d3@fCubg({c zY2@KBCPTkB)P|AZ@_1XkbT#2HB0U5>&~83-(IN@HUa0k^TYhbea-Q`67qTSmaiY6& zJJ*n^Uv1Cjl%d_M(x-nYO?vXIrFpnTwRx%_+j0lrq0&-^gn>ME#*?BVZv-~Vnlwpp z%#57WV>Ygz5V)@#Q~mRScNU6FqREYyTzT7+0C20M4LxBlc5|^#lq0{`z))+0sAf=d zm2j}q{Kaj9tF`6plc2t|$S;ecb?)ZdgniuS_g^Pa%~sx?czGOSkT)r~PwV9DSFx?0 z4Sdb-B+8jark*a1#7gk_SQ($0gPpy_2H@!yNUmP?)YGi_UvHY$#TtL6zqfj&%c5az zGsgr5T8G4@t)C_T2?{F-gDDmGq>0S?6=Wf=cfY$W1D-yc_qLTL)uc@(wa5UCvb-%+FDfFQE@Hb3Y&QE6{ZEy*@@WuGw zchj2|H};RPMbY~|k3+F~u-D7;x{Pzyb|Fm<=;l6CEJ-I6TF;(jGA3kZStm!WH=Vv2 zJWeEW`%I%MsRxxhK8=xQ9^FwZXI{$sh6_+sQMQwjWVZbKTkQ6Pj2HjmeX4Uzl*BhJ zKn^b;oomD(=+ltUviy=*sYcWqNp{vgW-R(w`SQzJ*85=mFCtU9Rq?qtgSFrI2`bl5 zHYyY139bW^BAhx1$;Z5LQ~)=aCA|k2Ml*xh7hY?JH7Vv{sLQW6lSGmP)98XSOw8>) zsYe2kL0lBaet1G+m8tUnUKJlEd(_gI&dAb5GG_<5S$# zM@TTw{XZ}34n33x>~B?ttUHxv6B?j}80qk%q{W3bd$E$D2|?|n(#&9#oK>;Tl*RTa zRzGbelfbs7o~o$X(5?Av(==tiW}(U;D*X5DmrXK&Y0szTdGMFYOm}K6;Whj?hKe??SDVzFcj{&MsAeRePv>B|P6T zbs%*@y-`4MyB$DBvr~QDYaL>R=(KEX99fFMPo%zC$uMAOg0P+EW!UzDF5p-7^t+nE{K(=!LYa%;!i& z2pf|dtso$tsIY=RN1Z4^UU_t}?5F()LpNDTA;O8Wyek_oCEK*UB^YXD z)g&wx@-i8wgwIPnUM@J~xjBXjPrvRk>`0!iL;l5yFQ0DLI5r+yjtr z^|Jq-w1FzCbM$ISx^f29L`lGYs}l29bbhb)S|(w5Xzy zNGVb<8g@$dfb+4&_M+2UAm3Mqpi~yy@O_M~@eOcXfN{}?eFMemJ3PB8i}|~fSk4&F zhbo9UL(kHRj+tDMt9<6I&9OflZ|9f2cB}gu5fHzcajKk6r9xFjSJ8pSN9B*)p{eU= z$o#=FSSM6(7c~PPXAMW_eGemsK<_dlBGr8(qfLl{mZIo}6Jn#aInGHQpwS?XRUy_+ zxRF-TVK*03L2v8zuhPhnmx8y@#Noqho!LP1p!)UrY}KisO!R4LV>xuq5`A_)Xx1w= zeqr(Chb7SvzXGcx>VVKXGCm7GJfJJph+W>?YgA=)Pe?qXv~8^) zB^rx&rQx$5FSsWjqD^V1>#g_mspaG^WtWgRyLA&7-@vGlxord_oI0kkwF{YEn=C}% zDOIN`GXnEjFDh~Bic)R%f`oQFvKeHpzuE%k#n}RT^ozVFLm8~Dqs+|g9{luM_rM1=Y6>$Iof3-JRe??K2 z1>>(1x$nN9Tw_QjqBnvhR?KQJW`Pk?HOea`zzOp5v2JOM-Co&LZC9&3Yx>1ir*)l> z)UZD(0%Vr7YvDR^K7*7$3$$v{mvXgLVzaQkkyvjqRJ2L9=0q}})^edH-Y&dcVx$#Y zDg{+wK%X0~afZe3lWsZLel4j@MeL**TiAB(w#NIzpI&y#41hP4Q>qgpw@d`oYVg7H z6~nQ@`Hib{sfihYM-JL*XEz=TJ!3{S-{bbcO8iaDNzxoK!uh{0o=aoP*vU!y*txnl zN>8cKw9wPZFc8NVqX;=ENOwLZcc zPkEGp&>&Kz3qzk*Bh6&YNrcUE({x8Ta>ke|+VtpAx`NXWo?<|Kyy#G7U?no*L7(uu z8Uv{B>Fl7Wy()ryLMcD`t5L+vt{XFLc6=G%4|5(j4yu4!Ay--5M>s=;H^pU@f2cky zJ6jic5$3|9m?yCuQT6-64lhCF{#<{vh?7QArO_iHnKE+3im@v=EiX*hO`s;hOyJ5r zhB7SWA~HKmfqH_u3db$WQ*>eprk=A7O9^;qP7$!sP+csLok&{KZ4&!p9BGA@gDjGx zsi?9D%wpZw@=B%HJ9$N_c6a4!kxamI;vN<^fC-2K!%Ac`0~GTOqSK*_Bt#Y z#eYhdu#H7dp%xO1K8``^11hhvyNzCn%boXkx3aBB8_73b<=j{Yf@?2aR2*xXDL1z3xF6e>KN^Rm1QeMJ2c>F1~}b=sog#8;L16$%ogk>oBDs(XB(1Z(^Hl`FNSA(Lg8vu2EHgKT2)!Iel; zqB?x~x;f2x?YE$q7Gn!Mk_$VOQS_a1J0=5hQE$`7kzP&NzI5tKE{W;~oaVPAVF}$Q z3&os_5due{0juj*r$(kjL??44IEgOD#MbMAEm+BFmb;f? zP&VpE^J8~=-a^O&beNub>79CUtY|?oG2JG~y#1v=76(w^JhM+T_JNFE_f?y-2=M)T zP4))Z6unt!G%sYG37hEk5?YMnvBiE}@OL~e-GN@S%(7wJ?o$ui?ZDaBGy#E&mS3() z()|t5-;VFn<;2n{G=GpLDP z{$a^1qX9?p($|L|ULyxBC6iqA?nSo>kDu$@+ByxAd@T(Aqs;NO^C631;nIoL7_c=l zaa2LyVA7^~SE`-jG&D5z(FV=AI0kBJZR#Soc7C?2;PT9Q8_pnM!czl+m!tS_v0HJB zVIyoNj2AqoZ^Udj(cht@;TbW5+_)NxTF;RL(j_6U=$|M{kQWli&o;)4e?~pQ&@V@G zMUaLF%ns$c)FGZ?sA6^c@l!Vb?$6eK*dwG6{ zwZ2J=5rrcWgCoR}d}S@iUv$~#mJ_iLhKCzmU?Is+0W3d~{=0L{t%?DVzTUD_$5gyY z)4mv!7OLj|cVro+weAW^!PN?Fpzp+_(Vi1&t~hv7w;{j3bbNG$_37A>jH(wc-*(oU z=s7RO0zfu49@%S;xgcWXBZ7v%9_HodJN0Yi@LOWLgrNDX4jP}QJT0I7s??_t>oXO8 zgle6Q*l9xcerz?>IO$reKbh%sJfJKD6B&-ut00Q4fk&K9*%*(21EN{)jvel;sI}3D zTW?)aY`cPypieqoE8s{5%2iO)dcDBO^2V6LLD^B;O|B`TM~0n}GP^5Bp(>X4zZ`&F z&|&{GsgokXv=4<0khA#Aj!6DT}B#N&4d}-yNe5QflG8)$Q5TUJk5vBpJ&?zHGEI%$O2HfCF)4_5q;R_p2^WN7 zD$#)CdMNGUhgbT7nT{z1ML;82pciLY6)1%3K24lpL8j+ zKPV-i@X1g?8X3i6Q3c@&Gu;ZqtzGNdXEEAi-uk^ngAZm@*v5u;9z><&sBMBG1q#+M zS{U`g8DO=$hKC#l+K7B@ByL{&s!XYd)Tr~Kx=xvU3YtLf?xu_?_=~@X0T)BggO`ad zH1cQp+0&U;5ne@ZN>EP9nCMz4fhfREVtuc&zw#tYQg|c7zdN8R%J;OEU+;2piC5;{9k{td^>V3%23UjK$){) zy0N}|qO+{>O)M>FK>RkkP*NUkVrk@(4VIRV(HV;L`vcZNi08Yu05}7ob1$MF1#U%n zF6OP#6Ti-|QN4>@i*GJH@_8IaXnA&ug(>V&_%XdqGI>MAam|fayw3O8t>4CxaRuo+ z@xW@9)u2?Xu{gT8>;9&VyPO4Z*pO$%^8PHf8)Z#5_mGO#ZOHLo?OJ~xk&8d{LOF6I zzk<8qY0@2?z?x6F1+n<`>7&a#qkSAu*W8x(r*UjD)7!6jHxl~H4sy4rQz7a9#|-V! z(v4wxUVA{ECMm&9=Ol zw^Tml=Txh5(P;DNnpn!k?QhBxm=gtKlJ!PR-N^3lNSh`6atgFrNMU^%di)8nDG0UU zhRm!|@T;{uK|GMiJH>p@(NmCp-xTSCu008g7s9OarFx7uZ@{^_;jdGI*uJ7ab89}4 z!AFH>k?hF)WsjDZ(~2j1vpn`r{_}NSIS4Kzq;(2f5&dtBz~0E{CZmJ^R;UKdfgM2N+?j|oZ- zu4W=n)Ga`Y&A~(^LjL8xXt($(xBZhRT#_i#W&@boK{cHxPa5TjwwgQ({Gy=QsKD#ORL-yO zn?a^GO5{3h`XasR0FQT*sVL|`Bx~(+ws3|>%{EfZ-F}O7Oh`FTX>UDCsFD+17({c? zC5@!r`P0o$pB_63?RdKo&~mV!m(nNAE^Gi7HOb$IsBh--K(#Kk{(gJTXa!fMNUFm_ zs5Pc2&lTpJaH6O(!xPYoxgIwuHvcR6pPG%Q6m3HzvCYD(b!SV(bm`|y;!0N5Vo)cu ztumSc4Kl60?2#!!{6MFUp!tYy6WV}QGL{9pVN(yK9Nm?;?D~Y;&OX{z7WSActhX5V zFzBU?)G6oCNgl{{(cTpF-3_FVcTawZ=Wr5 zmBnIyv0gTs&K%%Y&uDb=Q#-^tSu%j3juT8oK~9`ij2nAl@GP!?AtWnKd=+9sNg8U% z<@~DjJwg@-tUQA`B`ei8LrpsR5o{fhM+xtH&8;xKbVdbL!bP}DHn$+Wp8W9{;YPTH zY)Fg6CwX#fC$l{UvDBw+RB=}C=DT$x~>z)dNpf*2yuYWjyIskc$uLTw_K;RRAd z*H%3j>6&ShkSg500;1LHXJ|hH)YvZ5_1%d}{_erN0;!+{YCc_b@SFEf3|4 zS396VT)}c>tHb+>d@5hAO{rs>sG0;}lUuri?#}o;m=+SaL@Dt(zeNf8gBbJAel$>k z@eFV4kd%S}DHGgF&Q*2?i?^D8xm1QwimVpkQqOfT#5k2&iC8)Cjw*I(WjGB^|JO!?dJ( z!br$>_pV*2{^w-iv;#o>WSAzNAs3UVdrQD73RyP5zX|SO<&1;CjLIP>hCLO#i^AfQ z;s-_R)fVmX`omTW>YvziMX5K3dHO!Yaz{(|AY2Jm^c#LT(b8Ie@}63vCMRU!A1nb- zymM}G;Fl^q;GCsM@kop>#c@~AdisHp#}a)%N`vFb-Y3P#f*#HPoS+Y+vNAagP*}8M~5Ws%J0MC7Q6gDHR0ZcYUibFhb`+KwTC<{Hy66?YzD9><4a^=2tr{rtL6(`)!Q_5|2z#I)c*X0Jzo3 zd)~9XbicoZ5B2lpbU*K64q?cCkC4@I?fQfa>nx^y@6z?OUtIKkDt5m?g#s-NZpkq$ zsj1fWR`3u-$7;tq1i?_`r-~A>TqiEmp63(W*BXFMNiZ$9h7}ODwGpgaGr^}ZPY9mT z#q8_I)9J*0dKj6qnWI1lw&j)D34m;eK&TL72H#d-!4rpsMmnSP+5Rb7rbT|6iMS6& z6L{rw(Q%IH8%mef%!i>xU(J<1GAtUGr2(rXNhM8V3(fGyU$tuDoOs%zKtDuf99zW> z!W<@coj8nvgR2s0(v)*Wc2wtHDzIfy&BSkkHh>kejiaT*jrLbXh$!`5DqK2G6D^e< zoXx@F`iP-G25;gSbh#hTz)dE1Q3v84nH4J(SBxrL{?IX~WN~>-;S1Iq_;lg8WVCSU zxQ(?%_&_h-)hvEEfX`^k4^A(OU0cV?VM001`fR{T&6j@}@=UIz=634o^IThf>zy7uvI0;=I(flxMf?j&=-!Ic)ht*aDKCfxZOcHzhJ8kas@F__8eVY{P3HyT9I7i2>EL|$z$ zH_eAQc!jw2G;I>%VK?3z!v&*aAa-95`7(yX8f~ge{iW=~B8MMQnkqR=iVZ|OL1{&V zBEb)_*$ynsq*vZmX8lwewh`D}dZQ54os6K661*IB2<0>HwbDOWrWA}|7Hp`7WMCH9 z8>$NOo38X#9w&0Fs}#bJtXi}tOSg3JI!c0O5|Hkhc-cOrfuf1LYU3w_bdXDcx=Ja5 zh^Adh^yRdiKL}0xPW#VZ2$1z(I+~=(l*%SXFV!K5 zGy&kZ>1_G#i#XrNH5HBr`y&pbm&q^D&-X9vmqB)t$ZzqPFaltqmBCw#$~BFFPJ%k< zDOTvn=lTHwq9*{6KN8FF4<{q}(A|Jn|K?u~sQO%ORIkWtxkg8MO6Cogfp|>?-@BfEz{n%P zil(g4Dw4P|^*(<(zyO*7!MI@hkW^z&H1g z0!FXY9#cwMfFUzglpkrd1Acx7SWZ49Fd zLrPypWf-WT%uGyq#FfZm^rHW*G3!@Y3dGPto^m}B!cR$7Bsxg-Rs*e5n>x0FXdrfH zy%uo^>|kWfP=(j-98zBc7K~P;fd`xPnEHUXENV}t3zpgg1VryFD>E5kn*Dg?p<&Xb z(-!$VO0F8$2VOp7BEiT$)S#`|Bw;Dq?v3DT#3riq-%Bn8N`~yBfCIf6=tLT`&Vf4S zNUiOl;n@37KOtj2vlz%8l3|VqpE3JP8)1Y1ED=y3h`i$fY>8$>mBy2H{Iv~RBLY~r zI%{U;3D_E=!X8tyd2|!}i-7w_|@C&N%7oP1AsI`<-U0W>CT(jMylS zMdehW0^kpi_!3t_c7*^+`I-9z^cRMz5Ojsb2ZsSvhrZ1(3vU9jVF&zC=6nElyUEDv z=*sKjbP9uaqV?Tj$!TV`ut5GNs`)(9Jm*TXNuz8IxEvTC1YB{~M6*C;sg?NDL&PJd zu5=ANNMmvfa8d(}FR#)GW~;5({Dakb z(-@@`J5@T*6Ubo9d5Xbz&?BJCE(1H|9nmf7#7{%w`7Yyaze#1kQ`v5^jpod#e4@$`{X_IlI&8DD7A4A@n`w(Lb3E6 z!0qhn&n7a>VUH~b(~G?LqsKmWf7Gl8yDkXk2foXZO<(8Qv{X(q0{Q+YF1;8$_|spnJN9b%NzD}F%O}7y6y9tzj&MWLp)NK1I50rnZLvo$Zc$t(t~&#XMOpR6*fL2pHV* z+1Q<`017o~Rj}bV7;|-JmtDfN+yDLHFAnmK+!w6hP?)3-%0TN*@IkspVwOy&VT_n| z|1W^*nM~F1X3NaXX`@W2pxiC{ss=CUxJ}&FE#IMlsp++|8X2>2|JMS13od>d`j4=- zZPhoC@jf&X_)(bQwYTmM!_!PGqExK?Of^dkZid2cL$=x9B_mKwldu4B%y`PjhEa2~ z#`GljL1QA8t%|PB@EaSC0`OMhrvPXfZL+8x#sg9X-ry15G^wh zPlNwpz&-FcJ=S_K6e`6_di)st)MNxWcO0(JLNSSnh<~M_k;OQmi)=!2c8I05Owr~q zIzyJ`1-)0-W}_Np4iqB;mjdqOxf%AZz>~-loZk;RYc;^$vqlWY;K}LaY-OItZLPko z1T(S^u)nSz=%;zB32vho+s-g*w5MbKvdDXWJ0OyWdU~D657l@-Kx5DT^7ElNuULg~ z`rC1{b5t0YC2Dw=SC~<{UI{B|Mt^j}uX2{x>iFe0!bK}WA=h3o!Rk_SgKA&bMG4}? z5Fr?HVPxjBKB1vygIfiX2dTPWhTSRstszR@w%>h}ju=a_t>f0tCGz`?d~$Rm;8VFf ztU9gnj&k^vXT}Jg6>o$TTPl3;G-ix@xGlqgby7NIOWi(UjExA z%OMBA)k~FGq^=&}hHk>s2}lJt;CAVP2tiGT=?oN*%^UA&6=oFFZq z;4wUGl%(j)YGGP1p-CNkdXjIAR}-Z$2Gy28P7Y@c-@0mb?ieQ_4A)Y6T`^H+EdoF) zUK&cXlhAPi6Wym&g%DW*Cg2W3j`WCvV5RQYxiB27<-orj)ET+!;KlO9KRg12Uw)_; zpY~TXp3<>d{8f~gf3Gk&jD<6Hx;?QQmd+)iIZju99p!^pq)iXMmETg9eQVu0qjn$M z$~CfLY)Qyra8K9Gy2e9t;R!i$8^NRSGSA;Ys;}a+pp|V2lQK-2bGa@oYb4?b*VtmD zIDumYBObe6of*09zJsh9ggJ1o-y%}$tw+ExKu0tX)D8VgC_45nk8G3@LeJ5r)W9rqaY2-tCc<}?l)>YQBi0jSSt3{?Zu3^y+c-9B6mntu}W zmM-Bp>2;v-h1vV)9T1?1z3Z}$*ol|}B>ZV!8JPZu50f?5KKoL%ufpTM<1=Qo3(QN~ z>N|C|Y5Ca6>_<3}5?i|iA60bI&d61G1-UL&aPx*YGx$L(sEL6 zB3*h@74Q?(|E$nW^nRKG(Z`S6bR5m5Rn3W?!$up$)-|%*sLeM;lDo4E(@7No?&Am} z(>4p!Hc@WGiLcGbyksW3?uNXZ`a7T(0gN_+WZ)k(nI`1`zdX^#`Y9=fK-N#Wia@0w zsy^M4?)6s$fF_jvc6Vn>VK~}E85PXc0LRC5EAeLDD<<&Hv?leu3Vl#_u&8A?RusWM zp835V>OZXg*xed;*5N^$Mn3}0dWK*7a=;bpC8SF3bcFCImvpN-m_`@(q(N=*Wr_|_ z5H*(Pm`>9(mVRh^{U=^qe{Wvr!AlR zqhs8OaCdM4*tirJMMngt?-#xvb&H|d1(}#4_rkqDaML!ghrdaepHOl#PDxdnSenA| z94TsBSR?M8Cy-^(bET_l`O@aSLOK*L?wj^UDco6WS+ zh&vH!2m!aq+YHxATOHke2UOmGoufYSQ5Sc(vi5tt6h5#_RQr$pSg6I?#GNj^IKecT z^#|PPKcd(Pw!CXHs~#7H)lgXn;z-t^+U4d%Jjcb9sI(x6_PXvHzGgYM+_-u33|kIR zA)U=a3_I;8`2=D_W+_EcbMMSvs+eqH4&U1^d}3FZWW3&2NS{I?vFMTR!wroKpW(-P zbk!kjmEx*#-&^53yxi($f@ORcP6fI@IsRI|ywlQ-FdnkrkY-ngvy!>~1jo3$027 zoUVb~#Zv&`k4GrGr6fUE0`Y+CVBd)Us0)UawG%dYVTsvfACR+5i_s`)?NvDuEt&KG z4jMwZxQjsv%2Fw~Pd&dJ=KX_0R!WE`2!K^y(|FRzk>t1Z7z&_SC z5`Lrdy40)f>mjs(T<;sTNVxl46f8vJ4k|=7b|hWKoDPEr1ruCGpL3w>SSqc-d$7Z) zx_9uU&6@|F7`~P8=E2emsGhX7Cr~!`UT)7F51wv#Iw{LUvAlWrL?8jrU^52IGU zUB@}`>dc=#AiNTr$`_zfhao|S?ZIEj+Y&kr>g98J_4zqXCohllH9hA_a>6bZ-GJCP ztK^c!ciyWB-f1mFB&{MDfiseryORUHZT++weHI{Hm$o?if)YqRGtpRN(TOSnBb>ew zWEONm>NO{eS^nkO5un{_1ARm5KPR$ywHy^5!JP){*=`LJSh&hC@$xZ193j=YO9jk4 zaWtZmB+{!*Oxgm%DbXst<_-*qjxn-)viJK`a_e1aHH2OaSooVIh_7IegZd2kt=Wv- zzg8Lmg(VzP1!BH2fd6yNc5lM$d_{iVhX!g04ow3EbC8dadHj8rUT-|)if+6&1S#Cf zTy-tAlp78I5Jo)Vsb{Y)D3`w`7OM5QX5ooYh@!%i;trka&WaY(WyB2lZOhy=$iX;)|HlInhqLV~`do~GU%PBwxAV3@D+d~<< z&Rfc+m+5;QQ-QSb?Iccb`@z}=;9zi5FY$lzi#E{r7f48zcI-N(DIgM(l~sREG9 zGqs=F*D>$E##gN={Z=}vDI$Zlq66^Jv|XaI3xadVO}4VOWqS>}%PtAXm5U#R2p%SC z1o@Hvh}XWBvH3onWd32fOhHFd*%I5#kaAI%c7`t<4Q7kWLXMmymzC#>m?MbCA?=FC zEfJ>?H2iCN9{t1~ze?00YLZUhofP9ekvh)_XwcSEk0&0Zf4dRaJnVdJUYd3-5VtDIf1GyFs;&NUcM@hvvy@oE& zZIFYQtt0XL&6uvZ{zq5r6=V6*e$4_^Kp2{5G6YrOW%!)AU~D^OP^ZF6sCa)6%!vi0 z18>}I$b$j!LBCwLRHQR1oO+^=i9I~>faXS4bKt~2#5-ACh-SmaOTDJa-|Wf^jm;aZ zmiDlK=3)0aywh+NYd**~ssj>1{{0X_OpyKGsy$wRAV$H<^Y>=B@Lq-|V!3^Y(4$&$ z5Y$1ERnCES>Dp3{Gm@%^I4dG-!&u#+|Fxg_ECB;9Jf+~2J1|*LNL5ILDeB=R>RYf$ z6i?_>|9AJvgJ%%c8zGk@N$KuqVndK->dq&)N!1}SW$-MH^|hk}^NQ_|u>+5Y#H{^6 zf4FoG$Tb56Wc$OsLo$k%6_RT!j`BnM0}uwzJ_qNd_NdphMX4paD>XnmemDrKAvMv5Cw z;fA(|ng0>qO#MbgD$%YNmxstw2wyMg5kT!|iMy9``$sLiFkx<%TSM!u?i8s2Ltm;v>?oO$7rK))G-GyN`s@EEV%yni|hCn|8z^ z)KK6oXFdlBhQ$Bb2U)n#7LolEMZqNhr}MXiG01N*dpV#8M}u|+POM?p5xD6vehrVI z-T)5KDD!(B#@Qh%#baG>(Hw~{cBPPQ5VImGY(l5Jbp2dP;IaAo>PP>A!eF;$qJ@~K zW)w<<*>=+$5C}n{f~kjCvYNDM54idv9cwovG#0-d2_w>K)MdB@ zJID66;LI}xi4?)>6p+|*!9M$_!29185Pj5Lb;#(wHfA|Zx)gZ4a&oCTEd0v0OpGTa zVH>pyY%Cr5MSR40SJ<37cGKM9fq>3P&=!7kr}i(qOl4#Bx^%0if@t{gn(BDx>k_K- z36HQJ(2aP-w)7OSau7^QILaq56DheIqjUM%;V7=jp#%5FpG)e)W#ASGwDT!0Icl7B za_*C5cXLt&D$fgKqfDhODI-PW=7Qcbu*&7K_kZ3{M1MNJG#>A4Cey!J?_f}4t?fq2 zAF{s4-H|OkN+|wpV;r@RNa{|5Y!&sp_d^wuXYl3nL1t5`tSiYFu;-_kX`+wE-58+Y z*QRR;;r5d21I3hIU|X)I1wl%PY8m%eJ~Ay#(b zKPWSCZ3?Lfv4JqHL(J*Y+8tcPNK7Gw|{&rZ%M|2|2jR$JIs4;{lU!_jcA zLB~!3X`y{QmxzJaf5^XUoiN<6Z{VXP$Xap$qE7xoaxRa^ z%o33dMu>vJK^fR9GNx$}yuYRVqF|kX&|g3xn;mEIS5AN~0~xUd9j!^MftEF0L%zRW z;4bv>i(45C2~*eJd&qj!oqS6&x)m}?oOU}+nL#Uqehd>VQ~_0F3E(GLC?IJ{ee=! z-HZD_q@6>sC@|Bek8RtwZQHhO+qP}nwteogeUEKx?)TT!EM_sY_)?WrDw}i`Nu|4! z_ldhp^h+S2Imw3aa`<|iUBUCVIaj(zS+X)-+h#zAGSyb<82e~wdGPPhHgPFxHv>hA z43x`mG;y6E{Mb(L$|W(8QV4Md4^XJAq%5?vyreTu@SuI02^7^7!0Q?U zWCBL|tFIb6t;=m1hQ}>$GQA~a~ zazE`-lpzpA(M*OLnK!>9Qu>nwfP`B9*rB74u#hQpUrBlg2p~%#2;n@8?_zMgR`wc` z2LJ)scQfdjns?lT_+&G$Ml*E>(>jW*VfW-&ajS?|HZsO>4=Su<1Vfi(sciYrdL&tU z`D9o7%;A^XFm>7U;R)NV20MFK;5Ix!r1sxHKFkm@J4J!ioIcFz`*s_={uTEdg0~XP z;$!kARw*u^6!=9`jM5v}a##rBRQnkAPW7eDScBZxizX?1n|G-k-j@sZb)^;~U-X3>gMt$r`wM7~(jKddGR6@ug zXv!%>M)otkh9u3%KdU!1pM4E>IHc;xTo6tph~Nbt*)M>ud{Jh$hH^IS+>GyDp}ttL zjV-JXdeier>d{&^-wCMEoCKI+%~fK#77a+iagN>@B&4d90}SM|RM|mRvpg)@iELBs1gQ1coyk}uUaJI2?X_3+!-DnIF}NwEy~4(dyA zsaf2vOA`hCiuv=zSP}db;{$%xh@UZeO-Yyyd)F}Ro&mJMIM#=}bw)gnsBfhugh3is z3Z06C^ZrLq4=g``U=`XC>4#At_!-NV-_~)j4!mvy707AYX040lx@784I%jZ1SEOE! z#*jA(PIP@ifMvmV0tzcGS&AtI3+(K&V_CHegKG96^_2-g-@@|L^00zF#qVgb!1g!q z;?SQ9dn~_S9L=8Y!^^g&=LK_1i)8S!DIfr-998$bH@*y&KkE41zo{SVgFT&E-UPPi z%a}VpmP+EsvVJZOWV3SFVfP7BM@)jU0!1jQdm|ptse!`j`fT{5&RU_dc#|s6pb*)A z%vNwSf{kY!rUUJ*5@~w1%CM6&PE$gv3-R4b&QnBtr5ef&oUi<7Z*b+Pbn)?!lzf_L!PAlp( zw}>32ClVM`ww9)IEwRPPP**P@m}vp|3x7FaZt`<>z>e0{Ia9}S(C)SOQS|2CfWzW$ z!Nd5G?s=zP06y^qO)3>MsJO5WPqNw{QSA*eySHc*;+czpB498Oq|eH0G7C+1WT&Tw zgtKiGyN$N8@4N&+32BpBGNO$v8YBz{p#$z%7LC~%k7ylSo4L^t@Q?kJ0%^a6i}k5%CL@!ls0^F2!JNfIi&(6+EsPEQf{w z&lBWk%*aheA`;%ThU{nuBz6WQr~PrM9c^@uPLoIYWZn3L48`mxU&wkh+ zzyeRXz?SYtR)Vm2fF^4|O6ua}B-wz|MEK;_W0jM=Y;zJqjY&R%C+>iy8Ezd2BxH98 zXCyF`Jdhti6wAkAYmouCCMG8#MCqbZuPyXIHV!BB(fE}T@nA6k+B93jfgD|+Ny0`A z5b2O9@O3+eYv6F3ZCO~ka4F@;oOr%0t$Lo~@XF^wMy|gKtHKyCcTbIn5YH|l7uoAh z|CZ@+nq-n2<-2Xe84hd9mae5+-GV&ne06Z0!`Gv`fxUzqK;v_s1-qhs#sIw+`!wm& z?#(vy48`6a27-Y-su{nXyBICxSVP+E7pUECqXM~gbWzBott?U_zwxocy73vM0MKh}$}Ja>H9j-c;iT3w&B?{A3~W{8PgPKi#p(c2i}F@ZPmW3C`HDw$Dv)Z! z#4-vRkdv2E z6=S7WYiA}SDUbomP(P@900++o1zrFiF#wzlnoDj1@>z7URKm7(34_Rb#b7F~W3ebU z^nS7LQDSMzn8%92w^LI_vkGfdC8RV32E|%$Kr`V8xXy)d`g#Cl0VH9UG=?|Zbj&#S zelT-$orKIoM%8e{QZDO!urOQEINbiyHIvp~kVYvesP@i_GIFqqEbrV0aGG{w$$S(| zhMBKA_ufZ4$MZ#!^p;vAG1sVLDu(3-=D!ByJ!5C}*>tm*A=&hYvooIxrI)e`rKqO| z#KdL$iB%wtJA$s>49BpGtdnmmfyn{0W}fGYi2C`jD6i(z>Jv#dngshq3P=?^H_BKpBYvnB$dhGcGVx>bA9aN}K~b2luC0Pv1(6PyG&t{W z?0&cQW#(^Aj@^I^b&Nz@>Jpqxwq+CTCG;=)86zjL>wjM>wCL`{ua-J$uQmOgZ2&hy zN_{X%p}>I?*f}=Pj^Mm>?6({H8>N;ORO?0wR4sHY0K~kewhIjW4>}({#43Uiyy7qR z=pmJ&{9h1Lu_jl-T6qa27|tZlCEV&<-W)~wnaWh2=93aXWG8dERZbm>mSE;9vy^lX zBeSt&EhFYgJxZ##BoFah9Tbt&g}bmI3>ipSNX+@jVyU65of`Ogc)x8%Y* z=ZL%h%v|Y$wMxRHCJfdAL3t`qL-53^!M$WT`YbdCyDM2?>8uf zC)UyW(MRG%_+fF{(Uo8Jn)!nDqQAmoV2!^9!LKF)j#UE~cIcf_!F zAPW!!3bp0-Am;=fjRFGckp8u_M-e+fwSgp0o%>ndfSBS z()p2OXK|c=hIu;X!5wIXJ@F21nh=Haiav3tI~Nyd!_7t&nz{U1#b43ZdUs#K`%_>` z+9jttP%w4>a_*(oXF6Asu@tY;dOZh&!RT5!FvZ4)lpi z(LTU@td~bTC3oJWKv*PIjY&Qr2IY4qEZ9nBD;_Yo3o`^F0(-+mg+bZdq8Ddmyjndy z8nm#A>RYm2d=dKq?$)F$#))%QYE>|ekCFVDxIGNXNEC2)4%!XVyA+N%L4V4b3>B*8 zi>Pk?8XxmzTri4Z^C3|Wz+F<(;N1thek-mPlOtZxpw~Eumds4$VI<5^3PjnsJ&GoJ(qdaQcM(;zfI=z=l~Y6_9;NvpK(-ym$Blo? zy)v2+-^7huc+bC96FyoznL(pX<;oMa??L04v2m%%qbhQi^wva@xy%bmZ(cvcgn9KO9W2}_Iv;DHh8)gMX4698)K zVoU%r?w86)-3gt6Uq)biXid4@n9+0c>Im)PrRF@vEMx~Dj(K%IvMt~vHqDA7EM(I%? z4L?Qu8GyuT`))Pz)y6%;p3t_zAV}7y2p#xxc1#+Ga{Czp+bO7%a(Rio8@tspZ+KR^ zH)V-gVXBjKL%4xq$u50Z>G#FuuV={0SSxV{wyxwA47b=oX@3J$H))bxqFQSc(oUvQ z9X3GchfmI|j3$PfoWVywkTgK1X(1cs0E%c14b7!Mu6gKH-hkc#Y;kN8>>!nOM54Le`B@G;AeaXogCDVOZLNP(LJRVS zQwtQ(r~uVekl9`l7i4JE_cF~(f4q+QcI$AjL85tBL?1kTH(jg9^4X!bP^>&8FsxXm z%_(WEs=(^ZxFkSGa@g9{UxPBWL!x;^c2KZ%m=v=c@3)J!CCLS9#CW6}iX{asCUwrk z>y!E^2>N{bFX5F^4hVOT^fD%~fMe~czl$xf5MP>i5vuE&`+<1Ra#QwH58Vl%O8HeQ zl8oO-`e|i$Q9fYP_FbDdQN6{+bCu;?A#?RQ^wKN449ChBgFd!8=Ypm8>}4k(le9gygjV><190$!U_<* zP?%3A&J8*R!07G;BG-;=HD6dZnu)G0sAR>)cIMPQZ6_W3;JzNHEr1iAWV##U;qyq$_$NZp%k~-IuAx?dOTa`2xA@@fj z70SO&HwChtlQyUrA(1xF630SzBPDmU2eT*UO&ZiJ1f@ZA@1Eq;2Y=C=#jgb)$^xtL`b>|Pb$+4>1H@*@J2zh0wNKA3L? zg}L>LZ0Gu|Q?m7Mr4o=dT~h@Ht4ieGqfb2@LTr2-8t)4 zaKS$shLtObLqI^$)(j^*=mDBYIc3+)KsyK*$tjv`7j}TWQE+>_ID@y)83;maG9Wmi zobQFP-A>CoEL~YOc^D*DMIxtMkQ`B2B`a}a7%Cta*6UUU z`XI?S@Qp!jva+klE@YI^rP^n?MRcHNzgMn8A%I(5w_hAs=rYLhE798mGn$ZwgH+Nn z8b@^>|8emWq^xRIF^FM7&YXQb@WwHyp9@&YZg(wHO`?IXz!-8*vkE5tdh&^Q&9&VV}Z!$43g=k1#vzM%2#K9 zJm7L0w|2?pvs7%|C7$^}LW9i1%?VV=t$Vt4|Cqy#JMz^UFp}L1c9kdOfNxcUl7g%7 zb2+vT^L=l6J4Am^mPAk{VWR5m4tx?}1^Z!ot78Z|p9egFU(My{1NvO7sIp3=_!CSSDMG=>K&rzx%syJY*9`bAG&(FZL!dmknNTh^s*qHwA zAzPBdk^;!m;NUf(5-7xua{y$k!y%e(Pt@wd)K|;4jS6RA`$R1UiNQFr;1+{_{bREzx-6xvGP2ZU6-XJ-b%!2*A`so`a@F?Rs|K!#&CH1vh z9OeiJ+OJz(WzJGY1po~ZVgtAeYKVvemTF_NBXQl=NuU?5@3Tmk9(^-5QAdEYn`p+a z0fmQX7!L-|^3PR5C>M#bJvvQ-ImT0c3OKcw6Uz-TfV0ejYh+f)ApT%xq&5cDmGM;s z7?Ao)ly5dvLIH5dR8eFz9v`qI|8PYEjpKFF>KQ_86;J0Lt;=axLq8>vHf_sYbY<+o zN|l@FKmGG2)mYsG2H3phT%^w{tL?=ed-wM?xq;=A22VqzM`b;tm2=Jh-L* zT^og<3RVa*<{*FC-mhz-da#MAG$A{bF>TYGHw^QvaUY+G$Z8#Be)Z~_hO*$`l5>^_ z@2P+&RXivBqNiBtXoU@9mKlp+V+&3%pt6lt>zPN+al4+EzDSFTjWUo7h1;e~G?RyH zULey5!!z3SoW8NnuIO_w5#b7jic~3<$C1H%GS~=`)^Aq9+{3(*4s|R+nmjD4t=Vvy z)#S$eLz>p2Tcjj^C}X_2!+Ks`Y(4qlm#@eS(``31+QV<12AQ3EA8^mrs>c#>)xu9q z+mu=A3eSz|Mf~QjZNjP#0=E%9c}ICTnULkorP%YPOMzFItEn}+%BJzXvwwJB)B4wr4r8_96+#K;YmIKyJBD)wTsBOW< z34Zw3n#><3z{XB=V*xRX$dt^t$sRFe9$n!S7Kj25M~tOIes63PFBYWrXAt;gwTPJl zB@|)iMf?>G3)s!0MmlnpJe``2yTaFGL_1X;x?{y3VP&#>azdSlk>8}dxAM`aTnz*qgNgMFla!(AVljj%_N%q7^d zhfnZXICvZ7dLPRc$%I!7RrLu~M7i%+W+q-PsGy4VMT=8Vhuqvg>D^y}OvoLg$_ZM) zvro^k1NB|zZOb&y+!juWBFl7hdQ4ORi}J`o(mSfrAWPDR0c*BXZpavVOt_N_uD}D@ zm{Mi9Fxa?gK@U~l44Mx^Yqx7*mxw6+z=Uh{>%MpW?@~<3^5tHgIDY0T(!Sm+zMtLs z@N80PBAvFQq8tg;B0*n_5$LjN5no+@Ug&=DuoZRT*s0k;C8^avQRx)YQwR)CrSqRe zgLGePR7yv3e}TNe=N8qN9QDOk856u2br^Xs3~{UU(u z`|91}O71I&MHaU8fyJA{h=OQFKm@FcQ;cQ z{?4ziDRBk=kVW%_)tdH*UDbP#twnB~H<1ouEPfh5Hy=NPlp6;zvm!gJ*02%JO@1^& zrm-3@y{-_Ppdz;F_Ak|f{JaZeR^C=9d*^cvFhWGD{ROQ#TEOF?s+Gn`Ya4JIvb^e<3>LnHOXbClqi+&Sb)i zA=09D;h?Hsa_G`)?9``#-AIUzJn0ROr>%4+Pp6d^Tv9n-0iI{0ca>Q&Ho7t0xEis= z_GKM<4DGWk*jW;Eg31*u59})UFD|pt#(6cEOJ*LdepTDu?%`>rSRq2co(6UWX4t;% z(2a*Oj12Q#k=0QPnQe~7q7x--(6c1CzYQ3O+r!zW6lAw9kGl;E@~!GKu*;4sBLSe z3fz9}9g2XrQ&d~>t61*m#z6AQvLAR(LRMOKHJv(`#0@5?^aVT5f~%HviedYcu-0yd zv&-a0P8VyvN-!*OYCVP}agJ%bqMW9-{^T>Me@9lQmoH|7Mrn&9*qWzGC8-ERoaj-h;AD4=>P*mu7qb=b~N5AppOebi|g&oXa^ip&6W zUoo<(Q%?zB0y%A>i?9nUHY3Qun4=UDS#TcQ24VvR0B`Z$p{Wf=L_zK^km7bwc*ruY z3}oQrs0g`thiH2#ze8fqxxeM$zt|v%C>E+|Z*OK;#SsR`E@d6|V4_w9%`UE}TQOZi zQ1Y@zg^+}(%a*5qz6+E;i2k0=W7}Y+7_1UAyKo77F_B$wlV~H1R+>@K%3`RGWHDy8 zsNb}Foox}@uwnpO?d`fKNWE7Cj=~mF*bx{4CuW*iI&SC$sI6W3n70EKQ=+lw^_?Ej zL-nA&v@Y+dH)yZBM;!Vy0f0iv{D2)tKzvJH5uUEur>^A{+cW2?kblBc=`)6|6xKs>X0nNt z`;o+hKnS+p{Da?vsLh#QoKGAz)-E+;1u}9w1;MdUuISz!dsh#&PgRG}^@&5aiDma_50i@*BH^d5oLVe@y)=KJo z^aAcXLB2LPf0OwnfZ_x&MoY5tx93v{EdqdOQ^SH*pu`g%1EF9VaL*4uda!rCKsBPd z1(pYB2BcLpj(DQlk{y5nd;j)*a-XR@F4k9`jK8a4$VaXe4M#Tb2e1imU;X_p zOg>9U+J#_>Td%LOnw1T4XD^()9AQqV3 zfF2l{6Cfr@iD{^k6AFue!xz}NmOhP~Vy?h9vXvlI%&=QB=3VcF3X%J1>N%o5peTDA zr$Ac+?3Bl2KsNira#Y?3ndeu03)YyuHccRo`uQf>+1TZ*D^0DAj7AGad@aWTTrl5qoG9%GSx zOF669l)2kkLGF$`S(u0$9o)q%Xo+BfkY%^e(LPFQI+J5WE`XMW^macIi2~V30d!F+ zJjm3R*-hB~J_#2Py21LENZrA59d(i{yQ~#@UBFEV^9kB#QYfz?n99UZ zE_WjWYucpNyUSCePy|GX4eP~@T{x%PnZ*Y3sltR0VV~hwAw7V>O^Zby+f{N@?PD1` zdV}&XrT4z(U?D59o4UQD0_iU0Ad#4{qd{J8&o!;w+xeMPv%SHMVB7m}V1=C;V>|Ib z={lRC{M3Se9l&U0((TD!9F{ePemn`xGZtMT8fVkpGiy`2cK@lV!MPO!SW@17ZMRQx8fyZeKr!b zjd!1Im|gWJNs_j;J7yp&4nTC#WMFVliwfa4jy(Vwd+fx53_oSVT$8KS*2d=*u$Qz`_r~=Lb z->5`>0#Pwjl193rdM=;=>HgBMt{U&E_iz7gx;<&oa1bjg@d7( z3;nwX*`x?>xN67+FLWCBXL0(b82=6j&FBzZab?<`wY*H0r{AS}ffP zW}9o+w~B$EGjr+$3!>Hi)ZJ$fT_p~&)}ket0fvutbCTV4SfD|`#7~r9;Do&D2X~3f zrya%6%0e6^k%^Mcw6%65I6g- zT$BSWB(t^_XP2-5BNLE2=f|>cX%ZkOn(0`|MgEH)57+){}EyZ%7sILR) z>8o8#Og*R&OWwn@!(3gh>IC!GMZhn4b*Roz@pyTICx;H+-mktm(_OmtHvW;T9Xn3A zFZPR|LqwQG*OxE;P91y)n{DD+V-OP&KD1J@t%hv@PhbwRJ0y2^`^2Ye6jpV2y;(lL zicDE+4(o!HmD0P$7{XcEg@KK#5^g4Jqj*i`pNy%pknbH5%@#x&w@Nv?3>ol1$;txa z2a7V~Vv%~K?7*cP`@JK`MT3!HcY>A;mSO57sy zuDNL)E?yUPAByd-SNV{!Q!k6L(Z5!MiV5JF#-GCi*@=Z;7GyIq>&EJNteBCm^nlTE zSqF zn_Puip-Q{b!BwZFQx33oy;)^m;jiP0Jq??mr^)fR{V}sT%|GMU$>kS{GG5v4^O)r524RQIQ63STd}beb zy)uz0My9pn2T1eg_w1y`fvr6yqQJD{2j+2lrWZW{6G19NaBm`&N+{;?%}#e0yVyU> z0R>|>1#U*BnO|<_ttGB<-HvBpqBV-gbKlQHJG!<}&Lh(IZCH9MT-MluxRBy>6e2l8<>UiN zdnv-#nE)V%YM)sGM7dv@j~qX;pvT7S_ftn2nO;vkbf~afR_TLB7_YCXJ0vP8xZwzV zH$$ERAlX+z@q_@efHny7t$0KRbQ+p(k60S* zcacrXY(Z#7MQbOM)JnjkEt41bWU0bQ-Y$j};2v3#@{jsquVWc#K(?B3`Aa?+8mfTs zWG$$2iATlLw*Ux7k*S!zI@nNQ>XP*F%6KqD;kNNG76JL+)bCO2?>wD-*~T-lVes9K z84@R-mOwp%xduBtRqn^0oVavSA=ze89O^8!q1|5LJ=sOweAAN603V<*Nh`xs?P0K@ zvh&Vmm6ryi0`b5Gp!x{|XmEs95`g2b+J_5QVPnXg9-+OlwMI3xwP2vsvFJEAPGkf~ zwTxsn4boKeRh!}u!Xo+sIJ?9-9(^lyidr$Q4H~*S>~OVPG0t58)O-h{qZ7Fv5H7l4 z?BpNOa!#{Q+g10aRE70}Rds}QKobV|``N%+N5!{nK*dKYZ#QIio~x>c-j35~xN?od zU{Hn%CGbm;%00A<6By4{8O9u?CjCD#KKEFs2k{edza5o0jyHx1MQil=nxald7I!{4z=axKvYQz1!#OamApc+ zhmWUdW*GOD1EJikTabwQ6*Rzp!QwhQFH@I7De)$V^u@Vx|BF z@&Z;MlBV9^YVI%tWgw7jFAav^UWB&%?KcN?W!reuw=!AK?Bz88_>Oty$StrBSLC^~Moe!J+nNLb zLR!djS%Lz~?r7~aw)Sif0TAtCC5`fduHmtPNy#uW9ATCXLQ~*gKg6LU2?Sm)WjGek zDSZmCko&;0tQVp{fx|DneVo(cZ@oHR-OrltPM4T^ng+GYI3Y~>LrT`CXOwQz_f zLbX2Xu?<(x;5#|XZD@g(NB$)2qCve7z}dDstt_vOOJvs}#bjm!k#J+ab*mKbsD>JHN{r71kE*pai7CKe8q_hFg$hO$a=4 zcd+QS1iYi02iSiM6^F5RPUKlcrce;9JT>1xI0u<|J9Ioq$5(71H#K!RH!8Uli<RKV^h~yMh*Uf&EhH@eQoSamh%g{s5+B znQRF1Q^nESRBzT`ixQOhnV?fa*G{j(3uvhM;j?8*@=XgB{)#o>+5dCt?e_nMqJ`!; zLUn=w0QjK;0Lc8eP&5-$XJaQzhyOt3>0CTq{+}FojV-%FHU!^S^&?<8KqdF{T3IZE zg&OvCknVV};`vAfNg3CTvG0#K5^K`Tlq*>_3aF>iRhT&)4+%K&O)15dP#She-O6h> zWzx);KQ{4=D=!8V$(dAcEE7J~scRd#G;CJg8^dg-r1{flGp%aIvm0HYew%(1(^5x` zBoSpp8>wFNEz104A}&F?{62Y6O*rCv)H7$(-`m8p%#*F^f6BPtIIW%;DW^&N--s4B zB^K98ymAZ*j@tk-E8=SsPEJ~E>P!)*dywFrAlYMmvt*yB_QW?9&|fEN%nToswIF_= z=EhcHW-cr*$jiDD{z6(V^wT71@biEJGn1>RJP0Dnlyrd)?=MfAE0S8_i)8i`V3 z%}8|Hh$qj_wq!2kb#0isQ;}l5!qa+xI}2<5ZacN2Pb}Q&m@AhD7dI+1mmQdRuo-KC zi3K69{Bi%+y`agO{fO_XP|0+8b)j!D@j=I8>w~7n?JW_-;mq*J7G!0K3Bgp69?@Vr z)S@zD?)FUbKW@r4``xbfq;HAdh2E*UE!8yN(Sjd>snWd`%n)?FyNFsX}0=8}cXS#%<`tnh2t^P=_T$Gb_0N%7%lyicH>5L^ic&0dXBPH8gb27;8 zsiY?9?Pv`Vr;qdOyZwF#fWOg`ukx6`vOdR?S;GR0ac$PHQgT@5H?(sVY8n>BujG)i z1>4?U<}S?|tG;vXE=wCIIV#%GFtpHNNCVCFxJ>sojn|a*CulTn<*Tt~(BzNw2q3*AwYr&4KJrz`J@ULH>M?O^}l4Y}O^|(y; zD<{pm!~RfkJOCjF@aaBU2kcEi_dZ?+JYGO}0|re~qrpZ4js_hKd>TA;0P2ug1E-CX z<*=JUcLVP_-xYuxMEKx=0}ThtLg3-h{r;N+5C@?SSo(15KWHo^A$lG7S~&Iq>;a<( zP9Er7*y%vA53dJb4}cy-{^02y?Hij9cpr3r+2Jt!emDdMP~3o!0kH!@2LulY%NBrO zL;~Rv1Z@bL5O`tqyzm(@2@@7u1e}-;15pSfaSVmw2qdu*M&nQfnpj5~+Ysvr>kR8M z>o)5V>lN!0qJIv=k=ii*L4>dziIY(xa%_&rkq8Mn8K>f8q_iD7v>-=OGID+n#PJ9P zxfG}46fT?I&Lry{O>`|)lUcP56Xn#6@nV`i6C%15lg@Qsr6H}V7CW~6qd9F>bDvgf z%brDhrK}TvZe*8Ie~P?iv!{Bdhh)={ebZ)CONubkQv4Z@fee1OkLWMBf;2D)6u^H9 zvBQDf|8)L$BmUnWpH8NRCbp*k=j3$Kz|#Fsb@hM-00j8~1_1b<=066Y{F@R2pen^z zJ_*;5vWEfyKxYO3K>oiqz|7Rp#ns7F-`L*S<^RvL+|kucJ{oiM+tVLJ?~mnD(0t9N zz=I2`EDh3`jE^E@Eh87Xp)yNvdcM_71y|6dV3{9Rf}$R|J_qZ<{wAb;oNo5}yhhje z|2V0(*YkgPn+|vH_xxSU&%@K-{du3J=kt9Wx9@*@G~e@iJ?U1j_xn80X5aIB?&j}% z|C@Z;e#_6(_kD0aH|PI;)cx@H(%(M5kLUaPeE2c^)ZL!n^Lu>H|NdG%zg1lS_u=tX zo__CZP5le=R-D}T`(|ADbW$?^`|0iH`(3%u$Ls5EdirK9m;3s4?skvA|NCA(p8wnF zr~AbEXYLgKxA(J-pQrch?&)RtFkU{ruetv$y8ot#eX3pe2{xR+m4~mF=hp%5_uY=) z&*S4>m-GF;+_~KUQ^O>I|K+}%{Z_C4^Zot$P=2d?@t%F!Tddsg=k4JBdkNfUJdEZ# z_hDtLpTD=qpX*K%d`{&4_aq!23Q3z9RQ`Q31w zKAi8Sn?2utd;Rw3{_*wJ4@-{=f3w4gldDgGv&+%f$K$Z+=!~8}KJI6no*sX<*ME1B z+v(rn>G0uY9=yFy2hLvCLqFg5+*Wz5baJtF#MA+-#PJalQUOGdhRs@$-Lwp1Q~VZ75v$E+5PB^Y+k%pH7a?&nGj$>NpRn-CeCd09%pT#x{7l!&Dl3M)t3e1qYNv67QHQKZ&j$lD=*vK7s*UVV%iTxiQ0RD}*!LuB*wzX)QJVW3cGRUi zX$z+pN|hlSn0xykC$jYc9UF=Kqm->k*X-Cg1dU15XU81fniogDq8~TfZE6r~_S%opRtG99CP%v%X1jP%cj&Va1$~-6IXYz@sH^HQ>Ml6_kmG zc2VU!7pS?Gp``{Ww=DMOzJXSxt}zyz0hgAuYpAB;f~?9PF^Irr*hO0v>(Lj6nC_rx=M0=N+4(NV7<6d z5t}7YAjBv!3I)u>Nh0-GuX`W=9&b=Xta)-!N`J>yiGR$VjKTVl@@|MZUUVxW`bGo= zCP7G9Bmt8(%7##KI}}l|==xF=%877P0r50FOQnb%BYW1$Uiv)rx{sh!&vX}czQ})h zu8H!0IL?rbS`1PBIcH-YT~$y6cqu)fe$tZM#gBWdA6pDH%3$w_zpR}-7-%Ue$C}k zzKZO)CA6h}@Urd)sY`BTU7Af4Q4&ui{eLvVEQyhoXvHiUZ&af<;X#&7Uf-#OAj(UL zlreW){?2GAR$+P3Efb(omFBN|iVhX)O;X8C zuQTOPVA46`BnQf))UuZBGPVKzJk)htqNQv>78`h5Gnb`U!(hoNTjn=j#jed%^U%Gn=J6i@c$D5$ zGq~v7DvnL%LhlQ8DChvD?O{LDo@jD|z?C`_Zg%fH!};bg9Z~<#@@kxS0G6a1?k}p^ zybh8Krd#z)7hvsB8UEM@$Qvw)yp%iv2;~I+f+<8GLJd^@_@iY}>ZS%4MN3Y+xl{%# zhBZd2YJecr+nW-PnQt!d8;;L(&&}w3^L00$UBG{{#G-`Fb(W7Y9c%thDwzPx3v8J% z(R-B}sm2A%Z>YE!hSbIGQ0Emh(hWMLE!_vyVm!wZrV*b&)0u!?W9CE%OB+%9*Gls# z2mSGW*?0^YtQx|-xPT*XP&P6s7(~M$t7!bKl8vJR>Q@4Bchqf%QO7o(cNC}%X-^rf z>2u=%q~ypZdud15o)m8q3u_H5^|leLuu|)hps=LK(BfYP<76jN(Gah}S8;h>KC+Nq zd6?QiF)acNo2=bM`y@P`hep|!@@EDU^uadUc9R1Le+F^ ze)dB3T|f-3u&Nh3{{+MetbJ{KcMS_Nm!i>T)W~V;0KHmm!Y8V>QE4v;?gA!iqdmZi zpfyca8f)--XB}M%Po_jG!GqsLOl&Qx-Gu z@l9j!56pQr(3At+`U4eXm8>vZ)Co$p1&bs}#5T5IMJM5;MCzCbm7CTiVTB_}Wx3YC z_EqK%ECbX7fQ+WhT6k`6O+94D+Yko<^1QhT&yj?lF?oiyL%P{yI?#|&6=Bl}D9Yxm zxUP^PM?n`@TW1wpt##B)5pWiO>V7$5p(SA#*fAs$KS?ka+yUUCgl+b>8YO6rhp--? zO*jBNsT#wFz7d&EE>MYJl3YIeJ2IQ+bE4GL4f6U{GmMW_z3s##Exq_wC%4G-%< z>YDshmWPo7tN;IE?HhwL3!*lIi8Zk%wv&l7v7L!0wr$(CZ992m z+qP{RZ?gHeYFS&mKfe8Q>-Mec?$fucx=x?-Jm-o1c_3d|bPPcZCHSIY>>Q2$5Vz5e zrfODXr?zC&jvd3HbPqdVc9GDEyPIc?Lsg z$tao-lC1SYZdDw5T_~?_q)RmFktOwwZFJCVLI%~DT*Vbi)`1ZtmmAfuxS0&y$(Q1gQxHEmI=6H(P- zKW|^_ALl6N8_V{{UH4xSX&`(GYCQ)=K>vgCRlT1%p(;csc_H$t#BCVn)a{CP517PU zJ5B!tO0?@VF%0}|jMz#q+&0QBo+r2A7^_Cw{3ZZr_EgBgVG&h^$7}b#@%?dl{jocoG{EN^FYH&^hIptH`bS{RWXGMs1uAAnWEMjl~;K9 zt`}*1E4645XG=HPSCDf&vaWExBYaQ!2CBO?T7@QBymOAWBryMs5a$Z0mK%8Y1!Vel zRn%-nu;WzF(&dwDV6OAui_@du#GdMmd05F65S>9txzd=wrADnowt!SWjtgrgnb4?+ z99hu}CRj$CYfv;h)HRi~u(b4Mxz;x&$2kQ(LQtx}53Sy`Ya~FG^);*!e{&LfZ-Jxt z7E7aGC@hASg}UepAW$cb!j^g>?s*jcn3AN*s$OVZ=7c9v@!++tEy8mw8MitN;+NcX z*jgm+rlnmb9Mk1i#mAmlR(A$kIF>u%v=mphz(H5z`L64ox3G?%W#<(>2=eNbftu85 zS!`Lr0j86Tvr9T4jnG=U>A>AhS7-WIlOuLu*Qp6p%l$Y__SFY1Qs2Pc+KR6|abMb! zFRRlcP$fPlHED)<0S`l=jAdd}j<)5CkzW`_3-Rl80$U}`#?Jjp&TdqfO??Y*`#?9l z&(1u;i<=Lua@3#wW#o>YizxIW=C_Sq$$hV=P;-|XL4&?b*W@-U57`6I?!l9xLveEH z`I2GM8{F;?4`v|1{NCX#W_i$S1`zF!s1-bb!VyOVVU?;+-p~LJN`qN(8 z?lX3&ktBrkeyGJx8iG0->J>()RqED0mJ)3;(y?#6YLlcfOvW1PT`vAh6!DKPPI*oy zb}^5_8hykq$%((!zG1a!eGF;RK>ASv-UG#QrmPTk6BCPF6RW>~CO+nzU+3W9|0eNj zNhTT^&E1YG#bmsQuR2wK$Gh$ncS$G^C)>GH1FeFMPVK|4ssHK)d3C^hl=WN~sqLNY z^&dpdDKPvuh2d?lEoa4sPgXYxPU;+Pr^QF9@JE&1(Um-f{kPs4bm7|c za+Z}EI~RjnWg#?+AIR_2KcCFCE3U0hJXmuo(w~uImWjViX{4kfj$Owzm5h6qPugnc zO#U4t#Ec<7b_8bbz<&h6RD!&S5QQ@KNIiY{`dk&m!u0G$?j2^!jp@G$?+R`V$~pL8yZ(H$^AuDWF8{ECw8w~e27k) zKO}Z(30uRLlE9Vn)*WKIohP~I;8Sk&w>G%#1OcafCsl@`znc5pssKuAVzWE2^v_=z zB5*K$lbuL5i|1)EJPR$20%v)epAb`mg?%0QK}?kdtDj&qw|jL4)J>T^n)ByNtZc)m zW}(>uF{!zQJ??BajlH9%*=Qe-*H9QC3TE4GkLNch0(8ir1y#s!zCxX-pc1Z;&>?lu z#X;5jdnes=q=@; zZvZs&vO*S#x+ia2OWacJC*s^H7e!Eu0&=*p1gp;UBOU;cbdyoPL#8QV3u|GMANJ(l z(upX`v{qq%i{j2njMhfQ=m47-*~mx5XdyELzkOhU+BnwNBAn%lb+xE8)08kY-5 z79iRYmQwblOJMqumBKHhtkb#>S5PBRF$-VnLID{MRZe4RY)QI8R)jV4JOu*e(v=5Q z=!->IPT!g6(33CU$&E2Wh_ac%&hZgn6I({PV}YWoGxa6;+(Kk}oVw$G1`@`rUjTzY zxFY{xLco{(qAFj*^tx=dVhSQ}q3hMSp=<{*ESrib9;CeEQ`}J7?mldEH2rKot#IhQ zoZYE2zFiaHY?R`n$$g@ZeICimlics=WrK!Jkf3duGNOmcuTR5TU0D*XVRIl%WFTtu zvc%oa%Mm(KlNVr}@z0k2$){kl97jHV2^uqe(CizGz1lYx8{7I14@ps*v8>e6!ycxT zxET7MT9>W8-!TR{`Rdc8TXY~~d0?GeY-(NSjs1(@G=Zbn;l)2^pQxt;dG(`wN4lxQ z${*-DE}*96i0o}>%WL=kHA7R72}PiyG9=Q{#B^s6B_WCML#Sbe>)s!^v&(KiX^%qE zNij_s;M;gN$;zJt!in6qah`2hEhh=orI7|IF?B0T)l3btC+Dqp1tsU&Vce;W7>Vq; zKQ@7OUfsh@Znjzmw!Cuf6pe^pMkL{M!a`|-{45I;*@em$u@QsgdQ;cs`D%zBI3Yyg zz-X@GUGD4KW%DOf#k1ovqvcp_mV{V11B5sf@V*iLS3>eQBk+B)vjY3=y#TQTeQQQB z7iBa9y%D5F`chs)t*@#+;z`>Zao%BNxQi+e?kV@ zpp@+D=MR}t*BAN453o_bejD42LS1Vh^qDro8WxWSVzkx|^E^4fl0;!W&CHY$3CO6H zxDH4qP;E?{UL2)=OY>kJDFJP2lFO2h;y(Fo>Yw%qJyqI%R-D7&CE`M3XESty>6CFy z@qIe181=jY!k4}1dO!KfK}t*-&}mUlO+sGx`t(x0F~BjS-L$zf8ACP zKe1E_yJlD7M4|)DT4sOhzl3X`=mP964!#vilVimPxg;Rt8!Dlnke8p2wV3QaU-a)hmBD>n*8|sbR`g*~wu=BJ4w^cfkmBa2&6- zxMiI#Ke1J|U#>$wi&!`=Shw|G_;-Dkb`HZCR$U-Y^+;g%&XdCDoc(~lp|~bAAn6M8 zGfC%+5Gx+YGModYcA;wclLd27+Fu{=dUSE7M_bui{c@|^UzdEtGu5;oEH46LXx{=G zy)uN%9i-Dx(1ZVDD{pw}Nz4K&PO}iY4rv^4iFIG0z$(uj5AodA6t5mojVz^AP`#|H zNcY=?U#A$iaiQr^L~`BvKEn7gx7xiDs}bMytAP;r*TiFDJVG)oV`Xg^6qiY4Fl{6AP=pm7$+dnJ`OMWQ! z=)3R=CMIqwy*bWX`bUW65F;D=N_y7IB?k(qC0PxjnBbnJqRDgX}m`S3-zrxOI(A zP*`b7njXWrrpKqAsfaQCyzfpq#M4Cp(LDY4DEFb0Pq-KT_MLe88*b?(u7w^9U(ec! zf}eD(H2#wp5z6~p{xqytClP}j;uMa5zvdw&@mC8>#PpG-5+>rLkz<`*xJBniazFV4 z++^wn6BDx^;|&PkSqvw zG=+AEaxB%>j)+wC0MIHMq>l1ZEFs^lmYIMVhOP#z~?H$0B**;W%tJ+H$Y!TusD(#;f7h&EVFJ{2>;uC{4L$ zyKB)lx1nd|U@Wq+`H8~X!4;4MCeo`v+sw`>f08i=*A&Rj_s?g{EM5<}cvEA^zP*%8 zC|UIKiKio)GhGr^aQklsQ zinV|eUIg0g-9B`_$vC|Pkr?X)i1X)XL1JTnoyq>aM2Lm{n~{Z;TRl9*M@q@M9+9h`@rz(ID>dltfRSv0!QRF8)gqu^k`AF@1jnx>K~ z77U;Bp6aG)^U4lEZA*7227#X#B;D(f5k`~Bjt3=RT%re*Kf_)jOtZKoB{>P4jK!No zQHB~_ImHrs=6Dvk-*lR!g>jLHJ@Pe5z>aNG7FW+E3y)9W-Dpylh|PFr#11l#tV_Vf zMGbU8YjmR1>xTibnnoPRMWs(%qVmO@uA*!^Ke~YNMBT?&RT?9vI)PO>BxkzXMTNoEW)lP|*Uj52^0h~ld!;KHzOz;;tvjmxO- zbDr(ZN4Q~=6_r^WZDt17Z9cdJN7PkpnYpQ2Qp>j4zG7ZU{J3 zpO|JDbnr5Igz!)zE{u$5J1YGEzc93$ZrYUrniKE((c=8kQejt=%!I?Xyu!XHHJq|D z&R2=0?PfrSo&8WhlE$Tb+H1-vhH!Vjsy0!f^Pz<=stT~^&K%8U_Aps-UX?MY3TVhw z-lZ-Kikj?%F8mQ(aD2SGao6V8(~DQu7FB9}*%1aVr@D~g*OrSP720vZwj|xr3%2D4 zZQ};}-Uvc^%kWv0(z*8%?@!b)=C|x**%nQE z5kW7kPus6Y^Avg>)Qc4>V?f{N_g&o_X69JSWC(~UvBp~QEYq$T$z#+=@t^_tuA2;s zu`>JJkJeMVUi=K9BhOMe`c`Ta?|O2>(kQ@e%Swal!C(Dn_t%T3r0+1^${{L(ak?aJ z*UVYUyaZup6L{J{Ije|6XWu6%c(e?qhkx3LSW5 zyBT8ZBr@MabF}R_cN`2QA`z{QhusoOOx@d9N(ftKkKUXvYGo_?C1UjrxxF=7K~??S zF{I1vO$qJCUdDd}O-Nc*!ohbmeHE9~>NR+5o|E(pmvyXJFkc!8fhY+b}(-+kg^H^nVF2RPUXxlr9W+fm~tj4Og zly>Zd>-AVv{G8^X=BZaY15IPsJ<#emh;59rpFUmU_G*-q8r~0iomFs4}I&6>Z zo-iQAUB3xK`7mF3C}YqETQiWnL^-qRsoa)p}m@WY?8oc~OPhf#)!8hYId3SNvR zcIh>+q4e8HT|5J1aC66^m*}O;#z{>0iGmuNAs-uGwKWe1RAeDYx1BjFj{~z5)x$78 zOC?2#cofN&sruNGZ|oLM>(&a^VcT7I)y0+wd|lM`46BW7q9{Vsjp`Q)*XKwXB}(zJ z2S9&^3Z;yiT|b;K*045OlhwcKn=-)6A<`=E)zeGxVrmIyTYU5Yg-nzqMkc+Yr z9050F^$&`X@)1y$ptLGGeA>me$z}~e4GYQZ%>LT``FKS7ARNXs9_54qbk0D9aTr0P z7%(Gqvh_KtwBEe*7FZ5+Q$-hEzJ{rgW;P+M4CCXE1ztc)60ky0ODu4~Ts&)$a0_0lbJYio5<=0CwHkD^2%UBTs->d)w&?w9 zX+EwghL>GBzT!n3vl|HMHdx_BLs1Eq2iyfBt#rdieU+?|o#7k#I=auCOZLah@*NJH zIzTP;g0?NITk-4V#||fERXFKFa_eH4o`firqTI;Hq#v`(LXprsV@VRbp|k`(O$IMKjt&)B8|Aw91XliE;g8r83E>9+7@zoaV>VdY}ZYzvJF~1@yU6&b-^w$ z&&h3nus+pL$>BA{Hpbe!CaJ!bBuCctdwLL#v9YwM{jL)_g*xL`7B%1Sx1WX|HXgy` zCg?!}8)0gPoJsrKf1kC<`5mES=k79)&;L#B$)HHYFB_j0%;31U=%nI2scc2f}8}a~u?% zWJ&hHwkspa8b^KRJrG$=h`57r-O#qBG&nA$;w&EBosS&MNX?}dUPhlO&nDi^@Rt`z zn1z&Icp>PE6T<+D)A6L4DsRknbcM#Wx-ILOoqgVrg_pn|^kN;j#P7&d+nHBOf&;&Y zo{;3YLWa2yOkMa6_YU64>VUY7_-K7%PTf`<3KQoV#(YIcb*Nj|%#)-Slv{&kQ~5*M z>>l;wmP3;^ABr59n%;58;Mx&RKN%GGrap&}++?U4xdf>+Fh`U8;MgkR7=>)>y12y$ z+Vh&BnYyP`IYYIEigm^!acOIPcQ}H(vQ8FZsklE~C&%$CSf0)Lfc|}w`OcFJVi}gO zTEBlC=l&nG#5}%#S%cWEYc-wGfy}0@W2cBGi&%1`&vu;=O1z?v7l=^Wo*#voYDkmw8+w z*LwOD0)8Fi;B2wdPQnzn$!$>$hi3K`m2*b^*~R}ilnf3D6@p7x9A<~vTSh4_bFBNma=gd} zoaVX0v2cjU$ijmPa;F{-ejJtWoQBcl)VluegN-7p=gDL1-M#}lu+7PA0I!l_t+T7; z?nPpdi%=70O-r`&c-5LE)VV;c1fQh}EKPTaEcv(xPx^Zoef$wpP3+;D!V+(o^{ry9^Z zT1c53A*VV>tKeiH(wV$`#-`r%HNkhyXK_%_I7Y1{_PY3L*~+o$!?vx&KBxK7v|`3G z+x@18rKT*=S=V10zxZ@JJ0hAQcjt^%6=ixA5(jvm`)IClD!vvRb5buUGEFCxbfCHz z3gj9BF+VOhQ}TLNPRt;Ldij0fL+wEz`Js^BR9V%MK=1&{hjb#=>l5fb{E6I=hrKRl zw!qcX<^?08cbH|(DuT`LM5UgxCJL3Y5R@(*%1(N@_@lqE%d8MknkW#nSi9jzS~dN+)_{B3$3CVDs3nIZS_iNL3p z)b#A7Fn5Yi$zJp4AM|OEmC=`|tEDpICf2vxRf@~dHvr1ft|b>tZamy0DPNdLx1bLN zTav|MV@64zIQC!q<>tGI%gaKT+%`)BCv=sB!&W&wk2B_k%l2c@%FW^?y5CNZrRM-j z>!Y<_CGD`U6^vt-!XLav_o=PFqdK`CQO^a>`0IK{9TlV@xg~-xVq90c($1=kxbvqu z_CO*@Ktuyu@#jsXu|Y`qCkjD6Q^5wDV1F`;F@3i+2|x!Pk@>SnK{_a;PMM=YJ3{K< zLGQ3Y;=xxq_CfGRIebCMsKQR2+t0e*Rkhi~_mF`CQ*$q=1C-ASb1~FnVNClE%VfjD5IUrS=w%s$=Tk(1Ip87aEy&`{hrTR;p3c^||pAUa<7%57Yg z?S1BFZ=%2tMbZ2X&tou<6Bnn%pKAEI49oI@EoPAN5r(AP-6<%22g5(f`N;L|Iun0- za|YjxGXjY2#`lupAb>_GOVJelO32qTOyN0>a)QEZpom=f0}SdH1kvlheJi$;-E;`FVG5 zqk3Kab$dR!81mfR?RLL^`1rWkp2Mrz&9lkp?a}J>;p*b>HGJxV-}UwS*gWl`bFF=) zbCrWHcg^?l_H?$nt?lFSez+|+{mhVq|9LR!GdkISIec8g=W+eouKjigTtDTL%VCgZ zct&!O+t#6%MRGxUdfuNOpS)bI(YfN=`n(_C96a5>zigJnEGbEmbD91ceDBuL*6A9J zpAY)zxfXd7C@fYxPmLcdHJXShnYIy4P@W()h!-_ClE5Z8R99zCp#43OuN&t;U=gis zXk_UTA*9ZpU{2mDZGF}$Ud9u{I65wkLr(U*;6Ot%isclpVOyl;_*ke^=AeQe942d>jVqtkMLaZ#X)*w;{URsj^mI@A)-oiWFgBaHMlB$Avr-!pax#7f4lr`7T zvBQ)#Df!`$pL%ZXBAdA2F%h=gl@+O{|2r@B8=X6?HQ?CKxY|13Hu*q$IkfN0t`T*{+j4zERM>sejbtyeYCnO#{)Wu$L!Lidu)l!SJCM~ zIKEhP*N#yaqQSp+YGfyUor|FXe49Y3(F#fdy4i5+hU9(%z7oojnFK*nXEP~*RgnWf z%#lsxF2FpC7cNF1e^$7U4dm~zK#wF8s43JdyAQ>pH z=$R{vH zg+09{IuADUi+YI^YWPd^o;aeznn*;(*xUrWwhRblkrQSGirAyJZ6g=-t*?`}$~XWQ z=TyD$4><82o-@+(EAfgk=2YYKa0Y{i@T^5O*5ES>dH6N+*9~!ILpyS2@2UL(Z}E0$ zl^pJqrhiIn1-sSXG~I8Du?kI#;Ty?rti~?mT!!a;Qo|b93S491juh6-3fQ$u(k|Q%mY&jHA8z=NeTV2g`32bB zD{DfiL2K3n5lOA~9nbQ!*6!y>WDP6@<#577$(7B2eXJaE%l9atW07y6Gp#%=7Ro- zf-5~T5r#BaHisbDlwxAtwtE~w=FYk}=)lxABQCC?8DCT5g4iw1Gy_ZB%|iwhwm~r- z(chV~f-`Q1SZ;XvYE|kjQGz&q6AkX$DU-FgoK~jOsWf>@7A?O(MO!MxnXeRV1LMl7 za2!mOzJBrgu#(s0C7@fvfm7VGc%EW7mIQgvfnnvab!C^GS|L{N_vyFcnMPTfY#L*y zeFIVVM2BJ1whjp#8ef=9$phe&NA*r)Rb$)+5BM<&*9$>qa6``A%;-vfHxi{pE)+0u zZ628oG{Nr(8=`S#RsU39MZuk4{_RWA=^IqQM3-=DzzBV+L5{@%zWX_?f&5G`1(Pe}EGg=23 zDIPkJ7435h@VgT7MsDD8m5}{JS-Lh5@%^POBLQerOma+ynG|d2q5vJrf;brHEr~@@ zR`oXb;u#^YEJda8)&(=%d+*a3|KNIqkTv=5*~MvnP}WnPjl8HacoWla7H z4fN7$wiZ$u?nY20J7*&#ZRt_o7<6IYG=P3|_uTq;dd-t*eB4KT@}ZqPGZo9f^eqh% zT~?93ZJwqCvvmF#^~$7sm?_YiX?qcjVXz0UP zjAeuqZy`b!Tlf|BPGik#$319>{`v5+o_%x(H;JUvpb4#;dBQXs!m9`j_6~CK=~)^1 zaq7Icl1>(UvVPGca~xxWiUUUa8vTskbn`2LdF`FF(l6?4A#iT$g|FmDDavTl>EUlMW7T%`?3o)+C!+p6hPu z9*t$zPYxP?u2{VS&L{Q%WEcQ!^gwZ8n|*!sP9QcQtXF|v(N12`dhNMeUiGB@M0>w= zgdV7dtR5|rFt+oPZrM0A%dKq2DqX0w49l|Q|8)=Q_&l?dWt<3$;WEvENs~Z|JT^Gd z>LDOw99){h`b787fjOgkl8aydDgYK-)BV!4 zOS5`r(d;len#{!gOzp<%?Aa=U3uA;KY|G5On9^hu=!6Y!X_((S%l{J~J zeOFo>R9E2?ZvINEb<@ew$k6gmRXS`nQd?&I4TGeEaTsy%Yu{RkLV9+{9JPAfJ-ra| zC$)?>4f%oUISIbNf^b<>MuD9YE7v{4B6o zeYpwYYhh!HhFjeh#B8bQQKBvUdLjepJz8t$*q>o#KVS8_hSXTWsNOe)g7d2SqkCep z-NPi1S8;8i2J!&rrPdj7&fsRdG$gHGd;U6sbA7*G>Bu`Wd(FbLC3USK zYDY0GCI(;e{bILA$r}#Z9W&hJ#;$18Wl6r3#d=_Wt{pi+h)I;29`6^^E4pdOE1=l; zE=VFCPsz^B8mW1BIeX`+Ra7S>0nt*BBBY;d_PitD&hhC_;hG_NFnC7OH?@Pv7|C-r z@31eL?hZaBgV@=*QLl3g)Y-=aQNu2Om0eV^muyG;^W%EEn@zsyw@hlG8;HDv#gQ2F zUUQXF)g@BGlfcb1Wx$H_!BYSzuw*S&HwnY+hT#M+7t;?~Ra26dN;NbB=!B-L=fm>R z_!V+-JqZKF$xr5AM_#drd;c9674}g3QJuXJnKy=J-LJ_Jtcp~y^vfQdPd?|5r5vG(M4ihb+ z4}^@y5Q7v_kyd0!NFDq1CqZT~;0}`P*8Z1gm%)qM?D9tOH|V4gZ4W9C*w$ph zjKpBZ(a5NTyaI!1c&^4q2Mt+}SW)Gy%t-`ACJvtixgD;+BWl2_h_ zy7Lh1cWbyOC;Z+VpZEP3pNyy!O_I7F zGa$Y!Ey!bqC@A8&S|A+U;2n~`TC_;r{SKeRe=O=_d)7@}GFn07+{8)^6EDB`j3Emx zz0!z6gqSjNfUKCYBhi8i6I@m#Qu~j6;x2{#ri>)FRVjRmsy?YsGC1* za$R;0?z9u=S3?xY4E+iVaDVOpacd#T8?alCJ#YoHjWv_wtS^Mr*G$6+TH)MMVOnkv z?9wSr7S=CDAI5?!kEs$_C2&9XoJ4%pF;nlg*TTz~(*b$e*ZK9z*91|qnsNC(MloLl zEwp*z*}=T^?I^;d??R8#AQ=@|8Cn2ohZ| zkbe$40UX}32wW(L29-c5dj!%tkQQldqZHarIm6T%IqABp_?82DxtRQs%>3)n4Gyud zh58nzwkN!CddR3>Qz{@By`83_NjSU*2G0;tfjTL%>tF}H3bDlglrR(`FG*rADHlvP z5HxPya*q4bABy9GQ}rvTX>oR92i7nbquOqbSb@;VXsE_9x4vg5OxXtQibr@_zwwG2 zHj_9^uIA{+MPr?>tgiennnRrDugrt^R8L{OEHB4o`3WZ6Xzm0_gR#QX=5YK?bsG@U ze{*P=N(L4u5;}?d+CXg{?Zge?=ms8CwP}kwhNL1HWdWaBeuVkKN=!{%EDB^~uTson zv$_Z$XF7^K5Q&gC-;(VG{m!?H_OqKz0!y{Yf*}&73HyO@!x%s zXDJXgAl`kbC{&2l=Ai8a#p*)N#pQFHm4t(p6(PwF+Zj$>3E%1K2Nn$kr@m-TzBI1e z|5?3cuGzWA!fLmJ7*QPG8#>k92A#jv3Nm9qc5f~wZQ36=icfP3MW~>ock3yOHDrkV zTU0j}XvY2eN`7c-p7*PRA|V{*GV{$zti4zaNt(z&t}Tjphh64R+;By=?g|KV2K`1jYc}FKG>k%Vu^l8qt`zfwjzC_ksZBv# zq#y)zu_KY$>ZAWGGmqdgX6F$3NZ%U6Z^UKtZ7j!SZew1EPr2ox@ zaH4m5Jo$Ki5K95L-A2JkyES78PGo_s>bu`EAfCIsDqd?HkvPT~1j-R%*mEc|%**K` z?P?_rM-t|AXv849WLD?tf8v^z2QdF#D5n`kau7-D8}ZeSj6NVCM0Z&fPha#Zdh`z` z1zfQ05E1C2g~AAOnkZR}MpwXE>J2N$FS=DBq_pz0jmZVl;8Gccnv>9xTDC~Co_{iuJ4Gx`51fgyZM;BaMZNd1c>rF^RM>DqDCGUUzufbscJY4YzOAruT3{ViM|HM7wzf~G>*0Xdn z`hUlPFFQ3FNLQ7sUZ-kyinfXSJvUSM@ErLY^$@sHNl(NoeJ$JI%U~<0v#dY8Vr(W3 zk+1%cNcKsi7<-LqwbJlxko0+J9XU-feO(;$0nc+j?rJ`Vr#~Mmwm)tzORm2jRdc%E zt}=XH#&bRoQhc88b3XgGzYc1?u1dOJ68K&lzI&wbeN}vYx^#b>=6C}qpZPo=C;7ZS zLu$TW<$UrmeV)2=roEqEw|T#A9+7l>-Y0WDFRnk2O1^vX@#O&Tb-W&d&wRk6kn1jB zvyYF*r%m_!6G8XeaE;I7BhuF=yAH6sd)_Fg+x^Sw_`K^gi~fC(;cIWv$mezuNyqE! zH6+KUsKmqRv!?4cC#U=U^WLWY;_GG7h0pUHxV+u%1{gkNpMK!@dQ*#@x5?hE>3%!P z@c}Nne5HFoH4yv(V&;6Eo?o+u@V!6f@O`nu?cKX{j~{k_T-_fNv|rVH?NRVKznSQG z0r~j8o@+jV_s@LJi_?!^Ha@`htBZA?SF8J6DeqzI`6Ryg$(pa*dzY{Mueb4O9pFvI z_14DDeSNpO$HAe~u!zs+B2v!R;reygXGx9sqXEHtOQ!PMe#!?ufrI9~k2kRY3&^hH z^Yv}^$N09-n3Ie|xAkT-hZ*9IlD12TwRlfosW?jAB&wbvX zGN!+`45;J%zPa`J^ld_6K=;>Kdrj*oyA2;;TE`o>`ONomnbX~RhWYjO`t^GBy$p9{ z&+jqrUiml18PkIUPhj}*jUOa!uu!(Nq`yA|knXDl&`8t^X+@t9J z7|l6Qus-eD1iW`$yB)MXXNDtcm^&m+c5^Olb6Jde9Y4?PI(OTPQCZtqIh>kG=Ag&S*^?n+ly z$;n6B#aDVKQqOJ88*@W0*3FgOMIN<|vF6%e8`(9>njW|^&+5aM30JfxUP~*ihTNi; z%0i7PfaN#W^H-b8XU%5?`^QHOTt&IA%XVHtb|2n9kGpTh0E(!^3Z(fB=V_}>m&r?7 z4?H^~8M7LVEt7TbIBpyB+x6k5rp)mLPy13TdZ$93lDg;|8=K}bPU}dS<%`11=U$Ts zPV?z>|LWfNfu6#Am~$sr#xyw@xm{##KQ)4tH@08`kCX9t?KcdIyp%0-8ZWtxs}&Xy zR;vQ*%0E@vh3ys|68MThp@8jT>xK>9$M)pXF3kwb4SJ|oUQz4H?#y$Uai@8_OZQTq zVshq!)6^@aGiUs5Cqb8*5E_S!GFGo9)#GBQ#ct7ZrVgDCndd^iGP2V+SMB6Fd@~F6 z!p{z+c}tvf1-HJnM($zW;mDXT`+SQh;S`V2bWOVIHV30Q{rT7uWHHTlhuTFh(dXOZ zGFhIOs_V2gaUfHx#C}rq?>#;*zpX;5`Kzlzm$YWKCWCPNRExtCi$?k8R6SC;47K%~h>)fD;<)zXSP$9O3%hBv?Yk2M`GuFQGQ9Gr zB7|vsK7u9>Y&s;b(Gx{n^>KMPt1CAdyYMK_?EU!>e2VxhPvB~2$zxmrF$>%EZ4@gj z>y4JflYQ5tZ+u|bdwH@%e3A^Yxhb6rUq73Jq=dooW?ss3eUg^k=9aW8&`YKTWAipy zhJdywRdgI`D2L^U6J^vF{&q zwO4#QfX#%J0;RhB2N>r0z``2Nnt1zm1@MA3hjYO>7Q@VpAW~(?ew7T4w7?N_xxhU| z6I*0VQ0iqfGKL+sOPe4DnKE1F656blloK;w`kY&W#xg$Sb@Ivhish~qNIjw=W*6ch^a%9H~sR{Bf}~u46kdu9>W2~E{nD|eyJlzepx0=XR{#q z>EDCIA6rL@>PmKT%=40vkL(VC3@?o7CSEPBe{We4WNg93vVD~UgF^LP5Aj~xZA?)K zpB94|GGFu=r0UDYG$+&u+Z&Ba*55`imrkjXDoFaUFLriipFO_C!svxnAQZ{R4=hcb zJ8kkW|2T@DZ^xs%WlJZoiosCeUvHL>lf4YRFjA;M3&&T`@AYqzEJ5@Vqqi}3``j{i z%QDCwmr2)b*9Y!Sk$F7K*3*^Spba+LXffx@i(p7NxFDG0bGKKfWET}b@*x@(MENty z;r>xnkoqGKo~sf8f~|+sN=r2D1;AGV&LVnOyhZJFVeP6h8GGNss zJX(LG+)m*-ttxwUevSGJRS8r12(LNFxAdH@KCM(G3fsFi4oWMO{L`dv19@CEVJCKj6qJHF>`9UPo z=*&edziosoarajpV%$XN_Hqp`TV|o2w#E`pNvHTa?r^#3i^CF2ZOh9uPGxa{TMAL_ z!r7}40x)?ev_!$V$r+rdm0Xbh?3|t3x%R0(34~(sTN7 zXMdqS{%mVqx=!lM&A4f|J5}v+l{>@7L#4uTLAR!fbsXhqo5-e>w;A^-;%@?4BQnql z1IdqT@B%K6UKcA`Y;9(bZS#L*6OIiUJ_Zp@+Zl8K2?JdbsWHi+gR;kFCn{A=aGdXg zHCB~=Bhh**2W?jHL5EF8%8=pd!{G{{>z^p}*9SRrAi>Xz_3M2c(STD(Hn2 zz82c4puA$9+2R9bv+A3*Xw#wUSQJ%C5vs$i6Lz~)|aW0N0|WsIVz6BFO#NA?@rUyqA5&jAuyq&S#X23mHSFx>guBS0ddJ`fIJkCV?iAv0;Fvxo$~YSi4r* zdfEcl9qOpb*XR)?k@S%xw6u!MR-UtH)+_`D3qxujPe~HGY2|CgB#_eWdQ=cqY}SUQ zHSfCljCWo}xVOYXat?iYV;AsGnSRgzkW7Pw z__d8M8D6mFhUZN)x=s0VJE1A!VuPSrXMk@XkIt6%M0iP|YWd{+l-v(XTj%;|AlK^z zzI+!|5-NZSA{z zJ)wPD8>dSaVEoA`v-?Ttq7v=17V!i&eZ|n&OA}SA9!z2xd?kV~nq6sXDKH{WH>uD2 zxN*Uq8Q?B&TDN!E9g-TYEzPMjQmRu@Tu*Q(d{Sgg=4Exrgwo~K0J{Xgye2F=Hm)^= z^u8?;J-^lq3nlckEo_8NSx-XTatpI-XBX+9NwZ&%GER#m#!$z^ zdQN*k%jWg^ux7%w!&dOh(5&iyN{s54v#ABH)hnH$+t7NKkki!Z6=@yBJWeuk55hm% z4H_{#2}!({VKhx=ddNZD`!=y-l5Z^JaXBV4VwE)!Y-q>q+FL*H48 zYC*D92|#KKZb(TDRtk$yLR^Di!d#L%!;X^A+*Iz%G?sFO7gQwk?L`lGr2!y@MF`A^H8mqQ7w|8GVQAuu%wr4 zi?p-qAst1lgYi%lqwmbd5+)wyQo;2HXDJb&p`B{LFTq zoqo2Lw4^j3n%)*}xou+P#r}G3Q7lROQtBn?9d?@v53K>i?AKt)qte{(>_^MbNqC+R z*t@+MF)J;atvmML!q<@TuIZG~9t!N?ygBY-X^zayD~{2y`y_cK(=5^x8vxf)681I; zNn6=#+1yH#IVDflku+%`4IhJ>NFZ64%BJdg|5$txlGeRwrS*`&(y6jW^liJhH5y3^ zM=v=gApYP4e2CJ z$S$)%W^TO`n9$1B8h#Vf7>@*^CQhGGR(wF3QaYA&*gV9soSmIqlI@0cIZvQP)`+ZC z?QR*7QuORz-J~`rMpCnQk(DHg$7(ISgP5R(Ty{OKk!C&bca0>5mhFk1oK7aoFsv7L zoOwda)dsOPuoO^|^JgP~)3U9lJ)M&vuDxT9`C^G`8J-P6o>)&()RLSs#?Lg&sygCcOn9J~eIlo1E zSP#PbwF{^{AfKQ~Ws$?d`jLFxq4kzxJl z+g8F-`i|nWdXv_>^z-sZ>oFx5pcs;6^{o23|w>e)dWhkMTS_~%>+aFpul}qp{{vmaV=BtQWU6RvX+YqO0 zkP4Rhhc!8WBw0+>73Y@Olxt4dr$t&yI@P;v5Ezg#)k$n?8pazxcg`b@!Imea+r#dC zSWo54xkfpyT=5EFA=os>l9ayb52@n2rDtNIP_c52oe&|vy(LN7VdbMa)=&^1a;`|; zyVO!6A7RIpykjNw8j?jax!Uh_CTUYC(|Eqe5<-OvgN!Q-@VUlt?3oDe7ZA-j>? z5wS&CEkur`e!L0uIdr{&deAUShB$Z4^;)@-(E6A?J?*wGbx=sbvW#v?TMAQ6D@ix6 zHC%|%cO>Ot*JECih^=o|!@le^Nkddli>MA3beE(H2t({`cFplJFDBiX+!HLLTRfh& z`j)s*Hi2AWA{@JHT-o>XE)2Sm1eHMRO}o>ElVlRMzBag(0SFYW>lW#jOu(l$f-Sd* zIZiD2Zr!edm4L@-b_#_+jy@!fK+fPG<8H49Aeq$|whioptQrEadUovN>k$#by*kgR z9n|k6IEY#W6k%E6ha{Izb{f!lHC!HTLA^<1YN3Q{kO&x*H#&PaLO>5d8k`k5aqlOI zQhO?S61S?@KLy;+4k)SSHzt`h{4uE#l!?S+q>u`-4nxlmlX`!Kl`+VG=a5M7=G!`9 za9L1epAo&(cB*(P+BjNUvPL_$EPu!_kgw8&)BuM_lWP(Ou8of+g7sE8w6S$1;H8)l z5LU>tSkaFxCMH@Yq;|PHGw*|`o6>AHfoda$e%$B#2aSjOuSYC_l+fv zTfrKZ+=@IpYgvkq+*x%RrxOSQC$(bEB=5AD`JQ)Ee>m3C#4@q8LoE(9lM*$AX(HFz zo_|8tZ1Oe>zy>)u0X@@{U`_y`i7W7?rgp3>NtxQV)vzz6-6y%!jhKa0f5ykw2Dh;9 z1Wt8o5W>LeW3z8I4AG^7%v`de!Kcc|*E09$X(1)Q5TR|*dmZ?ojc7?B$;yepZxho& zVSGUl8rp_>c74q*$L4lDx~3bo>#zkHIjNYTH1DT;qZIy^3CAVcGahowOZ3UumtvIg zS;3Vr{>p%;gcA)2BzmHYGqO2EDN$k>8#My8UGp>u`=qzCd^la&Peb3TEgQf{nOT76 zN(6O2SZms%a%u5mBXCi1T6Rpdh`vfRBqW`@o=&WeD2kp*Zi42)_#kpm8_s+qYNCM{ z`ZO(c?a|_0v6Vmz1hNAkZ{Q8gxHqgFg7H2p5j*6x^={W+Ho9&He=t-(ksC04+CCrO zOR$=lA+F12GyHUP4We|6hJ^1np9v?&N*KQB>9z8XUk6wKb&^0UW^7GvN7gCkjZ6-U z#=P=7l8@vu>tPQ8bD4oTd(4&ta!yvZF`JJf6gui!gpFnV6n(m$?WhlDFWZ~YhFp$)J3`PhO|&t#5Rw|W zDTN?8S{_IZrra^M{m9QFne^o{!vgs8xPS;>KkRxP6{2>e8FXj2jLROmc*Lv*aG;90H4H!NvyBPqk)>H&jtrRu9IIzZ; z%5>|1HEbfBjyfu*Zpbp3XLv`9T(X{j*u2{K$4rpsdJgAtBJT+8Ika5J%M{X<^4l?f zz+Mqea}fRICa&_S!I(AOFe`7@d$SyT(8D;nnI#NfiZBIuw!IQ*?62h{vzreM*<|^X za&O)Yb66=2z>8~JC#oJgC;%H-<$8r|m&bc{P~q6Q@!w5Ddq<%I{>`^hq{y|%*+Z7? z@>L}12I)>>AKJ4MT#3ECiEPyxsUr~313+FUD;moH%>q=#vYVWz0q@!KJ{j9V{3Fu7 z=l6ndz|A1_TyGx^dJ51{?pBRjI)Lm}U@qz#j=-d3(b;^7gXg2`i8Kv7sP<{@&QIy% z&^d2%f6}meo(gowAh1qZh{5|&AW)>sbU`OTRfxU<*3;;`-@k|8mX^st3QCXRzy>AP zR~~T;0A14EM{!BHUO{+%0QV)ylx{Lt?oYp5BevVQ(Uiz=tGnb3s0H#>h0uNn77I!? z;|ylWisjWv7&OT-Et{=AZY$E)oUH^bG5KsOX{Je%Xq($#l@=pTzG?H9kMI4|7LrOn zp+WBFBQUZxTw{sFxd1nyA)djEY@7x=Hybrr?<+}|mm&mUo8!0rJjoca_dAgj%(5zz zIx;l*bZuBb;KPUgpjAAwNdPsF2R0cW(p(Ebrav)rz%K+{2(HVZLUw>hGjL;4gir@? zGfiN9jhtmI9J-@yR3S;TCZ{oX#F&)L7P;C`;zr(+H3>K5dI{Ao)jqOpS?v<)l5W)c zOy4RwhejQVY}}HUx+Tt_O(2VL5ZIN_lK&$o+^QM86H%eDz|RdUVUsuJwErOQx&e@O zN~)r|3n90_b7*q{mz`lxpWpk5agmnnmbtgS_)PlP(ZBY^dp!ND~kLr+EhKyCO*zrbxUtZ3AsrCps6ThU4KU17+~ z{#t}{l}3Opur>fePE15HLpT=G!|RWtEQFkoMA!n$P)G#CbnNe^{D?~z0I8ZVmYkP; znp8-P_5j*Rpd<->@HkG+)$1h$rZ*hcA<@%7+LitOh;|&_4PYn~J4Wg9Zc5+D0@c7; zVXEt~c5pT)M`u_%OUAWq7}@#gig(=aWR`RmZ1H2`9f2e*$aZ#g_TR*iDw@r0oh298r21|r7(K5FG7QM z6lO>KS(p>f?0i>`T`zgXC=3S(cBcX)lIHDtbTxJpdYY<*z14Gx(8Ji(XkQBdjx&T+ z^>O-of}vVDV2?rlxVo=^JK0}br!Fkhcgj!H;}a4GY358~*t!MVH-k*5*u~^eJ5v93 zw3o@<4_F(9%-&o+cXJv!6KF*k9hQ)uRYDv*f-Rxz6|y4$ImO>wAEyA4B$0Qf>{8;x zrQWqJbgP6ULD-@gCegYqjdG`IL27V1$WOdknsUR=in}6tFYBm>Z9^o0SXY=jQVf-{ z+s&c9Nq`p2=&)^md9S7!3#l;=a=5QYS3^h^ouiGj_5~S!7(!v2rh1v6*jMN1S&lHZ z%;(KucFi}zlHA`wkf#IHsAEYf;vqQF)B<_3NQVR7#V8=-KnxA@^mV)sf9xRvD8iCn zl?Jpq#B^he^l3@h4k4XMf3yhY*XuPKW+iZfjWm}Qyd!A-BNZdJ@7^fr+V$SH4`hz) zPe=Q3(ks9Vsc~s{f*3_p5%D}sLZ`1hBEGL3v~mqV6)}JWI?;BI0q#;c0XC-$kPI#P zrH}6gU0#8S=taO4r-P3n{LDikg7#-*=@G)0GjUGVtk>6b$Fs}qLO76;$G;)iDSNbO zGrgYU3IJ6GlI(rpPTh%XNiS@qPsBM06#Kqn++A?8vDJS;JN>-p|PSaQYTG+3EJ5}RsCVwNgy z0BwZ~qCQ&}Mx8G4FTC)!Er`zmPZ~%ZgU=?VA`;lfNjk+Z?b5>2mH;EO)1r{?WYw) z<@Gk-pyS$|^*AxL`fgb---bMI-&f@yc$-2g8>h8^2-wW@I_u+^e#RX2vC<+bD)Wuz z1|e0)A+R^%e1yeY)R@)lCEA!SH!HwWZdKc(J+`uTm5ni%mus2%I{?91QqS4q1@09kgj*V`6rVFz}#8~$QBgc)J&(E7*s>dW+*h9OaAaXq>g zY423&Ng?1HKcf=Q8f`t(c^4liNSdekZ}*H!woTtJ9>4}zlJ9)&y6h8ug(;Y|I}%wd ztv8v+#X^sqR|il=B}gZ21uSHLQ>@GE9?oc@UW8K07E*2kX9kYi9)>J6H>(WHWj5DK z$XoPBfwt6CV}8>pfHDiXHMY8TC@!jmLMG#v2$?ywC?#D8Y~|G2$6!60I+N=KseU=a z@tZiLx(nkWYBB^jjZ+Ar#OT|m)YjQf{|9@L_w8}jVbkt$-xS^)^(#cYwM6E^8ZBi! z0V_)uudq5bVG=|M3&z`#z21*pSfbj=H7ZWeU(V}YW3Y8H5oto^IO1heFjUEe;Rclg8x=#)x)c+X`a0M!UY92` z#|PpoVT4oQnu3I;8J<=GQR{ozr(_gmBP{@EGY?=P0B9E6Q%+6Ybt|lR=}a=%BR~T( zStw)G@+k>hP(Efw19zJloqQmHLX$dF%IO>_1Jy~et4BSEwBkk))|j4T3bf>MGSRj=oQDb5Gt%Q@Q&%#G1W=@j5v9WZ2*Q3I{4#){it2BUQ>K=|rC-z3kkI6_E zFCwqqLfQP3E{QnmD!!#d>O-$B?8+5g z!p>!r+a)G91GlZko=LVj&AJ@ zu7_!TxP(O3JGE8eSAlP+1WC&ygT;gb`j-LY@XqxTpdS>BB&u1YMek*M?Dypol4g>I z)hhsflT`P<;0Snv_<6lOElX*gOm_K=ODI$2{?a8pPJD0)wZX_q$nd};1b-w2Utg1WJUl{K z7~P3(aC5SEbnnX}l)E2mCGDUxC^)LG1%M~_;1GZcDQdM^)ZkYh)4$i`W&o@c2wPCW zoUcfr8QO-8>2*K_7)eQxTsNvN?l}L6Bi6YYsD=T!EXYp8Q2$}55)XwiFE5}Ts|`ad zLWLy zVb@5K<=rb2UP=3$CV4OIF|Xwc3wxYuW=cROJ-ij`W3oKVmy(jRU?P z#WnSO$#0u%Knj2qLEC}bBD&=O)hEYOCwVB!`nVQXh?20_(puA#ikuvLH&pr$h%F)n z)kdqtiPq$s?TL!X$XDz3paj#dKA8DPm6M?Tqs_J!DhpT6n-iA~%~7=%EC4Zn34$OyJk~n1VH^Q77lTwsU_Q@k`;K6rga=NPJkp$ z&-CRH`7Er25l+YJTF zJ)!;&XVMJPHrJJ7vrC=5|_*-GI^vU{lEp|8Ma#Y*O*4QaNomAIs;Rjp$SI8to>QL;UJ(?|^_l+8G zT^l5wr~v9Z$6Tbl8kh_@Luw!2`$wB^`Huwm4Z<++I@@lIj6;UR$~1DXi3L30JNh!i zWlOyw+_jsFp&hHoUgikOI|-PC{9dz5(4V5F-LXx$UyaG;heH?je93R0 z?TKS5F;tUjEjtvqGnPDnw^4APfnjy3&j9(vs;eLlfliHeTk?JN0ePXz5E>+VkDHdk zpVr)Ou#NSFUBA5ZKWnn3qo}oPQI}0J%rV~f8uaPOrt+X*+jzF&7w!gOFE%(M><2u| zOt*$Hqaog^?tG+NGi7aHitFOade~)$H;wmd)2sv+MLl2gJ8W+1Qc0s%uTmBX+%2D6 z(AIQ@TDjq5Jf(triR3=9+)m6Zf|?M$k$epB3@}ESCoPE1cZz@|_#sn%@!n=;a@?pt zqN#SB@2(kw=(t2}cA;>hFSPo*tql{SZbH>SzUp8({}(+nnBq?eSoaf&Bc7|5(#(`?_pR#sNKL|?ZK()l5~BL zt7)16*%-v(P!b2GP<ZA$mbZEAghtN>wefN$`v!oer|u7kKM zL*JTVvu`Od|7f82`{f-?1o!&Op@moclyx;OC={oI+sppre|n z+N%dbW-I6MJW}hRw{ib=$2TZJBTTuX%QAGP@egmXyd>K?5#pk^%roRDD* zj;BE7_sL=y=HE&z6zQkUZ6>+J4Mon46wO>DL+1S2g6kWeIfs6uawA+bjmQ>NjvZQM zWVkg9w>lwr9A+u_Pw}qtKq&JE-WseTz%C&P-QGJ~_@P9`b3i=^S2_=81H329GVcV2`6-+*)&~4jS&8wz~x)2NYNgbDzSPFJ&$xbR+ zi0=NUnco}&SRvSH4ejO!X_7-IX~N;256?{SgVd>$WH~(?|9%hc{%`Nkc}R|&Jgg)W#_tj} zVnCYUy?GY0)$B$EHR{+d=b-_rn9jxfsq!SK-VWq*5548`OtUf)-bG9jIL@%pb&vx$ z89#D3VF?a8Fw*e@R@fRb5UAcjAS}#hH+9q2SCbb&u5A*Qasd>B3+Q$4>tS}{{M%}> zKs6We`-qla4#(XAXR|h&#R|0QbkmsU@^25Wa6R6o2;w-Cpp}!ctgW6Qs-=oEQ3PgY zN>hz!Z@e*FYKNPO`fIbqI!>6VUvH+WaucL!vR5<&NyS=G*0$VNV$mL4n7UBQJw)be97Br8@?SVZVZ9!?D0{Nb(8g`2h!c}UIB_2^ox zXpd}89`iBDk77m1iF3Mkz5aZ11gd5XlQyFKiI6z1@go2Ga)ODsLb(EHW!#`S-C%+p zZL2HC0O+Bc&KENe(M*-)+rVMT4_6ar7@(99Z--O`Jj@gFDUnqm@PT6OX8g$Wl1Wb% z;&j>zy9=I@94cq4fth4G6EdG~o$f-ws(6a%>o5@Q6Yg!T^Hwv?O^(c+_6R|yZEod! zQxRo_szDFH(i+EE(nBG~rP}bxOZED@yp>`mFbpK8sUSEVTq|W#0qGBlY7t{yDrJ%p z%I9`lac0g_XCC;5$g@M*SDm;fXWc=Sy_{f~AWlhl$_mHMY}3wci*_E9`n3%@A!Fiq zU^?Ai;M@5<61W?cl4KA{v{8V5phB~;%||SOGX6}eflw7we7#0@vF{s6v<~FCcXMe# z2q=?EQWMyKarWA3M728aF9Exlv{s<1UfaEcLTQH-iyPrN(&N4+WHP}Py;t*Vhl1T< z6@g~H#rnXW0Zy?+N2c>zKN%#|T&%h;$kgLV+qjUZPjHI@u4(KOa?a0beh}lq>3ZJA z@%q8=>8F=p2y$TZDbl-xAY52j6jdvXw;22337$Xv-=No1G z?R|K#)Pyp$9KEw1E>Yjr`S}X zNN2x4Dv<#rClgQ^XchrL4+*@<8(qt;o_S zC94abQRHmGohxfDXh9nEI~u3HJ?}&I@D|n2q;e@2j%r!Lg}GXfO9IN0f~W9Z8-UjxZo1 zS}C~2#mm`+5SGGnx)=~`vzyfx#EmdR-rNb{eTgyI-!+*uFasYGrYP2oB&b%!e)yFY zX7-5_7*q+TD`JDE8VDaU0g}LGPHKo$XeFRa;)68#C{l_kbJ~_5^O#ls*R= zM1Y-xeTa%CqQp^Jk=!~59y3ez4V-1A5F{vXQ#K1}Frmi7U;=b^Lj*ocB~EwE`1$A> z0i2j$4IwkzGgNd;s$8v)5x@iAveVh5l^_{P)Wtef`6d~U`$%gVQcwnXHd;%8IUPY_ zs{M+GmqUy1IMz`4Z6^@)$7y}w7S6szuH91-yeWyi-LG!8_VRHlTTS`{`kkz&-|~ zifH3brE73r7PuQ}w-Lm7#z@Iknz1(VtthK^xRj0-*(L)K-RseZ%5lgi8&&c1Omg{{ z6&C7ww{7=F-8kJ9sBDKejFYqggaZ^RnU_<%nYFuYy;ZB$Y_!C z{?tCB5IBOP4`Oj3))UT#3EP7k?u_g;4&Z4}*$H?w2vc{uJDAdEf$3oTraGVpC_Pi@ zxVe!evOEFVA`=ldcn4a?ZMNf?i1=1pTssW-nCu0M>wI+8zQZinQbQ$|X9@avO!Q@* z$73IFeED$4!fiFXAtO+>H|Z6BPxSHtD^?s|W=Vn#3RYrQS`@@{m(%3f6*#B?%A&$K zRL6c0R?ry;9&1f{q2d?r`QVzR9Z8{t&3kshwoZqrkWz%|31Y*p8)_G-5bF3$4M9yQ z{i=e{f{qu_F3DO~0tIeNq{HocbfqBPVJ%|Hw*|!yt^4qgaynbm>-7ry9%W6U5GIBU z1%~4Z?M-bQa>HGrH+sN+a?X|3$~(09pGGzZ)ui#;G~KLBl|d&nA?LjBF7MUQsUn<3X*lp7f9%m!^XJt0XO5XC#=4$1JK)txI!!1~Ej189A_9+|im zGQdeSEOu6=fBAllUtWJbk(Wquyy_>SAsdvsX8S>`NgvWLx$JMSFy#7>tl3aiQ0K0s zAqQ$HNjoRF?hiCR0X0m_w=%e$dc${1)jdq9MCXJ)=95=~ejpW)TJTD2rz>Jg;S_Yu z1&JkX9&FDbHzudMht=o9okU9m$LW=*si8q<3Pg_#;zWDBUB|nOVKA6et_1#@@ zKAES;$%_&REUkLtksIyB-tF8CuLuI}(pDExQev5|DWG=4H6Am%lkEFqDW$O$ypHcJ zsnz7J((SPWgpI!GZ?qXajIMkMe}|Uy}7+sa&T)yNib= z%cCzehl%AU@k?03>2HL4l~z9|+-!PQuC#ws*x)NJ@W}{sCjG<{l)2&=+g)aYZoKZNy@5IYC9xZ{@*f%7`W(1JFKEpx{`SU?cCE zVnq>sP3Gd*qeAs`NT(oH!S3Ym0ND(Fb{@5Q_J+ozPdF{*&r@@pLBMvb2sUFz=$r3< zH_)L{R#zylc@vp5CmqPPv}&W0WcE|{(b%}~BUB?cu#hS3+aoPva$r&{ zNR&pxWfB&RAaaY%kK5y0sRnS(8r=}Ud1_LqHnL$)+;WFtZ3~-<9y$;zh8Ix705|0o zO|>Tb9Uw)yU5}s(F{25wK6rp8acLOip7Q#b*V@nH>T)RmEsmN&e;-Lt5XosAtiNCU zb-zg5f7+Pb+zX&V1rwF198)L&`Jwa5gH~E3SJK<8cst^bG}N8ReTf3UU16Vu)&2-o zQ3|fb!caRMTCbM?Nu#eFCQ*0Cg(uaeIqqd*&@g4XjtZ_L7uK8jf%cx-Ei!)ScFVq!(8x*9b$h&~cL8aLVur5>~PS0<%h0(gt&IiGr?GOW{s4tMJ?1+tWE}H8#u0o4En8Q>k%z;2zwEjoO~RN>CqHEL-bc z(2A1A=58%@Sz4K13FT;40?>g@Goj>hzaTqVXd6oHfq8UeA zB>}>iQC>9wYyp_ZkthJRFAK#i#*jktouZbe>c;uuON%sPoZBqEh(NA;>SMwwA}F z%4a@%N(WJ?OY~&>4nit(`LpQY-IXi*Yv2+l6xrg5P3AKBO!Fbr_C-%BxGl(NMU=gm z1Ghqc^nMRNO)Pykd<`>DtFWe;!f!ky&$KI$7~H}mJYBH}yk2D#m_qWd_)xRQ76!{j zXT}tSrnFSo@2~?2EQ9+bVRSR8F!qTCDO{jA>?O?TAxfnTVz~!<*P#0|o>qzX78&Sp z5*tg~pms8)d+UGjtb)A*rn9A$PemyakVx;cz8+C@Jmsikb+m-4glqz3unV5NS>nUg z7&{(}%gEz(Rj{XcuKX`5h!Kha2@PTCNJ+Z9>Ox3_3TRtheFLD+$~5Wf!^Q>)UDE3=tAAqKbk}6_S#i;-=6N*W zD8{1(Pl%h7XdwbM_;PcW;r<{;*tRp+=5dDZhY%O=+vswDn1#SO?hAqNWFo?sGX^*Z z`vJ(N4OyU?nZCxc&Oy#Y4nWJRyMy%zzPDtusfWb`-f7Y*9^uTnN-H&tlW3rG^eEuB zk4twm7MK}-)i zW#_Fq1(MH<5*@3SZX$v6+KO-z~QIfww$yjKEWosVe7Og$-@4GX3b5VS+GC^DH>O~J7}^M&^RI8gu$6I^e5#B0cRBHl)f zw*!krDBqY-ELoZ#^r&D!;L;MN608IF3TctqGq8|`$C{3FZ}WfSYDp2@j^0VA9GbOk z+fxcpIuFXO-IvgPT(SMQ6gC?+)k=pU1<(3sWCT(eN>GGz2Y^tisVSjuY3nXyka#Wl%z;H&Kpwin0Wt#;&U4gT-Q6i$T68;7|HGwP zJWohqhqRg0fCVM||5|{Ky z07zD!#^Z59LqcUUzNDA91OYUm1R>B2ebR%ETASB0cmN;<{j&w=R(p=}N6E82ShFj* zGT0PNVY|ubqqRhOn-du2ycG>`h%mZIrA?>)*oh{$r^&jv%;Wr4!z-rsW@waRqztwl{Ao!#iqva>EHA*@Hln>Hn7^UtFrws@r zm2ZqUxmVNLjbNSk>at32Z23y~RP;8~ZZsCJ1VrU}bj|xr=uSyWU=coc9K8j{-VNPr}J2Clsg?$>0v9vYvUdzxDSBqNi{g4%lIUpHE zv#Lg&Xs{9kj&XaKBqdd;z%VDxDw~38%{(;Rkv0}q{0PgDIzaL(bqQN|8DQ+}!7#VE z*>K00`uJXZHHcKBEpOMOYthy27RS@k>?im1qw0!m(xp0Mm0dN{L^t@&nHt=4EQ9%&Y zAQi70xsw{i29QN>An@}dJ})+eqjv)^c7mNPdV@ksM!}jFBCNHH4&$-DB-JuMgatRQ{J$l8rF&a97g~m6|(s)5xH|QJ>Hw8i+qA$H0E8 z5&MjuvXU9F-|Y-Hd4Mg09Bbn}u3|iK75Js3A%a+HP`Obd(0}Cg@-+Sd2B=$zm*a~1 zADGl>{cDc*a6NXws1uf3X0_5MT_#ubT<8uW>?L|}V+rn1axT7t0j7iSKv30mno*RX z(Kk0u#+t3s!B%gCuc?p%{X7dWk z(k2g0lmq}3II8j9?tL(+{#PHn|CRdK!MwRzdmwG;)EV7;X;Me!O@4H6Ce2QphVhWf z6AsiO0cJ3bL@UC?j24lT^}A2QhYPa$QGKRZ4~cNboOay^@1J7mbzV! zK6Lhm3TVT89Sh(eA>v2=cHpL+HF2DIAOTbK=cuk(+ahsG$dE8p+tbUPX&Xl-71rwr zGc5r>q5n7x?w_392ISaRIhDqo7V%r)6kgBx1BQ<82Zlcq@jc4+0Q(+6WT{YCDmohD z=9;(j6>nh~yW1>Kk29hIhzuAIOX#PpnW1FeP0Px{Lz`_;yJFj$@OnT6AuW=$n2Ta) zut;IS3xM!6(qlI<{KT3-6|PrWL!Q@{&-N)evZQX~QvAB+GBlFahH< zZmWYY74*;n5g(vZhRQ=)>qZT10{V7;P5xv$W+!Dth>-YN!fq|VNZr6m z%YHnZI;y(6{h?>)rU5WeE6A;@;)%LbxGJG=Z0D3-8^gDpl%sX5N5)*d_a}ov=o&^2@6lwFLYec{fvNCc) z5lf<_dfOL)HaKz@#gEmbM}tNHEtYK|z?@wI$sw^=1WSa<2iR^f?E{Bw+)WKmhe}h4 zoR2=5TCY5#hpF|!F*@Q<)YLbdp=alC=aOWdihbN{Ifo5xHkVpm ztF#lzbL`>moIbo=*&EXxfGm9Jnhn)k=r9Z4jr7TMw|h3s^$It)h6EQDi|pxnRx=J) zDLd9LL893(4Gd(%Ifz!A>89Caxj|2P2f?xmO(3`p^$56W z+c|!Cz8I0FZdzSy7!C6@JX`U=w!0Axj#fO+D!OGiV^aZUkIL(f1pP)fEGST@N1^!E4@UJr-RtC>c?(m$xfgPh z(QuRmFZA!;M6d^=rl4p-)w;R@%|a*m%hA=Sf)XE72%3kdZT+yRWb9vZ@&_jF)Qty= z2hw`HIK#StxgybC<5F+-5+DqjIZB31YGzWJm6q3#p+Z_fCS+ksBv$&5zjcb|I0Y&T z7|}WceNjQ&1{mdtRJskJV!>@a0VTxilp!ujgpc%*4%F`hLTKHaRQEY=Nd zszOrA1`0K=U@XXS0oXbpW%uC%XaY!XlExm*vgI+)`Qgm&?UEBbCMvMxG^b5-;nB^g zvBS6ay3Cfppq42?L00)a6vW}$6Hjx#0OywOX*1-DCK;bq&-uPB&piAjlK7RPNYAlc zWkwoy27lKAd(3WX`?BuRm`SqEIk}%I6ul|B6r#Mm1Y~sF^ff@1JBTi?W*lLS_ zkTP%?tk68{GB6UY($Gx?=QCYk8XZcfS?P|xpAKceGNHhq3`~U#oVI!WWGKxinhpEw zA}W36Jt0YkJ;ms|wtCoZCO6o7KxQ*I6yn?lpB5MQ(wq7%GGaa)7DzY9lmw~|I~2y9 zF&~9m1oUZ%lOyw!&R0;+K}8I@y4#?wP(-+NmJWNn8^3n9)HD4#INpGmR~V5UE2JEm zHq>NW(--_N%wlpz?RGu7S}yQM?9;fd?OA?vYysJXrF zNOrDt_;E!)GNx4Y(2|k*P05`*ROfJ_(+tq5no?ckiJKZtX^77yc%-Y_NYa%PkW=|E z82}CqT`ADrT#w)sqsLc@c8b`!bP`NyKk1-H+fIkM&m+3d%Zkio733VBn@ z2t18e4)&UHe9^Ikb}~VsQr!KUq6$K_bGk+CNN+_LmF4J5;IL4Kk*+O%FNq$Aei}J$E~o3gW_<>| z7xxZ%lO8!56}uH6JXMC4g5@P8(0eFie3n5PeD!L9@(?FIQq{0Tfhq}17|b6=FS#h~ ze00Tx4HI+|7;Pu!m&2%#9y{-|6DEv8G+O*=9j;2`K?OT%_>I5@{Cq4jcjRYDMw&ZO} z(3pX-gnD@d4C%N>aDF#KdMW(X4r@8I2Hu|A_}UJ-T>eQnG;z@N4D{tZ9l)^-=6i#( z$VCo%i{^JO=|@1e9h((G%3UkeWzVFT(~nfO z?avGtF?FIT-k)~n^$_Ff29WC_>yg6^cwUiL0{ayxx4<*yUY;Y$PU*5xA)-pF87II- z3V!HALA54rM}WcJ48jCN4el~37%9E9UXEg8)~n0Kg3;C$eWci<>W%UTcCuYYIUMMv zYM{#>7?b;v+`|bmB7pGe1-lHr(}xkrFXuUvq@6RkD8RC%w{6?DZQHhO+qP}nHqN$f z+cwTSZ-SY5$xLvA?x3TN*4JIDYW+~5YQBi-=5O&a-^K+a7&f00^#I%@B@NzvpzC+y zYB4$D9M5fwphn>K>s0lfN+r0!FpIwgagXJr1=yKDls0TB2JIX5UM z3w0`s?V``y$DS%f5<7bboEd}FW^r_0R@cNzQ~-L7b7;xTR31jc9Hl^%joTw=qNgpk zMRS)?MGGjTf>1d{B;rw;j{;=dVSL>9C)_I|Ns-x{V7PxT9By(XS8o9&=I5KZaSI># z*J{E?iYGE?w5eQqqW1krwwzpY8We(UBML2{*+;odl}s7 zCC0LvAm#Awl}T7)8~_iDNUvul6;A=Et&1@M#JFE8BX!4h2L3Vv+e2&0?Z%8;kXJ`& z7cXVs7KnH5op>Phu~mEDAFHzSjak4!nvds+JR9>{dCY!Yas=NIIXc>(PVQBCpOS5^ z52t_p>y1hu3!-zX28{qirbUayF^$rrKpK9I_A>y9)ArqJ4tCv!;)S4w9@a3%U{osm9bXh z4s2b?D;RFEfzti~sBY3EyF#_rCZwH6r8;VW&JUlMUKvRYH93cmoPs?vxnLMo!afar zsoE-aG0pX=2Q8c_s)8TLjs_O2RSp$L6p#tw{^J^>vK;Pxl;-Z|tY=$kS=gd{qYz|~ zkfIiNu?!ep%oqWRtKA5~?-efzxouF!1{a8Um+cL2MPfxVvzTq4R7JK(E|@bN1Z52Z z_o{R2%w+CR8NEy)ZvFwZacBx>am21Gm0yf{`s4tW!!1~Rkqn{Z4 z<$bEu5O)(Kx}Pvxst5GZpF<_ywyZeBwH4uCqhgS-H z-0E)}V!#mG=JhOvT z)-j3Za_3h$@PJ?*bPRsPy0x|bZ3!*NJ5DW7K%)XwQ$c2XMO=`fQQzwnFa60n=KGz) zg9eG_Q4xLc)csVgBFk5Y+Cs7Nkif8Fl{TlOwWLlLFc_WIg$M2d>~A@YXe4Z0s2<;igv{aOldBUxwTk_@?Dr%CG0# zju*EH+y|y&a(@*-Qy!_PYgbXP>S%FHvqBSQ0Hjx^kwY@>TF?w88vod$r0^=R+wBT# zW`k;-kQ%6tu5p2Gz#2>BfZEnAXfAw7)|OG}ymH|#Aim)Dx*E`&p#CYi$Vn&V;Ru8h zXHx=&VUL7D5Is06DhF#rD*@nxHLPHd7E7?Qe5DjpccY^I8TM%A=vJgVNnoy@&c@hm zF*&NV3vm&z3IIGu17G*dPP~#|r3FJ6kjX<&5e-3`4e?JOnN&!=!}f3)xRKWLLW?D< z9gq$Xj2`o&8cOP1Plh=40d7^&XoTFXL@Jbjn{EnZJ11>WF+w73pe2rl>_$rNW)Egh z%)2zGSqMsl=-va#oAnqgJT(34`i)nKsO%JOLwIBl>P(BIh0P5nGta3S*n+1HI^1}q zdS?3ayNtV*@lEoO?1^ipki}5UOxw)4I&>-jO(&6&2QPP|h>&#BST|J5x6R)!U=u<_ z^p|3mVXy~Pd}r%t%*f9OQ2u(2R{3DQX%yzxYqFi2_fE;y!%8I}X}YY49CSq}ojN-u zBQ|WRtfoeLhHcO(sb*+NK;if7eY*?Rt>6MA2@)$;5Ql((qOEC8cF+Sfk#fqe+ktiv zE|N1e+b-+?d86R=cyR`Ap>q&~)?`3%LOI_{W4oP}by&KxYVt5hu8KrXxga^BvPxFs z#4uDqF0A2c++T{uq60aXtM`Eiu3VzhzV$(pAK+Vq+GJ%{kzL3rqbs$qa*OCd&wj65 zg+c(gx^BNXu+U|YlQ*LG1!goM4F{>D6Eu$MKK_&9B}iG-tYQ$uf}A<~c;Jl_P(K&2 zlHKlFrkX?pUx88No@fow4T+>u#Y^$Ly-wV|+`;)Nk^ayLLgdxL9tgiCDLYqb*w z!L`f1ngGP|_g=gbj!ey*nvjXudDh05s|8kzn8$I&4b8mw;P6YFvy9~^O#Z~YU4DkA zdEHBcz@oGcK`Z<4iY4!CeqKZf11hqE zaEl9N7!1w*m;I*zU-2-XkgoPCRFaa5sJxr@EO1}&WoT^2thRgyYg}tbxaFnjY#6IQ zD!l)Euw7!Vtbym|MDlUM9`Zbz@stQzOr3Q|laXg2y5Y=tuj#@LUHg4^bs~4%*x+^^MfrJK`$J;6%PTX*E^HDDyWSL`ZJ$N}G~1|KLK6pPnuCCP+S5(GFDsk1r{w~kp z>@ctvKX&;)ZB6T1hO}JqFFj0HjVzE&3A$1X)$6?*&t*2<7~~f7NV_-Pb>a+GN@0MV zh$lcI!)?BU&B7%7UFpj{yY*Kuzc7T_jYZr9nDMXts3E2Q>O=MADGLM}VoP3Rut2~7 z%J)@%P;j^eN?^#S1#$NgrN3#r{QR3DL(%eZ4Wd^x7ZXrz8*{$bYl3%bpNk?c51u1b zK~!?V-9xq{g(U@$rNO~#LM2d$9Txz|R!2iL-JYn` zg{g0rZ5tKN!1jq+3=)HJV!^dv{ttAVM_djSWED``efmJp6Y0T;3GT=?Lo;@(J&qip66evgitOLV|#R(1apj~`V??# zFDI58VgP5E1J}r`kU`91W~4R-)|K&91Q?L|OO$UmR6+r8$W&2eGoBo!?n1C8Nz z(&`yPY!y%C9y=s*AaO{%fF2@J4#$+<{hRLJGpNgT@J z^yj&hu17*BLFSxGr=$rQ2;vR`S3J6OJAl%#YP!OhQe)ACYs4ZHZPEAgy9)&dQRP1XIJ#OmxyqMLPe?+%j3x4 zJsWHUN$WSOVD4dFONahrHccFr)z)md%xH4s&61|I=oTr-AITVR?yz2z7h6v}`sFJ! z!*ttCkM!_cr$J`tJ_I~)wd%1%T(|HO(>7(6y25j#dJ(_7Yn!m@gTQTsPux=;O(bMF zb1C+`>r&tq=4xuqtg>nR?Cc-iILP3 zc4D!TN46!D|AK`c6uEfB6R_7bxVV9+8%x5G#HsMChFzV(oWd1p6A7*eWU0h+2L})= zS9p>Sz^)kBM(rYUdSpcGjFSR^F7WlKzG^y&#nriY(3}-}m6PI(b06P#bkif(&G0xz z;BO%2EH91Mt+o2Z^CNNu&h;wWc7ZoRSb`rNT9f(X1lZV#ZY&^X5t)+tHrXSF%%dxu z!2(g>;fS$x$nTAg;KhQp{tg14t`;#_GjHdEYXPGq;6PqR2AcoEjAsz@j`h zko1mfG{}24DoRNB zkn$0!@!<1~DJ6cw>m$Nw`_Wx( znvAmaAg%F|!mPg9Xh@TbL2_}85*Qms|q$K%{j(e)WV_~2@pw}=LLEjZ* zm~vi*lHEx>!=NvtIwmRjH%}PiYOayr_ZlDSzAQy7C09wVeUQ0~msz5SnYw8JjL{=_ z*c98|`%&Jm_$$#7&%7XmIH7_$R#NmCB?H-<1iWMUCn<-#dV216x4&8Vt!^kk-71_;|P%S95q{f#+AuO>KqL0L0@Pxl*wvtuY<7YeA} z3ijPENgcNH(?k4tK_78i#eN%hmq1P%?;`91i_Hi!Fy<(QL>8O} zw}IF|0l-^)aA<175mAsk1XA1%3J+Pvm4OU=8WAD)?htJ+<#$NTx$w6f{8t+U5ye6^ z?d{DBt2o90*`=(*9!%7#pxMP0bt|Sz2ufb|s1TA6b=mUt&v$|H2hrctd1@QX6oXY_ zW*07jFDA0H-M0>#oL42O(F6Wxyg_^2KjF}y3jh>S<_GLJ0^(cpitu#J zJa;Xx*q%FAh5QSiN}o1#rLZ27Gm}lE+>az41VXU&<{$hSL~YLe=6vR;v398$E$_lL zVJyW)U~NxLEf{2Vg<>6&K>$W>39kdoWJ!XR2)CrnkqEpA?@eA^blyUr)WsKkG>;~` zQDB%uidlX>%PQlA;ICP@L$=A##ZBW%A{MG5*mGFCyu1)LM8ZJUHk;5H^*%=;l0`rf zbn7APo)y2b>$l;sv)O_=@{|6$G&oUFv6H{u^4l1?0y%s+zf6)epM;VNv<-{P6pG>7 zbwvtagUEPAd*Maaa{Yd#JvStIp&T||@SaHq2DQo1V~>2G!LOqSavTU@Q@iM223b$- z;{7+2Dmas|0i@*BH^d5oLVfG))=KJo{0iKK0?Pw54brL^M?79_$qvAPy?^&HvCmW8Gl#9kdIs`8jft|n0zRb$F$(1`@g@WISs?x$90?d_)OvkHpF?^KEGf>>aB1A1U+PJoysC8nWDPADw?4PRj6TKY6{in#*c z$X0?-F~e@jm=C>IDn#z*$(M-wfTHYeoC0kPurnTy0om+J%Mp1aWS+m`8;=~b*}#wQ zko_6U*cqd30%0YxRcQ~e3vzel$-+e3=-@7AKuZJ*ge<## zj`vYg)0rG2asjj~q__K#NEFEa6hIfH!h=k1ncar%?~`x=p&P7kiPRk|*HI_Qvddaw z*HkJvpT!yQ$kdDv<6{4ibqOI~wHm z_T13Qy|X7%1zH%+8c2noX392<96*SkqcIea?-tzLHu(^ zCS|F@<@2yAKc8?RJ=u2oF$aE5fYhKNkj#e)iLxOh4{O^!)d^l9lJ_@wvJ>x2P;kI5 zlo`p^g80gIbp~}XP&nh%VNlrFA#JeG9@sf&ApH*}PL3o>fcOQ|?Ufie#tslU2e-Fqiu z?W0dOVzFI9kYM=|z6*(nnp^Rbu|69K+Qx^^Hq5U2vm{B|+C4Ln6$c=?XfiOkr$vSE zJIDLTtUV~HxF>n*ccj4Zi$!rwJl%EKaM`Ji66u%79ac)#cm2#rsnfvpW@HlIY&Rn$ z9iye? zO?Rga8V-U6tBF6cUZ3O?{B2y0cW^KibD@WOkWGs4hO35L@Pfyx*9`}M(T=eyRFI^a# zGr;h%ZcnqjjtVpgnD~hj44jZx{ot-}`Lv@LT3LvrBr@>;s@Y-d$&20`9119~StRN~ ztc%^D3)cnl?U9JW!_#~uZ|7qba1j~u;M1*Y#^nMruM${Ad*uMz#lf*Qnn+44v3=R4 zK<%SGNu+oa;;~D%Obni=1n)||9O7m^l#6nJg=E&&;_MQZXtTOmm6F3E;^&b>lIGJ~ zw4)a1XDo7$RtR$b#4sR50~jh?ADt{Lh7j3AYHm#bX|7UK-9^st^P$`r9)L)l6X-?m zU5vgWryvXadxN5F9?Hk%skd2_KWpP=eW9hEbMh~>`8xRX(6^b~WUh7IDw0)@nqYc< z?W$t&5c+T`Hz>m{hVc~>&{8ZHiuxvip1#_}#MFZdvE(C6JIvM9s!lL}T?G7!SBL5x z6_1xUcw*?_{p0$ZGu@?IZv*H})Uo4~`*ObsIz)t7bba~qaPr_M*lZKm8iSaK@UfMW zZ8dBQcpP((-66TV+b2F%qp+&G>)rC@O=Qwqb66Lotd!n0#t_cRE(~l$m2fj*8^voP z|8!K9g?#UbXr>_2xK+y8WypXBN>&ySKUkC@7mL&@Wd|V(YNC&QrQQ{7fcg;=fXz`|~`$%kmy~>A-oqAb}jsC3~R7?QZ zH2wk>$WAQ$svw(*SvOYCW5tYgr3Z|TBS&5j)>5u@F>lu;)GU%0dvn1O;#5L}7+6c5 z;S7}GEO~1}Iga7s^*}0Fz&?EnC)roc+~hjU3RT*b4z4;aopOM!>)k5z3ZEshW%#n` z7Y;q}As+%Pw`&ubVq{u7et~ zavqVs@59nt;j+dK#Dx@RBM`|MDyN@F+Dj3>&IABCRQt>lAjY~HfM^O zr~rz!S)}kWEKFb~tr5tt4ERwAT9@?2Pw3!Nikc|+BF{cks>Vnq+ z$%&7eF^T0Gzg9jCXTK;&sBFJ@jJR9hd#+};J02Vdtrjm`Vg2FkjXWMx9~(0%wVzW1 zw^QJ<6hvHkxNyQfHkkQ`nwo;<+as1n`@6^{WwszRt)jJ)NopnF(U!@Jd%9HNByShP z3h;oeNI9#1)azIV8j!7KT>hF5hK4F2JW&g(T;fsj{38IuQDiEnuMRd;n7Sl=vN9G7 zQMhe9j730xnEEqf{gbD&FWYzyHVnSoF-_v+(-NphFxOy*r^@}*lM|OtDkR%1ibI{H zHniI-yeGS;n{QgO8Q=pHCTV4OracT+RCdw1tn%7mR3IMM08~G201b}NN&;}wRr`49 zDr^jy(<8Juy4I+MwiXPOIvO44#)*sosg{wfra_u&zG_qaNmxWb0B4st$D?nhPEjky zwLwEyhaIkVC&sx8fST`MbbKn;1HwfYjGg=|TFz+}YP;&bl&Y|Pu&R!*4rsyv|1c9+ z>!|pi4XF4;x8hg>E) z6=1HQs%*2uAZ)xpeTP<(k$s_xQ(wkF=5g*(_* z?-I?h+*Lm?o}Lt$rh^}-5R5js!0yS!9K?Z-^Bo{!HAII?EJscv-$Wwq7_+k<2ebsE z_7up}@M7v(CDnxPw57kWudp!mYO&zZVUiZKDM*!dS96CMC5V6E9oON%%)x~jYEFEbShGEn09%i8?io)!--O6M^ zvzONZ;3wvdBe%dlT#@JA8Zo^^Y-<7l2x%e9WeEx_yQ8(!*xIu_1VFTll{Cr+x`xLF zCMCnj@DHFf9v(hBCmP3 zetogd5)fs+Umg(U;I33L9Rv$i$w@LteINekg*FHScIu^e0BesQgl{2#K;Ly3Eh+nU zfmHTw&;rs&6H(AoiT<@uw<25EYkZm7UOnI7%zoBXce=#n^AxCE#wlUaEInw(#?W1`FQkQNEAKW@1mbg4X8*TNB&2-W(C$2MF&gYU!)x1j}E9{IDdiw5;V0B76k zl(M`!E|Fb_6qA_^M8dHNyW)3lfIAZr6E-^-TJMOAmdS=7zf>H(P4#9BwkSb~UkExCbnWyiynu$9 zAHP~ACEvAB;jdW}Ui`n7-tYbb|4$y8oObk%DhL3;IvN0g_tgH39&tr;M`~xkl%(cRcVpQ1k0$=(QyzD=3X-vCNZ0E)kg13bu#}MXeh3a zZzK;hJH8gjd0|z5R*RZ^eng=J{)&IXLxD6cZE;GDlvY@|-wx zFxobkEU8!eW#Tq{m=|G%#D=>Z^Pq0grpDaZJXV)~aT^nD-ckBDb6n}^9>-QDN1#Uq*C8iCYJw|&F|6DlD-0CkQwIS0*bm&9{ zsF%CHX(I=lL93TgRG&u4`4lAOO_8XK^Vx|DLRki4reE3Z>j?0iD;9h-nfFeYoL0M? zlPf_>k)JNAacOT+p%Qo5tYm+iaTW{=1a(zpyZ#oVYNCwSk2onxx~8|hyGU}(ok(u% zok((A91{&3N=%Dv!6ge!1f)uG1fl5|Yh}js^=;;lY7^VE-Og$caaNJLi1rIGYh_KX ztGrA>met!IkgD3ZfTp9(D0$CSp3nYtsrwy1OhxhBh_vjWsI%;9{bWKz@mh=5*dMXC z83oh(YbJG?8I#c|??DZtli8`-_pluXDk(CVhru%4Df6E>=xR9Qsk*=#wTT3dt^s=2 z-RHU1f=auU(mq9j5S8|H*!t?@KywYwwE=if zV-3znn#OyWOyarZOp9Z$VHD&~vAL4T#b$kRhwoE^*}YYF;O{>NA2o@8YdW0{XAI0N z{w%YEcM`%XKcSb2QBT7wyeCGEG1>L_G7M_hS@xXj4O`nm#&FSv{gZ`JNNa7D&S1UJ z3$`%B_91UJ@=9+2`FsoSG<58?Qq{Vydc-@wHuAXfzv4goT$ex66WqB!emZ|8eG5D{ zcb8GDK>z-LJ@Dxo2|j%?s>+8t-XV9~?-L6|Krp5ez(D{y3i2S}lK?;p5+z`mK&}N= zo9cAn>EPP|u!mp|#vafeaNa}k)Gh{}4MXpT-V=E+_K^C3^99}wt)cn81AZg;f$)d* zi|i8+KB8ov6he(US5e1El9uqzyf=0M- zAQoXP{0o}SAaFzUj&K#(lrGvK(Zel7=-% zs(}_W{mK=)cag1jX{mnY=7o~VQ+JhI^H*!(6O9gCPF;3AzOqTynU{LdKX%sRM~e>X zGZB4lPupD7Y1$2cII~3Jj_YLqsiBFj>Hiiq2MsLU z|6Z;h&;WoSzrX+h|7-i7ikgILNZCUH0H8DdpONa#ObuOJolNzO?VVlz|A5IIUCrd< zQAfW${Xz8pSS|(4w`>YLxUkC7Af1W$C_>gUa*OVIZ zU|rZhgw#({&3<1u==%Pjr`7g){*Uid;qLvOf7kN!@bq_oKc?vUe4obb``@3;_k7+? zyVdLczAm!a_xxVE`TIT&lh4}k_<8z%4ld^A{6CJn9}lnm?c@7+er_&?pTf`F?fE@_ z#`gRlZq)Nz#r6L_KE27)?|rYSe`DT>ll%VMjtQSlNap`MzyJPxDEIkzecw+_-LBHWTcejPrFmrw6&?mv(2zb#^)Y}b8; z4d-v=;p^r3>j3xn!;at2HYzqiMq>s}IkPUPY5X*fOvz8oPj7dASbALen;k}+Tzv|hU5>s!9!E{b z=k)yXald2q^!U5I{=19ZPX7+ihL5iD;O%ufaQ3<$`}uz6w#sXzlZ&+@CJ$gGPVUjl ze0+`;6-fF#z79@y_I?>tKF8YqKRzB#>h=F!qjR_(KmYIVbN86P4TTHe)l)fs-X6N} z^XbXO#kgnv-p|R!mDP8i-fZr#t*6i1(_r$<)tWHr$Hx6zYI?tq=XasjT+#XN&v(1K zeKeKt-;?h2Yt8!Ki-(iW&4}3cXbYc@@5kR^e7-FX>02fL)#^RJ--~Q{{lHfEQx;yC zZ+(x?nPYsI->G_8Wu|A0X(yAcm=kkPzAcu!w)grme?z|E%Q^b|q-E)y06Chss zfy;WG(a#vlgX|PO53bfL&e#{9HIcMTXq)EyY7oLt+G*Ti)FCU<^TEIj`ZCa&s_jG% zUs?gppE0iySN4Y@09g}cUNFg`08@!xJRu<*@{L8HWadKiAl>6~o!psvx%;Rb3LUQ$ z`<`SC+ghO}N^^%{$6dM;ws3l(R2i~?xpyCNB3qx(v60BXO4*8Z&5nIT(3nJhc1)9v z^!XAHLV{(T2t3_%K{^hu5m!OS3dAN05IahC`M9c(cAz!b6(Q?1F$~7ps9CfEo&OG-QiUuduPPGaA*rpqa@4V2{=$}11bxwk#qFYdc~I-gKDA) zU{L~<+G})ZwDZt-e}$r0ZL1<$jZ6W7`4j(zg*K3h?n7|}{y{+uri|dpQfnNlq0kRx zhnka%c$Yrms{#3-3(zCWR}<_jRxDN$`V1}`VcSYT;1`yMR$19rRf`p1tn+{pWY5p!ol`o@AECsiwN}|(1nSY#4G#UM5 zGnPTazC$gEv0Nznm)@mZ>rU+=?{dC?x;OP1>OfaxryMwo!)gn8-Zvo*%H`=Jte6wB zd#u41c)aAT1{`>xf-?TtE~t_1JzVqkX89B z1`)VyJUw0x6;;C9ON?w}`w*bR$&XwmS7|b=0UQ!k)h6W`TNVGLj!jB%(|qrU_KP@` z;*R!PS4pl<3FPb@tQQw5VzUGagcv18p@5k*;|*$vHBSyo>F>BI@eiPr zF<2i`-VHIwi*7|k--w{VBnT;sBw(^e*$_%@haxH#U0;eqIUbHGAfBdYsT8qeWY1dJ zOP_~c_Zf8NneL*_7x~Y08ZZBc5ebF_(J}gnIR`+=UuwEzafV1zXj30V7cjiJ$_^V} z`l79ll(bZE$eq`#m%q8xO*itcUSK~?=MwfSVs3e^CXTyIQ4v5K_+)cnWQ?YoXKT8A zXGArD?~v2mE%QX-*IXXqtH_R9LR;zwFYA7ky5dIGrP)LgCGkYk|Az+5lK8U{t(Ya_ zjcW8RJjk-i>pQs+M0q8VGU|@Y-x)2%Dl9Sfkc`IB`^Q}ncECb$fPU=L*@i&(3Mbd@ zp2i;68<32Oc*M!7;Cxxg*d3HLx;>SP;7+(=)@qg5uu>OF%R z*tMBz9=g}nJpK~^kJ6iJ8W){g#j&Ye=wqP{1s%Y&J?wYN6HRUqxKfA0&F+I|INuzm zBg$^~9}wmqz>;*s{Z&<)*FloObgQ1}60996!yo$qd4na9my#y{p`5^9Fog(2sDa8K zf21r*-PGW+Xvv8;m&#zpu*OJL4G@HSdsE^m^WEiR!|{dgr5T-XzV7y`3-~ZgEK1m1 zXZZxvvF30>$pm0tV9SJw-mBb5H7;0wL&e1~q%L-cIyN<0Y0bjrbIr z&II%ZGbch=+KAe}R+>*aXx966<0)jYY6$o85{|q<*~p+^5DkN@qVaE)Y#bF(zY>VM zqi#ElI=1n=qd;v)d&*!dYdgaBgm{x!SZiRZw~b(hm0FJkg(XFX7XLCB zCp(dfhIkFWip$ILv4!l)<5X-rvt+>O-h4&ytKD&Ie>+bd$DVnvqg|e6u|xDF+dSsL z*w8!7kqVh|Ut)a?McDf9uc(sVh7kEH69tSH&e`AegoA#Q0;_bDM0TuobcW2NPdJV= zEFAMshEsd!#p_xwR87a`7cW%b1;pSAt9r4EFF>ro+PB6J*RUXSDH?4?jhwa)(CgJE ze4=U_mG+Y0E?}ZI+5@ZzTGM2u(FVT{){&*~WJhPDxoJ%jRydMWmTL`c-(~K=GC(~5$Y{!}g%|eL)I)~64RH`4FPoe297*UI6X#ev zq?=8q0}UBf5jLHGqHMm3>k1ii6m)^Lbyl&}TF1>40p|gz?pJ>-v?S~TJBCE!CkRG^ zI{;jiu+0vuQG(WZ2D`%x*Nb;{uUch`kJkZoZ-C&^ApDWJ| z(MPQj2W5oIs`SSxmFdR9%6On{hE-@Yt{?|LA_f8}qE$&$v)e7h;pdHOP?++aX#P@H zgz6Bhngta}T8rw_@UR}FzANBrCp%LG;R3JdXm0soc^E0M`X?Y9E7p{rff9iWy=$Af z#i2eYZg-=oTb4R%u9|kENuW?|rR-BS7y;1&8Ed9-00aYGuL%XYU-O^a$# zZ31O^?JySj8amv&86c9A15!W!HWT|7DgaE5lT?9B$Jx2jmaC)$sKyd%%t#QH&?SKC z>=_nC83h})lGUJID?+=;HVI_c+uliS|2~~oM_K;hodG`rQ`q3+6*y2UlQfutbF`X9 z{nED2qv@}j#u0##c3#VGNI+}}7Y>d0iN(IKWPGwsj95;~BD;~Pd4S7#3-Xy=kzvrl zQN+;*+2&A#m30_060HT+S6RQ!>_Icsj&}u4w_Q3>_OCwBtOa0+Y&;P2 zgDYynR0Z$_S2m_sNn5uGy1pKlX@x^x6-QX&7(w->IF**?Z+aNRy0R-}tzGX6@iL$- z`HEhdl16)EZ3~Xb^oTblDb%v4wI%G9xYhnAD)IV5)wyzTa+$D^-J^qrJ

Xt4ynyfHpivbSc9 zq#k!^*Shu3&mhEQsbZsb6MvI{r~(S7Z@cSUKTJwKy|GcEp?j3Dsrl{oq$p*0dGTSR ztV?UYK2`0b+TvFa9-J(pNQ;(NAEi-pH5TnnTfbt{YW+LIf5&fqQrt2{r7Qli`eD<_ zvFp#aufo2h^WC~($+Fo0X^5ezD%smOT#>Z;`mi`5mM;J3hEW@9@h3d7?QQA1z23F# zPH4(itGv`Ai$Kbm@@6cAYYf2ZyvkC=_f0h=n*jWs>BgVR8(->MDXX=nwj&w;8IT|T zLVVCa#Bcl;v8NDoTikMmYoN;)Qr7S^*N#;bQ^1u{D}75as%fFKPK8#=e)NuM6y6ek zw6dWn(T)1XoH#HA?IoEd7G@k(&B1yP{o1vgg=cwC?Q-b?+p$>6G=E+dS4s98SxGi3 z!J*pd3caTK{7NjHuee|N9{X8t#JYwXv7d0S@;%muys45$q|YX1HTIFlkqfmS(o69n zs}LQvpYqCt=lnwOS66y|@m7Q<-M{>({g)ec9$;hgJ@(I9rCBTM=ffuXZNz6A(%GR6 z7i2*a^a}}pqr2IT8-?B37EXCL)JHYL)UC)jU+GguCsS-M&kORk(3L>r;G~O^3>c4O z*iF30MqlPtttn5@JjW403^9OMNGIO1xeO)%vA|3Tz;6c7h$|EVvpD0wwGKYu$P1!C z-XuT|x%4G-9AHm)BMjgp20#+<2FEb~-Xuo=ASq?!rCaw^-lo1HNDdUfBS+U5yB3knO zqVP4cI{*pItH6^HW5CPr-+ST?`e?$PbiodK;R++nUX+RIHV%2P!3G9ibC(C;rG&Thw0zwAE(??d+a=`{{6gc5nGwUCqW=ZRG=5; z&M63V*#b4wh$64x7w|4A2#dfA#1|BVPVlkz^4^q$Yor4BdBq_Sc_>Ec`9dZV0eOPr zkch_szQJ)xB7Z!m)!Dul5C&5OL6pUcc6`o(0505JQxG)q3h37rg*q(36r)W@c>2?k zhEB#2$oR<&AA3^}{5Zqzrx`<#7vFnF3t@$gk>Fm&!U}s8`N2NJ8CUG3EOG6^^0>m~RbCfxzwWKHc#c)e~ zVlF8z#XE{i@{egt1w5tyXA8i&CN3!ma-a^2z~YkryARq=zb~-=>Lt%4YE#(&1pw%R z1ppBJZz-aojk&#(rHh5F{(m%)&d|xs?Elu0W2+`;pUn=_d#?W8l&v07BBIo8ro9Mi zp-dnF(+Ma_H6S$$8`N`|H-f z%fq{;+vojtcy{$V|K`q~U&pVf)6>_}+uQkhyL_9y`sd-J!SlPRcl)RHGa`K5UcTRV z%WoggEqwRa(e>x@Cp)`(U%r2L=WnzB7H%Kk_er|{^1R=}^HLpm4^RE?uD^CYTzB~G z?`eY`uMPg^`SGtq`^WE(-<#>vhhMjzujdwzzi$0&c>Q+yUxYXO^!9Xg853)-)U_75 zdWuel=0xa7=gI4zc4%sq7U2#-osb9fFOa8^U4S{ zBb)_zBkXaOUnNCzXSoxtD(-m?A`>eZC0Y)kP{&m*>%0u6xyzKSZema2rYjXsF=mF? zQ>C5gbdfV9s#s+TDbZXL2cec8>&(!CM3sh|$?794LXc4wYreXwK{-YgfW-C%(s`@Z z!a5XES_22qw@64s2lYym3+OHiyMh68NpXQ44Y>oJ_Ac|2QX%NbNnjFkCYj@VF&gyc znO*==&AKOthIPhR4M9g00TPxhDWu#@I2q!!8nvO(<6(26;)R_G(UF4cvZ*MOD;k7T z#Ns!T$*7bhX(I_UtLBiQ&ew!eqJvZ4$1EV?*!8J}R@yt}jK!8%)j9i-!h*kS|1KP|mIjlS_4a zS)?L)-F}BsAoG=&xPlGVx=`3rbGf#&6Hpi{=^qnF;ZsiL*r?$yPHHPKUFV*d7-NmT9-1` zma)L)0X@?#>^s;Aa?KZ;{i5%LMMx~?Kt}~Wr(#TBI97z?OBQM)-kA^syMQDp=Dt=Jl8SrvHPrZw$^P2)m6o8+&8hww=7OZTro}Ha5vdZ*1GPZCkss zjg4=xxnG=d zvr{ZpG9eW%Hu-y+(JEY82BAU~EW-<(e4#3hEH=dXupOcUW2d{E@quo1%loOAktLK# zlm*lpMlwL)qRJ9)xKjTVQqcl#$2a*_^E- z|7;QqoKCD_bJ&S38HR13<#;MDY%=3QWw?;eX@l5La3_VLT=!eG{Z*ez)ksr}HRAP3 z82_6QnDu_r#`@OAMkt3}*Jjhj;dVO~s@e+mvKRn#hORh{h;AiX91cE2Q9upRhL^C+ zjyA4DE%g4(6u3pxFk`V4K_LtElvn+?BKr=BWVk$66N)MR7oQ1BCT(8QNIQ;oE#<@f zFkiHoI_!JuxbUZR(=NwUHeNYQ3s3AIRYUoW*6id z&(t~izm+3lcUknz(KxVX9aIIb3^i^({r@{LsyxwG@qZ*n{ck8rj!x#L*3MrXPD6WZ zQzxLIy{nyzwW*!4voqs=*47r*rpEul@icUHGv)g~e^S|Pq>Kmq1v?BUnS&kiQC4X+WWZSEKB3Po2qFFpD2>F_W;=m{a5dc7oDgickO!!)J?H2&%HAc;@UAzU;PO~{` z%Gkt+-QSO~C>PY4d~)4=*%e%gs1<&Fa^-#COar;s)Wlw(YkJXHi|f7KU=-_ALtsgl zeL|`CDebny!VxpmOcxUYGw)P6D44O&aw*Wc{A z$L^e8N@fSVtx4ZY?&Xg>FQ*G%)|lk&pDO>Nclvs-KO81rv``no7Rk=`xSUb7!|B(5 zyrP{Qq766*N1s(Dvuv5f*KqCo);nxGHc2<^?4}g9^qH#Yd#BalhR>I%pL$`~?_llt zT>^Ewhb$Fb2+5|btm&ndU)OAstz-$4_qS%&4X~z`A2!P63d-AZtP{*`%HNJHkH<}3 zyC30p9ev`dW-3eU(>migPON<*R?b>WF1T*H0EUX2Ca#<%C7xVc`T)XpU9rmju6-?#`P{?qrDF=#r%Jv!@BQl7%3$Jeu|OF)n2V$NuvWB8pz19w#PjQw^(DcjS*3; zft``ux`$BvPA#&)9T{I?^C2tN zNDFj!EH%>Z{Tgn@8Qj$I5)_0mw= z8S^YGX`>1FRCv5Mb`G}Y%!2E?bn`}VKD(tB#-PyZ`{nQysI_w>5d=TU$J<5vh`8|r zp(djDsK5l)xwsop2^_jt_3y1}YIt6)#=ojko0V*Rz5rV$$6dVY_K+2OJ@2A0spoSE zx9P+6uR&%xuxu+|hrhYiGOW|@3ecb1Zb1%oA7(XuHG;nylD}uoWG04eMQ_P{S|7Fx z`)2*~pe<%N(BEBnLqCgq6)?kgP}S?(vtgOSYz5e34sodY1no(YJp@J9tlhY!ZQ zdU)Y-hFv{;yjF~FsU^clN`eWB{l2Z6c5kW3E-Q{XwT06PPHYWqncB`cKBWzJt4`zA zf>U`SET=Uv;+1A6ydutXYE!_&t!~nejwkxKO(YZON@i=3%p5;6=z1u+IQVvaKYzcl zO^;-o@_nL)=M~4FF#No00h;S|lAAZmt+3sucbNMH5r2Trd>i!+zWd1P<)7S@t3bn_4aI2=g?MQ5es;FHpg9 z5bhM7EwVtxsFBxAVqxYwTWz2HJZ}l1M7==@GZZGDI}YKiz+%BZnj^O zaKjiq0>1VX1mmt0J(L+?`NfBAl|nCpPu z`1ruY8e{dM0K_wPVKw|vLo@Ad&CiBJj~pyf(oYSP(vZYkJ_ro>ri~9nIXs2T08fbU zC>@N5P4AXKsWt*uYa0~sRlw?OgI8&o=XZv&am9$ezpCuaEQ=Si-?GgX*Gt?5s-3D0bSW5Y7#i)p z^w=1qIFtVoUL#F{YpBl2NXyXh$XHZ0L*Qf*L$5sj4ag(gl3Hdb>~)s+F}Z%5t%uRx zP4^J4Mel;cb6?t?SyQQPceCd~+FSMf9$!MJKepAL2f zy>d9E*x-|%W6t=gtTG>=Au>mcT8#;pFMxSsVGL~Yn9^`Ng-(-2?Nd>PZwHa^V@?E?b6#hXaG8Lr zCN&)=d*Qn^R?m_)eR947OXmSFVbb}o=p_$`?eZosErL?_^GNG~D)BKMQ^`GS)O+-u zfm?IXMLE$(re4`fs1NP}0wULMBU+Gj;Tr6t*0jHR%bFe-ZWUreCR8xSPu??2iZ7vj z*n!Z-A7IJOFaDAZw$=dL0;A?JO`?XJOhOHTJO#9d!XHzqqza9u5>a;Z!azf-*V!{n zNaz~atDrvQA9SYuI~CzBKmJ1fd;Lp*3;jnQeVfkrT#oNA$KpIp@hH~G+_vx0;)&e1 zkV+g@(?U{rpZn7eyQn-qFijoZHOW8c;@qOYPMH`O;-QO@ob^m5XJ28}CrI?Kwup3k zC>6wn8tEya=lz59)f$B!8BY(e|0NLPHHNg!mRj)#P}5N7Gl#%ag6Sb37+Gjp=jR86 zSW6_!DLuhnaq&?j&c_NmZwqN<{R-y7>yy7|fPY?gG{9iNY55 zs%bUHb<`m)NmE6YIS+hmDkMu?)mtxp`MtlZcq?v)%jTs;dSv7#9 z5qFvSdq(1g*a5!#TQmCso3(&sMJnEuHHRufwN6A-{DuWGGMjk7Jx2uJ!TboBdv!d# zlOkN={;&RoSXKO{pSgMKvi6o0JbdK)ty?#;bQR*Y7MZA`}fkPbC~ zO*-%on0lC{d5$q92K|K21=X+|#F9;E4EIlLC%nvEBHYFU;#T&}b^$S^aAPtX8jO^F zs{&zfPKHXfUYcD|a|9lGwt6mBwXj9RtVmT zn$dSULF3}riDQLQt7=$z5-Ko(BPrJ?DCMM~Fs=#lq5Wl<$(njcz}sp7*Fc=c(M?)eKal0So8s?9+` z?`P5{$!qqVMm1B>&Slt^#D%#4;fkLYL(g5SHK+*Xx?o#?1jV4Y+s!VTNZol@arqG| zAAAnlqNI)PlF|3$He_wez;TF`FuVf&Z0mjdC8*Ms7VWDg^yR{?rt8j3zg6GAx!-}> zGCD1UJT64mZp`c@H4%7ToEZb>o~o0WH8ez8KKA7{@YNI%kfG~XhOxA0O~bS;h88$2 zdSeTboK%^3LgZx|Kafnze+u{N(Lq^clEbv`{;|Oi{By4lxTQ<~7+Gkl3BtT*VC|FN zbA79+N0A(TImNGQ{iKVgX;9Ou+Qt1K+XB&xm1lJ z)dB)|oz&J0LA&dH&`xiMtajIHL{OnJO8kGel>%NGcc;2SOvNcKT^Ec5-$Cl>Q05fh z@<2cL2>-oJ?y({p9u)&0p<}x%`zbe`9GQ1o^+fK`wRfK$C9^Qqb#A~|;Wp*XGtMSE3_;mb%= zdu)VPfq;m(#R?zfCzb{FeD1LkG(%Nl?~?7OteJFYA^lt4I3XLs$AjzA2)~f(X*9<< zIcGU77V#Dt1;Gr5C`?J-bHEed{wRtH*55SdVHQ*3?S|ILE;OH+_$_jHxiOAT=;R5* znCWP|w`NqMCFk~aoCk8S|E^GPdG|5bnjueAN>EKe1Q(P4lK0(3zktD)WZjA>K>YES zV@(OwAmKk6s8T4yfdQc{M@!<|Y%qJmy`n?z@)%xF#`rK=y(t{v9V?I#a(Rc6EKp>d zuOI*HRbi^&;3*O%x=+%GZn$SBoCvHUD5aQ^!CUU5bZCl0dtQZ$gn+j;of79kk)D>tvg%to! zf~bdn9S2F4ld6e!-5AxQHk{?8(*ey(4-6>Mw#)(a(b138do+E*2v7YEyx!D%* zkG8E}!P7%G3Mk_UOvNt#K>nXsjD_>?q@(P>!0<4^!8HFrO|Ji|9*LW=ovZo(eGU1d zOSgf1O}*-Qy5>*O4(Wi;R*DdTi*TbcB5x}Bu~cP%T{}VhnP}r|0D|!~fqz z&d1l1@Y7w+$H30#e$D4)NzcCoq34FL9w|bf6`vpOJ#QyDe(zIHLOu^uLcSm2HJ{Il z{&`sbk3BgveoxOkf}ht9$cFx}Q#l{!S09HZU%iBca^CL@eIMSRgx(Lsue#q`{QbQ@ zfIY8|L_IGfHU19|$e$lvhVMN+3+6dJUZ1WzoR%kvHYXZyQcS-%mSg(RWZshZE5JNM7M&zFfA!}sfqtL@D{ zclA9w-unlxBNF}}OUOB&2OC%2A0;(@52i$~t(odCdns>>M9zA5{(kQRpYL3T{-0m4 z-zIkaAHJgSFT=0o$W!m0=;e6lS@iw00s3qt`v&;=M&@RI=g;TEuiNla_xZZU z?XwW~{t?&5h~)j-^o~~@*6U37`@_nP&qIZwU&rucg<)6Eed}(@NQZ&G7s4do+9UV7 zWV66@PqO{jldZqN`usTae|yvf_IPLcJ}sTyX8>awgg%CP{!P^oS$^)%eC$&7yp84T zt2mr=Z#BPmUwQ7gJ!M8B>Do9aPWA9C?(o`<`yM^b{&njzk@a=(-QPUQcCcU3y$9@d z+v}c~9&TVi)h+1_8%TEf$~^fV4;+qmowukQb+s@=&)cq)OszdN$Ew_Mx~_W932iLD z%5|@lKTRb*CGu3-FLOd){V{Nja&GRl-*lz(zB0Wkn(};AyV_V(?<(9}zwlDKv`?!iDb&0bv_}t8{S<&;xlYi0~xk$LAxAa|J2UUaCM|*mRq*?{c5Ip!X*D zX)bSFqq}Xn!5_~Lw7J<-t*YZ zqz6^^y$<#k=EI)3dN8Lc$}9dw*1Ah_@<6)2`; zD>zBLR6BJe+;J6kuL-Af&M4#XZPGj{{=U>BSi>QTGuW>>gaJ~UR% zrI57&Zcu9%^{krNfZ3@VQAIEwRDXgmKoW4&{GjHtZ$Z@Y}3Jh}*R z#z}~%$s30O*>~(%6;EeE8Q%WVQ~qaUv`_Zl!YCmX=+ftXt*hiAzJQdS^Xev=gM;H* z-}%w0`yl`n67gD|3;-p`liFA@Xb25(I?Dh|kGAqsp6ZkI6}PtKJl=ifTQRq8lI4kL z%QQiSF=pQkjn{rnru(su>2I|(@1AFEEJG}(#(?Mx(9are>*PXiXEQIoavJ-!*s6U& z9nGAU98_r49Z(QhXM>CDxa-m#I~DKe963CTZgH5_)VIuvQAZQuJ^n z#zkfSZT*hrLhm*pibbW)HoSnbZX@Tx%9lIi2hiDp!k?!eEiTz_+c3`xXbEVZ>A;nr z!F=u4-J?%_Zy7$!`HHc0>ItS_e0XQr=R^>6@6=;D!~V>o?*lD&<|wboM;LAuggt8A z1Na|x{*J)DeH8_+eKKZAub+}Ej zb*doSdVkaxUU>tMf<=z3q>N)=*CN` zykm|BxK+~mHeo4tbFog4Ex%Y#Ut|a`f3VW@$!&+Nz3uB0ud%eqFN>so?&jNw zdEDz4*#T0smj@CqkE0FnCINmQj>`=1mp!z&(rEHP;(p26*jEOQ z#u%^ex7#Cx9yA@QLx*M4It%H5OE`ot#;M;;Z&}>{AHNs=-lshd#puRdFU##T$zI!q zG(Vu3`$)DXo=|8xdy+|?keOwj9JSqY`qJ`nB8uH-8dXU-sL=Ikh&c1;ids4IQZ_JJ zfTD=9pNt@}=ilFE2jVkaMB;pCa7+}(H7-C7Eg+n0M#C9U5mB=+N~~1DYmX#3>l`x_ z{;hasw3YQf7$1gbDzhm%*P*xd8$UtjqGh8nC7j?oFfGKYg^+yA9Y+RmgIUvgfT1wA&J+ABMUxzM3Ww#hd@mFGWY+*^_$2+c1oc;@A(5PpC9k-ruX_!>?B@gtc?A zv3J8ix#Y;dU^QQy&Zc)Le{AY5yli-iy?PG`=DGjtW!tWgRFC zl9IwNC-Sn+ES%&l^S0(-sD`YuoTG4B{#)WEvaIgWi#74e8C16qmO7enf4TMg zKzV#9RnoEnuqPwz`<)Ct9>MB9HJNB(c>{q`gy8S*Q?dt~kJa`U9o_At~fa83$iw4XaC{Ev@*;QF|^$J2c6C59kAm(&^Ya1G7Qbn$^nb+2zeOWkv7=b!1 z?rQ`f+$x5tGB%ZRRTVu&M=Bo`Gr2=^*U^yqgJrM|sGd$rdOpr-j?nvV1~h@5Wq5ds z`vwNP5Cv^T(M>1#23t$4lUzW9VJxdcjDv6kwW8y04!VN=_RFx+$e@>kx6s7l!%MA2 zf75`*_4sV%sh`a6)0Bp?Z`Dh5S$P|?UMX=4izh#<2?qHUSbrk-3#}vKvhc$KI#Z0< zr6pF`u(3glx!wRoJFY=S&NwQ-2G?U{}>Bnlf}h$w4-c9>eXR|ZQvcG z0f-uKQA(nE!G`^MAl`u((9C{(!V$TBOWi2JSez>rA8}9W`7a*r)n?C@ZgJIVT{k2p%q&@e#F~07TsPKdfSfj8y9RYBM@JB;EM_I+w&X*cx2vVrM_A*?kK*C#MGEzx>2j;3nM^ndFj;OI@9;;? z7;;1#A3aJ|u=>E04JnTo9ZL;uL`FR5;?=9rKn9-9j*2>~B8VsC@}rCmA{Gu^=&7^g z%ea2%^VqRa`IHJd${Iex=_K6_;@hbP{23v)k zRFWzU9`Q-!5hFGXox!QOVR~)?)$tYrSMJf|VaXQ}S(yry6U>!ZZke8<6HCx_oV6HA zpgT*lfQ9<1B7v*~;_5Ebm}ip+8=P!Jk!&qRl|@h{>%O*E3fUH4xi}FWm)M5Z%o>Fj zYy-czE8ilGLn}i^m04+D$>+&?>AbAa&x}m#0U-lv_maX(##8KCiz*5t_BUm+t_lb0vS=2Wl`LVJh@3(##2ddKZ)o(Zyu|D_cqJ@% z+~3{WH~lcnlXV{_m@7_l^y<`f$~QtxU|A}4kboq?X-!%eqdAMU5EFIJO?>!;OLmmN zeou}tipJMP-I1q7p=HPYm-fzDd@{%%U1yQ>$vZ0eCbX%R(1F|`fAy|@B9r{x#Y3Px z^?kh;sfEXo-kdrwRR@6zeS=7cJ4yDM_JhPV;;`aNun)ALzkyO>7YCR89L~YxPp&Pu z{NpHjEzwqCwM0TkhIgsM9tAs&WeH$si#v=b7oFv8y^(#EGwZ9IaNzdz`VAIwusm^P@41%;7nqd)K*rAMn-zm4D z(-RiSY(_SPs z+^R^*WQHS`PsS;O|9%;vv=JkV&4^psA{b}4LQ$JmrU8rdDBW(}iMl4HV7iXpFEr#E zBP`o((5JA3{g+lI!sZ=Q4E&!s0xDwlCK!OViiGA|+;6TF8JdI41GpaVE)aR(RyW8^?LhPqO_smV}(2r$B35t&H zGEL&`EBUe5j|AhHb(+2pV(_}J+?s`h?b~Yv8e)?5WTH?#lXS#yq1H)gGl<6&`E|nH z@wjvadd)J+hH<-3J?ON7W?xbT1TLBxU6rK!>VJRz9Kfl{(*d|@IP`waxsx9lt(Y2s zepLZA2fEMM0b*(jQKd+jE-8iZFk*aR7cZQ1E*Tp+iO0alE&iza08-}s= z-*EC2MK|184-P#-dyy`HsZCypZ5#}CA{VbWm#8qZFmC1#o}X7#5h~uao@g!Qt3uy4 zXOO7N4!J1P*xy*n&=MmCO$+1T>XF1|kQ2Q8!;)A=1CHXPuManPjUBa>Omk4X7TwA{ zXxF)Qbn7Mgn(6&Vnd55aLl(ipr4ww?zE?-bQUrYo95&XuQv4}OMM2RRt=F21rKhCS zp)7Q3<7c}HF3Xs=;|vnUKQ+XAK8gz$yA?+pGR9;=d&Y74lB(UQ>v1Yr=*;!v(r1L7%~B1X3lH+eIXpVwA9 zGImqj6}JQ(27;=|WSab{G3`0qUDcm^d470r{ozu4P6Qfk4iFMKh~%+ zP!5}(AhWxXP^2RdMPzWXS+Y|vk3x60@&}7+Ms`4zRQ6wV%2+>=D|74PCmAWLv4G-) zDU;yOo-99Rf%||TioYB8PrxY3MM%(SX_9AukV`({k|1wrrWc7t<%cWGbSVtAbgt{1 zMeB@t8}twiJXla*ni$=A5R{N2w+f2nE7(G7qtykcgVpRB9dhLB!1J{dxq0oYGNl+% zBF~HJIc4lAXo0x98q+IbFNO~TE(Veza-1;FsfbWedt}1wCLUHnZ2%%5gdm z3^}a6)S~?uoqNwc>6IkTrDIgttw!PiwMWrhK2sy>5lFH%{>c#-a9+V;m80vi01AN? zWl2*EurSo13?!^PMl%fG_UwyNWJ{)vjM*@~7+*foSyuT*mSz+XZYxbFF^>+RG-B~4 zOY{5a3|ZR!0qX$7zq{4|7(=0RFM=QWZiP55mMy<07|$?~y^CCnZZ19YcpOKldHxg$ zlL3+V(Y;JFctgdpElpOu&iC1EU&j%#1?f6)z^a$kpj4|cI6Ap&hf~L0&H^~>NHb%2 zX-gbNS<@^%q<-twXZx>quKyd6i#zl}I{Ha^1#`jEs5d%+F`s<9!Q$6zfGY2d@_s;B zeOuO-%CW^v2VC)P!1tLQ;BHH!Kro18{^rr#g=T$Tb3opZfsD9j_MG4Z+(vNLqCxD9 zvel(3C*-9D!_z+S3bjia@pe=CyV`cMBQNGHl?VAT)uLQD+BCW*mV9xmPJRM?qF_R_ z-hi$b(bW}Uw}e|phB6B&Y(PbaI{`KYp*B>Xky!$MwRR_nvmx?EHs5{p6a?&@B7V>V z5+QlP%_?7NMCV{ZeIPTo(3ICIvJSGe$6Z^Lpnqozh@FxP8{`{8I zPe7Z2h=(886UA7f1FPnxVvinK^G4fT;;3Uq$38;ow_EklS-|-np8C8M~bn`2jRZ#RS&vtay#Q>01#)e@MCYeIF^Y<&?EBqNw7a(P6?T+g9r z8Zp!c#+6Hw-S^ur6Pk|;k3;K<4v?Bc3T2eB*YeIkhF$x_7mq$!Pn*EY%Kq$PuO<9v zApAX7oxdIc#u4|O6NhMC4_>%fh{d^=2}&QPdLmcUEkKIR(NrZw>ANGFypeBora1$C zdx4!A37~VqG45?J-4pt}y5L6GfF7o`4qg_1H`zTkLm}9Qry~VhPHlABq9$yjp1&YwZvc3*ieyskbV&(j% z#uSRtayIv)kpx_?uaARROi|)p*!~}&r`Mjj(rPiUNIwfjcMfo?Z#+8rp%dbqBpEz6vp+Bn>s_a(-3v_DdEEtSp^5IWxsKT}}G;BiK41 zmmJpjnp5KxZm~PZ;XY~0v(tY#FEZGIq?#6VFAdsA4)XmN1ac5{^Evw#2S6BGQ&V$0Sj(Z)1 zE7SKUaC5S$Ao^b@)qO*flsibRp>`3=umUNeY~Rx&p#Z47GDksq&9bccKkc45&?!;_ z{)}bJipHt3!O{`&{1fL?buM7~rG{S?5MfC~0lp?3hr9HxzKGeYkx4Yz4Z}j}O*<8R z`rJn|R-egph#KKLj9Uo=Iq2|fp2^{c?v-91+O^~yF*~rjWMv5V8N(K${_yj~D=1Hg z$0%0hy|uk2fN#xEw}g>?!F^nDaSt8syY-=*$!hzz4X$9h($%4TMLv~J*T$5wEo3dc zu*q#bL3d|d9&{@SY=Y#tY;{q5{vd|Dvmf&T8xb2v-ciL)?R2Ms>BtUJPo}fdaCc_UKNC-`i-f)E!IT!{av`KWtIsjAE~}+*Qq^W9=B`rpcozBeyB8 zKG5cla9j!GQmH9`W|xF}ei6d6UOF6ddx)BNPZ$C5?%uT%+5emboVp)mkObYxGw5O( zb#Dz=MIy-p_&35FtekP+Sx`6zMYE@1c9L0rko_QQx!R^aUVqqbMmCE%SCo2noTuxB zFZ*fj9)vBSih9E@Ct6a&Pug8W(CCCH{DUO`ig(T}7W`6$2b{A6Ar67zxhVGPo4!F{ z#IZ#0kCNb6lDA1QlAuRRvlG<*6tLeN9MVGfa{2bz$8=ua^a=>s5fVt{=h-*vvMR8 zUFiNFOw2*Ic9U;sBEnSU<|KzIF#Pl^zGu&u;#y8af7!LtMA0&A04B+_4q*172>F7# zSdv+cH%y(^%?!V0z!Y(%@q=e5i%8K{hXK4pOc(S03&~()4GZ~k=N_fKelk&LX2Y_p zRjP_o9^mR@&LH*2TUN99)$A5I=a}+{xWr z&wAiyX?k(cX4(nk2kvMd5P4*X(%_Xe1;DIM-t(UArTGoxKh({W($L;TA3~F;kC4=I z?fQfa=`N;zeWmNEj9hfRDh}!!1p>|WZb{KBDJiyfHn0$d$7;vAc)?Jlr-~9WTqiEm zp63%g*P4J1Nic1<`jrh#TjTF;O?V$BJRvy77qc%TPp1?2X<;PF7C!~rF|DuEP5>l- z@PrD`W^nBV7Cf8iNWMutRV zGc~^}Nm58t*?*%q^H;5$I47L8$~Op68OKy{fUty)Stksm=isV9m^9~HksZ~&mkMlN zR5SHkpblV#Z{=uiccW%34-uu@OMyw_X{4smhp{_YTpuwKNasyB`&Q=1(|?n}UDyu4 zM`FVY#TBgzlQ($0QM|akrttaQ8}xMXbIEw&^5+i5HvR*hcxRLN*MU?9bAE6-S6X+^FxYcEbN>Q(=%sk-$}iy2t~ zs3M#^qq)KlZ^UP_d1C-=#AS zQT>x16jF?ntpTBY=Dk*8hG9;|0A|I8Y(xTPg}JG!Aiw2GSLtyg$GS=;49TiZZMt+z z1FNefXdwaVo`I9)LmViYz^gWXf=>gv1gNc$68P1)D~Y^wb6>>zl5SGrcmN)85Ij$Q zihjI(Vm=SB6GeQPScKt$3TzBtqgAe{40Yo*HlAXHj(n~k;2^pK;Q1pkewuMIkPhDU zd-ZJ%b3oPQ=pcJVRLM2`l-J^g896a_+ApB#$mz>Q!6u~bDC}>Rwg>itsJ``2n6<;E20aHGod1=YvV%{O zmm`n{|ILU?wi=6m<~3v}NyH`GGOIhXfZ;}brb%b zQFm@M2?MWo;~J5EmRApK1(MhP09c9h~V78f#td6d{EKVoWdnef59TuNvWC;u8eIQ%T zBg}KIB$+nIW`oOt@j<{8bxt%1RFv3=Pd)s4MAwt9hTYJd+}`jG)-M0o((-fy+7a9e zn1?aas1z?fzAt5P<2rCs+ZbP7r4h_hTe185UH4UUlw9mo={#2;ogw=v8rM-Dk36ds z?38yzudo9*6@lk#8*k@TDg*c&3-yH9&wS0h;Sl{>ENpO`-Kpj)Gw<-dunw5Zc9Cp0 zWklr>RE!uLqIS?=T9ihkGOw?o`;lw1+1a7x_ZH%0*)-zz5TTZHaVgoKCGtYC?CQ@ZGR*Q}ujM*Qx z=*FxKg8qT)a%9)ru{JH0odid^|A9>>1`9qP0DXem!E`JGXA~}m^&c-<&P9YN?Ux#s zH3J_;3tKXOK#Q_tPN1=C`3%_eV0i^GQlLV#tM0Au1Gs121PU&>cWg#-U(WI-ur~T3 zRKKV5PRO>tsGzjTPB}%Mb&_TK5%>ONtoSz9Xv3rVFV-crF%u^@+4*<=Nx7CVmI9LP z@=KpWJvMWDqsX(}aj#YLaIWYln}td!EC~U_TRuC7Qx!mgW{nCa>?T8w-t4kVm`>YY zGyb9=?}&ZDx=n>ix}bEFu6Q4WYXoM=G%AK))9#T06#qz6{cg4`ESxq=h4RbXvaYIe zf{t6o?cMSm^O+i7I;s%S3-%coV4JaVQ&E3}weG0Cij4Q75W$W@53Rj+y&IioU=XBW z>}RN1qjA$0bQ!VDsuzzyF-?94h@;1mKh}?0S~R33x(}GRVFg&E6rrQx8Mj!e{l*T$ zA~`L-2WsmFdM&tvDW!|7iTDOoE=&#ioTI^dpj{bj372m58eR02oq}9^<0`Dv!Pl?{ z|0NAps#{Dhf%oBdW+2Bxk!=YV#Cz-l8*PNp8?vRZSMMVMc!uB4{b#PM({7 z?+QGTB;NV$prb|;>@9P|a154|M$TU5Y24oC3niE_FaUU6)!#?;S{>XDN!Ba06bCf^AhNsn zFu~0;NhNkM8XWD;ACv{bqbz$;rSz*h6VH)5B-x|@m^!IatCZCv?9eS(8Ud-mdhAZU z4g8Hpqcq3UM7wbSpApU|(hoVo!dd$b&8dpwh;|hCM*u-eq86N+^_T*!_OHv2lC&Ls z2rZITYs8Dg@7B6B!6^-hLD}QRx_UU}b=g7MKEY!+m`I7gGpdBC!GtDtfpkQd9G#|0 zVf3oaft(!9n!dGF8r;!NLTIifbb4Z<%-VQ>6r5C~CMTied?uO?sd6Ebe00DanjG;F z8Qw}Q)^$*1T!81{7*mRix1${jHh%=CVwS4`tTJRhlz0dpDs_# z`lWNpZycwqj7NFk<*Cy{uVuI7rC**|&d6N{w{i`vXxkEUXx!7av#xQFTsT5b+{Um- zyv*}AkQ%GFEGVVh!o>8GmRzn2%bE#T!qxU@$xh%H!SKhfS7*j z5KZJMLwjjjb5v3=E18gPPQNcyc=6VhW$$3;RiQvX=O5q1;RU) zu~-v{u?XjOx2_K#C#Z4@gapp~w}?3I>KoF}SErze7ST2cEA!1Gv_Uo1r!@zmjvu+{{?o06grWc`pU4^;Z0>eD6ZUU!8DXhhm?b9c5DhM`W7Q9)k~ z`1!bQBi_V&#RT4w+NhCRZm`i6ENUH&5ry}cXMV4n(u}navrF^NHauv{_(z~c_s~mk zHn>8agjDgJt`H9Sl3ryy)9B)!^hRr3siI>PM78xfy3_QGwI9k}--*}u@Xbr#^+`#J zA&isJEzMFW?pR{{v#W)^H-sAx&KVy2wDrHf-_h;_*nhAAnAl`!g-3YiZx_BEwTq!y z`5EXU_rg6tuv53LhrSf^KA_}eoDwTDFtmi@IFi-2F-F`wP9RIa&6TXGT~nQqUqQ9xt25Y0|KN;(1mE2D(0u&HNeFGWnY zAe--%5tq=_B?+hJ1=6R0Kh2*yA2 z+qTjDpT5I8IioY*lRen=>{_d8y{Z)urhi`=|Pa zBWDb|c4H$uwB)X0j>a>K?d`xE21K)L?sH_bGXsdw%Bk1l$SXC+0zc9*EZ?Hhu+?EI z=uAT^aG{AX!`tto(VN1^PW)T&X?`bP4%??Lz&Z{UurX@XFA;z9YL0Z}oO z#iJ^^$veDIgluxJ2zeG|$mG;cYMgL3%thlP#$fJVV&H;u6pCJRADOV1&nPRc` z+-u684phi56E*<{$3Iq>YdmDZ3ShZT+DVJCAYW+RJf#rs{`xAKH8XQg75Ldv2yEDW zr3-{B5WS(yy=oH3Lws9eoIoGX?@N>9K|#pU80%xjGCOBO z%uYO;4wO-!ap@_IoF>l4I_es)&a?!~O-caD?)75iw96zFp2p*|yz6Hc2IiGdhr_$n zF83}*rF|r4Txu&w7yz9%J5#MUyR9cxy6qL&z?0kpH^VrZ{a$iEF9ff(4)zYTK6_7f z(eU(DT?kv4vK+1UX6QesX^_5^4l>LOT@&GN3g0_}hQWS(d%)JN1-xa|UWbTj> zBvTjSHMHLmFx#PccX2lyhzB;xn=sz&&}v@Yd|69Y!I#G0<-9;BY5}#&&aPzgW`593 zQZy7!Zz$afhxX&_s1Hp&eLnb)tQ&~fYk&9E{t^0hRWXW8E0`~D`4@|V2{R%m|4-+M zVF!*L5;Em>u1kzxN-s|!Crab@TmU3vAEu3url2rU$fktrkKAtH`pZWSEx zT}KRxxO@i#U4N&qP4l&W6iRYJt(HD+v+vf*r;8u_)ZzV5TZ>5AMl+<$OQIdk4hM7& zQELraZ|l2vCejs`1NbaO6A(maY6VQN1}6|$Py{JAU9A`R*A^yH95H z{#7NTiXd=w;jr3m=0_tK^9QyUGiJAY(wjl# z?dH(%tnE+^(g_mp@ek>(mNTxnmKWn~#b??3-qrRhlQ9foxJ#ZU_PXLKg$F{R2JZ(J zo+!mw3LGi!@VUOcI5B+&G!j<`FvRz$nhXmx88W#F4YgT|Weg||_Q{DjI^|Fj{Mbx* ziA2w(T#|LYG(7CM|ace{ho01bAxq=}`hYMNb71N0o>AY|)Kz4rU;5a z_DKh$D7lMVv=U6{A`j_N{0tW#3W2<|1~~I3K83v+rMwze=~syK#70uilrXVJMxRnW z%4rW@I)(YAYYI{A*!ybMxBjxcw?t<118JZ>E2erq`iSf?UO-<835e|mz$*Nlfr$yS zA8t6|3&}9D@Ny~z5bnKunu#V-)lt`__?@U52Xu@l zxgQdcpu%&Cu7$(1#U&Ia1Zbk(zM=uey97!2u1(`d@7_EkNPe(|L}|*;w5iP@+L;G` zLCtE<2+2biuFAbBzN9aAK%Trv89_FtqbP|03xX=Z1MN&>vilrU36ubZv!jg? z(N}lr3S!NnyOVp6P53}h!ZzQxi1897Zidr&HMcyGLC;!fPBY1i-1}c#3q`7$TGaJE zXxZ%4(C0ypYoRe#S@@Ll$c3(Bq|M70oFc~2)@f}O9U4%#X*>{LOQh})zl@|TH3Dl<~vGYU&)^ehz1)lFewaM{R^$30$V>etjtHC z+mHz&Q_?^{sp2&ePPJObb7x_?z--Z-09#LnwU+x4A{drrezI-tPF+fJD2j+idZ;(v z%@7i}lD`&Iils%p4lUNa;F7ZIJpBNJs@V(*)*`F11nK4+o8i4>uwn(zm#|*KHiA|i z8!@9-Rk3v=CGghvaE~#xtT@tVlWHv{svV0MWx3zFn34iUq>5$$SH7%+r_qf8K*#;> z@@-&L?og4f=3#qxdzr_UgL7sww+pmMt;)scfN&==eygBgR+&hPUhmpqAtq(~%#p!E zM>pFL=G0mXY#~^Yt?0yMfS6Cm8c+5%*vH-Ml$m{zd`n_feJ?Oo~iT^!6TZ zTaPZIW3Gy+W@EN(YjGS*WPM$d+e10U&5U;h21E-^nFAeJf;<@0DwfJ6 zZX}{ddie0m#otxUu}VlHwh)$PKHTqiL|~fK!<4HOCXYv z0(}6NKLVR_O=Ee^uoFMod@EfN_RcUFzd;jpub~BcmgrOchjPmVCghBS)i`=sd8_*R zeIo#dKJ=U1#GS2*`7IifMjyy?Los_nB3lM;+6Cpe4*X^-(2Rb^#JO-r#gp3xda{=J zUH^Kh4e0}vx0IzTNzkAqN#8g36RNoLpH`ZAw1!oO*@Ia$!WH!D9F44C^AX0)Odfk^ zG*)=m|1i-;p~Qf|AedZA%ObF4BGM0i^zoNN6*Y(3h+cuAZtKfT2=Iflh4s1pKt@Bu z;}Z{FiAsmTTI(4E0D$w;?3Y&~3wOVxQB$Ss#p(cUn@VEcCRY2g;^qu@h?IyiQ_ax&w0I}lK`&laV zIypoymBzvcOQ_|jwSae6o8(Ql#-xaeoCG(g=U>ORGAO$Vdoq>yIc@gaYOqSYNYnMZ znF|5>O@BJkIq^7GWg|%-=D#e>!Bw%36B8bi1Cz+<&vKEvoqs;&Rtf#=FI6AWmu=0~ zcbKtYE%a17$G;j|pMG`fnY$NvSVEvi2P@__35DR7V+=d~#~Li2dJe)4s$cCw=OoVZ zr_q4Q&3N!p5dIgbI@WFk8WQ)V1S&!gg{1YE?9|P6v>>kmsJX`^$=6PjZ$7!Xta6we zRFHC26-|+&%{8N0k~hQ0Y_NomFk$yFC@TQccDiEN_(w*YCy0n(J z;*Im)ubO3@>_25c)L%GP9N94&z{O8}-RHjLFBD~DjZp+WkBb~REUa4IFAPp$x$8nB zI66F01bV#yEJk=$noIP&*vD@c7$h+NlIx~XShy|>8&h!;!|j$@j|Rn_4-Z3}+TlwPJPe{v=_<@(|-lwp2^zTQ{Nt z5?uVU$SE|c2AQemXmdks=lI!ok~uLof1qcQd4-?oJYe*^ag1+nhL}6LHk~K3zCH~7 zIHJ@`7@XEi>=sw=FejE5Z>Zj1O0Ht;m&$m}(hWG4#kt7o#yj**@qP(~$ipE4NO8&tVLc z3g`<#xJ6dKO`XIeKnZ1cSg4(**dDyFp*l!OhE@hqTGK6R;e0TRQV~I3A;xIH>q^BP zg-nugB7S%#6=dQJEAav_dxqpQ_=bwgNe<rswc^EKdRlR0P`7wFh0skNkX)3mAh0(7Ji+)JhA@;g{;(z$~=zwBFb8^`D2&x z?kwOrSc#UQXW6zyWk9L*qJM!%dPCvsVg_@k?8?|50$LvWAgsLVo}-cmT;$?X7|*F& z9MW_OYp6;B`y-}Ar-UHlU-F3=4{*Bsl!E(S+-Lvqhr7kcYh=a2;u1K&yhMqlkOmHd z!Cn2hcNf51^;e6i=fCu?jiKHyT|Z)n%N3kme;ZY)Q-wfxXUaLn+=$1d=@V8F1>s_J zjr~#Y*YseqOe0Pr3OC*Gc)}_5R|x3b-`5Z2JKE$AEFXxr@I@_c!a+68S>f6gR`8Ie`lo)H*&}xHbYO{;XcNJ!)hsAOoRC-Leb02(#AxIcfTC-wJtsA9HhTwu8 z*q6+>SWXz6JzDrMk%>+NRf8G+h?THTYhyGH!Zk|e<3n&T3tPUdXRjmgup{k*NN6;BeG^VPy29O;dzEAK6hEaXXRIPyd)kb!Xp+x3%`<}@vpd#=GGV+d zfT9oqRU@Xwbgv~5?7_Y~e<^$4GTRIfgI*>n&RSAgh({%T=ngwF3`*|~%FG1v=kZo| z_xvfcIxh!hR;HV*t2v79R_kQW7pq``)2R8^2|j@RdyCZ>o@`?-tJKN50#}F=9U#7f zO2;{46H5NP6ehY@j(n0g3HBb_M&APne?fshiovZtO&r7#i6G5(pfq$PD=7*==gCN^ z@2Bc#`?*%+B-&Gg!Y_P5%d>p?(5Pr$&~B)Z=mp?^LD6lVOYFo45t~_EMN#C6%Y3)7 zf;qWevBnZtOQk|3K^QU|NCxu^z@|u=w87=VrlB?*nQsA+_B#p)ijlHv(Rqo2Il2w} zC6QIHLoB>U%4=fS@DDFd$I!3tVOO~uuK%v-e4b*S9}}=+&l8E@z?rFQP}7Pw<#uy; znhN`wGj|5dn^c8GCc-qsfM-MZ>qoBvQP| zi~w&w!V{&LcUit^-X)^s12xnPt3cdICf)=w1J&gbr(*1x(X^6oJtx_VLMY9kV2TEs zu#=yC5&RD=$#QA3mEwZwu_@&L^a|aXqVu*D1O(|bv*1&V%9)rQ?sU~`o#EkSQv=u* z|Dpw!;Bq)a*P(w@Fj3)<`n(g8o(rejv$Bst#JHP6h?jycl3V3@L_?^bZ}S9t_hToQ zJL($PVIw@V^Xokhw)dbQBxv%_(~(BRNu);yg%;606!F(ao43|eh9V{vCGsJdSbt|_ zAn#3;To*@Sn5ltUCQe_+ga47_q^DM{lq&jILy~+I+Pyx>^MO%wwPS#(YVXL`Bc z6K{knDF<4Mu6B2W&52Td%q*@}hM*FuLFYtPIQ(g>GQTl$eS)IATZxB?gLOh02*J4u4)9c$eB(uoQ4$3+~pI)2|`eSBt7nL&9=2 z7}HO>fNt;-S-c;DS%XPCri>HLHJ`G~e;&@>-KL=NQ_?n`uvaL!9WKsQHjQ+=bDRBxl=0E!x=J|ifQ$Es*rRSUUEhKS0A%Crd z`OP}oemCDOWyv)E=IJV=Men2OMlbH|1vhu!dEpSw;EQ5xu*5g%rtA{jPGWV&sa@c| zVP!_GTUtSnpK?p7zz%^q|!tjr&W0u~C8#Kpadd4lMx@3hl2&?LLOs8K&g ztYNKh4I=3~y#qAn?w;3s4%0oBz;@nBjQdF)vtNv1C9TYvYlFa$Jr zG;9Z|v&@moBtpLX7BtE@QFh%Q8-jY-6A=hL+mHA)7cCf>oH_{j96sWqwVie-74e3Z zy6&-B7XBb#D}hGahO>9$JbYE{6W!s_#We~`C4n+Ed#ieY9&$bbqEWDtQVw`?<~VH6 z2xEGL%=5T{Ar3w<-f8wn_>w@-gbTKlGi8t@Shxe9H_S#>Q<$$0 zIQ(U&lye}Lm@m!GDxw`vE973m_Qw`txBg!lE_RoNSNNAxeuBX!gfqX;=1B=e-`F!R z#!D&Tc7j|C(b?;tHKJ8L-A}J&!rwp#if%=XLE!Y`+oi8=zr}oM)^ehH+wD9&Hj76w z&0eAB*(>t#C1lby>2>l5g12F^7T7lib;lsvsbK-xwBlv6GD)#)4Gz_yBy8Y?m`EF? zgH+Ja9{e!4IKmw(EjCSGt6_qv*;>uaSjgfUMnLIC$yNLllxMSnBoF>YnJsXJ2s`yV zd1nNMg*52l5~2sbZ#fcWlIff$6*gQm07c8{Ju&Xb3^;~u|0Uf3!dF__=+_UiaWADA zmnT)x$tJ7}ZaxUz6>=I?8REHRFA^CfVAMq6H50C1I>d8M`HOCkQ`jL0nkXsn79D-D zUVW)U;$`Q=M}1g&cmI$lYpBLDfzj9YhD?HY#%>gweGIA_@|X0W%ukBF2y!b>t;B&=@>HFJQY#urA>h;f$dj9$BU#P4qB; zr*q0!f-le%;Ha!c!RssjY35naw&_#l)tn<~i=#!}1MLZktFZiKYt)}mxRIqGZ>P!^ z+_qW(9BH+O)%ycd)2u^zjbW!p!Z4Lid)x?D7&$e&I+h%6et{G{gK%tq$vm!#cNY9w zymJj0_^@vkniS#?p@CtM@bf~f^YE`>c4zW&3 z(+s{`0gWwTiGm=|YXTSYO_YJzF)rss3`V)n^+U2Hw#SWTmsYjL5B`rtHfPzK z6LOuEuI~!Kphy8+98uJUot@ELDFgTiejQj)lQL{GFuS8NA;iSA|7}Kq>2w3<jw=h0l)dB^@K661fp|UtXLzMmfFeGkzKu&S;@kNc?coV8 zCYBXkg&TO}f1y0G-<7{Kz;%IW(EQYirxG<$eA`-HRSep<|EVjOtl8$|zsd2dl)rf& zerp+VxF>Fc>UOeDo;yuk!zah4=KUu+&3bvV#h52cK=-5LicDM5h&P!4K$DX6MFoBk zVly1}Le)avdS`Roc1Cpmy2;P0bAZGy78No|@j*<3uz2}`D|VpSE)#mhiDeC)TA!*TFrW*SK?= zv>POJVD*emi;RPIxZ;O&4xSk*bQ-G8lu+%7zz& zeK2@KwkJ*W^!eT_DY`>~>Xh*)#oyBDu)o?3(qKAx7(z;rlNo|-@U0a#)AF}^aeCuE zJQ8AIabCCODAbnASFKeM==2W(XQ1qkA#tT4k*5Z*kc8&om(yee|&qC!@8p zGgm(3y>!iPQ->8!J+eiuhvVkj=PtD1%7kvaNKL&3_@c!{rlb~R| zmavaNK57uT*}dRIe?>tEHE6dfg$mB1v$fq&?%sZM$+R6+se;Kd=0xRTsle*jJF1#; z;?d?bH#sryK+MRtz{!A$edHcEUUF=Q0#T*OZ9Tx9gMx~;XL-0F4l%_mXu9tPJHUC# z&oLdk@q(00Lpu_snEgaAz>(WhL7_+#1Frr>_O@;yFqYR)MZoY@Ci5tUD3X>}agZfP zV1V-Cj?5DLrEV%dRCK@o7<}Z-CoSvW7$W}zxHGOxRr3(vgNZS{*8Hxpjt%x1@Xc2) z0`aWx2~2SCEgu=-dhyAELK|!62Z@iGN@DOe^Pnkv5 zul8$$P%1w92ur!Mb@FP%rs5Ykn&Pe(IjrNJCRH|d3O+(3ukg>aR$_33k_+~Pn4cF6 zu8f0=Gdj^ueEQ~ddyf%{-DG!l5?1h#V-u~8ogyoj{c?o_P@s)zDGwv9 zFV*0&bq-z+o`V9UBK*U;JFYRvOD|&zZaZ?I0-#pl@LY1*3!Uu<>|796mSc0_ZAaAw z{`p{gWV|_pFD=PblO(-V1@x0?Q3^Qv8!+Z$FJNZn9UYmFzMh#TZ5z4D=g=&D>FVqX zs_yqM#hrvKK=JQcREukCvO;+DpRR#K(3N&Rz0&Jf+4%ZvLaV`~M){|^GuYC5uS~mv zapyZPw3~GRs^c48wGYf-Ky{<4vWM7f1)e|KV_#+m^gwTpbVxQyvc~%^QVK~W*KuZ> zOBk1+HxjW(B~+UA+l{JBQc$?VaviCXwno8jM^64c1fvHA%M-1X#?e5xPiSrg zWNRRo(rA10hOP+%Z{lk&D{d1zOmmWf>|)JE-{woXeGfL3^#bbNZBM-vv#n|bXcx*U znD|J0z)*`AxlniJivM22_3K}F61}EkK4P52HzAC$vVZzeLq*ym@uuXmFC_vP07UJf zIuI6-fLIk04Fj0)5LNa!V^2tEOMEz139(V)n*MSMwtaIx;AUOqUh`{7-2KsKj5dTe z0f5^}bqn|X9h^~AXB7g2n)C-DZlGt_fxM^`2vZgsx-MD@i_&=sf@XU>OyA>!QB#!u zZqvS5=>~90)@7C+N{|d~HC_OthGP6>)VDZ)UwVPA&V{86>A~9x77mPt#2I-Cbljr) zzzv*w{uzyS9XAwnf{h}WZKHp8q`4lSy_b#E!SX$+2c`5PSIXHtpK87QBWQpn9(Esr zB&TiaSk|}O_6Z%Pqhw|b?lsWPrtBTK+wcCaV#P?(ppqCZwdImH6(C&_RD({f zJnx!KWI(_ubCVnieIgGj=nhB5NVnB_o|w4fw#`HSB5h0<)C4&`h!VMD9J(mHR1c$BBE$3UG7sgRNcRWw=&dAI zm|z2PtN^w0tCY~cakA2zg6qo#D}#(Feo0qsHCDlbaLZRy=dzq0vZwy#jRl(^>|!u9 zf!;2e$v@dp)V70rNug-oQM~HT+C`A9FgJSn*G+12cnS}4`YO81T~;dQJ4zoZ;13kI zmu*BttHR`6$fae77>f`L0V@r|-^B;pAdD{%xqi$M-$QYWl%RG?yFnq~Ay!6aDjKC2$Bc~o{j;E4x+;~-b zKMbe|i}H1J=his2|Lh(d-{ui%w)fzMASIfU083?4BdF|dBP92W0-&=b`wTye7A3g@UN@`VNwuT=U_ z4I-=>JH+gv@OWoM?M_mFK`aXPYrN^WN+#5Mb<&@g_*PKhPx75SboMZz)z9&}L=kPG zvaq?9Lcu`VJd0!~C z;1%4-<1}kduW4cR5(qmqCAd40#1OU`9d=W_6Ne&DhF|OpCve<)f)T!tV}svgb}74l zlT3K3v5TJVX4++3@OO27Fx`ifC##CivQ*#{KoOzrrV412SO=O^zS?ch`Tl{qFzi*| zf&vN9`sw5vZP@fOLgOUqRiShbb?Frpc@+!YLBBn~bwD*2ki^hgYaNU(B1RHe?b*qRxa5XPlBF_!U* zX)?}{@n^=J>rxyxgBus?qC^CG!0<2O*YO9z{8+%q536UaCB+S*TmPz1<_6jOQHGa1ah4F zcyxo5p*9Vd0GyhD+-qUun{GfHlZC#Z&^e<(sU__loujEaGH|2Wt_GL50F!s{7 zFi(QP8N)Nkw>AmvwnH zxgiF#W#!*Sn&V;3?#8JlwW^Sul<~dAD+HaG3ez%FyZT<~!cRrPPN2Rcyu|(2FcE46 zS*nhwiOfR*ELu1eP(S<;C~Ntqfj{el7zYZ}@ie@p`A#8L^1hbI;+9$#LGY$eP!Thn z`ya;zds1(t$NUQ-%u*!69%$LDgmI+0bne`=HOtQ3hD}{Y)CikNvC(IJk%a$Ua^&=wPqvjeY!!x^<$v-o(Jc*v`lhY2DjHNhc7K8?rkOYL4rUzbnI{4{KR3FkZ z^4RmvC>e>z}a10$mb%GCw7XfA5TCZfphs^U?}(3Tv5k zq2!+MDP?bX1=hTE6w}N*-z0U8vpn7AcOrV*^ycy24`-3;xG~Yf3n>{_jx}yAWexxv z@n{u(WiA0==sLNgizJzk8*ld$`T(QiuZwM@vCHdM#OvdS`^#Q0mwXG(84?9+*BmFx z@I>rd5ZI3xzAlce1vZ{Vr8i6)F2!gv&;R1iuxg0UU(wgCJ~9J`5gnN;HbjGJwnGCB zO4COi0}W9=U$MqqR|stic4;Y%aSl|XtGf(UiKNjoCc8*?`jLo>qV9-PspzNVH z(Lo589-W)p@x_%DkHA!RLL$Rf2;^acpU1?h{5mB%%7mPg^Dckc4E@sv!6k4pEc*Jg zBPvg@!S`tD@rIIhE9v(LBs@zPlR{EgyembeCET}teigdI{sK4J*MDvw%9cb>W#bYn zMJge6>}!^2g4fM3EnZy;_m?Tb$(0D4QL1++<{VK8!mPXBkN|J+jlx&nMvXXuMCQTC zu*@U~pM-F5tQhxm#$`=5^SOO61beC(a*}hSVwG5=!WkCU6?ki|WdS}fKyh$gE>5O8 zhhIK>3KX9ng((mi3LMx)OfP7V*BS!;8EvhQN>={Dg-73}Lin|M0wU*V?()j#qROn6s4O zA#-%3rvryLJYYG7<&gnst&#PR+3YC@(vdbadD6kRk$tIaOK#g((*{H$Pgb#%x5~`( zsBtSV=Q-s9&_cC~_h|N61_T+rDP*G6#QV-mS67!}CaBnGdX`iA<9-*YqzcIBBA&e@ zJ-?-H9S7{W9qqSaj|1htuZ&OC)g6`YwgWeZufdOAFRoH#tfpZVgB>CgvPI(r_dHM| z*P*lih2IOH>3aOUF51x@z+48yI5jT^R=_vXy9NF& zrHagDZGx%#_78JFqto5_dA3mmoV-B=Oj4~6E!<_l2&21#j5Cn09`$~!Vn%_GZfRRH z2$%XGVIu=)K=%WYVu$(`fErOPtqA;Jv*2wy31pKsHe4Xgcn9~NQwOXS38?`}A2ATW z1iDk=%-JN6eCP9UQHff1?YdpZM(qpP_z5APxrn=>6SUIwkaGj_WnyBWy@hP%c9BdJ zK0r0n0SXYv5kb-mqYGLMVrj3Km@hZ-ay{{~Hacm3YLbde+>=E1Ucq+Z>YT1Um%ib- zUZTo;%AIKxC&!WMyaB4z)bgzavzsr_NfNw6qQ`uk#dzhRgZle?glG541i%B9y{Wy=O(9y#Do)R`kdx7I@eYVPkF~L~OMz-1APo zdrQYud{H8JlCsu2_v65+rtXnIp1!#`>nSwbF=bFu&jSOZ+la#@ zO?qVFJ`LY!QTSe_U!xj=igS1HfqL4I=ltG-3b|J{V@jrI{QpU9K5_rf1w4Jg3}mg~ zWsSEBN0chqBHXhJjb9$_k<%G{=(nGb%5V)f!laV(1VQ#8B$1N#6tecA=9iLuq^XDn zVRd{8@~IG=mLUIKHftm-U@3NLt>BQHw(`6H=I<&|Mu>YdB3{iwl!^dFZF>Aq4$#vx zSzV&?L3FL5#v9w>^@l6XXCyd`-w48B@BS?k~xrQP5%b99O%JkvyIk)|+VO%9H*i z{U$#}Od!U0t=0vcH>ojo&*fZLjcOw_eg`^3MI2^SX#sz)7w+xCO zmlv`vPR4g4?VlsTm5!P$9c2Ha>+FXM(}4r}ps{EaJ5zo5?CZ<}gwi+{>;|IrZWenN zb`sDjNoTZ=#P}YW*ph(}+j13-7t>(-V_riGc&`O~9=)ZCp{g;i1`ly4qxY1uHtM|o zk84T`Ntbfd?N^`kP#46gjoQMgf*3I9oAOEs4m~qnP?h2Z|AkI>5uS^P3_6Chp*mQT zT|2DJVGIR}Wt}+>iMcsv3>7&6-0}u9M{)4;WYB}8E?MucCHb&-DaoN2-^0`4iuue0 z_SZ1arKr?5LU!ibAmJVnk*5V%kQ|vEx8Ma#)Y7go_|XPsVou(+5e@G@x=`w#{CiMJ z9FsysDwc`d$;CB2OP0-yILR?LKmB*$_q1MQ$lKQ+*uZSLL9rxK0Z=~Hm0};y_g-2ekOKKAtZ^}o?&+JtxzQylx({g?q=Ellg2WPjUQv`qau)q)k9KcJ6 za2yoj&g{+Q_})mr`=DswSd7)8A}FZ-N`5^nqIJ9VGgo_?g( zn{hD_zm2;QyzyqNWSkzS0Xh%3Q;+@zqh+I^i1x(rS;Pc0_>XCA&1_F=VCSg${;W~k zS;TlP`A@v>7ZtToJFm+Qnh7tG$ce^H;cCr}hY(kJ4E`$cE;&*3fAT=JlbOX3ui5Fba zh6s@pDrsb7hQG|+LcB5o3UdKcrfuz{Vld8i5{E>eB4~#c6z*m-d8rLv0GBL;Q_L5+ ztUyvCUg@^E@iVQ+ed)Jz!rZ4?aUMWaetkW`F=?40r-wr|H6ki;0aZL@A;VoSW@%y0 zI{$c;IBztL88sHfMEU0Uba5$+)IM5gb9S_)T18_It+3ypW^-f^I(=Ta53O%0_L_>C zGUC5mbUmwZL0%ujt>VJp_MUc^x>|Xs|5BT8LcfjzTBywD+ZJr2IYj7)XBXD5E0>Po zj%M;hvK-@B-f%!|B=ceEZiASZYTV5&ylGKNKO^)aJUndcMG7~>p{@n=X)iDc1q4E; zh7UhJZ+>_(-Fpl-!QLfYI?wp74vOKz#MvY^R<4ew5C4Q(ZV}jFlaZ1Zf z_K!*DfYD}cvTp9f#{94f3ZO)x63qFy6uxP@h?!=ApQwmu{n9(h18Wo~0oLlg2;9E( ztndS78gp#IXFg#1h~+HO_b3AEp1Q|Nx5Yijk_Q{r{*+vFE0UZ{?==vT!UPtHmk3~v zl9AWITvj%Ncth`1ON!NANJj2FB|`)o#kQq_J@;_SXaT&fMH}cdX>l@ut`hS(1oe69 z_LN!z^W)p0Y^tzR<_vynfTorCO@u9moChOfO-3foAZPc7ZT2-0dvfc@Rr6m&tl-B& zXec#)VJ=aZ;eQ`BSDycNb`UG_e>v70(47+*hVWjzj96V}C7v6&s5>X6#&Hw^@VmY;Nt{87 zqn0CkHB-nYmGB1SW_n3p9USLDKyq0CTF~ehRyqXg$f`Yd61kQcOcM#c4tBvT(s;WL zs2>+=lUTqcuXIsiEoczy=y$yKQ0-g<*GR2-aNX|S(6IkX;S~vM+Gh%>=<_Q)xt4D= z_mzH9z?}Rph`p-{8D+62U=#eJRs4uIK_fos1M||AOxqm-A|r}AtNi{GTi2xM9Ubs7 zBDWo>VCGC#M14L6otmY7_Jyjq92MY343bBCz$Oi@HXtWRMHCI}wYU85TVD>1$xteM zxTr@#^^0GOu)nz{EG8wi@dRowONkmJH9%SAj2H#T5CZc7WRvhgm~`BlD{iI*DbZt> zCBn8gho81Xrn)v3!XR#2Hj+AJL=ugxcR|{Ne+jj7d`P^(HHAlbpIRg6X${YzjzMr> z${Cud<3jBfHh!usaB)waY9(?1bIkLXcr!1DTj|4g)aUb@8W;Pb4_bZN-)azadmA3QQpL%(9n*_rLzUQwC^-C@FX=rqGmbBz^F_#F#nr zc;3kywX;aBOJGt}$K_Mh{l@-F4VUA|&?H2SRM{%WAD+JGlL@Vf3G*_ic}*xsWnLQ* zl-0*eSArAcIq328892dxGC9oumN;cC7e!~)b$7EVY=ynsvjqswmMdMA9OF1Z9??{2 zerp}~xs*c$()j_D0dRM;u5eDZjwvaT^f*dYNUzR&vod|_4+A$l! zC8jz`|1)Oyr$GNeq3Hs01ZuBymfY39HQ11NzR{6TgYUUFFCmjcRG~!zpDsspc&|@v zUt!6h(4urJ$R8|1#@6IqZv?Wq{IY9B{jJdyC>7iY)-Y)d2Zh{54szOE_jKhUW(Jej zE4n|v-lU1S9txH|9-H9FgN6*FnU$)kO_6T3W?%9}Qp_}n=$JguZ)B@QT_?%ANzYi1 z7pZwK$+HK7QRr-Xa;De|&dV5zm-<(tg2yu4Va;nfU3uehO$&Jg)SMaVaW1&dMdc$G zROOl0&l8P{|E9XJuk$<>u|oSe6oR=@73x~NY9BM}4Bn?ro+VGU+30ti|0AveIqRlW zyqU8jht{ig@=)K>dtkBkoKpRNZn}TG^Rc}y6mh9auOvxM?93Df{dpeKBt7(Y)@fXpc_@tFD!lb*zzV{Hb03;8 z4;Zhq$ok%b;H7s1tUwwUL>-4`-g10^U#yLxw*1>+Yrd${CtwWn zC+?j)ALtaR!vA20lG!S`Jp}@Wx|rj>3=5gt+16!d=hG1eBGJu35#tY6%Wn^#mSt)( z%BB#4sm!-=jL%3O3|J{+K9$NVdkL~u{KByE$DFu<9{tDA8XA zqbUq50HYb&lPzI{W~VJZP33OrC;Gb7182rdztRih=naGpC=v?lzlmU=;MxJo=H7)Y zqJB1$hAfvF-T3#Ya7Mf(mTT@e2oB919JcKsaWdk)-@)Vn`N$7rA&lwEXLm z?7lLrOD^z>pu!NYObN1k@fcT}c4N$Y2eE-UU}}!f#2T}J>P5_5n{F|Pr+sZkO-YM@ z)Ui{R)zTh1>BO8%>3n+4xGX!VDDd&`V>t7#+4;t`3>0h#ADuCq+yqTNG3l5+$Ajn>cyOa$a4J&=YhFhM#Tc>3{ zbTN=_IFep}eJ_99{|EU0*=SAd1YEt~ARysbARuc0M{G263pX=Y8|VK=LT7aMcK=_g z+Nk1chewhEwp&d^Xr#O&^+f&h5;}+__ZXHhuYq^T zM=>Osya-ua7~)= z_Ld8zPcEG_S9=rXb`b<$hW5ono_2@ZAM}aRi!0}vPKK3GZ|SAdtpIt~3)x3gWm%J; zyu`)JdsedNUIJxNt_@p>fJes+p}H*l9g*(a*Lg(i->y?z zrlgV)xBU2gLVSe$92NwsNGrB-3u9tzm0N$Lc}w|xiBz5cRIt)%Q|}7y!ZYITMMuWH zipokdTcZF_WKL+65fQ=6!M4%ZvX=UCW9F7#^G^->p366dJskJpo{N4(dar%#m$iD= zq5n>4eG|(AyI%>VE%iH&)Uw<3{(JAGUGc<O zpjLJ{%c;exI4yqq9-fx0!spbXZs$WLkvt>HMFgMI=U@IS9VftA%o54JD=uPq5h75g zcX8f>#FTcGXE%d(yE@PD?nxJ;sAph&Ad1ummx}%1-%t^igwC#Edv_qkJ1@g@7ib_g z9>cVXWwJ=cCcXvX+-$jiodpG4_hM?Z>`{x6l=IIS@O^*W3-B{~_F0?pUv0nZ(VTI) z&8Rw8WEUlZ+8?ZPUAkE=<&Wfqi3P{rK9(W9dYj%e!x1}2m^fO-h;WREVHh)=m9|jb z@JrSRlmB7u9b=oypDIo5{V&eEW4YOPvL{azR)iI)D6Il^wqm{x7bE}Bnzq}yNCa(D?paqm6Oi-?TmH_p$9 zSMPG=pjWnDN($z9pD(b3AIcFS;u$uYJSOX1GX{O3pqK>SU{HP79FQ%&HujL4;4i&C zw?N#%L;49Z;1i)oy^aPRwchH$l}KzsQw9Jj+-A^?kgL9D1ISt-4mgA`qrSXw1U$@G z$dNGAUg*7Ft$5YoE1^=*ZT38^yqiF_P;q?+w{-4o+>iuE7kv+V;J1RWaJ*r5eJy)` zZgpN+@Cki^_=J0r?7=WVa07yRMRtjE*C3z-gW-t9%!wP3xuA4{2?Pm=6Xt3KoSF86 zkcp$Q41}RcWw8?a!_h?>nfjX=k*kTT4XX31_o|6&3~KV?UJrzcT5x=!#qbTTu z%?}4jDM>h)$08(UwH-S&p!$)sGG7mcsVGG`6~-f!u50(sW2*zI=-A3f)2r&o3#mHd zBsL6t#MCrUFfD`2UA4}&r2Z~XeQSMbL>-SZVvyVXS$DSlTUh|Bpmv^e&uQ~~OM=QM zk^Cv6ikpypA3yP`#f6BqFd|C0@B{oGAcXx0>j_semIT8;0=SqA%#?^6d~_uD?;q~(T>yZdYJY-Yyq?Xcs1|H98MuABSo>TKX4 z?8MEE&*N)k$M5b+EvHFL@8|yEMUHXjb4BeF`$mk?=j(bz=wwVH=j-wH`|C}q+uQT= zc4FdsCF{@C%goIVU(eT_TpZt*<9Elg)%VN^;!oE{H6M4^=k4S3z(Jf`N_RugX;ja3 zKKpo!&LeynUlTWP7x#}n!q1y6pRfD-oet;QU5QhP--o(!Jm2$O3Hyz1&&S)_)q&hb z$^0GrgqLWE@AvE8-PZ!7_edz+Ro4B|Mh{?jR>cCr&&#ZZTwECMbq9Nn-RA1e_ua$GjW3Qa7vXxVAtzV2JZHOu zkGK0l{oyGiUtH|>2qPomc9-Aw{2#}EKTie@F0v8rv|I6Z+V6XKzh*W{s-yq~TH)h+ z@Zv|e7{%V+2lMh|-R>WIM_W7J%t`MfEq-rrcSkjP|I%n1s3FYx`F`ve@w29K=Dm0* zA9Iy)QnsM+~CI=is^%+{UG`nK`#etGBvOkJ!9k-x3ozWhz;@%H%4)11ja z-TwM)akGn}@%cIGNV(Lg`98ZlYFiJFX^ArTe*3)t9w6l1;E=jj^jogj@%=u_kkboj zLOf>Sk^a#&b|xinclUD$uszmAIwBt8~#!Mj7bo z^!y$~2^%OI-2lJz5j%%~Btm!XMPSEqD3ft0@>FF#$lrG#F;i8aTB_ij80Jkhx1{fsYumnGXk*qf5;Z24nz@U5y|Mr&FQi$xra0LvaAqP>1bEm7; z^;c5q1+c@+$cDd49r0Fzy)pRfQsk%#bQdTTCr6t3!^RO^8YOoQ>t>Kb(V8JokQQ5cn`5>sI*lIn8stZfj;dX69eb+ z@D@_Y4BkFe=M6Yq@KOa0*i%Luy>Agwx^;$`Y3yI9g>g-1f9mdSLTMjn!RvKyJiUUc zFUUzhQS_XyRc!jz)gwhsGL^P_V2#tC)44q#rS=T}_ ze9O>|wXBOV8?*X7@WdmWcM*z+W1e&pVFHhX5~ zD4JQe`tw(2bYsL;S>5eo4>UfF#Ub9(jMxQ?g&xS_j(f?AKd3r%>u4fm9w>VMTnf|0 z2bZE0(xtu74PS-&Sk`%b#^(a5FT|6E-SGL^q9j>`#7FJ`7#v-LZUXSV<_f)xBkxYu zL^>CES+=)ycKBYv6f~qmj+VKni-JaO;H*(Cf4PWmgi5C^mq`uEbYL{iCA=nIQgny} z9CPs6@01!x!6VDeUUn4hOIPb90rfAFB`^?DnImL-N<*|V7VOeC{yp5Z)f*y3v7ri# z%fK(Y${nKs8^`3XZybtr*)sQsr&j7j@n$;G?>>{gYXyq{RehPi9oyz>1RIkVMK}Wx zfTRtxYtI6g2AWyeE*G=7cOU|4FPcew%sMw%h-_X>Nfm$k*JSRkV2QC74V$YJQzVw7{hL;qIZXu_W+NbNeHe5cvrt zk$?%-()bY$6-TO>=%42=IPzrC=r0*m8mg!QgVAoTi$A2jI=`(sJTW{qVDipZUw^cN z?x%}J3Ylpy9$`CF?vE)N1I_Yp82@7QEHPAx4U$_^b~XsEj@hEkE?{Qpb4*^i^RL2s ziXl!WJ%(j42EW423>T6zr1h(k;#CTq_Ih4>2<|KI$3H(upsZCg)X(k1z@jLv`ze=+ zr2*+t1aouHX@OJ2HJWwcuL^ES>Z|N_s}~Jz3Mlfj7AUn;?G&f7 zpi0%`TZG_bCs9@xt0Yu*ep);h-vvP;eWg{TN?Cidl=Kx!d=a=W0vJ$o2^k` zA9c<)i`_fY{|a}YOrg}BP*X`2y88VsqNuwjNcsGW3eFSnM5ug5soGDSIo9cLAj zDJ}6Gfg>3Q&+MJ)*ba97vWg2`!=d5H6WwPHIjGdKM)d3h7$>0WrS8ooG|)_vPK#MR zv$+-ga=D(6q{3Rcr7)-+l%$z{4=0@71fV!v>-)w!v=9cM#wa8}+(u4lI>LBWqN-Wg z;3y?=3s1IxYo{h@uFc6q=`d|#yrmrk!89%Udj?KowXp6ETrsc~o*?fT3vrK#fad}} z;E9mxWFoP4v1Me6X%M1Iy~uj_gaLP*Q`MeijY|x-TVGUr~l8e^Rl--6SnCaCfX7pKb7AGPO|}zB0<77 zJZH%u?x;xakN}gFTrX~kCqZMeQp@&P>;@_g(g}o$p~RYZW@km)Z@^O<3kCMHzK+O| zh?zQeinB$&UT@M{n_3=j-3Bbe=A*DGpDIhm5KvWZ8B?Ko*g)lf>W}VrF=(zSZX3|r zFB~^UG#u0l2r*b2E%TBx{OZ)zj2d3-H^6HOpK=#sX^hA)o0p**W!Jkp^;Wzh*Z z38dhwmZ@tj`eVXo2b!8ik%PvvNe6~F8qG%1E_JOT2tA0AMluIb5bzn>E(wF`U+v#A z9nBMyBAPVoAnBf4%(*@W_P4J3$dr`8wD;c)q<(qwK;xri3jv@!;A!2KkYOL558fmFSlWu&y%o{24ViHp!c9u)`SGP_Owc5 zwWg39O(v1w^ewX(ddnuUL=fa{mvU?3P#Z#d{iEHYF;6V1?`&g37Lzilt`w^7kg{F^ zyrvfvSab+fv2=nqnY0katp>~_D}mNyj~(*UVfo2@O!)B3EVM(C-jk^PzQIh+s_-Oj z6vHi#1~ha7-K&GNz& zQc2n|ENMEF93C-AxOSV=1xj@5v@whV?M&E8FFiKPtzM>fV412$+XE(>&mF0Imhb3R z{BeZW?nwC{6*S<={dt4R>Qc(3teOQ}o)3#P!=Nt;!Y%L&VY-qWi%N3V-HqT~*cH=P zE_Vfam@pQ6L@rFoqukRs1%{+M#p(fy38p>xbd@-4A)L^^Q5L?8E76y*Ro+=&uh+yB zD32ZcCMO>{tWZ!@;@X5O)`Hl&^6&k4T*Xb0S=RN-M-KnXqtb?UvFvqpAB{{(<=oN-i1$t!ju3IAk ztYWBTi|~t+*moNgy{}jr1w(Nuye!;JUjUxw?-)#}H^RPGA=tDebyoFa;|eDnsj3&R zZEX?0bIF9wQ3$`}meckUNe>;}3emVew;BQVRg&U(@W8P6!jU)*gCLPxH0ee%9ouU6>6TqSW#LPO}5e zA*=TF8*kj#_T;PTv?x@G&na!%5#Hv<;c%uhF=}V~@}=k>43ovg4SIp?k``0f z0cBSY>Z_*y#rK0R4~OrrJfh3nPpooOq=7ODXYVBx1`*4<#_r_)H&n>^tId!hf94ws zyVb|+K`77Asqo3P2WkS z0t|v(@ZZKxbL;5O`)PYG*rg_t;I0SZR=a8Nnrx`o7~wXl+Xq<6bjgS({)uW$lBUoZ z>#X;=1g|k9VBMSwoXqTEUWGM=2-}jA^40zkwP^i}Y0_T|V}!hiO6AO1VVY)UR(obP z^36?rzjN>|Kta`~@M}pY8yYP=PAbJ@d`Pal)P5zp?-qAUD3T;QxK)4IgqoZ=M%>WI z8-)0D!g-bTUYcm^pY9JF#w;iUsG_`|{iP`0HE~d2PxDk6N>GL4nj5_<#8ixG10#}R zFqFky0K;DO1w2ExeK~qff#07M>N~-!yo49H2@JqFb#_=^^8a*HzEGVssPS*$UG>No z803VHdm1a@jf_2XD+FfBM{_IL9r-qvyBwtVnLUi_#hdAo-U{!9s>d40`Ny1PhdhyA z<-T^MrxMCB&e`;lx(ssGm0G)(L)&Fxv`e4JA2dkMmbw);Hm6>!ITh(I$Z;zqKjySj z(%>iVcTa=5$H=(9!H()oFaW1n8WA(PvgI3bx@1&5~R^ z>LSZ9@qMuemEt3Gy8L0WD{GhtpZmCS|spk!NUH|{1E0!!nJRZ*}MHZ zBbuhnUhRd8WmdKkREzNJpt#iB!d_1{yT-n;vuw0ah#N?ZFhz?UkEe^N$J|S!(%QBvk=2Qo?}j;JClPwFFA&E6?Z3Wnws2(i|0csHvLCs2cPj$xmVLAI?rbkb{`k*ky2qVt^4RS$%@Rrm%#vT79sy16+lFuzf zZosKO@n;Y)QT^IH1jZHp2NN8w43D~e9nz1mcnQ_HjMCmZ)gMjju z#(wW{v$F}Q<*dS~?`m$h&h&0wgtJkKi#GR}CjMnKD^K#Ew~q}93Lrt(I&H!LonN1Z zwYIt}TEpfU7^uF-Ap7;u7tPOKF5TsGxbA?@T{^R0)Qz=LT$Ej=ZSTj5g0Vo0%l_8auCZRtEF9}P68%B*N z-0=O(om=tnOM4QM{u|en0lI^Ko2>FZD4fJyo8aA!)p`n`DUCK#jjLN-u4ZnKJ-uji zC@8tmjo?mg#7JV#1>5>^@aY+8^03!2vgeiSpln3&F(Hkl7Zyqz;%8Z;%q~>1ijNwa zFqpn6&(}ck!U-de{EFo&-s8TxTd{mLS2{l#H(80-WdX#)8o?)^fcB5_zY$R+n1CLT zpBFgp><5V*8rm|6xv8KT8H^$}n%2IgsWg))V21cso3w@EE6gZ8#m|QAU+-Haz~1y8 zl7&u8fc)2R}e6Z`d8G3t2*gs=RePoHLr-d1DG7N445H@W`}`eR-4K(OK~-)ma3 zwRo6v6}_wYo3uko`+?~@!(82soHC4+F@9Cr0v+0KKV%-99;l{_EHE;%a?gj>@;w^( z)l%@vu^g6B0XHfl07=N_DIHd?N2pgf%ySio&PwI7BR3i#Y7Sg)D}w@L{3n{5$dUui zgCuz^V6vIhZc^K*PF?sNCvG+)Ekd?_3t#7YYW0D-PGRWEPbu2Kj+9!DW+vWZnKhN9 z(#?b>Fu+3}h%)jNs1NNQ*zQXChERoe$OY&k=&r*PxV>VO9*~Zqe~^L0LXrI2kYWfs z{7?;@NGU^eOHSace!Z~Vz-8|>`76S!XvkGV_g4PD$^$<<_#Ba#LS^=k^Rl)HS3214(L7T=Q`CJ0}(`5{6`i2#h zAl0@TS^9m!K>aqL0I^g`hZc8|B;rHu+TVdR@x*rf8=kZ1}lgZHN|XzsfVC*Co35L_+pBM77ZKDeUEKt=`5@N;t%*2jiwBzprNLxvQkv{@u!E&8xs!ZcA zt?pgY7`~CGTPdc8P@rtAs8e>7Oc|+}^j8%vL+_ zA$A<8Yas+Q+`2{=NUStvZLbkr^OH00RD`$z-Vc`?l9{4r(LBSC7|-F;Z`fDEj@?9t zTW;xPuEkyqfA8AK0wj7?+Q6yHD3yclKw8%8)2Ja1aY|?Tp9Kg>g0%uOF+;>@z+{3n za=fb>x9I$6?l*sshfKX-Qd0I)q7l&tYdWDx-U0*za+;H92&}eNsxl=AVX&*skS!sm zlM^&#vKBfWgETpYoMaDQQIo_oS__3}BU&rj@ z>AC)-P6Mn_ACKZrQx{>sz}(TS!5yZ*NK3WJ1ZgbMcr-5@EQkGOd+tr0yO}srq6Ti= zEN^3 zG^;Bz($nCnc>F08709vGGc2JOju(N4Ete@eXg7)YV}BC>c6_^vxMntaWFmlfvq?oF zKI4T6JH#@&4uFe`8tjJF=t6HW0Nu=L9(5=elRkNc$`^OGhO*=O*s0P`cZkc0f-cLTvF+z+`Ph|4ag8h_nKAO4VhMJ4j1jm%lt2@J z3(dL-(?e}DA)|S~dA`38<$+CJRAv)Zx>~om6IbwrZ>mTN!J6N+kW!vMqZ($ z)CpB~tX1$`S*a8e|HD$@6gCs!i<1QO#8Yhz1#5 z{g&x3{MKD8JECc?BIt$nX$SRa-a;=!2JvELjOd#K{%cz!zd6=38H3_VY_V3o%XDi- z^O&?!yl9*KH_V2_SbqmRjMY=QUm}IkQ)DR~|8sB??|$~c(kj4g&q{;rC0J8;koVy! z=|4)eaf*pznkh-!vv8HNEWsK5o0RWeQaEPfR~QWS0!LR(W-bUXdrzC1*zZ;kJ(`GP zThbeGK#NcI>(eL|`21erk33@@SQSyaXv*5n_UgCLzW=+qnjn)Eb_3M$=aOTW{G|?8 z0r4l(0hU+1(4kMZhcUKZ((eZ-j`n@m&cop(MB=rHh&vLA=?6P&31RE(vD>pHootnW zB&_~nkM~9!$SR~=WBSa#l<)!U6@n*Vz~41x90K>hP9NH2fvjhx-lAu)-yeyilk(0T z=V<~4aFco=aZWL+i#0EM8BXXd*`)jpK_pg2%^&DHwzl`BoH;EXB02@2Kv!Sp*A}2t zce-;&r>mLxHkR$3RS9naP1>e85+$0|^0mYcj?vB)Qi=Er;3D;GhN9YHUTdr^CD?G3 z?fa*(tc2uB)mYWm(#~D5eO^mS-!mLEJoU=wz-jFIhdKjB@r^N#Gbd}I5Oc5rSs+tZti&W5`&bv1c}K2QD9RG>VCH5TZhH7y7ht$m=5aVN<7yMT7|QT; zllsNNjd?OAiBbaWLEvBELMdYwH;<=GHLQ)c&gylxaw;Nb)H4AynPSSsFbaQup<#fSvWGu5i#wTWwN?>@%rgI5{VN;4>G_*%J| zE&V`DVf*T&!20=fp0qlWIyNb2C!io5a=?q~Cg|E6DRQlI#`vq`25u)aTi$`zFw+9U z3^*?g_L`f3T$IhwDA;Kmbx0YX&N`($z29-A?bemAz)G-(8oKbx4RnPx+Zh`$Jbr!GXmi=TQ|tQQ zNnJLoJA?qc^uYteV}5q|Bh_(kfzTJ~T@By4@kf=6jf;3HN#SyEt_=I;&)SThf}!hF z$1kd;(Uv>lZKys69~gN)bNu&d5p8a$2sRavjCx^sXXF&cs&wP3FvbjtNc4^6H_+*& zn16&LddZG5&3%<*qJMT{hD5TPiZct_?!I)!&?MGWYTDn;IXk>4%4btmsF#|;&Ru)U zg$=%vJG#ZL;FIV|@74M>#)*k105l(FCYe`C^tI#Z#_v$TNRJ0 zS8Wi_qGAin!Kr;OtFeBJbwL>fI_|b>2HrF&mR0zDJyxG>1&4}V;p74vo@Yy+;#r^a zBJfktd=?cRu>@m)Yg=I$UQo4a3K1f#iAi`z_3`FX6Cty(J40y`1OGPurVxoG=_tbl zYtEI&Wmz3}7$j9xdUOW+t3q$&gouLL1!Qk?;%2iPt;fetU(>3fmTS#v3%_%zWK2L> zUw-72M$GP zWAthRdyv@EOc2EQT;xQiwKd1PYD_Fw2i}J>!>;Hoo>>odrb%KK+M462;J)ki+9C>=k66 zUD11~EPv5kK#^S*vP02Xw-|l!IXYEk?NsVM0Id9Ykx$r@fXG9EE$6L7ZIGvaboOmP zCJK*4Q_xy3b^0I$gE%;b5t$cT(gDvW zlJ+~n&hyCQ<`anABm;18BXsSsD_Otiuk&`ffMay*+&xB$1^Lw849X;2{s@h@j>_S| zy8eFL&HDYQk$+Y~4wb;I4x};_xctpAFJEl(CmQ`r<9qY))=f4zrwhMbcAdH|K(B8^ zl{m}~H0|J4766!gR_Zj_f(Gi1REEIr#AIyZj8woGkk~`Ai6($n+k=q~wr#tRPQ=DA zJ**7;#y!vN?oa~wxRZcU1Y4qED{e`w!%@k+#HuD-)y-y@%9|ivvH~4ODtRH#Hqj#r zEFIOJzp9;_dG>Vq?l$jHW-|wj&Pu~b`BmUd)mZhx`jk>4p$QPi%0#Om)ToT4=uyAK z(U`F~;J0dC5+La%OR^7l+?hz%IqEYXzL4dFNIHo&jO|-XLlaUe&J)o+`6xh4G~8<8 zWDJ?}>=GS}@qBc;jwjE3{@b?OD$q9P@^)d<6cW7wf?$ z{p(D%pZl}|ocO&Agd`^vGc5g}>mql#ckxfx2F2~f$Lf=E>b4V5m^s%m7AnH3!#yHq zpCx@DJsPZ=${*9__GzBBotkv{P~<=~3{E({aEK0xdfIcEM6Mc%BgH4}Gwmim+^pmCW#wP5dib~c;h zx0_>$o*QE&TaS?!+k3SaSzH?)7v>!j;?)RHPC-=`0FtmFuIQnf$?8vrLBi0vUWA-) zi;H16!Qf97$!1k&uZtF%w-QW7v#tLbsh4svB<$f7M?|%haIoi}?qw*N#iFeLU3^BN zsh_hM4Nsg)^Yh)#Uz;g;q?I-xDwUkdICNS7JJ%}X%_m?N!ct48^TlY zCYvj(_R<9CHr6Jb?I<1NC^QcxyO}ceV&@|zCw?_&$nFm#?AiQroP@`STfb}89Z2cEekSW}53O5<EW~Q8t~XIMadT_&Jpy;q%X87jP?^I8tm-VNMG-~znrg;|XnXchl_Iiv2&$LJ z7-lG$rzWqhFOpOeI2ULwkDd zQV}{F*%nr7W7+;*Nrn4l4lHX&rpT-9K8}SH&*ccx97Vh1e{vPApjeDH49^!vPn$J0 zn>GWI+HCai-622I1{b^`IkG@>Z%3@g^Q|jKm4iqo|2*G`LuPvd1^oGQ-_I@m^qX;3 zs1sE70_&l_96!1Nr)9oyJQ6%Qy6~`q!ll=XA4m1yOv7Yqdc$z<(M}1~`}C>p{=fMn{TvR;&@U zBhD?FSAIFE#P}@bZ1k>TFXB+;AYqKnH*G6>iGZU^YehPSdvWW^?=BCjBBrM&Z#L@c zo$%p zKeioZ_Id5krd11;xt@0eEDaTjuDXHJ#HHuExlz#+xqDZvsu=U@u!QE9`OlUbm*N}2 zaTm>!BJ*@2Nhj*d;b5*|Aj^|-3uT`dm81+J$k$(&el%W$lAns{O_f!x0K!LLKEzY8 zKEGh!k#FSAJnRiIi$$*9b{}XNgQF~4RuOD|7i!Ivb%DtG*|utBdP%#Xn_okS7KlS- zwfPCIw3lY20ZAB70G1fI5p-31t8L5+mkt*0g+A4@`BPkn0!fpc85Mj*nNL(jnaFti z3d3{E%8HY7(G=c-9;I8{N7+HE%0I|G#M@=BG1ipzW!%Eu*3nBb_jC?i=snP$3U}#w znCLy!=f>P8rvl$PQZsW`!rUo-CHpPkf6!-uR>xjru9wS9n^@oP)+n#S-g;zcaA#N@9Pm9BP$K9m#{m$*7>7SFId#ODYsY!_hng;%_&)NN!1VlU>LAsN z;(Q#9SOoLIuExVfnfPG;HPFz zNk|MC{FM{P!q1SWh|Wn!bD*=Nr&Pp_h%t6@Uev_P#G1*sYFehqa zK2mUd=15-~a3y|Tgg9%VoG}=OL1z-F>mL|!C3A89m}}PUJr-Q-6JvpoIzKh%%w_Wl;9&{bEt} z7MbOUK2df_B4beo7>Q|Gkb^6P8Z$|>xfT=>ISy8&ppkiiV(Jb!iXD<0Qxbin|46=L z&nwKwIfzN{4QomIKcxI81n_h{mn0M=NUM23L1E9$8~wZQ2gLu(HxE;>N|S~H0%Ar3 z0#f^L!bk&aGdo8MXLB39|DYs;fupJE|4u{{qt<7OHwNGJL3^B-QLnF3C%$a+cW@PJ z^$A4tjgD%Zh+uUdYyK7OaOXjt?s*a{gJ{833ahSSY-B|2ex&1tZ)e-@_37aH?&Iur zq=ixMC9Z}Mv4!vF@nS3Nqv!kH{^jhz?q>VT-OJDKWlt{Wdglg#(XM19rQ~a1@1|qt z_Gl!B;pe^mrpBO)&i!`p?e@|0 zvui}J=c7YzXUFx2+qZ2k3t{Ht;ALUz+T(}YuNzMeF>a^ZhliWz`|9G`^yTsW_4&=z zjoW9t=Nnew^XU2eS%?0w?KtAApAcVsjP0UyA(C%O#zBhP`h@vHe!ECRamb)~w1AM8 zTZU$&EC2#PA{i7MrkwmsRIkK2jRFp!8JebE<(_3()-HXXO0gwJoGDu@Ba^Buma2@P zH|Rs1g^PG0R0(%oP;DeBT=-3KwA}P}-C*}*gqmL0*mwNBe7Fx(j}?cl)gCigs;QBM zIsmP0YF1%acQ6012Bm6~mbx@kRFkRZU*&fy@~#Obip)h(d=*^mX{@Sd4hnB-1j+^% z;c%r&tmp%gdW1Cd<_fu{U`z7ly+X~h#C>BRIiQvz_6c(k&#P$lb^BG0ToxpL+^k!( z1?KVkxl!A+cnmOU2nY=`gDPyUs@7q+t<&=caUjMZ88< zz+ni!YRK8Ir}>v)YXxVP;j%Q2!(&!4t>?xz3XY?_=B@nAb*}h_{9cE2^HENi-sNXA z^=X|d&7pLCXh8O@2bplgOTBIE7Mm&pwIG3xp#&OLsOH=ZmiR9E`{Y;@CPTaq)v9|) zq~uHD5x>hc2h8`mt81La`}0H>h=)70z|*kp6(#)kGmrVz?rT|Ys?4ZGZf!{5ttCSQ zb1P-u*C_TaQBRqHej6&bQm-$ZAcI+QgFdulFlq=qSAd^!mRE32=L)rT{;I6gCI77_ z6CuxY3t1$Yh{aGhPxnKk08THD9v{*Pm&@OHwj$UqBj4v1^kWKvE=ZOU2#&)|p5G5t zu3Y#$tqpKRGq^3RsR(A9ft!aaUob@vs6B|}4%!c#x2+^vrkG41%;qwP;R?K%wdH7x zP}O1s^wMpu^%doH!I2BUZH z+s)1)7`7b`QPYN41E9e;`F4JV@FeyrMbg&QmkUB5FH?{i%u}-{hY`t5(#?*`Br=Q}12?+TER5L^Fh+ z3a6J3MRGLZ*g$ltplV$`CZAEDULu${OK{yots5pZT8)S`bj&d1BW58VsjqX@*6ocu z<$mFgug{?9Z>m0w)=~o6bamQsJY$4{zpehQzXqC6hO`x@JcbB0&g%zgM4|pd9;O2# z1(VgbCd~n#E6U1n(8wo2Rk*J&w=}1OoOBvHZ= zehS35OQ(;&5#P94%*-b)wofd8rOrfava}GXMOjL)GGu~diBmR*v}uK-uIS}roz;nd z8e^^npMS}f z3l3^F`ly~1e#->z0;IkJ1ae6AEMwT%`3*WvK(OC%9~QZz6J7R_9yYP1!ja4B6n@};cE?l$&1oJusT}`J(>!W@#r;J*%#+?9f`FdDid1U~q7V071LHV<}Ljl`gf-h5Rx9vk?I#OLD^Z`P81bVZbvAfBvj1OE?RZO_?0-+Zv zCU<(E#%K0MU}>#=Ef7h&g^g%ii6QiuGP|!|0~zhnqM>CWy@aFnW_%%>2N}-KCZRq- zRQmuaIgILUEyEVjy)jfM7lG>h*Y|(wot6$H9b+EKfJ7?|I=5*o>v3~!UKndmJz8t( zKGR!7i2>|kssfv4TG|^E_(3FFi(@jSwp2(}XL6=6aTKX1_I#%M!o~=S(;(P=L1!#y z4raYSPSl>o8ZzlqRnsmQzT!^_a8$M%a=e`Rhy~3i9gtya@-+9U@)>a zaB{LRwJ;DMf+qU<=hQHF$0#vY}`0nfoYPai6YteLkB>SvdU(tpL9OM}Wfx0`I z`h7Lu#zYwhLH;@5bY7ZF%f(34^m)@{F~+Vcpp$J_YN@BmAFtdFTKxuKTHsX)0fsyb zwFa1crxsR&^E!BDq+iq3PGB=wq}V%TUKNEM{vNOO+IGwbe^p0cNbN49G($E>rlKgs4EGy@Lqb&yCR?yD^dqO+H|VAO3YuK(>xTHPF0jhN?00>XE zw%tM9E0;&?t6SscKg%P#n~4-bPD1T)48(38nRQQ7-X+l=*1EW^wZ}()nX@*IO`UAAGZwl+5Voyw&#w13rYAPLWLz5bHr9P> z_HebYlRws2)_f6vugbTAf5Hke?lIU;EbOePx2#}2!&+{cWeGD0XWBI*`PBPry=fM% z-OP=< zq(TB|r^bUvmsRuwJg%3j(i#(48qyg_CFKa@*w!TJmTJ?$5yYP{bk^`e$Kbbh;`nin zu*1r?W%lxB&Sy5m_o7krcI=9nqsO2op~rp^ zGnLWC<_+jM=ir3&npxv??<8@#y{@v*5h1R$`4iq|1cYP2HNxt z618qU!0pY>W@0-%l_A5blayft=Fm0t2)ao>-dogNj1x?d`(g&9Ek$J>**Q2Tb+r+TwI zHHfnyd%Ju?(_mGPq^W`D8`bl4@ZTmQqOiW7px{`(P@ju_q#qwBd-_KCbffp7jI|Af z0w&-WQITM^CrVL)(zKCXRcpAt_;UGBfe>J)u0Ljt41b~&v<;<^R1(G4)N#THIo&PT zk-_KmqiRK22h`evRS?&2a!_*!XK{t<4+R)!6Gpqp38d>f~_o640o!8KEONMC8rL2 zWhLj!beLFc-C)Ubs1U<>(JPM*rU6$4OP->7O^*Gw--oh*s3h7=6e(T~g%NIbYUeBX z`3%ny?Lj9D?fdbzqW4qx${x)~Wev@NGnO`p0$5*SCcXjwn;EPyV&p4KSjFjUDv~)) zYslR(#3qw^A(Oes-9&dv7e#8oHnA`Mnil~)St9c3zgvTk>`c_1ENobeK7u8bOpFty z_KeweBLIxym9~|N492)&1T!1qcohVIER9)AbO5duhD9P<9ny$5#>?$@3|v5^Cl|n> z<)_CnnDcjg>KnV=tSV+S%10geC`k^&D&un2q1w$kmyLn5}l7c%gtWWz;!`2+f0DU zVkzTYl;&yK$-wH~`P?m!(&ZR{nx&#@G|l}J^Q3VYxaOlky@xOJ28ARP36)Pq4C6Zi z(Mur~&tpj5MColAgZlfN10|t>E>_so*q7uN^RJ#h+82xu8z$K8& zwT8kh53-vYu1=d&7oCGJA?qp8$W>m6MUK(OQwih|O`3a=y8uUGG;pxVzZR>=S873a z`(LJoK%~A1v_PlfkX80I`gtse!BR3L8?sw4hinazQZB4n{?+n%j^8TwV1Zbu5luj! zjE&$kU;j9?035rkrCJe~P9)6%MYKR`(BIx1L(mMNtwEDBHLzP``XU&$s!$WD)K$a` zkgER|XXn(NS+uO-*iJgO)3I%Su{w4-wvCR{@fX{+JGO1xwsmqY&c(UfW9%O==cu)6 zVpP?v=Y>mSkxF5s5K)2i5L2Yn56G@CJ%_G5C=l)^{w=j-36Cv#?|(V9P;>Ff1x*HB znUJAdn^O%caMFKtEGQHpbySyyBv%rRPPDZ(7aR_0BDOO3-N{;oQ5(}U6M&MJC>Efo z=vcl(Km^q*6u=@6LH-Nd;>5$>H{85A#Z1DQI00uQJ8Tntr1{Sobmfppd2n}ZycL{% zxK;!B8JVgwNdX8v5G8PG(5YWN5)P3G_SRiHeDHYK@C2g97lo}-w;2uP3MCGI1x_m$ zN`TP1%wJ7Q(Vh(#716vd{JBy?f>1Y-Vp06vIDn9NG#CRzF)I<`S(=CInECI;CD^|~ zjb1;lqQ_sdi4zp>R7S{UJ8s?z#>*2e0u?lX3$EIh&4Zt{mvPV7cq*}l4A{BS#)YtR z?ls{`NETQJE*`ac{c$!((2pQyH8qS}{s&;MEtf|+p$!<|O(rmPHb^u%)>)im4lo~0 zHU?zb&rd$=)&bM8i9e&mdUe(mVzhQXo zthF~bXubV`bo8v_x0wsO)slWTrOq(HUWo5q3>yR=GvVwpv#pC;YYdiBOmFy#4kqR=jIr zIj5_A%`T8X!(Zt)?Zzf>jY_J`6wDqRmyndefwVaCYUpj;lu667zZ3a!=oy**R7Q1P ztk(W=$3z8mk{b=*?`NlpiP(=Gr47a=Q8vh>7rXE^GnB>eI%$iRvM@o#TYOlP4TRUN z6^c!u&4VRPGu$cbeN&1pQ_aRJ6=B`*L83^>-w8o4t<2J-!p!4#Bo=wHpG3$g<2Hjc|S&c1kG-YW>LxX zvSuKwS?k?adn5jsABPt=fj%A`v&Rj-O)&2~#LGw(9`&X70n zwq}q+3|2IuQ`H=ApsBRE90g=T3~o&Rg3fe_ck&fDUnSEa*Z{gA2ea&l z;Q!Ag3+3WTMOcG?;9-J-Nc?y4&;Nl+Tn(*VO#XA?_@YC*o_yt3<;!ICcHtIjpZ7+x zAc3<`gCQbM3i+{Qg}-$xLMdGN&rI7-pBTIG1Jp}iGU;AfG*h2p-4A{?(}^gKAr?W4nwaxKbrmgygu!^-X4j%UWcmv9v+atJ~{P2 zy1M2}vb#LLT#n8`wHct5bRzfR7s*h2;1 z9YWtG{-s1YKXv^nE@A1;3uEKR@oC1YH-V9=`1SKGrVJ z*Zf{=?s8;&hj8W+1>YvBzi#f_zV^Oe$ENf@uG6nJ*SGKLy0pCZ4_t=C{XQ3vv%e13 zt~x)9t9>7giQfKY{CeF>erF(Z(z)~V{pkDp;MDi~`ZoK0Y|HQA+Y8U>zQso#I`>4c z$6GHVA7AxQXG2*xc3+7_w zx@CmZPOxc8-}htVN$~w5yQ}3C>+AL9>*etKGdzEJf6sCIBFMFW#Q8ZSasNKK#gd)?HwIeV+Nf zKdRexd1d-MEu7t_+eOz4ehzd!PgE0`eeF+u?oxHVk7VyF+n#i8G`)3RdF;15W&B0b zwscCE=;EH=;;|a_IeMDjaqTdY@v-&UUq8ySwOP`>2kdp)XrCA#u3$yZYHMQHUyU==F8DAAnc)Y1ztE zD)g#xjQ!)@Y%&WpgoM9!jGd(&)}Jl-o8^Zt1)rwl;KH+nzlf4G|0Ly7_0*Mc%SFVkI+WHa zy_DUjQT?b0dZA0AjJaL^UGAyCu$1B?&Rs95me9gVtKhR;bL!Yvo&8$Z>Cv(C!5JoJoHa2D5KYZl=VQ$(1rdse$} zx)0`5qkt}t#%pE-1xHAcd~ZenfM{LkJAY#mkpsMh(q998v*?dM;c{i45qTs zS~EaF{vzz$M7bQ}FQKwgPe7w|F_MoYgPp0z=ccJgrg7GoT$)as(Z|jth1dN|9etS{ z#z3>3E^D5WI3~c!4bhU2udO0EtFY)n5XmGzDu77=k5@%mhFAD+j#>l=jv;Of9m$jr z0)Kc&K9z;N)oD{bH@guD>b-#$lsKpR+x(@AF}or0;o1ZB#zQ+bg23yAYFDbo*Omy! zNzdP)1tIqnou%8Ex@5gdJ5I+mtw!Z;y+bMDlV?qh!%d2fQ+b)DJJ?p`rWyn^#E~=Z zBxN~6kYSdDalB(D#Dq@MQN6f7`$|!jKOcCeAxOj9woLEr<-fCwKAt3rj0vYDVi56 zeCd8(YUR%J2Gx!1V`wO?;u|);7JMg2EJSoB6rkhA((hN``8-}dZZ>o{daPbsmKqe} z&h^@U#u1HM>GVwiV*_!SQR%_`(uZaj8g*`P+;85EO~s$#Xv5|GRvU!igT}*OP$5}A zodmT&#cf09V}IREZkXTLJwg{m?*BXv#^}UcEzM~+%39e4H#wl0`Ao7P9+PiAdy-BY zlb&W7AGX?X{Dyou62A%|yJy@fHyTp`%|(d^9wp2#tk?<_6^!w!9~Gzi zB4jNKwI|HChB11m%NhB%G;~!&O#5%mUK=LKb2ajn`&jVEo7~nH|9s9!oZ4j^t4)ET zX%=v$s6lDDrZKI*tdR+k<_e%786F5$Q`h!5bl{{gK<1(*SYZiXg#60aOzO_o0&i|Xlt~ot$MD4EfJiSj4U0aQpDZIku@~@9aqb$H(#WK zfPxpbBvJsNwOSll^^?cWe&3=fzpNKS3fZq(Un1S~B>pw`3p*nDco<_{E2R2PeI39g zviS^Ce_nvZE=tW96q-V+PfH)C9IkCd*Y1SQImWh1Wc!CfH&c4307m!7!>u~U!Y|XH zap*k}c9nP?DDa`9vLogB#p30caAzpv#YxMLE*5>YexvCmD$0jBl9#q;;v{97v@``m z%ykAwwb`VI2hLp5YAmwjJ%f54$$84U1}D(5iU-lHjL#sP5wT#$nPxeUuwIG5>ewlh z8lCr3u%pg%jj>35;Xqx%k z5yea-xJo6Wwei8Er^%SAUeIVexV8VmRYR!mBj^9B`)gOb)|dxS(A2*Hj`Pqi>M?I1ID7`C zmu1j@l@rPu;doO7Fs12QSkf?&DsYxgz5R3O$;8=au+wUGTOk1AR?<(DvMQIUDC;QL zQ+X@@mOV6a84j8~SOjT<=xV2= zS&b4o3R5UD=P~2Bde1hW846;O#6-6=BP@mMe!&V^!rMv#5Y^x!6h(9b^?P-IJblr? zsr|V4BXXPO+F^o`SQjc@yV1OR!hY(c7MiYFU+-!TzG60U@v~c3{?T=`GU?lTK-{TA z5^IZ~$+huZoN0Eg7`Cz*vK ze94PKNETFTKG7+wf0H)zUQM4L0hX%H*f z5Myr_p3YHH3Qgq#%1~>c>nH|0}` z69U(CIOIyuzVju6k^I^9t8>Y*DS&$x%5qx=4l^x%S|#t}R_{{mP1Q-l3?bb4PCNId zkwx_QIBoPy%^SG~#T?x%Iv&W9651mNEI@n3YGqwIo|{5pF^)% z{Y%3e6}Z@8Uic8(JzH8Qac|Ss%#I6ymt^FoAOWW)As}zNz};IzGFpH9$#}J3IxP{q zAs6JdStYqT#1cz>6bDx)T%ZF*n^PslXv9H)$$Zmrhd*>ipDohx=w7^p)dQNOPkB6V zU!rd*JmgLr_p1^OsORBiub{OojCevWH_T8kY--zqo-#eUi0g|!iyZ@zM=77Jq~I>}z@^lWp*pi4oCqimqu)tx#yd*7J$E@XpgnENBW3p4Arc zPiY;#x?f8{td%iL-M6CUJc|ugN@Q+(j-ms3JgO zKVwFV%1}FPX7$3~#S555!lw{(afTnqYihm9uhF~pp7D!q_jk88jX!?p%D9aZ%oHWs zd$y}P<{2QyGcT0bii4Bj{7YOFr8$c+6%}#INq7juB|C~|yC+8&M&s?EZp+o6(6Hv( z`FUp{HXh)It~F2k!qpH0Me6H_8(;nBd6el5k4lp)bP~ z*P>;x8E`9_1!Aq2C~8tm)nIWRrCLqeQCCFejaSio1qXbhg=9MQdgK?dcYaETTYg}Q zfInCjA5_GeubO&c}pS zs{&0JiK-U6m!c5XYDcppcY9ufh`luE9yzIPx-l#$0Z~yM#)&*V#XshIkzhPBPt*2+ z^q%(>8`E&GJ$nsy`j}*087NdQByDjUsI}sn^kUJ4zU{Dg+|C_;Jg1ptLb%)}9<*A3 z)2}J~{1;6OE{ak;b>HtlwBc0bXaStn>^fmHZsZ3BOU8P@uu41izRq(tfT*f`L%EM|vA(+1+=L*w;#4yy{2_;WfaK)W=XV)RL5$*sO zTjmB?#lm1GaPoL@iU=VK;b#2ccDSMnSN8hpf!18MEC{(ig+yI?$Vr*X_Rd_2mJr@= zTo4Oaha@_M9PjBDlE^&le-tZqeYnPBXs@YgoQ>Ks?^@>mbCpX=yH0|)iOz4BDYkkx zXdWa~D&7hWx+*G$BH){R*ih?2v0a#gf}%EDr!f;lM@gwgS>W2j$9ff5nm%jI5g>$r zs*m?_6dNjfD~2{;h{=fdg5&tju`xe&64lR+sWPwj7>Xla5cG`viL?NIA%6U9 zZAAZj*aHOhayVNUp&!rmP_|tS>?w*OTDu1~Y5gxBkCo=%m~~AT++uVXFseqQN%E_P z)R!zb6+f=U*?yMV22pw>_INb*APdr^l`KDzMeAD*_--g}E>Qlt1Os`H+;H0O))?0c zIzZ}b(?Shn;RaR9d{j!Xs^8$yB2;tD6@}c57fPh|FHeLCaOPA*@rHjhsBQ zJxjhKn)+`Rz&7Bp=b6}10dLZq%rfr}_A48`^To`rZf&9vJVYHhk=-`p_&?4Hf`A(V zT2$Wsh6mdveU4UJXg?-Z!8kl7?^OY!Et#6M6q2ejQ`UE9XT&_`$dI5bj%-+}A!+Q+ zlat2VXnkl65Eq~jt*Nz~t>hxjVU#RV#9;d^FED1G`OFX*)G753x7T=zDx0Oe*P8pd z0M3#8?G`vp0FsG#9fHfDl(R2R@e6t?x+DZ1l|-Iyj6ua8S!_CjjLrr^;kH~9;r{t1 ziFTb_3hn8NAIvW4S^kxhSv%;IF}@^MCKks}(vs#Q{zdTmulTFr~mWGPrXm zC?-YzCm@_BZv~}^RvVZGQoU<%$eyPK&-;(a)pK8kG1-6;c~(TnF?~;71IX3UkX8YE zF?i^I(eKoE8Q(-Dcb1zunQj^8S>UP&;i!a;s)_Um3D`!c=UFl+N3pUvV!Ez*n8zW36VRzc!a zGD4O0&p-^I`Y4jaYiwXK1WvZXH$DUd$|F#ud~`kHPr=`;Bw>sJ5`r3#j)axV@EgOY zHS3}T*^F^5eL6%Z+J{$UnnkXGxd{b``;R7=m|Kfb3bAONx#?qgiY)d1fTa)Y`R<=T zjK1KxC&7?(IS$GCL$bbWh`So?tO-GovNX z^L;j}w^2lF0opbkkg7#h2$f0<_I9qC!IV+wGkTcavq`sW%)Z@vsB%sy9|x3Gx1~KP>>Et9c1vFM_}(h4SZPz05%N%j;AtLs23sc& zdATa?EVtZj$%%SN=7N7tG%FPhHx93eCSBbAB0qsTkvAe*tw+}h@8}4(UcfCSLzxB_ z(xalq9RryFQyr*F&nO1HTDcRzSrdLIo9#S$3b5;*Ab!xXBSP|on^wA1i_+rpKUXtg zIK_+ZF8DpO;vF7%lz$e^hR9d)XmL5IaI!beZRhAWTjQAp=R8DQBd;0O^QJ_sSTN=+ zOeORytP^Z;?znxcD|9<4X+#K4I%cOEiegEf&;bENcXmV40nnl+?CuNlL@|ezLR!v|VGICDs{dLpeepm;g) z*fp+b0V&C(5C-Xc%^!Rt*fmeQap>c9KjV2=*j}7%G=#SMLO*hT@znu9*khqNaENAg z;Dw3=nVq^BA#`D?#&Sel{UurLjg^BGq3v1a41A(8Oz81j^Q~1$0QHo`%x?WD2@{wt ztBSi4oAK0eXG1u*V zl&cKP5f^ALQLcftmw(KUZ-{*Sg=is}NsLpU%R5vo9FVGv!5GbFGd~(gKy`Y0*oj3I z#6N^=o&jCmHcS=f^SOn(nJC&bfLmR|;qgzcAg4qLf4UkD5Mg;)F&0rS%(=d^m^`|m zj2N+HurWm`h<@kutK#=C87z>}G^V7CWS=xusmMo=RX`3otj{%<{N%zJ1w;`i{vyfD zoX~3G$7h%;{wAUUH3qN5$*ryQ)(F@_x0YeqS*@!*2bH9m29L~O<99RZ%kf{g^{d}W z&Y0FW#(M;R$mxe&U7a7d2F6x0>&&&ag??;3$RBID)q=S&LO+3;kWB>8g`rgS3`kIJ zA^i)s4qt@jPYz~3dn7eVZnE>eql$aNb%nuNuL&pRbhptCE|V-<4|$R zXAD!tFZB;MBccFZk&4A#fUGNI@@!xf33SCUm3-GsL7zPLR*%tT^cbKYuIQd=Wx*W_PT|vi%M0fEa9`1^LBAdxUOfYHwYUvpgg^eZR{Qg==xY}<(9OAx zD$MVpqd{97${H=VLauQJ%9bn->?`mpf4MXyk8B`o;DwBD>Ik?w;c}y!i(?Zc#b*5y z!RHI0&prE5M+U+@u%%5@3<4yNb1OPm-t8;gY-DgQ4O75?rp2DMykXW}ik=p_A#~eSdvGMh&F2aE?Wkz=VS1qY7 zDutPuA65SdiQ|hcVwhg!nVPe#esruE&D1!4G-2R6!Px^`-x7)~245&K2GH!1aLvwx zdDKaTf^QB`6YmKjAl}`(v?KeSlYmn90`(H18o2wNjU(s&0`Jo}u?&3(KRN{cy%TO9V{NY(8-$azuv%Sao%zm196@7ihvc_u7GMQ%cJ zs0_nL$Lw?Vaw(?aIIzQ}kt%|gZV50-`e_Sd6O522pp7Mw$#BEie%(YLHU*-9D}^68 zMOjFSwmb;n8DPAa?VU>kA*-Lui#>NQ>GqY5K>Ix?vs|g7AnE>w(;*}@#*PS)5)R21 zwc_1>@?i32LR($1&brjfinS^VNpNsc{6p;EfnzIYZ#DCQi@EXDR+DingwJkE{eZ|l zU4#a&xX~YGdHkN|Y%kS!5dWcemXzk_UDP2I$*&=j8qQtspaJdqlwdD7#2Z0lMb&?W(*=~}n6hG}I8 z?b?XB*rFsG&*~SyhN1MX6;h*!sBBGK?D}J_n zij;1W+hQc_hSC6Dd0(`iqx%HYq%`uPY0_45rVb5=#AK*LD@ssEQQ1J!{q|F-7&|AN zG|$ruQXa)rwgoeTie4oQp=0MPM;JHZSdtmmzL)&dG_PvxJ4fx$0{@S_snwO5p)5#* zaxWPsmAiqOLKnvRV19MTfIp2V{tU9zm%H~SovWY~evibG1%fk51tz!uc&%uDaYgT&v5EgcE7sm9cG!zcZ^8#kD}z~G!^3V&KFac}&qB$Ydl~dhs;KIE z>fz~88Ol%ev24SNgH+;k`U2APSkZOoFEh=LBF9-AUrpiJ)%DVy zUEwfpBGVLP27`T2rG?9u%`wu(H!2+-WN7k-m1o4By)0&&%Nh=G-lSxwO*A{=n#xiOeCECgb9cakom*{@J1JJ(*yJj}EE5~Qe* zLM2&)#o`s0g(=|u5S?ztKu>t(S!U8preYm}-la7RQrS)m2r9zKQUg;u^I9qXjbTDY z4`R-WY(N5Hj=8QPFSp@BTj72p%d$)+1kR#KZM<+x1FNklU@8vomX4F@P5ehBo=0`` z1fK?c0Z>ye$sg9RD}lP0lJy&|Vc&88*%J=D_DfrXIFVe**zlz?D86*6SCYz99@tS^ z>+H9b-`jyS3@cmC=KuSYFj|W0QDP)!L<)AllNgq@L<&5fE06mp8*KHo_o9U#?{$O{+PVh4RCGz?Hh56FQMil<7ZV`e9%(v8k zi&DO(($|htTYHKYJo3JNfCKCFhvy5&aQMwZPuhRi>)Eq0$PQ7Pt%d9vUMXAeAg93t zGjw9;xSvncmfe$uf=&3dt)RC_%Eqo6NCnwDX%fFiLCFc;Tse>hX!_Ns!f=)71@pU2 z3PaXWu{h95EMp)4+ME9Ck*>)dqTi#x-s!o-m<@ECybOUfa3?)B$$TX0g-4&hI02V% z<9F?mDGY~F8BT$!0cTo8kf|pKcZX`U?FC|0sD;nJAhAW>#$P+?f9y&i0Apl<13_S~ z$)J1JlMiS)L>Q6eWts)zS0-NPPX}l~Q((vfEr?IN>Qm=7jC;WTGn5G+SFMyR=az~8 z(zI#su$z<({AZ;}++g6g^YRh~<&{&xaJ&8{JVLMuI5@6aPbe9^j76rb>r0djwaUTg zECrvuw>@lQ&oE&4x|Pec=YBiz>Bsm{&$YK$zQjmV1PB8P>VtE?K* zpXzBF#UGBwvQcY(Tb*#OYiZvUqyimATd^mjS}F%;H6-f9MF%z{6P8vK;=5g&F!qAcECva1qk zmE6#}89)WLJMB4-jb{rbZHg?ka%Z3X>OW_=Bn3R!phedMyk$^&FkUcMAHczTWmuX@ z3sLRID)kQ#C!99P-H~%vxjgXj8W9MDcOwUEPA3RSQg^HeR>C(>oc~&I##1z469F9P zRzb#7nYQ)TFokPw1q?*r2m1;d@tQ_~cM=V-KX{MWrCSTZ`C|yL{aGVzJpfst8d9P1 zpdNkwhp8S0tXZDbnv4INf$zLZVy;Rc`}^uUZvEO<4o3jao>(ROePGC6cEn;M%P&|V14wQnyL zCk5_~J~k2;c)(W6=xR;zCOa!u|Km#!J=%FeV!!$7!F#}NYyEcQxBVFhZLLWP5N5y4 zRK*lR=z|^;$)TW>Vl5B&!#%dh1)ojcpImP0{s8rbt|ACke*VF}7umjh1@IzO32=M`^tcUW|qo+-qi`-yBei!jTvlxSQplLaaZ!V3md*gn?C zUtVk}Ht`Vlh^`}51-qs`zPaWVs9E;h-28L`+!EOEpM^0{s}L(WzAvG756c%*WAL<0Pjqz9-&LC72>wgzo_C>hy&u>hY1wAiDGiwr`f3uQ(_8&u+vMG?4 z{<3mnq(AaeF50&~51<~oV<@=fUNPxOJ=u%vb~RBC!Ma`TcY;=Rh503o)=J58EaS|Z zkGS_IBSp7425at3J6M-chKw9sWarR)eb4au(wI^w($ zt`V3dQmN>}Cf)w}Q#_NX_}*-qnmVqR2%(Ol=!L85870cf=xey%<&#>8CH4-}ji{!NE-cD2ZkLR2lh+>-Xim;D= z#oWYz_czGGH`_<~F&QETcRBfK#jEAeBBr!QH+;%xIn54Vu0x#E!ep{7d1EZj zMK{QHHSOeUJZQpr{m%4EyjCYv)T}VeK+*sex66<_#lb3q#4WqsN2#!p1e+Qzt!x6{ zUx+71C;Z;!y8|kd>hDO0PdTRapc%1-2+_qt2TvnL*oRxvbQmYa6E>7B1Bl|2Q5X|C z`8fUpeOSVPh3n07u2oYF6lTc(6rNPl`|3R4_ILy2! zSwgq0HTLp%izJH_08=YjVxGKwgdMyAOT#burw+SaXAOU?!64QCG{Jflz-xdrjPyfR zpkUf&O?{%gD7+N~{t-ZsoS*^cYB3`JQ#0)Hvp97NA54Q}*#hz65ZXeUCNQ}kF(7Ny zP+JG5tTrn^(>rhk2NNkFGQCoW8bokh+m4p#lD*wnF@#Q~=?@3Hle$k$r5ab1qad0~ zF|CfM2$LorAQ>kGsnJpJIFFI$Q?g8uBo7^MhbBvWM25Fi!*DJH!(!39lZ8AbdmT7m z8vmP{AOFi2`Qp>=YRW?@I)kr*9DVQ#jonBnZM(w*vu@#B0+Ri7nc*lGv@B(E;H~tQ zyyRO<%L%#T;8wPt1#MGY7L9ANX4)kdoD)aTk;@PkiHB+S23&0!ml>sGQ;3*u+>Fy@ zZc#lROQ^~QEy)oSBM|=B-sG$Ne^jg1JtaZBYgm_;naC9<>=N9 zxyqh@vk+#ND?TSNzP6$=i=?dZFRK!Ssj0L|l(}6yvyNqG`rlWp`hFhyloEZrqB2MH z0*k$cf4?p5?pQ|k6mC&XN}_G!3h;Z5U0!wb-apVBoKCe?=}1TvEdX=ikM zf`#U9jhXijwr(Y=TyR%mqy0rF;zOlZ_PfnN(qPs~MUhyLc}?A%ih?5`$=gPK=oiRo z)s!^Ej-?3Y5!T;#DyM_fn@~NZCZ->bwJOYGtmO&sm`7ra#7DxNT3tIny&WOSOcCNa z^4`N^xvFkRzup`J!ka}}z|2k7bGSDQ0tjr%X@@|LbJHiWSdnLB6A#w94M&jG(M_>) z5>V|zRU!Gt(Qj$uei2{)F}g5)AHD+u6fk$4SK-^>vw-+N&C36z{^mtz$+pY96zMK= z|9gDKgmQs?X;XQp#yTk%J)ZdpBV1%-d*H2tY7$%~jNCnoJES=)N-|WqdOafZ@Y>d% zXhg|`Kwex*%tfF>YoZK#g8bJK)mZnZ2@rMk$W`0HbW+8P@Hu3-UUXGG^B<+zhHzp> zhCwQ^Jiu)fZfMeaZqho!l`#Ib5s`<)c-PHu zB;6mDPua3Ria%7mJ0#p{ukZj3Nc$~rP8LEi)bY~F=*#{NkE@nqjXYP3plvA)YB^^ZiWpR$~I5}nEi?Y{q;<-6^^V)NLQk<+0<7jY8vk;6sk`VXeVyf!} z=E{w8hQ~H(@!S&`M;E>q>iU75vT;4|jneys zkd<~!s7S}q5Q=3_Qr*NDa%(#QFM*sXURKSOGV2oj8G99Lc@g%&RDNt5!gh*+i3_WE=6vbL^q!fqU2}^*U%a^Z0Kcb&CxA2x- zly%LHnKetZVPDIqv7U=!qaG$5gAYqDCM#&{n%YYimC4WIePh5Sba76^>3RkC&LBm$qtQhsV6}m&L9G<17D2LBd+PCh_q_tu&7skEk#VzjSzY=}C z#$1>xaHPSCw$$tmZqcf4(-GB!jn<`I`@HGcJ{XT|CG)UbB0GQYPjjAv4VR$#LLhPV zF|s9mU3RuN>$9*q-Du~OOo&1t0A%4rGZB+&(fk|u&iVzTB=tJBVx!p9r!QdkM=^zffJz@Q7=Z4m#7W+J}3S#eGEs(R)sC5H3}@og&|@A1$LXtTZXBc zQ{>3(2C}_SD~G}8=*wO_`Q!h1gs@#m5P-%L^UnZyhDI^05iKV*J!FL{{vMf@xnC%(5}{b`^1y9^O?^jvNoyw-dv-ZULx*zvg@Lg;w@@nE&uBno;{b2Bk)RrPXrW zR8p6_{Y!|(T%iO<-8h>G9d#I{!uxe|C+ql<@&ohKquJ(3a)WE*M~QVPM`U~hNDw&2 z&(vg<_SD97-Rka6d~5L4?qTz|yU%%nd=##Ld}JdB;zjhy5ZJXqyvxXQ zcBE|!#T8h0HW(GRR^F6Zvp**WZ$&&g(A4}YC;wXF$m{q(S4h!OIozSOhHRSlQp29r zv~_q9o>R^tBhI{?7drdsmXt*)lFeXW-DU4hvWAR_?7Uy>hk9+;+epY1n>bH?_$l1q zg6u0i;esJ4>(YMwDV0(YrU!R~1+jc?c!I2c2z#B8QbdBW8j{H>L-yX7<8Yl+gj!i0 zoT633*EaGWD~`<-z)Lnsm+BYEW_!6QxfRAJJgM#Vah|q zN=E=O_!Q^PWl*z(k?@KV2(Q*rDRXeA1k11*+fZvX^x>uBUEimYYu8+}0pz^@++c

%b}HnbVz)SH*&V0)Kj#p$S0k!Ac0R z?G<28?aX9(Ur0XtAZcEhl@cse_>M)+?!2HV%tZcv_rh!^^es+@JP?BHKZ&sF9HZvl zZ1|dYG7_F}-IS+)=<4qnZmp+=FgbMl@bE=0OxbR=;XI_=|CjQ>lg$b9cw17&0@y*rRU057k zpFp@5n9^#U^L#(P#G+Xi8uXWYMvMVLEt<^m6<)AQ8R?+9Bh3ANU$Bb)_6h;wtA z(|uH^MZ_XVl4Rt9x*C9Ri7%iV!_>w`8YA_K0H}r&zJap%)yb)l3YSf)0nE0<6Sinw zn9vc0Br-C?Px@v)UI`zWDIYP@rglOh82d7bUA#{LxI+>WXQP>{)Ed{HQwH2I=7UUz zKPeHnbj!@>kw*Bo^wTL}_Fc6&7gR)UZ7sneX^B3&hg~H#A}VnnMJ#1L!%a74aeme+ z?`VZ6_jeo;zC$fG@olk4Nr)fXs5w?6wm>Ja zhd7;5dwTp5)3u7Kd%EAOpI^nMY_&)9!OF`R-Q$cAY#bsWM%9ToleW=(CJRo-)Y&Na zj!0$;qfOgnT-}C^_+aGafeAy!neuSRebROiGED>CQ4s!F^lc{(tdbx5TdDEDbNSG* zzzvwH&$0@gdV}a8lru}+BJ;0#=o~Fx7xx@V?5|h*QE<{OOK>p0)__Y0;hQG{;6WTD zA}CAW@TH2)xA2HzJz zL8|fzaf&z(|9hypaR1%jMySXObg0*-JtH&-;W>XAu>@o&2)6&RusJI@7u3rW6p1li z+3xq6M$0z^bQoh;T zSNcv4ef%ds_NFT2H?ti+tH1}1!dtvCD$#x)h^LN3+RjiR5`u`6@}KY6x+Vp$X#ckn z*{w)M{JG4S?` z*+b)XoG86QMh}(w&Tgrbt;BBMN8CR|8@btBif=Z*z2A?hmfab)Y5@F^{hcQq3_~gA z1^!m2FUcal!ohK_%Qnn-4WW`zApDu97QO5|fBSP!=%M7JB;hO>LsPmD^}uoyV`k0b zc_yw@Pa`=2g-KN%fQP8t^}VMWPKV>63Gf=pvK97k?!M^ban12@vogmy4M+zi9&2Ib zm3x2-{;|<4)Y#cHtiT?rEXLnO4jGFDkr_3eonPcOLSF5^`0-DdDqR#E;@E-iQI)Cx zXdd-BmxBl8XqZ;K6hL603yDtFfvc8!RX%+SfpHa^OBiZG3>T#@$)2o?he8!?n~vZR zQ68p$kJ^6c>+Q=ookNU3?sm?Qx%jmP8xYMkIuNMyKKAA&WRi=>w}|7>W@`-Z_KEJv zFX|VVmu?36fka5#7@z5mKopk)x|Y>m8chl%gBw8_CX8Sqk=n?BPP*$JF5E>;p>umh z_QuwlG%(geLDI)!6Fj(4k)SoQQZ=;5(=Au+Nz6kE+I*8g?Y`S!}g@;q0-p)9?SAU?J=l^^uuzE6{I*WF&Dq3*Ja z*1s_33@82p`bF1$APpXWZoeUAZbDK^20&fw{ML`TP{bE>khs@rxW2YN927 z6LQO`uEG+8CPk?bLqM;JUnKtM@eIQP>)v`WoR@tI8riUt4m2QCQg4S+E;b!#uCTg% zv(hMHVjy#eUYc2Os0OuQ+2j(~WqW{a?QCVUqsq1IsV}Df!il~%(UFZe)ZX9%!??oT zFgTHs9F?w95Tpo#KD5x`(aaLeNr3weC~7rKk4GX`L8`z^D&rWtYXA?t6sztO#M~G# zeWjLe#&Fs?P}E;k6n?o_c<3}mkMcxR$MG1V%7Lp#28M353|xbQ4^EY^EzefNID5Oc zgeopgWYpaq?&C!Z15=p-I~=8fcNwj=<&~iY9)EDMNK>>2Q}x6^zjJ=jua&nWoU2y- z$Igk~nBB@LM*Bi;x~d5~4LFz|xDuH>{RUrqhZQ6XiEMjmC=CB1y!CLv65O3*4~ihS zFkX3q<+TOgQ}?Q{0&xhr)FYC8Z@(LE=bMQ)FjKMt18cv$2)(gEK}laCc^IgE_X~s zd#sZRh??NXHXV@l#MeEMv(iU7vr+VYtn)!hc7H!@~wnvzlnG& z>A@E`FI67jAX=T5Oyn94;98d{hM_;zoPEs=W{tL}!AYNqx|H-C3@UvF!>zK)_%>pg=(XJzQ&TIUI2y`M+u&gDL>4cmnF=af}vfIoH9qBtx43 zVo_w>-B)AZ9|>f(*U=JdRz5F|Hclr_5OG%l;vwyvw=RI&Bf2~1I)y1gX#GDVeVTjI-HUkH2228}LnAu7r$%n_{K37_g z2gpU8|6>&1Sdhh;;{uej7SMhJP5>*9R zcYdMGNyYw{6Er-JxUw-hi-=Pbq7R7``!c~L^!Z_!*CIHJxinQc6E!v>I%4pum%7T&sfQ2dvMi_xShv2lI$w^f;szAVW<>-C8;P+$UjLiY z#8b5VluXI0|GwtmfriK43(t{Bn4EW zMBAmte7&DutXWZIDa#@UrVm8>FR5Acw9KaKjf>Ydf9g$5ROWT)R>$`ylgydb=O9AN z8VD>6$ixe@61PD1>q&?noP!LI?Vg|UVA|5GVDDtm>{P`$+#Kqh6!~_J4Mg$P<5Di~ zJGdH*dn@l6{L_vmN93WO>H-d+!lj>5wn!GP*uXO-tdq2dTrr% z7W)yr_`9flYbdh|xO%<*P^<=>b?{YHuEhU(h5h}h79B2iVXq@lw%IplJQM+nN#F+t zHH6It*)eGE1i20VI_Q4~#1}SdlpGH}9dR<~V(i`Mrv+S##1T4coGOdk4!RY7J@jG> z*(k~dhY)ExR1}4PhnWC59*H^#eZbO*R}a1xAq(B*#NR2f4deioICOMJ=f%MXNpNyG z^mqV%C-Md-5a~G7alm`0|Hg(-7yu+FHi+Z|h5>>b96BhzPh7YG0VNU!M=WVY+=|Qt zr5{EhLP(sf+#>A8bQp?E9EW8r22HAfl{_4UF44*~%-)JzPf>4CUtWJ&Ptjn}P@ed9 zBu3O>696rV=R%T<8k=i%{F{`LgqwLXT1G+7rN;nj7&$NZ?O2S8Qi5A~Dq8iX@!%q% zJ~(ftgGTYPu34sxYBN!~=c`dtbIJrxO6$gRxf_F~>Vm%WqXy-<%8(9b)4TO{ znPyiEvzTUuanx1C@kpG;;E?Snr?#Dh+N|Kun=20@*3!7d-qK&-|G+5>(0>}S(}BW& zPyWxK{J#gMF6PE&_U8W=NgcFs^#9ZUA0+*s$ex04Ox;Tb1f(|u1f>1Hw-pO>V>fpf zb3;={SGWJa@8yoZcIxq%bHJYA5avKUkCOIl4iy1HL{(Xc-eh77F?%_s*bR+EX7lr{ zembO*HWl0axC#vI?`r@=H|{qv?c;Pyz~?olVc^GUjiW)}!`pPE=YaR$wSs&C!`+|v zX+{D6$8pDjw@1r8zt__q&4z$aKn~|#z;lma|NCL;S;ws)U;p<3U~Vq({kZ4h@G{Ub zv7hhz8Zh!0dFJUT=>0vu7kGcISl!6AKzamgum|&f&pG1_xjxL_Z6-cfuCAtNrEr;6`Z#Q z1E25j*GGz56^r+r)4mcF0Y7gC_uos9e&Z2z*Le>sTLXf9y@5P;(vWjv_kT|#31Lul zX~7lWN2Ysj2?qjy0PiD$0e|`Q@@kd{{=UvzD<(z?-1Kl3IBu`s{@g#l-Ui?p@DOfx znsD>wXvz_@3LU zsFO`C(T$oqfR{SC!zlOjJ6cpC>-YLRIN90zVNUxP?+AQYFn4(XoCjh{Ncf;QI3Eg?s3DEa%w-D`U0 zfS>nQktptq5bkHXL0*;R32Vm1EIaPR(pzAQ?Y8}`VLZ@SVB}(s zu^?sHww*g<4+eU3jv){mlU_gvlW^NbV4_F=arQ}i(2`6*;%~$$T)#$UIoj zghChZFN3^&bS|aN7pi@4ipFi7a5I&;!-(T<{YiTSgK(NG`QW_U_XM%657_u<)E|`` zWrh~#{$W^bl70u4sV2q(DJT))@-8I49)=J-C->UGB2t0I_ft@CyOedkp(pB^!nIAr5XKMax ztATT(TeMK$lNeYMp#f01L{j#1mcZ1dt8t{Nm8(1vRlC9~NA}K0dJxbTo<>QRAro+6 z*!oo#SR>~cr1gp}H3rl~6Ck1lDzw+=(dp)4@qdJ(SZymKT8vDALHQH^%`mMe7u|>9 z3LHj54yKCW$y93?tftftWQUoPi+Gbh;j0FFrw`C0&r=iZEmAB}68Z=(9bwx_K;jpc zhv!hrCPD>oFww#t;-g}r3)XC|*lSZtP9v+k?m?~v@Z%RwaY4gGs7;5g<2L;*0?3zA zC6+*1Qzy}Dpv^x{Cz_0Yuo=ss>g|I1s*SXtAPd{sGyBMwuvg=yTHsf4=&ZixMi}x^!B%) zbdIv%_PaEn-@r5$@m{ zqZ$tf2#BZZSt>>B7}>K{^f2UN)P4k=d8WCj^F{vag^ic}vzr7%gXtK($D9M9ZTcaS1qs~r*R4U6*4zJSCPbB zrl<%Y_kXZCFfm0_&$BgNzA*tz5ZmSSc1u0c_%)YD_{y{5me7~_AWOR*rLMS9b!j)z zL`gkS^#2(PGbM&sq7^e`ya7gU!UHUuyuOnQK~z@~DWmRq{2kF!tilpw56S2pJ;UyT z@ckBw{S0Ft&NhU)SGYNL_q6tS-oWJ4BqL5%`RB_*#_r&((QUuE2=9c;XRTIA3@dbD zv@9gOr(e@_2?d?NyPU#H7pAf&U$NDq`pXk;zfWo!fb zcxY<3L`&kr6d6{5U-wnI#*=NGQhUC!$us3DJf2?KsgfjGX-U2ZOb>1qEd$jIWPf+< zT5RBNO<$E@4?!fSY?5t2j263B51WqG14;wuOC9 zd7{e=fL7>Gy4k(+4CR@_wMW_Q{`1t_16h)7xc^br=5>%{G~KHEbqUc1lkSgmfV#nw z$V3v%M+tl=0^BzTx;n z|I&oPH(z`E*$H}>DHbJcuCsiCrHfG6y)4lof;6HZ9v3+ek zwH$lqIgWO@nne!Lmu&M`{bPe~a7QZS%Dsto)s$iD-`}E2dK*F%e|}NIdEuV@q$M2m zndDogsU)&vuVXM~Bz+)oq+;Wme=wff!!BOeZ~-(On_j#Cz6;2~N~86D@2n$B;mK6!#rTN3 z$cZf{=x@rDbxT_u<;3n0sSfX*RK%_Ic?Bq4W-Sc&G{YbmW+lHbz=^GwHa&o=hBhJ+ z6};jg9uX06UBQRE5YnAZB{xo%VQPviK@P3ljaL5}hU!u6))}v!o~FNi@zC}uL3lS$ zCTt`I0C}LjfY69Hfl5*Wd1)2AmEab%YEpqBWS7SDe|*sz{F8TH_cvz4wtU0HSS2gW z6n219Z^5HT60?miSkX&3DUmrQ!sMhjN?74aQd_Rov;8e~2bBTo20}$wW-Yj|x2731 zz-XYs;H0`fXuZ*zi02XEQRa{p{m!qT)tf{q%t z0JvWbTWCqx1-1{0#7_{82DbybDB+kLR-py0@etJkwF(D7CUqg0>Z7}{GFQw>#gOJ* zo4i2ydU&9#gS)}Osy|np8)A%FBM-<3msaYJQz_Grg_ZKa+6<}CrC&i0yhrp0Qbwzi zs%EuWh9k}!SEK#PbE5sBDG${lQ8f!Hl(ZJrr{!Ti`2D7Ur=9Ff9fSwEqNBOxhwWjc z!0MlXbgWoiat2NeDfFgo<`xHdPTcN7Q@1Q})Lb>~LYF|J-b&f0sy70m12NW2 zzF^xYrdRu|!z|m?IyEh-MZF1<>9xa@?`!CA?`D8ZK>_0aB3OcUb zFht6WP$M!g2vj&)`v%1jm;Ly3C)bM^kq^F zSiw^LV12vY5Lc>#JV$?%E$OIJdxwMTeUrhr)r=E$v+y6?+#rIKcqI%q< zRcfKdyW;5l4HA?U?p6iaeh=rhj7-0!j+(0sa+VHUzIJ~1mG7o-eR2AW*hh=805i1; zyeAYfUk2ls^n_jb?iUr@{gu8(tJoWDhm~tP4L+@g+St+WV z+U4doPB;=ZZ$7*F5s@Qu*16wixnW1b55Y%-n`l+&rmnS_YRtj=nq{zk3P@ukZh2R-z&eRgk;*tf0_~T0J!PrVhTN zLN4BHhmHg=-I3d`Kj#iZd4 zyj+uj)cI=pPAM0p7wUokHgR6q1pGP7IC#Y>HwAFt=4`ZQ*Cm!@U9p)tJGnCz)<)w(>ly7MJxUzU@)}mE^Hs+AFCe~(HUD1DSs(E;RICLDlq>@Y-?eix( zOLGTs_dVlnm!}|B&H|_4Ol8O`*gkASQf}zFOY=!VT`zB~nNoCNq4#l5TOHTl_g`?s(HZXPkX~|q^AAR8tdj(P}&i<9A3iV-~ z=kVB-`{}Y2L$y3twndQA(>r}3<=hIgM4%u>=KC>h)~|Eoc-FewMQ(~TNZ_+V#e?02 zp-j~T8{Z)F4~GrL)GgWlI!jlptZZYbmJzwZ@#*=+{a$SL%>xq`xoCeO?jX^_l`Qu> zU#{-Y1?iB1i)xTz1B81}fh9d+pu!uV%ob*;7VsS9s1)*%)C5UM2m*6M;(s%13zpGU zU(h0eqhQl@Iyih4l^LWmGZ>JxapVj#lj0FNjNN*;mwE_sANq-j_@&^M*CucMqet*a`DMT2S~m7BsW73C(308xxzLqw8VsQC4s210z}|_rw)qrPH#!TETmmd6OClpf zOm_)h7M=n(iW*tG<@Yy#Va?M&<3(8JcYI40=pNpEs_OT!ND5DVqE9Dg`*|{Td5p1I ze8c)`EmM=+`BjHwQQ4JVBu{!XdJ20!*v^-uZ{Jvpr-QDs1D|{sMKgl0DM=Kah;YV; z0LwB(Zn3I$LiEU#;p|;yp(cVib~sVgR~%RA0ngpTn$;f*<;$~4)3pRWmgEFjWB5cA z(7|zmcS7<+Q_v%_%Oa<}!(j1aBRfWMcU3fF!*RrBv-;Nz)mCCfj8MN?(~dAa#X04d zg!%Bp+e6Dl*t`Ct{6X~CVEQfpE#68?p3 zTHj9a{WW@lwwz<2WfH=Zm-BQ&nUsl4NrU>V=#6}WA~ylh=Pz?5@9VMV%P*~O+dTi8 z{&B7aAeaf&A9ZcH+PsYUN&f}#S&l0xft!^OfFuwT5nY zRX~$v1EyMANK=C>f~EK@VRD%=?$SFb&s_zartY?*EJJsgMQ-!GvEWS|#29#sG)4}O?DnPoLMg+#10&gC+arLeY6WpD13(*imU5IEZ~4x+$^p%bUpZ+U;9b_J1h1`0@M|&10Yb%qP|!f~(D=ax--PH4yQa!6?%m zUBawc|1yhb*L1g1H)z=?twe13{pvqti;3Q(Lb`*sI>feB#bmbbb<#~Ot%oyLtNK@) z;i}f8CWdT{qdaB|{1HTEFSKAc$Ju7PXU@giAGYeQn=Ob}QEQiFyUwBOpx&SIo-tUH znrnpFeo3r>B~rM&OE9Q=iaSDMlHSNb^GwcY@zUWOlSN=^cd91t9O&cnp~m3%)9Xt^ z+Ukzlw+GdshSYoB*_NX)MNuFVr*@dQ^-*M=Q2nN&etfXqg0a~@;?|IfI>k^8h*PaM zm`_zotcpBI;IEx6Nm{|R$kMt+wQB~-bidpM^hRRvXOHWt)F#_wFl<<$=p!61RF`V}ygi5vreyNm{>9)!jp`_>?NLOD-*0C-(Tt z{M7K~_rIrQx7&k{uwzT#3L~K5G&H+HVrHo5c#q*&oL%^&Bg7B!eY)lm&y}=_6&QWS zdX1ic!@e1H?I+RS^T@1nE%&1b_|#7qA6tkgOYH5DP*}UUf`5aE4j3)8v2!Y( zXD!0Egz)fd_)l6U86ubNXskt^&*62wPWq^=Mj?-@A`OJb+oh<-^kWJtO#$r4HpMOJRQa$}9C#CkX{yqRS+*Kap zJuSRr+WO1`Bsxe(hTjuS%D|_14SpGx`-i1OlKhUw1gJkc@+a8sd)D>`G@i1l`JN5q&Hln>l@h${&BRfwJfJ z;ts+a`xI|mZHAEU3R3NyqMZS`mD4aV+^yQwaE!x=0+6HE-1YaedhW>}zeN_7${J%P zUxD49U;r)>!`Dj2fo9!?>7%lplGQrmygXcr_QWD9sj!VMUvF66i!XY?GgBggU@dG} zN~=t}Vz9q_C#zCc?uM#3)h_xj@x+xx;?bt_$=iN&YW_W4!|S_g?u{JWLwC{9ywtQo z46y1xLg|#jAlgz4PVS6l)@Ac#DQ9lCOK&4_@05eJ4Od&UYY_cbmcUuRow8J!oSfz39LRoI z5g!E7Vsq=Nu`5eu!(!RoVqKS;%sQGCswL3&F#uo|{%W1d;L!a!Y{@DG_j0?fHCLna zr-du23AXObp2%nRHeYw!khP);Zpv0Ype_!Lo#}xp1`8`XJ3H8Z>Gm1P`Qx=CA=VUPb#DfB5To-p+<4uN`wqpc;i5Q3L` zq)AU2bZ>+nPr|k<>yJF5!6W_k*Ek*c@==gmfuR7bhA>ksZR2it{ag4j;KM>)h|wCW z3F`Fkic^omwJui?(OIbT-Cr7%)wF5k7irQ8l-&_y6l0ph#{;s{1@Ql-y15}_#VHVKXpihSTD+b z#4cdJKa<9%6s}ACoB=GkBmzyr#MZ{GpMX6(yT@|Qyf#l! z-6Bw+n=gx7OVF7Iy@iwW^=v#_tIqD4#P`4!9kV>iGOb#LdLl=s7?&#PB)lbX(MC2S zF&%O54c4|YEI5kJ!}B;+0y5=V%vu{6mmb&w?-k|mISy*xMwLt840eNK-67+I=2)k> zvyCu_hwU9RRg5UAUxu^!p2nvP{%aTegsJ;xq}X?;%a)|BBR=%hoI&o&-RdVcJ5XH!`lo%1OGT9o{AY1Ca1f@UIBrv4aK_JHPLk$vOgTe(b8eJ(}+cdN@P>?Qp;AIU{fDT8ReEWh4-a46~ z``O%%Pmm4Fte^-z_A9-E)^>6}%63>Z?5wQ@BqQZZYellkhTPaiFV_y6T`Ov2ctKC@ z&mPjtDe1FFB=1zLEBcpP7Amy!I2y&UC7G*(|7o?|&W)eoT8O7QKxFL>x=M!af(;lR zud!#mwPMk^ee?IU9vkHYLZE%-@R8A}0K3A8+9Z!)*elh(rr*Nkvuf7XRRX1yNF_K| zmP6~``mDa9k=t~qFUpqjwg=!{r~yY`7zKU{ypLH?9UiDiHdTI)}YQLDd|k`4Y({BGaR+d8VAJPrydcf=&d$i zYuGe70u5QWNne-H^`KR~T4|4{qe zDo8o8tToH8C;ZC^DOT^g zFX_93VoGNRiV!Nv<274Fb>9zx0OFx#548Xd>;ETu6ttMx$+VGjoDu5gHf;F8Sbu75! zvXi6(@-hfu-%ZX&;gxI&-sq>w944oi07o|_^=3;sg1YfA{?s!F%n)Sn!--?rz&F&h zmM=(bcjRNcYuS)%(&bG`EqG`MbBBIS?c~P%)H*t#inqkIk+-;un2F{VvBvg z#O%87+;atbdncyMVR58o54X0IjB#MCL7gjPsL@Pm1nfaX$|k`;37iFqH6oW}n%r)8 zG}gtoYaiN;*c`5pnT6MU;I-Quh7TWq7C4SzM>uNDBZYZ9E>)0J(}JV6-3n8E7pzBG zq{~35Anes4c0!J+tJe2ht(&vJfws`Y_9NPS?ufxfc@(Lz2Ar`LvoXYgLRvH|5yC`; za2GUcfK(!VJ|V!n3C79n~1{1 zxrx416wRCQ5u3Kql-9aJl6 z5n6q=gKBzum%(xw2;(8AHO7nN69UOAIPkEAOn1EXNx#25!CE z?7$Py>}M=-V(}5Fv;EM~ z8A3x-B_Qepr%}jM3l+6~>Oj5b`oxPp6-TW+Um z7^J|0w^$!-6T5WQVad~(fp^8<93bJ`_Lf#>oZWIOsb;$HOl2Wj${f*}zvD;X ziI|dnX|x|tJtPX@`GDG1pkq5@62Uoc40n-pEj;cj>u2EeHt|T+_>E*1{C%nR8`o+^O3Nx200RDSM48mvBJip(6`yxte(}hBYzn z(Vvlv4x}97&W<^#W)H7fkIpdDTY)P*qzXFt!y>yKr%4=z7JM3|)2f4=Ai2PYqpQZh zs!JHlgw;n5^gE>^bvbe^tv4oe1H4m;4@n(aHcrft*E@Zkipl>}BFJ!*>`!v%D_KLa z7;hO}E{&hJYH77>2c~q`8a#SH{>>O(@`2>Y0nxi3vz{!psUBAeCZ6X0^B@74>jf0Z z&HXsYBlE({FfZH_OGX5Iy1XvbntAi zjOug#((!oY4EXAFwH(H)rr7N1ZGU`|9N{9=f!@@Wt3F%3Gs@Ec+M;;=`M*JVvYyKO zgnyv?r5zBE?tec{>EvqX?DCJS{Qo1BE7^+nha703d+z|x4R$vGcX5HeDNM|KvnBY& zweg9Py)|nj&6rD@){TE&IuRaAB^#Za_^Sj|B}h0!>s`m%K~nO`wT%)j{iB3U^>>da zWeMZUiw_%BZEDliiE1zPCck>{z(g@+YP7ui2(6NRe0jgeG0J%1SIU38kE=u17<-MuDu(DlJufUsY4G2qE8oUHeme6G;74%515w zX-_722IfaR7a#Bs@f-U_?k>RE61QCD>hJW0mNh)dv11j*5^$x`O4}5SYMk$=RiTrz zAG!NAf@q02Qc+)+=tgsGP7;`c{*ufR3pWN(bFkjUxN_}c;aM6`yI4HOaV(ND&6`uj zQq^fp+=%d``IjBGeRBimfYvA8VsBR~%vxAK9yTa$ zB0gGCP7iFjp!1VpUr6~Q&HIG>DcF8nuQX?s4N71s(66;UPT$-VL+5we5V!)=l21D1 z@w_jYlW)4t#Hx2nTj+kdzLa0JQrewv;+A#6yjL+!-iZ9=D|t%q_!Zm3^MZOMbScm< zFyW#k1HmI1b{+4r-kWh*W6D!F$8iW0Ljoig(t*EZE`tR`A~0PH^qmee>P6DSFEo#O}ye}W?bn2aj&!maDF_fuVuP2v#w zOK^7aEq$2sRcSGvT0D~J=$W~zs91lDQwn9GXDvt1b0#&{@9UWIVvSsL(g=*el<7;i zv`FM$5k2{SLHG*Q9f*|nkHC`=Q^3pj&pk;yLp0HLnqWJFaJdmyu!FwX&vtteX(EXi zl_Y79o)k*ho;3798c^9HE}2LRjKi-PMWHvc@Ox>5gS5XG?=eia*IMDa?y-1 z@`Owz0&)e#p^=Y(e1qeXL~cE&)Y(4gkp@x(!IZ@cw|&k)fX>}rQ;;5mkAp8H(>FiS;mQ`|eeV;J@4JF8fbqhF zo0ps-+_VrDbc-G&@eCET1%ti!dWn8b0kCJl|u3l!UM#8K*) z+MJf;7sD;}fwic(81E=9$v>(s74VeyA4>qwHgZWpQGm2t1Qr$d-M!O&_p4C+I7syG_I!G} z+G^|T?dtsT^ZWY`U!E)bEUR;``-e8KWwo{a{5`on{p#A&>-l(ixw)Cy>g(zAb$+>j zdH4Bw8M?SVc&*y=c-Z*|llyY{a_11tBF5|2?e_ef%>4Vc&*$~~i6CAc@ho2d`#5Fwg<^6&l0^V^q?@5{-vso&3sM{n29 zpPQ$l@K1kVK3#oZk52z@58oG$Zx62z2G_qwFE>}}WN9ur{`)_!A4IX$Ew3tFl@@DP zWAb%cGs0#f4^S5s60;K~a8)H95)&OWP(;L4B`I6wk|y08#hY-7LQ)4YVlk$23%$gX z>Bz3+i*F9C)mlFqh?HlPLF`52ttEmdhS}H(0`+;S2J*u(lJxwWS zrQmIYb6n@-F|F;QTBH<7iB*?+72=2?d2oT2G8Ru$i3xWW;D`f50L|e*_eS*(6X%;6 z`SjuZcsbbRU+1bM+!K+7!#6(~j$Js7%H-z2uik zqTed+U9^vhEKiB6fNfM0lQ?(XgkM3pCm`Zj;0Z*|99?IWQfbglD^|7mMhP9#8%&C6 zGU+*L;8xheGuqmELA-@cw2H%_FzbOs~eg6NLqMKAmR3 zu*RAK7SiV5M|vrNNGYBcU?xpr9UD1MPZ-bix9eMfR!_(n8CMuB1)GIuM?= z%RAXJRfsEUiawk|RwJ6Ns*y#Rf24#!0kuWYrR?QD;C1N*Q%s_ud1VC4!iO&-dWnQi? zwPKeeIG0+hfRvTjW z3v3^A2jn!4$r7JobH{3>OZVeBCtn+{@?|cIE^?79hrLy^?u5VqT^-XmmG&-C%Fsgg zO`VG72-%p~u;8Cb$3AR#kulby9!)Two6Et^WQFj8RkdzF?v6_cizSEI)U2L+~&v&IyIEC4SF^Vk`L1N z^v7_iY`Sn#MA$iP40}dKS-M~vni0N}`Wm9RMU(Z{Gs2$IVt(VjaFUylSrY9!Yys>N z>}8|ds}?17C@*vv;C^Wb(A$+(Q^?8~js<)W+R7O3Uh9Po3K7%`FYu@#JQ3h@jW%uS z*w|=oBXss(2Frv`gTZd9K-zmK(n5%Gn{kuxTN)BnE446?ck;ybiB3N546scK4( zgAK!ISv8l@E!c{UcyM{hy)~q)4uPCR&E2cQR4`&g^3hy$Y;Vu2aLm{9aR|5#7R(YA z(AAr@Fek!1c@Wl`$m`q}$o^)uuHY6|s3q<6R8_M@wf4!n16C(%GYS`uIQ&R4%7(xb zjFrG)U9U2zkae%$;9Q|tqodh%A3FwDVzB3fLWt1nfZ- zGy6c-2(v)#P>f?i<=pAsS47N0aCWXxj1R64&V1r5U+oBh-J=?l3 zr`F}Bu*J}m^elF?k5T%_NHyW^vt-7bBD?Js_%Rs?6H90exCr6s(bzKS{f?cmOscum zgCpilZZ)WLCZFuZUjG9M8$_2pbO9MbV#Cy$wWVOILHf}fU3ZmzZygiQ0VE@{Fl0#Y z17dO1x@<|-i>cgfyvSvC%TrzS@p0}~a53Di8}m(&R0s6&ATZE&b&{bAtohczA&0b~^lt&!N;Y`;IE_1(G4waN z#oT-iBNXk=nYS^!k~e(puTI$j!P-?>;Kw-hVpSTjG{oVz0Z7rvDU`Q|&#yhScJ6O2 zVMCzwZt~oAgI6e7&JLUv-bU!kAX9z|zgJ$n(Xc@gQ|!(x9l6=gEA8~2$`?EdH)sU- z<9Zr{SE*eXpfx-0Biv0M40gbsyY8!Pa%ZyeW}JrL`4`A!x9pe%X)K6iMvdY-nS&Q# zoYhw;k5SYSBzCDVY}vJ=bW+R4pR~f06DM-G6uyQ3E;;pZ+PR6bt^29rkQv$WMj&h5 zr;-7X(1?F&^d`MVToJoKXAS5TBOW4v@?M0K+egi8;eE#`4!L(+m}C5tb4J7Vl&{=r zjRSXDc~8Hs!&n`V0PW?SyRS9ZQF!k7Id9)r-Vv)6{dSPQPzt_I`kZio(G#t- zv97jwoLSv3`TIM)!=t&pPFX#uWu$b=PcU3QxI^@=`)>iKnS`fEM{t*6_-{E*mz*~1 zFJce~<9(i3?-9MA1l>ydKdcCUI{|*aA^%tKurkG0{(lc13jWu1m!X@flcBk(zJrsg zv8D6BWVXJMrJbRZC%v(ap|i84nWeGef8@3GO-${aO&R{Pyvi~?{(lDzMd}BBmH#XE z0#bp1l>a-HHnz7jvov>g`k&izrZ;wW`_Ik%SNU<>t)wckM&CU9MD9z!4SNdm+3p#h zZ00*Kx`mw(kR9}Yz*pn1waH0p-Z9PWx~0D_TL$veH5__;>!qHZzI$Q}pB}z4(LTqv zcj2U$dzmBQ!XHWGpR3+Fe6j2g&WgBkdxtT-hi|R$dNWBLc}LpBmzD}fI6ug?Njnd8 z9<1?6LrgoPV@CcKFgz5W-5zs|ER%T;FkNjP_m|RMF>asD>+ED~6I{IcMGKC3q+Hvx;9u}_{{nk6j3DkYc8dS{*+~#^kjMlX0KR|; zN!k7lUDZzJD*aCgMD&6%@shFy=oW7gRz7tluwNwQ7dx3Q%ph2H)VFa!fE0-yioJtF z(AJ=3@i3gYM}jc1hc|gkEjZ|=-@#d~f2%3D*<p z<9Yo1`tN+`0xN}U&P=i}^&9A0xkd82{crAD<)8GA)=ch&`b!pHj1yb(eg z^fAKkw(^ND=hj3Y2O(;0lhi$u1EcF6+;9+eJ^>BvSYD`^)yK{d^y))9YbL%uOCw75 zo(-~r_Ck#L&b&}yZqIA4q*3|{dyjLiogiyRcN4wFRlpU3BrxM{GbDtI7a#VF-@cF7 zH;2C4LVBMZduTvT;4_mB>9)~e!2;T(=S`o)ME*NzFrSxk+jRUp7zB?CLrJ_p+Za73 zSzGRK?em*gpuT6p^MbTJg5jp5RbK!@co(;xP&zk)& zDFhgO6rbg<$=dpRDg18PIs)Bi z*C4-wso}@fny9!(kw02-$hI@Qh&VN_KImrHD6)Ep_)IE^7L58tT|VVe@cggsUSc~5 zZ2yeZ*d<cr(C+ft+d=0-xD3-MRm&CXI%1Ypz=lY%`&W-s;aSR^U_AD2QFQq> zgyqp|nRL8(g64~t!5*^}eoU>c2s3%`{H7s7gC#|5aO-9%@ z)a|kRx{EnS9b;_Vh!IId9^y8*M^bKaAWGat1>5-A> z#{lepwKTjgpIcV*9F(m>z9WW&5O3sWPw<&I@@6FLyo7*+EhIy47|2_!2T(K1xh=eU z_UFailhUzAMm8WahQ^ZbL#$ojUr%E5)sU2Df zCdq(pKVOImS%srQ;`H{8DCl%-zabc8PfoEs?4Atm?H*my8S=aX8+vHVC4L+Y(JdT) zL8W8aDrx9{{MDl$*Rk8cn2Rtd$tw~O08{RCW1aa?gD2x0>E@{WtX8YS-_(U~r3*mw zJ{)wa2>re2WXj82CHvTuxpEmP*v~l9!k(b4#&oOx6C~e%W-}rgu%#4LlTG{MAca}h z<2>!XT@v2+lRK)uMLJtimp0J7*>MGrO$sQQDU%q@G1JnV0(T!2*2a)k<5Jq>uxvJ2 zv^oITTO@sx$wjHuDs`i4!4##ePX8iVUY>2u3Lt0qs8V$%mhnQdF@I?UP4<@_p*DG@ zfdzFqmmXm+xX$L<)~%s+ZToor=GAOTVrX({^SP!mFt4%MnfDUD$X@8HW96)a#*aI< z?%oRsUHW0WW-KQ7MSle5tn{l6GuV-?xZZzwqo9WAD(83;rnX}-cOn{O>b*|fT0s>X zcT{qy&FIl18e?8>;qQW6j$ZRJ2>dZHdb>Q^0uBoV&S$T_Y4|m@I@T6^6?k zJ)aj162@<=T(!kwGD&jr2%?M5kRdl6$7)t0iw>~ZzdDEcti}Z0BO@wfUC`yW_6e9+ zk7cZ~UAM(@)NICWlG3W3u&!Ruif>2GSB>OpGGXN`YLGHm=34-IC`m84P+OL=>ZI47 zA81JZr;{`j04`@IZ-6<4|;m8ithmU0t);hG&s}Qav#fvf82eeI5gu*LjT^V($ z{qWP6!yp`NZ;7x^yd5SrVDstj5m=Z_VGR*VqL0NiL{)v&&vMdt2Tv@XWfhqg?0$s~ z%#8YG^LXvZmb5fYxD0(1@GOh$hIQbS5uovD(}Q;j@WOye$Mj!}>2BNbozIY%bwyZY z)o8iF22@MnGd|s{RdLdN$2Q`9_?m+6QNH^8^c<<={G0_(jq=GeY_mnlzBH#LiXeX# zH6YvSx-#%&BLZlm(?Q%OW!8FiYj{;J7Cff0$PT)A;D+W*_q-HmUSl^ef1&He|K4`A zq|zCXXGR)SuJ49c&RmsrzyZtU=&pzA!T8$z#M~(!mFHn>&tFdrUFjYX=hz3-fqxlIjsSb4@=#JEvo)}d(>2B>Dy;(xP9#S2=2Ga32 z-Z55LB`C94yxVLadDD`{8%@ixa5z84ud7NqFCYj&2CTw}pAMY81;vXBgJiW! z#Xn9(8pMK2Ez}GttoCHe-Et`76)Xv=+Zl!~JvO#7m{=*jAa=S(J-K`VLzs&^3-jh= zUUPvNlC_)6lwd zXW*rD*k^=aCTQ)4=~>zifRIsmO%GN^M*z;ns8?qnu*q<$iq*}OdQqb~qGGD0I)EAG zmqvB-%p1tpHoZSo%Tzjo>k*yHaZOoH+n%$X3iGsY8Po8p2+hj;%vrv7J$kTH>#mI+ z!Vz$K|5f1HcYr=VLL&)oC`Oe|u+8jg-RL4aC7zU!h~M9Xni2Y}g?$^Uq$Zu8W0sjS ztwvvMy|>`nF7lWwuC}wxid2~z^FHA+X`;3P()n6!WyeC3mMc9c6ln~5Hy0D!rnqLv z{0ymN&7>hS0N;^S(XOyP04q_H4s>mz#}+7|2}?Q3Feqo0w4z~Gn}~Fl$bkch4QYJ2 zf^?n@!l}v~tsckMV|Ts^AdXVTgf!egI(SdgUyl?@y5$uJESQ9(M{@A+HPt;%f#z@< z7bKs|vQSj~XSN*30Ox@5p(DtHlHFa?>?FGIk}j@4tmU!D^7lCf=X-BLf8z_b!&g)1 z)Gqn$E{`5F34CA}6j&KU*8-wLaZEF5QtKEDPS}>I0()Wv>Pe44F*vZB;kw8X^#?s0 zKmXrjiF9D5ZtNdKMqUO=rnr8w7NEr_IiAKNS;y1L3A;E03GuWgBy!(aK@E&qZG_|7 z77}a$67k^UatB=Gd>x!k{=RCzgR_NjJO!z+CI0u(SWc+BtP+ z7Ia%Uwr#Ux+k9i&w(XA7>FAAZ+a0@O+v(UgPtL`;I9K1z_XBE=wQKEFwQH_X&jhsv z(TYOTaf-_1-BZG1Mj=VCOtHz5WYl@GIV+0L1ZJTF?3R`iUi?VJ*h&CGJlmwl~In(dfR^!T%T1@ubIw=uFn9DJC`}lU!*q1 zP%NQ9@Q9x=Z(#b3R%IlSNcFYKk`Lul7b2YLOK+(s;5=;Cm2p<}+_q7vnuhp}kRh6?g0v_m%T zv64R?-C{ip*R&LGCAg?64;~{Y>&1(bKX*@#nr`7-fV_#X4Y?5(Z)vUK}o?#(6DtZdg1MH}N&eG$^k`f-_U5z!dL zD+PUng|2>rf=3)SH8)|8D9`#?()#>6P+HU%f3<#3Qe6tS8%s8O5Jzj6T-mf&OUXMW z5PUB-lXQi^l{!JDS_mDKY%NDmZEqKd`Y|3fKfQ15247{xE3-+C(`yK8SaT2Jt5ruk z$jpdHGX(AlAG&#V{+{otaa72oituSUhqAGMlq~LZZLvL&@Bb9DCO8DX9?uS~cU3+KrTU#PC5Iq94Rj zifRscsDwws!SgB2g@w5B98CqFK|}ST{$G%BrY*GuV3;EzM~P)pFU(BR+oEqj?G}e@ zaRU3#fu`xbio02jgvh)N0D{lL-y%s@29q`J7nmk{`L%D|t*9%8ByQswmrk|LT{aJf5QBzWf@9=GT6VgHUHVF8%7@E$uS;&D9>R)XJIXa$VIi zA8JURMla`gsOyluF$9>T?>D~Oe%Akz+dKXGI?DZe-!AP}0`gq%lNEC>I~ zm>+J$Cd;7yo8LF+=NA~JQgRWt2HVc`Jl@bf+3sVSekGoTz|k?a$MSPqt3uYOR9kM9 zPHREiZp|EW9t73jIpjHz>}3#~9g0Q6KVe+Ho{F4K2V?cs=H398dx zSiJDCZ#R1%`2C7YI=D=RJKl64#syBJj$91L%mp)oB8g}rGm3O<@SrDNlLh0NbEP{u zT~)N=`WgSRI)I)w%0kYkj~Be8`dMV@W&#z|R;mKO#UKGXymyW6gbjAy?meQcKBa2O znk=+}J9)qS-G)#eF_+#XEcA;IoAbJB4|H1r7Yy zRG7ly5u=H)F>8Rw$ZZcsQT|uDDVCdu@TVEhAqfd;_M`p1UXQ8~aTo4UqSt1wXs%$C z1J;(+-FHddb!RdaXXbjY;k}WSa`QeO367tFb_D096`O-nSu>!gxx6DYE9vK!&@fp$ zyRZVCEq~2_Ftn~eeFigMVifL|JEdTqy`RW|3}b{3vBZF)S-49Zwo0^v3LlatY8g!T zv)t`xdo%?Hak*PG<-qbMmqz8W8C7}uUlh09G12&!)tr-;x6j>M6Soz}|5DquTQPx# z316NoE+$6E4@SxNzZ2f_O(atY7<6T(gsJ?|37g$LIz+Qfnv0Mv0-y z2B41yq3+|T-UxHqO@_+YvD%G0W&=TZdUi77ebJkGRc;T(vG=Q!&s0?SDcQND@x#0& z)>sB!>>Lai;JW`DrtLr>ww{Fj4$d%-pvXjOh;fO{+J2YJMM*bD!noS=K)(It!e z%Y_cJ462uX@@-Bd6ba|k3y1>s(u8x;yKvRp>#B11eH&-4E)V=No}a@3{rjFjva`Ym zF;2d}m!vZZhOI!O{lkXyDSK+k0~@33XMYxlq6$23(mD&Tv{dYK7>~pHkVfwM@ZPcFlco}PL0+#a7V_oK7!&uqrGA*Qe0Me}b79of+T>m?#L zu(;IM{j;|FB{#R{?eosD-w5pl<5?bj}~kjJZ~q5p@F z(AQJ#=f~ZXkjLWm!=$NJ^@dffFz$S^(A#9~*Ug>h*WTCb__X22b>`L9 z#?D8r%iDerm4*Qwe;+FiyL#^b?4}NP80h;TOyI9R za(_s+2u$^)*niJ#!v*%&=UL$Uqo!kzZ?^x_;@N$sV{D_)=TOh{WG%7f*Z%b9E_KiQ zXzsp>!%6pM%Uk!A_kP<`)-NPo8`q@C9-f75UfVJMqoq~EP-OCkElSxlWJXQ8foKROg2JX?WEuHoo?sUFardP$2-fwDG>kH~#MH_1uK5Cct zDJh2rC6~s>vQO=;8*_g>9a^h;ihb+c;%yARHgam0^?dQ=pLB*V5-;g3{g+la%=jfQ z)Ww=oTb5tF&R!fZp7fqnTpk{D@l_SOFFFK8xdH|G9{#tPGP)NDl-9@QIy$zN^EmvLUp_C&dg`;h z=dqd22(IaS8|*DAfIV~fVoq0-SNw;{|5KZI<&`s>*!Ot+&EOTwHa~UChR$Dc<8p-^ zgu}kjp^C3Mr>Mi$7eJ)?Ar`V-;?TGu_|TD3)~y$9x4{VgA}Hxl)suB5KkhzHc;Qnf zP(sC4c#?LhcIrX2?JnwB8$suqSA%ca2fQ9RXmG((S}rrp(i&SXBm6je&E!?kXaSMuqm zq+CHDuKFrHUHXHi4X~Ho%DOA$|8uK|cK-5m$TPi_v)S|)QObqhuSSKW$-UC~0t z&}a>pLiPgG9lGtov2BxbYnm~IVy5L!MR zgX3D?_0gsKAqW^6^;VGr04B?m*;p}X2n}$$$^cA{HuF=T8j|%DH@D=xKK$kXU~S%{ z$P@o4*8~>Dnn4;GuSQN}1h9_kZ??AVo@cKwfi0!Q0_h9U&zfxO~vMzmcoBFlb zYW#s6Eu5Ad)Mzyw5MbD6g9~eTYtkLtl^^FExjYLV@mSW@#J@F`TvjRJDGJ@NmkWJT z^>8J|L}i~hf5&m5cN-ALq0;0SUcgwlQSe|F$er;6=U_!nf_SQ&4T*8TCdsBl$_xu3jI}sLt+ix_dx&cc9xjL zPurnP`7g#y3Y}#Oy5k>++Z#=44&Om9mrZF?sK^9zEp~P1oIbq9!x~3bA{HwH2bU(! z+&6`nAr67_9fS-woEcQraabzC>#YDqg^S2@bCpVrUqmV#^CLjN%D1Gl|8yG4aF@{X=rpVJ8XQWKoIGo5 z9d1!>o+`+;+`+c1wA3M>A&#E$q^QUngN(2yP2e9hBPMlQj2R^S+*gjN{_(&&13@a$ z+m~Le1?$k`I1f7eO@tZ!_ z*;{A;o^Gwn)ytlGTQvXcNz=Yq<vZF&SYo-m>NmQj>-JpFMnutr`6;E$NR?n z*;oA>i8Wr?Z?{JXJ7_*shYHX6;VPs9D&Y{Z5U+kWwP|(Z_y}F}d;iCA7)CeNT6tcl zNzUpnxcLF?>}QHK$+$x6*^^AhxXcXe#E9*t%Qw@^g*a}XX+$OUpi%b%*^Tv5)!M-mG}3m_y`(Q zi(u{CZ0tPVDju7=i!K|V;;ueILwN50`PjDWBQ;=sTiayasWh8X z1FghJh8`uYE^OJ0l@v|!Yaf-S2cqR{ighQgwns4fXeyZmwzc$CMJ)zzEnl0aDDt%m zRR&n`DOx-?mVSQDN}f7q9cxU3qG=a#r)faxcw{hdysVQ8ljRGdAQ>MB)zZ}WId|cv zF+%2}CfQ;OU4*L_YNzz(>VS82sse1kY#m`Uad)%&mg+}u+`Co z`zx&11}YN5sFRlr9D6duq3>ki@d;PM4kJ2{20gemME*i0JAUOSpW>#d;)hmhQOmO`ugPAk*t!-$T$rQQDr{DfK_hsYm zFgohAdae=z@v0dn%h^;aR8{m8ov8g(%;XNu-A6*_4wgVVAbL8f==nHnIKu9`8PEiJ zmf+zj?;9EHLKUT5Ux#$Y|Td#kWh6jBVe1*mj zA71M$`kMzduE%DoP6K3qpQbjJL)I+PW#_NY_@u_qFPvan6Atn#usS363#}pIvGBtJ zI#Z3=rH`<^!v^{RBR^6oW*IB*$Y?+-2G=;&x}QJDdJ+=+R-*54eGGM zHt-J807MPAXeCj-5W{{wAn!mdaC$!>@rc5{wSI(fG~S(>&vC5ao@kIJrH!_yKESV* zlfRT*LgMV!LttzJtwQFu5s+}|oWj;7WPWWr|NBmF|82do)dx~Up)r&=L63cMBVGC%%!;bI$E_0 zD&J3K+eegYmqO?OaaU$(9W^{fY(UUoolRE8Xo|8Byfs44-4~>5G^s??#&wBhiyE{U zVB}bW*-Dc00Zehh`O*b?(+$=?a)S6~BLO0HDfZ|7ib}i~+ zu8vA<7KSeZ>kXQU4)NBkNCw1eF2wlTg^yc|v|>x8pbFIb=Z1TnQStkPM^1J?OKMXQ zJ4wbCmP4Dp$^Ou%kApHj;7#R}@`TVM6Arl=bl`m1XtZ!{$d>Y`DgR;`mh08+6 zkWtO|xZS@Te^YakG)n|`{;!kg(!@G;VuCJqw(gC>n{u9h4jmt4SsCq-6BeMmYP-52 zlgLA>h-1NhYDQkvMPRv@0}}eki56=Xyw2Iz)LalP?Cbv zkP=dKT;T1kBbjVG{$RRVw3v~M-;@t^-KvpV8)l8CI7)zP5Gm4wqRXq1W-{R<#A3N= zx+54qW5^Y4dh{w?#_j`6F{CA&*8*D6i_MTDr@)&XQ=R|xUKLH)@NmB>j5vq-FOu9B$gtp)Gr)x6IAZc z4K|B7sU=k!y%LfsqK0i4Iz!U(!u31^Y7#62t~_HX!c#7yva=MZ#+j?IJ+iz-#}}dM zIqNW#fOnSULGumO#RA!hBsE>8u`ec3Hn=&6A~{-$Dht3Y)_rZCRPs%}3UOjOF0pm3 z=~YTC*hYSFcfJK$$wh6Ezi0JD0@FH&uU;3}={JS(Lo2YNfVA;zf1Vx*A1&nm*Jk)K z0g~K2fO*0|-)kx_IZugu9jYjhB+!(}x;ny9mqoMKtaK5}MC24=KEe3ocwM7k@is}G zYP}mcW6~A&VPI1C33B;vN3o?zfl|FoOg0K>PZuR%&G?{@XGsZ#*ri5#t7AchoY;6c zxzm*L9uFwN+O~G(PGxPxWF79R6{Ff9n^=5sCDN3r0h_*NNqt`X?dqt_&;pC##tvcp z`%bwHot~(ur|IKJzou+oI`t)&SoH%&>)RK$g!Yq#d{)z$i|#}KS#kTOFvD!{?3jWG zO?!dNaI-Qwiy4kWJ_WZN9{Ms$X+2gJhY_!;RWROenX)duTmu&OQM%o{6LnQg!E_D1 zUueibR#>*%pif~D=id*RD4P!~G0q&87 zv}|esjI4Il9Oyo02Z*UDM3*69xuq7t!-$>RdraQ2GEr@&ZJ^T^>hkBbyr%q7;{2&+ zOh(XQr?>ia%ZSh(50NZF(#`oN859Pi`V8wNMXtA$Gnk<@l1G95*%(Mi=camh%AMjg6cmk-2CdmR zdMYX%sv?gzezvQS^2|9q&R}7JQ$zfhqxcB1TXD1@V=N}L7hIQbWOoni?MxWVM!Z2#FGq4k5C-ur4&^#Ez@B0#V|DxRQZ|0^ z^V(|virdh3$16pL0i$j0#Ud;&TyJ$7cuq|LBU%K z_wn$bWE?*95NI#KZ$7Jo#N#PX%V)nT^((~qOobh$Tw^108JE2uT}d@by4LPXX8Ifp zDhowNgrV>$h-Pcx5vNr)!NunQYu3MGhj}Y%ZS>>TUsDv@u3*6Ll}^_VI+B5K7u2#{ zD{!&CF(G$ScGmHbYl`faVW*(T?#xlBilzCL18@jF?0Y70QN*9}BeyB|iSx?N;C3;) zt6!fi3=h!&PVBgYIPr(8iZJ*_kPek^zv;nY+3?#`20D;gO(+4M*>6pdcw4qEBaO6X z+=A`h%?+`@?RR+S6=yCi^{@<1_sL1~ZLA@*7Kl4gn9jmZ-a%@K_Ao{cDf(~cEgvv$ zpXJOL8PqlXCy(z$ni{)}g73Q5gdpyb!tFLVOfZtUL<55Rp|o27Zs`kpI=U1DKDA_l zew7epvbcPR|D zcCP81#psOs8uSnjJXla-nHb%95tfo6{}B`^P_Tv4Myn6W0IA(II^-zOf#>@}?BTPo z%9LtEg*+#!=aRXnpataaYRagBz4&_=bTQ~UaGBUbEq|7uJ(Xz_=~LvP1mU8Lj;f9H z6A9QsWZ+ZwSDtuL3U_!2xNkabTx!}-C^g2PGnf|59PlGO5At*{5g#AfE2 zMmb*RfgzXGpGLGFqjT@2C!>ngwQQ6+=Z}#%KB55Raw#$10)pwpV17%zdh%o4B3)tJ#!{pFV>$=bcR*FiKPVvi1&v!jD$yr zNE)$ZgQewTWSTtv{(yA=?D_6b5R9SFxep;`fkzRpn`P_oamF(&WZz=<;+spad>*G^ z8lIhEVRA<#esmwxOx`eY97~gBpYwfo+qW@996`DcT#%Y2H3-#e4319jy1!{-Zf8Lp zc4S$xygy1EMp)A=y`+BYHRJ@Ybgn%Q%f%o1ARRfAUBO)NH0h0uW6Y)8uCoO68lcL% zqI?`s)!df%rEzRB(>X5tHWK*F3~;xlQz97rVuti;=|ZzUuRWk>%tS_9HG4^Paoj?1 z)uKi0jkeXLt{~#20m0Wk@Cma^9rpE5`nS?{vn?;?E0quaIoYaQG}1h>Dwc9_t4?tO zb)sNGyw-@W7uD4jWw(e|PL47IE^I(ehc^x~38psGkeO8qdbN5dh`TQGPCnOt^c3va zJ4y1O=SYm?12?05sS%^Y8+5K=#CVDy+goHdyXqGea#VO0#g52d_Go=MrFgP8!{g`@ zI9KPB1LrnOQm3FD+4rVQqEs~QCPFQ&9@!1HG=JQ=-4n5sk}@g`ClmLt7m9LOlgJqX zM1O8m${EmRAmSAO@6B7*|CpM26?LtInDhkcZVA+uW0fo^U&s~Uoy<;9N9xU%3L>0#v)hfw!TaKKCLfc0c8a9MJ|uX znHxCtOrwU{K)CWqbNYVUWkK_hm7ty2+6(Q}NCAyhr7WI@s7igzIhlN+U?H@c5 zl7vxq8^F|d%Behgl4xg?mE;-V7diDt1#Tz0a(;d9G$NgGBG+Nl7s*vSXuO9^ML|0} zX=|^ml`B|kwy|RF_FI&5LdtncTkBaum7M7O0E(L)NfgbFSrfogBXj)t{tjJ1%VE7H zlz#1G3@cQlrfv#3+ADF{wQ-M~eUz&#tWkGpUoq~X^_M>^k8gxP`pKPebSJUrYUw}-}8vm2~*b%imv9~6#t zJnO;SnV_FQ&B-T&=_65U`i3N_wvqmX*+nhE3Z#ayL8t$M0HE^990ljM$g&bR+r4n0 zQ>F#&jAqV=#;dZy(h>7Ki*u^F7BWSu5tIi-SrSu%u1d$_EkZUFGy60#iH3M!SV+BV zr=d@s`)S7MGkFiuAV9--l!8$J55Lcu93JRC85Ln&i>^^K11pPGhHzi8Y@zB8&aXbf z`8qsCaUvgo+G~UORtfMG5$W8S>9C8^}R;hPHKyOF@7X37#eAD!T*4Tg{Aa zm7x?OD+M@Iv+eXTE~PdiHcq@Fik;dSE(24)I>@}4&dMU}#VCTbG}Cxh5Rx0#C-b`i zL0A*Vb@l?w8W0{*LjwX#f zCb{~68{5M1rQnNYrU2SqQtr70Fz*KG2=J{T8j?L>1jM_0_fF)%b5c;6exN}zR1?pj zn`!jDHDCpaG#e1u1aq)_#({4^=@cBpo{H5;ZuLoyN#1(3MRUCNu+@TW7JIHJ_2x83 z*9%|nZ0#A0BcY0V!!IXVTFXz?T}#;Hf+&p15(L3J>k$Wfslo%wS&9&k!0=KWcLk|$ z@H6UIq8GC?B#!ibLX0%{(bDV$wLg{3Sz)PadD(Tiar|7AJ03XPL%Kri+honwm9qy2 zq7DT-_uo<2jIsqV*%c`c^1$rx&BP~W zVxkM%-@(Ef^k_GMJQES7rZ6WxRDt2AXYoIKxfIuO8T!Yrl`e{wX#+4x`QZR!ABIpM zsEaL`&3MDqdELSgISrzSCruDCO;t>Ww(=LiJH&J`*FT>ELf$xE5P$Af)*B!bjb`>& zcBNWXQOfHLw@X-foC6UeEeeu9X4P--`K0wC^fiOJn4s>s4`3UoRABY4AvnVM$H3t+#;%D>_y?*2NEjAUjo*h~+wQ zoAN#%-@euabV!0|do(PsW7!%*do<&Jn(&0;8eh!34nLia-=~L@DqA=Uv}0Lcsht2w zckqP@(Wde21?Ii6iK(SCN}nB{e#^ATZ!;0~LTLf7{4Uzh(fz|{)0+9vwCSq3(uapc zq@c#?OhStO^W5RmQMX9KbB0V%Lbm={dM65hlzzmt{wE@1=gW zEU1|V%+mz1!vEoDY4@OEtOymQ+DnB==V_v$)Q7PH{UpjANY!N)riFY=OANJ!hnDc|u$zs*k z@p70_jIll&vQqKoUxq%DDXDp!di!`+M+gvqtk|>RB9-}{zJT;SR`uKk$<7F%$a9q@ z)>8WP^t|-uxWjVrEPZ$B5C7ex8iBbJx~CM`O$ay8=<{>EXfI4G=+!*eR^R%j#||$8 zR1r>I(A?qwt|xSD#p!7ktc`N~P?$EHI@33;b7w`h7Cq$NHCJI)8x8#jXLf2^yn5-A z4s1pf#`Z4Fq#JQm8HR{*O)hL1TjexuA=egS0fT)|ql3$p%QMj>FexAT%h(bGtH6Xa zcf&6HSYG4St0x9EF*W3HROmqsFZzN=K$pm?1LC3e5C^Lex0a?uOf=-ddt)?jTnxnO z?Iv47b6TZIb*sOWeOTZKAWTywgG#Xji^ngmh*ZSK6q{+sKu>z*U12szrDhw3-la1R zRo%%54lTjW(Ew9E^Ia`9!!Rdj0I_02HX;SF!rD+(kl%EttMWRLV_hK^24~f#F-o9vqK22wx_? zL_goZuwDk(iKD*Fyu$H;g*Jw7F)G*8hPnwF>rb&lM}F52aA4g*@cdC2&SsnpWP^A8 zK7E^iIUwqDb&!3cs^uD;<+XTWhEI%L_6un{a{F>naEN|%6!o`A+dK9GsUiEP%oEos zskp#ftA}y`E$RcRj91CNFlHUn7;-L3r6IQBS^EUnehgoa^etWxgWiLUuFqwr?4T1A z6$oS@|1#rKtVUyAcnuj!lkkW(&FYUVU^ta4aEsK8xH76jEqp+Dy3}GFE)Z)Xto{Fl ziZAgstN+vd=~xB<7$*-I3I%&j1>L)zdO*t~#`sN9pdQa~CSCVKJni)!O+MmadS zHh8*Sjgu#J5Zh8-sZMbFOpnvE`6b6;PjLXoF!V zbm#_vw=61erVEzZ12}l!EE@|MVe0*O<-sA6q|+ApI|{BE_Xl1+6GFkLUgY4dnIvH; zny!tIYWOC~bM-|xd?h1xQNV$I4P+vYffdNry@-%H)GR756#NPgV)lj3T#%J z?1Z%;Q$XjlYp^>4orJ+W2KM4{)8OtH;(y}-57_IOJnSgndn)DF#8=AsumE!9}HMX&PC;v>jgkeulN#o0(ONU3i;{#1JoD# zs!&vgg$Jj8WT)QEFDqYskRd0$5$1dVR-5VY%EqZ=&tpVaaJ`wy;3{C$i-n z!W`#vvT37i4yYUm9~ew==XkR~WvPw$(dEvTW~XI z4#r5MO1$j&zKp?x>%c{AeQarkRxn#_+3p{-?wjTah1jXmd7eN9L(WqSo|8U4MRpm; zDetgeQ3qZc0?&6FZ~ILu)A1z^;t8>z`I>j#A?CMO_~013OYQg0yv_H@I$$o_MY_?P z8J$m9Ic#u<+ChtDQ5J*Byta%UK%vQIXNOkMTZEf!(}dSUj9S5w`;K(=;P#!*>yJxb z?|87qC8*!>ZU zZmhasC`>%JBfH*?)hVf*WH_?@PaHZiSkSp3s1wulL6S8e6E-Y)dQ%;pz7enVk`E;s|FE5Q%p8i83dothzX%JWwcjz|EG0h08{nP(isz_EimT z@bMpUdyjml0;ZLtSvOcT%marAhK$A%G0i^lXM&jAw;>>!KOVstco<5nxR-#Ee8 zq^BkKj@tS^edawulrltCMf`)R<|l{z&e34K(5{TNgv&O2jV}7iPl0a!@s-vYpsP58 z|CG?6OO*VHDV+ueY=dr#&Ssm>G1Hi|jlo)`!JY>GL4kVVZF;SCqbXF1mvjfv2dK#i zaPBx=p@g9m5fU+`p^(Nnp^9vRbGD16wM^3FFSvr2<^{i3*JdLdX7(2&0vChsW0;FZgj}wzV4T&hc^Q7E z^tXmEdE0UKQ95!o$-a(TCzmil9r5JoM8L0dcSvLhSH;E%*#ShbJPS(1l!&EoKBuJ(f_%-0TkrLMtz+ zCxHd+v*Jz7jWEp}^cG$fSuZ0g?B*7q-gCbAUsquP7)@SeOYlzW3DzsLR0lNvVDh_< zaKVi;NhNkMT3qc;Osc|=5tcovGWr#r@t0rQq&Z{&n0l!)tJIYvoUlz;S^=q_4LF^8 z>jdjfM(IwcNp@oZJ|o-_BuqKMq8a;j&B@A=sCE?iM*v}Jk`|nY^{B!R?a0f|()4Wt zFfGy*Ys8B~Xlq^Ckkm%R;G8jIT|L~2`kY{GzmQQ}ETp90nbpEHAVL$mj&#JA9G#|0 z;qGZ@znYHl&skmuK%`QU61x&P`QWZj^1?Ye~G&zzZ za{T2w#&cm9R_p$MImpv;*C7k#iDo1Yn%kOieYxS6Z4W2>#M4BNGpneu_;4XSjecxSUGmG z=-EVM{(7}-9OP9HN~sc=Crvff+#V`k%c$2w-9c#CRY7VD5uM9_BxONiS!JWWCt zRfHz;l&QTqr8y!gn4LmIH>=+lCcJQK%CdK`eJfexj<*II8zf4Z7$LK|-)jYu0kd8% zhQx}@XW{8u5*7_f(J|)Fut-6tp{yl-EKQ_LN-J|Q%&jq5w0wIyJ;5{myyXJ=M>&+!Ns#UZN%*uQtk7v^;n9#nGZW!b^ zKXVG34S7~B`Cz@*cobO^-2x{s3Dq$|4U&HX{gyUCo#gtb$%V!H$Q=-%h_&ms2Hydn z10?uiS@|>Fj1QeP*D?E2w70_R*YO!M$_4tRef6CN+mw9lMD`<$NQu3}fuAa>d02%A za_=18u=bo7>2UGd^{DK_Ye#3Y2^BK}MQJ$+H=!P#xeDkB@-G`yQ~e+2K-94#4_#-A z?<)gD&*3ADVr!b&f2b@sMUuO+jM7OI0G?xT!&7$iQ+ClFM2WA>h`gkxyPih8n+7}U zF9K+P@RNaNC^AjTK>>N9jrEgK^gmfYux$k$3Cn3%b4gd>>9IfaG-~9c;->YsR zEW02RUF2T42NNf4^Lpr;9Q+9(C*zV-m5HGx9M6%WwuLe5*>M71202^0qLwdh*(3BL z{wm()BJzQ`^4KAq{S*ZY4+u2cg>D}ziMQKKD~-Gpkp>g+h`P;iue8_I%XdQN4ca;C z6(4c)gehyg$4%h_$wYSi*pG!+s7>7I)Q=NPlUaMfnKBc_O0ef$m09t+D6EFaIuJ*& z4bv&NB;+|Rra-1y2WzYAy5Vb<^T>^xv&^vPSTCftn~!0q86g{okIXD3FKX_Y-b)pe zEzIG2W5gqJcT2|Yc?I_?BozBS+;zC2dEqzoSdXeYh^10oHRgXSe1}*)GDkyM37@09 zZ`YqqXUk|Mf_Kk{SK2LbCH8oYwK!emLW>=1qun3Yrc>LYCuRT}t53K7dDFFXF!8&c z+}m-P{QP||!)+QiN|N>qfz-p_#DVB_#m&ibz}o(Fvy)3IDF%TMkb@h`OhTqZ`)BA| z^$SBu>33|5(%ScD0lG`M1c7S;jow_2#qfulpW$9 z`HQMxP+2EogBO~JP4)pX%e)wcg2qvm6W*FR|L=ein46~?g6HJ(%P}5kR+=Fq zDcd@mnwwmOUUeL5FZg!D{Ay7$39ih-5U<(`6>i*{v@M>B5|kIGj_gk!LJ^aSk6#g~ zV$oOJOG+>H)F}7E*8V$t@0M6gJmf#+!7?4S;%B2lUeVimiXh$m^prI!$0r?23DP4G z*>JjwW&&8TD#mp?ahxgHBTkGeBqkpqJ42W|RV7gR_}0WY^SnLYPK*E;I%eGdwAycDA-_AANltYG}6YPd_# zn)EG|S%ZTIH6a{fsuJ|7^S&>~F_5l>R&uOk9b@4)N}o&p+TLygJMgvM5vzo|&jrCk z6zhU2wr=3GapZU2hb`Q`mtB6siTEf11$=#dh44V== z`n@;}_uF%HkdiC4aGhWVD&F6M>?=Oufg!2r(|!CbmsS;F0C#}}v3YKKf~B2^q5kDT3!1)O{YO%XaNXDgwrt5)WH~ZO`YFg!Q?Ij9V_VPQ5z0G z(U`%7A+nYC$=}7keA&q;CIsY%PWpQEwkSlD#jAv#!kP@!;M-tVpqxI-A&SV;epCd{ z1jk~}GLGtKfp=%V|-1|xssgFi$yoKZj)%fo;Rw9x%Q4GLoN%Y-` ze*ZsxG@89u>w0c~;^+!W!1*jhV-ZEis|1X(dxsHOPz9-0U94vKmu7~64l50G4XyuN zNaNLVRCok;8f<5}G)_0P;*4T;guv2UhQJi=HX5WS75bvpw?*_ zqRJrWp7~ZI$c3Q!zgdEK3Xa&w&j59;W~{!|(jW*7;n1pe<{Lv;vun0{Q)bsI zvh!Y)^@fnJwDk}UvSCuMzt7TZjfY&n8}E(Q74BrNIu~2YjfXIW;ZJz#*{ci6<*$i^ zYQ3&mcp?>|DRHH^!zR13ev9cbpp&{lf+0OcR;HMv1IT4d)m10V=P{u<*hhwc(TkVdcb7PIMOdS6FXz^(i{h|>RHvi1Tv z=sh$_0$&1t8yW-(B&13^cb-xgkm4t`TfB)>0f^_A+Rh#8nD<}ft5%iNm5yqPNTF?L z0esYdE|J*O`wV!>E(*w%iywsw9wurA2asUK>s-s&eJ@Tj|1jJpA)_em ziR`9HxTs1yA{LJZvc+Y=hfk8r%JW4m;l$$*cE#fsiPG>J{xv<1d}57VCF&A3NvH2l zi1D6Co#zBK=;&+26OGcn-H2-)cDzybjW2<-ICl{F(_s0SQwseG6HFUDd)U->7ECW4 z8I4ARrLXqcO~Ahx+Pc_4tc3x)oYTNoQZY%dq0RFc;2>gaPdtA!p)Ib*?2Nr)C|}&K znWqd2NAXSuqb$6Pn6(s)ZKDY8PyDEjk^tfFa$mrlbVNr`jTbVp zM?@V^-^gn9pE!p4CTj>$Z`k>0)HDTJTv?#7`GVBa92QVN>^?_y7|meJ1^Y+0gTu+c zAA*Sqvj1Cg#O?cuR`BxtT?`k#OR$72w-1r}l*>+nx(Kq$IgoChTN-i3QWcSBMFfA) zR(9wZ_cNa*pg@Hu6a*30a?tUaT z1Z$=4e1e)*9THK5%wXGIJ4-Mx+Yg#J@rX#wI3DyxNaw7(XCOh13r4_1UvGAw+7;Jd zU1la~fQC{CQiez0c;IYEM$^&F8NW)OY=GQ)k}-m;j73rrgU<*m=j~{v8j)XRm`I@d z%OCEp6pB8$Ntcpn^j+>>fvh3~cn~%FJcj=*RODtjm{xVo66y7*g5flkJkGod z(a@x+@kUQ)r-3;Na##xaWtm1mC5MvhGDOxef6OUj6lImvT-vG*eUZci`8r3Y79F|) zvTFZ8!;-(2Bo@fh@!&mVaz^GP(PUEyG<#HinqB#GO|C}HSr<=xu};ol;$oyiFP$XW zZ;pl)L*f!W8MnUoO%QdO#M?ya^|nzd<7_Zi+;E97vWL(7MZ}BpBOF_40EFuRQ@)w% zjgUm5O+PLVo~01BUeGIu%GnxcFX#3(ZFy~a@6?6niy<~cB#;=Oh%#zTP}O7j?%pH4 z5xADNu>qea0*RhnMxaUq@{)fy7NVg;p*go+<;Axf%f663>K_Hxqi>uWy7&pBt_)W* z)UU)xs8gE?0w}B{q*C@A4x?Ty=D9RCnPE0>kAfiHHs zkZl0HB079rx4d-iTuR`v`T7d8Z(d=b%R13YOjIixDbixQX%+|sBUVAzhcB7e##e90 z1joSpI(h6Fl>Mi`R(ZX?xjxTh!@)T|lGz4YuUhWxy+gDS5wn)lEu%!NNw0gRKl2Ot z_t1gCU0WyJ0QSI26Kv+E0$cvRa}NohwiUk2#ZPZH(*tJqS<>4|fvC-J_=UeBO6yq+ zKN9Z33J^gtJA*L}c4ymHZ-}X_)SU?;(`qzixCJ{#_qJdxGX)71LF*I{*m6NWdnrNt z-sa)`{)e@1iqS3Twr$(CZQHi7+jjTYwrz8_ZQC|>+qP})v;UKuo1FV{U(QOZQdwCK zRSz|?<{Wd5q2a1SOzX8Z$6?Z~!0nZrN5O9CSH5FnJS73yq*Z8Z<;W-OBgV7J>eP9V z;SQZ7-4(gHgVWNb%>c+!HrA;BZQWcL107ylli++;N`5uv5rz)lgk54sOByE!!nlm7 zd=4>{md8FmU!Wb1;F=mb_;mKOtT9p!Y?(;4kmi!B#!)ZlK3)DeFIlMawn#kASk{_0 zRy=7T;4K5GTp{~p`iUSyLV>n^5GjAc@-FvCy!axnNYhR~ZYiG9 zlL6W$;`ii-C@4?o%jpBps#H~9nmK6CM>^X~n}D@7NXn2& zC2lc_7E;=*wt8C+3QZUMNoM52TEX-Z2|=x!&wWKceMT%(0%zO->Ajl&$(pMX`HF#k z=754LyP5CCQsRB-e^(vU16tZtlBy_9jhrBL+t^E>?80|kZsOV;QW;_kVpb2E+pTpl zw1mY=5HmG?=&D{{=282_NE3k^1q2OmbS@!P`l#hNUwPum zwnsHu1fdv_m2622!v5yuoVNc%B)Qhgx_06ib`y?-dAE7y6p#_x&wT?McxOuTkL!fs zg8T#?FNN32&-~px+VDsCk0^rBR8?2-GgNPzX=6qB2-uF*0u90bbcYNrdrY!rzVj2+ zUR%7p#Y+AREdAM^#;b)p?&f=tnBMSq5t7AY5m*}KqXK^T)xH6_MFT#~CaoHAr3GO* zL>{x#8gB5U*%%c3sx(MvGDHW+>BXWfDG>8w%|$pp>FigTBI8C9bp9Cxne<#yQ(R`X5?LBX7s;NSEVf zw%Bf}!YJ|}PS$Rx&-v;$e7B=<;BqX>gcF0$ewdqrsbC$($K56RC6LjbWFdAse7(&q z6L{O6tK6e4TAQqHF=4`(>8P}ie$+QV__ynrx)ipVgQG+SDr7YX2H};W4+8$O1d1h} z0Ryxw!i!p!K>rpxz_TLM@`ID$&Zik^Fa-EB#z;}^LSdPd{Tx~`Q@aO@Xx{ecn zY$bT*keSLTg*Zb7C{giDL=ChEWG5cI@Rc^C@?xAT48vEZ}xn0AM$> zXnH-Nl*qBHM*)p<^v)n7SFh-2qMV`03bLNzW7|sPz|i=Dno8smdZ2ZM*73kLyt*1- zYU@~c7|Zy0)AweNP%EOhUoEnkU%tT@Tb#e5e0?gqh_YQM;W0_oV_y{Gd^L%7YZ>wV zOS3YV_3;ZvG(xx^-u4Ej^4tM3`B7c2TC0_)zmU*Za=`BP^;X%&OtDv#+6Z}buWdwK z=99G4G6^n|Aw<%*CkXx;N$om$92Xxsn9XjkdXjvz|ICVVFDVg92}p58yP%2VPCr6f z7-@+Jy$-h{8D|(GLEN6`?vX@*kt3wY9hAu}D2LuFSVUH0AXAyu^g~!;sy~GLZV@P0 z>_QpzR_OlxtWu0Uq&Q~Va}gmmlh<`|JikvlZ;livLXc{# z->(Y!GbXn&3A=vx8jjO5fIb+{=8(VEn9mXYt)!SZ=$ExphcfY;|IyO}+Yc~AxlTm- zVblj<#*)>yP28&kzgvGfYMPFD%L1hyh31p)83M@_xtF5}^o^1eLvIjpY4Dwp(y~jY za!UR@C#U>qX3hM7hJ8p~MFPmTs3NT*ys%HvJ4P(1{mr{1?C1P0+utv)CQtXFB|Ee8 z{Mp3?3Pkx7Fd%fU%KM!gU#5y5O~S6fsUK?t-5uKAM0V#(*xNom+>=#O{N{D0`096>3XzF zag#GnQ$nf=2;GX$Q^b2@>dOqBul(q5@D*tE2nkSBd|0Q)F)|D6&~BAnUaTyhgYZYw zI{!sVE-zs)mP0=HuzQ5W2a-Q-d!;$-j@h0>z^YHD<+WOyB#yG<2~296i&HsPxRMm; zE0<8L^uU4zf4Sgp^0K!fj@C4}Q%AEg?sfLi^k?5d!{Tls!vs<8`6pk1J_&?PE0liG z@ZcPtWVSt`+Z$nbZPF>lvlfCxz+oZDo|RSqE->AepPC#L&9YPOGTzL(^AZ9lrcY|l zh&HyYmogfF4Y*@lFyUlAqIYm@;>AEBJoZxwr2iHzVwwDfUf&N}FPVb}&dwxc{x8Cdt2&Wir!WDkffBhjAZF%HN>nzHf2A{ z1boN_s0wC8f0_eECd^j}n-tZ)lz_Jd`Ec`B^0;QQ8XN>ZPf(aPr!*6bNO;p81km?O zZTCx0`SE4*RCacK7g(H@0y8Pmj@MKjM0TpSGv$buF~X`>{fh+eK>ppu$}~6D5yxe! zL~Z^Hgt0atF9F5F%#krgA0BceZA<{K#8sTF>!!hHAN&t+zNbQ9b5{d9QCK`sqYW@M zZP8Mae86cUV)E;;+R1K~1sU9_~l~BU7!^DWbI``CI zMbMFaE(zmbW3YkO0}Gk z=td@xqL(*D1&-Uw`F$4n&tjYT+-M`o5yN#|(C6V9sy#{TWg{RTsK>;NS0N&EY2ET>DSd zE1@8s7v9M7Gb0UYXQJq`Faq5~71%s}@-i0eTY`g*N~v73;B5s_;#qL}@+jLAR<)-C z(2^n~9KWH*|4y7sBXW_|WF~{a595VYAlbNKh<$}cE`^BC|@juzNTy`vh?dnpx z6?of95tT^@81_0tx^YL~H6B8<*L@I65Gg>?D8WqQG4t&E{`Adt5-J}BP5lvDnY{D< z{7iYnP}@uAbXs428nv*n#ydaS@css>qH{0MY1)ky>rpfXcAom|doTSg-xpcZTWX=y zY=f?u1dbbo|0;<0G{E|^@n#`Iy73QpM;;AY4|OM6VRtu}sms3=7 zhrnh6ivxD`9N!fS6H3j(oIJ!StDYeazD$|cd0cNEf>|F+L4O{cTqs<2tLL{V)eL1O z{O2VJjql07yi6GX$XQpb*BXF=bIn`GoRtghU<)D-1M_tu>w})fSO5=0igP>#tP+tI zZ8V3OFjO1t$tp6L^fCF5rqZmiI9zAvW`2#5Si4Iaf_E2gpIh4!>o+&oPC$kxRw6!a zG2U;EB~zV6%rC}iV<(C0f2|eT40n=Oi|sYnT7J&9!0RC;KG@_ike~^iTFUYAl6U$*O{KQgB zXHw@JSvc}m5wqkT#Z{Y93#q6H zs_#tLKnU=9)N|Xq@q1`RW13NJQEMG8C~{VU=lPPG3gI2Iq@91Juk;{Vq!G~*25LcI zJXNQl_+m8>UNRkh<{N_DScD5yw!X6pVMJP-JH@Z1>_s_0J2X-YAQ<;6m~?!dN#7;s z;XKwSH(;Qj+Y4)POg$ctKo*T8oyxa+{7W}ekI!WH>XpJ1Yw7(MBMBn>aJT^sWtTnX zz7Rc_uLz#W1l&02UFbx7>S1X?$K>R5-c-guX~H-@&(?ttNR<34%k^S`G|+Nb(}WU? z9aPsoE+ZlEs4l2jc9bXSL*)qsythrL6fq)f+Fw@qwK7M-;Jnsvu`7<6(9)ST;Bc9| zL<1{Zt&qxMbxSp!Bh}1&0bUmT_11MKuZG!pD!-1j2Z!fZ$jue`OH^$wYC(07a_|uh z0u>cA!5Y&?VY-GGQp2PlN9Fag@rZDbf4_w;@CS@JV%a-T1W15{+VOf&3IN%}g5jXI z9@)vvWdt^aczJ@sowrLm_;HGQQGYEX*l;&P?Bs5~Z6I{&{z!AOInF)9JstDm_cy?w zcn3F*iz9eNpSUxeOA58(XQ7KsUw*9+F6(H&yDt*_$+si#R8a5FpS*uL_tNe)n=Q^* zj8|*9o`u6=b}gjd$#Xk-Mmju)NVp`qOdNvu&`;C^`9!B`>t{XI&n2ByIB!%UE|jUl zrtFu1@jDY0ZlSP~3>esf8w3-BzhR-lqV8$dk25w|shS!InqNWpEnX|Sh<$)`Yt)nA z#ycyq&YvR0O8!jT8iHmf3Aj54?}F=D3`ZVkJmpS?3DxjL*0gwykNGmmAHlNykg5aX zEiSJ2?gd}FmDGsImMm*$6;c8-?T6|JI*uq0a@(*K4iDfrXdri=3e_zh;6A1Br`_QY zvI~GBOvt`QLz}NvTWAx1+B))78Eh3!4N-bwkw>yh$@^%BNv9rE+mtT(s~f0*a{QiB|PR`9!ZMK;)cNgd+ut_rKP|_sl@e+GCajRur_pESl%#^Uk)+FnKa)ZE;U;MB( z=#9%;%aE6|QQ-}2S_jA^_ ztFS6)R=rjVvP?+P2s~c`jxJ)30LRy902B0zmxkUlEagB5M83`PMzkigCYxEvvQMg{ zSfCWno(_Vs0YiAvy>Vu-aHxo0qLQ?D2U$NbL$EyL)RW08LO*$QfZPyLfjnZ%8fCW? zQX5KQ9gV`9G%BGETI}l4!FsX5=&TC*%}4f+;4J86#+M@3!)|AS2`6 zmnnY6<2CHJTZj8!WLk%XjKNcPQ#HzLpY0m+MXG~BL&}vp+|oAcO6<= zmr<#CJR47g9bBRL_>$aaQBHWWomWPiymwvVPmv@0#54 zs!a~Qt4!~5xvST~m!=`RTcQTYPJ7G5ndA5sJTfdQo_|TFX?Hi)DAPo7s9rQ2;Yo99 zu?8bh;DiKS5&kdu=rp^XVAY_P?&(bDts$)+XSrE5cA)6_f;-9{LlbGT^2 zBg^7~%fK$TOPrZ?nl)ltkXnX@d4_%)9I<^`JGY?O@I`q$W|gyw`P+c_{GY2TU<;zU z$KXOI-IV)7FlxLFDKw^CGAd!r;LNCOoOSI4pm+AL{9Sq+;fk{55@@~k^13JZ!D|P^gE(n}H>w^Yb>TGw0B<((aWzuMb!XK$r82?ti6zDc?`k*4DMEXE0 zJWKiYl$?!j?CzL1S#a|Zw0iN~d$L!XF?K{)#+9{euVQieDg65I$ZqtRW@$^?Yiw4& z6Ag%YPhCue@kq_z=}T{N?%F2T$%FF8uD?Yr2V-VhXU;TXO9ZbwNQ^!Bc_YO{WRu3a zV4A;d|8|0!5+h?i7qJaN+^Z8h+dN@Menfx^*8OTx3>KJ1V{N&j*uH-2kZw7sPyvx; z$c)IwREE*51*jNv;L>C^HrO+5fltXa!Ab**zGdwJ&e=DE^HHS8tX;tz0s;y*r@1-7 z_c6rEs5@`^+rW6pPBH8{aRU^MgWKXInY=~Lz>r#!fgy<%d@oD@+s$k645d|+VbDC~ ziQEc73M8c!?4*fd=)gQUL(}*_R1Jmu3NDv#{r5aMBqhCT17zQzH-M;< zXYJ!b*N?&dTp)^fx@uUe6AgWZMp3(?e}S({C6y>&NapT#;P>Xt35Jr+4|-F&0R!6` zUb!P(;KE;N95V^8UhGx}AeX)M5EOI$*2=C9nTVZZZ-}{^XSa-b7*}4`%6$tCzrZ`q zSc<|HOw8R8WO|&_yD$taOlwCy^6Z(->OO=obe7rHid(`(ijJq^CDoO8*V7fYwhu2~ z^v)9GM}{(_q1X?%JXeLo(%O68dkpZE4D$);Y`a7!D?X3Py>82d^o3l4#dXYV&2zB9 zw{b*RT8z$uvmRFC|Hp&vkn&{qKQ$#%juUrN=F*L)M95?7u0flQJb{>$wzZ{0c)6t; zwX9_+okB79q^hyWtGL`g6}A&F=Zk$#qnKSJ(o-%f!}R5?J&n z)XP2GoWK;{x~JRpjXK=8qh76oq5xiSD?Oq6eJkr#lw3uh%W!>I?|ahQp!&KqrGkDF zC#t`0BPJ1-a~`I*IEHWvcpwr9mqVslzFetDCj^ArE!L1I{!;H)Bf{E(Zp+M>1!r(Y zXMUiOR6pqJ^bF1lgJ|~SR2*(?T+=q9=ZSyrX2EG-gKkXFlbNqt>sfy)we7;9w46iP zx$df!WU^KX1MWaR1`!)-^&MyuB@^sSU-H?hyL|qEBi3msXMtMr3Gz$a3HKt<1wyNf9KN!t+=+z=a#Rz#>5zoa{#fN5Qy^}SjZ zzSa0x5OcZr9H9xKi37#yro4vz{0vMhsIdrwMoIXFkLl|gv?D7h&WA1u4qg>0hCyyW z2ST+z9Hi^=M6W7HeYI*`FLwsDPt<0T8iy+c8D&*Bg z!3^^+7{}1tpgG7y0|OmW?JZxt-$mJQ2p%n}i`UAMT(ou!Nj0UII3 z2JqxplaK@~*2H8*;=8Yr!OmabXOb^I`etmP4})ek(v4mNiw@E;?+={iovVdVFOcGT zbeINnjivhJbL%W6mKkAzW?Fz&%Pmtt{lU&iZ3wI_6(|odB=?sp+o-RA0pgOYq{?DG z-e*hx;fV$rBj}*lH-g$Mn#ws^Q}|^A`;nXwHoQ)X)L^e>v!V0RPh=kQW+ zkv%V0$N@+l$m8|px|FO%La9J!pUI}A2^$LI4}g|GxTPPgjzG}_D+QTwQ9f<$)i%;R z*hW>DQXI;ewd&0og?U!Hk4;8owv4d8dUa00SaR_wI7@|hmqV2(pA&yEQZ2W)z=yHQ zjYe>A1gGcI*hOn}&!J|!UC+s0q(#L>8A^vDY*8ngD?&HTQ~U}eFy8Q-y0OVB?{zO0 z;|Ya{R4Y=%lOuRCTn~~pXi~%8#lDgabu31iI4rHHUU!+%;wAV)p4O~asG@i%XR@)) zeqL5&Gx6Y;r_2i11(+V`7PLu&&dRwDxaVonXN$OM79^!_{9WRT$cyep`sS`<%5DIL zupT~fM}0Vvkm<~$-2J9UMNp8Vr9HF4@#}kg@9;XCP@}aACkQd#lsLawCOM4K<~mGb zx4;)FL!w7NCOv+0ZU(2btR=BVv1;90Yt`pjZ7x%Sj@x*1etXw8*QB9B-WW z=&rqs5w&)P&oP2<9XWexalCf5#V1}6nJaL%N7b$qvJuJ(^680D7`g$V!D4iZuNq`HFx8>gG``8@WQ6 zPRqet?(0rLKH+hu*o2#VBZu9nF}b3N*^Mt`*AVAoPaH+iY_Q)+`AQTFPZ4^yD-_3m z?FK^dI*J8$i_xLv^hrGCrphLAvYl>|cE;Dy{>FG4N|vZ1GR<6uoeN2byq(Pdi`df7 zsQlSxea7n>`b@uDZ37a>SM#f#bGUBZ-2jz?xLcXRCD^fBK=@fScnj@%562G0lwSf} z{Rv%6wf9(VI$j|tzmok$n_F0y(!xIJ-Cv19#2u>A30BCnSKp`|{ax;D(=5)y4nc(~ z(`;jER9py$`p8h)JF3AjQ`(0KXQo48&;)i=w1Wa6-vic!T5YHx*rae?A6?NLRsc(T zr*nRXgd~06lxOAZzGv-VF{XIwa<^8J@b?PJp8hMLAK-jwCaENmLB~;9fsAH>s5izK zd`Z1fptdhJbgyX0nzmr{)O^2!-1=ABWeWKz6qcvT`A?!@x-Tv|wIij!Q0~FGC2s?7 zJtuf80Svju_YuS>*WvIQ34L`64n7zaKAHR6+9z$F7%=CaX4jaC`!aH&rCnWM(Z&$6 zForRR<}gf@i0A?J14_gFCt+YI)^x$AhHMg6V!C{4(g4xH+{&s7Pw)>#v_M#mS+~Sh zod?Bg4b(2kGp@M+0;kyCKW#GSLY-a*MA2N2prA z)i7{G`?vxejN_`Ds_X#0$)eI91C7y;Mi;~6qG}Zg4vN_B!X^B64EafE%57cuRH280 zKzm@XAs(XMOXx7w+zb`KaXiz2FS8~#Iph~#81hPvvESFK0Q#OhRV+16agKeEg`Agp zqL{g+Spb~z17z3~$L`xp<13#?DqZUklYT3Fh?_s4FPb)@{39Jd9CfnD4Uu#&Ys+fz68a zmEhqQl(lNfEN5H&zm+G$;;RRQ+6RqQkoqS!P2=BF(cJNFtK*Y)=M2TzCT0YB6cG3X z<;HvNI$C(CjFfLu(sEd{4#;iDrBn)0(}_X~;;u)no70t$_N(tOMEo7%I?`W73O_f7 z(pOe}ptDl)vhpkGw83Oh{sGs5NJ-R*g| zyJL5oUUBLWz)v6f{TXw_X^FrpcbkU75PMHKva&;8g-{ALZM>7X6CySv z$k2qV1R7O%4$>BC9Sw+J;ohOK6;Dh_;Q&N=D=0i<311F6@Nq~b~)V+zPMWvLqbsUl1I6Sl(@^Lr+=Ocj6ayc zuI^*&z;6k76;@8sV#Fd6Ku@E11Dtl6apCepsE>3Jc9yu`lwz%2A;*w%07lL2nmAaU zR|KBYCQ8^51QIuPnt3{Y=s36?plsCJ0f!~g#Pj-2ALOBGz+Tpwl0|%;5_?ccoByS; znmPY*4w9Yoe11IHA@uywjlb~tAVeNtpMTFLY;sPWtXd!RAMpn3b@zzJcqRl?K%Ez` z?FdY0#V^L!IrG%Hv}||gTp99Dcq)C`$d$@wP{CY2k$NwZbN~d&&RcNcdjP%Z_ZRmQ zSGA2x^=MfqzA1AFE)siNYHI!fyDJR)pd1n?YIArk#Ba7Fc&TtJ>TIdN>+qiBl?CTb z%t<{$;RlOoifbjNS(KQir_;<*ekj4}`CC-m3_bibo+MI{N}^qdg^P=GQ6m&AR2}mP z-BIr|6cTwPG-0=H;;uiE*ML4-E`aSO%%Pv`=Y`?1ni@dyX47wd@Dl9c`RpP|+F}w$ zAX_Q_$pM!3&t})s4l0t5wVc+r+|0;5Tgfr=JM4enwsJHK&HW3wBks|tE@PIm~i)Q z-zWB1%HooJ72l%3{rNkSVoX`Z5xr)!aZm`Gwrx6{Muu(kSosLRp*V>;BI7hubda)q zbEKo9pgaVvX0{QHqJwYF)*I4IWvsMPyj3L>7P!U< zZQTQHLe)5&y3V~qv)o0Lc$L~y$d3-g)p&eWsHo(c@u%0Hpc2G+21E{dISO%0gZp>& zFt0i|@Sg-Q+zaesAt?kq>j!-lN-*%`bANyv3HCJK-@+6_Q9P!FAKd@Nk`{DGM`pNK z;2|TT;%v`hf-8&11l)MqbxDqm!TeqY66Ahh1e%w7XGnNyw5JRAZCJ@<%@r4Qs zva5me;XUQMNpo5DWvv1W*RFITV|bz}R$tAl*T@qrNKKi5E#>A*^sL3TZVvvV#DP*kFczo_x6z3AoWhYxN-e)1th*0W(2_*}lL!+(BDZ<-zO?5z)i{<_e zp6nnv6&CIXgtDU8S(0Abt<0bg1d3*yI1Gq7JERR1*n>J}_ookI<7G>u1xTK=++0fV zVr^59K{C9CrotBTn(*(gV4O-)s;vcW&9p$k-XkDQ@-rg>m;f7a{6?xNm*{+`{nAlK zFPjMZw{IQDH4i>r$VGsJAmOq_LKiYI4Y#6269W!1jP-Y)Ew~-cCuy?Q)jL)YYc60+ z@nld)Ps?)AH?FslKlb3{lAe?;Uy(vX&z42i@eEg`L!~FSD&(JHw>T-8Uv)DhB~Ja* z8<9x@f4Z2V2|(?@iU_go6ktzmO=WpsNIrWYX;>=a}jzmuHJfJ8{MgD$w!)(R% zEKCJG5Q6MIiLh!Pq2}DIdz-a05*~Bil%{^@==2C^dndx;6QQZtlKY_5yTnIO&Ckj_ z#v_MXKYLBwrnjaZsCK8Fjm2)_tOc&U7%Cd3#;60&g5Ib_euB`jQj-9lNCD_X2c#8l>H5+=c3*D%UXtZL)gD8BYepSE! z7vl)0Tn#1a?v6(>cu>sd=uBLCE+bl1Zv~M98x56?pNT|#2+hx<(OI_bWlsQWZf8gm zGj`diuu_QQo2$7qzc8>ij&L_ROTsJS1!Tm9_q0g11;Q@JsCNa6Fnjvc2_8(l>#3{P z9=1{vYPDHgI0J$Z=jJ4<^DzGx5sM&6f}s=YiXXxyzJN{?Qwtkul+6^J&C46M2T*P$i>T!iY?8^jp@g4=>Hc3dF z^+vK{D_mbr8F2fk4>B44gm~QIO;f{18sXdGPlvdfch$mdP!YM+)i^-HB7J5TyGn9c zMEo3zSkhdYi%!(S+>B+;;WAP7a10YlG?03-)l4-i%o3DaD4t$#^O=nx? ztRvZlX^E!iRxisJ4qy+aa)L4dG0ZR6z*Z7DFtpbJjEq$-re+>A$i?qrI$^G^*0sWU zYhsX>{JJz}=mh-y!4re~Z|_%M-03b|`s*OC;*RYnycc_gupwfs;%iG62b25X!R8zI zHdv%2#1Adh94ld)pySvBoDRudT|V)tzX~e5I^V3GU&SVEw1)J+N=q1BV~h~20bvj$ z>ckrfTWDSrc_*XlY?Ql)Bs2MuCM`0~E`x@AF!J)igu&uWIXL8ADccC?CVuZI2q(Q# zTZw%uLoF zs1qqMQc!J0rc-dL)8x$w)i|d6mwlOJA^Y?xykuVu3)8DGYjjyx2861#bn1SN&Nu7d zmxOGI%|jQBKM0tC_jyo|s(eD6B94Rqe$gEWFM42pa^?FQjb%AlhXR8hiPdsz>zh0Tj6`|4t1cB1L1wA`x@!)Gt zNvN;^f}ng(&y3{57PNu3Pb( zi}c3v1nzs=AZ95%9eY&w^VJE=AQBhaC@^N!2sLzD?mH+pjshzrmfSebH!rAIKa#iw zLK?Q|g37vl@()g>8;w21@8r-&e{!R5DuRZYZSh$JK4=u)VvSIV_Ig0vwIx!v2l9~+ zL>!d=d`H(bD0oErz75H4hRd5ckQPv#jzA@6sGWSE=qyI~Iuik9)9kTIfvNV%3Q!V8 z=6Bng|NYdJMP<~N3>_@!l2`fQ6D8VS z_ko7jt0iTt0YU7*}u7aB9F#2 z$Hq)c>}NF~0ZKep!pO__=T7)XhBHv3r&F*3yQH!he-}7p%;!a>)wH*NlUoaUwEpJD zKUpkyQUt`X1Kp!4Q~%LC>~Smw56IFmDSOF-z(5xgou~mI^{?mY#PkslC)2=Sv3GgVc>1!a^do zkO3Wc);wIeikd)YcZ=+ft~O|3tOkRmjz-71aibzZYh)yA{31`aSg|eoATDI=M*t+w z@)=lbQq@TCtkW^n;)ZM7N^tK0q31amADt+4gYhr~<0k)zmvNhi+O4=RrYf!NuV^Bz z0h=-*-p>TqI4Zwo0V_Y!c)Ou;@?BNd_q3lzBb5C*3wQ zX*Bo~<8zOrN5--)8EfJIVAp)MOdRN0c=ao^oKdX(>#Y6lk%Q%arhr3Pd?7)6WMd-V z@56ncBH^mDwMs+XVH>G;VZ<3q`~&oxu5(`+Joe0PUCPcd9xN-rfI5sS2z?!MclYx7 zDS%z3tiSCyIVRM2Q|<=jrbA7c1qe-&Qa*-&ZUw(c?BU}nhB?-~RevZi`zAE9K{*|$ zU$CU^HlOav}e~VUix@iKvF-F-VmISCC?H73L{_z4$!7fb2 z69fJB*?#Y@oNeK3waP!X4)jLsmJU%`7jjb-4cIBbf&9Sb$mFRv_*&bnAQ?zxTZ;oB z_!ptg2YnXct{l5i1ljqq%JVF*P4MnISNUa#1IWd0;q1G6EiR5zW9cZPa7-J3JGlAk zC@S-xHfz&)tsZ_upzoMhuAF@PaAm$b8|3t6iOmTh5R~~$mqi$etoD`;6C2OA5FqhR zcJe46*lIpoxReZIqhVJ0APgnmwL?4xvOv(K5~gFx?2@MdON9>{t2z-XGz7we+s9dL z!IrDz1%8VzgSsN!MG)#dzg!UNfgPD51}HX~;^Snlx?aMMa~&`y+|&!50QPP_DBl9X zfZnSxdUDRKe3`79pm~({Mv|b#VuLH6E@h6em-teR-8zARnZ3-Zu5_u%rzvni#tCuK zA4d8_u~*@u!Z_#}XK85VO}sDvX^PUWwh0CyG=7F8&yMlE5ay6HqJSyZS$@6LD!G&KBC95BaD^E`fsYu#K&qN(c zdI0@$FOb2ehtK9o={Ie3#4GlMXaCQ|x7)v<|I-)^pIB~M8w?2O00Rg}>3_o*&D6}< z#L3FR#mXMQ;Nsz;t_BSR+TXFS_n+bF0Sg2S_5%t8^xxCJe}+aN$o) z7rj=1aRn}$us1amM-Eh?SbTA(hoYcNBh5syDW??^+*)jn^IMMp!=03lggSYK+-mB< zrd?%Y5ILs)8CUf6yvhI!!;9B^$PZoh;g_Ak)>^rRY`U0QUO^Go{}3b9Tx{hV<{$cmqp) zt>hfU~}ZNaf~K*sA(NKWlZi&0csnb2Xl4+Aoi=>BQ3sIX0GUrjd}|dDe=? z=k+>LE02L9^MM4~ggccG>fFgs?8g$*W#>T9LTn)$Vhu@cQFlm?dK;Q#Wta{pe zzXS0g?uOEbvxnXdRNU*jYY`{(0}>1&9Ejg%MCO8m0>KOn7ve=Df>1%B226sl5JDxM82+qbCXOXNx_?S4=XFTvHadtRXQ z&|Mjw^`kZa!AFmwvZ|(;QnoHpRgU&oT;TK>LJRGQoVKyAWhw0>>3JtcP%U}Qak^zb z{lXvUKNtrC^q+^=VPE0D7yoCH{olYiCo>~cJG1`_Z-8GodjA>!54`w z0@9rZ0#g0o*o3*6k&CO7nSqJDv&;YAfO1<;EBR>D(Qnsa0JATaM@j27i;4gttfC}H zcOpKDn7x!z?1siXz47^0FBMWri;8V-Oa+E^`1%~86Ze~#_HnAo@ADed!2jc<%3k09 z;cY71z0dRSYF;jZ!OqY76r+Ie@B2aWY1^$J zU+?$+`RuI!`%%}!!KJ@_d@tYk_4&|a_^G?Spy&74uK)eDW?qY=!QY3+S4GC%uT{-2 z>|04n-|w3-(bEa(yzi&CpYM0oULUWoyQ!(0)g0dI*V)@$!M^W%#dyJQr=PA9o1fWJ z#J@eCwSs&-Uw2P0Lx=H->Ag*TXVHB(g`AUZdQb4-f-QUkJ$!#15dOXc1pPcd?)A9e z@5`La{6Bx0CJ4UVmvP?e_kF&Pmd);=t-Oh(Tf$!PPvKrasBAtlIeR!$kJB(5vpTh+uvR;qR{p0Q3ALf*gu{Qtr z_xs~IgMZuT7^)-8`}_0MHRf+i$BHYUA*HYVYglfZbF-s8-ZbiW0e zfW+Uh6F9%81_5(i7C%v^H+h8WL;I64-C6euFt5D8CH;=*CoI(gPAZ>!SDR&L-1Cp> zNO~5G4U0VuDA7lqG+qeWkY(AqU{EFlIoRLoZ6x=f+5t@;F)tC9_6K4>nG+OVaLM98 zQ;A-DAt79f4Tazo79w+CUE>NJyubBx_RzVM+Fz*lJSpn8v_nl*W)H%SI`t;(5cET7 zGUNkuZr|g?Ha}ovBT;`;vXmK`9D4_0u}OLXERzk4c~Vd!!lfNZd|eDdx(==pmqABL zq^9#w+bV!Oe0695cr|W$$QoS?lSvkOCcTg+QQ{`bW^b~8=9s->U<#p!?h>%mB$U}C z6nTc40VM1FXY5SPmyQNFH-=>k%^j(s6)_q*3XfRIUd|$zrffB?Otos2C!%^+c;(3M zDQOP^#{AnX(dV0Z$D(U60wBlt2k8V0MW3<5b}W)&jdWRC@^!QL4H^eOT*gnNsW zi&R8Df=fp@HWQEpMHS(>RI-Ut!5d7q@rDGb+316{nk#nORFc!l>#ll`YtaP>3a5CW zVInl9L)P$`zKYNlOQ{n}AZ=)p7=EG6JxnK>j(%{M$YJ2#q8GCO@32YD*R9pk6Ag3^E*#MwJoKQ2_vb`cx| zIZA>`2|ICuM03XL-p9Y&8{7zIjuM>O-*H9ipTSPXKwU^#7t}02rZov;1CkPpFtj|1 zkm)LQeJCXWOCb@K{*AI#poXD*YS)hCI>F}jWeLs@L~_ce#f?{yYZJ{JY>%r&{09&LwKvT)J|?f4 zV`G`f`+O}LCXiWM*w2(FhQa`7g)WsF;GJ(M&jPMJ3b6CfFy;=(ihSMuMO}yAL7K^I zvySBgq75d)A9o*hoh^}{nlAvUjL2U&g#=8bp2nYWq%=y?%ssnL+bJOo6GyU<1@o^6Q;mi?agN==s~7Ll&FR7(lNGU^}&RSDbSqIrYQ@fSDCST zT(IK0nu}3LZR|E}ZV@xXfK%GyeL&5BvG$HZq6EQ~@YuF(&mG&g@7T6&+qP}nwr$(i z-nS9E`|THt*ogP5qdU5~vb&-yv$D>~gYg_qm_mF4O=kppjhPiDBxOMDTP?+_6fooY zvi=yMYOmc2qlRrbXU|_9)SA>^)#Jhr zNXec__R@;5H7?d58q(}v>}e%XW}(_8PGL@wrop!a#=%CU{8y|BU)kw->BvlG`C&4; zl}W8ahLI!Ur6C3Kov(2g8Xi^ukp&C#t%ALDO> zlj7uaP$J944L88b!S%3sd5;*d2Y5IfXV5_pxHLy&iS^?psOq8$paTn6gOxA+U>(Yx zTEmr-leFh=Zkk?2Fwdrmxb=8H05_CpAZoEjUZDHTw+k4;kNQsfeh7C*Z~3o&sq0~=v0%nm}ELzCZ|L+s5XEyJhmD0y!GwwT=Wph$pNV! zej15=^W_01#z-narebVdXiJro{8XX||4xe&7SY9lYVGP5L>L4bG?P`KUMWDk$TaY0 z)>_|6Zv8x-R7RM8b4@-JYArJTjF*vEh619_!v_9!m(G<5k6IL$|@(#3fxkS6%S#?J3 zd*Dc<1Mn%T_Z=C32OL$b>m%fbsS=vzhbp8Ib)Z>Lbt>6Ep%ZcLHUAYT)v4D)GYqgb zW-YsN->R^Do!*6Js2=O^pK7^qpzK|_r&;yG5?;S2<^xwyhpF)64J@xut&p;85paGv zD$xjqyetee$1#BFPI4$N&E0S_gmq?9%vinJ6Xan)Tl5yWG$xI7`?s(imhKX3NK}kB z>BXg~!eR~PfMh~i{5GmWUB*=TV0pV)7gMYRKSO(yDQ>S-eTRYRd6UMoR*w;Jv2vGv z2e`x~?TO$!!ShmRqQC$CLiF|JeTr1fzyzvQ;#&N5Li4b3XSgdVpV z9_G}lhRe65Q-w2DTS;{rG(-)q-=_X$8_VQHPJYpoAg^vYplQ9f<&GtE%WSeqPH7jI zF-lu6EwG2#`rH6ZO5`5YCKW+ig+GVsp~kRf+6SDf)GwOxQo<&!z)oqaiOZm}i#z3Y^T5)_;kUc}Pj^1S)!i3H1v0{5 zIhm8!G7_DL#eGvx%D_7^_`>y8@US1_Et&1wQ_c{CN62*8NW5G|fn>P!7PklJlR3b5 zLH}qrlLFu^J%G+v^cpVw#i}^C)_3zyQkejqU^ncKk>mUZ>dQg;{wrpgu_UO=VVLD! zI;;jO@(o&;b=uA$hB8eG{Hb4pTC=1HWab9TLmu8+G!aM-hXMy9o0w-&tpVJQIY{BgGbSe%83+_UN3%9 zWFHz>NYH2bs&u8u!m%w)UX@}hhIIkqNzrJ^V$O*po(+XO!#4f7y3GMUUzLA%gVuQQ zFR|n40CVeYF}~&hk(+!Vx~P!j-a)$?5Y5p@@t^k9SHqg{jBRwF|D33Vd_O1MQqgw{it%+FBHZLHz>k+rIXAdxu;1bx4-nh9IgLF=Y znaq>+^O86=&>`Q=kXkGVL#sABG90+b;vzpC_=pPoB;l0RBzll)X4EEDVND_lkQ>+9 z(gye!wJPzyGzKv?v>Q&hKD*s<=T5)Ru6sN!OtkaHxLr>5m+{awwzTrHfE9~+r0m*A z+*2MVVcn|~M^cHl&*)H!;noG2 zG>Rw)pXz!Vb*qpr1GE%&ZCxLm29>?GDD6eJaBwYjEBZJ#sSuDXgtI3oqv*?!K=&st zgIPt|q;Vyxq=Kbn628_0|7JK=J&&WdBkl=Z6V}T25(t#bP#IRGD-mHn|74^^O}YLg zGeHX_%wYn$#DjZJY8&T{`xaH3YbY(?79!Q>(3|8MN}Q~DYZ(UNjN(EEg(=6Otk^*J zzHYZ>3?^%%?bp1c=xm{1H4{-fO8vwmzoW9-f7Wyd-w@$wlH#P! zd!dSZ9m~#_JnZXdg@8zuplO>irh_bKNXJ-TTM?~gbtFilC+zUH!rsl#6*^H@5MY@L z$dN|iQ#4(TC!4(nj2%5{^$WpVADD=XYyXQ2ucX6JUS{R#09i&<0`XV9$Iik36b+SZ z{bkxcCJ3w|sNOv;t-kxt;Z1NB-^u&<>aUA$^vjWg#z}z_?aXl%2&%3tpjibRTL;SO z#$!P3$P8FwF`%dnv9vT1?FDFQXcEi_a(L0E&sX03s=IIcvygOhY;z{?F7925%FmE+ z5_es^R|iJhSt37`CMcUlu#5?da~_P4!v?`X5TowlJJ zuUsd26P&j(aRjZfQ2H=G^AdSZk&0zp@-vU;R9|zdJY8`)Tj#1!JaiNKeIaFHUx7eeazp7ed|$av+LkIE?kn-t*yB;@mzj%qf-|JF1v zaF&G3N#(I2HtEA_4BqT011HM(O|~?Xqy(4+O7faRd;*^}}KrU>bj z^VfmX*?`4!eM2idBpyfoBmsqnAo^Ff!~lBau?8}MT!!k7l+bN}=@ye7^6rvy_!H!% z?Rmoiss*9D=rZ{=TU0R?E&N=PZZwno5>U=6#3AMm0& z0mr~K!HH7|NNL?6>!xA(JUrU76*Mc_##N+1)%IIi+5^D=y>E~1YBiIV)-0!6b0s?hqG4{w>`R3uV`B^ z&Q`aIjo`UgfA~LoBaFASaWf`;OU&+_Q(cN(z@;NJ;?ZSyEB~-ljC4ljQtd2NLDntG zMl-dq6E3Q$-5fa@RexIbS2QNn(PgUbKp^hOZwU;Ed&2!qGdLo}N`|tH767SS zDI2-6A&<%i8v@@?t}gUxs@iMb?o|fsQ|@?Xnh!%1L;#E&+Mr_AMi9A!bsG!&@IZF* zM`vEdEWzWoiXiIYCtI#D9xD}D6u1*WUptx;Gy-c7rL+rcR`rx<|G4t&mS8t6H9w0; zZn`{1njGiVcvN9D;hFt55@6#@Jtrl=ra&=N)rEs|nnp!EVA@(Z$?Cm&Z;5_@6)#WT zF5ot&-qn1kZ)Lrm1OYD7W^nE$t#GlSE{=#E6H2=O#gMS#2WN|Uh^S;_6}Y6Tiha=Z}1uIF>>|;{bta)mq2&NExp3I)Q9HhRX0_LK+8fMFntxNa<~&f z&2n=VIm|9j?j--a2quZQUT7+206&vB6)%k#=i|sukM1YZ zT4Ov(978Y>#R~(?ZnxEucU$jfDvpq#j$J>8T|dS}BwSgRddvFIrek4C$HdN1Y-@{v z#L~qXm<%M+Z!q7=#-VVQxd7c9#Lf5DcfveDAF*UxbH$;vj8rH^^!kOTD~2O;S!S>0 zHo9#i>Urq}@5wUBW=rzM&fmYa!5u$B4=#layUoh|i5aOXiysOu3*P~R*>D^-@0!}E zYU)i^QuTT5y&qtxvn$=t^dcnZg}GRQ?3UW-)QcEt z^9WpESaw;m@{G!PQ?%Pg_&H>5DsDvwiEqz^=Un-C14uk4RPJ#kv8$F`T0iHv@Kq!! zyM$3A^TQ@2A2pGk^sN6Pl>NGRe5<(u7d}?e(U3oMb>uuk zd71{&RU+=h&o~h?u0us!BZo91A(3~hSw$i)^OXTJ*dnSv5gQvh$Q7l@nO1)gvW3Mg z@<=W^W9k~2FZO&LY1if16^JMLG1j`u1TM`PsLC-(GaY<0yS{&@OQo^?2%7^5RhCYD z$JfK+sXLwQ22n^dbCii}8G3J=9B-5QZyr&$)iR69~)+JW#WBHz_I4cEZeY)m%%fXhXQVC zY)r>V83g#s$bPnYU*_9_Xy2a(i%VOTO-V8f8q?|q^Qz2f#@?hrHIAm29u;c-OXEZu zoA%|PIkN=R!{w&NRF&432Bx?=(6TpcJderKbj@X5#)2}iF-v8iswg;mx*MVhB&6{4 zbbsri!@sW|x4a{|%;vf)99T|mDbv3r4=*~b>xy+nx~m^(#~;Gh9qO|QfaIS3r#Q9y z;4Q&j*d$J!L;*i#0j5W_nawD&rFsxvKeKzlBIA|cs+)ONH2qBkwWuNeumQzO=yg~> zPOO|Bb!*UXeS4INeItuLFt*eNW6i5vr*)_L0H)b>a>Ib*9OS31T34$f{=KSBq7A7*NT43?OU5GP;D4&Cxs+MI=iUkryBo^e7)-Z}0@n7TyzR4L=rQPwpJ3tRypH$=gZnTD*j0r10(JqP1fMm2zy4fS^cSDgP5y$&wz=^ z>&jSoZpn_HS|kB1=Ve}^=g>c&31d_8PMsI&0{Sphy1}uI(W*%Su19>{L7r z$`^p?YmgwGTiYfo=n<5E^k?$i4OvDV5Iquyq_`WlAt{~~tBz$1 z+X%9v3C`idLMHpV#7l3GQ=dQrI4bxrRCyR=7^xsOt-)c$sN+`NgBr{J1*yvyEt%Zh zaj2#Gsq^s?Q~siWCgzBzCN~|eqk)y#u+kkDE-I7X*-9GWXg_6=qJ%t3q^pzztSNW) zOXu|)g_}^FZu=TyEBJn{>IX(O#3_vMD{#d5X9@zUfX$H$sj<(1{>(C>H#rSyj)@U6-`Ru z@mgkUR6*+!)>%Kijs6Z@XVxgqBB|kO=4rGJ05F6e{3Qm|E1377)|S+^PDMEd2I`ap zT>5K_s>Plv*EVm2yGE+-dOEx96<`H5BOpwN^-5=_v6YyIv=tHwJ!AbBoPpxGr94r2 zU3T=mhjW|NrUf}XG`~CNcNgLLgycy$oM$rH8SUF86B*KR4268ioYdLQ_oT{Z``Slf zHOO5JRe1FlvQnD$oD~ojx1oEirF_A$Z6kR~hn3BrxK2stBb#vt%cxE}e;~9@uva?*{KqZCXp9Yu#f$Wyz?Bb4_ z4~-EdkrjoS)=x|BE-#Yu`E)h%m4>iW_x?&zqmSgSPKgWX6spokjb5!$Lc*!Q8(?W7 zMksQrB{q<&R~flDYuUo3ob&sx}crp9@ z7HozsMnv&QbfVP}ccDl-?WplU6^mqd#8!c>-Ydt7!|AF*mt(i?x3)%M$By;A`0eU* zmot+Zv~&@fO$lUQVl+~5UQ|?a15s*wHIJ%iT_DerVk`6EnOz@?kzTY_VL2Eo_Kr&? z&I~bzW!OUlM!!uZyNYem)FLa6M{B?0dB5@!;B(+YHU$o$1iimYdr>G(V2x@j0X(#^ zaad=~$<}f+KBKT3U0E|7{|@e!5RnD(7~LdG?zQ_>c|CV1I7MVeR3_V-LSMwBh=STB zcwbAxR*M|9`{!?e^O~ThOYK=Jzf+lHw10YkLBzCs0vLU|$V4hzCCOKJ)P5RsGHNR@qVr--2rA1qy*Dm{bEs*$1kfdTBThJ(n_fBHW5Re){w#4?rG{4LS1 z->mYd>I2IY`wOsE&DL0Fi%iaYj@_5QH@Bin>}H1=wlJ%UiRk;5f2nc=_5U_e7y!Bv zlCX-?QvhayV-CwE7$>&b9FBIf?$`!*!8e8KVr1er?R)I>gy6x(p8Ai$*$|9aa!XmE-L8F&2d9-R%{kh2V<6sOZ^(N5 zMwAmG>LS=QvTG{~iBGM(NI>=ABLgy4cddhwF<{KMO|Un@@dgODl=6=#0(^6(A7XYs zoimznCg@NP?VMK*pRI8>_ZVcvQyEMY#~*Jr0-eYW(8T3Vl=czQ#6 z`vRTP*}^lkJ=6Xggq}<1Cd>a=yZ`=&wTh-NU*21<4AdhrLuLp&Kh-4B3}GWjHL71b zqEBqnHR}mej7V%)@^mkFWST(VSP_6~-(dti#Y|bXkIG-CsV@F}SLp;xp=CJ5)r6rB zvj8C_;kzYM_Fx2V-_ zImJX9ma#NQLy0|7Jvnw5mXI;Qn_BbX#7(>aMvg59omG65$2DrBZ%{E1=M)QThlOSu zvZzCDhjKJ5tG~E{BZ_Mu@9#(oFc^3!nuSCo70xUMd<{nTyC{mdnU(lHo*VJig=kZV z%+Vr7O*X`mh$35U4Si#j9b1S>G06fL#cM<~BZSQJH43-VAcfr<9mXniY8=$5i^Fb? z>KiGt)D9h|+qv7(<;gx?K2PPz&Rb4+V_n)5kaJqu^lFGa9aD~UwJTL?!IoqaC`FH9 z#UeIvEM$1THD?ny`j9&M9oiFO;l89@%;^z3<;=kq%i$?TS_@!>n`C}FUr2bD{S>ji z;JjCZRBBbA12`w>P-NxUcU3V%sgT<6zHWz9xHfx^x#jwJj-O{z(E*7)^ZKy~;#!Bd zLlN0a1)MZ{@!kYio}wiLv*D(}#p2jmi-tz?mVZ*awcdjp_*eSSq8B)OHjvKUsO3a~ zRn?esAkh@p%e^>wjt7807uUl8xAZd;{hUx2u<->c2e=1n<71 zy05djpmG)E+Zsbf%{7^Wo?jUrE81GJgj0_?wQ5}Z=B5$iGFPzDx`@4sLsS5T(zo2U zukI%#9$#50($GDKTUGsZyHgZ1JU@G}Qr4t2ULLFTP;Kz31@?~@QKUr5sSVR8Iva{~ zr>tJEYBc{&@!j%S9u+oCP-%<3FTY#0vv2#d?kck_Xni%WnKRG#e&}PUt4MU$50)h? zzueD{iKfauxL{OAo85%Qx4bTVwbnY9+zL)OYm^q7We`X@QeKS&agG33oK~1Cd%vnA zWfFkD{kig`^2C??Qp{+ss%}ffe*)x#KNIWs4e}ZNLF~%M+!Qlk;_U12hLq7i&bDC@ z!Q^+Q)JWYBh-jE=uTiFzv>m?vGYoGIKU`i{5br{LWlHRyg!Y`s91Syys%mGsgMR7U z$;`djuX?_4hHYOcX`DN&f~zR=g{&wO5ocFvaDiS`d3qt5%3Ij0bcg*UJ7ihKh1g5D zQ~nlhMcz=(E!<-jwH*CGW6z1&3+bV7pOKG_+Dmz1#C>`u@S`m?JAW-VvvZ30N#Ny3Bot-lWMG=a|r!O0w7AF1zTRH zKmcd1&PfRBczN`z3W9CsU<#2&B;38JNCQWs2xNR@`VZYn2tFJkcaw}k$n$UA!}+j6 zhDdPFBO&?S3VdYy>fm-lZ5Yux*J@CwYGU3}F#Bc*-meM`Xh(tP5@{Qs zXfUPoa^81wiFciW=YY5&LQRVfVJ;eQ^RknKSSlRC&=jF&fX4{a$vAtbOEE}9BZL{$ z9Kw#IzbezoK1q2f&~Cj{?C{o^{q^89n77~tablmilozbFfsW* z(R!TK`fW7DV7fl3oz7(-UD`=fJtH^4%Ke7`wN90EP_^CS#F z<3zoV?AVKTx4VAb+&!P)J(}HlZg#u8eAjH?xTSvF;TYWXOeczvcPA*a(<=EhLUqiR= zHxGZk93NL->fSrLJ$bEeUFrEC?A)G9@pW}oeQ$ef)ke1uU4A{ZYVlfQzkf@=?`{Im$8l~oV$O0 z6i6doU;q=tqa7*L%azf*kPs|z6#)<>WGbPGl`W*uAgKKHeCNItZ*0fCBAIvOyA;;S zRFRR(M+S_8_Lm5oPa|oHDioSS}|S z5m_W1Ml)p3Z?P|nPE%P}R57Lc+b4)P63wM*(!ol^ zGH_@lTOy2ERJg?yYf#Z1zDcBD1~Y0r!S(a!BEI_iIR43#99IAmgeCqV)Ag9G^_ zuP!JGG0=kAWV%l!n`k*s+CC?r%Vvy>P`4Bk;+n=0oVBz6sW`kr0X>Kh@E5qQw!%~J>bOw zT5tw|*w_kg8d*G6bt2uZU>-!UKd`vIxovb+uwj!x4rjw37zYEIB@zMFBFnnJ{(kjk zC348248!AKEDV!izx|a{&4Poaq-8G|`hV<@Ro_ac(xdc|OtAt8+3q#=qLj(WCLxwS zKG4YIrRAZzj1UzNinKScJOL_;A^!~0JhPL;*Pl45;p!IN;SG&2FwY8=RF)z{aaM*% z7_PBo9EGHs<|_oc*s9W{QGrO#O&;NXG7xNMV7m=1Vhk@R0>KI`&rld`5&$Wo0D^{Q z6!dZyK_B<^FlO=6F<+xJB2s9gi`VpR*cs^M^r45?C~~g&aTbScN6^O4DFuJim`aKl za~8)jn}BvoL&X4Uru4OiS%j_Dg54dQ0tdC@P~o_*uDM@z!r|l*`C<_Z_Ce%|?$4=l4xV$e3|53y%|DJC3oG1E^{6`8kFgS7_G ztIq-1+WzCHKVgG@uQ|Ul^0o7iKh2@7^lY_~`%4(h4LQ zEx3jI=Q|q!02KcddN#DRF)=rFcKEO6IMNw9y8L%r{#S%pZC%I1HdxZ^ zlZ_s2e2?rN3lvQWW>YEN_4UoCMf4=&@r1I!4o0mr2%YP0GU7x62ygx%<)v=Orj^W| z?=5^N_k`~5x>Z}XUE*r@WReXkJubxxO&-&WDo7$f58P4(t6y_o^)ii4pDocjk#5f7 zk5)&PUY(GBDk12-&qG~TlC^EKj+2U&*MRSP^U%$z6^ z57r{E;t{g{WPo-o;Oc~DNrq!{PT6x4-|kdNK4aU(eP$I>YWa?&8Y#AKcFGWMmHk!X zhdz%CU%b1vtrNW6Kkc(Vx5;?Cranv)db;o|a4)vG-vvH&8g6?PZt=1{ZbLA-v>j&l zuH)ze<;Jw}dc!oeQbD$i@sMs2bmiK#M<@$$vHsADbjc=OAtVz|MtjvpeOd(?t_^0~ z(sM6vTKll-p1yN@YoG0RFU9{zg>*a`-sqHmtkXMN|5n`NcKLd8uAF3Dzfu&!6>HAf zdORU-2ivIo_6&7&4%u1BU-D);OUM2_-o$m!truoYs>w8jpIziTv55(%p*^S^ZV1U z(3tqgy4R@eP*eFLyq!a{4V;6pjTY?TEldwy-i4KA%MNulPEUVtaTV_>_j=OXms}Fg zu3Nh1X%;>W^!H*Dj>TncBw(*hx6h`Yp})>&VN{&D*}|P(e6otLL6WO{XfqVJTWJ#+ zd*1(q42X2&vu8B>9WFna{P0vjXRL%vLu+H$tgoI~)YwYCGAglEI$tg}0JS^=nRe}# zX}fF=H@KR)C;r9BPOPgt!$1U-E-~F-k@utldVgr_$cr^hw^i3L-jw`yT0EpVUejv2-S<%0bM8dX}N@9y+%d8 zkvCr70koJjKPp|Mk*aU?X@L`)Jw@srZ=s&7niGrQEdU?;Pf9)}H@mSAXqzN6XzD&D z&|25zb3gpok&!I=v-%1QBgG2Re;8IA2-^@_k!E%eVf!H$Cd2~^GucyBv zEoudGNLsN&o#_sw3|M&7H3-)X=kgKd83^E|KNXKmEKvh#3ykJ-&VM54&>-`?Zp2f-C zA`5b)i3~s1N`@C_E8}Hy^f}i6q145451?gvzy@67GzzA=B?Pu%L73d6he}a&R)iRy z2;S$GU#9f*nx0Hn%`w~;!jNE`cct#UMw0jC9~FA1$tU)bI1oabYDSSK2K1ZYuYCF# zKguEg7wM~Ip}K++WsIMMkCEdAQY+VJXPWKpu0ch1BZCHJ_*0vamwV*!MAjvOQ$<2~ z0_eL%w7ddwJE(VmlpEc=VP{0WX}dE8$W+miHaR70KzJJc@`&sCnu) zbkpcsSH(J_A250a<;4LBM$zl6DmOW*-JTpqWWZ&Ah5RVtNAA9(fte&)p>FLs^L1EG z7Jf2Ao>Y5HnmB_|B+INc0&TrYe_bsNW6-bPa-dW1PU%D9u)6d0jQ&=8yQmVvv~L&w zc8&qyHselb9d)J&Sx1OAS}(#YKIdvXRyMmvnSU&UkV%xvVKiEek1hn;1IVtpIawQI zW+sR@h=R}DJe--LI_nTgDT7yfEnBT;r<1SAXPVpmYp$UUJND%}f>DW~7^)IqkN3gzi`+fus2C{=> zkVbr&NoHm>=Jf*=5oq0spc=E8tJzACbBwU_vU!I8RCPjzd^eaAJhF>Igb8X5Z_Eg^D`jS}EJBNL&DRpoU$sxmzvvu~IQMaA-mGs9vu)JSZT_Uq$a<)=`z*;hshYM8^gGLcw~bF_*w z4I|cXscr${lfab7i`N8AdNzJUBm*t;v=W`cL6VXXtriVrS<77baGlvClW2m`hDK^h?U*aQ|hhcunK= zwoK#|7&TxJ@QohUIi=~&b^!ETvK|u z1$J!tkYAZL-5z7l6i31nK`Tq#X_Haj@)GXe-qdVM6}v<1MY|e-$#5N-i#mwqsgP38 zQBje7as+Bmkq)xe{DEx&;iGIWLI5&BFRDe=d_Is$Y^#)3UYN39#?BE^X_fT}=IzyT zO7Sw?)Fd=zq^5;_LDgEL^oFByb_6!2>1GO5nuB(Vfi@+euwIXbT$tyhjv=Y2nqrcP z2gc#LeEm#7!Z3c0Gye}(_Ev^P?P9U<)eRyqe_5s=aI5h|0k53AqnL~|PoAR#TUt05 z-{+@f8FErG3aBKM-InP1tQC{H*Ob$%(;=lh@&s-ib#n=VGS_B+GeU4s%G3P2k%-jsGLpqY~M zSc-KTTpE|>JQuTmvTM{orBM;oGYxge5~Q?PY&Tzv;knV_*9+I(iV>Qj)6q^6f7_H- zAcH%Vz=M*y@DV!)ArW!w6zL8vE!eFpATKKQ-9}ma-WcT<27zsn9oQ@y2we-wu=kv0 zUUI~U6Fch#{Sn5RXylxFGrh*jp~C6bnnx)9F}Jq2Jq1F=DF{RV-TV+~+0Z&hpJNX6 zxs>pZHfA+OP%|C^bC6-hpU;9Q=H@&zq)w}cpp&y$!h!nmdz=*Uc+!Tet2h5}*dJ#~ z?dSFJv91inN7J$fySm$SN3FJ~M<0PGbBFYFtyfyFe)zVYN)LRp@P&SUq zFbsmcT|RuDdFk)G$bFKr+eKEpC$I}VFc8T<1F@DpQ`ojh-4!)?=hE&1()VTxR|W*J zj_Mt7XVDd+cjJ1<)536*s;3ZAiq!6+nOoJc5d7~ig-!q#9L7|KBBane(}=`2g&sIZ zb^9fz|8Z8P_q)j0!fPjpirD+P?dcr-S|dNQU!E-DaP;N>YuP?IGG|CW)TWp)k8jh= zwX&HB@c4$n*(F^|2w}Tu2{CZ_gcWed+Z{sCCI26nmMkGoe2RArp^5$0GyFnn!ZY@4 zY+F4c^g%#0koUR~%J^+C8S>Mc!Q{7{6(k&o5&S7_0DFHEb1Gt?NtQ;~LXR}rSZ zAKF6ELeelxe$fBtv^9S|ws@o!001@`;Qy{b%Ky`%|G(8oanZMOHvYe7t*^T^8%fqx zs^4d7_lkFk2EDdZ`EZ^1oAhBh(@4(5s{E`vV9KE?sj_W;ykl*rjuCITh$RQ4kxabD zwA-n9wulG3wNIR<7=N!$`Mxi6zaDCTMrVJXDtEu`u1jx!pVe}EKW;L8-zIZ^j#7PJ zA9H^OcYlv+e{V{A-xB%W8~?RP<@>Gt{c-L6I?wg_o_^)?dY%)bC~}3VAfZ`}aLG*SEOT)A^^i z=RG&K_w(n`w)5)uZQ7O3>+^ecx7WR8^qg(>iT(FOJ!a81XTP@h<0RMjd)f6j!{?mj(LZ(Y1H-xcBSk@f5%FruO%MoX_RMRM-2PkMH-j_UHTY zmCt2)_W9TLUnqKWwdwnA{g@}^Gm5#G%=bB6`+NWB`g{2MF*&RIeV2K=v$glw(5vBj zbnHAT;`_4HGPTIs0$OzIA;* zw|9PC{+Un|*!z3YS=&C&X3N(ytLyW<{mS=so!i@ff&TmP{`-FN?=?JBy#77o;hm5D z=#=eeRP^a$y_br*q_`Qn$PJsqG+jv{Ogh17-?){e}How1sD; zH^utjmu3slxphBkf6a=3)3k6*n(pOT+U2yI@IHN=+jr?Ukn*=`PyZxb{%Ku}(=j(J8sn zKa+axXx&;EakXi!>Mi!HbBeRj`Q6H?UDfi$mU-0}y-vKLG4)|uy;c!wN^M#F zaJziBy?)htRdjfM*2Gql>$&da6=d_}<$B)#C}|;&Uao{++;W+K3v7q*r+qzj}24Jx+w5j5% z&ME4&^pwC;`W6b@EwO3b;(hK+DeKXSwA!Krf9Dmosp`$Tl$ms1#J%U6AI<`jLsFDaMhiLJg( zPZ$4YY?nApZvAt>=k32!M7el#Gwhn)%GzugftPZv^|G?D=2o}nVpX(MF+5(wCY!wk z_K0kGd}i6C(3+-CBA2PYnHw3pl28)gc^c;_+kRy~Yrgw%2CK02wo{H<5nT*B>%fQK z?1@PW?>&B|gsm~D0BwEqUjSM_rN4Piaf*$6p5xQV9w(CSeEr$Ir}dGeA~ZJd@r^Y# zHSk??_Hw@UfRcoX`>K{nl9f#&YG^H1Kf^Y2N=akq-JzHFvXxy=-QG-i{e4Zi7Txid zO&=<$SCXWTWCdSg*@(=Velp2j-M6;l?48@z0@j$0k}RVW>$GdJOoZ~#obGy`w8B_3 zu6>fbwl-%qClao^1OgZ9#MIgv+DNZtU-i*_LovKuKAP?Rdz)>ai5-`uoRZr7uF*Qm}}BvML$H`Tqqp6d1bkO^Uls|}@5lEl`` z>vi2g*97L0)4Lrl@iv+!t&bK`K-;%TPfqQK>}67`6p9{FV!?v7N~aEeM=oz>dH&vJ zd7WdP%T1bGyJG#}&Lw;F(XuV7ZxqG1Z(TE@QAHL>b9W7D9z46Om!74j^*;_}qm6C1ubz?PQ zAm-ckDBbnEC=5RO?Nq;+Y5(6sHtE5Ngw;TJ>0H%I5|2S+IS>Anokyh2zJWCE) z>~?*$7U;l!io23jZB1_;*2)sYI+TOJS?TL8rEOho39eO?B+s)izPwkHs{zuEN_@}d z;N#S?lIq^uOtDMqd1kfYznEF=**u$ndseG-)L^f*HOmqZx<%b>erY`E4l*AsWhDX3 zV@mw*1EV^7!FO#e8D2Ac-)dMT%yhF`e`AZa-C|OUg>d-S2U%SzBP%*boqa^_o+avzW6FUV4t<*QqKvx)0?y^GiD zdQo0r(*z}pZOn-wZ9L@OG;bU&?TQ)xRyE6}khIeS#nTMzSB(l@Cm$#=E=AX^3t7jN zb6V&vq~WXiE^AAB<;unjFR5y!>{={qe#+?&H!3qFV;;*lFzb6?J{N5&Ymo8s`jalILs zE(M_xlxr71?S@sOS(d<`S_8YhQ%P#Mg-xpoJLo2XCXumWfTg)^Nn2RER@!>n0@oet zsL9vp5haoIkt4LUip*A?vuM^V1O^L3Y9CKY61r*SYs4gw((QUw5LIl}hNU&{y7`QE zUPidL#6fZneR*RS@K2e3&;R8Jl{9_GDE{pLkk&}iV5MdSSI|o6nC9EvjB#CRj%Zcuq2N^AT4B}BcaU~oFjh~E8Jw%gE*Ba3#lSLw{=k3;h zT4WMjl~7KIawMs|of(#$Wx2M41k=8Sjk|4_MT61qD_GVw8~6cw=S_M{c7>EIGev|f z+04-l>mM>0k!GHB$usXn30!YdB4X^sB{dP$ILk6)hL;2Gl?J9edPI;+gM|3CjV~Eq zu;zy6O*6Vp`Eomjb`h7gZ8K z!m4T7!>W;mCzaM-Z@-&{2lGk`8y(o>C?i6osy?Xwo~1U7xe(AY~8RjVFMVi|lTf-st0X=*7jB2PD|&-=J>!JQf4 zE^k`5ciA118m%qOsWVclQ&L<{a3_3HWK8B|b;yL$<<7YroUym|Qj&4A+C{J+WpA$}9-ulK+$HaP0dq2zO z_4=@8!nMOz@XFAv>V8U$>X);r1+LXAouS*%dY6#X)aeyz9mG6NGI0;WKiUl%F+2%L zyq94#O=o(@LEZZ{v15{NEaY)HCNpA{Hl>&)ozxbQIKu1R_%5W6mPA9}S&M2xvQ-H{ zY71^iNexyCi%>#bgI~g2k~+hVlF#IkljkIDx3S0_$&hs?J8#OO^d7`0mbodvv3`87 zHaw>^MMdlKc0J165~@n_vq_Qaah5Z=KV|S^(~R>_t(Q?PlA$u~s~51Omu%yLKj3(s zEK6(uxjyj=(p;e6mf%Q4w1hD$aO728u+Y+sE z5j%lVYxWSWa-#=+L~}4mT2vZO7fFD|THP`(izKLn8rmIq;`t8n5=WB}v)e`2X08&@ zl8DW&QP-xD%Oa`Cy+{(`ze|@|b!y{lE7aI>+FOK|hGdpKA!#Vthg*+)!z3!AS+5b* zpITCmoD_QuGVu~pS|8rgL7D{Fp9IVM?Rs>LNKdw_K2iqT{@itsV5R)bcAlMnwwJV| zG$5MZ7H+w1V&ujCdTvoHN&8ajCFvb@n+gxD0mJOqV9BG>-0$p1%g;%8o)Fl(y%{kp zEt;)6_TR$SknyhRl+hjv?BTpQ?qX?<%*-o}(Xjg@c_q^<(i0m1*HIGoHVH{v*=yO{ zN|QMyPu7t%X(0_CgPTYoS(nPD>UjTHd=Zk?y=bNNkigQZvPSf6ySFtONef3WIVB+j zC?OpryX_j|+YVyf_9`#$)n?E}kljs$bKb5;+Q%}GBn)!`wDx$X*B8M7mR~05)NrU@YC*?%E0I;L5|Undb8t^u*g*~HBu&UJvq5HV zy%U(w%GMfw6Ve!u1fnKRpHWtPK$=oImUP%W#Ic;6om`UbhIBbkpheb*tX1u98Ie-- z>|WiZHYY|>vv`q}B#FmrExd!6poUy_J+6^vJ@0poB!-sliJhEICd)9a7j~R^Ld(?# zu{N+2P?Ga!BY@Mgt))GklOV3WV~+V^iE0_14MCn*Pg2y9oHFEDdY2>JJE}wp;i zSkKudSP8Q{M0GpVDMr;HOIwLuzb+8dak|2L$FKMlZZ(A`}rDUho7Ggo_1k;gW{pj0P!czK< z;VuX++F|e-m2NBKLcXgYkv6upVwCZlc)pvdf$# z{d9Oes+j%Szz(-LUo2%Pp_p0>CllKrS~!(U@GAZxb&2Myh+18e(_PyTr)-c4midP@ zIe#QsOx6|Wmf4hRPS~eKT1qBaXqAC#2iM?tNHK<;%H7 zIjvmr3SlAGG{=&ZzUdFC;=841Vxmy7a*dr3A-}yPN!nrMqdC@45Fc``NZz~DQX?N> z$CbQeCG;ASMKZbC?{y|=U@vKgb6Aa2vP+YtT&5}hF(c7eaW^aJJr7Uc@uQk##OvrJ z9uis9B46FBm7FjNBu$nXsS5}0iBQ{)OBgO1thYXpY}X~QnW_mRis1F7;BY1Dxk#{|c z9FoWdN!Z%&>S1NX9qG}g7n5}89&6e@wiK0AY2xlf1Z-C4(#iAVdp`}rDHD01nY&$& zG7y4|jlE-)I>DzE)WQx3d7bK-`3JJmEv~OP)Bfp#wC;1_}k=+roMOiIG zj-`IQ3G+F0y@7hrFiVCwcg^)$xsuTOm_0r1wk~y0NWrp z@-P3R8EVi4i}__<@iH$a-I&}HETdaIp0@gyxKK8M zTwx*{yKG$9_wp_bx{w5wK6T2ur#6Buw}?4TEckBS zu7Q<+$7yy7g+PuzB#l7M;2`5}uLmHR)fl!7?1HQs0L?<6>g zS_KqgS>cBymrr&Y(0Dam9&JIrNn>iEglmup7?d|UdpANr4?r566*+P5Cy7#fDtQvO zs@OjT+|LduspdB(nKb+{sS=cl#ABq83bGDE&kvJ&e}~GP-CAF zz0`K9cq-aBT3fP4JGLx;$S{zv(uC9ihe(ra5(lo0k0pZjRynk>btT}Xm=F+F$g)_` zk1Qr8S|+4+xjZxPgQ=U+Y&L>G9O_>i`R9;_L-Irt#bFjN68RZ&H>6R$9md3z#m_@u zpO3C+uX)FjAk{y5j4BS*1MNlV_-tY=WXO2{5$?#JK@7wjX68)1Rs#2pC5~Ic8kXFO zJUVMxijUk`bsDD=2m&XyV$LM*w3+#ycT;~j*3!f>v9v=i4mFb!HH2v**V&$bLe^~Z zHVeQ8IXD45)0ALN0HKL1@TR7AtSm{H+P2lOFQwflxzvrAg;amW$JPe7uI~iN9|X(?MZ;K@b|+ zhI)2=%`V60c0IbL8@21O1sXZ2n4vW9r+lLn{+9{ICE7C{a?4Bf$=H`-l<-->l`sCv zfT)BM4G1K9qKh-KIYcQ@Vi_AX0<~T9Gzj~ox3hdWUD{7W->NMez(|={fagjCbv{^Y z+M;r4@nR!zQE^&!Otgr;N;D)SoxGk-td1y(o=I+k=E3+Na!(u1d?RY2ff)KUEp+YC z;$5+oKnn!210QeT4a~SVtQ~^!J}VJB3(aC&x+{zUk?;@{V5zSO9gBKr3c!O>RflDdvq#4vWUT@;j1` zT9zny+CJ@kMTPmHwW=>R3FK_L<xIRImPHP5P60w7t0I%X;<~5>P3p=wq09{dv;La*tzlFO+$M}p#%QSw^5|XwaD2+mhJLYBEJoTmZr+4DXb+d=#z(!S^Sf^Wdh zAoW~t9}ap7&{6JIjaoW@>{ehd>Kl&0q-4?Ae2IhSqw9$@4Lqp!Y3|NX>EqBjZ*qUq zuzH>fbjKjDPFaY-`%xfJq|0?M=)B**hv1f$$v_H9kKw=uCD&ITaSQ-m z(%naKNx5D@czyu)CCQX-GFR?Tzg#1>+quz{$Z)H>PY z)kqjL$uTXPtv+rm($}1=1S~Q6Y%6J|Ns?%r+h3IyBTl|)^OukB{nQqcN|iN(19H=rS&!HjI220J$!HCXQ}Ntl-+1Yn!vxBWcH7_j#{krT|aDw8@gH2HLG zSU}*zhy9>cJhDjuHIN5386VPI3qYnnF>}B#1YHQO%b-GbfJZZMV^V}r2XHe@V1138 zWi1@Kqij?mNwX%WF?Yn6l+6~o+EC&~-jg*6H{^N=)h^XOvTRxH66%s})cQ={DmjNn z9f@q*l9#$A&Y(>oi*XRxmC%y^BPZOd8N3rwp|QZv4J%=jH|DheAn&>XkakL{qPhzq zx4?5~a{`y0VNajm`-yRpmh6_fx9gFtOhVJc5OiE1i74P?y*w+Kj>rXZ^3#B}<>a82 zz(_PeUkHoLJhKa4W_0x>+CSR-^6V{Y2&hjWTR9wA$xlr;hI5hWPg2i0fVUIckIX~T zE|uX_YwyMsj{`r&FoSf8xyD)805ML(^C~r&WWgC#fyIVeI$6T}! z!daCRXc1tvz9dX3??g-5%XZ-85bvB8$#n;NLpa6T`M5Fm`wcujr4ojnmM_o#*x@HK zHU(5Kg%r2z(G|2I0bIesH6TMzMg2f+_({LOZ7{58)S;zaoe*2mN%CD`$jts)gmaZf zfGn^!06|VnL^4A-7SqG)kD@GuoR37<0?SZH1jKag@2C8TOBVpCnlP4}mwlR4NR0LX z+DV`!34QQ5PR`ZqB?P879M&Px(?Hsl{r-q{9NrCJC=@$J>GEz$-^l{iz*=Fd>#=rl zHYZ1CSUO9_wQLyK`RIyw-0x(TbQWy!W8)oxBrV8xc69dN#mm{9vS?ddKo#HhPUu>9 zu>y532loMGBh7*=h!C#9#YQII4!DJEiPi}{mxVJ2nTiBS5jslef}yrcG8}Pi`85+I zn1JBPo6tg#Q!@!GA369$HdAw>FNjnesep-RA0+|VNRS%U1+t|udbBS>gLV{VNBmis z6VB{>SC3sUdBrFU2MBhj0wj{=?Rs=Ib`yG0dZGL&LrWp&VF%NRMuSZuyNEV%=jkERz8GaZ-VVkCUnV{HL=jd6EFtyC*&0u!T zH^GwJ-$0P31J$TwNh#tXIMUPtd9p}{1K!0bAmcy`4fFJMybpiuApt1Dl3tYtv^m6d zV~g}@N!Sh{ok@SR2<6x7H5+CnaDt6AmlnJuX#OJ=Be(C~DCpYt-nI{9j_glI`*6}L zzzeByX?KDcMN<*+JWN8TuRJ2YuN|~<4L}t!fCM_xc8>w>QaJ%Orwot`E%~L7?*(06 zfr;ovz!j&1k0JcbLm`6pXJqLS!k05~PS&i~*K^0S%j`lpkdnv0A=oKriEBwOY@|=bISAzA(2x$(zSgSV>;BH6q4x7Ynvd)X*2sG{nO(!>p!rdX zFDr%Q;1(9dcDiB(>p?&#BxNDyQV2XOF@5X#?nGE}#o;tqnL-kqYDr?2DsBL6g$$xT zTNg&1F7YqC@U|_8&j3#vNF0ODCZ!@0*v3gZ#V_sB!qb)jBeT<@knDJX^qGbsQD$*Hx)y2gROv|} z;2S@q63-fKJ=1v?A16qfr}%I8j7qjm-!C4(23V5seC@jI6Mcm#n6*0+Su3qKna9OK zkDONrP(~$4Cv62RWPekv%j_P`Xrf+(Qpy%mZUScpj@llEEHyW)49sOV*GtG-^hbfV z)Kp`B(jkNPIl}RqIHbA@ z;~{D?1UHRS2%*I2+osgk*-rlldy@CHSM7*^`=E52+Wjp~ZOBS!N zIyGY?$};$0GA28r?u7UvP*is6d3t%Rhdm$t*ljZz4yb+V`2fN8tiSVsOVB+KO*f~L zuO)iFzxlwPbqO^&*8g?q=Hc`aU;=d=b(W-T%s{dzT|77WS=7wbT(0=9t@3-Bj>p$Y zQw-_(5_Jp4+mXHAk6c)y+Q~I4PS0P?>s@28butlYLgqN)Wl}Iy$%NqwWa?|B+3tFo zlC=^*7kQ_fE68tB#{`LUm7})G**0UgBUwK&0txk0D+-@XpkiAbVyVc+F1COPPA?eJ z@iaj8sjhUuTtf!T&c$x$qjo0?Lir6GE0a1}0CrgQj|onX+L&4&9>sP#@nF+orIHrF zz9QBVaM>uAbg}MB_0%*cn;D&aAb~=YI#kN(94Q0UNwBL&J&3g8MiJJSo@5HNnDH#@|iQCchnbU*RoPI^>61j9r?`v zLjL^!7Wu<8ABjy&)`lCBqiSFKjZ*L@Ut5t@+xLz?=X~(>3Ie`>3p|Mwabz|vBE<8; zdcobsH3YGrS`T`OtxU6VO;|bz$LV2m6wpGJZF7K6l2xPlR6ijkZr-N%moa7wvd@cX>EVg(A#CgQCbNHxZGy%*c zsth87;hT__XvvNFI$tfPlo}r0WU=RF2sTlw8KhKTH=3-3W_kcQy;P2F?G3JnX??ha zMAkdCRpD2GZ>R)G%OiuugaZ1P0psw_^%9^T6pSRQS)@hpWqa)RorC->kGfC?#UwOZ8RS02;9*W_jZtP==ZP{Ew9NT3zpavMm$bUxf+qsv(0bGu8*T4n(3C(& zD81Z+tqZgz>{Zot8&F_0LP_Oat_71PX1l_$Fo{{;g2j&Xg?>L`J7$doz8}Rk^?b>1 zn`}S|fD}R7f!iXw-L}o)2=?4`AC(Mp#7uGwiYT2SI(Ogmk!NQwHGW0#nDTU1FtJnJ>9Kh z;DxylBnP21Ry1#ta6kff!{d+{mN>|GS zn~R|xtH)mE5xXfCW~hx3FxuA>ek4~jY-|#uz@!oQ^oACYa zI(d)+)rn3*S(3g1-7F9z0ztX$vdQIaWbo8h*4U}CXL_(HMrF4aL)@gRVBvMz;w+-R z6FH03fRUqwAGe>F^cU^r~FAcb9H|+LC!UA7EA`e4b&CBZ;s^1 zX1*4%f(8{DbmwfqyV=f1o|OtB$Jb<@RaG!x;b*j$*`NXQLAP#{}jdfe{ef0r(q00~&BzupWmcgIa+;6ar z^@Ux(yz@V6vZbS_wQNzBO)|_e-u4>w>B*+@pkUj0w&54<24OEYI3w%_Jj_hDhB2cd z-m30=q+By)ZD5M);>vp1WrsJ7_iEFu1Q$g;U-CO_Zt7A=qgSs|775%fpIp$^bcR~F z;bc6ef_sVNKC#?R%qxPL5WSIn4DbvvMwurqh|YJ4fF<}LQ-1N@W@d8Ss6V2qcAf98 z8G`7zL~VAVaH21?`n#RfqK(-08pAi*+G4PtkccKeC!elcoOenSU{-Vz+mmcsp^t+eUPhZngQ7u z#Nkj92c}ScA0=?Z!)t)@r0dbO>=l?VSz!XU&jR12(!tn6}L*q11B&r=WuTt*@OAUz@_E4U*M>^G!G` zU2SR2m z=kYvJ>!7!B|8~bWC_y7kxuVN5bf)nSZ?L>1+d2|K8^m-iFgt7MCnz%r*pecGQ+Mu$ z^H9dF9hKj2;`T_0$&_udI9i__epLqbyCLn#?qycH&+gUB)CN2n~=TFd#0-yxnNNeHn=sA!ki=NN_E2`N2CfwCC9 zT-zMmv|yQV>XdBCNjqQ}$PN`uKBUlX+gHu2rir=`3-(DJmy}ovc4^5@Dp`o`{->GW z90FJ&*l7*z<_Bq8DT9}@4T7G^VOv}os2L?lrt) zP=+j1wt+S^b60(^tyVaVaz7Gvrz>1TLNHkyNtYRz4z+SW=%Ozv{zs}mA)*sW^Ab!T z=OY`}urBOC2r_BH;hqoAOz?x$sgq8 z7P8gsMg=wM*e>Ux0jikJ#rvu9B&gmF9kG7{cJOcFTGu+Vjo12-8zayVfL z4mvQ>@dH-a8Zi*4-asHM%x5=s)7Dp$7eKCU5|(lS6oU)sb?@t8cH;coYO+8z7x4Rt zmR=6W-2rE_Hk-u?wCZ%znCJ3u53X=M-lYiQIFq22ld-I=o*}BGiZf9JW@buLjc9MY zFTxMXT?^D@d_Px29EsE%%tq#VKL2^|@{$Il=l ziKG%i_nTSO&R57rKAn~xE74uSg+SEuV=?39-||MTbsE|Uj(|cXlZQ&#BmF3&OVaX3y6J{8olo4--R0TZD6Y?pMRUq(zV(n)9$n%m(PZr{I+6%i2 zo{}6YXRCpkWIGcwpKqP+LcpqcisMx zz@7n4u|-Fw^IJa|B-LE3x-ZDo<4D`Mkf={^ivq4`>=Sa%&uM-TJSzhl9FKJ2fD?kkZj$Ur-<`GNCG`}iSn9tt8H6cp>Pj6fZ*rA zM96e^2&lRA8J0T$n-6dO@du@O_m7E-0G%6;-c^9`-Y(kLaX3!YKr zY{H!@Yc6O(8uU9Fr@lS!L-z0%)z74IDHo1vS;B?6TCclYLtY7vT`SxE@x9>X%#6At zd2iPv?EJ)ID=yf;UbjSL-0o*9lXAR<~RxW&cG z*@Y06!g9J85N)%Y)fU8!Fhkzl3E_Q-G1=cWnKUp19}}i1){G>mR>gk!l@(_8i4z!9 z38yP!gQpq@A2I=wz-CTrh*W4LpiAO|H2Ek}hzML?Ozro892NEiZq<}N2OC6yor8Ud ziYB7OQCgAQItLyzOZ5$$Wu*`#C~s3X3u!Q+#=~F&baz7pK1(G|cg^_u=o$f>m|qPc zGuty%bWEyTt&b7F1K+aK*`$>q8A{Z}I#l^48ISu&YZ_8e26#4FOMy8ZL1L=?iiej& zi|;trQ2A{q5cJ1sec#>elixS%Uiq{aV0c|>0qiw&GiMmgD|{PMG(O|+Cw&=d3O8hVUfT-2BeB;<4&b( za9$R;8)>%@#CgU@$yJ)MHu0?}t9Q7RjuzP_0}Up8hc=9pv;c$y6e^jQQ@okAyOT(@^$=ZlBxM?wV2L#EM&F!*8?>zo?g6fu z-cr$C<@HoQ7(gbb6RhmP|8L3wz0|iO7U)`GK>H@^PHX;9e-cr*x8ce*>6(r1C`VEd*zpa&>DQ|Y+5ktDJ_ z0ofuG5jJ=STE}g+@#NwD$$C^@#e>NKg(4 zYsX8Tst8X_PzKrB#?LEO9A9Qhf(;5*Vpm!e#B-O^$`@7pf5I_)HB!O)34Vg3yAF z7tt=sT2}%EZcL=Z?Rs>jAl_juV#>D##Sg9f@Q-pjThi^N* zxWOn_z-ZfCbhwWzqNW2oWK)h#Sl|ZRDJ_Yq z%)ecaK6by;w_pX-fYZjDJ{nGM7Z3PJRamta!P%#P1QbAotlKd0UkPOJGxTFHbMGST zz!2NAgN2yegEb<@A$Z34NZPaJH;l9NVB1dfO7FbYwy9yo6+jE=Ik7aYqNz>r9zU;m z@G&`vPKzZOM>vJey_}3gTrCqJas>ho_lT>OWfMswOQ%&+%w^LR2Q@Uv$BZjbgCQJ? zmkdRZcG8<6*ISeu80*XiZ8kk2NgEKwJL3+?@SxS5D@wrn$x;JoeY+l+xD_(MNi{5X zR;GXXevDsUe?5_xNO8RCC!!%6l)7g7L99t1(l5E}Z?G`r`jD*IP*qUpuB0IcYAH!O zC%EnpG(G_}Ow6}3xSe{#cT3ehOsPcYgg)kzSAu>Z6_8r+N^GYqVoKo@bj<~cC2byT z&mcD@r@M#M=fj;uO9RL0m8hwqL1zj?j|}2OkJTQS8?xcF_{jbi%I}j~9kfW9;><%j z=|}9IfU;U$R*)XaEBo<8SmwL?Cc(?r12J*C9(^bldSxX)iiJLy$?NsqU2#5{r^v~R z5(zA=dg75A?Zw{h+zqb?0`AgQ7f@1SnXW0IcEdFuGrE)P`(i1ju@$_I?=7j-#NB?uXH5K90F0g1b)ng+#%E2I*)6a5|6jg!NyN^5Na@@F@^>OG0NBciCLaUPK2f0HSeIZU@0wyo5q(YO z;@G1?^>j$5AXdTdxbP!XBQ~&*Dec=MEn;$DQY=W6M#5zh z7L6csi_MSQ<6Ef)aLpRs5WsnAQm8hvVNcw0hhS|Bn~EMf5GsZjP{RN>vnn8abNly^TX&kJ-U;K5yNZfzg znB3e8pg{!_m8cw3C;<7P^U8x(S|nG}+pKsy;*K=boymQP0>52hpM=%^2vt!EuEoMo zI~`iDmjFqluN@{)cgKY%)ulP^Wn$1UWx9?Et|J%LoA`o0gpg$9+h}wsq#@x#4ih5b zTgJLAf_~JU)_|lJS18vAL$c6ulHG91@Cg!DvH=3KN>$PZb8w56c-mdmXAC=z*P}DT z6RL}b%Uh|Z+U!R?_Os`=_lE&t4 zEp=I1nO+IyXjcNzflf1_@o zg`y1s6}*BJ<>Jx0+3_DIXjO3>h?uTcN;p2d$>ZJ#XPZ79PXVw_3^Shq{=g*x!TZw- zd{%W^AuD>^&^BYdPC0s>pC&bEeyHAKV#`Jcvst2SX>mM3d_+3eGjh7(n0E=1NxO;L z-Ws*LS}tyzQDRoGnQFA6j0uOXt&nr@rX}plc!m`Pvl*prDMa$E3<=-}OzEBkBU5+)Sc;)zY>GWksNA=CCnPb#=A$Y@2By_f^HLVomq z4?j&TeK&j!Gf=CrrkcWUJR{GvE07r6!XrFgu?W0gWfYh~@~-$$v&R+&%SC6#6ojU< zRM+pY0|_jH`y^p>GpR84i3TZLpgHU%%;+IXr3_-Z2Yc6``!k+aiT4&6=x`DnOWUA! zGNpU#fAFk=y#uDRrIk-bDG`uJ@3OugQFJ`zsAF}sgsOyW0%foZp1fJ&!_*i%9*oP# z<8@WAr+BXXFDi%;iU0`>Vd+Rp4KtSxEW!A_Jf;VsvW00v>H-qmTuH}0JOML=y>t8q zi3kiFAi(NENQ4S#TU~tvpwG%Q>FUGA1_@o#>n^K*V%v1rWb;{Z(W>ToG~g)4qXtii zo0DiE0yX$@bC%)$AV=7?GuY;FhVF+D7x3HYa)6kHz&Y*$79`Q29cF0@AqGDqEO1ehzd^}ZXoKY5QTa}5kqW`0 z8%v#!XvR!EDVhxnrV$XdL$W9`nO9B0u|4yJ_Ww9h01OjcZ+paR$af;%MvS)ui$o~j zm{BZQnjiG2U_ju~5~dQY1NRDPk=Zk_kcP*aj&pDGf8%ON5#5g7NvIr}wQSo{3Qsx@ z%C6m)(0yF7{kRl18#dKSham;e`etMVQW)cpj_{FwkR&YTn-98h{P@k)9(!!*Qt=ei zYGc>jFc%7CTT-~j`mnU}f4Ey@bfULr1rmvYPMfhmf*ht5!fyXEfkcz%05kzys&OlC zL^1uaT_m;mBxFiZ zgmVXgP^qaYp>ApGE@O~*E%?lVMOi=|y21f60}{@2)LY%%DO*}}J5v9{rCK~sNMMM3 z+=zp=V>fp+sWg2Cq<4|P?iaGV*GlA!pNA8_Ez^o&0tlkan06+6KXDS5^hf|mR-eY> zaY92vWi!5{m$(E0G@%3`&^iCM0%SO80EYb4RMGtx=E!?r~cT9Cby@_y0^^Z{8qy&ruF0IHt$$BH@bn`4*-c< z;(?hU662qTqmj5oPg z)7p(-o%iapN^flWO88XtHq>r37Owv>(EIJtD_GJ|z34#kL$`LU)=w7grS&7cp?kxMhux=bBR}(r2 za>Nk_pm>}10d+o#W^G|kY&SbG`Fe$Y8jZ2EHS%7|&=OaRTJHUj6^l6_8Ah|JMxAJ| z5(AELdzd68RjI%*C(SCGf@;k?G~AIk7FPTS%aJ-j@+)--TX-2@?CilXx4GGH$C&!~ zUVAl&RHH3#*Q0CE)$SI@)6wiF_w=Lcifq!QI%AbxHPb{l_hXB8wZ(n^D%QfA`dA7> zh(W*&Z0-uG>2L>=^Hof&S+*d+q91)n!ENvN?@?=IPrgGbVG-Nlx5ZUT`y-}kk_?(I zghze7f(`8uRPB?<8yyjYG;PqV$|c*t=xHb5l932d08iFhp|dvdUWida5Y!+QuN%3O z8pH;WMQQMvlK=JA5 zszfz}q>SBT?K!Ux+?!PXmsOIDF!69#(+-uIJ9X2@pubU{&?6d%KPbn*eyb7tjGnTR z8L;2&3^#dzErT3u<2|loJaHBHrKBN(SZYwYQ6bQOj*O~0Y9Pt zI1KKeoZSZG*jG7~#+(-MTi_I4&-ep|j_(JCKN9gh%Ju;J9zkTOP*^HD8sp}gxAPTm zVHvyIEKrX#q5_Bv7!XV7r>vQwWZg~6%ECjNZBe^o+nexuKm{Q!lC+qMVrZ~PVZjT4 z@HEn6H!L9?okOQD3stKH0uJy3*Q0BymUc76an&f1T0QCx$F~4_ zK2PJC#m7&4IwfIR7j8R7?2c@8+h6;?4gLY3c}{_}yRkJOhK~TUYXK=Mqtud3faj49 zllSFR=HHuVBq3PIK0x$4fYZ!7TnG{qcL;M51{9tI{s9ze^P_7-zz(u9azYVHqNRG< z7lAf7au>yq)ucy*MgT3AZ6Uy%T>{A=u~-C4gvtllZZPcwhiu$U4NiwjQ;D39KAKvu zJfnxH^}#VZ;!xH@7!2_AvEvB!bcE`g*m9v~=WyqeWSxqA+-y0A4Q)1;T3xHO6UlSz z;q9D0yj|HF(;a{;eCe7E)m!K=3*U|O$#l1SHq7-3H@Aia7Z!``>3LQ&4p%8V)-OS# z*)R$7IoP_eDd`OxRP1++JK+{y<=rNPKcVv%)+>GRROj0{et5nZ zk*021U27N(^E5nL@xZpb5e<%3JkKj4hKxm(iGYKlEAXK_tchf_t|o&HkfDz|4Pj{e zn3fRU$)oF{h!tZ~0cMZN>x~5cMm8)cP^d?t_|^|b^*`O~T^Qks>P*N~w?T0kabVM-)c`j5YLisv{5Dhn9VIs$!B zLEHuy<%m?e7NX2xvEEKstp1^z5I%sMo|LMLYF@2wW=u&ym<2Ge!Cm1F4>k%1pa+P+ z1Cs@vCu9WsqUO`2HrwkA5jE^EWk+0S(niAh>fm^!X=O2^PV&8V@%xAwZs zmcO8uDM3M2`8^cG;o1{VbG`uQmhNdYSM z*8+RYZfg6o?$Ve^vd%fVpDGl+DY_J*yu1WtblmhcK$bg*F0W=BVY(){KHHEoa2c%7 zJnS+s60OqEO$O&Pter!wC_uC4k8RtwZQHhO+qP}nwteogZQH!>elL?r-ezX=I_XNM z(~DYFx~sbWKV1VXT&Z%+i@3ggIMaTn!hw7knNXOVHqRduidph3*sm)hD&Nz8d6_WY z(ev&$uXQ^z_H}OoGiDBy!)>s9H1xN{>`z)IBR(873HFH;kScg?l(Ae!{7@~BXUoWB z;-}<4>I!p$qA*=uTLrZWA{{PiaNgb6{ci2c%s-qQy8#*M7>T&lB{-RE%O=`O=->1+ zMowZk|5_`w=1H2s`y05?KPeK1L(z=0FkIX2La;k{>qZGw zEp#jZ#Jr}q3k>`ZIv+p9DuNKa;xG5;A(f*1UlCKWCRf5*c?l&L&Lu7+-0EE39Yy(> z%2b}`lM+8=Cv&+~&K!!CVCE~clyr|Gv$13?Bj!jwN~*Uc7E_TEls_0S0pMVDDd%@| z;`dRC$JL|UqSiZIkYp_RFABuB5`o}5eXHz74_74Mao;r~lf?+JXnkAa)=3=;f^u8E$F4f6LrP}Vg2H5R6AZ3yw}C5))Gya| zjn**o1bCVAHdxi4z8U1;C}|vP4UH_UlA6i$mMYtr*8yuIA7DPw%OjqWyJ%7%ERw3mBp(oi@;et6Y$dZ5 z4;b8q83GZ3y=9`ppzLkYi!(A_t)3nYT3AK(Em<$VjC}-mYtj|t#5pguDwxK{Nd8LP z9)@Hj3b?-j?S|=H3P+rvKjTb>3RUw(R5yQ%kNGw(7{##pl&A;bE-7j7?gL%F6IYAL z5ijpx7Ek~&8Gz^vI*F(Va@({K3=iPdZzOe}4%I0c2kjeCJTrEP;jkgQJ;I`HM} zm^2XO_A>&uQ&1=6@)G$lcB^CF@T_!i$`Z4}{3mP+;Rc2!yYy+L-xrs^o*^q^t;8MJ zx{_Bg++qWz{R2?lq)B##YOPI3JDEy#)Bv3yJ~^{8niy(w4j(xUdt`FKFs6ik8u(JR zRqA4z>s1e0I9pT&Kad>_ELf`?Dvl^16T&^>8l$ot?tPTz?&qv$TWML?qI{zeWRZ}f z7I?7?7+uU50g9{L2*U3bFA2GAP{sxqhw~Z3Vp)r zZxWJI2H|F_H5C#9cSw%|q{#J?gVj=_;dwRWyN~oA{yE^Qj2(N&7UhO_p7(fW2dS)M z63yk#uX5l4!93^~{D^gHYyI02T99{~TA+YN1*oQi%=U`7AVZ_R*J)n*lXcAZJBJ4i z63wF``rzsN=~_jWuMV|^V&x%$VZ|zKPDyK31y*OqB>_T`qt>qe8kDIW63t_>gMy`_ zq?p}!zg?^?NiI+$#$)ABEGb|ysS6%npVTiw(3i7+1+SEHK)45_*D;9&9BWVgU2K7c z_|m+~P+iyDPs9h7+p_0+=uQAt%I{i{Wc)_bFDtXl@&TK+pW3{M>Mb^&>n!gInd`Tq z*XCi{JAy{=E_;i_*^~HH91;u)u763V8Fx44D3e4{h&~i7!6`FJkw!ycz=Q-HA>MD; z=rr5iVCA6Ko|!D??P1MdXPG$_R)FY*!hAY$ZqO+JMt3g|xpri$`NFc%OmuBQB`aRu zg@C7ydgZ%uT((XMXw#7O=XnOIAA| z9UvGz=0`P@)VZDvaq0uys-&?9xjz!AQ2uSYDUj`)v_ZuPiL`;1I2N)SDY=_Hm_0G? z(x7G`C=H@}4CH<-*kr)ppeo;v7o6Ornf z>C5jj?pnq-$wRUyu9-p>Lou^$v*+s2rTjOYL`ELG+>s(e(n;gpP%Ym!f4hK92ocd= zidlxi9#rw2t)DR?KO;c->or>CgZXAqm|L&Oc5dD~C0h?Gm4Kw_vLbTO6`^$M?39ey zu&J_|8toajL8qmfp(O!@-?R7aE?BpM3y>s8tXx4H0s@M*W;oeF570!)DZ6e5+CjKT z&d_YTumj|cg4^T88N7wgK@eJ#0l^97d@qgdc3Re9>B_3f!yvgT5;^69b-_ z0o>}k{o=qvmqAY6h~5{N(S$S{q>@h1II8>jPl}fyWmU6^K@1CW=IrBvH%>tPT);|p zyK9+h5)FI>#*llWH9$8cl1ddX#q;($ar<)T`9p~phP=t$00Hd{uH6wXv0<;(P8bB& zF868z5X;|t@k%%{HFIi0CS&JW8)L2(SS?~6Clog{^WKBQFLBN?mZLEF6Z3ZY8J^~K zFAV~V(mIfjJ$vV}dyZg>oTYX&v-2{5)H+p}`#Kv(2(=rFxCt=hU-?l(O8@1D>dR9W2sXr)yvSgIfB}^6 ztNftga0!&akWmZb?juTn({}m!H${e`<>4AcuV^kNq1ra)e6QC8@6{N2!9S z;()Pw$Zw#(z5>$c$ z-?98ApT0*ToyPQp?V%!YXIg3A>?p6sX6~e-HPO9}>j25zN#wJ3&n8`HrgT0bB{H06yAK-~M=m=!$ycKTf26wT=f>lNdpDrHrLK@18m>BmvqA=yZT z0|OmW?JZutKZIGZ@Sor?IdTPyDG|d!SP^gEWT|3d{^0qb4orqiS8h7SPx?v}2UH^v z1}IVz-KVs;EsbruGV@_O(F~&@;y1t)dnUM3-T9()%n=Z@-?X~QoTrQm02(6125=SB5D^6|)y8B; z;<~SsKrh@pWRWgC`DSdQjsRyj(Tv>y3J=jR9t@u6U#NsoE)rvVbeaTnjHmh(aB43n zmK$OKXPE=n$gGe-{K3pfZ49g{RtPfYAb;N8uWO=uw27)TAv=;WZPT4M z4D+mUAD@cIY8_>M^Xi(0vf$v7bCwA2semX|ydeChr&#G|g$-kt8H-?J3r;VfvW-^j znMclXyP221OpA(*GLQ^~+onu3lZR|xAkzrLGurf=zO~M-=yNX-;R=O{R4JCnk->X5 z*a(u=Z&tzF!@QObbu2-eJSwZL*>IWFMSfmfKTsWrRGrt!10e{_?Buh!O$6$Bq|LRe5Dl^jNHeG?|OSLh3oA=axGlndI4 z#Y!I8mQel+7J5+R;t@~4Uen;>2BK~(2}csA!m}E7bp~?^SENlOxFV3H63-nRK(Jil zNj?C(VqhD!i^%Db5wSBt3Iw{q*Qffb=_D3c=iWhcR_s+yiZj7|eBaSck6bs);~0Uz zfta(rG*P$K>J!h8$PqZ#t8Ci^-UML@espL}=8qF#V<)Sj~FtKu5bnm zM1hAR#?m3bH#UkF3)1>K2z9yp3|Rk7bKw!YhWV`iv@~+;<`~6E7E3 zP{sPH#VM#mZf>9S;jcg@%CMtkMd2Ar* z9o1-%CF#R}HQOmSWDGqf+(`yk-~nw+sWMy`Y+SUUhbnId&4;12+qJMuM3jDD!nOMS z(7S%P6jQQ%wO1#OpSg;(ulI)UXLm6?n^c-er|qaHM?$qo&=+F_x~y8nSJ$5xx?enO zMO`>{W_D0XYNgSBl|p(3f#Ioi@tbIn?u(5|=}7J`kau`t!QIH+zz*7m2Suv(a||}d zaWt|{L|c=Bg$qJ~OX5Di{zcs{0?59v-aW45zJgd}VOt+qyg7_0h-L(&J^~dbBz#Eu zh}8J-g&$akF;n=ZCY^+lm@b=|G)Qnbzq+Qx75qyU%@d`HJURp7BO&>qlhn2VtA3NlPN zFGI=hB%Wc=mr)&)6#Sbf3~@Er$nSfN4|QLbB9@Y?B-cL3T*k{RQN&E$Gyulv5j<>~ zZSVakZ&&=4=!j=tkU^YKz!f=@2``37i`s>Qs(Q(xORuq0p8|FxAv*H3H$0xU(w#h= zR$g#P zd9eCTZFjqeroUn*cF&z`>sPb9?CE>%y&h0b0s;GZ>utWHF)F|X}v}~+u26% zZ`J9D=-MH^)?rgMxZbIC^Tf||G-tfq+QgLY1zicIu_>M|85k~Jh0(sdwkA$0J^8zY zqzuNK17bU38HHTbOrn6CsOxdt)=U++{n`f<0dJ?Mw&ZuQ-0!V{aAek z{gTvSJ3l?dj~Db&r)50Lyd5es1I&HJ$f`~~C433ww23alF0j~)AOmBLQb=UMd2kzu z4HN*p#RrF`HXIQJxkDhu?V#|GWn3A^z^73Wa_}M43J&QI_$wjtqPi5Tv4}Tx`d$QWseFW2~n3VPyc)uD1Q+BJ)Nhv!Avn&C1!Tv z68K^wyWS?zMi{L$qoS3?P#?)+%xqD=Y56+aBDP`00JPe>by1LduLvB4Eu^qxFa%D_ zG_!Qv&Grmu&H@fk{UhWe}y+_ulpw)`f~w*LdyJr9Y;WXOI{J4uG#0V ztBcNC=u^7*f{*6WWH$;7 zb4W4E&u3X>yb$~~3wOvi8M?S>TuH=2RRntuihpMnSbzgXe zX1fb1a4U4AkRBg}t8n=$QBcUV;LdD3LnMfD4GJA`vlU^N1rO}$qF;Bi;Xd=Bxfj|) zgOl-hH4OR4m7?Lu=KTUT;q9w`yobq$B6-XRKDz&VOPbRl9GhZefQF1Z2jMf&F%_zu~h zv5cKD)+P{EB5MVE$HG5$aj;85t^cmmb~Yl(KF|P}Ou`ue)(e|JMAV(n)Qgl`LinDd zC>(&^{=vtsh<{Ry@VjK(fS1QuWZzQGDmG>Ac3zOXBTp74;zkE|ISX1MSRiEC?Q^`3 zlA6xs7?BI0Wg)%Yk3^zCHc|jxlnM_rwPki2w!crp1%z&}z9mw3uv|x-B+D*qgQh`^dQsrBLVoG26l5n{u7xnmd3>2_|hfqbSg z;X~MG_+3a3U~t=Fk;itO998>N#*W^gJWT1muQ^!AO6;a?@2Eh!OF2j+X6$H?*V}VL zEBAhJF4b&ra4XpMF&tQ7r^eV${7<^hW+*?kpkD_t8kux^vKNPCjiDb;0`r_jSBS>h zbobm^6e20%l8%r9(9y4>!33TSWCeQ+N8C6C*+r$jDTDauLrJ_d2*j!eo@ zh0Et*RenCnV{f+T_`h> ztp)Lw?dmM*V4!fusl%YKvqRcop*^s3&OrJICQgndN`UwU)9sZQH^vSb2{_$bXex9O zw=wVDD%zPixypLb_G~K{^aC8i6fYyZ9fRE_46mVb$`vXP@_=O2@#_|x-rajAV(p_( zH)63}LXcqj621$Gh?-mRlCeG;3EIYo&o<1i`m-cS+uA)dkQE0Yx@a;mxTi&h@H@x* z=pTDfQgKi6*6&Dx;TMbIns~bFvf;8*8zs^&kvpuEtnd2S(Nd>@na#)~zCYcJka)nh zAjSBYwsO#?HYU>CuS8$H;8d@S3UOviJjX(3_Z|>rCPIIIdZ4!BdKagI9`S+ppM{vU zj*)Y3H@rG|Vp5hThtzNt)@6y{+ z50!h;&c|c7u+{_DUk&69Qe#vB=YVfjBENvBm?=pk-B3Lj(13LR(yy)>@2dB2A2!{c zHfT5q8muP%#Cm;_Q}DNOIo`p+P|SrM?m;#w!W*s{a={B8t6n!8{6#y)s!&0Sy1(a= z4;~V+K0X(foX?0>)?I~X!$d)*;bkBa9Y*oVEF-vxlw{hgfUT63hU@ z$GSbu?m8;aAYkGrN-%IjUiE{!!sXMBVrXR{j*`g41E^+)ttT&fb8sl2z-E!C2eB@8 zhb~+f#J5Kx3J*{7k-VLcRlr4L$b(O}s+o`r#Joyi744M+Y!?T|+GrvvvBdUemjbnq z`XrI!O^C-X*)lPBq7u9-`ErPx{ZKB-0Tz;3TZ^+xSfb79W>rcKi-?~`5=oj*bJ31k zoS(JGJz62i8Hr&)iUu%LxIQ{rSPUVuiPYSf8ELLkRNY0+@AIMD7#@H~ofGIq?p=(& zBBvk=`+I|;Z63l>f)Z&H6%1J?G?~YV&pQ=b>*ixyfAXyj3KtAT`0v{MuE; z;vw|mbZ$_FT@2$ZCZMHQE)?}m06l%Ri;1ZR6=KOpn0A<}t5uy~{<;YG6|WA}IVv76 zZ}8;M!TZPcH)py_x84TOo2X;QDfi`m5p;+Mv*`Nr<>A!9Pq5i0t~CZR5#eJiCEIG) z7Vre-AiG0ycehV`szzZ|ch|e+%bUoQwdSxcNLeYpYm6bBm0cLvs4C%R!ZwQ6Wd7-x zDhv7E5z%Zxq;ad1v&)bH50tDdAbzkYLoODnSIQ1ty0PB}65MH@#CBr;D(SJWg$fre zhZhYKOuw<}EVIC=Cy)+Y8KcA>Xu4()Q zERda8_*FqR6SHotp2vzA=}He69Y>D59;~HY>tf!nOQ=~SFZSkwCB&(O2r;mhJi{3% z#aZ&!q;ed?!|Q=mvVeX1G)}Uwnz_k!m=&tDD;->QS~}$bTi3f)<`q6mV$1Ml(=Qx) z;6pwHxH69byO871zl)j^=ilvZxbj?oyE(RGR4TWcKiTo-u#}O)HtxUXG9d3cKpCRPA~ML zr(hyTWeDy~q*4jRT)x@q?qZkwM>(Ki?54oY$TaiI?Yy`Syp@p$h0J3yu> zT%G$A4+}L3j6h8P_&bP0~7{B7!g#v0e>HLa1JhG2Y zWt&aCB_E`a$A9voZ!3dF7;SKw`97)S-eV1s3HEz|+_l6~b_NR&;Dj6$|NKPPHp+QK z`o0fKZ-vVmI}jIAoQ*;xXQ-ThB55y0_&O5+N9qwUc&x~CbV;6UA$0jU;`}Ap$jRc6f43U({5YamcBR{@zTge->o{Sf z8%#3I_q8~EO%(DL42*GJwqnGo4;GIA;!Qs_>tW^k+m~}f3n3dJ4r5LqoYV!c1CkRT zHER;fHF2$c8qR)EkWkrv@fdNpzV}?sZg)I50a`6yy2ARy*&BI0u0B3)Qffb^25zUo zWhscb@^ImVdu%WZF?Kc$&9_G^jrMnuP0DORXhubACzI4lz@sgb7x#3j!b#pPh85re zS&{OO`cbcA8E8PZnsNDSJ{TIRfbe83sB(!%#q*B<2uG2rn7%sLP+{tl^vTM2Fht?D z@h}zv`C;nMsP#{t&c1BpIoL4xZpRFXlTS;a9>H9L9iA%pQ%_D@I;oKCKl(9smfFy6 zukfDiqHexv$!35LP?)5Z;hFX@SW($U=d#LcgHeHaU;|M7gaI@-LMsWtNmuRTrK_+p zWKNIJ-q>2B8roVgQ0iE8oEs-H0;F0-vYG~Is`;u-@h4#s{Q#U@;vA2@l{!VO7}o|3 zT^)9~+MO8ZE&yu2gVFJ+Tn`8rT`+d?uV^`^S*Y!*`%;9Cy0<>o+CNeTsMd^(l9La|3rXJ}>^50(R=+^k!Wi24;Yz<$BvIy>Yt(WwA) z1yyC66$W7w{pmZjl8pR=)yVnFMpuAN+x;wSXDgfSmCmity-{_S4zxA#_AK1NwtAOn zhUKpMf${XD$TS`NK!sqm!3B0tCgvaxe4Ot95vw6OTw*zL68R<)X~&qI{Wzc{7`3NB zriK^O*D9$dbf+!-g?)vEp;wCqhYnM;NY4bdY)?VTY}h&^AgBh*fYn&IpcHZ2vMkl~ zvv+Gt$fA-223=jDULMp?P!&ngLlJ5?SCJ}P9%&k&u?HuMRE2v`mCtmvJLd;|8o4`y zIVx3uY#e9}SuGr*G%sbQD;qIW00VgeD-cOjZ*etun1M17NVb;-LvSxcTMqlpL0#GQ zAnL&s7g+fIqLiHKQFXF7_d_>wF6jt{2+V_`2+f{!)Qs_w+p1Q zZ-W+)KAMPvmP+)meYzFd!d~Oc)b{H6250xPrn}Q6rkPPEDvds@cQGH>p_>wgLN$&VTn+!k9usw)id}`&T<=CpyiQ23%h7gF9dM5txhY; ztK$;cbx1Lp*+3*5o3JZ>=LWbl5iw!2gTck_0##p6O)_mdD|0=&eeiZjGsMrP!|l%R zaz=%|GFf7Lh3w1S?O?_YclN zX5I~*4ASux8^}#fUCoV3F2$mzy~%Uq_$pD3DTqSx=(1>wM7=gkKrMX}PQ3d%_k4Yi z%kS~i@XFL)eQ_y~8YeHLRRkA(KbEe2Cayj|%cUY{9ardIj!PS^N%A8dzA|mew&16X z5O7z}f-bOMDLuV|wKy&r$Jmu*w1Gii8XCz%C#&T z1=RECD$JaYhXkDXri9{3D2=+KZsn|*Drsg6olSh>=&1!oawe4<%Y=`0;@US z=CGP6X}hV*fmU?ksnG@yzb)UfDe0pIl8Exbp%|~#W+Of_5!Y}XexHn}CS36ys@e04 z-)&+U=E){CxiarNr{xPH1o>1mIRohjmU4^q4n z9DAH^p6oN#9{I)s`rAkinc-4$7UVC~+}KLY%%$U{c^P-|A*SU*KTVPbKaV&tGP#P% zqavbANf-F={`$1JBB>R=c*&bpWfKXyB@3%T)2-Mo{JDjdfsHRImEu&@s2Fi&Et+ts z8s%79^EvznyJm`$jiExDAgmW{+rj~7o9)dv%6Q57r9*WG?Q*b(#A3;&uZ+{V)Ps?N zlwrW`jy@()956<4UIP+681;kGP?Qo&Mxxtp99dzuC37*aYx^{wiWJK&zSisKSy;<& z=ZO`4Lh-P3Zfq_d4s31~BP@BiIdhq*0Re{MjSu{snM|%|vgTh!^i-IgEN($YzklZA7*sL$^@O@hFwm0Su{5^8|QIqz!rqAhc z)}YLCM1?)PlMGh*2fa*-dWK!$Juz;4-ma&YaZtO?vgcH9*xC*7C0f{LQC) z74VInx3Z!I&i5Pi$d^iFs6@7{7O&}M@0{Ua7%)1X4+z8{CI@)?fUN`gHt5@c?>zud z$f!YLEa-ID$$*ogXM>LhU=0FW@T_67GYi^ zF*y+@<7A|ioQ_ksCd3e8ZqEC$2nD$qr{Ywk@@>PxWn5igEe%`ML{@FnR0&0QyyTWq zzqop41T?FVYB%jmZK>3i+27jV8d2wC^ys8ETwAVIsnvzh3hEc>j~q5{kHpAy66s&E z>R1VB&k56S+FbCM3*+MWi@yN>38GK{{~1IM2Xg7aq7 z`=9CR0Sy2M@(TZj>uzpoo~egDtXYI{BZ$M@-Q_kPd6Yx#M2`n$g$)AW44PviFe?@#7? zK5wVp>h*qK7uoE4elOkpeIJL(XYF_VJbgb07jtv|AIIH~hgbgg@qIi$Hy6WC;b-pl z{GLDKd;SkM>iMnW`hOpv-sI``zSq>hG4I65eSdDph0i7>^M9V-e}6ud`+U5<@2974 z*K)aU-sbN1`1^kzpsJV^SAQw_4536fcyJl$M5Iy`Jl`B@lftu?*FA>lEDA^P|kj**Z=kLadRZURlfMZ zKJ6`5?)UqC@bI$)?lT@nbCdhHvenPu+vCr5F9|*;^6>XG93Kivn;KO9V|cpz4!__3 z_u^xi-|sJvPHy!Q-ru)*OZoV4zT0m0eEaS7yWfYWw>v*9Judvs4kJ#kJ_XJ$M_(V0 zqo(6?dj9yh-*I|+{M}yv-9>Jvf178+M^}09_BtIndtHzHd_Qwr<+aku#o7^52e1++ z_h@B4K1YiRBz+!V2PZpwzl`v2VM9InUD|NHygJ??Kq;lg+IRF0pw zhc5hldUA0w;aR`;b8>NI^_{2pC->LZ)93AJFnRWBO_=m!Z)j9DRP$vUMA0&>j@z z<{X_rCOR#j7CQd66W>I)?$hkE2eWxdYmXAI>*b_$;dSL+pL?2FHuNLnVeP4j&<2;nE~G;T2JkQM3qU|V?g_b0?o7SheN+yG zj#r9(PqKz>txyxCxx=vIF5O96IK5D+4B5ckyN@`LtxxFKNaSCoY(=_e$G#zGOrkzJ zrm05ydHNDkae0E2IFkhELs6i zg2XMPt-fUctZ{qCz!ZEBon=6$DG1Xk2+|A{eQ@T7uh`kzZ*4VDPBe>Fs(WGsOF|S> zBrcJZ{oExGb?F*xsT$>KPk7bt@T$?hGvZ!2w1uZJl4bA&94NK{l?B$wIeKZm;!BM| zHPHmHD1l1tH99ofd1$=9LQ$-?RS~U5rhvfwiT|`p8^}cWp|}D^P!NMDBY3jZ8i#5q z^aI(U=Hw#YrBC>3KtAXK^vLqn1pA5=i#6lyV4>K^sl9aEAFP zS!jbbTPpY3m6Fp*>#ute>rnaeil(_Bp(4~~Le_Dbzl%}j%P12|!L6y1=rmB~A7>Ix z#y;7MWzewiP)lMg7mEIghbh;(Q@hB!oG+m6O@D?u(AC%}2mZlfwS_$In-mA-^7Ii_ z%n8{&*5C^~Uh-B04m?mnnRsj$RlawDnrj(aYJhUfVt?ryXhrH8W5F44X*s`vYAP35EpGG5Ux(2SCYRYPw@_hDcIqQy)YZFub|S z4x3o|qOFdUv{Z1&o!6_Ezq!;+H}bAtU_Vah680-%Zh5XIj=M}z5kMUHWOHC-jHa4r zYr1@AL^Xl$kki{O^F-m-Tps1C$c|e=Tj~cd>wc8F;zrh`*+dZ~@kG-9M<&dY7+Hx{ z%#!g&HF_5wWZC5PomvQ@ypl*6bI0ZHjFw^*mKc9XM&sxmaTkOguuvSJAOCc=A<(_T z$+f$uvB&iWB%>l8b+RfrUluZU2W5?JPvs)G6R!AUwMuMQsSBlLA?ZEymaa=6=#-Ds z^`P7`0UA|l{@~UAHA#8W*NWzY6%a zuhKn{Z0nTP`-?@EC0FV3^wL3@B+*7g{5xoRaI0t;sAeFW+P!PBfwwhtRf;tXmYlL> ze(P22+DtVM-Ro)|{|SIc=}k3*i_Wd$*iJ$rFH3PT((?LIfhz zK;@4=S{9{lYH(S!0?5j=VwH$e>^l4TG$r@o$xE92HQ% z5{SE_Zaa)Rw(-29Ky65S%3w{O8wVgIM>g4OJHqy)c#~LIYhbCjjbMe9T8{*UB}Il7 z|1uaSJCTZpcn!Xa%ggeyh3v}XbZk4bWWed(d`0l9-EnMxJ5L?Qo_VgLU7lvKL-Zxv zJm$do&^yeL3Yl_WVtoxo*!u6UsFL1>5cw+;1&kNY+28bpgMO0&t8|q_cC2-DhRmc- zIF2+d9P>|xQ+w#e>sl^UO~>XJFI3+J#NZ06da;WyK&-&px5f|Gupo0O8f`|6oVE_o z>(wTFqG}tJ_LAT(V4^nK1FQ&I(`2Qw2EPy1(WUTYO0*I@_+7-r))TaMWs3TxEshEz z_lPuyk1k50wubydq;9iT`g`gTAat|R)C*7|>!nQ(!0O?Rh(raiIIu@}I2>2dVK2B0 zXH&_IlVzydk}9A>D|e&SZ^KYM%H4Y7)zj0AmmeOQekCyPmZ^k|!~g&flouds@n&F2 z3IH#y!uL{~!ZuAxV7Q#Jn1Rn98iRk3&g+4u9O%|xs2Hnch1sG`P^v9hBuOH+u>~tS z2`42|$3&>yv?d8F97!t6wFb8DGIwAZpdJ8ZG-cMp3wvwoAw%AVI0%rJ%}scYB=n5Q zbF3ZG%_h@znaovDIwfmd`ixBRd?j1*Y?6A+FSYf8^RiNJ;4 zwawh(P@faGyHV6FOC2>=O}o(~P^h+2_9+{TfM|h?HPbi%f&nks_KE1!QgxVRyW6H` zM75|kfwH`I7z=z29q!!>5Xs2_sULrviTw)|0H!8Lsz7Gq?A&O}RZ;>}V+l28B?wFC z5OVqaJ?KG`NmEoWqr-N@8Dz~#IJ`OL1! zFlgW?;%J0ybEv_}It&?!)`Dy%pSu`GqjJsKF9-l8v=L z8&c7fcDfSQt_<@Hb#lAKy4~A!#~gU!NM-`?D`^fKoBjkHS8W&~J3gZmaqYKi2$t(L>Yy11*_*OeTzhU;S-;KhK{M1&bOp||T{=phNBQ5=y)S#|ls(!M*-)@L2RfC_S zy~~z%)~kKM!1TS#;Mr=%iMrW%%6$M_;ga@6@t@-PC^l0*Zqg{V(%@ckbfp3XWrw>} zgLmA+cr7E+ZK)&YDFdBl09LG>-~AN0DO_KiG7eWjEsv2qA z!7*_X`t1Uv4wlIvp((9IR7SWP2*OgOPC`}qz#V#*fc%l7%&l8)S>uEuR`cewYbeEa zDWA4I2^Emqao$}a>ZhSuBbYMaQOCoaS<`a+v399)#p)=l?SO`;!wuLnyy{?`zRW8u zc^2Z+uLLx2)V1EVhHjfnHOnjS0W(GE=%)qtve=j(Voi(QhuWed$fyeBG(XZBwa)ki z_UtOV^TB@aO1r7cj7FCHp4Fiq=WBZ&i(se}r*v_sT8a6KX1biTMJu>l-freLtm5WL zdDA+y{CV`_>G<1QNN|1sjZuY+FkDII;$fegNQvmH7bz<5Vyzy6#z z0^t=l8!?tBpH(CkDYMPv1^R3W@KZE6p3AHVct;PQ_Z_>A3xByL0j~4Y`kPWANGH?_ z`)lI7u!;J5n0fGqSz#&#>UI=iy`KrI#fE%~7GayQdxW7vlLmhlkfh!!Wd@nO$@*A; z_Z~|G(#NUD$;dA5T~coZw<|THP!|x{fHFj%Df2@&iO+YeT*a6hu4Qg+ePC{@(ALV& zoR4z_46HGW+dw?i++yWAemHy_yQGp#5$*FDAWinEg@putUZ_S_jw}-2*5Xqwu4>#66qyo>rXuc| zJm%e0%sXl~n6KX&^!r_{u@|z=hku2gNC%kTXpixu@UM5u52A+(IpG7ew+Yb_jTHa+ zKyxjkg}$F=4bNQVWMM6@yU5mRpM&@@r=NbaY&$E;NAa^n?Nk%7=#;bam^bRD%Fn*y zTv8?0C66{rk51mEMtlEybhk2`dgUAOiwfb@O0W9P_S~B_zdGv;F@BBcuLZTV4CtB1 zl#Ytoz}k6N{i36xAWkP2=ESwdhSBlGL-Y&$hpK-1Fqme#~unJugmm z^2K>v%?wuX(lxiW^Ra@Jig~5&*-1W79;IMCsFp@kiM8c(VF=Y;8bv?1z0l6aGKJ4k zK$SE=r9B_Ye`XO!wOWJ#h2l2*X1A#KsP6ID~eQZkF&7=ZsUoTyzSP&*L!g|Ca~ z6#57T%V(*Ms?n8+vRr&I(xRr_e36-YCA>}K7D^or46;E$BumnC{4u42ENaTc*jQf` zt7mg2NTDa}^0mRBLEu+1UrQvLy8(1DCg z-eIu#v5_5vxVtKfvEc-Ki&?{4rfM6JB6_G_ooQznuHu~XOTv8k;q9SiBJ|zBQ9%<1 zae23usz7o!%BZy5f3fyW!J-7wcE`4D+qSKHY}>YRk8RtwZQHhO<-Szq1(ixZlAo!W zkDgvLUDdU#_rm%Qi0rugo8s~p&^Z5~oqcAJzU?=}g)Zy{2Co-fj1GupzI;Gwl8AwJ zR_d4}Sae%_7nm}jE;?2}ma_WFB9K>VP=|)ps?@WDUjduuk0WefwQhh7=O}Qggz&`0 z9Ia3WMLc7|fF3Ja1D~MCwLj$9^K9|^TC~~HbIaQn&%fq=j7vTcMqJfLZEKDeFGHS^ zceOyXb|~=xAZ=HetGkg?rm->_Q7u9DC>={$DCCIfiQ!J8dr;6z#f$(Cl4ltA+!DLxCR z9LDt9w04R!7Xin~+pP$TkZop>n>RLeqJOR^hR`FAHIND9 zvQ&4Zgzf{(x0v*hcbAmIpCB)7&l`?VEePF3cW)B+GzKH2cqpFxDexor2bcCQc`8)6j_EI&R$X6_vlW&qwT=BTHPx)Lgror;F5R*z+gYoEZCaF#XKG(3Tvb!MIde3tm|6{1G$%CBWvdA(7HiG6?-FHhbs;5Mh;)qJOKWxbsQ11{5MaP1|naI>Q>j))x-N_nVb zNLmYkv&TF{R5CL1P#P?7-qSsUu7;V|(T#N`F9Nlf#vA#=kr%Gio2JP&F=_SeDU9ZV z%3u(+cw(W40l^Tcory_WJyX`+fiHWPD6&f~E?UKR`^fxO^XB!vr)IU;fey1{N#6*= zp0vQh_5Uqz}M?gUb^-ke1ab4ZXoEBr2kN#U&*nu#02&m>O8%OJ+Ny77oD zjOG0Z1bN6d2qh)uJSP|te6nWXo8&Ko(IKWgiG@IGYo#fZ1K|g|+6>#`qdPf4f~RPq z(vZoQ*J=yz5$0NJY@86P8MHvCx~$Q!b=u#)0jnSh??dhmD7J)K?XPw6y5 z8};)l?KXGg4+zd5&l%jK`-`^Lm`oDK5KKhz!9a7^Z*}C|*1MZYAS7sD*Uw?sk8u-; zRFbxSR@!AmTYUS zIChqi3a5x&zwma&aAq#c?zP-Tw{1i{FTLPBStZ$SN!{271hh7I;791grI2B_S$jOO zAXQ}vK*43_~0@451xD<5wF zN#umdKaM1J)so8?To;dTH8vtj0i7}QOB-rls>xK4y>*>#B2A%jl1sTh>l5zVZ_ z=D||N+-95BO6=Al2W=Ckx@uc5`twHud+lb@LV04GYH9Yc@(-KWPc@36&08y=RoErQ zK!MW5Q+1#_L#e*iVA`__|Lp-=k#?i1siP>WjAw76shl3Wo366Uskg(qDlun69%j2=JA$!))`u?6)P+egF*?w~i|NAE_*8OzRuW zt1{ym2h#%8IGSF1RH*qc%@Y}H+Lwdo%o0#f*P9wMRa!q9nBwXntKO{fJZ3MmHP>}n zOUj_eER}t#qLAq6Zipg~(8ANx{jGcx?8cET)r0T`ncV}H8LtA? z-7LFe>2IQ_MGfhP4Jh8iufqm$;^p+HTZ8`V+oQ}J8(H*0v8A>cYu@F$wPX1VTB%;t zE&iKk!{V&W0gvMi6z*3DVYFn~N+U8X(6Kuxv~F!Vjr& zNtnKj(f}_W1i2OH^FgW!GQ?8XZ+F&ygb(~b%+-V#tS}oPPQI2MyA`f6 zY{07#_Kaz>`cwZ7Vy@yn112V~D`VlgCp&#=lLWG!mwAhwL;rjxj7=#xcV46m8o*5H zg~U2Vt1i{P?q@oovg8m8GzJk_8MS<(?%LWulyT;^dWh;20s~xso8MRfPv7g#AD^vd z;o4YsbXCW{2Q+J&=1P`o)+p2wIygo-S4t<~E`o|Suo;SJi+ioJww7YTkarxM#jxU& z{;9#Jv6gY}hVJ)T{_``-LB-pkd;yrwu79L6XcX5J?Kpe79tw89wQZ_`9zn@uFq7wD z#5(GT=$SYq&C{?AN%6E;bu4StMvxUva1Qr3bh58YqVxti^$8@9vqIoPm6t)5kqTnd z1{_A5I&S4XxUuYCk-B`*lF7pphgxcoIv+1N6(9y^YJqradehN58dRALE7Nh|sxtYV z{YNt#?WasijF9&a=_=&_Tgsio(s}(x;U-k4`@W|53ckOa#({B-iCr}L-wcz6rJ~IR z5(ddKJj@|LroY0e;}*A1XAHHhO}3;B?}p~|kPC1$DhCa8lKlA;qI;e#2om*ZukCzp zWDuZ#2OH?D>j5!Tyxm{~luS$D@mgkUR6*+!*4aLMjMay(Gi(0LBB|kOqzO?q@tVx19i#+E~%TKYICH@x6K>lu8|tJoz8B12Uh-=sNyQ#6mY z-UIGH^gH-KDe#-)e$0q!^FV~NsQ_g*h`>4{rYcou7*~hUXG%t(Zmzro&n!p(BO1|4 zb(U)$sH715ix@K`klRw4UEFc^r7?yivZhee{%OhGya23VSi5r$l9g$?B9T?da{_*RprIb@Rf7X?wLQBQ}!WxqwO zOzhAeb#Noy&pp-nx<}7nqL^cT3pT?JBcgaDI??)wr%<$=cGP5`idCvRVyi$;|CMva z@pM(O%c)!MTSv37W5?!R;&%19%Y|7DTBeB1wgj>-F&e2jFDfdzfhe`Tnpf4UE{JzY zsg>pM%)XD+SU=jjupA5(d&e~sXNDNV>hD7XM!#((hl*X%)FK;>XKTOGdB5@!;B(MI zHU$o$Bz=Hudr=rpP>pIT0X(#c$=}YJlda`ud`1y>y0T_EfgRi}VIoW7F}g|C+-r}k z@_L>yaEi!`s7&@Z#lDD1QAM>&@V=IWtrmG|kI&!!<~1QL*V?mI0p~KQ=z#S8f{1C4 z1TgwyLs63hbKp8~*Mxz62xop2qP*q7_iC$I#h`B0*!!Af%fLVJr7fAh-C^Ixs5J)m zKrv@oK=5&ShzShq>yGy|=oqdJx;tvrTPd9snLO_TTly^Jc+%&aYSv`rLtrW;fLOL} zO&M0z4z=e1*te!OYG={h7Fi!fkmN-COBJ*gw84g*6=HQRdy+oeNG7zlz;GdwJf1U^ zly`k#a6q1Vt}Gng^q5BNW7BG zLF;{#8AD`r5};^CBwlPuhY;89MxVO+0qKJ5z1T4<>v#sbR`U7rZ4P{Fx6SKvjXJyu zDf##Hp>B|`DIMGxKN`qnFxui<6P-QNl)o!dV;hEjeXysP7&=q|w~5`t-3e>U+8=nk zFQZRe&p`51bb!H4kaZ)jBmRULS=Qwr?v7jZdw-NI>=ECj&CkaI1rnHDt`U zOK>p8@c{_8k`9O{0(^6!A7XJiowiWpi@lAh)SA__XT5N6%pbAx5#&ZK(SuIGSQ%FH*CTxHMal=< zqtUv#;wgRpfa-b2sacmFNghbU;IwOa;{zq~6%FgEgXe3Wny&UXH$zXO83t^XMnNO)A3YY0~E9QY`SBoibhdGAW zg)v5o^*CvXy;n!E#f{-fQT`D=POTvMG(=TlViG3UH7!I7NyF&~P#7xLtFZH3NeL7u z2<({>$(-ukP4QyOcD%`0j`hFr^fC^*_e>$9a#)G`JH#nT%y+ZX7RE|y-I?U@eOAoSe2H(3G4I{o)QY*jRc`3gSz zWuTsk8L~sz`KhLf<_H@(s!{zq5q;v5Zdp&5;zZ)ZQm1<%Bhv&1CQ1NQ`;H^vDdx(m zeN+LuO?C0-yMIow6kCQ<+)NqzFbfb;624n9Qq;HUDr#Y9z-F!xAzk_)u#-oVZIAz{s=b zptDJ=^14NB^bINn;hbV&?Xc2JLl$+&?@*5Z&FU|%;Edwl$5S6k0R{sPL$j1@q{5lS zfUm*meiuWLFt?W2$8#sXx)5s$l|5R-sL6&{5>;Zat)Xv>vS$xfDJEF}qj-&oW`vM^ zzDD8sGe}|oMu)M=k{Snf>gu?gqxwdQEWJa=<$msdba}Fmm(N>yvh$V`-dLA51>}-e zHoY3EK*yXTQ|(68TCgRR1WM6kRI!K+919tqZ^PBZgFd8zeuwsiShz1`A9H%dK{<19 z#d>&(k=6oO;VzZm&L0}yp5qA+z|H+Iz$5d_Og|^w1uS=o z@i&rvKpS#HN3QC0<<>A$?|YN{>Gyvn#FKVc+{OJP#9!C~0I2?d5)c(YVe~Dx?W_9FY`e&K9I5o$Jw^5qL>0Ml$xmJLs1#oh&?y{i^2+XV?yfQYN{xD!59rU&u}wqn4u|XdJjudm%j)?=$kzQF|#b zjCoGa1b=j-XXmd)cvAgJ4_be?QD*_x#^0iER?1DASwHUA$*=!@v>=`ATXR9?B|txu z@Q0iA3i*+ZI?9D zGPyjLUA9ozo^0Thc0#>ZGE7{HeDM`OrnNIgck?_WUkY6a)c21&E6ITINQPd;y07)5 zUsRj$6wGoQ07MZ3hy}OfEt<(-0uT#K6#@LD0S&o8Aux+G{(IKO2ON4v6v!C|=pvUs zXO02v3af_!e82!m0AAxb1i%~T@CPKN3_o}6yy*E<(`A!5K>QY*S$InuqIgwWh@}z_ zXFPmj?kp_Q8|9Qj8t-1s*7cZ9$?^R@qBvhA)0i*>!8c+2)+s3zxl=?-yqg!kM0Nup zp?MK_G-ULD{`tKlZljMR+)5Q}qZckS#0;|66Z_q2D15ItAOSi(O?P#hBR5WpuW zCPC!JV^WRna}J?DNf6|ZSizR}DGBwC;}Nj znZZMM5`r&h=-nh^F!KCc_i#R}un`j6^GIlZw<15;z6Q9xa2rN+&b1oUshYTtG|av^ z!uKXjsh<}<)_N#sG}=+nxn$bLCmKxYyu8m{T;g44&^aJ(sBqJw<6l=zxOurrLM#13R})1??Bq7lLjYEBU+(qEP7WZxu65j=Th-bU*+AP@(_;{Q3W| zKN?t@**RJ`o7?F9#|}wn;Am?4e-aJ3s0-TZj>2|*s+}!lV$ryblfI%Avnm0H19LoR zD5wykSxbhJHg<-cre20+ZdP{wW_`!t_x!#; zwaT~Z+3DTr-r(WS?D%~>Ux&Hh=1zceek$rfxnqW|m%_%AcCW z(&yUd{)CbIcsO|6m;A9ieFFZ;YQ-;$Cbe>@XKRamc^sX!Q#Ox=IESzpn5e$QdeZ`z zeMyrmLS#Z%;6@&2NL==M40}XLM;t&5T%{Yi1uM|1jO!s1;W3U^C%0IF2w-C_a@sNZ z(D`c(JHI01;55AG0b?R{F}l|qx`0t)tc>V$6gefo&VS)?HxLV^YkNddwQQ{*sz9`i zFwoKli#r(=y&5V=Lrh_7CM!D%;9E4L&3W?&XnFmYsFl=z;3UykSh6&3ccO z#r!z(gpZ^z%gFW83Q0bTOiAK;fGeX6<4|oIfSit3`I3#>Tx?_8+!+jx(%f3tn_Yg}7ozYy!IujMzHI?%y zhRWrVYVID(&w*mMAk#Pl?R8}sDGiphkzITL&)@cC&b z(Pku|&c;&bI;9};fz2x}1@G9pp=fz1`3J!uR$hrW7bBr)I;7QSZBglzj3-Phd7^WV z8Wvhq$>tO)W`1~}pq`bf5l$;wLfGC@`-5rj6w1yA@ve1gkOlQ6ZU`BgE;rDpgpEFK z)(ND2i3L&}5Z8h>LcN<6a$8Ah0e6nuedC4-52VAq(iyY&Tz(dl<1jO9aqDC}q5f&A z7BRtwU?%y5XRu<4uyt;@x+Z#zA^I;X40qmd`9jp3osUa6eHeysX|axDQZ9zIZlFa4 zf97Y70)a`c93V`61}rUhqF0oy-4zRuZ#w74;1b9!xZ$7gXUf& zf0|~!crSx+kRj_t9>vV3wO@`Q?xRVQ+YivFZEI#M&lA*W(E~O!iq1tKY$5&rV>KaB zjgXaZg0a|ypRc*W5rct;zOEz4c#)x>R)|%UlC9JU>eFt(q6`r`!yFa`g$l^PI8Paw zAK90|TeUesm+i|kip@8kIsSdC_+sXCsw%DI_(H#6W&E9dv&pX4zZ05-lR&J87{ zjej zSB0=K*xt^HK#`|4Qk`^wj2Ge}MAj1N`fiObaV7}8aiM}T&AKD3Pk)sPD?va?V^5=p zn6ezkojlPTH*M4IgO;wYp;QWWFl!lrIQGMSg_kL;haOc#N2? zh+l~rH<9(8GgLobt=GFUDV3bD9-9)1Zs!zD?A*L>-CCLR$phGmGNia9BuQRd`5PK< zPfc`)~R65e4n0lJK$pX}cypn6e8cEvR4^kC+ zKZOle$BryCPp6hBo#9die2C0qAL zjz=G!YhNqTTV7C5vQ`Fkmi>duzwlhXI?((KyEg{?Z)5K()>OxIr^3) zJQS#VDj@OPn)aTcNH0Bz`sa9vp8m-F=nUa`;St0@Z=ZGhOn6RGPPz12!W;RIdtTY7 zurz{$zN)WXQ%KLpl##wNB-CiMSVE@he={hu|0n}$In16Tk6>s`1mln-!>cbJ~=y{ z^}BJC`e@BM$^Lj9I6GtdxO(lFGi!SOxGBr$wT(B6UzM79{9(iEwPW^p_|MkOV||k_ zx0znYBy8}(BmLHD*5QXm%UFK%djCzjTMY4Y8Cyn-D`<~Uq zm3b0=^q7m=_BD*E|4+}Z^{#5=vGrrH_*%DVwpniK=(=t1)=ec!q<0UGeqQ4t->hG{ z5x{#7;|-VO10%PD>FnM?@h9W4Qx5L#%lkeP>f0;NrZqbHc}nKGW4G5Jf$ss}$tMr@ zQR+DSx@YSI{Qc#pBA#pI@jcV|?fb*=^BbLTHT~?bf0y{fAbr|4apZ4JBmpO>O5jm2 z3#5{a?Lv`;c45cJ9+?BWSLd1LlnoM`fz4`y$GTahNe}1D!Nfsw?vX=gl@lzvL~9uJ ztBjmh1`Vr+#l#Z|nR%_O@kc__=nYNLBcrg^Ge z!}7fuyI>}I82%**v0UzK>%W{%j@B`~JXtQe9^*@w6^9$OwVkd@N%#7OnR5;u8Mn;D z(#L2*u^yY2OVT@8C@_{8@_@b=zt8yD2J|TQA5Am01Me5&U3Ozu!DUJj$c++r5(zBf zU)X`(Sv}KE>)hqIRlZG{763;r-DC@9Epkb$04_--vsOu^j}F=7m1WotE6)zuo#Mhy zO0AD}Sx;H^)!MU#$*@#WEw;lj8^MFv$8Cf5idpfmx3usRvuvReaYb->z@o-C5068;J{io5rW$_23zh)f&VHdKa7cybL?F;jEb)-D1OHJHH%#oH$(JggPN-l}ZFPC=BBqpzrE@|aIMVsGPE7!jd z>-VQO8*A=wjl;fPHtJO!n$iV9w#n7GXsZyDkk|Krtt%hbZN1J$%b9~U>3-7cl(-%; zHnOr`xA}IjTb<6%ZKgG9d~^ZN8XrI^qu0+*9uK5vKR!|_XDhz`T?$+;mv6!LR_M#t zLNj}aEZA(nOEN^;U!|dpoGx2QuIFg=RJC54>D^}l4g^)hPm5;xdu_O8mJob1j$YLy z%pX65EHSRCS}wiycr0GNy0-N3KIdwrPV>Pja~z&W*oxnSB`>yJC)<2O8D}q_Kqs=V z8A^ae-Lh#NoLA2W+MKg#>;K)@hW>W&%;-7&#piI6*eMd5`xa$IH&W$(p4~~B-@K64 z4rB4Dwfr>nsg3e4rFU5VbLlk=$@^Sv_NDx(|A zh)D`V&wSSVJBc2R^g{;o5bgr*(IcQ^a>GT(ah~kCjlv*^PTOVs#94dG#t816Dt8e4 zG`WM54sa0b zADJO#2*TpT8LEXD5cxNghu+Ut?**X2#F;~n-YN7;Yi2EA`+e?a#T{wd?iMNO;%DRd zUy798A&WSLI5c+Hi0~v;(+4{q96j#*H)5rIyVFBEIteDZ41_T-E0fno7|tW(Y_rU9 zQ!T}Qba2mi4xDxOzlP-_AR10 z6siSq)F@}b%8U1>Y;U&lPkO49lUe!{V}isOlNn6t<_Ef@5GlnshQA0&Dq^4&nwaHp zVLXh3ZEu;O*$xq7h$PwP;+$8KemR5|w^kkh)UQw0ASam4=OCZ89 zlAykcs4j|CzOkoEJBb4pht3>M8rk+11_Q>DH+lLJRt6zXmLxJ*cpXf8@O6U>?*}E4 z5wSuylClC5*oEr>h%3UeD8$@m6?UFroGuL98-icz5Z$Ji$ZD~!{@8UABT&Uji;3Th zk4Lu;ER(L5U14g$l{6_OGrx49SacemLgO6fL9RWZrH_!}-m342)Vuc=;i<1&JA|L) zo#JAE4OoHVU?S)M5y=L@83DW(7lE8T-Ae~BFeR{w2aY`IbEouDpSqs{KC>h& zNeR}!B8sL*J@+Nbhz)3d3bjVP772ZXyW~9Xp{9xabWzm}eqNH~ksy8!;_bb|43iG7tYi%HdFQbUyt|1NNCtj#7t?u>CEXr_JxQ@ooJipZVGVb&F zJ3>0&GH?lfQT?%}2>i9~>upmusUB%IxzK4?GMyBhT@nxRC%LJ_cbK1Ym1g6Zl@xsX zL19-r3Uc1pKeeV-?z34acsbCQ$FY?*AkN3Q_nTHM?H_7o5HL)NkXL|WB}?=4v^e<- zB`tAz?6HsX3cSvF39x^QBX+Y6u2dlUXhNG`sxFeEZJVd>#TBj5EF}U3+SQM(4L6&4@z-QFi3&O;}X-Ka1tpdhFY$jei3syk! zZTd0--N}xa|%8LJMs`rG$$e#7OxA{#a73x{X>vqDcX*QAh?vZ;*JN0L5_1^|gcT zv^c}9pt+GMAk@V%Go*lI`w6$DYz+d%>>R#Z-%Z$8OmS#%R3?re`H%zr7J_?$3q$N# z!&@}tVF0BlWusaLASOTL#21B(IOmg1wtx(VXrmtqTHP|z_svXTOS3Z&JH+9Wrf;rP zh1a>Gn{9p$-r%5!tW(hUQ&bbCBJEWp$x|6hI8GqoVaPdv!E*R2!zl1 zg6|1RormysMfTInJtv^wiJ<_hd?+HZt3M_@p5rM&uPZNzc0;oeq$zGBvJp~`(y!)A0)$&zc(T@`-@=r~}r9P=g zs8_Ppomj8*xC2P5`?g_|&}ytgv^TsZ@48;SZ#ZjHrod{8jKJ0~W}hbsg{+OvkNN^QJq zfoR(T^29C^#zLoaXVfN>1&r82GCK%?KQ#W%4zQz@q+$|dGGt^_*sE&1gRHJAx-T=a z76BvFFE`-Em;Y5b;mLq?4Xad=eS&rDY8TN{Kk`4E?WY<;(E*S}aaH6;cgmP*X= zwm&M*=mHtu4LVex=q=-VG%#|M&ubzcDR8-ZH4HvwBxqvmd5dt)p4==Uh5pT_=8 zXAqOz2}j&JwFFwAO*Ysbkog_d)d9X$z$x+gt09X=6@_-6oOr5}-YrW-R~wS~QrUq? z2?qGcxGbkJCg}CNO!mjIhkJ~hIy=>uTjIYcoNzd zOy~0Jk!qPy^K@DS(-Vf{EQ)v0-=rzt@G$Y`39>|Y&^B>-CvFdFP7-C%)BiI#9N*pi z{RTX?$vP7?`_=X-uRtKUO9>02Fnfe>^kp4beb2ADLIX(0z);a2>>F$p105j`-O0Cz zO5?jNf%|ZuTT-cLZ=5;CQgzE^v`?J+4}*FUm3j!i=KIAAt=GXLa5wVy)Xo<29qJtU z5g`B`ed@M2+;K(M?%^J= zVL43++PGMIndi*5UU4e`_JQ;eYPdP=N-EWYCO1G`i-3!8V^`6Z$1C!l!kI+Gr#PmV z%vF*gYe(@14jt{cmLRr?2E(E4BXb1|E0Y8t#}~E4#|ET-v~7(0ETJaie&^P5ZWK>e z7v@RC6sd7G0!`~Qq057!EI}bgFl*eDwcECeRX!Zs^Sjz_Rvm4qvtWy5ZNf;21)ueG z+1;dzGnHW8Ck)e0j&O!RJnj#Zm}A8QUjsB~>k%ZdZw-q{)t)%eTOsd1ei!b+9@D*Q zl1S*f>SJ(sWF?Z&aLh^T||b z#`&?UZc&I?@aHuT1nX(#|2QJu6fW_Byu~J}3qxW|6uTP-G)lagddPz03--MjM6^@- zV_i64^3sHY)^T%$J{Ox@5Pt}HtL#K&jh|~tsBem}ox$M*xPU}ice5r;`g>ZF7Gh8| zF^sP$(2O_eZfW*eZUs?QAc3wSy+Fq_BFs=<8ZrDq?w2GejGpg+&QIR`0W3go3#Jh9 zqRet!#+9n6+CHO1S}WAd_rPJ zasS3TLS?8;kx$bm3z7N^3JjOl8 zFZ!@yT3f2R+;dgM(z1$xko7k7Sv#H!^1~BR=60IhAy`UW8_oK!q$QWZ-4!e|q<*(Y zhr?7Wj;paa$?~B9_Bd>Vh(ZppIzvA=?5Z3^ss3p_PK}!&SR>9Uv@F6u$D|w^I89tK zWrTV@e`fh*IAj)|H&Y+2l#b|4<{CEy!+AtXX`Bn*ex&wT$w8{Vh$WnKRvli854@Uo z$dc6sm_L19R>gJw!)*dfnG0c;&v)P1M#8#|XG)T;MR+S@rP)X-7}#_4IOd1#?p-@1 zW&(#quQ}&$jV*K^WU_GE;?_Ck^!u5}mYz?+8Gm9aHm{LG=|LY_0WYo&uB)LK-d8mA zIfy;g{5g3IOQ8yz7y4RvAb29Jm&~8G`TrSE)_Ly>8DWA|j+}c*j@TQoSb(}9u&f`X z^V;dpGaPQUs9l=cm(M}KRZMXca|()xeFVI^5}z{l$fu0@D3KkS!Ws*4iuu}!MZxX* zwUG_=4jDrJCB-cZHX!zTdjM!nyRS1z-~YE?gS-S?y8 zFZNbtuhZduY>I17wTJ~D@>W7VuK_p5rT^A_oFIQwQq(pn-+}P?xd!tqdAo?&GLNk( zop>S^w}n)CkT?4A&CNCU#L2TAf7LUWdY)-c^t(6IuVXS&Z2%;RZI_h@*O$z_!`gEG z6zj0-{AOBN3%|O1mEd)QE3qSW(8@h;qsTmpEt&ir*p#&Vk~eIniPyC|o;fqka!Lo{ za2p1FYgP9eKStw}Xe^pD8C3i?8-nAkxQnc&q#g#DzmvO%X^V$I{ie_SzG?a8Z4gSa z3%Akrk0wJz*6|Ogz)E;DE-%|Q+}&02esNYeZfmIs%$Mx27S9;B%--A$)bwDdxJzFf zddT+WN*Lv>bj#L=kmD#+j2Fd?CxoAW8!r+&sV(>OmQa?PuA!j){Ha3h&ZpqFWi4A^ z{5NQ~3yDZWPl~uas%D#xt_6H;M4q&8lzywGNAf#dB@pJD)&du$jh)!1Y4IZPL%ot5 z?Y$|Q?aF{|-Ocj`G)%bc@N*BL9spDbxLxbpLf5+~{ms>DAS6GHJo4G>cVKuAC$sN$ z(TVEhUB0Y$5`3aOwqDr~1HZd1$Ulomw%5hq=8{eEx4MopuU$^ac(VR?C?+Yj$@t0E zF$a3qO46ZaD;pc$`HvsNLxrBTwL35*TQm#9`laf@YW#o4?GSk#gW0>}N~m~Q+ZfEv zo>=Sdg41^fc2?&PeK%{cdvDpK{f^YGZKx~FDY>X!RykT|_R9GSDbeZfP`8+h)?j^5 zA@mk7-bl9byh@3Cd-#fB%zcBnIkWpml>y19K(-pqeuC@IVbm2N0K2HYBt1V=+nT_@ zZ^xi!ltZiC2EL~ruiwB(#?@D_HlWWqI+zbUD=s!4#HQjS|4U(-JGJ#vW~U0a$gv0QyN+6Gxcp(S9O>e`t6F3=$i_;d!y>m3c>OU%DGJuC3t*N zlkMGv4M08_FJozoNGDmTc5=Gap3uanWYzJrk-}mMR~>#7C;jzh!OaYdO8(&@fp2TH z#5C4#6NzOwg)r2AZo_{ZC5_FlaqwRc6m%tg(S?d&idzt&EsH{H4jacE`E)qDx%$`m zwp-n?*1spS6rj>7v}76!Jh}O6O3ww9Ze7;Ia8~f$uP?|9q^EHCPxLs-a+U-z+)+CE$eZixk*Cfx zyn#rX^;*!-vwJGERP_AV_mM-r+=0Et4jA9dDD4zw`Te2$DU9g`VS zk`RiEGOW+AjJXn*oGYj94Kh%>-`Pr=e5}fk!iXSYgaVRq$EzpU!RenE?S_K^ve+cE z6H?8~7}zV3O)cgpctb<@2%(2MXby3zISo_9#pfPu*kNaQsHzBljMlcAa4uVFMwGwi z@LT&;xMFp^ut`5D_@IWNc?dH^w3NZ#I2WwJ;zQqcHc($(B0)^x>;#OqD!I?3HVhhg z<$0n)LbeA?xII7CQ<^F;x|lU$8qmdDabNJy%&+3qrKQ{VG1Iwa?{_g2c#wo{3QPuK zCa-QEVI}>9i4tt^2?TV%_mbq_j#tjh`#ds%JL)fmC4EYlIy4kqS}+%n84s8y5QdnE z&g-p&ny`}__6tD@h6w!B!akgQ5s7k7A+aI4;yQyQu8*XB$0U0Dy zpD~?wYThm40Uwq`F`)7;r0AJr*nU@i`hA;rz>`R3>UeXafZ zethM3U7mgZwe$Plyt&%+d$)PallC3OTukQwoUZ-7e{}mj{Qa1m)%(87yxrN_du-^{ z^g23r85Q;WS%%O3J>I#+VKh3-Qd|vu}zGTk+Te5FG z-_PxxpO=3Sih_E7FFI@6$Jy=pTW0lqzqeoczpit8+b__6Ki+@ePyT&|r;7K#cRalF za~z$r|BQ+~ea-B8)}w#U_Iy9D?s`90>iKq#yj1FS_dc~9q>gs#>UhFTVXwb%e2ca4 z&h)0({QI*F*XX}LmwsO_YIeO|**>q!mrt2?F^&8`!@Y0QwfJVgN3%Z%y`wQgZe6TN_hPzU{j{LN|XtJN4oErnV=I)vFKF+;-1xy?4#+ zEPEYsWld?XTi$2eJdr$aYVq60wY8kf)O>QNDK2%SJ*TmbpKoussX1J_rf##`KJ~knsJ>i_Y}_sf`94N&tiPP=P0MxD9qoFk zJ*Flzqv@FLRO)sf-*rNFuZJsho2fN@wO8k+2i3nusxiD@{7v_yVW}nQk$7H~u^wq_ zTBQEGRMTu4s#1Bv+O#~!KF5rY|8k;h_C%L^$(md(uDWw&(qhw)rx8b7yK}JBI!5>L zrm0RnkFD>SnMnOHxk=-dw=&{CeSzMjDbw!m#dn#vHnn4l9+vG~_SV|ic(B-ZY^2nx z#mTNWPMy>S^Al_3=xel5x0qohPn%iWpNogqlBSWn=a73&x$JSzY2Whb4N=ti-l-j` zjin9Gaz7undl@YceaYxk8(Paz3vKRsO>v5ie4gXe$Q~z>?tJ~(y{Gk&qarjm@9~W_ zH8t>EbM|t+^?;IuiTkRSNs^UKB5G(YRzJfwb4p2L=iQ-~_p+5;Pu<>3c>R4%xE9^< zmQ5cjsaKMuj${R2VcCexntn3LUEQ~~;_RK<)&ka;j*={+6YI2Vu}p;W(46jipR~eQ zGp>D-yS6rGH763Ty95Fk>%`RB8rn#^->d!XHJ>@uc90fnX&;PM)^gb;3nZg-7uTbD zms}Y|%0f%{-HeYn6TV&_j}a)IU+oBKxgNNh@rUhxyI+X6`idxfbiADjg~L zD*;jY0DW!Rca7c^=zMks!`j81>iK`8zQIf>g z%e=mwzD}kVj&UqU|I%6H+cKh?sa1| zVIbz)^(fu-yeJGl`t4M|nQ8yuLN@8aiiFibc zSy<-YCEe;yOSba~x>B~V3=$2=>p7NEO<@4YGL+04=r9eGzGli^k%8nVeu}%2RBcUf9@fed!#b3Mz**_*E~RZ2GgvXbiF+f1=b>Un0h;lG$!?b$q=e|uJ|bktz4wKdBU5V}R(ZGLGy=?*dw{SV$=!F!%7TT$x zykef&;sa&nDbfCxX&}o^x9j&xn#)SiGR(+p-E!tzk#Zl8v~aMVABL8i*3w_A#FV5-ZXC{pEnUMC+YF)l^dtqWPl zm2+C?Eu`VA`7Ucqd*#Z;3oof^rR-WPYktb<4>u|^C1W1TH|1lb7q9|G_MplSNjLy$ zD|i4BwJbyb8MnG?AQR?=bT0d2lOK^~jH0L$6W`-U_8Z$@kBc?rhmn)4Wl`kgtUDaC zQ=|!ObZTvO9O>>gqU|ze)XttPZmRaRzNGH6_mA)Wgo8Qs;d*PjT@JDqh#V}O^HQ~A zCz5DGAjS4ZXzgn>rBF{}8?W|I&%=vMYNcDvXy3>b!6>UQJ>OVeQ$l9M@@M+A&Lp8& zqf)~%8zi=O+}45q(MIZToak210TNlHIG9!jT6UT+-1*xhKq8?&5DsDGKd&gU!EwDA zm@WmO5tM5eKkbH9qgj@~pIQUEyi-YPxrI%u2|MT}fhLi$VSuH%Zb@5MyH?tI+5*=d z>Zr-r=n*B6^pPX9w2I7Dp0jAyECdD%LuwySNfNqgl(2aTVMPCZ1EPS+aICX+=XtLN?3 zep+M_T$NBxh;k&Uyqy`Aon^VUg9OvQg^jyym_>up?kiZ